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b/translated_images/zh/yolo.a2648ec82ee8bb4e.webp differ diff --git a/translations/bg/README.md b/translations/bg/README.md index d2a2512f..511a209c 100644 --- a/translations/bg/README.md +++ b/translations/bg/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # Изкуствен интелект за начинаещи - Учебна програма -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/bg/ai-overview.0857791951d19500.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/bg/ai-overview.0857791951d19500.webp)| |:---:| | AI For Beginners - _Sketchnote by [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/bg/lessons/1-Intro/README.md b/translations/bg/lessons/1-Intro/README.md index 7454b1c1..f72a9965 100644 --- a/translations/bg/lessons/1-Intro/README.md +++ b/translations/bg/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Въведение в AI -![Обобщение на съдържанието за въведение в AI в рисунка](../../../../translated_images/bg/ai-intro.bf28d1ac4235881c.png) +![Обобщение на съдържанието за въведение в AI в рисунка](../../../../translated_images/bg/ai-intro.bf28d1ac4235881c.webp) > Рисунка от [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Първоначално компютрите са били изобретени от [Чарлз Бабидж](https://en.wikipedia.org/wiki/Charles_Babbage), за да работят с числа, следвайки добре дефинирана процедура – алгоритъм. Съвременните компютри, макар и значително по-усъвършенствани от оригиналния модел, предложен през 19-ти век, все още следват същата идея за контролирани изчисления. Следователно е възможно да програмираме компютър да извърши нещо, ако знаем точната последователност от стъпки, които трябва да изпълним, за да постигнем целта. -![Снимка на човек](../../../../translated_images/bg/dsh_age.d212a30d4e54fb5f.png) +![Снимка на човек](../../../../translated_images/bg/dsh_age.d212a30d4e54fb5f.webp) > Снимка от [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ CO_OP_TRANSLATOR_METADATA: Един от проблемите при работа с термина **[Интелигентност](https://en.wikipedia.org/wiki/Intelligence)** е, че няма ясно определение за този термин. Може да се твърди, че интелигентността е свързана с **абстрактно мислене** или със **самосъзнание**, но не можем да я дефинираме правилно. -![Снимка на котка](../../../../translated_images/bg/photo-cat.8c8e8fb760ffe457.jpg) +![Снимка на котка](../../../../translated_images/bg/photo-cat.8c8e8fb760ffe457.webp) > [Снимка](https://unsplash.com/photos/75715CVEJhI) от [Amber Kipp](https://unsplash.com/@sadmax) от Unsplash @@ -98,13 +98,13 @@ CO_OP_TRANSLATOR_METADATA: > | А какво да кажем за ML? | | > |--------------|-----------| -> | Част от Изкуствения интелект, която се основава на компютърното обучение за решаване на проблем въз основа на някои данни, се нарича **Машинно обучение**. Няма да разглеждаме класическото машинно обучение в този курс – насочваме ви към отделната учебна програма [Машинно обучение за начинаещи](http://aka.ms/ml-beginners). | ![ML за начинаещи](../../../../translated_images/bg/ml-for-beginners.9e4fed176fd5817d.png) | +> | Част от Изкуствения интелект, която се основава на компютърното обучение за решаване на проблем въз основа на някои данни, се нарича **Машинно обучение**. Няма да разглеждаме класическото машинно обучение в този курс – насочваме ви към отделната учебна програма [Машинно обучение за начинаещи](http://aka.ms/ml-beginners). | ![ML за начинаещи](../../../../translated_images/bg/ml-for-beginners.9e4fed176fd5817d.webp) | ## Кратка история на AI Изкуственият интелект започва като област в средата на двадесети век. Първоначално символичното разсъждение е преобладаващ подход и води до редица важни успехи, като експертни системи – компютърни програми, които могат да действат като експерт в някои ограничени проблемни области. Въпреки това скоро става ясно, че такъв подход не се мащабира добре. Извличането на знания от експерт, представянето им в компютър и поддържането на точна база от знания се оказва много сложна задача и твърде скъпа, за да бъде практична в много случаи. Това води до така наречената [AI зима](https://en.wikipedia.org/wiki/AI_winter) през 70-те години. -Кратка история на AI +Кратка история на AI > Изображение от [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/bg/lessons/2-Symbolic/Animals.ipynb b/translations/bg/lessons/2-Symbolic/Animals.ipynb index 1e8a2cd8..5207b89e 100644 --- a/translations/bg/lessons/2-Symbolic/Animals.ipynb +++ b/translations/bg/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "В този пример ще изградим проста система, базирана на знания, за определяне на животно въз основа на някои физически характеристики. Системата може да бъде представена чрез следното AND-OR дърво (това е част от цялото дърво, лесно можем да добавим още правила):\n", "\n", - "![](../../../../translated_images/bg/AND-OR-Tree.5592d2c70187f283.png)\n" + "![](../../../../translated_images/bg/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { diff --git a/translations/bg/lessons/2-Symbolic/README.md b/translations/bg/lessons/2-Symbolic/README.md index cc7257bd..4448c223 100644 --- a/translations/bg/lessons/2-Symbolic/README.md +++ b/translations/bg/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Представяне на знания и експертни системи -![Обобщение на съдържанието за символен AI](../../../../translated_images/bg/ai-symbolic.715a30cb610411a6.png) +![Обобщение на съдържанието за символен AI](../../../../translated_images/bg/ai-symbolic.715a30cb610411a6.webp) > Скица от [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ CO_OP_TRANSLATOR_METADATA: Следователно, проблемът с **представянето на знания** е да се намери ефективен начин за представяне на знанията вътре в компютър под формата на данни, за да бъдат автоматично използваеми. Това може да се разглежда като спектър: -![Спектър на представяне на знания](../../../../translated_images/bg/knowledge-spectrum.b60df631852c0217.png) +![Спектър на представяне на знания](../../../../translated_images/bg/knowledge-spectrum.b60df631852c0217.webp) > Изображение от [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Python | синтаксис на блокове | отстъп Един от ранните успехи на символния AI бяха така наречените **експертни системи** - компютърни системи, които бяха проектирани да действат като експерт в някаква ограничена проблемна област. Те се основаваха на **база знания**, извлечена от един или повече човешки експерти, и съдържаха **инференционен двигател**, който извършваше разсъждения върху нея. -![Човешка архитектура](../../../../translated_images/bg/arch-human.5d4d35f1bba3ab1c.png) | ![Архитектура на система, базирана на знания](../../../../translated_images/bg/arch-kbs.3ec5c150b09fa8da.png) +![Човешка архитектура](../../../../translated_images/bg/arch-human.5d4d35f1bba3ab1c.webp) | ![Архитектура на система, базирана на знания](../../../../translated_images/bg/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ Опростена структура на човешката нервна система | Архитектура на система, базирана на знания @@ -106,7 +106,7 @@ Python | синтаксис на блокове | отстъп Като пример, нека разгледаме следната експертна система за определяне на животно въз основа на неговите физически характеристики: -![AND-OR дърво](../../../../translated_images/bg/AND-OR-Tree.5592d2c70187f283.png) +![AND-OR дърво](../../../../translated_images/bg/AND-OR-Tree.5592d2c70187f283.webp) > Изображение от [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/bg/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/bg/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index 2bde2178..9a51d869 100644 --- a/translations/bg/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/bg/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -1255,7 +1255,7 @@ "* Ниска загуба при обучението - моделът може добре да приближи обучаващите данни, защото има достатъчно изразителна мощност.\n", "* Загубата при валидация може да бъде много по-висока от загубата при обучение и може да започне да се увеличава по време на обучението - това е, защото моделът \"запомня\" обучаващите точки и губи \"общата картина\".\n", "\n", - "![Преобучаване](../../../../../translated_images/bg/overfit.a0bd57f717c15769.png)\n", + "![Преобучаване](../../../../../translated_images/bg/overfit.a0bd57f717c15769.webp)\n", "\n", "> На тази картинка, `x` представлява обучаващи данни, `o` - валидационни данни. Ляво - линеен модел (еднослоен), той приближава естеството на данните доста добре. Дясно - преобучен модел, моделът перфектно приближава обучаващите данни, но спира да има смисъл с всякакви други данни (грешката при валидация е много висока).\n" ] diff --git a/translations/bg/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/bg/lessons/3-NeuralNetworks/05-Frameworks/README.md index be71b717..429bd884 100644 --- a/translations/bg/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/bg/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Overfitting е изключително важно понятие в машин Разгледайте следния проблем за апроксимация на 5 точки (представени с `x` на графиките по-долу): -![линеен модел](../../../../../translated_images/bg/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/bg/overfit2.131f5800ae10ca5e.jpg) +![линеен модел](../../../../../translated_images/bg/overfit1.f24b71c6f652e59e.webp) | ![overfit](../../../../../translated_images/bg/overfit2.131f5800ae10ca5e.webp) -------------------------|-------------------------- **Линеен модел, 2 параметъра** | **Нелинеен модел, 7 параметъра** Грешка при обучение = 5.3 | Грешка при обучение = 0 @@ -79,7 +79,7 @@ Overfitting е изключително важно понятие в машин Както можете да видите от графиката по-горе, overfitting може да бъде открит чрез много ниска грешка при обучение и висока грешка при валидиране. Обикновено по време на обучение ще видим как грешките при обучение и валидиране започват да намаляват, но в даден момент грешката при валидиране може да спре да намалява и да започне да се увеличава. Това ще бъде знак за overfitting и индикатор, че вероятно трябва да спрем обучението на този етап (или поне да направим моментна снимка на модела). -![overfitting](../../../../../translated_images/bg/Overfitting.408ad91cd90b4371.png) +![overfitting](../../../../../translated_images/bg/Overfitting.408ad91cd90b4371.webp) ## Как да предотвратим overfitting diff --git a/translations/bg/lessons/3-NeuralNetworks/README.md b/translations/bg/lessons/3-NeuralNetworks/README.md index 6ef25819..69a57fc3 100644 --- a/translations/bg/lessons/3-NeuralNetworks/README.md +++ b/translations/bg/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Въведение в невронните мрежи -![Обобщение на съдържанието за въведение в невронните мрежи в рисунка](../../../../translated_images/bg/ai-neuralnetworks.1c687ae40bc86e83.png) +![Обобщение на съдържанието за въведение в невронните мрежи в рисунка](../../../../translated_images/bg/ai-neuralnetworks.1c687ae40bc86e83.webp) Както обсъдихме във въведението, един от начините за постигане на интелигентност е чрез обучение на **компютърен модел** или **изкуствен мозък**. От средата на 20-ти век изследователите опитват различни математически модели, докато в последните години този подход не се оказа изключително успешен. Тези математически модели на мозъка се наричат **невронни мрежи**. @@ -36,13 +36,13 @@ CO_OP_TRANSLATOR_METADATA: От биологията знаем, че нашият мозък се състои от нервни клетки (неврони), всяка от които има множество "входове" (дендрити) и един "изход" (аксон). Както дендритите, така и аксоните могат да провеждат електрически сигнали, а връзките между тях — известни като синапси — могат да имат различна степен на проводимост, която се регулира от невротрансмитери. -![Модел на неврон](../../../../translated_images/bg/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![Модел на неврон](../../../../translated_images/bg/artneuron.1a5daa88d20ebe6f.png) +![Модел на неврон](../../../../translated_images/bg/synapse-wikipedia.ed20a9e4726ea1c6.webp) | ![Модел на неврон](../../../../translated_images/bg/artneuron.1a5daa88d20ebe6f.webp) ----|---- Реален неврон *([Изображение](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) от Wikipedia)* | Изкуствен неврон *(Изображение от автора)* Следователно, най-простият математически модел на неврон съдържа няколко входа X1, ..., XN и един изход Y, както и серия от тегла W1, ..., WN. Изходът се изчислява като: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) където f е някаква нелинейна **активационна функция**. diff --git a/translations/bg/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/bg/lessons/4-ComputerVision/06-IntroCV/README.md index 82232666..27041994 100644 --- a/translations/bg/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/bg/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) * **Предварителна обработка на фотография на книга на Брайл**. Фокусираме се върху това как можем да използваме прагова обработка, откриване на характеристики, перспективна трансформация и манипулации с NumPy, за да отделим отделни символи на Брайл за по-нататъшна класификация от невронна мрежа. -![Изображение на Брайл](../../../../../translated_images/bg/braille.341962ff76b1bd70.jpeg) | ![Предварително обработено изображение на Брайл](../../../../../translated_images/bg/braille-result.46530fea020b03c7.png) | ![Символи на Брайл](../../../../../translated_images/bg/braille-symbols.0159185ab69d5339.png) +![Изображение на Брайл](../../../../../translated_images/bg/braille.341962ff76b1bd70.webp) | ![Предварително обработено изображение на Брайл](../../../../../translated_images/bg/braille-result.46530fea020b03c7.webp) | ![Символи на Брайл](../../../../../translated_images/bg/braille-symbols.0159185ab69d5339.webp) ----|-----|----- > Изображение от [OpenCV.ipynb](OpenCV.ipynb) * **Откриване на движение във видео чрез разлика между кадри**. Ако камерата е фиксирана, тогава кадрите от камерата трябва да са доста подобни един на друг. Тъй като кадрите се представят като масиви, просто като извадите тези масиви за два последователни кадъра, ще получите разликата в пикселите, която трябва да е ниска за статични кадри и да стане по-висока, когато има значително движение в изображението. -![Изображение на видео кадри и разлики между кадри](../../../../../translated_images/bg/frame-difference.706f805491a0883c.png) +![Изображение на видео кадри и разлики между кадри](../../../../../translated_images/bg/frame-difference.706f805491a0883c.webp) > Изображение от [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) - **Плътен оптичен поток** изчислява векторно поле, което показва за всеки пиксел къде се движи. - **Рядък оптичен поток** се основава на вземане на някои отличителни характеристики в изображението (например ръбове) и изграждане на тяхната траектория от кадър на кадър. -![Изображение на оптичен поток](../../../../../translated_images/bg/optical.1f4a94464579a83a.png) +![Изображение на оптичен поток](../../../../../translated_images/bg/optical.1f4a94464579a83a.webp) > Изображение от [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/bg/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/bg/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index cd5fcba5..8dd96d25 100644 --- a/translations/bg/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/bg/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 е мрежа, която постигна 92.7% точност в класификацията на ImageNet топ-5 през 2014 г. Тя има следната структура на слоевете: -![ImageNet Layers](../../../../../translated_images/bg/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet Layers](../../../../../translated_images/bg/vgg-16-arch1.d901a5583b3a51ba.webp) Както можете да видите, VGG следва традиционна пирамидална архитектура, която представлява последователност от слоеве за конволюция и пулуване. -![ImageNet Pyramid](../../../../../translated_images/bg/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet Pyramid](../../../../../translated_images/bg/vgg-16-arch.64ff2137f50dd49f.webp) > Изображение от [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/bg/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/bg/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index b2930543..5b4ead3a 100644 --- a/translations/bg/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/bg/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -262,7 +262,7 @@ "\n", "Така в типичен CNN ще има няколко слоя на свиване, със слоеве за пулуване между тях, за да се намалят размерите на изображението. Също така ще увеличим броя на филтрите, защото с напредването на моделите има повече възможни интересни комбинации, които трябва да търсим.\n", "\n", - "![Изображение, показващо няколко слоя на свиване със слоеве за пулуване.](../../../../../translated_images/bg/cnn-pyramid.85915455759ef0ce.png)\n", + "![Изображение, показващо няколко слоя на свиване със слоеве за пулуване.](../../../../../translated_images/bg/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "Поради намаляването на пространствените размери и увеличаването на размерите на характеристиките/филтрите, тази архитектура се нарича също **пирамидална архитектура**.\n" ] diff --git a/translations/bg/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/bg/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index a8f0c6d6..a44e2feb 100644 --- a/translations/bg/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/bg/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -360,7 +360,7 @@ "\n", "Така, в типичен CNN има няколко конволюционни слоя, със слоеве за пуллинг между тях, за да се намалят размерите на изображението. Също така увеличаваме броя на филтрите, защото с напредването на моделите има повече възможни интересни комбинации, които трябва да търсим.\n", "\n", - "![Изображение, показващо няколко конволюционни слоя със слоеве за пуллинг.](../../../../../translated_images/bg/cnn-pyramid.85915455759ef0ce.png)\n", + "![Изображение, показващо няколко конволюционни слоя със слоеве за пуллинг.](../../../../../translated_images/bg/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "Поради намаляването на пространствените размери и увеличаването на размерите на характеристиките/филтрите, тази архитектура се нарича **пирамидална архитектура**.\n" ] diff --git a/translations/bg/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/bg/lessons/4-ComputerVision/07-ConvNets/README.md index 85c8ab43..2106f2ae 100644 --- a/translations/bg/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/bg/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: За да извлечем шаблони, ще използваме понятието за **конволюционни филтри**. Както знаете, едно изображение се представя чрез 2D-матрица или 3D-тензор с цветова дълбочина. Прилагането на филтър означава, че вземаме сравнително малка матрица, наречена **ядро на филтъра**, и за всеки пиксел в оригиналното изображение изчисляваме претегленото средно с неговите съседни точки. Можем да си представим това като малък прозорец, който се плъзга по цялото изображение и осреднява всички пиксели според теглата в матрицата на ядрото на филтъра. -![Филтър за вертикални ръбове](../../../../../translated_images/bg/filter-vert.b7148390ca0bc356.png) | ![Филтър за хоризонтални ръбове](../../../../../translated_images/bg/filter-horiz.59b80ed4feb946ef.png) +![Филтър за вертикални ръбове](../../../../../translated_images/bg/filter-vert.b7148390ca0bc356.webp) | ![Филтър за хоризонтални ръбове](../../../../../translated_images/bg/filter-horiz.59b80ed4feb946ef.webp) ----|---- > Изображение от Дмитрий Сошников @@ -38,7 +38,7 @@ CO_OP_TRANSLATOR_METADATA: * Можем да проектираме мрежата така, че филтрите да се обучават автоматично * Можем да използваме същия подход, за да намираме шаблони в характеристики на високо ниво, а не само в оригиналното изображение. Така извличането на характеристики в CNN работи на йерархия от характеристики, започвайки от нискоуровневи комбинации от пиксели до по-високо ниво комбинации от части на изображението. -![Йерархично извличане на характеристики](../../../../../translated_images/bg/FeatureExtractionCNN.d9b456cbdae7cb64.png) +![Йерархично извличане на характеристики](../../../../../translated_images/bg/FeatureExtractionCNN.d9b456cbdae7cb64.webp) > Изображение от [статия на Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), базирано на [тяхното изследване](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ CO_OP_TRANSLATOR_METADATA: Като пример, нека разгледаме архитектурата на VGG-16, мрежа, която постигна 92.7% точност в класификацията на ImageNet (топ 5) през 2014 г.: -![Слоеве на ImageNet](../../../../../translated_images/bg/vgg-16-arch1.d901a5583b3a51ba.jpg) +![Слоеве на ImageNet](../../../../../translated_images/bg/vgg-16-arch1.d901a5583b3a51ba.webp) -![Пирамида на ImageNet](../../../../../translated_images/bg/vgg-16-arch.64ff2137f50dd49f.jpg) +![Пирамида на ImageNet](../../../../../translated_images/bg/vgg-16-arch.64ff2137f50dd49f.webp) > Изображение от [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/bg/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/bg/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 026e11a8..4ee84663 100644 --- a/translations/bg/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/bg/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ CO_OP_TRANSLATOR_METADATA: Ще използваме [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/), който съдържа изображения на 37 различни породи кучета и котки. -![Наборът от данни, с който ще работим](../../../../../../translated_images/bg/data.50b2a9d5484bdbf0.png) +![Наборът от данни, с който ще работим](../../../../../../translated_images/bg/data.50b2a9d5484bdbf0.webp) За да изтеглите набора от данни, използвайте този кодов фрагмент: diff --git a/translations/bg/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/bg/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index c5697fbf..d1ed45f1 100644 --- a/translations/bg/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/bg/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "За да визуализираме идеалната котка, ще започнем с изображение от случайни шумове и ще се опитаме да използваме техниката на оптимизация чрез градиентен спуск, за да коригираме изображението така, че мрежата да разпознае котка.\n", "\n", - "![Цикъл на оптимизация](../../../../../translated_images/bg/ideal-cat-loop.999fbb8ff306e044.png)\n", + "![Цикъл на оптимизация](../../../../../translated_images/bg/ideal-cat-loop.999fbb8ff306e044.webp)\n", "\n", "Ето нашето начално изображение:\n" ] diff --git a/translations/bg/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/bg/lessons/4-ComputerVision/08-TransferLearning/README.md index 256bf0c6..bb40ee64 100644 --- a/translations/bg/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/bg/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ CO_OP_TRANSLATOR_METADATA: Ето примерни характеристики, извлечени от снимка на котка от мрежата VGG-16: -![Характеристики, извлечени от VGG-16](../../../../../translated_images/bg/features.6291f9c7ba3a0b95.png) +![Характеристики, извлечени от VGG-16](../../../../../translated_images/bg/features.6291f9c7ba3a0b95.webp) ## Набор от данни за котки и кучета @@ -48,19 +48,19 @@ CO_OP_TRANSLATOR_METADATA: Един подход, който можем да използваме, е да започнем с произволно изображение и след това да използваме техниката **оптимизация чрез градиентен спуск**, за да го коригираме така, че мрежата да започне да мисли, че това е котка. -![Цикъл на оптимизация на изображение](../../../../../translated_images/bg/ideal-cat-loop.999fbb8ff306e044.png) +![Цикъл на оптимизация на изображение](../../../../../translated_images/bg/ideal-cat-loop.999fbb8ff306e044.webp) Ако направим това, ще получим нещо много подобно на случаен шум. Това е така, защото *има много начини мрежата да мисли, че входното изображение е котка*, включително такива, които визуално не изглеждат логични. Докато тези изображения съдържат много модели, типични за котка, няма нищо, което да ги ограничава да бъдат визуално разпознаваеми. За да подобрим резултата, можем да добавим друг термин към функцията за загуба, наречен **variation loss**. Това е метрика, която показва колко сходни са съседните пиксели на изображението. Минимизирането на variation loss прави изображението по-гладко и премахва шума, разкривайки по-визуално привлекателни модели. Ето пример за такива "идеални" изображения, които се класифицират като котка и като зебра с висока вероятност: -![Идеална котка](../../../../../translated_images/bg/ideal-cat.203dd4597643d6b0.png) | ![Идеална зебра](../../../../../translated_images/bg/ideal-zebra.7f70e8b54ee15a7a.png) +![Идеална котка](../../../../../translated_images/bg/ideal-cat.203dd4597643d6b0.webp) | ![Идеална зебра](../../../../../translated_images/bg/ideal-zebra.7f70e8b54ee15a7a.webp) -----|----- *Идеална котка* | *Идеална зебра* Подобен подход може да се използва за извършване на така наречените **адверсариални атаки** върху невронна мрежа. Да предположим, че искаме да заблудим невронната мрежа и да направим така, че куче да изглежда като котка. Ако вземем изображение на куче, което мрежата разпознава като куче, можем да го коригираме малко чрез оптимизация с градиентен спуск, докато мрежата започне да го класифицира като котка: -![Снимка на куче](../../../../../translated_images/bg/original-dog.8f68a67d2fe0911f.png) | ![Снимка на куче, класифицирано като котка](../../../../../translated_images/bg/adversarial-dog.d9fc7773b0142b89.png) +![Снимка на куче](../../../../../translated_images/bg/original-dog.8f68a67d2fe0911f.webp) | ![Снимка на куче, класифицирано като котка](../../../../../translated_images/bg/adversarial-dog.d9fc7773b0142b89.webp) -----|----- *Оригинална снимка на куче* | *Снимка на куче, класифицирано като котка* diff --git a/translations/bg/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/bg/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index 856984f0..d0f188d9 100644 --- a/translations/bg/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/bg/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "Тъй като обучаваме автоенкодера да улавя възможно най-много информация от оригиналното изображение за точно възстановяване, мрежата се опитва да намери най-доброто **вграждане** на входните изображения, за да улови тяхното значение.\n", "\n", - "![Диаграма на Автоенкодер](../../../../../translated_images/bg/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![Диаграма на Автоенкодер](../../../../../translated_images/bg/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "> Изображение от [Keras блог](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", diff --git a/translations/bg/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/bg/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index 25eb239c..885c12da 100644 --- a/translations/bg/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/bg/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "Тъй като тренираме автоенкодера да улавя колкото се може повече информация от оригиналното изображение за точно възстановяване, мрежата се опитва да намери най-доброто **вграждане** на входните изображения, за да улови тяхното значение.\n", "\n", - "![Диаграма на Автоенкодер](../../../../../translated_images/bg/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![Диаграма на Автоенкодер](../../../../../translated_images/bg/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "*Изображение от [Keras блог](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", diff --git a/translations/bg/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/bg/lessons/4-ComputerVision/09-Autoencoders/README.md index ac2343db..2e84439b 100644 --- a/translations/bg/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/bg/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Тъй като обучаваме автоенкодера да улавя максимално много информация от оригиналното изображение за точна реконструкция, мрежата се опитва да намери най-доброто **вграждане** на входните изображения, за да улови тяхното значение. -![Диаграма на автоенкодер](../../../../../translated_images/bg/autoencoder_schema.5e6fc9ad98a5eb61.jpg) +![Диаграма на автоенкодер](../../../../../translated_images/bg/autoencoder_schema.5e6fc9ad98a5eb61.webp) > Изображение от [Keras блог](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/bg/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/bg/lessons/4-ComputerVision/11-ObjectDetection/README.md index 10eaa9ec..f049447a 100644 --- a/translations/bg/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/bg/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: ## [Тест преди лекцията](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Разпознаване на обекти](../../../../../translated_images/bg/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) +![Разпознаване на обекти](../../../../../translated_images/bg/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp) > Изображение от [уебсайта на YOLO v2](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 2. Извършваме класификация на изображения върху всяка част. 3. Тези части, които водят до достатъчно висока активация, могат да се считат за съдържащи търсения обект. -![Наивно разпознаване на обекти](../../../../../translated_images/bg/naive-detection.e7f1ba220ccd08c6.png) +![Наивно разпознаване на обекти](../../../../../translated_images/bg/naive-detection.e7f1ba220ccd08c6.webp) > *Изображение от [тетрадката с упражнения](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ CO_OP_TRANSLATOR_METADATA: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 класа * [COCO](http://cocodataset.org/#home) - Общи обекти в контекст. 80 класа, рамки и маски за сегментация -![COCO](../../../../../translated_images/bg/coco-examples.71bc60380fa6cceb.jpg) +![COCO](../../../../../translated_images/bg/coco-examples.71bc60380fa6cceb.webp) ## Метрики за разпознаване на обекти @@ -50,7 +50,7 @@ CO_OP_TRANSLATOR_METADATA: Докато за класификация на изображения е лесно да се измери колко добре се представя алгоритъмът, за разпознаване на обекти трябва да измерим както коректността на класа, така и прецизността на местоположението на предвидената рамка. За последното използваме така наречената **Пресечна площ спрямо обединение** (IoU), която измерва колко добре се припокриват две рамки (или две произволни области). -![IoU](../../../../../translated_images/bg/iou_equation.9a4751d40fff4e11.png) +![IoU](../../../../../translated_images/bg/iou_equation.9a4751d40fff4e11.webp) > *Фигура 2 от [този отличен блог пост за IoU](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ $$ [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) използва [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf), за да генерира йерархична структура от региони ROI, които след това се обработват чрез CNN екстрактори на характеристики и SVM-класификатори, за да се определи класът на обекта, и линейна регресия за определяне на координатите на *рамката*. [Официална статия](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/bg/rcnn1.cae407020dfb1d1f.png) +![RCNN](../../../../../translated_images/bg/rcnn1.cae407020dfb1d1f.webp) > *Изображение от van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/bg/rcnn2.2d9530bb83516484.png) +![RCNN-1](../../../../../translated_images/bg/rcnn2.2d9530bb83516484.webp) > *Изображения от [този блог](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e) @@ -110,7 +110,7 @@ $$ Този подход е подобен на R-CNN, но регионите се определят след прилагане на слоевете за конволюция. -![FRCNN](../../../../../translated_images/bg/f-rcnn.3cda6d9bb4188875.png) +![FRCNN](../../../../../translated_images/bg/f-rcnn.3cda6d9bb4188875.webp) > Изображение от [официалната статия](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ $$ Основната идея на този подход е да се използва невронна мрежа за предсказване на ROI - така наречената *Мрежа за предложения на региони*. [Статия](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/bg/faster-rcnn.8d46c099b87ef30a.png) +![FasterRCNN](../../../../../translated_images/bg/faster-rcnn.8d46c099b87ef30a.webp) > Изображение от [официалната статия](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ $$ 2. Характеристиките се обработват от **Position-Sensitive Score Map**. Всеки обект от $C$ класове се разделя на $k\times k$ региони, и обучаваме мрежата да предсказва части от обекти. 3. За всяка част от $k\times k$ региони всички мрежи гласуват за класовете на обектите, и класът с максимален брой гласове се избира. -![r-fcn image](../../../../../translated_images/bg/r-fcn.13eb88158b99a3da.png) +![r-fcn image](../../../../../translated_images/bg/r-fcn.13eb88158b99a3da.webp) > Изображение от [официалната статия](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO е алгоритъм за разпознаване в реално вре * Изображението се разделя на $S\times S$ региони. * За всеки регион **CNN** предсказва $n$ възможни обекти, координати на *рамката* и *увереност*=*вероятност* * IoU. - ![YOLO](../../../../../translated_images/bg/yolo.a2648ec82ee8bb4e.png) + ![YOLO](../../../../../translated_images/bg/yolo.a2648ec82ee8bb4e.webp) > Изображение от [официалната статия](https://arxiv.org/abs/1506.02640) diff --git a/translations/bg/lessons/4-ComputerVision/README.md b/translations/bg/lessons/4-ComputerVision/README.md index 26592e12..bb85666f 100644 --- a/translations/bg/lessons/4-ComputerVision/README.md +++ b/translations/bg/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Компютърно зрение -![Обобщение на съдържанието за компютърно зрение в рисунка](../../../../translated_images/bg/ai-computervision.6506ebebac3fbf76.png) +![Обобщение на съдържанието за компютърно зрение в рисунка](../../../../translated_images/bg/ai-computervision.6506ebebac3fbf76.webp) В тази секция ще научим за: diff --git a/translations/bg/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/bg/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index 94ec4eaf..bb467da2 100644 --- a/translations/bg/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/bg/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**Чанта с думи** (BoW) е най-често използваното традиционно векторно представяне. Всяка дума е свързана с индекс във вектора, а елементът на вектора съдържа броя на срещанията на дадена дума в конкретен документ.\n", "\n", - "![Изображение, показващо как представянето чрез \"чанта с думи\" се съхранява в паметта.](../../../../../translated_images/bg/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![Изображение, показващо как представянето чрез \"чанта с думи\" се съхранява в паметта.](../../../../../translated_images/bg/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Note**: Можете също да мислите за BoW като за сума от всички едноразрядно кодирани вектори за отделните думи в текста.\n", "\n", diff --git a/translations/bg/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/bg/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index 259d7dca..3f6a50e6 100644 --- a/translations/bg/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/bg/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**Чанта с думи** (BoW) е най-простото за разбиране традиционно представяне на вектор. Всяка дума е свързана с индекс във вектора, а елементът на вектора съдържа броя на срещанията на всяка дума в даден документ.\n", "\n", - "![Изображение, показващо как представянето чрез чанта с думи се съхранява в паметта.](../../../../../translated_images/bg/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![Изображение, показващо как представянето чрез чанта с думи се съхранява в паметта.](../../../../../translated_images/bg/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Note**: Можете също да мислите за BoW като сума от всички едно-горещо-кодирани вектори за отделните думи в текста.\n", "\n", diff --git a/translations/bg/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/bg/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 4153e266..14073c94 100644 --- a/translations/bg/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/bg/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "Използвайки слой за вграждане като първи слой в нашата мрежа, можем да преминем от модел на чанта с думи към модел на **чанта с вграждания**, където първо преобразуваме всяка дума в текста в съответното вграждане, а след това изчисляваме някаква агрегатна функция върху всички тези вграждания, като например `sum`, `average` или `max`.\n", "\n", - "![Изображение, показващо класификатор с вграждане за пет последователни думи.](../../../../../translated_images/bg/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![Изображение, показващо класификатор с вграждане за пет последователни думи.](../../../../../translated_images/bg/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "Нашата невронна мрежа за класификация ще започне със слой за вграждане, след това слой за агрегиране и линеен класификатор върху него:\n" ] @@ -176,7 +176,7 @@ "\n", "В предишната архитектура трябваше да запълним всички последователности до еднаква дължина, за да ги включим в минипартида. Това не е най-ефективният начин за представяне на последователности с променлива дължина - друг подход би бил използването на **вектор на отместванията**, който съдържа отместванията на всички последователности, съхранени в един голям вектор.\n", "\n", - "![Изображение, показващо представяне на последователност с отмествания](../../../../../translated_images/bg/offset-sequence-representation.eb73fcefb29b46ee.png)\n", + "![Изображение, показващо представяне на последователност с отмествания](../../../../../translated_images/bg/offset-sequence-representation.eb73fcefb29b46ee.webp)\n", "\n", "> **Note**: На изображението по-горе е показана последователност от символи, но в нашия пример работим с последователности от думи. Въпреки това, основният принцип на представяне на последователности с вектор на отмествания остава същият.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW е по-бърз, докато скип-грам е по-бавен, но се справя по-добре с представянето на редки думи.\n", "\n", - "![Изображение, показващо алгоритмите CBoW и Skip-Gram за преобразуване на думи във вектори.](../../../../../translated_images/bg/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![Изображение, показващо алгоритмите CBoW и Skip-Gram за преобразуване на думи във вектори.](../../../../../translated_images/bg/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "За да експериментираме с вграждания word2vec, предварително обучени върху набора от данни Google News, можем да използваме библиотеката **gensim**. По-долу намираме думите, които са най-близки до 'neural'.\n", "\n", diff --git a/translations/bg/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/bg/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index b06a13ef..d9b018fb 100644 --- a/translations/bg/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/bg/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "Като използваме embedding слой като първи слой в нашата мрежа, можем да преминем от модел bag-of-words към модел **embedding bag**, където първо преобразуваме всяка дума в текста в съответния embedding, а след това изчисляваме някаква агрегираща функция върху всички тези embeddings, като например `sum`, `average` или `max`.\n", "\n", - "![Изображение, показващо embedding класификатор за пет последователни думи.](../../../../../translated_images/bg/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![Изображение, показващо embedding класификатор за пет последователни думи.](../../../../../translated_images/bg/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "Нашата невронна мрежа за класификация се състои от следните слоеве:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW е по-бърз, докато скип-грам е по-бавен, но се справя по-добре с представянето на редки думи.\n", "\n", - "![Изображение, показващо алгоритмите CBoW и Skip-Gram за преобразуване на думи във вектори.](../../../../../translated_images/bg/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![Изображение, показващо алгоритмите CBoW и Skip-Gram за преобразуване на думи във вектори.](../../../../../translated_images/bg/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "За да експериментираме с вграждането Word2Vec, предварително обучено върху набора от данни Google News, можем да използваме библиотеката **gensim**. По-долу намираме думите, които са най-близки до 'neural'.\n", "\n", diff --git a/translations/bg/lessons/5-NLP/14-Embeddings/README.md b/translations/bg/lessons/5-NLP/14-Embeddings/README.md index dc91dfdf..71cf5c8f 100644 --- a/translations/bg/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/bg/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Използвайки слой за вграждане като първи слой в нашата мрежа за класификация, можем да преминем от модел на чанта от думи към модел на **чанта от вграждания**, където първо преобразуваме всяка дума в текста в съответното вграждане и след това изчисляваме някаква агрегираща функция върху всички тези вграждания, като `sum`, `average` или `max`. -![Изображение, показващо класификатор с вграждания за пет думи от последователност.](../../../../../translated_images/bg/embedding-classifier-example.b77f021a7ee67eee.png) +![Изображение, показващо класификатор с вграждания за пет думи от последователност.](../../../../../translated_images/bg/embedding-classifier-example.b77f021a7ee67eee.webp) > Изображение от автора @@ -40,7 +40,7 @@ CO_OP_TRANSLATOR_METADATA: CBoW е по-бърз, докато скип-грам е по-бавен, но се справя по-добре с представянето на редки думи. -![Изображение, показващо алгоритмите CBoW и Skip-Gram за преобразуване на думи във вектори.](../../../../../translated_images/bg/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![Изображение, показващо алгоритмите CBoW и Skip-Gram за преобразуване на думи във вектори.](../../../../../translated_images/bg/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Изображение от [тази статия](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/bg/lessons/5-NLP/15-LanguageModeling/README.md b/translations/bg/lessons/5-NLP/15-LanguageModeling/README.md index 24ff04a5..61ecb9b8 100644 --- a/translations/bg/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/bg/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: * **Непрекъсната торба с думи** (CBoW), когато предсказваме средния токен $W_0$ в последователност от токени $W_{-N}$, ..., $W_N$. * **Skip-gram**, където предсказваме набор от съседни токени {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} от средния токен $W_0$. -![изображение от статия за преобразуване на думи във вектори](../../../../../translated_images/bg/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![изображение от статия за преобразуване на думи във вектори](../../../../../translated_images/bg/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Изображение от [тази статия](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/bg/lessons/5-NLP/16-RNN/README.md b/translations/bg/lessons/5-NLP/16-RNN/README.md index c41a9c47..2020479f 100644 --- a/translations/bg/lessons/5-NLP/16-RNN/README.md +++ b/translations/bg/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: За да уловим значението на текстовата последователност, трябва да използваме друга архитектура на невронна мрежа, наречена **рекурентна невронна мрежа** или RNN. В RNN подаваме изречението през мрежата символ по символ, а мрежата произвежда някакво **състояние**, което след това подаваме отново на мрежата заедно със следващия символ. -![RNN](../../../../../translated_images/bg/rnn.27f5c29c53d727b5.png) +![RNN](../../../../../translated_images/bg/rnn.27f5c29c53d727b5.webp) > Изображение от автора @@ -61,7 +61,7 @@ LSTM мрежата е организирана по начин, подобен Рекурентната мрежа, независимо дали е еднопосочна или двунаправлена, улавя определени модели в рамките на последователността и може да ги съхранява в състоянието или да ги предава като изход. Както при конволюционните мрежи, можем да изградим друг рекурентен слой върху първия, за да уловим модели на по-високо ниво и да изградим от модели на ниско ниво, извлечени от първия слой. Това ни води до понятието за **многослойна RNN**, която се състои от две или повече рекурентни мрежи, където изходът на предишния слой се подава на следващия слой като вход. -![Изображение, показващо многослойна LSTM RNN](../../../../../translated_images/bg/multi-layer-lstm.dd975e29bb2a59fe.jpg) +![Изображение, показващо многослойна LSTM RNN](../../../../../translated_images/bg/multi-layer-lstm.dd975e29bb2a59fe.webp) *Изображение от [този чудесен пост](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) от Фернандо Лопес* diff --git a/translations/bg/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/bg/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index 8ac33881..1a844b09 100644 --- a/translations/bg/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/bg/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "Рекурентната мрежа, независимо дали е еднопосочна или двунаправлена, улавя определени модели в рамките на последователността и може да ги съхранява в скрития вектор или да ги предава към изхода. Както при конволюционните мрежи, можем да изградим друг рекурентен слой върху първия, за да уловим модели на по-високо ниво, изградени от модели на ниско ниво, извлечени от първия слой. Това ни води до понятието **многослойна RNN**, която се състои от две или повече рекурентни мрежи, където изходът на предишния слой се подава като вход към следващия слой.\n", "\n", - "![Изображение, показващо многослойна дългосрочно-краткосрочна памет RNN](../../../../../translated_images/bg/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![Изображение, показващо многослойна дългосрочно-краткосрочна памет RNN](../../../../../translated_images/bg/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*Снимка от [този чудесен пост](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) от Фернандо Лопес*\n", "\n", diff --git a/translations/bg/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/bg/lessons/5-NLP/16-RNN/RNNTF.ipynb index 5420fa4d..5091b902 100644 --- a/translations/bg/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/bg/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "За да уловим значението на текстова последователност, ще използваме архитектура на невронна мрежа, наречена **рекурентна невронна мрежа** или RNN. Когато използваме RNN, подаваме изречението си през мрежата, една по една дума (токен), и мрежата произвежда някакво **състояние**, което след това подаваме отново на мрежата заедно със следващия токен.\n", "\n", - "![Изображение, показващо пример за генериране с рекурентна невронна мрежа.](../../../../../translated_images/bg/rnn.27f5c29c53d727b5.png)\n", + "![Изображение, показващо пример за генериране с рекурентна невронна мрежа.](../../../../../translated_images/bg/rnn.27f5c29c53d727b5.webp)\n", "\n", "Дадена входна последователност от токени $X_0,\\dots,X_n$, RNN създава последователност от блокове на невронната мрежа и обучава тази последователност от край до край, използвайки обратно разпространение. Всеки блок на мрежата приема двойка $(X_i,S_i)$ като вход и произвежда $S_{i+1}$ като резултат. Финалното състояние $S_n$ или изходът $Y_n$ се подава към линеен класификатор, за да се произведе резултатът. Всички блокове на мрежата споделят едни и същи тегла и се обучават от край до край с един цикъл на обратно разпространение.\n", "\n", @@ -369,7 +369,7 @@ "\n", "Рекурентните мрежи, еднопосочни или двунасочни, улавят модели в рамките на последователността и ги съхраняват в състояния или ги връщат като изход. Както при конволюционните мрежи, можем да изградим друг рекурентен слой след първия, за да уловим модели на по-високо ниво, изградени от модели на по-ниско ниво, извлечени от първия слой. Това ни води до понятието за **многослойна RNN**, която се състои от две или повече рекурентни мрежи, където изходът на предишния слой се предава на следващия слой като вход.\n", "\n", - "![Изображение, показващо многослойна дългосрочно-краткосрочна памет RNN](../../../../../translated_images/bg/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![Изображение, показващо многослойна дългосрочно-краткосрочна памет RNN](../../../../../translated_images/bg/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*Снимка от [този чудесен пост](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) от Фернандо Лопес.*\n", "\n", diff --git a/translations/bg/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/bg/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index 5eed8380..d604707d 100644 --- a/translations/bg/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/bg/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "Начинът, по който ще обучим RNN да генерира текст, е следният. На всяка стъпка ще вземем последователност от символи с дължина `nchars` и ще помолим мрежата да генерира следващия изходен символ за всеки входен символ:\n", "\n", - "![Изображение, показващо пример за генериране на думата 'HELLO' от RNN.](../../../../../translated_images/bg/rnn-generate.56c54afb52f9781d.png)\n", + "![Изображение, показващо пример за генериране на думата 'HELLO' от RNN.](../../../../../translated_images/bg/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "В зависимост от конкретния сценарий, може да искаме да включим и някои специални символи, като например *край на последователността* ``. В нашия случай просто искаме да обучим мрежата за безкрайно генериране на текст, затова ще фиксираме размера на всяка последователност да бъде равен на `nchars` токени. Следователно, всеки тренировъчен пример ще се състои от `nchars` входове и `nchars` изходи (които са входната последователност, изместена с един символ наляво). Минипартидата ще се състои от няколко такива последователности.\n", "\n", diff --git a/translations/bg/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/bg/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index 086ebd56..dfc6f6c2 100644 --- a/translations/bg/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/bg/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -113,7 +113,7 @@ "\n", "Начинът, по който ще обучим RNN да генерира новинарски заглавия, е следният. На всяка стъпка ще вземем едно заглавие, което ще бъде подадено в RNN, и за всеки входен символ ще поискаме от мрежата да генерира следващия изходен символ:\n", "\n", - "![Изображение, показващо пример за генериране на думата 'HELLO' с RNN.](../../../../../translated_images/bg/rnn-generate.56c54afb52f9781d.png)\n", + "![Изображение, показващо пример за генериране на думата 'HELLO' с RNN.](../../../../../translated_images/bg/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "За последния символ от нашата последователност ще поискаме от мрежата да генерира токен ``.\n", "\n", diff --git a/translations/bg/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/bg/lessons/5-NLP/17-GenerativeNetworks/README.md index 4fb542d0..d893a416 100644 --- a/translations/bg/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/bg/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Това позволява различни невронни архитектури, които са показани на изображението по-долу: -![Изображение, показващо общи модели на рекурентни невронни мрежи.](../../../../../translated_images/bg/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Изображение, показващо общи модели на рекурентни невронни мрежи.](../../../../../translated_images/bg/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > Изображение от блог пост [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) от [Андрей Карпати](http://karpathy.github.io/) @@ -32,7 +32,7 @@ CO_OP_TRANSLATOR_METADATA: Ще обучим тази RNN да генерира текст стъпка по стъпка. На всяка стъпка ще вземем последователност от символи с дължина `nchars` и ще помолим мрежата да генерира следващия изходен символ за всеки входен символ: -![Изображение, показващо пример за генериране на думата 'HELLO' с RNN.](../../../../../translated_images/bg/rnn-generate.56c54afb52f9781d.png) +![Изображение, показващо пример за генериране на думата 'HELLO' с RNN.](../../../../../translated_images/bg/rnn-generate.56c54afb52f9781d.webp) При генериране на текст (по време на инференция) започваме с някакъв **подсказка**, която се предава през RNN клетките, за да генерира междинното си състояние, и след това от това състояние започва генерирането. Генерираме един символ наведнъж и предаваме състоянието и генерирания символ на друга RNN клетка, за да генерира следващия, докато генерираме достатъчно символи. diff --git a/translations/bg/lessons/5-NLP/18-Transformers/README.md b/translations/bg/lessons/5-NLP/18-Transformers/README.md index c2eb612f..a3234d7c 100644 --- a/translations/bg/lessons/5-NLP/18-Transformers/README.md +++ b/translations/bg/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ CO_OP_TRANSLATOR_METADATA: **Механизмите за внимание** предоставят начин за претегляне на контекстуалното влияние на всеки входен вектор върху всяка прогноза на изхода на RNN. Това се реализира чрез създаване на преки връзки между междинните състояния на входния RNN и изходния RNN. По този начин, когато генерираме изходния символ yt, ще вземем предвид всички скрити състояния на входа hi, с различни теглови коефициенти αt,i. -![Изображение, показващо модел кодировач/декодировач с добавен слой за внимание](../../../../../translated_images/bg/encoder-decoder-attention.7a726296894fb567.png) +![Изображение, показващо модел кодировач/декодировач с добавен слой за внимание](../../../../../translated_images/bg/encoder-decoder-attention.7a726296894fb567.webp) > Моделът кодировач-декодировач с механизъм за добавено внимание в [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), цитиран от [този блог пост](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) Матрицата за внимание {αi,j} представлява степента, до която определени входни думи играят роля в генерирането на дадена дума в изходната последователност. По-долу е пример за такава матрица: -![Изображение, показващо пример за подравняване, намерено от RNNsearch-50, взето от Bahdanau - arviz.org](../../../../../translated_images/bg/bahdanau-fig3.09ba2d37f202a6af.png) +![Изображение, показващо пример за подравняване, намерено от RNNsearch-50, взето от Bahdanau - arviz.org](../../../../../translated_images/bg/bahdanau-fig3.09ba2d37f202a6af.webp) > Фигура от [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Фиг.3) @@ -66,7 +66,7 @@ CO_OP_TRANSLATOR_METADATA: Следващата стъпка е да уловим някои модели в рамките на нашата последователност. За да направят това, трансформерите използват механизъм за **самовнимание**, който по същество е внимание, приложено към една и съща последователност като вход и изход. Прилагането на самовнимание ни позволява да вземем предвид **контекста** в рамките на изречението и да видим кои думи са взаимосвързани. Например, това ни позволява да видим кои думи се отнасят до кореференции, като *it*, и също така да вземем контекста предвид: -![](../../../../../translated_images/bg/CoreferenceResolution.861924d6d384a7d6.png) +![](../../../../../translated_images/bg/CoreferenceResolution.861924d6d384a7d6.webp) > Изображение от [Блогът на Google](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ CO_OP_TRANSLATOR_METADATA: **BERT** (Bidirectional Encoder Representations from Transformers) е много голяма многослойна трансформерна мрежа с 12 слоя за *BERT-base* и 24 за *BERT-large*. Моделът първо се предварително обучава върху голям корпус от текстови данни (WikiPedia + книги) чрез неуправляемо обучение (предсказване на маскирани думи в изречение). По време на предварителното обучение моделът усвоява значителни нива на разбиране на езика, които след това могат да бъдат използвани с други набори от данни чрез фина настройка. Този процес се нарича **трансферно обучение**. -![изображение от http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/bg/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) +![изображение от http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/bg/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > Изображение [източник](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/bg/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/bg/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index df1136ba..392e6f4e 100644 --- a/translations/bg/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/bg/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**Механизмите на вниманието** предоставят начин за претегляне на контекстуалното влияние на всеки входен вектор върху всяка изходна прогноза на RNN. Това се реализира чрез създаване на преки връзки между междинните състояния на входната RNN и изходната RNN. По този начин, при генериране на изходен символ $y_t$, ще вземем предвид всички входни скрити състояния $h_i$, с различни теглови коефициенти $\\alpha_{t,i}$.\n", "\n", - "![Изображение, показващо модел encoder/decoder с добавен слой за внимание](../../../../../translated_images/bg/encoder-decoder-attention.7a726296894fb567.png)\n", + "![Изображение, показващо модел encoder/decoder с добавен слой за внимание](../../../../../translated_images/bg/encoder-decoder-attention.7a726296894fb567.webp)\n", "*Моделът encoder-decoder с механизъм за добавено внимание в [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), цитиран от [този блог пост](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Матрицата на вниманието $\\{\\alpha_{i,j}\\}$ представлява степента, до която определени входни думи участват в генерирането на дадена дума в изходната последователност. По-долу е даден пример за такава матрица:\n", "\n", - "![Изображение, показващо пример за подравняване, намерено от RNNsearch-50, взето от Bahdanau - arviz.org](../../../../../translated_images/bg/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![Изображение, показващо пример за подравняване, намерено от RNNsearch-50, взето от Bahdanau - arviz.org](../../../../../translated_images/bg/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*Фигура, взета от [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Фиг.3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) е много голяма многослойна трансформерна мрежа с 12 слоя за *BERT-base* и 24 за *BERT-large*. Моделът първо се предварително обучава върху голям корпус от текстови данни (Wikipedia + книги) с помощта на неконтролирано обучение (предсказване на маскирани думи в изречение). По време на предварителното обучение моделът усвоява значително ниво на езиково разбиране, което след това може да бъде използвано с други набори от данни чрез фина настройка. Този процес се нарича **трансферно обучение**.\n", "\n", - "![изображение от http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/bg/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![изображение от http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/bg/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "Съществуват много вариации на трансформерните архитектури, включително BERT, DistilBERT, BigBird, OpenGPT3 и други, които могат да бъдат фино настроени. Пакетът [HuggingFace](https://github.com/huggingface/) предоставя хранилище за обучение на много от тези архитектури с PyTorch.\n", "\n", diff --git a/translations/bg/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/bg/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index fcec9e21..d9d069fa 100644 --- a/translations/bg/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/bg/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**Механизмите на внимание** предоставят начин за претегляне на контекстуалното влияние на всеки входен вектор върху всяка изходна прогноза на RNN. Това се реализира чрез създаване на преки връзки между междинните състояния на входната RNN и изходната RNN. По този начин, при генериране на изходен символ $y_t$, ще вземем предвид всички входни скрити състояния $h_i$, с различни теглови коефициенти $\\alpha_{t,i}$.\n", "\n", - "![Изображение, показващо модел енкодер/декодер с добавен слой за внимание](../../../../../translated_images/bg/encoder-decoder-attention.7a726296894fb567.png)\n", + "![Изображение, показващо модел енкодер/декодер с добавен слой за внимание](../../../../../translated_images/bg/encoder-decoder-attention.7a726296894fb567.webp)\n", "*Моделът енкодер-декодер с механизъм за добавено внимание в [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), цитиран от [този блог пост](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Матрицата на внимание $\\{\\alpha_{i,j}\\}$ представлява степента, до която определени входни думи играят роля в генерирането на дадена дума в изходната последователност. По-долу е даден пример за такава матрица:\n", "\n", - "![Изображение, показващо пример за подравняване, намерено от RNNsearch-50, взето от Bahdanau - arviz.org](../../../../../translated_images/bg/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![Изображение, показващо пример за подравняване, намерено от RNNsearch-50, взето от Bahdanau - arviz.org](../../../../../translated_images/bg/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*Фигура, взета от [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Фиг.3)*\n", "\n", @@ -225,7 +225,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) е много голяма многослойна трансформаторна мрежа с 12 слоя за *BERT-base* и 24 за *BERT-large*. Моделът първо се предварително обучава върху голям корпус от текстови данни (WikiPedia + книги) чрез обучение без надзор (предсказване на маскирани думи в изречение). По време на предварителното обучение моделът усвоява значително ниво на езиково разбиране, което след това може да бъде използвано с други набори от данни чрез фина настройка. Този процес се нарича **трансферно обучение**.\n", "\n", - "![картина от http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/bg/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![картина от http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/bg/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "Съществуват много вариации на трансформаторни архитектури, включително BERT, DistilBERT, BigBird, OpenGPT3 и други, които могат да бъдат фино настроени.\n", "\n", diff --git a/translations/bg/lessons/5-NLP/19-NER/README.md b/translations/bg/lessons/5-NLP/19-NER/README.md index 311cc867..3ed16747 100644 --- a/translations/bg/lessons/5-NLP/19-NER/README.md +++ b/translations/bg/lessons/5-NLP/19-NER/README.md @@ -58,7 +58,7 @@ infant | O Тъй като трябва да изградим едно към едно съответствие между токените и класовете, можем да обучим **много към много** невронен мрежов модел от тази картина: -![Изображение, показващо общи архитектури на рекурентни невронни мрежи.](../../../../../translated_images/bg/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Изображение, показващо общи архитектури на рекурентни невронни мрежи.](../../../../../translated_images/bg/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > *Изображение от [този блог пост](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) от [Андрей Карпати](http://karpathy.github.io/). Моделите за класификация на токени за NER съответстват на най-дясната архитектура на мрежата на тази картина.* diff --git a/translations/bg/lessons/5-NLP/README.md b/translations/bg/lessons/5-NLP/README.md index 0db562c2..fa285625 100644 --- a/translations/bg/lessons/5-NLP/README.md +++ b/translations/bg/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Обработка на естествен език -![Обобщение на задачите в NLP в рисунка](../../../../translated_images/bg/ai-nlp.b22dcb8ca4707cea.png) +![Обобщение на задачите в NLP в рисунка](../../../../translated_images/bg/ai-nlp.b22dcb8ca4707cea.webp) В тази секция ще се фокусираме върху използването на невронни мрежи за решаване на задачи, свързани с **обработката на естествен език (NLP)**. Съществуват много NLP проблеми, които искаме компютрите да могат да решават: diff --git a/translations/bg/lessons/6-Other/23-MultiagentSystems/README.md b/translations/bg/lessons/6-Other/23-MultiagentSystems/README.md index bdfe761f..35d014f0 100644 --- a/translations/bg/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/bg/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ ask turtles [ След като отворите модела, ще бъдете отведени до основния екран на NetLogo. Ето примерен модел, който описва популацията на вълци и овце, при наличието на ограничени ресурси (трева). -![NetLogo Main Screen](../../../../../translated_images/bg/NetLogo-Main.32653711ec1a01b3.png) +![NetLogo Main Screen](../../../../../translated_images/bg/NetLogo-Main.32653711ec1a01b3.webp) > Екранна снимка от Дмитрий Сошников diff --git a/translations/bg/lessons/README.md b/translations/bg/lessons/README.md index c3e0d764..8558aca0 100644 --- a/translations/bg/lessons/README.md +++ b/translations/bg/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Преглед -![Преглед в рисунка](../../../translated_images/bg/ai-overview.0857791951d19500.png) +![Преглед в рисунка](../../../translated_images/bg/ai-overview.0857791951d19500.webp) > Рисунка от [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/bg/lessons/X-Extras/X1-MultiModal/README.md b/translations/bg/lessons/X-Extras/X1-MultiModal/README.md index 6379dcc3..12d4f391 100644 --- a/translations/bg/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/bg/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: Основната идея на CLIP е да може да сравнява текстови подсказки с изображение и да определя доколко изображението съответства на подсказката. -![Архитектура на CLIP](../../../../../translated_images/bg/clip-arch.b3dbf20b4e8ed8be.png) +![Архитектура на CLIP](../../../../../translated_images/bg/clip-arch.b3dbf20b4e8ed8be.webp) > *Снимка от [тази публикация в блог](https://openai.com/blog/clip/)* @@ -31,7 +31,7 @@ CO_OP_TRANSLATOR_METADATA: Да предположим, че трябва да класифицираме изображения между, например, котки, кучета и хора. В този случай можем да подадем на модела изображение и серия от текстови подсказки: "*снимка на котка*", "*снимка на куче*", "*снимка на човек*". В получения вектор от 3 вероятности просто трябва да изберем индекса с най-висока стойност. -![CLIP за класификация на изображения](../../../../../translated_images/bg/clip-class.3af42ef0b2b19369.png) +![CLIP за класификация на изображения](../../../../../translated_images/bg/clip-class.3af42ef0b2b19369.webp) > *Снимка от [тази публикация в блог](https://openai.com/blog/clip/)* @@ -55,13 +55,13 @@ CLIP може също да се използва за **генериране н Една от важните разлики между VQGAN и традиционните GAN е, че последните могат да произведат прилично изображение от всеки входен вектор, докато VQGAN е по-вероятно да произведе изображение, което не е кохерентно. Затова трябва допълнително да насочим процеса на създаване на изображението, което може да се направи с помощта на CLIP. -![Архитектура на VQGAN+CLIP](../../../../../translated_images/bg/vqgan.5027fe05051dfa31.png) +![Архитектура на VQGAN+CLIP](../../../../../translated_images/bg/vqgan.5027fe05051dfa31.webp) За да генерираме изображение, съответстващо на текстова подсказка, започваме с някакъв случаен вектор за кодиране, който се подава през VQGAN, за да се произведе изображение. След това CLIP се използва за създаване на функция на загуба, която показва доколко изображението съответства на текстовата подсказка. Целта е да минимизираме тази загуба, използвайки обратна пропагация за настройка на параметрите на входния вектор. Отлична библиотека, която реализира VQGAN+CLIP, е [Pixray](http://github.com/pixray/pixray). -![Снимка, генерирана от Pixray](../../../../../translated_images/bg/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Снимка, генерирана от Pixray](../../../../../translated_images/bg/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Снимка, генерирана от Pixray](../../../../../translated_images/bg/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) +![Снимка, генерирана от Pixray](../../../../../translated_images/bg/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp) | ![Снимка, генерирана от Pixray](../../../../../translated_images/bg/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp) | ![Снимка, генерирана от Pixray](../../../../../translated_images/bg/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp) ----|----|---- Снимка, генерирана от подсказка *близък акварелен портрет на млад мъж учител по литература с книга* | Снимка, генерирана от подсказка *близък маслен портрет на млада жена учител по компютърни науки с компютър* | Снимка, генерирана от подсказка *близък маслен портрет на възрастен мъж учител по математика пред черна дъска* diff --git a/translations/bn/README.md b/translations/bn/README.md index 2c7a1c1e..7a611fd3 100644 --- a/translations/bn/README.md +++ b/translations/bn/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # আর্টিফিশিয়াল ইন্টেলিজেন্স ফর বিগিনার্স - একটি কারিকুলাম -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/bn/ai-overview.0857791951d19500.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/bn/ai-overview.0857791951d19500.webp)| |:---:| | AI For Beginners - _স্কেচনোট [@girlie_mac](https://twitter.com/girlie_mac) দ্বারা_ | diff --git a/translations/bn/lessons/1-Intro/README.md b/translations/bn/lessons/1-Intro/README.md index f6449e4b..fe1db6e1 100644 --- a/translations/bn/lessons/1-Intro/README.md +++ b/translations/bn/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # AI পরিচিতি -![AI পরিচিতির সামগ্রিক বিষয়বস্তুর একটি ডুডল](../../../../translated_images/bn/ai-intro.bf28d1ac4235881c.png) +![AI পরিচিতির সামগ্রিক বিষয়বস্তুর একটি ডুডল](../../../../translated_images/bn/ai-intro.bf28d1ac4235881c.webp) > স্কেচনোট: [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: মূলত, কম্পিউটার আবিষ্কার করেছিলেন [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage) সংখ্যা নিয়ে কাজ করার জন্য, একটি সুস্পষ্ট প্রক্রিয়া অনুসরণ করে - একটি অ্যালগরিদম। আধুনিক কম্পিউটার, যদিও ১৯শ শতাব্দীতে প্রস্তাবিত মূল মডেলের তুলনায় অনেক বেশি উন্নত, তবুও নিয়ন্ত্রিত গণনার একই ধারণা অনুসরণ করে। তাই, যদি আমরা জানি লক্ষ্য অর্জনের জন্য প্রয়োজনীয় সঠিক ধাপগুলোর ক্রম, তাহলে কম্পিউটারকে কিছু করতে প্রোগ্রাম করা সম্ভব। -![একজন ব্যক্তির ছবি](../../../../translated_images/bn/dsh_age.d212a30d4e54fb5f.png) +![একজন ব্যক্তির ছবি](../../../../translated_images/bn/dsh_age.d212a30d4e54fb5f.webp) > ছবি: [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ CO_OP_TRANSLATOR_METADATA: **[বুদ্ধিমত্তা](https://en.wikipedia.org/wiki/Intelligence)** শব্দটি নিয়ে কাজ করার সময় একটি সমস্যা হলো এই শব্দটির কোনো স্পষ্ট সংজ্ঞা নেই। কেউ যুক্তি করতে পারে যে বুদ্ধিমত্তা **অমূর্ত চিন্তা** বা **আত্ম-সচেতনতার** সাথে সংযুক্ত, কিন্তু আমরা এটি সঠিকভাবে সংজ্ঞায়িত করতে পারি না। -![একটি বিড়ালের ছবি](../../../../translated_images/bn/photo-cat.8c8e8fb760ffe457.jpg) +![একটি বিড়ালের ছবি](../../../../translated_images/bn/photo-cat.8c8e8fb760ffe457.webp) > [ছবি](https://unsplash.com/photos/75715CVEJhI): [Amber Kipp](https://unsplash.com/@sadmax) Unsplash থেকে @@ -98,13 +98,13 @@ AGI নিয়ে কথা বলার সময় আমাদের এমন > | ML সম্পর্কে কী? | | > |--------------|-----------| -> | কৃত্রিম বুদ্ধিমত্তার অংশ যা কম্পিউটারকে কিছু ডেটার ভিত্তিতে একটি সমস্যা সমাধানে শেখানোর উপর ভিত্তি করে তাকে **মেশিন লার্নিং** বলা হয়। আমরা এই কোর্সে ক্লাসিক্যাল মেশিন লার্নিং বিবেচনা করব না - আমরা আপনাকে একটি পৃথক [Machine Learning for Beginners](http://aka.ms/ml-beginners) কারিকুলামে রেফার করছি। | ![ML for Beginners](../../../../translated_images/bn/ml-for-beginners.9e4fed176fd5817d.png) | +> | কৃত্রিম বুদ্ধিমত্তার অংশ যা কম্পিউটারকে কিছু ডেটার ভিত্তিতে একটি সমস্যা সমাধানে শেখানোর উপর ভিত্তি করে তাকে **মেশিন লার্নিং** বলা হয়। আমরা এই কোর্সে ক্লাসিক্যাল মেশিন লার্নিং বিবেচনা করব না - আমরা আপনাকে একটি পৃথক [Machine Learning for Beginners](http://aka.ms/ml-beginners) কারিকুলামে রেফার করছি। | ![ML for Beginners](../../../../translated_images/bn/ml-for-beginners.9e4fed176fd5817d.webp) | ## AI এর সংক্ষিপ্ত ইতিহাস কৃত্রিম বুদ্ধিমত্তা একটি ক্ষেত্র হিসেবে শুরু হয়েছিল বিংশ শতাব্দীর মাঝামাঝি। প্রথমদিকে, প্রতীকী যুক্তি একটি প্রভাবশালী পদ্ধতি ছিল, এবং এটি কিছু গুরুত্বপূর্ণ সাফল্য অর্জন করেছিল, যেমন বিশেষজ্ঞ সিস্টেম – কম্পিউটার প্রোগ্রাম যা কিছু সীমিত সমস্যার ক্ষেত্রে একজন বিশেষজ্ঞের মতো কাজ করতে সক্ষম ছিল। তবে, শীঘ্রই এটি স্পষ্ট হয়ে যায় যে এই পদ্ধতি ভালোভাবে স্কেল করে না। একজন বিশেষজ্ঞ থেকে জ্ঞান বের করা, কম্পিউটারে উপস্থাপন করা, এবং সেই জ্ঞানভাণ্ডারকে সঠিক রাখা একটি অত্যন্ত জটিল কাজ এবং অনেক ক্ষেত্রে ব্যবহারিকভাবে খুব ব্যয়বহুল। এটি ১৯৭০-এর দশকে তথাকথিত [AI Winter](https://en.wikipedia.org/wiki/AI_winter) এর দিকে নিয়ে যায়। -AI এর সংক্ষিপ্ত ইতিহাস +AI এর সংক্ষিপ্ত ইতিহাস > ছবি: [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/bn/lessons/2-Symbolic/Animals.ipynb b/translations/bn/lessons/2-Symbolic/Animals.ipynb index 55977051..027c6051 100644 --- a/translations/bn/lessons/2-Symbolic/Animals.ipynb +++ b/translations/bn/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "এই উদাহরণে, আমরা একটি সহজ জ্ঞান-ভিত্তিক সিস্টেম বাস্তবায়ন করব যা কিছু শারীরিক বৈশিষ্ট্যের উপর ভিত্তি করে একটি প্রাণী নির্ধারণ করবে। সিস্টেমটি নিম্নলিখিত AND-OR গাছ দ্বারা উপস্থাপন করা যেতে পারে (এটি পুরো গাছের একটি অংশ, আমরা সহজেই আরও কিছু নিয়ম যোগ করতে পারি):\n", "\n", - "![](../../../../translated_images/bn/AND-OR-Tree.5592d2c70187f283.png)\n" + "![](../../../../translated_images/bn/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { diff --git a/translations/bn/lessons/2-Symbolic/README.md b/translations/bn/lessons/2-Symbolic/README.md index d90694b9..0e873aeb 100644 --- a/translations/bn/lessons/2-Symbolic/README.md +++ b/translations/bn/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # জ্ঞান উপস্থাপন এবং বিশেষজ্ঞ সিস্টেম -![সিম্বলিক AI বিষয়বস্তুর সারাংশ](../../../../translated_images/bn/ai-symbolic.715a30cb610411a6.png) +![সিম্বলিক AI বিষয়বস্তুর সারাংশ](../../../../translated_images/bn/ai-symbolic.715a30cb610411a6.webp) > স্কেচনোট [Tomomi Imura](https://twitter.com/girlie_mac) দ্বারা @@ -41,7 +41,7 @@ AI-এর প্রাথমিক দিনগুলোতে, বুদ্ধ তাই, **জ্ঞান উপস্থাপনের** সমস্যাটি হল কম্পিউটারের ভিতরে ডেটার আকারে জ্ঞান উপস্থাপন করার কার্যকর উপায় খুঁজে বের করা, যাতে এটি স্বয়ংক্রিয়ভাবে ব্যবহারযোগ্য হয়। এটি একটি স্পেকট্রামের মতো দেখা যেতে পারে: -![জ্ঞান উপস্থাপনের স্পেকট্রাম](../../../../translated_images/bn/knowledge-spectrum.b60df631852c0217.png) +![জ্ঞান উপস্থাপনের স্পেকট্রাম](../../../../translated_images/bn/knowledge-spectrum.b60df631852c0217.webp) > চিত্র [Dmitry Soshnikov](http://soshnikov.com) দ্বারা @@ -94,7 +94,7 @@ Block Syntax | Indent | | | সিম্বলিক AI-এর প্রাথমিক সাফল্যগুলোর মধ্যে একটি ছিল তথাকথিত **বিশেষজ্ঞ সিস্টেম** - কম্পিউটার সিস্টেম যা একটি সীমিত সমস্যার ক্ষেত্রে একজন বিশেষজ্ঞের মতো কাজ করার জন্য ডিজাইন করা হয়েছিল। এগুলো একটি **জ্ঞানভিত্তি** এবং একটি **ইনফারেন্স ইঞ্জিন** নিয়ে গঠিত ছিল যা এর উপর যুক্তি করত। -![মানব স্থাপত্য](../../../../translated_images/bn/arch-human.5d4d35f1bba3ab1c.png) | ![জ্ঞানভিত্তিক সিস্টেম](../../../../translated_images/bn/arch-kbs.3ec5c150b09fa8da.png) +![মানব স্থাপত্য](../../../../translated_images/bn/arch-human.5d4d35f1bba3ab1c.webp) | ![জ্ঞানভিত্তিক সিস্টেম](../../../../translated_images/bn/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ মানব স্নায়ুতন্ত্রের সরলীকৃত কাঠামো | জ্ঞানভিত্তিক সিস্টেমের স্থাপত্য @@ -106,7 +106,7 @@ Block Syntax | Indent | | | একটি উদাহরণ হিসেবে, চলুন একটি বিশেষজ্ঞ সিস্টেমের কথা বিবেচনা করি যা শারীরিক বৈশিষ্ট্যের উপর ভিত্তি করে একটি প্রাণী নির্ধারণ করে: -![AND-OR গাছ](../../../../translated_images/bn/AND-OR-Tree.5592d2c70187f283.png) +![AND-OR গাছ](../../../../translated_images/bn/AND-OR-Tree.5592d2c70187f283.webp) > চিত্র [Dmitry Soshnikov](http://soshnikov.com) দ্বারা diff --git a/translations/bn/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/bn/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index c1f6f3d1..46391787 100644 --- a/translations/bn/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/bn/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -1259,7 +1259,7 @@ "* প্রশিক্ষণ ক্ষতি কম - মডেল প্রশিক্ষণ ডেটাকে ভালোভাবে অনুমান করতে পারে, কারণ এর যথেষ্ট প্রকাশক্ষমতা রয়েছে।\n", "* যাচাইকরণ ক্ষতি প্রশিক্ষণ ক্ষতির তুলনায় অনেক বেশি হতে পারে এবং প্রশিক্ষণের সময় বৃদ্ধি পেতে পারে - কারণ মডেল প্রশিক্ষণ পয়েন্টগুলোকে \"মনে রাখে\" এবং \"সামগ্রিক চিত্র\" হারিয়ে ফেলে।\n", "\n", - "![ওভারফিটিং](../../../../../translated_images/bn/overfit.a0bd57f717c15769.png)\n", + "![ওভারফিটিং](../../../../../translated_images/bn/overfit.a0bd57f717c15769.webp)\n", "\n", "> এই ছবিতে, `x` প্রশিক্ষণ ডেটাকে নির্দেশ করে, `o` - যাচাইকরণ ডেটা। বামদিকে - লিনিয়ার মডেল (এক-লেয়ার), এটি ডেটার প্রকৃতিকে বেশ ভালোভাবে অনুমান করে। ডানদিকে - ওভারফিটেড মডেল, মডেল প্রশিক্ষণ ডেটাকে পুরোপুরি ভালোভাবে অনুমান করে, কিন্তু অন্য কোনো ডেটার ক্ষেত্রে অর্থহীন হয়ে যায় (যাচাইকরণ ত্রুটি খুব বেশি)।\n" ] diff --git a/translations/bn/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/bn/lessons/3-NeuralNetworks/05-Frameworks/README.md index 37f92b43..53ca9413 100644 --- a/translations/bn/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/bn/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ High-level API| [Keras](IntroKeras.ipynb) | *PyTorch Lightning* নিচের ৫টি বিন্দু (গ্রাফে `x` দ্বারা চিহ্নিত) আনুমানিক করার সমস্যাটি বিবেচনা করুন: -![linear](../../../../../translated_images/bn/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/bn/overfit2.131f5800ae10ca5e.jpg) +![linear](../../../../../translated_images/bn/overfit1.f24b71c6f652e59e.webp) | ![overfit](../../../../../translated_images/bn/overfit2.131f5800ae10ca5e.webp) -------------------------|-------------------------- **লিনিয়ার মডেল, ২টি প্যারামিটার** | **নন-লিনিয়ার মডেল, ৭টি প্যারামিটার** প্রশিক্ষণ ত্রুটি = ৫.৩ | প্রশিক্ষণ ত্রুটি = ০ @@ -79,7 +79,7 @@ High-level API| [Keras](IntroKeras.ipynb) | *PyTorch Lightning* উপরের গ্রাফ থেকে আপনি দেখতে পাচ্ছেন, ওভারফিটিং খুব কম প্রশিক্ষণ ত্রুটি এবং উচ্চ ভ্যালিডেশন ত্রুটি দ্বারা সনাক্ত করা যায়। সাধারণত প্রশিক্ষণের সময় আমরা দেখতে পাবো প্রশিক্ষণ এবং ভ্যালিডেশন ত্রুটি উভয়ই কমতে শুরু করে, এবং তারপর কোনো এক সময় ভ্যালিডেশন ত্রুটি কমা বন্ধ করে এবং বাড়তে শুরু করে। এটি ওভারফিটিংয়ের একটি চিহ্ন হবে এবং এটি নির্দেশ করবে যে আমাদের সম্ভবত এই সময়ে প্রশিক্ষণ বন্ধ করা উচিত (অথবা অন্তত মডেলের একটি স্ন্যাপশট নেওয়া উচিত)। -![overfitting](../../../../../translated_images/bn/Overfitting.408ad91cd90b4371.png) +![overfitting](../../../../../translated_images/bn/Overfitting.408ad91cd90b4371.webp) ## কিভাবে ওভারফিটিং প্রতিরোধ করবেন diff --git a/translations/bn/lessons/3-NeuralNetworks/README.md b/translations/bn/lessons/3-NeuralNetworks/README.md index 85692c04..0fe8128f 100644 --- a/translations/bn/lessons/3-NeuralNetworks/README.md +++ b/translations/bn/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # নিউরাল নেটওয়ার্কের পরিচিতি -![নিউরাল নেটওয়ার্ক বিষয়বস্তুর সারাংশ একটি ডুডলে](../../../../translated_images/bn/ai-neuralnetworks.1c687ae40bc86e83.png) +![নিউরাল নেটওয়ার্ক বিষয়বস্তুর সারাংশ একটি ডুডলে](../../../../translated_images/bn/ai-neuralnetworks.1c687ae40bc86e83.webp) যেমনটি আমরা পরিচিতিতে আলোচনা করেছি, বুদ্ধিমত্তা অর্জনের একটি উপায় হল একটি **কম্পিউটার মডেল** বা একটি **কৃত্রিম মস্তিষ্ক** প্রশিক্ষণ দেওয়া। ২০শ শতাব্দীর মাঝামাঝি থেকে গবেষকরা বিভিন্ন গাণিতিক মডেল চেষ্টা করেছেন, এবং সাম্প্রতিক বছরগুলোতে এই দিকটি অত্যন্ত সফল প্রমাণিত হয়েছে। মস্তিষ্কের এই ধরনের গাণিতিক মডেলগুলোকে **নিউরাল নেটওয়ার্ক** বলা হয়। @@ -36,13 +36,13 @@ CO_OP_TRANSLATOR_METADATA: জীববিজ্ঞান থেকে আমরা জানি যে আমাদের মস্তিষ্ক নিউরাল কোষ (নিউরন) নিয়ে গঠিত, প্রতিটি কোষের একাধিক "ইনপুট" (ডেনড্রাইট) এবং একটি "আউটপুট" (অ্যাক্সন) থাকে। ডেনড্রাইট এবং অ্যাক্সন উভয়ই বৈদ্যুতিক সংকেত পরিচালনা করতে পারে, এবং তাদের মধ্যে সংযোগগুলো — যেগুলো সিন্যাপস নামে পরিচিত — বিভিন্ন মাত্রার পরিবাহিতা প্রদর্শন করতে পারে, যা নিউরোট্রান্সমিটার দ্বারা নিয়ন্ত্রিত হয়। -![একটি নিউরনের মডেল](../../../../translated_images/bn/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![একটি কৃত্রিম নিউরনের মডেল](../../../../translated_images/bn/artneuron.1a5daa88d20ebe6f.png) +![একটি নিউরনের মডেল](../../../../translated_images/bn/synapse-wikipedia.ed20a9e4726ea1c6.webp) | ![একটি কৃত্রিম নিউরনের মডেল](../../../../translated_images/bn/artneuron.1a5daa88d20ebe6f.webp) ----|---- আসল নিউরন *([উইকিপিডিয়া থেকে ইমেজ](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg))* | কৃত্রিম নিউরন *(লেখকের তৈরি ইমেজ)* তাই, একটি নিউরনের সবচেয়ে সহজ গাণিতিক মডেলটি কয়েকটি ইনপুট X1, ..., XN এবং একটি আউটপুট Y, এবং একটি সিরিজ ওজন W1, ..., WN নিয়ে গঠিত। আউটপুটটি নিম্নরূপ গণনা করা হয়: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) যেখানে f হল কিছু অরৈখিক **অ্যাক্টিভেশন ফাংশন**। diff --git a/translations/bn/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/bn/lessons/4-ComputerVision/06-IntroCV/README.md index 567655c6..bab6ca62 100644 --- a/translations/bn/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/bn/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ OpenCV ব্যবহার করে ভিডিও ফ্রেম-বাই * **ব্রেইল বইয়ের একটি ছবির প্রি-প্রসেসিং**। আমরা দেখিয়েছি কীভাবে থ্রেশহোল্ডিং, ফিচার ডিটেকশন, পার্সপেক্টিভ ট্রান্সফরমেশন এবং NumPy ম্যানিপুলেশন ব্যবহার করে ব্রেইল প্রতীকগুলোকে আলাদা করা যায়, যা পরে নিউরাল নেটওয়ার্ক দ্বারা শ্রেণীবদ্ধ করা হবে। -![Braille Image](../../../../../translated_images/bn/braille.341962ff76b1bd70.jpeg) | ![Braille Image Pre-processed](../../../../../translated_images/bn/braille-result.46530fea020b03c7.png) | ![Braille Symbols](../../../../../translated_images/bn/braille-symbols.0159185ab69d5339.png) +![Braille Image](../../../../../translated_images/bn/braille.341962ff76b1bd70.webp) | ![Braille Image Pre-processed](../../../../../translated_images/bn/braille-result.46530fea020b03c7.webp) | ![Braille Symbols](../../../../../translated_images/bn/braille-symbols.0159185ab69d5339.webp) ----|-----|----- > ছবি [OpenCV.ipynb](OpenCV.ipynb) থেকে * **ভিডিওতে ফ্রেম ডিফারেন্স ব্যবহার করে গতিবিধি সনাক্তকরণ**। যদি ক্যামেরা স্থির থাকে, তাহলে ক্যামেরা ফিডের ফ্রেমগুলো একে অপরের সাথে বেশ মিল থাকবে। যেহেতু ফ্রেমগুলো অ্যারে হিসেবে উপস্থাপিত হয়, দুটি পরবর্তী ফ্রেমের জন্য সেই অ্যারেগুলোকে বিয়োগ করলেই আমরা পিক্সেল পার্থক্য পাব, যা স্থির ফ্রেমের জন্য কম হবে এবং ছবিতে উল্লেখযোগ্য গতিবিধি থাকলে বেশি হবে। -![Image of video frames and frame differences](../../../../../translated_images/bn/frame-difference.706f805491a0883c.png) +![Image of video frames and frame differences](../../../../../translated_images/bn/frame-difference.706f805491a0883c.webp) > ছবি [OpenCV.ipynb](OpenCV.ipynb) থেকে @@ -89,7 +89,7 @@ OpenCV ব্যবহার করে ভিডিও ফ্রেম-বাই - **ডেন্স অপটিক্যাল ফ্লো** প্রতিটি পিক্সেলের জন্য একটি ভেক্টর ক্ষেত্র গণনা করে যা দেখায় এটি কোথায় স্থানান্তরিত হচ্ছে। - **স্পার্স অপটিক্যাল ফ্লো** ছবিতে কিছু বৈশিষ্ট্যপূর্ণ বৈশিষ্ট্য (যেমন: প্রান্ত) গ্রহণ করে এবং ফ্রেম থেকে ফ্রেমে তাদের গতিপথ তৈরি করে। -![Image of Optical Flow](../../../../../translated_images/bn/optical.1f4a94464579a83a.png) +![Image of Optical Flow](../../../../../translated_images/bn/optical.1f4a94464579a83a.webp) > ছবি [OpenCV.ipynb](OpenCV.ipynb) থেকে diff --git a/translations/bn/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/bn/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 8301d9a5..4d88d3df 100644 --- a/translations/bn/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/bn/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 একটি নেটওয়ার্ক যা ২০১৪ সালে ImageNet টপ-৫ শ্রেণীবিন্যাসে ৯২.৭% সঠিকতা অর্জন করেছিল। এর স্তর কাঠামো নিম্নরূপ: -![ImageNet Layers](../../../../../translated_images/bn/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet Layers](../../../../../translated_images/bn/vgg-16-arch1.d901a5583b3a51ba.webp) যেমনটি আপনি দেখতে পাচ্ছেন, VGG একটি ঐতিহ্যবাহী পিরামিড আর্কিটেকচার অনুসরণ করে, যা কনভোলিউশন-পুলিং স্তরের একটি ক্রম। -![ImageNet Pyramid](../../../../../translated_images/bn/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet Pyramid](../../../../../translated_images/bn/vgg-16-arch.64ff2137f50dd49f.webp) > ছবি [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) থেকে diff --git a/translations/bn/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/bn/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index 7240ff12..4edc372e 100644 --- a/translations/bn/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/bn/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -260,7 +260,7 @@ "\n", "সুতরাং, একটি সাধারণ CNN-এ কয়েকটি কনভল্যুশনাল লেয়ার থাকবে, যার মধ্যে পুলিং লেয়ার থাকবে যা ছবির মাত্রা কমিয়ে দেবে। আমরা ফিল্টারের সংখ্যাও বাড়াবো, কারণ প্যাটার্ন যত উন্নত হয় - তত বেশি সম্ভাব্য আকর্ষণীয় সংমিশ্রণ থাকে যা আমাদের খুঁজে বের করতে হবে।\n", "\n", - "![কয়েকটি কনভল্যুশনাল লেয়ার এবং পুলিং লেয়ার দেখানো একটি চিত্র।](../../../../../translated_images/bn/cnn-pyramid.85915455759ef0ce.png)\n", + "![কয়েকটি কনভল্যুশনাল লেয়ার এবং পুলিং লেয়ার দেখানো একটি চিত্র।](../../../../../translated_images/bn/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "স্থানিক মাত্রা কমানো এবং ফিচার/ফিল্টারের মাত্রা বাড়ানোর কারণে, এই আর্কিটেকচারকে **পিরামিড আর্কিটেকচার**ও বলা হয়।\n" ] diff --git a/translations/bn/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/bn/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index 7ee73580..ff3830a6 100644 --- a/translations/bn/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/bn/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -358,7 +358,7 @@ "\n", "এইভাবে, একটি সাধারণ CNN-এ কয়েকটি কনভোলিউশনাল লেয়ার থাকে, যার মধ্যে পুলিং লেয়ারগুলো ছবির মাত্রা কমানোর জন্য ব্যবহৃত হয়। আমরা ফিল্টারের সংখ্যা বাড়িয়ে দিই, কারণ প্যাটার্নগুলো আরও উন্নত হওয়ার সাথে সাথে আরও বেশি সম্ভাব্য আকর্ষণীয় সংমিশ্রণ থাকে যা আমাদের খুঁজে বের করতে হয়।\n", "\n", - "![কনভোলিউশনাল লেয়ার এবং পুলিং লেয়ার নিয়ে একটি চিত্র।](../../../../../translated_images/bn/cnn-pyramid.85915455759ef0ce.png)\n", + "![কনভোলিউশনাল লেয়ার এবং পুলিং লেয়ার নিয়ে একটি চিত্র।](../../../../../translated_images/bn/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "স্থানিক মাত্রা কমানো এবং ফিচার/ফিল্টার মাত্রা বাড়ানোর কারণে, এই আর্কিটেকচারকে **পিরামিড আর্কিটেকচার** বলা হয়।\n" ] diff --git a/translations/bn/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/bn/lessons/4-ComputerVision/07-ConvNets/README.md index 59d75968..ea729d79 100644 --- a/translations/bn/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/bn/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: প্যাটার্ন বের করার জন্য, আমরা **কনভোলিউশনাল ফিল্টার** ধারণাটি ব্যবহার করব। আপনি জানেন, একটি ছবি 2D-ম্যাট্রিক্স বা রঙের গভীরতা সহ 3D-টেনসর দ্বারা উপস্থাপিত হয়। একটি ফিল্টার প্রয়োগ করার অর্থ হলো আমরা একটি তুলনামূলকভাবে ছোট **ফিল্টার কের্নেল** ম্যাট্রিক্স নিই, এবং মূল ছবির প্রতিটি পিক্সেলের জন্য আমরা প্রতিবেশী পয়েন্টগুলির সাথে ওজনযুক্ত গড় গণনা করি। আমরা এটি একটি ছোট জানালা হিসেবে দেখতে পারি যা পুরো ছবির উপর দিয়ে স্লাইড করে এবং ফিল্টার কের্নেল ম্যাট্রিক্সের ওজন অনুযায়ী সমস্ত পিক্সেল গড় করে। -![ভার্টিকাল এজ ফিল্টার](../../../../../translated_images/bn/filter-vert.b7148390ca0bc356.png) | ![হরিজন্টাল এজ ফিল্টার](../../../../../translated_images/bn/filter-horiz.59b80ed4feb946ef.png) +![ভার্টিকাল এজ ফিল্টার](../../../../../translated_images/bn/filter-vert.b7148390ca0bc356.webp) | ![হরিজন্টাল এজ ফিল্টার](../../../../../translated_images/bn/filter-horiz.59b80ed4feb946ef.webp) ----|---- > ছবি: দিমিত্রি সশনিকভ @@ -38,7 +38,7 @@ CNN যেভাবে কাজ করে তা নিম্নলিখিত * আমরা নেটওয়ার্কটিকে এমনভাবে ডিজাইন করতে পারি যাতে ফিল্টারগুলি স্বয়ংক্রিয়ভাবে প্রশিক্ষিত হয় * আমরা একই পদ্ধতি ব্যবহার করে উচ্চ-স্তরের বৈশিষ্ট্যগুলিতে প্যাটার্ন খুঁজে বের করতে পারি, শুধুমাত্র মূল ছবিতে নয়। সুতরাং CNN বৈশিষ্ট্য বের করার কাজ বৈশিষ্ট্যের একটি শ্রেণিবিন্যাসে কাজ করে, যা নিম্ন-স্তরের পিক্সেল সংমিশ্রণ থেকে শুরু করে ছবির অংশগুলির উচ্চ-স্তরের সংমিশ্রণে পৌঁছায়। -![হায়ারারকিকাল ফিচার এক্সট্রাকশন](../../../../../translated_images/bn/FeatureExtractionCNN.d9b456cbdae7cb64.png) +![হায়ারারকিকাল ফিচার এক্সট্রাকশন](../../../../../translated_images/bn/FeatureExtractionCNN.d9b456cbdae7cb64.webp) > ছবি: [হিসলপ-লিঞ্চের গবেষণাপত্র](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), তাদের [গবেষণার উপর ভিত্তি করে](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ CNN যেভাবে কাজ করে তা নিম্নলিখিত উদাহরণস্বরূপ, আসুন VGG-16-এর আর্কিটেকচার দেখি, একটি নেটওয়ার্ক যা ২০১৪ সালে ImageNet-এর টপ-৫ শ্রেণীবিন্যাসে ৯২.৭% সঠিকতা অর্জন করেছিল: -![ইমেজনেট স্তর](../../../../../translated_images/bn/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ইমেজনেট স্তর](../../../../../translated_images/bn/vgg-16-arch1.d901a5583b3a51ba.webp) -![ইমেজনেট পিরামিড](../../../../../translated_images/bn/vgg-16-arch.64ff2137f50dd49f.jpg) +![ইমেজনেট পিরামিড](../../../../../translated_images/bn/vgg-16-arch.64ff2137f50dd49f.webp) > ছবি: [রিসার্চগেট](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/bn/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/bn/lessons/4-ComputerVision/07-ConvNets/lab/README.md index cd14c714..2cf9ffea 100644 --- a/translations/bn/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/bn/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ CO_OP_TRANSLATOR_METADATA: আমরা [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/) ব্যবহার করব, যেখানে ৩৭টি ভিন্ন জাতের কুকুর এবং বিড়ালের ছবি রয়েছে। -![আমরা যে ডেটাসেট নিয়ে কাজ করব](../../../../../../translated_images/bn/data.50b2a9d5484bdbf0.png) +![আমরা যে ডেটাসেট নিয়ে কাজ করব](../../../../../../translated_images/bn/data.50b2a9d5484bdbf0.webp) ডেটাসেট ডাউনলোড করতে, এই কোড স্নিপেটটি ব্যবহার করুন: diff --git a/translations/bn/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/bn/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index b6b13775..6d3d8272 100644 --- a/translations/bn/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/bn/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "আদর্শ বিড়ালকে কল্পনা করার জন্য, আমরা একটি এলোমেলো শব্দযুক্ত ছবি দিয়ে শুরু করব এবং গ্রেডিয়েন্ট ডিসেন্ট অপ্টিমাইজেশন কৌশল ব্যবহার করে ছবিটি সামঞ্জস্য করার চেষ্টা করব, যাতে নেটওয়ার্কটি বিড়ালকে চিনতে পারে।\n", "\n", - "![অপ্টিমাইজেশন লুপ](../../../../../translated_images/bn/ideal-cat-loop.999fbb8ff306e044.png)\n", + "![অপ্টিমাইজেশন লুপ](../../../../../translated_images/bn/ideal-cat-loop.999fbb8ff306e044.webp)\n", "\n", "এখানে আমাদের শুরুর ছবি:\n" ] diff --git a/translations/bn/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/bn/lessons/4-ComputerVision/08-TransferLearning/README.md index 991f7b7b..f053728a 100644 --- a/translations/bn/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/bn/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Keras এবং PyTorch-এ কিছু সাধারণ আর্কিট এখানে VGG-16 নেটওয়ার্ক দ্বারা একটি বিড়ালের ছবির থেকে বের করা বৈশিষ্ট্যের উদাহরণ দেওয়া হয়েছে: -![Features extracted by VGG-16](../../../../../translated_images/bn/features.6291f9c7ba3a0b95.png) +![Features extracted by VGG-16](../../../../../translated_images/bn/features.6291f9c7ba3a0b95.webp) ## বিড়াল বনাম কুকুর ডেটাসেট @@ -48,19 +48,19 @@ Keras এবং PyTorch-এ কিছু সাধারণ আর্কিট একটি পদ্ধতি হলো একটি র্যান্ডম ইমেজ দিয়ে শুরু করা এবং তারপর **গ্রেডিয়েন্ট ডিসেন্ট অপ্টিমাইজেশন** পদ্ধতি ব্যবহার করে সেই ইমেজটি এমনভাবে পরিবর্তন করা যাতে নেটওয়ার্কটি মনে করে এটি একটি বিড়াল। -![Image Optimization Loop](../../../../../translated_images/bn/ideal-cat-loop.999fbb8ff306e044.png) +![Image Optimization Loop](../../../../../translated_images/bn/ideal-cat-loop.999fbb8ff306e044.webp) তবে, যদি আমরা এটি করি, তাহলে আমরা এমন কিছু পাব যা র্যান্ডম নয়েজের মতো দেখায়। কারণ *নেটওয়ার্ককে মনে করানোর অনেক উপায় আছে যে ইনপুট ইমেজটি একটি বিড়াল*, যার মধ্যে কিছু ভিজুয়ালি অর্থপূর্ণ নয়। যদিও এই ইমেজগুলোতে বিড়ালের জন্য সাধারণ প্যাটার্ন থাকে, তবুও এগুলোকে ভিজুয়ালি আলাদা করার জন্য কিছু নেই। ফলাফল উন্নত করতে, আমরা লস ফাংশনে একটি নতুন টার্ম যোগ করতে পারি, যাকে **ভ্যারিয়েশন লস** বলা হয়। এটি একটি মেট্রিক যা দেখায় ইমেজের প্রতিবেশী পিক্সেলগুলো কতটা মিল। ভ্যারিয়েশন লস মিনিমাইজ করলে ইমেজটি মসৃণ হয় এবং নয়েজ দূর হয় - ফলে আরও ভিজুয়ালি আকর্ষণীয় প্যাটার্ন প্রকাশ পায়। এখানে এমন "আদর্শ" ইমেজের উদাহরণ দেওয়া হয়েছে, যা বিড়াল এবং জেব্রা হিসেবে উচ্চ সম্ভাবনায় শ্রেণীবদ্ধ করা হয়েছে: -![Ideal Cat](../../../../../translated_images/bn/ideal-cat.203dd4597643d6b0.png) | ![Ideal Zebra](../../../../../translated_images/bn/ideal-zebra.7f70e8b54ee15a7a.png) +![Ideal Cat](../../../../../translated_images/bn/ideal-cat.203dd4597643d6b0.webp) | ![Ideal Zebra](../../../../../translated_images/bn/ideal-zebra.7f70e8b54ee15a7a.webp) -----|----- *আদর্শ বিড়াল* | *আদর্শ জেব্রা* একই পদ্ধতি ব্যবহার করে তথাকথিত **অ্যাডভার্সিয়াল আক্রমণ** করা যেতে পারে নিউরাল নেটওয়ার্কে। ধরুন আমরা একটি কুকুরকে বিড়াল হিসেবে দেখাতে চাই। যদি আমরা কুকুরের একটি ছবি নিই, যা নেটওয়ার্ক দ্বারা কুকুর হিসেবে স্বীকৃত, আমরা তারপর এটি সামান্য পরিবর্তন করতে পারি গ্রেডিয়েন্ট ডিসেন্ট অপ্টিমাইজেশন ব্যবহার করে, যতক্ষণ না নেটওয়ার্ক এটি বিড়াল হিসেবে শ্রেণীবদ্ধ করে: -![Picture of a Dog](../../../../../translated_images/bn/original-dog.8f68a67d2fe0911f.png) | ![Picture of a dog classified as a cat](../../../../../translated_images/bn/adversarial-dog.d9fc7773b0142b89.png) +![Picture of a Dog](../../../../../translated_images/bn/original-dog.8f68a67d2fe0911f.webp) | ![Picture of a dog classified as a cat](../../../../../translated_images/bn/adversarial-dog.d9fc7773b0142b89.webp) -----|----- *কুকুরের আসল ছবি* | *কুকুরের ছবি যা বিড়াল হিসেবে শ্রেণীবদ্ধ* diff --git a/translations/bn/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/bn/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index b7ebef7e..15998575 100644 --- a/translations/bn/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/bn/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "যেহেতু আমরা অটোএনকোডারকে মূল ছবির যতটা সম্ভব তথ্য ধারণ করতে প্রশিক্ষণ দিচ্ছি সঠিক পুনর্গঠনের জন্য, নেটওয়ার্কটি ইনপুট ছবিগুলোর সেরা **এম্বেডিং** খুঁজে বের করার চেষ্টা করে অর্থ ধারণ করার জন্য।\n", "\n", - "![অটোএনকোডার ডায়াগ্রাম](../../../../../translated_images/bn/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![অটোএনকোডার ডায়াগ্রাম](../../../../../translated_images/bn/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "> ছবি [Keras ব্লগ](https://blog.keras.io/building-autoencoders-in-keras.html) থেকে\n", "\n", diff --git a/translations/bn/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/bn/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index 0f8f50e1..9a580f12 100644 --- a/translations/bn/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/bn/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -21,7 +21,7 @@ "\n", "যেহেতু আমরা অটোএনকোডারকে মূল ছবির যতটা সম্ভব তথ্য ধারণ করতে প্রশিক্ষণ দিচ্ছি সঠিক পুনর্গঠনের জন্য, নেটওয়ার্কটি ইনপুট ছবিগুলোর সেরা **এম্বেডিং** খুঁজে বের করার চেষ্টা করে অর্থ ধারণ করার জন্য।\n", "\n", - "![অটোএনকোডার ডায়াগ্রাম](../../../../../translated_images/bn/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![অটোএনকোডার ডায়াগ্রাম](../../../../../translated_images/bn/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "*ছবি [Keras ব্লগ](https://blog.keras.io/building-autoencoders-in-keras.html) থেকে*\n", "\n", diff --git a/translations/bn/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/bn/lessons/4-ComputerVision/09-Autoencoders/README.md index 723b706e..1d4ec09b 100644 --- a/translations/bn/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/bn/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ CNN প্রশিক্ষণের সময় একটি বড় সম আমরা যখন অটোএনকোডার প্রশিক্ষণ দিই, তখন এটি মূল ইমেজ থেকে যতটা সম্ভব তথ্য ধারণ করার চেষ্টা করে, যাতে সঠিকভাবে পুনর্গঠন করা যায়। নেটওয়ার্কটি ইনপুট ইমেজগুলোর সেরা **এম্বেডিং** খুঁজে বের করার চেষ্টা করে, যা অর্থপূর্ণ তথ্য ধারণ করে। -![অটোএনকোডার ডায়াগ্রাম](../../../../../translated_images/bn/autoencoder_schema.5e6fc9ad98a5eb61.jpg) +![অটোএনকোডার ডায়াগ্রাম](../../../../../translated_images/bn/autoencoder_schema.5e6fc9ad98a5eb61.webp) > ছবি [Keras ব্লগ](https://blog.keras.io/building-autoencoders-in-keras.html) থেকে diff --git a/translations/bn/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/bn/lessons/4-ComputerVision/11-ObjectDetection/README.md index f92a5579..6c7cc48f 100644 --- a/translations/bn/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/bn/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: ## [পূর্ব-লেকচার কুইজ](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![অবজেক্ট ডিটেকশন](../../../../../translated_images/bn/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) +![অবজেক্ট ডিটেকশন](../../../../../translated_images/bn/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp) > ছবি [YOLO v2 ওয়েবসাইট](https://pjreddie.com/darknet/yolov2/) থেকে নেওয়া @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 2. প্রতিটি টাইলের উপর ইমেজ ক্লাসিফিকেশন চালান। 3. যেসব টাইল যথেষ্ট উচ্চ অ্যাক্টিভেশন দেখায়, সেগুলোতে কাঙ্ক্ষিত বস্তুটি রয়েছে বলে বিবেচনা করুন। -![সাধারণ অবজেক্ট ডিটেকশন](../../../../../translated_images/bn/naive-detection.e7f1ba220ccd08c6.png) +![সাধারণ অবজেক্ট ডিটেকশন](../../../../../translated_images/bn/naive-detection.e7f1ba220ccd08c6.webp) > *ছবি [এক্সারসাইজ নোটবুক](ObjectDetection-TF.ipynb) থেকে নেওয়া* @@ -42,7 +42,7 @@ CO_OP_TRANSLATOR_METADATA: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - ২০টি ক্লাস * [COCO](http://cocodataset.org/#home) - সাধারণ বস্তুসমূহের প্রসঙ্গ। ৮০টি ক্লাস, বাউন্ডিং বক্স এবং সেগমেন্টেশন মাস্ক। -![COCO](../../../../../translated_images/bn/coco-examples.71bc60380fa6cceb.jpg) +![COCO](../../../../../translated_images/bn/coco-examples.71bc60380fa6cceb.webp) ## অবজেক্ট ডিটেকশন মেট্রিকস @@ -50,7 +50,7 @@ CO_OP_TRANSLATOR_METADATA: যেখানে ইমেজ ক্লাসিফিকেশনের ক্ষেত্রে অ্যালগরিদম কতটা ভালো কাজ করছে তা পরিমাপ করা সহজ, অবজেক্ট ডিটেকশনের ক্ষেত্রে আমাদের ক্লাসের সঠিকতা এবং অনুমিত বাউন্ডিং বক্সের অবস্থানের নির্ভুলতা উভয়ই পরিমাপ করতে হবে। এর জন্য আমরা **ইন্টারসেকশন ওভার ইউনিয়ন** (IoU) ব্যবহার করি, যা দুটি বক্স (বা দুটি এলাকা) কতটা ওভারল্যাপ করছে তা পরিমাপ করে। -![IoU](../../../../../translated_images/bn/iou_equation.9a4751d40fff4e11.png) +![IoU](../../../../../translated_images/bn/iou_equation.9a4751d40fff4e11.webp) > *[এই চমৎকার ব্লগ পোস্ট](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/) থেকে ফিগার ২* @@ -98,11 +98,11 @@ $$ [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) ব্যবহার করে ROI অঞ্চলের একটি হায়ারারকিকাল স্ট্রাকচার তৈরি করে, যা পরে CNN ফিচার এক্সট্রাক্টর এবং SVM-ক্লাসিফায়ারগুলোর মাধ্যমে বস্তু ক্লাস নির্ধারণ করে এবং লিনিয়ার রিগ্রেশন ব্যবহার করে *বাউন্ডিং বক্স* এর কোঅর্ডিনেট নির্ধারণ করে। [অফিশিয়াল পেপার](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/bn/rcnn1.cae407020dfb1d1f.png) +![RCNN](../../../../../translated_images/bn/rcnn1.cae407020dfb1d1f.webp) > *ছবি van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/bn/rcnn2.2d9530bb83516484.png) +![RCNN-1](../../../../../translated_images/bn/rcnn2.2d9530bb83516484.webp) > *ছবি [এই ব্লগ](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e) থেকে নেওয়া* @@ -110,7 +110,7 @@ $$ এই পদ্ধতি R-CNN এর মতোই, তবে রিজনগুলো কনভোলিউশন লেয়ার প্রয়োগ করার পরে নির্ধারণ করা হয়। -![FRCNN](../../../../../translated_images/bn/f-rcnn.3cda6d9bb4188875.png) +![FRCNN](../../../../../translated_images/bn/f-rcnn.3cda6d9bb4188875.webp) > ছবি [অফিশিয়াল পেপার](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), ২০১৫ @@ -118,7 +118,7 @@ $$ এই পদ্ধতির মূল ধারণাটি হল রিজন প্রেডিক্ট করতে নিউরাল নেটওয়ার্ক ব্যবহার করা - যাকে *রিজন প্রপোজাল নেটওয়ার্ক* বলা হয়। [পেপার](https://arxiv.org/pdf/1506.01497.pdf), ২০১৬ -![FasterRCNN](../../../../../translated_images/bn/faster-rcnn.8d46c099b87ef30a.png) +![FasterRCNN](../../../../../translated_images/bn/faster-rcnn.8d46c099b87ef30a.webp) > ছবি [অফিশিয়াল পেপার](https://arxiv.org/pdf/1506.01497.pdf) থেকে নেওয়া @@ -130,7 +130,7 @@ $$ 2. ফিচারগুলো **পজিশন-সেনসিটিভ স্কোর ম্যাপ** দ্বারা প্রক্রিয়াকৃত হয়। $C$ ক্লাসের প্রতিটি অবজেক্টকে $k\times k$ অঞ্চলে ভাগ করা হয় এবং আমরা অবজেক্টের অংশগুলো প্রেডিক্ট করতে প্রশিক্ষণ দিই। 3. $k\times k$ অঞ্চলের প্রতিটি অংশের জন্য সমস্ত নেটওয়ার্ক অবজেক্ট ক্লাসের জন্য ভোট দেয় এবং সর্বাধিক ভোট পাওয়া অবজেক্ট ক্লাসটি নির্বাচিত হয়। -![r-fcn image](../../../../../translated_images/bn/r-fcn.13eb88158b99a3da.png) +![r-fcn image](../../../../../translated_images/bn/r-fcn.13eb88158b99a3da.webp) > ছবি [অফিশিয়াল পেপার](https://arxiv.org/abs/1605.06409) থেকে নেওয়া @@ -141,7 +141,7 @@ YOLO একটি রিয়েলটাইম ওয়ান-পাস অ * ছবিটিকে $S\times S$ অঞ্চলে ভাগ করা হয়। * প্রতিটি অঞ্চলের জন্য, **CNN** $n$ সম্ভাব্য অবজেক্ট, *বাউন্ডিং বক্স* এর কোঅর্ডিনেট এবং *কনফিডেন্স*=*প্রোবাবিলিটি* * IoU প্রেডিক্ট করে। - ![YOLO](../../../../../translated_images/bn/yolo.a2648ec82ee8bb4e.png) + ![YOLO](../../../../../translated_images/bn/yolo.a2648ec82ee8bb4e.webp) > ছবি [অফিশিয়াল পেপার](https://arxiv.org/abs/1506.02640) থেকে নেওয়া diff --git a/translations/bn/lessons/4-ComputerVision/README.md b/translations/bn/lessons/4-ComputerVision/README.md index 70dca50d..d7609678 100644 --- a/translations/bn/lessons/4-ComputerVision/README.md +++ b/translations/bn/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # কম্পিউটার ভিশন -![কম্পিউটার ভিশন বিষয়বস্তুর সারাংশ একটি ডুডলে](../../../../translated_images/bn/ai-computervision.6506ebebac3fbf76.png) +![কম্পিউটার ভিশন বিষয়বস্তুর সারাংশ একটি ডুডলে](../../../../translated_images/bn/ai-computervision.6506ebebac3fbf76.webp) এই অংশে আমরা শিখব: diff --git a/translations/bn/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/bn/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index 77d6c4c9..d160c1e5 100644 --- a/translations/bn/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/bn/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**শব্দের ব্যাগ** (BoW) ভেক্টর উপস্থাপনাটি সবচেয়ে বেশি ব্যবহৃত ঐতিহ্যবাহী ভেক্টর উপস্থাপনাগুলোর একটি। প্রতিটি শব্দ একটি ভেক্টর সূচকের সাথে যুক্ত থাকে, এবং ভেক্টরের উপাদানটি একটি নির্দিষ্ট ডকুমেন্টে সেই শব্দটির উপস্থিতির সংখ্যা ধারণ করে।\n", "\n", - "![একটি শব্দের ব্যাগ ভেক্টর উপস্থাপনাটি মেমোরিতে কীভাবে উপস্থাপিত হয় তা দেখানো একটি চিত্র।](../../../../../translated_images/bn/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![একটি শব্দের ব্যাগ ভেক্টর উপস্থাপনাটি মেমোরিতে কীভাবে উপস্থাপিত হয় তা দেখানো একটি চিত্র।](../../../../../translated_images/bn/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Note**: BoW-কে আপনি টেক্সটের পৃথক শব্দগুলোর জন্য এক-হট-এনকোডেড ভেক্টরগুলোর যোগফল হিসেবেও ভাবতে পারেন।\n", "\n", diff --git a/translations/bn/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/bn/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index 9bb52a12..262da2c2 100644 --- a/translations/bn/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/bn/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**ব্যাগ-অফ-ওয়ার্ডস** (BoW) ভেক্টর উপস্থাপন হল সবচেয়ে সহজে বোঝা যায় এমন ঐতিহ্যবাহী ভেক্টর উপস্থাপন। প্রতিটি শব্দ একটি ভেক্টর সূচকের সাথে যুক্ত থাকে, এবং একটি ভেক্টর উপাদান একটি নির্দিষ্ট ডকুমেন্টে প্রতিটি শব্দের উপস্থিতির সংখ্যা ধারণ করে।\n", "\n", - "![ব্যাগ-অফ-ওয়ার্ডস ভেক্টর উপস্থাপন মেমোরিতে কীভাবে উপস্থাপিত হয় তা দেখানো একটি চিত্র।](../../../../../translated_images/bn/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![ব্যাগ-অফ-ওয়ার্ডস ভেক্টর উপস্থাপন মেমোরিতে কীভাবে উপস্থাপিত হয় তা দেখানো একটি চিত্র।](../../../../../translated_images/bn/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Note**: BoW কে আপনি টেক্সটের পৃথক শব্দগুলোর জন্য সমস্ত এক-হট-এনকোডেড ভেক্টরের যোগফল হিসেবেও ভাবতে পারেন।\n", "\n", diff --git a/translations/bn/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/bn/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 27dc8c69..936fb64e 100644 --- a/translations/bn/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/bn/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "আমাদের নেটওয়ার্কে প্রথম লেয়ার হিসেবে এমবেডিং লেয়ার ব্যবহার করে, আমরা ব্যাগ-অফ-ওয়ার্ডস থেকে **এমবেডিং ব্যাগ** মডেলে পরিবর্তন করতে পারি, যেখানে আমরা প্রথমে আমাদের টেক্সটের প্রতিটি শব্দকে সংশ্লিষ্ট এমবেডিংয়ে রূপান্তর করি এবং তারপর সেই সমস্ত এমবেডিংয়ের উপর কিছু সামগ্রিক ফাংশন গণনা করি, যেমন `sum`, `average` বা `max`।\n", "\n", - "![পাঁচটি ক্রমের শব্দের জন্য একটি এমবেডিং ক্লাসিফায়ার দেখানো চিত্র।](../../../../../translated_images/bn/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![পাঁচটি ক্রমের শব্দের জন্য একটি এমবেডিং ক্লাসিফায়ার দেখানো চিত্র।](../../../../../translated_images/bn/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "আমাদের ক্লাসিফায়ার নিউরাল নেটওয়ার্কটি এমবেডিং লেয়ার দিয়ে শুরু হবে, তারপর অ্যাগ্রিগেশন লেয়ার, এবং তার উপরে একটি লিনিয়ার ক্লাসিফায়ার:\n" ] @@ -176,7 +176,7 @@ "\n", "পূর্ববর্তী আর্কিটেকচারে, সমস্ত সিকোয়েন্সকে একই দৈর্ঘ্যে প্যাড করতে হতো যাতে সেগুলোকে একটি মিনিব্যাচে ফিট করানো যায়। এটি ভেরিয়েবল দৈর্ঘ্যের সিকোয়েন্স উপস্থাপনার সবচেয়ে কার্যকর পদ্ধতি নয় - আরেকটি পদ্ধতি হতে পারে **অফসেট** ভেক্টর ব্যবহার করা, যা একটি বড় ভেক্টরে সংরক্ষিত সমস্ত সিকোয়েন্সের অফসেট ধারণ করবে।\n", "\n", - "![অফসেট সিকোয়েন্স উপস্থাপনা দেখানো একটি চিত্র](../../../../../translated_images/bn/offset-sequence-representation.eb73fcefb29b46ee.png)\n", + "![অফসেট সিকোয়েন্স উপস্থাপনা দেখানো একটি চিত্র](../../../../../translated_images/bn/offset-sequence-representation.eb73fcefb29b46ee.webp)\n", "\n", "> **Note**: উপরের ছবিতে, আমরা একটি ক্যারেক্টারের সিকোয়েন্স দেখিয়েছি, কিন্তু আমাদের উদাহরণে আমরা শব্দের সিকোয়েন্স নিয়ে কাজ করছি। তবে, অফসেট ভেক্টর দিয়ে সিকোয়েন্স উপস্থাপনার সাধারণ নীতিটি একই থাকে।\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW দ্রুততর, তবে স্কিপ-গ্রাম ধীর হলেও কম ঘন ঘন ব্যবহৃত শব্দগুলোর উপস্থাপনা করতে ভালো কাজ করে।\n", "\n", - "![CBoW এবং Skip-Gram অ্যালগরিদমের মাধ্যমে শব্দগুলোকে ভেক্টরে রূপান্তর করার উদাহরণ চিত্র।](../../../../../translated_images/bn/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![CBoW এবং Skip-Gram অ্যালগরিদমের মাধ্যমে শব্দগুলোকে ভেক্টরে রূপান্তর করার উদাহরণ চিত্র।](../../../../../translated_images/bn/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Google News ডেটাসেটে প্রি-ট্রেন করা word2vec এম্বেডিং নিয়ে পরীক্ষা করার জন্য, আমরা **gensim** লাইব্রেরি ব্যবহার করতে পারি। নিচে 'neural' শব্দটির সাথে সবচেয়ে মিল থাকা শব্দগুলো খুঁজে বের করা হয়েছে:\n", "\n", diff --git a/translations/bn/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/bn/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index 4747d3c1..9cda0730 100644 --- a/translations/bn/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/bn/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "আমাদের নেটওয়ার্কে প্রথম লেয়ার হিসেবে একটি এম্বেডিং লেয়ার ব্যবহার করে, আমরা ব্যাগ-অফ-ওয়ার্ডস থেকে **এম্বেডিং ব্যাগ** মডেলে স্যুইচ করতে পারি, যেখানে আমরা প্রথমে আমাদের টেক্সটের প্রতিটি শব্দকে সংশ্লিষ্ট এম্বেডিং-এ রূপান্তর করি এবং তারপর সেই এম্বেডিংগুলোর উপর একটি সমষ্টিগত ফাংশন (যেমন `sum`, `average` বা `max`) গণনা করি।\n", "\n", - "![পাঁচটি সিকোয়েন্স শব্দের জন্য একটি এম্বেডিং ক্লাসিফায়ার দেখানো চিত্র।](../../../../../translated_images/bn/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![পাঁচটি সিকোয়েন্স শব্দের জন্য একটি এম্বেডিং ক্লাসিফায়ার দেখানো চিত্র।](../../../../../translated_images/bn/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "আমাদের ক্লাসিফায়ার নিউরাল নেটওয়ার্কে নিম্নলিখিত লেয়ারগুলো অন্তর্ভুক্ত রয়েছে:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW দ্রুততর, এবং স্কিপ-গ্রাম ধীর হলেও এটি কম ঘন ঘন ব্যবহৃত শব্দগুলোর উপস্থাপনা আরও ভালোভাবে করে।\n", "\n", - "![CBoW এবং স্কিপ-গ্রাম অ্যালগরিদমের মাধ্যমে শব্দগুলোকে ভেক্টরে রূপান্তর করার একটি চিত্র।](../../../../../translated_images/bn/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![CBoW এবং স্কিপ-গ্রাম অ্যালগরিদমের মাধ্যমে শব্দগুলোকে ভেক্টরে রূপান্তর করার একটি চিত্র।](../../../../../translated_images/bn/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "গুগল নিউজ ডেটাসেটে প্রি-ট্রেইন করা Word2Vec এম্বেডিং নিয়ে পরীক্ষা করার জন্য, আমরা **gensim** লাইব্রেরি ব্যবহার করতে পারি। নিচে আমরা 'neural' শব্দটির সাথে সবচেয়ে সাদৃশ্যপূর্ণ শব্দগুলো খুঁজে বের করব।\n", "\n", diff --git a/translations/bn/lessons/5-NLP/14-Embeddings/README.md b/translations/bn/lessons/5-NLP/14-Embeddings/README.md index 4764b215..93c11d1e 100644 --- a/translations/bn/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/bn/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ BoW বা TF/IDF ভিত্তিক ক্লাসিফায়ার প আমাদের ক্লাসিফায়ার নেটওয়ার্কে প্রথম লেয়ার হিসেবে একটি এমবেডিং লেয়ার ব্যবহার করে, আমরা ব্যাগ-অফ-ওয়ার্ডস থেকে **এমবেডিং ব্যাগ** মডেলে পরিবর্তন করতে পারি, যেখানে আমরা প্রথমে আমাদের টেক্সটের প্রতিটি শব্দকে সংশ্লিষ্ট এমবেডিংয়ে রূপান্তর করি এবং তারপর সেই এমবেডিংগুলোর উপর কিছু অ্যাগ্রিগেট ফাংশন গণনা করি, যেমন `sum`, `average` বা `max`। -![পাঁচটি সিকোয়েন্স শব্দের জন্য একটি এমবেডিং ক্লাসিফায়ার দেখানো হয়েছে।](../../../../../translated_images/bn/embedding-classifier-example.b77f021a7ee67eee.png) +![পাঁচটি সিকোয়েন্স শব্দের জন্য একটি এমবেডিং ক্লাসিফায়ার দেখানো হয়েছে।](../../../../../translated_images/bn/embedding-classifier-example.b77f021a7ee67eee.webp) > লেখকের তৈরি চিত্র @@ -40,7 +40,7 @@ BoW বা TF/IDF ভিত্তিক ক্লাসিফায়ার প CBoW দ্রুততর, তবে স্কিপ-গ্রাম ধীরতর, কিন্তু কম ঘন ঘন ব্যবহৃত শব্দগুলোকে ভালোভাবে রিপ্রেজেন্ট করে। -![CBoW এবং স্কিপ-গ্রাম অ্যালগরিদমের মাধ্যমে শব্দগুলোকে ভেক্টরে রূপান্তর করার উদাহরণ দেখানো হয়েছে।](../../../../../translated_images/bn/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![CBoW এবং স্কিপ-গ্রাম অ্যালগরিদমের মাধ্যমে শব্দগুলোকে ভেক্টরে রূপান্তর করার উদাহরণ দেখানো হয়েছে।](../../../../../translated_images/bn/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > [এই পেপার](https://arxiv.org/pdf/1301.3781.pdf) থেকে নেওয়া চিত্র diff --git a/translations/bn/lessons/5-NLP/15-LanguageModeling/README.md b/translations/bn/lessons/5-NLP/15-LanguageModeling/README.md index d3d48e31..99a7bca3 100644 --- a/translations/bn/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/bn/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: * **কন্টিনিউয়াস ব্যাগ-অফ-ওয়ার্ডস** (CBoW), যেখানে আমরা একটি টোকেন সিকোয়েন্স $W_{-N}$, ..., $W_N$ এর মধ্যে $W_0$ টোকেন পূর্বানুমান করি। * **স্কিপ-গ্রাম**, যেখানে আমরা মধ্যবর্তী টোকেন $W_0$ থেকে পার্শ্ববর্তী টোকেনগুলোর সেট {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} পূর্বানুমান করি। -![শব্দকে ভেক্টরে রূপান্তর করার পেপারের ছবি](../../../../../translated_images/bn/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![শব্দকে ভেক্টরে রূপান্তর করার পেপারের ছবি](../../../../../translated_images/bn/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > ছবি [এই পেপার](https://arxiv.org/pdf/1301.3781.pdf) থেকে diff --git a/translations/bn/lessons/5-NLP/16-RNN/README.md b/translations/bn/lessons/5-NLP/16-RNN/README.md index c7e57c74..cc0e2291 100644 --- a/translations/bn/lessons/5-NLP/16-RNN/README.md +++ b/translations/bn/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: টেক্সট সিকোয়েন্সের অর্থ ধারণ করতে, আমাদের আরেকটি নিউরাল নেটওয়ার্ক আর্কিটেকচার ব্যবহার করতে হবে, যেটিকে **রিকারেন্ট নিউরাল নেটওয়ার্ক** বা RNN বলা হয়। RNN-এ, আমরা আমাদের বাক্যটি নেটওয়ার্কের মাধ্যমে একবারে একটি প্রতীক পাঠাই, এবং নেটওয়ার্ক কিছু **স্টেট** তৈরি করে, যা আমরা পরবর্তী প্রতীকের সাথে আবার নেটওয়ার্কে পাঠাই। -![RNN](../../../../../translated_images/bn/rnn.27f5c29c53d727b5.png) +![RNN](../../../../../translated_images/bn/rnn.27f5c29c53d727b5.webp) > লেখকের তৈরি ছবি @@ -61,7 +61,7 @@ LSTM নেটওয়ার্ক RNN-এর মতোই সংগঠিত, একটি রিকারেন্ট নেটওয়ার্ক, একদিকে বা বাইডিরেকশনাল, একটি সিকোয়েন্সের নির্দিষ্ট প্যাটার্নগুলো ধারণ করে এবং সেগুলোকে স্টেট ভেক্টরে সংরক্ষণ করতে পারে বা আউটপুটে পাস করতে পারে। কনভোলিউশনাল নেটওয়ার্কের মতো, আমরা প্রথম লেয়ার দ্বারা এক্সট্র্যাক্ট করা নিম্ন-স্তরের প্যাটার্ন থেকে উচ্চ-স্তরের প্যাটার্ন ক্যাপচার করতে প্রথম লেয়ারের উপরে আরেকটি রিকারেন্ট লেয়ার তৈরি করতে পারি। এটি আমাদের **মাল্টিলেয়ার RNN** ধারণায় নিয়ে যায়, যা দুটি বা তার বেশি রিকারেন্ট নেটওয়ার্ক নিয়ে গঠিত, যেখানে পূর্ববর্তী লেয়ারের আউটপুট পরবর্তী লেয়ারের ইনপুট হিসেবে পাঠানো হয়। -![মাল্টিলেয়ার LSTM RNN](../../../../../translated_images/bn/multi-layer-lstm.dd975e29bb2a59fe.jpg) +![মাল্টিলেয়ার LSTM RNN](../../../../../translated_images/bn/multi-layer-lstm.dd975e29bb2a59fe.webp) *ছবি [এই অসাধারণ পোস্ট](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) থেকে ফার্নান্দো লোপেজের দ্বারা* diff --git a/translations/bn/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/bn/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index 9589eb3d..7415a7a8 100644 --- a/translations/bn/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/bn/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "পুনরাবৃত্ত নেটওয়ার্ক, একমুখী বা দ্বিমুখী, একটি সিকোয়েন্সের নির্দিষ্ট প্যাটার্নগুলো ধারণ করে এবং সেগুলো স্টেট ভেক্টরে সংরক্ষণ করতে পারে বা আউটপুটে পাস করতে পারে। কনভোলিউশনাল নেটওয়ার্কের মতো, আমরা প্রথম স্তরের উপর আরেকটি পুনরাবৃত্ত স্তর তৈরি করতে পারি, যা নিম্ন-স্তরের প্যাটার্ন থেকে উচ্চ-স্তরের প্যাটার্ন ধারণ করে। এটি আমাদের **বহুস্তর RNN** ধারণার দিকে নিয়ে যায়, যা দুই বা তার বেশি পুনরাবৃত্ত নেটওয়ার্ক নিয়ে গঠিত, যেখানে পূর্ববর্তী স্তরের আউটপুট পরবর্তী স্তরের ইনপুট হিসেবে পাস করা হয়।\n", "\n", - "![একটি বহুস্তর দীর্ঘ-স্বল্প-মেয়াদী-মেমরি RNN দেখানো চিত্র](../../../../../translated_images/bn/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![একটি বহুস্তর দীর্ঘ-স্বল্প-মেয়াদী-মেমরি RNN দেখানো চিত্র](../../../../../translated_images/bn/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*ছবি [এই অসাধারণ পোস্ট](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) থেকে, লেখক Fernando López*\n", "\n", diff --git a/translations/bn/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/bn/lessons/5-NLP/16-RNN/RNNTF.ipynb index 993a075d..378bfaa5 100644 --- a/translations/bn/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/bn/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "একটি টেক্সট সিকোয়েন্সের অর্থ ধরতে, আমরা **পুনরাবৃত্তি নিউরাল নেটওয়ার্ক** বা RNN নামে একটি নিউরাল নেটওয়ার্ক আর্কিটেকচার ব্যবহার করব। RNN ব্যবহার করার সময়, আমরা আমাদের বাক্যটি নেটওয়ার্কের মধ্য দিয়ে একবারে একটি টোকেন পাঠাই, এবং নেটওয়ার্কটি কিছু **অবস্থা** তৈরি করে, যা আমরা পরবর্তী টোকেনের সাথে আবার নেটওয়ার্কে পাঠাই।\n", "\n", - "![একটি উদাহরণ পুনরাবৃত্তি নিউরাল নেটওয়ার্ক জেনারেশন দেখানো চিত্র।](../../../../../translated_images/bn/rnn.27f5c29c53d727b5.png)\n", + "![একটি উদাহরণ পুনরাবৃত্তি নিউরাল নেটওয়ার্ক জেনারেশন দেখানো চিত্র।](../../../../../translated_images/bn/rnn.27f5c29c53d727b5.webp)\n", "\n", "টোকেনগুলির ইনপুট সিকোয়েন্স $X_0,\\dots,X_n$ দেওয়া হলে, RNN একটি নিউরাল নেটওয়ার্ক ব্লকের সিকোয়েন্স তৈরি করে এবং ব্যাকপ্রোপাগেশনের মাধ্যমে এই সিকোয়েন্সটি এন্ড-টু-এন্ড প্রশিক্ষণ দেয়। প্রতিটি নেটওয়ার্ক ব্লক $(X_i,S_i)$ জোড়াকে ইনপুট হিসাবে নেয় এবং ফলাফল হিসাবে $S_{i+1}$ তৈরি করে। চূড়ান্ত অবস্থা $S_n$ বা আউটপুট $Y_n$ একটি লিনিয়ার ক্লাসিফায়ারে যায় ফলাফল তৈরি করতে। সমস্ত নেটওয়ার্ক ব্লক একই ওজন ভাগ করে এবং একটি ব্যাকপ্রোপাগেশন পাস ব্যবহার করে এন্ড-টু-এন্ড প্রশিক্ষণ দেওয়া হয়।\n", "\n", @@ -371,7 +371,7 @@ "\n", "পুনরাবৃত্ত নেটওয়ার্ক, একমুখী বা দ্বিমুখী, সিকোয়েন্সের মধ্যে প্যাটার্নগুলো ধরে এবং সেগুলোকে স্টেট ভেক্টরে সংরক্ষণ করে বা আউটপুট হিসেবে ফেরত দেয়। কনভোলিউশনাল নেটওয়ার্কের মতো, আমরা প্রথম স্তরের মাধ্যমে নিম্ন স্তরের প্যাটার্নগুলো থেকে উচ্চ স্তরের প্যাটার্ন ধরার জন্য আরেকটি পুনরাবৃত্ত স্তর তৈরি করতে পারি। এটি আমাদের **বহুস্তর RNN** ধারণার দিকে নিয়ে যায়, যা দুটি বা তার বেশি পুনরাবৃত্ত নেটওয়ার্ক নিয়ে গঠিত, যেখানে আগের স্তরের আউটপুট পরবর্তী স্তরের ইনপুট হিসেবে ব্যবহৃত হয়।\n", "\n", - "![একটি বহুস্তর দীর্ঘ-মেয়াদী-মেমরি RNN-এর ছবি](../../../../../translated_images/bn/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![একটি বহুস্তর দীর্ঘ-মেয়াদী-মেমরি RNN-এর ছবি](../../../../../translated_images/bn/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*ছবি [এই চমৎকার পোস্ট](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) থেকে, লেখক Fernando López।*\n", "\n", diff --git a/translations/bn/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/bn/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index 5774f286..68080a91 100644 --- a/translations/bn/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/bn/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "আমরা RNN-কে টেক্সট জেনারেট করার জন্য যেভাবে প্রশিক্ষণ দেব তা হলো নিম্নরূপ। প্রতিটি ধাপে, আমরা `nchars` দৈর্ঘ্যের একটি অক্ষরের ক্রম নেব এবং নেটওয়ার্ককে প্রতিটি ইনপুট অক্ষরের জন্য পরবর্তী আউটপুট অক্ষর তৈরি করতে বলব:\n", "\n", - "![চিত্রটি 'HELLO' শব্দের একটি উদাহরণ RNN জেনারেশন দেখাচ্ছে।](../../../../../translated_images/bn/rnn-generate.56c54afb52f9781d.png)\n", + "![চিত্রটি 'HELLO' শব্দের একটি উদাহরণ RNN জেনারেশন দেখাচ্ছে।](../../../../../translated_images/bn/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "বাস্তব পরিস্থিতির উপর নির্ভর করে, আমরা কিছু বিশেষ অক্ষর অন্তর্ভুক্ত করতে চাইতে পারি, যেমন *end-of-sequence* ``। আমাদের ক্ষেত্রে, আমরা শুধু নেটওয়ার্ককে অবিরাম টেক্সট জেনারেশনের জন্য প্রশিক্ষণ দিতে চাই, তাই আমরা প্রতিটি ক্রমের আকারকে `nchars` টোকেনের সমান স্থির করব। ফলস্বরূপ, প্রতিটি প্রশিক্ষণ উদাহরণে থাকবে `nchars` ইনপুট এবং `nchars` আউটপুট (যা ইনপুট ক্রম এক প্রতীক বামে সরানো)। মিনিব্যাচে এমন কয়েকটি ক্রম থাকবে।\n", "\n", diff --git a/translations/bn/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/bn/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index 5beed615..b76caab7 100644 --- a/translations/bn/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/bn/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -113,7 +113,7 @@ "\n", "আমরা যেভাবে RNN প্রশিক্ষণ করব যাতে এটি সংবাদ শিরোনাম তৈরি করতে পারে তা হলো নিম্নরূপ। প্রতিটি ধাপে, আমরা একটি শিরোনাম নেব, যা RNN-এ প্রবেশ করানো হবে, এবং প্রতিটি ইনপুট অক্ষরের জন্য আমরা নেটওয়ার্ককে পরবর্তী আউটপুট অক্ষর তৈরি করতে বলব:\n", "\n", - "![চিত্রটি 'HELLO' শব্দটি তৈরি করার একটি উদাহরণ RNN দেখাচ্ছে।](../../../../../translated_images/bn/rnn-generate.56c54afb52f9781d.png)\n", + "![চিত্রটি 'HELLO' শব্দটি তৈরি করার একটি উদাহরণ RNN দেখাচ্ছে।](../../../../../translated_images/bn/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "আমাদের ক্রমের শেষ অক্ষরের জন্য, আমরা নেটওয়ার্ককে `` টোকেন তৈরি করতে বলব।\n", "\n", diff --git a/translations/bn/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/bn/lessons/5-NLP/17-GenerativeNetworks/README.md index dbb04134..e283005c 100644 --- a/translations/bn/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/bn/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ Recurrent Neural Networks (RNNs) এবং তাদের গেটেড স এটি বিভিন্ন নিউরাল আর্কিটেকচারের জন্য অনুমতি দেয়, যা নিচের ছবিতে দেখানো হয়েছে: -![RNN-এর সাধারণ প্যাটার্ন দেখানো একটি ছবি।](../../../../../translated_images/bn/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![RNN-এর সাধারণ প্যাটার্ন দেখানো একটি ছবি।](../../../../../translated_images/bn/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > ছবি ব্লগ পোস্ট [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) থেকে [Andrej Karpaty](http://karpathy.github.io/) দ্বারা @@ -32,7 +32,7 @@ Recurrent Neural Networks (RNNs) এবং তাদের গেটেড স আমরা এই RNN-কে ধাপে ধাপে টেক্সট তৈরি করতে প্রশিক্ষণ দেব। প্রতিটি ধাপে, আমরা `nchars` দৈর্ঘ্যের একটি চরিত্রের সিকোয়েন্স গ্রহণ করব এবং নেটওয়ার্ককে প্রতিটি ইনপুট চরিত্রের জন্য পরবর্তী আউটপুট চরিত্র তৈরি করতে বলব: -!['HELLO' শব্দটি তৈরি করার একটি উদাহরণ RNN দেখানো হয়েছে।](../../../../../translated_images/bn/rnn-generate.56c54afb52f9781d.png) +!['HELLO' শব্দটি তৈরি করার একটি উদাহরণ RNN দেখানো হয়েছে।](../../../../../translated_images/bn/rnn-generate.56c54afb52f9781d.webp) যখন টেক্সট তৈরি করা হয় (ইনফারেন্সের সময়), আমরা কিছু **প্রম্পট** দিয়ে শুরু করি, যা RNN সেলগুলোর মাধ্যমে পাস করে তার মধ্যবর্তী স্টেট তৈরি করে, এবং তারপর এই স্টেট থেকে জেনারেশন শুরু হয়। আমরা একবারে একটি চরিত্র তৈরি করি এবং স্টেট এবং তৈরি করা চরিত্রকে অন্য RNN সেলে পাস করি পরবর্তী চরিত্র তৈরি করার জন্য, যতক্ষণ না আমরা পর্যাপ্ত চরিত্র তৈরি করি। diff --git a/translations/bn/lessons/5-NLP/18-Transformers/README.md b/translations/bn/lessons/5-NLP/18-Transformers/README.md index 053a8a49..3914a583 100644 --- a/translations/bn/lessons/5-NLP/18-Transformers/README.md +++ b/translations/bn/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ RNN ব্যবহার করে সিকোয়েন্স-টু-সি **অ্যাটেনশন মেকানিজম** RNN-এর আউটপুট প্রেডিকশনে প্রতিটি ইনপুট ভেক্টরের প্রসঙ্গগত প্রভাবকে ওজন করার একটি উপায় প্রদান করে। এটি বাস্তবায়িত হয় ইনপুট RNN এবং আউটপুট RNN-এর মধ্যবর্তী স্টেটগুলোর মধ্যে শর্টকাট তৈরি করে। এইভাবে, আউটপুট প্রতীক yt তৈরি করার সময়, আমরা সমস্ত ইনপুট হিডেন স্টেট hi বিবেচনা করব, বিভিন্ন ওজন সহগ αt,i সহ। -![এনকোডার/ডিকোডার মডেল একটি অ্যাডিটিভ অ্যাটেনশন লেয়ার সহ](../../../../../translated_images/bn/encoder-decoder-attention.7a726296894fb567.png) +![এনকোডার/ডিকোডার মডেল একটি অ্যাডিটিভ অ্যাটেনশন লেয়ার সহ](../../../../../translated_images/bn/encoder-decoder-attention.7a726296894fb567.webp) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf)-এ অ্যাডিটিভ অ্যাটেনশন মেকানিজম সহ এনকোডার-ডিকোডার মডেল, [এই ব্লগ পোস্ট](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) থেকে উদ্ধৃত। অ্যাটেনশন ম্যাট্রিক্স {αi,j} একটি আউটপুট সিকোয়েন্সের একটি নির্দিষ্ট শব্দ তৈরিতে ইনপুট শব্দগুলো কীভাবে ভূমিকা রাখে তা উপস্থাপন করবে। নিচে এমন একটি ম্যাট্রিক্সের উদাহরণ দেওয়া হয়েছে: -![Bahdanau - arviz.org থেকে নেওয়া RNNsearch-50 দ্বারা পাওয়া একটি নমুনা অ্যালাইনমেন্ট](../../../../../translated_images/bn/bahdanau-fig3.09ba2d37f202a6af.png) +![Bahdanau - arviz.org থেকে নেওয়া RNNsearch-50 দ্বারা পাওয়া একটি নমুনা অ্যালাইনমেন্ট](../../../../../translated_images/bn/bahdanau-fig3.09ba2d37f202a6af.webp) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) থেকে চিত্র (Fig.3) @@ -66,7 +66,7 @@ RNN ব্যবহার করে সিকোয়েন্স-টু-সি এরপর, আমাদের সিকোয়েন্সের মধ্যে কিছু প্যাটার্ন ধরতে হবে। এটি করতে, ট্রান্সফর্মারগুলো **সেলফ-অ্যাটেনশন** মেকানিজম ব্যবহার করে, যা মূলত ইনপুট এবং আউটপুট হিসেবে একই সিকোয়েন্সে অ্যাটেনশন প্রয়োগ। সেলফ-অ্যাটেনশন প্রয়োগ করে আমরা বাক্যের প্রসঙ্গ বিবেচনা করতে পারি এবং কোন শব্দগুলো আন্তঃসম্পর্কিত তা দেখতে পারি। উদাহরণস্বরূপ, এটি আমাদের *it* দ্বারা উল্লেখিত শব্দগুলো দেখতে এবং প্রসঙ্গ বিবেচনা করতে সাহায্য করে: -![](../../../../../translated_images/bn/CoreferenceResolution.861924d6d384a7d6.png) +![](../../../../../translated_images/bn/CoreferenceResolution.861924d6d384a7d6.webp) > [গুগলের ব্লগ](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) থেকে চিত্র। @@ -91,7 +91,7 @@ RNN ব্যবহার করে সিকোয়েন্স-টু-সি **BERT** (Bidirectional Encoder Representations from Transformers) একটি খুব বড় মাল্টি লেয়ার ট্রান্সফর্মার নেটওয়ার্ক, যেখানে *BERT-base*-এর জন্য 12টি লেয়ার এবং *BERT-large*-এর জন্য 24টি লেয়ার রয়েছে। মডেলটি প্রথমে একটি বড় টেক্সট ডেটা কর্পাস (WikiPedia + বই) ব্যবহার করে আনসুপারভাইজড প্রশিক্ষণের মাধ্যমে প্রি-ট্রেইন করা হয় (একটি বাক্যে মাস্ক করা শব্দগুলো প্রেডিক্ট করা)। প্রি-ট্রেইনিংয়ের সময় মডেলটি উল্লেখযোগ্য ভাষা বোঝার স্তর অর্জন করে, যা পরে অন্যান্য ডেটাসেটের সাথে ফাইন টিউনিংয়ের মাধ্যমে ব্যবহার করা যায়। এই প্রক্রিয়াকে **ট্রান্সফার লার্নিং** বলা হয়। -![http://jalammar.github.io/illustrated-bert/ থেকে ছবি](../../../../../translated_images/bn/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) +![http://jalammar.github.io/illustrated-bert/ থেকে ছবি](../../../../../translated_images/bn/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > [উৎস](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/bn/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/bn/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index 04236077..3d998025 100644 --- a/translations/bn/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/bn/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**অ্যাটেনশন মেকানিজম** একটি পদ্ধতি প্রদান করে, যা প্রতিটি ইনপুট ভেক্টরের প্রাসঙ্গিক প্রভাবকে RNN-এর প্রতিটি আউটপুট প্রেডিকশনে ওজন দেয়। এটি বাস্তবায়িত হয় ইনপুট RNN-এর মধ্যবর্তী স্টেট এবং আউটপুট RNN-এর মধ্যে শর্টকাট তৈরি করে। এই পদ্ধতিতে, যখন আউটপুট প্রতীক $y_t$ তৈরি করা হয়, তখন আমরা সব ইনপুট হিডেন স্টেট $h_i$-কে বিভিন্ন ওজন সহগ $\\alpha_{t,i}$ সহ বিবেচনা করব।\n", "\n", - "![এনকোডার/ডিকোডার মডেল একটি অ্যাডিটিভ অ্যাটেনশন লেয়ার সহ](../../../../../translated_images/bn/encoder-decoder-attention.7a726296894fb567.png)\n", + "![এনকোডার/ডিকোডার মডেল একটি অ্যাডিটিভ অ্যাটেনশন লেয়ার সহ](../../../../../translated_images/bn/encoder-decoder-attention.7a726296894fb567.webp)\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) থেকে উদ্ধৃত, [এই ব্লগ পোস্ট](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) থেকে নেওয়া এনকোডার-ডিকোডার মডেল অ্যাডিটিভ অ্যাটেনশন মেকানিজম সহ]*\n", "\n", "অ্যাটেনশন ম্যাট্রিক্স $\\{\\alpha_{i,j}\\}$ নির্দেশ করে যে নির্দিষ্ট ইনপুট শব্দগুলো আউটপুট সিকোয়েন্সের একটি নির্দিষ্ট শব্দ তৈরিতে কতটা ভূমিকা রাখে। নিচে এমন একটি ম্যাট্রিক্সের উদাহরণ দেওয়া হলো:\n", "\n", - "![RNNsearch-50 দ্বারা পাওয়া একটি নমুনা অ্যালাইনমেন্ট, Bahdanau - arviz.org থেকে নেওয়া](../../../../../translated_images/bn/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![RNNsearch-50 দ্বারা পাওয়া একটি নমুনা অ্যালাইনমেন্ট, Bahdanau - arviz.org থেকে নেওয়া](../../../../../translated_images/bn/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) থেকে নেওয়া (Fig.3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) একটি খুব বড় মাল্টি-লেয়ার ট্রান্সফর্মার নেটওয়ার্ক, যেখানে *BERT-base*-এর জন্য ১২টি লেয়ার এবং *BERT-large*-এর জন্য ২৪টি লেয়ার রয়েছে। মডেলটি প্রথমে একটি বড় টেক্সট ডেটাসেট (উইকিপিডিয়া + বই) ব্যবহার করে আনসুপারভাইজড প্রশিক্ষণের মাধ্যমে প্রি-ট্রেইন করা হয় (একটি বাক্যে মাস্ক করা শব্দগুলো প্রেডিক্ট করা)। প্রি-ট্রেইনিংয়ের সময় মডেলটি উল্লেখযোগ্য ভাষাগত বোঝাপড়া অর্জন করে, যা পরে অন্যান্য ডেটাসেটের সাথে ফাইন টিউনিংয়ের মাধ্যমে ব্যবহার করা যায়। এই প্রক্রিয়াটিকে **ট্রান্সফার লার্নিং** বলা হয়।\n", "\n", - "![http://jalammar.github.io/illustrated-bert/ থেকে নেওয়া ছবি](../../../../../translated_images/bn/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![http://jalammar.github.io/illustrated-bert/ থেকে নেওয়া ছবি](../../../../../translated_images/bn/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "ট্রান্সফর্মার আর্কিটেকচারের অনেক ভেরিয়েশন রয়েছে, যেমন BERT, DistilBERT, BigBird, OpenGPT3 এবং আরও অনেক, যেগুলো ফাইন টিউন করা যায়। [HuggingFace প্যাকেজ](https://github.com/huggingface/) PyTorch ব্যবহার করে এই আর্কিটেকচারগুলোর অনেকগুলোর প্রশিক্ষণের জন্য একটি রিপোজিটরি প্রদান করে।\n", "\n", diff --git a/translations/bn/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/bn/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index 3b23e1b3..a4d2efe1 100644 --- a/translations/bn/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/bn/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**অ্যাটেনশন মেকানিজম** RNN-এর প্রতিটি আউটপুট প্রেডিকশনে প্রতিটি ইনপুট ভেক্টরের প্রাসঙ্গিক প্রভাবকে ওজন দেওয়ার একটি উপায় প্রদান করে। এটি বাস্তবায়িত হয় ইনপুট RNN-এর মধ্যবর্তী স্টেট এবং আউটপুট RNN-এর মধ্যে শর্টকাট তৈরি করে। এই পদ্ধতিতে, যখন আউটপুট প্রতীক $y_t$ তৈরি করা হয়, তখন আমরা বিভিন্ন ওজন সহগ $\\alpha_{t,i}$ সহ সমস্ত ইনপুট হিডেন স্টেট $h_i$ বিবেচনায় নেব। \n", "\n", - "![এনকোডার/ডিকোডার মডেল একটি অ্যাডিটিভ অ্যাটেনশন লেয়ার সহ](../../../../../translated_images/bn/encoder-decoder-attention.7a726296894fb567.png)\n", + "![এনকোডার/ডিকোডার মডেল একটি অ্যাডিটিভ অ্যাটেনশন লেয়ার সহ](../../../../../translated_images/bn/encoder-decoder-attention.7a726296894fb567.webp)\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) থেকে উদ্ধৃত, [এই ব্লগ পোস্ট](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) থেকে নেওয়া এনকোডার-ডিকোডার মডেল অ্যাডিটিভ অ্যাটেনশন মেকানিজম সহ*\n", "\n", "অ্যাটেনশন ম্যাট্রিক্স $\\{\\alpha_{i,j}\\}$ একটি নির্দিষ্ট আউটপুট সিকোয়েন্সের একটি শব্দ তৈরিতে কোন ইনপুট শব্দগুলো কতটা ভূমিকা রাখছে তা উপস্থাপন করে। নিচে এমন একটি ম্যাট্রিক্সের উদাহরণ দেওয়া হলো:\n", "\n", - "![RNNsearch-50 দ্বারা পাওয়া একটি নমুনা অ্যালাইনমেন্ট, Bahdanau - arviz.org থেকে নেওয়া](../../../../../translated_images/bn/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![RNNsearch-50 দ্বারা পাওয়া একটি নমুনা অ্যালাইনমেন্ট, Bahdanau - arviz.org থেকে নেওয়া](../../../../../translated_images/bn/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) থেকে নেওয়া (Fig.3)*\n", "\n", @@ -225,7 +225,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) একটি অত্যন্ত বড় মাল্টি-লেয়ার ট্রান্সফর্মার নেটওয়ার্ক, যেখানে *BERT-base* এর জন্য ১২টি স্তর এবং *BERT-large* এর জন্য ২৪টি স্তর রয়েছে। এই মডেলটি প্রথমে বিশাল পরিমাণ টেক্সট ডেটা (উইকিপিডিয়া + বই) ব্যবহার করে অ-পর্যবেক্ষণমূলক প্রশিক্ষণের মাধ্যমে (একটি বাক্যে মাস্ক করা শব্দ অনুমান করা) প্রি-ট্রেইন করা হয়। প্রি-ট্রেইনিংয়ের সময় মডেলটি উল্লেখযোগ্য ভাষাগত বোঝাপড়া অর্জন করে, যা পরে অন্যান্য ডেটাসেটের সাথে ফাইন টিউনিংয়ের মাধ্যমে ব্যবহার করা যায়। এই প্রক্রিয়াকে **ট্রান্সফার লার্নিং** বলা হয়।\n", "\n", - "![http://jalammar.github.io/illustrated-bert/ থেকে ছবি](../../../../../translated_images/bn/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![http://jalammar.github.io/illustrated-bert/ থেকে ছবি](../../../../../translated_images/bn/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "ট্রান্সফর্মার আর্কিটেকচারের অনেক বৈচিত্র্য রয়েছে, যেমন BERT, DistilBERT, BigBird, OpenGPT3 এবং আরও অনেক কিছু, যেগুলো ফাইন টিউন করা যেতে পারে।\n", "\n", diff --git a/translations/bn/lessons/5-NLP/19-NER/README.md b/translations/bn/lessons/5-NLP/19-NER/README.md index 053d7238..a0866822 100644 --- a/translations/bn/lessons/5-NLP/19-NER/README.md +++ b/translations/bn/lessons/5-NLP/19-NER/README.md @@ -58,7 +58,7 @@ infant | O যেহেতু আমাদের টোকেন এবং শ্রেণীগুলোর মধ্যে এক-একটি সম্পর্ক তৈরি করতে হবে, আমরা এই চিত্র থেকে একটি ডানদিকের **অনেক-থেকে-অনেক** নিউরাল নেটওয়ার্ক মডেল প্রশিক্ষণ দিতে পারি: -![সাধারণ পুনরাবৃত্ত নিউরাল নেটওয়ার্ক প্যাটার্ন দেখানো চিত্র।](../../../../../translated_images/bn/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![সাধারণ পুনরাবৃত্ত নিউরাল নেটওয়ার্ক প্যাটার্ন দেখানো চিত্র।](../../../../../translated_images/bn/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > *[এই ব্লগ পোস্ট](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) থেকে চিত্র, লেখক [Andrej Karpathy](http://karpathy.github.io/)। NER টোকেন শ্রেণীবিন্যাস মডেল এই চিত্রের ডানদিকের নেটওয়ার্ক আর্কিটেকচারের সাথে মিলে যায়।* diff --git a/translations/bn/lessons/5-NLP/README.md b/translations/bn/lessons/5-NLP/README.md index 325fc92b..8a234528 100644 --- a/translations/bn/lessons/5-NLP/README.md +++ b/translations/bn/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # প্রাকৃতিক ভাষা প্রক্রিয়াকরণ -![NLP কাজগুলোর সারাংশ একটি ডুডলে](../../../../translated_images/bn/ai-nlp.b22dcb8ca4707cea.png) +![NLP কাজগুলোর সারাংশ একটি ডুডলে](../../../../translated_images/bn/ai-nlp.b22dcb8ca4707cea.webp) এই অংশে, আমরা **প্রাকৃতিক ভাষা প্রক্রিয়াকরণ (NLP)** সম্পর্কিত কাজগুলো পরিচালনা করতে নিউরাল নেটওয়ার্ক ব্যবহার করার উপর মনোযোগ দেব। অনেক ধরনের NLP সমস্যা রয়েছে যা আমরা চাই কম্পিউটার সমাধান করতে সক্ষম হোক: diff --git a/translations/bn/lessons/6-Other/23-MultiagentSystems/README.md b/translations/bn/lessons/6-Other/23-MultiagentSystems/README.md index ef46b642..a0f1fb1f 100644 --- a/translations/bn/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/bn/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ NetLogo-এর একটি চমৎকার দিক হলো এটি এ মডেলটি খোলার পরে, আপনি NetLogo-এর প্রধান স্ক্রিনে নিয়ে যাওয়া হবে। এখানে একটি নমুনা মডেল রয়েছে যা সীমিত সম্পদ (ঘাস) দেওয়া হলে নেকড়ে এবং ভেড়ার জনসংখ্যা বর্ণনা করে। -![NetLogo Main Screen](../../../../../translated_images/bn/NetLogo-Main.32653711ec1a01b3.png) +![NetLogo Main Screen](../../../../../translated_images/bn/NetLogo-Main.32653711ec1a01b3.webp) > দিমিত্রি সশনিকভের স্ক্রিনশট diff --git a/translations/bn/lessons/README.md b/translations/bn/lessons/README.md index ce2610e4..2a5540fa 100644 --- a/translations/bn/lessons/README.md +++ b/translations/bn/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # সংক্ষিপ্ত বিবরণ -![ডুডলে সংক্ষিপ্ত বিবরণ](../../../translated_images/bn/ai-overview.0857791951d19500.png) +![ডুডলে সংক্ষিপ্ত বিবরণ](../../../translated_images/bn/ai-overview.0857791951d19500.webp) > স্কেচনোট করেছেন [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/bn/lessons/X-Extras/X1-MultiModal/README.md b/translations/bn/lessons/X-Extras/X1-MultiModal/README.md index ad18f694..95f45a67 100644 --- a/translations/bn/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/bn/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: CLIP-এর মূল ধারণা হলো টেক্সট প্রম্পটের সাথে একটি ইমেজ তুলনা করা এবং নির্ধারণ করা যে ইমেজটি প্রম্পটের সাথে কতটা সঙ্গতিপূর্ণ। -![CLIP আর্কিটেকচার](../../../../../translated_images/bn/clip-arch.b3dbf20b4e8ed8be.png) +![CLIP আর্কিটেকচার](../../../../../translated_images/bn/clip-arch.b3dbf20b4e8ed8be.webp) > *ছবি [এই ব্লগ পোস্ট](https://openai.com/blog/clip/) থেকে নেওয়া হয়েছে* @@ -31,7 +31,7 @@ CLIP মডেল/লাইব্রেরি [OpenAI GitHub](https://github.com ধরা যাক আমাদের ইমেজগুলোকে বিড়াল, কুকুর এবং মানুষের মধ্যে শ্রেণীবদ্ধ করতে হবে। এই ক্ষেত্রে, আমরা মডেলটিকে একটি ইমেজ এবং একটি সিরিজ টেক্সট প্রম্পট দিতে পারি: "*একটি বিড়ালের ছবি*", "*একটি কুকুরের ছবি*", "*একটি মানুষের ছবি*"। ফলাফল হিসেবে প্রাপ্ত ৩টি সম্ভাবনার ভেক্টরে আমরা সর্বোচ্চ মানের ইনডেক্সটি নির্বাচন করব। -![ইমেজ ক্লাসিফিকেশনের জন্য CLIP](../../../../../translated_images/bn/clip-class.3af42ef0b2b19369.png) +![ইমেজ ক্লাসিফিকেশনের জন্য CLIP](../../../../../translated_images/bn/clip-class.3af42ef0b2b19369.webp) > *ছবি [এই ব্লগ পোস্ট](https://openai.com/blog/clip/) থেকে নেওয়া হয়েছে* @@ -55,13 +55,13 @@ VQGAN সম্পর্কে আরও জানতে [Taming Transformers](h VQGAN এবং সাধারণ GAN-এর মধ্যে একটি গুরুত্বপূর্ণ পার্থক্য হলো, সাধারণ GAN যেকোনো ইনপুট ভেক্টর থেকে একটি ভালো ইমেজ তৈরি করতে পারে, কিন্তু VQGAN সম্ভবত একটি অসঙ্গতিপূর্ণ ইমেজ তৈরি করবে। তাই, ইমেজ তৈরির প্রক্রিয়াকে আরও নির্দেশনা দিতে হবে, এবং এটি CLIP ব্যবহার করে করা যেতে পারে। -![VQGAN+CLIP আর্কিটেকচার](../../../../../translated_images/bn/vqgan.5027fe05051dfa31.png) +![VQGAN+CLIP আর্কিটেকচার](../../../../../translated_images/bn/vqgan.5027fe05051dfa31.webp) টেক্সট প্রম্পটের সাথে সঙ্গতিপূর্ণ একটি ইমেজ তৈরি করতে, আমরা কিছু র্যান্ডম এনকোডিং ভেক্টর দিয়ে শুরু করি যা VQGAN-এর মাধ্যমে একটি ইমেজ তৈরি করে। তারপর CLIP ব্যবহার করে একটি লস ফাংশন তৈরি করা হয় যা দেখায় ইমেজটি টেক্সট প্রম্পটের সাথে কতটা সঙ্গতিপূর্ণ। এরপর লক্ষ্য হলো এই লসকে কমানো, ব্যাক প্রোপাগেশন ব্যবহার করে ইনপুট ভেক্টর প্যারামিটারগুলো সামঞ্জস্য করা। VQGAN+CLIP বাস্তবায়নের জন্য একটি চমৎকার লাইব্রেরি হলো [Pixray](http://github.com/pixray/pixray) -![Pixray দ্বারা তৈরি ছবি](../../../../../translated_images/bn/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Pixray দ্বারা তৈরি ছবি](../../../../../translated_images/bn/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Pixray দ্বারা তৈরি ছবি](../../../../../translated_images/bn/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) +![Pixray দ্বারা তৈরি ছবি](../../../../../translated_images/bn/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp) | ![Pixray দ্বারা তৈরি ছবি](../../../../../translated_images/bn/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp) | ![Pixray দ্বারা তৈরি ছবি](../../../../../translated_images/bn/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp) ----|----|---- প্রম্পট থেকে তৈরি ছবি *একটি বই সহ তরুণ পুরুষ সাহিত্য শিক্ষকের একটি ক্লোজআপ জলরঙের প্রতিকৃতি* | প্রম্পট থেকে তৈরি ছবি *একটি কম্পিউটার সহ তরুণ নারী কম্পিউটার বিজ্ঞান শিক্ষকের একটি ক্লোজআপ তেল প্রতিকৃতি* | প্রম্পট থেকে তৈরি ছবি *একটি ব্ল্যাকবোর্ডের সামনে বৃদ্ধ পুরুষ গণিত শিক্ষকের একটি ক্লোজআপ তেল প্রতিকৃতি* diff --git a/translations/br/README.md b/translations/br/README.md index 2b9aeaaf..13dddf60 100644 --- a/translations/br/README.md +++ b/translations/br/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # Inteligência Artificial para Iniciantes - Um Currículo -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/br/ai-overview.0857791951d19500.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/br/ai-overview.0857791951d19500.webp)| |:---:| | AI For Beginners - _Sketchnote por [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/br/lessons/1-Intro/README.md b/translations/br/lessons/1-Intro/README.md index 10e919fd..5db483cf 100644 --- a/translations/br/lessons/1-Intro/README.md +++ b/translations/br/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Introdução à IA -![Resumo do conteúdo de Introdução à IA em um doodle](../../../../translated_images/br/ai-intro.bf28d1ac4235881c.png) +![Resumo do conteúdo de Introdução à IA em um doodle](../../../../translated_images/br/ai-intro.bf28d1ac4235881c.webp) > Sketchnote por [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Originalmente, os computadores foram inventados por [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage) para operar com números seguindo um procedimento bem definido - um algoritmo. Os computadores modernos, embora significativamente mais avançados que o modelo original proposto no século XIX, ainda seguem a mesma ideia de cálculos controlados. Assim, é possível programar um computador para fazer algo se soubermos a sequência exata de passos necessários para alcançar o objetivo. -![Foto de uma pessoa](../../../../translated_images/br/dsh_age.d212a30d4e54fb5f.png) +![Foto de uma pessoa](../../../../translated_images/br/dsh_age.d212a30d4e54fb5f.webp) > Foto por [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ Para mais informações, consulte **[Inteligência Artificial Geral](https://en. Um dos problemas ao lidar com o termo **[Inteligência](https://en.wikipedia.org/wiki/Intelligence)** é que não há uma definição clara para esse termo. Pode-se argumentar que inteligência está conectada ao **pensamento abstrato** ou à **autoconsciência**, mas não conseguimos defini-la adequadamente. -![Foto de um gato](../../../../translated_images/br/photo-cat.8c8e8fb760ffe457.jpg) +![Foto de um gato](../../../../translated_images/br/photo-cat.8c8e8fb760ffe457.webp) > [Foto](https://unsplash.com/photos/75715CVEJhI) por [Amber Kipp](https://unsplash.com/@sadmax) do Unsplash @@ -98,13 +98,13 @@ Alternativamente, podemos tentar modelar os elementos mais simples dentro de nos > | E o ML? | | > |--------------|-----------| -> | Parte da Inteligência Artificial que se baseia no aprendizado do computador para resolver um problema com base em alguns dados é chamada de **Machine Learning**. Não consideraremos o aprendizado de máquina clássico neste curso - recomendamos o currículo separado [Machine Learning para Iniciantes](http://aka.ms/ml-beginners). | ![ML para Iniciantes](../../../../translated_images/br/ml-for-beginners.9e4fed176fd5817d.png) | +> | Parte da Inteligência Artificial que se baseia no aprendizado do computador para resolver um problema com base em alguns dados é chamada de **Machine Learning**. Não consideraremos o aprendizado de máquina clássico neste curso - recomendamos o currículo separado [Machine Learning para Iniciantes](http://aka.ms/ml-beginners). | ![ML para Iniciantes](../../../../translated_images/br/ml-for-beginners.9e4fed176fd5817d.webp) | ## Um Breve Histórico da IA A Inteligência Artificial começou como um campo no meio do século XX. Inicialmente, o raciocínio simbólico era a abordagem predominante, e isso levou a uma série de sucessos importantes, como sistemas especialistas – programas de computador capazes de atuar como especialistas em alguns domínios de problemas limitados. No entanto, logo ficou claro que essa abordagem não escala bem. Extrair o conhecimento de um especialista, representá-lo em um computador e manter essa base de conhecimento precisa acaba sendo uma tarefa muito complexa e cara demais para ser prática em muitos casos. Isso levou ao chamado [Inverno da IA](https://en.wikipedia.org/wiki/AI_winter) na década de 1970. -Breve Histórico da IA +Breve Histórico da IA > Imagem por [Dmitry Soshnikov](http://soshnikov.com) @@ -124,7 +124,7 @@ Da mesma forma, podemos ver como a abordagem para criar “programas que falam * Assistentes modernos, como Cortana, Siri ou Google Assistant, são todos sistemas híbridos que usam redes neurais para converter fala em texto e reconhecer nossa intenção, e então empregam algum raciocínio ou algoritmos explícitos para realizar as ações necessárias. * No futuro, podemos esperar um modelo completamente baseado em redes neurais para lidar com diálogos por conta própria. As recentes redes neurais da família GPT e [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) mostram grande sucesso nisso. -a evolução do teste de Turing +a evolução do teste de Turing > Imagem de Dmitry Soshnikov, [foto](https://unsplash.com/photos/r8LmVbUKgns) por [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto), Unsplash ## Pesquisas Recentes em IA diff --git a/translations/br/lessons/2-Symbolic/Animals.ipynb b/translations/br/lessons/2-Symbolic/Animals.ipynb index 6688d5b7..4f0cd96a 100644 --- a/translations/br/lessons/2-Symbolic/Animals.ipynb +++ b/translations/br/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "Neste exemplo, vamos implementar um sistema simples baseado em conhecimento para determinar um animal com base em algumas características físicas. O sistema pode ser representado pela seguinte árvore AND-OR (esta é uma parte da árvore completa, podemos facilmente adicionar mais regras):\n", "\n", - "![](../../../../translated_images/br/AND-OR-Tree.5592d2c70187f283.png)\n" + "![](../../../../translated_images/br/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { diff --git a/translations/br/lessons/2-Symbolic/README.md b/translations/br/lessons/2-Symbolic/README.md index ca30e8cc..4a5f3d02 100644 --- a/translations/br/lessons/2-Symbolic/README.md +++ b/translations/br/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Representação de Conhecimento e Sistemas Especialistas -![Resumo do conteúdo de IA Simbólica](../../../../translated_images/br/ai-symbolic.715a30cb610411a6.png) +![Resumo do conteúdo de IA Simbólica](../../../../translated_images/br/ai-symbolic.715a30cb610411a6.webp) > Sketchnote por [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ Na maioria das vezes, não definimos estritamente o conhecimento, mas o alinhamo Assim, o problema da **representação de conhecimento** é encontrar uma maneira eficaz de representar o conhecimento dentro de um computador na forma de dados, para torná-lo automaticamente utilizável. Isso pode ser visto como um espectro: -![Espectro de representação de conhecimento](../../../../translated_images/br/knowledge-spectrum.b60df631852c0217.png) +![Espectro de representação de conhecimento](../../../../translated_images/br/knowledge-spectrum.b60df631852c0217.webp) > Imagem por [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Sintaxe de Bloco | Indentação | | | Um dos primeiros sucessos da IA simbólica foram os chamados **sistemas especialistas** - sistemas computacionais projetados para agir como especialistas em um domínio de problema limitado. Eles eram baseados em uma **base de conhecimento** extraída de um ou mais especialistas humanos e continham um **motor de inferência** que realizava algum raciocínio sobre ela. -![Arquitetura Humana](../../../../translated_images/br/arch-human.5d4d35f1bba3ab1c.png) | ![Sistema Baseado em Conhecimento](../../../../translated_images/br/arch-kbs.3ec5c150b09fa8da.png) +![Arquitetura Humana](../../../../translated_images/br/arch-human.5d4d35f1bba3ab1c.webp) | ![Sistema Baseado em Conhecimento](../../../../translated_images/br/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ Estrutura simplificada do sistema neural humano | Arquitetura de um sistema baseado em conhecimento @@ -106,7 +106,7 @@ Os sistemas especialistas são construídos como o sistema de raciocínio humano Como exemplo, vamos considerar o seguinte sistema especialista para determinar um animal com base em suas características físicas: -![Árvore AND-OR](../../../../translated_images/br/AND-OR-Tree.5592d2c70187f283.png) +![Árvore AND-OR](../../../../translated_images/br/AND-OR-Tree.5592d2c70187f283.webp) > Imagem por [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/br/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/br/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index 30e19e92..60ba16e6 100644 --- a/translations/br/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/br/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -1256,7 +1256,7 @@ "* Baixa perda de treinamento - o modelo consegue aproximar bem os dados de treinamento, pois tem poder expressivo suficiente.\n", "* A perda de validação pode ser muito maior do que a perda de treinamento e pode começar a aumentar durante o treinamento - isso ocorre porque o modelo \"memoriza\" os pontos de treinamento e perde a \"visão geral\".\n", "\n", - "![Overfitting](../../../../../translated_images/br/overfit.a0bd57f717c15769.png)\n", + "![Overfitting](../../../../../translated_images/br/overfit.a0bd57f717c15769.webp)\n", "\n", "> Nesta imagem, `x` representa os dados de treinamento, e `o` os dados de validação. À esquerda - modelo linear (uma camada), que aproxima bem a natureza dos dados. À direita - modelo com overfitting, que aproxima perfeitamente os dados de treinamento, mas deixa de fazer sentido com qualquer outro dado (o erro de validação é muito alto).\n" ] diff --git a/translations/br/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/br/lessons/3-NeuralNetworks/05-Frameworks/README.md index d86eafa9..83bef40d 100644 --- a/translations/br/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/br/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Overfitting é um conceito extremamente importante em aprendizado de máquina, e Considere o seguinte problema de aproximar 5 pontos (representados por `x` nos gráficos abaixo): -![linear](../../../../../translated_images/br/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/br/overfit2.131f5800ae10ca5e.jpg) +![linear](../../../../../translated_images/br/overfit1.f24b71c6f652e59e.webp) | ![overfit](../../../../../translated_images/br/overfit2.131f5800ae10ca5e.webp) -------------------------|-------------------------- **Modelo linear, 2 parâmetros** | **Modelo não-linear, 7 parâmetros** Erro de treinamento = 5.3 | Erro de treinamento = 0 @@ -79,7 +79,7 @@ Erro de validação = 5.1 | Erro de validação = 20 Como você pode ver no gráfico acima, o overfitting pode ser detectado por um erro de treinamento muito baixo e um erro de validação alto. Normalmente, durante o treinamento, veremos tanto o erro de treinamento quanto o de validação começarem a diminuir, e então, em algum momento, o erro de validação pode parar de diminuir e começar a aumentar. Isso será um sinal de overfitting e um indicador de que provavelmente devemos parar o treinamento nesse ponto (ou pelo menos salvar um snapshot do modelo). -![overfitting](../../../../../translated_images/br/Overfitting.408ad91cd90b4371.png) +![overfitting](../../../../../translated_images/br/Overfitting.408ad91cd90b4371.webp) ## Como prevenir o overfitting diff --git a/translations/br/lessons/3-NeuralNetworks/README.md b/translations/br/lessons/3-NeuralNetworks/README.md index 54ed22c4..0695d4df 100644 --- a/translations/br/lessons/3-NeuralNetworks/README.md +++ b/translations/br/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Introdução às Redes Neurais -![Resumo do conteúdo de Introdução às Redes Neurais em um desenho](../../../../translated_images/br/ai-neuralnetworks.1c687ae40bc86e83.png) +![Resumo do conteúdo de Introdução às Redes Neurais em um desenho](../../../../translated_images/br/ai-neuralnetworks.1c687ae40bc86e83.webp) Como discutimos na introdução, uma das formas de alcançar inteligência é treinar um **modelo computacional** ou um **cérebro artificial**. Desde meados do século XX, pesquisadores experimentaram diferentes modelos matemáticos, até que, nos últimos anos, essa abordagem se mostrou extremamente bem-sucedida. Esses modelos matemáticos do cérebro são chamados de **redes neurais**. @@ -36,13 +36,13 @@ Neste currículo, focaremos apenas em modelos de redes neurais. Na biologia, sabemos que nosso cérebro é composto por células neurais (neurônios), cada uma delas tendo múltiplas "entradas" (dendritos) e uma única "saída" (axônio). Tanto os dendritos quanto os axônios podem conduzir sinais elétricos, e as conexões entre eles — conhecidas como sinapses — podem apresentar diferentes graus de condutividade, que são regulados por neurotransmissores. -![Modelo de um Neurônio](../../../../translated_images/br/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![Modelo de um Neurônio](../../../../translated_images/br/artneuron.1a5daa88d20ebe6f.png) +![Modelo de um Neurônio](../../../../translated_images/br/synapse-wikipedia.ed20a9e4726ea1c6.webp) | ![Modelo de um Neurônio](../../../../translated_images/br/artneuron.1a5daa88d20ebe6f.webp) ----|---- Neurônio Real *([Imagem](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) da Wikipedia)* | Neurônio Artificial *(Imagem do Autor)* Assim, o modelo matemático mais simples de um neurônio contém várias entradas X1, ..., XN e uma saída Y, e uma série de pesos W1, ..., WN. A saída é calculada como: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) onde f é alguma **função de ativação** não linear. diff --git a/translations/br/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/br/lessons/4-ComputerVision/06-IntroCV/README.md index 7343159e..61a024dc 100644 --- a/translations/br/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/br/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ Em nosso [OpenCV Notebook](OpenCV.ipynb), damos alguns exemplos de quando a vis * **Pré-processamento de uma fotografia de um livro em Braille**. Focamos em como podemos usar limiarização, detecção de características, transformação de perspectiva e manipulações NumPy para separar símbolos individuais em Braille para posterior classificação por uma rede neural. -![Imagem Braille](../../../../../translated_images/br/braille.341962ff76b1bd70.jpeg) | ![Imagem Braille Pré-processada](../../../../../translated_images/br/braille-result.46530fea020b03c7.png) | ![Símbolos Braille](../../../../../translated_images/br/braille-symbols.0159185ab69d5339.png) +![Imagem Braille](../../../../../translated_images/br/braille.341962ff76b1bd70.webp) | ![Imagem Braille Pré-processada](../../../../../translated_images/br/braille-result.46530fea020b03c7.webp) | ![Símbolos Braille](../../../../../translated_images/br/braille-symbols.0159185ab69d5339.webp) ----|-----|----- > Imagem de [OpenCV.ipynb](OpenCV.ipynb) * **Detectando movimento em vídeo usando diferença de quadros**. Se a câmera estiver fixa, os quadros do feed da câmera devem ser bastante semelhantes entre si. Como os quadros são representados como arrays, apenas subtraindo esses arrays de dois quadros subsequentes obteremos a diferença de pixels, que deve ser baixa para quadros estáticos e se tornar maior quando houver movimento substancial na imagem. -![Imagem de quadros de vídeo e diferenças de quadros](../../../../../translated_images/br/frame-difference.706f805491a0883c.png) +![Imagem de quadros de vídeo e diferenças de quadros](../../../../../translated_images/br/frame-difference.706f805491a0883c.webp) > Imagem de [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ Em nosso [OpenCV Notebook](OpenCV.ipynb), damos alguns exemplos de quando a vis - **Fluxo Óptico Denso** calcula o campo vetorial que mostra para cada pixel onde ele está se movendo. - **Fluxo Óptico Esparso** é baseado em pegar algumas características distintivas na imagem (por exemplo, bordas) e construir sua trajetória de quadro a quadro. -![Imagem de Fluxo Óptico](../../../../../translated_images/br/optical.1f4a94464579a83a.png) +![Imagem de Fluxo Óptico](../../../../../translated_images/br/optical.1f4a94464579a83a.webp) > Imagem de [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/br/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/br/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 49471e64..33d4c25b 100644 --- a/translations/br/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/br/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 é uma rede que alcançou 92,7% de precisão na classificação top-5 do ImageNet em 2014. Ela possui a seguinte estrutura de camadas: -![Camadas do ImageNet](../../../../../translated_images/br/vgg-16-arch1.d901a5583b3a51ba.jpg) +![Camadas do ImageNet](../../../../../translated_images/br/vgg-16-arch1.d901a5583b3a51ba.webp) Como você pode ver, a VGG segue uma arquitetura tradicional em forma de pirâmide, que é uma sequência de camadas de convolução e pooling. -![Pirâmide do ImageNet](../../../../../translated_images/br/vgg-16-arch.64ff2137f50dd49f.jpg) +![Pirâmide do ImageNet](../../../../../translated_images/br/vgg-16-arch.64ff2137f50dd49f.webp) > Imagem de [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/br/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/br/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index b7bf2d0e..56ff46a0 100644 --- a/translations/br/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/br/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -262,7 +262,7 @@ "\n", "Assim, em uma CNN típica, haveria várias camadas de convolução, com camadas de pooling entre elas para diminuir as dimensões da imagem. Também aumentaríamos o número de filtros, porque à medida que os padrões se tornam mais avançados, há mais combinações interessantes possíveis que precisamos buscar.\n", "\n", - "![Uma imagem mostrando várias camadas de convolução com camadas de pooling.](../../../../../translated_images/br/cnn-pyramid.85915455759ef0ce.png)\n", + "![Uma imagem mostrando várias camadas de convolução com camadas de pooling.](../../../../../translated_images/br/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "Devido à diminuição das dimensões espaciais e ao aumento das dimensões de características/filtros, essa arquitetura também é chamada de **arquitetura piramidal**.\n" ] diff --git a/translations/br/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/br/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index b315ddb6..63aa1100 100644 --- a/translations/br/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/br/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -358,7 +358,7 @@ "\n", "Assim, em uma CNN típica, haveria várias camadas de convolução, com camadas de pooling entre elas para diminuir as dimensões da imagem. Também aumentaríamos o número de filtros, porque, à medida que os padrões se tornam mais avançados, há mais combinações interessantes que precisamos procurar.\n", "\n", - "![Uma imagem mostrando várias camadas de convolução com camadas de pooling.](../../../../../translated_images/br/cnn-pyramid.85915455759ef0ce.png)\n", + "![Uma imagem mostrando várias camadas de convolução com camadas de pooling.](../../../../../translated_images/br/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "Devido à diminuição das dimensões espaciais e ao aumento das dimensões de características/filtros, essa arquitetura também é chamada de **arquitetura piramidal**.\n" ] diff --git a/translations/br/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/br/lessons/4-ComputerVision/07-ConvNets/README.md index 474a726d..f12866df 100644 --- a/translations/br/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/br/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ Na vida real, queremos ser capazes de reconhecer objetos em uma imagem independe Para extrair padrões, utilizaremos o conceito de **filtros convolucionais**. Como você sabe, uma imagem é representada por uma matriz 2D ou um tensor 3D com profundidade de cor. Aplicar um filtro significa que pegamos uma matriz relativamente pequena chamada **kernel do filtro**, e para cada pixel na imagem original calculamos a média ponderada com os pontos vizinhos. Podemos imaginar isso como uma pequena janela deslizando sobre toda a imagem e calculando a média de todos os pixels de acordo com os pesos na matriz do kernel do filtro. -![Filtro de Borda Vertical](../../../../../translated_images/br/filter-vert.b7148390ca0bc356.png) | ![Filtro de Borda Horizontal](../../../../../translated_images/br/filter-horiz.59b80ed4feb946ef.png) +![Filtro de Borda Vertical](../../../../../translated_images/br/filter-vert.b7148390ca0bc356.webp) | ![Filtro de Borda Horizontal](../../../../../translated_images/br/filter-horiz.59b80ed4feb946ef.webp) ----|---- > Imagem por Dmitry Soshnikov @@ -38,7 +38,7 @@ O funcionamento das CNNs é baseado nas seguintes ideias importantes: * Podemos projetar a rede de forma que os filtros sejam treinados automaticamente * Podemos usar a mesma abordagem para encontrar padrões em características de alto nível, não apenas na imagem original. Assim, a extração de características pelas CNNs funciona em uma hierarquia de características, começando com combinações de pixels de baixo nível até combinações de alto nível de partes da imagem. -![Extração Hierárquica de Características](../../../../../translated_images/br/FeatureExtractionCNN.d9b456cbdae7cb64.png) +![Extração Hierárquica de Características](../../../../../translated_images/br/FeatureExtractionCNN.d9b456cbdae7cb64.webp) > Imagem de [um artigo de Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), baseado em [sua pesquisa](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ A maioria das CNNs usadas para processamento de imagens segue a chamada arquitet Como exemplo, vejamos a arquitetura do VGG-16, uma rede que alcançou 92,7% de precisão na classificação top-5 do ImageNet em 2014: -![Camadas do ImageNet](../../../../../translated_images/br/vgg-16-arch1.d901a5583b3a51ba.jpg) +![Camadas do ImageNet](../../../../../translated_images/br/vgg-16-arch1.d901a5583b3a51ba.webp) -![Pirâmide do ImageNet](../../../../../translated_images/br/vgg-16-arch.64ff2137f50dd49f.jpg) +![Pirâmide do ImageNet](../../../../../translated_images/br/vgg-16-arch.64ff2137f50dd49f.webp) > Imagem de [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/br/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/br/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 9fa971b5..91cbb1f6 100644 --- a/translations/br/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/br/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ Você precisa treinar uma rede neural convolucional para classificar diferentes Usaremos o [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/), que contém imagens de 37 diferentes raças de cães e gatos. -![Conjunto de dados com o qual trabalharemos](../../../../../../translated_images/br/data.50b2a9d5484bdbf0.png) +![Conjunto de dados com o qual trabalharemos](../../../../../../translated_images/br/data.50b2a9d5484bdbf0.webp) Para baixar o conjunto de dados, use este trecho de código: diff --git a/translations/br/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/br/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index b04e1887..c3ab2259 100644 --- a/translations/br/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/br/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "Para visualizar o gato ideal, começaremos com uma imagem de ruído aleatório e tentaremos usar a técnica de otimização por descida de gradiente para ajustar a imagem de forma que a rede reconheça um gato.\n", "\n", - "![Loop de Otimização](../../../../../translated_images/br/ideal-cat-loop.999fbb8ff306e044.png)\n", + "![Loop de Otimização](../../../../../translated_images/br/ideal-cat-loop.999fbb8ff306e044.webp)\n", "\n", "Aqui está nossa imagem inicial:\n" ] diff --git a/translations/br/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/br/lessons/4-ComputerVision/08-TransferLearning/README.md index 373e2fff..3373fb51 100644 --- a/translations/br/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/br/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Tanto o Keras quanto o PyTorch possuem funções para carregar facilmente pesos Aqui estão características extraídas de uma imagem de um gato pela rede VGG-16: -![Características extraídas pelo VGG-16](../../../../../translated_images/br/features.6291f9c7ba3a0b95.png) +![Características extraídas pelo VGG-16](../../../../../translated_images/br/features.6291f9c7ba3a0b95.webp) ## Conjunto de Dados de Gatos vs. Cachorros @@ -48,19 +48,19 @@ Uma rede neural pré-treinada contém diferentes padrões em seu *cérebro*, inc Uma abordagem que podemos adotar é começar com uma imagem aleatória e, em seguida, tentar usar a técnica de **otimização por descida de gradiente** para ajustar essa imagem de forma que a rede comece a pensar que é um gato. -![Loop de Otimização de Imagem](../../../../../translated_images/br/ideal-cat-loop.999fbb8ff306e044.png) +![Loop de Otimização de Imagem](../../../../../translated_images/br/ideal-cat-loop.999fbb8ff306e044.webp) No entanto, se fizermos isso, receberemos algo muito semelhante a um ruído aleatório. Isso ocorre porque *existem muitas maneiras de fazer a rede pensar que a imagem de entrada é um gato*, incluindo algumas que não fazem sentido visualmente. Embora essas imagens contenham muitos padrões típicos de um gato, não há nada que as restrinja a serem visualmente distintas. Para melhorar o resultado, podemos adicionar outro termo à função de perda, chamado de **perda de variação**. É uma métrica que mostra quão semelhantes são os pixels vizinhos da imagem. Minimizar a perda de variação torna a imagem mais suave e elimina o ruído, revelando padrões mais visualmente atraentes. Aqui está um exemplo de tais imagens "ideais", classificadas como gato e zebra com alta probabilidade: -![Gato Ideal](../../../../../translated_images/br/ideal-cat.203dd4597643d6b0.png) | ![Zebra Ideal](../../../../../translated_images/br/ideal-zebra.7f70e8b54ee15a7a.png) +![Gato Ideal](../../../../../translated_images/br/ideal-cat.203dd4597643d6b0.webp) | ![Zebra Ideal](../../../../../translated_images/br/ideal-zebra.7f70e8b54ee15a7a.webp) -----|----- *Gato Ideal* | *Zebra Ideal* Uma abordagem semelhante pode ser usada para realizar os chamados **ataques adversariais** em uma rede neural. Suponha que queremos enganar uma rede neural e fazer um cachorro parecer um gato. Se pegarmos a imagem de um cachorro, que é reconhecida pela rede como um cachorro, podemos ajustá-la um pouco usando otimização por descida de gradiente até que a rede comece a classificá-la como um gato: -![Imagem de um Cachorro](../../../../../translated_images/br/original-dog.8f68a67d2fe0911f.png) | ![Imagem de um cachorro classificada como gato](../../../../../translated_images/br/adversarial-dog.d9fc7773b0142b89.png) +![Imagem de um Cachorro](../../../../../translated_images/br/original-dog.8f68a67d2fe0911f.webp) | ![Imagem de um cachorro classificada como gato](../../../../../translated_images/br/adversarial-dog.d9fc7773b0142b89.webp) -----|----- *Imagem original de um cachorro* | *Imagem de um cachorro classificada como gato* diff --git a/translations/br/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/br/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index 016f4116..eea3aa66 100644 --- a/translations/br/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/br/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "Como estamos treinando o autoencoder para capturar o máximo de informações da imagem original possível para uma reconstrução precisa, a rede tenta encontrar a melhor **representação** das imagens de entrada para capturar o significado.\n", "\n", - "![Diagrama do AutoEncoder](../../../../../translated_images/br/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![Diagrama do AutoEncoder](../../../../../translated_images/br/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "> Imagem do [blog do Keras](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", diff --git a/translations/br/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/br/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index 700622d1..bd60fc03 100644 --- a/translations/br/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/br/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "Como estamos treinando o autoencoder para capturar o máximo de informações possíveis da imagem original para uma reconstrução precisa, a rede tenta encontrar o melhor **embedding** das imagens de entrada para capturar o significado.\n", "\n", - "![Diagrama do AutoEncoder](../../../../../translated_images/br/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![Diagrama do AutoEncoder](../../../../../translated_images/br/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "*Imagem do [blog do Keras](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", diff --git a/translations/br/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/br/lessons/4-ComputerVision/09-Autoencoders/README.md index 6c8fdf70..9961c0cf 100644 --- a/translations/br/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/br/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ No entanto, podemos querer usar dados brutos (não rotulados) para treinar extra Como estamos treinando um autoencoder para capturar o máximo de informações da imagem original possível para uma reconstrução precisa, a rede tenta encontrar a melhor **representação** das imagens de entrada para capturar seu significado. -![Diagrama de AutoEncoder](../../../../../translated_images/br/autoencoder_schema.5e6fc9ad98a5eb61.jpg) +![Diagrama de AutoEncoder](../../../../../translated_images/br/autoencoder_schema.5e6fc9ad98a5eb61.webp) > Imagem do [blog do Keras](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/br/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/br/lessons/4-ComputerVision/11-ObjectDetection/README.md index e86e1eaa..0b28ddc9 100644 --- a/translations/br/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/br/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ Os modelos de classificação de imagens que abordamos até agora tomavam uma im ## [Quiz pré-aula](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Detecção de Objetos](../../../../../translated_images/br/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) +![Detecção de Objetos](../../../../../translated_images/br/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp) > Imagem do [site YOLO v2](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ Suponha que queremos encontrar um gato em uma imagem. Uma abordagem muito ingên 2. Executar a classificação de imagem em cada bloco. 3. Os blocos que resultarem em uma ativação suficientemente alta podem ser considerados como contendo o objeto em questão. -![Detecção Ingênua de Objetos](../../../../../translated_images/br/naive-detection.e7f1ba220ccd08c6.png) +![Detecção Ingênua de Objetos](../../../../../translated_images/br/naive-detection.e7f1ba220ccd08c6.webp) > *Imagem do [Notebook de Exercícios](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ Você pode encontrar os seguintes conjuntos de dados para essa tarefa: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 classes * [COCO](http://cocodataset.org/#home) - Objetos Comuns em Contexto. 80 classes, caixas delimitadoras e máscaras de segmentação -![COCO](../../../../../translated_images/br/coco-examples.71bc60380fa6cceb.jpg) +![COCO](../../../../../translated_images/br/coco-examples.71bc60380fa6cceb.webp) ## Métricas de Detecção de Objetos @@ -50,7 +50,7 @@ Você pode encontrar os seguintes conjuntos de dados para essa tarefa: Enquanto na classificação de imagens é fácil medir o desempenho do algoritmo, na detecção de objetos precisamos medir tanto a correção da classe quanto a precisão da localização inferida da caixa delimitadora. Para este último, usamos a chamada **Interseção sobre União** (IoU), que mede o quão bem duas caixas (ou duas áreas arbitrárias) se sobrepõem. -![IoU](../../../../../translated_images/br/iou_equation.9a4751d40fff4e11.png) +![IoU](../../../../../translated_images/br/iou_equation.9a4751d40fff4e11.webp) > *Figura 2 de [este excelente post sobre IoU](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ Existem duas grandes classes de algoritmos de detecção de objetos: [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) usa [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) para gerar uma estrutura hierárquica de regiões ROI, que são então passadas por extratores de características CNN e classificadores SVM para determinar a classe do objeto, e regressão linear para determinar as coordenadas da *caixa delimitadora*. [Artigo Oficial](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/br/rcnn1.cae407020dfb1d1f.png) +![RCNN](../../../../../translated_images/br/rcnn1.cae407020dfb1d1f.webp) > *Imagem de van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/br/rcnn2.2d9530bb83516484.png) +![RCNN-1](../../../../../translated_images/br/rcnn2.2d9530bb83516484.webp) > *Imagens de [este blog](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -110,7 +110,7 @@ Existem duas grandes classes de algoritmos de detecção de objetos: Essa abordagem é semelhante à R-CNN, mas as regiões são definidas após as camadas de convolução terem sido aplicadas. -![FRCNN](../../../../../translated_images/br/f-rcnn.3cda6d9bb4188875.png) +![FRCNN](../../../../../translated_images/br/f-rcnn.3cda6d9bb4188875.webp) > Imagem do [Artigo Oficial](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ Essa abordagem é semelhante à R-CNN, mas as regiões são definidas após as c A ideia principal dessa abordagem é usar uma rede neural para prever ROIs - a chamada *Rede de Proposta de Região*. [Artigo](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/br/faster-rcnn.8d46c099b87ef30a.png) +![FasterRCNN](../../../../../translated_images/br/faster-rcnn.8d46c099b87ef30a.webp) > Imagem do [Artigo Oficial](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ Este algoritmo é ainda mais rápido que o Faster R-CNN. A ideia principal é a 1. As características são processadas por **Position-Sensitive Score Map**. Cada objeto de $C$ classes é dividido em regiões $k\times k$, e treinamos para prever partes dos objetos. 1. Para cada parte das regiões $k\times k$, todas as redes votam pelas classes de objetos, e a classe de objeto com o maior número de votos é selecionada. -![r-fcn image](../../../../../translated_images/br/r-fcn.13eb88158b99a3da.png) +![r-fcn image](../../../../../translated_images/br/r-fcn.13eb88158b99a3da.webp) > Imagem do [Artigo Oficial](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO é um algoritmo de uma única passagem em tempo real. A ideia principal é * A imagem é dividida em regiões $S\times S$. * Para cada região, **CNN** prevê $n$ objetos possíveis, coordenadas da *caixa delimitadora* e *confiança*=*probabilidade* * IoU. - ![YOLO](../../../../../translated_images/br/yolo.a2648ec82ee8bb4e.png) + ![YOLO](../../../../../translated_images/br/yolo.a2648ec82ee8bb4e.webp) > Imagem do [Artigo Oficial](https://arxiv.org/abs/1506.02640) diff --git a/translations/br/lessons/4-ComputerVision/README.md b/translations/br/lessons/4-ComputerVision/README.md index e03be1dd..06d0728f 100644 --- a/translations/br/lessons/4-ComputerVision/README.md +++ b/translations/br/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Visão Computacional -![Resumo do conteúdo de Visão Computacional em um desenho](../../../../translated_images/br/ai-computervision.6506ebebac3fbf76.png) +![Resumo do conteúdo de Visão Computacional em um desenho](../../../../translated_images/br/ai-computervision.6506ebebac3fbf76.webp) Nesta seção, vamos aprender sobre: diff --git a/translations/br/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/br/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index c9613cda..39c28817 100644 --- a/translations/br/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/br/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "A representação vetorial **Bag of Words** (BoW) é a representação vetorial tradicional mais comumente usada. Cada palavra está vinculada a um índice do vetor, e o elemento do vetor contém o número de ocorrências de uma palavra em um determinado documento.\n", "\n", - "![Imagem mostrando como uma representação vetorial Bag of Words é representada na memória.](../../../../../translated_images/br/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![Imagem mostrando como uma representação vetorial Bag of Words é representada na memória.](../../../../../translated_images/br/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Note**: Você também pode pensar no BoW como a soma de todos os vetores one-hot-encoded para palavras individuais no texto.\n", "\n", diff --git a/translations/br/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/br/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index 8d3d8d47..2ec103e7 100644 --- a/translations/br/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/br/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "A representação vetorial **Bag-of-words** (BoW) é a forma tradicional mais simples de entender uma representação vetorial. Cada palavra é vinculada a um índice no vetor, e um elemento do vetor contém o número de ocorrências de cada palavra em um determinado documento.\n", "\n", - "![Imagem mostrando como uma representação vetorial Bag-of-words é armazenada na memória.](../../../../../translated_images/br/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![Imagem mostrando como uma representação vetorial Bag-of-words é armazenada na memória.](../../../../../translated_images/br/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Note**: Você também pode pensar no BoW como a soma de todos os vetores one-hot codificados para as palavras individuais no texto.\n", "\n", diff --git a/translations/br/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/br/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 714e2242..244795f0 100644 --- a/translations/br/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/br/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "Ao usar a camada de embedding como a primeira camada em nossa rede, podemos mudar do modelo bag-of-words para o modelo **embedding bag**, onde primeiro convertemos cada palavra em nosso texto no embedding correspondente e, em seguida, calculamos alguma função de agregação sobre todos esses embeddings, como `sum`, `average` ou `max`.\n", "\n", - "![Imagem mostrando um classificador de embedding para cinco palavras em sequência.](../../../../../translated_images/br/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![Imagem mostrando um classificador de embedding para cinco palavras em sequência.](../../../../../translated_images/br/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "Nossa rede neural classificadora começará com uma camada de embedding, seguida por uma camada de agregação e, por fim, um classificador linear no topo:\n" ] @@ -176,7 +176,7 @@ "\n", "Na arquitetura anterior, precisávamos preencher todas as sequências para que tivessem o mesmo comprimento, a fim de ajustá-las em um minibatch. Essa não é a maneira mais eficiente de representar sequências de comprimento variável - outra abordagem seria usar um vetor de **offset**, que armazenaria os deslocamentos de todas as sequências em um único vetor grande.\n", "\n", - "![Imagem mostrando uma representação de sequência com offset](../../../../../translated_images/br/offset-sequence-representation.eb73fcefb29b46ee.png)\n", + "![Imagem mostrando uma representação de sequência com offset](../../../../../translated_images/br/offset-sequence-representation.eb73fcefb29b46ee.webp)\n", "\n", "> **Note**: Na imagem acima, mostramos uma sequência de caracteres, mas em nosso exemplo estamos trabalhando com sequências de palavras. No entanto, o princípio geral de representar sequências com um vetor de offset permanece o mesmo.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW é mais rápido, enquanto skip-gram é mais lento, mas faz um trabalho melhor ao representar palavras menos frequentes.\n", "\n", - "![Imagem mostrando os algoritmos CBoW e Skip-Gram para converter palavras em vetores.](../../../../../translated_images/br/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![Imagem mostrando os algoritmos CBoW e Skip-Gram para converter palavras em vetores.](../../../../../translated_images/br/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Para experimentar com embeddings word2vec pré-treinados no conjunto de dados Google News, podemos usar a biblioteca **gensim**. Abaixo, encontramos as palavras mais semelhantes a 'neural'.\n", "\n", diff --git a/translations/br/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/br/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index 20fdfd17..0e871d1f 100644 --- a/translations/br/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/br/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "Ao usar uma camada de embedding como a primeira camada da nossa rede, podemos mudar de um modelo de bag-of-words para um modelo de **embedding bag**, onde primeiro convertemos cada palavra do nosso texto no embedding correspondente e, em seguida, calculamos alguma função de agregação sobre todos esses embeddings, como `sum`, `average` ou `max`.\n", "\n", - "![Imagem mostrando um classificador com embedding para cinco palavras em sequência.](../../../../../translated_images/br/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![Imagem mostrando um classificador com embedding para cinco palavras em sequência.](../../../../../translated_images/br/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "Nossa rede neural classificadora consiste nas seguintes camadas:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW é mais rápido, enquanto o skip-gram, embora mais lento, faz um trabalho melhor ao representar palavras menos frequentes.\n", "\n", - "![Imagem mostrando os algoritmos CBoW e Skip-Gram para converter palavras em vetores.](../../../../../translated_images/br/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![Imagem mostrando os algoritmos CBoW e Skip-Gram para converter palavras em vetores.](../../../../../translated_images/br/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Para experimentar o embedding Word2Vec pré-treinado no conjunto de dados do Google News, podemos usar a biblioteca **gensim**. Abaixo, encontramos as palavras mais semelhantes a 'neural'.\n", "\n", diff --git a/translations/br/lessons/5-NLP/14-Embeddings/README.md b/translations/br/lessons/5-NLP/14-Embeddings/README.md index 6e720d2f..53035ff0 100644 --- a/translations/br/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/br/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ Assim, a camada de embedding receberia uma palavra como entrada e produziria um Ao usar uma camada de embedding como a primeira camada em nossa rede de classificação, podemos mudar de um modelo bag-of-words para um modelo **embedding bag**, onde primeiro convertemos cada palavra em nosso texto no embedding correspondente e, em seguida, calculamos alguma função agregada sobre todos esses embeddings, como `sum`, `average` ou `max`. -![Imagem mostrando um classificador de embedding para cinco palavras de sequência.](../../../../../translated_images/br/embedding-classifier-example.b77f021a7ee67eee.png) +![Imagem mostrando um classificador de embedding para cinco palavras de sequência.](../../../../../translated_images/br/embedding-classifier-example.b77f021a7ee67eee.webp) > Imagem do autor @@ -40,7 +40,7 @@ Para isso, precisamos pré-treinar nosso modelo de embedding em uma grande cole CBoW é mais rápido, enquanto skip-gram é mais lento, mas faz um trabalho melhor ao representar palavras menos frequentes. -![Imagem mostrando os algoritmos CBoW e Skip-Gram para converter palavras em vetores.](../../../../../translated_images/br/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![Imagem mostrando os algoritmos CBoW e Skip-Gram para converter palavras em vetores.](../../../../../translated_images/br/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Imagem retirada [deste artigo](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/br/lessons/5-NLP/15-LanguageModeling/README.md b/translations/br/lessons/5-NLP/15-LanguageModeling/README.md index 35b44ad8..65639386 100644 --- a/translations/br/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/br/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ Nos nossos exemplos anteriores, usamos embeddings semânticos pré-treinados, ma * **Continuous Bag-of-Words** (CBoW), onde prevemos o token do meio $W_0$ em uma sequência de tokens $W_{-N}$, ..., $W_N$. * **Skip-gram**, onde prevemos um conjunto de tokens vizinhos {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} a partir do token do meio $W_0$. -![imagem do artigo sobre conversão de palavras em vetores](../../../../../translated_images/br/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![imagem do artigo sobre conversão de palavras em vetores](../../../../../translated_images/br/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Imagem retirada [deste artigo](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/br/lessons/5-NLP/16-RNN/README.md b/translations/br/lessons/5-NLP/16-RNN/README.md index 53a674a4..d19ef8eb 100644 --- a/translations/br/lessons/5-NLP/16-RNN/README.md +++ b/translations/br/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ Nas seções anteriores, utilizamos representações semânticas ricas de texto Para capturar o significado de uma sequência de texto, precisamos usar outra arquitetura de rede neural, chamada de **rede neural recorrente**, ou RNN. Na RNN, passamos nossa frase pela rede um símbolo de cada vez, e a rede produz algum **estado**, que então passamos novamente para a rede junto com o próximo símbolo. -![RNN](../../../../../translated_images/br/rnn.27f5c29c53d727b5.png) +![RNN](../../../../../translated_images/br/rnn.27f5c29c53d727b5.webp) > Imagem do autor @@ -61,7 +61,7 @@ Discutimos redes recorrentes que operam em uma direção, do início de uma sequ Uma rede recorrente, seja unidirecional ou bidirecional, captura certos padrões dentro de uma sequência e pode armazená-los em um vetor de estado ou passá-los para a saída. Assim como nas redes convolucionais, podemos construir outra camada recorrente sobre a primeira para capturar padrões de nível superior e construir a partir dos padrões de baixo nível extraídos pela primeira camada. Isso nos leva à noção de uma **RNN multicamada**, que consiste em duas ou mais redes recorrentes, onde a saída da camada anterior é passada para a próxima camada como entrada. -![Imagem mostrando uma RNN LSTM multicamada](../../../../../translated_images/br/multi-layer-lstm.dd975e29bb2a59fe.jpg) +![Imagem mostrando uma RNN LSTM multicamada](../../../../../translated_images/br/multi-layer-lstm.dd975e29bb2a59fe.webp) *Imagem retirada [deste post maravilhoso](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) de Fernando López* diff --git a/translations/br/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/br/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index 849d0812..a3ca811b 100644 --- a/translations/br/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/br/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "Uma rede recorrente, seja unidirecional ou bidirecional, captura certos padrões dentro de uma sequência e pode armazená-los no vetor de estado ou passá-los para a saída. Assim como nas redes convolucionais, podemos construir outra camada recorrente sobre a primeira para capturar padrões de nível mais alto, construídos a partir de padrões de baixo nível extraídos pela primeira camada. Isso nos leva à noção de **RNN multicamada**, que consiste em duas ou mais redes recorrentes, onde a saída da camada anterior é passada para a próxima camada como entrada.\n", "\n", - "![Imagem mostrando uma RNN LSTM multicamada](../../../../../translated_images/br/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![Imagem mostrando uma RNN LSTM multicamada](../../../../../translated_images/br/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*Imagem retirada [deste post maravilhoso](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) por Fernando López*\n", "\n", diff --git a/translations/br/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/br/lessons/5-NLP/16-RNN/RNNTF.ipynb index af3ea88d..0ad61fde 100644 --- a/translations/br/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/br/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "Para capturar o significado de uma sequência de texto, usaremos uma arquitetura de rede neural chamada **rede neural recorrente**, ou RNN. Ao usar uma RNN, passamos nossa sentença pela rede um token de cada vez, e a rede produz algum **estado**, que então passamos novamente para a rede junto com o próximo token.\n", "\n", - "![Imagem mostrando um exemplo de geração de rede neural recorrente.](../../../../../translated_images/br/rnn.27f5c29c53d727b5.png)\n", + "![Imagem mostrando um exemplo de geração de rede neural recorrente.](../../../../../translated_images/br/rnn.27f5c29c53d727b5.webp)\n", "\n", "Dada a sequência de entrada de tokens $X_0,\\dots,X_n$, a RNN cria uma sequência de blocos de rede neural e treina essa sequência de ponta a ponta usando retropropagação. Cada bloco de rede recebe um par $(X_i,S_i)$ como entrada e produz $S_{i+1}$ como resultado. O estado final $S_n$ ou a saída $Y_n$ é enviado para um classificador linear para produzir o resultado. Todos os blocos de rede compartilham os mesmos pesos e são treinados de ponta a ponta usando uma única passagem de retropropagação.\n", "\n", @@ -369,7 +369,7 @@ "\n", "Redes recorrentes, sejam unidirecionais ou bidirecionais, capturam padrões dentro de uma sequência e os armazenam em vetores de estado ou os retornam como saída. Assim como nas redes convolucionais, podemos construir outra camada recorrente após a primeira para capturar padrões de nível mais alto, construídos a partir de padrões de nível mais baixo extraídos pela primeira camada. Isso nos leva à noção de uma **RNN multicamada**, que consiste em duas ou mais redes recorrentes, onde a saída da camada anterior é passada para a próxima camada como entrada.\n", "\n", - "![Imagem mostrando uma RNN multicamada de memória de longo e curto prazo](../../../../../translated_images/br/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![Imagem mostrando uma RNN multicamada de memória de longo e curto prazo](../../../../../translated_images/br/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*Imagem retirada [deste post incrível](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) por Fernando López.*\n", "\n", diff --git a/translations/br/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/br/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index 43381200..af3e7725 100644 --- a/translations/br/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/br/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "A maneira como treinaremos a RNN para gerar texto é a seguinte. A cada etapa, pegaremos uma sequência de caracteres de comprimento `nchars` e pediremos à rede que gere o próximo caractere de saída para cada caractere de entrada:\n", "\n", - "![Imagem mostrando um exemplo de geração de RNN com a palavra 'HELLO'.](../../../../../translated_images/br/rnn-generate.56c54afb52f9781d.png)\n", + "![Imagem mostrando um exemplo de geração de RNN com a palavra 'HELLO'.](../../../../../translated_images/br/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "Dependendo do cenário real, também podemos querer incluir alguns caracteres especiais, como *fim de sequência* ``. No nosso caso, queremos apenas treinar a rede para geração contínua de texto, então fixaremos o tamanho de cada sequência para ser igual a `nchars` tokens. Consequentemente, cada exemplo de treinamento consistirá em `nchars` entradas e `nchars` saídas (que são a sequência de entrada deslocada um símbolo para a esquerda). O minibatch consistirá de várias dessas sequências.\n", "\n", diff --git a/translations/br/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/br/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index 3c23a2a8..54a63bd7 100644 --- a/translations/br/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/br/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -111,7 +111,7 @@ "\n", "A maneira como treinaremos a RNN para gerar títulos de notícias é a seguinte. A cada etapa, pegaremos um título, que será alimentado em uma RNN, e para cada caractere de entrada pediremos à rede que gere o próximo caractere de saída:\n", "\n", - "![Imagem mostrando um exemplo de geração de RNN da palavra 'HELLO'.](../../../../../translated_images/br/rnn-generate.56c54afb52f9781d.png)\n", + "![Imagem mostrando um exemplo de geração de RNN da palavra 'HELLO'.](../../../../../translated_images/br/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "Para o último caractere da nossa sequência, pediremos à rede que gere o token ``.\n", "\n", diff --git a/translations/br/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/br/lessons/5-NLP/17-GenerativeNetworks/README.md index 4d4ce5c9..856b4e34 100644 --- a/translations/br/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/br/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ Na arquitetura de RNN que discutimos na unidade anterior, cada unidade RNN produ Isso permite diferentes arquiteturas neurais, como mostrado na imagem abaixo: -![Imagem mostrando padrões comuns de redes neurais recorrentes.](../../../../../translated_images/br/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Imagem mostrando padrões comuns de redes neurais recorrentes.](../../../../../translated_images/br/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > Imagem do post no blog [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) por [Andrej Karpaty](http://karpathy.github.io/) @@ -32,7 +32,7 @@ Nesta unidade, focaremos em modelos generativos simples que nos ajudam a gerar t Treinaremos esta RNN para gerar texto passo a passo. Em cada etapa, pegaremos uma sequência de caracteres de comprimento `nchars` e pediremos à rede que gere o próximo caractere de saída para cada caractere de entrada: -![Imagem mostrando um exemplo de geração de RNN da palavra 'HELLO'.](../../../../../translated_images/br/rnn-generate.56c54afb52f9781d.png) +![Imagem mostrando um exemplo de geração de RNN da palavra 'HELLO'.](../../../../../translated_images/br/rnn-generate.56c54afb52f9781d.webp) Ao gerar texto (durante a inferência), começamos com algum **prompt**, que é passado pelas células RNN para gerar seu estado intermediário, e então a geração começa a partir desse estado. Geramos um caractere por vez e passamos o estado e o caractere gerado para outra célula RNN para gerar o próximo, até que tenhamos gerado caracteres suficientes. diff --git a/translations/br/lessons/5-NLP/18-Transformers/README.md b/translations/br/lessons/5-NLP/18-Transformers/README.md index 28ef6ef9..8dc8f499 100644 --- a/translations/br/lessons/5-NLP/18-Transformers/README.md +++ b/translations/br/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ Com RNNs, a tarefa de sequência para sequência é implementada por duas redes Os **Mecanismos de Atenção** fornecem um meio de ponderar o impacto contextual de cada vetor de entrada em cada previsão de saída da RNN. Isso é implementado criando atalhos entre os estados intermediários da RNN de entrada e a RNN de saída. Dessa forma, ao gerar o símbolo de saída yt, levamos em conta todos os estados ocultos de entrada hi, com diferentes coeficientes de peso αt,i. -![Imagem mostrando um modelo codificador/decodificador com uma camada de atenção aditiva](../../../../../translated_images/br/encoder-decoder-attention.7a726296894fb567.png) +![Imagem mostrando um modelo codificador/decodificador com uma camada de atenção aditiva](../../../../../translated_images/br/encoder-decoder-attention.7a726296894fb567.webp) > O modelo codificador-decodificador com mecanismo de atenção aditiva em [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citado deste [post no blog](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) A matriz de atenção {αi,j} representaria o grau em que certas palavras de entrada influenciam a geração de uma palavra específica na sequência de saída. Abaixo está um exemplo de tal matriz: -![Imagem mostrando um alinhamento de exemplo encontrado pelo RNNsearch-50, retirada de Bahdanau - arviz.org](../../../../../translated_images/br/bahdanau-fig3.09ba2d37f202a6af.png) +![Imagem mostrando um alinhamento de exemplo encontrado pelo RNNsearch-50, retirada de Bahdanau - arviz.org](../../../../../translated_images/br/bahdanau-fig3.09ba2d37f202a6af.webp) > Figura de [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) @@ -66,7 +66,7 @@ O resultado que obtemos com o embutimento posicional incorpora tanto o token ori Em seguida, precisamos capturar alguns padrões dentro de nossa sequência. Para isso, os transformers usam um mecanismo de **autoatenção**, que é essencialmente atenção aplicada à mesma sequência como entrada e saída. Aplicar autoatenção nos permite levar em conta o **contexto** dentro da sentença e ver quais palavras estão inter-relacionadas. Por exemplo, isso nos permite identificar quais palavras são referidas por correferências, como *it* (ele/ela), e também considerar o contexto: -![](../../../../../translated_images/br/CoreferenceResolution.861924d6d384a7d6.png) +![](../../../../../translated_images/br/CoreferenceResolution.861924d6d384a7d6.webp) > Imagem do [Blog do Google](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Como cada posição de entrada é mapeada independentemente para cada posição **BERT** (Bidirectional Encoder Representations from Transformers) é uma rede transformer muito grande com várias camadas: 12 camadas para o *BERT-base* e 24 para o *BERT-large*. O modelo é primeiro pré-treinado em um grande corpus de dados textuais (Wikipedia + livros) usando treinamento não supervisionado (prevendo palavras mascaradas em uma sentença). Durante o pré-treinamento, o modelo absorve níveis significativos de compreensão da linguagem, que podem ser aproveitados com outros conjuntos de dados usando ajuste fino. Esse processo é chamado de **aprendizado por transferência**. -![imagem de http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/br/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) +![imagem de http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/br/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > Imagem [fonte](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/br/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/br/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index d747cd95..6030dcd4 100644 --- a/translations/br/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/br/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**Mecanismos de Atenção** fornecem um meio de ponderar o impacto contextual de cada vetor de entrada em cada previsão de saída da RNN. Isso é implementado criando atalhos entre estados intermediários da RNN de entrada e da RNN de saída. Dessa forma, ao gerar o símbolo de saída $y_t$, levaremos em conta todos os estados ocultos de entrada $h_i$, com diferentes coeficientes de peso $\\alpha_{t,i}$.\n", "\n", - "![Imagem mostrando um modelo codificador/decodificador com uma camada de atenção aditiva](../../../../../translated_images/br/encoder-decoder-attention.7a726296894fb567.png)\n", + "![Imagem mostrando um modelo codificador/decodificador com uma camada de atenção aditiva](../../../../../translated_images/br/encoder-decoder-attention.7a726296894fb567.webp)\n", "*O modelo codificador-decodificador com mecanismo de atenção aditiva em [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citado deste [post de blog](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "A matriz de atenção $\\{\\alpha_{i,j}\\}$ representaria o grau em que certas palavras de entrada influenciam a geração de uma palavra específica na sequência de saída. Abaixo está um exemplo de tal matriz:\n", "\n", - "![Imagem mostrando um alinhamento de exemplo encontrado pelo RNNsearch-50, retirada de Bahdanau - arviz.org](../../../../../translated_images/br/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![Imagem mostrando um alinhamento de exemplo encontrado pelo RNNsearch-50, retirada de Bahdanau - arviz.org](../../../../../translated_images/br/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*Figura retirada de [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Representações de Codificador Bidirecional de Transformadores) é uma rede transformadora muito grande e multilayer com 12 camadas para *BERT-base* e 24 para *BERT-large*. O modelo é primeiro pré-treinado em um grande corpus de dados de texto (WikiPedia + livros) usando treinamento não supervisionado (prevendo palavras mascaradas em uma frase). Durante o pré-treinamento, o modelo absorve um nível significativo de compreensão da linguagem, que pode ser aproveitado com outros conjuntos de dados usando ajuste fino. Esse processo é chamado de **aprendizado por transferência**.\n", "\n", - "![Imagem de http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/br/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![Imagem de http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/br/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "Existem muitas variações de arquiteturas de transformadores, incluindo BERT, DistilBERT, BigBird, OpenGPT3 e outras que podem ser ajustadas. O pacote [HuggingFace](https://github.com/huggingface/) fornece um repositório para treinar muitas dessas arquiteturas com PyTorch.\n", "\n", diff --git a/translations/br/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/br/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index 59b5da37..4f6024b7 100644 --- a/translations/br/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/br/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**Mecanismos de Atenção** fornecem um meio de ponderar o impacto contextual de cada vetor de entrada em cada previsão de saída da RNN. Isso é implementado criando atalhos entre os estados intermediários da RNN de entrada e a RNN de saída. Dessa forma, ao gerar o símbolo de saída $y_t$, levaremos em conta todos os estados ocultos de entrada $h_i$, com diferentes coeficientes de peso $\\alpha_{t,i}$.\n", "\n", - "![Imagem mostrando um modelo codificador/decodificador com uma camada de atenção aditiva](../../../../../translated_images/br/encoder-decoder-attention.7a726296894fb567.png)\n", + "![Imagem mostrando um modelo codificador/decodificador com uma camada de atenção aditiva](../../../../../translated_images/br/encoder-decoder-attention.7a726296894fb567.webp)\n", "*O modelo codificador-decodificador com mecanismo de atenção aditiva em [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citado deste [post de blog](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "A matriz de atenção $\\{\\alpha_{i,j}\\}$ representaria o grau em que certas palavras de entrada influenciam a geração de uma determinada palavra na sequência de saída. Abaixo está um exemplo de tal matriz:\n", "\n", - "![Imagem mostrando um alinhamento de exemplo encontrado pelo RNNsearch-50, retirada de Bahdanau - arviz.org](../../../../../translated_images/br/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![Imagem mostrando um alinhamento de exemplo encontrado pelo RNNsearch-50, retirada de Bahdanau - arviz.org](../../../../../translated_images/br/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*Figura retirada de [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3)*\n", "\n", @@ -225,7 +225,7 @@ "\n", "**BERT** (Representações de Codificadores Bidirecionais de Transformers) é uma rede transformer muito grande e multilayer, com 12 camadas para o *BERT-base* e 24 para o *BERT-large*. O modelo é inicialmente pré-treinado em um grande corpus de dados textuais (WikiPedia + livros) usando treinamento não supervisionado (prevendo palavras mascaradas em uma sentença). Durante o pré-treinamento, o modelo adquire um nível significativo de compreensão da linguagem, que pode ser aproveitado com outros conjuntos de dados por meio de ajuste fino. Esse processo é chamado de **aprendizado por transferência**.\n", "\n", - "![imagem de http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/br/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![imagem de http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/br/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "Existem muitas variações de arquiteturas Transformer, incluindo BERT, DistilBERT, BigBird, OpenGPT3 e outras, que podem ser ajustadas.\n", "\n", diff --git a/translations/br/lessons/5-NLP/19-NER/README.md b/translations/br/lessons/5-NLP/19-NER/README.md index 5e32d72e..911f3e8d 100644 --- a/translations/br/lessons/5-NLP/19-NER/README.md +++ b/translations/br/lessons/5-NLP/19-NER/README.md @@ -58,7 +58,7 @@ infant | O Como precisamos construir uma correspondência um-para-um entre tokens e classes, podemos treinar um modelo neural **muitos-para-muitos** da seguinte forma: -![Imagem mostrando padrões comuns de redes neurais recorrentes.](../../../../../translated_images/br/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Imagem mostrando padrões comuns de redes neurais recorrentes.](../../../../../translated_images/br/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > *Imagem do [post no blog](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) de [Andrej Karpathy](http://karpathy.github.io/). Os modelos de classificação de tokens NER correspondem à arquitetura de rede mais à direita nesta imagem.* diff --git a/translations/br/lessons/5-NLP/README.md b/translations/br/lessons/5-NLP/README.md index 3b597ba3..8e044804 100644 --- a/translations/br/lessons/5-NLP/README.md +++ b/translations/br/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Processamento de Linguagem Natural -![Resumo das tarefas de PLN em um desenho](../../../../translated_images/br/ai-nlp.b22dcb8ca4707cea.png) +![Resumo das tarefas de PLN em um desenho](../../../../translated_images/br/ai-nlp.b22dcb8ca4707cea.webp) Nesta seção, vamos focar no uso de Redes Neurais para lidar com tarefas relacionadas ao **Processamento de Linguagem Natural (PLN)**. Existem muitos problemas de PLN que queremos que os computadores sejam capazes de resolver: diff --git a/translations/br/lessons/6-Other/23-MultiagentSystems/README.md b/translations/br/lessons/6-Other/23-MultiagentSystems/README.md index 7e40ef3e..b54718f7 100644 --- a/translations/br/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/br/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ Você pode abrir um dos modelos, por exemplo **Biology → Flocking**. Após abrir o modelo, você será levado à tela principal do NetLogo. Aqui está um modelo de exemplo que descreve a população de lobos e ovelhas, considerando recursos finitos (grama). -![Tela Principal do NetLogo](../../../../../translated_images/br/NetLogo-Main.32653711ec1a01b3.png) +![Tela Principal do NetLogo](../../../../../translated_images/br/NetLogo-Main.32653711ec1a01b3.webp) > Captura de tela por Dmitry Soshnikov diff --git a/translations/br/lessons/README.md b/translations/br/lessons/README.md index 7f2c8d84..5fb6dff0 100644 --- a/translations/br/lessons/README.md +++ b/translations/br/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Visão Geral -![Visão Geral em um rabisco](../../../translated_images/br/ai-overview.0857791951d19500.png) +![Visão Geral em um rabisco](../../../translated_images/br/ai-overview.0857791951d19500.webp) > Rabisco por [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/br/lessons/X-Extras/X1-MultiModal/README.md b/translations/br/lessons/X-Extras/X1-MultiModal/README.md index 58c3ccae..1549f17f 100644 --- a/translations/br/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/br/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ Após o sucesso dos modelos transformers na resolução de tarefas de PLN, as me A ideia principal do CLIP é ser capaz de comparar descrições textuais com uma imagem e determinar o quão bem a imagem corresponde à descrição. -![Arquitetura do CLIP](../../../../../translated_images/br/clip-arch.b3dbf20b4e8ed8be.png) +![Arquitetura do CLIP](../../../../../translated_images/br/clip-arch.b3dbf20b4e8ed8be.webp) > *Imagem retirada [deste post no blog](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ Uma vez que este modelo é pré-treinado, podemos fornecer a ele um lote de imag Suponha que precisamos classificar imagens entre, por exemplo, gatos, cachorros e humanos. Nesse caso, podemos fornecer ao modelo uma imagem e uma série de descrições textuais: "*uma foto de um gato*", "*uma foto de um cachorro*", "*uma foto de um humano*". No vetor resultante de 3 probabilidades, basta selecionar o índice com o maior valor. -![CLIP para Classificação de Imagens](../../../../../translated_images/br/clip-class.3af42ef0b2b19369.png) +![CLIP para Classificação de Imagens](../../../../../translated_images/br/clip-class.3af42ef0b2b19369.webp) > *Imagem retirada [deste post no blog](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ Saiba mais sobre o VQGAN no site [Taming Transformers](https://compvis.github.io Uma das diferenças importantes entre o VQGAN e um GAN tradicional é que o último pode produzir uma imagem decente a partir de qualquer vetor de entrada, enquanto o VQGAN provavelmente produzirá uma imagem incoerente. Assim, precisamos orientar ainda mais o processo de criação da imagem, e isso pode ser feito usando o CLIP. -![Arquitetura VQGAN+CLIP](../../../../../translated_images/br/vqgan.5027fe05051dfa31.png) +![Arquitetura VQGAN+CLIP](../../../../../translated_images/br/vqgan.5027fe05051dfa31.webp) Para gerar uma imagem correspondente a uma descrição textual, começamos com algum vetor de codificação aleatório que é passado pelo VQGAN para produzir uma imagem. Em seguida, o CLIP é usado para produzir uma função de perda que mostra o quão bem a imagem corresponde à descrição textual. O objetivo, então, é minimizar essa perda, usando retropropagação para ajustar os parâmetros do vetor de entrada. Uma ótima biblioteca que implementa o VQGAN+CLIP é o [Pixray](http://github.com/pixray/pixray). -![Imagem gerada pelo Pixray](../../../../../translated_images/br/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Imagem gerada pelo Pixray](../../../../../translated_images/br/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Imagem gerada pelo Pixray](../../../../../translated_images/br/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) +![Imagem gerada pelo Pixray](../../../../../translated_images/br/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp) | ![Imagem gerada pelo Pixray](../../../../../translated_images/br/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp) | ![Imagem gerada pelo Pixray](../../../../../translated_images/br/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp) ----|----|---- Imagem gerada a partir da descrição *um retrato em aquarela de um jovem professor de literatura com um livro* | Imagem gerada a partir da descrição *um retrato a óleo de uma jovem professora de ciência da computação com um computador* | Imagem gerada a partir da descrição *um retrato a óleo de um velho professor de matemática em frente a um quadro-negro* diff --git a/translations/cs/README.md b/translations/cs/README.md index 06b2f274..d2354735 100644 --- a/translations/cs/README.md +++ b/translations/cs/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # Umělá inteligence pro začátečníky - Kurz -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/cs/ai-overview.0857791951d19500.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/cs/ai-overview.0857791951d19500.webp)| |:---:| | Umělá inteligence pro začátečníky - _sketchnote od [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/cs/lessons/1-Intro/README.md b/translations/cs/lessons/1-Intro/README.md index 839c54c9..ee01a06d 100644 --- a/translations/cs/lessons/1-Intro/README.md +++ b/translations/cs/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Úvod do AI -![Shrnutí obsahu Úvodu do AI ve formě kresby](../../../../translated_images/cs/ai-intro.bf28d1ac4235881c.png) +![Shrnutí obsahu Úvodu do AI ve formě kresby](../../../../translated_images/cs/ai-intro.bf28d1ac4235881c.webp) > Kresba od [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Původně byly počítače vynalezeny [Charlesem Babbagem](https://en.wikipedia.org/wiki/Charles_Babbage) k práci s čísly podle přesně definovaného postupu – algoritmu. Moderní počítače, i když jsou mnohem pokročilejší než původní model navržený v 19. století, stále vycházejí ze stejné myšlenky řízených výpočtů. Proto je možné naprogramovat počítač, aby něco vykonal, pokud známe přesnou posloupnost kroků, které je třeba provést k dosažení cíle. -![Fotografie osoby](../../../../translated_images/cs/dsh_age.d212a30d4e54fb5f.png) +![Fotografie osoby](../../../../translated_images/cs/dsh_age.d212a30d4e54fb5f.webp) > Foto od [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ Pro více informací se podívejte na **[Obecná umělá inteligence](https://en Jedním z problémů při práci s termínem **[inteligence](https://en.wikipedia.org/wiki/Intelligence)** je, že neexistuje jasná definice tohoto pojmu. Lze argumentovat, že inteligence souvisí s **abstraktním myšlením** nebo **sebeuvědoměním**, ale nemůžeme ji přesně definovat. -![Fotografie kočky](../../../../translated_images/cs/photo-cat.8c8e8fb760ffe457.jpg) +![Fotografie kočky](../../../../translated_images/cs/photo-cat.8c8e8fb760ffe457.webp) > [Foto](https://unsplash.com/photos/75715CVEJhI) od [Amber Kipp](https://unsplash.com/@sadmax) z Unsplash @@ -98,13 +98,13 @@ Alternativně můžeme zkusit modelovat nejjednodušší prvky uvnitř našeho m > | Co je ML? | | > |--------------|-----------| -> | Část umělé inteligence, která je založena na tom, že se počítač učí řešit problém na základě určitých dat, se nazývá **strojové učení**. V tomto kurzu se nebudeme zabývat klasickým strojovým učením – odkazujeme vás na samostatný [kurikulum Strojové učení pro začátečníky](http://aka.ms/ml-beginners). | ![ML pro začátečníky](../../../../translated_images/cs/ml-for-beginners.9e4fed176fd5817d.png) | +> | Část umělé inteligence, která je založena na tom, že se počítač učí řešit problém na základě určitých dat, se nazývá **strojové učení**. V tomto kurzu se nebudeme zabývat klasickým strojovým učením – odkazujeme vás na samostatný [kurikulum Strojové učení pro začátečníky](http://aka.ms/ml-beginners). | ![ML pro začátečníky](../../../../translated_images/cs/ml-for-beginners.9e4fed176fd5817d.webp) | ## Stručná historie AI Umělá inteligence vznikla jako obor v polovině 20. století. Zpočátku byl převládajícím přístupem symbolický přístup, který vedl k řadě důležitých úspěchů, jako byly expertní systémy – počítačové programy, které dokázaly fungovat jako odborník v některých omezených oblastech problémů. Brzy se však ukázalo, že tento přístup není dobře škálovatelný. Extrakce znalostí od experta, jejich reprezentace v počítači a udržování této znalostní báze přesné se ukázalo být velmi složitým úkolem a v mnoha případech příliš nákladným na to, aby bylo praktické. To vedlo k takzvané [AI zimě](https://en.wikipedia.org/wiki/AI_winter) v 70. letech. -Stručná historie AI +Stručná historie AI > Obrázek od [Dmitry Soshnikov](http://soshnikov.com) @@ -124,7 +124,7 @@ Podobně můžeme vidět, jak se přístup k vytváření „mluvících program * Moderní asistenti, jako Cortana, Siri nebo Google Assistant, jsou všechny hybridní systémy, které používají neuronové sítě k převodu řeči na text a rozpoznání našeho záměru, a poté využívají určité uvažování nebo explicitní algoritmy k provedení požadovaných akcí. * V budoucnu můžeme očekávat kompletní model založený na neuronových sítích, který bude sám zvládat dialog. Nedávné GPT a [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) rodiny neuronových sítí ukazují velký úspěch v tomto směru. -evoluce Turingova testu +evoluce Turingova testu > Obrázek od Dmitry Soshnikov, [fotografie](https://unsplash.com/photos/r8LmVbUKgns) od [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto), Unsplash ## Nedávný výzkum v oblasti AI diff --git a/translations/cs/lessons/2-Symbolic/Animals.ipynb b/translations/cs/lessons/2-Symbolic/Animals.ipynb index 307a9289..d7ec212c 100644 --- a/translations/cs/lessons/2-Symbolic/Animals.ipynb +++ b/translations/cs/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "V tomto příkladu implementujeme jednoduchý systém založený na znalostech, který určí zvíře na základě některých fyzických charakteristik. Systém může být reprezentován následujícím AND-OR stromem (toto je část celého stromu, pravidla lze snadno rozšířit):\n", "\n", - "![](../../../../translated_images/cs/AND-OR-Tree.5592d2c70187f283.png)\n" + "![](../../../../translated_images/cs/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { diff --git a/translations/cs/lessons/2-Symbolic/README.md b/translations/cs/lessons/2-Symbolic/README.md index d112748e..727c436a 100644 --- a/translations/cs/lessons/2-Symbolic/README.md +++ b/translations/cs/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Reprezentace znalostí a expertní systémy -![Shrnutí obsahu Symbolické AI](../../../../translated_images/cs/ai-symbolic.715a30cb610411a6.png) +![Shrnutí obsahu Symbolické AI](../../../../translated_images/cs/ai-symbolic.715a30cb610411a6.webp) > Sketchnote od [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ Znalosti často nedefinujeme striktně, ale porovnáváme je s jinými souvisej Problém **reprezentace znalostí** tedy spočívá v nalezení efektivního způsobu, jak reprezentovat znalosti uvnitř počítače ve formě dat, aby byly automaticky použitelné. To lze chápat jako spektrum: -![Spektrum reprezentace znalostí](../../../../translated_images/cs/knowledge-spectrum.b60df631852c0217.png) +![Spektrum reprezentace znalostí](../../../../translated_images/cs/knowledge-spectrum.b60df631852c0217.webp) > Obrázek od [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Bloková syntaxe | Odsazení | | | Jedním z raných úspěchů symbolické AI byly tzv. **expertní systémy** - počítačové systémy navržené tak, aby fungovaly jako expert v omezené oblasti problémů. Byly založeny na **bázi znalostí** získané od jednoho nebo více lidských expertů a obsahovaly **inferenční stroj**, který na ní prováděl usuzování. -![Lidská architektura](../../../../translated_images/cs/arch-human.5d4d35f1bba3ab1c.png) | ![Systém založený na znalostech](../../../../translated_images/cs/arch-kbs.3ec5c150b09fa8da.png) +![Lidská architektura](../../../../translated_images/cs/arch-human.5d4d35f1bba3ab1c.webp) | ![Systém založený na znalostech](../../../../translated_images/cs/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ Zjednodušená struktura lidského nervového systému | Architektura systému založeného na znalostech @@ -106,7 +106,7 @@ Expertní systémy jsou postaveny podobně jako lidský systém usuzování, kte Jako příklad si vezměme následující expertní systém určování zvířete na základě jeho fyzických charakteristik: -![AND-OR strom](../../../../translated_images/cs/AND-OR-Tree.5592d2c70187f283.png) +![AND-OR strom](../../../../translated_images/cs/AND-OR-Tree.5592d2c70187f283.webp) > Obrázek od [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/cs/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/cs/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index b86b4095..82d8828e 100644 --- a/translations/cs/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/cs/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -1255,7 +1255,7 @@ "* Nízká trénovací ztráta – model dokáže dobře aproximovat trénovací data, protože má dostatečnou vyjadřovací schopnost.\n", "* Validační ztráta může být mnohem vyšší než trénovací ztráta a během trénování může začít růst – to je způsobeno tím, že model „si pamatuje“ trénovací body a ztrácí „celkový přehled“.\n", "\n", - "![Přeučení](../../../../../translated_images/cs/overfit.a0bd57f717c15769.png)\n", + "![Přeučení](../../../../../translated_images/cs/overfit.a0bd57f717c15769.webp)\n", "\n", "> Na tomto obrázku `x` označuje trénovací data, `o` validační data. Vlevo – lineární model (jednovrstvý), který přibližně odpovídá povaze dat. Vpravo – přeučený model, který dokonale aproximuje trénovací data, ale přestává dávat smysl pro jakákoli jiná data (validační chyba je velmi vysoká).\n" ] diff --git a/translations/cs/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/cs/lessons/3-NeuralNetworks/05-Frameworks/README.md index 80d028a6..b9d690ac 100644 --- a/translations/cs/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/cs/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Přeučení je extrémně důležitý koncept v strojovém učení a je velmi d Zvažte následující problém aproximace 5 bodů (reprezentovaných `x` na grafech níže): -![linear](../../../../../translated_images/cs/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/cs/overfit2.131f5800ae10ca5e.jpg) +![linear](../../../../../translated_images/cs/overfit1.f24b71c6f652e59e.webp) | ![overfit](../../../../../translated_images/cs/overfit2.131f5800ae10ca5e.webp) -------------------------|-------------------------- **Lineární model, 2 parametry** | **Nelineární model, 7 parametrů** Chyba trénování = 5.3 | Chyba trénování = 0 @@ -79,7 +79,7 @@ Je velmi důležité najít správnou rovnováhu mezi složitostí modelu (počt Jak můžete vidět z grafu výše, přeučení lze detekovat velmi nízkou chybou trénování a vysokou chybou validace. Během trénování obvykle vidíme, že chyby trénování i validace začínají klesat, a poté v určitém bodě může chyba validace přestat klesat a začít stoupat. To bude znakem přeučení a indikátorem, že bychom pravděpodobně měli v tomto bodě zastavit trénování (nebo alespoň vytvořit snímek modelu). -![overfitting](../../../../../translated_images/cs/Overfitting.408ad91cd90b4371.png) +![overfitting](../../../../../translated_images/cs/Overfitting.408ad91cd90b4371.webp) ## Jak zabránit přeučení diff --git a/translations/cs/lessons/3-NeuralNetworks/README.md b/translations/cs/lessons/3-NeuralNetworks/README.md index 39f926ec..87a4cd5e 100644 --- a/translations/cs/lessons/3-NeuralNetworks/README.md +++ b/translations/cs/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Úvod do neuronových sítí -![Shrnutí obsahu Úvodu do neuronových sítí v kresbě](../../../../translated_images/cs/ai-neuralnetworks.1c687ae40bc86e83.png) +![Shrnutí obsahu Úvodu do neuronových sítí v kresbě](../../../../translated_images/cs/ai-neuralnetworks.1c687ae40bc86e83.webp) Jak jsme si řekli v úvodu, jedním ze způsobů, jak dosáhnout inteligence, je trénovat **počítačový model** nebo **umělý mozek**. Od poloviny 20. století vědci zkoušeli různé matematické modely, až se v posledních letech ukázalo, že tento směr je velmi úspěšný. Tyto matematické modely mozku se nazývají **neuronové sítě**. @@ -36,13 +36,13 @@ V tomto kurzu se zaměříme pouze na modely neuronových sítí. Z biologie víme, že náš mozek se skládá z nervových buněk (neuronů), z nichž každá má několik "vstupů" (dendritů) a jeden "výstup" (axon). Dendrity i axony mohou vést elektrické signály a spojení mezi nimi — známá jako synapse — mohou vykazovat různé stupně vodivosti, které jsou regulovány neurotransmitery. -![Model neuronu](../../../../translated_images/cs/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![Model neuronu](../../../../translated_images/cs/artneuron.1a5daa88d20ebe6f.png) +![Model neuronu](../../../../translated_images/cs/synapse-wikipedia.ed20a9e4726ea1c6.webp) | ![Model neuronu](../../../../translated_images/cs/artneuron.1a5daa88d20ebe6f.webp) ----|---- Skutečný neuron *([Obrázek](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) z Wikipedie)* | Umělý neuron *(Obrázek od autora)* Nejjednodušší matematický model neuronu tedy obsahuje několik vstupů X1, ..., XN a jeden výstup Y, a řadu vah W1, ..., WN. Výstup se vypočítá jako: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) kde f je nějaká nelineární **aktivační funkce**. diff --git a/translations/cs/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/cs/lessons/4-ComputerVision/06-IntroCV/README.md index 428c286d..92796d61 100644 --- a/translations/cs/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/cs/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ V našem [OpenCV Notebook](OpenCV.ipynb) uvádíme některé příklady, kdy lze * **Předzpracování fotografie Braillovy knihy**. Zaměřujeme se na to, jak můžeme použít prahování, detekci prvků, perspektivní transformaci a manipulace s NumPy k oddělení jednotlivých Braillových symbolů pro další klasifikaci neuronovou sítí. -![Braillův obrázek](../../../../../translated_images/cs/braille.341962ff76b1bd70.jpeg) | ![Předzpracovaný Braillův obrázek](../../../../../translated_images/cs/braille-result.46530fea020b03c7.png) | ![Braillovy symboly](../../../../../translated_images/cs/braille-symbols.0159185ab69d5339.png) +![Braillův obrázek](../../../../../translated_images/cs/braille.341962ff76b1bd70.webp) | ![Předzpracovaný Braillův obrázek](../../../../../translated_images/cs/braille-result.46530fea020b03c7.webp) | ![Braillovy symboly](../../../../../translated_images/cs/braille-symbols.0159185ab69d5339.webp) ----|-----|----- > Obrázek z [OpenCV.ipynb](OpenCV.ipynb) * **Detekce pohybu ve videu pomocí rozdílu snímků**. Pokud je kamera pevná, pak by snímky z kamerového záznamu měly být velmi podobné. Protože snímky jsou reprezentovány jako pole, pouhým odečtením těchto polí pro dva po sobě jdoucí snímky získáme rozdíl pixelů, který by měl být nízký pro statické snímky a stoupat, jakmile dojde k výraznému pohybu na obrázku. -![Obrázek video snímků a rozdílů snímků](../../../../../translated_images/cs/frame-difference.706f805491a0883c.png) +![Obrázek video snímků a rozdílů snímků](../../../../../translated_images/cs/frame-difference.706f805491a0883c.webp) > Obrázek z [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ V našem [OpenCV Notebook](OpenCV.ipynb) uvádíme některé příklady, kdy lze - **Hustý optický tok** počítá vektorové pole, které ukazuje, kam se každý pixel pohybuje. - **Řídký optický tok** je založen na výběru některých výrazných prvků na obrázku (např. hran) a sestavení jejich trajektorie snímek po snímku. -![Obrázek optického toku](../../../../../translated_images/cs/optical.1f4a94464579a83a.png) +![Obrázek optického toku](../../../../../translated_images/cs/optical.1f4a94464579a83a.webp) > Obrázek z [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/cs/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/cs/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 8f6642d0..b521ff36 100644 --- a/translations/cs/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/cs/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 je síť, která dosáhla 92,7% přesnosti v top-5 klasifikaci ImageNet v roce 2014. Má následující strukturu vrstev: -![ImageNet Layers](../../../../../translated_images/cs/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet Layers](../../../../../translated_images/cs/vgg-16-arch1.d901a5583b3a51ba.webp) Jak můžete vidět, VGG sleduje tradiční pyramidovou architekturu, což je sekvence vrstev konvoluce a pooling. -![ImageNet Pyramid](../../../../../translated_images/cs/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet Pyramid](../../../../../translated_images/cs/vgg-16-arch.64ff2137f50dd49f.webp) > Obrázek z [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/cs/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/cs/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index 4fba326c..d0aeae90 100644 --- a/translations/cs/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/cs/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -260,7 +260,7 @@ "\n", "V typické CNN tedy existuje několik konvolučních vrstev, mezi nimiž jsou pooling vrstvy, které snižují rozměry obrazu. Zároveň zvyšujeme počet filtrů, protože jak se vzory stávají složitějšími, existuje více možných zajímavých kombinací, které je třeba hledat.\n", "\n", - "![Obrázek zobrazující několik konvolučních vrstev s pooling vrstvami.](../../../../../translated_images/cs/cnn-pyramid.85915455759ef0ce.png)\n", + "![Obrázek zobrazující několik konvolučních vrstev s pooling vrstvami.](../../../../../translated_images/cs/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "Kvůli snižování prostorových rozměrů a zvyšování rozměrů vlastností/filtrů se tato architektura také nazývá **pyramidová architektura**.\n" ] diff --git a/translations/cs/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/cs/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index 1b43992d..31380197 100644 --- a/translations/cs/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/cs/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -360,7 +360,7 @@ "\n", "V typické CNN tedy najdeme několik konvolučních vrstev, mezi nimiž jsou pooling vrstvy, které snižují rozměry obrazu. Zároveň zvyšujeme počet filtrů, protože jak se vzory stávají složitějšími, existuje více možných zajímavých kombinací, které je třeba hledat.\n", "\n", - "![Obrázek ukazující několik konvolučních vrstev s pooling vrstvami.](../../../../../translated_images/cs/cnn-pyramid.85915455759ef0ce.png)\n", + "![Obrázek ukazující několik konvolučních vrstev s pooling vrstvami.](../../../../../translated_images/cs/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "Kvůli snižování prostorových rozměrů a zvyšování rozměrů funkcí/filtrů se tato architektura také nazývá **pyramidová architektura**.\n" ] diff --git a/translations/cs/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/cs/lessons/4-ComputerVision/07-ConvNets/README.md index 2e0b87f4..05fbaa71 100644 --- a/translations/cs/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/cs/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ V reálném životě chceme být schopni rozpoznat objekty na obrázku bez ohled K extrakci vzorů použijeme koncept **konvolučních filtrů**. Jak víte, obrázek je reprezentován jako 2D-matice nebo 3D-tensor s barevnou hloubkou. Aplikace filtru znamená, že vezmeme relativně malou matici **filtračního jádra** a pro každý pixel v původním obrázku vypočítáme vážený průměr s okolními body. Můžeme si to představit jako malé okno, které se posouvá po celém obrázku a průměruje všechny pixely podle vah v matici filtračního jádra. -![Vertikální filtr hran](../../../../../translated_images/cs/filter-vert.b7148390ca0bc356.png) | ![Horizontální filtr hran](../../../../../translated_images/cs/filter-horiz.59b80ed4feb946ef.png) +![Vertikální filtr hran](../../../../../translated_images/cs/filter-vert.b7148390ca0bc356.webp) | ![Horizontální filtr hran](../../../../../translated_images/cs/filter-horiz.59b80ed4feb946ef.webp) ----|---- > Obrázek od Dmitry Soshnikov @@ -38,7 +38,7 @@ Fungování CNN je založeno na následujících důležitých principech: * Síť můžeme navrhnout tak, aby se filtry učily automaticky * Stejný přístup můžeme použít k hledání vzorů ve vysokoúrovňových rysech, nejen v původním obrázku. Extrakce rysů pomocí CNN tedy funguje na hierarchii rysů, počínaje nízkoúrovňovými kombinacemi pixelů až po vysokoúrovňové kombinace částí obrázku. -![Hierarchická extrakce rysů](../../../../../translated_images/cs/FeatureExtractionCNN.d9b456cbdae7cb64.png) +![Hierarchická extrakce rysů](../../../../../translated_images/cs/FeatureExtractionCNN.d9b456cbdae7cb64.webp) > Obrázek z [práce Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), založené na [jejich výzkumu](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ Většina CNN používaných pro zpracování obrázků následuje tzv. pyramido Jako příklad se podívejme na architekturu VGG-16, sítě, která dosáhla 92,7% přesnosti v top-5 klasifikaci ImageNetu v roce 2014: -![Vrstvy ImageNet](../../../../../translated_images/cs/vgg-16-arch1.d901a5583b3a51ba.jpg) +![Vrstvy ImageNet](../../../../../translated_images/cs/vgg-16-arch1.d901a5583b3a51ba.webp) -![Pyramida ImageNet](../../../../../translated_images/cs/vgg-16-arch.64ff2137f50dd49f.jpg) +![Pyramida ImageNet](../../../../../translated_images/cs/vgg-16-arch.64ff2137f50dd49f.webp) > Obrázek z [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/cs/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/cs/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 5dd9bbdc..145f73ce 100644 --- a/translations/cs/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/cs/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ Vaším úkolem je natrénovat konvoluční neuronovou síť, která bude klasif Použijeme [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/), který obsahuje obrázky 37 různých plemen psů a koček. -![Dataset, se kterým budeme pracovat](../../../../../../translated_images/cs/data.50b2a9d5484bdbf0.png) +![Dataset, se kterým budeme pracovat](../../../../../../translated_images/cs/data.50b2a9d5484bdbf0.webp) Pro stažení datasetu použijte tento kód: diff --git a/translations/cs/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/cs/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index 62a3ce91..ee3c420c 100644 --- a/translations/cs/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/cs/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "Abychom si představili ideální kočku, začneme s obrázkem náhodného šumu a pokusíme se použít optimalizační techniku gradientního sestupu k úpravě obrázku tak, aby síť rozpoznala kočku.\n", "\n", - "![Optimalizační smyčka](../../../../../translated_images/cs/ideal-cat-loop.999fbb8ff306e044.png)\n", + "![Optimalizační smyčka](../../../../../translated_images/cs/ideal-cat-loop.999fbb8ff306e044.webp)\n", "\n", "Zde je náš výchozí obrázek:\n" ] diff --git a/translations/cs/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/cs/lessons/4-ComputerVision/08-TransferLearning/README.md index e681c0c4..6786a023 100644 --- a/translations/cs/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/cs/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Keras i PyTorch obsahují funkce pro snadné načtení předtrénovaných vah ne Zde jsou ukázkové rysy extrahované z obrázku kočky pomocí sítě VGG-16: -![Rysy extrahované sítí VGG-16](../../../../../translated_images/cs/features.6291f9c7ba3a0b95.png) +![Rysy extrahované sítí VGG-16](../../../../../translated_images/cs/features.6291f9c7ba3a0b95.webp) ## Dataset Kočky vs. Psi @@ -48,19 +48,19 @@ Předtrénovaná neuronová síť obsahuje různé vzory uvnitř svého *mozku*, Jedním z přístupů, které můžeme použít, je začít s náhodným obrázkem a poté se pokusit pomocí techniky **optimalizace gradientního sestupu** upravit tento obrázek tak, aby si síť začala myslet, že je to kočka. -![Optimalizační smyčka obrázku](../../../../../translated_images/cs/ideal-cat-loop.999fbb8ff306e044.png) +![Optimalizační smyčka obrázku](../../../../../translated_images/cs/ideal-cat-loop.999fbb8ff306e044.webp) Pokud to však uděláme, obdržíme něco velmi podobného náhodnému šumu. To je proto, že *existuje mnoho způsobů, jak síť přimět myslet si, že vstupní obrázek je kočka*, včetně některých, které vizuálně nedávají smysl. Zatímco tyto obrázky obsahují mnoho vzorů typických pro kočku, nic je neomezuje, aby byly vizuálně rozlišitelné. Pro zlepšení výsledku můžeme do ztrátové funkce přidat další člen, který se nazývá **variation loss**. Je to metrika, která ukazuje, jak podobné jsou sousední pixely obrázku. Minimalizace variation loss činí obrázek hladším a zbavuje se šumu – tím odhaluje vizuálně přitažlivější vzory. Zde je příklad takových "ideálních" obrázků, které jsou klasifikovány jako kočka a jako zebra s vysokou pravděpodobností: -![Ideální kočka](../../../../../translated_images/cs/ideal-cat.203dd4597643d6b0.png) | ![Ideální zebra](../../../../../translated_images/cs/ideal-zebra.7f70e8b54ee15a7a.png) +![Ideální kočka](../../../../../translated_images/cs/ideal-cat.203dd4597643d6b0.webp) | ![Ideální zebra](../../../../../translated_images/cs/ideal-zebra.7f70e8b54ee15a7a.webp) -----|----- *Ideální kočka* | *Ideální zebra* Podobný přístup lze použít k provádění tzv. **adversarial útoků** na neuronovou síť. Představme si, že chceme oklamat neuronovou síť a přimět ji, aby psa považovala za kočku. Pokud vezmeme obrázek psa, který je sítí rozpoznán jako pes, můžeme jej trochu upravit pomocí optimalizace gradientního sestupu, dokud síť nezačne klasifikovat obrázek jako kočku: -![Obrázek psa](../../../../../translated_images/cs/original-dog.8f68a67d2fe0911f.png) | ![Obrázek psa klasifikovaný jako kočka](../../../../../translated_images/cs/adversarial-dog.d9fc7773b0142b89.png) +![Obrázek psa](../../../../../translated_images/cs/original-dog.8f68a67d2fe0911f.webp) | ![Obrázek psa klasifikovaný jako kočka](../../../../../translated_images/cs/adversarial-dog.d9fc7773b0142b89.webp) -----|----- *Původní obrázek psa* | *Obrázek psa klasifikovaný jako kočka* diff --git a/translations/cs/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/cs/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index c385a1f5..8038b71a 100644 --- a/translations/cs/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/cs/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "Protože trénujeme autoencoder, aby zachytil co nejvíce informací z původního obrázku pro přesnou rekonstrukci, síť se snaží najít nejlepší **embedding** vstupních obrázků, aby zachytila jejich význam.\n", "\n", - "![Schéma AutoEncoderu](../../../../../translated_images/cs/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![Schéma AutoEncoderu](../../../../../translated_images/cs/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "> Obrázek z [Keras blogu](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", diff --git a/translations/cs/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/cs/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index 67db20fc..52b448f8 100644 --- a/translations/cs/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/cs/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "Protože trénujeme autoencoder, aby zachytil co nejvíce informací z původního obrázku pro přesnou rekonstrukci, síť se snaží najít nejlepší **embedding** vstupních obrázků, aby zachytila jejich význam.\n", "\n", - "![Diagram AutoEncoderu](../../../../../translated_images/cs/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![Diagram AutoEncoderu](../../../../../translated_images/cs/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "*Obrázek z [Keras blogu](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", diff --git a/translations/cs/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/cs/lessons/4-ComputerVision/09-Autoencoders/README.md index 4e2e5345..be7e2a79 100644 --- a/translations/cs/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/cs/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ Nicméně bychom mohli chtít použít surová (neoznačená) data pro trénová Protože trénujeme autoenkodér, aby zachytil co nejvíce informací z původního obrázku pro přesnou rekonstrukci, síť se snaží najít nejlepší **embedding** vstupních obrázků, aby zachytila jejich význam. -![Schéma Autoenkodéru](../../../../../translated_images/cs/autoencoder_schema.5e6fc9ad98a5eb61.jpg) +![Schéma Autoenkodéru](../../../../../translated_images/cs/autoencoder_schema.5e6fc9ad98a5eb61.webp) > Obrázek z [blogu Keras](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/cs/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/cs/lessons/4-ComputerVision/11-ObjectDetection/README.md index a159ceea..3db96374 100644 --- a/translations/cs/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/cs/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ Modely pro klasifikaci obrázků, se kterými jsme se dosud zabývali, přijíma ## [Kvíz před lekcí](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Detekce objektů](../../../../../translated_images/cs/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) +![Detekce objektů](../../../../../translated_images/cs/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp) > Obrázek z [webu YOLO v2](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ Předpokládejme, že chceme najít kočku na obrázku. Velmi naivní přístup 2. Proveďte klasifikaci obrázků na každé dlaždici. 3. Dlaždice, které vykazují dostatečně vysokou aktivaci, lze považovat za obsahující hledaný objekt. -![Naivní detekce objektů](../../../../../translated_images/cs/naive-detection.e7f1ba220ccd08c6.png) +![Naivní detekce objektů](../../../../../translated_images/cs/naive-detection.e7f1ba220ccd08c6.webp) > *Obrázek z [cvičebního notebooku](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ Můžete narazit na následující datové sady pro tento úkol: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) – 20 tříd * [COCO](http://cocodataset.org/#home) – Common Objects in Context. 80 tříd, ohraničující rámečky a segmentační masky -![COCO](../../../../../translated_images/cs/coco-examples.71bc60380fa6cceb.jpg) +![COCO](../../../../../translated_images/cs/coco-examples.71bc60380fa6cceb.webp) ## Metriky pro detekci objektů @@ -50,7 +50,7 @@ Můžete narazit na následující datové sady pro tento úkol: Zatímco u klasifikace obrázků je snadné měřit, jak dobře algoritmus funguje, u detekce objektů musíme měřit jak správnost třídy, tak přesnost určené polohy ohraničujícího rámečku. Pro druhé zmíněné používáme tzv. **Průnik přes sjednocení** (IoU), který měří, jak dobře se dva rámečky (nebo dvě libovolné oblasti) překrývají. -![IoU](../../../../../translated_images/cs/iou_equation.9a4751d40fff4e11.png) +![IoU](../../../../../translated_images/cs/iou_equation.9a4751d40fff4e11.webp) > *Obrázek 2 z [tohoto skvělého blogového příspěvku o IoU](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ Existují dvě široké kategorie algoritmů pro detekci objektů: [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) používá [Selektivní vyhledávání](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) k vytvoření hierarchické struktury oblastí ROI, které jsou následně zpracovány extraktory funkcí CNN a klasifikátory SVM k určení třídy objektu, a lineární regresí k určení souřadnic *ohraničujícího rámečku*. [Oficiální článek](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/cs/rcnn1.cae407020dfb1d1f.png) +![RCNN](../../../../../translated_images/cs/rcnn1.cae407020dfb1d1f.webp) > *Obrázek od van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/cs/rcnn2.2d9530bb83516484.png) +![RCNN-1](../../../../../translated_images/cs/rcnn2.2d9530bb83516484.webp) > *Obrázky z [tohoto blogu](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -110,7 +110,7 @@ Existují dvě široké kategorie algoritmů pro detekci objektů: Tento přístup je podobný R-CNN, ale oblasti jsou definovány po aplikaci konvolučních vrstev. -![FRCNN](../../../../../translated_images/cs/f-rcnn.3cda6d9bb4188875.png) +![FRCNN](../../../../../translated_images/cs/f-rcnn.3cda6d9bb4188875.webp) > Obrázek z [oficiálního článku](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ Tento přístup je podobný R-CNN, ale oblasti jsou definovány po aplikaci konv Hlavní myšlenkou tohoto přístupu je použití neuronové sítě k předpovědi ROI – tzv. *Region Proposal Network*. [Článek](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/cs/faster-rcnn.8d46c099b87ef30a.png) +![FasterRCNN](../../../../../translated_images/cs/faster-rcnn.8d46c099b87ef30a.webp) > Obrázek z [oficiálního článku](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ Tento algoritmus je ještě rychlejší než Faster R-CNN. Hlavní myšlenka je 2. Funkce jsou zpracovány pomocí **Position-Sensitive Score Map**. Každý objekt z $C$ tříd je rozdělen na $k\times k$ oblasti a trénujeme na předpověď částí objektů. 3. Pro každou část z $k\times k$ oblastí všechny sítě hlasují pro třídy objektů a třída objektu s maximálním počtem hlasů je vybrána. -![r-fcn image](../../../../../translated_images/cs/r-fcn.13eb88158b99a3da.png) +![r-fcn image](../../../../../translated_images/cs/r-fcn.13eb88158b99a3da.webp) > Obrázek z [oficiálního článku](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO je algoritmus pro detekci v reálném čase s jedním průchodem. Hlavní m * Obrázek je rozdělen na $S\times S$ oblasti. * Pro každou oblast **CNN** předpovídá $n$ možných objektů, souřadnice *ohraničujícího rámečku* a *důvěru*=*pravděpodobnost* * IoU. - ![YOLO](../../../../../translated_images/cs/yolo.a2648ec82ee8bb4e.png) + ![YOLO](../../../../../translated_images/cs/yolo.a2648ec82ee8bb4e.webp) > Obrázek z [oficiálního článku](https://arxiv.org/abs/1506.02640) diff --git a/translations/cs/lessons/4-ComputerVision/README.md b/translations/cs/lessons/4-ComputerVision/README.md index 6f9f421a..b47412d0 100644 --- a/translations/cs/lessons/4-ComputerVision/README.md +++ b/translations/cs/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Počítačové vidění -![Shrnutí obsahu Počítačového vidění ve formě kresby](../../../../translated_images/cs/ai-computervision.6506ebebac3fbf76.png) +![Shrnutí obsahu Počítačového vidění ve formě kresby](../../../../translated_images/cs/ai-computervision.6506ebebac3fbf76.webp) V této sekci se naučíme: diff --git a/translations/cs/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/cs/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index fbd04708..38c5666e 100644 --- a/translations/cs/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/cs/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**Bag of Words** (BoW) je nejčastěji používaná tradiční vektorová reprezentace. Každé slovo je spojeno s indexem vektoru, prvek vektoru obsahuje počet výskytů daného slova v konkrétním dokumentu.\n", "\n", - "![Obrázek ukazující, jak je v paměti reprezentována vektorová reprezentace Bag of Words.](../../../../../translated_images/cs/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![Obrázek ukazující, jak je v paměti reprezentována vektorová reprezentace Bag of Words.](../../../../../translated_images/cs/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Note**: Na BoW můžete také nahlížet jako na součet všech vektorů zakódovaných metodou one-hot pro jednotlivá slova v textu.\n", "\n", diff --git a/translations/cs/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/cs/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index e500f47e..4e9333ad 100644 --- a/translations/cs/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/cs/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "Reprezentace vektoru **Bag-of-words** (BoW) je nejjednodušší tradiční vektorová reprezentace na pochopení. Každé slovo je spojeno s indexem vektoru a prvek vektoru obsahuje počet výskytů každého slova v daném dokumentu.\n", "\n", - "![Obrázek ukazující, jak je reprezentace Bag-of-words uložena v paměti.](../../../../../translated_images/cs/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![Obrázek ukazující, jak je reprezentace Bag-of-words uložena v paměti.](../../../../../translated_images/cs/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Note**: Na BoW můžete také nahlížet jako na součet všech one-hot-encoded vektorů pro jednotlivá slova v textu.\n", "\n", diff --git a/translations/cs/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/cs/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 741b2ce6..1282e572 100644 --- a/translations/cs/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/cs/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "Použitím embeddingové vrstvy jako první vrstvy v naší síti můžeme přejít od modelu bag-of-words k modelu **embedding bag**, kde nejprve převedeme každé slovo v našem textu na odpovídající embedding a poté vypočítáme nějakou agregační funkci přes všechny tyto embeddingy, například `sum`, `average` nebo `max`.\n", "\n", - "![Obrázek ukazující klasifikátor embeddingu pro pět slov v sekvenci.](../../../../../translated_images/cs/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![Obrázek ukazující klasifikátor embeddingu pro pět slov v sekvenci.](../../../../../translated_images/cs/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "Naše klasifikační neuronová síť začne embeddingovou vrstvou, poté agregační vrstvou a na vrcholu bude lineární klasifikátor:\n" ] @@ -176,7 +176,7 @@ "\n", "V předchozí architektuře jsme museli všechny sekvence doplnit na stejnou délku, aby se vešly do minibatch. To není nejefektivnější způsob, jak reprezentovat sekvence s proměnnou délkou – jiný přístup by byl použití **offsetového** vektoru, který by obsahoval offsety všech sekvencí uložených v jednom velkém vektoru.\n", "\n", - "![Obrázek znázorňující reprezentaci sekvencí pomocí offsetů](../../../../../translated_images/cs/offset-sequence-representation.eb73fcefb29b46ee.png)\n", + "![Obrázek znázorňující reprezentaci sekvencí pomocí offsetů](../../../../../translated_images/cs/offset-sequence-representation.eb73fcefb29b46ee.webp)\n", "\n", "> **Note**: Na obrázku výše je znázorněna sekvence znaků, ale v našem příkladu pracujeme se sekvencemi slov. Nicméně obecný princip reprezentace sekvencí pomocí offsetového vektoru zůstává stejný.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW je rychlejší, zatímco skip-gram je pomalejší, ale lépe reprezentuje méně častá slova.\n", "\n", - "![Obrázek ukazující algoritmy CBoW a Skip-Gram pro převod slov na vektory.](../../../../../translated_images/cs/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![Obrázek ukazující algoritmy CBoW a Skip-Gram pro převod slov na vektory.](../../../../../translated_images/cs/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Pro experimentování s Word2Vec vektory předem natrénovanými na datasetu Google News můžeme použít knihovnu **gensim**. Níže najdeme slova nejpodobnější slovu 'neural'.\n", "\n", diff --git a/translations/cs/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/cs/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index 7b7473f1..ac1b3080 100644 --- a/translations/cs/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/cs/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "Použitím embedding vrstvy jako první vrstvy v naší síti můžeme přejít od modelu bag-of-words k modelu **embedding bag**, kde nejprve převedeme každé slovo v našem textu na odpovídající embedding a poté vypočítáme nějakou agregační funkci nad všemi těmito embeddingy, například `sum`, `average` nebo `max`.\n", "\n", - "![Obrázek ukazující embedding klasifikátor pro pět slov v sekvenci.](../../../../../translated_images/cs/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![Obrázek ukazující embedding klasifikátor pro pět slov v sekvenci.](../../../../../translated_images/cs/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "Naše neuronová síť klasifikátoru se skládá z následujících vrstev:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW je rychlejší, zatímco skip-gram je pomalejší, ale lépe reprezentuje méně častá slova.\n", "\n", - "![Obrázek ukazující algoritmy CBoW a Skip-Gram pro převod slov na vektory.](../../../../../translated_images/cs/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![Obrázek ukazující algoritmy CBoW a Skip-Gram pro převod slov na vektory.](../../../../../translated_images/cs/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Pro experimentování s Word2Vec vektorizací předtrénovanou na datasetu Google News můžeme použít knihovnu **gensim**. Níže najdeme slova nejpodobnější slovu 'neural'.\n", "\n", diff --git a/translations/cs/lessons/5-NLP/14-Embeddings/README.md b/translations/cs/lessons/5-NLP/14-Embeddings/README.md index a7dd5cb1..2f02898f 100644 --- a/translations/cs/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/cs/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ Vrstva embedding tedy přijme slovo jako vstup a vytvoří výstupní vektor o s Použitím vrstvy embedding jako první vrstvy v naší klasifikační síti můžeme přejít od modelu bag-of-words k modelu **embedding bag**, kde nejprve převedeme každé slovo v textu na odpovídající embedding a poté vypočítáme nějakou agregační funkci nad všemi těmito embeddingy, například `sum`, `average` nebo `max`. -![Obrázek znázorňující klasifikátor s embeddingy pro pět slov v sekvenci.](../../../../../translated_images/cs/embedding-classifier-example.b77f021a7ee67eee.png) +![Obrázek znázorňující klasifikátor s embeddingy pro pět slov v sekvenci.](../../../../../translated_images/cs/embedding-classifier-example.b77f021a7ee67eee.webp) > Obrázek od autora @@ -40,7 +40,7 @@ K tomu je třeba předtrénovat model embedding na velké kolekci textů specifi CBoW je rychlejší, zatímco skip-gram je pomalejší, ale lépe reprezentuje méně častá slova. -![Obrázek znázorňující algoritmy CBoW a Skip-Gram pro převod slov na vektory.](../../../../../translated_images/cs/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![Obrázek znázorňující algoritmy CBoW a Skip-Gram pro převod slov na vektory.](../../../../../translated_images/cs/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Obrázek z [tohoto článku](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/cs/lessons/5-NLP/15-LanguageModeling/README.md b/translations/cs/lessons/5-NLP/15-LanguageModeling/README.md index b94ea9e0..36c734c4 100644 --- a/translations/cs/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/cs/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ V našich předchozích příkladech jsme používali předtrénované sémantic * **Continuous Bag-of-Words** (CBoW), kdy předpovídáme prostřední token $W_0$ v sekvenci tokenů $W_{-N}$, ..., $W_N$. * **Skip-gram**, kde předpovídáme sadu sousedních tokenů {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} na základě prostředního tokenu $W_0$. -![obrázek z článku o převodu slov na vektory](../../../../../translated_images/cs/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![obrázek z článku o převodu slov na vektory](../../../../../translated_images/cs/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Obrázek z [tohoto článku](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/cs/lessons/5-NLP/16-RNN/README.md b/translations/cs/lessons/5-NLP/16-RNN/README.md index aea81fc4..ec380c9f 100644 --- a/translations/cs/lessons/5-NLP/16-RNN/README.md +++ b/translations/cs/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ V předchozích sekcích jsme používali bohaté sémantické reprezentace text Abychom zachytili význam textové sekvence, musíme použít jinou architekturu neuronové sítě, která se nazývá **rekurentní neuronová síť** (RNN). V RNN prochází věta sítí jeden symbol po druhém a síť produkuje nějaký **stav**, který se poté předává síti spolu s dalším symbolem. -![RNN](../../../../../translated_images/cs/rnn.27f5c29c53d727b5.png) +![RNN](../../../../../translated_images/cs/rnn.27f5c29c53d727b5.webp) > Obrázek od autora @@ -61,7 +61,7 @@ Diskutovali jsme o rekurentních sítích, které fungují jedním směrem, od z Rekurentní síť, ať už jednosměrná nebo bidirekcionální, zachycuje určité vzory v sekvenci a může je uložit do stavového vektoru nebo předat do výstupu. Stejně jako u konvolučních sítí můžeme na první vrstvu postavit další rekurentní vrstvu, která zachytí vzory na vyšší úrovni a vytvoří vzory na nižší úrovni extrahované první vrstvou. To nás vede k pojmu **vícevrstvá RNN**, která se skládá ze dvou nebo více rekurentních sítí, kde výstup předchozí vrstvy je předán další vrstvě jako vstup. -![Obrázek ukazující vícevrstvou LSTM RNN](../../../../../translated_images/cs/multi-layer-lstm.dd975e29bb2a59fe.jpg) +![Obrázek ukazující vícevrstvou LSTM RNN](../../../../../translated_images/cs/multi-layer-lstm.dd975e29bb2a59fe.webp) *Obrázek z [tohoto skvělého příspěvku](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) od Fernanda Lópeze* diff --git a/translations/cs/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/cs/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index 7d9a6e9f..6d297394 100644 --- a/translations/cs/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/cs/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "Rekurentní síť, ať už jednosměrná nebo obousměrná, zachycuje určité vzory v rámci sekvence a může je uložit do stavu nebo předat do výstupu. Stejně jako u konvolučních sítí můžeme na první vrstvu postavit další rekurentní vrstvu, která zachytí vzory na vyšší úrovni, vytvořené z nízkoúrovňových vzorů extrahovaných první vrstvou. To nás přivádí k pojmu **vícevrstvé RNN**, která se skládá ze dvou nebo více rekurentních sítí, kde výstup předchozí vrstvy je předán jako vstup do následující vrstvy.\n", "\n", - "![Obrázek zobrazující vícevrstvou dlouhodobou krátkodobou paměťovou RNN](../../../../../translated_images/cs/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![Obrázek zobrazující vícevrstvou dlouhodobou krátkodobou paměťovou RNN](../../../../../translated_images/cs/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*Obrázek z [tohoto skvělého článku](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) od Fernanda Lópeze*\n", "\n", diff --git a/translations/cs/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/cs/lessons/5-NLP/16-RNN/RNNTF.ipynb index 093fff5a..849e1c3f 100644 --- a/translations/cs/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/cs/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "Abychom zachytili význam textové sekvence, použijeme architekturu neuronové sítě nazývanou **rekurentní neuronová síť** (RNN). Při použití RNN procházíme větou sítí po jednom tokenu a síť produkuje určitý **stav**, který poté předáváme síti spolu s dalším tokenem.\n", "\n", - "![Obrázek ukazující příklad generování rekurentní neuronové sítě.](../../../../../translated_images/cs/rnn.27f5c29c53d727b5.png)\n", + "![Obrázek ukazující příklad generování rekurentní neuronové sítě.](../../../../../translated_images/cs/rnn.27f5c29c53d727b5.webp)\n", "\n", "Při dané vstupní sekvenci tokenů $X_0,\\dots,X_n$ RNN vytváří sekvenci bloků neuronové sítě a trénuje tuto sekvenci end-to-end pomocí zpětné propagace. Každý blok sítě přijímá dvojici $(X_i,S_i)$ jako vstup a produkuje $S_{i+1}$ jako výsledek. Konečný stav $S_n$ nebo výstup $Y_n$ se předává do lineárního klasifikátoru, aby se vytvořil výsledek. Všechny bloky sítě sdílejí stejné váhy a jsou trénovány end-to-end jedním průchodem zpětné propagace.\n", "\n", @@ -369,7 +369,7 @@ "\n", "Rekurentní sítě, ať už jednosměrné nebo obousměrné, zachycují vzory v rámci sekvence a ukládají je do stavových vektorů nebo je vracejí jako výstup. Stejně jako u konvolučních sítí můžeme vytvořit další rekurentní vrstvu, která následuje po první, aby zachytila vzory na vyšší úrovni, vytvořené z nižších úrovní vzorů extrahovaných první vrstvou. To nás přivádí k pojmu **vícevrstvé RNN**, které se skládají ze dvou nebo více rekurentních sítí, kde výstup předchozí vrstvy je předán jako vstup další vrstvě.\n", "\n", - "![Obrázek znázorňující vícevrstvou dlouhodobou krátkodobou paměťovou RNN](../../../../../translated_images/cs/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![Obrázek znázorňující vícevrstvou dlouhodobou krátkodobou paměťovou RNN](../../../../../translated_images/cs/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*Obrázek z [tohoto skvělého příspěvku](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) od Fernanda Lópeze.*\n", "\n", diff --git a/translations/cs/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/cs/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index e688fa62..ce4a7e6a 100644 --- a/translations/cs/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/cs/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "Způsob, jakým budeme trénovat RNN pro generování textu, je následující. Při každém kroku vezmeme sekvenci znaků o délce `nchars` a požádáme síť, aby pro každý vstupní znak vygenerovala následující výstupní znak:\n", "\n", - "![Obrázek ukazující příklad generování slova 'HELLO' pomocí RNN.](../../../../../translated_images/cs/rnn-generate.56c54afb52f9781d.png)\n", + "![Obrázek ukazující příklad generování slova 'HELLO' pomocí RNN.](../../../../../translated_images/cs/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "V závislosti na konkrétním scénáři můžeme také chtít zahrnout některé speciální znaky, jako například *konec sekvence* ``. V našem případě chceme síť trénovat pouze pro nekonečné generování textu, a proto nastavíme velikost každé sekvence na `nchars` tokenů. Každý tréninkový příklad tedy bude sestávat z `nchars` vstupů a `nchars` výstupů (což je vstupní sekvence posunutá o jeden symbol doleva). Minibatch bude obsahovat několik takových sekvencí.\n", "\n", diff --git a/translations/cs/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/cs/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index 52eb8e0c..37296d99 100644 --- a/translations/cs/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/cs/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -113,7 +113,7 @@ "\n", "Způsob, jakým budeme trénovat RNN na generování nadpisů zpráv, je následující. V každém kroku vezmeme jeden nadpis, který bude předán do RNN, a pro každý vstupní znak požádáme síť, aby vygenerovala následující výstupní znak:\n", "\n", - "![Obrázek ukazující příklad generování slova 'HELLO' pomocí RNN.](../../../../../translated_images/cs/rnn-generate.56c54afb52f9781d.png)\n", + "![Obrázek ukazující příklad generování slova 'HELLO' pomocí RNN.](../../../../../translated_images/cs/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "Pro poslední znak naší sekvence požádáme síť, aby vygenerovala token ``.\n", "\n", diff --git a/translations/cs/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/cs/lessons/5-NLP/17-GenerativeNetworks/README.md index 74f84d8e..2e7bd5c9 100644 --- a/translations/cs/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/cs/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ V architektuře RNN, kterou jsme probírali v předchozí kapitole, každá jedn To umožňuje různé neuronové architektury, jak je znázorněno na obrázku níže: -![Obrázek zobrazující běžné vzory rekurentních neuronových sítí.](../../../../../translated_images/cs/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Obrázek zobrazující běžné vzory rekurentních neuronových sítí.](../../../../../translated_images/cs/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > Obrázek z blogového příspěvku [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) od [Andreje Karpatyho](http://karpathy.github.io/) @@ -32,7 +32,7 @@ V této kapitole se zaměříme na jednoduché generativní modely, které nám Tuto RNN budeme trénovat na generování textu krok za krokem. Na každém kroku vezmeme sekvenci znaků o délce `nchars` a požádáme síť, aby pro každý vstupní znak vygenerovala další výstupní znak: -![Obrázek zobrazující příklad generování slova 'HELLO' pomocí RNN.](../../../../../translated_images/cs/rnn-generate.56c54afb52f9781d.png) +![Obrázek zobrazující příklad generování slova 'HELLO' pomocí RNN.](../../../../../translated_images/cs/rnn-generate.56c54afb52f9781d.webp) Při generování textu (během inference) začínáme s nějakým **podnětem**, který je předán přes RNN buňky pro vytvoření mezistavu, a poté začíná samotné generování. Generujeme jeden znak po druhém a předáváme stav a vygenerovaný znak další RNN buňce, aby vygenerovala další znak, dokud nevygenerujeme dostatek znaků. diff --git a/translations/cs/lessons/5-NLP/18-Transformers/README.md b/translations/cs/lessons/5-NLP/18-Transformers/README.md index 1fe9460d..a7edc6f3 100644 --- a/translations/cs/lessons/5-NLP/18-Transformers/README.md +++ b/translations/cs/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ U RNN je sekvence na sekvenci implementována dvěma rekurentními sítěmi, kde **Mechanismy pozornosti** poskytují způsob, jak vážit kontextuální vliv každého vstupního vektoru na každou výstupní predikci RNN. Implementuje se to vytvořením zkratek mezi mezistavy vstupní RNN a výstupní RNN. Tímto způsobem při generování výstupního symbolu yt zohledníme všechny skryté stavy vstupu hi, s různými váhovými koeficienty αt,i. -![Obrázek zobrazující model enkodér/dekodér s vrstvou aditivní pozornosti](../../../../../translated_images/cs/encoder-decoder-attention.7a726296894fb567.png) +![Obrázek zobrazující model enkodér/dekodér s vrstvou aditivní pozornosti](../../../../../translated_images/cs/encoder-decoder-attention.7a726296894fb567.webp) > Model enkodér-dekodér s mechanismem aditivní pozornosti podle [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citováno z [tohoto blogového příspěvku](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) Matice pozornosti {αi,j} by reprezentovala míru, jakou určitá vstupní slova ovlivňují generování daného slova ve výstupní sekvenci. Níže je příklad takové matice: -![Obrázek zobrazující vzorové zarovnání nalezené RNNsearch-50, převzato z Bahdanau - arviz.org](../../../../../translated_images/cs/bahdanau-fig3.09ba2d37f202a6af.png) +![Obrázek zobrazující vzorové zarovnání nalezené RNNsearch-50, převzato z Bahdanau - arviz.org](../../../../../translated_images/cs/bahdanau-fig3.09ba2d37f202a6af.webp) > Obrázek z [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Obr.3) @@ -66,7 +66,7 @@ Výsledek, který získáme s pozičním embeddingem, zahrnuje jak původní tok Dále potřebujeme zachytit určité vzorce v rámci naší sekvence. K tomu transformery používají mechanismus **vlastní pozornosti**, což je v podstatě pozornost aplikovaná na stejnou sekvenci jako vstup a výstup. Aplikace vlastní pozornosti nám umožňuje zohlednit **kontext** v rámci věty a vidět, která slova jsou vzájemně propojená. Například nám umožňuje vidět, na která slova odkazují koreference, jako *to*, a také zohlednit kontext: -![](../../../../../translated_images/cs/CoreferenceResolution.861924d6d384a7d6.png) +![](../../../../../translated_images/cs/CoreferenceResolution.861924d6d384a7d6.webp) > Obrázek z [Google Blogu](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Protože každá vstupní pozice je mapována nezávisle na každou výstupní p **BERT** (Bidirectional Encoder Representations from Transformers) je velmi velká vícevstvá síť transformeru s 12 vrstvami pro *BERT-base* a 24 pro *BERT-large*. Model je nejprve předtrénován na velkém korpusu textových dat (WikiPedia + knihy) pomocí nesupervizovaného tréninku (predikce maskovaných slov ve větě). Během předtrénování model absorbuje významné úrovně porozumění jazyku, které lze následně využít s jinými datovými sadami pomocí jemného ladění. Tento proces se nazývá **transfer learning**. -![obrázek z http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/cs/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) +![obrázek z http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/cs/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > Obrázek [zdroj](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/cs/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/cs/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index 794d66f7..e1b4f6b0 100644 --- a/translations/cs/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/cs/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**Mechanismy pozornosti** poskytují způsob, jak vážit kontextuální vliv jednotlivých vstupních vektorů na každou výstupní predikci RNN. To se implementuje vytvořením zkratek mezi mezistavy vstupní RNN a výstupní RNN. Tímto způsobem, při generování výstupního symbolu $y_t$, bereme v úvahu všechny skryté stavy vstupu $h_i$ s různými váhovými koeficienty $\\alpha_{t,i}$.\n", "\n", - "![Obrázek zobrazující model encoder/decoder s aditivní vrstvou pozornosti](../../../../../translated_images/cs/encoder-decoder-attention.7a726296894fb567.png) \n", + "![Obrázek zobrazující model encoder/decoder s aditivní vrstvou pozornosti](../../../../../translated_images/cs/encoder-decoder-attention.7a726296894fb567.webp) \n", "*Model encoder-decoder s mechanismem aditivní pozornosti podle [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citováno z [tohoto blogového příspěvku](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Matice pozornosti $\\{\\alpha_{i,j}\\}$ reprezentuje míru, do jaké určitá vstupní slova ovlivňují generování konkrétního slova ve výstupní sekvenci. Níže je příklad takové matice:\n", "\n", - "![Obrázek zobrazující příklad zarovnání nalezeného RNNsearch-50, převzato z Bahdanau - arviz.org](../../../../../translated_images/cs/bahdanau-fig3.09ba2d37f202a6af.png) \n", + "![Obrázek zobrazující příklad zarovnání nalezeného RNNsearch-50, převzato z Bahdanau - arviz.org](../../../../../translated_images/cs/bahdanau-fig3.09ba2d37f202a6af.webp) \n", "\n", "*Obrázek převzatý z [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Obr. 3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) je velmi velká vícevrstvá síť Transformer s 12 vrstvami pro *BERT-base* a 24 pro *BERT-large*. Model je nejprve předtrénován na velkém korpusu textových dat (WikiPedia + knihy) pomocí neřízeného učení (predikce maskovaných slov ve větě). Během předtrénování model absorbuje významnou úroveň porozumění jazyku, kterou lze následně využít s jinými datovými sadami pomocí doladění. Tento proces se nazývá **transfer learning**.\n", "\n", - "![Obrázek z http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/cs/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![Obrázek z http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/cs/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "Existuje mnoho variant architektur Transformer, včetně BERT, DistilBERT, BigBird, OpenGPT3 a dalších, které lze doladit. Balíček [HuggingFace](https://github.com/huggingface/) poskytuje repozitář pro trénování mnoha těchto architektur pomocí PyTorch.\n", "\n", diff --git a/translations/cs/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/cs/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index 5492d92a..6a627efa 100644 --- a/translations/cs/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/cs/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**Mechanismy pozornosti** poskytují způsob, jak vážit kontextuální vliv jednotlivých vstupních vektorů na každou výstupní predikci RNN. Toho je dosaženo vytvořením zkratek mezi mezistavy vstupní RNN a výstupní RNN. Tímto způsobem, při generování výstupního symbolu $y_t$, bereme v úvahu všechny skryté stavy vstupu $h_i$ s různými váhovými koeficienty $\\alpha_{t,i}$. \n", "\n", - "![Obrázek zobrazující model encoder/decoder s aditivní vrstvou pozornosti](../../../../../translated_images/cs/encoder-decoder-attention.7a726296894fb567.png)\n", + "![Obrázek zobrazující model encoder/decoder s aditivní vrstvou pozornosti](../../../../../translated_images/cs/encoder-decoder-attention.7a726296894fb567.webp)\n", "*Model encoder-decoder s mechanismem aditivní pozornosti podle [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citováno z [tohoto blogového příspěvku](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Matice pozornosti $\\{\\alpha_{i,j}\\}$ reprezentuje míru, do jaké určitá vstupní slova ovlivňují generování konkrétního slova ve výstupní sekvenci. Níže je příklad takové matice:\n", "\n", - "![Obrázek zobrazující ukázkové zarovnání nalezené modelem RNNsearch-50, převzato z Bahdanau - arviz.org](../../../../../translated_images/cs/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![Obrázek zobrazující ukázkové zarovnání nalezené modelem RNNsearch-50, převzato z Bahdanau - arviz.org](../../../../../translated_images/cs/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*Obrázek převzatý z [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Obr. 3)*\n", "\n", @@ -225,7 +225,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) je velmi rozsáhlá vícevrstvá transformátorová síť s 12 vrstvami pro *BERT-base* a 24 vrstvami pro *BERT-large*. Model je nejprve předtrénován na velkém korpusu textových dat (WikiPedia + knihy) pomocí nesupervizovaného učení (předpovídání maskovaných slov ve větě). Během předtrénování model získává významnou úroveň porozumění jazyku, kterou lze následně využít s jinými datovými sadami pomocí jemného ladění. Tento proces se nazývá **transferové učení**.\n", "\n", - "![obrázek z http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/cs/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![obrázek z http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/cs/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "Existuje mnoho variant architektur Transformerů, včetně BERT, DistilBERT, BigBird, OpenGPT3 a dalších, které lze jemně doladit.\n", "\n", diff --git a/translations/cs/lessons/5-NLP/19-NER/README.md b/translations/cs/lessons/5-NLP/19-NER/README.md index c66d3439..5d5b664d 100644 --- a/translations/cs/lessons/5-NLP/19-NER/README.md +++ b/translations/cs/lessons/5-NLP/19-NER/README.md @@ -56,7 +56,7 @@ novorozence | O Protože potřebujeme vytvořit jednoznačnou korespondenci mezi tokeny a třídami, můžeme trénovat pravostranný **many-to-many** model neuronové sítě podle tohoto obrázku: -![Obrázek ukazující běžné vzory rekurentních neuronových sítí.](../../../../../translated_images/cs/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Obrázek ukazující běžné vzory rekurentních neuronových sítí.](../../../../../translated_images/cs/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > *Obrázek z [tohoto blogového příspěvku](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) od [Andreje Karpathyho](http://karpathy.github.io/). Modely pro klasifikaci tokenů v NER odpovídají pravostranné architektuře na tomto obrázku.* diff --git a/translations/cs/lessons/5-NLP/README.md b/translations/cs/lessons/5-NLP/README.md index 54723db9..dae03c4b 100644 --- a/translations/cs/lessons/5-NLP/README.md +++ b/translations/cs/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Zpracování přirozeného jazyka -![Shrnutí úkolů NLP na kresbě](../../../../translated_images/cs/ai-nlp.b22dcb8ca4707cea.png) +![Shrnutí úkolů NLP na kresbě](../../../../translated_images/cs/ai-nlp.b22dcb8ca4707cea.webp) V této sekci se zaměříme na použití neuronových sítí k řešení úkolů spojených se **zpracováním přirozeného jazyka (NLP)**. Existuje mnoho problémů v oblasti NLP, které bychom chtěli, aby počítače dokázaly vyřešit: diff --git a/translations/cs/lessons/6-Other/23-MultiagentSystems/README.md b/translations/cs/lessons/6-Other/23-MultiagentSystems/README.md index 49a35624..41e60125 100644 --- a/translations/cs/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/cs/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ Můžete otevřít jeden z modelů, například **Biology → Flocking* Po otevření modelu se dostanete na hlavní obrazovku NetLogo. Zde je ukázkový model, který popisuje populaci vlků a ovcí, vzhledem k omezeným zdrojům (tráva). -![NetLogo Main Screen](../../../../../translated_images/cs/NetLogo-Main.32653711ec1a01b3.png) +![NetLogo Main Screen](../../../../../translated_images/cs/NetLogo-Main.32653711ec1a01b3.webp) > Screenshot od Dmitry Soshnikov diff --git a/translations/cs/lessons/README.md b/translations/cs/lessons/README.md index ac665c77..691bc957 100644 --- a/translations/cs/lessons/README.md +++ b/translations/cs/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Přehled -![Přehled v kresbě](../../../translated_images/cs/ai-overview.0857791951d19500.png) +![Přehled v kresbě](../../../translated_images/cs/ai-overview.0857791951d19500.webp) > Sketchnote od [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/cs/lessons/X-Extras/X1-MultiModal/README.md b/translations/cs/lessons/X-Extras/X1-MultiModal/README.md index 36435456..b312fbe7 100644 --- a/translations/cs/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/cs/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ Po úspěchu modelů transformerů při řešení úloh NLP byly stejné nebo po Hlavní myšlenkou CLIP je schopnost porovnávat textové výzvy s obrázkem a určit, jak dobře obrázek odpovídá dané výzvě. -![CLIP Architektura](../../../../../translated_images/cs/clip-arch.b3dbf20b4e8ed8be.png) +![CLIP Architektura](../../../../../translated_images/cs/clip-arch.b3dbf20b4e8ed8be.webp) > *Obrázek z [tohoto blogového příspěvku](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ Jakmile je model předtrénován, můžeme mu předložit dávku obrázků a tex Předpokládejme, že potřebujeme klasifikovat obrázky například na kočky, psy a lidi. V tomto případě můžeme modelu předložit obrázek a sérii textových výzev: "*obrázek kočky*", "*obrázek psa*", "*obrázek člověka*". Ve výsledném vektoru s 3 pravděpodobnostmi stačí vybrat index s nejvyšší hodnotou. -![CLIP pro Klasifikaci Obrázků](../../../../../translated_images/cs/clip-class.3af42ef0b2b19369.png) +![CLIP pro Klasifikaci Obrázků](../../../../../translated_images/cs/clip-class.3af42ef0b2b19369.webp) > *Obrázek z [tohoto blogového příspěvku](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ Více o VQGAN se dozvíte na webu [Taming Transformers](https://compvis.github.i Jedním z důležitých rozdílů mezi VQGAN a tradičním GAN je, že tradiční GAN může vytvořit slušný obrázek z jakéhokoli vstupního vektoru, zatímco VQGAN pravděpodobně vytvoří obrázek, který nebude koherentní. Proto je třeba dále řídit proces tvorby obrázku, což lze provést pomocí CLIP. -![VQGAN+CLIP Architektura](../../../../../translated_images/cs/vqgan.5027fe05051dfa31.png) +![VQGAN+CLIP Architektura](../../../../../translated_images/cs/vqgan.5027fe05051dfa31.webp) Pro generování obrázku odpovídajícího textové výzvě začneme s nějakým náhodným kódovacím vektorem, který je předán přes VQGAN k vytvoření obrázku. Poté je použit CLIP k vytvoření ztrátové funkce, která ukazuje, jak dobře obrázek odpovídá textové výzvě. Cílem je minimalizovat tuto ztrátu pomocí zpětné propagace k úpravě parametrů vstupního vektoru. Skvělá knihovna, která implementuje VQGAN+CLIP, je [Pixray](http://github.com/pixray/pixray). -![Obrázek vytvořený Pixray](../../../../../translated_images/cs/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Obrázek vytvořený Pixray](../../../../../translated_images/cs/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Obrázek vytvořený Pixray](../../../../../translated_images/cs/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) +![Obrázek vytvořený Pixray](../../../../../translated_images/cs/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp) | ![Obrázek vytvořený Pixray](../../../../../translated_images/cs/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp) | ![Obrázek vytvořený Pixray](../../../../../translated_images/cs/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp) ----|----|---- Obrázek vytvořený na základě výzvy *detailní akvarelový portrét mladého učitele literatury s knihou* | Obrázek vytvořený na základě výzvy *detailní olejový portrét mladé učitelky informatiky s počítačem* | Obrázek vytvořený na základě výzvy *detailní olejový portrét starého učitele matematiky před tabulí* diff --git a/translations/da/README.md b/translations/da/README.md index a6b4c346..26f8cd9f 100644 --- a/translations/da/README.md +++ b/translations/da/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # Kunstig intelligens for begyndere - Et pensum -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/da/ai-overview.0857791951d19500.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/da/ai-overview.0857791951d19500.webp)| |:---:| | AI For Beginners - _Sketchnote af [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/da/lessons/1-Intro/README.md b/translations/da/lessons/1-Intro/README.md index b9225132..4901fb13 100644 --- a/translations/da/lessons/1-Intro/README.md +++ b/translations/da/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Introduktion til AI -![Oversigt over introduktion til AI-indhold i en doodle](../../../../translated_images/da/ai-intro.bf28d1ac4235881c.png) +![Oversigt over introduktion til AI-indhold i en doodle](../../../../translated_images/da/ai-intro.bf28d1ac4235881c.webp) > Sketchnote af [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Oprindeligt blev computere opfundet af [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage) til at arbejde med tal ved at følge en veldefineret procedure - en algoritme. Moderne computere, selvom de er betydeligt mere avancerede end den oprindelige model foreslået i det 19. århundrede, følger stadig den samme idé om kontrollerede beregninger. Derfor er det muligt at programmere en computer til at udføre noget, hvis vi kender den præcise rækkefølge af trin, der skal til for at nå målet. -![Foto af en person](../../../../translated_images/da/dsh_age.d212a30d4e54fb5f.png) +![Foto af en person](../../../../translated_images/da/dsh_age.d212a30d4e54fb5f.webp) > Foto af [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ For mere information henvises til **[Artificial General Intelligence](https://en Et af problemerne ved at arbejde med begrebet **[Intelligens](https://en.wikipedia.org/wiki/Intelligence)** er, at der ikke findes en klar definition af dette begreb. Man kan argumentere for, at intelligens er forbundet med **abstrakt tænkning** eller **selvbevidsthed**, men vi kan ikke definere det præcist. -![Foto af en kat](../../../../translated_images/da/photo-cat.8c8e8fb760ffe457.jpg) +![Foto af en kat](../../../../translated_images/da/photo-cat.8c8e8fb760ffe457.webp) > [Foto](https://unsplash.com/photos/75715CVEJhI) af [Amber Kipp](https://unsplash.com/@sadmax) fra Unsplash @@ -98,13 +98,13 @@ Alternativt kan vi forsøge at modellere de simpleste elementer i vores hjerne > | Hvad med ML? | | > |--------------|-----------| -> | En del af Kunstig Intelligens, der er baseret på, at computeren lærer at løse et problem baseret på nogle data, kaldes **Machine Learning**. Vi vil ikke overveje klassisk machine learning i dette kursus - vi henviser dig til en separat [Machine Learning for Beginners](http://aka.ms/ml-beginners) læseplan. | ![ML for Beginners](../../../../translated_images/da/ml-for-beginners.9e4fed176fd5817d.png) | +> | En del af Kunstig Intelligens, der er baseret på, at computeren lærer at løse et problem baseret på nogle data, kaldes **Machine Learning**. Vi vil ikke overveje klassisk machine learning i dette kursus - vi henviser dig til en separat [Machine Learning for Beginners](http://aka.ms/ml-beginners) læseplan. | ![ML for Beginners](../../../../translated_images/da/ml-for-beginners.9e4fed176fd5817d.webp) | ## En Kort Historie om AI Kunstig Intelligens blev startet som et felt i midten af det tyvende århundrede. Oprindeligt var symbolsk ræsonnement en fremherskende tilgang, og det førte til en række vigtige succeser, såsom ekspertsystemer – computerprogrammer, der kunne fungere som en ekspert inden for nogle begrænsede problemområder. Det blev dog hurtigt klart, at en sådan tilgang ikke skalerer godt. At udtrække viden fra en ekspert, repræsentere det i en computer og holde denne vidensbase nøjagtig viser sig at være en meget kompleks opgave og for dyr til at være praktisk i mange tilfælde. Dette førte til den såkaldte [AI-vinter](https://en.wikipedia.org/wiki/AI_winter) i 1970'erne. -Kort historie om AI +Kort historie om AI > Billede af [Dmitry Soshnikov](http://soshnikov.com) @@ -124,7 +124,7 @@ På samme måde kan vi se, hvordan tilgangen til at skabe "talende programmer" ( * Moderne assistenter, såsom Cortana, Siri eller Google Assistant, er alle hybride systemer, der bruger neurale netværk til at konvertere tale til tekst og genkende vores intention, og derefter anvender noget ræsonnement eller eksplicitte algoritmer til at udføre de nødvendige handlinger. * I fremtiden kan vi forvente en komplet neural-baseret model til at håndtere dialoger selvstændigt. De seneste GPT- og [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) familier af neurale netværk viser stor succes i dette. -Turing-testens udvikling +Turing-testens udvikling > Billede af Dmitry Soshnikov, [foto](https://unsplash.com/photos/r8LmVbUKgns) af [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto), Unsplash ## Nyere AI-forskning diff --git a/translations/da/lessons/2-Symbolic/Animals.ipynb b/translations/da/lessons/2-Symbolic/Animals.ipynb index 6f0ff074..8f7f48b4 100644 --- a/translations/da/lessons/2-Symbolic/Animals.ipynb +++ b/translations/da/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "I dette eksempel vil vi implementere et simpelt vidensbaseret system til at bestemme et dyr baseret på nogle fysiske egenskaber. Systemet kan repræsenteres ved følgende OG-ELLER-træ (dette er en del af hele træet, vi kan nemt tilføje flere regler):\n", "\n", - "![](../../../../translated_images/da/AND-OR-Tree.5592d2c70187f283.png)\n" + "![](../../../../translated_images/da/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { diff --git a/translations/da/lessons/2-Symbolic/README.md b/translations/da/lessons/2-Symbolic/README.md index 3be0a09b..4d49954e 100644 --- a/translations/da/lessons/2-Symbolic/README.md +++ b/translations/da/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Videnrepræsentation og Ekspertsystemer -![Oversigt over Symbolsk AI-indhold](../../../../translated_images/da/ai-symbolic.715a30cb610411a6.png) +![Oversigt over Symbolsk AI-indhold](../../../../translated_images/da/ai-symbolic.715a30cb610411a6.webp) > Sketchnote af [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ Ofte definerer vi ikke viden strengt, men vi relaterer det til andre begreber ve Problemet med **videnrepræsentation** er derfor at finde en effektiv måde at repræsentere viden i en computer i form af data, så den kan bruges automatisk. Dette kan ses som et spektrum: -![Spektrum for videnrepræsentation](../../../../translated_images/da/knowledge-spectrum.b60df631852c0217.png) +![Spektrum for videnrepræsentation](../../../../translated_images/da/knowledge-spectrum.b60df631852c0217.webp) > Billede af [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Blok-syntaks | Indrykning | | | En af de tidlige succeser inden for symbolsk AI var de såkaldte **ekspertsystemer** - computersystemer designet til at fungere som en ekspert inden for et begrænset problemområde. De var baseret på en **vidensbase** udtrukket fra en eller flere menneskelige eksperter og indeholdt en **slutningsmotor**, der udførte ræsonnement ovenpå den. -![Menneskelig Arkitektur](../../../../translated_images/da/arch-human.5d4d35f1bba3ab1c.png) | ![Videnbaseret System](../../../../translated_images/da/arch-kbs.3ec5c150b09fa8da.png) +![Menneskelig Arkitektur](../../../../translated_images/da/arch-human.5d4d35f1bba3ab1c.webp) | ![Videnbaseret System](../../../../translated_images/da/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ Forenklet struktur af et menneskeligt neuralt system | Arkitektur af et videnbaseret system @@ -106,7 +106,7 @@ Ekspertsystemer er bygget som det menneskelige ræsonnementsystem, der indeholde Som et eksempel kan vi overveje følgende ekspertsystem til at bestemme et dyr baseret på dets fysiske egenskaber: -![AND-OR Træ](../../../../translated_images/da/AND-OR-Tree.5592d2c70187f283.png) +![AND-OR Træ](../../../../translated_images/da/AND-OR-Tree.5592d2c70187f283.webp) > Billede af [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/da/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/da/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index 467c5a9e..17b25a32 100644 --- a/translations/da/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/da/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -1257,7 +1257,7 @@ "* Lav træningstab - modellen kan tilpasse sig træningsdata godt, fordi den har tilstrækkelig udtrykskraft.\n", "* Valideringstab kan være meget højere end træningstab og kan begynde at stige under træningen - dette skyldes, at modellen \"husker\" træningspunkterne og mister det \"overordnede billede\".\n", "\n", - "![Overfitting](../../../../../translated_images/da/overfit.a0bd57f717c15769.png)\n", + "![Overfitting](../../../../../translated_images/da/overfit.a0bd57f717c15769.webp)\n", "\n", "> På dette billede står `x` for træningsdata, `o` for valideringsdata. Til venstre - lineær model (en-lags), den afspejler dataenes natur ret godt. Til højre - overfitted model, modellen tilpasser sig træningsdata perfekt, men giver ingen mening med andre data (valideringsfejlen er meget høj).\n" ] diff --git a/translations/da/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/da/lessons/3-NeuralNetworks/05-Frameworks/README.md index e2a22ef4..cd60ad3f 100644 --- a/translations/da/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/da/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Overfitting er et ekstremt vigtigt begreb inden for maskinlæring, og det er meg Overvej følgende problem med at approximere 5 punkter (repræsenteret ved `x` på graferne nedenfor): -![linear](../../../../../translated_images/da/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/da/overfit2.131f5800ae10ca5e.jpg) +![linear](../../../../../translated_images/da/overfit1.f24b71c6f652e59e.webp) | ![overfit](../../../../../translated_images/da/overfit2.131f5800ae10ca5e.webp) -------------------------|-------------------------- **Lineær model, 2 parametre** | **Ikke-lineær model, 7 parametre** Træningsfejl = 5.3 | Træningsfejl = 0 @@ -79,7 +79,7 @@ Det er meget vigtigt at finde den rette balance mellem modellens kompleksitet (a Som du kan se på grafen ovenfor, kan overfitting opdages ved en meget lav træningsfejl og en høj valideringsfejl. Normalt under træning vil vi se både trænings- og valideringsfejl begynde at falde, og på et tidspunkt kan valideringsfejlen stoppe med at falde og begynde at stige. Dette vil være et tegn på overfitting og en indikator for, at vi sandsynligvis bør stoppe træningen på dette tidspunkt (eller i det mindste tage et snapshot af modellen). -![overfitting](../../../../../translated_images/da/Overfitting.408ad91cd90b4371.png) +![overfitting](../../../../../translated_images/da/Overfitting.408ad91cd90b4371.webp) ## Hvordan man forhindrer overfitting diff --git a/translations/da/lessons/3-NeuralNetworks/README.md b/translations/da/lessons/3-NeuralNetworks/README.md index 8b683007..4da08e80 100644 --- a/translations/da/lessons/3-NeuralNetworks/README.md +++ b/translations/da/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Introduktion til Neurale Netværk -![Oversigt over indholdet i Intro Neural Networks i en doodle](../../../../translated_images/da/ai-neuralnetworks.1c687ae40bc86e83.png) +![Oversigt over indholdet i Intro Neural Networks i en doodle](../../../../translated_images/da/ai-neuralnetworks.1c687ae40bc86e83.webp) Som vi diskuterede i introduktionen, er en af måderne at opnå intelligens på at træne en **computermodel** eller en **kunstig hjerne**. Siden midten af det 20. århundrede har forskere prøvet forskellige matematiske modeller, indtil denne tilgang i de seneste år har vist sig at være enormt succesfuld. Sådanne matematiske modeller af hjernen kaldes **neurale netværk**. @@ -36,13 +36,13 @@ I dette pensum vil vi kun fokusere på neurale netværksmodeller. Fra biologien ved vi, at vores hjerne består af nerveceller (neuroner), som hver har flere "inputs" (dendritter) og en enkelt "output" (axon). Både dendritter og axoner kan lede elektriske signaler, og forbindelserne mellem dem — kendt som synapser — kan udvise varierende grader af ledningsevne, som reguleres af neurotransmittere. -![Model af en neuron](../../../../translated_images/da/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![Model af en neuron](../../../../translated_images/da/artneuron.1a5daa88d20ebe6f.png) +![Model af en neuron](../../../../translated_images/da/synapse-wikipedia.ed20a9e4726ea1c6.webp) | ![Model af en neuron](../../../../translated_images/da/artneuron.1a5daa88d20ebe6f.webp) ----|---- Ægte neuron *([Billede](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) fra Wikipedia)* | Kunstig neuron *(Billede af forfatteren)* Den simpleste matematiske model af en neuron indeholder således flere inputs X1, ..., XN og et output Y samt en række vægte W1, ..., WN. Et output beregnes som: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) hvor f er en ikke-lineær **aktiveringsfunktion**. diff --git a/translations/da/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/da/lessons/4-ComputerVision/06-IntroCV/README.md index da8ea11b..980d7e82 100644 --- a/translations/da/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/da/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ I vores [OpenCV Notebook](OpenCV.ipynb) giver vi nogle eksempler på, hvornår c * **Forbehandling af et fotografi af en Braille-bog**. Vi fokuserer på, hvordan vi kan bruge thresholding, feature detection, perspektivtransformation og NumPy-manipulationer til at adskille individuelle Braille-symboler til videre klassifikation af et neuralt netværk. -![Braille Image](../../../../../translated_images/da/braille.341962ff76b1bd70.jpeg) | ![Braille Image Pre-processed](../../../../../translated_images/da/braille-result.46530fea020b03c7.png) | ![Braille Symbols](../../../../../translated_images/da/braille-symbols.0159185ab69d5339.png) +![Braille Image](../../../../../translated_images/da/braille.341962ff76b1bd70.webp) | ![Braille Image Pre-processed](../../../../../translated_images/da/braille-result.46530fea020b03c7.webp) | ![Braille Symbols](../../../../../translated_images/da/braille-symbols.0159185ab69d5339.webp) ----|-----|----- > Billede fra [OpenCV.ipynb](OpenCV.ipynb) * **Detektion af bevægelse i video ved hjælp af frame difference**. Hvis kameraet er fast, bør frames fra kameraets feed være ret ens. Da frames er repræsenteret som arrays, vil vi ved blot at trække disse arrays fra hinanden for to efterfølgende frames få pixel-forskellen, som bør være lav for statiske frames og blive højere, når der er betydelig bevægelse i billedet. -![Billede af video frames og frame differences](../../../../../translated_images/da/frame-difference.706f805491a0883c.png) +![Billede af video frames og frame differences](../../../../../translated_images/da/frame-difference.706f805491a0883c.webp) > Billede fra [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ I vores [OpenCV Notebook](OpenCV.ipynb) giver vi nogle eksempler på, hvornår c - **Dense Optical Flow** beregner vektorfeltet, der viser, hvor hver pixel bevæger sig hen. - **Sparse Optical Flow** er baseret på at tage nogle karakteristiske træk i billedet (f.eks. kanter) og bygge deres bane fra frame til frame. -![Billede af optisk flow](../../../../../translated_images/da/optical.1f4a94464579a83a.png) +![Billede af optisk flow](../../../../../translated_images/da/optical.1f4a94464579a83a.webp) > Billede fra [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/da/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/da/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index aab95b63..938f9a91 100644 --- a/translations/da/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/da/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 er et netværk, der opnåede 92,7% nøjagtighed i ImageNet top-5 klassifikation i 2014. Det har følgende lagstruktur: -![ImageNet Layers](../../../../../translated_images/da/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet Layers](../../../../../translated_images/da/vgg-16-arch1.d901a5583b3a51ba.webp) Som du kan se, følger VGG en traditionel pyramidearkitektur, som er en sekvens af konvolutions- og pooling-lag. -![ImageNet Pyramid](../../../../../translated_images/da/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet Pyramid](../../../../../translated_images/da/vgg-16-arch.64ff2137f50dd49f.webp) > Billede fra [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/da/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/da/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index 1c407fe6..baf33fb6 100644 --- a/translations/da/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/da/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -260,7 +260,7 @@ "\n", "Således vil der i en typisk CNN være flere konvolutionslag med pooling-lag imellem dem for at reducere dimensionerne af billedet. Vi vil også øge antallet af filtre, fordi mønstrene bliver mere avancerede – der er flere mulige interessante kombinationer, vi skal kigge efter.\n", "\n", - "![Et billede, der viser flere konvolutionslag med pooling-lag.](../../../../../translated_images/da/cnn-pyramid.85915455759ef0ce.png)\n", + "![Et billede, der viser flere konvolutionslag med pooling-lag.](../../../../../translated_images/da/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "På grund af de reducerede rumlige dimensioner og de øgede feature-/filterdimensioner kaldes denne arkitektur også **pyramidearkitektur**.\n" ] diff --git a/translations/da/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/da/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index 30694ba2..86682741 100644 --- a/translations/da/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/da/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -358,7 +358,7 @@ "\n", "Således vil en typisk CNN have flere konvolutionslag, med pooling-lag imellem dem for at reducere dimensionerne af billedet. Vi vil også øge antallet af filtre, fordi når mønstrene bliver mere avancerede, er der flere mulige interessante kombinationer, vi skal lede efter.\n", "\n", - "![Et billede, der viser flere konvolutionslag med pooling-lag.](../../../../../translated_images/da/cnn-pyramid.85915455759ef0ce.png)\n", + "![Et billede, der viser flere konvolutionslag med pooling-lag.](../../../../../translated_images/da/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "På grund af de faldende rumlige dimensioner og stigende feature/filtre-dimensioner kaldes denne arkitektur også **pyramidearkitektur**.\n" ] diff --git a/translations/da/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/da/lessons/4-ComputerVision/07-ConvNets/README.md index 36f86f15..1d13fa1f 100644 --- a/translations/da/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/da/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ I virkeligheden ønsker vi at kunne genkende objekter på et billede, uanset der For at udtrække mønstre vil vi bruge begrebet **konvolutionelle filtre**. Som du ved, repræsenteres et billede af en 2D-matrix eller en 3D-tensor med farvedybde. At anvende et filter betyder, at vi tager en relativt lille **filterkerne**-matrix, og for hver pixel i det oprindelige billede beregner vi det vægtede gennemsnit med nabopunkterne. Vi kan se dette som et lille vindue, der glider hen over hele billedet og udjævner alle pixels i henhold til vægtene i filterkernematrixen. -![Vertikalt Kantfilter](../../../../../translated_images/da/filter-vert.b7148390ca0bc356.png) | ![Horisontalt Kantfilter](../../../../../translated_images/da/filter-horiz.59b80ed4feb946ef.png) +![Vertikalt Kantfilter](../../../../../translated_images/da/filter-vert.b7148390ca0bc356.webp) | ![Horisontalt Kantfilter](../../../../../translated_images/da/filter-horiz.59b80ed4feb946ef.webp) ----|---- > Billede af Dmitry Soshnikov @@ -38,7 +38,7 @@ Måden CNN'er fungerer på, er baseret på følgende vigtige idéer: * Vi kan designe netværket, så filtrene trænes automatisk * Vi kan bruge den samme tilgang til at finde mønstre i højere niveauer af funktioner, ikke kun i det oprindelige billede. CNN's funktionsekstraktion arbejder således på en hierarki af funktioner, der starter fra lavniveau-pixelkombinationer og går op til højere niveau-kombinationer af billeddele. -![Hierarkisk Funktionsekstraktion](../../../../../translated_images/da/FeatureExtractionCNN.d9b456cbdae7cb64.png) +![Hierarkisk Funktionsekstraktion](../../../../../translated_images/da/FeatureExtractionCNN.d9b456cbdae7cb64.webp) > Billede fra [en artikel af Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), baseret på [deres forskning](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ De fleste CNN'er, der bruges til billedbehandling, følger en såkaldt pyramidea Som et eksempel kan vi se på arkitekturen af VGG-16, et netværk der opnåede 92,7% nøjagtighed i ImageNet's top-5 klassifikation i 2014: -![ImageNet Lag](../../../../../translated_images/da/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet Lag](../../../../../translated_images/da/vgg-16-arch1.d901a5583b3a51ba.webp) -![ImageNet Pyramide](../../../../../translated_images/da/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet Pyramide](../../../../../translated_images/da/vgg-16-arch.64ff2137f50dd49f.webp) > Billede fra [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/da/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/da/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 976d41eb..6f4965a1 100644 --- a/translations/da/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/da/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ Du skal træne et konvolutionelt neuralt netværk til at klassificere forskellig Vi vil bruge [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/), som indeholder billeder af 37 forskellige racer af hunde og katte. -![Datasæt vi skal arbejde med](../../../../../../translated_images/da/data.50b2a9d5484bdbf0.png) +![Datasæt vi skal arbejde med](../../../../../../translated_images/da/data.50b2a9d5484bdbf0.webp) For at downloade datasættet, brug denne kode: diff --git a/translations/da/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/da/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index e5d29c90..1c2fbfbc 100644 --- a/translations/da/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/da/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "For at visualisere den ideelle kat, starter vi med et tilfældigt støjbillede og forsøger at bruge gradient descent-optimeringsteknikken til at justere billedet, så netværket genkender en kat.\n", "\n", - "![Optimeringsloop](../../../../../translated_images/da/ideal-cat-loop.999fbb8ff306e044.png)\n", + "![Optimeringsloop](../../../../../translated_images/da/ideal-cat-loop.999fbb8ff306e044.webp)\n", "\n", "Her er vores startbillede:\n" ] diff --git a/translations/da/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/da/lessons/4-ComputerVision/08-TransferLearning/README.md index 4d0f874c..f5dfd4b0 100644 --- a/translations/da/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/da/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Både Keras og PyTorch indeholder funktioner til nemt at indlæse forudtrænede Her er eksempler på funktioner udtrukket fra et billede af en kat ved hjælp af VGG-16-netværket: -![Funktioner udtrukket af VGG-16](../../../../../translated_images/da/features.6291f9c7ba3a0b95.png) +![Funktioner udtrukket af VGG-16](../../../../../translated_images/da/features.6291f9c7ba3a0b95.webp) ## Datasæt: Katte vs. Hunde @@ -48,19 +48,19 @@ Et forudtrænet neuralt netværk indeholder forskellige mønstre i sin *hjerne*, En tilgang, vi kan tage, er at starte med et tilfældigt billede og derefter bruge **gradient descent-optimering** til at justere billedet på en sådan måde, at netværket begynder at tro, at det er en kat. -![Billedoptimeringsloop](../../../../../translated_images/da/ideal-cat-loop.999fbb8ff306e044.png) +![Billedoptimeringsloop](../../../../../translated_images/da/ideal-cat-loop.999fbb8ff306e044.webp) Hvis vi gør dette, vil vi dog få noget, der minder meget om tilfældig støj. Dette skyldes, at *der er mange måder at få netværket til at tro, at inputbilledet er en kat*, herunder nogle, der ikke giver mening visuelt. Selvom disse billeder indeholder mange mønstre, der er typiske for en kat, er der intet, der tvinger dem til at være visuelt genkendelige. For at forbedre resultatet kan vi tilføje et andet led til tab-funktionen, som kaldes **variationstab**. Det er en måling, der viser, hvor ens nabopixels i billedet er. Ved at minimere variationstabet bliver billedet glattere og fjerner støj – hvilket afslører mere visuelt tiltalende mønstre. Her er et eksempel på sådanne "ideelle" billeder, der klassificeres som henholdsvis kat og zebra med høj sandsynlighed: -![Ideel Kat](../../../../../translated_images/da/ideal-cat.203dd4597643d6b0.png) | ![Ideel Zebra](../../../../../translated_images/da/ideal-zebra.7f70e8b54ee15a7a.png) +![Ideel Kat](../../../../../translated_images/da/ideal-cat.203dd4597643d6b0.webp) | ![Ideel Zebra](../../../../../translated_images/da/ideal-zebra.7f70e8b54ee15a7a.webp) -----|----- *Ideel Kat* | *Ideel Zebra* En lignende tilgang kan bruges til at udføre såkaldte **adversarielle angreb** på et neuralt netværk. Antag, at vi vil narre et neuralt netværk og få en hund til at ligne en kat. Hvis vi tager et billede af en hund, som netværket genkender som en hund, kan vi justere det lidt ved hjælp af gradient descent-optimering, indtil netværket begynder at klassificere det som en kat: -![Billede af en Hund](../../../../../translated_images/da/original-dog.8f68a67d2fe0911f.png) | ![Billede af en hund klassificeret som en kat](../../../../../translated_images/da/adversarial-dog.d9fc7773b0142b89.png) +![Billede af en Hund](../../../../../translated_images/da/original-dog.8f68a67d2fe0911f.webp) | ![Billede af en hund klassificeret som en kat](../../../../../translated_images/da/adversarial-dog.d9fc7773b0142b89.webp) -----|----- *Originalt billede af en hund* | *Billede af en hund klassificeret som en kat* diff --git a/translations/da/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/da/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index 9d72e2a0..8ef84cb9 100644 --- a/translations/da/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/da/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "Da vi træner autoencoderen til at fange så meget information som muligt fra det originale billede for at opnå en præcis rekonstruktion, forsøger netværket at finde den bedste **indlejring** af inputbilleder for at fange meningen.\n", "\n", - "![AutoEncoder Diagram](../../../../../translated_images/da/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![AutoEncoder Diagram](../../../../../translated_images/da/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "> Billede fra [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", diff --git a/translations/da/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/da/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index 840c84a3..621fa997 100644 --- a/translations/da/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/da/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "Da vi træner autoencoderen til at fange så meget information som muligt fra det originale billede for at opnå en præcis rekonstruktion, forsøger netværket at finde den bedste **embedding** af inputbillederne for at fange meningen.\n", "\n", - "![AutoEncoder Diagram](../../../../../translated_images/da/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![AutoEncoder Diagram](../../../../../translated_images/da/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "*Billede fra [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", diff --git a/translations/da/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/da/lessons/4-ComputerVision/09-Autoencoders/README.md index e1161324..c5f8852e 100644 --- a/translations/da/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/da/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ Vi kan dog ønske at bruge rå (umærkede) data til at træne CNN-featureekstrak Da vi træner en autoencoder til at fange så meget information som muligt fra det oprindelige billede for at opnå en præcis rekonstruktion, forsøger netværket at finde den bedste **indlejring** af inputbilleder for at fange meningen. -![AutoEncoder Diagram](../../../../../translated_images/da/autoencoder_schema.5e6fc9ad98a5eb61.jpg) +![AutoEncoder Diagram](../../../../../translated_images/da/autoencoder_schema.5e6fc9ad98a5eb61.webp) > Billede fra [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/da/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/da/lessons/4-ComputerVision/11-ObjectDetection/README.md index 928d9ac2..3974c5ec 100644 --- a/translations/da/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/da/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ De billedklassifikationsmodeller, vi hidtil har arbejdet med, tog et billede og ## [Quiz før lektionen](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Objektgenkendelse](../../../../../translated_images/da/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) +![Objektgenkendelse](../../../../../translated_images/da/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp) > Billede fra [YOLO v2 hjemmeside](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ Hvis vi antager, at vi vil finde en kat på et billede, kunne en meget naiv tilg 2. Kør billedklassifikation på hver flise. 3. De fliser, der resulterer i tilstrækkelig høj aktivering, kan betragtes som indeholdende det ønskede objekt. -![Naiv objektgenkendelse](../../../../../translated_images/da/naive-detection.e7f1ba220ccd08c6.png) +![Naiv objektgenkendelse](../../../../../translated_images/da/naive-detection.e7f1ba220ccd08c6.webp) > *Billede fra [Øvelsesnotebook](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ Du kan støde på følgende datasæt til denne opgave: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 klasser * [COCO](http://cocodataset.org/#home) - Common Objects in Context. 80 klasser, afgrænsningsbokse og segmenteringsmasker -![COCO](../../../../../translated_images/da/coco-examples.71bc60380fa6cceb.jpg) +![COCO](../../../../../translated_images/da/coco-examples.71bc60380fa6cceb.webp) ## Metrikker for objektgenkendelse @@ -50,7 +50,7 @@ Du kan støde på følgende datasæt til denne opgave: Mens det er nemt at måle, hvor godt en algoritme klarer sig i billedklassifikation, skal vi i objektgenkendelse måle både korrektheden af klassen og præcisionen af den forudsagte placering af afgrænsningsboksen. Til det sidste bruger vi den såkaldte **Intersection over Union** (IoU), som måler, hvor godt to bokse (eller to vilkårlige områder) overlapper. -![IoU](../../../../../translated_images/da/iou_equation.9a4751d40fff4e11.png) +![IoU](../../../../../translated_images/da/iou_equation.9a4751d40fff4e11.webp) > *Figur 2 fra [dette fremragende blogindlæg om IoU](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ Der er to brede klasser af algoritmer til objektgenkendelse: [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) bruger [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) til at generere en hierarkisk struktur af ROI-regioner, som derefter sendes gennem CNN-featureekstraktorer og SVM-klassifikatorer for at bestemme objektklassen og lineær regression for at bestemme *afgrænsningsboksens* koordinater. [Officiel artikel](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/da/rcnn1.cae407020dfb1d1f.png) +![RCNN](../../../../../translated_images/da/rcnn1.cae407020dfb1d1f.webp) > *Billede fra van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/da/rcnn2.2d9530bb83516484.png) +![RCNN-1](../../../../../translated_images/da/rcnn2.2d9530bb83516484.webp) > *Billeder fra [denne blog](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -110,7 +110,7 @@ Der er to brede klasser af algoritmer til objektgenkendelse: Denne tilgang ligner R-CNN, men regioner defineres efter, at konvolutionslagene er blevet anvendt. -![FRCNN](../../../../../translated_images/da/f-rcnn.3cda6d9bb4188875.png) +![FRCNN](../../../../../translated_images/da/f-rcnn.3cda6d9bb4188875.webp) > Billede fra [den officielle artikel](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ Denne tilgang ligner R-CNN, men regioner defineres efter, at konvolutionslagene Hovedideen med denne tilgang er at bruge et neuralt netværk til at forudsige ROIs – det såkaldte *Region Proposal Network*. [Artikel](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/da/faster-rcnn.8d46c099b87ef30a.png) +![FasterRCNN](../../../../../translated_images/da/faster-rcnn.8d46c099b87ef30a.webp) > Billede fra [den officielle artikel](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ Denne algoritme er endnu hurtigere end Faster R-CNN. Hovedideen er følgende: 2. Features behandles af **Position-Sensitive Score Map**. Hvert objekt fra $C$ klasser opdeles i $k\times k$ regioner, og vi træner til at forudsige dele af objekter. 3. For hver del fra $k\times k$ regioner stemmer alle netværk for objektklasser, og den objektklasse med flest stemmer vælges. -![r-fcn billede](../../../../../translated_images/da/r-fcn.13eb88158b99a3da.png) +![r-fcn billede](../../../../../translated_images/da/r-fcn.13eb88158b99a3da.webp) > Billede fra [officiel artikel](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO er en realtids one-pass algoritme. Hovedideen er følgende: * Billedet opdeles i $S\times S$ regioner. * For hver region forudsiger **CNN** $n$ mulige objekter, *afgrænsningsboksens* koordinater og *tillid*=*sandsynlighed* * IoU. - ![YOLO](../../../../../translated_images/da/yolo.a2648ec82ee8bb4e.png) + ![YOLO](../../../../../translated_images/da/yolo.a2648ec82ee8bb4e.webp) > Billede fra [officiel artikel](https://arxiv.org/abs/1506.02640) diff --git a/translations/da/lessons/4-ComputerVision/README.md b/translations/da/lessons/4-ComputerVision/README.md index 20577cd9..49c4502f 100644 --- a/translations/da/lessons/4-ComputerVision/README.md +++ b/translations/da/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Computer Vision -![Oversigt over Computer Vision-indhold i en doodle](../../../../translated_images/da/ai-computervision.6506ebebac3fbf76.png) +![Oversigt over Computer Vision-indhold i en doodle](../../../../translated_images/da/ai-computervision.6506ebebac3fbf76.webp) I denne sektion vil vi lære om: diff --git a/translations/da/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/da/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index 7e54d0a4..63382afd 100644 --- a/translations/da/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/da/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**Bag of Words** (BoW) vektorrepræsentation er den mest almindeligt anvendte traditionelle vektorrepræsentation. Hvert ord er knyttet til en vektorindeks, og vektorelementet indeholder antallet af forekomster af et ord i et givet dokument.\n", "\n", - "![Billede, der viser, hvordan en bag of words-vektorrepræsentation er repræsenteret i hukommelsen.](../../../../../translated_images/da/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![Billede, der viser, hvordan en bag of words-vektorrepræsentation er repræsenteret i hukommelsen.](../../../../../translated_images/da/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Note**: Du kan også tænke på BoW som en sum af alle one-hot-kodede vektorer for de enkelte ord i teksten.\n", "\n", diff --git a/translations/da/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/da/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index 55e8554f..87f7726f 100644 --- a/translations/da/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/da/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**Bag-of-words** (BoW) vektorrepræsentation er den mest simple og letforståelige traditionelle vektorrepræsentation. Hvert ord er knyttet til en vektorindeks, og et vektorelement indeholder antallet af forekomster af hvert ord i et givent dokument.\n", "\n", - "![Billede, der viser, hvordan en bag-of-words vektorrepræsentation er repræsenteret i hukommelsen.](../../../../../translated_images/da/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![Billede, der viser, hvordan en bag-of-words vektorrepræsentation er repræsenteret i hukommelsen.](../../../../../translated_images/da/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Note**: Du kan også tænke på BoW som en sum af alle one-hot-enkodede vektorer for de enkelte ord i teksten.\n", "\n", diff --git a/translations/da/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/da/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index c0f25d9e..810f7cee 100644 --- a/translations/da/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/da/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "Ved at bruge embedding-laget som det første lag i vores netværk kan vi skifte fra bag-of-words til en **embedding bag**-model, hvor vi først konverterer hvert ord i vores tekst til den tilsvarende embedding og derefter beregner en eller anden aggregeringsfunktion over alle disse embeddings, såsom `sum`, `average` eller `max`.\n", "\n", - "![Billede, der viser en embedding-klassifikator for fem sekvensord.](../../../../../translated_images/da/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![Billede, der viser en embedding-klassifikator for fem sekvensord.](../../../../../translated_images/da/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "Vores klassifikator-neurale netværk vil starte med et embedding-lag, derefter et aggregeringslag og en lineær klassifikator ovenpå:\n" ] @@ -176,7 +176,7 @@ "\n", "I den tidligere arkitektur var vi nødt til at udfylde alle sekvenser til samme længde for at passe dem ind i en minibatch. Dette er ikke den mest effektive måde at repræsentere sekvenser med variabel længde på - en anden tilgang ville være at bruge en **offset**-vektor, som indeholder offsets for alle sekvenser, der er gemt i én stor vektor.\n", "\n", - "![Billede, der viser en offset-sekvensrepræsentation](../../../../../translated_images/da/offset-sequence-representation.eb73fcefb29b46ee.png)\n", + "![Billede, der viser en offset-sekvensrepræsentation](../../../../../translated_images/da/offset-sequence-representation.eb73fcefb29b46ee.webp)\n", "\n", "> **Note**: På billedet ovenfor viser vi en sekvens af tegn, men i vores eksempel arbejder vi med sekvenser af ord. Dog forbliver det generelle princip om at repræsentere sekvenser med en offset-vektor det samme.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW er hurtigere, mens skip-gram er langsommere, men gør et bedre stykke arbejde med at repræsentere sjældne ord.\n", "\n", - "![Billede, der viser både CBoW- og Skip-Gram-algoritmer til at konvertere ord til vektorer.](../../../../../translated_images/da/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![Billede, der viser både CBoW- og Skip-Gram-algoritmer til at konvertere ord til vektorer.](../../../../../translated_images/da/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "For at eksperimentere med word2vec embedding fortrænet på Google News-datasættet kan vi bruge **gensim**-biblioteket. Nedenfor finder vi de ord, der minder mest om 'neural'.\n", "\n", diff --git a/translations/da/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/da/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index cee8f9ad..5a21e348 100644 --- a/translations/da/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/da/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "Ved at bruge et embedding-lag som det første lag i vores netværk kan vi skifte fra bag-of-words til en **embedding bag**-model, hvor vi først konverterer hvert ord i vores tekst til den tilsvarende embedding og derefter beregner en aggregeringsfunktion over alle disse embeddings, såsom `sum`, `average` eller `max`.\n", "\n", - "![Billede, der viser en embedding-klassifikator for fem sekvensord.](../../../../../translated_images/da/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![Billede, der viser en embedding-klassifikator for fem sekvensord.](../../../../../translated_images/da/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "Vores klassifikator-neurale netværk består af følgende lag:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW er hurtigere, og selvom skip-gram er langsommere, er det bedre til at repræsentere sjældne ord.\n", "\n", - "![Billede, der viser både CBoW- og Skip-Gram-algoritmer til at konvertere ord til vektorer.](../../../../../translated_images/da/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![Billede, der viser både CBoW- og Skip-Gram-algoritmer til at konvertere ord til vektorer.](../../../../../translated_images/da/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "For at eksperimentere med Word2Vec-indlejringen, der er fortrænet på Google News-datasættet, kan vi bruge **gensim**-biblioteket. Nedenfor finder vi de ord, der minder mest om 'neural'.\n", "\n", diff --git a/translations/da/lessons/5-NLP/14-Embeddings/README.md b/translations/da/lessons/5-NLP/14-Embeddings/README.md index 61692f2d..955e8495 100644 --- a/translations/da/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/da/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ Så indlejringslaget vil tage et ord som input og producere en outputvektor med Ved at bruge et indlejringslag som det første lag i vores klassifikationsnetværk kan vi skifte fra en bag-of-words til en **embedding bag** model, hvor vi først konverterer hvert ord i vores tekst til den tilsvarende indlejring og derefter beregner en aggregeringsfunktion over alle disse indlejringer, såsom `sum`, `average` eller `max`. -![Billede, der viser en indlejringsklassifikator for fem sekvensord.](../../../../../translated_images/da/embedding-classifier-example.b77f021a7ee67eee.png) +![Billede, der viser en indlejringsklassifikator for fem sekvensord.](../../../../../translated_images/da/embedding-classifier-example.b77f021a7ee67eee.webp) > Billede af forfatteren @@ -40,7 +40,7 @@ For at gøre dette skal vi fortræne vores indlejringsmodel på en stor samling CBoW er hurtigere, mens skip-gram er langsommere, men gør et bedre arbejde med at repræsentere sjældne ord. -![Billede, der viser både CBoW og Skip-Gram algoritmer til at konvertere ord til vektorer.](../../../../../translated_images/da/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![Billede, der viser både CBoW og Skip-Gram algoritmer til at konvertere ord til vektorer.](../../../../../translated_images/da/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Billede fra [denne artikel](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/da/lessons/5-NLP/15-LanguageModeling/README.md b/translations/da/lessons/5-NLP/15-LanguageModeling/README.md index 36a5dfa3..a553522c 100644 --- a/translations/da/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/da/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ I vores tidligere eksempler brugte vi fortrænede semantiske indlejringer, men d * **Continuous Bag-of-Words** (CBoW), hvor vi forudsiger det midterste token $W_0$ i en token-sekvens $W_{-N}$, ..., $W_N$. * **Skip-gram**, hvor vi forudsiger et sæt af nabotokens {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} ud fra det midterste token $W_0$. -![billede fra artikel om konvertering af ord til vektorer](../../../../../translated_images/da/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![billede fra artikel om konvertering af ord til vektorer](../../../../../translated_images/da/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Billede fra [denne artikel](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/da/lessons/5-NLP/16-RNN/README.md b/translations/da/lessons/5-NLP/16-RNN/README.md index 0f2f84e0..2ce77e5f 100644 --- a/translations/da/lessons/5-NLP/16-RNN/README.md +++ b/translations/da/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ I de tidligere afsnit har vi brugt rige semantiske repræsentationer af tekst og For at fange betydningen af tekstsekvenser skal vi bruge en anden neural netværksarkitektur, som kaldes et **rekurrent neuralt netværk**, eller RNN. I RNN sender vi vores sætning gennem netværket én symbol ad gangen, og netværket producerer en **tilstand**, som vi derefter sender tilbage til netværket sammen med det næste symbol. -![RNN](../../../../../translated_images/da/rnn.27f5c29c53d727b5.png) +![RNN](../../../../../translated_images/da/rnn.27f5c29c53d727b5.webp) > Billede af forfatteren @@ -61,7 +61,7 @@ Vi har diskuteret rekurrente netværk, der opererer i én retning, fra begyndels Et rekurrent netværk, enten én-retnings eller bidirektionelt, fanger visse mønstre inden for en sekvens og kan gemme dem i en tilstandsvektor eller sende dem til output. Ligesom med konvolutionelle netværk kan vi bygge et andet rekurrent lag oven på det første for at fange højere niveau mønstre og bygge videre på lav-niveau mønstre, der er udtrukket af det første lag. Dette fører os til begrebet **flerlags RNN**, som består af to eller flere rekurrente netværk, hvor output fra det foregående lag sendes til det næste lag som input. -![Billede der viser et flerlags long-short-term-memory RNN](../../../../../translated_images/da/multi-layer-lstm.dd975e29bb2a59fe.jpg) +![Billede der viser et flerlags long-short-term-memory RNN](../../../../../translated_images/da/multi-layer-lstm.dd975e29bb2a59fe.webp) *Billede fra [denne vidunderlige artikel](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) af Fernando López* diff --git a/translations/da/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/da/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index 2bdf3039..303ff26f 100644 --- a/translations/da/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/da/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "Et rekurrent netværk, enten én-retnings eller bidirektionelt, fanger visse mønstre inden for en sekvens og kan gemme dem i en tilstandsvektor eller sende dem videre til output. Ligesom med konvolutionelle netværk kan vi bygge et andet rekurrent lag oven på det første for at fange mønstre på et højere niveau, bygget fra lav-niveau mønstre, som det første lag har udtrukket. Dette fører os til begrebet **flerlags RNN**, som består af to eller flere rekurrente netværk, hvor output fra det foregående lag sendes til det næste lag som input.\n", "\n", - "![Billede der viser en flerlags lang-kort-tids-hukommelses-RNN](../../../../../translated_images/da/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![Billede der viser en flerlags lang-kort-tids-hukommelses-RNN](../../../../../translated_images/da/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*Billede fra [denne fantastiske artikel](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) af Fernando López*\n", "\n", diff --git a/translations/da/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/da/lessons/5-NLP/16-RNN/RNNTF.ipynb index be9d0bf4..d97a9485 100644 --- a/translations/da/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/da/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "For at fange betydningen af en tekstsekvens vil vi bruge en neural netværksarkitektur kaldet **rekurrente neurale netværk**, eller RNN. Når vi bruger en RNN, sender vi vores sætning gennem netværket én token ad gangen, og netværket producerer en **tilstand**, som vi derefter sender videre til netværket sammen med den næste token.\n", "\n", - "![Billede, der viser et eksempel på generering med rekurrente neurale netværk.](../../../../../translated_images/da/rnn.27f5c29c53d727b5.png)\n", + "![Billede, der viser et eksempel på generering med rekurrente neurale netværk.](../../../../../translated_images/da/rnn.27f5c29c53d727b5.webp)\n", "\n", "Givet inputsekvensen af tokens $X_0,\\dots,X_n$, skaber RNN en sekvens af neurale netværksblokke og træner denne sekvens ende-til-ende ved hjælp af backpropagation. Hver netværksblok tager et par $(X_i,S_i)$ som input og producerer $S_{i+1}$ som resultat. Den endelige tilstand $S_n$ eller output $Y_n$ går ind i en lineær klassifikator for at producere resultatet. Alle netværksblokke deler de samme vægte og trænes ende-til-ende ved hjælp af én backpropagation-pass.\n", "\n", @@ -369,7 +369,7 @@ "\n", "Rekurrente netværk, enten unidirektionelle eller bidirektionelle, fanger mønstre inden for en sekvens og gemmer dem i tilstandsvektorer eller returnerer dem som output. Ligesom med konvolutionelle netværk kan vi bygge et andet rekurrent lag efter det første for at fange mønstre på et højere niveau, bygget fra mønstre på lavere niveau, som det første lag har udtrukket. Dette fører os til begrebet **flerlags RNN**, som består af to eller flere rekurrente netværk, hvor outputtet fra det foregående lag gives videre til det næste lag som input.\n", "\n", - "![Billede, der viser et flerlags lang-kort-tids-hukommelses-RNN](../../../../../translated_images/da/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![Billede, der viser et flerlags lang-kort-tids-hukommelses-RNN](../../../../../translated_images/da/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*Billede fra [denne fantastiske artikel](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) af Fernando López.*\n", "\n", diff --git a/translations/da/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/da/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index e773bc20..8a7d1d80 100644 --- a/translations/da/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/da/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "Måden, vi vil træne en RNN til at generere tekst, er som følger. Ved hvert trin tager vi en sekvens af tegn med længden `nchars` og beder netværket om at generere det næste outputtegn for hvert inputtegn:\n", "\n", - "![Billede, der viser et eksempel på RNN-generering af ordet 'HELLO'.](../../../../../translated_images/da/rnn-generate.56c54afb52f9781d.png)\n", + "![Billede, der viser et eksempel på RNN-generering af ordet 'HELLO'.](../../../../../translated_images/da/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "Afhængigt af det konkrete scenarie kan vi også ønske at inkludere nogle specialtegn, såsom *end-of-sequence* ``. I vores tilfælde ønsker vi blot at træne netværket til uendelig tekstgenerering, så vi vil fastsætte størrelsen af hver sekvens til at være lig med `nchars` tokens. Derfor vil hvert træningseksempel bestå af `nchars` input og `nchars` output (hvilket er inputsekvensen forskudt med ét symbol til venstre). En minibatch vil bestå af flere sådanne sekvenser.\n", "\n", diff --git a/translations/da/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/da/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index 4018f8ce..38d590bf 100644 --- a/translations/da/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/da/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -113,7 +113,7 @@ "\n", "Måden, vi vil træne RNN til at generere nyhedstitler, er som følger. Ved hvert trin tager vi en titel, som bliver fodret ind i en RNN, og for hvert inputtegn beder vi netværket om at generere det næste outputtegn:\n", "\n", - "![Billede, der viser et eksempel på RNN-generering af ordet 'HELLO'.](../../../../../translated_images/da/rnn-generate.56c54afb52f9781d.png)\n", + "![Billede, der viser et eksempel på RNN-generering af ordet 'HELLO'.](../../../../../translated_images/da/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "For det sidste tegn i vores sekvens beder vi netværket om at generere ``-token.\n", "\n", diff --git a/translations/da/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/da/lessons/5-NLP/17-GenerativeNetworks/README.md index 346f3e8e..dbfd8297 100644 --- a/translations/da/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/da/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ I RNN-arkitekturen, som vi diskuterede i den forrige enhed, producerede hver RNN Dette muliggør forskellige neurale arkitekturer, som vist på billedet nedenfor: -![Billede, der viser almindelige mønstre for rekurrente neurale netværk.](../../../../../translated_images/da/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Billede, der viser almindelige mønstre for rekurrente neurale netværk.](../../../../../translated_images/da/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > Billede fra blogindlægget [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) af [Andrej Karpaty](http://karpathy.github.io/) @@ -32,7 +32,7 @@ I denne enhed vil vi fokusere på simple generative modeller, der hjælper os me Vi vil træne denne RNN til at generere tekst trin for trin. Ved hvert trin tager vi en sekvens af tegn med længden `nchars` og beder netværket om at generere det næste outputtegn for hvert inputtegn: -![Billede, der viser et eksempel på RNN-generering af ordet 'HELLO'.](../../../../../translated_images/da/rnn-generate.56c54afb52f9781d.png) +![Billede, der viser et eksempel på RNN-generering af ordet 'HELLO'.](../../../../../translated_images/da/rnn-generate.56c54afb52f9781d.webp) Når vi genererer tekst (under inferens), starter vi med en **prompt**, som sendes gennem RNN-celler for at generere dens mellemliggende tilstand, og derefter starter genereringen fra denne tilstand. Vi genererer ét tegn ad gangen og sender tilstanden og det genererede tegn til en anden RNN-celle for at generere det næste, indtil vi har genereret nok tegn. diff --git a/translations/da/lessons/5-NLP/18-Transformers/README.md b/translations/da/lessons/5-NLP/18-Transformers/README.md index b662e775..32578ed9 100644 --- a/translations/da/lessons/5-NLP/18-Transformers/README.md +++ b/translations/da/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ Med RNN'er implementeres sekvens-til-sekvens med to rekurrente netværk, hvor de **Attention Mekanismer** giver en metode til at vægte den kontekstuelle indflydelse af hver inputvektor på hver outputforudsigelse i RNN. Dette implementeres ved at skabe genveje mellem de mellemliggende tilstande i input-RNN og output-RNN. På denne måde, når vi genererer outputsymbol yt, tager vi alle input skjulte tilstande hi i betragtning med forskellige vægtkoefficienter αt,i. -![Billede, der viser en encoder/decoder-model med et additivt attention-lag](../../../../../translated_images/da/encoder-decoder-attention.7a726296894fb567.png) +![Billede, der viser en encoder/decoder-model med et additivt attention-lag](../../../../../translated_images/da/encoder-decoder-attention.7a726296894fb567.webp) > Encoder-decoder modellen med additiv attention mekanisme i [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citeret fra [denne blogpost](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) Attention-matrixen {αi,j} repræsenterer graden af, hvor meget visse inputord spiller en rolle i genereringen af et givet ord i outputsekvensen. Nedenfor er et eksempel på en sådan matrix: -![Billede, der viser en prøvejustering fundet af RNNsearch-50, taget fra Bahdanau - arviz.org](../../../../../translated_images/da/bahdanau-fig3.09ba2d37f202a6af.png) +![Billede, der viser en prøvejustering fundet af RNNsearch-50, taget fra Bahdanau - arviz.org](../../../../../translated_images/da/bahdanau-fig3.09ba2d37f202a6af.webp) > Figur fra [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) @@ -66,7 +66,7 @@ Resultatet, vi får med positionskodning, embedder både det originale token og Dernæst skal vi fange nogle mønstre inden for vores sekvens. For at gøre dette bruger transformers en **self-attention** mekanisme, som i bund og grund er attention anvendt på den samme sekvens som input og output. Ved at anvende self-attention kan vi tage **kontekst** inden for sætningen i betragtning og se, hvilke ord der er relaterede. For eksempel giver det os mulighed for at se, hvilke ord der refereres til af coreferencer, såsom *det*, og også tage konteksten i betragtning: -![](../../../../../translated_images/da/CoreferenceResolution.861924d6d384a7d6.png) +![](../../../../../translated_images/da/CoreferenceResolution.861924d6d384a7d6.webp) > Billede fra [Google Blog](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Da hver inputposition uafhængigt kortlægges til hver outputposition, kan trans **BERT** (Bidirectional Encoder Representations from Transformers) er et meget stort multi-lags transformer-netværk med 12 lag for *BERT-base* og 24 for *BERT-large*. Modellen trænes først på en stor tekstkorpus (Wikipedia + bøger) ved hjælp af usuperviseret træning (forudsige maskerede ord i en sætning). Under pre-træning absorberer modellen betydelige niveauer af sprogforståelse, som derefter kan udnyttes med andre datasæt ved hjælp af finjustering. Denne proces kaldes **transfer learning**. -![billede fra http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/da/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) +![billede fra http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/da/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > Billede [kilde](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/da/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/da/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index 6d3f0e4d..b35c90bc 100644 --- a/translations/da/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/da/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**Opmærksomhedsmekanismer** giver en metode til at vægte den kontekstuelle indflydelse af hver inputvektor på hver outputforudsigelse i RNN. Dette implementeres ved at skabe genveje mellem de mellemliggende tilstande i input-RNN og output-RNN. På denne måde, når vi genererer outputsymbolet $y_t$, tager vi højde for alle skjulte inputtilstande $h_i$ med forskellige vægtkoefficienter $\\alpha_{t,i}$. \n", "\n", - "![Billede, der viser en encoder/decoder-model med et additivt opmærksomhedslag](../../../../../translated_images/da/encoder-decoder-attention.7a726296894fb567.png)\n", + "![Billede, der viser en encoder/decoder-model med et additivt opmærksomhedslag](../../../../../translated_images/da/encoder-decoder-attention.7a726296894fb567.webp)\n", "*Encoder-decoder-modellen med additiv opmærksomhedsmekanisme i [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citeret fra [denne blogpost](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Opmærksomhedsmatricen $\\{\\alpha_{i,j}\\}$ repræsenterer graden, hvormed visse inputord spiller en rolle i genereringen af et givet ord i outputsekvensen. Nedenfor er et eksempel på en sådan matrix:\n", "\n", - "![Billede, der viser en prøvejustering fundet af RNNsearch-50, taget fra Bahdanau - arviz.org](../../../../../translated_images/da/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![Billede, der viser en prøvejustering fundet af RNNsearch-50, taget fra Bahdanau - arviz.org](../../../../../translated_images/da/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*Figur taget fra [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) er et meget stort flerlagstransformernetværk med 12 lag for *BERT-base* og 24 for *BERT-large*. Modellen fortrænes først på en stor tekstkorpus (Wikipedia + bøger) ved hjælp af usuperviseret træning (forudsige maskerede ord i en sætning). Under fortræningen absorberer modellen et betydeligt niveau af sprogforståelse, som derefter kan udnyttes med andre datasæt ved hjælp af finjustering. Denne proces kaldes **transfer learning**. \n", "\n", - "![Billede fra http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/da/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![Billede fra http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/da/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "Der findes mange variationer af Transformer-arkitekturer, herunder BERT, DistilBERT, BigBird, OpenGPT3 og flere, som kan finjusteres. [HuggingFace-pakken](https://github.com/huggingface/) giver et bibliotek til træning af mange af disse arkitekturer med PyTorch. \n", "\n", diff --git a/translations/da/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/da/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index e475a00f..afe50744 100644 --- a/translations/da/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/da/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**Opmærksomhedsmekanismer** giver en metode til at vægte den kontekstuelle indflydelse af hver inputvektor på hver outputforudsigelse af RNN. Dette implementeres ved at skabe genveje mellem de mellemliggende tilstande i input-RNN og output-RNN. På denne måde, når vi genererer outputsymbolet $y_t$, tager vi højde for alle skjulte inputtilstande $h_i$ med forskellige vægtkoefficienter $\\alpha_{t,i}$.\n", "\n", - "![Billede, der viser en encoder/decoder-model med et additivt opmærksomhedslag](../../../../../translated_images/da/encoder-decoder-attention.7a726296894fb567.png)\n", + "![Billede, der viser en encoder/decoder-model med et additivt opmærksomhedslag](../../../../../translated_images/da/encoder-decoder-attention.7a726296894fb567.webp)\n", "*Encoder-decoder-modellen med additiv opmærksomhedsmekanisme i [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citeret fra [denne blogpost](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Opmærksomhedsmatrixen $\\{\\alpha_{i,j}\\}$ repræsenterer graden, som visse inputord spiller i genereringen af et givet ord i outputsekvensen. Nedenfor er et eksempel på en sådan matrix:\n", "\n", - "![Billede, der viser en prøvejustering fundet af RNNsearch-50, taget fra Bahdanau - arviz.org](../../../../../translated_images/da/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![Billede, der viser en prøvejustering fundet af RNNsearch-50, taget fra Bahdanau - arviz.org](../../../../../translated_images/da/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*Figur taget fra [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3)*\n", "\n", @@ -231,7 +231,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) er et meget stort multi-lags transformer-netværk med 12 lag for *BERT-base* og 24 for *BERT-large*. Modellen bliver først forudtrænet på en stor mængde tekstdata (Wikipedia + bøger) ved hjælp af usuperviseret træning (forudsige maskerede ord i en sætning). Under forudtræningen opnår modellen en betydelig forståelse af sproget, som derefter kan udnyttes med andre datasæt gennem finjustering. Denne proces kaldes **transfer learning**.\n", "\n", - "![billede fra http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/da/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![billede fra http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/da/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "Der findes mange variationer af Transformer-arkitekturer, herunder BERT, DistilBERT, BigBird, OpenGPT3 og flere, som kan finjusteres.\n", "\n", diff --git a/translations/da/lessons/5-NLP/19-NER/README.md b/translations/da/lessons/5-NLP/19-NER/README.md index b3205a5d..1a5c0a85 100644 --- a/translations/da/lessons/5-NLP/19-NER/README.md +++ b/translations/da/lessons/5-NLP/19-NER/README.md @@ -57,7 +57,7 @@ spædbarn | O Da vi skal opbygge en én-til-én-korrespondance mellem tokens og klasser, kan vi træne en højreorienteret **mange-til-mange** neuralt netværksmodel fra dette billede: -![Billede, der viser almindelige mønstre for rekurrente neurale netværk.](../../../../../translated_images/da/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Billede, der viser almindelige mønstre for rekurrente neurale netværk.](../../../../../translated_images/da/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > *Billede fra [denne blogpost](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) af [Andrej Karpathy](http://karpathy.github.io/). NER-tokenklassifikationsmodeller svarer til den højreorienterede netværksarkitektur på dette billede.* diff --git a/translations/da/lessons/5-NLP/README.md b/translations/da/lessons/5-NLP/README.md index 8fc959e8..b28e8046 100644 --- a/translations/da/lessons/5-NLP/README.md +++ b/translations/da/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Naturlig Sprogbehandling -![Oversigt over NLP-opgaver i en doodle](../../../../translated_images/da/ai-nlp.b22dcb8ca4707cea.png) +![Oversigt over NLP-opgaver i en doodle](../../../../translated_images/da/ai-nlp.b22dcb8ca4707cea.webp) I denne sektion vil vi fokusere på at bruge neurale netværk til at håndtere opgaver relateret til **naturlig sprogbehandling (NLP)**. Der er mange NLP-problemer, som vi ønsker, at computere skal kunne løse: diff --git a/translations/da/lessons/6-Other/23-MultiagentSystems/README.md b/translations/da/lessons/6-Other/23-MultiagentSystems/README.md index 5496e9ee..2bab8b34 100644 --- a/translations/da/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/da/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ Du kan åbne en af modellerne, for eksempel **Biology → Flocking**. Efter åbning af modellen kommer du til NetLogos hovedskærm. Her er en eksempelmodel, der beskriver populationen af ulve og får, givet begrænsede ressourcer (græs). -![NetLogo Main Screen](../../../../../translated_images/da/NetLogo-Main.32653711ec1a01b3.png) +![NetLogo Main Screen](../../../../../translated_images/da/NetLogo-Main.32653711ec1a01b3.webp) > Skærmbillede af Dmitry Soshnikov diff --git a/translations/da/lessons/README.md b/translations/da/lessons/README.md index cd3c29bc..c0f545b0 100644 --- a/translations/da/lessons/README.md +++ b/translations/da/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Oversigt -![Oversigt i en doodle](../../../translated_images/da/ai-overview.0857791951d19500.png) +![Oversigt i en doodle](../../../translated_images/da/ai-overview.0857791951d19500.webp) > Sketchnote af [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/da/lessons/X-Extras/X1-MultiModal/README.md b/translations/da/lessons/X-Extras/X1-MultiModal/README.md index 0d6b36cf..5065fcd7 100644 --- a/translations/da/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/da/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ Efter succesen med transformer-modeller til løsning af NLP-opgaver, er de samme Hovedideen med CLIP er at kunne sammenligne tekstprompter med et billede og afgøre, hvor godt billedet svarer til prompten. -![CLIP Arkitektur](../../../../../translated_images/da/clip-arch.b3dbf20b4e8ed8be.png) +![CLIP Arkitektur](../../../../../translated_images/da/clip-arch.b3dbf20b4e8ed8be.webp) > *Billede fra [denne blogpost](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ Når denne model er fortrænet, kan vi give den en batch af billeder og en batch Antag, at vi skal klassificere billeder mellem f.eks. katte, hunde og mennesker. I dette tilfælde kan vi give modellen et billede og en række tekstprompter: "*et billede af en kat*", "*et billede af en hund*", "*et billede af et menneske*". I den resulterende vektor med 3 sandsynligheder skal vi blot vælge det indeks med den højeste værdi. -![CLIP til Billedklassifikation](../../../../../translated_images/da/clip-class.3af42ef0b2b19369.png) +![CLIP til Billedklassifikation](../../../../../translated_images/da/clip-class.3af42ef0b2b19369.webp) > *Billede fra [denne blogpost](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ Lær mere om VQGAN på [Taming Transformers](https://compvis.github.io/taming-tr En af de vigtige forskelle mellem VQGAN og traditionelle GAN er, at sidstnævnte kan producere et anstændigt billede fra enhver inputvektor, mens VQGAN sandsynligvis vil producere et billede, der ikke er sammenhængende. Derfor skal vi yderligere vejlede billedskabelsesprocessen, og det kan gøres ved hjælp af CLIP. -![VQGAN+CLIP Arkitektur](../../../../../translated_images/da/vqgan.5027fe05051dfa31.png) +![VQGAN+CLIP Arkitektur](../../../../../translated_images/da/vqgan.5027fe05051dfa31.webp) For at generere et billede, der svarer til en tekstprompt, starter vi med en tilfældig kodningsvektor, der sendes gennem VQGAN for at producere et billede. Derefter bruges CLIP til at producere en tab-funktion, der viser, hvor godt billedet svarer til tekstprompten. Målet er derefter at minimere denne tab ved hjælp af backpropagation for at justere inputvektorens parametre. Et fantastisk bibliotek, der implementerer VQGAN+CLIP, er [Pixray](http://github.com/pixray/pixray). -![Billede produceret af Pixray](../../../../../translated_images/da/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Billede produceret af Pixray](../../../../../translated_images/da/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Billede produceret af Pixray](../../../../../translated_images/da/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) +![Billede produceret af Pixray](../../../../../translated_images/da/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp) | ![Billede produceret af Pixray](../../../../../translated_images/da/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp) | ![Billede produceret af Pixray](../../../../../translated_images/da/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp) ----|----|---- Billede genereret fra prompten *et nærbillede akvarelportræt af en ung mandlig litteraturlærer med en bog* | Billede genereret fra prompten *et nærbillede oliemaleri af en ung kvindelig lærer i datalogi med en computer* | Billede genereret fra prompten *et nærbillede oliemaleri af en ældre mandlig matematiklærer foran en tavle* diff --git a/translations/el/README.md b/translations/el/README.md index 8341986c..79e783f6 100644 --- a/translations/el/README.md +++ b/translations/el/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # Τεχνητή Νοημοσύνη για Αρχάριους - Ένα Αναλυτικό Πρόγραμμα -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/el/ai-overview.0857791951d19500.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/el/ai-overview.0857791951d19500.webp)| |:---:| | AI For Beginners - _Σημείωση σχεδίασης από [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/el/lessons/1-Intro/README.md b/translations/el/lessons/1-Intro/README.md index fd527dfe..cbb72537 100644 --- a/translations/el/lessons/1-Intro/README.md +++ b/translations/el/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Εισαγωγή στην Τεχνητή Νοημοσύνη -![Περίληψη του περιεχομένου της Εισαγωγής στην Τεχνητή Νοημοσύνη σε ένα σκίτσο](../../../../translated_images/el/ai-intro.bf28d1ac4235881c.png) +![Περίληψη του περιεχομένου της Εισαγωγής στην Τεχνητή Νοημοσύνη σε ένα σκίτσο](../../../../translated_images/el/ai-intro.bf28d1ac4235881c.webp) > Σκίτσο από την [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Αρχικά, οι υπολογιστές εφευρέθηκαν από τον [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage) για να λειτουργούν με αριθμούς ακολουθώντας μια καλά καθορισμένη διαδικασία - έναν αλγόριθμο. Οι σύγχρονοι υπολογιστές, αν και σημαντικά πιο προηγμένοι από το αρχικό μοντέλο που προτάθηκε τον 19ο αιώνα, εξακολουθούν να ακολουθούν την ίδια ιδέα των ελεγχόμενων υπολογισμών. Έτσι, είναι δυνατόν να προγραμματίσουμε έναν υπολογιστή να κάνει κάτι, αν γνωρίζουμε την ακριβή ακολουθία βημάτων που πρέπει να ακολουθήσουμε για να επιτύχουμε τον στόχο. -![Φωτογραφία ενός ατόμου](../../../../translated_images/el/dsh_age.d212a30d4e54fb5f.png) +![Φωτογραφία ενός ατόμου](../../../../translated_images/el/dsh_age.d212a30d4e54fb5f.webp) > Φωτογραφία από την [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ CO_OP_TRANSLATOR_METADATA: Ένα από τα προβλήματα όταν ασχολούμαστε με τον όρο **[Νοημοσύνη](https://en.wikipedia.org/wiki/Intelligence)** είναι ότι δεν υπάρχει σαφής ορισμός αυτού του όρου. Κάποιος μπορεί να υποστηρίξει ότι η νοημοσύνη συνδέεται με την **αφηρημένη σκέψη** ή με την **αυτογνωσία**, αλλά δεν μπορούμε να την ορίσουμε σωστά. -![Φωτογραφία μιας γάτας](../../../../translated_images/el/photo-cat.8c8e8fb760ffe457.jpg) +![Φωτογραφία μιας γάτας](../../../../translated_images/el/photo-cat.8c8e8fb760ffe457.webp) > [Φωτογραφία](https://unsplash.com/photos/75715CVEJhI) από την [Amber Kipp](https://unsplash.com/@sadmax) στο Unsplash @@ -98,13 +98,13 @@ CO_OP_TRANSLATOR_METADATA: > | Τι γίνεται με τη Μηχανική Μάθηση; | | > |--------------|-----------| -> | Μέρος της Τεχνητής Νοημοσύνης που βασίζεται στο να μαθαίνει ο υπολογιστής να λύνει ένα πρόβλημα με βάση κάποια δεδομένα ονομάζεται **Μηχανική Μάθηση**. Δεν θα εξετάσουμε την κλασική μηχανική μάθηση σε αυτό το μάθημα - σας παραπέμπουμε σε ένα ξεχωριστό πρόγραμμα σπουδών [Machine Learning for Beginners](http://aka.ms/ml-beginners). | ![ML for Beginners](../../../../translated_images/el/ml-for-beginners.9e4fed176fd5817d.png) | +> | Μέρος της Τεχνητής Νοημοσύνης που βασίζεται στο να μαθαίνει ο υπολογιστής να λύνει ένα πρόβλημα με βάση κάποια δεδομένα ονομάζεται **Μηχανική Μάθηση**. Δεν θα εξετάσουμε την κλασική μηχανική μάθηση σε αυτό το μάθημα - σας παραπέμπουμε σε ένα ξεχωριστό πρόγραμμα σπουδών [Machine Learning for Beginners](http://aka.ms/ml-beginners). | ![ML for Beginners](../../../../translated_images/el/ml-for-beginners.9e4fed176fd5817d.webp) | ## Μια Σύντομη Ιστορία της ΤΝ Η Τεχνητή Νοημοσύνη ξεκίνησε ως πεδίο στα μέσα του εικοστού αιώνα. Αρχικά, η συμβολική συλλογιστική ήταν η κυρίαρχη προσέγγιση, και οδήγησε σε μια σειρά από σημαντικές επιτυχίες, όπως τα συστήματα ειδικών – προγράμματα υπολογιστών που μπορούσαν να λειτουργούν ως ειδικοί σε ορισμένους περιορισμένους τομείς προβλημάτων. Ωστόσο, σύντομα έγινε σαφές ότι αυτή η προσέγγιση δεν κλιμακώνεται καλά. Η εξαγωγή γνώσης από έναν ειδικό, η αναπαράστασή της σε έναν υπολογιστή και η διατήρηση αυτής της βάσης γνώσης ακριβούς αποδείχθηκε μια πολύπλοκη εργασία και πολύ δαπανηρή για να είναι πρακτική σε πολλές περιπτώσεις. Αυτό οδήγησε στον λεγόμενο [Χειμώνα της ΤΝ](https://en.wikipedia.org/wiki/AI_winter) τη δεκαετία του 1970. -Σύντομη Ιστορία της ΤΝ +Σύντομη Ιστορία της ΤΝ > Εικόνα από τον [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/el/lessons/2-Symbolic/Animals.ipynb b/translations/el/lessons/2-Symbolic/Animals.ipynb index bb22b3ea..a2bb6876 100644 --- a/translations/el/lessons/2-Symbolic/Animals.ipynb +++ b/translations/el/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "Σε αυτό το παράδειγμα, θα υλοποιήσουμε ένα απλό σύστημα βασισμένο στη γνώση για να προσδιορίσουμε ένα ζώο με βάση ορισμένα φυσικά χαρακτηριστικά. Το σύστημα μπορεί να αναπαρασταθεί από το παρακάτω δέντρο AND-OR (αυτό είναι ένα μέρος του συνολικού δέντρου, μπορούμε εύκολα να προσθέσουμε περισσότερους κανόνες):\n", "\n", - "![](../../../../translated_images/el/AND-OR-Tree.5592d2c70187f283.png)\n" + "![](../../../../translated_images/el/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { diff --git a/translations/el/lessons/2-Symbolic/README.md b/translations/el/lessons/2-Symbolic/README.md index 9c668c35..079e5bf0 100644 --- a/translations/el/lessons/2-Symbolic/README.md +++ b/translations/el/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Αναπαράσταση Γνώσης και Συστήματα Ειδικών -![Περίληψη περιεχομένου Συμβολικής Τεχνητής Νοημοσύνης](../../../../translated_images/el/ai-symbolic.715a30cb610411a6.png) +![Περίληψη περιεχομένου Συμβολικής Τεχνητής Νοημοσύνης](../../../../translated_images/el/ai-symbolic.715a30cb610411a6.webp) > Σχεδιαστικό σημείωμα από [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ CO_OP_TRANSLATOR_METADATA: Έτσι, το πρόβλημα της **αναπαράστασης γνώσης** είναι να βρεθεί ένας αποτελεσματικός τρόπος να αναπαρασταθεί η γνώση μέσα σε έναν υπολογιστή με τη μορφή δεδομένων, ώστε να είναι αυτόματα χρησιμοποιήσιμη. Αυτό μπορεί να θεωρηθεί ως ένα φάσμα: -![Φάσμα αναπαράστασης γνώσης](../../../../translated_images/el/knowledge-spectrum.b60df631852c0217.png) +![Φάσμα αναπαράστασης γνώσης](../../../../translated_images/el/knowledge-spectrum.b60df631852c0217.webp) > Εικόνα από [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Python | σύνταξη μπλοκ | εσοχή Μία από τις πρώτες επιτυχίες της συμβολικής Τεχνητής Νοημοσύνης ήταν τα λεγόμενα **συστήματα ειδικών** - υπολογιστικά συστήματα που σχεδιάστηκαν για να λειτουργούν ως ειδικός σε κάποιο περιορισμένο πεδίο προβλημάτων. Βασίζονταν σε μια **βάση γνώσης** που εξαγόταν από έναν ή περισσότερους ανθρώπινους ειδικούς και περιείχαν μια **μηχανή συλλογιστικής** που εκτελούσε κάποια συλλογιστική πάνω σε αυτήν. -![Αρχιτεκτονική Ανθρώπου](../../../../translated_images/el/arch-human.5d4d35f1bba3ab1c.png) | ![Αρχιτεκτονική Συστήματος Βασισμένου στη Γνώση](../../../../translated_images/el/arch-kbs.3ec5c150b09fa8da.png) +![Αρχιτεκτονική Ανθρώπου](../../../../translated_images/el/arch-human.5d4d35f1bba3ab1c.webp) | ![Αρχιτεκτονική Συστήματος Βασισμένου στη Γνώση](../../../../translated_images/el/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ Απλοποιημένη δομή του ανθρώπινου νευρικού συστήματος | Αρχιτεκτονική ενός συστήματος βασισμένου στη γνώση @@ -106,7 +106,7 @@ Python | σύνταξη μπλοκ | εσοχή Ως παράδειγμα, ας εξετάσουμε το εξής σύστημα ειδικών για τον προσδιορισμό ενός ζώου βάσει των φυσικών του χαρακτηριστικών: -![Δέντρο AND-OR](../../../../translated_images/el/AND-OR-Tree.5592d2c70187f283.png) +![Δέντρο AND-OR](../../../../translated_images/el/AND-OR-Tree.5592d2c70187f283.webp) > Εικόνα από [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/el/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/el/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index 11dfdea4..bf14f78b 100644 --- a/translations/el/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/el/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -1261,7 +1261,7 @@ "* Χαμηλή απώλεια εκπαίδευσης - το μοντέλο μπορεί να προσεγγίσει καλά τα δεδομένα εκπαίδευσης, επειδή έχει αρκετή εκφραστική ισχύ.\n", "* Η απώλεια επικύρωσης μπορεί να είναι πολύ υψηλότερη από την απώλεια εκπαίδευσης και να αρχίσει να αυξάνεται κατά τη διάρκεια της εκπαίδευσης - αυτό συμβαίνει επειδή το μοντέλο \"απομνημονεύει\" τα σημεία εκπαίδευσης και χάνει τη \"γενική εικόνα\".\n", "\n", - "![Υπερπροσαρμογή](../../../../../translated_images/el/overfit.a0bd57f717c15769.png)\n", + "![Υπερπροσαρμογή](../../../../../translated_images/el/overfit.a0bd57f717c15769.webp)\n", "\n", "> Στην εικόνα αυτή, το `x` αντιπροσωπεύει δεδομένα εκπαίδευσης, το `o` - δεδομένα επικύρωσης. Αριστερά - γραμμικό μοντέλο (μονοεπίπεδο), προσεγγίζει τη φύση των δεδομένων αρκετά καλά. Δεξιά - υπερπροσαρμοσμένο μοντέλο, το μοντέλο προσεγγίζει τέλεια τα δεδομένα εκπαίδευσης, αλλά χάνει τη λογική με οποιαδήποτε άλλα δεδομένα (το σφάλμα επικύρωσης είναι πολύ υψηλό).\n" ] diff --git a/translations/el/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/el/lessons/3-NeuralNetworks/05-Frameworks/README.md index 85bc4df5..9de09a22 100644 --- a/translations/el/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/el/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ High-level API| [Keras](IntroKeras.ipynb) | *PyTorch Lightning* Ας εξετάσουμε το παρακάτω πρόβλημα προσέγγισης 5 σημείων (που αναπαρίστανται από `x` στα γραφήματα παρακάτω): -![linear](../../../../../translated_images/el/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/el/overfit2.131f5800ae10ca5e.jpg) +![linear](../../../../../translated_images/el/overfit1.f24b71c6f652e59e.webp) | ![overfit](../../../../../translated_images/el/overfit2.131f5800ae10ca5e.webp) -------------------------|-------------------------- **Γραμμικό μοντέλο, 2 παράμετροι** | **Μη γραμμικό μοντέλο, 7 παράμετροι** Σφάλμα εκπαίδευσης = 5.3 | Σφάλμα εκπαίδευσης = 0 @@ -79,7 +79,7 @@ High-level API| [Keras](IntroKeras.ipynb) | *PyTorch Lightning* Όπως μπορείτε να δείτε από το παραπάνω γράφημα, η υπερπροσαρμογή μπορεί να ανιχνευθεί από ένα πολύ χαμηλό σφάλμα εκπαίδευσης και ένα υψηλό σφάλμα επικύρωσης. Κανονικά κατά την εκπαίδευση θα βλέπουμε τόσο το σφάλμα εκπαίδευσης όσο και το σφάλμα επικύρωσης να αρχίζουν να μειώνονται, και στη συνέχεια σε κάποιο σημείο το σφάλμα επικύρωσης μπορεί να σταματήσει να μειώνεται και να αρχίσει να αυξάνεται. Αυτό θα είναι ένα σημάδι υπερπροσαρμογής και η ένδειξη ότι πιθανώς πρέπει να σταματήσουμε την εκπαίδευση σε αυτό το σημείο (ή τουλάχιστον να κάνουμε ένα στιγμιότυπο του μοντέλου). -![overfitting](../../../../../translated_images/el/Overfitting.408ad91cd90b4371.png) +![overfitting](../../../../../translated_images/el/Overfitting.408ad91cd90b4371.webp) ## Πώς να αποτρέψετε την υπερπροσαρμογή diff --git a/translations/el/lessons/3-NeuralNetworks/README.md b/translations/el/lessons/3-NeuralNetworks/README.md index 2eac35dc..acfcd0f5 100644 --- a/translations/el/lessons/3-NeuralNetworks/README.md +++ b/translations/el/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Εισαγωγή στα Νευρωνικά Δίκτυα -![Περίληψη του περιεχομένου της Εισαγωγής στα Νευρωνικά Δίκτυα σε ένα σκίτσο](../../../../translated_images/el/ai-neuralnetworks.1c687ae40bc86e83.png) +![Περίληψη του περιεχομένου της Εισαγωγής στα Νευρωνικά Δίκτυα σε ένα σκίτσο](../../../../translated_images/el/ai-neuralnetworks.1c687ae40bc86e83.webp) Όπως συζητήσαμε στην εισαγωγή, ένας από τους τρόπους για να επιτευχθεί η νοημοσύνη είναι η εκπαίδευση ενός **μοντέλου υπολογιστή** ή ενός **τεχνητού εγκεφάλου**. Από τα μέσα του 20ού αιώνα, οι ερευνητές δοκίμασαν διάφορα μαθηματικά μοντέλα, μέχρι που τα τελευταία χρόνια αυτή η κατεύθυνση αποδείχθηκε εξαιρετικά επιτυχημένη. Αυτά τα μαθηματικά μοντέλα του εγκεφάλου ονομάζονται **νευρωνικά δίκτυα**. @@ -36,13 +36,13 @@ CO_OP_TRANSLATOR_METADATA: Από τη βιολογία, γνωρίζουμε ότι ο εγκέφαλός μας αποτελείται από νευρικά κύτταρα (νευρώνες), καθένα από τα οποία έχει πολλαπλές "εισόδους" (δενδρίτες) και μία "έξοδο" (άξονα). Τόσο οι δενδρίτες όσο και οι άξονες μπορούν να μεταφέρουν ηλεκτρικά σήματα, και οι συνδέσεις μεταξύ τους — γνωστές ως συνάψεις — μπορούν να παρουσιάζουν διαφορετικούς βαθμούς αγωγιμότητας, οι οποίοι ρυθμίζονται από νευροδιαβιβαστές. -![Μοντέλο Νευρώνα](../../../../translated_images/el/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![Μοντέλο Νευρώνα](../../../../translated_images/el/artneuron.1a5daa88d20ebe6f.png) +![Μοντέλο Νευρώνα](../../../../translated_images/el/synapse-wikipedia.ed20a9e4726ea1c6.webp) | ![Μοντέλο Νευρώνα](../../../../translated_images/el/artneuron.1a5daa88d20ebe6f.webp) ----|---- Πραγματικός Νευρώνας *([Εικόνα](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) από τη Wikipedia)* | Τεχνητός Νευρώνας *(Εικόνα από τον Συγγραφέα)* Έτσι, το απλούστερο μαθηματικό μοντέλο ενός νευρώνα περιέχει αρκετές εισόδους X1, ..., XN και μία έξοδο Y, καθώς και μια σειρά από βάρη W1, ..., WN. Η έξοδος υπολογίζεται ως: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) όπου f είναι κάποια μη γραμμική **συνάρτηση ενεργοποίησης**. diff --git a/translations/el/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/el/lessons/4-ComputerVision/06-IntroCV/README.md index 5a80b8fe..52db44ac 100644 --- a/translations/el/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/el/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) * **Προεπεξεργασία μιας φωτογραφίας ενός βιβλίου Braille**. Εστιάζουμε στο πώς μπορούμε να χρησιμοποιήσουμε την κατωφλίωση, την ανίχνευση χαρακτηριστικών, τον μετασχηματισμό προοπτικής και τους χειρισμούς NumPy για να διαχωρίσουμε μεμονωμένα σύμβολα Braille για περαιτέρω ταξινόμηση από ένα νευρωνικό δίκτυο. -![Εικόνα Braille](../../../../../translated_images/el/braille.341962ff76b1bd70.jpeg) | ![Προεπεξεργασμένη Εικόνα Braille](../../../../../translated_images/el/braille-result.46530fea020b03c7.png) | ![Σύμβολα Braille](../../../../../translated_images/el/braille-symbols.0159185ab69d5339.png) +![Εικόνα Braille](../../../../../translated_images/el/braille.341962ff76b1bd70.webp) | ![Προεπεξεργασμένη Εικόνα Braille](../../../../../translated_images/el/braille-result.46530fea020b03c7.webp) | ![Σύμβολα Braille](../../../../../translated_images/el/braille-symbols.0159185ab69d5339.webp) ----|-----|----- > Εικόνα από [OpenCV.ipynb](OpenCV.ipynb) * **Ανίχνευση κίνησης σε βίντεο χρησιμοποιώντας διαφορά καρέ**. Αν η κάμερα είναι σταθερή, τότε τα καρέ από τη ροή της κάμερας θα πρέπει να είναι αρκετά παρόμοια μεταξύ τους. Επειδή τα καρέ αναπαρίστανται ως πίνακες, απλώς αφαιρώντας αυτούς τους πίνακες για δύο διαδοχικά καρέ θα πάρουμε τη διαφορά των pixels, η οποία θα πρέπει να είναι χαμηλή για στατικά καρέ και να αυξάνεται όταν υπάρχει σημαντική κίνηση στην εικόνα. -![Εικόνα καρέ βίντεο και διαφορές καρέ](../../../../../translated_images/el/frame-difference.706f805491a0883c.png) +![Εικόνα καρέ βίντεο και διαφορές καρέ](../../../../../translated_images/el/frame-difference.706f805491a0883c.webp) > Εικόνα από [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) - **Πυκνή Οπτική Ροή** υπολογίζει το πεδίο διανυσμάτων που δείχνει για κάθε pixel πού κινείται - **Αραιή Οπτική Ροή** βασίζεται στη λήψη κάποιων χαρακτηριστικών στην εικόνα (π.χ. άκρες) και στην κατασκευή της τροχιάς τους από καρέ σε καρέ. -![Εικόνα Οπτικής Ροής](../../../../../translated_images/el/optical.1f4a94464579a83a.png) +![Εικόνα Οπτικής Ροής](../../../../../translated_images/el/optical.1f4a94464579a83a.webp) > Εικόνα από [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/el/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/el/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index d1b13dda..395450e6 100644 --- a/translations/el/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/el/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: Το VGG-16 είναι ένα δίκτυο που πέτυχε ακρίβεια 92.7% στην ταξινόμηση top-5 του ImageNet το 2014. Έχει την εξής δομή στρώσεων: -![ImageNet Layers](../../../../../translated_images/el/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet Layers](../../../../../translated_images/el/vgg-16-arch1.d901a5583b3a51ba.webp) Όπως μπορείτε να δείτε, το VGG ακολουθεί μια παραδοσιακή πυραμιδική αρχιτεκτονική, η οποία είναι μια ακολουθία από στρώσεις συνελικτικής και pooling. -![ImageNet Pyramid](../../../../../translated_images/el/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet Pyramid](../../../../../translated_images/el/vgg-16-arch.64ff2137f50dd49f.webp) > Εικόνα από [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/el/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/el/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index 6d353abc..6f01ed6a 100644 --- a/translations/el/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/el/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -260,7 +260,7 @@ "\n", "Έτσι, σε ένα τυπικό CNN θα υπάρχουν αρκετά επίπεδα συνέλιξης, με επίπεδα pooling ανάμεσά τους για να μειώσουν τις διαστάσεις της εικόνας. Θα αυξήσουμε επίσης τον αριθμό των φίλτρων, επειδή καθώς τα μοτίβα γίνονται πιο σύνθετα - υπάρχουν περισσότερες πιθανές ενδιαφέρουσες συνδυαστικές μορφές που πρέπει να αναζητήσουμε.\n", "\n", - "![Μια εικόνα που δείχνει αρκετά επίπεδα συνέλιξης με επίπεδα pooling.](../../../../../translated_images/el/cnn-pyramid.85915455759ef0ce.png)\n", + "![Μια εικόνα που δείχνει αρκετά επίπεδα συνέλιξης με επίπεδα pooling.](../../../../../translated_images/el/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "Λόγω της μείωσης των χωρικών διαστάσεων και της αύξησης των χαρακτηριστικών/διαστάσεων φίλτρων, αυτή η αρχιτεκτονική ονομάζεται επίσης **αρχιτεκτονική πυραμίδας**.\n" ] diff --git a/translations/el/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/el/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index 962a5c17..0a85323a 100644 --- a/translations/el/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/el/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -358,7 +358,7 @@ "\n", "Έτσι, σε ένα τυπικό CNN υπάρχουν αρκετά συνελικτικά επίπεδα, με επίπεδα pooling ανάμεσά τους για να μειώσουν τις διαστάσεις της εικόνας. Παράλληλα, αυξάνουμε τον αριθμό των φίλτρων, επειδή καθώς τα μοτίβα γίνονται πιο σύνθετα, υπάρχουν περισσότεροι πιθανοί ενδιαφέροντες συνδυασμοί που πρέπει να αναζητήσουμε.\n", "\n", - "![Μια εικόνα που δείχνει αρκετά συνελικτικά επίπεδα με επίπεδα pooling.](../../../../../translated_images/el/cnn-pyramid.85915455759ef0ce.png)\n", + "![Μια εικόνα που δείχνει αρκετά συνελικτικά επίπεδα με επίπεδα pooling.](../../../../../translated_images/el/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "Λόγω της μείωσης των χωρικών διαστάσεων και της αύξησης των διαστάσεων χαρακτηριστικών/φίλτρων, αυτή η αρχιτεκτονική ονομάζεται επίσης **αρχιτεκτονική πυραμίδας**.\n" ] diff --git a/translations/el/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/el/lessons/4-ComputerVision/07-ConvNets/README.md index bb90b32a..173527b8 100644 --- a/translations/el/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/el/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: Για να εξάγουμε μοτίβα, θα χρησιμοποιήσουμε την έννοια των **συνελικτικών φίλτρων**. Όπως γνωρίζετε, μια εικόνα αναπαρίσταται από έναν δισδιάστατο πίνακα ή έναν τρισδιάστατο τανυστή με βάθος χρώματος. Η εφαρμογή ενός φίλτρου σημαίνει ότι παίρνουμε έναν σχετικά μικρό πίνακα **πυρήνα φίλτρου**, και για κάθε pixel στην αρχική εικόνα υπολογίζουμε τον σταθμισμένο μέσο όρο με τα γειτονικά σημεία. Μπορούμε να το δούμε σαν ένα μικρό παράθυρο που γλιστρά πάνω από ολόκληρη την εικόνα, και εξομαλύνει όλα τα pixel σύμφωνα με τα βάρη στον πίνακα πυρήνα φίλτρου. -![Φίλτρο Κάθετης Άκρης](../../../../../translated_images/el/filter-vert.b7148390ca0bc356.png) | ![Φίλτρο Οριζόντιας Άκρης](../../../../../translated_images/el/filter-horiz.59b80ed4feb946ef.png) +![Φίλτρο Κάθετης Άκρης](../../../../../translated_images/el/filter-vert.b7148390ca0bc356.webp) | ![Φίλτρο Οριζόντιας Άκρης](../../../../../translated_images/el/filter-horiz.59b80ed4feb946ef.webp) ----|---- > Εικόνα από τον Dmitry Soshnikov @@ -38,7 +38,7 @@ CO_OP_TRANSLATOR_METADATA: * Μπορούμε να σχεδιάσουμε το δίκτυο με τέτοιο τρόπο ώστε τα φίλτρα να εκπαιδεύονται αυτόματα * Μπορούμε να χρησιμοποιήσουμε την ίδια προσέγγιση για να βρούμε μοτίβα σε χαρακτηριστικά υψηλού επιπέδου, όχι μόνο στην αρχική εικόνα. Έτσι, η εξαγωγή χαρακτηριστικών από τα CNN λειτουργεί σε μια ιεραρχία χαρακτηριστικών, ξεκινώντας από συνδυασμούς pixel χαμηλού επιπέδου, μέχρι συνδυασμούς υψηλότερου επιπέδου τμημάτων της εικόνας. -![Ιεραρχική Εξαγωγή Χαρακτηριστικών](../../../../../translated_images/el/FeatureExtractionCNN.d9b456cbdae7cb64.png) +![Ιεραρχική Εξαγωγή Χαρακτηριστικών](../../../../../translated_images/el/FeatureExtractionCNN.d9b456cbdae7cb64.webp) > Εικόνα από [μια εργασία των Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), βασισμένη στην [έρευνά τους](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ CO_OP_TRANSLATOR_METADATA: Ως παράδειγμα, ας δούμε την αρχιτεκτονική του VGG-16, ενός δικτύου που πέτυχε ακρίβεια 92.7% στην ταξινόμηση top-5 του ImageNet το 2014: -![Στρώσεις ImageNet](../../../../../translated_images/el/vgg-16-arch1.d901a5583b3a51ba.jpg) +![Στρώσεις ImageNet](../../../../../translated_images/el/vgg-16-arch1.d901a5583b3a51ba.webp) -![Πυραμίδα ImageNet](../../../../../translated_images/el/vgg-16-arch.64ff2137f50dd49f.jpg) +![Πυραμίδα ImageNet](../../../../../translated_images/el/vgg-16-arch.64ff2137f50dd49f.webp) > Εικόνα από [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/el/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/el/lessons/4-ComputerVision/07-ConvNets/lab/README.md index b68c2305..316bc794 100644 --- a/translations/el/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/el/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ CO_OP_TRANSLATOR_METADATA: Θα χρησιμοποιήσουμε το [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/), το οποίο περιέχει εικόνες από 37 διαφορετικές φυλές σκύλων και γατών. -![Το σύνολο δεδομένων που θα χρησιμοποιήσουμε](../../../../../../translated_images/el/data.50b2a9d5484bdbf0.png) +![Το σύνολο δεδομένων που θα χρησιμοποιήσουμε](../../../../../../translated_images/el/data.50b2a9d5484bdbf0.webp) Για να κατεβάσετε το σύνολο δεδομένων, χρησιμοποιήστε αυτό το απόσπασμα κώδικα: diff --git a/translations/el/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/el/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index 1287e249..02505b03 100644 --- a/translations/el/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/el/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "Για να οπτικοποιήσουμε την ιδανική γάτα, θα ξεκινήσουμε με μια τυχαία εικόνα θορύβου και θα προσπαθήσουμε να χρησιμοποιήσουμε την τεχνική βελτιστοποίησης με καθοδική κλίση για να προσαρμόσουμε την εικόνα ώστε ένα δίκτυο να αναγνωρίσει μια γάτα.\n", "\n", - "![Βρόχος Βελτιστοποίησης](../../../../../translated_images/el/ideal-cat-loop.999fbb8ff306e044.png)\n", + "![Βρόχος Βελτιστοποίησης](../../../../../translated_images/el/ideal-cat-loop.999fbb8ff306e044.webp)\n", "\n", "Εδώ είναι η αρχική μας εικόνα:\n" ] diff --git a/translations/el/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/el/lessons/4-ComputerVision/08-TransferLearning/README.md index 033981d4..a13c2204 100644 --- a/translations/el/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/el/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ CO_OP_TRANSLATOR_METADATA: Ακολουθούν δείγματα χαρακτηριστικών που εξήχθησαν από μια εικόνα γάτας από το δίκτυο VGG-16: -![Χαρακτηριστικά που εξήχθησαν από το VGG-16](../../../../../translated_images/el/features.6291f9c7ba3a0b95.png) +![Χαρακτηριστικά που εξήχθησαν από το VGG-16](../../../../../translated_images/el/features.6291f9c7ba3a0b95.webp) ## Σύνολο Δεδομένων Γάτες vs. Σκύλοι @@ -48,19 +48,19 @@ CO_OP_TRANSLATOR_METADATA: Μια προσέγγιση που μπορούμε να ακολουθήσουμε είναι να ξεκινήσουμε με μια τυχαία εικόνα και στη συνέχεια να χρησιμοποιήσουμε την τεχνική **βελτιστοποίησης καθοδικής κλίσης** για να προσαρμόσουμε αυτή την εικόνα με τέτοιο τρόπο ώστε το δίκτυο να αρχίσει να πιστεύει ότι είναι γάτα. -![Βρόχος Βελτιστοποίησης Εικόνας](../../../../../translated_images/el/ideal-cat-loop.999fbb8ff306e044.png) +![Βρόχος Βελτιστοποίησης Εικόνας](../../../../../translated_images/el/ideal-cat-loop.999fbb8ff306e044.webp) Ωστόσο, αν το κάνουμε αυτό, θα λάβουμε κάτι που μοιάζει πολύ με τυχαίο θόρυβο. Αυτό συμβαίνει επειδή *υπάρχουν πολλοί τρόποι να κάνουμε το δίκτυο να πιστέψει ότι η είσοδος είναι γάτα*, συμπεριλαμβανομένων κάποιων που δεν έχουν οπτική λογική. Ενώ αυτές οι εικόνες περιέχουν πολλά μοτίβα τυπικά για μια γάτα, δεν υπάρχει τίποτα που να τις περιορίζει να είναι οπτικά διακριτές. Για να βελτιώσουμε το αποτέλεσμα, μπορούμε να προσθέσουμε έναν άλλο όρο στη συνάρτηση απώλειας, που ονομάζεται **απώλεια παραλλαγής**. Είναι μια μετρική που δείχνει πόσο παρόμοια είναι τα γειτονικά εικονοστοιχεία της εικόνας. Ελαχιστοποιώντας την απώλεια παραλλαγής, η εικόνα γίνεται πιο ομαλή και απαλλάσσεται από τον θόρυβο - αποκαλύπτοντας έτσι πιο οπτικά ελκυστικά μοτίβα. Εδώ είναι ένα παράδειγμα τέτοιων "ιδανικών" εικόνων, που ταξινομούνται ως γάτα και ως ζέβρα με υψηλή πιθανότητα: -![Ιδανική Γάτα](../../../../../translated_images/el/ideal-cat.203dd4597643d6b0.png) | ![Ιδανική Ζέβρα](../../../../../translated_images/el/ideal-zebra.7f70e8b54ee15a7a.png) +![Ιδανική Γάτα](../../../../../translated_images/el/ideal-cat.203dd4597643d6b0.webp) | ![Ιδανική Ζέβρα](../../../../../translated_images/el/ideal-zebra.7f70e8b54ee15a7a.webp) -----|----- *Ιδανική Γάτα* | *Ιδανική Ζέβρα* Παρόμοια προσέγγιση μπορεί να χρησιμοποιηθεί για την εκτέλεση των λεγόμενων **αντιφατικών επιθέσεων** σε ένα νευρωνικό δίκτυο. Ας υποθέσουμε ότι θέλουμε να ξεγελάσουμε ένα νευρωνικό δίκτυο και να κάνουμε έναν σκύλο να μοιάζει με γάτα. Αν πάρουμε την εικόνα ενός σκύλου, που αναγνωρίζεται από το δίκτυο ως σκύλος, μπορούμε στη συνέχεια να την τροποποιήσουμε λίγο χρησιμοποιώντας τη βελτιστοποίηση καθοδικής κλίσης, μέχρι το δίκτυο να αρχίσει να την ταξινομεί ως γάτα: -![Εικόνα Σκύλου](../../../../../translated_images/el/original-dog.8f68a67d2fe0911f.png) | ![Εικόνα σκύλου που ταξινομείται ως γάτα](../../../../../translated_images/el/adversarial-dog.d9fc7773b0142b89.png) +![Εικόνα Σκύλου](../../../../../translated_images/el/original-dog.8f68a67d2fe0911f.webp) | ![Εικόνα σκύλου που ταξινομείται ως γάτα](../../../../../translated_images/el/adversarial-dog.d9fc7773b0142b89.webp) -----|----- *Αρχική εικόνα σκύλου* | *Εικόνα σκύλου που ταξινομείται ως γάτα* diff --git a/translations/el/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/el/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index 97a6545e..b3e66af1 100644 --- a/translations/el/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/el/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "Επειδή εκπαιδεύουμε το autoencoder να συλλάβει όσο το δυνατόν περισσότερες πληροφορίες από την αρχική εικόνα για ακριβή ανακατασκευή, το δίκτυο προσπαθεί να βρει την καλύτερη **ενσωμάτωση** των εικόνων εισόδου ώστε να αποτυπώσει το νόημα.\n", "\n", - "![Διάγραμμα AutoEncoder](../../../../../translated_images/el/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![Διάγραμμα AutoEncoder](../../../../../translated_images/el/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "> Εικόνα από [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", diff --git a/translations/el/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/el/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index bafb86ab..875feaeb 100644 --- a/translations/el/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/el/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "Επειδή εκπαιδεύουμε το autoencoder να συλλάβει όσο το δυνατόν περισσότερες πληροφορίες από την αρχική εικόνα για ακριβή ανακατασκευή, το δίκτυο προσπαθεί να βρει την καλύτερη **ενσωμάτωση** των εικόνων εισόδου ώστε να συλλάβει το νόημα.\n", "\n", - "![Διάγραμμα AutoEncoder](../../../../../translated_images/el/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![Διάγραμμα AutoEncoder](../../../../../translated_images/el/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "*Εικόνα από [το blog του Keras](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", diff --git a/translations/el/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/el/lessons/4-ComputerVision/09-Autoencoders/README.md index 42ca0a39..d747ae5d 100644 --- a/translations/el/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/el/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Καθώς εκπαιδεύουμε έναν αυτόματο κωδικοποιητή για να συλλάβει όσο το δυνατόν περισσότερες πληροφορίες από την αρχική εικόνα για ακριβή ανακατασκευή, το δίκτυο προσπαθεί να βρει την καλύτερη **ενσωμάτωση** των εικόνων εισόδου για να αποτυπώσει το νόημα. -![Διάγραμμα Αυτόματου Κωδικοποιητή](../../../../../translated_images/el/autoencoder_schema.5e6fc9ad98a5eb61.jpg) +![Διάγραμμα Αυτόματου Κωδικοποιητή](../../../../../translated_images/el/autoencoder_schema.5e6fc9ad98a5eb61.webp) > Εικόνα από το [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/el/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/el/lessons/4-ComputerVision/11-ObjectDetection/README.md index 30661734..fd4264a7 100644 --- a/translations/el/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/el/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: ## [Προ-μάθημα κουίζ](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Ανίχνευση Αντικειμένων](../../../../../translated_images/el/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) +![Ανίχνευση Αντικειμένων](../../../../../translated_images/el/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp) > Εικόνα από [ιστοσελίδα YOLO v2](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 2. Εκτελέστε ταξινόμηση εικόνας σε κάθε πλακίδιο. 3. Τα πλακίδια που δίνουν αρκετά υψηλή ενεργοποίηση μπορούν να θεωρηθούν ότι περιέχουν το αντικείμενο που αναζητούμε. -![Αφελής Ανίχνευση Αντικειμένων](../../../../../translated_images/el/naive-detection.e7f1ba220ccd08c6.png) +![Αφελής Ανίχνευση Αντικειμένων](../../../../../translated_images/el/naive-detection.e7f1ba220ccd08c6.webp) > *Εικόνα από [Exercise Notebook](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ CO_OP_TRANSLATOR_METADATA: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 κατηγορίες * [COCO](http://cocodataset.org/#home) - Common Objects in Context. 80 κατηγορίες, πλαίσια και μάσκες τμηματοποίησης -![COCO](../../../../../translated_images/el/coco-examples.71bc60380fa6cceb.jpg) +![COCO](../../../../../translated_images/el/coco-examples.71bc60380fa6cceb.webp) ## Μετρικές Ανίχνευσης Αντικειμένων @@ -50,7 +50,7 @@ CO_OP_TRANSLATOR_METADATA: Ενώ για την ταξινόμηση εικόνων είναι εύκολο να μετρήσουμε πόσο καλά αποδίδει ο αλγόριθμος, για την ανίχνευση αντικειμένων πρέπει να μετρήσουμε τόσο την ορθότητα της κατηγορίας όσο και την ακρίβεια της προβλεπόμενης θέσης του πλαισίου. Για το τελευταίο, χρησιμοποιούμε τη λεγόμενη **Intersection over Union** (IoU), η οποία μετρά πόσο καλά δύο πλαίσια (ή δύο αυθαίρετες περιοχές) επικαλύπτονται. -![IoU](../../../../../translated_images/el/iou_equation.9a4751d40fff4e11.png) +![IoU](../../../../../translated_images/el/iou_equation.9a4751d40fff4e11.webp) > *Εικόνα 2 από [αυτό το εξαιρετικό άρθρο για το IoU](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ $$ Το [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) χρησιμοποιεί [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) για να δημιουργήσει ιεραρχική δομή περιοχών ROI, οι οποίες στη συνέχεια περνούν από εξαγωγείς χαρακτηριστικών CNN και ταξινομητές SVM για να προσδιοριστεί η κατηγορία του αντικειμένου, και γραμμική παλινδρόμηση για να προσδιοριστούν οι συντεταγμένες του *bounding box*. [Επίσημο Άρθρο](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/el/rcnn1.cae407020dfb1d1f.png) +![RCNN](../../../../../translated_images/el/rcnn1.cae407020dfb1d1f.webp) > *Εικόνα από van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/el/rcnn2.2d9530bb83516484.png) +![RCNN-1](../../../../../translated_images/el/rcnn2.2d9530bb83516484.webp) > *Εικόνες από [αυτό το άρθρο](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -110,7 +110,7 @@ $$ Αυτή η προσέγγιση είναι παρόμοια με το R-CNN, αλλά οι περιοχές ορίζονται αφού έχουν εφαρμοστεί τα επίπεδα συνελικτικής. -![FRCNN](../../../../../translated_images/el/f-rcnn.3cda6d9bb4188875.png) +![FRCNN](../../../../../translated_images/el/f-rcnn.3cda6d9bb4188875.webp) > Εικόνα από [το Επίσημο Άρθρο](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ $$ Η βασική ιδέα αυτής της προσέγγισης είναι να χρησιμοποιηθεί ένα νευρωνικό δίκτυο για να προβλέψει τα ROI - το λεγόμενο *Region Proposal Network*. [Άρθρο](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/el/faster-rcnn.8d46c099b87ef30a.png) +![FasterRCNN](../../../../../translated_images/el/faster-rcnn.8d46c099b87ef30a.webp) > Εικόνα από [το επίσημο άρθρο](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ $$ 1. Τα χαρακτηριστικά επεξεργάζονται από **Position-Sensitive Score Map**. Κάθε αντικείμενο από $C$ κατηγορίες διαιρείται σε $k\times k$ περιοχές, και εκπαιδεύουμε το δίκτυο να προβλέπει μέρη αντικειμένων. 1. Για κάθε μέρος από τις $k\times k$ περιοχές, όλα τα δίκτυα ψηφίζουν για τις κατηγορίες αντικειμένων, και η κατηγορία αντικειμένου με τη μέγιστη ψήφο επιλέγεται. -![r-fcn image](../../../../../translated_images/el/r-fcn.13eb88158b99a3da.png) +![r-fcn image](../../../../../translated_images/el/r-fcn.13eb88158b99a3da.webp) > Εικόνα από [επίσημο άρθρο](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ $$ * Η εικόνα διαιρείται σε $S\times S$ περιοχές. * Για κάθε περιοχή, **CNN** προβλέπει $n$ πιθανά αντικείμενα, τις συντεταγμένες του *bounding box* και την *confidence*=*πιθανότητα* * IoU. - ![YOLO](../../../../../translated_images/el/yolo.a2648ec82ee8bb4e.png) + ![YOLO](../../../../../translated_images/el/yolo.a2648ec82ee8bb4e.webp) > Εικόνα από [επίσημο άρθρο](https://arxiv.org/abs/1506.02640) diff --git a/translations/el/lessons/4-ComputerVision/README.md b/translations/el/lessons/4-ComputerVision/README.md index 5c344a6b..cc55aa73 100644 --- a/translations/el/lessons/4-ComputerVision/README.md +++ b/translations/el/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Υπολογιστική Όραση -![Περίληψη του περιεχομένου της Υπολογιστικής Όρασης σε ένα σκίτσο](../../../../translated_images/el/ai-computervision.6506ebebac3fbf76.png) +![Περίληψη του περιεχομένου της Υπολογιστικής Όρασης σε ένα σκίτσο](../../../../translated_images/el/ai-computervision.6506ebebac3fbf76.webp) Σε αυτήν την ενότητα θα μάθουμε για: diff --git a/translations/el/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/el/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index cfc70f64..a6df757d 100644 --- a/translations/el/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/el/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "Η **αναπαράσταση Bag of Words** (BoW) είναι η πιο συχνά χρησιμοποιούμενη παραδοσιακή μέθοδος αναπαράστασης με διανύσματα. Κάθε λέξη συνδέεται με έναν δείκτη διανύσματος, και το στοιχείο του διανύσματος περιέχει τον αριθμό εμφανίσεων μιας λέξης σε ένα συγκεκριμένο έγγραφο.\n", "\n", - "![Εικόνα που δείχνει πώς η αναπαράσταση διανύσματος Bag of Words αποθηκεύεται στη μνήμη.](../../../../../translated_images/el/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![Εικόνα που δείχνει πώς η αναπαράσταση διανύσματος Bag of Words αποθηκεύεται στη μνήμη.](../../../../../translated_images/el/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Note**: Μπορείτε επίσης να σκεφτείτε το BoW ως το άθροισμα όλων των διανυσμάτων one-hot-encoded για τις μεμονωμένες λέξεις του κειμένου.\n", "\n", diff --git a/translations/el/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/el/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index 818d83f3..266805cc 100644 --- a/translations/el/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/el/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "Η **αναπαράσταση Bag-of-words** (BoW) είναι η πιο απλή και κατανοητή παραδοσιακή μέθοδος αναπαράστασης με διανύσματα. Κάθε λέξη συνδέεται με έναν δείκτη διανύσματος, και κάθε στοιχείο του διανύσματος περιέχει τον αριθμό εμφανίσεων κάθε λέξης σε ένα συγκεκριμένο έγγραφο.\n", "\n", - "![Εικόνα που δείχνει πώς η αναπαράσταση Bag-of-words αποθηκεύεται στη μνήμη.](../../../../../translated_images/el/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![Εικόνα που δείχνει πώς η αναπαράσταση Bag-of-words αποθηκεύεται στη μνήμη.](../../../../../translated_images/el/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Note**: Μπορείτε επίσης να σκεφτείτε το BoW ως το άθροισμα όλων των διανυσμάτων one-hot-encoding για τις μεμονωμένες λέξεις του κειμένου.\n", "\n", diff --git a/translations/el/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/el/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 7bb8c655..743392c8 100644 --- a/translations/el/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/el/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "Χρησιμοποιώντας τη στρώση ενσωμάτωσης ως την πρώτη στρώση στο δίκτυό μας, μπορούμε να μεταβούμε από το μοντέλο bag-of-words στο μοντέλο **embedding bag**, όπου πρώτα μετατρέπουμε κάθε λέξη στο κείμενό μας στην αντίστοιχη ενσωμάτωσή της και στη συνέχεια υπολογίζουμε κάποια συνάρτηση συσσωμάτωσης πάνω σε όλες αυτές τις ενσωματώσεις, όπως `sum`, `average` ή `max`.\n", "\n", - "![Εικόνα που δείχνει έναν ταξινομητή ενσωμάτωσης για πέντε λέξεις ακολουθίας.](../../../../../translated_images/el/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![Εικόνα που δείχνει έναν ταξινομητή ενσωμάτωσης για πέντε λέξεις ακολουθίας.](../../../../../translated_images/el/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "Το νευρωνικό δίκτυο ταξινομητή μας θα ξεκινά με τη στρώση ενσωμάτωσης, στη συνέχεια τη στρώση συσσωμάτωσης και έναν γραμμικό ταξινομητή στην κορυφή:\n" ] @@ -176,7 +176,7 @@ "\n", "Στην προηγούμενη αρχιτεκτονική, έπρεπε να συμπληρώσουμε όλες τις ακολουθίες ώστε να έχουν το ίδιο μήκος για να τις εντάξουμε σε ένα minibatch. Αυτός δεν είναι ο πιο αποδοτικός τρόπος για να αναπαραστήσουμε ακολουθίες μεταβλητού μήκους - μια άλλη προσέγγιση θα ήταν να χρησιμοποιήσουμε έναν **διάνυσμα μετατοπίσεων (offset)**, το οποίο θα περιείχε τις μετατοπίσεις όλων των ακολουθιών που αποθηκεύονται σε ένα μεγάλο διάνυσμα.\n", "\n", - "![Εικόνα που δείχνει την αναπαράσταση ακολουθιών με μετατοπίσεις](../../../../../translated_images/el/offset-sequence-representation.eb73fcefb29b46ee.png)\n", + "![Εικόνα που δείχνει την αναπαράσταση ακολουθιών με μετατοπίσεις](../../../../../translated_images/el/offset-sequence-representation.eb73fcefb29b46ee.webp)\n", "\n", "> **Σημείωση**: Στην παραπάνω εικόνα, δείχνουμε μια ακολουθία χαρακτήρων, αλλά στο παράδειγμά μας δουλεύουμε με ακολουθίες λέξεων. Ωστόσο, η γενική αρχή της αναπαράστασης ακολουθιών με διάνυσμα μετατοπίσεων παραμένει η ίδια.\n", "\n", @@ -311,7 +311,7 @@ "\n", "Το CBoW είναι ταχύτερο, ενώ το skip-gram είναι πιο αργό, αλλά αποδίδει καλύτερα στην αναπαράσταση σπάνιων λέξεων.\n", "\n", - "![Εικόνα που δείχνει τους αλγορίθμους CBoW και Skip-Gram για τη μετατροπή λέξεων σε διανύσματα.](../../../../../translated_images/el/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![Εικόνα που δείχνει τους αλγορίθμους CBoW και Skip-Gram για τη μετατροπή λέξεων σε διανύσματα.](../../../../../translated_images/el/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Για να πειραματιστούμε με την ενσωμάτωση word2vec που έχει προεκπαιδευτεί στο σύνολο δεδομένων Google News, μπορούμε να χρησιμοποιήσουμε τη βιβλιοθήκη **gensim**. Παρακάτω βρίσκουμε τις λέξεις που είναι πιο παρόμοιες με τη λέξη 'neural'.\n", "\n", diff --git a/translations/el/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/el/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index 02b58efd..0140c817 100644 --- a/translations/el/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/el/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "Χρησιμοποιώντας ένα embedding layer ως το πρώτο layer στο δίκτυό μας, μπορούμε να μεταβούμε από το μοντέλο bag-of-words σε ένα μοντέλο **embedding bag**, όπου πρώτα μετατρέπουμε κάθε λέξη στο κείμενό μας στο αντίστοιχο embedding και στη συνέχεια υπολογίζουμε κάποια συνάρτηση συσσωμάτωσης πάνω σε όλα αυτά τα embeddings, όπως `sum`, `average` ή `max`.\n", "\n", - "![Εικόνα που δείχνει έναν ταξινομητή embedding για πέντε λέξεις ακολουθίας.](../../../../../translated_images/el/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![Εικόνα που δείχνει έναν ταξινομητή embedding για πέντε λέξεις ακολουθίας.](../../../../../translated_images/el/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "Το νευρωνικό δίκτυο ταξινομητή μας αποτελείται από τα εξής layers:\n", "\n", @@ -283,7 +283,7 @@ "\n", "Το CBoW είναι πιο γρήγορο, ενώ το skip-gram, αν και πιο αργό, αποδίδει καλύτερα στην αναπαράσταση σπάνιων λέξεων.\n", "\n", - "![Εικόνα που δείχνει τους αλγόριθμους CBoW και Skip-Gram για τη μετατροπή λέξεων σε διανύσματα.](../../../../../translated_images/el/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![Εικόνα που δείχνει τους αλγόριθμους CBoW και Skip-Gram για τη μετατροπή λέξεων σε διανύσματα.](../../../../../translated_images/el/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Για να πειραματιστούμε με την ενσωμάτωση Word2Vec που έχει προεκπαιδευτεί στο σύνολο δεδομένων Google News, μπορούμε να χρησιμοποιήσουμε τη βιβλιοθήκη **gensim**. Παρακάτω βρίσκουμε τις λέξεις που είναι πιο παρόμοιες με τη λέξη 'neural'.\n", "\n", diff --git a/translations/el/lessons/5-NLP/14-Embeddings/README.md b/translations/el/lessons/5-NLP/14-Embeddings/README.md index d8d1fc6a..1e2b8179 100644 --- a/translations/el/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/el/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Χρησιμοποιώντας ένα επίπεδο ενσωμάτωσης ως το πρώτο επίπεδο στο δίκτυο ταξινόμησής μας, μπορούμε να μεταβούμε από ένα μοντέλο bag-of-words σε ένα μοντέλο **embedding bag**, όπου πρώτα μετατρέπουμε κάθε λέξη στο κείμενό μας στην αντίστοιχη ενσωμάτωσή της και στη συνέχεια υπολογίζουμε κάποια συνάρτηση συσσωμάτωσης πάνω σε όλες αυτές τις ενσωματώσεις, όπως `sum`, `average` ή `max`. -![Εικόνα που δείχνει έναν ταξινομητή ενσωμάτωσης για πέντε λέξεις ακολουθίας.](../../../../../translated_images/el/embedding-classifier-example.b77f021a7ee67eee.png) +![Εικόνα που δείχνει έναν ταξινομητή ενσωμάτωσης για πέντε λέξεις ακολουθίας.](../../../../../translated_images/el/embedding-classifier-example.b77f021a7ee67eee.webp) > Εικόνα από τον συγγραφέα @@ -40,7 +40,7 @@ CO_OP_TRANSLATOR_METADATA: Το CBoW είναι ταχύτερο, ενώ το skip-gram είναι πιο αργό, αλλά αποδίδει καλύτερα στην αναπαράσταση σπάνιων λέξεων. -![Εικόνα που δείχνει τους αλγορίθμους CBoW και Skip-Gram για τη μετατροπή λέξεων σε διανύσματα.](../../../../../translated_images/el/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![Εικόνα που δείχνει τους αλγορίθμους CBoW και Skip-Gram για τη μετατροπή λέξεων σε διανύσματα.](../../../../../translated_images/el/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Εικόνα από [αυτό το άρθρο](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/el/lessons/5-NLP/15-LanguageModeling/README.md b/translations/el/lessons/5-NLP/15-LanguageModeling/README.md index 46b00a5b..9d0de00c 100644 --- a/translations/el/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/el/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: * **Continuous Bag-of-Words** (CBoW), όπου προβλέπουμε το μεσαίο σύμβολο $W_0$ σε μια ακολουθία συμβόλων $W_{-N}$, ..., $W_N$. * **Skip-gram**, όπου προβλέπουμε ένα σύνολο γειτονικών συμβόλων {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} από το μεσαίο σύμβολο $W_0$. -![εικόνα από άρθρο για τη μετατροπή λέξεων σε διανύσματα](../../../../../translated_images/el/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![εικόνα από άρθρο για τη μετατροπή λέξεων σε διανύσματα](../../../../../translated_images/el/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Εικόνα από [αυτό το άρθρο](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/el/lessons/5-NLP/16-RNN/README.md b/translations/el/lessons/5-NLP/16-RNN/README.md index 18246f99..8b802012 100644 --- a/translations/el/lessons/5-NLP/16-RNN/README.md +++ b/translations/el/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: Για να κατανοήσουμε τη σημασία μιας ακολουθίας κειμένου, πρέπει να χρησιμοποιήσουμε μια άλλη αρχιτεκτονική νευρωνικού δικτύου, που ονομάζεται **επαναλαμβανόμενο νευρωνικό δίκτυο** ή RNN. Στο RNN, περνάμε την πρότασή μας μέσα από το δίκτυο ένα σύμβολο τη φορά, και το δίκτυο παράγει μια **κατάσταση**, την οποία στη συνέχεια περνάμε ξανά στο δίκτυο μαζί με το επόμενο σύμβολο. -![RNN](../../../../../translated_images/el/rnn.27f5c29c53d727b5.png) +![RNN](../../../../../translated_images/el/rnn.27f5c29c53d727b5.webp) > Εικόνα από τον συγγραφέα @@ -61,7 +61,7 @@ CO_OP_TRANSLATOR_METADATA: Ένα επαναλαμβανόμενο δίκτυο, είτε μονοκατευθυντικό είτε διπλής κατεύθυνσης, καταγράφει ορισμένα μοτίβα μέσα σε μια ακολουθία και μπορεί να τα αποθηκεύσει σε ένα διάνυσμα κατάστασης ή να τα περάσει στην έξοδο. Όπως και με τα συνελικτικά δίκτυα, μπορούμε να κατασκευάσουμε ένα άλλο επαναλαμβανόμενο επίπεδο πάνω από το πρώτο για να καταγράψουμε μοτίβα υψηλότερου επιπέδου και να χτίσουμε από τα μοτίβα χαμηλού επιπέδου που εξάγονται από το πρώτο επίπεδο. Αυτό μας οδηγεί στην έννοια ενός **πολυεπίπεδου RNN**, που αποτελείται από δύο ή περισσότερα επαναλαμβανόμενα δίκτυα, όπου η έξοδος του προηγούμενου επιπέδου περνά στο επόμενο επίπεδο ως είσοδος. -![Εικόνα που δείχνει ένα Πολυεπίπεδο LSTM RNN](../../../../../translated_images/el/multi-layer-lstm.dd975e29bb2a59fe.jpg) +![Εικόνα που δείχνει ένα Πολυεπίπεδο LSTM RNN](../../../../../translated_images/el/multi-layer-lstm.dd975e29bb2a59fe.webp) *Εικόνα από [αυτή την υπέροχη ανάρτηση](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) του Fernando López* diff --git a/translations/el/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/el/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index fbf5304f..372bfffc 100644 --- a/translations/el/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/el/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "Το επαναλαμβανόμενο δίκτυο, είτε μονής κατεύθυνσης είτε διπλής κατεύθυνσης, καταγράφει ορισμένα μοτίβα μέσα σε μια ακολουθία και μπορεί να τα αποθηκεύσει σε ένα διάνυσμα κατάστασης ή να τα περάσει στην έξοδο. Όπως συμβαίνει με τα συνελικτικά δίκτυα, μπορούμε να χτίσουμε ένα άλλο επαναλαμβανόμενο επίπεδο πάνω από το πρώτο για να καταγράψουμε μοτίβα υψηλότερου επιπέδου, χτισμένα από μοτίβα χαμηλότερου επιπέδου που εξάγονται από το πρώτο επίπεδο. Αυτό μας οδηγεί στην έννοια του **πολυεπίπεδου RNN**, το οποίο αποτελείται από δύο ή περισσότερα επαναλαμβανόμενα δίκτυα, όπου η έξοδος του προηγούμενου επιπέδου περνά στο επόμενο επίπεδο ως είσοδος.\n", "\n", - "![Εικόνα που δείχνει ένα πολυεπίπεδο RNN με μακροχρόνια-βραχυχρόνια μνήμη](../../../../../translated_images/el/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![Εικόνα που δείχνει ένα πολυεπίπεδο RNN με μακροχρόνια-βραχυχρόνια μνήμη](../../../../../translated_images/el/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*Εικόνα από [αυτή την υπέροχη ανάρτηση](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) του Fernando López*\n", "\n", diff --git a/translations/el/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/el/lessons/5-NLP/16-RNN/RNNTF.ipynb index 2ee54f57..fb6df5d2 100644 --- a/translations/el/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/el/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "Για να αποτυπώσουμε τη σημασία μιας ακολουθίας κειμένου, θα χρησιμοποιήσουμε μια αρχιτεκτονική νευρωνικού δικτύου που ονομάζεται **επαναλαμβανόμενο νευρωνικό δίκτυο** (recurrent neural network ή RNN). Όταν χρησιμοποιούμε ένα RNN, περνάμε την πρότασή μας μέσα από το δίκτυο μία λέξη τη φορά, και το δίκτυο παράγει μια **κατάσταση**, την οποία στη συνέχεια περνάμε ξανά στο δίκτυο μαζί με την επόμενη λέξη.\n", "\n", - "![Εικόνα που δείχνει ένα παράδειγμα δημιουργίας επαναλαμβανόμενου νευρωνικού δικτύου.](../../../../../translated_images/el/rnn.27f5c29c53d727b5.png)\n", + "![Εικόνα που δείχνει ένα παράδειγμα δημιουργίας επαναλαμβανόμενου νευρωνικού δικτύου.](../../../../../translated_images/el/rnn.27f5c29c53d727b5.webp)\n", "\n", "Δεδομένης της εισόδου μιας ακολουθίας λέξεων $X_0,\\dots,X_n$, το RNN δημιουργεί μια ακολουθία μπλοκ νευρωνικού δικτύου και εκπαιδεύει αυτή την ακολουθία από άκρη σε άκρη χρησιμοποιώντας οπισθοδιάδοση. Κάθε μπλοκ δικτύου λαμβάνει ένα ζεύγος $(X_i,S_i)$ ως είσοδο και παράγει το $S_{i+1}$ ως αποτέλεσμα. Η τελική κατάσταση $S_n$ ή η έξοδος $Y_n$ περνάει σε έναν γραμμικό ταξινομητή για να παραχθεί το αποτέλεσμα. Όλα τα μπλοκ του δικτύου μοιράζονται τα ίδια βάρη και εκπαιδεύονται από άκρη σε άκρη με μία διαδικασία οπισθοδιάδοσης.\n", "\n", @@ -369,7 +369,7 @@ "\n", "Τα αναδρομικά δίκτυα, είτε μονής είτε διπλής κατεύθυνσης, εντοπίζουν μοτίβα μέσα σε μια ακολουθία και τα αποθηκεύουν σε διανύσματα κατάστασης ή τα επιστρέφουν ως έξοδο. Όπως και με τα συνελικτικά δίκτυα, μπορούμε να κατασκευάσουμε ένα ακόμη αναδρομικό επίπεδο μετά το πρώτο, για να εντοπίσουμε μοτίβα υψηλότερου επιπέδου, τα οποία προκύπτουν από μοτίβα χαμηλότερου επιπέδου που εξήγαγε το πρώτο επίπεδο. Αυτό μας οδηγεί στην έννοια του **πολυεπίπεδου RNN**, το οποίο αποτελείται από δύο ή περισσότερα αναδρομικά δίκτυα, όπου η έξοδος του προηγούμενου επιπέδου περνάει ως είσοδος στο επόμενο επίπεδο.\n", "\n", - "![Εικόνα που δείχνει ένα πολυεπίπεδο RNN με LSTM](../../../../../translated_images/el/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![Εικόνα που δείχνει ένα πολυεπίπεδο RNN με LSTM](../../../../../translated_images/el/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*Εικόνα από [αυτή την εξαιρετική ανάρτηση](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) του Fernando López.*\n", "\n", diff --git a/translations/el/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/el/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index f117b6f1..371c4315 100644 --- a/translations/el/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/el/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "Ο τρόπος με τον οποίο θα εκπαιδεύσουμε το RNN για να δημιουργεί κείμενο είναι ο εξής. Σε κάθε βήμα, θα παίρνουμε μια ακολουθία χαρακτήρων μήκους `nchars` και θα ζητάμε από το δίκτυο να παράγει τον επόμενο χαρακτήρα εξόδου για κάθε χαρακτήρα εισόδου:\n", "\n", - "![Εικόνα που δείχνει ένα παράδειγμα δημιουργίας της λέξης 'HELLO' από RNN.](../../../../../translated_images/el/rnn-generate.56c54afb52f9781d.png)\n", + "![Εικόνα που δείχνει ένα παράδειγμα δημιουργίας της λέξης 'HELLO' από RNN.](../../../../../translated_images/el/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "Ανάλογα με το συγκεκριμένο σενάριο, μπορεί να θέλουμε να συμπεριλάβουμε και ειδικούς χαρακτήρες, όπως το *τέλος ακολουθίας* ``. Στην περίπτωσή μας, θέλουμε απλώς να εκπαιδεύσουμε το δίκτυο για ατελείωτη δημιουργία κειμένου, επομένως θα ορίσουμε το μέγεθος κάθε ακολουθίας να είναι ίσο με `nchars` tokens. Συνεπώς, κάθε παράδειγμα εκπαίδευσης θα αποτελείται από `nchars` εισόδους και `nchars` εξόδους (που είναι η ακολουθία εισόδου μετατοπισμένη κατά ένα σύμβολο προς τα αριστερά). Το minibatch θα αποτελείται από αρκετές τέτοιες ακολουθίες.\n", "\n", diff --git a/translations/el/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/el/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index 84858860..1814d3f3 100644 --- a/translations/el/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/el/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -113,7 +113,7 @@ "\n", "Ο τρόπος με τον οποίο θα εκπαιδεύσουμε το RNN για να δημιουργεί τίτλους ειδήσεων είναι ο εξής. Σε κάθε βήμα, θα παίρνουμε έναν τίτλο, ο οποίος θα εισάγεται σε ένα RNN, και για κάθε χαρακτήρα εισόδου θα ζητάμε από το δίκτυο να παράγει τον επόμενο χαρακτήρα εξόδου:\n", "\n", - "![Εικόνα που δείχνει ένα παράδειγμα δημιουργίας της λέξης 'HELLO' από RNN.](../../../../../translated_images/el/rnn-generate.56c54afb52f9781d.png)\n", + "![Εικόνα που δείχνει ένα παράδειγμα δημιουργίας της λέξης 'HELLO' από RNN.](../../../../../translated_images/el/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "Για τον τελευταίο χαρακτήρα της ακολουθίας μας, θα ζητάμε από το δίκτυο να παράγει το token ``.\n", "\n", diff --git a/translations/el/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/el/lessons/5-NLP/17-GenerativeNetworks/README.md index 17486709..0bd39cf7 100644 --- a/translations/el/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/el/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Αυτό επιτρέπει διαφορετικές νευρωνικές αρχιτεκτονικές, όπως φαίνεται στην παρακάτω εικόνα: -![Εικόνα που δείχνει κοινά μοτίβα επαναληπτικών νευρωνικών δικτύων.](../../../../../translated_images/el/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Εικόνα που δείχνει κοινά μοτίβα επαναληπτικών νευρωνικών δικτύων.](../../../../../translated_images/el/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > Εικόνα από το blog post [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) του [Andrej Karpaty](http://karpathy.github.io/) @@ -32,7 +32,7 @@ CO_OP_TRANSLATOR_METADATA: Θα εκπαιδεύσουμε αυτό το RNN να δημιουργεί κείμενο βήμα προς βήμα. Σε κάθε βήμα, θα πάρουμε μια ακολουθία χαρακτήρων μήκους `nchars` και θα ζητήσουμε από το δίκτυο να παράγει τον επόμενο χαρακτήρα εξόδου για κάθε χαρακτήρα εισόδου: -![Εικόνα που δείχνει ένα παράδειγμα RNN που δημιουργεί τη λέξη 'HELLO'.](../../../../../translated_images/el/rnn-generate.56c54afb52f9781d.png) +![Εικόνα που δείχνει ένα παράδειγμα RNN που δημιουργεί τη λέξη 'HELLO'.](../../../../../translated_images/el/rnn-generate.56c54afb52f9781d.webp) Κατά τη δημιουργία κειμένου (κατά την πρόβλεψη), ξεκινάμε με κάποιο **προτροπή**, η οποία περνά μέσα από τα RNN cells για να δημιουργήσει την ενδιάμεση κατάσταση, και στη συνέχεια από αυτή την κατάσταση ξεκινά η δημιουργία. Παράγουμε έναν χαρακτήρα τη φορά και περνάμε την κατάσταση και τον παραγόμενο χαρακτήρα σε άλλο RNN cell για να δημιουργήσουμε τον επόμενο, μέχρι να δημιουργήσουμε αρκετούς χαρακτήρες. diff --git a/translations/el/lessons/5-NLP/18-Transformers/README.md b/translations/el/lessons/5-NLP/18-Transformers/README.md index 1a4220d9..99c9af3c 100644 --- a/translations/el/lessons/5-NLP/18-Transformers/README.md +++ b/translations/el/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ CO_OP_TRANSLATOR_METADATA: Οι **Μηχανισμοί Προσοχής** παρέχουν έναν τρόπο να δίνεται βάρος στην επίδραση κάθε εισαγωγικού διανύσματος στο κάθε αποτέλεσμα πρόβλεψης του RNN. Αυτό υλοποιείται δημιουργώντας συντομεύσεις μεταξύ των ενδιάμεσων καταστάσεων του εισαγωγικού RNN και του εξαγωγικού RNN. Με αυτόν τον τρόπο, κατά τη δημιουργία του εξαγωγικού συμβόλου yt, λαμβάνουμε υπόψη όλες τις κρυφές καταστάσεις εισόδου hi, με διαφορετικούς συντελεστές βάρους αt,i. -![Εικόνα που δείχνει ένα μοντέλο encoder/decoder με ένα πρόσθετο επίπεδο προσοχής](../../../../../translated_images/el/encoder-decoder-attention.7a726296894fb567.png) +![Εικόνα που δείχνει ένα μοντέλο encoder/decoder με ένα πρόσθετο επίπεδο προσοχής](../../../../../translated_images/el/encoder-decoder-attention.7a726296894fb567.webp) > Το μοντέλο encoder-decoder με μηχανισμό πρόσθετης προσοχής στο [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), από [αυτό το blog post](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) Ο πίνακας προσοχής {αi,j} αντιπροσωπεύει τον βαθμό στον οποίο συγκεκριμένες λέξεις εισόδου συμβάλλουν στη δημιουργία μιας δεδομένης λέξης στην εξαγωγική ακολουθία. Παρακάτω είναι ένα παράδειγμα ενός τέτοιου πίνακα: -![Εικόνα που δείχνει ένα δείγμα ευθυγράμμισης που βρέθηκε από το RNNsearch-50, από Bahdanau - arviz.org](../../../../../translated_images/el/bahdanau-fig3.09ba2d37f202a6af.png) +![Εικόνα που δείχνει ένα δείγμα ευθυγράμμισης που βρέθηκε από το RNNsearch-50, από Bahdanau - arviz.org](../../../../../translated_images/el/bahdanau-fig3.09ba2d37f202a6af.webp) > Εικόνα από [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) @@ -66,7 +66,7 @@ CO_OP_TRANSLATOR_METADATA: Στη συνέχεια, πρέπει να ανιχνεύσουμε κάποια μοτίβα μέσα στην ακολουθία μας. Για να το κάνουμε αυτό, οι transformers χρησιμοποιούν έναν μηχανισμό **αυτοπροσοχής**, που είναι ουσιαστικά προσοχή εφαρμοσμένη στην ίδια ακολουθία ως είσοδος και έξοδος. Η εφαρμογή αυτοπροσοχής μας επιτρέπει να λαμβάνουμε υπόψη το **πλαίσιο** μέσα στην πρόταση και να βλέπουμε ποιες λέξεις σχετίζονται μεταξύ τους. Για παράδειγμα, μας επιτρέπει να δούμε ποιες λέξεις αναφέρονται από συνυποδηλώσεις, όπως *αυτό*, και να λαμβάνουμε υπόψη το πλαίσιο: -![](../../../../../translated_images/el/CoreferenceResolution.861924d6d384a7d6.png) +![](../../../../../translated_images/el/CoreferenceResolution.861924d6d384a7d6.webp) > Εικόνα από το [Blog της Google](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ CO_OP_TRANSLATOR_METADATA: Το **BERT** (Bidirectional Encoder Representations from Transformers) είναι ένα πολύ μεγάλο πολυεπίπεδο δίκτυο transformer με 12 επίπεδα για το *BERT-base* και 24 για το *BERT-large*. Το μοντέλο εκπαιδεύεται αρχικά σε ένα μεγάλο σώμα δεδομένων κειμένου (WikiPedia + βιβλία) χρησιμοποιώντας μη επιβλεπόμενη εκπαίδευση (πρόβλεψη λέξεων που έχουν καλυφθεί σε μια πρόταση). Κατά τη διάρκεια της αρχικής εκπαίδευσης, το μοντέλο απορροφά σημαντικά επίπεδα κατανόησης της γλώσσας, τα οποία μπορούν στη συνέχεια να αξιοποιηθούν με άλλα σύνολα δεδομένων μέσω της προσαρμογής. Αυτή η διαδικασία ονομάζεται **μεταφορά μάθησης**. -![εικόνα από http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/el/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) +![εικόνα από http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/el/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > Εικόνα [πηγή](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/el/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/el/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index 616786e7..b585282d 100644 --- a/translations/el/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/el/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**Οι Μηχανισμοί Προσοχής** παρέχουν έναν τρόπο να δίνεται βάρος στην επίδραση κάθε εισόδου στην πρόβλεψη κάθε εξόδου του RNN. Αυτό υλοποιείται δημιουργώντας συντομεύσεις μεταξύ των ενδιάμεσων καταστάσεων του εισόδου RNN και του εξόδου RNN. Με αυτόν τον τρόπο, κατά τη δημιουργία του συμβόλου εξόδου $y_t$, λαμβάνουμε υπόψη όλες τις κρυφές καταστάσεις εισόδου $h_i$, με διαφορετικούς συντελεστές βάρους $\\alpha_{t,i}$.\n", "\n", - "![Εικόνα που δείχνει ένα μοντέλο encoder/decoder με ένα πρόσθετο στρώμα προσοχής](../../../../../translated_images/el/encoder-decoder-attention.7a726296894fb567.png)\n", + "![Εικόνα που δείχνει ένα μοντέλο encoder/decoder με ένα πρόσθετο στρώμα προσοχής](../../../../../translated_images/el/encoder-decoder-attention.7a726296894fb567.webp)\n", "*Το μοντέλο encoder-decoder με μηχανισμό πρόσθετης προσοχής στο [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), από [αυτή την ανάρτηση](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Ο πίνακας προσοχής $\\{\\alpha_{i,j}\\}$ αντιπροσωπεύει τον βαθμό στον οποίο συγκεκριμένες λέξεις εισόδου επηρεάζουν τη δημιουργία μιας δεδομένης λέξης στην ακολουθία εξόδου. Παρακάτω είναι ένα παράδειγμα ενός τέτοιου πίνακα:\n", "\n", - "![Εικόνα που δείχνει ένα δείγμα ευθυγράμμισης που βρέθηκε από το RNNsearch-50, από το Bahdanau - arviz.org](../../../../../translated_images/el/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![Εικόνα που δείχνει ένα δείγμα ευθυγράμμισης που βρέθηκε από το RNNsearch-50, από το Bahdanau - arviz.org](../../../../../translated_images/el/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*Εικόνα από [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) είναι ένα πολύ μεγάλο πολυεπίπεδο δίκτυο μετασχηματιστών με 12 επίπεδα για το *BERT-base* και 24 για το *BERT-large*. Το μοντέλο εκπαιδεύεται αρχικά σε μεγάλο σώμα δεδομένων κειμένου (WikiPedia + βιβλία) χρησιμοποιώντας μη επιβλεπόμενη εκπαίδευση (πρόβλεψη λέξεων που έχουν καλυφθεί σε μια πρόταση). Κατά τη διάρκεια της αρχικής εκπαίδευσης, το μοντέλο απορροφά σημαντικό επίπεδο κατανόησης της γλώσσας, το οποίο μπορεί στη συνέχεια να αξιοποιηθεί με άλλα σύνολα δεδομένων μέσω λεπτομερούς προσαρμογής. Αυτή η διαδικασία ονομάζεται **μεταφορά μάθησης**.\n", "\n", - "![Εικόνα από http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/el/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![Εικόνα από http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/el/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "Υπάρχουν πολλές παραλλαγές αρχιτεκτονικών μετασχηματιστών, όπως BERT, DistilBERT, BigBird, OpenGPT3 και άλλες, που μπορούν να προσαρμοστούν. Το [πακέτο HuggingFace](https://github.com/huggingface/) παρέχει αποθετήριο για την εκπαίδευση πολλών από αυτές τις αρχιτεκτονικές με PyTorch.\n", "\n", diff --git a/translations/el/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/el/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index 658532a1..d3e57603 100644 --- a/translations/el/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/el/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**Οι Μηχανισμοί Προσοχής** παρέχουν έναν τρόπο να δίνεται βάρος στην επίδραση κάθε εισόδου στο κάθε αποτέλεσμα του RNN. Αυτό υλοποιείται δημιουργώντας συντομεύσεις μεταξύ των ενδιάμεσων καταστάσεων του RNN εισόδου και του RNN εξόδου. Με αυτόν τον τρόπο, κατά τη δημιουργία του συμβόλου εξόδου $y_t$, λαμβάνουμε υπόψη όλες τις κρυφές καταστάσεις εισόδου $h_i$, με διαφορετικούς συντελεστές βάρους $\\alpha_{t,i}$.\n", "\n", - "![Εικόνα που δείχνει ένα μοντέλο encoder/decoder με ένα πρόσθετο επίπεδο προσοχής](../../../../../translated_images/el/encoder-decoder-attention.7a726296894fb567.png)\n", + "![Εικόνα που δείχνει ένα μοντέλο encoder/decoder με ένα πρόσθετο επίπεδο προσοχής](../../../../../translated_images/el/encoder-decoder-attention.7a726296894fb567.webp)\n", "*Το μοντέλο encoder-decoder με μηχανισμό πρόσθετης προσοχής στο [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), από [αυτή την ανάρτηση](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Ο πίνακας προσοχής $\\{\\alpha_{i,j}\\}$ αντιπροσωπεύει τον βαθμό στον οποίο συγκεκριμένες λέξεις εισόδου συμβάλλουν στη δημιουργία μιας δεδομένης λέξης στην ακολουθία εξόδου. Παρακάτω είναι ένα παράδειγμα ενός τέτοιου πίνακα:\n", "\n", - "![Εικόνα που δείχνει ένα δείγμα ευθυγράμμισης από το RNNsearch-50, από Bahdanau - arviz.org](../../../../../translated_images/el/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![Εικόνα που δείχνει ένα δείγμα ευθυγράμμισης από το RNNsearch-50, από Bahdanau - arviz.org](../../../../../translated_images/el/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*Εικόνα από [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3)*\n", "\n", @@ -225,7 +225,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) είναι ένα πολύ μεγάλο πολυεπίπεδο δίκτυο transformer με 12 επίπεδα για το *BERT-base* και 24 για το *BERT-large*. Το μοντέλο εκπαιδεύεται αρχικά σε ένα μεγάλο σύνολο δεδομένων κειμένου (WikiPedia + βιβλία) χρησιμοποιώντας μη επιβλεπόμενη εκπαίδευση (πρόβλεψη λέξεων που έχουν καλυφθεί σε μια πρόταση). Κατά τη διάρκεια της αρχικής εκπαίδευσης, το μοντέλο αποκτά σημαντικό επίπεδο κατανόησης της γλώσσας, το οποίο μπορεί στη συνέχεια να αξιοποιηθεί με άλλα σύνολα δεδομένων μέσω της διαδικασίας της λεπτομερούς προσαρμογής. Αυτή η διαδικασία ονομάζεται **μεταφορά μάθησης**.\n", "\n", - "![εικόνα από http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/el/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![εικόνα από http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/el/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "Υπάρχουν πολλές παραλλαγές αρχιτεκτονικών Transformer, όπως BERT, DistilBERT, BigBird, OpenGPT3 και άλλες, που μπορούν να προσαρμοστούν περαιτέρω.\n", "\n", diff --git a/translations/el/lessons/5-NLP/19-NER/README.md b/translations/el/lessons/5-NLP/19-NER/README.md index 34be8c83..c1e7ab85 100644 --- a/translations/el/lessons/5-NLP/19-NER/README.md +++ b/translations/el/lessons/5-NLP/19-NER/README.md @@ -58,7 +58,7 @@ infant | O Επειδή πρέπει να δημιουργήσουμε μια αντιστοιχία ένα προς ένα μεταξύ συμβόλων και κατηγοριών, μπορούμε να εκπαιδεύσουμε ένα δεξιότερο **πολλά-προς-πολλά** μοντέλο νευρωνικού δικτύου από αυτή την εικόνα: -![Εικόνα που δείχνει κοινά μοτίβα επαναλαμβανόμενων νευρωνικών δικτύων.](../../../../../translated_images/el/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Εικόνα που δείχνει κοινά μοτίβα επαναλαμβανόμενων νευρωνικών δικτύων.](../../../../../translated_images/el/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > *Εικόνα από [αυτό το άρθρο](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) του [Andrej Karpathy](http://karpathy.github.io/). Τα μοντέλα ταξινόμησης συμβόλων NER αντιστοιχούν στην αρχιτεκτονική του δικτύου που βρίσκεται δεξιά στην εικόνα.* diff --git a/translations/el/lessons/5-NLP/README.md b/translations/el/lessons/5-NLP/README.md index 9166dfa8..60c3cbdb 100644 --- a/translations/el/lessons/5-NLP/README.md +++ b/translations/el/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Επεξεργασία Φυσικής Γλώσσας -![Περίληψη των εργασιών NLP σε ένα σκίτσο](../../../../translated_images/el/ai-nlp.b22dcb8ca4707cea.png) +![Περίληψη των εργασιών NLP σε ένα σκίτσο](../../../../translated_images/el/ai-nlp.b22dcb8ca4707cea.webp) Σε αυτή την ενότητα, θα επικεντρωθούμε στη χρήση Νευρωνικών Δικτύων για την αντιμετώπιση εργασιών που σχετίζονται με την **Επεξεργασία Φυσικής Γλώσσας (NLP)**. Υπάρχουν πολλά προβλήματα NLP που θέλουμε οι υπολογιστές να μπορούν να λύσουν: diff --git a/translations/el/lessons/6-Other/23-MultiagentSystems/README.md b/translations/el/lessons/6-Other/23-MultiagentSystems/README.md index da00726a..dd341817 100644 --- a/translations/el/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/el/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ ask turtles [ Αφού ανοίξετε το μοντέλο, μεταφέρεστε στην κύρια οθόνη του NetLogo. Εδώ είναι ένα δείγμα μοντέλου που περιγράφει τον πληθυσμό λύκων και προβάτων, δεδομένων πεπερασμένων πόρων (γρασίδι). -![Κύρια Οθόνη NetLogo](../../../../../translated_images/el/NetLogo-Main.32653711ec1a01b3.png) +![Κύρια Οθόνη NetLogo](../../../../../translated_images/el/NetLogo-Main.32653711ec1a01b3.webp) > Στιγμιότυπο οθόνης από τον Dmitry Soshnikov diff --git a/translations/el/lessons/README.md b/translations/el/lessons/README.md index bd1b8abb..6494fa10 100644 --- a/translations/el/lessons/README.md +++ b/translations/el/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Επισκόπηση -![Επισκόπηση σε ένα σκίτσο](../../../translated_images/el/ai-overview.0857791951d19500.png) +![Επισκόπηση σε ένα σκίτσο](../../../translated_images/el/ai-overview.0857791951d19500.webp) > Σκίτσο από την [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/el/lessons/X-Extras/X1-MultiModal/README.md b/translations/el/lessons/X-Extras/X1-MultiModal/README.md index 250a4c70..3b3fd2d4 100644 --- a/translations/el/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/el/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: Η βασική ιδέα του CLIP είναι να μπορεί να συγκρίνει κείμενα με εικόνες και να καθορίζει πόσο καλά η εικόνα αντιστοιχεί στο κείμενο. -![Αρχιτεκτονική CLIP](../../../../../translated_images/el/clip-arch.b3dbf20b4e8ed8be.png) +![Αρχιτεκτονική CLIP](../../../../../translated_images/el/clip-arch.b3dbf20b4e8ed8be.webp) > *Εικόνα από [αυτήν την ανάρτηση στο blog](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ CO_OP_TRANSLATOR_METADATA: Ας υποθέσουμε ότι πρέπει να ταξινομήσουμε εικόνες μεταξύ, για παράδειγμα, γάτες, σκύλους και ανθρώπους. Σε αυτήν την περίπτωση, μπορούμε να δώσουμε στο μοντέλο μια εικόνα και μια σειρά από κείμενα: "*μια εικόνα μιας γάτας*", "*μια εικόνα ενός σκύλου*", "*μια εικόνα ενός ανθρώπου*". Στον προκύπτοντα διανυσματικό πίνακα με 3 πιθανότητες, απλώς πρέπει να επιλέξουμε τον δείκτη με τη μεγαλύτερη τιμή. -![CLIP για Ταξινόμηση Εικόνων](../../../../../translated_images/el/clip-class.3af42ef0b2b19369.png) +![CLIP για Ταξινόμηση Εικόνων](../../../../../translated_images/el/clip-class.3af42ef0b2b19369.webp) > *Εικόνα από [αυτήν την ανάρτηση στο blog](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ CO_OP_TRANSLATOR_METADATA: Μία από τις σημαντικές διαφορές μεταξύ VQGAN και παραδοσιακού GAN είναι ότι το τελευταίο μπορεί να παράγει μια αξιοπρεπή εικόνα από οποιοδήποτε διανυσματικό εισόδου, ενώ το VQGAN είναι πιθανό να παράγει μια εικόνα που δεν είναι συνεκτική. Επομένως, πρέπει να καθοδηγήσουμε περαιτέρω τη διαδικασία δημιουργίας εικόνας, και αυτό μπορεί να γίνει χρησιμοποιώντας το CLIP. -![Αρχιτεκτονική VQGAN+CLIP](../../../../../translated_images/el/vqgan.5027fe05051dfa31.png) +![Αρχιτεκτονική VQGAN+CLIP](../../../../../translated_images/el/vqgan.5027fe05051dfa31.webp) Για να δημιουργήσουμε μια εικόνα που αντιστοιχεί σε ένα κείμενο, ξεκινάμε με κάποιο τυχαίο διανυσματικό κωδικοποίησης που περνάει μέσω του VQGAN για να παράγει μια εικόνα. Στη συνέχεια, το CLIP χρησιμοποιείται για να παράγει μια συνάρτηση απώλειας που δείχνει πόσο καλά η εικόνα αντιστοιχεί στο κείμενο. Ο στόχος είναι να ελαχιστοποιήσουμε αυτήν την απώλεια, χρησιμοποιώντας back propagation για να προσαρμόσουμε τις παραμέτρους του διανυσματικού εισόδου. Μια εξαιρετική βιβλιοθήκη που υλοποιεί το VQGAN+CLIP είναι το [Pixray](http://github.com/pixray/pixray). -![Εικόνα που δημιουργήθηκε από το Pixray](../../../../../translated_images/el/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Εικόνα που δημιουργήθηκε από το Pixray](../../../../../translated_images/el/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Εικόνα που δημιουργήθηκε από το Pixray](../../../../../translated_images/el/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) +![Εικόνα που δημιουργήθηκε από το Pixray](../../../../../translated_images/el/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp) | ![Εικόνα που δημιουργήθηκε από το Pixray](../../../../../translated_images/el/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp) | ![Εικόνα που δημιουργήθηκε από το Pixray](../../../../../translated_images/el/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp) ----|----|---- Εικόνα που δημιουργήθηκε από το κείμενο *μια κοντινή ακουαρέλα πορτραίτο ενός νεαρού άνδρα δασκάλου λογοτεχνίας με ένα βιβλίο* | Εικόνα που δημιουργήθηκε από το κείμενο *μια κοντινή ελαιογραφία πορτραίτο μιας νεαρής γυναίκας δασκάλας πληροφορικής με έναν υπολογιστή* | Εικόνα που δημιουργήθηκε από το κείμενο *μια κοντινή ελαιογραφία πορτραίτο ενός ηλικιωμένου άνδρα δασκάλου μαθηματικών μπροστά από έναν πίνακα* diff --git a/translations/et/README.md b/translations/et/README.md index 1f0ded16..b32da925 100644 --- a/translations/et/README.md +++ b/translations/et/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # Tehisintellekt algajatele - õppekava -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/et/ai-overview.0857791951d19500.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/et/ai-overview.0857791951d19500.webp)| |:---:| | AI algajatele - _Sketchnote autor [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/et/lessons/1-Intro/README.md b/translations/et/lessons/1-Intro/README.md index cc199941..c9b222b2 100644 --- a/translations/et/lessons/1-Intro/README.md +++ b/translations/et/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Sissejuhatus tehisintellekti -![Sissejuhatuse kokkuvõte tehisintellekti teemal doodle'is](../../../../translated_images/et/ai-intro.bf28d1ac4235881c.png) +![Sissejuhatuse kokkuvõte tehisintellekti teemal doodle'is](../../../../translated_images/et/ai-intro.bf28d1ac4235881c.webp) > Sketchnote autor: [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Algselt leiutas [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage) arvutid, et need töötleksid numbreid kindla protseduuri ehk algoritmi järgi. Kaasaegsed arvutid, kuigi palju arenenumad kui 19. sajandil välja pakutud mudel, järgivad endiselt sama ideed kontrollitud arvutustest. Seega on võimalik programmeerida arvutit midagi tegema, kui me teame täpset sammude jada, mida eesmärgi saavutamiseks vaja on. -![Foto inimesest](../../../../translated_images/et/dsh_age.d212a30d4e54fb5f.png) +![Foto inimesest](../../../../translated_images/et/dsh_age.d212a30d4e54fb5f.webp) > Foto autor: [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ Lisateabe saamiseks vaata **[Üldine tehisintellekt](https://en.wikipedia.org/wi Üks probleem **[intelligentsuse](https://en.wikipedia.org/wiki/Intelligence)** mõistega tegelemisel on see, et sellel puudub selge määratlus. Võib väita, et intelligentsus on seotud **abstraktse mõtlemise** või **eneseteadvusega**, kuid me ei suuda seda korralikult defineerida. -![Foto kassist](../../../../translated_images/et/photo-cat.8c8e8fb760ffe457.jpg) +![Foto kassist](../../../../translated_images/et/photo-cat.8c8e8fb760ffe457.webp) > [Foto](https://unsplash.com/photos/75715CVEJhI) autor: [Amber Kipp](https://unsplash.com/@sadmax) Unsplashist @@ -98,13 +98,13 @@ Teise võimalusena võime proovida modelleerida meie aju kõige lihtsamaid eleme > | Aga ML? | | > |--------------|-----------| -> | Tehisintellekti osa, mis põhineb arvuti õppimisel probleemi lahendamiseks andmete põhjal, nimetatakse **masinõppeks**. Me ei käsitle selles kursuses klassikalist masinõpet – soovitame tutvuda eraldi [Masinõppe algajatele](http://aka.ms/ml-beginners) õppekavaga. | ![ML algajatele](../../../../translated_images/et/ml-for-beginners.9e4fed176fd5817d.png) | +> | Tehisintellekti osa, mis põhineb arvuti õppimisel probleemi lahendamiseks andmete põhjal, nimetatakse **masinõppeks**. Me ei käsitle selles kursuses klassikalist masinõpet – soovitame tutvuda eraldi [Masinõppe algajatele](http://aka.ms/ml-beginners) õppekavaga. | ![ML algajatele](../../../../translated_images/et/ml-for-beginners.9e4fed176fd5817d.webp) | ## Lühike ülevaade TI ajaloost Tehisintellekt kui valdkond sai alguse 20. sajandi keskel. Alguses oli sümboolne arutlemine valdav lähenemine ja see tõi kaasa mitmeid olulisi edusamme, näiteks ekspertsüsteemid – arvutiprogrammid, mis suutsid tegutseda eksperdina mõnes piiratud probleemivaldkonnas. Kuid peagi sai selgeks, et selline lähenemine ei ole hästi skaleeritav. Teadmiste eraldamine eksperdilt, nende esitamine arvutis ja teadmistebaasi täpsuse säilitamine osutus väga keeruliseks ja paljudel juhtudel liiga kulukaks. See viis nn [TI talveni](https://en.wikipedia.org/wiki/AI_winter) 1970. aastatel. -TI ajaloo lühikokkuvõte +TI ajaloo lühikokkuvõte > Pildi autor: [Dmitry Soshnikov](http://soshnikov.com) @@ -124,7 +124,7 @@ Samamoodi näeme, kuidas lähenemine "rääkivate programmide" (mis võiksid lä * Kaasaegsed assistendid, nagu Cortana, Siri või Google Assistant, on kõik hübriidsüsteemid, mis kasutavad närvivõrke kõne tekstiks teisendamiseks ja meie kavatsuste tuvastamiseks ning seejärel rakendavad mõningaid arutlusi või selgesõnalisi algoritme vajalike toimingute tegemiseks. * Tulevikus võime oodata täielikult närvivõrkudel põhinevat mudelit, mis suudab dialoogi iseseisvalt hallata. Hiljutised GPT ja [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) närvivõrkude perekonnad näitavad selles valdkonnas suurt edu. -Turingi testi areng +Turingi testi areng > Pilt Dmitry Soshnikovilt, [foto](https://unsplash.com/photos/r8LmVbUKgns) autoriks [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto), Unsplash ## Viimased tehisintellekti uuringud diff --git a/translations/et/lessons/2-Symbolic/Animals.ipynb b/translations/et/lessons/2-Symbolic/Animals.ipynb index 2a47b4fb..0df546e6 100644 --- a/translations/et/lessons/2-Symbolic/Animals.ipynb +++ b/translations/et/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "Selles näites rakendame lihtsat teadmistepõhist süsteemi, et määrata loom füüsiliste omaduste põhjal. Süsteemi saab kujutada järgmise JA-VÕI puuna (see on osa kogu puust, reegleid saab hõlpsasti juurde lisada):\n", "\n", - "![](../../../../translated_images/et/AND-OR-Tree.5592d2c70187f283.png)\n" + "![](../../../../translated_images/et/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { diff --git a/translations/et/lessons/2-Symbolic/README.md b/translations/et/lessons/2-Symbolic/README.md index 3a00bdbb..ba03759f 100644 --- a/translations/et/lessons/2-Symbolic/README.md +++ b/translations/et/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Teadmiste esitus ja ekspertsüsteemid -![Sümboolse AI sisu kokkuvõte](../../../../translated_images/et/ai-symbolic.715a30cb610411a6.png) +![Sümboolse AI sisu kokkuvõte](../../../../translated_images/et/ai-symbolic.715a30cb610411a6.webp) > Sketchnote autorilt [Tomomi Imura](https://twitter.com/girlie_mac) @@ -35,13 +35,13 @@ Enamasti me ei defineeri teadmisi rangelt, vaid seostame neid teiste seotud mõi * **Teadmised** on informatsioon, mis on integreeritud meie maailmamudelisse. Näiteks, kui õpime, mis on arvuti, hakkame mõistma, kuidas see töötab, kui palju see maksab ja milleks seda saab kasutada. See omavahel seotud mõistete võrgustik moodustab meie teadmised. * **Tarkus** on veel üks tasand meie arusaamisest maailmast ja esindab *meta-teadmisi*, näiteks arusaama, kuidas ja millal teadmisi kasutada. - + *Pilt [Wikipedia-st](https://commons.wikimedia.org/w/index.php?curid=37705247), autor Longlivetheux - Oma töö, CC BY-SA 4.0* Seega on **teadmiste esitamise** probleem leida tõhus viis teadmiste esitamiseks arvutis andmete kujul, et neid automaatselt kasutada. Seda võib vaadelda spektrina: -![Teadmiste esitamise spekter](../../../../translated_images/et/knowledge-spectrum.b60df631852c0217.png) +![Teadmiste esitamise spekter](../../../../translated_images/et/knowledge-spectrum.b60df631852c0217.webp) > Pilt autorilt [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Ploki süntaks | Taanded | | | Sümboolse AI varajased edusammud olid nn **ekspertsüsteemid** - arvutisüsteemid, mis olid loodud tegutsema eksperdina mõnes piiratud probleemivaldkonnas. Need põhinesid **teadmistebaasil**, mis oli saadud ühelt või mitmelt inimeksperdilt, ja sisaldasid **järeldusmootorit**, mis tegi selle põhjal järeldusi. -![Inimese arhitektuur](../../../../translated_images/et/arch-human.5d4d35f1bba3ab1c.png) | ![Teadmistepõhise süsteemi arhitektuur](../../../../translated_images/et/arch-kbs.3ec5c150b09fa8da.png) +![Inimese arhitektuur](../../../../translated_images/et/arch-human.5d4d35f1bba3ab1c.webp) | ![Teadmistepõhise süsteemi arhitektuur](../../../../translated_images/et/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ Lihtsustatud inimese närvisüsteemi struktuur | Teadmistepõhise süsteemi arhitektuur @@ -106,7 +106,7 @@ Ekspertsüsteemid on ehitatud nagu inimese järeldussüsteem, mis sisaldab **lü Näiteks vaatame järgmist ekspertsüsteemi, mis määrab looma füüsiliste omaduste põhjal: -![AND-OR puu](../../../../translated_images/et/AND-OR-Tree.5592d2c70187f283.png) +![AND-OR puu](../../../../translated_images/et/AND-OR-Tree.5592d2c70187f283.webp) > Pilt autorilt [Dmitry Soshnikov](http://soshnikov.com) @@ -175,7 +175,7 @@ Semantilise veebi keskne mõiste on **ontoloogia**. See viitab probleemivaldkonn Semantilises veebis põhinevad kõik esitusviisid kolmikutel. Iga objekt ja iga seos on unikaalselt identifitseeritud URI abil. Näiteks, kui soovime väita, et see AI õppekava on koostanud Dmitry Soshnikov 1. jaanuaril 2022, siis siin on kolmikud, mida saame kasutada: - + ``` http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 13, 2007” @@ -186,7 +186,7 @@ http://github.com/microsoft/ai-for-beginners http://purl.org/dc/elements/1.1/cre Keerukamal juhul, kui soovime määratleda loojate nimekirja, saame kasutada RDF-is määratletud andmestruktuure. - + > Ülaltoodud diagrammid: [Dmitry Soshnikov](http://soshnikov.com) @@ -210,7 +210,7 @@ GROUP BY ?eyeColorLabel > ✅ Kui soovite katsetada oma ontoloogiate loomist või olemasolevate avamist, on suurepärane visuaalne ontoloogia redaktor nimega [Protégé](https://protege.stanford.edu/). Laadige see alla või kasutage seda veebis. - + *Web Protégé redaktor avatud Romanovite perekonna ontoloogiaga. Ekraanipilt: Dmitry Soshnikov* diff --git a/translations/et/lessons/3-NeuralNetworks/03-Perceptron/README.md b/translations/et/lessons/3-NeuralNetworks/03-Perceptron/README.md index 79728ecd..439ff005 100644 --- a/translations/et/lessons/3-NeuralNetworks/03-Perceptron/README.md +++ b/translations/et/lessons/3-NeuralNetworks/03-Perceptron/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: | | | |--------------|-----------| -|Frank Rosenblatt | Mark 1 Perceptron| +|Frank Rosenblatt | Mark 1 Perceptron| > Pildid [Wikipediast](https://en.wikipedia.org/wiki/Perceptron) @@ -34,7 +34,7 @@ y(x) = f(wTx) kus f on astmelise aktiveerimise funktsioon - + ## Perceptroni treenimine diff --git a/translations/et/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/et/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index bfbf4b81..7f693d23 100644 --- a/translations/et/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/et/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -373,7 +373,7 @@ "\n", "Oleme loonud andmekogu binaarse klassifikatsiooni probleemi jaoks. Kuid võtame seda algusest peale kui mitmeklassilist klassifikatsiooni, et saaksime hiljem oma koodi hõlpsalt mitmeklassiliseks klassifikatsiooniks ümber lülitada. Sel juhul on meie ühekihilise perceptroni arhitektuur järgmine:\n", "\n", - "\n", + "\n", "\n", "Võrgu kaks väljundit vastavad kahele klassile ning klass, mille väljundväärtus on kahe hulgast suurim, vastab õigele lahendusele.\n", "\n", @@ -620,7 +620,7 @@ "\n", "## Arvutusgraaf\n", "\n", - "\n", + "\n", "\n", "Siiani oleme defineerinud erinevad klassid võrgu erinevate kihtide jaoks. Nende kihtide koostist saab kujutada kui **arvutusgraafi**. Nüüd saame kaotuse arvutada antud treeningandmekogu (või selle osa) jaoks järgmiselt:\n" ] @@ -687,7 +687,7 @@ "source": [ "## Tagurpidi levik\n", "\n", - "\n", + "\n", "\n", "$$\\def\\L{\\mathcal{L}}\\def\\zz#1#2{\\frac{\\partial#1}{\\partial#2}}\n", "\\begin{align}\n", @@ -1265,7 +1265,7 @@ "* Madal treeningkadu – mudel suudab treeningandmeid hästi jäljendada, kuna sellel on piisavalt väljendusvõimsust.\n", "* Valideerimiskadu võib olla palju suurem kui treeningkadu ja treeningu käigus hakata suurenema – see juhtub, kuna mudel \"mäletab\" treeningpunkte ja kaotab \"üldise pildi\".\n", "\n", - "![Üleõppimine](../../../../../translated_images/et/overfit.a0bd57f717c15769.png)\n", + "![Üleõppimine](../../../../../translated_images/et/overfit.a0bd57f717c15769.webp)\n", "\n", "> Sellel pildil tähistab `x` treeningandmeid ja `o` valideerimisandmeid. Vasakul – lineaarne mudel (ühekihiline), mis jäljendab andmete olemust üsna hästi. Paremal – üleõppinud mudel, mis jäljendab treeningandmeid täiuslikult, kuid kaotab igasuguse mõtte teiste andmete puhul (valideerimisviga on väga suur).\n" ] diff --git a/translations/et/lessons/3-NeuralNetworks/04-OwnFramework/README.md b/translations/et/lessons/3-NeuralNetworks/04-OwnFramework/README.md index 2c973406..15d29dac 100644 --- a/translations/et/lessons/3-NeuralNetworks/04-OwnFramework/README.md +++ b/translations/et/lessons/3-NeuralNetworks/04-OwnFramework/README.md @@ -65,7 +65,7 @@ Gradientlanguse algoritm jääb samaks, kuid gradientide arvutamine muutub keeru Pange tähele, et kõigi nende avaldiste vasakpoolne osa on sama, ja seega saame tuletised tõhusalt arvutada, alustades kaofunktsioonist ja liikudes "tagasi" läbi arvutusgraafi. Seetõttu nimetatakse mitmekihilise perceptroni treenimise meetodit **tagasilevikuks** ehk 'backprop'. -arvutusgraaf +arvutusgraaf > TODO: pildi viide diff --git a/translations/et/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/et/lessons/3-NeuralNetworks/05-Frameworks/README.md index 703851cd..64cb4bb3 100644 --- a/translations/et/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/et/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Pärast raamistikest arusaamist vaatame üle üleliigse sobitamise (overfitting) Vaatleme järgmist probleemi, kus tuleb ligikaudselt määrata 5 punkti (graafikutel tähistatud `x`-ga): -![lineaarne](../../../../../translated_images/et/overfit1.f24b71c6f652e59e.jpg) | ![üleliigne sobitamine](../../../../../translated_images/et/overfit2.131f5800ae10ca5e.jpg) +![lineaarne](../../../../../translated_images/et/overfit1.f24b71c6f652e59e.webp) | ![üleliigne sobitamine](../../../../../translated_images/et/overfit2.131f5800ae10ca5e.webp) -------------------------|-------------------------- **Lineaarne mudel, 2 parameetrit** | **Mitte-lineaarne mudel, 7 parameetrit** Treeningu viga = 5.3 | Treeningu viga = 0 @@ -79,7 +79,7 @@ On väga oluline leida õige tasakaal mudeli rikkuse (parameetrite arv) ja treen Nagu ülaltoodud graafikult näha, saab üleliigset sobitamist tuvastada väga madala treeningu vea ja kõrge valideerimise vea järgi. Tavaliselt näeme treenimise ajal, kuidas treeningu ja valideerimise vead hakkavad mõlemad vähenema, kuid mingil hetkel valideerimise viga võib lõpetada vähenemise ja hakata kasvama. See on märk üleliigsest sobitamisest ja indikaator, et treenimine tuleks tõenäoliselt lõpetada (või vähemalt mudelist hetkeseis salvestada). -![üleliigne sobitamine](../../../../../translated_images/et/Overfitting.408ad91cd90b4371.png) +![üleliigne sobitamine](../../../../../translated_images/et/Overfitting.408ad91cd90b4371.webp) ## Kuidas vältida üleliigset sobitamist diff --git a/translations/et/lessons/3-NeuralNetworks/README.md b/translations/et/lessons/3-NeuralNetworks/README.md index 5396a06f..0652c032 100644 --- a/translations/et/lessons/3-NeuralNetworks/README.md +++ b/translations/et/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Sissejuhatus tehisnärvivõrkudesse -![Kokkuvõte tehisnärvivõrkude sissejuhatuse sisust doodle'is](../../../../translated_images/et/ai-neuralnetworks.1c687ae40bc86e83.png) +![Kokkuvõte tehisnärvivõrkude sissejuhatuse sisust doodle'is](../../../../translated_images/et/ai-neuralnetworks.1c687ae40bc86e83.webp) Nagu me arutasime sissejuhatuses, on üks viis intelligentsuse saavutamiseks treenida **arvutimudelit** või **tehisaju**. Alates 20. sajandi keskpaigast on teadlased katsetanud erinevaid matemaatilisi mudeleid, kuni viimastel aastatel osutus see suund väga edukaks. Selliseid aju matemaatilisi mudeleid nimetatakse **närvivõrkudeks**. @@ -36,13 +36,13 @@ Selles õppekavas keskendume ainult närvivõrkude mudelitele. Bioloogiast teame, et meie aju koosneb närvirakkudest (neuronitest), millest igaühel on mitu "sisendit" (dendriidid) ja üks "väljund" (akson). Nii dendriidid kui aksonid suudavad juhtida elektrilisi signaale ning nende vahelised ühendused — sünapsid — võivad näidata erinevat juhtivust, mida reguleerivad neurotransmitterid. -![Neuroni mudel](../../../../translated_images/et/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![Neuroni mudel](../../../../translated_images/et/artneuron.1a5daa88d20ebe6f.png) +![Neuroni mudel](../../../../translated_images/et/synapse-wikipedia.ed20a9e4726ea1c6.webp) | ![Neuroni mudel](../../../../translated_images/et/artneuron.1a5daa88d20ebe6f.webp) ----|---- Päris neuron *([Pilt](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) Wikipediast)* | Tehisneuron *(Pilt autorilt)* Seega sisaldab neuroni lihtsaim matemaatiline mudel mitut sisendit X1, ..., XN ja ühte väljundit Y ning mitmeid kaale W1, ..., WN. Väljund arvutatakse järgmiselt: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) kus f on mingi mittelineaarne **aktiveerimisfunktsioon**. diff --git a/translations/et/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/et/lessons/4-ComputerVision/06-IntroCV/README.md index f130ab09..782e73d6 100644 --- a/translations/et/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/et/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ Meie [OpenCV Notebook](OpenCV.ipynb) näitab mõningaid näiteid, millal arvutin * **Pildi eeltöötlus Braille'i raamatu fotol**. Keskendume sellele, kuidas kasutada läve määramist, tunnuste tuvastamist, perspektiiviteisendust ja NumPy manipuleerimist, et eraldada individuaalsed Braille'i sümbolid edasiseks klassifitseerimiseks närvivõrgu abil. -![Braille'i pilt](../../../../../translated_images/et/braille.341962ff76b1bd70.jpeg) | ![Braille'i pilt eeltöödeldud](../../../../../translated_images/et/braille-result.46530fea020b03c7.png) | ![Braille'i sümbolid](../../../../../translated_images/et/braille-symbols.0159185ab69d5339.png) +![Braille'i pilt](../../../../../translated_images/et/braille.341962ff76b1bd70.webp) | ![Braille'i pilt eeltöödeldud](../../../../../translated_images/et/braille-result.46530fea020b03c7.webp) | ![Braille'i sümbolid](../../../../../translated_images/et/braille-symbols.0159185ab69d5339.webp) ----|-----|----- > Pilt [OpenCV.ipynb](OpenCV.ipynb) failist * **Liikumise tuvastamine videos kaadrite erinevuse abil**. Kui kaamera on fikseeritud, peaksid kaadrid kaamera voos olema üksteisega üsna sarnased. Kuna kaadreid esitatakse massiividena, siis lihtsalt lahutades need massiivid kahe järjestikuse kaadri jaoks saame pikslite erinevuse, mis peaks olema madal staatiliste kaadrite puhul ja muutuma suuremaks, kui pildil toimub märkimisväärne liikumine. -![Kaadrite ja kaadrite erinevuste pilt](../../../../../translated_images/et/frame-difference.706f805491a0883c.png) +![Kaadrite ja kaadrite erinevuste pilt](../../../../../translated_images/et/frame-difference.706f805491a0883c.webp) > Pilt [OpenCV.ipynb](OpenCV.ipynb) failist @@ -89,7 +89,7 @@ Meie [OpenCV Notebook](OpenCV.ipynb) näitab mõningaid näiteid, millal arvutin - **Tihe optiline vool** arvutab vektorvälja, mis näitab iga piksli liikumissuunda. - **Hõre optiline vool** põhineb mõningate eristuvate tunnuste (nt servade) võtmisele pildil ja nende trajektoori ehitamisele kaadrist kaadrisse. -![Optilise voolu pilt](../../../../../translated_images/et/optical.1f4a94464579a83a.png) +![Optilise voolu pilt](../../../../../translated_images/et/optical.1f4a94464579a83a.webp) > Pilt [OpenCV.ipynb](OpenCV.ipynb) failist @@ -115,7 +115,7 @@ Loe rohkem optilise voolu kohta [selles suurepärases juhendis](https://learnope Selles laboris teete video lihtsate žestidega ja teie eesmärk on optilise voolu abil tuvastada üles/alla/vasakule/paremale liikumised. -Käe liikumise kaader +Käe liikumise kaader --- diff --git a/translations/et/lessons/4-ComputerVision/06-IntroCV/lab/MovementDetection.ipynb b/translations/et/lessons/4-ComputerVision/06-IntroCV/lab/MovementDetection.ipynb index 829b434c..34d2cc0f 100644 --- a/translations/et/lessons/4-ComputerVision/06-IntroCV/lab/MovementDetection.ipynb +++ b/translations/et/lessons/4-ComputerVision/06-IntroCV/lab/MovementDetection.ipynb @@ -10,7 +10,7 @@ "\n", "Vaadake [seda videot](../../../../../../lessons/4-ComputerVision/06-IntroCV/lab/palm-movement.mp4), kus inimese peopesa liigub vasakule/paremale/üles/alla stabiilse tausta ees.\n", "\n", - "\"Peopesa\n", + "\"Peopesa\n", "\n", "**Teie eesmärk** on kasutada optilist voolu, et määrata, millised video osad sisaldavad üles/alla/vasakule/paremale liikumisi.\n", "\n", diff --git a/translations/et/lessons/4-ComputerVision/06-IntroCV/lab/README.md b/translations/et/lessons/4-ComputerVision/06-IntroCV/lab/README.md index 460ff7d9..b392d4d8 100644 --- a/translations/et/lessons/4-ComputerVision/06-IntroCV/lab/README.md +++ b/translations/et/lessons/4-ComputerVision/06-IntroCV/lab/README.md @@ -15,7 +15,7 @@ Laboriülesanne [AI algajatele mõeldud õppekavast](https://aka.ms/ai-beginners Vaadake [seda videot](../../../../../../lessons/4-ComputerVision/06-IntroCV/lab/palm-movement.mp4), kus inimese peopesa liigub vasakule/paremale/üles/alla stabiilse tausta ees. -Peopesa liikumise kaader +Peopesa liikumise kaader **Teie eesmärk** on kasutada optilist voolu, et määrata, millised video osad sisaldavad üles/alla/vasakule/paremale liikumisi. diff --git a/translations/et/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/et/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 52fe9f8d..6c761cb6 100644 --- a/translations/et/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/et/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 on võrk, mis saavutas 2014. aastal ImageNet top-5 klassifikatsioonis 92,7% täpsuse. Sellel on järgmine kihistruktuur: -![ImageNet kihid](../../../../../translated_images/et/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet kihid](../../../../../translated_images/et/vgg-16-arch1.d901a5583b3a51ba.webp) Nagu näha, järgib VGG traditsioonilist püramiidstruktuuri, mis koosneb järjestikustest konvolutsiooni- ja koondamiskihidest. -![ImageNet püramiid](../../../../../translated_images/et/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet püramiid](../../../../../translated_images/et/vgg-16-arch.64ff2137f50dd49f.webp) > Pilt [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) lehelt @@ -25,7 +25,7 @@ Nagu näha, järgib VGG traditsioonilist püramiidstruktuuri, mis koosneb järje ResNet on mudelite perekond, mille Microsoft Research esitas 2015. aastal. ResNeti peamine idee on kasutada **jääkblokke**: - + > Pilt [sellest artiklist](https://arxiv.org/pdf/1512.03385.pdf) @@ -37,7 +37,7 @@ Seda võrku võib mõelda ka kui võimet kohandada oma keerukust vastavalt andme Google Inception arhitektuur viib selle idee veelgi kaugemale ja ehitab iga võrgu kihi mitme erineva tee kombinatsioonina: - + > Pilt [Researchgate](https://www.researchgate.net/figure/Inception-module-with-dimension-reductions-left-and-schema-for-Inception-ResNet-v1_fig2_355547454) lehelt diff --git a/translations/et/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/et/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index 21027af6..4de22253 100644 --- a/translations/et/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/et/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -262,7 +262,7 @@ "\n", "Seega on tüüpilises CNN-is mitu konvolutsioonikihti, mille vahel on koondamiskihid, et pildi mõõtmeid vähendada. Samuti suurendame filtrite arvu, sest mustrid muutuvad keerukamaks – on rohkem huvitavaid kombinatsioone, mida tuleb otsida.\n", "\n", - "![Pilt, mis näitab mitut konvolutsioonikihti koos koondamiskihtidega.](../../../../../translated_images/et/cnn-pyramid.85915455759ef0ce.png)\n", + "![Pilt, mis näitab mitut konvolutsioonikihti koos koondamiskihtidega.](../../../../../translated_images/et/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "Kuna ruumilised mõõtmed vähenevad ja tunnuste/filtrite mõõtmed suurenevad, nimetatakse seda arhitektuuri ka **püramiidi arhitektuuriks**.\n" ] diff --git a/translations/et/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/et/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index 045906f0..45d4fbba 100644 --- a/translations/et/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/et/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -114,7 +114,7 @@ "\n", "Klassikalises arvutinägemises rakendati pildile mitmeid filtreid, et genereerida tunnuseid, mida seejärel kasutas masinõppe algoritm klassifikaatori loomiseks. Need filtrid on tegelikult sarnased närvisüsteemi struktuuridega, mis on olemas mõnede loomade nägemissüsteemis.\n", "\n", - "\n", + "\n", "\n", "Kuid süvaõppes konstrueerime võrgustikke, mis **õpivad** parimad konvolutsioonifiltrid klassifitseerimisprobleemi lahendamiseks. Selleks tutvustame **konvolutsioonikihte**.\n" ] @@ -360,7 +360,7 @@ "\n", "Seega on tüüpilises CNN-is mitu konvolutsioonikihti, mille vahel on koondamiskihid, et vähendada pildi dimensioone. Samuti suurendame filtrite arvu, sest mustrid muutuvad keerukamaks – on rohkem huvitavaid kombinatsioone, mida peame otsima.\n", "\n", - "![Pilt, mis näitab mitut konvolutsioonikihti koos koondamiskihtidega.](../../../../../translated_images/et/cnn-pyramid.85915455759ef0ce.png)\n", + "![Pilt, mis näitab mitut konvolutsioonikihti koos koondamiskihtidega.](../../../../../translated_images/et/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "Kuna ruumilised dimensioonid vähenevad ja tunnuste/filtrite dimensioonid suurenevad, nimetatakse seda arhitektuuri ka **püramiidi arhitektuuriks**.\n" ] diff --git a/translations/et/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/et/lessons/4-ComputerVision/07-ConvNets/README.md index 0d75b953..1b774109 100644 --- a/translations/et/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/et/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,14 +17,14 @@ Päriselus tahame olla võimelised tuvastama objekte pildil sõltumata nende tä Mustrite leidmiseks kasutame **konvolutsioonifiltrite** mõistet. Nagu teate, on pilt esitatud 2D-maatriksina või 3D-tensorina koos värvisügavusega. Filtri rakendamine tähendab, et võtame suhteliselt väikese **filtrituuma** maatriksi ja arvutame iga originaalpildi pikseli jaoks kaalutud keskmise koos naaberpunktidega. Seda võib vaadelda kui väikest akent, mis libiseb üle kogu pildi ja keskmistab kõik pikslid vastavalt filtrituuma maatriksi kaaludele. -![Vertikaalse serva filter](../../../../../translated_images/et/filter-vert.b7148390ca0bc356.png) | ![Horisontaalse serva filter](../../../../../translated_images/et/filter-horiz.59b80ed4feb946ef.png) +![Vertikaalse serva filter](../../../../../translated_images/et/filter-vert.b7148390ca0bc356.webp) | ![Horisontaalse serva filter](../../../../../translated_images/et/filter-horiz.59b80ed4feb946ef.webp) ----|---- > Pilt: Dmitry Soshnikov Näiteks, kui rakendame MNIST numbritele 3x3 vertikaalse ja horisontaalse serva filtreid, saame esile tõsta (nt kõrged väärtused) kohad, kus originaalpildil on vertikaalsed ja horisontaalsed servad. Seega saab neid kahte filtrit kasutada servade "otsimiseks". Samamoodi saame kujundada erinevaid filtreid, et otsida teisi madala taseme mustreid: - + > Pilt: [Leung-Malik filtripank](https://www.robots.ox.ac.uk/~vgg/research/texclass/filters.html) @@ -38,7 +38,7 @@ CNN-i töö põhineb järgmistel olulistel ideedel: * Võrgu saab kujundada nii, et filtrid treenitakse automaatselt * Sama lähenemist saab kasutada mustrite leidmiseks kõrgetasemelistes omadustes, mitte ainult originaalpildil. Seega töötab CNN-i omaduste tuvastamine hierarhias, alustades madala taseme pikslikombinatsioonidest kuni kõrgema taseme pildiosade kombinatsioonideni. -![Hierarhiline omaduste tuvastamine](../../../../../translated_images/et/FeatureExtractionCNN.d9b456cbdae7cb64.png) +![Hierarhiline omaduste tuvastamine](../../../../../translated_images/et/FeatureExtractionCNN.d9b456cbdae7cb64.webp) > Pilt: [Hislop-Lynchi artikkel](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), põhineb [nende uurimusel](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ Enamik pilttöötluseks kasutatavaid CNN-e järgib nn püramiidset arhitektuuri. Näiteks vaatame VGG-16 arhitektuuri, võrku, mis saavutas 2014. aastal ImageNeti top-5 klassifikatsioonis 92,7% täpsuse: -![ImageNeti kihid](../../../../../translated_images/et/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNeti kihid](../../../../../translated_images/et/vgg-16-arch1.d901a5583b3a51ba.webp) -![ImageNeti püramiid](../../../../../translated_images/et/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNeti püramiid](../../../../../translated_images/et/vgg-16-arch.64ff2137f50dd49f.webp) > Pilt: [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/et/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/et/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 1a2e031c..25b94a50 100644 --- a/translations/et/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/et/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ Te peate treenima konvolutsioonilise närvivõrgu, et klassifitseerida erinevaid Me kasutame [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/) andmestikku, mis sisaldab pilte 37 erinevast koerte ja kasside tõust. -![Andmestik, millega töötame](../../../../../../translated_images/et/data.50b2a9d5484bdbf0.png) +![Andmestik, millega töötame](../../../../../../translated_images/et/data.50b2a9d5484bdbf0.webp) Andmestiku allalaadimiseks kasutage järgmist koodilõiku: diff --git a/translations/et/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/et/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index f50e8db6..2be89a8e 100644 --- a/translations/et/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/et/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "Et kujutada ette ideaalset kassi, alustame juhusliku müra pildiga ja proovime kasutada gradientse languse optimeerimistehnikat, et kohandada pilti nii, et võrk tunneks ära kassi.\n", "\n", - "![Optimeerimise tsükkel](../../../../../translated_images/et/ideal-cat-loop.999fbb8ff306e044.png)\n", + "![Optimeerimise tsükkel](../../../../../translated_images/et/ideal-cat-loop.999fbb8ff306e044.webp)\n", "\n", "Siin on meie alguspilt:\n" ] diff --git a/translations/et/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/et/lessons/4-ComputerVision/08-TransferLearning/README.md index 54422d17..91650478 100644 --- a/translations/et/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/et/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Nii Keras kui PyTorch sisaldavad funktsioone, mis võimaldavad hõlpsalt laadida Siin on näide omadustest, mille VGG-16 võrk kassipildilt tuvastas: -![VGG-16 tuvastatud omadused](../../../../../translated_images/et/features.6291f9c7ba3a0b95.png) +![VGG-16 tuvastatud omadused](../../../../../translated_images/et/features.6291f9c7ba3a0b95.webp) ## Kasside ja koerte andmestik @@ -48,19 +48,19 @@ Eelnevalt treenitud närvivõrk sisaldab oma *ajus* erinevaid mustreid, sealhulg Üks lähenemine, mida saame kasutada, on alustada juhuslikust pildist ja seejärel proovida kasutada **gradientide optimeerimise** tehnikat, et kohandada seda pilti nii, et võrk hakkaks arvama, et see on kass. -![Pildi optimeerimise tsükkel](../../../../../translated_images/et/ideal-cat-loop.999fbb8ff306e044.png) +![Pildi optimeerimise tsükkel](../../../../../translated_images/et/ideal-cat-loop.999fbb8ff306e044.webp) Kui me seda teeme, saame tulemuseks midagi, mis on väga sarnane juhusliku müraga. See on tingitud sellest, et *on palju viise, kuidas panna võrk arvama, et sisendpilt on kass*, sealhulgas mõned, mis visuaalselt ei ole mõistlikud. Kuigi need pildid sisaldavad palju kassile tüüpilisi mustreid, pole midagi, mis sunniks neid olema visuaalselt eristatavad. Tulemuse parandamiseks saame lisada kaotuse funktsiooni teise termini, mida nimetatakse **variatsioonikaotuseks**. See on mõõdik, mis näitab, kui sarnased on pildi naaberpikslid. Variatsioonikaotuse minimeerimine muudab pildi sujuvamaks ja eemaldab müra – paljastades visuaalselt meeldivamad mustrid. Siin on näide sellistest "ideaalse" piltidest, mis klassifitseeritakse suure tõenäosusega kassiks ja sebraks: -![Ideaalne kass](../../../../../translated_images/et/ideal-cat.203dd4597643d6b0.png) | ![Ideaalne sebra](../../../../../translated_images/et/ideal-zebra.7f70e8b54ee15a7a.png) +![Ideaalne kass](../../../../../translated_images/et/ideal-cat.203dd4597643d6b0.webp) | ![Ideaalne sebra](../../../../../translated_images/et/ideal-zebra.7f70e8b54ee15a7a.webp) -----|----- *Ideaalne kass* | *Ideaalne sebra* Sarnast lähenemist saab kasutada nn **adversariaalsete rünnakute** läbiviimiseks närvivõrgule. Oletame, et tahame petta närvivõrku ja panna koera välja nägema nagu kass. Kui võtame koera pildi, mida võrk tuvastab koerana, saame seda veidi kohandada, kasutades gradientide optimeerimist, kuni võrk hakkab seda klassifitseerima kassina: -![Koera pilt](../../../../../translated_images/et/original-dog.8f68a67d2fe0911f.png) | ![Koera pilt, mis klassifitseeritakse kassina](../../../../../translated_images/et/adversarial-dog.d9fc7773b0142b89.png) +![Koera pilt](../../../../../translated_images/et/original-dog.8f68a67d2fe0911f.webp) | ![Koera pilt, mis klassifitseeritakse kassina](../../../../../translated_images/et/adversarial-dog.d9fc7773b0142b89.webp) -----|----- *Originaalne koera pilt* | *Koera pilt, mis klassifitseeritakse kassina* diff --git a/translations/et/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/et/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index 2a94f2f2..edfacf31 100644 --- a/translations/et/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/et/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "Kuna treenime autoenkoodrit, et see haaraks võimalikult palju teavet algsest pildist täpseks taastamiseks, püüab võrk leida parima **sisendpiltide representatsiooni**, et tabada nende tähendust.\n", "\n", - "![Autoenkoodri skeem](../../../../../translated_images/et/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![Autoenkoodri skeem](../../../../../translated_images/et/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "> Pilt [Kerase blogist](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", @@ -941,7 +941,7 @@ " * Valime juhusliku vektori `sample(z_val in code)` jaotusest $N(\\mathrm{z\\_mean},e^{\\mathrm{z\\_log\\_sigma}})$\n", " * Dekodeerija püüab dekodeerida algse pildi, kasutades `sample`-it sisendvektorina\n", "\n", - " \n", + " \n", "\n", " > Pilt [sellest blogipostitusest](https://ijdykeman.github.io/ml/2016/12/21/cvae.html) autorilt Isaak Dykeman\n" ] @@ -1264,7 +1264,7 @@ "\n", "Selles lähenemises on meil **kolm kaotusfunktsiooni**: generaatori kaotus, diskrimineerija kaotus GAN-i puhul ja rekonstrueerimise kaotus VAE puhul.\n", "\n", - " \n", + " \n", "\n", " > Pilt [sellest blogipostitusest](https://blog.paperspace.com/adversarial-autoencoders-with-pytorch/) autorilt Felipe Ducau\n" ] diff --git a/translations/et/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/et/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index 21d9482e..6dcf6641 100644 --- a/translations/et/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/et/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -21,7 +21,7 @@ "\n", "Kuna treenime autoenkoodrit, et ta haaraks võimalikult palju teavet algsest pildist täpseks taastamiseks, püüab võrk leida parima **sisendpiltide representatsiooni**, et tabada nende tähendust.\n", "\n", - "![Autoenkoodri skeem](../../../../../translated_images/et/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![Autoenkoodri skeem](../../../../../translated_images/et/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "*Pilt [Kerase blogist](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", @@ -888,7 +888,7 @@ " * Valime juhusliku vektori `sample` jaotusest $N(\\mathrm{z\\_mean},e^{\\mathrm{z\\_log\\_sigma}})$\n", " * Dekodeerija püüab dekodeerida algse pildi, kasutades `sample`-it sisendvektorina\n", "\n", - " \n" + " \n" ] }, { diff --git a/translations/et/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/et/lessons/4-ComputerVision/09-Autoencoders/README.md index 0d2b42ab..732e5b25 100644 --- a/translations/et/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/et/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ Siiski võime soovida kasutada toorandmeid (märgistamata) CNN-i funktsioonide e Kuna treenime autoenkoodrit, et haarata võimalikult palju teavet algsest pildist täpseks taastamiseks, püüab võrk leida parima **sisendpiltide representatsiooni**, et tabada nende tähendus. -![Autoenkoodri skeem](../../../../../translated_images/et/autoencoder_schema.5e6fc9ad98a5eb61.jpg) +![Autoenkoodri skeem](../../../../../translated_images/et/autoencoder_schema.5e6fc9ad98a5eb61.webp) > Pilt [Kerase blogist](https://blog.keras.io/building-autoencoders-in-keras.html) @@ -46,7 +46,7 @@ Kokkuvõtteks: * Valime vektori `sample` jaotusest N(zmean,exp(zlog\_sigma)) * Dekooder püüab dekodeerida algset pilti, kasutades `sample` sisendvektorina - + > Pilt [sellest blogipostitusest](https://ijdykeman.github.io/ml/2016/12/21/cvae.html) autorilt Isaak Dykeman @@ -57,13 +57,13 @@ Variatsioonilised autoenkoodrid kasutavad keerulist kaotusefunktsiooni, mis koos Üks oluline eelis VAE-de puhul on see, et need võimaldavad meil suhteliselt lihtsalt uusi pilte genereerida, kuna teame, millist jaotust latentvektorite valimiseks kasutada. Näiteks kui treenime VAE-d 2D latentvektoriga MNIST andmestikul, saame seejärel muuta latentvektori komponente, et saada erinevaid numbreid: -vaemnist +vaemnist > Pilt autorilt [Dmitry Soshnikov](http://soshnikov.com) Vaadake, kuidas pildid sulanduvad üksteisesse, kui hakkame saama latentvektoreid latentparameetrite ruumi erinevatest osadest. Samuti saame visualiseerida seda ruumi 2D-s: -vaemnist cluster +vaemnist cluster > Pilt autorilt [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/et/lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb b/translations/et/lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb index 58eb6391..42d499eb 100644 --- a/translations/et/lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb +++ b/translations/et/lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb @@ -15,7 +15,7 @@ " * **Generaator** võtab juhusliku vektori ja peaks sellest pildi genereerima.\n", " * **Diskrimineerija** on võrk, mis peaks eristama algset pilti (treeningandmestikust) generaatori loodud pildist.\n", "\n", - "\n" + "\n" ] }, { @@ -670,7 +670,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n", + "\n", "\n", "> Pilt pärineb [sellest juhendist](https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html)\n" ] diff --git a/translations/et/lessons/4-ComputerVision/10-GANs/GANTF.ipynb b/translations/et/lessons/4-ComputerVision/10-GANs/GANTF.ipynb index d79b1106..4bf61c49 100644 --- a/translations/et/lessons/4-ComputerVision/10-GANs/GANTF.ipynb +++ b/translations/et/lessons/4-ComputerVision/10-GANs/GANTF.ipynb @@ -15,7 +15,7 @@ " * **Generaator** võtab juhusliku vektori ja peaks sellest pildi genereerima\n", " * **Diskrimineerija** on võrk, mis peaks eristama originaalpildi (treeningandmestikust) generaatori loodud pildist.\n", "\n", - "\n" + "\n" ] }, { diff --git a/translations/et/lessons/4-ComputerVision/10-GANs/README.md b/translations/et/lessons/4-ComputerVision/10-GANs/README.md index 05add710..960775d3 100644 --- a/translations/et/lessons/4-ComputerVision/10-GANs/README.md +++ b/translations/et/lessons/4-ComputerVision/10-GANs/README.md @@ -17,7 +17,7 @@ Kui aga proovime luua midagi tõeliselt tähenduslikku, näiteks maali mõistlik GAN-i peamine idee on kasutada kahte närvivõrku, mis treenivad üksteise vastu: - + > Pilt: [Dmitry Soshnikov](http://soshnikov.com) @@ -41,7 +41,7 @@ Generaator on veidi keerulisem. Seda võib pidada pööratud diskrimineerijaks. > ✅ Kuna konvolutsioonikiht rakendatakse lineaarse filtrina, mis liigub üle pildi, on dekonvolutsioon sisuliselt sarnane konvolutsiooniga ja seda saab rakendada sama kihi loogikaga. - + > Pilt: [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/et/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/et/lessons/4-ComputerVision/11-ObjectDetection/README.md index c1993e0c..ece5c1e2 100644 --- a/translations/et/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/et/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ Pildiklassifikatsiooni mudelid, millega oleme seni tegelenud, võtsid pildi ja a ## [Eelloengu viktoriin](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Objektide tuvastamine](../../../../../translated_images/et/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) +![Objektide tuvastamine](../../../../../translated_images/et/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp) > Pilt [YOLO v2 veebilehelt](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ Oletame, et tahame leida kassi pildilt. Väga naiivne lähenemine objektide tuva 2. Käivitame pildiklassifikatsiooni igal osal. 3. Need osad, mis annavad piisavalt kõrge aktiveerimise, võib pidada objektiks, mida otsime. -![Naivne objektide tuvastamine](../../../../../translated_images/et/naive-detection.e7f1ba220ccd08c6.png) +![Naivne objektide tuvastamine](../../../../../translated_images/et/naive-detection.e7f1ba220ccd08c6.webp) > *Pilt [harjutuste märkmikust](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ Selle ülesande jaoks võite kohata järgmisi andmekogumeid: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) – 20 klassi * [COCO](http://cocodataset.org/#home) – Tavalised objektid kontekstis. 80 klassi, piiritlevad kastid ja segmentatsioonimaskid -![COCO](../../../../../translated_images/et/coco-examples.71bc60380fa6cceb.jpg) +![COCO](../../../../../translated_images/et/coco-examples.71bc60380fa6cceb.webp) ## Objektide tuvastamise mõõdikud @@ -50,7 +50,7 @@ Selle ülesande jaoks võite kohata järgmisi andmekogumeid: Kui pildiklassifikatsiooni puhul on lihtne mõõta, kui hästi algoritm töötab, siis objektide tuvastamise puhul peame mõõtma nii klassi õigsust kui ka tuvastatud piiritleva kasti asukoha täpsust. Viimase jaoks kasutame nn **ühisosa ja ühenduse suhet** (IoU), mis mõõdab, kui hästi kaks kasti (või kaks suvalist ala) kattuvad. -![IoU](../../../../../translated_images/et/iou_equation.9a4751d40fff4e11.png) +![IoU](../../../../../translated_images/et/iou_equation.9a4751d40fff4e11.webp) > *Joonis 2 [sellest suurepärasest blogipostitusest IoU kohta](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ Objektide tuvastamise algoritme on kahte laia klassi: [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) kasutab [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf), et genereerida ROI piirkondade hierarhiline struktuur, mis seejärel läbib CNN-i funktsioonide ekstraktorid ja SVM-klassi määrajad, et määrata objekti klass, ning lineaarse regressiooni, et määrata *piiritleva kasti* koordinaadid. [Ametlik artikkel](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/et/rcnn1.cae407020dfb1d1f.png) +![RCNN](../../../../../translated_images/et/rcnn1.cae407020dfb1d1f.webp) > *Pilt van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/et/rcnn2.2d9530bb83516484.png) +![RCNN-1](../../../../../translated_images/et/rcnn2.2d9530bb83516484.webp) > *Pildid [sellest blogist](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -110,7 +110,7 @@ Objektide tuvastamise algoritme on kahte laia klassi: See lähenemine on sarnane R-CNN-iga, kuid piirkonnad määratakse pärast konvolutsioonikihtide rakendamist. -![FRCNN](../../../../../translated_images/et/f-rcnn.3cda6d9bb4188875.png) +![FRCNN](../../../../../translated_images/et/f-rcnn.3cda6d9bb4188875.webp) > Pilt [ametlikust artiklist](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ See lähenemine on sarnane R-CNN-iga, kuid piirkonnad määratakse pärast konvo Selle lähenemise peamine idee on kasutada närvivõrku ROIside ennustamiseks – nn *piirkonna ettepanekuvõrk*. [Artikkel](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/et/faster-rcnn.8d46c099b87ef30a.png) +![FasterRCNN](../../../../../translated_images/et/faster-rcnn.8d46c099b87ef30a.webp) > Pilt [ametlikust artiklist](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ See algoritm on isegi kiirem kui Faster R-CNN. Peamine idee on järgmine: 2. Funktsioone töödeldakse **positsioonitundliku skoorikaardiga**. Iga objekt klassist $C$ jagatakse $k\times k$ piirkondadeks ja treenime ennustama objektide osi. 3. Iga osa $k\times k$ piirkondadest hääletavad kõik võrgud objektiklasside eest ja maksimaalse häälega objektiklass valitakse. -![r-fcn pilt](../../../../../translated_images/et/r-fcn.13eb88158b99a3da.png) +![r-fcn pilt](../../../../../translated_images/et/r-fcn.13eb88158b99a3da.webp) > Pilt [ametlikust artiklist](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO on reaalajas ühe läbimise algoritm. Peamine idee on järgmine: * Pilt jagatakse $S\times S$ piirkondadeks. * Iga piirkonna jaoks ennustab **CNN** $n$ võimalikku objekti, *piiritleva kasti* koordinaate ja *usaldusväärsust*=*tõenäosust* * IoU. - ![YOLO](../../../../../translated_images/et/yolo.a2648ec82ee8bb4e.png) + ![YOLO](../../../../../translated_images/et/yolo.a2648ec82ee8bb4e.webp) > Pilt [ametlikust artiklist](https://arxiv.org/abs/1506.02640) diff --git a/translations/et/lessons/4-ComputerVision/12-Segmentation/README.md b/translations/et/lessons/4-ComputerVision/12-Segmentation/README.md index f74a73e3..cb3f69f0 100644 --- a/translations/et/lessons/4-ComputerVision/12-Segmentation/README.md +++ b/translations/et/lessons/4-ComputerVision/12-Segmentation/README.md @@ -20,7 +20,7 @@ Segmenteerimist võib vaadelda kui **pikslite klassifikatsiooni**, kus **iga** p Instance segmenteerimise puhul on need lambad erinevad objektid, kuid semantilise segmenteerimise puhul esindavad kõik lambad ühte klassi. - + > Pilt [sellest blogipostitusest](https://nirmalamurali.medium.com/image-classification-vs-semantic-segmentation-vs-instance-segmentation-625c33a08d50) @@ -29,7 +29,7 @@ Segmenteerimiseks on erinevaid närvivõrkude arhitektuure, kuid neil kõigil on * **Kodeerija** ekstraheerib sisendpildist omadused. * **Dekodeerija** teisendab need omadused **maskipildiks**, millel on sama suurus ja kanalite arv, mis vastab klasside arvule. - + > Pilt [sellest publikatsioonist](https://arxiv.org/pdf/2001.05566.pdf) @@ -43,7 +43,7 @@ Selles õppetükis näeme segmenteerimist tegevuses, treenides võrku inimeste n > ✅ See tehnika sobib eriti hästi sellist tüüpi meditsiiniliste piltide jaoks, kuid milliseid muid reaalse maailma rakendusi võiksite ette kujutada? -navi +navi > Pilt PH2 andmebaasist diff --git a/translations/et/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb b/translations/et/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb index 387e282f..ce17dfbb 100644 --- a/translations/et/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb +++ b/translations/et/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb @@ -17,7 +17,7 @@ "\n", "Näiteks instantsi segmenteerimise puhul on 10 lammast erinevad objektid, semantilise segmenteerimise puhul esindavad kõik lambad ühte klassi.\n", "\n", - "\n", + "\n", "\n", "> Pilt [sellest blogipostitusest](https://nirmalamurali.medium.com/image-classification-vs-semantic-segmentation-vs-instance-segmentation-625c33a08d50)\n", "\n", @@ -26,7 +26,7 @@ "* **Kodeerija** eraldab sisendpildist omadused\n", "* **Dekodeerija** teisendab need omadused **maskipildiks**, mille suurus ja kanalite arv vastavad klasside arvule.\n", "\n", - "\n", + "\n", "\n", "> Pilt [sellest publikatsioonist](https://arxiv.org/pdf/2001.05566.pdf)\n" ] @@ -252,7 +252,7 @@ "\n", "Lihtsaim kodeerija-dekodeerija arhitektuur kannab nime **SegNet**. See kasutab standardset CNN-i koos konvolutsioonide ja koondamistega kodeerijas ning dekodeerijas dekonvolutsioonilist CNN-i, mis sisaldab konvolutsioone ja ülesproovimisi. Samuti toetub see partiinormaliseerimisele, et edukalt treenida mitmekihilist võrku.\n", "\n", - "\n", + "\n", "\n", "> Pilt sellest artiklist: Badrinarayanan, V., Kendall, A., & Cipolla, R. (2015). [SegNet: Sügav konvolutsiooniline kodeerija-dekodeerija arhitektuur pildisegmentatsiooniks](https://arxiv.org/pdf/1511.00561.pdf)\n" ] @@ -547,7 +547,7 @@ "\n", "Siin kasutame üsna lihtsat CNN arhitektuuri, kuid U-Net võib kasutada ka keerukamat kodeerijat omaduste eraldamiseks, näiteks ResNet-50.\n", "\n", - "\n", + "\n", "\n", "> Pilt artiklist: Ronneberger, Olaf, Philipp Fischer ja Thomas Brox. [U-Net: Convolutional networks for biomedical image segmentation.](https://arxiv.org/pdf/1505.04597.pdf)\n" ] diff --git a/translations/et/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb b/translations/et/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb index f18c8c71..3c1da75f 100644 --- a/translations/et/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb +++ b/translations/et/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb @@ -12,13 +12,13 @@ "\n", "Näiteks instantssegmentimisel on kümme autot **erinevad** objektid, semantilise segmentimise puhul on **kõik** autod üks klass.\n", "\n", - "\n", + "\n", "\n", "> Pilt [sellest blogipostitusest](https://nirmalamurali.medium.com/image-classification-vs-semantic-segmentation-vs-instance-segmentation-625c33a08d50)\n", "\n", "Peaaegu kõigil arhitektuuridel on sama struktuur. Esimene osa on **kodeerija**, mis eraldab sisendpildist omadused, teine osa on **dekodeerija**, mis teisendab need omadused pildiks, millel on sama kõrgus ja laius ning teatud arv kanaleid, mis võib olla võrdne klasside arvuga.\n", "\n", - "\n", + "\n", "\n", "> Pilt [sellest publikatsioonist](https://arxiv.org/pdf/2001.05566.pdf)\n" ] @@ -210,7 +210,7 @@ "\n", "Lihtne kodeerija-dekodeerija arhitektuur, mis kasutab konvolutsioone ja koondamisi kodeerijas ning konvolutsioone ja ülesmõõdistamisi dekodeerijas.\n", "\n", - "\n", + "\n", "\n", "* Badrinarayanan, V., Kendall, A., & Cipolla, R. (2015). [SegNet: Sügav konvolutsiooniline kodeerija-dekodeerija arhitektuur pildisegmentatsiooniks](https://arxiv.org/pdf/1511.00561.pdf)\n" ] @@ -601,7 +601,7 @@ "\n", "U-Netil on tavaliselt vaikimisi kodeerija omaduste eraldamiseks, näiteks resnet50.\n", "\n", - "\n", + "\n", "\n", "* Ronneberger, Olaf, Philipp Fischer ja Thomas Brox. [U-Net: Konvolutsioonivõrgud biomeditsiiniliste piltide segmentimiseks.](https://arxiv.org/pdf/1505.04597.pdf)\n" ] diff --git a/translations/et/lessons/4-ComputerVision/README.md b/translations/et/lessons/4-ComputerVision/README.md index 04e274bb..d2ba0513 100644 --- a/translations/et/lessons/4-ComputerVision/README.md +++ b/translations/et/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Arvutinägemine -![Arvutinägemise sisu kokkuvõte visandina](../../../../translated_images/et/ai-computervision.6506ebebac3fbf76.png) +![Arvutinägemise sisu kokkuvõte visandina](../../../../translated_images/et/ai-computervision.6506ebebac3fbf76.webp) Selles osas õpime: diff --git a/translations/et/lessons/5-NLP/13-TextRep/README.md b/translations/et/lessons/5-NLP/13-TextRep/README.md index 6893006a..a2e90317 100644 --- a/translations/et/lessons/5-NLP/13-TextRep/README.md +++ b/translations/et/lessons/5-NLP/13-TextRep/README.md @@ -25,7 +25,7 @@ Meie eesmärk on klassifitseerida uudisartikkel üheks kategooriaks, tuginedes t Kui tahame lahendada loomuliku keele töötlemise (NLP) ülesandeid närvivõrkudega, peame leidma viisi, kuidas teksti tensoritena esitada. Arvutid esindavad tekstimärke juba numbritena, mis kaardistuvad ekraanil olevate fontidega, kasutades kodeeringuid nagu ASCII või UTF-8. -Pilt, mis näitab skeemi, kuidas märk kaardistub ASCII ja binaarse esitusena +Pilt, mis näitab skeemi, kuidas märk kaardistub ASCII ja binaarse esitusena > [Pildi allikas](https://www.seobility.net/en/wiki/ASCII) @@ -48,7 +48,7 @@ Mõnel juhul võime kaaluda ka tri-grammide - kolme sõna kombinatsioonide - kas Tekstiklassifikatsiooni ülesannete lahendamisel peame suutma esitada teksti ühe fikseeritud suurusega vektorina, mida kasutame lõpliku tiheda klassifikaatori sisendina. Üks lihtsamaid viise seda teha on kombineerida kõik üksikud sõnaesitused, näiteks neid liites. Kui liidame iga sõna ühe-kuuma kodeeringud, saame sagedusvektori, mis näitab, mitu korda iga sõna tekstis esineb. Sellist teksti esitust nimetatakse **sõnakotiks** (BoW). - + > Pilt autori poolt diff --git a/translations/et/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/et/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index ee2cded2..955e1804 100644 --- a/translations/et/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/et/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**Sõnade koti** (BoW) vektoriesitus on kõige sagedamini kasutatav traditsiooniline vektoriesitus. Iga sõna on seotud vektori indeksiga, vektori element sisaldab sõna esinemiste arvu antud dokumendis.\n", "\n", - "![Pilt, mis näitab, kuidas sõnade koti vektoriesitust mälus kujutatakse.](../../../../../translated_images/et/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![Pilt, mis näitab, kuidas sõnade koti vektoriesitust mälus kujutatakse.](../../../../../translated_images/et/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Note**: BoW-d võib mõelda ka kui kõigi teksti üksiksõnade ühekuumkodeeritud vektorite summat.\n", "\n", diff --git a/translations/et/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/et/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index 28c4c964..6d213353 100644 --- a/translations/et/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/et/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**Sõnade kogumi** (BoW) vektor-esitus on kõige lihtsamini mõistetav traditsiooniline vektor-esitus. Iga sõna on seotud vektori indeksiga ning vektori element sisaldab iga sõna esinemiskordade arvu antud dokumendis.\n", "\n", - "![Pilt, mis näitab, kuidas sõnade kogumi vektor-esitust mälus kujutatakse.](../../../../../translated_images/et/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![Pilt, mis näitab, kuidas sõnade kogumi vektor-esitust mälus kujutatakse.](../../../../../translated_images/et/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Note**: BoW-d võib mõelda ka kui kõigi teksti üksiksõnade ühekuumkooditud vektorite summana.\n", "\n", diff --git a/translations/et/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/et/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 782029f2..b5842549 100644 --- a/translations/et/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/et/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "Kasutades sisestuskihti meie võrgu esimesena kihina, saame liikuda sõnakoti mudelilt **sisestuskoti** mudelile, kus esmalt teisendame iga sõna tekstis vastavaks sisestuseks ja seejärel arvutame nende sisestuste üle mingi koondfunktsiooni, näiteks `sum`, `average` või `max`.\n", "\n", - "![Pilt, mis näitab viie sõna järjestuse sisestuse klassifikaatorit.](../../../../../translated_images/et/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![Pilt, mis näitab viie sõna järjestuse sisestuse klassifikaatorit.](../../../../../translated_images/et/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "Meie klassifikaatori närvivõrk algab sisestuskihiga, millele järgneb koondamiskihiga ja lineaarne klassifikaator selle peal:\n" ] @@ -176,7 +176,7 @@ "\n", "Eelmises arhitektuuris pidime kõik järjestused täitma sama pikkuseni, et need minibatch'i sobiksid. See ei ole kõige tõhusam viis muutuva pikkusega järjestuste esitamiseks - teine lähenemine oleks kasutada **nihke** vektorit, mis sisaldaks kõigi järjestuste nihked, mis on salvestatud ühes suures vektoris.\n", "\n", - "![Pilt, mis näitab nihkevektori järjestuse esitust](../../../../../translated_images/et/offset-sequence-representation.eb73fcefb29b46ee.png)\n", + "![Pilt, mis näitab nihkevektori järjestuse esitust](../../../../../translated_images/et/offset-sequence-representation.eb73fcefb29b46ee.webp)\n", "\n", "> **Note**: Ülaloleval pildil on näidatud tähemärkide järjestus, kuid meie näites töötame sõnade järjestustega. Siiski jääb üldine põhimõte järjestuste esitamiseks nihkevektoriga samaks.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW on kiirem, samas kui vahegramm on aeglasem, kuid esindab haruldasi sõnu paremini.\n", "\n", - "![Pilt, mis näitab nii CBoW kui ka Skip-Gram algoritme sõnade vektoriteks teisendamiseks.](../../../../../translated_images/et/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![Pilt, mis näitab nii CBoW kui ka Skip-Gram algoritme sõnade vektoriteks teisendamiseks.](../../../../../translated_images/et/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Et katsetada Word2Vec-i sisestust, mis on eelnevalt treenitud Google News andmekogul, saame kasutada **gensim** teeki. Allpool otsime sõnu, mis on kõige sarnasemad sõnale 'neural'.\n", "\n", diff --git a/translations/et/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/et/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index b5fd26e5..efd9b16c 100644 --- a/translations/et/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/et/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "Kasutades embedding-kihti meie võrgu esimeseks kihiks, saame liikuda sõnakottide mudelilt **embedding-koti** mudelile, kus esmalt teisendame iga sõna tekstis vastavaks embedding'iks ja seejärel arvutame nende embedding'ite üle mingi koondfunktsiooni, näiteks `sum`, `average` või `max`.\n", "\n", - "![Pilt, mis näitab embedding-klassifikaatorit viie järjestikuse sõna jaoks.](../../../../../translated_images/et/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![Pilt, mis näitab embedding-klassifikaatorit viie järjestikuse sõna jaoks.](../../../../../translated_images/et/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "Meie klassifitseeriva närvivõrgu kihid on järgmised:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW on kiirem, samas kui vahelejätu-gramm on aeglasem, kuid esindab haruldasi sõnu paremini.\n", "\n", - "![Pilt, mis näitab nii CBoW kui ka Skip-Gram algoritme sõnade vektoriteks teisendamiseks.](../../../../../translated_images/et/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![Pilt, mis näitab nii CBoW kui ka Skip-Gram algoritme sõnade vektoriteks teisendamiseks.](../../../../../translated_images/et/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Et katsetada Word2Vec sisendit, mis on eelnevalt treenitud Google News andmestiku peal, saame kasutada **gensim** teeki. Allpool otsime sõnu, mis on kõige sarnasemad sõnale 'neural'.\n", "\n", diff --git a/translations/et/lessons/5-NLP/14-Embeddings/README.md b/translations/et/lessons/5-NLP/14-Embeddings/README.md index 7fc1dfe8..35fa1790 100644 --- a/translations/et/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/et/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ Sisestuskihi ülesanne on võtta sõna sisendina ja anda väljundvektor kindlaks Kasutades sisestuskihti meie klassifitseerimisvõrgu esimese kihina, saame liikuda sõnakotilt **sisestuskoti** mudelile, kus esmalt teisendame iga sõna meie tekstis vastavaks sisestuseks ja seejärel arvutame nende sisestuste üle mingi koondfunktsiooni, nagu `sum`, `average` või `max`. -![Pilt, mis näitab sisestuskihi klassifitseerijat viie järjestikuse sõna jaoks.](../../../../../translated_images/et/embedding-classifier-example.b77f021a7ee67eee.png) +![Pilt, mis näitab sisestuskihi klassifitseerijat viie järjestikuse sõna jaoks.](../../../../../translated_images/et/embedding-classifier-example.b77f021a7ee67eee.webp) > Pilt autori poolt @@ -40,7 +40,7 @@ Selleks peame oma sisestusmudeli eelnevalt treenima suure tekstikogu peal spetsi CBoW on kiirem, samas kui hüppegramm on aeglasem, kuid teeb paremat tööd harvaesinevate sõnade esindamisel. -![Pilt, mis näitab nii CBoW kui ka hüppegrammi algoritme sõnade vektoriteks teisendamiseks.](../../../../../translated_images/et/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![Pilt, mis näitab nii CBoW kui ka hüppegrammi algoritme sõnade vektoriteks teisendamiseks.](../../../../../translated_images/et/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Pilt [sellest artiklist](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/et/lessons/5-NLP/15-LanguageModeling/README.md b/translations/et/lessons/5-NLP/15-LanguageModeling/README.md index ad459761..397ba754 100644 --- a/translations/et/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/et/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ Eelnevates näidetes kasutasime eelnevalt treenitud semantilisi vektoreid, kuid * **Järjepidev sõnakott** (CBoW), kus ennustame keskmist tokenit $W_0$ tokenite järjestuses $W_{-N}$, ..., $W_N$. * **Skip-gramm**, kus ennustame keskmise tokeni $W_0$ põhjal naabertokenite komplekti {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$}. -![pilt artiklist, mis käsitleb sõnade teisendamist vektoriteks](../../../../../translated_images/et/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![pilt artiklist, mis käsitleb sõnade teisendamist vektoriteks](../../../../../translated_images/et/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Pilt [sellest artiklist](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/et/lessons/5-NLP/16-RNN/README.md b/translations/et/lessons/5-NLP/16-RNN/README.md index 7bca4ea1..e773b4e7 100644 --- a/translations/et/lessons/5-NLP/16-RNN/README.md +++ b/translations/et/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ Eelnevates osades oleme kasutanud tekstide rikkalikke semantilisi esitusi ja lih Tekstijärjestuse tähenduse tabamiseks peame kasutama teistsugust neuraalvõrgu arhitektuuri, mida nimetatakse **korduvaks neuraalvõrguks** ehk RNN-iks. RNN-is edastame oma lause läbi võrgu ühe sümboli kaupa, ja võrk genereerib mingi **oleku**, mille edastame võrku uuesti koos järgmise sümboliga. -![RNN](../../../../../translated_images/et/rnn.27f5c29c53d727b5.png) +![RNN](../../../../../translated_images/et/rnn.27f5c29c53d727b5.webp) > Pilt autori poolt @@ -31,7 +31,7 @@ Vaatame, kuidas lihtne RNN-rakk on üles ehitatud. See võtab sisendiks eelneva Lihtsal RNN-rakul on kaks kaalusid sisaldavat maatriksit: üks teisendab sisendsümbolit (nimetame seda W-ks) ja teine teisendab sisendolekut (H). Sellisel juhul arvutatakse võrgu väljund valemiga σ(W×Xi+H×Si-1+b), kus σ on aktivatsioonifunktsioon ja b on täiendav nihe. -RNN-raku anatoomia +RNN-raku anatoomia > Pilt autori poolt @@ -61,7 +61,7 @@ Oleme arutanud korduvaid võrke, mis töötavad ühes suunas, järjestuse alguse Korduv võrk, olgu see ühesuunaline või kahepoolne, tabab teatud mustreid järjestuses ja suudab neid salvestada olekuvektorisse või edastada väljundisse. Nagu konvolutsioonivõrkude puhul, saame ehitada teise korduva kihi esimese peale, et tabada kõrgema taseme mustreid ja ehitada madalama taseme mustritest, mida esimene kiht eraldas. See viib meid **mitmekihilise RNN-i** mõisteni, mis koosneb kahest või enamast korduvast võrgust, kus eelmise kihi väljund edastatakse järgmisele kihile sisendiks. -![Pilt, mis näitab mitmekihilist pikaajalise lühimälu RNN-i](../../../../../translated_images/et/multi-layer-lstm.dd975e29bb2a59fe.jpg) +![Pilt, mis näitab mitmekihilist pikaajalise lühimälu RNN-i](../../../../../translated_images/et/multi-layer-lstm.dd975e29bb2a59fe.webp) *Pilt [sellest suurepärasest postitusest](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) autorilt Fernando López* diff --git a/translations/et/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/et/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index 83e4e8b7..af5538f4 100644 --- a/translations/et/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/et/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -10,7 +10,7 @@ "\n", "Tekstijärjestuse tähenduse tabamiseks peame kasutama teistsugust närvivõrgu arhitektuuri, mida nimetatakse **korduvaks närvivõrguks** ehk RNN-iks. RNN-is edastame oma lause läbi võrgu ühe sümboli kaupa, ja võrk genereerib mingi **oleku**, mille edastame võrku uuesti koos järgmise sümboliga.\n", "\n", - "\"RNN\"\n", + "\"RNN\"\n", "\n", "Arvestades sisendjärjestust $X_0,\\dots,X_n$, loob RNN järjestuse närvivõrgu plokkidest ja treenib seda järjestust otsast lõpuni tagasileviku meetodil. Iga võrguplokk võtab sisendiks paari $(X_i,S_i)$ ja genereerib tulemuseks $S_{i+1}$. Lõplik olek $S_n$ või väljund $X_n$ suunatakse lineaarsele klassifikaatorile, et toota tulemus. Kõik võrguplokid jagavad samu kaalusid ja neid treenitakse otsast lõpuni ühe tagasileviku käigu abil.\n", "\n", @@ -428,7 +428,7 @@ "\n", "Korduv võrk, olgu see ühe- või kahepoolne, tuvastab teatud mustrid järjestuses ja suudab need salvestada oleku vektorisse või edastada väljundisse. Nagu konvolutsioonivõrkude puhul, saame ehitada esimese kihi peale teise korduva kihi, et tuvastada kõrgema taseme mustreid, mis on loodud esimese kihi poolt tuvastatud madalama taseme mustritest. See viib meid **mitmekihilise RNN-i** mõisteni, mis koosneb kahest või enamast korduvast võrgust, kus eelmise kihi väljund edastatakse järgmisele kihile sisendina.\n", "\n", - "![Pilt, mis näitab mitmekihilist pika-lühiajalise-mälu RNN-i](../../../../../translated_images/et/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![Pilt, mis näitab mitmekihilist pika-lühiajalise-mälu RNN-i](../../../../../translated_images/et/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*Pilt [sellest suurepärasest postitusest](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) autorilt Fernando López*\n", "\n", diff --git a/translations/et/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/et/lessons/5-NLP/16-RNN/RNNTF.ipynb index 146861c0..f2bb2efb 100644 --- a/translations/et/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/et/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "Tekstijada tähenduse tabamiseks kasutame närvivõrgu arhitektuuri, mida nimetatakse **korduvaks närvivõrguks** ehk RNN-iks. RNN-i kasutamisel edastame oma lause võrgu kaudu ühe tokeni korraga, ja võrk toodab mingi **seisundi**, mille edastame võrku uuesti koos järgmise tokeniga.\n", "\n", - "![Pilt, mis näitab korduva närvivõrgu genereerimise näidet.](../../../../../translated_images/et/rnn.27f5c29c53d727b5.png)\n", + "![Pilt, mis näitab korduva närvivõrgu genereerimise näidet.](../../../../../translated_images/et/rnn.27f5c29c53d727b5.webp)\n", "\n", "Arvestades sisendjada tokenitest $X_0,\\dots,X_n$, loob RNN närvivõrgu plokkide jada ja treenib seda jada otsast lõpuni tagasileviku abil. Iga võrguplokk võtab sisendiks paari $(X_i,S_i)$ ja annab tulemuseks $S_{i+1}$. Lõplik seisund $S_n$ või väljund $Y_n$ suunatakse lineaarse klassifikaatori kaudu tulemuse saamiseks. Kõik võrguplokid jagavad samu kaale ja neid treenitakse otsast lõpuni ühe tagasileviku käigu abil.\n", "\n", @@ -371,7 +371,7 @@ "\n", "Korduvad närvivõrgud, olgu need ühe- või kaksuunalised, püüavad kinni mustreid järjestuses ja salvestavad need olekuvektoritesse või tagastavad need väljundina. Nii nagu konvolutsioonivõrkude puhul, saame esimesele kihile lisada teise korduva kihi, et püüda kinni kõrgema taseme mustreid, mis on loodud esimese kihi poolt tuvastatud madalama taseme mustritest. See viib meid **mitmekihilise RNN-i** mõisteni, mis koosneb kahest või enamast korduvast võrgust, kus eelmise kihi väljund edastatakse järgmisele kihile sisendina.\n", "\n", - "![Pilt, mis näitab mitmekihilist pika lühimälu RNN-i](../../../../../translated_images/et/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![Pilt, mis näitab mitmekihilist pika lühimälu RNN-i](../../../../../translated_images/et/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*Pilt [sellest suurepärasest postitusest](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3), autor Fernando López.*\n", "\n", diff --git a/translations/et/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/et/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index 702c0734..12e98409 100644 --- a/translations/et/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/et/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "RNN-i treenimiseks teksti genereerimiseks toimime järgmiselt. Igal sammul võtame `nchars` pikkuse tähemärkide jada ja palume võrgul genereerida iga sisendtähemärgi jaoks järgmine väljundtähemärk:\n", "\n", - "![Pilt, mis näitab näidet RNN-i genereerimisest sõnaga 'HELLO'.](../../../../../translated_images/et/rnn-generate.56c54afb52f9781d.png)\n", + "![Pilt, mis näitab näidet RNN-i genereerimisest sõnaga 'HELLO'.](../../../../../translated_images/et/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "Olenevalt konkreetsest stsenaariumist võime soovida lisada ka erimärke, näiteks *järjestuse lõpp* ``. Meie puhul tahame lihtsalt treenida võrku lõputu teksti genereerimiseks, seega määrame iga jada suuruseks `nchars` tokenit. Järelikult koosneb iga treeningnäide `nchars` sisendist ja `nchars` väljundist (mis on sisendjada, nihutatud ühe sümboli võrra vasakule). Minipartii koosneb mitmest sellisest jadast.\n", "\n", diff --git a/translations/et/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/et/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index 0a6b9405..f8210a42 100644 --- a/translations/et/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/et/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -113,7 +113,7 @@ "\n", "RNN-i treenimine uudiste pealkirjade genereerimiseks toimub järgmiselt. Igal sammul võtame ühe pealkirja, mis sisestatakse RNN-i, ja iga sisendmärgi puhul palume võrgul genereerida järgmine väljundmärk:\n", "\n", - "![Pilt, mis näitab RNN-i näidet sõna 'HELLO' genereerimisel.](../../../../../translated_images/et/rnn-generate.56c54afb52f9781d.png)\n", + "![Pilt, mis näitab RNN-i näidet sõna 'HELLO' genereerimisel.](../../../../../translated_images/et/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "Meie järjestuse viimase märgi puhul palume võrgul genereerida `` tokeni.\n", "\n", diff --git a/translations/et/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/et/lessons/5-NLP/17-GenerativeNetworks/README.md index 0436ef78..740deb74 100644 --- a/translations/et/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/et/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ RNN arhitektuuris, mida käsitlesime eelmises üksuses, genereeris iga RNN üksu See võimaldab erinevaid närvivõrgu arhitektuure, mida on näidatud alloleval pildil: -![Pilt, mis näitab korduvate närvivõrkude levinud mustreid.](../../../../../translated_images/et/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Pilt, mis näitab korduvate närvivõrkude levinud mustreid.](../../../../../translated_images/et/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > Pilt blogipostitusest [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) autorilt [Andrej Karpaty](http://karpathy.github.io/) @@ -32,11 +32,11 @@ Selles üksuses keskendume lihtsatele generatiivsetele mudelitele, mis aitavad m Treename selle RNN-i teksti genereerimiseks samm-sammult. Igal sammul võtame tähemärkide järjestuse pikkusega `nchars` ja palume võrgul genereerida järgmise väljundtähemärgi iga sisendtähemärgi jaoks: -![Pilt, mis näitab RNN-i näidet sõna 'HELLO' genereerimisel.](../../../../../translated_images/et/rnn-generate.56c54afb52f9781d.png) +![Pilt, mis näitab RNN-i näidet sõna 'HELLO' genereerimisel.](../../../../../translated_images/et/rnn-generate.56c54afb52f9781d.webp) Teksti genereerimisel (järeldamisel) alustame mõne **alguspunktiga**, mis edastatakse RNN rakkude kaudu, et genereerida selle vaheolek, ja seejärel algab genereerimine sellest olekust. Genereerime ühe tähemärgi korraga ja edastame oleku ja genereeritud tähemärgi järgmisele RNN rakule, et genereerida järgmine, kuni oleme genereerinud piisavalt tähemärke. - + > Pilt autorilt diff --git a/translations/et/lessons/5-NLP/18-Transformers/README.md b/translations/et/lessons/5-NLP/18-Transformers/README.md index 96d111c6..0a997766 100644 --- a/translations/et/lessons/5-NLP/18-Transformers/README.md +++ b/translations/et/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ RNN-idega rakendatakse järjestusest-järjestusse meetodit kahe korduva võrgu a **Tähelepanu mehhanismid** pakuvad võimalust kaaluda iga sisendvektori kontekstuaalset mõju RNN-i iga väljundprognoosi puhul. Seda rakendatakse, luues otseteid sisend-RNN-i vaheolekute ja väljund-RNN-i vahel. Sel viisil, kui genereerime väljundisümbolit yt, võtame arvesse kõiki sisendvarjatud olekuid hi, erinevate kaalukoefitsientidega αt,i. -![Pilt, mis näitab kodeerija/dekodeerija mudelit koos aditiivse tähelepanu kihiga](../../../../../translated_images/et/encoder-decoder-attention.7a726296894fb567.png) +![Pilt, mis näitab kodeerija/dekodeerija mudelit koos aditiivse tähelepanu kihiga](../../../../../translated_images/et/encoder-decoder-attention.7a726296894fb567.webp) > Kodeerija-dekodeerija mudel aditiivse tähelepanu mehhanismiga [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), viidatud [sellest blogipostitusest](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) Tähelepanu maatriks {αi,j} esindab, millises ulatuses teatud sisendsõnad mõjutavad antud sõna genereerimist väljundjärjestuses. Allpool on näide sellisest maatriksist: -![Pilt, mis näitab näidisalini, mille leidis RNNsearch-50, võetud Bahdanau - arviz.org](../../../../../translated_images/et/bahdanau-fig3.09ba2d37f202a6af.png) +![Pilt, mis näitab näidisalini, mille leidis RNNsearch-50, võetud Bahdanau - arviz.org](../../../../../translated_images/et/bahdanau-fig3.09ba2d37f202a6af.webp) > Joonis [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Joonis 3) @@ -56,7 +56,7 @@ Positsioonilise kodeerimise idee on järgmine. * Treenitav embedimine, sarnane tokeni embedimisele. See on lähenemine, mida siin kaalume. Rakendame embedimise kihid nii tokenitele kui ka nende positsioonidele, mille tulemuseks on sama mõõtmetega embedimise vektorid, mille me seejärel kokku liidame. * Fikseeritud positsioonilise kodeerimise funktsioon, nagu on välja pakutud algses artiklis. - + > Pilt autorilt @@ -66,7 +66,7 @@ Positsioonilise embedimise tulemusena saame vektori, mis sisaldab nii algset tok Järgmine samm on mustrite tuvastamine järjestuses. Selleks kasutavad transformerid **isetähelepanu** mehhanismi, mis on sisuliselt tähelepanu rakendamine samale järjestusele sisendi ja väljundina. Isetähelepanu rakendamine võimaldab meil arvestada **konteksti** lauses ja näha, millised sõnad on omavahel seotud. Näiteks võimaldab see meil näha, millistele sõnadele viitavad kooreferentsid, nagu *see*, ja arvestada konteksti: -![](../../../../../translated_images/et/CoreferenceResolution.861924d6d384a7d6.png) +![](../../../../../translated_images/et/CoreferenceResolution.861924d6d384a7d6.webp) > Pilt [Google'i blogist](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Kuna iga sisendi positsioon kaardistatakse sõltumatult iga väljundi positsioon **BERT** (Bidirectional Encoder Representations from Transformers) on väga suur mitmekihiline transformer võrk, millel on 12 kihti *BERT-base* jaoks ja 24 kihti *BERT-large* jaoks. Mudel treenitakse esmalt suure tekstikorpuse (Wikipedia + raamatud) peal kasutades juhendamata treeningut (ennustades maskeeritud sõnu lauses). Treeningu käigus omandab mudel märkimisväärsel tasemel keele mõistmist, mida saab seejärel kasutada teiste andmekogumitega peenhäälestamise abil. Seda protsessi nimetatakse **ülekandeõppeks**. -![pilt aadressilt http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/et/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) +![pilt aadressilt http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/et/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > Pildi [allikas](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/et/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/et/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index f4adea54..d1ad9254 100644 --- a/translations/et/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/et/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**Tähelepanumehhanismid** pakuvad võimalust kaaluda iga sisendvektori kontekstuaalset mõju RNN-i iga väljundprognoosi puhul. Seda rakendatakse, luues otseteid sisend-RNN-i vaheolekute ja väljund-RNN-i vahel. Sel viisil, kui genereeritakse väljundisümbolit $y_t$, arvestame kõiki sisendi varjatud olekuid $h_i$, erinevate kaalukoefitsientidega $\\alpha_{t,i}$.\n", "\n", - "![Pilt, mis näitab kodeerija/dekodeerija mudelit koos aditiivse tähelepanukihiga](../../../../../translated_images/et/encoder-decoder-attention.7a726296894fb567.png)\n", + "![Pilt, mis näitab kodeerija/dekodeerija mudelit koos aditiivse tähelepanukihiga](../../../../../translated_images/et/encoder-decoder-attention.7a726296894fb567.webp)\n", "*Kodeerija-dekodeerija mudel koos aditiivse tähelepanumehhanismiga [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), viidatud [sellest blogipostitusest](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Tähelepanumaatriks $\\{\\alpha_{i,j}\\}$ esindab, millisel määral teatud sisendsõnad mõjutavad antud sõna genereerimist väljundjärjestuses. Allpool on näide sellisest maatriksist:\n", "\n", - "![Pilt, mis näitab näidisalineerimist, mille leidis RNNsearch-50, võetud Bahdanau - arviz.org](../../../../../translated_images/et/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![Pilt, mis näitab näidisalineerimist, mille leidis RNNsearch-50, võetud Bahdanau - arviz.org](../../../../../translated_images/et/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*Joonis võetud [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Joonis 3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) on väga suur mitmekihiline transformerivõrk, millel on 12 kihti *BERT-base* jaoks ja 24 kihti *BERT-large* jaoks. Mudel treenitakse esmalt suure tekstikorpuse (Wikipedia + raamatud) peal, kasutades juhendamata treeningut (maskeeritud sõnade ennustamine lauses). Treeningu käigus omandab mudel märkimisväärse taseme keele mõistmist, mida saab seejärel kasutada teiste andmekogumite peal peenhäälestamise abil. Seda protsessi nimetatakse **ülekandeõppeks**.\n", "\n", - "![Pilt aadressilt http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/et/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![Pilt aadressilt http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/et/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "Transformeriarhitektuuridel, nagu BERT, DistilBERT, BigBird, OpenGPT3 ja teised, on palju variatsioone, mida saab peenhäälestada. [HuggingFace pakett](https://github.com/huggingface/) pakub repositooriumi paljude nende arhitektuuride treenimiseks PyTorchiga.\n", "\n", diff --git a/translations/et/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/et/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index 99f67010..4ea7675a 100644 --- a/translations/et/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/et/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**Tähelepanumehhanismid** pakuvad võimalust kaaluda iga sisendvektori kontekstuaalset mõju RNN-i iga väljundprognoosi puhul. Selle rakendamine toimub lühenduste loomisega sisend-RNN-i vaheolekute ja väljund-RNN-i vahel. Sel viisil, kui genereerime väljundisümbolit $y_t$, võtame arvesse kõiki sisendi varjatud olekuid $h_i$, erinevate kaalukoefitsientidega $\\alpha_{t,i}$.\n", "\n", - "![Pilt, mis näitab kodeerija/dekodeerija mudelit koos aditiivse tähelepanukihiga](../../../../../translated_images/et/encoder-decoder-attention.7a726296894fb567.png)\n", + "![Pilt, mis näitab kodeerija/dekodeerija mudelit koos aditiivse tähelepanukihiga](../../../../../translated_images/et/encoder-decoder-attention.7a726296894fb567.webp)\n", "*Kodeerija-dekodeerija mudel koos aditiivse tähelepanumehhanismiga [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), viidatud [selles blogipostituses](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Tähelepanumaatriks $\\{\\alpha_{i,j}\\}$ esindab, millisel määral teatud sisendsõnad osalevad antud sõna genereerimisel väljundjärjestuses. Allpool on näide sellisest maatriksist:\n", "\n", - "![Pilt, mis näitab näidisalini, mille leidis RNNsearch-50, võetud Bahdanau - arviz.org](../../../../../translated_images/et/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![Pilt, mis näitab näidisalini, mille leidis RNNsearch-50, võetud Bahdanau - arviz.org](../../../../../translated_images/et/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*Joonis võetud [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Joonis 3)*\n", "\n", @@ -92,7 +92,7 @@ "source": [ "See kiht koosneb kahest `Embedding` kihist: üks on mõeldud tokenite sisestamiseks (nagu me varem arutasime) ja teine tokenite positsioonide jaoks. Tokenite positsioonid luuakse naturaalarvude järjestusena vahemikus 0 kuni `maxlen`, kasutades `tf.range`, ning seejärel edastatakse need sisestuskihile. Kaks saadud sisestusvektorit liidetakse, mille tulemusena saadakse positsiooniliselt sisestatud sisendi esitus kujuga `maxlen`$\\times$`embed_dim`.\n", "\n", - "\n", + "\n", "\n", "Nüüd rakendame transformer ploki. See võtab sisendiks varem määratletud sisestuskihi väljundi:\n" ] @@ -134,7 +134,7 @@ "\n", "Selle kihi väljund edastatakse seejärel läbi `Dense` võrgu (meie puhul - kahekihi perceptron), ja tulemus lisatakse lõplikule väljundile (mis läbib uuesti normaliseerimise).\n", "\n", - "\n", + "\n", "\n", "Nüüd oleme valmis defineerima täieliku transformer mudeli:\n" ] @@ -235,7 +235,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) on väga suur mitmekihiline transformer-võrk, millel on 12 kihti *BERT-base* jaoks ja 24 kihti *BERT-large* jaoks. Mudel treenitakse esmalt suurtel tekstikorpustel (WikiPedia + raamatud) kasutades juhendamata treenimist (ennustades lauses maskeeritud sõnu). Eeltreeningu käigus omandab mudel märkimisväärse taseme keele mõistmist, mida saab seejärel kasutada koos teiste andmekogumitega peenhäälestuse abil. Seda protsessi nimetatakse **ülekandeõppeks**.\n", "\n", - "![pilt aadressilt http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/et/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![pilt aadressilt http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/et/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "Transformer-arhitektuuridel, sealhulgas BERT, DistilBERT, BigBird, OpenGPT3 ja teistel, on palju variatsioone, mida saab peenhäälestada.\n", "\n", diff --git a/translations/et/lessons/5-NLP/19-NER/README.md b/translations/et/lessons/5-NLP/19-NER/README.md index 5ac516fd..65c27e9a 100644 --- a/translations/et/lessons/5-NLP/19-NER/README.md +++ b/translations/et/lessons/5-NLP/19-NER/README.md @@ -17,7 +17,7 @@ Siiani oleme peamiselt keskendunud ühele NLP ülesandele - klassifikatsioonile. Oletame, et soovite arendada loomuliku keele vestlusrobotit, sarnast Amazon Alexa või Google Assistantiga. Nutikad vestlusrobotid töötavad nii, et nad *mõistavad*, mida kasutaja tahab, tehes sisendlausele tekstiklassifikatsiooni. Selle klassifikatsiooni tulemus on nn **intent**, mis määrab, mida vestlusrobot peaks tegema. -Bot NER +Bot NER > Pilt autorilt @@ -54,7 +54,7 @@ vastsündinul | O Kuna peame looma üks-ühele vastavuse tokenite ja klasside vahel, saame treenida parempoolse **mitme-mitme** närvivõrgu mudeli sellest pildist: -![Pilt, mis näitab levinud korduvate närvivõrkude mustreid.](../../../../../translated_images/et/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Pilt, mis näitab levinud korduvate närvivõrkude mustreid.](../../../../../translated_images/et/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > *Pilt [sellest blogipostitusest](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) autorilt [Andrej Karpathy](http://karpathy.github.io/). NER tokenite klassifikatsioonimudelid vastavad parempoolsele võrgustiku arhitektuurile sellel pildil.* diff --git a/translations/et/lessons/5-NLP/README.md b/translations/et/lessons/5-NLP/README.md index 71d2910b..638c8e50 100644 --- a/translations/et/lessons/5-NLP/README.md +++ b/translations/et/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Loodusliku keele töötlemine -![NLP ülesannete kokkuvõte visandis](../../../../translated_images/et/ai-nlp.b22dcb8ca4707cea.png) +![NLP ülesannete kokkuvõte visandis](../../../../translated_images/et/ai-nlp.b22dcb8ca4707cea.webp) Selles osas keskendume **loodusliku keele töötlemise (NLP)** ülesannete lahendamisele, kasutades tehisnärvivõrke. On palju NLP probleeme, mida soovime, et arvutid suudaksid lahendada: diff --git a/translations/et/lessons/6-Other/22-DeepRL/README.md b/translations/et/lessons/6-Other/22-DeepRL/README.md index 8560e4a1..e2ef75ab 100644 --- a/translations/et/lessons/6-Other/22-DeepRL/README.md +++ b/translations/et/lessons/6-Other/22-DeepRL/README.md @@ -34,7 +34,7 @@ Tõenäoliselt olete näinud kaasaegseid tasakaalustusseadmeid, nagu *Segway* v Lihtsustatud versioon tasakaalustamisest on tuntud kui **CartPole** probleem. CartPole maailmas on meil horisontaalne liugur, mis saab liikuda vasakule või paremale, ja eesmärk on tasakaalustada vertikaalne post liuguri peal, kui see liigub. -cartpole +cartpole Selle keskkonna loomiseks ja kasutamiseks on vaja paar rida Python koodi: diff --git a/translations/et/lessons/6-Other/22-DeepRL/lab/README.md b/translations/et/lessons/6-Other/22-DeepRL/lab/README.md index 042fab05..da0a6c9f 100644 --- a/translations/et/lessons/6-Other/22-DeepRL/lab/README.md +++ b/translations/et/lessons/6-Other/22-DeepRL/lab/README.md @@ -15,7 +15,7 @@ Laboriülesanne [AI algajatele õppekavast](https://github.com/microsoft/ai-for- Sinu eesmärk on treenida RL-agent juhtima [Mountain Car](https://www.gymlibrary.ml/environments/classic_control/mountain_car/) OpenAI keskkonnas. -Mountain Car +Mountain Car ## Keskkond diff --git a/translations/et/lessons/6-Other/23-MultiagentSystems/README.md b/translations/et/lessons/6-Other/23-MultiagentSystems/README.md index f23bebbc..e630c8df 100644 --- a/translations/et/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/et/lessons/6-Other/23-MultiagentSystems/README.md @@ -60,7 +60,7 @@ NetLogo saate [alla laadida](https://ccl.northwestern.edu/netlogo/download.shtml NetLogo suurepärane omadus on see, et see sisaldab töötavate mudelite raamatukogu, mida saate proovida. Minge **File → Models Library**, ja teil on palju mudelikategooriaid, mille vahel valida. -NetLogo mudelite raamatukogu +NetLogo mudelite raamatukogu > Mudelite raamatukogu ekraanipilt Dmitry Soshnikovilt @@ -70,7 +70,7 @@ Saate avada ühe mudeli, näiteks **Biology → Flocking**. Pärast mudeli avamist jõuate NetLogo põhiekraanile. Siin on näidis, mis kirjeldab huntide ja lammaste populatsiooni piiratud ressursside (rohu) tingimustes. -![NetLogo põhiekraan](../../../../../translated_images/et/NetLogo-Main.32653711ec1a01b3.png) +![NetLogo põhiekraan](../../../../../translated_images/et/NetLogo-Main.32653711ec1a01b3.webp) > Ekraanipilt Dmitry Soshnikovilt diff --git a/translations/et/lessons/README.md b/translations/et/lessons/README.md index 5b03c2a4..aea52dd3 100644 --- a/translations/et/lessons/README.md +++ b/translations/et/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Ülevaade -![Ülevaade visandina](../../../translated_images/et/ai-overview.0857791951d19500.png) +![Ülevaade visandina](../../../translated_images/et/ai-overview.0857791951d19500.webp) > Visandmärkmed autorilt [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/et/lessons/X-Extras/X1-MultiModal/README.md b/translations/et/lessons/X-Extras/X1-MultiModal/README.md index cf6aa3de..d4c5a9d8 100644 --- a/translations/et/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/et/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ Pärast transformer-mudelite edu NLP-ülesannete lahendamisel on sama või sarna CLIP-i peamine idee on võrrelda tekstilisi juhiseid pildiga ja määrata, kui hästi pilt vastab juhisele. -![CLIP arhitektuur](../../../../../translated_images/et/clip-arch.b3dbf20b4e8ed8be.png) +![CLIP arhitektuur](../../../../../translated_images/et/clip-arch.b3dbf20b4e8ed8be.webp) > *Pilt [sellest blogipostitusest](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ Kui mudel on eelnevalt treenitud, saame anda sellele pildipartii ja tekstiliste Oletame, et peame klassifitseerima pilte näiteks kasside, koerte ja inimeste vahel. Sel juhul saame mudelile anda pildi ja rea tekstilisi juhiseid: "*kassi pilt*", "*koera pilt*", "*inimese pilt*". Kolme tõenäosuse vektoris peame lihtsalt valima indeksi, mille väärtus on kõige suurem. -![CLIP pildiklassifikatsiooniks](../../../../../translated_images/et/clip-class.3af42ef0b2b19369.png) +![CLIP pildiklassifikatsiooniks](../../../../../translated_images/et/clip-class.3af42ef0b2b19369.webp) > *Pilt [sellest blogipostitusest](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ Lisateavet VQGAN-i kohta leiate [Taming Transformers](https://compvis.github.io/ Üks oluline erinevus VQGAN-i ja traditsioonilise GAN-i vahel on see, et viimane suudab genereerida korraliku pildi mis tahes sisendvektorist, samas kui VQGAN-i puhul on tõenäoline, et pilt ei ole koherentne. Seetõttu peame pildiloome protsessi täiendavalt suunama, mida saab teha CLIP-i abil. -![VQGAN+CLIP arhitektuur](../../../../../translated_images/et/vqgan.5027fe05051dfa31.png) +![VQGAN+CLIP arhitektuur](../../../../../translated_images/et/vqgan.5027fe05051dfa31.webp) Tekstijuhisele vastava pildi genereerimiseks alustame juhusliku kodeerimisvektoriga, mis edastatakse VQGAN-ile, et luua pilt. Seejärel kasutatakse CLIP-i kaotusefunktsiooni loomiseks, mis näitab, kui hästi pilt vastab tekstilisele juhisele. Eesmärk on seejärel minimeerida kaotus, kasutades tagasipropageerimist sisendvektori parameetrite kohandamiseks. Suurepärane teek, mis rakendab VQGAN+CLIP-i, on [Pixray](http://github.com/pixray/pixray). -![Pixray loodud pilt](../../../../../translated_images/et/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Pixray loodud pilt](../../../../../translated_images/et/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Pixray loodud pilt](../../../../../translated_images/et/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) +![Pixray loodud pilt](../../../../../translated_images/et/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp) | ![Pixray loodud pilt](../../../../../translated_images/et/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp) | ![Pixray loodud pilt](../../../../../translated_images/et/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp) ----|----|---- Pilt genereeritud juhisest *noore meesõpetaja lähivaade, akvarellportree, kirjanduse õpetaja, raamatuga* | Pilt genereeritud juhisest *noore naisõpetaja lähivaade, õliportree, arvutiteaduse õpetaja, arvutiga* | Pilt genereeritud juhisest *vana meesõpetaja lähivaade, õliportree, matemaatika õpetaja, tahvli ees* diff --git a/translations/fi/README.md b/translations/fi/README.md index d8715a4d..1f300c3c 100644 --- a/translations/fi/README.md +++ b/translations/fi/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # Keinotekoisen älykkyyden perusteet - Opetussuunnitelma -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/fi/ai-overview.0857791951d19500.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/fi/ai-overview.0857791951d19500.webp)| |:---:| | AI For Beginners - _Sketchnote by [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/fi/lessons/1-Intro/README.md b/translations/fi/lessons/1-Intro/README.md index 7b46f67b..1dd25e91 100644 --- a/translations/fi/lessons/1-Intro/README.md +++ b/translations/fi/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Johdanto tekoälyyn -![Yhteenveto tekoälyn johdannon sisällöstä piirroksena](../../../../translated_images/fi/ai-intro.bf28d1ac4235881c.png) +![Yhteenveto tekoälyn johdannon sisällöstä piirroksena](../../../../translated_images/fi/ai-intro.bf28d1ac4235881c.webp) > Piirros: [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Alun perin tietokoneet keksittiin [Charles Babbagen](https://en.wikipedia.org/wiki/Charles_Babbage) toimesta käsittelemään numeroita ennalta määritellyn menettelyn - algoritmin - mukaisesti. Vaikka modernit tietokoneet ovat huomattavasti kehittyneempiä kuin 1800-luvulla ehdotettu alkuperäinen malli, ne noudattavat yhä samaa ohjattujen laskentojen periaatetta. Näin ollen tietokone voidaan ohjelmoida tekemään jotain, jos tiedämme tarkalleen, mitä vaiheita tavoitteen saavuttamiseksi tarvitaan. -![Henkilön valokuva](../../../../translated_images/fi/dsh_age.d212a30d4e54fb5f.png) +![Henkilön valokuva](../../../../translated_images/fi/dsh_age.d212a30d4e54fb5f.webp) > Kuva: [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ Lisätietoja löytyy **[Artificial General Intelligence](https://en.wikipedia.or Yksi ongelma käsiteltäessä termiä **[älykkyys](https://en.wikipedia.org/wiki/Intelligence)** on, ettei sille ole selkeää määritelmää. Voidaan väittää, että älykkyys liittyy **abstraktiin ajatteluun** tai **itsetietoisuuteen**, mutta emme pysty määrittelemään sitä kunnolla. -![Kissan valokuva](../../../../translated_images/fi/photo-cat.8c8e8fb760ffe457.jpg) +![Kissan valokuva](../../../../translated_images/fi/photo-cat.8c8e8fb760ffe457.webp) > [Kuva](https://unsplash.com/photos/75715CVEJhI) [Amber Kipp](https://unsplash.com/@sadmax) Unsplashista @@ -98,13 +98,13 @@ Vaihtoehtoisesti voimme yrittää mallintaa aivojemme yksinkertaisimpia elementt > | Entä koneoppiminen? | | > |--------------|-----------| -> | Tekoälyn osa-alue, jossa tietokone oppii ratkaisemaan ongelman jonkin datan perusteella, kutsutaan **koneoppimiseksi**. Emme käsittele perinteistä koneoppimista tässä kurssissa – suosittelemme erillistä [Koneoppiminen aloittelijoille](http://aka.ms/ml-beginners) -opetussuunnitelmaa. | ![Koneoppiminen aloittelijoille](../../../../translated_images/fi/ml-for-beginners.9e4fed176fd5817d.png) | +> | Tekoälyn osa-alue, jossa tietokone oppii ratkaisemaan ongelman jonkin datan perusteella, kutsutaan **koneoppimiseksi**. Emme käsittele perinteistä koneoppimista tässä kurssissa – suosittelemme erillistä [Koneoppiminen aloittelijoille](http://aka.ms/ml-beginners) -opetussuunnitelmaa. | ![Koneoppiminen aloittelijoille](../../../../translated_images/fi/ml-for-beginners.9e4fed176fd5817d.webp) | ## Lyhyt katsaus tekoälyn historiaan Tekoäly syntyi tieteenalana 1900-luvun puolivälissä. Aluksi symbolinen järkeily oli vallitseva lähestymistapa, ja se johti useisiin merkittäviin saavutuksiin, kuten asiantuntijajärjestelmiin – tietokoneohjelmiin, jotka pystyivät toimimaan asiantuntijana tietyillä rajatuilla ongelma-alueilla. Pian kuitenkin huomattiin, että tällainen lähestymistapa ei skaalaudu hyvin. Tiedon kerääminen asiantuntijalta, sen esittäminen tietokoneessa ja tietokannan pitäminen ajan tasalla osoittautui erittäin monimutkaiseksi ja liian kalliiksi monissa tapauksissa. Tämä johti niin sanottuun [tekoälytalveen](https://en.wikipedia.org/wiki/AI_winter) 1970-luvulla. -Tekoälyn historian lyhyt katsaus +Tekoälyn historian lyhyt katsaus > Kuva: [Dmitry Soshnikov](http://soshnikov.com) @@ -124,7 +124,7 @@ Samoin voimme nähdä, kuinka lähestymistapa "puhuvien ohjelmien" (jotka saatta * Modernit avustajat, kuten Cortana, Siri tai Google Assistant, ovat kaikki hybridijärjestelmiä, jotka käyttävät neuroverkkoja muuntamaan puheen tekstiksi ja tunnistamaan tarkoituksemme, ja sitten hyödyntävät järkeilyä tai eksplisiittisiä algoritmeja suorittaakseen tarvittavat toiminnot. * Tulevaisuudessa voimme odottaa täysin neuroverkkoihin perustuvaa mallia, joka käsittelee dialogia itsenäisesti. Viimeaikaiset GPT- ja [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) -neuroverkkojen perheet osoittavat suurta menestystä tässä. -Turingin testin kehitys +Turingin testin kehitys > Kuva: Dmitry Soshnikov, [valokuva](https://unsplash.com/photos/r8LmVbUKgns) [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto), Unsplash ## Viimeaikainen tekoälytutkimus diff --git a/translations/fi/lessons/2-Symbolic/Animals.ipynb b/translations/fi/lessons/2-Symbolic/Animals.ipynb index 3a753799..8ebd26f8 100644 --- a/translations/fi/lessons/2-Symbolic/Animals.ipynb +++ b/translations/fi/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "Tässä esimerkissä toteutamme yksinkertaisen tietopohjaisen järjestelmän, joka määrittää eläimen fyysisten ominaisuuksien perusteella. Järjestelmä voidaan esittää seuraavalla JA-TAI-puulla (tämä on osa koko puusta, sääntöjä voidaan helposti lisätä): \n", "\n", - "![](../../../../translated_images/fi/AND-OR-Tree.5592d2c70187f283.png)\n" + "![](../../../../translated_images/fi/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { diff --git a/translations/fi/lessons/2-Symbolic/README.md b/translations/fi/lessons/2-Symbolic/README.md index a80b20e3..a9e7ea0e 100644 --- a/translations/fi/lessons/2-Symbolic/README.md +++ b/translations/fi/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Tiedon esittäminen ja asiantuntijajärjestelmät -![Symbolisen tekoälyn sisällön yhteenveto](../../../../translated_images/fi/ai-symbolic.715a30cb610411a6.png) +![Symbolisen tekoälyn sisällön yhteenveto](../../../../translated_images/fi/ai-symbolic.715a30cb610411a6.webp) > Sketchnote: [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ Usein emme määrittele tietoa tarkasti, vaan yhdistämme sen muihin siihen liit Näin ollen **tiedon esittämisen** ongelma on löytää tehokas tapa esittää tieto tietokoneessa datan muodossa, jotta sitä voidaan käyttää automaattisesti. Tämä voidaan nähdä spektrinä: -![Tiedon esittämisen spektri](../../../../translated_images/fi/knowledge-spectrum.b60df631852c0217.png) +![Tiedon esittämisen spektri](../../../../translated_images/fi/knowledge-spectrum.b60df631852c0217.webp) > Kuva: [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Lohkorakenne | Sisennys | | | Symbolisen tekoälyn varhaisia menestyksiä olivat niin sanotut **asiantuntijajärjestelmät** - tietokonejärjestelmät, jotka suunniteltiin toimimaan asiantuntijana jollakin rajatulla ongelma-alueella. Ne perustuivat **tietokantaan**, joka oli kerätty yhdeltä tai useammalta ihmisasiantuntijalta, ja ne sisälsivät **päättelymoottorin**, joka suoritti päättelyä sen pohjalta. -![Ihmisen arkkitehtuuri](../../../../translated_images/fi/arch-human.5d4d35f1bba3ab1c.png) | ![Tietopohjaisen järjestelmän arkkitehtuuri](../../../../translated_images/fi/arch-kbs.3ec5c150b09fa8da.png) +![Ihmisen arkkitehtuuri](../../../../translated_images/fi/arch-human.5d4d35f1bba3ab1c.webp) | ![Tietopohjaisen järjestelmän arkkitehtuuri](../../../../translated_images/fi/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ Ihmisen hermojärjestelmän yksinkertaistettu rakenne | Tietopohjaisen järjestelmän arkkitehtuuri @@ -106,7 +106,7 @@ Asiantuntijajärjestelmät rakennetaan ihmisen päättelyjärjestelmän tapaan, Esimerkiksi tarkastellaan seuraavaa asiantuntijajärjestelmää, joka määrittää eläimen sen fyysisten ominaisuuksien perusteella: -![AND-OR-puu](../../../../translated_images/fi/AND-OR-Tree.5592d2c70187f283.png) +![AND-OR-puu](../../../../translated_images/fi/AND-OR-Tree.5592d2c70187f283.webp) > Kuva: [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/fi/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/fi/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index 9d8db9c1..7d0670d3 100644 --- a/translations/fi/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/fi/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -1259,7 +1259,7 @@ "* Matala harjoitusdatan häviö - malli pystyy lähentämään harjoitusdataa hyvin, koska sillä on tarpeeksi ilmaisukykyä.\n", "* Validointidatan häviö voi olla paljon korkeampi kuin harjoitusdatan häviö ja voi alkaa kasvaa harjoittelun aikana - tämä johtuu siitä, että malli \"muistaa\" harjoitusdatan pisteet ja menettää \"kokonaiskuvan\".\n", "\n", - "![Ylisovittaminen](../../../../../translated_images/fi/overfit.a0bd57f717c15769.png)\n", + "![Ylisovittaminen](../../../../../translated_images/fi/overfit.a0bd57f717c15769.webp)\n", "\n", "> Tässä kuvassa `x` edustaa harjoitusdataa ja `o` validointidataa. Vasemmalla - lineaarinen malli (yksikerroksinen), se lähentää datan luonnetta melko hyvin. Oikealla - ylisovitettu malli, joka lähentää harjoitusdataa täydellisesti, mutta menettää merkityksensä muun datan kanssa (validointivirhe on erittäin korkea).\n" ] diff --git a/translations/fi/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/fi/lessons/3-NeuralNetworks/05-Frameworks/README.md index 81f18bfe..9761e843 100644 --- a/translations/fi/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/fi/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Ylikoulutus on erittäin tärkeä käsite koneoppimisessa, ja on erittäin tärk Tarkastellaan seuraavaa ongelmaa, jossa pyritään approksimoimaan 5 pistettä (esitettynä `x`-merkeillä alla olevissa kaavioissa): -![lineaarinen](../../../../../translated_images/fi/overfit1.f24b71c6f652e59e.jpg) | ![ylikoulutus](../../../../../translated_images/fi/overfit2.131f5800ae10ca5e.jpg) +![lineaarinen](../../../../../translated_images/fi/overfit1.f24b71c6f652e59e.webp) | ![ylikoulutus](../../../../../translated_images/fi/overfit2.131f5800ae10ca5e.webp) -------------------------|-------------------------- **Lineaarinen malli, 2 parametria** | **Ei-lineaarinen malli, 7 parametria** Koulutusvirhe = 5.3 | Koulutusvirhe = 0 @@ -79,7 +79,7 @@ On erittäin tärkeää löytää oikea tasapaino mallin monimutkaisuuden (param Kuten yllä olevasta kaaviosta näkyy, ylikoulutus voidaan havaita erittäin pienestä koulutusvirheestä ja suuresta validointivirheestä. Normaalisti koulutuksen aikana näemme sekä koulutus- että validointivirheiden alkavan pienentyä, ja jossain vaiheessa validointivirhe saattaa lakata pienentymästä ja alkaa kasvaa. Tämä on merkki ylikoulutuksesta ja indikaattori siitä, että koulutus pitäisi todennäköisesti lopettaa tässä vaiheessa (tai ainakin tehdä mallista tilannekuva). -![ylikoulutus](../../../../../translated_images/fi/Overfitting.408ad91cd90b4371.png) +![ylikoulutus](../../../../../translated_images/fi/Overfitting.408ad91cd90b4371.webp) ## Miten ylikoulutusta estetään diff --git a/translations/fi/lessons/3-NeuralNetworks/README.md b/translations/fi/lessons/3-NeuralNetworks/README.md index 29d75e3a..0b652bc6 100644 --- a/translations/fi/lessons/3-NeuralNetworks/README.md +++ b/translations/fi/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Johdanto neuroverkkoihin -![Yhteenveto neuroverkkojen sisällöstä piirroksena](../../../../translated_images/fi/ai-neuralnetworks.1c687ae40bc86e83.png) +![Yhteenveto neuroverkkojen sisällöstä piirroksena](../../../../translated_images/fi/ai-neuralnetworks.1c687ae40bc86e83.webp) Kuten keskustelimme johdannossa, yksi tapa saavuttaa älykkyyttä on kouluttaa **tietokonemalli** tai **keinotekoinen aivot**. 1900-luvun puolivälistä lähtien tutkijat kokeilivat erilaisia matemaattisia malleja, kunnes viime vuosina tämä suunta osoittautui erittäin menestyksekkääksi. Näitä aivojen matemaattisia malleja kutsutaan **neuroverkoiksi**. @@ -36,13 +36,13 @@ Tässä oppimateriaalissa keskitymme vain neuroverkkopohjaisiin malleihin. Biologiasta tiedämme, että aivomme koostuvat hermosoluista (neuroneista), joilla jokaisella on useita "syötteitä" (dendriittejä) ja yksi "tulos" (aksoni). Sekä dendriitit että aksonit voivat johtaa sähköisiä signaaleja, ja niiden väliset yhteydet — synapsit — voivat osoittaa vaihtelevaa johtavuutta, jota säätelevät välittäjäaineet. -![Neuronin malli](../../../../translated_images/fi/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![Neuronin malli](../../../../translated_images/fi/artneuron.1a5daa88d20ebe6f.png) +![Neuronin malli](../../../../translated_images/fi/synapse-wikipedia.ed20a9e4726ea1c6.webp) | ![Neuronin malli](../../../../translated_images/fi/artneuron.1a5daa88d20ebe6f.webp) ----|---- Oikea neuroni *([Kuva](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) Wikipediasta)* | Keinotekoinen neuroni *(Kuva tekijältä)* Näin ollen yksinkertaisin matemaattinen malli neuronista sisältää useita syötteitä X1, ..., XN ja yhden tuloksen Y sekä joukon painoja W1, ..., WN. Tulos lasketaan seuraavasti: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) missä f on jokin epälineaarinen **aktivointifunktio**. diff --git a/translations/fi/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/fi/lessons/4-ComputerVision/06-IntroCV/README.md index 08bde087..0a342227 100644 --- a/translations/fi/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/fi/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ Ennen kuin kuva syötetään neuroverkkoon, voi olla hyödyllistä suorittaa use * **Pistekirjakuvan esikäsittely**. Keskitymme siihen, miten voimme käyttää kynnysarvoja, piirteiden tunnistusta, perspektiivimuunnoksia ja NumPy-manipulaatioita erottamaan yksittäiset pistekirjoitussymbolit, jotta ne voidaan luokitella neuroverkolla. -![Pistekirjakuvan esimerkki](../../../../../translated_images/fi/braille.341962ff76b1bd70.jpeg) | ![Esikäsitelty pistekirjakuva](../../../../../translated_images/fi/braille-result.46530fea020b03c7.png) | ![Pistekirjoitussymbolit](../../../../../translated_images/fi/braille-symbols.0159185ab69d5339.png) +![Pistekirjakuvan esimerkki](../../../../../translated_images/fi/braille.341962ff76b1bd70.webp) | ![Esikäsitelty pistekirjakuva](../../../../../translated_images/fi/braille-result.46530fea020b03c7.webp) | ![Pistekirjoitussymbolit](../../../../../translated_images/fi/braille-symbols.0159185ab69d5339.webp) ----|-----|----- > Kuva [OpenCV.ipynb](OpenCV.ipynb) * **Liikkeen tunnistaminen videosta kehysten erotuksen avulla**. Jos kamera on kiinteä, kameran syötteen kehysten pitäisi olla melko samanlaisia. Koska kehykset esitetään taulukoina, kahden peräkkäisen kehyksen taulukoiden vähentämisellä saadaan pikseliero, joka on pieni staattisille kehyksille ja kasvaa merkittävästi, kun kuvassa on huomattavaa liikettä. -![Kuva videokehysten ja kehysten erojen analyysistä](../../../../../translated_images/fi/frame-difference.706f805491a0883c.png) +![Kuva videokehysten ja kehysten erojen analyysistä](../../../../../translated_images/fi/frame-difference.706f805491a0883c.webp) > Kuva [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ Ennen kuin kuva syötetään neuroverkkoon, voi olla hyödyllistä suorittaa use - **Tiheä optinen virtaus** laskee vektorikentän, joka näyttää jokaisen pikselin liikkeen suunnan. - **Harva optinen virtaus** perustuu tiettyjen erottuvien piirteiden (esim. reunojen) valintaan kuvassa ja niiden liikeradan rakentamiseen kehyksestä toiseen. -![Kuva optisesta virtauksesta](../../../../../translated_images/fi/optical.1f4a94464579a83a.png) +![Kuva optisesta virtauksesta](../../../../../translated_images/fi/optical.1f4a94464579a83a.webp) > Kuva [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/fi/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/fi/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 76a0ea33..80caebfd 100644 --- a/translations/fi/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/fi/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 on verkko, joka saavutti 92,7 % tarkkuuden ImageNetin top-5-luokittelussa vuonna 2014. Sen kerrosrakenne on seuraava: -![ImageNet Layers](../../../../../translated_images/fi/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet Layers](../../../../../translated_images/fi/vgg-16-arch1.d901a5583b3a51ba.webp) Kuten näet, VGG noudattaa perinteistä pyramidirakennetta, joka koostuu konvoluutio- ja pooling-kerrosten sarjasta. -![ImageNet Pyramid](../../../../../translated_images/fi/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet Pyramid](../../../../../translated_images/fi/vgg-16-arch.64ff2137f50dd49f.webp) > Kuva [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) -sivustolta diff --git a/translations/fi/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/fi/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index 0c0d88e6..4a6823e6 100644 --- a/translations/fi/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/fi/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -260,7 +260,7 @@ "\n", "Näin ollen tyypillisessä CNN:ssä on useita konvoluutiokerroksia, joiden välissä on pooling-kerroksia kuvan ulottuvuuksien pienentämiseksi. Samalla lisäämme suotimien määrää, koska kuvioiden muuttuessa monimutkaisemmiksi on enemmän mahdollisia kiinnostavia yhdistelmiä, joita meidän täytyy etsiä.\n", "\n", - "![Kuva, joka näyttää useita konvoluutiokerroksia ja pooling-kerroksia.](../../../../../translated_images/fi/cnn-pyramid.85915455759ef0ce.png)\n", + "![Kuva, joka näyttää useita konvoluutiokerroksia ja pooling-kerroksia.](../../../../../translated_images/fi/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "Koska tilalliset ulottuvuudet pienenevät ja piirre-/suodatinulottuvuudet kasvavat, tätä arkkitehtuuria kutsutaan myös **pyramidiarkkitehtuuriksi**.\n" ] diff --git a/translations/fi/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/fi/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index 8118c476..a3862910 100644 --- a/translations/fi/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/fi/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -359,7 +359,7 @@ "\n", "Näin ollen tyypillisessä CNN:ssä on useita konvoluutiokerroksia, joiden välissä on pooling-kerroksia kuvan dimensioiden pienentämiseksi. Samalla lisäämme suodattimien määrää, koska kuvioiden muuttuessa monimutkaisemmiksi on enemmän mahdollisia kiinnostavia yhdistelmiä, joita meidän täytyy etsiä.\n", "\n", - "![Kuva, joka näyttää useita konvoluutiokerroksia ja pooling-kerroksia.](../../../../../translated_images/fi/cnn-pyramid.85915455759ef0ce.png)\n", + "![Kuva, joka näyttää useita konvoluutiokerroksia ja pooling-kerroksia.](../../../../../translated_images/fi/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "Koska tiladimensioita pienennetään ja piirre-/suodatin-dimensioita kasvatetaan, tätä arkkitehtuuria kutsutaan myös **pyramidiarkkitehtuuriksi**.\n" ] diff --git a/translations/fi/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/fi/lessons/4-ComputerVision/07-ConvNets/README.md index a7e332b5..42043818 100644 --- a/translations/fi/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/fi/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ Todellisessa elämässä haluamme pystyä tunnistamaan esineitä kuvasta niiden Kuvioiden tunnistamiseen käytämme **konvoluutiokertoimia**. Kuten tiedät, kuva esitetään 2D-matriisina tai 3D-tensorina värisyvyyden kanssa. Suodattimen soveltaminen tarkoittaa, että otamme suhteellisen pienen **suodatinytimen** matriisin, ja alkuperäisen kuvan jokaiselle pikselille laskemme painotetun keskiarvon naapuripisteiden kanssa. Voimme ajatella tämän olevan kuin pieni ikkuna, joka liukuu koko kuvan yli ja tasoittaa kaikki pikselit suodatinytimen matriisin painojen mukaan. -![Pystysuuntainen reunasuodatin](../../../../../translated_images/fi/filter-vert.b7148390ca0bc356.png) | ![Vaakasuuntainen reunasuodatin](../../../../../translated_images/fi/filter-horiz.59b80ed4feb946ef.png) +![Pystysuuntainen reunasuodatin](../../../../../translated_images/fi/filter-vert.b7148390ca0bc356.webp) | ![Vaakasuuntainen reunasuodatin](../../../../../translated_images/fi/filter-horiz.59b80ed4feb946ef.webp) ----|---- > Kuva: Dmitry Soshnikov @@ -38,7 +38,7 @@ CNN:ien toiminta perustuu seuraaviin tärkeisiin ideoihin: * Voimme suunnitella verkon siten, että suodattimet koulutetaan automaattisesti * Voimme käyttää samaa lähestymistapaa löytääksemme kuvioita korkeamman tason ominaisuuksista, ei vain alkuperäisestä kuvasta. Näin CNN:n ominaisuuksien tunnistus toimii hierarkiana, alkaen matalan tason pikseliyhdistelmistä ja päätyen korkeamman tason kuvan osien yhdistelmiin. -![Hierarkkinen ominaisuuksien tunnistus](../../../../../translated_images/fi/FeatureExtractionCNN.d9b456cbdae7cb64.png) +![Hierarkkinen ominaisuuksien tunnistus](../../../../../translated_images/fi/FeatureExtractionCNN.d9b456cbdae7cb64.webp) > Kuva [Hislop-Lynchin artikkelista](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), perustuen [heidän tutkimukseensa](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ Useimmat kuvankäsittelyyn käytetyt CNN:t noudattavat niin sanottua pyramidirak Esimerkiksi tarkastellaan VGG-16-arkkitehtuuria, verkkoa, joka saavutti 92,7 % tarkkuuden ImageNetin top-5-luokittelussa vuonna 2014: -![ImageNet-kerrokset](../../../../../translated_images/fi/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet-kerrokset](../../../../../translated_images/fi/vgg-16-arch1.d901a5583b3a51ba.webp) -![ImageNet-pyramidi](../../../../../translated_images/fi/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet-pyramidi](../../../../../translated_images/fi/vgg-16-arch.64ff2137f50dd49f.webp) > Kuva [Researchgate-sivustolta](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/fi/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/fi/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 600a6869..af2c2e96 100644 --- a/translations/fi/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/fi/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ Sinun tulee kouluttaa konvoluutio-neuroverkko luokittelemaan eri kissojen ja koi Käytämme [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/) -datakokonaisuutta, joka sisältää kuvia 37 eri koira- ja kissarodusta. -![Datakokonaisuus, jonka kanssa työskentelemme](../../../../../../translated_images/fi/data.50b2a9d5484bdbf0.png) +![Datakokonaisuus, jonka kanssa työskentelemme](../../../../../../translated_images/fi/data.50b2a9d5484bdbf0.webp) Ladataksesi datakokonaisuuden, käytä tätä koodinpätkää: diff --git a/translations/fi/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/fi/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index 0f3bd746..87108c7b 100644 --- a/translations/fi/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/fi/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "Visualisoidaksemme ihanteellisen kissan aloitamme satunnaisesta kohinakuvaasta ja yritämme käyttää gradienttilaskeutumisoptimointitekniikkaa säätääksemme kuvaa niin, että verkko tunnistaa kissan.\n", "\n", - "![Optimointisilmukka](../../../../../translated_images/fi/ideal-cat-loop.999fbb8ff306e044.png)\n", + "![Optimointisilmukka](../../../../../translated_images/fi/ideal-cat-loop.999fbb8ff306e044.webp)\n", "\n", "Tässä on aloituskuvamme:\n" ] diff --git a/translations/fi/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/fi/lessons/4-ComputerVision/08-TransferLearning/README.md index ad61f319..d81dcfcd 100644 --- a/translations/fi/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/fi/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Sekä Keras että PyTorch sisältävät toimintoja, joilla voi helposti ladata e Tässä on esimerkki piirteistä, jotka VGG-16-verkko on tunnistanut kissan kuvasta: -![Piirteet, jotka VGG-16 tunnisti](../../../../../translated_images/fi/features.6291f9c7ba3a0b95.png) +![Piirteet, jotka VGG-16 tunnisti](../../../../../translated_images/fi/features.6291f9c7ba3a0b95.webp) ## Kissojen ja koirien datasetti @@ -48,19 +48,19 @@ Esikoulutettu neuroverkko sisältää erilaisia kuvioita "aivoissaan", mukaan lu Yksi lähestymistapa on aloittaa satunnaisesta kuvasta ja yrittää käyttää **gradient descent -optimointitekniikkaa** säätämään kuvaa niin, että verkko alkaa ajatella sen olevan kissa. -![Kuvan optimointisilmukka](../../../../../translated_images/fi/ideal-cat-loop.999fbb8ff306e044.png) +![Kuvan optimointisilmukka](../../../../../translated_images/fi/ideal-cat-loop.999fbb8ff306e044.webp) Jos teemme näin, saamme jotain hyvin satunnaisen kohinan kaltaista. Tämä johtuu siitä, että *on monia tapoja saada verkko ajattelemaan, että syötekuva on kissa*, mukaan lukien sellaisia, jotka eivät ole visuaalisesti järkeviä. Vaikka nämä kuvat sisältävät paljon kissalle tyypillisiä kuvioita, mikään ei rajoita niitä olemaan visuaalisesti erottuvia. Tuloksen parantamiseksi voimme lisätä toisen termin häviöfunktioon, jota kutsutaan **variation loss** -termiksi. Se on mittari, joka osoittaa, kuinka samanlaisia kuvan vierekkäiset pikselit ovat. Variation lossin minimointi tekee kuvasta tasaisemman ja poistaa kohinaa - paljastaen visuaalisesti miellyttävämpiä kuvioita. Tässä esimerkki tällaisista "ihanteellisista" kuvista, jotka luokitellaan kissaksi ja seepraksi suurella todennäköisyydellä: -![Ihanteellinen kissa](../../../../../translated_images/fi/ideal-cat.203dd4597643d6b0.png) | ![Ihanteellinen seepra](../../../../../translated_images/fi/ideal-zebra.7f70e8b54ee15a7a.png) +![Ihanteellinen kissa](../../../../../translated_images/fi/ideal-cat.203dd4597643d6b0.webp) | ![Ihanteellinen seepra](../../../../../translated_images/fi/ideal-zebra.7f70e8b54ee15a7a.webp) -----|----- *Ihanteellinen kissa* | *Ihanteellinen seepra* Samaa lähestymistapaa voidaan käyttää suorittamaan niin sanottuja **adversaarisia hyökkäyksiä** neuroverkkoon. Oletetaan, että haluamme huijata neuroverkkoa ja saada koiran näyttämään kissalta. Jos otamme koiran kuvan, jonka verkko tunnistaa koiraksi, voimme sitten säätää sitä hieman gradient descent -optimoinnin avulla, kunnes verkko alkaa luokitella sen kissaksi: -![Koiran kuva](../../../../../translated_images/fi/original-dog.8f68a67d2fe0911f.png) | ![Kuva koirasta, joka luokitellaan kissaksi](../../../../../translated_images/fi/adversarial-dog.d9fc7773b0142b89.png) +![Koiran kuva](../../../../../translated_images/fi/original-dog.8f68a67d2fe0911f.webp) | ![Kuva koirasta, joka luokitellaan kissaksi](../../../../../translated_images/fi/adversarial-dog.d9fc7773b0142b89.webp) -----|----- *Alkuperäinen kuva koirasta* | *Kuva koirasta, joka luokitellaan kissaksi* diff --git a/translations/fi/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/fi/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index 964accf8..c3f2b9a3 100644 --- a/translations/fi/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/fi/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "Koska koulutamme autokooderia vangitsemaan mahdollisimman paljon alkuperäisen kuvan informaatiota tarkkaa rekonstruointia varten, verkko pyrkii löytämään parhaan mahdollisen **upotuksen** syötekuvien merkityksen vangitsemiseksi.\n", "\n", - "![Autokooderin kaavio](../../../../../translated_images/fi/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![Autokooderin kaavio](../../../../../translated_images/fi/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "> Kuva [Keras-blogista](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", diff --git a/translations/fi/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/fi/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index a2c23f90..173a455f 100644 --- a/translations/fi/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/fi/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "Koska koulutamme autokooderia vangitsemaan mahdollisimman paljon alkuperäisen kuvan informaatiota tarkkaa rekonstruointia varten, verkko pyrkii löytämään parhaan mahdollisen **upotuksen** syötekuvien merkityksen vangitsemiseksi.\n", "\n", - "![Autokooderin kaavio](../../../../../translated_images/fi/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![Autokooderin kaavio](../../../../../translated_images/fi/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "*Kuva [Keras-blogista](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", diff --git a/translations/fi/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/fi/lessons/4-ComputerVision/09-Autoencoders/README.md index d85e1a32..a2356fa6 100644 --- a/translations/fi/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/fi/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ Voimme kuitenkin haluta käyttää raakadataa (merkitsemätöntä) CNN-ominaisuu Koska koulutamme autokooderia tallentamaan mahdollisimman paljon alkuperäisen kuvan informaatiota tarkkaa rekonstruointia varten, verkko pyrkii löytämään parhaan **upotuksen** syötekuville merkityksen tallentamiseksi. -![Autokooderin kaavio](../../../../../translated_images/fi/autoencoder_schema.5e6fc9ad98a5eb61.jpg) +![Autokooderin kaavio](../../../../../translated_images/fi/autoencoder_schema.5e6fc9ad98a5eb61.webp) > Kuva [Keras-blogista](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/fi/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/fi/lessons/4-ComputerVision/11-ObjectDetection/README.md index c715cc51..aaeeb356 100644 --- a/translations/fi/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/fi/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ Kuvien luokittelumallit, joita olemme tähän mennessä käsitelleet, ottavat ku ## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Objektien tunnistus](../../../../../translated_images/fi/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) +![Objektien tunnistus](../../../../../translated_images/fi/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp) > Kuva [YOLO v2 -verkkosivustolta](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ Oletetaan, että haluaisimme löytää kissan kuvasta. Hyvin yksinkertainen läh 2. Suorita kuvien luokittelu jokaiselle ruudulle. 3. Ruudut, jotka tuottavat riittävän korkean aktivoinnin, voidaan katsoa sisältävän kyseisen objektin. -![Naiivi objektien tunnistus](../../../../../translated_images/fi/naive-detection.e7f1ba220ccd08c6.png) +![Naiivi objektien tunnistus](../../../../../translated_images/fi/naive-detection.e7f1ba220ccd08c6.webp) > *Kuva [harjoitusmuistiosta](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ Tässä tehtävässä saatat törmätä seuraaviin tietoaineistoihin: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) – 20 luokkaa * [COCO](http://cocodataset.org/#home) – Common Objects in Context. 80 luokkaa, rajauslaatikot ja segmentointimaskit -![COCO](../../../../../translated_images/fi/coco-examples.71bc60380fa6cceb.jpg) +![COCO](../../../../../translated_images/fi/coco-examples.71bc60380fa6cceb.webp) ## Objektien tunnistuksen mittarit @@ -50,7 +50,7 @@ Tässä tehtävässä saatat törmätä seuraaviin tietoaineistoihin: Kuvien luokittelussa algoritmin suorituskyvyn mittaaminen on helppoa, mutta objektien tunnistuksessa meidän täytyy mitata sekä luokan oikeellisuus että ennustetun rajauslaatikon sijainnin tarkkuus. Jälkimmäistä varten käytämme niin kutsuttua **Intersection over Union** (IoU) -mittaria, joka mittaa, kuinka hyvin kaksi laatikkoa (tai kaksi satunnaista aluetta) menevät päällekkäin. -![IoU](../../../../../translated_images/fi/iou_equation.9a4751d40fff4e11.png) +![IoU](../../../../../translated_images/fi/iou_equation.9a4751d40fff4e11.webp) > *Kuva 2 [tästä erinomaisesta IoU-blogikirjoituksesta](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ Objektien tunnistusalgoritmit voidaan jakaa kahteen pääluokkaan: [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) käyttää [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) -menetelmää luomaan hierarkkisen rakenteen ROI-alueista, jotka sitten syötetään CNN-ominaisuuksien erottimiin ja SVM-luokittelijoihin objektin luokan määrittämiseksi, sekä lineaariseen regressioon *rajauslaatikon* koordinaattien määrittämiseksi. [Virallinen artikkeli](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/fi/rcnn1.cae407020dfb1d1f.png) +![RCNN](../../../../../translated_images/fi/rcnn1.cae407020dfb1d1f.webp) > *Kuva van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/fi/rcnn2.2d9530bb83516484.png) +![RCNN-1](../../../../../translated_images/fi/rcnn2.2d9530bb83516484.webp) > *Kuvat [tästä blogista](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -110,7 +110,7 @@ Objektien tunnistusalgoritmit voidaan jakaa kahteen pääluokkaan: Tämä lähestymistapa on samanlainen kuin R-CNN, mutta alueet määritellään vasta konvoluutiokerrosten soveltamisen jälkeen. -![FRCNN](../../../../../translated_images/fi/f-rcnn.3cda6d9bb4188875.png) +![FRCNN](../../../../../translated_images/fi/f-rcnn.3cda6d9bb4188875.webp) > Kuva [virallisesta artikkelista](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ Tämä lähestymistapa on samanlainen kuin R-CNN, mutta alueet määritellään Tämän lähestymistavan pääidea on käyttää neuroverkkoa ennustamaan ROI:t – niin kutsuttu *Region Proposal Network*. [Artikkeli](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/fi/faster-rcnn.8d46c099b87ef30a.png) +![FasterRCNN](../../../../../translated_images/fi/faster-rcnn.8d46c099b87ef30a.webp) > Kuva [virallisesta artikkelista](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ Tämä algoritmi on jopa nopeampi kuin Faster R-CNN. Pääidea on seuraava: 2. Ominaisuudet käsitellään **Position-Sensitive Score Map** -kartalla. Jokainen objekti luokasta $C$ jaetaan $k\times k$ alueisiin, ja verkkoa koulutetaan ennustamaan objektien osia. 3. Jokaiselle osalle $k\times k$ alueista kaikki verkot äänestävät objektin luokista, ja eniten ääniä saanut luokka valitaan. -![r-fcn kuva](../../../../../translated_images/fi/r-fcn.13eb88158b99a3da.png) +![r-fcn kuva](../../../../../translated_images/fi/r-fcn.13eb88158b99a3da.webp) > Kuva [virallisesta artikkelista](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO on reaaliaikainen yhden passin algoritmi. Pääidea on seuraava: * Kuva jaetaan $S\times S$ alueisiin. * Jokaiselle alueelle **CNN** ennustaa $n$ mahdollista objektia, *rajauslaatikon* koordinaatit ja *luottamus*=*todennäköisyys* * IoU. - ![YOLO](../../../../../translated_images/fi/yolo.a2648ec82ee8bb4e.png) + ![YOLO](../../../../../translated_images/fi/yolo.a2648ec82ee8bb4e.webp) > Kuva [virallisesta artikkelista](https://arxiv.org/abs/1506.02640) diff --git a/translations/fi/lessons/4-ComputerVision/README.md b/translations/fi/lessons/4-ComputerVision/README.md index 9c3d2b83..49d4a363 100644 --- a/translations/fi/lessons/4-ComputerVision/README.md +++ b/translations/fi/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Tietokonenäkö -![Yhteenveto tietokonenäön sisällöstä piirroksena](../../../../translated_images/fi/ai-computervision.6506ebebac3fbf76.png) +![Yhteenveto tietokonenäön sisällöstä piirroksena](../../../../translated_images/fi/ai-computervision.6506ebebac3fbf76.webp) Tässä osiossa opimme seuraavista aiheista: diff --git a/translations/fi/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/fi/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index 588b6bb6..c0f96361 100644 --- a/translations/fi/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/fi/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**Bag of Words** (BoW) -vektoriesitys on yleisimmin käytetty perinteinen vektoriesitys. Jokainen sana on yhdistetty vektorin indeksiin, ja vektorin elementti sisältää sanan esiintymiskertojen määrän tietyssä dokumentissa.\n", "\n", - "![Kuva, joka näyttää, miten bag of words -vektoriesitys tallennetaan muistiin.](../../../../../translated_images/fi/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![Kuva, joka näyttää, miten bag of words -vektoriesitys tallennetaan muistiin.](../../../../../translated_images/fi/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Note**: Voit myös ajatella BoW:n olevan summa kaikista yksittäisten sanojen yksi-hot-koodatuista vektoreista tekstissä.\n", "\n", diff --git a/translations/fi/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/fi/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index f6489e80..7eccfd0e 100644 --- a/translations/fi/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/fi/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**Bag-of-words** (BoW) -vektoriesitys on perinteisistä vektoriesityksistä yksinkertaisin ymmärtää. Jokainen sana yhdistetään vektorin indeksiin, ja vektorin elementti sisältää kunkin sanan esiintymiskertojen määrän tietyssä dokumentissa.\n", "\n", - "![Kuva, joka näyttää, miten bag-of-words-vektoriesitys tallennetaan muistiin.](../../../../../translated_images/fi/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![Kuva, joka näyttää, miten bag-of-words-vektoriesitys tallennetaan muistiin.](../../../../../translated_images/fi/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Note**: Voit myös ajatella BoW:n olevan summa kaikista yksittäisten sanojen yksi-kuuma-koodatuista vektoreista tekstissä.\n", "\n", diff --git a/translations/fi/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/fi/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 17dd4751..d40247e2 100644 --- a/translations/fi/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/fi/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "Kun käytämme upotuskerrosta verkkomme ensimmäisenä kerroksena, voimme siirtyä bag-of-words-mallista **embedding bag** -malliin, jossa ensin muutamme jokaisen tekstimme sanan vastaavaksi upotukseksi ja sitten laskemme jonkin aggregaattifunktion kaikkien näiden upotusten yli, kuten `sum`, `average` tai `max`.\n", "\n", - "![Kuva, joka näyttää upotusluokittelijan viidelle sanajonolle.](../../../../../translated_images/fi/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![Kuva, joka näyttää upotusluokittelijan viidelle sanajonolle.](../../../../../translated_images/fi/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "Luokittelijaneuroverkkomme alkaa upotuskerroksella, sitten aggregaatiokerroksella ja sen päällä lineaarisella luokittelijalla:\n" ] @@ -176,7 +176,7 @@ "\n", "Edellisessä arkkitehtuurissa meidän täytyi täyttää kaikki sekvenssit samanpituisiksi, jotta ne sopisivat minibatchiin. Tämä ei ole tehokkain tapa esittää muuttuvan pituisia sekvenssejä – toinen lähestymistapa olisi käyttää **offset**-vektoria, joka sisältää kaikkien yhteen suureen vektoriin tallennettujen sekvenssien offsetit.\n", "\n", - "![Kuva, joka näyttää offset-sekvenssiesityksen](../../../../../translated_images/fi/offset-sequence-representation.eb73fcefb29b46ee.png)\n", + "![Kuva, joka näyttää offset-sekvenssiesityksen](../../../../../translated_images/fi/offset-sequence-representation.eb73fcefb29b46ee.webp)\n", "\n", "> **Huom**: Yllä olevassa kuvassa esitetään merkkijonosekvenssi, mutta esimerkissämme työskentelemme sanasekvenssien kanssa. Yleinen periaate sekvenssien esittämisestä offset-vektorilla pysyy kuitenkin samana.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW on nopeampi, kun taas skip-gram on hitaampi, mutta se edustaa harvinaisia sanoja paremmin.\n", "\n", - "![Kuva, joka näyttää sekä CBoW- että Skip-Gram-algoritmit sanojen muuntamiseksi vektoreiksi.](../../../../../translated_images/fi/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![Kuva, joka näyttää sekä CBoW- että Skip-Gram-algoritmit sanojen muuntamiseksi vektoreiksi.](../../../../../translated_images/fi/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Kokeillaksemme Word2Vec-upotusta, joka on esikoulutettu Google News -aineistolla, voimme käyttää **gensim**-kirjastoa. Alla etsimme sanoja, jotka ovat lähimpänä sanaa 'neural'.\n", "\n", diff --git a/translations/fi/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/fi/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index b2372a16..0f8b220d 100644 --- a/translations/fi/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/fi/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "Kun käytämme upotuskerrosta verkkomme ensimmäisenä kerroksena, voimme siirtyä sanojen pussi -mallista **upotuspussi**-malliin, jossa ensin muutamme tekstimme jokaisen sanan vastaavaksi upotukseksi ja sitten laskemme jonkin aggregaattifunktion näiden upotusten yli, kuten `sum`, `average` tai `max`.\n", "\n", - "![Kuva, joka näyttää upotusluokittelijan viidelle sekvenssisanalle.](../../../../../translated_images/fi/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![Kuva, joka näyttää upotusluokittelijan viidelle sekvenssisanalle.](../../../../../translated_images/fi/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "Luokittelijaneuroverkkomme koostuu seuraavista kerroksista:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW on nopeampi, kun taas skip-gram on hitaampi, mutta se edustaa harvinaisia sanoja paremmin.\n", "\n", - "![Kuva, joka näyttää sekä CBoW- että Skip-Gram-algoritmit sanojen muuntamiseksi vektoreiksi.](../../../../../translated_images/fi/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![Kuva, joka näyttää sekä CBoW- että Skip-Gram-algoritmit sanojen muuntamiseksi vektoreiksi.](../../../../../translated_images/fi/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Kokeillaksemme Word2Vec-upotusta, joka on esikoulutettu Google News -aineistolla, voimme käyttää **gensim**-kirjastoa. Alla etsimme sanoja, jotka ovat lähimpänä sanaa 'neural'.\n", "\n", diff --git a/translations/fi/lessons/5-NLP/14-Embeddings/README.md b/translations/fi/lessons/5-NLP/14-Embeddings/README.md index 30034ee8..dc222659 100644 --- a/translations/fi/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/fi/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ Upotuskerros ottaa sanan syötteenä ja tuottaa ulostulovektorin, jonka pituus o Käyttämällä upotuskerrosta luokittelijaverkkomme ensimmäisenä kerroksena voimme siirtyä bag-of-words-mallista **embedding bag** -malliin, jossa ensin muutamme tekstimme jokaisen sanan vastaavaksi upotukseksi ja laskemme sitten jonkin aggregaattifunktion näiden upotusten yli, kuten `sum`, `average` tai `max`. -![Kuva, joka näyttää upotusluokittelijan viidelle sanajonon sanalle.](../../../../../translated_images/fi/embedding-classifier-example.b77f021a7ee67eee.png) +![Kuva, joka näyttää upotusluokittelijan viidelle sanajonon sanalle.](../../../../../translated_images/fi/embedding-classifier-example.b77f021a7ee67eee.webp) > Kuva tekijältä @@ -40,7 +40,7 @@ Tämän saavuttamiseksi meidän täytyy esikouluttaa upotusmallimme suurella tek CBoW on nopeampi, kun taas skip-gram on hitaampi, mutta se edustaa harvinaisia sanoja paremmin. -![Kuva, joka näyttää sekä CBoW- että Skip-Gram-algoritmit sanojen muuntamiseksi vektoreiksi.](../../../../../translated_images/fi/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![Kuva, joka näyttää sekä CBoW- että Skip-Gram-algoritmit sanojen muuntamiseksi vektoreiksi.](../../../../../translated_images/fi/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Kuva [tästä artikkelista](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/fi/lessons/5-NLP/15-LanguageModeling/README.md b/translations/fi/lessons/5-NLP/15-LanguageModeling/README.md index f8adf102..0ef02353 100644 --- a/translations/fi/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/fi/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ Aiemmissa esimerkeissämme käytimme valmiiksi koulutettuja semanttisia upotuksi * **Continuous Bag-of-Words** (CBoW), jossa ennustamme keskimmäisen sanan $W_0$ sanajonossa $W_{-N}$, ..., $W_N$. * **Skip-gram**, jossa ennustamme joukon naapurisanoja {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} keskimmäisestä sanasta $W_0$. -![kuva paperista, jossa käsitellään sanojen muuntamista vektoreiksi](../../../../../translated_images/fi/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![kuva paperista, jossa käsitellään sanojen muuntamista vektoreiksi](../../../../../translated_images/fi/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Kuva [tästä paperista](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/fi/lessons/5-NLP/16-RNN/README.md b/translations/fi/lessons/5-NLP/16-RNN/README.md index 646cc29f..6f52a3b9 100644 --- a/translations/fi/lessons/5-NLP/16-RNN/README.md +++ b/translations/fi/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ Aiemmissa osioissa olemme käyttäneet tekstin semanttisia esityksiä ja yksinke Tekstijonon merkityksen vangitsemiseksi meidän on käytettävä toisenlaista neuronaaliverkkoarkkitehtuuria, jota kutsutaan **toistuvaksi neuronaaliverkoksi** eli RNN:ksi. RNN:ssä syötämme lauseen verkon läpi yksi symboli kerrallaan, ja verkko tuottaa jonkin **tilan**, jonka syötämme verkkoon uudelleen seuraavan symbolin kanssa. -![RNN](../../../../../translated_images/fi/rnn.27f5c29c53d727b5.png) +![RNN](../../../../../translated_images/fi/rnn.27f5c29c53d727b5.webp) > Kuva: kirjoittaja @@ -61,7 +61,7 @@ Olemme käsitelleet toistuvia verkkoja, jotka toimivat yhteen suuntaan, jaksosta Toistuva verkko, joko yksisuuntainen tai kaksisuuntainen, tunnistaa tiettyjä kuvioita jaksossa ja voi tallentaa ne tilavektoriin tai välittää ulostuloon. Kuten konvoluutiokerroksissa, voimme rakentaa toisen toistuvan kerroksen ensimmäisen päälle tunnistaaksemme korkeamman tason kuvioita ja rakentaaksemme matalan tason kuvioista, jotka ensimmäinen kerros on tunnistanut. Tämä johtaa käsitteeseen **monikerroksinen RNN**, joka koostuu kahdesta tai useammasta toistuvasta verkosta, joissa edellisen kerroksen ulostulo syötetään seuraavaan kerrokseen. -![Kuva, joka esittää monikerroksisen pitkäkestoisen muistiyksikön RNN:n](../../../../../translated_images/fi/multi-layer-lstm.dd975e29bb2a59fe.jpg) +![Kuva, joka esittää monikerroksisen pitkäkestoisen muistiyksikön RNN:n](../../../../../translated_images/fi/multi-layer-lstm.dd975e29bb2a59fe.webp) *Kuva [tästä upeasta artikkelista](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) kirjoittanut Fernando López* diff --git a/translations/fi/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/fi/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index ddbf7aa0..2d5b5efd 100644 --- a/translations/fi/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/fi/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "Toistuva verkko, olipa se yksisuuntainen tai kaksisuuntainen, tunnistaa tiettyjä kuvioita sekvenssissä ja voi tallentaa ne tilavektoriin tai välittää ne ulostuloon. Kuten konvoluutioneuroverkoissa, voimme rakentaa toisen toistuvan kerroksen ensimmäisen päälle tunnistamaan korkeamman tason kuvioita, jotka on muodostettu ensimmäisen kerroksen tunnistamista matalan tason kuvioista. Tämä johtaa käsitteeseen **monikerroksinen RNN**, joka koostuu kahdesta tai useammasta toistuvasta verkosta, joissa edellisen kerroksen ulostulo välitetään seuraavan kerroksen syötteeksi.\n", "\n", - "![Kuva, joka esittää monikerroksista long-short-term-memory-RNN:ää](../../../../../translated_images/fi/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![Kuva, joka esittää monikerroksista long-short-term-memory-RNN:ää](../../../../../translated_images/fi/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*Kuva [tästä upeasta artikkelista](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) kirjoittanut Fernando López*\n", "\n", diff --git a/translations/fi/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/fi/lessons/5-NLP/16-RNN/RNNTF.ipynb index 37d15800..2c1b3514 100644 --- a/translations/fi/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/fi/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "Tekstijonon merkityksen tallentamiseksi käytämme neuroverkkoarkkitehtuuria nimeltä **toistuva neuroverkko** (recurrent neural network, RNN). RNN:ää käytettäessä syötämme lauseen verkon läpi yksi token kerrallaan, ja verkko tuottaa jonkin **tilan**, jonka syötämme verkkoon uudelleen seuraavan tokenin kanssa.\n", "\n", - "![Kuva, joka esittää esimerkin toistuvan neuroverkon generoinnista.](../../../../../translated_images/fi/rnn.27f5c29c53d727b5.png)\n", + "![Kuva, joka esittää esimerkin toistuvan neuroverkon generoinnista.](../../../../../translated_images/fi/rnn.27f5c29c53d727b5.webp)\n", "\n", "Kun syötteenä on tokenien jono $X_0,\\dots,X_n$, RNN luo neuroverkkolohkojen sarjan ja kouluttaa tämän sarjan päästä päähän takaisinlevityksen avulla. Jokainen verkkolohko ottaa syötteenä parin $(X_i,S_i)$ ja tuottaa tuloksena $S_{i+1}$. Lopullinen tila $S_n$ tai tulos $Y_n$ syötetään lineaariseen luokittelijaan tuloksen tuottamiseksi. Kaikilla verkkolohkoilla on samat painot, ja ne koulutetaan päästä päähän yhden takaisinlevityskierroksen avulla.\n", "\n", @@ -369,7 +369,7 @@ "\n", "Toistuvat verkot, olivatpa ne yksisuuntaisia tai kaksisuuntaisia, tunnistavat sekvenssin sisäisiä kuvioita ja tallentavat ne tilavektoreihin tai palauttavat ne ulostulona. Kuten konvoluutiokerroksissa, voimme rakentaa toisen toistuvan kerroksen ensimmäisen jälkeen tunnistamaan korkeammantason kuvioita, jotka on muodostettu ensimmäisen kerroksen tunnistamista alemman tason kuvioista. Tämä johtaa käsitteeseen **monikerroksinen RNN**, joka koostuu kahdesta tai useammasta toistuvasta verkosta, joissa edellisen kerroksen ulostulo välitetään seuraavalle kerrokselle syötteenä.\n", "\n", - "![Kuva, joka esittää monikerroksista long-short-term-memory-RNN:ää](../../../../../translated_images/fi/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![Kuva, joka esittää monikerroksista long-short-term-memory-RNN:ää](../../../../../translated_images/fi/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*Kuva [tästä upeasta artikkelista](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) kirjoittanut Fernando López.*\n", "\n", diff --git a/translations/fi/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/fi/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index dcdb0a00..1fa469d2 100644 --- a/translations/fi/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/fi/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "Tapa, jolla koulutamme RNN:n tuottamaan tekstiä, on seuraava. Jokaisella askeleella otamme `nchars` merkin pituisen merkkijonon ja pyydämme verkkoa tuottamaan seuraavan ulostulomerkin jokaiselle syötemerkille:\n", "\n", - "![Kuva, joka näyttää esimerkin RNN:n tuottamasta sanasta 'HELLO'.](../../../../../translated_images/fi/rnn-generate.56c54afb52f9781d.png)\n", + "![Kuva, joka näyttää esimerkin RNN:n tuottamasta sanasta 'HELLO'.](../../../../../translated_images/fi/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "Riippuen todellisesta tilanteesta, saatamme haluta sisällyttää myös joitakin erikoismerkkejä, kuten *sekvenssin loppu* ``. Meidän tapauksessamme haluamme vain kouluttaa verkon loputtomaan tekstin tuottamiseen, joten kiinnitämme jokaisen sekvenssin koon `nchars`-merkkien pituiseksi. Näin ollen jokainen koulutusesimerkki koostuu `nchars` syötteistä ja `nchars` ulostuloista (jotka ovat syötesekvenssi siirrettynä yhden symbolin verran vasemmalle). Minibatch koostuu useista tällaisista sekvensseistä.\n", "\n", diff --git a/translations/fi/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/fi/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index 9c1a581c..4ab148cc 100644 --- a/translations/fi/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/fi/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -113,7 +113,7 @@ "\n", "Tapa, jolla koulutamme RNN:n luomaan uutisotsikoita, on seuraava. Jokaisella askeleella otamme yhden otsikon, joka syötetään RNN:ään, ja jokaiselle syötehahmolle pyydämme verkkoa tuottamaan seuraavan ulostulohahmon:\n", "\n", - "![Kuva, joka näyttää esimerkin RNN:n sanan 'HELLO' generoinnista.](../../../../../translated_images/fi/rnn-generate.56c54afb52f9781d.png)\n", + "![Kuva, joka näyttää esimerkin RNN:n sanan 'HELLO' generoinnista.](../../../../../translated_images/fi/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "Sekvenssimme viimeisen hahmon kohdalla pyydämme verkkoa tuottamaan ``-tokenin.\n", "\n", diff --git a/translations/fi/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/fi/lessons/5-NLP/17-GenerativeNetworks/README.md index d6c9edc8..43e2738c 100644 --- a/translations/fi/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/fi/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ RNN-arkkitehtuurissa, jota käsittelimme edellisessä osiossa, jokainen RNN-yksi Tämä mahdollistaa erilaiset neuroarkkitehtuurit, jotka näkyvät alla olevassa kuvassa: -![Kuva, joka näyttää yleisiä toistuvien neuroverkkojen malleja.](../../../../../translated_images/fi/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Kuva, joka näyttää yleisiä toistuvien neuroverkkojen malleja.](../../../../../translated_images/fi/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > Kuva blogikirjoituksesta [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) kirjoittanut [Andrej Karpaty](http://karpathy.github.io/) @@ -32,7 +32,7 @@ Tässä osiossa keskitymme yksinkertaisiin generatiivisiin malleihin, jotka autt Koulutamme tämän RNN:n tuottamaan tekstiä askel askeleelta. Jokaisessa vaiheessa otamme `nchars`-pituisen merkkisekvenssin ja pyydämme verkkoa tuottamaan seuraavan ulostulomerkin jokaiselle syötemerkille: -![Kuva, joka näyttää esimerkin RNN:n tuottamasta sanasta 'HELLO'.](../../../../../translated_images/fi/rnn-generate.56c54afb52f9781d.png) +![Kuva, joka näyttää esimerkin RNN:n tuottamasta sanasta 'HELLO'.](../../../../../translated_images/fi/rnn-generate.56c54afb52f9781d.webp) Kun tuotamme tekstiä (inferenssin aikana), aloitamme jollain **aloitustekstillä**, joka syötetään RNN-soluihin tuottamaan sen välimuistin, ja sitten tästä tilasta alkaa generointi. Tuotamme yhden merkin kerrallaan ja syötämme tilan ja tuotetun merkin seuraavaan RNN-soluun tuottamaan seuraavan, kunnes olemme tuottaneet tarpeeksi merkkejä. diff --git a/translations/fi/lessons/5-NLP/18-Transformers/README.md b/translations/fi/lessons/5-NLP/18-Transformers/README.md index f78d920d..a67fa3a7 100644 --- a/translations/fi/lessons/5-NLP/18-Transformers/README.md +++ b/translations/fi/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ RNN:ien avulla sekvenssi-sekvenssi toteutetaan kahdella toistuvalla verkolla, jo **Huomiomekanismit** tarjoavat tavan painottaa kunkin syötevektorin kontekstuaalista vaikutusta RNN:n kunkin ennusteen kohdalla. Tämä toteutetaan luomalla oikoteitä syötteen RNN:n välitilojen ja tuloksen RNN:n välille. Näin ollen, kun tuotetaan ulostulosymbolia yt, otamme huomioon kaikki syötteen piilotilat hi, eri painokertoimilla αt,i. -![Kuva, joka näyttää enkooderi/dekooderi-mallin additiivisella huomiokerroksella](../../../../../translated_images/fi/encoder-decoder-attention.7a726296894fb567.png) +![Kuva, joka näyttää enkooderi/dekooderi-mallin additiivisella huomiokerroksella](../../../../../translated_images/fi/encoder-decoder-attention.7a726296894fb567.webp) > Enkooderi-dekooderi-malli additiivisella huomiomekanismilla [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), lainattu [tästä blogikirjoituksesta](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) Huomiomatriisi {αi,j} edustaa sitä, kuinka paljon tietyt syötteen sanat vaikuttavat tietyn sanan tuottamiseen ulostulosekvenssissä. Alla on esimerkki tällaisesta matriisista: -![Kuva, joka näyttää esimerkkikohdistuksen RNNsearch-50:llä, otettu Bahdanau - arviz.org](../../../../../translated_images/fi/bahdanau-fig3.09ba2d37f202a6af.png) +![Kuva, joka näyttää esimerkkikohdistuksen RNNsearch-50:llä, otettu Bahdanau - arviz.org](../../../../../translated_images/fi/bahdanau-fig3.09ba2d37f202a6af.webp) > Kuva [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) @@ -66,7 +66,7 @@ Tuloksena saadaan positionaalinen upotus, joka upottaa sekä alkuperäisen token Seuraavaksi täytyy tunnistaa kuvioita sekvenssissä. Tätä varten transformerit käyttävät **itsehuomiomekanismia**, joka on käytännössä huomio, joka kohdistetaan samaan sekvenssiin syötteenä ja tuloksena. Itsehuomion soveltaminen mahdollistaa **kontekstin** huomioimisen lauseessa ja sen, mitkä sanat liittyvät toisiinsa. Esimerkiksi se auttaa tunnistamaan, mihin sanat kuten *se* viittaavat, ja ottaa kontekstin huomioon: -![](../../../../../translated_images/fi/CoreferenceResolution.861924d6d384a7d6.png) +![](../../../../../translated_images/fi/CoreferenceResolution.861924d6d384a7d6.webp) > Kuva [Googlen blogista](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Koska jokainen syötteen sijainti kartoitetaan itsenäisesti jokaiseen tuloksen **BERT** (Bidirectional Encoder Representations from Transformers) on erittäin suuri monikerroksinen transformer-verkko, jossa on 12 kerrosta *BERT-base*-mallissa ja 24 kerrosta *BERT-large*-mallissa. Malli esikoulutetaan ensin suurella tekstikorpuksella (Wikipedia + kirjat) käyttämällä valvomatonta koulutusta (ennustamalla peitettyjä sanoja lauseessa). Esikoulutuksen aikana malli omaksuu merkittävän määrän kielen ymmärrystä, jota voidaan hyödyntää muilla aineistoilla hienosäädön avulla. Tätä prosessia kutsutaan **siirto-oppimiseksi**. -![kuva osoitteesta http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/fi/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) +![kuva osoitteesta http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/fi/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > Kuva [lähde](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/fi/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/fi/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index 94c14e18..1097a445 100644 --- a/translations/fi/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/fi/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**Huomiomekanismit** tarjoavat tavan painottaa kunkin syötevektorin kontekstuaalista vaikutusta RNN:n jokaisessa tulosennusteessa. Tämä toteutetaan luomalla oikopolkuja syötteen RNN:n välitilojen ja tulos-RNN:n välille. Näin ollen, kun tuotetaan tulossymbolia $y_t$, otamme huomioon kaikki syötteen piilotilat $h_i$, eri painokertoimilla $\\alpha_{t,i}$. \n", "\n", - "![Kuva, joka näyttää enkooderi/dekooderi-mallin additiivisella huomiokerroksella](../../../../../translated_images/fi/encoder-decoder-attention.7a726296894fb567.png)\n", + "![Kuva, joka näyttää enkooderi/dekooderi-mallin additiivisella huomiokerroksella](../../../../../translated_images/fi/encoder-decoder-attention.7a726296894fb567.webp)\n", "*Enkooderi-dekooderi-malli additiivisella huomiomekanismilla [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), lainattu [tästä blogikirjoituksesta](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Huomiomatriisi $\\{\\alpha_{i,j}\\}$ edustaa sitä, kuinka paljon tietyt syötteen sanat vaikuttavat tietyn sanan muodostumiseen tulossekvenssissä. Alla on esimerkki tällaisesta matriisista:\n", "\n", - "![Kuva, joka näyttää esimerkkikohdistuksen RNNsearch-50:llä, otettu Bahdanau - arviz.org](../../../../../translated_images/fi/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![Kuva, joka näyttää esimerkkikohdistuksen RNNsearch-50:llä, otettu Bahdanau - arviz.org](../../../../../translated_images/fi/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*Kuva otettu [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Kuva 3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) on erittäin suuri monikerroksinen transformer-verkko, jossa on 12 kerrosta *BERT-base*-mallissa ja 24 kerrosta *BERT-large*-mallissa. Malli esikoulutetaan ensin suurella tekstikorpuksella (Wikipedia + kirjat) käyttämällä valvomattua koulutusta (ennustamalla peitettyjä sanoja lauseessa). Esikoulutuksen aikana malli omaksuu merkittävän määrän kielen ymmärrystä, jota voidaan hyödyntää muilla aineistoilla hienosäädön avulla. Tätä prosessia kutsutaan **siirto-oppimiseksi**. \n", "\n", - "![Kuva osoitteesta http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/fi/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![Kuva osoitteesta http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/fi/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "Transformer-arkkitehtuureista on monia variaatioita, kuten BERT, DistilBERT, BigBird, OpenGPT3 ja muita, joita voidaan hienosäätää. [HuggingFace-paketti](https://github.com/huggingface/) tarjoaa kirjaston monien näiden arkkitehtuurien kouluttamiseen PyTorchilla. \n", "\n", diff --git a/translations/fi/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/fi/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index d89a337d..6fb6fb6e 100644 --- a/translations/fi/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/fi/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**Huomiomekanismit** tarjoavat keinon painottaa kunkin syötevektorin kontekstuaalista vaikutusta RNN:n jokaisessa tulosennusteessa. Tämä toteutetaan luomalla oikopolkuja syötteen RNN:n välitilojen ja tulos-RNN:n välille. Näin ollen, kun tuotetaan tulossymbolia $y_t$, otamme huomioon kaikki syötteen piilotilat $h_i$, eri painokertoimilla $\\alpha_{t,i}$. \n", "\n", - "![Kuva, joka esittää enkooderi/dekooderi-mallin additiivisella huomiokerroksella](../../../../../translated_images/fi/encoder-decoder-attention.7a726296894fb567.png)\n", + "![Kuva, joka esittää enkooderi/dekooderi-mallin additiivisella huomiokerroksella](../../../../../translated_images/fi/encoder-decoder-attention.7a726296894fb567.webp)\n", "*Enkooderi-dekooderi-malli additiivisella huomiomekanismilla [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), lainattu [tästä blogikirjoituksesta](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Huomiomatriisi $\\{\\alpha_{i,j}\\}$ edustaa sitä, kuinka paljon tietyt syötteen sanat vaikuttavat tietyn sanan muodostumiseen tulosjaksossa. Alla on esimerkki tällaisesta matriisista:\n", "\n", - "![Kuva, joka näyttää esimerkkikohdistuksen RNNsearch-50:llä, otettu Bahdanau - arviz.org](../../../../../translated_images/fi/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![Kuva, joka näyttää esimerkkikohdistuksen RNNsearch-50:llä, otettu Bahdanau - arviz.org](../../../../../translated_images/fi/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*Kuva otettu [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Kuva 3)*\n", "\n", @@ -225,7 +225,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) on erittäin suuri monikerroksinen transformer-verkko, jossa on 12 kerrosta *BERT-base*-mallissa ja 24 kerrosta *BERT-large*-mallissa. Malli esikoulutetaan ensin suurella tekstiaineistolla (WikiPedia + kirjat) käyttämällä valvomatonta oppimista (ennustamalla peitettyjä sanoja lauseessa). Esikoulutuksen aikana malli omaksuu merkittävän määrän kielellistä ymmärrystä, jota voidaan hyödyntää muiden aineistojen kanssa hienosäädön avulla. Tätä prosessia kutsutaan **siirto-oppimiseksi**.\n", "\n", - "![kuva osoitteesta http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/fi/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![kuva osoitteesta http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/fi/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "Transformer-arkkitehtuureista on monia variaatioita, kuten BERT, DistilBERT, BigBird, OpenGPT3 ja muita, joita voidaan hienosäätää.\n", "\n", diff --git a/translations/fi/lessons/5-NLP/19-NER/README.md b/translations/fi/lessons/5-NLP/19-NER/README.md index b4ffe3eb..e8a39109 100644 --- a/translations/fi/lessons/5-NLP/19-NER/README.md +++ b/translations/fi/lessons/5-NLP/19-NER/README.md @@ -56,7 +56,7 @@ lapsella | O Koska meidän täytyy rakentaa yksi-yhteen vastaavuus tokenien ja luokkien välillä, voimme kouluttaa oikeanpuoleisen **moni-moniin** neuroverkkopohjaisen mallin tästä kuvasta: -![Kuva, joka esittää yleisiä toistuvien neuroverkkojen rakenteita.](../../../../../translated_images/fi/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Kuva, joka esittää yleisiä toistuvien neuroverkkojen rakenteita.](../../../../../translated_images/fi/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > *Kuva [tästä blogikirjoituksesta](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) kirjoittajalta [Andrej Karpathy](http://karpathy.github.io/). NER token-luokittelumallit vastaavat oikeanpuoleista verkkoarkkitehtuuria tässä kuvassa.* diff --git a/translations/fi/lessons/5-NLP/README.md b/translations/fi/lessons/5-NLP/README.md index ec5cd22a..57f7e776 100644 --- a/translations/fi/lessons/5-NLP/README.md +++ b/translations/fi/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Luonnollisen kielen käsittely -![Yhteenveto NLP-tehtävistä doodlena](../../../../translated_images/fi/ai-nlp.b22dcb8ca4707cea.png) +![Yhteenveto NLP-tehtävistä doodlena](../../../../translated_images/fi/ai-nlp.b22dcb8ca4707cea.webp) Tässä osiossa keskitymme käyttämään neuroverkkoja **luonnollisen kielen käsittelyyn (NLP)** liittyvien tehtävien ratkaisemiseen. On monia NLP-ongelmia, joita haluamme tietokoneiden pystyvän ratkaisemaan: diff --git a/translations/fi/lessons/6-Other/23-MultiagentSystems/README.md b/translations/fi/lessons/6-Other/23-MultiagentSystems/README.md index e1b100fb..7cb6e095 100644 --- a/translations/fi/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/fi/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ Voit avata yhden malleista, esimerkiksi **Biology → Flocking**. Kun avaat mallin, sinut ohjataan NetLogon pääruutuun. Tässä on esimerkkimalli, joka kuvaa susien ja lampaiden populaatiota rajallisten resurssien (ruohon) avulla. -![NetLogo Main Screen](../../../../../translated_images/fi/NetLogo-Main.32653711ec1a01b3.png) +![NetLogo Main Screen](../../../../../translated_images/fi/NetLogo-Main.32653711ec1a01b3.webp) > Kuvakaappaus Dmitry Soshnikovilta diff --git a/translations/fi/lessons/README.md b/translations/fi/lessons/README.md index 2c7a5c2a..082dffc8 100644 --- a/translations/fi/lessons/README.md +++ b/translations/fi/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Yleiskatsaus -![Yleiskatsaus piirroksena](../../../translated_images/fi/ai-overview.0857791951d19500.png) +![Yleiskatsaus piirroksena](../../../translated_images/fi/ai-overview.0857791951d19500.webp) > Piirrosmuistiinpano: [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/fi/lessons/X-Extras/X1-MultiModal/README.md b/translations/fi/lessons/X-Extras/X1-MultiModal/README.md index b2e8b1ec..71ee4781 100644 --- a/translations/fi/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/fi/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ Transformer-mallien menestyksen jälkeen NLP-tehtävissä samoja tai samankaltai CLIP:n pääidea on kyky verrata tekstikehotteita kuvaan ja määrittää, kuinka hyvin kuva vastaa kehotetta. -![CLIP-arkkitehtuuri](../../../../../translated_images/fi/clip-arch.b3dbf20b4e8ed8be.png) +![CLIP-arkkitehtuuri](../../../../../translated_images/fi/clip-arch.b3dbf20b4e8ed8be.webp) > *Kuva [tästä blogikirjoituksesta](https://openai.com/blog/clip/)* @@ -31,7 +31,7 @@ Kun tämä malli on esikoulutettu, sille voidaan antaa erä kuvia ja tekstikehot Oletetaan, että meidän täytyy luokitella kuvia esimerkiksi kissoihin, koiriin ja ihmisiin. Tässä tapauksessa voimme antaa mallille kuvan ja sarjan tekstikehotteita: "*kuva kissasta*", "*kuva koirasta*", "*kuva ihmisestä*". Tuloksena olevasta kolmen todennäköisyyden vektorista valitsemme vain indeksin, jolla on korkein arvo. -![CLIP kuvien luokitteluun](../../../../../translated_images/fi/clip-class.3af42ef0b2b19369.png) +![CLIP kuvien luokitteluun](../../../../../translated_images/fi/clip-class.3af42ef0b2b19369.webp) > *Kuva [tästä blogikirjoituksesta](https://openai.com/blog/clip/)* @@ -55,13 +55,13 @@ Lisätietoja VQGAN:sta löytyy [Taming Transformers](https://compvis.github.io/t Yksi tärkeä ero VQGAN:n ja perinteisen GAN:n välillä on, että jälkimmäinen voi tuottaa kelvollisen kuvan mistä tahansa syötevektorista, kun taas VQGAN todennäköisesti tuottaa kuvan, joka ei ole koherentti. Siksi kuvan luomisprosessia täytyy ohjata edelleen, ja tämä voidaan tehdä CLIP:llä. -![VQGAN+CLIP-arkkitehtuuri](../../../../../translated_images/fi/vqgan.5027fe05051dfa31.png) +![VQGAN+CLIP-arkkitehtuuri](../../../../../translated_images/fi/vqgan.5027fe05051dfa31.webp) Tuottaaksemme kuvan, joka vastaa tekstikehotetta, aloitamme satunnaisella koodausvektorilla, joka syötetään VQGAN:lle kuvan tuottamiseksi. Sitten CLIP:ä käytetään tuottamaan tappiofunktio, joka osoittaa, kuinka hyvin kuva vastaa tekstikehotetta. Tavoitteena on minimoida tämä tappio käyttämällä takaisinkytkentää syötevektorin parametrien säätämiseen. Loistava kirjasto, joka toteuttaa VQGAN+CLIP:n, on [Pixray](http://github.com/pixray/pixray). -![Pixray:n tuottama kuva](../../../../../translated_images/fi/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Pixray:n tuottama kuva](../../../../../translated_images/fi/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Pixray:n tuottama kuva](../../../../../translated_images/fi/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) +![Pixray:n tuottama kuva](../../../../../translated_images/fi/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp) | ![Pixray:n tuottama kuva](../../../../../translated_images/fi/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp) | ![Pixray:n tuottama kuva](../../../../../translated_images/fi/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp) ----|----|---- Kuva, joka on tuotettu kehotteesta *a closeup watercolor portrait of young male teacher of literature with a book* | Kuva, joka on tuotettu kehotteesta *a closeup oil portrait of young female teacher of computer science with a computer* | Kuva, joka on tuotettu kehotteesta *a closeup oil portrait of old male teacher of mathematics in front of blackboard* diff --git a/translations/he/README.md b/translations/he/README.md index 20d8e1cc..e1db5e0f 100644 --- a/translations/he/README.md +++ b/translations/he/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # בינה מלאכותית למתחילים - תוכנית לימודים -|![רישום סכמטי מאת @girlie_mac https://twitter.com/girlie_mac](../../translated_images/he/ai-overview.0857791951d19500.png)| +|![רישום סכמטי מאת @girlie_mac https://twitter.com/girlie_mac](../../translated_images/he/ai-overview.0857791951d19500.webp)| |:---:| | בינה מלאכותית למתחילים - _רישום סכמטי מאת [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/he/lessons/1-Intro/README.md b/translations/he/lessons/1-Intro/README.md index d83cdb9a..77360771 100644 --- a/translations/he/lessons/1-Intro/README.md +++ b/translations/he/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # מבוא לבינה מלאכותית -![סיכום תוכן מבוא לבינה מלאכותית בציור](../../../../translated_images/he/ai-intro.bf28d1ac4235881c.png) +![סיכום תוכן מבוא לבינה מלאכותית בציור](../../../../translated_images/he/ai-intro.bf28d1ac4235881c.webp) > ציור מאת [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: במקור, מחשבים הומצאו על ידי [צ'ארלס בבג'](https://en.wikipedia.org/wiki/Charles_Babbage) כדי לפעול על מספרים לפי תהליך מוגדר היטב - אלגוריתם. מחשבים מודרניים, למרות שהם מתקדמים בהרבה מהמודל המקורי שהוצע במאה ה-19, עדיין פועלים על פי אותו רעיון של חישובים מבוקרים. לכן, ניתן לתכנת מחשב לבצע משהו אם אנו יודעים את רצף הצעדים המדויק שעלינו לבצע כדי להשיג את המטרה. -![תמונה של אדם](../../../../translated_images/he/dsh_age.d212a30d4e54fb5f.png) +![תמונה של אדם](../../../../translated_images/he/dsh_age.d212a30d4e54fb5f.webp) > תמונה מאת [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ CO_OP_TRANSLATOR_METADATA: אחת הבעיות בהתמודדות עם המונח **[אינטליגנציה](https://en.wikipedia.org/wiki/Intelligence)** היא שאין הגדרה ברורה למונח זה. ניתן לטעון שאינטליגנציה קשורה ל**חשיבה מופשטת**, או ל**מודעות עצמית**, אך איננו יכולים להגדיר אותה כראוי. -![תמונה של חתול](../../../../translated_images/he/photo-cat.8c8e8fb760ffe457.jpg) +![תמונה של חתול](../../../../translated_images/he/photo-cat.8c8e8fb760ffe457.webp) > [תמונה](https://unsplash.com/photos/75715CVEJhI) מאת [Amber Kipp](https://unsplash.com/@sadmax) מ-Unsplash @@ -98,13 +98,13 @@ CO_OP_TRANSLATOR_METADATA: > | מה לגבי ML? | | > |--------------|-----------| -> | חלק מהבינה המלאכותית המבוסס על למידת מחשב לפתור בעיה על סמך נתונים מסוימים נקרא **למידת מכונה**. לא נעסוק בלמידת מכונה קלאסית בקורס זה - אנו מפנים אתכם לתוכנית הלימודים הנפרדת [למידת מכונה למתחילים](http://aka.ms/ml-beginners). | ![ML למתחילים](../../../../translated_images/he/ml-for-beginners.9e4fed176fd5817d.png) | +> | חלק מהבינה המלאכותית המבוסס על למידת מחשב לפתור בעיה על סמך נתונים מסוימים נקרא **למידת מכונה**. לא נעסוק בלמידת מכונה קלאסית בקורס זה - אנו מפנים אתכם לתוכנית הלימודים הנפרדת [למידת מכונה למתחילים](http://aka.ms/ml-beginners). | ![ML למתחילים](../../../../translated_images/he/ml-for-beginners.9e4fed176fd5817d.webp) | ## היסטוריה קצרה של בינה מלאכותית בינה מלאכותית החלה כתחום באמצע המאה ה-20. בתחילה, גישת ההסקה הסימבולית הייתה הגישה השלטת, והיא הובילה למספר הצלחות חשובות, כמו מערכות מומחה – תוכניות מחשב שיכלו לפעול כמומחה בתחומים מוגבלים. עם זאת, עד מהרה התברר שגישה זו אינה מתאימה להרחבה. חילוץ הידע ממומחה, ייצוגו במחשב, ושמירה על בסיס הידע מדויק מתבררים כמשימה מורכבת מאוד ויקרה מדי במקרים רבים. זה הוביל למה שנקרא [חורף הבינה המלאכותית](https://en.wikipedia.org/wiki/AI_winter) בשנות ה-70. -היסטוריה קצרה של AI +היסטוריה קצרה של AI > תמונה מאת [Dmitry Soshnikov](http://soshnikov.com) @@ -124,7 +124,7 @@ CO_OP_TRANSLATOR_METADATA: * עוזרים מודרניים, כמו Cortana, Siri או Google Assistant הם כולם מערכות היברידיות שמשתמשות ברשתות נוירונים כדי להמיר דיבור לטקסט ולהבין את כוונתנו, ואז משתמשות בהסקה או באלגוריתמים מפורשים כדי לבצע פעולות נדרשות. * בעתיד, אנו עשויים לצפות למודל מבוסס רשת נוירונים מלא שיטפל בדיאלוג בעצמו. משפחת הרשתות הנוירוניות GPT ו-[Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) מראה הצלחות גדולות בתחום זה. -התפתחות מבחן טיורינג +התפתחות מבחן טיורינג > תמונה מאת דמיטרי סושניקוב, [תמונה](https://unsplash.com/photos/r8LmVbUKgns) מאת [מרינה אברוסימובה](https://unsplash.com/@abrosimova_marina_foto), Unsplash ## מחקר AI עדכני diff --git a/translations/he/lessons/2-Symbolic/Animals.ipynb b/translations/he/lessons/2-Symbolic/Animals.ipynb index ee2613c2..6e47047b 100644 --- a/translations/he/lessons/2-Symbolic/Animals.ipynb +++ b/translations/he/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "בדוגמה זו, ניישם מערכת פשוטה מבוססת ידע כדי לזהות חיה על סמך כמה מאפיינים פיזיים. ניתן לייצג את המערכת באמצעות עץ AND-OR הבא (זהו חלק מהעץ המלא, ניתן להוסיף בקלות עוד חוקים):\n", "\n", - "![](../../../../translated_images/he/AND-OR-Tree.5592d2c70187f283.png)\n" + "![](../../../../translated_images/he/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { diff --git a/translations/he/lessons/2-Symbolic/README.md b/translations/he/lessons/2-Symbolic/README.md index eb22b6e6..31b93e9f 100644 --- a/translations/he/lessons/2-Symbolic/README.md +++ b/translations/he/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # ייצוג ידע ומערכות מומחה -![Summary of Symbolic AI content](../../../../translated_images/he/ai-symbolic.715a30cb610411a6.png) +![Summary of Symbolic AI content](../../../../translated_images/he/ai-symbolic.715a30cb610411a6.webp) > סקיצה מאת [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ CO_OP_TRANSLATOR_METADATA: לכן, הבעיה של **ייצוג ידע** היא למצוא דרך יעילה לייצג ידע בתוך מחשב בצורה של נתונים, כדי להפוך אותו לשימושי באופן אוטומטי. ניתן לראות זאת כספקטרום: -![Knowledge representation spectrum](../../../../translated_images/he/knowledge-spectrum.b60df631852c0217.png) +![Knowledge representation spectrum](../../../../translated_images/he/knowledge-spectrum.b60df631852c0217.webp) > תמונה מאת [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Block Syntax | Indent | | | אחד ההישגים הראשונים של AI סמלי היו מערכות מומחה - מערכות מחשב שנועדו לפעול כמומחה בתחום בעיה מוגבל. הן התבססו על **בסיס ידע** שהופק ממומחים אנושיים, וכללו **מנוע הסקה** שביצע הסקת מסקנות על בסיסו. -![Human Architecture](../../../../translated_images/he/arch-human.5d4d35f1bba3ab1c.png) | ![Knowledge-Based System](../../../../translated_images/he/arch-kbs.3ec5c150b09fa8da.png) +![Human Architecture](../../../../translated_images/he/arch-human.5d4d35f1bba3ab1c.webp) | ![Knowledge-Based System](../../../../translated_images/he/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ מבנה מפושט של מערכת עצבית אנושית | ארכיטקטורה של מערכת מבוססת ידע @@ -106,7 +106,7 @@ Block Syntax | Indent | | | לדוגמה, נבחן את מערכת המומחה הבאה לקביעת בעל חיים על בסיס מאפייניו הפיזיים: -![AND-OR Tree](../../../../translated_images/he/AND-OR-Tree.5592d2c70187f283.png) +![AND-OR Tree](../../../../translated_images/he/AND-OR-Tree.5592d2c70187f283.webp) > תמונה מאת [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/he/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/he/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index 9c34ca09..e38b993a 100644 --- a/translations/he/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/he/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -1255,7 +1255,7 @@ "* הפסד אימון נמוך - המודל יכול להתאים היטב לנתוני האימון, כי יש לו מספיק כוח ביטוי.\n", "* הפסד האימות יכול להיות גבוה בהרבה מהפסד האימון ואף להתחיל לעלות במהלך האימון - זה קורה כי המודל \"זוכר\" את נקודות האימון ומאבד את \"התמונה הכללית\".\n", "\n", - "![התאמת יתר](../../../../../translated_images/he/overfit.a0bd57f717c15769.png)\n", + "![התאמת יתר](../../../../../translated_images/he/overfit.a0bd57f717c15769.webp)\n", "\n", "> בתמונה הזו, `x` מייצג נתוני אימון, `o` - נתוני אימות. משמאל - מודל ליניארי (חד-שכבתי), הוא מתאים את טבע הנתונים בצורה טובה. מימין - מודל עם התאמת יתר, המודל מתאים בצורה מושלמת לנתוני האימון, אבל מפסיק להיות הגיוני עם כל נתון אחר (שגיאת האימות גבוהה מאוד).\n" ] diff --git a/translations/he/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/he/lessons/3-NeuralNetworks/05-Frameworks/README.md index f90d82a2..c7924ad3 100644 --- a/translations/he/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/he/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Overfitting הוא מושג חשוב ביותר בלמידת מכונה, וחש שקלו את הבעיה הבאה של התאמת 5 נקודות (מיוצגות על ידי `x` בגרפים למטה): -![linear](../../../../../translated_images/he/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/he/overfit2.131f5800ae10ca5e.jpg) +![linear](../../../../../translated_images/he/overfit1.f24b71c6f652e59e.webp) | ![overfit](../../../../../translated_images/he/overfit2.131f5800ae10ca5e.webp) -------------------------|-------------------------- **מודל ליניארי, 2 פרמטרים** | **מודל לא ליניארי, 7 פרמטרים** שגיאת אימון = 5.3 | שגיאת אימון = 0 @@ -79,7 +79,7 @@ Overfitting הוא מושג חשוב ביותר בלמידת מכונה, וחש כפי שניתן לראות מהגרף למעלה, ניתן לזהות Overfitting על ידי שגיאת אימון נמוכה מאוד ושגיאת ולידציה גבוהה. בדרך כלל במהלך האימון נראה ששגיאות האימון והוולידציה מתחילות לרדת, ואז בשלב מסוים שגיאת הוולידציה עשויה להפסיק לרדת ולהתחיל לעלות. זה יהיה סימן ל-Overfitting, ואינדיקציה לכך שכדאי להפסיק את האימון בנקודה זו (או לפחות לשמור עותק של המודל). -![overfitting](../../../../../translated_images/he/Overfitting.408ad91cd90b4371.png) +![overfitting](../../../../../translated_images/he/Overfitting.408ad91cd90b4371.webp) ## כיצד למנוע Overfitting diff --git a/translations/he/lessons/3-NeuralNetworks/README.md b/translations/he/lessons/3-NeuralNetworks/README.md index 1ac27032..95411e5b 100644 --- a/translations/he/lessons/3-NeuralNetworks/README.md +++ b/translations/he/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # מבוא לרשתות עצביות -![סיכום תוכן מבוא לרשתות עצביות בציור](../../../../translated_images/he/ai-neuralnetworks.1c687ae40bc86e83.png) +![סיכום תוכן מבוא לרשתות עצביות בציור](../../../../translated_images/he/ai-neuralnetworks.1c687ae40bc86e83.webp) כפי שדיברנו במבוא, אחת הדרכים להשיג אינטליגנציה היא לאמן **מודל מחשב** או **מוח מלאכותי**. מאז אמצע המאה ה-20, חוקרים ניסו מודלים מתמטיים שונים, עד שבשנים האחרונות כיוון זה הוכיח את עצמו כהצלחה גדולה. מודלים מתמטיים כאלה של המוח נקראים **רשתות עצביות**. @@ -36,13 +36,13 @@ CO_OP_TRANSLATOR_METADATA: מהביולוגיה אנו יודעים שהמוח שלנו מורכב מתאי עצב (נוירונים), שלכל אחד מהם יש מספר "קלטים" (דנדריטים) ו"פלט" אחד (אקסון). הן הדנדריטים והן האקסונים יכולים להוליך אותות חשמליים, והחיבורים ביניהם — הידועים כסינפסות — יכולים להציג דרגות שונות של מוליכות, שמוסדרות על ידי נוירוטרנסמיטורים. -![מודל של נוירון](../../../../translated_images/he/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![מודל של נוירון](../../../../translated_images/he/artneuron.1a5daa88d20ebe6f.png) +![מודל של נוירון](../../../../translated_images/he/synapse-wikipedia.ed20a9e4726ea1c6.webp) | ![מודל של נוירון](../../../../translated_images/he/artneuron.1a5daa88d20ebe6f.webp) ----|---- נוירון אמיתי *([תמונה](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) מוויקיפדיה)* | נוירון מלאכותי *(תמונה מאת המחבר)* לכן, המודל המתמטי הפשוט ביותר של נוירון מכיל מספר קלטים X1, ..., XN ופלט Y, וסדרה של משקלים W1, ..., WN. הפלט מחושב כך: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) כאשר f היא **פונקציית הפעלה** לא ליניארית. diff --git a/translations/he/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/he/lessons/4-ComputerVision/06-IntroCV/README.md index 69f6a491..8050a666 100644 --- a/translations/he/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/he/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) * **עיבוד מקדים של צילום ספר ברייל**. אנו מתמקדים כיצד ניתן להשתמש בסף, זיהוי תכונות, טרנספורמציית פרספקטיבה ומניפולציות NumPy כדי להפריד סמלי ברייל בודדים לסיווג נוסף על ידי רשת נוירונים. -![תמונה של ברייל](../../../../../translated_images/he/braille.341962ff76b1bd70.jpeg) | ![תמונה מעובדת של ברייל](../../../../../translated_images/he/braille-result.46530fea020b03c7.png) | ![סמלי ברייל](../../../../../translated_images/he/braille-symbols.0159185ab69d5339.png) +![תמונה של ברייל](../../../../../translated_images/he/braille.341962ff76b1bd70.webp) | ![תמונה מעובדת של ברייל](../../../../../translated_images/he/braille-result.46530fea020b03c7.webp) | ![סמלי ברייל](../../../../../translated_images/he/braille-symbols.0159185ab69d5339.webp) ----|-----|----- > תמונה מתוך [OpenCV.ipynb](OpenCV.ipynb) * **זיהוי תנועה בווידאו באמצעות הבדל פריימים**. אם המצלמה קבועה, אז הפריימים מהמצלמה צריכים להיות די דומים זה לזה. מכיוון שפריימים מיוצגים כמערכים, פשוט על ידי חיסור המערכים של שני פריימים עוקבים נקבל את ההבדל בפיקסלים, שאמור להיות נמוך עבור פריימים סטטיים, ולהפוך לגבוה יותר כאשר יש תנועה משמעותית בתמונה. -![תמונה של פריימים בווידאו והבדלי פריימים](../../../../../translated_images/he/frame-difference.706f805491a0883c.png) +![תמונה של פריימים בווידאו והבדלי פריימים](../../../../../translated_images/he/frame-difference.706f805491a0883c.webp) > תמונה מתוך [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) - **זרימה אופטית צפופה** מחשבת את שדה הווקטורים שמראה לכל פיקסל לאן הוא נע. - **זרימה אופטית דלילה** מבוססת על לקיחת תכונות ייחודיות בתמונה (למשל, קצוות), ובניית מסלולן מפריים לפריים. -![תמונה של זרימה אופטית](../../../../../translated_images/he/optical.1f4a94464579a83a.png) +![תמונה של זרימה אופטית](../../../../../translated_images/he/optical.1f4a94464579a83a.webp) > תמונה מתוך [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/he/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/he/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 474c5bad..bc3ad896 100644 --- a/translations/he/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/he/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 היא רשת שהשיגה דיוק של 92.7% בסיווג ImageNet top-5 בשנת 2014. יש לה את מבנה השכבות הבא: -![שכבות ImageNet](../../../../../translated_images/he/vgg-16-arch1.d901a5583b3a51ba.jpg) +![שכבות ImageNet](../../../../../translated_images/he/vgg-16-arch1.d901a5583b3a51ba.webp) כפי שניתן לראות, VGG עוקבת אחר ארכיטקטורת פירמידה מסורתית, שהיא רצף של שכבות קונבולוציה-פולינג. -![פירמידת ImageNet](../../../../../translated_images/he/vgg-16-arch.64ff2137f50dd49f.jpg) +![פירמידת ImageNet](../../../../../translated_images/he/vgg-16-arch.64ff2137f50dd49f.webp) > תמונה מ-[Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/he/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/he/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index 61e874ef..558c6084 100644 --- a/translations/he/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/he/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -260,7 +260,7 @@ "\n", "לכן, ברשת CNN טיפוסית יהיו כמה שכבות קונבולוציה, עם שכבות pooling ביניהן כדי להקטין את הממדים של התמונה. כמו כן, נגדיל את מספר הפילטרים, מכיוון שככל שהדפוסים הופכים למתקדמים יותר - יש יותר שילובים מעניינים שצריך לחפש.\n", "\n", - "![תמונה המציגה כמה שכבות קונבולוציה עם שכבות pooling.](../../../../../translated_images/he/cnn-pyramid.85915455759ef0ce.png)\n", + "![תמונה המציגה כמה שכבות קונבולוציה עם שכבות pooling.](../../../../../translated_images/he/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "בגלל הקטנת הממדים המרחביים והגדלת ממדי התכונות/הפילטרים, ארכיטקטורה זו נקראת גם **ארכיטקטורת פירמידה**.\n" ] diff --git a/translations/he/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/he/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index b78a4a12..462a586f 100644 --- a/translations/he/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/he/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -360,7 +360,7 @@ "\n", "לכן, ברשת CNN טיפוסית יהיו מספר שכבות קונבולוציוניות, עם שכבות pooling ביניהן כדי להקטין את ממדי התמונה. בנוסף, נגדיל את מספר הפילטרים, מכיוון שככל שהדפוסים הופכים למתקדמים יותר - יש יותר שילובים מעניינים שצריך לחפש.\n", "\n", - "![תמונה המציגה מספר שכבות קונבולוציוניות עם שכבות pooling.](../../../../../translated_images/he/cnn-pyramid.85915455759ef0ce.png)\n", + "![תמונה המציגה מספר שכבות קונבולוציוניות עם שכבות pooling.](../../../../../translated_images/he/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "בגלל הקטנת הממדים המרחביים והגדלת ממדי הפיצ'רים/פילטרים, הארכיטקטורה הזו נקראת גם **ארכיטקטורת פירמידה**.\n" ] diff --git a/translations/he/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/he/lessons/4-ComputerVision/07-ConvNets/README.md index 932d1073..ef264fd1 100644 --- a/translations/he/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/he/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: כדי לחלץ תבניות, נשתמש במושג של **פילטרים קונבולוציוניים**. כפי שאתם יודעים, תמונה מיוצגת על ידי מטריצה דו-ממדית, או טנזור תלת-ממדי עם עומק צבע. החלת פילטר פירושה שאנחנו לוקחים מטריצת **ליבת פילטר** קטנה יחסית, ולכל פיקסל בתמונה המקורית מחשבים ממוצע משוקלל עם הנקודות השכנות. ניתן לראות זאת כמו חלון קטן שגולש על פני כל התמונה, וממוצע את כל הפיקסלים לפי המשקלים במטריצת ליבת הפילטר. -![פילטר קצה אנכי](../../../../../translated_images/he/filter-vert.b7148390ca0bc356.png) | ![פילטר קצה אופקי](../../../../../translated_images/he/filter-horiz.59b80ed4feb946ef.png) +![פילטר קצה אנכי](../../../../../translated_images/he/filter-vert.b7148390ca0bc356.webp) | ![פילטר קצה אופקי](../../../../../translated_images/he/filter-horiz.59b80ed4feb946ef.webp) ----|---- > תמונה מאת דמיטרי סושניקוב @@ -38,7 +38,7 @@ CO_OP_TRANSLATOR_METADATA: * ניתן לעצב את הרשת כך שהפילטרים יותאמו באופן אוטומטי * ניתן להשתמש באותה גישה כדי למצוא תבניות בתכונות ברמה גבוהה, ולא רק בתמונה המקורית. כך, חילוץ התכונות ב-CNN עובד על היררכיה של תכונות, החל משילובי פיקסלים ברמה נמוכה ועד לשילובים ברמה גבוהה של חלקי תמונה. -![חילוץ תכונות היררכי](../../../../../translated_images/he/FeatureExtractionCNN.d9b456cbdae7cb64.png) +![חילוץ תכונות היררכי](../../../../../translated_images/he/FeatureExtractionCNN.d9b456cbdae7cb64.webp) > תמונה מתוך [מאמר של Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), המבוסס על [המחקר שלהם](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ CO_OP_TRANSLATOR_METADATA: לדוגמה, בואו נסתכל על הארכיטקטורה של VGG-16, רשת שהשיגה דיוק של 92.7% בסיווג הטופ-5 של ImageNet בשנת 2014: -![שכבות ImageNet](../../../../../translated_images/he/vgg-16-arch1.d901a5583b3a51ba.jpg) +![שכבות ImageNet](../../../../../translated_images/he/vgg-16-arch1.d901a5583b3a51ba.webp) -![פירמידת ImageNet](../../../../../translated_images/he/vgg-16-arch.64ff2137f50dd49f.jpg) +![פירמידת ImageNet](../../../../../translated_images/he/vgg-16-arch.64ff2137f50dd49f.webp) > תמונה מתוך [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/he/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/he/lessons/4-ComputerVision/07-ConvNets/lab/README.md index e2b8eea2..ab1afdd4 100644 --- a/translations/he/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/he/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ CO_OP_TRANSLATOR_METADATA: נשתמש ב-[מאגר הנתונים של Oxford-IIIT Pet](https://www.robots.ox.ac.uk/~vgg/data/pets/), המכיל תמונות של 37 גזעים שונים של כלבים וחתולים. -![מאגר הנתונים שבו נעסוק](../../../../../../translated_images/he/data.50b2a9d5484bdbf0.png) +![מאגר הנתונים שבו נעסוק](../../../../../../translated_images/he/data.50b2a9d5484bdbf0.webp) כדי להוריד את מאגר הנתונים, השתמשו בקטע הקוד הבא: diff --git a/translations/he/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/he/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index 85fed709..77785c0b 100644 --- a/translations/he/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/he/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "כדי לדמיין את החתול האידיאלי, נתחיל עם תמונה של רעש אקראי, וננסה להשתמש בטכניקת אופטימיזציה של ירידת גרדיאנט כדי להתאים את התמונה כך שרשת תזהה חתול.\n", "\n", - "![לולאת אופטימיזציה](../../../../../translated_images/he/ideal-cat-loop.999fbb8ff306e044.png)\n", + "![לולאת אופטימיזציה](../../../../../translated_images/he/ideal-cat-loop.999fbb8ff306e044.webp)\n", "\n", "הנה התמונה ההתחלתית שלנו:\n" ] diff --git a/translations/he/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/he/lessons/4-ComputerVision/08-TransferLearning/README.md index 084e742a..3a6409d1 100644 --- a/translations/he/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/he/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ CO_OP_TRANSLATOR_METADATA: להלן דוגמה לתכונות שחולצו מתמונה של חתול על ידי רשת VGG-16: -![תכונות שחולצו על ידי VGG-16](../../../../../translated_images/he/features.6291f9c7ba3a0b95.png) +![תכונות שחולצו על ידי VGG-16](../../../../../translated_images/he/features.6291f9c7ba3a0b95.webp) ## מערך נתונים של חתולים וכלבים @@ -48,19 +48,19 @@ CO_OP_TRANSLATOR_METADATA: גישה אחת שנוכל לקחת היא להתחיל עם תמונה אקראית, ואז לנסות להשתמש בטכניקת **אופטימיזציה בירידת גרדיאנט** כדי להתאים את התמונה כך שהרשת תתחיל לחשוב שמדובר בחתול. -![לולאת אופטימיזציה של תמונה](../../../../../translated_images/he/ideal-cat-loop.999fbb8ff306e044.png) +![לולאת אופטימיזציה של תמונה](../../../../../translated_images/he/ideal-cat-loop.999fbb8ff306e044.webp) עם זאת, אם נעשה זאת, נקבל משהו שדומה מאוד לרעש אקראי. זאת מכיוון ש*יש הרבה דרכים לגרום לרשת לחשוב שהתמונה הקלט היא חתול*, כולל כאלה שאינן הגיוניות מבחינה חזותית. למרות שהתמונות הללו מכילות הרבה דפוסים אופייניים לחתול, אין שום דבר שמגביל אותן להיות מובחנות חזותית. כדי לשפר את התוצאה, נוכל להוסיף מונח נוסף לפונקציית ההפסד, שנקרא **הפסד וריאציה**. זהו מדד שמראה עד כמה פיקסלים סמוכים בתמונה דומים זה לזה. צמצום הפסד וריאציה הופך את התמונה לחלקה יותר ומסלק רעש - ובכך חושף דפוסים מושכים יותר מבחינה חזותית. הנה דוגמה לתמונות "אידיאליות" כאלה, שמסווגות כחתול וכזברה בהסתברות גבוהה: -![חתול אידיאלי](../../../../../translated_images/he/ideal-cat.203dd4597643d6b0.png) | ![זברה אידיאלית](../../../../../translated_images/he/ideal-zebra.7f70e8b54ee15a7a.png) +![חתול אידיאלי](../../../../../translated_images/he/ideal-cat.203dd4597643d6b0.webp) | ![זברה אידיאלית](../../../../../translated_images/he/ideal-zebra.7f70e8b54ee15a7a.webp) -----|----- *חתול אידיאלי* | *זברה אידיאלית* גישה דומה יכולה לשמש לביצוע מה שנקרא **התקפות עוינות** על רשת עצבית. נניח שאנחנו רוצים להטעות רשת עצבית ולגרום לכלב להיראות כמו חתול. אם ניקח תמונה של כלב, שמזוהה על ידי הרשת ככלב, נוכל לשנות אותה מעט באמצעות אופטימיזציה בירידת גרדיאנט, עד שהרשת תתחיל לסווג אותה כחתול: -![תמונה של כלב](../../../../../translated_images/he/original-dog.8f68a67d2fe0911f.png) | ![תמונה של כלב שמסווג כחתול](../../../../../translated_images/he/adversarial-dog.d9fc7773b0142b89.png) +![תמונה של כלב](../../../../../translated_images/he/original-dog.8f68a67d2fe0911f.webp) | ![תמונה של כלב שמסווג כחתול](../../../../../translated_images/he/adversarial-dog.d9fc7773b0142b89.webp) -----|----- *תמונה מקורית של כלב* | *תמונה של כלב שמסווג כחתול* diff --git a/translations/he/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/he/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index 8c49f6c1..af742faf 100644 --- a/translations/he/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/he/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "מכיוון שאנחנו מאמנים את האוטואנקודר ללכוד כמה שיותר מידע מהתמונה המקורית כדי לשחזר אותה בצורה מדויקת, הרשת מנסה למצוא את ה**הטמעה** הטובה ביותר של תמונות הקלט כדי ללכוד את המשמעות שלהן.\n", "\n", - "![תרשים אוטואנקודר](../../../../../translated_images/he/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![תרשים אוטואנקודר](../../../../../translated_images/he/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "> תמונה מתוך [הבלוג של Keras](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", diff --git a/translations/he/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/he/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index da8f005d..ab0c3ecd 100644 --- a/translations/he/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/he/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -21,7 +21,7 @@ "\n", "מכיוון שאנחנו מאמנים את האוטואנקודר ללכוד כמה שיותר מידע מהתמונה המקורית לצורך שחזור מדויק, הרשת מנסה למצוא את ה**הטמעה** הטובה ביותר של תמונות הקלט כדי ללכוד את המשמעות.\n", "\n", - "![תרשים אוטואנקודר](../../../../../translated_images/he/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![תרשים אוטואנקודר](../../../../../translated_images/he/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "*תמונה מתוך [הבלוג של Keras](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", diff --git a/translations/he/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/he/lessons/4-ComputerVision/09-Autoencoders/README.md index 42c95a88..d0f38c51 100644 --- a/translations/he/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/he/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: מכיוון שאנו מאמנים אוטואנקודר כדי ללכוד כמה שיותר מידע מהתמונה המקורית לצורך שחזור מדויק, הרשת מנסה למצוא את ה**הטמעה** הטובה ביותר של תמונות הקלט כדי ללכוד את המשמעות. -![AutoEncoder Diagram](../../../../../translated_images/he/autoencoder_schema.5e6fc9ad98a5eb61.jpg) +![AutoEncoder Diagram](../../../../../translated_images/he/autoencoder_schema.5e6fc9ad98a5eb61.webp) > תמונה מתוך [בלוג Keras](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/he/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/he/lessons/4-ComputerVision/11-ObjectDetection/README.md index 06c5d08b..dc647592 100644 --- a/translations/he/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/he/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: ## [שאלון לפני השיעור](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![זיהוי אובייקטים](../../../../../translated_images/he/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) +![זיהוי אובייקטים](../../../../../translated_images/he/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp) > תמונה מתוך [אתר YOLO v2](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 2. להריץ סיווג תמונה על כל אריח. 3. אריחים שמפיקים הפעלה גבוהה מספיק יכולים להיחשב ככאלה שמכילים את האובייקט המדובר. -![זיהוי נאיבי של אובייקטים](../../../../../translated_images/he/naive-detection.e7f1ba220ccd08c6.png) +![זיהוי נאיבי של אובייקטים](../../../../../translated_images/he/naive-detection.e7f1ba220ccd08c6.webp) > *תמונה מתוך [מחברת התרגילים](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ CO_OP_TRANSLATOR_METADATA: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 קטגוריות * [COCO](http://cocodataset.org/#home) - אובייקטים נפוצים בהקשר. 80 קטגוריות, תיבות גבול ומסכות סגמנטציה -![COCO](../../../../../translated_images/he/coco-examples.71bc60380fa6cceb.jpg) +![COCO](../../../../../translated_images/he/coco-examples.71bc60380fa6cceb.webp) ## מדדים לזיהוי אובייקטים @@ -50,7 +50,7 @@ CO_OP_TRANSLATOR_METADATA: בעוד שבסיווג תמונות קל למדוד עד כמה האלגוריתם מצליח, בזיהוי אובייקטים עלינו למדוד גם את נכונות הקטגוריה וגם את דיוק מיקום תיבת הגבול שחוזה האלגוריתם. עבור האחרון, אנו משתמשים במדד שנקרא **חיתוך על איחוד** (IoU), שמודד עד כמה שתי תיבות (או שני אזורים שרירותיים) חופפים. -![IoU](../../../../../translated_images/he/iou_equation.9a4751d40fff4e11.png) +![IoU](../../../../../translated_images/he/iou_equation.9a4751d40fff4e11.webp) > *איור 2 מתוך [פוסט בלוג מצוין על IoU](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ $$ [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) משתמשת ב-[חיפוש סלקטיבי](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) כדי ליצור מבנה היררכי של אזורי ROI, אשר מועברים לאחר מכן דרך מחלצי תכונות של CNN ומסווגי SVM כדי לקבוע את קטגוריית האובייקט, ורגרסיה ליניארית כדי לקבוע את קואורדינטות *תיבת הגבול*. [מאמר רשמי](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/he/rcnn1.cae407020dfb1d1f.png) +![RCNN](../../../../../translated_images/he/rcnn1.cae407020dfb1d1f.webp) > *תמונה מתוך van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/he/rcnn2.2d9530bb83516484.png) +![RCNN-1](../../../../../translated_images/he/rcnn2.2d9530bb83516484.webp) > *תמונות מתוך [הבלוג הזה](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -110,7 +110,7 @@ $$ גישה זו דומה ל-R-CNN, אך האזורים מוגדרים לאחר יישום שכבות הקונבולוציה. -![FRCNN](../../../../../translated_images/he/f-rcnn.3cda6d9bb4188875.png) +![FRCNN](../../../../../translated_images/he/f-rcnn.3cda6d9bb4188875.webp) > תמונה מתוך [המאמר הרשמי](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ $$ הרעיון המרכזי בגישה זו הוא להשתמש ברשת עצבית כדי לנבא ROI - מה שנקרא *רשת הצעת אזורים*. [מאמר](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/he/faster-rcnn.8d46c099b87ef30a.png) +![FasterRCNN](../../../../../translated_images/he/faster-rcnn.8d46c099b87ef30a.webp) > תמונה מתוך [המאמר הרשמי](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ $$ 2. התכונות מעובדות על ידי **מפת ניקוד רגישה למיקום**. כל אובייקט מתוך $C$ קטגוריות מחולק ל-$k\times k$ אזורים, ואנו מאמנים לחזות חלקים של אובייקטים. 3. עבור כל חלק מתוך $k\times k$ אזורים כל הרשתות מצביעות על קטגוריות אובייקטים, וקטגוריית האובייקט עם ההצבעה המקסימלית נבחרת. -![r-fcn image](../../../../../translated_images/he/r-fcn.13eb88158b99a3da.png) +![r-fcn image](../../../../../translated_images/he/r-fcn.13eb88158b99a3da.webp) > תמונה מתוך [המאמר הרשמי](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO הוא אלגוריתם בזמן אמת במעבר אחד. הרעיון ה * התמונה מחולקת ל-$S\times S$ אזורים. * עבור כל אזור, **CNN** מנבא $n$ אובייקטים אפשריים, *קואורדינטות תיבת הגבול* ו-*ביטחון*=*הסתברות* * IoU. - ![YOLO](../../../../../translated_images/he/yolo.a2648ec82ee8bb4e.png) + ![YOLO](../../../../../translated_images/he/yolo.a2648ec82ee8bb4e.webp) > תמונה מתוך [המאמר הרשמי](https://arxiv.org/abs/1506.02640) diff --git a/translations/he/lessons/4-ComputerVision/README.md b/translations/he/lessons/4-ComputerVision/README.md index 11c9719d..413ad21e 100644 --- a/translations/he/lessons/4-ComputerVision/README.md +++ b/translations/he/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # ראייה ממוחשבת -![סיכום תוכן ראייה ממוחשבת באיור](../../../../translated_images/he/ai-computervision.6506ebebac3fbf76.png) +![סיכום תוכן ראייה ממוחשבת באיור](../../../../translated_images/he/ai-computervision.6506ebebac3fbf76.webp) בפרק זה נלמד על: diff --git a/translations/he/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/he/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index 03ee4944..8ddc7ba6 100644 --- a/translations/he/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/he/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "ייצוג וקטורי **Bag of Words** (BoW) הוא הייצוג הווקטורי המסורתי הנפוץ ביותר. כל מילה מקושרת לאינדקס בווקטור, והאלמנט בווקטור מכיל את מספר ההופעות של מילה מסוימת במסמך נתון.\n", "\n", - "![תמונה שמראה כיצד ייצוג וקטורי בשיטת Bag of Words מיוצג בזיכרון.](../../../../../translated_images/he/bag-of-words-example.606fc1738f1d7ba9.png)\n", + "![תמונה שמראה כיצד ייצוג וקטורי בשיטת Bag of Words מיוצג בזיכרון.](../../../../../translated_images/he/bag-of-words-example.606fc1738f1d7ba9.webp)\n", "\n", "> **Note**: ניתן גם לחשוב על BoW כסכום של כל הווקטורים המקודדים בשיטת one-hot עבור המילים הבודדות בטקסט.\n", "\n", diff --git a/translations/he/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/he/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index e6dbfd35..16766060 100644 --- a/translations/he/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/he/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "ייצוג וקטורי בשיטת **Bag-of-words** (BoW) הוא הייצוג הווקטורי המסורתי הפשוט ביותר להבנה. כל מילה מקושרת לאינדקס בווקטור, ואלמנט בווקטור מכיל את מספר הפעמים שהמילה מופיעה במסמך נתון.\n", "\n", - "![תמונה שמציגה כיצד ייצוג וקטורי בשיטת Bag-of-words מיוצג בזיכרון.](../../../../../translated_images/he/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![תמונה שמציגה כיצד ייצוג וקטורי בשיטת Bag-of-words מיוצג בזיכרון.](../../../../../translated_images/he/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Note**: ניתן גם לחשוב על BoW כסכום של כל הווקטורים המקודדים בשיטת one-hot עבור המילים הבודדות בטקסט.\n", "\n", diff --git a/translations/he/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/he/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 51d7d7b2..72f05f9f 100644 --- a/translations/he/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/he/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "על ידי שימוש בשכבת הטמעה כשכבה הראשונה ברשת שלנו, נוכל לעבור ממודל bag-of-words למודל **embedding bag**, שבו קודם כל נמיר כל מילה בטקסט שלנו להטמעה המתאימה שלה, ואז נחשב פונקציית צבירה כלשהי על כל ההטמעות הללו, כמו `sum`, `average` או `max`.\n", "\n", - "![תמונה המציגה מסווג הטמעות עבור חמש מילים ברצף.](../../../../../translated_images/he/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![תמונה המציגה מסווג הטמעות עבור חמש מילים ברצף.](../../../../../translated_images/he/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "רשת העצבים המסווגת שלנו תתחיל עם שכבת הטמעה, לאחר מכן שכבת צבירה, ולבסוף מסווג ליניארי מעליה:\n" ] @@ -176,7 +176,7 @@ "\n", "בארכיטקטורה הקודמת, היינו צריכים לרפד את כל הרצפים לאורך אחיד כדי להתאים אותם למיני-באטץ'. זו לא הדרך היעילה ביותר לייצג רצפים באורך משתנה - גישה אחרת תהיה להשתמש בוקטור **offset**, שמחזיק את ההיסטים של כל הרצפים המאוחסנים בוקטור גדול אחד.\n", "\n", - "![תמונה המציגה ייצוג רצף עם היסטים](../../../../../translated_images/he/offset-sequence-representation.eb73fcefb29b46ee.png)\n", + "![תמונה המציגה ייצוג רצף עם היסטים](../../../../../translated_images/he/offset-sequence-representation.eb73fcefb29b46ee.webp)\n", "\n", "> **Note**: בתמונה למעלה, אנו מציגים רצף של תווים, אך בדוגמה שלנו אנו עובדים עם רצפים של מילים. עם זאת, העיקרון הכללי של ייצוג רצפים באמצעות וקטור היסטים נשאר זהה.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW מהיר יותר, בעוד ש-Skip-Gram איטי יותר, אך מבצע עבודה טובה יותר בייצוג מילים נדירות.\n", "\n", - "![תמונה המציגה את האלגוריתמים CBoW ו-Skip-Gram להמרת מילים לוקטורים.](../../../../../translated_images/he/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![תמונה המציגה את האלגוריתמים CBoW ו-Skip-Gram להמרת מילים לוקטורים.](../../../../../translated_images/he/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "כדי להתנסות בהטמעת Word2Vec שאומנה מראש על מאגר הנתונים של Google News, נוכל להשתמש בספריית **gensim**. להלן נמצא את המילים שהכי דומות ל-'neural'\n", "\n", diff --git a/translations/he/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/he/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index 2d396003..582046aa 100644 --- a/translations/he/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/he/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "על ידי שימוש בשכבת embedding כשכבה הראשונה ברשת שלנו, אנחנו יכולים לעבור ממודל bag-of-words למודל **embedding bag**, שבו תחילה אנו ממירים כל מילה בטקסט שלנו ל-embedding המתאים לה, ואז מחשבים פונקציית צבירה כלשהי על כל ה-embeddings, כמו `sum`, `average` או `max`.\n", "\n", - "![תמונה המציגה מסווג embedding עבור חמישה רצפי מילים.](../../../../../translated_images/he/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![תמונה המציגה מסווג embedding עבור חמישה רצפי מילים.](../../../../../translated_images/he/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "רשת הנוירונים המסווגת שלנו מורכבת מהשכבות הבאות:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW מהיר יותר, בעוד ש-Skip-Gram איטי יותר, אך הוא עושה עבודה טובה יותר בייצוג מילים נדירות.\n", "\n", - "![תמונה המציגה את האלגוריתמים CBoW ו-Skip-Gram להמרת מילים לוקטורים.](../../../../../translated_images/he/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![תמונה המציגה את האלגוריתמים CBoW ו-Skip-Gram להמרת מילים לוקטורים.](../../../../../translated_images/he/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "כדי להתנסות בהטמעת Word2Vec שאומנה מראש על מאגר הנתונים של Google News, ניתן להשתמש בספריית **gensim**. להלן נאתר את המילים הדומות ביותר ל'neural'.\n", "\n", diff --git a/translations/he/lessons/5-NLP/14-Embeddings/README.md b/translations/he/lessons/5-NLP/14-Embeddings/README.md index 9555a4fc..a5416f77 100644 --- a/translations/he/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/he/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: על ידי שימוש בשכבת הטמעות כשכבה הראשונה ברשת המסווג שלנו, נוכל לעבור מתיק מילים למודל **תיק הטמעות** (embedding bag), שבו אנו קודם ממירים כל מילה בטקסט שלנו להטמעה המתאימה, ואז מחשבים פונקציית צבירה כלשהי על כל ההטמעות הללו, כמו `sum`, `average` או `max`. -![תמונה המציגה מסווג הטמעות עבור חמש מילים ברצף.](../../../../../translated_images/he/embedding-classifier-example.b77f021a7ee67eee.png) +![תמונה המציגה מסווג הטמעות עבור חמש מילים ברצף.](../../../../../translated_images/he/embedding-classifier-example.b77f021a7ee67eee.webp) > תמונה מאת המחבר @@ -40,7 +40,7 @@ CO_OP_TRANSLATOR_METADATA: CBoW מהיר יותר, בעוד ש-skip-gram איטי יותר, אך עושה עבודה טובה יותר בייצוג מילים נדירות. -![תמונה המציגה את האלגוריתמים CBoW ו-Skip-Gram להמרת מילים לוקטורים.](../../../../../translated_images/he/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![תמונה המציגה את האלגוריתמים CBoW ו-Skip-Gram להמרת מילים לוקטורים.](../../../../../translated_images/he/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > תמונה מתוך [המאמר הזה](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/he/lessons/5-NLP/15-LanguageModeling/README.md b/translations/he/lessons/5-NLP/15-LanguageModeling/README.md index dda4e622..18530b22 100644 --- a/translations/he/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/he/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: * **Continuous Bag-of-Words** (CBoW), שבו אנו חוזים את הטוקן האמצעי $W_0$ ברצף טוקנים $W_{-N}$, ..., $W_N$. * **Skip-gram**, שבו אנו חוזים סט של טוקנים סמוכים {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} מתוך הטוקן האמצעי $W_0$. -![תמונה מתוך מאמר על המרת מילים לווקטורים](../../../../../translated_images/he/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![תמונה מתוך מאמר על המרת מילים לווקטורים](../../../../../translated_images/he/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > תמונה מתוך [המאמר הזה](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/he/lessons/5-NLP/16-RNN/README.md b/translations/he/lessons/5-NLP/16-RNN/README.md index 4cfbfecf..b26c7ed7 100644 --- a/translations/he/lessons/5-NLP/16-RNN/README.md +++ b/translations/he/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: כדי לתפוס את המשמעות של רצף טקסט, עלינו להשתמש בארכיטקטורה אחרת של רשת עצבית, הנקראת **רשת עצבית חוזרת**, או RNN. ב-RNN, אנו מעבירים את המשפט דרך הרשת סמל אחד בכל פעם, והרשת מייצרת **מצב** מסוים, אותו אנו מעבירים שוב לרשת עם הסמל הבא. -![RNN](../../../../../translated_images/he/rnn.27f5c29c53d727b5.png) +![RNN](../../../../../translated_images/he/rnn.27f5c29c53d727b5.webp) > תמונה מאת המחבר @@ -61,7 +61,7 @@ CO_OP_TRANSLATOR_METADATA: רשת חוזרת, בין אם חד-כיוונית או דו-כיוונית, תופסת דפוסים מסוימים בתוך רצף, ויכולה לאחסן אותם בוקטור מצב או להעבירם לפלט. כמו ברשתות קונבולוציה, אנו יכולים לבנות שכבה חוזרת נוספת מעל הראשונה כדי לתפוס דפוסים ברמה גבוהה יותר ולבנות מדפוסים ברמה נמוכה שנלכדו על ידי השכבה הראשונה. זה מוביל אותנו למושג של **RNN רב-שכבתי** שמורכב משתי רשתות חוזרות או יותר, כאשר הפלט של השכבה הקודמת מועבר לשכבה הבאה כקלט. -![תמונה המציגה RNN רב-שכבתי מסוג LSTM](../../../../../translated_images/he/multi-layer-lstm.dd975e29bb2a59fe.jpg) +![תמונה המציגה RNN רב-שכבתי מסוג LSTM](../../../../../translated_images/he/multi-layer-lstm.dd975e29bb2a59fe.webp) *תמונה מתוך [הפוסט הנהדר הזה](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) מאת פרננדו לופז* diff --git a/translations/he/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/he/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index 53178bc7..ed497a45 100644 --- a/translations/he/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/he/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "רשת חוזרת, בין אם היא חד-כיוונית או דו-כיוונית, לוכדת דפוסים מסוימים בתוך רצף, ויכולה לאחסן אותם בווקטור מצב או להעביר אותם לפלט. כמו ברשתות קונבולוציה, ניתן לבנות שכבה חוזרת נוספת מעל הראשונה כדי ללכוד דפוסים ברמה גבוהה יותר, שנבנים מדפוסים ברמה נמוכה שהופקו על ידי השכבה הראשונה. זה מוביל אותנו למושג של **RNN רב-שכבתי**, שמורכב משתי רשתות חוזרות או יותר, כאשר הפלט של השכבה הקודמת מועבר לשכבה הבאה כקלט.\n", "\n", - "![תמונה המציגה RNN רב-שכבתי מסוג LSTM](../../../../../translated_images/he/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![תמונה המציגה RNN רב-שכבתי מסוג LSTM](../../../../../translated_images/he/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*תמונה מתוך [הפוסט הנהדר הזה](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) מאת Fernando López*\n", "\n", diff --git a/translations/he/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/he/lessons/5-NLP/16-RNN/RNNTF.ipynb index 201cbf1f..6b7cbef1 100644 --- a/translations/he/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/he/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "כדי לתפוס את המשמעות של רצף טקסט, נשתמש בארכיטקטורה של רשת עצבית הנקראת **רשת עצבית חוזרת**, או RNN. כאשר משתמשים ב-RNN, אנו מעבירים את המשפט דרך הרשת, טוקן אחד בכל פעם, והרשת מייצרת **מצב** מסוים, אותו אנו מעבירים שוב לרשת יחד עם הטוקן הבא.\n", "\n", - "![תמונה המציגה דוגמה ליצירת רשת עצבית חוזרת.](../../../../../translated_images/he/rnn.27f5c29c53d727b5.png)\n", + "![תמונה המציגה דוגמה ליצירת רשת עצבית חוזרת.](../../../../../translated_images/he/rnn.27f5c29c53d727b5.webp)\n", "\n", "בהינתן רצף הטוקנים $X_0,\\dots,X_n$, ה-RNN יוצר רצף של בלוקים של רשת עצבית, ומאמן את הרצף הזה מקצה לקצה באמצעות שיטת ה-backpropagation. כל בלוק ברשת מקבל זוג $(X_i,S_i)$ כקלט, ומייצר $S_{i+1}$ כתוצאה. המצב הסופי $S_n$ או הפלט $Y_n$ מועבר למסווג ליניארי כדי לייצר את התוצאה. כל בלוקי הרשת חולקים את אותם משקלים, ומאומנים מקצה לקצה באמצעות מעבר אחד של backpropagation.\n", "\n", @@ -371,7 +371,7 @@ "\n", "רשתות חוזרות, בין אם חד-כיווניות או דו-כיווניות, לוכדות תבניות בתוך רצף, ושומרות אותן בווקטורי מצב או מחזירות אותן כפלט. כמו ברשתות קונבולוציה, ניתן לבנות שכבה חוזרת נוספת אחרי הראשונה כדי ללכוד תבניות ברמה גבוהה יותר, שנבנות מתבניות ברמה נמוכה יותר שהשכבה הראשונה חילצה. זה מוביל אותנו למושג של **RNN רב-שכבתי**, שמורכב משתי רשתות חוזרות או יותר, כאשר הפלט של השכבה הקודמת מועבר לשכבה הבאה כקלט.\n", "\n", - "![תמונה המציגה RNN רב-שכבתי מסוג LSTM](../../../../../translated_images/he/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![תמונה המציגה RNN רב-שכבתי מסוג LSTM](../../../../../translated_images/he/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*תמונה מתוך [הפוסט הנהדר הזה](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) מאת Fernando López.*\n", "\n", diff --git a/translations/he/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/he/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index 042a5f2b..3e140e19 100644 --- a/translations/he/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/he/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "הדרך שבה נאמן RNN לייצר טקסט היא כדלקמן. בכל שלב, ניקח רצף של תווים באורך `nchars`, ונבקש מהרשת לייצר את התו הבא עבור כל תו קלט:\n", "\n", - "![תמונה המציגה דוגמה ליצירת המילה 'HELLO' באמצעות RNN.](../../../../../translated_images/he/rnn-generate.56c54afb52f9781d.png)\n", + "![תמונה המציגה דוגמה ליצירת המילה 'HELLO' באמצעות RNN.](../../../../../translated_images/he/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "בהתאם לתרחיש בפועל, ייתכן שנרצה לכלול גם תווים מיוחדים, כמו *סוף רצף* ``. במקרה שלנו, אנחנו רק רוצים לאמן את הרשת ליצירת טקסט אינסופי, ולכן נקבע את גודל כל רצף להיות שווה ל-`nchars` טוקנים. כתוצאה מכך, כל דוגמת אימון תכלול `nchars` קלטים ו-`nchars` פלטים (שהם רצף הקלט מוזז סמל אחד שמאלה). מיניבאץ' יכלול כמה רצפים כאלה.\n", "\n", diff --git a/translations/he/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/he/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index 0192d317..f5af1d15 100644 --- a/translations/he/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/he/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -113,7 +113,7 @@ "\n", "הדרך שבה נאמן RNN לייצר כותרות חדשות היא כדלקמן. בכל שלב, ניקח כותרת אחת, שתוזן לתוך RNN, ולכל תו קלט נבקש מהרשת לייצר את תו הפלט הבא:\n", "\n", - "![תמונה המציגה דוגמה ליצירת המילה 'HELLO' באמצעות RNN.](../../../../../translated_images/he/rnn-generate.56c54afb52f9781d.png)\n", + "![תמונה המציגה דוגמה ליצירת המילה 'HELLO' באמצעות RNN.](../../../../../translated_images/he/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "עבור התו האחרון ברצף שלנו, נבקש מהרשת לייצר את הטוקן ``.\n", "\n", diff --git a/translations/he/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/he/lessons/5-NLP/17-GenerativeNetworks/README.md index 9dd3fc6c..ff966982 100644 --- a/translations/he/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/he/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: זה מאפשר ארכיטקטורות עצביות שונות, כפי שמוצג בתמונה הבאה: -![תמונה המציגה דפוסים נפוצים של רשתות עצביות חוזרות.](../../../../../translated_images/he/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![תמונה המציגה דפוסים נפוצים של רשתות עצביות חוזרות.](../../../../../translated_images/he/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > תמונה מתוך פוסט הבלוג [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) מאת [Andrej Karpaty](http://karpathy.github.io/) @@ -32,7 +32,7 @@ CO_OP_TRANSLATOR_METADATA: נאמן את ה-RNN הזה ליצירת טקסט שלב אחר שלב. בכל שלב, ניקח רצף של תווים באורך `nchars`, ונבקש מהרשת לייצר את התו הבא עבור כל תו קלט: -![תמונה המציגה דוגמה ליצירת המילה 'HELLO' באמצעות RNN.](../../../../../translated_images/he/rnn-generate.56c54afb52f9781d.png) +![תמונה המציגה דוגמה ליצירת המילה 'HELLO' באמצעות RNN.](../../../../../translated_images/he/rnn-generate.56c54afb52f9781d.webp) בעת יצירת טקסט (בזמן הסקת מסקנות), נתחיל עם **הנחיה** כלשהי, שתועבר דרך תאי RNN כדי ליצור את מצב הביניים שלה, ואז מהמצב הזה מתחילה היצירה. ניצור תו אחד בכל פעם, ונעביר את המצב ואת התו שנוצר לתא RNN נוסף כדי ליצור את הבא, עד שניצור מספיק תווים. diff --git a/translations/he/lessons/5-NLP/18-Transformers/README.md b/translations/he/lessons/5-NLP/18-Transformers/README.md index 29793e33..34df8704 100644 --- a/translations/he/lessons/5-NLP/18-Transformers/README.md +++ b/translations/he/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ CO_OP_TRANSLATOR_METADATA: **מנגנוני קשב** מספקים דרך לשקלל את ההשפעה ההקשרית של כל וקטור קלט על כל תחזית פלט של ה-RNN. זה מיושם על ידי יצירת קיצורי דרך בין המצבים הביניים של ה-RNN הקלט לבין ה-RNN הפלט. כך, בעת יצירת סמל פלט yt, ניקח בחשבון את כל המצבים המוסתרים של הקלט hi, עם מקדמי משקל שונים αt,i. -![תמונה המציגה מודל encoder/decoder עם שכבת קשב אדיטיבית](../../../../../translated_images/he/encoder-decoder-attention.7a726296894fb567.png) +![תמונה המציגה מודל encoder/decoder עם שכבת קשב אדיטיבית](../../../../../translated_images/he/encoder-decoder-attention.7a726296894fb567.webp) > מודל ה-encoder-decoder עם מנגנון קשב אדיטיבי מתוך [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), מצוטט מתוך [פוסט בבלוג זה](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) מטריצת הקשב {αi,j} מייצגת את המידה שבה מילים מסוימות בקלט משפיעות על יצירת מילה מסוימת בפלט. להלן דוגמה למטריצה כזו: -![תמונה המציגה יישור דוגמה שנמצא על ידי RNNsearch-50, מתוך Bahdanau - arviz.org](../../../../../translated_images/he/bahdanau-fig3.09ba2d37f202a6af.png) +![תמונה המציגה יישור דוגמה שנמצא על ידי RNNsearch-50, מתוך Bahdanau - arviz.org](../../../../../translated_images/he/bahdanau-fig3.09ba2d37f202a6af.webp) > איור מתוך [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (איור 3) @@ -66,7 +66,7 @@ CO_OP_TRANSLATOR_METADATA: כעת, עלינו לזהות תבניות בתוך הרצף שלנו. לשם כך, טרנספורמרים משתמשים במנגנון **קשב עצמי**, שהוא למעשה קשב המיושם על אותו רצף כקלט וכפלט. יישום קשב עצמי מאפשר לנו לקחת בחשבון **הקשר** בתוך המשפט ולראות אילו מילים קשורות זו לזו. לדוגמה, הוא מאפשר לנו לראות אילו מילים מתייחסות להן באמצעות התייחסויות כמו *it*, וגם לקחת את ההקשר בחשבון: -![](../../../../../translated_images/he/CoreferenceResolution.861924d6d384a7d6.png) +![](../../../../../translated_images/he/CoreferenceResolution.861924d6d384a7d6.webp) > תמונה מתוך [הבלוג של Google](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ CO_OP_TRANSLATOR_METADATA: **BERT** (Bidirectional Encoder Representations from Transformers) הוא רשת טרנספורמרים גדולה מאוד עם 12 שכבות עבור *BERT-base*, ו-24 עבור *BERT-large*. המודל מאומן תחילה על מאגר טקסט גדול (ויקיפדיה + ספרים) באמצעות אימון לא מפוקח (חיזוי מילים מוסתרות במשפט). במהלך האימון הראשוני, המודל סופג רמות משמעותיות של הבנת שפה, שניתן לאחר מכן לנצל עם מערכי נתונים אחרים באמצעות כוונון עדין. תהליך זה נקרא **למידת העברה**. -![תמונה מתוך http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/he/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) +![תמונה מתוך http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/he/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > מקור התמונה [כאן](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/he/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/he/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index 2a0c3211..bb0ecfbf 100644 --- a/translations/he/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/he/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**מנגנוני קשב** מספקים דרך לשקלל את ההשפעה ההקשרית של כל וקטור קלט על כל תחזית פלט של ה-RNN. זה מיושם על ידי יצירת קיצורי דרך בין המצבים הביניים של ה-RNN של הקלט לבין ה-RNN של הפלט. כך, בעת יצירת סמל פלט $y_t$, ניקח בחשבון את כל המצבים המוסתרים של הקלט $h_i$, עם מקדמי משקל שונים $\\alpha_{t,i}$.\n", "\n", - "![תמונה המציגה מודל מקודד/מפענח עם שכבת קשב אדיטיבית](../../../../../translated_images/he/encoder-decoder-attention.7a726296894fb567.png)\n", + "![תמונה המציגה מודל מקודד/מפענח עם שכבת קשב אדיטיבית](../../../../../translated_images/he/encoder-decoder-attention.7a726296894fb567.webp)\n", "*מודל מקודד-מפענח עם מנגנון קשב אדיטיבי מתוך [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), מצוטט מתוך [פוסט הבלוג הזה](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "מטריצת הקשב $\\{\\alpha_{i,j}\\}$ מייצגת את המידה שבה מילים מסוימות בקלט משפיעות על יצירת מילה מסוימת ברצף הפלט. להלן דוגמה למטריצה כזו:\n", "\n", - "![תמונה המציגה יישור דוגמה שנמצא על ידי RNNsearch-50, מתוך Bahdanau - arviz.org](../../../../../translated_images/he/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![תמונה המציגה יישור דוגמה שנמצא על ידי RNNsearch-50, מתוך Bahdanau - arviz.org](../../../../../translated_images/he/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*תמונה מתוך [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (איור 3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (ייצוגי מקודד דו-כיווניים מטרנספורמרים) הוא רשת טרנספורמרים גדולה מאוד עם 12 שכבות עבור *BERT-base* ו-24 עבור *BERT-large*. המודל מאומן מראש על מאגר טקסטים גדול (ויקיפדיה + ספרים) באמצעות אימון לא מפוקח (חיזוי מילים מוסתרות במשפט). במהלך האימון המוקדם, המודל סופג רמה משמעותית של הבנת שפה, שניתן לנצל לאחר מכן עם מערכי נתונים אחרים באמצעות כוונון עדין. תהליך זה נקרא **למידת העברה**.\n", "\n", - "![תמונה מתוך http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/he/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![תמונה מתוך http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/he/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "ישנן וריאציות רבות של ארכיטקטורות טרנספורמרים, כולל BERT, DistilBERT, BigBird, OpenGPT3 ועוד, שניתן לכוונן. חבילת [HuggingFace](https://github.com/huggingface/) מספקת מאגר לאימון רבות מהארכיטקטורות הללו עם PyTorch.\n", "\n", diff --git a/translations/he/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/he/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index b94ce148..5db33b69 100644 --- a/translations/he/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/he/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**מנגנוני קשב** מספקים דרך לשקלל את ההשפעה ההקשרית של כל וקטור קלט על כל תחזית פלט של ה-RNN. הדבר מיושם על ידי יצירת קיצורי דרך בין המצבים הביניים של ה-RNN של הקלט לבין ה-RNN של הפלט. כך, בעת יצירת סמל פלט $y_t$, ניקח בחשבון את כל המצבים המוסתרים של הקלט $h_i$, עם מקדמי משקל שונים $\\alpha_{t,i}$.\n", "\n", - "![תמונה המציגה מודל מקודד/מפענח עם שכבת קשב אדיטיבית](../../../../../translated_images/he/encoder-decoder-attention.7a726296894fb567.png)\n", + "![תמונה המציגה מודל מקודד/מפענח עם שכבת קשב אדיטיבית](../../../../../translated_images/he/encoder-decoder-attention.7a726296894fb567.webp)\n", "*מודל מקודד-מפענח עם מנגנון קשב אדיטיבי מתוך [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), מצוטט מתוך [פוסט הבלוג הזה](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "מטריצת הקשב $\\{\\alpha_{i,j}\\}$ מייצגת את המידה שבה מילים מסוימות בקלט משפיעות על יצירת מילה מסוימת ברצף הפלט. להלן דוגמה למטריצה כזו:\n", "\n", - "![תמונה המציגה יישור דוגמה שנמצא על ידי RNNsearch-50, מתוך Bahdanau - arviz.org](../../../../../translated_images/he/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![תמונה המציגה יישור דוגמה שנמצא על ידי RNNsearch-50, מתוך Bahdanau - arviz.org](../../../../../translated_images/he/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*תמונה מתוך [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (איור 3)*\n", "\n", @@ -225,7 +225,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) הוא רשת טרנספורמר רב שכבתית גדולה מאוד עם 12 שכבות עבור *BERT-base*, ו-24 עבור *BERT-large*. המודל עובר תחילה אימון מוקדם על מאגר נתונים גדול של טקסט (ויקיפדיה + ספרים) באמצעות אימון לא מפוקח (ניבוי מילים מוסתרות במשפט). במהלך האימון המוקדם, המודל סופג רמה משמעותית של הבנת שפה, שניתן לאחר מכן לנצל עם מערכי נתונים אחרים באמצעות כיוונון עדין. תהליך זה נקרא **למידה מעוברת**.\n", "\n", - "![תמונה מתוך http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/he/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![תמונה מתוך http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/he/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "ישנם וריאציות רבות של ארכיטקטורות טרנספורמר, כולל BERT, DistilBERT, BigBird, OpenGPT3 ועוד, שניתן לבצע עליהן כיוונון עדין.\n", "\n", diff --git a/translations/he/lessons/5-NLP/19-NER/README.md b/translations/he/lessons/5-NLP/19-NER/README.md index 7dfed10e..df2a9d59 100644 --- a/translations/he/lessons/5-NLP/19-NER/README.md +++ b/translations/he/lessons/5-NLP/19-NER/README.md @@ -58,7 +58,7 @@ infant | O מכיוון שעלינו לבנות התאמה של אחד-לאחד בין טוקנים לקטגוריות, נוכל לאמן מודל רשת עצבית **רב-לרב** מהתמונה הבאה: -![תמונה המציגה דפוסים נפוצים של רשתות עצביות חוזרות.](../../../../../translated_images/he/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![תמונה המציגה דפוסים נפוצים של רשתות עצביות חוזרות.](../../../../../translated_images/he/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > *תמונה מתוך [פוסט הבלוג הזה](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) מאת [אנדריי קרפת'י](http://karpathy.github.io/). מודלים של סיווג טוקנים ב-NER תואמים לארכיטקטורת הרשת הימנית ביותר בתמונה זו.* diff --git a/translations/he/lessons/5-NLP/README.md b/translations/he/lessons/5-NLP/README.md index 670f4c86..9945a46c 100644 --- a/translations/he/lessons/5-NLP/README.md +++ b/translations/he/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # עיבוד שפה טבעית -![סיכום משימות NLP בציור](../../../../translated_images/he/ai-nlp.b22dcb8ca4707cea.png) +![סיכום משימות NLP בציור](../../../../translated_images/he/ai-nlp.b22dcb8ca4707cea.webp) בפרק זה נתמקד בשימוש ברשתות נוירונים לטיפול במשימות הקשורות ל**עיבוד שפה טבעית (NLP)**. ישנם הרבה בעיות NLP שאנו רוצים שמחשבים יוכלו לפתור: diff --git a/translations/he/lessons/6-Other/23-MultiagentSystems/README.md b/translations/he/lessons/6-Other/23-MultiagentSystems/README.md index 94843e98..1c4793fa 100644 --- a/translations/he/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/he/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ ask turtles [ לאחר פתיחת המודל, תועברו למסך הראשי של NetLogo. הנה דוגמת מודל שמתאר את אוכלוסיית הזאבים והכבשים, בהתחשב במשאבים מוגבלים (דשא). -![מסך ראשי של NetLogo](../../../../../translated_images/he/NetLogo-Main.32653711ec1a01b3.png) +![מסך ראשי של NetLogo](../../../../../translated_images/he/NetLogo-Main.32653711ec1a01b3.webp) > צילום מסך מאת דמיטרי סושניקוב diff --git a/translations/he/lessons/README.md b/translations/he/lessons/README.md index 50e2582a..da7210b1 100644 --- a/translations/he/lessons/README.md +++ b/translations/he/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # סקירה כללית -![סקירה כללית באיור](../../../translated_images/he/ai-overview.0857791951d19500.png) +![סקירה כללית באיור](../../../translated_images/he/ai-overview.0857791951d19500.webp) > איור מאת [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/he/lessons/X-Extras/X1-MultiModal/README.md b/translations/he/lessons/X-Extras/X1-MultiModal/README.md index 37e1e323..dccb8494 100644 --- a/translations/he/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/he/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: הרעיון המרכזי של CLIP הוא היכולת להשוות בין טקסט לתמונה ולקבוע עד כמה התמונה מתאימה לטקסט. -![ארכיטקטורת CLIP](../../../../../translated_images/he/clip-arch.b3dbf20b4e8ed8be.png) +![ארכיטקטורת CLIP](../../../../../translated_images/he/clip-arch.b3dbf20b4e8ed8be.webp) > *תמונה מתוך [הפוסט הזה](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ CO_OP_TRANSLATOR_METADATA: נניח שעלינו לסווג תמונות בין חתולים, כלבים ובני אדם. במקרה זה, ניתן להזין למודל תמונה וסדרה של טקסטים: "*תמונה של חתול*", "*תמונה של כלב*", "*תמונה של אדם*". בווקטור התוצאות של 3 ההסתברויות, נבחר את האינדקס עם הערך הגבוה ביותר. -![CLIP לסיווג תמונות](../../../../../translated_images/he/clip-class.3af42ef0b2b19369.png) +![CLIP לסיווג תמונות](../../../../../translated_images/he/clip-class.3af42ef0b2b19369.webp) > *תמונה מתוך [הפוסט הזה](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ CO_OP_TRANSLATOR_METADATA: אחת ההבדלים החשובים בין VQGAN ל-GAN מסורתי היא שהאחרון יכול לייצר תמונה סבירה מכל וקטור קלט, בעוד ש-VQGAN עשוי לייצר תמונה שאינה קוהרנטית. לכן, יש להנחות את תהליך יצירת התמונה, וזה נעשה באמצעות CLIP. -![ארכיטקטורת VQGAN+CLIP](../../../../../translated_images/he/vqgan.5027fe05051dfa31.png) +![ארכיטקטורת VQGAN+CLIP](../../../../../translated_images/he/vqgan.5027fe05051dfa31.webp) כדי ליצור תמונה שמתאימה לטקסט, מתחילים עם וקטור קידוד אקראי שמועבר דרך VQGAN ליצירת תמונה. לאחר מכן, CLIP משמש ליצירת פונקציית הפסד שמראה עד כמה התמונה מתאימה לטקסט. המטרה היא למזער את ההפסד הזה באמצעות back propagation כדי להתאים את פרמטרי וקטור הקלט. ספרייה מצוינת שמממשת VQGAN+CLIP היא [Pixray](http://github.com/pixray/pixray). -![תמונה שנוצרה על ידי Pixray](../../../../../translated_images/he/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![תמונה שנוצרה על ידי Pixray](../../../../../translated_images/he/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![תמונה שנוצרה על ידי Pixray](../../../../../translated_images/he/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) +![תמונה שנוצרה על ידי Pixray](../../../../../translated_images/he/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp) | ![תמונה שנוצרה על ידי Pixray](../../../../../translated_images/he/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp) | ![תמונה שנוצרה על ידי Pixray](../../../../../translated_images/he/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp) ----|----|---- תמונה שנוצרה מהטקסט *דיוקן בצבעי מים של מורה צעיר לספרות עם ספר* | תמונה שנוצרה מהטקסט *דיוקן בשמן של מורה צעירה למדעי המחשב עם מחשב* | תמונה שנוצרה מהטקסט *דיוקן בשמן של מורה מבוגר למתמטיקה מול לוח שחור* diff --git a/translations/hi/README.md b/translations/hi/README.md index cf542420..7c7b4a88 100644 --- a/translations/hi/README.md +++ b/translations/hi/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # शुरुआती लोगों के लिए आर्टिफिशियल इंटेलिजेंस - एक पाठ्यक्रम -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/hi/ai-overview.0857791951d19500.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/hi/ai-overview.0857791951d19500.webp)| |:---:| | शुरुआत करने वालों के लिए AI - _स्केचनोट [@girlie_mac](https://twitter.com/girlie_mac) द्वारा_ | diff --git a/translations/hi/lessons/1-Intro/README.md b/translations/hi/lessons/1-Intro/README.md index c843da40..c981b34b 100644 --- a/translations/hi/lessons/1-Intro/README.md +++ b/translations/hi/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # एआई का परिचय -![एआई परिचय सामग्री का सारांश एक डूडल में](../../../../translated_images/hi/ai-intro.bf28d1ac4235881c.png) +![एआई परिचय सामग्री का सारांश एक डूडल में](../../../../translated_images/hi/ai-intro.bf28d1ac4235881c.webp) > स्केच नोट [टोमोमी इमुरा](https://twitter.com/girlie_mac) द्वारा @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: मूल रूप से, कंप्यूटर को [चार्ल्स बैबेज](https://en.wikipedia.org/wiki/Charles_Babbage) द्वारा संख्याओं पर एक निश्चित प्रक्रिया - एक एल्गोरिदम - के अनुसार काम करने के लिए आविष्कार किया गया था। आधुनिक कंप्यूटर, हालांकि 19वीं सदी में प्रस्तावित मूल मॉडल की तुलना में काफी उन्नत हैं, फिर भी नियंत्रित गणनाओं के उसी विचार का पालन करते हैं। इसलिए, यदि हमें उस लक्ष्य को प्राप्त करने के लिए आवश्यक चरणों का सटीक क्रम पता है, तो कंप्यूटर को कुछ करने के लिए प्रोग्राम करना संभव है। -![एक व्यक्ति की तस्वीर](../../../../translated_images/hi/dsh_age.d212a30d4e54fb5f.png) +![एक व्यक्ति की तस्वीर](../../../../translated_images/hi/dsh_age.d212a30d4e54fb5f.webp) > फोटो [विकी सॉश्निकोवा](http://twitter.com/vickievalerie) द्वारा @@ -46,7 +46,7 @@ CO_OP_TRANSLATOR_METADATA: **[बुद्धिमत्ता](https://en.wikipedia.org/wiki/Intelligence)** शब्द से निपटने में एक समस्या यह है कि इस शब्द की कोई स्पष्ट परिभाषा नहीं है। कोई तर्क कर सकता है कि बुद्धिमत्ता **सार्वभौमिक सोच** या **आत्म-जागरूकता** से जुड़ी है, लेकिन हम इसे ठीक से परिभाषित नहीं कर सकते। -![एक बिल्ली की तस्वीर](../../../../translated_images/hi/photo-cat.8c8e8fb760ffe457.jpg) +![एक बिल्ली की तस्वीर](../../../../translated_images/hi/photo-cat.8c8e8fb760ffe457.webp) > [फोटो](https://unsplash.com/photos/75715CVEJhI) [एंबर किप्प](https://unsplash.com/@sadmax) द्वारा Unsplash से @@ -98,13 +98,13 @@ CO_OP_TRANSLATOR_METADATA: > | एमएल के बारे में क्या? | | > |--------------|-----------| -> | कृत्रिम बुद्धिमत्ता का वह हिस्सा जो किसी समस्या को कुछ डेटा के आधार पर हल करने के लिए कंप्यूटर सीखने पर आधारित है, उसे **मशीन लर्निंग** कहा जाता है। हम इस पाठ्यक्रम में क्लासिकल मशीन लर्निंग पर विचार नहीं करेंगे - हम आपको एक अलग [मशीन लर्निंग फॉर बिगिनर्स](http://aka.ms/ml-beginners) पाठ्यक्रम की ओर संदर्भित करते हैं। | ![एमएल फॉर बिगिनर्स](../../../../translated_images/hi/ml-for-beginners.9e4fed176fd5817d.png) | +> | कृत्रिम बुद्धिमत्ता का वह हिस्सा जो किसी समस्या को कुछ डेटा के आधार पर हल करने के लिए कंप्यूटर सीखने पर आधारित है, उसे **मशीन लर्निंग** कहा जाता है। हम इस पाठ्यक्रम में क्लासिकल मशीन लर्निंग पर विचार नहीं करेंगे - हम आपको एक अलग [मशीन लर्निंग फॉर बिगिनर्स](http://aka.ms/ml-beginners) पाठ्यक्रम की ओर संदर्भित करते हैं। | ![एमएल फॉर बिगिनर्स](../../../../translated_images/hi/ml-for-beginners.9e4fed176fd5817d.webp) | ## एआई का संक्षिप्त इतिहास कृत्रिम बुद्धिमत्ता को 20वीं सदी के मध्य में एक क्षेत्र के रूप में शुरू किया गया था। प्रारंभ में, प्रतीकात्मक तर्क एक प्रमुख दृष्टिकोण था, और इसने कुछ महत्वपूर्ण सफलताओं को जन्म दिया, जैसे कि विशेषज्ञ प्रणालियाँ – कंप्यूटर प्रोग्राम जो कुछ सीमित समस्या डोमेन में एक विशेषज्ञ के रूप में कार्य करने में सक्षम थे। हालांकि, जल्द ही यह स्पष्ट हो गया कि ऐसा दृष्टिकोण अच्छी तरह से स्केल नहीं करता। किसी विशेषज्ञ से ज्ञान निकालना, उसे कंप्यूटर में प्रस्तुत करना, और उस ज्ञान आधार को सटीक बनाए रखना एक बहुत ही जटिल कार्य और कई मामलों में व्यावहारिक रूप से बहुत महंगा साबित हुआ। इसने 1970 के दशक में तथाकथित [एआई विंटर](https://en.wikipedia.org/wiki/AI_winter) को जन्म दिया। -एआई का संक्षिप्त इतिहास +एआई का संक्षिप्त इतिहास > छवि [दिमित्री सॉश्निकोव](http://soshnikov.com) द्वारा @@ -124,7 +124,7 @@ CO_OP_TRANSLATOR_METADATA: * आधुनिक सहायक, जैसे कि कोरटाना, सिरी या गूगल असिस्टेंट सभी हाइब्रिड सिस्टम हैं जो भाषण को टेक्स्ट में बदलने और हमारे इरादे को पहचानने के लिए न्यूरल नेटवर्क का उपयोग करते हैं, और फिर आवश्यक कार्यों को करने के लिए कुछ तर्क या स्पष्ट एल्गोरिदम का उपयोग करते हैं। * भविष्य में, हम उम्मीद कर सकते हैं कि एक पूर्ण न्यूरल-आधारित मॉडल स्वयं संवाद को संभालेगा। हाल ही में GPT और [ट्यूरिंग-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) न्यूरल नेटवर्क परिवार इस क्षेत्र में बड़ी सफलता दिखा रहे हैं। -ट्यूरिंग टेस्ट का विकास +ट्यूरिंग टेस्ट का विकास > चित्र Dmitry Soshnikov द्वारा, [फोटो](https://unsplash.com/photos/r8LmVbUKgns) Marina Abrosimova द्वारा [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto), Unsplash ## हालिया AI अनुसंधान diff --git a/translations/hi/lessons/2-Symbolic/README.md b/translations/hi/lessons/2-Symbolic/README.md index 4e927e9d..fa77d648 100644 --- a/translations/hi/lessons/2-Symbolic/README.md +++ b/translations/hi/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # ज्ञान प्रतिनिधित्व और विशेषज्ञ प्रणाली -![सिंबोलिक AI सामग्री का सारांश](../../../../translated_images/hi/ai-symbolic.715a30cb610411a6.png) +![सिंबोलिक AI सामग्री का सारांश](../../../../translated_images/hi/ai-symbolic.715a30cb610411a6.webp) > स्केच नोट [Tomomi Imura](https://twitter.com/girlie_mac) द्वारा @@ -41,7 +41,7 @@ AI के शुरुआती दिनों में, बुद्धिम इस प्रकार, **ज्ञान प्रतिनिधित्व** की समस्या यह है कि कंप्यूटर के अंदर डेटा के रूप में ज्ञान को प्रभावी ढंग से कैसे प्रस्तुत किया जाए, ताकि इसे स्वचालित रूप से उपयोग किया जा सके। इसे एक स्पेक्ट्रम के रूप में देखा जा सकता है: -![ज्ञान प्रतिनिधित्व स्पेक्ट्रम](../../../../translated_images/hi/knowledge-spectrum.b60df631852c0217.png) +![ज्ञान प्रतिनिधित्व स्पेक्ट्रम](../../../../translated_images/hi/knowledge-spectrum.b60df631852c0217.webp) > छवि [Dmitry Soshnikov](http://soshnikov.com) द्वारा @@ -94,7 +94,7 @@ Untyped-Language | नहीं है | टाइप डिफिनिशन सिंबोलिक AI की शुरुआती सफलताओं में से एक **विशेषज्ञ प्रणाली** थीं - कंप्यूटर प्रणाली जो किसी सीमित समस्या क्षेत्र में विशेषज्ञ के रूप में कार्य करने के लिए डिज़ाइन की गई थीं। ये **ज्ञान आधार** पर आधारित थीं जो एक या अधिक मानव विशेषज्ञों से निकाली गई थीं, और इनमें एक **तर्क इंजन** था जो इसके ऊपर कुछ तर्क करता था। -![मानव संरचना](../../../../translated_images/hi/arch-human.5d4d35f1bba3ab1c.png) | ![ज्ञान-आधारित प्रणाली](../../../../translated_images/hi/arch-kbs.3ec5c150b09fa8da.png) +![मानव संरचना](../../../../translated_images/hi/arch-human.5d4d35f1bba3ab1c.webp) | ![ज्ञान-आधारित प्रणाली](../../../../translated_images/hi/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------|-------------------------------------------- मानव तंत्रिका प्रणाली की सरलीकृत संरचना | ज्ञान-आधारित प्रणाली की संरचना @@ -106,7 +106,7 @@ Untyped-Language | नहीं है | टाइप डिफिनिशन उदाहरण के लिए, आइए एक जानवर की शारीरिक विशेषताओं के आधार पर उसे निर्धारित करने वाली विशेषज्ञ प्रणाली पर विचार करें: -![AND-OR ट्री](../../../../translated_images/hi/AND-OR-Tree.5592d2c70187f283.png) +![AND-OR ट्री](../../../../translated_images/hi/AND-OR-Tree.5592d2c70187f283.webp) > छवि [Dmitry Soshnikov](http://soshnikov.com) द्वारा diff --git a/translations/hi/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/hi/lessons/3-NeuralNetworks/05-Frameworks/README.md index efc4cde8..5604ac61 100644 --- a/translations/hi/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/hi/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ CO_OP_TRANSLATOR_METADATA: 5 बिंदुओं को (ग्राफ में `x` द्वारा दर्शाए गए) अनुमानित करने की निम्नलिखित समस्या पर विचार करें: -![linear](../../../../../translated_images/hi/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/hi/overfit2.131f5800ae10ca5e.jpg) +![linear](../../../../../translated_images/hi/overfit1.f24b71c6f652e59e.webp) | ![overfit](../../../../../translated_images/hi/overfit2.131f5800ae10ca5e.webp) -------------------------|-------------------------- **लिनियर मॉडल, 2 पैरामीटर्स** | **नॉन-लिनियर मॉडल, 7 पैरामीटर्स** ट्रेनिंग एरर = 5.3 | ट्रेनिंग एरर = 0 @@ -79,7 +79,7 @@ CO_OP_TRANSLATOR_METADATA: जैसा कि आप ऊपर दिए गए ग्राफ से देख सकते हैं, ओवरफिटिंग का पता बहुत कम ट्रेनिंग एरर और उच्च वैलिडेशन एरर से लगाया जा सकता है। आमतौर पर ट्रेनिंग के दौरान हम देखेंगे कि ट्रेनिंग और वैलिडेशन एरर दोनों कम होने लगते हैं, और फिर किसी बिंदु पर वैलिडेशन एरर कम होना बंद कर सकता है और बढ़ने लग सकता है। यह ओवरफिटिंग का संकेत होगा, और यह संकेत होगा कि हमें शायद इस बिंदु पर ट्रेनिंग रोक देनी चाहिए (या कम से कम मॉडल का स्नैपशॉट लेना चाहिए)। -![overfitting](../../../../../translated_images/hi/Overfitting.408ad91cd90b4371.png) +![overfitting](../../../../../translated_images/hi/Overfitting.408ad91cd90b4371.webp) ## ओवरफिटिंग को कैसे रोकें diff --git a/translations/hi/lessons/3-NeuralNetworks/README.md b/translations/hi/lessons/3-NeuralNetworks/README.md index c186d6c9..3d2a2817 100644 --- a/translations/hi/lessons/3-NeuralNetworks/README.md +++ b/translations/hi/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # न्यूरल नेटवर्क्स का परिचय -![न्यूरल नेटवर्क्स के परिचय की सामग्री का सारांश एक डूडल में](../../../../translated_images/hi/ai-neuralnetworks.1c687ae40bc86e83.png) +![न्यूरल नेटवर्क्स के परिचय की सामग्री का सारांश एक डूडल में](../../../../translated_images/hi/ai-neuralnetworks.1c687ae40bc86e83.webp) जैसा कि हमने परिचय में चर्चा की थी, बुद्धिमत्ता प्राप्त करने के तरीकों में से एक है **कंप्यूटर मॉडल** या **कृत्रिम मस्तिष्क** को प्रशिक्षित करना। 20वीं सदी के मध्य से, शोधकर्ताओं ने विभिन्न गणितीय मॉडलों की कोशिश की, और हाल के वर्षों में यह दिशा अत्यधिक सफल साबित हुई। मस्तिष्क के इन गणितीय मॉडलों को **न्यूरल नेटवर्क्स** कहा जाता है। @@ -36,13 +36,13 @@ CO_OP_TRANSLATOR_METADATA: जीवविज्ञान से, हम जानते हैं कि हमारा मस्तिष्क न्यूरल कोशिकाओं (न्यूरॉन्स) से बना है, जिनमें से प्रत्येक के पास कई "इनपुट" (डेंड्राइट्स) और एक "आउटपुट" (एक्सॉन) होता है। डेंड्राइट्स और एक्सॉन दोनों विद्युत संकेतों का संचालन कर सकते हैं, और उनके बीच के कनेक्शन — जिन्हें सिनैप्स कहा जाता है — विभिन्न स्तरों की चालकता प्रदर्शित कर सकते हैं, जो न्यूरोट्रांसमीटर द्वारा नियंत्रित होती है। -![न्यूरॉन का मॉडल](../../../../translated_images/hi/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![न्यूरॉन का मॉडल](../../../../translated_images/hi/artneuron.1a5daa88d20ebe6f.png) +![न्यूरॉन का मॉडल](../../../../translated_images/hi/synapse-wikipedia.ed20a9e4726ea1c6.webp) | ![न्यूरॉन का मॉडल](../../../../translated_images/hi/artneuron.1a5daa88d20ebe6f.webp) ----|---- वास्तविक न्यूरॉन *([छवि](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) विकिपीडिया से)* | कृत्रिम न्यूरॉन *(लेखक द्वारा बनाई गई छवि)* इस प्रकार, न्यूरॉन का सबसे सरल गणितीय मॉडल कई इनपुट्स X1, ..., XN और एक आउटपुट Y, और वज़नों की एक श्रृंखला W1, ..., WN को शामिल करता है। आउटपुट की गणना इस प्रकार की जाती है: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) जहां f एक गैर-रेखीय **सक्रियण फ़ंक्शन** (activation function) है। diff --git a/translations/hi/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/hi/lessons/4-ComputerVision/06-IntroCV/README.md index defd228c..c4a88ceb 100644 --- a/translations/hi/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/hi/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) * **ब्रेल पुस्तक की तस्वीर का पूर्व-प्रसंस्करण**। हम इस पर ध्यान केंद्रित करते हैं कि थ्रेशोल्डिंग, फीचर डिटेक्शन, परिप्रेक्ष्य रूपांतरण और NumPy हेरफेर का उपयोग करके ब्रेल प्रतीकों को अलग कैसे किया जा सकता है ताकि उन्हें न्यूरल नेटवर्क द्वारा आगे वर्गीकृत किया जा सके। -![ब्रेल छवि](../../../../../translated_images/hi/braille.341962ff76b1bd70.jpeg) | ![ब्रेल छवि पूर्व-प्रसंस्कृत](../../../../../translated_images/hi/braille-result.46530fea020b03c7.png) | ![ब्रेल प्रतीक](../../../../../translated_images/hi/braille-symbols.0159185ab69d5339.png) +![ब्रेल छवि](../../../../../translated_images/hi/braille.341962ff76b1bd70.webp) | ![ब्रेल छवि पूर्व-प्रसंस्कृत](../../../../../translated_images/hi/braille-result.46530fea020b03c7.webp) | ![ब्रेल प्रतीक](../../../../../translated_images/hi/braille-symbols.0159185ab69d5339.webp) ----|-----|----- > छवि [OpenCV.ipynb](OpenCV.ipynb) से * **फ्रेम अंतर का उपयोग करके वीडियो में गति का पता लगाना**। यदि कैमरा स्थिर है, तो कैमरा फीड से फ्रेम एक-दूसरे के समान होने चाहिए। चूंकि फ्रेम arrays के रूप में दर्शाए जाते हैं, केवल दो लगातार फ्रेम के लिए उन arrays को घटाकर हमें पिक्सल अंतर मिलेगा, जो स्थिर फ्रेम के लिए कम होना चाहिए और छवि में महत्वपूर्ण गति होने पर अधिक हो जाएगा। -![वीडियो फ्रेम और फ्रेम अंतर की छवि](../../../../../translated_images/hi/frame-difference.706f805491a0883c.png) +![वीडियो फ्रेम और फ्रेम अंतर की छवि](../../../../../translated_images/hi/frame-difference.706f805491a0883c.webp) > छवि [OpenCV.ipynb](OpenCV.ipynb) से @@ -89,7 +89,7 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) - **डेंस ऑप्टिकल फ्लो** वह वेक्टर फील्ड गणना करता है जो दिखाता है कि प्रत्येक पिक्सल कहां जा रहा है। - **स्पार्स ऑप्टिकल फ्लो** छवि में कुछ विशिष्ट विशेषताओं (जैसे किनारों) को लेता है और फ्रेम से फ्रेम तक उनकी प्रक्षेपवक्र बनाता है। -![ऑप्टिकल फ्लो की छवि](../../../../../translated_images/hi/optical.1f4a94464579a83a.png) +![ऑप्टिकल फ्लो की छवि](../../../../../translated_images/hi/optical.1f4a94464579a83a.webp) > छवि [OpenCV.ipynb](OpenCV.ipynb) से diff --git a/translations/hi/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/hi/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index faa226da..4cf6b187 100644 --- a/translations/hi/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/hi/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 एक नेटवर्क है जिसने 2014 में ImageNet टॉप-5 क्लासिफिकेशन में 92.7% सटीकता हासिल की। इसका लेयर स्ट्रक्चर निम्नलिखित है: -![ImageNet Layers](../../../../../translated_images/hi/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet Layers](../../../../../translated_images/hi/vgg-16-arch1.d901a5583b3a51ba.webp) जैसा कि आप देख सकते हैं, VGG एक पारंपरिक पिरामिड आर्किटेक्चर का अनुसरण करता है, जो कि कॉन्वोल्यूशन-पूलिंग लेयर्स का अनुक्रम है। -![ImageNet Pyramid](../../../../../translated_images/hi/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet Pyramid](../../../../../translated_images/hi/vgg-16-arch.64ff2137f50dd49f.webp) > चित्र [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) से लिया गया है diff --git a/translations/hi/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/hi/lessons/4-ComputerVision/07-ConvNets/README.md index 785453e1..875e7862 100644 --- a/translations/hi/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/hi/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: पैटर्न्स निकालने के लिए, हम **कॉन्वोल्यूशनल फिल्टर्स** की अवधारणा का उपयोग करेंगे। जैसा कि आप जानते हैं, एक इमेज को 2D-मैट्रिक्स या रंग गहराई के साथ 3D-टेंसर के रूप में दर्शाया जाता है। फिल्टर लागू करने का मतलब है कि हम एक अपेक्षाकृत छोटा **फिल्टर कर्नल** मैट्रिक्स लेते हैं, और मूल इमेज के प्रत्येक पिक्सल के लिए पड़ोसी बिंदुओं के साथ भारित औसत की गणना करते हैं। इसे हम ऐसे देख सकते हैं जैसे एक छोटी विंडो पूरी इमेज पर स्लाइड कर रही हो और फिल्टर कर्नल मैट्रिक्स में वज़न के अनुसार सभी पिक्सल्स को औसत कर रही हो। -![वर्टिकल एज फिल्टर](../../../../../translated_images/hi/filter-vert.b7148390ca0bc356.png) | ![हॉरिज़ॉन्टल एज फिल्टर](../../../../../translated_images/hi/filter-horiz.59b80ed4feb946ef.png) +![वर्टिकल एज फिल्टर](../../../../../translated_images/hi/filter-vert.b7148390ca0bc356.webp) | ![हॉरिज़ॉन्टल एज फिल्टर](../../../../../translated_images/hi/filter-horiz.59b80ed4feb946ef.webp) ----|---- > छवि: दिमित्री सोश्निकोव द्वारा @@ -38,7 +38,7 @@ CNNs जिस तरह से काम करते हैं, वह नि * हम नेटवर्क को इस तरह डिज़ाइन कर सकते हैं कि फिल्टर्स स्वचालित रूप से प्रशिक्षित हों * हम केवल मूल इमेज में ही नहीं, बल्कि उच्च-स्तरीय फीचर्स में भी पैटर्न्स ढूंढने के लिए इसी दृष्टिकोण का उपयोग कर सकते हैं। इस प्रकार, CNN फीचर एक्सट्रैक्शन फीचर्स की एक पदानुक्रम पर काम करता है, जो लो-लेवल पिक्सल संयोजनों से शुरू होकर, इमेज के हिस्सों के उच्च-स्तरीय संयोजनों तक जाता है। -![पदानुक्रमिक फीचर एक्सट्रैक्शन](../../../../../translated_images/hi/FeatureExtractionCNN.d9b456cbdae7cb64.png) +![पदानुक्रमिक फीचर एक्सट्रैक्शन](../../../../../translated_images/hi/FeatureExtractionCNN.d9b456cbdae7cb64.webp) > छवि: [Hislop-Lynch के पेपर](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d) से, उनके [अनुसंधान](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) पर आधारित @@ -55,9 +55,9 @@ CNNs जिस तरह से काम करते हैं, वह नि उदाहरण के लिए, आइए VGG-16 की आर्किटेक्चर पर नज़र डालें, एक नेटवर्क जिसने 2014 में ImageNet के टॉप-5 वर्गीकरण में 92.7% सटीकता प्राप्त की: -![ImageNet लेयर्स](../../../../../translated_images/hi/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet लेयर्स](../../../../../translated_images/hi/vgg-16-arch1.d901a5583b3a51ba.webp) -![ImageNet पिरामिड](../../../../../translated_images/hi/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet पिरामिड](../../../../../translated_images/hi/vgg-16-arch.64ff2137f50dd49f.webp) > छवि: [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) से diff --git a/translations/hi/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/hi/lessons/4-ComputerVision/07-ConvNets/lab/README.md index b2d90d25..ce3e446e 100644 --- a/translations/hi/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/hi/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ CO_OP_TRANSLATOR_METADATA: हम [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/) का उपयोग करेंगे, जिसमें कुत्तों और बिल्लियों की 37 विभिन्न नस्लों की छवियां शामिल हैं। -![हम जिस डेटासेट पर काम करेंगे](../../../../../../translated_images/hi/data.50b2a9d5484bdbf0.png) +![हम जिस डेटासेट पर काम करेंगे](../../../../../../translated_images/hi/data.50b2a9d5484bdbf0.webp) डेटासेट डाउनलोड करने के लिए, इस कोड स्निपेट का उपयोग करें: diff --git a/translations/hi/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/hi/lessons/4-ComputerVision/08-TransferLearning/README.md index 7e8875bc..09fc45e7 100644 --- a/translations/hi/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/hi/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Keras और PyTorch दोनों में कुछ सामान्य यहाँ VGG-16 नेटवर्क द्वारा एक बिल्ली की तस्वीर से निकाले गए फीचर्स का एक उदाहरण है: -![VGG-16 द्वारा निकाले गए फीचर्स](../../../../../translated_images/hi/features.6291f9c7ba3a0b95.png) +![VGG-16 द्वारा निकाले गए फीचर्स](../../../../../translated_images/hi/features.6291f9c7ba3a0b95.webp) ## कैट्स बनाम डॉग्स डेटासेट @@ -48,19 +48,19 @@ Keras और PyTorch दोनों में कुछ सामान्य एक दृष्टिकोण यह हो सकता है कि हम एक रैंडम इमेज से शुरू करें, और फिर **ग्रेडिएंट डिसेंट ऑप्टिमाइज़ेशन** तकनीक का उपयोग करके उस इमेज को इस तरह से समायोजित करें कि नेटवर्क इसे बिल्ली मानने लगे। -![इमेज ऑप्टिमाइज़ेशन लूप](../../../../../translated_images/hi/ideal-cat-loop.999fbb8ff306e044.png) +![इमेज ऑप्टिमाइज़ेशन लूप](../../../../../translated_images/hi/ideal-cat-loop.999fbb8ff306e044.webp) हालांकि, यदि हम ऐसा करते हैं, तो हमें कुछ ऐसा मिलेगा जो रैंडम नॉइज़ के बहुत करीब होगा। ऐसा इसलिए है क्योंकि *नेटवर्क को यह सोचने के लिए कई तरीके हैं कि इनपुट इमेज एक बिल्ली है*, जिनमें से कुछ दृश्य रूप से समझ में नहीं आते। जबकि उन इमेज में बिल्ली के लिए विशिष्ट कई पैटर्न होते हैं, उन्हें दृश्य रूप से विशिष्ट बनाने के लिए कुछ भी बाध्य नहीं करता। परिणाम को बेहतर बनाने के लिए, हम लॉस फंक्शन में एक और टर्म जोड़ सकते हैं, जिसे **वेरिएशन लॉस** कहा जाता है। यह एक मीट्रिक है जो दिखाता है कि इमेज के पड़ोसी पिक्सल कितने समान हैं। वेरिएशन लॉस को कम करने से इमेज स्मूथ हो जाती है और नॉइज़ हट जाता है - जिससे अधिक दृश्य रूप से आकर्षक पैटर्न सामने आते हैं। यहाँ ऐसे "आदर्श" इमेज का एक उदाहरण है, जिन्हें उच्च संभावना के साथ बिल्ली और ज़ेब्रा के रूप में वर्गीकृत किया गया है: -![आदर्श बिल्ली](../../../../../translated_images/hi/ideal-cat.203dd4597643d6b0.png) | ![आदर्श ज़ेब्रा](../../../../../translated_images/hi/ideal-zebra.7f70e8b54ee15a7a.png) +![आदर्श बिल्ली](../../../../../translated_images/hi/ideal-cat.203dd4597643d6b0.webp) | ![आदर्श ज़ेब्रा](../../../../../translated_images/hi/ideal-zebra.7f70e8b54ee15a7a.webp) -----|----- *आदर्श बिल्ली* | *आदर्श ज़ेब्रा* इसी तरह का दृष्टिकोण तथाकथित **एडवर्सेरियल अटैक्स** करने के लिए उपयोग किया जा सकता है। मान लें कि हम एक न्यूरल नेटवर्क को धोखा देना चाहते हैं और कुत्ते को बिल्ली जैसा दिखाना चाहते हैं। यदि हम कुत्ते की इमेज लें, जिसे नेटवर्क द्वारा कुत्ते के रूप में पहचाना जाता है, तो हम इसे थोड़ा सा समायोजित कर सकते हैं, जब तक कि नेटवर्क इसे बिल्ली के रूप में वर्गीकृत करना शुरू न कर दे: -![कुत्ते की तस्वीर](../../../../../translated_images/hi/original-dog.8f68a67d2fe0911f.png) | ![कुत्ते की तस्वीर जिसे बिल्ली के रूप में वर्गीकृत किया गया](../../../../../translated_images/hi/adversarial-dog.d9fc7773b0142b89.png) +![कुत्ते की तस्वीर](../../../../../translated_images/hi/original-dog.8f68a67d2fe0911f.webp) | ![कुत्ते की तस्वीर जिसे बिल्ली के रूप में वर्गीकृत किया गया](../../../../../translated_images/hi/adversarial-dog.d9fc7773b0142b89.webp) -----|----- *कुत्ते की मूल तस्वीर* | *कुत्ते की तस्वीर जिसे बिल्ली के रूप में वर्गीकृत किया गया* diff --git a/translations/hi/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/hi/lessons/4-ComputerVision/09-Autoencoders/README.md index 92f24e4a..f4f34776 100644 --- a/translations/hi/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/hi/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: चूंकि हम ऑटोएन्कोडर को मूल इमेज से अधिकतम जानकारी को कैप्चर करने के लिए ट्रेन कर रहे हैं ताकि सटीक पुनर्निर्माण हो सके, नेटवर्क इनपुट इमेज का सबसे अच्छा **एम्बेडिंग** खोजने की कोशिश करता है। -![ऑटोएन्कोडर डायग्राम](../../../../../translated_images/hi/autoencoder_schema.5e6fc9ad98a5eb61.jpg) +![ऑटोएन्कोडर डायग्राम](../../../../../translated_images/hi/autoencoder_schema.5e6fc9ad98a5eb61.webp) > इमेज [Keras ब्लॉग](https://blog.keras.io/building-autoencoders-in-keras.html) से diff --git a/translations/hi/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/hi/lessons/4-ComputerVision/11-ObjectDetection/README.md index bbccfa1a..4232577c 100644 --- a/translations/hi/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/hi/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: ## [प्री-लेक्चर क्विज़](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![ऑब्जेक्ट डिटेक्शन](../../../../../translated_images/hi/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) +![ऑब्जेक्ट डिटेक्शन](../../../../../translated_images/hi/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp) > इमेज [YOLO v2 वेबसाइट](https://pjreddie.com/darknet/yolov2/) से @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 2. प्रत्येक टाइल पर इमेज क्लासिफिकेशन चलाएं। 3. जिन टाइल्स में पर्याप्त उच्च सक्रियता होती है, उन्हें उस वस्तु को शामिल करने वाला माना जा सकता है। -![साधारण ऑब्जेक्ट डिटेक्शन](../../../../../translated_images/hi/naive-detection.e7f1ba220ccd08c6.png) +![साधारण ऑब्जेक्ट डिटेक्शन](../../../../../translated_images/hi/naive-detection.e7f1ba220ccd08c6.webp) > *इमेज [एक्सरसाइज नोटबुक](ObjectDetection-TF.ipynb) से* @@ -42,7 +42,7 @@ CO_OP_TRANSLATOR_METADATA: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 क्लासेस * [COCO](http://cocodataset.org/#home) - कॉमन ऑब्जेक्ट्स इन कॉन्टेक्स्ट। 80 क्लासेस, बॉक्स और सेगमेंटेशन मास्क्स -![COCO](../../../../../translated_images/hi/coco-examples.71bc60380fa6cceb.jpg) +![COCO](../../../../../translated_images/hi/coco-examples.71bc60380fa6cceb.webp) ## ऑब्जेक्ट डिटेक्शन मेट्रिक्स @@ -50,7 +50,7 @@ CO_OP_TRANSLATOR_METADATA: जहां इमेज क्लासिफिकेशन के लिए एल्गोरिदम के प्रदर्शन को मापना आसान है, वहीं ऑब्जेक्ट डिटेक्शन के लिए हमें क्लास की सही पहचान और बॉक्स की लोकेशन की सटीकता दोनों को मापना होता है। लोकेशन की सटीकता के लिए, हम **इंटरसेक्शन ओवर यूनियन** (IoU) का उपयोग करते हैं, जो मापता है कि दो बॉक्स (या दो क्षेत्र) कितने अच्छे से ओवरलैप करते हैं। -![IoU](../../../../../translated_images/hi/iou_equation.9a4751d40fff4e11.png) +![IoU](../../../../../translated_images/hi/iou_equation.9a4751d40fff4e11.webp) > *फिगर 2 [इस उत्कृष्ट ब्लॉग पोस्ट](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/) से* @@ -98,11 +98,11 @@ $$ [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) का उपयोग करता है ताकि ROI क्षेत्रों की एक पदानुक्रमित संरचना उत्पन्न की जा सके, जिन्हें फिर CNN फीचर एक्सट्रैक्टर्स और SVM-क्लासिफायर के माध्यम से पास किया जाता है ताकि ऑब्जेक्ट क्लास निर्धारित किया जा सके, और *बॉक्स* निर्देशांक निर्धारित करने के लिए लीनियर रिग्रेशन का उपयोग किया जाता है। [आधिकारिक पेपर](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/hi/rcnn1.cae407020dfb1d1f.png) +![RCNN](../../../../../translated_images/hi/rcnn1.cae407020dfb1d1f.webp) > *इमेज van de Sande et al. ICCV’11 से* -![RCNN-1](../../../../../translated_images/hi/rcnn2.2d9530bb83516484.png) +![RCNN-1](../../../../../translated_images/hi/rcnn2.2d9530bb83516484.webp) > *इमेज [इस ब्लॉग](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e) से* @@ -110,7 +110,7 @@ $$ यह दृष्टिकोण R-CNN के समान है, लेकिन रीजन को परिभाषित किया जाता है जब कॉन्वोल्यूशन लेयर्स लागू हो चुकी होती हैं। -![FRCNN](../../../../../translated_images/hi/f-rcnn.3cda6d9bb4188875.png) +![FRCNN](../../../../../translated_images/hi/f-rcnn.3cda6d9bb4188875.webp) > इमेज [आधिकारिक पेपर](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 से @@ -118,7 +118,7 @@ $$ इस दृष्टिकोण का मुख्य विचार है कि रीजन की भविष्यवाणी करने के लिए न्यूरल नेटवर्क का उपयोग किया जाए - जिसे *रीजन प्रपोजल नेटवर्क* कहा जाता है। [पेपर](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/hi/faster-rcnn.8d46c099b87ef30a.png) +![FasterRCNN](../../../../../translated_images/hi/faster-rcnn.8d46c099b87ef30a.webp) > इमेज [आधिकारिक पेपर](https://arxiv.org/pdf/1506.01497.pdf) से @@ -130,7 +130,7 @@ $$ 2. फीचर्स को **पोजिशन-सेंसिटिव स्कोर मैप** द्वारा प्रोसेस किया जाता है। $C$ क्लासेस के प्रत्येक ऑब्जेक्ट को $k\times k$ क्षेत्रों में विभाजित किया जाता है, और हम ऑब्जेक्ट्स के भागों की भविष्यवाणी करने के लिए प्रशिक्षण देते हैं। 3. $k\times k$ क्षेत्रों के प्रत्येक भाग के लिए सभी नेटवर्क ऑब्जेक्ट क्लासेस के लिए वोट करते हैं, और अधिकतम वोट वाले ऑब्जेक्ट क्लास को चुना जाता है। -![r-fcn इमेज](../../../../../translated_images/hi/r-fcn.13eb88158b99a3da.png) +![r-fcn इमेज](../../../../../translated_images/hi/r-fcn.13eb88158b99a3da.webp) > इमेज [आधिकारिक पेपर](https://arxiv.org/abs/1605.06409) से @@ -141,7 +141,7 @@ YOLO एक रियलटाइम वन-पास एल्गोरिद * इमेज को $S\times S$ क्षेत्रों में विभाजित किया जाता है। * प्रत्येक क्षेत्र के लिए, **CNN** $n$ संभावित ऑब्जेक्ट्स, *बॉक्स* निर्देशांक और *कॉन्फिडेंस*=*प्रोबेबिलिटी* * IoU की भविष्यवाणी करता है। - ![YOLO](../../../../../translated_images/hi/yolo.a2648ec82ee8bb4e.png) + ![YOLO](../../../../../translated_images/hi/yolo.a2648ec82ee8bb4e.webp) > इमेज [आधिकारिक पेपर](https://arxiv.org/abs/1506.02640) से diff --git a/translations/hi/lessons/5-NLP/14-Embeddings/README.md b/translations/hi/lessons/5-NLP/14-Embeddings/README.md index a80aca52..b8b35b25 100644 --- a/translations/hi/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/hi/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: हमारे क्लासिफायर नेटवर्क में एम्बेडिंग लेयर को पहली लेयर के रूप में उपयोग करके, हम बैग-ऑफ-वर्ड्स से **एम्बेडिंग बैग** मॉडल में स्विच कर सकते हैं, जहां हम पहले अपने टेक्स्ट में प्रत्येक शब्द को संबंधित एम्बेडिंग में बदलते हैं और फिर उन सभी एम्बेडिंग्स पर कुछ समग्र फ़ंक्शन की गणना करते हैं, जैसे `sum`, `average` या `max`। -![पांच अनुक्रम शब्दों के लिए एम्बेडिंग क्लासिफायर दिखाने वाली छवि।](../../../../../translated_images/hi/embedding-classifier-example.b77f021a7ee67eee.png) +![पांच अनुक्रम शब्दों के लिए एम्बेडिंग क्लासिफायर दिखाने वाली छवि।](../../../../../translated_images/hi/embedding-classifier-example.b77f021a7ee67eee.webp) > लेखक द्वारा बनाई गई छवि @@ -40,7 +40,7 @@ CO_OP_TRANSLATOR_METADATA: CBoW तेज है, जबकि स्किप-ग्राम धीमा है, लेकिन यह दुर्लभ शब्दों को बेहतर तरीके से रिप्रेजेंट करता है। -![CBoW और स्किप-ग्राम एल्गोरिदम को शब्दों को वेक्टर में बदलने के लिए दिखाने वाली छवि।](../../../../../translated_images/hi/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![CBoW और स्किप-ग्राम एल्गोरिदम को शब्दों को वेक्टर में बदलने के लिए दिखाने वाली छवि।](../../../../../translated_images/hi/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > [इस पेपर](https://arxiv.org/pdf/1301.3781.pdf) से ली गई छवि diff --git a/translations/hi/lessons/5-NLP/15-LanguageModeling/README.md b/translations/hi/lessons/5-NLP/15-LanguageModeling/README.md index 804f38d9..400d1f66 100644 --- a/translations/hi/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/hi/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: * **कंटीन्युअस बैग-ऑफ-वर्ड्स** (CBoW), जिसमें हम टोकन अनुक्रम $W_{-N}$, ..., $W_N$ में मध्य टोकन $W_0$ की भविष्यवाणी करते हैं। * **स्किप-ग्राम**, जिसमें हम मध्य टोकन $W_0$ से पड़ोसी टोकन {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} का सेट भविष्यवाणी करते हैं। -![शब्दों को वेक्टर में बदलने के लिए पेपर से छवि](../../../../../translated_images/hi/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![शब्दों को वेक्टर में बदलने के लिए पेपर से छवि](../../../../../translated_images/hi/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > छवि [इस पेपर](https://arxiv.org/pdf/1301.3781.pdf) से diff --git a/translations/hi/lessons/5-NLP/16-RNN/README.md b/translations/hi/lessons/5-NLP/16-RNN/README.md index b89afea4..719bb2a0 100644 --- a/translations/hi/lessons/5-NLP/16-RNN/README.md +++ b/translations/hi/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: टेक्स्ट अनुक्रम के अर्थ को पकड़ने के लिए, हमें एक अन्य न्यूरल नेटवर्क आर्किटेक्चर का उपयोग करना होगा, जिसे **पुनरावर्ती न्यूरल नेटवर्क** या RNN कहा जाता है। RNN में, हम अपने वाक्य को नेटवर्क के माध्यम से एक समय में एक प्रतीक पास करते हैं, और नेटवर्क कुछ **स्थिति** उत्पन्न करता है, जिसे हम अगले प्रतीक के साथ नेटवर्क में फिर से पास करते हैं। -![RNN](../../../../../translated_images/hi/rnn.27f5c29c53d727b5.png) +![RNN](../../../../../translated_images/hi/rnn.27f5c29c53d727b5.webp) > चित्र लेखक द्वारा @@ -61,7 +61,7 @@ LSTM नेटवर्क RNN के समान तरीके से सं एक पुनरावर्ती नेटवर्क, चाहे वह एक-दिशात्मक हो या बाईडायरेक्शनल, अनुक्रम के भीतर कुछ पैटर्न को कैप्चर करता है, और उन्हें स्थिति वेक्टर में संग्रहीत कर सकता है या आउटपुट में पास कर सकता है। जैसे कि कन्वोल्यूशनल नेटवर्क्स के साथ, हम पहले लेयर द्वारा निकाले गए निम्न-स्तरीय पैटर्न से उच्च-स्तरीय पैटर्न को कैप्चर करने के लिए पहले लेयर के ऊपर एक और पुनरावर्ती लेयर बना सकते हैं। यह हमें **मल्टी-लेयर RNN** की धारणा तक ले जाता है, जिसमें दो या अधिक पुनरावर्ती नेटवर्क होते हैं, जहां पिछले लेयर का आउटपुट अगले लेयर में इनपुट के रूप में पास होता है। -![मल्टीलेयर लॉन्ग-शॉर्ट-टर्म-मेमोरी RNN का चित्र](../../../../../translated_images/hi/multi-layer-lstm.dd975e29bb2a59fe.jpg) +![मल्टीलेयर लॉन्ग-शॉर्ट-टर्म-मेमोरी RNN का चित्र](../../../../../translated_images/hi/multi-layer-lstm.dd975e29bb2a59fe.webp) *चित्र [इस शानदार पोस्ट](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) से लिया गया है, लेखक: फर्नांडो लोपेज़* diff --git a/translations/hi/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/hi/lessons/5-NLP/17-GenerativeNetworks/README.md index a25dfacf..a2dad3f0 100644 --- a/translations/hi/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/hi/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ Recurrent Neural Networks (RNNs) और उनके gated cell वेरिए यह विभिन्न neural architectures की अनुमति देता है, जो नीचे दी गई तस्वीर में दिखाए गए हैं: -![Image showing common recurrent neural network patterns.](../../../../../translated_images/hi/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Image showing common recurrent neural network patterns.](../../../../../translated_images/hi/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > चित्र ब्लॉग पोस्ट [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) से लिया गया है, लेखक [Andrej Karpaty](http://karpathy.github.io/)। @@ -32,7 +32,7 @@ Recurrent Neural Networks (RNNs) और उनके gated cell वेरिए हम इस RNN को टेक्स्ट step-by-step उत्पन्न करने के लिए प्रशिक्षित करेंगे। प्रत्येक चरण में, हम `nchars` लंबाई के characters का एक sequence लेंगे, और नेटवर्क से प्रत्येक इनपुट character के लिए अगला आउटपुट character उत्पन्न करने के लिए कहेंगे: -![Image showing an example RNN generation of the word 'HELLO'.](../../../../../translated_images/hi/rnn-generate.56c54afb52f9781d.png) +![Image showing an example RNN generation of the word 'HELLO'.](../../../../../translated_images/hi/rnn-generate.56c54afb52f9781d.webp) जब टेक्स्ट उत्पन्न किया जाता है (inference के दौरान), हम कुछ **prompt** के साथ शुरू करते हैं, जिसे RNN cells के माध्यम से पास किया जाता है ताकि इसका intermediate state उत्पन्न हो सके, और फिर इस state से जनरेशन शुरू होती है। हम एक बार में एक character उत्पन्न करते हैं, और state और उत्पन्न character को अगले RNN cell में पास करते हैं ताकि अगला character उत्पन्न हो सके, जब तक कि हम पर्याप्त characters उत्पन्न न कर लें। diff --git a/translations/hi/lessons/5-NLP/18-Transformers/README.md b/translations/hi/lessons/5-NLP/18-Transformers/README.md index 9f660533..95456408 100644 --- a/translations/hi/lessons/5-NLP/18-Transformers/README.md +++ b/translations/hi/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ RNNs के साथ, सीक्वेंस-टू-सीक्वेंस **ध्यान तंत्र** प्रत्येक इनपुट वेक्टर के संदर्भ प्रभाव को प्रत्येक आउटपुट भविष्यवाणी पर वेटिंग प्रदान करने का एक तरीका है। इसे लागू करने का तरीका यह है कि इनपुट RNN और आउटपुट RNN की मध्यवर्ती अवस्थाओं के बीच शॉर्टकट बनाए जाते हैं। इस प्रकार, जब आउटपुट प्रतीक yt उत्पन्न किया जाता है, तो हम सभी इनपुट हिडन स्टेट्स hi को विभिन्न वेट कोएफिशिएंट्स αt,i के साथ ध्यान में रखते हैं। -![एन्कोडर/डिकोडर मॉडल जिसमें एडिटिव ध्यान लेयर है](../../../../../translated_images/hi/encoder-decoder-attention.7a726296894fb567.png) +![एन्कोडर/डिकोडर मॉडल जिसमें एडिटिव ध्यान लेयर है](../../../../../translated_images/hi/encoder-decoder-attention.7a726296894fb567.webp) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) में एडिटिव ध्यान तंत्र के साथ एन्कोडर-डिकोडर मॉडल, [इस ब्लॉग पोस्ट](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) से लिया गया। ध्यान मैट्रिक्स {αi,j} यह दर्शाता है कि आउटपुट सीक्वेंस में दिए गए शब्द के निर्माण में कुछ इनपुट शब्दों की भूमिका कितनी है। नीचे एक उदाहरण दिया गया है: -![Bahdanau - arviz.org से लिया गया RNNsearch-50 द्वारा पाया गया एक नमूना संरेखण](../../../../../translated_images/hi/bahdanau-fig3.09ba2d37f202a6af.png) +![Bahdanau - arviz.org से लिया गया RNNsearch-50 द्वारा पाया गया एक नमूना संरेखण](../../../../../translated_images/hi/bahdanau-fig3.09ba2d37f202a6af.webp) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) से चित्र (Fig.3) @@ -66,7 +66,7 @@ RNNs के साथ, सीक्वेंस-टू-सीक्वेंस अब हमें अपने सीक्वेंस के भीतर कुछ पैटर्न कैप्चर करने की आवश्यकता है। ऐसा करने के लिए, ट्रांसफॉर्मर्स **सेल्फ-अटेंशन** तंत्र का उपयोग करते हैं, जो मूल रूप से इनपुट और आउटपुट के रूप में एक ही सीक्वेंस पर लागू ध्यान है। सेल्फ-अटेंशन लागू करने से हमें वाक्य के भीतर **संदर्भ** को ध्यान में रखने और यह देखने की अनुमति मिलती है कि कौन से शब्द आपस में संबंधित हैं। उदाहरण के लिए, यह हमें यह देखने की अनुमति देता है कि कौन से शब्द *it* जैसे कोरफेरेंस द्वारा संदर्भित हैं, और संदर्भ को ध्यान में रखता है: -![](../../../../../translated_images/hi/CoreferenceResolution.861924d6d384a7d6.png) +![](../../../../../translated_images/hi/CoreferenceResolution.861924d6d384a7d6.webp) > [Google ब्लॉग](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) से छवि @@ -91,7 +91,7 @@ RNNs के साथ, सीक्वेंस-टू-सीक्वेंस **BERT** (Bidirectional Encoder Representations from Transformers) एक बहुत बड़ा मल्टी लेयर ट्रांसफॉर्मर नेटवर्क है जिसमें *BERT-base* के लिए 12 लेयर और *BERT-large* के लिए 24 लेयर हैं। मॉडल को पहले एक बड़े टेक्स्ट डेटा कॉर्पस (WikiPedia + किताबें) पर अनसुपरवाइज्ड ट्रेनिंग (वाक्य में मास्क किए गए शब्दों की भविष्यवाणी) का उपयोग करके प्री-ट्रेन किया जाता है। प्री-ट्रेनिंग के दौरान मॉडल महत्वपूर्ण स्तर की भाषा समझ को अवशोषित करता है, जिसे फिर अन्य डेटासेट्स के साथ फाइन ट्यूनिंग का उपयोग करके लाभ उठाया जा सकता है। इस प्रक्रिया को **ट्रांसफर लर्निंग** कहा जाता है। -![चित्र http://jalammar.github.io/illustrated-bert/ से](../../../../../translated_images/hi/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) +![चित्र http://jalammar.github.io/illustrated-bert/ से](../../../../../translated_images/hi/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > छवि [स्रोत](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/hi/lessons/5-NLP/18-Transformers/READMEtransformers.md b/translations/hi/lessons/5-NLP/18-Transformers/READMEtransformers.md index 2aa3e208..0ce3331f 100644 --- a/translations/hi/lessons/5-NLP/18-Transformers/READMEtransformers.md +++ b/translations/hi/lessons/5-NLP/18-Transformers/READMEtransformers.md @@ -11,13 +11,13 @@ RNNs के साथ, क्रम-से-क्रम को दो आवर **ध्यान तंत्र** प्रत्येक इनपुट वेक्टर के संदर्भात्मक प्रभाव को RNN के प्रत्येक आउटपुट भविष्यवाणी पर वजन देने का एक साधन प्रदान करते हैं। इसे लागू करने का तरीका इनपुट RNN और आउटपुट RNN के मध्यवर्ती राज्यों के बीच शॉर्टकट बनाना है। इस तरह, जब आउटपुट प्रतीक yt उत्पन्न करते हैं, तो हम सभी इनपुट छिपी हुई स्थितियों hi को विभिन्न वजन गुणांक αt,i के साथ ध्यान में रखते हैं। -![एक एन्कोडर/डिकोडर मॉडल को दर्शाने वाली छवि जिसमें एक जोड़ात्मक ध्यान परत है](../../../../../translated_images/hi/encoder-decoder-attention.7a726296894fb567.png) +![एक एन्कोडर/डिकोडर मॉडल को दर्शाने वाली छवि जिसमें एक जोड़ात्मक ध्यान परत है](../../../../../translated_images/hi/encoder-decoder-attention.7a726296894fb567.webp) > एन्कोडर-डिकोडर मॉडल जिसमें जोड़ात्मक ध्यान तंत्र है [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) से, [इस ब्लॉग पोस्ट](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) से उद्धृत। ध्यान मैट्रिक्स {αi,j} यह दर्शाएगा कि कुछ इनपुट शब्दों का एक दिए गए शब्द के उत्पादन में क्या योगदान है। नीचे एक ऐसे मैट्रिक्स का उदाहरण दिया गया है: -![RNNsearch-50 द्वारा पाए गए एक नमूना संरेखण को दर्शाने वाली छवि, Bahdanau - arviz.org से ली गई](../../../../../translated_images/hi/bahdanau-fig3.09ba2d37f202a6af.png) +![RNNsearch-50 द्वारा पाए गए एक नमूना संरेखण को दर्शाने वाली छवि, Bahdanau - arviz.org से ली गई](../../../../../translated_images/hi/bahdanau-fig3.09ba2d37f202a6af.webp) > चित्र [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) से (चित्र 3) @@ -57,7 +57,7 @@ RNNs के साथ, क्रम-से-क्रम को दो आवर अगला, हमें अपने अनुक्रम में कुछ पैटर्न कैप्चर करने की आवश्यकता है। ऐसा करने के लिए, ट्रांसफार्मर **आत्म-ध्यान** तंत्र का उपयोग करते हैं, जो मूल रूप से इनपुट और आउटपुट के रूप में उसी अनुक्रम पर लागू ध्यान है। आत्म-ध्यान लागू करने से हमें वाक्य के भीतर **संदर्भ** को ध्यान में रखने की अनुमति मिलती है, और यह देखने की अनुमति मिलती है कि कौन से शब्द आपस में संबंधित हैं। उदाहरण के लिए, यह हमें यह देखने की अनुमति देता है कि कौन से शब्द सहसंबंधों द्वारा संदर्भित हैं, जैसे *यह*, और संदर्भ को भी ध्यान में रखते हैं: -![](../../../../../translated_images/hi/CoreferenceResolution.861924d6d384a7d6.png) +![](../../../../../translated_images/hi/CoreferenceResolution.861924d6d384a7d6.webp) > चित्र [Google ब्लॉग](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) से @@ -82,7 +82,7 @@ RNNs के साथ, क्रम-से-क्रम को दो आवर **BERT** (Bidirectional Encoder Representations from Transformers) एक बहुत बड़ा मल्टी लेयर ट्रांसफार्मर नेटवर्क है जिसमें *BERT-base* के लिए 12 परतें और *BERT-large* के लिए 24 परतें हैं। मॉडल को पहले एक बड़े पाठ डेटा (WikiPedia + पुस्तकें) के कॉर्पस पर बिना पर्यवेक्षण प्रशिक्षण का उपयोग करके पूर्व-प्रशिक्षित किया जाता है (एक वाक्य में मास्क किए गए शब्दों की भविष्यवाणी करना)। पूर्व-प्रशिक्षण के दौरान, मॉडल भाषा समझने के महत्वपूर्ण स्तरों को अवशोषित करता है जिसे फिर अन्य डेटासेट के साथ फाइन ट्यूनिंग के माध्यम से लाभ उठाया जा सकता है। इस प्रक्रिया को **हस्तांतरण शिक्षण** कहा जाता है। -![चित्र http://jalammar.github.io/illustrated-bert/ से](../../../../../translated_images/hi/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) +![चित्र http://jalammar.github.io/illustrated-bert/ से](../../../../../translated_images/hi/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > चित्र [स्रोत](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/hi/lessons/5-NLP/19-NER/README.md b/translations/hi/lessons/5-NLP/19-NER/README.md index 1f27cf02..bc80bcef 100644 --- a/translations/hi/lessons/5-NLP/19-NER/README.md +++ b/translations/hi/lessons/5-NLP/19-NER/README.md @@ -58,7 +58,7 @@ infant | O चूंकि हमें टोकन और वर्गों के बीच एक-से-एक पत्राचार बनाना है, हम इस चित्र से एक सही **कई-से-कई** तंत्रिका नेटवर्क मॉडल को प्रशिक्षित कर सकते हैं: -![सामान्य पुनरावर्ती तंत्रिका नेटवर्क पैटर्न दिखाने वाली छवि।](../../../../../translated_images/hi/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![सामान्य पुनरावर्ती तंत्रिका नेटवर्क पैटर्न दिखाने वाली छवि।](../../../../../translated_images/hi/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > *[Andrej Karpathy](http://karpathy.github.io/) द्वारा [इस ब्लॉग पोस्ट](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) से छवि। NER टोकन वर्गीकरण मॉडल इस चित्र में सबसे दाईं ओर नेटवर्क आर्किटेक्चर से मेल खाते हैं।* diff --git a/translations/hi/lessons/6-Other/23-MultiagentSystems/README.md b/translations/hi/lessons/6-Other/23-MultiagentSystems/README.md index 65776039..164bd6a8 100644 --- a/translations/hi/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/hi/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ NetLogo की एक शानदार बात यह है कि इस मॉडल खोलने के बाद, आप मुख्य NetLogo स्क्रीन पर ले जाए जाते हैं। यहां एक नमूना मॉडल है जो सीमित संसाधनों (घास) को ध्यान में रखते हुए भेड़ियों और भेड़ों की आबादी का वर्णन करता है। -![NetLogo Main Screen](../../../../../translated_images/hi/NetLogo-Main.32653711ec1a01b3.png) +![NetLogo Main Screen](../../../../../translated_images/hi/NetLogo-Main.32653711ec1a01b3.webp) > दिमित्री सोश्निकोव द्वारा स्क्रीनशॉट diff --git a/translations/hk/README.md b/translations/hk/README.md index 1fdae7d7..3372aeaa 100644 --- a/translations/hk/README.md +++ b/translations/hk/README.md @@ -1,8 +1,8 @@ -[阿拉伯語](../ar/README.md) | [孟加拉語](../bn/README.md) | [保加利亞語](../bg/README.md) | [緬甸語](../my/README.md) | [中文（簡體）](../zh/README.md) | [中文（繁體，香港）](./README.md) | [中文（繁體，澳門）](../mo/README.md) | [中文（繁體，台灣）](../tw/README.md) | [克羅地亞語](../hr/README.md) | [捷克語](../cs/README.md) | [丹麥語](../da/README.md) | [荷蘭語](../nl/README.md) | [愛沙尼亞語](../et/README.md) | [芬蘭語](../fi/README.md) | [法語](../fr/README.md) | [德語](../de/README.md) | [希臘語](../el/README.md) | [希伯來語](../he/README.md) | [印地語](../hi/README.md) | [匈牙利語](../hu/README.md) | [印尼語](../id/README.md) | [意大利語](../it/README.md) | [日語](../ja/README.md) | [坎那達語](../kn/README.md) | [韓語](../ko/README.md) | [立陶宛語](../lt/README.md) | [馬來語](../ms/README.md) | [馬拉雅拉姆語](../ml/README.md) | [馬拉地語](../mr/README.md) | [尼泊爾語](../ne/README.md) | [奈及利亞皮欽語](../pcm/README.md) | [挪威語](../no/README.md) | [波斯語（法爾西語）](../fa/README.md) | [波蘭語](../pl/README.md) | [葡萄牙語（巴西）](../br/README.md) | [葡萄牙語（葡萄牙）](../pt/README.md) | [旁遮普語（古魯穆奇）](../pa/README.md) | [羅馬尼亞語](../ro/README.md) | [俄語](../ru/README.md) | [塞爾維亞語（西里爾字母）](../sr/README.md) | [斯洛伐克語](../sk/README.md) | [斯洛文尼亞語](../sl/README.md) | [西班牙語](../es/README.md) | [斯瓦希里語](../sw/README.md) | [瑞典語](../sv/README.md) | [他加祿語（菲律賓語）](../tl/README.md) | [泰米爾語](../ta/README.md) | [泰盧固語](../te/README.md) | [泰語](../th/README.md) | [土耳其語](../tr/README.md) | [烏克蘭語](../uk/README.md) | [烏爾都語](../ur/README.md) | [越南語](../vi/README.md) +[阿拉伯語](../ar/README.md) | [孟加拉語](../bn/README.md) | [保加利亞語](../bg/README.md) | [緬甸語 (Myanmar)](../my/README.md) | [中文（簡體）](../zh/README.md) | [中文（繁體，香港）](./README.md) | [中文（繁體，澳門）](../mo/README.md) | [中文（繁體，台灣）](../tw/README.md) | [克羅地亞語](../hr/README.md) | [捷克語](../cs/README.md) | [丹麥語](../da/README.md) | [荷蘭語](../nl/README.md) | [愛沙尼亞語](../et/README.md) | [芬蘭語](../fi/README.md) | [法語](../fr/README.md) | [德語](../de/README.md) | [希臘語](../el/README.md) | [希伯來語](../he/README.md) | [印地語](../hi/README.md) | [匈牙利語](../hu/README.md) | [印尼語](../id/README.md) | [義大利語](../it/README.md) | [日語](../ja/README.md) | [坎納達語](../kn/README.md) | [韓語](../ko/README.md) | [立陶宛語](../lt/README.md) | [馬來語](../ms/README.md) | [馬拉亞拉姆語](../ml/README.md) | [馬拉地語](../mr/README.md) | [尼泊爾語](../ne/README.md) | [奈及利亞皮欽語](../pcm/README.md) | [挪威語](../no/README.md) | [波斯語 (Farsi)](../fa/README.md) | [波蘭語](../pl/README.md) | [葡萄牙語（巴西）](../br/README.md) | [葡萄牙語（葡萄牙）](../pt/README.md) | [旁遮普語 (Gurmukhi)](../pa/README.md) | [羅馬尼亞語](../ro/README.md) | [俄語](../ru/README.md) | [塞爾維亞語（西里爾字母）](../sr/README.md) | [斯洛伐克語](../sk/README.md) | [斯洛維尼亞語](../sl/README.md) | [西班牙語](../es/README.md) | [斯瓦希里語](../sw/README.md) | [瑞典語](../sv/README.md) | [他加祿語（菲律賓語）](../tl/README.md) | [泰米爾語](../ta/README.md) | [泰盧固語](../te/README.md) | [泰語](../th/README.md) | [土耳其語](../tr/README.md) | [烏克蘭語](../uk/README.md) | [烏爾都語](../ur/README.md) | [越南語](../vi/README.md) -> **偏好本地克隆？** +> **希望本地端克隆？** -> 本儲存庫包含 50 多種語言翻譯，顯著增加下載大小。欲不包含翻譯而克隆，請使用稀疏簽出： +> 本存放庫包含 50 多種語言翻譯，會顯著增加下載大小。若想在無翻譯內容的情況下克隆，請使用稀疏檢出： > ```bash > git clone --filter=blob:none --sparse https://github.com/microsoft/AI-For-Beginners.git > cd AI-For-Beginners > git sparse-checkout set --no-cone '/*' '!translations' '!translated_images' > ``` -> 這樣您將以更快的速度取得完成課程所需的一切內容。 +> 可讓你快速下載，並獲得完成課程所需的一切。 -**如果您希望增添更多語言翻譯，支援語言列表見 [這裡](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** +**若希望支援其他翻譯語言，請參閱此處列出的語言清單 [here](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** ## 加入社群 [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -## 你將學習到的內容 +## 你將學到什麼 **[課程心智圖](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** -在此課程中，你將學習： +在這個課程中，你將學到： -* 不同的人工智能方法，包括「經典」的符號法，涉及 **知識表示** 與推理 ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence))。 -* **神經網絡** 與 **深度學習**，這些是現代 AI 的核心。我們將使用兩個最受歡迎的框架 — [TensorFlow](http://Tensorflow.org) 和 [PyTorch](http://pytorch.org) — 用程式碼示範這些重要主題背後的概念。 -* 用於處理圖像和文字的 **神經結構**。我們會涵蓋近期模型，但可能略欠缺最新最先進的內容。 -* 較不流行的 AI 方法，例如 **遺傳算法** 和 **多智能體系統**。 +* 不同的人工智能方法，包括以 **知識表示** 和推理為基礎的「經典」符號派方法 ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence))。 +* 現代 AI 核心的 **神經網絡** 與 **深度學習**。我們將使用兩大熱門框架 [TensorFlow](http://Tensorflow.org) 和 [PyTorch](http://pytorch.org) 以程式碼展示這些重要主題背後的概念。 +* 運用於圖像和文本的 **神經架構**。我們會介紹較新的模型，但在尖端技術上可能略有不足。 +* 較少熱門的 AI 方法，如 **遺傳算法** 和 **多代理系統**。 -本課程不涵蓋： +本課程不涵蓋的內容： -> [在 Microsoft Learn 收藏中查找本課程的所有額外資源](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) +> [在我們的 Microsoft Learn 集合中找到此課程的所有額外資源](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) -* **商業 AI** 的應用案例。建議參考 Microsoft Learn 上的 [業務用戶 AI 入門](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) 學習路徑，或與 [INSEAD](https://www.insead.edu/) 合作開發的 [AI 商業學院](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum)。 -* **經典機器學習**，我們有完善介紹的 [機器學習初學者課程](http://github.com/Microsoft/ML-for-Beginners)。 -* 使用 **[認知服務](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)** 建立的實務 AI 應用。建議從 Microsoft Learn 模組開始學習 [視覺](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum)、[自然語言處理](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum)、**[Azure OpenAI 服務的生成式 AI](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** 等。 -* 特定的 ML **雲端框架**，如 [Azure 機器學習](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-bethanycheum)、[Microsoft Fabric](https://learn.microsoft.com/en-us/training/paths/get-started-fabric/?WT.mc_id=academic-77998-bethanycheum)，或 [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum)。建議參考 [使用 Azure ML 建構與操作機器學習解決方案](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) 及 [使用 Azure Databricks 建構與操作機器學習解決方案](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum) 學習路徑。 -* **對話式 AI** 與 **聊天機器人**。有專門的 [建立對話式 AI 解決方案](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum) 學習路徑，也可參考 [這篇部落格文章](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) 獲得更多細節。 -* 深度學習背後的 **深厚數學基礎**。推薦閱讀 Ian Goodfellow、Yoshua Bengio 和 Aaron Courville 所著的 [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618)，線上版可見於 [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/)。 +* 使用 **AI 在商業中的應用** 案例。建議參考 Microsoft Learn 上的 [商業用戶的 AI 入門](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) 學習路徑，或與 [INSEAD](https://www.insead.edu/) 合作開發的 [AI 商業學校](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum)。 +* 在我們的 [機器學習初學者課程](http://github.com/Microsoft/ML-for-Beginners) 有詳盡說明的 **經典機器學習**。 +* 使用 **[認知服務](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)** 建立的實用 AI 應用。建議從 Microsoft Learn 上的 [視覺](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum)、[自然語言處理](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum) 以及 **[Azure OpenAI 服務的生成式 AI](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** 課程模組開始。 +* 特定的機器學習 **雲框架**，例如 [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-bethanycheum)、[Microsoft Fabric](https://learn.microsoft.com/en-us/training/paths/get-started-fabric/?WT.mc_id=academic-77998-bethanycheum)，或 [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum)。可以考慮使用 [使用 Azure Machine Learning 建立和運營機器學習解決方案](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) 和 [使用 Azure Databricks 建立及運營機器學習解決方案](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum) 學習路徑。 +* **會話式 AI** 和 **聊天機器人**。有專門的 [建立會話式 AI 解決方案](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum) 學習路徑，另可參考 [此篇部落格文章](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) 深入了解。 +* 深度學習背後的 **深度數學**。建議閱讀 Ian Goodfellow、Yoshua Bengio 和 Aaron Courville 的著作 [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618)，線上版本可見於 [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/)。 -若想輕鬆入門 _雲端 AI_ 主題，建議參考 [Azure 人工智能入門](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum) 學習路徑。 +若想輕鬆入門 _Azure 雲端 AI_ 主題，可考慮修習 [Azure 上的人工智能入門](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum) 學習路徑。 # 課程內容 -| | 課堂連結 | PyTorch/Keras/TensorFlow | 實驗室 | +| | 課程連結 | PyTorch/Keras/TensorFlow | 實驗室 | | :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | -| 0 | [課程設定](./lessons/0-course-setup/setup.md) | [設定您的開發環境](./lessons/0-course-setup/how-to-run.md) | | +| 0 | [課程設定](./lessons/0-course-setup/setup.md) | [設定你的開發環境](./lessons/0-course-setup/how-to-run.md) | | | I | [**人工智能簡介**](./lessons/1-Intro/README.md) | | | -| 01 | [人工智能介紹與歷史](./lessons/1-Intro/README.md) | - | - | +| 01 | [人工智能歷史與簡介](./lessons/1-Intro/README.md) | - | - | | II | **符號 AI** | | 02 | [知識表示與專家系統](./lessons/2-Symbolic/README.md) | [專家系統](./lessons/2-Symbolic/Animals.ipynb) / [本體論](./lessons/2-Symbolic/FamilyOntology.ipynb) /[概念圖](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | -| III | [**神經網絡簡介**](./lessons/3-NeuralNetworks/README.md) ||| +| III | [**神經網絡入門**](./lessons/3-NeuralNetworks/README.md) ||| | 03 | [感知器](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [筆記本](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [實驗室](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | -| 04 | [多層感知器與創建我們自己的框架](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [筆記本](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [實驗室](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | -| 05 | [框架介紹 (PyTorch/TensorFlow) 與過擬合](./lessons/3-NeuralNetworks/05-Frameworks/README.md) | [PyTorch](./lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb) / [Keras](./lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [實驗室](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | +| 04 | [多層感知器及創建自家框架](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [筆記本](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [實驗室](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | +| 05 | [框架入門（PyTorch/TensorFlow）及過度擬合](./lessons/3-NeuralNetworks/05-Frameworks/README.md) | [PyTorch](./lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb) / [Keras](./lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [實驗室](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | | IV | [**電腦視覺**](./lessons/4-ComputerVision/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-77998-cacaste) / [TensorFlow](https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-77998-cacaste)| [在 Microsoft Azure 探索電腦視覺](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | | 06 | [電腦視覺入門。OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [筆記本](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [實驗室](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | | 07 | [卷積神經網絡](./lessons/4-ComputerVision/07-ConvNets/README.md) & [CNN 架構](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [實驗室](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | -| 08 | [預訓練網絡與轉移學習](./lessons/4-ComputerVision/08-TransferLearning/README.md) 及 [訓練技巧](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [實驗室](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | -| 09 | [自編碼器與變分自編碼器(VAE)](./lessons/4-ComputerVision/09-Autoencoders/README.md) | [PyTorch](./lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb) | | -| 10 | [生成對抗網絡與藝術風格轉換](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | -| 11 | [物件檢測](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [實驗室](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | +| 08 | [預訓練網絡及遷移學習](./lessons/4-ComputerVision/08-TransferLearning/README.md) 和 [訓練技巧](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [實驗室](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | +| 09 | [自編碼器和變分自編碼器](./lessons/4-ComputerVision/09-Autoencoders/README.md) | [PyTorch](./lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb) | | +| 10 | [生成對抗網絡與藝術風格轉移](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | +| 11 | [目標檢測](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [實驗室](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | | 12 | [語義分割。U-Net](./lessons/4-ComputerVision/12-Segmentation/README.md) | [PyTorch](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb) | | | V | [**自然語言處理**](./lessons/5-NLP/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-77998-cacaste) /[TensorFlow](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-77998-cacaste) | [在 Microsoft Azure 探索自然語言處理](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| -| 13 | [文字表示。詞袋/TF-IDF](./lessons/5-NLP/13-TextRep/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb) | | +| 13 | [文本表示。詞袋模型/TF-IDF](./lessons/5-NLP/13-TextRep/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb) | | | 14 | [語義詞嵌入。Word2Vec 與 GloVe](./lessons/5-NLP/14-Embeddings/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb) | | | 15 | [語言模型。訓練你自己的詞嵌入](./lessons/5-NLP/15-LanguageModeling/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-PyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-TF.ipynb) | [實驗室](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | | 16 | [循環神經網絡](./lessons/5-NLP/16-RNN/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNTF.ipynb) | | | 17 | [生成式循環網絡](./lessons/5-NLP/17-GenerativeNetworks/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb) | [實驗室](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | -| 18 | [變換器。BERT。](./lessons/5-NLP/18-Transformers/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb) /[TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersTF.ipynb) | | +| 18 | [變壓器模型。BERT。](./lessons/5-NLP/18-Transformers/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb) /[TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersTF.ipynb) | | | 19 | [命名實體識別](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [實驗室](./lessons/5-NLP/19-NER/lab/README.md) | | 20 | [大型語言模型、提示編程與少量示例任務](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | | VI | **其他 AI 技術** || | @@ -111,68 +111,68 @@ CO_OP_TRANSLATOR_METADATA: | 22 | [深度強化學習](./lessons/6-Other/22-DeepRL/README.md) | [PyTorch](./lessons/6-Other/22-DeepRL/CartPole-RL-PyTorch.ipynb) /[TensorFlow](./lessons/6-Other/22-DeepRL/CartPole-RL-TF.ipynb) | [實驗室](./lessons/6-Other/22-DeepRL/lab/README.md) | | 23 | [多代理系統](./lessons/6-Other/23-MultiagentSystems/README.md) | | | | VII | **AI 倫理** | | | -| 24 | [AI 倫理與負責任的 AI](./lessons/7-Ethics/README.md) | [Microsoft Learn: 負責任的 AI 原則](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | +| 24 | [AI 倫理與負責任 AI](./lessons/7-Ethics/README.md) | [Microsoft Learn：負責任 AI 原則](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | | IX | **額外內容** | | | | 25 | [多模態網絡、CLIP 與 VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [筆記本](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | -## 每課程包含 +## 每課內容包含 -* 預習材料 -* 可執行的 Jupyter 筆記本，通常針對特定框架（**PyTorch** 或 **TensorFlow**）。可執行的筆記本還包含大量理論內容，因此要理解主題，您需要至少完成筆記本的其中一個版本（PyTorch 或 TensorFlow）。 -* 部分主題提供 **實驗室**，讓您有機會將所學應用到具體問題上。 -* 部分章節包含指向相關主題的 [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) 模組的連結。 +* 預讀資料 +* 可執行的 Jupyter 筆記本，通常針對特定框架（**PyTorch** 或 **TensorFlow**）。可執行筆記本也包含許多理論內容，要理解主題需至少瀏覽其中一個版本（PyTorch 或 TensorFlow）。 +* 某些主題提供的 **實驗室**，讓你有機會嘗試將所學應用到具體問題。 +* 部分章節包含指向涵蓋相關主題的 [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) 模組的連結。 -## 入門 +## 快速開始 -### 🎯 AI 新手？從這開始！ +### 🎯 AI 新手？從這裡開始！ -如果您完全沒有 AI 基礎並想快速嘗試實戰範例，請查看我們的 [**適合初學者的範例**](./examples/README.md)！這些包括： +如果你對 AI 完全陌生，想快速動手嘗試範例，請參考我們的 [**適合初學者的範例**](./examples/README.md)！內容包括： - 🌟 **Hello AI World** - 你的第一個 AI 程式（模式識別） - 🧠 **簡單神經網絡** - 從零開始構建神經網絡 -- 🖼️ **圖像分類器** - 詳盡註解的圖像分類器 -- 💬 **文字情感分析** - 分析文本的正面/負面情感 +- 🖼️ **影像分類器** - 詳細註解的圖像分類器 +- 💬 **文本情感分析** - 分析正面/負面文本 -這些範例旨在幫助你在深入完整課程之前理解 AI 概念。 +這些範例旨在幫助你在深入完整課程之前理解 AI 的概念。 ### 📚 完整課程設置 -- 我們已建立一個[設置課程](./lessons/0-course-setup/setup.md)來協助你設置開發環境。 - 對教師而言，我們也建立了[課程設置教學](./lessons/0-course-setup/for-teachers.md)！ -- 如何[在 VSCode 或 Codepace 執行程式碼](./lessons/0-course-setup/how-to-run.md) +- 我們已建立一個[設置課程](./lessons/0-course-setup/setup.md)來協助你設定開發環境。- 對教育工作者來說，我們也準備了一個[課程設置課程](./lessons/0-course-setup/for-teachers.md)！ +- 如何在 VSCode 或 Codespace 中[運行代碼](./lessons/0-course-setup/how-to-run.md) 請依照以下步驟： -分叉倉庫：點擊本頁右上角的「Fork」按鈕。 +Fork 倉庫：點擊本頁右上角的「Fork」按鈕。 -克隆倉庫：`git clone https://github.com/microsoft/AI-For-Beginners.git` +Clone 倉庫：`git clone https://github.com/microsoft/AI-For-Beginners.git` -別忘了收藏 (🌟) 此倉庫以便日後更容易找到。 +別忘了給這個倉庫點星 (🌟)，日後更容易找到。 ## 認識其他學習者 -加入我們的[官方 AI Discord 伺服器](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum)，與同學們交流並獲得支援。 +加入我們的[官方 AI Discord 伺服器](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum)，與其他正在學習本課程的人交流並獲得支援。 -若開發時有產品回饋或疑問，請造訪我們的[Azure AI Foundry 開發者論壇](https://aka.ms/foundry/forum) +在建立過程中若有產品反饋或疑問，歡迎造訪我們的[Azure AI Foundry 開發者論壇](https://aka.ms/foundry/forum) -## 測驗 +## 小測驗 -> **關於測驗**：所有測驗均包含於 etc\quiz-app 的 Quiz-app 資料夾中，或可[線上進行](https://ff-quizzes.netlify.app/)。它們已從課程中連結，quiz app 可以本地執行或部署到 Azure，請參考 `quiz-app` 資料夾內說明。測驗正逐步在本地化。 +> **有關小測驗的說明**：所有小測驗均包含於 etc\quiz-app 資料夾中的 Quiz-app 裡，或可在[此線上平台](https://ff-quizzes.netlify.app/)使用。它們都在課程內有連結，quiz 應用程式可在本地運行或部署到 Azure；請依照 `quiz-app` 資料夾內的指示操作。目前測驗正在逐步本地化。 -## 招募協助 +## 尋求協助 -有建議或發現拼寫／程式錯誤嗎？請提出 issue 或 pull request。 +你有建議或發現拼寫、程式碼錯誤嗎？歡迎提出問題單或發送 pull request。 ## 特別感謝 * **✍️ 主要作者：** [Dmitry Soshnikov](http://soshnikov.com), 博士 * **🔥 編輯：** [Jen Looper](https://twitter.com/jenlooper), 博士 -* **🎨 手繪插畫師：** [Tomomi Imura](https://twitter.com/girlie_mac) -* **✅ 測驗創建者：** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) +* **🎨 筆記插畫師：** [Tomomi Imura](https://twitter.com/girlie_mac) +* **✅ 小測驗創建者：** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) * **🙏 核心貢獻者：** [Evgenii Pishchik](https://github.com/Pe4enIks) ## 其他課程 -我們團隊亦製作了其他課程！歡迎參考： +我們團隊還製作了其他課程！歡迎查看： ### LangChain @@ -216,11 +216,11 @@ CO_OP_TRANSLATOR_METADATA: ## 尋求協助 -如果在開發 AI 應用程式時卡關或有任何疑問，歡迎加入 MCP 的討論，與其他學習者和資深開發者一同交流。這是一個支持友善的社群，歡迎提問並自由分享知識。 +如果遇到困難或有任何關於建立 AI 應用的問題，歡迎加入學習者與經驗豐富開發者的討論，一同探討 MCP。這是一個支持性的社群，歡迎提出問題並自由分享知識。 [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -若在開發時有產品回饋或遇到錯誤，請造訪： +在建置過程中若有產品回饋或錯誤，歡迎造訪： [![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) @@ -228,5 +228,5 @@ CO_OP_TRANSLATOR_METADATA: **免責聲明**： -本文件使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。雖然我們力求準確，但請注意，自動翻譯可能包含錯誤或不準確之處。文件原文應視為權威來源。如涉及重要資訊，建議聘請專業人工翻譯。我們不對因使用此翻譯而產生的任何誤解或誤釋負責。 +本文件使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。雖然我們致力於保持準確性，但請注意自動翻譯可能存在錯誤或不準確之處。原始文件的母語版本應被視為權威來源。對於關鍵資訊，建議採用專業人工翻譯。我們不對因使用本翻譯而產生的任何誤解或曲解承擔責任。 \ No newline at end of file diff --git a/translations/hk/lessons/0-course-setup/how-to-run.md b/translations/hk/lessons/0-course-setup/how-to-run.md index 0f7b3adc..1969b811 100644 --- a/translations/hk/lessons/0-course-setup/how-to-run.md +++ b/translations/hk/lessons/0-course-setup/how-to-run.md @@ -1,21 +1,21 @@ # 如何執行程式碼 -這份課程包含許多可執行的範例和實驗室，您可能會希望執行它們。為了做到這一點，您需要能夠在課程提供的 Jupyter Notebooks 中執行 Python 程式碼。以下是幾種執行程式碼的選項： +本課程包含了很多可執行的範例和實驗室，你會想要運行它們。為此，你需要能在本課程提供的 Jupyter 筆記本中執行 Python 程式碼。你有幾種選擇來執行程式碼： -## 在本地電腦上執行 +## 在你的電腦本地執行 -若要在本地電腦上執行程式碼，您需要安裝某個版本的 Python。我個人推薦安裝 **[miniconda](https://conda.io/en/latest/miniconda.html)** —— 它是一個輕量級的安裝包，支援 `conda` 套件管理器，用於建立不同的 Python **虛擬環境**。 +要在你的電腦本地執行程式碼，需要安裝 Python。推薦安裝 **[miniconda](https://conda.io/en/latest/miniconda.html)** —— 它是個相當輕量的安裝，支援 `conda` 套件管理器來處理不同的 Python **虛擬環境**。 -安裝 miniconda 後，您需要克隆此課程的存儲庫並建立一個虛擬環境以供使用： +安裝 miniconda 後，請複製這個資料庫並建立一個虛擬環境，用於本課程： ```bash git clone http://github.com/microsoft/ai-for-beginners @@ -24,53 +24,61 @@ conda env create --name ai4beg --file .devcontainer/environment.yml conda activate ai4beg ``` -### 使用 Visual Studio Code 和 Python 擴展 -使用課程的最佳方式可能是透過 [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) 和 [Python 擴展](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste) 打開課程。 +### 使用含 Python 擴充功能的 Visual Studio Code -> **注意**：當您克隆並在 VS Code 中打開目錄時，它會自動建議您安裝 Python 擴展。您還需要按照上述步驟安裝 miniconda。 +本課程最適合在 [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) 中開啟，並搭配 [Python 擴充功能](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste) 使用。 -> **注意**：如果 VS Code 建議您在容器中重新打開存儲庫，請拒絕此建議以使用本地的 Python 安裝。 +> **注意**：當你複製並在 VS Code 中開啟目錄後，它會自動建議你安裝 Python 擴充功能。你也需要照上述說明安裝 miniconda。 + +> **注意**：如果 VS Code 建議你在容器中重新開啟倉庫，請拒絕此建議以使用本地 Python 安裝。 ### 在瀏覽器中使用 Jupyter -您也可以直接在瀏覽器中使用 Jupyter 環境。事實上，傳統的 Jupyter 和 Jupyter Hub 都提供了相當方便的開發環境，包括自動完成、程式碼高亮等功能。 +你也可以在自己的電腦瀏覽器中使用 Jupyter 環境。傳統 Jupyter 和 JupyterHub 都提供方便的開發環境，包括自動補全、程式碼高亮等功能。 -若要在本地啟動 Jupyter，請進入課程目錄並執行以下指令： +要在本地啟動 Jupyter，前往課程目錄並執行： ```bash jupyter notebook ``` -或 + 或 ```bash jupyterhub ``` -接著，您可以導航到任何 `.ipynb` 文件，打開並開始工作。 -### 在容器中執行 -另一個替代 Python 安裝的方式是使用容器執行程式碼。由於我們的存儲庫包含特殊的 `.devcontainer` 資料夾，指示如何為此存儲庫建立容器，VS Code 會建議您在容器中重新打開程式碼。這需要安裝 Docker，並且操作會更複雜，因此我們建議有經驗的使用者使用此方法。 +之後，你可以導航到任意 `.ipynb` 檔案，開啟它們並開始工作。 + +### 在容器中運行 + +另一種選擇是不安裝 Python，而是在容器中運行程式碼。由於我們的資料庫包含一個特殊的 `.devcontainer` 資料夾，說明如何為此倉庫建置容器，VS Code 提供在容器中重新開啟程式碼的選項。這需要安裝 Docker，且過程較複雜，因此建議有經驗的使用者使用。 ## 在雲端執行 -如果您不想在本地安裝 Python，並且擁有一些雲端資源，另一個不錯的選擇是在雲端執行程式碼。有幾種方式可以做到： +如果你不想在本地安裝 Python，且能存取一些雲端資源，另一個好選擇是在雲端運行程式碼。你有幾種方式可以做到： -* 使用 **[GitHub Codespaces](https://github.com/features/codespaces)**，這是一個在 GitHub 上為您建立的虛擬環境，可透過 VS Code 的瀏覽器介面訪問。如果您有 Codespaces 的使用權限，只需點擊存儲庫中的 **Code** 按鈕，啟動一個 Codespace，即可快速開始使用。 -* 使用 **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**。 [Binder](https://mybinder.org) 是一個免費的雲端計算資源，供像您這樣的使用者在 GitHub 上測試程式碼。首頁上有一個按鈕可以在 Binder 中打開存儲庫——這會快速將您帶到 Binder 網站，並無縫啟動 Jupyter 網頁介面。 +* 使用 **[GitHub Codespaces](https://github.com/features/codespaces)**，這是在 GitHub 上為你建立的虛擬環境，通過 VS Code 瀏覽器介面存取。如果你有 Codespaces 權限，只需點選倉庫內的 **Code** 按鈕，啟動 codespace，即可快速開始。 -> **注意**：為了防止濫用，Binder 對某些網路資源的訪問進行了限制。這可能會導致某些程式碼無法正常運行，特別是那些從公共互聯網抓取模型和/或數據集的程式碼。您可能需要找到一些替代方法。此外，Binder 提供的計算資源相對基本，因此在後期更複雜的課程中，訓練速度可能會很慢。 +* 使用 **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**。 [Binder](https://mybinder.org) 為用戶提供免費的雲端運算資源，方便你測試 GitHub 上的程式碼。首頁有個按鈕可在 Binder 中開啟該倉庫——它會快速帶你進入 Binder 網站，建立底層容器並無縫啟動 Jupyter 網頁介面。 + +> **注意**：為防止濫用，Binder 有些網路資源的訪問被阻擋，這可能會導致部分從公共網際網路抓取模型和/或資料集的程式碼無法運作。你可能需要尋找替代方案。此外，Binder 提供的運算資源相當基礎，訓練速度會慢，尤其是在後面較複雜的課程中。 ## 在雲端使用 GPU 執行 -課程中的某些後期章節會因 GPU 支援而受益匪淺，否則訓練速度會非常慢。如果您透過 [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) 或您的機構擁有雲端資源，以下是幾個選項： +本課程後期的某些課程會從 GPU 支援中大幅獲益。例如模型訓練否則會非常緩慢。若你透過 [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) 或你的學校取得雲端資源，可參考以下選項： -* 建立 [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste)，並透過 Jupyter 連接到它。您可以直接在該機器上克隆存儲庫並開始學習。NC 系列虛擬機器支援 GPU。 +* 建立 [Data Science 虛擬機](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) 並透過 Jupyter 連接它。你可直接將倉庫複製到該機器，然後開始學習。NC 系列虛擬機支援 GPU。 -> **注意**：某些訂閱（包括 Azure for Students）並未預設提供 GPU 支援。您可能需要透過技術支援請求額外的 GPU 核心。 +> **注意**：部分訂閱方案（包括 Azure for Students）預設不提供 GPU 支援，你可能需透過技術支援請求額外核配 GPU 核心。 -* 建立 [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste)，然後使用其中的 Notebook 功能。[這段影片](https://azure-for-academics.github.io/quickstart/azureml-papers/) 顯示如何將存儲庫克隆到 Azure ML Notebook 並開始使用。 +* 建立 [Azure 機器學習工作區](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste)，並使用該處的筆記本功能。[這段影片](https://azure-for-academics.github.io/quickstart/azureml-papers/) 介紹如何將倉庫克隆到 Azure ML 筆記本並開始使用。 -您也可以使用 Google Colab，它提供一些免費的 GPU 支援，並將 Jupyter Notebooks 上傳到其中逐一執行。 +你也可以使用 Google Colab，它提供一些免費的 GPU 支援，並可將 Jupyter 筆記本逐一上傳執行。 +--- + + **免責聲明**： -本文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。應以原文文件作為權威來源。對於關鍵資訊，建議尋求專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解釋概不負責。 \ No newline at end of file +本文件由 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。儘管我們盡力確保準確性，但請注意，自動翻譯可能存在錯誤或不準確之處。原始文件的母語版本應視為權威來源。對於重要資訊，建議採用專業人工翻譯。我們不對因使用本翻譯所引起的任何誤解或誤釋承擔責任。 + \ No newline at end of file diff --git a/translations/hk/lessons/1-Intro/README.md b/translations/hk/lessons/1-Intro/README.md index be1fde8c..29df50e5 100644 --- a/translations/hk/lessons/1-Intro/README.md +++ b/translations/hk/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 人工智能簡介 -![人工智能簡介內容的手繪圖](../../../../translated_images/hk/ai-intro.bf28d1ac4235881c.png) +![人工智能簡介內容的手繪圖](../../../../translated_images/hk/ai-intro.bf28d1ac4235881c.webp) > 手繪筆記由 [Tomomi Imura](https://twitter.com/girlie_mac) 提供 @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: 最初，電腦是由 [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage) 發明的，用於按照明確定義的程序（即算法）處理數字。現代電腦雖然比19世紀提出的原型先進得多，但仍然遵循受控計算的理念。因此，如果我們知道實現目標所需的精確步驟，就可以編程讓電腦完成某些事情。 -![一個人的照片](../../../../translated_images/hk/dsh_age.d212a30d4e54fb5f.png) +![一個人的照片](../../../../translated_images/hk/dsh_age.d212a30d4e54fb5f.webp) > 照片由 [Vickie Soshnikova](http://twitter.com/vickievalerie) 提供 @@ -46,7 +46,7 @@ CO_OP_TRANSLATOR_METADATA: 當討論 **[智能](https://en.wikipedia.org/wiki/Intelligence)** 這個術語時，問題之一是我們對這個術語沒有明確的定義。有人認為智能與 **抽象思維** 或 **自我意識** 有關，但我們無法準確定義它。 -![一隻貓的照片](../../../../translated_images/hk/photo-cat.8c8e8fb760ffe457.jpg) +![一隻貓的照片](../../../../translated_images/hk/photo-cat.8c8e8fb760ffe457.webp) > [照片](https://unsplash.com/photos/75715CVEJhI) 由 [Amber Kipp](https://unsplash.com/@sadmax) 提供，來自 Unsplash @@ -98,13 +98,13 @@ CO_OP_TRANSLATOR_METADATA: > | 那麼機器學習呢？ | | > |--------------|-----------| -> | 基於電腦通過一些數據學習解決問題的人工智能部分被稱為 **機器學習**。我們不會在本課程中考慮經典的機器學習——我們建議你參考單獨的 [機器學習初學者課程](http://aka.ms/ml-beginners)。 | ![機器學習初學者課程](../../../../translated_images/hk/ml-for-beginners.9e4fed176fd5817d.png) | +> | 基於電腦通過一些數據學習解決問題的人工智能部分被稱為 **機器學習**。我們不會在本課程中考慮經典的機器學習——我們建議你參考單獨的 [機器學習初學者課程](http://aka.ms/ml-beginners)。 | ![機器學習初學者課程](../../../../translated_images/hk/ml-for-beginners.9e4fed176fd5817d.webp) | ## 人工智能的簡史 人工智能作為一個領域始於20世紀中葉。最初，符號推理是一種流行的方法，並且它帶來了一些重要的成功，例如專家系統——能夠在某些有限問題領域中充當專家的電腦程序。然而，很快就發現這種方法並不適用於大規模應用。從專家那裡提取知識、將其表示在電腦中並保持知識庫的準確性，事實證明這是一項非常複雜且在許多情況下成本過高的任務。這導致了20世紀70年代所謂的 [人工智能寒冬](https://en.wikipedia.org/wiki/AI_winter)。 -人工智能簡史 +人工智能簡史 > 圖片由 [Dmitry Soshnikov](http://soshnikov.com) 提供 @@ -124,7 +124,7 @@ CO_OP_TRANSLATOR_METADATA: * 現代助手，例如 Cortana、Siri 或 Google Assistant，都是混合系統，使用神經網絡將語音轉換為文本並識別我們的意圖，然後使用一些推理或明確的算法執行所需的操作。 * 未來，我們可能會期待一個完全基於神經網絡的模型能夠自行處理對話。最近的 GPT 和 [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) 系列神經網絡在這方面表現出色。 -圖靈測試的演變 +圖靈測試的演變 > 圖片由 Dmitry Soshnikov 提供，[照片](https://unsplash.com/photos/r8LmVbUKgns) 由 [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto) 提供，Unsplash ## 最近的人工智能研究 diff --git a/translations/hk/lessons/2-Symbolic/Animals.ipynb b/translations/hk/lessons/2-Symbolic/Animals.ipynb index 3ac04f6b..11e03dc5 100644 --- a/translations/hk/lessons/2-Symbolic/Animals.ipynb +++ b/translations/hk/lessons/2-Symbolic/Animals.ipynb @@ -6,25 +6,25 @@ "collapsed": true }, "source": [ - "# 實現動物專家系統\n", + "# 實作動物專家系統\n", "\n", "來自 [AI for Beginners Curriculum](http://github.com/microsoft/ai-for-beginners) 的範例。\n", "\n", - "在這個範例中，我們將實現一個簡單的知識型系統，根據一些外觀特徵來判斷動物的種類。該系統可以用以下的 AND-OR 樹來表示（這只是整棵樹的一部分，我們可以輕鬆地添加更多規則）：\n", + "在此範例中，我們將實作一個簡單的基於知識的系統，以根據一些物理特徵判斷動物。該系統可由以下 AND-OR 樹表示（這是整棵樹的一部分，我們可以輕易新增更多規則）：\n", "\n", - "![](../../../../lessons/2-Symbolic/images/AND-OR-Tree.png)\n" + "![](../../../../../../translated_images/hk/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## 我們自己的專家系統殼層，具備反向推理功能\n", + "## 我們自己的基於後向推理的專家系統外殼\n", "\n", - "讓我們嘗試定義一種基於生成規則的簡單知識表示語言。我們將使用 Python 類作為關鍵字來定義規則。基本上會有三種類型的類別：\n", - "* `Ask` 代表需要向使用者提問的問題。它包含一組可能的答案。\n", - "* `If` 代表一條規則，它只是用來存儲規則內容的語法糖。\n", - "* `AND`/`OR` 是用來表示樹的 AND/OR 分支的類別。它們僅存儲內部的參數列表。為了簡化程式碼，所有功能都定義在父類別 `Content` 中。\n" + "讓我們嘗試定義一種基於產生規則的知識表示簡單語言。我們將使用 Python 類作為關鍵字來定義規則。主要有三種類型的類別：\n", + "* `Ask` 代表需要向用戶提問的問題。它包含可能答案的集合。\n", + "* `If` 代表規則，它只是存儲規則內容的語法糖\n", + "* `AND`/`OR` 是用來表示樹中 AND/OR 分支的類別。它們只是存儲內部參數的列表。為簡化代碼，所有功能都在父類 `Content` 中定義\n" ] }, { @@ -66,7 +66,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "在我們的系統中，工作記憶將包含**事實**列表，作為**屬性-值對**。知識庫可以定義為一個大的字典，將行動（應插入工作記憶中的新事實）映射到條件，表達為AND-OR表達式。此外，一些事實可以被`詢問`。\n" + "在我們的系統中，工作記憶會包含作為**屬性-值對**的**事實**列表。知識庫可以定義為一個大型字典，將行動（應該插入工作記憶的新事實）映射到條件，條件以AND-OR 表達式表示。同時，某些事實可以被`問`。\n" ] }, { @@ -99,13 +99,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "為了進行反向推理，我們將定義 `Knowledgebase` 類別。它將包含：\n", - "* 工作的 `記憶體` - 一個字典，用於映射屬性到其值\n", - "* 知識庫的 `規則`，格式如上所述\n", + "要執行逆向推理，我們將定義 `Knowledgebase` 類別。它將包含：\n", + "* 工作 `memory` - 一個將屬性映射到值的字典\n", + "* 知識庫 `rules`，格式如上述所定義\n", "\n", "兩個主要方法是：\n", - "* `get` 用於獲取屬性的值，必要時執行推理。例如，`get('color')` 會獲取顏色槽的值（如果需要，它會詢問並將值存儲在工作記憶中以供後續使用）。如果我們詢問 `get('color:blue')`，它會詢問顏色，然後根據顏色返回 `y`/`n` 的值。\n", - "* `eval` 執行實際的推理，即遍歷 AND/OR 樹，評估子目標等。\n" + "* `get` 用來獲取屬性的值，必要時執行推理。例如，`get('color')` 會獲取顏色欄位的值（如有必要會詢問，並將該值儲存於工作記憶中以便後續使用）。如果我們詢問 `get('color:blue')`，它會先詢問顏色，然後根據該顏色返回 `y`/`n` 值。\n", + "* `eval` 執行實際的推理操作，即遍歷 AND/OR 樹，評估子目標等等。\n" ] }, { @@ -172,7 +172,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "現在讓我們定義我們的動物知識庫並進行諮詢。請注意，此操作將向您提問。您可以通過輸入 `y`/`n` 來回答是非問題，或者通過指定數字（0..N）來回答有多個選項的問題。\n" + "現在讓我們定義我們的動物知識庫並進行諮詢。請注意，這個過程會向你提問。你可以透過輸入 `y`/`n` 回答是非題，或者對於有多個選項的問題，輸入數字（0..N）來回答。\n" ] }, { @@ -229,11 +229,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 使用 PyKnow 進行前向推理\n", + "## 使用 Experta 進行正向推論\n", "\n", - "在以下的例子中，我們將嘗試使用其中一個知識表示庫 [PyKnow](https://github.com/buguroo/pyknow/) 來實現前向推理。**PyKnow** 是一個用於在 Python 中建立前向推理系統的庫，其設計理念與經典的舊系統 [CLIPS](http://www.clipsrules.net/index.html) 相似。\n", + "在下一個範例中，我們將嘗試使用其中一個知識表示的函式庫，[Experta](https://github.com/nilp0inter/experta) 來實現正向推論。**Experta** 是一個用 Python 建立正向推論系統的函式庫，設計上類似經典的舊系統 [CLIPS](http://www.clipsrules.net/index.html)。\n", "\n", - "我們其實也可以自己實現前向鏈推理，並不會有太大的困難，但簡單的實現通常效率不高。為了更有效地進行規則匹配，會使用一種特殊的算法 [Rete](https://en.wikipedia.org/wiki/Rete_algorithm)。\n" + "我們本也可以自己實作正向鏈結，並不困難，但天真的實作通常效率不高。為了更有效率地進行規則匹配，會使用一種特殊的演算法 [Rete](https://en.wikipedia.org/wiki/Rete_algorithm)。\n" ] }, { @@ -247,32 +247,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "Collecting git+https://github.com/buguroo/pyknow/\n", - " Cloning https://github.com/buguroo/pyknow/ to /tmp/pip-req-build-3cqeulyl\n", - " Running command git clone --filter=blob:none --quiet https://github.com/buguroo/pyknow/ /tmp/pip-req-build-3cqeulyl\n", - " Resolved https://github.com/buguroo/pyknow/ to commit 48818336f2e9a126f1964f2d8dc22d37ff800fe8\n", - " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hCollecting frozendict==1.2\n", - " Using cached frozendict-1.2.tar.gz (2.6 kB)\n", - " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hCollecting schema==0.6.7\n", - " Using cached schema-0.6.7-py2.py3-none-any.whl (14 kB)\n", - "Building wheels for collected packages: pyknow, frozendict\n", - " Building wheel for pyknow (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for pyknow: filename=pyknow-1.7.0-py3-none-any.whl size=34228 sha256=b7de5b09292c4007667c72f69b98d5a1b5f7324ff15f9dd8e077c3d5f7aade42\n", - " Stored in directory: /tmp/pip-ephem-wheel-cache-k7jpave7/wheels/81/1a/d3/f6c15dbe1955598a37755215f2a10449e7418500d7bd4b9508\n", - " Building wheel for frozendict (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for frozendict: filename=frozendict-1.2-py3-none-any.whl size=3148 sha256=2863d55c240d2409cddf05ccfe600591f8478681549fc97555c47c90dc6bb160\n", - " Stored in directory: /home/rg/.cache/pip/wheels/49/ac/f8/cb8120244e710bdb479c86198b03c7b08c3c2d3d2bf448fd6e\n", - "Successfully built pyknow frozendict\n", - "Installing collected packages: schema, frozendict, pyknow\n", - "Successfully installed frozendict-1.2 pyknow-1.7.0 schema-0.6.7\n" + "Collecting git+https://github.com/nilp0inter/experta\n", + " Cloning https://github.com/nilp0inter/experta to /tmp/pip-req-build-7qurtwk3\n", + " Running command git clone --filter=blob:none --quiet https://github.com/nilp0inter/experta /tmp/pip-req-build-7qurtwk3\n", + " Resolved https://github.com/nilp0inter/experta to commit c6d5834b123861f5ae09e7d07027dc98bec58741\n", + " Installing build dependencies ... \u001b[?25ldone\n", + "\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n", + "\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25hRequirement already satisfied: frozendict~=2.4.6 in /opt/conda/envs/ai4beg/lib/python3.12/site-packages (from experta==1.9.5.dev1) (2.4.7)\n", + "Collecting schema~=0.6.7 (from experta==1.9.5.dev1)\n", + " Downloading schema-0.6.8-py2.py3-none-any.whl.metadata (14 kB)\n", + "Downloading schema-0.6.8-py2.py3-none-any.whl (14 kB)\n", + "Building wheels for collected packages: experta\n", + " Building wheel for experta (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25h Created wheel for experta: filename=experta-1.9.5.dev1-py3-none-any.whl size=34804 sha256=888c459512a5e713f4b674caa9a0f96cfdf07ec0d6eb56cc318ce0653d218014\n", + " Stored in directory: /tmp/pip-ephem-wheel-cache-1eeii9zy/wheels/3d/e8/bb/22d7956359603fa8dd679aa09f5b8efb3f29991c3986fdc787\n", + "Successfully built experta\n", + "Installing collected packages: schema, experta\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2/2\u001b[0m [experta]\n", + "\u001b[1A\u001b[2KSuccessfully installed experta-1.9.5.dev1 schema-0.6.8\n" ] } ], "source": [ "import sys\n", - "!{sys.executable} -m pip install git+https://github.com/buguroo/pyknow/" + "!{sys.executable} -m pip install git+https://github.com/nilp0inter/experta" ] }, { @@ -283,15 +282,15 @@ }, "outputs": [], "source": [ - "from pyknow import *\n", - "#import pyknow" + "from experta import *\n", + "#import experta" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "我們將把我們的系統定義為一個繼承自 `KnowledgeEngine` 的類別。每條規則由帶有 `@Rule` 註解的單獨函數定義，該註解指定了規則應該何時觸發。在規則內，我們可以使用 `declare` 函數添加新事實，添加這些事實將導致前向推理引擎調用更多規則。\n" + "我們將系統定義為一個繼承自 `KnowledgeEngine` 的類別。每條規則由一個帶有 `@Rule` 註解的獨立函數定義，該註解指定規則應該在何時觸發。在規則內，我們可以使用 `declare` 函數添加新的事實，添加這些事實將導致前向推理引擎調用更多規則。\n" ] }, { @@ -378,7 +377,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "一旦我們定義了一個知識庫，我們會用一些初始事實填充工作記憶，然後調用 `run()` 方法來執行推理。結果你可以看到新的推導事實被添加到工作記憶中，包括關於動物的最終事實（如果我們正確設置了所有初始事實）。\n" + "一旦我們定義了知識庫，我們會用一些初始事實來填充工作記憶，然後調用 `run()` 方法來進行推理。你可以看到結果是新的推斷事實被添加到工作記憶中，包括關於動物的最終事實（如果我們正確設置了所有初始事實）。\n" ] }, { @@ -440,7 +439,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n---\n\n**免責聲明**： \n本文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。應以原文文件作為權威來源。對於關鍵資訊，建議尋求專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解釋概不負責。\n" + "---\n\n\n**免責聲明**： \n本文件由 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。雖然我們力求準確，但請注意自動翻譯可能包含錯誤或不準確之處。原始文件以其原文版本為權威來源。對於重要資訊，建議採用專業人工翻譯。我們不對因使用本翻譯而引起的任何誤解或錯誤詮釋承擔責任。\n\n" ] } ], @@ -467,8 +466,8 @@ "version": "3.11.2" }, "coopTranslator": { - "original_hash": "ab2bd97b0453415b89a469284609a8ce", - "translation_date": "2025-08-31T10:08:03+00:00", + "original_hash": "8ef43db4b9182239fd150a76bd494fdb", + "translation_date": "2026-01-15T11:44:05+00:00", "source_file": "lessons/2-Symbolic/Animals.ipynb", "language_code": "hk" } diff --git a/translations/hk/lessons/2-Symbolic/README.md b/translations/hk/lessons/2-Symbolic/README.md index ecc623b0..d15f51f2 100644 --- a/translations/hk/lessons/2-Symbolic/README.md +++ b/translations/hk/lessons/2-Symbolic/README.md @@ -1,116 +1,116 @@ -# 知識表示與專家系統 +# 知識表徵與專家系統 -![Symbolic AI內容摘要](../../../../translated_images/hk/ai-symbolic.715a30cb610411a6.png) +![Symbolic AI 內容摘要](../../../../../../translated_images/hk/ai-symbolic.715a30cb610411a6.webp) -> Sketchnote由 [Tomomi Imura](https://twitter.com/girlie_mac) 創作 +> 速寫筆記作者：[Tomomi Imura](https://twitter.com/girlie_mac) -人工智能的探索基於尋求知識，試圖像人類一樣理解世界。但要如何實現這一目標呢？ +人工智能的追求基於對知識的探索，以類似人類的方式理解世界。但我們應該如何著手呢？ ## [課前測驗](https://ff-quizzes.netlify.app/en/ai/quiz/3) -在人工智能的早期，創建智能系統的自上而下方法（在上一課中討論過）非常流行。這種方法的理念是將人類的知識提取成機器可讀的形式，然後用它來自動解決問題。這種方法基於兩個重要的概念： +在 AI 早期，建立智能系統的自上而下方法（前一課提及）很受歡迎。這個理念是將人類知識轉換為機器可讀格式，然後用來自動解決問題。此方法基於兩個重要概念： -* 知識表示 +* 知識表徵 * 推理 -## 知識表示 +## 知識表徵 -Symbolic AI的一個重要概念是**知識**。需要將知識與*信息*或*數據*區分開。例如，人們可以說書籍包含知識，因為人們可以通過學習書籍成為專家。然而，書籍實際上包含的是*數據*，通過閱讀書籍並將這些數據整合到我們的世界模型中，我們將數據轉化為知識。 +符號 AI 中的一大重點是**知識**。需要區分知識和*信息*或*數據*。舉例來說，可以說書本包含知識，因為人可以通過研讀書本成為專家。然而，書中實則包含稱為*數據*的內容，通過閱讀書本並將這些數據整合進我們的世界模型，我們將數據轉化為知識。 -> ✅ **知識**是我們頭腦中所包含的東西，代表我們對世界的理解。它是通過主動的**學習**過程獲得的，這個過程將我們接收到的信息片段整合到我們的世界模型中。 +> ✅ **知識**是儲存在我們腦中並代表我們對世界理解的東西。它是通過主動的**學習**過程獲得的，該過程將接收到的信息整合進我們對世界的主動模型中。 -通常，我們不會嚴格定義知識，而是通過[DIKW金字塔](https://en.wikipedia.org/wiki/DIKW_pyramid)將其與其他相關概念對齊。金字塔包含以下概念： +通常我們不會嚴格定義知識，而是通過[DIKW金字塔](https://en.wikipedia.org/wiki/DIKW_pyramid)將其與其他相關概念對齊。金字塔包含以下概念： -* **數據**是以物理媒介表示的東西，例如書面文字或口頭語言。數據獨立於人類存在，可以在人與人之間傳遞。 -* **信息**是我們在頭腦中對數據的解釋。例如，當我們聽到“電腦”這個詞時，我們對它有一定的理解。 -* **知識**是信息被整合到我們的世界模型中。例如，一旦我們了解了什麼是電腦，我們就開始對它的工作原理、價格以及用途有一些想法。這些相互關聯的概念網絡構成了我們的知識。 -* **智慧**是我們對世界理解的更高層次，代表*元知識*，例如關於如何以及何時使用知識的概念。 +* **數據**是以物理媒介表達的，如書寫文字或口語。數據獨立於人類存在，且可以在人與人之間傳遞。 +* **信息**是我們在頭腦中對數據的解讀。例如，當聽到「電腦」一詞，我們對其有一定的理解。 +* **知識**是信息被整合進我們的世界模型。例如，一旦了解電腦的概念，我們開始知道它的運作方式、成本和用途。這一互相關聯的概念網形成了我們的知識。 +* **智慧**則是對世界理解的更高層次，代表*元知識*，例如什麼時候以及如何使用該知識的概念。 - + -*圖片來源：[維基百科](https://commons.wikimedia.org/w/index.php?curid=37705247)，作者：Longlivetheux - 自創作品，CC BY-SA 4.0* +*圖片來源：[維基百科](https://commons.wikimedia.org/w/index.php?curid=37705247)，作者 Longlivetheux，CC BY-SA 4.0* -因此，**知識表示**的問題是找到某種有效的方法，將知識以數據的形式表示在計算機中，使其能夠自動使用。這可以看作是一個光譜： +因此，**知識表徵**問題就是尋找某種有效方式在計算機中以數據形式表示知識，讓它自動可用。這可以被視為一個光譜： -![知識表示光譜](../../../../translated_images/hk/knowledge-spectrum.b60df631852c0217.png) +![知識表徵光譜](../../../../../../translated_images/hk/knowledge-spectrum.b60df631852c0217.webp) -> 圖片由 [Dmitry Soshnikov](http://soshnikov.com) 創作 +> 圖片作者：[Dmitry Soshnikov](http://soshnikov.com) -* 在左側，有非常簡單的知識表示類型，可以被計算機有效使用。最簡單的是算法式，當知識以計算機程序的形式表示時。然而，這並不是表示知識的最佳方式，因為它不靈活。人類頭腦中的知識通常是非算法式的。 -* 在右側，有像自然文本這樣的表示方式。它是最強大的，但無法用於自動推理。 +* 左側是計算機能高效使用的簡單知識表徵類型。最簡單的是算法式，即知識由計算機程序表達。但這並非最佳方法，因為不夠靈活。人腦中的知識往往不是算法式的。 +* 右側是自然語言文本這類表徵。它最強大，但無法用於自動推理。 -> ✅ 想一想你如何在頭腦中表示知識並將其轉化為筆記。是否有某種格式能幫助你更好地記憶？ +> ✅ 請花一分鐘思考你如何在腦中表徵知識並轉化為筆記？有沒有特定的格式幫助你記憶？ -## 計算機知識表示的分類 +## 電腦知識表示的分類 -我們可以將不同的計算機知識表示方法分為以下類別： +我們可以將不同電腦知識表徵方法分為以下幾類： -* **網絡表示**基於我們頭腦中有一個相互關聯的概念網絡。我們可以嘗試在計算機中以圖的形式重現相同的網絡——即所謂的**語義網絡**。 +* **網絡表示**基於人腦中存在一張互相關聯概念的網絡。我們可以嘗試在計算機中重建這種網絡，稱為**語義網絡**。 -1. **對象-屬性-值三元組**或**屬性-值對**。由於圖可以在計算機中表示為節點和邊的列表，我們可以通過三元組列表來表示語義網絡，包含對象、屬性和值。例如，我們可以建立以下關於編程語言的三元組： +1. **物件-屬性-值三元組**或**屬性-值對**。因為圖形可以以節點和邊的列表實現，語義網絡可用三元組表示，包含物件、屬性和值。例如，我們構建以下關於程式語言的三元組： -對象 | 屬性 | 值 ------|------|----- -Python | 是 | Untyped-Language +物件 | 屬性 | 值 +-------|-----------|------ +Python | 是 | 未類型語言 Python | 發明者 | Guido van Rossum -Python | 塊語法 | 縮排 -Untyped-Language | 沒有 | 類型定義 +Python | 區塊語法 | 縮排 +未類型語言 | 沒有 | 型別定義 -> ✅ 想一想三元組如何用於表示其他類型的知識。 +> ✅ 思考三元組如何被用來表示其它類型的知識。 -2. **層次表示**強調我們通常在頭腦中創建對象的層次結構。例如，我們知道金絲雀是一種鳥類，所有鳥類都有翅膀。我們也知道金絲雀通常是什麼顏色，以及它們的飛行速度。 +2. **階層表示**強調我們在腦中常建構物件階層。例如，我們知道金絲雀是鳥，所有鳥都有翅膀。我們還知道金絲雀通常顏色與飛行速度。 - - **框架表示**基於將每個對象或對象類表示為一個**框架**，框架包含**槽**。槽可以有可能的默認值、值限制或存儲的程序，這些程序可以被調用以獲得槽的值。所有框架形成一個層次結構，類似於面向對象編程語言中的對象層次結構。 - - **場景**是表示可以隨時間展開的複雜情境的特殊框架。 + - **框架表示**將物件或物件類別表達為帶有**欄位**的**框架**。欄位有預設值、值限制，或可呼叫程序獲取欄位值。所有框架構成類似物件導向程式語言中的物件階層。 + - **情境**是特殊框架，代表可隨時間展開的複雜情境。 **Python** -槽 | 值 | 默認值 | 範圍 ----|----|--------|----- +欄位 | 值 | 預設值 | 範圍 +-----|-------|---------------|---------- 名稱 | Python | | | -是 | Untyped-Language | | | -變量命名方式 | | CamelCase | | -程序長度 | | | 5-5000行 | -塊語法 | 縮排 | | | +種類 | 未類型語言 | | | +變數命名慣例 | | 駝峰式 | | +程式長度 | | | 5-5000 行 | +區塊語法 | 縮排 | | | -3. **程序表示**基於通過一系列動作來表示知識，當某些條件發生時可以執行這些動作。 - - 生成規則是if-then語句，允許我們得出結論。例如，一位醫生可能有一條規則說**如果**患者有高燒**或**血液檢測中C反應蛋白水平高**那麼**他有炎症。一旦我們遇到其中一個條件，我們就可以得出關於炎症的結論，然後在進一步推理中使用它。 - - 算法可以被認為是另一種程序表示形式，儘管它們幾乎從未直接用於基於知識的系統。 +3. **程序表示**基於用可執行的動作列表表示知識，當某條件出現時可以執行。 + - 生產規則是 if-then 陳述，允許推斷結論。例如，醫生有規則說：**若**患者有高燒**或**血液C反應蛋白過高**則**患者有發炎反應**。當遇到條件成立，可做出發炎判斷，並用於後續推理。 + - 演算法可視為另一種程序表示，但幾乎不直接用於知識系統。 -4. **邏輯**最初由亞里士多德提出，作為表示普遍人類知識的一種方式。 - - 謂詞邏輯作為一種數學理論過於豐富而無法計算，因此通常使用它的一些子集，例如Prolog中使用的Horn子句。 - - 描述邏輯是一系列邏輯系統，用於表示和推理分佈式知識表示中的對象層次結構，例如*語義網絡*。 +4. **邏輯**最初由亞里斯多德提出，作為表示普遍人類知識的方式。 + - 謂詞邏輯數學理論過於龐大難以計算，故使用其子集，如 Prolog 使用的Horn子句。 + - 描述邏輯是一系列邏輯系統，用於表示與推理物件階層及分散知識表示，如*語義網*。 ## 專家系統 -Symbolic AI的一個早期成功是所謂的**專家系統**——設計用於在某些有限問題領域中充當專家的計算機系統。它們基於從一位或多位人類專家提取的**知識庫**，並包含一個**推理引擎**，在其上進行一些推理。 +符號 AI 的早期成功之一是所謂的**專家系統**－設計用於有限問題領域做專家決策的計算機系統。基於從一位或多位專家手動提取的**知識庫**，並包含執行推理的**推理引擎**。 -![人類架構](../../../../translated_images/hk/arch-human.5d4d35f1bba3ab1c.png) | ![基於知識的系統架構](../../../../translated_images/hk/arch-kbs.3ec5c150b09fa8da.png) -----------------------------------|----------------------------------------- -人類神經系統的簡化結構 | 基於知識的系統的架構 +![人類架構](../../../../../../translated_images/hk/arch-human.5d4d35f1bba3ab1c.webp) | ![知識系統架構](../../../../../../translated_images/hk/arch-kbs.3ec5c150b09fa8da.webp) +------------------------------|------------------------------ +簡化的人類神經系統結構 | 知識系統架構 -專家系統的構建類似於人類的推理系統，該系統包含**短期記憶**和**長期記憶**。同樣，在基於知識的系統中，我們區分以下組件： +專家系統類似人類推理系統，具備**短期記憶**和**長期記憶**。同理，知識系統區分以下元件： -* **問題記憶**：包含當前正在解決的問題的知識，例如患者的體溫或血壓，是否有炎症等。這些知識也被稱為**靜態知識**，因為它包含了我們目前對問題的了解的快照——即所謂的*問題狀態*。 -* **知識庫**：表示關於問題領域的長期知識。它是從人類專家手動提取的，並且不會因諮詢而改變。由於它使我們能夠從一個問題狀態導航到另一個問題狀態，它也被稱為**動態知識**。 -* **推理引擎**：負責協調在問題狀態空間中的搜索過程，必要時向用戶提問。它還負責找到適合每個狀態的規則。 +* **問題記憶**：包含目前正在解決問題的知識，如病人溫度、血壓、是否發炎等。此知識稱為**靜態知識**，因為它是當前問題情況的快照－所謂*問題狀態*。 +* **知識庫**：表示問題領域的長期知識。由人類專家手動提取，於不同諮詢不變。因為它支持從一種問題狀態導航到另一種，也稱**動態知識**。 +* **推理引擎**：協調整個問題狀態空間的搜索過程，必要時詢問使用者。也負責尋找適用規則於每種狀態。 -例如，讓我們考慮以下基於動物物理特徵的專家系統： +舉例，考慮以下依據動物物理特徵判斷的專家系統： -![AND-OR樹](../../../../translated_images/hk/AND-OR-Tree.5592d2c70187f283.png) +![AND-OR 樹](../../../../../../translated_images/hk/AND-OR-Tree.5592d2c70187f283.webp) -> 圖片由 [Dmitry Soshnikov](http://soshnikov.com) 創作 +> 圖片作者：[Dmitry Soshnikov](http://soshnikov.com) -此圖表稱為**AND-OR樹**，它是生成規則集的圖形表示。在提取專家知識的初期，繪製樹是有用的。要在計算機中表示知識，使用規則會更方便： +此圖稱為**AND-OR樹**，是生產規則集的圖形表達。繪製樹形有助於知識提取初期。電腦內部表示知識時，更常用規則表示： ``` IF the animal eats meat @@ -121,78 +121,78 @@ OR (animal has sharp teeth THEN the animal is a carnivore ``` -你會注意到規則的左側條件和動作本質上是對象-屬性-值（OAV）三元組。**工作記憶**包含與當前正在解決的問題相關的OAV三元組集合。**規則引擎**尋找條件滿足的規則並應用它們，將另一個三元組添加到工作記憶中。 +你會注意到規則左側的每個條件和動作本質上都是物件-屬性-值（OAV）三元組。**工作記憶**包含當前解決問題相符的 OAV 三元組集合。**規則引擎**尋找條件符合的規則並執行，增添新的三元組到工作記憶。 -> ✅ 試著畫一個你喜歡的主題的AND-OR樹！ +> ✅ 在你喜歡的主題上寫寫自己的 AND-OR 樹吧！ -### 前向推理與後向推理 +### 正向推理與反向推理 -上述過程稱為**前向推理**。它從工作記憶中可用的初始數據開始，然後執行以下推理循環： +上述過程稱為**正向推理**。從工作記憶中初始問題資料開始，然後進入下述推理循環： -1. 如果目標屬性存在於工作記憶中——停止並給出結果 -2. 查找所有條件目前滿足的規則——獲得**衝突集**規則。 -3. 執行**衝突解決**——選擇一條將在此步驟中執行的規則。可能有不同的衝突解決策略： - - 選擇知識庫中第一個適用的規則 - - 隨機選擇一條規則 - - 選擇*更具體*的規則，即滿足“左側條件”（LHS）中最多條件的規則 -4. 應用選定的規則並將新的知識片段插入問題狀態 -5. 從第1步重複。 +1. 若目標屬性已在工作記憶中，停止並呈現結果 +2. 查找所有條件符合的規則－得到**競爭集**（conflict set） +3. 執行**競爭解決**－選出當前步驟要執行的規則。可有不同策略： + - 選擇知識庫中第一條可用規則 + - 隨機選擇規則 + - 選擇*更特定*的規則，滿足左側最多條件者 +4. 執行選中規則，並將新知識插入問題狀態 +5. 重複步驟 1 -然而，在某些情況下，我們可能希望從對問題的空白知識開始，並提出問題以幫助我們得出結論。例如，在進行醫學診斷時，我們通常不會在診斷患者之前提前進行所有醫學分析。我們更希望在需要做出決定時進行分析。 +不過，有時可能想從空白問題知識開始，詢問使用者問題以導向結論。例如，醫療診斷時，通常不會在診斷開始前先做完整檢查，而是在決策需要時，再進行分析。 -此過程可以使用**後向推理**建模。它由**目標**驅動——即我們希望找到的屬性值： +此過程可用**反向推理**建模。它以**目標**為驅動－尋找的屬性值： -1. 選擇所有可以給出目標值的規則（即目標在右側條件（RHS）中）——衝突集 -1. 如果該屬性沒有規則，或者有規則表明我們應該向用戶詢問該值——詢問用戶，否則： -1. 使用衝突解決策略選擇一條規則作為*假設*——我們將嘗試證明它 -1. 對規則左側條件（LHS）中的所有屬性重複此過程，嘗試將它們作為目標證明 -1. 如果過程在任何時候失敗——在第3步使用另一條規則。 +1. 選擇所有可給出該目標值的規則（目標在右側）－形成競爭集 +2. 若此屬性無規則，或規則指示應向使用者詢問，則提問，否則： +3. 依競爭解決策略選規則作為*假設*，試圖證明它 +4. 對該規則左側所有屬性重複此過程，嘗試證明它們為目標 +5. 若過程某處失敗，回第 3 步嘗試其它規則 -> ✅ 在哪些情況下前向推理更合適？後向推理又適合哪些情況？ +> ✅ 哪些情況下正向推理較適合？反向推理又如何？ -### 專家系統的實現 +### 實作專家系統 -專家系統可以使用不同的工具來實現： +專家系統可用不同工具實作： -* 直接使用某些高級編程語言進行編程。這不是最好的方法，因為基於知識的系統的主要優勢是知識與推理分離，並且問題領域的專家應該能夠在不理解推理過程細節的情況下編寫規則。 -* 使用**專家系統外殼**，即專門設計用於使用某種知識表示語言填充知識的系統。 +* 直接用高階程式語言編寫。這不理想，因為知識系統的主優點是將知識和推理分離，專家可以不用理解推理細節就撰寫規則 +* 使用**專家系統殼層**，即專為知識導入設計的系統，採用特定知識表示語言。 ## ✍️ 練習：動物推理 -請參考 [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb)，了解如何實現前向和後向推理的專家系統。 +查看 [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) 了解正向與反向推理專家系統實例。 -> **注意**：此示例相對簡單，只能提供專家系統的基本概念。一旦你開始創建這樣的系統，只有當規則數量達到一定程度（約200+）時，你才會注意到系統的一些*智能*行為。在某些時候，規則變得過於複雜，難以全部記住，此時你可能會開始思考為什麼系統會做出某些決定。然而，基於知識的系統的一個重要特徵是你始終可以*解釋*任何決策是如何做出的。 +> **注意**：本例較為簡單，只給出專家系統樣貌。一旦開始創建此類系統，需要累積約 200 條規則後才會出現某種*智能*行為。規則變得太複雜難以完全記憶時，你可能會開始想為何系統會做出某些決策。但知識系統重要特質是你總能*解釋*任何決策的來由。 -## 本體論與語義網 +## 本體與語義網 -20世紀末，有一項倡議使用知識表示來標註互聯網資源，以便能夠找到符合非常特定查詢的資源。這一運動被稱為**語義網**，它依賴於幾個概念： +20 世紀末，有一項提案主張用知識表示註釋互聯網資源，使其可根據非常具體的查詢找到相應資源。這運動稱為**語義網**，依賴以下幾個概念： -- 基於**[描述邏輯](https://en.wikipedia.org/wiki/Description_logic)**（DL）的特殊知識表示。它類似於框架知識表示，因為它構建了一個具有屬性的對象層次結構，但它具有正式的邏輯語義和推理。描述邏輯有一整個家族，平衡了表達能力與推理的算法複雜性。 -- 分佈式知識表示，其中所有概念都由全局URI標識符表示，使得能夠創建跨互聯網的知識層次結構。 -- 一組基於 XML 的知識描述語言：RDF（資源描述框架）、RDFS（RDF 架構）、OWL（本體網絡語言）。 +- 基於**[描述邏輯](https://en.wikipedia.org/wiki/Description_logic)** (DL) 的特殊知識表示。它類似框架知識表示，建立有屬性的物件階層，但具邏輯語義與推理。描述邏輯有一整套子系統，於表達能力與推理算法複雜度間取捨平衡。 +- 分散式知識表示，所有概念由全球 URI 識別符表示，使可建立跨 Internet 的知識階層。 +- 一系列基於 XML 的知識描述語言：RDF（資源描述框架）、RDFS（RDF 架構）、OWL（本體網絡語言）。 -在語義網中，一個核心概念是 **本體（Ontology）**。它指的是使用某種形式化的知識表示來明確描述一個問題領域。最簡單的本體可能只是問題領域中的對象層次結構，但更複雜的本體會包含可用於推理的規則。 +語意網的一個核心概念是 **本體（Ontology）**。它指的是使用某種形式化知識表示對問題域的明確規範。最簡單的本體可以只是一個問題域中物件的層次結構，但較複雜的本體會包含可以用於推理的規則。 -在語義網中，所有的表示都基於三元組。每個對象和每個關係都由 URI 唯一標識。例如，如果我們想表達這個 AI 課程是由 Dmitry Soshnikov 在 2022 年 1 月 1 日開發的事實，我們可以使用以下三元組： +在語意網中，所有表示皆基於三元組。每個物件和每個關係都由 URI 唯一標識。例如，如果我們想表述這個 AI 課程是由 Dmitry Soshnikov 於 2022 年 1 月 1 日開發的——以下是我們可以使用的三元組： - + ``` -http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 13, 2007” +http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 1, 2022” http://github.com/microsoft/ai-for-beginners http://purl.org/dc/elements/1.1/creator http://soshnikov.com ``` -> ✅ 這裡的 `http://www.example.com/terms/creation-date` 和 `http://purl.org/dc/elements/1.1/creator` 是一些廣為接受的 URI，用來表達 *創建者* 和 *創建日期* 的概念。 +> ✅ 這裡的 `http://www.example.com/terms/creation-date` 和 `http://purl.org/dc/elements/1.1/creator` 是一些眾所周知且被普遍接受的 URI，用以表達*創建者*和*創建日期*的概念。 -在更複雜的情況下，如果我們想定義一個創建者列表，我們可以使用 RDF 中定義的一些數據結構。 +在更加複雜的情況下，如果我們想定義一個創建者列表，可以使用 RDF 中定義的一些資料結構。 - + -> 上述圖表由 [Dmitry Soshnikov](http://soshnikov.com) 提供 +> 以上圖表由 [Dmitry Soshnikov](http://soshnikov.com) 提供 -語義網的發展因搜索引擎和自然語言處理技術的成功而有所放緩，這些技術能夠從文本中提取結構化數據。然而，在某些領域，仍然有大量的努力在維護本體和知識庫。一些值得注意的項目包括： +語意網的建設進度在某種程度上因搜尋引擎和自然語言處理技術的成功而放緩，這些技術允許從文本中提取結構化資料。然而，在某些領域仍有重大努力用於維護本體和知識庫。值得注意的幾個項目： -* [WikiData](https://wikidata.org/) 是一個與維基百科相關的機器可讀知識庫集合。大部分數據來自維基百科的 *信息框（InfoBoxes）*，即維基頁面中的結構化內容片段。你可以使用語義網的專用查詢語言 SPARQL [查詢](https://query.wikidata.org/) WikiData。以下是一個示例查詢，顯示人類中最常見的眼睛顏色： +* [WikiData](https://wikidata.org/) 是與維基百科相關聯的機器可讀知識庫合集。大部分資料都從維基百科的 *資訊框（InfoBoxes）* 採集，即維基百科頁面內的結構化內容片段。你可以用 SPARQL —— 一種專門針對語意網的查詢語言 —— 來[查詢](https://query.wikidata.org/) wikidata。以下是一個顯示人類中最常見眼睛顏色的範例查詢： ```sparql #defaultView:BubbleChart @@ -208,45 +208,49 @@ GROUP BY ?eyeColorLabel * [DBpedia](https://www.dbpedia.org/) 是另一個類似於 WikiData 的努力。 -> ✅ 如果你想嘗試構建自己的本體，或者打開現有的本體，有一個很棒的可視化本體編輯器叫 [Protégé](https://protege.stanford.edu/)。你可以下載它，或者在線使用。 +> ✅ 如果你想嘗試建立自己的本體，或打開現有本體，有一個很棒的視覺本體編輯器叫做 [Protégé](https://protege.stanford.edu/)。下載它，或在線使用。 - + -*Web Protégé 編輯器打開了羅曼諾夫家族的本體。截圖由 Dmitry Soshnikov 提供* +*Web Protégé 編輯器開啟羅曼諾夫家族本體。截圖由 Dmitry Soshnikov 提供* -## ✍️ 練習：一個家庭本體 +## ✍️ 練習：家族本體 -參見 [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb)，了解如何使用語義網技術推理家庭關係。我們將採用以常見 GEDCOM 格式表示的家譜和家庭關係的本體，並為給定的一組個體構建所有家庭關係的圖。 +請參閱 [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb)，此範例使用語意網技術來推理家族關係。我們將採用以通用 GEDCOM 格式表示的家族系譜，以及家族關係的本體，構建指定個體集合中所有家族關係的圖譜。 -## 微軟概念圖 +## Microsoft 概念圖譜 -在大多數情況下，本體是由人工精心創建的。然而，也可以從非結構化數據中**挖掘**本體，例如從自然語言文本中。 +大多數情況下，本體是由人手精心建立的。然而，也可以從非結構化資料中 **挖掘** 本體，例如從自然語言文本。 -微軟研究院曾進行過這樣的嘗試，並推出了 [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste)。 +微軟研究院進行了這樣的嘗試，成果為 [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste)。 -這是一個基於 `is-a` 繼承關係將實體分組的大型集合。它可以回答像「什麼是微軟？」這樣的問題——答案可能是「一家公司，概率為 0.87；一個品牌，概率為 0.75」。 +這是個將實體依靠 `is-a` 繼承關係分組的大型集合。它允許回答「什麼是微軟？」這類問題——答案可能是「微軟是一家公司，概率 0.87，並且是一個品牌，概率 0.75」。 -該圖可以通過 REST API 獲取，也可以作為一個列出所有實體對的大型可下載文本文件。 +此圖譜可透過 REST API 存取，或以大型可下載純文字檔形式列出所有實體對。 -## ✍️ 練習：一個概念圖 +## ✍️ 練習：概念圖譜 -試試 [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) 筆記本，看看我們如何使用 Microsoft Concept Graph 將新聞文章分組到幾個類別中。 +請嘗試 [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) 筆記本，了解如何利用 Microsoft 概念圖譜將新聞文章分組到若干類別。 ## 結論 -如今，AI 通常被認為是 *機器學習* 或 *神經網絡* 的同義詞。然而，人類還表現出明確的推理能力，這是目前神經網絡無法處理的。在實際項目中，明確的推理仍然被用來執行需要解釋的任務，或者在受控方式下修改系統行為。 +當今，人工智慧通常被視為 *機器學習* 或 *神經網絡* 的同義語。然而，人類也展現明確推理能力，這是神經網絡目前尚未處理的。在現實世界的專案中，仍會使用明確推理來完成需要說明或能以受控方式修改系統行為的任務。 ## 🚀 挑戰 -在與本課程相關的家庭本體筆記本中，有機會嘗試其他家庭關係。試著發現家譜中人與人之間的新聯繫。 +在本課程附帶的 Family Ontology 筆記本中，有機會嘗試探索其他家族關係。試著發現家譜中人物間的新連結。 ## [課後測驗](https://ff-quizzes.netlify.app/en/ai/quiz/4) -## 回顧與自學 +## 複習與自學 -在互聯網上做一些研究，了解人類嘗試量化和編碼知識的領域。看看布魯姆的分類學，回顧歷史，了解人類如何試圖理解他們的世界。探索林奈創建生物分類學的工作，觀察門捷列夫如何創建描述和分組化學元素的方法。你還能找到哪些有趣的例子？ +上網搜尋人類嘗試量化和編纂知識的領域。看看布魯姆的分類法，回顧人類如何在歷史上嘗試理解世界。探索林奈（Linnaeus）創建生物分類學的工作，觀察德米特里·孟德列夫如何創造化學元素的描述與分組方法。你還能找到哪些其他有趣的例子？ -**作業**: [構建一個本體](assignment.md) +**作業**: [建立一個本體](assignment.md) --- + +**免責聲明**： +本文件由 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。雖然我們致力於確保翻譯的準確性，但自動翻譯可能存在錯誤或不準確之處。原文文件應被視為正式及權威的版本。如涉及重要資訊，建議尋求專業人士進行人工翻譯。我們不對因使用此翻譯而產生的任何誤解或誤釋負責。 + \ No newline at end of file diff --git a/translations/hk/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/hk/lessons/3-NeuralNetworks/05-Frameworks/README.md index 602b8991..094c6e07 100644 --- a/translations/hk/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/hk/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ CO_OP_TRANSLATOR_METADATA: 考慮以下近似 5 個點（圖中的 `x`）的問題： -![線性模型](../../../../../translated_images/hk/overfit1.f24b71c6f652e59e.jpg) | ![過擬合模型](../../../../../translated_images/hk/overfit2.131f5800ae10ca5e.jpg) +![線性模型](../../../../../translated_images/hk/overfit1.f24b71c6f652e59e.webp) | ![過擬合模型](../../../../../translated_images/hk/overfit2.131f5800ae10ca5e.webp) -------------------------|-------------------------- **線性模型，2 個參數** | **非線性模型，7 個參數** 訓練誤差 = 5.3 | 訓練誤差 = 0 @@ -79,7 +79,7 @@ CO_OP_TRANSLATOR_METADATA: 如上圖所示，過擬合可以通過非常低的訓練誤差和非常高的驗證誤差來檢測。通常在訓練過程中，我們會看到訓練和驗證誤差都開始下降，然後在某個時候驗證誤差可能停止下降並開始上升。這就是過擬合的跡象，表明我們可能應該停止訓練（或者至少保存模型的快照）。 -![過擬合](../../../../../translated_images/hk/Overfitting.408ad91cd90b4371.png) +![過擬合](../../../../../translated_images/hk/Overfitting.408ad91cd90b4371.webp) ## 如何防止過擬合 diff --git a/translations/hk/lessons/3-NeuralNetworks/README.md b/translations/hk/lessons/3-NeuralNetworks/README.md index 70efe38a..0435a69a 100644 --- a/translations/hk/lessons/3-NeuralNetworks/README.md +++ b/translations/hk/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 神經網絡簡介 -![神經網絡簡介內容的手繪圖](../../../../translated_images/hk/ai-neuralnetworks.1c687ae40bc86e83.png) +![神經網絡簡介內容的手繪圖](../../../../translated_images/hk/ai-neuralnetworks.1c687ae40bc86e83.webp) 正如我們在介紹中所討論的，實現智能的一種方法是訓練一個**計算機模型**或**人工大腦**。自20世紀中期以來，研究人員嘗試了不同的數學模型，直到近年來這一方向證明非常成功。這些模仿大腦的數學模型被稱為**神經網絡**。 @@ -36,13 +36,13 @@ CO_OP_TRANSLATOR_METADATA: 從生物學中我們知道，大腦由神經細胞（神經元）組成，每個神經元有多個“輸入”（樹突）和一個“輸出”（軸突）。樹突和軸突都可以傳導電信號，而它們之間的連接——稱為突觸——可以表現出不同程度的導電性，這由神經遞質調節。 -![神經元模型](../../../../translated_images/hk/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![神經元模型](../../../../translated_images/hk/artneuron.1a5daa88d20ebe6f.png) +![神經元模型](../../../../translated_images/hk/synapse-wikipedia.ed20a9e4726ea1c6.webp) | ![神經元模型](../../../../translated_images/hk/artneuron.1a5daa88d20ebe6f.webp) ----|---- 真實神經元 *（[圖片](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) 來自維基百科）* | 人工神經元 *（作者提供圖片）* 因此，神經元的最簡單數學模型包含若干輸入 X1, ..., XN 和一個輸出 Y，以及一系列權重 W1, ..., WN。輸出計算公式為： -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) 其中 f 是某種非線性的**激活函數**。 diff --git a/translations/hk/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/hk/lessons/4-ComputerVision/06-IntroCV/README.md index 3762220d..ca2e3bcb 100644 --- a/translations/hk/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/hk/lessons/4-ComputerVision/06-IntroCV/README.md @@ -75,14 +75,14 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) * **預處理盲文書籍的照片**。我們專注於如何使用閾值處理、特徵檢測、透視變換和 NumPy 操作來分離單個盲文符號，以便神經網絡進一步分類。 -![盲文圖像](../../../../../translated_images/hk/braille.341962ff76b1bd70.jpeg) | ![盲文圖像預處理](../../../../../translated_images/hk/braille-result.46530fea020b03c7.png) | ![盲文符號](../../../../../translated_images/hk/braille-symbols.0159185ab69d5339.png) +![盲文圖像](../../../../../translated_images/hk/braille.341962ff76b1bd70.webp) | ![盲文圖像預處理](../../../../../translated_images/hk/braille-result.46530fea020b03c7.webp) | ![盲文符號](../../../../../translated_images/hk/braille-symbols.0159185ab69d5339.webp) ----|-----|----- > 圖片來自 [OpenCV.ipynb](OpenCV.ipynb) * **使用幀差檢測視頻中的運動**。如果攝像機是固定的，那麼來自攝像機的幀應該彼此非常相似。由於幀表示為數組，只需對兩個連續幀的數組進行減法運算，我們就能得到像素差異，靜態幀的差異應該很小，而當圖像中有顯著運動時，差異會變大。 -![視頻幀和幀差的圖像](../../../../../translated_images/hk/frame-difference.706f805491a0883c.png) +![視頻幀和幀差的圖像](../../../../../translated_images/hk/frame-difference.706f805491a0883c.webp) > 圖片來自 [OpenCV.ipynb](OpenCV.ipynb) @@ -91,7 +91,7 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) - **密集光流** 計算顯示每個像素移動方向的向量場 - **稀疏光流** 基於圖像中的一些顯著特徵（例如邊緣），並從幀到幀構建它們的軌跡。 -![光流圖像](../../../../../translated_images/hk/optical.1f4a94464579a83a.png) +![光流圖像](../../../../../translated_images/hk/optical.1f4a94464579a83a.webp) > 圖片來自 [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/hk/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/hk/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index ca9e7bda..4e4be43e 100644 --- a/translations/hk/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/hk/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 是一個在 2014 年 ImageNet top-5 分類中達到 92.7% 準確率的網絡。它的層結構如下： -![ImageNet Layers](../../../../../translated_images/hk/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet Layers](../../../../../translated_images/hk/vgg-16-arch1.d901a5583b3a51ba.webp) 如你所見，VGG 採用了傳統的金字塔架構，即一系列的卷積-池化層。 -![ImageNet Pyramid](../../../../../translated_images/hk/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet Pyramid](../../../../../translated_images/hk/vgg-16-arch.64ff2137f50dd49f.webp) > 圖片來源：[Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/hk/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/hk/lessons/4-ComputerVision/07-ConvNets/README.md index d2668599..19c319be 100644 --- a/translations/hk/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/hk/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: 為了提取模式，我們將使用**卷積濾波器**的概念。正如你所知，圖像是由一個二維矩陣或帶有色彩深度的三維張量表示的。應用濾波器意味著我們取一個相對較小的**濾波核**矩陣，並對原始圖像中的每個像素與其鄰近點進行加權平均。我們可以將其視為一個小窗口在整個圖像上滑動，並根據濾波核矩陣中的權重對所有像素進行平均。 -![垂直邊緣濾波器](../../../../../translated_images/hk/filter-vert.b7148390ca0bc356.png) | ![水平邊緣濾波器](../../../../../translated_images/hk/filter-horiz.59b80ed4feb946ef.png) +![垂直邊緣濾波器](../../../../../translated_images/hk/filter-vert.b7148390ca0bc356.webp) | ![水平邊緣濾波器](../../../../../translated_images/hk/filter-horiz.59b80ed4feb946ef.webp) ----|---- > 圖片由 Dmitry Soshnikov 提供 @@ -38,7 +38,7 @@ CNN 的工作方式基於以下重要思想： * 我們可以設計網絡，使濾波器能夠自動訓練 * 我們可以使用相同的方法來在高層次特徵中尋找模式，而不僅僅是在原始圖像中。因此，CNN 的特徵提取在特徵層次上工作，從低層次的像素組合開始，到更高層次的圖像部分組合。 -![層次特徵提取](../../../../../translated_images/hk/FeatureExtractionCNN.d9b456cbdae7cb64.png) +![層次特徵提取](../../../../../translated_images/hk/FeatureExtractionCNN.d9b456cbdae7cb64.webp) > 圖片來自 [Hislop-Lynch 的論文](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d)，基於[他們的研究](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ CNN 的工作方式基於以下重要思想： 例如，讓我們看看 VGG-16 的架構，這是一個在 2014 年 ImageNet 的前五名分類中達到 92.7% 準確率的網絡： -![ImageNet 層](../../../../../translated_images/hk/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet 層](../../../../../translated_images/hk/vgg-16-arch1.d901a5583b3a51ba.webp) -![ImageNet 金字塔](../../../../../translated_images/hk/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet 金字塔](../../../../../translated_images/hk/vgg-16-arch.64ff2137f50dd49f.webp) > 圖片來自 [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/hk/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/hk/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 47a8b7df..7102677e 100644 --- a/translations/hk/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/hk/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ CO_OP_TRANSLATOR_METADATA: 我們將使用 [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/)，該數據集包含37種不同品種的狗和貓的圖片。 -![我們將處理的數據集](../../../../../../translated_images/hk/data.50b2a9d5484bdbf0.png) +![我們將處理的數據集](../../../../../../translated_images/hk/data.50b2a9d5484bdbf0.webp) 要下載數據集，請使用以下代碼片段： diff --git a/translations/hk/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/hk/lessons/4-ComputerVision/08-TransferLearning/README.md index d48305b8..b5fb9a51 100644 --- a/translations/hk/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/hk/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Keras 和 PyTorch 都包含了方便的函數，可以輕鬆加載一些常見 以下是 VGG-16 網絡從一張貓的圖片中提取的特徵示例： -![VGG-16 提取的特徵](../../../../../translated_images/hk/features.6291f9c7ba3a0b95.png) +![VGG-16 提取的特徵](../../../../../translated_images/hk/features.6291f9c7ba3a0b95.webp) ## 貓與狗數據集 @@ -48,19 +48,19 @@ Keras 和 PyTorch 都包含了方便的函數，可以輕鬆加載一些常見 我們可以採取的一種方法是從一張隨機圖像開始，然後嘗試使用**梯度下降優化**技術調整該圖像，使得網絡認為它是一隻貓。 -![圖像優化循環](../../../../../translated_images/hk/ideal-cat-loop.999fbb8ff306e044.png) +![圖像優化循環](../../../../../translated_images/hk/ideal-cat-loop.999fbb8ff306e044.webp) 然而，如果我們這樣做，我們會得到一些非常接近隨機噪聲的東西。這是因為*有很多方法可以讓網絡認為輸入圖像是一隻貓*，其中一些方法在視覺上並不合理。雖然這些圖像包含了許多典型於貓的模式，但並沒有任何約束使它們在視覺上具有辨識度。 為了改善結果，我們可以在損失函數中添加另一個項，稱為**變化損失**。這是一種衡量圖像中相鄰像素相似程度的指標。最小化變化損失可以使圖像更平滑，並消除噪聲——從而揭示出更具視覺吸引力的模式。以下是一些這樣的「理想」圖像的例子，它們被高概率分類為貓和斑馬： -![理想貓](../../../../../translated_images/hk/ideal-cat.203dd4597643d6b0.png) | ![理想斑馬](../../../../../translated_images/hk/ideal-zebra.7f70e8b54ee15a7a.png) +![理想貓](../../../../../translated_images/hk/ideal-cat.203dd4597643d6b0.webp) | ![理想斑馬](../../../../../translated_images/hk/ideal-zebra.7f70e8b54ee15a7a.webp) -----|----- *理想貓* | *理想斑馬* 類似的方法可以用於對神經網絡進行所謂的**對抗性攻擊**。假設我們想要欺騙神經網絡，讓一隻狗看起來像一隻貓。如果我們拿一張狗的圖片，該圖片被網絡識別為狗，然後稍微調整它，使用梯度下降優化，直到網絡開始將其分類為貓： -![狗的圖片](../../../../../translated_images/hk/original-dog.8f68a67d2fe0911f.png) | ![被分類為貓的狗圖片](../../../../../translated_images/hk/adversarial-dog.d9fc7773b0142b89.png) +![狗的圖片](../../../../../translated_images/hk/original-dog.8f68a67d2fe0911f.webp) | ![被分類為貓的狗圖片](../../../../../translated_images/hk/adversarial-dog.d9fc7773b0142b89.webp) -----|----- *原始狗圖片* | *被分類為貓的狗圖片* diff --git a/translations/hk/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/hk/lessons/4-ComputerVision/09-Autoencoders/README.md index 53be7178..5185151e 100644 --- a/translations/hk/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/hk/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: 由於我們訓練自動編碼器以捕捉原始圖像中的盡可能多的信息以進行準確重建，網絡會嘗試找到最佳的**嵌入**方式來捕捉輸入圖像的含義。 -![自動編碼器示意圖](../../../../../translated_images/hk/autoencoder_schema.5e6fc9ad98a5eb61.jpg) +![自動編碼器示意圖](../../../../../translated_images/hk/autoencoder_schema.5e6fc9ad98a5eb61.webp) > 圖片來源：[Keras 博客](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/hk/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/hk/lessons/4-ComputerVision/11-ObjectDetection/README.md index 478b5c79..cb8a979e 100644 --- a/translations/hk/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/hk/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: ## [課前測驗](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![物件偵測](../../../../../translated_images/hk/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) +![物件偵測](../../../../../translated_images/hk/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp) > 圖片來源：[YOLO v2 官方網站](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 2. 對每個區塊進行影像分類。 3. 將分類結果中激活值足夠高的區塊視為包含目標物件的區域。 -![簡單的物件偵測](../../../../../translated_images/hk/naive-detection.e7f1ba220ccd08c6.png) +![簡單的物件偵測](../../../../../translated_images/hk/naive-detection.e7f1ba220ccd08c6.webp) > *圖片來源：[練習筆記本](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ CO_OP_TRANSLATOR_METADATA: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 包含 20 個類別 * [COCO](http://cocodataset.org/#home) - 常見物件上下文數據集，包含 80 個類別、邊界框和分割遮罩 -![COCO](../../../../../translated_images/hk/coco-examples.71bc60380fa6cceb.jpg) +![COCO](../../../../../translated_images/hk/coco-examples.71bc60380fa6cceb.webp) ## 物件偵測的評估指標 @@ -50,7 +50,7 @@ CO_OP_TRANSLATOR_METADATA: 對於影像分類來說，衡量算法表現的方式很簡單，但對於物件偵測，我們需要同時衡量類別的正確性以及推測邊界框位置的精確性。後者使用所謂的**交集比聯集** (IoU) 來衡量，這是一種用來測量兩個框（或任意兩個區域）重疊程度的方法。 -![IoU](../../../../../translated_images/hk/iou_equation.9a4751d40fff4e11.png) +![IoU](../../../../../translated_images/hk/iou_equation.9a4751d40fff4e11.webp) > *圖片來源：[這篇優秀的 IoU 部落格文章](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -97,11 +97,11 @@ $$ [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) 使用 [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) 生成 ROI 區域的層次結構，然後通過 CNN 特徵提取器和 SVM 分類器來確定物件類別，並通過線性回歸確定*邊界框*座標。[官方論文](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/hk/rcnn1.cae407020dfb1d1f.png) +![RCNN](../../../../../translated_images/hk/rcnn1.cae407020dfb1d1f.webp) > *圖片來源：van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/hk/rcnn2.2d9530bb83516484.png) +![RCNN-1](../../../../../translated_images/hk/rcnn2.2d9530bb83516484.webp) > *圖片來源：[這篇部落格](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -109,7 +109,7 @@ $$ 這種方法與 R-CNN 類似，但區域是在卷積層應用後定義的。 -![FRCNN](../../../../../translated_images/hk/f-rcnn.3cda6d9bb4188875.png) +![FRCNN](../../../../../translated_images/hk/f-rcnn.3cda6d9bb4188875.webp) > 圖片來源：[官方論文](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf)，[arXiv](https://arxiv.org/pdf/1504.08083.pdf)，2015 @@ -117,7 +117,7 @@ $$ 這種方法的主要思想是使用神經網絡來預測 ROI，即所謂的*區域提議網絡* (Region Proposal Network)。[論文](https://arxiv.org/pdf/1506.01497.pdf)，2016 -![FasterRCNN](../../../../../translated_images/hk/faster-rcnn.8d46c099b87ef30a.png) +![FasterRCNN](../../../../../translated_images/hk/faster-rcnn.8d46c099b87ef30a.webp) > 圖片來源：[官方論文](https://arxiv.org/pdf/1506.01497.pdf) @@ -129,7 +129,7 @@ $$ 2. 特徵經過**位置敏感得分圖** (Position-Sensitive Score Map) 處理。每個來自 $C$ 類別的物件被劃分為 $k\times k$ 區域，我們訓練網絡來預測物件的部分。 3. 對於 $k\times k$ 區域中的每個部分，所有網絡對物件類別進行投票，選擇得票最多的物件類別。 -![r-fcn 圖片](../../../../../translated_images/hk/r-fcn.13eb88158b99a3da.png) +![r-fcn 圖片](../../../../../translated_images/hk/r-fcn.13eb88158b99a3da.webp) > 圖片來源：[官方論文](https://arxiv.org/abs/1605.06409) @@ -140,7 +140,7 @@ YOLO 是一種實時單次通過算法。主要思想如下： * 將圖片劃分為 $S\times S$ 區域。 * 對於每個區域，**CNN** 預測 $n$ 個可能的物件、*邊界框*座標以及*置信度*=*概率* * IoU。 - ![YOLO](../../../../../translated_images/hk/yolo.a2648ec82ee8bb4e.png) + ![YOLO](../../../../../translated_images/hk/yolo.a2648ec82ee8bb4e.webp) > 圖片來源：[官方論文](https://arxiv.org/abs/1506.02640) diff --git a/translations/hk/lessons/4-ComputerVision/README.md b/translations/hk/lessons/4-ComputerVision/README.md index b7b74236..58212b66 100644 --- a/translations/hk/lessons/4-ComputerVision/README.md +++ b/translations/hk/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 電腦視覺 -![電腦視覺內容摘要的手繪圖](../../../../translated_images/hk/ai-computervision.6506ebebac3fbf76.png) +![電腦視覺內容摘要的手繪圖](../../../../translated_images/hk/ai-computervision.6506ebebac3fbf76.webp) 在這部分，我們將學習以下內容： diff --git a/translations/hk/lessons/5-NLP/14-Embeddings/README.md b/translations/hk/lessons/5-NLP/14-Embeddings/README.md index 5552792f..9aa34d06 100644 --- a/translations/hk/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/hk/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: 通過在分類器網絡中使用嵌入層作為第一層，我們可以從詞袋模型切換到 **嵌入袋** 模型。在嵌入袋模型中，我們首先將文本中的每個詞轉換為相應的嵌入，然後對所有嵌入計算某種聚合函數，例如 `sum`、`average` 或 `max`。 -![展示五個序列詞嵌入分類器的圖片。](../../../../../translated_images/hk/embedding-classifier-example.b77f021a7ee67eee.png) +![展示五個序列詞嵌入分類器的圖片。](../../../../../translated_images/hk/embedding-classifier-example.b77f021a7ee67eee.webp) > 圖片由作者提供 @@ -40,7 +40,7 @@ CO_OP_TRANSLATOR_METADATA: CBoW 的速度更快，而 Skip-Gram 雖然較慢，但在表示不常見詞方面效果更好。 -![展示 CBoW 和 Skip-Gram 將詞轉換為向量的算法圖片。](../../../../../translated_images/hk/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![展示 CBoW 和 Skip-Gram 將詞轉換為向量的算法圖片。](../../../../../translated_images/hk/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > 圖片來源：[這篇論文](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/hk/lessons/5-NLP/15-LanguageModeling/README.md b/translations/hk/lessons/5-NLP/15-LanguageModeling/README.md index 0787023d..3dc4dfe4 100644 --- a/translations/hk/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/hk/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: * **連續詞袋模型** (CBoW)，通過預測詞元序列 $W_{-N}$, ..., $W_N$ 中的中間詞元 $W_0$。 * **Skip-gram**，通過中間詞元 $W_0$ 預測一組相鄰詞元 {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$}。 -![來自論文的將詞語轉換為向量的算法示例](../../../../../translated_images/hk/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![來自論文的將詞語轉換為向量的算法示例](../../../../../translated_images/hk/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > 圖片來源：[這篇論文](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/hk/lessons/5-NLP/16-RNN/README.md b/translations/hk/lessons/5-NLP/16-RNN/README.md index 2a7bf9d5..25e3deb6 100644 --- a/translations/hk/lessons/5-NLP/16-RNN/README.md +++ b/translations/hk/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: 為了捕捉文本序列的意義，我們需要使用另一種神經網絡架構，稱為**循環神經網絡**（Recurrent Neural Network，簡稱 RNN）。在 RNN 中，我們將句子逐個符號地輸入網絡，網絡會生成某種**狀態**，然後將該狀態與下一個符號一起再次輸入網絡。 -![RNN](../../../../../translated_images/hk/rnn.27f5c29c53d727b5.png) +![RNN](../../../../../translated_images/hk/rnn.27f5c29c53d727b5.webp) > 圖片由作者提供 @@ -61,7 +61,7 @@ LSTM 網絡的組織方式與 RNN 類似，但有兩個狀態會從層到層傳 循環網絡，無論是單向還是雙向，都能捕捉序列中的某些模式，並將其存儲到狀態向量中或傳遞到輸出中。與卷積網絡類似，我們可以在第一層之上構建另一個循環層，以捕捉更高層次的模式，並基於第一層提取的低層次模式進行構建。這引出了**多層 RNN**的概念，它由兩個或更多循環網絡組成，其中前一層的輸出作為下一層的輸入。 -![顯示多層長短期記憶 RNN 的圖片](../../../../../translated_images/hk/multi-layer-lstm.dd975e29bb2a59fe.jpg) +![顯示多層長短期記憶 RNN 的圖片](../../../../../translated_images/hk/multi-layer-lstm.dd975e29bb2a59fe.webp) *圖片來自 [這篇精彩文章](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) 作者 Fernando López* diff --git a/translations/hk/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/hk/lessons/5-NLP/17-GenerativeNetworks/README.md index c9d0113d..87b26be2 100644 --- a/translations/hk/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/hk/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: 這使得不同的神經架構成為可能，如下圖所示： -![展示常見循環神經網絡模式的圖片。](../../../../../translated_images/hk/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![展示常見循環神經網絡模式的圖片。](../../../../../translated_images/hk/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > 圖片來自 [Andrej Karpaty](http://karpathy.github.io/) 的博客文章 [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) @@ -32,7 +32,7 @@ CO_OP_TRANSLATOR_METADATA: 我們將訓練這個 RNN 逐步生成文本。在每一步中，我們將取一個長度為 `nchars` 的字符序列，並要求網絡為每個輸入字符生成下一個輸出字符： -![展示 RNN 生成單詞 'HELLO' 的示例圖片。](../../../../../translated_images/hk/rnn-generate.56c54afb52f9781d.png) +![展示 RNN 生成單詞 'HELLO' 的示例圖片。](../../../../../translated_images/hk/rnn-generate.56c54afb52f9781d.webp) 在生成文本（推理過程中）時，我們從某個**提示**開始，將其通過 RNN 單元生成中間狀態，然後從該狀態開始生成。我們一次生成一個字符，並將狀態和生成的字符傳遞給另一個 RNN 單元以生成下一個字符，直到生成足夠的字符。 diff --git a/translations/hk/lessons/5-NLP/18-Transformers/README.md b/translations/hk/lessons/5-NLP/18-Transformers/README.md index 5accdcbe..7c683bff 100644 --- a/translations/hk/lessons/5-NLP/18-Transformers/README.md +++ b/translations/hk/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ CO_OP_TRANSLATOR_METADATA: **注意力機制**提供了一種方法，能夠對每個輸入向量對RNN輸出預測的上下文影響進行加權。其實現方式是通過在輸入RNN的中間狀態與輸出RNN之間創建捷徑。這樣，在生成輸出符號yt時，我們會考慮所有輸入隱藏狀態hi，並賦予不同的權重係數αt,i。 -![顯示帶有加性注意力層的編碼器/解碼器模型的圖片](../../../../../translated_images/hk/encoder-decoder-attention.7a726296894fb567.png) +![顯示帶有加性注意力層的編碼器/解碼器模型的圖片](../../../../../translated_images/hk/encoder-decoder-attention.7a726296894fb567.webp) > [Bahdanau等人, 2015](https://arxiv.org/pdf/1409.0473.pdf)中的加性注意力機制編碼器-解碼器模型，圖片來源於[這篇博客文章](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) 注意力矩陣{αi,j}表示某些輸入詞在生成給定輸出序列中的某個詞時所起的作用程度。以下是一個這樣的矩陣示例： -![顯示RNNsearch-50找到的示例對齊的圖片，取自Bahdanau - arviz.org](../../../../../translated_images/hk/bahdanau-fig3.09ba2d37f202a6af.png) +![顯示RNNsearch-50找到的示例對齊的圖片，取自Bahdanau - arviz.org](../../../../../translated_images/hk/bahdanau-fig3.09ba2d37f202a6af.webp) > 圖片來自[Bahdanau等人, 2015](https://arxiv.org/pdf/1409.0473.pdf)（圖3） @@ -66,7 +66,7 @@ Transformer的主要思想之一是避免RNN的序列性質，並創建一個在 接下來，我們需要捕捉序列中的一些模式。為此，Transformer使用了**自注意力**機制，這本質上是將注意力應用於相同的輸入和輸出序列。應用自注意力使我們能夠考慮句子中的**上下文**，並查看哪些詞是相互關聯的。例如，它使我們能夠看到哪些詞由指代詞（如*it*）指代，並考慮上下文： -![](../../../../../translated_images/hk/CoreferenceResolution.861924d6d384a7d6.png) +![](../../../../../translated_images/hk/CoreferenceResolution.861924d6d384a7d6.webp) > 圖片來自[Google博客](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Transformer的主要思想之一是避免RNN的序列性質，並創建一個在 **BERT**（Bidirectional Encoder Representations from Transformers）是一個非常大的多層Transformer網絡，*BERT-base*有12層，*BERT-large*有24層。該模型首先在大規模文本數據語料庫（維基百科+書籍）上進行無監督預訓練（預測句子中的被遮蔽詞）。在預訓練過程中，模型吸收了大量的語言理解能力，這些能力可以通過微調其他數據集來利用。這個過程被稱為**遷移學習**。 -![圖片來自http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/hk/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) +![圖片來自http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/hk/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > 圖片[來源](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/hk/lessons/5-NLP/19-NER/README.md b/translations/hk/lessons/5-NLP/19-NER/README.md index 047be71f..a93b9689 100644 --- a/translations/hk/lessons/5-NLP/19-NER/README.md +++ b/translations/hk/lessons/5-NLP/19-NER/README.md @@ -57,7 +57,7 @@ NER 模型本質上是 **標記分類模型**，因為對於每個輸入標記 由於我們需要在標記和類別之間建立一對一的對應關係，我們可以從這張圖中訓練一個最右側的 **多對多** 神經網絡模型： -![展示常見循環神經網絡模式的圖片。](../../../../../translated_images/hk/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![展示常見循環神經網絡模式的圖片。](../../../../../translated_images/hk/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > *圖片來自 [這篇博客文章](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) 作者 [Andrej Karpathy](http://karpathy.github.io/)。NER 標記分類模型對應於圖片中最右側的網絡架構。* diff --git a/translations/hk/lessons/5-NLP/README.md b/translations/hk/lessons/5-NLP/README.md index 64902324..472e4581 100644 --- a/translations/hk/lessons/5-NLP/README.md +++ b/translations/hk/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 自然語言處理 -![NLP 任務的手繪圖概述](../../../../translated_images/hk/ai-nlp.b22dcb8ca4707cea.png) +![NLP 任務的手繪圖概述](../../../../translated_images/hk/ai-nlp.b22dcb8ca4707cea.webp) 在本節中，我們將專注於使用神經網絡來處理與**自然語言處理 (NLP)** 相關的任務。我們希望計算機能夠解決許多 NLP 問題： diff --git a/translations/hk/lessons/6-Other/23-MultiagentSystems/README.md b/translations/hk/lessons/6-Other/23-MultiagentSystems/README.md index d0555a51..f72bbfae 100644 --- a/translations/hk/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/hk/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ NetLogo的一大優勢是它包含一個可供試用的工作模型庫。進入* 打開模型後，你會進入NetLogo的主屏幕。以下是一個描述狼和羊的種群模型，給定有限資源（草地）。 -![NetLogo主屏幕](../../../../../translated_images/hk/NetLogo-Main.32653711ec1a01b3.png) +![NetLogo主屏幕](../../../../../translated_images/hk/NetLogo-Main.32653711ec1a01b3.webp) > Dmitry Soshnikov提供的截圖 diff --git a/translations/hk/lessons/README.md b/translations/hk/lessons/README.md index 05c47103..5f65959b 100644 --- a/translations/hk/lessons/README.md +++ b/translations/hk/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 概覽 -![概覽的手繪圖](../../../translated_images/hk/ai-overview.0857791951d19500.png) +![概覽的手繪圖](../../../translated_images/hk/ai-overview.0857791951d19500.webp) > 手繪筆記由 [Tomomi Imura](https://twitter.com/girlie_mac) 提供 diff --git a/translations/hk/lessons/X-Extras/X1-MultiModal/README.md b/translations/hk/lessons/X-Extras/X1-MultiModal/README.md index 933bdd75..5ed8ed79 100644 --- a/translations/hk/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/hk/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: CLIP 的主要理念是能夠比較文本提示與圖像，並判斷圖像與提示的匹配程度。 -![CLIP 架構](../../../../../translated_images/hk/clip-arch.b3dbf20b4e8ed8be.png) +![CLIP 架構](../../../../../translated_images/hk/clip-arch.b3dbf20b4e8ed8be.webp) > *圖片來源：[這篇博客文章](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ CLIP 模型/庫可從 [OpenAI GitHub](https://github.com/openai/CLIP) 獲得。 假設我們需要將圖像分類為例如貓、狗和人。在這種情況下，我們可以向模型提供一張圖像，以及一系列文本提示：“*一張貓的照片*”、“*一張狗的照片*”、“*一張人的照片*”。在結果的三個概率向量中，我們只需選擇值最高的索引。 -![CLIP 用於圖像分類](../../../../../translated_images/hk/clip-class.3af42ef0b2b19369.png) +![CLIP 用於圖像分類](../../../../../translated_images/hk/clip-class.3af42ef0b2b19369.webp) > *圖片來源：[這篇博客文章](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ VQGAN 與普通 [GAN](../../4-ComputerVision/10-GANs/README.md) 的主要區別 VQGAN 與傳統 GAN 的一個重要區別是，後者可以從任何輸入向量生成一張像樣的圖像，而 VQGAN 則可能生成不連貫的圖像。因此，我們需要進一步引導圖像創建過程，這可以通過 CLIP 來完成。 -![VQGAN+CLIP 架構](../../../../../translated_images/hk/vqgan.5027fe05051dfa31.png) +![VQGAN+CLIP 架構](../../../../../translated_images/hk/vqgan.5027fe05051dfa31.webp) 為了生成與文本提示相對應的圖像，我們首先使用一些隨機編碼向量，通過 VQGAN 生成一張圖像。然後使用 CLIP 生成損失函數，顯示圖像與文本提示的匹配程度。目標是最小化該損失，通過反向傳播調整輸入向量的參數。 一個實現 VQGAN+CLIP 的優秀庫是 [Pixray](http://github.com/pixray/pixray)。 -![由 Pixray 生成的圖片](../../../../../translated_images/hk/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![由 Pixray 生成的圖片](../../../../../translated_images/hk/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![由 Pixray 生成的圖片](../../../../../translated_images/hk/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) +![由 Pixray 生成的圖片](../../../../../translated_images/hk/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp) | ![由 Pixray 生成的圖片](../../../../../translated_images/hk/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp) | ![由 Pixray 生成的圖片](../../../../../translated_images/hk/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp) ----|----|---- 根據提示 *一張年輕男性文學教師拿著書的水彩特寫肖像* 生成的圖片 | 根據提示 *一張年輕女性計算機科學教師拿著電腦的油畫特寫肖像* 生成的圖片 | 根據提示 *一張老年男性數學教師站在黑板前的油畫特寫肖像* 生成的圖片 diff --git a/translations/hr/README.md b/translations/hr/README.md index 99633172..73a17b7d 100644 --- a/translations/hr/README.md +++ b/translations/hr/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # Umjetna inteligencija za početnike - Nastavni plan -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/hr/ai-overview.0857791951d19500.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/hr/ai-overview.0857791951d19500.webp)| |:---:| | AI za početnike - _Sketchnote autora [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/hr/lessons/1-Intro/README.md b/translations/hr/lessons/1-Intro/README.md index 6464b10e..1a0b7c03 100644 --- a/translations/hr/lessons/1-Intro/README.md +++ b/translations/hr/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Uvod u AI -![Sažetak sadržaja uvoda u AI u obliku crteža](../../../../translated_images/hr/ai-intro.bf28d1ac4235881c.png) +![Sažetak sadržaja uvoda u AI u obliku crteža](../../../../translated_images/hr/ai-intro.bf28d1ac4235881c.webp) > Crtež od [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Izvorno, računala je izumio [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage) kako bi radila s brojevima slijedeći dobro definirani postupak - algoritam. Moderna računala, iako znatno naprednija od originalnog modela predloženog u 19. stoljeću, i dalje slijede istu ideju kontroliranih izračuna. Stoga je moguće programirati računalo da nešto učini ako znamo točan slijed koraka koji trebamo poduzeti kako bismo postigli cilj. -![Fotografija osobe](../../../../translated_images/hr/dsh_age.d212a30d4e54fb5f.png) +![Fotografija osobe](../../../../translated_images/hr/dsh_age.d212a30d4e54fb5f.webp) > Fotografija od [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -45,7 +45,7 @@ Za više informacija pogledajte **[Umjetna Opća Inteligencija](https://en.wikip Jedan od problema pri bavljenju pojmom **[inteligencija](https://en.wikipedia.org/wiki/Intelligence)** jest taj što ne postoji jasna definicija ovog pojma. Može se tvrditi da je inteligencija povezana s **apstraktnim razmišljanjem** ili **samosviješću**, ali je ne možemo pravilno definirati. -![Fotografija mačke](../../../../translated_images/hr/photo-cat.8c8e8fb760ffe457.jpg) +![Fotografija mačke](../../../../translated_images/hr/photo-cat.8c8e8fb760ffe457.webp) > [Fotografija](https://unsplash.com/photos/75715CVEJhI) od [Amber Kipp](https://unsplash.com/@sadmax) s Unsplash-a @@ -97,13 +97,13 @@ Alternativno, možemo pokušati modelirati najjednostavnije elemente unutar naš > | Što je s ML? | | > |--------------|-----------| -> | Dio umjetne inteligencije koji se temelji na učenju računala da riješi problem na temelju nekih podataka naziva se **strojno učenje**. Nećemo razmatrati klasično strojno učenje u ovom tečaju - upućujemo vas na zasebni kurikulum [Strojno učenje za početnike](http://aka.ms/ml-beginners). | ![ML za početnike](../../../../translated_images/hr/ml-for-beginners.9e4fed176fd5817d.png) | +> | Dio umjetne inteligencije koji se temelji na učenju računala da riješi problem na temelju nekih podataka naziva se **strojno učenje**. Nećemo razmatrati klasično strojno učenje u ovom tečaju - upućujemo vas na zasebni kurikulum [Strojno učenje za početnike](http://aka.ms/ml-beginners). | ![ML za početnike](../../../../translated_images/hr/ml-for-beginners.9e4fed176fd5817d.webp) | ## Kratka povijest AI-a Umjetna inteligencija započela je kao područje sredinom dvadesetog stoljeća. U početku je simboličko zaključivanje bilo prevladavajući pristup, što je dovelo do brojnih važnih uspjeha, poput stručnih sustava – računalnih programa koji su mogli djelovati kao stručnjaci u nekim ograničenim domenama problema. Međutim, ubrzo je postalo jasno da se takav pristup ne skalira dobro. Izdvajanje znanja od stručnjaka, njegovo predstavljanje u računalu i održavanje te baze znanja točnom pokazalo se vrlo složenim zadatkom i preskupim za praktičnu primjenu u mnogim slučajevima. To je dovelo do takozvane [AI zime](https://en.wikipedia.org/wiki/AI_winter) 1970-ih. -Kratka povijest AI-a +Kratka povijest AI-a > Slika od [Dmitry Soshnikov](http://soshnikov.com) @@ -123,7 +123,7 @@ Slično, možemo vidjeti kako se pristup stvaranju "govornih programa" (koji bi * Moderni asistenti, poput Cortane, Siri ili Google Assistanta, svi su hibridni sustavi koji koriste neuronske mreže za pretvaranje govora u tekst i prepoznavanje naše namjere, a zatim koriste neko zaključivanje ili eksplicitne algoritme za obavljanje potrebnih radnji. * U budućnosti možemo očekivati potpuni model temeljen na neuronskim mrežama koji će samostalno upravljati dijalogom. Nedavne GPT i [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) obitelji neuronskih mreža pokazuju veliki uspjeh u tome. -evolucija Turingovog testa +evolucija Turingovog testa > Slika Dmitry Soshnikov, [fotografija](https://unsplash.com/photos/r8LmVbUKgns) od [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto), Unsplash ## Nedavna istraživanja u području umjetne inteligencije diff --git a/translations/hr/lessons/2-Symbolic/Animals.ipynb b/translations/hr/lessons/2-Symbolic/Animals.ipynb index 239fb768..c05c8f12 100644 --- a/translations/hr/lessons/2-Symbolic/Animals.ipynb +++ b/translations/hr/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "U ovom primjeru implementirat ćemo jednostavan sustav temeljen na znanju za određivanje životinje na temelju nekih fizičkih karakteristika. Sustav se može prikazati sljedećim AND-OR stablom (ovo je dio cijelog stabla, lako možemo dodati još pravila):\n", "\n", - "![](../../../../translated_images/hr/AND-OR-Tree.5592d2c70187f283.png)\n" + "![](../../../../translated_images/hr/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { diff --git a/translations/hr/lessons/2-Symbolic/README.md b/translations/hr/lessons/2-Symbolic/README.md index 8130e7a3..a4362088 100644 --- a/translations/hr/lessons/2-Symbolic/README.md +++ b/translations/hr/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Predstavljanje znanja i ekspertni sustavi -![Sažetak sadržaja o simboličkoj umjetnoj inteligenciji](../../../../translated_images/hr/ai-symbolic.715a30cb610411a6.png) +![Sažetak sadržaja o simboličkoj umjetnoj inteligenciji](../../../../translated_images/hr/ai-symbolic.715a30cb610411a6.webp) > Sketchnote autorice [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ Najčešće ne definiramo strogo znanje, već ga povezujemo s drugim srodnim poj Dakle, problem **predstavljanja znanja** je pronaći učinkovit način za predstavljanje znanja unutar računala u obliku podataka kako bi se ono moglo automatski koristiti. To se može promatrati kao spektar: -![Spektar predstavljanja znanja](../../../../translated_images/hr/knowledge-spectrum.b60df631852c0217.png) +![Spektar predstavljanja znanja](../../../../translated_images/hr/knowledge-spectrum.b60df631852c0217.webp) > Slika autora [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Sintaksa bloka | Uvlačenje | | | Jedan od ranih uspjeha simboličke umjetne inteligencije bili su tzv. **ekspertni sustavi** - računalni sustavi dizajnirani da djeluju kao stručnjaci u nekom ograničenom problematičnom području. Temeljili su se na **bazi znanja** izvučenoj od jednog ili više ljudskih stručnjaka i sadržavali su **mehanizam zaključivanja** koji je provodio zaključivanje na temelju te baze. -![Ljudska arhitektura](../../../../translated_images/hr/arch-human.5d4d35f1bba3ab1c.png) | ![Arhitektura sustava temeljenog na znanju](../../../../translated_images/hr/arch-kbs.3ec5c150b09fa8da.png) +![Ljudska arhitektura](../../../../translated_images/hr/arch-human.5d4d35f1bba3ab1c.webp) | ![Arhitektura sustava temeljenog na znanju](../../../../translated_images/hr/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ Pojednostavljena struktura ljudskog živčanog sustava | Arhitektura sustava temeljenog na znanju @@ -106,7 +106,7 @@ Ekspertni sustavi izgrađeni su poput ljudskog sustava zaključivanja, koji sadr Kao primjer, razmotrimo sljedeći ekspertni sustav za određivanje životinje na temelju njezinih fizičkih karakteristika: -![AND-OR stablo](../../../../translated_images/hr/AND-OR-Tree.5592d2c70187f283.png) +![AND-OR stablo](../../../../translated_images/hr/AND-OR-Tree.5592d2c70187f283.webp) > Slika autora [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/hr/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/hr/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index f2ad55ec..37950210 100644 --- a/translations/hr/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/hr/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -1248,7 +1248,7 @@ "* Nizak gubitak na podacima za treniranje - model može dobro aproksimirati podatke za treniranje jer ima dovoljno izražajne moći.\n", "* Gubitak na validaciji može biti znatno veći od gubitka na treniranju i može početi rasti tijekom treniranja - to je zato što model \"pamti\" točke za treniranje i gubi \"širu sliku\".\n", "\n", - "![Prekomjerno prilagođavanje](../../../../../translated_images/hr/overfit.a0bd57f717c15769.png)\n", + "![Prekomjerno prilagođavanje](../../../../../translated_images/hr/overfit.a0bd57f717c15769.webp)\n", "\n", "> Na ovoj slici, `x` označava podatke za treniranje, `o` - podatke za validaciju. Lijevo - linearni model (jednoslojni), dobro aproksimira prirodu podataka. Desno - model s prekomjernim prilagođavanjem, savršeno aproksimira podatke za treniranje, ali gubi smisao s bilo kojim drugim podacima (pogreška validacije je vrlo visoka).\n" ] diff --git a/translations/hr/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/hr/lessons/3-NeuralNetworks/05-Frameworks/README.md index d798a639..5836016c 100644 --- a/translations/hr/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/hr/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Pretreniranje je iznimno važan koncept u strojnom učenju, i vrlo je važno raz Razmotrimo sljedeći problem aproksimacije 5 točaka (prikazanih kao `x` na grafovima dolje): -![linear](../../../../../translated_images/hr/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/hr/overfit2.131f5800ae10ca5e.jpg) +![linear](../../../../../translated_images/hr/overfit1.f24b71c6f652e59e.webp) | ![overfit](../../../../../translated_images/hr/overfit2.131f5800ae10ca5e.webp) -------------------------|-------------------------- **Linearni model, 2 parametra** | **Nelinearni model, 7 parametara** Pogreška na treningu = 5.3 | Pogreška na treningu = 0 @@ -79,7 +79,7 @@ Vrlo je važno pronaći ispravnu ravnotežu između složenosti modela (broja pa Kao što možete vidjeti na grafu iznad, pretreniranje se može otkriti vrlo niskom pogreškom na treningu i visokom pogreškom na validaciji. Obično tijekom treninga vidimo kako pogreške na treningu i validaciji počinju opadati, a zatim u nekom trenutku pogreška na validaciji prestaje opadati i počinje rasti. To će biti znak pretreniranja i pokazatelj da bismo trebali prestati trenirati u tom trenutku (ili barem napraviti snimku modela). -![pretreniranje](../../../../../translated_images/hr/Overfitting.408ad91cd90b4371.png) +![pretreniranje](../../../../../translated_images/hr/Overfitting.408ad91cd90b4371.webp) ## Kako spriječiti pretreniranje diff --git a/translations/hr/lessons/3-NeuralNetworks/README.md b/translations/hr/lessons/3-NeuralNetworks/README.md index 315d8ae0..ad0949e3 100644 --- a/translations/hr/lessons/3-NeuralNetworks/README.md +++ b/translations/hr/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Uvod u neuronske mreže -![Sažetak sadržaja o uvodu u neuronske mreže u obliku crteža](../../../../translated_images/hr/ai-neuralnetworks.1c687ae40bc86e83.png) +![Sažetak sadržaja o uvodu u neuronske mreže u obliku crteža](../../../../translated_images/hr/ai-neuralnetworks.1c687ae40bc86e83.webp) Kao što smo raspravili u uvodu, jedan od načina za postizanje inteligencije je treniranje **računalnog modela** ili **umjetnog mozga**. Od sredine 20. stoljeća, istraživači su isprobavali različite matematičke modele, sve dok se u posljednjim godinama ovaj smjer nije pokazao izuzetno uspješnim. Takvi matematički modeli mozga nazivaju se **neuronske mreže**. @@ -36,13 +36,13 @@ U ovom kurikulumu fokusirat ćemo se isključivo na modele neuronskih mreža. Iz biologije znamo da naš mozak sastoji se od neuralnih stanica (neurona), od kojih svaka ima više "ulaza" (dendrita) i jedan "izlaz" (akson). I dendriti i aksoni mogu provoditi električne signale, a veze između njih — poznate kao sinapse — mogu pokazivati različite stupnjeve provodljivosti, koje reguliraju neurotransmiteri. -![Model neurona](../../../../translated_images/hr/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![Model neurona](../../../../translated_images/hr/artneuron.1a5daa88d20ebe6f.png) +![Model neurona](../../../../translated_images/hr/synapse-wikipedia.ed20a9e4726ea1c6.webp) | ![Model neurona](../../../../translated_images/hr/artneuron.1a5daa88d20ebe6f.webp) ----|---- Stvarni neuron *([Slika](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) s Wikipedije)* | Umjetni neuron *(Slika autora)* Dakle, najjednostavniji matematički model neurona sadrži nekoliko ulaza X1, ..., XN i jedan izlaz Y, te niz težina W1, ..., WN. Izlaz se računa kao: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) gdje je f neka nelinearna **funkcija aktivacije**. diff --git a/translations/hr/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/hr/lessons/4-ComputerVision/06-IntroCV/README.md index 2c2dbdb2..e8f871df 100644 --- a/translations/hr/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/hr/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ U našem [OpenCV Notebook](OpenCV.ipynb) dajemo neke primjere kada se računalni * **Predobrada fotografije Brailleove knjige**. Fokusiramo se na to kako možemo koristiti pragove, detekciju značajki, perspektivne transformacije i manipulacije NumPy nizovima kako bismo odvojili pojedinačne Brailleove simbole za daljnju klasifikaciju neuronskom mrežom. -![Braille slika](../../../../../translated_images/hr/braille.341962ff76b1bd70.jpeg) | ![Predobrađena Braille slika](../../../../../translated_images/hr/braille-result.46530fea020b03c7.png) | ![Braille simboli](../../../../../translated_images/hr/braille-symbols.0159185ab69d5339.png) +![Braille slika](../../../../../translated_images/hr/braille.341962ff76b1bd70.webp) | ![Predobrađena Braille slika](../../../../../translated_images/hr/braille-result.46530fea020b03c7.webp) | ![Braille simboli](../../../../../translated_images/hr/braille-symbols.0159185ab69d5339.webp) ----|-----|----- > Slika iz [OpenCV.ipynb](OpenCV.ipynb) * **Detekcija kretanja u videu pomoću razlike između okvira**. Ako je kamera fiksna, tada bi okviri iz videozapisa trebali biti prilično slični. Budući da su okviri predstavljeni kao nizovi, jednostavnim oduzimanjem tih nizova za dva uzastopna okvira dobit ćemo razliku piksela, koja bi trebala biti mala za statične okvire, a postati veća kada postoji značajno kretanje na slici. -![Slika video okvira i razlika između okvira](../../../../../translated_images/hr/frame-difference.706f805491a0883c.png) +![Slika video okvira i razlika između okvira](../../../../../translated_images/hr/frame-difference.706f805491a0883c.webp) > Slika iz [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ U našem [OpenCV Notebook](OpenCV.ipynb) dajemo neke primjere kada se računalni - **Gusti optički tok** izračunava vektorsko polje koje pokazuje za svaki piksel kamo se kreće - **Rijetki optički tok** temelji se na uzimanju nekih prepoznatljivih značajki na slici (npr. rubova) i praćenju njihove putanje od okvira do okvira. -![Slika optičkog toka](../../../../../translated_images/hr/optical.1f4a94464579a83a.png) +![Slika optičkog toka](../../../../../translated_images/hr/optical.1f4a94464579a83a.webp) > Slika iz [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/hr/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/hr/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 11d96f5c..449c78fd 100644 --- a/translations/hr/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/hr/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 je mreža koja je postigla 92.7% točnosti u ImageNet top-5 klasifikaciji 2014. godine. Ima sljedeću strukturu slojeva: -![ImageNet Layers](../../../../../translated_images/hr/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet Layers](../../../../../translated_images/hr/vgg-16-arch1.d901a5583b3a51ba.webp) Kao što možete vidjeti, VGG slijedi tradicionalnu piramidalnu arhitekturu, koja je niz slojeva konvolucije i pooling-a. -![ImageNet Pyramid](../../../../../translated_images/hr/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet Pyramid](../../../../../translated_images/hr/vgg-16-arch.64ff2137f50dd49f.webp) > Slika preuzeta s [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/hr/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/hr/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index 915241e4..1ff9890b 100644 --- a/translations/hr/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/hr/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -260,7 +260,7 @@ "\n", "Dakle, u tipičnom CNN-u postoji nekoliko slojeva konvolucije, sa slojevima za pooling između njih kako bi se smanjile dimenzije slike. Također bismo povećali broj filtera, jer kako uzorci postaju napredniji - postoji više mogućih zanimljivih kombinacija koje trebamo tražiti.\n", "\n", - "![Slika koja prikazuje nekoliko slojeva konvolucije sa slojevima za pooling.](../../../../../translated_images/hr/cnn-pyramid.85915455759ef0ce.png)\n", + "![Slika koja prikazuje nekoliko slojeva konvolucije sa slojevima za pooling.](../../../../../translated_images/hr/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "Zbog smanjenja prostorne dimenzije i povećanja dimenzije značajki/filtera, ova se arhitektura također naziva **piramidalna arhitektura**.\n" ] diff --git a/translations/hr/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/hr/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index 7a4c265c..95f92413 100644 --- a/translations/hr/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/hr/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -359,7 +359,7 @@ "\n", "Dakle, u tipičnom CNN-u postoji nekoliko konvolucijskih slojeva, s pooling slojevima između njih kako bi se smanjile dimenzije slike. Također bismo povećali broj filtera, jer kako uzorci postaju napredniji - postoji više mogućih zanimljivih kombinacija koje trebamo tražiti.\n", "\n", - "![Slika koja prikazuje nekoliko konvolucijskih slojeva sa slojevima za pooling.](../../../../../translated_images/hr/cnn-pyramid.85915455759ef0ce.png)\n", + "![Slika koja prikazuje nekoliko konvolucijskih slojeva sa slojevima za pooling.](../../../../../translated_images/hr/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "Zbog smanjenja prostorne dimenzije i povećanja dimenzije značajki/filtera, ova se arhitektura također naziva **piramidalna arhitektura**.\n" ] diff --git a/translations/hr/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/hr/lessons/4-ComputerVision/07-ConvNets/README.md index dbcee906..b47bf7c7 100644 --- a/translations/hr/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/hr/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ U stvarnom životu želimo biti u mogućnosti prepoznati objekte na slici bez ob Za izdvajanje uzoraka koristit ćemo pojam **konvolucijskih filtera**. Kao što znate, slika je predstavljena 2D-matricom ili 3D-tenzorom s dubinom boje. Primjena filtera znači da uzimamo relativno malu matricu **jezgre filtera** i za svaki piksel u originalnoj slici izračunavamo ponderirani prosjek s okolnim točkama. To možemo zamisliti kao mali prozor koji klizi preko cijele slike i izračunava prosjek svih piksela prema težinama u matrici jezgre filtera. -![Vertikalni rubni filter](../../../../../translated_images/hr/filter-vert.b7148390ca0bc356.png) | ![Horizontalni rubni filter](../../../../../translated_images/hr/filter-horiz.59b80ed4feb946ef.png) +![Vertikalni rubni filter](../../../../../translated_images/hr/filter-vert.b7148390ca0bc356.webp) | ![Horizontalni rubni filter](../../../../../translated_images/hr/filter-horiz.59b80ed4feb946ef.webp) ----|---- > Slika: Dmitry Soshnikov @@ -38,7 +38,7 @@ Način na koji CNN funkcionira temelji se na sljedećim važnim idejama: * Možemo dizajnirati mrežu na način da se filteri automatski treniraju * Možemo koristiti isti pristup za pronalaženje uzoraka u visokorazinskim značajkama, ne samo u originalnoj slici. Tako ekstrakcija značajki u CNN-u funkcionira na hijerarhiji značajki, počevši od niskorazinskih kombinacija piksela do visokorazinskih kombinacija dijelova slike. -![Hijerarhijska ekstrakcija značajki](../../../../../translated_images/hr/FeatureExtractionCNN.d9b456cbdae7cb64.png) +![Hijerarhijska ekstrakcija značajki](../../../../../translated_images/hr/FeatureExtractionCNN.d9b456cbdae7cb64.webp) > Slika iz [rada Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), temeljenog na [njihovom istraživanju](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ Većina CNN-a koji se koriste za obradu slika slijedi tzv. piramidalnu arhitektu Kao primjer, pogledajmo arhitekturu VGG-16, mreže koja je postigla 92.7% točnosti u ImageNet-ovoj top-5 klasifikaciji 2014. godine: -![ImageNet slojevi](../../../../../translated_images/hr/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet slojevi](../../../../../translated_images/hr/vgg-16-arch1.d901a5583b3a51ba.webp) -![ImageNet piramida](../../../../../translated_images/hr/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet piramida](../../../../../translated_images/hr/vgg-16-arch.64ff2137f50dd49f.webp) > Slika: [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/hr/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/hr/lessons/4-ComputerVision/07-ConvNets/lab/README.md index b7c4816f..b9213ea9 100644 --- a/translations/hr/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/hr/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ Vaš zadatak je trenirati konvolucijsku neuronsku mrežu za klasifikaciju razli Koristit ćemo [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/), koji sadrži slike 37 različitih pasmina pasa i mačaka. -![Skup podataka s kojim ćemo raditi](../../../../../../translated_images/hr/data.50b2a9d5484bdbf0.png) +![Skup podataka s kojim ćemo raditi](../../../../../../translated_images/hr/data.50b2a9d5484bdbf0.webp) Za preuzimanje skupa podataka, koristite ovaj isječak koda: diff --git a/translations/hr/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/hr/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index 4a6adcab..cf190a40 100644 --- a/translations/hr/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/hr/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "Kako bismo vizualizirali idealnu mačku, započet ćemo s nasumičnom slikom šuma i pokušati koristiti tehniku optimizacije gradijentnog spuštanja kako bismo prilagodili sliku tako da mreža prepozna mačku.\n", "\n", - "![Optimizacijska petlja](../../../../../translated_images/hr/ideal-cat-loop.999fbb8ff306e044.png)\n", + "![Optimizacijska petlja](../../../../../translated_images/hr/ideal-cat-loop.999fbb8ff306e044.webp)\n", "\n", "Ovo je naša početna slika:\n" ] diff --git a/translations/hr/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/hr/lessons/4-ComputerVision/08-TransferLearning/README.md index 022af723..43ab7887 100644 --- a/translations/hr/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/hr/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ I Keras i PyTorch sadrže funkcije za jednostavno učitavanje unaprijed treniran Evo primjera značajki koje je VGG-16 mreža izdvojila iz slike mačke: -![Features extracted by VGG-16](../../../../../translated_images/hr/features.6291f9c7ba3a0b95.png) +![Features extracted by VGG-16](../../../../../translated_images/hr/features.6291f9c7ba3a0b95.webp) ## Skup podataka Mačke vs. Psi @@ -48,19 +48,19 @@ Unaprijed trenirana neuronska mreža sadrži različite uzorke unutar svog *mozg Jedan pristup koji možemo koristiti je započeti s nasumičnom slikom, a zatim pokušati koristiti tehniku **optimizacije gradijentnog spuštanja** kako bismo prilagodili tu sliku na način da mreža počne misliti da je to mačka. -![Image Optimization Loop](../../../../../translated_images/hr/ideal-cat-loop.999fbb8ff306e044.png) +![Image Optimization Loop](../../../../../translated_images/hr/ideal-cat-loop.999fbb8ff306e044.webp) Međutim, ako to učinimo, dobit ćemo nešto vrlo slično nasumičnom šumu. To je zato što *postoji mnogo načina da mreža pomisli da je ulazna slika mačka*, uključujući neke koji vizualno nemaju smisla. Iako te slike sadrže mnogo uzoraka tipičnih za mačku, ništa ih ne ograničava da budu vizualno prepoznatljive. Kako bismo poboljšali rezultat, možemo dodati još jedan član u funkciju gubitka, koji se naziva **gubitak varijacije**. To je metrika koja pokazuje koliko su slični susjedni pikseli slike. Minimiziranje gubitka varijacije čini sliku glađom i uklanja šum - otkrivajući tako vizualno privlačnije uzorke. Evo primjera takvih "idealnih" slika koje se klasificiraju kao mačka i zebra s visokom vjerojatnošću: -![Ideal Cat](../../../../../translated_images/hr/ideal-cat.203dd4597643d6b0.png) | ![Ideal Zebra](../../../../../translated_images/hr/ideal-zebra.7f70e8b54ee15a7a.png) +![Ideal Cat](../../../../../translated_images/hr/ideal-cat.203dd4597643d6b0.webp) | ![Ideal Zebra](../../../../../translated_images/hr/ideal-zebra.7f70e8b54ee15a7a.webp) -----|----- *Idealna mačka* | *Idealna zebra* Sličan pristup može se koristiti za izvođenje takozvanih **adversarijalnih napada** na neuronsku mrežu. Pretpostavimo da želimo zavarati neuronsku mrežu i učiniti da pas izgleda kao mačka. Ako uzmemo sliku psa, koju mreža prepoznaje kao psa, možemo je malo prilagoditi koristeći optimizaciju gradijentnog spuštanja dok mreža ne počne klasificirati sliku kao mačku: -![Picture of a Dog](../../../../../translated_images/hr/original-dog.8f68a67d2fe0911f.png) | ![Picture of a dog classified as a cat](../../../../../translated_images/hr/adversarial-dog.d9fc7773b0142b89.png) +![Picture of a Dog](../../../../../translated_images/hr/original-dog.8f68a67d2fe0911f.webp) | ![Picture of a dog classified as a cat](../../../../../translated_images/hr/adversarial-dog.d9fc7773b0142b89.webp) -----|----- *Izvorna slika psa* | *Slika psa klasificirana kao mačka* diff --git a/translations/hr/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/hr/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index dbe2c66f..d5cc666f 100644 --- a/translations/hr/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/hr/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "Budući da treniramo autoenkoder kako bi uhvatio što više informacija iz izvorne slike za točnu rekonstrukciju, mreža pokušava pronaći najbolju **ugradnju** ulaznih slika kako bi uhvatila njihovo značenje.\n", "\n", - "![Dijagram Autoenkodera](../../../../../translated_images/hr/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![Dijagram Autoenkodera](../../../../../translated_images/hr/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "> Slika preuzeta s [Keras bloga](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", diff --git a/translations/hr/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/hr/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index a9c9eebe..40660e94 100644 --- a/translations/hr/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/hr/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "Budući da treniramo autoenkoder kako bi uhvatio što više informacija iz originalne slike za točnu rekonstrukciju, mreža pokušava pronaći najbolju **ugradnju** ulaznih slika kako bi uhvatila njihovo značenje.\n", "\n", - "![AutoEncoder Dijagram](../../../../../translated_images/hr/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![AutoEncoder Dijagram](../../../../../translated_images/hr/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "*Slika preuzeta s [Keras bloga](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", diff --git a/translations/hr/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/hr/lessons/4-ComputerVision/09-Autoencoders/README.md index d5548be4..e6643cee 100644 --- a/translations/hr/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/hr/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ Međutim, možda bismo željeli koristiti sirove (neoznačene) podatke za trenir Budući da treniramo autoenkoder kako bi uhvatio što više informacija iz originalne slike za točnu rekonstrukciju, mreža pokušava pronaći najbolju **ugradnju** ulaznih slika kako bi uhvatila njihovo značenje. -![Dijagram Autoenkodera](../../../../../translated_images/hr/autoencoder_schema.5e6fc9ad98a5eb61.jpg) +![Dijagram Autoenkodera](../../../../../translated_images/hr/autoencoder_schema.5e6fc9ad98a5eb61.webp) > Slika s [Keras bloga](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/hr/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/hr/lessons/4-ComputerVision/11-ObjectDetection/README.md index ef0eeadd..abd4be20 100644 --- a/translations/hr/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/hr/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ Modeli za klasifikaciju slika s kojima smo se dosad susretali uzimaju sliku i pr ## [Prethodni kviz](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Detekcija objekata](../../../../../translated_images/hr/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) +![Detekcija objekata](../../../../../translated_images/hr/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp) > Slika s [YOLO v2 web stranice](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ Pretpostavimo da želimo pronaći mačku na slici. Vrlo naivan pristup detekciji 2. Provoditi klasifikaciju slike na svakoj pločici. 3. Pločice koje rezultiraju dovoljno visokom aktivacijom mogu se smatrati da sadrže traženi objekt. -![Naivna detekcija objekata](../../../../../translated_images/hr/naive-detection.e7f1ba220ccd08c6.png) +![Naivna detekcija objekata](../../../../../translated_images/hr/naive-detection.e7f1ba220ccd08c6.webp) > *Slika iz [vježbenice](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ Možete naići na sljedeće skupove podataka za ovu zadaću: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) – 20 klasa * [COCO](http://cocodataset.org/#home) – Uobičajeni objekti u kontekstu. 80 klasa, okviri i maske za segmentaciju -![COCO](../../../../../translated_images/hr/coco-examples.71bc60380fa6cceb.jpg) +![COCO](../../../../../translated_images/hr/coco-examples.71bc60380fa6cceb.webp) ## Metrike za detekciju objekata @@ -50,7 +50,7 @@ Možete naići na sljedeće skupove podataka za ovu zadaću: Dok je za klasifikaciju slika lako izmjeriti koliko dobro algoritam radi, za detekciju objekata moramo mjeriti i točnost klase, kao i preciznost lokacije predviđenog okvira. Za ovo drugo koristimo metodu **Presjek kroz uniju** (IoU), koja mjeri koliko se dobro dva okvira (ili dva proizvoljna područja) preklapaju. -![IoU](../../../../../translated_images/hr/iou_equation.9a4751d40fff4e11.png) +![IoU](../../../../../translated_images/hr/iou_equation.9a4751d40fff4e11.webp) > *Slika 2 iz [ovog izvrsnog blog posta o IoU](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ Postoje dvije široke kategorije algoritama za detekciju objekata: [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) koristi [Selektivno pretraživanje](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) za generiranje hijerarhijske strukture ROI regija, koje se zatim prosljeđuju kroz CNN ekstraktore značajki i SVM klasifikatore za određivanje klase objekta, te linearnu regresiju za određivanje koordinata *okvira*. [Službeni rad](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/hr/rcnn1.cae407020dfb1d1f.png) +![RCNN](../../../../../translated_images/hr/rcnn1.cae407020dfb1d1f.webp) > *Slika iz van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/hr/rcnn2.2d9530bb83516484.png) +![RCNN-1](../../../../../translated_images/hr/rcnn2.2d9530bb83516484.webp) > *Slike iz [ovog bloga](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -110,7 +110,7 @@ Postoje dvije široke kategorije algoritama za detekciju objekata: Ovaj pristup je sličan R-CNN-u, ali regije se definiraju nakon što su primijenjeni slojevi konvolucije. -![FRCNN](../../../../../translated_images/hr/f-rcnn.3cda6d9bb4188875.png) +![FRCNN](../../../../../translated_images/hr/f-rcnn.3cda6d9bb4188875.webp) > Slika iz [službenog rada](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ Ovaj pristup je sličan R-CNN-u, ali regije se definiraju nakon što su primijen Glavna ideja ovog pristupa je korištenje neuronske mreže za predviđanje ROI – takozvane *Mreže za predlaganje regija*. [Rad](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/hr/faster-rcnn.8d46c099b87ef30a.png) +![FasterRCNN](../../../../../translated_images/hr/faster-rcnn.8d46c099b87ef30a.webp) > Slika iz [službenog rada](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ Ovaj algoritam je čak brži od Faster R-CNN-a. Glavna ideja je sljedeća: 2. Značajke se obrađuju pomoću **Pozicijski osjetljive mape rezultata**. Svaki objekt iz $C$ klasa dijeli se na $k\times k$ regije, i treniramo mrežu da predviđa dijelove objekata. 3. Za svaki dio iz $k\times k$ regija sve mreže glasaju za klase objekata, a klasa objekta s najviše glasova se odabire. -![r-fcn slika](../../../../../translated_images/hr/r-fcn.13eb88158b99a3da.png) +![r-fcn slika](../../../../../translated_images/hr/r-fcn.13eb88158b99a3da.webp) > Slika iz [službenog rada](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO je algoritam za detekciju u stvarnom vremenu s jednim prolazom. Glavna idej * Slika se dijeli na $S\times S$ regije. * Za svaku regiju, **CNN** predviđa $n$ mogućih objekata, koordinate *okvira* i *povjerenje*=*vjerojatnost* * IoU. - ![YOLO](../../../../../translated_images/hr/yolo.a2648ec82ee8bb4e.png) + ![YOLO](../../../../../translated_images/hr/yolo.a2648ec82ee8bb4e.webp) > Slika iz [službenog rada](https://arxiv.org/abs/1506.02640) diff --git a/translations/hr/lessons/4-ComputerVision/README.md b/translations/hr/lessons/4-ComputerVision/README.md index f322a147..f74f8956 100644 --- a/translations/hr/lessons/4-ComputerVision/README.md +++ b/translations/hr/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Računalni vid -![Sažetak sadržaja o računalnom vidu u obliku crteža](../../../../translated_images/hr/ai-computervision.6506ebebac3fbf76.png) +![Sažetak sadržaja o računalnom vidu u obliku crteža](../../../../translated_images/hr/ai-computervision.6506ebebac3fbf76.webp) U ovom dijelu ćemo naučiti o: diff --git a/translations/hr/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/hr/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index 59f0af09..03c19aeb 100644 --- a/translations/hr/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/hr/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**Vreća riječi** (BoW) vektorsko predstavljanje najčešće je korišteno tradicionalno vektorsko predstavljanje. Svaka riječ povezana je s indeksom vektora, a element vektora sadrži broj pojavljivanja riječi u određenom dokumentu.\n", "\n", - "![Slika koja prikazuje kako je vektorsko predstavljanje vreće riječi prikazano u memoriji.](../../../../../translated_images/hr/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![Slika koja prikazuje kako je vektorsko predstavljanje vreće riječi prikazano u memoriji.](../../../../../translated_images/hr/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Napomena**: Možete također razmišljati o BoW kao o zbroju svih one-hot kodiranih vektora za pojedinačne riječi u tekstu.\n", "\n", diff --git a/translations/hr/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/hr/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index 6596a023..54589081 100644 --- a/translations/hr/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/hr/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**Vreća riječi** (BoW) je najjednostavnija tradicionalna reprezentacija vektora za razumijevanje. Svaka riječ povezana je s indeksom vektora, a element vektora sadrži broj pojavljivanja svake riječi u danom dokumentu.\n", "\n", - "![Slika koja prikazuje kako je reprezentacija vektora vreće riječi prikazana u memoriji.](../../../../../translated_images/hr/bag-of-words-example.606fc1738f1d7ba9.png)\n", + "![Slika koja prikazuje kako je reprezentacija vektora vreće riječi prikazana u memoriji.](../../../../../translated_images/hr/bag-of-words-example.606fc1738f1d7ba9.webp)\n", "\n", "> **Note**: BoW možete zamisliti i kao zbroj svih vektora kodiranih metodom \"jedan na jedan\" za pojedinačne riječi u tekstu.\n", "\n", diff --git a/translations/hr/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/hr/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 2e3405d8..5eb8fa86 100644 --- a/translations/hr/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/hr/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "Korištenjem sloja za ugradnju kao prvog sloja u našoj mreži, možemo prijeći s modela vreće riječi na model **vreće ugradnji**, gdje prvo pretvaramo svaku riječ u našem tekstu u odgovarajuću ugradnju, a zatim izračunavamo neku agregatnu funkciju nad svim tim ugradnjama, poput `sum`, `average` ili `max`.\n", "\n", - "![Slika koja prikazuje klasifikator s ugradnjom za pet riječi u nizu.](../../../../../translated_images/hr/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![Slika koja prikazuje klasifikator s ugradnjom za pet riječi u nizu.](../../../../../translated_images/hr/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "Naša neuronska mreža za klasifikaciju započet će slojem za ugradnju, zatim slojem za agregaciju, i linearnim klasifikatorom na vrhu:\n" ] @@ -176,7 +176,7 @@ "\n", "U prethodnoj arhitekturi morali smo popuniti sve sekvence na istu duljinu kako bismo ih uklopili u minibatch. Ovo nije najučinkovitiji način za prikazivanje sekvenci promjenjive duljine - drugi pristup bio bi korištenje **offset** vektora, koji bi sadržavao pomake svih sekvenci pohranjenih u jednom velikom vektoru.\n", "\n", - "![Slika koja prikazuje prikaz sekvenci pomoću offseta](../../../../../translated_images/hr/offset-sequence-representation.eb73fcefb29b46ee.png)\n", + "![Slika koja prikazuje prikaz sekvenci pomoću offseta](../../../../../translated_images/hr/offset-sequence-representation.eb73fcefb29b46ee.webp)\n", "\n", "> **Napomena**: Na slici iznad prikazana je sekvenca znakova, ali u našem primjeru radimo sa sekvencama riječi. Međutim, osnovni princip prikazivanja sekvenci pomoću offset vektora ostaje isti.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW je brži, dok je skip-gram sporiji, ali bolje predstavlja riječi koje se rjeđe pojavljuju.\n", "\n", - "![Slika koja prikazuje algoritme CBoW i Skip-Gram za pretvaranje riječi u vektore.](../../../../../translated_images/hr/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![Slika koja prikazuje algoritme CBoW i Skip-Gram za pretvaranje riječi u vektore.](../../../../../translated_images/hr/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Za eksperimentiranje s Word2Vec ugradnjom unaprijed obučenom na Google News skupu podataka, možemo koristiti biblioteku **gensim**. Ispod nalazimo riječi najsličnije riječi 'neural':\n", "\n", diff --git a/translations/hr/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/hr/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index 444fa9c4..22e90ecf 100644 --- a/translations/hr/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/hr/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "Korištenjem embedding sloja kao prvog sloja u našoj mreži, možemo prijeći s modela vreće riječi (bag-of-words) na model **embedding vreće** (embedding bag), gdje prvo pretvaramo svaku riječ u našem tekstu u odgovarajući embedding, a zatim izračunavamo neku agregacijsku funkciju nad svim tim embeddingima, poput `sum`, `average` ili `max`.\n", "\n", - "![Slika koja prikazuje embedding klasifikator za pet riječi u nizu.](../../../../../translated_images/hr/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![Slika koja prikazuje embedding klasifikator za pet riječi u nizu.](../../../../../translated_images/hr/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "Naša neuronska mreža klasifikatora sastoji se od sljedećih slojeva:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW je brži, dok je skip-gram sporiji, ali bolje predstavlja riječi koje se rjeđe pojavljuju.\n", "\n", - "![Slika koja prikazuje algoritme CBoW i Skip-Gram za pretvaranje riječi u vektore.](../../../../../translated_images/hr/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![Slika koja prikazuje algoritme CBoW i Skip-Gram za pretvaranje riječi u vektore.](../../../../../translated_images/hr/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Kako bismo eksperimentirali s Word2Vec ugradnjom unaprijed istreniranom na Google News skupu podataka, možemo koristiti biblioteku **gensim**. Ispod nalazimo riječi koje su najsličnije riječi 'neural'.\n", "\n", diff --git a/translations/hr/lessons/5-NLP/14-Embeddings/README.md b/translations/hr/lessons/5-NLP/14-Embeddings/README.md index 3a8cc823..125d47ca 100644 --- a/translations/hr/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/hr/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ Dakle, sloj za ugrađivanje uzima riječ kao ulaz i proizvodi izlazni vektor odr Koristeći sloj za ugrađivanje kao prvi sloj u našoj mreži klasifikatora, možemo se prebaciti s modela vreće riječi na model **vreće ugrađivanja**, gdje prvo svaku riječ u našem tekstu pretvaramo u odgovarajuće ugrađivanje, a zatim izračunavamo neku agregatnu funkciju preko svih tih ugrađivanja, poput `sum`, `average` ili `max`. -![Slika koja prikazuje klasifikator ugrađivanja za pet riječi u nizu.](../../../../../translated_images/hr/embedding-classifier-example.b77f021a7ee67eee.png) +![Slika koja prikazuje klasifikator ugrađivanja za pet riječi u nizu.](../../../../../translated_images/hr/embedding-classifier-example.b77f021a7ee67eee.webp) > Slika autora @@ -40,7 +40,7 @@ Da bismo to postigli, trebamo unaprijed trenirati naš model za ugrađivanje na CBoW je brži, dok je skip-gram sporiji, ali bolje predstavlja rijetke riječi. -![Slika koja prikazuje algoritme CBoW i Skip-Gram za pretvaranje riječi u vektore.](../../../../../translated_images/hr/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![Slika koja prikazuje algoritme CBoW i Skip-Gram za pretvaranje riječi u vektore.](../../../../../translated_images/hr/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Slika iz [ovog rada](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/hr/lessons/5-NLP/15-LanguageModeling/README.md b/translations/hr/lessons/5-NLP/15-LanguageModeling/README.md index 4d4d9767..cc2d1285 100644 --- a/translations/hr/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/hr/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ U našim prethodnim primjerima koristili smo unaprijed trenirane semantičke ugr * **Kontinuirana vreća riječi** (CBoW), gdje predviđamo srednji token $W_0$ u nizu tokena $W_{-N}$, ..., $W_N$. * **Skip-gram**, gdje predviđamo skup susjednih tokena {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} iz srednjeg tokena $W_0$. -![slika iz rada o pretvaranju riječi u vektore](../../../../../translated_images/hr/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![slika iz rada o pretvaranju riječi u vektore](../../../../../translated_images/hr/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Slika iz [ovog rada](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/hr/lessons/5-NLP/16-RNN/README.md b/translations/hr/lessons/5-NLP/16-RNN/README.md index 5de06255..45c2ef63 100644 --- a/translations/hr/lessons/5-NLP/16-RNN/README.md +++ b/translations/hr/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ U prethodnim odjeljcima koristili smo bogate semantičke reprezentacije teksta i Kako bismo uhvatili značenje sekvenci teksta, trebamo koristiti drugu arhitekturu neuronske mreže, koja se naziva **rekurentna neuronska mreža** ili RNN. U RNN-u, rečenicu prosljeđujemo kroz mrežu jedan simbol po simbol, a mreža proizvodi određeno **stanje**, koje zatim ponovno prosljeđujemo mreži s idućim simbolom. -![RNN](../../../../../translated_images/hr/rnn.27f5c29c53d727b5.png) +![RNN](../../../../../translated_images/hr/rnn.27f5c29c53d727b5.webp) > Slika autora @@ -61,7 +61,7 @@ Razgovarali smo o rekurentnim mrežama koje djeluju u jednom smjeru, od početka Rekurentna mreža, bilo jednosmjerna ili dvosmjerna, hvata određene uzorke unutar sekvence i može ih pohraniti u vektor stanja ili proslijediti u izlaz. Kao i kod konvolucijskih mreža, možemo izgraditi drugi rekurentni sloj na vrhu prvog kako bismo uhvatili uzorke višeg nivoa i izgradili na temelju uzoraka nižeg nivoa koje je izvukao prvi sloj. To nas dovodi do pojma **višeslojnog RNN-a**, koji se sastoji od dva ili više rekurentnih mreža, gdje se izlaz prethodnog sloja prosljeđuje sljedećem sloju kao ulaz. -![Slika koja prikazuje višeslojni LSTM RNN](../../../../../translated_images/hr/multi-layer-lstm.dd975e29bb2a59fe.jpg) +![Slika koja prikazuje višeslojni LSTM RNN](../../../../../translated_images/hr/multi-layer-lstm.dd975e29bb2a59fe.webp) *Slika iz [ovog izvrsnog posta](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) Fernanda Lópeza* diff --git a/translations/hr/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/hr/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index 91218ff3..ef0ba9a4 100644 --- a/translations/hr/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/hr/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "Rekurentna mreža, bilo jednosmjerna ili dvosmjerna, hvata određene uzorke unutar sekvence i može ih pohraniti u vektor stanja ili proslijediti u izlaz. Kao i kod konvolucijskih mreža, možemo izgraditi još jedan rekurentni sloj na vrhu prvog kako bismo uhvatili uzorke višeg nivoa, izgrađene od uzoraka nižeg nivoa koje je izvukao prvi sloj. To nas dovodi do pojma **višeslojni RNN**, koji se sastoji od dvije ili više rekurentnih mreža, gdje se izlaz prethodnog sloja prosljeđuje sljedećem sloju kao ulaz.\n", "\n", - "![Slika koja prikazuje višeslojni long-short-term-memory RNN](../../../../../translated_images/hr/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![Slika koja prikazuje višeslojni long-short-term-memory RNN](../../../../../translated_images/hr/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*Slika iz [ovog sjajnog posta](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) autora Fernanda Lópeza*\n", "\n", diff --git a/translations/hr/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/hr/lessons/5-NLP/16-RNN/RNNTF.ipynb index bdb3ecc0..182796b4 100644 --- a/translations/hr/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/hr/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "Kako bismo uhvatili značenje sekvence teksta, koristit ćemo arhitekturu neuronske mreže zvanu **rekurentna neuronska mreža** ili RNN. Kada koristimo RNN, prosljeđujemo našu rečenicu kroz mrežu jedan token po jedan, a mreža proizvodi određeno **stanje**, koje zatim ponovno prosljeđujemo mreži s idućim tokenom.\n", "\n", - "![Slika koja prikazuje primjer generiranja rekurentne neuronske mreže.](../../../../../translated_images/hr/rnn.27f5c29c53d727b5.png)\n", + "![Slika koja prikazuje primjer generiranja rekurentne neuronske mreže.](../../../../../translated_images/hr/rnn.27f5c29c53d727b5.webp)\n", "\n", "S obzirom na ulaznu sekvencu tokena $X_0,\\dots,X_n$, RNN stvara sekvencu blokova neuronske mreže i trenira ovu sekvencu od početka do kraja koristeći unatrag širenje pogreške (backpropagation). Svaki blok mreže uzima par $(X_i,S_i)$ kao ulaz i proizvodi $S_{i+1}$ kao rezultat. Konačno stanje $S_n$ ili izlaz $Y_n$ ide u linearni klasifikator kako bi se proizveo rezultat. Svi blokovi mreže dijele iste težine i treniraju se od početka do kraja koristeći jedan prolaz unatrag širenja pogreške.\n", "\n", @@ -369,7 +369,7 @@ "\n", "Rekurentne mreže, bilo jednosmjerne ili dvosmjerne, prepoznaju uzorke unutar sekvence i pohranjuju ih u vektore stanja ili ih vraćaju kao izlaz. Kao i kod konvolucijskih mreža, možemo izgraditi još jedan rekurentni sloj nakon prvog kako bismo prepoznali uzorke višeg nivoa, izgrađene na temelju uzoraka nižeg nivoa koje je izdvojio prvi sloj. To nas dovodi do pojma **višeslojnog RNN-a**, koji se sastoji od dvije ili više rekurentnih mreža, gdje se izlaz prethodnog sloja prosljeđuje sljedećem sloju kao ulaz.\n", "\n", - "![Slika koja prikazuje višeslojni LSTM RNN](../../../../../translated_images/hr/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![Slika koja prikazuje višeslojni LSTM RNN](../../../../../translated_images/hr/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*Slika preuzeta iz [ovog izvrsnog članka](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) autora Fernanda Lópeza.*\n", "\n", diff --git a/translations/hr/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/hr/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index efd9dc50..712e5dcc 100644 --- a/translations/hr/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/hr/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "Način na koji ćemo trenirati RNN za generiranje teksta je sljedeći. U svakom koraku uzet ćemo niz znakova duljine `nchars` i tražiti od mreže da generira sljedeći izlazni znak za svaki ulazni znak:\n", "\n", - "![Slika koja prikazuje primjer RNN generiranja riječi 'HELLO'.](../../../../../translated_images/hr/rnn-generate.56c54afb52f9781d.png)\n", + "![Slika koja prikazuje primjer RNN generiranja riječi 'HELLO'.](../../../../../translated_images/hr/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "Ovisno o stvarnom scenariju, možda ćemo htjeti uključiti i neke posebne znakove, poput *kraj-sekvence* ``. U našem slučaju, želimo trenirati mrežu za beskonačno generiranje teksta, stoga ćemo fiksirati veličinu svakog niza na `nchars` tokena. Posljedično, svaki primjer za treniranje sastojat će se od `nchars` ulaza i `nchars` izlaza (što je ulazni niz pomaknut za jedan simbol ulijevo). Minibatch će se sastojati od nekoliko takvih nizova.\n", "\n", diff --git a/translations/hr/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/hr/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index 4a600cf8..32d0e458 100644 --- a/translations/hr/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/hr/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -113,7 +113,7 @@ "\n", "Način na koji ćemo trenirati RNN za generiranje naslova vijesti je sljedeći. U svakom koraku uzet ćemo jedan naslov, koji će se proslijediti u RNN, i za svaki ulazni znak tražit ćemo od mreže da generira sljedeći izlazni znak:\n", "\n", - "![Slika koja prikazuje primjer generiranja riječi 'HELLO' pomoću RNN-a.](../../../../../translated_images/hr/rnn-generate.56c54afb52f9781d.png)\n", + "![Slika koja prikazuje primjer generiranja riječi 'HELLO' pomoću RNN-a.](../../../../../translated_images/hr/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "Za posljednji znak našeg niza tražit ćemo od mreže da generira `` token.\n", "\n", diff --git a/translations/hr/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/hr/lessons/5-NLP/17-GenerativeNetworks/README.md index da678c09..3ebe9839 100644 --- a/translations/hr/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/hr/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ U arhitekturi RNN-a koju smo raspravili u prethodnoj jedinici, svaka RNN jedinic To omogućuje različite neuronske arhitekture prikazane na slici ispod: -![Slika koja prikazuje uobičajene uzorke rekurentnih neuronskih mreža.](../../../../../translated_images/hr/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Slika koja prikazuje uobičajene uzorke rekurentnih neuronskih mreža.](../../../../../translated_images/hr/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > Slika iz blog posta [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) autora [Andrej Karpaty](http://karpathy.github.io/) @@ -32,7 +32,7 @@ U ovoj jedinici fokusirat ćemo se na jednostavne generativne modele koji nam po Trenirat ćemo ovaj RNN da generira tekst korak po korak. Na svakom koraku uzet ćemo niz znakova duljine `nchars` i tražiti od mreže da generira sljedeći izlazni znak za svaki ulazni znak: -![Slika koja prikazuje primjer generiranja riječi 'HELLO' pomoću RNN-a.](../../../../../translated_images/hr/rnn-generate.56c54afb52f9781d.png) +![Slika koja prikazuje primjer generiranja riječi 'HELLO' pomoću RNN-a.](../../../../../translated_images/hr/rnn-generate.56c54afb52f9781d.webp) Tijekom generiranja teksta (tijekom inferencije), počinjemo s nekim **poticajem**, koji se prosljeđuje kroz RNN ćelije kako bi se generiralo njegovo međustanje, a zatim iz tog stanja počinje generiranje. Generiramo jedan znak po jedan, prosljeđujemo stanje i generirani znak sljedećoj RNN ćeliji kako bismo generirali sljedeći znak, sve dok ne generiramo dovoljno znakova. diff --git a/translations/hr/lessons/5-NLP/18-Transformers/README.md b/translations/hr/lessons/5-NLP/18-Transformers/README.md index 5077ab96..7959e07c 100644 --- a/translations/hr/lessons/5-NLP/18-Transformers/README.md +++ b/translations/hr/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ Kod RNN-ova, sekvenca-u-sekvencu se implementira pomoću dvije rekurentne mreže **Mehanizmi pažnje** omogućuju ponderiranje kontekstualnog utjecaja svakog ulaznog vektora na svaku izlaznu predikciju RNN-a. To se implementira stvaranjem prečaca između međustanja ulaznog RNN-a i izlaznog RNN-a. Na taj način, pri generiranju izlaznog simbola yt, uzet ćemo u obzir sva skrivena stanja ulaza hi, s različitim težinskim koeficijentima αt,i. -![Slika koja prikazuje enkoder/dekoder model s aditivnim slojem pažnje](../../../../../translated_images/hr/encoder-decoder-attention.7a726296894fb567.png) +![Slika koja prikazuje enkoder/dekoder model s aditivnim slojem pažnje](../../../../../translated_images/hr/encoder-decoder-attention.7a726296894fb567.webp) > Enkoder-dekoder model s aditivnim mehanizmom pažnje u [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citirano iz [ovog blog posta](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) Matrica pažnje {αi,j} predstavlja stupanj u kojem određene ulazne riječi sudjeluju u generiranju određene riječi u izlaznoj sekvenci. Ispod je primjer takve matrice: -![Slika koja prikazuje uzorak poravnanja pronađen od strane RNNsearch-50, preuzeto iz Bahdanau - arviz.org](../../../../../translated_images/hr/bahdanau-fig3.09ba2d37f202a6af.png) +![Slika koja prikazuje uzorak poravnanja pronađen od strane RNNsearch-50, preuzeto iz Bahdanau - arviz.org](../../../../../translated_images/hr/bahdanau-fig3.09ba2d37f202a6af.webp) > Slika iz [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Slika 3) @@ -66,7 +66,7 @@ Rezultat koji dobivamo s pozicijskim ugrađivanjem uključuje i originalni token Zatim trebamo uhvatiti neke uzorke unutar naše sekvence. Da bismo to učinili, transformeri koriste mehanizam **samopozornosti**, koji je u osnovi pažnja primijenjena na istu sekvencu kao ulaz i izlaz. Primjena samopozornosti omogućuje nam uzimanje u obzir **konteksta** unutar rečenice i uvid u međusobne odnose između riječi. Na primjer, omogućuje nam da vidimo na koje riječi se odnose zamjenice poput *to*, i također uzimamo kontekst u obzir: -![](../../../../../translated_images/hr/CoreferenceResolution.861924d6d384a7d6.png) +![](../../../../../translated_images/hr/CoreferenceResolution.861924d6d384a7d6.webp) > Slika iz [Googleovog bloga](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Budući da se svaka ulazna pozicija neovisno mapira na svaku izlaznu poziciju, t **BERT** (Bidirectional Encoder Representations from Transformers) je vrlo velika višeslojna transformer mreža s 12 slojeva za *BERT-base*, i 24 za *BERT-large*. Model se prvo unaprijed trenira na velikom korpusu tekstualnih podataka (WikiPedia + knjige) koristeći nenadzirano učenje (predviđanje maskiranih riječi u rečenici). Tijekom unaprijed treniranja model usvaja značajne razine razumijevanja jezika koje se zatim mogu iskoristiti s drugim skupovima podataka pomoću finog podešavanja. Ovaj proces naziva se **transferno učenje**. -![slika s http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/hr/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) +![slika s http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/hr/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > Slika [izvor](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/hr/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/hr/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index 40ee2a0a..67d5c83c 100644 --- a/translations/hr/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/hr/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**Mehanizmi pažnje** omogućuju ponderiranje kontekstualnog utjecaja svakog ulaznog vektora na svaku izlaznu predikciju RNN-a. To se implementira stvaranjem prečaca između međustanja ulaznog RNN-a i izlaznog RNN-a. Na taj način, prilikom generiranja izlaznog simbola $y_t$, uzimamo u obzir sva skrivena stanja ulaza $h_i$, s različitim težinskim koeficijentima $\\alpha_{t,i}$.\n", "\n", - "![Slika koja prikazuje encoder/decoder model s aditivnim slojem pažnje](../../../../../translated_images/hr/encoder-decoder-attention.7a726296894fb567.png)\n", + "![Slika koja prikazuje encoder/decoder model s aditivnim slojem pažnje](../../../../../translated_images/hr/encoder-decoder-attention.7a726296894fb567.webp)\n", "*Encoder-decoder model s mehanizmom aditivne pažnje iz [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citirano iz [ovog blog posta](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Matrica pažnje $\\{\\alpha_{i,j}\\}$ predstavlja stupanj u kojem određene ulazne riječi sudjeluju u generiranju određene riječi u izlaznom nizu. Ispod je primjer takve matrice:\n", "\n", - "![Slika koja prikazuje uzorak poravnanja pronađen od strane RNNsearch-50, preuzeto iz Bahdanau - arviz.org](../../../../../translated_images/hr/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![Slika koja prikazuje uzorak poravnanja pronađen od strane RNNsearch-50, preuzeto iz Bahdanau - arviz.org](../../../../../translated_images/hr/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*Slika preuzeta iz [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Slika 3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) je vrlo velika višeslojna mreža transformera s 12 slojeva za *BERT-base* i 24 za *BERT-large*. Model se prvo unaprijed trenira na velikom korpusu tekstualnih podataka (WikiPedia + knjige) koristeći nenadzirano učenje (predviđanje maskiranih riječi u rečenici). Tijekom unaprijed treniranja model usvaja značajnu razinu razumijevanja jezika, koja se zatim može iskoristiti s drugim skupovima podataka pomoću finog podešavanja. Ovaj proces naziva se **prijenosno učenje**.\n", "\n", - "![Slika s http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/hr/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![Slika s http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/hr/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "Postoji mnogo varijacija arhitektura transformera, uključujući BERT, DistilBERT, BigBird, OpenGPT3 i druge, koje se mogu fino podešavati. Paket [HuggingFace](https://github.com/huggingface/) pruža repozitorij za treniranje mnogih od ovih arhitektura s PyTorchom.\n", "\n", diff --git a/translations/hr/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/hr/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index 6fb5b85a..d33f970c 100644 --- a/translations/hr/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/hr/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**Mehanizmi pažnje** omogućuju ponderiranje kontekstualnog utjecaja svakog ulaznog vektora na svaku izlaznu predikciju RNN-a. To se implementira stvaranjem prečaca između međustanja ulaznog RNN-a i izlaznog RNN-a. Na taj način, prilikom generiranja izlaznog simbola $y_t$, uzimamo u obzir sva ulazna skrivena stanja $h_i$, s različitim težinskim koeficijentima $\\alpha_{t,i}$.\n", "\n", - "![Slika koja prikazuje encoder/decoder model s aditivnim slojem pažnje](../../../../../translated_images/hr/encoder-decoder-attention.7a726296894fb567.png)\n", + "![Slika koja prikazuje encoder/decoder model s aditivnim slojem pažnje](../../../../../translated_images/hr/encoder-decoder-attention.7a726296894fb567.webp)\n", "*Encoder-decoder model s aditivnim mehanizmom pažnje u [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citirano iz [ovog blog posta](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Matrica pažnje $\\{\\alpha_{i,j}\\}$ predstavlja stupanj u kojem određene ulazne riječi sudjeluju u generiranju određene riječi u izlaznom nizu. Ispod je primjer takve matrice:\n", "\n", - "![Slika koja prikazuje uzorak poravnanja pronađenog pomoću RNNsearch-50, preuzeto iz Bahdanau - arviz.org](../../../../../translated_images/hr/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![Slika koja prikazuje uzorak poravnanja pronađenog pomoću RNNsearch-50, preuzeto iz Bahdanau - arviz.org](../../../../../translated_images/hr/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*Slika preuzeta iz [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Slika 3)*\n", "\n", @@ -231,7 +231,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) je vrlo velika višeslojna transformacijska mreža s 12 slojeva za *BERT-base* i 24 za *BERT-large*. Model se prvo unaprijed trenira na velikom korpusu tekstualnih podataka (WikiPedia + knjige) koristeći nenadzirano učenje (predviđanje maskiranih riječi u rečenici). Tijekom unaprijednog treniranja model usvaja značajnu razinu razumijevanja jezika, što se kasnije može iskoristiti s drugim skupovima podataka putem finog podešavanja. Ovaj proces naziva se **transferno učenje**.\n", "\n", - "![slika s http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/hr/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![slika s http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/hr/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "Postoji mnogo varijacija transformacijskih arhitektura, uključujući BERT, DistilBERT, BigBird, OpenGPT3 i druge, koje se mogu fino podešavati.\n", "\n", diff --git a/translations/hr/lessons/5-NLP/19-NER/README.md b/translations/hr/lessons/5-NLP/19-NER/README.md index 68de7ab8..8fd14219 100644 --- a/translations/hr/lessons/5-NLP/19-NER/README.md +++ b/translations/hr/lessons/5-NLP/19-NER/README.md @@ -56,7 +56,7 @@ novorođenčeta | O Budući da trebamo izgraditi jedno-na-jedno korespondenciju između tokena i klasa, možemo trenirati desno **mnogostruko-na-mnogostruko** neuronski mrežni model iz ove slike: -![Slika koja prikazuje uobičajene obrasce rekurentnih neuronskih mreža.](../../../../../translated_images/hr/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Slika koja prikazuje uobičajene obrasce rekurentnih neuronskih mreža.](../../../../../translated_images/hr/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > *Slika iz [ovog blog posta](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) autora [Andreja Karpathyja](http://karpathy.github.io/). NER modeli za klasifikaciju tokena odgovaraju desno najudaljenijoj arhitekturi mreže na ovoj slici.* diff --git a/translations/hr/lessons/5-NLP/README.md b/translations/hr/lessons/5-NLP/README.md index c7451f28..3590ac81 100644 --- a/translations/hr/lessons/5-NLP/README.md +++ b/translations/hr/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Obrada Prirodnog Jezika -![Sažetak NLP zadataka u crtežu](../../../../translated_images/hr/ai-nlp.b22dcb8ca4707cea.png) +![Sažetak NLP zadataka u crtežu](../../../../translated_images/hr/ai-nlp.b22dcb8ca4707cea.webp) U ovom dijelu fokusirat ćemo se na korištenje neuronskih mreža za rješavanje zadataka povezanih s **Obradom Prirodnog Jezika (NLP)**. Postoji mnogo NLP problema koje želimo da računala mogu riješiti: diff --git a/translations/hr/lessons/6-Other/23-MultiagentSystems/README.md b/translations/hr/lessons/6-Other/23-MultiagentSystems/README.md index 3e301574..120813a0 100644 --- a/translations/hr/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/hr/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ Možete otvoriti jedan od modela, na primjer **Biology → Flocking**. Nakon otvaranja modela, dolazite na glavni ekran NetLoga. Evo uzorka modela koji opisuje populaciju vukova i ovaca, s obzirom na ograničene resurse (trava). -![NetLogo Main Screen](../../../../../translated_images/hr/NetLogo-Main.32653711ec1a01b3.png) +![NetLogo Main Screen](../../../../../translated_images/hr/NetLogo-Main.32653711ec1a01b3.webp) > Snimka zaslona Dmitryja Soshnikova diff --git a/translations/hr/lessons/README.md b/translations/hr/lessons/README.md index 9c50f9ab..88d59651 100644 --- a/translations/hr/lessons/README.md +++ b/translations/hr/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Pregled -![Pregled u crtežu](../../../translated_images/hr/ai-overview.0857791951d19500.png) +![Pregled u crtežu](../../../translated_images/hr/ai-overview.0857791951d19500.webp) > Crtež bilješki od [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/hr/lessons/X-Extras/X1-MultiModal/README.md b/translations/hr/lessons/X-Extras/X1-MultiModal/README.md index e237df41..fca85ca9 100644 --- a/translations/hr/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/hr/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ Nakon uspjeha transformera u rješavanju zadataka obrade prirodnog jezika (NLP), Glavna ideja CLIP-a je usporediti tekstualne upite sa slikom i odrediti koliko dobro slika odgovara upitu. -![CLIP Arhitektura](../../../../../translated_images/hr/clip-arch.b3dbf20b4e8ed8be.png) +![CLIP Arhitektura](../../../../../translated_images/hr/clip-arch.b3dbf20b4e8ed8be.webp) > *Slika iz [ovog blog posta](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ Nakon što je ovaj model prethodno treniran, možemo mu dati batch slika i batch Pretpostavimo da trebamo klasificirati slike, primjerice, između mačaka, pasa i ljudi. U tom slučaju možemo modelu dati sliku i niz tekstualnih upita: "*slika mačke*", "*slika psa*", "*slika čovjeka*". U rezultirajućem vektoru s 3 vjerojatnosti samo trebamo odabrati indeks s najvećom vrijednošću. -![CLIP za klasifikaciju slika](../../../../../translated_images/hr/clip-class.3af42ef0b2b19369.png) +![CLIP za klasifikaciju slika](../../../../../translated_images/hr/clip-class.3af42ef0b2b19369.webp) > *Slika iz [ovog blog posta](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ Više o VQGAN-u saznajte na web stranici [Taming Transformers](https://compvis.g Jedna od važnih razlika između VQGAN-a i tradicionalnog GAN-a je ta što potonji može proizvesti pristojnu sliku iz bilo kojeg ulaznog vektora, dok VQGAN vjerojatno neće proizvesti koherentnu sliku. Stoga je potrebno dodatno usmjeriti proces stvaranja slike, a to se može učiniti pomoću CLIP-a. -![VQGAN+CLIP Arhitektura](../../../../../translated_images/hr/vqgan.5027fe05051dfa31.png) +![VQGAN+CLIP Arhitektura](../../../../../translated_images/hr/vqgan.5027fe05051dfa31.webp) Za generiranje slike koja odgovara tekstualnom upitu, počinjemo s nekim nasumičnim vektorskim kodiranjem koje se prosljeđuje kroz VQGAN kako bi se proizvela slika. Zatim se CLIP koristi za stvaranje funkcije gubitka koja pokazuje koliko dobro slika odgovara tekstualnom upitu. Cilj je minimizirati taj gubitak koristeći backpropagation za prilagodbu parametara ulaznog vektora. Odlična biblioteka koja implementira VQGAN+CLIP je [Pixray](http://github.com/pixray/pixray). -![Slika generirana Pixrayem](../../../../../translated_images/hr/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Slika generirana Pixrayem](../../../../../translated_images/hr/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Slika generirana Pixrayem](../../../../../translated_images/hr/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) +![Slika generirana Pixrayem](../../../../../translated_images/hr/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp) | ![Slika generirana Pixrayem](../../../../../translated_images/hr/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp) | ![Slika generirana Pixrayem](../../../../../translated_images/hr/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp) ----|----|---- Slika generirana iz upita *bliski akvarelni portret mladog učitelja književnosti s knjigom* | Slika generirana iz upita *bliski uljani portret mlade učiteljice računalnih znanosti s računalom* | Slika generirana iz upita *bliski uljani portret starijeg učitelja matematike ispred ploče* diff --git a/translations/hu/README.md b/translations/hu/README.md index 72b3826b..d344c4f9 100644 --- a/translations/hu/README.md +++ b/translations/hu/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # Mesterséges intelligencia kezdőknek – Tananyag -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/hu/ai-overview.0857791951d19500.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/hu/ai-overview.0857791951d19500.webp)| |:---:| | Mesterséges intelligencia kezdőknek – _Vázlat [@girlie_mac](https://twitter.com/girlie_mac) tollából_ | diff --git a/translations/hu/lessons/1-Intro/README.md b/translations/hu/lessons/1-Intro/README.md index c7e67306..b4ffdd2d 100644 --- a/translations/hu/lessons/1-Intro/README.md +++ b/translations/hu/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Bevezetés a mesterséges intelligenciába -![A mesterséges intelligencia bevezetésének összefoglalása egy rajzban](../../../../translated_images/hu/ai-intro.bf28d1ac4235881c.png) +![A mesterséges intelligencia bevezetésének összefoglalása egy rajzban](../../../../translated_images/hu/ai-intro.bf28d1ac4235881c.webp) > Rajz: [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Eredetileg a számítógépeket [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage) találta fel, hogy számokkal dolgozzanak egy jól meghatározott eljárás – algoritmus – alapján. A modern számítógépek, bár jelentősen fejlettebbek, mint a 19. században javasolt eredeti modell, még mindig ugyanazt az irányított számítási elvet követik. Ezért lehetséges egy számítógépet programozni, hogy valamit elvégezzen, ha pontosan ismerjük a cél eléréséhez szükséges lépések sorrendjét. -![Egy személy fotója](../../../../translated_images/hu/dsh_age.d212a30d4e54fb5f.png) +![Egy személy fotója](../../../../translated_images/hu/dsh_age.d212a30d4e54fb5f.webp) > Fotó: [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ További információért lásd **[Mesterséges általános intelligencia](https Az egyik probléma az **[intelligencia](https://en.wikipedia.org/wiki/Intelligence)** kifejezéssel kapcsolatban az, hogy nincs egyértelmű definíciója. Egyesek szerint az intelligencia kapcsolódik az **absztrakt gondolkodáshoz**, vagy az **önismerethez**, de nem tudjuk megfelelően meghatározni. -![Macska fotója](../../../../translated_images/hu/photo-cat.8c8e8fb760ffe457.jpg) +![Macska fotója](../../../../translated_images/hu/photo-cat.8c8e8fb760ffe457.webp) > [Fotó](https://unsplash.com/photos/75715CVEJhI) készítette [Amber Kipp](https://unsplash.com/@sadmax) az Unsplash-en @@ -98,13 +98,13 @@ Alternatívaként megpróbálhatjuk modellezni az agyunk legegyszerűbb elemeit > | Mi a helyzet az ML-lel? | | > |--------------|-----------| -> | A mesterséges intelligencia azon része, amely a számítógép tanulásán alapul, hogy egy problémát megoldjon bizonyos adatok alapján, **gépi tanulásnak** nevezzük. Ebben a kurzusban nem foglalkozunk a klasszikus gépi tanulással – erre külön [Gépi tanulás kezdőknek](http://aka.ms/ml-beginners) tananyagot ajánlunk. | ![ML kezdőknek](../../../../translated_images/hu/ml-for-beginners.9e4fed176fd5817d.png) | +> | A mesterséges intelligencia azon része, amely a számítógép tanulásán alapul, hogy egy problémát megoldjon bizonyos adatok alapján, **gépi tanulásnak** nevezzük. Ebben a kurzusban nem foglalkozunk a klasszikus gépi tanulással – erre külön [Gépi tanulás kezdőknek](http://aka.ms/ml-beginners) tananyagot ajánlunk. | ![ML kezdőknek](../../../../translated_images/hu/ml-for-beginners.9e4fed176fd5817d.webp) | ## A mesterséges intelligencia rövid története A mesterséges intelligencia mint terület a huszadik század közepén indult. Kezdetben a szimbolikus érvelés volt az uralkodó megközelítés, és számos fontos sikert eredményezett, például szakértői rendszereket – számítógépes programokat, amelyek képesek voltak szakértőként működni bizonyos korlátozott problématerületeken. Azonban hamar világossá vált, hogy ez a megközelítés nem skálázható jól. A tudás kinyerése egy szakértőtől, annak számítógépes ábrázolása és a tudásbázis pontosan tartása rendkívül összetett feladatnak bizonyult, és sok esetben túl drága volt ahhoz, hogy gyakorlati legyen. Ez az úgynevezett [MI télhez](https://en.wikipedia.org/wiki/AI_winter) vezetett az 1970-es években. -A mesterséges intelligencia rövid története +A mesterséges intelligencia rövid története > Kép: [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/hu/lessons/2-Symbolic/Animals.ipynb b/translations/hu/lessons/2-Symbolic/Animals.ipynb index 9e40c0cf..eba7fad2 100644 --- a/translations/hu/lessons/2-Symbolic/Animals.ipynb +++ b/translations/hu/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "Ebben a példában egy egyszerű tudásalapú rendszert valósítunk meg, amely fizikai jellemzők alapján határozza meg az állatot. A rendszert az alábbi AND-OR fa képviseli (ez csak egy része a teljes fának, könnyen hozzáadhatunk további szabályokat):\n", "\n", - "![](../../../../translated_images/hu/AND-OR-Tree.5592d2c70187f283.png)\n" + "![](../../../../translated_images/hu/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { diff --git a/translations/hu/lessons/2-Symbolic/README.md b/translations/hu/lessons/2-Symbolic/README.md index 991fe72a..d9c28af1 100644 --- a/translations/hu/lessons/2-Symbolic/README.md +++ b/translations/hu/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Tudásábrázolás és szakértői rendszerek -![A szimbolikus AI tartalmának összefoglalása](../../../../translated_images/hu/ai-symbolic.715a30cb610411a6.png) +![A szimbolikus AI tartalmának összefoglalása](../../../../translated_images/hu/ai-symbolic.715a30cb610411a6.webp) > Sketchnote készítette: [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ Leggyakrabban nem határozzuk meg szigorúan a tudást, hanem más kapcsolódó Így a **tudásábrázolás** problémája az, hogy hatékony módot találjunk a tudás számítógépen belüli adatként való ábrázolására, hogy automatikusan használható legyen. Ez egy spektrumként értelmezhető: -![Tudásábrázolási spektrum](../../../../translated_images/hu/knowledge-spectrum.b60df631852c0217.png) +![Tudásábrázolási spektrum](../../../../translated_images/hu/knowledge-spectrum.b60df631852c0217.webp) > Kép készítette: [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Blokk szintaxis | Indent | | | A szimbolikus AI korai sikerei közé tartoztak az úgynevezett **szakértői rendszerek** - olyan számítógépes rendszerek, amelyeket arra terveztek, hogy egy korlátozott problématerületen szakértőként működjenek. Ezek egy **tudásbázison** alapultak, amelyet egy vagy több emberi szakértőtől nyertek ki, és tartalmaztak egy **következtető motort**, amely ezen tudás alapján végzett következtetéseket. -![Emberi architektúra](../../../../translated_images/hu/arch-human.5d4d35f1bba3ab1c.png) | ![Tudásalapú rendszer](../../../../translated_images/hu/arch-kbs.3ec5c150b09fa8da.png) +![Emberi architektúra](../../../../translated_images/hu/arch-human.5d4d35f1bba3ab1c.webp) | ![Tudásalapú rendszer](../../../../translated_images/hu/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ Az emberi idegrendszer egyszerűsített szerkezete | Tudásalapú rendszer architektúrája @@ -106,7 +106,7 @@ A szakértői rendszerek felépítése hasonló az emberi következtetési rends Példaként vegyük a következő szakértői rendszert, amely egy állatot határoz meg fizikai jellemzői alapján: -![AND-OR fa](../../../../translated_images/hu/AND-OR-Tree.5592d2c70187f283.png) +![AND-OR fa](../../../../translated_images/hu/AND-OR-Tree.5592d2c70187f283.webp) > Kép készítette: [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/hu/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/hu/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index ddfc9354..b60a463f 100644 --- a/translations/hu/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/hu/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -1257,7 +1257,7 @@ "* Alacsony tanulási veszteség – a modell jól tudja közelíteni a tanulási adatokat, mert elegendő kifejezőerővel rendelkezik.\n", "* Az érvényesítési veszteség sokkal magasabb lehet, mint a tanulási veszteség, és az edzés során növekedhet is – ez azért van, mert a modell \"megjegyzi\" a tanulási pontokat, és elveszíti az \"összképet\".\n", "\n", - "![Overfitting](../../../../../translated_images/hu/overfit.a0bd57f717c15769.png)\n", + "![Overfitting](../../../../../translated_images/hu/overfit.a0bd57f717c15769.webp)\n", "\n", "> Ezen a képen az `x` a tanulási adatokat, az `o` az érvényesítési adatokat jelöli. Bal oldalon – lineáris modell (egyrétegű), amely elég jól közelíti az adatok természetét. Jobb oldalon – túlilleszkedett modell, amely tökéletesen közelíti a tanulási adatokat, de bármilyen más adathalmaz esetén (érvényesítési hiba nagyon magas) már nem működik megfelelően.\n" ] diff --git a/translations/hu/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/hu/lessons/3-NeuralNetworks/05-Frameworks/README.md index 1759b562..ce15a6a7 100644 --- a/translations/hu/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/hu/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Az overfitting rendkívül fontos fogalom a gépi tanulásban, és nagyon fontos Vegyük például az alábbi problémát, amelyben 5 pontot próbálunk közelíteni (a grafikonokon `x` jelöli a pontokat): -![lineáris](../../../../../translated_images/hu/overfit1.f24b71c6f652e59e.jpg) | ![overfitting](../../../../../translated_images/hu/overfit2.131f5800ae10ca5e.jpg) +![lineáris](../../../../../translated_images/hu/overfit1.f24b71c6f652e59e.webp) | ![overfitting](../../../../../translated_images/hu/overfit2.131f5800ae10ca5e.webp) -------------------------|-------------------------- **Lineáris modell, 2 paraméter** | **Nemlineáris modell, 7 paraméter** Tanítási hiba = 5.3 | Tanítási hiba = 0 @@ -79,7 +79,7 @@ Nagyon fontos megtalálni a megfelelő egyensúlyt a modell gazdagsága (paramé Ahogy a fenti grafikonon látható, az overfittinget nagyon alacsony tanítási hiba és magas validációs hiba jelezheti. Általában a tanítás során mind a tanítási, mind a validációs hibák csökkenni kezdenek, majd egy ponton a validációs hiba megállhat a csökkenésben, és növekedni kezdhet. Ez az overfitting jele, és annak indikátora, hogy valószínűleg abba kell hagynunk a tanítást (vagy legalábbis készítenünk kell egy pillanatképet a modellről). -![overfitting](../../../../../translated_images/hu/Overfitting.408ad91cd90b4371.png) +![overfitting](../../../../../translated_images/hu/Overfitting.408ad91cd90b4371.webp) ## Hogyan előzhető meg az overfitting? diff --git a/translations/hu/lessons/3-NeuralNetworks/README.md b/translations/hu/lessons/3-NeuralNetworks/README.md index ddf264df..de2f0d39 100644 --- a/translations/hu/lessons/3-NeuralNetworks/README.md +++ b/translations/hu/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Bevezetés a neurális hálózatokba -![Összefoglaló a neurális hálózatok bevezetőjének tartalmáról egy rajzban](../../../../translated_images/hu/ai-neuralnetworks.1c687ae40bc86e83.png) +![Összefoglaló a neurális hálózatok bevezetőjének tartalmáról egy rajzban](../../../../translated_images/hu/ai-neuralnetworks.1c687ae40bc86e83.webp) Ahogy a bevezetőben tárgyaltuk, az intelligencia egyik elérési módja egy **számítógépes modell** vagy egy **mesterséges agy** tanítása. A 20. század közepe óta a kutatók különböző matematikai modelleket próbáltak ki, míg az utóbbi években ez az irány rendkívül sikeresnek bizonyult. Az agy ilyen matematikai modelljeit **neurális hálózatoknak** nevezzük. @@ -36,13 +36,13 @@ Ebben a tananyagban kizárólag neurális hálózati modellekre fogunk összpont A biológiából tudjuk, hogy az agyunk neurális sejtekből (neuronokból) áll, amelyek mindegyike több "bemenettel" (dendritek) és egyetlen "kimenettel" (axon) rendelkezik. Mind a dendritek, mind az axonok képesek elektromos jeleket vezetni, és a köztük lévő kapcsolatok — szinapszisok — különböző vezetőképességet mutathatnak, amelyeket neurotranszmitterek szabályoznak. -![Neuron modellje](../../../../translated_images/hu/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![Neuron modellje](../../../../translated_images/hu/artneuron.1a5daa88d20ebe6f.png) +![Neuron modellje](../../../../translated_images/hu/synapse-wikipedia.ed20a9e4726ea1c6.webp) | ![Neuron modellje](../../../../translated_images/hu/artneuron.1a5daa88d20ebe6f.webp) ----|---- Valódi neuron *([Kép](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) a Wikipédiáról)* | Mesterséges neuron *(Kép a szerzőtől)* Így a neuron legegyszerűbb matematikai modellje több bemenetet tartalmaz X1, ..., XN, egy kimenetet Y, valamint egy sor súlyt W1, ..., WN. A kimenet a következőképpen számítható: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) ahol f egy nemlineáris **aktivációs függvény**. diff --git a/translations/hu/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/hu/lessons/4-ComputerVision/06-IntroCV/README.md index 0686aa2a..3c5868db 100644 --- a/translations/hu/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/hu/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ Az [OpenCV Notebook](OpenCV.ipynb) példákban bemutatjuk, hogy a számítógép * **Egy Braille könyv fényképének előfeldolgozása**. Arra összpontosítunk, hogyan használhatjuk a küszöbérték alkalmazást, jellemzők detektálását, perspektíva transzformációt és NumPy manipulációkat az egyes Braille szimbólumok elkülönítésére, hogy azokat később neurális hálózat osztályozza. -![Braille kép](../../../../../translated_images/hu/braille.341962ff76b1bd70.jpeg) | ![Braille kép előfeldolgozva](../../../../../translated_images/hu/braille-result.46530fea020b03c7.png) | ![Braille szimbólumok](../../../../../translated_images/hu/braille-symbols.0159185ab69d5339.png) +![Braille kép](../../../../../translated_images/hu/braille.341962ff76b1bd70.webp) | ![Braille kép előfeldolgozva](../../../../../translated_images/hu/braille-result.46530fea020b03c7.webp) | ![Braille szimbólumok](../../../../../translated_images/hu/braille-symbols.0159185ab69d5339.webp) ----|-----|----- > Kép az [OpenCV.ipynb](OpenCV.ipynb)-ből * **Mozgás detektálása videóban képkocka különbséggel**. Ha a kamera fix, akkor a kamera képkockái elég hasonlóak kell legyenek egymáshoz. Mivel a képkockák tömbként vannak ábrázolva, egyszerűen a két egymást követő képkocka tömbjeinek kivonásával megkapjuk a pixelkülönbséget, amely alacsony lesz statikus képkockák esetén, és magasabb lesz, ha jelentős mozgás van a képen. -![Videó képkockák és képkocka különbségek képe](../../../../../translated_images/hu/frame-difference.706f805491a0883c.png) +![Videó képkockák és képkocka különbségek képe](../../../../../translated_images/hu/frame-difference.706f805491a0883c.webp) > Kép az [OpenCV.ipynb](OpenCV.ipynb)-ből @@ -89,7 +89,7 @@ Az [OpenCV Notebook](OpenCV.ipynb) példákban bemutatjuk, hogy a számítógép - **Sűrű optikai áramlás** kiszámítja a vektormezőt, amely megmutatja, hogy minden pixel hova mozog - **Ritka optikai áramlás** az alapján működik, hogy néhány jellegzetes jellemzőt vesz a képen (pl. élek), és ezek pályáját építi fel képkockáról képkockára. -![Optikai áramlás képe](../../../../../translated_images/hu/optical.1f4a94464579a83a.png) +![Optikai áramlás képe](../../../../../translated_images/hu/optical.1f4a94464579a83a.webp) > Kép az [OpenCV.ipynb](OpenCV.ipynb)-ből diff --git a/translations/hu/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/hu/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 338108dc..502618dc 100644 --- a/translations/hu/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/hu/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: A VGG-16 egy hálózat, amely 2014-ben 92,7%-os pontosságot ért el az ImageNet top-5 osztályozásban. Az alábbi rétegstruktúrával rendelkezik: -![ImageNet rétegek](../../../../../translated_images/hu/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet rétegek](../../../../../translated_images/hu/vgg-16-arch1.d901a5583b3a51ba.webp) Ahogy látható, a VGG egy hagyományos piramis architektúrát követ, amely egy konvolúciós és pooling rétegek sorozata. -![ImageNet piramis](../../../../../translated_images/hu/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet piramis](../../../../../translated_images/hu/vgg-16-arch.64ff2137f50dd49f.webp) > Kép forrása: [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/hu/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/hu/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index 03043789..4ed6ffb3 100644 --- a/translations/hu/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/hu/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -260,7 +260,7 @@ "\n", "Így egy tipikus CNN-ben több konvolúciós réteg található, amelyek között pooling rétegek csökkentik a kép dimenzióit. Emellett növeljük a szűrők számát is, mivel ahogy a minták egyre összetettebbé válnak, több érdekes kombinációt kell keresnünk.\n", "\n", - "![Egy ábra, amely több konvolúciós réteget és pooling réteget mutat be.](../../../../../translated_images/hu/cnn-pyramid.85915455759ef0ce.png)\n", + "![Egy ábra, amely több konvolúciós réteget és pooling réteget mutat be.](../../../../../translated_images/hu/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "A térbeli dimenziók csökkenése és a jellemzők/szűrők dimenzióinak növekedése miatt ezt az architektúrát **piramis architektúrának** is nevezik.\n" ] diff --git a/translations/hu/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/hu/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index 4d3e1f79..b884a6b9 100644 --- a/translations/hu/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/hu/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -358,7 +358,7 @@ "\n", "Így egy tipikus CNN-ben több konvolúciós réteg található, közöttük pooling rétegekkel, amelyek csökkentik a kép dimenzióit. Emellett növeljük a szűrők számát is, mert ahogy a minták bonyolultabbá válnak, több érdekes kombinációt kell keresnünk.\n", "\n", - "![Egy kép, amely több konvolúciós réteget mutat pooling rétegekkel.](../../../../../translated_images/hu/cnn-pyramid.85915455759ef0ce.png)\n", + "![Egy kép, amely több konvolúciós réteget mutat pooling rétegekkel.](../../../../../translated_images/hu/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "A térbeli dimenziók csökkenése és a jellemzők/szűrők dimenzióinak növekedése miatt ezt az architektúrát **piramis architektúrának** is nevezik.\n" ] diff --git a/translations/hu/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/hu/lessons/4-ComputerVision/07-ConvNets/README.md index 54b62c33..cc90085a 100644 --- a/translations/hu/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/hu/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ A valóságban azt szeretnénk, hogy képesek legyünk felismerni tárgyakat egy A mintázatok kinyeréséhez a **konvolúciós szűrők** fogalmát használjuk. Mint tudjuk, egy kép egy 2D-mátrixként vagy egy színes mélységgel rendelkező 3D-tenzorként van ábrázolva. Egy szűrő alkalmazása azt jelenti, hogy veszünk egy viszonylag kicsi **szűrőmag** mátrixot, és az eredeti kép minden egyes pixelénél kiszámítjuk a súlyozott átlagot a szomszédos pontokkal. Ezt úgy képzelhetjük el, mint egy kis ablakot, amely végigcsúszik az egész képen, és az összes pixelt az ablakban lévő súlyok szerint átlagolja. -![Függőleges él szűrő](../../../../../translated_images/hu/filter-vert.b7148390ca0bc356.png) | ![Vízszintes él szűrő](../../../../../translated_images/hu/filter-horiz.59b80ed4feb946ef.png) +![Függőleges él szűrő](../../../../../translated_images/hu/filter-vert.b7148390ca0bc356.webp) | ![Vízszintes él szűrő](../../../../../translated_images/hu/filter-horiz.59b80ed4feb946ef.webp) ----|---- > Kép: Dmitry Soshnikov @@ -38,7 +38,7 @@ A CNN-ek működése a következő fontos ötleteken alapul: * A hálózatot úgy tervezhetjük meg, hogy a szűrők automatikusan tanuljanak. * Ugyanezt a megközelítést használhatjuk magas szintű jellemzők mintázatainak megtalálására is, nem csak az eredeti képen. Így a CNN jellemzők kinyerése egy hierarchikus folyamatban működik, az alacsony szintű pixelkombinációktól kezdve a kép részeinek magasabb szintű kombinációjáig. -![Hierarchikus jellemzők kinyerése](../../../../../translated_images/hu/FeatureExtractionCNN.d9b456cbdae7cb64.png) +![Hierarchikus jellemzők kinyerése](../../../../../translated_images/hu/FeatureExtractionCNN.d9b456cbdae7cb64.webp) > Kép a [Hislop-Lynch tanulmányból](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), az [ő kutatásuk alapján](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ A legtöbb képfeldolgozásra használt CNN az úgynevezett piramis architektúr Példaként nézzük meg a VGG-16 architektúráját, amely 92,7%-os pontosságot ért el az ImageNet top-5 osztályozásában 2014-ben: -![ImageNet Rétegek](../../../../../translated_images/hu/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet Rétegek](../../../../../translated_images/hu/vgg-16-arch1.d901a5583b3a51ba.webp) -![ImageNet Piramis](../../../../../translated_images/hu/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet Piramis](../../../../../translated_images/hu/vgg-16-arch.64ff2137f50dd49f.webp) > Kép a [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) oldalról diff --git a/translations/hu/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/hu/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 1687a793..1ab7f187 100644 --- a/translations/hu/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/hu/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ Egy konvolúciós neurális hálózatot kell betanítanod, amely képes különb Az [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/) adathalmazt fogjuk használni, amely 37 különböző kutya- és macskafajta képeit tartalmazza. -![Az adathalmaz, amellyel dolgozni fogunk](../../../../../../translated_images/hu/data.50b2a9d5484bdbf0.png) +![Az adathalmaz, amellyel dolgozni fogunk](../../../../../../translated_images/hu/data.50b2a9d5484bdbf0.webp) Az adathalmaz letöltéséhez használd az alábbi kódrészletet: diff --git a/translations/hu/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/hu/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index 89cf3509..5bbb407c 100644 --- a/translations/hu/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/hu/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "Ahhoz, hogy elképzeljük az ideális macskát, egy véletlenszerű zajképpel kezdünk, majd a gradiens-descent optimalizációs technikát alkalmazzuk, hogy a képet úgy módosítsuk, hogy a hálózat felismerjen egy macskát.\n", "\n", - "![Optimalizációs ciklus](../../../../../translated_images/hu/ideal-cat-loop.999fbb8ff306e044.png)\n", + "![Optimalizációs ciklus](../../../../../translated_images/hu/ideal-cat-loop.999fbb8ff306e044.webp)\n", "\n", "Íme a kiindulási képünk:\n" ] diff --git a/translations/hu/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/hu/lessons/4-ComputerVision/08-TransferLearning/README.md index 3cb68acc..15afb47f 100644 --- a/translations/hu/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/hu/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Mind a Keras, mind a PyTorch tartalmaz funkciókat, amelyekkel könnyen betölth Íme egy példa a VGG-16 hálózat által egy macska képéből kinyert jellemzőkre: -![A VGG-16 által kinyert jellemzők](../../../../../translated_images/hu/features.6291f9c7ba3a0b95.png) +![A VGG-16 által kinyert jellemzők](../../../../../translated_images/hu/features.6291f9c7ba3a0b95.webp) ## Macskák és kutyák adathalmaz @@ -48,19 +48,19 @@ Egy előre betanított neurális hálózat különböző mintákat tartalmaz az Egy megközelítés az lehet, hogy egy véletlenszerű képpel kezdünk, majd a **gradiens-deszcendens optimalizációs** technikát alkalmazzuk, hogy úgy módosítsuk a képet, hogy a hálózat elkezdje azt macskának gondolni. -![Képoptimalizációs ciklus](../../../../../translated_images/hu/ideal-cat-loop.999fbb8ff306e044.png) +![Képoptimalizációs ciklus](../../../../../translated_images/hu/ideal-cat-loop.999fbb8ff306e044.webp) Ha azonban ezt tesszük, akkor valami nagyon hasonlót kapunk, mint egy véletlenszerű zaj. Ennek oka, hogy *sokféleképpen lehet a hálózatot rávenni arra, hogy a bemeneti képet macskának gondolja*, beleértve olyanokat is, amelyek vizuálisan nem értelmezhetők. Bár ezek a képek sok, a macskákra jellemző mintát tartalmaznak, semmi sem kényszeríti őket arra, hogy vizuálisan megkülönböztethetők legyenek. Az eredmény javítása érdekében hozzáadhatunk egy másik tagot a veszteségfüggvényhez, amelyet **variációs veszteségnek** nevezünk. Ez egy olyan metrika, amely megmutatja, mennyire hasonlóak a kép szomszédos pixelei. A variációs veszteség minimalizálása simábbá teszi a képet, és megszabadítja a zajtól – így vizuálisan vonzóbb mintákat tár fel. Íme egy példa az ilyen "ideális" képekre, amelyeket nagy valószínűséggel macskának és zebrának osztályoznak: -![Ideális macska](../../../../../translated_images/hu/ideal-cat.203dd4597643d6b0.png) | ![Ideális zebra](../../../../../translated_images/hu/ideal-zebra.7f70e8b54ee15a7a.png) +![Ideális macska](../../../../../translated_images/hu/ideal-cat.203dd4597643d6b0.webp) | ![Ideális zebra](../../../../../translated_images/hu/ideal-zebra.7f70e8b54ee15a7a.webp) -----|----- *Ideális macska* | *Ideális zebra* Hasonló megközelítést lehet alkalmazni úgynevezett **adverzális támadások** végrehajtására egy neurális hálózaton. Tegyük fel, hogy szeretnénk megtéveszteni egy neurális hálózatot, és egy kutyát macskának láttatni. Ha veszünk egy kutya képét, amelyet a hálózat kutyaként ismer fel, akkor azt egy kicsit módosíthatjuk a gradiens-deszcendens optimalizáció segítségével, amíg a hálózat macskaként nem kezdi osztályozni: -![Kép egy kutyáról](../../../../../translated_images/hu/original-dog.8f68a67d2fe0911f.png) | ![Kép egy kutyáról, amelyet macskának osztályoznak](../../../../../translated_images/hu/adversarial-dog.d9fc7773b0142b89.png) +![Kép egy kutyáról](../../../../../translated_images/hu/original-dog.8f68a67d2fe0911f.webp) | ![Kép egy kutyáról, amelyet macskának osztályoznak](../../../../../translated_images/hu/adversarial-dog.d9fc7773b0142b89.webp) -----|----- *Eredeti kép egy kutyáról* | *Kép egy kutyáról, amelyet macskának osztályoznak* diff --git a/translations/hu/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/hu/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index 5dc85e1d..9e6c087a 100644 --- a/translations/hu/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/hu/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "Mivel az autoencoder-t arra tanítjuk, hogy minél több információt megőrizzen az eredeti képből a pontos rekonstrukció érdekében, a hálózat megpróbálja megtalálni a legjobb **beágyazást** a bemeneti képek jelentésének megragadására.\n", "\n", - "![AutoEncoder Diagram](../../../../../translated_images/hu/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![AutoEncoder Diagram](../../../../../translated_images/hu/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "> Kép forrása: [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", diff --git a/translations/hu/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/hu/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index 33dfda25..b106c548 100644 --- a/translations/hu/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/hu/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "Mivel az autoencoder-t arra tanítjuk, hogy minél több információt megőrizzen az eredeti képből a pontos rekonstrukció érdekében, a hálózat megpróbálja megtalálni a legjobb **beágyazást** a bemeneti képek jelentésének megragadásához.\n", "\n", - "![AutoEncoder Diagram](../../../../../translated_images/hu/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![AutoEncoder Diagram](../../../../../translated_images/hu/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "*Kép a [Keras blogból](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", diff --git a/translations/hu/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/hu/lessons/4-ComputerVision/09-Autoencoders/README.md index e21d980e..95b0efd7 100644 --- a/translations/hu/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/hu/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ Azonban előfordulhat, hogy nyers (címkézetlen) adatokat szeretnénk használn Mivel az autoenkódert arra tanítjuk, hogy minél több információt megőrizzen az eredeti képből a pontos rekonstrukció érdekében, a hálózat megpróbálja megtalálni a legjobb **beágyazást** a bemeneti képek jelentésének megragadásához. -![Autoenkóder diagram](../../../../../translated_images/hu/autoencoder_schema.5e6fc9ad98a5eb61.jpg) +![Autoenkóder diagram](../../../../../translated_images/hu/autoencoder_schema.5e6fc9ad98a5eb61.webp) > Kép a [Keras blogból](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/hu/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/hu/lessons/4-ComputerVision/11-ObjectDetection/README.md index 1ca30e95..aaa4b848 100644 --- a/translations/hu/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/hu/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ Az eddig tárgyalt képosztályozási modellek egy képet vettek bemenetként, ## [Előadás előtti kvíz](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Objektumfelismerés](../../../../../translated_images/hu/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) +![Objektumfelismerés](../../../../../translated_images/hu/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp) > Kép a [YOLO v2 weboldaláról](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ Tegyük fel, hogy egy képen szeretnénk megtalálni egy macskát. Egy nagyon eg 2. Képosztályozást futtatunk minden csempén. 3. Azok a csempék, amelyeknél elég magas aktivációt kapunk, tartalmazhatják a keresett objektumot. -![Naiv objektumfelismerés](../../../../../translated_images/hu/naive-detection.e7f1ba220ccd08c6.png) +![Naiv objektumfelismerés](../../../../../translated_images/hu/naive-detection.e7f1ba220ccd08c6.webp) > *Kép az [Exercise Notebook](ObjectDetection-TF.ipynb)-ból* @@ -42,7 +42,7 @@ Az alábbi adatállományokkal találkozhatsz ezen a területen: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 osztály * [COCO](http://cocodataset.org/#home) - Közönséges tárgyak kontextusban. 80 osztály, körvonalak és szegmentációs maszkok -![COCO](../../../../../translated_images/hu/coco-examples.71bc60380fa6cceb.jpg) +![COCO](../../../../../translated_images/hu/coco-examples.71bc60380fa6cceb.webp) ## Objektumfelismerési metrikák @@ -50,7 +50,7 @@ Az alábbi adatállományokkal találkozhatsz ezen a területen: Míg a képosztályozásnál könnyű mérni az algoritmus teljesítményét, az objektumfelismerésnél nemcsak az osztály helyességét kell mérni, hanem az előre jelzett körvonal helyének pontosságát is. Ehhez az úgynevezett **Metszet az unióhoz viszonyítva** (IoU) metrikát használjuk, amely azt méri, hogy két doboz (vagy két tetszőleges terület) mennyire fedik egymást. -![IoU](../../../../../translated_images/hu/iou_equation.9a4751d40fff4e11.png) +![IoU](../../../../../translated_images/hu/iou_equation.9a4751d40fff4e11.webp) > *2. ábra [ebből a kiváló blogbejegyzésből az IoU-ról](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ Az objektumfelismerési algoritmusoknak két fő típusa van: Az [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) a [Szelektív Keresést](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) használja, hogy hierarchikus ROI régiókat generáljon, amelyeket aztán CNN jellemzőkivonókon és SVM-osztályozókon futtatunk, hogy meghatározzuk az objektum osztályát, valamint lineáris regresszióval a *körvonal* koordinátáit. [Hivatalos tanulmány](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/hu/rcnn1.cae407020dfb1d1f.png) +![RCNN](../../../../../translated_images/hu/rcnn1.cae407020dfb1d1f.webp) > *Kép van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/hu/rcnn2.2d9530bb83516484.png) +![RCNN-1](../../../../../translated_images/hu/rcnn2.2d9530bb83516484.webp) > *Képek [ebből a blogból](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -110,7 +110,7 @@ Az [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) a [Sze Ez a megközelítés hasonló az R-CNN-hez, de a régiókat a konvolúciós rétegek alkalmazása után határozzuk meg. -![FRCNN](../../../../../translated_images/hu/f-rcnn.3cda6d9bb4188875.png) +![FRCNN](../../../../../translated_images/hu/f-rcnn.3cda6d9bb4188875.webp) > Kép a [Hivatalos tanulmányból](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ Ez a megközelítés hasonló az R-CNN-hez, de a régiókat a konvolúciós rét Ennek a megközelítésnek az alapötlete, hogy neurális hálózatot használunk az ROI-k előrejelzésére - az úgynevezett *Régiójavasló Hálózat*. [Tanulmány](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/hu/faster-rcnn.8d46c099b87ef30a.png) +![FasterRCNN](../../../../../translated_images/hu/faster-rcnn.8d46c099b87ef30a.webp) > Kép a [hivatalos tanulmányból](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ Ez az algoritmus még gyorsabb, mint a Gyorsabb R-CNN. Az alapötlet a következ 2. A jellemzőket **Pozíció-Érzékeny Pontszám Térképen** dolgozzuk fel. Minden objektumot $C$ osztályból $k\times k$ régiókra osztunk, és az objektumok részeinek előrejelzésére tanítjuk a hálózatot. 3. Minden részre a $k\times k$ régiókból a hálózatok szavaznak az objektumosztályokra, és a maximális szavazatot kapó osztályt választjuk. -![r-fcn kép](../../../../../translated_images/hu/r-fcn.13eb88158b99a3da.png) +![r-fcn kép](../../../../../translated_images/hu/r-fcn.13eb88158b99a3da.webp) > Kép a [hivatalos tanulmányból](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ A YOLO egy valós idejű egyszeri futtatású algoritmus. Az alapötlet a követ * A képet $S\times S$ régiókra osztjuk. * Minden régióra **CNN** előrejelzi $n$ lehetséges objektumot, *körvonal* koordinátákat és *bizalmi szintet*=*valószínűség* * IoU. - ![YOLO](../../../../../translated_images/hu/yolo.a2648ec82ee8bb4e.png) + ![YOLO](../../../../../translated_images/hu/yolo.a2648ec82ee8bb4e.webp) > Kép a [hivatalos tanulmányból](https://arxiv.org/abs/1506.02640) diff --git a/translations/hu/lessons/4-ComputerVision/README.md b/translations/hu/lessons/4-ComputerVision/README.md index 0a01e4fb..028d9159 100644 --- a/translations/hu/lessons/4-ComputerVision/README.md +++ b/translations/hu/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Számítógépes látás -![A számítógépes látás tartalmának összefoglalása egy rajzban](../../../../translated_images/hu/ai-computervision.6506ebebac3fbf76.png) +![A számítógépes látás tartalmának összefoglalása egy rajzban](../../../../translated_images/hu/ai-computervision.6506ebebac3fbf76.webp) Ebben a részben megtanuljuk: diff --git a/translations/hu/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/hu/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index 319d05be..da15a61e 100644 --- a/translations/hu/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/hu/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "A **Szavak zsákja** (BoW) vektorábrázolás a leggyakrabban használt hagyományos vektorábrázolás. Minden szó egy vektorindexhez van rendelve, a vektor elemei pedig azt mutatják, hogy egy adott dokumentumban hányszor fordul elő az adott szó.\n", "\n", - "![Kép, amely bemutatja, hogyan van ábrázolva a szavak zsákja vektor a memóriában.](../../../../../translated_images/hu/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![Kép, amely bemutatja, hogyan van ábrázolva a szavak zsákja vektor a memóriában.](../../../../../translated_images/hu/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Megjegyzés**: A BoW-t úgy is elképzelheted, mint az egyes szavak egy-egy one-hot-kódolt vektorának összegét a szövegben.\n", "\n", diff --git a/translations/hu/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/hu/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index d5fdebd1..4076ed1f 100644 --- a/translations/hu/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/hu/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "A **szótásképzés** (BoW) vektori ábrázolás a legegyszerűbben érthető hagyományos vektori ábrázolás. Minden szó egy vektor indexhez van kötve, és a vektor elemei azt mutatják, hogy egy adott dokumentumban hányszor fordul elő az adott szó.\n", "\n", - "![Kép, amely bemutatja, hogyan van ábrázolva a szótásképzés vektori reprezentációja a memóriában.](../../../../../translated_images/hu/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![Kép, amely bemutatja, hogyan van ábrázolva a szótásképzés vektori reprezentációja a memóriában.](../../../../../translated_images/hu/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Megjegyzés**: A BoW-t úgy is elképzelhetjük, mint az egyes szavak egy-egy one-hot kódolt vektorának összegét a szövegben.\n", "\n", diff --git a/translations/hu/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/hu/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 39ba1655..8047401a 100644 --- a/translations/hu/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/hu/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "Ha az embedding réteget használjuk hálózatunk első rétegeként, akkor átállhatunk a bag-of-words modellről az **embedding bag** modellre. Ebben először minden szót a szövegünkben a megfelelő embeddingre konvertálunk, majd valamilyen aggregáló függvényt számítunk ki az összes embedding felett, például `sum`, `average` vagy `max`.\n", "\n", - "![Kép, amely egy embedding osztályozót mutat öt szekvencia szóra.](../../../../../translated_images/hu/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![Kép, amely egy embedding osztályozót mutat öt szekvencia szóra.](../../../../../translated_images/hu/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "Az osztályozó neurális hálózatunk embedding réteggel kezdődik, majd egy aggregáló réteggel, és végül egy lineáris osztályozóval a tetején:\n" ] @@ -176,7 +176,7 @@ "\n", "Az előző architektúrában minden szekvenciát ugyanarra a hosszra kellett kiegészíteni, hogy illeszkedjenek egy minibatch-be. Ez nem a leghatékonyabb módja a változó hosszúságú szekvenciák reprezentálásának – egy másik megközelítés az **offset** vektor használata, amely egy nagy vektorban tárolt összes szekvencia eltolásait tartalmazza.\n", "\n", - "![Kép, amely egy offset szekvencia reprezentációt mutat](../../../../../translated_images/hu/offset-sequence-representation.eb73fcefb29b46ee.png)\n", + "![Kép, amely egy offset szekvencia reprezentációt mutat](../../../../../translated_images/hu/offset-sequence-representation.eb73fcefb29b46ee.webp)\n", "\n", "> **Note**: A fenti képen karakterek szekvenciáját mutatjuk, de példánkban szavak szekvenciáival dolgozunk. Azonban a szekvenciák offset vektorral történő reprezentálásának általános elve ugyanaz marad.\n", "\n", @@ -311,7 +311,7 @@ "\n", "A CBoW gyorsabb, míg a skip-gram lassabb, de jobban reprezentálja a ritkábban előforduló szavakat.\n", "\n", - "![Kép, amely a CBoW és a Skip-Gram algoritmusokat mutatja be a szavak vektorokká alakításához.](../../../../../translated_images/hu/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![Kép, amely a CBoW és a Skip-Gram algoritmusokat mutatja be a szavak vektorokká alakításához.](../../../../../translated_images/hu/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Ahhoz, hogy kísérletezzünk a Google News adathalmazon előre betanított word2vec beágyazással, használhatjuk a **gensim** könyvtárat. Az alábbiakban megkeressük a 'neural' szóhoz leginkább hasonló szavakat.\n", "\n", diff --git a/translations/hu/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/hu/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index 1219c034..81a2f533 100644 --- a/translations/hu/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/hu/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "Ha az embedding réteget használjuk a hálózatunk első rétegeként, akkor áttérhetünk a bag-of-words modellről egy **embedding bag** modellre. Ebben az esetben először minden szót a szövegünkben a megfelelő embeddingre alakítunk, majd valamilyen aggregáló függvényt számítunk ki az összes embedding felett, például `sum`, `average` vagy `max`.\n", "\n", - "![Kép, amely egy embedding osztályozót mutat öt szekvencia szóra.](../../../../../translated_images/hu/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![Kép, amely egy embedding osztályozót mutat öt szekvencia szóra.](../../../../../translated_images/hu/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "Az osztályozó neurális hálózatunk a következő rétegekből áll:\n", "\n", @@ -283,7 +283,7 @@ "\n", "A CBoW gyorsabb, míg az ugrógram lassabb, de jobban reprezentálja a ritka szavakat.\n", "\n", - "![Kép, amely bemutatja a CBoW és az ugrógram algoritmusokat a szavak vektorokká alakításához.](../../../../../translated_images/hu/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![Kép, amely bemutatja a CBoW és az ugrógram algoritmusokat a szavak vektorokká alakításához.](../../../../../translated_images/hu/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Ahhoz, hogy kísérletezzünk a Google News adathalmazon előtanított Word2Vec beágyazással, használhatjuk a **gensim** könyvtárat. Az alábbiakban megkeressük a 'neural' szóhoz leginkább hasonló szavakat.\n", "\n", diff --git a/translations/hu/lessons/5-NLP/14-Embeddings/README.md b/translations/hu/lessons/5-NLP/14-Embeddings/README.md index 4f9bb829..4c3033df 100644 --- a/translations/hu/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/hu/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ A beágyazási réteg tehát egy szót vesz bemenetként, és egy meghatározott Ha a beágyazási réteget használjuk osztályozó hálózatunk első rétegeként, akkor a szavak zsákja modellről áttérhetünk az **embedding bag** modellre, ahol először minden szót a megfelelő beágyazásra alakítunk, majd valamilyen aggregált függvényt számítunk ki az összes beágyazás felett, például `sum`, `average` vagy `max`. -![Kép, amely egy beágyazási osztályozót mutat öt szekvencia szóra.](../../../../../translated_images/hu/embedding-classifier-example.b77f021a7ee67eee.png) +![Kép, amely egy beágyazási osztályozót mutat öt szekvencia szóra.](../../../../../translated_images/hu/embedding-classifier-example.b77f021a7ee67eee.webp) > Kép a szerzőtől @@ -40,7 +40,7 @@ Ehhez elő kell tanítanunk a beágyazási modellünket egy nagy szöveggyűjtem A CBoW gyorsabb, míg a skip-gram lassabb, de jobban reprezentálja a ritka szavakat. -![Kép, amely a CBoW és Skip-Gram algoritmusokat mutatja a szavak vektorokká alakításához.](../../../../../translated_images/hu/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![Kép, amely a CBoW és Skip-Gram algoritmusokat mutatja a szavak vektorokká alakításához.](../../../../../translated_images/hu/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Kép ebből a [tanulmányból](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/hu/lessons/5-NLP/15-LanguageModeling/README.md b/translations/hu/lessons/5-NLP/15-LanguageModeling/README.md index e23b1cb3..1e9c0637 100644 --- a/translations/hu/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/hu/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ Korábbi példáinkban előre betanított szemantikai beágyazásokat használtu * **Folytonos Szózsák** (CBoW), amikor a középső token-t $W_0$ jósoljuk meg egy token sorozatban $W_{-N}$, ..., $W_N$. * **Skip-gram**, ahol a középső token $W_0$ alapján egy szomszédos tokenek halmazát {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} jósoljuk meg. -![kép a szavak vektorokká alakításáról szóló tanulmányból](../../../../../translated_images/hu/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![kép a szavak vektorokká alakításáról szóló tanulmányból](../../../../../translated_images/hu/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Kép [ebből a tanulmányból](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/hu/lessons/5-NLP/16-RNN/README.md b/translations/hu/lessons/5-NLP/16-RNN/README.md index 0f23e583..054c7d6d 100644 --- a/translations/hu/lessons/5-NLP/16-RNN/README.md +++ b/translations/hu/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ Az előző szekciókban gazdag szemantikai reprezentációkat használtunk a sz Ahhoz, hogy a szövegszekvencia jelentését megragadjuk, egy másik neurális hálózati architektúrát kell használnunk, amelyet **rekurrens neurális hálózatnak** (RNN) nevezünk. Az RNN-ben mondatunkat egy szimbólumonként adjuk át a hálózaton, és a hálózat egy **állapotot** hoz létre, amelyet aztán a következő szimbólummal együtt újra átadunk a hálózatnak. -![RNN](../../../../../translated_images/hu/rnn.27f5c29c53d727b5.png) +![RNN](../../../../../translated_images/hu/rnn.27f5c29c53d727b5.webp) > Kép a szerzőtől @@ -61,7 +61,7 @@ Olyan rekurrens hálózatokat tárgyaltunk, amelyek egy irányban működnek, a Egy rekurrens hálózat, akár egyirányú, akár kétirányú, bizonyos mintákat ragad meg egy szekvenciában, és ezeket az állapotvektorba menti vagy a kimenetbe továbbítja. Akárcsak a konvolúciós hálózatok esetében, egy másik rekurrens réteget építhetünk az első fölé, hogy magasabb szintű mintákat ragadjunk meg, és az első réteg által kinyert alacsony szintű mintákból építkezzünk. Ez vezet minket a **többrétegű RNN** fogalmához, amely két vagy több rekurrens hálózatból áll, ahol az előző réteg kimenete bemenetként kerül a következő rétegbe. -![Többrétegű hosszú-rövid távú memória RNN](../../../../../translated_images/hu/multi-layer-lstm.dd975e29bb2a59fe.jpg) +![Többrétegű hosszú-rövid távú memória RNN](../../../../../translated_images/hu/multi-layer-lstm.dd975e29bb2a59fe.webp) *Kép Fernando López [ezen csodálatos bejegyzéséből](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3)* diff --git a/translations/hu/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/hu/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index ca8e5bcb..0f01523d 100644 --- a/translations/hu/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/hu/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "A rekurrens hálózat, legyen az egyirányú vagy kétirányú, bizonyos mintázatokat rögzít egy szekvencián belül, és ezeket tárolhatja az állapotvektorban vagy továbbíthatja a kimenetbe. Akárcsak a konvolúciós hálózatok esetében, egy másik rekurrens réteget építhetünk az első fölé, hogy magasabb szintű mintázatokat rögzítsünk, amelyeket az első réteg által kinyert alacsony szintű mintázatokból építünk fel. Ez vezet el minket a **többrétegű RNN** fogalmához, amely két vagy több rekurrens hálózatból áll, ahol az előző réteg kimenete bemenetként kerül a következő réteghez.\n", "\n", - "![Kép egy többrétegű hosszú-rövid távú memória RNN-ről](../../../../../translated_images/hu/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![Kép egy többrétegű hosszú-rövid távú memória RNN-ről](../../../../../translated_images/hu/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*Kép Fernando López [ezen csodálatos bejegyzéséből](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3)*\n", "\n", diff --git a/translations/hu/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/hu/lessons/5-NLP/16-RNN/RNNTF.ipynb index 4695c4b7..8cb1ca4b 100644 --- a/translations/hu/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/hu/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "Ahhoz, hogy egy szövegszekvencia jelentését megragadjuk, egy **rekurrens neurális hálózatnak** (angolul recurrent neural network, RNN) nevezett neurális hálózati architektúrát fogunk használni. Az RNN használatakor a mondatot egyesével, tokenenként vezetjük át a hálózaton, amely minden lépésben előállít egy **állapotot**, amit aztán a következő tokennel együtt ismét átadunk a hálózatnak.\n", "\n", - "![Kép, amely egy rekurrens neurális hálózat generálását mutatja.](../../../../../translated_images/hu/rnn.27f5c29c53d727b5.png)\n", + "![Kép, amely egy rekurrens neurális hálózat generálását mutatja.](../../../../../translated_images/hu/rnn.27f5c29c53d727b5.webp)\n", "\n", "A tokenekből álló bemeneti szekvencia $X_0,\\dots,X_n$ alapján az RNN egy neurális hálózati blokkokból álló szekvenciát hoz létre, és ezt a szekvenciát végig, visszaterjesztéses tanulással (backpropagation) tanítja. Minden hálózati blokk egy $(X_i,S_i)$ párt kap bemenetként, és eredményként előállítja $S_{i+1}$-et. A végső állapot $S_n$ vagy a kimenet $Y_n$ egy lineáris osztályozóba kerül, amely előállítja az eredményt. Az összes hálózati blokk ugyanazokat a súlyokat osztja meg, és egyetlen visszaterjesztési lépés során tanulják meg azokat.\n", "\n", @@ -369,7 +369,7 @@ "\n", "A rekurrens hálózatok, legyenek egyirányúak vagy kétirányúak, mintákat ragadnak meg egy szekvenciában, és ezeket állapotvektorokba tárolják vagy kimenetként adják vissza. Akárcsak a konvolúciós hálózatok esetében, építhetünk egy másik rekurrens réteget az első után, hogy magasabb szintű mintákat ragadjunk meg, amelyeket az első réteg által kinyert alacsonyabb szintű mintákból építünk fel. Ez vezet el minket a **többrétegű RNN** fogalmához, amely két vagy több rekurrens hálózatból áll, ahol az előző réteg kimenete bemenetként kerül a következő réteghez.\n", "\n", - "![Kép egy többrétegű hosszú-rövid távú memória RNN-ről](../../../../../translated_images/hu/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![Kép egy többrétegű hosszú-rövid távú memória RNN-ről](../../../../../translated_images/hu/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*Kép [ebből a nagyszerű bejegyzésből](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) Fernando López tollából.*\n", "\n", diff --git a/translations/hu/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/hu/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index 38a5a945..d7b165a1 100644 --- a/translations/hu/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/hu/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "Az RNN szöveg generálására való tanításának módja a következő. Minden lépésben veszünk egy `nchars` hosszúságú karakterláncot, és megkérjük a hálózatot, hogy minden bemeneti karakterhez generálja a következő kimeneti karaktert:\n", "\n", - "![Kép, amely az 'HELLO' szó RNN általi generálását mutatja.](../../../../../translated_images/hu/rnn-generate.56c54afb52f9781d.png)\n", + "![Kép, amely az 'HELLO' szó RNN általi generálását mutatja.](../../../../../translated_images/hu/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "A konkrét helyzettől függően előfordulhat, hogy speciális karaktereket is be kell vonnunk, például *sorvége* ``. A mi esetünkben azonban csak végtelen szöveg generálására szeretnénk tanítani a hálózatot, ezért minden szekvencia méretét fixen `nchars` tokenre állítjuk. Ennek megfelelően minden tanítási példában `nchars` bemenet és `nchars` kimenet lesz (a bemeneti szekvencia egy szimbólummal balra eltolva). Egy minibatch több ilyen szekvenciából fog állni.\n", "\n", diff --git a/translations/hu/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/hu/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index 5756d20d..1a5350a9 100644 --- a/translations/hu/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/hu/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -113,7 +113,7 @@ "\n", "Az RNN betanításának módja a hírcímek generálására a következő. Minden lépésben veszünk egy címet, amelyet betáplálunk egy RNN-be, és minden bemeneti karakterhez megkérjük a hálózatot, hogy generálja a következő kimeneti karaktert:\n", "\n", - "![Kép, amely bemutatja az 'HELLO' szó generálását egy RNN segítségével.](../../../../../translated_images/hu/rnn-generate.56c54afb52f9781d.png)\n", + "![Kép, amely bemutatja az 'HELLO' szó generálását egy RNN segítségével.](../../../../../translated_images/hu/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "A szekvenciánk utolsó karakterénél megkérjük a hálózatot, hogy generálja a `` tokent.\n", "\n", diff --git a/translations/hu/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/hu/lessons/5-NLP/17-GenerativeNetworks/README.md index 60b3fd37..928c7e02 100644 --- a/translations/hu/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/hu/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ Az előző egységben tárgyalt RNN architektúrában minden RNN egység a köve Ez különböző neurális architektúrákat tesz lehetővé, amelyeket az alábbi képen láthatunk: -![Kép, amely a rekurrens neurális hálózatok gyakori mintázatait mutatja.](../../../../../translated_images/hu/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Kép, amely a rekurrens neurális hálózatok gyakori mintázatait mutatja.](../../../../../translated_images/hu/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > Kép Andrej Karpaty [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) című blogbejegyzéséből [Andrej Karpaty](http://karpathy.github.io/) által @@ -32,7 +32,7 @@ Ebben az egységben egyszerű generatív modellekre fogunk összpontosítani, am Ezt az RNN-t arra fogjuk tanítani, hogy lépésről lépésre generáljon szöveget. Minden lépésben egy `nchars` hosszúságú karakter sorozatot veszünk, és megkérjük a hálózatot, hogy generálja a következő kimeneti karaktert minden bemeneti karakterhez: -![Kép, amely az 'HELLO' szó RNN általi generálását mutatja.](../../../../../translated_images/hu/rnn-generate.56c54afb52f9781d.png) +![Kép, amely az 'HELLO' szó RNN általi generálását mutatja.](../../../../../translated_images/hu/rnn-generate.56c54afb52f9781d.webp) Szöveg generálásakor (következtetés során) egy **indítószöveggel** kezdünk, amelyet RNN cellákon keresztül adunk át, hogy előállítsuk annak köztes állapotát, majd ebből az állapotból kezdődik a generálás. Egy karaktert generálunk egyszerre, és az állapotot és a generált karaktert átadjuk egy másik RNN cellának, hogy generálja a következőt, amíg elegendő karaktert nem generálunk. diff --git a/translations/hu/lessons/5-NLP/18-Transformers/README.md b/translations/hu/lessons/5-NLP/18-Transformers/README.md index 1f734222..2ed09268 100644 --- a/translations/hu/lessons/5-NLP/18-Transformers/README.md +++ b/translations/hu/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ Az RNN-ekkel a sorozat-sorozat feladatot két rekurzív hálózat valósítja me A **figyelem mechanizmusok** lehetőséget adnak arra, hogy súlyozzuk az egyes bemeneti vektorok kontextuális hatását az RNN kimeneti előrejelzéseire. Ez úgy valósul meg, hogy rövidítéseket hozunk létre a bemeneti RNN köztes állapotai és a kimeneti RNN között. Ily módon, amikor a yt kimeneti szimbólumot generáljuk, figyelembe vesszük az összes bemeneti rejtett állapotot hi, különböző súlyozási együtthatókkal αt,i. -![Kép egy enkóder/dekóder modellről additív figyelemréteggel](../../../../../translated_images/hu/encoder-decoder-attention.7a726296894fb567.png) +![Kép egy enkóder/dekóder modellről additív figyelemréteggel](../../../../../translated_images/hu/encoder-decoder-attention.7a726296894fb567.webp) > Az enkóder-dekóder modell additív figyelem mechanizmussal [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), idézve [ebből a blogbejegyzésből](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) A figyelem mátrix {αi,j} azt mutatja, hogy a bemeneti szavak milyen mértékben játszanak szerepet egy adott szó generálásában a kimeneti sorozatban. Az alábbiakban egy ilyen mátrix példáját láthatjuk: -![Kép egy mintázott igazításról, amelyet az RNNsearch-50 talált, Bahdanau - arviz.org](../../../../../translated_images/hu/bahdanau-fig3.09ba2d37f202a6af.png) +![Kép egy mintázott igazításról, amelyet az RNNsearch-50 talált, Bahdanau - arviz.org](../../../../../translated_images/hu/bahdanau-fig3.09ba2d37f202a6af.webp) > Ábra [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (3. ábra) @@ -66,7 +66,7 @@ Az eredmény, amelyet a pozíciós beágyazással kapunk, beágyazza mind az ere Ezután meg kell ragadnunk néhány mintát a sorozatunkon belül. Ehhez a transzformerek **önfigyelem** mechanizmust használnak, amely lényegében figyelem, amelyet ugyanarra a sorozatra alkalmazunk bemenetként és kimenetként. Az önfigyelem alkalmazása lehetővé teszi számunkra, hogy figyelembe vegyük a mondaton belüli **kontekztust**, és lássuk, mely szavak kapcsolódnak egymáshoz. Például lehetővé teszi számunkra, hogy lássuk, mely szavakra utalnak visszautalások, mint például *az*, és figyelembe vegyük a kontextust is: -![](../../../../../translated_images/hu/CoreferenceResolution.861924d6d384a7d6.png) +![](../../../../../translated_images/hu/CoreferenceResolution.861924d6d384a7d6.webp) > Kép a [Google Blogból](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Mivel minden bemeneti pozíciót függetlenül térképezünk a kimeneti pozíci A **BERT** (Bidirectional Encoder Representations from Transformers) egy nagyon nagy, többrétegű transzformer hálózat, amelynek 12 rétege van a *BERT-base* esetében, és 24 a *BERT-large* esetében. A modellt először egy nagy szövegkorpuszra (WikiPedia + könyvek) tanítják be felügyelet nélküli tanulással (maszkolt szavak előrejelzése egy mondatban). Az előképzés során a modell jelentős nyelvi megértést szerez, amelyet más adathalmazokkal finomhangolással lehet kihasználni. Ezt a folyamatot **transzfer tanulásnak** nevezzük. -![kép a http://jalammar.github.io/illustrated-bert/ oldalról](../../../../../translated_images/hu/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) +![kép a http://jalammar.github.io/illustrated-bert/ oldalról](../../../../../translated_images/hu/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > Kép [forrása](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/hu/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/hu/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index 2ed55de2..39dd71a2 100644 --- a/translations/hu/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/hu/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**Figyelemmechanizmusok** lehetőséget adnak arra, hogy súlyozzuk az egyes input vektorok kontextuális hatását az RNN minden egyes kimeneti előrejelzésére. Ez úgy valósul meg, hogy rövidítéseket hozunk létre az input RNN köztes állapotai és a kimeneti RNN között. Ily módon, amikor a $y_t$ kimeneti szimbólumot generáljuk, figyelembe vesszük az összes input rejtett állapotot $h_i$, különböző súlykoefficiensekkel $\\alpha_{t,i}$. \n", "\n", - "![Kép egy kódoló/dekódoló modellről additív figyelemréteggel](../../../../../translated_images/hu/encoder-decoder-attention.7a726296894fb567.png)\n", + "![Kép egy kódoló/dekódoló modellről additív figyelemréteggel](../../../../../translated_images/hu/encoder-decoder-attention.7a726296894fb567.webp)\n", "*A kódoló-dekódoló modell additív figyelemmechanizmussal [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), idézve [ebből a blogbejegyzésből](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "A figyelem mátrix $\\{\\alpha_{i,j}\\}$ azt mutatja, hogy az egyes input szavak milyen mértékben játszanak szerepet egy adott szó generálásában a kimeneti szekvenciában. Az alábbiakban egy ilyen mátrix példáját láthatjuk:\n", "\n", - "![Kép egy mintaillesztésről, amelyet az RNNsearch-50 talált, Bahdanau - arviz.org](../../../../../translated_images/hu/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![Kép egy mintaillesztésről, amelyet az RNNsearch-50 talált, Bahdanau - arviz.org](../../../../../translated_images/hu/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*Ábra [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (3. ábra) alapján*\n", "\n", @@ -35,7 +35,7 @@ "\n", "A **BERT** (Bidirectional Encoder Representations from Transformers) egy nagyon nagy, többrétegű transzformer hálózat, amelynek 12 rétege van a *BERT-base* esetében, és 24 a *BERT-large* esetében. A modellt először egy nagy szövegkorpuszra (WikiPedia + könyvek) tanítják be felügyelet nélküli tanítással (maszkolt szavak előrejelzése egy mondatban). Az előtanítás során a modell jelentős nyelvi megértést sajátít el, amelyet aztán más adathalmazokkal lehet finomhangolni. Ezt a folyamatot **transzfer tanulásnak** nevezzük. \n", "\n", - "![Kép a http://jalammar.github.io/illustrated-bert/ oldalról](../../../../../translated_images/hu/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![Kép a http://jalammar.github.io/illustrated-bert/ oldalról](../../../../../translated_images/hu/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "Számos transzformer architektúra létezik, például BERT, DistilBERT, BigBird, OpenGPT3 és még sok más, amelyek finomhangolhatók. A [HuggingFace csomag](https://github.com/huggingface/) lehetőséget biztosít ezeknek az architektúráknak a tanítására PyTorch segítségével. \n", "\n", diff --git a/translations/hu/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/hu/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index e9b7762b..f8dfab10 100644 --- a/translations/hu/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/hu/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "A **figyelemmechanizmusok** lehetőséget adnak arra, hogy súlyozzuk az egyes input vektorok kontextuális hatását az RNN kimeneti előrejelzéseire. Ez úgy valósul meg, hogy rövidítéseket hozunk létre az input RNN köztes állapotai és a kimeneti RNN között. Ily módon, amikor a $y_t$ kimeneti szimbólumot generáljuk, figyelembe vesszük az összes input rejtett állapotot $h_i$, különböző súlykoefficiensekkel $\\alpha_{t,i}$. \n", "\n", - "![Kép egy kódoló/dekódoló modellről additív figyelemréteggel](../../../../../translated_images/hu/encoder-decoder-attention.7a726296894fb567.png)\n", + "![Kép egy kódoló/dekódoló modellről additív figyelemréteggel](../../../../../translated_images/hu/encoder-decoder-attention.7a726296894fb567.webp)\n", "*A kódoló-dekódoló modell additív figyelemmechanizmussal [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) alapján, idézve [ebből a blogbejegyzésből](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "A figyelem mátrix $\\{\\alpha_{i,j}\\}$ azt mutatja, hogy az egyes input szavak milyen mértékben járulnak hozzá egy adott szó generálásához a kimeneti szekvenciában. Az alábbiakban egy ilyen mátrix példáját láthatjuk:\n", "\n", - "![Kép egy mintaillesztésről, amelyet az RNNsearch-50 talált, Bahdanau - arviz.org](../../../../../translated_images/hu/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![Kép egy mintaillesztésről, amelyet az RNNsearch-50 talált, Bahdanau - arviz.org](../../../../../translated_images/hu/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*Ábra [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (3. ábra) alapján*\n", "\n", @@ -225,7 +225,7 @@ "\n", "A **BERT** (Bidirectional Encoder Representations from Transformers) egy nagyon nagy, többrétegű transformer hálózat, amely *BERT-base* esetén 12 rétegből, míg *BERT-large* esetén 24 rétegből áll. A modellt először egy nagy szövegkorpuszra (WikiPedia + könyvek) tanítják be felügyelet nélküli tanulással (maszkolt szavak előrejelzése egy mondatban). Az előképzés során a modell jelentős nyelvi megértést sajátít el, amelyet később más adathalmazokkal finomhangolással lehet hasznosítani. Ezt a folyamatot **transzfer tanulásnak** nevezzük.\n", "\n", - "![kép forrása: http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/hu/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![kép forrása: http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/hu/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "Számos Transformer architektúra létezik, például BERT, DistilBERT, BigBird, OpenGPT3 és még sok más, amelyek finomhangolhatók.\n", "\n", diff --git a/translations/hu/lessons/5-NLP/19-NER/README.md b/translations/hu/lessons/5-NLP/19-NER/README.md index e21c304e..c5dbd7fb 100644 --- a/translations/hu/lessons/5-NLP/19-NER/README.md +++ b/translations/hu/lessons/5-NLP/19-NER/README.md @@ -57,7 +57,7 @@ csecsemőben | O Mivel egy-egy megfeleltetést kell létrehoznunk a tokenek és osztályok között, egy **sok-sokhoz** neurális hálózati modellt tudunk tanítani az alábbi ábráról: -![Kép, amely a gyakori rekurzív neurális hálózati mintákat mutatja.](../../../../../translated_images/hu/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Kép, amely a gyakori rekurzív neurális hálózati mintákat mutatja.](../../../../../translated_images/hu/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > *Kép [ebből a blogbejegyzésből](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) [Andrej Karpathy](http://karpathy.github.io/) tollából. A NER token klasszifikációs modellek megfelelnek az ábra jobb szélső hálózati architektúrájának.* diff --git a/translations/hu/lessons/5-NLP/README.md b/translations/hu/lessons/5-NLP/README.md index faf92568..592c02f3 100644 --- a/translations/hu/lessons/5-NLP/README.md +++ b/translations/hu/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Természetes Nyelvfeldolgozás -![Összefoglaló az NLP feladatokról egy rajzban](../../../../translated_images/hu/ai-nlp.b22dcb8ca4707cea.png) +![Összefoglaló az NLP feladatokról egy rajzban](../../../../translated_images/hu/ai-nlp.b22dcb8ca4707cea.webp) Ebben a részben a neurális hálózatok használatára összpontosítunk, hogy megoldjuk a **természetes nyelvfeldolgozással (NLP)** kapcsolatos feladatokat. Számos NLP probléma van, amelyeket szeretnénk, ha a számítógépek meg tudnának oldani: diff --git a/translations/hu/lessons/6-Other/23-MultiagentSystems/README.md b/translations/hu/lessons/6-Other/23-MultiagentSystems/README.md index 584d2de9..c11b8658 100644 --- a/translations/hu/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/hu/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ Megnyithatsz egy modellt, például **Biology → Flocking**. A modell megnyitása után a NetLogo fő képernyőjére kerülsz. Itt egy minta modell látható, amely a farkasok és juhok populációját írja le véges erőforrások (fű) mellett. -![NetLogo Main Screen](../../../../../translated_images/hu/NetLogo-Main.32653711ec1a01b3.png) +![NetLogo Main Screen](../../../../../translated_images/hu/NetLogo-Main.32653711ec1a01b3.webp) > Dmitry Soshnikov által készített képernyőkép diff --git a/translations/hu/lessons/README.md b/translations/hu/lessons/README.md index 7dfdebdc..3389d2d1 100644 --- a/translations/hu/lessons/README.md +++ b/translations/hu/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Áttekintés -![Áttekintés egy rajzban](../../../translated_images/hu/ai-overview.0857791951d19500.png) +![Áttekintés egy rajzban](../../../translated_images/hu/ai-overview.0857791951d19500.webp) > Vázlatrajz: [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/hu/lessons/X-Extras/X1-MultiModal/README.md b/translations/hu/lessons/X-Extras/X1-MultiModal/README.md index cc012a4e..9b567004 100644 --- a/translations/hu/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/hu/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ A transzformer modellek sikerét követően az NLP feladatok megoldásában, has A CLIP fő ötlete, hogy képes legyen összehasonlítani szöveges utasításokat egy képpel, és meghatározni, mennyire felel meg a kép az utasításnak. -![CLIP Architektúra](../../../../../translated_images/hu/clip-arch.b3dbf20b4e8ed8be.png) +![CLIP Architektúra](../../../../../translated_images/hu/clip-arch.b3dbf20b4e8ed8be.webp) > *Kép [ebből a blogbejegyzésből](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ Miután a modellt betanították, megadhatunk neki egy batch képet és egy batc Tegyük fel, hogy képeket kell osztályoznunk például macskák, kutyák és emberek között. Ebben az esetben megadhatjuk a modellnek a képet, és egy sor szöveges utasítást: "*egy macska képe*", "*egy kutya képe*", "*egy ember képe*". A kapott 3 valószínűségi vektorban csak ki kell választanunk a legmagasabb értékű indexet. -![CLIP Képosztályozáshoz](../../../../../translated_images/hu/clip-class.3af42ef0b2b19369.png) +![CLIP Képosztályozáshoz](../../../../../translated_images/hu/clip-class.3af42ef0b2b19369.webp) > *Kép [ebből a blogbejegyzésből](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ További információ a VQGAN-ról a [Taming Transformers](https://compvis.githu A VQGAN és a hagyományos GAN egyik fontos különbsége, hogy az utóbbi bármilyen bemeneti vektorból képes elfogadható képet előállítani, míg a VQGAN valószínűleg nem koherens képet hoz létre. Ezért tovább kell irányítanunk a képalkotási folyamatot, amit a CLIP segítségével tehetünk meg. -![VQGAN+CLIP Architektúra](../../../../../translated_images/hu/vqgan.5027fe05051dfa31.png) +![VQGAN+CLIP Architektúra](../../../../../translated_images/hu/vqgan.5027fe05051dfa31.webp) Ahhoz, hogy egy szöveges utasításhoz illeszkedő képet generáljunk, egy véletlenszerű kódoló vektorral kezdünk, amelyet a VQGAN-on keresztül egy képpé alakítunk. Ezután a CLIP-et használjuk egy veszteségfüggvény előállítására, amely megmutatja, mennyire felel meg a kép a szöveges utasításnak. A cél ennek a veszteségnek a minimalizálása, a visszaterjesztés segítségével a bemeneti vektor paramétereinek módosításával. Egy nagyszerű könyvtár, amely megvalósítja a VQGAN+CLIP-et, a [Pixray](http://github.com/pixray/pixray). -![Pixray által készített kép](../../../../../translated_images/hu/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Pixray által készített kép](../../../../../translated_images/hu/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Pixray által készített kép](../../../../../translated_images/hu/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) +![Pixray által készített kép](../../../../../translated_images/hu/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp) | ![Pixray által készített kép](../../../../../translated_images/hu/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp) | ![Pixray által készített kép](../../../../../translated_images/hu/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp) ----|----|---- Kép generálva az *egy fiatal irodalomtanár akvarell portréja könyvvel* utasítás alapján | Kép generálva az *egy fiatal női informatikatanár olajportréja számítógéppel* utasítás alapján | Kép generálva az *egy idős matematikatanár olajportréja táblával* utasítás alapján diff --git a/translations/id/README.md b/translations/id/README.md index aae77581..56b98da6 100644 --- a/translations/id/README.md +++ b/translations/id/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # Kecerdasan Buatan untuk Pemula - Kurikulum -|![Sketchnote oleh @girlie_mac https://twitter.com/girlie_mac](../../translated_images/id/ai-overview.0857791951d19500.png)| +|![Sketchnote oleh @girlie_mac https://twitter.com/girlie_mac](../../translated_images/id/ai-overview.0857791951d19500.webp)| |:---:| | AI Untuk Pemula - _Sketchnote oleh [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/id/lessons/1-Intro/README.md b/translations/id/lessons/1-Intro/README.md index d9d1707c..6d6567b4 100644 --- a/translations/id/lessons/1-Intro/README.md +++ b/translations/id/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Pengantar AI -![Ringkasan konten Pengantar AI dalam bentuk doodle](../../../../translated_images/id/ai-intro.bf28d1ac4235881c.png) +![Ringkasan konten Pengantar AI dalam bentuk doodle](../../../../translated_images/id/ai-intro.bf28d1ac4235881c.webp) > Sketchnote oleh [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Awalnya, komputer ditemukan oleh [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage) untuk mengolah angka dengan mengikuti prosedur yang terdefinisi dengan baik - sebuah algoritma. Komputer modern, meskipun jauh lebih canggih daripada model awal yang diusulkan pada abad ke-19, masih mengikuti ide yang sama tentang perhitungan terkontrol. Oleh karena itu, kita dapat memprogram komputer untuk melakukan sesuatu jika kita mengetahui urutan langkah-langkah yang tepat yang perlu dilakukan untuk mencapai tujuan. -![Foto seseorang](../../../../translated_images/id/dsh_age.d212a30d4e54fb5f.png) +![Foto seseorang](../../../../translated_images/id/dsh_age.d212a30d4e54fb5f.webp) > Foto oleh [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ Untuk informasi lebih lanjut, lihat **[Artificial General Intelligence](https:// Salah satu masalah ketika membahas istilah **[Kecerdasan](https://en.wikipedia.org/wiki/Intelligence)** adalah tidak adanya definisi yang jelas untuk istilah ini. Seseorang dapat berargumen bahwa kecerdasan terkait dengan **pemikiran abstrak**, atau dengan **kesadaran diri**, tetapi kita tidak dapat mendefinisikannya dengan tepat. -![Foto Kucing](../../../../translated_images/id/photo-cat.8c8e8fb760ffe457.jpg) +![Foto Kucing](../../../../translated_images/id/photo-cat.8c8e8fb760ffe457.webp) > [Foto](https://unsplash.com/photos/75715CVEJhI) oleh [Amber Kipp](https://unsplash.com/@sadmax) dari Unsplash @@ -98,13 +98,13 @@ Sebaliknya, kita dapat mencoba memodelkan elemen-elemen paling sederhana di dala > | Bagaimana dengan ML? | | > |--------------|-----------| -> | Bagian dari Kecerdasan Buatan yang didasarkan pada komputer yang belajar menyelesaikan masalah berdasarkan beberapa data disebut **Machine Learning**. Kita tidak akan membahas pembelajaran mesin klasik dalam kursus ini - kami merujuk Anda ke kurikulum [Machine Learning for Beginners](http://aka.ms/ml-beginners). | ![ML for Beginners](../../../../translated_images/id/ml-for-beginners.9e4fed176fd5817d.png) | +> | Bagian dari Kecerdasan Buatan yang didasarkan pada komputer yang belajar menyelesaikan masalah berdasarkan beberapa data disebut **Machine Learning**. Kita tidak akan membahas pembelajaran mesin klasik dalam kursus ini - kami merujuk Anda ke kurikulum [Machine Learning for Beginners](http://aka.ms/ml-beginners). | ![ML for Beginners](../../../../translated_images/id/ml-for-beginners.9e4fed176fd5817d.webp) | ## Sejarah Singkat AI Kecerdasan Buatan dimulai sebagai sebuah bidang pada pertengahan abad ke-20. Awalnya, penalaran simbolik adalah pendekatan yang dominan, dan ini menghasilkan sejumlah keberhasilan penting, seperti sistem pakar – program komputer yang mampu bertindak sebagai ahli dalam beberapa domain masalah terbatas. Namun, segera menjadi jelas bahwa pendekatan semacam itu tidak dapat berkembang dengan baik. Mengekstraksi pengetahuan dari seorang ahli, merepresentasikannya di dalam komputer, dan menjaga basis pengetahuan tersebut tetap akurat ternyata menjadi tugas yang sangat kompleks, dan terlalu mahal untuk praktis dalam banyak kasus. Hal ini menyebabkan apa yang disebut [AI Winter](https://en.wikipedia.org/wiki/AI_winter) pada tahun 1970-an. -Sejarah Singkat AI +Sejarah Singkat AI > Gambar oleh [Dmitry Soshnikov](http://soshnikov.com) @@ -124,7 +124,7 @@ Demikian pula, kita dapat melihat bagaimana pendekatan terhadap pembuatan “pro * Asisten modern, seperti Cortana, Siri, atau Google Assistant semuanya adalah sistem hibrida yang menggunakan jaringan saraf untuk mengubah ucapan menjadi teks dan mengenali niat kita, lalu menggunakan beberapa penalaran atau algoritma eksplisit untuk melakukan tindakan yang diperlukan. * Di masa depan, kita mungkin mengharapkan model berbasis jaringan sepenuhnya untuk menangani dialog secara mandiri. Keluarga jaringan saraf GPT dan [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) baru-baru ini menunjukkan keberhasilan besar dalam hal ini. -evolusi Tes Turing +evolusi Tes Turing > Gambar oleh Dmitry Soshnikov, [foto](https://unsplash.com/photos/r8LmVbUKgns) oleh [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto), Unsplash ## Penelitian AI Terkini diff --git a/translations/id/lessons/2-Symbolic/Animals.ipynb b/translations/id/lessons/2-Symbolic/Animals.ipynb index 7a6e139d..5a006726 100644 --- a/translations/id/lessons/2-Symbolic/Animals.ipynb +++ b/translations/id/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "Dalam contoh ini, kita akan menerapkan sistem berbasis pengetahuan sederhana untuk menentukan jenis hewan berdasarkan beberapa karakteristik fisik. Sistem ini dapat direpresentasikan dengan pohon AND-OR berikut (ini adalah bagian dari keseluruhan pohon, kita dapat dengan mudah menambahkan beberapa aturan lagi):\n", "\n", - "![](../../../../translated_images/id/AND-OR-Tree.5592d2c70187f283.png)\n" + "![](../../../../translated_images/id/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { diff --git a/translations/id/lessons/2-Symbolic/README.md b/translations/id/lessons/2-Symbolic/README.md index 3f5cb312..ccbad73b 100644 --- a/translations/id/lessons/2-Symbolic/README.md +++ b/translations/id/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Representasi Pengetahuan dan Sistem Pakar -![Ringkasan konten AI simbolik](../../../../translated_images/id/ai-symbolic.715a30cb610411a6.png) +![Ringkasan konten AI simbolik](../../../../translated_images/id/ai-symbolic.715a30cb610411a6.webp) > Sketchnote oleh [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ Sering kali, kita tidak secara ketat mendefinisikan pengetahuan, tetapi kita men Dengan demikian, masalah **representasi pengetahuan** adalah menemukan cara yang efektif untuk merepresentasikan pengetahuan di dalam komputer dalam bentuk data, agar dapat digunakan secara otomatis. Ini dapat dilihat sebagai spektrum: -![Spektrum representasi pengetahuan](../../../../translated_images/id/knowledge-spectrum.b60df631852c0217.png) +![Spektrum representasi pengetahuan](../../../../translated_images/id/knowledge-spectrum.b60df631852c0217.webp) > Gambar oleh [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Sintaks Blok | Indentasi | | | Salah satu keberhasilan awal AI simbolik adalah **sistem pakar** - sistem komputer yang dirancang untuk bertindak sebagai pakar dalam domain masalah yang terbatas. Sistem ini didasarkan pada **basis pengetahuan** yang diekstraksi dari satu atau lebih pakar manusia, dan mereka memiliki **mesin inferensi** yang melakukan penalaran di atasnya. -![Arsitektur Manusia](../../../../translated_images/id/arch-human.5d4d35f1bba3ab1c.png) | ![Sistem Berbasis Pengetahuan](../../../../translated_images/id/arch-kbs.3ec5c150b09fa8da.png) +![Arsitektur Manusia](../../../../translated_images/id/arch-human.5d4d35f1bba3ab1c.webp) | ![Sistem Berbasis Pengetahuan](../../../../translated_images/id/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ Struktur sederhana sistem saraf manusia | Arsitektur sistem berbasis pengetahuan @@ -106,7 +106,7 @@ Sistem pakar dibangun seperti sistem penalaran manusia, yang mengandung **memori Sebagai contoh, mari kita pertimbangkan sistem pakar berikut untuk menentukan hewan berdasarkan karakteristik fisiknya: -![Pohon AND-OR](../../../../translated_images/id/AND-OR-Tree.5592d2c70187f283.png) +![Pohon AND-OR](../../../../translated_images/id/AND-OR-Tree.5592d2c70187f283.webp) > Gambar oleh [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/id/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/id/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index c8fcdeaf..12f80dfe 100644 --- a/translations/id/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/id/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -1255,7 +1255,7 @@ "* Loss pelatihan rendah - model dapat mendekati data pelatihan dengan baik karena memiliki kekuatan ekspresif yang cukup.\n", "* Loss validasi bisa jauh lebih tinggi daripada loss pelatihan dan dapat mulai meningkat selama pelatihan - ini terjadi karena model \"mengingat\" titik-titik pelatihan, dan kehilangan \"gambaran keseluruhan.\"\n", "\n", - "![Overfitting](../../../../../translated_images/id/overfit.a0bd57f717c15769.png)\n", + "![Overfitting](../../../../../translated_images/id/overfit.a0bd57f717c15769.webp)\n", "\n", "> Pada gambar ini, `x` mewakili data pelatihan, `o` - data validasi. Kiri - model linear (satu layer), mendekati sifat data dengan cukup baik. Kanan - model yang overfitting, model mendekati data pelatihan dengan sangat baik, tetapi kehilangan makna untuk data lainnya (error validasi sangat tinggi).\n" ] diff --git a/translations/id/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/id/lessons/3-NeuralNetworks/05-Frameworks/README.md index 9d3a9bb3..14a5a26e 100644 --- a/translations/id/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/id/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Overfitting adalah konsep yang sangat penting dalam pembelajaran mesin, dan sang Pertimbangkan masalah berikut dalam mendekati 5 titik (diwakili oleh `x` pada grafik di bawah): -![linear](../../../../../translated_images/id/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/id/overfit2.131f5800ae10ca5e.jpg) +![linear](../../../../../translated_images/id/overfit1.f24b71c6f652e59e.webp) | ![overfit](../../../../../translated_images/id/overfit2.131f5800ae10ca5e.webp) -------------------------|-------------------------- **Model linear, 2 parameter** | **Model non-linear, 7 parameter** Error pelatihan = 5.3 | Error pelatihan = 0 @@ -79,7 +79,7 @@ Sangat penting untuk menemukan keseimbangan yang tepat antara kompleksitas model Seperti yang dapat Anda lihat dari grafik di atas, overfitting dapat dideteksi dengan error pelatihan yang sangat rendah, dan error validasi yang tinggi. Biasanya selama pelatihan kita akan melihat error pelatihan dan validasi mulai menurun, dan kemudian pada suatu titik error validasi mungkin berhenti menurun dan mulai meningkat. Ini akan menjadi tanda overfitting, dan indikator bahwa kita mungkin harus menghentikan pelatihan pada titik ini (atau setidaknya membuat snapshot model). -![overfitting](../../../../../translated_images/id/Overfitting.408ad91cd90b4371.png) +![overfitting](../../../../../translated_images/id/Overfitting.408ad91cd90b4371.webp) ## Cara mencegah overfitting diff --git a/translations/id/lessons/3-NeuralNetworks/README.md b/translations/id/lessons/3-NeuralNetworks/README.md index ec0bf51f..c3f7eace 100644 --- a/translations/id/lessons/3-NeuralNetworks/README.md +++ b/translations/id/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Pengantar Jaringan Neural -![Ringkasan konten pengantar Jaringan Neural dalam bentuk doodle](../../../../translated_images/id/ai-neuralnetworks.1c687ae40bc86e83.png) +![Ringkasan konten pengantar Jaringan Neural dalam bentuk doodle](../../../../translated_images/id/ai-neuralnetworks.1c687ae40bc86e83.webp) Seperti yang telah kita bahas dalam pengantar, salah satu cara untuk mencapai kecerdasan adalah dengan melatih **model komputer** atau **otak buatan**. Sejak pertengahan abad ke-20, para peneliti mencoba berbagai model matematika, hingga beberapa tahun terakhir arah ini terbukti sangat berhasil. Model matematika otak ini disebut **jaringan neural**. @@ -36,13 +36,13 @@ Dalam kurikulum ini, kita hanya akan fokus pada model jaringan neural. Dari biologi, kita tahu bahwa otak kita terdiri dari sel-sel neural (neuron), masing-masing memiliki beberapa "input" (dendrit) dan satu "output" (akson). Baik dendrit maupun akson dapat menghantarkan sinyal listrik, dan koneksi di antara mereka — yang dikenal sebagai sinaps — dapat menunjukkan tingkat konduktivitas yang bervariasi, yang diatur oleh neurotransmiter. -![Model Neuron](../../../../translated_images/id/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![Model Neuron](../../../../translated_images/id/artneuron.1a5daa88d20ebe6f.png) +![Model Neuron](../../../../translated_images/id/synapse-wikipedia.ed20a9e4726ea1c6.webp) | ![Model Neuron](../../../../translated_images/id/artneuron.1a5daa88d20ebe6f.webp) ----|---- Neuron Asli *([Gambar](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) dari Wikipedia)* | Neuron Buatan *(Gambar oleh Penulis)* Dengan demikian, model matematika paling sederhana dari neuron memiliki beberapa input X1, ..., XN dan satu output Y, serta serangkaian bobot W1, ..., WN. Output dihitung sebagai: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) di mana f adalah beberapa **fungsi aktivasi** non-linear. diff --git a/translations/id/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/id/lessons/4-ComputerVision/06-IntroCV/README.md index e8468321..f7f13226 100644 --- a/translations/id/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/id/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ Dalam [OpenCV Notebook](OpenCV.ipynb), kami memberikan beberapa contoh kapan com * **Pra-pemrosesan foto buku Braille**. Kami fokus pada bagaimana kami dapat menggunakan thresholding, deteksi fitur, transformasi perspektif, dan manipulasi NumPy untuk memisahkan simbol Braille individu untuk klasifikasi lebih lanjut oleh jaringan saraf. -![Gambar Braille](../../../../../translated_images/id/braille.341962ff76b1bd70.jpeg) | ![Gambar Braille yang Diproses](../../../../../translated_images/id/braille-result.46530fea020b03c7.png) | ![Simbol Braille](../../../../../translated_images/id/braille-symbols.0159185ab69d5339.png) +![Gambar Braille](../../../../../translated_images/id/braille.341962ff76b1bd70.webp) | ![Gambar Braille yang Diproses](../../../../../translated_images/id/braille-result.46530fea020b03c7.webp) | ![Simbol Braille](../../../../../translated_images/id/braille-symbols.0159185ab69d5339.webp) ----|-----|----- > Gambar dari [OpenCV.ipynb](OpenCV.ipynb) * **Mendeteksi gerakan dalam video menggunakan perbedaan frame**. Jika kamera tetap, maka frame dari umpan kamera seharusnya cukup mirip satu sama lain. Karena frame direpresentasikan sebagai array, hanya dengan mengurangi array untuk dua frame berturut-turut kita akan mendapatkan perbedaan piksel, yang seharusnya rendah untuk frame statis, dan menjadi lebih tinggi ketika ada gerakan yang signifikan dalam gambar. -![Gambar frame video dan perbedaan frame](../../../../../translated_images/id/frame-difference.706f805491a0883c.png) +![Gambar frame video dan perbedaan frame](../../../../../translated_images/id/frame-difference.706f805491a0883c.webp) > Gambar dari [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ Dalam [OpenCV Notebook](OpenCV.ipynb), kami memberikan beberapa contoh kapan com - **Dense Optical Flow** menghitung medan vektor yang menunjukkan untuk setiap piksel ke mana ia bergerak. - **Sparse Optical Flow** didasarkan pada mengambil beberapa fitur khas dalam gambar (misalnya, tepi), dan membangun trajektorinya dari frame ke frame. -![Gambar Optical Flow](../../../../../translated_images/id/optical.1f4a94464579a83a.png) +![Gambar Optical Flow](../../../../../translated_images/id/optical.1f4a94464579a83a.webp) > Gambar dari [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/id/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/id/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 0988bea6..34e62a22 100644 --- a/translations/id/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/id/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 adalah jaringan yang mencapai akurasi 92,7% dalam klasifikasi top-5 ImageNet pada tahun 2014. Jaringan ini memiliki struktur lapisan sebagai berikut: -![Lapisan ImageNet](../../../../../translated_images/id/vgg-16-arch1.d901a5583b3a51ba.jpg) +![Lapisan ImageNet](../../../../../translated_images/id/vgg-16-arch1.d901a5583b3a51ba.webp) Seperti yang dapat Anda lihat, VGG mengikuti arsitektur piramida tradisional, yaitu urutan lapisan konvolusi-pooling. -![Piramida ImageNet](../../../../../translated_images/id/vgg-16-arch.64ff2137f50dd49f.jpg) +![Piramida ImageNet](../../../../../translated_images/id/vgg-16-arch.64ff2137f50dd49f.webp) > Gambar dari [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/id/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/id/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index c1eec964..f0c8ea7d 100644 --- a/translations/id/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/id/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -262,7 +262,7 @@ "\n", "Dengan demikian, dalam CNN yang khas, akan ada beberapa lapisan konvolusi, dengan lapisan pooling di antaranya untuk mengurangi dimensi gambar. Kita juga akan meningkatkan jumlah filter, karena seiring pola menjadi lebih kompleks, ada lebih banyak kombinasi menarik yang perlu kita cari.\n", "\n", - "![Gambar yang menunjukkan beberapa lapisan konvolusi dengan lapisan pooling.](../../../../../translated_images/id/cnn-pyramid.85915455759ef0ce.png)\n", + "![Gambar yang menunjukkan beberapa lapisan konvolusi dengan lapisan pooling.](../../../../../translated_images/id/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "Karena dimensi spasial yang berkurang dan dimensi fitur/filter yang meningkat, arsitektur ini juga disebut sebagai **arsitektur piramida**.\n" ] diff --git a/translations/id/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/id/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index b8f38be9..46b7cf30 100644 --- a/translations/id/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/id/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -358,7 +358,7 @@ "\n", "Dengan demikian, dalam CNN yang khas akan ada beberapa lapisan konvolusi, dengan lapisan pooling di antaranya untuk mengurangi dimensi gambar. Kita juga akan meningkatkan jumlah filter, karena saat pola menjadi lebih kompleks - ada lebih banyak kombinasi menarik yang perlu kita cari.\n", "\n", - "![Gambar yang menunjukkan beberapa lapisan konvolusi dengan lapisan pooling.](../../../../../translated_images/id/cnn-pyramid.85915455759ef0ce.png)\n", + "![Gambar yang menunjukkan beberapa lapisan konvolusi dengan lapisan pooling.](../../../../../translated_images/id/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "Karena pengurangan dimensi spasial dan peningkatan dimensi fitur/filter, arsitektur ini juga disebut **arsitektur piramida**.\n" ] diff --git a/translations/id/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/id/lessons/4-ComputerVision/07-ConvNets/README.md index c265b4a3..a0be66ba 100644 --- a/translations/id/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/id/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ Dalam kehidupan nyata, kita ingin dapat mengenali objek dalam gambar tanpa pedul Untuk mengekstrak pola, kita akan menggunakan konsep **filter konvolusi**. Seperti yang Anda ketahui, gambar direpresentasikan sebagai matriks 2D, atau tensor 3D dengan kedalaman warna. Menerapkan filter berarti kita mengambil matriks **filter kernel** yang relatif kecil, dan untuk setiap piksel dalam gambar asli kita menghitung rata-rata berbobot dengan titik-titik tetangga. Kita dapat melihat ini seperti jendela kecil yang meluncur di seluruh gambar, dan meratakan semua piksel sesuai dengan bobot dalam matriks filter kernel. -![Filter Tepi Vertikal](../../../../../translated_images/id/filter-vert.b7148390ca0bc356.png) | ![Filter Tepi Horizontal](../../../../../translated_images/id/filter-horiz.59b80ed4feb946ef.png) +![Filter Tepi Vertikal](../../../../../translated_images/id/filter-vert.b7148390ca0bc356.webp) | ![Filter Tepi Horizontal](../../../../../translated_images/id/filter-horiz.59b80ed4feb946ef.webp) ----|---- > Gambar oleh Dmitry Soshnikov @@ -38,7 +38,7 @@ Cara kerja CNN didasarkan pada ide-ide penting berikut: * Kita dapat merancang jaringan sedemikian rupa sehingga filter dilatih secara otomatis * Kita dapat menggunakan pendekatan yang sama untuk menemukan pola dalam fitur tingkat tinggi, bukan hanya dalam gambar asli. Dengan demikian, ekstraksi fitur CNN bekerja pada hierarki fitur, mulai dari kombinasi piksel tingkat rendah hingga kombinasi tingkat tinggi dari bagian gambar. -![Ekstraksi Fitur Hierarkis](../../../../../translated_images/id/FeatureExtractionCNN.d9b456cbdae7cb64.png) +![Ekstraksi Fitur Hierarkis](../../../../../translated_images/id/FeatureExtractionCNN.d9b456cbdae7cb64.webp) > Gambar dari [makalah oleh Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), berdasarkan [penelitian mereka](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ Sebagian besar CNN yang digunakan untuk pemrosesan gambar mengikuti apa yang dis Sebagai contoh, mari kita lihat arsitektur VGG-16, sebuah jaringan yang mencapai akurasi 92.7% dalam klasifikasi top-5 ImageNet pada tahun 2014: -![Lapisan ImageNet](../../../../../translated_images/id/vgg-16-arch1.d901a5583b3a51ba.jpg) +![Lapisan ImageNet](../../../../../translated_images/id/vgg-16-arch1.d901a5583b3a51ba.webp) -![Piramida ImageNet](../../../../../translated_images/id/vgg-16-arch.64ff2137f50dd49f.jpg) +![Piramida ImageNet](../../../../../translated_images/id/vgg-16-arch.64ff2137f50dd49f.webp) > Gambar dari [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/id/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/id/lessons/4-ComputerVision/07-ConvNets/lab/README.md index e1265a61..74a174e3 100644 --- a/translations/id/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/id/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ Anda perlu melatih jaringan saraf konvolusi untuk mengklasifikasikan berbagai ra Kita akan menggunakan [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/), yang berisi gambar dari 37 ras anjing dan kucing yang berbeda. -![Dataset yang akan kita gunakan](../../../../../../translated_images/id/data.50b2a9d5484bdbf0.png) +![Dataset yang akan kita gunakan](../../../../../../translated_images/id/data.50b2a9d5484bdbf0.webp) Untuk mengunduh dataset, gunakan cuplikan kode berikut: diff --git a/translations/id/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/id/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index e0683672..30dab601 100644 --- a/translations/id/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/id/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "Untuk memvisualisasikan kucing yang ideal, kita akan memulai dengan gambar acak berupa noise, dan mencoba menggunakan teknik optimasi gradient descent untuk menyesuaikan gambar agar jaringan dapat mengenali kucing.\n", "\n", - "![Optimization Loop](../../../../../translated_images/id/ideal-cat-loop.999fbb8ff306e044.png)\n", + "![Optimization Loop](../../../../../translated_images/id/ideal-cat-loop.999fbb8ff306e044.webp)\n", "\n", "Berikut adalah gambar awal kita:\n" ] diff --git a/translations/id/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/id/lessons/4-ComputerVision/08-TransferLearning/README.md index 17a167be..3d3d0c98 100644 --- a/translations/id/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/id/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Baik Keras maupun PyTorch memiliki fungsi untuk dengan mudah memuat bobot jaring Berikut adalah contoh fitur yang diekstrak dari gambar kucing oleh jaringan VGG-16: -![Fitur yang diekstrak oleh VGG-16](../../../../../translated_images/id/features.6291f9c7ba3a0b95.png) +![Fitur yang diekstrak oleh VGG-16](../../../../../translated_images/id/features.6291f9c7ba3a0b95.webp) ## Dataset Kucing vs. Anjing @@ -48,19 +48,19 @@ Jaringan neural pra-latih mengandung berbagai pola di dalam "otaknya", termasuk Salah satu pendekatan yang bisa kita ambil adalah memulai dengan gambar acak, lalu mencoba menggunakan teknik **optimisasi penurunan gradien** untuk menyesuaikan gambar tersebut sedemikian rupa sehingga jaringan mulai berpikir bahwa itu adalah kucing. -![Loop Optimisasi Gambar](../../../../../translated_images/id/ideal-cat-loop.999fbb8ff306e044.png) +![Loop Optimisasi Gambar](../../../../../translated_images/id/ideal-cat-loop.999fbb8ff306e044.webp) Namun, jika kita melakukan ini, kita akan mendapatkan sesuatu yang sangat mirip dengan noise acak. Hal ini terjadi karena *ada banyak cara untuk membuat jaringan berpikir bahwa gambar input adalah kucing*, termasuk beberapa yang tidak masuk akal secara visual. Meskipun gambar-gambar tersebut mengandung banyak pola khas untuk kucing, tidak ada yang membatasi mereka untuk menjadi visual yang jelas. Untuk meningkatkan hasil, kita dapat menambahkan istilah lain ke dalam fungsi loss, yang disebut **variation loss**. Ini adalah metrik yang menunjukkan seberapa mirip piksel-piksel yang berdekatan dalam gambar. Meminimalkan variation loss membuat gambar lebih halus, dan menghilangkan noise - sehingga mengungkapkan pola yang lebih menarik secara visual. Berikut adalah contoh gambar "ideal" yang diklasifikasikan sebagai kucing dan zebra dengan probabilitas tinggi: -![Kucing Ideal](../../../../../translated_images/id/ideal-cat.203dd4597643d6b0.png) | ![Zebra Ideal](../../../../../translated_images/id/ideal-zebra.7f70e8b54ee15a7a.png) +![Kucing Ideal](../../../../../translated_images/id/ideal-cat.203dd4597643d6b0.webp) | ![Zebra Ideal](../../../../../translated_images/id/ideal-zebra.7f70e8b54ee15a7a.webp) -----|----- *Kucing Ideal* | *Zebra Ideal* Pendekatan serupa dapat digunakan untuk melakukan apa yang disebut **serangan adversarial** pada jaringan neural. Misalkan kita ingin mengelabui jaringan neural dan membuat gambar anjing terlihat seperti kucing. Jika kita mengambil gambar anjing, yang dikenali oleh jaringan sebagai anjing, kita kemudian dapat sedikit menyesuaikannya menggunakan optimisasi penurunan gradien, hingga jaringan mulai mengklasifikasikannya sebagai kucing: -![Gambar Anjing](../../../../../translated_images/id/original-dog.8f68a67d2fe0911f.png) | ![Gambar anjing yang diklasifikasikan sebagai kucing](../../../../../translated_images/id/adversarial-dog.d9fc7773b0142b89.png) +![Gambar Anjing](../../../../../translated_images/id/original-dog.8f68a67d2fe0911f.webp) | ![Gambar anjing yang diklasifikasikan sebagai kucing](../../../../../translated_images/id/adversarial-dog.d9fc7773b0142b89.webp) -----|----- *Gambar asli anjing* | *Gambar anjing yang diklasifikasikan sebagai kucing* diff --git a/translations/id/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/id/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index 4c6b29de..10574b7a 100644 --- a/translations/id/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/id/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "Karena kita melatih autoencoder untuk menangkap sebanyak mungkin informasi dari gambar asli agar rekonstruksi akurat, jaringan mencoba menemukan **embedding** terbaik dari gambar input untuk menangkap maknanya.\n", "\n", - "![Diagram AutoEncoder](../../../../../translated_images/id/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![Diagram AutoEncoder](../../../../../translated_images/id/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "> Gambar dari [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", diff --git a/translations/id/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/id/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index 00a33b1e..40c7091b 100644 --- a/translations/id/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/id/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "Karena kita melatih autoencoder untuk menangkap sebanyak mungkin informasi dari gambar asli agar rekonstruksi akurat, jaringan mencoba menemukan **embedding** terbaik dari gambar input untuk menangkap maknanya.\n", "\n", - "![Diagram AutoEncoder](../../../../../translated_images/id/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![Diagram AutoEncoder](../../../../../translated_images/id/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "*Gambar dari [blog Keras](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", diff --git a/translations/id/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/id/lessons/4-ComputerVision/09-Autoencoders/README.md index c011c4d9..983a1239 100644 --- a/translations/id/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/id/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ Namun, kita mungkin ingin menggunakan data mentah (tidak berlabel) untuk melatih Karena kita melatih autoencoder untuk menangkap sebanyak mungkin informasi dari gambar asli agar dapat merekonstruksi dengan akurat, jaringan ini mencoba menemukan **embedding** terbaik dari gambar input untuk menangkap maknanya. -![Diagram AutoEncoder](../../../../../translated_images/id/autoencoder_schema.5e6fc9ad98a5eb61.jpg) +![Diagram AutoEncoder](../../../../../translated_images/id/autoencoder_schema.5e6fc9ad98a5eb61.webp) > Gambar dari [blog Keras](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/id/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/id/lessons/4-ComputerVision/11-ObjectDetection/README.md index fb553c6d..20881df1 100644 --- a/translations/id/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/id/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ Model klasifikasi gambar yang telah kita bahas sejauh ini mengambil gambar dan m ## [Kuis sebelum pelajaran](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Deteksi Objek](../../../../../translated_images/id/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) +![Deteksi Objek](../../../../../translated_images/id/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp) > Gambar dari [situs web YOLO v2](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ Misalkan kita ingin menemukan seekor kucing dalam sebuah gambar, pendekatan yang 2. Melakukan klasifikasi gambar pada setiap ubin. 3. Ubin yang menghasilkan aktivasi yang cukup tinggi dapat dianggap mengandung objek yang dimaksud. -![Deteksi Objek Naif](../../../../../translated_images/id/naive-detection.e7f1ba220ccd08c6.png) +![Deteksi Objek Naif](../../../../../translated_images/id/naive-detection.e7f1ba220ccd08c6.webp) > *Gambar dari [Notebook Latihan](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ Anda mungkin menemukan dataset berikut untuk tugas ini: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 kelas * [COCO](http://cocodataset.org/#home) - Common Objects in Context. 80 kelas, kotak pembatas, dan masker segmentasi -![COCO](../../../../../translated_images/id/coco-examples.71bc60380fa6cceb.jpg) +![COCO](../../../../../translated_images/id/coco-examples.71bc60380fa6cceb.webp) ## Metrik Deteksi Objek @@ -50,7 +50,7 @@ Anda mungkin menemukan dataset berikut untuk tugas ini: Sementara untuk klasifikasi gambar mudah untuk mengukur seberapa baik algoritma bekerja, untuk deteksi objek kita perlu mengukur baik kebenaran kelas maupun ketepatan lokasi kotak pembatas yang dihasilkan. Untuk yang terakhir, kita menggunakan **Intersection over Union** (IoU), yang mengukur seberapa baik dua kotak (atau dua area arbitrer) saling tumpang tindih. -![IoU](../../../../../translated_images/id/iou_equation.9a4751d40fff4e11.png) +![IoU](../../../../../translated_images/id/iou_equation.9a4751d40fff4e11.webp) > *Gambar 2 dari [blog yang sangat bagus tentang IoU](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ Ada dua kelas besar algoritma deteksi objek: [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) menggunakan [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) untuk menghasilkan struktur hierarkis dari wilayah ROI, yang kemudian diteruskan melalui ekstraktor fitur CNN dan pengklasifikasi SVM untuk menentukan kelas objek, serta regresi linier untuk menentukan koordinat *bounding box*. [Makalah Resmi](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/id/rcnn1.cae407020dfb1d1f.png) +![RCNN](../../../../../translated_images/id/rcnn1.cae407020dfb1d1f.webp) > *Gambar dari van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/id/rcnn2.2d9530bb83516484.png) +![RCNN-1](../../../../../translated_images/id/rcnn2.2d9530bb83516484.webp) > *Gambar dari [blog ini](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e) @@ -110,7 +110,7 @@ Ada dua kelas besar algoritma deteksi objek: Pendekatan ini mirip dengan R-CNN, tetapi wilayah didefinisikan setelah lapisan konvolusi diterapkan. -![FRCNN](../../../../../translated_images/id/f-rcnn.3cda6d9bb4188875.png) +![FRCNN](../../../../../translated_images/id/f-rcnn.3cda6d9bb4188875.webp) > Gambar dari [Makalah Resmi](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ Pendekatan ini mirip dengan R-CNN, tetapi wilayah didefinisikan setelah lapisan Ide utama pendekatan ini adalah menggunakan jaringan saraf untuk memprediksi ROI - yang disebut *Region Proposal Network*. [Makalah](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/id/faster-rcnn.8d46c099b87ef30a.png) +![FasterRCNN](../../../../../translated_images/id/faster-rcnn.8d46c099b87ef30a.webp) > Gambar dari [makalah resmi](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ Algoritma ini bahkan lebih cepat daripada Faster R-CNN. Ide utamanya adalah seba 2. Fitur diproses oleh **Position-Sensitive Score Map**. Setiap objek dari $C$ kelas dibagi menjadi $k\times k$ wilayah, dan kita melatih untuk memprediksi bagian-bagian objek. 3. Untuk setiap bagian dari wilayah $k\times k$, semua jaringan memberikan suara untuk kelas objek, dan kelas objek dengan suara maksimum dipilih. -![r-fcn image](../../../../../translated_images/id/r-fcn.13eb88158b99a3da.png) +![r-fcn image](../../../../../translated_images/id/r-fcn.13eb88158b99a3da.webp) > Gambar dari [makalah resmi](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO adalah algoritma satu kali pengolahan secara real-time. Ide utamanya adalah * Gambar dibagi menjadi $S\times S$ wilayah. * Untuk setiap wilayah, **CNN** memprediksi $n$ objek yang mungkin, koordinat *bounding box*, dan *confidence*=*probabilitas* * IoU. - ![YOLO](../../../../../translated_images/id/yolo.a2648ec82ee8bb4e.png) + ![YOLO](../../../../../translated_images/id/yolo.a2648ec82ee8bb4e.webp) > Gambar dari [makalah resmi](https://arxiv.org/abs/1506.02640) diff --git a/translations/id/lessons/4-ComputerVision/README.md b/translations/id/lessons/4-ComputerVision/README.md index 22dfa4ed..be75a2a6 100644 --- a/translations/id/lessons/4-ComputerVision/README.md +++ b/translations/id/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Penglihatan Komputer -![Ringkasan konten Penglihatan Komputer dalam bentuk doodle](../../../../translated_images/id/ai-computervision.6506ebebac3fbf76.png) +![Ringkasan konten Penglihatan Komputer dalam bentuk doodle](../../../../translated_images/id/ai-computervision.6506ebebac3fbf76.webp) Di bagian ini kita akan mempelajari tentang: diff --git a/translations/id/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/id/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index 3777532d..6f1d6b74 100644 --- a/translations/id/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/id/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "Representasi vektor **Bag of Words** (BoW) adalah representasi vektor tradisional yang paling umum digunakan. Setiap kata dikaitkan dengan indeks vektor, elemen vektor berisi jumlah kemunculan sebuah kata dalam dokumen tertentu.\n", "\n", - "![Gambar yang menunjukkan bagaimana representasi vektor bag of words disimpan dalam memori.](../../../../../translated_images/id/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![Gambar yang menunjukkan bagaimana representasi vektor bag of words disimpan dalam memori.](../../../../../translated_images/id/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Note**: Anda juga dapat memikirkan BoW sebagai jumlah dari semua vektor one-hot-encoded untuk kata-kata individual dalam teks.\n", "\n", diff --git a/translations/id/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/id/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index ab363522..a36d15b2 100644 --- a/translations/id/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/id/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "Representasi vektor **Bag-of-words** (BoW) adalah representasi vektor tradisional yang paling sederhana untuk dipahami. Setiap kata dikaitkan dengan indeks vektor, dan elemen vektor berisi jumlah kemunculan setiap kata dalam dokumen tertentu.\n", "\n", - "![Gambar yang menunjukkan bagaimana representasi vektor bag-of-words disimpan dalam memori.](../../../../../translated_images/id/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![Gambar yang menunjukkan bagaimana representasi vektor bag-of-words disimpan dalam memori.](../../../../../translated_images/id/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Note**: Anda juga dapat memikirkan BoW sebagai jumlah dari semua vektor one-hot-encoded untuk setiap kata dalam teks.\n", "\n", diff --git a/translations/id/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/id/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 13737254..a7de90ea 100644 --- a/translations/id/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/id/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "Dengan menggunakan lapisan embedding sebagai lapisan pertama dalam jaringan kita, kita dapat beralih dari model bag-of-words ke model **embedding bag**, di mana kita pertama-tama mengonversi setiap kata dalam teks kita menjadi embedding yang sesuai, lalu menghitung beberapa fungsi agregat atas semua embedding tersebut, seperti `sum`, `average`, atau `max`.\n", "\n", - "![Gambar yang menunjukkan pengklasifikasi embedding untuk lima kata dalam urutan.](../../../../../translated_images/id/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![Gambar yang menunjukkan pengklasifikasi embedding untuk lima kata dalam urutan.](../../../../../translated_images/id/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "Jaringan saraf pengklasifikasi kita akan dimulai dengan lapisan embedding, kemudian lapisan agregasi, dan pengklasifikasi linear di atasnya:\n" ] @@ -176,7 +176,7 @@ "\n", "Dalam arsitektur sebelumnya, kita perlu menambahkan padding pada semua urutan agar memiliki panjang yang sama untuk dimasukkan ke dalam minibatch. Ini bukan cara yang paling efisien untuk merepresentasikan urutan dengan panjang variabel - pendekatan lain adalah menggunakan vektor **offset**, yang akan menyimpan offset dari semua urutan yang disimpan dalam satu vektor besar.\n", "\n", - "![Gambar yang menunjukkan representasi urutan dengan offset](../../../../../translated_images/id/offset-sequence-representation.eb73fcefb29b46ee.png)\n", + "![Gambar yang menunjukkan representasi urutan dengan offset](../../../../../translated_images/id/offset-sequence-representation.eb73fcefb29b46ee.webp)\n", "\n", "> **Note**: Pada gambar di atas, kami menunjukkan urutan karakter, tetapi dalam contoh ini kami bekerja dengan urutan kata. Namun, prinsip umum merepresentasikan urutan dengan vektor offset tetap sama.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW lebih cepat, sedangkan skip-gram lebih lambat, tetapi lebih baik dalam merepresentasikan kata-kata yang jarang muncul.\n", "\n", - "![Gambar menunjukkan algoritma CBoW dan Skip-Gram untuk mengonversi kata-kata menjadi vektor.](../../../../../translated_images/id/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![Gambar menunjukkan algoritma CBoW dan Skip-Gram untuk mengonversi kata-kata menjadi vektor.](../../../../../translated_images/id/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Untuk bereksperimen dengan embedding word2vec yang telah dilatih sebelumnya pada dataset Google News, kita dapat menggunakan pustaka **gensim**. Di bawah ini kita menemukan kata-kata yang paling mirip dengan 'neural'\n", "\n", diff --git a/translations/id/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/id/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index b7f51eda..eb7b30a2 100644 --- a/translations/id/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/id/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "Dengan menggunakan lapisan embedding sebagai lapisan pertama dalam jaringan kita, kita dapat beralih dari model bag-of-words ke model **embedding bag**, di mana kita pertama-tama mengonversi setiap kata dalam teks kita ke embedding yang sesuai, lalu menghitung fungsi agregat tertentu dari semua embedding tersebut, seperti `sum`, `average`, atau `max`.\n", "\n", - "![Gambar menunjukkan pengklasifikasi embedding untuk lima kata dalam urutan.](../../../../../translated_images/id/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![Gambar menunjukkan pengklasifikasi embedding untuk lima kata dalam urutan.](../../../../../translated_images/id/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "Jaringan neural pengklasifikasi kita terdiri dari lapisan-lapisan berikut:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW lebih cepat, sedangkan skip-gram lebih lambat, tetapi skip-gram lebih baik dalam merepresentasikan kata-kata yang jarang muncul.\n", "\n", - "![Gambar menunjukkan algoritma CBoW dan Skip-Gram untuk mengonversi kata-kata menjadi vektor.](../../../../../translated_images/id/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![Gambar menunjukkan algoritma CBoW dan Skip-Gram untuk mengonversi kata-kata menjadi vektor.](../../../../../translated_images/id/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Untuk bereksperimen dengan embedding Word2Vec yang telah dilatih sebelumnya pada dataset Google News, kita dapat menggunakan pustaka **gensim**. Di bawah ini kita menemukan kata-kata yang paling mirip dengan 'neural'.\n", "\n", diff --git a/translations/id/lessons/5-NLP/14-Embeddings/README.md b/translations/id/lessons/5-NLP/14-Embeddings/README.md index 4e550e4c..ec851097 100644 --- a/translations/id/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/id/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ Jadi, lapisan embedding akan mengambil sebuah kata sebagai input, dan menghasilk Dengan menggunakan lapisan embedding sebagai lapisan pertama dalam jaringan classifier kita, kita dapat beralih dari model bag-of-words ke model **embedding bag**, di mana kita pertama-tama mengonversi setiap kata dalam teks kita ke embedding yang sesuai, dan kemudian menghitung beberapa fungsi agregat dari semua embedding tersebut, seperti `sum`, `average`, atau `max`. -![Gambar menunjukkan classifier embedding untuk lima kata dalam urutan.](../../../../../translated_images/id/embedding-classifier-example.b77f021a7ee67eee.png) +![Gambar menunjukkan classifier embedding untuk lima kata dalam urutan.](../../../../../translated_images/id/embedding-classifier-example.b77f021a7ee67eee.webp) > Gambar oleh penulis @@ -40,7 +40,7 @@ Untuk mencapai itu, kita perlu melatih model embedding kita terlebih dahulu pada CBoW lebih cepat, sedangkan skip-gram lebih lambat, tetapi lebih baik dalam merepresentasikan kata-kata yang jarang muncul. -![Gambar menunjukkan algoritma CBoW dan Skip-Gram untuk mengonversi kata-kata menjadi vektor.](../../../../../translated_images/id/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![Gambar menunjukkan algoritma CBoW dan Skip-Gram untuk mengonversi kata-kata menjadi vektor.](../../../../../translated_images/id/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Gambar dari [makalah ini](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/id/lessons/5-NLP/15-LanguageModeling/README.md b/translations/id/lessons/5-NLP/15-LanguageModeling/README.md index f4ba0221..f8f33f8e 100644 --- a/translations/id/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/id/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ Dalam contoh sebelumnya, kita menggunakan embedding semantik yang sudah dilatih * **Continuous Bag-of-Words** (CBoW), di mana kita memprediksi token tengah $W_0$ dalam urutan token $W_{-N}$, ..., $W_N$. * **Skip-gram**, di mana kita memprediksi sekumpulan token tetangga {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} dari token tengah $W_0$. -![gambar dari makalah tentang mengonversi kata menjadi vektor](../../../../../translated_images/id/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![gambar dari makalah tentang mengonversi kata menjadi vektor](../../../../../translated_images/id/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Gambar dari [makalah ini](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/id/lessons/5-NLP/16-RNN/README.md b/translations/id/lessons/5-NLP/16-RNN/README.md index ffd27e3e..3d8aa08b 100644 --- a/translations/id/lessons/5-NLP/16-RNN/README.md +++ b/translations/id/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ Pada bagian sebelumnya, kita telah menggunakan representasi semantik yang kaya d Untuk menangkap makna dari urutan teks, kita perlu menggunakan arsitektur jaringan saraf lain yang disebut **jaringan saraf rekurens**, atau RNN. Dalam RNN, kita melewatkan kalimat kita melalui jaringan satu simbol pada satu waktu, dan jaringan menghasilkan beberapa **state**, yang kemudian kita lewati kembali ke jaringan bersama simbol berikutnya. -![RNN](../../../../../translated_images/id/rnn.27f5c29c53d727b5.png) +![RNN](../../../../../translated_images/id/rnn.27f5c29c53d727b5.webp) > Gambar oleh penulis @@ -61,7 +61,7 @@ Kita telah membahas jaringan rekurens yang beroperasi dalam satu arah, dari awal Jaringan rekurens, baik satu arah maupun bidirectional, menangkap pola tertentu dalam urutan, dan dapat menyimpannya ke dalam vektor state atau melewatkannya ke output. Seperti pada jaringan konvolusi, kita dapat membangun lapisan rekurens lain di atas yang pertama untuk menangkap pola tingkat tinggi dan membangun dari pola tingkat rendah yang diekstraksi oleh lapisan pertama. Ini membawa kita pada konsep **RNN multilayer** yang terdiri dari dua atau lebih jaringan rekurens, di mana output dari lapisan sebelumnya dilewatkan ke lapisan berikutnya sebagai input. -![Gambar menunjukkan RNN LSTM multilayer](../../../../../translated_images/id/multi-layer-lstm.dd975e29bb2a59fe.jpg) +![Gambar menunjukkan RNN LSTM multilayer](../../../../../translated_images/id/multi-layer-lstm.dd975e29bb2a59fe.webp) *Gambar dari [postingan luar biasa ini](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) oleh Fernando López* diff --git a/translations/id/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/id/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index 43a657b4..55ba344f 100644 --- a/translations/id/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/id/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "Jaringan rekuren, baik satu arah maupun bidirectional, menangkap pola tertentu dalam sebuah urutan, dan dapat menyimpannya ke dalam vektor state atau meneruskannya ke output. Seperti pada jaringan konvolusional, kita dapat membangun lapisan rekuren lain di atas lapisan pertama untuk menangkap pola tingkat yang lebih tinggi, yang dibangun dari pola tingkat rendah yang diekstraksi oleh lapisan pertama. Ini membawa kita pada konsep **RNN multilayer**, yang terdiri dari dua atau lebih jaringan rekuren, di mana output dari lapisan sebelumnya diteruskan ke lapisan berikutnya sebagai input.\n", "\n", - "![Gambar yang menunjukkan Multilayer long-short-term-memory- RNN](../../../../../translated_images/id/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![Gambar yang menunjukkan Multilayer long-short-term-memory- RNN](../../../../../translated_images/id/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*Gambar dari [postingan luar biasa ini](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) oleh Fernando López*\n", "\n", diff --git a/translations/id/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/id/lessons/5-NLP/16-RNN/RNNTF.ipynb index 3f2082ae..f8e7335c 100644 --- a/translations/id/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/id/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "Untuk menangkap makna dari urutan teks, kita akan menggunakan arsitektur jaringan saraf yang disebut **jaringan saraf berulang**, atau RNN. Saat menggunakan RNN, kita melewatkan kalimat kita melalui jaringan satu token pada satu waktu, dan jaringan menghasilkan beberapa **state**, yang kemudian kita lewati kembali ke jaringan bersama token berikutnya.\n", "\n", - "![Gambar menunjukkan contoh pembuatan jaringan saraf berulang.](../../../../../translated_images/id/rnn.27f5c29c53d727b5.png)\n", + "![Gambar menunjukkan contoh pembuatan jaringan saraf berulang.](../../../../../translated_images/id/rnn.27f5c29c53d727b5.webp)\n", "\n", "Diberikan urutan input token $X_0,\\dots,X_n$, RNN menciptakan urutan blok jaringan saraf, dan melatih urutan ini secara end-to-end menggunakan backpropagation. Setiap blok jaringan mengambil pasangan $(X_i,S_i)$ sebagai input, dan menghasilkan $S_{i+1}$ sebagai hasil. State akhir $S_n$ atau output $Y_n$ masuk ke dalam pengklasifikasi linier untuk menghasilkan hasil. Semua blok jaringan berbagi bobot yang sama, dan dilatih secara end-to-end menggunakan satu langkah backpropagation.\n", "\n", @@ -369,7 +369,7 @@ "\n", "Jaringan rekuren, baik unidirectional maupun bidirectional, menangkap pola dalam sebuah urutan, dan menyimpannya ke dalam vektor status atau mengembalikannya sebagai output. Seperti halnya jaringan konvolusi, kita dapat membangun lapisan rekuren lain setelah lapisan pertama untuk menangkap pola tingkat yang lebih tinggi, yang dibangun dari pola tingkat rendah yang diekstraksi oleh lapisan pertama. Hal ini membawa kita pada konsep **RNN multilayer**, yang terdiri dari dua atau lebih jaringan rekuren, di mana output dari lapisan sebelumnya diteruskan ke lapisan berikutnya sebagai input.\n", "\n", - "![Gambar menunjukkan RNN LSTM multilayer](../../../../../translated_images/id/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![Gambar menunjukkan RNN LSTM multilayer](../../../../../translated_images/id/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*Gambar dari [postingan luar biasa ini](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) oleh Fernando López.*\n", "\n", diff --git a/translations/id/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/id/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index 9c70464a..dd60bb61 100644 --- a/translations/id/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/id/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "Cara kita melatih RNN untuk menghasilkan teks adalah sebagai berikut. Pada setiap langkah, kita akan mengambil urutan karakter dengan panjang `nchars`, dan meminta jaringan untuk menghasilkan karakter keluaran berikutnya untuk setiap karakter masukan:\n", "\n", - "![Gambar menunjukkan contoh RNN menghasilkan kata 'HELLO'.](../../../../../translated_images/id/rnn-generate.56c54afb52f9781d.png)\n", + "![Gambar menunjukkan contoh RNN menghasilkan kata 'HELLO'.](../../../../../translated_images/id/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "Bergantung pada skenario sebenarnya, kita mungkin juga ingin menyertakan beberapa karakter khusus, seperti *akhir urutan* ``. Dalam kasus kita, kita hanya ingin melatih jaringan untuk menghasilkan teks tanpa henti, sehingga kita akan menetapkan ukuran setiap urutan sama dengan `nchars` token. Akibatnya, setiap contoh pelatihan akan terdiri dari `nchars` masukan dan `nchars` keluaran (yang merupakan urutan masukan yang digeser satu simbol ke kiri). Minibatch akan terdiri dari beberapa urutan seperti itu.\n", "\n", diff --git a/translations/id/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/id/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index 0f61f829..252f5c88 100644 --- a/translations/id/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/id/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -113,7 +113,7 @@ "\n", "Cara kita melatih RNN untuk menghasilkan judul berita adalah sebagai berikut. Pada setiap langkah, kita akan mengambil satu judul, yang akan dimasukkan ke dalam RNN, dan untuk setiap karakter input, kita akan meminta jaringan untuk menghasilkan karakter output berikutnya:\n", "\n", - "![Gambar yang menunjukkan contoh RNN menghasilkan kata 'HELLO'.](../../../../../translated_images/id/rnn-generate.56c54afb52f9781d.png)\n", + "![Gambar yang menunjukkan contoh RNN menghasilkan kata 'HELLO'.](../../../../../translated_images/id/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "Untuk karakter terakhir dari urutan kita, kita akan meminta jaringan untuk menghasilkan token ``.\n", "\n", diff --git a/translations/id/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/id/lessons/5-NLP/17-GenerativeNetworks/README.md index 6c8ab014..ecddacae 100644 --- a/translations/id/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/id/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ Dalam arsitektur RNN yang kita bahas di unit sebelumnya, setiap unit RNN menghas Hal ini memungkinkan berbagai arsitektur neural yang ditunjukkan pada gambar di bawah: -![Gambar menunjukkan pola umum jaringan neural rekuren.](../../../../../translated_images/id/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Gambar menunjukkan pola umum jaringan neural rekuren.](../../../../../translated_images/id/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > Gambar dari blog post [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) oleh [Andrej Karpaty](http://karpathy.github.io/) @@ -32,7 +32,7 @@ Di unit ini, kita akan fokus pada model generatif sederhana yang membantu kita m Kita akan melatih RNN ini untuk menghasilkan teks langkah demi langkah. Pada setiap langkah, kita akan mengambil urutan karakter dengan panjang `nchars`, dan meminta jaringan untuk menghasilkan karakter output berikutnya untuk setiap karakter input: -![Gambar menunjukkan contoh RNN menghasilkan kata 'HELLO'.](../../../../../translated_images/id/rnn-generate.56c54afb52f9781d.png) +![Gambar menunjukkan contoh RNN menghasilkan kata 'HELLO'.](../../../../../translated_images/id/rnn-generate.56c54afb52f9781d.webp) Saat menghasilkan teks (selama inferensi), kita mulai dengan beberapa **prompt**, yang dilewatkan melalui sel RNN untuk menghasilkan status intermediate-nya, dan kemudian dari status ini proses generasi dimulai. Kita menghasilkan satu karakter pada satu waktu, dan melewatkan status serta karakter yang dihasilkan ke sel RNN lainnya untuk menghasilkan karakter berikutnya, hingga kita menghasilkan cukup karakter. diff --git a/translations/id/lessons/5-NLP/18-Transformers/README.md b/translations/id/lessons/5-NLP/18-Transformers/README.md index 2b67c1c3..19d7ef12 100644 --- a/translations/id/lessons/5-NLP/18-Transformers/README.md +++ b/translations/id/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ Dengan RNN, sequence-to-sequence diimplementasikan oleh dua jaringan berulang, d **Mekanisme Perhatian** memberikan cara untuk memberi bobot pada dampak kontekstual dari setiap vektor input terhadap setiap prediksi output dari RNN. Cara ini diimplementasikan dengan membuat jalur pintas antara keadaan menengah dari RNN input dan RNN output. Dengan cara ini, saat menghasilkan simbol output yt, kita akan mempertimbangkan semua keadaan tersembunyi input hi, dengan koefisien bobot yang berbeda αt,i. -![Gambar menunjukkan model encoder/decoder dengan lapisan perhatian aditif](../../../../../translated_images/id/encoder-decoder-attention.7a726296894fb567.png) +![Gambar menunjukkan model encoder/decoder dengan lapisan perhatian aditif](../../../../../translated_images/id/encoder-decoder-attention.7a726296894fb567.webp) > Model encoder-decoder dengan mekanisme perhatian aditif dalam [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), dikutip dari [blog ini](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) Matriks perhatian {αi,j} akan mewakili tingkat di mana kata-kata tertentu dalam input berperan dalam menghasilkan kata tertentu dalam urutan output. Di bawah ini adalah contoh matriks semacam itu: -![Gambar menunjukkan contoh alignment yang ditemukan oleh RNNsearch-50, diambil dari Bahdanau - arviz.org](../../../../../translated_images/id/bahdanau-fig3.09ba2d37f202a6af.png) +![Gambar menunjukkan contoh alignment yang ditemukan oleh RNNsearch-50, diambil dari Bahdanau - arviz.org](../../../../../translated_images/id/bahdanau-fig3.09ba2d37f202a6af.webp) > Gambar dari [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) @@ -66,7 +66,7 @@ Hasil yang kita dapatkan dengan embedding posisi menggabungkan token asli dan po Selanjutnya, kita perlu menangkap beberapa pola dalam urutan kita. Untuk melakukan ini, transformer menggunakan mekanisme **perhatian diri**, yang pada dasarnya adalah perhatian yang diterapkan pada urutan yang sama sebagai input dan output. Menerapkan perhatian diri memungkinkan kita mempertimbangkan **konteks** dalam kalimat, dan melihat kata-kata mana yang saling terkait. Misalnya, ini memungkinkan kita melihat kata-kata yang dirujuk oleh coreferensi, seperti *itu*, dan juga mempertimbangkan konteks: -![](../../../../../translated_images/id/CoreferenceResolution.861924d6d384a7d6.png) +![](../../../../../translated_images/id/CoreferenceResolution.861924d6d384a7d6.webp) > Gambar dari [Blog Google](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Karena setiap posisi input dipetakan secara independen ke setiap posisi output, **BERT** (Bidirectional Encoder Representations from Transformers) adalah jaringan transformer multi-layer yang sangat besar dengan 12 lapisan untuk *BERT-base*, dan 24 untuk *BERT-large*. Model ini pertama kali dilatih pada korpus teks besar (WikiPedia + buku) menggunakan pelatihan tanpa pengawasan (memprediksi kata yang disembunyikan dalam sebuah kalimat). Selama pelatihan awal, model menyerap tingkat pemahaman bahasa yang signifikan yang kemudian dapat dimanfaatkan dengan dataset lain menggunakan fine tuning. Proses ini disebut **transfer learning**. -![gambar dari http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/id/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) +![gambar dari http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/id/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > Gambar [sumber](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/id/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/id/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index 64fc0489..3bb6179f 100644 --- a/translations/id/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/id/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**Mekanisme Perhatian** menyediakan cara untuk memberikan bobot pada dampak kontekstual dari setiap vektor input terhadap setiap prediksi output dari RNN. Cara ini diimplementasikan dengan menciptakan jalur pintas antara state antara dari RNN input dan RNN output. Dengan cara ini, saat menghasilkan simbol output $y_t$, kita akan mempertimbangkan semua state tersembunyi input $h_i$, dengan koefisien bobot yang berbeda $\\alpha_{t,i}$. \n", "\n", - "![Gambar yang menunjukkan model encoder/decoder dengan lapisan perhatian aditif](../../../../../translated_images/id/encoder-decoder-attention.7a726296894fb567.png)\n", + "![Gambar yang menunjukkan model encoder/decoder dengan lapisan perhatian aditif](../../../../../translated_images/id/encoder-decoder-attention.7a726296894fb567.webp)\n", "*Model encoder-decoder dengan mekanisme perhatian aditif dalam [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), dikutip dari [blog ini](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Matriks perhatian $\\{\\alpha_{i,j}\\}$ akan merepresentasikan sejauh mana kata-kata tertentu dalam input berperan dalam menghasilkan kata tertentu dalam urutan output. Berikut adalah contoh matriks seperti itu:\n", "\n", - "![Gambar yang menunjukkan contoh alignment yang ditemukan oleh RNNsearch-50, diambil dari Bahdanau - arviz.org](../../../../../translated_images/id/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![Gambar yang menunjukkan contoh alignment yang ditemukan oleh RNNsearch-50, diambil dari Bahdanau - arviz.org](../../../../../translated_images/id/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*Gambar diambil dari [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) adalah jaringan transformer multi-layer yang sangat besar dengan 12 lapisan untuk *BERT-base*, dan 24 untuk *BERT-large*. Model ini pertama kali dilatih pada korpus teks besar (WikiPedia + buku) menggunakan pelatihan tanpa pengawasan (memprediksi kata yang disembunyikan dalam sebuah kalimat). Selama pelatihan awal, model menyerap tingkat pemahaman bahasa yang signifikan yang kemudian dapat dimanfaatkan dengan dataset lain menggunakan fine tuning. Proses ini disebut **transfer learning**. \n", "\n", - "![gambar dari http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/id/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![gambar dari http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/id/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "Ada banyak variasi arsitektur Transformer termasuk BERT, DistilBERT, BigBird, OpenGPT3, dan lainnya yang dapat disesuaikan. Paket [HuggingFace](https://github.com/huggingface/) menyediakan repositori untuk melatih banyak arsitektur ini dengan PyTorch. \n", "\n", diff --git a/translations/id/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/id/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index 2bad587d..f5b938e8 100644 --- a/translations/id/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/id/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**Mekanisme Perhatian** menyediakan cara untuk memberikan bobot pada dampak kontekstual dari setiap vektor input terhadap setiap prediksi output dari RNN. Cara ini diimplementasikan dengan menciptakan jalur pintas antara state antara dari RNN input dan RNN output. Dengan cara ini, saat menghasilkan simbol output $y_t$, kita akan mempertimbangkan semua state tersembunyi input $h_i$, dengan koefisien bobot yang berbeda $\\alpha_{t,i}$. \n", "\n", - "![Gambar menunjukkan model encoder/decoder dengan lapisan perhatian aditif](../../../../../translated_images/id/encoder-decoder-attention.7a726296894fb567.png)\n", + "![Gambar menunjukkan model encoder/decoder dengan lapisan perhatian aditif](../../../../../translated_images/id/encoder-decoder-attention.7a726296894fb567.webp)\n", "*Model encoder-decoder dengan mekanisme perhatian aditif dalam [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), dikutip dari [blog ini](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Matriks perhatian $\\{\\alpha_{i,j}\\}$ akan merepresentasikan sejauh mana kata-kata tertentu dalam input berperan dalam menghasilkan kata tertentu dalam urutan output. Di bawah ini adalah contoh matriks seperti itu:\n", "\n", - "![Gambar menunjukkan contoh alignment yang ditemukan oleh RNNsearch-50, diambil dari Bahdanau - arviz.org](../../../../../translated_images/id/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![Gambar menunjukkan contoh alignment yang ditemukan oleh RNNsearch-50, diambil dari Bahdanau - arviz.org](../../../../../translated_images/id/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*Gambar diambil dari [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Gambar 3)*\n", "\n", @@ -225,7 +225,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) adalah jaringan transformer multi-layer yang sangat besar dengan 12 lapisan untuk *BERT-base*, dan 24 lapisan untuk *BERT-large*. Model ini pertama kali dilatih pada korpus teks yang sangat besar (WikiPedia + buku) menggunakan pelatihan tanpa pengawasan (memprediksi kata-kata yang disembunyikan dalam sebuah kalimat). Selama pelatihan awal, model ini menyerap pemahaman bahasa yang signifikan yang kemudian dapat dimanfaatkan dengan dataset lain melalui proses penyetelan ulang. Proses ini disebut **transfer learning**.\n", "\n", - "![gambar dari http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/id/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![gambar dari http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/id/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "Ada banyak variasi arsitektur Transformer termasuk BERT, DistilBERT, BigBird, OpenGPT3, dan lainnya yang dapat disesuaikan lebih lanjut.\n", "\n", diff --git a/translations/id/lessons/5-NLP/19-NER/README.md b/translations/id/lessons/5-NLP/19-NER/README.md index 4f93ce62..fe0fa1eb 100644 --- a/translations/id/lessons/5-NLP/19-NER/README.md +++ b/translations/id/lessons/5-NLP/19-NER/README.md @@ -58,7 +58,7 @@ infant | O Karena kita perlu membangun korespondensi satu-ke-satu antara token dan kelas, kita dapat melatih model jaringan neural **many-to-many** yang paling kanan dari gambar ini: -![Gambar menunjukkan pola umum jaringan neural berulang.](../../../../../translated_images/id/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Gambar menunjukkan pola umum jaringan neural berulang.](../../../../../translated_images/id/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > *Gambar dari [blog post ini](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) oleh [Andrej Karpathy](http://karpathy.github.io/). Model klasifikasi token NER sesuai dengan arsitektur jaringan paling kanan pada gambar ini.* diff --git a/translations/id/lessons/5-NLP/README.md b/translations/id/lessons/5-NLP/README.md index eafe3415..a504137b 100644 --- a/translations/id/lessons/5-NLP/README.md +++ b/translations/id/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Pemrosesan Bahasa Alami -![Ringkasan tugas NLP dalam bentuk doodle](../../../../translated_images/id/ai-nlp.b22dcb8ca4707cea.png) +![Ringkasan tugas NLP dalam bentuk doodle](../../../../translated_images/id/ai-nlp.b22dcb8ca4707cea.webp) Di bagian ini, kita akan fokus pada penggunaan Jaringan Saraf untuk menangani tugas-tugas yang berkaitan dengan **Pemrosesan Bahasa Alami (Natural Language Processing/NLP)**. Ada banyak masalah NLP yang ingin kita selesaikan dengan bantuan komputer: diff --git a/translations/id/lessons/6-Other/23-MultiagentSystems/README.md b/translations/id/lessons/6-Other/23-MultiagentSystems/README.md index b84c075d..d0397f1d 100644 --- a/translations/id/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/id/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ Anda dapat membuka salah satu model, misalnya **Biology → Flocking**. Setelah membuka model, Anda akan dibawa ke layar utama NetLogo. Berikut adalah contoh model yang menggambarkan populasi serigala dan domba, dengan sumber daya yang terbatas (rumput). -![NetLogo Main Screen](../../../../../translated_images/id/NetLogo-Main.32653711ec1a01b3.png) +![NetLogo Main Screen](../../../../../translated_images/id/NetLogo-Main.32653711ec1a01b3.webp) > Tangkapan layar oleh Dmitry Soshnikov diff --git a/translations/id/lessons/README.md b/translations/id/lessons/README.md index 260b4821..da01d59c 100644 --- a/translations/id/lessons/README.md +++ b/translations/id/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Gambaran Umum -![Gambaran Umum dalam sebuah sketsa](../../../translated_images/id/ai-overview.0857791951d19500.png) +![Gambaran Umum dalam sebuah sketsa](../../../translated_images/id/ai-overview.0857791951d19500.webp) > Sketsa oleh [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/id/lessons/X-Extras/X1-MultiModal/README.md b/translations/id/lessons/X-Extras/X1-MultiModal/README.md index 89172fbe..895057bb 100644 --- a/translations/id/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/id/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ Setelah keberhasilan model transformer dalam menyelesaikan tugas NLP, arsitektur Ide utama dari CLIP adalah untuk dapat membandingkan teks dengan gambar dan menentukan seberapa baik gambar tersebut sesuai dengan teks. -![Arsitektur CLIP](../../../../../translated_images/id/clip-arch.b3dbf20b4e8ed8be.png) +![Arsitektur CLIP](../../../../../translated_images/id/clip-arch.b3dbf20b4e8ed8be.webp) > *Gambar dari [blog ini](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ Setelah model ini dilatih sebelumnya, kita dapat memberikannya batch gambar dan Misalkan kita perlu mengklasifikasikan gambar antara, misalnya, kucing, anjing, dan manusia. Dalam kasus ini, kita dapat memberikan model sebuah gambar, dan serangkaian teks: "*gambar seekor kucing*", "*gambar seekor anjing*", "*gambar seorang manusia*". Dalam vektor hasil dengan 3 probabilitas, kita hanya perlu memilih indeks dengan nilai tertinggi. -![CLIP untuk Klasifikasi Gambar](../../../../../translated_images/id/clip-class.3af42ef0b2b19369.png) +![CLIP untuk Klasifikasi Gambar](../../../../../translated_images/id/clip-class.3af42ef0b2b19369.webp) > *Gambar dari [blog ini](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ Pelajari lebih lanjut tentang VQGAN di situs web [Taming Transformers](https://c Salah satu perbedaan penting antara VQGAN dan GAN tradisional adalah bahwa GAN tradisional dapat menghasilkan gambar yang cukup baik dari vektor input apa pun, sementara VQGAN cenderung menghasilkan gambar yang tidak koheren. Oleh karena itu, kita perlu membimbing lebih lanjut proses pembuatan gambar, dan itu dapat dilakukan menggunakan CLIP. -![Arsitektur VQGAN+CLIP](../../../../../translated_images/id/vqgan.5027fe05051dfa31.png) +![Arsitektur VQGAN+CLIP](../../../../../translated_images/id/vqgan.5027fe05051dfa31.webp) Untuk menghasilkan gambar yang sesuai dengan teks, kita mulai dengan vektor encoding acak yang diteruskan melalui VQGAN untuk menghasilkan gambar. Kemudian CLIP digunakan untuk menghasilkan fungsi loss yang menunjukkan seberapa baik gambar sesuai dengan teks. Tujuannya adalah meminimalkan loss ini, menggunakan backpropagation untuk menyesuaikan parameter vektor input. Pustaka hebat yang mengimplementasikan VQGAN+CLIP adalah [Pixray](http://github.com/pixray/pixray). -![Gambar yang dihasilkan oleh Pixray](../../../../../translated_images/id/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Gambar yang dihasilkan oleh Pixray](../../../../../translated_images/id/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Gambar yang dihasilkan oleh Pixray](../../../../../translated_images/id/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) +![Gambar yang dihasilkan oleh Pixray](../../../../../translated_images/id/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp) | ![Gambar yang dihasilkan oleh Pixray](../../../../../translated_images/id/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp) | ![Gambar yang dihasilkan oleh Pixray](../../../../../translated_images/id/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp) ----|----|---- Gambar yang dihasilkan dari teks *a closeup watercolor portrait of young male teacher of literature with a book* | Gambar yang dihasilkan dari teks *a closeup oil portrait of young female teacher of computer science with a computer* | Gambar yang dihasilkan dari teks *a closeup oil portrait of old male teacher of mathematics in front of blackboard* diff --git a/translations/it/README.md b/translations/it/README.md index d4ccdbde..c90da403 100644 --- a/translations/it/README.md +++ b/translations/it/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # Intelligenza Artificiale per Principianti - Un Curriculum -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/it/ai-overview.0857791951d19500.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/it/ai-overview.0857791951d19500.webp)| |:---:| | AI For Beginners - _Sketchnote di [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/it/lessons/1-Intro/README.md b/translations/it/lessons/1-Intro/README.md index ef4d1a13..1f6b447a 100644 --- a/translations/it/lessons/1-Intro/README.md +++ b/translations/it/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Introduzione all'Intelligenza Artificiale -![Riepilogo del contenuto dell'introduzione all'IA in uno schizzo](../../../../translated_images/it/ai-intro.bf28d1ac4235881c.png) +![Riepilogo del contenuto dell'introduzione all'IA in uno schizzo](../../../../translated_images/it/ai-intro.bf28d1ac4235881c.webp) > Schizzo di [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Originariamente, i computer furono inventati da [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage) per operare sui numeri seguendo una procedura ben definita - un algoritmo. I computer moderni, anche se significativamente più avanzati rispetto al modello originale proposto nel XIX secolo, seguono ancora lo stesso principio di calcoli controllati. Pertanto, è possibile programmare un computer per fare qualcosa se conosciamo la sequenza esatta di passaggi necessari per raggiungere l'obiettivo. -![Foto di una persona](../../../../translated_images/it/dsh_age.d212a30d4e54fb5f.png) +![Foto di una persona](../../../../translated_images/it/dsh_age.d212a30d4e54fb5f.webp) > Foto di [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ Per maggiori informazioni, consulta **[Intelligenza Artificiale Generale](https: Uno dei problemi nel trattare il termine **[Intelligenza](https://en.wikipedia.org/wiki/Intelligence)** è che non esiste una definizione chiara di questo termine. Si potrebbe sostenere che l'intelligenza sia collegata al **pensiero astratto**, o alla **autoconsapevolezza**, ma non possiamo definirla correttamente. -![Foto di un gatto](../../../../translated_images/it/photo-cat.8c8e8fb760ffe457.jpg) +![Foto di un gatto](../../../../translated_images/it/photo-cat.8c8e8fb760ffe457.webp) > [Foto](https://unsplash.com/photos/75715CVEJhI) di [Amber Kipp](https://unsplash.com/@sadmax) da Unsplash @@ -98,13 +98,13 @@ In alternativa, possiamo cercare di modellare gli elementi più semplici all'int > | E il ML? | | > |--------------|-----------| -> | Parte dell'Intelligenza Artificiale basata sull'apprendimento del computer per risolvere un problema basato su alcuni dati è chiamata **Machine Learning**. Non considereremo il machine learning classico in questo corso - ti rimandiamo al curriculum separato [Machine Learning for Beginners](http://aka.ms/ml-beginners). | ![ML per Principianti](../../../../translated_images/it/ml-for-beginners.9e4fed176fd5817d.png) | +> | Parte dell'Intelligenza Artificiale basata sull'apprendimento del computer per risolvere un problema basato su alcuni dati è chiamata **Machine Learning**. Non considereremo il machine learning classico in questo corso - ti rimandiamo al curriculum separato [Machine Learning for Beginners](http://aka.ms/ml-beginners). | ![ML per Principianti](../../../../translated_images/it/ml-for-beginners.9e4fed176fd5817d.webp) | ## Breve Storia dell'IA L'Intelligenza Artificiale è iniziata come campo a metà del ventesimo secolo. Inizialmente, il ragionamento simbolico era l'approccio prevalente e portò a una serie di successi importanti, come i sistemi esperti – programmi informatici in grado di agire come esperti in alcuni domini di problemi limitati. Tuttavia, presto divenne chiaro che tale approccio non si scala bene. Estrarre la conoscenza da un esperto, rappresentarla in un computer e mantenere accurata quella base di conoscenza si rivelò un compito molto complesso e troppo costoso per essere pratico in molti casi. Questo portò al cosiddetto [AI Winter](https://en.wikipedia.org/wiki/AI_winter) negli anni '70. -Breve Storia dell'IA +Breve Storia dell'IA > Immagine di [Dmitry Soshnikov](http://soshnikov.com) @@ -124,7 +124,7 @@ Allo stesso modo, possiamo vedere come l'approccio verso la creazione di "progra * Gli assistenti moderni, come Cortana, Siri o Google Assistant, sono tutti sistemi ibridi che utilizzano reti neurali per convertire il discorso in testo e riconoscere il nostro intento, e poi impiegano qualche ragionamento o algoritmi espliciti per eseguire le azioni richieste. * In futuro, possiamo aspettarci un modello completamente basato su reti neurali per gestire il dialogo autonomamente. Le recenti famiglie di reti neurali GPT e [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) mostrano grandi successi in questo. -l'evoluzione del Test di Turing +l'evoluzione del Test di Turing > Immagine di Dmitry Soshnikov, [foto](https://unsplash.com/photos/r8LmVbUKgns) di [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto), Unsplash ## Ricerca recente sull'IA diff --git a/translations/it/lessons/2-Symbolic/Animals.ipynb b/translations/it/lessons/2-Symbolic/Animals.ipynb index d28ce9d3..1eedbc4e 100644 --- a/translations/it/lessons/2-Symbolic/Animals.ipynb +++ b/translations/it/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "In questo esempio, implementeremo un semplice sistema basato sulla conoscenza per determinare un animale in base ad alcune caratteristiche fisiche. Il sistema può essere rappresentato dal seguente albero AND-OR (questa è solo una parte dell'intero albero, possiamo facilmente aggiungere altre regole):\n", "\n", - "![](../../../../translated_images/it/AND-OR-Tree.5592d2c70187f283.png)\n" + "![](../../../../translated_images/it/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { diff --git a/translations/it/lessons/2-Symbolic/README.md b/translations/it/lessons/2-Symbolic/README.md index 7b8b34e2..02df9204 100644 --- a/translations/it/lessons/2-Symbolic/README.md +++ b/translations/it/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Rappresentazione della Conoscenza e Sistemi Esperti -![Riepilogo del contenuto sull'IA simbolica](../../../../translated_images/it/ai-symbolic.715a30cb610411a6.png) +![Riepilogo del contenuto sull'IA simbolica](../../../../translated_images/it/ai-symbolic.715a30cb610411a6.webp) > Sketchnote di [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ Spesso non definiamo rigorosamente la conoscenza, ma la allineiamo ad altri conc Pertanto, il problema della **rappresentazione della conoscenza** è trovare un modo efficace per rappresentare la conoscenza all'interno di un computer sotto forma di dati, per renderla automaticamente utilizzabile. Questo può essere visto come uno spettro: -![Spettro della rappresentazione della conoscenza](../../../../translated_images/it/knowledge-spectrum.b60df631852c0217.png) +![Spettro della rappresentazione della conoscenza](../../../../translated_images/it/knowledge-spectrum.b60df631852c0217.webp) > Immagine di [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Sintassi Blocco | Indentazione | | | Uno dei primi successi dell'IA simbolica furono i cosiddetti **sistemi esperti** - sistemi informatici progettati per agire come esperti in un dominio di problemi limitato. Si basavano su una **base di conoscenza** estratta da uno o più esperti umani e contenevano un **motore di inferenza** che eseguiva un ragionamento su di essa. -![Architettura umana](../../../../translated_images/it/arch-human.5d4d35f1bba3ab1c.png) | ![Sistema basato sulla conoscenza](../../../../translated_images/it/arch-kbs.3ec5c150b09fa8da.png) +![Architettura umana](../../../../translated_images/it/arch-human.5d4d35f1bba3ab1c.webp) | ![Sistema basato sulla conoscenza](../../../../translated_images/it/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ Struttura semplificata del sistema neurale umano | Architettura di un sistema basato sulla conoscenza @@ -106,7 +106,7 @@ I sistemi esperti sono costruiti come il sistema di ragionamento umano, che cont Come esempio, consideriamo il seguente sistema esperto per determinare un animale basandosi sulle sue caratteristiche fisiche: -![Albero AND-OR](../../../../translated_images/it/AND-OR-Tree.5592d2c70187f283.png) +![Albero AND-OR](../../../../translated_images/it/AND-OR-Tree.5592d2c70187f283.webp) > Immagine di [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/it/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/it/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index e7937dad..9b625371 100644 --- a/translations/it/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/it/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -1256,7 +1256,7 @@ "* Bassa perdita sui dati di addestramento - il modello può approssimare bene i dati di addestramento, perché ha abbastanza potenza espressiva.\n", "* La perdita sui dati di validazione può essere molto più alta rispetto a quella sui dati di addestramento e può iniziare ad aumentare durante l'addestramento - questo accade perché il modello \"memorizza\" i punti di addestramento, perdendo la \"visione d'insieme\".\n", "\n", - "![Overfitting](../../../../../translated_images/it/overfit.a0bd57f717c15769.png)\n", + "![Overfitting](../../../../../translated_images/it/overfit.a0bd57f717c15769.webp)\n", "\n", "> In questa immagine, `x` rappresenta i dati di addestramento, `o` i dati di validazione. A sinistra - modello lineare (a uno strato), approssima abbastanza bene la natura dei dati. A destra - modello sovradattato, il modello approssima perfettamente i dati di addestramento, ma perde di significato con qualsiasi altro dato (l'errore di validazione è molto alto).\n" ] diff --git a/translations/it/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/it/lessons/3-NeuralNetworks/05-Frameworks/README.md index 76d8a0fe..976b43b7 100644 --- a/translations/it/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/it/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ L'overfitting è un concetto estremamente importante nel machine learning, ed è Considera il seguente problema di approssimazione di 5 punti (rappresentati da `x` nei grafici sottostanti): -![linear](../../../../../translated_images/it/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/it/overfit2.131f5800ae10ca5e.jpg) +![linear](../../../../../translated_images/it/overfit1.f24b71c6f652e59e.webp) | ![overfit](../../../../../translated_images/it/overfit2.131f5800ae10ca5e.webp) -------------------------|-------------------------- **Modello lineare, 2 parametri** | **Modello non lineare, 7 parametri** Errore di training = 5.3 | Errore di training = 0 @@ -79,7 +79,7 @@ Errore di validazione = 5.1 | Errore di validazione = 20 Come puoi vedere dal grafico sopra, l'overfitting può essere rilevato da un errore di training molto basso e un errore di validazione molto alto. Normalmente durante l'allenamento vedremo sia l'errore di training che quello di validazione iniziare a diminuire, e poi a un certo punto l'errore di validazione potrebbe smettere di diminuire e iniziare a salire. Questo sarà un segnale di overfitting e un indicatore che probabilmente dovremmo interrompere l'allenamento a questo punto (o almeno fare uno snapshot del modello). -![overfitting](../../../../../translated_images/it/Overfitting.408ad91cd90b4371.png) +![overfitting](../../../../../translated_images/it/Overfitting.408ad91cd90b4371.webp) ## Come prevenire l'overfitting diff --git a/translations/it/lessons/3-NeuralNetworks/README.md b/translations/it/lessons/3-NeuralNetworks/README.md index 6a99d120..22d1679d 100644 --- a/translations/it/lessons/3-NeuralNetworks/README.md +++ b/translations/it/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Introduzione alle Reti Neurali -![Riassunto del contenuto di Introduzione alle Reti Neurali in uno schizzo](../../../../translated_images/it/ai-neuralnetworks.1c687ae40bc86e83.png) +![Riassunto del contenuto di Introduzione alle Reti Neurali in uno schizzo](../../../../translated_images/it/ai-neuralnetworks.1c687ae40bc86e83.webp) Come discusso nell'introduzione, uno dei modi per raggiungere l'intelligenza è addestrare un **modello informatico** o un **cervello artificiale**. Dalla metà del XX secolo, i ricercatori hanno sperimentato diversi modelli matematici, finché negli ultimi anni questa direzione si è dimostrata estremamente promettente. Questi modelli matematici del cervello sono chiamati **reti neurali**. @@ -36,13 +36,13 @@ In questo curriculum, ci concentreremo solo sui modelli di reti neurali. Dalla biologia, sappiamo che il nostro cervello è composto da cellule neurali (neuroni), ciascuna delle quali ha molteplici "input" (dendriti) e un singolo "output" (assone). Sia i dendriti che gli assoni possono condurre segnali elettrici, e le connessioni tra di loro — note come sinapsi — possono mostrare diversi gradi di conduttività, regolati dai neurotrasmettitori. -![Modello di un Neurone](../../../../translated_images/it/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![Modello di un Neurone](../../../../translated_images/it/artneuron.1a5daa88d20ebe6f.png) +![Modello di un Neurone](../../../../translated_images/it/synapse-wikipedia.ed20a9e4726ea1c6.webp) | ![Modello di un Neurone](../../../../translated_images/it/artneuron.1a5daa88d20ebe6f.webp) ----|---- Neurone Reale *([Immagine](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) da Wikipedia)* | Neurone Artificiale *(Immagine dell'autore)* Pertanto, il modello matematico più semplice di un neurone contiene diversi input X1, ..., XN e un output Y, e una serie di pesi W1, ..., WN. L'output viene calcolato come: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) dove f è una **funzione di attivazione** non lineare. diff --git a/translations/it/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/it/lessons/4-ComputerVision/06-IntroCV/README.md index 6149db42..2609d7a3 100644 --- a/translations/it/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/it/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ Nel nostro [OpenCV Notebook](OpenCV.ipynb), forniamo alcuni esempi di quando la * **Pre-elaborazione di una fotografia di un libro in Braille**. Ci concentriamo su come possiamo utilizzare thresholding, rilevamento delle caratteristiche, trasformazione prospettica e manipolazioni NumPy per separare i singoli simboli Braille per una successiva classificazione tramite una rete neurale. -![Immagine Braille](../../../../../translated_images/it/braille.341962ff76b1bd70.jpeg) | ![Immagine Braille Pre-elaborata](../../../../../translated_images/it/braille-result.46530fea020b03c7.png) | ![Simboli Braille](../../../../../translated_images/it/braille-symbols.0159185ab69d5339.png) +![Immagine Braille](../../../../../translated_images/it/braille.341962ff76b1bd70.webp) | ![Immagine Braille Pre-elaborata](../../../../../translated_images/it/braille-result.46530fea020b03c7.webp) | ![Simboli Braille](../../../../../translated_images/it/braille-symbols.0159185ab69d5339.webp) ----|-----|----- > Immagine da [OpenCV.ipynb](OpenCV.ipynb) * **Rilevamento del movimento in video utilizzando la differenza tra fotogrammi**. Se la fotocamera è fissa, i fotogrammi del feed della fotocamera dovrebbero essere abbastanza simili tra loro. Poiché i fotogrammi sono rappresentati come array, semplicemente sottraendo questi array per due fotogrammi consecutivi otterremo la differenza dei pixel, che dovrebbe essere bassa per fotogrammi statici e diventare più alta quando c'è un movimento sostanziale nell'immagine. -![Immagine dei fotogrammi video e differenze tra fotogrammi](../../../../../translated_images/it/frame-difference.706f805491a0883c.png) +![Immagine dei fotogrammi video e differenze tra fotogrammi](../../../../../translated_images/it/frame-difference.706f805491a0883c.webp) > Immagine da [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ Nel nostro [OpenCV Notebook](OpenCV.ipynb), forniamo alcuni esempi di quando la - **Dense Optical Flow** calcola il campo vettoriale che mostra per ogni pixel dove si sta muovendo. - **Sparse Optical Flow** si basa sull'individuazione di alcune caratteristiche distintive nell'immagine (ad esempio, bordi) e sulla costruzione della loro traiettoria da fotogramma a fotogramma. -![Immagine di Optical Flow](../../../../../translated_images/it/optical.1f4a94464579a83a.png) +![Immagine di Optical Flow](../../../../../translated_images/it/optical.1f4a94464579a83a.webp) > Immagine da [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/it/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/it/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index daf0bd43..9dc53e97 100644 --- a/translations/it/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/it/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 è una rete che ha raggiunto il 92,7% di accuratezza nella classificazione top-5 di ImageNet nel 2014. Ha la seguente struttura di livelli: -![ImageNet Layers](../../../../../translated_images/it/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet Layers](../../../../../translated_images/it/vgg-16-arch1.d901a5583b3a51ba.webp) Come puoi vedere, VGG segue una tradizionale architettura a piramide, che consiste in una sequenza di livelli di convoluzione e pooling. -![ImageNet Pyramid](../../../../../translated_images/it/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet Pyramid](../../../../../translated_images/it/vgg-16-arch.64ff2137f50dd49f.webp) > Immagine da [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/it/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/it/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index 446b2a4e..fd6abfea 100644 --- a/translations/it/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/it/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -262,7 +262,7 @@ "\n", "Pertanto, in una CNN tipica ci sarebbero diversi livelli di convoluzione, con livelli di pooling tra di essi per ridurre le dimensioni dell'immagine. Aumenteremmo anche il numero di filtri, perché man mano che gli schemi diventano più complessi, ci sono più combinazioni interessanti che dobbiamo cercare.\n", "\n", - "![Un'immagine che mostra diversi livelli di convoluzione con livelli di pooling.](../../../../../translated_images/it/cnn-pyramid.85915455759ef0ce.png)\n", + "![Un'immagine che mostra diversi livelli di convoluzione con livelli di pooling.](../../../../../translated_images/it/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "A causa della diminuzione delle dimensioni spaziali e dell'aumento delle dimensioni delle caratteristiche/filtri, questa architettura è anche chiamata **architettura a piramide**.\n" ] diff --git a/translations/it/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/it/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index c9b275f3..377ed888 100644 --- a/translations/it/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/it/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -358,7 +358,7 @@ "\n", "Pertanto, in una CNN tipica ci sarebbero diversi strati convoluzionali, con livelli di pooling tra di essi per ridurre le dimensioni dell'immagine. Inoltre, aumenteremmo il numero di filtri, perché man mano che gli schemi diventano più avanzati, ci sono più possibili combinazioni interessanti che dobbiamo cercare.\n", "\n", - "![Un'immagine che mostra diversi strati convoluzionali con livelli di pooling.](../../../../../translated_images/it/cnn-pyramid.85915455759ef0ce.png)\n", + "![Un'immagine che mostra diversi strati convoluzionali con livelli di pooling.](../../../../../translated_images/it/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "A causa della diminuzione delle dimensioni spaziali e dell'aumento delle dimensioni delle caratteristiche/filtri, questa architettura è anche chiamata **architettura a piramide**.\n" ] diff --git a/translations/it/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/it/lessons/4-ComputerVision/07-ConvNets/README.md index e15d7791..b5ea039c 100644 --- a/translations/it/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/it/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ Nella vita reale, vogliamo essere in grado di riconoscere oggetti in un'immagine Per estrarre schemi, utilizzeremo il concetto di **filtri convoluzionali**. Come sapete, un'immagine è rappresentata da una matrice 2D o da un tensore 3D con profondità di colore. Applicare un filtro significa prendere una matrice relativamente piccola chiamata **kernel del filtro**, e per ogni pixel dell'immagine originale calcolare la media ponderata con i punti vicini. Possiamo immaginare questo processo come una piccola finestra che scorre su tutta l'immagine, mediando tutti i pixel secondo i pesi nella matrice del kernel del filtro. -![Filtro per bordi verticali](../../../../../translated_images/it/filter-vert.b7148390ca0bc356.png) | ![Filtro per bordi orizzontali](../../../../../translated_images/it/filter-horiz.59b80ed4feb946ef.png) +![Filtro per bordi verticali](../../../../../translated_images/it/filter-vert.b7148390ca0bc356.webp) | ![Filtro per bordi orizzontali](../../../../../translated_images/it/filter-horiz.59b80ed4feb946ef.webp) ----|---- > Immagine di Dmitry Soshnikov @@ -38,7 +38,7 @@ Il funzionamento delle CNN si basa sulle seguenti idee fondamentali: * Possiamo progettare la rete in modo che i filtri vengano addestrati automaticamente * Possiamo utilizzare lo stesso approccio per trovare schemi in caratteristiche di alto livello, non solo nell'immagine originale. Pertanto, l'estrazione delle caratteristiche nelle CNN funziona su una gerarchia di caratteristiche, partendo da combinazioni di pixel di basso livello fino ad arrivare a combinazioni di alto livello di parti dell'immagine. -![Estrazione gerarchica delle caratteristiche](../../../../../translated_images/it/FeatureExtractionCNN.d9b456cbdae7cb64.png) +![Estrazione gerarchica delle caratteristiche](../../../../../translated_images/it/FeatureExtractionCNN.d9b456cbdae7cb64.webp) > Immagine tratta da [un articolo di Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), basato sulla [loro ricerca](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ La maggior parte delle CNN utilizzate per l'elaborazione delle immagini segue un Ad esempio, osserviamo l'architettura di VGG-16, una rete che ha raggiunto il 92,7% di accuratezza nella classificazione top-5 di ImageNet nel 2014: -![Strati di ImageNet](../../../../../translated_images/it/vgg-16-arch1.d901a5583b3a51ba.jpg) +![Strati di ImageNet](../../../../../translated_images/it/vgg-16-arch1.d901a5583b3a51ba.webp) -![Piramide di ImageNet](../../../../../translated_images/it/vgg-16-arch.64ff2137f50dd49f.jpg) +![Piramide di ImageNet](../../../../../translated_images/it/vgg-16-arch.64ff2137f50dd49f.webp) > Immagine tratta da [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/it/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/it/lessons/4-ComputerVision/07-ConvNets/lab/README.md index bfa744f5..a2408e4d 100644 --- a/translations/it/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/it/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ Devi addestrare una rete neurale convoluzionale per classificare le diverse razz Utilizzeremo il [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/), che contiene immagini di 37 diverse razze di cani e gatti. -![Dataset con cui lavoreremo](../../../../../../translated_images/it/data.50b2a9d5484bdbf0.png) +![Dataset con cui lavoreremo](../../../../../../translated_images/it/data.50b2a9d5484bdbf0.webp) Per scaricare il dataset, utilizza questo frammento di codice: diff --git a/translations/it/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/it/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index 27116a57..72903de0 100644 --- a/translations/it/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/it/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "Per visualizzare il gatto ideale, inizieremo con un'immagine di rumore casuale e cercheremo di utilizzare la tecnica di ottimizzazione della discesa del gradiente per modificare l'immagine in modo che una rete riconosca un gatto.\n", "\n", - "![Ciclo di Ottimizzazione](../../../../../translated_images/it/ideal-cat-loop.999fbb8ff306e044.png)\n", + "![Ciclo di Ottimizzazione](../../../../../translated_images/it/ideal-cat-loop.999fbb8ff306e044.webp)\n", "\n", "Ecco la nostra immagine di partenza:\n" ] diff --git a/translations/it/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/it/lessons/4-ComputerVision/08-TransferLearning/README.md index 8b8e1959..fe283c84 100644 --- a/translations/it/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/it/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Sia Keras che PyTorch contengono funzioni per caricare facilmente i pesi di reti Ecco alcune caratteristiche estratte da un'immagine di un gatto dalla rete VGG-16: -![Caratteristiche estratte da VGG-16](../../../../../translated_images/it/features.6291f9c7ba3a0b95.png) +![Caratteristiche estratte da VGG-16](../../../../../translated_images/it/features.6291f9c7ba3a0b95.webp) ## Dataset Gatti vs. Cani @@ -48,19 +48,19 @@ Una rete neurale pre-addestrata contiene diversi schemi all'interno del suo *cer Un approccio che possiamo adottare è partire da un'immagine casuale e poi cercare di utilizzare la tecnica di **ottimizzazione con discesa del gradiente** per modificare quell'immagine in modo tale che la rete inizi a pensare che sia un gatto. -![Ciclo di Ottimizzazione dell'Immagine](../../../../../translated_images/it/ideal-cat-loop.999fbb8ff306e044.png) +![Ciclo di Ottimizzazione dell'Immagine](../../../../../translated_images/it/ideal-cat-loop.999fbb8ff306e044.webp) Tuttavia, se facciamo questo, otterremo qualcosa di molto simile a un rumore casuale. Questo perché *ci sono molti modi per far pensare alla rete che l'immagine di input sia un gatto*, inclusi alcuni che non hanno senso visivamente. Sebbene queste immagini contengano molti schemi tipici di un gatto, non c'è nulla che le vincoli a essere visivamente distintive. Per migliorare il risultato, possiamo aggiungere un altro termine alla funzione di perdita, chiamato **variation loss**. È una metrica che mostra quanto sono simili i pixel vicini dell'immagine. Minimizzare la variation loss rende l'immagine più liscia e elimina il rumore, rivelando così schemi più visivamente piacevoli. Ecco un esempio di queste immagini "ideali", classificate come gatto e zebra con alta probabilità: -![Gatto Ideale](../../../../../translated_images/it/ideal-cat.203dd4597643d6b0.png) | ![Zebra Ideale](../../../../../translated_images/it/ideal-zebra.7f70e8b54ee15a7a.png) +![Gatto Ideale](../../../../../translated_images/it/ideal-cat.203dd4597643d6b0.webp) | ![Zebra Ideale](../../../../../translated_images/it/ideal-zebra.7f70e8b54ee15a7a.webp) -----|----- *Gatto Ideale* | *Zebra Ideale* Un approccio simile può essere utilizzato per eseguire i cosiddetti **attacchi avversari** su una rete neurale. Supponiamo di voler ingannare una rete neurale e far sembrare un cane un gatto. Se prendiamo l'immagine di un cane, che è riconosciuta dalla rete come un cane, possiamo modificarla leggermente utilizzando l'ottimizzazione con discesa del gradiente, fino a quando la rete inizia a classificarla come un gatto: -![Immagine di un Cane](../../../../../translated_images/it/original-dog.8f68a67d2fe0911f.png) | ![Immagine di un cane classificata come gatto](../../../../../translated_images/it/adversarial-dog.d9fc7773b0142b89.png) +![Immagine di un Cane](../../../../../translated_images/it/original-dog.8f68a67d2fe0911f.webp) | ![Immagine di un cane classificata come gatto](../../../../../translated_images/it/adversarial-dog.d9fc7773b0142b89.webp) -----|----- *Immagine originale di un cane* | *Immagine di un cane classificata come gatto* diff --git a/translations/it/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/it/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index 243111c8..d35f3116 100644 --- a/translations/it/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/it/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "Poiché stiamo addestrando l'autoencoder per catturare quante più informazioni possibili dall'immagine originale al fine di ottenere una ricostruzione accurata, la rete cerca di trovare il miglior **embedding** delle immagini di input per coglierne il significato.\n", "\n", - "![Diagramma AutoEncoder](../../../../../translated_images/it/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![Diagramma AutoEncoder](../../../../../translated_images/it/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "> Immagine dal [blog di Keras](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", diff --git a/translations/it/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/it/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index bc657cb6..9a7f73b7 100644 --- a/translations/it/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/it/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "Poiché stiamo addestrando l'autoencoder per catturare quante più informazioni possibili dall'immagine originale al fine di ottenere una ricostruzione accurata, la rete cerca di trovare il miglior **embedding** delle immagini di input per rappresentarne il significato.\n", "\n", - "![Diagramma AutoEncoder](../../../../../translated_images/it/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![Diagramma AutoEncoder](../../../../../translated_images/it/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "*Immagine tratta dal [blog di Keras](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", diff --git a/translations/it/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/it/lessons/4-ComputerVision/09-Autoencoders/README.md index 49d94d0a..c1d84cab 100644 --- a/translations/it/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/it/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ Tuttavia, potremmo voler utilizzare dati grezzi (non etichettati) per allenare g Poiché stiamo allenando un autoencoder per catturare quante più informazioni possibili dall'immagine originale per una ricostruzione accurata, la rete cerca di trovare il miglior **embedding** delle immagini di input per catturarne il significato. -![Diagramma AutoEncoder](../../../../../translated_images/it/autoencoder_schema.5e6fc9ad98a5eb61.jpg) +![Diagramma AutoEncoder](../../../../../translated_images/it/autoencoder_schema.5e6fc9ad98a5eb61.webp) > Immagine dal [blog di Keras](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/it/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/it/lessons/4-ComputerVision/11-ObjectDetection/README.md index 0653028e..80adc91c 100644 --- a/translations/it/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/it/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ I modelli di classificazione delle immagini che abbiamo trattato finora prendeva ## [Quiz pre-lezione](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Rilevamento degli Oggetti](../../../../../translated_images/it/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) +![Rilevamento degli Oggetti](../../../../../translated_images/it/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp) > Immagine dal [sito web di YOLO v2](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ Supponendo di voler trovare un gatto in un'immagine, un approccio molto semplice 2. Eseguire la classificazione delle immagini su ciascun riquadro. 3. I riquadri che producono un'attivazione sufficientemente alta possono essere considerati contenere l'oggetto in questione. -![Rilevamento Naïf](../../../../../translated_images/it/naive-detection.e7f1ba220ccd08c6.png) +![Rilevamento Naïf](../../../../../translated_images/it/naive-detection.e7f1ba220ccd08c6.webp) > *Immagine dal [Notebook di Esercizi](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ Potresti imbatterti nei seguenti dataset per questo compito: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 classi * [COCO](http://cocodataset.org/#home) - Common Objects in Context. 80 classi, riquadri delimitatori e maschere di segmentazione -![COCO](../../../../../translated_images/it/coco-examples.71bc60380fa6cceb.jpg) +![COCO](../../../../../translated_images/it/coco-examples.71bc60380fa6cceb.webp) ## Metriche per il Rilevamento degli Oggetti @@ -50,7 +50,7 @@ Potresti imbatterti nei seguenti dataset per questo compito: Mentre per la classificazione delle immagini è facile misurare quanto bene l'algoritmo performa, per il rilevamento degli oggetti dobbiamo misurare sia la correttezza della classe, sia la precisione della posizione del riquadro delimitatore inferito. Per quest'ultimo, utilizziamo la cosiddetta **Intersezione su Unione** (IoU), che misura quanto bene due riquadri (o due aree arbitrarie) si sovrappongono. -![IoU](../../../../../translated_images/it/iou_equation.9a4751d40fff4e11.png) +![IoU](../../../../../translated_images/it/iou_equation.9a4751d40fff4e11.webp) > *Figura 2 da [questo eccellente post sul blog su IoU](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ Esistono due grandi classi di algoritmi di rilevamento degli oggetti: [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) utilizza [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) per generare una struttura gerarchica di regioni ROI, che vengono poi passate attraverso estrattori di caratteristiche CNN e classificatori SVM per determinare la classe dell'oggetto, e regressione lineare per determinare le coordinate del *riquadro delimitatore*. [Paper ufficiale](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/it/rcnn1.cae407020dfb1d1f.png) +![RCNN](../../../../../translated_images/it/rcnn1.cae407020dfb1d1f.webp) > *Immagine da van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/it/rcnn2.2d9530bb83516484.png) +![RCNN-1](../../../../../translated_images/it/rcnn2.2d9530bb83516484.webp) > *Immagini da [questo blog](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e) @@ -110,7 +110,7 @@ Esistono due grandi classi di algoritmi di rilevamento degli oggetti: Questo approccio è simile a R-CNN, ma le regioni vengono definite dopo che i livelli di convoluzione sono stati applicati. -![FRCNN](../../../../../translated_images/it/f-rcnn.3cda6d9bb4188875.png) +![FRCNN](../../../../../translated_images/it/f-rcnn.3cda6d9bb4188875.webp) > Immagine dal [Paper ufficiale](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ Questo approccio è simile a R-CNN, ma le regioni vengono definite dopo che i li L'idea principale di questo approccio è utilizzare una rete neurale per prevedere le ROI - la cosiddetta *Rete di Proposta di Regione*. [Paper](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/it/faster-rcnn.8d46c099b87ef30a.png) +![FasterRCNN](../../../../../translated_images/it/faster-rcnn.8d46c099b87ef30a.webp) > Immagine dal [Paper ufficiale](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ Questo algoritmo è ancora più veloce di Faster R-CNN. L'idea principale è la 1. Le caratteristiche vengono elaborate da **Position-Sensitive Score Map**. Ogni oggetto delle classi $C$ è suddiviso in regioni $k\times k$, e si addestra per prevedere parti degli oggetti. 1. Per ogni parte delle regioni $k\times k$ tutte le reti votano per le classi degli oggetti, e la classe dell'oggetto con il massimo voto viene selezionata. -![r-fcn image](../../../../../translated_images/it/r-fcn.13eb88158b99a3da.png) +![r-fcn image](../../../../../translated_images/it/r-fcn.13eb88158b99a3da.webp) > Immagine dal [Paper ufficiale](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO è un algoritmo in tempo reale a passaggio unico. L'idea principale è la s * L'immagine è suddivisa in regioni $S\times S$ * Per ogni regione, **CNN** prevede $n$ oggetti possibili, le coordinate del *riquadro delimitatore* e la *confidence*=*probabilità* * IoU. - ![YOLO](../../../../../translated_images/it/yolo.a2648ec82ee8bb4e.png) + ![YOLO](../../../../../translated_images/it/yolo.a2648ec82ee8bb4e.webp) > Immagine dal [Paper ufficiale](https://arxiv.org/abs/1506.02640) diff --git a/translations/it/lessons/4-ComputerVision/README.md b/translations/it/lessons/4-ComputerVision/README.md index 5b816f65..8287e79b 100644 --- a/translations/it/lessons/4-ComputerVision/README.md +++ b/translations/it/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Visione Artificiale -![Riassunto dei contenuti sulla Visione Artificiale in uno schizzo](../../../../translated_images/it/ai-computervision.6506ebebac3fbf76.png) +![Riassunto dei contenuti sulla Visione Artificiale in uno schizzo](../../../../translated_images/it/ai-computervision.6506ebebac3fbf76.webp) In questa sezione impareremo: diff --git a/translations/it/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/it/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index a926625f..36d1b6e4 100644 --- a/translations/it/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/it/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "La rappresentazione vettoriale **Bag of Words** (BoW) è la rappresentazione vettoriale tradizionale più comunemente utilizzata. Ogni parola è collegata a un indice del vettore, e l'elemento del vettore contiene il numero di occorrenze di una parola in un determinato documento.\n", "\n", - "![Immagine che mostra come una rappresentazione vettoriale Bag of Words sia rappresentata in memoria.](../../../../../translated_images/it/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![Immagine che mostra come una rappresentazione vettoriale Bag of Words sia rappresentata in memoria.](../../../../../translated_images/it/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Nota**: Puoi anche pensare al BoW come alla somma di tutti i vettori one-hot-encoded per le singole parole nel testo.\n", "\n", diff --git a/translations/it/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/it/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index ccdeb12f..e5bf365a 100644 --- a/translations/it/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/it/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "La rappresentazione vettoriale **Bag-of-words** (BoW) è la più semplice da comprendere tra le rappresentazioni vettoriali tradizionali. Ogni parola è collegata a un indice vettoriale, e un elemento del vettore contiene il numero di occorrenze di ciascuna parola in un determinato documento.\n", "\n", - "![Immagine che mostra come una rappresentazione vettoriale bag-of-words è rappresentata in memoria.](../../../../../translated_images/it/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![Immagine che mostra come una rappresentazione vettoriale bag-of-words è rappresentata in memoria.](../../../../../translated_images/it/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Nota**: Puoi anche pensare al BoW come alla somma di tutti i vettori one-hot-encoded per le singole parole nel testo.\n", "\n", diff --git a/translations/it/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/it/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index c3c8f50a..7c98fcb8 100644 --- a/translations/it/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/it/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "Utilizzando il livello di embedding come primo livello nella nostra rete, possiamo passare dal modello bag-of-words al modello **embedding bag**, dove prima convertiamo ogni parola nel nostro testo nel corrispondente embedding e poi calcoliamo una funzione aggregata su tutti questi embedding, come `sum`, `average` o `max`.\n", "\n", - "![Immagine che mostra un classificatore embedding per cinque parole in sequenza.](../../../../../translated_images/it/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![Immagine che mostra un classificatore embedding per cinque parole in sequenza.](../../../../../translated_images/it/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "La nostra rete neurale classificatrice inizierà con un livello di embedding, seguito da un livello di aggregazione e un classificatore lineare sopra di esso:\n" ] @@ -176,7 +176,7 @@ "\n", "Nell'architettura precedente, era necessario riempire tutte le sequenze fino alla stessa lunghezza per adattarle a un minibatch. Questo non è il modo più efficiente per rappresentare sequenze a lunghezza variabile - un altro approccio sarebbe utilizzare un vettore di **offset**, che contiene gli offset di tutte le sequenze memorizzate in un unico grande vettore.\n", "\n", - "![Immagine che mostra una rappresentazione di sequenza con offset](../../../../../translated_images/it/offset-sequence-representation.eb73fcefb29b46ee.png)\n", + "![Immagine che mostra una rappresentazione di sequenza con offset](../../../../../translated_images/it/offset-sequence-representation.eb73fcefb29b46ee.webp)\n", "\n", "> **Nota**: Nell'immagine sopra, mostriamo una sequenza di caratteri, ma nel nostro esempio stiamo lavorando con sequenze di parole. Tuttavia, il principio generale di rappresentare le sequenze con un vettore di offset rimane lo stesso.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW è più veloce, mentre skip-gram è più lento, ma rappresenta meglio le parole meno frequenti.\n", "\n", - "![Immagine che mostra entrambi gli algoritmi CBoW e Skip-Gram per convertire parole in vettori.](../../../../../translated_images/it/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![Immagine che mostra entrambi gli algoritmi CBoW e Skip-Gram per convertire parole in vettori.](../../../../../translated_images/it/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Per sperimentare con embedding word2vec pre-addestrati sul dataset Google News, possiamo utilizzare la libreria **gensim**. Di seguito troviamo le parole più simili a 'neural'.\n", "\n", diff --git a/translations/it/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/it/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index aa1f1b2c..07f31e97 100644 --- a/translations/it/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/it/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "Utilizzando un livello di embedding come primo livello nella nostra rete, possiamo passare da un modello bag-of-words a un modello **embedding bag**, dove prima convertiamo ogni parola del nostro testo nel corrispondente embedding e poi calcoliamo una funzione aggregata su tutti questi embedding, come `sum`, `average` o `max`.\n", "\n", - "![Immagine che mostra un classificatore con embedding per cinque parole di una sequenza.](../../../../../translated_images/it/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![Immagine che mostra un classificatore con embedding per cinque parole di una sequenza.](../../../../../translated_images/it/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "La nostra rete neurale classificatrice è composta dai seguenti livelli:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW è più veloce, mentre skip-gram, pur essendo più lento, rappresenta meglio le parole meno frequenti.\n", "\n", - "![Immagine che mostra entrambi gli algoritmi CBoW e Skip-Gram per convertire le parole in vettori.](../../../../../translated_images/it/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![Immagine che mostra entrambi gli algoritmi CBoW e Skip-Gram per convertire le parole in vettori.](../../../../../translated_images/it/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Per sperimentare con l'embedding Word2Vec pre-addestrato sul dataset di Google News, possiamo utilizzare la libreria **gensim**. Di seguito troviamo le parole più simili a 'neural'.\n", "\n", diff --git a/translations/it/lessons/5-NLP/14-Embeddings/README.md b/translations/it/lessons/5-NLP/14-Embeddings/README.md index c7554783..19fc07b3 100644 --- a/translations/it/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/it/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ Quindi, il livello di embedding prenderebbe una parola come input e produrrebbe Utilizzando un livello di embedding come primo livello nella nostra rete di classificazione, possiamo passare da un modello bag-of-words a un modello **embedding bag**, dove prima convertiamo ogni parola nel nostro testo nel corrispondente embedding, e poi calcoliamo una funzione aggregata su tutti questi embedding, come `sum`, `average` o `max`. -![Immagine che mostra un classificatore embedding per cinque parole di una sequenza.](../../../../../translated_images/it/embedding-classifier-example.b77f021a7ee67eee.png) +![Immagine che mostra un classificatore embedding per cinque parole di una sequenza.](../../../../../translated_images/it/embedding-classifier-example.b77f021a7ee67eee.webp) > Immagine dell'autore @@ -40,7 +40,7 @@ Per fare ciò, dobbiamo pre-addestrare il nostro modello di embedding su una gra CBoW è più veloce, mentre skip-gram è più lento, ma rappresenta meglio le parole meno frequenti. -![Immagine che mostra gli algoritmi CBoW e Skip-Gram per convertire parole in vettori.](../../../../../translated_images/it/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![Immagine che mostra gli algoritmi CBoW e Skip-Gram per convertire parole in vettori.](../../../../../translated_images/it/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Immagine tratta da [questo articolo](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/it/lessons/5-NLP/15-LanguageModeling/README.md b/translations/it/lessons/5-NLP/15-LanguageModeling/README.md index 1e9333d1..159ee7b4 100644 --- a/translations/it/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/it/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ Nei nostri esempi precedenti, abbiamo utilizzato embedding semantici pre-addestr * **Continuous Bag-of-Words** (CBoW), in cui si predice il token centrale $W_0$ in una sequenza di token $W_{-N}$, ..., $W_N$. * **Skip-gram**, in cui si predice un insieme di token vicini {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} a partire dal token centrale $W_0$. -![immagine tratta da un articolo sulla conversione di parole in vettori](../../../../../translated_images/it/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![immagine tratta da un articolo sulla conversione di parole in vettori](../../../../../translated_images/it/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Immagine tratta da [questo articolo](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/it/lessons/5-NLP/16-RNN/README.md b/translations/it/lessons/5-NLP/16-RNN/README.md index 7fb39a60..3985ad5a 100644 --- a/translations/it/lessons/5-NLP/16-RNN/README.md +++ b/translations/it/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ Nelle sezioni precedenti, abbiamo utilizzato rappresentazioni semantiche ricche Per catturare il significato di una sequenza di testo, dobbiamo utilizzare un'altra architettura di rete neurale, chiamata **rete neurale ricorrente**, o RNN. Nelle RNN, passiamo la nostra frase attraverso la rete un simbolo alla volta, e la rete produce uno **stato**, che poi passiamo nuovamente alla rete insieme al simbolo successivo. -![RNN](../../../../../translated_images/it/rnn.27f5c29c53d727b5.png) +![RNN](../../../../../translated_images/it/rnn.27f5c29c53d727b5.webp) > Immagine dell'autore @@ -61,7 +61,7 @@ Abbiamo discusso reti ricorrenti che operano in una direzione, dall'inizio di un Una rete ricorrente, sia unidirezionale che bidirezionale, cattura certi schemi all'interno di una sequenza e può memorizzarli in un vettore di stato o passarli all'output. Come con le reti convoluzionali, possiamo costruire un altro strato ricorrente sopra il primo per catturare schemi di livello superiore e costruire a partire dagli schemi di basso livello estratti dal primo strato. Questo ci porta al concetto di **RNN multistrato**, che consiste in due o più reti ricorrenti, dove l'output del livello precedente viene passato al livello successivo come input. -![Immagine che mostra una RNN LSTM multistrato](../../../../../translated_images/it/multi-layer-lstm.dd975e29bb2a59fe.jpg) +![Immagine che mostra una RNN LSTM multistrato](../../../../../translated_images/it/multi-layer-lstm.dd975e29bb2a59fe.webp) *Immagine tratta da [questo meraviglioso post](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) di Fernando López* diff --git a/translations/it/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/it/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index 0ea4901b..7290255b 100644 --- a/translations/it/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/it/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "Una rete ricorrente, sia unidirezionale che bidirezionale, cattura determinati schemi all'interno di una sequenza e può memorizzarli nel vettore di stato o passarli all'output. Come con le reti convoluzionali, possiamo costruire un altro livello ricorrente sopra il primo per catturare schemi di livello superiore, costruiti a partire dagli schemi di basso livello estratti dal primo livello. Questo ci porta al concetto di **RNN multilivello**, che consiste in due o più reti ricorrenti, dove l'output del livello precedente viene passato al livello successivo come input.\n", "\n", - "![Immagine che mostra una rete RNN LSTM multilivello](../../../../../translated_images/it/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![Immagine che mostra una rete RNN LSTM multilivello](../../../../../translated_images/it/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*Immagine tratta da [questo fantastico articolo](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) di Fernando López*\n", "\n", diff --git a/translations/it/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/it/lessons/5-NLP/16-RNN/RNNTF.ipynb index 526b2028..1d1aea5d 100644 --- a/translations/it/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/it/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "Per catturare il significato di una sequenza di testo, utilizzeremo un'architettura di rete neurale chiamata **rete neurale ricorrente**, o RNN. Quando utilizziamo una RNN, passiamo la nostra frase attraverso la rete un token alla volta, e la rete produce uno **stato**, che poi passiamo nuovamente alla rete insieme al token successivo.\n", "\n", - "![Immagine che mostra un esempio di generazione con rete neurale ricorrente.](../../../../../translated_images/it/rnn.27f5c29c53d727b5.png)\n", + "![Immagine che mostra un esempio di generazione con rete neurale ricorrente.](../../../../../translated_images/it/rnn.27f5c29c53d727b5.webp)\n", "\n", "Data la sequenza di input di token $X_0,\\dots,X_n$, la RNN crea una sequenza di blocchi di rete neurale e allena questa sequenza end-to-end utilizzando la retropropagazione. Ogni blocco di rete prende una coppia $(X_i,S_i)$ come input e produce $S_{i+1}$ come risultato. Lo stato finale $S_n$ o l'output $Y_n$ viene inviato a un classificatore lineare per produrre il risultato. Tutti i blocchi di rete condividono gli stessi pesi e vengono allenati end-to-end con un unico passaggio di retropropagazione.\n", "\n", @@ -369,7 +369,7 @@ "\n", "Le reti ricorrenti, unidirezionali o bidirezionali, catturano schemi all'interno di una sequenza e li memorizzano in vettori di stato o li restituiscono come output. Come per le reti convoluzionali, possiamo costruire un altro livello ricorrente dopo il primo per catturare schemi di livello superiore, costruiti a partire dagli schemi di livello inferiore estratti dal primo livello. Questo ci porta al concetto di **RNN multilivello**, che consiste in due o più reti ricorrenti, dove l'output del livello precedente viene passato al livello successivo come input.\n", "\n", - "![Immagine che mostra una RNN multilivello con memoria a lungo termine](../../../../../translated_images/it/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![Immagine che mostra una RNN multilivello con memoria a lungo termine](../../../../../translated_images/it/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*Immagine tratta da [questo fantastico articolo](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) di Fernando López.*\n", "\n", diff --git a/translations/it/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/it/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index 9a4ee6e3..44cd16ac 100644 --- a/translations/it/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/it/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "Il modo in cui addestreremo l'RNN per generare testo è il seguente. A ogni passo, prenderemo una sequenza di caratteri di lunghezza `nchars` e chiederemo alla rete di generare il carattere successivo per ogni carattere di input:\n", "\n", - "![Immagine che mostra un esempio di generazione RNN della parola 'HELLO'.](../../../../../translated_images/it/rnn-generate.56c54afb52f9781d.png)\n", + "![Immagine che mostra un esempio di generazione RNN della parola 'HELLO'.](../../../../../translated_images/it/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "A seconda dello scenario specifico, potremmo anche voler includere alcuni caratteri speciali, come *fine-sequenza* ``. Nel nostro caso, vogliamo semplicemente addestrare la rete per una generazione di testo continua, quindi fisseremo la dimensione di ogni sequenza uguale a `nchars` token. Di conseguenza, ogni esempio di addestramento sarà composto da `nchars` input e `nchars` output (che sono la sequenza di input spostata di un simbolo a sinistra). Il minibatch sarà composto da diverse di queste sequenze.\n", "\n", diff --git a/translations/it/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/it/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index 8ca2175c..30f3818e 100644 --- a/translations/it/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/it/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -111,7 +111,7 @@ "\n", "Il modo in cui addestreremo l'RNN per generare titoli di notizie è il seguente. A ogni passo, prenderemo un titolo, che verrà fornito a un RNN, e per ogni carattere di input chiederemo alla rete di generare il carattere di output successivo:\n", "\n", - "![Immagine che mostra un esempio di generazione RNN della parola 'HELLO'.](../../../../../translated_images/it/rnn-generate.56c54afb52f9781d.png)\n", + "![Immagine che mostra un esempio di generazione RNN della parola 'HELLO'.](../../../../../translated_images/it/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "Per l'ultimo carattere della nostra sequenza, chiederemo alla rete di generare il token ``.\n", "\n", diff --git a/translations/it/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/it/lessons/5-NLP/17-GenerativeNetworks/README.md index b7384d01..7416b89f 100644 --- a/translations/it/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/it/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ Nell'architettura RNN discussa nell'unità precedente, ogni unità RNN produceva Questo consente diverse architetture neurali, come mostrato nell'immagine seguente: -![Immagine che mostra i modelli comuni di reti neurali ricorrenti.](../../../../../translated_images/it/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Immagine che mostra i modelli comuni di reti neurali ricorrenti.](../../../../../translated_images/it/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > Immagine tratta dal post del blog [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) di [Andrej Karpaty](http://karpathy.github.io/) @@ -32,7 +32,7 @@ In questa unità, ci concentreremo su modelli generativi semplici che ci aiutano Addestreremo questa RNN per generare testo passo dopo passo. A ogni passo, prenderemo una sequenza di caratteri di lunghezza `nchars` e chiederemo alla rete di generare il carattere successivo per ciascun carattere di input: -![Immagine che mostra un esempio di generazione RNN della parola 'HELLO'.](../../../../../translated_images/it/rnn-generate.56c54afb52f9781d.png) +![Immagine che mostra un esempio di generazione RNN della parola 'HELLO'.](../../../../../translated_images/it/rnn-generate.56c54afb52f9781d.webp) Durante la generazione del testo (in fase di inferenza), iniziamo con un **prompt**, che viene passato attraverso le celle RNN per generare il suo stato intermedio, e da questo stato inizia la generazione. Generiamo un carattere alla volta, passando lo stato e il carattere generato a un'altra cella RNN per generare il successivo, fino a quando non abbiamo generato un numero sufficiente di caratteri. diff --git a/translations/it/lessons/5-NLP/18-Transformers/README.md b/translations/it/lessons/5-NLP/18-Transformers/README.md index 4691c93e..9b33d04b 100644 --- a/translations/it/lessons/5-NLP/18-Transformers/README.md +++ b/translations/it/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ Con gli RNN, il sequence-to-sequence viene implementato da due reti ricorrenti, I **Meccanismi di Attenzione** forniscono un mezzo per pesare l'impatto contestuale di ciascun vettore di input su ciascuna previsione di output dell'RNN. Questo viene implementato creando scorciatoie tra gli stati intermedi dell'RNN di input e l'RNN di output. In questo modo, quando si genera il simbolo di output yt, si prendono in considerazione tutti gli stati nascosti di input hi, con diversi coefficienti di peso αt,i. -![Immagine che mostra un modello encoder/decoder con uno strato di attenzione additiva](../../../../../translated_images/it/encoder-decoder-attention.7a726296894fb567.png) +![Immagine che mostra un modello encoder/decoder con uno strato di attenzione additiva](../../../../../translated_images/it/encoder-decoder-attention.7a726296894fb567.webp) > Il modello encoder-decoder con meccanismo di attenzione additiva in [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citato da [questo blog post](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) La matrice di attenzione {αi,j} rappresenta il grado in cui alcune parole di input influenzano la generazione di una determinata parola nella sequenza di output. Di seguito è riportato un esempio di tale matrice: -![Immagine che mostra un allineamento di esempio trovato da RNNsearch-50, tratto da Bahdanau - arviz.org](../../../../../translated_images/it/bahdanau-fig3.09ba2d37f202a6af.png) +![Immagine che mostra un allineamento di esempio trovato da RNNsearch-50, tratto da Bahdanau - arviz.org](../../../../../translated_images/it/bahdanau-fig3.09ba2d37f202a6af.webp) > Figura da [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) @@ -66,7 +66,7 @@ Il risultato che otteniamo con l'embedding posizionale incorpora sia il token or Successivamente, dobbiamo catturare alcuni schemi all'interno della nostra sequenza. Per fare ciò, i transformers utilizzano un meccanismo di **auto-attenzione**, che è essenzialmente attenzione applicata alla stessa sequenza come input e output. Applicare l'auto-attenzione ci consente di tenere conto del **contesto** all'interno della frase e vedere quali parole sono interconnesse. Ad esempio, ci consente di vedere quali parole sono riferite da coreferenze, come *it*, e di considerare il contesto: -![](../../../../../translated_images/it/CoreferenceResolution.861924d6d384a7d6.png) +![](../../../../../translated_images/it/CoreferenceResolution.861924d6d384a7d6.webp) > Immagine dal [Blog di Google](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Poiché ogni posizione di input viene mappata indipendentemente a ogni posizione **BERT** (Bidirectional Encoder Representations from Transformers) è una rete transformer multi-strato molto grande con 12 strati per *BERT-base* e 24 per *BERT-large*. Il modello viene prima pre-addestrato su un ampio corpus di dati testuali (Wikipedia + libri) utilizzando un addestramento non supervisionato (predizione di parole mascherate in una frase). Durante il pre-addestramento, il modello acquisisce livelli significativi di comprensione del linguaggio che possono poi essere sfruttati con altri dataset utilizzando il fine tuning. Questo processo è chiamato **transfer learning**. -![immagine da http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/it/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) +![immagine da http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/it/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > Immagine [fonte](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/it/lessons/5-NLP/18-Transformers/READMEtransformers.md b/translations/it/lessons/5-NLP/18-Transformers/READMEtransformers.md index 7d792b97..c40cb8b8 100644 --- a/translations/it/lessons/5-NLP/18-Transformers/READMEtransformers.md +++ b/translations/it/lessons/5-NLP/18-Transformers/READMEtransformers.md @@ -11,13 +11,13 @@ Avec les RNN, la séquence-à-séquence est mise en œuvre par deux réseaux ré **Les Mécanismes d'Attention** fournissent un moyen de pondérer l'impact contextuel de chaque vecteur d'entrée sur chaque prédiction de sortie du RNN. La façon dont cela est mis en œuvre consiste à créer des raccourcis entre les états intermédiaires du RNN d'entrée et du RNN de sortie. De cette manière, lors de la génération du symbole de sortie yt, nous prendrons en compte tous les états cachés d'entrée hi, avec différents coefficients de poids αt,i. -![Image montrant un modèle encodeur/décodeur avec une couche d'attention additive](../../../../../translated_images/it/encoder-decoder-attention.7a726296894fb567.png) +![Image montrant un modèle encodeur/décodeur avec une couche d'attention additive](../../../../../translated_images/it/encoder-decoder-attention.7a726296894fb567.webp) > Le modèle encodeur-décodeur avec un mécanisme d'attention additive dans [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), cité depuis [ce billet de blog](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) La matrice d'attention {αi,j} représenterait le degré d'importance de certains mots d'entrée dans la génération d'un mot donné dans la séquence de sortie. Ci-dessous se trouve un exemple de telle matrice : -![Image montrant un alignement d'exemple trouvé par RNNsearch-50, tirée de Bahdanau - arviz.org](../../../../../translated_images/it/bahdanau-fig3.09ba2d37f202a6af.png) +![Image montrant un alignement d'exemple trouvé par RNNsearch-50, tirée de Bahdanau - arviz.org](../../../../../translated_images/it/bahdanau-fig3.09ba2d37f202a6af.webp) > Figure de [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) @@ -57,7 +57,7 @@ Le résultat que nous obtenons avec l'embedding positionnel incorpore à la fois Ensuite, nous devons capturer certains motifs au sein de notre séquence. Pour ce faire, les transformateurs utilisent un mécanisme d'**auto-attention**, qui est essentiellement une attention appliquée à la même séquence en tant qu'entrée et sortie. L'application de l'auto-attention nous permet de prendre en compte le **contexte** au sein de la phrase et de voir quels mots sont inter-reliés. Par exemple, cela nous permet de voir quels mots sont référés par des co-références, telles que *il*, et également de prendre en compte le contexte : -![](../../../../../translated_images/it/CoreferenceResolution.861924d6d384a7d6.png) +![](../../../../../translated_images/it/CoreferenceResolution.861924d6d384a7d6.webp) > Image provenant du [Blog de Google](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -82,7 +82,7 @@ Puisque chaque position d'entrée est mappée indépendamment à chaque position **BERT** (Représentations d'Encodeur Bidirectionnelles à partir de Transformateurs) est un très grand réseau de transformateurs à plusieurs couches avec 12 couches pour *BERT-base*, et 24 pour *BERT-large*. Le modèle est d'abord pré-entraîné sur un grand corpus de données textuelles (WikiPedia + livres) en utilisant un entraînement non supervisé (prédiction de mots masqués dans une phrase). Au cours de la pré-formation, le modèle absorbe des niveaux significatifs de compréhension linguistique qui peuvent ensuite être exploités avec d'autres ensembles de données via un ajustement fin. Ce processus est appelé **apprentissage par transfert**. -![image de http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/it/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) +![image de http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/it/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > Image [source](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/it/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/it/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index 12eac6ce..6e968d93 100644 --- a/translations/it/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/it/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**I meccanismi di attenzione** forniscono un mezzo per pesare l'impatto contestuale di ciascun vettore di input su ciascuna previsione di output dell'RNN. Il modo in cui viene implementato è creando scorciatoie tra gli stati intermedi dell'RNN di input e l'RNN di output. In questo modo, quando si genera il simbolo di output $y_t$, si prenderanno in considerazione tutti gli stati nascosti di input $h_i$, con diversi coefficienti di peso $\\alpha_{t,i}$.\n", "\n", - "![Immagine che mostra un modello encoder/decoder con uno strato di attenzione additiva](../../../../../translated_images/it/encoder-decoder-attention.7a726296894fb567.png)\n", + "![Immagine che mostra un modello encoder/decoder con uno strato di attenzione additiva](../../../../../translated_images/it/encoder-decoder-attention.7a726296894fb567.webp)\n", "*Il modello encoder-decoder con meccanismo di attenzione additiva in [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citato da [questo post sul blog](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "La matrice di attenzione $\\{\\alpha_{i,j}\\}$ rappresenta il grado in cui certe parole di input influenzano la generazione di una determinata parola nella sequenza di output. Di seguito è riportato un esempio di tale matrice:\n", "\n", - "![Immagine che mostra un allineamento campione trovato da RNNsearch-50, tratto da Bahdanau - arviz.org](../../../../../translated_images/it/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![Immagine che mostra un allineamento campione trovato da RNNsearch-50, tratto da Bahdanau - arviz.org](../../../../../translated_images/it/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*Figura tratta da [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) è una rete Transformer multi-strato molto grande con 12 strati per *BERT-base* e 24 per *BERT-large*. Il modello viene prima pre-addestrato su un ampio corpus di dati testuali (Wikipedia + libri) utilizzando un addestramento non supervisionato (predizione di parole mascherate in una frase). Durante il pre-addestramento, il modello acquisisce un livello significativo di comprensione del linguaggio che può poi essere sfruttato con altri dataset tramite il fine-tuning. Questo processo è chiamato **transfer learning**.\n", "\n", - "![Immagine da http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/it/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![Immagine da http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/it/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "Esistono molte varianti delle architetture Transformer, tra cui BERT, DistilBERT, BigBird, OpenGPT3 e altre, che possono essere ottimizzate. Il pacchetto [HuggingFace](https://github.com/huggingface/) fornisce un repository per l'addestramento di molte di queste architetture con PyTorch.\n", "\n", diff --git a/translations/it/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/it/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index f8e99207..7d44b3d4 100644 --- a/translations/it/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/it/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "I **meccanismi di attenzione** forniscono un mezzo per pesare l'impatto contestuale di ciascun vettore di input su ciascuna previsione di output dell'RNN. Questo viene implementato creando scorciatoie tra gli stati intermedi dell'RNN di input e l'RNN di output. In questo modo, quando si genera il simbolo di output $y_t$, si tiene conto di tutti gli stati nascosti di input $h_i$, con diversi coefficienti di peso $\\alpha_{t,i}$. \n", "\n", - "![Immagine che mostra un modello encoder/decoder con uno strato di attenzione additiva](../../../../../translated_images/it/encoder-decoder-attention.7a726296894fb567.png)\n", + "![Immagine che mostra un modello encoder/decoder con uno strato di attenzione additiva](../../../../../translated_images/it/encoder-decoder-attention.7a726296894fb567.webp)\n", "*Il modello encoder-decoder con meccanismo di attenzione additiva in [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citato da [questo post sul blog](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "La matrice di attenzione $\\{\\alpha_{i,j}\\}$ rappresenta il grado in cui certe parole di input influenzano la generazione di una determinata parola nella sequenza di output. Di seguito è riportato un esempio di tale matrice:\n", "\n", - "![Immagine che mostra un allineamento campione trovato da RNNsearch-50, tratto da Bahdanau - arviz.org](../../../../../translated_images/it/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![Immagine che mostra un allineamento campione trovato da RNNsearch-50, tratto da Bahdanau - arviz.org](../../../../../translated_images/it/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*Figura tratta da [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3)*\n", "\n", @@ -225,7 +225,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) è una rete transformer multilivello molto grande con 12 livelli per *BERT-base* e 24 per *BERT-large*. Il modello viene inizialmente pre-addestrato su un ampio corpus di dati testuali (WikiPedia + libri) utilizzando un addestramento non supervisionato (predizione di parole mascherate in una frase). Durante la fase di pre-addestramento, il modello acquisisce un livello significativo di comprensione del linguaggio che può essere poi sfruttato con altri dataset attraverso il fine tuning. Questo processo è chiamato **apprendimento trasferibile**.\n", "\n", - "![immagine da http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/it/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![immagine da http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/it/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "Esistono molte varianti delle architetture Transformer, tra cui BERT, DistilBERT, BigBird, OpenGPT3 e altre, che possono essere ottimizzate.\n", "\n", diff --git a/translations/it/lessons/5-NLP/19-NER/README.md b/translations/it/lessons/5-NLP/19-NER/README.md index 2b894b45..076cd4e8 100644 --- a/translations/it/lessons/5-NLP/19-NER/README.md +++ b/translations/it/lessons/5-NLP/19-NER/README.md @@ -59,7 +59,7 @@ neonato | O Poiché dobbiamo costruire una corrispondenza uno-a-uno tra token e classi, possiamo allenare un modello neurale **molti-a-molti** come mostrato in questa immagine: -![Immagine che mostra i modelli comuni di reti neurali ricorrenti.](../../../../../translated_images/it/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Immagine che mostra i modelli comuni di reti neurali ricorrenti.](../../../../../translated_images/it/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > *Immagine tratta da [questo post sul blog](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) di [Andrej Karpathy](http://karpathy.github.io/). I modelli di classificazione dei token NER corrispondono all'architettura di rete più a destra in questa immagine.* diff --git a/translations/it/lessons/5-NLP/README.md b/translations/it/lessons/5-NLP/README.md index 0b5eb4b6..bb919306 100644 --- a/translations/it/lessons/5-NLP/README.md +++ b/translations/it/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Elaborazione del Linguaggio Naturale -![Riepilogo dei compiti NLP in uno schizzo](../../../../translated_images/it/ai-nlp.b22dcb8ca4707cea.png) +![Riepilogo dei compiti NLP in uno schizzo](../../../../translated_images/it/ai-nlp.b22dcb8ca4707cea.webp) In questa sezione ci concentreremo sull'utilizzo delle reti neurali per gestire compiti legati all'**Elaborazione del Linguaggio Naturale (NLP)**. Ci sono molti problemi di NLP che vogliamo che i computer siano in grado di risolvere: diff --git a/translations/it/lessons/6-Other/23-MultiagentSystems/README.md b/translations/it/lessons/6-Other/23-MultiagentSystems/README.md index c30f5155..8ccbaea7 100644 --- a/translations/it/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/it/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ Puoi aprire uno dei modelli, ad esempio **Biology → Flocking**. Dopo aver aperto il modello, verrai portato alla schermata principale di NetLogo. Ecco un esempio di modello che descrive la popolazione di lupi e pecore, date risorse finite (erba). -![Schermata Principale di NetLogo](../../../../../translated_images/it/NetLogo-Main.32653711ec1a01b3.png) +![Schermata Principale di NetLogo](../../../../../translated_images/it/NetLogo-Main.32653711ec1a01b3.webp) > Screenshot di Dmitry Soshnikov diff --git a/translations/it/lessons/README.md b/translations/it/lessons/README.md index 86ce5ea7..8a3eba7e 100644 --- a/translations/it/lessons/README.md +++ b/translations/it/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Panoramica -![Panoramica in uno schizzo](../../../translated_images/it/ai-overview.0857791951d19500.png) +![Panoramica in uno schizzo](../../../translated_images/it/ai-overview.0857791951d19500.webp) > Schizzo di [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/it/lessons/X-Extras/X1-MultiModal/README.md b/translations/it/lessons/X-Extras/X1-MultiModal/README.md index 1de3f143..a31b6a29 100644 --- a/translations/it/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/it/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ Dopo il successo dei modelli transformer per risolvere compiti di NLP, le stesse L'idea principale di CLIP è quella di confrontare i prompt testuali con un'immagine e determinare quanto bene l'immagine corrisponda al prompt. -![Architettura CLIP](../../../../../translated_images/it/clip-arch.b3dbf20b4e8ed8be.png) +![Architettura CLIP](../../../../../translated_images/it/clip-arch.b3dbf20b4e8ed8be.webp) > *Immagine tratta da [questo post sul blog](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ Una volta che questo modello è stato pre-addestrato, possiamo fornire un batch Supponiamo di dover classificare immagini tra, ad esempio, gatti, cani e esseri umani. In questo caso, possiamo fornire al modello un'immagine e una serie di prompt testuali: "*una foto di un gatto*", "*una foto di un cane*", "*una foto di un essere umano*". Nel vettore risultante di 3 probabilità, dobbiamo semplicemente selezionare l'indice con il valore più alto. -![CLIP per la Classificazione delle Immagini](../../../../../translated_images/it/clip-class.3af42ef0b2b19369.png) +![CLIP per la Classificazione delle Immagini](../../../../../translated_images/it/clip-class.3af42ef0b2b19369.webp) > *Immagine tratta da [questo post sul blog](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ Scopri di più su VQGAN sul sito web [Taming Transformers](https://compvis.githu Una delle differenze importanti tra VQGAN e i GAN tradizionali è che questi ultimi possono produrre un'immagine decente da qualsiasi vettore di input, mentre VQGAN è più propenso a produrre un'immagine incoerente. Pertanto, è necessario guidare ulteriormente il processo di creazione dell'immagine, e questo può essere fatto utilizzando CLIP. -![Architettura VQGAN+CLIP](../../../../../translated_images/it/vqgan.5027fe05051dfa31.png) +![Architettura VQGAN+CLIP](../../../../../translated_images/it/vqgan.5027fe05051dfa31.webp) Per generare un'immagine corrispondente a un prompt testuale, iniziamo con un vettore di codifica casuale che viene passato attraverso VQGAN per produrre un'immagine. Successivamente, CLIP viene utilizzato per produrre una funzione di perdita che mostra quanto bene l'immagine corrisponda al prompt testuale. L'obiettivo è quindi minimizzare questa perdita, utilizzando la retropropagazione per regolare i parametri del vettore di input. Una grande libreria che implementa VQGAN+CLIP è [Pixray](http://github.com/pixray/pixray). -![Immagine prodotta da Pixray](../../../../../translated_images/it/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Immagine prodotta da Pixray](../../../../../translated_images/it/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Immagine prodotta da Pixray](../../../../../translated_images/it/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) +![Immagine prodotta da Pixray](../../../../../translated_images/it/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp) | ![Immagine prodotta da Pixray](../../../../../translated_images/it/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp) | ![Immagine prodotta da Pixray](../../../../../translated_images/it/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp) ----|----|---- Immagine generata dal prompt *un ritratto ravvicinato ad acquerello di un giovane insegnante di letteratura con un libro* | Immagine generata dal prompt *un ritratto ravvicinato a olio di una giovane insegnante di informatica con un computer* | Immagine generata dal prompt *un ritratto ravvicinato a olio di un anziano insegnante di matematica davanti a una lavagna* diff --git a/translations/ja/README.md b/translations/ja/README.md index 161a1998..88917f65 100644 --- a/translations/ja/README.md +++ b/translations/ja/README.md @@ -1,8 +1,8 @@ -[アラビア語](../ar/README.md) | [ベンガル語](../bn/README.md) | [ブルガリア語](../bg/README.md) | [ビルマ語（ミャンマー）](../my/README.md) | [中国語（簡体字）](../zh/README.md) | [中国語（繁体字、香港）](../hk/README.md) | [中国語（繁体字、マカオ）](../mo/README.md) | [中国語（繁体字、台湾）](../tw/README.md) | [クロアチア語](../hr/README.md) | [チェコ語](../cs/README.md) | [デンマーク語](../da/README.md) | [オランダ語](../nl/README.md) | [エストニア語](../et/README.md) | [フィンランド語](../fi/README.md) | [フランス語](../fr/README.md) | [ドイツ語](../de/README.md) | [ギリシャ語](../el/README.md) | [ヘブライ語](../he/README.md) | [ヒンディー語](../hi/README.md) | [ハンガリー語](../hu/README.md) | [インドネシア語](../id/README.md) | [イタリア語](../it/README.md) | [日本語](./README.md) | [カンナダ語](../kn/README.md) | [韓国語](../ko/README.md) | [リトアニア語](../lt/README.md) | [マレー語](../ms/README.md) | [マラヤーラム語](../ml/README.md) | [マラーティー語](../mr/README.md) | [ネパール語](../ne/README.md) | [ナイジェリア・ピジン](../pcm/README.md) | [ノルウェー語](../no/README.md) | [ペルシャ語（ファルシ）](../fa/README.md) | [ポーランド語](../pl/README.md) | [ポルトガル語（ブラジル）](../br/README.md) | [ポルトガル語（ポルトガル）](../pt/README.md) | [パンジャーブ語（グルムキー）](../pa/README.md) | [ルーマニア語](../ro/README.md) | [ロシア語](../ru/README.md) | [セルビア語（キリル文字）](../sr/README.md) | [スロバキア語](../sk/README.md) | [スロベニア語](../sl/README.md) | [スペイン語](../es/README.md) | [スワヒリ語](../sw/README.md) | [スウェーデン語](../sv/README.md) | [タガログ語（フィリピン語）](../tl/README.md) | [タミル語](../ta/README.md) | [テルグ語](../te/README.md) | [タイ語](../th/README.md) | [トルコ語](../tr/README.md) | [ウクライナ語](../uk/README.md) | [ウルドゥー語](../ur/README.md) | [ベトナム語](../vi/README.md) +[アラビア語](../ar/README.md) | [ベンガル語](../bn/README.md) | [ブルガリア語](../bg/README.md) | [ビルマ語（ミャンマー）](../my/README.md) | [中国語（簡体字）](../zh/README.md) | [中国語（繁体字、香港）](../hk/README.md) | [中国語（繁体字、マカオ）](../mo/README.md) | [中国語（繁体字、台湾）](../tw/README.md) | [クロアチア語](../hr/README.md) | [チェコ語](../cs/README.md) | [デンマーク語](../da/README.md) | [オランダ語](../nl/README.md) | [エストニア語](../et/README.md) | [フィンランド語](../fi/README.md) | [フランス語](../fr/README.md) | [ドイツ語](../de/README.md) | [ギリシャ語](../el/README.md) | [ヘブライ語](../he/README.md) | [ヒンディー語](../hi/README.md) | [ハンガリー語](../hu/README.md) | [インドネシア語](../id/README.md) | [イタリア語](../it/README.md) | [日本語](./README.md) | [カンナダ語](../kn/README.md) | [韓国語](../ko/README.md) | [リトアニア語](../lt/README.md) | [マレー語](../ms/README.md) | [マラヤーラム語](../ml/README.md) | [マラーティー語](../mr/README.md) | [ネパール語](../ne/README.md) | [ナイジェリア・ピジン語](../pcm/README.md) | [ノルウェー語](../no/README.md) | [ペルシャ語（ファルシ）](../fa/README.md) | [ポーランド語](../pl/README.md) | [ポルトガル語（ブラジル）](../br/README.md) | [ポルトガル語（ポルトガル）](../pt/README.md) | [パンジャブ語（グルムキー）](../pa/README.md) | [ルーマニア語](../ro/README.md) | [ロシア語](../ru/README.md) | [セルビア語（キリル文字）](../sr/README.md) | [スロバキア語](../sk/README.md) | [スロベニア語](../sl/README.md) | [スペイン語](../es/README.md) | [スワヒリ語](../sw/README.md) | [スウェーデン語](../sv/README.md) | [タガログ語（フィリピン）](../tl/README.md) | [タミル語](../ta/README.md) | [テルグ語](../te/README.md) | [タイ語](../th/README.md) | [トルコ語](../tr/README.md) | [ウクライナ語](../uk/README.md) | [ウルドゥー語](../ur/README.md) | [ベトナム語](../vi/README.md) -> **ローカルにクローンしたい場合？** +> **ローカルでクローンしたいですか？** -> このリポジトリには50以上の言語翻訳が含まれており、ダウンロードサイズが大幅に増加します。翻訳なしでクローンする場合はスパースチェックアウトを使用してください： +> このリポジトリには50以上の言語翻訳が含まれており、ダウンロードサイズが大きくなります。翻訳なしでクローンしたい場合は、スパースチェックアウトを使用してください： > ```bash > git clone --filter=blob:none --sparse https://github.com/microsoft/AI-For-Beginners.git > cd AI-For-Beginners > git sparse-checkout set --no-cone '/*' '!translations' '!translated_images' > ``` -> これにより、コースを完了するために必要なすべてが、より高速なダウンロードで得られます。 +> これにより、コースを完了するために必要なものすべてがより高速にダウンロードできます。 -**追加の翻訳言語サポートをご希望の場合は[こちら](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)をご参照ください** +**追加翻訳言語のサポートを希望する場合は、[こちら](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)をご覧ください。** ## コミュニティに参加する [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -## 学べること +## 学べる内容 **[コースのマインドマップ](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** -このカリキュラムでは以下を学習します： +このカリキュラムでは次のことを学びます： -* 「古典的」記号的アプローチである**知識表現**と推論（[GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)）を含む異なる人工知能のアプローチ。 -* 現代AIの中心である**ニューラルネットワーク**と**ディープラーニング**。これらの主要なテーマの背後にある概念を、最も人気のあるフレームワーク2つ—[TensorFlow](http://Tensorflow.org) と [PyTorch](http://pytorch.org) のコードを使って説明します。 -* 画像やテキストの扱いに用いる**ニューラルアーキテクチャ**。最新のモデルをカバーしますが、最先端の部分ではやや不足があるかもしれません。 -* あまり知られていないAIのアプローチ、例えば**遺伝的アルゴリズム**や**マルチエージェントシステム**。 +* 「古き良き」記号的アプローチである**知識表現**や推論を含む、人工知能のさまざまなアプローチ（[GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)）。 +* 現代AIの中核である**ニューラルネットワーク**と**ディープラーニング**。最も人気のある2つのフレームワーク、[TensorFlow](http://Tensorflow.org)と[PyTorch](http://pytorch.org)のコードを使ってこれら重要なトピックの概念を説明します。 +* 画像やテキストを扱うための**ニューラルアーキテクチャ**。最近のモデルも取り上げますが、最先端を完全にカバーしているわけではありません。 +* **遺伝的アルゴリズム**や**マルチエージェントシステム**など、あまり知られていないAIアプローチ。 このカリキュラムで扱わない内容： -> [Microsoft Learnコレクションでこのコースに関連するすべての追加リソースを見つける](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) +> [Microsoft Learnコレクションでこのコースの追加リソースすべてを見つける](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) -* **ビジネスにおけるAI**のビジネスケース。Microsoft Learnの [ビジネスユーザーのためのAI入門](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) 学習パスや、[INSEAD](https://www.insead.edu/) と共同開発した [AI Business School](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum) の受講を検討してください。 -* クラシックな機械学習は、[初心者向け機械学習カリキュラム](http://github.com/Microsoft/ML-for-Beginners)で詳しく説明されています。 -* **[Cognitive Services](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)** を使用した実践的なAIアプリケーション。これには、Microsoft Learnの [視覚](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum)、[自然言語処理](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum)、**[Azure OpenAIサービスを使用した生成AI](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** などのモジュールから始めることをお勧めします。 -* [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-bethanycheum)、[Microsoft Fabric](https://learn.microsoft.com/en-us/training/paths/get-started-fabric/?WT.mc_id=academic-77998-bethanycheum)、[Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum)など、特定のMLクラウドフレームワーク。 [Azure Machine Learningで機械学習ソリューションを構築および運用する](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) と [Azure Databricksで機械学習ソリューションを構築および運用する](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum) の学習パスを検討してください。 -* **会話型AI**や**チャットボット**。別途 [会話型AIソリューションを作成する](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum) の学習パスがあり、詳細は [このブログ記事](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) も参照できます。 -* ディープラーニングの背後にある**高度な数学**。これにはIan Goodfellow, Yoshua Bengio, Aaron Courvilleの [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) をお勧めします。オンラインでも [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/) で利用可能です。 +* **ビジネスにおけるAIの活用事例**。Microsoft Learnの[ビジネスユーザー向けAI入門](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum)や、[INSEAD](https://www.insead.edu/)と共同開発した[AIビジネススクール](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum)をご検討ください。 +* 当リポジトリの[機械学習初心者カリキュラム](http://github.com/Microsoft/ML-for-Beginners)で詳しく解説されている**古典的機械学習**。 +* **[Cognitive Services](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**を使用した実践的なAIアプリケーション。これには、Microsoft Learnの[ビジョン](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum)、[自然言語処理](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum)、**[Azure OpenAI Serviceによる生成AI](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)**などのモジュールから始めることをお勧めします。 +* [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-bethanycheum)、[Microsoft Fabric](https://learn.microsoft.com/en-us/training/paths/get-started-fabric/?WT.mc_id=academic-77998-bethanycheum)、[Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum)などの特定のML**クラウドフレームワーク**。[Azure Machine Learningを使用した機械学習ソリューションの構築と運用](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum)や[Azure Databricksを使用した機械学習ソリューションの構築と運用](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum)のラーニングパスがおすすめです。 +* **対話型AI**および**チャットボット**。これには独立した[対話型AIソリューションの作成](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum)のラーニングパスがあり、詳細は[こちらのブログ記事](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/)もご参照ください。 +* 深層学習の**深い数学的背景**。これには、Ian Goodfellow、Yoshua Bengio、Aaron Courville著の[Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618)をお勧めします。オンライン版は[https://www.deeplearningbook.org/](https://www.deeplearningbook.org/)からも入手可能です。 -クラウドにおけるAIの優しい入門としては、[Azureで人工知能を始める](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum) 学習パスの受講を検討できます。 +_クラウド上のAI_ に優しく触れたい場合は、[Azureで人工知能をはじめよう](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum)ラーニングパスをご検討ください。 # コンテンツ @@ -84,95 +85,95 @@ CO_OP_TRANSLATOR_METADATA: | I | [**AI入門**](./lessons/1-Intro/README.md) | | | | 01 | [AIの紹介と歴史](./lessons/1-Intro/README.md) | - | - | | II | **記号的AI** | -| 02 | [知識表現とエキスパートシステム](./lessons/2-Symbolic/README.md) | [エキスパートシステム](./lessons/2-Symbolic/Animals.ipynb) / [オントロジー](./lessons/2-Symbolic/FamilyOntology.ipynb) /[概念グラフ](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | +| 02 | [知識表現とエキスパートシステム](./lessons/2-Symbolic/README.md) | [エキスパートシステム](./lessons/2-Symbolic/Animals.ipynb) / [オントロジー](./lessons/2-Symbolic/FamilyOntology.ipynb) /[コンセプトグラフ](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | | III | [**ニューラルネットワーク入門**](./lessons/3-NeuralNetworks/README.md) ||| | 03 | [パーセプトロン](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [ノートブック](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [ラボ](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | | 04 | [多層パーセプトロンと独自フレームワークの作成](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [ノートブック](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [ラボ](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | | 05 | [フレームワーク入門（PyTorch/TensorFlow）と過学習](./lessons/3-NeuralNetworks/05-Frameworks/README.md) | [PyTorch](./lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb) / [Keras](./lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [ラボ](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | -| IV | [**コンピュータビジョン**](./lessons/4-ComputerVision/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-77998-cacaste) / [TensorFlow](https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-77998-cacaste)| [Microsoft Azureでコンピュータビジョンを探る](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | +| IV | [**コンピュータビジョン**](./lessons/4-ComputerVision/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-77998-cacaste) / [TensorFlow](https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-77998-cacaste)| [Microsoft Azureでコンピュータビジョンを探索する](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | | 06 | [コンピュータビジョン入門。OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [ノートブック](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [ラボ](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | | 07 | [畳み込みニューラルネットワーク](./lessons/4-ComputerVision/07-ConvNets/README.md) & [CNNアーキテクチャ](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [ラボ](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | -| 08 | [事前学習済みネットワークと転移学習](./lessons/4-ComputerVision/08-TransferLearning/README.md) と [トレーニングのコツ](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [ラボ](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | -| 09 | [オートエンコーダーとVAE](./lessons/4-ComputerVision/09-Autoencoders/README.md) | [PyTorch](./lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb) | | -| 10 | [敵対的生成ネットワーク＆アーティスティックスタイル転送](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | +| 08 | [事前学習ネットワークと転移学習](./lessons/4-ComputerVision/08-TransferLearning/README.md) と [学習のコツ](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [ラボ](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | +| 09 | [オートエンコーダと変分オートエンコーダ（VAE）](./lessons/4-ComputerVision/09-Autoencoders/README.md) | [PyTorch](./lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb) | | +| 10 | [生成敵対ネットワークとアートスタイル転送](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | | 11 | [物体検出](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [ラボ](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | | 12 | [セマンティックセグメンテーション。U-Net](./lessons/4-ComputerVision/12-Segmentation/README.md) | [PyTorch](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb) | | -| V | [**自然言語処理**](./lessons/5-NLP/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-77998-cacaste) /[TensorFlow](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-77998-cacaste) | [Microsoft Azureで自然言語処理を探る](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| -| 13 | [テキスト表現。Bag of Words/TF-IDF](./lessons/5-NLP/13-TextRep/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb) | | -| 14 | [意味的単語埋め込み。Word2Vec と GloVe](./lessons/5-NLP/14-Embeddings/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb) | | -| 15 | [言語モデリング。独自埋め込みのトレーニング](./lessons/5-NLP/15-LanguageModeling/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-PyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-TF.ipynb) | [ラボ](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | -| 16 | [リカレントニューラルネットワーク](./lessons/5-NLP/16-RNN/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNTF.ipynb) | | -| 17 | [生成リカレントネットワーク](./lessons/5-NLP/17-GenerativeNetworks/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb) | [ラボ](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | +| V | [**自然言語処理**](./lessons/5-NLP/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-77998-cacaste) /[TensorFlow](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-77998-cacaste) | [Microsoft Azureで自然言語処理を探索する](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| +| 13 | [テキスト表現。Bag of Words（BoW）/TF-IDF](./lessons/5-NLP/13-TextRep/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb) | | +| 14 | [セマンティック単語埋め込み。Word2VecとGloVe](./lessons/5-NLP/14-Embeddings/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb) | | +| 15 | [言語モデル。独自の埋め込みの学習](./lessons/5-NLP/15-LanguageModeling/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-PyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-TF.ipynb) | [ラボ](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | +| 16 | [再帰型ニューラルネットワーク（RNN）](./lessons/5-NLP/16-RNN/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNTF.ipynb) | | +| 17 | [生成再帰型ネットワーク](./lessons/5-NLP/17-GenerativeNetworks/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb) | [ラボ](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | | 18 | [トランスフォーマー。BERT](./lessons/5-NLP/18-Transformers/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb) /[TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersTF.ipynb) | | | 19 | [固有表現抽出](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [ラボ](./lessons/5-NLP/19-NER/lab/README.md) | -| 20 | [大規模言語モデル、プロンプトプログラミングと少数ショット学習](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | +| 20 | [大規模言語モデル、プロンプトプログラミング、少数ショットタスク](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | | VI | **その他のAI技術** || | | 21 | [遺伝的アルゴリズム](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [ノートブック](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | -| 22 | [深層強化学習](./lessons/6-Other/22-DeepRL/README.md) | [PyTorch](./lessons/6-Other/22-DeepRL/CartPole-RL-PyTorch.ipynb) /[TensorFlow](./lessons/6-Other/22-DeepRL/CartPole-RL-TF.ipynb) | [ラボ](./lessons/6-Other/22-DeepRL/lab/README.md) | +| 22 | [ディープ強化学習](./lessons/6-Other/22-DeepRL/README.md) | [PyTorch](./lessons/6-Other/22-DeepRL/CartPole-RL-PyTorch.ipynb) /[TensorFlow](./lessons/6-Other/22-DeepRL/CartPole-RL-TF.ipynb) | [ラボ](./lessons/6-Other/22-DeepRL/lab/README.md) | | 23 | [マルチエージェントシステム](./lessons/6-Other/23-MultiagentSystems/README.md) | | | | VII | **AI倫理** | | | | 24 | [AI倫理と責任あるAI](./lessons/7-Ethics/README.md) | [Microsoft Learn: 責任あるAIの原則](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | | IX | **エクストラ** | | | -| 25 | [マルチモーダルネットワーク、CLIP と VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [ノートブック](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | +| 25 | [マルチモーダルネットワーク、CLIP、VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [ノートブック](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | -## 各レッスンには以下が含まれます +## 各レッスンに含まれるもの -* 予備学習用資料 -* 実行可能なJupyterノートブックが含まれており、多くは特定のフレームワーク（**PyTorch** または **TensorFlow**）向けです。実行可能なノートブックには理論的な内容も多く含まれているため、トピックを理解するにはノートブックのどちらか一方（PyTorchまたはTensorFlow）を最後まで学習する必要があります。 -* 一部のトピックには**ラボ**が用意されており、学んだ内容を具体的な課題に適用して試すことができます。 +* 事前学習用資料 +* 実行可能なJupyterノートブックで、多くは特定のフレームワーク（**PyTorch**または**TensorFlow**）に対応しています。実行可能なノートブックには理論的な解説も多く含まれているため、トピックを理解するにはノートブックのどちらか一方（PyTorchまたはTensorFlow）を一通り行うことが必要です。 +* 一部のトピックでは、学んだ内容を特定の問題に適用してみることができる**ラボ**があります。 * 一部のセクションには、関連トピックを扱う[**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)モジュールへのリンクがあります。 ## はじめに -### 🎯 AI初心者の方へ！まずはこちらから始めましょう +### 🎯 AIが初めてですか？ここから始めましょう！ -AIがまったく初めてで、手早く実践的な例を試したい方は、[**初心者向けの例**](./examples/README.md)をご覧ください！以下が含まれています： +もしAIがまったくはじめてで手早くハンズオンを試したいなら、[**初心者向けの例**](./examples/README.md)をチェックしてください！ここには次の内容が含まれます： - 🌟 **Hello AI World** - あなたの最初のAIプログラム（パターン認識） -- 🧠 **シンプルなニューラルネットワーク** - 最初からニューラルネットワークを構築する +- 🧠 **シンプルニューラルネットワーク** - 1からニューラルネットワークを作る - 🖼️ **画像分類器** - 詳細なコメント付き画像分類 -- 💬 **テキスト感情分析** - ポジティブ/ネガティブなテキストを解析する +- 💬 **テキスト感情分析** - ポジティブ/ネガティブなテキストを分析します -これらの例は、完全なカリキュラムに進む前にAIの概念を理解するのに役立つように設計されています。 +これらの例は、フルカリキュラムに入る前にAIの概念を理解するのに役立つよう設計されています。 -### 📚 フルカリキュラムのセットアップ +### 📚 フルカリキュラムセットアップ -- 開発環境のセットアップを支援するための[セットアップレッスン](./lessons/0-course-setup/setup.md)を用意しました。- 教育者向けには、[カリキュラムセットアップレッスン](./lessons/0-course-setup/for-teachers.md)もご用意しています！ -- VSCodeまたはCodepaceでの[コードの実行方法](./lessons/0-course-setup/how-to-run.md) +- 開発環境のセットアップを支援するために[セットアップレッスン](./lessons/0-course-setup/setup.md)を作成しました。 - 教育者向けには、[カリキュラムセットアップレッスン](./lessons/0-course-setup/for-teachers.md)も用意しています！ +- [VSCodeまたはCodespaceでコードを実行する方法](./lessons/0-course-setup/how-to-run.md) 以下の手順に従ってください： -リポジトリをフォークする: このページの右上にある「Fork」ボタンをクリックしてください。 +リポジトリをフォークする：このページ右上の「Fork」ボタンをクリックします。 -リポジトリをクローンする: `git clone https://github.com/microsoft/AI-For-Beginners.git` +リポジトリをクローンする：`git clone https://github.com/microsoft/AI-For-Beginners.git` -あとで見つけやすくするために、このリポジトリにスター(🌟)を忘れずにつけてください。 +後で見つけやすくするために、このリポジトリにスター（🌟）を付けるのを忘れないでください。 -## 他の学習者と出会う +## 他の学習者と交流 -このコースを受講している他の学習者と交流し、サポートを受けるために[公式AI Discordサーバー](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum)に参加しましょう。 +このコースを受講している他の学習者と出会い、ネットワークを築き、サポートを受けたい場合は、[公式AI Discordサーバー](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum)に参加してください。 -製品のフィードバックや質問がある場合は、[Azure AI Foundry Developer Forum](https://aka.ms/foundry/forum)をご利用ください。 +製品に関するフィードバックや質問がある場合は、[Azure AI Foundry デベロッパーフォーラム](https://aka.ms/foundry/forum)をご覧ください。 ## クイズ -> **クイズについての注意**: 全てのクイズはetc\quiz-appフォルダー内のQuiz-appフォルダーにあります。または[こちらのオンライン版](https://ff-quizzes.netlify.app/)でアクセス可能です。クイズはレッスン内からリンクされています。クイズアプリはローカルで実行することも、Azureにデプロイすることもできます；`quiz-app`フォルダーの指示に従ってください。順次ローカライズされています。 +> **クイズについての注意**：すべてのクイズはetc\quiz-app内のQuiz-appフォルダーに含まれているか、[オンラインはこちら](https://ff-quizzes.netlify.app/)からアクセスできます。クイズはレッスンからリンクされており、quiz-appフォルダーの指示に従ってローカルで実行するかAzureにデプロイできます。クイズは徐々にローカライズされています。 ## ヘルプ募集 -提案やスペルミスやコードの誤りを見つけた場合は、Issueを投稿するかプルリクエストを作成してください。 +提案やスペルミス、コードの誤りを見つけた場合は、イシューを投稿するかプルリクエストを作成してください。 ## 特別な感謝 -* **✍️ 主筆者:** [Dmitry Soshnikov](http://soshnikov.com), PhD -* **🔥 編集者:** [Jen Looper](https://twitter.com/jenlooper), PhD +* **✍️ 主著者:** [Dmitry Soshnikov](http://soshnikov.com)、PhD +* **🔥 編集者:** [Jen Looper](https://twitter.com/jenlooper)、PhD * **🎨 スケッチノートイラストレーター:** [Tomomi Imura](https://twitter.com/girlie_mac) -* **✅ クイズ作成者:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) -* **🙏 コア貢献者:** [Evgenii Pishchik](https://github.com/Pe4enIks) +* **✅ クイズ作成者:** [Lateefah Bello](https://github.com/CinnamonXI)、[MLSA](https://studentambassadors.microsoft.com/) +* **🙏 コアコントリビューター:** [Evgenii Pishchik](https://github.com/Pe4enIks) ## その他のカリキュラム -私たちのチームは他のカリキュラムも制作しています！ぜひご覧ください： +私たちのチームは他のカリキュラムも制作しています！こちらをチェックしてください： ### LangChain @@ -181,7 +182,7 @@ AIがまったく初めてで、手早く実践的な例を試したい方は、 --- -### Azure / Edge / MCP / エージェント +### Azure / Edge / MCP / Agents [![AZD for Beginners](https://img.shields.io/badge/AZD%20for%20Beginners-0078D4?style=for-the-badge&labelColor=E5E7EB&color=0078D4)](https://github.com/microsoft/AZD-for-beginners?WT.mc_id=academic-105485-koreyst) [![Edge AI for Beginners](https://img.shields.io/badge/Edge%20AI%20for%20Beginners-00B8E4?style=for-the-badge&labelColor=E5E7EB&color=00B8E4)](https://github.com/microsoft/edgeai-for-beginners?WT.mc_id=academic-105485-koreyst) [![MCP for Beginners](https://img.shields.io/badge/MCP%20for%20Beginners-009688?style=for-the-badge&labelColor=E5E7EB&color=009688)](https://github.com/microsoft/mcp-for-beginners?WT.mc_id=academic-105485-koreyst) @@ -208,19 +209,19 @@ AIがまったく初めてで、手早く実践的な例を試したい方は、 --- -### コパイロットシリーズ +### Copilotシリーズ [![Copilot for AI Paired Programming](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst) [![Copilot for C#/.NET](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst) [![Copilot Adventure](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst) -## ヘルプを得る +## ヘルプを得るには -AIアプリの構築で迷ったり質問がある場合は、MCPに関する議論に参加して他の学習者や経験豊富な開発者と交流しましょう。ここは質問を歓迎し、知識を自由に共有する支援的なコミュニティです。 +AIアプリの構築中に詰まったり質問がある場合は、他の学習者や経験豊富な開発者とともにMCPについてのディスカッションに参加してください。質問が歓迎され、知識が自由に共有される支援的なコミュニティです。 [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -製品に関するフィードバックや構築中のエラーがある場合は以下をご利用ください： +構築中に製品へのフィードバックやエラーがある場合は、こちらをご覧ください： [![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) @@ -228,5 +229,5 @@ AIアプリの構築で迷ったり質問がある場合は、MCPに関する議 **免責事項**： -本書類はAI翻訳サービス「Co-op Translator」（https://github.com/Azure/co-op-translator）を使用して翻訳されました。正確性の確保に努めておりますが、自動翻訳には誤りや不正確な部分が含まれる場合があります。原文の言語による文書が正式な内容とみなされます。重要な情報については専門の人間による翻訳を推奨いたします。本翻訳の使用により生じた誤解や解釈の相違について、当方は一切の責任を負いかねますのでご了承ください。 +本書類はAI翻訳サービス「Co-op Translator」（https://github.com/Azure/co-op-translator）を使用して翻訳されました。正確性には努めておりますが、自動翻訳には誤りや不正確な部分が含まれる可能性があります。原文のネイティブ言語版を正本としてご参照ください。重要な情報については、専門の人間による翻訳を推奨いたします。本翻訳の利用により生じた誤解や誤訳について、一切の責任を負いかねますのでご了承ください。 \ No newline at end of file diff --git a/translations/ja/lessons/0-course-setup/how-to-run.md b/translations/ja/lessons/0-course-setup/how-to-run.md index 5a29cca2..2fae9cb2 100644 --- a/translations/ja/lessons/0-course-setup/how-to-run.md +++ b/translations/ja/lessons/0-course-setup/how-to-run.md @@ -1,21 +1,21 @@ -# コードを実行する方法 +# コードの実行方法 -このカリキュラムには、実行可能な例やラボが多数含まれており、それらを実行したいと思うでしょう。そのためには、このカリキュラムの一部として提供されるJupyter NotebookでPythonコードを実行する環境が必要です。コードを実行するには、いくつかの選択肢があります。 +このカリキュラムには、多くの実行可能な例とラボが含まれており、それらを実行したいと思うでしょう。そのためには、このカリキュラムの一部として提供されるJupyterノートブックでPythonコードを実行する能力が必要です。コードを実行する方法はいくつかあります： -## ローカル環境で実行する +## お使いのコンピューターでローカルに実行する -コードをローカル環境で実行するには、何らかのバージョンのPythonをインストールする必要があります。個人的には、**[miniconda](https://conda.io/en/latest/miniconda.html)** のインストールをお勧めします。これは軽量なインストールで、`conda`パッケージマネージャを使用してさまざまなPythonの**仮想環境**をサポートします。 +お使いのコンピューターでコードをローカルに実行するには、Pythonのインストールが必要です。おすすめの一つは**[miniconda](https://conda.io/en/latest/miniconda.html)**のインストールです。これは比較的軽量なインストールで、異なるPythonの**仮想環境**用の`conda`パッケージマネージャーをサポートします。 -minicondaをインストールした後、このコース用の仮想環境を作成するためにリポジトリをクローンし、以下を実行します： +minicondaをインストールした後に、リポジトリをクローンし、このコース用に仮想環境を作成します： ```bash git clone http://github.com/microsoft/ai-for-beginners @@ -26,51 +26,55 @@ conda activate ai4beg ### Python拡張機能を使ったVisual Studio Codeの利用 -おそらく、このカリキュラムを利用する最良の方法は、[Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste)と[Python Extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste)を使って開くことです。 +このカリキュラムは、[Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste)で[Python拡張機能](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste)を開いて使うのが最適です。 -> **Note**: リポジトリをクローンしてVS Codeでディレクトリを開くと、Python拡張機能のインストールを自動的に提案されます。また、上記のようにminicondaをインストールする必要があります。 +> **注意**: リポジトリをクローンしてVS Codeでディレクトリを開くと、自動的にPython拡張機能のインストールを提案されます。また、上記のようにminicondaもインストールしておく必要があります。 -> **Note**: VS Codeがリポジトリをコンテナで再オープンすることを提案した場合、ローカルのPythonインストールを使用するためにこれを拒否してください。 +> **注意**: VS Codeがリポジトリをコンテナで再オープンするよう提案してきた場合は、ローカルのPythonインストールを利用するためにこれを拒否してください。 -### ブラウザでJupyterを使用する +### ブラウザーでのJupyterの使用 -ブラウザ上でJupyter環境を直接使用することもできます。実際、クラシックなJupyterやJupyter Hubは、オートコンプリートやコードのハイライトなど、非常に便利な開発環境を提供します。 +ご自身のコンピューターのブラウザーからJupyter環境を使うこともできます。従来のJupyterとJupyterHubの両方が、コード補完やコードのハイライトなど便利な開発環境を提供します。 -ローカルでJupyterを起動するには、コースのディレクトリに移動し、以下を実行します： +ローカルでJupyterを開始するには、コースのディレクトリに移動して、以下を実行します： ```bash jupyter notebook -``` -または +``` + または ```bash jupyterhub -``` -その後、任意の`.ipynb`ファイルに移動して開き、作業を開始できます。 +``` +`.ipynb`ファイルに移動して開き、作業を始めることができます。 -### コンテナで実行する +### コンテナでの実行 -Pythonをインストールする代わりの方法として、コンテナ内でコードを実行することもできます。このリポジトリには、`.devcontainer`フォルダが含まれており、このリポジトリ用のコンテナを構築する方法が記載されています。そのため、VS Codeはコードをコンテナで再オープンすることを提案します。ただし、これにはDockerのインストールが必要で、より複雑になるため、経験豊富なユーザーにお勧めします。 +Pythonのインストールの代替案として、コードをコンテナで実行する方法があります。本リポジトリは、このリポジトリ用のコンテナ作成方法を指示する特別な`.devcontainer`フォルダーを提供しており、VS Codeはコードをコンテナで再オープンする機能を提供しています。これはDockerのインストールが必要で、より複雑になるため、経験豊富なユーザー向けをお勧めします。 -## クラウドで実行する +## クラウドでの実行 -Pythonをローカルにインストールしたくない場合や、クラウドリソースにアクセスできる場合は、クラウドでコードを実行するのも良い選択肢です。以下の方法があります： +Pythonをローカルにインストールしたくない場合やクラウドリソースにアクセスできる場合、クラウドでコードを実行する良い代替手段があります。いくつかの方法を紹介します： -* **[GitHub Codespaces](https://github.com/features/codespaces)** を使用する方法。これはGitHub上で作成される仮想環境で、VS Codeのブラウザインターフェースを通じてアクセスできます。Codespacesにアクセスできる場合は、リポジトリの**Code**ボタンをクリックし、Codespaceを開始するだけで、すぐに実行を開始できます。 -* **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)** を使用する方法。[Binder](https://mybinder.org)は、GitHub上のコードを試すために提供される無料のクラウドコンピューティングリソースです。リポジトリのフロントページにあるボタンをクリックすると、Binderサイトに移動し、基盤となるコンテナを構築してJupyterのウェブインターフェースをシームレスに開始できます。 +* **[GitHub Codespaces](https://github.com/features/codespaces)**を利用する。これはGitHub上に用意された仮想環境で、VS Codeのブラウザーインターフェースからアクセス可能です。Codespacesにアクセスできる場合、リポジトリの**Code**ボタンをクリックしてCodespaceを開始し、すぐに作業を始められます。 +* **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**を利用する。[Binder](https://mybinder.org)は、GitHub上のコードを試すために無料でクラウドの計算資源を提供します。リポジトリのフロントページにあるボタンからBinderに移動でき、即座に基盤となるコンテナを構築してJupyterのウェブインターフェースをシームレスに開始します。 -> **Note**: 不正利用を防ぐため、Binderでは一部のウェブリソースへのアクセスが制限されています。これにより、モデルやデータセットをインターネットから取得するコードが動作しない場合があります。回避策を見つける必要があるかもしれません。また、Binderが提供する計算リソースは非常に基本的なものなので、特に後半の複雑なレッスンではトレーニングが遅くなる可能性があります。 +> **注意**: 不正使用防止のため、Binderは一部のウェブリソースへのアクセスをブロックしています。これにより、モデルやデータセットをパブリックインターネットから取得するコードの一部が動作しない場合があります。回避策を検討する必要があるかもしれません。また、Binderの提供する計算資源はかなり基本的なものなので、特に複雑な後のレッスンのトレーニングは遅くなります。 -## GPUを使用したクラウドでの実行 +## GPU付きクラウドでの実行 -このカリキュラムの後半のレッスンでは、GPUサポートがあると非常に便利です。GPUがないとトレーニングが非常に遅くなるためです。クラウドにアクセスできる場合（[Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste)や所属機関を通じて）、以下のオプションを検討できます： +このカリキュラムの後半のレッスンではGPUサポートがあると大いに役立ちます。例えばモデルのトレーニングは、そうでないと非常に遅くなります。特に[Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste)や所属機関を通じてクラウドにアクセスできる場合、次のような選択肢があります： -* [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste)を作成し、Jupyterを通じて接続します。その後、リポジトリをマシン上にクローンして学習を開始できます。NCシリーズのVMはGPUサポートがあります。 +* [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste)を作成し、Jupyterを通じて接続します。マシンにリポジトリをクローンして学習を始められます。NCシリーズVMはGPUサポートがあります。 -> **Note**: 一部のサブスクリプション（Azure for Studentsを含む）では、デフォルトでGPUサポートが提供されていません。追加のGPUコアをリクエストするために技術サポートに問い合わせる必要がある場合があります。 +> **注意**: Azure for Studentsを含む一部のサブスクリプションはGPUサポートを標準で提供していません。追加のGPUコアを技術サポートにリクエストする必要がある場合があります。 -* [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste)を作成し、そこでNotebook機能を使用します。[このビデオ](https://azure-for-academics.github.io/quickstart/azureml-papers/)では、Azure MLノートブックにリポジトリをクローンして使用を開始する方法を示しています。 +* [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste)を作成し、そこでノートブック機能を使用します。[このビデオ](https://azure-for-academics.github.io/quickstart/azureml-papers/)は、Azure MLノートブックにリポジトリをクローンして使い始める方法を示しています。 -また、Google Colabを使用することもできます。Google Colabは無料のGPUサポートを提供しており、Jupyter Notebookをアップロードして1つずつ実行することができます。 +またGoogle Colabも、無料のGPUサポートが付いており、Jupyterノートブックを一つずつアップロードして実行できます。 -**免責事項**: -この文書は、AI翻訳サービス [Co-op Translator](https://github.com/Azure/co-op-translator) を使用して翻訳されています。正確性を追求しておりますが、自動翻訳には誤りや不正確な部分が含まれる可能性があることをご承知おきください。元の言語で記載された文書が公式な情報源とみなされるべきです。重要な情報については、専門の人間による翻訳をお勧めします。本翻訳の使用に起因する誤解や誤認について、当方は一切の責任を負いません。 \ No newline at end of file +--- + + +**免責事項**： +本書類はAI翻訳サービス「Co-op Translator」（https://github.com/Azure/co-op-translator）を使用して翻訳されています。正確性には努めておりますが、自動翻訳は誤りや不正確な部分を含む可能性があります。原文の言語による原文書が正式な情報源とみなされるべきです。重要な情報については、専門の人間翻訳を推奨します。本翻訳の利用によって生じたいかなる誤解や誤訳についても、当方は一切責任を負いかねます。 + \ No newline at end of file diff --git a/translations/ja/lessons/1-Intro/README.md b/translations/ja/lessons/1-Intro/README.md index 9b18b2ed..b6435206 100644 --- a/translations/ja/lessons/1-Intro/README.md +++ b/translations/ja/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # AIの紹介 -![AIの紹介内容をまとめたスケッチノート](../../../../translated_images/ja/ai-intro.bf28d1ac4235881c.png) +![AIの紹介内容をまとめたスケッチノート](../../../../translated_images/ja/ai-intro.bf28d1ac4235881c.webp) > スケッチノート: [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: もともとコンピュータは、[チャールズ・バベッジ](https://en.wikipedia.org/wiki/Charles_Babbage)によって、明確に定義された手順（アルゴリズム）に従って数値を操作するために発明されました。現代のコンピュータは、19世紀に提案された元のモデルよりもはるかに高度ですが、依然として制御された計算という同じ考えに基づいています。そのため、目標を達成するために必要な手順を正確に知っていれば、コンピュータに何かをプログラムすることが可能です。 -![人物の写真](../../../../translated_images/ja/dsh_age.d212a30d4e54fb5f.png) +![人物の写真](../../../../translated_images/ja/dsh_age.d212a30d4e54fb5f.webp) > 写真: [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ CO_OP_TRANSLATOR_METADATA: **[知能](https://en.wikipedia.org/wiki/Intelligence)** という用語を扱う際の問題の1つは、この用語に明確な定義がないことです。知能は**抽象的思考**や**自己認識**に関連していると主張することができますが、それを適切に定義することはできません。 -![猫の写真](../../../../translated_images/ja/photo-cat.8c8e8fb760ffe457.jpg) +![猫の写真](../../../../translated_images/ja/photo-cat.8c8e8fb760ffe457.webp) > [写真](https://unsplash.com/photos/75715CVEJhI): [Amber Kipp](https://unsplash.com/@sadmax) (Unsplashより) @@ -98,13 +98,13 @@ AGIについて話すとき、私たちは本当に知能を持つシステム > | 機械学習については？ | | > |--------------|-----------| -> | データに基づいて問題を解決する方法をコンピュータが学ぶ人工知能の一部は、**機械学習**と呼ばれます。このコースでは古典的な機械学習は扱いません。別のカリキュラム [Machine Learning for Beginners](http://aka.ms/ml-beginners) を参照してください。 | ![ML for Beginners](../../../../translated_images/ja/ml-for-beginners.9e4fed176fd5817d.png) | +> | データに基づいて問題を解決する方法をコンピュータが学ぶ人工知能の一部は、**機械学習**と呼ばれます。このコースでは古典的な機械学習は扱いません。別のカリキュラム [Machine Learning for Beginners](http://aka.ms/ml-beginners) を参照してください。 | ![ML for Beginners](../../../../translated_images/ja/ml-for-beginners.9e4fed176fd5817d.webp) | ## AIの簡単な歴史 人工知能は20世紀中頃に分野として始まりました。当初は記号的推論が主流のアプローチであり、専門家システムのような重要な成功を収めました。専門家システムは、限られた問題領域で専門家として行動できるコンピュータプログラムです。しかし、このアプローチがスケールしにくいことがすぐに明らかになりました。専門家から知識を抽出し、それをコンピュータに表現し、その知識ベースを正確に保つことは非常に複雑で、多くの場合実用的ではないほど高価な作業であることが判明しました。この結果、1970年代にいわゆる[AIの冬](https://en.wikipedia.org/wiki/AI_winter)が訪れました。 -AIの簡単な歴史 +AIの簡単な歴史 > 画像: [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/ja/lessons/2-Symbolic/Animals.ipynb b/translations/ja/lessons/2-Symbolic/Animals.ipynb index 31498d48..5b06a943 100644 --- a/translations/ja/lessons/2-Symbolic/Animals.ipynb +++ b/translations/ja/lessons/2-Symbolic/Animals.ipynb @@ -6,25 +6,25 @@ "collapsed": true }, "source": [ - "# 動物専門家システムの実装\n", + "# 動物エキスパートシステムの実装\n", "\n", - "[AI for Beginners Curriculum](http://github.com/microsoft/ai-for-beginners) の例。\n", + "[AI for Beginners Curriculum](http://github.com/microsoft/ai-for-beginners)からの例です。\n", "\n", - "このサンプルでは、いくつかの物理的特徴に基づいて動物を特定するための簡単な知識ベースシステムを実装します。このシステムは以下の AND-OR ツリーで表すことができます（これはツリーの一部であり、簡単にさらにルールを追加することができます）:\n", + "このサンプルでは、いくつかの物理的特徴に基づいて動物を判断する単純な知識ベースシステムを実装します。システムは以下のようなAND-ORツリーで表現できます（これは全体のツリーの一部で、簡単にルールを追加できます）:\n", "\n", - "![](../../../../translated_images/ja/AND-OR-Tree.5592d2c70187f283.png)\n" + "![](../../../../../../translated_images/ja/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## 後方推論を用いた独自のエキスパートシステムシェル\n", + "## 自作の後退推論を持つエキスパートシステムシェル\n", "\n", - "生産規則に基づいた知識表現のための簡単な言語を定義してみましょう。ルールを定義するためにPythonのクラスをキーワードとして使用します。基本的に以下の3種類のクラスがあります:\n", - "* `Ask` はユーザーに尋ねる必要がある質問を表します。可能な回答のセットを含みます。\n", - "* `If` はルールを表し、ルールの内容を保存するための単なる構文糖衣です。\n", - "* `AND`/`OR` はツリーのAND/OR分岐を表すクラスです。これらは引数のリストを内部に保存するだけです。コードを簡素化するために、すべての機能は親クラス `Content` に定義されています。\n" + "生産規則に基づいた知識表現のための簡単な言語を定義してみましょう。ルールを定義するキーワードとしてPythonクラスを使用します。基本的に3種類のクラスがあります：\n", + "* `Ask` はユーザーに質問する必要がある質問を表します。可能な回答のセットを含んでいます。\n", + "* `If` はルールを表し、ルールの内容を格納するための構文糖衣です。\n", + "* `AND`/`OR` はツリーのAND/ORの枝を表すクラスです。引数のリストを内部に格納します。コードを簡素化するために、すべての機能は親クラス `Content` に定義されています。\n" ] }, { @@ -66,7 +66,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "私たちのシステムでは、作業記憶は**属性-値ペア**としての**事実**のリストを含みます。ナレッジベースは、条件（AND-OR式として表現される）に基づいてアクション（作業記憶に挿入されるべき新しい事実）をマッピングする1つの大きな辞書として定義できます。また、一部の事実は`Ask`されることがあります。\n" + "私たちのシステムでは、作業記憶は**属性-値のペア**としての**事実**のリストを含みます。知識ベースは、行動（作業記憶に挿入されるべき新しい事実）を条件にマッピングする大きな辞書として定義できます。条件はAND-OR式で表現されます。また、一部の事実は`Ask`することができます。\n" ] }, { @@ -99,13 +99,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "逆推論を行うために、`Knowledgebase` クラスを定義します。このクラスには以下が含まれます：\n", - "* 作業用の`memory` - 属性と値をマッピングする辞書\n", - "* 上記で定義された形式の知識ベースの`rules`\n", + "逆推論を行うために、`Knowledgebase` クラスを定義します。これには以下が含まれます：\n", + "* 動作中の `memory` - 属性を値にマッピングする辞書\n", + "* 先に定義した形式の Knowledgebase の `rules`\n", "\n", "主なメソッドは以下の2つです：\n", - "* `get` - 必要に応じて推論を行い、属性の値を取得します。例えば、`get('color')`は色スロットの値を取得します（必要であれば尋ね、作業メモリに値を保存します）。`get('color:blue')`を尋ねた場合、色を尋ねた後、その色に応じて`y`/`n`の値を返します。\n", - "* `eval` - 実際の推論を行います。つまり、AND/ORツリーをたどり、サブゴールを評価するなどの処理を行います。\n" + "* `get` は属性の値を取得し、必要に応じて推論を行います。例えば、`get('color')` は色のスロットの値を取得します（必要に応じて問い合わせを行い、後で使用するために作業メモリに値を保存します）。`get('color:blue')` と指定した場合は色を問い合わせ、その色に応じて「y」または「n」の値を返します。\n", + "* `eval` は実際の推論を行います。つまり、AND/ORツリーを辿り、サブゴールを評価するなどの処理を行います。\n" ] }, { @@ -172,7 +172,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "さて、動物の知識ベースを定義し、相談を行いましょう。この呼び出しでは質問が行われることに注意してください。はい/いいえの質問には `y`/`n` を入力して答えるか、複数選択肢の質問には番号 (0..N) を指定して答えることができます。\n" + "さて、動物の知識ベースを定義し、相談を行いましょう。この呼び出しでは質問が行われます。はい・いいえの質問には `y`/`n` と入力して答え、複数選択肢の質問には番号（0..N）を指定して答えてください。\n" ] }, { @@ -229,11 +229,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## PyKnowを使った前向き推論\n", + "## Expertaを使った順方向推論\n", "\n", - "次の例では、知識表現のためのライブラリの1つである[PyKnow](https://github.com/buguroo/pyknow/)を使用して、前向き推論を実装してみます。**PyKnow**は、Pythonで前向き推論システムを作成するためのライブラリで、古典的なシステムである[CLIPS](http://www.clipsrules.net/index.html)に似た設計になっています。\n", + "次の例では、知識表現用のライブラリの一つである[Experta](https://github.com/nilp0inter/experta)を使って順方向推論を実装してみます。**Experta**は、Pythonで順方向推論システムを作成するためのライブラリで、古典的なシステムである[CLIPS](http://www.clipsrules.net/index.html)に似た設計がされています。\n", "\n", - "もちろん、自分で前向き推論（フォワードチェイニング）を実装することも可能ですが、素朴な実装では通常あまり効率的ではありません。より効果的なルールマッチングのために、特別なアルゴリズムである[Rete](https://en.wikipedia.org/wiki/Rete_algorithm)が使用されます。\n" + "順方向連鎖を自分で実装することも多くの問題なく可能ですが、素朴な実装は通常効率的ではありません。より効果的なルールマッチングには、特別なアルゴリズムである[Rete](https://en.wikipedia.org/wiki/Rete_algorithm)が使われます。\n" ] }, { @@ -247,32 +247,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "Collecting git+https://github.com/buguroo/pyknow/\n", - " Cloning https://github.com/buguroo/pyknow/ to /tmp/pip-req-build-3cqeulyl\n", - " Running command git clone --filter=blob:none --quiet https://github.com/buguroo/pyknow/ /tmp/pip-req-build-3cqeulyl\n", - " Resolved https://github.com/buguroo/pyknow/ to commit 48818336f2e9a126f1964f2d8dc22d37ff800fe8\n", - " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hCollecting frozendict==1.2\n", - " Using cached frozendict-1.2.tar.gz (2.6 kB)\n", - " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hCollecting schema==0.6.7\n", - " Using cached schema-0.6.7-py2.py3-none-any.whl (14 kB)\n", - "Building wheels for collected packages: pyknow, frozendict\n", - " Building wheel for pyknow (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for pyknow: filename=pyknow-1.7.0-py3-none-any.whl size=34228 sha256=b7de5b09292c4007667c72f69b98d5a1b5f7324ff15f9dd8e077c3d5f7aade42\n", - " Stored in directory: /tmp/pip-ephem-wheel-cache-k7jpave7/wheels/81/1a/d3/f6c15dbe1955598a37755215f2a10449e7418500d7bd4b9508\n", - " Building wheel for frozendict (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for frozendict: filename=frozendict-1.2-py3-none-any.whl size=3148 sha256=2863d55c240d2409cddf05ccfe600591f8478681549fc97555c47c90dc6bb160\n", - " Stored in directory: /home/rg/.cache/pip/wheels/49/ac/f8/cb8120244e710bdb479c86198b03c7b08c3c2d3d2bf448fd6e\n", - "Successfully built pyknow frozendict\n", - "Installing collected packages: schema, frozendict, pyknow\n", - "Successfully installed frozendict-1.2 pyknow-1.7.0 schema-0.6.7\n" + "Collecting git+https://github.com/nilp0inter/experta\n", + " Cloning https://github.com/nilp0inter/experta to /tmp/pip-req-build-7qurtwk3\n", + " Running command git clone --filter=blob:none --quiet https://github.com/nilp0inter/experta /tmp/pip-req-build-7qurtwk3\n", + " Resolved https://github.com/nilp0inter/experta to commit c6d5834b123861f5ae09e7d07027dc98bec58741\n", + " Installing build dependencies ... \u001b[?25ldone\n", + "\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n", + "\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25hRequirement already satisfied: frozendict~=2.4.6 in /opt/conda/envs/ai4beg/lib/python3.12/site-packages (from experta==1.9.5.dev1) (2.4.7)\n", + "Collecting schema~=0.6.7 (from experta==1.9.5.dev1)\n", + " Downloading schema-0.6.8-py2.py3-none-any.whl.metadata (14 kB)\n", + "Downloading schema-0.6.8-py2.py3-none-any.whl (14 kB)\n", + "Building wheels for collected packages: experta\n", + " Building wheel for experta (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25h Created wheel for experta: filename=experta-1.9.5.dev1-py3-none-any.whl size=34804 sha256=888c459512a5e713f4b674caa9a0f96cfdf07ec0d6eb56cc318ce0653d218014\n", + " Stored in directory: /tmp/pip-ephem-wheel-cache-1eeii9zy/wheels/3d/e8/bb/22d7956359603fa8dd679aa09f5b8efb3f29991c3986fdc787\n", + "Successfully built experta\n", + "Installing collected packages: schema, experta\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2/2\u001b[0m [experta]\n", + "\u001b[1A\u001b[2KSuccessfully installed experta-1.9.5.dev1 schema-0.6.8\n" ] } ], "source": [ "import sys\n", - "!{sys.executable} -m pip install git+https://github.com/buguroo/pyknow/" + "!{sys.executable} -m pip install git+https://github.com/nilp0inter/experta" ] }, { @@ -283,15 +282,15 @@ }, "outputs": [], "source": [ - "from pyknow import *\n", - "#import pyknow" + "from experta import *\n", + "#import experta" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "私たちは、`KnowledgeEngine`をサブクラス化するクラスとしてシステムを定義します。各ルールは、ルールが発火する条件を指定する`@Rule`アノテーションを持つ個別の関数によって定義されます。ルール内では、`declare`関数を使用して新しい事実を追加することができ、それらの事実を追加することで、前向き推論エンジンによってさらに多くのルールが呼び出される結果となります。\n" + "私たちはシステムを `KnowledgeEngine` をサブクラス化したクラスとして定義します。各ルールは `@Rule` アノテーションが付いた別々の関数で定義されており、このアノテーションはルールがいつ発動すべきかを指定します。ルール内では `declare` 関数を使って新しいファクトを追加でき、これらのファクトを追加すると順方向推論エンジンによってさらにいくつかのルールが呼び出されます。\n" ] }, { @@ -378,7 +377,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "知識ベースを定義したら、いくつかの初期事実で作業メモリを埋め、次に推論を実行するために`run()`メソッドを呼び出します。その結果として、新たに推論された事実が作業メモリに追加され、動物に関する最終的な事実も含まれます（初期事実を正しく設定した場合）。\n" + "ナレッジベースを定義したら、いくつかの初期事実でワーキングメモリを埋め、次に推論を実行するために `run()` メソッドを呼び出します。結果として、新しく推論された事実がワーキングメモリに追加されるのがわかります。これには、動物に関する最終事実も含まれます（すべての初期事実を正しく設定した場合）。\n" ] }, { @@ -440,7 +439,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n---\n\n**免責事項**: \nこの文書は、AI翻訳サービス [Co-op Translator](https://github.com/Azure/co-op-translator) を使用して翻訳されています。正確性を追求しておりますが、自動翻訳には誤りや不正確な部分が含まれる可能性があることをご承知ください。元の言語で記載された文書が正式な情報源とみなされるべきです。重要な情報については、専門の人間による翻訳を推奨します。この翻訳の使用に起因する誤解や誤解釈について、当方は責任を負いません。\n" + "---\n\n\n**免責事項**： \n本書類はAI翻訳サービス「Co-op Translator」（https://github.com/Azure/co-op-translator）を使用して翻訳されました。正確性を期しておりますが、自動翻訳には誤りや不正確な表現が含まれる可能性があります。原文の言語によるオリジナル文書が正式な情報源とみなされます。重要な情報については、専門の人間による翻訳を推奨します。本翻訳の利用によって生じたいかなる誤解や解釈の違いについても、一切責任を負いかねます。\n\n" ] } ], @@ -467,8 +466,8 @@ "version": "3.11.2" }, "coopTranslator": { - "original_hash": "ab2bd97b0453415b89a469284609a8ce", - "translation_date": "2025-08-30T09:41:24+00:00", + "original_hash": "8ef43db4b9182239fd150a76bd494fdb", + "translation_date": "2026-01-15T11:44:57+00:00", "source_file": "lessons/2-Symbolic/Animals.ipynb", "language_code": "ja" } diff --git a/translations/ja/lessons/2-Symbolic/README.md b/translations/ja/lessons/2-Symbolic/README.md index 9799524d..86b729dd 100644 --- a/translations/ja/lessons/2-Symbolic/README.md +++ b/translations/ja/lessons/2-Symbolic/README.md @@ -1,116 +1,116 @@ # 知識表現とエキスパートシステム -![Symbolic AIの内容の概要](../../../../translated_images/ja/ai-symbolic.715a30cb610411a6.png) +![記号的AIの内容の概要](../../../../../../translated_images/ja/ai-symbolic.715a30cb610411a6.webp) -> スケッチノート: [Tomomi Imura](https://twitter.com/girlie_mac) +> スケッチノート作成者：[Tomomi Imura](https://twitter.com/girlie_mac) -人工知能の探求は、世界を人間のように理解するための知識を追求することに基づいています。しかし、これをどのように実現するのでしょうか？ +人工知能の探求は、人間と同様に世界を理解するための知識の探求に基づいています。しかし、それをどうやって行えばよいのでしょうか？ -## [講義前のクイズ](https://ff-quizzes.netlify.app/en/ai/quiz/3) +## [講義前クイズ](https://ff-quizzes.netlify.app/en/ai/quiz/3) -AIの初期には、知的システムを構築するためのトップダウンアプローチ（前回のレッスンで説明）が人気でした。このアプローチでは、人間から知識を抽出して機械が読み取れる形式に変換し、それを使って問題を自動的に解決するというアイデアに基づいていました。この方法は以下の2つの大きな概念に基づいています： +AIの初期には、インテリジェントシステムを作成するためのトップダウンアプローチ（前のレッスンで議論した）が人気でした。この考え方は、人から知識を抽出し、それを機械で読み取れる形式にし、それを使って問題を自動的に解決するというものです。このアプローチは二つの大きなアイデアに基づいていました： * 知識表現 * 推論 ## 知識表現 -Symbolic AIにおける重要な概念の1つが**知識**です。知識を*情報*や*データ*と区別することが重要です。例えば、本には知識が含まれていると言えますが、実際には本に含まれているのは*データ*です。本を読んでそのデータを自分の世界モデルに統合することで、データを知識に変換します。 +記号的AIにおける重要な概念の一つが**知識**です。知識を*情報*や*データ*と区別することが重要です。例えば、本は知識を含むと言えます。なぜなら、本を学ぶことで専門家になることができるからです。しかし、実際に本に含まれているのは*データ*であり、本を読み、このデータを自分の世界モデルに取り込むことでそのデータを知識に変換しているのです。 -> ✅ **知識**とは、私たちの頭の中に存在し、世界の理解を表すものです。これは、受け取った情報を自分の世界モデルに統合する**学習**プロセスによって得られます。 +> ✅ **知識**とは、頭の中に含まれていて私たちの世界の理解を表すものです。情報の断片を自分の積極的な世界モデルに統合する、能動的な**学習**プロセスによって得られます。 -通常、知識を厳密に定義することはありませんが、[DIKWピラミッド](https://en.wikipedia.org/wiki/DIKW_pyramid)を使って関連する概念と整合させます。このピラミッドには以下の概念が含まれています： +多くの場合、知識を厳密に定義することはしませんが、[DIKWピラミッド](https://en.wikipedia.org/wiki/DIKW_pyramid)を使って関連した他の概念と整合させます。ピラミッドには以下の概念が含まれます： -* **データ**は、書かれたテキストや話された言葉など、物理的な媒体に表現されたものです。データは人間とは独立して存在し、人々の間で共有することができます。 -* **情報**は、頭の中でデータを解釈したものです。例えば、「コンピュータ」という言葉を聞いたときに、それが何であるかについての理解を持つことです。 -* **知識**は、情報が世界モデルに統合されたものです。例えば、コンピュータが何であるかを学ぶと、それがどのように動作するか、価格、用途などについてのアイデアを持つようになります。この相互に関連する概念のネットワークが知識を形成します。 -* **知恵**は、さらに一段階進んだ世界の理解であり、*メタ知識*を表します。例えば、知識をどのように、いつ使うべきかについての概念です。 +* **データ**は、書かれたテキストや話された言葉のように物理的な媒体で表現されるものです。データは人間とは独立して存在し、人から人へ渡すことができます。 +* **情報**は、私たちが頭の中でデータを解釈したものです。例えば、「コンピューター」という言葉を聞くと、それが何かを理解します。 +* **知識**は、情報が私たちの世界モデルに統合されたものです。例えば、コンピューターが何かを学ぶと、その動作や価格、用途についてのアイデアを持ち始めます。この相互関連した概念のネットワークが私たちの知識を形成します。 +* **知恵**は、さらに私たちの世界理解の一段階上で、*メタ知識*を表します。例えば、知識をどのように、いつ使うべきかという概念です。 - + -*画像 [Wikipediaより](https://commons.wikimedia.org/w/index.php?curid=37705247), By Longlivetheux - Own work, CC BY-SA 4.0* +*画像 [Wikipediaより](https://commons.wikimedia.org/w/index.php?curid=37705247)、Longlivetheuxによるオリジナル作品、CC BY-SA 4.0* -したがって、**知識表現**の問題は、コンピュータ内で知識をデータとして効果的に表現し、自動的に利用可能にする方法を見つけることです。これは以下のようなスペクトラムとして見ることができます： +したがって、**知識表現**の問題は、知識を自動的に利用可能にするために、コンピューター内でデータという形で効果的に表現する方法を見つけることです。これは次のようなスペクトルとして見ることができます： -![知識表現のスペクトラム](../../../../translated_images/ja/knowledge-spectrum.b60df631852c0217.png) +![知識表現のスペクトル](../../../../../../translated_images/ja/knowledge-spectrum.b60df631852c0217.webp) -> 画像: [Dmitry Soshnikov](http://soshnikov.com) +> 画像提供：[Dmitry Soshnikov](http://soshnikov.com) -* 左側には、コンピュータが効果的に利用できる非常に単純なタイプの知識表現があります。最も単純なものはアルゴリズムで、知識がコンピュータプログラムとして表現されます。しかし、これは知識を表現する最良の方法ではありません。なぜなら、柔軟性がないからです。私たちの頭の中の知識はしばしば非アルゴリズム的です。 -* 右側には、自然言語のテキストのような表現があります。これは最も強力ですが、自動推論には使用できません。 +* 左側には、コンピューターが効果的に利用できる非常に単純な種類の知識表現があります。最も単純なのはアルゴリズム型で、知識はコンピュータープログラムで表現されます。しかしこれは柔軟性がなく、最良の方法ではありません。私たちの頭の中の知識はしばしば非アルゴリズム的です。 +* 右側には自然言語テキストのような表現があります。これは最も強力ですが、自動推論には利用できません。 -> ✅ 自分の頭の中で知識をどのように表現し、それをノートに変換するかについて少し考えてみてください。記憶を助けるために特に効果的な形式はありますか？ +> ✅ あなたの頭の中での知識表現とノートに変換する方法について考えてみてください。記憶保持に役立つ特定のフォーマットはありますか？ -## コンピュータの知識表現の分類 +## コンピュータ知識表現の分類 -コンピュータの知識表現方法を以下のカテゴリに分類できます： +異なるコンピュータの知識表現方法は、以下のカテゴリに分類できます： -* **ネットワーク表現**は、頭の中に相互に関連する概念のネットワークがあるという事実に基づいています。同じネットワークをコンピュータ内でグラフとして再現することができます。これを**セマンティックネットワーク**と呼びます。 +* **ネットワーク表現**は、頭の中に相互に関連する概念のネットワークがある事実に基づいています。これと同じネットワークをコンピューター内のグラフとして再現することができます。これを**セマンティックネットワーク**と呼びます。 -1. **オブジェクト-属性-値の三つ組**または**属性-値ペア**。グラフはコンピュータ内でノードとエッジのリストとして表現できるため、セマンティックネットワークをオブジェクト、属性、値を含む三つ組のリストとして表現できます。例えば、プログラミング言語について以下の三つ組を構築します： +1. **オブジェクト・属性・値のトリプレット**または**属性値ペア**。グラフはノードとエッジのリストとしてコンピューターに表現できるため、セマンティックネットワークはオブジェクト、属性、値を含むトリプレットのリストとして表現できます。例えば、プログラミング言語についての以下のトリプレットを作ります： オブジェクト | 属性 | 値 --------|-----------|------ +------------|------|----- Python | is | Untyped-Language Python | invented-by | Guido van Rossum Python | block-syntax | indentation Untyped-Language | doesn't have | type definitions -> ✅ 三つ組を使って他の種類の知識をどのように表現できるか考えてみてください。 +> ✅ トリプレットが他のタイプの知識をどのように表現できるか考えてみましょう。 -2. **階層的表現**は、頭の中でオブジェクトの階層を作成することが多いという事実を強調します。例えば、カナリアは鳥であり、すべての鳥には翼があることを知っています。また、カナリアの色や飛行速度についてのアイデアも持っています。 +2. **階層表現**は、頭の中にオブジェクトの階層を作ることを強調します。例えば、カナリアは鳥であり、すべての鳥は翼を持っていることを知っています。カナリアの通常の色や飛行速度についてもある程度知っています。 - - **フレーム表現**は、各オブジェクトまたはオブジェクトのクラスを**フレーム**として表現し、**スロット**を含みます。スロットにはデフォルト値、値の制限、または値を取得するために呼び出される手続きが含まれることがあります。すべてのフレームは、オブジェクト指向プログラミング言語のオブジェクト階層に似た階層を形成します。 - - **シナリオ**は、時間の経過とともに展開する複雑な状況を表す特別な種類のフレームです。 + - **フレーム表現**は各オブジェクトまたはオブジェクトクラスを**フレーム**として表現し、その中に**スロット**があります。スロットは可能なデフォルト値、値の制約、または値を得るために呼び出せる格納手続きがあります。すべてのフレームはオブジェクト指向プログラミング言語のオブジェクト階層に似た階層を形成します。 + - **シナリオ**は時間経過に伴い展開する複雑な状況を表す特別なフレームの種類です。 **Python** スロット | 値 | デフォルト値 | 範囲 | ------|-------|---------------|----------| -名前 | Python | | | +--------|----|--------------|------| +Name | Python | | | Is-A | Untyped-Language | | | -変数のケース | | CamelCase | | -プログラムの長さ | | | 5-5000行 | -ブロック構文 | Indent | | | +Variable Case | | CamelCase | | +Program Length | | | 5-5000行 | +Block Syntax | Indent | | | -3. **手続き的表現**は、特定の条件が発生したときに実行できるアクションのリストとして知識を表現することに基づいています。 - - 生産規則は、結論を導き出すためのif-then文です。例えば、医師は「**もし**患者が高熱を持っている**または**血液検査でC反応性タンパク質のレベルが高い場合、**ならば**炎症がある」とする規則を持つことができます。条件のいずれかに遭遇すると、炎症についての結論を導き出し、それをさらに推論に使用できます。 - - アルゴリズムは、手続き的表現の別の形式と見なすことができますが、知識ベースのシステムではほとんど直接使用されることはありません。 +3. **手続き的表現**は、ある条件が発生したときに実行されるアクションのリストとして知識を表現します。 + - 生産ルールは if-then 文で結論を導きます。例えば、医者は「もし」患者が高熱「または」血液検査でCRP値が高い「ならば」炎症がある、というルールを持つことができます。これらの条件が満たされると、炎症の結論を導き、さらに理由付けに使います。 + - アルゴリズムも手続き的表現の一種と考えられますが、知識ベースシステムで直接使われることはほとんどありません。 -4. **論理**は、普遍的な人間の知識を表現する方法としてアリストテレスによって最初に提案されました。 - - 述語論理は数学的理論として非常に豊かであるため、計算可能ではありません。そのため、通常はPrologで使用されるHorn節のようなサブセットが使用されます。 - - 記述論理は、*セマンティックウェブ*のような分散知識表現を表すために使用される論理システムのファミリーです。 +4. **論理**はもともとアリストテレスによって普遍的な人間の知識を表現する方法として提案されました。 + - 数学的理論としての述語論理は計算可能には豊か過ぎるため、通常は部分集合が使われます。例えば、Prologで使われるホーン節が代表的です。 + - 記述論理（Descriptive Logic）は、オブジェクトの階層や分散知識表現（*セマンティックウェブ*など）を表現・推論するための論理システム群です。 ## エキスパートシステム -Symbolic AIの初期の成功例の1つが、**エキスパートシステム**と呼ばれるものでした。これは、限定された問題領域で専門家として機能するように設計されたコンピュータシステムです。これらは、1人以上の人間の専門家から抽出された**知識ベース**に基づいており、その上で推論を行う**推論エンジン**を含んでいました。 +記号的AIの初期の成功例の一つが**エキスパートシステム**です。これは限定された問題領域の専門家として振る舞うよう設計されたコンピューターシステムです。複数の人間専門家から抽出された**知識ベース**を基にし、その上で推論を行う**推論エンジン**を備えています。 -![人間の構造](../../../../translated_images/ja/arch-human.5d4d35f1bba3ab1c.png) | ![知識ベースシステムの構造](../../../../translated_images/ja/arch-kbs.3ec5c150b09fa8da.png) +![人間の構造](../../../../../../translated_images/ja/arch-human.5d4d35f1bba3ab1c.webp) | ![知識ベースシステム](../../../../../../translated_images/ja/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ -人間の神経系の簡略化された構造 | 知識ベースシステムの構造 +人間の神経系の簡略化された構造 | 知識ベースシステムのアーキテクチャ -エキスパートシステムは、人間の推論システムのように構築されています。人間の推論システムには**短期記憶**と**長期記憶**が含まれています。同様に、知識ベースシステムには以下のコンポーネントが区別されます： +エキスパートシステムは、人間の推論システムのように**短期記憶**と**長期記憶**を含む形で構築されます。同様に、知識ベースシステムでは以下のコンポーネントに分けられます： -* **問題記憶**: 現在解決中の問題に関する知識を含みます。例えば、患者の体温や血圧、炎症があるかどうかなど。この知識は**静的知識**とも呼ばれ、現在の問題について知っていることのスナップショット、つまり*問題状態*を含みます。 -* **知識ベース**: 問題領域に関する長期的な知識を表します。これは人間の専門家から手動で抽出され、相談ごとに変更されません。問題状態から別の状態に移動することを可能にするため、**動的知識**とも呼ばれます。 -* **推論エンジン**: 問題状態空間での検索プロセス全体を調整し、必要に応じてユーザーに質問をします。また、各状態に適用されるべき適切な規則を見つける責任も負います。 +* **問題メモリ**：現在解決中の問題についての知識を含みます。例えば、患者の体温や血圧、炎症があるかどうかなど。この知識は**静的知識**とも呼ばれ、現在の問題状態のスナップショットを含みます。 +* **知識ベース**：問題ドメインに関する長期知識を表します。人間の専門家から手動で抽出され、診療ごとに変わりません。これにより問題状態間の移動が可能になるため、**動的知識**とも呼ばれます。 +* **推論エンジン**：問題状態空間の探索プロセスを統括し、必要に応じてユーザーに質問します。各状態で適用すべきルールを見つける責任も持っています。 -例として、動物の物理的特徴に基づいて動物を特定するエキスパートシステムを考えてみましょう： +例として、動物の身体的特徴から動物を決定する以下のエキスパートシステムを考えましょう： -![AND-ORツリー](../../../../translated_images/ja/AND-OR-Tree.5592d2c70187f283.png) +![AND-ORツリー](../../../../../../translated_images/ja/AND-OR-Tree.5592d2c70187f283.webp) -> 画像: [Dmitry Soshnikov](http://soshnikov.com) +> 画像提供：[Dmitry Soshnikov](http://soshnikov.com) -この図は**AND-ORツリー**と呼ばれ、生成規則のセットのグラフィカル表現です。専門家から知識を抽出する際にツリーを描くことは有用です。コンピュータ内で知識を表現するには、規則を使用する方が便利です： +この図は**AND-ORツリー**と呼ばれ、生産ルールの集合をグラフで表現しています。知識を専門家から抽出する初期段階で樹形図を描くのは有用です。コンピューター内で知識を表現するには、ルールを使う方が便利です： ``` IF the animal eats meat @@ -121,78 +121,78 @@ OR (animal has sharp teeth THEN the animal is a carnivore ``` -各規則の左辺の条件とアクションが本質的にオブジェクト-属性-値（OAV）の三つ組であることに気づくでしょう。**作業メモリ**には、現在解決中の問題に対応するOAV三つ組のセットが含まれています。**規則エンジン**は条件が満たされている規則を探し、それを適用して作業メモリに新しい三つ組を追加します。 +各ルールの左辺の条件とアクションは基本的にオブジェクト・属性・値（OAV）トリプレットであることが分かります。**作業メモリ**は現在解決中の問題に対応するOAVトリプレットの集合を含みます。**ルールエンジン**は条件が満たされているルールを探し、それを適用して作業メモリにトリプレットを追加します。 -> ✅ 好きなトピックで自分自身のAND-ORツリーを作成してみてください！ +> ✅ あなた自身の好きなテーマでAND-ORツリーを書いてみましょう！ -### 順推論 vs. 逆推論 +### 順方向推論と逆方向推論 -上記のプロセスは**順推論**と呼ばれます。作業メモリに問題に関する初期データがある状態から始まり、次の推論ループを実行します： +上記のプロセスは**順方向推論**と呼ばれます。これは作業メモリにある初期データから開始し、以下の推論ループを実行します： -1. 目標属性が作業メモリに存在する場合 - 結果を出して終了 -2. 現在条件が満たされている規則をすべて探す - **競合セット**を取得 -3. **競合解決**を実行 - このステップで実行される規則を1つ選択。競合解決戦略には以下のようなものがあります： - - 知識ベース内で適用可能な最初の規則を選択 - - ランダムな規則を選択 - - *より具体的な*規則を選択、つまり左辺（LHS）で最も多くの条件を満たす規則 -4. 選択した規則を適用し、問題状態に新しい知識を挿入 -5. ステップ1から繰り返す +1. ターゲット属性が作業メモリにあれば処理を止めて結果を出す +2. 条件が満たされているすべてのルールを探し、**競合セット**を得る +3. **競合解決**を行い、このステップで実行する1つのルールを選ぶ。競合解決戦略には以下がある： + - 知識ベースの中で適用できる最初のルールを選ぶ + - ランダムにルールを選ぶ + - *より具体的な* すなわち左辺(LHS)の条件を最も多く満たすルールを選ぶ +4. 選んだルールを適用し、新しい知識を問題状態に挿入する +5. ステップ1に戻る -しかし、場合によっては問題についての知識が空の状態から始まり、結論に到達するために役立つ質問をしたい場合があります。例えば、医療診断を行う際には、患者を診断する前にすべての医療分析を事前に実施することは通常ありません。むしろ、決定を下す必要があるときに分析を行いたいのです。 +しかし、場合によっては問題についての知識が空の場合から始め、結論に至るのに役立つ質問をしたいこともあります。例えば医療診断では、通常患者を診る前にすべての検査をしていません。決定を下す必要が生じたときに検査を行いたいのです。 -このプロセスは**逆推論**を使用してモデル化できます。これは**目標**、つまり探している属性値によって駆動されます： +このプロセスは**逆方向推論**でモデル化できます。これは**ゴール**（探している属性値）に駆動されます： -1. 目標の値を提供できる規則をすべて選択（つまり、目標が右辺（RHS）にある規則） - 競合セット -1. この属性に対する規則がない場合、またはユーザーに値を尋ねるべきだとする規則がある場合 - ユーザーに尋ねる。それ以外の場合： -1. 競合解決戦略を使用して仮説として使用する規則を1つ選択 - 証明を試みる -1. 規則の左辺（LHS）にあるすべての属性について再帰的にプロセスを繰り返し、それらを目標として証明しようとする -1. プロセスがどこかで失敗した場合 - ステップ3で別の規則を使用 +1. ゴールの値を与えうるすべてのルール（右辺（RHS）にゴールがあるもの）を選ぶ - 競合セット +2. もしこの属性にルールがなければ、または値をユーザーに尋ねるルールがあれば、ユーザーに質問する。そうでなければ： +3. 競合解決戦略を使って、*仮説*として使用するルールを1つ選ぶ。これを証明しようとする +4. ルールの左辺（LHS）のすべての属性に対して再帰的にゴールとしてこのプロセスを繰り返す +5. 途中で失敗したら、ステップ3で別のルールを使う -> ✅ 順推論が適している状況はどのような場合ですか？逆推論はどうでしょうか？ +> ✅ どんな場合に順方向推論がより適しているでしょうか？逆方向推論はどうですか？ ### エキスパートシステムの実装 -エキスパートシステムは以下の方法で実装できます： +エキスパートシステムは様々なツールで実装できます： -* 高水準プログラミング言語で直接プログラミングする。この方法は最良ではありません。なぜなら、知識ベースシステムの主な利点は、知識が推論から分離されていることであり、問題領域の専門家が推論プロセスの詳細を理解せずに規則を書くことができる可能性があるからです。 -* **エキスパートシステムシェル**を使用する。これは、知識表現言語を使用して知識を入力するために特別に設計されたシステムです。 +* 高水準プログラミング言語で直接プログラミングする方法。これはあまりよい方法ではありません。なぜなら、知識ベースシステムの主な利点は推論から知識が分離されていることであり、問題ドメインの専門家が推論の詳細を理解せずにルールを書けるようにすべきだからです。 +* **エキスパートシステムシェル**を使う。これは特別に設計されたシステムで、特定の知識表現言語を使って知識を投入します。 -## ✍️ 演習: 動物推論 +## ✍️ 演習：動物推論 -[Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb)を参照して、順推論と逆推論エキスパートシステムの実装例を確認してください。 +順方向推論と逆方向推論エキスパートシステムの実装例については、[Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb)を参照してください。 -> **注意**: この例は非常に簡単で、エキスパートシステムがどのように見えるかのアイデアを提供するだけです。このようなシステムを作成し始めると、規則が200以上に達する頃に初めて*知的*な振る舞いを感じることができます。規則が複雑になりすぎてすべてを頭に入れておくことができなくなると、システムがなぜ特定の決定を下したのか疑問に思うかもしれません。しかし、知識ベースシステムの重要な特徴は、どの決定がどのように行われたかを常に*説明*できることです。 +> **注**：この例はかなり簡単で、エキスパートシステムのイメージを掴むためのものです。実際にこの種のシステムを作り始めると、200以上のルールに達するまでは*知的な*振る舞いを感じないでしょう。ある段階でルールが複雑になりすぎて全体を把握できなくなり、システムがなぜ特定の決定をしたのか疑問に思い始めます。しかし、知識ベースシステムの重要な特徴は、どんな決定がどのように行われたかを常に*説明*できることです。 ## オントロジーとセマンティックウェブ -20世紀末に、知識表現を使用してインターネットリソースに注釈を付け、非常に具体的なクエリに対応するリソースを見つけることが可能になるというイニシアチブがありました。この動きは**セマンティックウェブ**と呼ばれ、いくつかの概念に依存していました： +20世紀末に、知識表現を使ってインターネット上のリソースに注釈をつけ、非常に具体的な検索クエリに対応できるようにする取り組みがありました。これは**セマンティックウェブ**と呼ばれ、以下の概念に依拠しています： -- **[記述論理](https://en.wikipedia.org/wiki/Description_logic)**（DL）に基づく特別な知識表現。これはフレーム知識表現に似ていますが、オブジェクトの階層を構築し、論理的な意味論と推論を持っています。DLには、表現力と推論のアルゴリズム的複雑さのバランスを取るためのさまざまなファミリーがあります。 -- 分散知識表現。すべての概念がグローバ -- 知識記述のためのXMLベースの言語ファミリー: RDF (Resource Description Framework)、RDFS (RDF Schema)、OWL (Ontology Web Language)。 +- **[記述論理](https://en.wikipedia.org/wiki/Description_logic)**（DL）に基づく特別な知識表現。これはフレーム表現に似ており、属性を持つオブジェクトの階層を構築しますが、形式的な論理意味論と推論を持っています。推論の表現力と計算複雑性のバランスをとった様々なDLのファミリーがあります。 +- 分散知識表現で、すべての概念がグローバルなURI識別子で表現されており、インターネット全体に及ぶ知識階層を作成できます。 +- 知識記述のためのXMLベース言語のファミリー：RDF（Resource Description Framework）、RDFS（RDF Schema）、OWL（Ontology Web Language）。 -セマンティックウェブの中心的な概念は、**オントロジー**という概念です。これは、問題領域を形式的な知識表現を用いて明示的に仕様化することを指します。最も単純なオントロジーは、問題領域内のオブジェクトの階層である場合がありますが、より複雑なオントロジーでは推論に使用できるルールを含むことがあります。 +セマンティックウェブの核心概念の一つが**オントロジー**の概念です。これは、ある問題領域を形式的な知識表現を用いて明示的に仕様化することを指します。最も単純なオントロジーは問題領域内のオブジェクトの階層でしかありませんが、より複雑なオントロジーは推論に使えるルールも含みます。 -セマンティックウェブでは、すべての表現がトリプレットに基づいています。各オブジェクトおよび各関係はURIによって一意に識別されます。例えば、このAIカリキュラムが2022年1月1日にDmitry Soshnikovによって開発されたという事実を述べたい場合、以下のようなトリプレットを使用できます。 +セマンティックウェブでは、すべての表現が三つ組（トリプレット）に基づいています。各オブジェクトと各関係はURIによって一意に識別されます。例えば、このAIカリキュラムが2022年1月1日にDmitry Soshnikovによって作成されたという事実を述べたい場合、次のようなトリプレットが使えます： - + ``` -http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 13, 2007” +http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 1, 2022” http://github.com/microsoft/ai-for-beginners http://purl.org/dc/elements/1.1/creator http://soshnikov.com ``` -> ✅ ここで、`http://www.example.com/terms/creation-date` および `http://purl.org/dc/elements/1.1/creator` は、*作成者*や*作成日*の概念を表現するための、よく知られた普遍的に受け入れられているURIです。 +> ✅ ここで `http://www.example.com/terms/creation-date` と `http://purl.org/dc/elements/1.1/creator` は、*作成者* と *作成日時* の概念を表すためによく知られた普遍的に受け入れられているURIです。 -より複雑なケースでは、作成者のリストを定義したい場合、RDFで定義されたデータ構造を使用できます。 +より複雑な場合、作成者の一覧を定義したい場合は、RDFで定義されたいくつかのデータ構造を使うことができます。 - + -> 上記の図は[Dmitry Soshnikov](http://soshnikov.com)によるものです。 +> 上記の図は[Dmitry Soshnikov](http://soshnikov.com)によるものです -セマンティックウェブの構築の進展は、検索エンジンや自然言語処理技術の成功によってある程度遅れました。これらの技術は、テキストから構造化データを抽出することを可能にします。しかし、いくつかの分野では、オントロジーや知識ベースを維持するための重要な努力が続けられています。注目すべきプロジェクトをいくつか挙げます： +セマンティックウェブの構築の進展は、検索エンジンや自然言語処理技術の成功によってある程度遅れました。これらの技術はテキストから構造化データを抽出可能にしました。しかし、いくつかの分野では依然としてオントロジーや知識ベースの維持にかなりの努力が払われています。注目すべきプロジェクトをいくつか紹介します： -* [WikiData](https://wikidata.org/) は、Wikipediaに関連付けられた機械可読な知識ベースのコレクションです。ほとんどのデータは、Wikipediaページ内の構造化コンテンツである*InfoBoxes*から抽出されています。SPARQLというセマンティックウェブ専用のクエリ言語を使って、[クエリ](https://query.wikidata.org/)を実行できます。以下は、人間の間で最も一般的な目の色を表示するサンプルクエリです： +* [WikiData](https://wikidata.org/) はウィキペディアに関連した機械可読の知識ベースの集合です。ほとんどのデータはウィキペディアの*インフォボックス*（ページ内の構造化コンテンツ）から抽出されています。SPARQLというセマンティックウェブ専用のクエリ言語で[クエリ](https://query.wikidata.org/)が可能です。以下は人間の最も人気のある目の色を表示するサンプルクエリです： ```sparql #defaultView:BubbleChart @@ -206,47 +206,51 @@ WHERE GROUP BY ?eyeColorLabel ``` -* [DBpedia](https://www.dbpedia.org/) は、WikiDataに似た別の取り組みです。 +* [DBpedia](https://www.dbpedia.org/) はWikiDataに似たもう一つのプロジェクトです。 -> ✅ 自分のオントロジーを構築したり、既存のものを開くことに興味がある場合、[Protégé](https://protege.stanford.edu/)という優れたビジュアルオントロジーエディタがあります。ダウンロードするか、オンラインで使用してみてください。 +> ✅ オントロジーの構築や既存オントロジーの開示を試したい場合は、優れたビジュアルオントロジーエディタである[Protégé](https://protege.stanford.edu/)があります。ダウンロードするかオンラインで利用してください。 - + -*Web ProtégéエディタがRomanov家族のオントロジーを開いた状態。Dmitry Soshnikovによるスクリーンショット* +*Web ProtégéエディタでRomanov Familyオントロジーを開いた様子。スクリーンショット提供：Dmitry Soshnikov* -## ✍️ 演習: 家族オントロジー +## ✍️ 練習問題：家族オントロジー -セマンティックウェブ技術を使用して家族関係を推論する例については、[FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) を参照してください。一般的なGEDCOM形式で表現された家系図と家族関係のオントロジーを使用して、指定された個人の家族関係のグラフを構築します。 +[FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) では、セマンティックウェブ技術を使って家族関係を推論する例を示しています。一般的なGEDCOM形式で表現された家系図と家族関係のオントロジーを使い、指定された個人群に対するすべての家族関係のグラフを構築します。 ## Microsoft Concept Graph -ほとんどの場合、オントロジーは手作業で慎重に作成されます。しかし、自然言語テキストなどの非構造化データからオントロジーを**抽出**することも可能です。 +多くの場合、オントロジーは注意深く手作業で作成されます。しかし、自然言語テキストのような非構造化データからオントロジーを**マイニング**することも可能です。 -そのような試みの一つがMicrosoft Researchによって行われ、[Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste)が生まれました。 +この試みの一つはMicrosoft Researchによってなされ、[Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste)として発表されました。 -これは、`is-a` 継承関係を使用してグループ化されたエンティティの大規模なコレクションです。例えば、「Microsoftとは何か？」という質問に対して、「会社（確率0.87）であり、ブランド（確率0.75）」というような答えを提供します。 +これは`is-a`継承関係を用いてグルーピングされた大量のエンティティのコレクションで、「Microsoftとは何か？」という問いに「0.87の確率で会社であり、0.75の確率でブランドである」などの回答を可能にします。 -このグラフは、REST APIとして利用可能であるか、またはすべてのエンティティペアをリスト化した大規模なテキストファイルとしてダウンロード可能です。 +このグラフはREST APIとして利用可能であるほか、すべてのエンティティペアをリストした大きなテキストファイルとしてもダウンロード可能です。 -## ✍️ 演習: コンセプトグラフ +## ✍️ 練習問題：コンセプトグラフ -[MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) ノートブックを試して、Microsoft Concept Graphを使用してニュース記事をいくつかのカテゴリにグループ化する方法を確認してください。 +[MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb)のノートブックで、Microsoft Concept Graphを使ってニュース記事をいくつかのカテゴリに分類する方法を試してみてください。 ## 結論 -現在、AIはしばしば*機械学習*や*ニューラルネットワーク*の同義語と見なされています。しかし、人間は明示的な推論も行います。これは、ニューラルネットワークでは現在扱われていないものです。実世界のプロジェクトでは、説明が必要なタスクや、システムの挙動を制御可能な方法で変更する必要があるタスクを実行するために、明示的な推論が依然として使用されています。 +今日、AIはしばしば*機械学習*や*ニューラルネットワーク*の同義語として見なされます。しかし、人間は明示的な推論も行い、これは現在ニューラルネットワークでは扱いきれていないものです。実際のプロジェクトでは、説明を要するタスクやシステムの振る舞いを制御可能に変更する必要がある場面で明示的推論が今も利用されています。 ## 🚀 チャレンジ -このレッスンに関連するFamily Ontologyノートブックでは、他の家族関係を試す機会があります。家系図内の人々の間に新しいつながりを発見してみてください。 +このレッスンに関連したFamily Ontologyノートブックでは、他の家族関係について実験できる機会があります。家系図内の人物同士の新しい繋がりを発見してみてください。 -## [講義後のクイズ](https://ff-quizzes.netlify.app/en/ai/quiz/4) +## [講義後クイズ](https://ff-quizzes.netlify.app/en/ai/quiz/4) -## 復習と自己学習 +## 復習と自主学習 -インターネットで調査を行い、人間が知識を定量化し、体系化しようとした分野を発見してください。ブルームのタキソノミーを調べ、歴史を遡って、人間がどのようにして世界を理解しようとしたかを学んでください。リンネによる生物の分類法の作成や、ドミトリ・メンデレーエフが化学元素を記述し分類する方法を作成した方法を探ってみてください。他にどのような興味深い例が見つかるでしょうか？ +インターネットで人類が知識を定量化・体系化しようとした領域を調べてみましょう。ブルームのタキソノミーを見て、人間がどのように世界を理解しようと試みてきたか歴史を遡って学んでください。リンネによる生物分類の研究を探り、ドミトリ・メンデレーエフが化学元素を記述・分類した方法を観察しましょう。ほかに興味深い例は何がありますか？ -**課題**: [オントロジーを構築する](assignment.md) +**課題**：[オントロジーを作成しよう](assignment.md) --- + +**免責事項**： +本書類はAI翻訳サービス「Co-op Translator」（https://github.com/Azure/co-op-translator）を使用して翻訳されました。正確性を期しておりますが、自動翻訳には誤りや不正確な部分が含まれる可能性があることをご理解ください。原文のオリジナル版が正式な情報源とみなされます。重要な情報については、専門の人間翻訳者による翻訳を推奨いたします。本翻訳の利用による誤解や誤訳に関して、当方は一切の責任を負いかねます。 + \ No newline at end of file diff --git a/translations/ja/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/ja/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index 4cddb4c9..b98b4585 100644 --- a/translations/ja/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/ja/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -1259,7 +1259,7 @@ "* トレーニング損失が低い - モデルは十分な表現力を持っているため、トレーニングデータを正確に近似できます。\n", "* 検証損失がトレーニング損失よりもはるかに高くなり、トレーニング中に増加し始めることがあります。これは、モデルがトレーニングデータを「記憶」してしまい、「全体像」を見失うためです。\n", "\n", - "![過学習](../../../../../translated_images/ja/overfit.a0bd57f717c15769.png)\n", + "![過学習](../../../../../translated_images/ja/overfit.a0bd57f717c15769.webp)\n", "\n", "> この図では、`x`はトレーニングデータ、`o`は検証データを表しています。左側は線形モデル（一層）で、データの本質をうまく近似しています。右側は過学習したモデルで、トレーニングデータを完全に近似していますが、他のデータに対しては意味をなさなくなっています（検証誤差が非常に高い）。\n" ] diff --git a/translations/ja/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/ja/lessons/3-NeuralNetworks/05-Frameworks/README.md index d51628e2..53bdbdd9 100644 --- a/translations/ja/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/ja/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ CO_OP_TRANSLATOR_METADATA: 以下の5つの点（グラフ上の`x`で表される）を近似する問題を考えてみましょう： -![linear](../../../../../translated_images/ja/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/ja/overfit2.131f5800ae10ca5e.jpg) +![linear](../../../../../translated_images/ja/overfit1.f24b71c6f652e59e.webp) | ![overfit](../../../../../translated_images/ja/overfit2.131f5800ae10ca5e.webp) -------------------------|-------------------------- **線形モデル、2つのパラメータ** | **非線形モデル、7つのパラメータ** 学習誤差 = 5.3 | 学習誤差 = 0 @@ -79,7 +79,7 @@ CO_OP_TRANSLATOR_METADATA: 上記のグラフからわかるように、過学習は非常に低い学習誤差と高い検証誤差によって検出できます。通常、学習中は学習誤差と検証誤差の両方が減少し始めますが、ある時点で検証誤差が減少を止めて上昇し始めることがあります。これが過学習の兆候であり、この時点で学習を停止するべき（または少なくともモデルのスナップショットを作成するべき）という指標になります。 -![overfitting](../../../../../translated_images/ja/Overfitting.408ad91cd90b4371.png) +![overfitting](../../../../../translated_images/ja/Overfitting.408ad91cd90b4371.webp) ## 過学習を防ぐ方法 diff --git a/translations/ja/lessons/3-NeuralNetworks/README.md b/translations/ja/lessons/3-NeuralNetworks/README.md index c1636df2..bb2a1dfb 100644 --- a/translations/ja/lessons/3-NeuralNetworks/README.md +++ b/translations/ja/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # ニューラルネットワーク入門 -![ニューラルネットワーク入門の内容をまとめたイラスト](../../../../translated_images/ja/ai-neuralnetworks.1c687ae40bc86e83.png) +![ニューラルネットワーク入門の内容をまとめたイラスト](../../../../translated_images/ja/ai-neuralnetworks.1c687ae40bc86e83.webp) 序章で述べたように、知能を実現する方法の一つは、**コンピュータモデル**や**人工の脳**を訓練することです。20世紀中頃から研究者たちはさまざまな数学的モデルを試みてきましたが、近年になってこの方向性が非常に成功を収めることが証明されました。このような脳の数学的モデルは**ニューラルネットワーク**と呼ばれます。 @@ -36,13 +36,13 @@ CO_OP_TRANSLATOR_METADATA: 生物学から、私たちの脳はニューロン（神経細胞）で構成されており、それぞれが複数の「入力」（樹状突起）と1つの「出力」（軸索）を持っていることがわかっています。樹状突起と軸索は電気信号を伝達することができ、これらの間の接続—シナプスとして知られる—は、神経伝達物質によって調節されるさまざまな伝導度を示すことができます。 -![ニューロンのモデル](../../../../translated_images/ja/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![ニューロンのモデル](../../../../translated_images/ja/artneuron.1a5daa88d20ebe6f.png) +![ニューロンのモデル](../../../../translated_images/ja/synapse-wikipedia.ed20a9e4726ea1c6.webp) | ![ニューロンのモデル](../../../../translated_images/ja/artneuron.1a5daa88d20ebe6f.webp) ----|---- 実際のニューロン *([画像](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) Wikipediaより)* | 人工ニューロン *(著者による画像)* したがって、ニューロンの最も単純な数学的モデルは、いくつかの入力X1, ..., XNと出力Y、および一連の重みW1, ..., WNを含みます。出力は次のように計算されます： -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) ここで、fは非線形の**活性化関数**です。 diff --git a/translations/ja/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/ja/lessons/4-ComputerVision/06-IntroCV/README.md index 0a8e12aa..889ba825 100644 --- a/translations/ja/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/ja/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ OpenCVを使用してビデオをフレームごとに読み込むことも可 * **点字本の写真の前処理**。閾値処理、特徴検出、透視変換、NumPy操作を使用して、個々の点字記号を分離し、ニューラルネットワークによる分類に備える方法に焦点を当てています。 -![点字画像](../../../../../translated_images/ja/braille.341962ff76b1bd70.jpeg) | ![前処理された点字画像](../../../../../translated_images/ja/braille-result.46530fea020b03c7.png) | ![点字記号](../../../../../translated_images/ja/braille-symbols.0159185ab69d5339.png) +![点字画像](../../../../../translated_images/ja/braille.341962ff76b1bd70.webp) | ![前処理された点字画像](../../../../../translated_images/ja/braille-result.46530fea020b03c7.webp) | ![点字記号](../../../../../translated_images/ja/braille-symbols.0159185ab69d5339.webp) ----|-----|----- > 画像は[OpenCV.ipynb](OpenCV.ipynb)から引用 * **フレーム差分を使用したビデオ内の動きの検出**。カメラが固定されている場合、カメラフィードのフレームは互いに非常に似ているはずです。フレームが配列として表現されているため、2つの連続するフレームの配列を引き算するだけでピクセル差分が得られます。静的なフレームでは差分は低く、画像内に大きな動きがあると差分が高くなります。 -![ビデオフレームとフレーム差分の画像](../../../../../translated_images/ja/frame-difference.706f805491a0883c.png) +![ビデオフレームとフレーム差分の画像](../../../../../translated_images/ja/frame-difference.706f805491a0883c.webp) > 画像は[OpenCV.ipynb](OpenCV.ipynb)から引用 @@ -89,7 +89,7 @@ OpenCVを使用してビデオをフレームごとに読み込むことも可 - **密なオプティカルフロー**は、各ピクセルがどこに移動しているかを示すベクトルフィールドを計算します。 - **疎なオプティカルフロー**は、画像内の特徴的な部分（例：エッジ）を取り、それらのフレーム間の軌跡を構築します。 -![オプティカルフローの画像](../../../../../translated_images/ja/optical.1f4a94464579a83a.png) +![オプティカルフローの画像](../../../../../translated_images/ja/optical.1f4a94464579a83a.webp) > 画像は[OpenCV.ipynb](OpenCV.ipynb)から引用 diff --git a/translations/ja/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/ja/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 9d07ebbe..6a8fecc9 100644 --- a/translations/ja/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/ja/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16は、2014年にImageNetのトップ5分類で92.7%の精度を達成したネットワークです。そのレイヤー構造は以下の通りです： -![ImageNet Layers](../../../../../translated_images/ja/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet Layers](../../../../../translated_images/ja/vgg-16-arch1.d901a5583b3a51ba.webp) ご覧の通り、VGGは従来のピラミッド型アーキテクチャを採用しており、畳み込み層とプーリング層が順番に並んでいます。 -![ImageNet Pyramid](../../../../../translated_images/ja/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet Pyramid](../../../../../translated_images/ja/vgg-16-arch.64ff2137f50dd49f.webp) > 画像出典: [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/ja/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/ja/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index 66a52d13..f6ded55b 100644 --- a/translations/ja/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/ja/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -262,7 +262,7 @@ "\n", "このようにして、典型的なCNNではいくつかの畳み込み層があり、その間にプーリング層を挟むことで画像の次元を減少させます。また、フィルターの数を増やします。これは、パターンがより高度になるにつれて、探すべき興味深い組み合わせが増えるためです。\n", "\n", - "![プーリング層を含む複数の畳み込み層を示す画像。](../../../../../translated_images/ja/cnn-pyramid.85915455759ef0ce.png)\n", + "![プーリング層を含む複数の畳み込み層を示す画像。](../../../../../translated_images/ja/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "空間次元が減少し、特徴やフィルターの次元が増加するため、このアーキテクチャは**ピラミッドアーキテクチャ**とも呼ばれます。\n" ] diff --git a/translations/ja/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/ja/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index b6232934..129eeab3 100644 --- a/translations/ja/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/ja/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -358,7 +358,7 @@ "\n", "このようにして、典型的なCNNではいくつかの畳み込み層があり、その間にプーリング層を挟むことで画像の次元を縮小します。また、フィルターの数を増やします。パターンがより高度になるにつれて、注目すべき興味深い組み合わせが増えるためです。\n", "\n", - "![複数の畳み込み層とプーリング層を示す画像。](../../../../../translated_images/ja/cnn-pyramid.85915455759ef0ce.png)\n", + "![複数の畳み込み層とプーリング層を示す画像。](../../../../../translated_images/ja/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "空間次元が縮小し、特徴/フィルター次元が増加するため、このアーキテクチャは**ピラミッドアーキテクチャ**とも呼ばれます。\n" ] diff --git a/translations/ja/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/ja/lessons/4-ComputerVision/07-ConvNets/README.md index b18f6f6e..a86e652a 100644 --- a/translations/ja/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/ja/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: パターンを抽出するために、**畳み込みフィルター**という概念を使用します。ご存じの通り、画像は2D行列、または色深度を持つ3Dテンソルとして表されます。フィルターを適用するとは、比較的小さな**フィルターカーネル**行列を取り、元の画像の各ピクセルに対して隣接する点との加重平均を計算することを意味します。これは、小さな窓が画像全体をスライドし、フィルターカーネル行列の重みに従ってすべてのピクセルを平均化するようなものと考えることができます。 -![垂直エッジフィルター](../../../../../translated_images/ja/filter-vert.b7148390ca0bc356.png) | ![水平エッジフィルター](../../../../../translated_images/ja/filter-horiz.59b80ed4feb946ef.png) +![垂直エッジフィルター](../../../../../translated_images/ja/filter-vert.b7148390ca0bc356.webp) | ![水平エッジフィルター](../../../../../translated_images/ja/filter-horiz.59b80ed4feb946ef.webp) ----|---- > Dmitry Soshnikovによる画像 @@ -38,7 +38,7 @@ CNNが機能する仕組みは、以下の重要なアイデアに基づいて * フィルターが自動的に学習されるようにネットワークを設計できる * 元の画像だけでなく、高レベルの特徴におけるパターンを見つけるためにも同じアプローチを使用できる。そのため、CNNの特徴抽出は低レベルのピクセルの組み合わせから始まり、画像の部分の高レベルの組み合わせに至るまで、特徴の階層で機能する。 -![階層的特徴抽出](../../../../../translated_images/ja/FeatureExtractionCNN.d9b456cbdae7cb64.png) +![階層的特徴抽出](../../../../../translated_images/ja/FeatureExtractionCNN.d9b456cbdae7cb64.webp) > Hislop-Lynchによる論文からの画像 [研究に基づく](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ CNNが機能する仕組みは、以下の重要なアイデアに基づいて 例として、2014年にImageNetのトップ5分類で92.7%の精度を達成したVGG-16のアーキテクチャを見てみましょう： -![ImageNet層](../../../../../translated_images/ja/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet層](../../../../../translated_images/ja/vgg-16-arch1.d901a5583b3a51ba.webp) -![ImageNetピラミッド](../../../../../translated_images/ja/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNetピラミッド](../../../../../translated_images/ja/vgg-16-arch.64ff2137f50dd49f.webp) > [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493)からの画像 diff --git a/translations/ja/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/ja/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 75629605..5d388d60 100644 --- a/translations/ja/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/ja/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ CO_OP_TRANSLATOR_METADATA: [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/) を使用します。このデータセットには、37種類の犬と猫の品種の画像が含まれています。 -![扱うデータセット](../../../../../../translated_images/ja/data.50b2a9d5484bdbf0.png) +![扱うデータセット](../../../../../../translated_images/ja/data.50b2a9d5484bdbf0.webp) データセットをダウンロードするには、以下のコードスニペットを使用してください： diff --git a/translations/ja/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/ja/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index 50a27bfc..6d922f6c 100644 --- a/translations/ja/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/ja/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "理想的な猫を視覚化するために、ランダムなノイズ画像から始めます。そして、勾配降下法の最適化技術を使って画像を調整し、ネットワークが猫を認識できるようにします。\n", "\n", - "![最適化ループ](../../../../../translated_images/ja/ideal-cat-loop.999fbb8ff306e044.png)\n", + "![最適化ループ](../../../../../translated_images/ja/ideal-cat-loop.999fbb8ff306e044.webp)\n", "\n", "こちらが初期の画像です:\n" ] diff --git a/translations/ja/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/ja/lessons/4-ComputerVision/08-TransferLearning/README.md index 6c64d032..caa5f12f 100644 --- a/translations/ja/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/ja/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ KerasとPyTorchには、一般的なアーキテクチャの事前学習済み 以下は、VGG-16ネットワークによって猫の画像から抽出された特徴の例です： -![VGG-16による特徴抽出](../../../../../translated_images/ja/features.6291f9c7ba3a0b95.png) +![VGG-16による特徴抽出](../../../../../translated_images/ja/features.6291f9c7ba3a0b95.webp) ## 猫 vs 犬データセット @@ -48,19 +48,19 @@ KerasとPyTorchには、一般的なアーキテクチャの事前学習済み 一つのアプローチとして、ランダムな画像から始めて、**勾配降下法**を使用してその画像を調整し、ネットワークがそれを猫だと認識するようにする方法があります。 -![画像最適化ループ](../../../../../translated_images/ja/ideal-cat-loop.999fbb8ff306e044.png) +![画像最適化ループ](../../../../../translated_images/ja/ideal-cat-loop.999fbb8ff306e044.webp) しかし、この方法ではランダムノイズに非常に近いものが得られます。これは、*ネットワークが入力画像を猫だと認識する方法が多数存在する*ためであり、その中には視覚的に意味をなさないものも含まれます。これらの画像には猫に典型的な多くのパターンが含まれていますが、視覚的に特徴的であるように制約するものはありません。 結果を改善するために、**変動損失**と呼ばれる項を損失関数に追加することができます。これは、画像の隣接するピクセルがどれだけ似ているかを示す指標です。変動損失を最小化することで画像が滑らかになり、ノイズが除去され、より視覚的に魅力的なパターンが現れます。以下は、猫とシマウマとして高い確率で分類される「理想的な」画像の例です： -![理想的な猫](../../../../../translated_images/ja/ideal-cat.203dd4597643d6b0.png) | ![理想的なシマウマ](../../../../../translated_images/ja/ideal-zebra.7f70e8b54ee15a7a.png) +![理想的な猫](../../../../../translated_images/ja/ideal-cat.203dd4597643d6b0.webp) | ![理想的なシマウマ](../../../../../translated_images/ja/ideal-zebra.7f70e8b54ee15a7a.webp) -----|----- *理想的な猫* | *理想的なシマウマ* 同様のアプローチを使用して、いわゆる**敵対的攻撃**をニューラルネットワークに対して行うことができます。例えば、犬を猫のように見せてネットワークを欺きたい場合、ネットワークが犬として認識する犬の画像を取り、それを少し調整してネットワークがそれを猫として分類するようにすることができます： -![犬の画像](../../../../../translated_images/ja/original-dog.8f68a67d2fe0911f.png) | ![猫として分類される犬の画像](../../../../../translated_images/ja/adversarial-dog.d9fc7773b0142b89.png) +![犬の画像](../../../../../translated_images/ja/original-dog.8f68a67d2fe0911f.webp) | ![猫として分類される犬の画像](../../../../../translated_images/ja/adversarial-dog.d9fc7773b0142b89.webp) -----|----- *元の犬の画像* | *猫として分類される犬の画像* diff --git a/translations/ja/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/ja/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index 3758cd08..cec96490 100644 --- a/translations/ja/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/ja/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "オートエンコーダをトレーニングする際には、元の画像から可能な限り多くの情報を正確に再構築するためにネットワークが最適な**埋め込み**を見つけようとします。これにより、入力画像の意味を捉えることができます。\n", "\n", - "![オートエンコーダの図](../../../../../translated_images/ja/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![オートエンコーダの図](../../../../../translated_images/ja/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "> 画像出典: [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", diff --git a/translations/ja/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/ja/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index ffed6a18..9bbdad06 100644 --- a/translations/ja/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/ja/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "オートエンコーダをトレーニングする際には、元の画像から可能な限り多くの情報を正確に再構築するために捉える必要があるため、ネットワークは入力画像の意味を捉える最適な**埋め込み**を見つけようとします。\n", "\n", - "![オートエンコーダの図](../../../../../translated_images/ja/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![オートエンコーダの図](../../../../../translated_images/ja/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "*画像出典: [Kerasブログ](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", diff --git a/translations/ja/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/ja/lessons/4-ComputerVision/09-Autoencoders/README.md index 547c234f..09a5aca3 100644 --- a/translations/ja/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/ja/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ CNNをトレーニングする際の問題の一つは、多くのラベル付 オートエンコーダーをトレーニングして、元の画像から可能な限り多くの情報をキャプチャし、正確に再構築するため、ネットワークは入力画像の意味を捉える最適な**埋め込み**を見つけようとします。 -![オートエンコーダーの図](../../../../../translated_images/ja/autoencoder_schema.5e6fc9ad98a5eb61.jpg) +![オートエンコーダーの図](../../../../../translated_images/ja/autoencoder_schema.5e6fc9ad98a5eb61.webp) > 画像は[Kerasブログ](https://blog.keras.io/building-autoencoders-in-keras.html)より diff --git a/translations/ja/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/ja/lessons/4-ComputerVision/11-ObjectDetection/README.md index 235fc3a2..d53b01a2 100644 --- a/translations/ja/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/ja/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: ## [事前クイズ](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![オブジェクト検出](../../../../../translated_images/ja/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) +![オブジェクト検出](../../../../../translated_images/ja/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp) > 画像出典: [YOLO v2 ウェブサイト](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 2. 各タイルに対して画像分類を実行する 3. 十分に高い活性化を示したタイルを、対象の物体を含むとみなす -![単純なオブジェクト検出](../../../../../translated_images/ja/naive-detection.e7f1ba220ccd08c6.png) +![単純なオブジェクト検出](../../../../../translated_images/ja/naive-detection.e7f1ba220ccd08c6.webp) > *画像出典: [演習ノートブック](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ CO_OP_TRANSLATOR_METADATA: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20クラス * [COCO](http://cocodataset.org/#home) - コンテキスト内の一般的な物体。80クラス、境界ボックスとセグメンテーションマスクを含む -![COCO](../../../../../translated_images/ja/coco-examples.71bc60380fa6cceb.jpg) +![COCO](../../../../../translated_images/ja/coco-examples.71bc60380fa6cceb.webp) ## オブジェクト検出の評価指標 @@ -50,7 +50,7 @@ CO_OP_TRANSLATOR_METADATA: 画像分類ではアルゴリズムの性能を測定するのは簡単ですが、オブジェクト検出ではクラスの正確性だけでなく、推定された境界ボックスの位置の精度も測定する必要があります。そのために使用されるのが**Intersection over Union** (IoU)です。これは、2つのボックス（または任意の領域）がどれだけ重なっているかを測定します。 -![IoU](../../../../../translated_images/ja/iou_equation.9a4751d40fff4e11.png) +![IoU](../../../../../translated_images/ja/iou_equation.9a4751d40fff4e11.webp) > *図2 出典: [IoUに関する優れたブログ記事](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -97,11 +97,11 @@ IoUが一定値以上の検出のみを考慮します。例えば、PASCAL VOC [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf)は、[Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) を使用してROI領域の階層構造を生成します。それらはCNN特徴抽出器とSVM分類器を通じて物体クラスを決定し、線形回帰を使用して*境界ボックス*の座標を決定します。[公式論文](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/ja/rcnn1.cae407020dfb1d1f.png) +![RCNN](../../../../../translated_images/ja/rcnn1.cae407020dfb1d1f.webp) > *画像出典: van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/ja/rcnn2.2d9530bb83516484.png) +![RCNN-1](../../../../../translated_images/ja/rcnn2.2d9530bb83516484.webp) > *画像出典: [このブログ](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -109,7 +109,7 @@ IoUが一定値以上の検出のみを考慮します。例えば、PASCAL VOC このアプローチはR-CNNに似ていますが、領域は畳み込み層が適用された後に定義されます。 -![FRCNN](../../../../../translated_images/ja/f-rcnn.3cda6d9bb4188875.png) +![FRCNN](../../../../../translated_images/ja/f-rcnn.3cda6d9bb4188875.webp) > 画像出典: [公式論文](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -117,7 +117,7 @@ IoUが一定値以上の検出のみを考慮します。例えば、PASCAL VOC このアプローチの主なアイデアは、ROIを予測するためにニューラルネットワークを使用することです。これを*領域提案ネットワーク* (Region Proposal Network) と呼びます。[論文](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/ja/faster-rcnn.8d46c099b87ef30a.png) +![FasterRCNN](../../../../../translated_images/ja/faster-rcnn.8d46c099b87ef30a.webp) > 画像出典: [公式論文](https://arxiv.org/pdf/1506.01497.pdf) @@ -129,7 +129,7 @@ IoUが一定値以上の検出のみを考慮します。例えば、PASCAL VOC 1. 特徴は**位置感知スコアマップ**で処理されます。$C$クラスの各物体は$k\times k$領域に分割され、物体の部分を予測するように学習します。 1. $k\times k$領域の各部分について、すべてのネットワークが物体クラスに投票し、最大票を得た物体クラスが選択されます。 -![r-fcn image](../../../../../translated_images/ja/r-fcn.13eb88158b99a3da.png) +![r-fcn image](../../../../../translated_images/ja/r-fcn.13eb88158b99a3da.webp) > 画像出典: [公式論文](https://arxiv.org/abs/1605.06409) @@ -140,7 +140,7 @@ YOLOはリアルタイムのワンパスアルゴリズムです。主なアイ * 画像を$S\times S$領域に分割 * 各領域について、**CNN**が$n$個の可能な物体、*境界ボックス*の座標、*信頼度*=*確率* * IoUを予測 - ![YOLO](../../../../../translated_images/ja/yolo.a2648ec82ee8bb4e.png) + ![YOLO](../../../../../translated_images/ja/yolo.a2648ec82ee8bb4e.webp) > 画像出典: [公式論文](https://arxiv.org/abs/1506.02640) diff --git a/translations/ja/lessons/4-ComputerVision/README.md b/translations/ja/lessons/4-ComputerVision/README.md index 840a305e..c4f5346c 100644 --- a/translations/ja/lessons/4-ComputerVision/README.md +++ b/translations/ja/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # コンピュータビジョン -![コンピュータビジョンの内容をまとめたイラスト](../../../../translated_images/ja/ai-computervision.6506ebebac3fbf76.png) +![コンピュータビジョンの内容をまとめたイラスト](../../../../translated_images/ja/ai-computervision.6506ebebac3fbf76.webp) このセクションでは以下について学びます: diff --git a/translations/ja/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/ja/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index 9ed03646..6215c929 100644 --- a/translations/ja/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/ja/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**Bag of Words** (BoW) ベクトル表現は、最も一般的に使用される伝統的なベクトル表現です。各単語はベクトルのインデックスにリンクされ、ベクトル要素には特定の文書内での単語の出現回数が含まれます。\n", "\n", - "![Bag of Words ベクトル表現がメモリ内でどのように表現されるかを示す画像。](../../../../../translated_images/ja/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![Bag of Words ベクトル表現がメモリ内でどのように表現されるかを示す画像。](../../../../../translated_images/ja/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Note**: BoW は、テキスト内の個々の単語に対するすべてのワンホットエンコードされたベクトルの合計として考えることもできます。\n", "\n", diff --git a/translations/ja/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/ja/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index 25bcdee8..79fedece 100644 --- a/translations/ja/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/ja/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**Bag-of-words** (BoW)ベクトル表現は、最も理解しやすい伝統的なベクトル表現です。各単語がベクトルのインデックスにリンクされ、ベクトルの要素には、特定の文書内で各単語が出現した回数が含まれます。\n", "\n", - "![Bag-of-wordsベクトル表現がメモリ内でどのように表現されるかを示す画像。](../../../../../translated_images/ja/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![Bag-of-wordsベクトル表現がメモリ内でどのように表現されるかを示す画像。](../../../../../translated_images/ja/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Note**: BoWは、テキスト内の個々の単語に対するすべてのone-hotエンコードされたベクトルの合計として考えることもできます。\n", "\n", diff --git a/translations/ja/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/ja/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 7381388f..d2c7207d 100644 --- a/translations/ja/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/ja/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "ネットワークの最初の層として埋め込み層を使用することで、バッグオブワードモデルから**埋め込みバッグ**モデルに切り替えることができます。このモデルでは、まずテキスト内の各単語を対応する埋め込みに変換し、それらの埋め込み全体に対して`sum`、`average`、`max`などの集約関数を計算します。\n", "\n", - "![5つのシーケンス単語に対する埋め込み分類器を示す画像。](../../../../../translated_images/ja/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![5つのシーケンス単語に対する埋め込み分類器を示す画像。](../../../../../translated_images/ja/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "私たちの分類器ニューラルネットワークは、埋め込み層、集約層、そしてその上に線形分類器を持つ構造になります。\n" ] @@ -176,7 +176,7 @@ "\n", "以前のアーキテクチャでは、すべてのシーケンスを同じ長さにパディングしてミニバッチに収める必要がありました。しかし、これは可変長シーケンスを表現する最も効率的な方法ではありません。別のアプローチとして、**オフセット**ベクトルを使用する方法があります。このベクトルは、1つの大きなベクトルに格納されたすべてのシーケンスのオフセットを保持します。\n", "\n", - "![オフセットシーケンス表現を示す画像](../../../../../translated_images/ja/offset-sequence-representation.eb73fcefb29b46ee.png)\n", + "![オフセットシーケンス表現を示す画像](../../../../../translated_images/ja/offset-sequence-representation.eb73fcefb29b46ee.webp)\n", "\n", "> **Note**: 上の図では文字のシーケンスを示していますが、この例では単語のシーケンスを扱っています。ただし、オフセットベクトルでシーケンスを表現するという基本的な原則は同じです。\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoWは高速ですが、スキップグラムは遅いものの、頻度の低い単語をより良く表現することができます。\n", "\n", - "![単語をベクトルに変換するためのCBoWとスキップグラムアルゴリズムを示す画像。](../../../../../translated_images/ja/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![単語をベクトルに変換するためのCBoWとスキップグラムアルゴリズムを示す画像。](../../../../../translated_images/ja/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Google Newsデータセットで事前学習されたWord2Vec埋め込みを試すには、**gensim** ライブラリを使用することができます。以下は「neural」に最も類似した単語を見つける例です。\n", "\n", diff --git a/translations/ja/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/ja/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index c920ceb2..d40774d6 100644 --- a/translations/ja/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/ja/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "ネットワークの最初の層として埋め込み層を使用することで、バッグオブワード（bag-of-words）モデルから **埋め込みバッグ（embedding bag）** モデルに切り替えることができます。このモデルでは、まずテキスト内の各単語を対応する埋め込みに変換し、その後、`sum`、`average`、`max` などの集約関数をこれらの埋め込み全体に対して計算します。\n", "\n", - "![5つのシーケンス単語に対する埋め込み分類器を示す画像。](../../../../../translated_images/ja/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![5つのシーケンス単語に対する埋め込み分類器を示す画像。](../../../../../translated_images/ja/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "私たちの分類器ニューラルネットワークは以下の層で構成されています：\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoWは高速ですが、スキップグラムは処理が遅いものの、頻度の低い単語をより良く表現することができます。\n", "\n", - "![単語をベクトルに変換するCBoWとスキップグラムのアルゴリズムを示す画像。](../../../../../translated_images/ja/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![単語をベクトルに変換するCBoWとスキップグラムのアルゴリズムを示す画像。](../../../../../translated_images/ja/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Googleニュースのデータセットで事前学習されたWord2Vec埋め込みを試すには、**gensim**ライブラリを使用することができます。以下に、'neural'に最も類似した単語を見つける例を示します。\n", "\n", diff --git a/translations/ja/lessons/5-NLP/14-Embeddings/README.md b/translations/ja/lessons/5-NLP/14-Embeddings/README.md index bf8cf7ea..1afb43a5 100644 --- a/translations/ja/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/ja/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ BoWやTF/IDFに基づく分類器を訓練する際、高次元の単語袋ベ 分類器ネットワークの最初の層として埋め込み層を使用することで、単語袋モデルから**埋め込み袋**モデルに切り替えることができます。このモデルでは、テキスト内の各単語を対応する埋め込みに変換し、それらの埋め込み全体に対して`sum`、`average`、`max`などの集約関数を計算します。 -![5つの単語シーケンスに対する埋め込み分類器を示す画像。](../../../../../translated_images/ja/embedding-classifier-example.b77f021a7ee67eee.png) +![5つの単語シーケンスに対する埋め込み分類器を示す画像。](../../../../../translated_images/ja/embedding-classifier-example.b77f021a7ee67eee.webp) > 著者による画像 @@ -40,7 +40,7 @@ BoWやTF/IDFに基づく分類器を訓練する際、高次元の単語袋ベ CBoWは高速ですが、スキップグラムは遅いものの、頻度の低い単語をより良く表現します。 -![単語をベクトルに変換するためのCBoWとスキップグラムアルゴリズムを示す画像。](../../../../../translated_images/ja/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![単語をベクトルに変換するためのCBoWとスキップグラムアルゴリズムを示す画像。](../../../../../translated_images/ja/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > [この論文](https://arxiv.org/pdf/1301.3781.pdf)からの画像 diff --git a/translations/ja/lessons/5-NLP/15-LanguageModeling/README.md b/translations/ja/lessons/5-NLP/15-LanguageModeling/README.md index 06fdf547..369f6aea 100644 --- a/translations/ja/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/ja/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ Word2VecやGloVeのようなセマンティック埋め込みは、実際には* * **Continuous Bag-of-Words** (CBoW)：トークン列$W_{-N}$, ..., $W_N$の中間トークン$W_0$を予測する。 * **Skip-gram**：中間トークン$W_0$から、隣接するトークンの集合{$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$}を予測する。 -![単語をベクトルに変換するアルゴリズムに関する論文の画像](../../../../../translated_images/ja/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![単語をベクトルに変換するアルゴリズムに関する論文の画像](../../../../../translated_images/ja/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > 画像出典：[この論文](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/ja/lessons/5-NLP/16-RNN/README.md b/translations/ja/lessons/5-NLP/16-RNN/README.md index f687713e..62fa843b 100644 --- a/translations/ja/lessons/5-NLP/16-RNN/README.md +++ b/translations/ja/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: テキストシーケンスの意味を捉えるためには、**リカレントニューラルネットワーク**（RNN）と呼ばれる別のニューラルネットワークアーキテクチャを使用する必要があります。RNNでは、文をネットワークに1つずつシンボルを通し、ネットワークは**状態**を生成します。この状態を次のシンボルとともに再びネットワークに渡します。 -![RNN](../../../../../translated_images/ja/rnn.27f5c29c53d727b5.png) +![RNN](../../../../../translated_images/ja/rnn.27f5c29c53d727b5.webp) > 著者による画像 @@ -61,7 +61,7 @@ LSTMネットワークはRNNと似た構成ですが、層から層へ渡され リカレントネットワークは、1方向または双方向のいずれであっても、シーケンス内の特定のパターンを捉え、それを状態ベクトルに保存するか、出力に渡すことができます。畳み込みネットワークと同様に、最初の層によって抽出された低レベルのパターンから構築し、高レベルのパターンを捉えるために、最初の層の上に別のリカレント層を構築することができます。これにより、**多層RNN**の概念に至ります。これは2つ以上のリカレントネットワークで構成され、前の層の出力が次の層の入力として渡されます。 -![多層長短期記憶RNNを示す画像](../../../../../translated_images/ja/multi-layer-lstm.dd975e29bb2a59fe.jpg) +![多層長短期記憶RNNを示す画像](../../../../../translated_images/ja/multi-layer-lstm.dd975e29bb2a59fe.webp) *Fernando Lópezによる[この素晴らしい投稿](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3)からの画像* diff --git a/translations/ja/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/ja/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index af0dbc92..b61682de 100644 --- a/translations/ja/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/ja/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "リカレントネットワーク（単方向でも双方向でも）は、シーケンス内の特定のパターンを捉え、それを状態ベクトルに保存したり、出力に渡したりすることができます。畳み込みネットワークと同様に、最初の層によって抽出された低レベルのパターンを基に、より高次のパターンを捉えるために、もう1つのリカレント層をその上に構築することができます。これにより、**多層RNN**という概念が生まれます。これは2つ以上のリカレントネットワークで構成され、前の層の出力が次の層の入力として渡されます。\n", "\n", - "![多層長短期記憶RNNを示す画像](../../../../../translated_images/ja/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![多層長短期記憶RNNを示す画像](../../../../../translated_images/ja/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*Fernando Lópezによる[素晴らしい投稿](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3)からの画像*\n", "\n", diff --git a/translations/ja/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/ja/lessons/5-NLP/16-RNN/RNNTF.ipynb index 0c47cf10..a4df70d9 100644 --- a/translations/ja/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/ja/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "テキストシーケンスの意味を捉えるために、**再帰型ニューラルネットワーク**（Recurrent Neural Network、RNN）と呼ばれるニューラルネットワークのアーキテクチャを使用します。RNNを使用する際には、文をネットワークに1トークンずつ通し、ネットワークが生成する**状態**を次のトークンとともに再びネットワークに渡します。\n", "\n", - "![再帰型ニューラルネットワーク生成の例を示す画像](../../../../../translated_images/ja/rnn.27f5c29c53d727b5.png)\n", + "![再帰型ニューラルネットワーク生成の例を示す画像](../../../../../translated_images/ja/rnn.27f5c29c53d727b5.webp)\n", "\n", "トークンの入力シーケンス $X_0,\\dots,X_n$ が与えられると、RNNはニューラルネットワークブロックのシーケンスを作成し、このシーケンスをバックプロパゲーションを使用してエンドツーエンドで学習します。各ネットワークブロックは、入力としてペア $(X_i,S_i)$ を受け取り、結果として $S_{i+1}$ を生成します。最終状態 $S_n$ または出力 $Y_n$ は線形分類器に渡され、結果を生成します。すべてのネットワークブロックは同じ重みを共有し、1回のバックプロパゲーションパスでエンドツーエンドで学習されます。\n", "\n", @@ -369,7 +369,7 @@ "\n", "リカレントネットワーク（単方向でも双方向でも）は、シーケンス内のパターンを捉え、それを状態ベクトルに保存したり、出力として返したりします。畳み込みネットワークと同様に、最初の層で抽出された低レベルのパターンから構築された高レベルのパターンを捉えるために、最初の層の後に別のリカレント層を追加することができます。これにより、**多層RNN**という概念が生まれます。これは、2つ以上のリカレントネットワークで構成され、前の層の出力が次の層の入力として渡されます。\n", "\n", - "![多層長短期記憶RNNを示す画像](../../../../../translated_images/ja/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![多層長短期記憶RNNを示す画像](../../../../../translated_images/ja/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*Fernando Lópezによる[素晴らしい投稿](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3)からの画像。*\n", "\n", diff --git a/translations/ja/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/ja/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index 77be5afc..371e6a2a 100644 --- a/translations/ja/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/ja/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "RNNを使ってテキストを生成する方法は以下の通りです。各ステップで、`nchars`の長さの文字列を入力として取り、ネットワークに対して各入力文字に対する次の出力文字を生成するように求めます。\n", "\n", - "![単語 'HELLO' を生成するRNNの例を示す画像。](../../../../../translated_images/ja/rnn-generate.56c54afb52f9781d.png)\n", + "![単語 'HELLO' を生成するRNNの例を示す画像。](../../../../../translated_images/ja/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "実際のシナリオによっては、*end-of-sequence* `` のような特別な文字を含めることもあります。しかし、今回の場合は無限にテキストを生成するネットワークをトレーニングしたいので、各シーケンスのサイズを`nchars`トークンに固定します。その結果、各トレーニング例は`nchars`の入力と`nchars`の出力（入力シーケンスを1文字左にシフトしたもの）で構成されます。ミニバッチはこのようなシーケンスをいくつかまとめたものになります。\n", "\n", diff --git a/translations/ja/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/ja/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index 83c33af0..a2bd1655 100644 --- a/translations/ja/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/ja/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -111,7 +111,7 @@ "\n", "ニュースタイトルを生成するためにRNNをトレーニングする方法は以下の通りです。各ステップで1つのタイトルを取り出し、それをRNNに入力します。そして、各入力文字に対してネットワークに次の出力文字を生成させます。\n", "\n", - "![単語 'HELLO' を生成するRNNの例を示す画像。](../../../../../translated_images/ja/rnn-generate.56c54afb52f9781d.png)\n", + "![単語 'HELLO' を生成するRNNの例を示す画像。](../../../../../translated_images/ja/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "シーケンスの最後の文字に対しては、ネットワークに `` トークンを生成させます。\n", "\n", diff --git a/translations/ja/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/ja/lessons/5-NLP/17-GenerativeNetworks/README.md index a335e453..0960f987 100644 --- a/translations/ja/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/ja/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: これにより、以下の図に示されるようなさまざまなニューラルアーキテクチャが可能になります： -![一般的なリカレントニューラルネットワークのパターンを示す画像](../../../../../translated_images/ja/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![一般的なリカレントニューラルネットワークのパターンを示す画像](../../../../../translated_images/ja/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > 画像は [Andrej Karpaty](http://karpathy.github.io/) のブログ記事 [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) より引用 @@ -32,7 +32,7 @@ CO_OP_TRANSLATOR_METADATA: このRNNを訓練してステップごとにテキストを生成します。各ステップで、`nchars`の長さの文字列を取り、ネットワークに各入力文字に対して次の出力文字を生成させます： -![単語 'HELLO' を生成するRNNの例を示す画像](../../../../../translated_images/ja/rnn-generate.56c54afb52f9781d.png) +![単語 'HELLO' を生成するRNNの例を示す画像](../../../../../translated_images/ja/rnn-generate.56c54afb52f9781d.webp) テキスト生成（推論中）では、まず**プロンプト**を使用し、それをRNNセルに通して中間状態を生成します。その後、この状態から生成が始まります。1文字ずつ生成し、状態と生成された文字を次のRNNセルに渡して次の文字を生成します。このプロセスを繰り返して十分な文字数を生成します。 diff --git a/translations/ja/lessons/5-NLP/18-Transformers/README.md b/translations/ja/lessons/5-NLP/18-Transformers/README.md index a6a308e2..42ec3a48 100644 --- a/translations/ja/lessons/5-NLP/18-Transformers/README.md +++ b/translations/ja/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ RNNを使用したシーケンス間変換は、2つのリカレントネット **注意機構**は、RNNの各出力予測に対する各入力ベクトルの文脈的な影響を重み付けする手段を提供します。これを実現する方法は、入力RNNの中間状態と出力RNNの間にショートカットを作成することです。この方法では、出力記号ytを生成する際に、異なる重み係数αt,iを用いてすべての入力隠れ状態hiを考慮します。 -![エンコーダ/デコーダモデルと加法型注意層を示す画像](../../../../../translated_images/ja/encoder-decoder-attention.7a726296894fb567.png) +![エンコーダ/デコーダモデルと加法型注意層を示す画像](../../../../../translated_images/ja/encoder-decoder-attention.7a726296894fb567.webp) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf)の加法型注意機構を持つエンコーダ-デコーダモデル。引用元：[このブログ記事](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) 注意行列{αi,j}は、出力シーケンス内の特定の単語の生成において、入力単語がどの程度関与しているかを表します。以下はそのような行列の例です： -![BahdanauによるRNNsearch-50のサンプルアラインメントを示す画像](../../../../../translated_images/ja/bahdanau-fig3.09ba2d37f202a6af.png) +![BahdanauによるRNNsearch-50のサンプルアラインメントを示す画像](../../../../../translated_images/ja/bahdanau-fig3.09ba2d37f202a6af.webp) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf)からの図（Fig.3） @@ -66,7 +66,7 @@ RNNを使用したシーケンス間変換は、2つのリカレントネット 次に、シーケンス内のパターンを捉える必要があります。これを行うために、トランスフォーマーは**自己注意**機構を使用します。これは入力と出力が同じシーケンスに対して適用される注意です。自己注意を適用することで、文内の**文脈**を考慮し、どの単語が相互に関連しているかを確認できます。例えば、*it*のような共参照が指す単語を確認したり、文脈を考慮することができます： -![](../../../../../translated_images/ja/CoreferenceResolution.861924d6d384a7d6.png) +![](../../../../../translated_images/ja/CoreferenceResolution.861924d6d384a7d6.webp) > [Googleのブログ](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html)からの画像 @@ -91,7 +91,7 @@ RNNを使用したシーケンス間変換は、2つのリカレントネット **BERT**（Bidirectional Encoder Representations from Transformers）は非常に大規模な多層トランスフォーマーネットワークで、*BERT-base*では12層、*BERT-large*では24層を持ちます。このモデルはまず、WikiPediaや書籍などの大規模なテキストデータコーパスで教師なし学習（文中のマスクされた単語を予測する）を使用して事前学習されます。事前学習中にモデルは言語理解の重要なレベルを吸収し、その後他のデータセットで微調整することで活用できます。このプロセスは**転移学習**と呼ばれます。 -![http://jalammar.github.io/illustrated-bert/からの画像](../../../../../translated_images/ja/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) +![http://jalammar.github.io/illustrated-bert/からの画像](../../../../../translated_images/ja/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > 画像の[出典](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/ja/lessons/5-NLP/18-Transformers/READMEtransformers.md b/translations/ja/lessons/5-NLP/18-Transformers/READMEtransformers.md index 6d501824..ee249d57 100644 --- a/translations/ja/lessons/5-NLP/18-Transformers/READMEtransformers.md +++ b/translations/ja/lessons/5-NLP/18-Transformers/READMEtransformers.md @@ -11,13 +11,13 @@ RNNを使用したシーケンスからシーケンスの実装は、二つの **注意機構**は、RNNの各出力予測に対する各入力ベクトルの文脈的影響を重み付けする手段を提供します。これは、入力RNNの中間状態と出力RNNの間にショートカットを作成することによって実装されます。この方法では、出力シンボルytを生成する際に、異なる重み係数αt,iを持つすべての入力隠れ状態hiを考慮します。 -![加法注意層を持つエンコーダ/デコーダモデルの画像](../../../../../translated_images/ja/encoder-decoder-attention.7a726296894fb567.png) +![加法注意層を持つエンコーダ/デコーダモデルの画像](../../../../../translated_images/ja/encoder-decoder-attention.7a726296894fb567.webp) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf)の加法注意機構を持つエンコーダ-デコーダモデル、[このブログ投稿](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)から引用 注意行列 {αi,j} は、特定の入力単語が出力シーケンス内の特定の単語の生成にどの程度寄与しているかを表します。以下はそのような行列の例です： -![RNNsearch-50によって見つかったサンプルアラインメントの画像、Bahdanau - arviz.orgから](../../../../../translated_images/ja/bahdanau-fig3.09ba2d37f202a6af.png) +![RNNsearch-50によって見つかったサンプルアラインメントの画像、Bahdanau - arviz.orgから](../../../../../translated_images/ja/bahdanau-fig3.09ba2d37f202a6af.webp) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf)からの図（Fig.3） @@ -57,7 +57,7 @@ RNNを使用したシーケンスからシーケンスの実装は、二つの 次に、シーケンス内のパターンをキャプチャする必要があります。これを行うために、トランスフォーマーは**自己注意**機構を使用します。これは基本的に、同じシーケンスに対して入力と出力に適用される注意です。自己注意を適用することで、文内の**コンテキスト**を考慮し、どの単語が相互関連しているかを確認できます。例えば、*it*のようなコリファレンスによって参照される単語を確認し、コンテキストも考慮に入れることができます： -![](../../../../../translated_images/ja/CoreferenceResolution.861924d6d384a7d6.png) +![](../../../../../translated_images/ja/CoreferenceResolution.861924d6d384a7d6.webp) > [Googleブログ](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html)からの画像 @@ -82,7 +82,7 @@ RNNを使用したシーケンスからシーケンスの実装は、二つの **BERT**（Bidirectional Encoder Representations from Transformers）は、*BERT-base*用の12層、*BERT-large*用の24層を持つ非常に大きなマルチレイヤートランスフォーマーネットワークです。このモデルは、無監督トレーニング（文中のマスクされた単語を予測）を使用して、大規模なテキストデータコーパス（WikiPedia + 書籍）で事前トレーニングされます。事前トレーニング中に、モデルは言語理解の重要なレベルを吸収し、その後ファインチューニングを使用して他のデータセットと活用できます。このプロセスは**転移学習**と呼ばれます。 -![http://jalammar.github.io/illustrated-bert/からの画像](../../../../../translated_images/ja/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) +![http://jalammar.github.io/illustrated-bert/からの画像](../../../../../translated_images/ja/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > 画像 [出典](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/ja/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/ja/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index b2e0b661..0448fb8a 100644 --- a/translations/ja/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/ja/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**注意メカニズム**は、RNNの各出力予測に対する各入力ベクトルの文脈的影響を重み付けする手段を提供します。これを実現する方法は、入力RNNの中間状態と出力RNNの間にショートカットを作成することです。この方法では、出力記号$y_t$を生成する際に、異なる重み係数$\\alpha_{t,i}$を用いてすべての入力隠れ状態$h_i$を考慮します。\n", "\n", - "![エンコーダーデコーダーモデルと加法型注意層を示す画像](../../../../../translated_images/ja/encoder-decoder-attention.7a726296894fb567.png)\n", + "![エンコーダーデコーダーモデルと加法型注意層を示す画像](../../../../../translated_images/ja/encoder-decoder-attention.7a726296894fb567.webp)\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf)の加法型注意メカニズムを持つエンコーダーデコーダーモデル。画像は[このブログ記事](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)から引用。]*\n", "\n", "注意行列$\\{\\alpha_{i,j}\\}$は、出力シーケンス内の特定の単語の生成において、入力単語がどの程度影響を与えるかを表します。以下はそのような行列の例です：\n", "\n", - "![Bahdanau - arviz.orgから引用されたRNNsearch-50によるサンプルアラインメントを示す画像](../../../../../translated_images/ja/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![Bahdanau - arviz.orgから引用されたRNNsearch-50によるサンプルアラインメントを示す画像](../../../../../translated_images/ja/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3)から引用された図]*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT**（Bidirectional Encoder Representations from Transformers）は、非常に大規模な多層トランスフォーマーネットワークであり、*BERT-base*では12層、*BERT-large*では24層を持ちます。このモデルは、まず大規模なテキストデータ（Wikipedia + 書籍）を使用して教師なし学習（文中のマスクされた単語を予測する）で事前学習されます。事前学習中にモデルは言語理解の重要なレベルを吸収し、その後、他のデータセットで微調整することで活用できます。このプロセスは**転移学習**と呼ばれます。\n", "\n", - "![http://jalammar.github.io/illustrated-bert/から引用された画像](../../../../../translated_images/ja/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![http://jalammar.github.io/illustrated-bert/から引用された画像](../../../../../translated_images/ja/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "BERT、DistilBERT、BigBird、OpenGPT3など、微調整可能なトランスフォーマーアーキテクチャには多くのバリエーションがあります。[HuggingFaceパッケージ](https://github.com/huggingface/)は、PyTorchを使用してこれらのアーキテクチャの多くをトレーニングするためのリポジトリを提供しています。\n", "\n", diff --git a/translations/ja/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/ja/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index 41047714..eaae13cc 100644 --- a/translations/ja/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/ja/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**注意メカニズム**は、RNNの各出力予測に対する各入力ベクトルの文脈的影響を重み付けする手段を提供します。このメカニズムは、入力RNNの中間状態と出力RNNの間にショートカットを作成することで実装されます。この方法では、出力記号$y_t$を生成する際に、異なる重み係数$\\alpha_{t,i}$を用いてすべての入力隠れ状態$h_i$を考慮します。\n", "\n", - "![エンコーダー/デコーダーモデルと加法型注意層を示す画像](../../../../../translated_images/ja/encoder-decoder-attention.7a726296894fb567.png)\n", + "![エンコーダー/デコーダーモデルと加法型注意層を示す画像](../../../../../translated_images/ja/encoder-decoder-attention.7a726296894fb567.webp)\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf)の加法型注意メカニズムを備えたエンコーダーデコーダーモデル。画像は[このブログ記事](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)から引用。]*\n", "\n", "注意行列$\\{\\alpha_{i,j}\\}$は、出力シーケンス内の特定の単語の生成において、入力単語がどの程度影響を与えるかを表します。以下はそのような行列の例です：\n", "\n", - "![Bahdanau - arviz.orgから引用されたRNNsearch-50によるサンプルアラインメントを示す画像](../../../../../translated_images/ja/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![Bahdanau - arviz.orgから引用されたRNNsearch-50によるサンプルアラインメントを示す画像](../../../../../translated_images/ja/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf)（図3）から引用された図]*\n", "\n", @@ -231,7 +231,7 @@ "\n", "**BERT**（Bidirectional Encoder Representations from Transformers）は、非常に大規模な多層トランスフォーマーネットワークで、*BERT-base* では12層、*BERT-large* では24層の構造を持っています。このモデルは、まず大規模なテキストデータ（Wikipedia + 書籍）を使用して、教師なし学習（文中のマスクされた単語を予測する）で事前学習されます。事前学習の過程で、モデルは言語理解の高度な知識を吸収し、その後、他のデータセットで微調整を行うことで活用できます。このプロセスは**転移学習**と呼ばれます。\n", "\n", - "![http://jalammar.github.io/illustrated-bert/ からの画像](../../../../../translated_images/ja/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![http://jalammar.github.io/illustrated-bert/ からの画像](../../../../../translated_images/ja/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "BERT、DistilBERT、BigBird、OpenGPT3 など、微調整可能なトランスフォーマーアーキテクチャには多くのバリエーションがあります。\n", "\n", diff --git a/translations/ja/lessons/5-NLP/19-NER/README.md b/translations/ja/lessons/5-NLP/19-NER/README.md index db547f17..ac85ee54 100644 --- a/translations/ja/lessons/5-NLP/19-NER/README.md +++ b/translations/ja/lessons/5-NLP/19-NER/README.md @@ -57,7 +57,7 @@ Token | Tag トークンとクラスの1対1の対応を構築する必要があるため、この図から右端の**多対多**ニューラルネットワークモデルをトレーニングできます： -![Image showing common recurrent neural network patterns.](../../../../../translated_images/ja/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Image showing common recurrent neural network patterns.](../../../../../translated_images/ja/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > *[Andrej Karpathy](http://karpathy.github.io/)による[このブログ記事](http://karpathy.github.io/2015/05/21/rnn-effectiveness/)からの画像。NERトークン分類モデルは、この画像の右端のネットワークアーキテクチャに対応します。* diff --git a/translations/ja/lessons/5-NLP/README.md b/translations/ja/lessons/5-NLP/README.md index a2f54bb9..34cab250 100644 --- a/translations/ja/lessons/5-NLP/README.md +++ b/translations/ja/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 自然言語処理 -![NLPタスクの概要を示すイラスト](../../../../translated_images/ja/ai-nlp.b22dcb8ca4707cea.png) +![NLPタスクの概要を示すイラスト](../../../../translated_images/ja/ai-nlp.b22dcb8ca4707cea.webp) このセクションでは、**自然言語処理 (NLP)** に関連するタスクを扱うためにニューラルネットワークを使用する方法に焦点を当てます。コンピュータに解決してほしい多くのNLP問題があります。 diff --git a/translations/ja/lessons/6-Other/23-MultiagentSystems/README.md b/translations/ja/lessons/6-Other/23-MultiagentSystems/README.md index 1cd31628..931ed7f2 100644 --- a/translations/ja/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/ja/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ NetLogoの素晴らしい点は、試すことができる動作するモデル モデルを開くと、NetLogoのメイン画面に移動します。ここでは、有限の資源（草）を考慮した狼と羊の個体数を記述するサンプルモデルを見てみましょう。 -![NetLogo Main Screen](../../../../../translated_images/ja/NetLogo-Main.32653711ec1a01b3.png) +![NetLogo Main Screen](../../../../../translated_images/ja/NetLogo-Main.32653711ec1a01b3.webp) > Dmitry Soshnikovによるスクリーンショット diff --git a/translations/ja/lessons/README.md b/translations/ja/lessons/README.md index d8f36b34..1c313a6d 100644 --- a/translations/ja/lessons/README.md +++ b/translations/ja/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 概要 -![概要のイラスト](../../../translated_images/ja/ai-overview.0857791951d19500.png) +![概要のイラスト](../../../translated_images/ja/ai-overview.0857791951d19500.webp) > スケッチノート作成者：[Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/ja/lessons/X-Extras/X1-MultiModal/README.md b/translations/ja/lessons/X-Extras/X1-MultiModal/README.md index aa2e608a..89b72192 100644 --- a/translations/ja/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/ja/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: CLIPの主なアイデアは、テキストプロンプトと画像を比較し、画像がプロンプトにどれだけ対応しているかを判断できるようにすることです。 -![CLIP アーキテクチャ](../../../../../translated_images/ja/clip-arch.b3dbf20b4e8ed8be.png) +![CLIP アーキテクチャ](../../../../../translated_images/ja/clip-arch.b3dbf20b4e8ed8be.webp) > *画像は[このブログ記事](https://openai.com/blog/clip/)から引用* @@ -29,7 +29,7 @@ CLIPモデル/ライブラリは[OpenAI GitHub](https://github.com/openai/CLIP) 例えば、画像を猫、犬、人間に分類する必要があるとします。この場合、モデルに画像と一連のテキストプロンプト（例: "*a picture of a cat*"、"*a picture of a dog*"、"*a picture of a human*"）を与えます。結果として得られる3つの確率のベクトルの中で、最も高い値を持つインデックスを選択すればよいのです。 -![CLIPによる画像分類](../../../../../translated_images/ja/clip-class.3af42ef0b2b19369.png) +![CLIPによる画像分類](../../../../../translated_images/ja/clip-class.3af42ef0b2b19369.webp) > *画像は[このブログ記事](https://openai.com/blog/clip/)から引用* @@ -53,13 +53,13 @@ VQGANの詳細については、[Taming Transformers](https://compvis.github.io/ VQGANと従来のGANの重要な違いの一つは、後者が任意の入力ベクトルから適切な画像を生成できるのに対し、VQGANは一貫性のない画像を生成する可能性が高いことです。そのため、画像生成プロセスをさらに誘導する必要があり、それがCLIPを使用して行われます。 -![VQGAN+CLIP アーキテクチャ](../../../../../translated_images/ja/vqgan.5027fe05051dfa31.png) +![VQGAN+CLIP アーキテクチャ](../../../../../translated_images/ja/vqgan.5027fe05051dfa31.webp) テキストプロンプトに対応する画像を生成するには、まずランダムなエンコーディングベクトルを用意し、それをVQGANに通して画像を生成します。その後、CLIPを使用して、画像がテキストプロンプトにどれだけ対応しているかを示す損失関数を生成します。その損失を最小化することを目指し、逆伝播を使用して入力ベクトルのパラメータを調整します。 VQGAN+CLIPを実装した優れたライブラリとして[Pixray](http://github.com/pixray/pixray)があります。 -![Pixrayによる生成画像](../../../../../translated_images/ja/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Pixrayによる生成画像](../../../../../translated_images/ja/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Pixrayによる生成画像](../../../../../translated_images/ja/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) +![Pixrayによる生成画像](../../../../../translated_images/ja/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp) | ![Pixrayによる生成画像](../../../../../translated_images/ja/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp) | ![Pixrayによる生成画像](../../../../../translated_images/ja/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp) ----|----|---- プロンプト *a closeup watercolor portrait of young male teacher of literature with a book* から生成された画像 | プロンプト *a closeup oil portrait of young female teacher of computer science with a computer* から生成された画像 | プロンプト *a closeup oil portrait of old male teacher of mathematics in front of blackboard* から生成された画像 diff --git a/translations/kn/README.md b/translations/kn/README.md index 36a0d815..3685413c 100644 --- a/translations/kn/README.md +++ b/translations/kn/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # ಹೊಸವರಿಗಾಗಿ искусственный ಬುದ್ಧಿಮತ್ತೆ - ಒಂದು ಪಠ್ಯಕ್ರಮ -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/kn/ai-overview.0857791951d19500.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/kn/ai-overview.0857791951d19500.webp)| |:---:| | ಹೊಸವರಿಗಾಗಿ искусственный ಬುದ್ಧಿಮತ್ತೆ - _ಸ್ಕೆಎಚ್‌ನೋಟ್ [@girlie_mac](https://twitter.com/girlie_mac) ಅವರಿಂದ_ | diff --git a/translations/kn/lessons/1-Intro/README.md b/translations/kn/lessons/1-Intro/README.md index 017d6287..93a419ad 100644 --- a/translations/kn/lessons/1-Intro/README.md +++ b/translations/kn/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # AI ಪರಿಚಯ -![AI ವಿಷಯದ ಪರಿಚಯದ ಸಾರಾಂಶ ಡೂಡಲ್‌ನಲ್ಲಿ](../../../../translated_images/kn/ai-intro.bf28d1ac4235881c.png) +![AI ವಿಷಯದ ಪರಿಚಯದ ಸಾರಾಂಶ ಡೂಡಲ್‌ನಲ್ಲಿ](../../../../translated_images/kn/ai-intro.bf28d1ac4235881c.webp) > ಸ್ಕೆಚ್‌ನೋಟ್: [ಟೊಮೊಮಿ ಇಮುರು](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: ಆರಂಭದಲ್ಲಿ, ಕಂಪ್ಯೂಟರ್‌ಗಳನ್ನು [ಚಾರ್ಲ್ಸ್ ಬ್ಯಾಬೇಜ್](https://en.wikipedia.org/wiki/Charles_Babbage) ಸಂಖ್ಯೆಗಳ ಮೇಲೆ ನಿರ್ದಿಷ್ಟ ಕ್ರಮವನ್ನು ಅನುಸರಿಸಿ ಕಾರ್ಯನಿರ್ವಹಿಸಲು ಕಂಡುಹಿಡಿದರು - ಅಲ್ಗೋರಿದಮ್. 19ನೇ ಶತಮಾನದಲ್ಲಿ ಪ್ರಸ್ತಾಪಿಸಲಾದ ಮೂಲ ಮಾದರಿಗಿಂತ ಬಹಳ ಮುಂದುವರೆದಿದ್ದರೂ, ಆಧುನಿಕ ಕಂಪ್ಯೂಟರ್‌ಗಳು ಇನ್ನೂ ನಿಯಂತ್ರಿತ ಗಣನೆಗಳ ಆಲೋಚನೆಯನ್ನು ಅನುಸರಿಸುತ್ತವೆ. ಆದ್ದರಿಂದ, ಗುರಿಯನ್ನು ಸಾಧಿಸಲು ಬೇಕಾದ ಕ್ರಮಗಳನ್ನು ನಾವು ತಿಳಿದಿದ್ದರೆ, ಕಂಪ್ಯೂಟರ್‌ಗೆ ಆ ಕಾರ್ಯವನ್ನು ಪ್ರೋಗ್ರಾಮ್ ಮಾಡಬಹುದು. -![ವ್ಯಕ್ತಿಯ ಫೋಟೋ](../../../../translated_images/kn/dsh_age.d212a30d4e54fb5f.png) +![ವ್ಯಕ್ತಿಯ ಫೋಟೋ](../../../../translated_images/kn/dsh_age.d212a30d4e54fb5f.webp) > ಫೋಟೋ: [ವಿಕ್ಕಿ ಸೋಶ್ನಿಕೋವಾ](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ CO_OP_TRANSLATOR_METADATA: **[ಬುದ್ಧಿಮತ್ತೆ](https://en.wikipedia.org/wiki/Intelligence)** ಎಂಬ ಪದದ ಸ್ಪಷ್ಟ ವ್ಯಾಖ್ಯಾನ ಇಲ್ಲದಿರುವುದು ಒಂದು ಸಮಸ್ಯೆ. ಬುದ್ಧಿಮತ್ತೆ abstract thinking ಅಥವಾ self-awareness ಗೆ ಸಂಬಂಧಿಸಿದೆ ಎಂದು ಹೇಳಬಹುದು, ಆದರೆ ಸರಿಯಾಗಿ ವ್ಯಾಖ್ಯಾನಿಸಲು ಸಾಧ್ಯವಿಲ್ಲ. -![ಬೆಕ್ಕಿನ ಫೋಟೋ](../../../../translated_images/kn/photo-cat.8c8e8fb760ffe457.jpg) +![ಬೆಕ್ಕಿನ ಫೋಟೋ](../../../../translated_images/kn/photo-cat.8c8e8fb760ffe457.webp) > [ಫೋಟೋ](https://unsplash.com/photos/75715CVEJhI) - [ಅಂಬರ್ ಕಿಪ್](https://unsplash.com/@sadmax) ಅವರಿಂದ Unsplash @@ -98,13 +98,13 @@ AGI ಬಗ್ಗೆ ಮಾತನಾಡುವಾಗ, ನಾವು ನಿಜವಾ > | ML ಬಗ್ಗೆ ಏನು? | | > |--------------|-----------| -> | ಕೆಲವು ಡೇಟಾ ಆಧಾರಿತ ಸಮಸ್ಯೆ ಪರಿಹಾರಕ್ಕಾಗಿ ಕಂಪ್ಯೂಟರ್ ಕಲಿಕೆಯನ್ನು ಆಧರಿಸಿದ ಕೃತಕ ಬುದ್ಧಿಮತ್ತೆಯ ಭಾಗವನ್ನು **ಮಷೀನ್ ಲರ್ನಿಂಗ್** ಎಂದು ಕರೆಯುತ್ತಾರೆ. ಈ ಕೋರ್ಸ್‌ನಲ್ಲಿ ನಾವು ಸಾಂಪ್ರದಾಯಿಕ ಮಷೀನ್ ಲರ್ನಿಂಗ್ ಅನ್ನು ಒಳಗೊಂಡಿಲ್ಲ - ನೀವು ಪ್ರತ್ಯೇಕ [ಮಷೀನ್ ಲರ್ನಿಂಗ್ ಫಾರ್ ಬಿಗಿನರ್ಸ್](http://aka.ms/ml-beginners) ಪಠ್ಯಕ್ರಮವನ್ನು ನೋಡಿ.| ![ಮಷೀನ್ ಲರ್ನಿಂಗ್ ಫಾರ್ ಬಿಗಿನರ್ಸ್](../../../../translated_images/kn/ml-for-beginners.9e4fed176fd5817d.png) | +> | ಕೆಲವು ಡೇಟಾ ಆಧಾರಿತ ಸಮಸ್ಯೆ ಪರಿಹಾರಕ್ಕಾಗಿ ಕಂಪ್ಯೂಟರ್ ಕಲಿಕೆಯನ್ನು ಆಧರಿಸಿದ ಕೃತಕ ಬುದ್ಧಿಮತ್ತೆಯ ಭಾಗವನ್ನು **ಮಷೀನ್ ಲರ್ನಿಂಗ್** ಎಂದು ಕರೆಯುತ್ತಾರೆ. ಈ ಕೋರ್ಸ್‌ನಲ್ಲಿ ನಾವು ಸಾಂಪ್ರದಾಯಿಕ ಮಷೀನ್ ಲರ್ನಿಂಗ್ ಅನ್ನು ಒಳಗೊಂಡಿಲ್ಲ - ನೀವು ಪ್ರತ್ಯೇಕ [ಮಷೀನ್ ಲರ್ನಿಂಗ್ ಫಾರ್ ಬಿಗಿನರ್ಸ್](http://aka.ms/ml-beginners) ಪಠ್ಯಕ್ರಮವನ್ನು ನೋಡಿ.| ![ಮಷೀನ್ ಲರ್ನಿಂಗ್ ಫಾರ್ ಬಿಗಿನರ್ಸ್](../../../../translated_images/kn/ml-for-beginners.9e4fed176fd5817d.webp) | ## AI ಇತಿಹಾಸದ ಸಂಕ್ಷಿಪ್ತ ಪರಿಚಯ ಕೃತಕ ಬುದ್ಧಿಮತ್ತೆ 20ನೇ ಶತಮಾನ ಮಧ್ಯದಲ್ಲಿ ಕ್ಷೇತ್ರವಾಗಿ ಪ್ರಾರಂಭವಾಯಿತು. ಆರಂಭದಲ್ಲಿ, ಪ್ರತೀಕಾತ್ಮಕ ತರ್ಕವು ಪ್ರಮುಖ ದೃಷ್ಠಿಕೋನವಾಗಿತ್ತು ಮತ್ತು ತಜ್ಞ ವ್ಯವಸ್ಥೆಗಳಂತಹ ಯಶಸ್ಸುಗಳನ್ನು ತಂದಿತು. ಆದರೆ ಈ ವಿಧಾನವು ವ್ಯಾಪಕವಾಗಿ ಕಾರ್ಯನಿರ್ವಹಿಸುವುದಿಲ್ಲ ಎಂದು ಸ್ಪಷ್ಟವಾಯಿತು. ತಜ್ಞರಿಂದ ಜ್ಞಾನ ತೆಗೆದು, ಅದನ್ನು ಕಂಪ್ಯೂಟರ್‌ನಲ್ಲಿ ಪ್ರತಿನಿಧಿಸಿ, ಜ್ಞಾನವನ್ನು ನಿಖರವಾಗಿರಿಸುವುದು ಬಹಳ ಸಂಕೀರ್ಣ ಮತ್ತು ದುಬಾರಿ ಕೆಲಸ. ಇದರಿಂದ 1970ರ ದಶಕದಲ್ಲಿ [AI ವಿಂಟರ್](https://en.wikipedia.org/wiki/AI_winter) ಉಂಟಾಯಿತು. -AI ಇತಿಹಾಸದ ಸಂಕ್ಷಿಪ್ತ ಚಿತ್ರ +AI ಇತಿಹಾಸದ ಸಂಕ್ಷಿಪ್ತ ಚಿತ್ರ > ಚಿತ್ರ: [ಡ್ಮಿತ್ರಿ ಸೋಶ್ನಿಕೋವ್](http://soshnikov.com) diff --git a/translations/kn/lessons/2-Symbolic/Animals.ipynb b/translations/kn/lessons/2-Symbolic/Animals.ipynb index 4ec72ca4..e1336b8c 100644 --- a/translations/kn/lessons/2-Symbolic/Animals.ipynb +++ b/translations/kn/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "ಈ ಉದಾಹರಣೆಯಲ್ಲಿ, ಕೆಲವು ದೈಹಿಕ ಲಕ್ಷಣಗಳ ಆಧಾರದ ಮೇಲೆ ಪ್ರಾಣಿಯನ್ನು ನಿರ್ಧರಿಸಲು ಸರಳ ಜ್ಞಾನಾಧಾರಿತ ವ್ಯವಸ್ಥೆಯನ್ನು ಅನುಷ್ಠಾನಗೊಳಿಸುವೆವು. ಈ ವ್ಯವಸ್ಥೆಯನ್ನು ಕೆಳಗಿನ AND-OR ಮರದಿಂದ ಪ್ರತಿನಿಧಿಸಬಹುದು (ಇದು ಸಂಪೂರ್ಣ ಮರದ ಒಂದು ಭಾಗ, ನಾವು ಸುಲಭವಾಗಿ ಇನ್ನಷ್ಟು ನಿಯಮಗಳನ್ನು ಸೇರಿಸಬಹುದು):\n", "\n", - "![](../../../../translated_images/kn/AND-OR-Tree.5592d2c70187f283.png)\n" + "![](../../../../translated_images/kn/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { diff --git a/translations/kn/lessons/2-Symbolic/README.md b/translations/kn/lessons/2-Symbolic/README.md index 69db57ec..b8cd749a 100644 --- a/translations/kn/lessons/2-Symbolic/README.md +++ b/translations/kn/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # ಜ್ಞಾನ ಪ್ರತಿನಿಧಾನ ಮತ್ತು ತಜ್ಞ ವ್ಯವಸ್ಥೆಗಳು -![ಸಾಂಕೆತಿಕ AI ವಿಷಯದ ಸಾರಾಂಶ](../../../../translated_images/kn/ai-symbolic.715a30cb610411a6.png) +![ಸಾಂಕೆತಿಕ AI ವಿಷಯದ ಸಾರಾಂಶ](../../../../translated_images/kn/ai-symbolic.715a30cb610411a6.webp) > ಸ್ಕೆಚ್‌ನೋಟ್ [ಟೊಮೊಮಿ ಇಮುರು](https://twitter.com/girlie_mac) ಅವರಿಂದ @@ -35,13 +35,13 @@ AI ಆರಂಭಿಕ ದಿನಗಳಲ್ಲಿ, ಬುದ್ಧಿವಂತ * **ಜ್ಞಾನ** ಎಂದರೆ ಮಾಹಿತಿಯನ್ನು ನಮ್ಮ ಜಗತ್ತಿನ ಮಾದರಿಯಲ್ಲಿ ಸಂಯೋಜಿಸುವುದು. ಉದಾಹರಣೆಗೆ, ಕಂಪ್ಯೂಟರ್ ಎಂದರೇನು ಎಂದು ಕಲಿತ ಮೇಲೆ, ಅದು ಹೇಗೆ ಕೆಲಸ ಮಾಡುತ್ತದೆ, ಅದರ ಬೆಲೆ ಎಷ್ಟು, ಮತ್ತು ಅದನ್ನು ಏಕೆ ಬಳಸಬಹುದು ಎಂಬುದರ ಬಗ್ಗೆ ನಮಗೆ ಕೆಲವು ಕಲ್ಪನೆಗಳು ಬರುತ್ತವೆ. ಈ ಪರಸ್ಪರ ಸಂಬಂಧಿತ ಕಲ್ಪನೆಗಳ ಜಾಲವೇ ನಮ್ಮ ಜ್ಞಾನ. * **ಜ್ಞಾನತೆ** ಎಂದರೆ ಜಗತ್ತಿನ ನಮ್ಮ ಇನ್ನೊಂದು ಮಟ್ಟದ ಅರ್ಥ, ಮತ್ತು ಇದು *ಮೆಟಾ-ಜ್ಞಾನ* ಅನ್ನು ಪ್ರತಿನಿಧಿಸುತ್ತದೆ, ಉದಾ. ಜ್ಞಾನವನ್ನು ಯಾವಾಗ ಮತ್ತು ಹೇಗೆ ಬಳಸಬೇಕು ಎಂಬ ಕಲ್ಪನೆ. - + *ಚಿತ್ರ [ವಿಕಿಪೀಡಿಯದಿಂದ](https://commons.wikimedia.org/w/index.php?curid=37705247), ಲಾಂಗ್ಲಿವಥಿಯುಕ್ಸ್ ಅವರ ಸ್ವಂತ ಕೆಲಸ, CC BY-SA 4.0* ಹೀಗಾಗಿ, **ಜ್ಞಾನ ಪ್ರತಿನಿಧಾನದ** ಸಮಸ್ಯೆ ಎಂದರೆ ಜ್ಞಾನವನ್ನು ಡೇಟಾ ರೂಪದಲ್ಲಿ ಕಂಪ್ಯೂಟರ್ ಒಳಗೆ ಪರಿಣಾಮಕಾರಿಯಾಗಿ ಪ್ರತಿನಿಧಿಸುವ ವಿಧಾನವನ್ನು ಕಂಡುಹಿಡಿಯುವುದು, ಅದನ್ನು ಸ್ವಯಂಚಾಲಿತವಾಗಿ ಬಳಸಲು ಸಾಧ್ಯವಾಗುವಂತೆ ಮಾಡುವುದು. ಇದನ್ನು ಒಂದು ಸ್ಪೆಕ್ಟ್ರಮ್ ಆಗಿ ನೋಡಬಹುದು: -![ಜ್ಞಾನ ಪ್ರತಿನಿಧಾನ ಸ್ಪೆಕ್ಟ್ರಮ್](../../../../translated_images/kn/knowledge-spectrum.b60df631852c0217.png) +![ಜ್ಞಾನ ಪ್ರತಿನಿಧಾನ ಸ್ಪೆಕ್ಟ್ರಮ್](../../../../translated_images/kn/knowledge-spectrum.b60df631852c0217.webp) > ಚಿತ್ರ [ಡ್ಮಿತ್ರಿ ಸೋಶ್ನಿಕೋವ್](http://soshnikov.com) ಅವರಿಂದ @@ -94,7 +94,7 @@ Python | ಬ್ಲಾಕ್-ಸಿಂಟ್ಯಾಕ್ಸ್ | ಇನ್‌ಡ ಸಾಂಕೆತಿಕ AIಯ ಮೊದಲ ಯಶಸ್ಸುಗಳಲ್ಲಿ ಒಂದಾಗಿದ್ದವು **ತಜ್ಞ ವ್ಯವಸ್ಥೆಗಳು** - ಕೆಲವು ನಿರ್ದಿಷ್ಟ ಸಮಸ್ಯಾ ಕ್ಷೇತ್ರದಲ್ಲಿ ತಜ್ಞರಂತೆ ಕಾರ್ಯನಿರ್ವಹಿಸಲು ವಿನ್ಯಾಸಗೊಳಿಸಿದ ಕಂಪ್ಯೂಟರ್ ವ್ಯವಸ್ಥೆಗಳು. ಅವು ಮಾನವ ತಜ್ಞರಿಂದ ತೆಗೆದುಕೊಂಡ **ಜ್ಞಾನ ಭಂಡಾರ** ಮತ್ತು ಅದರಲ್ಲಿ ತರ್ಕ ನಡೆಸುವ **ನಿರ್ಣಯ ಯಂತ್ರ** ಹೊಂದಿದ್ದವು. -![ಮಾನವ ವಾಸ್ತುಶಿಲ್ಪ](../../../../translated_images/kn/arch-human.5d4d35f1bba3ab1c.png) | ![ಜ್ಞಾನ ಆಧಾರಿತ ವ್ಯವಸ್ಥೆ](../../../../translated_images/kn/arch-kbs.3ec5c150b09fa8da.png) +![ಮಾನವ ವಾಸ್ತುಶಿಲ್ಪ](../../../../translated_images/kn/arch-human.5d4d35f1bba3ab1c.webp) | ![ಜ್ಞಾನ ಆಧಾರಿತ ವ್ಯವಸ್ಥೆ](../../../../translated_images/kn/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ ಮಾನವ ನ್ಯೂರಲ್ ವ್ಯವಸ್ಥೆಯ ಸರಳೀಕೃತ ರಚನೆ | ಜ್ಞಾನ ಆಧಾರಿತ ವ್ಯವಸ್ಥೆಯ ವಾಸ್ತುಶಿಲ್ಪ @@ -106,7 +106,7 @@ Python | ಬ್ಲಾಕ್-ಸಿಂಟ್ಯಾಕ್ಸ್ | ಇನ್‌ಡ ಉದಾಹರಣೆಗೆ, ಪ್ರಾಣಿಯನ್ನು ಅದರ ಭೌತಿಕ ಲಕ್ಷಣಗಳ ಆಧಾರದ ಮೇಲೆ ಗುರುತಿಸುವ ತಜ್ಞ ವ್ಯವಸ್ಥೆಯನ್ನು ಪರಿಗಣಿಸೋಣ: -![AND-OR ಮರ](../../../../translated_images/kn/AND-OR-Tree.5592d2c70187f283.png) +![AND-OR ಮರ](../../../../translated_images/kn/AND-OR-Tree.5592d2c70187f283.webp) > ಚಿತ್ರ [ಡ್ಮಿತ್ರಿ ಸೋಶ್ನಿಕೋವ್](http://soshnikov.com) ಅವರಿಂದ @@ -166,7 +166,7 @@ THEN the animal is a carnivore ಸೆಮ್ಯಾಂಟಿಕ್ ವೆಬ್‌ನಲ್ಲಿ ಎಲ್ಲಾ ಪ್ರತಿನಿಧಾನಗಳು ತ್ರಿಪುಟಗಳ ಮೇಲೆ ಆಧಾರಿತವಾಗಿವೆ. ಪ್ರತಿ ವಸ್ತು ಮತ್ತು ಪ್ರತಿ ಸಂಬಂಧವನ್ನು ಯುಆರ್‌ಐ ಮೂಲಕ ವಿಶಿಷ್ಟವಾಗಿ ಗುರುತಿಸಲಾಗುತ್ತದೆ. ಉದಾಹರಣೆಗೆ, ಈ AI ಪಠ್ಯಕ್ರಮವನ್ನು ಡಿಮಿಟ್ರಿ ಸೋಶ್ನಿಕೋವ್ ಜನವರಿ 1, 2022 ರಂದು ಅಭಿವೃದ್ಧಿಪಡಿಸಿದ್ದೆಂದು ಹೇಳಬೇಕಾದರೆ, ನಾವು ಬಳಸಬಹುದಾದ ತ್ರಿಪುಟಗಳು ಇವು: - + ``` http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 13, 2007” @@ -177,7 +177,7 @@ http://github.com/microsoft/ai-for-beginners http://purl.org/dc/elements/1.1/cre ಹೆಚ್ಚು ಸಂಕೀರ್ಣ ಪ್ರಕರಣದಲ್ಲಿ, ರಚಯಿತೃಗಳ ಪಟ್ಟಿಯನ್ನು ನಿರ್ಧರಿಸಲು RDF ನಲ್ಲಿ ನಿರ್ದಿಷ್ಟಪಡಿಸಿದ ಡೇಟಾ ರಚನೆಗಳನ್ನು ಬಳಸಬಹುದು. - + > ಮೇಲಿನ ಚಿತ್ರಗಳು [ಡಿಮಿಟ್ರಿ ಸೋಶ್ನಿಕೋವ್](http://soshnikov.com) ಅವರವರು ರಚಿಸಿದ್ದಾರೆ @@ -201,7 +201,7 @@ GROUP BY ?eyeColorLabel > ✅ ನಿಮ್ಮದೇ ಒಂಟಾಲಜಿಗಳನ್ನು ನಿರ್ಮಿಸಲು ಅಥವಾ ಇತ್ತೀಚಿನ ಒಂಟಾಲಜಿಗಳನ್ನು ತೆರೆಯಲು ಆಸಕ್ತಿ ಇದ್ದರೆ, [Protégé](https://protege.stanford.edu/) ಎಂಬ ಅದ್ಭುತ ದೃಶ್ಯ ಒಂಟಾಲಜಿ ಸಂಪಾದಕವನ್ನು ಬಳಸಬಹುದು. ಅದನ್ನು ಡೌನ್‌ಲೋಡ್ ಮಾಡಿ ಅಥವಾ ಆನ್‌ಲೈನ್‌ನಲ್ಲಿ ಬಳಸಿ. - + *Web Protégé ಸಂಪಾದಕ ರೋಮ್ಯಾನೋವ್ ಕುಟುಂಬ ಒಂಟಾಲಜಿಯೊಂದಿಗೆ ತೆರೆಯಲಾಗಿದೆ. ಚಿತ್ರ ಡಿಮಿಟ್ರಿ ಸೋಶ್ನಿಕೋವ್ ಅವರವರು ತೆಗೆದಿದ್ದಾರೆ* diff --git a/translations/kn/lessons/3-NeuralNetworks/03-Perceptron/README.md b/translations/kn/lessons/3-NeuralNetworks/03-Perceptron/README.md index 26764c54..4fe5b422 100644 --- a/translations/kn/lessons/3-NeuralNetworks/03-Perceptron/README.md +++ b/translations/kn/lessons/3-NeuralNetworks/03-Perceptron/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: | | | |--------------|-----------| -|Frank Rosenblatt | The Mark 1 Perceptron| +|Frank Rosenblatt | The Mark 1 Perceptron| > ಚಿತ್ರಗಳು [ವಿಕಿಪೀಡಿಯದಿಂದ](https://en.wikipedia.org/wiki/Perceptron) @@ -34,7 +34,7 @@ y(x) = f(wTx) ಇಲ್ಲಿ f ಒಂದು ಸ್ಟೆಪ್ ಸಕ್ರಿಯತೆ ಕಾರ್ಯವಾಗಿದೆ - + ## ಪರ್ಸೆಪ್ಟ್ರಾನ್ ತರಬೇತಿ diff --git a/translations/kn/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/kn/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index fddec4b1..8a720775 100644 --- a/translations/kn/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/kn/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -373,7 +373,7 @@ "\n", "ನಾವು ದ್ವಿಮೌಲ್ಯ ವರ್ಗೀಕರಣ ಸಮಸ್ಯೆಗೆ ಡೇಟಾಸೆಟ್ ಅನ್ನು ರಚಿಸಿದ್ದೇವೆ. ಆದಾಗ್ಯೂ, ನಾವು ಅದನ್ನು ಆರಂಭದಿಂದಲೇ ಬಹು-ವರ್ಗ ವರ್ಗೀಕರಣವೆಂದು ಪರಿಗಣಿಸೋಣ, ಇದರಿಂದ ನಾವು ನಮ್ಮ ಕೋಡ್ ಅನ್ನು ಸುಲಭವಾಗಿ ಬಹು-ವರ್ಗ ವರ್ಗೀಕರಣಕ್ಕೆ ಬದಲಾಯಿಸಬಹುದು. ಈ ಸಂದರ್ಭದಲ್ಲಿ, ನಮ್ಮ ಒನ್-ಲೆಯರ್ ಪರ್ಸೆಪ್ಟ್ರಾನ್ ಕೆಳಗಿನ ವಾಸ್ತುಶಿಲ್ಪವನ್ನು ಹೊಂದಿರುತ್ತದೆ:\n", "\n", - "\n", + "\n", "\n", "ನೆಟ್‌ವರ್ಕ್‌ನ ಎರಡು ಔಟ್‌ಪುಟ್‌ಗಳು ಎರಡು ವರ್ಗಗಳಿಗೆ ಹೊಂದಿವೆ, ಮತ್ತು ಎರಡು ಔಟ್‌ಪುಟ್‌ಗಳಲ್ಲಿನ ಅತ್ಯಧಿಕ ಮೌಲ್ಯ ಹೊಂದಿರುವ ವರ್ಗವೇ ಸರಿಯಾದ ಪರಿಹಾರವಾಗುತ್ತದೆ.\n", "\n", @@ -489,7 +489,7 @@ "\n", "ನಾವು 2 ಕ್ಕಿಂತ ಹೆಚ್ಚು ವರ್ಗಗಳಿದ್ದರೆ, ಸಾಫ್ಟ್‌ಮ್ಯಾಕ್ಸ್ ಎಲ್ಲಾ ವರ್ಗಗಳಲ್ಲಿಯೂ ಪ್ರಾಬಬಿಲಿಟಿಗಳನ್ನು ಸಾಮಾನ್ಯೀಕರಿಸುತ್ತದೆ. ಇಲ್ಲಿ MNIST ಅಂಕಿ ವರ್ಗೀಕರಣ ಮಾಡುವ ನೆಟ್‌ವರ್ಕ್ ವಾಸ್ತುಶಿಲ್ಪದ ಚಿತ್ರಣ ಇದೆ:\n", "\n", - "![MNIST Classifier](../../../../../translated_images/kn/Cross-Entropy-Loss.dc7ba633d2467ef3.png)\n" + "![MNIST Classifier](../../../../../translated_images/kn/Cross-Entropy-Loss.dc7ba633d2467ef3.webp)\n" ] }, { @@ -620,7 +620,7 @@ "\n", "## ಗಣನಾತ್ಮಕ ಗ್ರಾಫ್\n", "\n", - "\n", + "\n", "\n", "ಈ ಕ್ಷಣದವರೆಗೆ, ನಾವು ನೆಟ್‌ವರ್ಕ್‌ನ ವಿಭಿನ್ನ ಲೇಯರ್‌ಗಳಿಗೆ ವಿಭಿನ್ನ ಕ್ಲಾಸ್‌ಗಳನ್ನು ವ್ಯಾಖ್ಯಾನಿಸಿದ್ದೇವೆ. ಆ ಲೇಯರ್‌ಗಳ ಸಂಯೋಜನೆಯನ್ನು **ಗಣನಾತ್ಮಕ ಗ್ರಾಫ್** ಎಂದು ಪ್ರತಿನಿಧಿಸಬಹುದು. ಈಗ ನಾವು ಕೆಳಗಿನ ರೀತಿಯಲ್ಲಿ ನೀಡಲಾದ ತರಬೇತಿ ಡೇಟಾಸೆಟ್ (ಅಥವಾ ಅದರ ಭಾಗ) ಗೆ ಲಾಸ್ ಅನ್ನು ಲೆಕ್ಕಹಾಕಬಹುದು:\n" ] @@ -687,7 +687,7 @@ "source": [ "## ಹಿಂದುಮುಖ ಪ್ರಸರಣ\n", "\n", - "\n", + "\n", "\n", "$$\\def\\L{\\mathcal{L}}\\def\\zz#1#2{\\frac{\\partial#1}{\\partial#2}}\n", "\\begin{align}\n", @@ -712,7 +712,7 @@ "* ಇದು ನೋಡ್ $z$ ಗೆ $\\Delta z = (\\partial\\mathcal{p}/\\partial z)\\Delta p$ ಬದಲಾವಣೆಗಳಿಗೆ ಹೊಂದಿಕೆಯಾಗುತ್ತದೆ\n", "* ಈ ದೋಷವನ್ನು ಕಡಿಮೆ ಮಾಡಲು, ನಾವು ಪ್ಯಾರಾಮೀಟರ್‌ಗಳನ್ನು ಅನುಗುಣವಾಗಿ ಸರಿಹೊಂದಿಸಬೇಕಾಗುತ್ತದೆ: $\\Delta W = (\\partial\\mathcal{z}/\\partial W)\\Delta z$ (ಮತ್ತು $b$ ಗಾಗಿ ಕೂಡ ಇದೇ)\n", "\n", - "\n", + "\n", "\n", "ಈ ಪ್ರಕ್ರಿಯೆ ನೆಟ್‌ವರ್ಕ್‌ನ ಔಟ್‌ಪುಟ್‌ನಿಂದ ಅದರ ಪ್ಯಾರಾಮೀಟರ್‌ಗಳ ಕಡೆಗೆ ಲಾಸ್ ದೋಷವನ್ನು ಹಂಚಿಕೊಳ್ಳಲು ಪ್ರಾರಂಭವಾಗುತ್ತದೆ. ಆದ್ದರಿಂದ ಈ ಪ್ರಕ್ರಿಯೆಯನ್ನು **ಬ್ಯಾಕ್ ಪ್ರೋಪಾಗೇಶನ್** ಎಂದು ಕರೆಯುತ್ತಾರೆ.\n", "\n", @@ -1265,7 +1265,7 @@ "* ತರಬೇತಿ ನಷ್ಟ ಕಡಿಮೆ - ಮಾದರಿ ತರಬೇತಿ ಡೇಟಾವನ್ನು ಚೆನ್ನಾಗಿ ಅಂದಾಜು ಮಾಡಬಹುದು, ಏಕೆಂದರೆ ಅದಕ್ಕೆ ಸಾಕಷ್ಟು ವ್ಯಕ್ತಪಡಿಸುವ ಶಕ್ತಿ ಇದೆ.\n", "* ಮಾನ್ಯತೆ ನಷ್ಟ ತರಬೇತಿ ನಷ್ಟಕ್ಕಿಂತ ಬಹಳ ಹೆಚ್ಚು ಇರಬಹುದು ಮತ್ತು ತರಬೇತಿ ಸಮಯದಲ್ಲಿ ಹೆಚ್ಚಾಗಬಹುದು - ಇದಕ್ಕೆ ಕಾರಣ ಮಾದರಿ ತರಬೇತಿ ಬಿಂದುಗಳನ್ನು \"ಸ್ಮರಿಸುತ್ತದೆ\", ಮತ್ತು \"ಒಟ್ಟು ಚಿತ್ರ\" ಕಳೆದುಕೊಳ್ಳುತ್ತದೆ.\n", "\n", - "![Overfitting](../../../../../translated_images/kn/overfit.a0bd57f717c15769.png)\n", + "![Overfitting](../../../../../translated_images/kn/overfit.a0bd57f717c15769.webp)\n", "\n", "> ಈ ಚಿತ್ರದಲ್ಲಿ, `x` ತರಬೇತಿ ಡೇಟಾವನ್ನು ಸೂಚಿಸುತ್ತದೆ, `o` - ಮಾನ್ಯತೆ ಡೇಟಾವನ್ನು. ಎಡಭಾಗ - ರೇಖೀಯ ಮಾದರಿ (ಒಂದು-ಪರತೆಯ), ಇದು ಡೇಟಾದ ಸ್ವಭಾವವನ್ನು ಚೆನ್ನಾಗಿ ಅಂದಾಜು ಮಾಡುತ್ತದೆ. ಬಲಭಾಗ - ಅತಿವೈಯಕ್ತಿಕ ಮಾದರಿ, ಮಾದರಿ ತರಬೇತಿ ಡೇಟಾವನ್ನು ಪರಿಪೂರ್ಣವಾಗಿ ಅಂದಾಜು ಮಾಡುತ್ತದೆ, ಆದರೆ ಯಾವುದೇ ಇತರ ಡೇಟಾದೊಂದಿಗೆ ಅರ್ಥವಿಲ್ಲದಂತೆ ನಿಲ್ಲುತ್ತದೆ (ಮಾನ್ಯತೆ ದೋಷ ಬಹಳ ಹೆಚ್ಚು).\n" ] diff --git a/translations/kn/lessons/3-NeuralNetworks/04-OwnFramework/README.md b/translations/kn/lessons/3-NeuralNetworks/04-OwnFramework/README.md index 6a1570b8..95686b71 100644 --- a/translations/kn/lessons/3-NeuralNetworks/04-OwnFramework/README.md +++ b/translations/kn/lessons/3-NeuralNetworks/04-OwnFramework/README.md @@ -65,7 +65,7 @@ CO_OP_TRANSLATOR_METADATA: ಎಲ್ಲಾ ಅಭಿವ್ಯಕ್ತಿಗಳ ಎಡಭಾಗವು ಒಂದೇ ಆಗಿರುವುದರಿಂದ, ನಾವು ಲಾಸ್ ಫಂಕ್ಷನ್‌ನಿಂದ ಪ್ರಾರಂಭಿಸಿ ಗಣನಾತ್ಮಕ ಗ್ರಾಫ್ ಮೂಲಕ "ಹಿಂದಕ್ಕೆ" ಹೋಗಿ ಡೆರಿವೇಟಿವ್‌ಗಳನ್ನು ಪರಿಣಾಮಕಾರಿಯಾಗಿ ಲೆಕ್ಕಿಸಬಹುದು. ಆದ್ದರಿಂದ ಬಹು-ಪರತೆಯ ಪರ್ಸೆಪ್ಟ್ರಾನ್ ತರಬೇತಿಯ ವಿಧಾನವನ್ನು **ಬ್ಯಾಕ್‌ಪ್ರೊಪಾಗೇಶನ್** ಅಥವಾ 'ಬ್ಯಾಕ್‌ಪ್ರೊಪ್' ಎಂದು ಕರೆಯುತ್ತಾರೆ. -compute graph +compute graph > TODO: ಚಿತ್ರ ಉಲ್ಲೇಖ diff --git a/translations/kn/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/kn/lessons/3-NeuralNetworks/05-Frameworks/README.md index f66d1c12..9b635abb 100644 --- a/translations/kn/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/kn/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ CO_OP_TRANSLATOR_METADATA: ಕೆಳಗಿನ 5 ಬಿಂದುಗಳನ್ನು (ಗ್ರಾಫ್‌ಗಳಲ್ಲಿ `x` ಮೂಲಕ ಪ್ರತಿನಿಧಿಸಲಾಗಿದೆ) ಅಂದಾಜಿಸುವ ಸಮಸ್ಯೆಯನ್ನು ಪರಿಗಣಿಸಿ: -![linear](../../../../../translated_images/kn/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/kn/overfit2.131f5800ae10ca5e.jpg) +![linear](../../../../../translated_images/kn/overfit1.f24b71c6f652e59e.webp) | ![overfit](../../../../../translated_images/kn/overfit2.131f5800ae10ca5e.webp) -------------------------|-------------------------- **ರೇಖೀಯ ಮಾದರಿ, 2 ಪ್ಯಾರಾಮೀಟರ್‌ಗಳು** | **ಅರೇಖೀಯ ಮಾದರಿ, 7 ಪ್ಯಾರಾಮೀಟರ್‌ಗಳು** ತರಬೇತಿ ದೋಷ = 5.3 | ತರಬೇತಿ ದೋಷ = 0 @@ -79,7 +79,7 @@ CO_OP_TRANSLATOR_METADATA: ಮೇಲಿನ ಗ್ರಾಫ್‌ನಿಂದ ನೀವು ನೋಡಬಹುದು, ಓವರ್‌ಫಿಟಿಂಗ್ ಅನ್ನು ತುಂಬಾ ಕಡಿಮೆ ತರಬೇತಿ ದೋಷ ಮತ್ತು ಹೆಚ್ಚು ಮಾನ್ಯತೆ ದೋಷದಿಂದ ಪತ್ತೆಮಾಡಬಹುದು. ಸಾಮಾನ್ಯವಾಗಿ ತರಬೇತಿ ಸಮಯದಲ್ಲಿ ತರಬೇತಿ ಮತ್ತು ಮಾನ್ಯತೆ ದೋಷಗಳು ಎರಡೂ ಕಡಿಮೆಯಾಗುತ್ತವೆ, ನಂತರ ಕೆಲವೊಂದು ಸಮಯದಲ್ಲಿ ಮಾನ್ಯತೆ ದೋಷ ಕಡಿಮೆಯಾಗುವುದನ್ನು ನಿಲ್ಲಿಸಿ ಏರಿಕೆಯಾಗಬಹುದು. ಇದು ಓವರ್‌ಫಿಟಿಂಗ್ ಸೂಚನೆ ಆಗಿದ್ದು, ಈ ಸಮಯದಲ್ಲಿ ತರಬೇತಿಯನ್ನು ನಿಲ್ಲಿಸುವುದು (ಅಥವಾ ಕನಿಷ್ಠ ಮಾದರಿಯ ಸ্নಾಪ್‌ಶಾಟ್ ತೆಗೆದುಕೊಳ್ಳುವುದು) ಸೂಕ್ತ. -![overfitting](../../../../../translated_images/kn/Overfitting.408ad91cd90b4371.png) +![overfitting](../../../../../translated_images/kn/Overfitting.408ad91cd90b4371.webp) ## ಓವರ್‌ಫಿಟಿಂಗ್ ಅನ್ನು ತಡೆಯುವುದು ಹೇಗೆ diff --git a/translations/kn/lessons/3-NeuralNetworks/README.md b/translations/kn/lessons/3-NeuralNetworks/README.md index 2530fb45..9f80edd6 100644 --- a/translations/kn/lessons/3-NeuralNetworks/README.md +++ b/translations/kn/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # ನ್ಯೂರಲ್ ನೆಟ್‌ವರ್ಕ್‌ಗಳಿಗೆ ಪರಿಚಯ -![ಡೂಡಲ್‌ನಲ್ಲಿ ಇಂಟ್ರೋ ನ್ಯೂರಲ್ ನೆಟ್‌ವರ್ಕ್ ವಿಷಯದ ಸಾರಾಂಶ](../../../../translated_images/kn/ai-neuralnetworks.1c687ae40bc86e83.png) +![ಡೂಡಲ್‌ನಲ್ಲಿ ಇಂಟ್ರೋ ನ್ಯೂರಲ್ ನೆಟ್‌ವರ್ಕ್ ವಿಷಯದ ಸಾರಾಂಶ](../../../../translated_images/kn/ai-neuralnetworks.1c687ae40bc86e83.webp) ನಾವು ಪರಿಚಯದಲ್ಲಿ ಚರ್ಚಿಸಿದಂತೆ, ಬುದ್ಧಿಮತ್ತೆಯನ್ನು ಸಾಧಿಸುವ ಒಂದು ಮಾರ್ಗವೆಂದರೆ **ಕಂಪ್ಯೂಟರ್ ಮಾದರಿ** ಅಥವಾ **ಕೃತಕ ಮೆದುಳು** ಅನ್ನು ತರಬೇತುಗೊಳಿಸುವುದು. 20ನೇ ಶತಮಾನ ಮಧ್ಯಭಾಗದಿಂದ, ಸಂಶೋಧಕರು ವಿವಿಧ ಗಣಿತ ಮಾದರಿಗಳನ್ನು ಪ್ರಯತ್ನಿಸಿದರು, ಇತ್ತೀಚಿನ ವರ್ಷಗಳಲ್ಲಿ ಈ ದಿಕ್ಕು ಬಹುಮಟ್ಟಿಗೆ ಯಶಸ್ವಿಯಾಗಿದೆ. ಮೆದುಳಿನ ಇಂತಹ ಗಣಿತ ಮಾದರಿಗಳನ್ನು **ನ್ಯೂರಲ್ ನೆಟ್‌ವರ್ಕ್‌ಗಳು** ಎಂದು ಕರೆಯುತ್ತಾರೆ. @@ -36,13 +36,13 @@ CO_OP_TRANSLATOR_METADATA: ಜೈವಶಾಸ್ತ್ರದಿಂದ, ನಮ್ಮ ಮೆದುಳು ನ್ಯೂರಲ್ ಸೆಲ್‌ಗಳು (ನ್ಯೂರಾನ್‌ಗಳು) ಹೊಂದಿದೆ ಎಂದು ತಿಳಿದಿದೆ, ಪ್ರತಿಯೊಂದು ನ್ಯೂರಾನ್‌ಗೂ ಹಲವಾರು "ಇನ್‌ಪುಟ್‌ಗಳು" (ಡೆಂಡ್ರೈಟ್‌ಗಳು) ಮತ್ತು ಒಂದು "ಔಟ್‌ಪುಟ್" (ಆಕ್ಸಾನ್) ಇರುತ್ತದೆ. ಡೆಂಡ್ರೈಟ್‌ಗಳು ಮತ್ತು ಆಕ್ಸಾನ್‌ಗಳು ವಿದ್ಯುತ್ ಸಂಕೆತಗಳನ್ನು ಸಾಗಿಸಬಹುದು, ಮತ್ತು ಅವುಗಳ ನಡುವಿನ ಸಂಪರ್ಕಗಳು — ಸೈನಾಪ್ಸ್ ಎಂದು ಕರೆಯಲ್ಪಡುವವು — ವಿವಿಧ ಮಟ್ಟದ ಚಾಲಕತೆಯನ್ನು ತೋರಬಹುದು, ಅವು ನ್ಯೂರೋ ಟ್ರಾನ್ಸ್‌ಮಿಟರ್‌ಗಳ ಮೂಲಕ ನಿಯಂತ್ರಿಸಲ್ಪಡುತ್ತವೆ. -![ನ್ಯೂರಾನ್ ಮಾದರಿ](../../../../translated_images/kn/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![ನ್ಯೂರಾನ್ ಮಾದರಿ](../../../../translated_images/kn/artneuron.1a5daa88d20ebe6f.png) +![ನ್ಯೂರಾನ್ ಮಾದರಿ](../../../../translated_images/kn/synapse-wikipedia.ed20a9e4726ea1c6.webp) | ![ನ್ಯೂರಾನ್ ಮಾದರಿ](../../../../translated_images/kn/artneuron.1a5daa88d20ebe6f.webp) ----|---- ನಿಜವಾದ ನ್ಯೂರಾನ್ *([ಚಿತ್ರ](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) ವಿಕಿಪೀಡಿಯದಿಂದ)* | ಕೃತಕ ನ್ಯೂರಾನ್ *(ಲೇಖಕರ ಚಿತ್ರ)* ಹೀಗಾಗಿ, ನ್ಯೂರಾನ್‌ನ ಸರಳ ಗಣಿತ ಮಾದರಿಯಲ್ಲಿ ಹಲವಾರು ಇನ್‌ಪುಟ್‌ಗಳು X1, ..., XN ಮತ್ತು ಒಂದು ಔಟ್‌ಪುಟ್ Y, ಮತ್ತು ಸರಣಿಯಾದ ತೂಕಗಳು W1, ..., WN ಇರುತ್ತವೆ. ಔಟ್‌ಪುಟ್ ಅನ್ನು ಹೀಗೆ ಲೆಕ್ಕಹಾಕಲಾಗುತ್ತದೆ: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) ಇಲ್ಲಿ f ಎಂಬುದು ಕೆಲವು ರೇಖೀಯವಲ್ಲದ **ಸಕ್ರಿಯ ಕಾರ್ಯ**. diff --git a/translations/kn/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/kn/lessons/4-ComputerVision/06-IntroCV/README.md index 22286261..d0cdf17b 100644 --- a/translations/kn/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/kn/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ OpenCV ಬಳಸಿ ವಿಡಿಯೋವನ್ನು ಫ್ರೇಮ್-ಬೈ- * **ಬ್ರೈಲ್ ಪುಸ್ತಕದ ಫೋಟೋವನ್ನು ಪೂರ್ವ-ಸಂಸ್ಕರಿಸುವುದು**. ನಾವು ಥ್ರೆಶೋಲ್ಡಿಂಗ್, ವೈಶಿಷ್ಟ್ಯ ಪತ್ತೆ, ಪರ್ಸ್ಪೆಕ್ಟಿವ್ ಪರಿವರ್ತನೆ ಮತ್ತು NumPy ಮ್ಯಾನಿಪ್ಯುಲೇಶನ್‌ಗಳನ್ನು ಬಳಸಿಕೊಂಡು ಪ್ರತ್ಯೇಕ ಬ್ರೈಲ್ ಚಿಹ್ನೆಗಳನ್ನು neural network ಮೂಲಕ ವರ್ಗೀಕರಿಸಲು ಹೇಗೆ ಪ್ರಕ್ರಿಯೆ ಮಾಡಬಹುದು ಎಂಬುದರ ಮೇಲೆ ಗಮನಹರಿಸುತ್ತೇವೆ. -![Braille Image](../../../../../translated_images/kn/braille.341962ff76b1bd70.jpeg) | ![Braille Image Pre-processed](../../../../../translated_images/kn/braille-result.46530fea020b03c7.png) | ![Braille Symbols](../../../../../translated_images/kn/braille-symbols.0159185ab69d5339.png) +![Braille Image](../../../../../translated_images/kn/braille.341962ff76b1bd70.webp) | ![Braille Image Pre-processed](../../../../../translated_images/kn/braille-result.46530fea020b03c7.webp) | ![Braille Symbols](../../../../../translated_images/kn/braille-symbols.0159185ab69d5339.webp) ----|-----|----- > ಚಿತ್ರ [OpenCV.ipynb](OpenCV.ipynb) ನಿಂದ * **ವೀಡಿಯೋದಲ್ಲಿ ಚಲನವಲನ ಪತ್ತೆಹಚ್ಚುವುದು ಫ್ರೇಮ್ ವ್ಯತ್ಯಾಸ ಬಳಸಿ**. ಕ್ಯಾಮೆರಾ ಸ್ಥಿರವಾಗಿದ್ದರೆ, ಕ್ಯಾಮೆರಾ ಫೀಡ್‌ನ ಫ್ರೇಮ್‌ಗಳು ಪರಸ್ಪರ ಬಹಳ ಸಮಾನವಾಗಿರುತ್ತವೆ. ಫ್ರೇಮ್‌ಗಳು ಅರೆಗಳಾಗಿ ಪ್ರತಿನಿಧಿಸಲ್ಪಟ್ಟಿರುವುದರಿಂದ, ಎರಡು ಕ್ರಮಬದ್ಧ ಫ್ರೇಮ್‌ಗಳ ಅರೆಗಳನ್ನು ವಜಾ ಮಾಡಿದರೆ ಪಿಕ್ಸೆಲ್ ವ್ಯತ್ಯಾಸ ಸಿಗುತ್ತದೆ, ಇದು ಸ್ಥಿರ ಫ್ರೇಮ್‌ಗಳಿಗೆ ಕಡಿಮೆ ಮತ್ತು ಚಲನೆಯಾಗಿರುವ ಚಿತ್ರಗಳಿಗೆ ಹೆಚ್ಚು ಆಗುತ್ತದೆ. -![Image of video frames and frame differences](../../../../../translated_images/kn/frame-difference.706f805491a0883c.png) +![Image of video frames and frame differences](../../../../../translated_images/kn/frame-difference.706f805491a0883c.webp) > ಚಿತ್ರ [OpenCV.ipynb](OpenCV.ipynb) ನಿಂದ @@ -89,7 +89,7 @@ OpenCV ಬಳಸಿ ವಿಡಿಯೋವನ್ನು ಫ್ರೇಮ್-ಬೈ- - **Dense Optical Flow** ಪ್ರತಿ ಪಿಕ್ಸೆಲ್ ಯಾವ ಕಡೆಗೆ ಚಲಿಸುತ್ತಿದೆ ಎಂಬುದನ್ನು ತೋರಿಸುವ ವೆಕ್ಟರ್ ಕ್ಷೇತ್ರವನ್ನು ಲೆಕ್ಕಹಾಕುತ್ತದೆ - **Sparse Optical Flow** ಚಿತ್ರದಲ್ಲಿ ಕೆಲವು ವಿಶಿಷ್ಟ ಲಕ್ಷಣಗಳನ್ನು (ಉದಾ: ಅಂಚುಗಳು) ತೆಗೆದು, ಅವುಗಳ ಪಥವನ್ನು ಫ್ರೇಮ್‌ಗಳಿಂದ ಫ್ರೇಮ್‌ಗೆ ನಿರ್ಮಿಸುತ್ತದೆ. -![Image of Optical Flow](../../../../../translated_images/kn/optical.1f4a94464579a83a.png) +![Image of Optical Flow](../../../../../translated_images/kn/optical.1f4a94464579a83a.webp) > ಚಿತ್ರ [OpenCV.ipynb](OpenCV.ipynb) ನಿಂದ @@ -115,7 +115,7 @@ AI ಶೋದಿಂದ [ಈ ವೀಡಿಯೋವನ್ನು](https://docs.micro ಈ ಪ್ರಯೋಗಶಾಲೆಯಲ್ಲಿ, ನೀವು ಸರಳ ಸಂಕೆತಗಳೊಂದಿಗೆ ಒಂದು ವೀಡಿಯೋ ತೆಗೆದುಕೊಳ್ಳುತ್ತೀರಿ, ಮತ್ತು ನಿಮ್ಮ ಗುರಿ ಆಪ್ಟಿಕಲ್ ಫ್ಲೋ ಬಳಸಿ ಮೇಲಕ್ಕೆ/ಕೆಳಗೆ/ಎಡಕ್ಕೆ/ಬಲಕ್ಕೆ ಚಲನೆಗಳನ್ನು ಹೊರತೆಗೆಯುವುದು. -Palm Movement Frame +Palm Movement Frame --- diff --git a/translations/kn/lessons/4-ComputerVision/06-IntroCV/lab/MovementDetection.ipynb b/translations/kn/lessons/4-ComputerVision/06-IntroCV/lab/MovementDetection.ipynb index 8f9d02c7..cb5fc315 100644 --- a/translations/kn/lessons/4-ComputerVision/06-IntroCV/lab/MovementDetection.ipynb +++ b/translations/kn/lessons/4-ComputerVision/06-IntroCV/lab/MovementDetection.ipynb @@ -10,7 +10,7 @@ "\n", "ಸ್ಥಿರ ಹಿನ್ನೆಲೆಯ ಮೇಲೆ ಒಬ್ಬ ವ್ಯಕ್ತಿಯ ಕೈಬುಟ್ಟಿ ಎಡಕ್ಕೆ/ಬಲಕ್ಕೆ/ಮೇಲಕ್ಕೆ/ಕೆಳಗೆ ಚಲಿಸುವ [ಈ ವೀಡಿಯೋ](../../../../../../lessons/4-ComputerVision/06-IntroCV/lab/palm-movement.mp4) ಯನ್ನು ಪರಿಗಣಿಸಿ.\n", "\n", - "\"Palm\n", + "\"Palm\n", "\n", "**ನಿಮ್ಮ ಗುರಿ** ಆಪ್ಟಿಕಲ್ ಫ್ಲೋ ಬಳಸಿ, ವೀಡಿಯೋದಲ್ಲಿ ಯಾವ ಭಾಗಗಳು ಮೇಲಕ್ಕೆ/ಕೆಳಗೆ/ಎಡಕ್ಕೆ/ಬಲಕ್ಕೆ ಚಲಿಸುತ್ತಿವೆ ಎಂದು ನಿರ್ಧರಿಸುವುದು.\n", "\n", diff --git a/translations/kn/lessons/4-ComputerVision/06-IntroCV/lab/README.md b/translations/kn/lessons/4-ComputerVision/06-IntroCV/lab/README.md index 6684b504..8e1c3b78 100644 --- a/translations/kn/lessons/4-ComputerVision/06-IntroCV/lab/README.md +++ b/translations/kn/lessons/4-ComputerVision/06-IntroCV/lab/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: [ಈ ವೀಡಿಯೋ](../../../../../../lessons/4-ComputerVision/06-IntroCV/lab/palm-movement.mp4) ಯನ್ನು ಪರಿಗಣಿಸಿ, ಇದರಲ್ಲಿ ಒಬ್ಬ ವ್ಯಕ್ತಿಯ ಹಸ್ತದ ತೊಡೆಯು ಸ್ಥಿರ ಹಿನ್ನೆಲೆಯ ಮೇಲೆ ಎಡಕ್ಕೆ/ಬಲಕ್ಕೆ/ಮೇಲಕ್ಕೆ/ಕೆಳಗೆ ಚಲಿಸುತ್ತದೆ. -ಹಸ್ತದ ಚಲನೆ ಫ್ರೇಮ್ +ಹಸ್ತದ ಚಲನೆ ಫ್ರೇಮ್ **ನಿಮ್ಮ ಗುರಿ** ಆಪ್ಟಿಕಲ್ ಫ್ಲೋ ಬಳಸಿ ವೀಡಿಯೋದಲ್ಲಿ ಯಾವ ಭಾಗಗಳಲ್ಲಿ ಮೇಲಕ್ಕೆ/ಕೆಳಗೆ/ಎಡಕ್ಕೆ/ಬಲಕ್ಕೆ ಚಲನೆಗಳಿವೆ ಎಂದು ನಿರ್ಧರಿಸುವುದು. diff --git a/translations/kn/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/kn/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 264a8379..8bea7d1e 100644 --- a/translations/kn/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/kn/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 2014 ರಲ್ಲಿ ImageNet ಟಾಪ್-5 ವರ್ಗೀಕರಣದಲ್ಲಿ 92.7% ನಿಖರತೆಯನ್ನು ಸಾಧಿಸಿದ ನೆಟ್‌ವರ್ಕ್ ಆಗಿದೆ. ಇದರ ಲೇಯರ್ ರಚನೆ ಹೀಗಿದೆ: -![ImageNet Layers](../../../../../translated_images/kn/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet Layers](../../../../../translated_images/kn/vgg-16-arch1.d901a5583b3a51ba.webp) ನೀವು ನೋಡಬಹುದು, VGG ಪರಂಪರাগত ಪಿರಮಿಡ್ ವಾಸ್ತುಶಿಲ್ಪವನ್ನು ಅನುಸರಿಸುತ್ತದೆ, ಇದು ಸಂಯೋಜನೆ-ಪೂಲಿಂಗ್ ಲೇಯರ್‌ಗಳ ಸರಣಿಯಾಗಿದೆ. -![ImageNet Pyramid](../../../../../translated_images/kn/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet Pyramid](../../../../../translated_images/kn/vgg-16-arch.64ff2137f50dd49f.webp) > ಚಿತ್ರ [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) ನಿಂದ @@ -25,7 +25,7 @@ VGG-16 2014 ರಲ್ಲಿ ImageNet ಟಾಪ್-5 ವರ್ಗೀಕರಣದ ResNet 2015 ರಲ್ಲಿ Microsoft Research ಪ್ರಸ್ತಾಪಿಸಿದ ಮಾದರಿಗಳ ಕುಟುಂಬವಾಗಿದೆ. ResNet ನ ಮುಖ್ಯ ಆಲೋಚನೆ **residual blocks** ಬಳಕೆ: - + > ಚಿತ್ರ [ಈ ಪೇಪರ್](https://arxiv.org/pdf/1512.03385.pdf) ನಿಂದ @@ -37,7 +37,7 @@ ResNet 2015 ರಲ್ಲಿ Microsoft Research ಪ್ರಸ್ತಾಪಿಸಿ Google Inception ವಾಸ್ತುಶಿಲ್ಪ ಈ ಆಲೋಚನೆಯನ್ನು ಇನ್ನೊಂದು ಹಂತಕ್ಕೆ ತೆಗೆದುಕೊಂಡು ಹೋಗುತ್ತದೆ ಮತ್ತು ಪ್ರತಿ ನೆಟ್‌ವರ್ಕ್ ಲೇಯರ್ ಅನ್ನು ಹಲವು ವಿಭಿನ್ನ ಮಾರ್ಗಗಳ ಸಂಯೋಜನೆಯಾಗಿ ನಿರ್ಮಿಸುತ್ತದೆ: - + > ಚಿತ್ರ [Researchgate](https://www.researchgate.net/figure/Inception-module-with-dimension-reductions-left-and-schema-for-Inception-ResNet-v1_fig2_355547454) ನಿಂದ diff --git a/translations/kn/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/kn/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index 0a71ddb0..47e4bb99 100644 --- a/translations/kn/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/kn/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -260,7 +260,7 @@ "\n", "ಹೀಗಾಗಿ, ಸಾಮಾನ್ಯ CNNನಲ್ಲಿ ಹಲವಾರು ಕನ್ವಲ್ಯೂಷನಲ್ ಲೇಯರ್‌ಗಳು ಇರುತ್ತವೆ, ಅವುಗಳ ನಡುವೆ ಪೂಲಿಂಗ್ ಲೇಯರ್‌ಗಳು ಚಿತ್ರದ ಆಯಾಮಗಳನ್ನು ಕಡಿಮೆ ಮಾಡಲು ಇರುತ್ತವೆ. ನಾವು ಫಿಲ್ಟರ್‌ಗಳ ಸಂಖ್ಯೆಯನ್ನು ಕೂಡ ಹೆಚ್ಚಿಸುತ್ತೇವೆ, ಏಕೆಂದರೆ ಮಾದರಿಗಳು ಹೆಚ್ಚು ಉನ್ನತ ಮಟ್ಟದಾಗುತ್ತವೆ - ನಾವು ಹುಡುಕಬೇಕಾದ ಹೆಚ್ಚಿನ ಆಸಕ್ತಿದಾಯಕ ಸಂಯೋಜನೆಗಳಿವೆ.\n", "\n", - "![ಹಲವಾರು ಕನ್ವಲ್ಯೂಷನಲ್ ಲೇಯರ್‌ಗಳು ಮತ್ತು ಪೂಲಿಂಗ್ ಲೇಯರ್‌ಗಳನ್ನು ತೋರಿಸುವ ಚಿತ್ರ.](../../../../../translated_images/kn/cnn-pyramid.85915455759ef0ce.png)\n", + "![ಹಲವಾರು ಕನ್ವಲ್ಯೂಷನಲ್ ಲೇಯರ್‌ಗಳು ಮತ್ತು ಪೂಲಿಂಗ್ ಲೇಯರ್‌ಗಳನ್ನು ತೋರಿಸುವ ಚಿತ್ರ.](../../../../../translated_images/kn/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "ಸ್ಥಳೀಯ ಆಯಾಮಗಳು ಕಡಿಮೆಯಾಗುತ್ತಾ ಮತ್ತು ವೈಶಿಷ್ಟ್ಯ/ಫಿಲ್ಟರ್ ಆಯಾಮಗಳು ಹೆಚ್ಚಾಗುತ್ತಾ ಇರುವುದರಿಂದ, ಈ ವಾಸ್ತುಶಿಲ್ಪವನ್ನು **ಪಿರಮಿಡ್ ವಾಸ್ತುಶಿಲ್ಪ** ಎಂದು ಕೂಡ ಕರೆಯುತ್ತಾರೆ.\n" ] diff --git a/translations/kn/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/kn/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index 84b9bcab..c4683a7e 100644 --- a/translations/kn/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/kn/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -340,7 +340,7 @@ "\n", "ಹೀಗಾಗಿ, ಸಾಮಾನ್ಯ CNNನಲ್ಲಿ ಹಲವಾರು ಕನ್ವಲ್ಯೂಷನಲ್ ಲೇಯರ್‌ಗಳು ಇರುತ್ತವೆ, ಅವುಗಳ ನಡುವೆ ಪೂಲಿಂಗ್ ಲೇಯರ್‌ಗಳು ಚಿತ್ರದ ಆಯಾಮಗಳನ್ನು ಕಡಿಮೆ ಮಾಡಲು ಇರುತ್ತವೆ. ನಾವು ಫಿಲ್ಟರ್‌ಗಳ ಸಂಖ್ಯೆಯನ್ನು ಹೆಚ್ಚಿಸುತ್ತೇವೆ, ಏಕೆಂದರೆ ಮಾದರಿಗಳು ಹೆಚ್ಚು ಸುಧಾರಿತವಾಗುತ್ತವೆ - ನಾವು ಹುಡುಕಬೇಕಾದ ಹೆಚ್ಚಿನ ಆಸಕ್ತಿದಾಯಕ ಸಂಯೋಜನೆಗಳಿವೆ.\n", "\n", - "![ಹಲವಾರು ಕನ್ವಲ್ಯೂಷನಲ್ ಲೇಯರ್‌ಗಳು ಮತ್ತು ಪೂಲಿಂಗ್ ಲೇಯರ್‌ಗಳನ್ನು ತೋರಿಸುವ ಚಿತ್ರ.](../../../../../translated_images/kn/cnn-pyramid.85915455759ef0ce.png)\n", + "![ಹಲವಾರು ಕನ್ವಲ್ಯೂಷನಲ್ ಲೇಯರ್‌ಗಳು ಮತ್ತು ಪೂಲಿಂಗ್ ಲೇಯರ್‌ಗಳನ್ನು ತೋರಿಸುವ ಚಿತ್ರ.](../../../../../translated_images/kn/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "ಸ್ಥಳೀಯ ಆಯಾಮಗಳನ್ನು ಕಡಿಮೆ ಮಾಡುವುದು ಮತ್ತು ವೈಶಿಷ್ಟ್ಯ/ಫಿಲ್ಟರ್ ಆಯಾಮಗಳನ್ನು ಹೆಚ್ಚಿಸುವುದರಿಂದ, ಈ ವಾಸ್ತುಶಿಲ್ಪವನ್ನು **ಪಿರಮಿಡ್ ವಾಸ್ತುಶಿಲ್ಪ** ಎಂದು ಕರೆಯಲಾಗುತ್ತದೆ.\n" ] diff --git a/translations/kn/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/kn/lessons/4-ComputerVision/07-ConvNets/README.md index f1697343..8e7fb4c8 100644 --- a/translations/kn/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/kn/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,14 +17,14 @@ CO_OP_TRANSLATOR_METADATA: ನಮೂನೆಗಳನ್ನು ಹೊರತೆಗೆಯಲು, ನಾವು **ಕನ್ವಲ್ಯೂಷನಲ್ ಫಿಲ್ಟರ್‌ಗಳು** ಎಂಬ ಕಲ್ಪನೆಯನ್ನು ಬಳಸುತ್ತೇವೆ. ನೀವು ತಿಳಿದಿರುವಂತೆ, ಚಿತ್ರವನ್ನು 2D-ಮ್ಯಾಟ್ರಿಕ್ಸ್ ಅಥವಾ ಬಣ್ಣ ಆಳದ 3D-ಟೆನ್ಸರ್ ಮೂಲಕ ಪ್ರತಿನಿಧಿಸಲಾಗುತ್ತದೆ. ಫಿಲ್ಟರ್ ಅನ್ವಯಿಸುವುದು ಎಂದರೆ, ನಾವು ಸಾಪೇಕ್ಷವಾಗಿ ಸಣ್ಣ **ಫಿಲ್ಟರ್ ಕರ್ಣಲ್** ಮ್ಯಾಟ್ರಿಕ್ಸ್ ತೆಗೆದುಕೊಳ್ಳುತ್ತೇವೆ, ಮತ್ತು ಮೂಲ ಚಿತ್ರದಲ್ಲಿನ ಪ್ರತಿ ಪಿಕ್ಸೆಲ್‌ಗೆ ಸಮೀಪದ ಬಿಂದುಗಳೊಂದಿಗೆ ತೂಕಿತ ಸರಾಸರಿಯನ್ನು ಲೆಕ್ಕಿಸುತ್ತೇವೆ. ಇದನ್ನು ನಾವು ಒಂದು ಸಣ್ಣ ಕಿಟಕಿ ಚಿತ್ರದ ಮೇಲೆ ಸ್ಲೈಡ್ ಆಗುತ್ತಾ, ಫಿಲ್ಟರ್ ಕರ್ಣಲ್ ಮ್ಯಾಟ್ರಿಕ್ಸ್‌ನ ತೂಕಗಳ ಪ್ರಕಾರ ಎಲ್ಲಾ ಪಿಕ್ಸೆಲ್‌ಗಳನ್ನು ಸರಾಸರಿಗೊಳಿಸುವಂತೆ ನೋಡಬಹುದು. -![Vertical Edge Filter](../../../../../translated_images/kn/filter-vert.b7148390ca0bc356.png) | ![Horizontal Edge Filter](../../../../../translated_images/kn/filter-horiz.59b80ed4feb946ef.png) +![Vertical Edge Filter](../../../../../translated_images/kn/filter-vert.b7148390ca0bc356.webp) | ![Horizontal Edge Filter](../../../../../translated_images/kn/filter-horiz.59b80ed4feb946ef.webp) ----|---- > ಚಿತ್ರ: Dmitry Soshnikov ಉದಾಹರಣೆಗೆ, ನಾವು 3x3 ಲಂಬ ಮತ್ತು ಆಡುವ ಎಡ್ಜ್ ಫಿಲ್ಟರ್‌ಗಳನ್ನು MNIST ಅಂಕಿಗಳ ಮೇಲೆ ಅನ್ವಯಿಸಿದರೆ, ಮೂಲ ಚಿತ್ರದಲ್ಲಿ ಲಂಬ ಮತ್ತು ಆಡುವ ಎಡ್ಜ್‌ಗಳಿರುವ ಸ್ಥಳಗಳಲ್ಲಿ ಹೈಲೈಟ್ಸ್ (ಹೆಚ್ಚಿನ ಮೌಲ್ಯಗಳು) ಸಿಗುತ್ತವೆ. ಆದ್ದರಿಂದ ಆ ಎರಡು ಫಿಲ್ಟರ್‌ಗಳನ್ನು ಎಡ್ಜ್‌ಗಳನ್ನು "ಹುಡುಕಲು" ಬಳಸಬಹುದು. ಹಾಗೆಯೇ, ನಾವು ಬೇರೆ ಕಡಿಮೆ ಮಟ್ಟದ ನಮೂನೆಗಳನ್ನು ಹುಡುಕಲು ವಿಭಿನ್ನ ಫಿಲ್ಟರ್‌ಗಳನ್ನು ವಿನ್ಯಾಸಗೊಳಿಸಬಹುದು: - + > [Leung-Malik ಫಿಲ್ಟರ್ ಬ್ಯಾಂಕ್](https://www.robots.ox.ac.uk/~vgg/research/texclass/filters.html) ಚಿತ್ರ @@ -38,7 +38,7 @@ CNNಗಳು ಕಾರ್ಯನಿರ್ವಹಿಸುವ ವಿಧಾನವು * ಫಿಲ್ಟರ್‌ಗಳನ್ನು ಸ್ವಯಂಚಾಲಿತವಾಗಿ ತರಬೇತುಗೊಳಿಸುವಂತೆ ನೆಟ್‌ವರ್ಕ್ ವಿನ್ಯಾಸಗೊಳಿಸಬಹುದು * ನಾವು ಮೂಲ ಚಿತ್ರದಲ್ಲಿನ ಮಾತ್ರವಲ್ಲ, ಹೆಚ್ಚಿನ ಮಟ್ಟದ ವೈಶಿಷ್ಟ್ಯಗಳಲ್ಲಿ ನಮೂನೆಗಳನ್ನು ಹುಡುಕಲು ಇದೇ ವಿಧಾನವನ್ನು ಬಳಸಬಹುದು. ಆದ್ದರಿಂದ CNN ವೈಶಿಷ್ಟ್ಯ ಹೊರತೆಗೆಯುವಿಕೆ ಕಡಿಮೆ ಮಟ್ಟದ ಪಿಕ್ಸೆಲ್ ಸಂಯೋಜನೆಗಳಿಂದ ಪ್ರಾರಂಭಿಸಿ, ಚಿತ್ರ ಭಾಗಗಳ ಹೆಚ್ಚಿನ ಮಟ್ಟದ ಸಂಯೋಜನೆಗಳವರೆಗೆ ವೈಶಿಷ್ಟ್ಯಗಳ ಹಿರarchy ಮೇಲೆ ಕಾರ್ಯನಿರ್ವಹಿಸುತ್ತದೆ. -![Hierarchical Feature Extraction](../../../../../translated_images/kn/FeatureExtractionCNN.d9b456cbdae7cb64.png) +![Hierarchical Feature Extraction](../../../../../translated_images/kn/FeatureExtractionCNN.d9b456cbdae7cb64.webp) > [Hislop-Lynch ಅವರ ಪೇಪರ್](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d) ನಿಂದ ಚಿತ್ರ, ಅವರ [ಶೋಧನೆ](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) ಆಧಾರಿತ @@ -55,9 +55,9 @@ CNNಗಳು ಕಾರ್ಯನಿರ್ವಹಿಸುವ ವಿಧಾನವು ಉದಾಹರಣೆಗೆ, 2014 ರಲ್ಲಿ ImageNet ಟಾಪ್-5 ವರ್ಗೀಕರಣದಲ್ಲಿ 92.7% ನಿಖರತೆಯನ್ನು ಸಾಧಿಸಿದ VGG-16 ನೆಟ್‌ವರ್ಕ್‌ನ ವಾಸ್ತುಶಿಲ್ಪವನ್ನು ನೋಡೋಣ: -![ImageNet Layers](../../../../../translated_images/kn/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet Layers](../../../../../translated_images/kn/vgg-16-arch1.d901a5583b3a51ba.webp) -![ImageNet Pyramid](../../../../../translated_images/kn/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet Pyramid](../../../../../translated_images/kn/vgg-16-arch.64ff2137f50dd49f.webp) > [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) ನಿಂದ ಚಿತ್ರ diff --git a/translations/kn/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/kn/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 5054e953..2984dded 100644 --- a/translations/kn/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/kn/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ CO_OP_TRANSLATOR_METADATA: ನಾವು [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/) ಅನ್ನು ಬಳಸಲಿದ್ದೇವೆ, ಇದರಲ್ಲಿ 37 ವಿಭಿನ್ನ ನಾಯಿ ಮತ್ತು ಬೆಕ್ಕು ಜಾತಿಗಳ ಚಿತ್ರಗಳಿವೆ. -![ನಾವು ಕೈಗಾರಿಕೆ ಮಾಡಲಿರುವ ಡೇಟಾಸೆಟ್](../../../../../../translated_images/kn/data.50b2a9d5484bdbf0.png) +![ನಾವು ಕೈಗಾರಿಕೆ ಮಾಡಲಿರುವ ಡೇಟಾಸೆಟ್](../../../../../../translated_images/kn/data.50b2a9d5484bdbf0.webp) ಡೇಟಾಸೆಟ್ ಡೌನ್‌ಲೋಡ್ ಮಾಡಲು, ಈ ಕೋಡ್ ಸ್ನಿಪೆಟ್ ಬಳಸಿ: diff --git a/translations/kn/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/kn/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index b0702fb7..3e106697 100644 --- a/translations/kn/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/kn/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "ಆದರ್ಶ ಬೆಕ್ಕನ್ನು ದೃಶ್ಯೀಕರಿಸಲು, ನಾವು ಯಾದೃಚ್ಛಿಕ ಶಬ್ದ ಚಿತ್ರದಿಂದ ಪ್ರಾರಂಭಿಸಿ, ಗ್ರೇಡಿಯಂಟ್ ಡಿಸೆಂಟ್ ಆಪ್ಟಿಮೈಜೆಷನ್ ತಂತ್ರವನ್ನು ಬಳಸಿಕೊಂಡು ಚಿತ್ರವನ್ನು ಸರಿಹೊಂದಿಸಿ ನೆಟ್‌ವರ್ಕ್‌ಗೆ ಬೆಕ್ಕನ್ನು ಗುರುತಿಸಲು ಪ್ರಯತ್ನಿಸುವೆವು.\n", "\n", - "![Optimization Loop](../../../../../translated_images/kn/ideal-cat-loop.999fbb8ff306e044.png)\n", + "![Optimization Loop](../../../../../translated_images/kn/ideal-cat-loop.999fbb8ff306e044.webp)\n", "\n", "ಇದು ನಮ್ಮ ಪ್ರಾರಂಭಿಕ ಚಿತ್ರ:\n" ] diff --git a/translations/kn/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/kn/lessons/4-ComputerVision/08-TransferLearning/README.md index 73d1383c..30c3b5dd 100644 --- a/translations/kn/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/kn/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ CNNಗಳನ್ನು ತರಬೇತಿಗೊಳಿಸಲು ಬಹಳ ಸಮ ಇಲ್ಲಿ VGG-16 ನೆಟ್‌ವರ್ಕ್ ಮೂಲಕ ಬೆಕ್ಕಿನ ಚಿತ್ರದಿಂದ ಹೊರತೆಗೆಯಲಾದ ಕೆಲವು ಲಕ್ಷಣಗಳ ಉದಾಹರಣೆ ಇದೆ: -![Features extracted by VGG-16](../../../../../translated_images/kn/features.6291f9c7ba3a0b95.png) +![Features extracted by VGG-16](../../../../../translated_images/kn/features.6291f9c7ba3a0b95.webp) ## ಬೆಕ್ಕು ಮತ್ತು ನಾಯಿ ಡೇಟಾಸೆಟ್ @@ -48,19 +48,19 @@ CNNಗಳನ್ನು ತರಬೇತಿಗೊಳಿಸಲು ಬಹಳ ಸಮ ನಾವು ತೆಗೆದುಕೊಳ್ಳಬಹುದಾದ ಒಂದು ವಿಧಾನವೆಂದರೆ, ಯಾದೃಚ್ಛಿಕ ಚಿತ್ರದಿಂದ ಪ್ರಾರಂಭಿಸಿ, ನಂತರ ಆ ಚಿತ್ರವನ್ನು **ಗ್ರೇಡಿಯಂಟ್ ಡಿಸೆಂಟ್ ಆಪ್ಟಿಮೈಜೆಷನ್** ತಂತ್ರವನ್ನು ಬಳಸಿ ಸರಿಹೊಂದಿಸುವುದು, ಹೀಗೆ ನೆಟ್‌ವರ್ಕ್ ಅದನ್ನು ಬೆಕ್ಕಾಗಿ ಭಾವಿಸಲು ಪ್ರಾರಂಭಿಸುತ್ತದೆ. -![Image Optimization Loop](../../../../../translated_images/kn/ideal-cat-loop.999fbb8ff306e044.png) +![Image Optimization Loop](../../../../../translated_images/kn/ideal-cat-loop.999fbb8ff306e044.webp) ಆದರೆ, ನಾವು ಇದನ್ನು ಮಾಡಿದರೆ, ಯಾದೃಚ್ಛಿಕ ಶಬ್ದದಂತೆ ಕಾಣುವ ಏನನ್ನಾದರೂ ಪಡೆಯುತ್ತೇವೆ. ಇದಕ್ಕೆ ಕಾರಣವೆಂದರೆ *ನೆಟ್‌ವರ್ಕ್ ಇನ್ಪುಟ್ ಚಿತ್ರವನ್ನು ಬೆಕ್ಕಾಗಿ ಭಾವಿಸಲು ಅನೇಕ ಮಾರ್ಗಗಳಿವೆ*, ಅವುಗಳಲ್ಲಿ ಕೆಲವು ದೃಶ್ಯವಾಗಿ ಅರ್ಥವಿಲ್ಲದವುಗಳೂ ಇರುತ್ತವೆ. ಆ ಚಿತ್ರಗಳಲ್ಲಿ ಬೆಕ್ಕಿಗೆ ಸಾಮಾನ್ಯವಾದ ಹಲವಾರು ಮಾದರಿಗಳು ಇದ್ದರೂ, ಅವು ದೃಶ್ಯವಾಗಿ ವಿಭಿನ್ನವಾಗಿರಬೇಕೆಂದು ಯಾವುದೇ ನಿಯಂತ್ರಣವಿಲ್ಲ. ಫಲಿತಾಂಶವನ್ನು ಸುಧಾರಿಸಲು, ನಾವು ನಷ್ಟ ಕಾರ್ಯದಲ್ಲಿ ಮತ್ತೊಂದು ಪದವನ್ನು ಸೇರಿಸಬಹುದು, ಅದನ್ನು **ವೈವಿಧ್ಯ ನಷ್ಟ** ಎಂದು ಕರೆಯುತ್ತಾರೆ. ಇದು ಚಿತ್ರದಲ್ಲಿನ ಹತ್ತಿರದ ಪಿಕ್ಸೆಲ್‌ಗಳು ಎಷ್ಟು ಸಮಾನವಾಗಿವೆ ಎಂಬುದನ್ನು ತೋರಿಸುವ ಮಾನದಂಡ. ವೈವಿಧ್ಯ ನಷ್ಟವನ್ನು ಕಡಿಮೆ ಮಾಡುವುದು ಚಿತ್ರವನ್ನು ಮೃದುಗೊಳಿಸುತ್ತದೆ ಮತ್ತು ಶಬ್ದವನ್ನು ದೂರ ಮಾಡುತ್ತದೆ - ಹೀಗಾಗಿ ದೃಶ್ಯವಾಗಿ ಆಕರ್ಷಕ ಮಾದರಿಗಳನ್ನು ಬಹಿರಂಗಪಡಿಸುತ್ತದೆ. ಇಲ್ಲಿ "ಆದರ್ಶ" ಚಿತ್ರಗಳ ಉದಾಹರಣೆ ಇದೆ, ಅವು ಬೆಕ್ಕಾಗಿ ಮತ್ತು ಜೆಬ್ರಾ ಆಗಿ ಹೆಚ್ಚಿನ ಸಾಧ್ಯತೆಯಿಂದ ವರ್ಗೀಕರಿಸಲ್ಪಟ್ಟಿವೆ: -![Ideal Cat](../../../../../translated_images/kn/ideal-cat.203dd4597643d6b0.png) | ![Ideal Zebra](../../../../../translated_images/kn/ideal-zebra.7f70e8b54ee15a7a.png) +![Ideal Cat](../../../../../translated_images/kn/ideal-cat.203dd4597643d6b0.webp) | ![Ideal Zebra](../../../../../translated_images/kn/ideal-zebra.7f70e8b54ee15a7a.webp) -----|----- *ಆದರ್ಶ ಬೆಕ್ಕು* | *ಆದರ್ಶ ಜೆಬ್ರಾ* ಇದೇ ವಿಧಾನವನ್ನು ನ್ಯೂರಲ್ ನೆಟ್‌ವರ್ಕ್ ಮೇಲೆ **ವಿರೋಧಾತ್ಮಕ ದಾಳಿಗಳು** ನಡೆಸಲು ಬಳಸಬಹುದು. ನಾವು ನ್ಯೂರಲ್ ನೆಟ್‌ವರ್ಕ್ ಅನ್ನು ಮೋಸಗೊಳಿಸಿ ನಾಯಿಯನ್ನು ಬೆಕ್ಕಾಗಿ ತೋರಿಸಲು ಬಯಸಿದರೆ, ನೆಟ್‌ವರ್ಕ್ ನಾಯಿಯಾಗಿ ಗುರುತಿಸಿದ ನಾಯಿಯ ಚಿತ್ರವನ್ನು ತೆಗೆದುಕೊಂಡು, ಗ್ರೇಡಿಯಂಟ್ ಡಿಸೆಂಟ್ ಆಪ್ಟಿಮೈಜೆಷನ್ ಬಳಸಿ ಸ್ವಲ್ಪ ತಿದ್ದುಪಡಿ ಮಾಡಬಹುದು, ಹೀಗೆ ನೆಟ್‌ವರ್ಕ್ ಅದನ್ನು ಬೆಕ್ಕಾಗಿ ವರ್ಗೀಕರಿಸಲು ಪ್ರಾರಂಭಿಸುತ್ತದೆ: -![Picture of a Dog](../../../../../translated_images/kn/original-dog.8f68a67d2fe0911f.png) | ![Picture of a dog classified as a cat](../../../../../translated_images/kn/adversarial-dog.d9fc7773b0142b89.png) +![Picture of a Dog](../../../../../translated_images/kn/original-dog.8f68a67d2fe0911f.webp) | ![Picture of a dog classified as a cat](../../../../../translated_images/kn/adversarial-dog.d9fc7773b0142b89.webp) -----|----- *ನಾಯಿಯ ಮೂಲ ಚಿತ್ರ* | *ಬೆಕ್ಕಾಗಿ ವರ್ಗೀಕರಿಸಲ್ಪಟ್ಟ ನಾಯಿಯ ಚಿತ್ರ* diff --git a/translations/kn/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/kn/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index 34b43f53..45e63a1e 100644 --- a/translations/kn/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/kn/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "ನಾವು ಆಟೋಎನ್‌ಕೋಡರ್ ಅನ್ನು ಮೂಲ ಚಿತ್ರದಿಂದ ಸಾಧ್ಯವಾದಷ್ಟು ಮಾಹಿತಿ ಹಿಡಿಯಲು ತರಬೇತಿಗೊಳಿಸುತ್ತಿದ್ದೇವೆ, ಆದ್ದರಿಂದ ನೆಟ್‌ವರ್ಕ್ ಇನ್‌ಪುಟ್ ಚಿತ್ರಗಳ ಅರ್ಥವನ್ನು ಹಿಡಿಯಲು ಅತ್ಯುತ್ತಮ **ಎಂಬೆಡ್ಡಿಂಗ್** ಅನ್ನು ಹುಡುಕಲು ಪ್ರಯತ್ನಿಸುತ್ತದೆ.\n", "\n", - "![AutoEncoder Diagram](../../../../../translated_images/kn/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![AutoEncoder Diagram](../../../../../translated_images/kn/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "> ಚಿತ್ರ [Keras ಬ್ಲಾಗ್](https://blog.keras.io/building-autoencoders-in-keras.html) ನಿಂದ\n", "\n", @@ -941,7 +941,7 @@ " * ನಾವು $N(\\mathrm{z\\_mean},e^{\\mathrm{z\\_log\\_sigma}})$ ವಿತರಣೆಯಿಂದ `sample(z_val in code)` ಎಂಬ ವೆಕ್ಟರ್ ಅನ್ನು ಮಾದರಿಮಾಡುತ್ತೇವೆ\n", " * ಡಿಕೋಡರ್ `sample` ಅನ್ನು ಇನ್‌ಪುಟ್ ವೆಕ್ಟರ್ ಆಗಿ ಬಳಸಿಕೊಂಡು ಮೂಲ ಚಿತ್ರವನ್ನು ಡಿಕೋಡ್ ಮಾಡಲು ಪ್ರಯತ್ನಿಸುತ್ತದೆ\n", "\n", - " \n", + " \n", "\n", " > ಚಿತ್ರ [ಈ ಬ್ಲಾಗ್ ಪೋಸ್ಟ್](https://ijdykeman.github.io/ml/2016/12/21/cvae.html) ನಿಂದ, ಇಸಾಕ್ ಡೈಕೆಮನ್ ಅವರಿಂದ\n" ] @@ -1264,7 +1264,7 @@ "\n", "ಈ ವಿಧಾನದಲ್ಲಿ ನಮಗೆ **ಮೂರು ನಷ್ಟ ಕಾರ್ಯಗಳು** ಇವೆ: GAN ಗಳಿಂದ ಜನರೇಟರ್ ನಷ್ಟ, ಡಿಸ್ಕ್ರಿಮಿನೇಟರ್ ನಷ್ಟ ಮತ್ತು VAE ನಿಂದ ಪುನರ್ ನಿರ್ಮಾಣ ನಷ್ಟ.\n", "\n", - "\n", + "\n", "\n", "> ಚಿತ್ರ [ಈ ಬ್ಲಾಗ್ ಪೋಸ್ಟ್](https://blog.paperspace.com/adversarial-autoencoders-with-pytorch/) ನಿಂದ ಫೆಲಿಪೆ ಡುಕೋ ಅವರಿಂದ\n" ] diff --git a/translations/kn/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/kn/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index 864cfaa4..794f697d 100644 --- a/translations/kn/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/kn/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -21,7 +21,7 @@ "\n", "ನಾವು ಆಟೋಎನ್‌ಕೋಡರ್ ಅನ್ನು ಮೂಲ ಚಿತ್ರದಿಂದ ಸಾಧ್ಯವಾದಷ್ಟು ಮಾಹಿತಿ ಹಿಡಿಯಲು ತರಬೇತುಗೊಳಿಸುತ್ತಿದ್ದೇವೆ, ಆದ್ದರಿಂದ ನೆಟ್‌ವರ್ಕ್ ಇನ್‌ಪುಟ್ ಚಿತ್ರಗಳ ಅರ್ಥವನ್ನು ಹಿಡಿಯಲು ಅತ್ಯುತ್ತಮ **ಎಂಬೆಡ್ಡಿಂಗ್** ಅನ್ನು ಹುಡುಕಲು ಪ್ರಯತ್ನಿಸುತ್ತದೆ.\n", "\n", - "![AutoEncoder Diagram](../../../../../translated_images/kn/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![AutoEncoder Diagram](../../../../../translated_images/kn/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "*ಚಿತ್ರ [Keras ಬ್ಲಾಗ್](https://blog.keras.io/building-autoencoders-in-keras.html) ನಿಂದ*\n", "\n", @@ -888,7 +888,7 @@ " * ನಾವು $N(\\mathrm{z\\_mean},e^{\\mathrm{z\\_log\\_sigma}})$ ವಿತರಣೆಯಿಂದ `sample` ಎಂಬ ವೆಕ್ಟರ್ ಅನ್ನು ಮಾದರಿಮಾಡುತ್ತೇವೆ\n", " * ಡಿಕೋಡರ್ `sample` ಅನ್ನು ಇನ್‌ಪುಟ್ ವೆಕ್ಟರ್ ಆಗಿ ಬಳಸಿಕೊಂಡು ಮೂಲ ಚಿತ್ರವನ್ನು ಡಿಕೋಡ್ ಮಾಡಲು ಪ್ರಯತ್ನಿಸುತ್ತದೆ\n", "\n", - " \n" + " \n" ] }, { diff --git a/translations/kn/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/kn/lessons/4-ComputerVision/09-Autoencoders/README.md index e2757a15..557fb346 100644 --- a/translations/kn/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/kn/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ CNNಗಳನ್ನು ತರಬೇತುಗೊಳಿಸುವಾಗ, ಒಂದ ನಾವು ಮೂಲ ಚಿತ್ರದಿಂದ ಸಾಧ್ಯವಾದಷ್ಟು ಮಾಹಿತಿ ಹಿಡಿಯಲು ಆಟೋಎನ್‌ಕೋಡರ್ ತರಬೇತುಗೊಳಿಸುತ್ತಿದ್ದೇವೆ, ಆದ್ದರಿಂದ ನೆಟ್‌ವರ್ಕ್ ಇನ್‌ಪುಟ್ ಚಿತ್ರಗಳ ಅರ್ಥವನ್ನು ಹಿಡಿಯಲು ಉತ್ತಮ **ಎಂಬೆಡ್ಡಿಂಗ್** ಅನ್ನು ಹುಡುಕಲು ಪ್ರಯತ್ನಿಸುತ್ತದೆ. -![AutoEncoder Diagram](../../../../../translated_images/kn/autoencoder_schema.5e6fc9ad98a5eb61.jpg) +![AutoEncoder Diagram](../../../../../translated_images/kn/autoencoder_schema.5e6fc9ad98a5eb61.webp) > ಚಿತ್ರ [Keras ಬ್ಲಾಗ್](https://blog.keras.io/building-autoencoders-in-keras.html) ನಿಂದ @@ -46,7 +46,7 @@ VAE ಒಂದು ಆಟೋಎನ್‌ಕೋಡರ್ ಆಗಿದ್ದು, ಲ * ನಾವು ವಿತರಣೆಯಿಂದ `sample` ಎಂಬ ವೆಕ್ಟರ್ ಅನ್ನು ಆಯ್ಕೆ ಮಾಡುತ್ತೇವೆ N(zmean,exp(zlog_sigma)) * ಡಿಕೋಡರ್ `sample` ಅನ್ನು ಇನ್‌ಪುಟ್ ವೆಕ್ಟರ್ ಆಗಿ ಬಳಸಿಕೊಂಡು ಮೂಲ ಚಿತ್ರವನ್ನು ಮರುನಿರ್ಮಿಸಲು ಪ್ರಯತ್ನಿಸುತ್ತದೆ - + > ಚಿತ್ರ [ಈ ಬ್ಲಾಗ್ ಪೋಸ್ಟ್](https://ijdykeman.github.io/ml/2016/12/21/cvae.html) ನಿಂದ, ಇಸಾಕ್ ಡೈಕೆಮನ್ ರಚನೆ @@ -57,13 +57,13 @@ VAE ಒಂದು ಆಟೋಎನ್‌ಕೋಡರ್ ಆಗಿದ್ದು, ಲ VAEಗಳ ಪ್ರಮುಖ ಲಾಭವೆಂದರೆ, ನಾವು ಲ್ಯಾಟೆಂಟ್ ವೆಕ್ಟರ್‌ಗಳನ್ನು ಯಾವ ವಿತರಣೆಯಿಂದ ಆಯ್ಕೆ ಮಾಡಬೇಕೆಂದು ತಿಳಿದಿರುವುದರಿಂದ, ಹೊಸ ಚಿತ್ರಗಳನ್ನು ಸುಲಭವಾಗಿ ರಚಿಸಬಹುದು. ಉದಾಹರಣೆಗೆ, 2D ಲ್ಯಾಟೆಂಟ್ ವೆಕ್ಟರ್‌ನೊಂದಿಗೆ MNIST ಮೇಲೆ VAE ತರಬೇತಿಗೊಳಿಸಿದರೆ, ಲ್ಯಾಟೆಂಟ್ ವೆಕ್ಟರ್‌ನ ಅಂಶಗಳನ್ನು ಬದಲಾಯಿಸಿ ವಿಭಿನ್ನ ಅಂಕಿಗಳನ್ನು ಪಡೆಯಬಹುದು: -vaemnist +vaemnist > ಚಿತ್ರ [ಡ್ಮಿತ್ರಿ ಸೋಶ್ನಿಕೋವ್](http://soshnikov.com) ರಚನೆ ಲ್ಯಾಟೆಂಟ್ ಪರಿಮಾಣ ಸ್ಥಳದ ವಿಭಿನ್ನ ಭಾಗಗಳಿಂದ ಲ್ಯಾಟೆಂಟ್ ವೆಕ್ಟರ್‌ಗಳನ್ನು ಪಡೆಯಲು ಪ್ರಾರಂಭಿಸಿದಂತೆ ಚಿತ್ರಗಳು ಪರಸ್ಪರ ಮಿಶ್ರಣವಾಗುತ್ತಿರುವುದನ್ನು ಗಮನಿಸಿ. ನಾವು ಈ ಸ್ಥಳವನ್ನು 2Dಯಲ್ಲಿ ದೃಶ್ಯೀಕರಿಸಬಹುದು: -vaemnist cluster +vaemnist cluster > ಚಿತ್ರ [ಡ್ಮಿತ್ರಿ ಸೋಶ್ನಿಕೋವ್](http://soshnikov.com) ರಚನೆ diff --git a/translations/kn/lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb b/translations/kn/lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb index b429009c..96a8672e 100644 --- a/translations/kn/lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb +++ b/translations/kn/lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb @@ -15,7 +15,7 @@ "* **ಜನರೇಟರ್** ಒಂದು ಯಾದೃಚ್ಛಿಕ ವೆಕ್ಟರ್ ಅನ್ನು ತೆಗೆದು, ಅದರಿಂದ ಒಂದು ಚಿತ್ರವನ್ನು ರಚಿಸಬೇಕು\n", "* **ಡಿಸ್ಕ್ರಿಮಿನೇಟರ್** ಒಂದು ನೆಟ್‌ವರ್ಕ್ ಆಗಿದ್ದು, ಮೂಲ ಚಿತ್ರ (ತರಬೇತಿ ಡೇಟಾಸೆಟ್‌ನಿಂದ) ಮತ್ತು ಜನರೇಟರ್ ರಚಿಸಿದ ಚಿತ್ರವನ್ನು ವಿಭಿನ್ನಗೊಳಿಸಬೇಕು.\n", "\n", - "\n" + "\n" ] }, { @@ -670,7 +670,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n", + "\n", "\n", "> ಚಿತ್ರ [ಈ ಟ್ಯುಟೋರಿಯಲ್](https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html) ನಿಂದ\n" ] diff --git a/translations/kn/lessons/4-ComputerVision/10-GANs/GANTF.ipynb b/translations/kn/lessons/4-ComputerVision/10-GANs/GANTF.ipynb index 12976264..350bdd75 100644 --- a/translations/kn/lessons/4-ComputerVision/10-GANs/GANTF.ipynb +++ b/translations/kn/lessons/4-ComputerVision/10-GANs/GANTF.ipynb @@ -15,7 +15,7 @@ "* **ಜನರೇಟರ್** ಒಂದು ಯಾದೃಚ್ಛಿಕ ವೆಕ್ಟರ್ ಅನ್ನು ತೆಗೆದು, ಅದರಿಂದ ಚಿತ್ರವನ್ನು ರಚಿಸಬೇಕು\n", "* **ಡಿಸ್ಕ್ರಿಮಿನೇಟರ್** ಒಂದು ನೆಟ್‌ವರ್ಕ್ ಆಗಿದ್ದು, ಮೂಲ ಚಿತ್ರ (ತರಬೇತಿ ಡೇಟಾಸೆಟ್‌ನಿಂದ) ಮತ್ತು ಜನರೇಟರ್ ರಚಿಸಿದ ಚಿತ್ರವನ್ನು ವಿಭಿನ್ನಗೊಳಿಸಬೇಕು.\n", "\n", - "\n" + "\n" ] }, { diff --git a/translations/kn/lessons/4-ComputerVision/10-GANs/README.md b/translations/kn/lessons/4-ComputerVision/10-GANs/README.md index 0fea65ea..971ddbe7 100644 --- a/translations/kn/lessons/4-ComputerVision/10-GANs/README.md +++ b/translations/kn/lessons/4-ComputerVision/10-GANs/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: GANನ ಮುಖ್ಯ ಕಲ್ಪನೆ ಎರಡು ನ್ಯೂರಲ್ ನೆಟ್‌ವರ್ಕ್‌ಗಳನ್ನು ಪರಸ್ಪರ ಎದುರಾಗಿ ತರಬೇತಿಗೊಳಿಸುವುದು: - + > ಚಿತ್ರ: [Dmitry Soshnikov](http://soshnikov.com) @@ -41,7 +41,7 @@ CNN ಡಿಸ್ಕ್ರಿಮಿನೇಟರ್‌ನಲ್ಲಿ ಹಲವಾ > ✅ ಕನ್ವಲ್ಯೂಷನ್ ಪದರವು ಚಿತ್ರವನ್ನು ತಲುಪುವ ರೇಖೀಯ ಫಿಲ್ಟರ್ ಆಗಿರುವುದರಿಂದ, ಡಿಕನ್ವಲ್ಯೂಷನ್ ಮೂಲತಃ ಕನ್ವಲ್ಯೂಷನ್‌ಗೆ ಸಮಾನವಾಗಿದ್ದು, ಅದೇ ಪದರ ಲಾಜಿಕ್ ಬಳಸಿ ಅನುಷ್ಠಾನಗೊಳ್ಳಬಹುದು. - + > ಚಿತ್ರ: [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/kn/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/kn/lessons/4-ComputerVision/11-ObjectDetection/README.md index 5d169ba9..459210ef 100644 --- a/translations/kn/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/kn/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: ## [ಪೂರ್ವ-ಪಾಠ ಪ್ರಶ್ನೋತ್ತರ](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![ವಸ್ತು ಪತ್ತೆ](../../../../../translated_images/kn/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) +![ವಸ್ತು ಪತ್ತೆ](../../../../../translated_images/kn/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp) > ಚಿತ್ರ [YOLO v2 ವೆಬ್ ಸೈಟ್](https://pjreddie.com/darknet/yolov2/) ನಿಂದ @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 2. ಪ್ರತಿ ಟೈಲ್ನಲ್ಲಿ ಚಿತ್ರ ವರ್ಗೀಕರಣವನ್ನು ನಡೆಸಿ. 3. ಸಾಕಷ್ಟು ಉತ್ಸಾಹದೊಂದಿಗೆ ಫಲಿತಾಂಶ ನೀಡುವ ಟೈಲ್ಗಳನ್ನು ಆ ವಸ್ತು ಹೊಂದಿದೆ ಎಂದು ಪರಿಗಣಿಸಬಹುದು. -![ಸರಳ ವಸ್ತು ಪತ್ತೆ](../../../../../translated_images/kn/naive-detection.e7f1ba220ccd08c6.png) +![ಸರಳ ವಸ್ತು ಪತ್ತೆ](../../../../../translated_images/kn/naive-detection.e7f1ba220ccd08c6.webp) > *ಚಿತ್ರ [ಅಭ್ಯಾಸ ನೋಟ್ಬುಕ್](ObjectDetection-TF.ipynb) ನಿಂದ* @@ -42,7 +42,7 @@ CO_OP_TRANSLATOR_METADATA: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 ವರ್ಗಗಳು * [COCO](http://cocodataset.org/#home) - ಸಾಮಾನ್ಯ ವಸ್ತುಗಳು ಸನ್ನಿವೇಶದಲ್ಲಿ. 80 ವರ್ಗಗಳು, ಸುತ್ತುವರೆದ ಬಾಕ್ಸ್‌ಗಳು ಮತ್ತು ವಿಭಾಗ ಚಿಹ್ನೆಗಳು -![COCO](../../../../../translated_images/kn/coco-examples.71bc60380fa6cceb.jpg) +![COCO](../../../../../translated_images/kn/coco-examples.71bc60380fa6cceb.webp) ## ವಸ್ತು ಪತ್ತೆ ಮೌಲ್ಯಮಾಪನ @@ -50,7 +50,7 @@ CO_OP_TRANSLATOR_METADATA: ಚಿತ್ರ ವರ್ಗೀಕರಣದಲ್ಲಿ ಆಲ್ಗಾರಿದಮ್ ಎಷ್ಟು ಚೆನ್ನಾಗಿ ಕಾರ್ಯನಿರ್ವಹಿಸುತ್ತದೆ ಎಂದು ಅಳೆಯುವುದು ಸುಲಭ, ಆದರೆ ವಸ್ತು ಪತ್ತೆಯಲ್ಲಿ ವರ್ಗದ ಸರಿಯಾದತೆ ಮತ್ತು ಸುತ್ತುವರೆದ ಬಾಕ್ಸ್ ನಿಖರತೆಯನ್ನು ಎರಡನ್ನೂ ಅಳೆಯಬೇಕಾಗುತ್ತದೆ. ನಂತರದದಕ್ಕಾಗಿ ನಾವು **Intersection over Union** (IoU) ಅನ್ನು ಬಳಸುತ್ತೇವೆ, ಇದು ಎರಡು ಬಾಕ್ಸ್‌ಗಳು (ಅಥವಾ ಎರಡು ಯಾವುದೇ ಪ್ರದೇಶಗಳು) ಎಷ್ಟು ಒಟ್ಟಿಗೆ ಬರುತ್ತವೆ ಎಂದು ಅಳೆಯುತ್ತದೆ. -![IoU](../../../../../translated_images/kn/iou_equation.9a4751d40fff4e11.png) +![IoU](../../../../../translated_images/kn/iou_equation.9a4751d40fff4e11.webp) > *ಚಿತ್ರ 2 [ಈ ಅದ್ಭುತ ಬ್ಲಾಗ್ ಪೋಸ್ಟ್](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/) ನಿಂದ* @@ -97,11 +97,11 @@ $$ [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) ಬಳಸಿ ROI ಪ್ರದೇಶಗಳ ಹೈರಾರ್ಕಿಕ ರಚನೆಯನ್ನು ರಚಿಸುತ್ತದೆ, ಅವುಗಳನ್ನು ನಂತರ CNN ವೈಶಿಷ್ಟ್ಯ ಸಂಗ್ರಾಹಕ ಮತ್ತು SVM ವರ್ಗೀಕರಣಕಾರರ ಮೂಲಕ ವಸ್ತು ವರ್ಗವನ್ನು ನಿರ್ಧರಿಸಲು ಮತ್ತು ರೇಖೀಯ ರಿಗ್ರೆಷನ್ ಮೂಲಕ *ಸುತ್ತುವರೆದ ಬಾಕ್ಸ್* ಸಂಯೋಜನೆಗಳನ್ನು ಊಹಿಸಲು ಬಳಸಲಾಗುತ್ತದೆ. [ಅಧಿಕೃತ ಪೇಪರ್](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/kn/rcnn1.cae407020dfb1d1f.png) +![RCNN](../../../../../translated_images/kn/rcnn1.cae407020dfb1d1f.webp) > *ಚಿತ್ರ van de Sande et al. ICCV’11 ನಿಂದ* -![RCNN-1](../../../../../translated_images/kn/rcnn2.2d9530bb83516484.png) +![RCNN-1](../../../../../translated_images/kn/rcnn2.2d9530bb83516484.webp) > *ಚಿತ್ರಗಳು [ಈ ಬ್ಲಾಗ್](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e) ನಿಂದ* @@ -109,7 +109,7 @@ $$ ಈ ವಿಧಾನ R-CNN ಗೆ ಸಮಾನ, ಆದರೆ ಪ್ರದೇಶಗಳನ್ನು ಕನ್ವಲ್ಯೂಷನ್ ಲೇಯರ್‌ಗಳ ನಂತರ ನಿರ್ಧರಿಸಲಾಗುತ್ತದೆ. -![FRCNN](../../../../../translated_images/kn/f-rcnn.3cda6d9bb4188875.png) +![FRCNN](../../../../../translated_images/kn/f-rcnn.3cda6d9bb4188875.webp) > ಚಿತ್ರ [ಅಧಿಕೃತ ಪೇಪರ್](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 ನಿಂದ @@ -117,7 +117,7 @@ $$ ಈ ವಿಧಾನದಲ್ಲಿ ಮುಖ್ಯ ಯೋಚನೆ ROI ಗಳನ್ನು ಊಹಿಸಲು ನ್ಯೂರಲ್ ನೆಟ್‌ವರ್ಕ್ ಬಳಸುವುದು - ಇದನ್ನು *ಪ್ರದೇಶ ಪ್ರಸ್ತಾವನೆ ಜಾಲ* ಎಂದು ಕರೆಯುತ್ತಾರೆ. [ಪೇಪರ್](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/kn/faster-rcnn.8d46c099b87ef30a.png) +![FasterRCNN](../../../../../translated_images/kn/faster-rcnn.8d46c099b87ef30a.webp) > ಚಿತ್ರ [ಅಧಿಕೃತ ಪೇಪರ್](https://arxiv.org/pdf/1506.01497.pdf) ನಿಂದ @@ -129,7 +129,7 @@ $$ 2. ವೈಶಿಷ್ಟ್ಯಗಳನ್ನು **ಸ್ಥಾನ-ಸಂವೇದನಾತ್ಮಕ ಸ್ಕೋರ್ ನಕ್ಷೆ** ಮೂಲಕ ಪ್ರಕ್ರಿಯೆಗೊಳಿಸುವುದು. $C$ ವರ್ಗಗಳ ಪ್ರತಿಯೊಂದು ವಸ್ತುವನ್ನು $k\times k$ ಪ್ರದೇಶಗಳಾಗಿ ವಿಭಜಿಸಿ, ವಸ್ತುಗಳ ಭಾಗಗಳನ್ನು ಊಹಿಸಲು ತರಬೇತಿ ನೀಡಲಾಗುತ್ತದೆ. 3. $k\times k$ ಪ್ರದೇಶಗಳ ಪ್ರತಿಯೊಂದು ಭಾಗಕ್ಕೆ ಎಲ್ಲಾ ಜಾಲಗಳು ವಸ್ತು ವರ್ಗಗಳಿಗೆ ಮತದಾನ ಮಾಡುತ್ತವೆ, ಗರಿಷ್ಠ ಮತ ಪಡೆದ ವಸ್ತು ವರ್ಗ ಆಯ್ಕೆಮಾಡಲಾಗುತ್ತದೆ. -![r-fcn image](../../../../../translated_images/kn/r-fcn.13eb88158b99a3da.png) +![r-fcn image](../../../../../translated_images/kn/r-fcn.13eb88158b99a3da.webp) > ಚಿತ್ರ [ಅಧಿಕೃತ ಪೇಪರ್](https://arxiv.org/abs/1605.06409) ನಿಂದ @@ -140,7 +140,7 @@ YOLO ಒಂದು ರಿಯಲ್-ಟೈಮ್ ಒಂದು-ಪಾಸ್ ಆಲ * ಚಿತ್ರವನ್ನು $S\times S$ ಪ್ರದೇಶಗಳಾಗಿ ವಿಭಜಿಸುವುದು * ಪ್ರತಿ ಪ್ರದೇಶಕ್ಕೆ, **CNN** $n$ ಸಾಧ್ಯವಿರುವ ವಸ್ತುಗಳನ್ನು, *ಸುತ್ತುವರೆದ ಬಾಕ್ಸ್* ಸಂಯೋಜನೆಗಳನ್ನು ಮತ್ತು *ನಂಬಿಕೆ* = *ಸಂಭಾವ್ಯತೆ* * IoU ಅನ್ನು ಊಹಿಸುವುದು. - ![YOLO](../../../../../translated_images/kn/yolo.a2648ec82ee8bb4e.png) + ![YOLO](../../../../../translated_images/kn/yolo.a2648ec82ee8bb4e.webp) > ಚಿತ್ರ [ಅಧಿಕೃತ ಪೇಪರ್](https://arxiv.org/abs/1506.02640) ನಿಂದ diff --git a/translations/kn/lessons/4-ComputerVision/12-Segmentation/README.md b/translations/kn/lessons/4-ComputerVision/12-Segmentation/README.md index 4e1e25df..2c994073 100644 --- a/translations/kn/lessons/4-ComputerVision/12-Segmentation/README.md +++ b/translations/kn/lessons/4-ComputerVision/12-Segmentation/README.md @@ -20,7 +20,7 @@ CO_OP_TRANSLATOR_METADATA: ಘಟಕ ವಿಭಾಗೀಕರಣದಲ್ಲಿ, ಈ ಕುರಿಗಳು ವಿಭಿನ್ನ ವಸ್ತುಗಳಾಗಿವೆ, ಆದರೆ ಸಾಮಾನ್ಯ ವಿಭಾಗೀಕರಣದಲ್ಲಿ ಎಲ್ಲಾ ಕುರಿಗಳು ಒಂದೇ ವರ್ಗದಿಂದ ಪ್ರತಿನಿಧಿಸಲಾಗುತ್ತವೆ. - + > ಚಿತ್ರ [ಈ ಬ್ಲಾಗ್ ಪೋಸ್ಟ್](https://nirmalamurali.medium.com/image-classification-vs-semantic-segmentation-vs-instance-segmentation-625c33a08d50) ನಿಂದ @@ -29,7 +29,7 @@ CO_OP_TRANSLATOR_METADATA: * **ಎನ್‌ಕೋಡರ್** ಇನ್‌ಪುಟ್ ಚಿತ್ರದಿಂದ ಲಕ್ಷಣಗಳನ್ನು ತೆಗೆದುಕೊಳ್ಳುತ್ತದೆ * **ಡಿಕೋಡರ್** ಆ ಲಕ್ಷಣಗಳನ್ನು **ಮಾಸ್ಕ್ ಚಿತ್ರ** ಆಗಿ ಪರಿವರ್ತಿಸುತ್ತದೆ, ಅದೇ ಗಾತ್ರ ಮತ್ತು ವರ್ಗಗಳ ಸಂಖ್ಯೆಗೆ ಹೊಂದಿಕೊಂಡ ಚಾನೆಲ್‌ಗಳೊಂದಿಗೆ. - + > ಚಿತ್ರ [ಈ ಪ್ರಕಟಣೆಯಿಂದ](https://arxiv.org/pdf/2001.05566.pdf) @@ -43,7 +43,7 @@ CO_OP_TRANSLATOR_METADATA: > ✅ ಈ ತಂತ್ರಜ್ಞಾನ ವೈದ್ಯಕೀಯ ಚಿತ್ರಣಕ್ಕೆ ವಿಶೇಷವಾಗಿ ಸೂಕ್ತವಾಗಿದೆ, ಆದರೆ ನೀವು ಇನ್ನೇನು ನೈಜ ಜಗತ್ತಿನ ಅನ್ವಯಗಳನ್ನು ಊಹಿಸಬಹುದು? -navi +navi > ಚಿತ್ರ PH2 ಡೇಟಾಬೇಸ್‌ನಿಂದ diff --git a/translations/kn/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb b/translations/kn/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb index a6e7286a..5f47d6f9 100644 --- a/translations/kn/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb +++ b/translations/kn/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb @@ -17,7 +17,7 @@ "\n", "ಉದಾಹರಣೆಗೆ, ಘಟಕ ವಿಭಾಗೀಕರಣದಲ್ಲಿ 10 ಕುರಿಗಳು ವಿಭಿನ್ನ ವಸ್ತುಗಳಾಗಿವೆ, ಆದರೆ ಅರ್ಥಪೂರ್ಣ ವಿಭಾಗೀಕರಣದಲ್ಲಿ ಎಲ್ಲಾ ಕುರಿಗಳು ಒಂದೇ ವರ್ಗದಿಂದ ಪ್ರತಿನಿಧಿಸಲಾಗುತ್ತವೆ.\n", "\n", - "\n", + "\n", "\n", "> ಚಿತ್ರ [ಈ ಬ್ಲಾಗ್ ಪೋಸ್ಟ್](https://nirmalamurali.medium.com/image-classification-vs-semantic-segmentation-vs-instance-segmentation-625c33a08d50) ನಿಂದ\n", "\n", @@ -26,7 +26,7 @@ "* **ಎನ್‌ಕೋಡರ್** ಇನ್‌ಪುಟ್ ಚಿತ್ರದಿಂದ ವೈಶಿಷ್ಟ್ಯಗಳನ್ನು ತೆಗೆದುಕೊಳ್ಳುತ್ತದೆ\n", "* **ಡಿಕೋಡರ್** ಆ ವೈಶಿಷ್ಟ್ಯಗಳನ್ನು **ಮಾಸ್ಕ್ ಚಿತ್ರ** ಆಗಿ ಪರಿವರ್ತಿಸುತ್ತದೆ, ಅದೇ ಗಾತ್ರ ಮತ್ತು ವರ್ಗಗಳ ಸಂಖ್ಯೆಗೆ ಹೊಂದಿಕೊಂಡ ಚಾನೆಲ್‌ಗಳೊಂದಿಗೆ.\n", "\n", - "\n", + "\n", "\n", "> ಚಿತ್ರ [ಈ ಪ್ರಕಟಣೆಯಿಂದ](https://arxiv.org/pdf/2001.05566.pdf)\n" ] @@ -252,7 +252,7 @@ "\n", "ಎಲ್ಲಾದರೂ ಸರಳವಾದ ಎನ್‌ಕೋಡರ್-ಡಿಕೋಡರ್ ವಾಸ್ತುಶಿಲ್ಪವನ್ನು **SegNet** ಎಂದು ಕರೆಯುತ್ತಾರೆ. ಇದು ಎನ್‌ಕೋಡರ್‌ನಲ್ಲಿ ಸಂಪ್ರದಾಯಬದ್ಧ CNN ಅನ್ನು ಸಂವೇದನೆಗಳು ಮತ್ತು ಪೂಲಿಂಗ್‌ಗಳೊಂದಿಗೆ ಬಳಸುತ್ತದೆ, ಮತ್ತು ಡಿಕೋಡರ್‌ನಲ್ಲಿ ಸಂವೇದನೆಗಳು ಮತ್ತು ಅಪ್ಸ್ಯಾಂಪಿಂಗ್‌ಗಳನ್ನು ಒಳಗೊಂಡ ಡಿಕೋನ್ವಲ್ಯೂಷನ್ CNN ಅನ್ನು ಬಳಸುತ್ತದೆ. ಬಹು-ಮಟ್ಟದ ನೆಟ್‌ವರ್ಕ್ ಅನ್ನು ಯಶಸ್ವಿಯಾಗಿ ತರಬೇತುಗೊಳಿಸಲು ಇದು ಬ್ಯಾಚ್ ನಾರ್ಮಲೈಜೆಶನ್ ಮೇಲೆ ಅವಲಂಬಿತವಾಗಿದೆ.\n", "\n", - "\n", + "\n", "\n", "> ಈ ಚಿತ್ರ ಈ ಪೇಪರ್‌ನಿಂದ: Badrinarayanan, V., Kendall, A., & Cipolla, R. (2015). [SegNet: A deep convolutional\n", "encoder-decoder architecture for image segmentation](https://arxiv.org/pdf/1511.00561.pdf)\n" @@ -548,7 +548,7 @@ "\n", "ನಾವು ಇಲ್ಲಿ ಬಹಳ ಸರಳ CNN ವಿನ್ಯಾಸವನ್ನು ಬಳಸುತ್ತೇವೆ, ಆದರೆ U-Net ಹೆಚ್ಚು ಸಂಕೀರ್ಣ ಎನ್‌ಕೋಡರ್ ಅನ್ನು ವೈಶಿಷ್ಟ್ಯಗಳ ಹೊರತೆಗೆಯಲು ಬಳಸಬಹುದು, ಉದಾಹರಣೆಗೆ ResNet-50.\n", "\n", - "\n", + "\n", "\n", "> ಚಿತ್ರ ಮೂಲ: Ronneberger, Olaf, Philipp Fischer, ಮತ್ತು Thomas Brox. [U-Net: Convolutional networks for biomedical image segmentation.](https://arxiv.org/pdf/1505.04597.pdf)\n" ] diff --git a/translations/kn/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb b/translations/kn/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb index ec55f9c4..dddb72b6 100644 --- a/translations/kn/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb +++ b/translations/kn/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb @@ -12,13 +12,13 @@ "\n", "ಉದಾಹರಣೆಗೆ, ಇನ್ಸ್ಟಾನ್ಸ್ ಸೆಗ್ಮೆಂಟೇಶನ್‌ನಲ್ಲಿ ಹತ್ತು ಕಾರುಗಳು **ವಿಭಿನ್ನ** ವಸ್ತುಗಳಾಗಿವೆ, ಆದರೆ ಸಿಮೆಂಟಿಕ್ ಸೆಗ್ಮೆಂಟೇಶನ್‌ನಲ್ಲಿ **ಎಲ್ಲಾ** ಕಾರುಗಳು ಒಂದೇ ವರ್ಗ.\n", "\n", - "\n", + "\n", "\n", "> ಚಿತ್ರ [ಈ ಬ್ಲಾಗ್ ಪೋಸ್ಟ್](https://nirmalamurali.medium.com/image-classification-vs-semantic-segmentation-vs-instance-segmentation-625c33a08d50) ನಿಂದ\n", "\n", "ಸುಮಾರು ಎಲ್ಲಾ ವಾಸ್ತುಶಿಲ್ಪಗಳು ಒಂದೇ ರಚನೆಯನ್ನು ಹೊಂದಿವೆ. ಮೊದಲ ಭಾಗವು **ಎನ್‌ಕೋಡರ್** ಆಗಿದ್ದು, ಇನ್‌ಪುಟ್ ಚಿತ್ರದಿಂದ ವೈಶಿಷ್ಟ್ಯಗಳನ್ನು ತೆಗೆದುಕೊಳ್ಳುತ್ತದೆ, ಎರಡನೇ ಭಾಗವು **ಡಿಕೋಡರ್** ಆಗಿದ್ದು, ಈ ವೈಶಿಷ್ಟ್ಯಗಳನ್ನು ಅದೇ ಎತ್ತರ ಮತ್ತು ಅಗಲದ ಚಿತ್ರವಾಗಿ ಮತ್ತು ಕೆಲವು ಚಾನೆಲ್‌ಗಳ ಸಂಖ್ಯೆಯೊಂದಿಗೆ (ವರ್ಗಗಳ ಸಂಖ್ಯೆಗೆ ಸಮಾನವಾಗಬಹುದು) ಪರಿವರ್ತಿಸುತ್ತದೆ.\n", "\n", - "\n", + "\n", "\n", "> ಚಿತ್ರ [ಈ ಪ್ರಕಟಣೆಯಿಂದ](https://arxiv.org/pdf/2001.05566.pdf)\n" ] @@ -210,7 +210,7 @@ "\n", "ಸರಳ ಎನ್‌ಕೋಡರ್ - ಡಿಕೋಡರ್ ವಾಸ್ತುಶಿಲ್ಪ, ಎನ್‌ಕೋಡರ್‌ನಲ್ಲಿ ಕಾಂವೊಲ್ಯೂಶನ್ಗಳು ಮತ್ತು ಪೂಲಿಂಗ್‌ಗಳು, ಡಿಕೋಡರ್‌ನಲ್ಲಿ ಕಾಂವೊಲ್ಯೂಶನ್ಗಳು ಮತ್ತು ಅಪ್ಸ್ಯಾಂಪ್ಲಿಂಗ್‌ಗಳೊಂದಿಗೆ.\n", "\n", - "\n", + "\n", "\n", "* ಬದ್ರಿನಾರಾಯಣನ್, ವಿ., ಕೆಂಡಾಲ್, ಎ., & ಸಿಪೊಲ್ಲಾ, ಆರ್. (2015). [SegNet: ಚಿತ್ರ ವಿಭಾಗಕ್ಕಾಗಿ ಆಳವಾದ ಕಾಂವೊಲ್ಯೂಶನಲ್ ಎನ್‌ಕೋಡರ್-ಡಿಕೋಡರ್ ವಾಸ್ತುಶಿಲ್ಪ](https://arxiv.org/pdf/1511.00561.pdf)\n" ] @@ -601,7 +601,7 @@ "\n", "U-Net ಸಾಮಾನ್ಯವಾಗಿ ವೈಶಿಷ್ಟ್ಯಗಳನ್ನು ಹೊರತೆಗೆಯಲು ಡೀಫಾಲ್ಟ್ ಎನ್‌ಕೋಡರ್ ಹೊಂದಿರುತ್ತದೆ, ಉದಾಹರಣೆಗೆ resnet50.\n", "\n", - "\n", + "\n", "\n", "* Ronneberger, Olaf, Philipp Fischer, ಮತ್ತು Thomas Brox. [U-Net: Convolutional networks for biomedical image segmentation.](https://arxiv.org/pdf/1505.04597.pdf)\n" ] diff --git a/translations/kn/lessons/4-ComputerVision/README.md b/translations/kn/lessons/4-ComputerVision/README.md index dc0a579a..6e173c13 100644 --- a/translations/kn/lessons/4-ComputerVision/README.md +++ b/translations/kn/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # ಕಂಪ್ಯೂಟರ್ ವೀಕ್ಷಣೆ -![ಕಂಪ್ಯೂಟರ್ ವೀಕ್ಷಣೆ ವಿಷಯದ ಸಾರಾಂಶ ಡೂಡಲ್‌ನಲ್ಲಿ](../../../../translated_images/kn/ai-computervision.6506ebebac3fbf76.png) +![ಕಂಪ್ಯೂಟರ್ ವೀಕ್ಷಣೆ ವಿಷಯದ ಸಾರಾಂಶ ಡೂಡಲ್‌ನಲ್ಲಿ](../../../../translated_images/kn/ai-computervision.6506ebebac3fbf76.webp) ಈ ವಿಭಾಗದಲ್ಲಿ ನಾವು ಕಲಿಯಲಿದ್ದೇವೆ: diff --git a/translations/kn/lessons/5-NLP/13-TextRep/README.md b/translations/kn/lessons/5-NLP/13-TextRep/README.md index 54e08905..4a66c780 100644 --- a/translations/kn/lessons/5-NLP/13-TextRep/README.md +++ b/translations/kn/lessons/5-NLP/13-TextRep/README.md @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: ನಾವು ನ್ಯೂರಲ್ ನೆಟ್‌ವರ್ಕ್‌ಗಳೊಂದಿಗೆ ನೈಸರ್ಗಿಕ ಭಾಷಾ ಪ್ರಕ್ರಿಯೆ (NLP) ಕಾರ್ಯಗಳನ್ನು ಪರಿಹರಿಸಲು ಬಯಸಿದರೆ, ಪಠ್ಯವನ್ನು ಟೆನ್ಸರ್‌ಗಳಾಗಿ ಪ್ರತಿನಿಧಿಸುವ ವಿಧಾನ ಬೇಕಾಗುತ್ತದೆ. ಕಂಪ್ಯೂಟರ್‌ಗಳು ಈಗಾಗಲೇ ASCII ಅಥವಾ UTF-8 ಎಂಬ ಎನ್ಕೋಡಿಂಗ್‌ಗಳನ್ನು ಬಳಸಿ ನಿಮ್ಮ ಪರದೆ上的 ಫಾಂಟ್‌ಗಳಿಗೆ ನಕ್ಷೆ ಮಾಡಲಾದ ಸಂಖ್ಯೆಗಳಾಗಿ ಪಠ್ಯ ಅಕ್ಷರಗಳನ್ನು ಪ್ರತಿನಿಧಿಸುತ್ತವೆ. -ಅಕ್ಷರವನ್ನು ASCII ಮತ್ತು ಬೈನರಿ ಪ್ರತಿನಿಧಿಸುವ ಡಯಾಗ್ರಾಂ ತೋರಿಸುವ ಚಿತ್ರ +ಅಕ್ಷರವನ್ನು ASCII ಮತ್ತು ಬೈನರಿ ಪ್ರತಿನಿಧಿಸುವ ಡಯಾಗ್ರಾಂ ತೋರಿಸುವ ಚಿತ್ರ > [ಚಿತ್ರ ಮೂಲ](https://www.seobility.net/en/wiki/ASCII) @@ -48,7 +48,7 @@ CO_OP_TRANSLATOR_METADATA: ಪಠ್ಯ ವರ್ಗೀಕರಣದಂತಹ ಕಾರ್ಯಗಳನ್ನು ಪರಿಹರಿಸುವಾಗ, ನಾವು ಪಠ್ಯವನ್ನು ಒಂದು ನಿಶ್ಚಿತ ಗಾತ್ರದ ವೆಕ್ಟರ್ ಮೂಲಕ ಪ್ರತಿನಿಧಿಸಬೇಕಾಗುತ್ತದೆ, ಇದನ್ನು ಅಂತಿಮ ಡೆನ್ಸ್ ವರ್ಗೀಕರಣಕ್ಕೆ ಇನ್ಪುಟ್ ಆಗಿ ಬಳಸುತ್ತೇವೆ. ಇದಕ್ಕೆ ಸರಳ ವಿಧಾನಗಳಲ್ಲಿ ಒಂದಾಗಿದೆ ಎಲ್ಲಾ ಪದಗಳ ಪ್ರತಿನಿಧನೆಗಳನ್ನು ಸೇರಿಸುವುದು. ಪ್ರತಿಯೊಂದು ಪದದ ಒನ್-ಹಾಟ್ ಎನ್ಕೋಡಿಂಗ್‌ಗಳನ್ನು ಸೇರಿಸಿದರೆ, ನಾವು ಪದಗಳ ಆವರ್ತನೆಗಳ ವೆಕ್ಟರ್ ಅನ್ನು ಪಡೆಯುತ್ತೇವೆ, ಇದು ಪಠ್ಯದಲ್ಲಿ ಪ್ರತಿ ಪದ ಎಷ್ಟು ಬಾರಿ ಬರುತ್ತದೆ ಎಂಬುದನ್ನು ತೋರಿಸುತ್ತದೆ. ಈ ರೀತಿಯ ಪಠ್ಯ ಪ್ರತಿನಿಧನೆಯನ್ನು **ಬ್ಯಾಗ್ ಆಫ್ ವರ್ಡ್ಸ್** (BoW) ಎಂದು ಕರೆಯುತ್ತಾರೆ. - + > ಚಿತ್ರ ಲೇಖಕರಿಂದ diff --git a/translations/kn/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/kn/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index 01085cd7..134f3cc7 100644 --- a/translations/kn/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/kn/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**ಪದಗಳ ಬ್ಯಾಗ್** (BoW) ವೆಕ್ಟರ್ ಪ್ರತಿನಿಧಾನವು ಅತ್ಯಂತ ಸಾಮಾನ್ಯವಾಗಿ ಬಳಸುವ ಪರಂಪರাগত ವೆಕ್ಟರ್ ಪ್ರತಿನಿಧಾನವಾಗಿದೆ. ಪ್ರತಿ ಪದವು ಒಂದು ವೆಕ್ಟರ್ ಸೂಚ್ಯಂಕಕ್ಕೆ ಜೋಡಿಸಲಾಗುತ್ತದೆ, ಮತ್ತು ವೆಕ್ಟರ್ ಅಂಶವು ನೀಡಲಾದ ದಾಖಲೆಗಳಲ್ಲಿ ಆ ಪದದ ಸಂಭವನೆಯ ಸಂಖ್ಯೆಯನ್ನು ಹೊಂದಿರುತ್ತದೆ.\n", "\n", - "![ಪದಗಳ ಬ್ಯಾಗ್ ವೆಕ್ಟರ್ ಪ್ರತಿನಿಧಾನವನ್ನು ಮೆಮೊರಿಯಲ್ಲಿ ಹೇಗೆ ಪ್ರತಿನಿಧಿಸಲಾಗುತ್ತದೆ ಎಂಬುದನ್ನು ತೋರಿಸುವ ಚಿತ್ರ.](../../../../../translated_images/kn/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![ಪದಗಳ ಬ್ಯಾಗ್ ವೆಕ್ಟರ್ ಪ್ರತಿನಿಧಾನವನ್ನು ಮೆಮೊರಿಯಲ್ಲಿ ಹೇಗೆ ಪ್ರತಿನಿಧಿಸಲಾಗುತ್ತದೆ ಎಂಬುದನ್ನು ತೋರಿಸುವ ಚಿತ್ರ.](../../../../../translated_images/kn/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Note**: BoW ಅನ್ನು ಪಠ್ಯದ ಪ್ರತ್ಯೇಕ ಪದಗಳ ಒನ್-ಹಾಟ್-ಎನ್‌ಕೋಡ್ ಮಾಡಿದ ವೆಕ್ಟರ್‌ಗಳ ಮೊತ್ತವೆಂದು ಕೂಡ ಪರಿಗಣಿಸಬಹುದು.\n", "\n", diff --git a/translations/kn/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/kn/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index 0b6d2f2c..d5c5cc07 100644 --- a/translations/kn/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/kn/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**ಪದಗಳ ಬ್ಯಾಗ್** (BoW) ವೆಕ್ಟರ್ ಪ್ರತಿನಿಧಾನವು ಅತಿ ಸರಳವಾಗಿ ಅರ್ಥಮಾಡಿಕೊಳ್ಳಬಹುದಾದ ಪರಂಪರাগত ವೆಕ್ಟರ್ ಪ್ರತಿನಿಧಾನವಾಗಿದೆ. ಪ್ರತಿ ಪದವು ಒಂದು ವೆಕ್ಟರ್ ಸೂಚ್ಯಂಕಕ್ಕೆ ಜೋಡಿಸಲಾಗುತ್ತದೆ, ಮತ್ತು ಒಂದು ವೆಕ್ಟರ್ ಅಂಶವು ನೀಡಲಾದ ದಾಖಲೆಗಳಲ್ಲಿ ಪ್ರತಿ ಪದದ ಸಂಭವನಗಳ ಸಂಖ್ಯೆಯನ್ನು ಹೊಂದಿರುತ್ತದೆ.\n", "\n", - "![ಪದಗಳ ಬ್ಯಾಗ್ ವೆಕ್ಟರ್ ಪ್ರತಿನಿಧಾನವನ್ನು ಮೆಮೊರಿಯಲ್ಲಿ ಹೇಗೆ ಪ್ರತಿನಿಧಿಸಲಾಗುತ್ತದೆ ಎಂಬುದನ್ನು ತೋರಿಸುವ ಚಿತ್ರ.](../../../../../translated_images/kn/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![ಪದಗಳ ಬ್ಯಾಗ್ ವೆಕ್ಟರ್ ಪ್ರತಿನಿಧಾನವನ್ನು ಮೆಮೊರಿಯಲ್ಲಿ ಹೇಗೆ ಪ್ರತಿನಿಧಿಸಲಾಗುತ್ತದೆ ಎಂಬುದನ್ನು ತೋರಿಸುವ ಚಿತ್ರ.](../../../../../translated_images/kn/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Note**: BoW ಅನ್ನು ಪಠ್ಯದ ಪ್ರತ್ಯೇಕ ಪದಗಳ ಒನ್-ಹಾಟ್-ಎನ್‌ಕೋಡ್ ಮಾಡಿದ ವೆಕ್ಟರ್‌ಗಳ ಮೊತ್ತವೆಂದು ಕೂಡ ಪರಿಗಣಿಸಬಹುದು.\n", "\n", diff --git a/translations/kn/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/kn/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 79c026d0..92a9d8e5 100644 --- a/translations/kn/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/kn/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "ನಮ್ಮ ನೆಟ್‌ವರ್ಕ್‌ನಲ್ಲಿ ಮೊದಲ ಲೇಯರ್ ಆಗಿ ಎम्बೆಡ್ಡಿಂಗ್ ಲೇಯರ್ ಬಳಸಿ, ನಾವು ಬ್ಯಾಗ್-ಆಫ್-ವರ್ಡ್ಸ್‌ನಿಂದ **ಎಂಬೆಡ್ಡಿಂಗ್ ಬ್ಯಾಗ್** ಮಾದರಿಗೆ ಬದಲಾಗಬಹುದು, ಅಲ್ಲಿ ನಾವು ಮೊದಲಿಗೆ ನಮ್ಮ ಪಠ್ಯದ ಪ್ರತಿಯೊಂದು ಪದವನ್ನು ಅದರ ಸಂಬಂಧಿತ ಎम्बೆಡ್ಡಿಂಗ್‌ಗೆ ಪರಿವರ್ತಿಸಿ, ನಂತರ ಆ ಎಲ್ಲ ಎम्बೆಡ್ಡಿಂಗ್‌ಗಳ ಮೇಲೆ `sum`, `average` ಅಥವಾ `max` ಮುಂತಾದ ಸಂಗ್ರಹಣಾ ಕಾರ್ಯವನ್ನು ಲೆಕ್ಕಹಾಕುತ್ತೇವೆ.\n", "\n", - "![ಐದು ಕ್ರಮ ಪದಗಳಿಗಾಗಿ ಎम्बೆಡ್ಡಿಂಗ್ ವರ್ಗೀಕರಣವನ್ನು ತೋರಿಸುವ ಚಿತ್ರ.](../../../../../translated_images/kn/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![ಐದು ಕ್ರಮ ಪದಗಳಿಗಾಗಿ ಎम्बೆಡ್ಡಿಂಗ್ ವರ್ಗೀಕರಣವನ್ನು ತೋರಿಸುವ ಚಿತ್ರ.](../../../../../translated_images/kn/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "ನಮ್ಮ ವರ್ಗೀಕರಣ ನ್ಯೂರಲ್ ನೆಟ್‌ವರ್ಕ್ ಎಂಬೆಡ್ಡಿಂಗ್ ಲೇಯರ್‌ನಿಂದ ಪ್ರಾರಂಭವಾಗಿ, ನಂತರ ಸಂಗ್ರಹಣಾ ಲೇಯರ್ ಮತ್ತು ಅದರ ಮೇಲೆ ಲೀನಿಯರ್ ವರ್ಗೀಕರಣ ಲೇಯರ್ ಇರುತ್ತದೆ:\n" ] @@ -176,7 +176,7 @@ "\n", "ಹಿಂದಿನ ವಾಸ್ತುಶಿಲ್ಪದಲ್ಲಿ, ನಾವು ಎಲ್ಲಾ ಕ್ರಮಗಳನ್ನು ಒಂದೇ ಉದ್ದಕ್ಕೆ ಪ್ಯಾಡ್ ಮಾಡಬೇಕಾಗಿತ್ತು ताकि ಅವುಗಳನ್ನು ಒಂದು ಮಿನಿಬ್ಯಾಚ್‌ಗೆ ಹೊಂದಿಸಬಹುದು. ಇದು ಬದಲಾಗುವ ಉದ್ದದ ಕ್ರಮಗಳನ್ನು ಪ್ರತಿನಿಧಿಸುವ ಅತ್ಯಂತ ಪರಿಣಾಮಕಾರಿ ವಿಧಾನವಲ್ಲ - ಇನ್ನೊಂದು ವಿಧಾನವೆಂದರೆ **offset** ವೆಕ್ಟರ್ ಅನ್ನು ಬಳಸುವುದು, ಇದು ಒಂದು ದೊಡ್ಡ ವೆಕ್ಟರ್‌ನಲ್ಲಿ ಸಂಗ್ರಹಿಸಲಾದ ಎಲ್ಲಾ ಕ್ರಮಗಳ ಆಫ್‌ಸೆಟ್‌ಗಳನ್ನು ಹಿಡಿದಿರುತ್ತದೆ.\n", "\n", - "![offset ಕ್ರಮದ ಪ್ರತಿನಿಧಾನವನ್ನು ತೋರಿಸುವ ಚಿತ್ರ](../../../../../translated_images/kn/offset-sequence-representation.eb73fcefb29b46ee.png)\n", + "![offset ಕ್ರಮದ ಪ್ರತಿನಿಧಾನವನ್ನು ತೋರಿಸುವ ಚಿತ್ರ](../../../../../translated_images/kn/offset-sequence-representation.eb73fcefb29b46ee.webp)\n", "\n", "> **Note**: ಮೇಲಿನ ಚಿತ್ರದಲ್ಲಿ, ನಾವು ಅಕ್ಷರಗಳ ಕ್ರಮವನ್ನು ತೋರಿಸಿದ್ದೇವೆ, ಆದರೆ ನಮ್ಮ ಉದಾಹರಣೆಯಲ್ಲಿ ನಾವು ಪದಗಳ ಕ್ರಮಗಳೊಂದಿಗೆ ಕೆಲಸ ಮಾಡುತ್ತಿದ್ದೇವೆ. ಆದಾಗ್ಯೂ, ಆಫ್‌ಸೆಟ್ ವೆಕ್ಟರ್‌ನೊಂದಿಗೆ ಕ್ರಮಗಳನ್ನು ಪ್ರತಿನಿಧಿಸುವ ಸಾಮಾನ್ಯ ತತ್ವ ಅದೇ ಆಗಿರುತ್ತದೆ.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW ವೇಗವಾಗಿ ಕಾರ್ಯನಿರ್ವಹಿಸುತ್ತದೆ, ಆದರೆ ಸ್ಕಿಪ್-ಗ್ರಾಮ್ ನಿಧಾನವಾಗಿದ್ದು, ಅಪರೂಪದ ಪದಗಳನ್ನು ಉತ್ತಮವಾಗಿ ಪ್ರತಿನಿಧಿಸುತ್ತದೆ.\n", "\n", - "![ಪದಗಳನ್ನು ವೆಕ್ಟರ್‌ಗಳಿಗೆ ಪರಿವರ್ತಿಸುವ CBoW ಮತ್ತು ಸ್ಕಿಪ್-ಗ್ರಾಮ್ ಆಲ್ಗಾರಿಥಮ್‌ಗಳನ್ನು ತೋರಿಸುವ ಚಿತ್ರ.](../../../../../translated_images/kn/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![ಪದಗಳನ್ನು ವೆಕ್ಟರ್‌ಗಳಿಗೆ ಪರಿವರ್ತಿಸುವ CBoW ಮತ್ತು ಸ್ಕಿಪ್-ಗ್ರಾಮ್ ಆಲ್ಗಾರಿಥಮ್‌ಗಳನ್ನು ತೋರಿಸುವ ಚಿತ್ರ.](../../../../../translated_images/kn/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Google News ಡೇಟಾಸೆಟ್‌ನಲ್ಲಿ ಪೂರ್ವ-ಪ್ರಶಿಕ್ಷಿತ word2vec ಎम्बೆಡ್ಡಿಂಗ್‌ನೊಂದಿಗೆ ಪ್ರಯೋಗ ಮಾಡಲು, ನಾವು **gensim** ಗ್ರಂಥಾಲಯವನ್ನು ಬಳಸಬಹುದು. ಕೆಳಗೆ 'neural' ಗೆ ಅತ್ಯಂತ ಸಮಾನವಾದ ಪದಗಳನ್ನು ಕಂಡುಹಿಡಿಯಲಾಗಿದೆ\n", "\n", diff --git a/translations/kn/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/kn/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index 812bea82..86591dd1 100644 --- a/translations/kn/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/kn/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "ನಮ್ಮ ನೆಟ್‌ವರ್ಕ್‌ನಲ್ಲಿ ಮೊದಲ ಲೇಯರ್ ಆಗಿ ಎम्बೆಡ್ಡಿಂಗ್ ಲೇಯರ್ ಅನ್ನು ಬಳಸುವುದರಿಂದ, ನಾವು ಬ್ಯಾಗ್-ಆಫ್-ವರ್ಡ್ಸ್‌ನಿಂದ **ಎಂಬೆಡ್ಡಿಂಗ್ ಬ್ಯಾಗ್** ಮಾದರಿಯ ಕಡೆಗೆ ಬದಲಾಗಬಹುದು, ಅಲ್ಲಿ ನಾವು ಮೊದಲಿಗೆ ನಮ್ಮ ಪಠ್ಯದ ಪ್ರತಿಯೊಂದು ಪದವನ್ನು ಅದರ ಸಂಬಂಧಿಸಿದ ಎम्बೆಡ್ಡಿಂಗ್‌ಗೆ ಪರಿವರ್ತಿಸುತ್ತೇವೆ, ನಂತರ ಆ ಎಲ್ಲಾ ಎम्बೆಡ್ಡಿಂಗ್‌ಗಳ ಮೇಲೆ `sum`, `average` ಅಥವಾ `max` ಮುಂತಾದ ಸಂಗ್ರಹಣಾ ಕಾರ್ಯವನ್ನು ಲೆಕ್ಕಹಾಕುತ್ತೇವೆ.\n", "\n", - "![ಐದು ಕ್ರಮ ಪದಗಳಿಗಾಗಿ ಎम्बೆಡ್ಡಿಂಗ್ ವರ್ಗೀಕರಣವನ್ನು ತೋರಿಸುವ ಚಿತ್ರ.](../../../../../translated_images/kn/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![ಐದು ಕ್ರಮ ಪದಗಳಿಗಾಗಿ ಎम्बೆಡ್ಡಿಂಗ್ ವರ್ಗೀಕರಣವನ್ನು ತೋರಿಸುವ ಚಿತ್ರ.](../../../../../translated_images/kn/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "ನಮ್ಮ ವರ್ಗೀಕರಣ ನ್ಯೂರಲ್ ನೆಟ್‌ವರ್ಕ್ ಕೆಳಗಿನ ಲೇಯರ್‌ಗಳಿಂದ ಕೂಡಿದೆ:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW ವೇಗವಾಗಿ ಕಾರ್ಯನಿರ್ವಹಿಸುತ್ತದೆ, ಮತ್ತು ಸ್ಕಿಪ್-ಗ್ರಾಮ್ ನಿಧಾನವಾಗಿದ್ದರೂ ಅಪರೂಪದ ಪದಗಳನ್ನು ಪ್ರತಿನಿಧಿಸುವಲ್ಲಿ ಉತ್ತಮ ಕಾರ್ಯನಿರ್ವಹಿಸುತ್ತದೆ.\n", "\n", - "![ಪದಗಳನ್ನು ವೆಕ್ಟರ್‌ಗಳಿಗೆ ಪರಿವರ್ತಿಸಲು CBoW ಮತ್ತು ಸ್ಕಿಪ್-ಗ್ರಾಮ್ ಆಲ್ಗಾರಿಥಮ್‌ಗಳನ್ನು ತೋರಿಸುವ ಚಿತ್ರ.](../../../../../translated_images/kn/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![ಪದಗಳನ್ನು ವೆಕ್ಟರ್‌ಗಳಿಗೆ ಪರಿವರ್ತಿಸಲು CBoW ಮತ್ತು ಸ್ಕಿಪ್-ಗ್ರಾಮ್ ಆಲ್ಗಾರಿಥಮ್‌ಗಳನ್ನು ತೋರಿಸುವ ಚಿತ್ರ.](../../../../../translated_images/kn/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Google News ಡೇಟಾಸೆಟ್‌ನಲ್ಲಿ ಪೂರ್ವಪ್ರಶಿಕ್ಷಿತ Word2Vec ಎम्बೆಡ್ಡಿಂಗ್‌ನೊಂದಿಗೆ ಪ್ರಯೋಗ ಮಾಡಲು, ನಾವು **gensim** ಗ್ರಂಥಾಲಯವನ್ನು ಬಳಸಬಹುದು. ಕೆಳಗೆ 'neural' ಗೆ ಅತ್ಯಂತ ಸಮಾನವಾದ ಪದಗಳನ್ನು ಕಂಡುಹಿಡಿಯಲಾಗಿದೆ.\n", "\n", diff --git a/translations/kn/lessons/5-NLP/14-Embeddings/README.md b/translations/kn/lessons/5-NLP/14-Embeddings/README.md index 57f5ca5e..9d37c296 100644 --- a/translations/kn/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/kn/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ BoW ಅಥವಾ TF/IDF ಆಧಾರಿತ ವರ್ಗೀಕರಣಗಳನ್ ನಮ್ಮ ವರ್ಗೀಕರಣ ಜಾಲದಲ್ಲಿ ಮೊದಲ ಲೇಯರ್ ಆಗಿ ಎम्बೆಡ್ಡಿಂಗ್ ಲೇಯರ್ ಬಳಸಿ, ನಾವು ಬ್ಯಾಗ್-ಆಫ್-ವರ್ಡ್ಸ್‌ನಿಂದ **ಎಂಬೆಡ್ಡಿಂಗ್ ಬ್ಯಾಗ್** ಮಾದರಿಗೆ ಬದಲಾಗಬಹುದು, ಅಲ್ಲಿ ಮೊದಲಿಗೆ ನಮ್ಮ ಪಠ್ಯದಲ್ಲಿನ ಪ್ರತಿ ಪದವನ್ನು ಸಂಬಂಧಿತ ಎम्बೆಡ್ಡಿಂಗ್‌ಗೆ ಪರಿವರ್ತಿಸಿ, ನಂತರ ಆ ಎಲ್ಲ ಎम्बೆಡ್ಡಿಂಗ್‌ಗಳ ಮೇಲೆ `sum`, `average` ಅಥವಾ `max` ಮುಂತಾದ ಸಂಗ್ರಹ ಕಾರ್ಯವನ್ನು ಲೆಕ್ಕಹಾಕುತ್ತೇವೆ. -![ಐದು ಕ್ರಮ ಪದಗಳಿಗಾಗಿ ಎम्बೆಡ್ಡಿಂಗ್ ವರ್ಗೀಕರಣವನ್ನು ತೋರಿಸುವ ಚಿತ್ರ.](../../../../../translated_images/kn/embedding-classifier-example.b77f021a7ee67eee.png) +![ಐದು ಕ್ರಮ ಪದಗಳಿಗಾಗಿ ಎम्बೆಡ್ಡಿಂಗ್ ವರ್ಗೀಕರಣವನ್ನು ತೋರಿಸುವ ಚಿತ್ರ.](../../../../../translated_images/kn/embedding-classifier-example.b77f021a7ee67eee.webp) > ಚಿತ್ರ ಲೇಖಕರಿಂದ @@ -40,7 +40,7 @@ BoW ಅಥವಾ TF/IDF ಆಧಾರಿತ ವರ್ಗೀಕರಣಗಳನ್ CBoW ವೇಗವಾಗಿ ಕಾರ್ಯನಿರ್ವಹಿಸುತ್ತದೆ, ಆದರೆ ಸ್ಕಿಪ್-ಗ್ರಾಮ್ ನಿಧಾನವಾಗಿದ್ದು, ಅಪರೂಪದ ಪದಗಳನ್ನು ಉತ್ತಮವಾಗಿ ಪ್ರತಿನಿಧಿಸುತ್ತದೆ. -![ಪದಗಳನ್ನು ವೆಕ್ಟರ್‌ಗಳಿಗೆ ಪರಿವರ್ತಿಸುವ CBoW ಮತ್ತು ಸ್ಕಿಪ್-ಗ್ರಾಮ್ ಆಲ್ಗಾರಿಥಮ್‌ಗಳನ್ನು ತೋರಿಸುವ ಚಿತ್ರ.](../../../../../translated_images/kn/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![ಪದಗಳನ್ನು ವೆಕ್ಟರ್‌ಗಳಿಗೆ ಪರಿವರ್ತಿಸುವ CBoW ಮತ್ತು ಸ್ಕಿಪ್-ಗ್ರಾಮ್ ಆಲ್ಗಾರಿಥಮ್‌ಗಳನ್ನು ತೋರಿಸುವ ಚಿತ್ರ.](../../../../../translated_images/kn/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > ಚಿತ್ರ [ಈ ಪೇಪರ್](https://arxiv.org/pdf/1301.3781.pdf) ನಿಂದ diff --git a/translations/kn/lessons/5-NLP/15-LanguageModeling/README.md b/translations/kn/lessons/5-NLP/15-LanguageModeling/README.md index e08b5812..67fa01ec 100644 --- a/translations/kn/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/kn/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: * **ಕಂಟಿನ್ಯೂಯಸ್ ಬ್ಯಾಗ್-ಆಫ್-ವರ್ಡ್ಸ್** (CBoW), ಇಲ್ಲಿ ನಾವು ಟೋಕನ್ ಸರಣಿಯಲ್ಲಿ ಮಧ್ಯದ ಟೋಕನ್ $W_0$ ಅನ್ನು ಊಹಿಸುತ್ತೇವೆ $W_{-N}$, ..., $W_N$. * **ಸ್ಕಿಪ್-ಗ್ರಾಮ್**, ಇಲ್ಲಿ ನಾವು ಮಧ್ಯದ ಟೋಕನ್ $W_0$ ರಿಂದ ಸುತ್ತಲೂ ಇರುವ ಟೋಕನ್‌ಗಳ ಸಮೂಹ {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} ಅನ್ನು ಊಹಿಸುತ್ತೇವೆ. -![ಪದಗಳನ್ನು ವೆಕ್ಟರ್‌ಗಳಿಗೆ ಪರಿವರ್ತಿಸುವ ಕುರಿತು ಪೇಪರ್‌ನ ಚಿತ್ರ](../../../../../translated_images/kn/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![ಪದಗಳನ್ನು ವೆಕ್ಟರ್‌ಗಳಿಗೆ ಪರಿವರ್ತಿಸುವ ಕುರಿತು ಪೇಪರ್‌ನ ಚಿತ್ರ](../../../../../translated_images/kn/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > ಚಿತ್ರ [ಈ ಪೇಪರ್](https://arxiv.org/pdf/1301.3781.pdf) ನಿಂದ diff --git a/translations/kn/lessons/5-NLP/16-RNN/README.md b/translations/kn/lessons/5-NLP/16-RNN/README.md index 856f4ccc..0e538239 100644 --- a/translations/kn/lessons/5-NLP/16-RNN/README.md +++ b/translations/kn/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: ಪಠ್ಯದ ಕ್ರಮದ ಅರ್ಥವನ್ನು ಹಿಡಿಯಲು, ನಾವು ಮತ್ತೊಂದು ನ್ಯೂರಲ್ ನೆಟ್‌ವರ್ಕ್ ವಾಸ್ತುಶಿಲ್ಪವನ್ನು ಬಳಸಬೇಕಾಗುತ್ತದೆ, ಇದನ್ನು **ಪುನರಾವರ್ತಿತ ನ್ಯೂರಲ್ ನೆಟ್‌ವರ್ಕ್** ಅಥವಾ RNN ಎಂದು ಕರೆಯುತ್ತಾರೆ. RNN ನಲ್ಲಿ, ನಾವು ನಮ್ಮ ವಾಕ್ಯವನ್ನು ಒಂದು ಸಂಕೇತವನ್ನು ಒಂದೇ ಸಮಯದಲ್ಲಿ ನೆಟ್‌ವರ್ಕ್ ಮೂಲಕ ಹಾದುಹೋಗಿಸುತ್ತೇವೆ, ಮತ್ತು ನೆಟ್‌ವರ್ಕ್ ಕೆಲವು **ಸ್ಥಿತಿ**ಗಳನ್ನು ಉತ್ಪಾದಿಸುತ್ತದೆ, ಅದನ್ನು ಮುಂದಿನ ಸಂಕೇತದೊಂದಿಗೆ ಮತ್ತೆ ನೆಟ್‌ವರ್ಕ್‌ಗೆ ನೀಡುತ್ತೇವೆ. -![RNN](../../../../../translated_images/kn/rnn.27f5c29c53d727b5.png) +![RNN](../../../../../translated_images/kn/rnn.27f5c29c53d727b5.webp) > ಚಿತ್ರ ಲೇಖಕರಿಂದ @@ -31,7 +31,7 @@ CO_OP_TRANSLATOR_METADATA: ಸರಳ RNN ಸೆಲ್ ಒಳಗೆ ಎರಡು ತೂಕ ಮ್ಯಾಟ್ರಿಕ್ಸ್‌ಗಳಿವೆ: ಒಂದು ಇನ್‌ಪುಟ್ ಸಂಕೇತವನ್ನು ಪರಿವರ್ತಿಸುತ್ತದೆ (ನಾವು ಅದನ್ನು W ಎಂದು ಕರೆಯೋಣ), ಮತ್ತೊಂದು ಇನ್‌ಪುಟ್ ಸ್ಥಿತಿಯನ್ನು ಪರಿವರ್ತಿಸುತ್ತದೆ (H). ಈ ಸಂದರ್ಭದಲ್ಲಿ ನೆಟ್‌ವರ್ಕ್ ಔಟ್‌ಪುಟ್ ಅನ್ನು σ(W×Xi+H×Si-1+b) ಎಂದು ಲೆಕ್ಕಹಾಕಲಾಗುತ್ತದೆ, ಇಲ್ಲಿ σ ಸಕ್ರಿಯಕರಣ ಕಾರ್ಯ ಮತ್ತು b ಹೆಚ್ಚುವರಿ ಬಯಾಸ್. -RNN Cell Anatomy +RNN Cell Anatomy > ಚಿತ್ರ ಲೇಖಕರಿಂದ @@ -61,7 +61,7 @@ LSTM ನೆಟ್‌ವರ್ಕ್ RNN ಗೆ ಹೋಲುವ ರೀತಿಯ ಒಂದು ಪುನರಾವರ್ತಿತ ನೆಟ್‌ವರ್ಕ್, ಏತಾದರೂ ಒಂದು ದಿಕ್ಕಿನ ಅಥವಾ ದ್ವಿಮುಖಿ, ಸರಣಿಯೊಳಗಿನ ಕೆಲವು ಮಾದರಿಗಳನ್ನು ಹಿಡಿದುಕೊಳ್ಳುತ್ತದೆ ಮತ್ತು ಅವುಗಳನ್ನು ಸ್ಥಿತಿ ವೆಕ್ಟರ್ ಅಥವಾ ಔಟ್‌ಪುಟ್‌ಗೆ ಸಂಗ್ರಹಿಸುತ್ತದೆ. ಕಾಂವಲ್ಯೂಷನಲ್ ನೆಟ್‌ವರ್ಕ್‌ಗಳಂತೆ, ನಾವು ಮೊದಲ ಲೇಯರ್‌ನ ಮೇಲೆ ಮತ್ತೊಂದು ಪುನರಾವರ್ತಿತ ಲೇಯರ್ ನಿರ್ಮಿಸಬಹುದು, ಇದರಿಂದ ಹೆಚ್ಚಿನ ಮಟ್ಟದ ಮಾದರಿಗಳನ್ನು ಹಿಡಿದುಕೊಳ್ಳಬಹುದು ಮತ್ತು ಮೊದಲ ಲೇಯರ್ ತೆಗೆದುಕೊಂಡ ಕಡಿಮೆ ಮಟ್ಟದ ಮಾದರಿಗಳಿಂದ ನಿರ್ಮಿಸಬಹುದು. ಇದರಿಂದ **ಬಹು-ಲೇಯರ್ RNN** ಎಂಬ ಕಲ್ಪನೆ ಬರುತ್ತದೆ, ಇದು ಎರಡು ಅಥವಾ ಹೆಚ್ಚು ಪುನರಾವರ್ತಿತ ನೆಟ್‌ವರ್ಕ್‌ಗಳಿಂದ ಕೂಡಿದೆ, ಇಲ್ಲಿ ಹಿಂದಿನ ಲೇಯರ್‌ನ ಔಟ್‌ಪುಟ್ ಮುಂದಿನ ಲೇಯರ್‌ಗೆ ಇನ್‌ಪುಟ್ ಆಗಿ ನೀಡಲಾಗುತ್ತದೆ. -![Image showing a Multilayer long-short-term-memory- RNN](../../../../../translated_images/kn/multi-layer-lstm.dd975e29bb2a59fe.jpg) +![Image showing a Multilayer long-short-term-memory- RNN](../../../../../translated_images/kn/multi-layer-lstm.dd975e29bb2a59fe.webp) *ಚಿತ್ರ [ಈ ಅದ್ಭುತ ಪೋಸ್ಟ್](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) ನಿಂದ ಫೆರ್ನಾಂಡೋ ಲೋಪೆಜ್ ಅವರಿಂದ* diff --git a/translations/kn/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/kn/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index 28f3240e..ce5ab782 100644 --- a/translations/kn/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/kn/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -10,7 +10,7 @@ "\n", "ಪಠ್ಯದ ಕ್ರಮದ ಅರ್ಥವನ್ನು ಹಿಡಿಯಲು, ನಾವು ಮತ್ತೊಂದು ನ್ಯೂರಲ್ ನೆಟ್‌ವರ್ಕ್ ವಾಸ್ತುಶಿಲ್ಪವನ್ನು ಬಳಸಬೇಕಾಗುತ್ತದೆ, ಇದನ್ನು **ಪುನರಾವರ್ತಿತ ನ್ಯೂರಲ್ ನೆಟ್‌ವರ್ಕ್** ಅಥವಾ RNN ಎಂದು ಕರೆಯುತ್ತಾರೆ. RNN ನಲ್ಲಿ, ನಾವು ನಮ್ಮ ವಾಕ್ಯವನ್ನು ಒಂದು ಸಂಕೇತವನ್ನು ಪ್ರತಿ ಬಾರಿ ನೆಟ್‌ವರ್ಕ್ ಮೂಲಕ ಕಳುಹಿಸುತ್ತೇವೆ, ಮತ್ತು ನೆಟ್‌ವರ್ಕ್ ಕೆಲವು **ಸ್ಥಿತಿ** ಅನ್ನು ಉತ್ಪಾದಿಸುತ್ತದೆ, ಅದನ್ನು ನಂತರ ಮುಂದಿನ ಸಂಕೇತದೊಂದಿಗೆ ಮತ್ತೆ ನೆಟ್‌ವರ್ಕ್‌ಗೆ ಕಳುಹಿಸುತ್ತೇವೆ.\n", "\n", - "\"RNN\"\n", + "\"RNN\"\n", "\n", "ನಮೂದಿಸಿದ ಟೋಕನ್ ಸರಣಿಯಾದ $X_0,\\dots,X_n$ ನೀಡಿದಾಗ, RNN ನ್ಯೂರಲ್ ನೆಟ್‌ವರ್ಕ್ ಬ್ಲಾಕ್‌ಗಳ ಸರಣಿಯನ್ನು ರಚಿಸುತ್ತದೆ ಮತ್ತು ಬ್ಯಾಕ್ ಪ್ರೋಪಗೇಶನ್ ಬಳಸಿ ಈ ಸರಣಿಯನ್ನು ಅಂತ್ಯದಿಂದ ಅಂತ್ಯಕ್ಕೆ ತರಬೇತುಗೊಳಿಸುತ್ತದೆ. ಪ್ರತಿ ನೆಟ್‌ವರ್ಕ್ ಬ್ಲಾಕ್ ಒಂದು ಜೋಡಿ $(X_i,S_i)$ ಅನ್ನು ಇನ್‌ಪುಟ್ ಆಗಿ ತೆಗೆದುಕೊಳ್ಳುತ್ತದೆ ಮತ್ತು $S_{i+1}$ ಅನ್ನು ಫಲಿತಾಂಶವಾಗಿ ಉತ್ಪಾದಿಸುತ್ತದೆ. ಅಂತಿಮ ಸ್ಥಿತಿ $S_n$ ಅಥವಾ ಔಟ್‌ಪುಟ್ $X_n$ ರೇಖೀಯ ವರ್ಗೀಕರಣಕ್ಕೆ ಹೋಗಿ ಫಲಿತಾಂಶವನ್ನು ಉತ್ಪಾದಿಸುತ್ತದೆ. ಎಲ್ಲಾ ನೆಟ್‌ವರ್ಕ್ ಬ್ಲಾಕ್‌ಗಳು ಒಂದೇ ತೂಕಗಳನ್ನು ಹಂಚಿಕೊಳ್ಳುತ್ತವೆ ಮತ್ತು ಒಂದು ಬ್ಯಾಕ್ ಪ್ರೋಪಗೇಶನ್ ಪಾಸ್ ಬಳಸಿ ಅಂತ್ಯದಿಂದ ಅಂತ್ಯಕ್ಕೆ ತರಬೇತುಗೊಳ್ಳುತ್ತವೆ.\n", "\n", @@ -428,7 +428,7 @@ "\n", "ಪುನರಾವರ್ತಿತ ನೆಟ್‌ವರ್ಕ್, ಒಂದು ದಿಕ್ಕಿನ ಅಥವಾ ದ್ವಿಮುಖಿ, ಕ್ರಮದೊಳಗಿನ ನಿರ್ದಿಷ್ಟ ಮಾದರಿಗಳನ್ನು ಹಿಡಿದುಕೊಳ್ಳುತ್ತದೆ ಮತ್ತು ಅವುಗಳನ್ನು ಸ್ಥಿತಿ ವೆಕ್ಟರ್‌ನಲ್ಲಿ ಸಂಗ್ರಹಿಸಬಹುದು ಅಥವಾ ಔಟ್‌ಪುಟ್‌ಗೆ ಪಾಸ್ ಮಾಡಬಹುದು. ಸಂಯೋಜಿತ ನೆಟ್‌ವರ್ಕ್‌ಗಳಂತೆ, ನಾವು ಮೊದಲನೆಯದಿನ ಮೇಲೆ ಮತ್ತೊಂದು ಪುನರಾವರ್ತಿತ ಪದರವನ್ನು ನಿರ್ಮಿಸಬಹುದು, ಮೊದಲನೆಯ ಪದರದಿಂದ ತೆಗೆದುಕೊಂಡ ಕಡಿಮೆ ಮಟ್ಟದ ಮಾದರಿಗಳಿಂದ ನಿರ್ಮಿತ ಉನ್ನತ ಮಟ್ಟದ ಮಾದರಿಗಳನ್ನು ಹಿಡಿಯಲು. ಇದರಿಂದ ನಮಗೆ **ಬಹುಮಟ್ಟದ RNN** ಎಂಬ ಕಲ್ಪನೆ ಬರುತ್ತದೆ, ಇದು ಎರಡು ಅಥವಾ ಹೆಚ್ಚು ಪುನರಾವರ್ತಿತ ನೆಟ್‌ವರ್ಕ್‌ಗಳಿಂದ ಕೂಡಿದ್ದು, ಹಿಂದಿನ ಪದರದ ಔಟ್‌ಪುಟ್ ಮುಂದಿನ ಪದರಕ್ಕೆ ಇನ್‌ಪುಟ್ ಆಗಿ ಪಾಸ್ ಆಗುತ್ತದೆ.\n", "\n", - "![ಬಹುಮಟ್ಟದ ದೀರ್ಘಕಾಲಿಕ-ಸ್ಮೃತಿ- RNN ಅನ್ನು ತೋರಿಸುವ ಚಿತ್ರ](../../../../../translated_images/kn/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![ಬಹುಮಟ್ಟದ ದೀರ್ಘಕಾಲಿಕ-ಸ್ಮೃತಿ- RNN ಅನ್ನು ತೋರಿಸುವ ಚಿತ್ರ](../../../../../translated_images/kn/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*ಫೆರ್ನಾಂಡೋ ಲೋಪೆಜ್ ಅವರ [ಈ ಅದ್ಭುತ ಪೋಸ್ಟ್](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) ನಿಂದ ಚಿತ್ರ*\n", "\n", diff --git a/translations/kn/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/kn/lessons/5-NLP/16-RNN/RNNTF.ipynb index ffc60ba3..3bbe146e 100644 --- a/translations/kn/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/kn/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "ಪಠ್ಯ ಕ್ರಮದ ಅರ್ಥವನ್ನು ಹಿಡಿಯಲು, ನಾವು **ಪುನರಾವರ್ತಿತ ನ್ಯೂರಲ್ ನೆಟ್‌ವರ್ಕ್** ಅಥವಾ RNN ಎಂದು ಕರೆಯುವ ನ್ಯೂರಲ್ ನೆಟ್‌ವರ್ಕ್ ವಾಸ್ತುಶಿಲ್ಪವನ್ನು ಬಳಸುತ್ತೇವೆ. RNN ಬಳಸುವಾಗ, ನಾವು ನಮ್ಮ ವಾಕ್ಯವನ್ನು ಒಂದು ಟೋಕನ್‌ವನ್ನೊಂದು ಸಮಯದಲ್ಲಿ ನೆಟ್‌ವರ್ಕ್ ಮೂಲಕ ಹಾದುಹೋಗಿಸುತ್ತೇವೆ, ಮತ್ತು ನೆಟ್‌ವರ್ಕ್ ಕೆಲವು **ಸ್ಥಿತಿ** ಅನ್ನು ಉತ್ಪಾದಿಸುತ್ತದೆ, ಅದನ್ನು ನಂತರ ಮುಂದಿನ ಟೋಕನ್ ಜೊತೆಗೆ ಮತ್ತೆ ನೆಟ್‌ವರ್ಕ್‌ಗೆ ನೀಡುತ್ತೇವೆ.\n", "\n", - "![ಪುನರಾವರ್ತಿತ ನ್ಯೂರಲ್ ನೆಟ್‌ವರ್ಕ್ ರಚನೆಯ ಉದಾಹರಣೆಯನ್ನು ತೋರಿಸುವ ಚಿತ್ರ.](../../../../../translated_images/kn/rnn.27f5c29c53d727b5.png)\n", + "![ಪುನರಾವರ್ತಿತ ನ್ಯೂರಲ್ ನೆಟ್‌ವರ್ಕ್ ರಚನೆಯ ಉದಾಹರಣೆಯನ್ನು ತೋರಿಸುವ ಚಿತ್ರ.](../../../../../translated_images/kn/rnn.27f5c29c53d727b5.webp)\n", "\n", "ನಮೂದಿಸಿದ ಟೋಕನ್ ಕ್ರಮ $X_0,\\dots,X_n$ ನೀಡಿದಾಗ, RNN ನ್ಯೂರಲ್ ನೆಟ್‌ವರ್ಕ್ ಬ್ಲಾಕ್‌ಗಳ ಸರಣಿಯನ್ನು ರಚಿಸುತ್ತದೆ ಮತ್ತು ಬ್ಯಾಕ್ಪ್ರೊಪಗೇಶನ್ ಬಳಸಿ ಈ ಸರಣಿಯನ್ನು ಅಂತ್ಯದಿಂದ ಅಂತ್ಯಕ್ಕೆ ತರಬೇತುಗೊಳಿಸುತ್ತದೆ. ಪ್ರತಿ ನೆಟ್‌ವರ್ಕ್ ಬ್ಲಾಕ್ ಒಂದು ಜೋಡಿ $(X_i,S_i)$ ಅನ್ನು ಇನ್ಪುಟ್ ಆಗಿ ತೆಗೆದುಕೊಳ್ಳುತ್ತದೆ ಮತ್ತು $S_{i+1}$ ಅನ್ನು ಫಲಿತಾಂಶವಾಗಿ ಉತ್ಪಾದಿಸುತ್ತದೆ. ಅಂತಿಮ ಸ್ಥಿತಿ $S_n$ ಅಥವಾ ಔಟ್‌ಪುಟ್ $Y_n$ ಅನ್ನು ಲೀನಿಯರ್ ವರ್ಗೀಕರಣಕಾರಿಗೆ ನೀಡಲಾಗುತ್ತದೆ ಫಲಿತಾಂಶವನ್ನು ಉತ್ಪಾದಿಸಲು. ಎಲ್ಲಾ ನೆಟ್‌ವರ್ಕ್ ಬ್ಲಾಕ್‌ಗಳು ಒಂದೇ ತೂಕಗಳನ್ನು ಹಂಚಿಕೊಳ್ಳುತ್ತವೆ ಮತ್ತು ಒಂದು ಬ್ಯಾಕ್ಪ್ರೊಪಗೇಶನ್ ಪಾಸ್ ಬಳಸಿ ಅಂತ್ಯದಿಂದ ಅಂತ್ಯಕ್ಕೆ ತರಬೇತುಗೊಳ್ಳುತ್ತವೆ.\n", "\n", @@ -371,7 +371,7 @@ "\n", "ಪುನರಾವರ್ತಿತ ಜಾಲಗಳು, ಏಕದಿಕ್ಕಿ ಅಥವಾ ದ್ವಿಮುಖವಾಗಿರಲಿ, ಕ್ರಮದೊಳಗಿನ ಮಾದರಿಗಳನ್ನು ಹಿಡಿದುಕೊಳ್ಳುತ್ತವೆ ಮತ್ತು ಅವುಗಳನ್ನು ಸ್ಥಿತಿ ವೆಕ್ಟರ್‌ಗಳಲ್ಲಿ ಸಂಗ್ರಹಿಸುತ್ತವೆ ಅಥವಾ ಔಟ್‌ಪುಟ್ ಆಗಿ ನೀಡುತ್ತವೆ. ಸಂಯೋಜಕ ಜಾಲಗಳಂತೆ, ನಾವು ಮೊದಲ ಲೇಯರ್‌ನ ನಂತರ ಮತ್ತೊಂದು ಪುನರಾವರ್ತಿತ ಲೇಯರ್ ಅನ್ನು ನಿರ್ಮಿಸಿ, ಮೊದಲ ಲೇಯರ್ ತೆಗೆದುಕೊಂಡ ಕಡಿಮೆ ಮಟ್ಟದ ಮಾದರಿಗಳಿಂದ ನಿರ್ಮಿತ ಉನ್ನತ ಮಟ್ಟದ ಮಾದರಿಗಳನ್ನು ಹಿಡಿಯಬಹುದು. ಇದರಿಂದ ನಮಗೆ **ಬಹುಮಟ್ಟದ RNN** ಎಂಬ ಕಲ್ಪನೆ ಬರುತ್ತದೆ, ಇದು ಎರಡು ಅಥವಾ ಹೆಚ್ಚು ಪುನರಾವರ್ತಿತ ಜಾಲಗಳನ್ನು ಒಳಗೊಂಡಿದ್ದು, ಹಿಂದಿನ ಲೇಯರ್‌ನ ಔಟ್‌ಪುಟ್ ಮುಂದಿನ ಲೇಯರ್‌ಗೆ ಇನ್ಪುಟ್ ಆಗಿ ನೀಡಲಾಗುತ್ತದೆ.\n", "\n", - "![ಬಹುಮಟ್ಟದ ದೀರ್ಘಕಾಲಿಕ-ಸ್ಮೃತಿ- RNN ಅನ್ನು ತೋರಿಸುವ ಚಿತ್ರ](../../../../../translated_images/kn/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![ಬಹುಮಟ್ಟದ ದೀರ್ಘಕಾಲಿಕ-ಸ್ಮೃತಿ- RNN ಅನ್ನು ತೋರಿಸುವ ಚಿತ್ರ](../../../../../translated_images/kn/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*ಚಿತ್ರ [ಈ ಅದ್ಭುತ ಪೋಸ್ಟ್](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) ನಿಂದ ಫೆರ್ನಾಂಡೋ ಲೋಪೆಜ್ ಅವರಿಂದ.*\n", "\n", diff --git a/translations/kn/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/kn/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index 64e782f0..e00302d1 100644 --- a/translations/kn/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/kn/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "ನಾವು RNN ಅನ್ನು ಪಠ್ಯವನ್ನು ರಚಿಸಲು ತರಬೇತಿಗೊಳಿಸುವ ವಿಧಾನ ಹೀಗಿದೆ. ಪ್ರತಿ ಹಂತದಲ್ಲಿ, ನಾವು `nchars` ಉದ್ದದ ಅಕ್ಷರಗಳ ಸರಣಿಯನ್ನು ತೆಗೆದು, ಪ್ರತಿಯೊಂದು ಇನ್‌ಪುಟ್ ಅಕ್ಷರಕ್ಕೆ ಮುಂದಿನ ಔಟ್‌ಪುಟ್ ಅಕ್ಷರವನ್ನು ನೆಟ್‌ವರ್ಕ್ ರಚಿಸಲು ಕೇಳುತ್ತೇವೆ:\n", "\n", - "!['HELLO' ಎಂಬ ಪದದ ಉದಾಹರಣೆಯಾಗಿ RNN ರಚನೆಯನ್ನು ತೋರಿಸುವ ಚಿತ್ರ.](../../../../../translated_images/kn/rnn-generate.56c54afb52f9781d.png)\n", + "!['HELLO' ಎಂಬ ಪದದ ಉದಾಹರಣೆಯಾಗಿ RNN ರಚನೆಯನ್ನು ತೋರಿಸುವ ಚಿತ್ರ.](../../../../../translated_images/kn/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "ವಾಸ್ತವಿಕ ಪರಿಸ್ಥಿತಿಯ ಮೇಲೆ ಅವಲಂಬಿಸಿ, ನಾವು ಕೆಲವು ವಿಶೇಷ ಅಕ್ಷರಗಳನ್ನು ಸೇರಿಸಲು ಬಯಸಬಹುದು, ಉದಾಹರಣೆಗೆ *end-of-sequence* ``. ನಮ್ಮ ಪ್ರಕರಣದಲ್ಲಿ, ನಾವು ನಿರಂತರ ಪಠ್ಯ ರಚನೆಗಾಗಿ ನೆಟ್‌ವರ್ಕ್ ಅನ್ನು ತರಬೇತಿಗೊಳಿಸಲು ಬಯಸುತ್ತೇವೆ, ಆದ್ದರಿಂದ ಪ್ರತಿ ಸರಣಿಯ ಗಾತ್ರವನ್ನು `nchars` ಟೋಕನ್‌ಗಳಿಗೆ ಸಮಾನವಾಗಿರಿಸುವೆವು. ಪರಿಣಾಮವಾಗಿ, ಪ್ರತಿ ತರಬೇತಿ ಉದಾಹರಣೆ `nchars` ಇನ್‌ಪುಟ್‌ಗಳು ಮತ್ತು `nchars` ಔಟ್‌ಪುಟ್‌ಗಳಿಂದ (ಇನ್‌ಪುಟ್ ಸರಣಿಯನ್ನು ಎಡಕ್ಕೆ ಒಂದು ಚಿಹ್ನೆ ಸರಿಸಿದವು) ಕೂಡಿರುತ್ತದೆ. ಮಿನಿಬ್ಯಾಚ್ ಹಲವಾರು ಇಂತಹ ಸರಣಿಗಳಿಂದ ಕೂಡಿರುತ್ತದೆ.\n", "\n", diff --git a/translations/kn/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/kn/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index 1fbb96f3..401dce38 100644 --- a/translations/kn/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/kn/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -113,7 +113,7 @@ "\n", "ನಾವು ಸುದ್ದಿಯ ಶೀರ್ಷಿಕೆಗಳನ್ನು ರಚಿಸಲು RNN ಅನ್ನು ತರಬೇತಿಗೊಳಿಸುವ ವಿಧಾನ ಹೀಗಿದೆ. ಪ್ರತಿ ಹಂತದಲ್ಲಿ, ನಾವು ಒಂದು ಶೀರ್ಷಿಕೆಯನ್ನು ತೆಗೆದುಕೊಳ್ಳುತ್ತೇವೆ, ಅದನ್ನು RNN ಗೆ ನೀಡಲಾಗುತ್ತದೆ, ಮತ್ತು ಪ್ರತಿ ಇನ್‌ಪುಟ್ ಅಕ್ಷರಕ್ಕೆ ನೆಟ್‌ವರ್ಕ್ ಮುಂದಿನ ಔಟ್‌ಪುಟ್ ಅಕ್ಷರವನ್ನು ರಚಿಸಲು ಕೇಳಲಾಗುತ್ತದೆ:\n", "\n", - "!['HELLO' ಎಂಬ ಪದದ ಉದಾಹರಣೆಯಾಗಿ RNN ರಚನೆಯನ್ನು ತೋರಿಸುವ ಚಿತ್ರ.](../../../../../translated_images/kn/rnn-generate.56c54afb52f9781d.png)\n", + "!['HELLO' ಎಂಬ ಪದದ ಉದಾಹರಣೆಯಾಗಿ RNN ರಚನೆಯನ್ನು ತೋರಿಸುವ ಚಿತ್ರ.](../../../../../translated_images/kn/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "ನಮ್ಮ ಕ್ರಮದ ಕೊನೆಯ ಅಕ್ಷರಕ್ಕೆ, ನಾವು ನೆಟ್‌ವರ್ಕ್ `` ಟೋಕನ್ ರಚಿಸಲು ಕೇಳುತ್ತೇವೆ.\n", "\n", diff --git a/translations/kn/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/kn/lessons/5-NLP/17-GenerativeNetworks/README.md index ae4c03fd..f628ab74 100644 --- a/translations/kn/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/kn/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: ಇದು ಕೆಳಗಿನ ಚಿತ್ರದಲ್ಲಿ ತೋರಿಸಿದಂತೆ ವಿಭಿನ್ನ ನ್ಯೂರಲ್ ವಾಸ್ತುಶಿಲ್ಪಗಳಿಗೆ ಅವಕಾಶ ನೀಡುತ್ತದೆ: -![Image showing common recurrent neural network patterns.](../../../../../translated_images/kn/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Image showing common recurrent neural network patterns.](../../../../../translated_images/kn/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > ಚಿತ್ರವು [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) ಬ್ಲಾಗ್ ಪೋಸ್ಟ್‌ನಿಂದ [Andrej Karpaty](http://karpathy.github.io/) ಅವರಿಂದ @@ -32,11 +32,11 @@ CO_OP_TRANSLATOR_METADATA: ನಾವು ಈ RNN ಅನ್ನು ಹಂತ ಹಂತವಾಗಿ ಪಠ್ಯ ರಚಿಸಲು ತರಬೇತುಗೊಳಿಸುವೆವು. ಪ್ರತಿ ಹಂತದಲ್ಲಿ, ನಾವು `nchars` ಉದ್ದದ ಅಕ್ಷರ ಸರಣಿಯನ್ನು ತೆಗೆದು, ಪ್ರತಿ ಇನ್‌ಪುಟ್ ಅಕ್ಷರಕ್ಕೆ ಮುಂದಿನ ಔಟ್‌ಪುಟ್ ಅಕ್ಷರವನ್ನು ರಚಿಸಲು ನೆಟ್‌ವರ್ಕ್‌ಗೆ ಕೇಳುತ್ತೇವೆ: -![Image showing an example RNN generation of the word 'HELLO'.](../../../../../translated_images/kn/rnn-generate.56c54afb52f9781d.png) +![Image showing an example RNN generation of the word 'HELLO'.](../../../../../translated_images/kn/rnn-generate.56c54afb52f9781d.webp) ಪಠ್ಯ ರಚಿಸುವಾಗ (ಇನ್ಫರೆನ್ಸ್ ಸಮಯದಲ್ಲಿ), ನಾವು ಕೆಲವು **ಪ್ರಾಂಪ್ಟ್**ನಿಂದ ಪ್ರಾರಂಭಿಸುತ್ತೇವೆ, ಅದನ್ನು RNN ಸೆಲ್‌ಗಳ ಮೂಲಕ ಹೋದರೆ ಮಧ್ಯಂತರ ಸ್ಥಿತಿಯನ್ನು ರಚಿಸಲಾಗುತ್ತದೆ, ಮತ್ತು ಆ ಸ್ಥಿತಿಯಿಂದ ರಚನೆ ಪ್ರಾರಂಭವಾಗುತ್ತದೆ. ನಾವು ಒಂದೊಂದು ಅಕ್ಷರವನ್ನು ರಚಿಸಿ, ಆ ಸ್ಥಿತಿಯನ್ನು ಮತ್ತು ರಚಿಸಿದ ಅಕ್ಷರವನ್ನು ಮತ್ತೊಂದು RNN ಸೆಲ್‌ಗೆ ನೀಡುತ್ತೇವೆ ಮುಂದಿನ ಅಕ್ಷರವನ್ನು ರಚಿಸಲು, ಇದನ್ನು ಬೇಕಾದಷ್ಟು ಅಕ್ಷರಗಳು ರಚಿಸುವವರೆಗೆ ಮುಂದುವರಿಸುತ್ತೇವೆ. - + > ಚಿತ್ರ ಲೇಖಕರಿಂದ diff --git a/translations/kn/lessons/5-NLP/18-Transformers/README.md b/translations/kn/lessons/5-NLP/18-Transformers/README.md index bb2ef635..1cf634be 100644 --- a/translations/kn/lessons/5-NLP/18-Transformers/README.md +++ b/translations/kn/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ RNN ಗಳೊಂದಿಗೆ, ಕ್ರಮ-ದಿಂದ-ಕ್ರಮ ಕಾರ **ಗಮನ ಯಂತ್ರಗಳು** RNN ನ ಪ್ರತಿ ಔಟ್‌ಪುಟ್ ಭವಿಷ್ಯವಾಣಿಗೆ ಪ್ರತಿ ಇನ್‌ಪುಟ್ ವೆಕ್ಟರ್‌ನ ಸಾಂದರ್ಭಿಕ ಪ್ರಭಾವವನ್ನು ತೂಕ ನೀಡುವ ವಿಧಾನವನ್ನು ಒದಗಿಸುತ್ತವೆ. ಇದನ್ನು ಅನುಷ್ಠಾನಗೊಳಿಸುವ ವಿಧಾನವೆಂದರೆ ಇನ್‌ಪುಟ್ RNN ಮತ್ತು ಔಟ್‌ಪುಟ್ RNN ನಡುವಿನ ಮಧ್ಯಂತರ ಸ್ಥಿತಿಗಳ ನಡುವೆ ಶಾರ್ಟ್‌ಕಟ್‌ಗಳನ್ನು ಸೃಷ್ಟಿಸುವುದು. ಈ ರೀತಿಯಲ್ಲಿ, ಔಟ್‌ಪುಟ್ ಚಿಹ್ನೆ yt ಅನ್ನು ರಚಿಸುವಾಗ, ನಾವು ಎಲ್ಲಾ ಇನ್‌ಪುಟ್ ಗುಪ್ತ ಸ್ಥಿತಿಗಳು hiಗಳನ್ನು ವಿಭಿನ್ನ ತೂಕ ಗುಣಾಂಕಗಳು αt,iಗಳೊಂದಿಗೆ ಪರಿಗಣಿಸುತ್ತೇವೆ. -![ಎನ್‌ಕೋಡರ್/ಡಿಕೋಡರ್ ಮಾದರಿಯನ್ನು ತೋರಿಸುವ ಚಿತ್ರ, ಜೊತೆಗೆ ಸೇರಿಸಿದ ಗಮನ ಪದರ](../../../../../translated_images/kn/encoder-decoder-attention.7a726296894fb567.png) +![ಎನ್‌ಕೋಡರ್/ಡಿಕೋಡರ್ ಮಾದರಿಯನ್ನು ತೋರಿಸುವ ಚಿತ್ರ, ಜೊತೆಗೆ ಸೇರಿಸಿದ ಗಮನ ಪದರ](../../../../../translated_images/kn/encoder-decoder-attention.7a726296894fb567.webp) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) ನಲ್ಲಿ ಸೇರಿಸಿದ ಗಮನ ಯಂತ್ರದೊಂದಿಗೆ ಎನ್‌ಕೋಡರ್-ಡಿಕೋಡರ್ ಮಾದರಿ, [ಈ ಬ್ಲಾಗ್ ಪೋಸ್ಟ್](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) ನಿಂದ ಉಲ್ಲೇಖಿಸಲಾಗಿದೆ ಗಮನ ಮ್ಯಾಟ್ರಿಕ್ಸ್ {αi,j} ಒಂದು ನಿರ್ದಿಷ್ಟ ಔಟ್‌ಪುಟ್ ಪದವನ್ನು ರಚಿಸುವಲ್ಲಿ ಕೆಲವು ಇನ್‌ಪುಟ್ ಪದಗಳು ಎಷ್ಟು ಪ್ರಭಾವ ಬೀರುತ್ತವೆ ಎಂಬುದನ್ನು ಪ್ರತಿನಿಧಿಸುತ್ತದೆ. ಕೆಳಗಿನ ಚಿತ್ರದಲ್ಲಿ ಇಂತಹ ಮ್ಯಾಟ್ರಿಕ್ಸ್ ಉದಾಹರಣೆ ನೀಡಲಾಗಿದೆ: -![RNNsearch-50 ಮೂಲಕ ಕಂಡುಬಂದ ಮಾದರಿ ಹೊಂದಾಣಿಕೆ ಚಿತ್ರ, Bahdanau - arviz.org ನಿಂದ ತೆಗೆದಿದೆ](../../../../../translated_images/kn/bahdanau-fig3.09ba2d37f202a6af.png) +![RNNsearch-50 ಮೂಲಕ ಕಂಡುಬಂದ ಮಾದರಿ ಹೊಂದಾಣಿಕೆ ಚಿತ್ರ, Bahdanau - arviz.org ನಿಂದ ತೆಗೆದಿದೆ](../../../../../translated_images/kn/bahdanau-fig3.09ba2d37f202a6af.webp) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (ಚಿತ್ರ 3) ನಿಂದ @@ -56,7 +56,7 @@ RNN ಗಳೊಂದಿಗೆ, ಕ್ರಮ-ದಿಂದ-ಕ್ರಮ ಕಾರ * ಟೋಕನ್ ಎಂಬೆಡ್ಡಿಂಗ್‌ಗೆ ಹೋಲುವ ತರಬೇತಿಗೊಳ್ಳಬಹುದಾದ ಎಂಬೆಡ್ಡಿಂಗ್. ಇದನ್ನು ಇಲ್ಲಿ ನಾವು ಪರಿಗಣಿಸುತ್ತೇವೆ. ಟೋಕನ್‌ಗಳು ಮತ್ತು ಅವುಗಳ ಸ್ಥಾನಗಳ ಮೇಲೆ ಎಂಬೆಡ್ಡಿಂಗ್ ಪದರಗಳನ್ನು ಅನ್ವಯಿಸಿ, ಸಮಾನ ಆಯಾಮಗಳ ಎಂಬೆಡ್ಡಿಂಗ್ ವೆಕ್ಟರ್‌ಗಳನ್ನು ಪಡೆಯುತ್ತೇವೆ ಮತ್ತು ಅವುಗಳನ್ನು ಸೇರಿಸುತ್ತೇವೆ. * ಮೂಲ ಕಾಗದದಲ್ಲಿ ಪ್ರಸ್ತಾಪಿಸಿದ ಸ್ಥಿರ ಸ್ಥಾನ ಎನ್‌ಕೋಡಿಂಗ್ ಕಾರ್ಯ. - + > ಲೇಖಕರಿಂದ ಚಿತ್ರ @@ -66,7 +66,7 @@ RNN ಗಳೊಂದಿಗೆ, ಕ್ರಮ-ದಿಂದ-ಕ್ರಮ ಕಾರ ಮುಂದೆ, ನಾವು ಕ್ರಮದೊಳಗಿನ ಕೆಲವು ಮಾದರಿಗಳನ್ನು ಹಿಡಿಯಬೇಕಾಗುತ್ತದೆ. ಇದಕ್ಕಾಗಿ, ಟ್ರಾನ್ಸ್‌ಫಾರ್ಮರ್‌ಗಳು **ಸ್ವ-ಗಮನ** ಯಂತ್ರವನ್ನು ಬಳಸುತ್ತವೆ, ಇದು ಇನ್‌ಪುಟ್ ಮತ್ತು ಔಟ್‌ಪುಟ್ ಕ್ರಮ ಒಂದೇ ಆಗಿರುವ ಗಮನವಾಗಿದೆ. ಸ್ವ-ಗಮನವನ್ನು ಅನ್ವಯಿಸುವುದರಿಂದ ವಾಕ್ಯದ **ಸಂದರ್ಭ**ವನ್ನು ಪರಿಗಣಿಸಲು ಸಾಧ್ಯವಾಗುತ್ತದೆ ಮತ್ತು ಯಾವ ಪದಗಳು ಪರಸ್ಪರ ಸಂಬಂಧ ಹೊಂದಿವೆ ಎಂದು ನೋಡಬಹುದು. ಉದಾಹರಣೆಗೆ, ಇದು *it* ಮುಂತಾದ ಕೋರೆಫರೆನ್ಸ್‌ಗಳ ಮೂಲಕ ಯಾವ ಪದಗಳನ್ನು ಸೂಚಿಸಲಾಗುತ್ತಿದೆ ಎಂಬುದನ್ನು ತಿಳಿಯಲು ಸಹಾಯ ಮಾಡುತ್ತದೆ ಮತ್ತು ಸಾಂದರ್ಭಿಕ ಮಾಹಿತಿಯನ್ನು ಪರಿಗಣಿಸುತ್ತದೆ: -![](../../../../../translated_images/kn/CoreferenceResolution.861924d6d384a7d6.png) +![](../../../../../translated_images/kn/CoreferenceResolution.861924d6d384a7d6.webp) > [Google ಬ್ಲಾಗ್](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) ನಿಂದ ಚಿತ್ರ @@ -91,7 +91,7 @@ RNN ಗಳೊಂದಿಗೆ, ಕ್ರಮ-ದಿಂದ-ಕ್ರಮ ಕಾರ **BERT** (Bidirectional Encoder Representations from Transformers) ಒಂದು ಬಹುಮಟ್ಟದ ಟ್ರಾನ್ಸ್‌ಫಾರ್ಮರ್ ಜಾಲವಾಗಿದೆ, *BERT-base* ಗೆ 12 ಪದರಗಳು ಮತ್ತು *BERT-large* ಗೆ 24 ಪದರಗಳಿವೆ. ಈ ಮಾದರಿಯನ್ನು ಮೊದಲು ದೊಡ್ಡ ಪಠ್ಯ ಸಂಗ್ರಹ (ವಿಕಿಪೀಡಿಯ + ಪುಸ್ತಕಗಳು) ಮೇಲೆ ಸ್ವಯಂನಿರೀಕ್ಷಿತ ತರಬೇತಿಯಲ್ಲಿ (ವಾಕ್ಯದಲ್ಲಿ ಮಸ್ಕ್ ಮಾಡಿದ ಪದಗಳನ್ನು ಊಹಿಸುವುದು) ಪೂರ್ವ-ತರಬೇತಿ ಮಾಡಲಾಗುತ್ತದೆ. ಪೂರ್ವ-ತರಬೇತಿಯ ಸಮಯದಲ್ಲಿ, ಮಾದರಿ ಭಾಷಾ ಅರ್ಥಮಾಡಿಕೊಳ್ಳುವ ಮಹತ್ವಪೂರ್ಣ ಮಟ್ಟವನ್ನು ಅಳವಡಿಸಿಕೊಂಡು, ನಂತರ ಇತರ ಡೇಟಾಸೆಟ್‌ಗಳೊಂದಿಗೆ ಸೂಕ್ಷ್ಮ-ತರಬೇತಿ ಮೂಲಕ ಉಪಯೋಗಿಸಬಹುದು. ಈ ಪ್ರಕ್ರಿಯೆಯನ್ನು **ಟ್ರಾನ್ಸ್‌ಫರ್ ಲರ್ನಿಂಗ್** ಎಂದು ಕರೆಯುತ್ತಾರೆ. -![ಚಿತ್ರ http://jalammar.github.io/illustrated-bert/ ನಿಂದ](../../../../../translated_images/kn/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) +![ಚಿತ್ರ http://jalammar.github.io/illustrated-bert/ ನಿಂದ](../../../../../translated_images/kn/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > ಚಿತ್ರ [ಮೂಲ](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/kn/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/kn/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index d266373c..0f0ba843 100644 --- a/translations/kn/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/kn/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**ಗಮನ ಯಂತ್ರಗಳು** RNNನ ಪ್ರತಿ ಔಟ್‌ಪುಟ್ ಭವಿಷ್ಯವಾಣಿ ಮೇಲೆ ಪ್ರತಿ ಇನ್‌ಪುಟ್ ವೆಕ್ಟರ್‌ನ ಸಾಂದರ್ಭಿಕ ಪ್ರಭಾವವನ್ನು ತೂಕ ನೀಡುವ ವಿಧಾನವನ್ನು ಒದಗಿಸುತ್ತವೆ. ಇದನ್ನು ಅನುಷ್ಠಾನಗೊಳಿಸುವ ವಿಧಾನವೆಂದರೆ ಇನ್‌ಪುಟ್ RNN ಮತ್ತು ಔಟ್‌ಪುಟ್ RNNನ ಮಧ್ಯಂತರ ಸ್ಥಿತಿಗಳ ನಡುವೆ ಶಾರ್ಟ್‌ಕಟ್‌ಗಳನ್ನು ಸೃಷ್ಟಿಸುವುದು. ಈ ರೀತಿಯಲ್ಲಿ, ಔಟ್‌ಪುಟ್ ಚಿಹ್ನೆ $y_t$ ರಚಿಸುವಾಗ, ನಾವು ಎಲ್ಲಾ ಇನ್‌ಪುಟ್ ಗುಪ್ತ ಸ್ಥಿತಿಗಳು $h_i$ಗಳನ್ನು ವಿಭಿನ್ನ ತೂಕ ಗುಣಾಂಕಗಳು $\\alpha_{t,i}$ ಜೊತೆಗೆ ಪರಿಗಣಿಸುತ್ತೇವೆ.\n", "\n", - "![ಎನ್‌ಕೋಡರ್/ಡಿಕೋಡರ್ ಮಾದರಿಯನ್ನು ತೋರಿಸುವ ಚಿತ್ರ, ಜೊತೆಗೆ ಸೇರಿಸಿದ ಗಮನ ಪದರ](../../../../../translated_images/kn/encoder-decoder-attention.7a726296894fb567.png)\n", + "![ಎನ್‌ಕೋಡರ್/ಡಿಕೋಡರ್ ಮಾದರಿಯನ್ನು ತೋರಿಸುವ ಚಿತ್ರ, ಜೊತೆಗೆ ಸೇರಿಸಿದ ಗಮನ ಪದರ](../../../../../translated_images/kn/encoder-decoder-attention.7a726296894fb567.webp)\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) ನಲ್ಲಿನ ಸೇರಿಸಿದ ಗಮನ ಯಂತ್ರದೊಂದಿಗೆ ಎನ್‌ಕೋಡರ್-ಡಿಕೋಡರ್ ಮಾದರಿ, [ಈ ಬ್ಲಾಗ್ ಪೋಸ್ಟ್](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) ನಿಂದ ಉಲ್ಲೇಖಿಸಲಾಗಿದೆ*\n", "\n", "ಗಮನ ಮ್ಯಾಟ್ರಿಕ್ಸ್ $\\{\\alpha_{i,j}\\}$ ನಿರ್ದಿಷ್ಟ ಇನ್‌ಪುಟ್ ಪದಗಳು ಔಟ್‌ಪುಟ್ ಸರಣಿಯ ನಿರ್ದಿಷ್ಟ ಪದ ರಚನೆಯಲ್ಲಿ ಎಷ್ಟು ಪಾತ್ರ ವಹಿಸುತ್ತವೆ ಎಂಬ ಮಟ್ಟವನ್ನು ಪ್ರತಿನಿಧಿಸುತ್ತದೆ. ಕೆಳಗಿನ ಚಿತ್ರವು ಇಂತಹ ಮ್ಯಾಟ್ರಿಕ್ಸ್‌ನ ಉದಾಹರಣೆಯಾಗಿದೆ:\n", "\n", - "![RNNsearch-50 ಮೂಲಕ ಕಂಡುಬಂದ ಮಾದರಿ ಹೊಂದಾಣಿಕೆ ಚಿತ್ರ, Bahdanau - arviz.org ನಿಂದ ತೆಗೆದಿದೆ](../../../../../translated_images/kn/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![RNNsearch-50 ಮೂಲಕ ಕಂಡುಬಂದ ಮಾದರಿ ಹೊಂದಾಣಿಕೆ ಚಿತ್ರ, Bahdanau - arviz.org ನಿಂದ ತೆಗೆದಿದೆ](../../../../../translated_images/kn/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (ಚಿತ್ರ 3) ನಿಂದ ತೆಗೆದಿದೆ*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) ಒಂದು ಬಹಳ ದೊಡ್ಡ ಬಹುಮಟ್ಟದ ಟ್ರಾನ್ಸ್‌ಫಾರ್ಮರ್ ನೆಟ್‌ವರ್ಕ್ ಆಗಿದ್ದು, *BERT-base* ಗೆ 12 ಮಟ್ಟಗಳು ಮತ್ತು *BERT-large* ಗೆ 24 ಮಟ್ಟಗಳಿವೆ. ಈ ಮಾದರಿಯನ್ನು ಮೊದಲು ದೊಡ್ಡ ಪಠ್ಯ ಸಂಗ್ರಹ (ವಿಕಿಪೀಡಿಯಾ + ಪುಸ್ತಕಗಳು) ಮೇಲೆ ಸ್ವಯಂನಿರೀಕ್ಷಿತ ತರಬೇತಿಯಲ್ಲಿ (ವಾಕ್ಯದಲ್ಲಿ ಮಸ್ಕ್ ಮಾಡಲಾದ ಪದಗಳನ್ನು ಭವಿಷ್ಯವಾಣಿ ಮಾಡುವ ಮೂಲಕ) ಪೂರ್ವ-ತರಬೇತಿ ಮಾಡಲಾಗುತ್ತದೆ. ಪೂರ್ವ-ತರಬೇತಿಯ ಸಮಯದಲ್ಲಿ ಮಾದರಿ ಭಾಷಾ ಅರ್ಥಮಾಡಿಕೊಳ್ಳುವ ಮಹತ್ವಪೂರ್ಣ ಮಟ್ಟವನ್ನು ಶೋಷಿಸುತ್ತದೆ, ಇದನ್ನು ನಂತರ ಇತರ ಡೇಟಾಸೆಟ್‌ಗಳೊಂದಿಗೆ ಸೂಕ್ಷ್ಮ-ಸಂಯೋಜನೆಯ ಮೂಲಕ ಉಪಯೋಗಿಸಬಹುದು. ಈ ಪ್ರಕ್ರಿಯೆಯನ್ನು **ಟ್ರಾನ್ಸ್‌ಫರ್ ಲರ್ನಿಂಗ್** ಎಂದು ಕರೆಯುತ್ತಾರೆ.\n", "\n", - "![http://jalammar.github.io/illustrated-bert/ ನಿಂದ ಚಿತ್ರ](../../../../../translated_images/kn/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![http://jalammar.github.io/illustrated-bert/ ನಿಂದ ಚಿತ್ರ](../../../../../translated_images/kn/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "BERT, DistilBERT, BigBird, OpenGPT3 ಮತ್ತು ಇನ್ನಷ್ಟು ಟ್ರಾನ್ಸ್‌ಫಾರ್ಮರ್ ವಾಸ್ತುಶಿಲ್ಪಗಳ ಅನೇಕ ರೂಪಾಂತರಗಳಿವೆ, ಅವುಗಳನ್ನು ಸೂಕ್ಷ್ಮ-ಸಂಯೋಜನೆ ಮಾಡಬಹುದು. [HuggingFace ಪ್ಯಾಕೇಜ್](https://github.com/huggingface/) PyTorch ಬಳಸಿ ಈ ವಾಸ್ತುಶಿಲ್ಪಗಳ ಬಹುತೇಕ ತರಬೇತಿಗಾಗಿ ಸಂಗ್ರಹಾಲಯವನ್ನು ಒದಗಿಸುತ್ತದೆ.\n", "\n", diff --git a/translations/kn/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/kn/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index 1cdaa8d0..7c9837cc 100644 --- a/translations/kn/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/kn/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**ಗಮನ ಯಂತ್ರಗಳು** RNNನ ಪ್ರತಿ ಔಟ್‌ಪುಟ್ ಭವಿಷ್ಯವಾಣಿ ಮೇಲೆ ಪ್ರತಿ ಇನ್‌ಪುಟ್ ವೆಕ್ಟರ್‌ನ ಸಾಂದರ್ಭಿಕ ಪ್ರಭಾವವನ್ನು ತೂಕ ನೀಡುವ ವಿಧಾನವನ್ನು ಒದಗಿಸುತ್ತವೆ. ಇದನ್ನು ಅನುಷ್ಠಾನಗೊಳಿಸುವ ವಿಧಾನವೆಂದರೆ ಇನ್‌ಪುಟ್ RNNನ ಮಧ್ಯಂತರ ಸ್ಥಿತಿಗಳ ಮತ್ತು ಔಟ್‌ಪುಟ್ RNNನ ನಡುವೆ ಶಾರ್ಟ್‌ಕಟ್‌ಗಳನ್ನು ಸೃಷ್ಟಿಸುವುದು. ಈ ರೀತಿಯಲ್ಲಿ, ಔಟ್‌ಪುಟ್ ಚಿಹ್ನೆ $y_t$ ರಚಿಸುವಾಗ, ನಾವು ಎಲ್ಲಾ ಇನ್‌ಪುಟ್ ಗುಪ್ತ ಸ್ಥಿತಿಗಳು $h_i$ಗಳನ್ನು ವಿಭಿನ್ನ ತೂಕ ಗುಣಾಂಕಗಳ $\\alpha_{t,i}$ೊಂದಿಗೆ ಪರಿಗಣಿಸುತ್ತೇವೆ.\n", "\n", - "![ಚಿತ್ರವು ಎನ್‌ಕೋಡರ್/ಡಿಕೋಡರ್ ಮಾದರಿಯನ್ನು ಸೇರಿಸಿದ ಗಮನ ಪದರದೊಂದಿಗೆ ತೋರಿಸುತ್ತದೆ](../../../../../translated_images/kn/encoder-decoder-attention.7a726296894fb567.png)\n", + "![ಚಿತ್ರವು ಎನ್‌ಕೋಡರ್/ಡಿಕೋಡರ್ ಮಾದರಿಯನ್ನು ಸೇರಿಸಿದ ಗಮನ ಪದರದೊಂದಿಗೆ ತೋರಿಸುತ್ತದೆ](../../../../../translated_images/kn/encoder-decoder-attention.7a726296894fb567.webp)\n", "*ಎನ್‌ಕೋಡರ್-ಡಿಕೋಡರ್ ಮಾದರಿಯೊಂದಿಗೆ ಸೇರಿಸಿದ ಗಮನ ಯಂತ್ರವನ್ನು [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) ನಲ್ಲಿ ವಿವರಿಸಲಾಗಿದೆ, [ಈ ಬ್ಲಾಗ್ ಪೋಸ್ಟ್](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) ನಿಂದ ಉಲ್ಲೇಖಿಸಲಾಗಿದೆ*\n", "\n", "ಗಮನ ಮ್ಯಾಟ್ರಿಕ್ಸ್ $\\{\\alpha_{i,j}\\}$ ನಿರ್ದಿಷ್ಟ ಇನ್‌ಪುಟ್ ಪದಗಳು ಔಟ್‌ಪುಟ್ ಸರಣಿಯ ನಿರ್ದಿಷ್ಟ ಪದ ರಚನೆಯಲ್ಲಿ ಎಷ್ಟು ಪಾತ್ರ ವಹಿಸುತ್ತವೆ ಎಂಬುದನ್ನು ಪ್ರತಿನಿಧಿಸುತ್ತದೆ. ಕೆಳಗಿನ ಚಿತ್ರದಲ್ಲಿ ಇಂತಹ ಮ್ಯಾಟ್ರಿಕ್ಸ್ ಉದಾಹರಣೆ ನೀಡಲಾಗಿದೆ:\n", "\n", - "![ಚಿತ್ರವು RNNsearch-50 ಮೂಲಕ ಕಂಡುಬಂದ ಮಾದರಿ ಹೊಂದಾಣಿಕೆಯನ್ನು ತೋರಿಸುತ್ತದೆ, Bahdanau - arviz.org ನಿಂದ ತೆಗೆದುಕೊಂಡಿದೆ](../../../../../translated_images/kn/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![ಚಿತ್ರವು RNNsearch-50 ಮೂಲಕ ಕಂಡುಬಂದ ಮಾದರಿ ಹೊಂದಾಣಿಕೆಯನ್ನು ತೋರಿಸುತ್ತದೆ, Bahdanau - arviz.org ನಿಂದ ತೆಗೆದುಕೊಂಡಿದೆ](../../../../../translated_images/kn/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*ಚಿತ್ರ [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (ಚಿತ್ರ 3) ನಿಂದ ತೆಗೆದುಕೊಂಡಿದೆ*\n", "\n", @@ -92,7 +92,7 @@ "source": [ "ಈ ಲೇಯರ್ ಎರಡು `Embedding` ಲೇಯರ್‌ಗಳಿಂದ ಕೂಡಿದೆ: ಟೋಕನ್‌ಗಳನ್ನು ಎम्बೆಡ್ ಮಾಡಲು (ನಾವು ಹಿಂದಿನಂತೆ ಚರ್ಚಿಸಿದ ರೀತಿಯಲ್ಲಿ) ಮತ್ತು ಟೋಕನ್ ಸ್ಥಾನಗಳನ್ನು ಎम्बೆಡ್ ಮಾಡಲು. ಟೋಕನ್ ಸ್ಥಾನಗಳನ್ನು 0 ರಿಂದ `maxlen` ವರೆಗೆ ನೈಸರ್ಗಿಕ ಸಂಖ್ಯೆಗಳ ಸರಣಿಯಾಗಿ `tf.range` ಬಳಸಿ ರಚಿಸಲಾಗುತ್ತದೆ, ನಂತರ ಅದನ್ನು ಎम्बೆಡಿಂಗ್ ಲೇಯರ್ ಮೂಲಕ ಹಾದುಹೋಗುತ್ತದೆ. ಎರಡು ಫಲಿತಾಂಶ ಎम्बೆಡಿಂಗ್ ವೆಕ್ಟರ್‌ಗಳನ್ನು ಸೇರಿಸಲಾಗುತ್ತದೆ, ಇದರಿಂದ `maxlen`$\\times$`embed_dim` ಆಕಾರದ ಸ್ಥಾನಾನುಕ್ರಮಿತ ಎम्बೆಡಿಂಗ್ ಪ್ರತಿನಿಧಾನ ಸೃಷ್ಟಿಯಾಗುತ್ತದೆ.\n", "\n", - "\n", + "\n", "\n", "ಈಗ, ಟ್ರಾನ್ಸ್‌ಫಾರ್ಮರ್ ಬ್ಲಾಕ್ ಅನ್ನು ಅನುಷ್ಠಾನಗೊಳಿಸೋಣ. ಇದು ಹಿಂದಿನದಾಗಿ ವ್ಯಾಖ್ಯಾನಿಸಿದ ಎम्बೆಡಿಂಗ್ ಲೇಯರ್‌ನ ಔಟ್‌ಪುಟ್ ಅನ್ನು ತೆಗೆದುಕೊಳ್ಳುತ್ತದೆ:\n" ] @@ -134,7 +134,7 @@ "\n", "ಈ ಲೇಯರ್‌ನ ಔಟ್‌ಪುಟ್ ನಂತರ `Dense` ನೆಟ್‌ವರ್ಕ್ (ನಮ್ಮ ಪ್ರಕರಣದಲ್ಲಿ - ಎರಡು ಲೇಯರ್ ಪರ್ಸೆಪ್ಟ್ರಾನ್) ಮೂಲಕ ಸಾಗಿಸಲಾಗುತ್ತದೆ, ಮತ್ತು ಫಲಿತಾಂಶವನ್ನು ಅಂತಿಮ ಔಟ್‌ಪುಟ್‌ಗೆ ಸೇರಿಸಲಾಗುತ್ತದೆ (ಅದು ಮತ್ತೆ ಸಾಮಾನ್ಯೀಕರಣಕ್ಕೆ ಒಳಗಾಗುತ್ತದೆ).\n", "\n", - "\n", + "\n", "\n", "ಈಗ, ನಾವು ಸಂಪೂರ್ಣ ಟ್ರಾನ್ಸ್‌ಫಾರ್ಮರ್ ಮಾದರಿಯನ್ನು ವ್ಯಾಖ್ಯಾನಿಸಲು ಸಿದ್ಧರಾಗಿದ್ದೇವೆ:\n" ] @@ -235,7 +235,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) ಒಂದು ಬಹಳ ದೊಡ್ಡ ಬಹುಮಟ್ಟದ ಟ್ರಾನ್ಸ್‌ಫಾರ್ಮರ್ ನೆಟ್‌ವರ್ಕ್ ಆಗಿದ್ದು, *BERT-base* ಗೆ 12 ಲೇಯರ್‌ಗಳು ಮತ್ತು *BERT-large* ಗೆ 24 ಲೇಯರ್‌ಗಳಿವೆ. ಈ ಮಾದರಿಯನ್ನು ಮೊದಲು ದೊಡ್ಡ ಪಠ್ಯ ಡೇಟಾ (ವಿಕಿಪೀಡಿಯಾ + ಪುಸ್ತಕಗಳು) ಮೇಲೆ ಅನ್‌ಸೂಪರ್ವೈಸ್‌ಡ್ ತರಬೇತಿಯಲ್ಲಿ (ವಾಕ್ಯದಲ್ಲಿ ಮಸ್ಕ್ ಮಾಡಲಾದ ಪದಗಳನ್ನು ಊಹಿಸುವ ಮೂಲಕ) ಪೂರ್ವ-ತರಬೇತಿ ಮಾಡಲಾಗುತ್ತದೆ. ಪೂರ್ವ-ತರಬೇತಿ ಸಮಯದಲ್ಲಿ, ಮಾದರಿ ಭಾಷೆಯ ಅರ್ಥಮಾಡಿಕೊಳ್ಳುವ ಮಹತ್ವದ ಮಟ್ಟವನ್ನು ಅಳವಡಿಸಿಕೊಂಡು, ನಂತರ ಇತರ ಡೇಟಾಸೆಟ್‌ಗಳೊಂದಿಗೆ ಫೈನ್ ಟ್ಯೂನಿಂಗ್ ಮೂಲಕ ಉಪಯೋಗಿಸಬಹುದು. ಈ ಪ್ರಕ್ರಿಯೆಯನ್ನು **ಟ್ರಾನ್ಸ್‌ಫರ್ ಲರ್ನಿಂಗ್** ಎಂದು ಕರೆಯುತ್ತಾರೆ.\n", "\n", - "![picture from http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/kn/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![picture from http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/kn/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "ಟ್ರಾನ್ಸ್‌ಫಾರ್ಮರ್ ವಾಸ್ತುಶಿಲ್ಪಗಳ ಹಲವು ಬದಲಾವಣೆಗಳಿವೆ, ಅವುಗಳಲ್ಲಿ BERT, DistilBERT, BigBird, OpenGPT3 ಮತ್ತು ಇನ್ನಷ್ಟು ಫೈನ್ ಟ್ಯೂನಿಂಗ್ ಮಾಡಬಹುದಾದವುಗಳಾಗಿವೆ.\n", "\n", diff --git a/translations/kn/lessons/5-NLP/19-NER/README.md b/translations/kn/lessons/5-NLP/19-NER/README.md index e60a7602..d4441b6a 100644 --- a/translations/kn/lessons/5-NLP/19-NER/README.md +++ b/translations/kn/lessons/5-NLP/19-NER/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: ನೀವು ಅಮೆಜಾನ್ ಅಲೆಕ್ಸಾ ಅಥವಾ ಗೂಗಲ್ ಅಸಿಸ್ಟೆಂಟ್‌ನಂತೆ ನೈಸರ್ಗಿಕ ಭಾಷೆ ಚಾಟ್ ಬಾಟ್ ಅಭಿವೃದ್ಧಿಪಡಿಸಲು ಬಯಸಿದರೆ, ಬುದ್ಧಿವಂತ ಚಾಟ್ ಬಾಟ್‌ಗಳು ಬಳಕೆದಾರನು ಏನು ಬಯಸುತ್ತಾನೆ ಎಂಬುದನ್ನು *ಅರ್ಥಮಾಡಿಕೊಳ್ಳಲು* ಇನ್‌ಪುಟ್ ವಾಕ್ಯದಲ್ಲಿ ಪಠ್ಯ ವರ್ಗೀಕರಣವನ್ನು ಮಾಡುತ್ತವೆ. ಈ ವರ್ಗೀಕರಣದ ಫಲಿತಾಂಶವನ್ನು **ಉದ್ದೇಶ** ಎಂದು ಕರೆಯುತ್ತಾರೆ, ಇದು ಚಾಟ್ ಬಾಟ್ ಏನು ಮಾಡಬೇಕು ಎಂದು ನಿರ್ಧರಿಸುತ್ತದೆ. -Bot NER +Bot NER > ಚಿತ್ರ ಲೇಖಕರಿಂದ @@ -58,7 +58,7 @@ infant | O ಟೋಕನ್‌ಗಳು ಮತ್ತು ವರ್ಗಗಳ ನಡುವೆ ಒಂದರೊಂದರ ಹೊಂದಾಣಿಕೆಯನ್ನು ನಿರ್ಮಿಸಬೇಕಾಗಿರುವುದರಿಂದ, ಈ ಚಿತ್ರದಿಂದ ನಾವು ಬಲಭಾಗದ **ಬಹು-ದಿಂದ-ಬಹು** ನ್ಯೂರಲ್ ನೆಟ್‌ವರ್ಕ್ ಮಾದರಿಯನ್ನು ತರಬೇತುಗೊಳಿಸಬಹುದು: -![Image showing common recurrent neural network patterns.](../../../../../translated_images/kn/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Image showing common recurrent neural network patterns.](../../../../../translated_images/kn/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > *ಚಿತ್ರ [ಈ ಬ್ಲಾಗ್ ಪೋಸ್ಟ್](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) ನಿಂದ [ಅಂದ್ರೇಜ್ ಕಾರ್ಪಥಿ](http://karpathy.github.io/) ಅವರಿಂದ. NER ಟೋಕನ್ ವರ್ಗೀಕರಣ ಮಾದರಿಗಳು ಈ ಚಿತ್ರದಲ್ಲಿ ಬಲಭಾಗದ ನೆಟ್‌ವರ್ಕ್ ವಾಸ್ತುಶಿಲ್ಪಕ್ಕೆ ಹೊಂದಿಕೆಯಾಗುತ್ತವೆ.* diff --git a/translations/kn/lessons/5-NLP/README.md b/translations/kn/lessons/5-NLP/README.md index aa0ab1d5..58bba849 100644 --- a/translations/kn/lessons/5-NLP/README.md +++ b/translations/kn/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # ನೈಸರ್ಗಿಕ ಭಾಷಾ ಪ್ರಕ್ರಿಯೆ -![NLP ಕಾರ್ಯಗಳ ಸಾರಾಂಶವನ್ನು ಡೂಡಲ್‌ನಲ್ಲಿ](../../../../translated_images/kn/ai-nlp.b22dcb8ca4707cea.png) +![NLP ಕಾರ್ಯಗಳ ಸಾರಾಂಶವನ್ನು ಡೂಡಲ್‌ನಲ್ಲಿ](../../../../translated_images/kn/ai-nlp.b22dcb8ca4707cea.webp) ಈ ವಿಭಾಗದಲ್ಲಿ, ನಾವು ನ್ಯೂರಲ್ ನೆಟ್‌ವರ್ಕ್‌ಗಳನ್ನು ಬಳಸಿಕೊಂಡು **ನೈಸರ್ಗಿಕ ಭಾಷಾ ಪ್ರಕ್ರಿಯೆ (NLP)** ಸಂಬಂಧಿತ ಕಾರ್ಯಗಳನ್ನು ನಿರ್ವಹಿಸುವುದರ ಮೇಲೆ ಗಮನಹರಿಸುವೆವು. ಕಂಪ್ಯೂಟರ್‌ಗಳು ಪರಿಹರಿಸಬೇಕಾದ ಅನೇಕ NLP ಸಮಸ್ಯೆಗಳಿವೆ: diff --git a/translations/kn/lessons/6-Other/22-DeepRL/README.md b/translations/kn/lessons/6-Other/22-DeepRL/README.md index 0ca4fe1d..00d25fc6 100644 --- a/translations/kn/lessons/6-Other/22-DeepRL/README.md +++ b/translations/kn/lessons/6-Other/22-DeepRL/README.md @@ -34,7 +34,7 @@ RL ಗೆ ಅತ್ಯುತ್ತಮ ಸಾಧನವೆಂದರೆ [OpenAI Gym ಬ್ಯಾಲೆನ್ಸಿಂಗ್‌ನ ಸರಳೀಕೃತ ಆವೃತ್ತಿಯನ್ನು **ಕಾರ್ಟ್‌ಪೋಲ್** ಸಮಸ್ಯೆ ಎಂದು ಕರೆಯಲಾಗುತ್ತದೆ. ಕಾರ್ಟ್‌ಪೋಲ್ ಜಗತ್ತಿನಲ್ಲಿ, ನಾವು ಎಡಕ್ಕೆ ಅಥವಾ ಬಲಕ್ಕೆ ಚಲಿಸುವ ಹೋರಿಜಾಂಟಲ್ ಸ್ಲೈಡರ್ ಹೊಂದಿದ್ದೇವೆ, ಮತ್ತು ಗುರಿ ಸ್ಲೈಡರ್ ಮೇಲ್ಭಾಗದಲ್ಲಿ ಲಂಬ ಪೋಲ್ ಅನ್ನು ಬ್ಯಾಲೆನ್ಸ್ ಮಾಡುವುದು. -a cartpole +a cartpole ಈ ಪರಿಸರವನ್ನು ರಚಿಸಲು ಮತ್ತು ಬಳಸಲು, ನಮಗೆ ಕೆಲವು ಪೈಥಾನ್ ಕೋಡ್ ಸಾಲುಗಳು ಬೇಕಾಗುತ್ತವೆ: diff --git a/translations/kn/lessons/6-Other/22-DeepRL/lab/README.md b/translations/kn/lessons/6-Other/22-DeepRL/lab/README.md index c0d3a3a8..77e1e823 100644 --- a/translations/kn/lessons/6-Other/22-DeepRL/lab/README.md +++ b/translations/kn/lessons/6-Other/22-DeepRL/lab/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: ನಿಮ್ಮ ಗುರಿ RL ಏಜೆಂಟ್ ಅನ್ನು OpenAI ಪರಿಸರದಲ್ಲಿ [ಪರ್ವತ ಕಾರ್](https://www.gymlibrary.ml/environments/classic_control/mountain_car/) ಅನ್ನು ನಿಯಂತ್ರಿಸಲು ತರಬೇತಿ ನೀಡುವುದು. -Mountain Car +Mountain Car ## ಪರಿಸರ diff --git a/translations/kn/lessons/6-Other/23-MultiagentSystems/README.md b/translations/kn/lessons/6-Other/23-MultiagentSystems/README.md index 874297b4..ea19959d 100644 --- a/translations/kn/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/kn/lessons/6-Other/23-MultiagentSystems/README.md @@ -60,7 +60,7 @@ ask turtles [ ನೆಟ್‌ಲೋಗೋದಲ್ಲಿ ಒಂದು ಅದ್ಭುತ ವಿಷಯವೆಂದರೆ, ನೀವು ಪ್ರಯತ್ನಿಸಬಹುದಾದ ಕಾರ್ಯನಿರ್ವಹಿಸುವ ಮಾದರಿಗಳ ಗ್ರಂಥಾಲಯವಿದೆ. **File → Models Library** ಗೆ ಹೋಗಿ, ಮತ್ತು ನೀವು ಅನೇಕ ವರ್ಗಗಳ ಮಾದರಿಗಳನ್ನು ಆಯ್ಕೆಮಾಡಬಹುದು. -NetLogo Models Library +NetLogo Models Library > ಡಿಮಿಟ್ರಿ ಸೋಶ್ನಿಕೋವ್ ಅವರ ಮಾದರಿ ಗ್ರಂಥಾಲಯದ ಸ್ಕ್ರೀನ್‌ಶಾಟ್ @@ -70,7 +70,7 @@ ask turtles [ ಮಾದರಿಯನ್ನು ತೆರೆಯುವ ನಂತರ, ನೀವು ಮುಖ್ಯ ನೆಟ್‌ಲೋಗೋ ಪರದೆಗೆ ಹೋಗುತ್ತೀರಿ. ಇಲ್ಲಿ ನಾಯಿ ಮತ್ತು ಕುರಿಗಳ ಜನಸಂಖ್ಯೆಯನ್ನು ವಿವರಿಸುವ ಮಾದರಿ ಇದೆ, ನಿರ್ದಿಷ್ಟ ಸಂಪನ್ಮೂಲಗಳ (ಹುಲ್ಲು)ೊಂದಿಗೆ. -![NetLogo Main Screen](../../../../../translated_images/kn/NetLogo-Main.32653711ec1a01b3.png) +![NetLogo Main Screen](../../../../../translated_images/kn/NetLogo-Main.32653711ec1a01b3.webp) > ಡಿಮಿಟ್ರಿ ಸೋಶ್ನಿಕೋವ್ ಅವರ ಸ್ಕ್ರೀನ್‌ಶಾಟ್ diff --git a/translations/kn/lessons/README.md b/translations/kn/lessons/README.md index c7251622..b4ad6c7c 100644 --- a/translations/kn/lessons/README.md +++ b/translations/kn/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # ಅವಲೋಕನ -![ಡೂಡಲ್‌ನಲ್ಲಿ ಅವಲೋಕನ](../../../translated_images/kn/ai-overview.0857791951d19500.png) +![ಡೂಡಲ್‌ನಲ್ಲಿ ಅವಲೋಕನ](../../../translated_images/kn/ai-overview.0857791951d19500.webp) > ಸ್ಕೆಚ್‌ನೋಟ್ [ಟೊಮೊಮಿ ಇಮುರು](https://twitter.com/girlie_mac) ಅವರಿಂದ diff --git a/translations/kn/lessons/X-Extras/X1-MultiModal/README.md b/translations/kn/lessons/X-Extras/X1-MultiModal/README.md index c632a4a5..e78a118d 100644 --- a/translations/kn/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/kn/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ NLP ಕಾರ್ಯಗಳನ್ನು ಪರಿಹರಿಸಲು ಟ್ರಾ CLIP ನ ಮುಖ್ಯ ಆಲೋಚನೆ ಎಂದರೆ ಪಠ್ಯ ಪ್ರಾಂಪ್ಟ್‌ಗಳನ್ನು ಚಿತ್ರದೊಂದಿಗೆ ಹೋಲಿಸಿ, ಚಿತ್ರವು ಪ್ರಾಂಪ್ಟ್‌ಗೆ ಎಷ್ಟು ಹೊಂದಿಕೆಯಾಗುತ್ತದೆ ಎಂದು ನಿರ್ಧರಿಸುವುದು. -![CLIP ವಾಸ್ತುಶಿಲ್ಪ](../../../../../translated_images/kn/clip-arch.b3dbf20b4e8ed8be.png) +![CLIP ವಾಸ್ತುಶಿಲ್ಪ](../../../../../translated_images/kn/clip-arch.b3dbf20b4e8ed8be.webp) > *ಚಿತ್ರ [ಈ ಬ್ಲಾಗ್ ಪೋಸ್ಟ್](https://openai.com/blog/clip/) ನಿಂದ* @@ -29,7 +29,7 @@ CLIP ಮಾದರಿ/ಲೈಬ್ರರಿ [OpenAI GitHub](https://github.com/op ನಾವು ಚಿತ್ರಗಳನ್ನು, ಉದಾಹರಣೆಗೆ, ಬೆಕ್ಕುಗಳು, ನಾಯಿ ಮತ್ತು ಮಾನವರ ನಡುವೆ ವರ್ಗೀಕರಿಸಬೇಕಾದರೆ, ಈ ಸಂದರ್ಭದಲ್ಲಿ ನಾವು ಮಾದರಿಗೆ ಒಂದು ಚಿತ್ರ ಮತ್ತು ಪಠ್ಯ ಪ್ರಾಂಪ್ಟ್‌ಗಳ ಸರಣಿಯನ್ನು ನೀಡಬಹುದು: "*ಬೆಕ್ಕಿನ ಚಿತ್ರ*", "*ನಾಯಿಯ ಚಿತ್ರ*", "*ಮಾನವರ ಚಿತ್ರ*". 3 ಪ್ರಾಬಬಿಲಿಟಿಗಳ ಫಲಿತಾಂಶ ವೆಕ್ಟರ್‌ನಲ್ಲಿ ಅತ್ಯಧಿಕ ಮೌಲ್ಯದ ಸೂಚ್ಯಂಕವನ್ನು ಆಯ್ಕೆ ಮಾಡಬೇಕಾಗುತ್ತದೆ. -![ಚಿತ್ರ ವರ್ಗೀಕರಣಕ್ಕೆ CLIP](../../../../../translated_images/kn/clip-class.3af42ef0b2b19369.png) +![ಚಿತ್ರ ವರ್ಗೀಕರಣಕ್ಕೆ CLIP](../../../../../translated_images/kn/clip-class.3af42ef0b2b19369.webp) > *ಚಿತ್ರ [ಈ ಬ್ಲಾಗ್ ಪೋಸ್ಟ್](https://openai.com/blog/clip/) ನಿಂದ* @@ -53,13 +53,13 @@ VQGAN ಬಗ್ಗೆ ಹೆಚ್ಚಿನ ಮಾಹಿತಿಗಾಗಿ [Tami VQGAN ಮತ್ತು ಸಾಂಪ್ರದಾಯಿಕ GAN ನಡುವಿನ ಪ್ರಮುಖ ವ್ಯತ್ಯಾಸವೆಂದರೆ, GAN ಯಾವುದೇ ಇನ್‌ಪುಟ್ ವೆಕ್ಟರ್‌ನಿಂದ ಸಮರ್ಪಕ ಚಿತ್ರವನ್ನು ಉತ್ಪಾದಿಸಬಹುದು, ಆದರೆ VQGAN ಅಸಂಬದ್ಧ ಚಿತ್ರವನ್ನು ಉತ್ಪಾದಿಸುವ ಸಾಧ್ಯತೆ ಇದೆ. ಆದ್ದರಿಂದ, ಚಿತ್ರ ರಚನೆ ಪ್ರಕ್ರಿಯೆಯನ್ನು ಇನ್ನಷ್ಟು ಮಾರ್ಗದರ್ಶನ ಮಾಡಬೇಕಾಗುತ್ತದೆ, ಮತ್ತು ಅದನ್ನು CLIP ಬಳಸಿ ಮಾಡಬಹುದು. -![VQGAN+CLIP ವಾಸ್ತುಶಿಲ್ಪ](../../../../../translated_images/kn/vqgan.5027fe05051dfa31.png) +![VQGAN+CLIP ವಾಸ್ತುಶಿಲ್ಪ](../../../../../translated_images/kn/vqgan.5027fe05051dfa31.webp) ಪಠ್ಯ ಪ್ರಾಂಪ್ಟ್‌ಗೆ ಹೊಂದುವ ಚಿತ್ರವನ್ನು ರಚಿಸಲು, ನಾವು ಕೆಲವು ಯಾದೃಚ್ಛಿಕ ಎನ್‌ಕೋಡಿಂಗ್ ವೆಕ್ಟರ್‌ನಿಂದ ಪ್ರಾರಂಭಿಸಿ ಅದನ್ನು VQGAN ಮೂಲಕ ಚಿತ್ರವಾಗಿ ಉತ್ಪಾದಿಸುತ್ತೇವೆ. ನಂತರ CLIP ಅನ್ನು ಬಳಸಿಕೊಂಡು ಚಿತ್ರವು ಪಠ್ಯ ಪ್ರಾಂಪ್ಟ್‌ಗೆ ಎಷ್ಟು ಹೊಂದಿಕೆಯಾಗುತ್ತದೆ ಎಂಬುದನ್ನು ತೋರಿಸುವ ಲಾಸ್ ಫಂಕ್ಷನ್ ರಚಿಸಲಾಗುತ್ತದೆ. ಗುರಿ ಈ ಲಾಸ್ ಅನ್ನು ಕನಿಷ್ಠಗೊಳಿಸುವುದು, ಬ್ಯಾಕ್ ಪ್ರೋಪಗೇಶನ್ ಬಳಸಿ ಇನ್‌ಪುಟ್ ವೆಕ್ಟರ್ ಪರಿಮಾಣಗಳನ್ನು ಸರಿಹೊಂದಿಸುವುದು. VQGAN+CLIP ಅನ್ನು ಅನುಷ್ಠಾನಗೊಳಿಸುವ ಅತ್ಯುತ್ತಮ ಲೈಬ್ರರಿ [Pixray](http://github.com/pixray/pixray) -![Pixray ಮೂಲಕ ರಚಿಸಲಾದ ಚಿತ್ರ](../../../../../translated_images/kn/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Pixray ಮೂಲಕ ರಚಿಸಲಾದ ಚಿತ್ರ](../../../../../translated_images/kn/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Pixray ಮೂಲಕ ರಚಿಸಲಾದ ಚಿತ್ರ](../../../../../translated_images/kn/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) +![Pixray ಮೂಲಕ ರಚಿಸಲಾದ ಚಿತ್ರ](../../../../../translated_images/kn/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp) | ![Pixray ಮೂಲಕ ರಚಿಸಲಾದ ಚಿತ್ರ](../../../../../translated_images/kn/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp) | ![Pixray ಮೂಲಕ ರಚಿಸಲಾದ ಚಿತ್ರ](../../../../../translated_images/kn/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp) ----|----|---- ಪ್ರಾಂಪ್ಟ್ *ಪುಸ್ತಕದೊಂದಿಗೆ ಯುವ ಸಾಹಿತ್ಯ ಶಿಕ್ಷಕರ ನೀರಾವರಿ ಚಿತ್ರ* ನಿಂದ ರಚಿಸಲಾದ ಚಿತ್ರ | ಪ್ರಾಂಪ್ಟ್ *ಕಂಪ್ಯೂಟರ್ ವಿಜ್ಞಾನ ಯುವ ಶಿಕ್ಷಕಿ ಕಂಪ್ಯೂಟರ್ ಜೊತೆಗೆ ತೈಲ ಚಿತ್ರ* ನಿಂದ ರಚಿಸಲಾದ ಚಿತ್ರ | ಪ್ರಾಂಪ್ಟ್ *ಹಳೆಯ ಗಣಿತ ಶಿಕ್ಷಕ ಬ್ಲ್ಯಾಕ್ಬೋರ್ಡ್ ಮುಂದೆ ತೈಲ ಚಿತ್ರ* ನಿಂದ ರಚಿಸಲಾದ ಚಿತ್ರ diff --git a/translations/ko/README.md b/translations/ko/README.md index 13ff1ef0..ac89e15a 100644 --- a/translations/ko/README.md +++ b/translations/ko/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # 초보자를 위한 인공 지능 - 커리큘럼 -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ko/ai-overview.0857791951d19500.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ko/ai-overview.0857791951d19500.webp)| |:---:| | AI For Beginners - _스케치노트 by [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/ko/lessons/1-Intro/README.md b/translations/ko/lessons/1-Intro/README.md index fe5900b4..aded377c 100644 --- a/translations/ko/lessons/1-Intro/README.md +++ b/translations/ko/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # AI 소개 -![AI 소개 내용 요약을 담은 스케치](../../../../translated_images/ko/ai-intro.bf28d1ac4235881c.png) +![AI 소개 내용 요약을 담은 스케치](../../../../translated_images/ko/ai-intro.bf28d1ac4235881c.webp) > 스케치노트: [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: 원래 컴퓨터는 [찰스 배비지](https://en.wikipedia.org/wiki/Charles_Babbage)에 의해 명확히 정의된 절차, 즉 알고리즘을 따라 숫자를 처리하기 위해 발명되었습니다. 현대의 컴퓨터는 19세기에 제안된 원래 모델보다 훨씬 더 발전했지만, 여전히 제어된 계산이라는 동일한 아이디어를 따릅니다. 따라서 목표를 달성하기 위해 필요한 정확한 단계의 순서를 알고 있다면 컴퓨터를 프로그래밍하여 작업을 수행할 수 있습니다. -![한 사람의 사진](../../../../translated_images/ko/dsh_age.d212a30d4e54fb5f.png) +![한 사람의 사진](../../../../translated_images/ko/dsh_age.d212a30d4e54fb5f.webp) > 사진 제공: [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ CO_OP_TRANSLATOR_METADATA: **[지능](https://en.wikipedia.org/wiki/Intelligence)**이라는 용어를 다룰 때의 문제 중 하나는 이 용어에 대한 명확한 정의가 없다는 점입니다. 지능이 **추상적 사고** 또는 **자기 인식**과 연결되어 있다고 주장할 수 있지만, 이를 제대로 정의할 수는 없습니다. -![고양이 사진](../../../../translated_images/ko/photo-cat.8c8e8fb760ffe457.jpg) +![고양이 사진](../../../../translated_images/ko/photo-cat.8c8e8fb760ffe457.webp) > [사진](https://unsplash.com/photos/75715CVEJhI) 제공: [Amber Kipp](https://unsplash.com/@sadmax) (Unsplash) @@ -98,13 +98,13 @@ AGI에 대해 이야기할 때, 우리가 진정으로 지능적인 시스템을 > | 기계 학습은? | | > |--------------|-----------| -> | 일부 데이터를 기반으로 문제를 해결하도록 컴퓨터가 학습하는 인공지능의 한 부분을 **기계 학습**이라고 합니다. 이 강의에서는 고전적인 기계 학습을 다루지 않습니다. 별도의 [기계 학습 초보자용](http://aka.ms/ml-beginners) 커리큘럼을 참조하세요. | ![기계 학습 초보자용](../../../../translated_images/ko/ml-for-beginners.9e4fed176fd5817d.png) | +> | 일부 데이터를 기반으로 문제를 해결하도록 컴퓨터가 학습하는 인공지능의 한 부분을 **기계 학습**이라고 합니다. 이 강의에서는 고전적인 기계 학습을 다루지 않습니다. 별도의 [기계 학습 초보자용](http://aka.ms/ml-beginners) 커리큘럼을 참조하세요. | ![기계 학습 초보자용](../../../../translated_images/ko/ml-for-beginners.9e4fed176fd5817d.webp) | ## AI의 간략한 역사 인공지능은 20세기 중반에 하나의 분야로 시작되었습니다. 초기에는 기호적 추론이 우세한 접근법이었으며, 제한된 문제 영역에서 전문가처럼 행동할 수 있는 컴퓨터 프로그램인 전문가 시스템과 같은 중요한 성공을 거두었습니다. 그러나 곧 이러한 접근법이 잘 확장되지 않는다는 것이 명확해졌습니다. 전문가로부터 지식을 추출하고, 이를 컴퓨터에 표현하며, 지식 기반을 정확하게 유지하는 것은 매우 복잡하고 많은 경우 실용적이지 않을 정도로 비용이 많이 드는 작업임이 밝혀졌습니다. 이는 1970년대의 소위 [AI 겨울](https://en.wikipedia.org/wiki/AI_winter)로 이어졌습니다. -AI의 간략한 역사 +AI의 간략한 역사 > 이미지 제공: [Dmitry Soshnikov](http://soshnikov.com) @@ -124,7 +124,7 @@ AGI에 대해 이야기할 때, 우리가 진정으로 지능적인 시스템을 * Cortana, Siri, Google Assistant와 같은 현대의 비서는 모두 음성을 텍스트로 변환하고 우리의 의도를 인식하기 위해 신경망을 사용하는 하이브리드 시스템이며, 이후 필요한 작업을 수행하기 위해 일부 추론이나 명시적 알고리즘을 사용합니다. * 미래에는 대화 자체를 처리할 수 있는 완전한 신경망 기반 모델을 기대할 수 있습니다. 최근의 GPT 및 [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) 계열의 신경망은 이 분야에서 큰 성공을 보여주고 있습니다. -튜링 테스트의 진화 +튜링 테스트의 진화 > 이미지 제공: Dmitry Soshnikov, [사진](https://unsplash.com/photos/r8LmVbUKgns) 제공: [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto), Unsplash ## 최근 AI 연구 diff --git a/translations/ko/lessons/2-Symbolic/README.md b/translations/ko/lessons/2-Symbolic/README.md index 9cad7b31..032b2498 100644 --- a/translations/ko/lessons/2-Symbolic/README.md +++ b/translations/ko/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 지식 표현과 전문가 시스템 -![Symbolic AI 내용 요약](../../../../translated_images/ko/ai-symbolic.715a30cb610411a6.png) +![Symbolic AI 내용 요약](../../../../translated_images/ko/ai-symbolic.715a30cb610411a6.webp) > 스케치노트 제공: [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ Symbolic AI에서 중요한 개념 중 하나는 **지식**입니다. 지식을 따라서 **지식 표현**의 문제는 컴퓨터 내부에서 데이터를 통해 지식을 효과적으로 표현하여 자동으로 사용할 수 있도록 하는 방법을 찾는 것입니다. 이는 다음과 같은 스펙트럼으로 볼 수 있습니다: -![지식 표현 스펙트럼](../../../../translated_images/ko/knowledge-spectrum.b60df631852c0217.png) +![지식 표현 스펙트럼](../../../../translated_images/ko/knowledge-spectrum.b60df631852c0217.webp) > 이미지 제공: [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Is-A | Untyped-Language | | | Symbolic AI의 초기 성공 사례 중 하나는 **전문가 시스템**이라 불리는 컴퓨터 시스템이었습니다. 이는 제한된 문제 영역에서 전문가처럼 행동하도록 설계되었습니다. 이러한 시스템은 인간 전문가로부터 추출된 **지식 기반**과 이를 기반으로 추론을 수행하는 **추론 엔진**을 포함하고 있었습니다. -![인간 구조](../../../../translated_images/ko/arch-human.5d4d35f1bba3ab1c.png) | ![지식 기반 시스템](../../../../translated_images/ko/arch-kbs.3ec5c150b09fa8da.png) +![인간 구조](../../../../translated_images/ko/arch-human.5d4d35f1bba3ab1c.webp) | ![지식 기반 시스템](../../../../translated_images/ko/arch-kbs.3ec5c150b09fa8da.webp) ----------------------------------|-------------------------------------- 인간 신경 시스템의 단순화된 구조 | 지식 기반 시스템의 구조 @@ -106,7 +106,7 @@ Symbolic AI의 초기 성공 사례 중 하나는 **전문가 시스템**이라 예를 들어, 동물의 신체적 특성을 기반으로 동물을 결정하는 다음 전문가 시스템을 고려해 보겠습니다: -![AND-OR 트리](../../../../translated_images/ko/AND-OR-Tree.5592d2c70187f283.png) +![AND-OR 트리](../../../../translated_images/ko/AND-OR-Tree.5592d2c70187f283.webp) > 이미지 제공: [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/ko/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/ko/lessons/3-NeuralNetworks/05-Frameworks/README.md index 3f189960..81f743d7 100644 --- a/translations/ko/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/ko/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ CO_OP_TRANSLATOR_METADATA: 다음 그래프에서 5개의 점(`x`로 표시된 점)을 근사하는 문제를 고려해봅시다: -![linear](../../../../../translated_images/ko/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/ko/overfit2.131f5800ae10ca5e.jpg) +![linear](../../../../../translated_images/ko/overfit1.f24b71c6f652e59e.webp) | ![overfit](../../../../../translated_images/ko/overfit2.131f5800ae10ca5e.webp) -------------------------|-------------------------- **선형 모델, 2개의 매개변수** | **비선형 모델, 7개의 매개변수** 학습 오류 = 5.3 | 학습 오류 = 0 @@ -79,7 +79,7 @@ CO_OP_TRANSLATOR_METADATA: 위 그래프에서 볼 수 있듯이, 과적합은 매우 낮은 학습 오류와 높은 검증 오류로 감지할 수 있습니다. 일반적으로 학습 중에는 학습 오류와 검증 오류가 모두 감소하다가, 어느 시점에서 검증 오류가 감소를 멈추고 증가하기 시작할 수 있습니다. 이는 과적합의 신호이며, 이 시점에서 학습을 멈추거나 모델의 스냅샷을 저장해야 한다는 표시입니다. -![overfitting](../../../../../translated_images/ko/Overfitting.408ad91cd90b4371.png) +![overfitting](../../../../../translated_images/ko/Overfitting.408ad91cd90b4371.webp) ## 과적합을 방지하는 방법 diff --git a/translations/ko/lessons/3-NeuralNetworks/README.md b/translations/ko/lessons/3-NeuralNetworks/README.md index a8de557e..f537569e 100644 --- a/translations/ko/lessons/3-NeuralNetworks/README.md +++ b/translations/ko/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 신경망 소개 -![신경망 소개 내용 요약을 담은 그림](../../../../translated_images/ko/ai-neuralnetworks.1c687ae40bc86e83.png) +![신경망 소개 내용 요약을 담은 그림](../../../../translated_images/ko/ai-neuralnetworks.1c687ae40bc86e83.webp) 소개에서 논의했듯이, 지능을 구현하는 방법 중 하나는 **컴퓨터 모델** 또는 **인공 두뇌**를 훈련시키는 것입니다. 20세기 중반부터 연구자들은 다양한 수학적 모델을 시도했으며, 최근 몇 년간 이 방향이 큰 성공을 거두었습니다. 이러한 두뇌의 수학적 모델을 **신경망**이라고 합니다. @@ -36,13 +36,13 @@ CO_OP_TRANSLATOR_METADATA: 생물학적으로 우리의 뇌는 신경 세포(뉴런)로 구성되어 있으며, 각 뉴런은 여러 개의 "입력"(수상돌기)과 하나의 "출력"(축삭)을 가지고 있습니다. 수상돌기와 축삭은 모두 전기 신호를 전달할 수 있으며, 이들 간의 연결(시냅스)은 신경전달물질에 의해 전도도가 조절될 수 있습니다. -![뉴런 모델](../../../../translated_images/ko/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![뉴런 모델](../../../../translated_images/ko/artneuron.1a5daa88d20ebe6f.png) +![뉴런 모델](../../../../translated_images/ko/synapse-wikipedia.ed20a9e4726ea1c6.webp) | ![뉴런 모델](../../../../translated_images/ko/artneuron.1a5daa88d20ebe6f.webp) ----|---- 실제 뉴런 *([이미지](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) 출처: 위키피디아)* | 인공 뉴런 *(이미지 제공: 저자)* 따라서 뉴런의 가장 간단한 수학적 모델은 여러 입력 X1, ..., XN과 출력 Y, 그리고 일련의 가중치 W1, ..., WN을 포함합니다. 출력은 다음과 같이 계산됩니다: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) 여기서 f는 비선형 **활성화 함수**입니다. diff --git a/translations/ko/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/ko/lessons/4-ComputerVision/06-IntroCV/README.md index 2d839ef3..b8b69d89 100644 --- a/translations/ko/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/ko/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ OpenCV를 사용하여 비디오를 프레임별로 로드할 수도 있습니 * **점자 책 사진 전처리**. 임계값 처리, 특징 탐지, 투시 변환 및 NumPy 조작을 사용하여 개별 점자 기호를 분리하고, 이를 신경망으로 추가 분류하는 방법에 초점을 맞춥니다. -![점자 이미지](../../../../../translated_images/ko/braille.341962ff76b1bd70.jpeg) | ![전처리된 점자 이미지](../../../../../translated_images/ko/braille-result.46530fea020b03c7.png) | ![점자 기호](../../../../../translated_images/ko/braille-symbols.0159185ab69d5339.png) +![점자 이미지](../../../../../translated_images/ko/braille.341962ff76b1bd70.webp) | ![전처리된 점자 이미지](../../../../../translated_images/ko/braille-result.46530fea020b03c7.webp) | ![점자 기호](../../../../../translated_images/ko/braille-symbols.0159185ab69d5339.webp) ----|-----|----- > 이미지 출처: [OpenCV.ipynb](OpenCV.ipynb) * **프레임 차이를 사용한 비디오에서의 움직임 탐지**. 카메라가 고정되어 있다면, 카메라 피드의 프레임은 서로 매우 유사해야 합니다. 프레임이 배열로 표현되므로, 두 연속 프레임의 배열을 빼면 픽셀 차이를 얻을 수 있습니다. 정적인 프레임에서는 차이가 작고, 이미지에 상당한 움직임이 있을 때 차이가 커집니다. -![비디오 프레임 및 프레임 차이 이미지](../../../../../translated_images/ko/frame-difference.706f805491a0883c.png) +![비디오 프레임 및 프레임 차이 이미지](../../../../../translated_images/ko/frame-difference.706f805491a0883c.webp) > 이미지 출처: [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ OpenCV를 사용하여 비디오를 프레임별로 로드할 수도 있습니 - **밀집 광학 흐름**: 각 픽셀이 어디로 이동하는지 보여주는 벡터 필드를 계산합니다. - **희소 광학 흐름**: 이미지에서 일부 특징적인 요소(예: 가장자리)를 선택하고, 프레임 간의 궤적을 생성합니다. -![광학 흐름 이미지](../../../../../translated_images/ko/optical.1f4a94464579a83a.png) +![광학 흐름 이미지](../../../../../translated_images/ko/optical.1f4a94464579a83a.webp) > 이미지 출처: [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/ko/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/ko/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 85a97d1f..9f744004 100644 --- a/translations/ko/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/ko/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16은 2014년 ImageNet의 top-5 분류에서 92.7%의 정확도를 달성한 네트워크입니다. 이 네트워크는 다음과 같은 계층 구조를 가지고 있습니다: -![ImageNet Layers](../../../../../translated_images/ko/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet Layers](../../../../../translated_images/ko/vgg-16-arch1.d901a5583b3a51ba.webp) 보시다시피, VGG는 전통적인 피라미드 아키텍처를 따르며, 이는 컨볼루션-풀링 계층의 연속입니다. -![ImageNet Pyramid](../../../../../translated_images/ko/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet Pyramid](../../../../../translated_images/ko/vgg-16-arch.64ff2137f50dd49f.webp) > 이미지 출처: [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/ko/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/ko/lessons/4-ComputerVision/07-ConvNets/README.md index ff1d81be..397dee87 100644 --- a/translations/ko/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/ko/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: 패턴을 추출하기 위해 **컨볼루션 필터**라는 개념을 사용할 것입니다. 이미지는 2D-매트릭스 또는 색상 깊이를 가진 3D-텐서로 표현됩니다. 필터를 적용한다는 것은 비교적 작은 **필터 커널** 매트릭스를 가져와 원본 이미지의 각 픽셀에 대해 이웃한 점들과 가중 평균을 계산하는 것을 의미합니다. 이를 작은 창이 전체 이미지를 슬라이딩하며 필터 커널 매트릭스의 가중치에 따라 모든 픽셀을 평균화하는 것으로 볼 수 있습니다. -![수직 엣지 필터](../../../../../translated_images/ko/filter-vert.b7148390ca0bc356.png) | ![수평 엣지 필터](../../../../../translated_images/ko/filter-horiz.59b80ed4feb946ef.png) +![수직 엣지 필터](../../../../../translated_images/ko/filter-vert.b7148390ca0bc356.webp) | ![수평 엣지 필터](../../../../../translated_images/ko/filter-horiz.59b80ed4feb946ef.webp) ----|---- > 이미지 제공: Dmitry Soshnikov @@ -38,7 +38,7 @@ CNN이 작동하는 방식은 다음과 같은 중요한 아이디어를 기반 * 필터가 자동으로 학습되도록 네트워크를 설계할 수 있다. * 원본 이미지뿐만 아니라 고수준 특징에서도 패턴을 찾는 데 동일한 접근 방식을 사용할 수 있다. 따라서 CNN 특징 추출은 저수준 픽셀 조합에서 시작하여 이미지 부분의 고수준 조합까지 특징의 계층 구조에서 작동한다. -![계층적 특징 추출](../../../../../translated_images/ko/FeatureExtractionCNN.d9b456cbdae7cb64.png) +![계층적 특징 추출](../../../../../translated_images/ko/FeatureExtractionCNN.d9b456cbdae7cb64.webp) > 이미지 출처: [Hislop-Lynch의 논문](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), [그들의 연구](https://dl.acm.org/doi/abs/10.1145/1553374.1553453)를 기반으로 함 @@ -55,9 +55,9 @@ CNN이 작동하는 방식은 다음과 같은 중요한 아이디어를 기반 예를 들어, 2014년 ImageNet의 상위 5개 분류에서 92.7%의 정확도를 달성한 VGG-16 네트워크의 아키텍처를 살펴봅시다: -![ImageNet 계층](../../../../../translated_images/ko/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet 계층](../../../../../translated_images/ko/vgg-16-arch1.d901a5583b3a51ba.webp) -![ImageNet 피라미드](../../../../../translated_images/ko/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet 피라미드](../../../../../translated_images/ko/vgg-16-arch.64ff2137f50dd49f.webp) > 이미지 출처: [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/ko/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/ko/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 1c4e1925..c2aa3102 100644 --- a/translations/ko/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/ko/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ CO_OP_TRANSLATOR_METADATA: 우리는 [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/)을 사용할 것입니다. 이 데이터셋은 37가지 다른 품종의 개와 고양이 이미지를 포함하고 있습니다. -![우리가 다룰 데이터셋](../../../../../../translated_images/ko/data.50b2a9d5484bdbf0.png) +![우리가 다룰 데이터셋](../../../../../../translated_images/ko/data.50b2a9d5484bdbf0.webp) 데이터셋을 다운로드하려면 아래 코드 스니펫을 사용하세요: diff --git a/translations/ko/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/ko/lessons/4-ComputerVision/08-TransferLearning/README.md index d7e1626b..cd74ad33 100644 --- a/translations/ko/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/ko/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Keras와 PyTorch는 대부분 ImageNet 이미지로 훈련된 일반적인 아 다음은 VGG-16 네트워크가 고양이 사진에서 추출한 특징의 예입니다: -![VGG-16이 추출한 특징](../../../../../translated_images/ko/features.6291f9c7ba3a0b95.png) +![VGG-16이 추출한 특징](../../../../../translated_images/ko/features.6291f9c7ba3a0b95.webp) ## 고양이 vs. 개 데이터셋 @@ -48,19 +48,19 @@ Keras와 PyTorch는 대부분 ImageNet 이미지로 훈련된 일반적인 아 한 가지 접근법은 랜덤 이미지를 시작점으로 사용하여 **경사 하강 최적화** 기법을 통해 이미지를 조정하는 것입니다. 이를 통해 네트워크가 해당 이미지를 고양이라고 생각하도록 만들 수 있습니다. -![이미지 최적화 루프](../../../../../translated_images/ko/ideal-cat-loop.999fbb8ff306e044.png) +![이미지 최적화 루프](../../../../../translated_images/ko/ideal-cat-loop.999fbb8ff306e044.webp) 하지만 이렇게 하면 랜덤 노이즈와 매우 유사한 결과를 얻을 수 있습니다. 이는 *네트워크가 입력 이미지를 고양이라고 생각하도록 만드는 방법이 많기 때문*이며, 그중 일부는 시각적으로 의미가 없을 수 있습니다. 이러한 이미지는 고양이에 일반적인 많은 패턴을 포함하고 있지만, 시각적으로 뚜렷하게 보이도록 제한하는 요소가 없습니다. 결과를 개선하기 위해 손실 함수에 **변동 손실**이라는 항목을 추가할 수 있습니다. 이는 이미지의 인접 픽셀이 얼마나 유사한지를 보여주는 메트릭입니다. 변동 손실을 최소화하면 이미지가 더 부드러워지고 노이즈가 제거되어 시각적으로 더 매력적인 패턴이 드러납니다. 아래는 고양이와 얼룩말로 높은 확률로 분류된 "이상적인" 이미지의 예입니다: -![이상적인 고양이](../../../../../translated_images/ko/ideal-cat.203dd4597643d6b0.png) | ![이상적인 얼룩말](../../../../../translated_images/ko/ideal-zebra.7f70e8b54ee15a7a.png) +![이상적인 고양이](../../../../../translated_images/ko/ideal-cat.203dd4597643d6b0.webp) | ![이상적인 얼룩말](../../../../../translated_images/ko/ideal-zebra.7f70e8b54ee15a7a.webp) -----|----- *이상적인 고양이* | *이상적인 얼룩말* 유사한 접근법은 **적대적 공격**을 수행하는 데도 사용할 수 있습니다. 예를 들어, 신경망을 속여 개를 고양이처럼 보이게 만들고 싶다고 가정해봅시다. 네트워크가 개로 인식하는 개 이미지를 가져와 경사 하강 최적화를 사용해 약간 조정하면 네트워크가 이를 고양이로 분류하기 시작할 때까지 조정할 수 있습니다: -![개 사진](../../../../../translated_images/ko/original-dog.8f68a67d2fe0911f.png) | ![고양이로 분류된 개 사진](../../../../../translated_images/ko/adversarial-dog.d9fc7773b0142b89.png) +![개 사진](../../../../../translated_images/ko/original-dog.8f68a67d2fe0911f.webp) | ![고양이로 분류된 개 사진](../../../../../translated_images/ko/adversarial-dog.d9fc7773b0142b89.webp) -----|----- *원래 개 사진* | *고양이로 분류된 개 사진* diff --git a/translations/ko/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/ko/lessons/4-ComputerVision/09-Autoencoders/README.md index 5f619503..576e7db4 100644 --- a/translations/ko/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/ko/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ CNN을 훈련할 때, 문제 중 하나는 많은 라벨링된 데이터가 필 오토인코더를 훈련하여 원본 이미지의 정보를 최대한 많이 캡처하여 정확히 재구성하려고 할 때, 네트워크는 입력 이미지를 가장 잘 표현할 수 있는 **임베딩**을 찾으려고 합니다. -![AutoEncoder Diagram](../../../../../translated_images/ko/autoencoder_schema.5e6fc9ad98a5eb61.jpg) +![AutoEncoder Diagram](../../../../../translated_images/ko/autoencoder_schema.5e6fc9ad98a5eb61.webp) > 이미지 출처: [Keras 블로그](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/ko/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/ko/lessons/4-ComputerVision/11-ObjectDetection/README.md index 6e4f54bb..a0192a2b 100644 --- a/translations/ko/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/ko/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: ## [강의 전 퀴즈](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![객체 탐지](../../../../../translated_images/ko/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) +![객체 탐지](../../../../../translated_images/ko/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp) > 이미지 출처: [YOLO v2 웹사이트](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 2. 각 타일에 대해 이미지 분류를 실행합니다. 3. 충분히 높은 활성화를 보이는 타일은 해당 객체를 포함하고 있다고 간주합니다. -![단순 객체 탐지](../../../../../translated_images/ko/naive-detection.e7f1ba220ccd08c6.png) +![단순 객체 탐지](../../../../../translated_images/ko/naive-detection.e7f1ba220ccd08c6.webp) > *이미지 출처: [실습 노트북](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ CO_OP_TRANSLATOR_METADATA: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20개의 클래스 * [COCO](http://cocodataset.org/#home) - 컨텍스트 내 일반 객체. 80개의 클래스, 경계 상자 및 세분화 마스크 제공 -![COCO](../../../../../translated_images/ko/coco-examples.71bc60380fa6cceb.jpg) +![COCO](../../../../../translated_images/ko/coco-examples.71bc60380fa6cceb.webp) ## 객체 탐지 평가 지표 @@ -50,7 +50,7 @@ CO_OP_TRANSLATOR_METADATA: 이미지 분류에서는 알고리즘의 성능을 측정하기 쉽지만, 객체 탐지에서는 클래스의 정확성과 예측된 경계 상자 위치의 정밀도를 모두 측정해야 합니다. 후자를 위해 **교집합 비율**(IoU)을 사용합니다. 이는 두 상자(또는 임의의 두 영역)가 얼마나 잘 겹치는지를 측정합니다. -![IoU](../../../../../translated_images/ko/iou_equation.9a4751d40fff4e11.png) +![IoU](../../../../../translated_images/ko/iou_equation.9a4751d40fff4e11.webp) > *출처: [이 훌륭한 IoU 블로그 글](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -97,11 +97,11 @@ IoU가 특정 값 이상인 탐지만 고려합니다. 예를 들어, PASCAL VOC [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf)은 [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf)를 사용하여 ROI 영역의 계층 구조를 생성합니다. 그런 다음 이를 CNN 특징 추출기와 SVM 분류기를 통해 객체 클래스를 결정하고, 선형 회귀를 통해 *경계 상자* 좌표를 결정합니다. [공식 논문](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/ko/rcnn1.cae407020dfb1d1f.png) +![RCNN](../../../../../translated_images/ko/rcnn1.cae407020dfb1d1f.webp) > *이미지 출처: van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/ko/rcnn2.2d9530bb83516484.png) +![RCNN-1](../../../../../translated_images/ko/rcnn2.2d9530bb83516484.webp) > *이미지 출처: [이 블로그](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -109,7 +109,7 @@ IoU가 특정 값 이상인 탐지만 고려합니다. 예를 들어, PASCAL VOC 이 접근법은 R-CNN과 유사하지만, 영역이 합성곱 계층이 적용된 후에 정의됩니다. -![FRCNN](../../../../../translated_images/ko/f-rcnn.3cda6d9bb4188875.png) +![FRCNN](../../../../../translated_images/ko/f-rcnn.3cda6d9bb4188875.webp) > 이미지 출처: [공식 논문](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -117,7 +117,7 @@ IoU가 특정 값 이상인 탐지만 고려합니다. 예를 들어, PASCAL VOC 이 접근법의 주요 아이디어는 ROI를 예측하기 위해 신경망을 사용하는 것입니다. 이를 *영역 제안 네트워크*라고 합니다. [논문](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/ko/faster-rcnn.8d46c099b87ef30a.png) +![FasterRCNN](../../../../../translated_images/ko/faster-rcnn.8d46c099b87ef30a.webp) > 이미지 출처: [공식 논문](https://arxiv.org/pdf/1506.01497.pdf) @@ -129,7 +129,7 @@ IoU가 특정 값 이상인 탐지만 고려합니다. 예를 들어, PASCAL VOC 2. 특징은 **위치 민감 점수 맵**으로 처리됩니다. $C$ 클래스의 각 객체는 $k\times k$ 영역으로 나뉘며, 객체의 부분을 예측하도록 학습합니다. 3. $k\times k$ 영역의 각 부분에 대해 모든 네트워크가 객체 클래스를 투표하며, 최대 투표를 받은 객체 클래스가 선택됩니다. -![r-fcn 이미지](../../../../../translated_images/ko/r-fcn.13eb88158b99a3da.png) +![r-fcn 이미지](../../../../../translated_images/ko/r-fcn.13eb88158b99a3da.webp) > 이미지 출처: [공식 논문](https://arxiv.org/abs/1605.06409) @@ -140,7 +140,7 @@ YOLO는 실시간 한 번 처리 알고리즘입니다. 주요 아이디어는 * 이미지를 $S\times S$ 영역으로 나눕니다. * 각 영역에 대해 **CNN**이 $n$개의 가능한 객체, *경계 상자* 좌표 및 *신뢰도*=*확률* * IoU를 예측합니다. - ![YOLO](../../../../../translated_images/ko/yolo.a2648ec82ee8bb4e.png) + ![YOLO](../../../../../translated_images/ko/yolo.a2648ec82ee8bb4e.webp) > 이미지 출처: [공식 논문](https://arxiv.org/abs/1506.02640) diff --git a/translations/ko/lessons/4-ComputerVision/README.md b/translations/ko/lessons/4-ComputerVision/README.md index 4d40dc98..739c698e 100644 --- a/translations/ko/lessons/4-ComputerVision/README.md +++ b/translations/ko/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 컴퓨터 비전 -![컴퓨터 비전 내용 요약을 담은 스케치](../../../../translated_images/ko/ai-computervision.6506ebebac3fbf76.png) +![컴퓨터 비전 내용 요약을 담은 스케치](../../../../translated_images/ko/ai-computervision.6506ebebac3fbf76.webp) 이 섹션에서는 다음 내용을 학습합니다: diff --git a/translations/ko/lessons/5-NLP/14-Embeddings/README.md b/translations/ko/lessons/5-NLP/14-Embeddings/README.md index 3c34d21c..981fdb84 100644 --- a/translations/ko/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/ko/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ BoW 또는 TF/IDF 기반의 분류기를 훈련할 때, 우리는 `vocab_size` 분류기 네트워크의 첫 번째 레이어로 임베딩 레이어를 사용하면 bag-of-words 모델에서 **embedding bag** 모델로 전환할 수 있습니다. 여기서 텍스트의 각 단어를 해당 임베딩으로 변환한 후, 이러한 모든 임베딩에 대해 `sum`, `average`, `max`와 같은 집계 함수를 계산합니다. -![다섯 개의 시퀀스 단어에 대한 임베딩 분류기를 보여주는 이미지.](../../../../../translated_images/ko/embedding-classifier-example.b77f021a7ee67eee.png) +![다섯 개의 시퀀스 단어에 대한 임베딩 분류기를 보여주는 이미지.](../../../../../translated_images/ko/embedding-classifier-example.b77f021a7ee67eee.webp) > 작성자 제공 이미지 @@ -40,7 +40,7 @@ BoW 또는 TF/IDF 기반의 분류기를 훈련할 때, 우리는 `vocab_size` CBoW는 더 빠르지만, skip-gram은 더 느리며 드문 단어를 더 잘 표현합니다. -![단어를 벡터로 변환하는 CBoW와 Skip-Gram 알고리즘을 보여주는 이미지.](../../../../../translated_images/ko/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![단어를 벡터로 변환하는 CBoW와 Skip-Gram 알고리즘을 보여주는 이미지.](../../../../../translated_images/ko/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > [이 논문](https://arxiv.org/pdf/1301.3781.pdf)에서 제공된 이미지 diff --git a/translations/ko/lessons/5-NLP/15-LanguageModeling/README.md b/translations/ko/lessons/5-NLP/15-LanguageModeling/README.md index 08fee905..e7265e4f 100644 --- a/translations/ko/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/ko/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ Word2Vec와 GloVe 같은 의미 임베딩은 사실 **언어 모델링**의 첫 * **연속적인 단어 묶음** (CBoW): 토큰 시퀀스 $W_{-N}$, ..., $W_N$에서 가운데 토큰 $W_0$을 예측하는 방식 * **스킵그램**: 가운데 토큰 $W_0$에서 주변 토큰 {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$}을 예측하는 방식 -![단어를 벡터로 변환하는 알고리즘에 대한 논문 이미지](../../../../../translated_images/ko/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![단어를 벡터로 변환하는 알고리즘에 대한 논문 이미지](../../../../../translated_images/ko/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > 이미지 출처: [이 논문](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/ko/lessons/5-NLP/16-RNN/README.md b/translations/ko/lessons/5-NLP/16-RNN/README.md index f1ea8e6f..9d47cc60 100644 --- a/translations/ko/lessons/5-NLP/16-RNN/README.md +++ b/translations/ko/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: 텍스트 시퀀스의 의미를 포착하려면 **순환 신경망**(Recurrent Neural Network, RNN)이라는 다른 신경망 아키텍처를 사용해야 합니다. RNN에서는 문장을 네트워크에 한 번에 하나의 기호씩 전달하고, 네트워크는 **상태**를 생성하며, 이를 다음 기호와 함께 네트워크에 다시 전달합니다. -![RNN](../../../../../translated_images/ko/rnn.27f5c29c53d727b5.png) +![RNN](../../../../../translated_images/ko/rnn.27f5c29c53d727b5.webp) > 저자 제공 이미지 @@ -61,7 +61,7 @@ LSTM 네트워크는 RNN과 유사하게 구성되지만, 레이어 간에 전 순환 네트워크는 단일 방향이든 양방향이든 시퀀스 내 특정 패턴을 캡처하고 이를 상태 벡터에 저장하거나 출력으로 전달할 수 있습니다. 합성곱 네트워크와 마찬가지로 첫 번째 레이어에서 추출한 저수준 패턴을 기반으로 더 높은 수준의 패턴을 캡처하기 위해 첫 번째 레이어 위에 또 다른 순환 레이어를 구축할 수 있습니다. 이는 **다층 RNN**의 개념으로 이어지며, 두 개 이상의 순환 네트워크로 구성되며 이전 레이어의 출력이 다음 레이어의 입력으로 전달됩니다. -![다층 장단기 메모리 RNN을 보여주는 이미지](../../../../../translated_images/ko/multi-layer-lstm.dd975e29bb2a59fe.jpg) +![다층 장단기 메모리 RNN을 보여주는 이미지](../../../../../translated_images/ko/multi-layer-lstm.dd975e29bb2a59fe.webp) *Fernando López의 [이 훌륭한 글](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3)에서 가져온 그림* diff --git a/translations/ko/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/ko/lessons/5-NLP/17-GenerativeNetworks/README.md index 8e081f4f..4163c1f8 100644 --- a/translations/ko/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/ko/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: 이를 통해 아래 그림에 표시된 다양한 신경망 아키텍처를 구현할 수 있습니다: -![일반적인 순환 신경망 패턴을 보여주는 이미지.](../../../../../translated_images/ko/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![일반적인 순환 신경망 패턴을 보여주는 이미지.](../../../../../translated_images/ko/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > 이미지 출처: [Andrej Karpaty](http://karpathy.github.io/)의 블로그 게시물 [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) @@ -32,7 +32,7 @@ CO_OP_TRANSLATOR_METADATA: 이 RNN을 훈련시켜 단계별로 텍스트를 생성할 것입니다. 각 단계에서 `nchars` 길이의 문자 시퀀스를 받아 각 입력 문자에 대해 다음 출력 문자를 생성하도록 네트워크에 요청합니다: -![단어 'HELLO'를 생성하는 RNN 예제를 보여주는 이미지.](../../../../../translated_images/ko/rnn-generate.56c54afb52f9781d.png) +![단어 'HELLO'를 생성하는 RNN 예제를 보여주는 이미지.](../../../../../translated_images/ko/rnn-generate.56c54afb52f9781d.webp) 텍스트를 생성할 때(추론 중), **프롬프트**를 시작점으로 사용하여 RNN 셀을 통해 중간 상태를 생성한 후 이 상태에서 생성을 시작합니다. 한 번에 한 문자씩 생성하며, 상태와 생성된 문자를 다음 RNN 셀에 전달하여 다음 문자를 생성합니다. 이 과정을 필요한 문자 수만큼 반복합니다. diff --git a/translations/ko/lessons/5-NLP/18-Transformers/README.md b/translations/ko/lessons/5-NLP/18-Transformers/README.md index 19ab881a..c04f3748 100644 --- a/translations/ko/lessons/5-NLP/18-Transformers/README.md +++ b/translations/ko/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ RNN을 사용하면 시퀀스-투-시퀀스 작업은 두 개의 순환 신경 **어텐션 메커니즘**은 RNN의 각 출력 예측에 대해 각 입력 벡터의 맥락적 영향을 가중치로 부여하는 방법을 제공합니다. 이는 입력 RNN의 중간 상태와 출력 RNN 사이에 지름길을 만드는 방식으로 구현됩니다. 이렇게 하면 출력 심볼 yt를 생성할 때, 서로 다른 가중치 계수 αt,i를 사용하여 모든 입력 은닉 상태 hi를 고려하게 됩니다. -![어텐션 레이어가 포함된 인코더/디코더 모델](../../../../../translated_images/ko/encoder-decoder-attention.7a726296894fb567.png) +![어텐션 레이어가 포함된 인코더/디코더 모델](../../../../../translated_images/ko/encoder-decoder-attention.7a726296894fb567.webp) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf)의 어텐션 메커니즘이 포함된 인코더-디코더 모델. [이 블로그 글](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)에서 인용. 어텐션 행렬 {αi,j}는 출력 시퀀스의 특정 단어를 생성하는 데 특정 입력 단어가 얼마나 중요한지를 나타냅니다. 아래는 이러한 행렬의 예입니다: -![Bahdanau - arviz.org에서 가져온 RNNsearch-50의 샘플 정렬](../../../../../translated_images/ko/bahdanau-fig3.09ba2d37f202a6af.png) +![Bahdanau - arviz.org에서 가져온 RNNsearch-50의 샘플 정렬](../../../../../translated_images/ko/bahdanau-fig3.09ba2d37f202a6af.webp) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf)에서 발췌 (그림 3) @@ -66,7 +66,7 @@ RNN을 사용하면 시퀀스-투-시퀀스 작업은 두 개의 순환 신경 다음으로, 시퀀스 내에서 패턴을 캡처해야 합니다. 이를 위해 트랜스포머는 **셀프 어텐션** 메커니즘을 사용합니다. 이는 입력과 출력이 동일한 시퀀스에 어텐션을 적용하는 것입니다. 셀프 어텐션을 적용하면 문장 내 **맥락**을 고려하고, 어떤 단어들이 서로 관련이 있는지 확인할 수 있습니다. 예를 들어, *it*과 같은 대명사가 참조하는 단어를 확인하고, 맥락을 반영할 수 있습니다: -![](../../../../../translated_images/ko/CoreferenceResolution.861924d6d384a7d6.png) +![](../../../../../translated_images/ko/CoreferenceResolution.861924d6d384a7d6.webp) > [Google 블로그](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html)에서 발췌. @@ -91,7 +91,7 @@ RNN을 사용하면 시퀀스-투-시퀀스 작업은 두 개의 순환 신경 **BERT**(Bidirectional Encoder Representations from Transformers)는 매우 큰 다층 트랜스포머 네트워크로, *BERT-base*는 12개 층, *BERT-large*는 24개 층으로 구성됩니다. 이 모델은 대규모 텍스트 데이터(Wikipedia + 책) 코퍼스에서 비지도 학습(문장에서 마스킹된 단어 예측)을 통해 사전 학습됩니다. 사전 학습 동안 모델은 상당한 수준의 언어 이해를 흡수하며, 이후 다른 데이터셋에서 미세 조정을 통해 이를 활용할 수 있습니다. 이 과정을 **전이 학습**이라고 합니다. -![http://jalammar.github.io/illustrated-bert/에서 가져온 그림](../../../../../translated_images/ko/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) +![http://jalammar.github.io/illustrated-bert/에서 가져온 그림](../../../../../translated_images/ko/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > 이미지 [출처](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/ko/lessons/5-NLP/18-Transformers/READMEtransformers.md b/translations/ko/lessons/5-NLP/18-Transformers/READMEtransformers.md index 2d989f20..30d30888 100644 --- a/translations/ko/lessons/5-NLP/18-Transformers/READMEtransformers.md +++ b/translations/ko/lessons/5-NLP/18-Transformers/READMEtransformers.md @@ -11,13 +11,13 @@ RNN을 사용하면 시퀀스-투-시퀀스는 두 개의 순환 네트워크로 **주의 메커니즘**은 RNN의 각 출력 예측에 대한 각 입력 벡터의 맥락적 영향을 가중치로 조정하는 수단을 제공합니다. 이를 구현하는 방법은 입력 RNN의 중간 상태와 출력 RNN 사이에 단축 경로를 생성하는 것입니다. 이렇게 하면 출력 기호 yt를 생성할 때 모든 입력 숨겨진 상태 hi를 서로 다른 가중치 계수 αt,i와 함께 고려합니다. -![인코더/디코더 모델과 추가적인 주의 레이어를 보여주는 이미지](../../../../../translated_images/ko/encoder-decoder-attention.7a726296894fb567.png) +![인코더/디코더 모델과 추가적인 주의 레이어를 보여주는 이미지](../../../../../translated_images/ko/encoder-decoder-attention.7a726296894fb567.webp) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf)에서 인용된 추가적인 주의 메커니즘을 가진 인코더-디코더 모델, [이 블로그 게시물](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)에서 인용됨 주의 행렬 {αi,j}는 특정 입력 단어가 출력 시퀀스의 주어진 단어 생성에 기여하는 정도를 나타냅니다. 아래는 이러한 행렬의 예입니다: -![RNNsearch-50에 의해 발견된 샘플 정렬을 보여주는 이미지, Bahdanau - arviz.org에서 가져옴](../../../../../translated_images/ko/bahdanau-fig3.09ba2d37f202a6af.png) +![RNNsearch-50에 의해 발견된 샘플 정렬을 보여주는 이미지, Bahdanau - arviz.org에서 가져옴](../../../../../translated_images/ko/bahdanau-fig3.09ba2d37f202a6af.webp) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf)에서의 그림 (Fig.3) @@ -57,7 +57,7 @@ RNN을 사용하면 시퀀스-투-시퀀스는 두 개의 순환 네트워크로 다음으로, 우리는 시퀀스 내에서 몇 가지 패턴을 포착해야 합니다. 이를 위해 트랜스포머는 **자기 주의** 메커니즘을 사용하며, 이는 기본적으로 입력과 출력으로 동일한 시퀀스에 적용되는 주의입니다. 자기 주의를 적용하면 문장 내의 **맥락**을 고려하고 어떤 단어가 서로 관련되어 있는지를 확인할 수 있습니다. 예를 들어, 이는 *it*와 같은 대명사가 지칭하는 단어를 확인하고 맥락을 고려할 수 있게 해줍니다: -![](../../../../../translated_images/ko/CoreferenceResolution.861924d6d384a7d6.png) +![](../../../../../translated_images/ko/CoreferenceResolution.861924d6d384a7d6.webp) > [Google 블로그](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html)에서의 이미지 @@ -82,7 +82,7 @@ RNN을 사용하면 시퀀스-투-시퀀스는 두 개의 순환 네트워크로 **BERT** (Bidirectional Encoder Representations from Transformers)는 *BERT-base*의 경우 12층, *BERT-large*의 경우 24층으로 구성된 매우 큰 다층 트랜스포머 네트워크입니다. 이 모델은 먼저 대규모 텍스트 데이터(위키피디아 + 책)에서 비지도 학습(문장에서 마스킹된 단어 예측)을 사용하여 사전 훈련됩니다. 사전 훈련 동안 모델은 상당한 수준의 언어 이해를 흡수하며, 이는 이후 다른 데이터 세트와 함께 미세 조정하여 활용될 수 있습니다. 이 과정을 **전이 학습**이라고 합니다. -![http://jalammar.github.io/illustrated-bert/에서 가져온 이미지](../../../../../translated_images/ko/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) +![http://jalammar.github.io/illustrated-bert/에서 가져온 이미지](../../../../../translated_images/ko/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > 이미지 [출처](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/ko/lessons/5-NLP/19-NER/README.md b/translations/ko/lessons/5-NLP/19-NER/README.md index c5f1d67d..79edac0f 100644 --- a/translations/ko/lessons/5-NLP/19-NER/README.md +++ b/translations/ko/lessons/5-NLP/19-NER/README.md @@ -58,7 +58,7 @@ infant | O 토큰과 클래스 간의 일대일 대응을 구축해야 하므로, 아래 그림에서 보이는 것처럼 **다대다** 신경망 모델을 훈련할 수 있습니다: -![일반적인 순환 신경망 패턴을 보여주는 이미지.](../../../../../translated_images/ko/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![일반적인 순환 신경망 패턴을 보여주는 이미지.](../../../../../translated_images/ko/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > *이미지 출처: [Andrej Karpathy](http://karpathy.github.io/)의 [블로그 글](http://karpathy.github.io/2015/05/21/rnn-effectiveness/). NER 토큰 분류 모델은 이 그림의 가장 오른쪽 네트워크 아키텍처에 해당합니다.* diff --git a/translations/ko/lessons/5-NLP/README.md b/translations/ko/lessons/5-NLP/README.md index 676ec4af..16d3a969 100644 --- a/translations/ko/lessons/5-NLP/README.md +++ b/translations/ko/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 자연어 처리 -![NLP 작업 요약 스케치](../../../../translated_images/ko/ai-nlp.b22dcb8ca4707cea.png) +![NLP 작업 요약 스케치](../../../../translated_images/ko/ai-nlp.b22dcb8ca4707cea.webp) 이 섹션에서는 **자연어 처리(NLP)**와 관련된 작업을 처리하기 위해 신경망을 사용하는 방법에 대해 집중적으로 다룹니다. 컴퓨터가 해결할 수 있기를 바라는 많은 NLP 문제들이 있습니다: diff --git a/translations/ko/lessons/6-Other/23-MultiagentSystems/README.md b/translations/ko/lessons/6-Other/23-MultiagentSystems/README.md index d1f3d12b..75e51036 100644 --- a/translations/ko/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/ko/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ NetLogo의 훌륭한 점은 작동하는 모델 라이브러리를 포함하고 모델을 열면 NetLogo의 주요 화면으로 이동합니다. 여기에는 유한한 자원(풀)을 고려한 늑대와 양의 개체군을 설명하는 샘플 모델이 있습니다. -![NetLogo 주요 화면](../../../../../translated_images/ko/NetLogo-Main.32653711ec1a01b3.png) +![NetLogo 주요 화면](../../../../../translated_images/ko/NetLogo-Main.32653711ec1a01b3.webp) > Dmitry Soshnikov의 스크린샷 diff --git a/translations/ko/lessons/README.md b/translations/ko/lessons/README.md index 1532861b..8d9ce783 100644 --- a/translations/ko/lessons/README.md +++ b/translations/ko/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 개요 -![낙서로 그린 개요](../../../translated_images/ko/ai-overview.0857791951d19500.png) +![낙서로 그린 개요](../../../translated_images/ko/ai-overview.0857791951d19500.webp) > 스케치노트: [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/ko/lessons/X-Extras/X1-MultiModal/README.md b/translations/ko/lessons/X-Extras/X1-MultiModal/README.md index 867ae3fb..17b9ac20 100644 --- a/translations/ko/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/ko/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ Transformer 모델이 NLP 작업을 해결하는 데 성공한 이후, 동일하 CLIP의 주요 아이디어는 텍스트 프롬프트와 이미지를 비교하여 이미지가 프롬프트와 얼마나 잘 일치하는지 판단하는 것입니다. -![CLIP 아키텍처](../../../../../translated_images/ko/clip-arch.b3dbf20b4e8ed8be.png) +![CLIP 아키텍처](../../../../../translated_images/ko/clip-arch.b3dbf20b4e8ed8be.webp) > *[이 블로그 글](https://openai.com/blog/clip/)에서 가져온 이미지* @@ -31,7 +31,7 @@ CLIP 모델/라이브러리는 [OpenAI GitHub](https://github.com/openai/CLIP) 예를 들어, 고양이, 개, 인간을 분류해야 한다고 가정해 봅시다. 이 경우 모델에 이미지를 제공하고, "*고양이 사진*", "*개 사진*", "*인간 사진*"과 같은 일련의 텍스트 프롬프트를 제공합니다. 결과 벡터에서 가장 높은 값을 가진 인덱스를 선택하면 됩니다. -![CLIP을 이용한 이미지 분류](../../../../../translated_images/ko/clip-class.3af42ef0b2b19369.png) +![CLIP을 이용한 이미지 분류](../../../../../translated_images/ko/clip-class.3af42ef0b2b19369.webp) > *[이 블로그 글](https://openai.com/blog/clip/)에서 가져온 이미지* @@ -55,13 +55,13 @@ VQGAN에 대해 더 알아보려면 [Taming Transformers](https://compvis.github VQGAN과 전통적인 GAN의 중요한 차이점 중 하나는 후자가 어떤 입력 벡터로도 괜찮은 이미지를 생성할 수 있는 반면, VQGAN은 일관성이 없는 이미지를 생성할 가능성이 높다는 점입니다. 따라서 이미지 생성 과정을 추가로 안내해야 하며, 이를 CLIP을 사용하여 수행할 수 있습니다. -![VQGAN+CLIP 아키텍처](../../../../../translated_images/ko/vqgan.5027fe05051dfa31.png) +![VQGAN+CLIP 아키텍처](../../../../../translated_images/ko/vqgan.5027fe05051dfa31.webp) 텍스트 프롬프트에 해당하는 이미지를 생성하려면, 먼저 VQGAN을 통해 이미지를 생성하는 임의의 인코딩 벡터로 시작합니다. 그런 다음 CLIP을 사용하여 이미지가 텍스트 프롬프트와 얼마나 잘 일치하는지 보여주는 손실 함수를 생성합니다. 이후 목표는 이 손실을 최소화하는 것이며, 역전파를 사용하여 입력 벡터 매개변수를 조정합니다. VQGAN+CLIP을 구현한 훌륭한 라이브러리는 [Pixray](http://github.com/pixray/pixray)입니다. -![Pixray로 생성된 이미지](../../../../../translated_images/ko/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Pixray로 생성된 이미지](../../../../../translated_images/ko/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Pixray로 생성된 이미지](../../../../../translated_images/ko/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) +![Pixray로 생성된 이미지](../../../../../translated_images/ko/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp) | ![Pixray로 생성된 이미지](../../../../../translated_images/ko/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp) | ![Pixray로 생성된 이미지](../../../../../translated_images/ko/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp) ----|----|---- 프롬프트 *문학을 가르치는 젊은 남성 교사의 근접 수채화 초상화, 책을 들고 있음*으로 생성된 이미지 | 프롬프트 *컴퓨터 과학을 가르치는 젊은 여성 교사의 근접 유화 초상화, 컴퓨터와 함께 있음*으로 생성된 이미지 | 프롬프트 *수학을 가르치는 나이 든 남성 교사의 근접 유화 초상화, 칠판 앞에 있음*으로 생성된 이미지 diff --git a/translations/lt/README.md b/translations/lt/README.md index b3f48860..7c64a6e7 100644 --- a/translations/lt/README.md +++ b/translations/lt/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # Dirbtinis intelektas pradedantiesiems - mokymo programa -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/lt/ai-overview.0857791951d19500.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/lt/ai-overview.0857791951d19500.webp)| |:---:| | Dirbtinis intelektas pradedantiesiems - _Sketchnote by [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/lt/lessons/1-Intro/README.md b/translations/lt/lessons/1-Intro/README.md index fbde8d4b..c07ab61b 100644 --- a/translations/lt/lessons/1-Intro/README.md +++ b/translations/lt/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Įvadas į dirbtinį intelektą -![Santrauka apie dirbtinio intelekto įvadą piešinyje](../../../../translated_images/lt/ai-intro.bf28d1ac4235881c.png) +![Santrauka apie dirbtinio intelekto įvadą piešinyje](../../../../translated_images/lt/ai-intro.bf28d1ac4235881c.webp) > Piešinys sukurtas [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Iš pradžių kompiuterius išrado [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage), kad jie galėtų atlikti skaičiavimus pagal aiškiai apibrėžtą procedūrą – algoritmą. Šiuolaikiniai kompiuteriai, nors ir gerokai pažangesni nei XIX amžiuje pasiūlytas modelis, vis dar remiasi ta pačia kontroliuojamų skaičiavimų idėja. Todėl galima užprogramuoti kompiuterį atlikti tam tikrą užduotį, jei žinome tikslų veiksmų seką, reikalingą tikslui pasiekti. -![Asmens nuotrauka](../../../../translated_images/lt/dsh_age.d212a30d4e54fb5f.png) +![Asmens nuotrauka](../../../../translated_images/lt/dsh_age.d212a30d4e54fb5f.webp) > Nuotrauka [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ Daugiau informacijos rasite **[Dirbtinis bendrasis intelektas](https://en.wikipe Viena iš problemų, susijusių su terminu **[intelektas](https://en.wikipedia.org/wiki/Intelligence)**, yra ta, kad nėra aiškaus šio termino apibrėžimo. Galima teigti, kad intelektas susijęs su **abstrakčiu mąstymu** arba **savimone**, tačiau mes negalime jo tinkamai apibrėžti. -![Katės nuotrauka](../../../../translated_images/lt/photo-cat.8c8e8fb760ffe457.jpg) +![Katės nuotrauka](../../../../translated_images/lt/photo-cat.8c8e8fb760ffe457.webp) > [Nuotrauka](https://unsplash.com/photos/75715CVEJhI) [Amber Kipp](https://unsplash.com/@sadmax) iš Unsplash @@ -98,13 +98,13 @@ Kita vertus, galime pabandyti modeliuoti paprasčiausius mūsų smegenų element > | O kaip su ML? | | > |--------------|-----------| -> | Dirbtinio intelekto dalis, pagrįsta kompiuterio mokymusi spręsti problemą remiantis tam tikrais duomenimis, vadinama **mašininiu mokymusi**. Šiame kurse neaptarsime klasikinio mašininio mokymosi – siūlome atskirą [Mašininio mokymosi pradedantiesiems](http://aka.ms/ml-beginners) mokymo programą. | ![ML pradedantiesiems](../../../../translated_images/lt/ml-for-beginners.9e4fed176fd5817d.png) | +> | Dirbtinio intelekto dalis, pagrįsta kompiuterio mokymusi spręsti problemą remiantis tam tikrais duomenimis, vadinama **mašininiu mokymusi**. Šiame kurse neaptarsime klasikinio mašininio mokymosi – siūlome atskirą [Mašininio mokymosi pradedantiesiems](http://aka.ms/ml-beginners) mokymo programą. | ![ML pradedantiesiems](../../../../translated_images/lt/ml-for-beginners.9e4fed176fd5817d.webp) | ## Trumpa DI istorija Dirbtinis intelektas kaip sritis pradėtas XX amžiaus viduryje. Iš pradžių simbolinis samprotavimas buvo vyraujantis požiūris, ir jis lėmė nemažai svarbių pasiekimų, tokių kaip ekspertų sistemos – kompiuterinės programos, galinčios veikti kaip ekspertas tam tikrose ribotose problemų srityse. Tačiau netrukus tapo aišku, kad toks požiūris nėra gerai pritaikomas. Žinių išgavimas iš eksperto, jų pateikimas kompiuteryje ir žinių bazės tikslumo palaikymas pasirodė esąs labai sudėtingas ir per brangus daugeliu atvejų. Tai lėmė vadinamąją [DI žiemą](https://en.wikipedia.org/wiki/AI_winter) 1970-aisiais. -Trumpa DI istorija +Trumpa DI istorija > Vaizdas sukurtas [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/lt/lessons/2-Symbolic/README.md b/translations/lt/lessons/2-Symbolic/README.md index 864ddeee..04815f5c 100644 --- a/translations/lt/lessons/2-Symbolic/README.md +++ b/translations/lt/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Žinių Atvaizdavimas ir Ekspertinės Sistemos -![Santrauka apie simbolinį AI](../../../../translated_images/lt/ai-symbolic.715a30cb610411a6.png) +![Santrauka apie simbolinį AI](../../../../translated_images/lt/ai-symbolic.715a30cb610411a6.webp) > Sketchnote sukūrė [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ Dažniausiai mes griežtai neapibrėžiame žinių, bet jas susiejame su kitais Taigi, **žinių atvaizdavimo** problema yra rasti efektyvų būdą atvaizduoti žinias kompiuteryje duomenų forma, kad jos būtų automatiškai naudojamos. Tai galima laikyti spektru: -![Žinių atvaizdavimo spektras](../../../../translated_images/lt/knowledge-spectrum.b60df631852c0217.png) +![Žinių atvaizdavimo spektras](../../../../translated_images/lt/knowledge-spectrum.b60df631852c0217.webp) > Vaizdas sukurtas [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Blokų sintaksė | Įtraukimas | | | Vienas iš ankstyvųjų simbolinio AI pasiekimų buvo vadinamosios **ekspertinės sistemos** - kompiuterinės sistemos, sukurtos veikti kaip ekspertas tam tikroje ribotoje problemų srityje. Jos buvo pagrįstos **žinių baze**, išgauta iš vieno ar daugiau žmonių ekspertų, ir turėjo **išvadų variklį**, kuris atliko tam tikrą samprotavimą remdamasis šia baze. -![Žmogaus architektūra](../../../../translated_images/lt/arch-human.5d4d35f1bba3ab1c.png) | ![Žinių pagrindu veikianti sistema](../../../../translated_images/lt/arch-kbs.3ec5c150b09fa8da.png) +![Žmogaus architektūra](../../../../translated_images/lt/arch-human.5d4d35f1bba3ab1c.webp) | ![Žinių pagrindu veikianti sistema](../../../../translated_images/lt/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ Supaprastinta žmogaus nervų sistemos struktūra | Žinių pagrindu veikiančios sistemos architektūra @@ -106,7 +106,7 @@ Ekspertinės sistemos yra sukurtos panašiai kaip žmogaus samprotavimo sistema, Kaip pavyzdį, apsvarstykime šią ekspertinę sistemą, skirtą gyvūnui nustatyti pagal jo fizines charakteristikas: -![AND-OR medis](../../../../translated_images/lt/AND-OR-Tree.5592d2c70187f283.png) +![AND-OR medis](../../../../translated_images/lt/AND-OR-Tree.5592d2c70187f283.webp) > Vaizdas sukurtas [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/lt/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/lt/lessons/3-NeuralNetworks/05-Frameworks/README.md index 5d0aa6fe..a504ff6c 100644 --- a/translations/lt/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/lt/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Per didelis pritaikymas yra itin svarbi sąvoka mašininio mokymosi srityje, ir Apsvarstykite šią problemą, kurioje reikia aproksimuoti 5 taškus (grafikuose pažymėtus `x`): -![linear](../../../../../translated_images/lt/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/lt/overfit2.131f5800ae10ca5e.jpg) +![linear](../../../../../translated_images/lt/overfit1.f24b71c6f652e59e.webp) | ![overfit](../../../../../translated_images/lt/overfit2.131f5800ae10ca5e.webp) -------------------------|-------------------------- **Linijinis modelis, 2 parametrai** | **Nelinijinis modelis, 7 parametrai** Mokymo klaida = 5.3 | Mokymo klaida = 0 @@ -79,7 +79,7 @@ Labai svarbu rasti tinkamą pusiausvyrą tarp modelio sudėtingumo (parametrų s Kaip matote iš aukščiau pateikto grafiko, per didelį pritaikymą galima aptikti pagal labai mažą mokymo klaidą ir didelę validacijos klaidą. Paprastai mokymo metu matysime, kaip tiek mokymo, tiek validacijos klaidos pradeda mažėti, o tada tam tikru momentu validacijos klaida gali nustoti mažėti ir pradėti didėti. Tai bus per didelio pritaikymo ženklas ir indikatorius, kad turėtume sustabdyti mokymą (arba bent jau išsaugoti modelio būseną). -![overfitting](../../../../../translated_images/lt/Overfitting.408ad91cd90b4371.png) +![overfitting](../../../../../translated_images/lt/Overfitting.408ad91cd90b4371.webp) ## Kaip išvengti per didelio pritaikymo diff --git a/translations/lt/lessons/3-NeuralNetworks/README.md b/translations/lt/lessons/3-NeuralNetworks/README.md index 9bfaf990..160bd398 100644 --- a/translations/lt/lessons/3-NeuralNetworks/README.md +++ b/translations/lt/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Įvadas į neuroninius tinklus -![Santrauka apie neuroninių tinklų įvadą piešinyje](../../../../translated_images/lt/ai-neuralnetworks.1c687ae40bc86e83.png) +![Santrauka apie neuroninių tinklų įvadą piešinyje](../../../../translated_images/lt/ai-neuralnetworks.1c687ae40bc86e83.webp) Kaip aptarėme įvade, vienas iš būdų pasiekti intelektą yra treniruoti **kompiuterinį modelį** arba **dirbtinį smegenų modelį**. Nuo XX amžiaus vidurio mokslininkai bandė įvairius matematinius modelius, kol pastaraisiais metais šis metodas pasirodė itin sėkmingas. Tokie smegenų matematiniai modeliai vadinami **neuroniniais tinklais**. @@ -36,13 +36,13 @@ Mes apsvarstysime dvi dažniausiai pasitaikančias mašininio mokymosi problemas Iš biologijos žinome, kad mūsų smegenys susideda iš neuroninių ląstelių (neuronų), kiekviena iš jų turi kelis "įėjimus" (dendritus) ir vieną "išėjimą" (aksoną). Tiek dendritai, tiek aksonai gali perduoti elektrinius signalus, o jungtys tarp jų — vadinamos sinapsėmis — gali turėti skirtingą laidumą, kurį reguliuoja neurotransmiteriai. -![Neurono modelis](../../../../translated_images/lt/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![Neurono modelis](../../../../translated_images/lt/artneuron.1a5daa88d20ebe6f.png) +![Neurono modelis](../../../../translated_images/lt/synapse-wikipedia.ed20a9e4726ea1c6.webp) | ![Neurono modelis](../../../../translated_images/lt/artneuron.1a5daa88d20ebe6f.webp) ----|---- Tikras neuronas *([Vaizdas](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) iš Vikipedijos)* | Dirbtinis neuronas *(Vaizdas autoriaus)* Taigi, paprasčiausias matematinis neurono modelis turi kelis įėjimus X1, ..., XN ir vieną išėjimą Y, bei svorių seriją W1, ..., WN. Išėjimas apskaičiuojamas taip: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) kur f yra tam tikra nelinijinė **aktyvavimo funkcija**. diff --git a/translations/lt/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/lt/lessons/4-ComputerVision/06-IntroCV/README.md index 011ee593..63214b05 100644 --- a/translations/lt/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/lt/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ Mūsų [OpenCV Notebook](OpenCV.ipynb) pateikiame keletą pavyzdžių, kada komp * **Brailio knygos nuotraukos išankstinis apdorojimas**. Mes sutelkiame dėmesį į tai, kaip galima naudoti slenksčio nustatymą, funkcijų aptikimą, perspektyvos transformaciją ir NumPy manipuliacijas, kad atskirtume atskirus Brailio simbolius tolimesnei klasifikacijai neuroniniu tinklu. -![Brailio vaizdas](../../../../../translated_images/lt/braille.341962ff76b1bd70.jpeg) | ![Brailio vaizdas apdorotas](../../../../../translated_images/lt/braille-result.46530fea020b03c7.png) | ![Brailio simboliai](../../../../../translated_images/lt/braille-symbols.0159185ab69d5339.png) +![Brailio vaizdas](../../../../../translated_images/lt/braille.341962ff76b1bd70.webp) | ![Brailio vaizdas apdorotas](../../../../../translated_images/lt/braille-result.46530fea020b03c7.webp) | ![Brailio simboliai](../../../../../translated_images/lt/braille-symbols.0159185ab69d5339.webp) ----|-----|----- > Vaizdas iš [OpenCV.ipynb](OpenCV.ipynb) * **Judėjimo aptikimas vaizdo įraše naudojant kadrų skirtumą**. Jei kamera yra fiksuota, tuomet kadrai iš kameros turėtų būti gana panašūs vienas į kitą. Kadangi kadrai atvaizduojami kaip masyvai, tiesiog atimant šiuos masyvus dviejų iš eilės einančių kadrų atveju gausime pikselių skirtumą, kuris turėtų būti mažas statiniams kadrams ir didėti, kai vaizde yra reikšmingas judėjimas. -![Vaizdo kadrų ir kadrų skirtumų vaizdas](../../../../../translated_images/lt/frame-difference.706f805491a0883c.png) +![Vaizdo kadrų ir kadrų skirtumų vaizdas](../../../../../translated_images/lt/frame-difference.706f805491a0883c.webp) > Vaizdas iš [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ Mūsų [OpenCV Notebook](OpenCV.ipynb) pateikiame keletą pavyzdžių, kada komp - **Tankus optinis srautas** apskaičiuoja vektorių lauką, kuris rodo, kur kiekvienas pikselis juda. - **Retas optinis srautas** remiasi tam tikrų išskirtinių vaizdo bruožų (pvz., kraštų) paėmimu ir jų trajektorijos kūrimu nuo kadro iki kadro. -![Optinio srauto vaizdas](../../../../../translated_images/lt/optical.1f4a94464579a83a.png) +![Optinio srauto vaizdas](../../../../../translated_images/lt/optical.1f4a94464579a83a.webp) > Vaizdas iš [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/lt/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/lt/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index bbeee05a..e25fc68f 100644 --- a/translations/lt/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/lt/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 yra tinklas, kuris 2014 m. pasiekė 92,7% tikslumą ImageNet top-5 klasifikacijoje. Jo sluoksnių struktūra yra tokia: -![ImageNet Layers](../../../../../translated_images/lt/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet Layers](../../../../../translated_images/lt/vgg-16-arch1.d901a5583b3a51ba.webp) Kaip matote, VGG seka tradicinę piramidės architektūrą, kuri yra konvoliucinių ir kaupimo sluoksnių seka. -![ImageNet Pyramid](../../../../../translated_images/lt/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet Pyramid](../../../../../translated_images/lt/vgg-16-arch.64ff2137f50dd49f.webp) > Vaizdas iš [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/lt/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/lt/lessons/4-ComputerVision/07-ConvNets/README.md index e2b25f00..61b1e93b 100644 --- a/translations/lt/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/lt/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ Realiame gyvenime norime atpažinti objektus nuotraukoje, nepaisant jų tikslios Norėdami išgauti raštus, naudosime **konvoliucinių filtrų** sąvoką. Kaip žinote, vaizdas yra pateikiamas kaip 2D-matrica arba 3D-tensoras su spalvų gylio dimensija. Filtrą taikyti reiškia, kad imame palyginti mažą **filtrų branduolio** matricą ir kiekvienam pikseliui originaliame vaizde apskaičiuojame svertinį vidurkį su kaimyniniais taškais. Galime tai įsivaizduoti kaip mažą langą, kuris slysta per visą vaizdą ir vidutiniškai apskaičiuoja pikselius pagal filtrų branduolio matricos svorius. -![Vertikalus kraštų filtras](../../../../../translated_images/lt/filter-vert.b7148390ca0bc356.png) | ![Horizontalus kraštų filtras](../../../../../translated_images/lt/filter-horiz.59b80ed4feb946ef.png) +![Vertikalus kraštų filtras](../../../../../translated_images/lt/filter-vert.b7148390ca0bc356.webp) | ![Horizontalus kraštų filtras](../../../../../translated_images/lt/filter-horiz.59b80ed4feb946ef.webp) ----|---- > Vaizdas: Dmitry Soshnikov @@ -38,7 +38,7 @@ CNN veikimas grindžiamas šiomis svarbiomis idėjomis: * Galime sukurti tinklą taip, kad filtrai būtų mokomi automatiškai * Galime naudoti tą patį metodą aukšto lygio savybių raštų paieškai, ne tik originaliame vaizde. Taigi CNN savybių išgavimas veikia hierarchijos principu – pradedant nuo žemo lygio pikselių kombinacijų iki aukšto lygio vaizdo dalių kombinacijų. -![Hierarchinis savybių išgavimas](../../../../../translated_images/lt/FeatureExtractionCNN.d9b456cbdae7cb64.png) +![Hierarchinis savybių išgavimas](../../../../../translated_images/lt/FeatureExtractionCNN.d9b456cbdae7cb64.webp) > Vaizdas iš [Hislop-Lynch straipsnio](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), remiantis [jų tyrimu](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ Dauguma CNN, naudojamų vaizdų apdorojimui, seka vadinamąją piramidės archit Pavyzdžiui, pažvelkime į VGG-16 architektūrą – tinklą, kuris 2014 m. pasiekė 92,7% tikslumą ImageNet top-5 klasifikacijoje: -![ImageNet sluoksniai](../../../../../translated_images/lt/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet sluoksniai](../../../../../translated_images/lt/vgg-16-arch1.d901a5583b3a51ba.webp) -![ImageNet piramidė](../../../../../translated_images/lt/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet piramidė](../../../../../translated_images/lt/vgg-16-arch.64ff2137f50dd49f.webp) > Vaizdas iš [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/lt/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/lt/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 91af04de..37ddea92 100644 --- a/translations/lt/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/lt/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ Jums reikia išmokyti konvoliucinį neuroninį tinklą klasifikuoti skirtingas k Naudosime [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/), kuriame yra 37 skirtingų šunų ir kačių veislių nuotraukos. -![Duomenų rinkinys, su kuriuo dirbsime](../../../../../../translated_images/lt/data.50b2a9d5484bdbf0.png) +![Duomenų rinkinys, su kuriuo dirbsime](../../../../../../translated_images/lt/data.50b2a9d5484bdbf0.webp) Norėdami atsisiųsti duomenų rinkinį, naudokite šį kodo fragmentą: diff --git a/translations/lt/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/lt/lessons/4-ComputerVision/08-TransferLearning/README.md index 63b1e94d..ef570d19 100644 --- a/translations/lt/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/lt/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Tiek Keras, tiek PyTorch turi funkcijas, leidžiančias lengvai įkelti iš anks Štai pavyzdinės savybės, išgautos iš katės nuotraukos naudojant VGG-16 tinklą: -![Savybės, išgautos naudojant VGG-16](../../../../../translated_images/lt/features.6291f9c7ba3a0b95.png) +![Savybės, išgautos naudojant VGG-16](../../../../../translated_images/lt/features.6291f9c7ba3a0b95.webp) ## Kačių ir šunų duomenų rinkinys @@ -48,19 +48,19 @@ Iš anksto apmokytas neuroninis tinklas savo „smegenyse“ turi įvairius šab Vienas iš būdų, kurį galime naudoti, yra pradėti nuo atsitiktinio vaizdo ir tada bandyti naudoti **gradientinio nusileidimo optimizavimo** techniką, kad pakoreguotume tą vaizdą taip, jog tinklas pradėtų manyti, kad tai yra katė. -![Vaizdo optimizavimo ciklas](../../../../../translated_images/lt/ideal-cat-loop.999fbb8ff306e044.png) +![Vaizdo optimizavimo ciklas](../../../../../translated_images/lt/ideal-cat-loop.999fbb8ff306e044.webp) Tačiau, jei tai padarysime, gausime kažką labai panašaus į atsitiktinį triukšmą. Taip yra todėl, kad *yra daug būdų, kaip tinklas gali manyti, kad įvesties vaizdas yra katė*, įskaitant kai kuriuos, kurie vizualiai neturi prasmės. Nors tie vaizdai turi daug šablonų, būdingų katei, nėra nieko, kas juos apribotų vizualiai išskirtiniais. Norėdami pagerinti rezultatą, galime pridėti dar vieną terminą į nuostolių funkciją, vadinamą **variacijos nuostoliu**. Tai metrika, rodanti, kaip panašūs yra kaimyniniai vaizdo pikseliai. Mažinant variacijos nuostolį vaizdas tampa lygesnis ir atsikratoma triukšmo – taip atskleidžiami vizualiai patrauklesni šablonai. Štai pavyzdys tokių „idealių“ vaizdų, kurie su didele tikimybe klasifikuojami kaip katė ir kaip zebra: -![Ideali katė](../../../../../translated_images/lt/ideal-cat.203dd4597643d6b0.png) | ![Ideali zebra](../../../../../translated_images/lt/ideal-zebra.7f70e8b54ee15a7a.png) +![Ideali katė](../../../../../translated_images/lt/ideal-cat.203dd4597643d6b0.webp) | ![Ideali zebra](../../../../../translated_images/lt/ideal-zebra.7f70e8b54ee15a7a.webp) -----|----- *Ideali katė* | *Ideali zebra* Panašus metodas gali būti naudojamas atliekant vadinamuosius **priešiškus išpuolius** prieš neuroninį tinklą. Tarkime, norime apgauti neuroninį tinklą ir priversti šunį atrodyti kaip katę. Jei paimsime šuns vaizdą, kurį tinklas atpažįsta kaip šunį, galime šiek tiek jį pakoreguoti naudodami gradientinio nusileidimo optimizavimą, kol tinklas pradės jį klasifikuoti kaip katę: -![Šuns nuotrauka](../../../../../translated_images/lt/original-dog.8f68a67d2fe0911f.png) | ![Šuns nuotrauka, klasifikuojama kaip katė](../../../../../translated_images/lt/adversarial-dog.d9fc7773b0142b89.png) +![Šuns nuotrauka](../../../../../translated_images/lt/original-dog.8f68a67d2fe0911f.webp) | ![Šuns nuotrauka, klasifikuojama kaip katė](../../../../../translated_images/lt/adversarial-dog.d9fc7773b0142b89.webp) -----|----- *Originali šuns nuotrauka* | *Šuns nuotrauka, klasifikuojama kaip katė* diff --git a/translations/lt/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/lt/lessons/4-ComputerVision/09-Autoencoders/README.md index a66c7809..c94b2990 100644 --- a/translations/lt/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/lt/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ Tačiau galime norėti naudoti neapdorotus (nepažymėtus) duomenis CNN funkcij Kadangi mokome autoenkoderį užfiksuoti kuo daugiau informacijos iš originalaus vaizdo, kad būtų galima tiksliai jį atkurti, tinklas stengiasi rasti geriausią **įterpimą** įvesties vaizdams, kad užfiksuotų jų prasmę. -![Autoenkoderio schema](../../../../../translated_images/lt/autoencoder_schema.5e6fc9ad98a5eb61.jpg) +![Autoenkoderio schema](../../../../../translated_images/lt/autoencoder_schema.5e6fc9ad98a5eb61.webp) > Vaizdas iš [Keras tinklaraščio](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/lt/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/lt/lessons/4-ComputerVision/11-ObjectDetection/README.md index f3dd1be3..462e2395 100644 --- a/translations/lt/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/lt/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ Vaizdų klasifikavimo modeliai, su kuriais dirbome iki šiol, paimdavo vaizdą i ## [Prieš paskaitą: testas](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Objektų atpažinimas](../../../../../translated_images/lt/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) +![Objektų atpažinimas](../../../../../translated_images/lt/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp) > Vaizdas iš [YOLO v2 svetainės](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ Tarkime, norime rasti katę paveikslėlyje. Labai naivus požiūris į objektų 2. Atlikti vaizdų klasifikavimą kiekvienoje plytelėje. 3. Tos plytelės, kurios duoda pakankamai aukštą aktyvaciją, gali būti laikomos turinčiomis ieškomą objektą. -![Naivus objektų atpažinimas](../../../../../translated_images/lt/naive-detection.e7f1ba220ccd08c6.png) +![Naivus objektų atpažinimas](../../../../../translated_images/lt/naive-detection.e7f1ba220ccd08c6.webp) > *Vaizdas iš [užduočių sąsiuvinio](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ Galite susidurti su šiais duomenų rinkiniais: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) – 20 klasių * [COCO](http://cocodataset.org/#home) – Įprasti objektai kontekste. 80 klasių, ribų dėžutės ir segmentavimo kaukės -![COCO](../../../../../translated_images/lt/coco-examples.71bc60380fa6cceb.jpg) +![COCO](../../../../../translated_images/lt/coco-examples.71bc60380fa6cceb.webp) ## Objektų atpažinimo metrikos @@ -50,7 +50,7 @@ Galite susidurti su šiais duomenų rinkiniais: Vaizdų klasifikavimui lengva išmatuoti, kaip gerai veikia algoritmas, tačiau objektų atpažinimui reikia įvertinti tiek klasės teisingumą, tiek numatytos ribų dėžutės vietos tikslumą. Pastarajam naudojama vadinamoji **Sankirta per sąjungą** (IoU), kuri matuoja, kaip gerai sutampa dvi dėžutės (arba dvi savavališkos sritys). -![IoU](../../../../../translated_images/lt/iou_equation.9a4751d40fff4e11.png) +![IoU](../../../../../translated_images/lt/iou_equation.9a4751d40fff4e11.webp) > *2 paveikslas iš [puikaus tinklaraščio apie IoU](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ Yra dvi pagrindinės objektų atpažinimo algoritmų klasės: [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) naudoja [Selektyvų paiešką](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf), kad sukurtų hierarchinę ROI regionų struktūrą, kuri vėliau perduodama per CNN funkcijų ištraukėjus ir SVM klasifikatorius, kad būtų nustatyta objekto klasė, o linijinė regresija naudojama *ribų dėžutės* koordinatėms nustatyti. [Oficialus straipsnis](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/lt/rcnn1.cae407020dfb1d1f.png) +![RCNN](../../../../../translated_images/lt/rcnn1.cae407020dfb1d1f.webp) > *Vaizdas iš van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/lt/rcnn2.2d9530bb83516484.png) +![RCNN-1](../../../../../translated_images/lt/rcnn2.2d9530bb83516484.webp) > *Vaizdai iš [šio tinklaraščio](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -110,7 +110,7 @@ Yra dvi pagrindinės objektų atpažinimo algoritmų klasės: Šis metodas panašus į R-CNN, tačiau regionai apibrėžiami po konvoliucinių sluoksnių taikymo. -![FRCNN](../../../../../translated_images/lt/f-rcnn.3cda6d9bb4188875.png) +![FRCNN](../../../../../translated_images/lt/f-rcnn.3cda6d9bb4188875.webp) > Vaizdas iš [oficialaus straipsnio](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ Yra dvi pagrindinės objektų atpažinimo algoritmų klasės: Pagrindinė šio metodo idėja – naudoti neuroninį tinklą ROI prognozavimui – vadinamąjį *Regionų pasiūlymo tinklą*. [Straipsnis](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/lt/faster-rcnn.8d46c099b87ef30a.png) +![FasterRCNN](../../../../../translated_images/lt/faster-rcnn.8d46c099b87ef30a.webp) > Vaizdas iš [oficialaus straipsnio](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ Pagrindinė šio metodo idėja – naudoti neuroninį tinklą ROI prognozavimui 2. Funkcijos apdorojamos **Pozicijos jautriu rezultatų žemėlapiu**. Kiekvienas objektas iš $C$ klasių padalijamas į $k\times k$ regionus, ir mes treniruojame tinklą prognozuoti objektų dalis. 3. Kiekvienai daliai iš $k\times k$ regionų visi tinklai balsuoja už objektų klases, ir klasė su didžiausiu balsų skaičiumi yra pasirinkta. -![r-fcn image](../../../../../translated_images/lt/r-fcn.13eb88158b99a3da.png) +![r-fcn image](../../../../../translated_images/lt/r-fcn.13eb88158b99a3da.webp) > Vaizdas iš [oficialaus straipsnio](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO yra realaus laiko vieno perėjimo algoritmas. Pagrindinė idėja yra tokia: * Vaizdas padalijamas į $S\times S$ regionus. * Kiekvienam regionui **CNN** prognozuoja $n$ galimų objektų, *ribų dėžutės* koordinates ir *pasitikėjimą*=*tikimybę* * IoU. - ![YOLO](../../../../../translated_images/lt/yolo.a2648ec82ee8bb4e.png) + ![YOLO](../../../../../translated_images/lt/yolo.a2648ec82ee8bb4e.webp) > Vaizdas iš [oficialaus straipsnio](https://arxiv.org/abs/1506.02640) diff --git a/translations/lt/lessons/4-ComputerVision/README.md b/translations/lt/lessons/4-ComputerVision/README.md index 3102619c..57ef4ce4 100644 --- a/translations/lt/lessons/4-ComputerVision/README.md +++ b/translations/lt/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Kompiuterinis matymas -![Kompiuterinio matymo turinio santrauka piešinyje](../../../../translated_images/lt/ai-computervision.6506ebebac3fbf76.png) +![Kompiuterinio matymo turinio santrauka piešinyje](../../../../translated_images/lt/ai-computervision.6506ebebac3fbf76.webp) Šiame skyriuje sužinosime apie: diff --git a/translations/lt/lessons/5-NLP/14-Embeddings/README.md b/translations/lt/lessons/5-NLP/14-Embeddings/README.md index e58f0182..059697bf 100644 --- a/translations/lt/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/lt/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ Taigi, įterpimo sluoksnis priims žodį kaip įvestį ir pateiks išvesties vek Naudodami įterpimo sluoksnį kaip pirmąjį sluoksnį mūsų klasifikatoriaus tinkle, galime pereiti nuo žodžių maišo prie **įterpinių maišo** modelio, kuriame pirmiausia kiekvieną žodį mūsų tekste konvertuojame į atitinkamą įterpinį, o tada apskaičiuojame tam tikrą agregavimo funkciją visiems tiems įterpiniams, pvz., `sum`, `average` arba `max`. -![Vaizdas, rodantis įterpinio klasifikatorių penkiems sekos žodžiams.](../../../../../translated_images/lt/embedding-classifier-example.b77f021a7ee67eee.png) +![Vaizdas, rodantis įterpinio klasifikatorių penkiems sekos žodžiams.](../../../../../translated_images/lt/embedding-classifier-example.b77f021a7ee67eee.webp) > Vaizdas sukurtas autoriaus @@ -40,7 +40,7 @@ Norėdami tai pasiekti, turime iš anksto apmokyti savo įterpimo modelį didel CBoW yra greitesnis, o praleidimo gramų modelis yra lėtesnis, tačiau geriau reprezentuoja retus žodžius. -![Vaizdas, rodantis CBoW ir praleidimo gramų algoritmus žodžių konvertavimui į vektorius.](../../../../../translated_images/lt/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![Vaizdas, rodantis CBoW ir praleidimo gramų algoritmus žodžių konvertavimui į vektorius.](../../../../../translated_images/lt/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Vaizdas iš [šio straipsnio](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/lt/lessons/5-NLP/15-LanguageModeling/README.md b/translations/lt/lessons/5-NLP/15-LanguageModeling/README.md index 8e5bad56..b9d99ed8 100644 --- a/translations/lt/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/lt/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ Ankstesniuose pavyzdžiuose naudojome iš anksto apmokytus semantinius įterpini * **Nuolatinis žodžių maišas** (CBoW), kai prognozuojame vidurinį žodį $W_0$ žodžių sekoje $W_{-N}$, ..., $W_N$. * **Skip-gram**, kai prognozuojame kaimyninių žodžių rinkinį {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} iš vidurinio žodžio $W_0$. -![vaizdas iš straipsnio apie žodžių konvertavimą į vektorius](../../../../../translated_images/lt/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![vaizdas iš straipsnio apie žodžių konvertavimą į vektorius](../../../../../translated_images/lt/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Vaizdas iš [šio straipsnio](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/lt/lessons/5-NLP/16-RNN/README.md b/translations/lt/lessons/5-NLP/16-RNN/README.md index 461ae351..609c326f 100644 --- a/translations/lt/lessons/5-NLP/16-RNN/README.md +++ b/translations/lt/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ Ankstesnėse dalyse naudojome turtingas semantines teksto reprezentacijas ir pap Norint užfiksuoti teksto sekos prasmę, reikia naudoti kitą neuroninio tinklo architektūrą, vadinamą **rekurentiniais neuroniniais tinklais** (RNN). RNN tinkluose sakinį perduodame per tinklą po vieną simbolį, o tinklas generuoja tam tikrą **būseną**, kurią vėliau perduodame tinklui kartu su kitu simboliu. -![RNN](../../../../../translated_images/lt/rnn.27f5c29c53d727b5.png) +![RNN](../../../../../translated_images/lt/rnn.27f5c29c53d727b5.webp) > Vaizdas sukurtas autoriaus @@ -61,7 +61,7 @@ Aptarėme rekurentinius tinklus, kurie veikia viena kryptimi, nuo sekos pradžio Rekurentinis tinklas, nesvarbu, ar vienkryptis, ar dvikryptis, užfiksuoja tam tikrus sekos modelius ir gali juos saugoti būsenos vektoriuje arba perduoti į išvestį. Kaip ir konvoliuciniuose tinkluose, galime sukurti kitą rekurentinį sluoksnį virš pirmojo, kad užfiksuotume aukštesnio lygio modelius ir sukurtume iš žemo lygio modelių, kuriuos ištraukė pirmasis sluoksnis. Tai veda mus prie **daugiasluoksnio RNN** sąvokos, kurią sudaro du ar daugiau rekurentinių tinklų, kur ankstesnio sluoksnio išvestis perduodama kitam sluoksniui kaip įvestis. -![Vaizdas, rodantis daugiasluoksnį ilgalaikės trumpalaikės atminties RNN](../../../../../translated_images/lt/multi-layer-lstm.dd975e29bb2a59fe.jpg) +![Vaizdas, rodantis daugiasluoksnį ilgalaikės trumpalaikės atminties RNN](../../../../../translated_images/lt/multi-layer-lstm.dd975e29bb2a59fe.webp) *Paveikslas iš [šio puikaus įrašo](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) Fernando López* diff --git a/translations/lt/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/lt/lessons/5-NLP/17-GenerativeNetworks/README.md index 1b480ed7..2c7f7ae3 100644 --- a/translations/lt/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/lt/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ RNN architektūroje, kurią aptarėme ankstesniame skyriuje, kiekvienas RNN vien Tai leidžia sukurti skirtingas neuronines architektūras, kurios parodytos žemiau esančiame paveikslėlyje: -![Paveikslėlis, rodantis įprastus pasikartojančių neuroninių tinklų modelius.](../../../../../translated_images/lt/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Paveikslėlis, rodantis įprastus pasikartojančių neuroninių tinklų modelius.](../../../../../translated_images/lt/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > Paveikslėlis iš tinklaraščio įrašo [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) autoriaus [Andrej Karpaty](http://karpathy.github.io/) @@ -32,7 +32,7 @@ Tai leidžia sukurti skirtingas neuronines architektūras, kurios parodytos žem Mes išmokysime šį RNN generuoti tekstą žingsnis po žingsnio. Kiekviename žingsnyje imsime simbolių seką, kurios ilgis yra `nchars`, ir paprašysime tinklo generuoti kitą išvesties simbolį kiekvienam įvesties simboliui: -![Paveikslėlis, rodantis RNN generavimo pavyzdį su žodžiu 'HELLO'.](../../../../../translated_images/lt/rnn-generate.56c54afb52f9781d.png) +![Paveikslėlis, rodantis RNN generavimo pavyzdį su žodžiu 'HELLO'.](../../../../../translated_images/lt/rnn-generate.56c54afb52f9781d.webp) Generuojant tekstą (inference metu), pradedame nuo tam tikro **pradžios taško**, kuris perduodamas per RNN ląsteles, kad būtų generuojama tarpinė būsena, o tada iš šios būsenos prasideda generavimas. Generuojame po vieną simbolį, perduodame būseną ir sugeneruotą simbolį kitai RNN ląstelei, kad sugeneruotume kitą, kol sugeneruojame pakankamai simbolių. diff --git a/translations/lt/lessons/5-NLP/18-Transformers/README.md b/translations/lt/lessons/5-NLP/18-Transformers/README.md index a86fde6d..2d587c16 100644 --- a/translations/lt/lessons/5-NLP/18-Transformers/README.md +++ b/translations/lt/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ Naudojant RNN, sekos į seką užduotis įgyvendinama naudojant du rekursinius t **Dėmesio mechanizmai** suteikia galimybę įvertinti kiekvieno įvesties vektoriaus kontekstinį poveikį kiekvienai RNN išvesties prognozei. Tai įgyvendinama sukuriant trumpesnius ryšius tarp įvesties RNN tarpinių būsenų ir išvesties RNN. Tokiu būdu, generuojant išvesties simbolį yt, atsižvelgiama į visas įvesties paslėptas būsenas hi, su skirtingais svorio koeficientais αt,i. -![Vaizdas, rodantis koduotojo/dekoduotojo modelį su papildomu dėmesio sluoksniu](../../../../../translated_images/lt/encoder-decoder-attention.7a726296894fb567.png) +![Vaizdas, rodantis koduotojo/dekoduotojo modelį su papildomu dėmesio sluoksniu](../../../../../translated_images/lt/encoder-decoder-attention.7a726296894fb567.webp) > Koduotojo-dekoduotojo modelis su papildomu dėmesio mechanizmu [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), cituota iš [šio tinklaraščio įrašo](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) Dėmesio matrica {αi,j} atspindėtų, kokiu mastu tam tikri įvesties žodžiai dalyvauja generuojant tam tikrą žodį išvesties sekoje. Žemiau pateiktas tokios matricos pavyzdys: -![Vaizdas, rodantis pavyzdinį suderinimą, rastą RNNsearch-50, paimta iš Bahdanau - arviz.org](../../../../../translated_images/lt/bahdanau-fig3.09ba2d37f202a6af.png) +![Vaizdas, rodantis pavyzdinį suderinimą, rastą RNNsearch-50, paimta iš Bahdanau - arviz.org](../../../../../translated_images/lt/bahdanau-fig3.09ba2d37f202a6af.webp) > Paveikslas iš [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) @@ -66,7 +66,7 @@ Rezultatas, kurį gauname su poziciniu įterpimu, įterpia tiek originalų žeto Toliau mums reikia užfiksuoti tam tikrus modelius mūsų sekoje. Tam transformatoriai naudoja **savidėmesio** mechanizmą, kuris iš esmės yra dėmesys, taikomas tai pačiai sekai kaip įvestis ir išvestis. Taikant savidėmesį, galime atsižvelgti į **kontekstą** sakinyje ir pamatyti, kurie žodžiai yra tarpusavyje susiję. Pavyzdžiui, tai leidžia pamatyti, į ką nurodo koreferencijos, tokios kaip *tai*, ir taip pat atsižvelgti į kontekstą: -![](../../../../../translated_images/lt/CoreferenceResolution.861924d6d384a7d6.png) +![](../../../../../translated_images/lt/CoreferenceResolution.861924d6d384a7d6.webp) > Vaizdas iš [Google tinklaraščio](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Kadangi kiekviena įvesties pozicija yra nepriklausomai susieta su kiekviena iš **BERT** (Bidirectional Encoder Representations from Transformers) yra labai didelis daugiasluoksnis transformatorių tinklas su 12 sluoksnių *BERT-base* ir 24 sluoksniais *BERT-large*. Modelis pirmiausia iš anksto apmokomas naudojant didelį tekstų korpusą (WikiPedia + knygos) taikant nesupervizuotą mokymą (prognozuojant užmaskuotus žodžius sakinyje). Per išankstinį mokymą modelis įgyja reikšmingą kalbos supratimą, kurį vėliau galima panaudoti su kitais duomenų rinkiniais taikant smulkų derinimą. Šis procesas vadinamas **perkėlimo mokymu**. -![paveikslas iš http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/lt/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) +![paveikslas iš http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/lt/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > Vaizdo [šaltinis](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/lt/lessons/5-NLP/19-NER/README.md b/translations/lt/lessons/5-NLP/19-NER/README.md index 9ce80d96..944b69e3 100644 --- a/translations/lt/lessons/5-NLP/19-NER/README.md +++ b/translations/lt/lessons/5-NLP/19-NER/README.md @@ -55,7 +55,7 @@ naujagimiui | O Kadangi reikia sukurti vienas prie vieno atitikimą tarp žodžių ir klasių, galime treniruoti tinkamą **daugelio prie daugelio** neuroninio tinklo modelį pagal šį paveikslą: -![Vaizdas, rodantis įprastus pasikartojančių neuroninių tinklų modelius.](../../../../../translated_images/lt/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Vaizdas, rodantis įprastus pasikartojančių neuroninių tinklų modelius.](../../../../../translated_images/lt/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > *Vaizdas iš [šio tinklaraščio įrašo](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) autoriaus [Andrejaus Karpathy](http://karpathy.github.io/). NER žodžių klasifikacijos modeliai atitinka dešiniausią tinklo architektūrą šiame paveikslėlyje.* diff --git a/translations/lt/lessons/5-NLP/README.md b/translations/lt/lessons/5-NLP/README.md index 5dd2dea4..d6cb5143 100644 --- a/translations/lt/lessons/5-NLP/README.md +++ b/translations/lt/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Natūralios kalbos apdorojimas -![NLP užduočių santrauka piešinyje](../../../../translated_images/lt/ai-nlp.b22dcb8ca4707cea.png) +![NLP užduočių santrauka piešinyje](../../../../translated_images/lt/ai-nlp.b22dcb8ca4707cea.webp) Šiame skyriuje mes sutelksime dėmesį į neuroninių tinklų naudojimą užduotims, susijusioms su **natūralios kalbos apdorojimu (NLP)**. Yra daugybė NLP problemų, kurias norime, kad kompiuteriai galėtų išspręsti: diff --git a/translations/lt/lessons/6-Other/23-MultiagentSystems/README.md b/translations/lt/lessons/6-Other/23-MultiagentSystems/README.md index 4a8cee77..3244e78c 100644 --- a/translations/lt/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/lt/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ Galite atidaryti vieną iš modelių, pavyzdžiui, **Biology → Flocki Atidarius modelį, būsite nukreipti į pagrindinį NetLogo ekraną. Čia pateikiamas pavyzdinis modelis, aprašantis vilkų ir avių populiaciją, turint ribotus išteklius (žolę). -![NetLogo Pagrindinis ekranas](../../../../../translated_images/lt/NetLogo-Main.32653711ec1a01b3.png) +![NetLogo Pagrindinis ekranas](../../../../../translated_images/lt/NetLogo-Main.32653711ec1a01b3.webp) > Dmitry Soshnikov ekrano kopija diff --git a/translations/lt/lessons/README.md b/translations/lt/lessons/README.md index 7fe9501b..83649325 100644 --- a/translations/lt/lessons/README.md +++ b/translations/lt/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Apžvalga -![Apžvalga piešinyje](../../../translated_images/lt/ai-overview.0857791951d19500.png) +![Apžvalga piešinyje](../../../translated_images/lt/ai-overview.0857791951d19500.webp) > Piešinys sukurtas [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/lt/lessons/X-Extras/X1-MultiModal/README.md b/translations/lt/lessons/X-Extras/X1-MultiModal/README.md index 046ae040..2b32ff29 100644 --- a/translations/lt/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/lt/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ Po transformatorių modelių sėkmės sprendžiant NLP užduotis, tos pačios ar Pagrindinė CLIP idėja yra gebėjimas palyginti tekstinius užklausimus su vaizdu ir nustatyti, kaip gerai vaizdas atitinka užklausimą. -![CLIP Architektūra](../../../../../translated_images/lt/clip-arch.b3dbf20b4e8ed8be.png) +![CLIP Architektūra](../../../../../translated_images/lt/clip-arch.b3dbf20b4e8ed8be.webp) > *Paveikslėlis iš [šio tinklaraščio įrašo](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ Kai modelis yra iš anksto apmokytas, galime jam pateikti vaizdų paketą ir tek Tarkime, mums reikia klasifikuoti vaizdus, pavyzdžiui, į kategorijas: katės, šunys ir žmonės. Tokiu atveju galime modeliui pateikti vaizdą ir seriją tekstinių užklausų: "*katės paveikslas*", "*šuns paveikslas*", "*žmogaus paveikslas*". Gautame 3 tikimybių vektoriuje tiesiog reikia pasirinkti indeksą su didžiausia reikšme. -![CLIP vaizdų klasifikavimui](../../../../../translated_images/lt/clip-class.3af42ef0b2b19369.png) +![CLIP vaizdų klasifikavimui](../../../../../translated_images/lt/clip-class.3af42ef0b2b19369.webp) > *Paveikslėlis iš [šio tinklaraščio įrašo](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ Daugiau apie VQGAN sužinokite [Taming Transformers](https://compvis.github.io/t Vienas svarbus skirtumas tarp VQGAN ir tradicinio GAN yra tas, kad pastarasis gali sukurti padorų vaizdą iš bet kokio įvesties vektoriaus, o VQGAN greičiausiai sukurs vaizdą, kuris nebus nuoseklus. Todėl reikia papildomai vadovauti vaizdo kūrimo procesui, ir tai galima padaryti naudojant CLIP. -![VQGAN+CLIP Architektūra](../../../../../translated_images/lt/vqgan.5027fe05051dfa31.png) +![VQGAN+CLIP Architektūra](../../../../../translated_images/lt/vqgan.5027fe05051dfa31.webp) Norint sugeneruoti vaizdą, atitinkantį tekstinę užklausą, pradedame nuo atsitiktinio kodavimo vektoriaus, kuris perduodamas per VQGAN, kad būtų sukurtas vaizdas. Tada CLIP naudojamas nuostolių funkcijai sukurti, kuri parodo, kaip gerai vaizdas atitinka tekstinę užklausą. Tikslas yra sumažinti šiuos nuostolius, naudojant atgalinį sklidimą, kad būtų koreguojami įvesties vektoriaus parametrai. Puiki biblioteka, įgyvendinanti VQGAN+CLIP, yra [Pixray](http://github.com/pixray/pixray). -![Vaizdas sukurtas Pixray](../../../../../translated_images/lt/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Vaizdas sukurtas Pixray](../../../../../translated_images/lt/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Vaizdas sukurtas Pixray](../../../../../translated_images/lt/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) +![Vaizdas sukurtas Pixray](../../../../../translated_images/lt/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp) | ![Vaizdas sukurtas Pixray](../../../../../translated_images/lt/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp) | ![Vaizdas sukurtas Pixray](../../../../../translated_images/lt/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp) ----|----|---- Vaizdas sukurtas pagal užklausą *artimas akvarelės portretas jauno literatūros mokytojo su knyga* | Vaizdas sukurtas pagal užklausą *artimas aliejinis portretas jaunos kompiuterių mokslų mokytojos su kompiuteriu* | Vaizdas sukurtas pagal užklausą *artimas aliejinis portretas seno matematikos mokytojo priešais lentą* diff --git a/translations/ml/README.md b/translations/ml/README.md index 73c5c322..56466fc2 100644 --- a/translations/ml/README.md +++ b/translations/ml/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # ആരംഭക്കാർക്കായി കൃത്രിമ ബുദ്ധിമുട്ട് - ഒരു പ്രോഗ്രാം -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ml/ai-overview.0857791951d19500.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ml/ai-overview.0857791951d19500.webp)| |:---:| | ആരംഭക്കാർക്കായുള്ള AI - _സ്കെച്ച് നോട്ടുകൾ [@girlie_mac](https://twitter.com/girlie_mac) തുടങ്ങിയവശേഷങ്ങൾ_ | diff --git a/translations/ml/lessons/1-Intro/README.md b/translations/ml/lessons/1-Intro/README.md index bfff6c86..d6bc4c35 100644 --- a/translations/ml/lessons/1-Intro/README.md +++ b/translations/ml/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # എഐയുടെ പരിചയം -![എഐയുടെ പരിചയത്തിന്റെ സംഗ്രഹം ഒരു ഡ്രോയിങ്ങിൽ](../../../../translated_images/ml/ai-intro.bf28d1ac4235881c.png) +![എഐയുടെ പരിചയത്തിന്റെ സംഗ്രഹം ഒരു ഡ്രോയിങ്ങിൽ](../../../../translated_images/ml/ai-intro.bf28d1ac4235881c.webp) > സ്കെച്ച്നോട്ട്: [ടോമോമി ഇമുര](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: ആദ്യമായി, കമ്പ്യൂട്ടറുകൾ [ചാൾസ് ബാബേജ്](https://en.wikipedia.org/wiki/Charles_Babbage) നിർമിച്ചത് സംഖ്യകളിൽ നിശ്ചിത ക്രമത്തിൽ പ്രവർത്തിക്കുന്നതിനായി - ഒരു ആൽഗോരിതം. 19-ാം നൂറ്റാണ്ടിൽ നിർദ്ദേശിച്ച ആദ്യ മാതൃകയേക്കാൾ ആധുനിക കമ്പ്യൂട്ടറുകൾ വളരെ മുന്നേറ്റം നേടിയിട്ടുണ്ടെങ്കിലും, അവ ഇപ്പോഴും നിയന്ത്രിത കണക്കുകൂട്ടലുകൾ പിന്തുടരുന്നു. അതിനാൽ, ലക്ഷ്യം നേടാൻ വേണ്ട കൃത്യമായ നടപടികൾ അറിയുകയാണെങ്കിൽ, കമ്പ്യൂട്ടറിനെ പ്രോഗ്രാം ചെയ്യാൻ കഴിയും. -![ഒരു വ്യക്തിയുടെ ഫോട്ടോ](../../../../translated_images/ml/dsh_age.d212a30d4e54fb5f.png) +![ഒരു വ്യക്തിയുടെ ഫോട്ടോ](../../../../translated_images/ml/dsh_age.d212a30d4e54fb5f.webp) > ഫോട്ടോ: [വിക്കി സോഷ്നിക്കോവ](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ CO_OP_TRANSLATOR_METADATA: **[ബുദ്ധിമുട്ട്](https://en.wikipedia.org/wiki/Intelligence)** എന്ന പദം ഉപയോഗിക്കുമ്പോൾ ഒരു പ്രശ്നം ഉണ്ട്, അതായത് ഈ പദത്തിന് വ്യക്തമായ നിർവചനമില്ല. ബുദ്ധിമുട്ട് **അബ്സ്ട്രാക്ട് ചിന്തന**യുമായി ബന്ധപ്പെട്ടു എന്ന് പറയാം, അല്ലെങ്കിൽ **സ്വയം ബോധം**യുമായി ബന്ധപ്പെട്ടു എന്ന് പറയാം, പക്ഷേ നാം അതിനെ ശരിയായി നിർവചിക്കാൻ കഴിയുന്നില്ല. -![ഒരു പൂച്ചയുടെ ഫോട്ടോ](../../../../translated_images/ml/photo-cat.8c8e8fb760ffe457.jpg) +![ഒരു പൂച്ചയുടെ ഫോട്ടോ](../../../../translated_images/ml/photo-cat.8c8e8fb760ffe457.webp) > [ഫോട്ടോ](https://unsplash.com/photos/75715CVEJhI) - [ആംബർ കിപ്പ്](https://unsplash.com/@sadmax) Unsplash-ൽ നിന്നു @@ -98,13 +98,13 @@ AGIയെക്കുറിച്ച് സംസാരിക്കുമ്പ > | ML എന്ത്? | | > |--------------|-----------| -> | കൃത്രിമ ബുദ്ധിമുട്ടിന്റെ ഭാഗമായും, ഡാറ്റയുടെ അടിസ്ഥാനത്തിൽ പ്രശ്നം പരിഹരിക്കാൻ കമ്പ്യൂട്ടർ പഠിക്കുന്നതിനെ **മെഷീൻ ലേണിംഗ്** എന്ന് വിളിക്കുന്നു. ഈ കോഴ്സിൽ ക്ലാസിക്കൽ മെഷീൻ ലേണിംഗ് പരിഗണിക്കില്ല - നിങ്ങൾക്ക് വേറെ [Machine Learning for Beginners](http://aka.ms/ml-beginners) പാഠ്യപദ്ധതി കാണാം. | ![ML for Beginners](../../../../translated_images/ml/ml-for-beginners.9e4fed176fd5817d.png) | +> | കൃത്രിമ ബുദ്ധിമുട്ടിന്റെ ഭാഗമായും, ഡാറ്റയുടെ അടിസ്ഥാനത്തിൽ പ്രശ്നം പരിഹരിക്കാൻ കമ്പ്യൂട്ടർ പഠിക്കുന്നതിനെ **മെഷീൻ ലേണിംഗ്** എന്ന് വിളിക്കുന്നു. ഈ കോഴ്സിൽ ക്ലാസിക്കൽ മെഷീൻ ലേണിംഗ് പരിഗണിക്കില്ല - നിങ്ങൾക്ക് വേറെ [Machine Learning for Beginners](http://aka.ms/ml-beginners) പാഠ്യപദ്ധതി കാണാം. | ![ML for Beginners](../../../../translated_images/ml/ml-for-beginners.9e4fed176fd5817d.webp) | ## എഐയുടെ ഒരു സംക്ഷിപ്ത ചരിത്രം കൃത്രിമ ബുദ്ധിമുട്ട് 20-ാം നൂറ്റാണ്ടിന്റെ മധ്യത്തിൽ ഒരു ശാഖയായി ആരംഭിച്ചു. ആദ്യം, സിംബോളിക് റീസണിംഗ് പ്രചാരത്തിലായിരുന്നു, ഇത് ചില പ്രധാന വിജയങ്ങൾക്കു വഴിതെളിച്ചു, ഉദാഹരണത്തിന് വിദഗ്ധ സിസ്റ്റങ്ങൾ – ചില പരിമിത പ്രശ്ന മേഖലകളിൽ വിദഗ്ധൻപോലെ പ്രവർത്തിക്കുന്ന കമ്പ്യൂട്ടർ പ്രോഗ്രാമുകൾ. എന്നാൽ, ഈ സമീപനം വലിയ തോതിൽ വ്യാപിപ്പിക്കാൻ കഴിയില്ലെന്ന് ഉടൻ മനസ്സിലായി. വിദഗ്ധനിൽ നിന്നുള്ള ജ്ഞാനം എടുക്കുകയും, അത് കമ്പ്യൂട്ടറിലേക്കു പ്രതിനിധാനം ചെയ്യുകയും, ആ ജ്ഞാനശേഖരം കൃത്യമായി നിലനിർത്തുകയും ചെയ്യുന്നത് വളരെ സങ്കീർണ്ണവും ചെലവേറിയതുമായ ജോലി ആയിരുന്നു. ഇതാണ് 1970-കളിലെ [AI വിന്റർ](https://en.wikipedia.org/wiki/AI_winter) ന്റെ കാരണമായത്. -എഐയുടെ സംക്ഷിപ്ത ചരിത്രം +എഐയുടെ സംക്ഷിപ്ത ചരിത്രം > ചിത്രം: [ഡ്മിത്രി സോഷ്നിക്കോവ്](http://soshnikov.com) diff --git a/translations/ml/lessons/2-Symbolic/Animals.ipynb b/translations/ml/lessons/2-Symbolic/Animals.ipynb index f92bd22a..aed50cae 100644 --- a/translations/ml/lessons/2-Symbolic/Animals.ipynb +++ b/translations/ml/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "ഈ സാമ്പിളിൽ, ചില ശാരീരിക ലക്ഷണങ്ങളുടെ അടിസ്ഥാനത്തിൽ ഒരു മൃഗം നിർണയിക്കുന്ന ഒരു ലളിതമായ അറിവ് അടിസ്ഥാനമാക്കിയ സിസ്റ്റം നടപ്പിലാക്കും. സിസ്റ്റം താഴെ കാണുന്ന AND-OR വൃക്ഷം ഉപയോഗിച്ച് പ്രതിനിധീകരിക്കാം (ഇത് മുഴുവൻ വൃക്ഷത്തിന്റെ ഒരു ഭാഗമാണ്, നാം എളുപ്പത്തിൽ കൂടുതൽ നിയമങ്ങൾ ചേർക്കാം):\n", "\n", - "![](../../../../translated_images/ml/AND-OR-Tree.5592d2c70187f283.png)\n" + "![](../../../../translated_images/ml/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { diff --git a/translations/ml/lessons/2-Symbolic/README.md b/translations/ml/lessons/2-Symbolic/README.md index 8fd24d59..6439e9d2 100644 --- a/translations/ml/lessons/2-Symbolic/README.md +++ b/translations/ml/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # അറിവ് പ്രതിനിധാനം ചെയ്യലും വിദഗ്ധ സിസ്റ്റങ്ങളും -![സിംബോളിക് AI ഉള്ളടക്കത്തിന്റെ സംഗ്രഹം](../../../../translated_images/ml/ai-symbolic.715a30cb610411a6.png) +![സിംബോളിക് AI ഉള്ളടക്കത്തിന്റെ സംഗ്രഹം](../../../../translated_images/ml/ai-symbolic.715a30cb610411a6.webp) > സ്കെച്ച്നോട്ട്: [Tomomi Imura](https://twitter.com/girlie_mac) @@ -35,13 +35,13 @@ AIയുടെ ആദ്യകാലങ്ങളിൽ, ബുദ്ധിമു * **അറിവ്**: വിവരങ്ങൾ നമ്മുടെ ലോക മോഡലിൽ ചേർക്കപ്പെടുന്നത്. ഉദാഹരണത്തിന്, കമ്പ്യൂട്ടർ എന്താണെന്ന് പഠിച്ചാൽ, അത് എങ്ങനെ പ്രവർത്തിക്കുന്നു, വില എത്രയാണ്, എന്തിന് ഉപയോഗിക്കാം തുടങ്ങിയ ആശയങ്ങൾ ഉണ്ടാകുന്നു. ഈ ബന്ധമുള്ള ആശയങ്ങളുടെ ശൃംഖലയാണ് അറിവ്. * **ബുദ്ധി**: ലോകത്തെ മനസ്സിലാക്കലിന്റെ ഒരു ഉയർന്ന തലമാണ്, ഇത് *മെറ്റാ-അറിവ്* പ്രതിനിധാനം ചെയ്യുന്നു, ഉദാ: അറിവ് എപ്പോൾ എങ്ങനെ ഉപയോഗിക്കണം എന്ന ധാരണ. - + *ചിത്രം [വിക്കിപീഡിയയിൽ നിന്ന്](https://commons.wikimedia.org/w/index.php?curid=37705247), Longlivetheux - സ്വന്തം കൃതി, CC BY-SA 4.0* അതിനാൽ, **അറിവ് പ്രതിനിധാനം** എന്ന പ്രശ്നം കമ്പ്യൂട്ടറിനുള്ളിൽ അറിവ് ഡാറ്റ രൂപത്തിൽ പ്രതിനിധാനം ചെയ്ത് അത് സ്വയം ഉപയോഗിക്കാൻ കഴിയുന്ന വിധം കണ്ടെത്തലാണ്. ഇത് ഒരു സ്പെക്ട്രം ആയി കാണാം: -![അറിവ് പ്രതിനിധാന സ്പെക്ട്രം](../../../../translated_images/ml/knowledge-spectrum.b60df631852c0217.png) +![അറിവ് പ്രതിനിധാന സ്പെക്ട്രം](../../../../translated_images/ml/knowledge-spectrum.b60df631852c0217.webp) > ചിത്രം: [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Block Syntax | Indent | | | സിംബോളിക് AIയുടെ ആദ്യ വിജയങ്ങളിൽ ഒന്നായിരുന്നു **വിദഗ്ധ സിസ്റ്റങ്ങൾ** - ചില പരിമിത പ്രശ്ന മേഖലകളിൽ വിദഗ്ധനായി പ്രവർത്തിക്കാൻ രൂപകൽപ്പന ചെയ്ത കമ്പ്യൂട്ടർ സിസ്റ്റങ്ങൾ. ഇവ മനുഷ്യ വിദഗ്ധരിൽ നിന്നുള്ള **അറിവ് ബേസ്** ഉപയോഗിച്ച് നിർമ്മിക്കപ്പെട്ടിരുന്നു, അതിന്മേൽ പ്രവർത്തിക്കുന്ന **ഇൻഫറൻസ് എഞ്ചിൻ** ഉണ്ടായിരുന്നു. -![മനുഷ്യൻ്റെ ഘടന](../../../../translated_images/ml/arch-human.5d4d35f1bba3ab1c.png) | ![അറിവ് അടിസ്ഥാനമാക്കിയ സിസ്റ്റം](../../../../translated_images/ml/arch-kbs.3ec5c150b09fa8da.png) +![മനുഷ്യൻ്റെ ഘടന](../../../../translated_images/ml/arch-human.5d4d35f1bba3ab1c.webp) | ![അറിവ് അടിസ്ഥാനമാക്കിയ സിസ്റ്റം](../../../../translated_images/ml/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ മനുഷ്യ നാഡീകുഴപ്പത്തിന്റെ ലളിതമായ ഘടന | അറിവ് അടിസ്ഥാനമാക്കിയ സിസ്റ്റത്തിന്റെ ഘടന @@ -106,7 +106,7 @@ Block Syntax | Indent | | | ഉദാഹരണമായി, ഒരു ജീവിയെ അതിന്റെ ശാരീരിക സവിശേഷതകളുടെ അടിസ്ഥാനത്തിൽ തിരിച്ചറിയുന്ന വിദഗ്ധ സിസ്റ്റം പരിഗണിക്കാം: -![AND-OR ട്രീ](../../../../translated_images/ml/AND-OR-Tree.5592d2c70187f283.png) +![AND-OR ട്രീ](../../../../translated_images/ml/AND-OR-Tree.5592d2c70187f283.webp) > ചിത്രം: [Dmitry Soshnikov](http://soshnikov.com) @@ -168,7 +168,7 @@ THEN the animal is a carnivore സെമാന്റിക് വെബിൽ എല്ലാ പ്രതിനിധാനങ്ങളും ട്രിപ്പിളുകളിലാണ് അടിസ്ഥാനമാക്കുന്നത്. ഓരോ വസ്തുവും ഓരോ ബന്ധവും യുണീക്ക് ആയി URI ഉപയോഗിച്ച് തിരിച്ചറിയപ്പെടുന്നു. ഉദാഹരണത്തിന്, ഈ AI പാഠ്യപദ്ധതി 2022 ജനുവരി 1-ന് Dmitry Soshnikov വികസിപ്പിച്ചുവെന്ന് പറയാൻ താഴെ കാണുന്ന ട്രിപ്പിളുകൾ ഉപയോഗിക്കാം: - + ``` http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 13, 2007” @@ -179,7 +179,7 @@ http://github.com/microsoft/ai-for-beginners http://purl.org/dc/elements/1.1/cre കൂടുതൽ സങ്കീർണ്ണമായ സാഹചര്യത്തിൽ, സൃഷ്ടാക്കളുടെ പട്ടിക നിർവചിക്കാൻ RDF ൽ നിർവചിച്ചിട്ടുള്ള ചില ഡാറ്റാ ഘടനകൾ ഉപയോഗിക്കാം. - + > മുകളിൽ കാണുന്ന ചിത്രങ്ങൾ [Dmitry Soshnikov](http://soshnikov.com) യുടെതാണ് @@ -203,7 +203,7 @@ GROUP BY ?eyeColorLabel > ✅ നിങ്ങളുടെ സ്വന്തം ഓന്റോളജികൾ നിർമ്മിക്കാൻ, അല്ലെങ്കിൽ നിലവിലുള്ളവ തുറക്കാൻ ആഗ്രഹിക്കുന്നുവെങ്കിൽ, [Protégé](https://protege.stanford.edu/) എന്ന മികച്ച ദൃശ്യ ഓന്റോളജി എഡിറ്റർ ഉണ്ട്. ഡൗൺലോഡ് ചെയ്യുക അല്ലെങ്കിൽ ഓൺലൈനിൽ ഉപയോഗിക്കുക. - + *Web Protégé എഡിറ്റർ റോമാനോവ് കുടുംബ ഓന്റോളജിയുമായി തുറന്നിരിക്കുന്ന ചിത്രം. Dmitry Soshnikov യുടെ സ്ക്രീൻഷോട്ട്* diff --git a/translations/ml/lessons/3-NeuralNetworks/03-Perceptron/README.md b/translations/ml/lessons/3-NeuralNetworks/03-Perceptron/README.md index 91358a81..2202aed6 100644 --- a/translations/ml/lessons/3-NeuralNetworks/03-Perceptron/README.md +++ b/translations/ml/lessons/3-NeuralNetworks/03-Perceptron/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: | | | |--------------|-----------| -|Frank Rosenblatt | The Mark 1 Perceptron| +|Frank Rosenblatt | The Mark 1 Perceptron| > ചിത്രങ്ങൾ [വിക്കിപീഡിയയിൽ നിന്ന്](https://en.wikipedia.org/wiki/Perceptron) @@ -34,7 +34,7 @@ y(x) = f(wTx) ഇവിടെ f ഒരു സ്റ്റെപ്പ് ആക്ടിവേഷൻ ഫംഗ്ഷനാണ് - + ## പേഴ്സെപ്ട്രോൺ പരിശീലനം diff --git a/translations/ml/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/ml/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index a16e113d..08424a21 100644 --- a/translations/ml/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/ml/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -373,7 +373,7 @@ "\n", "ബൈനറി ക്ലാസിഫിക്കേഷൻ പ്രശ്നത്തിനായി ഒരു ഡാറ്റാസെറ്റ് ഞങ്ങൾ സൃഷ്ടിച്ചിട്ടുണ്ട്. എങ്കിലും, തുടക്കത്തിൽ തന്നെ ഇത് മൾട്ടി-ക്ലാസ് ക്ലാസിഫിക്കേഷനായി പരിഗണിക്കാം, അതിനാൽ പിന്നീട് നമുക്ക് എളുപ്പത്തിൽ കോഡ് മൾട്ടി-ക്ലാസ് ക്ലാസിഫിക്കേഷനായി മാറ്റാൻ കഴിയും. ഈ സാഹചര്യത്തിൽ, നമ്മുടെ ഒറ്റ-ലെയർ പെർസെപ്ട്രോൺ താഴെ കാണുന്ന ആർക്കിടെക്ചർ ഉണ്ടാകും:\n", "\n", - "\n", + "\n", "\n", "നെറ്റ്‌വർക്കിന്റെ രണ്ട് ഔട്ട്പുട്ടുകൾ രണ്ട് ക്ലാസുകളെ പ്രതിനിധീകരിക്കുന്നു, രണ്ട് ഔട്ട്പുട്ടുകളിൽ ഏറ്റവും ഉയർന്ന മൂല്യമുള്ള ക്ലാസ് ശരിയായ പരിഹാരമായി കണക്കാക്കപ്പെടുന്നു.\n", "\n", @@ -489,7 +489,7 @@ "\n", "ക്ലാസുകൾ 2-ലധികമുണ്ടെങ്കിൽ, softmax അവയിൽ എല്ലാം സാധ്യതകൾ സാധാരണവത്കരിക്കും. MNIST അക്കങ്ങൾ തിരിച്ചറിയുന്ന ഒരു നെറ്റ്‌വർക്ക് ആർക്കിടെക്ചറിന്റെ ചിത്രരൂപം ഇതാ:\n", "\n", - "![MNIST Classifier](../../../../../translated_images/ml/Cross-Entropy-Loss.dc7ba633d2467ef3.png)\n" + "![MNIST Classifier](../../../../../translated_images/ml/Cross-Entropy-Loss.dc7ba633d2467ef3.webp)\n" ] }, { @@ -620,7 +620,7 @@ "\n", "## കംപ്യൂട്ടേഷണൽ ഗ്രാഫ്\n", "\n", - "\n", + "\n", "\n", "ഇപ്പോൾവരെ, നാം നെറ്റ്‌വർക്കിലെ വ്യത്യസ്ത ലെയറുകൾക്കായി വ്യത്യസ്ത ക്ലാസുകൾ നിർവചിച്ചിട്ടുണ്ട്. ആ ലെയറുകളുടെ സംയോജനം **കംപ്യൂട്ടേഷണൽ ഗ്രാഫ്** ആയി പ്രതിനിധീകരിക്കാം. ഇപ്പോൾ നാം നൽകിയ ട്രെയിനിംഗ് ഡാറ്റാസെറ്റിനോ അതിന്റെ ഭാഗത്തിനോ ലോസ് കണക്കാക്കാൻ താഴെ പറയുന്ന രീതിയിൽ കഴിയും:\n" ] @@ -687,7 +687,7 @@ "source": [ "## ബാക്ക്വർഡ് പ്രൊപ്പഗേഷൻ\n", "\n", - "\n", + "\n", "\n", "$$\\def\\L{\\mathcal{L}}\\def\\zz#1#2{\\frac{\\partial#1}{\\partial#2}}\n", "\\begin{align}\n", @@ -712,7 +712,7 @@ "* ഇത് നോഡ് $z$-യിലെ മാറ്റങ്ങളുമായി ബന്ധപ്പെട്ടിരിക്കുന്നു: $\\Delta z = (\\partial\\mathcal{p}/\\partial z)\\Delta p$\n", "* ഈ പിശക് കുറയ്ക്കാൻ, പാരാമീറ്ററുകൾ അനുസരിച്ച് ക്രമീകരിക്കണം: $\\Delta W = (\\partial\\mathcal{z}/\\partial W)\\Delta z$ (അതുപോലെ $b$-ക്കും)\n", "\n", - "\n", + "\n", "\n", "ഈ പ്രക്രിയ നെറ്റ്‌വർക്കിന്റെ ഔട്ട്പുട്ടിൽ നിന്നുള്ള നഷ്ട പിശക് പാരാമീറ്ററുകളിലേക്ക് തിരിച്ച് വിതരണം ചെയ്യുന്നതായി ആരംഭിക്കുന്നു. അതുകൊണ്ടുതന്നെ ഈ പ്രക്രിയയെ **ബാക്ക് പ്രൊപ്പഗേഷൻ** എന്ന് വിളിക്കുന്നു.\n", "\n", @@ -1265,7 +1265,7 @@ "* പരിശീലന നഷ്ടം കുറവാണ് - മോഡലിന് പരിശീലന ഡാറ്റയെ നന്നായി അനുകരിക്കാൻ മതിയായ പ്രകടനശക്തി ഉണ്ട്.\n", "* വാലിഡേഷൻ നഷ്ടം പരിശീലന നഷ്ടത്തേക്കാൾ വളരെ കൂടുതലായിരിക്കാം, കൂടാതെ പരിശീലനത്തിനിടെ ഇത് വർദ്ധിക്കാനും തുടങ്ങാം - കാരണം മോഡൽ പരിശീലന പോയിന്റുകൾ \"ഓർമ്മിച്ച്\" വെക്കുന്നു, അതിനാൽ \"മൊത്തത്തിലുള്ള ചിത്രം\" നഷ്ടപ്പെടുന്നു.\n", "\n", - "![Overfitting](../../../../../translated_images/ml/overfit.a0bd57f717c15769.png)\n", + "![Overfitting](../../../../../translated_images/ml/overfit.a0bd57f717c15769.webp)\n", "\n", "> ഈ ചിത്രത്തിൽ, `x` പരിശീലന ഡാറ്റയെ സൂചിപ്പിക്കുന്നു, `o` - വാലിഡേഷൻ ഡാറ്റ. ഇടത് ഭാഗം - ലീനിയർ മോഡൽ (ഒറ്റ ലെയർ), ഇത് ഡാറ്റയുടെ സ്വഭാവം നന്നായി അനുകരിക്കുന്നു. വലത് ഭാഗം - ഓവർഫിറ്റഡ് മോഡൽ, മോഡൽ പരിശീലന ഡാറ്റയെ പൂർണ്ണമായും അനുകരിക്കുന്നു, പക്ഷേ മറ്റ് ഡാറ്റയുമായി (വാലിഡേഷൻ പിശക് വളരെ ഉയർന്നതാണ്) യോജിപ്പിക്കാനാകുന്നില്ല.\n" ] diff --git a/translations/ml/lessons/3-NeuralNetworks/04-OwnFramework/README.md b/translations/ml/lessons/3-NeuralNetworks/04-OwnFramework/README.md index da298e89..032cda3f 100644 --- a/translations/ml/lessons/3-NeuralNetworks/04-OwnFramework/README.md +++ b/translations/ml/lessons/3-NeuralNetworks/04-OwnFramework/README.md @@ -65,7 +65,7 @@ CO_OP_TRANSLATOR_METADATA: ഈ എല്ലാ വ്യഞ്ജനങ്ങളുടെയും ഇടത്തരം ഭാഗം ഒരുപോലെയാണ്, അതിനാൽ ലോസ് ഫംഗ്ഷനിൽ നിന്ന് "പിന്നിലേക്ക്" കണക്കുകൂട്ടി ഡെരിവേറ്റീവുകൾ എളുപ്പത്തിൽ കണ്ടെത്താം. അതുകൊണ്ട് മൾട്ടി-ലെയർഡ് പേഴ്സെപ്ട്രോൺ പരിശീലന രീതി **ബാക്ക്‌പ്രൊപ്പഗേഷൻ** അല്ലെങ്കിൽ 'ബാക്ക്‌പ്രോപ്പ്' എന്ന് വിളിക്കുന്നു. -compute graph +compute graph > TODO: ചിത്രം സൈറ്റേഷൻ diff --git a/translations/ml/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/ml/lessons/3-NeuralNetworks/05-Frameworks/README.md index 704e619b..b08f4c53 100644 --- a/translations/ml/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/ml/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ CO_OP_TRANSLATOR_METADATA: 5 പോയിന്റുകൾ (ഗ്രാഫുകളിൽ `x` ആയി പ്രതിനിധീകരിച്ചിരിക്കുന്നു) ഏകീകരിക്കുന്ന പ്രശ്നം പരിഗണിക്കൂ: -![linear](../../../../../translated_images/ml/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/ml/overfit2.131f5800ae10ca5e.jpg) +![linear](../../../../../translated_images/ml/overfit1.f24b71c6f652e59e.webp) | ![overfit](../../../../../translated_images/ml/overfit2.131f5800ae10ca5e.webp) -------------------------|-------------------------- **രേഖീയ മോഡൽ, 2 പാരാമീറ്ററുകൾ** | **അരേഖീയ മോഡൽ, 7 പാരാമീറ്ററുകൾ** പരിശീലന പിശക് = 5.3 | പരിശീലന പിശക് = 0 @@ -79,7 +79,7 @@ CO_OP_TRANSLATOR_METADATA: മുകളിൽ കാണിച്ച ഗ്രാഫിൽ നിന്ന്, ഓവർഫിറ്റിംഗ് വളരെ കുറഞ്ഞ പരിശീലന പിശക്, കൂടിയ വാലിഡേഷൻ പിശക് എന്നിവയാൽ കണ്ടെത്താം. സാധാരണയായി പരിശീലന സമയത്ത് പരിശീലനവും വാലിഡേഷനും പിശകുകൾ കുറയാൻ തുടങ്ങും, പിന്നീട് വാലിഡേഷൻ പിശക് കുറയുന്നത് നിർത്തി ഉയരാൻ തുടങ്ങും. ഇത് ഓവർഫിറ്റിംഗിന്റെ സൂചനയാണ്, ഈ ഘട്ടത്തിൽ പരിശീലനം നിർത്തേണ്ടതായിരിക്കും (അല്ലെങ്കിൽ മോഡലിന്റെ സ്നാപ്ഷോട്ട് എടുക്കാം). -![overfitting](../../../../../translated_images/ml/Overfitting.408ad91cd90b4371.png) +![overfitting](../../../../../translated_images/ml/Overfitting.408ad91cd90b4371.webp) ## ഓവർഫിറ്റിംഗ് തടയാനുള്ള മാർഗങ്ങൾ diff --git a/translations/ml/lessons/3-NeuralNetworks/README.md b/translations/ml/lessons/3-NeuralNetworks/README.md index ebc10b03..cdf8f14d 100644 --- a/translations/ml/lessons/3-NeuralNetworks/README.md +++ b/translations/ml/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # ന്യൂറൽ നെറ്റ്വർക്കുകളിലേക്ക് പരിചയം -![Intro Neural Networks ഉള്ളടക്കത്തിന്റെ ഒരു ഡൂഡിൽ സംഗ്രഹം](../../../../translated_images/ml/ai-neuralnetworks.1c687ae40bc86e83.png) +![Intro Neural Networks ഉള്ളടക്കത്തിന്റെ ഒരു ഡൂഡിൽ സംഗ്രഹം](../../../../translated_images/ml/ai-neuralnetworks.1c687ae40bc86e83.webp) പരിചയത്തിൽ ചർച്ച ചെയ്തതുപോലെ, ബുദ്ധിമുട്ട് നേടാനുള്ള ഒരു മാർഗം **കമ്പ്യൂട്ടർ മോഡൽ** അല്ലെങ്കിൽ **കൃത്രിമ മസ്തിഷ്കം** പരിശീലിപ്പിക്കുകയാണ്. 20-ആം നൂറ്റാണ്ടിന്റെ മധ്യത്തിൽ നിന്ന് ഗവേഷകർ വിവിധ ഗണിത മോഡലുകൾ പരീക്ഷിച്ചു, അടുത്തിടെ ഈ ദിശ വളരെ വിജയകരമായി തെളിഞ്ഞു. മസ്തിഷ്കത്തിന്റെ ഇത്തരം ഗണിത മോഡലുകൾ **ന്യൂറൽ നെറ്റ്വർക്കുകൾ** എന്ന് വിളിക്കുന്നു. @@ -36,13 +36,13 @@ CO_OP_TRANSLATOR_METADATA: ജീവശാസ്ത്രത്തിൽനിന്ന്, നമ്മുടെ മസ്തിഷ്കം ന്യൂറൽ സെല്ലുകൾ (ന്യൂറോണുകൾ) കൊണ്ട് നിർമ്മിതമാണ്, ഓരോന്നിനും നിരവധി "ഇൻപുട്ടുകൾ" (ഡെൻഡ്രൈറ്റുകൾ) ഉണ്ട്, ഒറ്റ "ഔട്ട്പുട്ട്" (ആക്സൺ) ഉണ്ട്. ഡെൻഡ്രൈറ്റുകളും ആക്സണുകളും വൈദ്യുത സിഗ്നലുകൾ കൈമാറാൻ കഴിയും, അവ തമ്മിലുള്ള ബന്ധങ്ങൾ — സിനാപ്സുകൾ എന്ന് അറിയപ്പെടുന്നു — വൈദ്യുത ചാലകതയുടെ വ്യത്യസ്ത നിലകൾ കാണിക്കുന്നു, ഇത് ന്യൂറോട്രാൻസ്മിറ്ററുകൾ നിയന്ത്രിക്കുന്നു. -![ന്യൂറോണിന്റെ മോഡൽ](../../../../translated_images/ml/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![ന്യൂറോണിന്റെ മോഡൽ](../../../../translated_images/ml/artneuron.1a5daa88d20ebe6f.png) +![ന്യൂറോണിന്റെ മോഡൽ](../../../../translated_images/ml/synapse-wikipedia.ed20a9e4726ea1c6.webp) | ![ന്യൂറോണിന്റെ മോഡൽ](../../../../translated_images/ml/artneuron.1a5daa88d20ebe6f.webp) ----|---- യഥാർത്ഥ ന്യൂറോൺ *([ചിത്രം](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) വിക്കിപീഡിയയിൽ നിന്നുള്ളത്)* | കൃത്രിമ ന്യൂറോൺ *(ചിത്രം രചയിതാവ്)* അതിനാൽ, ഒരു ന്യൂറോണിന്റെ ഏറ്റവും ലളിതമായ ഗണിത മോഡൽ X1, ..., XN എന്ന നിരവധി ഇൻപുട്ടുകളും Y എന്ന ഔട്ട്പുട്ടും, W1, ..., WN എന്ന ഭാരങ്ങൾ ഉൾക്കൊള്ളുന്നു. ഔട്ട്പുട്ട് കണക്കാക്കുന്നത്: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) ഇവിടെ f ഒരു നോൺ-ലിനിയർ **ആക്ടിവേഷൻ ഫംഗ്ഷൻ** ആണ്. diff --git a/translations/ml/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/ml/lessons/4-ComputerVision/06-IntroCV/README.md index c8c5d3c8..b7682c41 100644 --- a/translations/ml/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/ml/lessons/4-ComputerVision/06-IntroCV/README.md @@ -75,14 +75,14 @@ OpenCV ഉപയോഗിച്ച് വീഡിയോ ഫ്രെയിമ * **ബ്രെയിൽ പുസ്തകത്തിന്റെ ഫോട്ടോ പ്രീ-പ്രോസസ്സിംഗ്**. thresholding, ഫീച്ചർ കണ്ടെത്തൽ, perspective transformation, NumPy മാനിപ്പുലേഷനുകൾ ഉപയോഗിച്ച് വ്യക്തിഗത ബ്രെയിൽ ചിഹ്നങ്ങൾ വേർതിരിച്ച് പിന്നീട് ന്യൂറൽ നെറ്റ്വർക്കിൽ വർഗ്ഗീകരിക്കാൻ തയ്യാറാക്കൽ. -![Braille Image](../../../../../translated_images/ml/braille.341962ff76b1bd70.jpeg) | ![Braille Image Pre-processed](../../../../../translated_images/ml/braille-result.46530fea020b03c7.png) | ![Braille Symbols](../../../../../translated_images/ml/braille-symbols.0159185ab69d5339.png) +![Braille Image](../../../../../translated_images/ml/braille.341962ff76b1bd70.webp) | ![Braille Image Pre-processed](../../../../../translated_images/ml/braille-result.46530fea020b03c7.webp) | ![Braille Symbols](../../../../../translated_images/ml/braille-symbols.0159185ab69d5339.webp) ----|-----|----- > ചിത്രം [OpenCV.ipynb](OpenCV.ipynb) നിന്നാണ് * **ഫ്രെയിം വ്യത്യാസം ഉപയോഗിച്ച് വീഡിയോയിൽ ചലനം കണ്ടെത്തൽ**. ക്യാമറ സ്ഥിരമാണെങ്കിൽ, ക്യാമറ ഫീഡിലെ ഫ്രെയിമുകൾ തമ്മിൽ വളരെ സമാനമായിരിക്കും. ഫ്രെയിമുകൾ അറേകളായി പ്രതിനിധീകരിക്കപ്പെടുന്നതിനാൽ, രണ്ട് തുടർച്ചയായ ഫ്രെയിമുകളുടെ അറേകൾ തമ്മിൽ വ്യത്യാസം എടുത്താൽ പിക്‌സൽ വ്യത്യാസം കിട്ടും, ഇത് സ്ഥിരമായ ഫ്രെയിമുകൾക്കായി കുറവായിരിക്കും, ചിത്രത്തിൽ വലിയ ചലനം ഉണ്ടാകുമ്പോൾ ഉയരും. -![Image of video frames and frame differences](../../../../../translated_images/ml/frame-difference.706f805491a0883c.png) +![Image of video frames and frame differences](../../../../../translated_images/ml/frame-difference.706f805491a0883c.webp) > ചിത്രം [OpenCV.ipynb](OpenCV.ipynb) നിന്നാണ് @@ -91,7 +91,7 @@ OpenCV ഉപയോഗിച്ച് വീഡിയോ ഫ്രെയിമ - **Dense Optical Flow** ഓരോ പിക്‌സലും എവിടെ പോകുന്നു എന്ന് കാണിക്കുന്ന വെക്ടർ ഫീൽഡ് കണക്കാക്കുന്നു - **Sparse Optical Flow** ചിത്രത്തിലെ ചില വ്യത്യസ്തമായ ഫീച്ചറുകൾ (ഉദാ: അരികുകൾ) എടുത്ത്, അവയുടെ ട്രാജക്ടറി ഫ്രെയിമിൽ നിന്ന് ഫ്രെയിമിലേക്ക് നിർമ്മിക്കുന്നു. -![Image of Optical Flow](../../../../../translated_images/ml/optical.1f4a94464579a83a.png) +![Image of Optical Flow](../../../../../translated_images/ml/optical.1f4a94464579a83a.webp) > ചിത്രം [OpenCV.ipynb](OpenCV.ipynb) നിന്നാണ് @@ -117,7 +117,7 @@ AI ഷോയിൽ നിന്നുള്ള [ഈ വീഡിയോ](https:// ഈ ലാബിൽ, ലളിതമായ ജെസ്റ്ററുകളുള്ള ഒരു വീഡിയോ എടുത്ത്, optical flow ഉപയോഗിച്ച് മുകളിൽ/താഴെ/ഇടത്തേക്ക്/വലത്തേക്ക് ചലനങ്ങൾ കണ്ടെത്തുക. -Palm Movement Frame +Palm Movement Frame --- diff --git a/translations/ml/lessons/4-ComputerVision/06-IntroCV/lab/MovementDetection.ipynb b/translations/ml/lessons/4-ComputerVision/06-IntroCV/lab/MovementDetection.ipynb index 4b4ea33d..703a12d8 100644 --- a/translations/ml/lessons/4-ComputerVision/06-IntroCV/lab/MovementDetection.ipynb +++ b/translations/ml/lessons/4-ComputerVision/06-IntroCV/lab/MovementDetection.ipynb @@ -10,7 +10,7 @@ "\n", "ഒരു വ്യക്തിയുടെ കൈവിരൽ സ്ഥിരമായ പശ്ചാത്തലത്തിൽ ഇടത്തേക്ക്/വലത്തേക്ക്/മുകളിലേക്ക്/താഴേക്ക് ചലിക്കുന്ന [ഈ വീഡിയോ](../../../../../../lessons/4-ComputerVision/06-IntroCV/lab/palm-movement.mp4) പരിഗണിക്കുക.\n", "\n", - "\"Palm\n", + "\"Palm\n", "\n", "**നിങ്ങളുടെ ലക്ഷ്യം** ഓപ്റ്റിക്കൽ ഫ്ലോ ഉപയോഗിച്ച് വീഡിയോയിലെ ഏത് ഭാഗങ്ങളിൽ മുകളിലേക്ക്/താഴേക്ക്/ഇടത്തേക്ക്/വലത്തേക്ക് ചലനങ്ങൾ ഉണ്ടെന്ന് കണ്ടെത്തുക എന്നതാണ്.\n", "\n", diff --git a/translations/ml/lessons/4-ComputerVision/06-IntroCV/lab/README.md b/translations/ml/lessons/4-ComputerVision/06-IntroCV/lab/README.md index 66c22c27..8857db78 100644 --- a/translations/ml/lessons/4-ComputerVision/06-IntroCV/lab/README.md +++ b/translations/ml/lessons/4-ComputerVision/06-IntroCV/lab/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: ഒരു വ്യക്തിയുടെ കൈവിരൽ സ്ഥിരമായ പശ്ചാത്തലത്തിൽ ഇടത്തേക്ക്/വലത്തേക്ക്/മുകളിലേക്ക്/താഴേക്ക് ചലിക്കുന്ന [ഈ വീഡിയോ](../../../../../../lessons/4-ComputerVision/06-IntroCV/lab/palm-movement.mp4) പരിഗണിക്കുക. -Palm Movement Frame +Palm Movement Frame **നിങ്ങളുടെ ലക്ഷ്യം** ഓപ്റ്റിക്കൽ ഫ്ലോ ഉപയോഗിച്ച് വീഡിയോയിലെ ഏത് ഭാഗങ്ങളിൽ മുകളിലേക്ക്/താഴേക്ക്/ഇടത്തേക്ക്/വലത്തേക്ക് ചലനങ്ങൾ ഉണ്ടെന്ന് കണ്ടെത്താൻ കഴിയുക എന്നതാണ്. diff --git a/translations/ml/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/ml/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 772d83c9..72de3450 100644 --- a/translations/ml/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/ml/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 2014-ൽ ImageNet ടോപ്പ്-5 ക്ലാസിഫിക്കേഷനിൽ 92.7% കൃത്യത നേടിയ ഒരു നെറ്റ്‌വർക്കാണ്. ഇതിന് താഴെ പറയുന്ന ലെയർ ഘടനയുണ്ട്: -![ImageNet Layers](../../../../../translated_images/ml/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet Layers](../../../../../translated_images/ml/vgg-16-arch1.d901a5583b3a51ba.webp) നിങ്ങൾക്ക് കാണാമല്ലോ, VGG പരമ്പരാഗത പിരമിഡ് ആർക്കിടെക്ചർ പിന്തുടരുന്നു, convolution-pooling ലെയറുകളുടെ ഒരു ശ്രേണിയാണ് ഇത്. -![ImageNet Pyramid](../../../../../translated_images/ml/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet Pyramid](../../../../../translated_images/ml/vgg-16-arch.64ff2137f50dd49f.webp) > ചിത്രം [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) നിന്നാണ് @@ -25,7 +25,7 @@ VGG-16 2014-ൽ ImageNet ടോപ്പ്-5 ക്ലാസിഫിക്ക ResNet മൈക്രോസോഫ്റ്റ് റിസർച്ച് 2015-ൽ നിർദ്ദേശിച്ച മോഡലുകളുടെ ഒരു കുടുംബമാണ്. ResNet-ന്റെ പ്രധാന ആശയം **residual blocks** ഉപയോഗിക്കുകയാണ്: - + > ചിത്രം [ഈ പേപ്പർ](https://arxiv.org/pdf/1512.03385.pdf) നിന്നാണ് @@ -37,7 +37,7 @@ ResNet മൈക്രോസോഫ്റ്റ് റിസർച്ച് 2015- Google Inception ആർക്കിടെക്ചർ ഈ ആശയം ഒരു പടി മുന്നോട്ട് കൊണ്ടുപോകുന്നു, ഓരോ നെറ്റ്‌വർക്ക് ലെയറും പല വ്യത്യസ്ത പാതകളുടെ സംയോജനം ആയി നിർമ്മിക്കുന്നു: - + > ചിത്രം [Researchgate](https://www.researchgate.net/figure/Inception-module-with-dimension-reductions-left-and-schema-for-Inception-ResNet-v1_fig2_355547454) നിന്നാണ് diff --git a/translations/ml/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/ml/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index 8bd637cd..34a59fab 100644 --- a/translations/ml/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/ml/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -260,7 +260,7 @@ "\n", "അതിനാൽ, സാധാരണ CNN-ൽ നിരവധി കോൺവല്യൂഷണൽ ലെയറുകൾ ഉണ്ടാകും, അവയുടെ ഇടയിൽ ചിത്രത്തിന്റെ വലുപ്പം കുറയ്ക്കാൻ പൂലിംഗ് ലെയറുകൾ ഉപയോഗിക്കും. പാറ്റേണുകൾ കൂടുതൽ സങ്കീർണ്ണമാകുമ്പോൾ, കൂടുതൽ ഫിൽട്ടറുകളുടെ എണ്ണം വർദ്ധിപ്പിക്കും, കാരണം അന്വേഷിക്കേണ്ട രസകരമായ സംയോജനങ്ങൾ കൂടുതലാകും.\n", "\n", - "![പൂളിംഗ് ലെയറുകളോടുകൂടിയ നിരവധി കോൺവല്യൂഷണൽ ലെയറുകൾ കാണിക്കുന്ന ചിത്രം.](../../../../../translated_images/ml/cnn-pyramid.85915455759ef0ce.png)\n", + "![പൂളിംഗ് ലെയറുകളോടുകൂടിയ നിരവധി കോൺവല്യൂഷണൽ ലെയറുകൾ കാണിക്കുന്ന ചിത്രം.](../../../../../translated_images/ml/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "സ്ഥലം വലുപ്പം കുറയുകയും ഫീച്ചർ/ഫിൽട്ടർ വലുപ്പം വർദ്ധിക്കുകയും ചെയ്യുന്നതിനാൽ, ഈ ആർക്കിടെക്ചർ **പിരമിഡ് ആർക്കിടെക്ചർ** എന്നും വിളിക്കുന്നു.\n" ] diff --git a/translations/ml/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/ml/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index cc871370..1497863f 100644 --- a/translations/ml/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/ml/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -112,7 +112,7 @@ "\n", "പരമ്പരാഗത കമ്പ്യൂട്ടർ ദൃശ്യശാസ്ത്രത്തിൽ, ചിത്രത്തിൽ നിന്ന് ഫീച്ചറുകൾ സൃഷ്ടിക്കാൻ പല ഫിൽട്ടറുകളും ഉപയോഗിച്ചിരുന്നു, പിന്നീട് അവ മെഷീൻ ലേണിംഗ് ആൽഗോരിതം ഉപയോഗിച്ച് ക്ലാസിഫയർ നിർമ്മിക്കാൻ ഉപയോഗിച്ചിരുന്നു. ആ ഫിൽട്ടറുകൾ ചില മൃഗങ്ങളുടെ ദൃശ്യ സംവിധാനത്തിൽ ലഭ്യമായ ന്യുറൽ ഘടനകളോട് സാമ്യമുള്ളതാണ്.\n", "\n", - "\n", + "\n", "\n", "എങ്കിലും, ഡീപ്പ് ലേണിങ്ങിൽ, ക്ലാസിഫിക്കേഷൻ പ്രശ്നം പരിഹരിക്കാൻ ഏറ്റവും മികച്ച കോൺവല്യൂഷണൽ ഫിൽട്ടറുകൾ **കൽപ്പിക്കാൻ പഠിക്കുന്ന** നെറ്റ്‌വർക്കുകൾ നിർമ്മിക്കുന്നു. അതിനായി, **കോൺവല്യൂഷണൽ ലെയറുകൾ** പരിചയപ്പെടുത്തുന്നു.\n" ] @@ -358,7 +358,7 @@ "\n", "അതിനാൽ, സാധാരണ CNN-ൽ നിരവധി കോൺവല്യൂഷണൽ ലെയറുകൾ ഉണ്ടാകും, അവയുടെ ഇടയിൽ പൂലിംഗ് ലെയറുകൾ ചിത്രത്തിന്റെ വലുപ്പം കുറയ്ക്കാൻ ഉപയോഗിക്കും. പാറ്റേണുകൾ കൂടുതൽ സങ്കീർണ്ണമായതിനാൽ, കൂടുതൽ ഫിൽട്ടറുകളുടെ എണ്ണം വർദ്ധിപ്പിക്കും, കാരണം കൂടുതൽ സങ്കീർണ്ണമായ സംയോജനങ്ങൾ കണ്ടെത്തേണ്ടതുണ്ട്.\n", "\n", - "![പൂളിംഗ് ലെയറുകളോടുകൂടിയ നിരവധി കോൺവല്യൂഷണൽ ലെയറുകൾ കാണിക്കുന്ന ചിത്രം.](../../../../../translated_images/ml/cnn-pyramid.85915455759ef0ce.png)\n", + "![പൂളിംഗ് ലെയറുകളോടുകൂടിയ നിരവധി കോൺവല്യൂഷണൽ ലെയറുകൾ കാണിക്കുന്ന ചിത്രം.](../../../../../translated_images/ml/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "സ്ഥലം വലുപ്പം കുറയുകയും ഫീച്ചർ/ഫിൽട്ടർ വലുപ്പം വർദ്ധിക്കുകയും ചെയ്യുന്നതിനാൽ, ഈ ആർക്കിടെക്ചർ **പിരമിഡ് ആർക്കിടെക്ചർ** എന്നും വിളിക്കുന്നു.\n" ] diff --git a/translations/ml/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/ml/lessons/4-ComputerVision/07-ConvNets/README.md index 718b2caf..635e5e7e 100644 --- a/translations/ml/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/ml/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,14 +17,14 @@ CO_OP_TRANSLATOR_METADATA: പാറ്റേണുകൾ എടുക്കാൻ, നാം **കോൺവല്യൂഷണൽ ഫിൽട്ടറുകൾ** എന്ന ആശയം ഉപയോഗിക്കും. നിങ്ങൾക്ക് അറിയാമല്ലോ, ഒരു ചിത്രം 2D-മാട്രിക്സ് അല്ലെങ്കിൽ നിറത്തിന്റെ ആഴമുള്ള 3D-ടെൻസർ ആയി പ്രതിനിധീകരിക്കപ്പെടുന്നു. ഒരു ഫിൽട്ടർ പ്രയോഗിക്കുന്നത് അർത്ഥമാക്കുന്നത്, നാം ചെറിയ **ഫിൽട്ടർ കർണൽ** മാട്രിക്സ് എടുത്ത്, യഥാർത്ഥ ചിത്രത്തിലെ ഓരോ പിക്‌സലിനും സമീപമുള്ള പോയിന്റുകളുമായി ഭാരിത ശരാശരി കണക്കാക്കുകയാണ്. ഇത് ഒരു ചെറിയ വിൻഡോ മുഴുവൻ ചിത്രത്തിലൂടെ സ്ലൈഡ് ചെയ്യുന്നതുപോലെ കാണാം, ഫിൽട്ടർ കർണൽ മാട്രിക്സിലെ ഭാരങ്ങൾ അനുസരിച്ച് എല്ലാ പിക്‌സലുകളും ശരാശരി ചെയ്യുന്നു. -![Vertical Edge Filter](../../../../../translated_images/ml/filter-vert.b7148390ca0bc356.png) | ![Horizontal Edge Filter](../../../../../translated_images/ml/filter-horiz.59b80ed4feb946ef.png) +![Vertical Edge Filter](../../../../../translated_images/ml/filter-vert.b7148390ca0bc356.webp) | ![Horizontal Edge Filter](../../../../../translated_images/ml/filter-horiz.59b80ed4feb946ef.webp) ----|---- > ചിത്രം: Dmitry Soshnikov ഉദാഹരണത്തിന്, 3x3 വെർട്ടിക്കൽ എഡ്ജ്, ഹോരിസോണ്ടൽ എഡ്ജ് ഫിൽട്ടറുകൾ MNIST അക്കങ്ങളിൽ പ്രയോഗിച്ചാൽ, യഥാർത്ഥ ചിത്രത്തിലെ വെർട്ടിക്കൽ, ഹോരിസോണ്ടൽ എഡ്ജുകൾ ഉള്ള സ്ഥലങ്ങളിൽ ഹൈലൈറ്റുകൾ (ഉയർന്ന മൂല്യങ്ങൾ) ലഭിക്കും. അതിനാൽ ആ രണ്ട് ഫിൽട്ടറുകൾ എഡ്ജുകൾ "തിരയാൻ" ഉപയോഗിക്കാം. അതുപോലെ, നാം മറ്റ് താഴ്ന്ന തലത്തിലുള്ള പാറ്റേണുകൾ കണ്ടെത്താൻ വ്യത്യസ്ത ഫിൽട്ടറുകൾ രൂപകൽപ്പന ചെയ്യാം: - + > [Leung-Malik Filter Bank](https://www.robots.ox.ac.uk/~vgg/research/texclass/filters.html) ചിത്രം @@ -38,7 +38,7 @@ CNN-കൾ പ്രവർത്തിക്കുന്നത് താഴെ * ഫിൽട്ടറുകൾ സ്വയം പരിശീലിക്കപ്പെടുന്ന വിധത്തിൽ നെറ്റ്വർക്ക് രൂപകൽപ്പന ചെയ്യാം * നാം ഈ സമീപനം ഉപയോഗിച്ച് ഉയർന്ന തലത്തിലുള്ള ഫീച്ചറുകളിലും പാറ്റേണുകൾ കണ്ടെത്താം, യഥാർത്ഥ ചിത്രത്തിൽ മാത്രമല്ല. അതായത് CNN ഫീച്ചർ എക്സ്ട്രാക്ഷൻ പിക്‌സൽ സംയോജനങ്ങളിൽ നിന്നാരംഭിച്ച് ചിത്രഭാഗങ്ങളുടെ ഉയർന്ന തലത്തിലുള്ള സംയോജനങ്ങളിലേക്കുള്ള ഫീച്ചറുകളുടെ ഹയർആർക്കിയിൽ പ്രവർത്തിക്കുന്നു. -![Hierarchical Feature Extraction](../../../../../translated_images/ml/FeatureExtractionCNN.d9b456cbdae7cb64.png) +![Hierarchical Feature Extraction](../../../../../translated_images/ml/FeatureExtractionCNN.d9b456cbdae7cb64.webp) > [Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d) എന്ന പേപ്പറിൽ നിന്നുള്ള ചിത്രം, അവരുടെ [ഗവേഷണത്തെ അടിസ്ഥാനമാക്കി](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ CNN-കൾ പ്രവർത്തിക്കുന്നത് താഴെ ഉദാഹരണമായി, 2014-ൽ ImageNet ടോപ്പ്-5 ക്ലാസിഫിക്കേഷനിൽ 92.7% കൃത്യത നേടിയ VGG-16 നെറ്റ്വർക്ക് ആർക്കിടെക്ചർ നോക്കാം: -![ImageNet Layers](../../../../../translated_images/ml/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet Layers](../../../../../translated_images/ml/vgg-16-arch1.d901a5583b3a51ba.webp) -![ImageNet Pyramid](../../../../../translated_images/ml/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet Pyramid](../../../../../translated_images/ml/vgg-16-arch.64ff2137f50dd49f.webp) > [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) നിന്നുള്ള ചിത്രം diff --git a/translations/ml/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/ml/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 2acd7888..baa5ee4a 100644 --- a/translations/ml/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/ml/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ CO_OP_TRANSLATOR_METADATA: നാം ഉപയോഗിക്കുന്നത് [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/) ആണ്, ഇതിൽ 37 വ്യത്യസ്ത നായയും പൂച്ചയും ജാതികളുടെ ചിത്രങ്ങൾ ഉൾപ്പെടുന്നു. -![നാം കൈകാര്യം ചെയ്യാൻ പോകുന്ന ഡാറ്റാസെറ്റ്](../../../../../../translated_images/ml/data.50b2a9d5484bdbf0.png) +![നാം കൈകാര്യം ചെയ്യാൻ പോകുന്ന ഡാറ്റാസെറ്റ്](../../../../../../translated_images/ml/data.50b2a9d5484bdbf0.webp) ഡാറ്റാസെറ്റ് ഡൗൺലോഡ് ചെയ്യാൻ, ഈ കോഡ് സ്നിപ്പെറ്റ് ഉപയോഗിക്കുക: diff --git a/translations/ml/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/ml/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index 028016e2..934415b2 100644 --- a/translations/ml/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/ml/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "ആദർശമായ പൂച്ചയെ ദൃശ്യവൽക്കരിക്കാൻ, നാം ഒരു യാദൃച്ഛിക ശബ്ദചിത്രം ഉപയോഗിച്ച് തുടങ്ങും, പിന്നീട് ഗ്രേഡിയന്റ് ഡിസെന്റ് മെച്ചപ്പെടുത്തൽ സാങ്കേതിക വിദ്യ ഉപയോഗിച്ച് ചിത്രം ക്രമീകരിച്ച് ഒരു നെറ്റ്‌വർക്ക് പൂച്ചയെ തിരിച്ചറിയാൻ ശ്രമിക്കും.\n", "\n", - "![Optimization Loop](../../../../../translated_images/ml/ideal-cat-loop.999fbb8ff306e044.png)\n", + "![Optimization Loop](../../../../../translated_images/ml/ideal-cat-loop.999fbb8ff306e044.webp)\n", "\n", "ഇതാണ് നമ്മുടെ ആരംഭചിത്രം:\n" ] diff --git a/translations/ml/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/ml/lessons/4-ComputerVision/08-TransferLearning/README.md index d5045e8b..ed3d7677 100644 --- a/translations/ml/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/ml/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Keras-നും PyTorch-നും ImageNet ചിത്രങ്ങളിൽ പ ഇവിടെ VGG-16 നെറ്റ്‌വർക്ക് ഒരു പൂച്ചയുടെ ചിത്രത്തിൽ നിന്നെടുത്ത ഫീച്ചറുകളുടെ ഉദാഹരണം കാണാം: -![Features extracted by VGG-16](../../../../../translated_images/ml/features.6291f9c7ba3a0b95.png) +![Features extracted by VGG-16](../../../../../translated_images/ml/features.6291f9c7ba3a0b95.webp) ## പൂച്ചകളും നായകളും ഡാറ്റാസെറ്റ് @@ -48,19 +48,19 @@ Keras-നും PyTorch-നും ImageNet ചിത്രങ്ങളിൽ പ ഒരു സമീപനം, ഒരു യാദൃച്ഛിക ചിത്രം കൊണ്ട് ആരംഭിച്ച്, **ഗ്രേഡിയന്റ് ഡിസെന്റ് ഓപ്റ്റിമൈസേഷൻ** സാങ്കേതിക വിദ്യ ഉപയോഗിച്ച് ആ ചിത്രം അങ്ങനെ ക്രമീകരിക്കാൻ ശ്രമിക്കുക എന്നതാണ്, നെറ്റ്‌വർക്ക് അത് പൂച്ചയാണെന്ന് കരുതാൻ തുടങ്ങും വിധം. -![Image Optimization Loop](../../../../../translated_images/ml/ideal-cat-loop.999fbb8ff306e044.png) +![Image Optimization Loop](../../../../../translated_images/ml/ideal-cat-loop.999fbb8ff306e044.webp) എങ്കിലും, ഇത് ചെയ്താൽ, നമുക്ക് യാദൃച്ഛിക ശബ്ദം പോലുള്ള ഒന്നാണ് ലഭിക്കുന്നത്. കാരണം *നെറ്റ്‌വർക്ക് ഇൻപുട്ട് ചിത്രം പൂച്ചയാണെന്ന് കരുതാൻ നിരവധി മാർഗ്ഗങ്ങൾ ഉണ്ട്*, ചിലത് ദൃശ്യമായി അർത്ഥവത്തല്ലാത്തവയും. ആ ചിത്രങ്ങളിൽ പൂച്ചയ്ക്ക് സാധാരണമായ പല പാറ്റേണുകളും ഉണ്ടെങ്കിലും, അവ ദൃശ്യമായി വ്യത്യസ്തമാകാൻ യാതൊരു നിയന്ത്രണവും ഇല്ല. ഫലം മെച്ചപ്പെടുത്താൻ, നാം നഷ്ട ഫംഗ്ഷനിൽ മറ്റൊരു പദം ചേർക്കാം, അത് **വേരിയേഷൻ ലോസ്** എന്ന് വിളിക്കുന്നു. ഇത് ചിത്രത്തിലെ സമീപമുള്ള പിക്‌സലുകൾ എത്രത്തോളം സമാനമാണെന്ന് കാണിക്കുന്ന ഒരു മെട്രിക് ആണ്. വേരിയേഷൻ ലോസ് കുറയ്ക്കുന്നത് ചിത്രം മൃദുവാക്കുകയും ശബ്ദം നീക്കം ചെയ്യുകയും ചെയ്യുന്നു - അതിലൂടെ കൂടുതൽ ദൃശ്യപരമായി ആകർഷകമായ പാറ്റേണുകൾ വെളിപ്പെടുത്തുന്നു. താഴെ "ആദർശ" ചിത്രങ്ങളുടെ ഉദാഹരണം കാണാം, പൂച്ചയും സീബ്രയും ഉയർന്ന സാധ്യതയോടെ വർഗ്ഗീകരിക്കപ്പെട്ടവ: -![Ideal Cat](../../../../../translated_images/ml/ideal-cat.203dd4597643d6b0.png) | ![Ideal Zebra](../../../../../translated_images/ml/ideal-zebra.7f70e8b54ee15a7a.png) +![Ideal Cat](../../../../../translated_images/ml/ideal-cat.203dd4597643d6b0.webp) | ![Ideal Zebra](../../../../../translated_images/ml/ideal-zebra.7f70e8b54ee15a7a.webp) -----|----- *ആദർശ പൂച്ച* | *ആദർശ സീബ്ര* ഇത്തരത്തിലുള്ള സമീപനം ന്യൂറൽ നെറ്റ്‌വർക്കിൽ **എതിരാളി ആക്രമണങ്ങൾ** നടത്താനും ഉപയോഗിക്കാം. ഒരു നായയെ പൂച്ചയെന്നു തോന്നിക്കാൻ നമുക്ക് ആഗ്രഹമുണ്ടെന്ന് കരുതുക. ഒരു നായയുടെ ചിത്രം, നെറ്റ്‌വർക്ക് നായയെന്നു തിരിച്ചറിയുന്ന ചിത്രം, നാം ഗ്രേഡിയന്റ് ഡിസെന്റ് ഓപ്റ്റിമൈസേഷൻ ഉപയോഗിച്ച് ചെറിയ മാറ്റങ്ങൾ വരുത്തി, നെറ്റ്‌വർക്ക് അത് പൂച്ചയെന്നു തിരിച്ചറിയാൻ തുടങ്ങും വരെ ക്രമീകരിക്കാം: -![Picture of a Dog](../../../../../translated_images/ml/original-dog.8f68a67d2fe0911f.png) | ![Picture of a dog classified as a cat](../../../../../translated_images/ml/adversarial-dog.d9fc7773b0142b89.png) +![Picture of a Dog](../../../../../translated_images/ml/original-dog.8f68a67d2fe0911f.webp) | ![Picture of a dog classified as a cat](../../../../../translated_images/ml/adversarial-dog.d9fc7773b0142b89.webp) -----|----- *നായയുടെ യഥാർത്ഥ ചിത്രം* | *പൂച്ചയെന്നു തിരിച്ചറിയപ്പെട്ട നായയുടെ ചിത്രം* diff --git a/translations/ml/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/ml/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index 7845f29f..bfebddb6 100644 --- a/translations/ml/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/ml/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "ഓട്ടോഎൻകോഡർ യഥാർത്ഥ ചിത്രത്തിൽ നിന്നുള്ള വിവരങ്ങൾ പരമാവധി പിടിച്ചുപറ്റി കൃത്യമായ പുനഃസൃഷ്ടിക്ക് പരിശീലിപ്പിക്കപ്പെടുന്നതിനാൽ, നെറ്റ്‌വർക്ക് ഇൻപുട്ട് ചിത്രങ്ങളുടെ അർത്ഥം പിടിച്ചുപറ്റാൻ ഏറ്റവും മികച്ച **എംബെഡ്ഡിംഗ്** കണ്ടെത്താൻ ശ്രമിക്കുന്നു.\n", "\n", - "![AutoEncoder Diagram](../../../../../translated_images/ml/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![AutoEncoder Diagram](../../../../../translated_images/ml/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "> ചിത്രം [Keras ബ്ലോഗിൽ നിന്നുള്ളത്](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", @@ -941,7 +941,7 @@ " * നാം $N(\\mathrm{z\\_mean},e^{\\mathrm{z\\_log\\_sigma}})$ എന്ന വിതരണത്തിൽ നിന്ന് `sample(z_val in code)` എന്ന വെക്ടർ സാമ്പിൾ ചെയ്യുന്നു\n", " * ഡീകോഡർ `sample` എന്ന ഇൻപുട്ട് വെക്ടർ ഉപയോഗിച്ച് ഒറിജിനൽ ചിത്രം പുനരുദ്ധരിക്കാൻ ശ്രമിക്കുന്നു\n", "\n", - " \n", + " \n", "\n", " > ഈ ചിത്രം [ഈ ബ്ലോഗ് പോസ്റ്റ്](https://ijdykeman.github.io/ml/2016/12/21/cvae.html) ഇസാക്ക് ഡൈക്മാൻ എഴുതിയത്\n" ] @@ -1264,7 +1264,7 @@ "\n", "ഈ സമീപനത്തിൽ നമുക്ക് **മൂന്ന് നഷ്ട ഫംഗ്ഷനുകൾ** ഉണ്ട്: GAN-കളിൽ നിന്നുള്ള generator നഷ്ടം, discriminator നഷ്ടം, കൂടാതെ VAE-യിൽ നിന്നുള്ള പുനർനിർമ്മാണ നഷ്ടം.\n", "\n", - "\n", + "\n", "\n", "> ചിത്രം Felipe Ducau എഴുതിയ [ഈ ബ്ലോഗ് പോസ്റ്റ്](https://blog.paperspace.com/adversarial-autoencoders-with-pytorch/) ൽ നിന്നാണ്\n" ] diff --git a/translations/ml/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/ml/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index f875df69..a2482afd 100644 --- a/translations/ml/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/ml/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -21,7 +21,7 @@ "\n", "ഓട്ടോഎൻകോഡർ യഥാർത്ഥ ചിത്രത്തിൽ നിന്നുള്ള വിവരങ്ങൾ പരമാവധി പിടിച്ചുപറ്റി കൃത്യമായ പുനഃസൃഷ്ടിക്ക് പരിശീലിപ്പിക്കപ്പെടുന്നതിനാൽ, നെറ്റ്‌വർക്ക് ഇൻപുട്ട് ചിത്രങ്ങളുടെ അർത്ഥം പിടിച്ചുപറ്റാൻ മികച്ച **എംബെഡ്ഡിംഗ്** കണ്ടെത്താൻ ശ്രമിക്കുന്നു.\n", "\n", - "![AutoEncoder Diagram](../../../../../translated_images/ml/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![AutoEncoder Diagram](../../../../../translated_images/ml/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "*ചിത്രം [Keras ബ്ലോഗിൽ നിന്നുള്ളത്](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", @@ -888,7 +888,7 @@ " * നാം $N(\\mathrm{z\\_mean},e^{\\mathrm{z\\_log\\_sigma}})$ എന്ന വിതരണത്തിൽ നിന്ന് ഒരു വെക്ടർ `sample` സാമ്പിൾ ചെയ്യുന്നു\n", " * ഡീകോഡർ `sample` എന്ന ഇൻപുട്ട് വെക്ടർ ഉപയോഗിച്ച് യഥാർത്ഥ ചിത്രം പുനഃസംരചിക്കാൻ ശ്രമിക്കുന്നു\n", "\n", - " \n" + " \n" ] }, { diff --git a/translations/ml/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/ml/lessons/4-ComputerVision/09-Autoencoders/README.md index 89902894..055cfc5e 100644 --- a/translations/ml/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/ml/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ CNN-കൾ പരിശീലിപ്പിക്കുമ്പോൾ, ഒര യഥാർത്ഥ ചിത്രത്തിൽ നിന്നുള്ള വിവരങ്ങൾ പൂർണ്ണമായി പിടിച്ചുപറ്റി കൃത്യമായി പുനഃസൃഷ്ടിക്കാൻ ഓട്ടോഎൻകോഡർ പരിശീലിപ്പിക്കുമ്പോൾ, നെറ്റ്‌വർക്ക് ഇൻപുട്ട് ചിത്രങ്ങളുടെ അർത്ഥം പിടിച്ചുപറ്റാൻ മികച്ച **എംബെഡ്ഡിംഗ്** കണ്ടെത്താൻ ശ്രമിക്കുന്നു. -![AutoEncoder Diagram](../../../../../translated_images/ml/autoencoder_schema.5e6fc9ad98a5eb61.jpg) +![AutoEncoder Diagram](../../../../../translated_images/ml/autoencoder_schema.5e6fc9ad98a5eb61.webp) > ചിത്രം [Keras ബ്ലോഗിൽ നിന്നുള്ളത്](https://blog.keras.io/building-autoencoders-in-keras.html) @@ -46,7 +46,7 @@ VAE ഒരു ഓട്ടോഎൻകോഡറാണ്, അത് ലാറ് * N(zmean, exp(zlog_sigma)) എന്ന വിതരണത്തിൽ നിന്ന് ഒരു `sample` വെക്ടർ സാമ്പിൾ ചെയ്യുന്നു * ഡീകോഡർ `sample` ഉപയോഗിച്ച് യഥാർത്ഥ ചിത്രം പുനഃസൃഷ്ടിക്കാൻ ശ്രമിക്കുന്നു - + > ചിത്രം [ഈ ബ്ലോഗ് പോസ്റ്റിൽ നിന്നുള്ളത്](https://ijdykeman.github.io/ml/2016/12/21/cvae.html) - Isaak Dykeman @@ -57,13 +57,13 @@ VAE ഒരു ഓട്ടോഎൻകോഡറാണ്, അത് ലാറ് VAE-കളുടെ പ്രധാന ഗുണം, നാം ലാറ്റന്റ് വെക്ടറുകൾ സാമ്പിൾ ചെയ്യേണ്ട വിതരണത്തെ അറിയുന്നതിനാൽ, പുതിയ ചിത്രങ്ങൾ സൃഷ്ടിക്കുന്നത് എളുപ്പമാണ്. ഉദാഹരണത്തിന്, 2D ലാറ്റന്റ് വെക്ടർ ഉപയോഗിച്ച് MNIST-ൽ VAE പരിശീലിപ്പിച്ചാൽ, ലാറ്റന്റ് വെക്ടറിന്റെ ഘടകങ്ങൾ മാറ്റി വ്യത്യസ്ത അക്കങ്ങൾ ലഭിക്കാം: -vaemnist +vaemnist > ചിത്രം [Dmitry Soshnikov](http://soshnikov.com) എന്നവന്റെ ലാറ്റന്റ് പാരാമീറ്റർ സ്പേസിന്റെ വ്യത്യസ്ത ഭാഗങ്ങളിൽ നിന്നുള്ള ലാറ്റന്റ് വെക്ടറുകൾ ഉപയോഗിച്ച് ചിത്രങ്ങൾ എങ്ങനെ പരസ്പരം മിശ്രിതമാകുന്നു എന്ന് ശ്രദ്ധിക്കുക. നാം ഈ സ്പേസ് 2D-ൽ ദൃശ്യവൽക്കരിക്കാനും കഴിയും: -vaemnist cluster +vaemnist cluster > ചിത്രം [Dmitry Soshnikov](http://soshnikov.com) എന്നവന്റെ diff --git a/translations/ml/lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb b/translations/ml/lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb index c1e4a89d..4f5523ef 100644 --- a/translations/ml/lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb +++ b/translations/ml/lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb @@ -15,7 +15,7 @@ "* **ജനറേറ്റർ** ഒരു റാൻഡം വെക്ടർ സ്വീകരിച്ച് അതിൽ നിന്ന് ഒരു ചിത്രം സൃഷ്ടിക്കണം\n", "* **ഡിസ്ക്രിമിനേറ്റർ** ഒരു നെറ്റ്‌വർക്ക് ആണ്, അത് യഥാർത്ഥ ചിത്രം (പരിശീലന ഡാറ്റാസെറ്റിൽ നിന്നുള്ളത്)യും ജനറേറ്റർ സൃഷ്ടിച്ച ചിത്രവും വേർതിരിക്കണം.\n", "\n", - "\n" + "\n" ] }, { @@ -670,7 +670,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n", + "\n", "\n", "> ചിത്രം [ഈ ട്യൂട്ടോറിയൽ](https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html) നിന്നാണ്\n" ] diff --git a/translations/ml/lessons/4-ComputerVision/10-GANs/GANTF.ipynb b/translations/ml/lessons/4-ComputerVision/10-GANs/GANTF.ipynb index 2024b47b..d754a5a3 100644 --- a/translations/ml/lessons/4-ComputerVision/10-GANs/GANTF.ipynb +++ b/translations/ml/lessons/4-ComputerVision/10-GANs/GANTF.ipynb @@ -15,7 +15,7 @@ "* **ജനറേറ്റർ** ഒരു യാദൃച്ഛിക വെക്ടർ സ്വീകരിച്ച് അതിൽ നിന്ന് ഒരു ചിത്രം സൃഷ്ടിക്കണം\n", "* **ഡിസ്ക്രിമിനേറ്റർ** ഒരു നെറ്റ്വർക്ക് ആണ്, അത് യഥാർത്ഥ ചിത്രം (പരിശീലന ഡാറ്റാസെറ്റിൽ നിന്നുള്ളത്)യും ജനറേറ്റർ സൃഷ്ടിച്ച ചിത്രവും വേർതിരിക്കണം.\n", "\n", - "\n" + "\n" ] }, { diff --git a/translations/ml/lessons/4-ComputerVision/10-GANs/README.md b/translations/ml/lessons/4-ComputerVision/10-GANs/README.md index b1d00a06..4629e95b 100644 --- a/translations/ml/lessons/4-ComputerVision/10-GANs/README.md +++ b/translations/ml/lessons/4-ComputerVision/10-GANs/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: GAN-ന്റെ പ്രധാന ആശയം രണ്ട് ന്യൂറൽ നെറ്റ്വർക്കുകൾ തമ്മിൽ പരസ്പരം മത്സരിച്ച് പരിശീലിക്കപ്പെടുക എന്നതാണ്: - + > ചിത്രം: [Dmitry Soshnikov](http://soshnikov.com) @@ -41,7 +41,7 @@ CNN ഡിസ്ക്രിമിനേറ്റർ താഴെപ്പറയ > ✅ കോൺവല്യൂഷൻ ലെയർ ചിത്രം താണ്ടുന്ന ലീനിയർ ഫിൽട്ടറായതിനാൽ, ഡീകോൺവല്യൂഷൻ അടിസ്ഥാനപരമായി കോൺവല്യൂഷനോട് സമാനമാണ്, അതേ ലെയർ ലജിക് ഉപയോഗിച്ച് നടപ്പിലാക്കാം. - + > ചിത്രം: [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/ml/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/ml/lessons/4-ComputerVision/11-ObjectDetection/README.md index 401a322b..2f1fb543 100644 --- a/translations/ml/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/ml/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: ## [പ്രീ-ലെക്ചർ ക്വിസ്](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Object Detection](../../../../../translated_images/ml/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) +![Object Detection](../../../../../translated_images/ml/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp) > ചിത്രം [YOLO v2 വെബ്‌സൈറ്റ്](https://pjreddie.com/darknet/yolov2/) നിന്നാണ് @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 2. ഓരോ ടൈലിലും ഇമേജ് ക്ലാസിഫിക്കേഷൻ നടത്തുക 3. ഉയർന്ന ആക്ടിവേഷൻ ലഭിക്കുന്ന ടൈലുകൾ ആ വസ്തു അടങ്ങിയതായി കരുതാം -![Naive Object Detection](../../../../../translated_images/ml/naive-detection.e7f1ba220ccd08c6.png) +![Naive Object Detection](../../../../../translated_images/ml/naive-detection.e7f1ba220ccd08c6.webp) > *ചിത്രം [Exercise Notebook](ObjectDetection-TF.ipynb) നിന്നാണ്* @@ -42,7 +42,7 @@ CO_OP_TRANSLATOR_METADATA: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 ക്ലാസുകൾ * [COCO](http://cocodataset.org/#home) - Common Objects in Context. 80 ക്ലാസുകൾ, ബൗണ്ടിംഗ് ബോക്സുകളും സെഗ്മെന്റേഷൻ മാസ്കുകളും -![COCO](../../../../../translated_images/ml/coco-examples.71bc60380fa6cceb.jpg) +![COCO](../../../../../translated_images/ml/coco-examples.71bc60380fa6cceb.webp) ## ഒബ്ജക്റ്റ് ഡിറ്റക്ഷൻ മെട്രിക്‌സ് @@ -50,7 +50,7 @@ CO_OP_TRANSLATOR_METADATA: ഇമേജ് ക്ലാസിഫിക്കേഷനിൽ ആൽഗോരിതത്തിന്റെ പ്രകടനം എളുപ്പത്തിൽ അളക്കാമെങ്കിലും, ഒബ്ജക്റ്റ് ഡിറ്റക്ഷനിൽ ക്ലാസിന്റെ ശരിതത്വവും ബൗണ്ടിംഗ് ബോക്സിന്റെ കൃത്യതയും അളക്കേണ്ടതുണ്ട്. ഇതിന് **Intersection over Union** (IoU) ഉപയോഗിക്കുന്നു, ഇത് രണ്ട് ബോക്സുകൾ (അഥവാ രണ്ട് ഏരിയകൾ) എത്രമാത്രം ഒതുക്കപ്പെടുന്നുവെന്ന് അളക്കുന്നു. -![IoU](../../../../../translated_images/ml/iou_equation.9a4751d40fff4e11.png) +![IoU](../../../../../translated_images/ml/iou_equation.9a4751d40fff4e11.webp) > *ചിത്രം [ഈ മികച്ച ബ്ലോഗ് പോസ്റ്റ്](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/) നിന്നാണ്* @@ -97,11 +97,11 @@ IoU ഒരു നിശ്ചിത മൂല്യത്തിന് മുക [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) ഉപയോഗിച്ച് ROI പ്രദേശങ്ങളുടെ ഹയർആർക്കിക്കൽ ഘടന സൃഷ്ടിക്കുന്നു, പിന്നീട് CNN ഫീച്ചർ എക്സ്ട്രാക്ടറുകളും SVM ക്ലാസിഫയറുകളും ഉപയോഗിച്ച് വസ്തു ക്ലാസ് നിർണയിക്കുന്നു, ലീനിയർ റെഗ്രഷൻ ഉപയോഗിച്ച് *ബൗണ്ടിംഗ് ബോക്സ്* കോഓർഡിനേറ്റുകൾ കണ്ടെത്തുന്നു. [അധികൃത പേപ്പർ](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/ml/rcnn1.cae407020dfb1d1f.png) +![RCNN](../../../../../translated_images/ml/rcnn1.cae407020dfb1d1f.webp) > *ചിത്രം van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/ml/rcnn2.2d9530bb83516484.png) +![RCNN-1](../../../../../translated_images/ml/rcnn2.2d9530bb83516484.webp) > *ചിത്രങ്ങൾ [ഈ ബ്ലോഗ്](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e) നിന്നാണ്* @@ -109,7 +109,7 @@ IoU ഒരു നിശ്ചിത മൂല്യത്തിന് മുക R-CNN പോലെയാണ്, പക്ഷേ പ്രദേശങ്ങൾ കോൺവല്യൂഷൻ ലെയറുകൾ പ്രയോഗിച്ചതിന് ശേഷം നിർവചിക്കുന്നു. -![FRCNN](../../../../../translated_images/ml/f-rcnn.3cda6d9bb4188875.png) +![FRCNN](../../../../../translated_images/ml/f-rcnn.3cda6d9bb4188875.webp) > ചിത്രം [അധികൃത പേപ്പർ](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -117,7 +117,7 @@ R-CNN പോലെയാണ്, പക്ഷേ പ്രദേശങ്ങൾ ഈ സമീപനത്തിന്റെ പ്രധാന ആശയം ROIകൾ പ്രവചിക്കാൻ ഒരു ന്യൂറൽ നെറ്റ്‌വർക്ക് ഉപയോഗിക്കുക എന്നതാണ് - ഇതാണ് *Region Proposal Network*. [പേപ്പർ](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/ml/faster-rcnn.8d46c099b87ef30a.png) +![FasterRCNN](../../../../../translated_images/ml/faster-rcnn.8d46c099b87ef30a.webp) > ചിത്രം [അധികൃത പേപ്പർ](https://arxiv.org/pdf/1506.01497.pdf) @@ -129,7 +129,7 @@ Faster R-CNN-നേക്കാൾ വേഗത്തിൽ പ്രവർത 2. ഫീച്ചറുകൾ **Position-Sensitive Score Map** ഉപയോഗിച്ച് പ്രോസസ്സ് ചെയ്യുന്നു. $C$ ക്ലാസുകളിലുള്ള ഓരോ വസ്തുവും $k\times k$ പ്രദേശങ്ങളായി വിഭജിച്ച്, വസ്തുവിന്റെ ഭാഗങ്ങൾ പ്രവചിക്കാൻ പരിശീലനം നൽകുന്നു. 3. $k\times k$ പ്രദേശങ്ങളിലെ ഓരോ ഭാഗത്തിനും എല്ലാ നെറ്റ്‌വർക്കുകളും വസ്തു ക്ലാസുകൾക്ക് വോട്ട് ചെയ്യുന്നു, പരമാവധി വോട്ട് ലഭിച്ച ക്ലാസ് തിരഞ്ഞെടുക്കുന്നു. -![r-fcn image](../../../../../translated_images/ml/r-fcn.13eb88158b99a3da.png) +![r-fcn image](../../../../../translated_images/ml/r-fcn.13eb88158b99a3da.webp) > ചിത്രം [അധികൃത പേപ്പർ](https://arxiv.org/abs/1605.06409) @@ -140,7 +140,7 @@ YOLO ഒരു റിയൽടൈം ഒന്ന്-പാസ്സ് ആൽ * ചിത്രം $S\times S$ പ്രദേശങ്ങളായി വിഭജിക്കുന്നു * ഓരോ പ്രദേശത്തിനും **CNN** $n$ സാധ്യതയുള്ള വസ്തുക്കൾ, *ബൗണ്ടിംഗ് ബോക്സ്* കോഓർഡിനേറ്റുകൾ, *confidence*=*probability* * IoU പ്രവചിക്കുന്നു. - ![YOLO](../../../../../translated_images/ml/yolo.a2648ec82ee8bb4e.png) + ![YOLO](../../../../../translated_images/ml/yolo.a2648ec82ee8bb4e.webp) > ചിത്രം [അധികൃത പേപ്പർ](https://arxiv.org/abs/1506.02640) diff --git a/translations/ml/lessons/4-ComputerVision/12-Segmentation/README.md b/translations/ml/lessons/4-ComputerVision/12-Segmentation/README.md index 8b33bd5d..70c60bd4 100644 --- a/translations/ml/lessons/4-ComputerVision/12-Segmentation/README.md +++ b/translations/ml/lessons/4-ComputerVision/12-Segmentation/README.md @@ -20,7 +20,7 @@ CO_OP_TRANSLATOR_METADATA: ഇൻസ്റ്റൻസ് സെഗ്മെന്റേഷനിൽ, ഈ ആടുകൾ വ്യത്യസ്ത വസ്തുക്കളാണ്, എന്നാൽ സെമാന്റിക് സെഗ്മെന്റേഷനിൽ എല്ലാ ആടുകളും ഒരേ ക്ലാസ്സായി പ്രതിനിധീകരിക്കുന്നു. - + > ചിത്രം [ഈ ബ്ലോഗ് പോസ്റ്റ്](https://nirmalamurali.medium.com/image-classification-vs-semantic-segmentation-vs-instance-segmentation-625c33a08d50) നിന്നാണ് @@ -29,7 +29,7 @@ CO_OP_TRANSLATOR_METADATA: * **എൻകോഡർ** ഇൻപുട്ട് ചിത്രത്തിൽ നിന്ന് ഫീച്ചറുകൾ എടുക്കുന്നു * **ഡികോഡർ** ആ ഫീച്ചറുകൾ **മാസ്‌ക് ചിത്രം** ആക്കുന്നു, അതിന്റെ വലിപ്പവും ചാനലുകളുടെ എണ്ണം ക്ലാസുകളുടെ എണ്ണം അനുസരിച്ചുള്ളതാണ്. - + > ചിത്രം [ഈ പ്രസിദ്ധീകരണം](https://arxiv.org/pdf/2001.05566.pdf) നിന്നാണ് @@ -43,7 +43,7 @@ CO_OP_TRANSLATOR_METADATA: > ✅ ഈ സാങ്കേതികവിദ്യ ഈ തരത്തിലുള്ള മെഡിക്കൽ ഇമേജിംഗിന് പ്രത്യേകിച്ച് അനുയോജ്യമാണ്, എന്നാൽ മറ്റേതെങ്കിലും യാഥാർത്ഥ്യപ്രയോഗങ്ങൾ നിങ്ങൾക്ക് കാണാമോ? -navi +navi > ചിത്രം PH2 ഡാറ്റാബേസിൽ നിന്നാണ് diff --git a/translations/ml/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb b/translations/ml/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb index 13f2705e..7e294098 100644 --- a/translations/ml/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb +++ b/translations/ml/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb @@ -17,7 +17,7 @@ "\n", "ഉദാഹരണത്തിന്, ഇൻസ്റ്റൻസ് സെഗ്മെന്റേഷനിൽ 10 ആട് വ്യത്യസ്ത ഒബ്ജക്റ്റുകളാണ്, സെമാന്റിക് സെഗ്മെന്റേഷനിൽ എല്ലാ ആടുകളും ഒരേ ക്ലാസ്സായി പ്രതിനിധീകരിക്കുന്നു.\n", "\n", - "\n", + "\n", "\n", "> ചിത്രം [ഈ ബ്ലോഗ് പോസ്റ്റ്](https://nirmalamurali.medium.com/image-classification-vs-semantic-segmentation-vs-instance-segmentation-625c33a08d50) നിന്നാണ്\n", "\n", @@ -26,7 +26,7 @@ "* **എൻകോഡർ** ഇൻപുട്ട് ചിത്രത്തിൽ നിന്ന് ഫീച്ചറുകൾ എടുക്കുന്നു\n", "* **ഡികോഡർ** ആ ഫീച്ചറുകൾ **മാസ്‌ക് ഇമേജായി** മാറ്റുന്നു, അതിന്റെ വലിപ്പവും ചാനലുകളുടെ എണ്ണം ക്ലാസുകളുടെ എണ്ണത്തോടനുസരിച്ചുള്ളതാണ്.\n", "\n", - "\n", + "\n", "\n", "> ചിത്രം [ഈ പ്രസിദ്ധീകരണം](https://arxiv.org/pdf/2001.05566.pdf) നിന്നാണ്\n" ] @@ -252,7 +252,7 @@ "\n", "ഏറ്റവും ലളിതമായ എൻകോഡർ-ഡീകോഡർ ആർക്കിടെക്ചർ **സെഗ്‌നെറ്റ്** എന്നാണ് വിളിക്കുന്നത്. എൻകോഡറിൽ കോൺവല്യൂഷനുകളും പൂലിംഗുകളും ഉപയോഗിക്കുന്ന സ്റ്റാൻഡേർഡ് CNN ആണ് ഇത് ഉപയോഗിക്കുന്നത്, ഡീകോഡറിൽ കോൺവല്യൂഷനുകളും അപ്സാമ്പ്ലിംഗുകളും ഉൾക്കൊള്ളുന്ന ഡീകോൺവല്യൂഷൻ CNN ആണ് ഉപയോഗിക്കുന്നത്. ബാച്ച് നോർമലൈസേഷൻ ഉപയോഗിച്ച് മൾട്ടി-ലെയർ നെറ്റ്‌വർക്ക് വിജയകരമായി പരിശീലിപ്പിക്കാനും ഇത് ആശ്രയിക്കുന്നു.\n", "\n", - "\n", + "\n", "\n", "> ഈ ചിത്രം ഈ പേപ്പറിൽ നിന്നാണ്: Badrinarayanan, V., Kendall, A., & Cipolla, R. (2015). [SegNet: A deep convolutional\n", "encoder-decoder architecture for image segmentation](https://arxiv.org/pdf/1511.00561.pdf)\n" @@ -548,7 +548,7 @@ "\n", "ഇവിടെ നാം വളരെ ലളിതമായ CNN ആർക്കിടെക്ചർ ഉപയോഗിക്കും, പക്ഷേ U-Net കൂടുതൽ സങ്കീർണ്ണമായ എൻകോഡർ ഫീച്ചർ എക്സ്ട്രാക്ഷനായി ഉപയോഗിക്കാം, ഉദാഹരണത്തിന് ResNet-50.\n", "\n", - "\n", + "\n", "\n", "> ചിത്രം പേപ്പറിൽ നിന്നാണ്: Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. [U-Net: Convolutional networks for biomedical image segmentation.](https://arxiv.org/pdf/1505.04597.pdf)\n" ] diff --git a/translations/ml/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb b/translations/ml/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb index beacb927..3dcf0736 100644 --- a/translations/ml/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb +++ b/translations/ml/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb @@ -12,13 +12,13 @@ "\n", "ഉദാഹരണത്തിന്, ഇൻസ്റ്റൻസ് സെഗ്മെന്റേഷനിൽ പത്ത് കാറുകൾ **വ്യത്യസ്ത** വസ്തുക്കളാണ്, സെമാന്റിക് സെഗ്മെന്റേഷനിൽ **എല്ലാ** കാറുകളും ഒരേ ക്ലാസായി കണക്കാക്കപ്പെടുന്നു.\n", "\n", - "\n", + "\n", "\n", "> ചിത്രം [ഈ ബ്ലോഗ് പോസ്റ്റ്](https://nirmalamurali.medium.com/image-classification-vs-semantic-segmentation-vs-instance-segmentation-625c33a08d50) നിന്നാണ്\n", "\n", "ഏതാണ്ട് എല്ലാ ആർക്കിടെക്ചറുകളും ഒരേ ഘടനയുള്ളതാണ്. ആദ്യഭാഗം **എൻകോഡർ** ആണ്, ഇത് ഇൻപുട്ട് ചിത്രത്തിൽ നിന്ന് ഫീച്ചറുകൾ എടുക്കുന്നു, രണ്ടാം ഭാഗം **ഡികോഡർ** ആണ്, ഇത് ഈ ഫീച്ചറുകൾ ചിത്രത്തിന്റെ സമാന ഉയരം, വീതി, ചിലപ്പോൾ ക്ലാസുകളുടെ എണ്ണം തുല്യമായ ചാനലുകളുള്ള ചിത്രമായി മാറ്റുന്നു.\n", "\n", - "\n", + "\n", "\n", "> ചിത്രം [ഈ പ്രസിദ്ധീകരണം](https://arxiv.org/pdf/2001.05566.pdf) നിന്നാണ്\n" ] @@ -210,7 +210,7 @@ "\n", "എൻകോഡറിൽ കോൺവല്യൂഷനുകളും പൂലിംഗുകളും, ഡികോഡറിൽ കോൺവല്യൂഷനുകളും അപ്സാമ്പ്ലിംഗുകളും ഉള്ള ലളിതമായ എൻകോഡർ-ഡികോഡർ ആർക്കിടെക്ചർ.\n", "\n", - "\n", + "\n", "\n", "* Badrinarayanan, V., Kendall, A., & Cipolla, R. (2015). [SegNet: A deep convolutional\n", "encoder-decoder architecture for image segmentation](https://arxiv.org/pdf/1511.00561.pdf)\n" @@ -602,7 +602,7 @@ "\n", "U-Net സാധാരണയായി ഫീച്ചർ എക്സ്ട്രാക്ഷനിനായി ഒരു ഡിഫോൾട്ട് എൻകോഡർ ഉപയോഗിക്കുന്നു, ഉദാഹരണത്തിന് resnet50.\n", "\n", - "\n", + "\n", "\n", "* Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. [U-Net: Convolutional networks for biomedical image segmentation.](https://arxiv.org/pdf/1505.04597.pdf)\n" ] diff --git a/translations/ml/lessons/4-ComputerVision/README.md b/translations/ml/lessons/4-ComputerVision/README.md index 831c3cba..ad5aaa2e 100644 --- a/translations/ml/lessons/4-ComputerVision/README.md +++ b/translations/ml/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # കമ്പ്യൂട്ടർ വിഷൻ -![കമ്പ്യൂട്ടർ വിഷൻ ഉള്ളടക്കത്തിന്റെ സംഗ്രഹം ഒരു ഡ്രോയിങ്ങിൽ](../../../../translated_images/ml/ai-computervision.6506ebebac3fbf76.png) +![കമ്പ്യൂട്ടർ വിഷൻ ഉള്ളടക്കത്തിന്റെ സംഗ്രഹം ഒരു ഡ്രോയിങ്ങിൽ](../../../../translated_images/ml/ai-computervision.6506ebebac3fbf76.webp) ഈ വിഭാഗത്തിൽ നാം പഠിക്കാനിരിക്കുന്നവ: diff --git a/translations/ml/lessons/5-NLP/13-TextRep/README.md b/translations/ml/lessons/5-NLP/13-TextRep/README.md index db892b66..a1c0737d 100644 --- a/translations/ml/lessons/5-NLP/13-TextRep/README.md +++ b/translations/ml/lessons/5-NLP/13-TextRep/README.md @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: നാചുറൽ ലാംഗ്വേജ് പ്രോസസ്സിംഗ് (NLP) ടാസ്കുകൾ ന്യൂറൽ നെറ്റ്‌വർക്കുകളിലൂടെ പരിഹരിക്കാൻ, ടെക്സ്റ്റ് ടെൻസറുകളായി പ്രതിനിധാനം ചെയ്യാനുള്ള മാർഗ്ഗം വേണം. കമ്പ്യൂട്ടറുകൾ ഇതിനകം തന്നെ ASCII അല്ലെങ്കിൽ UTF-8 പോലുള്ള എൻകോഡിങ്ങുകൾ ഉപയോഗിച്ച് സ്ക്രീനിലെ ഫോണ്ടുകളുമായി മാപ്പ് ചെയ്യുന്ന സംഖ്യകളായി ടെക്സ്റ്റ് അക്ഷരങ്ങളെ പ്രതിനിധാനം ചെയ്യുന്നു. -ഒരു അക്ഷരത്തെ ASCII, ബൈനറി പ്രതിനിധാനങ്ങളായി മാപ്പ് ചെയ്യുന്ന ഡയഗ്രാം കാണിക്കുന്ന ചിത്രം +ഒരു അക്ഷരത്തെ ASCII, ബൈനറി പ്രതിനിധാനങ്ങളായി മാപ്പ് ചെയ്യുന്ന ഡയഗ്രാം കാണിക്കുന്ന ചിത്രം > [ചിത്രം സ്രോതസ്സ്](https://www.seobility.net/en/wiki/ASCII) @@ -48,7 +48,7 @@ CO_OP_TRANSLATOR_METADATA: ടെക്സ്റ്റ് ക്ലാസിഫിക്കേഷൻ പോലുള്ള ടാസ്കുകൾ പരിഹരിക്കുമ്പോൾ, ടെക്സ്റ്റ് ഒരു സ്ഥിരമായ വലിപ്പമുള്ള വെക്ടറായി പ്രതിനിധാനം ചെയ്യാൻ കഴിയണം, ഇത് ഫൈനൽ ഡെൻസ് ക്ലാസിഫയറിലേക്ക് ഇൻപുട്ടായി ഉപയോഗിക്കും. ഏറ്റവും ലളിതമായ മാർഗ്ഗങ്ങളിൽ ഒന്ന് എല്ലാ വ്യക്തിഗത വാക്കുകളുടെ പ്രതിനിധാനങ്ങൾ ചേർക്കലാണ്. ഓരോ വാക്കിന്റെയും ഒന്ന്-ഹോട്ട് എൻകോഡിങ്ങുകൾ ചേർത്താൽ, ഓരോ വാക്കും ടെക്സ്റ്റിൽ എത്ര തവണ വന്നുവെന്ന് കാണിക്കുന്ന ഫ്രീക്വൻസി വെക്ടർ ലഭിക്കും. ഈ ടെക്സ്റ്റ് പ്രതിനിധാനം **ബാഗ് ഓഫ് വേർഡ്സ്** (BoW) എന്ന് വിളിക്കുന്നു. - + > ചിത്രകാരൻ: ലേഖകൻ diff --git a/translations/ml/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/ml/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index 4e98717f..81b73942 100644 --- a/translations/ml/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/ml/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**Bag of Words** (BoW) വെക്ടർ പ്രതിനിധാനം ഏറ്റവും സാധാരണമായി ഉപയോഗിക്കുന്ന പരമ്പരാഗത വെക്ടർ പ്രതിനിധാനമാണ്. ഓരോ വാക്കും ഒരു വെക്ടർ ഇൻഡക്സുമായി ബന്ധിപ്പിച്ചിരിക്കുന്നു, വെക്ടർ ഘടകം ഒരു നൽകിയ ഡോക്യുമെന്റിൽ ആ വാക്കിന്റെ ആവർത്തനങ്ങളുടെ എണ്ണം ഉൾക്കൊള്ളുന്നു.\n", "\n", - "![Image showing how a bag of words vector representation is represented in memory.](../../../../../translated_images/ml/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![Image showing how a bag of words vector representation is represented in memory.](../../../../../translated_images/ml/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Note**: BoW-നെ ടെക്സ്റ്റിലെ ഓരോ വാക്കിനും ഉള്ള ഒന്ന്-ഹോട്ട്-എൻകോഡഡ് വെക്ടറുകളുടെ മൊത്തം കൂട്ടമായി കാണാം.\n", "\n", diff --git a/translations/ml/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/ml/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index c2bd527f..e5c3fb92 100644 --- a/translations/ml/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/ml/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**Bag-of-words** (BoW) വെക്ടർ പ്രതിനിധാനം ഏറ്റവും എളുപ്പത്തിൽ മനസ്സിലാക്കാവുന്ന പരമ്പരാഗത വെക്ടർ പ്രതിനിധാനമാണ്. ഓരോ വാക്കിനും ഒരു വെക്ടർ ഇൻഡക്സ് ബന്ധിപ്പിച്ചിരിക്കുന്നു, ഒരു വെക്ടർ ഘടകം ഒരു നൽകിയ ഡോക്യുമെന്റിൽ ഓരോ വാക്കിന്റെയും ആവർത്തനങ്ങളുടെ എണ്ണം ഉൾക്കൊള്ളുന്നു.\n", "\n", - "![Image showing how a bag of words vector representation is represented in memory.](../../../../../translated_images/ml/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![Image showing how a bag of words vector representation is represented in memory.](../../../../../translated_images/ml/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Note**: BoW-നെ ടെക്സ്റ്റിലെ ഓരോ വാക്കിനും ഉള്ള ഒന്ന്-ഹോട്ട്-എൻകോഡഡ് വെക്ടറുകളുടെ മൊത്തം കൂട്ടമായി കാണാം.\n", "\n", diff --git a/translations/ml/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/ml/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 4ed062b6..398c6e6f 100644 --- a/translations/ml/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/ml/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "നമ്മുടെ നെറ്റ്‌വർക്കിലെ ആദ്യ ലെയറായി എമ്പെഡ്ഡിംഗ് ലെയർ ഉപയോഗിച്ച്, നാം ബാഗ്-ഓഫ്-വേർഡ്സ് മോഡലിൽ നിന്ന് **എമ്പെഡ്ഡിംഗ് ബാഗ്** മോഡലിലേക്ക് മാറാം, ഇവിടെ ആദ്യം നമ്മുടെ ടെക്സ്റ്റിലെ ഓരോ വാക്കും അനുയോജ്യമായ എമ്പെഡ്ഡിങ്ങിലേക്ക് മാറ്റുകയും, പിന്നീട് ആ എമ്പെഡ്ഡിങ്ങുകളുടെ മേൽ `sum`, `average` അല്ലെങ്കിൽ `max` പോലുള്ള ഏതെങ്കിലും സംഗ്രഹ ഫംഗ്ഷൻ കണക്കാക്കുകയും ചെയ്യും.\n", "\n", - "![അഞ്ച് സീക്വൻസ് വാക്കുകൾക്കുള്ള എമ്പെഡ്ഡിംഗ് ക്ലാസിഫയർ കാണിക്കുന്ന ചിത്രം.](../../../../../translated_images/ml/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![അഞ്ച് സീക്വൻസ് വാക്കുകൾക്കുള്ള എമ്പെഡ്ഡിംഗ് ക്ലാസിഫയർ കാണിക്കുന്ന ചിത്രം.](../../../../../translated_images/ml/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "നമ്മുടെ ക്ലാസിഫയർ ന്യൂറൽ നെറ്റ്‌വർക്ക് എമ്പെഡ്ഡിംഗ് ലെയറോടെ ആരംഭിച്ച്, തുടർന്ന് അഗ്രിഗേഷൻ ലെയർ, അതിന്റെ മുകളിൽ ലീനിയർ ക്ലാസിഫയർ എന്നിവയാകും:\n" ] @@ -176,7 +176,7 @@ "\n", "മുൻവർഷത്തെ ആർക്കിടെക്ചറിൽ, മിനിബാച്ചിൽ ഫിറ്റ് ചെയ്യാൻ എല്ലാ സീക്വൻസുകളും ഒരേ നീളത്തിലേക്ക് പാഡ് ചെയ്യേണ്ടിവന്നു. വ്യത്യസ്ത നീളമുള്ള സീക്വൻസുകൾ പ്രതിനിധാനം ചെയ്യാനുള്ള ഏറ്റവും കാര്യക്ഷമമായ മാർഗം ഇത് അല്ല - മറ്റൊരു സമീപനം **ഓഫ്സെറ്റ്** വെക്ടർ ഉപയോഗിക്കുകയാണ്, ഇത് ഒരു വലിയ വെക്ടറിൽ സൂക്ഷിച്ചിരിക്കുന്ന എല്ലാ സീക്വൻസുകളുടെ ഓഫ്സെറ്റുകൾ സൂക്ഷിക്കും.\n", "\n", - "![ഓഫ്സെറ്റ് സീക്വൻസ് പ്രതിനിധാനം കാണിക്കുന്ന ചിത്രം](../../../../../translated_images/ml/offset-sequence-representation.eb73fcefb29b46ee.png)\n", + "![ഓഫ്സെറ്റ് സീക്വൻസ് പ്രതിനിധാനം കാണിക്കുന്ന ചിത്രം](../../../../../translated_images/ml/offset-sequence-representation.eb73fcefb29b46ee.webp)\n", "\n", "> **Note**: മുകളിൽ കാണുന്ന ചിത്രത്തിൽ, ഒരു അക്ഷരങ്ങളുടെ സീക്വൻസ് കാണിക്കുന്നു, എന്നാൽ നമ്മുടെ ഉദാഹരണത്തിൽ നാം വാക്കുകളുടെ സീക്വൻസുകളുമായി പ്രവർത്തിക്കുന്നു. എന്നിരുന്നാലും, ഓഫ്സെറ്റ് വെക്ടർ ഉപയോഗിച്ച് സീക്വൻസുകൾ പ്രതിനിധാനം ചെയ്യാനുള്ള പൊതുവായ സിദ്ധാന്തം അതേപോലെ തുടരുന്നു.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW വേഗത്തിൽ പ്രവർത്തിക്കുന്നു, സ്കിപ്പ്-ഗ്രാം മന്ദഗതിയിലാണ്, പക്ഷേ അപൂർവമായ വാക്കുകൾ പ്രതിനിധാനം ചെയ്യുന്നതിൽ മികച്ചതാണ്.\n", "\n", - "![വാക്കുകളെ വെക്ടറുകളാക്കി മാറ്റാൻ CBoWയും സ്കിപ്പ്-ഗ്രാം ആൽഗോരിതങ്ങളും കാണിക്കുന്ന ചിത്രം.](../../../../../translated_images/ml/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![വാക്കുകളെ വെക്ടറുകളാക്കി മാറ്റാൻ CBoWയും സ്കിപ്പ്-ഗ്രാം ആൽഗോരിതങ്ങളും കാണിക്കുന്ന ചിത്രം.](../../../../../translated_images/ml/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Google News ഡാറ്റാസെറ്റിൽ പ്രീ-ട്രെയിൻ ചെയ്ത word2vec എംബെഡ്ഡിംഗ് പരീക്ഷിക്കാൻ, നാം **gensim** ലൈബ്രറി ഉപയോഗിക്കാം. താഴെ 'neural' എന്ന വാക്കിനോട് ഏറ്റവും സമാനമായ വാക്കുകൾ കാണിക്കുന്നു\n", "\n", diff --git a/translations/ml/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/ml/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index 546ad34f..1d9e4c16 100644 --- a/translations/ml/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/ml/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "നമ്മുടെ നെറ്റ്‌വർക്കിലെ ആദ്യ ലെയറായി ഒരു എമ്പെഡ്ഡിംഗ് ലെയർ ഉപയോഗിച്ച്, നാം ബാഗ്-ഓഫ്-വേർഡ്സ് മോഡലിൽ നിന്ന് **എമ്പെഡ്ഡിംഗ് ബാഗ്** മോഡലിലേക്ക് മാറാം, ഇവിടെ ആദ്യം നമ്മുടെ ടെക്സ്റ്റിലെ ഓരോ വാക്കും അനുയോജ്യമായ എമ്പെഡ്ഡിങ്ങിലേക്ക് മാറ്റുകയും, പിന്നീട് ആ എമ്പെഡ്ഡിങ്ങുകളുടെ മേൽ `sum`, `average` അല്ലെങ്കിൽ `max` പോലുള്ള ഒരു സംഗ്രഹ ഫംഗ്ഷൻ കണക്കാക്കുകയും ചെയ്യും.\n", "\n", - "![അഞ്ച് സീക്വൻസ് വാക്കുകൾക്കുള്ള ഒരു എമ്പെഡ്ഡിംഗ് ക്ലാസിഫയർ കാണിക്കുന്ന ചിത്രം.](../../../../../translated_images/ml/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![അഞ്ച് സീക്വൻസ് വാക്കുകൾക്കുള്ള ഒരു എമ്പെഡ്ഡിംഗ് ക്ലാസിഫയർ കാണിക്കുന്ന ചിത്രം.](../../../../../translated_images/ml/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "നമ്മുടെ ക്ലാസിഫയർ ന്യൂറൽ നെറ്റ്‌വർക്ക് താഴെപ്പറയുന്ന ലെയറുകൾ ഉൾക്കൊള്ളുന്നു:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW വേഗത്തിലാണ്, സ്കിപ്പ്-ഗ്രാം മന്ദഗതിയിലാണ്, എന്നാൽ അപൂർവമായ വാക്കുകൾ പ്രതിനിധാനം ചെയ്യുന്നതിൽ മികച്ചതാണ്.\n", "\n", - "![വാക്കുകൾ വെക്ടറുകളായി മാറ്റാൻ CBoWയും സ്കിപ്പ്-ഗ്രാം ആൽഗോരിതങ്ങളും കാണിക്കുന്ന ചിത്രം.](../../../../../translated_images/ml/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![വാക്കുകൾ വെക്ടറുകളായി മാറ്റാൻ CBoWയും സ്കിപ്പ്-ഗ്രാം ആൽഗോരിതങ്ങളും കാണിക്കുന്ന ചിത്രം.](../../../../../translated_images/ml/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Google News ഡാറ്റാസെറ്റിൽ പ്രീട്രെയിൻ ചെയ്ത Word2Vec എംബെഡിംഗ് പരീക്ഷിക്കാൻ, **gensim** ലൈബ്രറി ഉപയോഗിക്കാം. താഴെ 'neural' എന്ന വാക്കിനോട് ഏറ്റവും സമാനമായ വാക്കുകൾ കാണിക്കുന്നു.\n", "\n", diff --git a/translations/ml/lessons/5-NLP/14-Embeddings/README.md b/translations/ml/lessons/5-NLP/14-Embeddings/README.md index fbdb2273..7cfa04b8 100644 --- a/translations/ml/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/ml/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ BoW അല്ലെങ്കിൽ TF/IDF അടിസ്ഥാനമാക് ക്ലാസിഫയർ നെറ്റ്വർക്കിലെ ആദ്യ ലെയറായി എംബെഡിംഗ് ലെയർ ഉപയോഗിച്ച്, നാം ബാഗ്-ഓഫ്-വേർഡിൽ നിന്ന് **എംബെഡിംഗ് ബാഗ്** മോഡലിലേക്ക് മാറാം, ഇവിടെ ആദ്യം ടെക്സ്റ്റിലെ ഓരോ വാക്കും അനുയോജ്യമായ എംബെഡിംഗിലേക്ക് മാറ്റുകയും, പിന്നീട് ആ എംബെഡിംഗുകളുടെ മേൽ `sum`, `average` അല്ലെങ്കിൽ `max` പോലുള്ള ഒരു സംഗ്രഹ ഫംഗ്ഷൻ കണക്കാക്കുകയും ചെയ്യും. -![അഞ്ച് സീക്വൻസ് വാക്കുകൾക്കുള്ള എംബെഡിംഗ് ക്ലാസിഫയർ കാണിക്കുന്ന ചിത്രം.](../../../../../translated_images/ml/embedding-classifier-example.b77f021a7ee67eee.png) +![അഞ്ച് സീക്വൻസ് വാക്കുകൾക്കുള്ള എംബെഡിംഗ് ക്ലാസിഫയർ കാണിക്കുന്ന ചിത്രം.](../../../../../translated_images/ml/embedding-classifier-example.b77f021a7ee67eee.webp) > ചിത്രം: രചയിതാവ് @@ -40,7 +40,7 @@ BoW അല്ലെങ്കിൽ TF/IDF അടിസ്ഥാനമാക് CBoW വേഗത്തിൽ പ്രവർത്തിക്കുന്നു, എന്നാൽ സ്കിപ്പ്-ഗ്രാം മന്ദഗതിയിലാണ്, പക്ഷേ അപൂർവമായ വാക്കുകൾ പ്രതിനിധീകരിക്കുന്നതിൽ മികച്ചതാണ്. -![CBoWയും സ്കിപ്പ്-ഗ്രാം ആൽഗോരിതങ്ങളും വാക്കുകളെ വെക്ടറുകളാക്കി മാറ്റുന്നത് കാണിക്കുന്ന ചിത്രം.](../../../../../translated_images/ml/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![CBoWയും സ്കിപ്പ്-ഗ്രാം ആൽഗോരിതങ്ങളും വാക്കുകളെ വെക്ടറുകളാക്കി മാറ്റുന്നത് കാണിക്കുന്ന ചിത്രം.](../../../../../translated_images/ml/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > ചിത്രം ഈ [പേപ്പറിൽ](https://arxiv.org/pdf/1301.3781.pdf) നിന്നാണ് diff --git a/translations/ml/lessons/5-NLP/15-LanguageModeling/README.md b/translations/ml/lessons/5-NLP/15-LanguageModeling/README.md index 44bf7943..49109b74 100644 --- a/translations/ml/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/ml/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: * **കണ്ടിന്യൂവസ് ബാഗ്-ഓഫ്-വേർഡ്സ്** (CBoW), ടോക്കൺ ശ്രേണിയിൽ മധ്യത്തിലുള്ള ടോക്കൺ $W_0$ പ്രവചിക്കുന്നത്, $W_{-N}$, ..., $W_N$. * **സ്കിപ്പ്-ഗ്രാം**, മധ്യത്തിലുള്ള ടോക്കൺ $W_0$ ഉപയോഗിച്ച് സമീപവരുന്ന ടോക്കണുകളുടെ സെറ്റ് {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} പ്രവചിക്കുന്നത്. -![വാക്കുകൾ വെക്ടറുകളാക്കി മാറ്റുന്നതിന് പത്രത്തിൽ നിന്നുള്ള ചിത്രം](../../../../../translated_images/ml/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![വാക്കുകൾ വെക്ടറുകളാക്കി മാറ്റുന്നതിന് പത്രത്തിൽ നിന്നുള്ള ചിത്രം](../../../../../translated_images/ml/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > ചിത്രം [ഈ പത്രത്തിൽ നിന്നുള്ളത്](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/ml/lessons/5-NLP/16-RNN/README.md b/translations/ml/lessons/5-NLP/16-RNN/README.md index 1c886655..48ddf8a3 100644 --- a/translations/ml/lessons/5-NLP/16-RNN/README.md +++ b/translations/ml/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: ടെക്സ്റ്റ് സീക്വൻസിന്റെ അർത്ഥം പിടികൂടാൻ, നമുക്ക് മറ്റൊരു ന്യൂറൽ നെറ്റ്വർക്ക് ആർക്കിടെക്ചർ ഉപയോഗിക്കേണ്ടതുണ്ട്, അതാണ് **റികറന്റ് ന്യൂറൽ നെറ്റ്വർക്ക്** അല്ലെങ്കിൽ RNN. RNN-ൽ, നാം വാക്യം ഒരു സിംബോളായി ഒരു സമയം നെറ്റ്വർക്കിലൂടെ കടത്തുന്നു, നെറ്റ്വർക്ക് ചില **സ്റ്റേറ്റ്** ഉൽപ്പാദിപ്പിക്കുന്നു, അത് പിന്നീട് അടുത്ത സിംബോളിനൊപ്പം വീണ്ടും നെറ്റ്വർക്കിലേക്ക് കടത്തുന്നു. -![RNN](../../../../../translated_images/ml/rnn.27f5c29c53d727b5.png) +![RNN](../../../../../translated_images/ml/rnn.27f5c29c53d727b5.webp) > ചിത്രകാരൻ: ലേഖകൻ @@ -31,7 +31,7 @@ CO_OP_TRANSLATOR_METADATA: ഒരു ലളിതമായ RNN സെലിൽ രണ്ട് വെയ്റ്റ് മാട്രിസുകൾ ഉണ്ട്: ഒന്ന് ഇൻപുട്ട് സിംബോളിനെ മാറ്റുന്നു (W എന്ന് വിളിക്കാം), മറ്റൊന്ന് ഇൻപുട്ട് സ്റ്റേറ്റ് മാറ്റുന്നു (H). ഈ സാഹചര്യത്തിൽ, നെറ്റ്വർക്ക് ഔട്ട്പുട്ട് σ(W×Xi+H×Si-1+b) ആയി കണക്കാക്കുന്നു, ഇവിടെ σ ആക്ടിവേഷൻ ഫംഗ്ഷനും b അധിക ബയാസും ആണ്. -RNN Cell Anatomy +RNN Cell Anatomy > ചിത്രകാരൻ: ലേഖകൻ @@ -61,7 +61,7 @@ LSTM നെറ്റ്വർക്ക് RNN-നെപ്പോലെ ക്ര ഒരു റികറന്റ് നെറ്റ്വർക്ക്, ഒരോ ദിശയിലായാലും, സീക്വൻസിൽ ചില പാറ്റേണുകൾ പിടികൂടുകയും അവ സ്റ്റേറ്റ് വെക്ടറിലോ ഔട്ട്പുട്ടിലോ സൂക്ഷിക്കുകയും ചെയ്യുന്നു. കോൺവല്യൂഷണൽ നെറ്റ്വർക്കുകളെപ്പോലെ, നാം ആദ്യ ലെയറിന്റെ ഔട്ട്പുട്ട് അടുത്ത ലെയറിന്റെ ഇൻപുട്ടായി ഉപയോഗിച്ച് ഉയർന്ന തലത്തിലുള്ള പാറ്റേണുകൾ പിടികൂടാൻ മറ്റൊരു റികറന്റ് ലെയർ നിർമ്മിക്കാം. ഇതാണ് **മൾട്ടി-ലെയർ RNN** എന്ന ആശയം, ഇത് രണ്ട് അല്ലെങ്കിൽ കൂടുതൽ റികറന്റ് നെറ്റ്വർക്കുകൾ ഉൾക്കൊള്ളുന്നു, മുൻ ലെയറിന്റെ ഔട്ട്പുട്ട് അടുത്ത ലെയറിന്റെ ഇൻപുട്ടായി കടത്തുന്നു. -![Image showing a Multilayer long-short-term-memory- RNN](../../../../../translated_images/ml/multi-layer-lstm.dd975e29bb2a59fe.jpg) +![Image showing a Multilayer long-short-term-memory- RNN](../../../../../translated_images/ml/multi-layer-lstm.dd975e29bb2a59fe.webp) *ചിത്രം Fernando López-ന്റെ [ഈ മനോഹരമായ പോസ്റ്റ്](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) നിന്നാണ്* diff --git a/translations/ml/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/ml/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index b0087cbe..3cbf6fa0 100644 --- a/translations/ml/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/ml/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -10,7 +10,7 @@ "\n", "ടെക്സ്റ്റ് സീക്വൻസിന്റെ അർത്ഥം പിടിച്ചുപറ്റാൻ, നാം മറ്റൊരു ന്യൂറൽ നെറ്റ്‌വർക്ക് ആർക്കിടെക്ചർ ഉപയോഗിക്കേണ്ടതുണ്ട്, അതാണ് **പുനരാവർത്തന ന്യൂറൽ നെറ്റ്‌വർക്ക്** അല്ലെങ്കിൽ RNN. RNN-ൽ, നാം നമ്മുടെ വാക്യം ഒരു സിംബോളായി ഓരോ തവണയും നെറ്റ്‌വർക്കിലൂടെ കടത്തുന്നു, നെറ്റ്‌വർക്ക് ചില **സ്റ്റേറ്റ്** ഉൽപ്പാദിപ്പിക്കുന്നു, അത് പിന്നീട് അടുത്ത സിംബോളിനൊപ്പം വീണ്ടും നെറ്റ്‌വർക്കിലേക്ക് നൽകുന്നു.\n", "\n", - "\"RNN\"\n", + "\"RNN\"\n", "\n", "ഇൻപുട്ട് ടോക്കൺ സീക്വൻസ് $X_0,\\dots,X_n$ നൽകിയാൽ, RNN ഒരു ന്യൂറൽ നെറ്റ്‌വർക്ക് ബ്ലോക്കുകളുടെ സീക്വൻസ് സൃഷ്ടിക്കുന്നു, ഈ സീക്വൻസ് എന്റു-ടു-എൻഡ് ബാക്ക് പ്രൊപ്പഗേഷൻ ഉപയോഗിച്ച് പരിശീലിപ്പിക്കുന്നു. ഓരോ നെറ്റ്‌വർക്ക് ബ്ലോക്കും ഒരു ജോഡി $(X_i,S_i)$ ഇൻപുട്ടായി സ്വീകരിച്ച്, $S_{i+1}$ ഫലമായി ഉൽപ്പാദിപ്പിക്കുന്നു. അന്തിമ സ്റ്റേറ്റ് $S_n$ അല്ലെങ്കിൽ ഔട്ട്പുട്ട് $X_n$ ഒരു ലീനിയർ ക്ലാസിഫയറിലേക്ക് പോകുന്നു ഫലം ഉൽപ്പാദിപ്പിക്കാൻ. എല്ലാ നെറ്റ്‌വർക്ക് ബ്ലോക്കുകളും ഒരേ വെയ്റ്റുകൾ പങ്കുവെക്കുന്നു, ഒറ്റ ബാക്ക് പ്രൊപ്പഗേഷൻ പാസിലൂടെ എന്റു-ടു-എൻഡ് പരിശീലനം നടത്തുന്നു.\n", "\n", @@ -428,7 +428,7 @@ "\n", "റികറന്റ് നെറ്റ്വർക്ക്, ഒരുദിശയിലോ ദ്വിദിശയിലോ, ഒരു സീക്വൻസിനുള്ളിൽ ചില പാറ്റേണുകൾ പിടിച്ചുപറ്റുകയും അവ സ്റ്റേറ്റ് വെക്ടറിലോ ഔട്ട്പുട്ടിലോ സൂക്ഷിക്കുകയും ചെയ്യുന്നു. കോൺവല്യൂഷണൽ നെറ്റ്വർക്കുകളെപ്പോലെ, നാം ആദ്യ ലെയറിന്റെ മുകളിൽ മറ്റൊരു റികറന്റ് ലെയർ നിർമ്മിച്ച് ഉയർന്ന തലത്തിലുള്ള പാറ്റേണുകൾ പിടിച്ചുപറ്റാം, ആദ്യ ലെയർ കണ്ടെത്തിയ താഴ്ന്ന തലത്തിലുള്ള പാറ്റേണുകളിൽ നിന്നാണ് ഇത് നിർമ്മിക്കുന്നത്. ഇതാണ് **ബഹുസ്തര RNN** എന്ന ആശയം, ഇത് രണ്ട് അല്ലെങ്കിൽ അതിലധികം റികറന്റ് നെറ്റ്വർക്കുകൾ ഉൾക്കൊള്ളുന്നു, മുൻ ലെയറിന്റെ ഔട്ട്പുട്ട് അടുത്ത ലെയറിന്റെ ഇൻപുട്ടായി പാസ്സ് ചെയ്യപ്പെടുന്നു.\n", "\n", - "![Multilayer long-short-term-memory- RNN കാണിക്കുന്ന ചിത്രം](../../../../../translated_images/ml/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![Multilayer long-short-term-memory- RNN കാണിക്കുന്ന ചിത്രം](../../../../../translated_images/ml/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*ഫെർണാണ്ടോ ലോപ്പസ് എഴുതിയ [ഈ മനോഹരമായ പോസ്റ്റ്](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) നിന്നുള്ള ചിത്രം*\n", "\n", diff --git a/translations/ml/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/ml/lessons/5-NLP/16-RNN/RNNTF.ipynb index 7795daac..d952cc2b 100644 --- a/translations/ml/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/ml/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "ഒരു വാചക ശ്രേണിയുടെ അർത്ഥം പിടിച്ചുപറ്റാൻ, നാം **പുനരാവർത്തിത ന്യൂറൽ നെറ്റ്‌വർക്ക്** എന്നറിയപ്പെടുന്ന ഒരു ന്യൂറൽ നെറ്റ്‌വർക്ക് ആർക്കിടെക്ചർ ഉപയോഗിക്കും, അതായത് RNN. RNN ഉപയോഗിക്കുമ്പോൾ, നാം വാചകം ഓരോ ടോക്കണും ഒരിക്കൽ씩 നെറ്റ്‌വർക്കിലൂടെ കടത്തുന്നു, നെറ്റ്‌വർക്ക് ചില **സ്റ്റേറ്റ്** ഉൽപ്പാദിപ്പിക്കുന്നു, അത് പിന്നീട് അടുത്ത ടോക്കണുമായി വീണ്ടും നെറ്റ്‌വർക്കിലേക്ക് നൽകുന്നു.\n", "\n", - "![പുനരാവർത്തിത ന്യൂറൽ നെറ്റ്‌വർക്ക് ഉദാഹരണ സൃഷ്ടി കാണിക്കുന്ന ചിത്രം.](../../../../../translated_images/ml/rnn.27f5c29c53d727b5.png)\n", + "![പുനരാവർത്തിത ന്യൂറൽ നെറ്റ്‌വർക്ക് ഉദാഹരണ സൃഷ്ടി കാണിക്കുന്ന ചിത്രം.](../../../../../translated_images/ml/rnn.27f5c29c53d727b5.webp)\n", "\n", "ഇൻപുട്ട് ടോക്കൺ ശ്രേണി $X_0,\\dots,X_n$ നൽകിയാൽ, RNN ന്യൂറൽ നെറ്റ്‌വർക്ക് ബ്ലോക്കുകളുടെ ഒരു ശ്രേണി സൃഷ്ടിക്കുന്നു, ഈ ശ്രേണി ബാക്ക്‌പ്രൊപ്പഗേഷൻ ഉപയോഗിച്ച് എന്റു-ടു-എൻഡ് പരിശീലിപ്പിക്കുന്നു. ഓരോ നെറ്റ്‌വർക്ക് ബ്ലോക്കും $(X_i,S_i)$ എന്ന ജോഡി ഇൻപുട്ടായി സ്വീകരിച്ച് $S_{i+1}$ എന്ന ഔട്ട്പുട്ട് നൽകുന്നു. അവസാന സ്റ്റേറ്റ് $S_n$ അല്ലെങ്കിൽ ഔട്ട്പുട്ട് $Y_n$ ഒരു ലീനിയർ ക്ലാസിഫയറിലേക്ക് പോകുന്നു ഫലം ഉൽപ്പാദിപ്പിക്കാൻ. എല്ലാ നെറ്റ്‌വർക്ക് ബ്ലോക്കുകളും ഒരേ ഭാരങ്ങൾ പങ്കുവെക്കുന്നു, ഒറ്റ ബാക്ക്‌പ്രൊപ്പഗേഷൻ പാസിലൂടെ എന്റു-ടു-എൻഡ് പരിശീലിപ്പിക്കുന്നു.\n", "\n", @@ -371,7 +371,7 @@ "\n", "യൂണിഡിരക്ഷണോ ദ്വിദിശ RNN-കളോ സീക്വൻസിനുള്ളിൽ പാറ്റേണുകൾ പിടിച്ചുപറ്റി അവയെ സ്റ്റേറ്റ് വെക്ടറുകളിലോ ഔട്ട്പുട്ടിലോ സൂക്ഷിക്കുന്നു. കോൺവല്യൂഷണൽ നെറ്റ്വർക്കുകളെപ്പോലെ, ആദ്യ ലെയറിന്റെ താഴ്ന്ന തലത്തിലുള്ള പാറ്റേണുകൾ പിടിച്ചുപറ്റി ഉയർന്ന തലത്തിലുള്ള പാറ്റേണുകൾ പിടിക്കാൻ മറ്റൊരു റികറന്റ് ലെയർ ചേർക്കാം. ഇതാണ് **ബഹുസ്തര RNN** എന്ന ആശയം, ഇത് രണ്ട് അല്ലെങ്കിൽ അതിലധികം റികറന്റ് നെറ്റ്വർക്കുകൾ ഉൾക്കൊള്ളുന്നു, മുൻ ലെയറിന്റെ ഔട്ട്പുട്ട് അടുത്ത ലെയറിന്റെ ഇൻപുട്ടായി നൽകുന്നു.\n", "\n", - "![ബഹുസ്തര ലോങ്-ഷോർട്ട്-ടേം-മെമ്മറി RNN-നെ കാണിക്കുന്ന ചിത്രം](../../../../../translated_images/ml/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![ബഹുസ്തര ലോങ്-ഷോർട്ട്-ടേം-മെമ്മറി RNN-നെ കാണിക്കുന്ന ചിത്രം](../../../../../translated_images/ml/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*ഫെർണാണ്ടോ ലോപ്പസ് എഴുതിയ [ഈ മനോഹരമായ പോസ്റ്റ്](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) നിന്നുള്ള ചിത്രം.*\n", "\n", diff --git a/translations/ml/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/ml/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index 08227327..fababd4b 100644 --- a/translations/ml/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/ml/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "RNN ഉപയോഗിച്ച് ടെക്സ്റ്റ് ജനറേറ്റ് ചെയ്യാൻ നാം സ്വീകരിക്കുന്ന മാർഗം ഇപ്രകാരമാണ്. ഓരോ ഘട്ടത്തിലും, നീളം `nchars` ഉള്ള ഒരു അക്ഷരക്രമം എടുത്ത്, ഓരോ ഇൻപുട്ട് അക്ഷരത്തിനും അടുത്ത ഔട്ട്പുട്ട് അക്ഷരം ജനറേറ്റ് ചെയ്യാൻ നെറ്റ്‌വർക്ക് ആവശ്യപ്പെടും:\n", "\n", - "!['HELLO' എന്ന വാക്ക് RNN ഉപയോഗിച്ച് ജനറേറ്റ് ചെയ്യുന്നതിന്റെ ഉദാഹരണം കാണിക്കുന്ന ചിത്രം.](../../../../../translated_images/ml/rnn-generate.56c54afb52f9781d.png)\n", + "!['HELLO' എന്ന വാക്ക് RNN ഉപയോഗിച്ച് ജനറേറ്റ് ചെയ്യുന്നതിന്റെ ഉദാഹരണം കാണിക്കുന്ന ചിത്രം.](../../../../../translated_images/ml/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "യഥാർത്ഥ സാഹചര്യത്തെ ആശ്രയിച്ച്, *end-of-sequence* `` പോലുള്ള ചില പ്രത്യേക അക്ഷരങ്ങൾ ഉൾപ്പെടുത്തേണ്ടതുണ്ടാകാം. നമ്മുടെ കേസിൽ, നാം അനന്തമായ ടെക്സ്റ്റ് ജനറേഷനായി നെറ്റ്‌വർക്ക് പരിശീലിപ്പിക്കാനാണ് ഉദ്ദേശിക്കുന്നത്, അതിനാൽ ഓരോ സീക്വൻസിന്റെയും വലിപ്പം `nchars` ടോക്കണുകളായി നിശ്ചയിക്കും. അതിനാൽ, ഓരോ പരിശീലന ഉദാഹരണവും `nchars` ഇൻപുട്ടുകളും `nchars` ഔട്ട്പുട്ടുകളും (ഇൻപുട്ട് സീക്വൻസ് ഒരു സിംബോളിന് ഇടത്തേക്ക് ഷിഫ്റ്റ് ചെയ്തതും) ഉൾക്കൊള്ളും. മിനിബാച്ച് ഇത്തരത്തിലുള്ള നിരവധി സീക്വൻസുകൾ അടങ്ങിയിരിക്കും.\n", "\n", diff --git a/translations/ml/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/ml/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index 84e6dfc9..906d537e 100644 --- a/translations/ml/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/ml/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -113,7 +113,7 @@ "\n", "നാം വാർത്താ തലക്കെട്ടുകൾ സൃഷ്ടിക്കാൻ RNN എങ്ങനെ പരിശീലിപ്പിക്കുമെന്ന് പറയുന്നത് ഇങ്ങനെ ആണ്. ഓരോ ഘട്ടത്തിലും, ഒരു തലക്കെട്ട് എടുത്ത് അത് RNN-ലേക്ക് നൽകും, ഓരോ ഇൻപുട്ട് അക്ഷരത്തിനും നെറ്റ്‌വർക്കിന് അടുത്ത ഔട്ട്പുട്ട് അക്ഷരം സൃഷ്ടിക്കാൻ ആവശ്യപ്പെടും:\n", "\n", - "!['HELLO' എന്ന വാക്കിന്റെ ഉദാഹരണ RNN സൃഷ്ടി കാണിക്കുന്ന ചിത്രം.](../../../../../translated_images/ml/rnn-generate.56c54afb52f9781d.png)\n", + "!['HELLO' എന്ന വാക്കിന്റെ ഉദാഹരണ RNN സൃഷ്ടി കാണിക്കുന്ന ചിത്രം.](../../../../../translated_images/ml/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "നമ്മുടെ സീക്വൻസിലെ അവസാന അക്ഷരത്തിന്, നെറ്റ്‌വർക്കിന് `` ടോക്കൺ സൃഷ്ടിക്കാൻ ആവശ്യപ്പെടും.\n", "\n", diff --git a/translations/ml/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/ml/lessons/5-NLP/17-GenerativeNetworks/README.md index fb611854..6f08d3c8 100644 --- a/translations/ml/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/ml/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: ഇത് താഴെ കാണുന്ന ചിത്രത്തിൽ കാണുന്ന വ്യത്യസ്ത ന്യൂറൽ ആർക്കിടെക്ചറുകൾക്ക് വഴിയൊരുക്കുന്നു: -![Image showing common recurrent neural network patterns.](../../../../../translated_images/ml/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Image showing common recurrent neural network patterns.](../../../../../translated_images/ml/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > [Andrej Karpaty](http://karpathy.github.io/) എഴുതിയ [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) എന്ന ബ്ലോഗ് പോസ്റ്റിൽ നിന്നുള്ള ചിത്രം @@ -32,11 +32,11 @@ CO_OP_TRANSLATOR_METADATA: നാം ഈ RNN ഓരോ ഘട്ടത്തിലും ടെക്സ്റ്റ് ജനറേറ്റ് ചെയ്യാൻ പരിശീലിപ്പിക്കും. ഓരോ ഘട്ടത്തിലും, നീളം `nchars` ഉള്ള കറക്റ്ററുകളുടെ ഒരു സീക്വൻസ് എടുത്ത്, ഓരോ ഇൻപുട്ട് കറക്റ്ററിനും അടുത്ത ഔട്ട്പുട്ട് കറക്റ്റർ ജനറേറ്റ് ചെയ്യാൻ നെറ്റ്വർക്കിനെ ചോദിക്കും: -![Image showing an example RNN generation of the word 'HELLO'.](../../../../../translated_images/ml/rnn-generate.56c54afb52f9781d.png) +![Image showing an example RNN generation of the word 'HELLO'.](../../../../../translated_images/ml/rnn-generate.56c54afb52f9781d.webp) ടെക്സ്റ്റ് ജനറേറ്റ് ചെയ്യുമ്പോൾ (ഇൻഫറൻസ് സമയത്ത്), നാം ഒരു **പ്രോംപ്റ്റ്** ഉപയോഗിച്ച് തുടങ്ങുന്നു, അത് RNN സെല്ലുകൾ വഴി കടന്നുപോകുന്നു, ഇടക്കാല സ്റ്റേറ്റ് ഉത്പാദിപ്പിക്കുന്നു, തുടർന്ന് ആ സ്റ്റേറ്റിൽ നിന്നാണ് ജനറേഷൻ ആരംഭിക്കുന്നത്. ഓരോ കറക്റ്ററും ഒറ്റത്തവണയായി ജനറേറ്റ് ചെയ്ത്, സ്റ്റേറ്റ് കൂടാതെ ജനറേറ്റ് ചെയ്ത കറക്റ്റർ മറ്റൊരു RNN സെലിലേക്ക് നൽകുന്നു, അടുത്തത് ജനറേറ്റ് ചെയ്യാൻ, ആവശ്യമായ കറക്റ്ററുകൾ വരെ തുടരും. - + > എഴുത്തുകാരന്റെ ചിത്രം diff --git a/translations/ml/lessons/5-NLP/18-Transformers/README.md b/translations/ml/lessons/5-NLP/18-Transformers/README.md index c6ab76db..a23f3c07 100644 --- a/translations/ml/lessons/5-NLP/18-Transformers/README.md +++ b/translations/ml/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ RNN-കളിൽ, sequence-to-sequence രണ്ട് റിക്കറന് **ശ്രദ്ധാ യന്ത്രങ്ങൾ** RNN-ന്റെ ഓരോ ഔട്ട്പുട്ട് പ്രവചനത്തിലും ഓരോ ഇൻപുട്ട് വെക്ടറിന്റെ സാന്ദർഭിക സ്വാധീനം തൂക്കത്തോടെ നൽകാനുള്ള മാർഗമാണ്. ഇത് നടപ്പിലാക്കുന്നത് ഇൻപുട്ട് RNN-ന്റെ ഇടനില സ്റ്റേറ്റുകളും ഔട്ട്പുട്ട് RNN-ന്റെ ഇടനില സ്റ്റേറ്റുകളും തമ്മിൽ ഷോർട്ട്കട്ടുകൾ സൃഷ്ടിച്ച് ആണ്. ഈ രീതിയിൽ, ഔട്ട്പുട്ട് ചിഹ്നം yt സൃഷ്ടിക്കുമ്പോൾ, എല്ലാ ഇൻപുട്ട് ഹിഡൻ സ്റ്റേറ്റുകളും hi വ്യത്യസ്ത തൂക്കം കോഫിഷ്യന്റുകളായ αt,i ഉപയോഗിച്ച് പരിഗണിക്കും. -![എൻകോഡർ/ഡീകോഡർ മോഡലും അഡിറ്റീവ് ശ്രദ്ധാ ലെയറും കാണിക്കുന്ന ചിത്രം](../../../../../translated_images/ml/encoder-decoder-attention.7a726296894fb567.png) +![എൻകോഡർ/ഡീകോഡർ മോഡലും അഡിറ്റീവ് ശ്രദ്ധാ ലെയറും കാണിക്കുന്ന ചിത്രം](../../../../../translated_images/ml/encoder-decoder-attention.7a726296894fb567.webp) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) ലെ അഡിറ്റീവ് ശ്രദ്ധാ യന്ത്രം ഉൾപ്പെടുത്തിയ എൻകോഡർ-ഡീകോഡർ മോഡൽ, [ഈ ബ്ലോഗ് പോസ്റ്റ്](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) നിന്നുള്ള ഉദ്ധരണം ശ്രദ്ധാ മാട്രിക്സ് {αi,j} ഒരു ഔട്ട്പുട്ട് വാക്കിന്റെ സൃഷ്ടിയിൽ ചില ഇൻപുട്ട് വാക്കുകൾ എത്രമാത്രം പങ്കുവഹിക്കുന്നുവെന്ന് പ്രതിനിധീകരിക്കും. താഴെ ഒരു ഉദാഹരണ മാട്രിക്സ് കാണിക്കുന്നു: -![RNNsearch-50 ഉപയോഗിച്ച് കണ്ടെത്തിയ ഒരു സാംപിൾ അലൈന്മെന്റ്, Bahdanau - arviz.org നിന്നുള്ള ചിത്രം](../../../../../translated_images/ml/bahdanau-fig3.09ba2d37f202a6af.png) +![RNNsearch-50 ഉപയോഗിച്ച് കണ്ടെത്തിയ ഒരു സാംപിൾ അലൈന്മെന്റ്, Bahdanau - arviz.org നിന്നുള്ള ചിത്രം](../../../../../translated_images/ml/bahdanau-fig3.09ba2d37f202a6af.webp) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (ചിത്രം 3) @@ -56,7 +56,7 @@ RNN-കളിൽ, sequence-to-sequence രണ്ട് റിക്കറന് * ടോക്കൺ എംബെഡിംഗിനോട് സമാനമായ ട്രെയിനബിൾ എംബെഡിംഗ്. ഇതാണ് ഇവിടെ പരിഗണിക്കുന്നത്. ടോക്കണുകൾക്കും അവരുടെ സ്ഥാനങ്ങൾക്കും എംബെഡിംഗ് ലെയറുകൾ പ്രയോഗിച്ച് ഒരേ ഡൈമെൻഷനിലുള്ള എംബെഡിംഗ് വെക്ടറുകൾ ലഭിച്ച് അവ ചേർക്കുന്നു. * ഒറിജിനൽ പേപ്പറിൽ നിർദ്ദേശിച്ച സ്ഥിരം പൊസിഷൻ എൻകോഡിംഗ് ഫംഗ്ഷൻ. - + > എഴുത്തുകാരന്റെ ചിത്രം @@ -66,7 +66,7 @@ RNN-കളിൽ, sequence-to-sequence രണ്ട് റിക്കറന് അടുത്തത്, സീക്വൻസിനുള്ളിൽ ചില പാറ്റേണുകൾ പിടിക്കേണ്ടതാണ്. ഇതിന് ട്രാൻസ്ഫോർമറുകൾ **സെൽഫ്-അറ്റൻഷൻ** മെക്കാനിസം ഉപയോഗിക്കുന്നു, അതായത് ഇൻപുട്ടും ഔട്ട്പുട്ടും ഒരേ സീക്വൻസായാണ് ശ്രദ്ധ പ്രയോഗിക്കുന്നത്. സെൽഫ്-അറ്റൻഷൻ ഉപയോഗിച്ച് വാചകത്തിലെ **സന്ദർഭം** പരിഗണിക്കാനും വാക്കുകൾ തമ്മിലുള്ള ബന്ധം കാണാനും കഴിയും. ഉദാഹരണത്തിന്, *it* പോലുള്ള കോറഫറൻസുകൾ ഏത് വാക്കുകളെ സൂചിപ്പിക്കുന്നു എന്ന് കാണാനും, കൂടാതെ സന്ദർഭം പരിഗണിക്കാനും ഇത് സഹായിക്കുന്നു: -![](../../../../../translated_images/ml/CoreferenceResolution.861924d6d384a7d6.png) +![](../../../../../translated_images/ml/CoreferenceResolution.861924d6d384a7d6.webp) > [Google Blog](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) നിന്നുള്ള ചിത്രം @@ -91,7 +91,7 @@ RNN-കളിൽ, sequence-to-sequence രണ്ട് റിക്കറന് **BERT** (Bidirectional Encoder Representations from Transformers) വളരെ വലിയ 12 ലെയർ *BERT-base* ഉം 24 ലെയർ *BERT-large* ഉം ഉള്ള മൾട്ടി-ലെയർ ട്രാൻസ്ഫോർമർ നെറ്റ്വർക്ക് ആണ്. മോഡൽ ആദ്യം വലിയ ടെക്സ്റ്റ് കോർപ്പസിൽ (വിക്കിപീഡിയ + പുസ്തകങ്ങൾ) അൺസൂപ്പർവൈസ്ഡ് ട്രെയിനിംഗിലൂടെ (വാചകത്തിലെ മറച്ചുവച്ച വാക്കുകൾ പ്രവചിച്ച്) പ്രീ-ട്രെയിൻ ചെയ്യപ്പെടുന്നു. പ്രീ-ട്രെയിനിംഗിൽ മോഡൽ ഭാഷാ ബോധം ഗഹനമായി ഉൾക്കൊള്ളുന്നു, പിന്നീട് ഫൈൻ ട്യൂണിങ്ങ് വഴി മറ്റ് ഡാറ്റാസെറ്റുകളുമായി ഉപയോഗിക്കാം. ഈ പ്രക്രിയ **ട്രാൻസ്ഫർ ലേണിംഗ്** എന്ന് വിളിക്കുന്നു. -![http://jalammar.github.io/illustrated-bert/ നിന്നുള്ള ചിത്രം](../../../../../translated_images/ml/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) +![http://jalammar.github.io/illustrated-bert/ നിന്നുള്ള ചിത്രം](../../../../../translated_images/ml/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > ചിത്രം [മൂലം](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/ml/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/ml/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index 19f68310..43807370 100644 --- a/translations/ml/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/ml/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**ശ്രദ്ധാ യന്ത്രങ്ങൾ** RNN-ന്റെ ഓരോ ഔട്ട്പുട്ട് പ്രവചനത്തിലും ഓരോ ഇൻപുട്ട് വെക്ടറിന്റെ സാന്ദർഭിക സ്വാധീനം തൂക്കമിടാനുള്ള മാർഗം നൽകുന്നു. ഇത് നടപ്പിലാക്കുന്നത് ഇൻപുട്ട് RNN-ന്റെ ഇടനിലവസ്ഥകളും ഔട്ട്പുട്ട് RNN-ന്റെ ഇടനിലവസ്ഥകളും തമ്മിൽ ഷോർട്ട്കട്ടുകൾ സൃഷ്ടിച്ച് ആണ്. ഈ രീതിയിൽ, ഔട്ട്പുട്ട് ചിഹ്നം $y_t$ സൃഷ്ടിക്കുമ്പോൾ, വ്യത്യസ്ത തൂക്കം കോഫിഷ്യന്റുകളായ $\\alpha_{t,i}$ ഉപയോഗിച്ച് എല്ലാ ഇൻപുട്ട് ഹിഡൻ സ്റ്റേറ്റുകളും $h_i$ പരിഗണിക്കും.\n", "\n", - "![Image showing an encoder/decoder model with an additive attention layer](../../../../../translated_images/ml/encoder-decoder-attention.7a726296894fb567.png)\n", + "![Image showing an encoder/decoder model with an additive attention layer](../../../../../translated_images/ml/encoder-decoder-attention.7a726296894fb567.webp)\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) ലെ ആഡിറ്റീവ് ശ്രദ്ധാ യന്ത്രം ഉൾപ്പെടുത്തിയ എൻകോഡർ-ഡികോഡർ മോഡൽ, [ഈ ബ്ലോഗ് പോസ്റ്റ്](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) നിന്നെടുത്തത്]*\n", "\n", "ശ്രദ്ധാ മാട്രിക്സ് $\\{\\alpha_{i,j}\\}$ ഒരു പ്രത്യേക ഔട്ട്പുട്ട് വാക്കിന്റെ സൃഷ്ടിയിൽ ചില ഇൻപുട്ട് വാക്കുകൾ എത്രമാത്രം പങ്കുവഹിക്കുന്നുവെന്ന് പ്രതിനിധീകരിക്കും. താഴെ ഒരു ഉദാഹരണ മാട്രിക്സ് കാണിക്കുന്നു:\n", "\n", - "![Image showing a sample alignment found by RNNsearch-50, taken from Bahdanau - arviz.org](../../../../../translated_images/ml/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![Image showing a sample alignment found by RNNsearch-50, taken from Bahdanau - arviz.org](../../../../../translated_images/ml/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (ചിത്രം 3) നിന്നെടുത്ത ചിത്രം]*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) വളരെ വലിയ, 12 ലെയറുള്ള *BERT-base*നും 24 ലെയറുള്ള *BERT-large*നും ഉള്ള മൾട്ടി ലെയർ ട്രാൻസ്ഫോർമർ നെറ്റ്‌വർക്കാണ്. മോഡൽ ആദ്യം വലിയ ടെക്സ്റ്റ് കോർപ്പസ് (വിക്കിപീഡിയ + പുസ്തകങ്ങൾ) ഉപയോഗിച്ച് സ്വയംപരിശീലനത്തിലൂടെ (വാക്യത്തിലെ മറച്ചുവച്ച വാക്കുകൾ പ്രവചിച്ച്) പ്രീ-ട്രെയിൻ ചെയ്യപ്പെടുന്നു. പ്രീ-ട്രെയിനിംഗിൽ മോഡൽ ഭാഷാ ബോധം ഗഹനമായി ഉൾക്കൊള്ളുന്നു, പിന്നീട് ഇത് മറ്റ് ഡാറ്റാസെറ്റുകൾ ഉപയോഗിച്ച് ഫൈൻ ട്യൂണിങ്ങിലൂടെ പ്രയോജനപ്പെടുത്താം. ഈ പ്രക്രിയ **ട്രാൻസ്ഫർ ലേണിംഗ്** എന്ന് വിളിക്കുന്നു.\n", "\n", - "![picture from http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/ml/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![picture from http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/ml/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "BERT, DistilBERT, BigBird, OpenGPT3 തുടങ്ങിയ നിരവധി ട്രാൻസ്ഫോർമർ ആർക്കിടെക്ചറുകളുടെ വ്യത്യാസങ്ങൾ ഉണ്ട്, ഇവ ഫൈൻ ട്യൂൺ ചെയ്യാവുന്നതാണ്. [HuggingFace പാക്കേജ്](https://github.com/huggingface/) PyTorch ഉപയോഗിച്ച് ഈ ആർക്കിടെക്ചറികളുടെ പരിശീലനത്തിനുള്ള റിപോസിറ്ററി നൽകുന്നു.\n", "\n", diff --git a/translations/ml/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/ml/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index c4034abc..b8025194 100644 --- a/translations/ml/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/ml/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**ശ്രദ്ധാ യന്ത്രങ്ങൾ** RNN-ന്റെ ഓരോ ഔട്ട്പുട്ട് പ്രവചനത്തിലും ഓരോ ഇൻപുട്ട് വെക്ടറിന്റെ സാന്ദർഭിക സ്വാധീനം തൂക്കമിടാനുള്ള മാർഗം നൽകുന്നു. ഇത് നടപ്പിലാക്കുന്നത് ഇൻപുട്ട് RNN-ന്റെ ഇടനില സ്റ്റേറ്റുകളും ഔട്ട്പുട്ട് RNN-ന്റെ ഇടനില സ്റ്റേറ്റുകളും തമ്മിൽ ഷോർട്ട്കട്ടുകൾ സൃഷ്ടിച്ച് ആണ്. ഈ രീതിയിൽ, ഔട്ട്പുട്ട് ചിഹ്നം $y_t$ സൃഷ്ടിക്കുമ്പോൾ, വ്യത്യസ്ത തൂക്കം കോഫിഷ്യന്റുകളായ $\\alpha_{t,i}$ ഉപയോഗിച്ച് എല്ലാ ഇൻപുട്ട് ഹിഡൻ സ്റ്റേറ്റുകളും $h_i$ പരിഗണിക്കും.\n", "\n", - "![Image showing an encoder/decoder model with an additive attention layer](../../../../../translated_images/ml/encoder-decoder-attention.7a726296894fb567.png)\n", + "![Image showing an encoder/decoder model with an additive attention layer](../../../../../translated_images/ml/encoder-decoder-attention.7a726296894fb567.webp)\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) ലെ കൂട്ടിച്ചേർത്ത ശ്രദ്ധാ യന്ത്രം ഉള്ള എൻകോഡർ-ഡികോഡർ മോഡൽ, [ഈ ബ്ലോഗ് പോസ്റ്റ്](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) നിന്നെടുത്തത്]*\n", "\n", "ശ്രദ്ധാ മാട്രിക്സ് $\\{\\alpha_{i,j}\\}$ ഒരു പ്രത്യേക ഇൻപുട്ട് വാക്ക് ഔട്ട്പുട്ട് ശൃംഖലയിൽ ഒരു വാക്ക് സൃഷ്ടിക്കുന്നതിൽ എത്രമാത്രം പങ്കുവഹിക്കുന്നുവെന്ന് പ്രതിനിധീകരിക്കും. താഴെ ഒരു ഉദാഹരണ മാട്രിക്സ് കാണിക്കുന്നു:\n", "\n", - "![Image showing a sample alignment found by RNNsearch-50, taken from Bahdanau - arviz.org](../../../../../translated_images/ml/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![Image showing a sample alignment found by RNNsearch-50, taken from Bahdanau - arviz.org](../../../../../translated_images/ml/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (ചിത്രം 3) നിന്നെടുത്ത ചിത്രം]*\n", "\n", @@ -92,7 +92,7 @@ "source": [ "ഈ ലെയർ രണ്ട് `Embedding` ലെയറുകളടങ്ങിയതാണ്: ടോക്കണുകൾ (മുൻപ് ചർച്ച ചെയ്ത രീതിയിൽ) എമ്പെഡ് ചെയ്യുന്നതിനും ടോക്കൺ സ്ഥാനങ്ങൾ എമ്പെഡ് ചെയ്യുന്നതിനും. ടോക്കൺ സ്ഥാനങ്ങൾ 0 മുതൽ `maxlen` വരെ സ്വാഭാവിക സംഖ്യകളുടെ ഒരു ശ്രേണിയായി `tf.range` ഉപയോഗിച്ച് സൃഷ്ടിക്കപ്പെടുന്നു, പിന്നീട് എമ്പെഡിംഗ് ലെയറിലൂടെ കടന്നുപോകുന്നു. രണ്ട് ഫലമായ എമ്പെഡിംഗ് വെക്ടറുകളും ചേർത്ത്, `maxlen`$\\times$`embed_dim` ആകൃതിയിലുള്ള സ്ഥാനാനുസൃതമായി എമ്പെഡുചെയ്ത ഇൻപുട്ട് പ്രതിനിധാനം സൃഷ്ടിക്കുന്നു.\n", "\n", - "\n", + "\n", "\n", "ഇപ്പോൾ, ട്രാൻസ്ഫോർമർ ബ്ലോക്ക് നടപ്പിലാക്കാം. ഇത് മുൻപ് നിർവചിച്ച എമ്പെഡിംഗ് ലെയറിന്റെ ഔട്ട്പുട്ട് സ്വീകരിക്കും:\n" ] @@ -134,7 +134,7 @@ "\n", "ഈ ലെയറിന്റെ ഔട്ട്പുട്ട് പിന്നീട് `Dense` നെറ്റ്‌വർക്കിലൂടെ (നമ്മുടെ കേസിൽ - രണ്ട് ലെയർ പെർസെപ്ട്രോൺ) കടന്നുപോകുകയും, ഫലം അന്തിമ ഔട്ട്പുട്ടിൽ ചേർക്കുകയും ചെയ്യുന്നു (അത് വീണ്ടും സാധാരണവത്കരണത്തിലൂടെ കടന്നുപോകുന്നു).\n", "\n", - "\n", + "\n", "\n", "ഇപ്പോൾ, നാം പൂർണ്ണമായ transformer മോഡൽ നിർവചിക്കാൻ തയ്യാറാണ്:\n" ] @@ -235,7 +235,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) ഒരു വളരെ വലിയ, 12 ലെയറുള്ള *BERT-base* മൾട്ടി ലെയർ ട്രാൻസ്ഫോർമർ നെറ്റ്‌വർക്കും, 24 ലെയറുള്ള *BERT-large* മോഡലും ആണ്. മോഡൽ ആദ്യം വലിയ വാചക ഡാറ്റാ കോർപ്പസിൽ (വിക്കിപീഡിയ + പുസ്തകങ്ങൾ) അനിയന്ത്രിത പരിശീലനത്തിലൂടെ (വാചകത്തിലെ മറച്ചുവച്ച വാക്കുകൾ പ്രവചിച്ച്) പ്രീ-ട്രെയിൻ ചെയ്യപ്പെടുന്നു. പ്രീ-ട്രെയിനിംഗിനിടെ മോഡൽ ഭാഷാ മനസ്സിലാക്കലിന്റെ ഒരു വലിയ തോതിൽ അറിവ് സമ്പാദിക്കുന്നു, പിന്നീട് ഇത് മറ്റ് ഡാറ്റാസെറ്റുകളുമായി ഫൈൻ ട്യൂണിങ്ങ് ഉപയോഗിച്ച് പ്രയോജനപ്പെടുത്താം. ഈ പ്രക്രിയയെ **ട്രാൻസ്ഫർ ലേണിംഗ്** എന്ന് വിളിക്കുന്നു.\n", "\n", - "![picture from http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/ml/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![picture from http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/ml/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "BERT, DistilBERT, BigBird, OpenGPT3 തുടങ്ങിയ നിരവധി ട്രാൻസ്ഫോർമർ ആർക്കിടെക്ചറുകളുടെ വ്യത്യാസങ്ങൾ ഉണ്ട്, അവ ഫൈൻ ട്യൂൺ ചെയ്യാവുന്നതാണ്.\n", "\n", diff --git a/translations/ml/lessons/5-NLP/19-NER/README.md b/translations/ml/lessons/5-NLP/19-NER/README.md index 940e3b85..6987ceb9 100644 --- a/translations/ml/lessons/5-NLP/19-NER/README.md +++ b/translations/ml/lessons/5-NLP/19-NER/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: നിങ്ങൾക്ക് ആമസോൺ അലക്സാ അല്ലെങ്കിൽ ഗൂഗിൾ അസിസ്റ്റന്റ് പോലുള്ള ഒരു നാച്ചുറൽ ലാംഗ്വേജ് ചാറ്റ് ബോട്ട് വികസിപ്പിക്കണമെന്ന് കരുതുക. ബുദ്ധിമുട്ടുള്ള ചാറ്റ് ബോട്ടുകൾ പ്രവർത്തിക്കുന്നത് ഉപയോക്താവ് എന്ത് ആഗ്രഹിക്കുന്നു എന്ന് *അർത്ഥമാക്കുന്നതിലൂടെ* ആണ്, ഇൻപുട്ട് വാക്യത്തിൽ ടെക്സ്റ്റ് ക്ലാസിഫിക്കേഷൻ നടത്തിയാണ് ഇത് സാധ്യമാകുന്നത്. ഈ ക്ലാസിഫിക്കേഷന്റെ ഫലം **ഇന്റന്റ്** എന്നറിയപ്പെടുന്നു, ഇത് ചാറ്റ് ബോട്ട് എന്ത് ചെയ്യണമെന്ന് നിർണ്ണയിക്കുന്നു. -Bot NER +Bot NER > ചിത്രകാരൻ: ലേഖകൻ @@ -58,7 +58,7 @@ infant | O ടോക്കണുകളും ക്ലാസുകളും തമ്മിൽ ഒന്ന് ഒന്ന് പൊരുത്തപ്പെടുത്തേണ്ടതിനാൽ, ഈ ചിത്രത്തിൽ നിന്ന് ഒരു വലതുവശത്തെ **many-to-many** ന്യൂറൽ നെറ്റ്വർക്ക് മോഡൽ പരിശീലിപ്പിക്കാം: -![Image showing common recurrent neural network patterns.](../../../../../translated_images/ml/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Image showing common recurrent neural network patterns.](../../../../../translated_images/ml/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > *ചിത്രം [ഈ ബ്ലോഗ് പോസ്റ്റ്](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) ൽ നിന്നുള്ളതാണ്, ലേഖകൻ [Andrej Karpathy](http://karpathy.github.io/). NER ടോക്കൺ ക്ലാസിഫിക്കേഷൻ മോഡലുകൾ ഈ ചിത്രത്തിലെ വലതുവശത്തെ നെറ്റ്വർക്ക് ആർക്കിടെക്ചറിനോട് പൊരുത്തപ്പെടുന്നു.* diff --git a/translations/ml/lessons/5-NLP/README.md b/translations/ml/lessons/5-NLP/README.md index 9575be17..ad828d50 100644 --- a/translations/ml/lessons/5-NLP/README.md +++ b/translations/ml/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # സ്വാഭാവിക ഭാഷാ പ്രോസസ്സിംഗ് -![NLP ടാസ്കുകളുടെ സംഗ്രഹം ഒരു ഡ്രോയിങ്ങിൽ](../../../../translated_images/ml/ai-nlp.b22dcb8ca4707cea.png) +![NLP ടാസ്കുകളുടെ സംഗ്രഹം ഒരു ഡ്രോയിങ്ങിൽ](../../../../translated_images/ml/ai-nlp.b22dcb8ca4707cea.webp) ഈ ഭാഗത്തിൽ, നാം **സ്വാഭാവിക ഭാഷാ പ്രോസസ്സിംഗ് (NLP)** സംബന്ധിച്ച ടാസ്കുകൾ കൈകാര്യം ചെയ്യാൻ ന്യൂറൽ നെറ്റ്‌വർക്കുകൾ ഉപയോഗിക്കുന്നതിൽ ശ്രദ്ധ കേന്ദ്രീകരിക്കും. കമ്പ്യൂട്ടറുകൾക്ക് പരിഹരിക്കാൻ കഴിയേണ്ട നിരവധി NLP പ്രശ്നങ്ങൾ ഉണ്ട്: diff --git a/translations/ml/lessons/6-Other/22-DeepRL/README.md b/translations/ml/lessons/6-Other/22-DeepRL/README.md index 6ed3eff6..3213a0bc 100644 --- a/translations/ml/lessons/6-Other/22-DeepRL/README.md +++ b/translations/ml/lessons/6-Other/22-DeepRL/README.md @@ -34,7 +34,7 @@ RL-ക്കായി മികച്ച ഉപകരണം [OpenAI Gym](https:/ ബാലൻസിംഗ് ഒരു ലളിതമായ പതിപ്പ് **കാർട്ട്‌പോൾ** പ്രശ്നമായി അറിയപ്പെടുന്നു. കാർട്ട്‌പോൾ ലോകത്ത്, ഒരു ഹോരിസോണ്ടൽ സ്ലൈഡർ ഇടത്തോ വലത്തോ നീങ്ങാൻ കഴിയും, സ്ലൈഡറിന്റെ മുകളിൽ ഒരു വെർട്ടിക്കൽ പോൾ ബാലൻസ് ചെയ്യുകയാണ് ലക്ഷ്യം. -a cartpole +a cartpole ഈ പരിസ്ഥിതി സൃഷ്ടിക്കുകയും ഉപയോഗിക്കുകയും ചെയ്യാൻ, നമുക്ക് പൈതൺ കോഡിന്റെ കുറച്ച് വരികൾ ആവശ്യമാണ്: diff --git a/translations/ml/lessons/6-Other/22-DeepRL/lab/README.md b/translations/ml/lessons/6-Other/22-DeepRL/lab/README.md index 44658f55..8e83e3a2 100644 --- a/translations/ml/lessons/6-Other/22-DeepRL/lab/README.md +++ b/translations/ml/lessons/6-Other/22-DeepRL/lab/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: OpenAI പരിസ്ഥിതിയിൽ [Mountain Car](https://www.gymlibrary.ml/environments/classic_control/mountain_car/) നിയന്ത്രിക്കാൻ RL ഏജന്റിനെ പരിശീലിപ്പിക്കുക. -Mountain Car +Mountain Car ## പരിസ്ഥിതി diff --git a/translations/ml/lessons/6-Other/23-MultiagentSystems/README.md b/translations/ml/lessons/6-Other/23-MultiagentSystems/README.md index d5410496..44962bcf 100644 --- a/translations/ml/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/ml/lessons/6-Other/23-MultiagentSystems/README.md @@ -61,7 +61,7 @@ ask turtles [ നെറ്റ്‌ലോഗോയുടെ മികച്ച പ്രത്യേകത ഇതിൽ പ്രവർത്തനക്ഷമമായ മോഡലുകളുടെ ലൈബ്രറി ഉള്ളതാണ്. **File → Models Library** എന്ന വഴി പോകുക, അവിടെ നിരവധി മോഡൽ വിഭാഗങ്ങൾ തിരഞ്ഞെടുക്കാം. -NetLogo Models Library +NetLogo Models Library > മോഡൽ ലൈബ്രറിയുടെ സ്ക്രീൻഷോട്ട് - Dmitry Soshnikov @@ -71,7 +71,7 @@ ask turtles [ മോഡൽ തുറന്ന ശേഷം, നിങ്ങൾ പ്രധാന നെറ്റ്‌ലോഗോ സ്ക്രീനിലേക്ക് എത്തും. ഇവിടെ finite resources (പുല്ല്) ഉള്ള ഒരു വുൾഫ്-ഷീപ്പ് ജനസംഖ്യയെ വിവരിക്കുന്ന ഒരു മാതൃകയാണ്. -![NetLogo Main Screen](../../../../../translated_images/ml/NetLogo-Main.32653711ec1a01b3.png) +![NetLogo Main Screen](../../../../../translated_images/ml/NetLogo-Main.32653711ec1a01b3.webp) > സ്ക്രീൻഷോട്ട് - Dmitry Soshnikov diff --git a/translations/ml/lessons/README.md b/translations/ml/lessons/README.md index 807d430f..27eb998c 100644 --- a/translations/ml/lessons/README.md +++ b/translations/ml/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # അവലോകനം -![ഒരു ഡ്രോയിങ്ങിൽ അവലോകനം](../../../translated_images/ml/ai-overview.0857791951d19500.png) +![ഒരു ഡ്രോയിങ്ങിൽ അവലോകനം](../../../translated_images/ml/ai-overview.0857791951d19500.webp) > സ്കെച്ച്നോട്ട് [ടോമോമി ഇമുര](https://twitter.com/girlie_mac) എഴുതിയത് diff --git a/translations/ml/lessons/X-Extras/X1-MultiModal/README.md b/translations/ml/lessons/X-Extras/X1-MultiModal/README.md index 9672fbc7..81aa35cc 100644 --- a/translations/ml/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/ml/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ NLP ടാസ്കുകൾ പരിഹരിക്കുന്നതിന് CLIP-ന്റെ പ്രധാന ആശയം ഒരു ടെക്സ്റ്റ് പ്രോംപ്റ്റും ഒരു ചിത്രവും താരതമ്യം ചെയ്ത് ചിത്രം പ്രോംപ്റ്റിനോട് എത്രത്തോളം പൊരുത്തപ്പെടുന്നു എന്ന് നിർണ്ണയിക്കാനാകുക എന്നതാണ്. -![CLIP Architecture](../../../../../translated_images/ml/clip-arch.b3dbf20b4e8ed8be.png) +![CLIP Architecture](../../../../../translated_images/ml/clip-arch.b3dbf20b4e8ed8be.webp) > *ഈ ചിത്രം [ഈ ബ്ലോഗ് പോസ്റ്റിൽ നിന്നാണ്](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ CLIP മോഡൽ/ലൈബ്രറി [OpenAI GitHub](https://github.com/opena ഉദാഹരണത്തിന്, പൂച്ചകൾ, നായകൾ, മനുഷ്യർ എന്നിങ്ങനെ ചിത്രങ്ങൾ വർഗ്ഗീകരിക്കേണ്ടതുണ്ടെങ്കിൽ, മോഡലിന് ഒരു ചിത്രം നൽകുകയും, "*ഒരു പൂച്ചയുടെ ചിത്രം*", "*ഒരു നായയുടെ ചിത്രം*", "*ഒരു മനുഷ്യന്റെ ചിത്രം*" എന്നിങ്ങനെ ടെക്സ്റ്റ് പ്രോംപ്റ്റുകൾ നൽകുകയും ചെയ്യാം. 3 പ്രോബബിലിറ്റികളുള്ള വെക്ടറിൽ ഏറ്റവും ഉയർന്ന മൂല്യമുള്ള ഇൻഡക്സ് തിരഞ്ഞെടുക്കുക മതി. -![CLIP for Image Classification](../../../../../translated_images/ml/clip-class.3af42ef0b2b19369.png) +![CLIP for Image Classification](../../../../../translated_images/ml/clip-class.3af42ef0b2b19369.webp) > *ഈ ചിത്രം [ഈ ബ്ലോഗ് പോസ്റ്റിൽ നിന്നാണ്](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ VQGAN-നെ കുറിച്ച് കൂടുതൽ അറിയാൻ [Tam VQGAN-നും പരമ്പരാഗത GAN-നും ഇടയിലെ പ്രധാന വ്യത്യാസം, GAN ഏതെങ്കിലും ഇൻപുട്ട് വെക്ടറിൽ നിന്ന് നല്ല ചിത്രം സൃഷ്ടിക്കാമെങ്കിലും, VQGAN സൃഷ്ടിക്കുന്ന ചിത്രം സുസംയോജിതമല്ലായിരിക്കാം. അതിനാൽ, ചിത്രം സൃഷ്ടിക്കൽ പ്രക്രിയ കൂടുതൽ മാർഗ്ഗനിർദ്ദേശം ആവശ്യമാണ്, അത് CLIP ഉപയോഗിച്ച് സാധ്യമാക്കാം. -![VQGAN+CLIP Architecture](../../../../../translated_images/ml/vqgan.5027fe05051dfa31.png) +![VQGAN+CLIP Architecture](../../../../../translated_images/ml/vqgan.5027fe05051dfa31.webp) ഒരു ടെക്സ്റ്റ് പ്രോംപ്റ്റിനോട് പൊരുത്തമുള്ള ചിത്രം സൃഷ്ടിക്കാൻ, ആദ്യം ഒരു യാദൃച്ഛിക എൻകോഡിംഗ് വെക്ടർ എടുത്ത് VQGAN വഴി ചിത്രം സൃഷ്ടിക്കുന്നു. തുടർന്ന് CLIP ഉപയോഗിച്ച് ചിത്രം പ്രോംപ്റ്റിനോട് എത്രത്തോളം പൊരുത്തപ്പെടുന്നു എന്ന് കാണിക്കുന്ന ലോസ് ഫംഗ്ഷൻ നിർമ്മിക്കുന്നു. ഈ ലോസ് കുറയ്ക്കാൻ ബാക്ക് പ്രൊപ്പഗേഷൻ ഉപയോഗിച്ച് ഇൻപുട്ട് വെക്ടർ പാരാമീറ്ററുകൾ ക്രമീകരിക്കുന്നു. VQGAN+CLIP നടപ്പിലാക്കുന്ന മികച്ച ലൈബ്രറിയാണ് [Pixray](http://github.com/pixray/pixray) -![Picture produced by Pixray](../../../../../translated_images/ml/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Picture produced by pixray](../../../../../translated_images/ml/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Picture produced by Pixray](../../../../../translated_images/ml/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) +![Picture produced by Pixray](../../../../../translated_images/ml/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp) | ![Picture produced by pixray](../../../../../translated_images/ml/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp) | ![Picture produced by Pixray](../../../../../translated_images/ml/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp) ----|----|---- *ഒരു പുസ്തകമുള്ള സാഹിത്യ അധ്യാപകനായ യുവ പുരുഷന്റെ ക്ലോസ്അപ്പ് വാട്ടർകോളർ പോർട്രെയിറ്റ്* എന്ന പ്രോംപ്റ്റിൽ നിന്നുള്ള ചിത്രം | *ഒരു കമ്പ്യൂട്ടർ ഉള്ള യുവ വനിതാ കമ്പ്യൂട്ടർ സയൻസ് അധ്യാപകന്റെ ക്ലോസ്അപ്പ് ഓയിൽ പോർട്രെയിറ്റ്* എന്ന പ്രോംപ്റ്റിൽ നിന്നുള്ള ചിത്രം | *ബ്ലാക്ക്ബോർഡിന് മുന്നിലുള്ള പ്രായമായ പുരുഷ ഗണിത അധ്യാപകന്റെ ക്ലോസ്അപ്പ് ഓയിൽ പോർട്രെയിറ്റ്* എന്ന പ്രോംപ്റ്റിൽ നിന്നുള്ള ചിത്രം diff --git a/translations/mo/README.md b/translations/mo/README.md index 254279d2..5fadddb4 100644 --- a/translations/mo/README.md +++ b/translations/mo/README.md @@ -1,8 +1,8 @@ -[阿拉伯文](../ar/README.md) | [孟加拉文](../bn/README.md) | [保加利亞文](../bg/README.md) | [緬甸文 (Myanmar)](../my/README.md) | [中文 (簡體)](../zh/README.md) | [中文 (繁體，香港)](../hk/README.md) | [中文 (繁體，澳門)](./README.md) | [中文 (繁體，台灣)](../tw/README.md) | [克羅埃西亞文](../hr/README.md) | [捷克文](../cs/README.md) | 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-**如果您希望支援更多翻譯語言，請參考[這裡](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** +**如果您希望支援其他語言翻譯，列表請見 [此處](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** ## 加入社群 [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -## 您將學到什麼 +## 你將會學到什麼 **[課程心智圖](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** -本課程將教授： +在此課程中，你將學會： -* 不同人工智能的方法，包括「老派」的符號方法，結合**知識表徵**及推理（[GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)）。 -* 神經網絡與深度學習，是現代 AI 的核心。我們會使用兩個當紅框架的程式碼示範這些重要概念——[TensorFlow](http://Tensorflow.org) 和 [PyTorch](http://pytorch.org)。 -* 針對圖片與文字的**神經架構**。我們會涵蓋近代模型，但可能未達最新研究水準。 -* 較不常用的 AI 方法，例如**遺傳算法**與**多代理系統**。 +* 理解人工智能的不同方法，包括「傳統」符號方法的**知識表示**及推理 ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence))。 +* 現代AI核心的**神經網絡**與**深度學習**。我們將利用兩個最熱門框架的程式碼來說明這些重要主題 - [TensorFlow](http://Tensorflow.org)與[PyTorch](http://pytorch.org)。 +* 針對影像與文字處理的**神經網絡架構**。將會涵蓋最新模型，但可能較少涉及最先進技術。 +* 不那麼常見的AI方法，如**基因演算法**及**多智能體系統**。 -本課程不包含： +此課程不涵蓋： -> [在 Microsoft Learn 上查看此課程所有額外資源](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) +> [在我們的 Microsoft Learn 集合中尋找本課程的所有額外資源](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) -* 商業中**AI 的應用案例**。建議可參考 Microsoft Learn 上的 [企業用戶 AI 入門](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) 學習路徑，或與 [INSEAD](https://www.insead.edu/) 合作開發的 [AI 商業學校](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum)。 -* **經典機器學習**，請參閱我們的 [初學者機器學習課程](http://github.com/Microsoft/ML-for-Beginners)。 -* 使用 **[認知服務](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)** 架構的實務 AI 應用。我們建議從 Microsoft Learn 的[視覺](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum)、[自然語言處理](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum)、**[Azure OpenAI 服務的生成式 AI](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)**等模組開始學習。 -* 特定機器學習**雲端框架**，如 [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-bethanycheum)、[Microsoft Fabric](https://learn.microsoft.com/en-us/training/paths/get-started-fabric/?WT.mc_id=academic-77998-bethanycheum) 或 [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum)。可參考 [使用 Azure ML 服務建立與操作機器學習解決方案](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) 與 [使用 Azure Databricks 建立與操作機器學習解決方案](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum)。 -* **對話式 AI** 與 **聊天機器人**。有獨立的 [建立對話式 AI 解決方案](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum) 學習路徑，您也可參考[此部落格](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/)獲得更多資訊。 -* 深度學習背後的**高深數學**。建議閱讀 Ian Goodfellow、Yoshua Bengio 及 Aaron Courville 著作的[深度學習](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618)，並可線上查閱 [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/)。 +* 商業案例中的**AI應用**。建議學習 Microsoft Learn 上的 [Introduction to AI for business users](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) 學習路徑，或與[INSEAD](https://www.insead.edu/)合作開發的[AI Business School](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum)。 +* **經典機器學習**，詳見本[初學者機器學習課程](http://github.com/Microsoft/ML-for-Beginners)。 +* 利用 **[認知服務](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)** 實作的AI應用。我們建議先從 Microsoft Learn 的視覺([vision](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum))、自然語言處理([natural language processing](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum))、**[Azure OpenAI 服務生成式AI](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)**等模組開始。 +* 特定的機器學習**雲端框架**，如 [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-bethanycheum)、[Microsoft Fabric](https://learn.microsoft.com/en-us/training/paths/get-started-fabric/?WT.mc_id=academic-77998-bethanycheum)、或[Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum)。可參考 [使用 Azure Machine Learning 建立及操作機器學習解決方案](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) 與 [使用 Azure Databricks 建立及操作機器學習解決方案](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum) 學習路徑。 +* **會話式AI**及**聊天機器人**。有專門的 [建立聊天式AI解決方案](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum) 學習路徑，也可參考 [此博客文章](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) 獲取更多資訊。 +* 深入的**深度學習數學**。推薦閱讀 Ian Goodfellow、Yoshua Bengio 與 Aaron Courville 合著的[深度學習](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618)，線上版本可見於 [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/)。 -若想輕鬆入門 _雲端 AI_ 主題，您可參考 Microsoft Learn 的 [Azure 人工智能快速入門](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum) 學習路徑。 +如欲溫和入門_雲端AI_主題，建議採用[在 Azure 上開始人工智能](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum)學習路徑。 # 課程內容 -| | 課程連結 | PyTorch/Keras/TensorFlow | 實驗室 | +| | 課程連結 | PyTorch/Keras/TensorFlow | 實驗室 | | :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | -| 0 | [課程設定](./lessons/0-course-setup/setup.md) | [設定您的開發環境](./lessons/0-course-setup/how-to-run.md) | | -| I | [**AI 簡介**](./lessons/1-Intro/README.md) | | | -| 01 | [人工智能介紹及歷史](./lessons/1-Intro/README.md) | - | - | -| II | **符號 AI** | -| 02 | [知識表徵與專家系統](./lessons/2-Symbolic/README.md) | [專家系統](./lessons/2-Symbolic/Animals.ipynb) / [本體論](./lessons/2-Symbolic/FamilyOntology.ipynb) /[概念圖](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | -| III | [**神經網絡導論**](./lessons/3-NeuralNetworks/README.md) ||| -| 03 | [感知機](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [筆記本](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [實驗室](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | -| 04 | [多層感知機及建立自家框架](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [筆記本](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [實驗室](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | -| 05 | [框架簡介（PyTorch/TensorFlow）及過擬合](./lessons/3-NeuralNetworks/05-Frameworks/README.md) | [PyTorch](./lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb) / [Keras](./lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [實驗室](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | -| IV | [**電腦視覺**](./lessons/4-ComputerVision/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-77998-cacaste) / [TensorFlow](https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-77998-cacaste)| [在 Microsoft Azure 探索電腦視覺](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | -| 06 | [電腦視覺簡介。OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [筆記本](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [實驗室](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | -| 07 | [卷積神經網絡](./lessons/4-ComputerVision/07-ConvNets/README.md) & [CNN 架構](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [實驗室](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | -| 08 | [預訓練網絡及遷移學習](./lessons/4-ComputerVision/08-TransferLearning/README.md) 和 [訓練技巧](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [實驗室](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | -| 09 | [自動編碼器與變分自動編碼器](./lessons/4-ComputerVision/09-Autoencoders/README.md) | [PyTorch](./lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb) | | -| 10 | [生成對抗網絡與藝術風格轉換](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | -| 11 | [物體偵測](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [實驗室](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | -| 12 | [語義分割。U-Net](./lessons/4-ComputerVision/12-Segmentation/README.md) | [PyTorch](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb) | | -| V | [**自然語言處理**](./lessons/5-NLP/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-77998-cacaste) /[TensorFlow](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-77998-cacaste) | [在 Microsoft Azure 探索自然語言處理](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| -| 13 | [文本表示法。詞袋模型/TF-IDF](./lessons/5-NLP/13-TextRep/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb) | | -| 14 | [語義詞向量嵌入。Word2Vec 與 GloVe](./lessons/5-NLP/14-Embeddings/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb) | | -| 15 | [語言模型。訓練自己的嵌入](./lessons/5-NLP/15-LanguageModeling/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-PyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-TF.ipynb) | [實驗室](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | -| 16 | [遞歸神經網絡](./lessons/5-NLP/16-RNN/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNTF.ipynb) | | -| 17 | [生成遞歸網絡](./lessons/5-NLP/17-GenerativeNetworks/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb) | [實驗室](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | -| 18 | [變換器。BERT。](./lessons/5-NLP/18-Transformers/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb) /[TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersTF.ipynb) | | -| 19 | [命名實體識別](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [實驗室](./lessons/5-NLP/19-NER/lab/README.md) | -| 20 | [大型語言模型、提示編程與少量示例任務](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | -| VI | **其他 AI 技術** || | +| 0 | [課程設定](./lessons/0-course-setup/setup.md) | [設定你的開發環境](./lessons/0-course-setup/how-to-run.md) | | +| I | [**AI 簡介**](./lessons/1-Intro/README.md) | | | +| 01 | [人工智能概述與歷史](./lessons/1-Intro/README.md) | - | - | +| II | **符號式人工智能** | +| 02 | [知識表示與專家系統](./lessons/2-Symbolic/README.md) | [專家系統](./lessons/2-Symbolic/Animals.ipynb) / [本體論](./lessons/2-Symbolic/FamilyOntology.ipynb) /[概念圖](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | +| III | [**神經網絡簡介**](./lessons/3-NeuralNetworks/README.md) ||| +| 03 | [感知器](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [筆記本](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [實驗](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | +| 04 | [多層感知器與打造我們自己的框架](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [筆記本](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [實驗](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | +| 05 | [框架簡介（PyTorch/TensorFlow）與過度擬合](./lessons/3-NeuralNetworks/05-Frameworks/README.md) | [PyTorch](./lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb) / [Keras](./lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [實驗](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | +| IV | [**電腦視覺**](./lessons/4-ComputerVision/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-77998-cacaste) / [TensorFlow](https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-77998-cacaste)| [在 Microsoft Azure 上探索電腦視覺](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | +| 06 | [電腦視覺入門。OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [筆記本](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [實驗](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | +| 07 | [卷積神經網絡](./lessons/4-ComputerVision/07-ConvNets/README.md) & [CNN 架構](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [實驗](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | +| 08 | [預訓練網絡與轉移學習](./lessons/4-ComputerVision/08-TransferLearning/README.md) 和 [訓練技巧](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [實驗](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | +| 09 | [自編碼器與變分自編碼器（VAE）](./lessons/4-ComputerVision/09-Autoencoders/README.md) | [PyTorch](./lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb) | | +| 10 | [生成對抗網絡與藝術風格轉移](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | +| 11 | [物件偵測](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [實驗](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | +| 12 | [語意分割。U-Net](./lessons/4-ComputerVision/12-Segmentation/README.md) | [PyTorch](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb) | | +| V | [**自然语言處理**](./lessons/5-NLP/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-77998-cacaste) /[TensorFlow](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-77998-cacaste) | [在 Microsoft Azure 上探索自然語言處理](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| +| 13 | [文本表示。詞袋模型（Bow）/ TF-IDF](./lessons/5-NLP/13-TextRep/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb) | | +| 14 | [語意詞嵌入。Word2Vec 和 GloVe](./lessons/5-NLP/14-Embeddings/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb) | | +| 15 | [語言模型。訓練你自己的詞嵌入](./lessons/5-NLP/15-LanguageModeling/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-PyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-TF.ipynb) | [實驗](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | +| 16 | [循環神經網絡](./lessons/5-NLP/16-RNN/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNTF.ipynb) | | +| 17 | [生成式循環網絡](./lessons/5-NLP/17-GenerativeNetworks/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb) | [實驗](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | +| 18 | [Transformer。BERT。](./lessons/5-NLP/18-Transformers/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb) /[TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersTF.ipynb) | | +| 19 | [命名實體識別](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [實驗](./lessons/5-NLP/19-NER/lab/README.md) | +| 20 | [大型語言模型、提示程式設計與少量示範任務](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | +| VI | **其他人工智能技術** || | | 21 | [基因算法](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [筆記本](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | -| 22 | [深度強化學習](./lessons/6-Other/22-DeepRL/README.md) | [PyTorch](./lessons/6-Other/22-DeepRL/CartPole-RL-PyTorch.ipynb) /[TensorFlow](./lessons/6-Other/22-DeepRL/CartPole-RL-TF.ipynb) | [實驗室](./lessons/6-Other/22-DeepRL/lab/README.md) | +| 22 | [深度強化學習](./lessons/6-Other/22-DeepRL/README.md) | [PyTorch](./lessons/6-Other/22-DeepRL/CartPole-RL-PyTorch.ipynb) /[TensorFlow](./lessons/6-Other/22-DeepRL/CartPole-RL-TF.ipynb) | [實驗](./lessons/6-Other/22-DeepRL/lab/README.md) | | 23 | [多智能體系統](./lessons/6-Other/23-MultiagentSystems/README.md) | | | -| VII | **AI 倫理** | | | -| 24 | [AI 倫理與負責任的 AI](./lessons/7-Ethics/README.md) | [Microsoft Learn：負責任的 AI 原則](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | +| VII | **人工智能倫理** | | | +| 24 | [人工智能倫理與負責任的人工智能](./lessons/7-Ethics/README.md) | [Microsoft Learn: 負責任的人工智能原則](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | | IX | **額外內容** | | | -| 25 | [多模態網絡、CLIP 與 VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [筆記本](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | +| 25 | [多模態網絡，CLIP 及 VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [筆記本](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | -## 每堂課包含 +## 每個課程包含 -* 預習材料 -* 可執行的 Jupyter 筆記本，通常針對某種框架（**PyTorch** 或 **TensorFlow**）。可執行筆記本也包含大量理論內容，因此要理解主題，需要至少完成其中一個版本（PyTorch 或 TensorFlow）。 -* 部份主題有**實驗室**，讓你有機會將所學知識應用到特定問題。 -* 有些章節包含連結至[**Microsoft Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)相關主題的模組。 +* 預讀教材 +* 可執行的 Jupyter 筆記本，通常針對特定框架（**PyTorch** 或 **TensorFlow**）。執行筆記本內含大量理論資料，理解主題需至少通過其中一個版本的筆記本（PyTorch 或 TensorFlow）。 +* 部分主題提供 **實驗**，給你機會將所學材料嘗試應用於特定問題。 +* 部分章節包含指向相關主題的 [**Microsoft Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) 模組的連結。 -## 開始學習 +## 開始入門 -### 🎯 對 AI 完全新手？從這裡開始！ +### 🎯 AI 新手？從這裡開始！ -如果你對 AI 完全陌生，想要快速實操範例，請查看我們的[**新手友好範例**](./examples/README.md)！內容包括： +如果你完全是 AI 初學者，想要快速、動手操作的範例，請查看我們的[**新手友好範例**](./examples/README.md)！包括： - 🌟 **Hello AI World** - 你的第一個 AI 程式（模式識別） -- 🧠 **簡單神經網絡** - 從零建構一個神經網絡 -- 🖼️ **影像分類器** - 詳細註釋的影像分類器 -- 💬 **文字情感分析** - 分析正面/負面文字 +- 🧠 **簡單神經網絡** - 從零開始建立神經網絡 +- 🖼️ **影像分類器** - 詳細註解的影像分類器 +- 💬 **文本情感分析** - 分析正面/負面文本 -這些範例旨在幫助您了解 AI 概念，然後再深入完整課程。 +這些範例旨在幫助您理解 AI 概念，然後再深入完整課程。 ### 📚 完整課程設置 -- 我們建立了一個[設置課程](./lessons/0-course-setup/setup.md) 來幫助您設置開發環境。 - 對於教育者，我們也為您準備了一個[課程設置課程](./lessons/0-course-setup/for-teachers.md)！ -- 如何在 VSCode 或 Codepace 中[執行程式碼](./lessons/0-course-setup/how-to-run.md) +- 我們已建立一個[設置課程](./lessons/0-course-setup/setup.md)以幫助您設定開發環境。 - 對教育工作者，我們也製作了一個[課程設置課程](./lessons/0-course-setup/for-teachers.md)！ +- 如何在 VSCode 或 Codespace 中[執行程式碼](./lessons/0-course-setup/how-to-run.md) -請按以下步驟操作： +請依以下步驟操作： -分叉倉庫：點擊本頁面右上角的「Fork」按鈕。 +複製儲存庫：點擊本頁右上角的「Fork」按鈕。 -克隆倉庫：`git clone https://github.com/microsoft/AI-For-Beginners.git` +克隆儲存庫：`git clone https://github.com/microsoft/AI-For-Beginners.git` -別忘了給這個倉庫加星（🌟），以便日後更容易找到。 +別忘了為此儲存庫點星 (🌟)，方便日後查找。 ## 認識其他學習者 -加入我們的[官方 AI Discord 伺服器](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum)，與其他參加此課程的學習者交流聯繫並獲得支援。 +加入我們的[正式 AI Discord 伺服器](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum)，與其他正在學習此課程的人交流並獲得支持。 -如果您在建置過程中有產品反饋或疑問，請造訪我們的[Azure AI Foundry 開發人員論壇](https://aka.ms/foundry/forum) +若您在開發過程中有產品反饋或問題，請造訪我們的[Azure AI Foundry 開發者論壇](https://aka.ms/foundry/forum) ## 測驗 -> **關於測驗的說明**：所有測驗均包含在 etc\quiz-app 裡的 Quiz-app 資料夾，或可[線上此處](https://ff-quizzes.netlify.app/)查看。它們與課程中串連，測驗應用程式可在本地端執行或部署至 Azure；請遵循 `quiz-app` 資料夾中的指示。測驗正逐步在地化中。 +> **關於測驗的說明**：所有測驗均包含於 etc\quiz-app 資料夾下的 Quiz-app 中，或可至[線上查看](https://ff-quizzes.netlify.app/)。測驗連結嵌入於課程中，Quiz 應用程式可在本機運行或部署到 Azure；請遵照 `quiz-app` 資料夾內的指引。測驗正逐步進行本地化。 ## 需要協助 -您有建議或發現拼字或程式碼錯誤嗎？請提出議題或建立拉取請求。 +您有建議或發現拼寫或程式碼錯誤嗎？歡迎提出 issue 或建立 pull request。 ## 特別感謝 -* **✍️ 主要作者：** [Dmitry Soshnikov](http://soshnikov.com), PhD -* **🔥 編輯：** [Jen Looper](https://twitter.com/jenlooper), PhD -* **🎨 筆記插畫家：** [Tomomi Imura](https://twitter.com/girlie_mac) -* **✅ 測驗創作者：** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) +* **✍️ 主要作者：** [Dmitry Soshnikov](http://soshnikov.com), 博士 +* **🔥 編輯：** [Jen Looper](https://twitter.com/jenlooper), 博士 +* **🎨 繪圖插畫：** [Tomomi Imura](https://twitter.com/girlie_mac) +* **✅ 測驗創建者：** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) * **🙏 核心貢獻者：** [Evgenii Pishchik](https://github.com/Pe4enIks) ## 其他課程 -我們團隊還製作了其他課程！快來看看： +我們團隊還有製作其他課程！敬請查看： ### LangChain @@ -214,13 +214,13 @@ CO_OP_TRANSLATOR_METADATA: [![Copilot Adventure](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst) -## 尋求協助 +## 尋求幫助 -如果您卡關或對建置 AI 應用程式有任何疑問，歡迎加入與 MCP 有關的討論，與其他學友和經驗豐富的開發者交流。這是一個支持性的社群，歡迎提問且知識自由分享。 +如果您遇到困難或對建立 AI 應用有任何疑問，加入與 MCP 相關的同學與經驗豐富開發者討論。這是一個支持性強的社群，歡迎提問並自由分享知識。 [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -如果您在建置過程中有產品反饋或錯誤，請造訪： +若您在開發過程中有產品反饋或錯誤，請造訪： [![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) @@ -228,5 +228,5 @@ CO_OP_TRANSLATOR_METADATA: **免責聲明**： -本文件乃使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 翻譯而成。雖然我們致力於確保準確性，惟請注意自動翻譯可能包含錯誤或不準確之處。原始文件的母語版本應視為權威來源。如涉及重要資訊，建議採用專業人工翻譯。對於因使用本翻譯而引致的任何誤解或曲解，我們概不負責。 +本文件經由人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 翻譯。 雖然我們致力於維持準確性，但請注意自動翻譯可能包含錯誤或不準確之處。文件以原始語言版本為準。 如涉及重要資料，請尋求專業人工翻譯。 我們不對因使用本翻譯而導致之任何誤解或錯釋承擔責任。 \ No newline at end of file diff --git a/translations/mo/lessons/0-course-setup/how-to-run.md b/translations/mo/lessons/0-course-setup/how-to-run.md index bde767d6..1ec8a60b 100644 --- a/translations/mo/lessons/0-course-setup/how-to-run.md +++ b/translations/mo/lessons/0-course-setup/how-to-run.md @@ -1,21 +1,21 @@ # 如何執行程式碼 -這份課程包含許多可執行的範例和實驗室，您可能會想要執行它們。為了做到這一點，您需要能夠在這份課程提供的 Jupyter Notebooks 中執行 Python 程式碼。以下是幾種執行程式碼的選項： +本課程包含許多可執行的範例和實驗室，您會想要執行它們。為此，您需要能夠在本課程所提供的 Jupyter 筆記本中執行 Python 程式碼。您有幾種選擇可以執行程式碼： -## 在本地電腦上執行 +## 在您電腦本地執行 -若要在本地電腦上執行程式碼，您需要安裝某個版本的 Python。我個人推薦安裝 **[miniconda](https://conda.io/en/latest/miniconda.html)**，這是一個輕量級的安裝包，支援使用 `conda` 套件管理器來建立不同的 Python **虛擬環境**。 +要在您電腦本地執行程式碼，需要安裝 Python。建議安裝 **[miniconda](https://conda.io/en/latest/miniconda.html)** — 這是一個相對輕量的安裝，支援使用 `conda` 套件管理器管理不同的 Python **虛擬環境**。 -安裝 miniconda 後，您需要克隆這個儲存庫並建立一個虛擬環境來用於這門課程： +安裝 miniconda 後，克隆此倉庫並建立一個虛擬環境供本課程使用： ```bash git clone http://github.com/microsoft/ai-for-beginners @@ -24,19 +24,19 @@ conda env create --name ai4beg --file .devcontainer/environment.yml conda activate ai4beg ``` -### 使用 Visual Studio Code 和 Python 擴展 +### 使用安裝了 Python 擴充功能的 Visual Studio Code -最好的方式可能是使用 [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) 搭配 [Python 擴展](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste)來開啟這份課程。 +本課程最適合在安裝了 [Python 擴充功能](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste) 的 [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) 中開啟進行使用。 -> **注意**：當您克隆並在 VS Code 中開啟目錄時，系統會自動建議您安裝 Python 擴展。您還需要按照上述步驟安裝 miniconda。 +> **注意**：當您克隆並在 VS Code 中開啟目錄時，它會自動建議您安裝 Python 擴充功能。您同時也需要依前述安裝 miniconda。 -> **注意**：如果 VS Code 建議您在容器中重新開啟儲存庫，請拒絕此建議，改用本地的 Python 安裝。 +> **注意**：如果 VS Code 建議您在容器中重新開啟倉庫，您應該拒絕，以使用本地的 Python 安裝。 ### 在瀏覽器中使用 Jupyter -您也可以直接在自己的電腦瀏覽器中使用 Jupyter 環境。事實上，無論是傳統的 Jupyter 還是 Jupyter Hub，都提供了相當方便的開發環境，包括自動補全、程式碼高亮等功能。 +您也可以在自己電腦的瀏覽器中使用 Jupyter 環境。傳統的 Jupyter 與 JupyterHub 都提供了方便的開發環境，包括自動補完、程式碼高亮等功能。 -若要在本地啟動 Jupyter，請進入課程的目錄，然後執行以下指令： +要在本地啟動 Jupyter，請前往課程目錄並執行： ```bash jupyter notebook @@ -45,33 +45,36 @@ jupyter notebook ```bash jupyterhub ``` -接著，您可以瀏覽任何 `.ipynb` 檔案，開啟並開始工作。 +然後您可以導航到任何 `.ipynb` 檔案，開啟並開始操作。 ### 在容器中執行 -另一種替代安裝 Python 的方式是使用容器來執行程式碼。由於我們的儲存庫包含一個特殊的 `.devcontainer` 資料夾，指示如何為此儲存庫建立容器，VS Code 會提示您在容器中重新開啟程式碼。這需要安裝 Docker，並且操作會更為複雜，因此我們建議有經驗的使用者採用此方法。 +另一種替代安裝 Python 的方法是使用容器來執行程式碼。由於我們的倉庫提供了一個特殊的 `.devcontainer` 資料夾，說明了如何為此倉庫建置容器，VS Code 提供機會讓您在容器中重新打開程式碼。這需要安裝 Docker，且流程較複雜，因此我們建議較有經驗的使用者採用此方式。 ## 在雲端執行 -如果您不想在本地安裝 Python，並且有一些雲端資源可用，那麼在雲端執行程式碼是一個不錯的選擇。以下是幾種方法： +如果您不想在本地安裝 Python，且能使用某些雲端資源，一個不錯的選擇是直接在雲端執行程式碼。您可以用以下幾種方法： -* 使用 **[GitHub Codespaces](https://github.com/features/codespaces)**，這是一個在 GitHub 上為您建立的虛擬環境，可透過 VS Code 的瀏覽器介面訪問。如果您有 Codespaces 的存取權，只需點擊儲存庫中的 **Code** 按鈕，啟動一個 Codespace，即可快速開始。 +* 使用 **[GitHub Codespaces](https://github.com/features/codespaces)**，這是在 GitHub 上為您建立的虛擬環境，可透過 VS Code 瀏覽器介面存取。如果您有 Codespaces 的使用權，您只需點選倉庫中的 **Code** 按鈕，啟動 codespace，即可迅速開始。 +* 使用 **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**。[Binder](https://mybinder.org) 免費提供雲端計算資源，方便您測試 GitHub 上的程式碼。在首頁有個按鈕可在 Binder 中開啟此倉庫 — 這會快速導向 Binder 網站，並自動建構一個底層容器，順暢啟動 Jupyter 網頁介面。 -* 使用 **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**。[Binder](https://mybinder.org) 是一個免費的雲端計算資源，供像您這樣的使用者在 GitHub 上測試一些程式碼。首頁上有一個按鈕可以將儲存庫開啟於 Binder，這會快速將您帶到 Binder 網站，該網站會建立底層容器並無縫啟動 Jupyter 網頁介面。 - -> **注意**：為了防止濫用，Binder 對某些網路資源的存取是受限的。這可能會導致某些需要從公共網路下載模型或數據集的程式碼無法正常運行，您可能需要尋找替代方案。此外，Binder 提供的計算資源相對基礎，因此在後續更複雜的課程中，訓練速度可能會較慢。 +> **注意**：為防止濫用，Binder 對部分網絡資源有限制，有時會阻擋某些從公共網際網路下載模型或資料集的程式碼，可能導致部分程式無法正常運行，您可能需要尋找替代方案。此外，Binder 提供的計算資源較基本，訓練過程會比較慢，尤其在後續較複雜課程中。 ## 在雲端使用 GPU 執行 -課程中的某些後續章節會因為需要大量計算而非常適合使用 GPU，否則訓練過程可能會非常緩慢。如果您有雲端資源的存取權，例如透過 [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) 或您的機構，以下是幾個選項： +本課程後面的一些章節會大大受益於 GPU 支援。例如模型訓練，否則會非常緩慢。您可以選擇以下幾種方法，特別是您有透過 [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) 或您的機構取得雲端資源： -* 建立 [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste)，並透過 Jupyter 連接到它。您可以直接將儲存庫克隆到該機器上，然後開始學習。NC 系列的虛擬機器支援 GPU。 +* 建立 [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) 並透過 Jupyter 連線。您可以直接將倉庫克隆到此虛擬機，開始學習。NC 系列虛擬機支援 GPU。 -> **注意**：某些訂閱（包括 Azure for Students）並未預設提供 GPU 支援。您可能需要透過技術支援請求額外的 GPU 核心。 +> **注意**：部分訂閱服務，包括 Azure for Students 預設不提供 GPU 支援，您可能需要透過技術支援申請額外 GPU 核心。 -* 建立 [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste)，然後使用其中的 Notebook 功能。[這段影片](https://azure-for-academics.github.io/quickstart/azureml-papers/)展示了如何將儲存庫克隆到 Azure ML Notebook 並開始使用。 +* 建立 [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) 並使用其 Notebook 功能。[此影片](https://azure-for-academics.github.io/quickstart/azureml-papers/) 示範如何將倉庫克隆到 Azure ML Notebook 並開始使用。 -您也可以使用 Google Colab，它提供一些免費的 GPU 支援，並將 Jupyter Notebooks 上傳到那裡，逐一執行。 +您亦可使用 Google Colab，它提供部分免費 GPU 支援，並能上傳 Jupyter 筆記本，一個一個地執行。 +--- + + **免責聲明**： -本文件已使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。應以原始語言的文件作為權威來源。對於關鍵資訊，建議尋求專業人工翻譯。我們對因使用此翻譯而產生的任何誤解或錯誤解讀概不負責。 \ No newline at end of file +本文件經由 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。儘管我們力求準確，請注意自動翻譯可能包含錯誤或不準確之處。原始文件的母語版本應被視為權威來源。對於重要資訊，建議使用專業人工翻譯。本公司不對使用本翻譯所引致的任何誤解或誤釋承擔責任。 + \ No newline at end of file diff --git a/translations/mo/lessons/1-Intro/README.md b/translations/mo/lessons/1-Intro/README.md index e34d8bbe..e2d8e984 100644 --- a/translations/mo/lessons/1-Intro/README.md +++ b/translations/mo/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 人工智能簡介 -![人工智能簡介內容的手繪圖](../../../../translated_images/mo/ai-intro.bf28d1ac4235881c.png) +![人工智能簡介內容的手繪圖](../../../../translated_images/mo/ai-intro.bf28d1ac4235881c.webp) > 手繪筆記由 [Tomomi Imura](https://twitter.com/girlie_mac) 提供 @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: 最初，電腦是由 [查爾斯·巴貝奇](https://en.wikipedia.org/wiki/Charles_Babbage) 發明的，用於按照明確定義的程序（算法）處理數字。現代電腦雖然比19世紀提出的原始模型先進得多，但仍然遵循受控計算的理念。因此，如果我們知道實現目標所需的精確步驟序列，就可以編程讓電腦完成某些事情。 -![一個人的照片](../../../../translated_images/mo/dsh_age.d212a30d4e54fb5f.png) +![一個人的照片](../../../../translated_images/mo/dsh_age.d212a30d4e54fb5f.webp) > 照片由 [Vickie Soshnikova](http://twitter.com/vickievalerie) 提供 @@ -46,7 +46,7 @@ CO_OP_TRANSLATOR_METADATA: 處理 **[智能](https://en.wikipedia.org/wiki/Intelligence)** 這個術語時的一個問題是，對於這個術語並沒有明確的定義。有人認為智能與 **抽象思維** 或 **自我意識** 有關，但我們無法準確定義它。 -![一隻貓的照片](../../../../translated_images/mo/photo-cat.8c8e8fb760ffe457.jpg) +![一隻貓的照片](../../../../translated_images/mo/photo-cat.8c8e8fb760ffe457.webp) > [照片](https://unsplash.com/photos/75715CVEJhI) 由 [Amber Kipp](https://unsplash.com/@sadmax) 提供，來自 Unsplash @@ -98,13 +98,13 @@ CO_OP_TRANSLATOR_METADATA: > | 那麼機器學習呢？ | | > |--------------|-----------| -> | 基於計算機學習如何根據某些數據解決問題的人工智能部分稱為 **機器學習**。我們不會在本課程中考慮經典的機器學習——我們建議參考單獨的 [機器學習初學者課程](http://aka.ms/ml-beginners)。 | ![機器學習初學者課程](../../../../translated_images/mo/ml-for-beginners.9e4fed176fd5817d.png) | +> | 基於計算機學習如何根據某些數據解決問題的人工智能部分稱為 **機器學習**。我們不會在本課程中考慮經典的機器學習——我們建議參考單獨的 [機器學習初學者課程](http://aka.ms/ml-beginners)。 | ![機器學習初學者課程](../../../../translated_images/mo/ml-for-beginners.9e4fed176fd5817d.webp) | ## 人工智能的簡史 人工智能作為一個領域始於20世紀中葉。最初，符號推理是一種流行的方法，並且它帶來了一些重要的成功，例如專家系統——能夠在某些有限問題領域中充當專家的計算機程序。然而，很快就發現這種方法並不適用於大規模應用。從專家那裡提取知識、將其表示在計算機中並保持知識庫的準確性，事實證明這是一項非常複雜且在許多情況下成本過高的任務。這導致了20世紀70年代所謂的 [人工智能寒冬](https://en.wikipedia.org/wiki/AI_winter)。 -人工智能簡史 +人工智能簡史 > 圖片由 [Dmitry Soshnikov](http://soshnikov.com) 提供 @@ -124,7 +124,7 @@ CO_OP_TRANSLATOR_METADATA: * 現代助手（如 Cortana、Siri 或 Google Assistant）都是混合系統，使用神經網絡將語音轉換為文本並識別我們的意圖，然後使用一些推理或明確的算法執行所需的操作。 * 未來，我們可能會期待一個完全基於神經網絡的模型能夠自行處理對話。最近的 GPT 和 [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) 神經網絡家族在這方面表現出色。 -圖靈測試的演變 +圖靈測試的演變 > 圖片由 Dmitry Soshnikov 提供，[照片](https://unsplash.com/photos/r8LmVbUKgns) 由 [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto) 提供，Unsplash ## 最近的人工智能研究 diff --git a/translations/mo/lessons/2-Symbolic/Animals.ipynb b/translations/mo/lessons/2-Symbolic/Animals.ipynb index f87df3dc..77607738 100644 --- a/translations/mo/lessons/2-Symbolic/Animals.ipynb +++ b/translations/mo/lessons/2-Symbolic/Animals.ipynb @@ -8,23 +8,23 @@ "source": [ "# 實作動物專家系統\n", "\n", - "範例取自 [AI for Beginners Curriculum](http://github.com/microsoft/ai-for-beginners)。\n", + "取自 [AI for Beginners Curriculum](http://github.com/microsoft/ai-for-beginners) 的一個範例。\n", "\n", - "在這個範例中，我們將實作一個簡單的知識型系統，根據一些外觀特徵來判斷動物。此系統可以用以下的 AND-OR 樹來表示（這只是整個樹的一部分，我們可以輕鬆地添加更多規則）：\n", + "在本範例中，我們將實作一個簡單的知識庫系統，用來根據一些物理特徵判定動物。系統可以用以下的 AND-OR 樹來表示（這是整棵樹的一部分，我們可以輕易加入更多規則）：\n", "\n", - "![](../../../../translated_images/mo/AND-OR-Tree.5592d2c70187f283.png)\n" + "![](../../../../../../translated_images/mo/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## 我們自己的專家系統殼層與反向推理\n", + "## 我們自己的專家系統外殼與逆向推理\n", "\n", - "讓我們嘗試基於生成規則定義一種簡單的知識表示語言。我們將使用 Python 類作為關鍵字來定義規則。基本上會有三種類型的類別：\n", - "* `Ask` 代表需要向使用者提問的問題。它包含可能的答案集合。\n", - "* `If` 代表一條規則，它只是用來存儲規則內容的語法糖。\n", - "* `AND`/`OR` 是用來表示樹的 AND/OR 分支的類別。它們僅存儲內部的參數列表。為了簡化程式碼，所有功能都定義在父類別 `Content` 中。\n" + "讓我們嘗試定義一種基於產生規則的簡單知識表示語言。我們將使用 Python 類作為關鍵字來定義規則。基本上會有三種類型的類別：\n", + "* `Ask` 代表一個需要向使用者提問的問題。它包含可能答案的集合。\n", + "* `If` 代表一條規則，它只是儲存規則內容的語法糖。\n", + "* `AND`/`OR` 是表示樹狀結構中 AND/OR 分支的類別。它們只是儲存內部參數的列表。為了簡化程式碼，所有功能都定義在父類別 `Content` 中。\n" ] }, { @@ -66,7 +66,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "在我們的系統中，工作記憶將包含**事實**列表作為**屬性-值對**。知識庫可以定義為一個大的字典，將行動（應插入到工作記憶中的新事實）映射到條件，表達為AND-OR表達式。此外，一些事實可以被`詢問`。\n" + "在我們的系統中，工作記憶會包含作為**屬性-值對**的**事實**清單。知識庫可以定義為一個大型字典，將動作（應該插入工作記憶的新事實）映射到條件，這些條件以 AND-OR 表達式表示。此外，某些事實可以被 `Ask`。\n" ] }, { @@ -99,13 +99,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "為了執行反向推理，我們將定義一個 `Knowledgebase` 類別。這個類別將包含以下內容：\n", - "* 運作中的 `memory` - 一個字典，用於將屬性映射到對應的值\n", - "* 知識庫中的 `rules` - 以上述定義的格式表示的規則\n", + "為執行逆向推理，我們將定義 `Knowledgebase` 類別。它將包含：\n", + "* 工作的 `memory` - 一個將屬性映射到值的字典\n", + "* 以上述格式定義的知識庫 `rules`\n", "\n", - "主要的兩個方法是：\n", - "* `get` 方法，用於獲取屬性的值，必要時執行推理。例如，`get('color')` 將獲取顏色槽位的值（如果需要，會詢問並將值存儲到運作記憶中以供後續使用）。如果我們詢問 `get('color:blue')`，它會詢問顏色，然後根據顏色返回 `y`/`n` 的值。\n", - "* `eval` 方法執行實際的推理，即遍歷 AND/OR 樹，評估子目標等。\n" + "兩個主要方法為：\n", + "* `get` 用於獲取屬性的值，必要時執行推理。例如，`get('color')` 會取得顏色槽的值（必要時會詢問，並將值儲存在工作記憶體中以供日後使用）。如果我們詢問 `get('color:blue')`，它會詢問顏色，然後根據顏色返回 `y`/`n` 的值。\n", + "* `eval` 執行實際的推理，即遍歷 AND/OR 樹、評估子目標，等等。\n" ] }, { @@ -172,7 +172,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "現在讓我們定義我們的動物知識庫並進行諮詢。請注意，此操作將向您提問。您可以通過輸入 `y`/`n` 來回答是非問題，或者通過指定數字（0..N）來回答具有較多選擇的問題。\n" + "現在讓我們定義我們的動物知識庫並進行諮詢。請注意，此過程會向您提問。對於是非問題，您可以輸入 `y`/`n` 回答，對於有多個選項的問題，您可以輸入數字（0..N）來選擇答案。\n" ] }, { @@ -229,11 +229,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 使用 PyKnow 進行前向推理\n", + "## 使用 Experta 進行前向推論\n", "\n", - "在下一個範例中，我們將嘗試使用一個知識表示庫 [PyKnow](https://github.com/buguroo/pyknow/) 來實現前向推理。**PyKnow** 是一個用於在 Python 中建立前向推理系統的庫，其設計理念與經典的舊系統 [CLIPS](http://www.clipsrules.net/index.html) 相似。\n", + "在下一個範例中，我們將嘗試使用其中一個知識表示庫 [Experta](https://github.com/nilp0inter/experta) 來實現前向推論。**Experta** 是一個用於在 Python 中創建前向推論系統的庫，其設計與經典舊系統 [CLIPS](http://www.clipsrules.net/index.html) 類似。\n", "\n", - "我們也可以自己實現前向鏈推理，並不會遇到太多問題，但簡單的實現通常效率不高。為了更有效地進行規則匹配，會使用一種特殊的演算法 [Rete](https://en.wikipedia.org/wiki/Rete_algorithm)。\n" + "我們本來也可以自行實現前向鏈式推論，並不會有太大問題，但天真的實現通常效率不高。為了更有效地進行規則匹配，使用了一種特殊算法 [Rete](https://en.wikipedia.org/wiki/Rete_algorithm)。\n" ] }, { @@ -247,32 +247,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "Collecting git+https://github.com/buguroo/pyknow/\n", - " Cloning https://github.com/buguroo/pyknow/ to /tmp/pip-req-build-3cqeulyl\n", - " Running command git clone --filter=blob:none --quiet https://github.com/buguroo/pyknow/ /tmp/pip-req-build-3cqeulyl\n", - " Resolved https://github.com/buguroo/pyknow/ to commit 48818336f2e9a126f1964f2d8dc22d37ff800fe8\n", - " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hCollecting frozendict==1.2\n", - " Using cached frozendict-1.2.tar.gz (2.6 kB)\n", - " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hCollecting schema==0.6.7\n", - " Using cached schema-0.6.7-py2.py3-none-any.whl (14 kB)\n", - "Building wheels for collected packages: pyknow, frozendict\n", - " Building wheel for pyknow (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for pyknow: filename=pyknow-1.7.0-py3-none-any.whl size=34228 sha256=b7de5b09292c4007667c72f69b98d5a1b5f7324ff15f9dd8e077c3d5f7aade42\n", - " Stored in directory: /tmp/pip-ephem-wheel-cache-k7jpave7/wheels/81/1a/d3/f6c15dbe1955598a37755215f2a10449e7418500d7bd4b9508\n", - " Building wheel for frozendict (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for frozendict: filename=frozendict-1.2-py3-none-any.whl size=3148 sha256=2863d55c240d2409cddf05ccfe600591f8478681549fc97555c47c90dc6bb160\n", - " Stored in directory: /home/rg/.cache/pip/wheels/49/ac/f8/cb8120244e710bdb479c86198b03c7b08c3c2d3d2bf448fd6e\n", - "Successfully built pyknow frozendict\n", - "Installing collected packages: schema, frozendict, pyknow\n", - "Successfully installed frozendict-1.2 pyknow-1.7.0 schema-0.6.7\n" + "Collecting git+https://github.com/nilp0inter/experta\n", + " Cloning https://github.com/nilp0inter/experta to /tmp/pip-req-build-7qurtwk3\n", + " Running command git clone --filter=blob:none --quiet https://github.com/nilp0inter/experta /tmp/pip-req-build-7qurtwk3\n", + " Resolved https://github.com/nilp0inter/experta to commit c6d5834b123861f5ae09e7d07027dc98bec58741\n", + " Installing build dependencies ... \u001b[?25ldone\n", + "\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n", + "\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25hRequirement already satisfied: frozendict~=2.4.6 in /opt/conda/envs/ai4beg/lib/python3.12/site-packages (from experta==1.9.5.dev1) (2.4.7)\n", + "Collecting schema~=0.6.7 (from experta==1.9.5.dev1)\n", + " Downloading schema-0.6.8-py2.py3-none-any.whl.metadata (14 kB)\n", + "Downloading schema-0.6.8-py2.py3-none-any.whl (14 kB)\n", + "Building wheels for collected packages: experta\n", + " Building wheel for experta (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25h Created wheel for experta: filename=experta-1.9.5.dev1-py3-none-any.whl size=34804 sha256=888c459512a5e713f4b674caa9a0f96cfdf07ec0d6eb56cc318ce0653d218014\n", + " Stored in directory: /tmp/pip-ephem-wheel-cache-1eeii9zy/wheels/3d/e8/bb/22d7956359603fa8dd679aa09f5b8efb3f29991c3986fdc787\n", + "Successfully built experta\n", + "Installing collected packages: schema, experta\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2/2\u001b[0m [experta]\n", + "\u001b[1A\u001b[2KSuccessfully installed experta-1.9.5.dev1 schema-0.6.8\n" ] } ], "source": [ "import sys\n", - "!{sys.executable} -m pip install git+https://github.com/buguroo/pyknow/" + "!{sys.executable} -m pip install git+https://github.com/nilp0inter/experta" ] }, { @@ -283,15 +282,15 @@ }, "outputs": [], "source": [ - "from pyknow import *\n", - "#import pyknow" + "from experta import *\n", + "#import experta" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "我們將把系統定義為一個繼承 `KnowledgeEngine` 的類別。每個規則由一個帶有 `@Rule` 註解的獨立函數定義，該註解指定了規則應該觸發的時機。在規則內部，我們可以使用 `declare` 函數添加新的事實，添加這些事實將導致前向推理引擎調用更多的規則。\n" + "我們將定義我們的系統為一個繼承自 `KnowledgeEngine` 的類別。每條規則由一個帶有 `@Rule` 註解的獨立函數定義，該註解指定了規則應該在何時觸發。在規則內，我們可以使用 `declare` 函數新增事實，新增這些事實將導致前向推理引擎調用更多規則。\n" ] }, { @@ -378,7 +377,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "一旦我們定義了一個知識庫，我們會用一些初始事實填充工作記憶，然後調用 `run()` 方法來執行推理。結果可以看到新的推斷事實被添加到工作記憶中，包括關於動物的最終事實（如果我們正確設置了所有初始事實）。\n" + "一旦我們定義了一個知識庫，我們就會用一些初始事實填充我們的工作記憶，然後調用 `run()` 方法來執行推理。你可以看到結果中新的推斷事實被添加到工作記憶中，包括關於動物的最終事實（如果我們正確設置了所有初始事實）。\n" ] }, { @@ -440,7 +439,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n---\n\n**免責聲明**： \n本文件已使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。雖然我們努力確保翻譯的準確性，但請注意，自動翻譯可能包含錯誤或不準確之處。原始文件的母語版本應被視為權威來源。對於關鍵信息，建議尋求專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解釋不承擔責任。\n" + "---\n\n\n**免責聲明**： \n本文件乃使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 所翻譯。儘管我們力求準確，但請注意自動翻譯可能存在錯誤或不準確之處。原始文件的母語版本應視為權威來源。對於重要資訊，建議採用專業人工翻譯。我們對因使用本翻譯而導致的任何誤解或誤譯概不負責。\n\n" ] } ], @@ -467,8 +466,8 @@ "version": "3.11.2" }, "coopTranslator": { - "original_hash": "ab2bd97b0453415b89a469284609a8ce", - "translation_date": "2025-08-28T11:31:02+00:00", + "original_hash": "8ef43db4b9182239fd150a76bd494fdb", + "translation_date": "2026-01-15T11:43:41+00:00", "source_file": "lessons/2-Symbolic/Animals.ipynb", "language_code": "mo" } diff --git a/translations/mo/lessons/2-Symbolic/README.md b/translations/mo/lessons/2-Symbolic/README.md index 2ef830a7..ceadbba3 100644 --- a/translations/mo/lessons/2-Symbolic/README.md +++ b/translations/mo/lessons/2-Symbolic/README.md @@ -1,116 +1,116 @@ # 知識表示與專家系統 -![象徵式 AI 內容摘要](../../../../translated_images/mo/ai-symbolic.715a30cb610411a6.png) +![符號AI內容摘要](../../../../../../translated_images/mo/ai-symbolic.715a30cb610411a6.webp) -> Sketchnote by [Tomomi Imura](https://twitter.com/girlie_mac) +> 手繪筆記由 [Tomomi Imura](https://twitter.com/girlie_mac) 製作 -人工智慧的追求基於對知識的探索，目的是讓機器能像人類一樣理解世界。但該如何實現呢？ +人工智能的追求基於對知識的探索，旨在像人類一樣理解世界。但你該如何著手呢？ ## [課前測驗](https://ff-quizzes.netlify.app/en/ai/quiz/3) -在人工智慧的早期，採用自上而下的方法來創建智能系統（在上一課中討論過）非常流行。這種方法的核心思想是將人類的知識提取成機器可讀的形式，然後用它來自動解決問題。這種方法基於兩個重要概念： +在AI的早期階段，自上而下的創建智能系統方法（前一課討論過）很受歡迎。這個想法是從人們身上提取知識，轉換成機器可讀的形式，然後用以自動解決問題。這種方法基於兩個重要的理念： * 知識表示 * 推理 ## 知識表示 -象徵式 AI 的一個重要概念是**知識**。需要將知識與*信息*或*數據*區分開。例如，人們可以說書籍包含知識，因為可以通過學習書籍成為專家。然而，書籍實際上包含的是*數據*，而通過閱讀書籍並將這些數據整合到我們的世界模型中，我們將數據轉化為知識。 +符號AI中的一個重要概念是**知識**。區分知識與*資訊*或*資料*很重要。例如，人們常說書本包含知識，因為閱讀書本可成為專家。然而，書本裡實際包含的是稱為*資料*的東西，通過閱讀書本並整合這些資料進入我們的世界模型，我們將資料轉變成知識。 -> ✅ **知識**是存在於我們頭腦中的東西，代表我們對世界的理解。它是通過主動的**學習**過程獲得的，這個過程將我們接收到的信息片段整合到我們的世界模型中。 +> ✅ **知識**是存在於我們腦海中，代表我們對世界理解的東西。它是透過一個積極的**學習**過程獲得，將我們接收到的資訊片段整合進我們對世界的主動模型中。 -通常，我們不會嚴格定義知識，而是通過 [DIKW 金字塔](https://en.wikipedia.org/wiki/DIKW_pyramid)將其與其他相關概念對齊。金字塔包含以下概念： +我們通常不會嚴格定義知識，而是通過[DIKW金字塔](https://en.wikipedia.org/wiki/DIKW_pyramid)將它與其他相關概念對應，其包含以下概念： -* **數據**是以物理媒介表示的東西，例如書面文字或口頭語言。數據獨立於人類存在，可以在人與人之間傳遞。 -* **信息**是我們在頭腦中對數據的解釋。例如，當我們聽到“電腦”這個詞時，我們對它有一定的理解。 -* **知識**是信息被整合到我們的世界模型中。例如，一旦我們學會了什麼是電腦，我們就開始對它的工作原理、價格以及用途有一些想法。這些相互關聯的概念網絡構成了我們的知識。 -* **智慧**是我們對世界理解的更高層次，代表著*元知識*，例如關於如何以及何時使用知識的概念。 +* **資料**是以物理媒介表示的東西，如書寫文本或口語詞彙。資料獨立於人類存在，且可在人與人之間傳遞。 +* **資訊**是在我們腦中如何解讀資料。例如，當聽到「電腦」一詞時，我們對它有一定理解。 +* **知識**是被整合到我們的世界模型中的資訊。例如，當我們知道電腦是什麼，我們開始對它如何運作、價格多少、用途等有想法。這些相互關聯的概念網絡形成了我們的知識。 +* **智慧**是我們對世界理解的更高一層，代表*元知識*，例如關於何時及如何使用知識的一些觀念。 - + -*圖片 [來自維基百科](https://commons.wikimedia.org/w/index.php?curid=37705247)，作者 Longlivetheux - 自製作品，CC BY-SA 4.0* +*圖片來自 [維基百科](https://commons.wikimedia.org/w/index.php?curid=37705247)，作者 Longlivetheux - 自作，CC BY-SA 4.0* -因此，**知識表示**的問題是找到某種有效的方法，將知識以數據的形式表示在計算機中，使其能夠自動使用。這可以看作是一個光譜： +因此，**知識表示**問題是找到有效的方式，將知識以資料形式表示在電腦內，讓其可自動使用。這可看作是一個光譜： -![知識表示光譜](../../../../translated_images/mo/knowledge-spectrum.b60df631852c0217.png) +![知識表示光譜](../../../../../../translated_images/mo/knowledge-spectrum.b60df631852c0217.webp) > 圖片由 [Dmitry Soshnikov](http://soshnikov.com) 提供 -* 在左側，有非常簡單的知識表示類型，可以被計算機有效使用。最簡單的是算法式表示，當知識以計算機程序的形式表示時。然而，這並不是表示知識的最佳方式，因為它不靈活。我們頭腦中的知識通常是非算法式的。 -* 在右側，有像自然文本這樣的表示方式。它是最強大的，但無法用於自動推理。 +* 左側是電腦可有效使用的非常簡單的知識表示類型。最簡單的是算法表示，知識由電腦程式代表。但這不是呈現知識的最佳方式，因為它不夠靈活。存在於我們腦中的知識常常是非算法性的。 +* 右側是自然文字表示。它最強大，但無法用於自動推理。 -> ✅ 花一分鐘思考你如何在頭腦中表示知識並將其轉化為筆記。是否有某種格式能幫助你更好地記憶？ +> ✅ 想一下你如何在腦中表示知識並轉換成筆記？有沒有特定的格式能幫助你記憶？ -## 計算機知識表示的分類 +## 電腦知識表示的分類 -我們可以將不同的計算機知識表示方法分為以下幾類： +我們可將不同的電腦知識表示方法分類如下： -* **網絡表示**基於我們頭腦中有一個相互關聯的概念網絡。我們可以嘗試在計算機中以圖的形式重現這些網絡——即所謂的**語義網絡**。 +* **網路表示**基於我們腦中存在的相互關聯概念網絡。我們可嘗試在電腦中重現這些網絡，作為圖形表示——所謂的**語義網絡**。 -1. **對象-屬性-值三元組**或**屬性-值對**。由於圖可以在計算機中表示為節點和邊的列表，我們可以用三元組的列表來表示語義網絡，包含對象、屬性和值。例如，我們可以建立以下關於程式語言的三元組： +1. **物件-屬性-值三元組**或**屬性-值對**。由於圖形可在電腦中表示為節點與邊列表，我們可用三元組列表表示語義網絡，其中包含物件、屬性、值。例如，我們構建以下關於程式語言的三元組： -對象 | 屬性 | 值 ------|------|----- -Python | 是 | 無類型語言 +物件 | 屬性 | 值 +-------|-----------|------ +Python | 是 | 非型別語言 Python | 發明者 | Guido van Rossum Python | 區塊語法 | 縮排 -無類型語言 | 沒有 | 類型定義 +非型別語言 | 沒有 | 型別定義 -> ✅ 思考如何使用三元組來表示其他類型的知識。 +> ✅ 想想三元組如何用於表示其他類型的知識。 -2. **層次表示**強調我們通常在頭腦中創建對象的層次結構。例如，我們知道金絲雀是一種鳥類，所有鳥類都有翅膀。我們還知道金絲雀通常是什麼顏色，以及它們的飛行速度。 +2. **層次式表示**強調我們經常在腦中建立物件層次結構。例如，我們知道金絲雀是鳥，所有鳥都有翅膀，我們也知道金絲雀通常的顏色及飛行速度。 - - **框架表示**基於將每個對象或對象類表示為一個**框架**，框架包含**槽**。槽可以有可能的默認值、值限制或存儲的程序，這些程序可以被調用以獲得槽的值。所有框架形成一個層次結構，類似於面向對象程式語言中的對象層次結構。 - - **場景**是表示可以隨時間展開的複雜情境的特殊框架。 + - **框架表示**基於將每個物件或物件類別表示為包含**槽位**的**框架**。槽位可有預設值、值限制或可調用的程序以獲得槽位值。所有框架形成的層次結構類似物件導向程式語言中的物件層次結構。 + - **劇本**是特殊類型的框架，用於表示可隨時間展開的複雜情境。 **Python** -槽 | 值 | 默認值 | 範圍 ----|----|--------|----- -名稱 | Python | | -是 | 無類型語言 | | -變數命名方式 | | 駝峰式命名 | -程式長度 | | | 5-5000 行 -區塊語法 | 縮排 | | +槽位 | 值 | 預設值 | 範圍 | +-----|-------|---------------|----------| +名稱 | Python | | | +是-一個 | 非型別語言 | | | +變數寫法 | | 駝峰式 | | +程式長度 | | | 5-5000 行 | +區塊語法 | 縮排 | | | -3. **程序表示**基於用一系列動作表示知識，當某些條件發生時可以執行。 - - 產生規則是 if-then 語句，允許我們得出結論。例如，醫生可以有一條規則說**如果**患者有高燒**或**血液檢測中 C 反應蛋白水平高**那麼**他有炎症。一旦我們遇到其中一個條件，我們就可以得出關於炎症的結論，然後在進一步推理中使用它。 - - 算法可以被認為是另一種程序表示形式，儘管它們幾乎從未直接用於基於知識的系統。 +3. **程序式表示**基於以可在條件觸發時執行的一系列動作表示知識。 + - 生產規則是if-then語句，允許我們推斷結論。例如，一位醫生可能有規則：**如果**患者有高燒**或**血液檢驗中C反應蛋白偏高，**那麼**他有發炎情況。一旦符合條件之一，就能推論發炎，並用於後續推理。 + - 算法可視為另一種程序式表示，儘管它們幾乎不直接用於基於知識的系統。 -4. **邏輯**最初由亞里士多德提出，作為表示普遍人類知識的一種方式。 - - 謂詞邏輯作為數學理論過於豐富而無法計算，因此通常使用它的一些子集，例如 Prolog 中使用的 Horn 子句。 - - 描述邏輯是一系列邏輯系統，用於表示和推理分佈式知識表示中的對象層次結構，例如*語義網*。 +4. **邏輯**最初由亞里斯多德提出，作為代表普遍人類知識的方法。 + - 謂詞邏輯作為數學理論過於豐富而難以計算，因此通常使用其子集，如Prolog中使用的Horn子句。 + - 描述邏輯屬於一組邏輯系統，用於表示與推理物件層次結構的分散式知識表示，如*語義網路*。 ## 專家系統 -象徵式 AI 的早期成功之一是所謂的**專家系統**——設計用於在某些有限問題領域中充當專家的計算機系統。它們基於從一位或多位人類專家提取的**知識庫**，並包含一個在其上執行推理的**推理引擎**。 +符號AI的早期成功之一是所謂的**專家系統**——設計用於充當有限問題領域專家的計算機系統。它基於從一個或多個人類專家提取的**知識庫**，並包含執行推理的**推理引擎**。 -![人類架構](../../../../translated_images/mo/arch-human.5d4d35f1bba3ab1c.png) | ![基於知識的系統架構](../../../../translated_images/mo/arch-kbs.3ec5c150b09fa8da.png) -----------------------------------|---------------------------------------- -人類神經系統的簡化結構 | 基於知識的系統的架構 +![人類架構](../../../../../../translated_images/mo/arch-human.5d4d35f1bba3ab1c.webp) | ![基於知識系統](../../../../../../translated_images/mo/arch-kbs.3ec5c150b09fa8da.webp) +---------------------------------------------|------------------------------------------------ +簡化的人類神經系統結構 | 基於知識系統的架構 -專家系統的構建類似於人類的推理系統，該系統包含**短期記憶**和**長期記憶**。同樣，在基於知識的系統中，我們區分以下組件： +專家系統是按照人類推理系統建造的，該系統包含**短期記憶**與**長期記憶**。類似地，在基於知識的系統中，我們區分以下組件： -* **問題記憶**：包含當前正在解決的問題的知識，例如患者的體溫或血壓、是否有炎症等。這些知識也被稱為**靜態知識**，因為它包含了我們目前對問題的了解的快照——即所謂的*問題狀態*。 -* **知識庫**：表示關於問題領域的長期知識。它是從人類專家手動提取的，並且不會因諮詢而改變。由於它使我們能夠從一個問題狀態導航到另一個問題狀態，它也被稱為**動態知識**。 -* **推理引擎**：負責協調在問題狀態空間中的搜索過程，必要時向用戶提問。它還負責找到適用於每個狀態的正確規則。 +* **問題記憶**：包含當前解決問題的知識，例如果病人體溫或血壓，是否有發炎等。這類知識又稱為**靜態知識**，因為它是表示我們目前對問題了解的快照——即所謂的*問題狀態*。 +* **知識庫**：代表有關問題領域的長期知識。它是從人類專家手動提取的，且在諮詢期間不會改變。由於可引導我們從一個問題狀態轉移到另一狀態，故亦稱為**動態知識**。 +* **推理引擎**：調度整個搜索問題狀態空間的過程，在必要時向用戶提問。並負責找出應用於每個狀態的正確規則。 -例如，讓我們考慮以下基於動物物理特徵的專家系統： +舉例來說，考慮以下透過動物的物理特徵判定動物的專家系統： -![AND-OR 樹](../../../../translated_images/mo/AND-OR-Tree.5592d2c70187f283.png) +![AND-OR樹](../../../../../../translated_images/mo/AND-OR-Tree.5592d2c70187f283.webp) > 圖片由 [Dmitry Soshnikov](http://soshnikov.com) 提供 -此圖表稱為**AND-OR 樹**，它是生產規則集的圖形表示。在提取專家知識的初期，繪製樹是有用的。要在計算機中表示知識，使用規則會更方便： +此圖被稱為**AND-OR樹**，是生產規則集合的圖形表述。繪製樹狀圖在從專家處提取知識時非常有用。在電腦中表示知識，則更方便使用規則： ``` IF the animal eats meat @@ -121,78 +121,78 @@ OR (animal has sharp teeth THEN the animal is a carnivore ``` -你會注意到規則左側的每個條件和動作本質上都是對象-屬性-值（OAV）三元組。**工作記憶**包含與當前正在解決的問題相對應的 OAV 三元組集。**規則引擎**尋找條件滿足的規則並應用它們，將另一個三元組添加到工作記憶中。 +你會看到，每個規則的左側條件與動作本質上是物件-屬性-值（OAV）三元組。**工作記憶**包含與當前解決問題相關的OAV三元組集合。**規則引擎**尋找條件符合的規則並執行它們，將新的三元組加入工作記憶。 -> ✅ 嘗試畫出你喜歡的主題的 AND-OR 樹！ +> ✅ 自己擬定一棵你感興趣主題的AND-OR樹吧！ -### 前向推理與後向推理 +### 正向與反向推理 -上述過程稱為**前向推理**。它從工作記憶中可用的初始數據開始，然後執行以下推理循環： +上述過程稱為**正向推理**。它從工作記憶中可用的初始問題數據開始，然後執行以下推理循環： -1. 如果目標屬性存在於工作記憶中——停止並給出結果 -2. 查找所有條件目前滿足的規則——獲得**衝突集**規則。 -3. 執行**衝突解決**——選擇一條將在此步驟中執行的規則。可能有不同的衝突解決策略： - - 選擇知識庫中第一個適用的規則 +1. 若工作記憶中有目標屬性，停止並輸出結果 +2. 搜尋條件當前被滿足的所有規則——獲得**衝突集**規則 +3. 執行**衝突解決**——從衝突集選擇一條執行規則。可能的衝突解決策略： + - 選擇知識庫中第一個合適規則 - 隨機選擇一條規則 - - 選擇*更具體*的規則，即滿足“左側”（LHS）中最多條件的規則 -4. 應用選定的規則並將新知識片段插入問題狀態 -5. 從第 1 步重複。 + - 選擇*較特定*的規則，即「左側」條件最多條件被滿足的規則 +4. 應用所選規則並將新知識插入問題狀態 +5. 從第1步重複 -然而，在某些情況下，我們可能希望從對問題的空白知識開始，並提出問題以幫助我們得出結論。例如，在進行醫學診斷時，我們通常不會在開始診斷患者之前提前進行所有醫學分析。我們更希望在需要做出決定時進行分析。 +然而，有時我們想從問題的空白知識開始，通過提問推理以得出結論。例如醫學診斷時，我們通常不會在診斷前事先做所有醫學檢查，而是根據需要決定做哪些檢查。 -此過程可以使用**後向推理**建模。它由**目標**驅動——即我們尋找的屬性值： +這個流程可以用**反向推理**建模。它由**目標**驅動——我們想找到的屬性值： -1. 選擇所有可以給出目標值的規則（即目標在 RHS（右側））——衝突集 -1. 如果沒有針對此屬性的規則，或者有規則表明我們應該向用戶詢問該值——詢問該值，否則： -1. 使用衝突解決策略選擇一條規則作為*假設*——我們將嘗試證明它 -1. 對規則 LHS 中的所有屬性重複此過程，嘗試將它們作為目標證明 -1. 如果過程在任何時候失敗——在第 3 步使用另一條規則。 +1. 選擇所有可給定目標值的規則（即目標在「右側」）——形成衝突集 +2. 若該屬性無規則或規則要求向使用者提問該值，則詢問使用者，否則： +3. 使用衝突解決策略選擇一條規則作為*假設*，將嘗試證明它 +4. 遞迴地重複此過程，嘗試證明該規則左側（條件）的所有屬性目標 +5. 若過程中任一步失敗，返回步驟3選用另一規則 -> ✅ 在哪些情況下前向推理更合適？後向推理又適用於哪些情況？ +> ✅ 正向推理在何種情況較適合？反向推理又如何？ -### 專家系統的實現 +### 實作專家系統 -專家系統可以使用不同的工具來實現： +專家系統可使用不同工具實現： -* 直接使用某些高級程式語言進行編程。這不是最好的方法，因為基於知識的系統的主要優勢是知識與推理分離，並且問題領域的專家應該能夠在不理解推理過程細節的情況下編寫規則。 -* 使用**專家系統外殼**，即專門設計用於使用某種知識表示語言填充知識的系統。 +* 直接用高階程式語言編程。但這不是理想方法，因為基於知識系統的最大優點是知識和推理分離，而理想上問題領域專家應能不懂推理細節便撰寫規則。 +* 使用**專家系統殼層**，即專門設計用知識表示語言填充知識的系統。 ## ✍️ 練習：動物推理 -請參閱 [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb)，了解實現前向和後向推理專家系統的示例。 +見[Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb)，範例展示如何實作正向與反向推理專家系統。 -> **注意**：此示例相對簡單，只能提供專家系統的基本概念。一旦你開始創建這樣的系統，只有當規則數量達到一定程度（大約 200+）時，你才會注意到一些*智能*行為。在某些時候，規則變得過於複雜，無法全部記住，此時你可能會開始思考系統為什麼做出某些決定。然而，基於知識的系統的一個重要特徵是你始終可以*解釋*每個決定是如何做出的。 +> **注意**：此範例較簡單，僅展示專家系統的基本樣貌。當你開始打造此系統時，只有達到一定數量的規則（約200條以上）時，才會看到一些*智能*行為。某階段規則過於複雜，難以全盤掌握，你會開始質疑系統為何做出某些決策。然而，基於知識系統的重要特性是，你總能*解釋*任何決定是如何做出的。 -## 本體論與語義網 +## 本體論與語義網路 -20 世紀末，有一項倡議使用知識表示來標註互聯網資源，以便能夠找到符合非常特定查詢的資源。這一運動被稱為**語義網**，並依賴於以下幾個概念： +20世紀末期曾有一項倡議，利用知識表示為網際網路資源加註解，使其可以找到符合非常具體查詢的資源。此倡議稱為**語義網路**，並依賴以下幾個概念： -- 基於**[描述邏輯](https://en.wikipedia.org/wiki/Description_logic)**（DL）的特殊知識表示。它類似於框架知識表示，因為它構建了一個具有屬性的對象層次結構，但它具有正式的邏輯語義和推理。描述邏輯有一整個家族，它們在表達能力和推理的算法複雜性之間取得平衡。 -- 分佈式知識表示，其中所有概念都由全局 URI 標識符表示，使得能夠創建跨越互聯網的知識層次結構。 -- 一系列基於 XML 的知識描述語言：RDF（資源描述框架）、RDFS（RDF Schema）、OWL（本體網絡語言）。 +- 基於**[描述邏輯](https://en.wikipedia.org/wiki/Description_logic)**(DL)的特殊知識表示。它類似框架知識表示，因為建立了帶屬性的物件層次結構，但擁有正式的邏輯語義與推理。描述邏輯有整個家族，平衡了表達力與推理算法複雜度。 +- 分散式知識表示，所有概念以全球URI識別，使得可建立跨網路的知識層次結構。 +- 一組基於 XML 的知識描述語言家族：RDF（資源描述框架）、RDFS（RDF 架構）、OWL（本體網路語言）。 -語義網的一個核心概念是 **本體**。它指的是使用某種形式化知識表示對問題領域進行明確的規範。最簡單的本體可能僅僅是問題領域中的對象層次結構，但更複雜的本體會包含可用於推理的規則。 +語意網的一個核心概念是**本體**。它指的是使用某種形式化知識表示對問題域的明確規範。最簡單的本體可以只是問題域中物件的階層結構，但更複雜的本體將包括可用於推理的規則。 -在語義網中，所有表示都基於三元組。每個對象和每個關係都由 URI 唯一標識。例如，如果我們想表達這個 AI 課程是由 Dmitry Soshnikov 在 2022 年 1 月 1 日開發的——以下是我們可以使用的三元組： +在語意網中，所有表示都是基於三元組。每個物件和每個關係都由 URI 唯一標識。例如，如果我們想表示這個 AI 課程是由 Dmitry Soshnikov 於 2022 年 1 月 1 日開發的——這裡是我們可以使用的三元組： - + ``` -http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 13, 2007” +http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 1, 2022” http://github.com/microsoft/ai-for-beginners http://purl.org/dc/elements/1.1/creator http://soshnikov.com ``` -> ✅ 這裡 `http://www.example.com/terms/creation-date` 和 `http://purl.org/dc/elements/1.1/creator` 是一些公認且通用的 URI，用於表達 *創建者* 和 *創建日期* 的概念。 +> ✅ 這裡 `http://www.example.com/terms/creation-date` 和 `http://purl.org/dc/elements/1.1/creator` 是一些眾所周知且被普遍接受的 URI，用於表達*創建者*和*創建日期*的概念。 -在更複雜的情況下，如果我們想定義一個創建者列表，我們可以使用 RDF 中定義的一些數據結構。 +在更複雜的情況下，如果我們想定義一個創建者列表，可以使用 RDF 定義的一些資料結構。 - + -> 上述圖表由 [Dmitry Soshnikov](http://soshnikov.com) 提供 +> 上方圖示由 [Dmitry Soshnikov](http://soshnikov.com) 製作 -語義網的發展因搜索引擎和自然語言處理技術的成功而有所放緩，這些技術能夠從文本中提取結構化數據。然而，在某些領域仍然有重要的努力來維護本體和知識庫。以下是幾個值得注意的項目： +語意網的建設進展在某種程度上因搜尋引擎和自然語言處理技術的成功而放緩，這些技術可以從文本中提取結構化數據。然而，在某些領域仍有大量努力來維護本體和知識庫。值得注意的幾個項目： -* [WikiData](https://wikidata.org/) 是與 Wikipedia 相關的機器可讀知識庫集合。大部分數據是從 Wikipedia 的 *InfoBoxes*（頁面內的結構化內容）中挖掘出來的。你可以使用 SPARQL（一種語義網專用的查詢語言）來[查詢](https://query.wikidata.org/) WikiData。以下是一個示例查詢，顯示人類中最常見的眼睛顏色： +* [WikiData](https://wikidata.org/) 是一個與維基百科關聯的機器可讀知識庫集合。大部分數據來自維基百科的 *資訊框*，即維基百科頁面中的結構化內容片段。你可以使用 SPARQL，一種語意網專用查詢語言，來[查詢](https://query.wikidata.org/) wikidata。這裡有一個示例查詢，顯示人類中最流行的眼睛顏色： ```sparql #defaultView:BubbleChart @@ -206,47 +206,51 @@ WHERE GROUP BY ?eyeColorLabel ``` -* [DBpedia](https://www.dbpedia.org/) 是另一個類似於 WikiData 的努力。 +* [DBpedia](https://www.dbpedia.org/) 是另一個類似 WikiData 的項目。 -> ✅ 如果你想嘗試構建自己的本體，或打開現有的本體，有一個很棒的可視化本體編輯器叫 [Protégé](https://protege.stanford.edu/)。你可以下載它，或者在線使用。 +> ✅ 如果你想嘗試自己建立本體，或打開現有的本體，有一個很棒的視覺本體編輯器叫做 [Protégé](https://protege.stanford.edu/)。你可以下載使用，也可以線上使用。 - + -*Web Protégé 編輯器打開了 Romanov 家族本體。截圖由 Dmitry Soshnikov 提供* +*Web Protégé 編輯器開啟了羅曼諾夫家族本體。截圖由 Dmitry Soshnikov 提供* ## ✍️ 練習：家族本體 -請參考 [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb)，了解如何使用語義網技術推理家族關係。我們將使用常見的 GEDCOM 格式表示的家族樹和家族關係的本體，為給定的一組個體構建所有家族關係的圖。 +參考 [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) 了解如何使用語意網技術來推理家族關係。我們將採用以常見的 GEDCOM 格式表示的家譜和家族關係本體，構建給定個人集的所有家族關係圖譜。 ## 微軟概念圖 -在大多數情況下，本體是由人工仔細創建的。然而，也可以從非結構化數據中**挖掘**本體，例如從自然語言文本中。 +在大多數情況下，本體是由人工精心創建的。然而，也可以從非結構化資料中**挖掘**本體，例如從自然語言文本中挖掘。 -微軟研究院曾進行過這樣的嘗試，並創建了 [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste)。 +微軟研究院進行過這樣的嘗試，並產生了[微軟概念圖](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste)。 -這是一個使用 `is-a` 繼承關係將實體分組的大型集合。它可以回答像「什麼是微軟？」這樣的問題——答案可能是「一家公司，概率為 0.87；一個品牌，概率為 0.75」。 +這是一個使用「is-a」繼承關係將實體分組的大型集合。它允許回答「什麼是微軟？」這樣的問題——答案會是「以 0.87 的概率是一家公司，以 0.75 的概率是一個品牌」等。 -該圖可以通過 REST API 獲取，也可以作為一個大型可下載的文本文件，其中列出了所有實體對。 +該圖可通過 REST API 獲取，或下載一個列出所有實體對的大型文本文件。 ## ✍️ 練習：概念圖 -試試 [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) 筆記本，看看我們如何使用 Microsoft Concept Graph 將新聞文章分組到幾個類別中。 +嘗試使用 [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) 筆記本，看看如何使用微軟概念圖將新聞文章分組到幾個類別中。 ## 結論 -如今，AI 通常被認為是 *機器學習* 或 *神經網絡* 的同義詞。然而，人類也表現出明確的推理能力，這是目前神經網絡無法處理的。在實際項目中，明確的推理仍然被用來執行需要解釋或能夠以可控方式修改系統行為的任務。 +現在，AI 常被認為是*機器學習*或*神經網絡*的同義詞。然而，人類也表現出明確的推理能力，這是目前神經網絡尚未處理的。在現實世界的專案中，明確推理仍用於執行需要解釋或能以可控方式修改系統行為的任務。 ## 🚀 挑戰 -在與本課程相關的家族本體筆記本中，有機會嘗試其他家族關係。試著發現家族樹中人物之間的新聯繫。 +在本課程相關的家族本體筆記本中，有機會嘗試其他家族關係。嘗試發掘家譜中人物之間的新連結。 ## [課後測驗](https://ff-quizzes.netlify.app/en/ai/quiz/4) -## 回顧與自學 +## 複習與自學 -在互聯網上進行一些研究，探索人類試圖量化和編碼知識的領域。了解布魯姆的教育目標分類法，回顧歷史，看看人類如何試圖理解世界。研究林奈斯如何創建生物分類法，以及觀察德米特里·門捷列夫如何描述和分組化學元素。你還能找到哪些有趣的例子？ +在網路上做些調查，發掘人類嘗試量化和編碼知識的領域。看看布魯姆的分類法，回顧歷史，了解人類如何試圖理解世界。探索林奈創建生物分類法的工作，觀察德米特里·門捷列夫如何創造化學元素的描述和分組方式。你還可以找到哪些有趣的例子？ -**作業**: [構建本體](assignment.md) +**作業**: [建立一個本體](assignment.md) --- + +**免責聲明**： +本文件係使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。儘管我們力求準確，但請注意，自動翻譯可能包含錯誤或不準確之處。原始文件的母語版本應被視為權威資料。對於重要資訊，建議採用專業人工翻譯。本公司對因使用此翻譯所產生的任何誤解或誤釋不承擔任何責任。 + \ No newline at end of file diff --git a/translations/mo/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/mo/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index 1f4c7bee..6b929e28 100644 --- a/translations/mo/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/mo/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -1259,7 +1259,7 @@ "* 訓練損失低——模型能很好地接近訓練數據，因為它具有足夠的表達能力。\n", "* 驗證損失可能比訓練損失高得多，並且在訓練過程中可能開始增加——這是因為模型“記住”了訓練數據點，卻失去了對“整體情況”的把握。\n", "\n", - "![過擬合](../../../../../translated_images/mo/overfit.a0bd57f717c15769.png)\n", + "![過擬合](../../../../../translated_images/mo/overfit.a0bd57f717c15769.webp)\n", "\n", "> 在這張圖中，`x` 代表訓練數據，`o` 代表驗證數據。左邊是線性模型（單層），它很好地接近了數據的本質。右邊是過擬合模型，該模型完美地接近了訓練數據，但對其他數據（驗證數據）的表現卻變得毫無意義（驗證誤差非常高）。\n" ] diff --git a/translations/mo/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/mo/lessons/3-NeuralNetworks/05-Frameworks/README.md index 98a4200e..6b1b7e27 100644 --- a/translations/mo/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/mo/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ CO_OP_TRANSLATOR_METADATA: 考慮以下近似 5 個點（圖中的 `x`）的問題： -![線性模型](../../../../../translated_images/mo/overfit1.f24b71c6f652e59e.jpg) | ![過擬合模型](../../../../../translated_images/mo/overfit2.131f5800ae10ca5e.jpg) +![線性模型](../../../../../translated_images/mo/overfit1.f24b71c6f652e59e.webp) | ![過擬合模型](../../../../../translated_images/mo/overfit2.131f5800ae10ca5e.webp) -------------------------|-------------------------- **線性模型，2 個參數** | **非線性模型，7 個參數** 訓練誤差 = 5.3 | 訓練誤差 = 0 @@ -79,7 +79,7 @@ CO_OP_TRANSLATOR_METADATA: 如上圖所示，過擬合可以通過非常低的訓練誤差和非常高的驗證誤差來檢測。通常在訓練過程中，我們會看到訓練誤差和驗證誤差都開始下降，然後在某個時候驗證誤差可能停止下降並開始上升。這將是過擬合的跡象，也是我們應該停止訓練的指示（或者至少保存模型的快照）。 -![過擬合](../../../../../translated_images/mo/Overfitting.408ad91cd90b4371.png) +![過擬合](../../../../../translated_images/mo/Overfitting.408ad91cd90b4371.webp) ## 如何防止過擬合 diff --git a/translations/mo/lessons/3-NeuralNetworks/README.md b/translations/mo/lessons/3-NeuralNetworks/README.md index dc2ca5f6..a72f1ec0 100644 --- a/translations/mo/lessons/3-NeuralNetworks/README.md +++ b/translations/mo/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 神經網絡簡介 -![神經網絡簡介內容摘要的手繪圖](../../../../translated_images/mo/ai-neuralnetworks.1c687ae40bc86e83.png) +![神經網絡簡介內容摘要的手繪圖](../../../../translated_images/mo/ai-neuralnetworks.1c687ae40bc86e83.webp) 如我們在介紹中所討論的，實現智能的一種方法是訓練一個**計算機模型**或**人工大腦**。自20世紀中期以來，研究人員嘗試了不同的數學模型，直到最近這一方向取得了巨大成功。這些模仿大腦的數學模型被稱為**神經網絡**。 @@ -36,13 +36,13 @@ CO_OP_TRANSLATOR_METADATA: 從生物學中，我們知道大腦由神經細胞（神經元）組成，每個神經元都有多個“輸入”（樹突）和一個“輸出”（軸突）。樹突和軸突都可以傳導電信號，而它們之間的連接——稱為突觸——可以表現出不同程度的導電性，這些導電性由神經遞質調節。 -![神經元模型](../../../../translated_images/mo/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![神經元模型](../../../../translated_images/mo/artneuron.1a5daa88d20ebe6f.png) +![神經元模型](../../../../translated_images/mo/synapse-wikipedia.ed20a9e4726ea1c6.webp) | ![神經元模型](../../../../translated_images/mo/artneuron.1a5daa88d20ebe6f.webp) ----|---- 真實神經元 *（[圖片](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) 來自維基百科）* | 人工神經元 *（作者提供圖片）* 因此，神經元的最簡單數學模型包含幾個輸入 X1, ..., XN 和一個輸出 Y，以及一系列權重 W1, ..., WN。輸出計算公式為： -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) 其中 f 是某種非線性的**激活函數**。 diff --git a/translations/mo/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/mo/lessons/4-ComputerVision/06-IntroCV/README.md index b0126ccc..c2c46dbf 100644 --- a/translations/mo/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/mo/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) * **預處理盲文書的照片**。我們專注於如何使用閾值處理、特徵檢測、透視變換和 NumPy 操作來分離單個盲文符號，以便進一步由神經網路進行分類。 -![盲文影像](../../../../../translated_images/mo/braille.341962ff76b1bd70.jpeg) | ![盲文影像預處理結果](../../../../../translated_images/mo/braille-result.46530fea020b03c7.png) | ![盲文符號](../../../../../translated_images/mo/braille-symbols.0159185ab69d5339.png) +![盲文影像](../../../../../translated_images/mo/braille.341962ff76b1bd70.webp) | ![盲文影像預處理結果](../../../../../translated_images/mo/braille-result.46530fea020b03c7.webp) | ![盲文符號](../../../../../translated_images/mo/braille-symbols.0159185ab69d5339.webp) ----|-----|----- > 圖片來自 [OpenCV.ipynb](OpenCV.ipynb) * **使用幀差檢測影片中的運動**。如果相機固定，則來自相機的幀應該彼此非常相似。由於幀表示為陣列，只需對兩個連續幀的陣列進行相減，我們就能得到像素差異，靜態幀的差異應該很低，而當影像中有顯著運動時，差異會變高。 -![影片幀和幀差的影像](../../../../../translated_images/mo/frame-difference.706f805491a0883c.png) +![影片幀和幀差的影像](../../../../../translated_images/mo/frame-difference.706f805491a0883c.webp) > 圖片來自 [OpenCV.ipynb](OpenCV.ipynb) @@ -88,7 +88,7 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) - **密集光流** 計算顯示每個像素移動方向的向量場 - **稀疏光流** 基於提取影像中的一些顯著特徵（例如邊緣），並從幀到幀構建它們的軌跡。 -![光流影像](../../../../../translated_images/mo/optical.1f4a94464579a83a.png) +![光流影像](../../../../../translated_images/mo/optical.1f4a94464579a83a.webp) > 圖片來自 [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/mo/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/mo/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 6f4124bb..91c9d360 100644 --- a/translations/mo/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/mo/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 是一個在 2014 年 ImageNet top-5 分類中達到 92.7% 準確率的網路。它的層結構如下： -![ImageNet Layers](../../../../../translated_images/mo/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet Layers](../../../../../translated_images/mo/vgg-16-arch1.d901a5583b3a51ba.webp) 如圖所示，VGG 採用傳統的金字塔架構，即一系列的卷積-池化層。 -![ImageNet Pyramid](../../../../../translated_images/mo/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet Pyramid](../../../../../translated_images/mo/vgg-16-arch.64ff2137f50dd49f.webp) > 圖片來源：[Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/mo/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/mo/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index a90e6f39..43bf8832 100644 --- a/translations/mo/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/mo/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -260,7 +260,7 @@ "\n", "因此，在典型的 CNN 中，會有多個卷積層，並在它們之間加入池化層以減少影像的維度。我們還會增加濾波器的數量，因為隨著模式變得更複雜，我們需要尋找的可能組合也會更多。\n", "\n", - "![一張展示多個卷積層和池化層的圖片。](../../../../../translated_images/mo/cnn-pyramid.85915455759ef0ce.png)\n", + "![一張展示多個卷積層和池化層的圖片。](../../../../../translated_images/mo/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "由於空間維度減少以及特徵/濾波器維度增加，這種架構也被稱為 **金字塔架構**。\n" ] diff --git a/translations/mo/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/mo/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index ef541c45..dc73ecae 100644 --- a/translations/mo/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/mo/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -359,7 +359,7 @@ "\n", "因此，在典型的 CNN 中，會有多個卷積層，並在它們之間插入池化層以減少圖片的尺寸。同時，我們還會增加濾波器的數量，因為隨著模式變得更加複雜，我們需要尋找的可能組合也會更多。\n", "\n", - "![一張展示多個卷積層與池化層的圖片。](../../../../../translated_images/mo/cnn-pyramid.85915455759ef0ce.png)\n", + "![一張展示多個卷積層與池化層的圖片。](../../../../../translated_images/mo/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "由於空間尺寸逐漸減小，而特徵/濾波器的維度逐漸增加，這種架構也被稱為 **金字塔架構**。\n" ] diff --git a/translations/mo/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/mo/lessons/4-ComputerVision/07-ConvNets/README.md index 78a552bb..6bcbb9af 100644 --- a/translations/mo/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/mo/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: 為了提取模式，我們將使用**卷積濾波器**的概念。正如你所知，圖像可以用二維矩陣或帶有色彩深度的三維張量來表示。應用濾波器意味著我們取一個相對較小的**濾波核**矩陣，並對原始圖像中的每個像素與其鄰近點進行加權平均。我們可以將其視為一個小窗口在整個圖像上滑動，並根據濾波核矩陣中的權重對所有像素進行平均。 -![垂直邊緣濾波器](../../../../../translated_images/mo/filter-vert.b7148390ca0bc356.png) | ![水平邊緣濾波器](../../../../../translated_images/mo/filter-horiz.59b80ed4feb946ef.png) +![垂直邊緣濾波器](../../../../../translated_images/mo/filter-vert.b7148390ca0bc356.webp) | ![水平邊緣濾波器](../../../../../translated_images/mo/filter-horiz.59b80ed4feb946ef.webp) ----|---- > 圖片來源：Dmitry Soshnikov @@ -38,7 +38,7 @@ CNN 的工作方式基於以下重要思想： * 我們可以設計網絡，使濾波器能夠自動訓練 * 我們可以使用相同的方法來在高層次特徵中找到模式，而不僅僅是在原始圖像中。因此，CNN 的特徵提取在特徵層次上工作，從低層次的像素組合開始，到更高層次的圖片部分組合。 -![層次特徵提取](../../../../../translated_images/mo/FeatureExtractionCNN.d9b456cbdae7cb64.png) +![層次特徵提取](../../../../../translated_images/mo/FeatureExtractionCNN.d9b456cbdae7cb64.webp) > 圖片來源：[Hislop-Lynch 的論文](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d)，基於[他們的研究](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ CNN 的工作方式基於以下重要思想： 例如，讓我們看看 VGG-16 的架構，這是一個在 2014 年 ImageNet 的 top-5 分類中達到 92.7% 準確率的網絡： -![ImageNet 層](../../../../../translated_images/mo/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet 層](../../../../../translated_images/mo/vgg-16-arch1.d901a5583b3a51ba.webp) -![ImageNet 金字塔](../../../../../translated_images/mo/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet 金字塔](../../../../../translated_images/mo/vgg-16-arch.64ff2137f50dd49f.webp) > 圖片來源：[Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/mo/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/mo/lessons/4-ComputerVision/07-ConvNets/lab/README.md index b0533466..bcfcb330 100644 --- a/translations/mo/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/mo/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ CO_OP_TRANSLATOR_METADATA: 我們將使用 [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/)，該數據集包含 37 種不同品種的狗和貓的圖片。 -![我們將處理的數據集](../../../../../../translated_images/mo/data.50b2a9d5484bdbf0.png) +![我們將處理的數據集](../../../../../../translated_images/mo/data.50b2a9d5484bdbf0.webp) 要下載數據集，請使用以下程式碼片段： diff --git a/translations/mo/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/mo/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index 4d879215..d40faa20 100644 --- a/translations/mo/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/mo/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "為了呈現理想的貓，我們將從一張隨機噪點圖片開始，並嘗試使用梯度下降優化技術來調整圖片，使網絡能夠識別出貓。\n", "\n", - "![優化循環](../../../../../translated_images/mo/ideal-cat-loop.999fbb8ff306e044.png)\n", + "![優化循環](../../../../../translated_images/mo/ideal-cat-loop.999fbb8ff306e044.webp)\n", "\n", "以下是我們的起始圖片：\n" ] diff --git a/translations/mo/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/mo/lessons/4-ComputerVision/08-TransferLearning/README.md index 64f4d00a..51acb903 100644 --- a/translations/mo/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/mo/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Keras 和 PyTorch 都包含函數，可以輕鬆加載一些常見架構的預 以下是 VGG-16 網絡從一張貓的圖片中提取的特徵示例： -![VGG-16 提取的特徵](../../../../../translated_images/mo/features.6291f9c7ba3a0b95.png) +![VGG-16 提取的特徵](../../../../../translated_images/mo/features.6291f9c7ba3a0b95.webp) ## 貓與狗數據集 @@ -48,19 +48,19 @@ Keras 和 PyTorch 都包含函數，可以輕鬆加載一些常見架構的預 我們可以採取的一種方法是從一張隨機圖像開始，然後嘗試使用**梯度下降優化**技術調整該圖像，使網絡開始認為它是一隻貓。 -![圖像優化循環](../../../../../translated_images/mo/ideal-cat-loop.999fbb8ff306e044.png) +![圖像優化循環](../../../../../translated_images/mo/ideal-cat-loop.999fbb8ff306e044.webp) 然而，如果我們這樣做，結果會非常接近隨機噪聲。這是因為*有很多方法可以讓網絡認為輸入圖像是一隻貓*，包括一些在視覺上沒有意義的方式。雖然這些圖像包含了許多典型的貓的模式，但並沒有任何約束使它們在視覺上更具辨識性。 為了改善結果，我們可以在損失函數中添加另一個項，稱為**變異損失**。它是一種度量，顯示圖像中相鄰像素的相似程度。最小化變異損失可以使圖像更平滑，並消除噪聲——從而揭示更具視覺吸引力的模式。以下是一些“理想”圖像的示例，它們被高概率分類為貓和斑馬： -![理想貓](../../../../../translated_images/mo/ideal-cat.203dd4597643d6b0.png) | ![理想斑馬](../../../../../translated_images/mo/ideal-zebra.7f70e8b54ee15a7a.png) +![理想貓](../../../../../translated_images/mo/ideal-cat.203dd4597643d6b0.webp) | ![理想斑馬](../../../../../translated_images/mo/ideal-zebra.7f70e8b54ee15a7a.webp) -----|----- *理想貓* | *理想斑馬* 類似的方法可以用於對神經網絡進行所謂的**對抗性攻擊**。假設我們想要欺騙神經網絡，使一隻狗看起來像一隻貓。如果我們拿一張狗的圖片，該圖片被網絡識別為狗，然後稍微調整它，使用梯度下降優化，直到網絡開始將其分類為貓： -![狗的圖片](../../../../../translated_images/mo/original-dog.8f68a67d2fe0911f.png) | ![被分類為貓的狗圖片](../../../../../translated_images/mo/adversarial-dog.d9fc7773b0142b89.png) +![狗的圖片](../../../../../translated_images/mo/original-dog.8f68a67d2fe0911f.webp) | ![被分類為貓的狗圖片](../../../../../translated_images/mo/adversarial-dog.d9fc7773b0142b89.webp) -----|----- *狗的原始圖片* | *被分類為貓的狗圖片* diff --git a/translations/mo/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/mo/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index b3f6bb2b..8ab0953b 100644 --- a/translations/mo/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/mo/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "由於我們訓練自編碼器的目的是儘可能捕捉原始圖像中的信息以進行準確的重建，網絡會嘗試找到輸入圖像的最佳**嵌入（embedding）**來捕捉其含義。\n", "\n", - "![自編碼器示意圖](../../../../../translated_images/mo/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![自編碼器示意圖](../../../../../translated_images/mo/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "> 圖片來源：[Keras 博客](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", diff --git a/translations/mo/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/mo/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index 1badcdb9..b6d58f9e 100644 --- a/translations/mo/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/mo/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "由於我們訓練自編碼器的目的是捕捉原始圖像中的盡可能多的信息以進行準確的重建，網絡會嘗試找到輸入圖像的最佳**嵌入**來捕捉其含義。\n", "\n", - "![自編碼器示意圖](../../../../../translated_images/mo/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![自編碼器示意圖](../../../../../translated_images/mo/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "*圖片來源：[Keras 部落格](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", diff --git a/translations/mo/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/mo/lessons/4-ComputerVision/09-Autoencoders/README.md index 8b7f1ad2..9f5d01b4 100644 --- a/translations/mo/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/mo/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: 由於我們訓練自動編碼器的目的是捕捉原始圖像中的盡可能多的信息以進行準確的重建，網絡會嘗試找到最佳的**嵌入**方式來捕捉輸入圖像的含義。 -![自動編碼器示意圖](../../../../../translated_images/mo/autoencoder_schema.5e6fc9ad98a5eb61.jpg) +![自動編碼器示意圖](../../../../../translated_images/mo/autoencoder_schema.5e6fc9ad98a5eb61.webp) > 圖片來源：[Keras 博客](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/mo/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/mo/lessons/4-ComputerVision/11-ObjectDetection/README.md index 9ac3af8e..cd75d16c 100644 --- a/translations/mo/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/mo/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: ## [課前測驗](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![物件偵測](../../../../../translated_images/mo/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) +![物件偵測](../../../../../translated_images/mo/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp) > 圖片來源：[YOLO v2 網站](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 2. 對每個區塊進行影像分類。 3. 將分類結果中激活值足夠高的區塊視為包含目標物件的區域。 -![簡單的物件偵測](../../../../../translated_images/mo/naive-detection.e7f1ba220ccd08c6.png) +![簡單的物件偵測](../../../../../translated_images/mo/naive-detection.e7f1ba220ccd08c6.webp) > *圖片來源：[練習筆記本](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ CO_OP_TRANSLATOR_METADATA: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 包含 20 個類別 * [COCO](http://cocodataset.org/#home) - 常見物件的上下文。包含 80 個類別、邊界框和分割遮罩 -![COCO](../../../../../translated_images/mo/coco-examples.71bc60380fa6cceb.jpg) +![COCO](../../../../../translated_images/mo/coco-examples.71bc60380fa6cceb.webp) ## 物件偵測的評估指標 @@ -50,7 +50,7 @@ CO_OP_TRANSLATOR_METADATA: 對於影像分類來說，衡量演算法的表現相對簡單；但對於物件偵測，我們需要同時衡量類別的正確性以及推測邊界框位置的精確性。後者使用所謂的**交集比聯集**（IoU）來衡量，這是一種用來評估兩個框（或任意兩個區域）重疊程度的方法。 -![IoU](../../../../../translated_images/mo/iou_equation.9a4751d40fff4e11.png) +![IoU](../../../../../translated_images/mo/iou_equation.9a4751d40fff4e11.webp) > *圖片來源：[這篇優秀的 IoU 部落格文章](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ $$ [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) 使用 [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) 生成層次結構的 ROI 區域，然後通過 CNN 特徵提取器和 SVM 分類器來確定物件類別，並通過線性迴歸確定*邊界框*座標。[官方論文](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/mo/rcnn1.cae407020dfb1d1f.png) +![RCNN](../../../../../translated_images/mo/rcnn1.cae407020dfb1d1f.webp) > *圖片來源：van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/mo/rcnn2.2d9530bb83516484.png) +![RCNN-1](../../../../../translated_images/mo/rcnn2.2d9530bb83516484.webp) > *圖片來源：[這篇部落格](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -110,7 +110,7 @@ $$ 這種方法與 R-CNN 類似，但區域是在卷積層應用之後定義的。 -![FRCNN](../../../../../translated_images/mo/f-rcnn.3cda6d9bb4188875.png) +![FRCNN](../../../../../translated_images/mo/f-rcnn.3cda6d9bb4188875.webp) > 圖片來源：[官方論文](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf)，[arXiv](https://arxiv.org/pdf/1504.08083.pdf)，2015 @@ -118,7 +118,7 @@ $$ 這種方法的主要思想是使用神經網路來預測 ROI，即所謂的*區域提議網路*。[論文](https://arxiv.org/pdf/1506.01497.pdf)，2016 -![FasterRCNN](../../../../../translated_images/mo/faster-rcnn.8d46c099b87ef30a.png) +![FasterRCNN](../../../../../translated_images/mo/faster-rcnn.8d46c099b87ef30a.webp) > 圖片來源：[官方論文](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ $$ 2. 特徵經過**位置敏感分數圖**處理。每個來自 $C$ 類別的物件被劃分為 $k\times k$ 區域，並訓練網路預測物件的部分。 3. 對於 $k\times k$ 區域中的每個部分，所有網路對物件類別進行投票，選擇得票最多的物件類別。 -![r-fcn 圖片](../../../../../translated_images/mo/r-fcn.13eb88158b99a3da.png) +![r-fcn 圖片](../../../../../translated_images/mo/r-fcn.13eb88158b99a3da.webp) > 圖片來源：[官方論文](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO 是一種實時的單次通過演算法。主要思想如下： * 將圖片劃分為 $S\times S$ 區域。 * 對於每個區域，**CNN** 預測 $n$ 個可能的物件、*邊界框*座標以及*置信度*=*概率* * IoU。 - ![YOLO](../../../../../translated_images/mo/yolo.a2648ec82ee8bb4e.png) + ![YOLO](../../../../../translated_images/mo/yolo.a2648ec82ee8bb4e.webp) > 圖片來源：[官方論文](https://arxiv.org/abs/1506.02640) diff --git a/translations/mo/lessons/4-ComputerVision/README.md b/translations/mo/lessons/4-ComputerVision/README.md index 243c3a13..0a1a408e 100644 --- a/translations/mo/lessons/4-ComputerVision/README.md +++ b/translations/mo/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 電腦視覺 -![電腦視覺內容摘要的手繪圖](../../../../translated_images/mo/ai-computervision.6506ebebac3fbf76.png) +![電腦視覺內容摘要的手繪圖](../../../../translated_images/mo/ai-computervision.6506ebebac3fbf76.webp) 在本章節中，我們將學習以下內容： diff --git a/translations/mo/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/mo/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index e30ff6e3..d9d2e1f6 100644 --- a/translations/mo/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/mo/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**詞袋** (BoW) 向量表示法是最常用的傳統向量表示法。每個詞語都與一個向量索引相關聯，向量元素包含某個詞語在特定文檔中出現的次數。\n", "\n", - "![顯示詞袋向量表示法在記憶體中如何表示的圖片。](../../../../../translated_images/mo/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![顯示詞袋向量表示法在記憶體中如何表示的圖片。](../../../../../translated_images/mo/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Note**: 你也可以將 BoW 理解為文本中每個詞語的單熱編碼向量的總和。\n", "\n", diff --git a/translations/mo/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/mo/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index 322f66df..9be7de31 100644 --- a/translations/mo/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/mo/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**詞袋**（Bag-of-words, BoW）向量表示法是最簡單易懂的傳統向量表示法。每個詞語都對應到向量中的一個索引，而向量中的元素則表示該詞語在特定文檔中出現的次數。\n", "\n", - "![顯示詞袋向量表示法在記憶體中如何表示的圖片。](../../../../../translated_images/mo/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![顯示詞袋向量表示法在記憶體中如何表示的圖片。](../../../../../translated_images/mo/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **注意**：你也可以將 BoW 理解為文本中每個詞語的單熱編碼（one-hot-encoded）向量的總和。\n", "\n", diff --git a/translations/mo/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/mo/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 3ef91809..884cac4c 100644 --- a/translations/mo/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/mo/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "通過將嵌入層作為我們網絡的第一層，我們可以從詞袋模型（bag-of-words）切換到 **嵌入袋模型**（embedding bag model）。在這種模型中，我們首先將文本中的每個單詞轉換為對應的嵌入向量，然後對所有這些嵌入向量執行某種聚合函數，例如 `sum`、`average` 或 `max`。\n", "\n", - "![展示一個針對五個序列單詞的嵌入分類器的圖片。](../../../../../translated_images/mo/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![展示一個針對五個序列單詞的嵌入分類器的圖片。](../../../../../translated_images/mo/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "我們的分類器神經網絡將以嵌入層開始，接著是聚合層，最後在其上添加一個線性分類器：\n" ] @@ -176,7 +176,7 @@ "\n", "在之前的架構中，我們需要將所有序列填充（pad）到相同的長度，才能將它們放入一個小批次中。這並不是表示變長序列最有效率的方法——另一種方法是使用 **offset** 向量，該向量會保存所有序列在一個大型向量中的偏移量。\n", "\n", - "![顯示偏移序列表示法的圖片](../../../../../translated_images/mo/offset-sequence-representation.eb73fcefb29b46ee.png)\n", + "![顯示偏移序列表示法的圖片](../../../../../translated_images/mo/offset-sequence-representation.eb73fcefb29b46ee.webp)\n", "\n", "> **注意**：在上圖中，我們展示的是一個字符序列，但在我們的例子中，我們處理的是單詞序列。然而，使用偏移向量表示序列的基本原則是相同的。\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW 的訓練速度較快，而 Skip-Gram 雖然較慢，但在表示不常見單詞方面表現更好。\n", "\n", - "![顯示 CBoW 和 Skip-Gram 算法如何將單詞轉換為向量的圖片。](../../../../../translated_images/mo/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![顯示 CBoW 和 Skip-Gram 算法如何將單詞轉換為向量的圖片。](../../../../../translated_images/mo/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "為了試驗在 Google News 數據集上預訓練的 Word2Vec 嵌入，我們可以使用 **gensim** 庫。以下是找到與 'neural' 最相似的單詞的示例：\n", "\n", diff --git a/translations/mo/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/mo/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index 0ecb1e0d..383f40e2 100644 --- a/translations/mo/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/mo/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "通過將嵌入層作為我們網絡的第一層，我們可以從詞袋模型（bag-of-words）切換到 **嵌入袋模型**（embedding bag），在這裡我們首先將文本中的每個單詞轉換為對應的嵌入，然後對所有這些嵌入計算某種聚合函數，例如 `sum`、`average` 或 `max`。\n", "\n", - "![顯示五個序列單詞的嵌入分類器的圖片。](../../../../../translated_images/mo/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![顯示五個序列單詞的嵌入分類器的圖片。](../../../../../translated_images/mo/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "我們的分類器神經網絡由以下幾層組成：\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW 的速度較快，而 Skip-Gram 雖然較慢，但在表示不常見詞方面表現更好。\n", "\n", - "![展示 CBoW 和 Skip-Gram 算法如何將詞轉換為向量的圖片。](../../../../../translated_images/mo/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![展示 CBoW 和 Skip-Gram 算法如何將詞轉換為向量的圖片。](../../../../../translated_images/mo/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "為了試驗基於 Google News 數據集預訓練的 Word2Vec 嵌入，我們可以使用 **gensim** 庫。以下是我們找到與「neural」最相似的詞。\n", "\n", diff --git a/translations/mo/lessons/5-NLP/14-Embeddings/README.md b/translations/mo/lessons/5-NLP/14-Embeddings/README.md index eecae6c8..b558137b 100644 --- a/translations/mo/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/mo/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: 通過在分類器網絡中使用嵌入層作為第一層，我們可以從詞袋模型切換到 **嵌入袋** 模型。在嵌入袋模型中，我們首先將文本中的每個詞轉換為相應的嵌入，然後對所有嵌入計算某種聚合函數，例如 `sum`、`average` 或 `max`。 -![嵌入分類器處理五個序列詞的示例圖。](../../../../../translated_images/mo/embedding-classifier-example.b77f021a7ee67eee.png) +![嵌入分類器處理五個序列詞的示例圖。](../../../../../translated_images/mo/embedding-classifier-example.b77f021a7ee67eee.webp) > 圖片由作者提供 @@ -40,7 +40,7 @@ CO_OP_TRANSLATOR_METADATA: CBoW 的速度更快，而 Skip-Gram 雖然較慢，但在表示不常見詞方面效果更好。 -![展示 CBoW 和 Skip-Gram 將詞轉換為向量的算法示例圖。](../../../../../translated_images/mo/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![展示 CBoW 和 Skip-Gram 將詞轉換為向量的算法示例圖。](../../../../../translated_images/mo/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > 圖片來源：[這篇論文](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/mo/lessons/5-NLP/15-LanguageModeling/README.md b/translations/mo/lessons/5-NLP/15-LanguageModeling/README.md index abab2d12..30c4a56a 100644 --- a/translations/mo/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/mo/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: * **連續詞袋模型**（CBoW）：在標記序列 $W_{-N}$, ..., $W_N$ 中預測中間的標記 $W_0$。 * **Skip-gram**：從中間標記 $W_0$ 預測一組相鄰的標記 {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$}。 -![來自將單詞轉換為向量的論文的圖片](../../../../../translated_images/mo/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![來自將單詞轉換為向量的論文的圖片](../../../../../translated_images/mo/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > 圖片來源：[這篇論文](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/mo/lessons/5-NLP/16-RNN/README.md b/translations/mo/lessons/5-NLP/16-RNN/README.md index 6bed1bde..4460466c 100644 --- a/translations/mo/lessons/5-NLP/16-RNN/README.md +++ b/translations/mo/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: 為了捕捉文本序列的意義，我們需要使用另一種神經網絡架構，稱為**循環神經網絡**（Recurrent Neural Network，簡稱 RNN）。在 RNN 中，我們將句子逐個符號地傳遞給網絡，網絡會生成某種**狀態**，然後將該狀態與下一個符號一起再次傳遞給網絡。 -![RNN](../../../../../translated_images/mo/rnn.27f5c29c53d727b5.png) +![RNN](../../../../../translated_images/mo/rnn.27f5c29c53d727b5.webp) > 圖片由作者提供 @@ -61,7 +61,7 @@ LSTM 網絡的組織方式與 RNN 類似，但有兩個狀態從層到層傳遞 循環網絡，無論是單向還是雙向，都能捕捉序列中的某些模式，並將它們存儲到狀態向量中或傳遞到輸出中。與卷積網絡類似，我們可以在第一層之上構建另一個循環層，以捕捉更高層次的模式，並基於第一層提取的低層次模式進行構建。這引出了**多層 RNN** 的概念，它由兩個或更多循環網絡組成，其中前一層的輸出作為輸入傳遞到下一層。 -![顯示多層長短期記憶 RNN 的圖片](../../../../../translated_images/mo/multi-layer-lstm.dd975e29bb2a59fe.jpg) +![顯示多層長短期記憶 RNN 的圖片](../../../../../translated_images/mo/multi-layer-lstm.dd975e29bb2a59fe.webp) *圖片來自 Fernando López 的[這篇精彩文章](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3)* diff --git a/translations/mo/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/mo/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index 691955ff..07ce9a47 100644 --- a/translations/mo/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/mo/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "無論是單向還是雙向的循環網路，都能捕捉序列中的某些模式，並將其存儲到狀態向量中或傳遞到輸出中。與卷積網路類似，我們可以在第一層之上構建另一個循環層，以捕捉更高層次的模式，這些模式是由第一層提取的低層次模式構成的。這引出了 **多層 RNN** 的概念，它由兩層或更多的循環網路組成，前一層的輸出作為下一層的輸入。\n", "\n", - "![顯示多層長短期記憶 RNN 的圖片](../../../../../translated_images/mo/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![顯示多層長短期記憶 RNN 的圖片](../../../../../translated_images/mo/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*圖片來源：[這篇精彩的文章](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) 作者 Fernando López*\n", "\n", diff --git a/translations/mo/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/mo/lessons/5-NLP/16-RNN/RNNTF.ipynb index d2f7d542..77d37d64 100644 --- a/translations/mo/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/mo/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "為了捕捉文本序列的意義，我們將使用一種稱為**循環神經網路**（Recurrent Neural Network，簡稱 RNN）的神經網路架構。在使用 RNN 時，我們會將句子逐個標記（token）傳遞給網路，網路會生成某種**狀態**，然後我們將該狀態與下一個標記一起再次傳遞給網路。\n", "\n", - "![顯示循環神經網路生成示例的圖片。](../../../../../translated_images/mo/rnn.27f5c29c53d727b5.png)\n", + "![顯示循環神經網路生成示例的圖片。](../../../../../translated_images/mo/rnn.27f5c29c53d727b5.webp)\n", "\n", "給定標記輸入序列 $X_0,\\dots,X_n$，RNN 會創建一個神經網路區塊的序列，並通過反向傳播對該序列進行端到端訓練。每個網路區塊接受一對 $(X_i,S_i)$ 作為輸入，並生成 $S_{i+1}$ 作為結果。最終狀態 $S_n$ 或輸出 $Y_n$ 會進入線性分類器以生成結果。所有網路區塊共享相同的權重，並通過一次反向傳播訓練完成端到端學習。\n", "\n", @@ -369,7 +369,7 @@ "\n", "無論是單向還是雙向的循環網絡，都能捕捉序列中的模式，並將其存儲到狀態向量中或作為輸出返回。與卷積網絡類似，我們可以在第一層循環層之後再構建另一層循環層，以捕捉更高層次的模式，這些模式是由第一層提取的低層次模式構建而成的。這引出了 **多層 RNN** 的概念，它由兩層或更多層循環網絡組成，其中前一層的輸出作為下一層的輸入。\n", "\n", - "![顯示多層長短期記憶 RNN 的圖片](../../../../../translated_images/mo/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![顯示多層長短期記憶 RNN 的圖片](../../../../../translated_images/mo/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*圖片來源：[這篇精彩的文章](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3)，作者 Fernando López。*\n", "\n", diff --git a/translations/mo/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/mo/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index 90ad201f..ece3ecaa 100644 --- a/translations/mo/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/mo/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "我們將以以下方式訓練 RNN 來生成文本。在每一步中，我們會取一段長度為 `nchars` 的字元序列，並讓網路為每個輸入字元生成下一個輸出字元：\n", "\n", - "![顯示 RNN 生成單詞 'HELLO' 的範例圖像。](../../../../../translated_images/mo/rnn-generate.56c54afb52f9781d.png)\n", + "![顯示 RNN 生成單詞 'HELLO' 的範例圖像。](../../../../../translated_images/mo/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "根據實際情況，我們可能還需要加入一些特殊字元，例如 *序列結束符* ``。在我們的例子中，我們只希望訓練網路進行無限文本生成，因此我們會將每個序列的大小固定為 `nchars` 個標記。因此，每個訓練樣本將包含 `nchars` 個輸入和 `nchars` 個輸出（輸出是將輸入序列向左移動一個符號後的結果）。一個小批次（minibatch）將由多個這樣的序列組成。\n", "\n", diff --git a/translations/mo/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/mo/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index d6cdef61..797f7153 100644 --- a/translations/mo/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/mo/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -111,7 +111,7 @@ "\n", "我們將以以下方式訓練 RNN 來生成新聞標題。在每一步中，我們會取一個標題，將其輸入到 RNN 中，並對於每個輸入的字元，要求網路生成下一個輸出的字元：\n", "\n", - "![顯示 RNN 生成單詞 'HELLO' 的範例圖片。](../../../../../translated_images/mo/rnn-generate.56c54afb52f9781d.png)\n", + "![顯示 RNN 生成單詞 'HELLO' 的範例圖片。](../../../../../translated_images/mo/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "對於序列中的最後一個字元，我們會要求網路生成 `` 標記。\n", "\n", diff --git a/translations/mo/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/mo/lessons/5-NLP/17-GenerativeNetworks/README.md index 28cd11aa..12654c3c 100644 --- a/translations/mo/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/mo/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: 這使得不同的神經網絡架構成為可能，如下圖所示： -![展示常見循環神經網絡模式的圖片。](../../../../../translated_images/mo/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![展示常見循環神經網絡模式的圖片。](../../../../../translated_images/mo/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > 圖片來源：[Andrej Karpaty](http://karpathy.github.io/) 的博客文章 [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) @@ -32,7 +32,7 @@ CO_OP_TRANSLATOR_METADATA: 我們將訓練這個 RNN 逐步生成文本。在每一步中，我們將取一個長度為 `nchars` 的字符序列，並要求網絡為每個輸入字符生成下一個輸出字符： -![展示 RNN 生成單詞 'HELLO' 的示例圖片。](../../../../../translated_images/mo/rnn-generate.56c54afb52f9781d.png) +![展示 RNN 生成單詞 'HELLO' 的示例圖片。](../../../../../translated_images/mo/rnn-generate.56c54afb52f9781d.webp) 在生成文本（推理過程）時，我們從某個**提示**開始，將其通過 RNN 單元生成中間狀態，然後從該狀態開始生成。我們一次生成一個字符，並將狀態和生成的字符傳遞給另一個 RNN 單元以生成下一個字符，直到生成足夠的字符。 diff --git a/translations/mo/lessons/5-NLP/18-Transformers/README.md b/translations/mo/lessons/5-NLP/18-Transformers/README.md index f5e2c167..40351150 100644 --- a/translations/mo/lessons/5-NLP/18-Transformers/README.md +++ b/translations/mo/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ CO_OP_TRANSLATOR_METADATA: **注意力機制**提供了一種方法，能夠對每個輸入向量對 RNN 每個輸出預測的上下文影響進行加權。其實現方式是通過在輸入 RNN 的中間狀態與輸出 RNN 之間創建捷徑。這樣，在生成輸出符號 yt 時，我們會考慮所有輸入隱藏狀態 hi，並賦予不同的權重係數 αt,i。 -![顯示具有加性注意力層的編碼器/解碼器模型的圖片](../../../../../translated_images/mo/encoder-decoder-attention.7a726296894fb567.png) +![顯示具有加性注意力層的編碼器/解碼器模型的圖片](../../../../../translated_images/mo/encoder-decoder-attention.7a726296894fb567.webp) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) 中的加性注意力機制編碼器-解碼器模型，圖片來源於[這篇博客文章](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) 注意力矩陣 {αi,j} 表示某些輸入詞在生成輸出序列中特定詞時所起的作用程度。以下是一個這樣的矩陣示例： -![顯示 RNNsearch-50 發現的樣本對齊的圖片，取自 Bahdanau - arviz.org](../../../../../translated_images/mo/bahdanau-fig3.09ba2d37f202a6af.png) +![顯示 RNNsearch-50 發現的樣本對齊的圖片，取自 Bahdanau - arviz.org](../../../../../translated_images/mo/bahdanau-fig3.09ba2d37f202a6af.webp) > 圖片來自 [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (圖3) @@ -66,7 +66,7 @@ Transformer 的核心思想之一是避免 RNN 的序列性質，並創建一個 接下來，我們需要捕捉序列中的一些模式。為此，Transformer 使用了**自注意力**機制，這本質上是將注意力應用於相同的輸入和輸出序列。應用自注意力使我們能夠考慮句子中的**上下文**，並查看哪些詞是相互關聯的。例如，它可以幫助我們理解哪些詞是由代詞（如 *it*）指代的，並考慮上下文： -![](../../../../../translated_images/mo/CoreferenceResolution.861924d6d384a7d6.png) +![](../../../../../translated_images/mo/CoreferenceResolution.861924d6d384a7d6.webp) > 圖片來自 [Google 博客](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Transformer 的核心思想之一是避免 RNN 的序列性質，並創建一個 **BERT**（Bidirectional Encoder Representations from Transformers）是一個非常大的多層 Transformer 網路，*BERT-base* 有 12 層，*BERT-large* 有 24 層。該模型首先在大規模文本語料庫（維基百科 + 書籍）上進行無監督訓練（預測句子中的遮蔽詞）。在預訓練過程中，模型吸收了大量的語言理解能力，這些能力可以通過微調其他數據集來利用。這個過程稱為**遷移學習**。 -![圖片來自 http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/mo/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) +![圖片來自 http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/mo/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > 圖片 [來源](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/mo/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/mo/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index d19acfa3..b8b67d2d 100644 --- a/translations/mo/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/mo/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**注意力機制**提供了一種方法，能夠對每個輸入向量對RNN每個輸出預測的上下文影響進行加權。其實現方式是通過在輸入RNN的中間狀態和輸出RNN之間創建捷徑。這樣，在生成輸出符號 $y_t$ 時，我們會考慮所有輸入隱藏狀態 $h_i$，並賦予不同的權重係數 $\\alpha_{t,i}$。\n", "\n", - "![顯示具有加性注意力層的編碼器/解碼器模型的圖片](../../../../../translated_images/mo/encoder-decoder-attention.7a726296894fb567.png)\n", + "![顯示具有加性注意力層的編碼器/解碼器模型的圖片](../../../../../translated_images/mo/encoder-decoder-attention.7a726296894fb567.webp)\n", "*來自 [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) 的加性注意力機制編碼器-解碼器模型，圖片引用自[這篇部落格文章](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "注意力矩陣 $\\{\\alpha_{i,j}\\}$ 表示某些輸入詞在生成輸出序列中特定詞時所起的作用程度。以下是這樣一個矩陣的示例：\n", "\n", - "![顯示由 RNNsearch-50 找到的樣本對齊的圖片，取自 Bahdanau - arviz.org](../../../../../translated_images/mo/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![顯示由 RNNsearch-50 找到的樣本對齊的圖片，取自 Bahdanau - arviz.org](../../../../../translated_images/mo/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*圖片取自 [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf)（圖3）*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT**（Bidirectional Encoder Representations from Transformers，雙向編碼器表示）是一個非常大的多層Transformer網路，*BERT-base*有12層，*BERT-large*有24層。該模型首先在大規模文本數據（維基百科+書籍）上進行無監督訓練（預測句子中的被遮蔽詞）。在預訓練過程中，模型吸收了大量的語言理解能力，這些能力可以通過微調其他數據集來加以利用。這個過程被稱為**遷移學習**。\n", "\n", - "![圖片來自 http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/mo/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![圖片來自 http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/mo/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "Transformer架構有許多變體，包括BERT、DistilBERT、BigBird、OpenGPT3等，這些模型都可以進行微調。[HuggingFace套件](https://github.com/huggingface/) 提供了用PyTorch訓練這些架構的資源庫。\n", "\n", diff --git a/translations/mo/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/mo/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index 3ac0e346..47060bea 100644 --- a/translations/mo/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/mo/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**注意力機制**提供了一種方法，能夠對每個輸入向量在RNN的每個輸出預測中的上下文影響進行加權。其實現方式是通過在輸入RNN的中間狀態和輸出RNN之間創建捷徑。在生成輸出符號$y_t$時，我們會考慮所有輸入隱藏狀態$h_i$，並使用不同的權重係數$\\alpha_{t,i}$。\n", "\n", - "![顯示具有加性注意力層的編碼器/解碼器模型的圖片](../../../../../translated_images/mo/encoder-decoder-attention.7a726296894fb567.png)\n", + "![顯示具有加性注意力層的編碼器/解碼器模型的圖片](../../../../../translated_images/mo/encoder-decoder-attention.7a726296894fb567.webp)\n", "*[Bahdanau等人，2015](https://arxiv.org/pdf/1409.0473.pdf)中的加性注意力機制編碼器-解碼器模型，圖片引用自[這篇博客文章](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "注意力矩陣$\\{\\alpha_{i,j}\\}$表示某些輸入詞語在生成輸出序列中的某個詞語時所起的作用程度。以下是這樣一個矩陣的示例：\n", "\n", - "![顯示RNNsearch-50找到的示例對齊的圖片，取自Bahdanau - arviz.org](../../../../../translated_images/mo/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![顯示RNNsearch-50找到的示例對齊的圖片，取自Bahdanau - arviz.org](../../../../../translated_images/mo/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*圖片取自[Bahdanau等人，2015](https://arxiv.org/pdf/1409.0473.pdf)（圖3）*\n", "\n", @@ -225,7 +225,7 @@ "\n", "**BERT**（雙向編碼器表示來自 Transformer）是一個非常大型的多層 Transformer 網絡，*BERT-base* 有 12 層，*BERT-large* 則有 24 層。該模型首先在大量文本數據（維基百科 + 書籍）上進行無監督訓練（預測句子中的被遮蔽詞）。在預訓練過程中，模型吸收了大量的語言理解能力，之後可以通過微調與其他數據集結合使用。這個過程被稱為 **遷移學習**。\n", "\n", - "![圖片來源：http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/mo/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![圖片來源：http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/mo/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "Transformer 架構有許多變體，包括 BERT、DistilBERT、BigBird、OpenGPT3 等，它們都可以進行微調。\n", "\n", diff --git a/translations/mo/lessons/5-NLP/19-NER/README.md b/translations/mo/lessons/5-NLP/19-NER/README.md index 144a8e0d..2d98a652 100644 --- a/translations/mo/lessons/5-NLP/19-NER/README.md +++ b/translations/mo/lessons/5-NLP/19-NER/README.md @@ -57,7 +57,7 @@ NER 模型本質上是 **標記分類模型**，因為對於每個輸入標記 由於我們需要在標記和類別之間建立一對一的對應關係，我們可以從這張圖中訓練一個右側的 **多對多** 神經網絡模型： -![顯示常見循環神經網絡模式的圖片。](../../../../../translated_images/mo/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![顯示常見循環神經網絡模式的圖片。](../../../../../translated_images/mo/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > *圖片來自 [這篇部落格文章](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) 作者 [Andrej Karpathy](http://karpathy.github.io/)。NER 標記分類模型對應於此圖片中的最右側網絡架構。* diff --git a/translations/mo/lessons/5-NLP/README.md b/translations/mo/lessons/5-NLP/README.md index a2e85a7a..5d1e15ed 100644 --- a/translations/mo/lessons/5-NLP/README.md +++ b/translations/mo/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 自然語言處理 -![NLP 任務的手繪摘要](../../../../translated_images/mo/ai-nlp.b22dcb8ca4707cea.png) +![NLP 任務的手繪摘要](../../../../translated_images/mo/ai-nlp.b22dcb8ca4707cea.webp) 在本節中，我們將專注於使用神經網絡來處理與**自然語言處理 (NLP)** 相關的任務。我們希望計算機能夠解決許多 NLP 問題： diff --git a/translations/mo/lessons/6-Other/23-MultiagentSystems/README.md b/translations/mo/lessons/6-Other/23-MultiagentSystems/README.md index 0cf2f730..d28528bb 100644 --- a/translations/mo/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/mo/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ NetLogo的一大優勢是它包含一個可供試用的工作模型庫。進入* 打開模型後，你會進入NetLogo的主界面。以下是一個描述狼和羊在有限資源（草地）條件下的種群模型示例。 -![NetLogo主界面](../../../../../translated_images/mo/NetLogo-Main.32653711ec1a01b3.png) +![NetLogo主界面](../../../../../translated_images/mo/NetLogo-Main.32653711ec1a01b3.webp) > Dmitry Soshnikov提供的截圖 diff --git a/translations/mo/lessons/README.md b/translations/mo/lessons/README.md index 040347d5..6a641a9d 100644 --- a/translations/mo/lessons/README.md +++ b/translations/mo/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 概述 -![概述的手繪圖](../../../translated_images/mo/ai-overview.0857791951d19500.png) +![概述的手繪圖](../../../translated_images/mo/ai-overview.0857791951d19500.webp) > 手繪筆記由 [Tomomi Imura](https://twitter.com/girlie_mac) 提供 diff --git a/translations/mo/lessons/X-Extras/X1-MultiModal/README.md b/translations/mo/lessons/X-Extras/X1-MultiModal/README.md index d77457c4..eb704a04 100644 --- a/translations/mo/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/mo/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: CLIP 的核心思想是能夠比較文本提示與圖像，並判斷圖像與提示的匹配程度。 -![CLIP 架構](../../../../../translated_images/mo/clip-arch.b3dbf20b4e8ed8be.png) +![CLIP 架構](../../../../../translated_images/mo/clip-arch.b3dbf20b4e8ed8be.webp) > *圖片來源於[這篇博客](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ CLIP 模型/庫可以從 [OpenAI GitHub](https://github.com/openai/CLIP) 獲取 假設我們需要在貓、狗和人之間對圖像進行分類。在這種情況下，我們可以將圖像和一系列文本提示輸入模型，例如：“*一張貓的照片*”、“*一張狗的照片*”、“*一張人的照片*”。在結果的 3 個概率向量中，我們只需選擇值最大的索引。 -![CLIP 用於圖像分類](../../../../../translated_images/mo/clip-class.3af42ef0b2b19369.png) +![CLIP 用於圖像分類](../../../../../translated_images/mo/clip-class.3af42ef0b2b19369.webp) > *圖片來源於[這篇博客](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ VQGAN 與普通 [GAN](../../4-ComputerVision/10-GANs/README.md) 的主要區別 VQGAN 與傳統 GAN 的一個重要區別是，後者可以從任何輸入向量生成一個體面的圖像，而 VQGAN 可能生成不連貫的圖像。因此，我們需要進一步引導圖像創建過程，這可以通過 CLIP 完成。 -![VQGAN+CLIP 架構](../../../../../translated_images/mo/vqgan.5027fe05051dfa31.png) +![VQGAN+CLIP 架構](../../../../../translated_images/mo/vqgan.5027fe05051dfa31.webp) 為了生成與文本提示相符的圖像，我們從一些隨機編碼向量開始，將其通過 VQGAN 生成圖像。然後使用 CLIP 生成一個損失函數，該函數顯示圖像與文本提示的匹配程度。接下來的目標是最小化這個損失，通過反向傳播調整輸入向量參數。 一個實現 VQGAN+CLIP 的優秀庫是 [Pixray](http://github.com/pixray/pixray)。 -![Pixray 生成的圖片](../../../../../translated_images/mo/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Pixray 生成的圖片](../../../../../translated_images/mo/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Pixray 生成的圖片](../../../../../translated_images/mo/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) +![Pixray 生成的圖片](../../../../../translated_images/mo/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp) | ![Pixray 生成的圖片](../../../../../translated_images/mo/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp) | ![Pixray 生成的圖片](../../../../../translated_images/mo/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp) ----|----|---- 根據提示 *一幅年輕男性文學教師手持書本的水彩特寫肖像* 生成的圖片 | 根據提示 *一幅年輕女性計算機科學教師手持電腦的油畫特寫肖像* 生成的圖片 | 根據提示 *一幅年長男性數學教師站在黑板前的油畫特寫肖像* 生成的圖片 diff --git a/translations/mr/README.md b/translations/mr/README.md index 582730d7..a7a9f987 100644 --- a/translations/mr/README.md +++ b/translations/mr/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # नवशिक्यांसाठी कृत्रिम बुद्धिमत्ता - एक अभ्यासक्रम -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/mr/ai-overview.0857791951d19500.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/mr/ai-overview.0857791951d19500.webp)| |:---:| | नवशिक्यांसाठी कृत्रिम बुद्धिमत्ता - _स्केचनोट [@girlie_mac](https://twitter.com/girlie_mac) कडून_ | diff --git a/translations/mr/lessons/1-Intro/README.md b/translations/mr/lessons/1-Intro/README.md index 1f32974b..0a06a89d 100644 --- a/translations/mr/lessons/1-Intro/README.md +++ b/translations/mr/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # AI ची ओळख -![AI ची ओळख यावरील सामग्रीचा डूडलमध्ये सारांश](../../../../translated_images/mr/ai-intro.bf28d1ac4235881c.png) +![AI ची ओळख यावरील सामग्रीचा डूडलमध्ये सारांश](../../../../translated_images/mr/ai-intro.bf28d1ac4235881c.webp) > स्केच नोट [Tomomi Imura](https://twitter.com/girlie_mac) यांच्याकडून @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: मूळतः, संगणक [चार्ल्स बॅबेज](https://en.wikipedia.org/wiki/Charles_Babbage) यांनी संख्यांवर कार्य करण्यासाठी आणि एक निश्चित प्रक्रिया - अल्गोरिदम अनुसरण करण्यासाठी शोधले होते. आधुनिक संगणक, जरी 19व्या शतकात प्रस्तावित केलेल्या मूळ मॉडेलपेक्षा लक्षणीय अधिक प्रगत असले तरी, नियंत्रित गणनांच्या त्याच कल्पनेचे अनुसरण करतात. त्यामुळे जर आपल्याला एखादे लक्ष्य साध्य करण्यासाठी आवश्यक असलेल्या अचूक चरणांची क्रमवारी माहित असेल तर संगणकाला काहीतरी करण्यासाठी प्रोग्राम करणे शक्य आहे. -![व्यक्तीचा फोटो](../../../../translated_images/mr/dsh_age.d212a30d4e54fb5f.png) +![व्यक्तीचा फोटो](../../../../translated_images/mr/dsh_age.d212a30d4e54fb5f.webp) > फोटो [Vickie Soshnikova](http://twitter.com/vickievalerie) यांच्याकडून @@ -46,7 +46,7 @@ CO_OP_TRANSLATOR_METADATA: **[बुद्धिमत्ता](https://en.wikipedia.org/wiki/Intelligence)** या संज्ञेशी संबंधित असताना एक समस्या अशी आहे की या संज्ञेची स्पष्ट व्याख्या नाही. एखाद्याला वाटू शकते की बुद्धिमत्ता **गूढ विचारांशी** किंवा **आत्म-जाणिवेशी** संबंधित आहे, परंतु आपण याची योग्य व्याख्या करू शकत नाही. -![मांजराचा फोटो](../../../../translated_images/mr/photo-cat.8c8e8fb760ffe457.jpg) +![मांजराचा फोटो](../../../../translated_images/mr/photo-cat.8c8e8fb760ffe457.webp) > [फोटो](https://unsplash.com/photos/75715CVEJhI) [Amber Kipp](https://unsplash.com/@sadmax) यांच्याकडून Unsplash वरून @@ -98,13 +98,13 @@ AGI बद्दल बोलताना आपल्याला काही > | ML बद्दल काय? | | > |--------------|-----------| -> | संगणकाला काही डेटा आधारित समस्या सोडवण्यासाठी शिकवण्यावर आधारित कृत्रिम बुद्धिमत्तेचा भाग **मशीन लर्निंग** म्हणून ओळखला जातो. आम्ही या अभ्यासक्रमात पारंपरिक मशीन लर्निंगचा विचार करणार नाही - आम्ही तुम्हाला स्वतंत्र [Machine Learning for Beginners](http://aka.ms/ml-beginners) अभ्यासक्रमाकडे संदर्भित करतो. | ![ML for Beginners](../../../../translated_images/mr/ml-for-beginners.9e4fed176fd5817d.png) | +> | संगणकाला काही डेटा आधारित समस्या सोडवण्यासाठी शिकवण्यावर आधारित कृत्रिम बुद्धिमत्तेचा भाग **मशीन लर्निंग** म्हणून ओळखला जातो. आम्ही या अभ्यासक्रमात पारंपरिक मशीन लर्निंगचा विचार करणार नाही - आम्ही तुम्हाला स्वतंत्र [Machine Learning for Beginners](http://aka.ms/ml-beginners) अभ्यासक्रमाकडे संदर्भित करतो. | ![ML for Beginners](../../../../translated_images/mr/ml-for-beginners.9e4fed176fd5817d.webp) | ## AI चा थोडक्यात इतिहास कृत्रिम बुद्धिमत्ता ही एक शाखा म्हणून विसाव्या शतकाच्या मध्यात सुरू झाली. सुरुवातीला, प्रतीकात्मक तर्कसंगतता हा एक प्रचलित दृष्टिकोन होता आणि यामुळे काही महत्त्वाच्या यशस्वीतेसाठी, जसे की तज्ज्ञ प्रणाली - मर्यादित समस्या क्षेत्रांमध्ये तज्ज्ञ म्हणून कार्य करण्यास सक्षम संगणक प्रोग्राम्स. तथापि, लवकरच हे स्पष्ट झाले की अशा दृष्टिकोनाचा चांगला विस्तार होत नाही. तज्ज्ञाकडून ज्ञान काढणे, संगणकात सादर करणे आणि त्या ज्ञानाच्या अचूकतेची खात्री करणे हे एक अत्यंत जटिल कार्य आहे आणि अनेक प्रकरणांमध्ये व्यावहारिकदृष्ट्या खूप महाग आहे. यामुळे 1970 च्या दशकात तथाकथित [AI Winter](https://en.wikipedia.org/wiki/AI_winter) आले. -AI चा थोडक्यात इतिहास +AI चा थोडक्यात इतिहास > प्रतिमा [Dmitry Soshnikov](http://soshnikov.com) यांच्याकडून diff --git a/translations/mr/lessons/2-Symbolic/Animals.ipynb b/translations/mr/lessons/2-Symbolic/Animals.ipynb index 73b86246..d42466d4 100644 --- a/translations/mr/lessons/2-Symbolic/Animals.ipynb +++ b/translations/mr/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "या नमुन्यात, आपण काही शारीरिक वैशिष्ट्यांवर आधारित प्राणी ओळखण्यासाठी एक साधी ज्ञान-आधारित प्रणाली अंमलात आणू. ही प्रणाली खालील AND-OR झाडाद्वारे दर्शविली जाऊ शकते (हे संपूर्ण झाडाचा एक भाग आहे, आपण सहजपणे आणखी काही नियम जोडू शकतो):\n", "\n", - "![](../../../../translated_images/mr/AND-OR-Tree.5592d2c70187f283.png)\n" + "![](../../../../translated_images/mr/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { diff --git a/translations/mr/lessons/2-Symbolic/README.md b/translations/mr/lessons/2-Symbolic/README.md index 9b00b9aa..385d8a33 100644 --- a/translations/mr/lessons/2-Symbolic/README.md +++ b/translations/mr/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # ज्ञानाचे प्रतिनिधित्व आणि तज्ज्ञ प्रणाली -![Symbolic AI content चा सारांश](../../../../translated_images/mr/ai-symbolic.715a30cb610411a6.png) +![Symbolic AI content चा सारांश](../../../../translated_images/mr/ai-symbolic.715a30cb610411a6.webp) > [Tomomi Imura](https://twitter.com/girlie_mac) यांचे स्केच नोट @@ -41,7 +41,7 @@ Symbolic AI मधील एक महत्त्वाची संकल् म्हणून, **ज्ञानाचे प्रतिनिधित्व** करण्याची समस्या म्हणजे संगणकाच्या आत डेटा स्वरूपात ज्ञानाचे प्रतिनिधित्व करण्याचा काही प्रभावी मार्ग शोधणे, जेणेकरून ते स्वयंचलितपणे वापरता येईल. याकडे एक स्पेक्ट्रम म्हणून पाहिले जाऊ शकते: -![ज्ञानाचे प्रतिनिधित्व स्पेक्ट्रम](../../../../translated_images/mr/knowledge-spectrum.b60df631852c0217.png) +![ज्ञानाचे प्रतिनिधित्व स्पेक्ट्रम](../../../../translated_images/mr/knowledge-spectrum.b60df631852c0217.webp) > Image by [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Block Syntax | Indent | | | Symbolic AI च्या सुरुवातीच्या यशांपैकी एक म्हणजे **तज्ज्ञ प्रणाली** - संगणक प्रणाली जी मर्यादित समस्या क्षेत्रात तज्ज्ञ म्हणून कार्य करण्यासाठी डिझाइन केली गेली होती. त्या **ज्ञान बेस** वर आधारित होत्या, जे एका किंवा अधिक मानवी तज्ज्ञांकडून काढले गेले होते, आणि त्यामध्ये **तर्क इंजिन** होते जे त्यावर काही तर्कशक्ती अंमलात आणत होते. -![मानवी आर्किटेक्चर](../../../../translated_images/mr/arch-human.5d4d35f1bba3ab1c.png) | ![ज्ञान-आधारित प्रणाली](../../../../translated_images/mr/arch-kbs.3ec5c150b09fa8da.png) +![मानवी आर्किटेक्चर](../../../../translated_images/mr/arch-human.5d4d35f1bba3ab1c.webp) | ![ज्ञान-आधारित प्रणाली](../../../../translated_images/mr/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ मानवी न्यूरल प्रणालीची साधी रचना | ज्ञान-आधारित प्रणालीची आर्किटेक्चर @@ -106,7 +106,7 @@ Symbolic AI च्या सुरुवातीच्या यशांपै उदाहरण म्हणून, खालील तज्ज्ञ प्रणाली विचार करूया जी प्राण्याचे शारीरिक वैशिष्ट्यांवर आधारित निर्धारण करते: -![AND-OR Tree](../../../../translated_images/mr/AND-OR-Tree.5592d2c70187f283.png) +![AND-OR Tree](../../../../translated_images/mr/AND-OR-Tree.5592d2c70187f283.webp) > Image by [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/mr/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/mr/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index 8a6d7dbc..17ef631d 100644 --- a/translations/mr/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/mr/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -1255,7 +1255,7 @@ "* कमी प्रशिक्षण नुकसान - मॉडेल प्रशिक्षण डेटा चांगल्या प्रकारे अंदाज करू शकते, कारण त्याच्याकडे पुरेशी अभिव्यक्ती क्षमता असते.\n", "* व्हॅलिडेशन नुकसान प्रशिक्षण नुकसानापेक्षा खूप जास्त असू शकते आणि प्रशिक्षणादरम्यान वाढू शकते - कारण मॉडेल \"प्रशिक्षण बिंदू\" लक्षात ठेवते आणि \"संपूर्ण चित्र\" गमावते.\n", "\n", - "![ओव्हरफिटिंग](../../../../../translated_images/mr/overfit.a0bd57f717c15769.png)\n", + "![ओव्हरफिटिंग](../../../../../translated_images/mr/overfit.a0bd57f717c15769.webp)\n", "\n", "> या चित्रात, `x` प्रशिक्षण डेटा दर्शवतो, `o` - व्हॅलिडेशन डेटा. डावीकडे - रेखीय मॉडेल (एक-स्तरीय), ते डेटाच्या स्वरूपाचा चांगल्या प्रकारे अंदाज करते. उजवीकडे - ओव्हरफिटेड मॉडेल, मॉडेल प्रशिक्षण डेटा उत्तम प्रकारे अंदाज करते, पण इतर कोणत्याही डेटासह अर्थपूर्ण राहात नाही (व्हॅलिडेशन त्रुटी खूप जास्त आहे).\n" ] diff --git a/translations/mr/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/mr/lessons/3-NeuralNetworks/05-Frameworks/README.md index 31f90065..73a5607a 100644 --- a/translations/mr/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/mr/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ CO_OP_TRANSLATOR_METADATA: खालीलप्रमाणे 5 डॉट्स (ग्राफ्सवर `x` ने दर्शविलेले) अंदाज लावण्याच्या समस्येचा विचार करा: -![linear](../../../../../translated_images/mr/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/mr/overfit2.131f5800ae10ca5e.jpg) +![linear](../../../../../translated_images/mr/overfit1.f24b71c6f652e59e.webp) | ![overfit](../../../../../translated_images/mr/overfit2.131f5800ae10ca5e.webp) -------------------------|-------------------------- **रेखीय मॉडेल, 2 पॅरामिटर्स** | **नॉन-रेखीय मॉडेल, 7 पॅरामिटर्स** प्रशिक्षण त्रुटी = 5.3 | प्रशिक्षण त्रुटी = 0 @@ -79,7 +79,7 @@ CO_OP_TRANSLATOR_METADATA: वरील ग्राफवरून आपण पाहू शकतो की, ओव्हरफिटिंग खूप कमी प्रशिक्षण त्रुटी आणि जास्त व्हॅलिडेशन त्रुटीने ओळखले जाऊ शकते. सामान्यतः प्रशिक्षणादरम्यान आपण पाहतो की प्रशिक्षण आणि व्हॅलिडेशन त्रुटी कमी होऊ लागतात, आणि नंतर काही टप्प्यावर व्हॅलिडेशन त्रुटी कमी होणे थांबवते आणि वाढू लागते. हे ओव्हरफिटिंगचे चिन्ह असेल, आणि यावेळी प्रशिक्षण थांबवावे (किंवा किमान मॉडेलचा स्नॅपशॉट घ्यावा) याचा संकेत असेल. -![overfitting](../../../../../translated_images/mr/Overfitting.408ad91cd90b4371.png) +![overfitting](../../../../../translated_images/mr/Overfitting.408ad91cd90b4371.webp) ## ओव्हरफिटिंग कसे टाळावे diff --git a/translations/mr/lessons/3-NeuralNetworks/README.md b/translations/mr/lessons/3-NeuralNetworks/README.md index 732083c2..2edc06a1 100644 --- a/translations/mr/lessons/3-NeuralNetworks/README.md +++ b/translations/mr/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # न्यूरल नेटवर्क्सची ओळख -![न्यूरल नेटवर्क्सच्या परिचयाचा सारांश एका चित्रात](../../../../translated_images/mr/ai-neuralnetworks.1c687ae40bc86e83.png) +![न्यूरल नेटवर्क्सच्या परिचयाचा सारांश एका चित्रात](../../../../translated_images/mr/ai-neuralnetworks.1c687ae40bc86e83.webp) जसे आपण परिचयात चर्चा केली, बुद्धिमत्ता मिळवण्याचा एक मार्ग म्हणजे **कंप्यूटर मॉडेल** किंवा **कृत्रिम मेंदू** तयार करणे. विसाव्या शतकाच्या मध्यापासून संशोधकांनी विविध गणितीय मॉडेल्स वापरून पाहिले, आणि अलीकडच्या वर्षांत हा दृष्टिकोन अत्यंत यशस्वी ठरला. मेंदूचे असे गणितीय मॉडेल्स **न्यूरल नेटवर्क्स** म्हणून ओळखले जातात. @@ -36,13 +36,13 @@ CO_OP_TRANSLATOR_METADATA: जीवशास्त्रानुसार, आपला मेंदू न्यूरल पेशींनी (न्यूरॉन्स) बनलेला असतो, ज्यामध्ये अनेक "इनपुट्स" (डेंड्राइट्स) आणि एक "आउटपुट" (अॅक्सॉन) असतो. डेंड्राइट्स आणि अॅक्सॉन्स दोन्ही विद्युत संकेत वाहून नेऊ शकतात, आणि त्यांच्यातील कनेक्शन्स — ज्यांना सायनॅप्स म्हणतात — विविध प्रकारच्या चालकतेचे प्रदर्शन करू शकतात, जे न्यूरोट्रान्समीटरद्वारे नियंत्रित केले जातात. -![न्यूरॉनचे मॉडेल](../../../../translated_images/mr/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![न्यूरॉनचे मॉडेल](../../../../translated_images/mr/artneuron.1a5daa88d20ebe6f.png) +![न्यूरॉनचे मॉडेल](../../../../translated_images/mr/synapse-wikipedia.ed20a9e4726ea1c6.webp) | ![न्यूरॉनचे मॉडेल](../../../../translated_images/mr/artneuron.1a5daa88d20ebe6f.webp) ----|---- वास्तविक न्यूरॉन *([Wikipedia](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) मधील प्रतिमा)* | कृत्रिम न्यूरॉन *(लेखकाने तयार केलेली प्रतिमा)* त्यामुळे, न्यूरॉनचे सर्वात सोपे गणितीय मॉडेलमध्ये अनेक इनपुट्स X1, ..., XN आणि एक आउटपुट Y, तसेच वजनांची मालिका W1, ..., WN असते. आउटपुट खालीलप्रमाणे गणना केली जाते: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) जिथे f ही काही नॉन-लिनियर **अॅक्टिवेशन फंक्शन** आहे. diff --git a/translations/mr/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/mr/lessons/4-ComputerVision/06-IntroCV/README.md index 638ecc39..8bfd8ebb 100644 --- a/translations/mr/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/mr/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) * **ब्रेल पुस्तकाच्या छायाचित्राची पूर्व-प्रक्रिया**. आम्ही थ्रेशोल्डिंग, वैशिष्ट्य शोध, परिप्रेक्ष्य रूपांतरण आणि NumPy हेरफेर कसे वापरून ब्रेल चिन्हे वेगळी करू शकतो यावर लक्ष केंद्रित करतो, जेणेकरून न्यूरल नेटवर्कद्वारे पुढील वर्गीकरण करता येईल. -![ब्रेल प्रतिमा](../../../../../translated_images/mr/braille.341962ff76b1bd70.jpeg) | ![पूर्व-प्रक्रिया केलेली ब्रेल प्रतिमा](../../../../../translated_images/mr/braille-result.46530fea020b03c7.png) | ![ब्रेल चिन्हे](../../../../../translated_images/mr/braille-symbols.0159185ab69d5339.png) +![ब्रेल प्रतिमा](../../../../../translated_images/mr/braille.341962ff76b1bd70.webp) | ![पूर्व-प्रक्रिया केलेली ब्रेल प्रतिमा](../../../../../translated_images/mr/braille-result.46530fea020b03c7.webp) | ![ब्रेल चिन्हे](../../../../../translated_images/mr/braille-symbols.0159185ab69d5339.webp) ----|-----|----- > प्रतिमा [OpenCV.ipynb](OpenCV.ipynb) मधून * **फ्रेम फरक वापरून व्हिडिओमध्ये हालचाल शोधणे**. जर कॅमेरा स्थिर असेल, तर कॅमेरा फीडमधील फ्रेम्स एकमेकांशी खूप समान असाव्यात. फ्रेम्स arrays म्हणून दर्शविल्या जात असल्याने, दोन अनुक्रमिक फ्रेम्ससाठी त्या arrays वजा करून आम्हाला पिक्सेल फरक मिळेल, जो स्थिर फ्रेम्ससाठी कमी असावा, आणि प्रतिमेमध्ये लक्षणीय हालचाल झाल्यावर जास्त होईल. -![व्हिडिओ फ्रेम्स आणि फ्रेम फरक प्रतिमा](../../../../../translated_images/mr/frame-difference.706f805491a0883c.png) +![व्हिडिओ फ्रेम्स आणि फ्रेम फरक प्रतिमा](../../../../../translated_images/mr/frame-difference.706f805491a0883c.webp) > प्रतिमा [OpenCV.ipynb](OpenCV.ipynb) मधून @@ -89,7 +89,7 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) - **घन ऑप्टिकल फ्लो** प्रत्येक पिक्सेलसाठी तो कुठे हलतो हे दर्शविणारा वेक्टर फील्ड गणना करतो. - **दुर्मिळ ऑप्टिकल फ्लो** प्रतिमेतील काही वेगळ्या वैशिष्ट्यांवर आधारित असतो (उदा. कडा), आणि फ्रेम ते फ्रेम त्यांचा मार्ग तयार करतो. -![ऑप्टिकल फ्लो प्रतिमा](../../../../../translated_images/mr/optical.1f4a94464579a83a.png) +![ऑप्टिकल फ्लो प्रतिमा](../../../../../translated_images/mr/optical.1f4a94464579a83a.webp) > प्रतिमा [OpenCV.ipynb](OpenCV.ipynb) मधून diff --git a/translations/mr/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/mr/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index d492a1b3..b208201c 100644 --- a/translations/mr/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/mr/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 हा एक नेटवर्क आहे ज्याने 2014 मध्ये ImageNet टॉप-5 वर्गीकरणात 92.7% अचूकता मिळवली. यामध्ये खालील स्तर रचना आहे: -![ImageNet Layers](../../../../../translated_images/mr/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet Layers](../../../../../translated_images/mr/vgg-16-arch1.d901a5583b3a51ba.webp) जसे तुम्ही पाहू शकता, VGG पारंपरिक पिरॅमिड आर्किटेक्चरचे अनुसरण करते, जे कन्व्होल्यूशन-पूलिंग स्तरांचा क्रम आहे. -![ImageNet Pyramid](../../../../../translated_images/mr/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet Pyramid](../../../../../translated_images/mr/vgg-16-arch.64ff2137f50dd49f.webp) > प्रतिमा [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) वरून घेतलेली आहे. diff --git a/translations/mr/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/mr/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index 432fdb4c..5c29f68d 100644 --- a/translations/mr/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/mr/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -260,7 +260,7 @@ "\n", "म्हणून, एका सामान्य CNN मध्ये अनेक कॉनव्होल्यूशन लेयर्स असतात, ज्यांच्या दरम्यान पूलिंग लेयर्स असतात जे प्रतिमेचे परिमाण कमी करतात. आपण फिल्टर्सची संख्या देखील वाढवतो, कारण पॅटर्न्स अधिक प्रगत होत जातात - आपल्याला शोधायच्या असलेल्या संभाव्य रचनांची संख्या वाढते.\n", "\n", - "![काही कॉनव्होल्यूशन लेयर्स आणि पूलिंग लेयर्स दाखवणारी प्रतिमा.](../../../../../translated_images/mr/cnn-pyramid.85915455759ef0ce.png)\n", + "![काही कॉनव्होल्यूशन लेयर्स आणि पूलिंग लेयर्स दाखवणारी प्रतिमा.](../../../../../translated_images/mr/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "स्थानिक परिमाण कमी होणे आणि वैशिष्ट्ये/फिल्टर्सचे परिमाण वाढणे यामुळे, या आर्किटेक्चरला **पिरॅमिड आर्किटेक्चर** असेही म्हणतात.\n" ] diff --git a/translations/mr/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/mr/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index a1d7cf1a..e34570d8 100644 --- a/translations/mr/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/mr/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -359,7 +359,7 @@ "\n", "म्हणून, एका सामान्य CNN मध्ये अनेक कॉनव्होल्यूशन लेयर्स असतात, ज्यामध्ये प्रतिमेचे परिमाण कमी करण्यासाठी पूलिंग लेयर्स असतात. तसेच, आम्ही फिल्टर्सची संख्या वाढवतो, कारण नमुने अधिक प्रगत होत जातात - आपल्याला शोधण्यासाठी अधिक संभाव्य मनोरंजक संयोजन असतात.\n", "\n", - "![कॉनव्होल्यूशन लेयर्स आणि पूलिंग लेयर्स असलेला पिरॅमिड आर्किटेक्चर दर्शवणारी प्रतिमा.](../../../../../translated_images/mr/cnn-pyramid.85915455759ef0ce.png)\n", + "![कॉनव्होल्यूशन लेयर्स आणि पूलिंग लेयर्स असलेला पिरॅमिड आर्किटेक्चर दर्शवणारी प्रतिमा.](../../../../../translated_images/mr/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "जागात्मक परिमाण कमी होणे आणि वैशिष्ट्य/फिल्टर्स परिमाण वाढणे यामुळे, या आर्किटेक्चरला **पिरॅमिड आर्किटेक्चर** असेही म्हणतात.\n" ] diff --git a/translations/mr/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/mr/lessons/4-ComputerVision/07-ConvNets/README.md index 0b3e862e..3ff2a500 100644 --- a/translations/mr/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/mr/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: पॅटर्न्स काढण्यासाठी, आपण **कॉनव्होल्यूशनल फिल्टर्स** चा उपयोग करू. तुम्हाला माहीत आहेच की, प्रतिमा 2D-मॅट्रिक्स किंवा रंग खोलीसह 3D-टेंसरद्वारे दर्शवली जाते. फिल्टर लागू करणे म्हणजे आपण तुलनेने लहान **फिल्टर कर्नल** मॅट्रिक्स घेतो, आणि मूळ प्रतिमेतील प्रत्येक पिक्सेलसाठी शेजारील बिंदूंसह भारित सरासरीची गणना करतो. आपण याला असे पाहू शकतो की एक छोटी विंडो संपूर्ण प्रतिमेवर सरकत आहे, आणि फिल्टर कर्नल मॅट्रिक्समधील वजनांनुसार सर्व पिक्सेल्स सरासरी करत आहे. -![आडव्या कडांचा फिल्टर](../../../../../translated_images/mr/filter-vert.b7148390ca0bc356.png) | ![उभ्या कडांचा फिल्टर](../../../../../translated_images/mr/filter-horiz.59b80ed4feb946ef.png) +![आडव्या कडांचा फिल्टर](../../../../../translated_images/mr/filter-vert.b7148390ca0bc356.webp) | ![उभ्या कडांचा फिल्टर](../../../../../translated_images/mr/filter-horiz.59b80ed4feb946ef.webp) ----|---- > प्रतिमा: दिमित्री सोश्निकोव्ह @@ -38,7 +38,7 @@ CNN कसे कार्य करते यामागील महत्त * आपण नेटवर्क अशा प्रकारे डिझाइन करू शकतो की फिल्टर्स आपोआप प्रशिक्षित होतील * आपण मूळ प्रतिमेतील पॅटर्न्स शोधण्यासाठीच नव्हे तर उच्च-स्तरीय वैशिष्ट्यांमध्ये पॅटर्न्स शोधण्यासाठीही याच पद्धतीचा उपयोग करू शकतो. त्यामुळे CNN वैशिष्ट्य काढणे वैशिष्ट्यांच्या श्रेणीवर कार्य करते, कमी-स्तरीय पिक्सेल संयोजनांपासून ते प्रतिमेच्या भागांच्या उच्च-स्तरीय संयोजनांपर्यंत. -![हायरार्किकल वैशिष्ट्य काढणे](../../../../../translated_images/mr/FeatureExtractionCNN.d9b456cbdae7cb64.png) +![हायरार्किकल वैशिष्ट्य काढणे](../../../../../translated_images/mr/FeatureExtractionCNN.d9b456cbdae7cb64.webp) > प्रतिमा: [हिस्लॉप-लिंच यांच्या पेपरमधून](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), त्यांच्या [संशोधनावर आधारित](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ CNN कसे कार्य करते यामागील महत्त उदाहरण म्हणून, VGG-16 च्या आर्किटेक्चरकडे पाहूया, ज्याने 2014 मध्ये ImageNet च्या टॉप-5 वर्गीकरणात 92.7% अचूकता मिळवली: -![ImageNet स्तर](../../../../../translated_images/mr/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet स्तर](../../../../../translated_images/mr/vgg-16-arch1.d901a5583b3a51ba.webp) -![ImageNet पिरॅमिड](../../../../../translated_images/mr/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet पिरॅमिड](../../../../../translated_images/mr/vgg-16-arch.64ff2137f50dd49f.webp) > प्रतिमा: [रिसर्चगेट](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/mr/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/mr/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 2e0180ff..beea7973 100644 --- a/translations/mr/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/mr/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ CO_OP_TRANSLATOR_METADATA: आम्ही [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/) वापरणार आहोत, ज्यामध्ये 37 वेगवेगळ्या जातींच्या कुत्र्यांच्या आणि मांजरींच्या प्रतिमा आहेत. -![आपण हाताळत असलेला डेटासेट](../../../../../../translated_images/mr/data.50b2a9d5484bdbf0.png) +![आपण हाताळत असलेला डेटासेट](../../../../../../translated_images/mr/data.50b2a9d5484bdbf0.webp) डेटासेट डाउनलोड करण्यासाठी, हा कोड स्निपेट वापरा: diff --git a/translations/mr/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/mr/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index a14ed80a..04b2ff24 100644 --- a/translations/mr/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/mr/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "आदर्श मांजर पाहण्यासाठी, आपण एका यादृच्छिक आवाजाच्या प्रतिमेसह सुरुवात करू आणि ग्रेडियंट डिसेंट ऑप्टिमायझेशन तंत्राचा वापर करून प्रतिमा समायोजित करण्याचा प्रयत्न करू, जेणेकरून नेटवर्कला मांजर ओळखता येईल.\n", "\n", - "![ऑप्टिमायझेशन लूप](../../../../../translated_images/mr/ideal-cat-loop.999fbb8ff306e044.png)\n", + "![ऑप्टिमायझेशन लूप](../../../../../translated_images/mr/ideal-cat-loop.999fbb8ff306e044.webp)\n", "\n", "ही आहे आपली सुरुवातीची प्रतिमा:\n" ] diff --git a/translations/mr/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/mr/lessons/4-ComputerVision/08-TransferLearning/README.md index 834ac9d1..5e42f372 100644 --- a/translations/mr/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/mr/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Keras आणि PyTorch मध्ये काही सामान्य आ VGG-16 नेटवर्कद्वारे मांजराच्या प्रतिमेतून काढलेली नमुना वैशिष्ट्ये येथे आहेत: -![Features extracted by VGG-16](../../../../../translated_images/mr/features.6291f9c7ba3a0b95.png) +![Features extracted by VGG-16](../../../../../translated_images/mr/features.6291f9c7ba3a0b95.webp) ## मांजरे विरुद्ध कुत्रे डेटासेट @@ -48,19 +48,19 @@ VGG-16 नेटवर्कद्वारे मांजराच्या आपण एक पद्धत वापरू शकतो, जिथे आपण एका रँडम प्रतिमेसह सुरुवात करतो आणि नंतर **ग्रेडियंट डिसेंट ऑप्टिमायझेशन** तंत्र वापरून ती प्रतिमा समायोजित करण्याचा प्रयत्न करतो, ज्यामुळे नेटवर्कला वाटते की ती मांजर आहे. -![Image Optimization Loop](../../../../../translated_images/mr/ideal-cat-loop.999fbb8ff306e044.png) +![Image Optimization Loop](../../../../../translated_images/mr/ideal-cat-loop.999fbb8ff306e044.webp) मात्र, जर आपण असे केले, तर आपल्याला रँडम नॉइजसारखे काहीतरी मिळेल. कारण *नेटवर्कला वाटावे की इनपुट प्रतिमा मांजर आहे असे करण्याचे अनेक मार्ग आहेत*, ज्यामध्ये काही दृश्यदृष्ट्या अर्थपूर्ण नाहीत. जरी त्या प्रतिमांमध्ये मांजरीसाठी विशिष्ट नमुने असले तरी, त्यांना दृश्यदृष्ट्या वेगळे करण्यासाठी काहीही बंधन नाही. परिणाम सुधारण्यासाठी, आपण लॉस फंक्शनमध्ये आणखी एक टर्म जोडू शकतो, ज्याला **व्हेरिएशन लॉस** म्हणतात. हे एक मेट्रिक आहे जे प्रतिमेचे शेजारी असलेले पिक्सेल किती समान आहेत हे दर्शवते. व्हेरिएशन लॉस कमी केल्याने प्रतिमा गुळगुळीत होते आणि नॉइज दूर होते - त्यामुळे अधिक दृश्यदृष्ट्या आकर्षक नमुने उलगडतात. येथे अशा "आदर्श" प्रतिमांचे उदाहरण आहे, ज्यांना उच्च संभाव्यतेसह मांजर आणि झेब्रा म्हणून वर्गीकृत केले जाते: -![Ideal Cat](../../../../../translated_images/mr/ideal-cat.203dd4597643d6b0.png) | ![Ideal Zebra](../../../../../translated_images/mr/ideal-zebra.7f70e8b54ee15a7a.png) +![Ideal Cat](../../../../../translated_images/mr/ideal-cat.203dd4597643d6b0.webp) | ![Ideal Zebra](../../../../../translated_images/mr/ideal-zebra.7f70e8b54ee15a7a.webp) -----|----- *आदर्श मांजर* | *आदर्श झेब्रा* समान पद्धत वापरून तथाकथित **अड्व्हर्सेरियल हल्ले** न्यूरल नेटवर्कवर करता येतात. समजा आपण न्यूरल नेटवर्कला मूर्ख बनवायचे आहे आणि कुत्र्याला मांजरासारखे बनवायचे आहे. जर आपण कुत्र्याची प्रतिमा घेतली, जी नेटवर्कद्वारे कुत्रा म्हणून ओळखली जाते, तर आपण ती थोडीशी समायोजित करू शकतो, ग्रेडियंट डिसेंट ऑप्टिमायझेशन वापरून, जोपर्यंत नेटवर्क ती मांजर म्हणून वर्गीकृत करत नाही: -![Picture of a Dog](../../../../../translated_images/mr/original-dog.8f68a67d2fe0911f.png) | ![Picture of a dog classified as a cat](../../../../../translated_images/mr/adversarial-dog.d9fc7773b0142b89.png) +![Picture of a Dog](../../../../../translated_images/mr/original-dog.8f68a67d2fe0911f.webp) | ![Picture of a dog classified as a cat](../../../../../translated_images/mr/adversarial-dog.d9fc7773b0142b89.webp) -----|----- *कुत्र्याची मूळ प्रतिमा* | *कुत्र्याची प्रतिमा जी मांजर म्हणून वर्गीकृत केली जाते* diff --git a/translations/mr/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/mr/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index a2158273..a4401cb3 100644 --- a/translations/mr/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/mr/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "ऑटोएनकोडरला मूळ प्रतिमेतील जास्तीत जास्त माहिती अचूक पुनर्रचना करण्यासाठी कॅप्चर करण्यासाठी प्रशिक्षण दिले जात असल्याने, नेटवर्क इनपुट प्रतिमांचे अर्थ कॅप्चर करण्यासाठी सर्वोत्तम **एम्बेडिंग** शोधण्याचा प्रयत्न करते.\n", "\n", - "![ऑटोएनकोडर आकृती](../../../../../translated_images/mr/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![ऑटोएनकोडर आकृती](../../../../../translated_images/mr/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "> प्रतिमा [Keras ब्लॉग](https://blog.keras.io/building-autoencoders-in-keras.html) वरून\n", "\n", diff --git a/translations/mr/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/mr/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index d3dfa684..bf47034f 100644 --- a/translations/mr/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/mr/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "ऑटोएनकोडरला मूळ प्रतिमेतील जास्तीत जास्त माहिती अचूक पुनर्रचना करण्यासाठी कॅप्चर करण्यासाठी प्रशिक्षण दिले जात असल्याने, नेटवर्क इनपुट प्रतिमांचे अर्थ कॅप्चर करण्यासाठी सर्वोत्तम **एम्बेडिंग** शोधण्याचा प्रयत्न करते.\n", "\n", - "![ऑटोएनकोडर आकृती](../../../../../translated_images/mr/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![ऑटोएनकोडर आकृती](../../../../../translated_images/mr/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "*प्रतिमा [Keras ब्लॉग](https://blog.keras.io/building-autoencoders-in-keras.html) मधून*\n", "\n", diff --git a/translations/mr/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/mr/lessons/4-ComputerVision/09-Autoencoders/README.md index 767c546c..9a74c21d 100644 --- a/translations/mr/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/mr/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: आम्ही ऑटोएन्कोडरला मूळ प्रतिमेतील माहिती अचूक पुनर्रचना करण्यासाठी शक्य तितकी माहिती कॅप्चर करण्यासाठी प्रशिक्षण देत असल्याने, नेटवर्क इनपुट प्रतिमांचे सर्वोत्तम **एम्बेडिंग** शोधण्याचा प्रयत्न करते. -![ऑटोएन्कोडर आकृती](../../../../../translated_images/mr/autoencoder_schema.5e6fc9ad98a5eb61.jpg) +![ऑटोएन्कोडर आकृती](../../../../../translated_images/mr/autoencoder_schema.5e6fc9ad98a5eb61.webp) > प्रतिमा [Keras ब्लॉग](https://blog.keras.io/building-autoencoders-in-keras.html) मधून diff --git a/translations/mr/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/mr/lessons/4-ComputerVision/11-ObjectDetection/README.md index 086a3f85..adfffcd1 100644 --- a/translations/mr/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/mr/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: ## [पूर्व-व्याख्यान क्विझ](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![ऑब्जेक्ट डिटेक्शन](../../../../../translated_images/mr/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) +![ऑब्जेक्ट डिटेक्शन](../../../../../translated_images/mr/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp) > प्रतिमा [YOLO v2 वेबसाइट](https://pjreddie.com/darknet/yolov2/) वरून @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 2. प्रत्येक टाइलवर इमेज क्लासिफिकेशन चालवा. 3. ज्या टाइल्समध्ये पुरेसे उच्च सक्रियता दिसते, त्या टाइल्समध्ये संबंधित वस्तू असल्याचे मानले जाऊ शकते. -![साधा ऑब्जेक्ट डिटेक्शन](../../../../../translated_images/mr/naive-detection.e7f1ba220ccd08c6.png) +![साधा ऑब्जेक्ट डिटेक्शन](../../../../../translated_images/mr/naive-detection.e7f1ba220ccd08c6.webp) > *प्रतिमा [व्यायाम नोटबुक](ObjectDetection-TF.ipynb) मधून* @@ -42,7 +42,7 @@ CO_OP_TRANSLATOR_METADATA: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 वर्ग * [COCO](http://cocodataset.org/#home) - कॉमन ऑब्जेक्ट्स इन कॉन्टेक्स्ट. 80 वर्ग, बॉक्सेस आणि सेगमेंटेशन मास्क -![COCO](../../../../../translated_images/mr/coco-examples.71bc60380fa6cceb.jpg) +![COCO](../../../../../translated_images/mr/coco-examples.71bc60380fa6cceb.webp) ## ऑब्जेक्ट डिटेक्शन मेट्रिक्स @@ -50,7 +50,7 @@ CO_OP_TRANSLATOR_METADATA: इमेज क्लासिफिकेशनसाठी अल्गोरिदम किती चांगले कार्य करते हे मोजणे सोपे आहे, परंतु ऑब्जेक्ट डिटेक्शनसाठी आपल्याला वर्गाची अचूकता आणि बॉक्सच्या स्थानाची अचूकता दोन्ही मोजावी लागते. यासाठी **इंटरसेक्शन ओव्हर युनियन** (IoU) वापरले जाते, जे दोन बॉक्सेस (किंवा दोन क्षेत्रे) किती चांगले ओव्हरलॅप होतात हे मोजते. -![IoU](../../../../../translated_images/mr/iou_equation.9a4751d40fff4e11.png) +![IoU](../../../../../translated_images/mr/iou_equation.9a4751d40fff4e11.webp) > *[IoU वर उत्कृष्ट ब्लॉग पोस्ट](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/) मधून आकृती 2* @@ -97,11 +97,11 @@ $$ [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) वापरते ROI क्षेत्रांची संरचना तयार करण्यासाठी, जी नंतर CNN फीचर एक्स्ट्रॅक्टर्स आणि SVM-क्लासिफायर्सद्वारे वस्तूचा वर्ग निश्चित करण्यासाठी आणि *बॉक्स* समन्वय निश्चित करण्यासाठी रेषीय रेग्रेशनद्वारे प्रक्रिया केली जाते. [अधिकृत पेपर](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/mr/rcnn1.cae407020dfb1d1f.png) +![RCNN](../../../../../translated_images/mr/rcnn1.cae407020dfb1d1f.webp) > *van de Sande et al. ICCV’11 मधून प्रतिमा* -![RCNN-1](../../../../../translated_images/mr/rcnn2.2d9530bb83516484.png) +![RCNN-1](../../../../../translated_images/mr/rcnn2.2d9530bb83516484.webp) > *[या ब्लॉग](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e) मधून प्रतिमा* @@ -109,7 +109,7 @@ $$ हा दृष्टिकोन R-CNN सारखाच आहे, परंतु क्षेत्रे कॉन्व्होल्यूशन लेयर्स लागू केल्यानंतर निश्चित केली जातात. -![FRCNN](../../../../../translated_images/mr/f-rcnn.3cda6d9bb4188875.png) +![FRCNN](../../../../../translated_images/mr/f-rcnn.3cda6d9bb4188875.webp) > प्रतिमा [अधिकृत पेपर](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 मधून @@ -117,7 +117,7 @@ $$ या दृष्टिकोनाची मुख्य कल्पना म्हणजे ROIs अंदाज लावण्यासाठी न्यूरल नेटवर्क वापरणे - ज्याला *Region Proposal Network* म्हणतात. [पेपर](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/mr/faster-rcnn.8d46c099b87ef30a.png) +![FasterRCNN](../../../../../translated_images/mr/faster-rcnn.8d46c099b87ef30a.webp) > प्रतिमा [अधिकृत पेपर](https://arxiv.org/pdf/1506.01497.pdf) मधून @@ -129,7 +129,7 @@ $$ 2. फीचर्स **Position-Sensitive Score Map** द्वारे प्रक्रिया केली जातात. $C$ वर्गातील प्रत्येक वस्तू $k\times k$ क्षेत्रांमध्ये विभागली जाते, आणि वस्तूंचे भाग अंदाज लावण्यासाठी प्रशिक्षण दिले जाते. 3. $k\times k$ क्षेत्रांमधील प्रत्येक भागासाठी सर्व नेटवर्क्स वस्तू वर्गांसाठी मतदान करतात, आणि जास्तीत जास्त मत असलेला वर्ग निवडला जातो. -![r-fcn प्रतिमा](../../../../../translated_images/mr/r-fcn.13eb88158b99a3da.png) +![r-fcn प्रतिमा](../../../../../translated_images/mr/r-fcn.13eb88158b99a3da.webp) > प्रतिमा [अधिकृत पेपर](https://arxiv.org/abs/1605.06409) मधून @@ -140,7 +140,7 @@ YOLO हा एक रिअलटाइम वन-पास अल्गोर * प्रतिमा $S\times S$ क्षेत्रांमध्ये विभागली जाते. * प्रत्येक क्षेत्रासाठी, **CNN** $n$ शक्य वस्तू, *बॉक्स* समन्वय आणि *कॉन्फिडन्स*=*प्रोबॅबिलिटी* * IoU अंदाज लावते. - ![YOLO](../../../../../translated_images/mr/yolo.a2648ec82ee8bb4e.png) + ![YOLO](../../../../../translated_images/mr/yolo.a2648ec82ee8bb4e.webp) > प्रतिमा [अधिकृत पेपर](https://arxiv.org/abs/1506.02640) मधून diff --git a/translations/mr/lessons/4-ComputerVision/README.md b/translations/mr/lessons/4-ComputerVision/README.md index 1e41ab36..d475761c 100644 --- a/translations/mr/lessons/4-ComputerVision/README.md +++ b/translations/mr/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # संगणकीय दृष्टिकोन -![संगणकीय दृष्टिकोन विषयाची रेखाचित्रात संक्षिप्त माहिती](../../../../translated_images/mr/ai-computervision.6506ebebac3fbf76.png) +![संगणकीय दृष्टिकोन विषयाची रेखाचित्रात संक्षिप्त माहिती](../../../../translated_images/mr/ai-computervision.6506ebebac3fbf76.webp) या विभागात आपण शिकणार आहोत: diff --git a/translations/mr/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/mr/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index 767a0f0a..f5dfa4e3 100644 --- a/translations/mr/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/mr/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**शब्दांची पिशवी** (BoW) वेक्टर प्रतिनिधित्व हे पारंपरिक वेक्टर प्रतिनिधित्वांपैकी सर्वात जास्त वापरले जाणारे आहे. प्रत्येक शब्द एका वेक्टर निर्देशांकाशी जोडलेला असतो, आणि वेक्टर घटक दिलेल्या दस्तऐवजात त्या शब्दाच्या उपस्थितीची संख्या दर्शवतो.\n", "\n", - "![शब्दांची पिशवी वेक्टर प्रतिनिधित्व स्मृतीत कसे सादर केले जाते हे दाखवणारी प्रतिमा.](../../../../../translated_images/mr/bag-of-words-example.606fc1738f1d7ba9.png)\n", + "![शब्दांची पिशवी वेक्टर प्रतिनिधित्व स्मृतीत कसे सादर केले जाते हे दाखवणारी प्रतिमा.](../../../../../translated_images/mr/bag-of-words-example.606fc1738f1d7ba9.webp)\n", "\n", "> **टीप**: तुम्ही BoW ला मजकूरातील स्वतंत्र शब्दांसाठी असलेल्या सर्व एक-हॉट-एन्कोडेड वेक्टरच्या बेरीजप्रमाणे देखील विचार करू शकता.\n", "\n", diff --git a/translations/mr/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/mr/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index 815694aa..4221c566 100644 --- a/translations/mr/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/mr/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**बॅग-ऑफ-वर्ड्स** (BoW) वेक्टर प्रतिनिधित्व हे पारंपरिक वेक्टर प्रतिनिधित्वांपैकी सर्वात सोपे आणि समजण्यास सोपे आहे. प्रत्येक शब्द एका वेक्टर निर्देशांकाशी जोडलेला असतो, आणि वेक्टर घटक दिलेल्या दस्तऐवजात प्रत्येक शब्द किती वेळा आढळतो ते दर्शवतो.\n", "\n", - "![बॅग-ऑफ-वर्ड्स वेक्टर प्रतिनिधित्व मेमरीमध्ये कसे सादर केले जाते हे दाखवणारी प्रतिमा.](../../../../../translated_images/mr/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![बॅग-ऑफ-वर्ड्स वेक्टर प्रतिनिधित्व मेमरीमध्ये कसे सादर केले जाते हे दाखवणारी प्रतिमा.](../../../../../translated_images/mr/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Note**: BoW चा विचार आपण मजकूरातील स्वतंत्र शब्दांसाठी एकत्रित केलेल्या सर्व वन-हॉट-एन्कोडेड वेक्टरच्या बेरीज म्हणून करू शकतो.\n", "\n", diff --git a/translations/mr/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/mr/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index acd0f886..413114e4 100644 --- a/translations/mr/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/mr/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "आपल्या नेटवर्कमध्ये एम्बेडिंग लेयर प्रथम लेयर म्हणून वापरल्याने, आपण बॅग-ऑफ-वर्ड्स मॉडेलवरून **एम्बेडिंग बॅग** मॉडेलकडे स्विच करू शकतो, जिथे आपण प्रथम आपल्या मजकुरातील प्रत्येक शब्द संबंधित एम्बेडिंगमध्ये रूपांतरित करतो आणि नंतर त्या सर्व एम्बेडिंगवर काही एकत्रित फंक्शन गणना करतो, जसे की `sum`, `average` किंवा `max`.\n", "\n", - "![पाच अनुक्रम शब्दांसाठी एम्बेडिंग वर्गीकरणकर्ता दर्शवणारी प्रतिमा.](../../../../../translated_images/mr/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![पाच अनुक्रम शब्दांसाठी एम्बेडिंग वर्गीकरणकर्ता दर्शवणारी प्रतिमा.](../../../../../translated_images/mr/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "आपले वर्गीकरणकर्ता न्यूरल नेटवर्क एम्बेडिंग लेयरने सुरू होईल, त्यानंतर एकत्रीकरण लेयर आणि त्यावर लीनियर वर्गीकरणकर्ता असेल:\n" ] @@ -176,7 +176,7 @@ "\n", "मागील आर्किटेक्चरमध्ये, सर्व अनुक्रम समान लांबीचे करण्यासाठी त्यांना पॅड करणे आवश्यक होते, जेणेकरून ते मिनीबॅचमध्ये बसतील. बदलत्या लांबीच्या अनुक्रमांचे प्रतिनिधित्व करण्याचा हा सर्वात कार्यक्षम मार्ग नाही - दुसरा दृष्टिकोन म्हणजे **ऑफसेट** व्हेक्टर वापरणे, जो एका मोठ्या व्हेक्टरमध्ये संग्रहित केलेल्या सर्व अनुक्रमांचे ऑफसेट ठेवेल.\n", "\n", - "![ऑफसेट अनुक्रमाचे प्रतिनिधित्व दर्शवणारी प्रतिमा](../../../../../translated_images/mr/offset-sequence-representation.eb73fcefb29b46ee.png)\n", + "![ऑफसेट अनुक्रमाचे प्रतिनिधित्व दर्शवणारी प्रतिमा](../../../../../translated_images/mr/offset-sequence-representation.eb73fcefb29b46ee.webp)\n", "\n", "> **Note**: वरील चित्रात, आम्ही अक्षरांच्या अनुक्रमाचे प्रदर्शन केले आहे, परंतु आमच्या उदाहरणात आम्ही शब्दांच्या अनुक्रमांवर काम करत आहोत. तथापि, ऑफसेट व्हेक्टरसह अनुक्रमांचे प्रतिनिधित्व करण्याचा सामान्य तत्त्व समान राहतो.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW जलद आहे, तर स्किप-ग्राम थोडा धीमा आहे, परंतु दुर्मिळ शब्दांचे प्रतिनिधित्व करण्याचे काम अधिक चांगल्या प्रकारे करतो.\n", "\n", - "![CBoW आणि स्किप-ग्राम अल्गोरिदम्स शब्दांना व्हेक्टरमध्ये रूपांतरित करण्यासाठी दाखवणारी प्रतिमा.](../../../../../translated_images/mr/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![CBoW आणि स्किप-ग्राम अल्गोरिदम्स शब्दांना व्हेक्टरमध्ये रूपांतरित करण्यासाठी दाखवणारी प्रतिमा.](../../../../../translated_images/mr/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Google News डेटासेटवर प्री-ट्रेन केलेल्या word2vec एम्बेडिंगसह प्रयोग करण्यासाठी, आपण **gensim** लायब्ररीचा वापर करू शकतो. खाली 'neural' या शब्दाशी सर्वाधिक समान शब्द शोधले आहेत:\n", "\n", diff --git a/translations/mr/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/mr/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index 6027a907..f90957fd 100644 --- a/translations/mr/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/mr/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "आपल्या नेटवर्कमध्ये पहिल्या लेयर म्हणून एम्बेडिंग लेयर वापरल्याने, आपण बॅग-ऑफ-वर्ड्स मॉडेलऐवजी **एम्बेडिंग बॅग** मॉडेलकडे स्विच करू शकतो, जिथे आपण प्रथम आपल्या मजकुरातील प्रत्येक शब्द त्याच्या संबंधित एम्बेडिंगमध्ये रूपांतरित करतो, आणि नंतर त्या सर्व एम्बेडिंग्सवर काही एकत्रित फंक्शन (जसे की `sum`, `average` किंवा `max`) गणना करतो.\n", "\n", - "![पाच अनुक्रम शब्दांसाठी एम्बेडिंग वर्गीकरण दाखवणारी प्रतिमा.](../../../../../translated_images/mr/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![पाच अनुक्रम शब्दांसाठी एम्बेडिंग वर्गीकरण दाखवणारी प्रतिमा.](../../../../../translated_images/mr/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "आपल्या वर्गीकरण न्यूरल नेटवर्कमध्ये खालील लेयर्स असतात:\n", "\n", @@ -281,7 +281,7 @@ "\n", "CBoW जलद आहे, आणि स्किप-ग्राम जरी हळू असला तरी, तो दुर्मिळ शब्दांचे प्रतिनिधित्व अधिक चांगल्या प्रकारे करतो.\n", "\n", - "![CBoW आणि स्किप-ग्राम अल्गोरिदम्स शब्दांना व्हेक्टरमध्ये रूपांतरित करण्यासाठी दाखवणारी प्रतिमा.](../../../../../translated_images/mr/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![CBoW आणि स्किप-ग्राम अल्गोरिदम्स शब्दांना व्हेक्टरमध्ये रूपांतरित करण्यासाठी दाखवणारी प्रतिमा.](../../../../../translated_images/mr/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Google News डेटासेटवर प्रीट्रेन केलेल्या Word2Vec एम्बेडिंगसह प्रयोग करण्यासाठी, आपण **gensim** लायब्ररीचा वापर करू शकतो. खाली 'neural' शब्दाशी सर्वाधिक समान असलेले शब्द शोधले आहेत.\n", "\n", diff --git a/translations/mr/lessons/5-NLP/14-Embeddings/README.md b/translations/mr/lessons/5-NLP/14-Embeddings/README.md index 03b7ddc4..1eb24eb2 100644 --- a/translations/mr/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/mr/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ BoW किंवा TF/IDF आधारित वर्गीकरण प्र आमच्या वर्गीकरण नेटवर्कमध्ये पहिल्या लेयर म्हणून एम्बेडिंग लेयर वापरून, आपण बॅग-ऑफ-वर्ड्स मॉडेलवरून **एम्बेडिंग बॅग** मॉडेलवर स्विच करू शकतो, जिथे आपण प्रथम आमच्या मजकुरातील प्रत्येक शब्द संबंधित एम्बेडिंगमध्ये रूपांतरित करतो आणि नंतर त्या सर्व एम्बेडिंग्सवर काही एकत्रित फंक्शन गणना करतो, जसे की `sum`, `average` किंवा `max`. -![पाच अनुक्रम शब्दांसाठी एम्बेडिंग वर्गीकरणकर्ता दर्शवणारी प्रतिमा.](../../../../../translated_images/mr/embedding-classifier-example.b77f021a7ee67eee.png) +![पाच अनुक्रम शब्दांसाठी एम्बेडिंग वर्गीकरणकर्ता दर्शवणारी प्रतिमा.](../../../../../translated_images/mr/embedding-classifier-example.b77f021a7ee67eee.webp) > लेखकाने तयार केलेली प्रतिमा @@ -40,7 +40,7 @@ BoW किंवा TF/IDF आधारित वर्गीकरण प्र CBoW जलद आहे, तर स्किप-ग्राम हळू आहे, पण दुर्मिळ शब्दांचे प्रतिनिधित्व चांगल्या प्रकारे करते. -![शब्दांना व्हेक्टरमध्ये रूपांतरित करण्यासाठी CBoW आणि स्किप-ग्राम अल्गोरिदम दर्शवणारी प्रतिमा.](../../../../../translated_images/mr/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![शब्दांना व्हेक्टरमध्ये रूपांतरित करण्यासाठी CBoW आणि स्किप-ग्राम अल्गोरिदम दर्शवणारी प्रतिमा.](../../../../../translated_images/mr/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > [या पेपरमधून](https://arxiv.org/pdf/1301.3781.pdf) घेतलेली प्रतिमा diff --git a/translations/mr/lessons/5-NLP/15-LanguageModeling/README.md b/translations/mr/lessons/5-NLP/15-LanguageModeling/README.md index 68c921fd..e931771a 100644 --- a/translations/mr/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/mr/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: * **कंटिन्युअस बॅग-ऑफ-वर्ड्स** (CBoW), जिथे आपण टोकन अनुक्रमातील मध्यवर्ती टोकन $W_0$ ची भविष्यवाणी करतो $W_{-N}$, ..., $W_N$. * **स्किप-ग्राम**, जिथे आपण मध्यवर्ती टोकन $W_0$ पासून शेजारील टोकनांचा संच {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} ची भविष्यवाणी करतो. -![शब्दांना वेक्टरमध्ये रूपांतरित करण्यासाठी वापरलेले अल्गोरिदम](../../../../../translated_images/mr/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![शब्दांना वेक्टरमध्ये रूपांतरित करण्यासाठी वापरलेले अल्गोरिदम](../../../../../translated_images/mr/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > [या पेपरमधून](https://arxiv.org/pdf/1301.3781.pdf) घेतलेली प्रतिमा diff --git a/translations/mr/lessons/5-NLP/16-RNN/README.md b/translations/mr/lessons/5-NLP/16-RNN/README.md index 92aece0c..a3cb3ea4 100644 --- a/translations/mr/lessons/5-NLP/16-RNN/README.md +++ b/translations/mr/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: मजकूर अनुक्रमाचा अर्थ पकडण्यासाठी, आपल्याला एक वेगळी तंत्रिका नेटवर्क रचना वापरावी लागते, ज्याला **पुनरावृत्ती तंत्रिका नेटवर्क** किंवा RNN म्हणतात. RNN मध्ये, आपण आपले वाक्य नेटवर्कमधून एकावेळी एक चिन्ह पाठवतो, आणि नेटवर्क काही **स्थिती** तयार करते, जी आपण पुढील चिन्हासह पुन्हा नेटवर्कमध्ये पाठवतो. -![RNN](../../../../../translated_images/mr/rnn.27f5c29c53d727b5.png) +![RNN](../../../../../translated_images/mr/rnn.27f5c29c53d727b5.webp) > लेखकाने तयार केलेले चित्र @@ -61,7 +61,7 @@ LSTM नेटवर्क RNN प्रमाणेच आयोजित क पुनरावृत्ती नेटवर्क, एकदिशात्मक किंवा द्विदिशात्मक, अनुक्रमातील विशिष्ट नमुने पकडतो आणि त्यांना स्थिती वेक्टरमध्ये संग्रहित करतो किंवा आउटपुटमध्ये पास करतो. जसे की कॉनव्होल्यूशन नेटवर्क्ससह, आपण पहिल्या स्तराद्वारे काढलेल्या कमी-स्तरीय नमुन्यांपासून उच्च-स्तरीय नमुने पकडण्यासाठी पहिल्या स्तरावर आणखी एक पुनरावृत्ती स्तर तयार करू शकतो. यामुळे **बहुस्तरीय RNN** ची संकल्पना तयार होते, ज्यामध्ये दोन किंवा अधिक पुनरावृत्ती नेटवर्क्स असतात, जिथे मागील स्तराचा आउटपुट पुढील स्तराला इनपुट म्हणून पास केला जातो. -![बहुस्तरीय लांब-आवधी स्मृती RNN दर्शवणारे चित्र](../../../../../translated_images/mr/multi-layer-lstm.dd975e29bb2a59fe.jpg) +![बहुस्तरीय लांब-आवधी स्मृती RNN दर्शवणारे चित्र](../../../../../translated_images/mr/multi-layer-lstm.dd975e29bb2a59fe.webp) *चित्र [या उत्कृष्ट पोस्टमधून](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) फर्नांडो लोपेझ यांनी* diff --git a/translations/mr/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/mr/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index f9214702..5f070c06 100644 --- a/translations/mr/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/mr/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -420,7 +420,7 @@ "\n", "पुनरावृत्ती नेटवर्क, एकदिशात्मक किंवा द्विदिशात्मक, अनुक्रमातील विशिष्ट नमुने कॅप्चर करते आणि त्यांना स्टेट व्हेक्टरमध्ये साठवते किंवा आउटपुटमध्ये पास करते. जसे कॉनव्होल्यूशनल नेटवर्क्समध्ये होते, तसेच आपण पहिल्या लेयरने काढलेल्या कमी-स्तरीय नमुन्यांवर आधारित उच्च-स्तरीय नमुने कॅप्चर करण्यासाठी पहिल्या लेयरच्या वर आणखी एक पुनरावृत्ती लेयर तयार करू शकतो. यामुळे **बहुपरत RNN** ची संकल्पना तयार होते, ज्यामध्ये दोन किंवा अधिक पुनरावृत्ती नेटवर्क्स असतात, जिथे मागील लेयरचा आउटपुट पुढील लेयरला इनपुट म्हणून दिला जातो.\n", "\n", - "![मल्टीलेयर लाँग-शॉर्ट-टर्म-मेमरी RNN दर्शवणारी प्रतिमा](../../../../../translated_images/mr/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![मल्टीलेयर लाँग-शॉर्ट-टर्म-मेमरी RNN दर्शवणारी प्रतिमा](../../../../../translated_images/mr/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*फर्नांडो लोपेझ यांच्या [या अप्रतिम पोस्टमधून](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) घेतलेली प्रतिमा*\n", "\n", diff --git a/translations/mr/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/mr/lessons/5-NLP/16-RNN/RNNTF.ipynb index 5302ccba..fdb1267a 100644 --- a/translations/mr/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/mr/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "मजकूर अनुक्रमाचा अर्थ पकडण्यासाठी, आपण **पुनरावृत्ती तंत्रिका नेटवर्क** किंवा RNN नावाची तंत्रिका नेटवर्क आर्किटेक्चर वापरणार आहोत. RNN वापरताना, आपण आपले वाक्य नेटवर्कमधून एकावेळी एक टोकन पास करतो, आणि नेटवर्क काही **स्थिती** तयार करते, जी आपण पुढील टोकनसह पुन्हा नेटवर्कमध्ये पास करतो.\n", "\n", - "![पुनरावृत्ती तंत्रिका नेटवर्क निर्मितीचे उदाहरण दर्शवणारी प्रतिमा.](../../../../../translated_images/mr/rnn.27f5c29c53d727b5.png)\n", + "![पुनरावृत्ती तंत्रिका नेटवर्क निर्मितीचे उदाहरण दर्शवणारी प्रतिमा.](../../../../../translated_images/mr/rnn.27f5c29c53d727b5.webp)\n", "\n", "टोकनच्या इनपुट अनुक्रम $X_0,\\dots,X_n$ दिल्यास, RNN तंत्रिका नेटवर्क ब्लॉक्सची एक अनुक्रम तयार करते आणि बॅकप्रोपोगेशन वापरून हे अनुक्रम एंड-टू-एंड प्रशिक्षित करते. प्रत्येक नेटवर्क ब्लॉक $(X_i,S_i)$ ही जोडी इनपुट म्हणून घेतो आणि $S_{i+1}$ परिणाम म्हणून तयार करतो. अंतिम स्थिती $S_n$ किंवा आउटपुट $Y_n$ रेषीय वर्गीकरणामध्ये जाते जेणेकरून परिणाम तयार होतो. सर्व नेटवर्क ब्लॉक्स समान वजन सामायिक करतात आणि एकाच बॅकप्रोपोगेशन पासद्वारे एंड-टू-एंड प्रशिक्षित केले जातात.\n", "\n", @@ -369,7 +369,7 @@ "\n", "पुनरावृत्ती नेटवर्क्स, एकदिशात्मक किंवा द्विदिशात्मक, अनुक्रमातील नमुने पकडतात आणि त्यांना स्टेट व्हेक्टरमध्ये साठवतात किंवा आउटपुट म्हणून परत करतात. जसे कॉनव्होल्यूशन नेटवर्क्समध्ये होते, तसेच आपण पहिल्या लेयरने काढलेल्या कमी स्तरातील नमुन्यांमधून उच्च स्तराचे नमुने पकडण्यासाठी पहिल्या लेयरनंतर आणखी एक पुनरावृत्ती लेयर तयार करू शकतो. यामुळे **बहुपरत RNN** ची संकल्पना तयार होते, ज्यामध्ये दोन किंवा अधिक पुनरावृत्ती नेटवर्क्स असतात, जिथे मागील लेयरचे आउटपुट पुढील लेयरला इनपुट म्हणून दिले जाते.\n", "\n", - "![मल्टीलेयर लाँग-शॉर्ट-टर्म-मेमरी RNN दर्शवणारी प्रतिमा](../../../../../translated_images/mr/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![मल्टीलेयर लाँग-शॉर्ट-टर्म-मेमरी RNN दर्शवणारी प्रतिमा](../../../../../translated_images/mr/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*फर्नांडो लोपेझ यांनी लिहिलेल्या [या अप्रतिम पोस्टमधून](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) घेतलेली प्रतिमा.*\n", "\n", diff --git a/translations/mr/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/mr/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index 38b227a6..da4fca30 100644 --- a/translations/mr/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/mr/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "RNNला मजकूर तयार करण्यासाठी प्रशिक्षण देण्याची पद्धत पुढीलप्रमाणे आहे. प्रत्येक चरणावर, आम्ही `nchars` लांबीचा अक्षरांचा क्रम घेऊ आणि नेटवर्कला प्रत्येक इनपुट अक्षरासाठी पुढील आउटपुट अक्षर तयार करण्यास सांगू:\n", "\n", - "!['HELLO' शब्द तयार करणाऱ्या RNNचे उदाहरण दाखवणारी प्रतिमा.](../../../../../translated_images/mr/rnn-generate.56c54afb52f9781d.png)\n", + "!['HELLO' शब्द तयार करणाऱ्या RNNचे उदाहरण दाखवणारी प्रतिमा.](../../../../../translated_images/mr/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "खऱ्या परिस्थितीनुसार, आपल्याला काही विशेष अक्षरे समाविष्ट करायची असू शकतात, जसे की *end-of-sequence* ``. आपल्या बाबतीत, आम्हाला फक्त अखंड मजकूर तयार करण्यासाठी नेटवर्कला प्रशिक्षण द्यायचे आहे, त्यामुळे आम्ही प्रत्येक क्रमाची लांबी `nchars` टोकन इतकी निश्चित करू. परिणामी, प्रत्येक प्रशिक्षण उदाहरणामध्ये `nchars` इनपुट्स आणि `nchars` आउटपुट्स (जे इनपुट क्रम एका चिन्हाने डावीकडे सरकवलेले असतील) असतील. मिनीबॅचमध्ये अशा अनेक क्रमांचा समावेश असेल.\n", "\n", diff --git a/translations/mr/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/mr/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index 83c1fa6c..8be5a071 100644 --- a/translations/mr/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/mr/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -111,7 +111,7 @@ "\n", "आपण RNN कसे प्रशिक्षण देणार आहोत जेणेकरून ती बातम्यांची शीर्षके तयार करू शकेल, ते पुढीलप्रमाणे आहे. प्रत्येक टप्प्यावर, आपण एक शीर्षक घेऊ, जे RNN मध्ये दिले जाईल, आणि प्रत्येक इनपुट अक्षरासाठी आपण नेटवर्कला पुढील आउटपुट अक्षर तयार करण्यास सांगू:\n", "\n", - "!['HELLO' शब्दाच्या RNN जनरेशनचे उदाहरण दाखवणारी प्रतिमा.](../../../../../translated_images/mr/rnn-generate.56c54afb52f9781d.png)\n", + "!['HELLO' शब्दाच्या RNN जनरेशनचे उदाहरण दाखवणारी प्रतिमा.](../../../../../translated_images/mr/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "आमच्या अनुक्रमाच्या शेवटच्या अक्षरासाठी, आम्ही नेटवर्कला `` टोकन तयार करण्यास सांगू.\n", "\n", diff --git a/translations/mr/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/mr/lessons/5-NLP/17-GenerativeNetworks/README.md index 2c4aa5d1..bfd199de 100644 --- a/translations/mr/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/mr/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: यामुळे खालील चित्रात दाखवलेल्या विविध न्यूरल आर्किटेक्चर्स शक्य होतात: -![रीकरंट न्यूरल नेटवर्क्सचे सामान्य नमुने दाखवणारी प्रतिमा.](../../../../../translated_images/mr/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![रीकरंट न्यूरल नेटवर्क्सचे सामान्य नमुने दाखवणारी प्रतिमा.](../../../../../translated_images/mr/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > प्रतिमा ब्लॉग पोस्ट [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) मधून [Andrej Karpaty](http://karpathy.github.io/) यांनी तयार केलेली. @@ -32,7 +32,7 @@ CO_OP_TRANSLATOR_METADATA: आम्ही या RNN ला टप्प्याटप्प्याने मजकूर तयार करण्यासाठी प्रशिक्षित करू. प्रत्येक टप्प्यावर, आम्ही `nchars` लांबीचा कॅरेक्टर अनुक्रम घेऊ आणि नेटवर्कला प्रत्येक इनपुट कॅरेक्टरसाठी पुढील आउटपुट कॅरेक्टर तयार करण्यास सांगू: -!['HELLO' शब्द तयार करण्यासाठी RNN चा उदाहरण दाखवणारी प्रतिमा.](../../../../../translated_images/mr/rnn-generate.56c54afb52f9781d.png) +!['HELLO' शब्द तयार करण्यासाठी RNN चा उदाहरण दाखवणारी प्रतिमा.](../../../../../translated_images/mr/rnn-generate.56c54afb52f9781d.webp) मजकूर तयार करताना (इन्फरन्स दरम्यान), आम्ही काही **प्रॉम्प्ट** पासून सुरुवात करतो, जे RNN सेल्समधून पास होते आणि त्याचा इंटरमीडिएट स्टेट तयार होतो, आणि नंतर त्या स्टेटमधून निर्मिती सुरू होते. आम्ही एकावेळी एक कॅरेक्टर तयार करतो आणि स्टेट आणि तयार केलेला कॅरेक्टर पुढील RNN सेलला पास करतो, जोपर्यंत पुरेसे कॅरेक्टर्स तयार होत नाहीत. diff --git a/translations/mr/lessons/5-NLP/18-Transformers/README.md b/translations/mr/lessons/5-NLP/18-Transformers/README.md index ab3cd005..863e481d 100644 --- a/translations/mr/lessons/5-NLP/18-Transformers/README.md +++ b/translations/mr/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ RNNs वापरून, क्रम-ते-क्रम दोन पुनर **लक्षवेध यंत्रणा** प्रत्येक इनपुट व्हेक्टरच्या संदर्भात्मक प्रभावाचे वजन प्रत्येक RNN च्या आउटपुट अंदाजावर देण्याचा मार्ग प्रदान करते. हे अंमलात आणण्याचा मार्ग म्हणजे इनपुट RNN च्या मध्यवर्ती स्थिती आणि आउटपुट RNN दरम्यान शॉर्टकट तयार करणे. अशा प्रकारे, आउटपुट चिन्ह yt तयार करताना, आपण सर्व इनपुट लपवलेल्या स्थिती hi विचारात घेऊ, वेगवेगळ्या वजन गुणांक αt,i सह. -![एन्कोडर/डिकोडर मॉडेलसह अ‍ॅडिटिव्ह लक्षवेध स्तर दर्शवणारी प्रतिमा](../../../../../translated_images/mr/encoder-decoder-attention.7a726296894fb567.png) +![एन्कोडर/डिकोडर मॉडेलसह अ‍ॅडिटिव्ह लक्षवेध स्तर दर्शवणारी प्रतिमा](../../../../../translated_images/mr/encoder-decoder-attention.7a726296894fb567.webp) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) मधील अ‍ॅडिटिव्ह लक्षवेध यंत्रणा असलेले एन्कोडर-डिकोडर मॉडेल, [या ब्लॉग पोस्टमधून](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) उद्धृत. लक्षवेध मॅट्रिक्स {αi,j} आउटपुट क्रमातील दिलेल्या शब्दाच्या निर्मितीत विशिष्ट इनपुट शब्द किती महत्त्वाची भूमिका बजावतात हे दर्शवेल. खाली अशा मॅट्रिक्सचे उदाहरण दिले आहे: -![Bahdanau - arviz.org मधून घेतलेले RNNsearch-50 द्वारे आढळलेले नमुना संरेखन दर्शवणारी प्रतिमा](../../../../../translated_images/mr/bahdanau-fig3.09ba2d37f202a6af.png) +![Bahdanau - arviz.org मधून घेतलेले RNNsearch-50 द्वारे आढळलेले नमुना संरेखन दर्शवणारी प्रतिमा](../../../../../translated_images/mr/bahdanau-fig3.09ba2d37f202a6af.webp) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) मधील आकृती (Fig.3) @@ -66,7 +66,7 @@ RNNs वापरून, क्रम-ते-क्रम दोन पुनर पुढे, आपल्याला आपल्या क्रमातील काही नमुने पकडणे आवश्यक आहे. हे करण्यासाठी, ट्रान्सफॉर्मर्स **स्व-लक्षवेध** यंत्रणा वापरतात, जी इनपुट आणि आउटपुट म्हणून समान क्रमावर लागू केलेले लक्षवेध आहे. स्व-लक्षवेध लागू केल्याने आपल्याला वाक्याच्या **संदर्भ** विचारात घेता येतो, आणि कोणते शब्द परस्पर संबंधित आहेत हे पाहता येते. उदाहरणार्थ, हे आपल्याला *it* सारख्या कोरफेरन्सद्वारे संदर्भित केलेले शब्द पाहण्यास अनुमती देते, तसेच संदर्भ विचारात घेण्यास मदत करते: -![](../../../../../translated_images/mr/CoreferenceResolution.861924d6d384a7d6.png) +![](../../../../../translated_images/mr/CoreferenceResolution.861924d6d384a7d6.webp) > [Google ब्लॉग](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) मधून प्रतिमा @@ -91,7 +91,7 @@ RNNs वापरून, क्रम-ते-क्रम दोन पुनर **BERT** (Bidirectional Encoder Representations from Transformers) हे 12 स्तरांसाठी *BERT-base* आणि 24 स्तरांसाठी *BERT-large* असलेले एक मोठे मल्टी-लेयर ट्रान्सफॉर्मर नेटवर्क आहे. मॉडेल प्रथम मोठ्या मजकूर डेटाच्या संग्रहावर (WikiPedia + पुस्तके) असंरचित प्रशिक्षण वापरून (वाक्यातील लपवलेले शब्द अंदाज करणे) पूर्व-प्रशिक्षित केले जाते. पूर्व-प्रशिक्षणादरम्यान मॉडेल महत्त्वपूर्ण स्तरांचा भाषा समज आत्मसात करते, ज्याचा नंतर इतर डेटासेटसह सूक्ष्म-ट्यूनिंगद्वारे लाभ घेतला जाऊ शकतो. या प्रक्रियेला **ट्रान्सफर लर्निंग** म्हणतात. -![http://jalammar.github.io/illustrated-bert/ मधून प्रतिमा](../../../../../translated_images/mr/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) +![http://jalammar.github.io/illustrated-bert/ मधून प्रतिमा](../../../../../translated_images/mr/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > प्रतिमा [स्रोत](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/mr/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/mr/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index 5be26fc7..827975db 100644 --- a/translations/mr/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/mr/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**लक्षवेधी यंत्रणा (Attention Mechanisms)** RNN च्या प्रत्येक आउटपुट अंदाजावर प्रत्येक इनपुट वेक्टरच्या संदर्भात्मक प्रभावाचे वजन करण्याचा एक मार्ग प्रदान करतात. हे अंमलात आणण्याचा मार्ग म्हणजे इनपुट RNN च्या मध्यवर्ती स्थिती आणि आउटपुट RNN यांच्यात शॉर्टकट तयार करणे. अशा प्रकारे, आउटपुट चिन्ह $y_t$ तयार करताना, आपण सर्व इनपुट लपलेल्या स्थिती $h_i$ विचारात घेऊ, वेगवेगळ्या वजन गुणांक $\\alpha_{t,i}$ सह.\n", "\n", - "![एन्कोडर/डिकोडर मॉडेल आणि अ‍ॅडिटिव्ह लक्षवेधी स्तर दर्शविणारी प्रतिमा](../../../../../translated_images/mr/encoder-decoder-attention.7a726296894fb567.png)\n", + "![एन्कोडर/डिकोडर मॉडेल आणि अ‍ॅडिटिव्ह लक्षवेधी स्तर दर्शविणारी प्रतिमा](../../../../../translated_images/mr/encoder-decoder-attention.7a726296894fb567.webp)\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) मधील अ‍ॅडिटिव्ह लक्षवेधी यंत्रणेसह एन्कोडर-डिकोडर मॉडेल, [या ब्लॉग पोस्टमधून](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) उद्धृत]*\n", "\n", "लक्षवेधी मॅट्रिक्स $\\{\\alpha_{i,j}\\}$ हे दर्शवेल की आउटपुट अनुक्रमातील विशिष्ट शब्द तयार करण्यात कोणत्या इनपुट शब्दांचा किती प्रभाव आहे. खाली अशा मॅट्रिक्सचे उदाहरण दिले आहे:\n", "\n", - "![RNNsearch-50 ने सापडलेले नमुना संरेखन दर्शविणारी प्रतिमा, Bahdanau - arviz.org कडून घेतलेली](../../../../../translated_images/mr/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![RNNsearch-50 ने सापडलेले नमुना संरेखन दर्शविणारी प्रतिमा, Bahdanau - arviz.org कडून घेतलेली](../../../../../translated_images/mr/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (आकृती 3) मधून घेतलेली आकृती]*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) हे 12 स्तरांसाठी *BERT-base* आणि 24 स्तरांसाठी *BERT-large* असलेले खूप मोठे बहुस्तरीय ट्रान्सफॉर्मर नेटवर्क आहे. हे मॉडेल मोठ्या प्रमाणावर मजकूर डेटावर (विकिपीडिया + पुस्तके) असंरचित प्रशिक्षण (unsupervised training) वापरून आधी प्रशिक्षणित केले जाते (वाक्यातील लपवलेले शब्द ओळखणे). पूर्व-प्रशिक्षणादरम्यान मॉडेल महत्त्वपूर्ण भाषिक समज आत्मसात करते, ज्याचा नंतर इतर डेटासेट्ससह सूक्ष्म-ट्यूनिंगद्वारे उपयोग केला जाऊ शकतो. या प्रक्रियेला **स्थानांतरण शिक्षण (transfer learning)** म्हणतात.\n", "\n", - "![http://jalammar.github.io/illustrated-bert/ येथून घेतलेली प्रतिमा](../../../../../translated_images/mr/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![http://jalammar.github.io/illustrated-bert/ येथून घेतलेली प्रतिमा](../../../../../translated_images/mr/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "BERT, DistilBERT, BigBird, OpenGPT3 आणि इतर अनेक ट्रान्सफॉर्मर आर्किटेक्चर्सच्या विविध आवृत्त्या आहेत, ज्या सूक्ष्म-ट्यूनिंगसाठी वापरता येतात. [HuggingFace पॅकेज](https://github.com/huggingface/) PyTorch सह या आर्किटेक्चर्सपैकी अनेकांचे प्रशिक्षण घेण्यासाठी रिपॉझिटरी प्रदान करते.\n", "\n", diff --git a/translations/mr/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/mr/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index 285041f9..997f1e0c 100644 --- a/translations/mr/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/mr/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**अटेन्शन मेकॅनिझम्स** RNN च्या प्रत्येक आउटपुट प्रेडिक्शनवर प्रत्येक इनपुट व्हेक्टरच्या संदर्भात्मक प्रभावाचे वजन करण्याचा एक मार्ग प्रदान करतात. हे अंमलात आणण्याचा मार्ग म्हणजे इनपुट RNN च्या इंटरमिजिएट स्टेट्स आणि आउटपुट RNN दरम्यान शॉर्टकट तयार करणे. अशा प्रकारे, जेव्हा आउटपुट चिन्ह $y_t$ तयार करतो, तेव्हा आपण सर्व इनपुट हिडन स्टेट्स $h_i$ विचारात घेऊ, वेगवेगळ्या वजन गुणांक $\\alpha_{t,i}$ सह.\n", "\n", - "![एन्कोडर/डिकोडर मॉडेल अ‍ॅडिटिव्ह अटेन्शन लेयरसह दर्शविणारी प्रतिमा](../../../../../translated_images/mr/encoder-decoder-attention.7a726296894fb567.png)\n", + "![एन्कोडर/डिकोडर मॉडेल अ‍ॅडिटिव्ह अटेन्शन लेयरसह दर्शविणारी प्रतिमा](../../../../../translated_images/mr/encoder-decoder-attention.7a726296894fb567.webp)\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) मधील अ‍ॅडिटिव्ह अटेन्शन मेकॅनिझमसह एन्कोडर-डिकोडर मॉडेल, [या ब्लॉग पोस्टमधून](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) घेतलेले*\n", "\n", "अटेन्शन मॅट्रिक्स $\\{\\alpha_{i,j}\\}$ हे दर्शवते की आउटपुट अनुक्रमातील विशिष्ट शब्द तयार करण्यात कोणत्या इनपुट शब्दांचा किती प्रभाव आहे. खाली अशा मॅट्रिक्सचे उदाहरण दिले आहे:\n", "\n", - "![RNNsearch-50 ने सापडलेले नमुना अलाइनमेंट दर्शविणारी प्रतिमा, Bahdanau - arviz.org कडून घेतलेली](../../../../../translated_images/mr/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![RNNsearch-50 ने सापडलेले नमुना अलाइनमेंट दर्शविणारी प्रतिमा, Bahdanau - arviz.org कडून घेतलेली](../../../../../translated_images/mr/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) मधून घेतलेली आकृती]*\n", "\n", @@ -225,7 +225,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) हा एक मोठा मल्टी लेयर ट्रान्सफॉर्मर नेटवर्क आहे ज्यामध्ये *BERT-base* साठी 12 स्तर आणि *BERT-large* साठी 24 स्तर आहेत. हा मॉडेल प्रथम मोठ्या प्रमाणावर मजकूर डेटावर (WikiPedia + पुस्तके) असुपरवाइज्ड ट्रेनिंगचा वापर करून (वाक्यातील मास्क केलेल्या शब्दांची भविष्यवाणी करणे) प्री-ट्रेन केला जातो. प्री-ट्रेनिंग दरम्यान मॉडेल महत्त्वपूर्ण भाषिक समज आत्मसात करते, ज्याचा नंतर इतर डेटासेटसह फाइन ट्यूनिंगद्वारे उपयोग केला जाऊ शकतो. या प्रक्रियेला **ट्रान्सफर लर्निंग** म्हणतात.\n", "\n", - "![picture from http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/mr/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![picture from http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/mr/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "BERT, DistilBERT, BigBird, OpenGPT3 आणि इतर अनेक ट्रान्सफॉर्मर आर्किटेक्चरचे प्रकार आहेत, जे फाइन ट्यून केले जाऊ शकतात.\n", "\n", diff --git a/translations/mr/lessons/5-NLP/19-NER/README.md b/translations/mr/lessons/5-NLP/19-NER/README.md index 3e59d364..1e17b4e5 100644 --- a/translations/mr/lessons/5-NLP/19-NER/README.md +++ b/translations/mr/lessons/5-NLP/19-NER/README.md @@ -58,7 +58,7 @@ infant | O टोकन्स आणि वर्गांमध्ये एक-ते-एक संबंध तयार करणे आवश्यक असल्याने, आपण या चित्रातून उजव्या बाजूला असलेल्या **अनेक-ते-अनेक** न्यूरल नेटवर्क मॉडेलला प्रशिक्षण देऊ शकतो: -![सामान्य पुनरावृत्तीशील न्यूरल नेटवर्क नमुन्यांचे चित्र.](../../../../../translated_images/mr/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![सामान्य पुनरावृत्तीशील न्यूरल नेटवर्क नमुन्यांचे चित्र.](../../../../../translated_images/mr/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > *[Andrej Karpathy](http://karpathy.github.io/) यांच्या [या ब्लॉग पोस्टमधून](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) घेतलेले चित्र. NER टोकन वर्गीकरण मॉडेल्स या चित्रातील उजव्या बाजूच्या नेटवर्क आर्किटेक्चरशी संबंधित आहेत.* diff --git a/translations/mr/lessons/5-NLP/README.md b/translations/mr/lessons/5-NLP/README.md index 5c376603..bfd8e708 100644 --- a/translations/mr/lessons/5-NLP/README.md +++ b/translations/mr/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # नैसर्गिक भाषा प्रक्रिया -![NLP कार्यांचा संक्षेप एका चित्रात](../../../../translated_images/mr/ai-nlp.b22dcb8ca4707cea.png) +![NLP कार्यांचा संक्षेप एका चित्रात](../../../../translated_images/mr/ai-nlp.b22dcb8ca4707cea.webp) या विभागात, आपण **नैसर्गिक भाषा प्रक्रिया (NLP)** संबंधित कार्ये हाताळण्यासाठी न्यूरल नेटवर्क्सचा वापर कसा करायचा यावर लक्ष केंद्रित करू. अनेक NLP समस्या आहेत ज्या संगणकांनी सोडवाव्या अशी आपली इच्छा आहे: diff --git a/translations/mr/lessons/6-Other/23-MultiagentSystems/README.md b/translations/mr/lessons/6-Other/23-MultiagentSystems/README.md index 025cc88e..20f3e170 100644 --- a/translations/mr/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/mr/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ NetLogo ची एक उत्तम गोष्ट म्हणजे त् मॉडेल उघडल्यानंतर, तुम्हाला मुख्य NetLogo स्क्रीनवर नेले जाते. येथे एक नमुना मॉडेल आहे जो मर्यादित संसाधने (गवत) दिल्यास लांडगा आणि मेंढ्यांच्या लोकसंख्येचे वर्णन करतो. -![NetLogo Main Screen](../../../../../translated_images/mr/NetLogo-Main.32653711ec1a01b3.png) +![NetLogo Main Screen](../../../../../translated_images/mr/NetLogo-Main.32653711ec1a01b3.webp) > स्क्रीनशॉट - Dmitry Soshnikov diff --git a/translations/mr/lessons/README.md b/translations/mr/lessons/README.md index c4aac771..22d67c6c 100644 --- a/translations/mr/lessons/README.md +++ b/translations/mr/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # आढावा -![डूडलमधील आढावा](../../../translated_images/mr/ai-overview.0857791951d19500.png) +![डूडलमधील आढावा](../../../translated_images/mr/ai-overview.0857791951d19500.webp) > स्केच नोट: [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/mr/lessons/X-Extras/X1-MultiModal/README.md b/translations/mr/lessons/X-Extras/X1-MultiModal/README.md index c47287fe..c6a3dfc6 100644 --- a/translations/mr/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/mr/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ NLP कार्यांसाठी ट्रान्सफॉर्मर CLIP ची मुख्य कल्पना म्हणजे टेक्स्ट प्रॉम्प्ट्सची प्रतिमा सोबत तुलना करणे आणि प्रतिमा प्रॉम्प्टशी किती चांगली जुळते हे ठरवणे. -![CLIP आर्किटेक्चर](../../../../../translated_images/mr/clip-arch.b3dbf20b4e8ed8be.png) +![CLIP आर्किटेक्चर](../../../../../translated_images/mr/clip-arch.b3dbf20b4e8ed8be.webp) > *[या ब्लॉग पोस्ट](https://openai.com/blog/clip/) मधून चित्र* @@ -31,7 +31,7 @@ CLIP मॉडेल/लायब्ररी [OpenAI GitHub](https://github.com समजा आपल्याला प्रतिमा वर्गीकृत करायच्या आहेत, जसे की मांजरी, कुत्रे आणि माणसे. अशा परिस्थितीत, आपण मॉडेलला एक प्रतिमा आणि टेक्स्ट प्रॉम्प्ट्सची मालिका देऊ शकतो: "*मांजरीचे चित्र*", "*कुत्र्याचे चित्र*", "*माणसाचे चित्र*". परिणामी 3 संभाव्यतेच्या व्हेक्टरमध्ये आपल्याला फक्त सर्वाधिक मूल्य असलेल्या निर्देशांक निवडायचा आहे. -![प्रतिमा वर्गीकरणासाठी CLIP](../../../../../translated_images/mr/clip-class.3af42ef0b2b19369.png) +![प्रतिमा वर्गीकरणासाठी CLIP](../../../../../translated_images/mr/clip-class.3af42ef0b2b19369.webp) > *[या ब्लॉग पोस्ट](https://openai.com/blog/clip/) मधून चित्र* @@ -55,13 +55,13 @@ VQGAN बद्दल अधिक जाणून घेण्यासाठ VQGAN आणि पारंपरिक GAN मधील एक महत्त्वाचा फरक म्हणजे पारंपरिक GAN कोणत्याही इनपुट व्हेक्टरवरून चांगली प्रतिमा तयार करू शकते, तर VQGAN कदाचित सुसंगत प्रतिमा तयार करणार नाही. त्यामुळे, प्रतिमा निर्मिती प्रक्रियेला पुढे मार्गदर्शन करणे आवश्यक आहे, आणि ते CLIP वापरून केले जाऊ शकते. -![VQGAN+CLIP आर्किटेक्चर](../../../../../translated_images/mr/vqgan.5027fe05051dfa31.png) +![VQGAN+CLIP आर्किटेक्चर](../../../../../translated_images/mr/vqgan.5027fe05051dfa31.webp) टेक्स्ट प्रॉम्प्टशी संबंधित प्रतिमा तयार करण्यासाठी, आपण काही रँडम एन्कोडिंग व्हेक्टरसह सुरुवात करतो जो VQGAN द्वारे प्रतिमा तयार करण्यासाठी पास केला जातो. नंतर CLIP चा वापर लॉस फंक्शन तयार करण्यासाठी केला जातो जो प्रतिमा टेक्स्ट प्रॉम्प्टशी किती चांगली जुळते हे दर्शवतो. त्यानंतर उद्दिष्ट म्हणजे हा लॉस कमी करणे, बॅक प्रोपोगेशन वापरून इनपुट व्हेक्टर पॅरामीटर्स समायोजित करणे. VQGAN+CLIP लागू करणारी एक उत्कृष्ट लायब्ररी [Pixray](http://github.com/pixray/pixray) आहे. -![Pixray द्वारे तयार केलेले चित्र](../../../../../translated_images/mr/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Pixray द्वारे तयार केलेले चित्र](../../../../../translated_images/mr/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Pixray द्वारे तयार केलेले चित्र](../../../../../translated_images/mr/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) +![Pixray द्वारे तयार केलेले चित्र](../../../../../translated_images/mr/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp) | ![Pixray द्वारे तयार केलेले चित्र](../../../../../translated_images/mr/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp) | ![Pixray द्वारे तयार केलेले चित्र](../../../../../translated_images/mr/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp) ----|----|---- प्रॉम्प्ट *साहित्याच्या तरुण पुरुष शिक्षकाचा जलरंग पोर्ट्रेट जवळून* वरून तयार केलेले चित्र | प्रॉम्प्ट *संगणक विज्ञानाच्या तरुण महिला शिक्षकाचा तेल पोर्ट्रेट जवळून* वरून तयार केलेले चित्र | प्रॉम्प्ट *गणिताच्या वृद्ध पुरुष शिक्षकाचा तेल पोर्ट्रेट जवळून* वरून तयार केलेले चित्र diff --git a/translations/ms/README.md b/translations/ms/README.md index 20a6c39e..4d966bee 100644 --- a/translations/ms/README.md +++ b/translations/ms/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # Kecerdasan Buatan untuk Pemula - Kurikulum -|![Sketchnote oleh @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ms/ai-overview.0857791951d19500.png)| +|![Sketchnote oleh @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ms/ai-overview.0857791951d19500.webp)| |:---:| | AI Untuk Pemula - _Sketchnote oleh [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/ms/lessons/1-Intro/README.md b/translations/ms/lessons/1-Intro/README.md index 9d3c1018..7a04ef46 100644 --- a/translations/ms/lessons/1-Intro/README.md +++ b/translations/ms/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Pengenalan kepada AI -![Ringkasan kandungan Pengenalan AI dalam bentuk doodle](../../../../translated_images/ms/ai-intro.bf28d1ac4235881c.png) +![Ringkasan kandungan Pengenalan AI dalam bentuk doodle](../../../../translated_images/ms/ai-intro.bf28d1ac4235881c.webp) > Sketchnote oleh [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Pada asalnya, komputer dicipta oleh [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage) untuk beroperasi pada nombor mengikut prosedur yang jelas - iaitu algoritma. Komputer moden, walaupun jauh lebih maju daripada model asal yang dicadangkan pada abad ke-19, masih mengikuti idea yang sama iaitu pengiraan terkawal. Oleh itu, adalah mungkin untuk memprogram komputer untuk melakukan sesuatu jika kita tahu urutan langkah yang tepat yang perlu dilakukan untuk mencapai matlamat tersebut. -![Foto seorang individu](../../../../translated_images/ms/dsh_age.d212a30d4e54fb5f.png) +![Foto seorang individu](../../../../translated_images/ms/dsh_age.d212a30d4e54fb5f.webp) > Foto oleh [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ Untuk maklumat lanjut, rujuk **[Artificial General Intelligence](https://en.wiki Salah satu masalah apabila berurusan dengan istilah **[Kecerdasan](https://en.wikipedia.org/wiki/Intelligence)** ialah tiada definisi yang jelas untuk istilah ini. Ada yang berpendapat bahawa kecerdasan berkaitan dengan **pemikiran abstrak**, atau **kesedaran diri**, tetapi kita tidak dapat mendefinisikannya dengan tepat. -![Foto seekor kucing](../../../../translated_images/ms/photo-cat.8c8e8fb760ffe457.jpg) +![Foto seekor kucing](../../../../translated_images/ms/photo-cat.8c8e8fb760ffe457.webp) > [Foto](https://unsplash.com/photos/75715CVEJhI) oleh [Amber Kipp](https://unsplash.com/@sadmax) dari Unsplash @@ -98,13 +98,13 @@ Sebaliknya, kita boleh cuba memodelkan elemen paling mudah dalam otak kita – i > | Bagaimana dengan ML? | | > |--------------|-----------| -> | Bahagian Kecerdasan Buatan yang berdasarkan komputer belajar untuk menyelesaikan masalah berdasarkan beberapa data dipanggil **Pembelajaran Mesin**. Kita tidak akan mempertimbangkan pembelajaran mesin klasik dalam kursus ini - kita merujuk anda kepada kurikulum [Machine Learning for Beginners](http://aka.ms/ml-beginners) yang berasingan. | ![ML for Beginners](../../../../translated_images/ms/ml-for-beginners.9e4fed176fd5817d.png) | +> | Bahagian Kecerdasan Buatan yang berdasarkan komputer belajar untuk menyelesaikan masalah berdasarkan beberapa data dipanggil **Pembelajaran Mesin**. Kita tidak akan mempertimbangkan pembelajaran mesin klasik dalam kursus ini - kita merujuk anda kepada kurikulum [Machine Learning for Beginners](http://aka.ms/ml-beginners) yang berasingan. | ![ML for Beginners](../../../../translated_images/ms/ml-for-beginners.9e4fed176fd5817d.webp) | ## Sejarah Ringkas AI Kecerdasan Buatan bermula sebagai satu bidang pada pertengahan abad ke-20. Pada mulanya, pendekatan penaakulan simbolik adalah pendekatan yang dominan, dan ia membawa kepada beberapa kejayaan penting, seperti sistem pakar – program komputer yang mampu bertindak sebagai pakar dalam beberapa domain masalah yang terhad. Walau bagaimanapun, tidak lama kemudian menjadi jelas bahawa pendekatan sedemikian tidak berskala dengan baik. Mengekstrak pengetahuan daripada pakar, mewakilkannya dalam komputer, dan memastikan pangkalan pengetahuan itu tepat ternyata menjadi tugas yang sangat kompleks, dan terlalu mahal untuk praktikal dalam banyak kes. Ini membawa kepada apa yang dipanggil [AI Winter](https://en.wikipedia.org/wiki/AI_winter) pada tahun 1970-an. -Sejarah Ringkas AI +Sejarah Ringkas AI > Imej oleh [Dmitry Soshnikov](http://soshnikov.com) @@ -124,7 +124,7 @@ Begitu juga, kita boleh melihat bagaimana pendekatan terhadap mencipta “progra * Pembantu moden, seperti Cortana, Siri atau Google Assistant semuanya adalah sistem hibrid yang menggunakan rangkaian neural untuk menukar pertuturan kepada teks dan mengenali niat kita, dan kemudian menggunakan beberapa penaakulan atau algoritma eksplisit untuk melaksanakan tindakan yang diperlukan. * Pada masa depan, kita mungkin menjangkakan model berasaskan neural sepenuhnya untuk mengendalikan dialog dengan sendirinya. Keluarga GPT dan [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) rangkaian neural baru-baru ini menunjukkan kejayaan besar dalam hal ini. -evolusi ujian Turing +evolusi ujian Turing > Gambar oleh Dmitry Soshnikov, [foto](https://unsplash.com/photos/r8LmVbUKgns) oleh [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto), Unsplash ## Penyelidikan AI Terkini diff --git a/translations/ms/lessons/2-Symbolic/Animals.ipynb b/translations/ms/lessons/2-Symbolic/Animals.ipynb index 48da5bc4..268e4bfa 100644 --- a/translations/ms/lessons/2-Symbolic/Animals.ipynb +++ b/translations/ms/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "Dalam contoh ini, kita akan melaksanakan sistem berasaskan pengetahuan yang mudah untuk menentukan haiwan berdasarkan beberapa ciri fizikal. Sistem ini boleh diwakili oleh pokok AND-OR berikut (ini adalah sebahagian daripada keseluruhan pokok, kita boleh menambah lebih banyak peraturan dengan mudah):\n", "\n", - "![](../../../../translated_images/ms/AND-OR-Tree.5592d2c70187f283.png)\n" + "![](../../../../translated_images/ms/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { diff --git a/translations/ms/lessons/2-Symbolic/README.md b/translations/ms/lessons/2-Symbolic/README.md index 8073d031..5c2f4f59 100644 --- a/translations/ms/lessons/2-Symbolic/README.md +++ b/translations/ms/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Perwakilan Pengetahuan dan Sistem Pakar -![Ringkasan kandungan AI Simbolik](../../../../translated_images/ms/ai-symbolic.715a30cb610411a6.png) +![Ringkasan kandungan AI Simbolik](../../../../translated_images/ms/ai-symbolic.715a30cb610411a6.webp) > Sketchnote oleh [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ Kebiasaannya, kita tidak mentakrifkan pengetahuan secara ketat, tetapi kita meny Oleh itu, masalah **perwakilan pengetahuan** adalah untuk mencari cara yang berkesan untuk mewakili pengetahuan dalam komputer dalam bentuk data, supaya ia boleh digunakan secara automatik. Ini boleh dilihat sebagai spektrum: -![Spektrum perwakilan pengetahuan](../../../../translated_images/ms/knowledge-spectrum.b60df631852c0217.png) +![Spektrum perwakilan pengetahuan](../../../../translated_images/ms/knowledge-spectrum.b60df631852c0217.webp) > Imej oleh [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Sintaks Blok | Indentasi | | | Salah satu kejayaan awal AI simbolik ialah **sistem pakar** - sistem komputer yang direka untuk bertindak sebagai pakar dalam domain masalah yang terhad. Ia berdasarkan **pangkalan pengetahuan** yang diekstrak daripada satu atau lebih pakar manusia, dan mengandungi **enjin inferens** yang melakukan beberapa penaakulan di atasnya. -![Seni Bina Manusia](../../../../translated_images/ms/arch-human.5d4d35f1bba3ab1c.png) | ![Sistem Berasaskan Pengetahuan](../../../../translated_images/ms/arch-kbs.3ec5c150b09fa8da.png) +![Seni Bina Manusia](../../../../translated_images/ms/arch-human.5d4d35f1bba3ab1c.webp) | ![Sistem Berasaskan Pengetahuan](../../../../translated_images/ms/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ Struktur ringkas sistem saraf manusia | Seni bina sistem berasaskan pengetahuan @@ -106,7 +106,7 @@ Sistem pakar dibina seperti sistem penaakulan manusia, yang mengandungi **memori Sebagai contoh, mari kita pertimbangkan sistem pakar berikut untuk menentukan haiwan berdasarkan ciri fizikalnya: -![Pokok AND-OR](../../../../translated_images/ms/AND-OR-Tree.5592d2c70187f283.png) +![Pokok AND-OR](../../../../translated_images/ms/AND-OR-Tree.5592d2c70187f283.webp) > Imej oleh [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/ms/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/ms/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index 6211a2d5..e873cf34 100644 --- a/translations/ms/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/ms/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -1255,7 +1255,7 @@ "* Kehilangan latihan yang rendah - model dapat menghampiri data latihan dengan baik kerana ia mempunyai kuasa ekspresi yang mencukupi.\n", "* Kehilangan validasi boleh menjadi jauh lebih tinggi daripada kehilangan latihan dan boleh mula meningkat semasa latihan - ini kerana model \"mengingati\" titik-titik latihan, dan kehilangan \"gambaran keseluruhan.\"\n", "\n", - "![Overfitting](../../../../../translated_images/ms/overfit.a0bd57f717c15769.png)\n", + "![Overfitting](../../../../../translated_images/ms/overfit.a0bd57f717c15769.webp)\n", "\n", "> Dalam gambar ini, `x` mewakili data latihan, `o` - data validasi. Kiri - model linear (satu lapisan), ia menghampiri sifat data dengan baik. Kanan - model yang overfitting, model ini menghampiri data latihan dengan sempurna, tetapi tidak lagi masuk akal untuk data lain (kesalahan validasi sangat tinggi).\n" ] diff --git a/translations/ms/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/ms/lessons/3-NeuralNetworks/05-Frameworks/README.md index 3c4936af..32eb8a0a 100644 --- a/translations/ms/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/ms/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Overfitting adalah konsep yang sangat penting dalam pembelajaran mesin, dan sang Pertimbangkan masalah berikut untuk menghampiri 5 titik (diwakili oleh `x` pada graf di bawah): -![linear](../../../../../translated_images/ms/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/ms/overfit2.131f5800ae10ca5e.jpg) +![linear](../../../../../translated_images/ms/overfit1.f24b71c6f652e59e.webp) | ![overfit](../../../../../translated_images/ms/overfit2.131f5800ae10ca5e.webp) -------------------------|-------------------------- **Model Linear, 2 parameter** | **Model Tidak Linear, 7 parameter** Ralat latihan = 5.3 | Ralat latihan = 0 @@ -79,7 +79,7 @@ Adalah sangat penting untuk mencapai keseimbangan yang betul antara kekayaan mod Seperti yang anda lihat daripada graf di atas, overfitting boleh dikesan melalui ralat latihan yang sangat rendah, dan ralat validasi yang tinggi. Biasanya semasa latihan kita akan melihat kedua-dua ralat latihan dan validasi mula berkurangan, dan kemudian pada satu ketika ralat validasi mungkin berhenti berkurangan dan mula meningkat. Ini akan menjadi tanda overfitting, dan petunjuk bahawa kita mungkin perlu menghentikan latihan pada ketika ini (atau sekurang-kurangnya membuat snapshot model). -![overfitting](../../../../../translated_images/ms/Overfitting.408ad91cd90b4371.png) +![overfitting](../../../../../translated_images/ms/Overfitting.408ad91cd90b4371.webp) ## Cara mencegah overfitting diff --git a/translations/ms/lessons/3-NeuralNetworks/README.md b/translations/ms/lessons/3-NeuralNetworks/README.md index 9daed6ac..3f796a16 100644 --- a/translations/ms/lessons/3-NeuralNetworks/README.md +++ b/translations/ms/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Pengenalan kepada Rangkaian Neural -![Ringkasan kandungan Pengenalan Rangkaian Neural dalam bentuk doodle](../../../../translated_images/ms/ai-neuralnetworks.1c687ae40bc86e83.png) +![Ringkasan kandungan Pengenalan Rangkaian Neural dalam bentuk doodle](../../../../translated_images/ms/ai-neuralnetworks.1c687ae40bc86e83.webp) Seperti yang telah kita bincangkan dalam pengenalan, salah satu cara untuk mencapai kecerdasan adalah dengan melatih **model komputer** atau **otak tiruan**. Sejak pertengahan abad ke-20, para penyelidik telah mencuba pelbagai model matematik, sehingga beberapa tahun kebelakangan ini arah ini terbukti sangat berjaya. Model matematik otak seperti ini dipanggil **rangkaian neural**. @@ -36,13 +36,13 @@ Dalam kurikulum ini, kita hanya akan memberi tumpuan kepada model rangkaian neur Daripada biologi, kita tahu bahawa otak kita terdiri daripada sel-sel neural (neuron), setiap satunya mempunyai pelbagai "input" (dendrit) dan satu "output" (akson). Kedua-dua dendrit dan akson boleh menghantar isyarat elektrik, dan sambungan di antara mereka — dikenali sebagai sinaps — boleh menunjukkan tahap kekonduksian yang berbeza-beza, yang dikawal oleh neurotransmitter. -![Model Neuron](../../../../translated_images/ms/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![Model Neuron](../../../../translated_images/ms/artneuron.1a5daa88d20ebe6f.png) +![Model Neuron](../../../../translated_images/ms/synapse-wikipedia.ed20a9e4726ea1c6.webp) | ![Model Neuron](../../../../translated_images/ms/artneuron.1a5daa88d20ebe6f.webp) ----|---- Neuron Sebenar *([Imej](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) dari Wikipedia)* | Neuron Tiruan *(Imej oleh Penulis)* Oleh itu, model matematik paling mudah bagi neuron mengandungi beberapa input X1, ..., XN dan satu output Y, serta satu siri pemberat W1, ..., WN. Output dikira sebagai: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) di mana f adalah beberapa **fungsi pengaktifan** bukan linear. diff --git a/translations/ms/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/ms/lessons/4-ComputerVision/06-IntroCV/README.md index f3cd9bb1..18caaf63 100644 --- a/translations/ms/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/ms/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ Dalam [OpenCV Notebook](OpenCV.ipynb) kami, kami memberikan beberapa contoh di m * **Pra-pemprosesan gambar buku Braille**. Kami memberi tumpuan kepada bagaimana kami boleh menggunakan thresholding, pengesanan ciri, transformasi perspektif dan manipulasi NumPy untuk memisahkan simbol Braille individu untuk pengelasan selanjutnya oleh rangkaian neural. -![Imej Braille](../../../../../translated_images/ms/braille.341962ff76b1bd70.jpeg) | ![Imej Braille Pra-pemprosesan](../../../../../translated_images/ms/braille-result.46530fea020b03c7.png) | ![Simbol Braille](../../../../../translated_images/ms/braille-symbols.0159185ab69d5339.png) +![Imej Braille](../../../../../translated_images/ms/braille.341962ff76b1bd70.webp) | ![Imej Braille Pra-pemprosesan](../../../../../translated_images/ms/braille-result.46530fea020b03c7.webp) | ![Simbol Braille](../../../../../translated_images/ms/braille-symbols.0159185ab69d5339.webp) ----|-----|----- > Imej dari [OpenCV.ipynb](OpenCV.ipynb) * **Mengesan pergerakan dalam video menggunakan perbezaan bingkai**. Jika kamera tetap, maka bingkai dari suapan kamera seharusnya agak serupa antara satu sama lain. Oleh kerana bingkai diwakili sebagai array, hanya dengan menolak array tersebut untuk dua bingkai berturut-turut kita akan mendapat perbezaan piksel, yang seharusnya rendah untuk bingkai statik, dan menjadi lebih tinggi apabila terdapat pergerakan yang ketara dalam imej. -![Imej bingkai video dan perbezaan bingkai](../../../../../translated_images/ms/frame-difference.706f805491a0883c.png) +![Imej bingkai video dan perbezaan bingkai](../../../../../translated_images/ms/frame-difference.706f805491a0883c.webp) > Imej dari [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ Dalam [OpenCV Notebook](OpenCV.ipynb) kami, kami memberikan beberapa contoh di m - **Dense Optical Flow** mengira medan vektor yang menunjukkan untuk setiap piksel ke mana ia bergerak - **Sparse Optical Flow** berdasarkan mengambil beberapa ciri yang jelas dalam imej (contohnya, tepi), dan membina trajektori mereka dari bingkai ke bingkai. -![Imej Optical Flow](../../../../../translated_images/ms/optical.1f4a94464579a83a.png) +![Imej Optical Flow](../../../../../translated_images/ms/optical.1f4a94464579a83a.webp) > Imej dari [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/ms/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/ms/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index ac791498..23c5de5e 100644 --- a/translations/ms/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/ms/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 adalah rangkaian yang mencapai ketepatan 92.7% dalam klasifikasi top-5 ImageNet pada tahun 2014. Ia mempunyai struktur lapisan seperti berikut: -![Lapisan ImageNet](../../../../../translated_images/ms/vgg-16-arch1.d901a5583b3a51ba.jpg) +![Lapisan ImageNet](../../../../../translated_images/ms/vgg-16-arch1.d901a5583b3a51ba.webp) Seperti yang anda lihat, VGG mengikuti senibina piramid tradisional, iaitu urutan lapisan penumpuan dan pengumpulan. -![Piramid ImageNet](../../../../../translated_images/ms/vgg-16-arch.64ff2137f50dd49f.jpg) +![Piramid ImageNet](../../../../../translated_images/ms/vgg-16-arch.64ff2137f50dd49f.webp) > Imej daripada [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/ms/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/ms/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index 5c4382d2..f9427f4c 100644 --- a/translations/ms/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/ms/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -262,7 +262,7 @@ "\n", "Oleh itu, dalam CNN biasa, akan terdapat beberapa lapisan pengenalan, dengan lapisan pengumpulan di antara mereka untuk mengurangkan dimensi gambar. Kita juga akan meningkatkan bilangan penapis, kerana apabila corak menjadi lebih kompleks - terdapat lebih banyak kombinasi menarik yang perlu kita cari.\n", "\n", - "![Imej menunjukkan beberapa lapisan pengenalan dengan lapisan pengumpulan.](../../../../../translated_images/ms/cnn-pyramid.85915455759ef0ce.png)\n", + "![Imej menunjukkan beberapa lapisan pengenalan dengan lapisan pengumpulan.](../../../../../translated_images/ms/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "Disebabkan oleh pengurangan dimensi ruang dan peningkatan dimensi ciri/penapis, seni bina ini juga dipanggil **seni bina piramid**.\n" ] diff --git a/translations/ms/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/ms/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index 08ba0e2c..31ecda49 100644 --- a/translations/ms/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/ms/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -360,7 +360,7 @@ "\n", "Oleh itu, dalam CNN tipikal, akan terdapat beberapa lapisan convolutional, dengan lapisan pooling di antara mereka untuk mengurangkan dimensi imej. Kita juga akan meningkatkan bilangan penapis, kerana apabila corak menjadi lebih kompleks - terdapat lebih banyak kombinasi menarik yang perlu kita cari.\n", "\n", - "![Imej menunjukkan beberapa lapisan convolutional dengan lapisan pooling.](../../../../../translated_images/ms/cnn-pyramid.85915455759ef0ce.png)\n", + "![Imej menunjukkan beberapa lapisan convolutional dengan lapisan pooling.](../../../../../translated_images/ms/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "Disebabkan oleh pengurangan dimensi ruang dan peningkatan dimensi ciri/penapis, seni bina ini juga dipanggil **seni bina piramid**.\n" ] diff --git a/translations/ms/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/ms/lessons/4-ComputerVision/07-ConvNets/README.md index b365a6a9..f2144572 100644 --- a/translations/ms/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/ms/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ Dalam kehidupan sebenar, kita ingin dapat mengenali objek dalam gambar tanpa men Untuk mengekstrak pola, kita akan menggunakan konsep **penapis konvolusi**. Seperti yang anda tahu, imej diwakili oleh matriks 2D, atau tensor 3D dengan kedalaman warna. Menggunakan penapis bermaksud kita mengambil matriks **kernel penapis** yang agak kecil, dan untuk setiap piksel dalam imej asal, kita mengira purata berwajaran dengan titik-titik jiran. Kita boleh melihat ini seperti tingkap kecil yang meluncur di seluruh imej, dan meratakan semua piksel mengikut berat dalam matriks kernel penapis. -![Penapis Tepi Menegak](../../../../../translated_images/ms/filter-vert.b7148390ca0bc356.png) | ![Penapis Tepi Mendatar](../../../../../translated_images/ms/filter-horiz.59b80ed4feb946ef.png) +![Penapis Tepi Menegak](../../../../../translated_images/ms/filter-vert.b7148390ca0bc356.webp) | ![Penapis Tepi Mendatar](../../../../../translated_images/ms/filter-horiz.59b80ed4feb946ef.webp) ----|---- > Imej oleh Dmitry Soshnikov @@ -38,7 +38,7 @@ Cara CNN berfungsi berdasarkan idea penting berikut: * Kita boleh mereka bentuk rangkaian sedemikian rupa sehingga penapis dilatih secara automatik * Kita boleh menggunakan pendekatan yang sama untuk mencari pola dalam ciri tahap tinggi, bukan hanya dalam imej asal. Oleh itu, pengekstrakan ciri CNN berfungsi pada hierarki ciri, bermula daripada gabungan piksel tahap rendah, sehingga gabungan tahap tinggi bahagian gambar. -![Pengekstrakan Ciri Hierarki](../../../../../translated_images/ms/FeatureExtractionCNN.d9b456cbdae7cb64.png) +![Pengekstrakan Ciri Hierarki](../../../../../translated_images/ms/FeatureExtractionCNN.d9b456cbdae7cb64.webp) > Imej daripada [kertas kerja oleh Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), berdasarkan [penyelidikan mereka](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ Kebanyakan CNN yang digunakan untuk pemprosesan imej mengikuti seni bina yang di Sebagai contoh, mari kita lihat seni bina VGG-16, rangkaian yang mencapai ketepatan 92.7% dalam klasifikasi top-5 ImageNet pada tahun 2014: -![Lapisan ImageNet](../../../../../translated_images/ms/vgg-16-arch1.d901a5583b3a51ba.jpg) +![Lapisan ImageNet](../../../../../translated_images/ms/vgg-16-arch1.d901a5583b3a51ba.webp) -![Piramid ImageNet](../../../../../translated_images/ms/vgg-16-arch.64ff2137f50dd49f.jpg) +![Piramid ImageNet](../../../../../translated_images/ms/vgg-16-arch.64ff2137f50dd49f.webp) > Imej daripada [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/ms/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/ms/lessons/4-ComputerVision/07-ConvNets/lab/README.md index c7fe3722..ed0b513f 100644 --- a/translations/ms/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/ms/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ Anda perlu melatih rangkaian neural konvolusi untuk mengklasifikasikan pelbagai Kita akan menggunakan [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/), yang mengandungi imej 37 baka anjing dan kucing yang berbeza. -![Dataset yang akan kita gunakan](../../../../../../translated_images/ms/data.50b2a9d5484bdbf0.png) +![Dataset yang akan kita gunakan](../../../../../../translated_images/ms/data.50b2a9d5484bdbf0.webp) Untuk memuat turun dataset, gunakan kod berikut: diff --git a/translations/ms/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/ms/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index 261d4655..14c2ca99 100644 --- a/translations/ms/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/ms/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "Untuk menggambarkan kucing yang ideal, kita akan bermula dengan imej bunyi rawak, dan akan cuba menggunakan teknik pengoptimuman penurunan kecerunan untuk melaraskan imej supaya rangkaian dapat mengenali kucing.\n", "\n", - "![Gelung Pengoptimuman](../../../../../translated_images/ms/ideal-cat-loop.999fbb8ff306e044.png)\n", + "![Gelung Pengoptimuman](../../../../../translated_images/ms/ideal-cat-loop.999fbb8ff306e044.webp)\n", "\n", "Berikut adalah imej permulaan kita:\n" ] diff --git a/translations/ms/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/ms/lessons/4-ComputerVision/08-TransferLearning/README.md index b0e947cf..3c63f087 100644 --- a/translations/ms/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/ms/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Kedua-dua Keras dan PyTorch mengandungi fungsi untuk memuatkan berat rangkaian n Berikut adalah contoh ciri-ciri yang diekstrak daripada gambar kucing oleh rangkaian VGG-16: -![Ciri-ciri yang diekstrak oleh VGG-16](../../../../../translated_images/ms/features.6291f9c7ba3a0b95.png) +![Ciri-ciri yang diekstrak oleh VGG-16](../../../../../translated_images/ms/features.6291f9c7ba3a0b95.webp) ## Dataset Kucing vs. Anjing @@ -48,19 +48,19 @@ Rangkaian neural pra-latih mengandungi pelbagai corak dalam "otaknya", termasuk Satu pendekatan yang boleh kita ambil adalah bermula dengan imej rawak, dan kemudian cuba menggunakan teknik **pengoptimuman penurunan kecerunan** untuk menyesuaikan imej tersebut sedemikian rupa sehingga rangkaian mula berfikir bahawa ia adalah kucing. -![Gelung Pengoptimuman Imej](../../../../../translated_images/ms/ideal-cat-loop.999fbb8ff306e044.png) +![Gelung Pengoptimuman Imej](../../../../../translated_images/ms/ideal-cat-loop.999fbb8ff306e044.webp) Walau bagaimanapun, jika kita melakukan ini, kita akan mendapat sesuatu yang sangat mirip dengan bunyi rawak. Ini kerana *terdapat banyak cara untuk membuat rangkaian berfikir imej input adalah kucing*, termasuk beberapa yang tidak masuk akal secara visual. Walaupun imej-imej tersebut mengandungi banyak corak yang tipikal untuk kucing, tiada apa yang menghalang mereka daripada menjadi jelas secara visual. Untuk memperbaiki hasilnya, kita boleh menambah satu lagi istilah ke dalam fungsi kehilangan, yang dipanggil **kehilangan variasi**. Ia adalah metrik yang menunjukkan betapa serupa piksel-piksel yang bersebelahan dalam imej. Meminimumkan kehilangan variasi menjadikan imej lebih licin, dan menghilangkan bunyi - dengan itu mendedahkan corak yang lebih menarik secara visual. Berikut adalah contoh imej "ideal" seperti itu, yang diklasifikasikan sebagai kucing dan zebra dengan kebarangkalian tinggi: -![Kucing Ideal](../../../../../translated_images/ms/ideal-cat.203dd4597643d6b0.png) | ![Zebra Ideal](../../../../../translated_images/ms/ideal-zebra.7f70e8b54ee15a7a.png) +![Kucing Ideal](../../../../../translated_images/ms/ideal-cat.203dd4597643d6b0.webp) | ![Zebra Ideal](../../../../../translated_images/ms/ideal-zebra.7f70e8b54ee15a7a.webp) -----|----- *Kucing Ideal* | *Zebra Ideal* Pendekatan serupa boleh digunakan untuk melakukan apa yang dipanggil **serangan adversarial** pada rangkaian neural. Katakan kita ingin mengelirukan rangkaian neural dan membuat anjing kelihatan seperti kucing. Jika kita mengambil imej anjing, yang dikenali oleh rangkaian sebagai anjing, kita kemudian boleh mengubahnya sedikit menggunakan pengoptimuman penurunan kecerunan, sehingga rangkaian mula mengklasifikasikannya sebagai kucing: -![Gambar Anjing](../../../../../translated_images/ms/original-dog.8f68a67d2fe0911f.png) | ![Gambar anjing yang diklasifikasikan sebagai kucing](../../../../../translated_images/ms/adversarial-dog.d9fc7773b0142b89.png) +![Gambar Anjing](../../../../../translated_images/ms/original-dog.8f68a67d2fe0911f.webp) | ![Gambar anjing yang diklasifikasikan sebagai kucing](../../../../../translated_images/ms/adversarial-dog.d9fc7773b0142b89.webp) -----|----- *Gambar asal anjing* | *Gambar anjing yang diklasifikasikan sebagai kucing* diff --git a/translations/ms/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/ms/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index 1b97784a..9ee9231c 100644 --- a/translations/ms/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/ms/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "Memandangkan kita melatih autoencoder untuk menangkap sebanyak mungkin maklumat daripada imej asal bagi tujuan pembinaan semula yang tepat, rangkaian cuba mencari **embedding** terbaik bagi imej input untuk menangkap maksudnya.\n", "\n", - "![Rajah AutoEncoder](../../../../../translated_images/ms/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![Rajah AutoEncoder](../../../../../translated_images/ms/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "> Imej daripada [blog Keras](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", diff --git a/translations/ms/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/ms/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index dc9e3f0c..b5298b1a 100644 --- a/translations/ms/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/ms/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "Oleh kerana kita melatih autoencoder untuk menangkap sebanyak mungkin maklumat daripada imej asal bagi menghasilkan pembinaan semula yang tepat, rangkaian cuba mencari **embedding** terbaik bagi imej input untuk menangkap maksudnya.\n", "\n", - "![AutoEncoder Diagram](../../../../../translated_images/ms/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![AutoEncoder Diagram](../../../../../translated_images/ms/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "*Imej daripada [blog Keras](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", diff --git a/translations/ms/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/ms/lessons/4-ComputerVision/09-Autoencoders/README.md index 42ff1072..d0f5eaf6 100644 --- a/translations/ms/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/ms/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ Namun, kita mungkin ingin menggunakan data mentah (tidak berlabel) untuk melatih Oleh kerana kita melatih autoencoder untuk menangkap sebanyak mungkin maklumat daripada imej asal bagi tujuan pembinaan semula yang tepat, rangkaian cuba mencari **embedding** terbaik bagi imej input untuk menangkap maknanya. -![AutoEncoder Diagram](../../../../../translated_images/ms/autoencoder_schema.5e6fc9ad98a5eb61.jpg) +![AutoEncoder Diagram](../../../../../translated_images/ms/autoencoder_schema.5e6fc9ad98a5eb61.webp) > Imej daripada [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/ms/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/ms/lessons/4-ComputerVision/11-ObjectDetection/README.md index 6e7e6925..26e9813d 100644 --- a/translations/ms/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/ms/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ Model klasifikasi imej yang telah kita pelajari sebelum ini mengambil imej dan m ## [Kuiz Pra-Kuliah](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Pengesanan Objek](../../../../../translated_images/ms/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) +![Pengesanan Objek](../../../../../translated_images/ms/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp) > Imej dari [laman web YOLO v2](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ Jika kita ingin mencari seekor kucing dalam gambar, pendekatan naif untuk penges 2. Jalankan klasifikasi imej pada setiap jubin. 3. Jubin yang menghasilkan pengaktifan yang cukup tinggi boleh dianggap mengandungi objek yang dicari. -![Pengesanan Objek Naif](../../../../../translated_images/ms/naive-detection.e7f1ba220ccd08c6.png) +![Pengesanan Objek Naif](../../../../../translated_images/ms/naive-detection.e7f1ba220ccd08c6.webp) > *Imej dari [Buku Latihan](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ Anda mungkin akan menemui dataset berikut untuk tugas ini: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 kelas * [COCO](http://cocodataset.org/#home) - Common Objects in Context. 80 kelas, kotak sempadan dan topeng segmentasi -![COCO](../../../../../translated_images/ms/coco-examples.71bc60380fa6cceb.jpg) +![COCO](../../../../../translated_images/ms/coco-examples.71bc60380fa6cceb.webp) ## Metrik Pengesanan Objek @@ -50,7 +50,7 @@ Anda mungkin akan menemui dataset berikut untuk tugas ini: Untuk klasifikasi imej, mudah untuk mengukur sejauh mana algoritma berfungsi, tetapi untuk pengesanan objek kita perlu mengukur kedua-dua ketepatan kelas dan ketepatan lokasi kotak sempadan yang diramalkan. Untuk yang terakhir, kita menggunakan **Intersection over Union** (IoU), yang mengukur sejauh mana dua kotak (atau dua kawasan arbitrari) bertindih. -![IoU](../../../../../translated_images/ms/iou_equation.9a4751d40fff4e11.png) +![IoU](../../../../../translated_images/ms/iou_equation.9a4751d40fff4e11.webp) > *Rajah 2 dari [blog post yang sangat baik tentang IoU](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ Terdapat dua kelas utama algoritma pengesanan objek: [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) menggunakan [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) untuk menghasilkan struktur hierarki kawasan ROI, yang kemudian dilalui oleh pengekstrak ciri CNN dan pengklasifikasi SVM untuk menentukan kelas objek, dan regresi linear untuk menentukan koordinat *kotak sempadan*. [Kertas Rasmi](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/ms/rcnn1.cae407020dfb1d1f.png) +![RCNN](../../../../../translated_images/ms/rcnn1.cae407020dfb1d1f.webp) > *Imej dari van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/ms/rcnn2.2d9530bb83516484.png) +![RCNN-1](../../../../../translated_images/ms/rcnn2.2d9530bb83516484.webp) > *Imej dari [blog ini](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e) @@ -110,7 +110,7 @@ Terdapat dua kelas utama algoritma pengesanan objek: Pendekatan ini serupa dengan R-CNN, tetapi kawasan ditentukan selepas lapisan konvolusi diterapkan. -![FRCNN](../../../../../translated_images/ms/f-rcnn.3cda6d9bb4188875.png) +![FRCNN](../../../../../translated_images/ms/f-rcnn.3cda6d9bb4188875.webp) > Imej dari [Kertas Rasmi](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ Pendekatan ini serupa dengan R-CNN, tetapi kawasan ditentukan selepas lapisan ko Idea utama pendekatan ini adalah menggunakan rangkaian neural untuk meramalkan ROI - yang dipanggil *Region Proposal Network*. [Kertas](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/ms/faster-rcnn.8d46c099b87ef30a.png) +![FasterRCNN](../../../../../translated_images/ms/faster-rcnn.8d46c099b87ef30a.webp) > Imej dari [kertas rasmi](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ Algoritma ini lebih pantas daripada Faster R-CNN. Idea utama adalah seperti beri 1. Ciri-ciri diproses oleh **Position-Sensitive Score Map**. Setiap objek dari $C$ kelas dibahagikan kepada $k\times k$ kawasan, dan kita melatih untuk meramalkan bahagian objek. 1. Untuk setiap bahagian dari $k\times k$ kawasan, semua rangkaian mengundi untuk kelas objek, dan kelas objek dengan undian maksimum dipilih. -![r-fcn image](../../../../../translated_images/ms/r-fcn.13eb88158b99a3da.png) +![r-fcn image](../../../../../translated_images/ms/r-fcn.13eb88158b99a3da.webp) > Imej dari [kertas rasmi](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO adalah algoritma satu-laluan masa nyata. Idea utama adalah seperti berikut: * Imej dibahagikan kepada $S\times S$ kawasan. * Untuk setiap kawasan, **CNN** meramalkan $n$ objek yang mungkin, koordinat *kotak sempadan* dan *confidence*=*probability* * IoU. - ![YOLO](../../../../../translated_images/ms/yolo.a2648ec82ee8bb4e.png) + ![YOLO](../../../../../translated_images/ms/yolo.a2648ec82ee8bb4e.webp) > Imej dari [kertas rasmi](https://arxiv.org/abs/1506.02640) diff --git a/translations/ms/lessons/4-ComputerVision/README.md b/translations/ms/lessons/4-ComputerVision/README.md index dda84866..11705437 100644 --- a/translations/ms/lessons/4-ComputerVision/README.md +++ b/translations/ms/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Penglihatan Komputer -![Ringkasan kandungan Penglihatan Komputer dalam bentuk doodle](../../../../translated_images/ms/ai-computervision.6506ebebac3fbf76.png) +![Ringkasan kandungan Penglihatan Komputer dalam bentuk doodle](../../../../translated_images/ms/ai-computervision.6506ebebac3fbf76.webp) Dalam bahagian ini, kita akan mempelajari tentang: diff --git a/translations/ms/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/ms/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index 985a2ea3..07ec0cd5 100644 --- a/translations/ms/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/ms/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**Bag of Words** (BoW) adalah representasi vektor yang paling biasa digunakan dalam kaedah tradisional. Setiap perkataan dikaitkan dengan indeks vektor, dan elemen vektor mengandungi bilangan kemunculan sesuatu perkataan dalam dokumen tertentu.\n", "\n", - "![Imej menunjukkan bagaimana representasi vektor bag of words diwakili dalam memori.](../../../../../translated_images/ms/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![Imej menunjukkan bagaimana representasi vektor bag of words diwakili dalam memori.](../../../../../translated_images/ms/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Note**: Anda juga boleh menganggap BoW sebagai jumlah semua vektor satu-hot-encoded untuk setiap perkataan individu dalam teks.\n", "\n", diff --git a/translations/ms/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/ms/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index 7319a400..e7885194 100644 --- a/translations/ms/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/ms/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**Bag-of-words** (BoW) adalah representasi vektor tradisional yang paling mudah difahami. Setiap perkataan dihubungkan dengan indeks vektor, dan elemen vektor mengandungi bilangan kemunculan setiap perkataan dalam dokumen tertentu.\n", "\n", - "![Imej menunjukkan bagaimana representasi vektor bag-of-words diwakili dalam memori.](../../../../../translated_images/ms/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![Imej menunjukkan bagaimana representasi vektor bag-of-words diwakili dalam memori.](../../../../../translated_images/ms/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Note**: Anda juga boleh menganggap BoW sebagai jumlah semua vektor satu-hot-encoded untuk setiap perkataan dalam teks.\n", "\n", diff --git a/translations/ms/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/ms/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 060e1c79..34ee66c4 100644 --- a/translations/ms/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/ms/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "Dengan menggunakan lapisan embedding sebagai lapisan pertama dalam rangkaian kita, kita boleh beralih daripada model bag-of-words kepada model **embedding bag**, di mana kita mula-mula menukar setiap perkataan dalam teks kita kepada embedding yang sepadan, dan kemudian mengira beberapa fungsi agregat ke atas semua embedding tersebut, seperti `sum`, `average` atau `max`.\n", "\n", - "![Imej menunjukkan pengklasifikasi embedding untuk lima perkataan dalam urutan.](../../../../../translated_images/ms/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![Imej menunjukkan pengklasifikasi embedding untuk lima perkataan dalam urutan.](../../../../../translated_images/ms/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "Rangkaian neural pengklasifikasi kita akan bermula dengan lapisan embedding, kemudian lapisan agregasi, dan pengklasifikasi linear di atasnya:\n" ] @@ -176,7 +176,7 @@ "\n", "Dalam seni bina sebelumnya, kami perlu menambah semua jujukan kepada panjang yang sama untuk dimuatkan ke dalam minibatch. Ini bukan cara yang paling efisien untuk mewakili jujukan panjang berubah - pendekatan lain adalah menggunakan vektor **offset**, yang akan menyimpan offset semua jujukan yang disimpan dalam satu vektor besar.\n", "\n", - "![Imej menunjukkan representasi jujukan offset](../../../../../translated_images/ms/offset-sequence-representation.eb73fcefb29b46ee.png)\n", + "![Imej menunjukkan representasi jujukan offset](../../../../../translated_images/ms/offset-sequence-representation.eb73fcefb29b46ee.webp)\n", "\n", "> **Nota**: Dalam gambar di atas, kami menunjukkan jujukan watak, tetapi dalam contoh kami, kami bekerja dengan jujukan perkataan. Walau bagaimanapun, prinsip umum mewakili jujukan dengan vektor offset tetap sama.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW lebih pantas, manakala skip-gram lebih perlahan, tetapi lebih baik dalam mewakili perkataan yang jarang digunakan.\n", "\n", - "![Imej menunjukkan kedua-dua algoritma CBoW dan Skip-Gram untuk menukar perkataan kepada vektor.](../../../../../translated_images/ms/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![Imej menunjukkan kedua-dua algoritma CBoW dan Skip-Gram untuk menukar perkataan kepada vektor.](../../../../../translated_images/ms/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Untuk mencuba embedding word2vec yang telah dilatih terlebih dahulu pada dataset Google News, kita boleh menggunakan perpustakaan **gensim**. Di bawah ini, kita mencari perkataan yang paling serupa dengan 'neural'\n", "\n", diff --git a/translations/ms/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/ms/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index f8e360ea..29a48dbc 100644 --- a/translations/ms/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/ms/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "Dengan menggunakan lapisan embedding sebagai lapisan pertama dalam rangkaian kita, kita boleh beralih daripada model bag-of-words kepada model **embedding bag**, di mana kita mula-mula menukar setiap perkataan dalam teks kita kepada embedding yang sepadan, dan kemudian mengira beberapa fungsi agregat ke atas semua embedding tersebut, seperti `sum`, `average` atau `max`.\n", "\n", - "![Imej menunjukkan pengelasan embedding untuk lima perkataan dalam urutan.](../../../../../translated_images/ms/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![Imej menunjukkan pengelasan embedding untuk lima perkataan dalam urutan.](../../../../../translated_images/ms/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "Rangkaian neural pengelasan kita terdiri daripada lapisan-lapisan berikut:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW lebih pantas, manakala skip-gram lebih perlahan tetapi lebih baik dalam mewakili perkataan yang jarang digunakan.\n", "\n", - "![Imej menunjukkan algoritma CBoW dan Skip-Gram untuk menukar perkataan kepada vektor.](../../../../../translated_images/ms/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![Imej menunjukkan algoritma CBoW dan Skip-Gram untuk menukar perkataan kepada vektor.](../../../../../translated_images/ms/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Untuk mencuba embedding Word2Vec yang telah dilatih terlebih dahulu pada dataset Google News, kita boleh menggunakan pustaka **gensim**. Di bawah ini, kita mencari perkataan yang paling serupa dengan 'neural'.\n", "\n", diff --git a/translations/ms/lessons/5-NLP/14-Embeddings/README.md b/translations/ms/lessons/5-NLP/14-Embeddings/README.md index d1a851de..c46cec26 100644 --- a/translations/ms/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/ms/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ Jadi, lapisan pembenaman akan mengambil perkataan sebagai input, dan menghasilka Dengan menggunakan lapisan pembenaman sebagai lapisan pertama dalam rangkaian pengklasifikasi kita, kita boleh beralih daripada model bag-of-words kepada model **embedding bag**, di mana kita mula-mula menukar setiap perkataan dalam teks kita kepada pembenaman yang sepadan, dan kemudian mengira beberapa fungsi agregat ke atas semua pembenaman tersebut, seperti `sum`, `average` atau `max`. -![Imej menunjukkan pengklasifikasi pembenaman untuk lima perkataan dalam urutan.](../../../../../translated_images/ms/embedding-classifier-example.b77f021a7ee67eee.png) +![Imej menunjukkan pengklasifikasi pembenaman untuk lima perkataan dalam urutan.](../../../../../translated_images/ms/embedding-classifier-example.b77f021a7ee67eee.webp) > Imej oleh penulis @@ -40,7 +40,7 @@ Untuk mencapai itu, kita perlu melatih model pembenaman kita terlebih dahulu pad CBoW lebih pantas, manakala skip-gram lebih perlahan tetapi lebih baik dalam mewakili perkataan yang jarang digunakan. -![Imej menunjukkan kedua-dua algoritma CBoW dan Skip-Gram untuk menukar perkataan kepada vektor.](../../../../../translated_images/ms/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![Imej menunjukkan kedua-dua algoritma CBoW dan Skip-Gram untuk menukar perkataan kepada vektor.](../../../../../translated_images/ms/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Imej daripada [kertas ini](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/ms/lessons/5-NLP/15-LanguageModeling/README.md b/translations/ms/lessons/5-NLP/15-LanguageModeling/README.md index 935e4134..9d8f4eaf 100644 --- a/translations/ms/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/ms/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ Dalam contoh sebelumnya, kita menggunakan pemerangkapan semantik yang telah dila * **Continuous Bag-of-Words** (CBoW), di mana kita meramalkan token tengah $W_0$ dalam urutan token $W_{-N}$, ..., $W_N$. * **Skip-gram**, di mana kita meramalkan satu set token berdekatan {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} daripada token tengah $W_0$. -![imej daripada kertas kerja tentang menukar perkataan kepada vektor](../../../../../translated_images/ms/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![imej daripada kertas kerja tentang menukar perkataan kepada vektor](../../../../../translated_images/ms/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Imej daripada [kertas kerja ini](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/ms/lessons/5-NLP/16-RNN/README.md b/translations/ms/lessons/5-NLP/16-RNN/README.md index f03a0199..9f10f3f1 100644 --- a/translations/ms/lessons/5-NLP/16-RNN/README.md +++ b/translations/ms/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ Dalam bahagian sebelum ini, kita telah menggunakan representasi semantik teks ya Untuk menangkap makna urutan teks, kita perlu menggunakan seni bina rangkaian neural lain, yang dipanggil **rangkaian neural berulang**, atau RNN. Dalam RNN, kita lalui ayat kita melalui rangkaian satu simbol pada satu masa, dan rangkaian menghasilkan beberapa **keadaan**, yang kemudian kita lalui semula ke rangkaian bersama simbol seterusnya. -![RNN](../../../../../translated_images/ms/rnn.27f5c29c53d727b5.png) +![RNN](../../../../../translated_images/ms/rnn.27f5c29c53d727b5.webp) > Gambar oleh penulis @@ -61,7 +61,7 @@ Kita telah membincangkan rangkaian berulang yang beroperasi dalam satu arah, dar Rangkaian berulang, sama ada satu arah atau dua arah, menangkap corak tertentu dalam urutan, dan boleh menyimpannya ke dalam vektor keadaan atau menghantarnya ke output. Seperti rangkaian konvolusi, kita boleh membina lapisan berulang lain di atas yang pertama untuk menangkap corak tahap lebih tinggi dan membina daripada corak tahap rendah yang diekstrak oleh lapisan pertama. Ini membawa kita kepada konsep **RNN berlapis** yang terdiri daripada dua atau lebih rangkaian berulang, di mana output lapisan sebelumnya dihantar ke lapisan seterusnya sebagai input. -![Gambar menunjukkan RNN LSTM berlapis pelbagai](../../../../../translated_images/ms/multi-layer-lstm.dd975e29bb2a59fe.jpg) +![Gambar menunjukkan RNN LSTM berlapis pelbagai](../../../../../translated_images/ms/multi-layer-lstm.dd975e29bb2a59fe.webp) *Gambar daripada [post yang hebat ini](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) oleh Fernando López* diff --git a/translations/ms/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/ms/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index 053f5e25..a01da55a 100644 --- a/translations/ms/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/ms/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "Rangkaian berulang, sama ada satu hala atau dwihala, menangkap corak tertentu dalam urutan, dan boleh menyimpannya ke dalam vektor keadaan atau menghantarnya ke output. Seperti rangkaian konvolusi, kita boleh membina lapisan berulang lain di atas lapisan pertama untuk menangkap corak tahap lebih tinggi, yang dibina daripada corak tahap rendah yang diekstrak oleh lapisan pertama. Ini membawa kita kepada konsep **RNN berlapis**, yang terdiri daripada dua atau lebih rangkaian berulang, di mana output daripada lapisan sebelumnya dihantar ke lapisan seterusnya sebagai input.\n", "\n", - "![Imej menunjukkan RNN LSTM berlapis panjang-pendek](../../../../../translated_images/ms/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![Imej menunjukkan RNN LSTM berlapis panjang-pendek](../../../../../translated_images/ms/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*Gambar daripada [post yang hebat ini](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) oleh Fernando López*\n", "\n", diff --git a/translations/ms/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/ms/lessons/5-NLP/16-RNN/RNNTF.ipynb index f040a620..e7a17065 100644 --- a/translations/ms/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/ms/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "Untuk menangkap makna urutan teks, kita akan menggunakan seni bina rangkaian neural yang dipanggil **rangkaian neural berulang**, atau RNN. Apabila menggunakan RNN, kita menghantar ayat kita melalui rangkaian satu token pada satu masa, dan rangkaian menghasilkan beberapa **keadaan**, yang kemudian kita hantar semula ke rangkaian bersama token seterusnya.\n", "\n", - "![Imej menunjukkan contoh penjanaan rangkaian neural berulang.](../../../../../translated_images/ms/rnn.27f5c29c53d727b5.png)\n", + "![Imej menunjukkan contoh penjanaan rangkaian neural berulang.](../../../../../translated_images/ms/rnn.27f5c29c53d727b5.webp)\n", "\n", "Diberikan urutan input token $X_0,\\dots,X_n$, RNN mencipta urutan blok rangkaian neural, dan melatih urutan ini secara hujung ke hujung menggunakan backpropagation. Setiap blok rangkaian mengambil pasangan $(X_i,S_i)$ sebagai input, dan menghasilkan $S_{i+1}$ sebagai hasil. Keadaan akhir $S_n$ atau output $Y_n$ dimasukkan ke dalam pengelas linear untuk menghasilkan keputusan. Semua blok rangkaian berkongsi berat yang sama, dan dilatih secara hujung ke hujung menggunakan satu laluan backpropagation.\n", "\n", @@ -369,7 +369,7 @@ "\n", "Rangkaian berulang, sama ada sehala atau dwihala, menangkap corak dalam urutan, dan menyimpannya ke dalam vektor keadaan atau mengembalikannya sebagai output. Seperti rangkaian konvolusi, kita boleh membina lapisan berulang lain selepas yang pertama untuk menangkap corak tahap lebih tinggi, yang dibina daripada corak tahap lebih rendah yang diekstrak oleh lapisan pertama. Ini membawa kita kepada konsep **RNN berlapis**, yang terdiri daripada dua atau lebih rangkaian berulang, di mana output lapisan sebelumnya dihantar ke lapisan seterusnya sebagai input.\n", "\n", - "![Imej menunjukkan RNN LSTM berlapis panjang-pendek](../../../../../translated_images/ms/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![Imej menunjukkan RNN LSTM berlapis panjang-pendek](../../../../../translated_images/ms/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*Gambar daripada [post yang hebat ini](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) oleh Fernando López.*\n", "\n", diff --git a/translations/ms/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/ms/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index 92fa43ab..3ca23f8d 100644 --- a/translations/ms/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/ms/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "Cara kita akan melatih RNN untuk menghasilkan teks adalah seperti berikut. Pada setiap langkah, kita akan mengambil satu urutan watak dengan panjang `nchars`, dan meminta rangkaian untuk menghasilkan watak output seterusnya bagi setiap watak input:\n", "\n", - "![Imej menunjukkan contoh RNN menghasilkan perkataan 'HELLO'.](../../../../../translated_images/ms/rnn-generate.56c54afb52f9781d.png)\n", + "![Imej menunjukkan contoh RNN menghasilkan perkataan 'HELLO'.](../../../../../translated_images/ms/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "Bergantung kepada senario sebenar, kita mungkin juga ingin memasukkan beberapa watak khas, seperti *end-of-sequence* ``. Dalam kes kita, kita hanya ingin melatih rangkaian untuk menghasilkan teks tanpa henti, oleh itu kita akan menetapkan saiz setiap urutan sama dengan token `nchars`. Akibatnya, setiap contoh latihan akan terdiri daripada `nchars` input dan `nchars` output (iaitu urutan input yang dialihkan satu simbol ke kiri). Minibatch akan terdiri daripada beberapa urutan seperti ini.\n", "\n", diff --git a/translations/ms/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/ms/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index 6d860091..8946d2e9 100644 --- a/translations/ms/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/ms/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -113,7 +113,7 @@ "\n", "Cara kita akan melatih RNN untuk menjana tajuk berita adalah seperti berikut. Pada setiap langkah, kita akan mengambil satu tajuk, yang akan dimasukkan ke dalam RNN, dan untuk setiap aksara input, kita akan meminta rangkaian untuk menjana aksara output seterusnya:\n", "\n", - "![Imej menunjukkan contoh penjanaan RNN untuk perkataan 'HELLO'.](../../../../../translated_images/ms/rnn-generate.56c54afb52f9781d.png)\n", + "![Imej menunjukkan contoh penjanaan RNN untuk perkataan 'HELLO'.](../../../../../translated_images/ms/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "Untuk aksara terakhir dalam urutan kita, kita akan meminta rangkaian untuk menjana token ``.\n", "\n", diff --git a/translations/ms/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/ms/lessons/5-NLP/17-GenerativeNetworks/README.md index 6049d046..025af31f 100644 --- a/translations/ms/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/ms/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ Dalam seni bina RNN yang kita bincangkan dalam unit sebelumnya, setiap unit RNN Ini membolehkan pelbagai seni bina neural yang ditunjukkan dalam gambar di bawah: -![Imej menunjukkan corak rangkaian neural berulang yang biasa.](../../../../../translated_images/ms/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Imej menunjukkan corak rangkaian neural berulang yang biasa.](../../../../../translated_images/ms/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > Imej daripada blog post [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) oleh [Andrej Karpaty](http://karpathy.github.io/) @@ -32,7 +32,7 @@ Dalam unit ini, kita akan memberi tumpuan kepada model generatif mudah yang memb Kita akan melatih RNN ini untuk menjana teks langkah demi langkah. Pada setiap langkah, kita akan mengambil urutan aksara sepanjang `nchars`, dan meminta rangkaian untuk menghasilkan aksara output seterusnya untuk setiap aksara input: -![Imej menunjukkan contoh penjanaan RNN untuk perkataan 'HELLO'.](../../../../../translated_images/ms/rnn-generate.56c54afb52f9781d.png) +![Imej menunjukkan contoh penjanaan RNN untuk perkataan 'HELLO'.](../../../../../translated_images/ms/rnn-generate.56c54afb52f9781d.webp) Semasa menjana teks (semasa inferens), kita bermula dengan beberapa **prompt**, yang dilalui melalui sel RNN untuk menghasilkan keadaan perantaraannya, dan kemudian daripada keadaan ini penjanaan bermula. Kita menjana satu aksara pada satu masa, dan menghantar keadaan dan aksara yang dijana kepada sel RNN lain untuk menjana aksara seterusnya, sehingga kita menjana aksara yang mencukupi. diff --git a/translations/ms/lessons/5-NLP/18-Transformers/README.md b/translations/ms/lessons/5-NLP/18-Transformers/README.md index 8d0b2a6d..257a47ca 100644 --- a/translations/ms/lessons/5-NLP/18-Transformers/README.md +++ b/translations/ms/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ Dengan RNN, urutan-ke-urutan dilaksanakan oleh dua rangkaian berulang, di mana s **Mekanisme Perhatian** menyediakan cara untuk memberi berat kepada kesan kontekstual setiap vektor input terhadap setiap ramalan output RNN. Cara ia dilaksanakan adalah dengan mencipta pintasan antara keadaan perantaraan RNN input dan RNN output. Dengan cara ini, apabila menghasilkan simbol output yt, kita akan mengambil kira semua keadaan tersembunyi input hi, dengan pekali berat yang berbeza αt,i. -![Imej menunjukkan model encoder/decoder dengan lapisan perhatian tambahan](../../../../../translated_images/ms/encoder-decoder-attention.7a726296894fb567.png) +![Imej menunjukkan model encoder/decoder dengan lapisan perhatian tambahan](../../../../../translated_images/ms/encoder-decoder-attention.7a726296894fb567.webp) > Model encoder-decoder dengan mekanisme perhatian tambahan dalam [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), dipetik daripada [blog post ini](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) Matriks perhatian {αi,j} akan mewakili tahap di mana perkataan input tertentu memainkan peranan dalam penjanaan perkataan tertentu dalam urutan output. Berikut adalah contoh matriks sedemikian: -![Imej menunjukkan penjajaran sampel yang ditemui oleh RNNsearch-50, diambil daripada Bahdanau - arviz.org](../../../../../translated_images/ms/bahdanau-fig3.09ba2d37f202a6af.png) +![Imej menunjukkan penjajaran sampel yang ditemui oleh RNNsearch-50, diambil daripada Bahdanau - arviz.org](../../../../../translated_images/ms/bahdanau-fig3.09ba2d37f202a6af.webp) > Rajah daripada [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Rajah 3) @@ -66,7 +66,7 @@ Hasil yang kita peroleh dengan pemadanan kedudukan menggabungkan kedua-dua token Seterusnya, kita perlu menangkap beberapa corak dalam urutan kita. Untuk melakukan ini, transformer menggunakan mekanisme **perhatian kendiri**, yang pada dasarnya adalah perhatian yang diterapkan pada urutan yang sama sebagai input dan output. Menerapkan perhatian kendiri membolehkan kita mengambil kira **konteks** dalam ayat, dan melihat perkataan mana yang saling berkaitan. Sebagai contoh, ia membolehkan kita melihat perkataan mana yang dirujuk oleh koreferensi, seperti *ia*, dan juga mengambil kira konteks: -![](../../../../../translated_images/ms/CoreferenceResolution.861924d6d384a7d6.png) +![](../../../../../translated_images/ms/CoreferenceResolution.861924d6d384a7d6.webp) > Imej daripada [Blog Google](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Oleh kerana setiap kedudukan input dipetakan secara bebas ke setiap kedudukan ou **BERT** (Bidirectional Encoder Representations from Transformers) ialah rangkaian transformer berlapis besar dengan 12 lapisan untuk *BERT-base*, dan 24 untuk *BERT-large*. Model ini mula-mula dilatih awal pada korpus teks yang besar (WikiPedia + buku) menggunakan latihan tanpa pengawasan (meramalkan perkataan yang disembunyikan dalam ayat). Semasa latihan awal, model menyerap tahap pemahaman bahasa yang ketara yang kemudiannya boleh dimanfaatkan dengan set data lain menggunakan penalaan halus. Proses ini dipanggil **pembelajaran pemindahan**. -![gambar dari http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/ms/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) +![gambar dari http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/ms/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > Sumber imej [di sini](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/ms/lessons/5-NLP/18-Transformers/READMEtransformers.md b/translations/ms/lessons/5-NLP/18-Transformers/READMEtransformers.md index 6b3886a2..9e2e4fa1 100644 --- a/translations/ms/lessons/5-NLP/18-Transformers/READMEtransformers.md +++ b/translations/ms/lessons/5-NLP/18-Transformers/READMEtransformers.md @@ -11,13 +11,13 @@ Dengan RNN, urutan-ke-urutan diimplementasikan oleh dua jaringan berulang, di ma **Mekanisme Perhatian** menyediakan cara untuk memberikan bobot pada dampak kontekstual dari setiap vektor masukan terhadap setiap prediksi keluaran dari RNN. Cara ini diimplementasikan dengan membuat jalur pendek antara keadaan sementara dari RNN masukan dan RNN keluaran. Dengan cara ini, saat menghasilkan simbol keluaran yt, kita akan mempertimbangkan semua keadaan tersembunyi masukan hi, dengan koefisien bobot yang berbeda αt,i. -![Gambar menunjukkan model encoder/decoder dengan lapisan perhatian aditif](../../../../../translated_images/ms/encoder-decoder-attention.7a726296894fb567.png) +![Gambar menunjukkan model encoder/decoder dengan lapisan perhatian aditif](../../../../../translated_images/ms/encoder-decoder-attention.7a726296894fb567.webp) > Model encoder-decoder dengan mekanisme perhatian aditif dalam [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), dikutip dari [posting blog ini](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) Matriks perhatian {αi,j} akan mewakili sejauh mana kata-kata masukan tertentu berperan dalam penghasilan kata tertentu dalam urutan keluaran. Di bawah ini adalah contoh matriks semacam itu: -![Gambar menunjukkan contoh keselarasan yang ditemukan oleh RNNsearch-50, diambil dari Bahdanau - arviz.org](../../../../../translated_images/ms/bahdanau-fig3.09ba2d37f202a6af.png) +![Gambar menunjukkan contoh keselarasan yang ditemukan oleh RNNsearch-50, diambil dari Bahdanau - arviz.org](../../../../../translated_images/ms/bahdanau-fig3.09ba2d37f202a6af.webp) > Gambar dari [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) @@ -57,7 +57,7 @@ Hasil yang kita dapatkan dengan embedding posisi menggabungkan baik token asli m Selanjutnya, kita perlu menangkap beberapa pola dalam urutan kita. Untuk melakukan ini, transformer menggunakan mekanisme **perhatian diri**, yang pada dasarnya adalah perhatian yang diterapkan pada urutan yang sama sebagai masukan dan keluaran. Menerapkan perhatian diri memungkinkan kita untuk mempertimbangkan **konteks** dalam kalimat, dan melihat kata-kata mana yang saling terkait. Misalnya, ini memungkinkan kita untuk melihat kata-kata mana yang dirujuk oleh ko-referensi, seperti *itu*, dan juga mempertimbangkan konteks: -![](../../../../../translated_images/ms/CoreferenceResolution.861924d6d384a7d6.png) +![](../../../../../translated_images/ms/CoreferenceResolution.861924d6d384a7d6.webp) > Gambar dari [Blog Google](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -82,7 +82,7 @@ Karena setiap posisi masukan dipetakan secara independen ke setiap posisi keluar **BERT** (Bidirectional Encoder Representations from Transformers) adalah jaringan transformer multi-layer yang sangat besar dengan 12 lapisan untuk *BERT-base*, dan 24 untuk *BERT-large*. Model ini pertama kali dilatih pada korpus data teks yang besar (WikiPedia + buku) menggunakan pelatihan tanpa pengawasan (memprediksi kata-kata yang disembunyikan dalam kalimat). Selama pelatihan awal, model menyerap tingkat pemahaman bahasa yang signifikan yang kemudian dapat dimanfaatkan dengan dataset lain menggunakan penyempurnaan. Proses ini disebut **transfer learning**. -![gambar dari http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/ms/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) +![gambar dari http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/ms/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > Gambar [sumber](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/ms/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/ms/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index e946704f..fb5d73c5 100644 --- a/translations/ms/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/ms/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**Mekanisme Perhatian** menyediakan cara untuk memberi pemberat kepada kesan kontekstual setiap vektor input terhadap setiap ramalan output RNN. Cara ia dilaksanakan adalah dengan mencipta pintasan antara keadaan perantaraan RNN input dan RNN output. Dengan cara ini, apabila menghasilkan simbol output $y_t$, kita akan mengambil kira semua keadaan tersembunyi input $h_i$, dengan pekali pemberat yang berbeza $\\alpha_{t,i}$.\n", "\n", - "![Imej menunjukkan model encoder/decoder dengan lapisan perhatian tambahan](../../../../../translated_images/ms/encoder-decoder-attention.7a726296894fb567.png)\n", + "![Imej menunjukkan model encoder/decoder dengan lapisan perhatian tambahan](../../../../../translated_images/ms/encoder-decoder-attention.7a726296894fb567.webp)\n", "*Model encoder-decoder dengan mekanisme perhatian tambahan dalam [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), dipetik daripada [catatan blog ini](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Matriks perhatian $\\{\\alpha_{i,j}\\}$ akan mewakili tahap di mana perkataan input tertentu memainkan peranan dalam penjanaan perkataan tertentu dalam urutan output. Di bawah adalah contoh matriks seperti itu:\n", "\n", - "![Imej menunjukkan penjajaran sampel yang ditemui oleh RNNsearch-50, diambil daripada Bahdanau - arviz.org](../../../../../translated_images/ms/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![Imej menunjukkan penjajaran sampel yang ditemui oleh RNNsearch-50, diambil daripada Bahdanau - arviz.org](../../../../../translated_images/ms/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*Rajah diambil daripada [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Rajah 3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) ialah rangkaian transformer berlapis besar dengan 12 lapisan untuk *BERT-base*, dan 24 untuk *BERT-large*. Model ini mula-mula dilatih awal pada korpus teks yang besar (WikiPedia + buku) menggunakan latihan tanpa pengawasan (meramalkan perkataan yang disembunyikan dalam satu ayat). Semasa latihan awal, model menyerap tahap pemahaman bahasa yang signifikan yang kemudiannya boleh dimanfaatkan dengan set data lain menggunakan penalaan halus. Proses ini dipanggil **pembelajaran pemindahan**.\n", "\n", - "![gambar dari http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/ms/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![gambar dari http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/ms/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "Terdapat banyak variasi seni bina Transformer termasuk BERT, DistilBERT, BigBird, OpenGPT3 dan banyak lagi yang boleh ditala halus. Pakej [HuggingFace](https://github.com/huggingface/) menyediakan repositori untuk melatih banyak seni bina ini dengan PyTorch.\n", "\n", diff --git a/translations/ms/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/ms/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index a2e7fb30..5aca263d 100644 --- a/translations/ms/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/ms/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**Mekanisme Perhatian** menyediakan cara untuk memberi pemberat kepada kesan kontekstual setiap vektor input terhadap setiap ramalan output RNN. Cara ia dilaksanakan adalah dengan mencipta jalan pintas antara keadaan perantaraan RNN input dan RNN output. Dengan cara ini, apabila menghasilkan simbol output $y_t$, kita akan mengambil kira semua keadaan tersembunyi input $h_i$, dengan pekali pemberat yang berbeza $\\alpha_{t,i}$. \n", "\n", - "![Imej menunjukkan model encoder/decoder dengan lapisan perhatian tambahan](../../../../../translated_images/ms/encoder-decoder-attention.7a726296894fb567.png)\n", + "![Imej menunjukkan model encoder/decoder dengan lapisan perhatian tambahan](../../../../../translated_images/ms/encoder-decoder-attention.7a726296894fb567.webp)\n", "*Model encoder-decoder dengan mekanisme perhatian tambahan dalam [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), dipetik daripada [catatan blog ini](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Matriks perhatian $\\{\\alpha_{i,j}\\}$ akan mewakili tahap di mana perkataan input tertentu memainkan peranan dalam penjanaan perkataan tertentu dalam urutan output. Di bawah adalah contoh matriks seperti itu:\n", "\n", - "![Imej menunjukkan penjajaran sampel yang ditemui oleh RNNsearch-50, diambil daripada Bahdanau - arviz.org](../../../../../translated_images/ms/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![Imej menunjukkan penjajaran sampel yang ditemui oleh RNNsearch-50, diambil daripada Bahdanau - arviz.org](../../../../../translated_images/ms/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*Rajah diambil daripada [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Rajah 3)*\n", "\n", @@ -231,7 +231,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) ialah rangkaian transformer berbilang lapisan yang sangat besar dengan 12 lapisan untuk *BERT-base*, dan 24 lapisan untuk *BERT-large*. Model ini mula-mula dilatih awal menggunakan korpus teks yang besar (WikiPedia + buku) melalui latihan tanpa pengawasan (meramalkan perkataan yang disembunyikan dalam ayat). Semasa latihan awal, model ini menyerap tahap pemahaman bahasa yang signifikan yang kemudiannya boleh dimanfaatkan dengan dataset lain melalui penalaan halus. Proses ini dipanggil **pembelajaran pemindahan**.\n", "\n", - "![gambar dari http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/ms/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![gambar dari http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/ms/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "Terdapat banyak variasi seni bina Transformer termasuk BERT, DistilBERT, BigBird, OpenGPT3 dan banyak lagi yang boleh ditala halus.\n", "\n", diff --git a/translations/ms/lessons/5-NLP/19-NER/README.md b/translations/ms/lessons/5-NLP/19-NER/README.md index 8cdfc24c..1a60b6c8 100644 --- a/translations/ms/lessons/5-NLP/19-NER/README.md +++ b/translations/ms/lessons/5-NLP/19-NER/README.md @@ -58,7 +58,7 @@ infant | O Oleh kerana kita perlu membina korespondensi satu-ke-satu antara token dan kelas, kita boleh melatih model rangkaian neural **banyak-ke-banyak** paling kanan daripada gambar ini: -![Imej menunjukkan corak rangkaian neural berulang yang biasa.](../../../../../translated_images/ms/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Imej menunjukkan corak rangkaian neural berulang yang biasa.](../../../../../translated_images/ms/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > *Imej daripada [blog post ini](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) oleh [Andrej Karpathy](http://karpathy.github.io/). Model klasifikasi token NER sepadan dengan seni bina rangkaian paling kanan dalam gambar ini.* diff --git a/translations/ms/lessons/5-NLP/README.md b/translations/ms/lessons/5-NLP/README.md index 4e95b5f8..d9409206 100644 --- a/translations/ms/lessons/5-NLP/README.md +++ b/translations/ms/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Pemprosesan Bahasa Semula Jadi -![Ringkasan tugas NLP dalam bentuk doodle](../../../../translated_images/ms/ai-nlp.b22dcb8ca4707cea.png) +![Ringkasan tugas NLP dalam bentuk doodle](../../../../translated_images/ms/ai-nlp.b22dcb8ca4707cea.webp) Dalam bahagian ini, kita akan memberi tumpuan kepada penggunaan Rangkaian Neural untuk menangani tugas-tugas berkaitan dengan **Pemprosesan Bahasa Semula Jadi (NLP)**. Terdapat banyak masalah NLP yang kita mahu komputer dapat selesaikan: diff --git a/translations/ms/lessons/6-Other/23-MultiagentSystems/README.md b/translations/ms/lessons/6-Other/23-MultiagentSystems/README.md index 9a2bc9a1..0d59f248 100644 --- a/translations/ms/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/ms/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ Anda boleh membuka salah satu model, contohnya **Biology → Flocking** Selepas membuka model, anda akan dibawa ke skrin utama NetLogo. Berikut adalah contoh model yang menerangkan populasi serigala dan kambing, dengan sumber yang terhad (rumput). -![NetLogo Main Screen](../../../../../translated_images/ms/NetLogo-Main.32653711ec1a01b3.png) +![NetLogo Main Screen](../../../../../translated_images/ms/NetLogo-Main.32653711ec1a01b3.webp) > Tangkapan skrin oleh Dmitry Soshnikov diff --git a/translations/ms/lessons/README.md b/translations/ms/lessons/README.md index c892436f..42acfb74 100644 --- a/translations/ms/lessons/README.md +++ b/translations/ms/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Gambaran Keseluruhan -![Gambaran Keseluruhan dalam bentuk lakaran](../../../translated_images/ms/ai-overview.0857791951d19500.png) +![Gambaran Keseluruhan dalam bentuk lakaran](../../../translated_images/ms/ai-overview.0857791951d19500.webp) > Lakaran oleh [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/ms/lessons/X-Extras/X1-MultiModal/README.md b/translations/ms/lessons/X-Extras/X1-MultiModal/README.md index 72296480..ee9b98ee 100644 --- a/translations/ms/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/ms/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ Selepas kejayaan model transformer dalam menyelesaikan tugas NLP, seni bina yang Idea utama CLIP adalah untuk membandingkan arahan teks dengan imej dan menentukan sejauh mana imej tersebut sesuai dengan arahan. -![Seni Bina CLIP](../../../../../translated_images/ms/clip-arch.b3dbf20b4e8ed8be.png) +![Seni Bina CLIP](../../../../../translated_images/ms/clip-arch.b3dbf20b4e8ed8be.webp) > *Gambar dari [catatan blog ini](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ Setelah model ini dilatih awal, kita boleh memberikannya sekumpulan imej dan sek Katakan kita perlu mengklasifikasikan imej antara, contohnya, kucing, anjing dan manusia. Dalam kes ini, kita boleh memberikan model imej, dan satu siri arahan teks: "*gambar seekor kucing*", "*gambar seekor anjing*", "*gambar seorang manusia*". Dalam vektor kebarangkalian yang dihasilkan, kita hanya perlu memilih indeks dengan nilai tertinggi. -![CLIP untuk Klasifikasi Imej](../../../../../translated_images/ms/clip-class.3af42ef0b2b19369.png) +![CLIP untuk Klasifikasi Imej](../../../../../translated_images/ms/clip-class.3af42ef0b2b19369.webp) > *Gambar dari [catatan blog ini](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ Ketahui lebih lanjut tentang VQGAN di laman web [Taming Transformers](https://co Salah satu perbezaan penting antara VQGAN dan GAN tradisional ialah yang terakhir boleh menghasilkan imej yang baik daripada sebarang vektor input, manakala VQGAN cenderung menghasilkan imej yang tidak koheren. Oleh itu, kita perlu membimbing proses penciptaan imej dengan lebih lanjut, dan itu boleh dilakukan menggunakan CLIP. -![Seni Bina VQGAN+CLIP](../../../../../translated_images/ms/vqgan.5027fe05051dfa31.png) +![Seni Bina VQGAN+CLIP](../../../../../translated_images/ms/vqgan.5027fe05051dfa31.webp) Untuk menghasilkan imej yang sepadan dengan arahan teks, kita bermula dengan beberapa vektor pengekodan rawak yang dihantar melalui VQGAN untuk menghasilkan imej. Kemudian CLIP digunakan untuk menghasilkan fungsi kehilangan yang menunjukkan sejauh mana imej tersebut sesuai dengan arahan teks. Matlamatnya kemudian adalah untuk meminimumkan kehilangan ini, menggunakan propagasi balik untuk menyesuaikan parameter vektor input. Pustaka hebat yang melaksanakan VQGAN+CLIP ialah [Pixray](http://github.com/pixray/pixray). -![Gambar yang dihasilkan oleh Pixray](../../../../../translated_images/ms/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Gambar yang dihasilkan oleh Pixray](../../../../../translated_images/ms/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Gambar yang dihasilkan oleh Pixray](../../../../../translated_images/ms/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) +![Gambar yang dihasilkan oleh Pixray](../../../../../translated_images/ms/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp) | ![Gambar yang dihasilkan oleh Pixray](../../../../../translated_images/ms/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp) | ![Gambar yang dihasilkan oleh Pixray](../../../../../translated_images/ms/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp) ----|----|---- Gambar yang dihasilkan daripada arahan *potret closeup cat air guru lelaki muda dalam bidang kesusasteraan dengan sebuah buku* | Gambar yang dihasilkan daripada arahan *potret closeup minyak guru wanita muda dalam bidang sains komputer dengan sebuah komputer* | Gambar yang dihasilkan daripada arahan *potret closeup minyak guru lelaki tua dalam bidang matematik di hadapan papan hitam* diff --git a/translations/my/README.md b/translations/my/README.md index 37716775..6d06b17e 100644 --- a/translations/my/README.md +++ b/translations/my/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # စက်မှုအသိပညာစနစ် အတွက် ရှေ့မီသူများ - သင်ရိုးညွှန်းတမ်း -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/my/ai-overview.0857791951d19500.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/my/ai-overview.0857791951d19500.webp)| |:---:| | AI For Beginners - _Sketchnote by [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/my/lessons/1-Intro/README.md b/translations/my/lessons/1-Intro/README.md index 55685708..8f99c27e 100644 --- a/translations/my/lessons/1-Intro/README.md +++ b/translations/my/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # AI အကြောင်းအကျဉ်း -![AI အကြောင်းအကျဉ်းအကြောင်းအရာကို ရေးဆွဲထားသော ပုံ](../../../../translated_images/my/ai-intro.bf28d1ac4235881c.png) +![AI အကြောင်းအကျဉ်းအကြောင်းအရာကို ရေးဆွဲထားသော ပုံ](../../../../translated_images/my/ai-intro.bf28d1ac4235881c.webp) > [Tomomi Imura](https://twitter.com/girlie_mac) မှ Sketchnote @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: မူလကတော့ ကွန်ပျူတာတွေကို [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage) က အလွန်သေချာတဲ့ နည်းလမ်းတစ်ခုဖြင့် နံပါတ်တွေကို လုပ်ဆောင်နိုင်ဖို့ တီထွင်ခဲ့တာပါ။ ယနေ့ခေတ်ကွန်ပျူတာတွေဟာ ၁၉ ရာစုက မူလပုံစံထက် အလွန်တိုးတက်လာပြီးသားဖြစ်ပေမယ့်လည်း ထိန်းချုပ်ထားတဲ့တွက်ချက်မှုတွေကို အခြေခံထားတဲ့ အယူအဆကိုပဲ ဆက်လက်လိုက်နာနေပါတယ်။ ဒါကြောင့် ရည်မှန်းချက်ကို ရရှိဖို့ လိုအပ်တဲ့ အဆင့်ဆင့်လုပ်ဆောင်မှုတွေကို သိထားရင် ကွန်ပျူတာကို အစီအစဉ်ရေးသားပြီး လုပ်ဆောင်နိုင်ပါတယ်။ -![လူတစ်ဦး၏ ဓာတ်ပုံ](../../../../translated_images/my/dsh_age.d212a30d4e54fb5f.png) +![လူတစ်ဦး၏ ဓာတ်ပုံ](../../../../translated_images/my/dsh_age.d212a30d4e54fb5f.webp) > [Vickie Soshnikova](http://twitter.com/vickievalerie) မှ ဓာတ်ပုံ @@ -46,7 +46,7 @@ CO_OP_TRANSLATOR_METADATA: **[Intelligence](https://en.wikipedia.org/wiki/Intelligence)** ဆိုတဲ့ စကားလုံးကို သုံးတဲ့အခါမှာ အဓိပ္ပါယ်ရှင်းလင်းမှုမရှိတာက ပြဿနာတစ်ခုဖြစ်ပါတယ်။ ဉာဏ်ရည်ဆိုတာ **အထွေထွေစဉ်းစားနိုင်စွမ်း** သို့မဟုတ် **ကိုယ့်ကိုယ်ကိုသိမြင်မှု** နဲ့ ဆက်စပ်နေတယ်လို့ ဆိုနိုင်ပေမယ့် အတိအကျ သတ်မှတ်လို့မရပါဘူး။ -![ကြောင်တစ်ကောင်ရဲ့ ဓာတ်ပုံ](../../../../translated_images/my/photo-cat.8c8e8fb760ffe457.jpg) +![ကြောင်တစ်ကောင်ရဲ့ ဓာတ်ပုံ](../../../../translated_images/my/photo-cat.8c8e8fb760ffe457.webp) > [Amber Kipp](https://unsplash.com/@sadmax) မှ [ဓာတ်ပုံ](https://unsplash.com/photos/75715CVEJhI) (Unsplash မှ) diff --git a/translations/my/lessons/2-Symbolic/Animals.ipynb b/translations/my/lessons/2-Symbolic/Animals.ipynb index d1b307a2..e0fbde1c 100644 --- a/translations/my/lessons/2-Symbolic/Animals.ipynb +++ b/translations/my/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "ဒီနမူနာမှာတော့ ရုပ်ပိုင်းဆိုင်ရာ အင်္ဂါရပ်အချို့အပေါ် မူတည်ပြီး တိရစ္ဆာန်ကို သတ်မှတ်နိုင်ရန် ရိုးရှင်းတဲ့ အသိပညာအခြေပြု စနစ်တစ်ခုကို တည်ဆောက်သွားမှာ ဖြစ်ပါတယ်။ ဒီစနစ်ကို အောက်ပါ AND-OR သစ်ပင်ဖြင့် ကိုယ်စားပြုနိုင်ပါတယ် (ဒီဟာက သစ်ပင်တစ်ခုလုံးရဲ့ အစိတ်အပိုင်းတစ်ခုသာဖြစ်ပြီး နောက်ထပ် စည်းမျဉ်းများကို လွယ်ကူစွာ ထပ်ထည့်နိုင်ပါတယ်)။\n", "\n", - "![](../../../../translated_images/my/AND-OR-Tree.5592d2c70187f283.png)\n" + "![](../../../../translated_images/my/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { diff --git a/translations/my/lessons/2-Symbolic/README.md b/translations/my/lessons/2-Symbolic/README.md index ba206b37..2b1dd2b9 100644 --- a/translations/my/lessons/2-Symbolic/README.md +++ b/translations/my/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Knowledge Representation and Expert Systems -![Summary of Symbolic AI content](../../../../translated_images/my/ai-symbolic.715a30cb610411a6.png) +![Summary of Symbolic AI content](../../../../translated_images/my/ai-symbolic.715a30cb610411a6.webp) > Sketchnote by [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ Symbolic AI ရဲ့ အရေးကြီးတဲ့အယူအဆတစ် ဒါကြောင့် **အသိပညာကိုဖော်ပြခြင်း** ဆိုတာ စက်ထဲမှာ ဒေတာအဖြစ် အသိပညာကို ထိရောက်စွာ ဖော်ပြနိုင်တဲ့ နည်းလမ်းတစ်ခုကို ရှာဖွေဖို့ ပြဿနာဖြစ်ပါတယ်၊ အလိုအလျောက် အသုံးပြုနိုင်စေဖို့ပါ။ ဒါကို အောက်ပါအတိုင်း စက်ရုပ်နည်းလမ်းအဖြစ် မြင်နိုင်ပါတယ်- -![Knowledge representation spectrum](../../../../translated_images/my/knowledge-spectrum.b60df631852c0217.png) +![Knowledge representation spectrum](../../../../translated_images/my/knowledge-spectrum.b60df631852c0217.webp) > Image by [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Block Syntax | Indent | | | Symbolic AI ရဲ့ အစောပိုင်းအောင်မြင်မှုတွေထဲမှာ **expert systems** လို့ခေါ်တဲ့ စနစ်တွေ ပါဝင်ပါတယ်။ ဒီစနစ်တွေက အကန့်အသတ်ရှိတဲ့ ပြဿနာဒေသတစ်ခုမှာ ကျွမ်းကျင်သူတစ်ဦးအဖြစ် လုပ်ဆောင်ဖို့ ဖန်တီးထားတဲ့ computer systems တွေဖြစ်ပါတယ်။ ဒီစနစ်တွေမှာ **knowledge base** ကို လူသားကျွမ်းကျင်သူတစ်ဦး သို့မဟုတ် အများအပြားကနေ ထုတ်ယူပြီး **inference engine** ကို အသုံးပြုပြီး reasoning လုပ်ပါတယ်။ -![Human Architecture](../../../../translated_images/my/arch-human.5d4d35f1bba3ab1c.png) | ![Knowledge-Based System](../../../../translated_images/my/arch-kbs.3ec5c150b09fa8da.png) +![Human Architecture](../../../../translated_images/my/arch-human.5d4d35f1bba3ab1c.webp) | ![Knowledge-Based System](../../../../translated_images/my/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ လူသားရဲ့ ဦးနှောက်စနစ်ရဲ့ ရိုးရှင်းတဲ့ဖွဲ့စည်းမှု | အသိပညာအခြေခံစနစ်ရဲ့ ဖွဲ့စည်းမှု @@ -106,7 +106,7 @@ Expert systems တွေကို လူသားရဲ့ reasoning စနစ ဥပမာအားဖြင့် အောက်ပါ expert system ကို သက်ရှိတစ်ခုရဲ့ ရုပ်ပိုင်းဆိုင်ရာလက္ခဏာအပေါ် အခြေခံပြီး သတ်မှတ်ခြင်းလုပ်ငန်းစဉ်အဖြစ် တွေးဆနိုင်ပါတယ်- -![AND-OR Tree](../../../../translated_images/my/AND-OR-Tree.5592d2c70187f283.png) +![AND-OR Tree](../../../../translated_images/my/AND-OR-Tree.5592d2c70187f283.webp) > Image by [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/my/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/my/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index 44648bf1..06e6793d 100644 --- a/translations/my/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/my/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -1265,7 +1265,7 @@ "* သင်ကြားမှုအရှုံး (training loss) နည်းတယ် - မော်ဒယ်မှာ အစွမ်းထက်တဲ့ ဖော်ပြနိုင်စွမ်းရှိလို့ သင်ကြားမှုဒေတာကို မှန်ကန်စွာ ခန့်မှန်းနိုင်တယ်။\n", "* Validation loss က training loss ထက် ပိုမြင့်တတ်တယ်၊ သင်ကြားမှုအတွင်းမှာတောင် မြင့်တက်လာတတ်တယ် - ဒီအခြေအနေက မော်ဒယ်က သင်ကြားမှုဒေတာကို \"မှတ်မိ\" သွားပြီး \"အထွေထွေသဘော\" ကို ပျောက်ဆုံးသွားတာကြောင့် ဖြစ်တယ်။\n", "\n", - "![Overfitting](../../../../../translated_images/my/overfit.a0bd57f717c15769.png)\n", + "![Overfitting](../../../../../translated_images/my/overfit.a0bd57f717c15769.webp)\n", "\n", "> ဒီပုံမှာ `x` က သင်ကြားမှုဒေတာကို ဆိုလိုတာဖြစ်ပြီး၊ `o` က စစ်ဆေးမှုဒေတာ (validation data) ကို ဆိုလိုတယ်။ ဘယ်ဘက်မှာ - linear မော်ဒယ် (တစ်လွှာမော်ဒယ်) က ဒေတာရဲ့ သဘာဝကို ကောင်းမွန်စွာ ခန့်မှန်းနိုင်တယ်။ ညာဘက်မှာ - overfitted မော်ဒယ်က သင်ကြားမှုဒေတာကို ပြည့်စုံစွာ ခန့်မှန်းနိုင်ပေမယ့် အခြားဒေတာတွေနဲ့တော့ အဓိပ္ပာယ်မရှိတော့ဘူး (validation error အလွန်မြင့်တက်တယ်)။\n" ] diff --git a/translations/my/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/my/lessons/3-NeuralNetworks/05-Frameworks/README.md index 46e90b7c..0b8f6165 100644 --- a/translations/my/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/my/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Overfitting သည် machine learning တွင် အလွန်အရေး အောက်ပါ 5 dots (graph ပေါ်တွင် `x` ဖြင့် ဖော်ပြထားသည်) ကို approximation လုပ်ရန် ပြဿနာကို စဉ်းစားပါ- -![linear](../../../../../translated_images/my/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/my/overfit2.131f5800ae10ca5e.jpg) +![linear](../../../../../translated_images/my/overfit1.f24b71c6f652e59e.webp) | ![overfit](../../../../../translated_images/my/overfit2.131f5800ae10ca5e.webp) -------------------------|-------------------------- **Linear model, 2 parameters** | **Non-linear model, 7 parameters** Training error = 5.3 | Training error = 0 @@ -79,7 +79,7 @@ Model ၏ richness (parameter အရေအတွက်) နှင့် training အထက်ပါ graph မှာမြင်နိုင်သည့်အတိုင်း overfitting ကို training error အလွန်နည်းပြီး validation error အလွန်မြင့်ခြင်းဖြင့် ရှာဖွေနိုင်သည်။ Training အတွင်း training error နှင့် validation error နှစ်ခုစလုံး လျော့နည်းလာပြီး validation error သည် တစ်ချိန်တွင် လျော့နည်းမှုရပ်ပြီး မြင့်တက်လာနိုင်သည်။ ဤအချိန်သည် overfitting ဖြစ်နေသည်ဟု သက်သေပြသည်။ Training ကို ရပ်တန့်ရန် သို့မဟုတ် model ၏ snapshot ကို သိမ်းဆည်းရန် အချိန်ဖြစ်သည်။ -![overfitting](../../../../../translated_images/my/Overfitting.408ad91cd90b4371.png) +![overfitting](../../../../../translated_images/my/Overfitting.408ad91cd90b4371.webp) ## How to prevent overfitting diff --git a/translations/my/lessons/3-NeuralNetworks/README.md b/translations/my/lessons/3-NeuralNetworks/README.md index 5c4b6fe5..ce68712a 100644 --- a/translations/my/lessons/3-NeuralNetworks/README.md +++ b/translations/my/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # နယူးရယ်နက်ဝါ့ခ်များအကြောင်း အကျဉ်းချုပ် -![နယူးရယ်နက်ဝါ့ခ်များအကြောင်း အကျဉ်းချုပ်ကို ရေးဆွဲထားသော ပုံ](../../../../translated_images/my/ai-neuralnetworks.1c687ae40bc86e83.png) +![နယူးရယ်နက်ဝါ့ခ်များအကြောင်း အကျဉ်းချုပ်ကို ရေးဆွဲထားသော ပုံ](../../../../translated_images/my/ai-neuralnetworks.1c687ae40bc86e83.webp) ကျွန်ုပ်တို့ အကျဉ်းချုပ်တွင် ဆွေးနွေးခဲ့သည့်အတိုင်း၊ ဉာဏ်ရည်ကို ရရှိစေရန် နည်းလမ်းတစ်ခုမှာ **ကွန်ပျူတာမော်ဒယ်** သို့မဟုတ် **အတုဉာဏ်ရည်** တစ်ခုကို လေ့ကျင့်ခြင်းဖြစ်သည်။ ၂၀ ရာစု အလယ်ပိုင်းမှစ၍ သုတေသနပြုသူများသည် သင်္ချာမော်ဒယ်အမျိုးမျိုးကို စမ်းသပ်ခဲ့ကြပြီး၊ မကြာသေးမီနှစ်များတွင် ဤလမ်းကြောင်းသည် အလွန်အောင်မြင်ကြောင်း သက်သေပြခဲ့သည်။ ဉာဏ်ရည်၏ သင်္ချာမော်ဒယ်များကို **နယူးရယ်နက်ဝါ့ခ်များ** ဟု ခေါ်သည်။ @@ -36,13 +36,13 @@ CO_OP_TRANSLATOR_METADATA: ဇီဝဗေဒမှ ကျွန်ုပ်တို့သိရှိထားသည်မှာ၊ ကျွန်ုပ်တို့၏ ဦးနှောက်သည် နယူးရွန်ဆဲလ်များ (neurons) ဖြင့် ဖွဲ့စည်းထားပြီး၊ ၎င်းတို့တွင် "ထည့်သွင်းမှုများ" (dendrites) အများအပြားနှင့် "ထွက်ရှိမှု" (axon) တစ်ခုရှိသည်။ Dendrites နှင့် Axons နှစ်ခုစလုံးသည် လျှပ်စစ်သံကို သယ်ဆောင်နိုင်ပြီး၊ ၎င်းတို့အကြားရှိ ဆက်သွယ်မှုများ — synapses ဟုခေါ်သည် — သည် လျှပ်စစ်သံသယ်ဆောင်နိုင်စွမ်းအမျိုးမျိုးကို ပြသနိုင်ပြီး၊ ၎င်းတို့ကို neurotransmitters များက ထိန်းညှိသည်။ -![နယူးရွန်၏ မော်ဒယ်](../../../../translated_images/my/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![နယူးရွန်၏ မော်ဒယ်](../../../../translated_images/my/artneuron.1a5daa88d20ebe6f.png) +![နယူးရွန်၏ မော်ဒယ်](../../../../translated_images/my/synapse-wikipedia.ed20a9e4726ea1c6.webp) | ![နယူးရွန်၏ မော်ဒယ်](../../../../translated_images/my/artneuron.1a5daa88d20ebe6f.webp) ----|---- အမှန်တကယ် နယူးရွန် *([ပုံ](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) Wikipedia မှ)* | အတုနယူးရွန် *(ရေးသားသူမှ ပုံဆွဲသည်)* ထို့ကြောင့်၊ နယူးရွန်၏ အလွယ်ဆုံး သင်္ချာမော်ဒယ်တွင် ထည့်သွင်းမှုများ X1, ..., XN နှင့် ထွက်ရှိမှု Y တို့နှင့် အလေးချိန်များ W1, ..., WN တို့ပါဝင်သည်။ ထွက်ရှိမှုကို အောက်ပါအတိုင်းတွက်ချက်သည်- -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) ဤတွင် f သည် အချို့သော မလိုက်လျောသော **activation function** ဖြစ်သည်။ diff --git a/translations/my/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/my/lessons/4-ComputerVision/06-IntroCV/README.md index c7e58ee8..f6bd5f13 100644 --- a/translations/my/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/my/lessons/4-ComputerVision/06-IntroCV/README.md @@ -75,14 +75,14 @@ OpenCV ကို အသုံးပြုပြီး ဗီဒီယို fram * **Braille စာအုပ်ရဲ့ ဓာတ်ပုံကို Pre-processing လုပ်ခြင်း**။ thresholding, feature detection, perspective transformation နဲ့ NumPy ကို ကိုင်တွယ်ခြင်းတို့ကို အသုံးပြုပြီး Braille အက္ခရာတစ်ခုချင်းစီကို neural network နဲ့ classification လုပ်ဖို့ ခွဲထုတ်ပေးနိုင်ပါတယ်။ -![Braille Image](../../../../../translated_images/my/braille.341962ff76b1bd70.jpeg) | ![Braille Image Pre-processed](../../../../../translated_images/my/braille-result.46530fea020b03c7.png) | ![Braille Symbols](../../../../../translated_images/my/braille-symbols.0159185ab69d5339.png) +![Braille Image](../../../../../translated_images/my/braille.341962ff76b1bd70.webp) | ![Braille Image Pre-processed](../../../../../translated_images/my/braille-result.46530fea020b03c7.webp) | ![Braille Symbols](../../../../../translated_images/my/braille-symbols.0159185ab69d5339.webp) ----|-----|----- > [OpenCV.ipynb](OpenCV.ipynb) မှ ပုံရိပ် * **Frame difference ကို အသုံးပြုပြီး ဗီဒီယိုထဲမှာ လှုပ်ရှားမှုကို ရှာဖွေခြင်း**။ ကင်မရာက တည်နေရာမှာရှိရင် ကင်မရာ feed ရဲ့ frame တွေဟာ တူညီနေတတ်ပါတယ်။ Frame တွေကို array အနေနဲ့ ကိုယ်စားပြုထားတဲ့အတွက် frame 2 ခုကို လျော့ချက်လုပ်လိုက်ရင် pixel difference ကို ရရှိမှာဖြစ်ပြီး static frame တွေမှာ pixel difference နည်းနည်းရှိပြီး ပုံရိပ်ထဲမှာ လှုပ်ရှားမှုများလာတဲ့အခါ pixel difference ပိုများလာတတ်ပါတယ်။ -![Image of video frames and frame differences](../../../../../translated_images/my/frame-difference.706f805491a0883c.png) +![Image of video frames and frame differences](../../../../../translated_images/my/frame-difference.706f805491a0883c.webp) > [OpenCV.ipynb](OpenCV.ipynb) မှ ပုံရိပ် @@ -91,7 +91,7 @@ OpenCV ကို အသုံးပြုပြီး ဗီဒီယို fram - **Dense Optical Flow** က pixel တစ်ခုချင်းစီ ဘယ်နေရာကိုရွေ့လျားနေတယ်ဆိုတာကို ပြသတဲ့ vector field ကို တွက်ချက်ပေးပါတယ်။ - **Sparse Optical Flow** က ပုံရိပ်ထဲမှာ အထူးသတ်မှတ်ချက်တွေ (ဥပမာ - အနားသတ်) ကို ရွေးပြီး frame-to-frame trajectory ကို တည်ဆောက်ပေးပါတယ်။ -![Image of Optical Flow](../../../../../translated_images/my/optical.1f4a94464579a83a.png) +![Image of Optical Flow](../../../../../translated_images/my/optical.1f4a94464579a83a.webp) > [OpenCV.ipynb](OpenCV.ipynb) မှ ပုံရိပ် diff --git a/translations/my/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/my/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 9c26ba71..2ee77b53 100644 --- a/translations/my/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/my/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 သည် 2014 ခုနှစ်တွင် ImageNet top-5 classification တွင် 92.7% တိကျမှုရရှိခဲ့သော network တစ်ခုဖြစ်သည်။ ၎င်းတွင် အောက်ပါ layer အဆောက်အအုံပါရှိသည်- -![ImageNet Layers](../../../../../translated_images/my/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet Layers](../../../../../translated_images/my/vgg-16-arch1.d901a5583b3a51ba.webp) VGG သည် convolution-pooling layers များ၏ အစဉ်အတိုင်း pyramid architecture ကို လိုက်နာသည်ကို သင်တွေ့နိုင်ပါသည်။ -![ImageNet Pyramid](../../../../../translated_images/my/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet Pyramid](../../../../../translated_images/my/vgg-16-arch.64ff2137f50dd49f.webp) > [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) မှရရှိသော ပုံ diff --git a/translations/my/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/my/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index 8044639f..d7770877 100644 --- a/translations/my/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/my/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -258,7 +258,7 @@ "\n", "ထို့ကြောင့်၊ ပုံမှန် CNN တစ်ခုတွင် convolutional layer အတော်များများ ရှိပြီး၊ ၎င်းတို့အကြား dimension ကို လျှော့ချရန် pooling layer များပါဝင်လေ့ရှိသည်။ ထို့အပြင်၊ pattern များ ပိုမိုရှုပ်ထွေးလာသည့်အခါ - ရှာဖွေရမည့် စိတ်ဝင်စားဖွယ် ပုံစံပေါင်းစပ်မှုများ ပိုမိုများလာသည့်အတွက် filter များ၏ အရေအတွက်ကိုလည်း တိုးမြှင့်လေ့ရှိသည်။\n", "\n", - "![An image showing several convolutional layers with pooling layers.](../../../../../translated_images/my/cnn-pyramid.85915455759ef0ce.png)\n", + "![An image showing several convolutional layers with pooling layers.](../../../../../translated_images/my/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "Spatial dimensions ကို လျှော့ချခြင်းနှင့် feature/filter dimensions ကို တိုးမြှင့်ခြင်းကြောင့်၊ ဒီ architecture ကို **pyramid architecture** ဟုလည်း ခေါ်ဆိုကြသည်။\n" ] diff --git a/translations/my/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/my/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index 7c6be664..306be720 100644 --- a/translations/my/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/my/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -362,7 +362,7 @@ "\n", "ထို့ကြောင့်၊ အခြားသော CNN architecture များတွင် convolutional layer များစွာရှိပြီး၊ pooling layer များကို အကြားတွင် ထည့်သွင်းထားသည်။ ထိုကဲ့သို့ dimension များကို လျှော့ချခြင်းနှင့် filter များ၏ အရေအတွက်ကို တိုးမြှင့်ခြင်းဖြင့် pattern များသည် ပိုမိုအဆင့်မြင့်လာသည်နှင့်အမျှ - ရှာဖွေရမည့် combination များ ပိုမိုများလာသည်။\n", "\n", - "![Convolutional layer များနှင့် pooling layer များပါဝင်သော ပုံတစ်ပုံကို ပြသထားသည်။](../../../../../translated_images/my/cnn-pyramid.85915455759ef0ce.png)\n", + "![Convolutional layer များနှင့် pooling layer များပါဝင်သော ပုံတစ်ပုံကို ပြသထားသည်။](../../../../../translated_images/my/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "Spatial dimension များကို လျှော့ချခြင်းနှင့် feature/filter dimension များကို တိုးမြှင့်ခြင်းကြောင့်၊ architecture ကို **pyramid architecture** ဟုလည်း ခေါ်ဆိုပါသည်။\n" ] diff --git a/translations/my/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/my/lessons/4-ComputerVision/07-ConvNets/README.md index f0fd1772..09df45c5 100644 --- a/translations/my/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/my/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: Patterns တွေကို ရှာဖွေဖို့ **convolutional filters** ဆိုတဲ့ အယူအဆကို အသုံးပြုပါမယ်။ သင်သိပြီးသားဖြစ်တဲ့အတိုင်း၊ ပုံတစ်ပုံဟာ 2D-matrix, ဒါမှမဟုတ် color depth ပါတဲ့ 3D-tensor အနေနဲ့ ဖော်ပြထားပါတယ်။ Filter ကို အသုံးပြုတဲ့အခါမှာ **filter kernel** matrix လေးတစ်ခုကို ယူပြီး၊ မူရင်းပုံထဲက pixel တစ်ခုစီအတွက် အနီးအနားမှာရှိတဲ့ point တွေနဲ့ weighted average ကိုတွက်ချက်ပါတယ်။ ဒါကို ပုံတစ်ပုံလုံးကို sliding လုပ်ပြီး၊ filter kernel matrix ထဲက weight တွေအတိုင်း pixel တွေကို averaging လုပ်နေတဲ့ window လေးတစ်ခုလိုမျိုး မြင်နိုင်ပါတယ်။ -![Vertical Edge Filter](../../../../../translated_images/my/filter-vert.b7148390ca0bc356.png) | ![Horizontal Edge Filter](../../../../../translated_images/my/filter-horiz.59b80ed4feb946ef.png) +![Vertical Edge Filter](../../../../../translated_images/my/filter-vert.b7148390ca0bc356.webp) | ![Horizontal Edge Filter](../../../../../translated_images/my/filter-horiz.59b80ed4feb946ef.webp) ----|---- > Image by Dmitry Soshnikov @@ -38,7 +38,7 @@ CNN တွေဟာ အောက်ပါ အရေးကြီးတဲ့ အ * Filters တွေကို အလိုအလျောက် သင်ယူနိုင်အောင် network ကို design လုပ်နိုင်တယ် * မူရင်းပုံထဲမှာသာမက၊ high-level features တွေထဲမှာ patterns တွေကို ရှာဖွေနိုင်ဖို့ အတူတူပုံစံကို အသုံးပြုနိုင်တယ်။ ဒါကြောင့် CNN feature extraction ဟာ low-level pixel combinations တွေကနေ စပြီး၊ ပုံရဲ့ အပိုင်းပိုင်းတွေကို ပေါင်းစပ်ထားတဲ့ higher-level features တွေထိ hierarchy အတိုင်း အလုပ်လုပ်ပါတယ်။ -![Hierarchical Feature Extraction](../../../../../translated_images/my/FeatureExtractionCNN.d9b456cbdae7cb64.png) +![Hierarchical Feature Extraction](../../../../../translated_images/my/FeatureExtractionCNN.d9b456cbdae7cb64.webp) > Image from [a paper by Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), based on [their research](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ Convolutional neural networks တွေ ဘယ်လိုအလုပ်လု ဥပမာအနေနဲ့၊ VGG-16 ရဲ့ architecture ကို ကြည့်ကြမယ်၊ ဒီ network ဟာ 2014 မှာ ImageNet ရဲ့ top-5 classification မှာ 92.7% accuracy ရရှိခဲ့ပါတယ်- -![ImageNet Layers](../../../../../translated_images/my/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet Layers](../../../../../translated_images/my/vgg-16-arch1.d901a5583b3a51ba.webp) -![ImageNet Pyramid](../../../../../translated_images/my/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet Pyramid](../../../../../translated_images/my/vgg-16-arch.64ff2137f50dd49f.webp) > Image from [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/my/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/my/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 0560d54a..1d3df1b8 100644 --- a/translations/my/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/my/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ CO_OP_TRANSLATOR_METADATA: ကျွန်ုပ်တို့သည် [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/) ကို အသုံးပြုမည်ဖြစ်ပြီး၊ ၎င်းတွင် ခွေးနှင့် ကြောင်အမျိုးအစား ၃၇ မျိုး၏ ဓာတ်ပုံများ ပါဝင်ပါသည်။ -![ကျွန်ုပ်တို့ကို ကိုင်တွယ်ရမည့် Dataset](../../../../../../translated_images/my/data.50b2a9d5484bdbf0.png) +![ကျွန်ုပ်တို့ကို ကိုင်တွယ်ရမည့် Dataset](../../../../../../translated_images/my/data.50b2a9d5484bdbf0.webp) Dataset ကို download လုပ်ရန် အောက်ပါ code snippet ကို အသုံးပြုပါ: diff --git a/translations/my/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/my/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index 70f75609..de671abf 100644 --- a/translations/my/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/my/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "ကြိုက်နှစ်သက်သောကြောင်ပုံကို ရှင်းလင်းမြင်သာစေရန်အတွက် ကျွန်ုပ်တို့ random noise ပုံတစ်ပုံကို စတင်အသုံးပြုမည်ဖြစ်ပြီး၊ gradient descent optimization နည်းလမ်းကို အသုံးပြု၍ ပုံကို ပြင်ဆင်ကာ network တစ်ခုက ကြောင်ကို အသိအမှတ်ပြုနိုင်အောင် လုပ်ဆောင်မည်ဖြစ်သည်။\n", "\n", - "![Optimization Loop](../../../../../translated_images/my/ideal-cat-loop.999fbb8ff306e044.png)\n", + "![Optimization Loop](../../../../../translated_images/my/ideal-cat-loop.999fbb8ff306e044.webp)\n", "\n", "ဤသည်မှာ ကျွန်ုပ်တို့၏ စတင်ပုံဖြစ်သည်:\n" ] diff --git a/translations/my/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/my/lessons/4-ComputerVision/08-TransferLearning/README.md index 1cb05cd2..2863df68 100644 --- a/translations/my/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/my/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Keras နှင့် PyTorch တို့တွင် ImageNet ပုံမျ VGG-16 network မှ ကြောင်ပုံတစ်ပုံမှ ထုတ်ယူထားသော sample feature များကို အောက်တွင် ဖော်ပြထားသည်- -![Features extracted by VGG-16](../../../../../translated_images/my/features.6291f9c7ba3a0b95.png) +![Features extracted by VGG-16](../../../../../translated_images/my/features.6291f9c7ba3a0b95.webp) ## Cats vs. Dogs Dataset @@ -48,19 +48,19 @@ Pre-trained neural network တွင် ၎င်း၏ *brain* အတွင် တစ်ခုသော နည်းလမ်းကို အသုံးပြုနိုင်သည်မှာ random image တစ်ပုံကို စတင်ပြီး၊ **gradient descent optimization** နည်းလမ်းကို အသုံးပြု၍ ၎င်းပုံကို network သည် ၎င်းကို ကြောင်ဟု ထင်ရအောင် ပြောင်းလဲရန် ကြိုးစားခြင်းဖြစ်သည်။ -![Image Optimization Loop](../../../../../translated_images/my/ideal-cat-loop.999fbb8ff306e044.png) +![Image Optimization Loop](../../../../../translated_images/my/ideal-cat-loop.999fbb8ff306e044.webp) သို့သော် ဤနည်းလမ်းကို အသုံးပြုပါက random noise ကဲ့သို့သော အရာတစ်ခုကို ရရှိမည်ဖြစ်သည်။ အကြောင်းမှာ *network ကို input image ကို ကြောင်ဟု ထင်ရအောင် ပြုလုပ်ရန် နည်းလမ်းများစွာရှိသည်*၊ ၎င်းတို့အနက် visual sense မရှိသော အရာများပါဝင်သည်။ ဤပုံများတွင် ကြောင်အတွက် အထူးသက်သက် pattern များစွာပါဝင်သော်လည်း၊ visually distinctive ဖြစ်ရန် အကန့်အသတ်မရှိပါ။ ရလဒ်ကို တိုးတက်စေရန် loss function တွင် **variation loss** ဟုခေါ်သော term တစ်ခုကို ထည့်သွင်းနိုင်သည်။ ၎င်းသည် ပုံ၏ အနီးအနား pixel များ၏ တူညီမှုကို ပြသသော metric ဖြစ်သည်။ Variation loss ကို လျှော့ချခြင်းဖြင့် ပုံကို ပိုမိုချောမွေ့စေပြီး၊ noise ကို ဖယ်ရှားနိုင်သည် - visual pattern များကို ပိုမိုရှင်းလင်းစွာ ဖော်ပြနိုင်သည်။ အောက်တွင် ကြောင်နှင့် zebra အဖြစ် classified ဖြစ်သော "ideal" ပုံများကို ဖော်ပြထားသည်- -![Ideal Cat](../../../../../translated_images/my/ideal-cat.203dd4597643d6b0.png) | ![Ideal Zebra](../../../../../translated_images/my/ideal-zebra.7f70e8b54ee15a7a.png) +![Ideal Cat](../../../../../translated_images/my/ideal-cat.203dd4597643d6b0.webp) | ![Ideal Zebra](../../../../../translated_images/my/ideal-zebra.7f70e8b54ee15a7a.webp) -----|----- *Ideal Cat* | *Ideal Zebra* အလားတူနည်းလမ်းကို neural network တွင် **adversarial attacks** ပြုလုပ်ရန် အသုံးပြုနိုင်သည်။ ကြောင်ကဲ့သို့သော ပုံကို ဖန်တီးရန် ကြိုးစားပါက၊ ကြောင်ဟု network မှ ခွဲခြားထားသော ကြောင်ပုံကို gradient descent optimization အသုံးပြု၍ network သည် ၎င်းကို ကြောင်ဟု ထင်ရအောင် ပြောင်းလဲနိုင်သည်- -![Picture of a Dog](../../../../../translated_images/my/original-dog.8f68a67d2fe0911f.png) | ![Picture of a dog classified as a cat](../../../../../translated_images/my/adversarial-dog.d9fc7773b0142b89.png) +![Picture of a Dog](../../../../../translated_images/my/original-dog.8f68a67d2fe0911f.webp) | ![Picture of a dog classified as a cat](../../../../../translated_images/my/adversarial-dog.d9fc7773b0142b89.webp) -----|----- *Original picture of a dog* | *Picture of a dog classified as a cat* diff --git a/translations/my/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/my/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index 3dff4b37..b263db05 100644 --- a/translations/my/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/my/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "Autoencoder ကို မူရင်းပုံမှ အချက်အလက်များကို အတတ်နိုင်ဆုံး ဖမ်းယူပြီး တိကျစွာ ပြန်လည်ဖန်တီးနိုင်ရန် လေ့ကျင့်နေသောကြောင့်၊ network သည် input ပုံများ၏ အဓိပ္ပာယ်ကို ဖမ်းယူနိုင်ရန် အကောင်းဆုံးသော **embedding** ကို ရှာဖွေကြိုးစားသည်။\n", "\n", - "![AutoEncoder Diagram](../../../../../translated_images/my/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![AutoEncoder Diagram](../../../../../translated_images/my/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "> ပုံကို [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html) မှ ယူဆောင်ထားသည်။\n", "\n", diff --git a/translations/my/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/my/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index c901d049..0347ba2a 100644 --- a/translations/my/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/my/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "Autoencoder ကို မူရင်းပုံရိပ်မှ အချက်အလက်များကို အတိအကျ ပြန်လည်ဖော်ထုတ်နိုင်ရန် အများဆုံး ဖမ်းယူနိုင်ရန် သင်ကြားနေသောကြောင့်၊ network သည် input ပုံရိပ်များ၏ အဓိပ္ပါယ်ကို ဖမ်းယူနိုင်ရန် အကောင်းဆုံး **embedding** ကို ရှာဖွေကြိုးစားသည်။\n", "\n", - "![AutoEncoder Diagram](../../../../../translated_images/my/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![AutoEncoder Diagram](../../../../../translated_images/my/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "*ပုံရင်း [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html) မှ*\n", "\n", diff --git a/translations/my/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/my/lessons/4-ComputerVision/09-Autoencoders/README.md index ce5f518c..8a2ee488 100644 --- a/translations/my/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/my/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ CNN များကို လေ့ကျင့်ရာတွင် တစ် Autoencoder ကို မူရင်းပုံရိပ်မှ အချက်အလက်များကို အများဆုံး ဖမ်းယူရန် လေ့ကျင့်နေသောကြောင့်၊ ကွန်ယက်သည် input ပုံရိပ်များ၏ အဓိပ္ပါယ်ကို ဖမ်းယူရန် အကောင်းဆုံး **embedding** ကို ရှာဖွေသည်။ -![AutoEncoder Diagram](../../../../../translated_images/my/autoencoder_schema.5e6fc9ad98a5eb61.jpg) +![AutoEncoder Diagram](../../../../../translated_images/my/autoencoder_schema.5e6fc9ad98a5eb61.webp) > ပုံရိပ် - [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/my/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/my/lessons/4-ComputerVision/11-ObjectDetection/README.md index 1e0c5e35..d6b02eee 100644 --- a/translations/my/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/my/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: ## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Object Detection](../../../../../translated_images/my/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) +![Object Detection](../../../../../translated_images/my/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp) > ပုံကို [YOLO v2 web site](https://pjreddie.com/darknet/yolov2/) မှရယူထားသည်။ @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 2. တစ်ခုချင်းစီ tile တွေမှာ image classification ကို run လုပ်ပါ။ 3. activation အဆင့်မြင့်တဲ့ tiles တွေကို object ရှိတယ်လို့ယူဆနိုင်ပါတယ်။ -![Naive Object Detection](../../../../../translated_images/my/naive-detection.e7f1ba220ccd08c6.png) +![Naive Object Detection](../../../../../translated_images/my/naive-detection.e7f1ba220ccd08c6.webp) > *ပုံကို [Exercise Notebook](ObjectDetection-TF.ipynb) မှရယူထားသည်* @@ -42,7 +42,7 @@ CO_OP_TRANSLATOR_METADATA: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 classes * [COCO](http://cocodataset.org/#home) - Common Objects in Context. 80 classes, bounding boxes နှင့် segmentation masks -![COCO](../../../../../translated_images/my/coco-examples.71bc60380fa6cceb.jpg) +![COCO](../../../../../translated_images/my/coco-examples.71bc60380fa6cceb.webp) ## Object Detection Metrics @@ -50,7 +50,7 @@ CO_OP_TRANSLATOR_METADATA: Image classification အတွက် algorithm ရဲ့ performance ကိုတိုင်းတာရလွယ်ကူသလို၊ object detection အတွက် class ရဲ့တိကျမှုနှင့် bounding box ရဲ့တိကျမှုကိုတိုင်းတာဖို့လိုအပ်ပါတယ်။ Bounding box ရဲ့တိကျမှုကိုတိုင်းတာဖို့ **Intersection over Union** (IoU) ကိုသုံးပါတယ်၊ ဒါဟာ box နှစ်ခု (သို့မဟုတ် arbitrary areas နှစ်ခု) overlap ဖြစ်ပုံကိုတိုင်းတာပေးပါတယ်။ -![IoU](../../../../../translated_images/my/iou_equation.9a4751d40fff4e11.png) +![IoU](../../../../../translated_images/my/iou_equation.9a4751d40fff4e11.webp) > *ပုံကို [IoU အကြောင်း blog post](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/) မှရယူထားသည်* @@ -97,11 +97,11 @@ Object detection algorithms တွေကို broad classes နှစ်ခု [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) က [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) ကိုသုံးပြီး ROI regions တွေကို hierarchical structure အဖြစ် generate လုပ်ပါတယ်၊ အဲ့ဒီ regions တွေကို CNN feature extractors နှင့် SVM-classifiers တွေက object class ကိုသတ်မှတ်ဖို့၊ linear regression က *bounding box* coordinates ကိုသတ်မှတ်ဖို့သုံးပါတယ်။ [Official Paper](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/my/rcnn1.cae407020dfb1d1f.png) +![RCNN](../../../../../translated_images/my/rcnn1.cae407020dfb1d1f.webp) > *ပုံကို van de Sande et al. ICCV’11 မှရယူထားသည်* -![RCNN-1](../../../../../translated_images/my/rcnn2.2d9530bb83516484.png) +![RCNN-1](../../../../../translated_images/my/rcnn2.2d9530bb83516484.webp) > *ပုံကို [ဒီ blog](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e) မှရယူထားသည်* @@ -109,7 +109,7 @@ Object detection algorithms တွေကို broad classes နှစ်ခု ဒီနည်းလမ်းက R-CNN နဲ့တူပေမယ့် regions တွေကို convolution layers apply လုပ်ပြီးမှသတ်မှတ်ပါတယ်။ -![FRCNN](../../../../../translated_images/my/f-rcnn.3cda6d9bb4188875.png) +![FRCNN](../../../../../translated_images/my/f-rcnn.3cda6d9bb4188875.webp) > ပုံကို [Official Paper](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 မှရယူထားသည်။ @@ -117,7 +117,7 @@ Object detection algorithms တွေကို broad classes နှစ်ခု ဒီနည်းလမ်းရဲ့အဓိကအကြောင်းအရာက neural network ကိုသုံးပြီး ROIs ကို predict လုပ်တာဖြစ်ပါတယ် - *Region Proposal Network* လို့ခေါ်ပါတယ်။ [Paper](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/my/faster-rcnn.8d46c099b87ef30a.png) +![FasterRCNN](../../../../../translated_images/my/faster-rcnn.8d46c099b87ef30a.webp) > ပုံကို [Official Paper](https://arxiv.org/pdf/1506.01497.pdf) မှရယူထားသည်။ @@ -129,7 +129,7 @@ Object detection algorithms တွေကို broad classes နှစ်ခု 2. Features တွေကို **Position-Sensitive Score Map** မှာ process လုပ်ပါ။ $C$ classes ထဲက object တစ်ခုစီကို $k\times k$ regions တွေခွဲပြီး၊ object parts တွေကို predict လုပ်ဖို့ training လုပ်ပါတယ်။ 3. $k\times k$ regions ထဲက part တစ်ခုစီအတွက် networks အားလုံးက object classes အတွက် vote လုပ်ပြီး၊ maximum vote ရတဲ့ object class ကိုရွေးချယ်ပါတယ်။ -![r-fcn image](../../../../../translated_images/my/r-fcn.13eb88158b99a3da.png) +![r-fcn image](../../../../../translated_images/my/r-fcn.13eb88158b99a3da.webp) > ပုံကို [Official Paper](https://arxiv.org/abs/1605.06409) မှရယူထားသည်။ @@ -140,7 +140,7 @@ YOLO က realtime one-pass algorithm ဖြစ်ပါတယ်။ အဓိက * ပုံကို $S\times S$ regions တွေခွဲပါ။ * Region တစ်ခုစီအတွက် **CNN** က $n$ possible objects, *bounding box* coordinates နှင့် *confidence*=*probability* * IoU ကို predict လုပ်ပါတယ်။ - ![YOLO](../../../../../translated_images/my/yolo.a2648ec82ee8bb4e.png) + ![YOLO](../../../../../translated_images/my/yolo.a2648ec82ee8bb4e.webp) > ပုံကို [Official Paper](https://arxiv.org/abs/1506.02640) မှရယူထားသည်။ diff --git a/translations/my/lessons/4-ComputerVision/README.md b/translations/my/lessons/4-ComputerVision/README.md index aa3a7b52..972ae173 100644 --- a/translations/my/lessons/4-ComputerVision/README.md +++ b/translations/my/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # ကွန်ပျူတာမြင်ကြည့်မှု -![ကွန်ပျူတာမြင်ကြည့်မှုအကြောင်းအရာကို ရေးဆွဲထားသောပုံ](../../../../translated_images/my/ai-computervision.6506ebebac3fbf76.png) +![ကွန်ပျူတာမြင်ကြည့်မှုအကြောင်းအရာကို ရေးဆွဲထားသောပုံ](../../../../translated_images/my/ai-computervision.6506ebebac3fbf76.webp) ဤအပိုင်းတွင် ကျွန်ုပ်တို့ သင်ယူမည့်အကြောင်းအရာများမှာ - diff --git a/translations/my/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/my/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index 09a83af1..e373937a 100644 --- a/translations/my/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/my/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -197,7 +197,7 @@ "\n", "**စကားလုံးအိတ်** (BoW) ဗက်တာကိုယ်စားပြုမှုဟာ အရင်က အသုံးများခဲ့တဲ့ ဗက်တာကိုယ်စားပြုမှုအနက် အများဆုံး အသုံးပြုတဲ့နည်းလမ်းဖြစ်ပါတယ်။ စကားလုံးတစ်လုံးချင်းစီကို ဗက်တာအညွှန်းနဲ့ ချိတ်ဆက်ထားပြီး၊ ဗက်တာအခန်းက စာရွက်တစ်ခုထဲမှာ စကားလုံးတစ်လုံးရဲ့ ဖြစ်ပေါ်မှုအရေအတွက်ကို ထည့်သွင်းထားပါတယ်။\n", "\n", - "![စကားလုံးအိတ် ဗက်တာကိုယ်စားပြုမှုကို မှတ်ဉာဏ်ထဲမှာ ဘယ်လို ကိုယ်စားပြုထားတယ်ဆိုတာ ဖော်ပြတဲ့ ပုံ။](../../../../../translated_images/my/bag-of-words-example.606fc1738f1d7ba9.png)\n", + "![စကားလုံးအိတ် ဗက်တာကိုယ်စားပြုမှုကို မှတ်ဉာဏ်ထဲမှာ ဘယ်လို ကိုယ်စားပြုထားတယ်ဆိုတာ ဖော်ပြတဲ့ ပုံ။](../../../../../translated_images/my/bag-of-words-example.606fc1738f1d7ba9.webp)\n", "\n", "> **Note**: BoW ကို စာသားထဲမှာ စကားလုံးတစ်လုံးချင်းစီအတွက် one-hot-encoded ဗက်တာတွေကို စုပေါင်းထားတဲ့အနေနဲ့လည်း စဉ်းစားနိုင်ပါတယ်။\n", "\n", diff --git a/translations/my/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/my/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index ca2bdcfe..3ba6c75f 100644 --- a/translations/my/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/my/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**စကားလုံးအထုပ်** (BoW) ဗက်တာကိုယ်စားပြုမှုသည် နားလည်ရန် အလွယ်ဆုံးသော ရိုးရာဗက်တာကိုယ်စားပြုမှုဖြစ်သည်။ စကားလုံးတစ်ခုချင်းစီကို ဗက်တာအညွှန်းနှင့် ချိတ်ဆက်ထားပြီး၊ ဗက်တာအခန်းကဏ္ဍတစ်ခုတွင် သတ်မှတ်ထားသော စာရွက်စာတမ်းအတွင်း စကားလုံးတစ်ခုချင်းစီ၏ ဖြစ်ပေါ်မှုအရေအတွက်ကို ပါဝင်ထားသည်။\n", "\n", - "![စကားလုံးအထုပ်ဗက်တာကိုယ်စားပြုမှုကို မှတ်ဉာဏ်တွင် ဘယ်လိုကိုယ်စားပြုထားသည်ကို ဖော်ပြထားသော ပုံ။](../../../../../translated_images/my/bag-of-words-example.606fc1738f1d7ba9.png)\n", + "![စကားလုံးအထုပ်ဗက်တာကိုယ်စားပြုမှုကို မှတ်ဉာဏ်တွင် ဘယ်လိုကိုယ်စားပြုထားသည်ကို ဖော်ပြထားသော ပုံ။](../../../../../translated_images/my/bag-of-words-example.606fc1738f1d7ba9.webp)\n", "\n", "> **Note**: BoW ကို စာသားအတွင်း စကားလုံးတစ်ခုချင်းစီအတွက် တစ်ခုချင်းစီ *one-hot-encoded* ဗက်တာများ၏ စုစုပေါင်းအဖြစ်လည်း စဉ်းစားနိုင်ပါသည်။\n", "\n", diff --git a/translations/my/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/my/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index d1cd5d2d..708288ed 100644 --- a/translations/my/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/my/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "ကျွန်တော်တို့ network ရဲ့ ပထမဆုံး layer အနေနဲ့ embedding layer ကို သုံးခြင်းအားဖြင့်၊ bag-of-words မှ **embedding bag** မော်ဒယ်ဆီကို ပြောင်းနိုင်ပါတယ်။ ဒီမှာ ကျွန်တော်တို့ရဲ့ စာသားထဲက စကားလုံးတစ်လုံးစီကို သက်ဆိုင်ရာ embedding ကို ပြောင်းပြီး၊ ထို embedding တွေကို `sum`၊ `average` သို့မဟုတ် `max` ကဲ့သို့သော aggregate function တစ်ခုခုကို တွက်ချက်ပေးပါမယ်။\n", "\n", - "![Image showing an embedding classifier for five sequence words.](../../../../../translated_images/my/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![Image showing an embedding classifier for five sequence words.](../../../../../translated_images/my/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "ကျွန်တော်တို့ရဲ့ classifier neural network က embedding layer နဲ့ စပြီး၊ aggregation layer နဲ့ linear classifier ကို အပေါ်မှာ ထည့်သွင်းထားပါမယ်။\n" ] @@ -176,7 +176,7 @@ "\n", "ယခင် အဆောက်အအုံတွင်၊ minibatch ထဲသို့ ထည့်သွင်းနိုင်ရန် အစီအစဉ်အားလုံးကို အရှည်တူအောင် pad လုပ်ရန် လိုအပ်ခဲ့သည်။ သို့သော်၊ အရှည်မတူညီသော အစီအစဉ်များကို ကိုယ်စားပြုရန်အတွက် ဤနည်းလမ်းသည် အကျိုးရှိဆုံးမဟုတ်ပါ။ အခြားနည်းလမ်းတစ်ခုမှာ **offset** vector ကို အသုံးပြုခြင်းဖြစ်ပြီး၊ ၎င်းသည် အစီအစဉ်အားလုံး၏ offsets ကို တစ်ခုတည်းသော vector အကြီးထဲတွင် သိမ်းဆည်းထားမည်ဖြစ်သည်။\n", "\n", - "![Offset sequence ကို ကိုယ်စားပြုထားသော ပုံ](../../../../../translated_images/my/offset-sequence-representation.eb73fcefb29b46ee.png)\n", + "![Offset sequence ကို ကိုယ်စားပြုထားသော ပုံ](../../../../../translated_images/my/offset-sequence-representation.eb73fcefb29b46ee.webp)\n", "\n", "> **Note**: အထက်ပါ ပုံတွင် အက္ခရာများ၏ အစီအစဉ်ကို ပြထားသော်လည်း၊ ကျွန်ုပ်တို့၏ ဥပမာတွင် စကားလုံးများ၏ အစီအစဉ်များနှင့် အလုပ်လုပ်နေပါသည်။ သို့သော်၊ offset vector ဖြင့် အစီအစဉ်များကို ကိုယ်စားပြုခြင်း၏ အခြေခံသဘောတရားမှာ မပြောင်းလဲပါ။\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW က ပိုမြန်ပါတယ်၊ skip-gram က ပိုနှေးပေမယ့် မကြာခဏ မတွေ့ရတဲ့ စကားလုံးများကို ပိုကောင်းစွာ ဖော်ပြနိုင်ပါတယ်။\n", "\n", - "![CBoW နဲ့ Skip-Gram algorithm နှစ်ခုစလုံးကို စကားလုံးများကို ဗက်တာအဖြစ် ပြောင်းလဲဖို့ အသုံးပြုနေတဲ့ ပုံ။](../../../../../translated_images/my/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![CBoW နဲ့ Skip-Gram algorithm နှစ်ခုစလုံးကို စကားလုံးများကို ဗက်တာအဖြစ် ပြောင်းလဲဖို့ အသုံးပြုနေတဲ့ ပုံ။](../../../../../translated_images/my/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Google News dataset ပေါ်မှာ ကြိုတင်သင်ယူထားတဲ့ word2vec embedding ကို စမ်းသပ်ဖို့၊ **gensim** library ကို အသုံးပြုနိုင်ပါတယ်။ အောက်မှာ 'neural' နဲ့ အနီးဆုံးသော စကားလုံးများကို ရှာဖွေထားပါတယ်-\n", "\n", diff --git a/translations/my/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/my/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index 26c24f51..bef22d19 100644 --- a/translations/my/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/my/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "ကျွန်တော်တို့ network ရဲ့ ပထမဆုံး layer အနေနဲ့ embedding layer ကို အသုံးပြုခြင်းအားဖြင့် bag-of-words နည်းလမ်းကနေ **embedding bag** မော်ဒယ်ဆီကို ပြောင်းနိုင်ပါတယ်။ ဒီမှာ ကျွန်တော်တို့ စာသားထဲက စကားလုံးတစ်လုံးချင်းစီကို သက်ဆိုင်ရာ embedding ကို ပြောင်းပြီး၊ အဲဒီ embedding တွေကို `sum`, `average`, `max` စတဲ့ aggregate function တစ်ခုခုနဲ့ တွက်ချက်ပေးနိုင်ပါတယ်။\n", "\n", - "![Image showing an embedding classifier for five sequence words.](../../../../../translated_images/my/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![Image showing an embedding classifier for five sequence words.](../../../../../translated_images/my/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "ကျွန်တော်တို့ classifier neural network ရဲ့ layer တွေက အောက်ပါအတိုင်းဖြစ်ပါတယ်-\n", "\n", @@ -279,7 +279,7 @@ "\n", "CBoW သည် မြန်ဆန်သော်လည်း၊ skip-gram သည် နှေးကွေးသော်လည်း မကြာခဏမတွေ့ရသော စကားလုံးများကို ပိုမိုကောင်းမွန်စွာ ဖော်ပြနိုင်သည်။\n", "\n", - "![CBoW နှင့် Skip-Gram algorithm များကို အသုံးပြု၍ စကားလုံးများကို ဗက်တာအဖြစ် ပြောင်းလဲနေသော ပုံ။](../../../../../translated_images/my/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![CBoW နှင့် Skip-Gram algorithm များကို အသုံးပြု၍ စကားလုံးများကို ဗက်တာအဖြစ် ပြောင်းလဲနေသော ပုံ။](../../../../../translated_images/my/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Google News dataset ပေါ်တွင် ကြိုတင်သင်ကြားထားသော Word2Vec embedding ကို စမ်းသပ်ရန် **gensim** library ကို အသုံးပြုနိုင်သည်။ အောက်တွင် 'neural' နှင့် နီးစပ်သော စကားလုံးများကို ရှာဖွေထားသည်။\n", "\n", diff --git a/translations/my/lessons/5-NLP/14-Embeddings/README.md b/translations/my/lessons/5-NLP/14-Embeddings/README.md index c3388f2b..592d1c3c 100644 --- a/translations/my/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/my/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ Embedding layer က စကားလုံးကို input အနေနဲ့ Embedding layer ကို classifier network ရဲ့ ပထမဆုံး layer အနေနဲ့ အသုံးပြုခြင်းအားဖြင့်၊ bag-of-words model ကနေ **embedding bag** model သို့ ပြောင်းလဲနိုင်ပါတယ်။ ဒီမှာ စကားလုံးတစ်လုံးချင်းစီကို သက်ဆိုင်ရာ embedding သို့ ပြောင်းပြီး၊ အဲဒီ embeddings အားလုံးအပေါ်မှာ `sum`, `average` သို့မဟုတ် `max` ကဲ့သို့သော aggregate function တစ်ခုခုကို တွက်ချက်ပါမယ်။ -![Image showing an embedding classifier for five sequence words.](../../../../../translated_images/my/embedding-classifier-example.b77f021a7ee67eee.png) +![Image showing an embedding classifier for five sequence words.](../../../../../translated_images/my/embedding-classifier-example.b77f021a7ee67eee.webp) > ပုံကို စာရေးသူမှ ဖန်တီးထားသည် @@ -40,7 +40,7 @@ Embedding layer က စကားလုံးတွေကို vector representa CBoW က ပိုမြန်ပြီး၊ skip-gram က ပိုနှေးပေမယ့်၊ မကြာခဏ မတွေ့ရတဲ့ စကားလုံးတွေကို ပိုကောင်းစွာ ကိုယ်စားပြုနိုင်ပါတယ်။ -![Image showing both CBoW and Skip-Gram algorithms to convert words to vectors.](../../../../../translated_images/my/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![Image showing both CBoW and Skip-Gram algorithms to convert words to vectors.](../../../../../translated_images/my/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > ပုံကို [ဒီစာတမ်း](https://arxiv.org/pdf/1301.3781.pdf) မှာ ရရှိခဲ့သည် diff --git a/translations/my/lessons/5-NLP/15-LanguageModeling/README.md b/translations/my/lessons/5-NLP/15-LanguageModeling/README.md index 627429a2..700c4133 100644 --- a/translations/my/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/my/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ Semantic embeddings, Word2Vec နှင့် GloVe က **ဘာသာစကာ * **Continuous Bag-of-Words** (CBoW)၊ token အစဉ် $W_{-N}$, ..., $W_N$ တွင် အလယ် token $W_0$ ကို ခန့်မှန်းခြင်း။ * **Skip-gram**၊ အလယ် token $W_0$ မှ အနီးအနား token များ {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} ကို ခန့်မှန်းခြင်း။ -![image from paper on converting words to vectors](../../../../../translated_images/my/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![image from paper on converting words to vectors](../../../../../translated_images/my/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Image from [this paper](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/my/lessons/5-NLP/16-RNN/README.md b/translations/my/lessons/5-NLP/16-RNN/README.md index a9e56820..b93e0155 100644 --- a/translations/my/lessons/5-NLP/16-RNN/README.md +++ b/translations/my/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: စာသားအစီအစဉ်၏ အဓိပ္ပါယ်ကို ဖမ်းဆီးရန် **recurrent neural network** (RNN) ဟုခေါ်သော neural network architecture တစ်ခုကို အသုံးပြုရန် လိုအပ်သည်။ RNN တွင် ကျွန်ုပ်တို့၏ စာကြောင်းကို network အတွင်းသို့ သင်္ကေတတစ်ခုစီဖြင့် ဖြတ်သွားပြီး network သည် **state** တစ်ခုကို ထုတ်လုပ်သည်၊ ထို့နောက် ကျွန်ုပ်တို့သည် နောက်ထပ်သင်္ကေတနှင့်အတူ network သို့ ပြန်လည်ပေးပို့သည်။ -![RNN](../../../../../translated_images/my/rnn.27f5c29c53d727b5.png) +![RNN](../../../../../translated_images/my/rnn.27f5c29c53d727b5.webp) > ပုံကို စာရေးသူမှ ဖန်တီးသည် @@ -61,7 +61,7 @@ State C ၏ components များကို flags အဖြစ် switch on န Recurrent network တစ်ခုသည် direction တစ်ခုဖြစ်စေ bidirectional ဖြစ်စေ sequence အတွင်း certain patterns များကို ဖမ်းဆီးပြီး state vector သို့မဟုတ် output သို့ ပေးပို့နိုင်သည်။ Convolutional networks များနှင့်တူပင်၊ ပထမ layer မှ low-level patterns များကို extract လုပ်ပြီး အဆင့်မြင့် patterns များကို ဖမ်းဆီးရန် ပထမ layer အပေါ်တွင် recurrent layer တစ်ခုတိုးတက်စေပြီး **multi-layer RNN** ကို ဖန်တီးနိုင်သည်။ Multi-layer RNN သည် recurrent networks နှစ်ခု သို့မဟုတ် အများကြီးပါဝင်ပြီး ယခင် layer ၏ output ကို နောက်တစ်ခု layer ၏ input အဖြစ် ပေးပို့သည်။ -![Image showing a Multilayer long-short-term-memory- RNN](../../../../../translated_images/my/multi-layer-lstm.dd975e29bb2a59fe.jpg) +![Image showing a Multilayer long-short-term-memory- RNN](../../../../../translated_images/my/multi-layer-lstm.dd975e29bb2a59fe.webp) *Fernando López ရေးသားသော [this wonderful post](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) မှ ပုံ* diff --git a/translations/my/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/my/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index 7aa14157..b26eaefe 100644 --- a/translations/my/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/my/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -420,7 +420,7 @@ "\n", "Recurrent network, one-directional ဖြစ်စေ bidirectional ဖြစ်စေ၊ sequence တစ်ခုအတွင်းမှာ pattern အချို့ကို capture လုပ်နိုင်ပြီး၊ state vector ထဲမှာ သိမ်းဆည်းနိုင်သလို output ကို pass လုပ်နိုင်ပါတယ်။ Convolutional networks တွေနဲ့တူတူပဲ၊ ပထမ layer က low-level patterns တွေကို extract လုပ်ပြီး၊ အဲ့ဒီ patterns တွေကို အသုံးပြုပြီး higher level patterns တွေကို capture လုပ်ဖို့ ပထမ layer ရဲ့အပေါ်မှာ recurrent layer တစ်ခုတိုးဖွဲ့နိုင်ပါတယ်။ ဒီအရာက **multi-layer RNN** ဆိုတဲ့အယူအဆကို ရောက်လာစေပြီး၊ ဒါဟာ recurrent networks နှစ်ခု သို့မဟုတ် အများကြီးပါဝင်ပြီး၊ အရင် layer ရဲ့ output ကို နောက် layer ရဲ့ input အဖြစ် pass လုပ်ပေးတဲ့ network ဖြစ်ပါတယ်။\n", "\n", - "![Multilayer long-short-term-memory- RNN ကို ပြသတဲ့ပုံ](../../../../../translated_images/my/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![Multilayer long-short-term-memory- RNN ကို ပြသတဲ့ပုံ](../../../../../translated_images/my/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*Fernando López ရဲ့ [ဒီ post](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) မှာရရှိတဲ့ပုံ*\n", "\n", diff --git a/translations/my/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/my/lessons/5-NLP/16-RNN/RNNTF.ipynb index b11e47cd..18895ec0 100644 --- a/translations/my/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/my/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "စာသားအစီအစဉ်၏ အဓိပ္ပါယ်ကို ဖမ်းဆီးရန်၊ **recurrent neural network** (RNN) ဟုခေါ်သော နယူးရယ်နက်ဝက် architecture ကို အသုံးပြုမည်ဖြစ်သည်။ RNN ကို အသုံးပြုသောအခါ၊ ကျွန်ုပ်တို့၏ စာကြောင်းကို network အတွင်းသို့ token တစ်ခုစီဖြင့် အဆင့်ဆင့် ဖြတ်သန်းပြီး၊ network သည် **state** တစ်ခုကို ထုတ်ပေးမည်ဖြစ်သည်။ ထို state ကို နောက် token နှင့်အတူ network သို့ ပြန်လည်ထည့်သွင်းမည်ဖြစ်သည်။\n", "\n", - "![Recurrent neural network ထုတ်လုပ်မှု၏ ဥပမာကို ပြသသော ပုံ။](../../../../../translated_images/my/rnn.27f5c29c53d727b5.png)\n", + "![Recurrent neural network ထုတ်လုပ်မှု၏ ဥပမာကို ပြသသော ပုံ။](../../../../../translated_images/my/rnn.27f5c29c53d727b5.webp)\n", "\n", "tokens များ၏ input အစီအစဉ် $X_0,\\dots,X_n$ ကို ပေးသောအခါ၊ RNN သည် နယူးရယ်နက်ဝက် block များ၏ အစီအစဉ်တစ်ခုကို ဖန်တီးပြီး၊ ထိုအစီအစဉ်ကို backpropagation အသုံးပြု၍ အဆုံးမှ အဆုံးသို့ လေ့ကျင့်သည်။ network block တစ်ခုစီသည် $(X_i,S_i)$ ကို input အနေဖြင့် လက်ခံပြီး၊ $S_{i+1}$ ကို ရလဒ်အဖြစ် ထုတ်ပေးသည်။ နောက်ဆုံး state $S_n$ သို့မဟုတ် output $Y_n$ ကို linear classifier သို့ ပေးပို့ပြီး ရလဒ်ကို ထုတ်ယူသည်။ network block အားလုံးသည် တူညီသော weight များကို မျှဝေထားပြီး၊ တစ်ကြိမ်တည်းသော backpropagation pass ဖြင့် အဆုံးမှ အဆုံးသို့ လေ့ကျင့်သည်။\n", "\n", @@ -369,7 +369,7 @@ "\n", "Recurrent network များ၊ unidirectional ဖြစ်စေ bidirectional ဖြစ်စေ၊ sequence အတွင်းရှိ pattern များကို ဖမ်းဆီးပြီး state vector များအဖြစ် သိမ်းဆည်းသို့မဟုတ် output အဖြစ် ပြန်လည်ပေးသည်။ Convolutional network များနှင့်တူပင်၊ ပထမ layer မှ အနိမ့်ဆုံး level pattern များကို ဖမ်းဆီးပြီး၊ အမြင့်ဆုံး level pattern များကို ဖမ်းဆီးရန် ပိုမိုမြင့်မားသော recurrent layer တစ်ခုကို တည်ဆောက်နိုင်သည်။ ၎င်းသည် **multi-layer RNN** ၏ အယူအဆသို့ ဦးတည်ပြီး၊ ၎င်းသည် recurrent network နှစ်ခု သို့မဟုတ် အများကြီးပါဝင်ပြီး၊ ယခင် layer ၏ output ကို နောက် layer ၏ input အဖြစ် ပေးပို့သည်။\n", "\n", - "![Multilayer long-short-term-memory- RNN ကို ပြသထားသော ပုံ](../../../../../translated_images/my/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![Multilayer long-short-term-memory- RNN ကို ပြသထားသော ပုံ](../../../../../translated_images/my/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*Fernando López ရေးသားထားသော [ဤအလွန်အမိုက်ဆုံး post](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) မှ ရိုက်ယူထားသော ပုံ။*\n", "\n", diff --git a/translations/my/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/my/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index 7c0400ea..567cdf51 100644 --- a/translations/my/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/my/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "RNN ကို စာသားထုတ်လုပ်ရန် လေ့ကျင့်ပုံမှာ အောက်ပါအတိုင်းဖြစ်ပါမည်။ အဆင့်တစ်ခုစီတွင် `nchars` အရှည်ရှိသော စာလုံးများ၏ အစဉ်အတိုင်းယူပြီး၊ နောက်ထွက်စာလုံးကို အဝင်စာလုံးတစ်ခုစီအတွက် ကွန်ယက်အားဖြင့် ထုတ်လုပ်ရန် တောင်းဆိုပါမည်။\n", "\n", - "![RNN သုံး၍ 'HELLO' စကားလုံးကို ထုတ်လုပ်နေသော ဥပမာပုံ။](../../../../../translated_images/my/rnn-generate.56c54afb52f9781d.png)\n", + "![RNN သုံး၍ 'HELLO' စကားလုံးကို ထုတ်လုပ်နေသော ဥပမာပုံ။](../../../../../translated_images/my/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "အခြေအနေအလိုက်၊ *အဆုံးအမှတ်အသား* `` ကဲ့သို့သော အထူးအက္ခရာများကိုလည်း ထည့်သွင်းလိုတတ်ပါသည်။ သို့သော် ကျွန်ုပ်တို့အနေဖြင့် အဆုံးမရှိသော စာသားထုတ်လုပ်မှုအတွက် ကွန်ယက်ကိုသာ လေ့ကျင့်လိုသောကြောင့်၊ အစဉ်တစ်ခုစီ၏ အရွယ်အစားကို `nchars` အက္ခရာများအဖြစ် သတ်မှတ်ထားမည်ဖြစ်သည်။ ထို့ကြောင့်၊ လေ့ကျင့်မှုဥပမာတစ်ခုစီတွင် `nchars` အဝင်များနှင့် `nchars` အထွက်များ (အဝင်အစဉ်ကို ဘယ်ဘက်သို့ အက္ခရာတစ်လုံးရွှေ့ထားသောအတိုင်း) ပါဝင်မည်ဖြစ်သည်။ Minibatch တစ်ခုတွင် ဤအစဉ်များစွာ ပါဝင်မည်ဖြစ်သည်။\n", "\n", diff --git a/translations/my/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/my/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index edb84d8b..c34efe93 100644 --- a/translations/my/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/my/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -111,7 +111,7 @@ "\n", "RNN ကို သတင်းခေါင်းစဉ်များ ဖန်တီးရန် သင်ကြားပုံမှာ အောက်ပါအတိုင်း ဖြစ်ပါတယ်။ တစ်ခုချင်းစီအဆင့်မှာ RNN ထဲသို့ ထည့်မည့် ခေါင်းစဉ်တစ်ခုကို ရွေးပြီး၊ အင်ပွတ်အက္ခရာတစ်ခုစီအတွက် နောက်ထွက်အက္ခရာကို ဖန်တီးရန် ကွန်ယက်ကို မေးမြန်းပါမည်။\n", "\n", - "![RNN က 'HELLO' စကားလုံးကို ဖန်တီးနေသည်ကို ပြသသော ပုံ။](../../../../../translated_images/my/rnn-generate.56c54afb52f9781d.png)\n", + "![RNN က 'HELLO' စကားလုံးကို ဖန်တီးနေသည်ကို ပြသသော ပုံ။](../../../../../translated_images/my/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "အကြောင်းအရာ၏ နောက်ဆုံးအက္ခရာအတွက် `` token ကို ဖန်တီးရန် ကွန်ယက်ကို မေးမြန်းပါမည်။\n", "\n", diff --git a/translations/my/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/my/lessons/5-NLP/17-GenerativeNetworks/README.md index 825b0480..24500b2f 100644 --- a/translations/my/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/my/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ Recurrent Neural Networks (RNNs) နှင့် Long Short Term Memory Cells (L ဤအရာသည် အောက်ပါပုံတွင် ဖော်ပြထားသော neural architectures များကို ဖန်တီးနိုင်စေသည်- -![Image showing common recurrent neural network patterns.](../../../../../translated_images/my/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Image showing common recurrent neural network patterns.](../../../../../translated_images/my/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > ပုံကို [Andrej Karpaty](http://karpathy.github.io/) ရဲ့ [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) ဆိုတဲ့ blog post မှရယူထားသည်။ @@ -32,7 +32,7 @@ Recurrent Neural Networks (RNNs) နှင့် Long Short Term Memory Cells (L ဤ RNN ကို စာသားကို အဆင့်ဆင့် ထုတ်လုပ်ရန် training လုပ်မည်။ အဆင့်တစ်ခုစီတွင် `nchars` အရှည်ရှိသော စာလုံးများ၏ sequence ကိုယူပြီး၊ input character တစ်ခုစီအတွက် နောက်ထွက် character ကို network မှ ထုတ်ပေးရန် မေးမြန်းမည်- -![Image showing an example RNN generation of the word 'HELLO'.](../../../../../translated_images/my/rnn-generate.56c54afb52f9781d.png) +![Image showing an example RNN generation of the word 'HELLO'.](../../../../../translated_images/my/rnn-generate.56c54afb52f9781d.webp) စာသားထုတ်လုပ်ခြင်း (inference အတွင်း) တွင် **prompt** တစ်ခုကို RNN cells မှတဆင့် hidden state ကို ရယူပြီး၊ ထို့နောက် generation ကို စတင်မည်။ စာလုံးတစ်လုံးစီကို အဆင့်ဆင့် ထုတ်လုပ်ပြီး၊ state နှင့် ထုတ်လုပ်ထားသော စာလုံးကို နောက် RNN cell သို့ ပေးပို့ကာ နောက် character ကို ထုတ်လုပ်မည်။ ထိုနောက် လိုအပ်သော စာလုံးများကို ထုတ်လုပ်ပြီးမှ ရပ်မည်။ diff --git a/translations/my/lessons/5-NLP/18-Transformers/README.md b/translations/my/lessons/5-NLP/18-Transformers/README.md index f1fe1d29..1d1e9fd0 100644 --- a/translations/my/lessons/5-NLP/18-Transformers/README.md +++ b/translations/my/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ RNNs အသုံးပြု၍ sequence-to-sequence ကို **encoder** န **Attention Mechanisms** သည် RNN ၏ output prediction အပေါ် input vector တစ်ခုချင်းစီ၏ context သက်ရောက်မှုကို အလေးပေးရန် နည်းလမ်းတစ်ခုဖြစ်သည်။ ဒီနည်းလမ်းကို input RNN ၏ intermediate states နှင့် output RNN အကြား shortcut များဖန်တီးခြင်းဖြင့် အကောင်အထည်ဖော်သည်။ ထို့ကြောင့် output symbol yt ကို ဖန်တီးသောအခါ input hidden states hi အားလုံးကို အလေးပေး coefficient αt,i များဖြင့် ထည့်သွင်းစဉ်းစားမည်။ -![Image showing an encoder/decoder model with an additive attention layer](../../../../../translated_images/my/encoder-decoder-attention.7a726296894fb567.png) +![Image showing an encoder/decoder model with an additive attention layer](../../../../../translated_images/my/encoder-decoder-attention.7a726296894fb567.webp) > Encoder-decoder မော်ဒယ်နှင့် additive attention mechanism ကို [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) မှ ရယူထားသည်။ [ဒီ blog post](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) မှာလည်း ရှင်းပြထားသည်။ Attention matrix {αi,j} သည် output sequence အတွင်း စကားလုံးတစ်ခုကို ဖန်တီးရာတွင် input စကားလုံးတစ်ခုချင်းစီ၏ သက်ရောက်မှုကို ကိုယ်စားပြုသည်။ အောက်တွင် matrix ၏ ဥပမာတစ်ခုကို ဖော်ပြထားသည်- -![Image showing a sample alignment found by RNNsearch-50, taken from Bahdanau - arviz.org](../../../../../translated_images/my/bahdanau-fig3.09ba2d37f202a6af.png) +![Image showing a sample alignment found by RNNsearch-50, taken from Bahdanau - arviz.org](../../../../../translated_images/my/bahdanau-fig3.09ba2d37f202a6af.webp) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) မှာပါရှိသော (Fig.3) ပုံ @@ -66,7 +66,7 @@ Positional embedding ရလဒ်သည် original token နှင့် sequen ထို့နောက် sequence အတွင်း pattern များကို ဖမ်းဆီးရန်လိုအပ်သည်။ Transformers တွင် **self-attention** mechanism ကို အသုံးပြုသည်။ Self-attention သည် input နှင့် output အဖြစ် တူညီသော sequence အပေါ် attention ကို အသုံးပြုခြင်းဖြစ်သည်။ Self-attention ကို အသုံးပြုခြင်းဖြင့် sentence အတွင်း context ကို စဉ်းစားနိုင်ပြီး၊ စကားလုံးများ၏ inter-relationship ကို တွေ့နိုင်သည်။ ဥပမာ- *it* ကဲ့သို့သော coreferences ကို ရှာဖွေနိုင်သည်။ -![](../../../../../translated_images/my/CoreferenceResolution.861924d6d384a7d6.png) +![](../../../../../translated_images/my/CoreferenceResolution.861924d6d384a7d6.webp) > [Google Blog](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) မှ ပုံ @@ -91,7 +91,7 @@ Input position တစ်ခုချင်းစီကို output position တ **BERT** (Bidirectional Encoder Representations from Transformers) သည် *BERT-base* အတွက် 12 layers နှင့် *BERT-large* အတွက် 24 layers ပါဝင်သော အလွန်ကြီးမားသော multi-layer transformer network ဖြစ်သည်။ မော်ဒယ်ကို WikiPedia နှင့် books ကဲ့သို့သော text data အကြီးအကျယ်ကို unsupervised training (sentence အတွင်း masked words များကို ခန့်မှန်းခြင်း) ဖြင့် ပထမဦးဆုံး pre-train လုပ်သည်။ Pre-training အတွင်း မော်ဒယ်သည် language understanding အဆင့်များကို စွမ်းဆောင်နိုင်ပြီး၊ အခြား datasets များနှင့် fine-tuning ဖြင့် အသုံးပြုနိုင်သည်။ ဒီလုပ်ငန်းစဉ်ကို **transfer learning** ဟု ခေါ်သည်။ -![picture from http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/my/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) +![picture from http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/my/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > ပုံ [source](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/my/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/my/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index e224588d..fcafad61 100644 --- a/translations/my/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/my/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**Attention Mechanisms (အာရုံစူးစိုက်မှုစနစ်များ)** ကတော့ RNN ရဲ့ output prediction (အထွက်ခန့်မှန်းမှု) တစ်ခုစီအပေါ် input vector (အဝင်ဗက်တာ) တစ်ခုစီရဲ့ အကြောင်းအရာသက်ရောက်မှုကို အလေးပေးနိုင်တဲ့ နည်းလမ်းတစ်ခုကို ပံ့ပိုးပေးပါတယ်။ ဒီစနစ်ကို အကောင်အထည်ဖော်တဲ့နည်းလမ်းက input RNN ရဲ့ အလယ်အလတ်အခြေအနေတွေနဲ့ output RNN အကြား shortcut (တိုက်ရိုက်လမ်းကြောင်း) တွေ ဖန်တီးခြင်းဖြစ်ပါတယ်။ ဒီနည်းလမ်းနဲ့ $y_t$ output symbol ကို ဖန်တီးတဲ့အခါမှာ input hidden states $h_i$ အားလုံးကို အလေးချိန်ကွဲပြားမှု $\\alpha_{t,i}$ နဲ့အတူ စဉ်းစားပါမယ်။\n", "\n", - "![Image showing an encoder/decoder model with an additive attention layer](../../../../../translated_images/my/encoder-decoder-attention.7a726296894fb567.png)\n", + "![Image showing an encoder/decoder model with an additive attention layer](../../../../../translated_images/my/encoder-decoder-attention.7a726296894fb567.webp)\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) မှ additive attention mechanism ပါတဲ့ encoder-decoder မော်ဒယ်ကို [ဒီ blog post](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) မှ ရယူထားသည်။*\n", "\n", "Attention matrix $\\{\\alpha_{i,j}\\}$ က output sequence (အထွက်အဆက်မပြတ်အချက်အလက်) ရဲ့ စကားလုံးတစ်လုံးကို ဖန်တီးရာမှာ input words (အဝင်စကားလုံး) တစ်ချို့ရဲ့ သက်ရောက်မှုအဆင့်ကို ကိုယ်စားပြုပါတယ်။ အောက်မှာ ဒီလို matrix ရဲ့ ဥပမာကို ကြည့်နိုင်ပါတယ်။\n", "\n", - "![Image showing a sample alignment found by RNNsearch-50, taken from Bahdanau - arviz.org](../../../../../translated_images/my/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![Image showing a sample alignment found by RNNsearch-50, taken from Bahdanau - arviz.org](../../../../../translated_images/my/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) မှ (ပုံ ၃) ကို ရယူထားသည်။*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) က အလွှာ ၁၂ လွှာပါတဲ့ *BERT-base* နဲ့ အလွှာ ၂၄ လွှာပါတဲ့ *BERT-large* တို့ဖြင့် ဖွဲ့စည်းထားတဲ့ အလွန်ကြီးမားတဲ့ multi-layer transformer network တစ်ခုဖြစ်ပါတယ်။ ဒီမော်ဒယ်ကို စာကြောင်းထဲက masked words (ဖုံးထားသောစကားလုံးများ) ကို ခန့်မှန်းတဲ့ unsupervised training (မကြီးကြပ်သောလေ့ကျင့်မှု) နည်းလမ်းနဲ့ စာကြောင်းအများအပြား (WikiPedia + စာအုပ်များ) အပေါ်မှာ အရင်ဆုံး pre-train လုပ်ထားပါတယ်။ Pre-training လုပ်စဉ်မှာ မော်ဒယ်က ဘာသာစကားနားလည်မှုအဆင့်မြင့်တစ်ခုကို စုပ်ယူထားပြီး၊ အဲ့ဒီနားလည်မှုကို အခြား dataset တွေနဲ့ fine-tuning (အသေးစိတ်ချိန်ညှိမှု) လုပ်နိုင်ပါတယ်။ ဒီလုပ်ငန်းစဉ်ကို **transfer learning** လို့ ခေါ်ပါတယ်။\n", "\n", - "![picture from http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/my/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![picture from http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/my/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "Transformer architecture တွေမှာ BERT, DistilBERT, BigBird, OpenGPT3 စတဲ့ မော်ဒယ်အမျိုးအစားအများအပြားရှိပြီး၊ အဲ့ဒီမော်ဒယ်တွေကို fine-tune လုပ်နိုင်ပါတယ်။ [HuggingFace package](https://github.com/huggingface/) က PyTorch နဲ့ အဲ့ဒီ architecture တွေကို training လုပ်ဖို့ repository တစ်ခုကို ပံ့ပိုးပေးထားပါတယ်။\n", "\n", diff --git a/translations/my/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/my/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index 9555ee27..e183787b 100644 --- a/translations/my/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/my/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**အာရုံစူးစိုက်မှု မော်ကွန်းများ** သည် RNN ၏ output များကို ခန့်မှန်းရာတွင် input vector တစ်ခုချင်းစီ၏ context သက်ရောက်မှုကို အလေးပေးနိုင်စေသော နည်းလမ်းတစ်ခုဖြစ်သည်။ ၎င်းကို အကောင်အထည်ဖော်ရာတွင် input RNN ၏ အလယ်အလတ် states များနှင့် output RNN အကြား shortcut များ ဖန်တီးခြင်းဖြင့် ပြုလုပ်သည်။ ဒီနည်းလမ်းဖြင့် output symbol $y_t$ ကို ထုတ်လုပ်စဉ်တွင် input hidden states $h_i$ အားလုံးကို အလေးချိန် coefficient များ $\\alpha_{t,i}$ ဖြင့် သက်ဆိုင်စွာ ထည့်သွင်းစဉ်းစားမည်ဖြစ်သည်။\n", "\n", - "![Image showing an encoder/decoder model with an additive attention layer](../../../../../translated_images/my/encoder-decoder-attention.7a726296894fb567.png)\n", + "![Image showing an encoder/decoder model with an additive attention layer](../../../../../translated_images/my/encoder-decoder-attention.7a726296894fb567.webp)\n", "*Additive attention mechanism ပါဝင်သည့် encoder-decoder မော်ဒယ် ([Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf)) ကို [ဒီ blog post](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) မှ ရယူထားသည်*\n", "\n", "Attention matrix $\\{\\alpha_{i,j}\\}$ သည် output စာကြောင်းအတွင်း စကားလုံးတစ်လုံးကို ဖန်တီးရာတွင် သက်ဆိုင်သော input စကားလုံးများ၏ သက်ရောက်မှုအဆင့်ကို ကိုယ်စားပြုသည်။ အောက်တွင် ထို matrix ၏ ဥပမာကို ဖော်ပြထားသည်-\n", "\n", - "![Image showing a sample alignment found by RNNsearch-50, taken from Bahdanau - arviz.org](../../../../../translated_images/my/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![Image showing a sample alignment found by RNNsearch-50, taken from Bahdanau - arviz.org](../../../../../translated_images/my/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) မှ ရယူထားသော ပုံ]*\n", "\n", @@ -225,7 +225,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) သည် *BERT-base* အတွက် အလွှာ 12 လွှာနှင့် *BERT-large* အတွက် အလွှာ 24 လွှာပါဝင်သော အလွန်ကြီးမားသော multi-layer transformer network တစ်ခုဖြစ်သည်။ ဤမော်ဒယ်ကို ပထမဦးစွာ အကြီးမားသော စာသားဒေတာများ (WikiPedia + စာအုပ်များ) ကို အသုံးပြု၍ unsupervised training (ဝါကျအတွင်းရှိ masked စကားလုံးများကို ခန့်မှန်းခြင်း) ဖြင့် pre-training ပြုလုပ်သည်။ Pre-training လုပ်စဉ်အတွင်း မော်ဒယ်သည် ဘာသာစကားနားလည်မှုအဆင့်အတန်းများကို အလွန်အမင်း စွမ်းဆောင်နိုင်စွမ်း ရရှိလာပြီး၊ ထို့နောက် အခြားသောဒေတာများနှင့်အတူ fine tuning ဖြင့် အသုံးချနိုင်သည်။ ဤလုပ်ငန်းစဉ်ကို **transfer learning** ဟုခေါ်သည်။\n", "\n", - "![picture from http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/my/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![picture from http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/my/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "BERT, DistilBERT, BigBird, OpenGPT3 စသည်တို့အပါအဝင် Transformer architecture များ၏ အမျိုးအစားများစွာရှိပြီး၊ ထိုမော်ဒယ်များကို fine tuning ပြုလုပ်နိုင်သည်။\n", "\n", diff --git a/translations/my/lessons/5-NLP/19-NER/README.md b/translations/my/lessons/5-NLP/19-NER/README.md index ae652a40..81c41f67 100644 --- a/translations/my/lessons/5-NLP/19-NER/README.md +++ b/translations/my/lessons/5-NLP/19-NER/README.md @@ -58,7 +58,7 @@ infant | O Token နှင့် class များအကြား တစ်ခုချင်းစီကို တိုက်ရိုက်ဆက်စပ်မှုတစ်ခုတည်ဆောက်ရန် လိုအပ်သဖြင့်၊ ဒီပုံစံမှ **many-to-many** neural network model တစ်ခုကို တည်ဆောက်နိုင်ပါသည်။ -![Image showing common recurrent neural network patterns.](../../../../../translated_images/my/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Image showing common recurrent neural network patterns.](../../../../../translated_images/my/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > *ပုံကို [ဒီ blog post](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) မှ [Andrej Karpathy](http://karpathy.github.io/) ရေးသားထားသည်။ NER token classification model များသည် ဒီပုံရဲ့ ညာဘက်ဆုံး network architecture ကို ကိုယ်စားပြုသည်။* diff --git a/translations/my/lessons/5-NLP/README.md b/translations/my/lessons/5-NLP/README.md index 5db04d23..87153baf 100644 --- a/translations/my/lessons/5-NLP/README.md +++ b/translations/my/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # သဘာဝဘာသာစကားလုပ်ငန်းဆောင်တာ -![NLP လုပ်ငန်းဆောင်တာများကို ရေးဆွဲထားသော ပုံ](../../../../translated_images/my/ai-nlp.b22dcb8ca4707cea.png) +![NLP လုပ်ငန်းဆောင်တာများကို ရေးဆွဲထားသော ပုံ](../../../../translated_images/my/ai-nlp.b22dcb8ca4707cea.webp) ဤအပိုင်းတွင် **သဘာဝဘာသာစကားလုပ်ငန်းဆောင်တာ (NLP)** နှင့်ဆက်စပ်သောအလုပ်များကို Neural Networks အသုံးပြု၍ ဖြေရှင်းပုံကို အဓိကထားဆွေးနွေးသွားမည်ဖြစ်သည်။ ကွန်ပျူတာများကို ဖြေရှင်းစေလိုသော NLP ပြဿနာများစွာရှိသည်။ diff --git a/translations/my/lessons/6-Other/23-MultiagentSystems/README.md b/translations/my/lessons/6-Other/23-MultiagentSystems/README.md index c7fcfe80..0758491a 100644 --- a/translations/my/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/my/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ models တစ်ခုကို ဖွင့်နိုင်သည်၊ ဥ model ကို ဖွင့်ပြီးနောက် NetLogo ၏ အဓိက screen သို့ ရောက်ရှိသည်။ ဤနေရာတွင် finite resources (grass) ရှိသော wolves နှင့် sheep ၏ population ကို ဖော်ပြသော sample model တစ်ခုကို တွေ့နိုင်သည်။ -![NetLogo Main Screen](../../../../../translated_images/my/NetLogo-Main.32653711ec1a01b3.png) +![NetLogo Main Screen](../../../../../translated_images/my/NetLogo-Main.32653711ec1a01b3.webp) > Dmitry Soshnikov ၏ screenshot diff --git a/translations/my/lessons/README.md b/translations/my/lessons/README.md index c209f7bf..41de2207 100644 --- a/translations/my/lessons/README.md +++ b/translations/my/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # အကျဉ်းချုပ် -![အကျဉ်းချုပ်ကို ရေးဆွဲထားသော ပုံ](../../../translated_images/my/ai-overview.0857791951d19500.png) +![အကျဉ်းချုပ်ကို ရေးဆွဲထားသော ပုံ](../../../translated_images/my/ai-overview.0857791951d19500.webp) > [Tomomi Imura](https://twitter.com/girlie_mac) မှ ရေးဆွဲထားသော Sketchnote diff --git a/translations/my/lessons/X-Extras/X1-MultiModal/README.md b/translations/my/lessons/X-Extras/X1-MultiModal/README.md index 621d4395..9373acca 100644 --- a/translations/my/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/my/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ NLP အလုပ်များကို ဖြေရှင်းရန် trans CLIP ၏ အဓိကအကြောင်းအရာမှာ စာသား prompt များနှင့် ပုံတစ်ပုံကို နှိုင်းယှဉ်ပြီး ပုံသည် prompt နှင့် ဘယ်လောက်တိကျမှုရှိသည်ကို သတ်မှတ်နိုင်ရန်ဖြစ်သည်။ -![CLIP Architecture](../../../../../translated_images/my/clip-arch.b3dbf20b4e8ed8be.png) +![CLIP Architecture](../../../../../translated_images/my/clip-arch.b3dbf20b4e8ed8be.webp) > *ဤ blog post မှ ပုံ [ဒီမှာ](https://openai.com/blog/clip/)* @@ -31,7 +31,7 @@ CLIP မော်ဒယ်/စာကြည့်တိုက်ကို [OpenAI ဥပမာအားဖြင့် ပုံများကို ကြောင်၊ ခွေး၊ လူတို့အကြား ခွဲခြားရန် လိုအပ်သည်ဟု ယူဆပါစို့။ ဤအခါတွင် မော်ဒယ်ကို ပုံတစ်ပုံနှင့် စာသား prompt များ "*a picture of a cat*", "*a picture of a dog*", "*a picture of a human*" တို့ကို ပေးပါ။ 3 probabilities ရလဒ် vector တွင် အမြင့်ဆုံးတန်ဖိုးရှိသော index ကို ရွေးချယ်ရမည်ဖြစ်သည်။ -![CLIP for Image Classification](../../../../../translated_images/my/clip-class.3af42ef0b2b19369.png) +![CLIP for Image Classification](../../../../../translated_images/my/clip-class.3af42ef0b2b19369.webp) > *ဤ blog post မှ ပုံ [ဒီမှာ](https://openai.com/blog/clip/)* @@ -55,13 +55,13 @@ VQGAN အကြောင်းကို [Taming Transformers](https://compvis.gi VQGAN နှင့် ရိုးရာ GAN အကြား အရေးကြီးသော ကွာခြားချက်တစ်ခုမှာ၊ နောက်ဆုံးပုံကို မည်သည့် input vector မှမဆို ရိုးရာ GAN သည် သင့်တော်သောပုံကို ဖန်တီးနိုင်သော်လည်း၊ VQGAN သည် coherence မရှိသောပုံကို ဖန်တီးနိုင်သည်။ ထို့ကြောင့် ပုံဖန်တီးမှုလုပ်ငန်းစဉ်ကို CLIP ကို အသုံးပြု၍ ထပ်မံလမ်းညွှန်ရန် လိုအပ်သည်။ -![VQGAN+CLIP Architecture](../../../../../translated_images/my/vqgan.5027fe05051dfa31.png) +![VQGAN+CLIP Architecture](../../../../../translated_images/my/vqgan.5027fe05051dfa31.webp) စာသား prompt နှင့် ကိုက်ညီသော ပုံတစ်ပုံကို ဖန်တီးရန်၊ random encoding vector တစ်ခုဖြင့် စတင်ပြီး၊ ၎င်းကို VQGAN မှတဆင့် ပုံတစ်ပုံထုတ်လုပ်သည်။ ထို့နောက် CLIP ကို loss function တစ်ခုဖန်တီးရန် အသုံးပြုသည်။ ၎င်းသည် ပုံသည် စာသား prompt နှင့် ဘယ်လောက်ကိုက်ညီသည်ကို ပြသသည်။ ထို့နောက် loss ကို အနည်းဆုံးဖြစ်အောင်လုပ်ရန်၊ back propagation ကို အသုံးပြု၍ input vector parameters များကို ပြင်ဆင်သည်။ VQGAN+CLIP ကို အကောင်အထည်ဖော်ထားသော စာကြည့်တိုက်တစ်ခုမှာ [Pixray](http://github.com/pixray/pixray) ဖြစ်သည်။ -![Picture produced by Pixray](../../../../../translated_images/my/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Picture produced by pixray](../../../../../translated_images/my/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Picture produced by Pixray](../../../../../translated_images/my/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) +![Picture produced by Pixray](../../../../../translated_images/my/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp) | ![Picture produced by pixray](../../../../../translated_images/my/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp) | ![Picture produced by Pixray](../../../../../translated_images/my/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp) ----|----|---- Prompt *a closeup watercolor portrait of young male teacher of literature with a book* မှ ဖန်တီးထားသော ပုံ | Prompt *a closeup oil portrait of young female teacher of computer science with a computer* မှ ဖန်တီးထားသော ပုံ | Prompt *a closeup oil portrait of old male teacher of mathematics in front of blackboard* မှ ဖန်တီးထားသော ပုံ diff --git a/translations/ne/README.md b/translations/ne/README.md index 90f97dfe..aef74274 100644 --- a/translations/ne/README.md +++ b/translations/ne/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # शुरुवातीहरूको लागि कृत्रिम बुद्धिमत्ता - एक पाठ्यक्रम -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ne/ai-overview.0857791951d19500.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ne/ai-overview.0857791951d19500.webp)| |:---:| | शुरुवातीहरूको लागि कृत्रिम बुद्धिमत्ता - _स्केन्चोटो द्वारा [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/ne/lessons/1-Intro/README.md b/translations/ne/lessons/1-Intro/README.md index 4252d651..515011e9 100644 --- a/translations/ne/lessons/1-Intro/README.md +++ b/translations/ne/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # एआईको परिचय -![एआईको परिचयको सामग्रीको डूडलमा सारांश](../../../../translated_images/ne/ai-intro.bf28d1ac4235881c.png) +![एआईको परिचयको सामग्रीको डूडलमा सारांश](../../../../translated_images/ne/ai-intro.bf28d1ac4235881c.webp) > स्केच नोट [टोमोमी इमुरा](https://twitter.com/girlie_mac) द्वारा @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: सुरुमा, कम्प्युटरहरू [चार्ल्स बैबेज](https://en.wikipedia.org/wiki/Charles_Babbage) द्वारा संख्याहरूमा काम गर्न र एक राम्रो परिभाषित प्रक्रिया - एल्गोरिदम - अनुसरण गर्न आविष्कार गरिएको थियो। आधुनिक कम्प्युटरहरू, यद्यपि १९औं शताब्दीमा प्रस्तावित मूल मोडेलभन्दा धेरै उन्नत छन्, अझै पनि नियन्त्रित गणनाको उही विचारलाई अनुसरण गर्छन्। त्यसैले, यदि हामीलाई लक्ष्य प्राप्त गर्न आवश्यक चरणहरूको ठ्याक्कै क्रम थाहा छ भने, कम्प्युटरलाई कुनै काम गर्न प्रोग्राम गर्न सम्भव छ। -![व्यक्तिको फोटो](../../../../translated_images/ne/dsh_age.d212a30d4e54fb5f.png) +![व्यक्तिको फोटो](../../../../translated_images/ne/dsh_age.d212a30d4e54fb5f.webp) > फोटो [भिक्की सोश्निकोभा](http://twitter.com/vickievalerie) द्वारा @@ -46,7 +46,7 @@ CO_OP_TRANSLATOR_METADATA: **[बुद्धिमत्ता](https://en.wikipedia.org/wiki/Intelligence)** शब्दसँग व्यवहार गर्दा एउटा समस्या यो हो कि यस शब्दको कुनै स्पष्ट परिभाषा छैन। कसैले तर्क गर्न सक्छ कि बुद्धिमत्ता **सार सोचाइ** वा **आत्म-जागरूकता**सँग सम्बन्धित छ, तर हामी यसलाई ठीकसँग परिभाषित गर्न सक्दैनौं। -![बिरालोको फोटो](../../../../translated_images/ne/photo-cat.8c8e8fb760ffe457.jpg) +![बिरालोको फोटो](../../../../translated_images/ne/photo-cat.8c8e8fb760ffe457.webp) > [फोटो](https://unsplash.com/photos/75715CVEJhI) [एम्बर किप](https://unsplash.com/@sadmax) द्वारा अनस्प्लाशबाट @@ -98,13 +98,13 @@ CO_OP_TRANSLATOR_METADATA: > | एमएलको बारेमा के? | | > |--------------|-----------| -> | केही डेटा आधारित समस्याहरू समाधान गर्न कम्प्युटर सिकाइमा आधारित कृत्रिम बुद्धिमत्ताको भागलाई **मेसिन लर्निङ** भनिन्छ। हामी यस पाठ्यक्रममा शास्त्रीय मेसिन लर्निङलाई विचार गर्ने छैनौं - हामी तपाईंलाई छुट्टै [मेसिन लर्निङका लागि शुरुआती](http://aka.ms/ml-beginners) पाठ्यक्रममा सन्दर्भ दिन्छौं। | ![मेसिन लर्निङका लागि शुरुआती](../../../../translated_images/ne/ml-for-beginners.9e4fed176fd5817d.png) | +> | केही डेटा आधारित समस्याहरू समाधान गर्न कम्प्युटर सिकाइमा आधारित कृत्रिम बुद्धिमत्ताको भागलाई **मेसिन लर्निङ** भनिन्छ। हामी यस पाठ्यक्रममा शास्त्रीय मेसिन लर्निङलाई विचार गर्ने छैनौं - हामी तपाईंलाई छुट्टै [मेसिन लर्निङका लागि शुरुआती](http://aka.ms/ml-beginners) पाठ्यक्रममा सन्दर्भ दिन्छौं। | ![मेसिन लर्निङका लागि शुरुआती](../../../../translated_images/ne/ml-for-beginners.9e4fed176fd5817d.webp) | ## एआईको संक्षिप्त इतिहास कृत्रिम बुद्धिमत्ता २०औं शताब्दीको मध्यमा एउटा क्षेत्रको रूपमा सुरु भएको थियो। सुरुमा, प्रतीकात्मक तर्क एक प्रमुख दृष्टिकोण थियो, र यसले विशेषज्ञ प्रणालीहरू जस्ता केही महत्त्वपूर्ण सफलताहरू ल्यायो - कम्प्युटर प्रोग्रामहरू जसले केही सिमित समस्या क्षेत्रहरूमा विशेषज्ञको रूपमा काम गर्न सक्षम थिए। तर, चाँडै यो स्पष्ट भयो कि यस्तो दृष्टिकोण राम्रोसँग स्केल हुँदैन। विशेषज्ञबाट ज्ञान निकाल्नु, यसलाई कम्प्युटरमा प्रतिनिधित्व गर्नु, र त्यो ज्ञान आधारलाई सटीक राख्नु धेरै जटिल कार्य हो, र धेरै अवस्थामा व्यावहारिक हुन धेरै महँगो छ। यसले १९७० को दशकमा तथाकथित [एआई जाडो](https://en.wikipedia.org/wiki/AI_winter) ल्यायो। -एआईको संक्षिप्त इतिहास +एआईको संक्षिप्त इतिहास > छवि [दिमित्री सोश्निकोभ](http://soshnikov.com) द्वारा diff --git a/translations/ne/lessons/2-Symbolic/Animals.ipynb b/translations/ne/lessons/2-Symbolic/Animals.ipynb index 88e81db1..fce7642f 100644 --- a/translations/ne/lessons/2-Symbolic/Animals.ipynb +++ b/translations/ne/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "यस नमूनामा, हामी केही भौतिक विशेषताहरूको आधारमा जनावर पहिचान गर्नको लागि एक साधारण ज्ञान-आधारित प्रणाली कार्यान्वयन गर्नेछौं। प्रणालीलाई निम्न AND-OR रूखद्वारा प्रतिनिधित्व गर्न सकिन्छ (यो सम्पूर्ण रूखको एक भाग हो, हामी सजिलै थप नियमहरू थप्न सक्छौं):\n", "\n", - "![](../../../../translated_images/ne/AND-OR-Tree.5592d2c70187f283.png)\n" + "![](../../../../translated_images/ne/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { diff --git a/translations/ne/lessons/2-Symbolic/README.md b/translations/ne/lessons/2-Symbolic/README.md index 1666833e..b5cf0e9a 100644 --- a/translations/ne/lessons/2-Symbolic/README.md +++ b/translations/ne/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # ज्ञान प्रतिनिधित्व र विशेषज्ञ प्रणाली -![सिम्बोलिक AI सामग्रीको सारांश](../../../../translated_images/ne/ai-symbolic.715a30cb610411a6.png) +![सिम्बोलिक AI सामग्रीको सारांश](../../../../translated_images/ne/ai-symbolic.715a30cb610411a6.webp) > स्केच नोट [Tomomi Imura](https://twitter.com/girlie_mac) द्वारा @@ -41,7 +41,7 @@ AI को सुरुवाती दिनहरूमा, बुद्धि त्यसैले, **ज्ञान प्रतिनिधित्व** को समस्या भनेको कम्प्युटर भित्र ज्ञानलाई डाटाको रूपमा प्रतिनिधित्व गर्ने केही प्रभावकारी तरिका पत्ता लगाउनु हो, ताकि यसलाई स्वचालित रूपमा प्रयोग गर्न सकियोस्। यसलाई एक स्पेक्ट्रमको रूपमा हेर्न सकिन्छ: -![ज्ञान प्रतिनिधित्व स्पेक्ट्रम](../../../../translated_images/ne/knowledge-spectrum.b60df631852c0217.png) +![ज्ञान प्रतिनिधित्व स्पेक्ट्रम](../../../../translated_images/ne/knowledge-spectrum.b60df631852c0217.webp) > छवि [Dmitry Soshnikov](http://soshnikov.com) द्वारा @@ -94,7 +94,7 @@ Untyped-Language | छैन | प्रकार परिभाषा सिम्बोलिक AI को प्रारम्भिक सफलताहरू मध्ये एक **विशेषज्ञ प्रणालीहरू** थिए - कम्प्युटर प्रणालीहरू जसलाई सीमित समस्या क्षेत्रमा विशेषज्ञको रूपमा कार्य गर्न डिजाइन गरिएको थियो। तिनीहरू **ज्ञान आधार** मा आधारित थिए, जुन एक वा बढी मानव विशेषज्ञहरूबाट निकालिएको थियो, र तिनीहरूमा **तर्क इन्जिन** समावेश थियो जसले यसमा केही तर्क प्रदर्शन गर्थ्यो। -![मानव वास्तुकला](../../../../translated_images/ne/arch-human.5d4d35f1bba3ab1c.png) | ![ज्ञान-आधारित प्रणाली](../../../../translated_images/ne/arch-kbs.3ec5c150b09fa8da.png) +![मानव वास्तुकला](../../../../translated_images/ne/arch-human.5d4d35f1bba3ab1c.webp) | ![ज्ञान-आधारित प्रणाली](../../../../translated_images/ne/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ मानव न्युरल प्रणालीको सरलीकृत संरचना | ज्ञान-आधारित प्रणालीको वास्तुकला @@ -106,7 +106,7 @@ Untyped-Language | छैन | प्रकार परिभाषा उदाहरणका लागि, निम्न विशेषज्ञ प्रणालीलाई विचार गरौं जसले शारीरिक विशेषताहरूको आधारमा जनावर निर्धारण गर्दछ: -![AND-OR ट्री](../../../../translated_images/ne/AND-OR-Tree.5592d2c70187f283.png) +![AND-OR ट्री](../../../../translated_images/ne/AND-OR-Tree.5592d2c70187f283.webp) > छवि [Dmitry Soshnikov](http://soshnikov.com) द्वारा diff --git a/translations/ne/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/ne/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index 7cb301ca..c3802c5a 100644 --- a/translations/ne/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/ne/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -1259,7 +1259,7 @@ "* कम प्रशिक्षण हानि - मोडेलले प्रशिक्षण डेटा राम्रोसँग अनुमान गर्न सक्छ, किनभने यससँग पर्याप्त अभिव्यक्तिपूर्ण शक्ति छ। \n", "* मान्यकरण हानि प्रशिक्षण हानिभन्दा धेरै उच्च हुन सक्छ र प्रशिक्षणको क्रममा बढ्न थाल्न सक्छ - यसको कारण मोडेलले \"प्रशिक्षण बिन्दुहरू\" सम्झिन्छ, र \"समग्र चित्र\" गुमाउँछ।\n", "\n", - "![ओभरफिटिङ](../../../../../translated_images/ne/overfit.a0bd57f717c15769.png)\n", + "![ओभरफिटिङ](../../../../../translated_images/ne/overfit.a0bd57f717c15769.webp)\n", "\n", "> यस चित्रमा, `x` ले प्रशिक्षण डेटा जनाउँछ, `o` ले मान्यकरण डेटा। बायाँ - रेखीय मोडेल (एक-स्तरीय), यसले डेटा प्रकृतिलाई राम्रोसँग अनुमान गर्छ। दायाँ - ओभरफिट गरिएको मोडेल, यसले प्रशिक्षण डेटा पूर्ण रूपमा अनुमान गर्छ, तर अन्य कुनै पनि डेटा (मान्यकरण त्रुटि धेरै उच्च छ) सँग अर्थपूर्ण हुँदैन।\n" ] diff --git a/translations/ne/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/ne/lessons/3-NeuralNetworks/05-Frameworks/README.md index 781b6026..a4e29e8b 100644 --- a/translations/ne/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/ne/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ High-level API| [Keras](IntroKeras.ipynb) | *PyTorch Lightning* तलको समस्या विचार गर्नुहोस् जहाँ ५ बिन्दुहरूलाई (ग्राफमा `x` ले प्रतिनिधित्व गरिएको) अनुमान गर्नुपर्छ: -![linear](../../../../../translated_images/ne/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/ne/overfit2.131f5800ae10ca5e.jpg) +![linear](../../../../../translated_images/ne/overfit1.f24b71c6f652e59e.webp) | ![overfit](../../../../../translated_images/ne/overfit2.131f5800ae10ca5e.webp) -------------------------|-------------------------- **रेखीय मोडेल, २ प्यारामिटरहरू** | **गैर-रेखीय मोडेल, ७ प्यारामिटरहरू** प्रशिक्षण त्रुटि = ५.३ | प्रशिक्षण त्रुटि = ० @@ -79,7 +79,7 @@ High-level API| [Keras](IntroKeras.ipynb) | *PyTorch Lightning* जसरी माथिको ग्राफबाट देख्न सकिन्छ, ओभरफिटिङलाई धेरै कम प्रशिक्षण त्रुटि र उच्च मान्यकरण त्रुटिबाट पत्ता लगाउन सकिन्छ। सामान्यतया प्रशिक्षणको क्रममा हामीले प्रशिक्षण र मान्यकरण त्रुटिहरू दुवै घट्न थालेको देख्छौं, र त्यसपछि कुनै बिन्दुमा मान्यकरण त्रुटि घट्न रोक्न सक्छ र बढ्न थाल्न सक्छ। यो ओभरफिटिङको संकेत हुनेछ, र यो बिन्दुमा प्रशिक्षण रोक्नुपर्छ (वा कम्तीमा मोडेलको स्न्यापशट लिनुपर्छ) भन्ने सूचक हुनेछ। -![overfitting](../../../../../translated_images/ne/Overfitting.408ad91cd90b4371.png) +![overfitting](../../../../../translated_images/ne/Overfitting.408ad91cd90b4371.webp) ## ओभरफिटिङ रोक्न कसरी diff --git a/translations/ne/lessons/3-NeuralNetworks/README.md b/translations/ne/lessons/3-NeuralNetworks/README.md index 7030fb93..7c120fbc 100644 --- a/translations/ne/lessons/3-NeuralNetworks/README.md +++ b/translations/ne/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # न्युरल नेटवर्कको परिचय -![न्युरल नेटवर्कको सामग्रीको सारांश एक चित्रमा](../../../../translated_images/ne/ai-neuralnetworks.1c687ae40bc86e83.png) +![न्युरल नेटवर्कको सामग्रीको सारांश एक चित्रमा](../../../../translated_images/ne/ai-neuralnetworks.1c687ae40bc86e83.webp) जसरी हामीले परिचयमा चर्चा गर्यौं, बुद्धिमत्ता प्राप्त गर्ने एक तरिका भनेको **कम्प्युटर मोडेल** वा **कृत्रिम मस्तिष्क**लाई प्रशिक्षण दिनु हो। २०औं शताब्दीको मध्यदेखि, अनुसन्धानकर्ताहरूले विभिन्न गणितीय मोडेलहरू प्रयास गरे, र पछिल्लो केही वर्षमा यो दिशा अत्यन्त सफल साबित भयो। मस्तिष्कको यस्ता गणितीय मोडेलहरूलाई **न्युरल नेटवर्क** भनिन्छ। @@ -36,13 +36,13 @@ CO_OP_TRANSLATOR_METADATA: जैविक विज्ञानबाट, हामीलाई थाहा छ कि हाम्रो मस्तिष्क न्युरल कोषहरू (न्युरोनहरू) बाट बनेको छ, जसको प्रत्येकमा धेरै "इनपुट" (डेंड्राइटहरू) र एक "आउटपुट" (एक्सन) हुन्छ। डेंड्राइटहरू र एक्सनहरू दुवैले विद्युतीय संकेतहरू प्रवाह गर्न सक्छन्, र तिनीहरू बीचको जडानहरू — जसलाई सिन्याप्स भनिन्छ — विभिन्न स्तरको प्रवाहशीलता देखाउन सक्छन्, जुन न्युरोट्रान्समिटरहरूले नियमन गर्छन्। -![न्युरोनको मोडेल](../../../../translated_images/ne/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![न्युरोनको मोडेल](../../../../translated_images/ne/artneuron.1a5daa88d20ebe6f.png) +![न्युरोनको मोडेल](../../../../translated_images/ne/synapse-wikipedia.ed20a9e4726ea1c6.webp) | ![न्युरोनको मोडेल](../../../../translated_images/ne/artneuron.1a5daa88d20ebe6f.webp) ----|---- वास्तविक न्युरोन *([छवि](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) विकिपीडियाबाट)* | कृत्रिम न्युरोन *(लेखकद्वारा बनाइएको छवि)* त्यसैले, न्युरोनको सबैभन्दा सरल गणितीय मोडेलमा धेरै इनपुटहरू X1, ..., XN र एक आउटपुट Y हुन्छ, र तौलहरूको श्रृंखला W1, ..., WN हुन्छ। आउटपुट यसरी गणना गरिन्छ: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) जहाँ f भनेको केही गैर-रेखीय **सक्रियता फलन** हो। diff --git a/translations/ne/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/ne/lessons/4-ComputerVision/06-IntroCV/README.md index b1da174a..2e5e5e94 100644 --- a/translations/ne/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/ne/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) * **ब्रेल पुस्तकको तस्बिर पूर्व-प्रशोधन गर्ने**। हामी थ्रेसहोल्डिङ, फिचर डिटेक्सन, परिप्रेक्ष्य रूपान्तरण र NumPy हेरफेर प्रयोग गरेर व्यक्तिगत ब्रेल प्रतीकहरू अलग गर्नेमा केन्द्रित छौं, जसलाई न्यूरल नेटवर्कद्वारा थप वर्गीकरण गर्न सकिन्छ। -![ब्रेल छवि](../../../../../translated_images/ne/braille.341962ff76b1bd70.jpeg) | ![ब्रेल छवि पूर्व-प्रशोधित](../../../../../translated_images/ne/braille-result.46530fea020b03c7.png) | ![ब्रेल प्रतीकहरू](../../../../../translated_images/ne/braille-symbols.0159185ab69d5339.png) +![ब्रेल छवि](../../../../../translated_images/ne/braille.341962ff76b1bd70.webp) | ![ब्रेल छवि पूर्व-प्रशोधित](../../../../../translated_images/ne/braille-result.46530fea020b03c7.webp) | ![ब्रेल प्रतीकहरू](../../../../../translated_images/ne/braille-symbols.0159185ab69d5339.webp) ----|-----|----- > छवि [OpenCV.ipynb](OpenCV.ipynb) बाट * **फ्रेम भिन्नता प्रयोग गरेर भिडियोमा गति पत्ता लगाउने**। यदि क्यामेरा स्थिर छ भने, क्यामेरा फिडका फ्रेमहरू एकअर्कासँग धेरै समान हुनुपर्छ। किनभने फ्रेमहरू एरेको रूपमा प्रतिनिधित्व गरिन्छ, दुई लगातार फ्रेमहरूको लागि ती एरेहरू घटाएर मात्र हामी पिक्सेल भिन्नता प्राप्त गर्नेछौं, जुन स्थिर फ्रेमहरूको लागि कम हुनुपर्छ, र छविमा पर्याप्त गति हुँदा उच्च हुन्छ। -![भिडियो फ्रेम र फ्रेम भिन्नताको छवि](../../../../../translated_images/ne/frame-difference.706f805491a0883c.png) +![भिडियो फ्रेम र फ्रेम भिन्नताको छवि](../../../../../translated_images/ne/frame-difference.706f805491a0883c.webp) > छवि [OpenCV.ipynb](OpenCV.ipynb) बाट @@ -89,7 +89,7 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) - **डेंस अप्टिकल फ्लो** प्रत्येक पिक्सेलको लागि भेक्टर फिल्ड गणना गर्दछ जसले देखाउँछ कि यो कहाँ सर्दैछ। - **स्पार्स अप्टिकल फ्लो** छविमा केही विशिष्ट फिचरहरू (जस्तै किनाराहरू) लिनेमा आधारित छ, र फ्रेमबाट फ्रेममा तिनीहरूको ट्राजेक्टोरी निर्माण गर्ने। -![अप्टिकल फ्लोको छवि](../../../../../translated_images/ne/optical.1f4a94464579a83a.png) +![अप्टिकल फ्लोको छवि](../../../../../translated_images/ne/optical.1f4a94464579a83a.webp) > छवि [OpenCV.ipynb](OpenCV.ipynb) बाट diff --git a/translations/ne/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/ne/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 1951e9fa..f8342a8b 100644 --- a/translations/ne/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/ne/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 एक नेटवर्क हो जसले २०१४ मा ImageNet टप-५ वर्गीकरणमा ९२.७% शुद्धता प्राप्त गर्यो। यसको लेयर संरचना निम्नानुसार छ: -![ImageNet Layers](../../../../../translated_images/ne/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet Layers](../../../../../translated_images/ne/vgg-16-arch1.d901a5583b3a51ba.webp) जस्तो कि तपाईं देख्न सक्नुहुन्छ, VGG ले परम्परागत पिरामिड आर्किटेक्चर अनुसरण गर्दछ, जुन कनभोल्युसन-पूलिङ लेयरहरूको क्रम हो। -![ImageNet Pyramid](../../../../../translated_images/ne/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet Pyramid](../../../../../translated_images/ne/vgg-16-arch.64ff2137f50dd49f.webp) > [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) बाट छवि diff --git a/translations/ne/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/ne/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index 448ac48e..14ad6466 100644 --- a/translations/ne/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/ne/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -260,7 +260,7 @@ "\n", "त्यसैले, एउटा सामान्य CNN मा धेरै कनभोल्युसनल तहहरू हुन्छन्, जसको बीचमा पूलिङ तहहरू हुन्छन् तस्बिरको आयाम घटाउनका लागि। हामी फिल्टरहरूको संख्या पनि बढाउँछौं, किनकि ढाँचाहरू जति जटिल बन्दै जान्छन् - त्यति नै धेरै सम्भावित रोचक संयोजनहरू हुन्छन् जसलाई हामी खोज्नुपर्ने हुन्छ।\n", "\n", - "![कनभोल्युसनल तहहरू र पूलिङ तहहरूको पिरामिड देखाउने तस्बिर।](../../../../../translated_images/ne/cnn-pyramid.85915455759ef0ce.png)\n", + "![कनभोल्युसनल तहहरू र पूलिङ तहहरूको पिरामिड देखाउने तस्बिर।](../../../../../translated_images/ne/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "स्थानिक आयाम घटाउने र विशेषता/फिल्टर आयाम बढाउने कारणले गर्दा, यो आर्किटेक्चरलाई **पिरामिड आर्किटेक्चर** पनि भनिन्छ।\n" ] diff --git a/translations/ne/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/ne/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index b0d7b4b2..f8cabe71 100644 --- a/translations/ne/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/ne/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -359,7 +359,7 @@ "\n", "त्यसैले, एउटा सामान्य CNN मा धेरै कन्भोल्युसनल तहहरू हुनेछन्, जसको बीचमा पूलिङ तहहरू तस्बिरको आयाम घटाउन प्रयोग गरिन्छ। हामी फिल्टरहरूको संख्या पनि बढाउँछौं, किनभने ढाँचाहरू जति जटिल बन्दै जान्छन् - त्यति नै धेरै सम्भावित चाखलाग्दा संयोजनहरू हुन्छन् जसलाई हामी खोज्न आवश्यक हुन्छ।\n", "\n", - "![कन्भोल्युसनल तहहरू र पूलिङ तहहरूको साथमा पिरामिड आर्किटेक्चर देखाउने तस्बिर।](../../../../../translated_images/ne/cnn-pyramid.85915455759ef0ce.png)\n", + "![कन्भोल्युसनल तहहरू र पूलिङ तहहरूको साथमा पिरामिड आर्किटेक्चर देखाउने तस्बिर।](../../../../../translated_images/ne/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "स्थानिक आयाम घट्दै र विशेषता/फिल्टर आयाम बढ्दै जाने भएकाले, यस आर्किटेक्चरलाई **पिरामिड आर्किटेक्चर** पनि भनिन्छ।\n" ] diff --git a/translations/ne/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/ne/lessons/4-ComputerVision/07-ConvNets/README.md index e32ad037..8370327e 100644 --- a/translations/ne/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/ne/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: ढाँचाहरू निकाल्न, हामी **कन्भोल्युसनल फिल्टरहरू** को धारणा प्रयोग गर्नेछौं। तपाईंलाई थाहा छ, छवि 2D-म्याट्रिक्स वा रंग गहिराइ भएको 3D-टेन्सरद्वारा प्रतिनिधित्व गरिन्छ। फिल्टर लागू गर्नुको मतलब हामी सानो **फिल्टर कर्नेल** म्याट्रिक्स लिन्छौं, र मूल छविको प्रत्येक पिक्सेलको लागि हामी छिमेकी बिन्दुहरूसँग तौलित औसत गणना गर्छौं। हामी यसलाई सानो झ्यालले सम्पूर्ण छविमा स्लाइड गर्दै, र फिल्टर कर्नेल म्याट्रिक्समा तौलहरू अनुसार सबै पिक्सेलहरू औसत गर्दै हेर्न सक्छौं। -![Vertical Edge Filter](../../../../../translated_images/ne/filter-vert.b7148390ca0bc356.png) | ![Horizontal Edge Filter](../../../../../translated_images/ne/filter-horiz.59b80ed4feb946ef.png) +![Vertical Edge Filter](../../../../../translated_images/ne/filter-vert.b7148390ca0bc356.webp) | ![Horizontal Edge Filter](../../../../../translated_images/ne/filter-horiz.59b80ed4feb946ef.webp) ----|---- > छवि: दिमित्री सोश्निकोभ @@ -38,7 +38,7 @@ CNN काम गर्ने तरिका निम्न महत्त् * हामी नेटवर्कलाई यसरी डिजाइन गर्न सक्छौं कि फिल्टरहरू स्वचालित रूपमा प्रशिक्षित हुन्छन् * हामी मूल छविमा मात्र होइन, उच्च-स्तरका विशेषताहरूमा ढाँचाहरू फेला पार्न उस्तै दृष्टिकोण प्रयोग गर्न सक्छौं। यसरी CNN विशेषता निकाल्ने काम विशेषताको पदानुक्रममा हुन्छ, तल्लो-स्तरका पिक्सेल संयोजनबाट सुरु गर्दै, चित्रका भागहरूको उच्च-स्तर संयोजनसम्म। -![Hierarchical Feature Extraction](../../../../../translated_images/ne/FeatureExtractionCNN.d9b456cbdae7cb64.png) +![Hierarchical Feature Extraction](../../../../../translated_images/ne/FeatureExtractionCNN.d9b456cbdae7cb64.webp) > छवि: [Hislop-Lynch को पेपर](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d) बाट, [तिनीहरूको अनुसन्धान](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) मा आधारित @@ -55,9 +55,9 @@ CNN काम गर्ने तरिका निम्न महत्त् उदाहरणका लागि, VGG-16 को आर्किटेक्चरलाई हेरौं, जसले 2014 मा ImageNet को शीर्ष-5 वर्गीकरणमा 92.7% शुद्धता प्राप्त गर्यो: -![ImageNet Layers](../../../../../translated_images/ne/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet Layers](../../../../../translated_images/ne/vgg-16-arch1.d901a5583b3a51ba.webp) -![ImageNet Pyramid](../../../../../translated_images/ne/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet Pyramid](../../../../../translated_images/ne/vgg-16-arch.64ff2137f50dd49f.webp) > छवि: [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) बाट diff --git a/translations/ne/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/ne/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 7f3bb440..15a02273 100644 --- a/translations/ne/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/ne/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ CO_OP_TRANSLATOR_METADATA: हामी [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/) प्रयोग गर्नेछौं, जसमा कुकुर र बिरालोका ३७ विभिन्न प्रजातिहरूका छविहरू समावेश छन्। -![हामीले प्रयोग गर्ने डेटासेट](../../../../../../translated_images/ne/data.50b2a9d5484bdbf0.png) +![हामीले प्रयोग गर्ने डेटासेट](../../../../../../translated_images/ne/data.50b2a9d5484bdbf0.webp) डेटासेट डाउनलोड गर्न, यो कोड स्निपेट प्रयोग गर्नुहोस्: diff --git a/translations/ne/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/ne/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index ca413930..121cbf33 100644 --- a/translations/ne/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/ne/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "आदर्श बिरालोलाई देखाउनका लागि, हामी एउटा जथाभावी आवाज भएको तस्बिरबाट सुरु गर्नेछौं, र ग्रेडियन्ट डिसेन्ट अनुकूलन प्रविधि प्रयोग गरेर तस्बिरलाई समायोजन गर्ने प्रयास गर्नेछौं ताकि नेटवर्कले बिरालोलाई चिन्न सकोस्।\n", "\n", - "![अनुकूलन लूप](../../../../../translated_images/ne/ideal-cat-loop.999fbb8ff306e044.png)\n", + "![अनुकूलन लूप](../../../../../translated_images/ne/ideal-cat-loop.999fbb8ff306e044.webp)\n", "\n", "यहाँ हाम्रो सुरुवाती तस्बिर छ:\n" ] diff --git a/translations/ne/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/ne/lessons/4-ComputerVision/08-TransferLearning/README.md index 5362a888..3316c981 100644 --- a/translations/ne/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/ne/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Keras र PyTorch दुवैमा सामान्य आर्किटे यहाँ VGG-16 नेटवर्कले बिरालोको तस्बिरबाट निकालेका विशेषताहरूको उदाहरण छ: -![Features extracted by VGG-16](../../../../../translated_images/ne/features.6291f9c7ba3a0b95.png) +![Features extracted by VGG-16](../../../../../translated_images/ne/features.6291f9c7ba3a0b95.webp) ## बिरालो र कुकुर डेटासेट @@ -48,19 +48,19 @@ Keras र PyTorch दुवैमा सामान्य आर्किटे हामी एउटा विधि लिन सक्छौं, जहाँ हामी एउटा र्यान्डम छविबाट सुरु गर्छौं, र त्यसपछि **ग्रेडियन्ट डिसेन्ट अप्टिमाइजेसन** प्रविधि प्रयोग गरेर त्यो छवि समायोजन गर्ने प्रयास गर्छौं, ताकि नेटवर्कले सोच्न थाल्छ कि यो बिरालो हो। -![Image Optimization Loop](../../../../../translated_images/ne/ideal-cat-loop.999fbb8ff306e044.png) +![Image Optimization Loop](../../../../../translated_images/ne/ideal-cat-loop.999fbb8ff306e044.webp) तर, यदि हामी यसो गर्छौं भने, हामीले र्यान्डम आवाजसँग धेरै मिल्दोजुल्दो केही प्राप्त गर्नेछौं। यसको कारण हो कि *नेटवर्कलाई इनपुट छवि बिरालो हो भनेर सोच्न बनाउने धेरै तरिकाहरू छन्*, जसमा केही दृश्य रूपमा अर्थपूर्ण छैनन्। ती छविहरूमा बिरालोको लागि सामान्य ढाँचाहरू धेरै हुन्छन्, तर तिनीहरूलाई दृश्य रूपमा विशिष्ट बनाउने कुनै बाध्यता छैन। नतिजा सुधार गर्न, हामी हानि कार्यमा अर्को पद थप्न सक्छौं, जसलाई **भेरिएसन हानि** भनिन्छ। यो एउटा मेट्रिक हो, जसले छविको छेउछाउका पिक्सेलहरू कति समान छन् भनेर देखाउँछ। भेरिएसन हानि न्यूनतम गर्दा छवि चिल्लो हुन्छ, र आवाज हट्छ - जसले दृश्य रूपमा आकर्षक ढाँचाहरू प्रकट गर्छ। यहाँ उच्च सम्भावनाका साथ बिरालो र जेब्रा भनेर वर्गीकृत गरिएका "आदर्श" छविहरूको उदाहरण छ: -![Ideal Cat](../../../../../translated_images/ne/ideal-cat.203dd4597643d6b0.png) | ![Ideal Zebra](../../../../../translated_images/ne/ideal-zebra.7f70e8b54ee15a7a.png) +![Ideal Cat](../../../../../translated_images/ne/ideal-cat.203dd4597643d6b0.webp) | ![Ideal Zebra](../../../../../translated_images/ne/ideal-zebra.7f70e8b54ee15a7a.webp) -----|----- *आदर्श बिरालो* | *आदर्श जेब्रा* यस्तै विधि प्रयोग गरेर न्यूरल नेटवर्कमा **adversarial attacks** गर्न सकिन्छ। मानौं हामी न्यूरल नेटवर्कलाई मूर्ख बनाउन चाहन्छौं र कुकुरलाई बिरालो जस्तो देखाउन चाहन्छौं। यदि हामी कुकुरको छवि लिन्छौं, जुन नेटवर्कले कुकुर भनेर पहिचान गर्छ, हामी त्यसलाई थोरै समायोजन गर्न सक्छौं ग्रेडियन्ट डिसेन्ट अप्टिमाइजेसन प्रयोग गरेर, जबसम्म नेटवर्कले यसलाई बिरालो भनेर वर्गीकृत गर्न थाल्दैन: -![Picture of a Dog](../../../../../translated_images/ne/original-dog.8f68a67d2fe0911f.png) | ![Picture of a dog classified as a cat](../../../../../translated_images/ne/adversarial-dog.d9fc7773b0142b89.png) +![Picture of a Dog](../../../../../translated_images/ne/original-dog.8f68a67d2fe0911f.webp) | ![Picture of a dog classified as a cat](../../../../../translated_images/ne/adversarial-dog.d9fc7773b0142b89.webp) -----|----- *कुकुरको मूल छवि* | *कुकुरको छवि बिरालो भनेर वर्गीकृत गरिएको* diff --git a/translations/ne/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/ne/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index 92144c8c..8273b59d 100644 --- a/translations/ne/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/ne/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "किनभने हामी ऑटोएन्कोडरलाई मूल छविबाट सकेसम्म धेरै जानकारी समात्न प्रशिक्षण दिइरहेका छौं ताकि सही पुनर्निर्माण गर्न सकियोस्, नेटवर्कले इनपुट छविहरूको अर्थ समात्नको लागि उत्तम **एम्बेडिङ** खोज्ने प्रयास गर्छ।\n", "\n", - "![ऑटोएन्कोडर चित्र](../../../../../translated_images/ne/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![ऑटोएन्कोडर चित्र](../../../../../translated_images/ne/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "> छवि [Keras ब्लग](https://blog.keras.io/building-autoencoders-in-keras.html) बाट\n", "\n", diff --git a/translations/ne/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/ne/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index 1d064ac5..375b263a 100644 --- a/translations/ne/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/ne/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "किनकि हामी ऑटोएन्कोडरलाई मूल छविबाट यथासम्भव धेरै जानकारी समात्न प्रशिक्षण गरिरहेका छौं ताकि सही पुनर्निर्माण गर्न सकियोस्, नेटवर्कले इनपुट छविहरूको अर्थ समात्नको लागि उत्तम **एम्बेडिङ** खोज्ने प्रयास गर्छ।\n", "\n", - "![ऑटोएन्कोडर डायग्राम](../../../../../translated_images/ne/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![ऑटोएन्कोडर डायग्राम](../../../../../translated_images/ne/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "*छवि [Keras ब्लग](https://blog.keras.io/building-autoencoders-in-keras.html) बाट*\n", "\n", diff --git a/translations/ne/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/ne/lessons/4-ComputerVision/09-Autoencoders/README.md index 953c043d..a665177a 100644 --- a/translations/ne/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/ne/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ CNNs प्रशिक्षण गर्दा, एउटा समस्य किनकि हामी अटोएनकोडरलाई मूल छविबाट सकेसम्म धेरै जानकारी समात्न प्रशिक्षण गर्दैछौं ताकि सही पुनर्निर्माण गर्न सकियोस्, नेटवर्कले इनपुट छविहरूको उत्तम **एम्बेडिङ** पत्ता लगाउन प्रयास गर्छ। -![अटोएनकोडर आरेख](../../../../../translated_images/ne/autoencoder_schema.5e6fc9ad98a5eb61.jpg) +![अटोएनकोडर आरेख](../../../../../translated_images/ne/autoencoder_schema.5e6fc9ad98a5eb61.webp) > छवि [Keras ब्लग](https://blog.keras.io/building-autoencoders-in-keras.html) बाट diff --git a/translations/ne/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/ne/lessons/4-ComputerVision/11-ObjectDetection/README.md index 20853f09..79758f0c 100644 --- a/translations/ne/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/ne/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: ## [पूर्व-व्याख्यान क्विज](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![वस्तु पहिचान](../../../../../translated_images/ne/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) +![वस्तु पहिचान](../../../../../translated_images/ne/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp) > छवि [YOLO v2 वेबसाइट](https://pjreddie.com/darknet/yolov2/) बाट @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 2. प्रत्येक टाइलमा छवि वर्गीकरण चलाउनुहोस्। 3. ती टाइलहरू, जसले पर्याप्त उच्च सक्रियता देखाउँछन्, तिनीहरूमा खोजिएको वस्तु भएको मान्न सकिन्छ। -![साधारण वस्तु पहिचान](../../../../../translated_images/ne/naive-detection.e7f1ba220ccd08c6.png) +![साधारण वस्तु पहिचान](../../../../../translated_images/ne/naive-detection.e7f1ba220ccd08c6.webp) > *छवि [व्यायाम नोटबुक](ObjectDetection-TF.ipynb) बाट* @@ -42,7 +42,7 @@ CO_OP_TRANSLATOR_METADATA: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - २० वर्गहरू * [COCO](http://cocodataset.org/#home) - सामान्य वस्तुहरू सन्दर्भमा। ८० वर्गहरू, सीमाना बक्सहरू र खण्डन मास्कहरू -![COCO](../../../../../translated_images/ne/coco-examples.71bc60380fa6cceb.jpg) +![COCO](../../../../../translated_images/ne/coco-examples.71bc60380fa6cceb.webp) ## वस्तु पहिचान मेट्रिक्स @@ -50,7 +50,7 @@ CO_OP_TRANSLATOR_METADATA: जहाँ छवि वर्गीकरणका लागि एल्गोरिदमको प्रदर्शन मापन गर्न सजिलो छ, वस्तु पहिचानका लागि वर्गको शुद्धता र अनुमानित सीमाना बक्स स्थानको सटीकता दुवै मापन गर्न आवश्यक छ। पछिल्लोका लागि, हामी **Intersection over Union** (IoU) प्रयोग गर्छौं, जसले दुई बक्सहरू (वा दुई मनमानी क्षेत्रहरू) कत्तिको ओभरल्याप गर्छन् भनेर मापन गर्छ। -![IoU](../../../../../translated_images/ne/iou_equation.9a4751d40fff4e11.png) +![IoU](../../../../../translated_images/ne/iou_equation.9a4751d40fff4e11.webp) > *[IoU सम्बन्धी उत्कृष्ट ब्लग पोस्ट](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/) बाट चित्र २* @@ -97,11 +97,11 @@ $$ [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) ले [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) प्रयोग गरी ROI क्षेत्रहरूको पदानुक्रम संरचना उत्पन्न गर्छ, जसलाई CNN फिचर एक्स्ट्र्याक्टरहरू र SVM वर्गीकरणकर्ताहरू मार्फत पास गरिन्छ, वस्तु वर्ग निर्धारण गर्न, र *सीमाना बक्स* समन्वय निर्धारण गर्न रेखीय रिग्रेसन प्रयोग गरिन्छ। [आधिकारिक पेपर](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/ne/rcnn1.cae407020dfb1d1f.png) +![RCNN](../../../../../translated_images/ne/rcnn1.cae407020dfb1d1f.webp) > *van de Sande et al. ICCV’11 बाट छवि* -![RCNN-1](../../../../../translated_images/ne/rcnn2.2d9530bb83516484.png) +![RCNN-1](../../../../../translated_images/ne/rcnn2.2d9530bb83516484.webp) > *[यो ब्लग](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e) बाट छविहरू* @@ -109,7 +109,7 @@ $$ यो दृष्टिकोण R-CNN जस्तै हो, तर क्षेत्रहरू कन्भोल्युसन तहहरू लागू भएपछि परिभाषित गरिन्छन्। -![FRCNN](../../../../../translated_images/ne/f-rcnn.3cda6d9bb4188875.png) +![FRCNN](../../../../../translated_images/ne/f-rcnn.3cda6d9bb4188875.webp) > [आधिकारिक पेपर](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 बाट छवि @@ -117,7 +117,7 @@ $$ यस दृष्टिकोणको मुख्य विचार भनेको क्षेत्रहरू भविष्यवाणी गर्न न्यूरल नेटवर्क प्रयोग गर्नु हो - जसलाई *क्षेत्र प्रस्ताव नेटवर्क* भनिन्छ। [पेपर](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/ne/faster-rcnn.8d46c099b87ef30a.png) +![FasterRCNN](../../../../../translated_images/ne/faster-rcnn.8d46c099b87ef30a.webp) > [आधिकारिक पेपर](https://arxiv.org/pdf/1506.01497.pdf) बाट छवि @@ -129,7 +129,7 @@ $$ 2. फिचरहरू **पोजिसन-सेंसिटिभ स्कोर म्याप** द्वारा प्रशोधन गरिन्छ। $C$ वर्गका प्रत्येक वस्तु $k\times k$ क्षेत्रहरूमा विभाजन गरिन्छ, र हामी वस्तुका भागहरू भविष्यवाणी गर्न प्रशिक्षण गर्छौं। 3. $k\times k$ क्षेत्रका प्रत्येक भागका लागि सबै नेटवर्कहरूले वस्तु वर्गहरूको लागि मतदान गर्छन्, र अधिकतम भोट भएको वस्तु वर्ग चयन गरिन्छ। -![r-fcn छवि](../../../../../translated_images/ne/r-fcn.13eb88158b99a3da.png) +![r-fcn छवि](../../../../../translated_images/ne/r-fcn.13eb88158b99a3da.webp) > [आधिकारिक पेपर](https://arxiv.org/abs/1605.06409) बाट छवि @@ -140,7 +140,7 @@ YOLO एक वास्तविक-समय एक-पास एल्गो * छवि $S\times S$ क्षेत्रहरूमा विभाजन गरिन्छ। * प्रत्येक क्षेत्रका लागि, **CNN** ले $n$ सम्भावित वस्तुहरू, *सीमाना बक्स* समन्वय र *विश्वास* = *संभाव्यता* * IoU भविष्यवाणी गर्छ। - ![YOLO](../../../../../translated_images/ne/yolo.a2648ec82ee8bb4e.png) + ![YOLO](../../../../../translated_images/ne/yolo.a2648ec82ee8bb4e.webp) > [आधिकारिक पेपर](https://arxiv.org/abs/1506.02640) बाट छवि diff --git a/translations/ne/lessons/4-ComputerVision/README.md b/translations/ne/lessons/4-ComputerVision/README.md index 00722a9d..4d2756cf 100644 --- a/translations/ne/lessons/4-ComputerVision/README.md +++ b/translations/ne/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # कम्प्युटर भिजन -![कम्प्युटर भिजन सामग्रीको सारांश डुडलमा](../../../../translated_images/ne/ai-computervision.6506ebebac3fbf76.png) +![कम्प्युटर भिजन सामग्रीको सारांश डुडलमा](../../../../translated_images/ne/ai-computervision.6506ebebac3fbf76.webp) यस खण्डमा हामी सिक्नेछौं: diff --git a/translations/ne/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/ne/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index 03e3fa16..9093f96d 100644 --- a/translations/ne/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/ne/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**शब्दहरूको झोला** (BoW) भेक्टर प्रतिनिधित्व परम्परागत भेक्टर प्रतिनिधित्वमा सबैभन्दा धेरै प्रयोग गरिने विधि हो। प्रत्येक शब्दलाई भेक्टरको सूचकसँग जोडिन्छ, र भेक्टरको तत्त्वले कुनै विशेष दस्तावेजमा शब्दको उपस्थितिको सङ्ख्या समावेश गर्दछ।\n", "\n", - "![शब्दहरूको झोला भेक्टर प्रतिनिधित्व मेमोरीमा कसरी देखिन्छ भन्ने देखाउने छवि।](../../../../../translated_images/ne/bag-of-words-example.606fc1738f1d7ba9.png)\n", + "![शब्दहरूको झोला भेक्टर प्रतिनिधित्व मेमोरीमा कसरी देखिन्छ भन्ने देखाउने छवि।](../../../../../translated_images/ne/bag-of-words-example.606fc1738f1d7ba9.webp)\n", "\n", "> **Note**: तपाईं BoW लाई पाठका व्यक्तिगत शब्दहरूको लागि सबै एक-तर्फ-कोड गरिएको भेक्टरहरूको योगको रूपमा पनि सोच्न सक्नुहुन्छ।\n", "\n", diff --git a/translations/ne/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/ne/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index 764d5eb3..7c1d6c00 100644 --- a/translations/ne/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/ne/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**शब्दहरूको झोला** (BoW) भेक्टर प्रतिनिधित्व परम्परागत भेक्टर प्रतिनिधित्वहरू मध्ये सबैभन्दा सरल र बुझ्न सजिलो हो। प्रत्येक शब्दलाई भेक्टरको सूचकसँग जोडिन्छ, र भेक्टरको तत्वले कुनै पनि दस्तावेजमा प्रत्येक शब्दको उपस्थितिको संख्या समावेश गर्दछ।\n", "\n", - "![शब्दहरूको झोला भेक्टर प्रतिनिधित्व मेमोरीमा कसरी प्रतिनिधित्व गरिन्छ भन्ने देखाउने चित्र।](../../../../../translated_images/ne/bag-of-words-example.606fc1738f1d7ba9.png)\n", + "![शब्दहरूको झोला भेक्टर प्रतिनिधित्व मेमोरीमा कसरी प्रतिनिधित्व गरिन्छ भन्ने देखाउने चित्र।](../../../../../translated_images/ne/bag-of-words-example.606fc1738f1d7ba9.webp)\n", "\n", "> **Note**: तपाईं BoW लाई पाठमा व्यक्तिगत शब्दहरूको लागि एक-हट-एन्कोड गरिएको भेक्टरहरूको योगको रूपमा पनि सोच्न सक्नुहुन्छ।\n", "\n", diff --git a/translations/ne/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/ne/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 16f68fe4..60cb6a7c 100644 --- a/translations/ne/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/ne/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "हाम्रो नेटवर्कमा पहिलो लेयरको रूपमा एम्बेडिङ लेयर प्रयोग गरेर, हामी ब्याग-अफ-वर्ड्स मोडेलबाट **एम्बेडिङ ब्याग** मोडेलमा स्विच गर्न सक्छौं, जहाँ हामी पहिलो पटक हाम्रो पाठका प्रत्येक शब्दलाई सम्बन्धित एम्बेडिङमा रूपान्तरण गर्छौं, र त्यसपछि ती सबै एम्बेडिङहरूमा `sum`, `average` वा `max` जस्ता कुनै एग्रिगेट फङ्सन गणना गर्छौं। \n", "\n", - "![पाँच अनुक्रम शब्दहरूको लागि एम्बेडिङ क्लासिफायर देखाउने छवि।](../../../../../translated_images/ne/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![पाँच अनुक्रम शब्दहरूको लागि एम्बेडिङ क्लासिफायर देखाउने छवि।](../../../../../translated_images/ne/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "हाम्रो क्लासिफायर न्यूरल नेटवर्क एम्बेडिङ लेयरबाट सुरु हुनेछ, त्यसपछि एग्रिगेसन लेयर, र यसको माथि लीनियर क्लासिफायर हुनेछ:\n" ] @@ -176,7 +176,7 @@ "\n", "अघिल्लो आर्किटेक्चरमा, हामीले सबै अनुक्रमहरूलाई एउटै लम्बाइमा ल्याउनका लागि padding गर्नुपर्ने हुन्थ्यो ताकि तिनीहरूलाई मिनिब्याचमा फिट गर्न सकियोस्। यो भिन्न-लम्बाइ अनुक्रमहरूको प्रतिनिधित्व गर्ने सबैभन्दा प्रभावकारी तरिका होइन - अर्को तरिका भनेको **offset** भेक्टर प्रयोग गर्नु हो, जसले एउटै ठूलो भेक्टरमा भण्डारण गरिएका सबै अनुक्रमहरूको offsets राख्छ।\n", "\n", - "![Offset अनुक्रम प्रतिनिधित्व देखाउने चित्र](../../../../../translated_images/ne/offset-sequence-representation.eb73fcefb29b46ee.png)\n", + "![Offset अनुक्रम प्रतिनिधित्व देखाउने चित्र](../../../../../translated_images/ne/offset-sequence-representation.eb73fcefb29b46ee.webp)\n", "\n", "> **Note**: माथिको चित्रमा, हामीले क्यारेक्टरहरूको अनुक्रम देखाएका छौं, तर हाम्रो उदाहरणमा हामी शब्दहरूको अनुक्रमसँग काम गरिरहेका छौं। यद्यपि, offset भेक्टर प्रयोग गरेर अनुक्रमहरूको प्रतिनिधित्व गर्ने सामान्य सिद्धान्त उस्तै रहन्छ।\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW छिटो हुन्छ, जबकि स्किप-ग्राम ढिलो हुन्छ, तर दुर्लभ शब्दहरूको प्रतिनिधित्व गर्न राम्रो काम गर्छ।\n", "\n", - "![CBoW र स्किप-ग्राम एल्गोरिदमहरू शब्दहरूलाई भेक्टरमा रूपान्तरण गर्ने तरिका देखाउने चित्र।](../../../../../translated_images/ne/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![CBoW र स्किप-ग्राम एल्गोरिदमहरू शब्दहरूलाई भेक्टरमा रूपान्तरण गर्ने तरिका देखाउने चित्र।](../../../../../translated_images/ne/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Google News डेटासेटमा प्रि-ट्रेन गरिएको word2vec एम्बेडिङसँग प्रयोग गर्न, हामी **gensim** लाइब्रेरी प्रयोग गर्न सक्छौं। तल हामी 'neural' शब्दसँग सबैभन्दा मिल्दो शब्दहरू फेला पार्छौं।\n", "\n", diff --git a/translations/ne/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/ne/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index b199245c..c9adb083 100644 --- a/translations/ne/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/ne/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "हाम्रो नेटवर्कको पहिलो लेयरको रूपमा एम्बेडिङ लेयर प्रयोग गरेर, हामी ब्याग-ऑफ-वर्ड्स मोडेलबाट **एम्बेडिङ ब्याग** मोडेलमा स्विच गर्न सक्छौं, जहाँ हामी पहिलो पटक हाम्रो पाठको प्रत्येक शब्दलाई सम्बन्धित एम्बेडिङमा रूपान्तरण गर्छौं, र त्यसपछि ती सबै एम्बेडिङहरूमा केही समग्र कार्य गणना गर्छौं, जस्तै `sum`, `average` वा `max`। \n", "\n", - "![पाँच अनुक्रम शब्दहरूको लागि एम्बेडिङ वर्गीकरण देखाउने छवि।](../../../../../translated_images/ne/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![पाँच अनुक्रम शब्दहरूको लागि एम्बेडिङ वर्गीकरण देखाउने छवि।](../../../../../translated_images/ne/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "हाम्रो वर्गीकरण न्युरल नेटवर्क निम्न लेयरहरू समावेश गर्दछ:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW छिटो हुन्छ, जबकि स्किप-ग्राम ढिलो भए पनि यो दुर्लभ शब्दहरूको प्रतिनिधित्व गर्न राम्रो काम गर्छ।\n", "\n", - "![CBoW र स्किप-ग्राम एल्गोरिदमहरूलाई शब्दहरूलाई भेक्टरमा रूपान्तरण गर्न देखाउने छवि।](../../../../../translated_images/ne/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![CBoW र स्किप-ग्राम एल्गोरिदमहरूलाई शब्दहरूलाई भेक्टरमा रूपान्तरण गर्न देखाउने छवि।](../../../../../translated_images/ne/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Google News डेटासेटमा प्रि-ट्रेन गरिएको Word2Vec एम्बेडिङसँग प्रयोग गर्न, हामी **gensim** लाइब्रेरी प्रयोग गर्न सक्छौं। तल हामी 'neural' शब्दसँग सबैभन्दा मिल्दोजुल्दो शब्दहरू फेला पार्छौं।\n", "\n", diff --git a/translations/ne/lessons/5-NLP/14-Embeddings/README.md b/translations/ne/lessons/5-NLP/14-Embeddings/README.md index d7aba910..db514579 100644 --- a/translations/ne/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/ne/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: हाम्रो वर्गीकरणकर्ता नेटवर्कमा पहिलो लेयरको रूपमा एम्बेडिङ लेयर प्रयोग गरेर, हामी बाग-ऑफ-वर्ड्सबाट **embedding bag** मोडेलमा स्विच गर्न सक्छौं, जहाँ हामी हाम्रो पाठमा प्रत्येक शब्दलाई सम्बन्धित एम्बेडिङमा रूपान्तरण गर्छौं, र त्यसपछि ती सबै एम्बेडिङ्समा केही समग्र कार्य जस्तै `sum`, `average` वा `max` गणना गर्छौं। -![पाँच अनुक्रम शब्दहरूको लागि एम्बेडिङ वर्गीकरणकर्ताको छवि।](../../../../../translated_images/ne/embedding-classifier-example.b77f021a7ee67eee.png) +![पाँच अनुक्रम शब्दहरूको लागि एम्बेडिङ वर्गीकरणकर्ताको छवि।](../../../../../translated_images/ne/embedding-classifier-example.b77f021a7ee67eee.webp) > लेखकद्वारा प्रदान गरिएको छवि @@ -40,7 +40,7 @@ CO_OP_TRANSLATOR_METADATA: CBoW छिटो छ, जबकि स्किप-ग्राम ढिलो छ, तर दुर्लभ शब्दहरूको प्रतिनिधित्व गर्न राम्रो काम गर्छ। -![शब्दहरूलाई भेक्टरमा रूपान्तरण गर्न CBoW र स्किप-ग्राम एल्गोरिदमहरूको छवि।](../../../../../translated_images/ne/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![शब्दहरूलाई भेक्टरमा रूपान्तरण गर्न CBoW र स्किप-ग्राम एल्गोरिदमहरूको छवि।](../../../../../translated_images/ne/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > [यस पेपर](https://arxiv.org/pdf/1301.3781.pdf) बाट लिइएको छवि diff --git a/translations/ne/lessons/5-NLP/15-LanguageModeling/README.md b/translations/ne/lessons/5-NLP/15-LanguageModeling/README.md index 4cee2eca..ab64d189 100644 --- a/translations/ne/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/ne/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ Word2Vec र GloVe जस्ता सेम्यान्टिक एम् * **Continuous Bag-of-Words** (CBoW), जहाँ हामी टोकन अनुक्रम $W_{-N}$, ..., $W_N$ को बीचको टोकन $W_0$ भविष्यवाणी गर्छौं। * **Skip-gram**, जहाँ हामी बीचको टोकन $W_0$ बाट छेउछाउका टोकनहरूको सेट {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} भविष्यवाणी गर्छौं। -![शब्दहरूलाई भेक्टरमा रूपान्तरण गर्ने एल्गोरिदमको कागजबाट चित्र](../../../../../translated_images/ne/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![शब्दहरूलाई भेक्टरमा रूपान्तरण गर्ने एल्गोरिदमको कागजबाट चित्र](../../../../../translated_images/ne/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > चित्र [यस कागज](https://arxiv.org/pdf/1301.3781.pdf) बाट diff --git a/translations/ne/lessons/5-NLP/16-RNN/README.md b/translations/ne/lessons/5-NLP/16-RNN/README.md index c61b0524..cdd7d851 100644 --- a/translations/ne/lessons/5-NLP/16-RNN/README.md +++ b/translations/ne/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: पाठ अनुक्रमको अर्थ समेट्नका लागि, हामीले अर्को न्यूरल नेटवर्क वास्तुकला प्रयोग गर्नुपर्छ, जसलाई **पुनरावर्ती न्यूरल नेटवर्क** (RNN) भनिन्छ। RNN मा, हामी हाम्रो वाक्यलाई नेटवर्कमार्फत एक पटकमा एउटा प्रतीक पठाउँछौं, र नेटवर्कले केही **स्थिति** उत्पादन गर्छ, जसलाई हामी अर्को प्रतीकसँग पुनः नेटवर्कमा पठाउँछौं। -![RNN](../../../../../translated_images/ne/rnn.27f5c29c53d727b5.png) +![RNN](../../../../../translated_images/ne/rnn.27f5c29c53d727b5.webp) > लेखकद्वारा तयार गरिएको छवि @@ -61,7 +61,7 @@ LSTM नेटवर्क RNN जस्तै व्यवस्थित छ, पुनरावर्ती नेटवर्क, चाहे एक-दिशात्मक होस् वा द्विदिशात्मक, अनुक्रमभित्रका निश्चित ढाँचाहरू समात्छ, र तिनीहरूलाई स्थिति भेक्टरमा भण्डारण गर्न वा आउटपुटमा पास गर्न सक्छ। कन्भोल्युसनल नेटवर्कहरूको जस्तै, हामी पहिलो तहले निकालेका तल्लो-स्तरका ढाँचाहरूबाट उच्च-स्तरका ढाँचाहरू समात्न अर्को पुनरावर्ती तह निर्माण गर्न सक्छौं। यसले हामीलाई **बहु-तह RNN** को अवधारणामा पुर्‍याउँछ, जसमा दुई वा बढी पुनरावर्ती नेटवर्कहरू हुन्छन्, जहाँ अघिल्लो तहको आउटपुटलाई अर्को तहमा इनपुटको रूपमा पास गरिन्छ। -![बहु-तह लामो-छोटो-समय-स्मृति RNN देखाउने छवि](../../../../../translated_images/ne/multi-layer-lstm.dd975e29bb2a59fe.jpg) +![बहु-तह लामो-छोटो-समय-स्मृति RNN देखाउने छवि](../../../../../translated_images/ne/multi-layer-lstm.dd975e29bb2a59fe.webp) *फर्नान्डो लोपेजको [यो अद्भुत पोस्ट](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) बाट लिइएको चित्र* diff --git a/translations/ne/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/ne/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index 21b30539..4f125807 100644 --- a/translations/ne/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/ne/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "पुनरावर्ती नेटवर्क, एक-दिशात्मक होस् वा द्विदिशात्मक, अनुक्रमभित्रका केही ढाँचाहरू समात्छ, र तिनीहरूलाई अवस्था भेक्टरमा भण्डारण गर्न वा आउटपुटमा पास गर्न सक्छ। जस्तै कनभोल्युसनल नेटवर्कहरूमा, हामी पहिलो तहले निकालेका तल्लो-स्तरीय ढाँचाहरूबाट उच्च-स्तरीय ढाँचाहरू समात्न अर्को पुनरावर्ती तह माथि निर्माण गर्न सक्छौं। यसले हामीलाई **बहु-स्तरीय RNN** को अवधारणामा पुर्‍याउँछ, जसमा दुई वा बढी पुनरावर्ती नेटवर्कहरू हुन्छन्, जहाँ अघिल्लो तहको आउटपुटलाई अर्को तहमा इनपुटको रूपमा पास गरिन्छ।\n", "\n", - "![बहु-स्तरीय लामो-छोटो-अवधि-स्मृति RNN देखाउने चित्र](../../../../../translated_images/ne/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![बहु-स्तरीय लामो-छोटो-अवधि-स्मृति RNN देखाउने चित्र](../../../../../translated_images/ne/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*फर्नान्डो लोपेजको [यो उत्कृष्ट पोस्ट](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) बाट चित्र*\n", "\n", diff --git a/translations/ne/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/ne/lessons/5-NLP/16-RNN/RNNTF.ipynb index e0695a78..a4c87c63 100644 --- a/translations/ne/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/ne/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "पाठ अनुक्रमको अर्थ समेट्न, हामी **पुनरावर्ती न्युरल नेटवर्क** भनिने न्युरल नेटवर्क आर्किटेक्चर प्रयोग गर्नेछौं। RNN प्रयोग गर्दा, हामी हाम्रो वाक्यलाई नेटवर्कमा एक पटकमा एक टोकन पास गर्छौं, र नेटवर्कले केही **स्थिति** उत्पादन गर्छ, जुन हामी अर्को टोकनसँग फेरि नेटवर्कमा पास गर्छौं।\n", "\n", - "![पुनरावर्ती न्युरल नेटवर्क उत्पादनको उदाहरण देखाउने चित्र।](../../../../../translated_images/ne/rnn.27f5c29c53d727b5.png)\n", + "![पुनरावर्ती न्युरल नेटवर्क उत्पादनको उदाहरण देखाउने चित्र।](../../../../../translated_images/ne/rnn.27f5c29c53d727b5.webp)\n", "\n", "टोकनहरूको इनपुट अनुक्रम $X_0,\\dots,X_n$ दिइएको अवस्थामा, RNN ले न्युरल नेटवर्क ब्लकहरूको अनुक्रम सिर्जना गर्छ, र यो अनुक्रमलाई ब्याकप्रोपोगेसन प्रयोग गरेर अन्त-देखि-अन्तसम्म प्रशिक्षण गर्छ। प्रत्येक नेटवर्क ब्लकले $(X_i,S_i)$ जोडीलाई इनपुटको रूपमा लिन्छ, र परिणामस्वरूप $S_{i+1}$ उत्पादन गर्छ। अन्तिम स्थिति $S_n$ वा आउटपुट $Y_n$ लाई रैखिक वर्गीकरणकर्तामा पठाइन्छ ताकि परिणाम उत्पादन गर्न सकियोस्। सबै नेटवर्क ब्लकहरूले समान तौलहरू साझा गर्छन्, र एक ब्याकप्रोपोगेसन पास प्रयोग गरेर अन्त-देखि-अन्तसम्म प्रशिक्षण गरिन्छ।\n", "\n", @@ -369,7 +369,7 @@ "\n", "पुनरावर्ती नेटवर्कहरूले, एकदिशात्मक होस् वा द्विदिशात्मक, अनुक्रमभित्रका ढाँचाहरूलाई समात्छन्, र तिनीहरूलाई अवस्था भेक्टरहरूमा भण्डारण गर्छन् वा तिनीहरूलाई आउटपुटको रूपमा फिर्ता दिन्छन्। कन्भोल्युसनल नेटवर्कहरूको जस्तै, हामी पहिलो तहले निकालेका तल्लो स्तरका ढाँचाहरूबाट उच्च स्तरका ढाँचाहरू समात्न अर्को पुनरावर्ती तह निर्माण गर्न सक्छौं। यसले हामीलाई **बहु-स्तरीय RNN** को अवधारणामा पुर्‍याउँछ, जसमा दुई वा बढी पुनरावर्ती नेटवर्कहरू हुन्छन्, जहाँ अघिल्लो तहको आउटपुटलाई अर्को तहमा इनपुटको रूपमा पठाइन्छ।\n", "\n", - "![बहु-स्तरीय लामो-छोटो-अवधि-स्मृति RNN देखाउने चित्र](../../../../../translated_images/ne/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![बहु-स्तरीय लामो-छोटो-अवधि-स्मृति RNN देखाउने चित्र](../../../../../translated_images/ne/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*फर्नान्डो लोपेजको [यो उत्कृष्ट पोस्ट](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) बाट लिइएको चित्र।*\n", "\n", diff --git a/translations/ne/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/ne/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index cf955d99..5f356c15 100644 --- a/translations/ne/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/ne/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "हामी RNN लाई पाठ उत्पन्न गर्न प्रशिक्षण दिने तरिका निम्नानुसार हुनेछ। प्रत्येक चरणमा, हामी `nchars` लम्बाइको अक्षरहरूको श्रृंखला लिनेछौं, र प्रत्येक इनपुट अक्षरको लागि नेटवर्कलाई अर्को आउटपुट अक्षर उत्पन्न गर्न अनुरोध गर्नेछौं:\n", "\n", - "![RNN ले 'HELLO' शब्द उत्पन्न गरिरहेको उदाहरण देखाउने छवि।](../../../../../translated_images/ne/rnn-generate.56c54afb52f9781d.png)\n", + "![RNN ले 'HELLO' शब्द उत्पन्न गरिरहेको उदाहरण देखाउने छवि।](../../../../../translated_images/ne/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "वास्तविक परिदृश्यमा निर्भर गर्दै, हामीले केही विशेष अक्षरहरू पनि समावेश गर्न चाहन सक्छौं, जस्तै *end-of-sequence* ``। हाम्रो अवस्थामा, हामी केवल नेटवर्कलाई अन्तहीन पाठ उत्पन्न गर्न प्रशिक्षण दिन चाहन्छौं, त्यसैले हामी प्रत्येक श्रृंखलाको आकारलाई `nchars` टोकनहरूको बराबरमा स्थिर गर्नेछौं। तदनुसार, प्रत्येक प्रशिक्षण उदाहरण `nchars` इनपुटहरू र `nchars` आउटपुटहरू (जसले इनपुट श्रृंखलालाई एक प्रतीक बायाँ सिफ्ट गरेको हुन्छ) बाट बनेको हुनेछ। मिनिब्याचमा यस्ता धेरै श्रृंखलाहरू हुनेछन्।\n", "\n", diff --git a/translations/ne/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/ne/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index f60c954e..b2e2a9f5 100644 --- a/translations/ne/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/ne/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -111,7 +111,7 @@ "\n", "हामीले RNN लाई समाचार शीर्षकहरू उत्पन्न गर्न प्रशिक्षण गर्ने तरिका यस प्रकार हुनेछ। प्रत्येक चरणमा, हामी एउटा शीर्षक लिनेछौं, जसलाई RNN मा खुवाइनेछ, र प्रत्येक इनपुट अक्षरको लागि, हामी नेटवर्कलाई अर्को आउटपुट अक्षर उत्पन्न गर्न अनुरोध गर्नेछौं:\n", "\n", - "![शब्द 'HELLO' को RNN द्वारा उत्पन्न गर्ने उदाहरण देखाउने छवि।](../../../../../translated_images/ne/rnn-generate.56c54afb52f9781d.png)\n", + "![शब्द 'HELLO' को RNN द्वारा उत्पन्न गर्ने उदाहरण देखाउने छवि।](../../../../../translated_images/ne/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "हाम्रो अनुक्रमको अन्तिम अक्षरको लागि, हामी नेटवर्कलाई `` टोकन उत्पन्न गर्न अनुरोध गर्नेछौं।\n", "\n", diff --git a/translations/ne/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/ne/lessons/5-NLP/17-GenerativeNetworks/README.md index c18b2875..d7aab15c 100644 --- a/translations/ne/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/ne/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ Recurrent Neural Networks (RNNs) र तिनका गेटेड सेल यसले विभिन्न न्युरल आर्किटेक्चरहरूलाई अनुमति दिन्छ जुन तलको चित्रमा देखाइएको छ: -![सामान्य पुनरावर्ती न्युरल नेटवर्क ढाँचाहरू देखाउने चित्र।](../../../../../translated_images/ne/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![सामान्य पुनरावर्ती न्युरल नेटवर्क ढाँचाहरू देखाउने चित्र।](../../../../../translated_images/ne/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > [Andrej Karpaty](http://karpathy.github.io/) द्वारा [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) ब्लग पोस्टबाट चित्र @@ -32,7 +32,7 @@ Recurrent Neural Networks (RNNs) र तिनका गेटेड सेल हामीले यो RNN लाई चरण-दर-चरण पाठ उत्पन्न गर्न तालिम दिनेछौं। प्रत्येक चरणमा, हामी `nchars` लम्बाइको क्यारेक्टरहरूको अनुक्रम लिनेछौं, र नेटवर्कलाई प्रत्येक इनपुट क्यारेक्टरको लागि अर्को आउटपुट क्यारेक्टर उत्पन्न गर्न सोध्नेछौं: -![शब्द 'HELLO' को RNN उत्पादनको उदाहरण देखाउने चित्र।](../../../../../translated_images/ne/rnn-generate.56c54afb52f9781d.png) +![शब्द 'HELLO' को RNN उत्पादनको उदाहरण देखाउने चित्र।](../../../../../translated_images/ne/rnn-generate.56c54afb52f9781d.webp) पाठ उत्पन्न गर्दा (इनफरेन्सको समयमा), हामी केही **प्रम्प्ट** बाट सुरु गर्छौं, जुन RNN सेलहरू मार्फत पास गरिन्छ यसको मध्यवर्ती अवस्था उत्पन्न गर्न, र त्यसपछि यो अवस्थाबाट उत्पादन सुरु हुन्छ। हामी एक पटकमा एक क्यारेक्टर उत्पन्न गर्छौं, र अर्को RNN सेलमा अवस्था र उत्पन्न क्यारेक्टर पास गर्छौं अर्को क्यारेक्टर उत्पन्न गर्न, जबसम्म हामी पर्याप्त क्यारेक्टरहरू उत्पन्न गर्दैनौं। diff --git a/translations/ne/lessons/5-NLP/18-Transformers/README.md b/translations/ne/lessons/5-NLP/18-Transformers/README.md index a20335b2..b496e1c0 100644 --- a/translations/ne/lessons/5-NLP/18-Transformers/README.md +++ b/translations/ne/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ RNNs प्रयोग गर्दा, sequence-to-sequence दुई पु **ध्यान मेकानिज्महरू** प्रत्येक इनपुट भेक्टरको सन्दर्भात्मक प्रभावलाई प्रत्येक RNN को आउटपुट भविष्यवाणीमा तौल दिने माध्यम प्रदान गर्छ। यसलाई कार्यान्वयन गर्ने तरिका भनेको इनपुट RNN र आउटपुट RNN का बीचमा छोटो मार्गहरू सिर्जना गर्नु हो। यस प्रकार, आउटपुट प्रतीक yt उत्पन्न गर्दा, हामी सबै इनपुट लुकाइएको अवस्थाहरू hi लाई विभिन्न तौल गुणांकहरू αt,i सहित विचार गर्नेछौं। -![Image showing an encoder/decoder model with an additive attention layer](../../../../../translated_images/ne/encoder-decoder-attention.7a726296894fb567.png) +![Image showing an encoder/decoder model with an additive attention layer](../../../../../translated_images/ne/encoder-decoder-attention.7a726296894fb567.webp) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) मा additive attention मेकानिज्मसहितको encoder-decoder मोडेल, [यो ब्लग पोस्ट](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) बाट उद्धृत। ध्यान म्याट्रिक्स {αi,j} ले इनपुट अनुक्रमका निश्चित शब्दहरूले आउटपुट अनुक्रमको कुनै शब्दको उत्पत्तिमा कति भूमिका खेल्छन् भन्ने प्रतिनिधित्व गर्दछ। तल यस्तो म्याट्रिक्सको उदाहरण छ: -![Image showing a sample alignment found by RNNsearch-50, taken from Bahdanau - arviz.org](../../../../../translated_images/ne/bahdanau-fig3.09ba2d37f202a6af.png) +![Image showing a sample alignment found by RNNsearch-50, taken from Bahdanau - arviz.org](../../../../../translated_images/ne/bahdanau-fig3.09ba2d37f202a6af.webp) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) बाट चित्र (Fig.3) @@ -66,7 +66,7 @@ Positional embedding को परिणामले मूल टोकन र अब, हामीले हाम्रो अनुक्रमभित्र केही ढाँचाहरू कब्जा गर्न आवश्यक छ। यो गर्न ट्रान्सफर्मरहरूले **self-attention** मेकानिज्म प्रयोग गर्छन्, जुन इनपुट र आउटपुटको रूपमा समान अनुक्रममा लागू गरिएको ध्यान हो। Self-attention लागू गर्दा हामी वाक्यभित्रको **सन्दर्भ**लाई विचार गर्न सक्छौं, र कुन शब्दहरू परस्पर सम्बन्धित छन् हेर्न सक्छौं। उदाहरणका लागि, यसले *it* जस्ता coreferences द्वारा उल्लेख गरिएका शब्दहरू हेर्न र सन्दर्भलाई विचार गर्न अनुमति दिन्छ: -![](../../../../../translated_images/ne/CoreferenceResolution.861924d6d384a7d6.png) +![](../../../../../translated_images/ne/CoreferenceResolution.861924d6d384a7d6.webp) > [Google Blog](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) बाट चित्र। @@ -91,7 +91,7 @@ Encoder-decoder attention RNNs मा प्रयोग गरिएको ध **BERT** (Bidirectional Encoder Representations from Transformers) एक धेरै ठूलो बहु-लेयर ट्रान्सफर्मर नेटवर्क हो, *BERT-base* को लागि 12 लेयरहरू, र *BERT-large* को लागि 24। मोडेललाई पहिलो पटक ठूलो पाठ डाटाको संग्रह (WikiPedia + किताबहरू) मा unsupervised प्रशिक्षण (वाक्यमा masked शब्दहरूको भविष्यवाणी गर्दै) प्रयोग गरेर प्रि-ट्रेन गरिन्छ। प्रि-ट्रेनिङको क्रममा मोडेलले भाषा बुझ्ने महत्त्वपूर्ण स्तरहरू अवशोषित गर्छ, जसलाई अन्य डाटासेटहरूसँग fine tuning गरेर उपयोग गर्न सकिन्छ। यस प्रक्रियालाई **transfer learning** भनिन्छ। -![picture from http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/ne/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) +![picture from http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/ne/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > [स्रोत](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/ne/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/ne/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index decc2b06..e582f1bb 100644 --- a/translations/ne/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/ne/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**ध्यान केन्द्रित गर्ने प्रणालीहरू** (Attention Mechanisms) ले प्रत्येक इनपुट भेक्टरको सन्दर्भात्मक प्रभावलाई RNN को प्रत्येक आउटपुट भविष्यवाणीमा तौल दिने माध्यम प्रदान गर्छ। यसलाई कार्यान्वयन गर्ने तरिका भनेको इनपुट RNN का मध्यवर्ती अवस्थाहरू र आउटपुट RNN बीच छोटो मार्गहरू सिर्जना गर्नु हो। यसरी, आउटपुट प्रतीक $y_t$ उत्पन्न गर्दा, हामी सबै इनपुट लुकेका अवस्थाहरू $h_i$ लाई विभिन्न तौल गुणांक $\\alpha_{t,i}$ का साथ विचार गर्नेछौं।\n", "\n", - "![एन्कोडर/डिकोडर मोडेलमा एडिटिभ ध्यान तह देखाउने छवि](../../../../../translated_images/ne/encoder-decoder-attention.7a726296894fb567.png)\n", + "![एन्कोडर/डिकोडर मोडेलमा एडिटिभ ध्यान तह देखाउने छवि](../../../../../translated_images/ne/encoder-decoder-attention.7a726296894fb567.webp)\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) मा एडिटिभ ध्यान प्रणाली भएको एन्कोडर-डिकोडर मोडेल, [यो ब्लग पोस्ट](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) बाट उद्धृत]*\n", "\n", "ध्यान म्याट्रिक्स $\\{\\alpha_{i,j}\\}$ ले इनपुटका निश्चित शब्दहरूले आउटपुट अनुक्रमको कुनै शब्द उत्पन्न गर्न कत्तिको भूमिका खेल्छन् भन्ने प्रतिनिधित्व गर्दछ। तल यस्तो म्याट्रिक्सको उदाहरण दिइएको छ:\n", "\n", - "![RNNsearch-50 द्वारा फेला परेको नमूना संरेखण देखाउने छवि, Bahdanau - arviz.org बाट लिइएको](../../../../../translated_images/ne/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![RNNsearch-50 द्वारा फेला परेको नमूना संरेखण देखाउने छवि, Bahdanau - arviz.org बाट लिइएको](../../../../../translated_images/ne/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) बाट लिइएको चित्र (Fig.3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) एक धेरै ठूलो बहु-तह ट्रान्सफर्मर नेटवर्क हो, जसमा *BERT-base* का लागि १२ तहहरू र *BERT-large* का लागि २४ तहहरू छन्। मोडेललाई पहिलो पटक ठूलो पाठ डाटाको कर्पस (WikiPedia + पुस्तकहरू) मा असुपरभाइज्ड प्रशिक्षण (वाक्यमा लुकेका शब्दहरूको भविष्यवाणी) प्रयोग गरेर पूर्व-प्रशिक्षण गरिन्छ। पूर्व-प्रशिक्षणको क्रममा मोडेलले महत्त्वपूर्ण स्तरको भाषा बुझाइ प्राप्त गर्छ, जसलाई अन्य डाटासेटहरूसँग फाइन ट्युनिङ गरेर उपयोग गर्न सकिन्छ। यस प्रक्रियालाई **ट्रान्सफर लर्निङ** भनिन्छ।\n", "\n", - "![http://jalammar.github.io/illustrated-bert/ बाट चित्र](../../../../../translated_images/ne/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![http://jalammar.github.io/illustrated-bert/ बाट चित्र](../../../../../translated_images/ne/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "BERT, DistilBERT, BigBird, OpenGPT3 जस्ता ट्रान्सफर्मर आर्किटेक्चरका धेरै भेरिएसनहरू छन्, जसलाई फाइन ट्युन गर्न सकिन्छ। [HuggingFace package](https://github.com/huggingface/) ले यी आर्किटेक्चरहरूलाई PyTorch का साथ प्रशिक्षण गर्नका लागि रिपोजिटरी प्रदान गर्दछ।\n", "\n", diff --git a/translations/ne/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/ne/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index 593ed455..9c924154 100644 --- a/translations/ne/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/ne/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**ध्यान मेकानिज्महरू** RNN को प्रत्येक आउटपुट भविष्यवाणीमा प्रत्येक इनपुट भेक्टरको सन्दर्भात्मक प्रभावलाई तौल दिने माध्यम प्रदान गर्छन्। यसलाई कार्यान्वयन गर्ने तरिका भनेको इनपुट RNN का मध्यवर्ती अवस्थाहरू र आउटपुट RNN बीच छोटो मार्गहरू सिर्जना गर्नु हो। यसरी, आउटपुट प्रतीक $y_t$ उत्पन्न गर्दा, हामी सबै इनपुट लुकेका अवस्थाहरू $h_i$ लाई विभिन्न तौल गुणांक $\\alpha_{t,i}$ का साथ विचार गर्नेछौं।\n", "\n", - "![एन्कोडर/डिकोडर मोडेलमा एडिटिभ ध्यान तह देखाउने छवि](../../../../../translated_images/ne/encoder-decoder-attention.7a726296894fb567.png)\n", + "![एन्कोडर/डिकोडर मोडेलमा एडिटिभ ध्यान तह देखाउने छवि](../../../../../translated_images/ne/encoder-decoder-attention.7a726296894fb567.webp)\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) मा एडिटिभ ध्यान मेकानिज्म भएको एन्कोडर-डिकोडर मोडेल, [यो ब्लग पोस्ट](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) बाट उद्धृत]*\n", "\n", "ध्यान म्याट्रिक्स $\\{\\alpha_{i,j}\\}$ ले इनपुटका निश्चित शब्दहरूले आउटपुट अनुक्रमको कुनै शब्द उत्पन्न गर्न कत्तिको भूमिका खेल्छन् भन्ने प्रतिनिधित्व गर्दछ। तल यस्तो म्याट्रिक्सको उदाहरण दिइएको छ:\n", "\n", - "![RNNsearch-50 द्वारा फेला परेको नमूना एलाइनमेन्ट देखाउने छवि, Bahdanau - arviz.org बाट लिइएको](../../../../../translated_images/ne/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![RNNsearch-50 द्वारा फेला परेको नमूना एलाइनमेन्ट देखाउने छवि, Bahdanau - arviz.org बाट लिइएको](../../../../../translated_images/ne/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) बाट लिइएको चित्र (Fig.3)*\n", "\n", @@ -225,7 +225,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) एक धेरै ठूलो बहु-स्तरीय ट्रान्सफर्मर नेटवर्क हो जसमा *BERT-base* का लागि १२ तहहरू र *BERT-large* का लागि २४ तहहरू छन्। यो मोडेललाई पहिलो चरणमा ठूलो पाठ डाटाको संग्रह (WikiPedia + किताबहरू) मा असुपरभाइज्ड तालिम (वाक्यमा लुकेका शब्दहरूको भविष्यवाणी गर्दै) प्रयोग गरेर प्रि-ट्रेन गरिन्छ। प्रि-ट्रेनिङको क्रममा मोडेलले भाषाको गहिरो समझ हासिल गर्छ, जसलाई अन्य डाटासेटहरूसँग फाइन ट्युनिङ गरेर उपयोग गर्न सकिन्छ। यस प्रक्रियालाई **ट्रान्सफर लर्निङ** भनिन्छ।\n", "\n", - "![picture from http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/ne/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![picture from http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/ne/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "BERT, DistilBERT, BigBird, OpenGPT3 लगायतका ट्रान्सफर्मर आर्किटेक्चरका धेरै भेरिएसनहरू छन् जसलाई फाइन ट्युन गर्न सकिन्छ।\n", "\n", diff --git a/translations/ne/lessons/5-NLP/19-NER/README.md b/translations/ne/lessons/5-NLP/19-NER/README.md index 792c8881..b175300f 100644 --- a/translations/ne/lessons/5-NLP/19-NER/README.md +++ b/translations/ne/lessons/5-NLP/19-NER/README.md @@ -58,7 +58,7 @@ infant | O किनकि हामीले टोकनहरू र वर्गहरू बीच एक-देखि-एक सम्बन्ध निर्माण गर्नुपर्छ, हामी यस चित्रबाट सही **many-to-many** न्यूरल नेटवर्क मोडेल प्रशिक्षण गर्न सक्छौं: -![सामान्य पुनरावर्ती न्यूरल नेटवर्क ढाँचाहरू देखाउने छवि।](../../../../../translated_images/ne/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![सामान्य पुनरावर्ती न्यूरल नेटवर्क ढाँचाहरू देखाउने छवि।](../../../../../translated_images/ne/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > *[Andrej Karpathy](http://karpathy.github.io/) द्वारा [यो ब्लग पोस्ट](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) बाट छवि। NER टोकन वर्गीकरण मोडेलहरू यस चित्रको दायाँ-तर्फको नेटवर्क आर्किटेक्चरसँग मेल खान्छ।* diff --git a/translations/ne/lessons/5-NLP/README.md b/translations/ne/lessons/5-NLP/README.md index efc23b7a..5a4d3533 100644 --- a/translations/ne/lessons/5-NLP/README.md +++ b/translations/ne/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # प्राकृतिक भाषा प्रशोधन -![NLP कार्यहरूको सारांश](../../../../translated_images/ne/ai-nlp.b22dcb8ca4707cea.png) +![NLP कार्यहरूको सारांश](../../../../translated_images/ne/ai-nlp.b22dcb8ca4707cea.webp) यस खण्डमा, हामी **प्राकृतिक भाषा प्रशोधन (NLP)** सम्बन्धित कार्यहरू समाधान गर्न न्युरल नेटवर्कहरू प्रयोग गर्ने कुरामा ध्यान केन्द्रित गर्नेछौं। कम्प्युटरले समाधान गर्नुपर्ने धेरै NLP समस्याहरू छन्: diff --git a/translations/ne/lessons/6-Other/23-MultiagentSystems/README.md b/translations/ne/lessons/6-Other/23-MultiagentSystems/README.md index d8705553..ffacea2c 100644 --- a/translations/ne/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/ne/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ NetLogo को एक उत्कृष्ट पक्ष यो हो कि मोडेल खोलिसकेपछि, तपाईंलाई मुख्य NetLogo स्क्रिनमा लगिन्छ। यहाँ सीमित स्रोतहरू (घाँस) दिइएको भेडा र ब्वाँसोको जनसंख्यालाई वर्णन गर्ने नमूना मोडेल छ। -![NetLogo Main Screen](../../../../../translated_images/ne/NetLogo-Main.32653711ec1a01b3.png) +![NetLogo Main Screen](../../../../../translated_images/ne/NetLogo-Main.32653711ec1a01b3.webp) > Dmitry Soshnikov द्वारा स्क्रिनशट diff --git a/translations/ne/lessons/README.md b/translations/ne/lessons/README.md index 0b49b1f5..86c24757 100644 --- a/translations/ne/lessons/README.md +++ b/translations/ne/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # अवलोकन -![डुडलमा अवलोकन](../../../translated_images/ne/ai-overview.0857791951d19500.png) +![डुडलमा अवलोकन](../../../translated_images/ne/ai-overview.0857791951d19500.webp) > स्केच नोट [Tomomi Imura](https://twitter.com/girlie_mac) द्वारा diff --git a/translations/ne/lessons/X-Extras/X1-MultiModal/README.md b/translations/ne/lessons/X-Extras/X1-MultiModal/README.md index 8c40bfa7..00aae04f 100644 --- a/translations/ne/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/ne/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ NLP कार्यहरू समाधान गर्न ट्रान् CLIP को मुख्य विचार भनेको टेक्स्ट प्रम्प्टहरूलाई तस्बिरसँग तुलना गर्न र तस्बिरले प्रम्प्टसँग कत्तिको मेल खान्छ भनेर निर्धारण गर्न सक्षम हुनु हो। -![CLIP आर्किटेक्चर](../../../../../translated_images/ne/clip-arch.b3dbf20b4e8ed8be.png) +![CLIP आर्किटेक्चर](../../../../../translated_images/ne/clip-arch.b3dbf20b4e8ed8be.webp) > *[यो ब्लग पोस्ट](https://openai.com/blog/clip/) बाट तस्बिर* @@ -31,7 +31,7 @@ CLIP मोडेल/लाइब्रेरी [OpenAI GitHub](https://github. मानौं हामीलाई तस्बिरहरूलाई बिरालो, कुकुर र मानिसहरू बीच वर्गीकरण गर्नुपर्छ। यस अवस्थामा, हामी मोडेललाई तस्बिर र टेक्स्ट प्रम्प्टहरूको श्रृंखला दिन सक्छौं: "*बिरालोको तस्बिर*", "*कुकुरको तस्बिर*", "*मानिसको तस्बिर*"। परिणामस्वरूप 3 सम्भावनाहरूको भेक्टरमा हामीले सबैभन्दा उच्च मान भएको इन्डेक्स चयन गर्नुपर्छ। -![इमेज क्लासिफिकेशनको लागि CLIP](../../../../../translated_images/ne/clip-class.3af42ef0b2b19369.png) +![इमेज क्लासिफिकेशनको लागि CLIP](../../../../../translated_images/ne/clip-class.3af42ef0b2b19369.webp) > *[यो ब्लग पोस्ट](https://openai.com/blog/clip/) बाट तस्बिर* @@ -55,13 +55,13 @@ VQGAN को बारेमा थप जान्न [Taming Transformers](htt VQGAN र पारम्परिक GAN बीचको महत्त्वपूर्ण भिन्नता भनेको पछिल्लोले कुनै पनि इनपुट भेक्टरबाट राम्रो तस्बिर उत्पादन गर्न सक्छ, जबकि VQGAN ले सुसंगत तस्बिर उत्पादन गर्न सक्दैन। त्यसैले, हामीले तस्बिर निर्माण प्रक्रियालाई थप मार्गदर्शन गर्न आवश्यक छ, र त्यो CLIP प्रयोग गरेर गर्न सकिन्छ। -![VQGAN+CLIP आर्किटेक्चर](../../../../../translated_images/ne/vqgan.5027fe05051dfa31.png) +![VQGAN+CLIP आर्किटेक्चर](../../../../../translated_images/ne/vqgan.5027fe05051dfa31.webp) टेक्स्ट प्रम्प्टसँग मेल खाने तस्बिर निर्माण गर्न, हामी केही र्यान्डम इन्कोडिङ भेक्टरबाट सुरु गर्छौं जुन VQGAN मार्फत पास गरिन्छ र तस्बिर उत्पादन गरिन्छ। त्यसपछि CLIP प्रयोग गरेर लस फङ्क्सन उत्पादन गरिन्छ जसले तस्बिर टेक्स्ट प्रम्प्टसँग कत्तिको मेल खान्छ भनेर देखाउँछ। त्यसपछि यो लसलाई न्यूनतम बनाउने लक्ष्य राखिन्छ, ब्याक प्रोपोगेसन प्रयोग गरेर इनपुट भेक्टर प्यारामिटरहरू समायोजन गरिन्छ। VQGAN+CLIP कार्यान्वयन गर्ने उत्कृष्ट लाइब्रेरी [Pixray](http://github.com/pixray/pixray) हो। -![Pixray द्वारा उत्पादन गरिएको तस्बिर](../../../../../translated_images/ne/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Pixray द्वारा उत्पादन गरिएको तस्बिर](../../../../../translated_images/ne/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Pixray द्वारा उत्पादन गरिएको तस्बिर](../../../../../translated_images/ne/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) +![Pixray द्वारा उत्पादन गरिएको तस्बिर](../../../../../translated_images/ne/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp) | ![Pixray द्वारा उत्पादन गरिएको तस्बिर](../../../../../translated_images/ne/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp) | ![Pixray द्वारा उत्पादन गरिएको तस्बिर](../../../../../translated_images/ne/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp) ----|----|---- प्रम्प्ट *साहित्यको युवा पुरुष शिक्षकको पुस्तकसहितको नजिकको वाटरकलर पोर्ट्रेट* बाट उत्पन्न तस्बिर | प्रम्प्ट *कम्प्युटर विज्ञानको युवा महिला शिक्षकको कम्प्युटरसहितको नजिकको तेल पोर्ट्रेट* बाट उत्पन्न तस्बिर | प्रम्प्ट *गणितको वृद्ध पुरुष शिक्षकको ब्ल्याकबोर्ड अगाडि नजिकको तेल पोर्ट्रेट* बाट उत्पन्न तस्बिर diff --git a/translations/nl/README.md b/translations/nl/README.md index a3a790dd..e7b1d633 100644 --- a/translations/nl/README.md +++ b/translations/nl/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # Kunstmatige Intelligentie voor Beginners - Een Curriculum -|![Sketchnote door @girlie_mac https://twitter.com/girlie_mac](../../translated_images/nl/ai-overview.0857791951d19500.png)| +|![Sketchnote door @girlie_mac https://twitter.com/girlie_mac](../../translated_images/nl/ai-overview.0857791951d19500.webp)| |:---:| | AI Voor Beginners - _Sketchnote door [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/nl/lessons/1-Intro/README.md b/translations/nl/lessons/1-Intro/README.md index e7e530d7..4bd59e32 100644 --- a/translations/nl/lessons/1-Intro/README.md +++ b/translations/nl/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Introductie tot AI -![Samenvatting van de inhoud van de introductie van AI in een schets](../../../../translated_images/nl/ai-intro.bf28d1ac4235881c.png) +![Samenvatting van de inhoud van de introductie van AI in een schets](../../../../translated_images/nl/ai-intro.bf28d1ac4235881c.webp) > Sketchnote door [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Oorspronkelijk werden computers uitgevonden door [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage) om met cijfers te werken volgens een goed gedefinieerde procedure - een algoritme. Moderne computers, hoewel aanzienlijk geavanceerder dan het oorspronkelijke model dat in de 19e eeuw werd voorgesteld, volgen nog steeds hetzelfde idee van gecontroleerde berekeningen. Het is dus mogelijk om een computer te programmeren om iets te doen als we de exacte reeks stappen kennen die nodig zijn om het doel te bereiken. -![Foto van een persoon](../../../../translated_images/nl/dsh_age.d212a30d4e54fb5f.png) +![Foto van een persoon](../../../../translated_images/nl/dsh_age.d212a30d4e54fb5f.webp) > Foto door [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ Voor meer informatie, zie **[Algemene Kunstmatige Intelligentie](https://en.wiki Een van de problemen bij het omgaan met de term **[Intelligentie](https://en.wikipedia.org/wiki/Intelligence)** is dat er geen duidelijke definitie van deze term is. Men kan stellen dat intelligentie verbonden is met **abstract denken**, of met **zelfbewustzijn**, maar we kunnen het niet goed definiëren. -![Foto van een kat](../../../../translated_images/nl/photo-cat.8c8e8fb760ffe457.jpg) +![Foto van een kat](../../../../translated_images/nl/photo-cat.8c8e8fb760ffe457.webp) > [Foto](https://unsplash.com/photos/75715CVEJhI) door [Amber Kipp](https://unsplash.com/@sadmax) van Unsplash @@ -98,13 +98,13 @@ Alternatief kunnen we proberen de eenvoudigste elementen in ons brein te modelle > | Wat betreft ML? | | > |--------------|-----------| -> | Een onderdeel van Kunstmatige Intelligentie dat gebaseerd is op het leren van een computer om een probleem op te lossen op basis van bepaalde data wordt **Machine Learning** genoemd. We zullen klassieke machine learning niet behandelen in deze cursus - we verwijzen je naar een aparte [Machine Learning voor Beginners](http://aka.ms/ml-beginners) curriculum. | ![ML voor Beginners](../../../../translated_images/nl/ml-for-beginners.9e4fed176fd5817d.png) | +> | Een onderdeel van Kunstmatige Intelligentie dat gebaseerd is op het leren van een computer om een probleem op te lossen op basis van bepaalde data wordt **Machine Learning** genoemd. We zullen klassieke machine learning niet behandelen in deze cursus - we verwijzen je naar een aparte [Machine Learning voor Beginners](http://aka.ms/ml-beginners) curriculum. | ![ML voor Beginners](../../../../translated_images/nl/ml-for-beginners.9e4fed176fd5817d.webp) | ## Een korte geschiedenis van AI Kunstmatige Intelligentie begon als een vakgebied in het midden van de twintigste eeuw. Aanvankelijk was symbolisch redeneren een veelgebruikte benadering, en dit leidde tot een aantal belangrijke successen, zoals expertsystemen – computerprogramma's die als expert konden optreden in enkele beperkte probleemdomeinen. Het werd echter al snel duidelijk dat deze benadering niet goed schaalbaar is. Het extraheren van kennis van een expert, het representeren ervan in een computer en het nauwkeurig houden van die kennisbank blijkt een zeer complexe taak te zijn, en te duur om in veel gevallen praktisch te zijn. Dit leidde tot de zogenaamde [AI Winter](https://en.wikipedia.org/wiki/AI_winter) in de jaren '70. -Korte geschiedenis van AI +Korte geschiedenis van AI > Afbeelding door [Dmitry Soshnikov](http://soshnikov.com) @@ -124,7 +124,7 @@ Evenzo kunnen we zien hoe de benadering van het creëren van "sprekende programm * Moderne assistenten, zoals Cortana, Siri of Google Assistant, zijn allemaal hybride systemen die neurale netwerken gebruiken om spraak om te zetten in tekst en onze intentie te herkennen, en vervolgens enige redenering of expliciete algoritmen gebruiken om de vereiste acties uit te voeren. * In de toekomst kunnen we een volledig neurale model verwachten dat zelf een dialoog kan afhandelen. De recente GPT- en [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) familie van neurale netwerken tonen hierin grote successen. -de evolutie van de Turing-test +de evolutie van de Turing-test > Afbeelding door Dmitry Soshnikov, [foto](https://unsplash.com/photos/r8LmVbUKgns) door [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto), Unsplash ## Recente AI-onderzoeken diff --git a/translations/nl/lessons/2-Symbolic/Animals.ipynb b/translations/nl/lessons/2-Symbolic/Animals.ipynb index 6cbd7621..f1021abe 100644 --- a/translations/nl/lessons/2-Symbolic/Animals.ipynb +++ b/translations/nl/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "In dit voorbeeld implementeren we een eenvoudig kennisgebaseerd systeem om een dier te bepalen op basis van enkele fysieke kenmerken. Het systeem kan worden weergegeven door de volgende AND-OR-boom (dit is een deel van de volledige boom, we kunnen eenvoudig meer regels toevoegen):\n", "\n", - "![](../../../../translated_images/nl/AND-OR-Tree.5592d2c70187f283.png)\n" + "![](../../../../translated_images/nl/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { diff --git a/translations/nl/lessons/2-Symbolic/README.md b/translations/nl/lessons/2-Symbolic/README.md index 781a0d29..eee102f4 100644 --- a/translations/nl/lessons/2-Symbolic/README.md +++ b/translations/nl/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Kennisrepresentatie en Expertsystemen -![Samenvatting van Symbolische AI-inhoud](../../../../translated_images/nl/ai-symbolic.715a30cb610411a6.png) +![Samenvatting van Symbolische AI-inhoud](../../../../translated_images/nl/ai-symbolic.715a30cb610411a6.webp) > Sketchnote door [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ Meestal definiëren we kennis niet strikt, maar brengen we het in lijn met ander Het probleem van **kennisrepresentatie** is dus om een effectieve manier te vinden om kennis binnen een computer in de vorm van data te representeren, zodat het automatisch bruikbaar is. Dit kan worden gezien als een spectrum: -![Spectrum van kennisrepresentatie](../../../../translated_images/nl/knowledge-spectrum.b60df631852c0217.png) +![Spectrum van kennisrepresentatie](../../../../translated_images/nl/knowledge-spectrum.b60df631852c0217.webp) > Afbeelding door [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Blok-syntaxis | Inspringing | | | Een van de vroege successen van symbolische AI waren de zogenaamde **expertsystemen** - computersystemen die waren ontworpen om als expert te functioneren in een beperkt probleemgebied. Ze waren gebaseerd op een **kennisbasis** die was geëxtraheerd van een of meer menselijke experts, en ze bevatten een **inferentie-engine** die enige redenering uitvoerde bovenop deze basis. -![Menselijke Architectuur](../../../../translated_images/nl/arch-human.5d4d35f1bba3ab1c.png) | ![Kennisgebaseerd Systeem](../../../../translated_images/nl/arch-kbs.3ec5c150b09fa8da.png) +![Menselijke Architectuur](../../../../translated_images/nl/arch-human.5d4d35f1bba3ab1c.webp) | ![Kennisgebaseerd Systeem](../../../../translated_images/nl/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ Vereenvoudigde structuur van een menselijk neuraal systeem | Architectuur van een kennisgebaseerd systeem @@ -106,7 +106,7 @@ Expertsystemen zijn gebouwd zoals het menselijke redeneersysteem, dat **korteter Als voorbeeld bekijken we het volgende expertsysteem om een dier te bepalen op basis van zijn fysieke kenmerken: -![AND-OR Boom](../../../../translated_images/nl/AND-OR-Tree.5592d2c70187f283.png) +![AND-OR Boom](../../../../translated_images/nl/AND-OR-Tree.5592d2c70187f283.webp) > Afbeelding door [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/nl/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/nl/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index 8b50aeb2..b71c49e5 100644 --- a/translations/nl/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/nl/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -1259,7 +1259,7 @@ "* Lage trainingsfout - het model kan trainingsdata goed benaderen, omdat het voldoende expressieve kracht heeft.\n", "* Validatiefout kan veel hoger zijn dan de trainingsfout en kan tijdens het trainen beginnen te stijgen - dit komt doordat het model de trainingspunten \"onthoudt\" en het \"overzicht\" verliest.\n", "\n", - "![Overfitting](../../../../../translated_images/nl/overfit.a0bd57f717c15769.png)\n", + "![Overfitting](../../../../../translated_images/nl/overfit.a0bd57f717c15769.webp)\n", "\n", "> Op deze afbeelding staat `x` voor trainingsdata, `o` voor validatiedata. Links - lineair model (één laag), het benadert de aard van de data redelijk goed. Rechts - overfitted model, het model benadert de trainingsdata perfect, maar verliest betekenis bij andere data (validatiefout is erg hoog).\n" ] diff --git a/translations/nl/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/nl/lessons/3-NeuralNetworks/05-Frameworks/README.md index 58c79ecc..daac5b04 100644 --- a/translations/nl/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/nl/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Overfitting is een uiterst belangrijk concept in machine learning, en het is ess Bekijk het volgende probleem van het benaderen van 5 punten (weergegeven door `x` op de grafieken hieronder): -![linear](../../../../../translated_images/nl/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/nl/overfit2.131f5800ae10ca5e.jpg) +![linear](../../../../../translated_images/nl/overfit1.f24b71c6f652e59e.webp) | ![overfit](../../../../../translated_images/nl/overfit2.131f5800ae10ca5e.webp) -------------------------|-------------------------- **Lineair model, 2 parameters** | **Niet-lineair model, 7 parameters** Trainingsfout = 5.3 | Trainingsfout = 0 @@ -79,7 +79,7 @@ Het is erg belangrijk om een juiste balans te vinden tussen de rijkdom van het m Zoals je kunt zien op de bovenstaande grafiek, kan overfitting worden gedetecteerd door een zeer lage trainingsfout en een hoge validatiefout. Normaal gesproken zien we tijdens het trainen zowel de trainings- als validatiefouten afnemen, en op een gegeven moment kan de validatiefout stoppen met afnemen en beginnen te stijgen. Dit is een teken van overfitting en een indicatie dat we waarschijnlijk op dit punt moeten stoppen met trainen (of op zijn minst een snapshot van het model moeten maken). -![overfitting](../../../../../translated_images/nl/Overfitting.408ad91cd90b4371.png) +![overfitting](../../../../../translated_images/nl/Overfitting.408ad91cd90b4371.webp) ## Hoe overfitting te voorkomen diff --git a/translations/nl/lessons/3-NeuralNetworks/README.md b/translations/nl/lessons/3-NeuralNetworks/README.md index fcc92212..cb9b9546 100644 --- a/translations/nl/lessons/3-NeuralNetworks/README.md +++ b/translations/nl/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Introductie tot Neurale Netwerken -![Samenvatting van de inhoud van Intro Neural Networks in een schets](../../../../translated_images/nl/ai-neuralnetworks.1c687ae40bc86e83.png) +![Samenvatting van de inhoud van Intro Neural Networks in een schets](../../../../translated_images/nl/ai-neuralnetworks.1c687ae40bc86e83.webp) Zoals we in de introductie hebben besproken, is een van de manieren om intelligentie te bereiken het trainen van een **computermodel** of een **kunstmatig brein**. Sinds het midden van de 20e eeuw hebben onderzoekers verschillende wiskundige modellen geprobeerd, totdat deze richting in de afgelopen jaren enorm succesvol bleek te zijn. Dergelijke wiskundige modellen van het brein worden **neurale netwerken** genoemd. @@ -36,13 +36,13 @@ In dit curriculum richten we ons uitsluitend op neurale netwerkmodellen. Uit de biologie weten we dat ons brein bestaat uit neurale cellen (neuronen), die elk meerdere "inputs" (dendrieten) en een enkele "output" (axon) hebben. Zowel dendrieten als axonen kunnen elektrische signalen geleiden, en de verbindingen daartussen — bekend als synapsen — kunnen verschillende graden van geleiding vertonen, die worden gereguleerd door neurotransmitters. -![Model van een Neuron](../../../../translated_images/nl/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![Model van een Neuron](../../../../translated_images/nl/artneuron.1a5daa88d20ebe6f.png) +![Model van een Neuron](../../../../translated_images/nl/synapse-wikipedia.ed20a9e4726ea1c6.webp) | ![Model van een Neuron](../../../../translated_images/nl/artneuron.1a5daa88d20ebe6f.webp) ----|---- Echt Neuron *([Afbeelding](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) van Wikipedia)* | Kunstmatig Neuron *(Afbeelding door Auteur)* Het eenvoudigste wiskundige model van een neuron bevat dus meerdere inputs X1, ..., XN en een output Y, en een reeks gewichten W1, ..., WN. Een output wordt berekend als: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) waarbij f een niet-lineaire **activatiefunctie** is. diff --git a/translations/nl/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/nl/lessons/4-ComputerVision/06-IntroCV/README.md index de813e01..954065d6 100644 --- a/translations/nl/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/nl/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ In ons [OpenCV Notebook](OpenCV.ipynb) geven we enkele voorbeelden van wanneer c * **Voorbewerking van een foto van een Braille-boek**. We richten ons op hoe we thresholding, kenmerkdetectie, perspectieftransformatie en NumPy-manipulaties kunnen gebruiken om individuele Braille-symbolen te scheiden voor verdere classificatie door een neuraal netwerk. -![Braille Afbeelding](../../../../../translated_images/nl/braille.341962ff76b1bd70.jpeg) | ![Braille Afbeelding Voorbewerkt](../../../../../translated_images/nl/braille-result.46530fea020b03c7.png) | ![Braille Symbolen](../../../../../translated_images/nl/braille-symbols.0159185ab69d5339.png) +![Braille Afbeelding](../../../../../translated_images/nl/braille.341962ff76b1bd70.webp) | ![Braille Afbeelding Voorbewerkt](../../../../../translated_images/nl/braille-result.46530fea020b03c7.webp) | ![Braille Symbolen](../../../../../translated_images/nl/braille-symbols.0159185ab69d5339.webp) ----|-----|----- > Afbeelding uit [OpenCV.ipynb](OpenCV.ipynb) * **Beweging detecteren in video met behulp van frameverschillen**. Als de camera vast staat, zouden frames van de camerafeed vrij vergelijkbaar met elkaar moeten zijn. Omdat frames worden weergegeven als arrays, krijg je door die arrays van twee opeenvolgende frames van elkaar af te trekken het pixelverschil, dat laag zou moeten zijn voor statische frames, en hoger wordt zodra er aanzienlijke beweging in de afbeelding is. -![Afbeelding van videoframes en frameverschillen](../../../../../translated_images/nl/frame-difference.706f805491a0883c.png) +![Afbeelding van videoframes en frameverschillen](../../../../../translated_images/nl/frame-difference.706f805491a0883c.webp) > Afbeelding uit [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ In ons [OpenCV Notebook](OpenCV.ipynb) geven we enkele voorbeelden van wanneer c - **Dense Optical Flow** berekent het vectorveld dat voor elke pixel laat zien waar deze naartoe beweegt. - **Sparse Optical Flow** is gebaseerd op het nemen van enkele onderscheidende kenmerken in de afbeelding (bijv. randen) en het opbouwen van hun traject van frame tot frame. -![Afbeelding van Optische Stroom](../../../../../translated_images/nl/optical.1f4a94464579a83a.png) +![Afbeelding van Optische Stroom](../../../../../translated_images/nl/optical.1f4a94464579a83a.webp) > Afbeelding uit [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/nl/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/nl/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index d2f94bc8..157540be 100644 --- a/translations/nl/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/nl/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 is een netwerk dat in 2014 een nauwkeurigheid van 92,7% behaalde in de ImageNet top-5 classificatie. Het heeft de volgende laagstructuur: -![ImageNet Lagen](../../../../../translated_images/nl/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet Lagen](../../../../../translated_images/nl/vgg-16-arch1.d901a5583b3a51ba.webp) Zoals je kunt zien, volgt VGG een traditionele piramide-architectuur, wat een opeenvolging is van convolutie- en poolinglagen. -![ImageNet Piramide](../../../../../translated_images/nl/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet Piramide](../../../../../translated_images/nl/vgg-16-arch.64ff2137f50dd49f.webp) > Afbeelding van [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/nl/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/nl/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index dc9a2ef3..31998f11 100644 --- a/translations/nl/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/nl/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -260,7 +260,7 @@ "\n", "In een typische CNN zijn er dus meerdere convolutionele lagen, met pooling-lagen ertussen om de afmetingen van de afbeelding te verkleinen. We zouden ook het aantal filters verhogen, omdat er, naarmate patronen complexer worden, meer mogelijke interessante combinaties zijn waar we naar moeten zoeken.\n", "\n", - "![Een afbeelding die meerdere convolutionele lagen met pooling-lagen toont.](../../../../../translated_images/nl/cnn-pyramid.85915455759ef0ce.png)\n", + "![Een afbeelding die meerdere convolutionele lagen met pooling-lagen toont.](../../../../../translated_images/nl/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "Vanwege de afnemende ruimtelijke afmetingen en toenemende kenmerken/filters wordt deze architectuur ook wel **piramide-architectuur** genoemd.\n" ] diff --git a/translations/nl/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/nl/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index 2028c605..6cd6cabe 100644 --- a/translations/nl/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/nl/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -360,7 +360,7 @@ "\n", "In een typische CNN zijn er dus meerdere convolutionele lagen, met pooling-lagen ertussen om de afmetingen van de afbeelding te verkleinen. We zouden ook het aantal filters verhogen, omdat er naarmate patronen geavanceerder worden, meer mogelijke interessante combinaties zijn waar we naar moeten zoeken.\n", "\n", - "![Een afbeelding die verschillende convolutionele lagen met pooling-lagen toont.](../../../../../translated_images/nl/cnn-pyramid.85915455759ef0ce.png)\n", + "![Een afbeelding die verschillende convolutionele lagen met pooling-lagen toont.](../../../../../translated_images/nl/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "Vanwege de afnemende ruimtelijke dimensies en toenemende feature-/filterdimensies wordt deze architectuur ook wel **piramide-architectuur** genoemd.\n" ] diff --git a/translations/nl/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/nl/lessons/4-ComputerVision/07-ConvNets/README.md index 886cb6e4..8bae1f01 100644 --- a/translations/nl/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/nl/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ In het echte leven willen we objecten op een afbeelding kunnen herkennen, ongeac Om patronen te extraheren, maken we gebruik van het concept van **convolutionele filters**. Zoals je weet, wordt een afbeelding weergegeven door een 2D-matrix, of een 3D-tensor met kleurendiepte. Het toepassen van een filter betekent dat we een relatief kleine **filterkernel**-matrix nemen, en voor elke pixel in de originele afbeelding het gewogen gemiddelde berekenen met naburige punten. Dit kun je zien als een klein venster dat over de hele afbeelding schuift en alle pixels gemiddeld volgens de gewichten in de filterkernel-matrix. -![Verticaal Randfilter](../../../../../translated_images/nl/filter-vert.b7148390ca0bc356.png) | ![Horizontaal Randfilter](../../../../../translated_images/nl/filter-horiz.59b80ed4feb946ef.png) +![Verticaal Randfilter](../../../../../translated_images/nl/filter-vert.b7148390ca0bc356.webp) | ![Horizontaal Randfilter](../../../../../translated_images/nl/filter-horiz.59b80ed4feb946ef.webp) ----|---- > Afbeelding door Dmitry Soshnikov @@ -38,7 +38,7 @@ De werking van CNN's is gebaseerd op de volgende belangrijke ideeën: * We kunnen het netwerk zo ontwerpen dat de filters automatisch worden getraind. * We kunnen dezelfde aanpak gebruiken om patronen te vinden in hoog-niveau kenmerken, niet alleen in de originele afbeelding. Hierdoor werkt de CNN-functie-extractie op een hiërarchie van kenmerken, beginnend bij laag-niveau pixelcombinaties tot hoog-niveau combinaties van afbeeldingsonderdelen. -![Hiërarchische Functie-extractie](../../../../../translated_images/nl/FeatureExtractionCNN.d9b456cbdae7cb64.png) +![Hiërarchische Functie-extractie](../../../../../translated_images/nl/FeatureExtractionCNN.d9b456cbdae7cb64.webp) > Afbeelding uit [een paper van Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), gebaseerd op [hun onderzoek](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ De meeste CNN's die worden gebruikt voor beeldverwerking volgen een zogenaamde p Als voorbeeld bekijken we de architectuur van VGG-16, een netwerk dat in 2014 een nauwkeurigheid van 92,7% behaalde in de top-5 classificatie van ImageNet: -![ImageNet Lagen](../../../../../translated_images/nl/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet Lagen](../../../../../translated_images/nl/vgg-16-arch1.d901a5583b3a51ba.webp) -![ImageNet Piramide](../../../../../translated_images/nl/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet Piramide](../../../../../translated_images/nl/vgg-16-arch.64ff2137f50dd49f.webp) > Afbeelding van [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/nl/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/nl/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 69f276a5..e6b2e99e 100644 --- a/translations/nl/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/nl/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ Je moet een convolutioneel neuraal netwerk trainen om verschillende rassen van k We gebruiken de [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/), die afbeeldingen bevat van 37 verschillende rassen van honden en katten. -![Dataset waarmee we werken](../../../../../../translated_images/nl/data.50b2a9d5484bdbf0.png) +![Dataset waarmee we werken](../../../../../../translated_images/nl/data.50b2a9d5484bdbf0.webp) Om de dataset te downloaden, gebruik deze codefragment: diff --git a/translations/nl/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/nl/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index a48e795c..b1419e4f 100644 --- a/translations/nl/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/nl/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "Om de ideale kat te visualiseren, beginnen we met een willekeurige ruisafbeelding en proberen we de optimalisatietechniek van gradient descent te gebruiken om de afbeelding aan te passen zodat een netwerk een kat herkent.\n", "\n", - "![Optimalisatie Loop](../../../../../translated_images/nl/ideal-cat-loop.999fbb8ff306e044.png)\n", + "![Optimalisatie Loop](../../../../../translated_images/nl/ideal-cat-loop.999fbb8ff306e044.webp)\n", "\n", "Hier is onze startafbeelding:\n" ] diff --git a/translations/nl/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/nl/lessons/4-ComputerVision/08-TransferLearning/README.md index 751a36ea..dc8676af 100644 --- a/translations/nl/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/nl/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Zowel Keras als PyTorch bevatten functies om eenvoudig voorgetrainde neurale net Hier zijn voorbeeldkenmerken die door een VGG-16 netwerk uit een afbeelding van een kat zijn gehaald: -![Kenmerken geëxtraheerd door VGG-16](../../../../../translated_images/nl/features.6291f9c7ba3a0b95.png) +![Kenmerken geëxtraheerd door VGG-16](../../../../../translated_images/nl/features.6291f9c7ba3a0b95.webp) ## Cats vs. Dogs Dataset @@ -48,19 +48,19 @@ Een voorgetraind neuraal netwerk bevat verschillende patronen in zijn *brein*, w Een aanpak die we kunnen nemen is om te beginnen met een willekeurige afbeelding en vervolgens de techniek van **gradient descent optimalisatie** te gebruiken om die afbeelding zo aan te passen dat het netwerk begint te denken dat het een kat is. -![Afbeelding Optimalisatie Loop](../../../../../translated_images/nl/ideal-cat-loop.999fbb8ff306e044.png) +![Afbeelding Optimalisatie Loop](../../../../../translated_images/nl/ideal-cat-loop.999fbb8ff306e044.webp) Als we dit doen, krijgen we echter iets dat erg lijkt op willekeurige ruis. Dit komt omdat *er veel manieren zijn om een netwerk te laten denken dat de invoerafbeelding een kat is*, inclusief enkele die visueel geen zin hebben. Hoewel deze afbeeldingen veel patronen bevatten die typisch zijn voor een kat, is er niets dat hen dwingt visueel onderscheidend te zijn. Om het resultaat te verbeteren, kunnen we een andere term toevoegen aan de verliesfunctie, genaamd **variation loss**. Dit is een maatstaf die aangeeft hoe vergelijkbaar naburige pixels van de afbeelding zijn. Het minimaliseren van variation loss maakt de afbeelding gladder en verwijdert ruis, waardoor meer visueel aantrekkelijke patronen zichtbaar worden. Hier is een voorbeeld van dergelijke "ideale" afbeeldingen, die met hoge waarschijnlijkheid als kat en als zebra worden geclassificeerd: -![Ideale Kat](../../../../../translated_images/nl/ideal-cat.203dd4597643d6b0.png) | ![Ideale Zebra](../../../../../translated_images/nl/ideal-zebra.7f70e8b54ee15a7a.png) +![Ideale Kat](../../../../../translated_images/nl/ideal-cat.203dd4597643d6b0.webp) | ![Ideale Zebra](../../../../../translated_images/nl/ideal-zebra.7f70e8b54ee15a7a.webp) -----|----- *Ideale Kat* | *Ideale Zebra* Een soortgelijke aanpak kan worden gebruikt om zogenaamde **adversarial attacks** op een neuraal netwerk uit te voeren. Stel dat we een neuraal netwerk willen misleiden en een hond eruit willen laten zien als een kat. Als we een afbeelding van een hond nemen, die door een netwerk wordt herkend als een hond, kunnen we deze vervolgens een beetje aanpassen met behulp van gradient descent optimalisatie, totdat het netwerk deze begint te classificeren als een kat: -![Afbeelding van een Hond](../../../../../translated_images/nl/original-dog.8f68a67d2fe0911f.png) | ![Afbeelding van een hond geclassificeerd als een kat](../../../../../translated_images/nl/adversarial-dog.d9fc7773b0142b89.png) +![Afbeelding van een Hond](../../../../../translated_images/nl/original-dog.8f68a67d2fe0911f.webp) | ![Afbeelding van een hond geclassificeerd als een kat](../../../../../translated_images/nl/adversarial-dog.d9fc7773b0142b89.webp) -----|----- *Originele afbeelding van een hond* | *Afbeelding van een hond geclassificeerd als een kat* diff --git a/translations/nl/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/nl/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index 2c199387..29a6a32f 100644 --- a/translations/nl/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/nl/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "Omdat we de autoencoder trainen om zoveel mogelijk informatie uit de originele afbeelding vast te leggen voor een nauwkeurige reconstructie, probeert het netwerk de beste **embedding** van invoerafbeeldingen te vinden om de betekenis vast te leggen.\n", "\n", - "![AutoEncoder Diagram](../../../../../translated_images/nl/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![AutoEncoder Diagram](../../../../../translated_images/nl/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "> Afbeelding afkomstig van [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", diff --git a/translations/nl/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/nl/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index de310e27..c191b763 100644 --- a/translations/nl/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/nl/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "Omdat we de autoencoder trainen om zoveel mogelijk informatie uit de originele afbeelding vast te leggen voor een nauwkeurige reconstructie, probeert het netwerk de beste **embedding** van inputafbeeldingen te vinden om de betekenis te vatten.\n", "\n", - "![AutoEncoder Diagram](../../../../../translated_images/nl/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![AutoEncoder Diagram](../../../../../translated_images/nl/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "*Afbeelding afkomstig van [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", diff --git a/translations/nl/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/nl/lessons/4-ComputerVision/09-Autoencoders/README.md index e4bc5900..d9d9b039 100644 --- a/translations/nl/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/nl/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ We willen echter mogelijk ruwe (ongelabelde) data gebruiken om CNN-feature extra Omdat we een autoencoder trainen om zoveel mogelijk informatie uit de originele afbeelding vast te leggen voor een nauwkeurige reconstructie, probeert het netwerk de beste **embedding** van invoerafbeeldingen te vinden om de betekenis vast te leggen. -![AutoEncoder Diagram](../../../../../translated_images/nl/autoencoder_schema.5e6fc9ad98a5eb61.jpg) +![AutoEncoder Diagram](../../../../../translated_images/nl/autoencoder_schema.5e6fc9ad98a5eb61.webp) > Afbeelding van [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/nl/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/nl/lessons/4-ComputerVision/11-ObjectDetection/README.md index 1c44bfbc..fc098284 100644 --- a/translations/nl/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/nl/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ De beeldclassificatiemodellen die we tot nu toe hebben behandeld, namen een afbe ## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Objectdetectie](../../../../../translated_images/nl/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) +![Objectdetectie](../../../../../translated_images/nl/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp) > Afbeelding van [YOLO v2 website](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ Stel dat we een kat op een afbeelding willen vinden, een zeer eenvoudige aanpak 2. Voer beeldclassificatie uit op elke tegel. 3. De tegels die een voldoende hoge activatie opleveren, kunnen worden beschouwd als tegels die het betreffende object bevatten. -![Eenvoudige objectdetectie](../../../../../translated_images/nl/naive-detection.e7f1ba220ccd08c6.png) +![Eenvoudige objectdetectie](../../../../../translated_images/nl/naive-detection.e7f1ba220ccd08c6.webp) > *Afbeelding uit [Exercise Notebook](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ Je kunt de volgende datasets tegenkomen voor deze taak: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 klassen * [COCO](http://cocodataset.org/#home) - Common Objects in Context. 80 klassen, begrenzingskaders en segmentatiemaskers -![COCO](../../../../../translated_images/nl/coco-examples.71bc60380fa6cceb.jpg) +![COCO](../../../../../translated_images/nl/coco-examples.71bc60380fa6cceb.webp) ## Objectdetectie-metrics @@ -50,7 +50,7 @@ Je kunt de volgende datasets tegenkomen voor deze taak: Bij beeldclassificatie is het eenvoudig om te meten hoe goed het algoritme presteert, maar bij objectdetectie moeten we zowel de juistheid van de klasse als de precisie van de voorspelde locatie van het begrenzingskader meten. Voor dat laatste gebruiken we de zogenaamde **Intersection over Union** (IoU), die meet hoe goed twee kaders (of twee willekeurige gebieden) overlappen. -![IoU](../../../../../translated_images/nl/iou_equation.9a4751d40fff4e11.png) +![IoU](../../../../../translated_images/nl/iou_equation.9a4751d40fff4e11.webp) > *Figuur 2 uit [deze uitstekende blogpost over IoU](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ Er zijn twee brede categorieën van objectdetectie-algoritmen: [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) gebruikt [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) om een hiërarchische structuur van ROI-regio's te genereren, die vervolgens door CNN-feature extractors en SVM-classificators worden geleid om de objectklasse te bepalen, en lineaire regressie om de coördinaten van *begrenzingskaders* te bepalen. [Officiële paper](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/nl/rcnn1.cae407020dfb1d1f.png) +![RCNN](../../../../../translated_images/nl/rcnn1.cae407020dfb1d1f.webp) > *Afbeelding van van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/nl/rcnn2.2d9530bb83516484.png) +![RCNN-1](../../../../../translated_images/nl/rcnn2.2d9530bb83516484.webp) > *Afbeeldingen uit [deze blog](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -110,7 +110,7 @@ Er zijn twee brede categorieën van objectdetectie-algoritmen: Deze aanpak lijkt op R-CNN, maar regio's worden gedefinieerd nadat convolutielagen zijn toegepast. -![FRCNN](../../../../../translated_images/nl/f-rcnn.3cda6d9bb4188875.png) +![FRCNN](../../../../../translated_images/nl/f-rcnn.3cda6d9bb4188875.webp) > Afbeelding uit [de officiële paper](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ Deze aanpak lijkt op R-CNN, maar regio's worden gedefinieerd nadat convolutielag Het belangrijkste idee van deze aanpak is om een neuraal netwerk te gebruiken om ROI's te voorspellen - de zogenaamde *Region Proposal Network*. [Paper](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/nl/faster-rcnn.8d46c099b87ef30a.png) +![FasterRCNN](../../../../../translated_images/nl/faster-rcnn.8d46c099b87ef30a.webp) > Afbeelding uit [de officiële paper](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ Dit algoritme is zelfs sneller dan Faster R-CNN. Het belangrijkste idee is als v 2. Features worden verwerkt door **Position-Sensitive Score Map**. Elk object uit $C$ klassen wordt verdeeld in $k\times k$ regio's, en we trainen om delen van objecten te voorspellen. 3. Voor elk deel uit $k\times k$ regio's stemmen alle netwerken op objectklassen, en de objectklasse met de meeste stemmen wordt geselecteerd. -![r-fcn afbeelding](../../../../../translated_images/nl/r-fcn.13eb88158b99a3da.png) +![r-fcn afbeelding](../../../../../translated_images/nl/r-fcn.13eb88158b99a3da.webp) > Afbeelding uit [officiële paper](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO is een realtime one-pass algoritme. Het belangrijkste idee is als volgt: * De afbeelding wordt verdeeld in $S\times S$ regio's. * Voor elke regio voorspelt **CNN** $n$ mogelijke objecten, *begrenzingskader*-coördinaten en *vertrouwen*=*waarschijnlijkheid* * IoU. - ![YOLO](../../../../../translated_images/nl/yolo.a2648ec82ee8bb4e.png) + ![YOLO](../../../../../translated_images/nl/yolo.a2648ec82ee8bb4e.webp) > Afbeelding uit [officiële paper](https://arxiv.org/abs/1506.02640) diff --git a/translations/nl/lessons/4-ComputerVision/README.md b/translations/nl/lessons/4-ComputerVision/README.md index b61451cb..abb851c1 100644 --- a/translations/nl/lessons/4-ComputerVision/README.md +++ b/translations/nl/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Computer Vision -![Samenvatting van Computer Vision-inhoud in een schets](../../../../translated_images/nl/ai-computervision.6506ebebac3fbf76.png) +![Samenvatting van Computer Vision-inhoud in een schets](../../../../translated_images/nl/ai-computervision.6506ebebac3fbf76.webp) In deze sectie leren we over: diff --git a/translations/nl/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/nl/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index 62031c99..3e0ba735 100644 --- a/translations/nl/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/nl/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**Zak met Woorden** (BoW) vectorrepresentatie is de meest gebruikte traditionele vectorrepresentatie. Elk woord is gekoppeld aan een vectorindex, en het vectorelement bevat het aantal keren dat een woord voorkomt in een bepaald document.\n", "\n", - "![Afbeelding die laat zien hoe een zak met woorden vectorrepresentatie in het geheugen wordt weergegeven.](../../../../../translated_images/nl/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![Afbeelding die laat zien hoe een zak met woorden vectorrepresentatie in het geheugen wordt weergegeven.](../../../../../translated_images/nl/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Note**: Je kunt BoW ook zien als de som van alle one-hot-gecodeerde vectoren voor individuele woorden in de tekst.\n", "\n", diff --git a/translations/nl/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/nl/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index 536b3115..e2274760 100644 --- a/translations/nl/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/nl/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**Bag-of-words** (BoW) vectorrepresentatie is de meest eenvoudige traditionele vectorrepresentatie om te begrijpen. Elk woord is gekoppeld aan een vectorindex, en een element in de vector bevat het aantal keren dat elk woord voorkomt in een bepaald document.\n", "\n", - "![Afbeelding die laat zien hoe een bag-of-words vectorrepresentatie in het geheugen wordt weergegeven.](../../../../../translated_images/nl/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![Afbeelding die laat zien hoe een bag-of-words vectorrepresentatie in het geheugen wordt weergegeven.](../../../../../translated_images/nl/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Note**: Je kunt BoW ook zien als de som van alle one-hot-gecodeerde vectoren voor individuele woorden in de tekst.\n", "\n", diff --git a/translations/nl/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/nl/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index e345e5c7..49547e4c 100644 --- a/translations/nl/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/nl/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "Door een embedding-laag als eerste laag in ons netwerk te gebruiken, kunnen we overschakelen van een bag-of-words naar een **embedding bag**-model, waarbij we eerst elk woord in onze tekst omzetten naar de bijbehorende embedding en vervolgens een aggregatiefunctie toepassen op al deze embeddings, zoals `sum`, `average` of `max`.\n", "\n", - "![Afbeelding die een embedding-classificator toont voor vijf sequentiewoorden.](../../../../../translated_images/nl/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![Afbeelding die een embedding-classificator toont voor vijf sequentiewoorden.](../../../../../translated_images/nl/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "Ons classifier-neuraal netwerk zal beginnen met een embedding-laag, gevolgd door een aggregatielaag en een lineaire classifier bovenop:\n" ] @@ -176,7 +176,7 @@ "\n", "In de vorige architectuur moesten we alle sequenties op dezelfde lengte opvullen om ze in een minibatch te passen. Dit is niet de meest efficiënte manier om sequenties met variabele lengte te representeren - een andere aanpak zou zijn om een **offset**-vector te gebruiken, die de offsets van alle sequenties in één grote vector bevat.\n", "\n", - "![Afbeelding die een offset-sequentierepresentatie toont](../../../../../translated_images/nl/offset-sequence-representation.eb73fcefb29b46ee.png)\n", + "![Afbeelding die een offset-sequentierepresentatie toont](../../../../../translated_images/nl/offset-sequence-representation.eb73fcefb29b46ee.webp)\n", "\n", "> **Note**: Op de afbeelding hierboven tonen we een sequentie van karakters, maar in ons voorbeeld werken we met sequenties van woorden. Het algemene principe van het representeren van sequenties met een offset-vector blijft echter hetzelfde.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW is sneller, terwijl skip-gram langzamer is, maar beter presteert bij het representeren van zeldzame woorden.\n", "\n", - "![Afbeelding die zowel CBoW- als Skip-Gram-algoritmen toont om woorden naar vectoren te converteren.](../../../../../translated_images/nl/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![Afbeelding die zowel CBoW- als Skip-Gram-algoritmen toont om woorden naar vectoren te converteren.](../../../../../translated_images/nl/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Om te experimenteren met Word2Vec-embedding die is voorgetraind op de Google News dataset, kunnen we de **gensim**-bibliotheek gebruiken. Hieronder vinden we de woorden die het meest lijken op 'neural'.\n", "\n", diff --git a/translations/nl/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/nl/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index 3b91f611..bbd55858 100644 --- a/translations/nl/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/nl/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "Door een embedding-laag als de eerste laag in ons netwerk te gebruiken, kunnen we overschakelen van een bag-of-words-model naar een **embedding bag**-model, waarbij we eerst elk woord in onze tekst omzetten naar de bijbehorende embedding en vervolgens een aggregatiefunctie toepassen op al deze embeddings, zoals `sum`, `average` of `max`.\n", "\n", - "![Afbeelding die een embedding-classificator toont voor vijf sequentiewoorden.](../../../../../translated_images/nl/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![Afbeelding die een embedding-classificator toont voor vijf sequentiewoorden.](../../../../../translated_images/nl/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "Ons classifier-neuraal netwerk bestaat uit de volgende lagen:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW is sneller, terwijl skip-gram langzamer is, maar beter presteert bij het representeren van zeldzame woorden.\n", "\n", - "![Afbeelding die zowel de CBoW- als Skip-Gram-algoritmen toont om woorden naar vectoren om te zetten.](../../../../../translated_images/nl/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![Afbeelding die zowel de CBoW- als Skip-Gram-algoritmen toont om woorden naar vectoren om te zetten.](../../../../../translated_images/nl/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Om te experimenteren met de Word2Vec-embedding die vooraf is getraind op de Google News-dataset, kunnen we de **gensim**-bibliotheek gebruiken. Hieronder vinden we de woorden die het meest lijken op 'neural'.\n", "\n", diff --git a/translations/nl/lessons/5-NLP/14-Embeddings/README.md b/translations/nl/lessons/5-NLP/14-Embeddings/README.md index 13d21956..f4861e02 100644 --- a/translations/nl/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/nl/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ De embeddinglaag neemt een woord als invoer en produceert een uitvoervector met Door een embeddinglaag als eerste laag in ons classifier-netwerk te gebruiken, kunnen we overschakelen van een bag-of-words naar een **embedding bag** model, waarbij we eerst elk woord in onze tekst omzetten in de bijbehorende embedding, en vervolgens een aggregatiefunctie berekenen over al deze embeddings, zoals `sum`, `average` of `max`. -![Afbeelding die een embedding classifier toont voor vijf woorden in een reeks.](../../../../../translated_images/nl/embedding-classifier-example.b77f021a7ee67eee.png) +![Afbeelding die een embedding classifier toont voor vijf woorden in een reeks.](../../../../../translated_images/nl/embedding-classifier-example.b77f021a7ee67eee.webp) > Afbeelding door de auteur @@ -40,7 +40,7 @@ Om dit te bereiken, moeten we ons embeddingmodel op een grote tekstcollectie op CBoW is sneller, terwijl skip-gram langzamer is, maar beter presteert bij het representeren van zeldzame woorden. -![Afbeelding die zowel de CBoW- als Skip-Gram-algoritmen toont om woorden naar vectoren te converteren.](../../../../../translated_images/nl/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![Afbeelding die zowel de CBoW- als Skip-Gram-algoritmen toont om woorden naar vectoren te converteren.](../../../../../translated_images/nl/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Afbeelding uit [dit artikel](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/nl/lessons/5-NLP/15-LanguageModeling/README.md b/translations/nl/lessons/5-NLP/15-LanguageModeling/README.md index 6a7527ab..2917f5e7 100644 --- a/translations/nl/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/nl/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ In onze eerdere voorbeelden hebben we gebruik gemaakt van vooraf getrainde seman * **Continuous Bag-of-Words** (CBoW), waarbij we het middelste token $W_0$ voorspellen in een reeks tokens $W_{-N}$, ..., $W_N$. * **Skip-gram**, waarbij we een set van naburige tokens {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} voorspellen vanuit het middelste token $W_0$. -![afbeelding uit paper over het converteren van woorden naar vectoren](../../../../../translated_images/nl/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![afbeelding uit paper over het converteren van woorden naar vectoren](../../../../../translated_images/nl/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Afbeelding uit [deze paper](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/nl/lessons/5-NLP/16-RNN/README.md b/translations/nl/lessons/5-NLP/16-RNN/README.md index 23a685e6..2ae8b7f2 100644 --- a/translations/nl/lessons/5-NLP/16-RNN/README.md +++ b/translations/nl/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ In de vorige secties hebben we gebruik gemaakt van rijke semantische representat Om de betekenis van een tekstsequentie vast te leggen, moeten we een andere neurale netwerkarchitectuur gebruiken, genaamd een **recurrent neural network**, of RNN. In een RNN sturen we onze zin één symbool tegelijk door het netwerk, en het netwerk produceert een **toestand**, die we vervolgens weer doorgeven aan het netwerk samen met het volgende symbool. -![RNN](../../../../../translated_images/nl/rnn.27f5c29c53d727b5.png) +![RNN](../../../../../translated_images/nl/rnn.27f5c29c53d727b5.webp) > Afbeelding door de auteur @@ -61,7 +61,7 @@ We hebben recurrente netwerken besproken die in één richting werken, van het b Een recurrent netwerk, of het nu éénrichtings of bidirectioneel is, legt bepaalde patronen binnen een sequentie vast en kan deze opslaan in een toestandsvector of doorgeven aan de uitvoer. Net zoals bij convolutionele netwerken, kunnen we een andere recurrente laag bovenop de eerste bouwen om hogere niveau patronen vast te leggen en te bouwen op laag-niveau patronen die door de eerste laag zijn geëxtraheerd. Dit leidt ons naar het concept van een **multi-layer RNN**, die bestaat uit twee of meer recurrente netwerken, waarbij de uitvoer van de vorige laag wordt doorgegeven aan de volgende laag als invoer. -![Afbeelding van een multilayer long-short-term-memory RNN](../../../../../translated_images/nl/multi-layer-lstm.dd975e29bb2a59fe.jpg) +![Afbeelding van een multilayer long-short-term-memory RNN](../../../../../translated_images/nl/multi-layer-lstm.dd975e29bb2a59fe.webp) *Afbeelding uit [dit geweldige artikel](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) door Fernando López* diff --git a/translations/nl/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/nl/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index 763cd800..e6531a70 100644 --- a/translations/nl/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/nl/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "Een recurrent netwerk, of het nu eendirectioneel of bidirectioneel is, legt bepaalde patronen binnen een reeks vast en kan deze opslaan in de toestandsvector of doorgeven aan de uitvoer. Net als bij convolutionele netwerken kunnen we een andere recurrente laag bovenop de eerste bouwen om patronen van een hoger niveau vast te leggen, opgebouwd uit de laag-niveau patronen die door de eerste laag zijn geëxtraheerd. Dit brengt ons bij het concept van een **meerlaagse RNN**, die bestaat uit twee of meer recurrente netwerken, waarbij de uitvoer van de vorige laag wordt doorgegeven aan de volgende laag als invoer.\n", "\n", - "![Afbeelding van een meerlaagse long-short-term-memory-RNN](../../../../../translated_images/nl/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![Afbeelding van een meerlaagse long-short-term-memory-RNN](../../../../../translated_images/nl/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*Afbeelding afkomstig uit [deze geweldige post](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) van Fernando López*\n", "\n", diff --git a/translations/nl/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/nl/lessons/5-NLP/16-RNN/RNNTF.ipynb index b777f148..103963df 100644 --- a/translations/nl/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/nl/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "Om de betekenis van een tekstsequentie vast te leggen, gebruiken we een neurale netwerkarchitectuur genaamd **recurrente neurale netwerken**, of RNN. Bij het gebruik van een RNN voeren we onze zin één token tegelijk door het netwerk, en het netwerk produceert een bepaalde **toestand**, die we vervolgens weer doorgeven aan het netwerk met het volgende token.\n", "\n", - "![Afbeelding die een voorbeeld van generatie door een recurrent neuraal netwerk toont.](../../../../../translated_images/nl/rnn.27f5c29c53d727b5.png)\n", + "![Afbeelding die een voorbeeld van generatie door een recurrent neuraal netwerk toont.](../../../../../translated_images/nl/rnn.27f5c29c53d727b5.webp)\n", "\n", "Gegeven de invoersequentie van tokens $X_0,\\dots,X_n$, creëert de RNN een reeks neurale netwerkblokken en traint deze reeks end-to-end met behulp van backpropagation. Elk netwerkblok neemt een paar $(X_i,S_i)$ als invoer en produceert $S_{i+1}$ als resultaat. De uiteindelijke toestand $S_n$ of uitvoer $Y_n$ gaat naar een lineaire classifier om het resultaat te produceren. Alle netwerkblokken delen dezelfde gewichten en worden end-to-end getraind met één backpropagation-pass.\n", "\n", @@ -369,7 +369,7 @@ "\n", "Recurrente netwerken, unidirectioneel of bidirectioneel, leggen patronen binnen een reeks vast en slaan deze op in toestandsvectoren of geven ze terug als output. Net zoals bij convolutionele netwerken kunnen we een andere recurrente laag bouwen na de eerste om hogere-orde patronen vast te leggen, opgebouwd uit lagere-orde patronen die door de eerste laag zijn geëxtraheerd. Dit leidt ons naar het concept van een **meerlaagse RNN**, die bestaat uit twee of meer recurrente netwerken, waarbij de output van de vorige laag als invoer wordt doorgegeven aan de volgende laag.\n", "\n", - "![Afbeelding van een meerlaagse long-short-term-memory RNN](../../../../../translated_images/nl/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![Afbeelding van een meerlaagse long-short-term-memory RNN](../../../../../translated_images/nl/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*Afbeelding afkomstig uit [deze geweldige post](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) van Fernando López.*\n", "\n", diff --git a/translations/nl/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/nl/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index 2a3c60c0..fa2994f7 100644 --- a/translations/nl/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/nl/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "De manier waarop we een RNN zullen trainen om tekst te genereren, is als volgt. Bij elke stap nemen we een reeks karakters van lengte `nchars` en vragen we het netwerk om het volgende uitvoerkarakter te genereren voor elk invoerkarakter:\n", "\n", - "![Afbeelding die een voorbeeld toont van een RNN die het woord 'HELLO' genereert.](../../../../../translated_images/nl/rnn-generate.56c54afb52f9781d.png)\n", + "![Afbeelding die een voorbeeld toont van een RNN die het woord 'HELLO' genereert.](../../../../../translated_images/nl/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "Afhankelijk van het specifieke scenario willen we mogelijk ook enkele speciale karakters opnemen, zoals *einde-van-sequentie* ``. In ons geval willen we het netwerk gewoon trainen voor eindeloze tekstgeneratie, dus stellen we de grootte van elke sequentie vast op `nchars` tokens. Bijgevolg zal elk trainingsvoorbeeld bestaan uit `nchars` invoer en `nchars` uitvoer (wat de invoersequentie is, verschoven met één symbool naar links). Een minibatch zal bestaan uit meerdere van dergelijke sequenties.\n", "\n", diff --git a/translations/nl/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/nl/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index 23c6fbb5..3c76625a 100644 --- a/translations/nl/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/nl/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -111,7 +111,7 @@ "\n", "De manier waarop we een RNN zullen trainen om nieuwstitels te genereren is als volgt. Bij elke stap nemen we één titel, die wordt ingevoerd in een RNN, en voor elk invoerkarakter vragen we het netwerk om het volgende uitvoerkarakter te genereren:\n", "\n", - "![Afbeelding die een voorbeeld toont van RNN-generatie van het woord 'HELLO'.](../../../../../translated_images/nl/rnn-generate.56c54afb52f9781d.png)\n", + "![Afbeelding die een voorbeeld toont van RNN-generatie van het woord 'HELLO'.](../../../../../translated_images/nl/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "Voor het laatste karakter van onze reeks vragen we het netwerk om een ``-token te genereren.\n", "\n", diff --git a/translations/nl/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/nl/lessons/5-NLP/17-GenerativeNetworks/README.md index fc6b22ff..0612be7c 100644 --- a/translations/nl/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/nl/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ In de RNN-architectuur die we in de vorige eenheid hebben besproken, produceerde Dit maakt verschillende neurale architecturen mogelijk, zoals weergegeven in de onderstaande afbeelding: -![Afbeelding met veelvoorkomende patronen van recurrente neurale netwerken.](../../../../../translated_images/nl/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Afbeelding met veelvoorkomende patronen van recurrente neurale netwerken.](../../../../../translated_images/nl/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > Afbeelding uit de blogpost [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) door [Andrej Karpaty](http://karpathy.github.io/) @@ -32,7 +32,7 @@ In deze eenheid richten we ons op eenvoudige generatieve modellen die ons helpen We trainen deze RNN om tekst stap voor stap te genereren. Bij elke stap nemen we een reeks karakters van lengte `nchars` en vragen we het netwerk om het volgende uitvoerkarakter te genereren voor elk invoerkarakter: -![Afbeelding met een voorbeeld van RNN-generatie van het woord 'HELLO'.](../../../../../translated_images/nl/rnn-generate.56c54afb52f9781d.png) +![Afbeelding met een voorbeeld van RNN-generatie van het woord 'HELLO'.](../../../../../translated_images/nl/rnn-generate.56c54afb52f9781d.webp) Bij het genereren van tekst (tijdens inferentie) beginnen we met een **prompt**, die door de RNN-cellen wordt doorgegeven om de tussenliggende toestand te genereren. Vanuit deze toestand begint de generatie. We genereren één karakter tegelijk en geven de toestand en het gegenereerde karakter door aan een andere RNN-cel om het volgende te genereren, totdat we genoeg karakters hebben gegenereerd. diff --git a/translations/nl/lessons/5-NLP/18-Transformers/README.md b/translations/nl/lessons/5-NLP/18-Transformers/README.md index 478b405b..1a64f5a6 100644 --- a/translations/nl/lessons/5-NLP/18-Transformers/README.md +++ b/translations/nl/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ Met RNNs wordt sequence-to-sequence geïmplementeerd door twee recurrente netwer **Aandachtsmechanismen** bieden een manier om het contextuele effect van elke invoervector op elke outputvoorspelling van de RNN te wegen. Dit wordt geïmplementeerd door shortcuts te creëren tussen de tussenliggende toestanden van de input-RNN en de output-RNN. Op deze manier nemen we bij het genereren van outputsymbool yt alle verborgen toestanden hi van de input in aanmerking, met verschillende gewichtcoëfficiënten αt,i. -![Afbeelding van een encoder/decoder-model met een additieve aandachtlaag](../../../../../translated_images/nl/encoder-decoder-attention.7a726296894fb567.png) +![Afbeelding van een encoder/decoder-model met een additieve aandachtlaag](../../../../../translated_images/nl/encoder-decoder-attention.7a726296894fb567.webp) > Het encoder-decoder model met additief aandachtsmechanisme in [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), geciteerd uit [deze blogpost](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) De aandachtmatrix {αi,j} vertegenwoordigt de mate waarin bepaalde invoerwoorden een rol spelen bij het genereren van een bepaald woord in de outputsequentie. Hieronder staat een voorbeeld van zo'n matrix: -![Afbeelding van een voorbeelduitlijning gevonden door RNNsearch-50, afkomstig van Bahdanau - arviz.org](../../../../../translated_images/nl/bahdanau-fig3.09ba2d37f202a6af.png) +![Afbeelding van een voorbeelduitlijning gevonden door RNNsearch-50, afkomstig van Bahdanau - arviz.org](../../../../../translated_images/nl/bahdanau-fig3.09ba2d37f202a6af.webp) > Afbeelding uit [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) @@ -66,7 +66,7 @@ Het resultaat dat we krijgen met positionele embedding embedt zowel het oorspron Vervolgens moeten we enkele patronen binnen onze sequentie vastleggen. Om dit te doen, gebruiken transformers een **zelf-aandachtsmechanisme**, wat in wezen aandacht is toegepast op dezelfde sequentie als input en output. Het toepassen van zelf-aandacht stelt ons in staat om **context** binnen de zin in aanmerking te nemen en te zien welke woorden met elkaar verbonden zijn. Bijvoorbeeld, het stelt ons in staat om te zien welke woorden worden verwezen door coreferenties, zoals *het*, en ook de context in aanmerking te nemen: -![](../../../../../translated_images/nl/CoreferenceResolution.861924d6d384a7d6.png) +![](../../../../../translated_images/nl/CoreferenceResolution.861924d6d384a7d6.webp) > Afbeelding van de [Google Blog](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Omdat elke invoerpositie onafhankelijk wordt gekoppeld aan elke uitvoerpositie, **BERT** (Bidirectional Encoder Representations from Transformers) is een zeer groot meerlagig transformernetwerk met 12 lagen voor *BERT-base*, en 24 voor *BERT-large*. Het model wordt eerst voorgetraind op een grote corpus van tekstdata (WikiPedia + boeken) met behulp van ongesuperviseerde training (voorspellen van gemaskeerde woorden in een zin). Tijdens het voortrainen absorbeert het model aanzienlijke niveaus van taalbegrip, die vervolgens kunnen worden benut met andere datasets door middel van fine-tuning. Dit proces wordt **transfer learning** genoemd. -![Afbeelding van http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/nl/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) +![Afbeelding van http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/nl/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > Afbeelding [bron](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/nl/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/nl/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index 7a841441..884ccf58 100644 --- a/translations/nl/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/nl/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**Aandachtsmechanismen** bieden een manier om het contextuele effect van elke invoervector op elke uitvoervoorspelling van het RNN te wegen. Dit wordt geïmplementeerd door shortcuts te creëren tussen de tussenliggende toestanden van het invoer-RNN en het uitvoer-RNN. Op deze manier houden we bij het genereren van het uitvoersymbool $y_t$ rekening met alle verborgen toestanden van de invoer $h_i$, met verschillende gewichtscoëfficiënten $\\alpha_{t,i}$.\n", "\n", - "![Afbeelding van een encoder/decoder model met een additieve aandachtlaag](../../../../../translated_images/nl/encoder-decoder-attention.7a726296894fb567.png)\n", + "![Afbeelding van een encoder/decoder model met een additieve aandachtlaag](../../../../../translated_images/nl/encoder-decoder-attention.7a726296894fb567.webp)\n", "*Het encoder-decoder model met additief aandachtsmechanisme in [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), geciteerd uit [deze blogpost](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "De aandachtsmatrix $\\{\\alpha_{i,j}\\}$ vertegenwoordigt de mate waarin bepaalde invoerwoorden een rol spelen bij het genereren van een bepaald woord in de uitvoerreeks. Hieronder staat een voorbeeld van zo'n matrix:\n", "\n", - "![Afbeelding van een voorbeelduitlijning gevonden door RNNsearch-50, afkomstig van Bahdanau - arviz.org](../../../../../translated_images/nl/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![Afbeelding van een voorbeelduitlijning gevonden door RNNsearch-50, afkomstig van Bahdanau - arviz.org](../../../../../translated_images/nl/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*Afbeelding afkomstig uit [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) is een zeer groot meerlagig transformer-netwerk met 12 lagen voor *BERT-base* en 24 voor *BERT-large*. Het model wordt eerst voorgetraind op een grote corpus van tekstdata (Wikipedia + boeken) met behulp van ongesuperviseerde training (voorspellen van gemaskeerde woorden in een zin). Tijdens het voortrainen absorbeert het model een significant niveau van taalbegrip, dat vervolgens kan worden benut met andere datasets door middel van fine-tuning. Dit proces wordt **transfer learning** genoemd.\n", "\n", - "![Afbeelding van http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/nl/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![Afbeelding van http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/nl/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "Er zijn veel variaties van Transformer-architecturen, waaronder BERT, DistilBERT, BigBird, OpenGPT3 en meer, die kunnen worden gefinetuned. Het [HuggingFace-pakket](https://github.com/huggingface/) biedt een repository voor het trainen van veel van deze architecturen met PyTorch.\n", "\n", diff --git a/translations/nl/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/nl/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index cbe8bf7a..495b90a5 100644 --- a/translations/nl/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/nl/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**Aandachtsmechanismen** bieden een manier om het contextuele gewicht van elke invoervector op elke uitvoervoorspelling van het RNN te bepalen. Dit wordt geïmplementeerd door shortcuts te creëren tussen de tussenliggende toestanden van het invoer-RNN en het uitvoer-RNN. Op deze manier houden we bij het genereren van het uitvoersymbool $y_t$ rekening met alle verborgen toestanden van de invoer $h_i$, met verschillende gewichtscoëfficiënten $\\alpha_{t,i}$. \n", "\n", - "![Afbeelding van een encoder/decoder model met een additieve aandachtlaag](../../../../../translated_images/nl/encoder-decoder-attention.7a726296894fb567.png)\n", + "![Afbeelding van een encoder/decoder model met een additieve aandachtlaag](../../../../../translated_images/nl/encoder-decoder-attention.7a726296894fb567.webp)\n", "*Het encoder-decoder model met additief aandachtsmechanisme in [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), geciteerd uit [deze blogpost](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "De aandachtsmatrix $\\{\\alpha_{i,j}\\}$ vertegenwoordigt de mate waarin bepaalde invoerwoorden bijdragen aan de generatie van een specifiek woord in de uitvoerreeks. Hieronder staat een voorbeeld van zo'n matrix:\n", "\n", - "![Afbeelding van een voorbeelduitlijning gevonden door RNNsearch-50, afkomstig uit Bahdanau - arviz.org](../../../../../translated_images/nl/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![Afbeelding van een voorbeelduitlijning gevonden door RNNsearch-50, afkomstig uit Bahdanau - arviz.org](../../../../../translated_images/nl/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*Afbeelding afkomstig uit [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3)*\n", "\n", @@ -225,7 +225,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) is een zeer groot meerlagig transformernetwerk met 12 lagen voor *BERT-base* en 24 lagen voor *BERT-large*. Het model wordt eerst voorgetraind op een grote hoeveelheid tekstdata (WikiPedia + boeken) met behulp van ongesuperviseerd leren (voorspellen van gemaskeerde woorden in een zin). Tijdens de voortraining absorbeert het model een aanzienlijk niveau van taalbegrip, wat vervolgens kan worden benut met andere datasets door middel van fine-tuning. Dit proces wordt **transfer learning** genoemd.\n", "\n", - "![afbeelding van http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/nl/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![afbeelding van http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/nl/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "Er zijn veel variaties van Transformer-architecturen, waaronder BERT, DistilBERT, BigBird, OpenGPT3 en meer, die kunnen worden fijn afgestemd.\n", "\n", diff --git a/translations/nl/lessons/5-NLP/19-NER/README.md b/translations/nl/lessons/5-NLP/19-NER/README.md index 482766f8..57951ea5 100644 --- a/translations/nl/lessons/5-NLP/19-NER/README.md +++ b/translations/nl/lessons/5-NLP/19-NER/README.md @@ -58,7 +58,7 @@ infant | O Omdat we een één-op-één-correspondentie tussen tokens en klassen moeten opbouwen, kunnen we een rechtse **veel-op-veel** neurale netwerkmodel trainen zoals in deze afbeelding: -![Afbeelding die veelvoorkomende patronen van recurrente neurale netwerken toont.](../../../../../translated_images/nl/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Afbeelding die veelvoorkomende patronen van recurrente neurale netwerken toont.](../../../../../translated_images/nl/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > *Afbeelding uit [deze blogpost](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) door [Andrej Karpathy](http://karpathy.github.io/). NER-tokenclassificatiemodellen komen overeen met de rechtse netwerkarchitectuur op deze afbeelding.* diff --git a/translations/nl/lessons/5-NLP/README.md b/translations/nl/lessons/5-NLP/README.md index 0e4c7417..f11335c7 100644 --- a/translations/nl/lessons/5-NLP/README.md +++ b/translations/nl/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Natuurlijke Taalverwerking -![Samenvatting van NLP-taken in een schets](../../../../translated_images/nl/ai-nlp.b22dcb8ca4707cea.png) +![Samenvatting van NLP-taken in een schets](../../../../translated_images/nl/ai-nlp.b22dcb8ca4707cea.webp) In deze sectie richten we ons op het gebruik van neurale netwerken om taken met betrekking tot **Natuurlijke Taalverwerking (NLP)** uit te voeren. Er zijn veel NLP-problemen die we willen dat computers kunnen oplossen: diff --git a/translations/nl/lessons/6-Other/23-MultiagentSystems/README.md b/translations/nl/lessons/6-Other/23-MultiagentSystems/README.md index b20f26ce..fd85a8b3 100644 --- a/translations/nl/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/nl/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ Je kunt een van de modellen openen, bijvoorbeeld **Biology → Flocking Na het openen van het model kom je op het hoofdscherm van NetLogo. Hier is een voorbeeldmodel dat de populatie van wolven en schapen beschrijft, gegeven eindige middelen (gras). -![NetLogo Main Screen](../../../../../translated_images/nl/NetLogo-Main.32653711ec1a01b3.png) +![NetLogo Main Screen](../../../../../translated_images/nl/NetLogo-Main.32653711ec1a01b3.webp) > Screenshot door Dmitry Soshnikov diff --git a/translations/nl/lessons/README.md b/translations/nl/lessons/README.md index 97dc79ec..fa9a918d 100644 --- a/translations/nl/lessons/README.md +++ b/translations/nl/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Overzicht -![Overzicht in een schets](../../../translated_images/nl/ai-overview.0857791951d19500.png) +![Overzicht in een schets](../../../translated_images/nl/ai-overview.0857791951d19500.webp) > Schetsnotitie door [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/nl/lessons/X-Extras/X1-MultiModal/README.md b/translations/nl/lessons/X-Extras/X1-MultiModal/README.md index 202855d0..c54872f9 100644 --- a/translations/nl/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/nl/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ Na het succes van transformer-modellen voor het oplossen van NLP-taken, zijn dez Het belangrijkste idee van CLIP is om tekstprompts te kunnen vergelijken met een afbeelding en te bepalen hoe goed de afbeelding overeenkomt met de prompt. -![CLIP Architectuur](../../../../../translated_images/nl/clip-arch.b3dbf20b4e8ed8be.png) +![CLIP Architectuur](../../../../../translated_images/nl/clip-arch.b3dbf20b4e8ed8be.webp) > *Afbeelding uit [deze blogpost](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ Zodra dit model is voorgetraind, kunnen we het een batch afbeeldingen en een bat Stel dat we afbeeldingen moeten classificeren tussen bijvoorbeeld katten, honden en mensen. In dit geval kunnen we het model een afbeelding geven en een reeks tekstprompts: "*een afbeelding van een kat*", "*een afbeelding van een hond*", "*een afbeelding van een mens*". In de resulterende vector van 3 waarschijnlijkheden hoeven we alleen de index met de hoogste waarde te selecteren. -![CLIP voor Afbeeldingsclassificatie](../../../../../translated_images/nl/clip-class.3af42ef0b2b19369.png) +![CLIP voor Afbeeldingsclassificatie](../../../../../translated_images/nl/clip-class.3af42ef0b2b19369.webp) > *Afbeelding uit [deze blogpost](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ Meer informatie over VQGAN vind je op de [Taming Transformers](https://compvis.g Een belangrijk verschil tussen VQGAN en traditionele GAN is dat de laatste een behoorlijke afbeelding kan produceren vanuit elke invoervector, terwijl VQGAN waarschijnlijk een afbeelding produceert die niet coherent is. Daarom moeten we het proces van afbeeldingcreatie verder sturen, en dat kan worden gedaan met behulp van CLIP. -![VQGAN+CLIP Architectuur](../../../../../translated_images/nl/vqgan.5027fe05051dfa31.png) +![VQGAN+CLIP Architectuur](../../../../../translated_images/nl/vqgan.5027fe05051dfa31.webp) Om een afbeelding te genereren die overeenkomt met een tekstprompt, beginnen we met een willekeurige coderingsvector die door VQGAN wordt doorgegeven om een afbeelding te produceren. Vervolgens wordt CLIP gebruikt om een verliesfunctie te produceren die aangeeft hoe goed de afbeelding overeenkomt met de tekstprompt. Het doel is dan om dit verlies te minimaliseren, door middel van backpropagation om de parameters van de invoervector aan te passen. Een geweldige bibliotheek die VQGAN+CLIP implementeert is [Pixray](http://github.com/pixray/pixray). -![Afbeelding gegenereerd door Pixray](../../../../../translated_images/nl/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Afbeelding gegenereerd door Pixray](../../../../../translated_images/nl/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Afbeelding gegenereerd door Pixray](../../../../../translated_images/nl/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) +![Afbeelding gegenereerd door Pixray](../../../../../translated_images/nl/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp) | ![Afbeelding gegenereerd door Pixray](../../../../../translated_images/nl/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp) | ![Afbeelding gegenereerd door Pixray](../../../../../translated_images/nl/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp) ----|----|---- Afbeelding gegenereerd vanuit prompt *een close-up aquarelportret van een jonge mannelijke leraar literatuur met een boek* | Afbeelding gegenereerd vanuit prompt *een close-up olieverfportret van een jonge vrouwelijke leraar informatica met een computer* | Afbeelding gegenereerd vanuit prompt *een close-up olieverfportret van een oude mannelijke leraar wiskunde voor een schoolbord* diff --git a/translations/no/README.md b/translations/no/README.md index 405edf5c..b4c7b71c 100644 --- a/translations/no/README.md +++ b/translations/no/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # Kunstig intelligens for nybegynnere - Et undervisningsopplegg -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/no/ai-overview.0857791951d19500.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/no/ai-overview.0857791951d19500.webp)| |:---:| | AI For Beginners - _Sketchnote av [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/no/lessons/1-Intro/README.md b/translations/no/lessons/1-Intro/README.md index ef9cbd2b..5eab5462 100644 --- a/translations/no/lessons/1-Intro/README.md +++ b/translations/no/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Introduksjon til AI -![Oppsummering av innholdet i Introduksjon til AI i en tegning](../../../../translated_images/no/ai-intro.bf28d1ac4235881c.png) +![Oppsummering av innholdet i Introduksjon til AI i en tegning](../../../../translated_images/no/ai-intro.bf28d1ac4235881c.webp) > Tegning av [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Opprinnelig ble datamaskiner oppfunnet av [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage) for å operere på tall ved å følge en veldefinert prosedyre – en algoritme. Moderne datamaskiner, selv om de er betydelig mer avanserte enn den opprinnelige modellen foreslått på 1800-tallet, følger fortsatt samme idé om kontrollerte beregninger. Dermed er det mulig å programmere en datamaskin til å gjøre noe hvis vi kjenner den nøyaktige sekvensen av trinn som må utføres for å oppnå målet. -![Bilde av en person](../../../../translated_images/no/dsh_age.d212a30d4e54fb5f.png) +![Bilde av en person](../../../../translated_images/no/dsh_age.d212a30d4e54fb5f.webp) > Foto av [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ For mer informasjon, se **[Artificial General Intelligence](https://en.wikipedia Et av problemene med begrepet **[intelligens](https://en.wikipedia.org/wiki/Intelligence)** er at det ikke finnes noen klar definisjon av dette begrepet. Man kan argumentere for at intelligens er knyttet til **abstrakt tenkning** eller **selvbevissthet**, men vi kan ikke definere det ordentlig. -![Bilde av en katt](../../../../translated_images/no/photo-cat.8c8e8fb760ffe457.jpg) +![Bilde av en katt](../../../../translated_images/no/photo-cat.8c8e8fb760ffe457.webp) > [Foto](https://unsplash.com/photos/75715CVEJhI) av [Amber Kipp](https://unsplash.com/@sadmax) fra Unsplash @@ -98,13 +98,13 @@ Alternativt kan vi prøve å modellere de enkleste elementene i hjernen vår – > | Hva med ML? | | > |--------------|-----------| -> | En del av kunstig intelligens som er basert på at datamaskiner lærer å løse et problem basert på noen data, kalles **maskinlæring**. Vi vil ikke gå inn på klassisk maskinlæring i dette kurset – vi henviser deg til et eget [Maskinlæring for nybegynnere](http://aka.ms/ml-beginners)-pensum. | ![ML for Beginners](../../../../translated_images/no/ml-for-beginners.9e4fed176fd5817d.png) | +> | En del av kunstig intelligens som er basert på at datamaskiner lærer å løse et problem basert på noen data, kalles **maskinlæring**. Vi vil ikke gå inn på klassisk maskinlæring i dette kurset – vi henviser deg til et eget [Maskinlæring for nybegynnere](http://aka.ms/ml-beginners)-pensum. | ![ML for Beginners](../../../../translated_images/no/ml-for-beginners.9e4fed176fd5817d.webp) | ## En Kort Historie om AI Kunstig intelligens startet som et felt på midten av det tjuende århundre. Opprinnelig var symbolsk resonnering en dominerende tilnærming, og det førte til en rekke viktige suksesser, som ekspertsystemer – dataprogrammer som kunne opptre som en ekspert innenfor noen begrensede problemområder. Det ble imidlertid raskt klart at en slik tilnærming ikke skalerer godt. Å trekke ut kunnskap fra en ekspert, representere det i en datamaskin og holde kunnskapsbasen nøyaktig viste seg å være en svært kompleks oppgave, og for kostbar til å være praktisk i mange tilfeller. Dette førte til den såkalte [AI-vinteren](https://en.wikipedia.org/wiki/AI_winter) på 1970-tallet. -Kort Historie om AI +Kort Historie om AI > Bilde av [Dmitry Soshnikov](http://soshnikov.com) @@ -124,7 +124,7 @@ På samme måte kan vi se hvordan tilnærmingen til å lage "snakkende programme * Moderne assistenter, som Cortana, Siri eller Google Assistant, er alle hybridsystemer som bruker nevrale nettverk for å konvertere tale til tekst og gjenkjenne vår intensjon, og deretter benytter noe resonnering eller eksplisitte algoritmer for å utføre nødvendige handlinger. * I fremtiden kan vi forvente en komplett nevrale-basert modell som håndterer dialog helt på egen hånd. De nylige GPT- og [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft)-familiene av nevrale nettverk viser stor suksess i dette. -Turing-testens utvikling +Turing-testens utvikling > Bilde av Dmitry Soshnikov, [foto](https://unsplash.com/photos/r8LmVbUKgns) av [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto), Unsplash ## Nyere AI-forskning diff --git a/translations/no/lessons/2-Symbolic/Animals.ipynb b/translations/no/lessons/2-Symbolic/Animals.ipynb index 62d67b14..5f4f209e 100644 --- a/translations/no/lessons/2-Symbolic/Animals.ipynb +++ b/translations/no/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "I dette eksempelet skal vi implementere et enkelt kunnskapsbasert system for å identifisere et dyr basert på noen fysiske egenskaper. Systemet kan representeres av følgende AND-OR-tre (dette er en del av hele treet, vi kan enkelt legge til flere regler):\n", "\n", - "![](../../../../translated_images/no/AND-OR-Tree.5592d2c70187f283.png)\n" + "![](../../../../translated_images/no/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { diff --git a/translations/no/lessons/2-Symbolic/README.md b/translations/no/lessons/2-Symbolic/README.md index e04c4edc..1f021465 100644 --- a/translations/no/lessons/2-Symbolic/README.md +++ b/translations/no/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Kunnskapsrepresentasjon og ekspertsystemer -![Oppsummering av Symbolic AI-innhold](../../../../translated_images/no/ai-symbolic.715a30cb610411a6.png) +![Oppsummering av Symbolic AI-innhold](../../../../translated_images/no/ai-symbolic.715a30cb610411a6.webp) > Sketchnote av [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ Ofte definerer vi ikke kunnskap strengt, men vi knytter det til andre relaterte Dermed er problemet med **kunnskapsrepresentasjon** å finne en effektiv måte å representere kunnskap inne i en datamaskin i form av data, slik at den kan brukes automatisk. Dette kan sees som et spektrum: -![Spektrum for kunnskapsrepresentasjon](../../../../translated_images/no/knowledge-spectrum.b60df631852c0217.png) +![Spektrum for kunnskapsrepresentasjon](../../../../translated_images/no/knowledge-spectrum.b60df631852c0217.webp) > Bilde av [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Blokk-syntaks | Innrykk | | | En av de tidlige suksessene til symbolsk AI var de såkalte **ekspertsystemene** - datasystemer som var designet for å fungere som en ekspert innenfor et begrenset problemområde. De var basert på en **kunnskapsbase** hentet fra en eller flere menneskelige eksperter, og de inneholdt en **slutningsmotor** som utførte resonnering basert på denne kunnskapen. -![Menneskelig arkitektur](../../../../translated_images/no/arch-human.5d4d35f1bba3ab1c.png) | ![Kunnskapsbasert system](../../../../translated_images/no/arch-kbs.3ec5c150b09fa8da.png) +![Menneskelig arkitektur](../../../../translated_images/no/arch-human.5d4d35f1bba3ab1c.webp) | ![Kunnskapsbasert system](../../../../translated_images/no/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ Forenklet struktur av et menneskelig nervesystem | Arkitektur av et kunnskapsbasert system @@ -106,7 +106,7 @@ Ekspertsystemer er bygget som det menneskelige resonnanssystemet, som inneholder Som et eksempel, la oss se på følgende ekspertsystem for å bestemme et dyr basert på dets fysiske egenskaper: -![AND-OR-tre](../../../../translated_images/no/AND-OR-Tree.5592d2c70187f283.png) +![AND-OR-tre](../../../../translated_images/no/AND-OR-Tree.5592d2c70187f283.webp) > Bilde av [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/no/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/no/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index fcbd3b1d..fa10f196 100644 --- a/translations/no/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/no/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -1257,7 +1257,7 @@ "* Lav treningsfeil - modellen kan tilpasse seg treningsdata godt, fordi den har nok uttrykkskraft.\n", "* Valideringsfeilen kan være mye høyere enn treningsfeilen og kan begynne å øke under treningen - dette skjer fordi modellen \"husker\" treningspunktene og mister \"helhetsbildet\".\n", "\n", - "![Overtilpasning](../../../../../translated_images/no/overfit.a0bd57f717c15769.png)\n", + "![Overtilpasning](../../../../../translated_images/no/overfit.a0bd57f717c15769.webp)\n", "\n", "> På dette bildet står `x` for treningsdata, `o` for valideringsdata. Til venstre - lineær modell (ett lag), den tilpasser seg naturen til dataen ganske bra. Til høyre - overtilpasset modell, modellen tilpasser seg treningsdata perfekt, men gir ingen mening for annen data (valideringsfeilen er veldig høy).\n" ] diff --git a/translations/no/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/no/lessons/3-NeuralNetworks/05-Frameworks/README.md index fa6e75b9..7b08afa9 100644 --- a/translations/no/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/no/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Overtilpasning er et ekstremt viktig konsept innen maskinlæring, og det er veld Tenk på følgende problem med å tilnærme 5 punkter (representert med `x` på grafene nedenfor): -![linear](../../../../../translated_images/no/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/no/overfit2.131f5800ae10ca5e.jpg) +![linear](../../../../../translated_images/no/overfit1.f24b71c6f652e59e.webp) | ![overfit](../../../../../translated_images/no/overfit2.131f5800ae10ca5e.webp) -------------------------|-------------------------- **Lineær modell, 2 parametere** | **Ikke-lineær modell, 7 parametere** Treningsfeil = 5.3 | Treningsfeil = 0 @@ -79,7 +79,7 @@ Det er veldig viktig å finne en riktig balanse mellom modellens kompleksitet (a Som du kan se fra grafen ovenfor, kan overtilpasning oppdages ved en veldig lav treningsfeil og en høy valideringsfeil. Normalt under trening vil vi se både trenings- og valideringsfeil begynne å avta, og deretter på et tidspunkt kan valideringsfeilen slutte å avta og begynne å stige. Dette vil være et tegn på overtilpasning og en indikator på at vi sannsynligvis bør stoppe treningen på dette punktet (eller i det minste ta et øyeblikksbilde av modellen). -![overfitting](../../../../../translated_images/no/Overfitting.408ad91cd90b4371.png) +![overfitting](../../../../../translated_images/no/Overfitting.408ad91cd90b4371.webp) ## Hvordan forhindre overtilpasning diff --git a/translations/no/lessons/3-NeuralNetworks/README.md b/translations/no/lessons/3-NeuralNetworks/README.md index b41109db..ff17a345 100644 --- a/translations/no/lessons/3-NeuralNetworks/README.md +++ b/translations/no/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Introduksjon til nevrale nettverk -![Oppsummering av innholdet i Intro Neural Networks i en tegning](../../../../translated_images/no/ai-neuralnetworks.1c687ae40bc86e83.png) +![Oppsummering av innholdet i Intro Neural Networks i en tegning](../../../../translated_images/no/ai-neuralnetworks.1c687ae40bc86e83.webp) Som vi diskuterte i introduksjonen, er en av måtene å oppnå intelligens på å trene en **datamodell** eller en **kunstig hjerne**. Siden midten av 1900-tallet har forskere prøvd ulike matematiske modeller, og i de senere år har denne retningen vist seg å være svært vellykket. Slike matematiske modeller av hjernen kalles **nevrale nettverk**. @@ -36,13 +36,13 @@ I dette pensumet vil vi kun fokusere på modeller for nevrale nettverk. Fra biologien vet vi at hjernen vår består av nerveceller (nevroner), som hver har flere "innganger" (dendritter) og en enkelt "utgang" (akson). Både dendritter og aksoner kan lede elektriske signaler, og forbindelsene mellom dem — kjent som synapser — kan ha varierende grad av ledningsevne, som reguleres av nevrotransmittere. -![Modell av et nevron](../../../../translated_images/no/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![Modell av et nevron](../../../../translated_images/no/artneuron.1a5daa88d20ebe6f.png) +![Modell av et nevron](../../../../translated_images/no/synapse-wikipedia.ed20a9e4726ea1c6.webp) | ![Modell av et nevron](../../../../translated_images/no/artneuron.1a5daa88d20ebe6f.webp) ----|---- Ekte nevron *([Bilde](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) fra Wikipedia)* | Kunstig nevron *(Bilde av forfatteren)* Dermed inneholder den enkleste matematiske modellen av et nevron flere innganger X1, ..., XN og en utgang Y, samt en serie vekter W1, ..., WN. En utgang beregnes som: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) der f er en ikke-lineær **aktiveringsfunksjon**. diff --git a/translations/no/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/no/lessons/4-ComputerVision/06-IntroCV/README.md index 9cc3410c..fd627c0a 100644 --- a/translations/no/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/no/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ I vår [OpenCV Notebook](OpenCV.ipynb) gir vi noen eksempler på når datamaskin * **Forhåndsbehandling av et fotografi av en Braille-bok**. Vi fokuserer på hvordan vi kan bruke terskling, funksjonsdeteksjon, perspektivtransformasjon og NumPy-manipulasjoner for å separere individuelle Braille-symboler for videre klassifisering av et nevralt nettverk. -![Braille-bilde](../../../../../translated_images/no/braille.341962ff76b1bd70.jpeg) | ![Forhåndsbehandlet Braille-bilde](../../../../../translated_images/no/braille-result.46530fea020b03c7.png) | ![Braille-symboler](../../../../../translated_images/no/braille-symbols.0159185ab69d5339.png) +![Braille-bilde](../../../../../translated_images/no/braille.341962ff76b1bd70.webp) | ![Forhåndsbehandlet Braille-bilde](../../../../../translated_images/no/braille-result.46530fea020b03c7.webp) | ![Braille-symboler](../../../../../translated_images/no/braille-symbols.0159185ab69d5339.webp) ----|-----|----- > Bilde fra [OpenCV.ipynb](OpenCV.ipynb) * **Deteksjon av bevegelse i video ved hjelp av rammeforskjell**. Hvis kameraet er fast, bør rammer fra kamerafeeden være ganske like hverandre. Siden rammer er representert som arrays, vil vi ved å trekke fra disse arrayene for to påfølgende rammer få pikselforskjellen, som bør være lav for statiske rammer, og bli høyere når det er betydelig bevegelse i bildet. -![Bilde av videorammer og rammeforskjeller](../../../../../translated_images/no/frame-difference.706f805491a0883c.png) +![Bilde av videorammer og rammeforskjeller](../../../../../translated_images/no/frame-difference.706f805491a0883c.webp) > Bilde fra [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ I vår [OpenCV Notebook](OpenCV.ipynb) gir vi noen eksempler på når datamaskin - **Tett optisk flyt** beregner vektorfeltet som viser hvor hver piksel beveger seg. - **Sparsom optisk flyt** er basert på å ta noen distinkte funksjoner i bildet (f.eks. kanter) og bygge deres bane fra ramme til ramme. -![Bilde av optisk flyt](../../../../../translated_images/no/optical.1f4a94464579a83a.png) +![Bilde av optisk flyt](../../../../../translated_images/no/optical.1f4a94464579a83a.webp) > Bilde fra [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/no/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/no/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index ec019299..32840dda 100644 --- a/translations/no/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/no/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 er et nettverk som oppnådde 92,7 % nøyaktighet i ImageNet top-5 klassifisering i 2014. Det har følgende lagstruktur: -![ImageNet Layers](../../../../../translated_images/no/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet Layers](../../../../../translated_images/no/vgg-16-arch1.d901a5583b3a51ba.webp) Som du kan se, følger VGG en tradisjonell pyramidearkitektur, som er en sekvens av konvolusjons- og pooling-lag. -![ImageNet Pyramid](../../../../../translated_images/no/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet Pyramid](../../../../../translated_images/no/vgg-16-arch.64ff2137f50dd49f.webp) > Bilde fra [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/no/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/no/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index 4c04b11c..5377eb34 100644 --- a/translations/no/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/no/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -262,7 +262,7 @@ "\n", "Dermed vil en typisk CNN ha flere konvolusjonslag, med pooling-lag mellom dem for å redusere dimensjonene på bildet. Vi vil også øke antallet filtre, fordi når mønstrene blir mer avanserte, er det flere mulige interessante kombinasjoner vi må se etter.\n", "\n", - "![Et bilde som viser flere konvolusjonslag med pooling-lag.](../../../../../translated_images/no/cnn-pyramid.85915455759ef0ce.png)\n", + "![Et bilde som viser flere konvolusjonslag med pooling-lag.](../../../../../translated_images/no/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "På grunn av reduserte romlige dimensjoner og økte funksjons-/filterdimensjoner, kalles denne arkitekturen også **pyramidearkitektur**.\n" ] diff --git a/translations/no/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/no/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index eb511605..793eec5c 100644 --- a/translations/no/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/no/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -360,7 +360,7 @@ "\n", "Dermed vil en typisk CNN ha flere konvolusjonslag, med pooling-lag mellom dem for å redusere dimensjonene på bildet. Vi vil også øke antallet filtre, fordi når mønstrene blir mer avanserte, er det flere mulige interessante kombinasjoner vi må se etter.\n", "\n", - "![Et bilde som viser flere konvolusjonslag med pooling-lag.](../../../../../translated_images/no/cnn-pyramid.85915455759ef0ce.png)\n", + "![Et bilde som viser flere konvolusjonslag med pooling-lag.](../../../../../translated_images/no/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "På grunn av reduserte romlige dimensjoner og økte funksjons-/filterdimensjoner, kalles denne arkitekturen også **pyramidearkitektur**.\n" ] diff --git a/translations/no/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/no/lessons/4-ComputerVision/07-ConvNets/README.md index ada5232a..f01067aa 100644 --- a/translations/no/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/no/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ I virkeligheten ønsker vi å kunne gjenkjenne objekter på et bilde uavhengig a For å trekke ut mønstre, vil vi bruke begrepet **konvolusjonsfiltre**. Som du vet, er et bilde representert av en 2D-matrise, eller en 3D-tensor med fargedybde. Å bruke et filter betyr at vi tar en relativt liten **filterkjerne**-matrise, og for hver piksel i det originale bildet beregner vi det vektede gjennomsnittet med nabopunktene. Vi kan se på dette som et lite vindu som glir over hele bildet og jevner ut alle pikslene i henhold til vektene i filterkjernematrisen. -![Vertikalt kantfilter](../../../../../translated_images/no/filter-vert.b7148390ca0bc356.png) | ![Horisontalt kantfilter](../../../../../translated_images/no/filter-horiz.59b80ed4feb946ef.png) +![Vertikalt kantfilter](../../../../../translated_images/no/filter-vert.b7148390ca0bc356.webp) | ![Horisontalt kantfilter](../../../../../translated_images/no/filter-horiz.59b80ed4feb946ef.webp) ----|---- > Bilde av Dmitry Soshnikov @@ -38,7 +38,7 @@ Måten CNN-er fungerer på er basert på følgende viktige ideer: * Vi kan designe nettverket slik at filtrene trenes automatisk * Vi kan bruke samme tilnærming for å finne mønstre i høyere nivå-funksjoner, ikke bare i det originale bildet. Dermed fungerer CNN-funksjonsekstraksjon på en hierarki av funksjoner, fra lavnivå pikselkombinasjoner til høyere nivå kombinasjoner av bildeelementer. -![Hierarkisk funksjonsekstraksjon](../../../../../translated_images/no/FeatureExtractionCNN.d9b456cbdae7cb64.png) +![Hierarkisk funksjonsekstraksjon](../../../../../translated_images/no/FeatureExtractionCNN.d9b456cbdae7cb64.webp) > Bilde fra [en artikkel av Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), basert på [deres forskning](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ De fleste CNN-er som brukes til bildebehandling følger en såkalt pyramidearkit Som et eksempel, la oss se på arkitekturen til VGG-16, et nettverk som oppnådde 92,7 % nøyaktighet i ImageNet's topp-5 klassifisering i 2014: -![ImageNet-lag](../../../../../translated_images/no/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet-lag](../../../../../translated_images/no/vgg-16-arch1.d901a5583b3a51ba.webp) -![ImageNet-pyramide](../../../../../translated_images/no/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet-pyramide](../../../../../translated_images/no/vgg-16-arch.64ff2137f50dd49f.webp) > Bilde fra [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/no/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/no/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 97001f75..6b50467e 100644 --- a/translations/no/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/no/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ Du må trene et konvolusjonsnevralt nettverk for å klassifisere forskjellige ra Vi skal bruke [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/), som inneholder bilder av 37 forskjellige raser av hunder og katter. -![Datasettet vi skal jobbe med](../../../../../../translated_images/no/data.50b2a9d5484bdbf0.png) +![Datasettet vi skal jobbe med](../../../../../../translated_images/no/data.50b2a9d5484bdbf0.webp) For å laste ned datasettet, bruk denne kodebiten: diff --git a/translations/no/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/no/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index face15ad..9a9e8065 100644 --- a/translations/no/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/no/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "For å visualisere den ideelle katten, starter vi med et tilfeldig støybilde og bruker gradientnedstigning som optimaliseringsteknikk for å justere bildet slik at nettverket gjenkjenner en katt.\n", "\n", - "![Optimaliseringssløyfe](../../../../../translated_images/no/ideal-cat-loop.999fbb8ff306e044.png)\n", + "![Optimaliseringssløyfe](../../../../../translated_images/no/ideal-cat-loop.999fbb8ff306e044.webp)\n", "\n", "Her er vårt startbilde:\n" ] diff --git a/translations/no/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/no/lessons/4-ComputerVision/08-TransferLearning/README.md index 66e8296a..adc01564 100644 --- a/translations/no/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/no/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Både Keras og PyTorch inneholder funksjoner for enkelt å laste inn forhåndstr Her er eksempler på funksjoner som er trukket ut fra et bilde av en katt av VGG-16-nettverket: -![Funksjoner trukket ut av VGG-16](../../../../../translated_images/no/features.6291f9c7ba3a0b95.png) +![Funksjoner trukket ut av VGG-16](../../../../../translated_images/no/features.6291f9c7ba3a0b95.webp) ## Datasett: Katter vs. Hunder @@ -48,19 +48,19 @@ Et forhåndstrent nevralt nettverk inneholder ulike mønstre i sin *hjerne*, ink En tilnærming vi kan bruke, er å starte med et tilfeldig bilde og deretter bruke **gradient descent-optimalisering** for å justere bildet slik at nettverket begynner å tro at det er en katt. -![Bildeoptimaliseringssløyfe](../../../../../translated_images/no/ideal-cat-loop.999fbb8ff306e044.png) +![Bildeoptimaliseringssløyfe](../../../../../translated_images/no/ideal-cat-loop.999fbb8ff306e044.webp) Men hvis vi gjør dette, vil vi få noe som ligner veldig på tilfeldig støy. Dette er fordi *det finnes mange måter å få nettverket til å tro at inngangsbilde er en katt*, inkludert noen som ikke gir mening visuelt. Selv om disse bildene inneholder mange mønstre som er typiske for en katt, er det ingenting som begrenser dem til å være visuelt distinkte. For å forbedre resultatet kan vi legge til et annet ledd i tapsfunksjonen, som kalles **variasjonstap**. Dette er en metrikk som viser hvor like nabopikslene i bildet er. Ved å minimere variasjonstap blir bildet jevnere og støy fjernes, noe som avslører mer visuelt tiltalende mønstre. Her er et eksempel på slike "ideelle" bilder, som klassifiseres som katt og som sebra med høy sannsynlighet: -![Ideell Katt](../../../../../translated_images/no/ideal-cat.203dd4597643d6b0.png) | ![Ideell Sebra](../../../../../translated_images/no/ideal-zebra.7f70e8b54ee15a7a.png) +![Ideell Katt](../../../../../translated_images/no/ideal-cat.203dd4597643d6b0.webp) | ![Ideell Sebra](../../../../../translated_images/no/ideal-zebra.7f70e8b54ee15a7a.webp) -----|----- *Ideell Katt* | *Ideell Sebra* En lignende tilnærming kan brukes til å utføre såkalte **adversarielle angrep** på et nevralt nettverk. Anta at vi ønsker å lure et nevralt nettverk og få en hund til å se ut som en katt. Hvis vi tar et bilde av en hund, som nettverket gjenkjenner som en hund, kan vi justere det litt ved hjelp av gradient descent-optimalisering, til nettverket begynner å klassifisere det som en katt: -![Bilde av en Hund](../../../../../translated_images/no/original-dog.8f68a67d2fe0911f.png) | ![Bilde av en hund klassifisert som en katt](../../../../../translated_images/no/adversarial-dog.d9fc7773b0142b89.png) +![Bilde av en Hund](../../../../../translated_images/no/original-dog.8f68a67d2fe0911f.webp) | ![Bilde av en hund klassifisert som en katt](../../../../../translated_images/no/adversarial-dog.d9fc7773b0142b89.webp) -----|----- *Originalbilde av en hund* | *Bilde av en hund klassifisert som en katt* diff --git a/translations/no/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/no/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index 288bac64..b3f61e6b 100644 --- a/translations/no/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/no/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "Siden vi trener autoencoderen til å fange så mye informasjon som mulig fra det originale bildet for nøyaktig rekonstruksjon, prøver nettverket å finne den beste **innkapslingen** av inngangsbildene for å fange meningen.\n", "\n", - "![AutoEncoder Diagram](../../../../../translated_images/no/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![AutoEncoder Diagram](../../../../../translated_images/no/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "> Bilde fra [Keras-bloggen](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", diff --git a/translations/no/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/no/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index 40dbdd9a..a4267ce1 100644 --- a/translations/no/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/no/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "Siden vi trener autoencoderen til å fange opp så mye informasjon som mulig fra det originale bildet for å oppnå nøyaktig rekonstruksjon, prøver nettverket å finne den beste **innkapslingen** av inngangsbilder for å fange meningen.\n", "\n", - "![AutoEncoder Diagram](../../../../../translated_images/no/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![AutoEncoder Diagram](../../../../../translated_images/no/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "*Bilde fra [Keras-bloggen](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", diff --git a/translations/no/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/no/lessons/4-ComputerVision/09-Autoencoders/README.md index 3a3de3c0..cd88734d 100644 --- a/translations/no/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/no/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ Vi kan imidlertid ønske å bruke rå (umerkede) data for å trene CNN-funksjons Siden vi trener en autoencoder til å fange så mye informasjon som mulig fra det originale bildet for nøyaktig rekonstruksjon, prøver nettverket å finne den beste **embedding** av input-bilder for å fange meningen. -![AutoEncoder Diagram](../../../../../translated_images/no/autoencoder_schema.5e6fc9ad98a5eb61.jpg) +![AutoEncoder Diagram](../../../../../translated_images/no/autoencoder_schema.5e6fc9ad98a5eb61.webp) > Bilde fra [Keras-blogg](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/no/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/no/lessons/4-ComputerVision/11-ObjectDetection/README.md index 36f43b5d..fa31b8ca 100644 --- a/translations/no/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/no/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ Bildklassifiseringsmodellene vi har jobbet med så langt tar et bilde og gir et ## [Quiz før forelesning](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Objektgjenkjenning](../../../../../translated_images/no/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) +![Objektgjenkjenning](../../../../../translated_images/no/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp) > Bilde fra [YOLO v2 nettside](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ Anta at vi ønsket å finne en katt på et bilde. En veldig naiv tilnærming til 2. Kjør bildklassifisering på hver flis. 3. De flisene som gir tilstrekkelig høy aktivering kan anses å inneholde det aktuelle objektet. -![Naiv objektgjenkjenning](../../../../../translated_images/no/naive-detection.e7f1ba220ccd08c6.png) +![Naiv objektgjenkjenning](../../../../../translated_images/no/naive-detection.e7f1ba220ccd08c6.webp) > *Bilde fra [Øvingsnotatbok](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ Du kan komme over følgende datasett for denne oppgaven: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) – 20 klasser * [COCO](http://cocodataset.org/#home) – Common Objects in Context. 80 klasser, avgrensningsbokser og segmenteringsmasker -![COCO](../../../../../translated_images/no/coco-examples.71bc60380fa6cceb.jpg) +![COCO](../../../../../translated_images/no/coco-examples.71bc60380fa6cceb.webp) ## Objektgjenkjenningsmetrikker @@ -50,7 +50,7 @@ Du kan komme over følgende datasett for denne oppgaven: Mens det er enkelt å måle hvor godt algoritmen presterer for bildklassifisering, må vi for objektgjenkjenning måle både korrektheten av klassen og presisjonen til den utledede plasseringen av avgrensningsboksen. For sistnevnte bruker vi den såkalte **Intersection over Union** (IoU), som måler hvor godt to bokser (eller to vilkårlige områder) overlapper. -![IoU](../../../../../translated_images/no/iou_equation.9a4751d40fff4e11.png) +![IoU](../../../../../translated_images/no/iou_equation.9a4751d40fff4e11.webp) > *Figur 2 fra [dette utmerkede blogginnlegget om IoU](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ Det finnes to brede klasser av algoritmer for objektgjenkjenning: [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) bruker [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) for å generere en hierarkisk struktur av ROI-regioner, som deretter sendes gjennom CNN-funksjonsekstraktorer og SVM-klassifisatorer for å bestemme objektklassen, og lineær regresjon for å bestemme *avgrensningsboks*-koordinater. [Offisiell artikkel](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/no/rcnn1.cae407020dfb1d1f.png) +![RCNN](../../../../../translated_images/no/rcnn1.cae407020dfb1d1f.webp) > *Bilde fra van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/no/rcnn2.2d9530bb83516484.png) +![RCNN-1](../../../../../translated_images/no/rcnn2.2d9530bb83516484.webp) > *Bilder fra [denne bloggen](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -110,7 +110,7 @@ Det finnes to brede klasser av algoritmer for objektgjenkjenning: Denne tilnærmingen ligner på R-CNN, men regioner defineres etter at konvolusjonslagene er blitt brukt. -![FRCNN](../../../../../translated_images/no/f-rcnn.3cda6d9bb4188875.png) +![FRCNN](../../../../../translated_images/no/f-rcnn.3cda6d9bb4188875.webp) > Bilde fra [den offisielle artikkelen](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ Denne tilnærmingen ligner på R-CNN, men regioner defineres etter at konvolusjo Hovedideen med denne tilnærmingen er å bruke et nevralt nettverk til å forutsi ROI-er – såkalte *Region Proposal Network*. [Artikkel](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/no/faster-rcnn.8d46c099b87ef30a.png) +![FasterRCNN](../../../../../translated_images/no/faster-rcnn.8d46c099b87ef30a.webp) > Bilde fra [den offisielle artikkelen](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ Denne algoritmen er enda raskere enn Faster R-CNN. Hovedideen er følgende: 2. Funksjonene behandles av **Position-Sensitive Score Map**. Hvert objekt fra $C$ klasser deles inn i $k\times k$ regioner, og vi trener på å forutsi deler av objekter. 3. For hver del fra $k\times k$ regioner stemmer alle nettverkene på objektklasser, og objektklassen med flest stemmer blir valgt. -![r-fcn bilde](../../../../../translated_images/no/r-fcn.13eb88158b99a3da.png) +![r-fcn bilde](../../../../../translated_images/no/r-fcn.13eb88158b99a3da.webp) > Bilde fra [offisiell artikkel](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO er en sanntids én-pass-algoritme. Hovedideen er følgende: * Bildet deles inn i $S\times S$ regioner. * For hver region forutsier **CNN** $n$ mulige objekter, *avgrensningsboks*-koordinater og *confidence*=*sannsynlighet* * IoU. - ![YOLO](../../../../../translated_images/no/yolo.a2648ec82ee8bb4e.png) + ![YOLO](../../../../../translated_images/no/yolo.a2648ec82ee8bb4e.webp) > Bilde fra [offisiell artikkel](https://arxiv.org/abs/1506.02640) diff --git a/translations/no/lessons/4-ComputerVision/README.md b/translations/no/lessons/4-ComputerVision/README.md index 8dee9d04..2877b89f 100644 --- a/translations/no/lessons/4-ComputerVision/README.md +++ b/translations/no/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Datamaskinsyn -![Sammendrag av innholdet om datamaskinsyn i en skisse](../../../../translated_images/no/ai-computervision.6506ebebac3fbf76.png) +![Sammendrag av innholdet om datamaskinsyn i en skisse](../../../../translated_images/no/ai-computervision.6506ebebac3fbf76.webp) I denne delen skal vi lære om: diff --git a/translations/no/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/no/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index 77ee0cac..69059cc9 100644 --- a/translations/no/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/no/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -197,7 +197,7 @@ "\n", "**Bag of Words** (BoW) vektorrepresentasjon er den mest brukte tradisjonelle vektorrepresentasjonen. Hvert ord er knyttet til en vektorindeks, og hvert element i vektoren inneholder antall forekomster av et ord i et gitt dokument.\n", "\n", - "![Bilde som viser hvordan en bag of words-vektorrepresentasjon lagres i minnet.](../../../../../translated_images/no/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![Bilde som viser hvordan en bag of words-vektorrepresentasjon lagres i minnet.](../../../../../translated_images/no/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Merk**: Du kan også tenke på BoW som en sum av alle én-hot-kodede vektorer for de individuelle ordene i teksten.\n", "\n", diff --git a/translations/no/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/no/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index 48990a9f..bbe65483 100644 --- a/translations/no/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/no/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**Bag-of-words** (BoW) vektorrepresentasjon er den enkleste tradisjonelle vektorrepresentasjonen å forstå. Hvert ord er knyttet til en vektorindeks, og et vektorelement inneholder antall forekomster av hvert ord i et gitt dokument.\n", "\n", - "![Bilde som viser hvordan en bag-of-words vektorrepresentasjon lagres i minnet.](../../../../../translated_images/no/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![Bilde som viser hvordan en bag-of-words vektorrepresentasjon lagres i minnet.](../../../../../translated_images/no/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Note**: Du kan også tenke på BoW som en sum av alle én-hot-kodede vektorer for individuelle ord i teksten.\n", "\n", diff --git a/translations/no/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/no/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 9efe9fe4..e50cddcc 100644 --- a/translations/no/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/no/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "Ved å bruke embedding-laget som det første laget i vårt nettverk, kan vi gå fra bag-of-words til **embedding bag**-modellen, hvor vi først konverterer hvert ord i teksten vår til tilsvarende embedding, og deretter beregner en aggregatfunksjon over alle disse embeddingene, som for eksempel `sum`, `average` eller `max`. \n", "\n", - "![Bilde som viser en embedding-klassifiserer for fem sekvensord.](../../../../../translated_images/no/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![Bilde som viser en embedding-klassifiserer for fem sekvensord.](../../../../../translated_images/no/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "Vårt klassifiserings-nevrale nettverk vil starte med embedding-lag, deretter et aggregasjonslag, og en lineær klassifiserer på toppen av det:\n" ] @@ -176,7 +176,7 @@ "\n", "I den tidligere arkitekturen måtte vi fylle alle sekvenser til samme lengde for å passe dem inn i en minibatch. Dette er ikke den mest effektive måten å representere sekvenser med variabel lengde på - en annen tilnærming ville være å bruke en **offset**-vektor, som holder offsetene til alle sekvenser lagret i én stor vektor.\n", "\n", - "![Bilde som viser en offset-sekvensrepresentasjon](../../../../../translated_images/no/offset-sequence-representation.eb73fcefb29b46ee.png)\n", + "![Bilde som viser en offset-sekvensrepresentasjon](../../../../../translated_images/no/offset-sequence-representation.eb73fcefb29b46ee.webp)\n", "\n", "> **Note**: På bildet ovenfor viser vi en sekvens av tegn, men i vårt eksempel jobber vi med sekvenser av ord. Prinsippet for å representere sekvenser med en offset-vektor forblir imidlertid det samme.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW er raskere, mens skip-gram er tregere, men gjør en bedre jobb med å representere sjeldne ord.\n", "\n", - "![Bilde som viser både CBoW- og Skip-Gram-algoritmer for å konvertere ord til vektorer.](../../../../../translated_images/no/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![Bilde som viser både CBoW- og Skip-Gram-algoritmer for å konvertere ord til vektorer.](../../../../../translated_images/no/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "For å eksperimentere med word2vec-embedding forhåndstrent på Google News-datasettet, kan vi bruke **gensim**-biblioteket. Nedenfor finner vi ordene som ligner mest på 'neural'\n", "\n", diff --git a/translations/no/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/no/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index cb54e2e0..33951e3e 100644 --- a/translations/no/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/no/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "Ved å bruke et embedding-lag som det første laget i nettverket vårt, kan vi gå fra bag-of-words til en **embedding bag**-modell, der vi først konverterer hvert ord i teksten vår til den tilsvarende embedding, og deretter beregner en aggregasjonsfunksjon over alle disse embeddingene, som for eksempel `sum`, `average` eller `max`. \n", "\n", - "![Bilde som viser en embedding-klassifiserer for fem sekvensord.](../../../../../translated_images/no/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![Bilde som viser en embedding-klassifiserer for fem sekvensord.](../../../../../translated_images/no/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "Vårt klassifiserings-nevrale nettverk består av følgende lag:\n", "\n", @@ -281,7 +281,7 @@ "\n", "CBoW er raskere, mens skip-gram er tregere, men det gjør en bedre jobb med å representere sjeldne ord.\n", "\n", - "![Bilde som viser både CBoW- og Skip-Gram-algoritmer for å konvertere ord til vektorer.](../../../../../translated_images/no/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![Bilde som viser både CBoW- og Skip-Gram-algoritmer for å konvertere ord til vektorer.](../../../../../translated_images/no/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "For å eksperimentere med Word2Vec-embedding forhåndstrent på Google News-datasettet, kan vi bruke **gensim**-biblioteket. Nedenfor finner vi ordene som ligner mest på 'neural'.\n", "\n", diff --git a/translations/no/lessons/5-NLP/14-Embeddings/README.md b/translations/no/lessons/5-NLP/14-Embeddings/README.md index f293e235..98b03b16 100644 --- a/translations/no/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/no/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ Så, innebyggingslaget vil ta et ord som input og produsere en output-vektor med Ved å bruke et innebyggingslag som første lag i vårt klassifikatornettverk, kan vi bytte fra en bag-of-words til **embedding bag**-modell, hvor vi først konverterer hvert ord i teksten vår til tilsvarende innebygging, og deretter beregner en aggregatfunksjon over alle disse innebyggingene, som `sum`, `average` eller `max`. -![Bilde som viser en innebyggingsklassifikator for fem sekvensord.](../../../../../translated_images/no/embedding-classifier-example.b77f021a7ee67eee.png) +![Bilde som viser en innebyggingsklassifikator for fem sekvensord.](../../../../../translated_images/no/embedding-classifier-example.b77f021a7ee67eee.webp) > Bilde av forfatteren @@ -40,7 +40,7 @@ For å oppnå dette må vi forhåndstrene innebyggingsmodellen vår på en stor CBoW er raskere, mens skip-gram er tregere, men gjør en bedre jobb med å representere sjeldne ord. -![Bilde som viser både CBoW- og Skip-Gram-algoritmer for å konvertere ord til vektorer.](../../../../../translated_images/no/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![Bilde som viser både CBoW- og Skip-Gram-algoritmer for å konvertere ord til vektorer.](../../../../../translated_images/no/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Bilde fra [denne artikkelen](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/no/lessons/5-NLP/15-LanguageModeling/README.md b/translations/no/lessons/5-NLP/15-LanguageModeling/README.md index fd3b951a..2f169b4a 100644 --- a/translations/no/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/no/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ I våre tidligere eksempler brukte vi forhåndstrente semantiske embeddinger, me * **Continuous Bag-of-Words** (CBoW), der vi forutsier det midterste tokenet $W_0$ i en sekvens av token $W_{-N}$, ..., $W_N$. * **Skip-gram**, der vi forutsier et sett av nabotoken {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} fra det midterste tokenet $W_0$. -![bilde fra artikkel om konvertering av ord til vektorer](../../../../../translated_images/no/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![bilde fra artikkel om konvertering av ord til vektorer](../../../../../translated_images/no/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Bilde fra [denne artikkelen](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/no/lessons/5-NLP/16-RNN/README.md b/translations/no/lessons/5-NLP/16-RNN/README.md index 18297499..5124a725 100644 --- a/translations/no/lessons/5-NLP/16-RNN/README.md +++ b/translations/no/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ I tidligere seksjoner har vi brukt rike semantiske representasjoner av tekst og For å fange betydningen av tekstsekvenser, må vi bruke en annen nevralt nettverksarkitektur, som kalles et **rekurrent nevralt nettverk**, eller RNN. I RNN sender vi setningen vår gjennom nettverket én symbol om gangen, og nettverket produserer en **tilstand**, som vi deretter sender tilbake til nettverket sammen med neste symbol. -![RNN](../../../../../translated_images/no/rnn.27f5c29c53d727b5.png) +![RNN](../../../../../translated_images/no/rnn.27f5c29c53d727b5.webp) > Bilde av forfatteren @@ -61,7 +61,7 @@ Vi har diskutert rekurrente nettverk som opererer i én retning, fra begynnelsen Et rekurrent nettverk, enten én-retning eller bidireksjonalt, fanger visse mønstre innen en sekvens og kan lagre dem i en tilstandsvektor eller sende dem til output. Som med konvolusjonsnettverk, kan vi bygge et annet rekurrent lag oppå det første for å fange høyere nivå mønstre og bygge fra lavnivå mønstre som er hentet ut av det første laget. Dette leder oss til begrepet **flerlags RNN**, som består av to eller flere rekurrente nettverk, der output fra det forrige laget sendes til neste lag som input. -![Bilde som viser et flerlags long-short-term-memory-RNN](../../../../../translated_images/no/multi-layer-lstm.dd975e29bb2a59fe.jpg) +![Bilde som viser et flerlags long-short-term-memory-RNN](../../../../../translated_images/no/multi-layer-lstm.dd975e29bb2a59fe.webp) *Bilde fra [denne fantastiske posten](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) av Fernando López* diff --git a/translations/no/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/no/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index fe4b8acf..e9b97f21 100644 --- a/translations/no/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/no/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -420,7 +420,7 @@ "\n", "Et rekurrent nettverk, enten det er énveis eller toveis, fanger visse mønstre innen en sekvens og kan lagre dem i tilstandsvektoren eller sende dem til utgangen. Som med konvolusjonsnettverk kan vi bygge et annet rekurrent lag oppå det første for å fange mønstre på høyere nivå, bygget fra lavnivåmønstre som det første laget har hentet ut. Dette leder oss til begrepet **flerlags RNN**, som består av to eller flere rekurrente nettverk, der utgangen fra det forrige laget sendes til det neste laget som inngang.\n", "\n", - "![Bilde som viser et flerlags lang-korttidsminne-RNN](../../../../../translated_images/no/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![Bilde som viser et flerlags lang-korttidsminne-RNN](../../../../../translated_images/no/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*Bilde fra [denne fantastiske artikkelen](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) av Fernando López*\n", "\n", diff --git a/translations/no/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/no/lessons/5-NLP/16-RNN/RNNTF.ipynb index 971d122a..44107ad6 100644 --- a/translations/no/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/no/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "For å fange meningen av en tekstsekvens, vil vi bruke en nevralt nettverksarkitektur kalt **rekurrente nevrale nettverk**, eller RNN. Når vi bruker en RNN, sender vi setningen vår gjennom nettverket én token om gangen, og nettverket produserer en **tilstand**, som vi deretter sender tilbake til nettverket sammen med neste token.\n", "\n", - "![Bilde som viser et eksempel på generering med rekurrente nevrale nettverk.](../../../../../translated_images/no/rnn.27f5c29c53d727b5.png)\n", + "![Bilde som viser et eksempel på generering med rekurrente nevrale nettverk.](../../../../../translated_images/no/rnn.27f5c29c53d727b5.webp)\n", "\n", "Gitt en inngangssekvens av tokenene $X_0,\\dots,X_n$, lager RNN-en en sekvens av nevrale nettverksblokker og trener denne sekvensen ende-til-ende ved hjelp av backpropagation. Hver nettverksblokk tar et par $(X_i,S_i)$ som input og produserer $S_{i+1}$ som resultat. Den endelige tilstanden $S_n$ eller utgangen $Y_n$ går inn i en lineær klassifiserer for å produsere resultatet. Alle nettverksblokker deler de samme vektene og trenes ende-til-ende med én backpropagation-passering.\n", "\n", @@ -369,7 +369,7 @@ "\n", "Rekurrente nettverk, enten de er enveis eller toveis, fanger opp mønstre innen en sekvens og lagrer dem i tilstandsvektorer eller returnerer dem som output. Akkurat som med konvolusjonsnettverk, kan vi bygge et annet rekurrent lag etter det første for å fange opp mønstre på et høyere nivå, bygget fra mønstre på lavere nivå som det første laget har hentet ut. Dette leder oss til begrepet **flerlags RNN**, som består av to eller flere rekurrente nettverk, der output fra det forrige laget sendes videre til det neste laget som input.\n", "\n", - "![Bilde som viser et flerlags lang-korttidsminne-RNN](../../../../../translated_images/no/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![Bilde som viser et flerlags lang-korttidsminne-RNN](../../../../../translated_images/no/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*Bilde fra [denne fantastiske artikkelen](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) av Fernando López.*\n", "\n", diff --git a/translations/no/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/no/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index 6b09b2ad..fd0c73c0 100644 --- a/translations/no/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/no/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "Måten vi skal trene en RNN til å generere tekst på, er som følger. For hvert steg tar vi en sekvens av tegn med lengde `nchars`, og ber nettverket generere neste utgangstegn for hvert inngangstegn:\n", "\n", - "![Bilde som viser et eksempel på RNN-generering av ordet 'HELLO'.](../../../../../translated_images/no/rnn-generate.56c54afb52f9781d.png)\n", + "![Bilde som viser et eksempel på RNN-generering av ordet 'HELLO'.](../../../../../translated_images/no/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "Avhengig av det faktiske scenariet, kan vi også ønske å inkludere noen spesialtegn, som *slutt-på-sekvens* ``. I vårt tilfelle ønsker vi bare å trene nettverket for uendelig tekstgenerering, så vi vil fastsette størrelsen på hver sekvens til å være lik `nchars` tokens. Følgelig vil hvert treningseksempel bestå av `nchars` innganger og `nchars` utganger (som er inngangssekvensen forskjøvet én symbol til venstre). Minibatcher vil bestå av flere slike sekvenser.\n", "\n", diff --git a/translations/no/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/no/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index 63d11999..26b579b4 100644 --- a/translations/no/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/no/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -111,7 +111,7 @@ "\n", "Måten vi skal trene RNN til å generere nyhetstitler på er som følger. For hvert steg tar vi én tittel, som mates inn i en RNN, og for hvert inndata-tegn ber vi nettverket om å generere neste utdata-tegn:\n", "\n", - "![Bilde som viser et eksempel på RNN-generering av ordet 'HELLO'.](../../../../../translated_images/no/rnn-generate.56c54afb52f9781d.png)\n", + "![Bilde som viser et eksempel på RNN-generering av ordet 'HELLO'.](../../../../../translated_images/no/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "For det siste tegnet i sekvensen vår ber vi nettverket om å generere ``-token.\n", "\n", diff --git a/translations/no/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/no/lessons/5-NLP/17-GenerativeNetworks/README.md index 41b2fd58..934d7afd 100644 --- a/translations/no/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/no/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ I RNN-arkitekturen vi diskuterte i forrige enhet, produserte hver RNN-enhet den Dette åpner for ulike nevrale arkitekturer som vist i bildet nedenfor: -![Bilde som viser vanlige mønstre for rekurrente nevrale nettverk.](../../../../../translated_images/no/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Bilde som viser vanlige mønstre for rekurrente nevrale nettverk.](../../../../../translated_images/no/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > Bilde fra blogginnlegget [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) av [Andrej Karpaty](http://karpathy.github.io/) @@ -32,7 +32,7 @@ I denne enheten vil vi fokusere på enkle generative modeller som hjelper oss me Vi vil trene denne RNN-en til å generere tekst steg for steg. På hvert steg tar vi en sekvens av tegn med lengde `nchars` og ber nettverket generere neste output-tegn for hvert input-tegn: -![Bilde som viser et eksempel på RNN-generering av ordet 'HELLO'.](../../../../../translated_images/no/rnn-generate.56c54afb52f9781d.png) +![Bilde som viser et eksempel på RNN-generering av ordet 'HELLO'.](../../../../../translated_images/no/rnn-generate.56c54afb52f9781d.webp) Når vi genererer tekst (under inferens), starter vi med en **prompt**, som sendes gjennom RNN-celler for å generere dens mellomliggende tilstand, og deretter starter genereringen fra denne tilstanden. Vi genererer ett tegn om gangen og sender tilstanden og det genererte tegnet til en annen RNN-celle for å generere det neste, helt til vi har generert nok tegn. diff --git a/translations/no/lessons/5-NLP/18-Transformers/README.md b/translations/no/lessons/5-NLP/18-Transformers/README.md index 54fcc808..a7b369d3 100644 --- a/translations/no/lessons/5-NLP/18-Transformers/README.md +++ b/translations/no/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ Med RNN-er implementeres sekvens-til-sekvens med to rekurrente nettverk, hvor et **Oppmerksomhetsmekanismer** gir en måte å vekte den kontekstuelle innvirkningen av hver inngangsvektor på hver utgangsprediksjon av RNN. Dette implementeres ved å lage snarveier mellom mellomliggende tilstander i inngangs-RNN og utgangs-RNN. På denne måten, når vi genererer utgangssymbolet yt, tar vi hensyn til alle skjulte inngangstilstander hi, med forskjellige vektkoeffisienter αt,i. -![Bilde som viser en enkoder/dekoder-modell med et additivt oppmerksomhetslag](../../../../../translated_images/no/encoder-decoder-attention.7a726296894fb567.png) +![Bilde som viser en enkoder/dekoder-modell med et additivt oppmerksomhetslag](../../../../../translated_images/no/encoder-decoder-attention.7a726296894fb567.webp) > Enkoder-dekoder-modellen med additiv oppmerksomhetsmekanisme i [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), sitert fra [denne bloggposten](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) Oppmerksomhetsmatrisen {αi,j} representerer graden av innflytelse visse inngangsord har på genereringen av et gitt ord i utgangssekvensen. Nedenfor er et eksempel på en slik matrise: -![Bilde som viser et eksempel på justering funnet av RNNsearch-50, hentet fra Bahdanau - arviz.org](../../../../../translated_images/no/bahdanau-fig3.09ba2d37f202a6af.png) +![Bilde som viser et eksempel på justering funnet av RNNsearch-50, hentet fra Bahdanau - arviz.org](../../../../../translated_images/no/bahdanau-fig3.09ba2d37f202a6af.webp) > Figur fra [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) @@ -66,7 +66,7 @@ Resultatet vi får med posisjonsembedding inkluderer både det originale tokenet Deretter må vi fange noen mønstre i vår sekvens. For å gjøre dette bruker transformere en **selvoppmerksomhetsmekanisme**, som i hovedsak er oppmerksomhet anvendt på samme sekvens som inngang og utgang. Å bruke selvoppmerksomhet lar oss ta hensyn til **kontekst** i setningen og se hvilke ord som er relaterte. For eksempel lar det oss se hvilke ord som refereres til av korreferanser, som *det*, og også ta konteksten i betraktning: -![](../../../../../translated_images/no/CoreferenceResolution.861924d6d384a7d6.png) +![](../../../../../translated_images/no/CoreferenceResolution.861924d6d384a7d6.webp) > Bilde fra [Google Blog](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Siden hver inngangsposisjon kartlegges uavhengig til hver utgangsposisjon, kan t **BERT** (Bidirectional Encoder Representations from Transformers) er et veldig stort flerlags transformernettverk med 12 lag for *BERT-base*, og 24 for *BERT-large*. Modellen er først forhåndstrent på en stor tekstkorpus (Wikipedia + bøker) ved hjelp av usupervisert trening (predikere maskerte ord i en setning). Under forhåndstreningen absorberer modellen betydelige nivåer av språkforståelse som deretter kan utnyttes med andre datasett ved hjelp av finjustering. Denne prosessen kalles **transfer learning**. -![bilde fra http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/no/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) +![bilde fra http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/no/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > Bilde [kilde](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/no/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/no/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index 0c66ea82..8eed209b 100644 --- a/translations/no/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/no/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**Oppmerksomhetsmekanismer** gir en måte å vekte den kontekstuelle innvirkningen av hver inngangsvektor på hver utgangsprediksjon av RNN. Dette implementeres ved å lage snarveier mellom mellomliggende tilstander i inngangs-RNN og utgangs-RNN. På denne måten, når vi genererer utgangssymbolet $y_t$, tar vi hensyn til alle skjulte inngangstilstander $h_i$, med forskjellige vektkoeffisienter $\\alpha_{t,i}$.\n", "\n", - "![Bilde som viser en encoder/decoder-modell med et additivt oppmerksomhetslag](../../../../../translated_images/no/encoder-decoder-attention.7a726296894fb567.png)\n", + "![Bilde som viser en encoder/decoder-modell med et additivt oppmerksomhetslag](../../../../../translated_images/no/encoder-decoder-attention.7a726296894fb567.webp)\n", "*Encoder-decoder-modellen med additiv oppmerksomhetsmekanisme i [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), sitert fra [denne bloggposten](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Oppmerksomhetsmatrisen $\\{\\alpha_{i,j}\\}$ representerer graden til hvilken visse inngangsord spiller en rolle i genereringen av et gitt ord i utgangssekvensen. Nedenfor er et eksempel på en slik matrise:\n", "\n", - "![Bilde som viser en eksempeljustering funnet av RNNsearch-50, hentet fra Bahdanau - arviz.org](../../../../../translated_images/no/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![Bilde som viser en eksempeljustering funnet av RNNsearch-50, hentet fra Bahdanau - arviz.org](../../../../../translated_images/no/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*Figur hentet fra [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) er et svært stort flerlags transformatornettverk med 12 lag for *BERT-base* og 24 for *BERT-large*. Modellen blir først forhåndstrent på en stor tekstkorpus (Wikipedia + bøker) ved hjelp av usupervisert trening (predikere maskerte ord i en setning). Under forhåndstreningen absorberer modellen et betydelig nivå av språkforståelse som deretter kan utnyttes med andre datasett ved hjelp av finjustering. Denne prosessen kalles **overføringslæring**.\n", "\n", - "![Bilde fra http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/no/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![Bilde fra http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/no/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "Det finnes mange varianter av transformator-arkitekturer, inkludert BERT, DistilBERT, BigBird, OpenGPT3 og flere, som kan finjusteres. [HuggingFace-pakken](https://github.com/huggingface/) gir et bibliotek for å trene mange av disse arkitekturene med PyTorch.\n", "\n", diff --git a/translations/no/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/no/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index 247bdf9e..d4fcd6f7 100644 --- a/translations/no/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/no/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**Oppmerksomhetsmekanismer** gir en måte å vekte den kontekstuelle påvirkningen av hver inngangsvektor på hver utgangsprediksjon i RNN. Dette implementeres ved å lage snarveier mellom mellomliggende tilstander i inngangs-RNN og utgangs-RNN. På denne måten, når vi genererer utgangssymbolet $y_t$, tar vi hensyn til alle skjulte inngangstilstander $h_i$, med forskjellige vektkoeffisienter $\\alpha_{t,i}$. \n", "\n", - "![Bilde som viser en encoder/decoder-modell med et additivt oppmerksomhetslag](../../../../../translated_images/no/encoder-decoder-attention.7a726296894fb567.png)\n", + "![Bilde som viser en encoder/decoder-modell med et additivt oppmerksomhetslag](../../../../../translated_images/no/encoder-decoder-attention.7a726296894fb567.webp)\n", "*Encoder-decoder-modellen med additiv oppmerksomhetsmekanisme i [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), sitert fra [denne bloggposten](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Oppmerksomhetsmatrisen $\\{\\alpha_{i,j}\\}$ representerer graden til hvilken visse inngangsord spiller en rolle i genereringen av et gitt ord i utgangssekvensen. Nedenfor er et eksempel på en slik matrise:\n", "\n", - "![Bilde som viser et eksempel på justering funnet av RNNsearch-50, hentet fra Bahdanau - arviz.org](../../../../../translated_images/no/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![Bilde som viser et eksempel på justering funnet av RNNsearch-50, hentet fra Bahdanau - arviz.org](../../../../../translated_images/no/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*Figur hentet fra [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3)*\n", "\n", @@ -225,7 +225,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) er et svært stort flerlags transformatornettverk med 12 lag for *BERT-base* og 24 for *BERT-large*. Modellen blir først forhåndstrent på en stor mengde tekstdata (Wikipedia + bøker) ved hjelp av usupervisert trening (forutsi maskerte ord i en setning). Under forhåndstreningen absorberer modellen et betydelig nivå av språkforståelse som deretter kan utnyttes med andre datasett ved hjelp av finjustering. Denne prosessen kalles **transfer learning**.\n", "\n", - "![bilde fra http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/no/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![bilde fra http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/no/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "Det finnes mange varianter av transformatorarkitekturer, inkludert BERT, DistilBERT, BigBird, OpenGPT3 og flere, som kan finjusteres.\n", "\n", diff --git a/translations/no/lessons/5-NLP/19-NER/README.md b/translations/no/lessons/5-NLP/19-NER/README.md index f6c71eab..5afa1890 100644 --- a/translations/no/lessons/5-NLP/19-NER/README.md +++ b/translations/no/lessons/5-NLP/19-NER/README.md @@ -57,7 +57,7 @@ barn | O Siden vi må bygge en én-til-én korrespondanse mellom tokens og klasser, kan vi trene en høyreorientert **mange-til-mange** nevralt nettverksmodell fra dette bildet: -![Bilde som viser vanlige mønstre for rekurrente nevrale nettverk.](../../../../../translated_images/no/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Bilde som viser vanlige mønstre for rekurrente nevrale nettverk.](../../../../../translated_images/no/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > *Bilde fra [denne bloggposten](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) av [Andrej Karpathy](http://karpathy.github.io/). NER-tokenklassifiseringsmodeller tilsvarer nettverksarkitekturen lengst til høyre på dette bildet.* diff --git a/translations/no/lessons/5-NLP/README.md b/translations/no/lessons/5-NLP/README.md index e44d12ea..b1d1f29f 100644 --- a/translations/no/lessons/5-NLP/README.md +++ b/translations/no/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Naturlig Språkbehandling -![Oppsummering av NLP-oppgaver i en skisse](../../../../translated_images/no/ai-nlp.b22dcb8ca4707cea.png) +![Oppsummering av NLP-oppgaver i en skisse](../../../../translated_images/no/ai-nlp.b22dcb8ca4707cea.webp) I denne delen vil vi fokusere på å bruke nevrale nettverk for å håndtere oppgaver relatert til **Naturlig Språkbehandling (NLP)**. Det finnes mange NLP-problemer vi ønsker at datamaskiner skal kunne løse: diff --git a/translations/no/lessons/6-Other/23-MultiagentSystems/README.md b/translations/no/lessons/6-Other/23-MultiagentSystems/README.md index 7e933fe2..cac620aa 100644 --- a/translations/no/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/no/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ Du kan åpne en av modellene, for eksempel **Biology → Flocking**. Etter å ha åpnet modellen, kommer du til hovedskjermen i NetLogo. Her er et eksempel på en modell som beskriver populasjonen av ulver og sauer, gitt begrensede ressurser (gress). -![NetLogo Main Screen](../../../../../translated_images/no/NetLogo-Main.32653711ec1a01b3.png) +![NetLogo Main Screen](../../../../../translated_images/no/NetLogo-Main.32653711ec1a01b3.webp) > Skjermbilde av Dmitry Soshnikov diff --git a/translations/no/lessons/README.md b/translations/no/lessons/README.md index a6aada5c..6eabc81b 100644 --- a/translations/no/lessons/README.md +++ b/translations/no/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Oversikt -![Oversikt i en skisse](../../../translated_images/no/ai-overview.0857791951d19500.png) +![Oversikt i en skisse](../../../translated_images/no/ai-overview.0857791951d19500.webp) > Skisse laget av [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/no/lessons/X-Extras/X1-MultiModal/README.md b/translations/no/lessons/X-Extras/X1-MultiModal/README.md index 30a4f94b..697c7996 100644 --- a/translations/no/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/no/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ Etter suksessen med transformer-modeller for å løse NLP-oppgaver, har de samme Hovedideen med CLIP er å kunne sammenligne tekstbeskrivelser med et bilde og avgjøre hvor godt bildet samsvarer med beskrivelsen. -![CLIP Arkitektur](../../../../../translated_images/no/clip-arch.b3dbf20b4e8ed8be.png) +![CLIP Arkitektur](../../../../../translated_images/no/clip-arch.b3dbf20b4e8ed8be.webp) > *Bilde fra [denne bloggposten](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ Når denne modellen er forhåndstrent, kan vi gi den en batch med bilder og en b Anta at vi må klassifisere bilder mellom for eksempel katter, hunder og mennesker. I dette tilfellet kan vi gi modellen et bilde og en serie tekstbeskrivelser: "*et bilde av en katt*", "*et bilde av en hund*", "*et bilde av et menneske*". I den resulterende vektoren med 3 sannsynligheter trenger vi bare å velge indeksen med høyest verdi. -![CLIP for Bildeklassifisering](../../../../../translated_images/no/clip-class.3af42ef0b2b19369.png) +![CLIP for Bildeklassifisering](../../../../../translated_images/no/clip-class.3af42ef0b2b19369.webp) > *Bilde fra [denne bloggposten](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ Lær mer om VQGAN på [Taming Transformers](https://compvis.github.io/taming-tra En av de viktige forskjellene mellom VQGAN og tradisjonelle GAN er at sistnevnte kan produsere et anstendig bilde fra hvilken som helst inputvektor, mens VQGAN sannsynligvis vil produsere et bilde som ikke er sammenhengende. Derfor må vi videre veilede bildeopprettingsprosessen, og det kan gjøres ved hjelp av CLIP. -![VQGAN+CLIP Arkitektur](../../../../../translated_images/no/vqgan.5027fe05051dfa31.png) +![VQGAN+CLIP Arkitektur](../../../../../translated_images/no/vqgan.5027fe05051dfa31.webp) For å generere et bilde som samsvarer med en tekstbeskrivelse, starter vi med en tilfeldig kodingsvektor som sendes gjennom VQGAN for å produsere et bilde. Deretter brukes CLIP til å produsere en tapfunksjon som viser hvor godt bildet samsvarer med tekstbeskrivelsen. Målet er da å minimere dette tapet, ved hjelp av backpropagation for å justere inputvektorens parametere. Et flott bibliotek som implementerer VQGAN+CLIP er [Pixray](http://github.com/pixray/pixray). -![Bilde produsert av Pixray](../../../../../translated_images/no/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Bilde produsert av Pixray](../../../../../translated_images/no/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Bilde produsert av Pixray](../../../../../translated_images/no/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) +![Bilde produsert av Pixray](../../../../../translated_images/no/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp) | ![Bilde produsert av Pixray](../../../../../translated_images/no/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp) | ![Bilde produsert av Pixray](../../../../../translated_images/no/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp) ----|----|---- Bilde generert fra beskrivelsen *et nærbilde akvarellportrett av ung mannlig lærer i litteratur med en bok* | Bilde generert fra beskrivelsen *et nærbilde oljemaleriportrett av ung kvinnelig lærer i informatikk med en datamaskin* | Bilde generert fra beskrivelsen *et nærbilde oljemaleriportrett av eldre mannlig lærer i matematikk foran en tavle* diff --git a/translations/pa/README.md b/translations/pa/README.md index 359c6b59..ae294ed6 100644 --- a/translations/pa/README.md +++ b/translations/pa/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # ਸ਼ੁਰੂਆਤੀ ਲਈ ਕৃত੍ਰਿਮ ਬੁੱਧੀ - ਇੱਕ ਕੋਰਸ -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/pa/ai-overview.0857791951d19500.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/pa/ai-overview.0857791951d19500.webp)| |:---:| |ਹੋਰ ਜਾਣਕਾਰੀ ਲਈ [@girlie_mac](https://twitter.com/girlie_mac) ਵੱਲੋਂ ਕਿਰਿਆ ਗਿਆ AI For Beginners - _ਸਕੈਚਨੋਟ_ | diff --git a/translations/pa/lessons/1-Intro/README.md b/translations/pa/lessons/1-Intro/README.md index e8d8f1dd..88736606 100644 --- a/translations/pa/lessons/1-Intro/README.md +++ b/translations/pa/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # AI ਦਾ ਪਰਿਚਯ -![AI ਸਮੱਗਰੀ ਦੇ ਪਰਿਚਯ ਦਾ ਡੂਡਲ ਵਿੱਚ ਸਾਰ](../../../../translated_images/pa/ai-intro.bf28d1ac4235881c.png) +![AI ਸਮੱਗਰੀ ਦੇ ਪਰਿਚਯ ਦਾ ਡੂਡਲ ਵਿੱਚ ਸਾਰ](../../../../translated_images/pa/ai-intro.bf28d1ac4235881c.webp) > ਸਕੈਚਨੋਟ [ਟੋਮੋਮੀ ਇਮੁਰਾ](https://twitter.com/girlie_mac) ਦੁਆਰਾ @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: ਮੂਲ ਰੂਪ ਵਿੱਚ, ਕੰਪਿਊਟਰਾਂ ਨੂੰ [ਚਾਰਲਸ ਬੈਬੇਜ](https://en.wikipedia.org/wiki/Charles_Babbage) ਦੁਆਰਾ ਸੰਖਿਆਵਾਂ 'ਤੇ ਕੰਮ ਕਰਨ ਲਈ ਬਣਾਇਆ ਗਿਆ ਸੀ, ਇੱਕ ਸਪਸ਼ਟ ਤਰੀਕੇ - ਇੱਕ ਐਲਗੋਰਿਦਮ ਦੀ ਪਾਲਣਾ ਕਰਦੇ ਹੋਏ। ਆਧੁਨਿਕ ਕੰਪਿਊਟਰ, ਹਾਲਾਂਕਿ 19ਵੀਂ ਸਦੀ ਵਿੱਚ ਪ੍ਰਸਤਾਵਿਤ ਮੂਲ ਮਾਡਲ ਨਾਲੋਂ ਕਾਫ਼ੀ ਅਗਰਗਾਮੀ ਹਨ, ਫਿਰ ਵੀ ਨਿਯੰਤਰਿਤ ਗਣਨਾਵਾਂ ਦੇ ਇੱਕੋ ਹੀ ਵਿਚਾਰ ਦੀ ਪਾਲਣਾ ਕਰਦੇ ਹਨ। ਇਸ ਲਈ, ਇਹ ਸੰਭਵ ਹੈ ਕਿ ਕੰਪਿਊਟਰ ਨੂੰ ਕੁਝ ਕਰਨ ਲਈ ਪ੍ਰੋਗਰਾਮ ਕੀਤਾ ਜਾ ਸਕੇ ਜੇਕਰ ਅਸੀਂ ਉਹ ਸਪਸ਼ਟ ਕਦਮਾਂ ਦੀ ਲੜੀ ਜਾਣਦੇ ਹਾਂ ਜੋ ਲਕਸ਼ ਪ੍ਰਾਪਤ ਕਰਨ ਲਈ ਕਰਨੇ ਹਨ। -![ਇੱਕ ਵਿਅਕਤੀ ਦੀ ਤਸਵੀਰ](../../../../translated_images/pa/dsh_age.d212a30d4e54fb5f.png) +![ਇੱਕ ਵਿਅਕਤੀ ਦੀ ਤਸਵੀਰ](../../../../translated_images/pa/dsh_age.d212a30d4e54fb5f.webp) > ਤਸਵੀਰ [ਵਿਕੀ ਸੋਸ਼ਨਿਕੋਵਾ](http://twitter.com/vickievalerie) ਦੁਆਰਾ @@ -46,7 +46,7 @@ CO_OP_TRANSLATOR_METADATA: **[Intelligence](https://en.wikipedia.org/wiki/Intelligence)** ਸ਼ਬਦ ਨਾਲ ਨਜਿੱਠਣ ਵੇਲੇ ਇੱਕ ਸਮੱਸਿਆ ਇਹ ਹੈ ਕਿ ਇਸ ਸ਼ਬਦ ਦੀ ਕੋਈ ਸਪਸ਼ਟ ਪਰਿਭਾਸ਼ਾ ਨਹੀਂ ਹੈ। ਕੋਈ ਦਲੀਲ ਕਰ ਸਕਦਾ ਹੈ ਕਿ ਬੁੱਧੀ **ਅਮੂਰ ਚਿੰਤਨ** ਜਾਂ **ਸਵੈ-ਜਾਗਰੂਕਤਾ** ਨਾਲ ਜੁੜੀ ਹੋਈ ਹੈ, ਪਰ ਅਸੀਂ ਇਸਨੂੰ ਢੰਗ ਨਾਲ ਪਰਿਭਾਸ਼ਿਤ ਨਹੀਂ ਕਰ ਸਕਦੇ। -![ਬਿੱਲੀ ਦੀ ਤਸਵੀਰ](../../../../translated_images/pa/photo-cat.8c8e8fb760ffe457.jpg) +![ਬਿੱਲੀ ਦੀ ਤਸਵੀਰ](../../../../translated_images/pa/photo-cat.8c8e8fb760ffe457.webp) > [ਤਸਵੀਰ](https://unsplash.com/photos/75715CVEJhI) [ਐਂਬਰ ਕਿਪ](https://unsplash.com/@sadmax) ਦੁਆਰਾ Unsplash ਤੋਂ diff --git a/translations/pa/lessons/2-Symbolic/Animals.ipynb b/translations/pa/lessons/2-Symbolic/Animals.ipynb index 0d8c9921..3657dd6d 100644 --- a/translations/pa/lessons/2-Symbolic/Animals.ipynb +++ b/translations/pa/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "ਇਸ ਉਦਾਹਰਨ ਵਿੱਚ, ਅਸੀਂ ਕੁਝ ਭੌਤਿਕ ਲੱਛਣਾਂ ਦੇ ਆਧਾਰ 'ਤੇ ਜਾਨਵਰ ਦੀ ਪਛਾਣ ਕਰਨ ਲਈ ਇੱਕ ਸਧਾਰਣ ਗਿਆਨ-ਅਧਾਰਿਤ ਪ੍ਰਣਾਲੀ ਲਾਗੂ ਕਰਾਂਗੇ। ਇਸ ਪ੍ਰਣਾਲੀ ਨੂੰ ਹੇਠਾਂ ਦਿੱਤੇ AND-OR ਟ੍ਰੀ ਦੁਆਰਾ ਦਰਸਾਇਆ ਜਾ ਸਕਦਾ ਹੈ (ਇਹ ਪੂਰੇ ਟ੍ਰੀ ਦਾ ਇੱਕ ਹਿੱਸਾ ਹੈ, ਅਸੀਂ ਆਸਾਨੀ ਨਾਲ ਹੋਰ ਨਿਯਮ ਸ਼ਾਮਲ ਕਰ ਸਕਦੇ ਹਾਂ):\n", "\n", - "![](../../../../translated_images/pa/AND-OR-Tree.5592d2c70187f283.png)\n" + "![](../../../../translated_images/pa/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { diff --git a/translations/pa/lessons/2-Symbolic/README.md b/translations/pa/lessons/2-Symbolic/README.md index 59bf97dc..fdd870d0 100644 --- a/translations/pa/lessons/2-Symbolic/README.md +++ b/translations/pa/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # ਗਿਆਨ ਪ੍ਰਸਤੁਤੀ ਅਤੇ ਮਾਹਰ ਪ੍ਰਣਾਲੀਆਂ -![ਸੰਕੇਤਕ AI ਸਮੱਗਰੀ ਦਾ ਸਾਰ](../../../../translated_images/pa/ai-symbolic.715a30cb610411a6.png) +![ਸੰਕੇਤਕ AI ਸਮੱਗਰੀ ਦਾ ਸਾਰ](../../../../translated_images/pa/ai-symbolic.715a30cb610411a6.webp) > ਸਕੈਚਨੋਟ [Tomomi Imura](https://twitter.com/girlie_mac) ਦੁਆਰਾ @@ -41,7 +41,7 @@ AI ਦੇ ਸ਼ੁਰੂਆਤੀ ਦਿਨਾਂ ਵਿੱਚ, ਬੁੱਧੀ ਇਸ ਤਰ੍ਹਾਂ, **ਗਿਆਨ ਪ੍ਰਸਤੁਤੀ** ਦੀ ਸਮੱਸਿਆ ਇਹ ਹੈ ਕਿ ਕੰਪਿਊਟਰ ਵਿੱਚ ਡਾਟਾ ਦੇ ਰੂਪ ਵਿੱਚ ਗਿਆਨ ਨੂੰ ਦਰਸਾਉਣ ਦਾ ਕੁਝ ਪ੍ਰਭਾਵਸ਼ਾਲੀ ਤਰੀਕਾ ਲੱਭਿਆ ਜਾਵੇ, ਤਾਂ ਜੋ ਇਹ ਸਵੈਚਾਲਿਤ ਤਰੀਕੇ ਨਾਲ ਵਰਤਿਆ ਜਾ ਸਕੇ। ਇਸਨੂੰ ਇੱਕ ਸਪੈਕਟ੍ਰਮ ਵਜੋਂ ਦੇਖਿਆ ਜਾ ਸਕਦਾ ਹੈ: -![ਗਿਆਨ ਪ੍ਰਸਤੁਤੀ ਸਪੈਕਟ੍ਰਮ](../../../../translated_images/pa/knowledge-spectrum.b60df631852c0217.png) +![ਗਿਆਨ ਪ੍ਰਸਤੁਤੀ ਸਪੈਕਟ੍ਰਮ](../../../../translated_images/pa/knowledge-spectrum.b60df631852c0217.webp) > ਚਿੱਤਰ [Dmitry Soshnikov](http://soshnikov.com) ਦੁਆਰਾ @@ -94,7 +94,7 @@ Block Syntax | Indent | | | ਸੰਕੇਤਕ AI ਦੀਆਂ ਸ਼ੁਰੂਆਤੀ ਸਫਲਤਾਵਾਂ ਵਿੱਚੋਂ ਇੱਕ **ਮਾਹਰ ਪ੍ਰਣਾਲੀਆਂ** ਸਨ - ਕੰਪਿਊਟਰ ਪ੍ਰਣਾਲੀਆਂ ਜੋ ਕੁਝ ਸੀਮਿਤ ਸਮੱਸਿਆ ਖੇਤਰ ਵਿੱਚ ਮਾਹਰ ਵਜੋਂ ਕੰਮ ਕਰਨ ਲਈ ਡਿਜ਼ਾਈਨ ਕੀਤੀਆਂ ਗਈਆਂ ਸਨ। ਇਹਨਾਂ ਦਾ ਆਧਾਰ **ਗਿਆਨ ਅਧਾਰ** ਸੀ ਜੋ ਇੱਕ ਜਾਂ ਵੱਧ ਮਨੁੱਖੀ ਮਾਹਰਾਂ ਤੋਂ ਕੱਢਿਆ ਗਿਆ ਸੀ, ਅਤੇ ਇਹਨਾਂ ਵਿੱਚ ਇੱਕ **ਤਰਕ ਇੰਜਣ** ਸੀ ਜੋ ਇਸ 'ਤੇ ਕੁਝ ਤਰਕ ਕਰਦਾ ਸੀ। -![ਮਨੁੱਖੀ ਆਰਕੀਟੈਕਚਰ](../../../../translated_images/pa/arch-human.5d4d35f1bba3ab1c.png) | ![ਗਿਆਨ-ਅਧਾਰਤ ਪ੍ਰਣਾਲੀ](../../../../translated_images/pa/arch-kbs.3ec5c150b09fa8da.png) +![ਮਨੁੱਖੀ ਆਰਕੀਟੈਕਚਰ](../../../../translated_images/pa/arch-human.5d4d35f1bba3ab1c.webp) | ![ਗਿਆਨ-ਅਧਾਰਤ ਪ੍ਰਣਾਲੀ](../../../../translated_images/pa/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ ਮਨੁੱਖੀ ਨਰਵ ਪ੍ਰਣਾਲੀ ਦੀ ਸਰਲ ਰਚਨਾ | ਗਿਆਨ-ਅਧਾਰਤ ਪ੍ਰਣਾਲੀ ਦੀ ਆਰਕੀਟੈਕਚਰ @@ -106,7 +106,7 @@ Block Syntax | Indent | | | ਉਦਾਹਰਣ ਵਜੋਂ, ਆਓ ਹੇਠਾਂ ਦਿੱਤੀ ਮਾਹਰ ਪ੍ਰਣਾਲੀ ਨੂੰ ਵੇਖੀਏ ਜੋ ਕਿਸੇ ਜਾਨਵਰ ਨੂੰ ਇਸਦੇ ਭੌਤਿਕ ਲੱਛਣਾਂ ਦੇ ਆਧਾਰ 'ਤੇ ਨਿਰਧਾਰਤ ਕਰਦੀ ਹੈ: -![AND-OR ਟ੍ਰੀ](../../../../translated_images/pa/AND-OR-Tree.5592d2c70187f283.png) +![AND-OR ਟ੍ਰੀ](../../../../translated_images/pa/AND-OR-Tree.5592d2c70187f283.webp) > ਚਿੱਤਰ [Dmitry Soshnikov](http://soshnikov.com) ਦੁਆਰਾ diff --git a/translations/pa/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/pa/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index 72f6d293..9274ed54 100644 --- a/translations/pa/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/pa/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -1255,7 +1255,7 @@ "* ਘੱਟ ਸਿਖਲਾਈ ਨੁਕਸਾਨ - ਮਾਡਲ ਸਿਖਲਾਈ ਡਾਟਾ ਨੂੰ ਚੰਗੀ ਤਰ੍ਹਾਂ ਅਨੁਕੂਲ ਕਰ ਸਕਦਾ ਹੈ, ਕਿਉਂਕਿ ਇਸਦੇ ਕੋਲ ਕਾਫ਼ੀ ਅਭਿਵੈਕਤਮਕ ਸ਼ਕਤੀ ਹੁੰਦੀ ਹੈ।\n", "* ਵੈਲੀਡੇਸ਼ਨ ਨੁਕਸਾਨ ਸਿਖਲਾਈ ਨੁਕਸਾਨ ਨਾਲੋਂ ਕਾਫ਼ੀ ਵੱਧ ਹੋ ਸਕਦਾ ਹੈ ਅਤੇ ਸਿਖਲਾਈ ਦੌਰਾਨ ਵਧਣਾ ਸ਼ੁਰੂ ਕਰ ਸਕਦਾ ਹੈ - ਇਹ ਇਸ ਲਈ ਹੁੰਦਾ ਹੈ ਕਿਉਂਕਿ ਮਾਡਲ \"ਸਿਖਲਾਈ ਬਿੰਦੂਆਂ\" ਨੂੰ ਯਾਦ ਕਰ ਲੈਂਦਾ ਹੈ ਅਤੇ \"ਕੁੱਲ ਤਸਵੀਰ\" ਨੂੰ ਗੁਆ ਲੈਂਦਾ ਹੈ।\n", "\n", - "![Overfitting](../../../../../translated_images/pa/overfit.a0bd57f717c15769.png)\n", + "![Overfitting](../../../../../translated_images/pa/overfit.a0bd57f717c15769.webp)\n", "\n", "> ਇਸ ਤਸਵੀਰ ਵਿੱਚ, `x` ਸਿਖਲਾਈ ਡਾਟਾ ਲਈ ਹੈ, `o` - ਵੈਲੀਡੇਸ਼ਨ ਡਾਟਾ ਲਈ। ਖੱਬੇ ਪਾਸੇ - ਰੇਖੀ ਮਾਡਲ (ਇੱਕ-ਪਰਤ), ਇਹ ਡਾਟਾ ਦੀ ਕੁਦਰਤ ਨੂੰ ਕਾਫ਼ੀ ਚੰਗੇ ਤਰੀਕੇ ਨਾਲ ਅਨੁਕੂਲ ਕਰਦਾ ਹੈ। ਸੱਜੇ ਪਾਸੇ - ਓਵਰਫਿਟ ਮਾਡਲ, ਮਾਡਲ ਸਿਖਲਾਈ ਡਾਟਾ ਨੂੰ ਬਿਲਕੁਲ ਸਹੀ ਤਰੀਕੇ ਨਾਲ ਅਨੁਕੂਲ ਕਰਦਾ ਹੈ, ਪਰ ਕਿਸੇ ਹੋਰ ਡਾਟਾ ਨਾਲ (ਵੈਲੀਡੇਸ਼ਨ ਗਲਤੀ ਬਹੁਤ ਜ਼ਿਆਦਾ ਹੈ) ਸਮਝ ਨਹੀਂ ਬਣਾਉਂਦਾ।\n" ] diff --git a/translations/pa/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/pa/lessons/3-NeuralNetworks/05-Frameworks/README.md index 76771641..e987d8e7 100644 --- a/translations/pa/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/pa/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ High-level API| [Keras](IntroKeras.ipynb) | *PyTorch Lightning* ਹੇਠਾਂ ਦਿੱਤੇ ਸਮੱਸਿਆ ਨੂੰ ਵਿਚਾਰੋ ਜਿਸ ਵਿੱਚ 5 ਬਿੰਦੂਆਂ ਨੂੰ ਅਨੁਮਾਨਿਤ ਕਰਨਾ ਹੈ (ਗ੍ਰਾਫ ਵਿੱਚ `x` ਨਾਲ ਦਰਸਾਇਆ ਗਿਆ ਹੈ): -![linear](../../../../../translated_images/pa/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/pa/overfit2.131f5800ae10ca5e.jpg) +![linear](../../../../../translated_images/pa/overfit1.f24b71c6f652e59e.webp) | ![overfit](../../../../../translated_images/pa/overfit2.131f5800ae10ca5e.webp) -------------------------|-------------------------- **ਲਿਨੀਅਰ ਮਾਡਲ, 2 ਪੈਰਾਮੀਟਰ** | **ਨਾਨ-ਲਿਨੀਅਰ ਮਾਡਲ, 7 ਪੈਰਾਮੀਟਰ** ਟ੍ਰੇਨਿੰਗ ਐਰਰ = 5.3 | ਟ੍ਰੇਨਿੰਗ ਐਰਰ = 0 @@ -79,7 +79,7 @@ High-level API| [Keras](IntroKeras.ipynb) | *PyTorch Lightning* ਜਿਵੇਂ ਕਿ ਉੱਪਰ ਦਿੱਤੇ ਗ੍ਰਾਫ ਤੋਂ ਦਿਖਾਈ ਦਿੰਦਾ ਹੈ, ਓਵਰਫਿਟਿੰਗ ਦੀ ਪਛਾਣ ਬਹੁਤ ਘੱਟ ਟ੍ਰੇਨਿੰਗ ਐਰਰ ਅਤੇ ਉੱਚੇ ਵੈਲੀਡੇਸ਼ਨ ਐਰਰ ਦੁਆਰਾ ਕੀਤੀ ਜਾ ਸਕਦੀ ਹੈ। ਆਮ ਤੌਰ 'ਤੇ ਟ੍ਰੇਨਿੰਗ ਦੌਰਾਨ ਅਸੀਂ ਦੋਵੇਂ ਟ੍ਰੇਨਿੰਗ ਅਤੇ ਵੈਲੀਡੇਸ਼ਨ ਐਰਰ ਨੂੰ ਘਟਦੇ ਹੋਏ ਦੇਖਾਂਗੇ, ਅਤੇ ਫਿਰ ਕਿਸੇ ਸਮੇਂ ਵੈਲੀਡੇਸ਼ਨ ਐਰਰ ਘਟਣਾ ਬੰਦ ਕਰ ਸਕਦਾ ਹੈ ਅਤੇ ਵਧਣਾ ਸ਼ੁਰੂ ਕਰ ਸਕਦਾ ਹੈ। ਇਹ ਓਵਰਫਿਟਿੰਗ ਦਾ ਸੰਕੇਤ ਹੋਵੇਗਾ, ਅਤੇ ਇਹ ਦਰਸਾਵੇਗਾ ਕਿ ਸਾਨੂੰ ਇਸ ਸਮੇਂ ਟ੍ਰੇਨਿੰਗ ਰੋਕ ਦੇਣੀ ਚਾਹੀਦੀ ਹੈ (ਜਾਂ ਘੱਟੋ-ਘੱਟ ਮਾਡਲ ਦਾ ਸਨੈਪਸ਼ਾਟ ਲੈਣਾ ਚਾਹੀਦਾ ਹੈ)। -![overfitting](../../../../../translated_images/pa/Overfitting.408ad91cd90b4371.png) +![overfitting](../../../../../translated_images/pa/Overfitting.408ad91cd90b4371.webp) ## ਓਵਰਫਿਟਿੰਗ ਨੂੰ ਰੋਕਣ ਦੇ ਤਰੀਕੇ diff --git a/translations/pa/lessons/3-NeuralNetworks/README.md b/translations/pa/lessons/3-NeuralNetworks/README.md index e2673646..c3e1c613 100644 --- a/translations/pa/lessons/3-NeuralNetworks/README.md +++ b/translations/pa/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # ਨਿਊਰਲ ਨੈੱਟਵਰਕਸ ਦਾ ਪਰਿਚਯ -![ਨਿਊਰਲ ਨੈੱਟਵਰਕਸ ਸਮੱਗਰੀ ਦਾ ਸਾਰ](../../../../translated_images/pa/ai-neuralnetworks.1c687ae40bc86e83.png) +![ਨਿਊਰਲ ਨੈੱਟਵਰਕਸ ਸਮੱਗਰੀ ਦਾ ਸਾਰ](../../../../translated_images/pa/ai-neuralnetworks.1c687ae40bc86e83.webp) ਜਿਵੇਂ ਕਿ ਅਸੀਂ ਪਰਿਚਯ ਵਿੱਚ ਚਰਚਾ ਕੀਤੀ ਸੀ, ਬੁੱਧੀ ਪ੍ਰਾਪਤ ਕਰਨ ਦੇ ਤਰੀਕਿਆਂ ਵਿੱਚੋਂ ਇੱਕ ਤਰੀਕਾ **ਕੰਪਿਊਟਰ ਮਾਡਲ** ਜਾਂ ਇੱਕ **ਕ੍ਰਿਤ੍ਰਿਮ ਦਿਮਾਗ** ਨੂੰ ਸਿਖਲਾਈ ਦੇਣਾ ਹੈ। 20ਵੀਂ ਸਦੀ ਦੇ ਮੱਧ ਤੋਂ, ਖੋਜਕਰਤਾਵਾਂ ਨੇ ਵੱਖ-ਵੱਖ ਗਣਿਤ ਮਾਡਲਾਂ ਦੀ ਕੋਸ਼ਿਸ਼ ਕੀਤੀ, ਜਦੋਂ ਤੱਕ ਕਿ ਹਾਲੀਆ ਸਾਲਾਂ ਵਿੱਚ ਇਹ ਦਿਸ਼ਾ ਬਹੁਤ ਸਫਲ ਸਾਬਤ ਨਹੀਂ ਹੋਈ। ਦਿਮਾਗ ਦੇ ਅਜਿਹੇ ਗਣਿਤ ਮਾਡਲਾਂ ਨੂੰ **ਨਿਊਰਲ ਨੈੱਟਵਰਕਸ** ਕਿਹਾ ਜਾਂਦਾ ਹੈ। @@ -36,13 +36,13 @@ CO_OP_TRANSLATOR_METADATA: ਜੀਵ ਵਿਗਿਆਨ ਤੋਂ, ਅਸੀਂ ਜਾਣਦੇ ਹਾਂ ਕਿ ਸਾਡਾ ਦਿਮਾਗ ਨਿਊਰਲ ਸੈਲ (ਨਿਊਰੋਨ) ਤੋਂ ਬਣਿਆ ਹੁੰਦਾ ਹੈ, ਜਿਨ੍ਹਾਂ ਵਿੱਚ ਹਰ ਇੱਕ ਦੇ ਕਈ "ਇਨਪੁੱਟ" (ਡੈਂਡਰਾਈਟਸ) ਅਤੇ ਇੱਕ "ਆਉਟਪੁੱਟ" (ਐਕਸੋਨ) ਹੁੰਦੇ ਹਨ। ਡੈਂਡਰਾਈਟਸ ਅਤੇ ਐਕਸੋਨ ਦੋਵੇਂ ਬਿਜਲਈ ਸੰਕੇਤਾਂ ਨੂੰ ਚਲਾਉਣ ਦੇ ਯੋਗ ਹੁੰਦੇ ਹਨ, ਅਤੇ ਉਨ੍ਹਾਂ ਦੇ ਵਿਚਕਾਰ ਦੇ ਸੰਪਰਕ — ਜਿਨ੍ਹਾਂ ਨੂੰ ਸਿਨੈਪਸ ਕਿਹਾ ਜਾਂਦਾ ਹੈ — ਵੱਖ-ਵੱਖ ਪੱਧਰ ਦੀ ਚਾਲਕਤਾ ਦਿਖਾ ਸਕਦੇ ਹਨ, ਜੋ ਨਿਊਰੋਟ੍ਰਾਂਸਮੀਟਰਾਂ ਦੁਆਰਾ ਨਿਯੰਤਰਿਤ ਹੁੰਦੀ ਹੈ। -![ਨਿਊਰੋਨ ਦਾ ਮਾਡਲ](../../../../translated_images/pa/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![ਨਿਊਰੋਨ ਦਾ ਮਾਡਲ](../../../../translated_images/pa/artneuron.1a5daa88d20ebe6f.png) +![ਨਿਊਰੋਨ ਦਾ ਮਾਡਲ](../../../../translated_images/pa/synapse-wikipedia.ed20a9e4726ea1c6.webp) | ![ਨਿਊਰੋਨ ਦਾ ਮਾਡਲ](../../../../translated_images/pa/artneuron.1a5daa88d20ebe6f.webp) ----|---- ਅਸਲੀ ਨਿਊਰੋਨ *([ਵਿਕੀਪੀਡੀਆ ਤੋਂ ਚਿੱਤਰ](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg))* | ਕ੍ਰਿਤ੍ਰਿਮ ਨਿਊਰੋਨ *(ਲੇਖਕ ਦੁਆਰਾ ਚਿੱਤਰ)* ਇਸ ਤਰ੍ਹਾਂ, ਨਿਊਰੋਨ ਦਾ ਸਭ ਤੋਂ ਸਧਾਰਨ ਗਣਿਤ ਮਾਡਲ ਕਈ ਇਨਪੁੱਟ X1, ..., XN ਅਤੇ ਇੱਕ ਆਉਟਪੁੱਟ Y, ਅਤੇ ਕਈ ਵਜ਼ਨ W1, ..., WN ਸ਼ਾਮਲ ਕਰਦਾ ਹੈ। ਆਉਟਪੁੱਟ ਇਸ ਤਰ੍ਹਾਂ ਗਣਨਾ ਕੀਤੀ ਜਾਂਦੀ ਹੈ: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) ਜਿੱਥੇ f ਕੁਝ ਗੈਰ-ਰੇਖਿਕ **ਐਕਟੀਵੇਸ਼ਨ ਫੰਕਸ਼ਨ** ਹੈ। diff --git a/translations/pa/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/pa/lessons/4-ComputerVision/06-IntroCV/README.md index ff424971..0bc0c0c7 100644 --- a/translations/pa/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/pa/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) * **ਬ੍ਰੇਲ ਬੁੱਕ ਦੀ ਤਸਵੀਰ ਦੀ ਪ੍ਰੀ-ਪ੍ਰੋਸੈਸਿੰਗ**। ਅਸੀਂ ਧਿਆਨ ਦਿੰਦੇ ਹਾਂ ਕਿ ਕਿਵੇਂ ਅਸੀਂ thresholding, feature detection, perspective transformation ਅਤੇ NumPy ਮੈਨਿਪੂਲੇਸ਼ਨ ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਵਿਅਕਤੀਗਤ ਬ੍ਰੇਲ ਚਿੰਨ੍ਹਾਂ ਨੂੰ neural network ਦੁਆਰਾ ਹੋਰ ਵਰਗੀਕਰਨ ਲਈ ਵੱਖ ਕਰ ਸਕਦੇ ਹਾਂ। -![Braille Image](../../../../../translated_images/pa/braille.341962ff76b1bd70.jpeg) | ![Braille Image Pre-processed](../../../../../translated_images/pa/braille-result.46530fea020b03c7.png) | ![Braille Symbols](../../../../../translated_images/pa/braille-symbols.0159185ab69d5339.png) +![Braille Image](../../../../../translated_images/pa/braille.341962ff76b1bd70.webp) | ![Braille Image Pre-processed](../../../../../translated_images/pa/braille-result.46530fea020b03c7.webp) | ![Braille Symbols](../../../../../translated_images/pa/braille-symbols.0159185ab69d5339.webp) ----|-----|----- > ਚਿੱਤਰ [OpenCV.ipynb](OpenCV.ipynb) ਤੋਂ * **ਫਰੇਮ ਡਿਫਰੈਂਸ ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਵੀਡੀਓ ਵਿੱਚ ਗਤੀ ਪਛਾਣਣਾ**। ਜੇਕਰ ਕੈਮਰਾ ਸਥਿਰ ਹੈ, ਤਾਂ ਕੈਮਰੇ ਫੀਡ ਤੋਂ ਫਰੇਮ ਇੱਕ ਦੂਜੇ ਨਾਲ ਕਾਫ਼ੀ ਮਿਲਦੇ-ਜੁਲਦੇ ਹੋਣੇ ਚਾਹੀਦੇ ਹਨ। ਕਿਉਂਕਿ ਫਰੇਮ ਐਰੇ ਵਜੋਂ ਦਰਸਾਏ ਜਾਂਦੇ ਹਨ, ਸਿਰਫ਼ ਉਹਨਾਂ ਐਰੇਜ਼ ਨੂੰ ਦੋ ਲਗਾਤਾਰ ਫਰੇਮਾਂ ਲਈ ਘਟਾ ਕੇ ਅਸੀਂ ਪਿਕਸਲ ਡਿਫਰੈਂਸ ਪ੍ਰਾਪਤ ਕਰਾਂਗੇ, ਜੋ ਸਥਿਰ ਫਰੇਮਾਂ ਲਈ ਘੱਟ ਹੋਣਾ ਚਾਹੀਦਾ ਹੈ, ਅਤੇ ਚਿੱਤਰ ਵਿੱਚ ਮਹੱਤਵਪੂਰਨ ਗਤੀ ਹੋਣ 'ਤੇ ਵਧ ਜਾਵੇਗਾ। -![Image of video frames and frame differences](../../../../../translated_images/pa/frame-difference.706f805491a0883c.png) +![Image of video frames and frame differences](../../../../../translated_images/pa/frame-difference.706f805491a0883c.webp) > ਚਿੱਤਰ [OpenCV.ipynb](OpenCV.ipynb) ਤੋਂ @@ -89,7 +89,7 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) - **Dense Optical Flow** ਹਰ ਪਿਕਸਲ ਲਈ ਵੇਕਟਰ ਫੀਲਡ ਦੀ ਗਣਨਾ ਕਰਦਾ ਹੈ ਜੋ ਦਿਖਾਉਂਦਾ ਹੈ ਕਿ ਇਹ ਕਿੱਥੇ ਹਿਲ ਰਿਹਾ ਹੈ। - **Sparse Optical Flow** ਚਿੱਤਰ ਵਿੱਚ ਕੁਝ ਵਿਸ਼ੇਸ਼ ਲੱਛਣ (ਜਿਵੇਂ ਕਿ edges) ਲੈਣ 'ਤੇ ਅਧਾਰਿਤ ਹੁੰਦਾ ਹੈ, ਅਤੇ ਫਰੇਮ ਤੋਂ ਫਰੇਮ ਤੱਕ ਉਹਨਾਂ ਦੀ ਰਾਹ ਬਣਾਉਂਦਾ ਹੈ। -![Image of Optical Flow](../../../../../translated_images/pa/optical.1f4a94464579a83a.png) +![Image of Optical Flow](../../../../../translated_images/pa/optical.1f4a94464579a83a.webp) > ਚਿੱਤਰ [OpenCV.ipynb](OpenCV.ipynb) ਤੋਂ diff --git a/translations/pa/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/pa/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 04e6eea9..b13b949b 100644 --- a/translations/pa/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/pa/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 ਇੱਕ ਨੈਟਵਰਕ ਹੈ ਜਿਸ ਨੇ 2014 ਵਿੱਚ ImageNet top-5 ਵਰਗੀਕਰਨ ਵਿੱਚ 92.7% ਸਹੀਤਾ ਹਾਸਲ ਕੀਤੀ। ਇਸ ਵਿੱਚ ਹੇਠਾਂ ਦਿੱਤੇ ਲੇਅਰ ਸਟ੍ਰਕਚਰ ਹਨ: -![ImageNet Layers](../../../../../translated_images/pa/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet Layers](../../../../../translated_images/pa/vgg-16-arch1.d901a5583b3a51ba.webp) ਜਿਵੇਂ ਤੁਸੀਂ ਦੇਖ ਸਕਦੇ ਹੋ, VGG ਇੱਕ ਪਰੰਪਰਾਗਤ ਪਿਰਾਮਿਡ ਆਰਕੀਟੈਕਚਰ ਦੀ ਪਾਲਣਾ ਕਰਦਾ ਹੈ, ਜੋ ਕਿ ਕਨਵੋਲੂਸ਼ਨ-ਪੂਲਿੰਗ ਲੇਅਰਾਂ ਦੀ ਲੜੀ ਹੈ। -![ImageNet Pyramid](../../../../../translated_images/pa/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet Pyramid](../../../../../translated_images/pa/vgg-16-arch.64ff2137f50dd49f.webp) > ਚਿੱਤਰ [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) ਤੋਂ diff --git a/translations/pa/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/pa/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index 046f81ab..5b7e65ea 100644 --- a/translations/pa/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/pa/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -262,7 +262,7 @@ "\n", "ਇਸ ਤਰ੍ਹਾਂ, ਇੱਕ ਆਮ CNN ਵਿੱਚ ਕਈ ਕਨਵੋਲੂਸ਼ਨਲ ਲੇਅਰ ਹੁੰਦੇ ਹਨ, ਜਿਨ੍ਹਾਂ ਦੇ ਵਿਚਕਾਰ ਪੂਲਿੰਗ ਲੇਅਰ ਹੁੰਦੇ ਹਨ ਜੋ ਤਸਵੀਰ ਦੇ ਆਕਾਰ ਨੂੰ ਘਟਾਉਂਦੇ ਹਨ। ਅਸੀਂ ਫਿਲਟਰਾਂ ਦੀ ਗਿਣਤੀ ਵੀ ਵਧਾਉਂਦੇ ਹਾਂ, ਕਿਉਂਕਿ ਜਿਵੇਂ ਪੈਟਰਨ ਹੋਰ ਅਡਵਾਂਸਡ ਹੁੰਦੇ ਹਨ - ਸਾਨੂੰ ਵੇਖਣ ਲਈ ਹੋਰ ਸੰਭਾਵਤ ਦਿਲਚਸਪ ਕੌਂਬੀਨੇਸ਼ਨ ਹੁੰਦੇ ਹਨ।\n", "\n", - "![ਕਈ ਕਨਵੋਲੂਸ਼ਨਲ ਲੇਅਰਜ਼ ਨੂੰ ਪੂਲਿੰਗ ਲੇਅਰਜ਼ ਨਾਲ ਦਿਖਾਉਂਦੀ ਇੱਕ ਤਸਵੀਰ।](../../../../../translated_images/pa/cnn-pyramid.85915455759ef0ce.png)\n", + "![ਕਈ ਕਨਵੋਲੂਸ਼ਨਲ ਲੇਅਰਜ਼ ਨੂੰ ਪੂਲਿੰਗ ਲੇਅਰਜ਼ ਨਾਲ ਦਿਖਾਉਂਦੀ ਇੱਕ ਤਸਵੀਰ।](../../../../../translated_images/pa/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "ਸਪੇਸ਼ਲ ਡਾਈਮੇੰਸ਼ਨ ਘਟਣ ਅਤੇ ਫੀਚਰ/ਫਿਲਟਰ ਡਾਈਮੇੰਸ਼ਨ ਵਧਣ ਦੇ ਕਾਰਨ, ਇਸ ਆਰਕੀਟੈਕਚਰ ਨੂੰ **ਪਿਰਾਮਿਡ ਆਰਕੀਟੈਕਚਰ** ਵੀ ਕਿਹਾ ਜਾਂਦਾ ਹੈ।\n" ] diff --git a/translations/pa/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/pa/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index bb5627ad..33aa9382 100644 --- a/translations/pa/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/pa/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -360,7 +360,7 @@ "\n", "ਇਸ ਤਰ੍ਹਾਂ, ਇੱਕ ਆਮ CNN ਵਿੱਚ ਕਈ ਕਨਵੋਲੂਸ਼ਨਲ ਲੇਅਰ ਹੁੰਦੇ ਹਨ, ਜਿਨ੍ਹਾਂ ਦੇ ਵਿਚਕਾਰ ਪੂਲਿੰਗ ਲੇਅਰ ਹੁੰਦੇ ਹਨ ਜੋ ਤਸਵੀਰ ਦੇ ਆਕਾਰ ਨੂੰ ਘਟਾਉਂਦੇ ਹਨ। ਅਸੀਂ ਫਿਲਟਰਾਂ ਦੀ ਗਿਣਤੀ ਵੀ ਵਧਾਉਂਦੇ ਹਾਂ, ਕਿਉਂਕਿ ਜਿਵੇਂ ਪੈਟਰਨ ਹੋਰ ਅਡਵਾਂਸਡ ਬਣਦੇ ਹਨ - ਸਾਨੂੰ ਭਾਲਣ ਲਈ ਹੋਰ ਸੰਭਾਵਿਤ ਦਿਲਚਸਪ ਸੰਯੋਜਨ ਮਿਲਦੇ ਹਨ।\n", "\n", - "![ਕਈ ਕਨਵੋਲੂਸ਼ਨਲ ਲੇਅਰਾਂ ਨੂੰ ਪੂਲਿੰਗ ਲੇਅਰਾਂ ਨਾਲ ਦਿਖਾਉਂਦੀ ਇੱਕ ਤਸਵੀਰ।](../../../../../translated_images/pa/cnn-pyramid.85915455759ef0ce.png)\n", + "![ਕਈ ਕਨਵੋਲੂਸ਼ਨਲ ਲੇਅਰਾਂ ਨੂੰ ਪੂਲਿੰਗ ਲੇਅਰਾਂ ਨਾਲ ਦਿਖਾਉਂਦੀ ਇੱਕ ਤਸਵੀਰ।](../../../../../translated_images/pa/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "ਸਪੇਸ਼ਲ ਡਾਈਮੇੰਸ਼ਨ ਘਟਣ ਅਤੇ ਫੀਚਰ/ਫਿਲਟਰ ਡਾਈਮੇੰਸ਼ਨ ਵਧਣ ਦੇ ਕਾਰਨ, ਇਸ ਆਰਕੀਟੈਕਚਰ ਨੂੰ **ਪਿਰਾਮਿਡ ਆਰਕੀਟੈਕਚਰ** ਵੀ ਕਿਹਾ ਜਾਂਦਾ ਹੈ।\n" ] diff --git a/translations/pa/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/pa/lessons/4-ComputerVision/07-ConvNets/README.md index 030fccd2..7845f8ca 100644 --- a/translations/pa/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/pa/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: ਪੈਟਰਨਜ਼ ਨੂੰ ਕੱਢਣ ਲਈ, ਅਸੀਂ **ਕਨਵੋਲੂਸ਼ਨਲ ਫਿਲਟਰਜ਼** ਦੀ ਧਾਰਨਾ ਦੀ ਵਰਤੋਂ ਕਰਾਂਗੇ। ਜਿਵੇਂ ਕਿ ਤੁਸੀਂ ਜਾਣਦੇ ਹੋ, ਇੱਕ ਚਿੱਤਰ ਨੂੰ 2D-ਮੈਟ੍ਰਿਕਸ ਜਾਂ ਰੰਗ ਦੀ ਗਹਿਰਾਈ ਵਾਲੇ 3D-ਟੈਂਸਰ ਦੁਆਰਾ ਦਰਸਾਇਆ ਜਾਂਦਾ ਹੈ। ਫਿਲਟਰ ਲਾਗੂ ਕਰਨ ਦਾ ਮਤਲਬ ਹੈ ਕਿ ਅਸੀਂ ਇੱਕ ਛੋਟੀ **ਫਿਲਟਰ ਕਰਨਲ** ਮੈਟ੍ਰਿਕਸ ਲੈਂਦੇ ਹਾਂ, ਅਤੇ ਮੂਲ ਚਿੱਤਰ ਵਿੱਚ ਹਰ ਪਿਕਸਲ ਲਈ ਅਸੀਂ ਪੜੋਸੀ ਬਿੰਦੂਆਂ ਨਾਲ ਵਜ਼ਨੀ ਔਸਤ ਦੀ ਗਣਨਾ ਕਰਦੇ ਹਾਂ। ਅਸੀਂ ਇਸਨੂੰ ਇਸ ਤਰ੍ਹਾਂ ਦੇਖ ਸਕਦੇ ਹਾਂ ਕਿ ਇੱਕ ਛੋਟੀ ਵਿੰਡੋ ਸਾਰੇ ਚਿੱਤਰ 'ਤੇ ਸਲਾਈਡ ਕਰ ਰਹੀ ਹੈ, ਅਤੇ ਫਿਲਟਰ ਕਰਨਲ ਮੈਟ੍ਰਿਕਸ ਵਿੱਚ ਵਜ਼ਨਾਂ ਦੇ ਅਨੁਸਾਰ ਸਾਰੇ ਪਿਕਸਲਾਂ ਨੂੰ ਔਸਤ ਕਰ ਰਹੀ ਹੈ। -![ਵਰਟਿਕਲ ਐਜ ਫਿਲਟਰ](../../../../../translated_images/pa/filter-vert.b7148390ca0bc356.png) | ![ਹੋਰਿਜ਼ਾਂਟਲ ਐਜ ਫਿਲਟਰ](../../../../../translated_images/pa/filter-horiz.59b80ed4feb946ef.png) +![ਵਰਟਿਕਲ ਐਜ ਫਿਲਟਰ](../../../../../translated_images/pa/filter-vert.b7148390ca0bc356.webp) | ![ਹੋਰਿਜ਼ਾਂਟਲ ਐਜ ਫਿਲਟਰ](../../../../../translated_images/pa/filter-horiz.59b80ed4feb946ef.webp) ----|---- > ਚਿੱਤਰ: ਦਿਮਿਤਰੀ ਸੋਸ਼ਨਿਕੋਵ @@ -38,7 +38,7 @@ CNNs ਦੇ ਕੰਮ ਕਰਨ ਦਾ ਤਰੀਕਾ ਹੇਠਾਂ ਦਿੱ * ਅਸੀਂ ਨੈਟਵਰਕ ਨੂੰ ਇਸ ਤਰ੍ਹਾਂ ਡਿਜ਼ਾਈਨ ਕਰ ਸਕਦੇ ਹਾਂ ਕਿ ਫਿਲਟਰਜ਼ ਆਪਣੇ ਆਪ ਸਿੱਖੇ ਜਾਣ * ਅਸੀਂ ਇਹੀ ਤਰੀਕਾ ਉੱਚ-ਸਤਰ ਦੇ ਫੀਚਰਜ਼ ਵਿੱਚ ਪੈਟਰਨਜ਼ ਲੱਭਣ ਲਈ ਵਰਤ ਸਕਦੇ ਹਾਂ, ਨਾ ਕਿ ਸਿਰਫ਼ ਮੂਲ ਚਿੱਤਰ ਵਿੱਚ। ਇਸ ਤਰ੍ਹਾਂ CNN ਫੀਚਰ ਕੱਢਣ ਦਾ ਕੰਮ ਫੀਚਰਜ਼ ਦੀ ਹਾਇਰਾਰਕੀ 'ਤੇ ਹੁੰਦਾ ਹੈ, ਜੋ ਨੀਚਲੇ-ਸਤਰ ਦੇ ਪਿਕਸਲ ਸੰਯੋਜਨਾਂ ਤੋਂ ਸ਼ੁਰੂ ਹੁੰਦਾ ਹੈ, ਅਤੇ ਚਿੱਤਰ ਦੇ ਹਿੱਸਿਆਂ ਦੇ ਉੱਚ-ਸਤਰ ਦੇ ਸੰਯੋਜਨ ਤੱਕ ਪਹੁੰਚਦਾ ਹੈ। -![ਹਾਇਰਾਰਕਲ ਫੀਚਰ ਕੱਢਣਾ](../../../../../translated_images/pa/FeatureExtractionCNN.d9b456cbdae7cb64.png) +![ਹਾਇਰਾਰਕਲ ਫੀਚਰ ਕੱਢਣਾ](../../../../../translated_images/pa/FeatureExtractionCNN.d9b456cbdae7cb64.webp) > ਚਿੱਤਰ: [ਹਿਸਲੋਪ-ਲਿੰਚ ਦੇ ਪੇਪਰ](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d) ਤੋਂ, [ਉਨ੍ਹਾਂ ਦੇ ਰਿਸਰਚ](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) ਦੇ ਆਧਾਰ 'ਤੇ @@ -55,9 +55,9 @@ CNNs ਦੇ ਕੰਮ ਕਰਨ ਦਾ ਤਰੀਕਾ ਹੇਠਾਂ ਦਿੱ ਉਦਾਹਰਣ ਵਜੋਂ, ਆਓ VGG-16 ਦੀ ਆਰਕੀਟੈਕਚਰ ਨੂੰ ਦੇਖੀਏ, ਇੱਕ ਨੈਟਵਰਕ ਜਿਸ ਨੇ 2014 ਵਿੱਚ ImageNet ਦੇ ਟਾਪ-5 ਵਰਗੀਕਰਨ ਵਿੱਚ 92.7% ਸਹੀਤਾ ਪ੍ਰਾਪਤ ਕੀਤੀ: -![ImageNet ਲੇਅਰਜ਼](../../../../../translated_images/pa/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet ਲੇਅਰਜ਼](../../../../../translated_images/pa/vgg-16-arch1.d901a5583b3a51ba.webp) -![ImageNet ਪਿਰਾਮਿਡ](../../../../../translated_images/pa/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet ਪਿਰਾਮਿਡ](../../../../../translated_images/pa/vgg-16-arch.64ff2137f50dd49f.webp) > ਚਿੱਤਰ: [ਰਿਸਰਚਗੇਟ](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) ਤੋਂ diff --git a/translations/pa/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/pa/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 46ec0e57..2fe779dd 100644 --- a/translations/pa/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/pa/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ CO_OP_TRANSLATOR_METADATA: ਅਸੀਂ [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/) ਦੀ ਵਰਤੋਂ ਕਰਾਂਗੇ, ਜਿਸ ਵਿੱਚ ਕੁੱਤਿਆਂ ਅਤੇ ਬਿੱਲੀਆਂ ਦੀਆਂ 37 ਵੱਖ-ਵੱਖ ਬਰੀਡਾਂ ਦੀਆਂ ਤਸਵੀਰਾਂ ਸ਼ਾਮਲ ਹਨ। -![ਜਿਸ ਡੇਟਾ ਨਾਲ ਅਸੀਂ ਕੰਮ ਕਰਨ ਜਾ ਰਹੇ ਹਾਂ](../../../../../../translated_images/pa/data.50b2a9d5484bdbf0.png) +![ਜਿਸ ਡੇਟਾ ਨਾਲ ਅਸੀਂ ਕੰਮ ਕਰਨ ਜਾ ਰਹੇ ਹਾਂ](../../../../../../translated_images/pa/data.50b2a9d5484bdbf0.webp) ਡੇਟਾਸੈਟ ਡਾਊਨਲੋਡ ਕਰਨ ਲਈ, ਇਸ ਕੋਡ ਸਨਿੱਪਟ ਦੀ ਵਰਤੋਂ ਕਰੋ: diff --git a/translations/pa/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/pa/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index 59148368..1abeaa22 100644 --- a/translations/pa/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/pa/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "ਆਦਰਸ਼ ਬਿੱਲੀ ਨੂੰ ਦਿਖਾਉਣ ਲਈ, ਅਸੀਂ ਇੱਕ ਰੈਂਡਮ ਸ਼ੋਰ ਵਾਲੀ ਤਸਵੀਰ ਨਾਲ ਸ਼ੁਰੂ ਕਰਾਂਗੇ ਅਤੇ ਗ੍ਰੇਡੀਅੰਟ ਡਿਸੈਂਟ ਅਨੁਕੂਲਤਾ ਤਕਨੀਕ ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਤਸਵੀਰ ਨੂੰ ਇਸ ਤਰ੍ਹਾਂ ਬਦਲਣ ਦੀ ਕੋਸ਼ਿਸ਼ ਕਰਾਂਗੇ ਕਿ ਨੈੱਟਵਰਕ ਬਿੱਲੀ ਨੂੰ ਪਛਾਣ ਸਕੇ।\n", "\n", - "![ਅਨੁਕੂਲਤਾ ਲੂਪ](../../../../../translated_images/pa/ideal-cat-loop.999fbb8ff306e044.png)\n", + "![ਅਨੁਕੂਲਤਾ ਲੂਪ](../../../../../translated_images/pa/ideal-cat-loop.999fbb8ff306e044.webp)\n", "\n", "ਇਹ ਸਾਡੀ ਸ਼ੁਰੂਆਤੀ ਤਸਵੀਰ ਹੈ:\n" ] diff --git a/translations/pa/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/pa/lessons/4-ComputerVision/08-TransferLearning/README.md index 66dcb8f1..f7bcc69d 100644 --- a/translations/pa/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/pa/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Keras ਅਤੇ PyTorch ਵਿੱਚ ਕੁਝ ਆਮ ਆਰਕੀਟੈਕਚ ਇੱਥੇ VGG-16 ਨੈਟਵਰਕ ਦੁਆਰਾ ਇੱਕ ਬਿੱਲੀ ਦੀ ਤਸਵੀਰ ਤੋਂ ਕਾਢੇ ਗਏ ਫੀਚਰਾਂ ਦੇ ਨਮੂਨੇ ਹਨ: -![Features extracted by VGG-16](../../../../../translated_images/pa/features.6291f9c7ba3a0b95.png) +![Features extracted by VGG-16](../../../../../translated_images/pa/features.6291f9c7ba3a0b95.webp) ## ਬਿੱਲੀਆਂ ਵਸੋਂ ਕੁੱਤੇ ਡਾਟਾਸੈਟ @@ -48,19 +48,19 @@ Keras ਅਤੇ PyTorch ਵਿੱਚ ਕੁਝ ਆਮ ਆਰਕੀਟੈਕਚ ਇੱਕ ਪਹੁੰਚ ਜੋ ਅਸੀਂ ਅਪਣਾਉ ਸਕਦੇ ਹਾਂ ਉਹ ਹੈ ਇੱਕ ਰੈਂਡਮ ਚਿੱਤਰ ਨਾਲ ਸ਼ੁਰੂ ਕਰਨਾ, ਅਤੇ ਫਿਰ **ਗ੍ਰੇਡੀਅੰਟ ਡਿਸੈਂਟ ਓਪਟੀਮਾਈਜ਼ੇਸ਼ਨ** ਤਕਨੀਕ ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਉਸ ਚਿੱਤਰ ਨੂੰ ਇਸ ਤਰੀਕੇ ਨਾਲ ਢਾਲਣਾ, ਕਿ ਨੈਟਵਰਕ ਇਹ ਸੋਚਣ ਲੱਗੇ ਕਿ ਇਹ ਬਿੱਲੀ ਹੈ। -![Image Optimization Loop](../../../../../translated_images/pa/ideal-cat-loop.999fbb8ff306e044.png) +![Image Optimization Loop](../../../../../translated_images/pa/ideal-cat-loop.999fbb8ff306e044.webp) ਹਾਲਾਂਕਿ, ਜੇ ਅਸੀਂ ਇਹ ਕਰਦੇ ਹਾਂ, ਤਾਂ ਸਾਨੂੰ ਕੁਝ ਬਹੁਤ ਹੀ ਰੈਂਡਮ ਸ਼ੋਰ ਵਰਗਾ ਮਿਲੇਗਾ। ਇਹ ਇਸ ਲਈ ਹੈ ਕਿ *ਨੈਟਵਰਕ ਨੂੰ ਇਹ ਸੋਚਣ ਲਈ ਬਹੁਤ ਸਾਰੇ ਤਰੀਕੇ ਹਨ ਕਿ ਇਨਪੁਟ ਚਿੱਤਰ ਬਿੱਲੀ ਹੈ*, ਜਿਨ੍ਹਾਂ ਵਿੱਚ ਕੁਝ ਵਿਜ਼ੁਅਲ ਤੌਰ 'ਤੇ ਸਮਝਦਾਰ ਨਹੀਂ ਹਨ। ਜਦੋਂ ਕਿ ਉਹ ਚਿੱਤਰ ਬਿੱਲੀ ਲਈ ਆਮ ਪੈਟਰਨਾਂ ਨੂੰ ਸ਼ਾਮਲ ਕਰਦੇ ਹਨ, ਉਨ੍ਹਾਂ ਨੂੰ ਵਿਜ਼ੁਅਲ ਤੌਰ 'ਤੇ ਵੱਖਰੇ ਹੋਣ ਲਈ ਕੁਝ ਵੀ ਬਾਧਾ ਨਹੀਂ ਹੈ। ਨਤੀਜੇ ਨੂੰ ਸੁਧਾਰਨ ਲਈ, ਅਸੀਂ ਲਾਸ ਫੰਕਸ਼ਨ ਵਿੱਚ ਇੱਕ ਹੋਰ ਟਰਮ ਸ਼ਾਮਲ ਕਰ ਸਕਦੇ ਹਾਂ, ਜਿਸ ਨੂੰ **ਵੈਰੀਏਸ਼ਨ ਲਾਸ** ਕਿਹਾ ਜਾਂਦਾ ਹੈ। ਇਹ ਇੱਕ ਮੈਟ੍ਰਿਕ ਹੈ ਜੋ ਦਿਖਾਉਂਦੀ ਹੈ ਕਿ ਚਿੱਤਰ ਦੇ ਪੜੋਸੀ ਪਿਕਸਲ ਕਿੰਨੇ ਸਮਾਨ ਹਨ। ਵੈਰੀਏਸ਼ਨ ਲਾਸ ਨੂੰ ਘਟਾਉਣ ਨਾਲ ਚਿੱਤਰ ਸਮੂਥ ਬਣਦਾ ਹੈ, ਅਤੇ ਸ਼ੋਰ ਨੂੰ ਦੂਰ ਕਰਦਾ ਹੈ - ਇਸ ਤਰ੍ਹਾਂ ਵਧੇਰੇ ਵਿਜ਼ੁਅਲ ਤੌਰ 'ਤੇ ਆਕਰਸ਼ਕ ਪੈਟਰਨਾਂ ਨੂੰ ਪ੍ਰਗਟ ਕਰਦਾ ਹੈ। ਇੱਥੇ ਉਹ "ਆਦਰਸ਼" ਚਿੱਤਰਾਂ ਦੇ ਉਦਾਹਰਨ ਹਨ, ਜੋ ਬਿੱਲੀ ਅਤੇ ਜ਼ੈਬਰਾ ਵਜੋਂ ਉੱਚ ਸੰਭਾਵਨਾ ਨਾਲ ਕਲਾਸੀਫਾਈ ਕੀਤੇ ਗਏ ਹਨ: -![Ideal Cat](../../../../../translated_images/pa/ideal-cat.203dd4597643d6b0.png) | ![Ideal Zebra](../../../../../translated_images/pa/ideal-zebra.7f70e8b54ee15a7a.png) +![Ideal Cat](../../../../../translated_images/pa/ideal-cat.203dd4597643d6b0.webp) | ![Ideal Zebra](../../../../../translated_images/pa/ideal-zebra.7f70e8b54ee15a7a.webp) -----|----- *ਆਦਰਸ਼ ਬਿੱਲੀ* | *ਆਦਰਸ਼ ਜ਼ੈਬਰਾ* ਇਹੀ ਪਹੁੰਚ **ਐਡਵਰਸਰੀਅਲ ਅਟੈਕਸ** ਕਰਨ ਲਈ ਵਰਤੀ ਜਾ ਸਕਦੀ ਹੈ। ਮੰਨ ਲਓ ਕਿ ਅਸੀਂ ਨਿਊਰਲ ਨੈਟਵਰਕ ਨੂੰ ਬੇਵਕੂਫ ਬਣਾਉਣਾ ਚਾਹੁੰਦੇ ਹਾਂ ਅਤੇ ਕੁੱਤੇ ਨੂੰ ਬਿੱਲੀ ਵਜੋਂ ਦਿਖਾਉਣਾ ਚਾਹੁੰਦੇ ਹਾਂ। ਜੇਕਰ ਅਸੀਂ ਕੁੱਤੇ ਦੀ ਤਸਵੀਰ ਲੈਂਦੇ ਹਾਂ, ਜਿਸਨੂੰ ਨੈਟਵਰਕ ਦੁਆਰਾ ਕੁੱਤੇ ਵਜੋਂ ਪਛਾਣਿਆ ਜਾਂਦਾ ਹੈ, ਤਾਂ ਅਸੀਂ ਇਸਨੂੰ ਥੋੜ੍ਹਾ ਜਿਹਾ ਢਾਲ ਸਕਦੇ ਹਾਂ **ਗ੍ਰੇਡੀਅੰਟ ਡਿਸੈਂਟ ਓਪਟੀਮਾਈਜ਼ੇਸ਼ਨ** ਦੀ ਵਰਤੋਂ ਕਰਕੇ, ਜਦੋਂ ਤੱਕ ਨੈਟਵਰਕ ਇਸਨੂੰ ਬਿੱਲੀ ਵਜੋਂ ਕਲਾਸੀਫਾਈ ਕਰਨਾ ਸ਼ੁਰੂ ਨਹੀਂ ਕਰਦਾ: -![Picture of a Dog](../../../../../translated_images/pa/original-dog.8f68a67d2fe0911f.png) | ![Picture of a dog classified as a cat](../../../../../translated_images/pa/adversarial-dog.d9fc7773b0142b89.png) +![Picture of a Dog](../../../../../translated_images/pa/original-dog.8f68a67d2fe0911f.webp) | ![Picture of a dog classified as a cat](../../../../../translated_images/pa/adversarial-dog.d9fc7773b0142b89.webp) -----|----- *ਕੁੱਤੇ ਦੀ ਮੂਲ ਤਸਵੀਰ* | *ਕੁੱਤੇ ਦੀ ਤਸਵੀਰ ਜੋ ਬਿੱਲੀ ਵਜੋਂ ਕਲਾਸੀਫਾਈ ਕੀਤੀ ਗਈ ਹੈ* diff --git a/translations/pa/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/pa/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index 9b50292c..82b61344 100644 --- a/translations/pa/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/pa/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "ਕਿਉਂਕਿ ਅਸੀਂ ਆਟੋਇੰਕੋਡਰ ਨੂੰ ਮੂਲ ਤਸਵੀਰ ਤੋਂ ਜਿੰਨੀ ਜ਼ਿਆਦਾ ਜਾਣਕਾਰੀ ਲੈ ਸਕੇ, ਉਤਨੀ ਸਹੀ ਰੀਕੰਸਟਰਕਸ਼ਨ ਲਈ ਟ੍ਰੇਨ ਕਰ ਰਹੇ ਹਾਂ, ਨੈਟਵਰਕ ਇਨਪੁਟ ਤਸਵੀਰਾਂ ਦੀ ਸਭ ਤੋਂ ਵਧੀਆ **ਐਮਬੈਡਿੰਗ** ਲੱਭਣ ਦੀ ਕੋਸ਼ਿਸ਼ ਕਰਦਾ ਹੈ ਤਾਂ ਜੋ ਅਸਲ ਮਤਲਬ ਨੂੰ ਕੈਪਚਰ ਕੀਤਾ ਜਾ ਸਕੇ।\n", "\n", - "![ਆਟੋਇੰਕੋਡਰ ਡਾਇਗ੍ਰਾਮ](../../../../../translated_images/pa/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![ਆਟੋਇੰਕੋਡਰ ਡਾਇਗ੍ਰਾਮ](../../../../../translated_images/pa/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "> ਤਸਵੀਰ [Keras ਬਲੌਗ](https://blog.keras.io/building-autoencoders-in-keras.html) ਤੋਂ\n", "\n", diff --git a/translations/pa/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/pa/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index 27a455a8..5dd2a247 100644 --- a/translations/pa/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/pa/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "ਜਿਵੇਂ ਕਿ ਅਸੀਂ ਆਟੋਇੰਕੋਡਰ ਨੂੰ ਮੂਲ ਚਿੱਤਰ ਤੋਂ ਜਿੰਨੀ ਜ਼ਿਆਦਾ ਜਾਣਕਾਰੀ ਪ੍ਰਾਪਤ ਕਰ ਸਕੀਏ, ਉਸਨੂੰ ਸਹੀ ਤਰੀਕੇ ਨਾਲ ਮੁੜ ਬਣਾਉਣ ਲਈ ਟ੍ਰੇਨ ਕਰ ਰਹੇ ਹਾਂ, ਨੈਟਵਰਕ ਇਨਪੁਟ ਚਿੱਤਰਾਂ ਦੀ ਸਭ ਤੋਂ ਵਧੀਆ **ਐਮਬੈਡਿੰਗ** ਲੱਭਣ ਦੀ ਕੋਸ਼ਿਸ਼ ਕਰਦਾ ਹੈ ਤਾਂ ਜੋ ਅਸਲ ਮਤਲਬ ਨੂੰ ਕੈਪਚਰ ਕੀਤਾ ਜਾ ਸਕੇ।\n", "\n", - "![ਆਟੋਇੰਕੋਡਰ ਡਾਇਗ੍ਰਾਮ](../../../../../translated_images/pa/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![ਆਟੋਇੰਕੋਡਰ ਡਾਇਗ੍ਰਾਮ](../../../../../translated_images/pa/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "*ਚਿੱਤਰ [Keras ਬਲੌਗ](https://blog.keras.io/building-autoencoders-in-keras.html) ਤੋਂ*\n", "\n", diff --git a/translations/pa/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/pa/lessons/4-ComputerVision/09-Autoencoders/README.md index 94936915..e2b3c711 100644 --- a/translations/pa/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/pa/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: ਜਦੋਂ ਅਸੀਂ ਆਟੋਇਨਕੋਡਰ ਨੂੰ ਮੂਲ ਚਿੱਤਰ ਤੋਂ ਜਿੰਨਾ ਜ਼ਿਆਦਾ ਜਾਣਕਾਰੀ ਕੈਪਚਰ ਕਰ ਸਕਦੇ ਹਾਂ ਉਸ ਲਈ ਟ੍ਰੇਨ ਕਰਦੇ ਹਾਂ, ਤਾਂ ਕਿ ਸਹੀ ਰੀਕੰਸਟ੍ਰਕਸ਼ਨ ਹੋ ਸਕੇ, ਨੈਟਵਰਕ ਇਨਪੁਟ ਚਿੱਤਰਾਂ ਦੀ **embedding** ਨੂੰ ਕੈਪਚਰ ਕਰਨ ਲਈ ਸਭ ਤੋਂ ਵਧੀਆ ਕੋਸ਼ਿਸ਼ ਕਰਦਾ ਹੈ। -![AutoEncoder Diagram](../../../../../translated_images/pa/autoencoder_schema.5e6fc9ad98a5eb61.jpg) +![AutoEncoder Diagram](../../../../../translated_images/pa/autoencoder_schema.5e6fc9ad98a5eb61.webp) > ਚਿੱਤਰ [Keras ਬਲੌਗ](https://blog.keras.io/building-autoencoders-in-keras.html) ਤੋਂ diff --git a/translations/pa/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/pa/lessons/4-ComputerVision/11-ObjectDetection/README.md index 30742e96..6be70046 100644 --- a/translations/pa/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/pa/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: ## [ਪ੍ਰੀ-ਲੈਕਚਰ ਕਵਿਜ਼](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Object Detection](../../../../../translated_images/pa/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) +![Object Detection](../../../../../translated_images/pa/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp) > ਤਸਵੀਰ [YOLO v2 ਵੈਬਸਾਈਟ](https://pjreddie.com/darknet/yolov2/) ਤੋਂ @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 2. ਹਰ ਟਾਈਲ 'ਤੇ ਇਮੇਜ ਕਲਾਸੀਫਿਕੇਸ਼ਨ ਚਲਾਓ। 3. ਉਹ ਟਾਈਲਾਂ ਜਿਨ੍ਹਾਂ ਵਿੱਚ ਕਾਫ਼ੀ ਉੱਚੀ ਐਕਟੀਵੇਸ਼ਨ ਹੁੰਦੀ ਹੈ, ਉਹਨਾਂ ਨੂੰ ਉਹ ਵਸਤੂ ਸ਼ਾਮਲ ਕਰਨ ਵਾਲੇ ਮੰਨਿਆ ਜਾ ਸਕਦਾ ਹੈ। -![Naive Object Detection](../../../../../translated_images/pa/naive-detection.e7f1ba220ccd08c6.png) +![Naive Object Detection](../../../../../translated_images/pa/naive-detection.e7f1ba220ccd08c6.webp) > *ਤਸਵੀਰ [ਐਕਸਰਸਾਈਜ਼ ਨੋਟਬੁੱਕ](ObjectDetection-TF.ipynb) ਤੋਂ* @@ -42,7 +42,7 @@ CO_OP_TRANSLATOR_METADATA: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 ਕਲਾਸਾਂ * [COCO](http://cocodataset.org/#home) - ਸਧਾਰਨ ਵਸਤੂਆਂ ਸੰਦਰਭ ਵਿੱਚ। 80 ਕਲਾਸਾਂ, ਬਾਊਂਡਿੰਗ ਬਾਕਸ ਅਤੇ ਸੈਗਮੈਂਟੇਸ਼ਨ ਮਾਸਕ -![COCO](../../../../../translated_images/pa/coco-examples.71bc60380fa6cceb.jpg) +![COCO](../../../../../translated_images/pa/coco-examples.71bc60380fa6cceb.webp) ## ਆਬਜੈਕਟ ਡਿਟੈਕਸ਼ਨ ਮੈਟ੍ਰਿਕਸ @@ -50,7 +50,7 @@ CO_OP_TRANSLATOR_METADATA: ਜਦੋਂ ਕਿ ਇਮੇਜ ਕਲਾਸੀਫਿਕੇਸ਼ਨ ਲਈ ਇਹ ਮਾਪਣਾ ਆਸਾਨ ਹੈ ਕਿ ਐਲਗੋਰਿਦਮ ਕਿੰਨਾ ਚੰਗਾ ਕੰਮ ਕਰਦਾ ਹੈ, ਆਬਜੈਕਟ ਡਿਟੈਕਸ਼ਨ ਲਈ ਸਾਨੂੰ ਕਲਾਸ ਦੀ ਸਹੀਤਾ ਦੇ ਨਾਲ-साथ ਅਨੁਮਾਨਿਤ ਬਾਊਂਡਿੰਗ ਬਾਕਸ ਸਥਿਤੀ ਦੀ ਸ਼ੁੱਧਤਾ ਨੂੰ ਮਾਪਣਾ ਪੈਂਦਾ ਹੈ। ਇਸ ਲਈ, ਅਸੀਂ **ਇੰਟਰਸੈਕਸ਼ਨ ਓਵਰ ਯੂਨੀਅਨ** (IoU) ਦੀ ਵਰਤੋਂ ਕਰਦੇ ਹਾਂ, ਜੋ ਮਾਪਦਾ ਹੈ ਕਿ ਦੋ ਬਾਕਸ (ਜਾਂ ਦੋ ਮਨਮਾਨੇ ਖੇਤਰ) ਕਿੰਨੇ ਚੰਗੇ ਤਰੀਕੇ ਨਾਲ ਓਵਰਲੈਪ ਕਰਦੇ ਹਨ। -![IoU](../../../../../translated_images/pa/iou_equation.9a4751d40fff4e11.png) +![IoU](../../../../../translated_images/pa/iou_equation.9a4751d40fff4e11.webp) > *ਫਿਗਰ 2 [ਇਹ ਸ਼ਾਨਦਾਰ ਬਲੌਗ ਪੋਸਟ IoU 'ਤੇ](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/) ਤੋਂ* @@ -97,11 +97,11 @@ $$ [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) ਦੀ ਵਰਤੋਂ ਕਰਦਾ ਹੈ ROI ਖੇਤਰਾਂ ਦੀ ਹਾਇਰਾਰਕੀਕਲ ਸਟ੍ਰਕਚਰ ਪੈਦਾ ਕਰਨ ਲਈ, ਜੋ ਫਿਰ CNN ਫੀਚਰ ਐਕਸਟ੍ਰੈਕਟਰ ਅਤੇ SVM-ਕਲਾਸੀਫਾਇਰਾਂ ਦੁਆਰਾ ਪਾਸ ਕੀਤੇ ਜਾਂਦੇ ਹਨ ਵਸਤੂਆਂ ਦੀ ਸ਼੍ਰੇਣੀ ਨੂੰ ਨਿਰਧਾਰਤ ਕਰਨ ਲਈ, ਅਤੇ *ਬਾਊਂਡਿੰਗ ਬਾਕਸ* ਕੋਆਰਡੀਨੇਟਸ ਨੂੰ ਨਿਰਧਾਰਤ ਕਰਨ ਲਈ ਲੀਨੀਅਰ ਰੇਗ੍ਰੈਸ਼ਨ। [ਆਧਿਕਾਰਿਕ ਪੇਪਰ](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/pa/rcnn1.cae407020dfb1d1f.png) +![RCNN](../../../../../translated_images/pa/rcnn1.cae407020dfb1d1f.webp) > *ਤਸਵੀਰ ਵੈਨ ਡੇ ਸੈਂਡ ਆਦਿ ICCV’11 ਤੋਂ* -![RCNN-1](../../../../../translated_images/pa/rcnn2.2d9530bb83516484.png) +![RCNN-1](../../../../../translated_images/pa/rcnn2.2d9530bb83516484.webp) > *ਤਸਵੀਰਾਂ [ਇਸ ਬਲੌਗ](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e) ਤੋਂ* @@ -109,7 +109,7 @@ $$ ਇਹ ਪਹੁੰਚ R-CNN ਦੇ ਸਮਾਨ ਹੈ, ਪਰ ਖੇਤਰ ਕਨਵੋਲੂਸ਼ਨ ਲੇਅਰਾਂ ਦੇ ਲਾਗੂ ਹੋਣ ਤੋਂ ਬਾਅਦ ਨਿਰਧਾਰਤ ਕੀਤੇ ਜਾਂਦੇ ਹਨ। -![FRCNN](../../../../../translated_images/pa/f-rcnn.3cda6d9bb4188875.png) +![FRCNN](../../../../../translated_images/pa/f-rcnn.3cda6d9bb4188875.webp) > ਤਸਵੀਰ [ਆਧਿਕਾਰਿਕ ਪੇਪਰ](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 ਤੋਂ @@ -117,7 +117,7 @@ $$ ਇਸ ਪਹੁੰਚ ਦਾ ਮੁੱਖ ਵਿਚਾਰ ROI ਦੀ ਪੇਸ਼ਗੂਈ ਕਰਨ ਲਈ ਨਿਊਰਲ ਨੈਟਵਰਕ ਦੀ ਵਰਤੋਂ ਕਰਨਾ ਹੈ - ਜਿਸਨੂੰ *Region Proposal Network* ਕਿਹਾ ਜਾਂਦਾ ਹੈ। [ਪੇਪਰ](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/pa/faster-rcnn.8d46c099b87ef30a.png) +![FasterRCNN](../../../../../translated_images/pa/faster-rcnn.8d46c099b87ef30a.webp) > ਤਸਵੀਰ [ਆਧਿਕਾਰਿਕ ਪੇਪਰ](https://arxiv.org/pdf/1506.01497.pdf) ਤੋਂ @@ -129,7 +129,7 @@ $$ 1. ਫੀਚਰ **ਪੋਜ਼ੀਸ਼ਨ-ਸੈਂਸਿਟਿਵ ਸਕੋਰ ਮੈਪ** ਦੁਆਰਾ ਪ੍ਰੋਸੈਸ ਕੀਤੇ ਜਾਂਦੇ ਹਨ। $C$ ਕਲਾਸਾਂ ਵਿੱਚੋਂ ਹਰ ਵਸਤੂ ਨੂੰ $k\times k$ ਖੇਤਰਾਂ ਦੁਆਰਾ ਵੰਡਿਆ ਜਾਂਦਾ ਹੈ, ਅਤੇ ਅਸੀਂ ਵਸਤੂਆਂ ਦੇ ਹਿੱਸਿਆਂ ਦੀ ਪੇਸ਼ਗੂਈ ਕਰਨ ਲਈ ਟ੍ਰੇਨਿੰਗ ਕਰਦੇ ਹਾਂ। 1. $k\times k$ ਖੇਤਰਾਂ ਵਿੱਚੋਂ ਹਰ ਹਿੱਸੇ ਲਈ ਸਾਰੇ ਨੈਟਵਰਕ ਵਸਤੂਆਂ ਦੀਆਂ ਕਲਾਸਾਂ ਲਈ ਵੋਟ ਕਰਦੇ ਹਨ, ਅਤੇ ਵਧੇਰੇ ਵੋਟ ਵਾਲੀ ਵਸਤੂ ਦੀ ਕਲਾਸ ਚੁਣੀ ਜਾਂਦੀ ਹੈ। -![r-fcn image](../../../../../translated_images/pa/r-fcn.13eb88158b99a3da.png) +![r-fcn image](../../../../../translated_images/pa/r-fcn.13eb88158b99a3da.webp) > ਤਸਵੀਰ [ਆਧਿਕਾਰਿਕ ਪੇਪਰ](https://arxiv.org/abs/1605.06409) ਤੋਂ @@ -140,7 +140,7 @@ YOLO ਇੱਕ ਰੀਅਲਟਾਈਮ ਵਨ-ਪਾਸ ਐਲਗੋਰਿਦ * ਤਸਵੀਰ ਨੂੰ $S\times S$ ਖੇਤਰਾਂ ਵਿੱਚ ਵੰਡਿਆ ਜਾਂਦਾ ਹੈ। * ਹਰ ਖੇਤਰ ਲਈ, **CNN** $n$ ਸੰਭਾਵਿਤ ਵਸਤੂਆਂ, *ਬਾਊਂਡਿੰਗ ਬਾਕਸ* ਕੋਆਰਡੀਨੇਟਸ ਅਤੇ *ਕਾਨਫਿਡੈਂਸ*=*ਪ੍ਰੋਬੈਬਿਲਿਟੀ* * IoU ਦੀ ਪੇਸ਼ਗੂਈ ਕਰਦਾ ਹੈ। - ![YOLO](../../../../../translated_images/pa/yolo.a2648ec82ee8bb4e.png) + ![YOLO](../../../../../translated_images/pa/yolo.a2648ec82ee8bb4e.webp) > ਤਸਵੀਰ [ਆਧਿਕਾਰਿਕ ਪੇਪਰ](https://arxiv.org/abs/1506.02640) ਤੋਂ diff --git a/translations/pa/lessons/4-ComputerVision/README.md b/translations/pa/lessons/4-ComputerVision/README.md index 956afa73..0ffcf8e6 100644 --- a/translations/pa/lessons/4-ComputerVision/README.md +++ b/translations/pa/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # ਕੰਪਿਊਟਰ ਵਿਜ਼ਨ -![ਕੰਪਿਊਟਰ ਵਿਜ਼ਨ ਸਮੱਗਰੀ ਦਾ ਇੱਕ ਡੂਡਲ ਵਿੱਚ ਸਾਰ](../../../../translated_images/pa/ai-computervision.6506ebebac3fbf76.png) +![ਕੰਪਿਊਟਰ ਵਿਜ਼ਨ ਸਮੱਗਰੀ ਦਾ ਇੱਕ ਡੂਡਲ ਵਿੱਚ ਸਾਰ](../../../../translated_images/pa/ai-computervision.6506ebebac3fbf76.webp) ਇਸ ਭਾਗ ਵਿੱਚ ਅਸੀਂ ਸਿੱਖਾਂਗੇ: diff --git a/translations/pa/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/pa/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index 9e77d46a..cecab879 100644 --- a/translations/pa/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/pa/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**ਸ਼ਬਦਾਂ ਦੀ ਥੈਲੀ** (BoW) ਵੇਕਟਰ ਪ੍ਰਤੀਨਿਧੀ ਸਭ ਤੋਂ ਆਮ ਤੌਰ 'ਤੇ ਵਰਤੀ ਜਾਣ ਵਾਲੀ ਰਵਾਇਤੀ ਵੇਕਟਰ ਪ੍ਰਤੀਨਿਧੀ ਹੈ। ਹਰ ਸ਼ਬਦ ਨੂੰ ਇੱਕ ਵੇਕਟਰ ਇੰਡੈਕਸ ਨਾਲ ਜੋੜਿਆ ਜਾਂਦਾ ਹੈ, ਅਤੇ ਵੇਕਟਰ ਤੱਤ ਵਿੱਚ ਦਿੱਤੇ ਗਏ ਦਸਤਾਵੇਜ਼ ਵਿੱਚ ਸ਼ਬਦ ਦੀ ਘਟਨਾ ਦੀ ਗਿਣਤੀ ਹੁੰਦੀ ਹੈ।\n", "\n", - "![ਇਮੇਜ ਦਿਖਾ ਰਿਹਾ ਹੈ ਕਿ ਸ਼ਬਦਾਂ ਦੀ ਥੈਲੀ ਵੇਕਟਰ ਪ੍ਰਤੀਨਿਧੀ ਮੈਮਰੀ ਵਿੱਚ ਕਿਵੇਂ ਦਰਸਾਈ ਜਾਂਦੀ ਹੈ।](../../../../../translated_images/pa/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![ਇਮੇਜ ਦਿਖਾ ਰਿਹਾ ਹੈ ਕਿ ਸ਼ਬਦਾਂ ਦੀ ਥੈਲੀ ਵੇਕਟਰ ਪ੍ਰਤੀਨਿਧੀ ਮੈਮਰੀ ਵਿੱਚ ਕਿਵੇਂ ਦਰਸਾਈ ਜਾਂਦੀ ਹੈ।](../../../../../translated_images/pa/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Note**: ਤੁਸੀਂ BoW ਨੂੰ ਟੈਕਸਟ ਵਿੱਚ ਵੱਖ-ਵੱਖ ਸ਼ਬਦਾਂ ਲਈ ਇੱਕ-ਹਾਟ-ਐਨਕੋਡ ਕੀਤੇ ਵੇਕਟਰਾਂ ਦੇ ਜੋੜ ਵਜੋਂ ਵੀ ਸੋਚ ਸਕਦੇ ਹੋ।\n", "\n", diff --git a/translations/pa/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/pa/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index 8f63410f..b6bf29bb 100644 --- a/translations/pa/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/pa/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**ਬੈਗ-ਆਫ-ਵਰਡਸ** (BoW) ਵੇਕਟਰ ਪ੍ਰਤੀਨਿਧਤਾ ਸਭ ਤੋਂ ਸਧਾਰਨ ਅਤੇ ਸਮਝਣ ਵਿੱਚ ਆਸਾਨ ਰਵਾਇਤੀ ਵੇਕਟਰ ਪ੍ਰਤੀਨਿਧਤਾ ਹੈ। ਹਰ ਸ਼ਬਦ ਨੂੰ ਇੱਕ ਵੇਕਟਰ ਇੰਡੈਕਸ ਨਾਲ ਜੋੜਿਆ ਜਾਂਦਾ ਹੈ, ਅਤੇ ਇੱਕ ਵੇਕਟਰ ਤੱਤ ਵਿੱਚ ਦਿੱਤੇ ਗਏ ਦਸਤਾਵੇਜ਼ ਵਿੱਚ ਹਰ ਸ਼ਬਦ ਦੇ ਆਵਿਰਤੀ ਦੀ ਗਿਣਤੀ ਹੁੰਦੀ ਹੈ।\n", "\n", - "![ਇਮੇਜ ਦਿਖਾ ਰਿਹਾ ਹੈ ਕਿ ਬੈਗ-ਆਫ-ਵਰਡਸ ਵੇਕਟਰ ਪ੍ਰਤੀਨਿਧਤਾ ਮੈਮੋਰੀ ਵਿੱਚ ਕਿਵੇਂ ਦਰਸਾਈ ਜਾਂਦੀ ਹੈ।](../../../../../translated_images/pa/bag-of-words-example.606fc1738f1d7ba9.png)\n", + "![ਇਮੇਜ ਦਿਖਾ ਰਿਹਾ ਹੈ ਕਿ ਬੈਗ-ਆਫ-ਵਰਡਸ ਵੇਕਟਰ ਪ੍ਰਤੀਨਿਧਤਾ ਮੈਮੋਰੀ ਵਿੱਚ ਕਿਵੇਂ ਦਰਸਾਈ ਜਾਂਦੀ ਹੈ।](../../../../../translated_images/pa/bag-of-words-example.606fc1738f1d7ba9.webp)\n", "\n", "> **Note**: ਤੁਸੀਂ BoW ਨੂੰ ਟੈਕਸਟ ਵਿੱਚ ਵੱਖ-ਵੱਖ ਸ਼ਬਦਾਂ ਲਈ ਇੱਕ-ਹਾਟ-ਐਨਕੋਡਡ ਵੇਕਟਰਾਂ ਦੇ ਜੋੜ ਵਜੋਂ ਵੀ ਸੋਚ ਸਕਦੇ ਹੋ।\n", "\n", diff --git a/translations/pa/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/pa/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 73fe0c7a..2da6698a 100644 --- a/translations/pa/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/pa/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "ਜਦੋਂ ਅਸੀਂ ਆਪਣੇ ਨੈੱਟਵਰਕ ਵਿੱਚ ਪਹਿਲੇ ਲੇਅਰ ਵਜੋਂ ਐਮਬੈਡਿੰਗ ਲੇਅਰ ਦੀ ਵਰਤੋਂ ਕਰਦੇ ਹਾਂ, ਤਾਂ ਅਸੀਂ ਬੈਗ-ਆਫ-ਵਰਡਜ਼ ਤੋਂ **ਐਮਬੈਡਿੰਗ ਬੈਗ** ਮਾਡਲ ਵਿੱਚ ਬਦਲ ਸਕਦੇ ਹਾਂ, ਜਿੱਥੇ ਅਸੀਂ ਪਹਿਲਾਂ ਆਪਣੇ ਪਾਠ ਵਿੱਚ ਹਰ ਸ਼ਬਦ ਨੂੰ ਉਸਦੇ ਸੰਬੰਧਿਤ ਐਮਬੈਡਿੰਗ ਵਿੱਚ ਬਦਲਦੇ ਹਾਂ, ਅਤੇ ਫਿਰ ਸਾਰੇ ਐਮਬੈਡਿੰਗਜ਼ 'ਤੇ ਕੁਝ ਸਮੂਹ ਫੰਕਸ਼ਨ ਗਣਨਾ ਕਰਦੇ ਹਾਂ, ਜਿਵੇਂ ਕਿ `sum`, `average` ਜਾਂ `max`।\n", "\n", - "![ਪੰਜ ਕ੍ਰਮ ਸ਼ਬਦਾਂ ਲਈ ਐਮਬੈਡਿੰਗ ਕਲਾਸੀਫਾਇਰ ਦਿਖਾਉਂਦੀ ਇੱਕ ਚਿੱਤਰ।](../../../../../translated_images/pa/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![ਪੰਜ ਕ੍ਰਮ ਸ਼ਬਦਾਂ ਲਈ ਐਮਬੈਡਿੰਗ ਕਲਾਸੀਫਾਇਰ ਦਿਖਾਉਂਦੀ ਇੱਕ ਚਿੱਤਰ।](../../../../../translated_images/pa/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "ਸਾਡਾ ਕਲਾਸੀਫਾਇਰ ਨਿਊਰਲ ਨੈੱਟਵਰਕ ਐਮਬੈਡਿੰਗ ਲੇਅਰ ਨਾਲ ਸ਼ੁਰੂ ਹੋਵੇਗਾ, ਫਿਰ ਐਗਰੀਗੇਸ਼ਨ ਲੇਅਰ, ਅਤੇ ਇਸਦੇ ਉੱਪਰ ਲੀਨਿਅਰ ਕਲਾਸੀਫਾਇਰ:\n" ] @@ -176,7 +176,7 @@ "\n", "ਪਿਛਲੀ ਆਰਕੀਟੈਕਚਰ ਵਿੱਚ, ਸਾਨੂੰ ਸਾਰੇ ਕ੍ਰਮਾਂ ਨੂੰ ਇੱਕੋ ਲੰਬਾਈ ਵਿੱਚ ਪੈਡ ਕਰਨਾ ਪੈਂਦਾ ਸੀ ਤਾਂ ਜੋ ਉਹਨਾਂ ਨੂੰ ਇੱਕ ਮਿਨੀਬੈਚ ਵਿੱਚ ਫਿੱਟ ਕੀਤਾ ਜਾ ਸਕੇ। ਇਹ ਵੱਖ-ਵੱਖ ਲੰਬਾਈ ਵਾਲੇ ਕ੍ਰਮਾਂ ਨੂੰ ਪ੍ਰਤੀਨਿਧਤ ਕਰਨ ਦਾ ਸਭ ਤੋਂ ਕੁਸ਼ਲ ਤਰੀਕਾ ਨਹੀਂ ਹੈ - ਇੱਕ ਹੋਰ ਤਰੀਕਾ **ਆਫਸੈਟ** ਵੇਕਟਰ ਦੀ ਵਰਤੋਂ ਕਰਨਾ ਹੋਵੇਗਾ, ਜੋ ਇੱਕ ਵੱਡੇ ਵੇਕਟਰ ਵਿੱਚ ਸਟੋਰ ਕੀਤੇ ਸਾਰੇ ਕ੍ਰਮਾਂ ਦੇ ਆਫਸੈਟ ਨੂੰ ਰੱਖੇਗਾ।\n", "\n", - "![ਆਫਸੈਟ ਕ੍ਰਮ ਦੀ ਪ੍ਰਤੀਨਿਧਤਾ ਦਿਖਾਉਂਦੀ ਤਸਵੀਰ](../../../../../translated_images/pa/offset-sequence-representation.eb73fcefb29b46ee.png)\n", + "![ਆਫਸੈਟ ਕ੍ਰਮ ਦੀ ਪ੍ਰਤੀਨਿਧਤਾ ਦਿਖਾਉਂਦੀ ਤਸਵੀਰ](../../../../../translated_images/pa/offset-sequence-representation.eb73fcefb29b46ee.webp)\n", "\n", "> **Note**: ਉੱਪਰ ਦਿੱਤੀ ਤਸਵੀਰ ਵਿੱਚ, ਅਸੀਂ ਅੱਖਰਾਂ ਦੇ ਕ੍ਰਮ ਨੂੰ ਦਿਖਾਇਆ ਹੈ, ਪਰ ਸਾਡੇ ਉਦਾਹਰਨ ਵਿੱਚ ਅਸੀਂ ਸ਼ਬਦਾਂ ਦੇ ਕ੍ਰਮਾਂ ਨਾਲ ਕੰਮ ਕਰ ਰਹੇ ਹਾਂ। ਹਾਲਾਂਕਿ, ਆਫਸੈਟ ਵੇਕਟਰ ਨਾਲ ਕ੍ਰਮਾਂ ਦੀ ਪ੍ਰਤੀਨਿਧਤਾ ਕਰਨ ਦਾ ਆਮ ਸਿਧਾਂਤ ਇੱਕੋ ਜਿਹਾ ਰਹਿੰਦਾ ਹੈ।\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW ਤੇਜ਼ ਹੈ, ਜਦਕਿ ਸਕਿਪ-ਗ੍ਰਾਮ ਹੌਲੀ ਹੈ, ਪਰ ਇਹ ਅਲਭ ਸ਼ਬਦਾਂ ਦੀ ਵਧੀਆ ਪ੍ਰਤੀਨਿਧੀ ਕਰਦਾ ਹੈ।\n", "\n", - "![ਦੋਵੇਂ CBoW ਅਤੇ ਸਕਿਪ-ਗ੍ਰਾਮ ਐਲਗੋਰਿਥਮਾਂ ਨੂੰ ਸ਼ਬਦਾਂ ਨੂੰ ਵੈਕਟਰ ਵਿੱਚ ਬਦਲਣ ਲਈ ਦਿਖਾਉਣ ਵਾਲੀ ਚਿੱਤਰ।](../../../../../translated_images/pa/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![ਦੋਵੇਂ CBoW ਅਤੇ ਸਕਿਪ-ਗ੍ਰਾਮ ਐਲਗੋਰਿਥਮਾਂ ਨੂੰ ਸ਼ਬਦਾਂ ਨੂੰ ਵੈਕਟਰ ਵਿੱਚ ਬਦਲਣ ਲਈ ਦਿਖਾਉਣ ਵਾਲੀ ਚਿੱਤਰ।](../../../../../translated_images/pa/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "ਗੂਗਲ ਨਿਊਜ਼ ਡੇਟਾਸੈਟ 'ਤੇ ਪ੍ਰੀ-ਟ੍ਰੇਨ ਕੀਤੇ ਵਰਡ2ਵੈਕ ਐਮਬੈਡਿੰਗ ਨਾਲ ਪ੍ਰਯੋਗ ਕਰਨ ਲਈ, ਅਸੀਂ **gensim** ਲਾਇਬ੍ਰੇਰੀ ਦੀ ਵਰਤੋਂ ਕਰ ਸਕਦੇ ਹਾਂ। ਹੇਠਾਂ ਅਸੀਂ 'neural' ਦੇ ਸਭ ਤੋਂ ਸਮਾਨ ਸ਼ਬਦਾਂ ਨੂੰ ਲੱਭਦੇ ਹਾਂ।\n", "\n", diff --git a/translations/pa/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/pa/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index 027b78fb..5a3ea54b 100644 --- a/translations/pa/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/pa/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "ਜਦੋਂ ਅਸੀਂ ਆਪਣੇ ਨੈਟਵਰਕ ਵਿੱਚ ਪਹਿਲੀ ਲੇਅਰ ਵਜੋਂ ਐਮਬੈਡਿੰਗ ਲੇਅਰ ਦੀ ਵਰਤੋਂ ਕਰਦੇ ਹਾਂ, ਤਾਂ ਅਸੀਂ ਬੈਗ-ਆਫ-ਵਰਡਸ ਮਾਡਲ ਤੋਂ **ਐਮਬੈਡਿੰਗ ਬੈਗ** ਮਾਡਲ ਵਿੱਚ ਸਵਿੱਚ ਕਰ ਸਕਦੇ ਹਾਂ, ਜਿੱਥੇ ਅਸੀਂ ਪਹਿਲਾਂ ਆਪਣੇ ਟੈਕਸਟ ਵਿੱਚ ਹਰ ਸ਼ਬਦ ਨੂੰ ਉਸ ਦੇ ਸੰਬੰਧਿਤ ਐਮਬੈਡਿੰਗ ਵਿੱਚ ਬਦਲਦੇ ਹਾਂ, ਅਤੇ ਫਿਰ ਉਹਨਾਂ ਸਾਰੀਆਂ ਐਮਬੈਡਿੰਗਜ਼ 'ਤੇ ਕੁਝ ਸਮੁੱਚੇ ਫੰਕਸ਼ਨ ਦੀ ਗਣਨਾ ਕਰਦੇ ਹਾਂ, ਜਿਵੇਂ ਕਿ `sum`, `average` ਜਾਂ `max`। \n", "\n", - "![ਪੰਜ ਕ੍ਰਮਵਾਰ ਸ਼ਬਦਾਂ ਲਈ ਐਮਬੈਡਿੰਗ ਕਲਾਸੀਫਾਇਰ ਦਿਖਾਉਣ ਵਾਲੀ ਚਿੱਤਰ।](../../../../../translated_images/pa/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![ਪੰਜ ਕ੍ਰਮਵਾਰ ਸ਼ਬਦਾਂ ਲਈ ਐਮਬੈਡਿੰਗ ਕਲਾਸੀਫਾਇਰ ਦਿਖਾਉਣ ਵਾਲੀ ਚਿੱਤਰ।](../../../../../translated_images/pa/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "ਸਾਡੇ ਕਲਾਸੀਫਾਇਰ ਨਿਊਰਲ ਨੈਟਵਰਕ ਵਿੱਚ ਹੇਠਾਂ ਦਿੱਤੀਆਂ ਲੇਅਰਾਂ ਸ਼ਾਮਲ ਹਨ:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW ਤੇਜ਼ ਹੈ, ਜਦਕਿ ਸਕਿਪ-ਗ੍ਰਾਮ ਹੌਲੀ ਹੈ, ਪਰ ਇਹ ਅਲਪ-ਵਰਤੋਂ ਵਾਲੇ ਸ਼ਬਦਾਂ ਦੀ ਵਧੀਆ ਪ੍ਰਤੀਨਿਧੀ ਕਰਦਾ ਹੈ।\n", "\n", - "![CBoW ਅਤੇ ਸਕਿਪ-ਗ੍ਰਾਮ ਐਲਗੋਰਿਥਮ ਦਿਖਾਉਣ ਵਾਲੀ ਚਿੱਤਰਕਾਰੀ ਜੋ ਸ਼ਬਦਾਂ ਨੂੰ ਵੈਕਟਰ ਵਿੱਚ ਬਦਲਦੀ ਹੈ।](../../../../../translated_images/pa/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![CBoW ਅਤੇ ਸਕਿਪ-ਗ੍ਰਾਮ ਐਲਗੋਰਿਥਮ ਦਿਖਾਉਣ ਵਾਲੀ ਚਿੱਤਰਕਾਰੀ ਜੋ ਸ਼ਬਦਾਂ ਨੂੰ ਵੈਕਟਰ ਵਿੱਚ ਬਦਲਦੀ ਹੈ।](../../../../../translated_images/pa/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Google News ਡੇਟਾਸੈਟ 'ਤੇ ਪ੍ਰੀ-ਟ੍ਰੇਨ ਕੀਤੇ ਵਰਡ2ਵੈਕ ਐਮਬੈਡਿੰਗ ਨਾਲ ਪ੍ਰਯੋਗ ਕਰਨ ਲਈ, ਅਸੀਂ **gensim** ਲਾਇਬ੍ਰੇਰੀ ਦੀ ਵਰਤੋਂ ਕਰ ਸਕਦੇ ਹਾਂ। ਹੇਠਾਂ ਅਸੀਂ 'neural' ਦੇ ਸਭ ਤੋਂ ਸਮਾਨ ਸ਼ਬਦਾਂ ਨੂੰ ਲੱਭਦੇ ਹਾਂ।\n", "\n", diff --git a/translations/pa/lessons/5-NLP/14-Embeddings/README.md b/translations/pa/lessons/5-NLP/14-Embeddings/README.md index 1c9e5923..8e40a988 100644 --- a/translations/pa/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/pa/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: ਐਮਬੈਡਿੰਗ ਲੇਅਰ ਨੂੰ ਸਾਡੇ ਕਲਾਸੀਫਾਇਰ ਨੈਟਵਰਕ ਵਿੱਚ ਪਹਿਲੇ ਲੇਅਰ ਵਜੋਂ ਵਰਤ ਕੇ, ਅਸੀਂ ਬੈਗ-ਆਫ-ਵਰਡਸ ਤੋਂ **embedding bag** ਮਾਡਲ ਵਿੱਚ ਸਵਿੱਚ ਕਰ ਸਕਦੇ ਹਾਂ, ਜਿੱਥੇ ਅਸੀਂ ਪਹਿਲਾਂ ਆਪਣੇ ਟੈਕਸਟ ਵਿੱਚ ਹਰ ਸ਼ਬਦ ਨੂੰ ਸੰਬੰਧਿਤ ਐਮਬੈਡਿੰਗ ਵਿੱਚ ਬਦਲਦੇ ਹਾਂ, ਅਤੇ ਫਿਰ ਸਾਰੇ ਐਮਬੈਡਿੰਗਸ 'ਤੇ ਕੁਝ ਸਮੁੱਚੇ ਫੰਕਸ਼ਨ ਦੀ ਗਣਨਾ ਕਰਦੇ ਹਾਂ, ਜਿਵੇਂ ਕਿ `sum`, `average` ਜਾਂ `max`। -![ਪੰਜ ਕ੍ਰਮ ਸ਼ਬਦਾਂ ਲਈ ਐਮਬੈਡਿੰਗ ਕਲਾਸੀਫਾਇਰ ਦਿਖਾਉਣ ਵਾਲੀ ਚਿੱਤਰ।](../../../../../translated_images/pa/embedding-classifier-example.b77f021a7ee67eee.png) +![ਪੰਜ ਕ੍ਰਮ ਸ਼ਬਦਾਂ ਲਈ ਐਮਬੈਡਿੰਗ ਕਲਾਸੀਫਾਇਰ ਦਿਖਾਉਣ ਵਾਲੀ ਚਿੱਤਰ।](../../../../../translated_images/pa/embedding-classifier-example.b77f021a7ee67eee.webp) > ਲੇਖਕ ਦੁਆਰਾ ਚਿੱਤਰ @@ -40,7 +40,7 @@ CO_OP_TRANSLATOR_METADATA: CBoW ਤੇਜ਼ ਹੈ, ਜਦਕਿ ਸਕਿਪ-ਗ੍ਰਾਮ ਹੌਲੀ ਹੈ, ਪਰ ਅਲਭ ਸ਼ਬਦਾਂ ਦੀ ਪ੍ਰਤੀਨਿਧੀ ਕਰਨ ਵਿੱਚ ਵਧੀਆ ਕੰਮ ਕਰਦਾ ਹੈ। -![CBoW ਅਤੇ ਸਕਿਪ-ਗ੍ਰਾਮ ਅਲਗੋਰਿਥਮ ਦਿਖਾਉਣ ਵਾਲੀ ਚਿੱਤਰ।](../../../../../translated_images/pa/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![CBoW ਅਤੇ ਸਕਿਪ-ਗ੍ਰਾਮ ਅਲਗੋਰਿਥਮ ਦਿਖਾਉਣ ਵਾਲੀ ਚਿੱਤਰ।](../../../../../translated_images/pa/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > [ਇਸ ਪੇਪਰ](https://arxiv.org/pdf/1301.3781.pdf) ਤੋਂ ਚਿੱਤਰ diff --git a/translations/pa/lessons/5-NLP/15-LanguageModeling/README.md b/translations/pa/lessons/5-NLP/15-LanguageModeling/README.md index 06f8c700..060d8742 100644 --- a/translations/pa/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/pa/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: * **ਕੰਟਿਨਿਊਅਸ ਬੈਗ-ਆਫ-ਵਰਡਸ** (CBoW), ਜਦੋਂ ਅਸੀਂ ਟੋਕਨ ਸੀਕਵੈਂਸ $W_{-N}$, ..., $W_N$ ਵਿੱਚ ਮੱਧਲੇ ਟੋਕਨ $W_0$ ਦੀ ਪੇਸ਼ਗੂਈ ਕਰਦੇ ਹਾਂ। * **ਸਕਿਪ-ਗ੍ਰਾਮ**, ਜਿੱਥੇ ਅਸੀਂ ਮੱਧਲੇ ਟੋਕਨ $W_0$ ਤੋਂ ਨੇੜਲੇ ਟੋਕਨਜ਼ {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} ਦੀ ਪੇਸ਼ਗੂਈ ਕਰਦੇ ਹਾਂ। -![ਸ਼ਬਦਾਂ ਨੂੰ ਵੈਕਟਰ ਵਿੱਚ ਬਦਲਣ ਲਈ ਪੇਪਰ ਤੋਂ ਚਿੱਤਰ](../../../../../translated_images/pa/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![ਸ਼ਬਦਾਂ ਨੂੰ ਵੈਕਟਰ ਵਿੱਚ ਬਦਲਣ ਲਈ ਪੇਪਰ ਤੋਂ ਚਿੱਤਰ](../../../../../translated_images/pa/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > ਚਿੱਤਰ [ਇਸ ਪੇਪਰ](https://arxiv.org/pdf/1301.3781.pdf) ਤੋਂ diff --git a/translations/pa/lessons/5-NLP/16-RNN/README.md b/translations/pa/lessons/5-NLP/16-RNN/README.md index 1680cf98..ee13b478 100644 --- a/translations/pa/lessons/5-NLP/16-RNN/README.md +++ b/translations/pa/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: ਟੈਕਸਟ ਕ੍ਰਮ ਦੇ ਅਰਥ ਨੂੰ ਕੈਪਚਰ ਕਰਨ ਲਈ, ਸਾਨੂੰ ਇੱਕ ਹੋਰ ਨਿਊਰਲ ਨੈਟਵਰਕ ਆਰਕੀਟੈਕਚਰ ਦੀ ਵਰਤੋਂ ਕਰਨ ਦੀ ਲੋੜ ਹੈ, ਜਿਸਨੂੰ **ਰਿਕਰੰਟ ਨਿਊਰਲ ਨੈਟਵਰਕ** ਜਾਂ RNN ਕਿਹਾ ਜਾਂਦਾ ਹੈ। RNN ਵਿੱਚ, ਅਸੀਂ ਆਪਣੇ ਵਾਕਾਂਸ਼ ਨੂੰ ਨੈਟਵਰਕ ਵਿੱਚ ਇੱਕ ਸਮੇਂ ਵਿੱਚ ਇੱਕ ਚਿੰਨ੍ਹ ਦੇ ਰਾਹੀਂ ਪਾਸ ਕਰਦੇ ਹਾਂ, ਅਤੇ ਨੈਟਵਰਕ ਕੁਝ **ਸਟੇਟ** ਪੈਦਾ ਕਰਦਾ ਹੈ, ਜਿਸਨੂੰ ਅਗਲੇ ਚਿੰਨ੍ਹ ਦੇ ਨਾਲ ਮੁੜ ਨੈਟਵਰਕ ਵਿੱਚ ਪਾਸ ਕੀਤਾ ਜਾਂਦਾ ਹੈ। -![RNN](../../../../../translated_images/pa/rnn.27f5c29c53d727b5.png) +![RNN](../../../../../translated_images/pa/rnn.27f5c29c53d727b5.webp) > ਲੇਖਕ ਦੁਆਰਾ ਚਿੱਤਰ @@ -61,7 +61,7 @@ LSTM ਨੈਟਵਰਕ RNN ਦੇ ਸਮਾਨ ਸੰਗਠਿਤ ਹੈ, ਪ ਇੱਕ ਰਿਕਰੰਟ ਨੈਟਵਰਕ, ਚਾਹੇ ਇੱਕ-ਦਿਸ਼ਾ ਵਾਲਾ ਹੋਵੇ ਜਾਂ ਬਾਈਡਾਇਰੈਕਸ਼ਨਲ, ਲੜੀ ਵਿੱਚ ਕੁਝ ਪੈਟਰਨਾਂ ਨੂੰ ਕੈਪਚਰ ਕਰਦਾ ਹੈ ਅਤੇ ਇਸਨੂੰ ਸਟੇਟ ਵੈਕਟਰ ਵਿੱਚ ਸਟੋਰ ਕਰਦਾ ਹੈ ਜਾਂ ਆਉਟਪੁਟ ਵਿੱਚ ਪਾਸ ਕਰਦਾ ਹੈ। ਜਿਵੇਂ ਕਿ ਕਨਵੋਲੂਸ਼ਨਲ ਨੈਟਵਰਕਸ ਦੇ ਨਾਲ, ਅਸੀਂ ਪਹਿਲੇ ਲੇਅਰ ਦੁਆਰਾ ਕੈਪਚਰ ਕੀਤੇ ਗਏ ਨੀਵਾਂ-ਸਤਹ ਪੈਟਰਨਾਂ ਤੋਂ ਉੱਚ-ਸਤਹ ਪੈਟਰਨਾਂ ਨੂੰ ਕੈਪਚਰ ਕਰਨ ਲਈ ਪਹਿਲੇ ਲੇਅਰ ਦੇ ਉੱਪਰ ਇੱਕ ਹੋਰ ਰਿਕਰੰਟ ਲੇਅਰ ਬਣਾਉਣ ਲਈ ਬਣਾਉਣ ਕਰ ਸਕਦੇ ਹਾਂ। ਇਸ ਨਾਲ ਸਾਨੂੰ **ਮਲਟੀ-ਲੇਅਰ RNN** ਦੀ ਧਾਰਨਾ ਮਿਲਦੀ ਹੈ ਜੋ ਦੋ ਜਾਂ ਵੱਧ ਰਿਕਰੰਟ ਨੈਟਵਰਕਸ ਤੋਂ ਬਣਦੀ ਹੈ, ਜਿੱਥੇ ਪਿਛਲੇ ਲੇਅਰ ਦਾ ਆਉਟਪੁਟ ਅਗਲੇ ਲੇਅਰ ਵਿੱਚ ਇਨਪੁਟ ਵਜੋਂ ਪਾਸ ਕੀਤਾ ਜਾਂਦਾ ਹੈ। -![ਮਲਟੀਲੇਅਰ ਲਾਂਗ-ਸ਼ਾਰਟ-ਟਰਮ-ਮੈਮੋਰੀ RNN ਦਿਖਾਉਂਦਾ ਚਿੱਤਰ](../../../../../translated_images/pa/multi-layer-lstm.dd975e29bb2a59fe.jpg) +![ਮਲਟੀਲੇਅਰ ਲਾਂਗ-ਸ਼ਾਰਟ-ਟਰਮ-ਮੈਮੋਰੀ RNN ਦਿਖਾਉਂਦਾ ਚਿੱਤਰ](../../../../../translated_images/pa/multi-layer-lstm.dd975e29bb2a59fe.webp) *ਚਿੱਤਰ [ਇਸ ਸ਼ਾਨਦਾਰ ਪੋਸਟ](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) ਫਰਨਾਂਡੋ ਲੋਪੇਜ਼ ਦੁਆਰਾ* diff --git a/translations/pa/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/pa/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index 699c02cf..857a80d7 100644 --- a/translations/pa/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/pa/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "ਰੀਕਰਨਟ ਨੈਟਵਰਕ, ਚਾਹੇ ਇੱਕ-ਦਿਸ਼ਾ ਹੋਵੇ ਜਾਂ ਦੋ-ਦਿਸ਼ਾ, ਕ੍ਰਮ ਵਿੱਚ ਕੁਝ ਪੈਟਰਨ ਨੂੰ ਕੈਪਚਰ ਕਰਦਾ ਹੈ, ਅਤੇ ਉਨ੍ਹਾਂ ਨੂੰ ਸਟੇਟ ਵੈਕਟਰ ਵਿੱਚ ਸਟੋਰ ਕਰਦਾ ਹੈ ਜਾਂ ਆਉਟਪੁਟ ਵਿੱਚ ਪਾਸ ਕਰਦਾ ਹੈ। ਜਿਵੇਂ ਕਿ ਕਨਵੋਲੂਸ਼ਨਲ ਨੈਟਵਰਕਸ ਵਿੱਚ, ਅਸੀਂ ਪਹਿਲੀ ਲੇਅਰ ਦੁਆਰਾ ਕੈਪਚਰ ਕੀਤੇ ਨੀਵੀਂ-ਪੱਧਰੀ ਪੈਟਰਨਾਂ ਤੋਂ ਉੱਚ-ਪੱਧਰੀ ਪੈਟਰਨਾਂ ਨੂੰ ਕੈਪਚਰ ਕਰਨ ਲਈ ਪਹਿਲੀ ਲੇਅਰ ਦੇ ਉੱਪਰ ਇੱਕ ਹੋਰ ਰੀਕਰਨਟ ਲੇਅਰ ਬਣਾਉਣ ਦੀ ਯੋਜਨਾ ਬਣਾ ਸਕਦੇ ਹਾਂ। ਇਸ ਨਾਲ **ਬਹੁ-ਪੱਧਰੀ RNN** ਦੀ ਧਾਰਨਾ ਬਣਦੀ ਹੈ, ਜਿਸ ਵਿੱਚ ਦੋ ਜਾਂ ਵੱਧ ਰੀਕਰਨਟ ਨੈਟਵਰਕਸ ਹੁੰਦੇ ਹਨ, ਜਿੱਥੇ ਪਿਛਲੀ ਲੇਅਰ ਦਾ ਆਉਟਪੁਟ ਅਗਲੀ ਲੇਅਰ ਨੂੰ ਇਨਪੁਟ ਵਜੋਂ ਪਾਸ ਕੀਤਾ ਜਾਂਦਾ ਹੈ।\n", "\n", - "![ਬਹੁ-ਪੱਧਰੀ ਲੰਬੇ-ਛੋਟੇ-ਅਵਧੀ-ਮੈਮੋਰੀ RNN ਦਿਖਾਉਣ ਵਾਲੀ ਚਿੱਤਰ](../../../../../translated_images/pa/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![ਬਹੁ-ਪੱਧਰੀ ਲੰਬੇ-ਛੋਟੇ-ਅਵਧੀ-ਮੈਮੋਰੀ RNN ਦਿਖਾਉਣ ਵਾਲੀ ਚਿੱਤਰ](../../../../../translated_images/pa/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*ਫਰਨਾਂਡੋ ਲੋਪੇਜ਼ ਦੁਆਰਾ [ਇਸ ਸ਼ਾਨਦਾਰ ਪੋਸਟ](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) ਤੋਂ ਚਿੱਤਰ*\n", "\n", diff --git a/translations/pa/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/pa/lessons/5-NLP/16-RNN/RNNTF.ipynb index 042e066c..972fa69b 100644 --- a/translations/pa/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/pa/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "ਪਾਠ ਕ੍ਰਮ ਦੇ ਅਰਥ ਨੂੰ ਕੈਪਚਰ ਕਰਨ ਲਈ, ਅਸੀਂ ਇੱਕ ਨਿਊਰਲ ਨੈਟਵਰਕ ਆਰਕੀਟੈਕਚਰ ਵਰਤਾਂਗੇ ਜਿਸਨੂੰ **ਰਿਕਰੰਟ ਨਿਊਰਲ ਨੈਟਵਰਕ** ਜਾਂ RNN ਕਿਹਾ ਜਾਂਦਾ ਹੈ। RNN ਵਰਤਦੇ ਸਮੇਂ, ਅਸੀਂ ਆਪਣੀ ਵਾਕ ਨੂੰ ਨੈਟਵਰਕ ਵਿੱਚ ਇੱਕ ਟੋਕਨ ਇੱਕ ਵਾਰ ਵਿੱਚ ਪਾਸ ਕਰਦੇ ਹਾਂ, ਅਤੇ ਨੈਟਵਰਕ ਕੁਝ **ਸਟੇਟ** ਤਿਆਰ ਕਰਦਾ ਹੈ, ਜਿਸਨੂੰ ਅਸੀਂ ਅਗਲੇ ਟੋਕਨ ਦੇ ਨਾਲ ਫਿਰ ਨੈਟਵਰਕ ਵਿੱਚ ਪਾਸ ਕਰਦੇ ਹਾਂ।\n", "\n", - "![ਇੱਕ ਉਦਾਹਰਣ ਰਿਕਰੰਟ ਨਿਊਰਲ ਨੈਟਵਰਕ ਜਨਰੇਸ਼ਨ ਦਿਖਾਉਂਦੀ ਤਸਵੀਰ।](../../../../../translated_images/pa/rnn.27f5c29c53d727b5.png)\n", + "![ਇੱਕ ਉਦਾਹਰਣ ਰਿਕਰੰਟ ਨਿਊਰਲ ਨੈਟਵਰਕ ਜਨਰੇਸ਼ਨ ਦਿਖਾਉਂਦੀ ਤਸਵੀਰ।](../../../../../translated_images/pa/rnn.27f5c29c53d727b5.webp)\n", "\n", "ਜਦੋਂ ਟੋਕਨ ਦਾ ਇਨਪੁਟ ਕ੍ਰਮ $X_0,\\dots,X_n$ ਦਿੱਤਾ ਜਾਂਦਾ ਹੈ, RNN ਨਿਊਰਲ ਨੈਟਵਰਕ ਬਲਾਕਾਂ ਦੀ ਇੱਕ ਲੜੀ ਬਣਾਉਂਦਾ ਹੈ ਅਤੇ ਇਸ ਲੜੀ ਨੂੰ ਬੈਕਪ੍ਰੋਪਾਗੇਸ਼ਨ ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਐਂਡ-ਟੂ-ਐਂਡ ਟ੍ਰੇਨ ਕਰਦਾ ਹੈ। ਹਰ ਨੈਟਵਰਕ ਬਲਾਕ ਇੱਕ ਜੋੜੇ $(X_i,S_i)$ ਨੂੰ ਇਨਪੁਟ ਵਜੋਂ ਲੈਂਦਾ ਹੈ ਅਤੇ ਨਤੀਜੇ ਵਜੋਂ $S_{i+1}$ ਤਿਆਰ ਕਰਦਾ ਹੈ। ਅੰਤਿਮ ਸਟੇਟ $S_n$ ਜਾਂ ਆਉਟਪੁਟ $Y_n$ ਨੂੰ ਨਤੀਜਾ ਤਿਆਰ ਕਰਨ ਲਈ ਇੱਕ ਲੀਨੀਅਰ ਕਲਾਸੀਫਾਇਰ ਵਿੱਚ ਪਾਸ ਕੀਤਾ ਜਾਂਦਾ ਹੈ। ਸਾਰੇ ਨੈਟਵਰਕ ਬਲਾਕ ਇੱਕੋ ਜਿਹੇ ਵਜ਼ਨ ਸਾਂਝੇ ਕਰਦੇ ਹਨ ਅਤੇ ਇੱਕੋ ਬੈਕਪ੍ਰੋਪਾਗੇਸ਼ਨ ਪਾਸ ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਐਂਡ-ਟੂ-ਐਂਡ ਟ੍ਰੇਨ ਕੀਤੇ ਜਾਂਦੇ ਹਨ।\n", "\n", @@ -369,7 +369,7 @@ "\n", "ਰਿਕਰੰਟ ਨੈਟਵਰਕ, ਚਾਹੇ ਇੱਕ-ਦਿਸ਼ਾਵਾਂ ਹੋਣ ਜਾਂ ਦੋ-ਦਿਸ਼ਾਵਾਂ, ਸ਼੍ਰੇਣੀ ਦੇ ਅੰਦਰ ਪੈਟਰਨ ਨੂੰ ਕੈਪਚਰ ਕਰਦੇ ਹਨ ਅਤੇ ਉਨ੍ਹਾਂ ਨੂੰ ਸਟੇਟ ਵੈਕਟਰਾਂ ਵਿੱਚ ਸਟੋਰ ਕਰਦੇ ਹਨ ਜਾਂ ਉਨ੍ਹਾਂ ਨੂੰ ਆਉਟਪੁਟ ਵਜੋਂ ਵਾਪਸ ਕਰਦੇ ਹਨ। ਜਿਵੇਂ ਕਿ ਕਨਵੋਲੂਸ਼ਨਲ ਨੈਟਵਰਕ ਵਿੱਚ ਹੁੰਦਾ ਹੈ, ਅਸੀਂ ਪਹਿਲੇ ਲੇਅਰ ਦੇ ਬਾਅਦ ਇੱਕ ਹੋਰ ਰਿਕਰੰਟ ਲੇਅਰ ਬਣਾਉਣ ਦੇ ਯੋਗ ਹੋ ਸਕਦੇ ਹਾਂ, ਜੋ ਉੱਚ ਪੱਧਰ ਦੇ ਪੈਟਰਨ ਨੂੰ ਕੈਪਚਰ ਕਰਦਾ ਹੈ, ਜੋ ਪਹਿਲੇ ਲੇਅਰ ਦੁਆਰਾ ਕੈਪਚਰ ਕੀਤੇ ਨੀਵੇਂ ਪੱਧਰ ਦੇ ਪੈਟਰਨ ਤੋਂ ਬਣੇ ਹੁੰਦੇ ਹਨ। ਇਸ ਨਾਲ ਸਾਨੂੰ **ਬਹੁ-ਪਤਰੀ RNN** ਦਾ ਧਾਰਨਾ ਮਿਲਦੀ ਹੈ, ਜਿਸ ਵਿੱਚ ਦੋ ਜਾਂ ਵੱਧ ਰਿਕਰੰਟ ਨੈਟਵਰਕ ਹੁੰਦੇ ਹਨ, ਜਿੱਥੇ ਪਿਛਲੇ ਲੇਅਰ ਦਾ ਆਉਟਪੁਟ ਅਗਲੇ ਲੇਅਰ ਨੂੰ ਇਨਪੁਟ ਵਜੋਂ ਦਿੱਤਾ ਜਾਂਦਾ ਹੈ।\n", "\n", - "![ਬਹੁ-ਪਤਰੀ ਲੰਬੇ-ਛੋਟੇ-ਸਮੇਂ-ਯਾਦاشت RNN ਦਿਖਾਉਣ ਵਾਲੀ ਚਿੱਤਰ](../../../../../translated_images/pa/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![ਬਹੁ-ਪਤਰੀ ਲੰਬੇ-ਛੋਟੇ-ਸਮੇਂ-ਯਾਦاشت RNN ਦਿਖਾਉਣ ਵਾਲੀ ਚਿੱਤਰ](../../../../../translated_images/pa/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*ਫਰਨਾਂਡੋ ਲੋਪੇਜ਼ ਦੁਆਰਾ [ਇਸ ਸ਼ਾਨਦਾਰ ਪੋਸਟ](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) ਤੋਂ ਚਿੱਤਰ।*\n", "\n", diff --git a/translations/pa/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/pa/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index 2343638b..3dc7f00d 100644 --- a/translations/pa/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/pa/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "ਅਸੀਂ RNN ਨੂੰ ਟੈਕਸਟ ਜਨਰੇਟ ਕਰਨ ਲਈ ਇਸ ਤਰੀਕੇ ਨਾਲ ਟ੍ਰੇਨ ਕਰਾਂਗੇ। ਹਰ ਕਦਮ 'ਤੇ, ਅਸੀਂ ਅੱਖਰਾਂ ਦੀ `nchars` ਲੰਬਾਈ ਦੀ ਲੜੀ ਲਵਾਂਗੇ ਅਤੇ ਨੈਟਵਰਕ ਨੂੰ ਹਰ ਇਨਪੁਟ ਅੱਖਰ ਲਈ ਅਗਲਾ ਆਉਟਪੁੱਟ ਅੱਖਰ ਜਨਰੇਟ ਕਰਨ ਲਈ ਕਹਾਂਗੇ:\n", "\n", - "![ਇਮੇਜ 'HELLO' ਸ਼ਬਦ ਦੇ RNN ਜਨਰੇਸ਼ਨ ਦਾ ਉਦਾਹਰਨ ਦਿਖਾਉਂਦੀ ਹੈ।](../../../../../translated_images/pa/rnn-generate.56c54afb52f9781d.png)\n", + "![ਇਮੇਜ 'HELLO' ਸ਼ਬਦ ਦੇ RNN ਜਨਰੇਸ਼ਨ ਦਾ ਉਦਾਹਰਨ ਦਿਖਾਉਂਦੀ ਹੈ।](../../../../../translated_images/pa/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "ਅਸਲ ਸਥਿਤੀ ਦੇ ਅਨੁਸਾਰ, ਅਸੀਂ ਕੁਝ ਖਾਸ ਅੱਖਰ ਵੀ ਸ਼ਾਮਲ ਕਰਨਾ ਚਾਹੁੰਦੇ ਹੋ ਸਕਦੇ ਹਾਂ, ਜਿਵੇਂ ਕਿ *end-of-sequence* ``। ਸਾਡੇ ਕੇਸ ਵਿੱਚ, ਅਸੀਂ ਨੈਟਵਰਕ ਨੂੰ ਅਨੰਤ ਟੈਕਸਟ ਜਨਰੇਸ਼ਨ ਲਈ ਟ੍ਰੇਨ ਕਰਨਾ ਚਾਹੁੰਦੇ ਹਾਂ, ਇਸ ਲਈ ਅਸੀਂ ਹਰ ਲੜੀ ਦਾ ਆਕਾਰ `nchars` ਟੋਕਨ ਦੇ ਬਰਾਬਰ ਰੱਖਾਂਗੇ। ਇਸ ਤਰ੍ਹਾਂ, ਹਰ ਟ੍ਰੇਨਿੰਗ ਉਦਾਹਰਨ `nchars` ਇਨਪੁਟ ਅਤੇ `nchars` ਆਉਟਪੁੱਟ (ਜੋ ਇਨਪੁਟ ਲੜੀ ਨੂੰ ਇੱਕ ਚਿੰਨ੍ਹ ਖੱਬੇ ਵੱਲ ਸ਼ਿਫਟ ਕਰਕੇ ਬਣਦੀ ਹੈ) 'ਤੇ ਮੁਸ਼ਤਮਿਲ ਹੋਵੇਗੀ। ਮਿਨੀਬੈਚ ਕਈ ਇਸ ਤਰ੍ਹਾਂ ਦੀਆਂ ਲੜੀਆਂ 'ਤੇ ਮੁਸ਼ਤਮਿਲ ਹੋਵੇਗਾ।\n", "\n", diff --git a/translations/pa/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/pa/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index 2fb37509..745973e9 100644 --- a/translations/pa/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/pa/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -111,7 +111,7 @@ "\n", "ਅਸੀਂ RNN ਨੂੰ ਖ਼ਬਰਾਂ ਦੇ ਸਿਰਲੇਖ ਬਣਾਉਣ ਲਈ ਇਸ ਤਰੀਕੇ ਨਾਲ ਟ੍ਰੇਨ ਕਰਾਂਗੇ। ਹਰ ਕਦਮ 'ਤੇ, ਅਸੀਂ ਇੱਕ ਸਿਰਲੇਖ ਲਵਾਂਗੇ, ਜਿਸਨੂੰ RNN ਵਿੱਚ ਫੀਡ ਕੀਤਾ ਜਾਵੇਗਾ, ਅਤੇ ਹਰ ਇਨਪੁਟ ਅੱਖਰ ਲਈ ਅਸੀਂ ਨੈੱਟਵਰਕ ਨੂੰ ਅਗਲਾ ਆਉਟਪੁਟ ਅੱਖਰ ਜਨਰੇਟ ਕਰਨ ਲਈ ਕਹਾਂਗੇ:\n", "\n", - "![ਇੱਕ ਉਦਾਹਰਣ RNN 'HELLO' ਸ਼ਬਦ ਦੀ ਜਨਰੇਸ਼ਨ ਦਿਖਾਉਂਦੀ ਤਸਵੀਰ।](../../../../../translated_images/pa/rnn-generate.56c54afb52f9781d.png)\n", + "![ਇੱਕ ਉਦਾਹਰਣ RNN 'HELLO' ਸ਼ਬਦ ਦੀ ਜਨਰੇਸ਼ਨ ਦਿਖਾਉਂਦੀ ਤਸਵੀਰ।](../../../../../translated_images/pa/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "ਸਾਡੇ ਕ੍ਰਮ ਦੇ ਆਖਰੀ ਅੱਖਰ ਲਈ, ਅਸੀਂ ਨੈੱਟਵਰਕ ਨੂੰ `` ਟੋਕਨ ਜਨਰੇਟ ਕਰਨ ਲਈ ਕਹਾਂਗੇ।\n", "\n", diff --git a/translations/pa/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/pa/lessons/5-NLP/17-GenerativeNetworks/README.md index 4c20e085..5629ba49 100644 --- a/translations/pa/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/pa/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: ਇਸ ਨਾਲ ਵੱਖ-ਵੱਖ ਨਿਊਰਲ ਆਰਕੀਟੈਕਚਰਸ ਦੀ ਆਗਿਆ ਮਿਲਦੀ ਹੈ ਜੋ ਹੇਠਾਂ ਦਿੱਤੇ ਚਿੱਤਰ ਵਿੱਚ ਦਿਖਾਏ ਗਏ ਹਨ: -![ਚਿੱਤਰ ਜੋ ਆਮ ਰੀਕਰਨਟ ਨਿਊਰਲ ਨੈਟਵਰਕ ਪੈਟਰਨ ਦਿਖਾਉਂਦਾ ਹੈ।](../../../../../translated_images/pa/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![ਚਿੱਤਰ ਜੋ ਆਮ ਰੀਕਰਨਟ ਨਿਊਰਲ ਨੈਟਵਰਕ ਪੈਟਰਨ ਦਿਖਾਉਂਦਾ ਹੈ।](../../../../../translated_images/pa/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > ਚਿੱਤਰ ਬਲੌਗ ਪੋਸਟ [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) ਤੋਂ [Andrej Karpaty](http://karpathy.github.io/) ਦੁਆਰਾ @@ -32,7 +32,7 @@ CO_OP_TRANSLATOR_METADATA: ਅਸੀਂ ਇਸ RNN ਨੂੰ ਕਦਮ-ਦਰ-ਕਦਮ ਟੈਕਸਟ ਪੈਦਾ ਕਰਨ ਲਈ ਟ੍ਰੇਨ ਕਰਾਂਗੇ। ਹਰ ਕਦਮ 'ਤੇ, ਅਸੀਂ `nchars` ਦੀ ਲੰਬਾਈ ਦੇ ਕਿਰਦਾਰਾਂ ਦੇ ਕ੍ਰਮ ਨੂੰ ਲਵਾਂਗੇ, ਅਤੇ ਨੈਟਵਰਕ ਤੋਂ ਹਰ ਇਨਪੁਟ ਕਿਰਦਾਰ ਲਈ ਅਗਲਾ ਆਉਟਪੁਟ ਕਿਰਦਾਰ ਪੈਦਾ ਕਰਨ ਲਈ ਕਹਾਂਗੇ: -![ਚਿੱਤਰ ਜੋ 'HELLO' ਸ਼ਬਦ ਦੇ RNN ਜਨਰੇਸ਼ਨ ਦਾ ਉਦਾਹਰਨ ਦਿਖਾਉਂਦਾ ਹੈ।](../../../../../translated_images/pa/rnn-generate.56c54afb52f9781d.png) +![ਚਿੱਤਰ ਜੋ 'HELLO' ਸ਼ਬਦ ਦੇ RNN ਜਨਰੇਸ਼ਨ ਦਾ ਉਦਾਹਰਨ ਦਿਖਾਉਂਦਾ ਹੈ।](../../../../../translated_images/pa/rnn-generate.56c54afb52f9781d.webp) ਜਦੋਂ ਟੈਕਸਟ ਪੈਦਾ ਕਰਨਾ (ਇਨਫਰੈਂਸ ਦੌਰਾਨ), ਅਸੀਂ ਕੁਝ **ਪ੍ਰਾਂਪਟ** ਨਾਲ ਸ਼ੁਰੂ ਕਰਦੇ ਹਾਂ, ਜਿਸਨੂੰ RNN ਸੈਲਸ ਵਿੱਚੋਂ ਪਾਸ ਕੀਤਾ ਜਾਂਦਾ ਹੈ ਤਾਂ ਜੋ ਇਸਦਾ ਮੱਧਵਰਤੀ ਸਟੇਟ ਪੈਦਾ ਕੀਤਾ ਜਾ ਸਕੇ, ਅਤੇ ਫਿਰ ਇਸ ਸਟੇਟ ਤੋਂ ਜਨਰੇਸ਼ਨ ਸ਼ੁਰੂ ਹੁੰਦੀ ਹੈ। ਅਸੀਂ ਇੱਕ ਸਮੇਂ ਵਿੱਚ ਇੱਕ ਕਿਰਦਾਰ ਪੈਦਾ ਕਰਦੇ ਹਾਂ, ਅਤੇ ਸਟੇਟ ਅਤੇ ਪੈਦਾ ਕੀਤੇ ਕਿਰਦਾਰ ਨੂੰ ਅਗਲੇ RNN ਸੈਲ ਵਿੱਚ ਪਾਸ ਕਰਦੇ ਹਾਂ ਤਾਂ ਜੋ ਅਗਲਾ ਪੈਦਾ ਕੀਤਾ ਜਾ ਸਕੇ, ਜਦੋਂ ਤੱਕ ਅਸੀਂ ਕਾਫ਼ੀ ਕਿਰਦਾਰ ਪੈਦਾ ਨਹੀਂ ਕਰ ਲੈਂਦੇ। diff --git a/translations/pa/lessons/5-NLP/18-Transformers/README.md b/translations/pa/lessons/5-NLP/18-Transformers/README.md index 6a2fafbb..da7eca75 100644 --- a/translations/pa/lessons/5-NLP/18-Transformers/README.md +++ b/translations/pa/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ RNNs ਨਾਲ, ਸੀਕਵੈਂਸ-ਟੂ-ਸੀਕਵੈਂਸ ਦੋ ਰਿ **ਧਿਆਨ ਮਕੈਨਿਜ਼ਮ** RNN ਦੇ ਹਰ ਆਉਟਪੁੱਟ ਅਨੁਮਾਨ 'ਤੇ ਹਰ ਇਨਪੁਟ ਵੇਕਟਰ ਦੇ ਸੰਦਰਭਕ ਪ੍ਰਭਾਵ ਨੂੰ ਵਜਨ ਦੇਣ ਦਾ ਇੱਕ ਢੰਗ ਪ੍ਰਦਾਨ ਕਰਦੇ ਹਨ। ਇਹ ਇਸ ਤਰੀਕੇ ਨਾਲ ਲਾਗੂ ਕੀਤਾ ਜਾਂਦਾ ਹੈ ਕਿ ਇਨਪੁਟ RNN ਦੇ ਮੱਧਵਰਤੀ ਸਟੇਟਾਂ ਅਤੇ ਆਉਟਪੁੱਟ RNN ਦੇ ਵਿਚਕਾਰ ਸ਼ਾਰਟਕਟ ਬਣਾਏ ਜਾਂਦੇ ਹਨ। ਇਸ ਤਰੀਕੇ ਨਾਲ, ਜਦੋਂ ਆਉਟਪੁੱਟ ਚਿੰਨ੍ਹ yt ਬਣਾਇਆ ਜਾਂਦਾ ਹੈ, ਅਸੀਂ ਸਾਰੇ ਇਨਪੁਟ ਹਿਡਨ ਸਟੇਟ hi ਨੂੰ ਵੱਖ-ਵੱਖ ਵਜਨ ਗੁਣਾਂਕ αt,i ਦੇ ਨਾਲ ਧਿਆਨ ਵਿੱਚ ਲਵਾਂਗੇ। -![ਇੱਕ ਐਨਕੋਡਰ/ਡਿਕੋਡਰ ਮਾਡਲ ਨੂੰ additive attention layer ਨਾਲ ਦਿਖਾਉਂਦੀ ਚਿੱਤਰ](../../../../../translated_images/pa/encoder-decoder-attention.7a726296894fb567.png) +![ਇੱਕ ਐਨਕੋਡਰ/ਡਿਕੋਡਰ ਮਾਡਲ ਨੂੰ additive attention layer ਨਾਲ ਦਿਖਾਉਂਦੀ ਚਿੱਤਰ](../../../../../translated_images/pa/encoder-decoder-attention.7a726296894fb567.webp) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) ਵਿੱਚ additive attention ਮਕੈਨਿਜ਼ਮ ਨਾਲ ਐਨਕੋਡਰ-ਡਿਕੋਡਰ ਮਾਡਲ, [ਇਸ ਬਲੌਗ ਪੋਸਟ](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) ਤੋਂ ਲਿਆ ਗਿਆ। Attention ਮੈਟ੍ਰਿਕਸ {αi,j} ਇਹ ਦਰਸਾਉਂਦੀ ਹੈ ਕਿ ਕੁਝ ਇਨਪੁਟ ਸ਼ਬਦਾਂ ਦਾ ਇੱਕ ਦਿੱਤੇ ਗਏ ਆਉਟਪੁੱਟ ਸੀਕਵੈਂਸ ਵਿੱਚ ਸ਼ਬਦ ਬਣਾਉਣ ਵਿੱਚ ਕਿੰਨਾ ਯੋਗਦਾਨ ਹੈ। ਹੇਠਾਂ ਇਸ ਮੈਟ੍ਰਿਕਸ ਦਾ ਇੱਕ ਉਦਾਹਰਨ ਦਿੱਤਾ ਗਿਆ ਹੈ: -![Bahdanau - arviz.org ਤੋਂ ਲਿਆ ਗਿਆ RNNsearch-50 ਦੁਆਰਾ ਪਾਈ ਗਈ ਇੱਕ ਨਮੂਨਾ ਅਲਾਈਨਮੈਂਟ ਦਿਖਾਉਂਦੀ ਚਿੱਤਰ](../../../../../translated_images/pa/bahdanau-fig3.09ba2d37f202a6af.png) +![Bahdanau - arviz.org ਤੋਂ ਲਿਆ ਗਿਆ RNNsearch-50 ਦੁਆਰਾ ਪਾਈ ਗਈ ਇੱਕ ਨਮੂਨਾ ਅਲਾਈਨਮੈਂਟ ਦਿਖਾਉਂਦੀ ਚਿੱਤਰ](../../../../../translated_images/pa/bahdanau-fig3.09ba2d37f202a6af.webp) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) ਤੋਂ ਚਿੱਤਰ @@ -66,7 +66,7 @@ Positional encoding ਦਾ ਵਿਚਾਰ ਹੇਠਾਂ ਦਿੱਤਾ ਗ ਅਗਲੇ, ਸਾਨੂੰ ਆਪਣੇ ਸੀਕਵੈਂਸ ਵਿੱਚ ਕੁਝ ਪੈਟਰਨ ਕੈਪਚਰ ਕਰਨ ਦੀ ਲੋੜ ਹੈ। ਇਹ ਕਰਨ ਲਈ, ਟ੍ਰਾਂਸਫਾਰਮਰ **self-attention** ਮਕੈਨਿਜ਼ਮ ਦੀ ਵਰਤੋਂ ਕਰਦੇ ਹਨ, ਜੋ ਮੂਲ ਤੌਰ 'ਤੇ attention ਹੈ ਜੋ ਇਨਪੁਟ ਅਤੇ ਆਉਟਪੁੱਟ ਦੇ ਤੌਰ 'ਤੇ ਇੱਕੋ ਸੀਕਵੈਂਸ 'ਤੇ ਲਾਗੂ ਹੁੰਦੀ ਹੈ। Self-attention ਲਾਗੂ ਕਰਨ ਨਾਲ ਸਾਨੂੰ **context** ਨੂੰ ਵਾਕ ਵਿੱਚ ਧਿਆਨ ਵਿੱਚ ਲੈਣ ਦੀ ਆਗਿਆ ਮਿਲਦੀ ਹੈ, ਅਤੇ ਵੇਖਣ ਦੀ ਆਗਿਆ ਮਿਲਦੀ ਹੈ ਕਿ ਕਿਹੜੇ ਸ਼ਬਦ ਆਪਸ ਵਿੱਚ ਜੁੜੇ ਹੋਏ ਹਨ। ਉਦਾਹਰਨ ਲਈ, ਇਹ ਸਾਨੂੰ ਇਹ ਵੇਖਣ ਦੀ ਆਗਿਆ ਦਿੰਦਾ ਹੈ ਕਿ ਕਿਹੜੇ ਸ਼ਬਦ coreferences ਦੁਆਰਾ ਦਰਸਾਏ ਜਾਂਦੇ ਹਨ, ਜਿਵੇਂ ਕਿ *it*, ਅਤੇ context ਨੂੰ ਵੀ ਧਿਆਨ ਵਿੱਚ ਲੈਣ ਦੀ ਆਗਿਆ ਦਿੰਦਾ ਹੈ: -![](../../../../../translated_images/pa/CoreferenceResolution.861924d6d384a7d6.png) +![](../../../../../translated_images/pa/CoreferenceResolution.861924d6d384a7d6.webp) > [Google Blog](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) ਤੋਂ ਚਿੱਤਰ @@ -91,7 +91,7 @@ Encoder-decoder attention RNNs ਵਿੱਚ ਵਰਤੇ attention ਮਕੈਨ **BERT** (Bidirectional Encoder Representations from Transformers) ਇੱਕ ਬਹੁਤ ਵੱਡਾ multi-layer ਟ੍ਰਾਂਸਫਾਰਮਰ ਨੈਟਵਰਕ ਹੈ ਜਿਸ ਵਿੱਚ *BERT-base* ਲਈ 12 ਲੇਅਰ ਹਨ, ਅਤੇ *BERT-large* ਲਈ 24। ਮਾਡਲ ਨੂੰ ਪਹਿਲਾਂ ਇੱਕ ਵੱਡੇ ਟੈਕਸਟ ਡਾਟਾ ਕੋਰਪਸ (WikiPedia + ਕਿਤਾਬਾਂ) 'ਤੇ unsupervised training (ਵਾਕ ਵਿੱਚ masked ਸ਼ਬਦਾਂ ਦੀ ਪੇਸ਼ਕਸ਼) ਦੀ ਵਰਤੋਂ ਕਰਕੇ pre-train ਕੀਤਾ ਜਾਂਦਾ ਹੈ। Pre-training ਦੌਰਾਨ ਮਾਡਲ ਭਾਸ਼ਾ ਸਮਝਣ ਦੇ ਮਹੱਤਵਪੂਰਨ ਪੱਧਰਾਂ ਨੂੰ ਅਪਣਾਉਂਦਾ ਹੈ, ਜਿਸਨੂੰ ਫਿਰ ਹੋਰ ਡਾਟਾਸੈਟਾਂ ਨਾਲ fine-tuning ਦੁਆਰਾ leveraged ਕੀਤਾ ਜਾ ਸਕਦਾ ਹੈ। ਇਸ ਪ੍ਰਕਿਰਿਆ ਨੂੰ **transfer learning** ਕਿਹਾ ਜਾਂਦਾ ਹੈ। -![http://jalammar.github.io/illustrated-bert/ ਤੋਂ ਚਿੱਤਰ](../../../../../translated_images/pa/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) +![http://jalammar.github.io/illustrated-bert/ ਤੋਂ ਚਿੱਤਰ](../../../../../translated_images/pa/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > ਚਿੱਤਰ [source](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/pa/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/pa/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index 6e2099c8..0df7b35e 100644 --- a/translations/pa/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/pa/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**ਧਿਆਨ ਮਕੈਨਿਜ਼ਮ** RNN ਦੇ ਹਰ ਆਉਟਪੁੱਟ ਅਨੁਮਾਨ 'ਤੇ ਹਰ ਇਨਪੁਟ ਵੇਕਟਰ ਦੇ ਸੰਦਰਭਕ ਪ੍ਰਭਾਵ ਨੂੰ ਵਜਨ ਦੇਣ ਦਾ ਇੱਕ ਢੰਗ ਪ੍ਰਦਾਨ ਕਰਦੇ ਹਨ। ਇਹ ਇਸ ਤਰੀਕੇ ਨਾਲ ਲਾਗੂ ਕੀਤਾ ਜਾਂਦਾ ਹੈ ਕਿ ਇਨਪੁਟ RNN ਦੇ ਮੱਧਵਰਤੀ ਸਟੇਟਸ ਅਤੇ ਆਉਟਪੁੱਟ RNN ਦੇ ਵਿਚਕਾਰ ਸ਼ਾਰਟਕਟ ਬਣਾਏ ਜਾਂਦੇ ਹਨ। ਇਸ ਤਰੀਕੇ ਨਾਲ, ਜਦੋਂ ਆਉਟਪੁੱਟ ਚਿੰਨ੍ਹ $y_t$ ਬਣਾਇਆ ਜਾ ਰਿਹਾ ਹੈ, ਤਾਂ ਅਸੀਂ ਸਾਰੇ ਇਨਪੁਟ ਹਿਡਨ ਸਟੇਟਸ $h_i$ ਨੂੰ ਵੱਖ-ਵੱਖ ਵਜਨ ਗੁਣਾਂਕ $\\alpha_{t,i}$ ਦੇ ਨਾਲ ਧਿਆਨ ਵਿੱਚ ਲਵਾਂਗੇ।\n", "\n", - "![ਇੱਕ ਐਨਕੋਡਰ/ਡਿਕੋਡਰ ਮਾਡਲ ਨੂੰ additive attention layer ਨਾਲ ਦਿਖਾਉਂਦੀ ਚਿੱਤਰ](../../../../../translated_images/pa/encoder-decoder-attention.7a726296894fb567.png)\n", + "![ਇੱਕ ਐਨਕੋਡਰ/ਡਿਕੋਡਰ ਮਾਡਲ ਨੂੰ additive attention layer ਨਾਲ ਦਿਖਾਉਂਦੀ ਚਿੱਤਰ](../../../../../translated_images/pa/encoder-decoder-attention.7a726296894fb567.webp)\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) ਵਿੱਚ additive attention ਮਕੈਨਿਜ਼ਮ ਵਾਲਾ ਐਨਕੋਡਰ-ਡਿਕੋਡਰ ਮਾਡਲ, [ਇਸ ਬਲੌਗ ਪੋਸਟ](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) ਤੋਂ ਲਿਆ ਗਿਆ।]*\n", "\n", "Attention ਮੈਟ੍ਰਿਕਸ $\\{\\alpha_{i,j}\\}$ ਇਹ ਦਰਸਾਉਂਦੀ ਹੈ ਕਿ ਕਿਸ ਹੱਦ ਤੱਕ ਕੁਝ ਇਨਪੁਟ ਸ਼ਬਦ ਆਉਟਪੁੱਟ ਕ੍ਰਮ ਵਿੱਚ ਦਿੱਤੇ ਗਏ ਸ਼ਬਦ ਦੇ ਜਨਰੇਸ਼ਨ ਵਿੱਚ ਭੂਮਿਕਾ ਨਿਭਾਉਂਦੇ ਹਨ। ਹੇਠਾਂ ਇਸ ਮੈਟ੍ਰਿਕਸ ਦਾ ਇੱਕ ਉਦਾਹਰਨ ਦਿੱਤਾ ਗਿਆ ਹੈ:\n", "\n", - "![Bahdanau - arviz.org ਤੋਂ ਲਿਆ ਗਿਆ RNNsearch-50 ਦੁਆਰਾ ਪਾਈ ਗਈ ਇੱਕ ਨਮੂਨਾ ਅਲਾਈਨਮੈਂਟ ਦਿਖਾਉਂਦੀ ਚਿੱਤਰ](../../../../../translated_images/pa/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![Bahdanau - arviz.org ਤੋਂ ਲਿਆ ਗਿਆ RNNsearch-50 ਦੁਆਰਾ ਪਾਈ ਗਈ ਇੱਕ ਨਮੂਨਾ ਅਲਾਈਨਮੈਂਟ ਦਿਖਾਉਂਦੀ ਚਿੱਤਰ](../../../../../translated_images/pa/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) ਤੋਂ ਲਿਆ ਗਿਆ ਚਿੱਤਰ]*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) ਇੱਕ ਬਹੁਤ ਵੱਡਾ ਮਲਟੀ ਲੇਅਰ ਟ੍ਰਾਂਸਫਾਰਮਰ ਨੈਟਵਰਕ ਹੈ, ਜਿਸ ਵਿੱਚ *BERT-base* ਲਈ 12 ਲੇਅਰ ਹਨ, ਅਤੇ *BERT-large* ਲਈ 24। ਮਾਡਲ ਨੂੰ ਪਹਿਲਾਂ ਵੱਡੇ ਟੈਕਸਟ ਡਾਟਾ (WikiPedia + ਕਿਤਾਬਾਂ) 'ਤੇ ਅਨਸੁਪਰਵਾਈਜ਼ਡ ਟ੍ਰੇਨਿੰਗ (ਵਾਕ ਵਿੱਚ ਮਾਸਕ ਕੀਤੇ ਸ਼ਬਦਾਂ ਦੀ ਪੇਸ਼ਕਸ਼) ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਪ੍ਰੀ-ਟ੍ਰੇਨ ਕੀਤਾ ਜਾਂਦਾ ਹੈ। ਪ੍ਰੀ-ਟ੍ਰੇਨਿੰਗ ਦੌਰਾਨ ਮਾਡਲ ਭਾਸ਼ਾ ਦੀ ਮਹੱਤਵਪੂਰਨ ਸਮਝ ਹਾਸਲ ਕਰਦਾ ਹੈ, ਜਿਸਨੂੰ ਫਿਰ ਹੋਰ ਡਾਟਾਸੈਟਸ ਨਾਲ ਫਾਈਨ ਟਿਊਨਿੰਗ ਦੁਆਰਾ ਲਾਭਕਾਰੀ ਬਣਾਇਆ ਜਾ ਸਕਦਾ ਹੈ। ਇਸ ਪ੍ਰਕਿਰਿਆ ਨੂੰ **ਟ੍ਰਾਂਸਫਰ ਲਰਨਿੰਗ** ਕਿਹਾ ਜਾਂਦਾ ਹੈ।\n", "\n", - "![http://jalammar.github.io/illustrated-bert/ ਤੋਂ ਚਿੱਤਰ](../../../../../translated_images/pa/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![http://jalammar.github.io/illustrated-bert/ ਤੋਂ ਚਿੱਤਰ](../../../../../translated_images/pa/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "ਟ੍ਰਾਂਸਫਾਰਮਰ ਆਰਕੀਟੈਕਚਰਾਂ ਦੇ ਕਈ ਰੂਪ ਹਨ, ਜਿਵੇਂ ਕਿ BERT, DistilBERT, BigBird, OpenGPT3 ਅਤੇ ਹੋਰ, ਜਿਨ੍ਹਾਂ ਨੂੰ ਫਾਈਨ ਟਿਊਨ ਕੀਤਾ ਜਾ ਸਕਦਾ ਹੈ। [HuggingFace ਪੈਕੇਜ](https://github.com/huggingface/) PyTorch ਨਾਲ ਇਨ੍ਹਾਂ ਆਰਕੀਟੈਕਚਰਾਂ ਨੂੰ ਟ੍ਰੇਨ ਕਰਨ ਲਈ ਰਿਪੋਜ਼ਟਰੀ ਪ੍ਰਦਾਨ ਕਰਦਾ ਹੈ।\n", "\n", diff --git a/translations/pa/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/pa/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index 5d70ac38..b3da1075 100644 --- a/translations/pa/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/pa/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**ਧਿਆਨ ਮਕੈਨਿਜ਼ਮ** RNN ਦੇ ਹਰ ਆਉਟਪੁੱਟ ਅਨੁਮਾਨ 'ਤੇ ਹਰ ਇਨਪੁਟ ਵੇਕਟਰ ਦੇ ਸੰਦਰਭਕ ਪ੍ਰਭਾਵ ਨੂੰ ਵਜਨ ਦੇਣ ਦਾ ਇੱਕ ਢੰਗ ਪ੍ਰਦਾਨ ਕਰਦੇ ਹਨ। ਇਹ ਇਸ ਤਰੀਕੇ ਨਾਲ ਲਾਗੂ ਕੀਤਾ ਜਾਂਦਾ ਹੈ ਕਿ ਇਨਪੁਟ RNN ਦੇ ਮੱਧਵਰਤੀ ਸਟੇਟਸ ਅਤੇ ਆਉਟਪੁੱਟ RNN ਦੇ ਵਿਚਕਾਰ ਸ਼ਾਰਟਕਟ ਬਣਾਏ ਜਾਂਦੇ ਹਨ। ਇਸ ਤਰੀਕੇ ਨਾਲ, ਜਦੋਂ ਆਉਟਪੁੱਟ ਚਿੰਨ੍ਹ $y_t$ ਬਣਾਇਆ ਜਾ ਰਿਹਾ ਹੈ, ਅਸੀਂ ਸਾਰੇ ਇਨਪੁਟ ਹਿਡਨ ਸਟੇਟਸ $h_i$ ਨੂੰ ਵੱਖ-ਵੱਖ ਵਜਨ ਗੁਣਾਂ $\\alpha_{t,i}$ ਦੇ ਨਾਲ ਧਿਆਨ ਵਿੱਚ ਲਵਾਂਗੇ।\n", "\n", - "![ਇੱਕ ਐਨਕੋਡਰ/ਡਿਕੋਡਰ ਮਾਡਲ ਨੂੰ additive attention layer ਦੇ ਨਾਲ ਦਿਖਾਉਂਦੀ ਚਿੱਤਰ](../../../../../translated_images/pa/encoder-decoder-attention.7a726296894fb567.png)\n", + "![ਇੱਕ ਐਨਕੋਡਰ/ਡਿਕੋਡਰ ਮਾਡਲ ਨੂੰ additive attention layer ਦੇ ਨਾਲ ਦਿਖਾਉਂਦੀ ਚਿੱਤਰ](../../../../../translated_images/pa/encoder-decoder-attention.7a726296894fb567.webp)\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) ਵਿੱਚ additive attention ਮਕੈਨਿਜ਼ਮ ਵਾਲਾ ਐਨਕੋਡਰ-ਡਿਕੋਡਰ ਮਾਡਲ, [ਇਸ ਬਲੌਗ ਪੋਸਟ](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) ਤੋਂ ਲਿਆ ਗਿਆ।]*\n", "\n", "Attention ਮੈਟ੍ਰਿਕਸ $\\{\\alpha_{i,j}\\}$ ਇਹ ਦਰਸਾਏਗੀ ਕਿ ਕਿਸ ਹੱਦ ਤੱਕ ਕੁਝ ਇਨਪੁਟ ਸ਼ਬਦ ਆਉਟਪੁੱਟ ਕ੍ਰਮ ਵਿੱਚ ਦਿੱਤੇ ਗਏ ਸ਼ਬਦ ਦੇ ਜਨਰੇਸ਼ਨ ਵਿੱਚ ਭੂਮਿਕਾ ਨਿਭਾਉਂਦੇ ਹਨ। ਹੇਠਾਂ ਇਸ ਮੈਟ੍ਰਿਕਸ ਦਾ ਇੱਕ ਉਦਾਹਰਨ ਦਿੱਤਾ ਗਿਆ ਹੈ:\n", "\n", - "![Bahdanau - arviz.org ਤੋਂ ਲਿਆ ਗਿਆ RNNsearch-50 ਦੁਆਰਾ ਪਾਈ ਗਈ ਇੱਕ ਨਮੂਨਾ ਅਲਾਈਨਮੈਂਟ ਦਿਖਾਉਂਦੀ ਚਿੱਤਰ](../../../../../translated_images/pa/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![Bahdanau - arviz.org ਤੋਂ ਲਿਆ ਗਿਆ RNNsearch-50 ਦੁਆਰਾ ਪਾਈ ਗਈ ਇੱਕ ਨਮੂਨਾ ਅਲਾਈਨਮੈਂਟ ਦਿਖਾਉਂਦੀ ਚਿੱਤਰ](../../../../../translated_images/pa/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) ਤੋਂ ਲਿਆ ਗਿਆ ਚਿੱਤਰ]*\n", "\n", @@ -225,7 +225,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) ਇੱਕ ਬਹੁਤ ਵੱਡਾ ਬਹੁ-ਪਰਤ ਟ੍ਰਾਂਸਫਾਰਮਰ ਨੈਟਵਰਕ ਹੈ ਜਿਸ ਵਿੱਚ *BERT-base* ਲਈ 12 ਪਰਤਾਂ ਹਨ ਅਤੇ *BERT-large* ਲਈ 24 ਪਰਤਾਂ। ਮਾਡਲ ਨੂੰ ਪਹਿਲਾਂ ਵੱਡੇ ਟੈਕਸਟ ਡਾਟਾ (WikiPedia + ਕਿਤਾਬਾਂ) ਦੇ ਕੋਰਪਸ 'ਤੇ ਅਨਸੁਪਰਵਾਈਜ਼ਡ ਟ੍ਰੇਨਿੰਗ (ਵਾਕ ਵਿੱਚ ਮਾਸਕ ਕੀਤੇ ਸ਼ਬਦਾਂ ਦੀ ਪੇਸ਼ਕਸ਼) ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਪ੍ਰੀ-ਟ੍ਰੇਨ ਕੀਤਾ ਜਾਂਦਾ ਹੈ। ਪ੍ਰੀ-ਟ੍ਰੇਨਿੰਗ ਦੌਰਾਨ, ਮਾਡਲ ਭਾਸ਼ਾ ਦੀ ਸਮਝ ਦੇ ਇੱਕ ਮਹੱਤਵਪੂਰਨ ਪੱਧਰ ਨੂੰ ਅਪਣਾਉਂਦਾ ਹੈ, ਜਿਸਨੂੰ ਫਿਰ ਫਾਈਨ ਟਿਊਨਿੰਗ ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਹੋਰ ਡਾਟਾਸੈਟਾਂ ਨਾਲ ਲਾਗੂ ਕੀਤਾ ਜਾ ਸਕਦਾ ਹੈ। ਇਸ ਪ੍ਰਕਿਰਿਆ ਨੂੰ **ਟ੍ਰਾਂਸਫਰ ਲਰਨਿੰਗ** ਕਿਹਾ ਜਾਂਦਾ ਹੈ।\n", "\n", - "![http://jalammar.github.io/illustrated-bert/ ਤੋਂ ਤਸਵੀਰ](../../../../../translated_images/pa/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![http://jalammar.github.io/illustrated-bert/ ਤੋਂ ਤਸਵੀਰ](../../../../../translated_images/pa/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "ਟ੍ਰਾਂਸਫਾਰਮਰ ਆਰਕੀਟੈਕਚਰ ਦੇ ਕਈ ਰੂਪ ਹਨ ਜਿਵੇਂ ਕਿ BERT, DistilBERT, BigBird, OpenGPT3 ਅਤੇ ਹੋਰ, ਜਿਨ੍ਹਾਂ ਨੂੰ ਫਾਈਨ ਟਿਊਨ ਕੀਤਾ ਜਾ ਸਕਦਾ ਹੈ।\n", "\n", diff --git a/translations/pa/lessons/5-NLP/19-NER/README.md b/translations/pa/lessons/5-NLP/19-NER/README.md index 9a44ec40..ab0286a5 100644 --- a/translations/pa/lessons/5-NLP/19-NER/README.md +++ b/translations/pa/lessons/5-NLP/19-NER/README.md @@ -58,7 +58,7 @@ infant | O ਕਿਉਂਕਿ ਸਾਨੂੰ ਟੋਕਨਾਂ ਅਤੇ ਵਰਗਾਂ ਦੇ ਵਿਚਕਾਰ ਇੱਕ-ਤੋਂ-ਇੱਕ ਸੰਬੰਧ ਬਣਾਉਣਾ ਹੁੰਦਾ ਹੈ, ਅਸੀਂ ਇਸ ਤਸਵੀਰ ਤੋਂ ਇੱਕ ਸਹੀ **ਬਹੁਤ-ਤੋਂ-ਬਹੁਤ** ਨਰਲ ਨੈੱਟਵਰਕ ਮਾਡਲ ਨੂੰ ਟ੍ਰੇਨ ਕਰ ਸਕਦੇ ਹਾਂ: -![ਸਧਾਰਨ ਰਿਕਰੰਟ ਨਰਲ ਨੈੱਟਵਰਕ ਪੈਟਰਨ ਦਿਖਾਉਂਦੀ ਤਸਵੀਰ।](../../../../../translated_images/pa/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![ਸਧਾਰਨ ਰਿਕਰੰਟ ਨਰਲ ਨੈੱਟਵਰਕ ਪੈਟਰਨ ਦਿਖਾਉਂਦੀ ਤਸਵੀਰ।](../../../../../translated_images/pa/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > *ਤਸਵੀਰ [ਇਸ ਬਲੌਗ ਪੋਸਟ](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) ਤੋਂ [Andrej Karpathy](http://karpathy.github.io/) ਦੁਆਰਾ। NER ਟੋਕਨ ਵਰਗੀਕਰਨ ਮਾਡਲ ਇਸ ਤਸਵੀਰ ਦੇ ਸੱਜੇ ਪਾਸੇ ਵਾਲੇ ਨੈੱਟਵਰਕ ਆਰਕੀਟੈਕਚਰ ਨਾਲ ਮਿਲਦੇ ਹਨ।* diff --git a/translations/pa/lessons/5-NLP/README.md b/translations/pa/lessons/5-NLP/README.md index 233bf2cd..4d4801f2 100644 --- a/translations/pa/lessons/5-NLP/README.md +++ b/translations/pa/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # ਨੈਚਰਲ ਲੈਂਗਵੇਜ ਪ੍ਰੋਸੈਸਿੰਗ -![NLP ਟਾਸਕਾਂ ਦਾ ਸਾਰ](../../../../translated_images/pa/ai-nlp.b22dcb8ca4707cea.png) +![NLP ਟਾਸਕਾਂ ਦਾ ਸਾਰ](../../../../translated_images/pa/ai-nlp.b22dcb8ca4707cea.webp) ਇਸ ਸੈਕਸ਼ਨ ਵਿੱਚ, ਅਸੀਂ **ਨੈਚਰਲ ਲੈਂਗਵੇਜ ਪ੍ਰੋਸੈਸਿੰਗ (NLP)** ਨਾਲ ਸੰਬੰਧਿਤ ਟਾਸਕਾਂ ਨੂੰ ਹੱਲ ਕਰਨ ਲਈ ਨਿਊਰਲ ਨੈਟਵਰਕਸ ਦੀ ਵਰਤੋਂ 'ਤੇ ਧਿਆਨ ਦੇਵਾਂਗੇ। ਬਹੁਤ ਸਾਰੇ NLP ਸਮੱਸਿਆਵਾਂ ਹਨ ਜਿਨ੍ਹਾਂ ਨੂੰ ਅਸੀਂ ਚਾਹੁੰਦੇ ਹਾਂ ਕਿ ਕੰਪਿਊਟਰ ਹੱਲ ਕਰ ਸਕਣ: diff --git a/translations/pa/lessons/6-Other/23-MultiagentSystems/README.md b/translations/pa/lessons/6-Other/23-MultiagentSystems/README.md index d652126f..2ae97831 100644 --- a/translations/pa/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/pa/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ ask turtles [ ਮਾਡਲ ਖੋਲ੍ਹਣ ਤੋਂ ਬਾਅਦ, ਤੁਹਾਨੂੰ ਨੈਟਲੋਗੋ ਦੇ ਮੁੱਖ ਸਕ੍ਰੀਨ 'ਤੇ ਲਿਆਂਦਾ ਜਾਂਦਾ ਹੈ। ਇੱਥੇ ਇੱਕ ਨਮੂਨਾ ਮਾਡਲ ਹੈ ਜੋ ਵੁਲਫ਼ ਅਤੇ ਭੇਡਾਂ ਦੀ ਆਬਾਦੀ ਦਾ ਵਰਣਨ ਕਰਦਾ ਹੈ, ਜਦੋਂ ਕਿ ਸੰਸਾਧਨ ਸੀਮਤ ਹਨ (ਘਾਹ)। -![ਨੈਟਲੋਗੋ ਮੁੱਖ ਸਕ੍ਰੀਨ](../../../../../translated_images/pa/NetLogo-Main.32653711ec1a01b3.png) +![ਨੈਟਲੋਗੋ ਮੁੱਖ ਸਕ੍ਰੀਨ](../../../../../translated_images/pa/NetLogo-Main.32653711ec1a01b3.webp) > ਦਿਮਿਤਰੀ ਸੋਸ਼ਨਿਕੋਵ ਦੁਆਰਾ ਸਕ੍ਰੀਨਸ਼ਾਟ diff --git a/translations/pa/lessons/README.md b/translations/pa/lessons/README.md index 9a161824..657134b4 100644 --- a/translations/pa/lessons/README.md +++ b/translations/pa/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # ਝਲਕ -![ਝਲਕ ਇੱਕ ਡੂਡਲ ਵਿੱਚ](../../../translated_images/pa/ai-overview.0857791951d19500.png) +![ਝਲਕ ਇੱਕ ਡੂਡਲ ਵਿੱਚ](../../../translated_images/pa/ai-overview.0857791951d19500.webp) > ਸਕੈਚਨੋਟ [Tomomi Imura](https://twitter.com/girlie_mac) ਦੁਆਰਾ diff --git a/translations/pa/lessons/X-Extras/X1-MultiModal/README.md b/translations/pa/lessons/X-Extras/X1-MultiModal/README.md index f25790d2..243ce194 100644 --- a/translations/pa/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/pa/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ NLP ਟਾਸਕਾਂ ਨੂੰ ਹੱਲ ਕਰਨ ਲਈ ਟ੍ਰਾਂਸ CLIP ਦਾ ਮੁੱਖ ਵਿਚਾਰ ਇਹ ਹੈ ਕਿ ਟੈਕਸਟ ਪ੍ਰੌਮਪਟਸ ਨੂੰ ਇੱਕ ਚਿੱਤਰ ਨਾਲ ਤੁਲਨਾ ਕਰਨ ਅਤੇ ਇਹ ਨਿਰਧਾਰਤ ਕਰਨ ਦੀ ਸਮਰਥਾ ਹੋਵੇ ਕਿ ਚਿੱਤਰ ਪ੍ਰੌਮਪਟ ਨਾਲ ਕਿੰਨਾ ਚੰਗਾ ਮੇਲ ਖਾਂਦਾ ਹੈ। -![CLIP ਆਰਕੀਟੈਕਚਰ](../../../../../translated_images/pa/clip-arch.b3dbf20b4e8ed8be.png) +![CLIP ਆਰਕੀਟੈਕਚਰ](../../../../../translated_images/pa/clip-arch.b3dbf20b4e8ed8be.webp) > *ਤਸਵੀਰ [ਇਸ ਬਲੌਗ ਪੋਸਟ](https://openai.com/blog/clip/) ਤੋਂ* @@ -31,7 +31,7 @@ CLIP ਮਾਡਲ/ਲਾਇਬ੍ਰੇਰੀ [OpenAI GitHub](https://github.com ਮੰਨ ਲਓ ਕਿ ਸਾਨੂੰ ਚਿੱਤਰਾਂ ਨੂੰ ਬਿੱਲੀਆਂ, ਕੁੱਤੇ ਅਤੇ ਮਨੁੱਖਾਂ ਵਿੱਚ ਵਰਗੀਕਰਣ ਦੀ ਲੋੜ ਹੈ। ਇਸ ਮਾਮਲੇ ਵਿੱਚ, ਅਸੀਂ ਮਾਡਲ ਨੂੰ ਇੱਕ ਚਿੱਤਰ ਅਤੇ ਟੈਕਸਟ ਪ੍ਰੌਮਪਟਸ ਦੀ ਲੜੀ ਦੇ ਸਕਦੇ ਹਾਂ: "*ਬਿੱਲੀ ਦੀ ਤਸਵੀਰ*", "*ਕੁੱਤੇ ਦੀ ਤਸਵੀਰ*", "*ਮਨੁੱਖ ਦੀ ਤਸਵੀਰ*"। 3 ਸੰਭਾਵਨਾਵਾਂ ਦੇ ਨਤੀਜੇ ਵਾਲੇ ਵੈਕਟਰ ਵਿੱਚ ਸਿਰਫ ਸਭ ਤੋਂ ਉੱਚੇ ਮੁੱਲ ਵਾਲੇ ਇੰਡੈਕਸ ਨੂੰ ਚੁਣਨ ਦੀ ਲੋੜ ਹੈ। -![ਚਿੱਤਰ ਵਰਗੀਕਰਨ ਲਈ CLIP](../../../../../translated_images/pa/clip-class.3af42ef0b2b19369.png) +![ਚਿੱਤਰ ਵਰਗੀਕਰਨ ਲਈ CLIP](../../../../../translated_images/pa/clip-class.3af42ef0b2b19369.webp) > *ਤਸਵੀਰ [ਇਸ ਬਲੌਗ ਪੋਸਟ](https://openai.com/blog/clip/) ਤੋਂ* @@ -55,13 +55,13 @@ VQGAN ਬਾਰੇ ਹੋਰ ਜਾਣਕਾਰੀ [Taming Transformers](https:/ VQGAN ਅਤੇ ਪ੍ਰੰਪਰਾਗਤ GAN ਦੇ ਵਿਚਕਾਰ ਇੱਕ ਮਹੱਤਵਪੂਰਨ ਅੰਤਰ ਇਹ ਹੈ ਕਿ ਪੁਰਾਣਾ ਕਿਸੇ ਵੀ ਇਨਪੁਟ ਵੈਕਟਰ ਤੋਂ ਇੱਕ ਢੰਗ ਦਾ ਚਿੱਤਰ ਪੈਦਾ ਕਰ ਸਕਦਾ ਹੈ, ਜਦਕਿ VQGAN ਸੰਭਾਵਨਾ ਹੈ ਕਿ ਇੱਕ ਚਿੱਤਰ ਪੈਦਾ ਕਰੇ ਜੋ ਸੰਗਤਿ ਵਾਲਾ ਨਾ ਹੋਵੇ। ਇਸ ਲਈ, ਸਾਨੂੰ ਚਿੱਤਰ ਬਣਾਉਣ ਦੀ ਪ੍ਰਕਿਰਿਆ ਨੂੰ ਹੋਰ ਮਾਰਗਦਰਸ਼ਨ ਕਰਨ ਦੀ ਲੋੜ ਹੈ, ਅਤੇ ਇਹ CLIP ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਕੀਤਾ ਜਾ ਸਕਦਾ ਹੈ। -![VQGAN+CLIP ਆਰਕੀਟੈਕਚਰ](../../../../../translated_images/pa/vqgan.5027fe05051dfa31.png) +![VQGAN+CLIP ਆਰਕੀਟੈਕਚਰ](../../../../../translated_images/pa/vqgan.5027fe05051dfa31.webp) ਟੈਕਸਟ ਪ੍ਰੌਮਪਟ ਦੇ ਅਨੁਕੂਲ ਚਿੱਤਰ ਬਣਾਉਣ ਲਈ, ਅਸੀਂ ਕੁਝ ਰੈਂਡਮ ਐਨਕੋਡਿੰਗ ਵੈਕਟਰ ਨਾਲ ਸ਼ੁਰੂ ਕਰਦੇ ਹਾਂ ਜੋ VQGAN ਦੁਆਰਾ ਚਿੱਤਰ ਪੈਦਾ ਕਰਨ ਲਈ ਪਾਸ ਕੀਤਾ ਜਾਂਦਾ ਹੈ। ਫਿਰ CLIP ਨੂੰ ਇੱਕ ਲੌਸ ਫੰਕਸ਼ਨ ਪੈਦਾ ਕਰਨ ਲਈ ਵਰਤਿਆ ਜਾਂਦਾ ਹੈ ਜੋ ਦਿਖਾਉਂਦਾ ਹੈ ਕਿ ਚਿੱਤਰ ਟੈਕਸਟ ਪ੍ਰੌਮਪਟ ਨਾਲ ਕਿੰਨਾ ਚੰਗਾ ਮੇਲ ਖਾਂਦਾ ਹੈ। ਫਿਰ ਉਦੇਸ਼ ਇਸ ਲੌਸ ਨੂੰ ਘਟਾਉਣਾ ਹੈ, ਬੈਕ ਪ੍ਰੋਪਾਗੇਸ਼ਨ ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਇਨਪੁਟ ਵੈਕਟਰ ਪੈਰਾਮੀਟਰਾਂ ਨੂੰ ਢਾਲਣਾ। VQGAN+CLIP ਨੂੰ ਲਾਗੂ ਕਰਨ ਵਾਲੀ ਇੱਕ ਸ਼ਾਨਦਾਰ ਲਾਇਬ੍ਰੇਰੀ [Pixray](http://github.com/pixray/pixray) ਹੈ। -![Pixray ਦੁਆਰਾ ਬਣਾਈ ਗਈ ਤਸਵੀਰ](../../../../../translated_images/pa/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Pixray ਦੁਆਰਾ ਬਣਾਈ ਗਈ ਤਸਵੀਰ](../../../../../translated_images/pa/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Pixray ਦੁਆਰਾ ਬਣਾਈ ਗਈ ਤਸਵੀਰ](../../../../../translated_images/pa/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) +![Pixray ਦੁਆਰਾ ਬਣਾਈ ਗਈ ਤਸਵੀਰ](../../../../../translated_images/pa/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp) | ![Pixray ਦੁਆਰਾ ਬਣਾਈ ਗਈ ਤਸਵੀਰ](../../../../../translated_images/pa/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp) | ![Pixray ਦੁਆਰਾ ਬਣਾਈ ਗਈ ਤਸਵੀਰ](../../../../../translated_images/pa/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp) ----|----|---- ਪ੍ਰੌਮਪਟ ਤੋਂ ਬਣਾਈ ਗਈ ਤਸਵੀਰ *ਸਾਹਿਤ ਦੇ ਇੱਕ ਨੌਜਵਾਨ ਪੁਰਸ਼ ਅਧਿਆਪਕ ਦਾ ਇੱਕ ਜਲਰੰਗ ਪੋਰਟਰੇਟ ਜਿਸਦੇ ਕੋਲ ਇੱਕ ਕਿਤਾਬ ਹੈ* | ਪ੍ਰੌਮਪਟ ਤੋਂ ਬਣਾਈ ਗਈ ਤਸਵੀਰ *ਕੰਪਿਊਟਰ ਵਿਗਿਆਨ ਦੀ ਇੱਕ ਨੌਜਵਾਨ ਮਹਿਲਾ ਅਧਿਆਪਕ ਦਾ ਇੱਕ ਤੇਲ ਪੋਰਟਰੇਟ ਜਿਸਦੇ ਕੋਲ ਇੱਕ ਕੰਪਿਊਟਰ ਹੈ* | ਪ੍ਰੌਮਪਟ ਤੋਂ ਬਣਾਈ ਗਈ ਤਸਵੀਰ *ਗਣਿਤ ਦੇ ਇੱਕ ਬੁਜ਼ੁਰਗ ਪੁਰਸ਼ ਅਧਿਆਪਕ ਦਾ ਇੱਕ ਤੇਲ ਪੋਰਟਰੇਟ ਜੋ ਬਲੈਕਬੋਰਡ ਦੇ ਸਾਹਮਣੇ ਹੈ* diff --git a/translations/pl/README.md b/translations/pl/README.md index b5e395cb..f9c6f551 100644 --- a/translations/pl/README.md +++ b/translations/pl/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # Artificial Intelligence for Beginners - Program nauczania -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/pl/ai-overview.0857791951d19500.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/pl/ai-overview.0857791951d19500.webp)| |:---:| | AI For Beginners - _Sketchnotka autorstwa [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/pl/lessons/1-Intro/README.md b/translations/pl/lessons/1-Intro/README.md index 723394c8..d1ec59b4 100644 --- a/translations/pl/lessons/1-Intro/README.md +++ b/translations/pl/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Wprowadzenie do AI -![Podsumowanie treści wprowadzenia do AI w formie rysunku](../../../../translated_images/pl/ai-intro.bf28d1ac4235881c.png) +![Podsumowanie treści wprowadzenia do AI w formie rysunku](../../../../translated_images/pl/ai-intro.bf28d1ac4235881c.webp) > Rysunek odręczny autorstwa [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Pierwotnie komputery zostały wynalezione przez [Charlesa Babbage'a](https://en.wikipedia.org/wiki/Charles_Babbage), aby operować na liczbach zgodnie z dobrze zdefiniowaną procedurą – algorytmem. Współczesne komputery, choć znacznie bardziej zaawansowane niż pierwotny model zaproponowany w XIX wieku, nadal opierają się na tej samej idei kontrolowanych obliczeń. Dlatego możliwe jest zaprogramowanie komputera do wykonania czegoś, jeśli znamy dokładną sekwencję kroków potrzebnych do osiągnięcia celu. -![Zdjęcie osoby](../../../../translated_images/pl/dsh_age.d212a30d4e54fb5f.png) +![Zdjęcie osoby](../../../../translated_images/pl/dsh_age.d212a30d4e54fb5f.webp) > Zdjęcie autorstwa [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ Więcej informacji znajdziesz w **[Ogólnej Sztucznej Inteligencji](https://en.w Jednym z problemów związanych z terminem **[Inteligencja](https://en.wikipedia.org/wiki/Intelligence)** jest brak jasnej definicji tego pojęcia. Można argumentować, że inteligencja jest związana z **myśleniem abstrakcyjnym** lub **samoświadomością**, ale nie potrafimy jej właściwie zdefiniować. -![Zdjęcie kota](../../../../translated_images/pl/photo-cat.8c8e8fb760ffe457.jpg) +![Zdjęcie kota](../../../../translated_images/pl/photo-cat.8c8e8fb760ffe457.webp) > [Zdjęcie](https://unsplash.com/photos/75715CVEJhI) autorstwa [Amber Kipp](https://unsplash.com/@sadmax) z Unsplash @@ -98,13 +98,13 @@ Alternatywnie, możemy spróbować modelować najprostsze elementy w naszym móz > | A co z ML? | | > |--------------|-----------| -> | Część Sztucznej Inteligencji, która opiera się na uczeniu komputera rozwiązywania problemu na podstawie danych, nazywa się **Uczeniem Maszynowym**. Nie będziemy rozważać klasycznego uczenia maszynowego w tym kursie – odsyłamy Cię do osobnego programu [Uczenie Maszynowe dla Początkujących](http://aka.ms/ml-beginners). | ![ML dla Początkujących](../../../../translated_images/pl/ml-for-beginners.9e4fed176fd5817d.png) | +> | Część Sztucznej Inteligencji, która opiera się na uczeniu komputera rozwiązywania problemu na podstawie danych, nazywa się **Uczeniem Maszynowym**. Nie będziemy rozważać klasycznego uczenia maszynowego w tym kursie – odsyłamy Cię do osobnego programu [Uczenie Maszynowe dla Początkujących](http://aka.ms/ml-beginners). | ![ML dla Początkujących](../../../../translated_images/pl/ml-for-beginners.9e4fed176fd5817d.webp) | ## Krótka historia AI Sztuczna Inteligencja jako dziedzina rozpoczęła się w połowie XX wieku. Początkowo podejście symboliczne było dominujące i doprowadziło do wielu ważnych sukcesów, takich jak systemy ekspertowe – programy komputerowe, które były w stanie działać jako ekspert w ograniczonych dziedzinach problemowych. Jednak szybko stało się jasne, że takie podejście nie jest skalowalne. Wydobycie wiedzy od eksperta, reprezentowanie jej w komputerze i utrzymanie tej bazy wiedzy w aktualności okazuje się bardzo złożonym zadaniem i zbyt kosztownym, aby było praktyczne w wielu przypadkach. Doprowadziło to do tzw. [Zimy AI](https://en.wikipedia.org/wiki/AI_winter) w latach 70. -Krótka historia AI +Krótka historia AI > Obraz autorstwa [Dmitry Soshnikov](http://soshnikov.com) @@ -124,7 +124,7 @@ Podobnie możemy zobaczyć, jak zmieniało się podejście do tworzenia „progr * Współczesne asystenty, takie jak Cortana, Siri czy Google Assistant, to hybrydowe systemy, które wykorzystują sieci neuronowe do konwersji mowy na tekst i rozpoznawania naszych intencji, a następnie stosują pewne rozumowanie lub explicite algorytmy do wykonywania wymaganych działań. * W przyszłości możemy oczekiwać pełnego modelu opartego na sieciach neuronowych, który samodzielnie obsłuży dialog. Ostatnie sieci neuronowe GPT i [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) wykazują wielkie sukcesy w tym zakresie. -Ewolucja testu Turinga +Ewolucja testu Turinga > Obraz autorstwa Dmitry Soshnikov, [zdjęcie](https://unsplash.com/photos/r8LmVbUKgns) autorstwa [Mariny Abrosimovej](https://unsplash.com/@abrosimova_marina_foto), Unsplash ## Ostatnie badania nad sztuczną inteligencją diff --git a/translations/pl/lessons/2-Symbolic/README.md b/translations/pl/lessons/2-Symbolic/README.md index ce553e3a..ee8ac934 100644 --- a/translations/pl/lessons/2-Symbolic/README.md +++ b/translations/pl/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Reprezentacja wiedzy i systemy ekspertowe -![Podsumowanie treści o Symbolicznym AI](../../../../translated_images/pl/ai-symbolic.715a30cb610411a6.png) +![Podsumowanie treści o Symbolicznym AI](../../../../translated_images/pl/ai-symbolic.715a30cb610411a6.webp) > Sketchnote autorstwa [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ Najczęściej nie definiujemy wiedzy w sposób ścisły, ale zestawiamy ją z in Problem **reprezentacji wiedzy** polega więc na znalezieniu skutecznego sposobu reprezentowania wiedzy w komputerze w formie danych, aby była automatycznie użyteczna. Można to postrzegać jako spektrum: -![Spektrum reprezentacji wiedzy](../../../../translated_images/pl/knowledge-spectrum.b60df631852c0217.png) +![Spektrum reprezentacji wiedzy](../../../../translated_images/pl/knowledge-spectrum.b60df631852c0217.webp) > Obraz autorstwa [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Składnia blokowa | Wcięcia | | | Jednym z wczesnych sukcesów symbolicznego AI były tzw. **systemy ekspertowe** - systemy komputerowe zaprojektowane do działania jako ekspert w ograniczonej dziedzinie problemowej. Opierały się na **bazie wiedzy** wydobytej od jednego lub więcej ludzkich ekspertów i zawierały **silnik wnioskowania**, który wykonywał wnioskowanie na jej podstawie. -![Architektura człowieka](../../../../translated_images/pl/arch-human.5d4d35f1bba3ab1c.png) | ![System oparty na wiedzy](../../../../translated_images/pl/arch-kbs.3ec5c150b09fa8da.png) +![Architektura człowieka](../../../../translated_images/pl/arch-human.5d4d35f1bba3ab1c.webp) | ![System oparty na wiedzy](../../../../translated_images/pl/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ Uproszczona struktura ludzkiego układu nerwowego | Architektura systemu opartego na wiedzy @@ -106,7 +106,7 @@ Systemy ekspertowe są zbudowane podobnie jak system wnioskowania człowieka, kt Na przykład rozważmy następujący system ekspertowy do określania zwierzęcia na podstawie jego cech fizycznych: -![Drzewo AND-OR](../../../../translated_images/pl/AND-OR-Tree.5592d2c70187f283.png) +![Drzewo AND-OR](../../../../translated_images/pl/AND-OR-Tree.5592d2c70187f283.webp) > Obraz autorstwa [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/pl/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/pl/lessons/3-NeuralNetworks/05-Frameworks/README.md index 9758aaab..34331b5d 100644 --- a/translations/pl/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/pl/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Nadmierne dopasowanie to niezwykle ważne pojęcie w uczeniu maszynowym i bardzo Rozważmy następujący problem aproksymacji 5 punktów (reprezentowanych przez `x` na poniższych wykresach): -![linear](../../../../../translated_images/pl/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/pl/overfit2.131f5800ae10ca5e.jpg) +![linear](../../../../../translated_images/pl/overfit1.f24b71c6f652e59e.webp) | ![overfit](../../../../../translated_images/pl/overfit2.131f5800ae10ca5e.webp) -------------------------|-------------------------- **Model liniowy, 2 parametry** | **Model nieliniowy, 7 parametrów** Błąd treningowy = 5.3 | Błąd treningowy = 0 @@ -79,7 +79,7 @@ Bardzo ważne jest znalezienie odpowiedniej równowagi między złożonością m Jak widać na powyższym wykresie, nadmierne dopasowanie można wykryć po bardzo niskim błędzie treningowym i wysokim błędzie walidacyjnym. Zazwyczaj podczas treningu widzimy, że zarówno błędy treningowe, jak i walidacyjne zaczynają się zmniejszać, a następnie w pewnym momencie błąd walidacyjny może przestać się zmniejszać i zacząć rosnąć. To będzie oznaka nadmiernego dopasowania i wskazówka, że powinniśmy prawdopodobnie zatrzymać trening w tym momencie (lub przynajmniej zrobić migawkę modelu). -![overfitting](../../../../../translated_images/pl/Overfitting.408ad91cd90b4371.png) +![overfitting](../../../../../translated_images/pl/Overfitting.408ad91cd90b4371.webp) ## Jak zapobiegać nadmiernemu dopasowaniu diff --git a/translations/pl/lessons/3-NeuralNetworks/README.md b/translations/pl/lessons/3-NeuralNetworks/README.md index 5fc804d2..758f7d3f 100644 --- a/translations/pl/lessons/3-NeuralNetworks/README.md +++ b/translations/pl/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Wprowadzenie do sieci neuronowych -![Podsumowanie treści wprowadzających do sieci neuronowych w formie rysunku](../../../../translated_images/pl/ai-neuralnetworks.1c687ae40bc86e83.png) +![Podsumowanie treści wprowadzających do sieci neuronowych w formie rysunku](../../../../translated_images/pl/ai-neuralnetworks.1c687ae40bc86e83.webp) Jak omówiliśmy we wstępie, jednym ze sposobów osiągnięcia inteligencji jest trenowanie **modelu komputerowego** lub **sztucznego mózgu**. Od połowy XX wieku badacze próbowali różnych modeli matematycznych, aż w ostatnich latach ten kierunek okazał się niezwykle skuteczny. Takie matematyczne modele mózgu nazywane są **sieciami neuronowymi**. @@ -36,13 +36,13 @@ W tym programie nauczania skupimy się wyłącznie na modelach sieci neuronowych Z biologii wiemy, że nasz mózg składa się z komórek nerwowych (neuronów), z których każda ma wiele "wejść" (dendrytów) i jedno "wyjście" (akson). Zarówno dendryty, jak i aksony mogą przewodzić sygnały elektryczne, a połączenia między nimi — znane jako synapsy — mogą wykazywać różne stopnie przewodnictwa, które są regulowane przez neuroprzekaźniki. -![Model neuronu](../../../../translated_images/pl/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![Model neuronu](../../../../translated_images/pl/artneuron.1a5daa88d20ebe6f.png) +![Model neuronu](../../../../translated_images/pl/synapse-wikipedia.ed20a9e4726ea1c6.webp) | ![Model neuronu](../../../../translated_images/pl/artneuron.1a5daa88d20ebe6f.webp) ----|---- Prawdziwy neuron *([Obraz](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) z Wikipedii)* | Sztuczny neuron *(Obraz autora)* Najprostszy matematyczny model neuronu zawiera kilka wejść X1, ..., XN oraz jedno wyjście Y, a także serię wag W1, ..., WN. Wyjście obliczane jest jako: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) gdzie f jest pewną nieliniową **funkcją aktywacji**. diff --git a/translations/pl/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/pl/lessons/4-ComputerVision/06-IntroCV/README.md index b35e112a..ef433345 100644 --- a/translations/pl/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/pl/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ W naszym [OpenCV Notebook](OpenCV.ipynb) przedstawiamy kilka przykładów, kiedy * **Wstępne przetwarzanie fotografii książki Braille'a**. Skupiamy się na tym, jak można użyć progowania, detekcji cech, transformacji perspektywicznej i manipulacji NumPy, aby oddzielić pojedyncze symbole Braille'a do dalszej klasyfikacji przez sieć neuronową. -![Obraz Braille'a](../../../../../translated_images/pl/braille.341962ff76b1bd70.jpeg) | ![Obraz Braille'a po przetworzeniu](../../../../../translated_images/pl/braille-result.46530fea020b03c7.png) | ![Symbole Braille'a](../../../../../translated_images/pl/braille-symbols.0159185ab69d5339.png) +![Obraz Braille'a](../../../../../translated_images/pl/braille.341962ff76b1bd70.webp) | ![Obraz Braille'a po przetworzeniu](../../../../../translated_images/pl/braille-result.46530fea020b03c7.webp) | ![Symbole Braille'a](../../../../../translated_images/pl/braille-symbols.0159185ab69d5339.webp) ----|-----|----- > Obraz z [OpenCV.ipynb](OpenCV.ipynb) * **Detekcja ruchu w wideo za pomocą różnicy klatek**. Jeśli kamera jest nieruchoma, klatki z jej strumienia powinny być dość podobne do siebie. Ponieważ klatki są reprezentowane jako tablice, wystarczy odjąć te tablice dla dwóch kolejnych klatek, aby uzyskać różnicę pikseli, która powinna być niska dla statycznych klatek, a wyższa, gdy w obrazie występuje znaczący ruch. -![Obraz klatek wideo i różnic klatek](../../../../../translated_images/pl/frame-difference.706f805491a0883c.png) +![Obraz klatek wideo i różnic klatek](../../../../../translated_images/pl/frame-difference.706f805491a0883c.webp) > Obraz z [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ W naszym [OpenCV Notebook](OpenCV.ipynb) przedstawiamy kilka przykładów, kiedy - **Gęsty optyczny przepływ** oblicza pole wektorowe, które pokazuje, gdzie każdy piksel się porusza. - **Rzadki optyczny przepływ** opiera się na wybraniu charakterystycznych cech obrazu (np. krawędzi) i budowaniu ich trajektorii od klatki do klatki. -![Obraz optycznego przepływu](../../../../../translated_images/pl/optical.1f4a94464579a83a.png) +![Obraz optycznego przepływu](../../../../../translated_images/pl/optical.1f4a94464579a83a.webp) > Obraz z [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/pl/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/pl/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 8d7d47ef..4f6099b8 100644 --- a/translations/pl/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/pl/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 to sieć, która osiągnęła 92,7% dokładności w klasyfikacji top-5 ImageNet w 2014 roku. Ma następującą strukturę warstw: -![ImageNet Layers](../../../../../translated_images/pl/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet Layers](../../../../../translated_images/pl/vgg-16-arch1.d901a5583b3a51ba.webp) Jak widać, VGG stosuje tradycyjną architekturę piramidy, czyli sekwencję warstw konwolucyjnych i poolingowych. -![ImageNet Pyramid](../../../../../translated_images/pl/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet Pyramid](../../../../../translated_images/pl/vgg-16-arch.64ff2137f50dd49f.webp) > Obraz z [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/pl/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/pl/lessons/4-ComputerVision/07-ConvNets/README.md index 7956119e..23181e28 100644 --- a/translations/pl/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/pl/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ W rzeczywistości chcemy być w stanie rozpoznawać obiekty na zdjęciu niezale Aby wyodrębnić wzory, użyjemy pojęcia **filtrów konwolucyjnych**. Jak wiadomo, obraz jest reprezentowany jako macierz 2D lub tensor 3D z głębią kolorów. Zastosowanie filtra oznacza, że bierzemy stosunkowo małą macierz **jądra filtra** i dla każdego piksela w oryginalnym obrazie obliczamy średnią ważoną z sąsiednich punktów. Możemy to sobie wyobrazić jako małe okno przesuwające się po całym obrazie, uśredniające wszystkie piksele zgodnie z wagami w macierzy jądra filtra. -![Filtr krawędzi pionowych](../../../../../translated_images/pl/filter-vert.b7148390ca0bc356.png) | ![Filtr krawędzi poziomych](../../../../../translated_images/pl/filter-horiz.59b80ed4feb946ef.png) +![Filtr krawędzi pionowych](../../../../../translated_images/pl/filter-vert.b7148390ca0bc356.webp) | ![Filtr krawędzi poziomych](../../../../../translated_images/pl/filter-horiz.59b80ed4feb946ef.webp) ----|---- > Obraz autorstwa Dmitry Soshnikov @@ -38,7 +38,7 @@ Działanie CNN opiera się na następujących ważnych założeniach: * Możemy zaprojektować sieć w taki sposób, aby filtry były trenowane automatycznie * Możemy użyć tego samego podejścia do znajdowania wzorów w cechach wysokiego poziomu, a nie tylko w oryginalnym obrazie. W ten sposób ekstrakcja cech w CNN działa na hierarchii cech, zaczynając od kombinacji pikseli niskiego poziomu, aż do kombinacji części obrazu na wyższym poziomie. -![Hierarchiczna Ekstrakcja Cech](../../../../../translated_images/pl/FeatureExtractionCNN.d9b456cbdae7cb64.png) +![Hierarchiczna Ekstrakcja Cech](../../../../../translated_images/pl/FeatureExtractionCNN.d9b456cbdae7cb64.webp) > Obraz z [artykułu Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), opartego na [ich badaniach](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ Większość CNN używanych do przetwarzania obrazów stosuje tzw. architekturę Na przykład, spójrzmy na architekturę VGG-16, sieci, która osiągnęła 92,7% dokładności w klasyfikacji top-5 ImageNet w 2014 roku: -![Warstwy ImageNet](../../../../../translated_images/pl/vgg-16-arch1.d901a5583b3a51ba.jpg) +![Warstwy ImageNet](../../../../../translated_images/pl/vgg-16-arch1.d901a5583b3a51ba.webp) -![Piramida ImageNet](../../../../../translated_images/pl/vgg-16-arch.64ff2137f50dd49f.jpg) +![Piramida ImageNet](../../../../../translated_images/pl/vgg-16-arch.64ff2137f50dd49f.webp) > Obraz z [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/pl/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/pl/lessons/4-ComputerVision/07-ConvNets/lab/README.md index ad58007f..48a3ac6a 100644 --- a/translations/pl/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/pl/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ Twoim zadaniem jest wytrenowanie konwolucyjnej sieci neuronowej do klasyfikacji Użyjemy [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/), który zawiera obrazy 37 różnych ras psów i kotów. -![Zestaw danych, z którym będziemy pracować](../../../../../../translated_images/pl/data.50b2a9d5484bdbf0.png) +![Zestaw danych, z którym będziemy pracować](../../../../../../translated_images/pl/data.50b2a9d5484bdbf0.webp) Aby pobrać zestaw danych, użyj tego fragmentu kodu: diff --git a/translations/pl/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/pl/lessons/4-ComputerVision/08-TransferLearning/README.md index 14d610a8..8850aaec 100644 --- a/translations/pl/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/pl/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Zarówno Keras, jak i PyTorch zawierają funkcje umożliwiające łatwe ładowan Oto przykładowe cechy wyodrębnione z obrazu kota przez sieć VGG-16: -![Cechy wyodrębnione przez VGG-16](../../../../../translated_images/pl/features.6291f9c7ba3a0b95.png) +![Cechy wyodrębnione przez VGG-16](../../../../../translated_images/pl/features.6291f9c7ba3a0b95.webp) ## Zbiór danych Koty vs. Psy @@ -48,19 +48,19 @@ Wstępnie wytrenowana sieć neuronowa zawiera różne wzorce w swoim *mózgu*, w Jednym z podejść, które możemy zastosować, jest rozpoczęcie od losowego obrazu, a następnie próba użycia techniki **optymalizacji metodą gradientu** w celu dostosowania tego obrazu w taki sposób, aby sieć zaczęła myśleć, że to kot. -![Pętla optymalizacji obrazu](../../../../../translated_images/pl/ideal-cat-loop.999fbb8ff306e044.png) +![Pętla optymalizacji obrazu](../../../../../translated_images/pl/ideal-cat-loop.999fbb8ff306e044.webp) Jednak jeśli to zrobimy, otrzymamy coś bardzo podobnego do losowego szumu. Dzieje się tak, ponieważ *istnieje wiele sposobów, aby sieć myślała, że obraz wejściowy to kot*, w tym takie, które nie mają sensu wizualnie. Chociaż te obrazy zawierają wiele wzorców typowych dla kota, nic nie zmusza ich do bycia wizualnie wyraźnymi. Aby poprawić wynik, możemy dodać kolejny składnik do funkcji straty, który nazywa się **stratą wariacji**. Jest to metryka pokazująca, jak podobne są sąsiadujące piksele obrazu. Minimalizowanie straty wariacji sprawia, że obraz staje się bardziej gładki i pozbywa się szumu - ujawniając bardziej atrakcyjne wizualnie wzorce. Oto przykład takich "idealnych" obrazów, które są klasyfikowane jako kot i jako zebra z dużym prawdopodobieństwem: -![Idealny kot](../../../../../translated_images/pl/ideal-cat.203dd4597643d6b0.png) | ![Idealna zebra](../../../../../translated_images/pl/ideal-zebra.7f70e8b54ee15a7a.png) +![Idealny kot](../../../../../translated_images/pl/ideal-cat.203dd4597643d6b0.webp) | ![Idealna zebra](../../../../../translated_images/pl/ideal-zebra.7f70e8b54ee15a7a.webp) -----|----- *Idealny kot* | *Idealna zebra* Podobne podejście można zastosować do przeprowadzania tzw. **ataków adversarialnych** na sieć neuronową. Załóżmy, że chcemy oszukać sieć neuronową i sprawić, by pies wyglądał jak kot. Jeśli weźmiemy obraz psa, który jest rozpoznawany przez sieć jako pies, możemy go nieco zmodyfikować za pomocą optymalizacji metodą gradientu, aż sieć zacznie klasyfikować go jako kota: -![Obraz psa](../../../../../translated_images/pl/original-dog.8f68a67d2fe0911f.png) | ![Obraz psa klasyfikowany jako kot](../../../../../translated_images/pl/adversarial-dog.d9fc7773b0142b89.png) +![Obraz psa](../../../../../translated_images/pl/original-dog.8f68a67d2fe0911f.webp) | ![Obraz psa klasyfikowany jako kot](../../../../../translated_images/pl/adversarial-dog.d9fc7773b0142b89.webp) -----|----- *Oryginalny obraz psa* | *Obraz psa klasyfikowany jako kot* diff --git a/translations/pl/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/pl/lessons/4-ComputerVision/09-Autoencoders/README.md index c60240a9..a83bc57a 100644 --- a/translations/pl/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/pl/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ Możemy jednak chcieć wykorzystać surowe (nieoznaczone) dane do trenowania eks Ponieważ trenujemy autoenkoder, aby uchwycić jak najwięcej informacji z oryginalnego obrazu w celu dokładnej rekonstrukcji, sieć stara się znaleźć najlepsze **osadzenie** obrazów wejściowych, aby uchwycić ich znaczenie. -![Schemat Autoenkodera](../../../../../translated_images/pl/autoencoder_schema.5e6fc9ad98a5eb61.jpg) +![Schemat Autoenkodera](../../../../../translated_images/pl/autoencoder_schema.5e6fc9ad98a5eb61.webp) > Obraz z [blogu Keras](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/pl/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/pl/lessons/4-ComputerVision/11-ObjectDetection/README.md index dc93e14d..dc907077 100644 --- a/translations/pl/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/pl/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ Modele klasyfikacji obrazów, które omawialiśmy do tej pory, przyjmowały obra ## [Quiz przed wykładem](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Wykrywanie Obiektów](../../../../../translated_images/pl/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) +![Wykrywanie Obiektów](../../../../../translated_images/pl/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp) > Obraz z [witryny YOLO v2](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ Załóżmy, że chcemy znaleźć kota na zdjęciu. Bardzo naiwne podejście do w 2. Uruchom klasyfikację obrazu na każdym kafelku. 3. Kafelki, które dają wystarczająco wysoką aktywację, można uznać za zawierające poszukiwany obiekt. -![Naiwne Wykrywanie Obiektów](../../../../../translated_images/pl/naive-detection.e7f1ba220ccd08c6.png) +![Naiwne Wykrywanie Obiektów](../../../../../translated_images/pl/naive-detection.e7f1ba220ccd08c6.webp) > *Obraz z [notatnika ćwiczeniowego](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ Możesz natknąć się na następujące zbiory danych do tego zadania: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) – 20 klas * [COCO](http://cocodataset.org/#home) – Common Objects in Context. 80 klas, ramki ograniczające i maski segmentacji -![COCO](../../../../../translated_images/pl/coco-examples.71bc60380fa6cceb.jpg) +![COCO](../../../../../translated_images/pl/coco-examples.71bc60380fa6cceb.webp) ## Metryki wykrywania obiektów @@ -50,7 +50,7 @@ Możesz natknąć się na następujące zbiory danych do tego zadania: Podczas gdy w klasyfikacji obrazów łatwo jest zmierzyć, jak dobrze działa algorytm, w wykrywaniu obiektów musimy ocenić zarówno poprawność klasy, jak i precyzję lokalizacji przewidywanej ramki ograniczającej. Do tego ostatniego używamy tzw. **Intersection over Union** (IoU), które mierzy, jak dobrze dwie ramki (lub dwa dowolne obszary) się pokrywają. -![IoU](../../../../../translated_images/pl/iou_equation.9a4751d40fff4e11.png) +![IoU](../../../../../translated_images/pl/iou_equation.9a4751d40fff4e11.webp) > *Rysunek 2 z [tego doskonałego wpisu na blogu o IoU](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ Istnieją dwie główne klasy algorytmów wykrywania obiektów: [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) wykorzystuje [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) do generowania hierarchicznej struktury regionów ROI, które następnie są przetwarzane przez ekstraktory cech CNN i klasyfikatory SVM w celu określenia klasy obiektu oraz regresję liniową w celu określenia współrzędnych *ramki ograniczającej*. [Oficjalny artykuł](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/pl/rcnn1.cae407020dfb1d1f.png) +![RCNN](../../../../../translated_images/pl/rcnn1.cae407020dfb1d1f.webp) > *Obraz z van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/pl/rcnn2.2d9530bb83516484.png) +![RCNN-1](../../../../../translated_images/pl/rcnn2.2d9530bb83516484.webp) > *Obrazy z [tego bloga](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -110,7 +110,7 @@ Istnieją dwie główne klasy algorytmów wykrywania obiektów: To podejście jest podobne do R-CNN, ale regiony są definiowane po zastosowaniu warstw konwolucyjnych. -![FRCNN](../../../../../translated_images/pl/f-rcnn.3cda6d9bb4188875.png) +![FRCNN](../../../../../translated_images/pl/f-rcnn.3cda6d9bb4188875.webp) > Obraz z [oficjalnego artykułu](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ To podejście jest podobne do R-CNN, ale regiony są definiowane po zastosowaniu Główna idea tego podejścia polega na użyciu sieci neuronowej do przewidywania ROI – tzw. *Region Proposal Network*. [Artykuł](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/pl/faster-rcnn.8d46c099b87ef30a.png) +![FasterRCNN](../../../../../translated_images/pl/faster-rcnn.8d46c099b87ef30a.webp) > Obraz z [oficjalnego artykułu](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ Ten algorytm jest jeszcze szybszy niż Faster R-CNN. Główna idea jest następu 2. Cechy są przetwarzane przez **Position-Sensitive Score Map**. Każdy obiekt z $C$ klas jest dzielony na $k\times k$ regiony, a sieć jest trenowana do przewidywania części obiektów. 3. Dla każdej części z $k\times k$ regionów wszystkie sieci głosują na klasy obiektów, a klasa z największą liczbą głosów jest wybierana. -![r-fcn image](../../../../../translated_images/pl/r-fcn.13eb88158b99a3da.png) +![r-fcn image](../../../../../translated_images/pl/r-fcn.13eb88158b99a3da.webp) > Obraz z [oficjalnego artykułu](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO to algorytm jednoprzebiegowy w czasie rzeczywistym. Główna idea jest nast * Obraz jest dzielony na $S\times S$ regiony. * Dla każdego regionu **CNN** przewiduje $n$ możliwych obiektów, współrzędne *ramki ograniczającej* oraz *pewność* = *prawdopodobieństwo* * IoU. - ![YOLO](../../../../../translated_images/pl/yolo.a2648ec82ee8bb4e.png) + ![YOLO](../../../../../translated_images/pl/yolo.a2648ec82ee8bb4e.webp) > Obraz z [oficjalnego artykułu](https://arxiv.org/abs/1506.02640) diff --git a/translations/pl/lessons/5-NLP/14-Embeddings/README.md b/translations/pl/lessons/5-NLP/14-Embeddings/README.md index dd3a600b..ddbec807 100644 --- a/translations/pl/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/pl/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ Warstwa osadzenia przyjmuje słowo jako wejście i generuje wektor wyjściowy o Używając warstwy osadzenia jako pierwszej warstwy w naszej sieci klasyfikatora, możemy przejść od modelu bag-of-words do modelu **embedding bag**, gdzie najpierw konwertujemy każde słowo w naszym tekście na odpowiadające mu osadzenie, a następnie obliczamy pewną funkcję agregującą dla wszystkich tych osadzeń, taką jak `sum`, `average` lub `max`. -![Obraz przedstawiający klasyfikator osadzeń dla pięciu słów w sekwencji.](../../../../../translated_images/pl/embedding-classifier-example.b77f021a7ee67eee.png) +![Obraz przedstawiający klasyfikator osadzeń dla pięciu słów w sekwencji.](../../../../../translated_images/pl/embedding-classifier-example.b77f021a7ee67eee.webp) > Obraz autorstwa autora @@ -40,7 +40,7 @@ Aby to osiągnąć, musimy wstępnie wytrenować nasz model osadzenia na dużym CBoW działa szybciej, podczas gdy skip-gram jest wolniejszy, ale lepiej reprezentuje rzadkie słowa. -![Obraz przedstawiający algorytmy CBoW i Skip-Gram do konwersji słów na wektory.](../../../../../translated_images/pl/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![Obraz przedstawiający algorytmy CBoW i Skip-Gram do konwersji słów na wektory.](../../../../../translated_images/pl/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Obraz z [tego artykułu](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/pl/lessons/5-NLP/15-LanguageModeling/README.md b/translations/pl/lessons/5-NLP/15-LanguageModeling/README.md index c3df220c..16412598 100644 --- a/translations/pl/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/pl/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ W naszych wcześniejszych przykładach korzystaliśmy z wstępnie wytrenowanych * **Continuous Bag-of-Words** (CBoW), gdzie przewidujemy środkowy token $W_0$ w sekwencji tokenów $W_{-N}$, ..., $W_N$. * **Skip-gram**, gdzie przewidujemy zestaw sąsiednich tokenów {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} na podstawie środkowego tokena $W_0$. -![obraz z artykułu o konwersji słów na wektory](../../../../../translated_images/pl/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![obraz z artykułu o konwersji słów na wektory](../../../../../translated_images/pl/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Obraz z [tego artykułu](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/pl/lessons/5-NLP/16-RNN/README.md b/translations/pl/lessons/5-NLP/16-RNN/README.md index b9394854..3c62bb48 100644 --- a/translations/pl/lessons/5-NLP/16-RNN/README.md +++ b/translations/pl/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ W poprzednich sekcjach korzystaliśmy z bogatych semantycznych reprezentacji tek Aby uchwycić znaczenie sekwencji tekstu, musimy użyć innej architektury sieci neuronowej, zwanej **siecią neuronową rekurencyjną** (RNN). W RNN przekazujemy nasze zdanie przez sieć symbol po symbolu, a sieć generuje pewien **stan**, który następnie przekazujemy z kolejnym symbolem. -![RNN](../../../../../translated_images/pl/rnn.27f5c29c53d727b5.png) +![RNN](../../../../../translated_images/pl/rnn.27f5c29c53d727b5.webp) > Obraz autorstwa autora @@ -61,7 +61,7 @@ Omówiliśmy sieci rekurencyjne, które działają w jednym kierunku, od począt Sieć rekurencyjna, czy to jednokierunkowa, czy dwukierunkowa, wychwytuje pewne wzorce w sekwencji i może je przechowywać w wektorze stanu lub przekazywać na wyjście. Podobnie jak w przypadku sieci konwolucyjnych, możemy zbudować kolejną warstwę rekurencyjną na szczycie pierwszej, aby uchwycić wzorce wyższego poziomu i budować na bazie wzorców niskiego poziomu wyodrębnionych przez pierwszą warstwę. Prowadzi to do pojęcia **wielowarstwowego RNN**, który składa się z dwóch lub więcej sieci rekurencyjnych, gdzie wyjście poprzedniej warstwy jest przekazywane jako wejście do następnej warstwy. -![Obraz przedstawiający wielowarstwowy LSTM RNN](../../../../../translated_images/pl/multi-layer-lstm.dd975e29bb2a59fe.jpg) +![Obraz przedstawiający wielowarstwowy LSTM RNN](../../../../../translated_images/pl/multi-layer-lstm.dd975e29bb2a59fe.webp) *Obraz z [tego wspaniałego artykułu](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) autorstwa Fernando Lópeza* diff --git a/translations/pl/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/pl/lessons/5-NLP/17-GenerativeNetworks/README.md index f623a0e5..3a2da89b 100644 --- a/translations/pl/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/pl/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ W architekturze RNN, którą omawialiśmy w poprzedniej jednostce, każda jednos To umożliwia różne architektury sieci neuronowych, które przedstawiono na poniższym obrazku: -![Obraz przedstawiający typowe wzorce rekurencyjnych sieci neuronowych.](../../../../../translated_images/pl/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Obraz przedstawiający typowe wzorce rekurencyjnych sieci neuronowych.](../../../../../translated_images/pl/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > Obraz z wpisu na blogu [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) autorstwa [Andreja Karpaty](http://karpathy.github.io/) @@ -32,7 +32,7 @@ W tej jednostce skupimy się na prostych modelach generatywnych, które pomagaj Wytrenujemy tę RNN do generowania tekstu krok po kroku. Na każdym kroku weźmiemy sekwencję znaków o długości `nchars` i poprosimy sieć o wygenerowanie kolejnego znaku wyjściowego dla każdego znaku wejściowego: -![Obraz przedstawiający przykład generowania słowa 'HELLO' przez RNN.](../../../../../translated_images/pl/rnn-generate.56c54afb52f9781d.png) +![Obraz przedstawiający przykład generowania słowa 'HELLO' przez RNN.](../../../../../translated_images/pl/rnn-generate.56c54afb52f9781d.webp) Podczas generowania tekstu (w trakcie inferencji) zaczynamy od jakiegoś **podpowiedzi** (prompt), która jest przepuszczana przez komórki RNN, aby wygenerować jej stan pośredni, a następnie z tego stanu rozpoczyna się generowanie. Generujemy jeden znak na raz, przekazujemy stan i wygenerowany znak do kolejnej komórki RNN, aby wygenerować następny znak, aż wygenerujemy wystarczającą liczbę znaków. diff --git a/translations/pl/lessons/5-NLP/18-Transformers/README.md b/translations/pl/lessons/5-NLP/18-Transformers/README.md index 897dd868..934910f5 100644 --- a/translations/pl/lessons/5-NLP/18-Transformers/README.md +++ b/translations/pl/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ W przypadku RNN, zadania sequence-to-sequence są realizowane za pomocą dwóch **Mechanizmy uwagi** umożliwiają ważenie kontekstowego wpływu każdego wektora wejściowego na każdą prognozę wyjściową RNN. Implementuje się to poprzez tworzenie skrótów między stanami pośrednimi wejściowego RNN a wyjściowego RNN. W ten sposób, generując symbol wyjściowy yt, uwzględniamy wszystkie stany ukryte wejścia hi, z różnymi współczynnikami wagowymi αt,i. -![Obraz przedstawiający model enkoder/dekoder z warstwą uwagi addytywnej](../../../../../translated_images/pl/encoder-decoder-attention.7a726296894fb567.png) +![Obraz przedstawiający model enkoder/dekoder z warstwą uwagi addytywnej](../../../../../translated_images/pl/encoder-decoder-attention.7a726296894fb567.webp) > Model enkoder-dekoder z mechanizmem uwagi addytywnej w [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), cytowany z [tego wpisu na blogu](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) Macierz uwagi {αi,j} reprezentuje stopień, w jakim określone słowa wejściowe wpływają na generowanie danego słowa w sekwencji wyjściowej. Poniżej znajduje się przykład takiej macierzy: -![Obraz przedstawiający przykładowe wyrównanie znalezione przez RNNsearch-50, zaczerpnięte z Bahdanau - arviz.org](../../../../../translated_images/pl/bahdanau-fig3.09ba2d37f202a6af.png) +![Obraz przedstawiający przykładowe wyrównanie znalezione przez RNNsearch-50, zaczerpnięte z Bahdanau - arviz.org](../../../../../translated_images/pl/bahdanau-fig3.09ba2d37f202a6af.webp) > Rysunek z [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) @@ -66,7 +66,7 @@ Wynik, który uzyskujemy dzięki osadzaniu pozycji, osadza zarówno oryginalny t Następnie musimy wychwycić pewne wzorce w naszej sekwencji. Aby to zrobić, modele Transformer używają mechanizmu **samo-uwagi**, który w zasadzie jest uwagą zastosowaną do tej samej sekwencji jako wejście i wyjście. Zastosowanie samo-uwagi pozwala nam uwzględnić **kontekst** w zdaniu i zobaczyć, które słowa są ze sobą powiązane. Na przykład pozwala nam zobaczyć, które słowa są odniesieniami do innych, takich jak *to*, oraz uwzględnić kontekst: -![](../../../../../translated_images/pl/CoreferenceResolution.861924d6d384a7d6.png) +![](../../../../../translated_images/pl/CoreferenceResolution.861924d6d384a7d6.webp) > Obraz z [Bloga Google](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Ponieważ każda pozycja wejściowa jest mapowana niezależnie na każdą pozycj **BERT** (Bidirectional Encoder Representations from Transformers) to bardzo duża wielowarstwowa sieć Transformer z 12 warstwami dla *BERT-base* i 24 dla *BERT-large*. Model jest najpierw wstępnie trenowany na dużym korpusie danych tekstowych (Wikipedia + książki) za pomocą treningu niesuperwizowanego (przewidywanie zamaskowanych słów w zdaniu). Podczas wstępnego treningu model przyswaja znaczące poziomy zrozumienia języka, które można następnie wykorzystać z innymi zestawami danych za pomocą dostrajania. Ten proces nazywa się **transfer learning**. -![obrazek z http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/pl/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) +![obrazek z http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/pl/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > Źródło obrazu [tutaj](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/pl/lessons/5-NLP/19-NER/README.md b/translations/pl/lessons/5-NLP/19-NER/README.md index a4284f3c..6b0fb2bb 100644 --- a/translations/pl/lessons/5-NLP/19-NER/README.md +++ b/translations/pl/lessons/5-NLP/19-NER/README.md @@ -56,7 +56,7 @@ noworodka | O Ponieważ musimy zbudować jednoznaczną korespondencję między tokenami a klasami, możemy wytrenować odpowiedni model sieci neuronowej **wielu-do-wielu** z tego obrazu: -![Obraz przedstawiający typowe wzorce sieci neuronowych rekurencyjnych.](../../../../../translated_images/pl/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Obraz przedstawiający typowe wzorce sieci neuronowych rekurencyjnych.](../../../../../translated_images/pl/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > *Obraz z [tego wpisu na blogu](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) autorstwa [Andreja Karpathy'ego](http://karpathy.github.io/). Modele klasyfikacji tokenów NER odpowiadają architekturze sieci po prawej stronie tego obrazu.* diff --git a/translations/pl/lessons/6-Other/23-MultiagentSystems/README.md b/translations/pl/lessons/6-Other/23-MultiagentSystems/README.md index 6140a782..bfd21a07 100644 --- a/translations/pl/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/pl/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ Możesz otworzyć jeden z modeli, na przykład **Biology → Flocking** Po otwarciu modelu zostaniesz przeniesiony na główny ekran NetLogo. Oto przykładowy model opisujący populację wilków i owiec, biorąc pod uwagę ograniczone zasoby (trawę). -![Główny ekran NetLogo](../../../../../translated_images/pl/NetLogo-Main.32653711ec1a01b3.png) +![Główny ekran NetLogo](../../../../../translated_images/pl/NetLogo-Main.32653711ec1a01b3.webp) > Zrzut ekranu autorstwa Dmitry Soshnikov diff --git a/translations/pt/README.md b/translations/pt/README.md index 24d913b2..cc8ed108 100644 --- a/translations/pt/README.md +++ b/translations/pt/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # Inteligência Artificial para Iniciantes - Um Currículo -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/pt/ai-overview.0857791951d19500.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/pt/ai-overview.0857791951d19500.webp)| |:---:| | Inteligência Artificial para Iniciantes - _Sketchnote por [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/pt/lessons/1-Intro/README.md b/translations/pt/lessons/1-Intro/README.md index b4296a65..add14a01 100644 --- a/translations/pt/lessons/1-Intro/README.md +++ b/translations/pt/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Introdução à IA -![Resumo do conteúdo de Introdução à IA em um desenho](../../../../translated_images/pt/ai-intro.bf28d1ac4235881c.png) +![Resumo do conteúdo de Introdução à IA em um desenho](../../../../translated_images/pt/ai-intro.bf28d1ac4235881c.webp) > Sketchnote por [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Originalmente, os computadores foram inventados por [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage) para operar com números seguindo um procedimento bem definido - um algoritmo. Os computadores modernos, embora significativamente mais avançados do que o modelo original proposto no século XIX, ainda seguem a mesma ideia de cálculos controlados. Assim, é possível programar um computador para fazer algo se soubermos a sequência exata de passos necessários para alcançar o objetivo. -![Foto de uma pessoa](../../../../translated_images/pt/dsh_age.d212a30d4e54fb5f.png) +![Foto de uma pessoa](../../../../translated_images/pt/dsh_age.d212a30d4e54fb5f.webp) > Foto por [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ Para mais informações, consulte **[Inteligência Artificial Geral](https://en. Um dos problemas ao lidar com o termo **[Inteligência](https://en.wikipedia.org/wiki/Intelligence)** é que não há uma definição clara para este termo. Pode-se argumentar que inteligência está conectada ao **pensamento abstrato** ou à **autoconsciência**, mas não conseguimos defini-la adequadamente. -![Foto de um gato](../../../../translated_images/pt/photo-cat.8c8e8fb760ffe457.jpg) +![Foto de um gato](../../../../translated_images/pt/photo-cat.8c8e8fb760ffe457.webp) > [Foto](https://unsplash.com/photos/75715CVEJhI) por [Amber Kipp](https://unsplash.com/@sadmax) do Unsplash @@ -98,13 +98,13 @@ Alternativamente, podemos tentar modelar os elementos mais simples dentro do nos > | E o ML? | | > |--------------|-----------| -> | Parte da Inteligência Artificial que se baseia no computador aprendendo a resolver um problema com base em alguns dados é chamada de **Machine Learning**. Não consideraremos o aprendizado de máquina clássico neste curso - recomendamos o currículo separado [Machine Learning para Iniciantes](http://aka.ms/ml-beginners). | ![ML para Iniciantes](../../../../translated_images/pt/ml-for-beginners.9e4fed176fd5817d.png) | +> | Parte da Inteligência Artificial que se baseia no computador aprendendo a resolver um problema com base em alguns dados é chamada de **Machine Learning**. Não consideraremos o aprendizado de máquina clássico neste curso - recomendamos o currículo separado [Machine Learning para Iniciantes](http://aka.ms/ml-beginners). | ![ML para Iniciantes](../../../../translated_images/pt/ml-for-beginners.9e4fed176fd5817d.webp) | ## Um Breve Histórico da IA A Inteligência Artificial começou como um campo no meio do século XX. Inicialmente, o raciocínio simbólico era a abordagem predominante, e isso levou a uma série de sucessos importantes, como sistemas especialistas – programas de computador que eram capazes de agir como especialistas em alguns domínios de problemas limitados. No entanto, logo ficou claro que essa abordagem não escala bem. Extrair o conhecimento de um especialista, representá-lo em um computador e manter essa base de conhecimento precisa acaba sendo uma tarefa muito complexa e cara demais para ser prática em muitos casos. Isso levou ao chamado [Inverno da IA](https://en.wikipedia.org/wiki/AI_winter) na década de 1970. -Breve Histórico da IA +Breve Histórico da IA > Imagem por [Dmitry Soshnikov](http://soshnikov.com) @@ -124,7 +124,7 @@ Da mesma forma, podemos ver como a abordagem para criar “programas falantes” * Assistentes modernos, como Cortana, Siri ou Google Assistant, são todos sistemas híbridos que usam redes neurais para converter fala em texto e reconhecer nossa intenção, e depois empregam algum raciocínio ou algoritmos explícitos para realizar as ações necessárias. * No futuro, podemos esperar um modelo completamente baseado em redes neurais para lidar com diálogos por conta própria. As recentes redes neurais da família GPT e [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) mostram grande sucesso nisso. -a evolução do Teste de Turing +a evolução do Teste de Turing > Imagem de Dmitry Soshnikov, [foto](https://unsplash.com/photos/r8LmVbUKgns) de [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto), Unsplash ## Investigação Recente em IA diff --git a/translations/pt/lessons/2-Symbolic/README.md b/translations/pt/lessons/2-Symbolic/README.md index 6a6a33e5..f579538a 100644 --- a/translations/pt/lessons/2-Symbolic/README.md +++ b/translations/pt/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Representação de Conhecimento e Sistemas Especialistas -![Resumo do conteúdo de IA Simbólica](../../../../translated_images/pt/ai-symbolic.715a30cb610411a6.png) +![Resumo do conteúdo de IA Simbólica](../../../../translated_images/pt/ai-symbolic.715a30cb610411a6.webp) > Sketchnote por [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ Na maioria das vezes, não definimos estritamente o conhecimento, mas alinhamos Assim, o problema da **representação de conhecimento** é encontrar uma forma eficaz de representar o conhecimento dentro de um computador na forma de dados, para torná-lo automaticamente utilizável. Isso pode ser visto como um espectro: -![Espectro de representação de conhecimento](../../../../translated_images/pt/knowledge-spectrum.b60df631852c0217.png) +![Espectro de representação de conhecimento](../../../../translated_images/pt/knowledge-spectrum.b60df631852c0217.webp) > Imagem por [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Sintaxe de Bloco | Indentação | | Um dos primeiros sucessos da IA simbólica foram os chamados **sistemas especialistas** - sistemas computacionais projetados para atuar como especialistas em um domínio de problema limitado. Eles baseavam-se em uma **base de conhecimento** extraída de um ou mais especialistas humanos e continham um **motor de inferência** que realizava algum raciocínio sobre ela. -![Arquitetura Humana](../../../../translated_images/pt/arch-human.5d4d35f1bba3ab1c.png) | ![Sistema Baseado em Conhecimento](../../../../translated_images/pt/arch-kbs.3ec5c150b09fa8da.png) +![Arquitetura Humana](../../../../translated_images/pt/arch-human.5d4d35f1bba3ab1c.webp) | ![Sistema Baseado em Conhecimento](../../../../translated_images/pt/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ Estrutura simplificada do sistema neural humano | Arquitetura de um sistema baseado em conhecimento @@ -106,7 +106,7 @@ Os sistemas especialistas são construídos como o sistema de raciocínio humano Como exemplo, vamos considerar o seguinte sistema especialista para determinar um animal com base nas suas características físicas: -![Árvore AND-OR](../../../../translated_images/pt/AND-OR-Tree.5592d2c70187f283.png) +![Árvore AND-OR](../../../../translated_images/pt/AND-OR-Tree.5592d2c70187f283.webp) > Imagem por [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/pt/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/pt/lessons/3-NeuralNetworks/05-Frameworks/README.md index 36f25468..13fb393f 100644 --- a/translations/pt/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/pt/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Overfitting é um conceito extremamente importante em aprendizagem automática, Considere o seguinte problema de aproximar 5 pontos (representados por `x` nos gráficos abaixo): -![linear](../../../../../translated_images/pt/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/pt/overfit2.131f5800ae10ca5e.jpg) +![linear](../../../../../translated_images/pt/overfit1.f24b71c6f652e59e.webp) | ![overfit](../../../../../translated_images/pt/overfit2.131f5800ae10ca5e.webp) -------------------------|-------------------------- **Modelo linear, 2 parâmetros** | **Modelo não-linear, 7 parâmetros** Erro de treino = 5.3 | Erro de treino = 0 @@ -79,7 +79,7 @@ Erro de validação = 5.1 | Erro de validação = 20 Como pode ver no gráfico acima, o overfitting pode ser detetado por um erro de treino muito baixo e um erro de validação elevado. Normalmente, durante o treino, vemos tanto os erros de treino quanto de validação começarem a diminuir, e então, em algum momento, o erro de validação pode parar de diminuir e começar a aumentar. Este será um sinal de overfitting e um indicador de que provavelmente devemos parar o treino nesse ponto (ou pelo menos fazer um snapshot do modelo). -![overfitting](../../../../../translated_images/pt/Overfitting.408ad91cd90b4371.png) +![overfitting](../../../../../translated_images/pt/Overfitting.408ad91cd90b4371.webp) ## Como prevenir o overfitting diff --git a/translations/pt/lessons/3-NeuralNetworks/README.md b/translations/pt/lessons/3-NeuralNetworks/README.md index 63f72bd0..decf2d7b 100644 --- a/translations/pt/lessons/3-NeuralNetworks/README.md +++ b/translations/pt/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Introdução às Redes Neuronais -![Resumo do conteúdo de Introdução às Redes Neuronais em um desenho](../../../../translated_images/pt/ai-neuralnetworks.1c687ae40bc86e83.png) +![Resumo do conteúdo de Introdução às Redes Neuronais em um desenho](../../../../translated_images/pt/ai-neuralnetworks.1c687ae40bc86e83.webp) Como discutimos na introdução, uma das formas de alcançar inteligência é treinar um **modelo computacional** ou um **cérebro artificial**. Desde meados do século XX, os investigadores experimentaram diferentes modelos matemáticos, até que, nos últimos anos, esta abordagem provou ser extremamente bem-sucedida. Esses modelos matemáticos do cérebro são chamados de **redes neuronais**. @@ -36,13 +36,13 @@ Neste currículo, focar-nos-emos apenas em modelos de redes neuronais. Na biologia, sabemos que o nosso cérebro é composto por células neuronais (neurónios), cada uma delas com múltiplas "entradas" (dendritos) e uma única "saída" (axónio). Tanto os dendritos como os axónios podem conduzir sinais elétricos, e as conexões entre eles — conhecidas como sinapses — podem apresentar diferentes graus de condutividade, que são regulados por neurotransmissores. -![Modelo de um Neurónio](../../../../translated_images/pt/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![Modelo de um Neurónio](../../../../translated_images/pt/artneuron.1a5daa88d20ebe6f.png) +![Modelo de um Neurónio](../../../../translated_images/pt/synapse-wikipedia.ed20a9e4726ea1c6.webp) | ![Modelo de um Neurónio](../../../../translated_images/pt/artneuron.1a5daa88d20ebe6f.webp) ----|---- Neurónio Real *([Imagem](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) da Wikipédia)* | Neurónio Artificial *(Imagem do Autor)* Assim, o modelo matemático mais simples de um neurónio contém várias entradas X1, ..., XN e uma saída Y, e uma série de pesos W1, ..., WN. A saída é calculada como: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) onde **f** é uma **função de ativação** não linear. diff --git a/translations/pt/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/pt/lessons/4-ComputerVision/06-IntroCV/README.md index e677a250..7494536f 100644 --- a/translations/pt/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/pt/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ No nosso [OpenCV Notebook](OpenCV.ipynb), damos alguns exemplos de quando a vis * **Pré-processamento de uma fotografia de um livro em Braille**. Focamos em como podemos usar thresholding, deteção de características, transformação de perspetiva e manipulações NumPy para separar símbolos individuais em Braille para posterior classificação por uma rede neuronal. -![Imagem Braille](../../../../../translated_images/pt/braille.341962ff76b1bd70.jpeg) | ![Imagem Braille Pré-processada](../../../../../translated_images/pt/braille-result.46530fea020b03c7.png) | ![Símbolos Braille](../../../../../translated_images/pt/braille-symbols.0159185ab69d5339.png) +![Imagem Braille](../../../../../translated_images/pt/braille.341962ff76b1bd70.webp) | ![Imagem Braille Pré-processada](../../../../../translated_images/pt/braille-result.46530fea020b03c7.webp) | ![Símbolos Braille](../../../../../translated_images/pt/braille-symbols.0159185ab69d5339.webp) ----|-----|----- > Imagem de [OpenCV.ipynb](OpenCV.ipynb) * **Deteção de movimento em vídeo usando diferença de frames**. Se a câmara estiver fixa, os frames do feed da câmara devem ser bastante semelhantes entre si. Como os frames são representados como arrays, apenas subtraindo esses arrays de dois frames subsequentes obteremos a diferença de pixels, que deve ser baixa para frames estáticos e tornar-se maior quando houver movimento substancial na imagem. -![Imagem de frames de vídeo e diferenças de frames](../../../../../translated_images/pt/frame-difference.706f805491a0883c.png) +![Imagem de frames de vídeo e diferenças de frames](../../../../../translated_images/pt/frame-difference.706f805491a0883c.webp) > Imagem de [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ No nosso [OpenCV Notebook](OpenCV.ipynb), damos alguns exemplos de quando a vis - **Fluxo Ótico Denso** calcula o campo vetorial que mostra para cada pixel onde ele está a mover-se. - **Fluxo Ótico Esparso** baseia-se em tomar algumas características distintivas na imagem (por exemplo, bordas) e construir a sua trajetória de frame para frame. -![Imagem de Fluxo Ótico](../../../../../translated_images/pt/optical.1f4a94464579a83a.png) +![Imagem de Fluxo Ótico](../../../../../translated_images/pt/optical.1f4a94464579a83a.webp) > Imagem de [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/pt/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/pt/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index cf88e7d7..65eeee96 100644 --- a/translations/pt/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/pt/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 é uma rede que alcançou 92,7% de precisão na classificação top-5 do ImageNet em 2014. Tem a seguinte estrutura de camadas: -![Camadas do ImageNet](../../../../../translated_images/pt/vgg-16-arch1.d901a5583b3a51ba.jpg) +![Camadas do ImageNet](../../../../../translated_images/pt/vgg-16-arch1.d901a5583b3a51ba.webp) Como pode ver, a VGG segue uma arquitetura tradicional em pirâmide, que consiste numa sequência de camadas de convolução e pooling. -![Pirâmide do ImageNet](../../../../../translated_images/pt/vgg-16-arch.64ff2137f50dd49f.jpg) +![Pirâmide do ImageNet](../../../../../translated_images/pt/vgg-16-arch.64ff2137f50dd49f.webp) > Imagem de [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/pt/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/pt/lessons/4-ComputerVision/07-ConvNets/README.md index 9af82b03..31d90669 100644 --- a/translations/pt/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/pt/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ Na vida real, queremos ser capazes de reconhecer objetos numa imagem independent Para extrair padrões, utilizaremos o conceito de **filtros convolucionais**. Como sabe, uma imagem é representada por uma matriz 2D ou um tensor 3D com profundidade de cor. Aplicar um filtro significa que utilizamos uma matriz relativamente pequena chamada **kernel do filtro**, e para cada pixel na imagem original calculamos a média ponderada com os pontos vizinhos. Podemos imaginar isto como uma pequena janela que desliza sobre toda a imagem, e que calcula a média de todos os pixels de acordo com os pesos na matriz kernel do filtro. -![Filtro de Borda Vertical](../../../../../translated_images/pt/filter-vert.b7148390ca0bc356.png) | ![Filtro de Borda Horizontal](../../../../../translated_images/pt/filter-horiz.59b80ed4feb946ef.png) +![Filtro de Borda Vertical](../../../../../translated_images/pt/filter-vert.b7148390ca0bc356.webp) | ![Filtro de Borda Horizontal](../../../../../translated_images/pt/filter-horiz.59b80ed4feb946ef.webp) ----|---- > Imagem por Dmitry Soshnikov @@ -38,7 +38,7 @@ O funcionamento das CNN baseia-se nas seguintes ideias importantes: * Podemos projetar a rede de forma a que os filtros sejam treinados automaticamente * Podemos usar a mesma abordagem para encontrar padrões em características de alto nível, não apenas na imagem original. Assim, a extração de características pelas CNN funciona numa hierarquia de características, começando por combinações de pixels de baixo nível até combinações de partes da imagem de nível mais alto. -![Extração Hierárquica de Características](../../../../../translated_images/pt/FeatureExtractionCNN.d9b456cbdae7cb64.png) +![Extração Hierárquica de Características](../../../../../translated_images/pt/FeatureExtractionCNN.d9b456cbdae7cb64.webp) > Imagem de [um artigo de Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), baseado na [sua pesquisa](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ A maioria das CNN utilizadas para processamento de imagens segue uma arquitetura Como exemplo, vejamos a arquitetura da VGG-16, uma rede que alcançou 92,7% de precisão na classificação top-5 do ImageNet em 2014: -![Camadas do ImageNet](../../../../../translated_images/pt/vgg-16-arch1.d901a5583b3a51ba.jpg) +![Camadas do ImageNet](../../../../../translated_images/pt/vgg-16-arch1.d901a5583b3a51ba.webp) -![Pirâmide do ImageNet](../../../../../translated_images/pt/vgg-16-arch.64ff2137f50dd49f.jpg) +![Pirâmide do ImageNet](../../../../../translated_images/pt/vgg-16-arch.64ff2137f50dd49f.webp) > Imagem de [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/pt/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/pt/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 5a636f18..ad50dceb 100644 --- a/translations/pt/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/pt/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ Precisa treinar uma rede neuronal convolucional para classificar diferentes raç Vamos utilizar o [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/), que contém imagens de 37 raças diferentes de cães e gatos. -![Dataset com que iremos trabalhar](../../../../../../translated_images/pt/data.50b2a9d5484bdbf0.png) +![Dataset com que iremos trabalhar](../../../../../../translated_images/pt/data.50b2a9d5484bdbf0.webp) Para descarregar o dataset, utilize este trecho de código: diff --git a/translations/pt/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/pt/lessons/4-ComputerVision/08-TransferLearning/README.md index ad22f76f..d4ba53cc 100644 --- a/translations/pt/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/pt/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Tanto o Keras como o PyTorch possuem funções para carregar facilmente pesos de Aqui estão características extraídas de uma imagem de um gato pela rede VGG-16: -![Características extraídas pela VGG-16](../../../../../translated_images/pt/features.6291f9c7ba3a0b95.png) +![Características extraídas pela VGG-16](../../../../../translated_images/pt/features.6291f9c7ba3a0b95.webp) ## Conjunto de Dados de Gatos vs. Cães @@ -48,19 +48,19 @@ Uma rede neural pré-treinada contém diferentes padrões no seu *cérebro*, inc Uma abordagem que podemos adotar é começar com uma imagem aleatória e tentar usar a técnica de **otimização por descida de gradiente** para ajustar essa imagem de forma que a rede comece a pensar que é um gato. -![Ciclo de Otimização de Imagem](../../../../../translated_images/pt/ideal-cat-loop.999fbb8ff306e044.png) +![Ciclo de Otimização de Imagem](../../../../../translated_images/pt/ideal-cat-loop.999fbb8ff306e044.webp) No entanto, se fizermos isso, obteremos algo muito semelhante a um ruído aleatório. Isso acontece porque *existem muitas maneiras de fazer a rede pensar que a imagem de entrada é um gato*, incluindo algumas que não fazem sentido visualmente. Embora essas imagens contenham muitos padrões típicos de um gato, não há nada que as obrigue a serem visualmente distintas. Para melhorar o resultado, podemos adicionar outro termo à função de perda, chamado **perda de variação**. É uma métrica que mostra quão semelhantes são os pixels vizinhos da imagem. Minimizar a perda de variação torna a imagem mais suave e elimina o ruído, revelando padrões mais visualmente apelativos. Aqui está um exemplo de imagens "ideais", classificadas como gato e zebra com alta probabilidade: -![Gato Ideal](../../../../../translated_images/pt/ideal-cat.203dd4597643d6b0.png) | ![Zebra Ideal](../../../../../translated_images/pt/ideal-zebra.7f70e8b54ee15a7a.png) +![Gato Ideal](../../../../../translated_images/pt/ideal-cat.203dd4597643d6b0.webp) | ![Zebra Ideal](../../../../../translated_images/pt/ideal-zebra.7f70e8b54ee15a7a.webp) -----|----- *Gato Ideal* | *Zebra Ideal* Uma abordagem semelhante pode ser usada para realizar os chamados **ataques adversariais** numa rede neural. Suponha que queremos enganar uma rede neural e fazer com que um cão pareça um gato. Se pegarmos na imagem de um cão, que é reconhecida pela rede como um cão, podemos ajustá-la ligeiramente usando otimização por descida de gradiente até que a rede comece a classificá-la como um gato: -![Imagem de um Cão](../../../../../translated_images/pt/original-dog.8f68a67d2fe0911f.png) | ![Imagem de um cão classificada como gato](../../../../../translated_images/pt/adversarial-dog.d9fc7773b0142b89.png) +![Imagem de um Cão](../../../../../translated_images/pt/original-dog.8f68a67d2fe0911f.webp) | ![Imagem de um cão classificada como gato](../../../../../translated_images/pt/adversarial-dog.d9fc7773b0142b89.webp) -----|----- *Imagem original de um cão* | *Imagem de um cão classificada como gato* diff --git a/translations/pt/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/pt/lessons/4-ComputerVision/09-Autoencoders/README.md index 79621355..609f0687 100644 --- a/translations/pt/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/pt/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ No entanto, podemos querer usar dados brutos (não etiquetados) para treinar ext Como estamos a treinar um autoencoder para capturar o máximo de informação possível da imagem original para uma reconstrução precisa, a rede tenta encontrar a melhor **representação** das imagens de entrada para captar o seu significado. -![Diagrama de Autoencoder](../../../../../translated_images/pt/autoencoder_schema.5e6fc9ad98a5eb61.jpg) +![Diagrama de Autoencoder](../../../../../translated_images/pt/autoencoder_schema.5e6fc9ad98a5eb61.webp) > Imagem retirada do [blog da Keras](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/pt/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/pt/lessons/4-ComputerVision/11-ObjectDetection/README.md index b2e8d7f6..cf2a25de 100644 --- a/translations/pt/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/pt/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ Os modelos de classificação de imagens que abordámos até agora recebiam uma ## [Questionário pré-aula](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Detecção de Objetos](../../../../../translated_images/pt/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) +![Detecção de Objetos](../../../../../translated_images/pt/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp) > Imagem do [site YOLO v2](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ Supondo que queremos encontrar um gato numa imagem, uma abordagem muito ingénua 2. Executar a classificação de imagem em cada bloco. 3. Os blocos que resultarem numa ativação suficientemente alta podem ser considerados como contendo o objeto em questão. -![Detecção Ingénua de Objetos](../../../../../translated_images/pt/naive-detection.e7f1ba220ccd08c6.png) +![Detecção Ingénua de Objetos](../../../../../translated_images/pt/naive-detection.e7f1ba220ccd08c6.webp) > *Imagem do [Caderno de Exercícios](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ Poderá encontrar os seguintes conjuntos de dados para esta tarefa: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 classes * [COCO](http://cocodataset.org/#home) - Objetos Comuns em Contexto. 80 classes, caixas delimitadoras e máscaras de segmentação -![COCO](../../../../../translated_images/pt/coco-examples.71bc60380fa6cceb.jpg) +![COCO](../../../../../translated_images/pt/coco-examples.71bc60380fa6cceb.webp) ## Métricas de Detecção de Objetos @@ -50,7 +50,7 @@ Poderá encontrar os seguintes conjuntos de dados para esta tarefa: Enquanto na classificação de imagens é fácil medir o desempenho do algoritmo, na detecção de objetos precisamos de medir tanto a correção da classe como a precisão da localização da caixa delimitadora inferida. Para esta última, utilizamos a chamada **Interseção sobre União** (IoU), que mede o quão bem duas caixas (ou duas áreas arbitrárias) se sobrepõem. -![IoU](../../../../../translated_images/pt/iou_equation.9a4751d40fff4e11.png) +![IoU](../../../../../translated_images/pt/iou_equation.9a4751d40fff4e11.webp) > *Figura 2 de [este excelente artigo sobre IoU](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ Existem duas grandes classes de algoritmos de detecção de objetos: [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) utiliza [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) para gerar uma estrutura hierárquica de regiões ROI, que são então passadas por extratores de características CNN e classificadores SVM para determinar a classe do objeto, e regressão linear para determinar as coordenadas da *caixa delimitadora*. [Artigo Oficial](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/pt/rcnn1.cae407020dfb1d1f.png) +![RCNN](../../../../../translated_images/pt/rcnn1.cae407020dfb1d1f.webp) > *Imagem de van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/pt/rcnn2.2d9530bb83516484.png) +![RCNN-1](../../../../../translated_images/pt/rcnn2.2d9530bb83516484.webp) > *Imagens de [este artigo](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -110,7 +110,7 @@ Existem duas grandes classes de algoritmos de detecção de objetos: Esta abordagem é semelhante à R-CNN, mas as regiões são definidas após as camadas de convolução terem sido aplicadas. -![FRCNN](../../../../../translated_images/pt/f-rcnn.3cda6d9bb4188875.png) +![FRCNN](../../../../../translated_images/pt/f-rcnn.3cda6d9bb4188875.webp) > Imagem do [Artigo Oficial](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ Esta abordagem é semelhante à R-CNN, mas as regiões são definidas após as c A ideia principal desta abordagem é usar uma rede neural para prever ROIs - a chamada *Rede de Proposta de Região*. [Artigo](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/pt/faster-rcnn.8d46c099b87ef30a.png) +![FasterRCNN](../../../../../translated_images/pt/faster-rcnn.8d46c099b87ef30a.webp) > Imagem do [artigo oficial](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ Este algoritmo é ainda mais rápido que o Faster R-CNN. A ideia principal é a 1. As características são processadas por **Position-Sensitive Score Map**. Cada objeto das classes $C$ é dividido em regiões $k\times k$, e treinamos para prever partes dos objetos. 1. Para cada parte das regiões $k\times k$, todas as redes votam pelas classes de objetos, e a classe de objeto com o voto máximo é selecionada. -![r-fcn image](../../../../../translated_images/pt/r-fcn.13eb88158b99a3da.png) +![r-fcn image](../../../../../translated_images/pt/r-fcn.13eb88158b99a3da.webp) > Imagem do [artigo oficial](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO é um algoritmo de uma única passagem em tempo real. A ideia principal é * A imagem é dividida em regiões $S\times S$. * Para cada região, **CNN** prevê $n$ objetos possíveis, coordenadas da *caixa delimitadora* e *confiança*=*probabilidade* * IoU. - ![YOLO](../../../../../translated_images/pt/yolo.a2648ec82ee8bb4e.png) + ![YOLO](../../../../../translated_images/pt/yolo.a2648ec82ee8bb4e.webp) > Imagem do [artigo oficial](https://arxiv.org/abs/1506.02640) diff --git a/translations/pt/lessons/5-NLP/14-Embeddings/README.md b/translations/pt/lessons/5-NLP/14-Embeddings/README.md index 969ee93a..ce409ea2 100644 --- a/translations/pt/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/pt/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ Assim, a camada de embedding receberia uma palavra como entrada e produziria um Ao usar uma camada de embedding como a primeira camada na nossa rede de classificação, podemos mudar de um modelo de bag-of-words para um modelo de **embedding bag**, onde primeiro convertemos cada palavra no nosso texto no embedding correspondente e, em seguida, calculamos alguma função agregada sobre todos esses embeddings, como `sum`, `average` ou `max`. -![Imagem mostrando um classificador de embedding para cinco palavras de sequência.](../../../../../translated_images/pt/embedding-classifier-example.b77f021a7ee67eee.png) +![Imagem mostrando um classificador de embedding para cinco palavras de sequência.](../../../../../translated_images/pt/embedding-classifier-example.b77f021a7ee67eee.webp) > Imagem do autor @@ -40,7 +40,7 @@ Para isso, precisamos de pré-treinar o nosso modelo de embedding numa grande co CBoW é mais rápido, enquanto skip-gram é mais lento, mas faz um trabalho melhor ao representar palavras menos frequentes. -![Imagem mostrando os algoritmos CBoW e Skip-Gram para converter palavras em vetores.](../../../../../translated_images/pt/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![Imagem mostrando os algoritmos CBoW e Skip-Gram para converter palavras em vetores.](../../../../../translated_images/pt/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Imagem retirada [deste artigo](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/pt/lessons/5-NLP/15-LanguageModeling/README.md b/translations/pt/lessons/5-NLP/15-LanguageModeling/README.md index 4d499a1d..9113413a 100644 --- a/translations/pt/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/pt/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ Nos nossos exemplos anteriores, utilizámos embeddings semânticos pré-treinado * **Continuous Bag-of-Words** (CBoW), onde prevemos o token central $W_0$ numa sequência de tokens $W_{-N}$, ..., $W_N$. * **Skip-gram**, onde prevemos um conjunto de tokens vizinhos {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} a partir do token central $W_0$. -![imagem do artigo sobre conversão de palavras em vetores](../../../../../translated_images/pt/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![imagem do artigo sobre conversão de palavras em vetores](../../../../../translated_images/pt/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Imagem retirada [deste artigo](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/pt/lessons/5-NLP/16-RNN/README.md b/translations/pt/lessons/5-NLP/16-RNN/README.md index d84ae842..fc78dd6e 100644 --- a/translations/pt/lessons/5-NLP/16-RNN/README.md +++ b/translations/pt/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ Nas secções anteriores, utilizámos representações semânticas ricas de text Para capturar o significado de uma sequência de texto, precisamos de usar outra arquitetura de rede neural, chamada **rede neural recorrente**, ou RNN. Numa RNN, passamos a nossa frase pela rede, um símbolo de cada vez, e a rede produz um **estado**, que depois passamos novamente à rede com o próximo símbolo. -![RNN](../../../../../translated_images/pt/rnn.27f5c29c53d727b5.png) +![RNN](../../../../../translated_images/pt/rnn.27f5c29c53d727b5.webp) > Imagem do autor @@ -61,7 +61,7 @@ Discutimos redes recorrentes que operam numa direção, do início de uma sequê Uma rede recorrente, seja unidirecional ou bidirecional, captura certos padrões dentro de uma sequência e pode armazená-los num vetor de estado ou passá-los para a saída. Tal como nas redes convolucionais, podemos construir outra camada recorrente sobre a primeira para capturar padrões de nível superior e construir a partir dos padrões de baixo nível extraídos pela primeira camada. Isto leva-nos à noção de uma **RNN multicamada**, que consiste em duas ou mais redes recorrentes, onde a saída da camada anterior é passada para a próxima camada como entrada. -![Imagem mostrando uma RNN multicamada com LSTM](../../../../../translated_images/pt/multi-layer-lstm.dd975e29bb2a59fe.jpg) +![Imagem mostrando uma RNN multicamada com LSTM](../../../../../translated_images/pt/multi-layer-lstm.dd975e29bb2a59fe.webp) *Imagem retirada [deste excelente artigo](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) de Fernando López* diff --git a/translations/pt/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/pt/lessons/5-NLP/17-GenerativeNetworks/README.md index 959cc59e..33f358f8 100644 --- a/translations/pt/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/pt/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ Na arquitetura de RNN discutida na unidade anterior, cada unidade RNN produzia o Isto permite diferentes arquiteturas neuronais, como mostrado na imagem abaixo: -![Imagem mostrando padrões comuns de redes neuronais recorrentes.](../../../../../translated_images/pt/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Imagem mostrando padrões comuns de redes neuronais recorrentes.](../../../../../translated_images/pt/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > Imagem do artigo [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) por [Andrej Karpaty](http://karpathy.github.io/) @@ -32,7 +32,7 @@ Nesta unidade, vamos focar-nos em modelos generativos simples que nos ajudam a g Vamos treinar esta RNN para gerar texto passo a passo. Em cada passo, tomaremos uma sequência de caracteres de comprimento `nchars` e pediremos à rede que gere o próximo carácter de saída para cada carácter de entrada: -![Imagem mostrando um exemplo de geração de RNN da palavra 'HELLO'.](../../../../../translated_images/pt/rnn-generate.56c54afb52f9781d.png) +![Imagem mostrando um exemplo de geração de RNN da palavra 'HELLO'.](../../../../../translated_images/pt/rnn-generate.56c54afb52f9781d.webp) Ao gerar texto (durante a inferência), começamos com um **prompt**, que é passado pelas células RNN para gerar o estado intermédio, e a partir deste estado começa a geração. Geramos um carácter de cada vez e passamos o estado e o carácter gerado para outra célula RNN para gerar o próximo, até gerarmos caracteres suficientes. diff --git a/translations/pt/lessons/5-NLP/18-Transformers/README.md b/translations/pt/lessons/5-NLP/18-Transformers/README.md index 37818f51..744e3ab2 100644 --- a/translations/pt/lessons/5-NLP/18-Transformers/README.md +++ b/translations/pt/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ Com RNNs, a tarefa de sequência para sequência é implementada por duas redes Os **Mecanismos de Atenção** fornecem um meio de ponderar o impacto contextual de cada vetor de entrada em cada previsão de saída da RNN. Isto é implementado criando atalhos entre estados intermediários da RNN de entrada e a RNN de saída. Desta forma, ao gerar o símbolo de saída yt, consideramos todos os estados ocultos de entrada hi, com diferentes coeficientes de peso αt,i. -![Imagem mostrando um modelo codificador/descodificador com uma camada de atenção aditiva](../../../../../translated_images/pt/encoder-decoder-attention.7a726296894fb567.png) +![Imagem mostrando um modelo codificador/descodificador com uma camada de atenção aditiva](../../../../../translated_images/pt/encoder-decoder-attention.7a726296894fb567.webp) > O modelo codificador-descodificador com mecanismo de atenção aditiva em [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citado deste [post de blog](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) A matriz de atenção {αi,j} representaria o grau em que certas palavras de entrada influenciam a geração de uma determinada palavra na sequência de saída. Abaixo está um exemplo de tal matriz: -![Imagem mostrando um alinhamento de exemplo encontrado por RNNsearch-50, retirada de Bahdanau - arviz.org](../../../../../translated_images/pt/bahdanau-fig3.09ba2d37f202a6af.png) +![Imagem mostrando um alinhamento de exemplo encontrado por RNNsearch-50, retirada de Bahdanau - arviz.org](../../../../../translated_images/pt/bahdanau-fig3.09ba2d37f202a6af.webp) > Figura de [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) @@ -66,7 +66,7 @@ O resultado que obtemos com o embedding posicional incorpora tanto o token origi A seguir, precisamos capturar alguns padrões dentro da nossa sequência. Para isso, os transformers usam um mecanismo de **auto-atenção**, que é essencialmente atenção aplicada à mesma sequência como entrada e saída. Aplicar auto-atenção permite-nos levar em conta o **contexto** dentro da frase e ver quais palavras estão inter-relacionadas. Por exemplo, permite-nos ver quais palavras são referidas por correferências, como *it*, e também considerar o contexto: -![](../../../../../translated_images/pt/CoreferenceResolution.861924d6d384a7d6.png) +![](../../../../../translated_images/pt/CoreferenceResolution.861924d6d384a7d6.webp) > Imagem do [Blog do Google](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Como cada posição de entrada é mapeada independentemente para cada posição **BERT** (Bidirectional Encoder Representations from Transformers) é uma rede transformer muito grande com várias camadas: 12 camadas para o *BERT-base* e 24 para o *BERT-large*. O modelo é primeiro pré-treinado num grande corpus de dados de texto (WikiPedia + livros) usando treino não supervisionado (prevendo palavras mascaradas numa frase). Durante o pré-treino, o modelo absorve níveis significativos de compreensão da linguagem, que podem ser aproveitados com outros conjuntos de dados usando ajuste fino. Este processo é chamado de **aprendizagem por transferência**. -![imagem de http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/pt/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) +![imagem de http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/pt/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > Imagem [fonte](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/pt/lessons/5-NLP/18-Transformers/READMEtransformers.md b/translations/pt/lessons/5-NLP/18-Transformers/READMEtransformers.md index 978f9cd5..34c2fcad 100644 --- a/translations/pt/lessons/5-NLP/18-Transformers/READMEtransformers.md +++ b/translations/pt/lessons/5-NLP/18-Transformers/READMEtransformers.md @@ -11,13 +11,13 @@ Com RNNs, a sequência-para-sequência é implementada por duas redes recorrente **Mecanismos de Atenção** fornecem um meio de ponderar o impacto contextual de cada vetor de entrada em cada previsão de saída da RNN. A forma como é implementado é criando atalhos entre estados intermediários da RNN de entrada e da RNN de saída. Dessa maneira, ao gerar o símbolo de saída yt, levaremos em conta todos os estados ocultos de entrada hi, com diferentes coeficientes de peso αt,i. -![Imagem mostrando um modelo codificador/decodificador com uma camada de atenção aditiva](../../../../../translated_images/pt/encoder-decoder-attention.7a726296894fb567.png) +![Imagem mostrando um modelo codificador/decodificador com uma camada de atenção aditiva](../../../../../translated_images/pt/encoder-decoder-attention.7a726296894fb567.webp) > O modelo codificador-decodificador com mecanismo de atenção aditiva em [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citado a partir [deste post de blog](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) A matriz de atenção {αi,j} representaria o grau em que certas palavras de entrada desempenham na geração de uma palavra específica na sequência de saída. Abaixo está um exemplo de tal matriz: -![Imagem mostrando um alinhamento de exemplo encontrado por RNNsearch-50, tirada de Bahdanau - arviz.org](../../../../../translated_images/pt/bahdanau-fig3.09ba2d37f202a6af.png) +![Imagem mostrando um alinhamento de exemplo encontrado por RNNsearch-50, tirada de Bahdanau - arviz.org](../../../../../translated_images/pt/bahdanau-fig3.09ba2d37f202a6af.webp) > Figura de [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) @@ -57,7 +57,7 @@ O resultado que obtemos com o embutimento posicional incorpora tanto o token ori Em seguida, precisamos capturar alguns padrões dentro da nossa sequência. Para fazer isso, os transformers usam um mecanismo de **autoatenção**, que é essencialmente atenção aplicada à mesma sequência como entrada e saída. A aplicação de autoatenção nos permite levar em conta o **contexto** dentro da sentença e ver quais palavras estão inter-relacionadas. Por exemplo, isso nos permite ver quais palavras são referidas por co-referências, como *isso*, e também levar o contexto em consideração: -![](../../../../../translated_images/pt/CoreferenceResolution.861924d6d384a7d6.png) +![](../../../../../translated_images/pt/CoreferenceResolution.861924d6d384a7d6.webp) > Imagem do [Blog do Google](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -82,7 +82,7 @@ Como cada posição de entrada é mapeada independentemente para cada posição **BERT** (Representações de Codificador Bidirecional de Transformers) é uma rede transformer multilayer muito grande com 12 camadas para *BERT-base* e 24 para *BERT-large*. O modelo é primeiro pré-treinado em um grande corpus de dados textuais (WikiPedia + livros) usando treinamento não supervisionado (previsão de palavras mascaradas em uma sentença). Durante o pré-treinamento, o modelo absorve níveis significativos de compreensão da linguagem, que podem ser aproveitados com outros conjuntos de dados usando ajuste fino. Este processo é chamado de **aprendizado por transferência**. -![imagem de http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/pt/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) +![imagem de http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/pt/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > Imagem [fonte](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/pt/lessons/5-NLP/19-NER/README.md b/translations/pt/lessons/5-NLP/19-NER/README.md index 6e18c33d..1b113d2e 100644 --- a/translations/pt/lessons/5-NLP/19-NER/README.md +++ b/translations/pt/lessons/5-NLP/19-NER/README.md @@ -58,7 +58,7 @@ infant | O Como precisamos construir uma correspondência um-para-um entre tokens e classes, podemos treinar um modelo de rede neural **muitos-para-muitos** da seguinte forma: -![Imagem mostrando padrões comuns de redes neuronais recorrentes.](../../../../../translated_images/pt/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Imagem mostrando padrões comuns de redes neuronais recorrentes.](../../../../../translated_images/pt/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > *Imagem retirada [deste artigo](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) por [Andrej Karpathy](http://karpathy.github.io/). Os modelos de classificação de tokens NER correspondem à arquitetura de rede mais à direita nesta imagem.* diff --git a/translations/pt/lessons/6-Other/23-MultiagentSystems/README.md b/translations/pt/lessons/6-Other/23-MultiagentSystems/README.md index 5dea4dbd..59b35b73 100644 --- a/translations/pt/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/pt/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ Pode abrir um dos modelos, por exemplo **Biology → Flocking**. Depois de abrir o modelo, será levado ao ecrã principal do NetLogo. Aqui está um modelo de exemplo que descreve a população de lobos e ovelhas, dado recursos finitos (relva). -![NetLogo Main Screen](../../../../../translated_images/pt/NetLogo-Main.32653711ec1a01b3.png) +![NetLogo Main Screen](../../../../../translated_images/pt/NetLogo-Main.32653711ec1a01b3.webp) > Captura de ecrã por Dmitry Soshnikov diff --git a/translations/ro/README.md b/translations/ro/README.md index 022e22d8..c28a5371 100644 --- a/translations/ro/README.md +++ b/translations/ro/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # Inteligență Artificială pentru Începători - Un Curriculum -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ro/ai-overview.0857791951d19500.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ro/ai-overview.0857791951d19500.webp)| |:---:| | AI For Beginners - _Sketchnote de [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/ro/lessons/1-Intro/README.md b/translations/ro/lessons/1-Intro/README.md index 692c765a..e0b1057e 100644 --- a/translations/ro/lessons/1-Intro/README.md +++ b/translations/ro/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Introducere în Inteligența Artificială -![Rezumat al conținutului Introducerii în AI într-un desen](../../../../translated_images/ro/ai-intro.bf28d1ac4235881c.png) +![Rezumat al conținutului Introducerii în AI într-un desen](../../../../translated_images/ro/ai-intro.bf28d1ac4235881c.webp) > Schiță de [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Inițial, computerele au fost inventate de [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage) pentru a opera pe numere urmând o procedură bine definită - un algoritm. Computerele moderne, deși semnificativ mai avansate decât modelul original propus în secolul al XIX-lea, încă urmează aceeași idee de calcule controlate. Astfel, este posibil să programăm un computer să facă ceva dacă știm exact secvența de pași necesară pentru a atinge scopul. -![Fotografie a unei persoane](../../../../translated_images/ro/dsh_age.d212a30d4e54fb5f.png) +![Fotografie a unei persoane](../../../../translated_images/ro/dsh_age.d212a30d4e54fb5f.webp) > Fotografie de [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ Pentru mai multe informații, consultați **[Inteligența Artificială Generală Una dintre problemele legate de termenul **[Inteligență](https://en.wikipedia.org/wiki/Intelligence)** este că nu există o definiție clară a acestui termen. Se poate argumenta că inteligența este legată de **gândirea abstractă** sau de **auto-conștientizare**, dar nu o putem defini corect. -![Fotografie a unei pisici](../../../../translated_images/ro/photo-cat.8c8e8fb760ffe457.jpg) +![Fotografie a unei pisici](../../../../translated_images/ro/photo-cat.8c8e8fb760ffe457.webp) > [Fotografie](https://unsplash.com/photos/75715CVEJhI) de [Amber Kipp](https://unsplash.com/@sadmax) de pe Unsplash @@ -98,13 +98,13 @@ Alternativ, putem încerca să modelăm cele mai simple elemente din creierul no > | Ce ziceți de ML? | | > |--------------|-----------| -> | O parte a Inteligenței Artificiale care se bazează pe învățarea computerului să rezolve o problemă pe baza unor date se numește **Învățare Automată**. Nu vom analiza învățarea automată clasică în acest curs - vă recomandăm un curriculum separat [Machine Learning for Beginners](http://aka.ms/ml-beginners). | ![ML pentru Începători](../../../../translated_images/ro/ml-for-beginners.9e4fed176fd5817d.png) | +> | O parte a Inteligenței Artificiale care se bazează pe învățarea computerului să rezolve o problemă pe baza unor date se numește **Învățare Automată**. Nu vom analiza învățarea automată clasică în acest curs - vă recomandăm un curriculum separat [Machine Learning for Beginners](http://aka.ms/ml-beginners). | ![ML pentru Începători](../../../../translated_images/ro/ml-for-beginners.9e4fed176fd5817d.webp) | ## O Scurtă Istorie a AI Inteligența Artificială a început ca un domeniu la mijlocul secolului XX. Inițial, raționamentul simbolic a fost o abordare predominantă și a condus la o serie de succese importante, cum ar fi sistemele expert – programe de computer care puteau acționa ca un expert în unele domenii limitate de probleme. Cu toate acestea, a devenit curând clar că o astfel de abordare nu se scalează bine. Extragerea cunoștințelor de la un expert, reprezentarea lor într-un computer și menținerea bazei de cunoștințe exacte s-a dovedit a fi o sarcină foarte complexă și prea costisitoare pentru a fi practică în multe cazuri. Acest lucru a dus la așa-numita [Iarnă AI](https://en.wikipedia.org/wiki/AI_winter) în anii 1970. -Scurtă Istorie a AI +Scurtă Istorie a AI > Imagine de [Dmitry Soshnikov](http://soshnikov.com) @@ -124,7 +124,7 @@ Putem observa cum s-au schimbat abordările, de exemplu, în crearea unui progra * Asistenții moderni, cum ar fi Cortana, Siri sau Google Assistant, sunt toate sisteme hibride care folosesc rețele neuronale pentru a converti vorbirea în text și a recunoaște intenția noastră, iar apoi utilizează un raționament sau algoritmi expliciți pentru a efectua acțiunile necesare. * În viitor, ne putem aștepta la un model complet bazat pe rețele neuronale care să gestioneze dialogul de unul singur. Familiile recente de rețele neuronale GPT și [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) arată un mare succes în acest sens. -evoluția testului Turing +evoluția testului Turing > Imagine de Dmitry Soshnikov, [fotografie](https://unsplash.com/photos/r8LmVbUKgns) de [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto), Unsplash ## Cercetări recente în domeniul AI diff --git a/translations/ro/lessons/2-Symbolic/Animals.ipynb b/translations/ro/lessons/2-Symbolic/Animals.ipynb index c1b14c00..42c6bc2e 100644 --- a/translations/ro/lessons/2-Symbolic/Animals.ipynb +++ b/translations/ro/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "În acest exemplu, vom implementa un sistem simplu bazat pe cunoștințe pentru a determina un animal pe baza unor caracteristici fizice. Sistemul poate fi reprezentat prin următorul arbore AND-OR (acesta este doar o parte din arborele complet, putem adăuga cu ușurință mai multe reguli):\n", "\n", - "![](../../../../translated_images/ro/AND-OR-Tree.5592d2c70187f283.png)\n" + "![](../../../../translated_images/ro/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { diff --git a/translations/ro/lessons/2-Symbolic/README.md b/translations/ro/lessons/2-Symbolic/README.md index cb6d509b..7bf9f0ab 100644 --- a/translations/ro/lessons/2-Symbolic/README.md +++ b/translations/ro/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Reprezentarea Cunoașterii și Sisteme Expert -![Rezumat al conținutului AI simbolic](../../../../translated_images/ro/ai-symbolic.715a30cb610411a6.png) +![Rezumat al conținutului AI simbolic](../../../../translated_images/ro/ai-symbolic.715a30cb610411a6.webp) > Sketchnote de [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ De cele mai multe ori, nu definim strict cunoașterea, ci o aliniem cu alte conc Astfel, problema **reprezentării cunoașterii** este de a găsi o modalitate eficientă de a reprezenta cunoașterea într-un computer sub formă de date, pentru a o face utilizabilă automat. Acest lucru poate fi văzut ca un spectru: -![Spectrul reprezentării cunoașterii](../../../../translated_images/ro/knowledge-spectrum.b60df631852c0217.png) +![Spectrul reprezentării cunoașterii](../../../../translated_images/ro/knowledge-spectrum.b60df631852c0217.webp) > Imagine de [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Sintaxă Bloc | Indentare | | | Unul dintre succesele timpurii ale AI simbolic au fost așa-numitele **sisteme expert** - sisteme de computer concepute să acționeze ca un expert într-un domeniu limitat de probleme. Acestea se bazau pe o **bază de cunoaștere** extrasă de la unul sau mai mulți experți umani și conțineau un **motor de inferență** care efectua raționamente pe baza acesteia. -![Arhitectura umană](../../../../translated_images/ro/arch-human.5d4d35f1bba3ab1c.png) | ![Sistem bazat pe cunoaștere](../../../../translated_images/ro/arch-kbs.3ec5c150b09fa8da.png) +![Arhitectura umană](../../../../translated_images/ro/arch-human.5d4d35f1bba3ab1c.webp) | ![Sistem bazat pe cunoaștere](../../../../translated_images/ro/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ Structura simplificată a sistemului neural uman | Arhitectura unui sistem bazat pe cunoaștere @@ -106,7 +106,7 @@ Sistemele expert sunt construite similar cu sistemul de raționament uman, care Ca exemplu, să luăm în considerare următorul sistem expert de determinare a unui animal pe baza caracteristicilor sale fizice: -![Arbore AND-OR](../../../../translated_images/ro/AND-OR-Tree.5592d2c70187f283.png) +![Arbore AND-OR](../../../../translated_images/ro/AND-OR-Tree.5592d2c70187f283.webp) > Imagine de [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/ro/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/ro/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index 1b08d2bb..860aee98 100644 --- a/translations/ro/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/ro/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -1259,7 +1259,7 @@ "* Pierdere mică în antrenament - modelul poate aproxima bine datele de antrenament, deoarece are suficientă putere expresivă.\n", "* Pierderea pe validare poate fi mult mai mare decât pierderea pe antrenament și poate începe să crească în timpul antrenamentului - acest lucru se întâmplă deoarece modelul \"memorizează\" punctele de antrenament și pierde \"imaginea de ansamblu\".\n", "\n", - "![Supraînvățare](../../../../../translated_images/ro/overfit.a0bd57f717c15769.png)\n", + "![Supraînvățare](../../../../../translated_images/ro/overfit.a0bd57f717c15769.webp)\n", "\n", "> În această imagine, `x` reprezintă datele de antrenament, iar `o` - datele de validare. Stânga - model liniar (cu un singur strat), care aproximează destul de bine natura datelor. Dreapta - model supraînvățat, care aproximează perfect datele de antrenament, dar nu mai are sens pentru alte date (eroarea pe validare este foarte mare).\n" ] diff --git a/translations/ro/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/ro/lessons/3-NeuralNetworks/05-Frameworks/README.md index 34170c28..ca31d4bb 100644 --- a/translations/ro/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/ro/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Overfitting este un concept extrem de important în învățarea automată și e Luați în considerare următoarea problemă de aproximare a 5 puncte (reprezentate de `x` pe graficele de mai jos): -![linear](../../../../../translated_images/ro/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/ro/overfit2.131f5800ae10ca5e.jpg) +![linear](../../../../../translated_images/ro/overfit1.f24b71c6f652e59e.webp) | ![overfit](../../../../../translated_images/ro/overfit2.131f5800ae10ca5e.webp) -------------------------|-------------------------- **Model liniar, 2 parametri** | **Model neliniar, 7 parametri** Eroare de antrenare = 5.3 | Eroare de antrenare = 0 @@ -79,7 +79,7 @@ Este foarte important să găsim un echilibru corect între complexitatea modelu Așa cum se vede din graficul de mai sus, overfitting poate fi detectat printr-o eroare de antrenare foarte mică și o eroare de validare mare. În mod normal, în timpul antrenării, vom vedea atât erorile de antrenare, cât și cele de validare începând să scadă, iar apoi, la un moment dat, eroarea de validare poate înceta să scadă și să înceapă să crească. Acesta va fi un semn de overfitting și un indicator că ar trebui să oprim antrenarea în acel moment (sau cel puțin să facem un snapshot al modelului). -![overfitting](../../../../../translated_images/ro/Overfitting.408ad91cd90b4371.png) +![overfitting](../../../../../translated_images/ro/Overfitting.408ad91cd90b4371.webp) ## Cum prevenim overfitting diff --git a/translations/ro/lessons/3-NeuralNetworks/README.md b/translations/ro/lessons/3-NeuralNetworks/README.md index 049f4a3b..d886d55a 100644 --- a/translations/ro/lessons/3-NeuralNetworks/README.md +++ b/translations/ro/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Introducere în Rețele Neuronale -![Rezumat al conținutului despre Introducerea în Rețele Neuronale într-un desen](../../../../translated_images/ro/ai-neuralnetworks.1c687ae40bc86e83.png) +![Rezumat al conținutului despre Introducerea în Rețele Neuronale într-un desen](../../../../translated_images/ro/ai-neuralnetworks.1c687ae40bc86e83.webp) Așa cum am discutat în introducere, una dintre modalitățile de a obține inteligență este să antrenăm un **model computerizat** sau un **creier artificial**. Începând cu mijlocul secolului al XX-lea, cercetătorii au încercat diferite modele matematice, până când, în ultimii ani, această direcție s-a dovedit a fi extrem de eficientă. Aceste modele matematice ale creierului sunt numite **rețele neuronale**. @@ -36,13 +36,13 @@ Vom analiza cele mai comune două probleme din Învățarea Automată: Din biologie, știm că creierul nostru este format din celule neuronale (neuroni), fiecare având multiple "intrări" (dendrite) și o singură "ieșire" (axon). Atât dendritele, cât și axonii pot conduce semnale electrice, iar conexiunile dintre ele — cunoscute sub numele de sinapse — pot prezenta grade variate de conductivitate, care sunt reglate de neurotransmițători. -![Model al unui Neuron](../../../../translated_images/ro/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![Model al unui Neuron](../../../../translated_images/ro/artneuron.1a5daa88d20ebe6f.png) +![Model al unui Neuron](../../../../translated_images/ro/synapse-wikipedia.ed20a9e4726ea1c6.webp) | ![Model al unui Neuron](../../../../translated_images/ro/artneuron.1a5daa88d20ebe6f.webp) ----|---- Neuron Real *([Imagine](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) de pe Wikipedia)* | Neuron Artificial *(Imagine de Autor)* Astfel, cel mai simplu model matematic al unui neuron conține mai multe intrări X1, ..., XN și o ieșire Y, precum și o serie de ponderi W1, ..., WN. Ieșirea este calculată astfel: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) unde f este o **funcție de activare** neliniară. diff --git a/translations/ro/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/ro/lessons/4-ComputerVision/06-IntroCV/README.md index ae6c4eb5..e5f6a409 100644 --- a/translations/ro/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/ro/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ De asemenea, poți folosi OpenCV pentru a încărca cadre video unul câte unul * **Preprocesarea unei fotografii a unei cărți Braille**. Ne concentrăm pe modul în care putem utiliza thresholding, detectarea caracteristicilor, transformarea de perspectivă și manipulările NumPy pentru a separa simbolurile individuale Braille pentru clasificarea ulterioară de către o rețea neuronală. -![Imagine Braille](../../../../../translated_images/ro/braille.341962ff76b1bd70.jpeg) | ![Imagine Braille Preprocesată](../../../../../translated_images/ro/braille-result.46530fea020b03c7.png) | ![Simboluri Braille](../../../../../translated_images/ro/braille-symbols.0159185ab69d5339.png) +![Imagine Braille](../../../../../translated_images/ro/braille.341962ff76b1bd70.webp) | ![Imagine Braille Preprocesată](../../../../../translated_images/ro/braille-result.46530fea020b03c7.webp) | ![Simboluri Braille](../../../../../translated_images/ro/braille-symbols.0159185ab69d5339.webp) ----|-----|----- > Imagine din [OpenCV.ipynb](OpenCV.ipynb) * **Detectarea mișcării în video folosind diferența dintre cadre**. Dacă camera este fixă, atunci cadrele din fluxul camerei ar trebui să fie destul de similare între ele. Deoarece cadrele sunt reprezentate ca matrice, doar prin scăderea acestor matrice pentru două cadre consecutive vom obține diferența de pixeli, care ar trebui să fie mică pentru cadre statice și să devină mai mare odată ce există o mișcare semnificativă în imagine. -![Imagine a cadrelor video și diferențelor dintre cadre](../../../../../translated_images/ro/frame-difference.706f805491a0883c.png) +![Imagine a cadrelor video și diferențelor dintre cadre](../../../../../translated_images/ro/frame-difference.706f805491a0883c.webp) > Imagine din [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ De asemenea, poți folosi OpenCV pentru a încărca cadre video unul câte unul - **Flux Optic Dens** calculează câmpul vectorial care arată pentru fiecare pixel unde se mișcă. - **Flux Optic Rar** se bazează pe luarea unor caracteristici distinctive din imagine (de exemplu, margini) și construirea traiectoriei lor de la un cadru la altul. -![Imagine a Fluxului Optic](../../../../../translated_images/ro/optical.1f4a94464579a83a.png) +![Imagine a Fluxului Optic](../../../../../translated_images/ro/optical.1f4a94464579a83a.webp) > Imagine din [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/ro/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/ro/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 2a5ba02e..6f93aedd 100644 --- a/translations/ro/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/ro/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 este o rețea care a atins o acuratețe de 92.7% în clasificarea top-5 ImageNet în 2014. Structura sa de straturi este următoarea: -![Straturi ImageNet](../../../../../translated_images/ro/vgg-16-arch1.d901a5583b3a51ba.jpg) +![Straturi ImageNet](../../../../../translated_images/ro/vgg-16-arch1.d901a5583b3a51ba.webp) După cum se poate observa, VGG urmează o arhitectură tradițională de tip piramidă, care constă într-o secvență de straturi de convoluție și pooling. -![Piramida ImageNet](../../../../../translated_images/ro/vgg-16-arch.64ff2137f50dd49f.jpg) +![Piramida ImageNet](../../../../../translated_images/ro/vgg-16-arch.64ff2137f50dd49f.webp) > Imagine de la [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/ro/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/ro/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index f643f148..35951051 100644 --- a/translations/ro/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/ro/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -260,7 +260,7 @@ "\n", "Astfel, într-un CNN tipic, ar exista mai multe straturi de convoluție, cu straturi de pooling între ele pentru a reduce dimensiunile imaginii. De asemenea, am crește numărul de filtre, deoarece, pe măsură ce modelele devin mai complexe, există mai multe combinații interesante pe care trebuie să le căutăm.\n", "\n", - "![O imagine care arată mai multe straturi de convoluție cu straturi de pooling.](../../../../../translated_images/ro/cnn-pyramid.85915455759ef0ce.png)\n", + "![O imagine care arată mai multe straturi de convoluție cu straturi de pooling.](../../../../../translated_images/ro/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "Datorită reducerii dimensiunilor spațiale și creșterii dimensiunilor caracteristicilor/filtrelor, această arhitectură este numită și **arhitectură piramidală**.\n" ] diff --git a/translations/ro/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/ro/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index 8040318e..f3b6b579 100644 --- a/translations/ro/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/ro/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -358,7 +358,7 @@ "\n", "Astfel, într-un CNN tipic, ar exista mai multe straturi de convoluție, cu straturi de pooling între ele pentru a reduce dimensiunile imaginii. De asemenea, am crește numărul de filtre, deoarece, pe măsură ce modelele devin mai complexe, există mai multe combinații interesante pe care trebuie să le căutăm.\n", "\n", - "![O imagine care arată mai multe straturi de convoluție cu straturi de pooling.](../../../../../translated_images/ro/cnn-pyramid.85915455759ef0ce.png)\n", + "![O imagine care arată mai multe straturi de convoluție cu straturi de pooling.](../../../../../translated_images/ro/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "Datorită reducerii dimensiunilor spațiale și creșterii dimensiunilor caracteristicilor/filtrelor, această arhitectură este numită și **arhitectură piramidală**.\n" ] diff --git a/translations/ro/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/ro/lessons/4-ComputerVision/07-ConvNets/README.md index 558c35f3..06cfe96d 100644 --- a/translations/ro/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/ro/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ Am văzut anterior că rețelele neuronale sunt destul de bune la procesarea ima Pentru a extrage tipare, vom folosi noțiunea de **filtre convoluționale**. După cum știți, o imagine este reprezentată printr-o matrice 2D sau un tensor 3D cu adâncime de culoare. Aplicarea unui filtru înseamnă că luăm o matrice relativ mică numită **kernel de filtru**, iar pentru fiecare pixel din imaginea originală calculăm media ponderată cu punctele vecine. Putem privi acest proces ca o fereastră mică care alunecă peste întreaga imagine și calculează media tuturor pixelilor conform greutăților din matricea kernelului de filtru. -![Filtru pentru margini verticale](../../../../../translated_images/ro/filter-vert.b7148390ca0bc356.png) | ![Filtru pentru margini orizontale](../../../../../translated_images/ro/filter-horiz.59b80ed4feb946ef.png) +![Filtru pentru margini verticale](../../../../../translated_images/ro/filter-vert.b7148390ca0bc356.webp) | ![Filtru pentru margini orizontale](../../../../../translated_images/ro/filter-horiz.59b80ed4feb946ef.webp) ----|---- > Imagine de Dmitry Soshnikov @@ -38,7 +38,7 @@ Modul în care funcționează CNN-urile se bazează pe următoarele idei importa * Putem proiecta rețeaua astfel încât filtrele să fie antrenate automat * Putem folosi aceeași abordare pentru a găsi tipare în caracteristici de nivel înalt, nu doar în imaginea originală. Astfel, extragerea caracteristicilor prin CNN funcționează pe o ierarhie de caracteristici, începând de la combinații de pixeli de nivel scăzut, până la combinații de nivel înalt ale părților imaginii. -![Extragerea ierarhică a caracteristicilor](../../../../../translated_images/ro/FeatureExtractionCNN.d9b456cbdae7cb64.png) +![Extragerea ierarhică a caracteristicilor](../../../../../translated_images/ro/FeatureExtractionCNN.d9b456cbdae7cb64.webp) > Imagine din [un articol de Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), bazat pe [cercetarea lor](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ Majoritatea CNN-urilor utilizate pentru procesarea imaginilor urmează o așa-nu Ca exemplu, să analizăm arhitectura VGG-16, o rețea care a obținut o acuratețe de 92.7% în clasificarea top-5 din ImageNet în 2014: -![Straturi ImageNet](../../../../../translated_images/ro/vgg-16-arch1.d901a5583b3a51ba.jpg) +![Straturi ImageNet](../../../../../translated_images/ro/vgg-16-arch1.d901a5583b3a51ba.webp) -![Piramida ImageNet](../../../../../translated_images/ro/vgg-16-arch.64ff2137f50dd49f.jpg) +![Piramida ImageNet](../../../../../translated_images/ro/vgg-16-arch.64ff2137f50dd49f.webp) > Imagine de pe [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/ro/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/ro/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 98a21093..08254cc3 100644 --- a/translations/ro/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/ro/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ Trebuie să antrenezi o rețea neuronală convoluțională pentru a clasifica di Vom folosi [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/), care conține imagini ale 37 de rase diferite de câini și pisici. -![Dataset-ul cu care vom lucra](../../../../../../translated_images/ro/data.50b2a9d5484bdbf0.png) +![Dataset-ul cu care vom lucra](../../../../../../translated_images/ro/data.50b2a9d5484bdbf0.webp) Pentru a descărca dataset-ul, folosește acest fragment de cod: diff --git a/translations/ro/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/ro/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index d469a739..5256562a 100644 --- a/translations/ro/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/ro/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "Pentru a vizualiza pisica ideală, vom începe cu o imagine de zgomot aleatoriu și vom încerca să folosim tehnica de optimizare prin gradient descent pentru a ajusta imaginea astfel încât o rețea să recunoască o pisică.\n", "\n", - "![Bucla de Optimizare](../../../../../translated_images/ro/ideal-cat-loop.999fbb8ff306e044.png)\n", + "![Bucla de Optimizare](../../../../../translated_images/ro/ideal-cat-loop.999fbb8ff306e044.webp)\n", "\n", "Iată imaginea noastră de început:\n" ] diff --git a/translations/ro/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/ro/lessons/4-ComputerVision/08-TransferLearning/README.md index 2e322342..e582da42 100644 --- a/translations/ro/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/ro/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Atât Keras, cât și PyTorch conțin funcții pentru a încărca cu ușurință Iată caracteristici extrase dintr-o imagine cu o pisică de către rețeaua VGG-16: -![Caracteristici extrase de VGG-16](../../../../../translated_images/ro/features.6291f9c7ba3a0b95.png) +![Caracteristici extrase de VGG-16](../../../../../translated_images/ro/features.6291f9c7ba3a0b95.webp) ## Setul de Date Pisici vs. Câini @@ -48,19 +48,19 @@ Rețeaua neuronală pre-antrenată conține diferite modele în "creierul" său, O abordare pe care o putem adopta este să începem cu o imagine aleatorie și apoi să folosim tehnica de optimizare **gradient descent** pentru a ajusta acea imagine astfel încât rețeaua să înceapă să creadă că este o pisică. -![Buclă de Optimizare a Imaginilor](../../../../../translated_images/ro/ideal-cat-loop.999fbb8ff306e044.png) +![Buclă de Optimizare a Imaginilor](../../../../../translated_images/ro/ideal-cat-loop.999fbb8ff306e044.webp) Totuși, dacă facem acest lucru, vom obține ceva foarte asemănător cu un zgomot aleatoriu. Acest lucru se întâmplă deoarece *există multe moduri prin care rețeaua poate crede că imaginea de intrare este o pisică*, inclusiv unele care nu au sens vizual. Deși aceste imagini conțin multe modele tipice pentru o pisică, nu există nimic care să le constrângă să fie distincte vizual. Pentru a îmbunătăți rezultatul, putem adăuga un alt termen în funcția de pierdere, numit **pierdere de variație**. Este o metrică care arată cât de similari sunt pixelii vecini ai imaginii. Minimizarea pierderii de variație face imaginea mai netedă și elimină zgomotul - dezvăluind astfel modele mai atractive vizual. Iată un exemplu de astfel de imagini "ideale", care sunt clasificate ca pisică și ca zebră cu o probabilitate mare: -![Pisică Ideală](../../../../../translated_images/ro/ideal-cat.203dd4597643d6b0.png) | ![Zebră Ideală](../../../../../translated_images/ro/ideal-zebra.7f70e8b54ee15a7a.png) +![Pisică Ideală](../../../../../translated_images/ro/ideal-cat.203dd4597643d6b0.webp) | ![Zebră Ideală](../../../../../translated_images/ro/ideal-zebra.7f70e8b54ee15a7a.webp) -----|----- *Pisică Ideală* | *Zebră Ideală* O abordare similară poate fi utilizată pentru a efectua așa-numitele **atacuri adversariale** asupra unei rețele neuronale. Să presupunem că dorim să păcălim o rețea neuronală și să facem un câine să arate ca o pisică. Dacă luăm imaginea unui câine, care este recunoscută de rețea ca fiind un câine, putem apoi să o ajustăm puțin folosind optimizarea gradient descent, până când rețeaua începe să o clasifice ca fiind o pisică: -![Imaginea unui Câine](../../../../../translated_images/ro/original-dog.8f68a67d2fe0911f.png) | ![Imaginea unui câine clasificat ca pisică](../../../../../translated_images/ro/adversarial-dog.d9fc7773b0142b89.png) +![Imaginea unui Câine](../../../../../translated_images/ro/original-dog.8f68a67d2fe0911f.webp) | ![Imaginea unui câine clasificat ca pisică](../../../../../translated_images/ro/adversarial-dog.d9fc7773b0142b89.webp) -----|----- *Imagine originală a unui câine* | *Imaginea unui câine clasificat ca pisică* diff --git a/translations/ro/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/ro/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index 362b1386..2115db84 100644 --- a/translations/ro/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/ro/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "Deoarece antrenăm autoencoderul să capteze cât mai multe informații din imaginea originală pentru o reconstrucție precisă, rețeaua încearcă să găsească cea mai bună **reprezentare** a imaginilor de intrare pentru a surprinde semnificația.\n", "\n", - "![Diagrama AutoEncoder](../../../../../translated_images/ro/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![Diagrama AutoEncoder](../../../../../translated_images/ro/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "> Imagine de pe [blogul Keras](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", diff --git a/translations/ro/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/ro/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index 9c1c2ded..dd0c92d6 100644 --- a/translations/ro/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/ro/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "Deoarece antrenăm autoencoderul să capteze cât mai multe informații din imaginea originală pentru o reconstrucție precisă, rețeaua încearcă să găsească cea mai bună **reprezentare** a imaginilor de intrare pentru a surprinde semnificația.\n", "\n", - "![Diagrama AutoEncoder](../../../../../translated_images/ro/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![Diagrama AutoEncoder](../../../../../translated_images/ro/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "*Imagine preluată de pe [blogul Keras](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", diff --git a/translations/ro/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/ro/lessons/4-ComputerVision/09-Autoencoders/README.md index cf74e4c1..a5ac5fe7 100644 --- a/translations/ro/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/ro/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ Totuși, s-ar putea să dorim să folosim date brute (neetichetate) pentru a ant Deoarece antrenăm un autoencoder pentru a captura cât mai multă informație din imaginea originală pentru o reconstrucție precisă, rețeaua încearcă să găsească cea mai bună **reprezentare** a imaginilor de intrare pentru a surprinde semnificația acestora. -![Diagrama Autoencoder](../../../../../translated_images/ro/autoencoder_schema.5e6fc9ad98a5eb61.jpg) +![Diagrama Autoencoder](../../../../../translated_images/ro/autoencoder_schema.5e6fc9ad98a5eb61.webp) > Imagine de pe [blogul Keras](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/ro/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/ro/lessons/4-ComputerVision/11-ObjectDetection/README.md index 20adf2ef..5abcd2e0 100644 --- a/translations/ro/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/ro/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ Modelele de clasificare a imaginilor pe care le-am abordat până acum au luat o ## [Chestionar înainte de lecție](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Detectarea Obiectelor](../../../../../translated_images/ro/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) +![Detectarea Obiectelor](../../../../../translated_images/ro/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp) > Imagine de pe [site-ul YOLO v2](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ Presupunând că dorim să găsim o pisică într-o imagine, o abordare foarte n 2. Aplicăm clasificarea imaginilor pe fiecare secțiune. 3. Secțiunile care generează o activare suficient de mare pot fi considerate ca conținând obiectul în cauză. -![Detectare Naivă a Obiectelor](../../../../../translated_images/ro/naive-detection.e7f1ba220ccd08c6.png) +![Detectare Naivă a Obiectelor](../../../../../translated_images/ro/naive-detection.e7f1ba220ccd08c6.webp) > *Imagine din [Notebook-ul de exerciții](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ Este posibil să întâlniți următoarele seturi de date pentru această sarcin * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 clase * [COCO](http://cocodataset.org/#home) - Obiecte Comune în Context. 80 clase, casete de delimitare și măști de segmentare -![COCO](../../../../../translated_images/ro/coco-examples.71bc60380fa6cceb.jpg) +![COCO](../../../../../translated_images/ro/coco-examples.71bc60380fa6cceb.webp) ## Metrice pentru Detectarea Obiectelor @@ -50,7 +50,7 @@ Este posibil să întâlniți următoarele seturi de date pentru această sarcin În timp ce pentru clasificarea imaginilor este ușor să măsurăm cât de bine performează algoritmul, pentru detectarea obiectelor trebuie să măsurăm atât corectitudinea clasei, cât și precizia locației casetei de delimitare inferate. Pentru aceasta din urmă, folosim așa-numita **Intersecția peste Uniune** (IoU), care măsoară cât de bine se suprapun două casete (sau două zone arbitrare). -![IoU](../../../../../translated_images/ro/iou_equation.9a4751d40fff4e11.png) +![IoU](../../../../../translated_images/ro/iou_equation.9a4751d40fff4e11.webp) > *Figura 2 din [acest articol excelent despre IoU](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ Există două clase largi de algoritmi de detectare a obiectelor: [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) folosește [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) pentru a genera o structură ierarhică de regiuni ROI, care sunt apoi trecute prin extractoare de caracteristici CNN și clasificatoare SVM pentru a determina clasa obiectului, și regresie liniară pentru a determina coordonatele *casetei de delimitare*. [Lucrare oficială](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/ro/rcnn1.cae407020dfb1d1f.png) +![RCNN](../../../../../translated_images/ro/rcnn1.cae407020dfb1d1f.webp) > *Imagine de van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/ro/rcnn2.2d9530bb83516484.png) +![RCNN-1](../../../../../translated_images/ro/rcnn2.2d9530bb83516484.webp) > *Imagini din [acest blog](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -110,7 +110,7 @@ Există două clase largi de algoritmi de detectare a obiectelor: Această abordare este similară cu R-CNN, dar regiunile sunt definite după ce straturile de convoluție au fost aplicate. -![FRCNN](../../../../../translated_images/ro/f-rcnn.3cda6d9bb4188875.png) +![FRCNN](../../../../../translated_images/ro/f-rcnn.3cda6d9bb4188875.webp) > Imagine din [Lucrarea Oficială](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ Această abordare este similară cu R-CNN, dar regiunile sunt definite după ce Ideea principală a acestei abordări este de a folosi o rețea neuronală pentru a prezice ROI-urile - așa-numita *Rețea de Propunere a Regiunilor*. [Lucrare](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/ro/faster-rcnn.8d46c099b87ef30a.png) +![FasterRCNN](../../../../../translated_images/ro/faster-rcnn.8d46c099b87ef30a.webp) > Imagine din [lucrarea oficială](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ Acest algoritm este chiar mai rapid decât Faster R-CNN. Ideea principală este 2. Caracteristicile sunt procesate de **Position-Sensitive Score Map**. Fiecare obiect din $C$ clase este împărțit în regiuni $k\times k$, și antrenăm pentru a prezice părți ale obiectelor. 3. Pentru fiecare parte din regiunile $k\times k$, toate rețelele votează pentru clasele de obiecte, iar clasa de obiect cu votul maxim este selectată. -![r-fcn image](../../../../../translated_images/ro/r-fcn.13eb88158b99a3da.png) +![r-fcn image](../../../../../translated_images/ro/r-fcn.13eb88158b99a3da.webp) > Imagine din [lucrarea oficială](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO este un algoritm în timp real, cu o singură trecere. Ideea principală es * Imaginea este împărțită în regiuni $S\times S$. * Pentru fiecare regiune, **CNN** prezice $n$ obiecte posibile, coordonatele *casetei de delimitare* și *încrederea*=*probabilitatea* * IoU. - ![YOLO](../../../../../translated_images/ro/yolo.a2648ec82ee8bb4e.png) + ![YOLO](../../../../../translated_images/ro/yolo.a2648ec82ee8bb4e.webp) > Imagine din [lucrarea oficială](https://arxiv.org/abs/1506.02640) diff --git a/translations/ro/lessons/4-ComputerVision/README.md b/translations/ro/lessons/4-ComputerVision/README.md index 1a8b3b71..6c1f6641 100644 --- a/translations/ro/lessons/4-ComputerVision/README.md +++ b/translations/ro/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Viziune Computerizată -![Rezumat al conținutului despre Viziune Computerizată într-un desen](../../../../translated_images/ro/ai-computervision.6506ebebac3fbf76.png) +![Rezumat al conținutului despre Viziune Computerizată într-un desen](../../../../translated_images/ro/ai-computervision.6506ebebac3fbf76.webp) În această secțiune vom învăța despre: diff --git a/translations/ro/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/ro/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index 1c6b2204..b904b7c9 100644 --- a/translations/ro/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/ro/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "Reprezentarea vectorială **Bag of Words** (BoW) este cea mai utilizată metodă tradițională de reprezentare vectorială. Fiecare cuvânt este asociat unui index vectorial, iar elementul vectorului conține numărul de apariții ale unui cuvânt într-un document dat.\n", "\n", - "![Imagine care arată cum este reprezentată în memorie metoda bag of words.](../../../../../translated_images/ro/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![Imagine care arată cum este reprezentată în memorie metoda bag of words.](../../../../../translated_images/ro/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Note**: Poți să te gândești la BoW și ca la o sumă a tuturor vectorilor one-hot-encoded pentru cuvintele individuale din text.\n", "\n", diff --git a/translations/ro/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/ro/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index c7f493ee..a1ceb586 100644 --- a/translations/ro/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/ro/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "Reprezentarea vectorială **Bag-of-words** (BoW) este cea mai simplă de înțeles dintre reprezentările vectoriale tradiționale. Fiecare cuvânt este asociat unui index vectorial, iar un element al vectorului conține numărul de apariții ale fiecărui cuvânt într-un document dat.\n", "\n", - "![Imagine care arată cum este reprezentată în memorie o reprezentare vectorială bag-of-words.](../../../../../translated_images/ro/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![Imagine care arată cum este reprezentată în memorie o reprezentare vectorială bag-of-words.](../../../../../translated_images/ro/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Note**: Poți considera BoW și ca o sumă a tuturor vectorilor one-hot-encoded pentru cuvintele individuale din text.\n", "\n", diff --git a/translations/ro/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/ro/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 24a1ed3c..38a3e3b2 100644 --- a/translations/ro/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/ro/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "Folosind stratul de embedding ca prim strat în rețeaua noastră, putem trece de la modelul bag-of-words la modelul **embedding bag**, unde mai întâi convertim fiecare cuvânt din textul nostru în embedding-ul corespunzător, iar apoi calculăm o funcție de agregare peste toate aceste embedding-uri, cum ar fi `sum`, `average` sau `max`.\n", "\n", - "![Imagine care arată un clasificator embedding pentru cinci cuvinte dintr-o secvență.](../../../../../translated_images/ro/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![Imagine care arată un clasificator embedding pentru cinci cuvinte dintr-o secvență.](../../../../../translated_images/ro/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "Rețeaua noastră neuronală de clasificare va începe cu un strat de embedding, urmat de un strat de agregare și un clasificator liniar deasupra acestuia:\n" ] @@ -176,7 +176,7 @@ "\n", "În arhitectura anterioară, a fost necesar să completăm toate secvențele la aceeași lungime pentru a le încadra într-un minibatch. Aceasta nu este cea mai eficientă metodă de a reprezenta secvențele de lungime variabilă - o altă abordare ar fi utilizarea unui vector de **offset**, care ar conține offset-urile tuturor secvențelor stocate într-un singur vector mare.\n", "\n", - "![Imagine care arată o reprezentare a secvențelor cu offset](../../../../../translated_images/ro/offset-sequence-representation.eb73fcefb29b46ee.png)\n", + "![Imagine care arată o reprezentare a secvențelor cu offset](../../../../../translated_images/ro/offset-sequence-representation.eb73fcefb29b46ee.webp)\n", "\n", "> **Note**: În imaginea de mai sus, este prezentată o secvență de caractere, dar în exemplul nostru lucrăm cu secvențe de cuvinte. Totuși, principiul general de reprezentare a secvențelor cu un vector de offset rămâne același.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW este mai rapid, în timp ce skip-gram este mai lent, dar oferă o reprezentare mai bună pentru cuvintele rare.\n", "\n", - "![Imagine care arată algoritmii CBoW și Skip-Gram pentru conversia cuvintelor în vectori.](../../../../../translated_images/ro/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![Imagine care arată algoritmii CBoW și Skip-Gram pentru conversia cuvintelor în vectori.](../../../../../translated_images/ro/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Pentru a experimenta cu încapsulările word2vec pre-antrenate pe setul de date Google News, putem folosi biblioteca **gensim**. Mai jos găsim cuvintele cele mai similare cu 'neural'.\n", "\n", diff --git a/translations/ro/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/ro/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index 178212d2..5e185f4a 100644 --- a/translations/ro/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/ro/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "Folosind un strat de embedding ca prim strat în rețeaua noastră, putem trece de la modelul bag-of-words la un model **embedding bag**, unde mai întâi convertim fiecare cuvânt din textul nostru în embedding-ul corespunzător, iar apoi calculăm o funcție agregată pentru toate aceste embedding-uri, cum ar fi `sum`, `average` sau `max`.\n", "\n", - "![Imagine care arată un clasificator embedding pentru cinci cuvinte dintr-o secvență.](../../../../../translated_images/ro/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![Imagine care arată un clasificator embedding pentru cinci cuvinte dintr-o secvență.](../../../../../translated_images/ro/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "Rețeaua noastră neurală de clasificare constă din următoarele straturi:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW este mai rapid, iar skip-gram, deși mai lent, face o treabă mai bună în reprezentarea cuvintelor rare.\n", "\n", - "![Imagine care ilustrează algoritmii CBoW și Skip-Gram pentru conversia cuvintelor în vectori.](../../../../../translated_images/ro/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![Imagine care ilustrează algoritmii CBoW și Skip-Gram pentru conversia cuvintelor în vectori.](../../../../../translated_images/ro/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Pentru a experimenta cu încapsularea Word2Vec preantrenată pe setul de date Google News, putem folosi biblioteca **gensim**. Mai jos găsim cuvintele cele mai similare cu 'neural'.\n", "\n", diff --git a/translations/ro/lessons/5-NLP/14-Embeddings/README.md b/translations/ro/lessons/5-NLP/14-Embeddings/README.md index 6d139e2a..ff7e8a59 100644 --- a/translations/ro/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/ro/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ Astfel, stratul de încapsulare ar lua un cuvânt ca intrare și ar produce un v Folosind un strat de încapsulare ca prim strat în rețeaua noastră de clasificare, putem trece de la un model bag-of-words la un model **embedding bag**, unde mai întâi convertim fiecare cuvânt din textul nostru în încapsularea corespunzătoare, și apoi calculăm o funcție agregată peste toate aceste încapsulări, cum ar fi `sum`, `average` sau `max`. -![Imagine care arată un clasificator bazat pe încapsulări pentru cinci cuvinte dintr-o secvență.](../../../../../translated_images/ro/embedding-classifier-example.b77f021a7ee67eee.png) +![Imagine care arată un clasificator bazat pe încapsulări pentru cinci cuvinte dintr-o secvență.](../../../../../translated_images/ro/embedding-classifier-example.b77f021a7ee67eee.webp) > Imagine realizată de autor @@ -40,7 +40,7 @@ Pentru a face acest lucru, trebuie să pre-antrenăm modelul de încapsulare pe CBoW este mai rapid, în timp ce skip-gram este mai lent, dar face o treabă mai bună în reprezentarea cuvintelor rare. -![Imagine care arată algoritmii CBoW și Skip-Gram pentru conversia cuvintelor în vectori.](../../../../../translated_images/ro/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![Imagine care arată algoritmii CBoW și Skip-Gram pentru conversia cuvintelor în vectori.](../../../../../translated_images/ro/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Imagine din [acest articol](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/ro/lessons/5-NLP/15-LanguageModeling/README.md b/translations/ro/lessons/5-NLP/15-LanguageModeling/README.md index ed66bec3..fc74277d 100644 --- a/translations/ro/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/ro/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ Ideea principală din spatele modelării limbajului este antrenarea acestora pe * **Continuous Bag-of-Words** (CBoW), când prezicem tokenul din mijloc $W_0$ într-o secvență de tokeni $W_{-N}$, ..., $W_N$. * **Skip-gram**, unde prezicem un set de tokeni vecini {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} pornind de la tokenul din mijloc $W_0$. -![imagine dintr-un articol despre convertirea cuvintelor în vectori](../../../../../translated_images/ro/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![imagine dintr-un articol despre convertirea cuvintelor în vectori](../../../../../translated_images/ro/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Imagine din [acest articol](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/ro/lessons/5-NLP/16-RNN/README.md b/translations/ro/lessons/5-NLP/16-RNN/README.md index d1a2745a..0db9d938 100644 --- a/translations/ro/lessons/5-NLP/16-RNN/README.md +++ b/translations/ro/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: Pentru a capta sensul unei secvențe de text, trebuie să utilizăm o altă arhitectură de rețea neuronală, numită **rețea neuronală recurentă**, sau RNN. În RNN, trecem propoziția prin rețea, un simbol la un moment dat, iar rețeaua produce un **stare**, pe care o trecem din nou prin rețea împreună cu următorul simbol. -![RNN](../../../../../translated_images/ro/rnn.27f5c29c53d727b5.png) +![RNN](../../../../../translated_images/ro/rnn.27f5c29c53d727b5.webp) > Imagine realizată de autor @@ -61,7 +61,7 @@ Am discutat despre rețelele recurente care operează într-o singură direcție O rețea recurentă, fie unidirecțională, fie bidirecțională, captează anumite modele dintr-o secvență și le poate stoca într-un vector de stare sau le poate transmite ca ieșire. La fel ca în cazul rețelelor convoluționale, putem construi un alt strat recurent deasupra primului pentru a capta modele de nivel superior și a construi din modelele de nivel inferior extrase de primul strat. Acest lucru ne conduce la noțiunea de **RNN multistrat**, care constă din două sau mai multe rețele recurente, unde ieșirea stratului anterior este transmisă stratului următor ca intrare. -![Imagine care arată un RNN LSTM multistrat](../../../../../translated_images/ro/multi-layer-lstm.dd975e29bb2a59fe.jpg) +![Imagine care arată un RNN LSTM multistrat](../../../../../translated_images/ro/multi-layer-lstm.dd975e29bb2a59fe.webp) *Imagine din [acest articol minunat](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) de Fernando López* diff --git a/translations/ro/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/ro/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index fd1bac21..4844cb6d 100644 --- a/translations/ro/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/ro/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "O rețea recurentă, fie unidirecțională, fie bidirecțională, captează anumite modele dintr-o secvență și le poate stoca în vectorul de stare sau le poate transmite în ieșire. La fel ca în cazul rețelelor convoluționale, putem construi un alt strat recurent deasupra primului pentru a capta modele de nivel superior, construite din modelele de nivel inferior extrase de primul strat. Acest lucru ne conduce la conceptul de **RNN multilayer**, care constă din două sau mai multe rețele recurente, unde ieșirea stratului anterior este transmisă stratului următor ca intrare.\n", "\n", - "![Imagine care arată un RNN multilayer cu memorie pe termen lung și scurt](../../../../../translated_images/ro/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![Imagine care arată un RNN multilayer cu memorie pe termen lung și scurt](../../../../../translated_images/ro/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*Imagine din [această postare minunată](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) de Fernando López*\n", "\n", diff --git a/translations/ro/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/ro/lessons/5-NLP/16-RNN/RNNTF.ipynb index cb47e9d1..b817a9a7 100644 --- a/translations/ro/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/ro/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "Pentru a capta semnificația unei secvențe de text, vom folosi o arhitectură de rețea neuronală numită **rețea neuronală recurentă**, sau RNN. Când utilizăm o RNN, trecem propoziția prin rețea, un token pe rând, iar rețeaua produce un **statut**, pe care îl transmitem din nou rețelei împreună cu următorul token.\n", "\n", - "![Imagine care arată un exemplu de generare a unei rețele neuronale recurente.](../../../../../translated_images/ro/rnn.27f5c29c53d727b5.png)\n", + "![Imagine care arată un exemplu de generare a unei rețele neuronale recurente.](../../../../../translated_images/ro/rnn.27f5c29c53d727b5.webp)\n", "\n", "Având secvența de intrare de tokeni $X_0,\\dots,X_n$, RNN creează o secvență de blocuri de rețea neuronală și antrenează această secvență cap-coadă folosind retropropagarea. Fiecare bloc de rețea ia o pereche $(X_i,S_i)$ ca intrare și produce $S_{i+1}$ ca rezultat. Statutul final $S_n$ sau ieșirea $Y_n$ este transmisă unui clasificator liniar pentru a produce rezultatul. Toate blocurile de rețea împărtășesc aceleași greutăți și sunt antrenate cap-coadă folosind o singură trecere de retropropagare.\n", "\n", @@ -369,7 +369,7 @@ "\n", "Rețelele recurente, unidirecționale sau bidirecționale, captează modele dintr-o secvență și le stochează în vectori de stare sau le returnează ca ieșire. La fel ca în cazul rețelelor convoluționale, putem construi un alt strat recurent care urmează primului pentru a capta modele de nivel superior, construite din modelele de nivel inferior extrase de primul strat. Acest lucru ne conduce la noțiunea de **RNN multilayer**, care constă din două sau mai multe rețele recurente, unde ieșirea stratului anterior este transmisă stratului următor ca intrare.\n", "\n", - "![Imagine care arată un RNN multilayer cu memorie pe termen lung și scurt](../../../../../translated_images/ro/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![Imagine care arată un RNN multilayer cu memorie pe termen lung și scurt](../../../../../translated_images/ro/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*Imagine din [această postare minunată](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) de Fernando López.*\n", "\n", diff --git a/translations/ro/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/ro/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index 5d5858b6..2c41f10c 100644 --- a/translations/ro/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/ro/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "Modul în care vom antrena RNN pentru a genera text este următorul. La fiecare pas, vom lua o secvență de caractere de lungime `nchars` și vom cere rețelei să genereze următorul caracter de ieșire pentru fiecare caracter de intrare:\n", "\n", - "![Imagine care arată un exemplu de generare RNN a cuvântului 'HELLO'.](../../../../../translated_images/ro/rnn-generate.56c54afb52f9781d.png)\n", + "![Imagine care arată un exemplu de generare RNN a cuvântului 'HELLO'.](../../../../../translated_images/ro/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "În funcție de scenariul concret, este posibil să dorim să includem și câteva caractere speciale, cum ar fi *sfârșit-de-secvență* ``. În cazul nostru, dorim doar să antrenăm rețeaua pentru generarea continuă de text, așa că vom fixa dimensiunea fiecărei secvențe să fie egală cu `nchars` tokeni. Prin urmare, fiecare exemplu de antrenament va consta din `nchars` intrări și `nchars` ieșiri (care sunt secvența de intrare deplasată cu un simbol spre stânga). Un minibatch va consta din mai multe astfel de secvențe.\n", "\n", diff --git a/translations/ro/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/ro/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index e237640d..eeef8fd5 100644 --- a/translations/ro/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/ro/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -111,7 +111,7 @@ "\n", "Modul în care vom antrena RNN pentru a genera titluri de știri este următorul. La fiecare pas, vom lua un titlu, care va fi introdus într-un RNN, iar pentru fiecare caracter de intrare vom cere rețelei să genereze următorul caracter de ieșire:\n", "\n", - "![Imagine care arată un exemplu de generare RNN a cuvântului 'HELLO'.](../../../../../translated_images/ro/rnn-generate.56c54afb52f9781d.png)\n", + "![Imagine care arată un exemplu de generare RNN a cuvântului 'HELLO'.](../../../../../translated_images/ro/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "Pentru ultimul caracter al secvenței noastre, vom cere rețelei să genereze token-ul ``.\n", "\n", diff --git a/translations/ro/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/ro/lessons/5-NLP/17-GenerativeNetworks/README.md index cda5ba64..24be20d2 100644 --- a/translations/ro/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/ro/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ Rețelele Neuronale Recurente (RNN) și variantele lor cu celule cu porți, cum Acest lucru permite diferite arhitecturi neuronale, așa cum sunt prezentate în imaginea de mai jos: -![Imagine care arată modele comune de rețele neuronale recurente.](../../../../../translated_images/ro/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Imagine care arată modele comune de rețele neuronale recurente.](../../../../../translated_images/ro/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > Imagine din articolul [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) de [Andrej Karpathy](http://karpathy.github.io/) @@ -32,7 +32,7 @@ Acest lucru permite diferite arhitecturi neuronale, așa cum sunt prezentate în Vom antrena acest RNN pentru a genera text pas cu pas. La fiecare pas, vom lua o secvență de caractere de lungime `nchars` și vom cere rețelei să genereze următorul caracter de ieșire pentru fiecare caracter de intrare: -![Imagine care arată un exemplu de generare RNN a cuvântului 'HELLO'.](../../../../../translated_images/ro/rnn-generate.56c54afb52f9781d.png) +![Imagine care arată un exemplu de generare RNN a cuvântului 'HELLO'.](../../../../../translated_images/ro/rnn-generate.56c54afb52f9781d.webp) Când generăm text (în timpul inferenței), începem cu un **prompt**, care este trecut prin celulele RNN pentru a genera starea intermediară, iar apoi, din această stare, începe generarea. Generăm un caracter pe rând și transmitem starea și caracterul generat unei alte celule RNN pentru a genera următorul, până când generăm suficiente caractere. diff --git a/translations/ro/lessons/5-NLP/18-Transformers/README.md b/translations/ro/lessons/5-NLP/18-Transformers/README.md index 8ef2f2f9..3824bf1c 100644 --- a/translations/ro/lessons/5-NLP/18-Transformers/README.md +++ b/translations/ro/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ Cu RNN-uri, sarcinile de tip sequence-to-sequence sunt implementate prin două r **Mecanismele de Atenție** oferă o modalitate de a pondera impactul contextual al fiecărui vector de intrare asupra fiecărei predicții de ieșire a RNN-ului. Modul în care este implementat constă în crearea unor scurtături între stările intermediare ale RNN-ului de intrare și RNN-ul de ieșire. Astfel, atunci când generăm simbolul de ieșire yt, vom lua în considerare toate stările ascunse de intrare hi, cu diferiți coeficienți de greutate αt,i. -![Imagine care arată un model encoder/decoder cu un strat de atenție aditiv](../../../../../translated_images/ro/encoder-decoder-attention.7a726296894fb567.png) +![Imagine care arată un model encoder/decoder cu un strat de atenție aditiv](../../../../../translated_images/ro/encoder-decoder-attention.7a726296894fb567.webp) > Modelul encoder-decoder cu mecanism de atenție aditiv din [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citat din [acest articol de blog](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) Matricea de atenție {αi,j} ar reprezenta gradul în care anumite cuvinte de intrare contribuie la generarea unui cuvânt dat în secvența de ieșire. Mai jos este un exemplu al unei astfel de matrice: -![Imagine care arată o aliniere exemplară găsită de RNNsearch-50, preluată din Bahdanau - arviz.org](../../../../../translated_images/ro/bahdanau-fig3.09ba2d37f202a6af.png) +![Imagine care arată o aliniere exemplară găsită de RNNsearch-50, preluată din Bahdanau - arviz.org](../../../../../translated_images/ro/bahdanau-fig3.09ba2d37f202a6af.webp) > Figură din [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) @@ -66,7 +66,7 @@ Rezultatul pe care îl obținem cu încorporarea pozițională încorporează at Următorul pas este capturarea unor tipare în cadrul secvenței noastre. Pentru a face acest lucru, transformerele folosesc un mecanism de **auto-atenție**, care este, în esență, atenție aplicată aceleași secvențe ca intrare și ieșire. Aplicarea auto-atenției ne permite să luăm în considerare **contextul** din propoziție și să vedem care cuvinte sunt inter-relaționate. De exemplu, ne permite să vedem care cuvinte sunt referite prin coreferințe, cum ar fi *it*, și să luăm contextul în considerare: -![](../../../../../translated_images/ro/CoreferenceResolution.861924d6d384a7d6.png) +![](../../../../../translated_images/ro/CoreferenceResolution.861924d6d384a7d6.webp) > Imagine din [Blogul Google](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Deoarece fiecare poziție de intrare este mapată independent la fiecare poziți **BERT** (Bidirectional Encoder Representations from Transformers) este o rețea transformer foarte mare, cu mai multe straturi: 12 straturi pentru *BERT-base* și 24 pentru *BERT-large*. Modelul este mai întâi pre-antrenat pe un corpus mare de date text (Wikipedia + cărți) folosind antrenare nesupravegheată (prezicerea cuvintelor mascate într-o propoziție). În timpul pre-antrenării, modelul dobândește niveluri semnificative de înțelegere a limbajului, care pot fi apoi utilizate cu alte seturi de date prin ajustare fină. Acest proces se numește **învățare transferabilă**. -![imagine de pe http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/ro/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) +![imagine de pe http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/ro/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > Imagine [sursă](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/ro/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/ro/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index bd1c9084..5d6b6e0d 100644 --- a/translations/ro/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/ro/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**Mecanismele de atenție** oferă o modalitate de a pondera impactul contextual al fiecărui vector de intrare asupra fiecărei predicții de ieșire a RNN-ului. Acest lucru este implementat prin crearea unor scurtături între stările intermediare ale RNN-ului de intrare și RNN-ului de ieșire. Astfel, atunci când generăm simbolul de ieșire $y_t$, vom lua în considerare toate stările ascunse de intrare $h_i$, cu coeficienți de greutate diferiți $\\alpha_{t,i}$.\n", "\n", - "![Imagine care arată un model encoder/decoder cu un strat de atenție aditiv](../../../../../translated_images/ro/encoder-decoder-attention.7a726296894fb567.png)\n", + "![Imagine care arată un model encoder/decoder cu un strat de atenție aditiv](../../../../../translated_images/ro/encoder-decoder-attention.7a726296894fb567.webp)\n", "*Modelul encoder-decoder cu mecanism de atenție aditiv din [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citat din [acest articol de blog](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Matricea de atenție $\\{\\alpha_{i,j}\\}$ ar reprezenta gradul în care anumite cuvinte de intrare contribuie la generarea unui cuvânt dat în secvența de ieșire. Mai jos este un exemplu al unei astfel de matrice:\n", "\n", - "![Imagine care arată o aliniere exemplară găsită de RNNsearch-50, preluată din Bahdanau - arviz.org](../../../../../translated_images/ro/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![Imagine care arată o aliniere exemplară găsită de RNNsearch-50, preluată din Bahdanau - arviz.org](../../../../../translated_images/ro/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*Figura preluată din [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) este o rețea transformatoare foarte mare, cu mai multe straturi: 12 straturi pentru *BERT-base* și 24 pentru *BERT-large*. Modelul este mai întâi pre-antrenat pe un corpus mare de date text (Wikipedia + cărți) folosind antrenare nesupravegheată (prezicerea cuvintelor mascate într-o propoziție). În timpul pre-antrenării, modelul absoarbe un nivel semnificativ de înțelegere a limbajului, care poate fi apoi valorificat cu alte seturi de date prin ajustare fină. Acest proces se numește **învățare transferabilă**.\n", "\n", - "![Imagine de pe http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/ro/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![Imagine de pe http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/ro/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "Există multe variații ale arhitecturilor Transformatoare, inclusiv BERT, DistilBERT, BigBird, OpenGPT3 și altele, care pot fi ajustate fin. Pachetul [HuggingFace](https://github.com/huggingface/) oferă un depozit pentru antrenarea multora dintre aceste arhitecturi cu PyTorch.\n", "\n", diff --git a/translations/ro/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/ro/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index dd106609..52023e00 100644 --- a/translations/ro/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/ro/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**Mecanismele de atenție** oferă o modalitate de a pondera impactul contextual al fiecărui vector de intrare asupra fiecărei predicții de ieșire a RNN-ului. Acest lucru este implementat prin crearea unor scurtături între stările intermediare ale RNN-ului de intrare și RNN-ului de ieșire. Astfel, atunci când generăm simbolul de ieșire $y_t$, vom lua în considerare toate stările ascunse de intrare $h_i$, cu coeficienți de greutate diferiți $\\alpha_{t,i}$. \n", "\n", - "![Imagine care arată un model encoder/decoder cu un strat de atenție aditiv](../../../../../translated_images/ro/encoder-decoder-attention.7a726296894fb567.png)\n", + "![Imagine care arată un model encoder/decoder cu un strat de atenție aditiv](../../../../../translated_images/ro/encoder-decoder-attention.7a726296894fb567.webp)\n", "*Modelul encoder-decoder cu mecanism de atenție aditiv din [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citat din [acest articol de blog](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Matricea de atenție $\\{\\alpha_{i,j}\\}$ reprezintă gradul în care anumite cuvinte de intrare contribuie la generarea unui cuvânt dat în secvența de ieșire. Mai jos este un exemplu al unei astfel de matrici:\n", "\n", - "![Imagine care arată o aliniere exemplară găsită de RNNsearch-50, preluată din Bahdanau - arviz.org](../../../../../translated_images/ro/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![Imagine care arată o aliniere exemplară găsită de RNNsearch-50, preluată din Bahdanau - arviz.org](../../../../../translated_images/ro/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*Figura preluată din [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3)*\n", "\n", @@ -229,7 +229,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) este o rețea de transformatoare foarte mare, cu mai multe straturi, având 12 straturi pentru *BERT-base* și 24 pentru *BERT-large*. Modelul este mai întâi pre-antrenat pe un corpus mare de date textuale (Wikipedia + cărți) folosind antrenare nesupravegheată (prezicerea cuvintelor mascate într-o propoziție). În timpul pre-antrenării, modelul dobândește un nivel semnificativ de înțelegere a limbajului, care poate fi apoi valorificat cu alte seturi de date prin ajustare fină. Acest proces se numește **învățare transferabilă**.\n", "\n", - "![imagine de pe http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/ro/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![imagine de pe http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/ro/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "Există multe variații ale arhitecturilor Transformer, inclusiv BERT, DistilBERT, BigBird, OpenGPT3 și altele, care pot fi ajustate fin.\n", "\n", diff --git a/translations/ro/lessons/5-NLP/19-NER/README.md b/translations/ro/lessons/5-NLP/19-NER/README.md index a389bf14..96ac9750 100644 --- a/translations/ro/lessons/5-NLP/19-NER/README.md +++ b/translations/ro/lessons/5-NLP/19-NER/README.md @@ -58,7 +58,7 @@ infant | O Deoarece trebuie să construim o corespondență unu-la-unu între token-uri și clase, putem antrena un model neuronal **many-to-many** din această imagine: -![Imagine care arată modele comune de rețele neuronale recurente.](../../../../../translated_images/ro/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Imagine care arată modele comune de rețele neuronale recurente.](../../../../../translated_images/ro/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > *Imagine din [acest articol de blog](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) de [Andrej Karpathy](http://karpathy.github.io/). Modelele de clasificare de token-uri NER corespund arhitecturii de rețea din partea dreaptă a imaginii.* diff --git a/translations/ro/lessons/5-NLP/README.md b/translations/ro/lessons/5-NLP/README.md index a8c4513a..e248bc1e 100644 --- a/translations/ro/lessons/5-NLP/README.md +++ b/translations/ro/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Procesarea Limbajului Natural -![Rezumat al sarcinilor NLP într-un desen](../../../../translated_images/ro/ai-nlp.b22dcb8ca4707cea.png) +![Rezumat al sarcinilor NLP într-un desen](../../../../translated_images/ro/ai-nlp.b22dcb8ca4707cea.webp) În această secțiune, ne vom concentra pe utilizarea Rețelelor Neuronale pentru a rezolva sarcini legate de **Procesarea Limbajului Natural (NLP)**. Există multe probleme NLP pe care ne dorim ca calculatoarele să le poată rezolva: diff --git a/translations/ro/lessons/6-Other/23-MultiagentSystems/README.md b/translations/ro/lessons/6-Other/23-MultiagentSystems/README.md index 7e8b443c..42e3140d 100644 --- a/translations/ro/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/ro/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ Poți deschide unul dintre modele, de exemplu **Biology → Flocking**. După ce deschizi modelul, vei fi dus la ecranul principal NetLogo. Iată un model exemplu care descrie populația de lupi și oi, având resurse finite (iarbă). -![Ecranul principal NetLogo](../../../../../translated_images/ro/NetLogo-Main.32653711ec1a01b3.png) +![Ecranul principal NetLogo](../../../../../translated_images/ro/NetLogo-Main.32653711ec1a01b3.webp) > Captură de ecran de Dmitry Soshnikov diff --git a/translations/ro/lessons/README.md b/translations/ro/lessons/README.md index 0fd97600..c1ddd4c4 100644 --- a/translations/ro/lessons/README.md +++ b/translations/ro/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Prezentare generală -![Prezentare generală într-un desen](../../../translated_images/ro/ai-overview.0857791951d19500.png) +![Prezentare generală într-un desen](../../../translated_images/ro/ai-overview.0857791951d19500.webp) > Notiță ilustrată de [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/ro/lessons/X-Extras/X1-MultiModal/README.md b/translations/ro/lessons/X-Extras/X1-MultiModal/README.md index a6f8b103..627b88bc 100644 --- a/translations/ro/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/ro/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ După succesul modelelor transformer în rezolvarea sarcinilor NLP, aceleași sa Ideea principală a CLIP este de a putea compara descrieri textuale cu o imagine și de a determina cât de bine corespunde imaginea descrierii. -![CLIP Architecture](../../../../../translated_images/ro/clip-arch.b3dbf20b4e8ed8be.png) +![CLIP Architecture](../../../../../translated_images/ro/clip-arch.b3dbf20b4e8ed8be.webp) > *Imagine din [acest articol](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ Odată ce acest model este pre-antrenat, putem să-i oferim un lot de imagini ș Să presupunem că trebuie să clasificăm imagini între, să zicem, pisici, câini și oameni. În acest caz, putem oferi modelului o imagine și o serie de descrieri textuale: "*o imagine cu o pisică*", "*o imagine cu un câine*", "*o imagine cu un om*". În vectorul rezultat de 3 probabilități, trebuie doar să selectăm indexul cu cea mai mare valoare. -![CLIP for Image Classification](../../../../../translated_images/ro/clip-class.3af42ef0b2b19369.png) +![CLIP for Image Classification](../../../../../translated_images/ro/clip-class.3af42ef0b2b19369.webp) > *Imagine din [acest articol](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ Aflați mai multe despre VQGAN pe site-ul [Taming Transformers](https://compvis. Una dintre diferențele importante între VQGAN și GAN-ul tradițional este că cel din urmă poate produce o imagine decentă din orice vector de intrare, în timp ce VQGAN este probabil să producă o imagine incoerentă. Astfel, trebuie să ghidăm procesul de creare a imaginii, iar acest lucru poate fi realizat folosind CLIP. -![VQGAN+CLIP Architecture](../../../../../translated_images/ro/vqgan.5027fe05051dfa31.png) +![VQGAN+CLIP Architecture](../../../../../translated_images/ro/vqgan.5027fe05051dfa31.webp) Pentru a genera o imagine corespunzătoare unei descrieri textuale, începem cu un vector de codificare aleatoriu care este transmis prin VQGAN pentru a produce o imagine. Apoi, CLIP este utilizat pentru a produce o funcție de pierdere care arată cât de bine corespunde imaginea descrierii textuale. Scopul este de a minimiza această pierdere, utilizând backpropagation pentru a ajusta parametrii vectorului de intrare. O bibliotecă excelentă care implementează VQGAN+CLIP este [Pixray](http://github.com/pixray/pixray). -![Imagine produsă de Pixray](../../../../../translated_images/ro/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Imagine produsă de Pixray](../../../../../translated_images/ro/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Imagine produsă de Pixray](../../../../../translated_images/ro/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) +![Imagine produsă de Pixray](../../../../../translated_images/ro/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp) | ![Imagine produsă de Pixray](../../../../../translated_images/ro/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp) | ![Imagine produsă de Pixray](../../../../../translated_images/ro/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp) ----|----|---- Imagine generată din descrierea *un portret în acuarelă, prim-plan, al unui tânăr profesor de literatură cu o carte* | Imagine generată din descrierea *un portret în ulei, prim-plan, al unei tinere profesoare de informatică cu un computer* | Imagine generată din descrierea *un portret în ulei, prim-plan, al unui profesor bătrân de matematică în fața unei table negre* diff --git a/translations/sk/README.md b/translations/sk/README.md index ae9b19b2..4c2a7382 100644 --- a/translations/sk/README.md +++ b/translations/sk/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # Učenie pre začiatočníkov v oblasti umelej inteligencie - Kurz -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/sk/ai-overview.0857791951d19500.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/sk/ai-overview.0857791951d19500.webp)| |:---:| | AI pre začiatočníkov - _Sketchnote od [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/sk/lessons/1-Intro/README.md b/translations/sk/lessons/1-Intro/README.md index 2adcbf53..fedb7539 100644 --- a/translations/sk/lessons/1-Intro/README.md +++ b/translations/sk/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Úvod do umelej inteligencie -![Zhrnutie obsahu Úvodu do umelej inteligencie v kresbe](../../../../translated_images/sk/ai-intro.bf28d1ac4235881c.png) +![Zhrnutie obsahu Úvodu do umelej inteligencie v kresbe](../../../../translated_images/sk/ai-intro.bf28d1ac4235881c.webp) > Kresba od [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Pôvodne boli počítače vynájdené [Charlesom Babbageom](https://en.wikipedia.org/wiki/Charles_Babbage) na prácu s číslami podľa presne definovaného postupu – algoritmu. Moderné počítače, hoci sú oveľa pokročilejšie ako pôvodný model navrhnutý v 19. storočí, stále fungujú na rovnakom princípe riadených výpočtov. Preto je možné naprogramovať počítač na vykonanie úlohy, ak poznáme presnú postupnosť krokov potrebných na dosiahnutie cieľa. -![Fotografia osoby](../../../../translated_images/sk/dsh_age.d212a30d4e54fb5f.png) +![Fotografia osoby](../../../../translated_images/sk/dsh_age.d212a30d4e54fb5f.webp) > Fotografia od [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ Viac informácií nájdete v článku **[Artificial General Intelligence](https: Jedným z problémov pri zaoberaní sa pojmom **[inteligencia](https://en.wikipedia.org/wiki/Intelligence)** je, že neexistuje jasná definícia tohto pojmu. Dá sa argumentovať, že inteligencia súvisí s **abstraktným myslením** alebo so **sebauvedomením**, ale nemôžeme ju presne definovať. -![Fotografia mačky](../../../../translated_images/sk/photo-cat.8c8e8fb760ffe457.jpg) +![Fotografia mačky](../../../../translated_images/sk/photo-cat.8c8e8fb760ffe457.webp) > [Fotografia](https://unsplash.com/photos/75715CVEJhI) od [Amber Kipp](https://unsplash.com/@sadmax) z Unsplash @@ -98,13 +98,13 @@ Alternatívne môžeme skúsiť modelovať najjednoduchšie prvky v našom mozgu > | Čo tak ML? | | > |--------------|-----------| -> | Časť umelej inteligencie, ktorá je založená na tom, že počítač sa učí riešiť problém na základe niektorých dát, sa nazýva **strojové učenie**. V tomto kurze sa nebudeme zaoberať klasickým strojovým učením – odkazujeme vás na samostatný [kurz Strojové učenie pre začiatočníkov](http://aka.ms/ml-beginners). | ![ML pre začiatočníkov](../../../../translated_images/sk/ml-for-beginners.9e4fed176fd5817d.png) | +> | Časť umelej inteligencie, ktorá je založená na tom, že počítač sa učí riešiť problém na základe niektorých dát, sa nazýva **strojové učenie**. V tomto kurze sa nebudeme zaoberať klasickým strojovým učením – odkazujeme vás na samostatný [kurz Strojové učenie pre začiatočníkov](http://aka.ms/ml-beginners). | ![ML pre začiatočníkov](../../../../translated_images/sk/ml-for-beginners.9e4fed176fd5817d.webp) | ## Stručná história AI Umelá inteligencia vznikla ako oblasť v polovici 20. storočia. Spočiatku bol symbolický prístup dominantný a viedol k niekoľkým dôležitým úspechom, ako napríklad expertné systémy – počítačové programy, ktoré dokázali pôsobiť ako odborníci v niektorých obmedzených problémových oblastiach. Avšak čoskoro sa ukázalo, že tento prístup nie je dobre škálovateľný. Extrahovanie vedomostí od experta, ich reprezentácia v počítači a udržiavanie tejto databázy vedomostí presnej sa ukázalo byť veľmi zložitou úlohou a príliš nákladnou na to, aby bola praktická v mnohých prípadoch. To viedlo k takzvanej [AI zime](https://en.wikipedia.org/wiki/AI_winter) v 70. rokoch. -Stručná história AI +Stručná história AI > Obrázok od [Dmitry Soshnikov](http://soshnikov.com) @@ -124,7 +124,7 @@ Podobne môžeme vidieť, ako sa prístup k vytváraniu „hovoriacich programov * Moderní asistenti, ako Cortana, Siri alebo Google Assistant, sú všetci hybridné systémy, ktoré používajú neurónové siete na prevod reči na text a rozpoznanie nášho zámeru, a potom využívajú nejaké uvažovanie alebo explicitné algoritmy na vykonanie požadovaných akcií. * V budúcnosti môžeme očakávať kompletný model založený na neurónových sieťach, ktorý bude sám zvládať dialóg. Nedávne GPT a [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) rodiny neurónových sietí ukazujú veľký úspech v tomto smere. -evolúcia Turingovho testu # Reprezentácia znalostí a expertné systémy -![Zhrnutie obsahu Symbolickej AI](../../../../translated_images/sk/ai-symbolic.715a30cb610411a6.png) +![Zhrnutie obsahu Symbolickej AI](../../../../translated_images/sk/ai-symbolic.715a30cb610411a6.webp) > Sketchnote od [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ Najčastejšie znalosti striktne nedefinujeme, ale zosúladíme ich s inými pr Problém **reprezentácie znalostí** teda spočíva v nájdení efektívneho spôsobu, ako reprezentovať znalosti v počítači vo forme údajov, aby boli automaticky použiteľné. To možno vnímať ako spektrum: -![Spektrum reprezentácie znalostí](../../../../translated_images/sk/knowledge-spectrum.b60df631852c0217.png) +![Spektrum reprezentácie znalostí](../../../../translated_images/sk/knowledge-spectrum.b60df631852c0217.webp) > Obrázok od [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Bloková syntax | Odsadenie | | | Jedným z prvých úspechov symbolickej AI boli tzv. **expertné systémy** - počítačové systémy navrhnuté tak, aby fungovali ako odborník v obmedzenej oblasti problémov. Boli založené na **báze znalostí** extrahovanej od jedného alebo viacerých ľudských odborníkov a obsahovali **odvodzovací mechanizmus**, ktorý vykonával odvodzovanie na jej základe. -![Štruktúra človeka](../../../../translated_images/sk/arch-human.5d4d35f1bba3ab1c.png) | ![Systém založený na znalostiach](../../../../translated_images/sk/arch-kbs.3ec5c150b09fa8da.png) +![Štruktúra človeka](../../../../translated_images/sk/arch-human.5d4d35f1bba3ab1c.webp) | ![Systém založený na znalostiach](../../../../translated_images/sk/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ Zjednodušená štruktúra ľudského nervového systému | Architektúra systému založeného na znalostiach @@ -106,7 +106,7 @@ Expertné systémy sú postavené podobne ako systém ľudského odvodzovania, k Ako príklad si vezmime nasledujúci expertný systém na určenie zvieraťa na základe jeho fyzických charakteristík: -![AND-OR strom](../../../../translated_images/sk/AND-OR-Tree.5592d2c70187f283.png) +![AND-OR strom](../../../../translated_images/sk/AND-OR-Tree.5592d2c70187f283.webp) > Obrázok od [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/sk/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/sk/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index 56669638..765b94a5 100644 --- a/translations/sk/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/sk/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -1259,7 +1259,7 @@ "* Nízka tréningová chyba - model dokáže dobre aproximovať tréningové dáta, pretože má dostatočnú vyjadrovaciu schopnosť.\n", "* Chyba na validačných dátach môže byť oveľa vyššia ako tréningová chyba a môže počas tréningu začať rásť - je to preto, že model si \"pamätá\" tréningové body a stráca \"celkový obraz\".\n", "\n", - "![Overfitting](../../../../../translated_images/sk/overfit.a0bd57f717c15769.png)\n", + "![Overfitting](../../../../../translated_images/sk/overfit.a0bd57f717c15769.webp)\n", "\n", "> Na tomto obrázku `x` predstavuje tréningové dáta, `o` - validačné dáta. Vľavo - lineárny model (jednovrstvový), ktorý pomerne dobre aproximuje povahu dát. Vpravo - model s overfittingom, ktorý dokonale aproximuje tréningové dáta, ale prestáva dávať zmysel pri akýchkoľvek iných dátach (validačná chyba je veľmi vysoká).\n" ] diff --git a/translations/sk/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/sk/lessons/3-NeuralNetworks/05-Frameworks/README.md index 9e051b98..beacfcf5 100644 --- a/translations/sk/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/sk/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Pretrénovanie je mimoriadne dôležitý koncept v strojovom učení, a je veľm Zvážte nasledujúci problém aproximácie 5 bodov (reprezentovaných `x` na grafoch nižšie): -![linear](../../../../../translated_images/sk/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/sk/overfit2.131f5800ae10ca5e.jpg) +![linear](../../../../../translated_images/sk/overfit1.f24b71c6f652e59e.webp) | ![overfit](../../../../../translated_images/sk/overfit2.131f5800ae10ca5e.webp) -------------------------|-------------------------- **Lineárny model, 2 parametre** | **Nelineárny model, 7 parametrov** Chyba trénovania = 5.3 | Chyba trénovania = 0 @@ -79,7 +79,7 @@ Je veľmi dôležité nájsť správnu rovnováhu medzi bohatstvom modelu (poče Ako môžete vidieť na grafe vyššie, pretrénovanie možno detekovať veľmi nízkou chybou trénovania a vysokou chybou validácie. Normálne počas trénovania vidíme, že chyby trénovania aj validácie začínajú klesať, a potom v určitom bode chyba validácie môže prestať klesať a začať stúpať. Toto bude znak pretrénovania a indikátor, že by sme mali pravdepodobne zastaviť trénovanie (alebo aspoň urobiť snímku modelu). -![overfitting](../../../../../translated_images/sk/Overfitting.408ad91cd90b4371.png) +![overfitting](../../../../../translated_images/sk/Overfitting.408ad91cd90b4371.webp) ## Ako predísť pretrénovaniu diff --git a/translations/sk/lessons/3-NeuralNetworks/README.md b/translations/sk/lessons/3-NeuralNetworks/README.md index 8a64bb62..b35c3a34 100644 --- a/translations/sk/lessons/3-NeuralNetworks/README.md +++ b/translations/sk/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Úvod do neurónových sietí -![Zhrnutie obsahu Úvodu do neurónových sietí v kresbe](../../../../translated_images/sk/ai-neuralnetworks.1c687ae40bc86e83.png) +![Zhrnutie obsahu Úvodu do neurónových sietí v kresbe](../../../../translated_images/sk/ai-neuralnetworks.1c687ae40bc86e83.webp) Ako sme si povedali v úvode, jedným zo spôsobov, ako dosiahnuť inteligenciu, je trénovať **počítačový model** alebo **umelý mozog**. Od polovice 20. storočia vedci skúšali rôzne matematické modely, až kým sa v posledných rokoch tento smer neukázal ako mimoriadne úspešný. Takéto matematické modely mozgu sa nazývajú **neurónové siete**. @@ -36,13 +36,13 @@ V tomto kurze sa zameriame iba na modely neurónových sietí. Z biológie vieme, že náš mozog pozostáva z neurónových buniek (neurónov), pričom každý z nich má viacero "vstupov" (dendritov) a jeden "výstup" (axon). Dendrity aj axóny môžu viesť elektrické signály a spojenia medzi nimi — známe ako synapsie — môžu vykazovať rôzne stupne vodivosti, ktoré sú regulované neurotransmitermi. -![Model neurónu](../../../../translated_images/sk/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![Model neurónu](../../../../translated_images/sk/artneuron.1a5daa88d20ebe6f.png) +![Model neurónu](../../../../translated_images/sk/synapse-wikipedia.ed20a9e4726ea1c6.webp) | ![Model neurónu](../../../../translated_images/sk/artneuron.1a5daa88d20ebe6f.webp) ----|---- Skutočný neurón *([Obrázok](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) z Wikipédie)* | Umelý neurón *(Obrázok od autora)* Najjednoduchší matematický model neurónu teda obsahuje niekoľko vstupov X1, ..., XN a jeden výstup Y, a sériu váh W1, ..., WN. Výstup sa vypočíta ako: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) kde f je nejaká nelineárna **aktivačná funkcia**. diff --git a/translations/sk/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/sk/lessons/4-ComputerVision/06-IntroCV/README.md index 3ed2164d..e5e09370 100644 --- a/translations/sk/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/sk/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ V našom [OpenCV Notebook](OpenCV.ipynb) uvádzame niekoľko príkladov, kedy sa * **Predspracovanie fotografie Braillovej knihy**. Zameriavame sa na to, ako môžeme použiť prahovanie, detekciu vlastností, perspektívnu transformáciu a manipuláciu s NumPy na oddelenie jednotlivých Braillových symbolov na ďalšiu klasifikáciu neurónovou sieťou. -![Braillov obrázok](../../../../../translated_images/sk/braille.341962ff76b1bd70.jpeg) | ![Predspracovaný Braillov obrázok](../../../../../translated_images/sk/braille-result.46530fea020b03c7.png) | ![Braillove symboly](../../../../../translated_images/sk/braille-symbols.0159185ab69d5339.png) +![Braillov obrázok](../../../../../translated_images/sk/braille.341962ff76b1bd70.webp) | ![Predspracovaný Braillov obrázok](../../../../../translated_images/sk/braille-result.46530fea020b03c7.webp) | ![Braillove symboly](../../../../../translated_images/sk/braille-symbols.0159185ab69d5339.webp) ----|-----|----- > Obrázok z [OpenCV.ipynb](OpenCV.ipynb) * **Detekcia pohybu vo videu pomocou rozdielu snímok**. Ak je kamera pevná, snímky z kamery by mali byť veľmi podobné. Keďže snímky sú reprezentované ako polia, jednoduchým odčítaním týchto polí pre dve po sebe idúce snímky získame rozdiel pixelov, ktorý by mal byť nízky pre statické snímky a vyšší, keď je v obrázku výrazný pohyb. -![Obrázok video snímok a rozdielov snímok](../../../../../translated_images/sk/frame-difference.706f805491a0883c.png) +![Obrázok video snímok a rozdielov snímok](../../../../../translated_images/sk/frame-difference.706f805491a0883c.webp) > Obrázok z [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ V našom [OpenCV Notebook](OpenCV.ipynb) uvádzame niekoľko príkladov, kedy sa - **Hustý optický tok** vypočíta vektorové pole, ktoré ukazuje, kam sa každý pixel pohybuje. - **Riedky optický tok** je založený na výbere niektorých výrazných vlastností na obrázku (napr. hrany) a budovaní ich trajektórie zo snímky na snímku. -![Obrázok optického toku](../../../../../translated_images/sk/optical.1f4a94464579a83a.png) +![Obrázok optického toku](../../../../../translated_images/sk/optical.1f4a94464579a83a.webp) > Obrázok z [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/sk/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/sk/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 9094ef24..3d2d47c5 100644 --- a/translations/sk/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/sk/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 je sieť, ktorá dosiahla 92,7% presnosť v klasifikácii ImageNet top-5 v roku 2014. Má nasledujúcu štruktúru vrstiev: -![ImageNet Layers](../../../../../translated_images/sk/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet Layers](../../../../../translated_images/sk/vgg-16-arch1.d901a5583b3a51ba.webp) Ako môžete vidieť, VGG nasleduje tradičnú pyramídovú architektúru, ktorá je sekvenciou vrstiev konvolúcie a pooling. -![ImageNet Pyramid](../../../../../translated_images/sk/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet Pyramid](../../../../../translated_images/sk/vgg-16-arch.64ff2137f50dd49f.webp) > Obrázok z [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/sk/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/sk/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index 19a8019b..0a241fdc 100644 --- a/translations/sk/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/sk/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -260,7 +260,7 @@ "\n", "Takže v typickej CNN by bolo niekoľko konvolučných vrstiev, medzi ktorými by boli pooling vrstvy na zmenšenie rozmerov obrázka. Zároveň by sme zvýšili počet filtrov, pretože ako sa vzory stávajú pokročilejšími, existuje viac možných zaujímavých kombinácií, ktoré musíme hľadať.\n", "\n", - "![Obrázok zobrazujúci niekoľko konvolučných vrstiev s pooling vrstvami.](../../../../../translated_images/sk/cnn-pyramid.85915455759ef0ce.png)\n", + "![Obrázok zobrazujúci niekoľko konvolučných vrstiev s pooling vrstvami.](../../../../../translated_images/sk/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "Kvôli zmenšovaniu priestorových rozmerov a zvyšovaniu rozmerov vlastností/filtrov sa táto architektúra nazýva aj **pyramídová architektúra**.\n" ] diff --git a/translations/sk/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/sk/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index 45084caa..ef2e872e 100644 --- a/translations/sk/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/sk/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -358,7 +358,7 @@ "\n", "Takže v typickej CNN by bolo niekoľko konvolučných vrstiev, pričom medzi nimi by boli pooling vrstvy na zmenšenie rozmerov obrázka. Zároveň by sme zvýšili počet filtrov, pretože ako sa vzory stávajú pokročilejšími, existuje viac možných zaujímavých kombinácií, ktoré musíme hľadať.\n", "\n", - "![Obrázok zobrazujúci niekoľko konvolučných vrstiev s pooling vrstvami.](../../../../../translated_images/sk/cnn-pyramid.85915455759ef0ce.png)\n", + "![Obrázok zobrazujúci niekoľko konvolučných vrstiev s pooling vrstvami.](../../../../../translated_images/sk/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "Kvôli zmenšovaniu priestorových rozmerov a zvyšovaniu rozmerov vlastností/filtrov sa táto architektúra nazýva aj **pyramídová architektúra**.\n" ] diff --git a/translations/sk/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/sk/lessons/4-ComputerVision/07-ConvNets/README.md index a24d3b9a..c751eb45 100644 --- a/translations/sk/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/sk/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ V reálnom živote chceme byť schopní rozpoznať objekty na obrázku bez ohľa Na extrakciu vzorov použijeme koncept **konvolučných filtrov**. Ako viete, obrázok je reprezentovaný ako 2D-matica alebo 3D-tenzor s farebnou hĺbkou. Aplikácia filtra znamená, že vezmeme relatívne malú **jadrovú maticu filtra** a pre každý pixel v pôvodnom obrázku vypočítame vážený priemer so susednými bodmi. Môžeme si to predstaviť ako malé okno, ktoré sa posúva po celom obrázku a spriemeruje všetky pixely podľa váh v jadrovej matici filtra. -![Vertikálny filter hrán](../../../../../translated_images/sk/filter-vert.b7148390ca0bc356.png) | ![Horizontálny filter hrán](../../../../../translated_images/sk/filter-horiz.59b80ed4feb946ef.png) +![Vertikálny filter hrán](../../../../../translated_images/sk/filter-vert.b7148390ca0bc356.webp) | ![Horizontálny filter hrán](../../../../../translated_images/sk/filter-horiz.59b80ed4feb946ef.webp) ----|---- > Obrázok od Dmitry Soshnikov @@ -38,7 +38,7 @@ Princíp fungovania CNN je založený na nasledujúcich dôležitých myšlienka * Môžeme navrhnúť sieť tak, aby sa filtre učili automaticky * Rovnaký prístup môžeme použiť na hľadanie vzorov vo vysokoúrovňových vlastnostiach, nielen v pôvodnom obrázku. Extrakcia vlastností pomocou CNN teda funguje na hierarchii vlastností, od nízkoúrovňových kombinácií pixelov až po vysokoúrovňové kombinácie častí obrázku. -![Hierarchická extrakcia vlastností](../../../../../translated_images/sk/FeatureExtractionCNN.d9b456cbdae7cb64.png) +![Hierarchická extrakcia vlastností](../../../../../translated_images/sk/FeatureExtractionCNN.d9b456cbdae7cb64.webp) > Obrázok z [práce Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), založený na [ich výskume](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ Väčšina CNN používaných na spracovanie obrázkov nasleduje tzv. pyramídov Ako príklad sa pozrime na architektúru VGG-16, siete, ktorá dosiahla 92,7% presnosť v top-5 klasifikácii ImageNet v roku 2014: -![Vrstvy ImageNet](../../../../../translated_images/sk/vgg-16-arch1.d901a5583b3a51ba.jpg) +![Vrstvy ImageNet](../../../../../translated_images/sk/vgg-16-arch1.d901a5583b3a51ba.webp) -![Pyramída ImageNet](../../../../../translated_images/sk/vgg-16-arch.64ff2137f50dd49f.jpg) +![Pyramída ImageNet](../../../../../translated_images/sk/vgg-16-arch.64ff2137f50dd49f.webp) > Obrázok z [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/sk/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/sk/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 8e6893d2..d5411614 100644 --- a/translations/sk/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/sk/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ Vašou úlohou je vytrénovať konvolučnú neurónovú sieť na klasifikáciu r Použijeme [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/), ktorý obsahuje obrázky 37 rôznych plemien psov a mačiek. -![Dataset, s ktorým budeme pracovať](../../../../../../translated_images/sk/data.50b2a9d5484bdbf0.png) +![Dataset, s ktorým budeme pracovať](../../../../../../translated_images/sk/data.50b2a9d5484bdbf0.webp) Na stiahnutie datasetu použite tento kód: diff --git a/translations/sk/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/sk/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index 16c94a96..0aa4ce02 100644 --- a/translations/sk/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/sk/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "Aby sme si predstavili ideálnu mačku, začneme s náhodným šumovým obrázkom a pokúsime sa použiť optimalizačnú techniku gradientného zostupu na úpravu obrázka tak, aby sieť rozpoznala mačku.\n", "\n", - "![Optimalizačná slučka](../../../../../translated_images/sk/ideal-cat-loop.999fbb8ff306e044.png)\n", + "![Optimalizačná slučka](../../../../../translated_images/sk/ideal-cat-loop.999fbb8ff306e044.webp)\n", "\n", "Tu je náš počiatočný obrázok:\n" ] diff --git a/translations/sk/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/sk/lessons/4-ComputerVision/08-TransferLearning/README.md index b0108696..a8afa2a4 100644 --- a/translations/sk/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/sk/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Keras aj PyTorch obsahujú funkcie na jednoduché načítanie predtrénovaných Tu sú ukážkové črty extrahované z obrázku mačky pomocou siete VGG-16: -![Črty extrahované VGG-16](../../../../../translated_images/sk/features.6291f9c7ba3a0b95.png) +![Črty extrahované VGG-16](../../../../../translated_images/sk/features.6291f9c7ba3a0b95.webp) ## Dataset Mačky vs. Psy @@ -48,19 +48,19 @@ Predtrénovaná neurónová sieť obsahuje rôzne vzory vo svojej *pamäti*, vr Jeden prístup, ktorý môžeme použiť, je začať s náhodným obrázkom a potom sa pokúsiť použiť techniku **optimalizácie pomocou gradientného zostupu**, aby sme upravili tento obrázok tak, že sieť začne myslieť, že je to mačka. -![Optimalizačný cyklus obrázku](../../../../../translated_images/sk/ideal-cat-loop.999fbb8ff306e044.png) +![Optimalizačný cyklus obrázku](../../../../../translated_images/sk/ideal-cat-loop.999fbb8ff306e044.webp) Ak to však urobíme, dostaneme niečo veľmi podobné náhodnému šumu. Je to preto, že *existuje mnoho spôsobov, ako presvedčiť sieť, že vstupný obrázok je mačka*, vrátane niektorých, ktoré vizuálne nedávajú zmysel. Hoci tieto obrázky obsahujú veľa vzorov typických pre mačku, nič ich neobmedzuje, aby boli vizuálne zreteľné. Na zlepšenie výsledku môžeme do funkcie straty pridať ďalší člen, ktorý sa nazýva **variácia straty**. Je to metrika, ktorá ukazuje, ako podobné sú susedné pixely obrázku. Minimalizácia variácie straty robí obrázok hladším a zbavuje sa šumu - čím odhaľuje vizuálne príťažlivejšie vzory. Tu je príklad takýchto "ideálnych" obrázkov, ktoré sú klasifikované ako mačka a zebra s vysokou pravdepodobnosťou: -![Ideálna mačka](../../../../../translated_images/sk/ideal-cat.203dd4597643d6b0.png) | ![Ideálna zebra](../../../../../translated_images/sk/ideal-zebra.7f70e8b54ee15a7a.png) +![Ideálna mačka](../../../../../translated_images/sk/ideal-cat.203dd4597643d6b0.webp) | ![Ideálna zebra](../../../../../translated_images/sk/ideal-zebra.7f70e8b54ee15a7a.webp) -----|----- *Ideálna mačka* | *Ideálna zebra* Podobný prístup môže byť použitý na vykonanie tzv. **adversariálnych útokov** na neurónovú sieť. Predpokladajme, že chceme oklamať neurónovú sieť a urobiť z psa mačku. Ak vezmeme obrázok psa, ktorý je sieťou rozpoznaný ako pes, môžeme ho trochu upraviť pomocou optimalizácie gradientného zostupu, až kým sieť nezačne klasifikovať obrázok ako mačku: -![Obrázok psa](../../../../../translated_images/sk/original-dog.8f68a67d2fe0911f.png) | ![Obrázok psa klasifikovaný ako mačka](../../../../../translated_images/sk/adversarial-dog.d9fc7773b0142b89.png) +![Obrázok psa](../../../../../translated_images/sk/original-dog.8f68a67d2fe0911f.webp) | ![Obrázok psa klasifikovaný ako mačka](../../../../../translated_images/sk/adversarial-dog.d9fc7773b0142b89.webp) -----|----- *Pôvodný obrázok psa* | *Obrázok psa klasifikovaný ako mačka* diff --git a/translations/sk/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/sk/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index d8ef7fa0..5f34e804 100644 --- a/translations/sk/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/sk/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "Keďže trénujeme autoenkóder, aby zachytil čo najviac informácií z pôvodného obrázku pre presnú rekonštrukciu, sieť sa snaží nájsť najlepšie **zabudovanie** vstupných obrázkov, aby zachytila ich význam.\n", "\n", - "![Schéma Autoenkódera](../../../../../translated_images/sk/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![Schéma Autoenkódera](../../../../../translated_images/sk/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "> Obrázok z [Keras blogu](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", diff --git a/translations/sk/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/sk/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index bfe97bc3..d394b747 100644 --- a/translations/sk/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/sk/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "Keďže trénujeme autoenkóder, aby zachytil čo najviac informácií z pôvodného obrázku pre presnú rekonštrukciu, sieť sa snaží nájsť najlepšie **zabudovanie** vstupných obrázkov, aby zachytila ich význam.\n", "\n", - "![Schéma Autoenkódera](../../../../../translated_images/sk/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![Schéma Autoenkódera](../../../../../translated_images/sk/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "*Obrázok z [Keras blogu](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", diff --git a/translations/sk/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/sk/lessons/4-ComputerVision/09-Autoencoders/README.md index f983d469..a5783bac 100644 --- a/translations/sk/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/sk/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ Avšak, môžeme chcieť použiť surové (neoznačené) dáta na trénovanie CN Keďže trénujeme autoenkodér, aby zachytil čo najviac informácií z pôvodného obrázku na presnú rekonštrukciu, sieť sa snaží nájsť najlepšie **zobrazenie** vstupných obrázkov, aby zachytila ich význam. -![Schéma Autoenkodéra](../../../../../translated_images/sk/autoencoder_schema.5e6fc9ad98a5eb61.jpg) +![Schéma Autoenkodéra](../../../../../translated_images/sk/autoencoder_schema.5e6fc9ad98a5eb61.webp) > Obrázok z [blogu Keras](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/sk/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/sk/lessons/4-ComputerVision/11-ObjectDetection/README.md index 0fb69ae9..e1c7e387 100644 --- a/translations/sk/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/sk/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ Modely klasifikácie obrázkov, s ktorými sme sa doteraz zaoberali, brali obrá ## [Kvíz pred prednáškou](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Detekcia objektov](../../../../../translated_images/sk/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) +![Detekcia objektov](../../../../../translated_images/sk/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp) > Obrázok zo stránky [YOLO v2](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ Predpokladajme, že chceme nájsť mačku na obrázku. Veľmi naivný prístup k 2. Spustiť klasifikáciu obrázkov na každej dlaždici. 3. Dlaždice, ktoré majú dostatočne vysokú aktiváciu, môžeme považovať za obsahujúce hľadaný objekt. -![Naivná detekcia objektov](../../../../../translated_images/sk/naive-detection.e7f1ba220ccd08c6.png) +![Naivná detekcia objektov](../../../../../translated_images/sk/naive-detection.e7f1ba220ccd08c6.webp) > *Obrázok z [cvičebného notebooku](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ Pri tejto úlohe sa môžete stretnúť s nasledujúcimi datasetmi: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 tried * [COCO](http://cocodataset.org/#home) - Common Objects in Context. 80 tried, ohraničujúce rámy a segmentačné masky -![COCO](../../../../../translated_images/sk/coco-examples.71bc60380fa6cceb.jpg) +![COCO](../../../../../translated_images/sk/coco-examples.71bc60380fa6cceb.webp) ## Metriky detekcie objektov @@ -50,7 +50,7 @@ Pri tejto úlohe sa môžete stretnúť s nasledujúcimi datasetmi: Zatiaľ čo pri klasifikácii obrázkov je jednoduché merať, ako dobre algoritmus funguje, pri detekcii objektov musíme merať správnosť triedy, ako aj presnosť polohy predpokladaného ohraničujúceho rámu. Na tento účel používame tzv. **Prienik cez zjednotenie** (IoU), ktorý meria, ako dobre sa dva rámy (alebo dve ľubovoľné oblasti) prekrývajú. -![IoU](../../../../../translated_images/sk/iou_equation.9a4751d40fff4e11.png) +![IoU](../../../../../translated_images/sk/iou_equation.9a4751d40fff4e11.webp) > *Obrázok 2 z [tohto výborného blogového príspevku o IoU](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ Existujú dve hlavné triedy algoritmov detekcie objektov: [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) používa [Selektívne vyhľadávanie](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) na generovanie hierarchickej štruktúry regiónov ROI, ktoré sú potom prechádzané extraktormi funkcií CNN a SVM klasifikátormi na určenie triedy objektu, a lineárnou regresiou na určenie súradníc *ohraničujúceho rámu*. [Oficiálny článok](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/sk/rcnn1.cae407020dfb1d1f.png) +![RCNN](../../../../../translated_images/sk/rcnn1.cae407020dfb1d1f.webp) > *Obrázok od van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/sk/rcnn2.2d9530bb83516484.png) +![RCNN-1](../../../../../translated_images/sk/rcnn2.2d9530bb83516484.webp) > *Obrázky z [tohto blogu](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -110,7 +110,7 @@ Existujú dve hlavné triedy algoritmov detekcie objektov: Tento prístup je podobný R-CNN, ale regióny sú definované po aplikovaní konvolučných vrstiev. -![FRCNN](../../../../../translated_images/sk/f-rcnn.3cda6d9bb4188875.png) +![FRCNN](../../../../../translated_images/sk/f-rcnn.3cda6d9bb4188875.webp) > Obrázok z [oficiálneho článku](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ Tento prístup je podobný R-CNN, ale regióny sú definované po aplikovaní ko Hlavnou myšlienkou tohto prístupu je použitie neurónovej siete na predpovedanie ROI - tzv. *Sieť na návrh regiónov*. [Článok](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/sk/faster-rcnn.8d46c099b87ef30a.png) +![FasterRCNN](../../../../../translated_images/sk/faster-rcnn.8d46c099b87ef30a.webp) > Obrázok z [oficiálneho článku](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ Tento algoritmus je ešte rýchlejší ako Faster R-CNN. Hlavná myšlienka je n 2. Funkcie sú spracované pomocou **Position-Sensitive Score Map**. Každý objekt z $C$ tried je rozdelený na $k\times k$ regióny, a trénujeme na predpovedanie častí objektov. 3. Pre každú časť z $k\times k$ regiónov všetky siete hlasujú za triedy objektov, a trieda objektu s maximálnym počtom hlasov je vybraná. -![r-fcn image](../../../../../translated_images/sk/r-fcn.13eb88158b99a3da.png) +![r-fcn image](../../../../../translated_images/sk/r-fcn.13eb88158b99a3da.webp) > Obrázok z [oficiálneho článku](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO je algoritmus na detekciu objektov v reálnom čase s jedným prechodom. Hl * Obrázok je rozdelený na $S\times S$ regióny. * Pre každý región **CNN** predpovedá $n$ možných objektov, *súradnice ohraničujúceho rámu* a *dôveru*=*pravdepodobnosť* * IoU. - ![YOLO](../../../../../translated_images/sk/yolo.a2648ec82ee8bb4e.png) + ![YOLO](../../../../../translated_images/sk/yolo.a2648ec82ee8bb4e.webp) > Obrázok z [oficiálneho článku](https://arxiv.org/abs/1506.02640) diff --git a/translations/sk/lessons/4-ComputerVision/README.md b/translations/sk/lessons/4-ComputerVision/README.md index 00d2625a..9b6a28f4 100644 --- a/translations/sk/lessons/4-ComputerVision/README.md +++ b/translations/sk/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Počítačové videnie -![Zhrnutie obsahu o počítačovom videní v kresbe](../../../../translated_images/sk/ai-computervision.6506ebebac3fbf76.png) +![Zhrnutie obsahu o počítačovom videní v kresbe](../../../../translated_images/sk/ai-computervision.6506ebebac3fbf76.webp) V tejto sekcii sa naučíme: diff --git a/translations/sk/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/sk/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index 320f7d9b..074249f9 100644 --- a/translations/sk/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/sk/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**Bag of Words** (BoW) je najčastejšie používaná tradičná vektorová reprezentácia. Každé slovo je priradené k indexu vektora a prvok vektora obsahuje počet výskytov daného slova v konkrétnom dokumente.\n", "\n", - "![Obrázok znázorňujúci, ako je reprezentácia Bag of Words uložená v pamäti.](../../../../../translated_images/sk/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![Obrázok znázorňujúci, ako je reprezentácia Bag of Words uložená v pamäti.](../../../../../translated_images/sk/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Note**: Na BoW sa môžete pozerať aj ako na súčet všetkých one-hot-enkódovaných vektorov pre jednotlivé slová v texte.\n", "\n", diff --git a/translations/sk/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/sk/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index e195b43d..5b4b80cf 100644 --- a/translations/sk/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/sk/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**Bag-of-words** (BoW) je najjednoduchšia tradičná metóda reprezentácie textu pomocou vektorov. Každé slovo je priradené k indexu vektora a prvok vektora obsahuje počet výskytov daného slova v konkrétnom dokumente.\n", "\n", - "![Obrázok znázorňujúci, ako je reprezentácia Bag-of-words uložená v pamäti.](../../../../../translated_images/sk/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![Obrázok znázorňujúci, ako je reprezentácia Bag-of-words uložená v pamäti.](../../../../../translated_images/sk/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Note**: Na metódu BoW sa môžete pozerať aj ako na súčet všetkých vektorov s jedným aktívnym prvkom (one-hot-encoded) pre jednotlivé slová v texte.\n", "\n", diff --git a/translations/sk/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/sk/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index a849f9b3..78b43610 100644 --- a/translations/sk/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/sk/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "Použitím embedding vrstvy ako prvej vrstvy v našej sieti môžeme prejsť od modelu bag-of-words k modelu **embedding bag**, kde najskôr každé slovo v našom texte prevedieme na zodpovedajúci embedding a potom vypočítame nejakú agregačnú funkciu nad všetkými týmito embeddingami, ako napríklad `sum`, `average` alebo `max`.\n", "\n", - "![Obrázok zobrazujúci embedding klasifikátor pre päť slov v sekvencii.](../../../../../translated_images/sk/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![Obrázok zobrazujúci embedding klasifikátor pre päť slov v sekvencii.](../../../../../translated_images/sk/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "Naša klasifikačná neurónová sieť začne embedding vrstvou, potom agregačnou vrstvou a na vrchu bude lineárny klasifikátor:\n" ] @@ -176,7 +176,7 @@ "\n", "V predchádzajúcej architektúre sme museli všetky sekvencie doplniť na rovnakú dĺžku, aby sa zmestili do minibatchu. Toto nie je najefektívnejší spôsob reprezentácie sekvencií s premenlivou dĺžkou - iný prístup by bol použiť **offset** vektor, ktorý by obsahoval posuny všetkých sekvencií uložených v jednom veľkom vektore.\n", "\n", - "![Obrázok zobrazujúci reprezentáciu sekvencií pomocou offset vektora](../../../../../translated_images/sk/offset-sequence-representation.eb73fcefb29b46ee.png)\n", + "![Obrázok zobrazujúci reprezentáciu sekvencií pomocou offset vektora](../../../../../translated_images/sk/offset-sequence-representation.eb73fcefb29b46ee.webp)\n", "\n", "> **Note**: Na obrázku vyššie je zobrazená sekvencia znakov, ale v našom príklade pracujeme so sekvenciami slov. Avšak, základný princíp reprezentácie sekvencií pomocou offset vektora zostáva rovnaký.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW je rýchlejší, zatiaľ čo skip-gram je pomalší, ale lepšie reprezentuje zriedkavé slová.\n", "\n", - "![Obrázok zobrazujúci algoritmy CBoW a Skip-Gram na konverziu slov na vektory.](../../../../../translated_images/sk/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![Obrázok zobrazujúci algoritmy CBoW a Skip-Gram na konverziu slov na vektory.](../../../../../translated_images/sk/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Na experimentovanie s Word2Vec vektormi predtrénovanými na Google News dataset môžeme použiť knižnicu **gensim**. Nižšie nájdeme slová najviac podobné slovu 'neural'.\n", "\n", diff --git a/translations/sk/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/sk/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index 3e330b30..fddbc099 100644 --- a/translations/sk/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/sk/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "Použitím embedding vrstvy ako prvej vrstvy v našej sieti môžeme prejsť z modelu bag-of-words na model **embedding bag**, kde najskôr každé slovo v našom texte prevedieme na zodpovedajúci embedding a potom vypočítame nejakú agregačnú funkciu nad všetkými týmito embeddingmi, ako napríklad `sum`, `average` alebo `max`.\n", "\n", - "![Obrázok zobrazujúci embedding klasifikátor pre päť sekvenčných slov.](../../../../../translated_images/sk/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![Obrázok zobrazujúci embedding klasifikátor pre päť sekvenčných slov.](../../../../../translated_images/sk/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "Naša klasifikačná neurónová sieť pozostáva z nasledujúcich vrstiev:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW je rýchlejší, zatiaľ čo skip-gram je pomalší, ale lepšie reprezentuje zriedkavé slová.\n", "\n", - "![Obrázok zobrazujúci algoritmy CBoW a Skip-Gram na konverziu slov na vektory.](../../../../../translated_images/sk/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![Obrázok zobrazujúci algoritmy CBoW a Skip-Gram na konverziu slov na vektory.](../../../../../translated_images/sk/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Na experimentovanie s embeddingom Word2Vec predtrénovaným na Google News dataset môžeme použiť knižnicu **gensim**. Nižšie nájdeme slová najviac podobné slovu 'neural'.\n", "\n", diff --git a/translations/sk/lessons/5-NLP/14-Embeddings/README.md b/translations/sk/lessons/5-NLP/14-Embeddings/README.md index 4f26fe4e..2a767af3 100644 --- a/translations/sk/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/sk/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ Vstavaná vrstva (embedding layer) teda prijíma slovo ako vstup a produkuje vý Použitím vstavanej vrstvy ako prvej vrstvy v našej klasifikačnej sieti môžeme prejsť z modelu bag-of-words na model **embedding bag**, kde najprv každé slovo v texte prevedieme na zodpovedajúcu vstavanú reprezentáciu a potom vypočítame nejakú agregačnú funkciu nad všetkými týmito reprezentáciami, ako napríklad `sum`, `average` alebo `max`. -![Obrázok zobrazujúci klasifikátor s použitím vstavaných reprezentácií pre päť slov v sekvencii.](../../../../../translated_images/sk/embedding-classifier-example.b77f021a7ee67eee.png) +![Obrázok zobrazujúci klasifikátor s použitím vstavaných reprezentácií pre päť slov v sekvencii.](../../../../../translated_images/sk/embedding-classifier-example.b77f021a7ee67eee.webp) > Obrázok od autora @@ -40,7 +40,7 @@ Na to potrebujeme predtrénovať náš model vstavaných reprezentácií na veľ CBoW je rýchlejší, zatiaľ čo skip-gram je pomalší, ale lepšie reprezentuje zriedkavé slová. -![Obrázok zobrazujúci algoritmy CBoW a Skip-Gram na konverziu slov na vektory.](../../../../../translated_images/sk/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![Obrázok zobrazujúci algoritmy CBoW a Skip-Gram na konverziu slov na vektory.](../../../../../translated_images/sk/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Obrázok z [tohto článku](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/sk/lessons/5-NLP/15-LanguageModeling/README.md b/translations/sk/lessons/5-NLP/15-LanguageModeling/README.md index e6b92a87..d8adca0b 100644 --- a/translations/sk/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/sk/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ V našich predchádzajúcich príkladoch sme používali predtrénované sémant * **Continuous Bag-of-Words** (CBoW), kde predpovedáme stredný token $W_0$ v sekvencii tokenov $W_{-N}$, ..., $W_N$. * **Skip-gram**, kde predpovedáme množinu susedných tokenov {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} zo stredného tokenu $W_0$. -![obrázok z článku o konverzii slov na vektory](../../../../../translated_images/sk/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![obrázok z článku o konverzii slov na vektory](../../../../../translated_images/sk/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Obrázok z [tohto článku](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/sk/lessons/5-NLP/16-RNN/README.md b/translations/sk/lessons/5-NLP/16-RNN/README.md index f9817d2e..994113cb 100644 --- a/translations/sk/lessons/5-NLP/16-RNN/README.md +++ b/translations/sk/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ V predchádzajúcich sekciách sme používali bohaté sémantické reprezentác Na zachytenie významu textovej sekvencie potrebujeme použiť inú architektúru neurónovej siete, ktorá sa nazýva **rekurentná neurónová sieť** alebo RNN. V RNN prechádzame vetou cez sieť jeden symbol po druhom a sieť produkuje určitý **stav**, ktorý následne posunieme do siete spolu s ďalším symbolom. -![RNN](../../../../../translated_images/sk/rnn.27f5c29c53d727b5.png) +![RNN](../../../../../translated_images/sk/rnn.27f5c29c53d727b5.webp) > Obrázok od autora @@ -61,7 +61,7 @@ Diskutovali sme o rekurentných sieťach, ktoré fungujú jedným smerom, od za Rekurentná sieť, či už jednosmerná alebo obojstranná, zachytáva určité vzory v rámci sekvencie a môže ich uložiť do stavového vektora alebo preniesť do výstupu. Rovnako ako pri konvolučných sieťach, môžeme na prvú vrstvu postaviť ďalšiu rekurentnú vrstvu, aby sme zachytili vzory vyššej úrovne a stavali na vzoroch nižšej úrovne extrahovaných prvou vrstvou. To nás privádza k pojmu **viacvrstvová RNN**, ktorá pozostáva z dvoch alebo viacerých rekurentných sietí, kde výstup predchádzajúcej vrstvy je posunutý do ďalšej vrstvy ako vstup. -![Obrázok zobrazujúci viacvrstvovú LSTM RNN](../../../../../translated_images/sk/multi-layer-lstm.dd975e29bb2a59fe.jpg) +![Obrázok zobrazujúci viacvrstvovú LSTM RNN](../../../../../translated_images/sk/multi-layer-lstm.dd975e29bb2a59fe.webp) *Obrázok z [tohto skvelého príspevku](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) od Fernanda Lópeza* diff --git a/translations/sk/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/sk/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index aa386a76..6acc51e1 100644 --- a/translations/sk/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/sk/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "Rekurentná sieť, či už jednosmerná alebo obojsmerná, zachytáva určité vzory v rámci sekvencie a môže ich uložiť do vektora stavu alebo odovzdať do výstupu. Rovnako ako pri konvolučných sieťach, môžeme na prvú vrstvu postaviť ďalšiu rekurentnú vrstvu, aby sme zachytili vzory na vyššej úrovni, ktoré sú vytvorené z nízkoúrovňových vzorov extrahovaných prvou vrstvou. To nás privádza k pojmu **viacvrstvová RNN**, ktorá pozostáva z dvoch alebo viacerých rekurentných sietí, kde výstup predchádzajúcej vrstvy je odovzdaný ako vstup do nasledujúcej vrstvy.\n", "\n", - "![Obrázok zobrazujúci viacvrstvovú LSTM-RNN](../../../../../translated_images/sk/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![Obrázok zobrazujúci viacvrstvovú LSTM-RNN](../../../../../translated_images/sk/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*Obrázok z [tohto skvelého článku](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) od Fernanda Lópeza*\n", "\n", diff --git a/translations/sk/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/sk/lessons/5-NLP/16-RNN/RNNTF.ipynb index 02eb1076..3f0b6afc 100644 --- a/translations/sk/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/sk/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "Na zachytenie významu textovej sekvencie použijeme architektúru neurónovej siete nazývanú **rekurentná neurónová sieť** (RNN). Pri použití RNN prechádzame vetou cez sieť po jednom tokene, pričom sieť produkuje určitý **stav**, ktorý následne odovzdáme s ďalším tokenom späť do siete.\n", "\n", - "![Obrázok znázorňujúci generovanie rekurentnej neurónovej siete.](../../../../../translated_images/sk/rnn.27f5c29c53d727b5.png)\n", + "![Obrázok znázorňujúci generovanie rekurentnej neurónovej siete.](../../../../../translated_images/sk/rnn.27f5c29c53d727b5.webp)\n", "\n", "Pri danej vstupnej sekvencii tokenov $X_0,\\dots,X_n$ RNN vytvára sekvenciu blokov neurónovej siete a trénuje túto sekvenciu end-to-end pomocou spätného šírenia. Každý blok siete prijíma ako vstup dvojicu $(X_i,S_i)$ a produkuje výsledok $S_{i+1}$. Konečný stav $S_n$ alebo výstup $Y_n$ sa odovzdáva do lineárneho klasifikátora na produkciu výsledku. Všetky bloky siete zdieľajú rovnaké váhy a sú trénované end-to-end jedným priechodom spätného šírenia.\n", "\n", @@ -369,7 +369,7 @@ "\n", "Rekurentné siete, či už jednosmerné alebo obojsmerné, zachytávajú vzory v rámci sekvencie a ukladajú ich do stavových vektorov alebo ich vracajú ako výstup. Podobne ako pri konvolučných sieťach, môžeme za prvú rekurentnú vrstvu pridať ďalšiu, aby sme zachytili vzory vyššej úrovne, ktoré sú vytvorené z nižších úrovní vzorov extrahovaných prvou vrstvou. To nás privádza k pojmu **viacvrstvová RNN**, ktorá pozostáva z dvoch alebo viacerých rekurentných sietí, kde výstup predchádzajúcej vrstvy slúži ako vstup pre nasledujúcu vrstvu.\n", "\n", - "![Obrázok zobrazujúci viacvrstvovú LSTM RNN](../../../../../translated_images/sk/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![Obrázok zobrazujúci viacvrstvovú LSTM RNN](../../../../../translated_images/sk/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*Obrázok pochádza z [tohto skvelého článku](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) od Fernanda Lópeza.*\n", "\n", diff --git a/translations/sk/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/sk/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index b486f7e8..556f0a69 100644 --- a/translations/sk/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/sk/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "Spôsob, akým budeme trénovať RNN na generovanie textu, je nasledovný. V každom kroku vezmeme sekvenciu znakov s dĺžkou `nchars` a požiadame sieť, aby pre každý vstupný znak vygenerovala nasledujúci výstupný znak:\n", "\n", - "![Obrázok zobrazujúci príklad generovania slova 'HELLO' pomocou RNN.](../../../../../translated_images/sk/rnn-generate.56c54afb52f9781d.png)\n", + "![Obrázok zobrazujúci príklad generovania slova 'HELLO' pomocou RNN.](../../../../../translated_images/sk/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "V závislosti od konkrétneho scenára môžeme chcieť zahrnúť aj špeciálne znaky, ako napríklad *koniec sekvencie* ``. V našom prípade chceme sieť trénovať na nekonečné generovanie textu, preto nastavíme veľkosť každej sekvencie na `nchars` tokenov. Každý tréningový príklad tak bude pozostávať z `nchars` vstupov a `nchars` výstupov (čo je vstupná sekvencia posunutá o jeden symbol doľava). Minibatch bude obsahovať niekoľko takýchto sekvencií.\n", "\n", diff --git a/translations/sk/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/sk/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index bf819db5..1e7d5a8c 100644 --- a/translations/sk/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/sk/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -111,7 +111,7 @@ "\n", "Spôsob, akým budeme trénovať RNN na generovanie nadpisov správ, je nasledovný. V každom kroku vezmeme jeden nadpis, ktorý bude poskytnutý RNN, a pre každý vstupný znak požiadame sieť, aby vygenerovala nasledujúci výstupný znak:\n", "\n", - "![Obrázok zobrazujúci príklad generovania slova 'HELLO' pomocou RNN.](../../../../../translated_images/sk/rnn-generate.56c54afb52f9781d.png)\n", + "![Obrázok zobrazujúci príklad generovania slova 'HELLO' pomocou RNN.](../../../../../translated_images/sk/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "Pre posledný znak našej sekvencie požiadame sieť, aby vygenerovala token ``.\n", "\n", diff --git a/translations/sk/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/sk/lessons/5-NLP/17-GenerativeNetworks/README.md index 04126ac6..63b8e1ba 100644 --- a/translations/sk/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/sk/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ V architektúre RNN, ktorú sme preberali v predchádzajúcej jednotke, každá To umožňuje rôzne neurónové architektúry, ktoré sú znázornené na obrázku nižšie: -![Obrázok zobrazujúci bežné vzory rekurentných neurónových sietí.](../../../../../translated_images/sk/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Obrázok zobrazujúci bežné vzory rekurentných neurónových sietí.](../../../../../translated_images/sk/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > Obrázok z blogového príspevku [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) od [Andreja Karpatyho](http://karpathy.github.io/) @@ -32,7 +32,7 @@ V tejto jednotke sa zameriame na jednoduché generatívne modely, ktoré nám po Budeme trénovať túto RNN na generovanie textu krok za krokom. Na každom kroku vezmeme sekvenciu znakov dĺžky `nchars` a požiadame sieť, aby pre každý vstupný znak vygenerovala ďalší výstupný znak: -![Obrázok zobrazujúci príklad generovania slova 'HELLO' pomocou RNN.](../../../../../translated_images/sk/rnn-generate.56c54afb52f9781d.png) +![Obrázok zobrazujúci príklad generovania slova 'HELLO' pomocou RNN.](../../../../../translated_images/sk/rnn-generate.56c54afb52f9781d.webp) Pri generovaní textu (počas inferencie) začíname s nejakým **podnetom**, ktorý prechádza cez RNN bunky na generovanie jeho medzistavu, a potom z tohto stavu začína generovanie. Generujeme jeden znak naraz a stav spolu s vygenerovaným znakom posielame ďalšej RNN bunke na generovanie ďalšieho znaku, až kým nevygenerujeme dostatok znakov. diff --git a/translations/sk/lessons/5-NLP/18-Transformers/README.md b/translations/sk/lessons/5-NLP/18-Transformers/README.md index 77a9e886..3349e484 100644 --- a/translations/sk/lessons/5-NLP/18-Transformers/README.md +++ b/translations/sk/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ Pri RNN sa sekvencia na sekvenciu implementuje pomocou dvoch rekurentných siet **Mechanizmy pozornosti** poskytujú spôsob váženia kontextového vplyvu každého vstupného vektora na každú výstupnú predikciu RNN. Implementuje sa to vytvorením skratiek medzi medzistavmi vstupného RNN a výstupného RNN. Týmto spôsobom, pri generovaní výstupného symbolu yt, zohľadníme všetky skryté stavy vstupu hi, s rôznymi váhovými koeficientmi αt,i. -![Obrázok zobrazujúci model enkodér/dekodér s aditívnou vrstvou pozornosti](../../../../../translated_images/sk/encoder-decoder-attention.7a726296894fb567.png) +![Obrázok zobrazujúci model enkodér/dekodér s aditívnou vrstvou pozornosti](../../../../../translated_images/sk/encoder-decoder-attention.7a726296894fb567.webp) > Model enkodér-dekodér s aditívnym mechanizmom pozornosti podľa [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citované z [tohto blogového príspevku](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) Maticu pozornosti {αi,j} by sme mohli interpretovať ako mieru, do akej určité vstupné slová ovplyvňujú generovanie daného slova vo výstupnej sekvencii. Nižšie je príklad takejto matice: -![Obrázok zobrazujúci vzorové zarovnanie nájdené RNNsearch-50, prevzaté z Bahdanau - arviz.org](../../../../../translated_images/sk/bahdanau-fig3.09ba2d37f202a6af.png) +![Obrázok zobrazujúci vzorové zarovnanie nájdené RNNsearch-50, prevzaté z Bahdanau - arviz.org](../../../../../translated_images/sk/bahdanau-fig3.09ba2d37f202a6af.webp) > Obrázok z [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Obr.3) @@ -66,7 +66,7 @@ Výsledok, ktorý získame s pozičným embeddingom, zahŕňa pôvodný token aj Ďalej potrebujeme zachytiť nejaké vzory v rámci našej sekvencie. Na tento účel transformery používajú mechanizmus **vlastnej pozornosti**, ktorý je v podstate pozornosť aplikovaná na tú istú sekvenciu ako vstup a výstup. Aplikovanie vlastnej pozornosti nám umožňuje zohľadniť **kontext** v rámci vety a vidieť, ktoré slová sú navzájom prepojené. Napríklad nám umožňuje vidieť, na ktoré slová odkazujú koreferencie, ako *to*, a tiež zohľadniť kontext: -![](../../../../../translated_images/sk/CoreferenceResolution.861924d6d384a7d6.png) +![](../../../../../translated_images/sk/CoreferenceResolution.861924d6d384a7d6.webp) > Obrázok z [Google Blogu](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Keďže každá vstupná pozícia je nezávisle mapovaná na každú výstupnú **BERT** (Bidirectional Encoder Representations from Transformers) je veľmi veľká viacvrstvová sieť transformera s 12 vrstvami pre *BERT-base* a 24 pre *BERT-large*. Model je najprv predtrénovaný na veľkom korpuse textových dát (WikiPedia + knihy) pomocou nesupervidovaného tréningu (predikcia maskovaných slov vo vete). Počas predtrénovania model absorbuje významné úrovne porozumenia jazyka, ktoré môžu byť následne využité s inými datasetmi pomocou jemného doladenia. Tento proces sa nazýva **transfer learning**. -![obrázok z http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/sk/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) +![obrázok z http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/sk/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > Obrázok [zdroj](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/sk/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/sk/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index 38268ce3..0bfb3d49 100644 --- a/translations/sk/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/sk/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**Mechanizmy pozornosti** poskytujú spôsob, ako vážiť kontextuálny vplyv každého vstupného vektora na každú výstupnú predikciu RNN. Implementuje sa to vytvorením skratiek medzi medzistavmi vstupnej RNN a výstupnej RNN. Týmto spôsobom, pri generovaní výstupného symbolu $y_t$, zohľadníme všetky skryté stavy vstupu $h_i$, s rôznymi váhovými koeficientmi $\\alpha_{t,i}$.\n", "\n", - "![Obrázok zobrazujúci model encoder/decoder s vrstvou aditívnej pozornosti](../../../../../translated_images/sk/encoder-decoder-attention.7a726296894fb567.png)\n", + "![Obrázok zobrazujúci model encoder/decoder s vrstvou aditívnej pozornosti](../../../../../translated_images/sk/encoder-decoder-attention.7a726296894fb567.webp)\n", "*Model encoder-decoder s mechanizmom aditívnej pozornosti podľa [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citované z [tohto blogového príspevku](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Maticu pozornosti $\\{\\alpha_{i,j}\\}$ môžeme interpretovať ako mieru, do akej konkrétne vstupné slová ovplyvňujú generovanie daného slova vo výstupnej sekvencii. Nižšie je príklad takejto matice:\n", "\n", - "![Obrázok zobrazujúci vzorové zarovnanie nájdené RNNsearch-50, prevzaté z Bahdanau - arviz.org](../../../../../translated_images/sk/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![Obrázok zobrazujúci vzorové zarovnanie nájdené RNNsearch-50, prevzaté z Bahdanau - arviz.org](../../../../../translated_images/sk/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*Obrázok prevzatý z [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Obr.3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) je veľmi veľká viacvrstvová sieť Transformer s 12 vrstvami pre *BERT-base* a 24 pre *BERT-large*. Model je najprv predtrénovaný na veľkom korpuse textových dát (Wikipedia + knihy) pomocou nesupervidovaného tréningu (predikcia maskovaných slov vo vete). Počas predtrénovania model absorbuje významnú úroveň porozumenia jazyka, ktorú je možné následne využiť s inými dátovými súbormi pomocou jemného doladenia. Tento proces sa nazýva **transfer learning**.\n", "\n", - "![Obrázok z http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/sk/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![Obrázok z http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/sk/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "Existuje mnoho variácií architektúr Transformer, vrátane BERT, DistilBERT, BigBird, OpenGPT3 a ďalších, ktoré je možné jemne doladiť. Balík [HuggingFace](https://github.com/huggingface/) poskytuje úložisko na tréning mnohých z týchto architektúr pomocou PyTorch.\n", "\n", diff --git a/translations/sk/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/sk/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index f3dda8db..ea48f5a3 100644 --- a/translations/sk/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/sk/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**Mechanizmy pozornosti** poskytujú spôsob, ako vážiť kontextuálny vplyv každého vstupného vektora na každú predikciu výstupu RNN. Implementuje sa to vytvorením skratiek medzi medzistavmi vstupnej RNN a výstupnej RNN. Týmto spôsobom, pri generovaní výstupného symbolu $y_t$, zohľadníme všetky skryté stavy vstupu $h_i$, s rôznymi váhovými koeficientmi $\\alpha_{t,i}$.\n", "\n", - "![Obrázok zobrazujúci model encoder/decoder s vrstvou aditívnej pozornosti](../../../../../translated_images/sk/encoder-decoder-attention.7a726296894fb567.png)\n", + "![Obrázok zobrazujúci model encoder/decoder s vrstvou aditívnej pozornosti](../../../../../translated_images/sk/encoder-decoder-attention.7a726296894fb567.webp)\n", "*Model encoder-decoder s mechanizmom aditívnej pozornosti podľa [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citované z [tohto blogového príspevku](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Matica pozornosti $\\{\\alpha_{i,j}\\}$ predstavuje mieru, do akej určité vstupné slová ovplyvňujú generovanie daného slova vo výstupnej sekvencii. Nižšie je príklad takejto matice:\n", "\n", - "![Obrázok zobrazujúci vzorové zarovnanie nájdené RNNsearch-50, prevzaté z Bahdanau - arviz.org](../../../../../translated_images/sk/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![Obrázok zobrazujúci vzorové zarovnanie nájdené RNNsearch-50, prevzaté z Bahdanau - arviz.org](../../../../../translated_images/sk/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*Obrázok prevzatý z [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Obr.3)*\n", "\n", @@ -231,7 +231,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) je veľmi veľká viacvrstvová transformerová sieť s 12 vrstvami pre *BERT-base* a 24 vrstvami pre *BERT-large*. Model je najskôr predtrénovaný na veľkom korpuse textových dát (WikiPedia + knihy) pomocou nesupervidovaného učenia (predpovedanie maskovaných slov vo vete). Počas predtrénovania model absorbuje významnú úroveň porozumenia jazyka, ktorú je možné následne využiť s inými datasetmi pomocou jemného doladenia. Tento proces sa nazýva **transferové učenie**.\n", "\n", - "![obrázok z http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/sk/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![obrázok z http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/sk/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "Existuje mnoho variácií transformerových architektúr vrátane BERT, DistilBERT, BigBird, OpenGPT3 a ďalších, ktoré je možné jemne doladiť.\n", "\n", diff --git a/translations/sk/lessons/5-NLP/19-NER/README.md b/translations/sk/lessons/5-NLP/19-NER/README.md index 4330909c..af6ea3dc 100644 --- a/translations/sk/lessons/5-NLP/19-NER/README.md +++ b/translations/sk/lessons/5-NLP/19-NER/README.md @@ -56,7 +56,7 @@ novorodenca | O Keďže potrebujeme vytvoriť jednoznačnú korešpondenciu medzi tokenmi a triedami, môžeme trénovať pravú **mnoho-na-mnoho** neurónovú sieť z tohto obrázku: -![Obrázok zobrazujúci bežné vzory rekurentných neurónových sietí.](../../../../../translated_images/sk/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Obrázok zobrazujúci bežné vzory rekurentných neurónových sietí.](../../../../../translated_images/sk/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > *Obrázok z [tohto blogového príspevku](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) od [Andreja Karpathyho](http://karpathy.github.io/). Modely klasifikácie tokenov NER zodpovedajú najpravšej architektúre siete na tomto obrázku.* diff --git a/translations/sk/lessons/5-NLP/README.md b/translations/sk/lessons/5-NLP/README.md index aca50226..e675c908 100644 --- a/translations/sk/lessons/5-NLP/README.md +++ b/translations/sk/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Spracovanie prirodzeného jazyka -![Zhrnutie úloh NLP v kresbe](../../../../translated_images/sk/ai-nlp.b22dcb8ca4707cea.png) +![Zhrnutie úloh NLP v kresbe](../../../../translated_images/sk/ai-nlp.b22dcb8ca4707cea.webp) V tejto sekcii sa zameriame na používanie neurónových sietí na riešenie úloh súvisiacich so **spracovaním prirodzeného jazyka (NLP)**. Existuje mnoho problémov v oblasti NLP, ktoré by sme chceli, aby počítače dokázali vyriešiť: diff --git a/translations/sk/lessons/6-Other/23-MultiagentSystems/README.md b/translations/sk/lessons/6-Other/23-MultiagentSystems/README.md index 909cdea1..e12e57cc 100644 --- a/translations/sk/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/sk/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ Môžete otvoriť jeden z modelov, napríklad **Biology → Flocking**. Po otvorení modelu sa dostanete na hlavnú obrazovku NetLogo. Tu je ukážkový model, ktorý popisuje populáciu vlkov a oviec, pričom zdroje (tráva) sú obmedzené. -![NetLogo Main Screen](../../../../../translated_images/sk/NetLogo-Main.32653711ec1a01b3.png) +![NetLogo Main Screen](../../../../../translated_images/sk/NetLogo-Main.32653711ec1a01b3.webp) > Snímka obrazovky od Dmitry Soshnikov diff --git a/translations/sk/lessons/README.md b/translations/sk/lessons/README.md index add695e4..d7b7e756 100644 --- a/translations/sk/lessons/README.md +++ b/translations/sk/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Prehľad -![Prehľad v kresbe](../../../translated_images/sk/ai-overview.0857791951d19500.png) +![Prehľad v kresbe](../../../translated_images/sk/ai-overview.0857791951d19500.webp) > Kresba od [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/sk/lessons/X-Extras/X1-MultiModal/README.md b/translations/sk/lessons/X-Extras/X1-MultiModal/README.md index e08c10a8..3cdd4d64 100644 --- a/translations/sk/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/sk/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ Po úspechu transformerových modelov pri riešení úloh spracovania prirodzen Hlavnou myšlienkou CLIP je schopnosť porovnávať textové podnety s obrázkom a určiť, ako dobre obrázok zodpovedá danému podnetu. -![Architektúra CLIP](../../../../../translated_images/sk/clip-arch.b3dbf20b4e8ed8be.png) +![Architektúra CLIP](../../../../../translated_images/sk/clip-arch.b3dbf20b4e8ed8be.webp) > *Obrázok z [tohto blogového príspevku](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ Po predtrénovaní modelu mu môžeme poskytnúť dávku obrázkov a dávku text Predpokladajme, že potrebujeme klasifikovať obrázky, napríklad medzi mačkami, psami a ľuďmi. V tomto prípade môžeme modelu poskytnúť obrázok a sériu textových podnetov: "*obrázok mačky*", "*obrázok psa*", "*obrázok človeka*". Vo výslednom vektore s 3 pravdepodobnosťami stačí vybrať index s najvyššou hodnotou. -![CLIP pre klasifikáciu obrázkov](../../../../../translated_images/sk/clip-class.3af42ef0b2b19369.png) +![CLIP pre klasifikáciu obrázkov](../../../../../translated_images/sk/clip-class.3af42ef0b2b19369.webp) > *Obrázok z [tohto blogového príspevku](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ Viac o VQGAN sa dozviete na webovej stránke [Taming Transformers](https://compv Jedným z dôležitých rozdielov medzi VQGAN a tradičným GAN je, že tradičný GAN dokáže vytvoriť slušný obrázok z akéhokoľvek vstupného vektora, zatiaľ čo VQGAN pravdepodobne vytvorí obrázok, ktorý nebude koherentný. Preto je potrebné ďalej usmerňovať proces tvorby obrázka, čo sa dá dosiahnuť pomocou CLIP. -![Architektúra VQGAN+CLIP](../../../../../translated_images/sk/vqgan.5027fe05051dfa31.png) +![Architektúra VQGAN+CLIP](../../../../../translated_images/sk/vqgan.5027fe05051dfa31.webp) Na generovanie obrázka zodpovedajúceho textovému podnetu začíname s náhodným kódovacím vektorom, ktorý prechádza cez VQGAN a vytvára obrázok. Potom sa použije CLIP na vytvorenie stratovej funkcie, ktorá ukazuje, ako dobre obrázok zodpovedá textovému podnetu. Cieľom je minimalizovať túto stratu pomocou spätného šírenia na úpravu parametrov vstupného vektora. Skvelá knižnica, ktorá implementuje VQGAN+CLIP, je [Pixray](http://github.com/pixray/pixray). -![Obrázok vytvorený Pixray](../../../../../translated_images/sk/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Obrázok vytvorený Pixray](../../../../../translated_images/sk/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Obrázok vytvorený Pixray](../../../../../translated_images/sk/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) +![Obrázok vytvorený Pixray](../../../../../translated_images/sk/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp) | ![Obrázok vytvorený Pixray](../../../../../translated_images/sk/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp) | ![Obrázok vytvorený Pixray](../../../../../translated_images/sk/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp) ----|----|---- Obrázok vytvorený z podnetu *detailný akvarelový portrét mladého učiteľa literatúry s knihou* | Obrázok vytvorený z podnetu *detailný olejový portrét mladej učiteľky informatiky s počítačom* | Obrázok vytvorený z podnetu *detailný olejový portrét starého učiteľa matematiky pred tabuľou* diff --git a/translations/sl/README.md b/translations/sl/README.md index 8ac42b8d..4825f441 100644 --- a/translations/sl/README.md +++ b/translations/sl/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # Umetna inteligenca za začetnike - učni načrt -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/sl/ai-overview.0857791951d19500.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/sl/ai-overview.0857791951d19500.webp)| |:---:| | AI za začetnike - _Sketchnote avtorja [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/sl/lessons/1-Intro/README.md b/translations/sl/lessons/1-Intro/README.md index 6b8a6ce9..53a868f3 100644 --- a/translations/sl/lessons/1-Intro/README.md +++ b/translations/sl/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Uvod v umetno inteligenco -![Povzetek vsebine uvoda v umetno inteligenco v skici](../../../../translated_images/sl/ai-intro.bf28d1ac4235881c.png) +![Povzetek vsebine uvoda v umetno inteligenco v skici](../../../../translated_images/sl/ai-intro.bf28d1ac4235881c.webp) > Skica avtorja [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Računalnike je prvotno izumil [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage) za obdelavo števil po natančno določenem postopku – algoritmu. Sodobni računalniki, čeprav bistveno naprednejši od prvotnega modela iz 19. stoletja, še vedno sledijo isti ideji nadzorovanih izračunov. Tako je mogoče programirati računalnik za izvedbo naloge, če poznamo natančen zaporedje korakov, potrebnih za dosego cilja. -![Fotografija osebe](../../../../translated_images/sl/dsh_age.d212a30d4e54fb5f.png) +![Fotografija osebe](../../../../translated_images/sl/dsh_age.d212a30d4e54fb5f.webp) > Fotografija avtorja [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ Za več informacij glejte **[Splošna umetna inteligenca](https://en.wikipedia.o Ena od težav pri obravnavi izraza **[inteligenca](https://en.wikipedia.org/wiki/Intelligence)** je, da ni jasne definicije tega pojma. Nekateri trdijo, da je inteligenca povezana z **abstraktnim razmišljanjem** ali **samozavedanjem**, vendar je ne moremo ustrezno opredeliti. -![Fotografija mačke](../../../../translated_images/sl/photo-cat.8c8e8fb760ffe457.jpg) +![Fotografija mačke](../../../../translated_images/sl/photo-cat.8c8e8fb760ffe457.webp) > [Fotografija](https://unsplash.com/photos/75715CVEJhI) avtorja [Amber Kipp](https://unsplash.com/@sadmax) iz Unsplash @@ -98,13 +98,13 @@ Alternativno lahko poskusimo modelirati najpreprostejše elemente v naših možg > | Kaj pa ML? | | > |--------------|-----------| -> | Del umetne inteligence, ki temelji na tem, da se računalnik nauči reševati problem na podlagi nekaterih podatkov, se imenuje **strojno učenje**. Klasičnega strojnega učenja v tem tečaju ne bomo obravnavali – napotujemo vas na ločen učni načrt [Strojno učenje za začetnike](http://aka.ms/ml-beginners). | ![ML za začetnike](../../../../translated_images/sl/ml-for-beginners.9e4fed176fd5817d.png) | +> | Del umetne inteligence, ki temelji na tem, da se računalnik nauči reševati problem na podlagi nekaterih podatkov, se imenuje **strojno učenje**. Klasičnega strojnega učenja v tem tečaju ne bomo obravnavali – napotujemo vas na ločen učni načrt [Strojno učenje za začetnike](http://aka.ms/ml-beginners). | ![ML za začetnike](../../../../translated_images/sl/ml-for-beginners.9e4fed176fd5817d.webp) | ## Kratka zgodovina umetne inteligence Umetna inteligenca se je kot področje začela sredi dvajsetega stoletja. Sprva je bil simbolični pristop prevladujoč in je prinesel številne pomembne uspehe, kot so ekspertni sistemi – računalniški programi, ki so lahko delovali kot strokovnjaki na nekaterih omejenih problematičnih področjih. Vendar se je kmalu izkazalo, da tak pristop ni dobro skalabilen. Pridobivanje znanja od strokovnjaka, njegovo predstavljanje v računalniku in ohranjanje točnosti baze znanja se je izkazalo za zelo zapleteno nalogo in predrago za praktično uporabo v mnogih primerih. To je privedlo do tako imenovane [zime umetne inteligence](https://en.wikipedia.org/wiki/AI_winter) v 70. letih. -Kratka zgodovina umetne inteligence +Kratka zgodovina umetne inteligence > Slika avtorja [Dmitry Soshnikov](http://soshnikov.com) @@ -124,7 +124,7 @@ Podobno lahko vidimo, kako se je spreminjal pristop k ustvarjanju "govorečih pr * Sodobni asistenti, kot so Cortana, Siri ali Google Assistant, so vsi hibridni sistemi, ki uporabljajo nevronske mreže za pretvorbo govora v besedilo in prepoznavanje našega namena, nato pa uporabijo nekaj razmišljanja ali eksplicitnih algoritmov za izvedbo zahtevanih dejanj. * V prihodnosti lahko pričakujemo popoln model, ki temelji na nevronskih mrežah, za samostojno obravnavo dialoga. Nedavne družine nevronskih mrež GPT in [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) kažejo velik uspeh pri tem. -evolucija Turingovega testa +evolucija Turingovega testa > Slika Dmitry Soshnikov, [fotografija](https://unsplash.com/photos/r8LmVbUKgns) avtorja [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto), Unsplash ## Nedavne raziskave na področju umetne inteligence diff --git a/translations/sl/lessons/2-Symbolic/Animals.ipynb b/translations/sl/lessons/2-Symbolic/Animals.ipynb index 153cd484..c3472b6d 100644 --- a/translations/sl/lessons/2-Symbolic/Animals.ipynb +++ b/translations/sl/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "V tem primeru bomo implementirali preprost sistem, ki temelji na znanju, za določanje živali na podlagi nekaterih fizičnih značilnosti. Sistem lahko predstavimo z naslednjim AND-OR drevesom (to je del celotnega drevesa, zlahka lahko dodamo še več pravil):\n", "\n", - "![](../../../../translated_images/sl/AND-OR-Tree.5592d2c70187f283.png)\n" + "![](../../../../translated_images/sl/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { diff --git a/translations/sl/lessons/2-Symbolic/README.md b/translations/sl/lessons/2-Symbolic/README.md index ca132102..12615b8e 100644 --- a/translations/sl/lessons/2-Symbolic/README.md +++ b/translations/sl/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Predstavitev znanja in ekspertni sistemi -![Povzetek vsebine simbolne umetne inteligence](../../../../translated_images/sl/ai-symbolic.715a30cb610411a6.png) +![Povzetek vsebine simbolne umetne inteligence](../../../../translated_images/sl/ai-symbolic.715a30cb610411a6.webp) > Sketchnote avtorja [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ Najpogosteje znanja ne definiramo strogo, ampak ga uskladimo z drugimi povezanim Tako je problem **predstavitve znanja** najti učinkovit način za predstavitev znanja znotraj računalnika v obliki podatkov, da bi bilo samodejno uporabno. To lahko vidimo kot spekter: -![Spekter predstavitve znanja](../../../../translated_images/sl/knowledge-spectrum.b60df631852c0217.png) +![Spekter predstavitve znanja](../../../../translated_images/sl/knowledge-spectrum.b60df631852c0217.webp) > Slika avtorja [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Blok-sintaksa | Zamik | | | Eden zgodnjih uspehov simbolne umetne inteligence so bili tako imenovani **ekspertni sistemi** - računalniški sistemi, zasnovani za delovanje kot strokovnjak na omejenem področju problemov. Temeljili so na **bazi znanja**, pridobljeni od enega ali več človeških strokovnjakov, in vsebovali **inferenčni mehanizem**, ki je izvajal razmišljanje na podlagi te baze. -![Človeška arhitektura](../../../../translated_images/sl/arch-human.5d4d35f1bba3ab1c.png) | ![Arhitektura sistema, ki temelji na znanju](../../../../translated_images/sl/arch-kbs.3ec5c150b09fa8da.png) +![Človeška arhitektura](../../../../translated_images/sl/arch-human.5d4d35f1bba3ab1c.webp) | ![Arhitektura sistema, ki temelji na znanju](../../../../translated_images/sl/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ Poenostavljena struktura človeškega nevronskega sistema | Arhitektura sistema, ki temelji na znanju @@ -106,7 +106,7 @@ Ekspertni sistemi so zgrajeni podobno kot človeški sistem razmišljanja, ki vs Kot primer si poglejmo naslednji ekspertni sistem za določanje živali na podlagi njihovih fizičnih značilnosti: -![AND-OR drevo](../../../../translated_images/sl/AND-OR-Tree.5592d2c70187f283.png) +![AND-OR drevo](../../../../translated_images/sl/AND-OR-Tree.5592d2c70187f283.webp) > Slika avtorja [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/sl/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/sl/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index dbdbb8d5..f78a76a4 100644 --- a/translations/sl/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/sl/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -1255,7 +1255,7 @@ "* Nizka izguba na učnih podatkih - model lahko dobro približa učne podatke, ker ima dovolj izražalne moči.\n", "* Izguba na validacijskih podatkih je lahko veliko višja kot izguba na učnih podatkih in se lahko med učenjem začne povečevati - to je zato, ker model \"zapomni\" učne točke in izgubi \"celotno sliko\".\n", "\n", - "![Prilagajanje na učne podatke](../../../../../translated_images/sl/overfit.a0bd57f717c15769.png)\n", + "![Prilagajanje na učne podatke](../../../../../translated_images/sl/overfit.a0bd57f717c15769.webp)\n", "\n", "> Na tej sliki `x` predstavlja učne podatke, `o` - validacijske podatke. Levo - linearen model (enoplastni), ki precej dobro približa naravo podatkov. Desno - model, ki je preveč prilagojen učnim podatkom, model popolnoma dobro približa učne podatke, vendar izgubi smisel pri drugih podatkih (napaka na validacijskih podatkih je zelo visoka).\n" ] diff --git a/translations/sl/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/sl/lessons/3-NeuralNetworks/05-Frameworks/README.md index b2f83a11..ce2b35fa 100644 --- a/translations/sl/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/sl/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Prenaučenje je izjemno pomemben koncept v strojnem učenju, zato je zelo pomemb Razmislimo o naslednjem problemu približevanja 5 točk (predstavljenih z `x` na spodnjih grafih): -![linear](../../../../../translated_images/sl/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/sl/overfit2.131f5800ae10ca5e.jpg) +![linear](../../../../../translated_images/sl/overfit1.f24b71c6f652e59e.webp) | ![overfit](../../../../../translated_images/sl/overfit2.131f5800ae10ca5e.webp) -------------------------|-------------------------- **Linearen model, 2 parametra** | **Nelinearen model, 7 parametrov** Napaka pri učenju = 5.3 | Napaka pri učenju = 0 @@ -79,7 +79,7 @@ Zelo pomembno je najti pravo ravnovesje med kompleksnostjo modela (številom par Kot lahko vidite na zgornjem grafu, lahko prenaučenje zaznamo z zelo nizko napako pri učenju in visoko napako pri validaciji. Običajno med učenjem vidimo, da se napake pri učenju in validaciji zmanjšujejo, nato pa se v nekem trenutku napaka pri validaciji preneha zmanjševati in začne naraščati. To bo znak prenaučenja in indikator, da bi morali verjetno ustaviti učenje (ali vsaj narediti posnetek modela). -![prenaučenje](../../../../../translated_images/sl/Overfitting.408ad91cd90b4371.png) +![prenaučenje](../../../../../translated_images/sl/Overfitting.408ad91cd90b4371.webp) ## Kako preprečiti prenaučenje diff --git a/translations/sl/lessons/3-NeuralNetworks/README.md b/translations/sl/lessons/3-NeuralNetworks/README.md index f66953d5..c4b1276a 100644 --- a/translations/sl/lessons/3-NeuralNetworks/README.md +++ b/translations/sl/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Uvod v nevronske mreže -![Povzetek vsebine uvoda v nevronske mreže v skici](../../../../translated_images/sl/ai-neuralnetworks.1c687ae40bc86e83.png) +![Povzetek vsebine uvoda v nevronske mreže v skici](../../../../translated_images/sl/ai-neuralnetworks.1c687ae40bc86e83.webp) Kot smo razpravljali v uvodu, je eden od načinov za dosego inteligence treniranje **računalniškega modela** ali **umetnih možganov**. Od sredine 20. stoletja so raziskovalci preizkušali različne matematične modele, dokler se v zadnjih letih ta smer ni izkazala za izjemno uspešno. Takšni matematični modeli možganov se imenujejo **nevronske mreže**. @@ -36,13 +36,13 @@ V tem učnem načrtu se bomo osredotočili le na modele nevronskih mrež. Iz biologije vemo, da naši možgani sestojijo iz nevralnih celic (nevronov), od katerih ima vsaka več "vhodov" (dendritov) in en "izhod" (akson). Tako dendriti kot aksoni lahko prenašajo električne signale, povezave med njimi — znane kot sinapse — pa lahko kažejo različne stopnje prevodnosti, ki jih uravnavajo nevrotransmiterji. -![Model nevrona](../../../../translated_images/sl/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![Model nevrona](../../../../translated_images/sl/artneuron.1a5daa88d20ebe6f.png) +![Model nevrona](../../../../translated_images/sl/synapse-wikipedia.ed20a9e4726ea1c6.webp) | ![Model nevrona](../../../../translated_images/sl/artneuron.1a5daa88d20ebe6f.webp) ----|---- Pravi nevron *([Slika](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) iz Wikipedije)* | Umetni nevron *(Slika avtorja)* Tako najpreprostejši matematični model nevrona vsebuje več vhodov X1, ..., XN in en izhod Y ter vrsto uteži W1, ..., WN. Izhod se izračuna kot: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) kjer je f neka nelinearna **aktivacijska funkcija**. diff --git a/translations/sl/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/sl/lessons/4-ComputerVision/06-IntroCV/README.md index f90a3e97..a3bec85d 100644 --- a/translations/sl/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/sl/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ V našem [OpenCV Notebook](OpenCV.ipynb) podajamo nekaj primerov, kdaj se račun * **Predobdelava fotografije Braillove knjige**. Osredotočamo se na to, kako lahko uporabimo pragovno obdelavo, zaznavanje značilnosti, perspektivno transformacijo in manipulacije z NumPy za ločevanje posameznih Braillovih simbolov za nadaljnjo klasifikacijo z nevronsko mrežo. -![Slika Braillove knjige](../../../../../translated_images/sl/braille.341962ff76b1bd70.jpeg) | ![Predobdelana slika Braillove knjige](../../../../../translated_images/sl/braille-result.46530fea020b03c7.png) | ![Braillovi simboli](../../../../../translated_images/sl/braille-symbols.0159185ab69d5339.png) +![Slika Braillove knjige](../../../../../translated_images/sl/braille.341962ff76b1bd70.webp) | ![Predobdelana slika Braillove knjige](../../../../../translated_images/sl/braille-result.46530fea020b03c7.webp) | ![Braillovi simboli](../../../../../translated_images/sl/braille-symbols.0159185ab69d5339.webp) ----|-----|----- > Slika iz [OpenCV.ipynb](OpenCV.ipynb) * **Zaznavanje gibanja v videu z razliko med okvirji**. Če je kamera fiksna, bi morali biti okvirji iz kamere med seboj precej podobni. Ker so okvirji predstavljeni kot polja, bomo z odštevanjem teh polj za dva zaporedna okvirja dobili razliko med piksli, ki bi morala biti nizka za statične okvirje in postati višja, ko je v sliki zaznano večje gibanje. -![Slika video okvirjev in razlik med okvirji](../../../../../translated_images/sl/frame-difference.706f805491a0883c.png) +![Slika video okvirjev in razlik med okvirji](../../../../../translated_images/sl/frame-difference.706f805491a0883c.webp) > Slika iz [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ V našem [OpenCV Notebook](OpenCV.ipynb) podajamo nekaj primerov, kdaj se račun - **Gost optični tok** izračuna vektorsko polje, ki kaže, kam se premika vsak piksel. - **Redek optični tok** temelji na zaznavanju nekaterih značilnih značilnosti slike (npr. robov) in gradnji njihove trajektorije od okvirja do okvirja. -![Slika optičnega toka](../../../../../translated_images/sl/optical.1f4a94464579a83a.png) +![Slika optičnega toka](../../../../../translated_images/sl/optical.1f4a94464579a83a.webp) > Slika iz [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/sl/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/sl/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 855d4cb1..6df5cdba 100644 --- a/translations/sl/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/sl/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 je mreža, ki je leta 2014 dosegla 92,7 % natančnost pri razvrščanju ImageNet top-5. Ima naslednjo strukturo slojev: -![ImageNet Layers](../../../../../translated_images/sl/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet Layers](../../../../../translated_images/sl/vgg-16-arch1.d901a5583b3a51ba.webp) Kot lahko vidite, VGG sledi tradicionalni piramidni arhitekturi, ki je zaporedje slojev konvolucije in združevanja. -![ImageNet Pyramid](../../../../../translated_images/sl/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet Pyramid](../../../../../translated_images/sl/vgg-16-arch.64ff2137f50dd49f.webp) > Slika iz [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/sl/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/sl/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index f8b22410..b0e118fb 100644 --- a/translations/sl/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/sl/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -260,7 +260,7 @@ "\n", "Tako bi v tipičnem CNN-ju imeli več konvolucijskih plasti, med katerimi bi bili sloji za združevanje, da zmanjšamo dimenzije slike. Prav tako bi povečali število filtrov, saj postajajo vzorci bolj kompleksni – obstaja več možnih zanimivih kombinacij, ki jih moramo iskati.\n", "\n", - "![Slika, ki prikazuje več konvolucijskih plasti s sloji za združevanje.](../../../../../translated_images/sl/cnn-pyramid.85915455759ef0ce.png)\n", + "![Slika, ki prikazuje več konvolucijskih plasti s sloji za združevanje.](../../../../../translated_images/sl/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "Zaradi zmanjševanja prostorskih dimenzij in povečevanja dimenzij značilnosti/filtrov se ta arhitektura imenuje tudi **piramidna arhitektura**.\n" ] diff --git a/translations/sl/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/sl/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index 04d9cb65..4fc4e016 100644 --- a/translations/sl/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/sl/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -358,7 +358,7 @@ "\n", "Tako bi v tipičnem CNN-ju imeli več konvolucijskih plasti, z vmesnimi pooling sloji za zmanjšanje dimenzij slike. Prav tako bi povečali število filtrov, saj z naprednejšimi vzorci obstaja več možnih zanimivih kombinacij, ki jih moramo iskati.\n", "\n", - "![Slika, ki prikazuje več konvolucijskih plasti s pooling sloji.](../../../../../translated_images/sl/cnn-pyramid.85915455759ef0ce.png)\n", + "![Slika, ki prikazuje več konvolucijskih plasti s pooling sloji.](../../../../../translated_images/sl/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "Zaradi zmanjševanja prostorskih dimenzij in povečevanja dimenzij značilnosti/filtrov se ta arhitektura imenuje tudi **piramidna arhitektura**.\n" ] diff --git a/translations/sl/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/sl/lessons/4-ComputerVision/07-ConvNets/README.md index 556fb8d1..6954e149 100644 --- a/translations/sl/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/sl/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ V resničnem življenju želimo prepoznati predmete na sliki ne glede na njihov Za ekstrakcijo vzorcev bomo uporabili koncept **konvolucijskih filtrov**. Kot veste, je slika predstavljena z 2D-matriko ali 3D-tenzorjem z barvno globino. Uporaba filtra pomeni, da vzamemo relativno majhno matriko **jedra filtra** in za vsak piksel v izvirni sliki izračunamo uteženo povprečje z okoliškimi točkami. To si lahko predstavljamo kot majhno okno, ki drsi čez celotno sliko in povpreči vse piksle glede na uteži v matriki jedra filtra. -![Filter za navpične robove](../../../../../translated_images/sl/filter-vert.b7148390ca0bc356.png) | ![Filter za vodoravne robove](../../../../../translated_images/sl/filter-horiz.59b80ed4feb946ef.png) +![Filter za navpične robove](../../../../../translated_images/sl/filter-vert.b7148390ca0bc356.webp) | ![Filter za vodoravne robove](../../../../../translated_images/sl/filter-horiz.59b80ed4feb946ef.webp) ----|---- > Slika: Dmitry Soshnikov @@ -38,7 +38,7 @@ Delovanje CNN temelji na naslednjih pomembnih idejah: * Mrežo lahko zasnujemo tako, da se filtri učijo samodejno * Enak pristop lahko uporabimo za iskanje vzorcev v visokih značilnostih, ne le v izvirni sliki. Tako ekstrakcija značilnosti v CNN deluje na hierarhiji značilnosti, začenši z nizkoročnimi kombinacijami pikslov do višjih kombinacij delov slike. -![Hierarhična ekstrakcija značilnosti](../../../../../translated_images/sl/FeatureExtractionCNN.d9b456cbdae7cb64.png) +![Hierarhična ekstrakcija značilnosti](../../../../../translated_images/sl/FeatureExtractionCNN.d9b456cbdae7cb64.webp) > Slika iz [članka Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), na podlagi [njihove raziskave](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ Večina CNN-jev, ki se uporabljajo za obdelavo slik, sledi tako imenovani pirami Na primer, poglejmo arhitekturo VGG-16, mreže, ki je leta 2014 dosegla 92,7 % natančnost v top-5 klasifikaciji na ImageNet: -![Sloji ImageNet](../../../../../translated_images/sl/vgg-16-arch1.d901a5583b3a51ba.jpg) +![Sloji ImageNet](../../../../../translated_images/sl/vgg-16-arch1.d901a5583b3a51ba.webp) -![Piramida ImageNet](../../../../../translated_images/sl/vgg-16-arch.64ff2137f50dd49f.jpg) +![Piramida ImageNet](../../../../../translated_images/sl/vgg-16-arch.64ff2137f50dd49f.webp) > Slika iz [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/sl/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/sl/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 05ec7086..2d7f9601 100644 --- a/translations/sl/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/sl/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ Vaša naloga je, da usposobite konvolucijsko nevronsko mrežo za razvrščanje r Uporabili bomo [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/), ki vsebuje slike 37 različnih pasem psov in mačk. -![Podatkovna zbirka, s katero bomo delali](../../../../../../translated_images/sl/data.50b2a9d5484bdbf0.png) +![Podatkovna zbirka, s katero bomo delali](../../../../../../translated_images/sl/data.50b2a9d5484bdbf0.webp) Za prenos podatkovne zbirke uporabite naslednji del kode: diff --git a/translations/sl/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/sl/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index e3567873..695b5b44 100644 --- a/translations/sl/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/sl/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "Za vizualizacijo idealne mačke bomo začeli s sliko naključnega šuma in poskusili uporabiti tehniko optimizacije z gradientnim spustom, da prilagodimo sliko tako, da bo mreža prepoznala mačko.\n", "\n", - "![Optimizacijska zanka](../../../../../translated_images/sl/ideal-cat-loop.999fbb8ff306e044.png)\n", + "![Optimizacijska zanka](../../../../../translated_images/sl/ideal-cat-loop.999fbb8ff306e044.webp)\n", "\n", "Tukaj je naša začetna slika:\n" ] diff --git a/translations/sl/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/sl/lessons/4-ComputerVision/08-TransferLearning/README.md index a0b8b8c8..e0a23ce9 100644 --- a/translations/sl/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/sl/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Tako Keras kot PyTorch vsebujeta funkcije za enostavno nalaganje vnaprej naučen Tukaj so primeri značilnosti, izvlečenih iz slike mačke z mrežo VGG-16: -![Značilnosti, izvlečene z VGG-16](../../../../../translated_images/sl/features.6291f9c7ba3a0b95.png) +![Značilnosti, izvlečene z VGG-16](../../../../../translated_images/sl/features.6291f9c7ba3a0b95.webp) ## Nabor podatkov Mačke proti psom @@ -48,19 +48,19 @@ Vnaprej naučena nevronska mreža vsebuje različne vzorce v svojem *možganu*, Eden od pristopov, ki ga lahko uporabimo, je začeti z naključno sliko in nato poskusiti uporabiti tehniko **optimizacije z gradientnim spustom**, da prilagodimo to sliko tako, da mreža začne misliti, da je to mačka. -![Zanka optimizacije slike](../../../../../translated_images/sl/ideal-cat-loop.999fbb8ff306e044.png) +![Zanka optimizacije slike](../../../../../translated_images/sl/ideal-cat-loop.999fbb8ff306e044.webp) Če to storimo, bomo dobili nekaj, kar je zelo podobno naključnemu šumu. To je zato, ker *obstaja veliko načinov, kako mreži narediti vtis, da je vhodna slika mačka*, vključno z nekaterimi, ki vizualno nimajo smisla. Čeprav te slike vsebujejo veliko vzorcev, značilnih za mačko, ni ničesar, kar bi jih omejevalo, da bi bile vizualno prepoznavne. Za izboljšanje rezultata lahko v funkcijo izgube dodamo še en člen, imenovan **izguba variacije**. To je metrika, ki kaže, kako podobni so sosednji piksli slike. Zmanjšanje izgube variacije naredi sliko bolj gladko in odstrani šum – s tem razkrije bolj vizualno privlačne vzorce. Tukaj je primer takšnih "idealnih" slik, ki so z visoko verjetnostjo razvrščene kot mačka in zebra: -![Idealna mačka](../../../../../translated_images/sl/ideal-cat.203dd4597643d6b0.png) | ![Idealna zebra](../../../../../translated_images/sl/ideal-zebra.7f70e8b54ee15a7a.png) +![Idealna mačka](../../../../../translated_images/sl/ideal-cat.203dd4597643d6b0.webp) | ![Idealna zebra](../../../../../translated_images/sl/ideal-zebra.7f70e8b54ee15a7a.webp) -----|----- *Idealna mačka* | *Idealna zebra* Podoben pristop lahko uporabimo za izvajanje tako imenovanih **adversarnih napadov** na nevronsko mrežo. Recimo, da želimo zavajati nevronsko mrežo in narediti, da pes izgleda kot mačka. Če vzamemo sliko psa, ki jo mreža prepozna kot psa, jo lahko nato nekoliko prilagodimo z optimizacijo gradientnega spusta, dokler mreža ne začne razvrščati slike kot mačko: -![Slika psa](../../../../../translated_images/sl/original-dog.8f68a67d2fe0911f.png) | ![Slika psa, razvrščena kot mačka](../../../../../translated_images/sl/adversarial-dog.d9fc7773b0142b89.png) +![Slika psa](../../../../../translated_images/sl/original-dog.8f68a67d2fe0911f.webp) | ![Slika psa, razvrščena kot mačka](../../../../../translated_images/sl/adversarial-dog.d9fc7773b0142b89.webp) -----|----- *Izvirna slika psa* | *Slika psa, razvrščena kot mačka* diff --git a/translations/sl/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/sl/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index d27d1898..d7034b98 100644 --- a/translations/sl/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/sl/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "Ker treniramo avtoenkoder, da zajame čim več informacij iz izvirne slike za natančno rekonstrukcijo, mreža poskuša najti najboljšo **vgraditev** vhodnih slik, da zajame njihov pomen.\n", "\n", - "![Diagram avtoenkoderja](../../../../../translated_images/sl/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![Diagram avtoenkoderja](../../../../../translated_images/sl/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "> Slika iz [Keras bloga](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", diff --git a/translations/sl/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/sl/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index c39ece07..a328c0be 100644 --- a/translations/sl/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/sl/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "Ker treniramo avtoenkoder, da zajame čim več informacij iz izvirne slike za natančno rekonstrukcijo, omrežje poskuša najti najboljšo **vgraditev** vhodnih slik, da zajame njihov pomen.\n", "\n", - "![Diagram avtoenkoderja](../../../../../translated_images/sl/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![Diagram avtoenkoderja](../../../../../translated_images/sl/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "*Slika iz [Keras bloga](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", diff --git a/translations/sl/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/sl/lessons/4-ComputerVision/09-Autoencoders/README.md index 33de3626..8a5c526d 100644 --- a/translations/sl/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/sl/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ Vendar pa bi morda želeli uporabiti surove (neoznačene) podatke za treniranje Ker treniramo avtoenkoder, da zajame čim več informacij iz izvirne slike za natančno rekonstrukcijo, mreža poskuša najti najboljšo **vgradnjo** vhodnih slik, da zajame njihov pomen. -![Diagram avtoenkoderja](../../../../../translated_images/sl/autoencoder_schema.5e6fc9ad98a5eb61.jpg) +![Diagram avtoenkoderja](../../../../../translated_images/sl/autoencoder_schema.5e6fc9ad98a5eb61.webp) > Slika iz [Keras bloga](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/sl/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/sl/lessons/4-ComputerVision/11-ObjectDetection/README.md index bc4415dc..8daccc06 100644 --- a/translations/sl/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/sl/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ Modeli za klasifikacijo slik, s katerimi smo se doslej ukvarjali, so vzeli sliko ## [Predhodni kviz](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Zaznavanje objektov](../../../../../translated_images/sl/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) +![Zaznavanje objektov](../../../../../translated_images/sl/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp) > Slika s [spletne strani YOLO v2](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ Modeli za klasifikacijo slik, s katerimi smo se doslej ukvarjali, so vzeli sliko 2. Na vsaki ploščici izvedemo klasifikacijo slik. 3. Tiste ploščice, ki imajo dovolj visoko aktivacijo, lahko štejemo, da vsebujejo iskani objekt. -![Naivno zaznavanje objektov](../../../../../translated_images/sl/naive-detection.e7f1ba220ccd08c6.png) +![Naivno zaznavanje objektov](../../../../../translated_images/sl/naive-detection.e7f1ba220ccd08c6.webp) > *Slika iz [zvezka z vajami](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ Pri tej nalogi lahko naletite na naslednje podatkovne nabore: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) – 20 razredov * [COCO](http://cocodataset.org/#home) – Pogosti objekti v kontekstu. 80 razredov, okvirji in maske za segmentacijo -![COCO](../../../../../translated_images/sl/coco-examples.71bc60380fa6cceb.jpg) +![COCO](../../../../../translated_images/sl/coco-examples.71bc60380fa6cceb.webp) ## Merila za zaznavanje objektov @@ -50,7 +50,7 @@ Pri tej nalogi lahko naletite na naslednje podatkovne nabore: Medtem ko je za klasifikacijo slik enostavno meriti, kako dobro deluje algoritem, moramo pri zaznavanju objektov meriti tako pravilnost razreda kot tudi natančnost določene lokacije okvirja. Za slednje uporabljamo tako imenovani **Presek nad unijo** (IoU), ki meri, kako dobro se dve škatli (ali dve poljubni območji) prekrivata. -![IoU](../../../../../translated_images/sl/iou_equation.9a4751d40fff4e11.png) +![IoU](../../../../../translated_images/sl/iou_equation.9a4751d40fff4e11.webp) > *Slika 2 iz [te odlične blog objave o IoU](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ Obstajata dve glavni skupini algoritmov za zaznavanje objektov: [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) uporablja [Selektivno iskanje](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) za generiranje hierarhične strukture regij ROI, ki se nato prenesejo skozi CNN ekstraktorje značilnosti in SVM-klasifikatorje za določanje razreda objekta ter linearno regresijo za določanje koordinat *okvirja*. [Uradni članek](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/sl/rcnn1.cae407020dfb1d1f.png) +![RCNN](../../../../../translated_images/sl/rcnn1.cae407020dfb1d1f.webp) > *Slika iz van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/sl/rcnn2.2d9530bb83516484.png) +![RCNN-1](../../../../../translated_images/sl/rcnn2.2d9530bb83516484.webp) > *Slike iz [tega bloga](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -110,7 +110,7 @@ Obstajata dve glavni skupini algoritmov za zaznavanje objektov: Ta pristop je podoben R-CNN, vendar se regije določijo po tem, ko so bile uporabljene konvolucijske plasti. -![FRCNN](../../../../../translated_images/sl/f-rcnn.3cda6d9bb4188875.png) +![FRCNN](../../../../../translated_images/sl/f-rcnn.3cda6d9bb4188875.webp) > Slika iz [uradnega članka](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ Ta pristop je podoben R-CNN, vendar se regije določijo po tem, ko so bile upora Glavna ideja tega pristopa je uporaba nevronske mreže za napovedovanje ROI – tako imenovane *Mreže za predlaganje regij*. [Članek](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/sl/faster-rcnn.8d46c099b87ef30a.png) +![FasterRCNN](../../../../../translated_images/sl/faster-rcnn.8d46c099b87ef30a.webp) > Slika iz [uradnega članka](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ Ta algoritem je še hitrejši od Faster R-CNN. Glavna ideja je naslednja: 1. Značilnosti obdelamo z **Zemljevidom občutljivih na položaj**. Vsak objekt iz $C$ razredov je razdeljen na $k\times k$ regij, in treniramo za napovedovanje delov objektov. 1. Za vsak del iz $k\times k$ regij vse mreže glasujejo za razrede objektov, in izbran je razred objekta z največ glasovi. -![r-fcn slika](../../../../../translated_images/sl/r-fcn.13eb88158b99a3da.png) +![r-fcn slika](../../../../../translated_images/sl/r-fcn.13eb88158b99a3da.webp) > Slika iz [uradnega članka](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO je algoritem za zaznavanje v realnem času z enim prehodom. Glavna ideja je * Slika je razdeljena na $S\times S$ regij. * Za vsako regijo **CNN** napove $n$ možnih objektov, koordinate *okvirja* in *zaupanje*=*verjetnost* * IoU. - ![YOLO](../../../../../translated_images/sl/yolo.a2648ec82ee8bb4e.png) + ![YOLO](../../../../../translated_images/sl/yolo.a2648ec82ee8bb4e.webp) > Slika iz [uradnega članka](https://arxiv.org/abs/1506.02640) diff --git a/translations/sl/lessons/4-ComputerVision/README.md b/translations/sl/lessons/4-ComputerVision/README.md index d8569a77..df55f7b1 100644 --- a/translations/sl/lessons/4-ComputerVision/README.md +++ b/translations/sl/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Računalniški vid -![Povzetek vsebine o računalniškem vidu v skici](../../../../translated_images/sl/ai-computervision.6506ebebac3fbf76.png) +![Povzetek vsebine o računalniškem vidu v skici](../../../../translated_images/sl/ai-computervision.6506ebebac3fbf76.webp) V tem poglavju bomo spoznali: diff --git a/translations/sl/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/sl/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index 1673dd9a..7417eae6 100644 --- a/translations/sl/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/sl/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**Bag of Words** (BoW) je najpogosteje uporabljena tradicionalna predstavitev vektorskih podatkov. Vsaka beseda je povezana z indeksom vektorja, element vektorja pa vsebuje število pojavitev besede v določenem dokumentu.\n", "\n", - "![Slika prikazuje, kako je predstavitev vektorja Bag of Words shranjena v pomnilniku.](../../../../../translated_images/sl/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![Slika prikazuje, kako je predstavitev vektorja Bag of Words shranjena v pomnilniku.](../../../../../translated_images/sl/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Note**: Na Bag of Words lahko gledate tudi kot na vsoto vseh vektorjev, kodiranih z metodo one-hot, za posamezne besede v besedilu.\n", "\n", diff --git a/translations/sl/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/sl/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index 5d54cd4d..38dd269f 100644 --- a/translations/sl/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/sl/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**Predstavitev z metodo vreče besed** (BoW) je najpreprostejša za razumevanje med tradicionalnimi vektorskimi predstavitvami. Vsaka beseda je povezana z indeksom vektorja, element vektorja pa vsebuje število pojavitev posamezne besede v določenem dokumentu.\n", "\n", - "![Slika, ki prikazuje, kako je predstavitev z metodo vreče besed predstavljena v pomnilniku.](../../../../../translated_images/sl/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![Slika, ki prikazuje, kako je predstavitev z metodo vreče besed predstavljena v pomnilniku.](../../../../../translated_images/sl/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Opomba**: Na metodo BoW lahko gledate tudi kot na vsoto vseh eno-vroče kodiranih vektorjev za posamezne besede v besedilu.\n", "\n", diff --git a/translations/sl/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/sl/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index bf762706..39901bab 100644 --- a/translations/sl/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/sl/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "Z uporabo vgrajevalne plasti kot prve plasti v naši mreži lahko preklopimo iz modela vreče besed na model **vreče vgrajevanja**, kjer najprej vsako besedo v našem besedilu pretvorimo v ustrezno vgrajevanje, nato pa izračunamo neko agregatno funkcijo nad vsemi temi vgrajevanji, kot so `sum`, `average` ali `max`.\n", "\n", - "![Slika, ki prikazuje klasifikator vgrajevanja za pet zaporednih besed.](../../../../../translated_images/sl/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![Slika, ki prikazuje klasifikator vgrajevanja za pet zaporednih besed.](../../../../../translated_images/sl/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "Naša nevronska mreža klasifikatorja se bo začela z vgrajevalno plastjo, nato agregatno plastjo in linearnim klasifikatorjem na vrhu:\n" ] @@ -176,7 +176,7 @@ "\n", "V prejšnji arhitekturi smo morali vsa zaporedja zapolniti do enake dolžine, da so ustrezala mini seriji. To ni najbolj učinkovit način za predstavitev zaporedij spremenljive dolžine – drugačen pristop bi bil uporaba **offset** vektorja, ki bi vseboval zamike vseh zaporedij, shranjenih v enem velikem vektorju.\n", "\n", - "![Slika, ki prikazuje predstavitev zaporedij z zamiki](../../../../../translated_images/sl/offset-sequence-representation.eb73fcefb29b46ee.png)\n", + "![Slika, ki prikazuje predstavitev zaporedij z zamiki](../../../../../translated_images/sl/offset-sequence-representation.eb73fcefb29b46ee.webp)\n", "\n", "> **Opomba**: Na zgornji sliki prikazujemo zaporedje znakov, vendar v našem primeru delamo z zaporedji besed. Kljub temu splošno načelo predstavitve zaporedij z offset vektorjem ostaja enako.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW je hitrejši, medtem ko je skip-gram počasnejši, vendar bolje predstavlja redke besede.\n", "\n", - "![Slika, ki prikazuje algoritma CBoW in Skip-Gram za pretvorbo besed v vektorje.](../../../../../translated_images/sl/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![Slika, ki prikazuje algoritma CBoW in Skip-Gram za pretvorbo besed v vektorje.](../../../../../translated_images/sl/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Za eksperimentiranje z vektorsko predstavitvijo Word2Vec, predhodno naučeno na zbirki podatkov Google News, lahko uporabimo knjižnico **gensim**. Spodaj poiščemo besede, ki so najbolj podobne 'neural'.\n", "\n", diff --git a/translations/sl/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/sl/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index 42903066..7d06ab2e 100644 --- a/translations/sl/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/sl/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "Z uporabo vdelavnega sloja kot prvega sloja v naši mreži lahko preklopimo iz modela vreče besed (bag-of-words) na model **vreče vdelav** (embedding bag), kjer najprej vsako besedo v našem besedilu pretvorimo v ustrezno vdelavo, nato pa izračunamo neko agregatno funkcijo nad vsemi temi vdelavami, na primer `sum`, `average` ali `max`.\n", "\n", - "![Slika, ki prikazuje klasifikator z vdelavami za pet zaporednih besed.](../../../../../translated_images/sl/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![Slika, ki prikazuje klasifikator z vdelavami za pet zaporednih besed.](../../../../../translated_images/sl/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "Naša nevronska mreža za klasifikacijo je sestavljena iz naslednjih slojev:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW je hitrejši, medtem ko je skip-gram počasnejši, vendar bolje predstavlja redke besede.\n", "\n", - "![Slika, ki prikazuje algoritma CBoW in Skip-Gram za pretvorbo besed v vektorje.](../../../../../translated_images/sl/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![Slika, ki prikazuje algoritma CBoW in Skip-Gram za pretvorbo besed v vektorje.](../../../../../translated_images/sl/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Za eksperimentiranje z Word2Vec vektorskimi predstavitvami, predhodno naučenimi na zbirki podatkov Google News, lahko uporabimo knjižnico **gensim**. Spodaj poiščemo besede, ki so najbolj podobne 'neural'.\n", "\n", diff --git a/translations/sl/lessons/5-NLP/14-Embeddings/README.md b/translations/sl/lessons/5-NLP/14-Embeddings/README.md index 5fae5918..59428818 100644 --- a/translations/sl/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/sl/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ Tako bi plast vdelave sprejela besedo kot vhod in ustvarila izhodni vektor dolo Z uporabo plasti vdelave kot prve plasti v našem klasifikacijskem omrežju lahko preklopimo iz modela vreče besed na model **vreče vdelav**, kjer najprej vsako besedo v našem besedilu pretvorimo v ustrezno vdelavo, nato pa izračunamo neko agregatno funkcijo nad vsemi temi vdelavami, kot so `sum`, `average` ali `max`. -![Slika prikazuje klasifikator vdelav za pet besed v zaporedju.](../../../../../translated_images/sl/embedding-classifier-example.b77f021a7ee67eee.png) +![Slika prikazuje klasifikator vdelav za pet besed v zaporedju.](../../../../../translated_images/sl/embedding-classifier-example.b77f021a7ee67eee.webp) > Slika avtorja @@ -40,7 +40,7 @@ Da bi to dosegli, moramo naš model vdelave predhodno trenirati na veliki zbirki CBoW je hitrejši, medtem ko je preskok-gram počasnejši, vendar bolje predstavlja redke besede. -![Slika prikazuje algoritma CBoW in Skip-Gram za pretvorbo besed v vektorje.](../../../../../translated_images/sl/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![Slika prikazuje algoritma CBoW in Skip-Gram za pretvorbo besed v vektorje.](../../../../../translated_images/sl/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Slika iz [tega članka](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/sl/lessons/5-NLP/15-LanguageModeling/README.md b/translations/sl/lessons/5-NLP/15-LanguageModeling/README.md index 3e670294..385c9eb5 100644 --- a/translations/sl/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/sl/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ V prejšnjih primerih smo uporabljali že vnaprej naučene semantične vektorske * **Neprekinjena vreča besed** (CBoW), kjer napovedujemo srednji token $W_0$ v zaporedju tokenov $W_{-N}$, ..., $W_N$. * **Skip-gram**, kjer napovedujemo niz sosednjih tokenov {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} iz srednjega tokena $W_0$. -![slika iz članka o pretvorbi besed v vektorje](../../../../../translated_images/sl/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![slika iz članka o pretvorbi besed v vektorje](../../../../../translated_images/sl/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Slika iz [tega članka](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/sl/lessons/5-NLP/16-RNN/README.md b/translations/sl/lessons/5-NLP/16-RNN/README.md index 5edbfe06..916ebf1c 100644 --- a/translations/sl/lessons/5-NLP/16-RNN/README.md +++ b/translations/sl/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ V prejšnjih poglavjih smo uporabljali bogate semantične reprezentacije besedil Da bi zajeli pomen zaporedja besedila, moramo uporabiti drugo arhitekturo nevronske mreže, imenovano **rekurentna nevronska mreža** ali RNN. Pri RNN stavke pošiljamo skozi mrežo en simbol naenkrat, mreža pa ustvari neko **stanje**, ki ga nato ponovno pošljemo v mrežo skupaj z naslednjim simbolom. -![RNN](../../../../../translated_images/sl/rnn.27f5c29c53d727b5.png) +![RNN](../../../../../translated_images/sl/rnn.27f5c29c53d727b5.webp) > Slika avtorja @@ -61,7 +61,7 @@ Razpravljali smo o rekurentnih mrežah, ki delujejo v eno smer, od začetka zapo Rekurentna mreža, bodisi enosmerna ali dvosmerna, zajame določene vzorce znotraj zaporedja in jih lahko shrani v vektorsko stanje ali prenese v izhod. Tako kot pri konvolucijskih mrežah lahko na prvo plast zgradimo drugo rekurentno plast, da zajamemo vzorce višje ravni in gradimo na nizkoročnih vzorcih, ki jih je zajela prva plast. To nas pripelje do pojma **večslojne RNN**, ki je sestavljena iz dveh ali več rekurentnih mrež, kjer se izhod prejšnje plasti prenese v naslednjo plast kot vhod. -![Slika, ki prikazuje večslojno LSTM RNN](../../../../../translated_images/sl/multi-layer-lstm.dd975e29bb2a59fe.jpg) +![Slika, ki prikazuje večslojno LSTM RNN](../../../../../translated_images/sl/multi-layer-lstm.dd975e29bb2a59fe.webp) *Slika iz [tega čudovitega prispevka](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) avtorja Fernanda Lópeza* diff --git a/translations/sl/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/sl/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index 5b0442d3..903152f1 100644 --- a/translations/sl/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/sl/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "Rekurzivna mreža, enosmerna ali dvosmerna, zajame določene vzorce znotraj zaporedja in jih lahko shrani v vektor stanja ali prenese v izhod. Tako kot pri konvolucijskih mrežah lahko na prvo plast zgradimo drugo rekurzivno plast, da zajamemo vzorce višje ravni, ki so zgrajeni iz vzorcev nižje ravni, ki jih je izločila prva plast. To nas pripelje do pojma **večplastne RNN**, ki je sestavljena iz dveh ali več rekurzivnih mrež, kjer se izhod prejšnje plasti prenese v naslednjo plast kot vhod.\n", "\n", - "![Slika, ki prikazuje večplastno dolgotrajno-kratkoročno pomnilniško RNN](../../../../../translated_images/sl/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![Slika, ki prikazuje večplastno dolgotrajno-kratkoročno pomnilniško RNN](../../../../../translated_images/sl/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*Slika iz [tega čudovitega prispevka](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) avtorja Fernanda Lópeza*\n", "\n", diff --git a/translations/sl/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/sl/lessons/5-NLP/16-RNN/RNNTF.ipynb index e70f6fb0..3b7ef555 100644 --- a/translations/sl/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/sl/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "Za zajemanje pomena zaporedja besedila bomo uporabili arhitekturo nevronske mreže, imenovano **rekurentna nevronska mreža** ali RNN. Pri uporabi RNN stavke pošiljamo skozi mrežo en token naenkrat, mreža pa ustvari neko **stanje**, ki ga nato skupaj z naslednjim tokenom ponovno pošljemo v mrežo.\n", "\n", - "![Slika, ki prikazuje primer generiranja z rekurentno nevronsko mrežo.](../../../../../translated_images/sl/rnn.27f5c29c53d727b5.png)\n", + "![Slika, ki prikazuje primer generiranja z rekurentno nevronsko mrežo.](../../../../../translated_images/sl/rnn.27f5c29c53d727b5.webp)\n", "\n", "Glede na vhodno zaporedje tokenov $X_0,\\dots,X_n$ RNN ustvari zaporedje blokov nevronske mreže in to zaporedje trenira od začetka do konca z uporabo povratnega razširjanja napake (backpropagation). Vsak blok mreže kot vhod prejme par $(X_i,S_i)$ in kot rezultat ustvari $S_{i+1}$. Končno stanje $S_n$ ali izhod $Y_n$ gre v linearni klasifikator, da ustvari rezultat. Vsi bloki mreže si delijo iste uteži in so trenirani od začetka do konca z enim prehodom povratnega razširjanja napake.\n", "\n", @@ -369,7 +369,7 @@ "\n", "Rekurentne mreže, enosmerne ali dvosmerne, zajamejo vzorce znotraj zaporedja in jih shranijo v vektorske stanja ali jih vrnejo kot izhod. Tako kot pri konvolucijskih mrežah lahko zgradimo še en rekurentni sloj, ki sledi prvemu, da zajame vzorce višje ravni, zgrajene iz vzorcev nižje ravni, ki jih je izvlekel prvi sloj. To nas pripelje do pojma **večplastnega RNN-ja**, ki je sestavljen iz dveh ali več rekurentnih mrež, kjer se izhod prejšnjega sloja posreduje naslednjemu sloju kot vhod.\n", "\n", - "![Slika, ki prikazuje večplastni dolgoročno-kratkoročni pomnilniški RNN](../../../../../translated_images/sl/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![Slika, ki prikazuje večplastni dolgoročno-kratkoročni pomnilniški RNN](../../../../../translated_images/sl/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*Slika iz [tega odličnega prispevka](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) Fernanda Lópeza.*\n", "\n", diff --git a/translations/sl/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/sl/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index d0ecc1ba..29ab804d 100644 --- a/translations/sl/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/sl/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "Način, kako bomo učili RNN za generiranje besedila, je naslednji. Na vsakem koraku bomo vzeli zaporedje znakov dolžine `nchars` in mreži naročili, naj za vsak vhodni znak ustvari naslednji izhodni znak:\n", "\n", - "![Slika, ki prikazuje primer generiranja besede 'HELLO' z RNN.](../../../../../translated_images/sl/rnn-generate.56c54afb52f9781d.png)\n", + "![Slika, ki prikazuje primer generiranja besede 'HELLO' z RNN.](../../../../../translated_images/sl/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "Odvisno od dejanskega scenarija bomo morda želeli vključiti tudi nekatere posebne znake, kot je *konec zaporedja* ``. V našem primeru želimo mrežo naučiti generiranja neskončnega besedila, zato bomo določili, da je velikost vsakega zaporedja enaka `nchars` znakom. Posledično bo vsak učni primer sestavljen iz `nchars` vhodov in `nchars` izhodov (kar je vhodno zaporedje, premaknjeno za en simbol v levo). Miniserija bo sestavljena iz več takšnih zaporedij.\n", "\n", diff --git a/translations/sl/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/sl/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index 5cc38e8b..8d380486 100644 --- a/translations/sl/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/sl/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -113,7 +113,7 @@ "\n", "Način, kako bomo učili RNN za generiranje novičarskih naslovov, je naslednji. Na vsakem koraku bomo vzeli en naslov, ki bo podan v RNN, in za vsak vhodni znak bomo od mreže zahtevali, da generira naslednji izhodni znak:\n", "\n", - "![Slika, ki prikazuje primer generiranja besede 'HELLO' z RNN.](../../../../../translated_images/sl/rnn-generate.56c54afb52f9781d.png)\n", + "![Slika, ki prikazuje primer generiranja besede 'HELLO' z RNN.](../../../../../translated_images/sl/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "Za zadnji znak našega zaporedja bomo od mreže zahtevali, da generira `` token.\n", "\n", diff --git a/translations/sl/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/sl/lessons/5-NLP/17-GenerativeNetworks/README.md index 2e0fdebb..a1b670d0 100644 --- a/translations/sl/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/sl/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ V arhitekturi RNN, ki smo jo obravnavali v prejšnji enoti, je vsaka enota RNN p To omogoča različne nevronske arhitekture, prikazane na spodnji sliki: -![Slika, ki prikazuje pogoste vzorce rekurentnih nevronskih mrež.](../../../../../translated_images/sl/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Slika, ki prikazuje pogoste vzorce rekurentnih nevronskih mrež.](../../../../../translated_images/sl/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > Slika iz blog objave [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) avtorja [Andrej Karpaty](http://karpathy.github.io/) @@ -32,7 +32,7 @@ V tej enoti se bomo osredotočili na preproste generativne modele, ki nam pomaga To RNN bomo trenirali za generiranje besedila korak za korakom. Na vsakem koraku bomo vzeli zaporedje znakov dolžine `nchars` in mreži naročili, naj za vsak vhodni znak ustvari naslednji izhodni znak: -![Slika, ki prikazuje primer generiranja besede 'HELLO' z RNN.](../../../../../translated_images/sl/rnn-generate.56c54afb52f9781d.png) +![Slika, ki prikazuje primer generiranja besede 'HELLO' z RNN.](../../../../../translated_images/sl/rnn-generate.56c54afb52f9781d.webp) Pri generiranju besedila (med inferenco) začnemo z nekim **pozivom**, ki ga prenesemo skozi RNN celice za generiranje vmesnega stanja, nato pa se začne generiranje. Generiramo en znak naenkrat, stanje in generirani znak pa prenesemo v drugo RNN celico za generiranje naslednjega, dokler ne generiramo dovolj znakov. diff --git a/translations/sl/lessons/5-NLP/18-Transformers/README.md b/translations/sl/lessons/5-NLP/18-Transformers/README.md index 1d0d200a..649f496a 100644 --- a/translations/sl/lessons/5-NLP/18-Transformers/README.md +++ b/translations/sl/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ Pri RNN-jih je zaporedje-v-zaporedje implementirano z dvema rekurzivnima mrežam **Mehanizmi pozornosti** omogočajo tehtanje kontekstualnega vpliva vsakega vhodnega vektorja na vsako napoved izhoda RNN. To se implementira z ustvarjanjem bližnjic med vmesnimi stanji vhodnega RNN in izhodnega RNN. Na ta način bomo pri generiranju izhodnega simbola yt upoštevali vsa vhodna skrita stanja hi, z različnimi utežnimi koeficienti αt,i. -![Slika, ki prikazuje model enkoder/dekoder z aditivno plastjo pozornosti](../../../../../translated_images/sl/encoder-decoder-attention.7a726296894fb567.png) +![Slika, ki prikazuje model enkoder/dekoder z aditivno plastjo pozornosti](../../../../../translated_images/sl/encoder-decoder-attention.7a726296894fb567.webp) > Model enkoder-dekoder z aditivnim mehanizmom pozornosti v [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), povzeto iz [tega bloga](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) Matrika pozornosti {αi,j} predstavlja stopnjo, do katere določene vhodne besede vplivajo na generiranje določene besede v izhodnem zaporedju. Spodaj je primer takšne matrike: -![Slika, ki prikazuje vzorčno poravnavo, najdeno z RNNsearch-50, povzeto iz Bahdanau - arviz.org](../../../../../translated_images/sl/bahdanau-fig3.09ba2d37f202a6af.png) +![Slika, ki prikazuje vzorčno poravnavo, najdeno z RNNsearch-50, povzeto iz Bahdanau - arviz.org](../../../../../translated_images/sl/bahdanau-fig3.09ba2d37f202a6af.webp) > Slika iz [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Slika 3) @@ -66,7 +66,7 @@ Rezultat, ki ga dobimo s kodiranjem položaja, ugnezdi tako izvirni token kot nj Nato moramo zajeti nekatere vzorce znotraj našega zaporedja. Da bi to dosegli, transformatorji uporabljajo mehanizem **samopozornosti**, ki je v bistvu pozornost, uporabljena na istem zaporedju kot vhod in izhod. Uporaba samopozornosti nam omogoča, da upoštevamo **kontekst** znotraj stavka in vidimo, katere besede so medsebojno povezane. Na primer, omogoča nam, da vidimo, na katere besede se nanašajo koreference, kot je *to*, in tudi upoštevamo kontekst: -![](../../../../../translated_images/sl/CoreferenceResolution.861924d6d384a7d6.png) +![](../../../../../translated_images/sl/CoreferenceResolution.861924d6d384a7d6.webp) > Slika iz [Googlovega bloga](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Ker je vsak vhodni položaj neodvisno preslikan na vsak izhodni položaj, lahko **BERT** (Bidirectional Encoder Representations from Transformers) je zelo velika večplastna mreža transformatorjev z 12 plastmi za *BERT-base* in 24 za *BERT-large*. Model je najprej predhodno naučen na velikem korpusu besedilnih podatkov (WikiPedia + knjige) z uporabo nenadzorovanega učenja (napovedovanje zamaskiranih besed v stavku). Med predhodnim učenjem model absorbira pomembne ravni razumevanja jezika, ki jih je nato mogoče uporabiti z drugimi nabori podatkov z uporabo finega uglaševanja. Ta proces se imenuje **prenosno učenje**. -![slika iz http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/sl/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) +![slika iz http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/sl/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > Slika [vir](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/sl/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/sl/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index 1500d435..101575ac 100644 --- a/translations/sl/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/sl/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**Mehanizmi pozornosti** omogočajo tehtanje kontekstualnega vpliva vsakega vhodnega vektorja na vsako izhodno napoved RNN. To se implementira z ustvarjanjem bližnjic med vmesnimi stanji vhodne RNN in izhodne RNN. Na ta način pri generiranju izhodnega simbola $y_t$ upoštevamo vsa vhodna skrita stanja $h_i$ z različnimi utežnimi koeficienti $\\alpha_{t,i}$.\n", "\n", - "![Slika, ki prikazuje model kodirnik/dekodirnik z aditivno plastjo pozornosti](../../../../../translated_images/sl/encoder-decoder-attention.7a726296894fb567.png)\n", + "![Slika, ki prikazuje model kodirnik/dekodirnik z aditivno plastjo pozornosti](../../../../../translated_images/sl/encoder-decoder-attention.7a726296894fb567.webp)\n", "*Model kodirnik-dekodirnik z mehanizmom aditivne pozornosti v [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), povzeto iz [tega bloga](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Matrika pozornosti $\\{\\alpha_{i,j}\\}$ predstavlja stopnjo, do katere določene vhodne besede vplivajo na generacijo določene besede v izhodnem zaporedju. Spodaj je primer takšne matrike:\n", "\n", - "![Slika, ki prikazuje vzorčno poravnavo, najdeno z RNNsearch-50, povzeto iz Bahdanau - arviz.org](../../../../../translated_images/sl/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![Slika, ki prikazuje vzorčno poravnavo, najdeno z RNNsearch-50, povzeto iz Bahdanau - arviz.org](../../../../../translated_images/sl/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*Slika povzeta iz [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Slika 3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) je zelo velik večplastni transformator z 12 plastmi za *BERT-base* in 24 za *BERT-large*. Model je najprej predhodno usposobljen na velikem korpusu besedilnih podatkov (Wikipedia + knjige) z uporabo nenadzorovanega učenja (napovedovanje zamaskiranih besed v stavku). Med predhodnim učenjem model pridobi pomembno raven razumevanja jezika, ki jo je nato mogoče uporabiti z drugimi nabori podatkov z uporabo prilagoditve. Ta proces se imenuje **prenosno učenje**.\n", "\n", - "![Slika s http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/sl/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![Slika s http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/sl/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "Obstaja veliko različic arhitektur transformatorjev, vključno z BERT, DistilBERT, BigBird, OpenGPT3 in drugimi, ki jih je mogoče prilagoditi. Paket [HuggingFace](https://github.com/huggingface/) ponuja repozitorij za učenje mnogih teh arhitektur s PyTorch.\n", "\n", diff --git a/translations/sl/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/sl/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index f175ec1f..faa7eb55 100644 --- a/translations/sl/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/sl/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**Mehanizmi pozornosti** omogočajo tehtanje kontekstualnega vpliva vsakega vhodnega vektorja na vsako izhodno napoved RNN. To se implementira z ustvarjanjem bližnjic med vmesnimi stanji vhodnega RNN in izhodnega RNN. Na ta način, ko generiramo izhodni simbol $y_t$, upoštevamo vsa skrita stanja vhodov $h_i$ z različnimi utežnimi koeficienti $\\alpha_{t,i}$.\n", "\n", - "![Slika prikazuje model kodirnik/dekodirnik z dodatno plastjo pozornosti](../../../../../translated_images/sl/encoder-decoder-attention.7a726296894fb567.png)\n", + "![Slika prikazuje model kodirnik/dekodirnik z dodatno plastjo pozornosti](../../../../../translated_images/sl/encoder-decoder-attention.7a726296894fb567.webp)\n", "*Model kodirnik-dekodirnik z mehanizmom dodatne pozornosti v [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citirano iz [tega bloga](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Matrika pozornosti $\\{\\alpha_{i,j}\\}$ predstavlja stopnjo, do katere določene vhodne besede vplivajo na generacijo določene besede v izhodnem zaporedju. Spodaj je primer takšne matrike:\n", "\n", - "![Slika prikazuje vzorčno poravnavo, ki jo je našel RNNsearch-50, vzeto iz Bahdanau - arviz.org](../../../../../translated_images/sl/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![Slika prikazuje vzorčno poravnavo, ki jo je našel RNNsearch-50, vzeto iz Bahdanau - arviz.org](../../../../../translated_images/sl/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*Slika vzeta iz [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3)*\n", "\n", @@ -225,7 +225,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) je zelo velik večplastni transformatorni model z 12 plastmi za *BERT-base* in 24 za *BERT-large*. Model je najprej predhodno usposobljen na velikem korpusu besedilnih podatkov (WikiPedia + knjige) z uporabo nenadzorovanega učenja (napovedovanje zakritih besed v stavku). Med predhodnim usposabljanjem model pridobi pomembno raven razumevanja jezika, ki jo je nato mogoče uporabiti z drugimi nabori podatkov prek finega prilagajanja. Ta proces se imenuje **prenosno učenje**.\n", "\n", - "![slika s http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/sl/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![slika s http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/sl/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "Obstaja veliko različic transformatornih arhitektur, vključno z BERT, DistilBERT, BigBird, OpenGPT3 in drugimi, ki jih je mogoče fino prilagoditi.\n", "\n", diff --git a/translations/sl/lessons/5-NLP/19-NER/README.md b/translations/sl/lessons/5-NLP/19-NER/README.md index dc54191a..adbeb555 100644 --- a/translations/sl/lessons/5-NLP/19-NER/README.md +++ b/translations/sl/lessons/5-NLP/19-NER/README.md @@ -56,7 +56,7 @@ novorojenčku | O Ker moramo vzpostaviti enako razmerje med tokeni in razredi, lahko iz te slike treniramo desno **mnogokratno-mnogokratno** nevronsko mrežo: -![Slika prikazuje običajne vzorce rekurzivnih nevronskih mrež.](../../../../../translated_images/sl/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Slika prikazuje običajne vzorce rekurzivnih nevronskih mrež.](../../../../../translated_images/sl/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > *Slika iz [tega bloga](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) avtorja [Andreja Karpathyja](http://karpathy.github.io/). Modeli klasifikacije tokenov NER ustrezajo desni arhitekturi mreže na tej sliki.* diff --git a/translations/sl/lessons/5-NLP/README.md b/translations/sl/lessons/5-NLP/README.md index 6dfe2e02..c88a43d2 100644 --- a/translations/sl/lessons/5-NLP/README.md +++ b/translations/sl/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Obdelava naravnega jezika -![Povzetek nalog NLP v skici](../../../../translated_images/sl/ai-nlp.b22dcb8ca4707cea.png) +![Povzetek nalog NLP v skici](../../../../translated_images/sl/ai-nlp.b22dcb8ca4707cea.webp) V tem poglavju se bomo osredotočili na uporabo nevronskih mrež za reševanje nalog, povezanih z **obdelavo naravnega jezika (NLP)**. Obstaja veliko NLP problemov, ki jih želimo, da jih računalniki rešujejo: diff --git a/translations/sl/lessons/6-Other/23-MultiagentSystems/README.md b/translations/sl/lessons/6-Other/23-MultiagentSystems/README.md index 60145928..4a46646a 100644 --- a/translations/sl/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/sl/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ Odprite enega od modelov, na primer **Biology → Flocking**. Ko odprete model, pridete na glavni zaslon NetLogo. Tukaj je primer modela, ki opisuje populacijo volkov in ovc ob omejenih virih (trava). -![NetLogo Main Screen](../../../../../translated_images/sl/NetLogo-Main.32653711ec1a01b3.png) +![NetLogo Main Screen](../../../../../translated_images/sl/NetLogo-Main.32653711ec1a01b3.webp) > Posnetek zaslona avtorja Dmitry Soshnikov diff --git a/translations/sl/lessons/README.md b/translations/sl/lessons/README.md index c715cc7f..1301e8bf 100644 --- a/translations/sl/lessons/README.md +++ b/translations/sl/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Pregled -![Pregled v skici](../../../translated_images/sl/ai-overview.0857791951d19500.png) +![Pregled v skici](../../../translated_images/sl/ai-overview.0857791951d19500.webp) > Skica avtorja [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/sl/lessons/X-Extras/X1-MultiModal/README.md b/translations/sl/lessons/X-Extras/X1-MultiModal/README.md index d3ed61d9..c394e458 100644 --- a/translations/sl/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/sl/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ Po uspehu modelov transformatorjev pri reševanju nalog NLP so bile iste ali pod Glavna ideja CLIP je primerjati besedilne pozive s sliko in ugotoviti, kako dobro slika ustreza pozivu. -![CLIP Arhitektura](../../../../../translated_images/sl/clip-arch.b3dbf20b4e8ed8be.png) +![CLIP Arhitektura](../../../../../translated_images/sl/clip-arch.b3dbf20b4e8ed8be.webp) > *Slika iz [tega blog prispevka](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ Ko je model predhodno treniran, mu lahko podamo paket slik in paket besedilnih p Recimo, da moramo razvrstiti slike med, na primer, mačke, pse in ljudi. V tem primeru lahko modelu podamo sliko in serijo besedilnih pozivov: "*slika mačke*", "*slika psa*", "*slika človeka*". V nastalem vektorju s tremi verjetnostmi moramo le izbrati indeks z najvišjo vrednostjo. -![CLIP za razvrščanje slik](../../../../../translated_images/sl/clip-class.3af42ef0b2b19369.png) +![CLIP za razvrščanje slik](../../../../../translated_images/sl/clip-class.3af42ef0b2b19369.webp) > *Slika iz [tega blog prispevka](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ Več o VQGAN si lahko preberete na spletni strani [Taming Transformers](https:// Ena pomembna razlika med VQGAN in tradicionalnim GAN je, da slednji lahko ustvari spodobno sliko iz katerega koli vhodnega vektorja, medtem ko VQGAN verjetno ustvari sliko, ki ni koherentna. Zato moramo dodatno usmerjati proces ustvarjanja slike, kar lahko storimo z uporabo CLIP. -![VQGAN+CLIP Arhitektura](../../../../../translated_images/sl/vqgan.5027fe05051dfa31.png) +![VQGAN+CLIP Arhitektura](../../../../../translated_images/sl/vqgan.5027fe05051dfa31.webp) Za generiranje slike, ki ustreza besedilnemu pozivu, začnemo z naključnim kodirnim vektorjem, ki ga posredujemo VQGAN za ustvarjanje slike. Nato uporabimo CLIP za ustvarjanje funkcije izgube, ki kaže, kako dobro slika ustreza besedilnemu pozivu. Cilj je nato minimizirati to izgubo z uporabo povratnega razširjanja za prilagoditev parametrov vhodnega vektorja. Odlična knjižnica, ki implementira VQGAN+CLIP, je [Pixray](http://github.com/pixray/pixray). -![Slika, ustvarjena s Pixray](../../../../../translated_images/sl/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Slika, ustvarjena s Pixray](../../../../../translated_images/sl/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Slika, ustvarjena s Pixray](../../../../../translated_images/sl/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) +![Slika, ustvarjena s Pixray](../../../../../translated_images/sl/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp) | ![Slika, ustvarjena s Pixray](../../../../../translated_images/sl/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp) | ![Slika, ustvarjena s Pixray](../../../../../translated_images/sl/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp) ----|----|---- Slika, ustvarjena iz poziva *bližnji akvarelni portret mladega učitelja književnosti z knjigo* | Slika, ustvarjena iz poziva *bližnji oljni portret mlade učiteljice računalništva z računalnikom* | Slika, ustvarjena iz poziva *bližnji oljni portret starega učitelja matematike pred tablo* diff --git a/translations/sr/README.md b/translations/sr/README.md index 19b61d12..cbc2be8b 100644 --- a/translations/sr/README.md +++ b/translations/sr/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # Вештачка интелигенција за почетнике - Наставни програм -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/sr/ai-overview.0857791951d19500.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/sr/ai-overview.0857791951d19500.webp)| |:---:| | Вештачка интелигенција за почетнике - _Скетчнот од [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/sr/lessons/1-Intro/README.md b/translations/sr/lessons/1-Intro/README.md index 4f53573e..3a8f2a23 100644 --- a/translations/sr/lessons/1-Intro/README.md +++ b/translations/sr/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Увод у вештачку интелигенцију -![Резиме садржаја увода у вештачку интелигенцију у виду цртежа](../../../../translated_images/sr/ai-intro.bf28d1ac4235881c.png) +![Резиме садржаја увода у вештачку интелигенцију у виду цртежа](../../../../translated_images/sr/ai-intro.bf28d1ac4235881c.webp) > Цртеж направио [Томоми Имура](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Првобитно, рачунаре је изумео [Чарлс Бебиџ](https://en.wikipedia.org/wiki/Charles_Babbage) да би обрађивали бројеве пратећи добро дефинисану процедуру - алгоритам. Савремени рачунари, иако значајно напреднији од оригиналног модела предложеног у 19. веку, и даље следе исту идеју контролисаних рачунања. Због тога је могуће програмирати рачунар да ради нешто ако знамо тачан низ корака који треба да се изврше да би се постигао циљ. -![Фотографија особе](../../../../translated_images/sr/dsh_age.d212a30d4e54fb5f.png) +![Фотографија особе](../../../../translated_images/sr/dsh_age.d212a30d4e54fb5f.webp) > Фотографија од [Вики Сошникова](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ CO_OP_TRANSLATOR_METADATA: Један од проблема када се бавимо термином **[интелигенција](https://en.wikipedia.org/wiki/Intelligence)** је то што не постоји јасна дефиниција овог термина. Може се тврдити да је интелигенција повезана са **апстрактним размишљањем**, или са **самосвешћу**, али не можемо је правилно дефинисати. -![Фотографија мачке](../../../../translated_images/sr/photo-cat.8c8e8fb760ffe457.jpg) +![Фотографија мачке](../../../../translated_images/sr/photo-cat.8c8e8fb760ffe457.webp) > [Фотографија](https://unsplash.com/photos/75715CVEJhI) од [Амбер Кип](https://unsplash.com/@sadmax) са Unsplash-а @@ -98,13 +98,13 @@ CO_OP_TRANSLATOR_METADATA: > | Шта је са МЛ? | | > |--------------|-----------| -> | Део Вештачке интелигенције који се заснива на томе да рачунар учи да реши проблем на основу неких података назива се **Машинско учење**. Нећемо разматрати класично машинско учење у овом курсу - упућујемо вас на посебан [Курс машинског учења за почетнике](http://aka.ms/ml-beginners). | ![МЛ за почетнике](../../../../translated_images/sr/ml-for-beginners.9e4fed176fd5817d.png) | +> | Део Вештачке интелигенције који се заснива на томе да рачунар учи да реши проблем на основу неких података назива се **Машинско учење**. Нећемо разматрати класично машинско учење у овом курсу - упућујемо вас на посебан [Курс машинског учења за почетнике](http://aka.ms/ml-beginners). | ![МЛ за почетнике](../../../../translated_images/sr/ml-for-beginners.9e4fed176fd5817d.webp) | ## Кратка историја ВИ Вештачка интелигенција је започета као област средином двадесетог века. У почетку је симболичко резоновање било доминантан приступ, и довело је до бројних важних успеха, као што су експертски системи – компјутерски програми који су могли да делују као експерт у неким ограниченим проблемским доменима. Међутим, убрзо је постало јасно да се такав приступ не скалира добро. Извлачење знања од експерта, представљање у рачунару и одржавање те базе знања тачном испоставило се као веома сложен задатак, и превише скуп да би био практичан у многим случајевима. То је довело до такозване [зиме ВИ](https://en.wikipedia.org/wiki/AI_winter) 1970-их. -Кратка историја ВИ +Кратка историја ВИ > Слика од [Дмитрија Сошникова](http://soshnikov.com) diff --git a/translations/sr/lessons/2-Symbolic/Animals.ipynb b/translations/sr/lessons/2-Symbolic/Animals.ipynb index 936a7bfc..dc8df4c9 100644 --- a/translations/sr/lessons/2-Symbolic/Animals.ipynb +++ b/translations/sr/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "У овом примеру, имплементираћемо једноставан систем заснован на знању који одређује животињу на основу неких физичких карактеристика. Систем се може представити следећим AND-OR стаблом (ово је део целог стабла, лако можемо додати још правила):\n", "\n", - "![](../../../../translated_images/sr/AND-OR-Tree.5592d2c70187f283.png)\n" + "![](../../../../translated_images/sr/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { diff --git a/translations/sr/lessons/2-Symbolic/README.md b/translations/sr/lessons/2-Symbolic/README.md index 3cc1f63c..e2e444fd 100644 --- a/translations/sr/lessons/2-Symbolic/README.md +++ b/translations/sr/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Представљање знања и експертски системи -![Резиме садржаја о симболичкој вештачкој интелигенцији](../../../../translated_images/sr/ai-symbolic.715a30cb610411a6.png) +![Резиме садржаја о симболичкој вештачкој интелигенцији](../../../../translated_images/sr/ai-symbolic.715a30cb610411a6.webp) > Скетч од [Томоми Имура](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ CO_OP_TRANSLATOR_METADATA: Дакле, проблем **представљања знања** је у проналажењу ефикасног начина за представљање знања унутар рачунара у облику података, како би оно било аутоматски употребљиво. Ово се може посматрати као спектар: -![Спектар представљања знања](../../../../translated_images/sr/knowledge-spectrum.b60df631852c0217.png) +![Спектар представљања знања](../../../../translated_images/sr/knowledge-spectrum.b60df631852c0217.webp) > Слика од [Дмитрија Сошњикова](http://soshnikov.com) @@ -94,7 +94,7 @@ Python | блок-синтакса | увлачење Један од раних успеха симболичке вештачке интелигенције били су такозвани **експертски системи** - рачунарски системи дизајнирани да делују као стручњаци у некој ограниченој области проблема. Они су се заснивали на **бази знања** извученој од једног или више људских стручњака и садржали су **инференцијски механизам** који је вршио закључивање на основу те базе. -![Људска архитектура](../../../../translated_images/sr/arch-human.5d4d35f1bba3ab1c.png) | ![Архитектура система заснованог на знању](../../../../translated_images/sr/arch-kbs.3ec5c150b09fa8da.png) +![Људска архитектура](../../../../translated_images/sr/arch-human.5d4d35f1bba3ab1c.webp) | ![Архитектура система заснованог на знању](../../../../translated_images/sr/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ Поједностављена структура људског нервног система | Архитектура система заснованог на знању @@ -106,7 +106,7 @@ Python | блок-синтакса | увлачење Као пример, размотримо следећи експертски систем за одређивање животиње на основу њених физичких карактеристика: -![AND-OR стабло](../../../../translated_images/sr/AND-OR-Tree.5592d2c70187f283.png) +![AND-OR стабло](../../../../translated_images/sr/AND-OR-Tree.5592d2c70187f283.webp) > Слика од [Дмитрија Сошњикова](http://soshnikov.com) diff --git a/translations/sr/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/sr/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index 115652e5..1a744676 100644 --- a/translations/sr/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/sr/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -1255,7 +1255,7 @@ "* Низак губитак на подацима за обуку - модел може добро да приближи податке за обуку, јер има довољно изражајне снаге.\n", "* Губитак на валидационим подацима може бити много већи од губитка на подацима за обуку и може почети да расте током обуке - то је зато што модел \"памти\" тачке из обуке и губи \"општу слику\".\n", "\n", - "![Претерано прилагођавање](../../../../../translated_images/sr/overfit.a0bd57f717c15769.png)\n", + "![Претерано прилагођавање](../../../../../translated_images/sr/overfit.a0bd57f717c15769.webp)\n", "\n", "> На овој слици, `x` представља податке за обуку, `o` - валидационе податке. Лево - линеарни модел (један слој), прилично добро приближава природу података. Десно - модел са претераним прилагођавањем, савршено добро приближава податке за обуку, али губи смисао са било којим другим подацима (валидациони губитак је веома висок).\n" ] diff --git a/translations/sr/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/sr/lessons/3-NeuralNetworks/05-Frameworks/README.md index 7c16917d..84fe28bc 100644 --- a/translations/sr/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/sr/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ CO_OP_TRANSLATOR_METADATA: Размотрите следећи проблем апроксимације 5 тачака (представљених са `x` на графицима испод): -![линеарно](../../../../../translated_images/sr/overfit1.f24b71c6f652e59e.jpg) | ![претренираност](../../../../../translated_images/sr/overfit2.131f5800ae10ca5e.jpg) +![линеарно](../../../../../translated_images/sr/overfit1.f24b71c6f652e59e.webp) | ![претренираност](../../../../../translated_images/sr/overfit2.131f5800ae10ca5e.webp) -------------------------|-------------------------- **Линеарни модел, 2 параметра** | **Нелинеарни модел, 7 параметара** Грешка на тренингу = 5.3 | Грешка на тренингу = 0 @@ -79,7 +79,7 @@ CO_OP_TRANSLATOR_METADATA: Као што можете видети на графику изнад, претренираност се може открити веома ниском грешком на тренингу и високом грешком на валидацији. Обично током тренинга видимо да и грешка на тренингу и грешка на валидацији почињу да опадају, а затим у једном тренутку грешка на валидацији може престати да опада и почети да расте. То ће бити знак претренираности и показатељ да би требало да зауставимо тренинг у том тренутку (или барем направимо снимак модела). -![претренираност](../../../../../translated_images/sr/Overfitting.408ad91cd90b4371.png) +![претренираност](../../../../../translated_images/sr/Overfitting.408ad91cd90b4371.webp) ## Како спречити претренираност diff --git a/translations/sr/lessons/3-NeuralNetworks/README.md b/translations/sr/lessons/3-NeuralNetworks/README.md index 13220bf2..0d9f6e85 100644 --- a/translations/sr/lessons/3-NeuralNetworks/README.md +++ b/translations/sr/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Увод у неуронске мреже -![Резиме садржаја увода у неуронске мреже у виду цртежа](../../../../translated_images/sr/ai-neuralnetworks.1c687ae40bc86e83.png) +![Резиме садржаја увода у неуронске мреже у виду цртежа](../../../../translated_images/sr/ai-neuralnetworks.1c687ae40bc86e83.webp) Као што смо разговарали у уводу, један од начина да се постигне интелигенција је да се обучи **рачунарски модел** или **вештачки мозак**. Од средине 20. века, истраживачи су испробавали различите математичке моделе, све док се у последњим годинама овај правац није показао као изузетно успешан. Такви математички модели мозга називају се **неуронске мреже**. @@ -36,13 +36,13 @@ CO_OP_TRANSLATOR_METADATA: Из биологије знамо да наш мозак састоји се од неуронских ћелија (неурона), од којих свака има више "улаза" (дендрита) и један "излаз" (аксон). И дендрити и аксони могу проводити електричне сигнале, а везе између њих — познате као синапсе — могу показивати различите степене проводљивости, које регулишу неуротрансмитери. -![Модел неурона](../../../../translated_images/sr/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![Модел неурона](../../../../translated_images/sr/artneuron.1a5daa88d20ebe6f.png) +![Модел неурона](../../../../translated_images/sr/synapse-wikipedia.ed20a9e4726ea1c6.webp) | ![Модел неурона](../../../../translated_images/sr/artneuron.1a5daa88d20ebe6f.webp) ----|---- Прави неурон *([Слика](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) са Википедије)* | Вештачки неурон *(Слика аутора)* Дакле, најједноставнији математички модел неурона садржи неколико улаза X1, ..., XN и један излаз Y, као и низ тежина W1, ..., WN. Излаз се рачуна као: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) где је f нека нелинеарна **активациона функција**. diff --git a/translations/sr/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/sr/lessons/4-ComputerVision/06-IntroCV/README.md index 3a5283b8..3627810f 100644 --- a/translations/sr/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/sr/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ OpenCV можете користити и за учитавање видео з * **Предобрада фотографије Брајеве књиге**. Фокусирамо се на то како можемо користити праговање, детекцију карактеристика, перспективну трансформацију и манипулације NumPy низовима да бисмо раздвојили појединачне Брајеве симболе за даљу класификацију помоћу неуронске мреже. -![Слика Брајеве књиге](../../../../../translated_images/sr/braille.341962ff76b1bd70.jpeg) | ![Предобрађена слика Брајеве књиге](../../../../../translated_images/sr/braille-result.46530fea020b03c7.png) | ![Брајеви симболи](../../../../../translated_images/sr/braille-symbols.0159185ab69d5339.png) +![Слика Брајеве књиге](../../../../../translated_images/sr/braille.341962ff76b1bd70.webp) | ![Предобрађена слика Брајеве књиге](../../../../../translated_images/sr/braille-result.46530fea020b03c7.webp) | ![Брајеви симболи](../../../../../translated_images/sr/braille-symbols.0159185ab69d5339.webp) ----|-----|----- > Слика из [OpenCV.ipynb](OpenCV.ipynb) * **Детекција кретања у видеу коришћењем разлике кадрова**. Ако је камера фиксирана, онда би кадрови из видео записа требало да буду прилично слични један другом. Пошто су кадрови представљени као низови, само одузимањем тих низова за два узастопна кадра добићемо разлику пиксела, која би требало да буде мала за статичне кадрове, а да постане већа када постоји значајно кретање на слици. -![Слика видео кадрова и разлике кадрова](../../../../../translated_images/sr/frame-difference.706f805491a0883c.png) +![Слика видео кадрова и разлике кадрова](../../../../../translated_images/sr/frame-difference.706f805491a0883c.webp) > Слика из [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ OpenCV можете користити и за учитавање видео з - **Густи оптички ток** израчунава векторско поље које показује за сваки пиксел где се креће - **Ретки оптички ток** заснован је на узимању неких карактеристичних елемената на слици (нпр. ивица) и изградњи њихове трајекторије од кадра до кадра. -![Слика оптичког тока](../../../../../translated_images/sr/optical.1f4a94464579a83a.png) +![Слика оптичког тока](../../../../../translated_images/sr/optical.1f4a94464579a83a.webp) > Слика из [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/sr/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/sr/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index f6046111..3e29c5ab 100644 --- a/translations/sr/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/sr/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 је мрежа која је постигла тачност од 92.7% у ImageNet top-5 класификацији 2014. године. Има следећу структуру слојева: -![ImageNet Layers](../../../../../translated_images/sr/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet Layers](../../../../../translated_images/sr/vgg-16-arch1.d901a5583b3a51ba.webp) Као што можете видети, VGG прати традиционалну пирамидалну архитектуру, која је низ слојева за конволуцију и пуловање. -![ImageNet Pyramid](../../../../../translated_images/sr/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet Pyramid](../../../../../translated_images/sr/vgg-16-arch.64ff2137f50dd49f.webp) > Слика са [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/sr/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/sr/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index a9ed8845..abd5a3e5 100644 --- a/translations/sr/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/sr/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -260,7 +260,7 @@ "\n", "Тако, у типичном CNN-у постоји неколико конволуционих слојева, са слојевима за пуловање између њих како би се смањиле димензије слике. Такође бисмо повећали број филтера, јер како обрасци постају сложенији, постоји више могућих занимљивих комбинација које треба тражити.\n", "\n", - "![Слика која приказује неколико конволуционих слојева са слојевима за пуловање.](../../../../../translated_images/sr/cnn-pyramid.85915455759ef0ce.png)\n", + "![Слика која приказује неколико конволуционих слојева са слојевима за пуловање.](../../../../../translated_images/sr/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "Због смањивања просторних димензија и повећавања димензија карактеристика/филтера, ова архитектура се такође назива **пирамидална архитектура**.\n" ] diff --git a/translations/sr/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/sr/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index fe03394c..ed45b248 100644 --- a/translations/sr/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/sr/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -359,7 +359,7 @@ "\n", "Тако би у типичном CNN-у постојало неколико конволуционих слојева, са pooling слојевима између њих како би се смањиле димензије слике. Такође бисмо повећали број филтера, јер како обрасци постају напреднији, постоји више могућих занимљивих комбинација које треба тражити.\n", "\n", - "![Слика која приказује неколико конволуционих слојева са pooling слојевима.](../../../../../translated_images/sr/cnn-pyramid.85915455759ef0ce.png)\n", + "![Слика која приказује неколико конволуционих слојева са pooling слојевима.](../../../../../translated_images/sr/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "Због смањења просторних димензија и повећања димензија карактеристика/филтера, ова архитектура се такође назива **пирамидална архитектура**.\n" ] diff --git a/translations/sr/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/sr/lessons/4-ComputerVision/07-ConvNets/README.md index ee6db5fd..88e750c3 100644 --- a/translations/sr/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/sr/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: Да бисмо извукли шаблоне, користићемо концепт **конволуционих филтера**. Као што знате, слика је представљена као 2D-матрица или 3D-тензор са дубином боје. Примена филтера значи да узимамо релативно малу матрицу **језгра филтера**, и за сваки пиксел у оригиналној слици израчунавамо пондерисани просек са суседним тачкама. Ово можемо замислити као мали прозор који клизи преко целе слике и израчунава просек свих пиксела према тежинама у матрици језгра филтера. -![Филтер за вертикалне ивице](../../../../../translated_images/sr/filter-vert.b7148390ca0bc356.png) | ![Филтер за хоризонталне ивице](../../../../../translated_images/sr/filter-horiz.59b80ed4feb946ef.png) +![Филтер за вертикалне ивице](../../../../../translated_images/sr/filter-vert.b7148390ca0bc356.webp) | ![Филтер за хоризонталне ивице](../../../../../translated_images/sr/filter-horiz.59b80ed4feb946ef.webp) ----|---- > Слика: Дмитриј Сошњиков @@ -38,7 +38,7 @@ CO_OP_TRANSLATOR_METADATA: * Можемо дизајнирати мрежу тако да се филтери аутоматски тренирају * Можемо користити исти приступ за проналажење шаблона у карактеристикама високог нивоа, а не само у оригиналној слици. Тако екстракција карактеристика у CNN-у функционише на хијерархији карактеристика, почевши од комбинација пиксела ниског нивоа, па све до комбинација делова слике високог нивоа. -![Хијерархијска екстракција карактеристика](../../../../../translated_images/sr/FeatureExtractionCNN.d9b456cbdae7cb64.png) +![Хијерархијска екстракција карактеристика](../../../../../translated_images/sr/FeatureExtractionCNN.d9b456cbdae7cb64.webp) > Слика из [рада Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), засновано на [њиховом истраживању](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ CO_OP_TRANSLATOR_METADATA: Као пример, погледајмо архитектуру VGG-16, мреже која је постигла 92.7% тачности у ImageNet топ-5 класификацији 2014. године: -![Слојеви ImageNet](../../../../../translated_images/sr/vgg-16-arch1.d901a5583b3a51ba.jpg) +![Слојеви ImageNet](../../../../../translated_images/sr/vgg-16-arch1.d901a5583b3a51ba.webp) -![Пирамида ImageNet](../../../../../translated_images/sr/vgg-16-arch.64ff2137f50dd49f.jpg) +![Пирамида ImageNet](../../../../../translated_images/sr/vgg-16-arch.64ff2137f50dd49f.webp) > Слика са [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/sr/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/sr/lessons/4-ComputerVision/07-ConvNets/lab/README.md index e3d2e61d..d958bc0c 100644 --- a/translations/sr/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/sr/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ CO_OP_TRANSLATOR_METADATA: Користићемо [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/), који садржи слике 37 различитих раса паса и мачака. -![Скуп података са којим ћемо радити](../../../../../../translated_images/sr/data.50b2a9d5484bdbf0.png) +![Скуп података са којим ћемо радити](../../../../../../translated_images/sr/data.50b2a9d5484bdbf0.webp) Да бисте преузели скуп података, користите овај код: diff --git a/translations/sr/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/sr/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index c99083f0..a7a492b3 100644 --- a/translations/sr/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/sr/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "Да бисмо визуализовали идеалну мачку, почећемо са сликом насумичног шума и покушаћемо да користимо технику оптимизације градијентног спуштања како бисмо прилагодили слику тако да мрежа препозна мачку.\n", "\n", - "![Оптимизациони циклус](../../../../../translated_images/sr/ideal-cat-loop.999fbb8ff306e044.png)\n", + "![Оптимизациони циклус](../../../../../translated_images/sr/ideal-cat-loop.999fbb8ff306e044.webp)\n", "\n", "Ово је наша почетна слика:\n" ] diff --git a/translations/sr/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/sr/lessons/4-ComputerVision/08-TransferLearning/README.md index 4669986f..20de3913 100644 --- a/translations/sr/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/sr/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ CO_OP_TRANSLATOR_METADATA: Ево примера карактеристика издвојених из слике мачке помоћу VGG-16 мреже: -![Карактеристике издвојене помоћу VGG-16](../../../../../translated_images/sr/features.6291f9c7ba3a0b95.png) +![Карактеристике издвојене помоћу VGG-16](../../../../../translated_images/sr/features.6291f9c7ba3a0b95.webp) ## Скуп података: Мачке и пси @@ -48,19 +48,19 @@ CO_OP_TRANSLATOR_METADATA: Један приступ који можемо применити је да почнемо са насумичном сликом и затим покушамо да користимо технику **оптимизације градијентног спуштања** како бисмо прилагодили ту слику на такав начин да мрежа почне да мисли да је то мачка. -![Петља оптимизације слике](../../../../../translated_images/sr/ideal-cat-loop.999fbb8ff306e044.png) +![Петља оптимизације слике](../../../../../translated_images/sr/ideal-cat-loop.999fbb8ff306e044.webp) Међутим, ако то урадимо, добићемо нешто веома слично насумичном шуму. То је зато што *постоји много начина да мрежа помисли да је улазна слика мачка*, укључујући неке који визуелно немају смисла. Иако те слике садрже много образаца типичних за мачку, ништа их не ограничава да буду визуелно препознатљиве. Да бисмо побољшали резултат, можемо додати још један члан у функцију губитка, који се назива **губитак варијације**. То је метрика која показује колико су слични суседни пиксели слике. Минимизирање губитка варијације чини слику глаткијом и уклања шум - чиме открива визуелно привлачније обрасце. Ево примера таквих "идеалних" слика, које се класификују као мачка и као зебра са високом вероватноћом: -![Идеална мачка](../../../../../translated_images/sr/ideal-cat.203dd4597643d6b0.png) | ![Идеална зебра](../../../../../translated_images/sr/ideal-zebra.7f70e8b54ee15a7a.png) +![Идеална мачка](../../../../../translated_images/sr/ideal-cat.203dd4597643d6b0.webp) | ![Идеална зебра](../../../../../translated_images/sr/ideal-zebra.7f70e8b54ee15a7a.webp) -----|----- *Идеална мачка* | *Идеална зебра* Сличан приступ може се користити за извођење такозваних **адверзаријалних напада** на неуронску мрежу. Претпоставимо да желимо да преваримо неуронску мрежу и учинимо да пас изгледа као мачка. Ако узмемо слику пса, коју мрежа препознаје као пса, можемо је мало изменити користећи оптимизацију градијентног спуштања, док мрежа не почне да је класификује као мачку: -![Слика пса](../../../../../translated_images/sr/original-dog.8f68a67d2fe0911f.png) | ![Слика пса класификована као мачка](../../../../../translated_images/sr/adversarial-dog.d9fc7773b0142b89.png) +![Слика пса](../../../../../translated_images/sr/original-dog.8f68a67d2fe0911f.webp) | ![Слика пса класификована као мачка](../../../../../translated_images/sr/adversarial-dog.d9fc7773b0142b89.webp) -----|----- *Оригинална слика пса* | *Слика пса класификована као мачка* diff --git a/translations/sr/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/sr/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index 8d34eb8b..8e85ae3b 100644 --- a/translations/sr/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/sr/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "Пошто тренирамо аутокодер да ухвати што више информација из оригиналне слике ради тачне реконструкције, мрежа покушава да пронађе најбољу **репрезентацију** улазних слика како би ухватила њихово значење.\n", "\n", - "![Дијаграм аутокодера](../../../../../translated_images/sr/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![Дијаграм аутокодера](../../../../../translated_images/sr/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "> Слика са [Keras блога](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", diff --git a/translations/sr/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/sr/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index ab8ace41..fc232a57 100644 --- a/translations/sr/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/sr/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "Пошто тренирамо аутокодер да ухвати што више информација из оригиналне слике ради тачне реконструкције, мрежа покушава да пронађе најбоље **уграђивање** улазних слика како би ухватила њихово значење.\n", "\n", - "![Шема аутокодера](../../../../../translated_images/sr/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![Шема аутокодера](../../../../../translated_images/sr/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "*Слика са [Keras блога](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", diff --git a/translations/sr/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/sr/lessons/4-ComputerVision/09-Autoencoders/README.md index 9552ace9..1bca0f96 100644 --- a/translations/sr/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/sr/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Пошто тренирамо аутоенкодер да ухвати што више информација из оригиналне слике ради тачне реконструкције, мрежа покушава да пронађе најбоље **уграђивање** улазних слика како би ухватила њихово значење. -![Дијаграм аутоенкодера](../../../../../translated_images/sr/autoencoder_schema.5e6fc9ad98a5eb61.jpg) +![Дијаграм аутоенкодера](../../../../../translated_images/sr/autoencoder_schema.5e6fc9ad98a5eb61.webp) > Слика са [Keras блога](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/sr/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/sr/lessons/4-ComputerVision/11-ObjectDetection/README.md index c09f7076..a797d0fe 100644 --- a/translations/sr/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/sr/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: ## [Квиз пре предавања](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Детекција објеката](../../../../../translated_images/sr/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) +![Детекција објеката](../../../../../translated_images/sr/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp) > Слика са [YOLO v2 веб сајта](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 2. Покрените класификацију слике на свакој плочици. 3. Плочице које резултирају довољно високом активацијом могу се сматрати да садрже тражени објекат. -![Наивна детекција објеката](../../../../../translated_images/sr/naive-detection.e7f1ba220ccd08c6.png) +![Наивна детекција објеката](../../../../../translated_images/sr/naive-detection.e7f1ba220ccd08c6.webp) > *Слика из [радне свеске за вежбе](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ CO_OP_TRANSLATOR_METADATA: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 класа * [COCO](http://cocodataset.org/#home) - Уобичајени објекти у контексту. 80 класа, оквири и маске за сегментацију -![COCO](../../../../../translated_images/sr/coco-examples.71bc60380fa6cceb.jpg) +![COCO](../../../../../translated_images/sr/coco-examples.71bc60380fa6cceb.webp) ## Метрике за детекцију објеката @@ -50,7 +50,7 @@ CO_OP_TRANSLATOR_METADATA: Док је за класификацију слика лако измерити колико добро алгоритам ради, за детекцију објеката морамо измерити и исправност класе, као и прецизност локације предвиђеног оквира. За ово друго користимо такозвани **Пресек преко уније** (IoU), који мери колико добро се два оквира (или две произвољне области) преклапају. -![IoU](../../../../../translated_images/sr/iou_equation.9a4751d40fff4e11.png) +![IoU](../../../../../translated_images/sr/iou_equation.9a4751d40fff4e11.webp) > *Фигура 2 из [овог одличног блог поста о IoU](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ $$ [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) користи [Селективно претраживање](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) за генерисање хијерархијске структуре ROI региона, који се затим прослеђују кроз CNN екстракторе карактеристика и SVM класификаторе за одређивање класе објекта, и линеарну регресију за одређивање координата *оквира*. [Званични рад](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/sr/rcnn1.cae407020dfb1d1f.png) +![RCNN](../../../../../translated_images/sr/rcnn1.cae407020dfb1d1f.webp) > *Слика из ван де Санде и др. ICCV’11* -![RCNN-1](../../../../../translated_images/sr/rcnn2.2d9530bb83516484.png) +![RCNN-1](../../../../../translated_images/sr/rcnn2.2d9530bb83516484.webp) > *Слике из [овог блога](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -110,7 +110,7 @@ $$ Овај приступ је сличан R-CNN-у, али региони се дефинишу након што су примењени слојеви конволуције. -![FRCNN](../../../../../translated_images/sr/f-rcnn.3cda6d9bb4188875.png) +![FRCNN](../../../../../translated_images/sr/f-rcnn.3cda6d9bb4188875.webp) > Слика из [званичног рада](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ $$ Главна идеја овог приступа је коришћење неуронске мреже за предвиђање ROI - такозване *мреже за предлог региона*. [Рад](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/sr/faster-rcnn.8d46c099b87ef30a.png) +![FasterRCNN](../../../../../translated_images/sr/faster-rcnn.8d46c099b87ef30a.webp) > Слика из [званичног рада](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ $$ 2. Карактеристике се обрађују помоћу **мапе оцена осетљивих на позицију**. Сваки објекат из $C$ класа се дели на $k\times k$ регије, и тренирамо мрежу да предвиђа делове објеката. 3. За сваки део из $k\times k$ регија све мреже гласају за класе објеката, и класа објекта са максималним бројем гласова се бира. -![r-fcn image](../../../../../translated_images/sr/r-fcn.13eb88158b99a3da.png) +![r-fcn image](../../../../../translated_images/sr/r-fcn.13eb88158b99a3da.webp) > Слика из [званичног рада](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO је алгоритам за реално време са једним пр * Слика се дели на $S\times S$ регије. * За сваку регију, **CNN** предвиђа $n$ могућих објеката, координате *оквира* и *поузданост*=*вероватноћа* * IoU. - ![YOLO](../../../../../translated_images/sr/yolo.a2648ec82ee8bb4e.png) + ![YOLO](../../../../../translated_images/sr/yolo.a2648ec82ee8bb4e.webp) > Слика из [званичног рада](https://arxiv.org/abs/1506.02640) diff --git a/translations/sr/lessons/4-ComputerVision/README.md b/translations/sr/lessons/4-ComputerVision/README.md index 1b972c04..58b6dd5b 100644 --- a/translations/sr/lessons/4-ComputerVision/README.md +++ b/translations/sr/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Рачунарски вид -![Резиме садржаја о рачунарском виду у виду цртежа](../../../../translated_images/sr/ai-computervision.6506ebebac3fbf76.png) +![Резиме садржаја о рачунарском виду у виду цртежа](../../../../translated_images/sr/ai-computervision.6506ebebac3fbf76.webp) У овом делу ћемо научити о: diff --git a/translations/sr/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/sr/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index 88d58598..02706ab8 100644 --- a/translations/sr/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/sr/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**Врећа речи** (BoW) представљање у виду вектора је најчешће коришћено традиционално представљање вектора. Свака реч је повезана са индексом вектора, а елемент вектора садржи број појављивања те речи у датом документу.\n", "\n", - "![Слика која приказује како је представљање вектора вреће речи приказано у меморији.](../../../../../translated_images/sr/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![Слика која приказује како је представљање вектора вреће речи приказано у меморији.](../../../../../translated_images/sr/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Напомена**: Можете такође размишљати о BoW као о збиру свих вектора кодираних једним битом за појединачне речи у тексту.\n", "\n", diff --git a/translations/sr/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/sr/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index 6f8b41b4..1dfa1f2f 100644 --- a/translations/sr/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/sr/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**Торба речи** (BoW) представљање вектора је најједноставније за разумевање међу традиционалним представљањима вектора. Свака реч је повезана са индексом вектора, а елемент вектора садржи број појављивања сваке речи у датом документу.\n", "\n", - "![Слика која приказује како је представљање вектора методом \"торба речи\" приказано у меморији.](../../../../../translated_images/sr/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![Слика која приказује како је представљање вектора методом \"торба речи\" приказано у меморији.](../../../../../translated_images/sr/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Note**: Можете такође размишљати о BoW као о збиру свих једноврсно-кодираних вектора за појединачне речи у тексту.\n", "\n", diff --git a/translations/sr/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/sr/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 61ab039d..a6b20502 100644 --- a/translations/sr/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/sr/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "Користећи слој за угњеждавање као први слој у нашој мрежи, можемо прећи са модела торбе речи на модел **торбе угњеждавања**, где прво конвертујемо сваку реч у нашем тексту у одговарајуће угњеждавање, а затим израчунавамо неку агрегатну функцију над свим тим угњеждавањима, као што су `sum`, `average` или `max`.\n", "\n", - "![Слика која приказује класификатор угњеждавања за пет речи у низу.](../../../../../translated_images/sr/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![Слика која приказује класификатор угњеждавања за пет речи у низу.](../../../../../translated_images/sr/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "Наша неуронска мрежа класификатора ће почети са слојем за угњеждавање, затим слојем за агрегирање, и линеарним класификатором на врху:\n" ] @@ -176,7 +176,7 @@ "\n", "У претходној архитектури, морали смо да попунимо све секвенце до исте дужине како би се уклопиле у мини серију. Ово није најефикаснији начин за представљање секвенци променљиве дужине - други приступ би био коришћење **вектора офсета**, који би садржао офсете свих секвенци смештених у један велики вектор.\n", "\n", - "![Слика која приказује репрезентацију секвенци са офсетом](../../../../../translated_images/sr/offset-sequence-representation.eb73fcefb29b46ee.png)\n", + "![Слика која приказује репрезентацију секвенци са офсетом](../../../../../translated_images/sr/offset-sequence-representation.eb73fcefb29b46ee.webp)\n", "\n", "> **Note**: На слици изнад приказујемо секвенцу карактера, али у нашем примеру радимо са секвенцама речи. Међутим, општи принцип представљања секвенци помоћу вектора офсета остаје исти.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW је бржи, док је skip-gram спорији, али боље представља речи које се ређе јављају.\n", "\n", - "![Слика која приказује и CBoW и Skip-Gram алгоритме за претварање речи у векторе.](../../../../../translated_images/sr/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![Слика која приказује и CBoW и Skip-Gram алгоритме за претварање речи у векторе.](../../../../../translated_images/sr/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Да бисмо експериментисали са Word2Vec угнежђењем претходно обученим на Google News скупу података, можемо користити библиотеку **gensim**. Испод налазимо речи које су најсличније речи 'neural'.\n", "\n", diff --git a/translations/sr/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/sr/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index 2535bf3b..ca195aa0 100644 --- a/translations/sr/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/sr/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "Коришћењем слоја за уграђивање као првог слоја у нашој мрежи, можемо прећи са модела „вреће речи“ на модел **вреће уграђивања**, где прво претварамо сваку реч у нашем тексту у одговарајуће уграђивање, а затим израчунавамо неку агрегатну функцију над свим тим уграђивањима, као што су `sum`, `average` или `max`.\n", "\n", - "![Слика која приказује класификатор са уграђивањем за пет речи у низу.](../../../../../translated_images/sr/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![Слика која приказује класификатор са уграђивањем за пет речи у низу.](../../../../../translated_images/sr/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "Наша неуронска мрежа класификатора састоји се од следећих слојева:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW је бржи, док је скип-грам спорији, али боље представља ретке речи.\n", "\n", - "![Слика која приказује алгоритме CBoW и Skip-Gram за конвертовање речи у векторе.](../../../../../translated_images/sr/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![Слика која приказује алгоритме CBoW и Skip-Gram за конвертовање речи у векторе.](../../../../../translated_images/sr/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Да бисмо експериментисали са Word2Vec уградњом претходно обученом на Google News скупу података, можемо користити библиотеку **gensim**. Испод налазимо речи које су најсличније 'neural'.\n", "\n", diff --git a/translations/sr/lessons/5-NLP/14-Embeddings/README.md b/translations/sr/lessons/5-NLP/14-Embeddings/README.md index 7edfec36..92c03142 100644 --- a/translations/sr/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/sr/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Коришћењем слоја угњеждења као првог слоја у нашој мрежи класификатора, можемо прећи са модела торбе речи на модел **торбе угњеждења**, где прво конвертујемо сваку реч у нашем тексту у одговарајуће угњеждење, а затим израчунавамо неку агрегатну функцију над свим тим угњеждењима, као што су `sum`, `average` или `max`. -![Слика која приказује класификатор заснован на угњеждењима за пет речи у низу.](../../../../../translated_images/sr/embedding-classifier-example.b77f021a7ee67eee.png) +![Слика која приказује класификатор заснован на угњеждењима за пет речи у низу.](../../../../../translated_images/sr/embedding-classifier-example.b77f021a7ee67eee.webp) > Слика аутора @@ -40,7 +40,7 @@ CO_OP_TRANSLATOR_METADATA: CBoW је бржи, док је скип-грам спорији, али боље представља ретке речи. -![Слика која приказује алгоритме CBoW и Skip-Gram за конвертовање речи у векторе.](../../../../../translated_images/sr/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![Слика која приказује алгоритме CBoW и Skip-Gram за конвертовање речи у векторе.](../../../../../translated_images/sr/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Слика из [овог рада](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/sr/lessons/5-NLP/15-LanguageModeling/README.md b/translations/sr/lessons/5-NLP/15-LanguageModeling/README.md index 4d2d6a7d..60363a07 100644 --- a/translations/sr/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/sr/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: * **Континуирана врећа речи** (CBoW), где предвиђамо средишњи токен $W_0$ у низу токена $W_{-N}$, ..., $W_N$. * **Скип-грам**, где предвиђамо скуп суседних токена {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} на основу средишњег токена $W_0$. -![слика из рада о претварању речи у векторе](../../../../../translated_images/sr/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![слика из рада о претварању речи у векторе](../../../../../translated_images/sr/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Слика из [овог рада](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/sr/lessons/5-NLP/16-RNN/README.md b/translations/sr/lessons/5-NLP/16-RNN/README.md index e3762a15..52f1823d 100644 --- a/translations/sr/lessons/5-NLP/16-RNN/README.md +++ b/translations/sr/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: Да бисмо ухватили значење секвенце текста, потребно је да користимо другу архитектуру неуронске мреже, која се назива **рекурентна неуронска мрежа**, или RNN. У RNN-у, реченицу пропуштамо кроз мрежу један симбол по један, а мрежа производи неко **стање**, које затим поново прослеђујемо мрежи са следећим симболом. -![RNN](../../../../../translated_images/sr/rnn.27f5c29c53d727b5.png) +![RNN](../../../../../translated_images/sr/rnn.27f5c29c53d727b5.webp) > Слика аутора @@ -61,7 +61,7 @@ LSTM мрежа је организована на начин сличан RNN- Рекурентна мрежа, било једносмерна или двосмерна, хвата одређене обрасце унутар секвенце и може их складиштити у вектор стања или проследити у излаз. Као и код конволуционих мрежа, можемо изградити још један рекурентни слој на врху првог да ухватимо обрасце вишег нивоа и изградимо од образаца нижег нивоа које је извукао први слој. Ово нас доводи до концепта **вишеслојног RNN-а**, који се састоји од два или више рекурентних мрежа, где се излаз претходног слоја прослеђује следећем слоју као улаз. -![Слика која приказује вишеслојни LSTM RNN](../../../../../translated_images/sr/multi-layer-lstm.dd975e29bb2a59fe.jpg) +![Слика која приказује вишеслојни LSTM RNN](../../../../../translated_images/sr/multi-layer-lstm.dd975e29bb2a59fe.webp) *Слика из [овог дивног чланка](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) аутора Фернанда Лопеза* diff --git a/translations/sr/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/sr/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index 385ac8ee..ea503b5a 100644 --- a/translations/sr/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/sr/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "Рекурентна мрежа, једносмерна или двосмерна, хвата одређене обрасце унутар секвенце, и може их складиштити у вектор стања или проследити у излаз. Као и код конволуционих мрежа, можемо изградити још један рекурентни слој на врху првог како бисмо ухватили обрасце вишег нивоа, изграђене од образаца нижег нивоа које је извукао први слој. Ово нас доводи до концепта **вишеслојне РНН**, која се састоји од два или више рекурентних мрежа, где се излаз претходног слоја прослеђује следећем слоју као улаз.\n", "\n", - "![Слика која приказује вишеслојну дугорочно-краткорочну меморијску РНН](../../../../../translated_images/sr/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![Слика која приказује вишеслојну дугорочно-краткорочну меморијску РНН](../../../../../translated_images/sr/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*Слика из [овог дивног чланка](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) аутора Фернанда Лопеза*\n", "\n", diff --git a/translations/sr/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/sr/lessons/5-NLP/16-RNN/RNNTF.ipynb index 5a6008e3..15409c11 100644 --- a/translations/sr/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/sr/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "Да бисмо ухватили значење секвенце текста, користићемо архитектуру неуронске мреже која се зове **рекурентна неуронска мрежа**, или RNN. Када користимо RNN, пролазимо кроз реченицу кроз мрежу један токен по један, а мрежа производи неко **стање**, које затим поново прослеђујемо мрежи са следећим токеном.\n", "\n", - "![Слика која приказује пример генерисања рекурентне неуронске мреже.](../../../../../translated_images/sr/rnn.27f5c29c53d727b5.png)\n", + "![Слика која приказује пример генерисања рекурентне неуронске мреже.](../../../../../translated_images/sr/rnn.27f5c29c53d727b5.webp)\n", "\n", "С обзиром на улазну секвенцу токена $X_0,\\dots,X_n$, RNN креира секвенцу блокова неуронске мреже и тренира ову секвенцу од почетка до краја користећи бацкпропагацију. Сваки блок мреже узима пар $(X_i,S_i)$ као улаз, и производи $S_{i+1}$ као резултат. Коначно стање $S_n$ или излаз $Y_n$ иде у линеарни класификатор да би произвео резултат. Сви блокови мреже деле исте тежине и тренирају се од почетка до краја користећи један пролаз бацкпропагације.\n", "\n", @@ -369,7 +369,7 @@ "\n", "Рекурентне мреже, било једносмерне или двосмерне, хватају обрасце унутар секвенце и чувају их у векторима стања или их враћају као излаз. Као и код конволуционих мрежа, можемо изградити још један рекурентни слој након првог како бисмо ухватили обрасце вишег нивоа, изграђене од образаца нижег нивоа које је извукао први слој. Ово нас доводи до појма **вишеслојног РНН-а**, који се састоји од два или више рекурентних мрежа, где се излаз претходног слоја прослеђује следећем слоју као улаз.\n", "\n", - "![Слика која приказује вишеслојни дугорочно-краткорочно-меморијски РНН](../../../../../translated_images/sr/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![Слика која приказује вишеслојни дугорочно-краткорочно-меморијски РНН](../../../../../translated_images/sr/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*Слика из [овог сјајног чланка](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) аутора Фернанда Лопеза.*\n", "\n", diff --git a/translations/sr/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/sr/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index 42ae3e80..1e022459 100644 --- a/translations/sr/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/sr/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "Начин на који ћемо тренирати RNN за генерисање текста је следећи. На сваком кораку, узимамо секвенцу карактера дужине `nchars` и тражимо од мреже да генерише следећи излазни карактер за сваки улазни карактер:\n", "\n", - "![Слика која приказује пример RNN генерисања речи 'HELLO'.](../../../../../translated_images/sr/rnn-generate.56c54afb52f9781d.png)\n", + "![Слика која приказује пример RNN генерисања речи 'HELLO'.](../../../../../translated_images/sr/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "У зависности од конкретног сценарија, можда ћемо желети да укључимо неке посебне карактере, као што је *крај секвенце* ``. У нашем случају, желимо само да обучимо мрежу за бесконачно генерисање текста, па ћемо фиксирати величину сваке секвенце да буде једнака `nchars` токенима. Сходно томе, сваки пример за тренирање ће се састојати од `nchars` улаза и `nchars` излаза (што је улазна секвенца померена за један симбол улево). Минибатч ће се састојати од неколико таквих секвенци.\n", "\n", diff --git a/translations/sr/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/sr/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index 49cfa4f4..da2cfa68 100644 --- a/translations/sr/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/sr/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -113,7 +113,7 @@ "\n", "Начин на који ћемо тренирати RNN да генерише наслове вести је следећи. У сваком кораку, узимамо један наслов, који ће бити унет у RNN, и за сваки улазни карактер тражимо од мреже да генерише следећи излазни карактер:\n", "\n", - "![Слика која приказује пример генерације речи 'HELLO' помоћу RNN.](../../../../../translated_images/sr/rnn-generate.56c54afb52f9781d.png)\n", + "![Слика која приказује пример генерације речи 'HELLO' помоћу RNN.](../../../../../translated_images/sr/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "За последњи карактер у нашој секвенци, тражићемо од мреже да генерише `` токен.\n", "\n", diff --git a/translations/sr/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/sr/lessons/5-NLP/17-GenerativeNetworks/README.md index b8e032c4..a78eaa5c 100644 --- a/translations/sr/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/sr/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Ово омогућава различите неуронске архитектуре које су приказане на слици испод: -![Слика која приказује уобичајене обрасце рекурентних неуронских мрежа.](../../../../../translated_images/sr/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Слика која приказује уобичајене обрасце рекурентних неуронских мрежа.](../../../../../translated_images/sr/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > Слика из блога [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) аутора [Андреја Карпатија](http://karpathy.github.io/) @@ -32,7 +32,7 @@ CO_OP_TRANSLATOR_METADATA: Обучаваћемо ову RNN да генерише текст корак по корак. На сваком кораку, узимамо секвенцу карактера дужине `nchars` и тражимо од мреже да генерише следећи излазни карактер за сваки улазни карактер: -![Слика која приказује пример RNN генерисања речи 'HELLO'.](../../../../../translated_images/sr/rnn-generate.56c54afb52f9781d.png) +![Слика која приказује пример RNN генерисања речи 'HELLO'.](../../../../../translated_images/sr/rnn-generate.56c54afb52f9781d.webp) Када генеришемо текст (током инференције), почињемо са неким **подстицајем** (prompt), који се прослеђује кроз RNN ћелије да би се генерисало његово интермедијарно стање, а затим из тог стања почиње генерисање. Генеришемо један карактер у исто време и прослеђујемо стање и генерисани карактер следећој RNN ћелији да генерише следећи, све док не генеришемо довољно карактера. diff --git a/translations/sr/lessons/5-NLP/18-Transformers/README.md b/translations/sr/lessons/5-NLP/18-Transformers/README.md index 244831a3..afdc515e 100644 --- a/translations/sr/lessons/5-NLP/18-Transformers/README.md +++ b/translations/sr/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ CO_OP_TRANSLATOR_METADATA: **Механизми пажње** пружају начин за одређивање тежине контекстуалног утицаја сваког улазног вектора на сваку предикцију излазног РНМ-а. Ово се имплементира стварањем пречица између међустојања улазног РНМ-а и излазног РНМ-а. На овај начин, када генеришемо излазни симбол yt, узимамо у обзир сва улазна скривена стања hi, са различитим тежинским коефицијентима αt,i. -![Слика која приказује енкодер/декодер модел са адитивним слојем пажње](../../../../../translated_images/sr/encoder-decoder-attention.7a726296894fb567.png) +![Слика која приказује енкодер/декодер модел са адитивним слојем пажње](../../../../../translated_images/sr/encoder-decoder-attention.7a726296894fb567.webp) > Енкодер-декодер модел са адитивним механизмом пажње у [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), цитиран из [овог блога](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) Матрица пажње {αi,j} представља степен у којем одређене улазне речи утичу на генерисање одређене речи у излазној секвенци. Испод је пример такве матрице: -![Слика која приказује пример поравнања пронађеног помоћу RNNsearch-50, преузета из Bahdanau - arviz.org](../../../../../translated_images/sr/bahdanau-fig3.09ba2d37f202a6af.png) +![Слика која приказује пример поравнања пронађеног помоћу RNNsearch-50, преузета из Bahdanau - arviz.org](../../../../../translated_images/sr/bahdanau-fig3.09ba2d37f202a6af.webp) > Фигура из [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Сл.3) @@ -66,7 +66,7 @@ CO_OP_TRANSLATOR_METADATA: Следеће, потребно је ухватити неке обрасце унутар наше секвенце. Да бисмо то урадили, трансформери користе механизам **само-пажње**, који је у суштини пажња примењена на исту секвенцу као улаз и излаз. Примена само-пажње омогућава нам да узмемо у обзир **контекст** унутар реченице и видимо које речи су међусобно повезане. На пример, омогућава нам да видимо на које речи се односе кореференце, као што је *оно*, и такође узмемо у обзир контекст: -![](../../../../../translated_images/sr/CoreferenceResolution.861924d6d384a7d6.png) +![](../../../../../translated_images/sr/CoreferenceResolution.861924d6d384a7d6.webp) > Слика из [Google блога](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ CO_OP_TRANSLATOR_METADATA: **BERT** (Bidirectional Encoder Representations from Transformers) је веома велика трансформер мрежа са више слојева, са 12 слојева за *BERT-base* и 24 за *BERT-large*. Модел се прво претходно тренира на великом корпусу текстуалних података (Wikipedia + књиге) користећи ненадгледано учење (предвиђање маскираних речи у реченици). Током претходног тренинга, модел апсорбује значајне нивое разумевања језика, који се затим могу искористити са другим скуповима података кроз фино подешавање. Овај процес се назива **трансфер учење**. -![слика са http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/sr/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) +![слика са http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/sr/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > Слика [извор](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/sr/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/sr/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index 2f796c51..1a5ddc9f 100644 --- a/translations/sr/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/sr/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**Механизми пажње** пружају начин да се тежински одреди контекстуални утицај сваког улазног вектора на сваку излазну предикцију RNN-а. Ово се имплементира стварањем пречица између интермедијарних стања улазног RNN-а и излазног RNN-а. На овај начин, када генеришемо излазни симбол $y_t$, узимамо у обзир сва улазна скривена стања $h_i$, са различитим тежинским коефицијентима $\\alpha_{t,i}$.\n", "\n", - "![Слика која приказује енкодер/декодер модел са адитивним слојем пажње](../../../../../translated_images/sr/encoder-decoder-attention.7a726296894fb567.png) \n", + "![Слика која приказује енкодер/декодер модел са адитивним слојем пажње](../../../../../translated_images/sr/encoder-decoder-attention.7a726296894fb567.webp) \n", "*Енкодер-декодер модел са адитивним механизмом пажње у [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), цитиран из [овог блога](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Матрица пажње $\\{\\alpha_{i,j}\\}$ представља степен у којем одређене улазне речи утичу на генерисање одређене речи у излазној секвенци. Испод је пример такве матрице:\n", "\n", - "![Слика која приказује пример поравнања пронађеног помоћу RNNsearch-50, преузета из Bahdanau - arviz.org](../../../../../translated_images/sr/bahdanau-fig3.09ba2d37f202a6af.png) \n", + "![Слика која приказује пример поравнања пронађеног помоћу RNNsearch-50, преузета из Bahdanau - arviz.org](../../../../../translated_images/sr/bahdanau-fig3.09ba2d37f202a6af.webp) \n", "\n", "*Слика преузета из [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Сл.3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) је веома велика трансформер мрежа са више слојева, са 12 слојева за *BERT-base*, и 24 за *BERT-large*. Модел се прво претходно тренира на великом корпусу текстуалних података (Википедија + књиге) користећи несупервизирано учење (предвиђање маскираних речи у реченици). Током претходног тренинга, модел апсорбује значајан ниво разумевања језика, који се затим може искористити са другим скуповима података кроз фино подешавање. Овај процес се назива **трансфер учење**.\n", "\n", - "![Слика са http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/sr/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![Слика са http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/sr/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "Постоји много варијација трансформер архитектура, укључујући BERT, DistilBERT, BigBird, OpenGPT3 и друге, које се могу фино подесити. [HuggingFace пакет](https://github.com/huggingface/) пружа репозиторијум за тренирање многих од ових архитектура са PyTorch-ом.\n", "\n", diff --git a/translations/sr/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/sr/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index 3c1bf77d..29cbebf6 100644 --- a/translations/sr/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/sr/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**Механизми пажње** пружају начин да се тежински одреди контекстуални утицај сваког улазног вектора на сваку излазну предикцију РНН-а. Ово се имплементира стварањем пречица између међустојања улазног РНН-а и излазног РНН-а. На овај начин, приликом генерисања излазног симбола $y_t$, узимамо у обзир сва улазна скривена стања $h_i$, са различитим тежинским коефицијентима $\\alpha_{t,i}$. \n", "\n", - "![Слика која приказује модел енкодера/декодера са адитивним слојем пажње](../../../../../translated_images/sr/encoder-decoder-attention.7a726296894fb567.png)\n", + "![Слика која приказује модел енкодера/декодера са адитивним слојем пажње](../../../../../translated_images/sr/encoder-decoder-attention.7a726296894fb567.webp)\n", "*Модел енкодера-декодера са механизмом адитивне пажње у [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), цитиран из [овог блога](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Матрица пажње $\\{\\alpha_{i,j}\\}$ представља степен у којем одређене улазне речи утичу на генерисање одређене речи у излазној секвенци. Испод је пример такве матрице:\n", "\n", - "![Слика која приказује пример поравнања које је пронашао RNNsearch-50, преузето из Bahdanau - arviz.org](../../../../../translated_images/sr/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![Слика која приказује пример поравнања које је пронашао RNNsearch-50, преузето из Bahdanau - arviz.org](../../../../../translated_images/sr/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*Слика преузета из [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Сл.3)*\n", "\n", @@ -225,7 +225,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) је веома велики трансформер мрежни модел са више слојева, који има 12 слојева за *BERT-base* и 24 за *BERT-large*. Модел се прво претходно тренира на великом корпусу текстуалних података (Википедија + књиге) користећи несупервизирано учење (предвиђање маскираних речи у реченици). Током претходног тренирања, модел усваја значајан ниво разумевања језика, који се затим може искористити са другим скуповима података кроз фино подешавање. Овај процес се назива **трансферно учење**.\n", "\n", - "![слика са http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/sr/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![слика са http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/sr/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "Постоји много варијација трансформер архитектура, укључујући BERT, DistilBERT, BigBird, OpenGPT3 и друге, које се могу фино подесити.\n", "\n", diff --git a/translations/sr/lessons/5-NLP/19-NER/README.md b/translations/sr/lessons/5-NLP/19-NER/README.md index 4724e7b1..e2e9c1e4 100644 --- a/translations/sr/lessons/5-NLP/19-NER/README.md +++ b/translations/sr/lessons/5-NLP/19-NER/README.md @@ -56,7 +56,7 @@ NER модели су у суштини **модели за класификац Пошто треба да изградимо један-на-један кореспонденцију између токена и класа, можемо обучити десни **многи-на-многе** модел неуронске мреже из ове слике: -![Слика која приказује уобичајене обрасце рекурентних неуронских мрежа.](../../../../../translated_images/sr/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Слика која приказује уобичајене обрасце рекурентних неуронских мрежа.](../../../../../translated_images/sr/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > *Слика из [овог блога](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) аутора [Андреја Карпатија](http://karpathy.github.io/). Модели за класификацију токена у NER-у одговарају десној архитектури мреже на овој слици.* diff --git a/translations/sr/lessons/5-NLP/README.md b/translations/sr/lessons/5-NLP/README.md index 4a106331..2b626065 100644 --- a/translations/sr/lessons/5-NLP/README.md +++ b/translations/sr/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Обрада природног језика -![Резиме NLP задатака у скици](../../../../translated_images/sr/ai-nlp.b22dcb8ca4707cea.png) +![Резиме NLP задатака у скици](../../../../translated_images/sr/ai-nlp.b22dcb8ca4707cea.webp) У овом делу ћемо се фокусирати на коришћење неуронских мрежа за решавање задатака везаних за **обраду природног језика (NLP)**. Постоји много NLP проблема које желимо да рачунари могу да реше: diff --git a/translations/sr/lessons/6-Other/23-MultiagentSystems/README.md b/translations/sr/lessons/6-Other/23-MultiagentSystems/README.md index ed5d175c..271cd36c 100644 --- a/translations/sr/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/sr/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ ask turtles [ Након отварања модела, бићете пребачени на главни екран NetLogo-а. Ево примера модела који описује популацију вукова и оваца, уз ограничене ресурсе (трава). -![NetLogo Main Screen](../../../../../translated_images/sr/NetLogo-Main.32653711ec1a01b3.png) +![NetLogo Main Screen](../../../../../translated_images/sr/NetLogo-Main.32653711ec1a01b3.webp) > Снимак екрана, Дмитриј Сошњиков diff --git a/translations/sr/lessons/README.md b/translations/sr/lessons/README.md index 10c0979c..03338320 100644 --- a/translations/sr/lessons/README.md +++ b/translations/sr/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Преглед -![Преглед у виду цртежа](../../../translated_images/sr/ai-overview.0857791951d19500.png) +![Преглед у виду цртежа](../../../translated_images/sr/ai-overview.0857791951d19500.webp) > Цртеж белешке од [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/sr/lessons/X-Extras/X1-MultiModal/README.md b/translations/sr/lessons/X-Extras/X1-MultiModal/README.md index b69104e1..0fba3a05 100644 --- a/translations/sr/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/sr/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: Главна идеја CLIP-а је могућност поређења текстуалних упита са сликом и одређивање колико добро слика одговара упиту. -![CLIP Архитектура](../../../../../translated_images/sr/clip-arch.b3dbf20b4e8ed8be.png) +![CLIP Архитектура](../../../../../translated_images/sr/clip-arch.b3dbf20b4e8ed8be.webp) > *Слика из [овог блога](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ CLIP модел/библиотека доступан је на [OpenAI GitHub]( Претпоставимо да треба да класификујемо слике, рецимо, између мачака, паса и људи. У том случају, можемо моделу дати слику и низ текстуалних упита: "*слика мачке*", "*слика пса*", "*слика човека*". У резултујућем вектору од 3 вероватноће само треба изабрати индекс са највишом вредношћу. -![CLIP за класификацију слика](../../../../../translated_images/sr/clip-class.3af42ef0b2b19369.png) +![CLIP за класификацију слика](../../../../../translated_images/sr/clip-class.3af42ef0b2b19369.webp) > *Слика из [овог блога](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ CLIP се такође може користити за **генерисање Једна од важних разлика између VQGAN-а и традиционалног GAN-а је та што други може произвести пристојну слику из било ког улазног вектора, док VQGAN вероватно производи слику која није кохерентна. Због тога је потребно додатно усмеравати процес креирања слике, а то се може урадити помоћу CLIP-а. -![VQGAN+CLIP Архитектура](../../../../../translated_images/sr/vqgan.5027fe05051dfa31.png) +![VQGAN+CLIP Архитектура](../../../../../translated_images/sr/vqgan.5027fe05051dfa31.webp) Да бисмо генерисали слику која одговара текстуалном упиту, почињемо са неким насумичним вектором кодирања који се прослеђује кроз VQGAN да би се произвела слика. Затим се CLIP користи за креирање функције губитка која показује колико добро слика одговара текстуалном упиту. Циљ је затим минимизовати овај губитак, користећи уназадно ширење да би се прилагодили параметри улазног вектора. Одлична библиотека која имплементира VQGAN+CLIP је [Pixray](http://github.com/pixray/pixray). -![Слика коју је произвео Pixray](../../../../../translated_images/sr/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Слика коју је произвео Pixray](../../../../../translated_images/sr/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Слика коју је произвео Pixray](../../../../../translated_images/sr/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) +![Слика коју је произвео Pixray](../../../../../translated_images/sr/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp) | ![Слика коју је произвео Pixray](../../../../../translated_images/sr/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp) | ![Слика коју је произвео Pixray](../../../../../translated_images/sr/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp) ----|----|---- Слика генерисана на основу упита *акварелски портрет младог мушког професора књижевности са књигом* | Слика генерисана на основу упита *уљани портрет младе женске професорке рачунарских наука са рачунаром* | Слика генерисана на основу упита *уљани портрет старог мушког професора математике испред табле* diff --git a/translations/sv/README.md b/translations/sv/README.md index 6875af55..462e414c 100644 --- a/translations/sv/README.md +++ b/translations/sv/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # Artificiell intelligens för nybörjare - Ett läroprogram -|![Sketchnote av @girlie_mac https://twitter.com/girlie_mac](../../translated_images/sv/ai-overview.0857791951d19500.png)| +|![Sketchnote av @girlie_mac https://twitter.com/girlie_mac](../../translated_images/sv/ai-overview.0857791951d19500.webp)| |:---:| | AI För Nybörjare - _Sketchnote av [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/sv/lessons/1-Intro/README.md b/translations/sv/lessons/1-Intro/README.md index b8b3b5f4..b3d95878 100644 --- a/translations/sv/lessons/1-Intro/README.md +++ b/translations/sv/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Introduktion till AI -![Sammanfattning av innehållet i Introduktion till AI i en skiss](../../../../translated_images/sv/ai-intro.bf28d1ac4235881c.png) +![Sammanfattning av innehållet i Introduktion till AI i en skiss](../../../../translated_images/sv/ai-intro.bf28d1ac4235881c.webp) > Skiss av [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Ursprungligen uppfanns datorer av [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage) för att arbeta med siffror enligt en väl definierad procedur - en algoritm. Moderna datorer, även om de är betydligt mer avancerade än den ursprungliga modellen som föreslogs på 1800-talet, följer fortfarande samma idé om kontrollerade beräkningar. Därför är det möjligt att programmera en dator att göra något om vi vet den exakta sekvensen av steg som behövs för att uppnå målet. -![Foto av en person](../../../../translated_images/sv/dsh_age.d212a30d4e54fb5f.png) +![Foto av en person](../../../../translated_images/sv/dsh_age.d212a30d4e54fb5f.webp) > Foto av [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ För mer information, se **[Artificiell General Intelligens](https://en.wikipedi Ett av problemen med att hantera termen **[intelligens](https://en.wikipedia.org/wiki/Intelligence)** är att det inte finns någon tydlig definition av termen. Man kan argumentera för att intelligens är kopplad till **abstrakt tänkande** eller **självmedvetenhet**, men vi kan inte definiera det ordentligt. -![Foto av en katt](../../../../translated_images/sv/photo-cat.8c8e8fb760ffe457.jpg) +![Foto av en katt](../../../../translated_images/sv/photo-cat.8c8e8fb760ffe457.webp) > [Foto](https://unsplash.com/photos/75715CVEJhI) av [Amber Kipp](https://unsplash.com/@sadmax) från Unsplash @@ -98,13 +98,13 @@ Alternativt kan vi försöka modellera de enklaste elementen i vår hjärna – > | Vad sägs om ML? | | > |--------------|-----------| -> | En del av artificiell intelligens som bygger på att datorn lär sig att lösa ett problem baserat på viss data kallas **Machine Learning**. Vi kommer inte att behandla klassisk maskininlärning i denna kurs - vi hänvisar dig till en separat [Machine Learning for Beginners](http://aka.ms/ml-beginners) läroplan. | ![ML för nybörjare](../../../../translated_images/sv/ml-for-beginners.9e4fed176fd5817d.png) | +> | En del av artificiell intelligens som bygger på att datorn lär sig att lösa ett problem baserat på viss data kallas **Machine Learning**. Vi kommer inte att behandla klassisk maskininlärning i denna kurs - vi hänvisar dig till en separat [Machine Learning for Beginners](http://aka.ms/ml-beginners) läroplan. | ![ML för nybörjare](../../../../translated_images/sv/ml-for-beginners.9e4fed176fd5817d.webp) | ## En kort historia om AI Artificiell intelligens startade som ett område i mitten av 1900-talet. Ursprungligen var symboliskt resonemang en dominerande ansats, och det ledde till ett antal viktiga framgångar, såsom expertsystem – datorprogram som kunde agera som en expert inom vissa begränsade problemområden. Men det blev snart klart att en sådan ansats inte skalar bra. Att extrahera kunskap från en expert, representera den i en dator och hålla den kunskapsbasen korrekt visade sig vara en mycket komplex uppgift och för dyr för att vara praktisk i många fall. Detta ledde till den så kallade [AI-vintern](https://en.wikipedia.org/wiki/AI_winter) på 1970-talet. -Kort historia om AI +Kort historia om AI > Bild av [Dmitry Soshnikov](http://soshnikov.com) @@ -124,7 +124,7 @@ På samma sätt kan vi se hur ansatsen för att skapa "talande program" (som kan * Moderna assistenter, såsom Cortana, Siri eller Google Assistant, är alla hybridsystem som använder neurala nätverk för att konvertera tal till text och känna igen vår avsikt, och sedan använder vissa resonemang eller explicita algoritmer för att utföra nödvändiga åtgärder. * I framtiden kan vi förvänta oss en komplett neuralbaserad modell som hanterar dialoger själv. Den senaste GPT- och [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft)-familjen av neurala nätverk visar stor framgång i detta. -Turing-testets utveckling +Turing-testets utveckling > Bild av Dmitry Soshnikov, [foto](https://unsplash.com/photos/r8LmVbUKgns) av [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto), Unsplash ## Senaste AI-forskningen diff --git a/translations/sv/lessons/2-Symbolic/Animals.ipynb b/translations/sv/lessons/2-Symbolic/Animals.ipynb index 7bd622de..ca2660c3 100644 --- a/translations/sv/lessons/2-Symbolic/Animals.ipynb +++ b/translations/sv/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "I detta exempel kommer vi att implementera ett enkelt kunskapsbaserat system för att identifiera ett djur baserat på vissa fysiska egenskaper. Systemet kan representeras av följande AND-OR-träd (detta är en del av hela trädet, vi kan enkelt lägga till fler regler):\n", "\n", - "![](../../../../translated_images/sv/AND-OR-Tree.5592d2c70187f283.png)\n" + "![](../../../../translated_images/sv/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { diff --git a/translations/sv/lessons/2-Symbolic/README.md b/translations/sv/lessons/2-Symbolic/README.md index d3189272..ba9551b2 100644 --- a/translations/sv/lessons/2-Symbolic/README.md +++ b/translations/sv/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Kunskapsrepresentation och Expertsystem -![Sammanfattning av Symbolisk AI-innehåll](../../../../translated_images/sv/ai-symbolic.715a30cb610411a6.png) +![Sammanfattning av Symbolisk AI-innehåll](../../../../translated_images/sv/ai-symbolic.715a30cb610411a6.webp) > Sketchnote av [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ Oftast definierar vi inte strikt vad kunskap är, utan vi relaterar den till and Således är problemet med **kunskapsrepresentation** att hitta ett effektivt sätt att representera kunskap i en dator i form av data, för att göra den automatiskt användbar. Detta kan ses som ett spektrum: -![Spektrum för kunskapsrepresentation](../../../../translated_images/sv/knowledge-spectrum.b60df631852c0217.png) +![Spektrum för kunskapsrepresentation](../../../../translated_images/sv/knowledge-spectrum.b60df631852c0217.webp) > Bild av [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Blocksyntax | Indragning | | | En av de tidiga framgångarna med symbolisk AI var de så kallade **expertsystemen** - datorsystem som designades för att agera som experter inom ett begränsat problemområde. De baserades på en **kunskapsbas** som extraherades från en eller flera mänskliga experter och innehöll en **slutsatsmotor** som utförde resonerande ovanpå den. -![Mänsklig arkitektur](../../../../translated_images/sv/arch-human.5d4d35f1bba3ab1c.png) | ![Kunskapsbaserat system](../../../../translated_images/sv/arch-kbs.3ec5c150b09fa8da.png) +![Mänsklig arkitektur](../../../../translated_images/sv/arch-human.5d4d35f1bba3ab1c.webp) | ![Kunskapsbaserat system](../../../../translated_images/sv/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ Förenklad struktur av ett mänskligt nervsystem | Arkitektur av ett kunskapsbaserat system @@ -106,7 +106,7 @@ Expertsystem är byggda som det mänskliga resoneringssystemet, som innehåller Som ett exempel, låt oss överväga följande expertsystem för att bestämma ett djur baserat på dess fysiska egenskaper: -![AND-OR-träd](../../../../translated_images/sv/AND-OR-Tree.5592d2c70187f283.png) +![AND-OR-träd](../../../../translated_images/sv/AND-OR-Tree.5592d2c70187f283.webp) > Bild av [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/sv/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/sv/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index 0daadaef..9b73673f 100644 --- a/translations/sv/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/sv/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -1259,7 +1259,7 @@ "* Låg träningsförlust - modellen kan approximera träningsdata väl eftersom den har tillräcklig uttryckskraft.\n", "* Valideringsförlust kan vara mycket högre än träningsförlust och kan börja öka under träningen - detta beror på att modellen \"memorerar\" träningspunkterna och förlorar \"helhetsbilden\".\n", "\n", - "![Överanpassning](../../../../../translated_images/sv/overfit.a0bd57f717c15769.png)\n", + "![Överanpassning](../../../../../translated_images/sv/overfit.a0bd57f717c15769.webp)\n", "\n", "> På denna bild står `x` för träningsdata, `o` - valideringsdata. Vänster - linjär modell (enlager), den approximera datans natur ganska väl. Höger - överanpassad modell, modellen approximera träningsdata perfekt, men slutar vara meningsfull för annan data (valideringsfelet är mycket högt).\n" ] diff --git a/translations/sv/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/sv/lessons/3-NeuralNetworks/05-Frameworks/README.md index d32d8f44..0255ef69 100644 --- a/translations/sv/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/sv/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Efter att ha bemästrat ramverken, låt oss repetera begreppet överanpassning. Tänk på följande problem med att approximera 5 punkter (representerade av `x` på graferna nedan): -![linear](../../../../../translated_images/sv/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/sv/overfit2.131f5800ae10ca5e.jpg) +![linear](../../../../../translated_images/sv/overfit1.f24b71c6f652e59e.webp) | ![overfit](../../../../../translated_images/sv/overfit2.131f5800ae10ca5e.webp) -------------------------|-------------------------- **Linjär modell, 2 parametrar** | **Icke-linjär modell, 7 parametrar** Träningsfel = 5.3 | Träningsfel = 0 @@ -79,7 +79,7 @@ Det är mycket viktigt att hitta en korrekt balans mellan modellens komplexitet Som du kan se från grafen ovan kan överanpassning upptäckas genom ett mycket lågt träningsfel och ett högt valideringsfel. Normalt under träning ser vi både tränings- och valideringsfel minska, och sedan vid någon punkt kan valideringsfelet sluta minska och börja öka. Detta är ett tecken på överanpassning och en indikation på att vi förmodligen bör sluta träna vid denna punkt (eller åtminstone spara en ögonblicksbild av modellen). -![overfitting](../../../../../translated_images/sv/Overfitting.408ad91cd90b4371.png) +![overfitting](../../../../../translated_images/sv/Overfitting.408ad91cd90b4371.webp) ## Hur man förhindrar överanpassning diff --git a/translations/sv/lessons/3-NeuralNetworks/README.md b/translations/sv/lessons/3-NeuralNetworks/README.md index 254efd7b..33e5cab0 100644 --- a/translations/sv/lessons/3-NeuralNetworks/README.md +++ b/translations/sv/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Introduktion till neurala nätverk -![Sammanfattning av innehållet i Introduktion till neurala nätverk i en skiss](../../../../translated_images/sv/ai-neuralnetworks.1c687ae40bc86e83.png) +![Sammanfattning av innehållet i Introduktion till neurala nätverk i en skiss](../../../../translated_images/sv/ai-neuralnetworks.1c687ae40bc86e83.webp) Som vi diskuterade i introduktionen är ett av sätten att uppnå intelligens att träna en **datormodell** eller en **artificiell hjärna**. Sedan mitten av 1900-talet har forskare testat olika matematiska modeller, och på senare år har denna riktning visat sig vara mycket framgångsrik. Sådana matematiska modeller av hjärnan kallas **neurala nätverk**. @@ -36,13 +36,13 @@ I denna kursplan kommer vi endast att fokusera på modeller för neurala nätver Från biologin vet vi att vår hjärna består av nervceller (neuroner), var och en med flera "ingångar" (dendriter) och en enda "utgång" (axon). Både dendriter och axoner kan leda elektriska signaler, och kopplingarna mellan dem — kända som synapser — kan uppvisa varierande grad av ledningsförmåga, som regleras av neurotransmittorer. -![Modell av en neuron](../../../../translated_images/sv/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![Modell av en neuron](../../../../translated_images/sv/artneuron.1a5daa88d20ebe6f.png) +![Modell av en neuron](../../../../translated_images/sv/synapse-wikipedia.ed20a9e4726ea1c6.webp) | ![Modell av en neuron](../../../../translated_images/sv/artneuron.1a5daa88d20ebe6f.webp) ----|---- Verklig neuron *([Bild](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) från Wikipedia)* | Artificiell neuron *(Bild av författaren)* Således innehåller den enklaste matematiska modellen av en neuron flera ingångar X1, ..., XN och en utgång Y, samt en serie vikter W1, ..., WN. En utgång beräknas som: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) där f är någon icke-linjär **aktiveringsfunktion**. diff --git a/translations/sv/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/sv/lessons/4-ComputerVision/06-IntroCV/README.md index 45ea061f..0fa880b5 100644 --- a/translations/sv/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/sv/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ I vår [OpenCV Notebook](OpenCV.ipynb) ger vi några exempel på när datorseend * **Förbehandling av ett fotografi av en Braille-bok**. Vi fokuserar på hur vi kan använda tröskling, funktionsdetektion, perspektivtransformation och NumPy-manipulationer för att separera individuella Braille-symboler för vidare klassificering av ett neuralt nätverk. -![Braille Image](../../../../../translated_images/sv/braille.341962ff76b1bd70.jpeg) | ![Braille Image Pre-processed](../../../../../translated_images/sv/braille-result.46530fea020b03c7.png) | ![Braille Symbols](../../../../../translated_images/sv/braille-symbols.0159185ab69d5339.png) +![Braille Image](../../../../../translated_images/sv/braille.341962ff76b1bd70.webp) | ![Braille Image Pre-processed](../../../../../translated_images/sv/braille-result.46530fea020b03c7.webp) | ![Braille Symbols](../../../../../translated_images/sv/braille-symbols.0159185ab69d5339.webp) ----|-----|----- > Bild från [OpenCV.ipynb](OpenCV.ipynb) * **Detektera rörelse i video med hjälp av bildruteskillnad**. Om kameran är fast, bör bildrutor från kameraflödet vara ganska lika varandra. Eftersom bildrutor representeras som arrayer, kan vi genom att subtrahera dessa arrayer för två efterföljande bildrutor få pixeldifferensen, som bör vara låg för statiska bildrutor och bli högre när det finns betydande rörelse i bilden. -![Image of video frames and frame differences](../../../../../translated_images/sv/frame-difference.706f805491a0883c.png) +![Image of video frames and frame differences](../../../../../translated_images/sv/frame-difference.706f805491a0883c.webp) > Bild från [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ I vår [OpenCV Notebook](OpenCV.ipynb) ger vi några exempel på när datorseend - **Tätt optiskt flöde** beräknar vektorfältet som visar för varje pixel var den rör sig. - **Gles optiskt flöde** baseras på att ta några distinkta funktioner i bilden (t.ex. kanter) och bygga deras bana från bildruta till bildruta. -![Image of Optical Flow](../../../../../translated_images/sv/optical.1f4a94464579a83a.png) +![Image of Optical Flow](../../../../../translated_images/sv/optical.1f4a94464579a83a.webp) > Bild från [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/sv/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/sv/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 8ec57129..ac571afc 100644 --- a/translations/sv/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/sv/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 är ett nätverk som uppnådde 92,7% noggrannhet i ImageNet top-5 klassificering år 2014. Det har följande lagerstruktur: -![ImageNet Layers](../../../../../translated_images/sv/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet Layers](../../../../../translated_images/sv/vgg-16-arch1.d901a5583b3a51ba.webp) Som du kan se följer VGG en traditionell pyramidarkitektur, vilket är en sekvens av konvolutions- och poolinglager. -![ImageNet Pyramid](../../../../../translated_images/sv/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet Pyramid](../../../../../translated_images/sv/vgg-16-arch.64ff2137f50dd49f.webp) > Bild från [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/sv/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/sv/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index fe4a422b..66fd755c 100644 --- a/translations/sv/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/sv/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -262,7 +262,7 @@ "\n", "Således skulle en typisk CNN innehålla flera konvolutionella lager, med pooling-lager mellan dem för att minska bildens dimensioner. Vi skulle också öka antalet filter, eftersom mönstren blir mer avancerade – det finns fler möjliga intressanta kombinationer som vi behöver leta efter.\n", "\n", - "![En bild som visar flera konvolutionella lager med pooling-lager.](../../../../../translated_images/sv/cnn-pyramid.85915455759ef0ce.png)\n", + "![En bild som visar flera konvolutionella lager med pooling-lager.](../../../../../translated_images/sv/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "På grund av de minskande rumsliga dimensionerna och de ökande funktions-/filterdimensionerna kallas denna arkitektur också för **pyramidarkitektur**.\n" ] diff --git a/translations/sv/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/sv/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index f55f02be..f6e7aa7e 100644 --- a/translations/sv/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/sv/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -358,7 +358,7 @@ "\n", "Således skulle en typisk CNN ha flera konvolutionella lager, med pooling-lager mellan dem för att minska bildens dimensioner. Vi skulle också öka antalet filter, eftersom mönstren blir mer avancerade – det finns fler möjliga intressanta kombinationer som vi behöver leta efter.\n", "\n", - "![En bild som visar flera konvolutionella lager med pooling-lager.](../../../../../translated_images/sv/cnn-pyramid.85915455759ef0ce.png)\n", + "![En bild som visar flera konvolutionella lager med pooling-lager.](../../../../../translated_images/sv/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "På grund av minskande rumsliga dimensioner och ökande funktions-/filterdimensioner kallas denna arkitektur också för **pyramidarkitektur**.\n" ] diff --git a/translations/sv/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/sv/lessons/4-ComputerVision/07-ConvNets/README.md index bc7dd8ce..a478dae1 100644 --- a/translations/sv/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/sv/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ I verkligheten vill vi kunna känna igen objekt på en bild oavsett deras exakta För att extrahera mönster kommer vi att använda begreppet **konvolutionella filter**. Som du vet representeras en bild av en 2D-matris eller en 3D-tensor med färgdjup. Att applicera ett filter innebär att vi tar en relativt liten **filterkärna**-matris, och för varje pixel i den ursprungliga bilden beräknar vi det viktade medelvärdet med angränsande punkter. Vi kan se detta som ett litet fönster som glider över hela bilden och jämnar ut alla pixlar enligt vikterna i filterkärnan. -![Vertikalt kantfilter](../../../../../translated_images/sv/filter-vert.b7148390ca0bc356.png) | ![Horisontellt kantfilter](../../../../../translated_images/sv/filter-horiz.59b80ed4feb946ef.png) +![Vertikalt kantfilter](../../../../../translated_images/sv/filter-vert.b7148390ca0bc356.webp) | ![Horisontellt kantfilter](../../../../../translated_images/sv/filter-horiz.59b80ed4feb946ef.webp) ----|---- > Bild av Dmitry Soshnikov @@ -38,7 +38,7 @@ Så här fungerar CNN baserat på följande viktiga idéer: * Vi kan designa nätverket så att filtren tränas automatiskt * Vi kan använda samma metod för att hitta mönster i hög-nivå egenskaper, inte bara i den ursprungliga bilden. Således arbetar CNN med en hierarki av egenskaper, från låg-nivå pixelkombinationer till högre nivå kombinationer av bilddelar. -![Hierarkisk egenskapsutvinning](../../../../../translated_images/sv/FeatureExtractionCNN.d9b456cbdae7cb64.png) +![Hierarkisk egenskapsutvinning](../../../../../translated_images/sv/FeatureExtractionCNN.d9b456cbdae7cb64.webp) > Bild från [en artikel av Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), baserad på [deras forskning](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ De flesta CNN som används för bildbehandling följer en så kallad pyramidarki Som exempel, låt oss titta på arkitekturen för VGG-16, ett nätverk som uppnådde 92,7% noggrannhet i ImageNet's topp-5 klassificering år 2014: -![ImageNet-lager](../../../../../translated_images/sv/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet-lager](../../../../../translated_images/sv/vgg-16-arch1.d901a5583b3a51ba.webp) -![ImageNet-pyramid](../../../../../translated_images/sv/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet-pyramid](../../../../../translated_images/sv/vgg-16-arch.64ff2137f50dd49f.webp) > Bild från [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/sv/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/sv/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 1403f161..d706abef 100644 --- a/translations/sv/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/sv/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ Du behöver träna ett konvolutionellt neuralt nätverk för att klassificera ol Vi kommer att använda [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/), som innehåller bilder av 37 olika raser av hundar och katter. -![Datasetet vi kommer att arbeta med](../../../../../../translated_images/sv/data.50b2a9d5484bdbf0.png) +![Datasetet vi kommer att arbeta med](../../../../../../translated_images/sv/data.50b2a9d5484bdbf0.webp) För att ladda ner datasetet, använd följande kodsnutt: diff --git a/translations/sv/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/sv/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index 55c7975b..2afa2724 100644 --- a/translations/sv/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/sv/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "För att visualisera den ideala katten börjar vi med en slumpmässig brusbild och försöker använda gradientnedstigningsoptimeringstekniken för att justera bilden så att nätverket känner igen en katt.\n", "\n", - "![Optimeringsloop](../../../../../translated_images/sv/ideal-cat-loop.999fbb8ff306e044.png)\n", + "![Optimeringsloop](../../../../../translated_images/sv/ideal-cat-loop.999fbb8ff306e044.webp)\n", "\n", "Här är vår startbild:\n" ] diff --git a/translations/sv/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/sv/lessons/4-ComputerVision/08-TransferLearning/README.md index 8bb8ccf3..f3f87fe1 100644 --- a/translations/sv/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/sv/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Både Keras och PyTorch innehåller funktioner för att enkelt ladda förtränad Här är exempel på funktioner som extraherats från en bild av en katt med VGG-16-nätverket: -![Funktioner extraherade av VGG-16](../../../../../translated_images/sv/features.6291f9c7ba3a0b95.png) +![Funktioner extraherade av VGG-16](../../../../../translated_images/sv/features.6291f9c7ba3a0b95.webp) ## Dataset för katter och hundar @@ -48,19 +48,19 @@ Ett förtränat neuralt nätverk innehåller olika mönster i sitt *"hjärna"*, En metod vi kan använda är att börja med en slumpmässig bild och sedan försöka använda **gradient descent-optimering** för att justera bilden så att nätverket börjar tro att det är en katt. -![Bildoptimeringsloop](../../../../../translated_images/sv/ideal-cat-loop.999fbb8ff306e044.png) +![Bildoptimeringsloop](../../../../../translated_images/sv/ideal-cat-loop.999fbb8ff306e044.webp) Om vi gör detta kommer vi dock att få något som liknar slumpmässigt brus. Detta beror på att *det finns många sätt att få nätverket att tro att inmatningsbilden är en katt*, inklusive sådana som inte är visuellt meningsfulla. Även om dessa bilder innehåller många mönster som är typiska för en katt, finns det inget som begränsar dem till att vara visuellt distinkta. För att förbättra resultatet kan vi lägga till en annan term i förlustfunktionen, kallad **variation loss**. Det är en metrik som visar hur lika angränsande pixlar i bilden är. Genom att minimera variation loss blir bilden mjukare och bruset försvinner - vilket avslöjar mer visuellt tilltalande mönster. Här är exempel på sådana "ideala" bilder som klassificeras som katt och zebra med hög sannolikhet: -![Ideal katt](../../../../../translated_images/sv/ideal-cat.203dd4597643d6b0.png) | ![Ideal zebra](../../../../../translated_images/sv/ideal-zebra.7f70e8b54ee15a7a.png) +![Ideal katt](../../../../../translated_images/sv/ideal-cat.203dd4597643d6b0.webp) | ![Ideal zebra](../../../../../translated_images/sv/ideal-zebra.7f70e8b54ee15a7a.webp) -----|----- *Ideal katt* | *Ideal zebra* En liknande metod kan användas för att utföra så kallade **adversarial attacks** på ett neuralt nätverk. Anta att vi vill lura ett neuralt nätverk och få en hund att se ut som en katt. Om vi tar en bild av en hund som nätverket känner igen som en hund, kan vi sedan justera den lite med gradient descent-optimering tills nätverket börjar klassificera den som en katt: -![Bild av en hund](../../../../../translated_images/sv/original-dog.8f68a67d2fe0911f.png) | ![Bild av en hund klassificerad som en katt](../../../../../translated_images/sv/adversarial-dog.d9fc7773b0142b89.png) +![Bild av en hund](../../../../../translated_images/sv/original-dog.8f68a67d2fe0911f.webp) | ![Bild av en hund klassificerad som en katt](../../../../../translated_images/sv/adversarial-dog.d9fc7773b0142b89.webp) -----|----- *Originalbild av en hund* | *Bild av en hund klassificerad som en katt* diff --git a/translations/sv/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/sv/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index c3c54230..b62f3b28 100644 --- a/translations/sv/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/sv/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "Eftersom vi tränar autoencodern för att fånga så mycket information som möjligt från den ursprungliga bilden för en korrekt rekonstruktion, försöker nätverket hitta den bästa **inbäddningen** av inmatningsbilder för att fånga meningen.\n", "\n", - "![AutoEncoder Diagram](../../../../../translated_images/sv/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![AutoEncoder Diagram](../../../../../translated_images/sv/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "> Bild från [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", diff --git a/translations/sv/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/sv/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index ed86aa0b..7634dbc4 100644 --- a/translations/sv/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/sv/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "Eftersom vi tränar autoencodern att fånga så mycket information som möjligt från den ursprungliga bilden för att kunna återskapa den korrekt, försöker nätverket hitta den bästa **inbäddningen** av inmatningsbilder för att fånga dess innebörd.\n", "\n", - "![AutoEncoder Diagram](../../../../../translated_images/sv/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![AutoEncoder Diagram](../../../../../translated_images/sv/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "*Bild från [Keras blogg](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", diff --git a/translations/sv/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/sv/lessons/4-ComputerVision/09-Autoencoders/README.md index 68c030a3..f22858f7 100644 --- a/translations/sv/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/sv/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ Men vi kanske vill använda rå (omärkt) data för att träna CNN-funktionsextr Eftersom vi tränar en autoencoder för att fånga så mycket information som möjligt från den ursprungliga bilden för en korrekt rekonstruktion, försöker nätverket hitta den bästa **embedding** av inmatningsbilder för att fånga dess betydelse. -![AutoEncoder Diagram](../../../../../translated_images/sv/autoencoder_schema.5e6fc9ad98a5eb61.jpg) +![AutoEncoder Diagram](../../../../../translated_images/sv/autoencoder_schema.5e6fc9ad98a5eb61.webp) > Bild från [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/sv/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/sv/lessons/4-ComputerVision/11-ObjectDetection/README.md index b064aced..5818775a 100644 --- a/translations/sv/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/sv/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ De bildklassificeringsmodeller vi har arbetat med hittills tar en bild och produ ## [Quiz före föreläsningen](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Objektigenkänning](../../../../../translated_images/sv/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) +![Objektigenkänning](../../../../../translated_images/sv/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp) > Bild från [YOLO v2 webbplats](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ Om vi ville hitta en katt på en bild, skulle en mycket naiv metod för objektig 2. Kör bildklassificering på varje ruta. 3. De rutor som resulterar i tillräckligt hög aktivering kan anses innehålla det aktuella objektet. -![Naiv objektigenkänning](../../../../../translated_images/sv/naive-detection.e7f1ba220ccd08c6.png) +![Naiv objektigenkänning](../../../../../translated_images/sv/naive-detection.e7f1ba220ccd08c6.webp) > *Bild från [Övningsanteckningsbok](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ Du kan stöta på följande dataset för denna uppgift: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 klasser * [COCO](http://cocodataset.org/#home) - Vanliga objekt i kontext. 80 klasser, begränsningsrutor och segmenteringsmasker -![COCO](../../../../../translated_images/sv/coco-examples.71bc60380fa6cceb.jpg) +![COCO](../../../../../translated_images/sv/coco-examples.71bc60380fa6cceb.webp) ## Mätvärden för objektigenkänning @@ -50,7 +50,7 @@ Du kan stöta på följande dataset för denna uppgift: Medan det är enkelt att mäta hur väl algoritmen presterar för bildklassificering, behöver vi för objektigenkänning mäta både korrektheten av klassen och precisionen av den förutsagda begränsningsrutans position. För det senare använder vi den så kallade **Intersection over Union** (IoU), som mäter hur väl två rutor (eller två godtyckliga områden) överlappar. -![IoU](../../../../../translated_images/sv/iou_equation.9a4751d40fff4e11.png) +![IoU](../../../../../translated_images/sv/iou_equation.9a4751d40fff4e11.webp) > *Figur 2 från [denna utmärkta bloggpost om IoU](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ Det finns två breda klasser av algoritmer för objektigenkänning: [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) använder [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) för att generera en hierarkisk struktur av ROI-regioner, som sedan passerar genom CNN-funktionsextraktorer och SVM-klassificerare för att bestämma objektklassen, och linjär regression för att bestämma *begränsningsrutans* koordinater. [Officiell artikel](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/sv/rcnn1.cae407020dfb1d1f.png) +![RCNN](../../../../../translated_images/sv/rcnn1.cae407020dfb1d1f.webp) > *Bild från van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/sv/rcnn2.2d9530bb83516484.png) +![RCNN-1](../../../../../translated_images/sv/rcnn2.2d9530bb83516484.webp) > *Bilder från [denna blogg](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -110,7 +110,7 @@ Det finns två breda klasser av algoritmer för objektigenkänning: Denna metod liknar R-CNN, men regioner definieras efter att konvolutionslager har applicerats. -![FRCNN](../../../../../translated_images/sv/f-rcnn.3cda6d9bb4188875.png) +![FRCNN](../../../../../translated_images/sv/f-rcnn.3cda6d9bb4188875.webp) > Bild från [den officiella artikeln](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ Denna metod liknar R-CNN, men regioner definieras efter att konvolutionslager ha Huvudidén med denna metod är att använda ett neuralt nätverk för att förutsäga ROI - så kallat *Region Proposal Network*. [Artikel](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/sv/faster-rcnn.8d46c099b87ef30a.png) +![FasterRCNN](../../../../../translated_images/sv/faster-rcnn.8d46c099b87ef30a.webp) > Bild från [den officiella artikeln](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ Denna algoritm är ännu snabbare än Faster R-CNN. Huvudidén är följande: 2. Funktionerna bearbetas av **Position-Sensitive Score Map**. Varje objekt från $C$ klasser delas upp i $k\times k$ regioner, och vi tränar för att förutsäga delar av objekt. 3. För varje del från $k\times k$ regioner röstar alla nätverk för objektklasser, och den objektklass med flest röster väljs. -![r-fcn bild](../../../../../translated_images/sv/r-fcn.13eb88158b99a3da.png) +![r-fcn bild](../../../../../translated_images/sv/r-fcn.13eb88158b99a3da.webp) > Bild från [officiell artikel](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO är en realtidsalgoritm med ett enda pass. Huvudidén är följande: * Bilden delas upp i $S\times S$ regioner. * För varje region förutsäger **CNN** $n$ möjliga objekt, *begränsningsrutans* koordinater och *confidence*=*sannolikhet* * IoU. - ![YOLO](../../../../../translated_images/sv/yolo.a2648ec82ee8bb4e.png) + ![YOLO](../../../../../translated_images/sv/yolo.a2648ec82ee8bb4e.webp) > Bild från [officiell artikel](https://arxiv.org/abs/1506.02640) diff --git a/translations/sv/lessons/4-ComputerVision/README.md b/translations/sv/lessons/4-ComputerVision/README.md index a82d9515..b8c5a1f7 100644 --- a/translations/sv/lessons/4-ComputerVision/README.md +++ b/translations/sv/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Datorseende -![Sammanfattning av innehållet om datorseende i en skiss](../../../../translated_images/sv/ai-computervision.6506ebebac3fbf76.png) +![Sammanfattning av innehållet om datorseende i en skiss](../../../../translated_images/sv/ai-computervision.6506ebebac3fbf76.webp) I den här delen kommer vi att lära oss om: diff --git a/translations/sv/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/sv/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index ea1ed663..a6ebf964 100644 --- a/translations/sv/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/sv/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**Påsmodell** (BoW) vektorrepresentation är den mest använda traditionella vektorrepresentationen. Varje ord är kopplat till ett vektorindex, och varje element i vektorn innehåller antalet förekomster av ett ord i ett givet dokument.\n", "\n", - "![Bild som visar hur en påsmodell-vektorrepresentation lagras i minnet.](../../../../../translated_images/sv/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![Bild som visar hur en påsmodell-vektorrepresentation lagras i minnet.](../../../../../translated_images/sv/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Note**: Du kan också tänka på BoW som en summa av alla enskilda one-hot-kodade vektorer för individuella ord i texten.\n", "\n", diff --git a/translations/sv/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/sv/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index 0e9f92da..218889d1 100644 --- a/translations/sv/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/sv/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**Bag-of-words** (BoW) vektorrepresentation är den mest lättförståeliga traditionella vektorrepresentationen. Varje ord är kopplat till ett vektorindex, och ett element i vektorn innehåller antalet förekomster av varje ord i ett givet dokument.\n", "\n", - "![Bild som visar hur en bag-of-words vektorrepresentation representeras i minnet.](../../../../../translated_images/sv/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![Bild som visar hur en bag-of-words vektorrepresentation representeras i minnet.](../../../../../translated_images/sv/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Note**: Du kan också tänka på BoW som en summa av alla enskilt one-hot-kodade vektorer för individuella ord i texten.\n", "\n", diff --git a/translations/sv/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/sv/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 3f11b772..9ccfcc4d 100644 --- a/translations/sv/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/sv/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "Genom att använda inbäddningslagret som det första lagret i vårt nätverk kan vi byta från bag-of-words till **embedding bag**-modellen, där vi först konverterar varje ord i vår text till motsvarande inbäddning och sedan beräknar någon aggregeringsfunktion över alla dessa inbäddningar, såsom `sum`, `average` eller `max`.\n", "\n", - "![Bild som visar en inbäddningsklassificerare för fem sekvensord.](../../../../../translated_images/sv/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![Bild som visar en inbäddningsklassificerare för fem sekvensord.](../../../../../translated_images/sv/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "Vårt klassificerande neurala nätverk kommer att börja med ett inbäddningslager, sedan ett aggregeringslager och en linjär klassificerare ovanpå det:\n" ] @@ -176,7 +176,7 @@ "\n", "I den tidigare arkitekturen behövde vi fylla ut alla sekvenser till samma längd för att passa in dem i en minibatch. Detta är inte det mest effektiva sättet att representera sekvenser med variabel längd - ett annat tillvägagångssätt skulle vara att använda en **offset**-vektor, som innehåller offset för alla sekvenser lagrade i en stor vektor.\n", "\n", - "![Bild som visar en offset-sekvensrepresentation](../../../../../translated_images/sv/offset-sequence-representation.eb73fcefb29b46ee.png)\n", + "![Bild som visar en offset-sekvensrepresentation](../../../../../translated_images/sv/offset-sequence-representation.eb73fcefb29b46ee.webp)\n", "\n", "> **Note**: På bilden ovan visar vi en sekvens av tecken, men i vårt exempel arbetar vi med sekvenser av ord. Principen för att representera sekvenser med en offset-vektor förblir dock densamma.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW är snabbare, medan skip-gram är långsammare men gör ett bättre jobb med att representera sällsynta ord.\n", "\n", - "![Bild som visar både CBoW- och Skip-Gram-algoritmer för att konvertera ord till vektorer.](../../../../../translated_images/sv/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![Bild som visar både CBoW- och Skip-Gram-algoritmer för att konvertera ord till vektorer.](../../../../../translated_images/sv/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "För att experimentera med Word2Vec-inbäddningar förtränade på Google News-datasetet kan vi använda **gensim**-biblioteket. Nedan hittar vi de ord som är mest lik 'neural'.\n", "\n", diff --git a/translations/sv/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/sv/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index a81129d4..d19d2315 100644 --- a/translations/sv/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/sv/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "Genom att använda ett embedding-lager som det första lagret i vårt nätverk kan vi byta från bag-of-words till en **embedding bag**-modell, där vi först konverterar varje ord i vår text till motsvarande embedding och sedan beräknar någon aggregeringsfunktion över alla dessa embeddings, såsom `sum`, `average` eller `max`.\n", "\n", - "![Bild som visar en embedding-klassificerare för fem sekvensord.](../../../../../translated_images/sv/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![Bild som visar en embedding-klassificerare för fem sekvensord.](../../../../../translated_images/sv/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "Vårt klassificeringsnätverk består av följande lager:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW är snabbare, medan skip-gram är långsammare men gör ett bättre jobb med att representera sällsynta ord.\n", "\n", - "![Bild som visar både CBoW- och Skip-Gram-algoritmer för att konvertera ord till vektorer.](../../../../../translated_images/sv/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![Bild som visar både CBoW- och Skip-Gram-algoritmer för att konvertera ord till vektorer.](../../../../../translated_images/sv/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "För att experimentera med Word2Vec-inbäddningen förtränad på Google News-datasetet kan vi använda **gensim**-biblioteket. Nedan hittar vi de ord som är mest liknande 'neural'.\n", "\n", diff --git a/translations/sv/lessons/5-NLP/14-Embeddings/README.md b/translations/sv/lessons/5-NLP/14-Embeddings/README.md index a74a66d4..5458c8f6 100644 --- a/translations/sv/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/sv/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ Så, inbäddningslagret skulle ta ett ord som indata och producera en utdata-vek Genom att använda ett inbäddningslager som det första lagret i vårt klassificeringsnätverk kan vi byta från en bag-of-words till en **embedding bag**-modell, där vi först konverterar varje ord i vår text till motsvarande inbäddning och sedan beräknar någon aggregeringsfunktion över alla dessa inbäddningar, såsom `sum`, `average` eller `max`. -![Bild som visar en inbäddningsklassificerare för fem sekvensord.](../../../../../translated_images/sv/embedding-classifier-example.b77f021a7ee67eee.png) +![Bild som visar en inbäddningsklassificerare för fem sekvensord.](../../../../../translated_images/sv/embedding-classifier-example.b77f021a7ee67eee.webp) > Bild av författaren @@ -40,7 +40,7 @@ För att göra detta behöver vi förträna vår inbäddningsmodell på en stor CBoW är snabbare, medan skip-gram är långsammare men gör ett bättre jobb med att representera sällsynta ord. -![Bild som visar både CBoW- och Skip-Gram-algoritmer för att konvertera ord till vektorer.](../../../../../translated_images/sv/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![Bild som visar både CBoW- och Skip-Gram-algoritmer för att konvertera ord till vektorer.](../../../../../translated_images/sv/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Bild från [denna artikel](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/sv/lessons/5-NLP/15-LanguageModeling/README.md b/translations/sv/lessons/5-NLP/15-LanguageModeling/README.md index 98c48798..7e80a8c4 100644 --- a/translations/sv/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/sv/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ I våra tidigare exempel använde vi förtränade semantiska inbäddningar, men * **Continuous Bag-of-Words** (CBoW), där vi förutspår den mittersta token $W_0$ i en sekvens av tokens $W_{-N}$, ..., $W_N$. * **Skip-gram**, där vi förutspår en uppsättning närliggande tokens {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} från den mittersta token $W_0$. -![bild från artikel om att konvertera ord till vektorer](../../../../../translated_images/sv/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![bild från artikel om att konvertera ord till vektorer](../../../../../translated_images/sv/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Bild från [denna artikel](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/sv/lessons/5-NLP/16-RNN/README.md b/translations/sv/lessons/5-NLP/16-RNN/README.md index b4ba33ed..6790808d 100644 --- a/translations/sv/lessons/5-NLP/16-RNN/README.md +++ b/translations/sv/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ I tidigare avsnitt har vi använt rika semantiska representationer av text och e För att fånga betydelsen av en textsekvens behöver vi använda en annan neural nätverksarkitektur, som kallas för ett **rekurrent neuralt nätverk**, eller RNN. I RNN skickar vi vår mening genom nätverket en symbol i taget, och nätverket producerar ett **tillstånd**, som vi sedan skickar tillbaka till nätverket tillsammans med nästa symbol. -![RNN](../../../../../translated_images/sv/rnn.27f5c29c53d727b5.png) +![RNN](../../../../../translated_images/sv/rnn.27f5c29c53d727b5.webp) > Bild av författaren @@ -61,7 +61,7 @@ Vi har diskuterat rekurrenta nätverk som arbetar i en riktning, från början a Ett rekurrent nätverk, antingen enkelriktat eller bidirektionellt, fångar vissa mönster inom en sekvens och kan lagra dem i en tillståndsvektor eller skicka dem till utgången. Precis som med konvolutionella nätverk kan vi bygga ett annat rekurrent lager ovanpå det första för att fånga högre nivåmönster och bygga från lågnivåmönster som extraherats av det första lagret. Detta leder oss till begreppet **flerskiktat RNN**, som består av två eller fler rekurrenta nätverk, där utgången från det föregående lagret skickas till nästa lager som inmatning. -![Bild som visar ett flerskiktat Long Short Term Memory-RNN](../../../../../translated_images/sv/multi-layer-lstm.dd975e29bb2a59fe.jpg) +![Bild som visar ett flerskiktat Long Short Term Memory-RNN](../../../../../translated_images/sv/multi-layer-lstm.dd975e29bb2a59fe.webp) *Bild från [detta fantastiska inlägg](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) av Fernando López* diff --git a/translations/sv/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/sv/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index d01ecce4..21afd66d 100644 --- a/translations/sv/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/sv/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "Ett rekurrent nätverk, oavsett om det är enkelriktat eller bidirektionellt, fångar vissa mönster inom en sekvens och kan lagra dem i en tillståndsvektor eller skicka dem vidare till utdata. Precis som med konvolutionella nätverk kan vi bygga ett annat rekurrent lager ovanpå det första för att fånga mönster på högre nivå, baserat på låg-nivå mönster som det första lagret har extraherat. Detta leder oss till begreppet **flerskiktad RNN**, som består av två eller fler rekurrenta nätverk, där utdata från det föregående lagret skickas som indata till nästa lager.\n", "\n", - "![Bild som visar en flerskiktad lång-korttidsminne-RNN](../../../../../translated_images/sv/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![Bild som visar en flerskiktad lång-korttidsminne-RNN](../../../../../translated_images/sv/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*Bild från [detta fantastiska inlägg](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) av Fernando López*\n", "\n", diff --git a/translations/sv/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/sv/lessons/5-NLP/16-RNN/RNNTF.ipynb index ee56c208..666cfa9a 100644 --- a/translations/sv/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/sv/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "För att fånga betydelsen av en textsekvens kommer vi att använda en neural nätverksarkitektur som kallas **rekurrenta neurala nätverk**, eller RNN. När vi använder en RNN skickar vi vår mening genom nätverket en token i taget, och nätverket producerar ett **tillstånd**, som vi sedan skickar vidare till nätverket tillsammans med nästa token.\n", "\n", - "![Bild som visar ett exempel på generering med rekurrenta neurala nätverk.](../../../../../translated_images/sv/rnn.27f5c29c53d727b5.png)\n", + "![Bild som visar ett exempel på generering med rekurrenta neurala nätverk.](../../../../../translated_images/sv/rnn.27f5c29c53d727b5.webp)\n", "\n", "Givet en inmatningssekvens av token $X_0,\\dots,X_n$, skapar RNN en sekvens av neurala nätverksblock och tränar denna sekvens från början till slut med hjälp av backpropagation. Varje nätverksblock tar ett par $(X_i,S_i)$ som indata och producerar $S_{i+1}$ som resultat. Det slutliga tillståndet $S_n$ eller utdata $Y_n$ skickas till en linjär klassificerare för att producera resultatet. Alla nätverksblock delar samma vikter och tränas från början till slut med en enda backpropagation-pass.\n", "\n", @@ -369,7 +369,7 @@ "\n", "Rekurrenta nätverk, oavsett om de är enkelriktade eller bidirektionella, fångar mönster inom en sekvens och lagrar dem i tillståndsvektorer eller returnerar dem som output. Precis som med konvolutionella nätverk kan vi bygga ett annat rekurrent lager efter det första för att fånga högre nivåmönster, byggda från lägre nivåmönster som extraherats av det första lagret. Detta leder oss till begreppet **flerskiktad RNN**, som består av två eller fler rekurrenta nätverk, där output från det föregående lagret skickas till nästa lager som input.\n", "\n", - "![Bild som visar ett flerskiktat lång-korttidsminnes-RNN](../../../../../translated_images/sv/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![Bild som visar ett flerskiktat lång-korttidsminnes-RNN](../../../../../translated_images/sv/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*Bild från [detta fantastiska inlägg](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) av Fernando López.*\n", "\n", diff --git a/translations/sv/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/sv/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index c3aca307..269d4d2c 100644 --- a/translations/sv/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/sv/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "Så här kommer vi att träna ett RNN för att generera text. Vid varje steg tar vi en sekvens av tecken med längden `nchars` och ber nätverket att generera nästa utmatningstecken för varje inmatningstecken:\n", "\n", - "![Bild som visar ett exempel på RNN-generering av ordet 'HELLO'.](../../../../../translated_images/sv/rnn-generate.56c54afb52f9781d.png)\n", + "![Bild som visar ett exempel på RNN-generering av ordet 'HELLO'.](../../../../../translated_images/sv/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "Beroende på det faktiska scenariot kan vi också vilja inkludera några specialtecken, såsom *slut-på-sekvens* ``. I vårt fall vill vi bara träna nätverket för oändlig textgenerering, därför kommer vi att fixa storleken på varje sekvens till att vara lika med `nchars` tokens. Följaktligen kommer varje tränings-exempel att bestå av `nchars` inmatningar och `nchars` utmatningar (vilka är inmatningssekvensen förskjuten ett tecken åt vänster). Minibatchen kommer att bestå av flera sådana sekvenser.\n", "\n", diff --git a/translations/sv/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/sv/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index f0eb8a31..ab33872a 100644 --- a/translations/sv/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/sv/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -113,7 +113,7 @@ "\n", "Så här kommer vi att träna ett RNN för att generera nyhetstitlar. Vid varje steg tar vi en titel, som matas in i ett RNN, och för varje inmatad tecken ber vi nätverket att generera nästa utmatade tecken:\n", "\n", - "![Bild som visar ett exempel på RNN-generering av ordet 'HELLO'.](../../../../../translated_images/sv/rnn-generate.56c54afb52f9781d.png)\n", + "![Bild som visar ett exempel på RNN-generering av ordet 'HELLO'.](../../../../../translated_images/sv/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "För det sista tecknet i vår sekvens kommer vi att be nätverket att generera ``-token.\n", "\n", diff --git a/translations/sv/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/sv/lessons/5-NLP/17-GenerativeNetworks/README.md index ec9c3a0e..2ee7b8ad 100644 --- a/translations/sv/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/sv/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ I RNN-arkitekturen som vi diskuterade i föregående enhet, producerade varje RN Detta möjliggör olika neurala arkitekturer som visas i bilden nedan: -![Bild som visar vanliga mönster för återkommande neurala nätverk.](../../../../../translated_images/sv/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Bild som visar vanliga mönster för återkommande neurala nätverk.](../../../../../translated_images/sv/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > Bild från blogginlägget [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) av [Andrej Karpaty](http://karpathy.github.io/) @@ -32,7 +32,7 @@ I denna enhet kommer vi att fokusera på enkla generativa modeller som hjälper Vi kommer att träna denna RNN att generera text steg för steg. Vid varje steg tar vi en sekvens av tecken med längden `nchars` och ber nätverket att generera nästa outputtecken för varje inputtecken: -![Bild som visar ett exempel på RNN-generering av ordet 'HELLO'.](../../../../../translated_images/sv/rnn-generate.56c54afb52f9781d.png) +![Bild som visar ett exempel på RNN-generering av ordet 'HELLO'.](../../../../../translated_images/sv/rnn-generate.56c54afb52f9781d.webp) När vi genererar text (under inferens), börjar vi med en **prompt**, som passeras genom RNN-celler för att generera dess mellanliggande tillstånd, och sedan börjar genereringen från detta tillstånd. Vi genererar ett tecken i taget och skickar tillståndet och det genererade tecknet till en annan RNN-cell för att generera nästa, tills vi har genererat tillräckligt många tecken. diff --git a/translations/sv/lessons/5-NLP/18-Transformers/README.md b/translations/sv/lessons/5-NLP/18-Transformers/README.md index 4eaa1749..f170c929 100644 --- a/translations/sv/lessons/5-NLP/18-Transformers/README.md +++ b/translations/sv/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ Med RNNs implementeras sekvens-till-sekvens med två rekurrenta nätverk, där e **Uppmärksamhetsmekanismer** ger ett sätt att vikta den kontextuella påverkan av varje inmatningsvektor på varje utgångsprediktion av RNN. Detta implementeras genom att skapa genvägar mellan mellanliggande tillstånd i inmatnings-RNN och utgångs-RNN. På detta sätt, när vi genererar utgångssymbolen yt, tar vi hänsyn till alla dolda inmatningstillstånd hi, med olika viktkoefficienter αt,i. -![Bild som visar en kodare/avkodare-modell med ett additivt uppmärksamhetslager](../../../../../translated_images/sv/encoder-decoder-attention.7a726296894fb567.png) +![Bild som visar en kodare/avkodare-modell med ett additivt uppmärksamhetslager](../../../../../translated_images/sv/encoder-decoder-attention.7a726296894fb567.webp) > Kodare-avkodare-modellen med additiv uppmärksamhetsmekanism i [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citerad från [denna bloggpost](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) Uppmärksamhetsmatrisen {αi,j} representerar graden av påverkan som vissa inmatningsord har på genereringen av ett givet ord i utgångssekvensen. Nedan är ett exempel på en sådan matris: -![Bild som visar ett exempel på justering funnen av RNNsearch-50, hämtad från Bahdanau - arviz.org](../../../../../translated_images/sv/bahdanau-fig3.09ba2d37f202a6af.png) +![Bild som visar ett exempel på justering funnen av RNNsearch-50, hämtad från Bahdanau - arviz.org](../../../../../translated_images/sv/bahdanau-fig3.09ba2d37f202a6af.webp) > Figur från [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) @@ -66,7 +66,7 @@ Resultatet vi får med positionsinbäddning inbäddar både den ursprungliga tok Nästa steg är att fånga vissa mönster inom vår sekvens. För att göra detta använder transformatorer en **självuppmärksamhetsmekanism**, som i grunden är uppmärksamhet applicerad på samma sekvens som inmatning och utgång. Att applicera självuppmärksamhet gör att vi kan ta hänsyn till **kontext** inom meningen och se vilka ord som är relaterade till varandra. Till exempel gör det att vi kan se vilka ord som refereras av korreferenser, såsom *det*, och också ta kontexten i beaktande: -![](../../../../../translated_images/sv/CoreferenceResolution.861924d6d384a7d6.png) +![](../../../../../translated_images/sv/CoreferenceResolution.861924d6d384a7d6.webp) > Bild från [Google Blog](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Eftersom varje inmatningsposition mappas oberoende till varje utgångsposition k **BERT** (Bidirectional Encoder Representations from Transformers) är ett mycket stort flerskikts transformatornätverk med 12 lager för *BERT-base* och 24 för *BERT-large*. Modellen förtränas först på en stor textkorpus (Wikipedia + böcker) med hjälp av oövervakad träning (förutsäga maskerade ord i en mening). Under förträningen absorberar modellen betydande nivåer av språkförståelse som sedan kan utnyttjas med andra dataset genom finjustering. Denna process kallas **transfer learning**. -![bild från http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/sv/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) +![bild från http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/sv/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > Bild [källa](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/sv/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/sv/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index bc6005a5..d854129c 100644 --- a/translations/sv/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/sv/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**Uppmärksamhetsmekanismer** ger ett sätt att vikta den kontextuella påverkan av varje ingångsvektor på varje utgångsprediktion i RNN. Detta implementeras genom att skapa genvägar mellan mellanliggande tillstånd i ingångs-RNN och utgångs-RNN. På detta sätt, när vi genererar utgångssymbolen $y_t$, tar vi hänsyn till alla dolda ingångstillstånd $h_i$, med olika viktkoefficienter $\\alpha_{t,i}$.\n", "\n", - "![Bild som visar en encoder/decoder-modell med ett additivt uppmärksamhetslager](../../../../../translated_images/sv/encoder-decoder-attention.7a726296894fb567.png)\n", + "![Bild som visar en encoder/decoder-modell med ett additivt uppmärksamhetslager](../../../../../translated_images/sv/encoder-decoder-attention.7a726296894fb567.webp)\n", "*Encoder-decoder-modellen med additiv uppmärksamhetsmekanism i [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citerad från [denna bloggpost](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Uppmärksamhetsmatrisen $\\{\\alpha_{i,j}\\}$ representerar graden till vilken vissa ingångsord bidrar till genereringen av ett givet ord i utgångssekvensen. Nedan är ett exempel på en sådan matris:\n", "\n", - "![Bild som visar ett exempel på justering funnen av RNNsearch-50, hämtad från Bahdanau - arviz.org](../../../../../translated_images/sv/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![Bild som visar ett exempel på justering funnen av RNNsearch-50, hämtad från Bahdanau - arviz.org](../../../../../translated_images/sv/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*Figur hämtad från [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) är ett mycket stort flerskikts-transformernätverk med 12 lager för *BERT-base* och 24 för *BERT-large*. Modellen förtränas först på en stor textkorpus (Wikipedia + böcker) med hjälp av oövervakad träning (förutsäga maskerade ord i en mening). Under förträningen absorberar modellen en betydande nivå av språkförståelse som sedan kan utnyttjas med andra dataset genom finjustering. Denna process kallas **transfer learning**.\n", "\n", - "![Bild från http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/sv/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![Bild från http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/sv/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "Det finns många variationer av Transformer-arkitekturer, inklusive BERT, DistilBERT, BigBird, OpenGPT3 och fler, som kan finjusteras. [HuggingFace-paketet](https://github.com/huggingface/) tillhandahåller ett bibliotek för att träna många av dessa arkitekturer med PyTorch.\n", "\n", diff --git a/translations/sv/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/sv/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index 029e6c8f..cda2ca6a 100644 --- a/translations/sv/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/sv/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**Uppmärksamhetsmekanismer** ger ett sätt att vikta den kontextuella påverkan av varje inmatningsvektor på varje utgångsprediktion av RNN. Detta implementeras genom att skapa genvägar mellan mellanliggande tillstånd i inmatnings-RNN och utgångs-RNN. På detta sätt, när vi genererar utgångssymbolen $y_t$, tar vi hänsyn till alla dolda tillstånd $h_i$ från inmatningen, med olika viktkoefficienter $\\alpha_{t,i}$. \n", "\n", - "![Bild som visar en encoder/decoder-modell med ett additivt uppmärksamhetslager](../../../../../translated_images/sv/encoder-decoder-attention.7a726296894fb567.png)\n", + "![Bild som visar en encoder/decoder-modell med ett additivt uppmärksamhetslager](../../../../../translated_images/sv/encoder-decoder-attention.7a726296894fb567.webp)\n", "*Encoder-decoder-modellen med additiv uppmärksamhetsmekanism i [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citerad från [denna bloggpost](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Uppmärksamhetsmatrisen $\\{\\alpha_{i,j}\\}$ representerar graden av påverkan som vissa inmatningsord har vid genereringen av ett visst ord i utgångssekvensen. Nedan är ett exempel på en sådan matris:\n", "\n", - "![Bild som visar ett exempel på justering funnen av RNNsearch-50, hämtad från Bahdanau - arviz.org](../../../../../translated_images/sv/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![Bild som visar ett exempel på justering funnen av RNNsearch-50, hämtad från Bahdanau - arviz.org](../../../../../translated_images/sv/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*Figur hämtad från [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3)*\n", "\n", @@ -231,7 +231,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) är ett mycket stort flerskiktat transformatornätverk med 12 lager för *BERT-base* och 24 för *BERT-large*. Modellen förtränas först på en stor mängd textdata (WikiPedia + böcker) med hjälp av osuperviserad träning (förutsäga maskerade ord i en mening). Under förträningen absorberar modellen en betydande nivå av språkförståelse som sedan kan utnyttjas med andra dataset genom finjustering. Denna process kallas **transfer learning**.\n", "\n", - "![bild från http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/sv/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![bild från http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/sv/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "Det finns många variationer av Transformer-arkitekturer, inklusive BERT, DistilBERT, BigBird, OpenGPT3 och fler som kan finjusteras.\n", "\n", diff --git a/translations/sv/lessons/5-NLP/19-NER/README.md b/translations/sv/lessons/5-NLP/19-NER/README.md index 9369b6e6..60bcfde1 100644 --- a/translations/sv/lessons/5-NLP/19-NER/README.md +++ b/translations/sv/lessons/5-NLP/19-NER/README.md @@ -57,7 +57,7 @@ barn | O Eftersom vi behöver bygga en en-till-en-korrespondens mellan tokens och klasser, kan vi träna en högerställd **många-till-många** neural nätverksmodell från denna bild: -![Bild som visar vanliga återkommande neurala nätverksmönster.](../../../../../translated_images/sv/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Bild som visar vanliga återkommande neurala nätverksmönster.](../../../../../translated_images/sv/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > *Bild från [denna bloggpost](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) av [Andrej Karpathy](http://karpathy.github.io/). NER tokenklassificeringsmodeller motsvarar den högra nätverksarkitekturen på denna bild.* diff --git a/translations/sv/lessons/5-NLP/README.md b/translations/sv/lessons/5-NLP/README.md index 6dc167f7..148c97ec 100644 --- a/translations/sv/lessons/5-NLP/README.md +++ b/translations/sv/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Naturlig Språkbearbetning -![Sammanfattning av NLP-uppgifter i en skiss](../../../../translated_images/sv/ai-nlp.b22dcb8ca4707cea.png) +![Sammanfattning av NLP-uppgifter i en skiss](../../../../translated_images/sv/ai-nlp.b22dcb8ca4707cea.webp) I den här delen kommer vi att fokusera på att använda neurala nätverk för att hantera uppgifter relaterade till **naturlig språkbearbetning (NLP)**. Det finns många NLP-problem som vi vill att datorer ska kunna lösa: diff --git a/translations/sv/lessons/6-Other/23-MultiagentSystems/README.md b/translations/sv/lessons/6-Other/23-MultiagentSystems/README.md index c63ba6e3..553729ea 100644 --- a/translations/sv/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/sv/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ Du kan öppna en av modellerna, till exempel **Biology → Flocking**. Efter att ha öppnat modellen tas du till huvudskärmen i NetLogo. Här är en exempelmodell som beskriver populationen av vargar och får, givet begränsade resurser (gräs). -![NetLogo Main Screen](../../../../../translated_images/sv/NetLogo-Main.32653711ec1a01b3.png) +![NetLogo Main Screen](../../../../../translated_images/sv/NetLogo-Main.32653711ec1a01b3.webp) > Skärmdump av Dmitry Soshnikov diff --git a/translations/sv/lessons/README.md b/translations/sv/lessons/README.md index 1168f258..0b545ab3 100644 --- a/translations/sv/lessons/README.md +++ b/translations/sv/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Översikt -![Översikt i en skiss](../../../translated_images/sv/ai-overview.0857791951d19500.png) +![Översikt i en skiss](../../../translated_images/sv/ai-overview.0857791951d19500.webp) > Skissanteckning av [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/sv/lessons/X-Extras/X1-MultiModal/README.md b/translations/sv/lessons/X-Extras/X1-MultiModal/README.md index 1fa6693d..f6e1c412 100644 --- a/translations/sv/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/sv/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ Efter framgången med transformer-modeller för att lösa NLP-uppgifter har samm Huvudidén med CLIP är att kunna jämföra textprompter med en bild och avgöra hur väl bilden motsvarar prompten. -![CLIP-arkitektur](../../../../../translated_images/sv/clip-arch.b3dbf20b4e8ed8be.png) +![CLIP-arkitektur](../../../../../translated_images/sv/clip-arch.b3dbf20b4e8ed8be.webp) > *Bild från [detta blogginlägg](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ När denna modell är förtränad kan vi ge den en batch av bilder och en batch Anta att vi behöver klassificera bilder mellan exempelvis katter, hundar och människor. I detta fall kan vi ge modellen en bild och en serie textprompter: "*en bild av en katt*", "*en bild av en hund*", "*en bild av en människa*". I den resulterande vektorn med tre sannolikheter behöver vi bara välja indexet med högst värde. -![CLIP för bildklassificering](../../../../../translated_images/sv/clip-class.3af42ef0b2b19369.png) +![CLIP för bildklassificering](../../../../../translated_images/sv/clip-class.3af42ef0b2b19369.webp) > *Bild från [detta blogginlägg](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ Lär dig mer om VQGAN på [Taming Transformers](https://compvis.github.io/taming En viktig skillnad mellan VQGAN och traditionella GAN är att den senare kan producera en hyfsad bild från vilken inputvektor som helst, medan VQGAN sannolikt kommer att producera en bild som inte är sammanhängande. Därför behöver vi ytterligare vägleda bildskapandeprocessen, och det kan göras med hjälp av CLIP. -![VQGAN+CLIP-arkitektur](../../../../../translated_images/sv/vqgan.5027fe05051dfa31.png) +![VQGAN+CLIP-arkitektur](../../../../../translated_images/sv/vqgan.5027fe05051dfa31.webp) För att generera en bild som motsvarar en textprompt börjar vi med någon slumpmässig kodningsvektor som skickas genom VQGAN för att producera en bild. Sedan används CLIP för att skapa en förlustfunktion som visar hur väl bilden motsvarar textprompten. Målet är sedan att minimera denna förlust genom att använda backpropagation för att justera parametrarna för inputvektorn. Ett utmärkt bibliotek som implementerar VQGAN+CLIP är [Pixray](http://github.com/pixray/pixray). -![Bild skapad av Pixray](../../../../../translated_images/sv/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Bild skapad av Pixray](../../../../../translated_images/sv/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Bild skapad av Pixray](../../../../../translated_images/sv/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) +![Bild skapad av Pixray](../../../../../translated_images/sv/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp) | ![Bild skapad av Pixray](../../../../../translated_images/sv/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp) | ![Bild skapad av Pixray](../../../../../translated_images/sv/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp) ----|----|---- Bild genererad från prompten *en närbild akvarellporträtt av en ung manlig litteraturlärare med en bok* | Bild genererad från prompten *en närbild oljeporträtt av en ung kvinnlig datavetenskapslärare med en dator* | Bild genererad från prompten *en närbild oljeporträtt av en äldre manlig matematiklärare framför en svart tavla* diff --git a/translations/sw/README.md b/translations/sw/README.md index 4da3e7c3..e68526ff 100644 --- a/translations/sw/README.md +++ b/translations/sw/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # Akili Bandia kwa Komesheni - Mtaala -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/sw/ai-overview.0857791951d19500.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/sw/ai-overview.0857791951d19500.webp)| |:---:| | AI Kwa Komesheni - _Sketchnote na [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/sw/lessons/1-Intro/README.md b/translations/sw/lessons/1-Intro/README.md index 4a79416f..f6317c72 100644 --- a/translations/sw/lessons/1-Intro/README.md +++ b/translations/sw/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Utangulizi wa AI -![Muhtasari wa maudhui ya Utangulizi wa AI katika mchoro](../../../../translated_images/sw/ai-intro.bf28d1ac4235881c.png) +![Muhtasari wa maudhui ya Utangulizi wa AI katika mchoro](../../../../translated_images/sw/ai-intro.bf28d1ac4235881c.webp) > Mchoro na [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Awali, kompyuta ziligunduliwa na [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage) ili kufanya kazi na namba kwa kufuata utaratibu ulioelezwa vizuri - algorithimu. Kompyuta za kisasa, ingawa zimeendelea sana kuliko mfano wa awali uliopendekezwa katika karne ya 19, bado zinafuata wazo lile lile la hesabu zinazoongozwa. Kwa hivyo inawezekana kuipanga kompyuta kufanya jambo fulani ikiwa tunajua mlolongo halisi wa hatua tunazohitaji kuchukua ili kufanikisha lengo. -![Picha ya mtu](../../../../translated_images/sw/dsh_age.d212a30d4e54fb5f.png) +![Picha ya mtu](../../../../translated_images/sw/dsh_age.d212a30d4e54fb5f.webp) > Picha na [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -45,7 +45,7 @@ Kwa maelezo zaidi rejelea **[Akili Bandia ya Kijumla](https://en.wikipedia.org/w Moja ya matatizo tunaposhughulikia neno **[Akili](https://en.wikipedia.org/wiki/Intelligence)** ni kwamba hakuna ufafanuzi wazi wa neno hili. Mtu anaweza kusema akili inahusiana na **fikra za dhahania**, au na **kujifahamu**, lakini hatuwezi kuifafanua ipasavyo. -![Picha ya Paka](../../../../translated_images/sw/photo-cat.8c8e8fb760ffe457.jpg) +![Picha ya Paka](../../../../translated_images/sw/photo-cat.8c8e8fb760ffe457.webp) > [Picha](https://unsplash.com/photos/75715CVEJhI) na [Amber Kipp](https://unsplash.com/@sadmax) kutoka Unsplash @@ -97,13 +97,13 @@ Vinginevyo, tunaweza kujaribu kuiga vipengele rahisi zaidi ndani ya ubongo wetu > | Je, kuhusu ML? | | > |--------------|-----------| -> | Sehemu ya Akili Bandia inayotegemea kompyuta kujifunza kutatua tatizo kwa msingi wa data fulani inaitwa **Machine Learning**. Hatutazingatia ujifunzaji wa mashine wa kawaida katika kozi hii - tunakuelekeza kwenye mtaala tofauti wa [Machine Learning for Beginners](http://aka.ms/ml-beginners). | ![ML for Beginners](../../../../translated_images/sw/ml-for-beginners.9e4fed176fd5817d.png) | +> | Sehemu ya Akili Bandia inayotegemea kompyuta kujifunza kutatua tatizo kwa msingi wa data fulani inaitwa **Machine Learning**. Hatutazingatia ujifunzaji wa mashine wa kawaida katika kozi hii - tunakuelekeza kwenye mtaala tofauti wa [Machine Learning for Beginners](http://aka.ms/ml-beginners). | ![ML for Beginners](../../../../translated_images/sw/ml-for-beginners.9e4fed176fd5817d.webp) | ## Historia Fupi ya AI Akili Bandia ilianza kama uwanja katikati ya karne ya ishirini. Awali, ufikiri wa kimaumbo ulikuwa njia maarufu, na ulisababisha mafanikio kadhaa muhimu, kama vile mifumo ya wataalamu – programu za kompyuta ambazo ziliweza kutenda kama mtaalamu katika baadhi ya maeneo ya tatizo yaliyopunguzwa. Hata hivyo, hivi karibuni ilibainika kuwa njia hiyo haipimi vizuri. Kutoa maarifa kutoka kwa mtaalamu, kuyaweka katika kompyuta, na kuweka msingi wa maarifa sahihi inageuka kuwa kazi ngumu sana, na ghali sana kuwa ya vitendo katika hali nyingi. Hii ilisababisha kile kinachoitwa [Majira ya Baridi ya AI](https://en.wikipedia.org/wiki/AI_winter) katika miaka ya 1970. -Historia Fupi ya AI +Historia Fupi ya AI > Picha na [Dmitry Soshnikov](http://soshnikov.com) @@ -123,7 +123,7 @@ Vivyo hivyo, tunaweza kuona jinsi mbinu za kuunda “programu zinazozungumza” * Wasaidizi wa kisasa, kama Cortana, Siri au Google Assistant ni mifumo mseto inayotumia Mitandao ya Neva kubadilisha hotuba kuwa maandishi na kutambua nia yetu, na kisha kutumia baadhi ya ufikiri au algorithimu wazi kutekeleza vitendo vinavyohitajika. * Katika siku zijazo, tunaweza kutarajia mfano kamili unaotegemea neva kushughulikia mazungumzo yenyewe. Familia ya hivi karibuni ya mitandao ya neva ya GPT na [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) inaonyesha mafanikio makubwa katika hili. -mabadiliko ya mtihani wa Turing +mabadiliko ya mtihani wa Turing > Picha na Dmitry Soshnikov, [picha](https://unsplash.com/photos/r8LmVbUKgns) na [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto), Unsplash ## Utafiti wa Hivi Karibuni wa AI diff --git a/translations/sw/lessons/2-Symbolic/Animals.ipynb b/translations/sw/lessons/2-Symbolic/Animals.ipynb index 9c580061..4e3a4ee3 100644 --- a/translations/sw/lessons/2-Symbolic/Animals.ipynb +++ b/translations/sw/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "Katika mfano huu, tutatekeleza mfumo rahisi unaotegemea maarifa ili kubaini mnyama kulingana na baadhi ya sifa za kimwili. Mfumo huu unaweza kuwakilishwa na mti wa AND-OR ufuatao (hii ni sehemu ya mti mzima, tunaweza kuongeza sheria zaidi kwa urahisi):\n", "\n", - "![](../../../../translated_images/sw/AND-OR-Tree.5592d2c70187f283.png)\n" + "![](../../../../translated_images/sw/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { diff --git a/translations/sw/lessons/2-Symbolic/README.md b/translations/sw/lessons/2-Symbolic/README.md index e7d27c85..77f0d92e 100644 --- a/translations/sw/lessons/2-Symbolic/README.md +++ b/translations/sw/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Uwakilishi wa Maarifa na Mifumo ya Wataalamu -![Muhtasari wa maudhui ya AI ya Kimaandishi](../../../../translated_images/sw/ai-symbolic.715a30cb610411a6.png) +![Muhtasari wa maudhui ya AI ya Kimaandishi](../../../../translated_images/sw/ai-symbolic.715a30cb610411a6.webp) > Sketchnote na [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ Mara nyingi, hatufafanui maarifa kwa ukali, lakini tunayalinganisha na dhana nyi Kwa hivyo, tatizo la **uwakilishi wa maarifa** ni kutafuta njia bora ya kuwakilisha maarifa ndani ya kompyuta kwa njia ya data, ili yaweze kutumika kiotomatiki. Hili linaweza kuonekana kama wigo: -![Wigo wa uwakilishi wa maarifa](../../../../translated_images/sw/knowledge-spectrum.b60df631852c0217.png) +![Wigo wa uwakilishi wa maarifa](../../../../translated_images/sw/knowledge-spectrum.b60df631852c0217.webp) > Picha na [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Syntax ya Block | Indent | | | Moja ya mafanikio ya awali ya AI ya kimaandishi yalikuwa mifumo ya **wataalamu** - mifumo ya kompyuta iliyoundwa kufanya kazi kama mtaalamu katika eneo fulani la tatizo. Ilitegemea **hifadhidata ya maarifa** iliyotolewa kutoka kwa mtaalamu mmoja au zaidi wa binadamu, na ilikuwa na **injini ya utoaji wa sababu** iliyofanya utoaji wa sababu juu yake. -![Muundo wa Binadamu](../../../../translated_images/sw/arch-human.5d4d35f1bba3ab1c.png) | ![Mfumo Unaotegemea Maarifa](../../../../translated_images/sw/arch-kbs.3ec5c150b09fa8da.png) +![Muundo wa Binadamu](../../../../translated_images/sw/arch-human.5d4d35f1bba3ab1c.webp) | ![Mfumo Unaotegemea Maarifa](../../../../translated_images/sw/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ Muundo rahisi wa mfumo wa neva wa binadamu | Muundo wa mfumo unaotegemea maarifa @@ -106,7 +106,7 @@ Mifumo ya wataalamu imejengwa kama mfumo wa utoaji wa sababu wa binadamu, ambao Kwa mfano, hebu tuchunguze mfumo wa wataalamu wa kuamua mnyama kulingana na sifa zake za kimwili: -![Mti wa AND-OR](../../../../translated_images/sw/AND-OR-Tree.5592d2c70187f283.png) +![Mti wa AND-OR](../../../../translated_images/sw/AND-OR-Tree.5592d2c70187f283.webp) > Picha na [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/sw/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/sw/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index ce4928e6..6563da49 100644 --- a/translations/sw/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/sw/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -1259,7 +1259,7 @@ "* Hasara ya mafunzo ni ndogo - mfano unaweza kufanana vizuri na data ya mafunzo kwa sababu una uwezo wa kujieleza wa kutosha.\n", "* Hasara ya uthibitishaji inaweza kuwa kubwa zaidi kuliko hasara ya mafunzo na inaweza kuanza kuongezeka wakati wa mafunzo - hii ni kwa sababu mfano \"unakumbuka\" pointi za mafunzo na kupoteza \"picha kubwa.\"\n", "\n", - "![Overfitting](../../../../../translated_images/sw/overfit.a0bd57f717c15769.png)\n", + "![Overfitting](../../../../../translated_images/sw/overfit.a0bd57f717c15769.webp)\n", "\n", "> Katika picha hii, `x` inawakilisha data ya mafunzo, `o` - data ya uthibitishaji. Kushoto - mfano wa mstari (tabaka moja), unafanana vizuri na asili ya data. Kulia - mfano uliopitiliza, mfano unafanana kikamilifu na data ya mafunzo, lakini hauleti maana yoyote kwa data nyingine yoyote (makosa ya uthibitishaji ni makubwa sana).\n" ] diff --git a/translations/sw/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/sw/lessons/3-NeuralNetworks/05-Frameworks/README.md index 4521e03b..c8e1ebef 100644 --- a/translations/sw/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/sw/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Overfitting ni dhana muhimu sana katika ujifunzaji wa mashine, na ni muhimu sana Fikiria tatizo lifuatalo la kukadiria alama 5 (zinazoonyeshwa na `x` kwenye grafu hapa chini): -![linear](../../../../../translated_images/sw/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/sw/overfit2.131f5800ae10ca5e.jpg) +![linear](../../../../../translated_images/sw/overfit1.f24b71c6f652e59e.webp) | ![overfit](../../../../../translated_images/sw/overfit2.131f5800ae10ca5e.webp) -------------------------|-------------------------- **Modeli ya mstari, vigezo 2** | **Modeli isiyo ya mstari, vigezo 7** Makosa ya mafunzo = 5.3 | Makosa ya mafunzo = 0 @@ -79,7 +79,7 @@ Ni muhimu sana kupata usawa sahihi kati ya utajiri wa modeli (idadi ya vigezo) n Kama unavyoona kutoka kwenye grafu hapo juu, overfitting inaweza kugunduliwa kwa makosa ya mafunzo ya chini sana, na makosa ya uthibitishaji ya juu. Kawaida wakati wa mafunzo tutaona makosa ya mafunzo na uthibitishaji yakianza kupungua, na kisha wakati fulani makosa ya uthibitishaji yanaweza kuacha kupungua na kuanza kuongezeka. Hii itakuwa ishara ya overfitting, na kiashiria kwamba tunapaswa labda kuacha mafunzo wakati huo (au angalau kufanya nakala ya modeli). -![overfitting](../../../../../translated_images/sw/Overfitting.408ad91cd90b4371.png) +![overfitting](../../../../../translated_images/sw/Overfitting.408ad91cd90b4371.webp) ## Jinsi ya kuzuia overfitting diff --git a/translations/sw/lessons/3-NeuralNetworks/README.md b/translations/sw/lessons/3-NeuralNetworks/README.md index 9709271e..6d4545ed 100644 --- a/translations/sw/lessons/3-NeuralNetworks/README.md +++ b/translations/sw/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Utangulizi wa Mitandao ya Neva -![Muhtasari wa maudhui ya Utangulizi wa Mitandao ya Neva katika mchoro](../../../../translated_images/sw/ai-neuralnetworks.1c687ae40bc86e83.png) +![Muhtasari wa maudhui ya Utangulizi wa Mitandao ya Neva katika mchoro](../../../../translated_images/sw/ai-neuralnetworks.1c687ae40bc86e83.webp) Kama tulivyojadili katika utangulizi, mojawapo ya njia za kufanikisha akili ni kufundisha **mfano wa kompyuta** au **ubongo bandia**. Tangu katikati ya karne ya 20, watafiti walijaribu mifano mbalimbali ya kihisabati, hadi miaka ya hivi karibuni ambapo mwelekeo huu ulionekana kufanikiwa sana. Mifano hii ya kihisabati ya ubongo inaitwa **mitandao ya neva**. @@ -36,13 +36,13 @@ Katika mtaala huu, tutazingatia tu mifano ya mitandao ya neva. Kutoka kwa biolojia, tunajua kuwa ubongo wetu unajumuisha seli za neva (neuroni), kila moja ikiwa na "viingizo" vingi (dendriti) na "matokeo" moja (aksoni). Dendriti na aksoni zote mbili zinaweza kusafirisha ishara za umeme, na miunganisho kati yao — inayojulikana kama sinapsi — inaweza kuonyesha viwango tofauti vya usafirishaji, ambavyo vinadhibitiwa na nyurotransmita. -![Mfano wa Neva](../../../../translated_images/sw/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![Mfano wa Neva](../../../../translated_images/sw/artneuron.1a5daa88d20ebe6f.png) +![Mfano wa Neva](../../../../translated_images/sw/synapse-wikipedia.ed20a9e4726ea1c6.webp) | ![Mfano wa Neva](../../../../translated_images/sw/artneuron.1a5daa88d20ebe6f.webp) ----|---- Neva Halisi *([Picha](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) kutoka Wikipedia)* | Neva Bandia *(Picha na Mwandishi)* Kwa hivyo, mfano rahisi wa kihisabati wa neva una viingizo kadhaa X1, ..., XN na matokeo Y, na mfululizo wa uzito W1, ..., WN. Matokeo huhesabiwa kama: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) ambapo f ni **kazi ya uanzishaji** isiyo ya mstari. diff --git a/translations/sw/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/sw/lessons/4-ComputerVision/06-IntroCV/README.md index f24695d2..b606d8e2 100644 --- a/translations/sw/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/sw/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ Katika [OpenCV Notebook](OpenCV.ipynb) yetu, tunatoa mifano ya wakati uelewa wa * **Usindikaji wa awali wa picha ya kitabu cha Braille**. Tunazingatia jinsi tunavyoweza kutumia thresholding, utambuzi wa vipengele, mabadiliko ya mtazamo na manipulations za NumPy kutenganisha alama za Braille kwa uainishaji zaidi na mtandao wa neva. -![Braille Image](../../../../../translated_images/sw/braille.341962ff76b1bd70.jpeg) | ![Braille Image Pre-processed](../../../../../translated_images/sw/braille-result.46530fea020b03c7.png) | ![Braille Symbols](../../../../../translated_images/sw/braille-symbols.0159185ab69d5339.png) +![Braille Image](../../../../../translated_images/sw/braille.341962ff76b1bd70.webp) | ![Braille Image Pre-processed](../../../../../translated_images/sw/braille-result.46530fea020b03c7.webp) | ![Braille Symbols](../../../../../translated_images/sw/braille-symbols.0159185ab69d5339.webp) ----|-----|----- > Picha kutoka [OpenCV.ipynb](OpenCV.ipynb) * **Kutambua harakati kwenye video kwa kutumia tofauti ya fremu**. Ikiwa kamera imetulia, basi fremu kutoka mlisho wa kamera zinapaswa kufanana sana. Kwa kuwa fremu zinawakilishwa kama safu, kwa kutoa tofauti ya safu hizo kwa fremu mbili mfululizo tutapata tofauti ya pikseli, ambayo inapaswa kuwa ndogo kwa fremu tuli, na kuwa kubwa zaidi mara kuna harakati kubwa kwenye picha. -![Image of video frames and frame differences](../../../../../translated_images/sw/frame-difference.706f805491a0883c.png) +![Image of video frames and frame differences](../../../../../translated_images/sw/frame-difference.706f805491a0883c.webp) > Picha kutoka [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ Katika [OpenCV Notebook](OpenCV.ipynb) yetu, tunatoa mifano ya wakati uelewa wa - **Dense Optical Flow** huhesabu uwanja wa vekta unaoonyesha kwa kila pikseli inahama wapi. - **Sparse Optical Flow** inategemea kuchukua vipengele vya kipekee kwenye picha (mfano, kingo), na kujenga mwelekeo wake kutoka fremu hadi fremu. -![Image of Optical Flow](../../../../../translated_images/sw/optical.1f4a94464579a83a.png) +![Image of Optical Flow](../../../../../translated_images/sw/optical.1f4a94464579a83a.webp) > Picha kutoka [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/sw/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/sw/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index e1e9fe35..09db4c41 100644 --- a/translations/sw/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/sw/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 ni mtandao uliopata usahihi wa 92.7% katika uainishaji wa ImageNet top-5 mwaka 2014. Una muundo wa tabaka zifuatazo: -![Tabaka za ImageNet](../../../../../translated_images/sw/vgg-16-arch1.d901a5583b3a51ba.jpg) +![Tabaka za ImageNet](../../../../../translated_images/sw/vgg-16-arch1.d901a5583b3a51ba.webp) Kama unavyoona, VGG inafuata muundo wa jadi wa piramidi, ambao ni mfululizo wa tabaka za convolution-pooling. -![Piramidi ya ImageNet](../../../../../translated_images/sw/vgg-16-arch.64ff2137f50dd49f.jpg) +![Piramidi ya ImageNet](../../../../../translated_images/sw/vgg-16-arch.64ff2137f50dd49f.webp) > Picha kutoka [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/sw/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/sw/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index d12b64be..882d2b9a 100644 --- a/translations/sw/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/sw/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -262,7 +262,7 @@ "\n", "Kwa hivyo, katika CNN ya kawaida kutakuwa na tabaka kadhaa za convolution, zikiwa na tabaka za pooling kati yao ili kupunguza vipimo vya picha. Pia tungeongeza idadi ya vichujio, kwa sababu kadri mifumo inavyokuwa ya hali ya juu - kuna mchanganyiko zaidi wa kuvutia ambao tunahitaji kutafuta.\n", "\n", - "![Picha inayoonyesha tabaka kadhaa za convolution zikiwa na tabaka za pooling.](../../../../../translated_images/sw/cnn-pyramid.85915455759ef0ce.png)\n", + "![Picha inayoonyesha tabaka kadhaa za convolution zikiwa na tabaka za pooling.](../../../../../translated_images/sw/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "Kwa sababu ya kupungua kwa vipimo vya anga na kuongezeka kwa vipimo vya sifa/vichujio, usanifu huu pia huitwa **usanifu wa piramidi**.\n" ] diff --git a/translations/sw/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/sw/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index c964c7ca..939eb1b2 100644 --- a/translations/sw/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/sw/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -360,7 +360,7 @@ "\n", "Kwa hivyo, katika CNN ya kawaida kutakuwa na tabaka kadhaa za convolutional, zikiwa na tabaka za pooling kati yao ili kupunguza vipimo vya picha. Pia tungeongeza idadi ya vichujio, kwa sababu mifumo inavyokuwa ya hali ya juu zaidi - kuna mchanganyiko mwingi wa kuvutia ambao tunahitaji kutafuta.\n", "\n", - "![Picha inayoonyesha tabaka kadhaa za convolutional zikiwa na tabaka za pooling.](../../../../../translated_images/sw/cnn-pyramid.85915455759ef0ce.png)\n", + "![Picha inayoonyesha tabaka kadhaa za convolutional zikiwa na tabaka za pooling.](../../../../../translated_images/sw/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "Kwa sababu ya kupungua kwa vipimo vya anga na kuongezeka kwa vipimo vya sifa/vichujio, usanifu huu pia huitwa **usanifu wa piramidi**.\n" ] diff --git a/translations/sw/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/sw/lessons/4-ComputerVision/07-ConvNets/README.md index 27503844..9901dea2 100644 --- a/translations/sw/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/sw/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ Katika maisha halisi, tunataka kuwa na uwezo wa kutambua vitu kwenye picha bila Ili kutoa mifumo, tutatumia dhana ya **vichujio vya convolutional**. Kama unavyojua, picha inawakilishwa na matriki ya 2D, au tensor ya 3D yenye kina cha rangi. Kutumia kichujio kunamaanisha kwamba tunachukua matriki ndogo ya **kernel ya kichujio**, na kwa kila pikseli kwenye picha ya awali tunahesabu wastani wa uzito na pointi za jirani. Tunaweza kuona hili kama dirisha dogo linalosonga juu ya picha nzima, na kujumlisha pikseli zote kulingana na uzito katika matriki ya kernel ya kichujio. -![Kichujio cha Mstari Wima](../../../../../translated_images/sw/filter-vert.b7148390ca0bc356.png) | ![Kichujio cha Mstari Mlalo](../../../../../translated_images/sw/filter-horiz.59b80ed4feb946ef.png) +![Kichujio cha Mstari Wima](../../../../../translated_images/sw/filter-vert.b7148390ca0bc356.webp) | ![Kichujio cha Mstari Mlalo](../../../../../translated_images/sw/filter-horiz.59b80ed4feb946ef.webp) ----|---- > Picha na Dmitry Soshnikov @@ -38,7 +38,7 @@ Njia CNN zinavyofanya kazi inategemea mawazo muhimu yafuatayo: * Tunaweza kubuni mtandao kwa njia ambayo vichujio vinajifunza kiotomatiki * Tunaweza kutumia mbinu hiyo hiyo kutafuta mifumo kwenye vipengele vya kiwango cha juu, si tu kwenye picha ya awali. Kwa hivyo uchimbaji wa vipengele vya CNN hufanya kazi kwenye uhierakia wa vipengele, kuanzia mchanganyiko wa pikseli za kiwango cha chini, hadi mchanganyiko wa kiwango cha juu wa sehemu za picha. -![Uchimbaji wa Vipengele vya Kihierakia](../../../../../translated_images/sw/FeatureExtractionCNN.d9b456cbdae7cb64.png) +![Uchimbaji wa Vipengele vya Kihierakia](../../../../../translated_images/sw/FeatureExtractionCNN.d9b456cbdae7cb64.webp) > Picha kutoka [karatasi ya Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), kulingana na [utafiti wao](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ CNN nyingi zinazotumika kwa usindikaji wa picha hufuata kile kinachoitwa muundo Kwa mfano, hebu tuangalie muundo wa VGG-16, mtandao uliopata usahihi wa 92.7% katika uainishaji wa juu-5 wa ImageNet mwaka 2014: -![Tabaka za ImageNet](../../../../../translated_images/sw/vgg-16-arch1.d901a5583b3a51ba.jpg) +![Tabaka za ImageNet](../../../../../translated_images/sw/vgg-16-arch1.d901a5583b3a51ba.webp) -![Piramidi ya ImageNet](../../../../../translated_images/sw/vgg-16-arch.64ff2137f50dd49f.jpg) +![Piramidi ya ImageNet](../../../../../translated_images/sw/vgg-16-arch.64ff2137f50dd49f.webp) > Picha kutoka [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/sw/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/sw/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 9b740d07..be14092c 100644 --- a/translations/sw/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/sw/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ Unahitaji kufundisha mtandao wa neva wa convolutional ili kuainisha aina tofauti Tutatumia [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/), ambayo ina picha za aina 37 tofauti za mbwa na paka. -![Dataset tutakayoshughulikia](../../../../../../translated_images/sw/data.50b2a9d5484bdbf0.png) +![Dataset tutakayoshughulikia](../../../../../../translated_images/sw/data.50b2a9d5484bdbf0.webp) Ili kupakua dataset, tumia kipande hiki cha msimbo: diff --git a/translations/sw/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/sw/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index b19c8fd0..fa37d48e 100644 --- a/translations/sw/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/sw/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "Ili kuonyesha paka bora, tutaanza na picha ya kelele ya nasibu, na tutajaribu kutumia mbinu ya uboreshaji ya gradient descent kurekebisha picha ili mtandao utambue paka.\n", "\n", - "![Mzunguko wa Uboreshaji](../../../../../translated_images/sw/ideal-cat-loop.999fbb8ff306e044.png)\n", + "![Mzunguko wa Uboreshaji](../../../../../translated_images/sw/ideal-cat-loop.999fbb8ff306e044.webp)\n", "\n", "Hii hapa ni picha yetu ya kuanzia:\n" ] diff --git a/translations/sw/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/sw/lessons/4-ComputerVision/08-TransferLearning/README.md index 02525dba..ea939869 100644 --- a/translations/sw/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/sw/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Keras na PyTorch zote zina kazi za kupakia kwa urahisi uzito wa mtandao wa neva Hapa kuna vipengele vya mfano vilivyotolewa kutoka kwa picha ya paka na mtandao wa VGG-16: -![Vipengele vilivyotolewa na VGG-16](../../../../../translated_images/sw/features.6291f9c7ba3a0b95.png) +![Vipengele vilivyotolewa na VGG-16](../../../../../translated_images/sw/features.6291f9c7ba3a0b95.webp) ## Seti ya Data ya Paka na Mbwa @@ -48,19 +48,19 @@ Mtandao wa neva uliyojifunza kabla una mifumo tofauti ndani ya "ubongo" wake, ik Njia moja tunayoweza kuchukua ni kuanza na picha ya nasibu, kisha kujaribu kutumia mbinu ya **ufanisi wa gradient descent** kurekebisha picha hiyo kwa namna ambayo mtandao unaanza kufikiria kuwa ni paka. -![Mzunguko wa Uboreshaji wa Picha](../../../../../translated_images/sw/ideal-cat-loop.999fbb8ff306e044.png) +![Mzunguko wa Uboreshaji wa Picha](../../../../../translated_images/sw/ideal-cat-loop.999fbb8ff306e044.webp) Hata hivyo, tukifanya hivyo, tutapata kitu kinachofanana sana na kelele ya nasibu. Hii ni kwa sababu *kuna njia nyingi za kufanya mtandao kufikiria picha ya ingizo ni paka*, ikiwa ni pamoja na baadhi ambazo hazina maana kwa macho. Ingawa picha hizo zina mifumo mingi inayotambulika kwa paka, hakuna kitu kinachozuia kuwa tofauti kwa macho. Ili kuboresha matokeo, tunaweza kuongeza kipengele kingine kwenye kazi ya hasara, kinachoitwa **variation loss**. Ni kipimo kinachoonyesha jinsi pikseli za jirani za picha zinavyofanana. Kupunguza variation loss hufanya picha kuwa laini, na kuondoa kelele - hivyo kufichua mifumo inayovutia zaidi kwa macho. Hapa kuna mfano wa picha "bora" zinazotambuliwa kama paka na kama punda milia kwa uwezekano mkubwa: -![Paka Bora](../../../../../translated_images/sw/ideal-cat.203dd4597643d6b0.png) | ![Punda Milia Bora](../../../../../translated_images/sw/ideal-zebra.7f70e8b54ee15a7a.png) +![Paka Bora](../../../../../translated_images/sw/ideal-cat.203dd4597643d6b0.webp) | ![Punda Milia Bora](../../../../../translated_images/sw/ideal-zebra.7f70e8b54ee15a7a.webp) -----|----- *Paka Bora* | *Punda Milia Bora* Njia sawa inaweza kutumika kufanya kile kinachoitwa **mashambulizi ya kihasama** kwenye mtandao wa neva. Tuseme tunataka kudanganya mtandao wa neva na kufanya mbwa aonekane kama paka. Ikiwa tutachukua picha ya mbwa, ambayo inatambuliwa na mtandao kama mbwa, tunaweza kuirekebisha kidogo kwa kutumia ufanisi wa gradient descent, hadi mtandao uanze kuainisha kama paka: -![Picha ya Mbwa](../../../../../translated_images/sw/original-dog.8f68a67d2fe0911f.png) | ![Picha ya mbwa inayotambuliwa kama paka](../../../../../translated_images/sw/adversarial-dog.d9fc7773b0142b89.png) +![Picha ya Mbwa](../../../../../translated_images/sw/original-dog.8f68a67d2fe0911f.webp) | ![Picha ya mbwa inayotambuliwa kama paka](../../../../../translated_images/sw/adversarial-dog.d9fc7773b0142b89.webp) -----|----- *Picha ya asili ya mbwa* | *Picha ya mbwa inayotambuliwa kama paka* diff --git a/translations/sw/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/sw/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index 3048e674..37d19f3e 100644 --- a/translations/sw/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/sw/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "Kwa kuwa tunafundisha autoencoder ili kunasa taarifa nyingi iwezekanavyo kutoka kwenye picha ya awali kwa ajili ya ujenzi sahihi, mtandao unajaribu kupata **embedding** bora ya picha za pembejeo ili kunasa maana yake.\n", "\n", - "![Mchoro wa AutoEncoder](../../../../../translated_images/sw/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![Mchoro wa AutoEncoder](../../../../../translated_images/sw/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "> Picha kutoka [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", diff --git a/translations/sw/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/sw/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index 40c1d14c..043e1f1d 100644 --- a/translations/sw/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/sw/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "Kwa kuwa tunafundisha autoencoder ili kunasa taarifa nyingi iwezekanavyo kutoka kwenye picha ya asili kwa ajili ya ujenzi sahihi, mtandao unajaribu kupata **embedding** bora ya picha za pembejeo ili kunasa maana.\n", "\n", - "![Mchoro wa AutoEncoder](../../../../../translated_images/sw/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![Mchoro wa AutoEncoder](../../../../../translated_images/sw/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "*Picha kutoka [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", diff --git a/translations/sw/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/sw/lessons/4-ComputerVision/09-Autoencoders/README.md index bcda26f5..39c63075 100644 --- a/translations/sw/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/sw/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ Hata hivyo, tunaweza kutaka kutumia data ghafi (isiyo na lebo) kwa kufundisha CN Kwa kuwa tunafundisha autoencoder ili kunasa taarifa nyingi kutoka kwenye picha ya asili iwezekanavyo kwa ajili ya ujenzi sahihi, mtandao unajaribu kupata **embedding** bora ya picha za pembejeo ili kunasa maana yake. -![AutoEncoder Diagram](../../../../../translated_images/sw/autoencoder_schema.5e6fc9ad98a5eb61.jpg) +![AutoEncoder Diagram](../../../../../translated_images/sw/autoencoder_schema.5e6fc9ad98a5eb61.webp) > Picha kutoka [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/sw/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/sw/lessons/4-ComputerVision/11-ObjectDetection/README.md index 5768246e..eed4d7e8 100644 --- a/translations/sw/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/sw/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ Mifano ya uainishaji wa picha tuliyojifunza hadi sasa ilichukua picha na kutoa m ## [Jaribio la awali la somo](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Utambuzi wa Vitu](../../../../../translated_images/sw/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) +![Utambuzi wa Vitu](../../../../../translated_images/sw/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp) > Picha kutoka [tovuti ya YOLO v2](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ Tukidhani tunataka kutambua paka kwenye picha, njia rahisi ya utambuzi wa vitu i 2. Fanya uainishaji wa picha kwenye kila kigae. 3. Vigae vile vinavyotoa matokeo ya juu vya kutosha vinaweza kuchukuliwa kuwa na kitu kinachotafutwa. -![Utambuzi Rahisi wa Vitu](../../../../../translated_images/sw/naive-detection.e7f1ba220ccd08c6.png) +![Utambuzi Rahisi wa Vitu](../../../../../translated_images/sw/naive-detection.e7f1ba220ccd08c6.webp) > *Picha kutoka [Daftari la Mazoezi](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ Unaweza kukutana na seti zifuatazo za data kwa kazi hii: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - madarasa 20 * [COCO](http://cocodataset.org/#home) - Vitu vya Kawaida katika Muktadha. Madarasa 80, maboksi ya mipaka na maski za kugawanya -![COCO](../../../../../translated_images/sw/coco-examples.71bc60380fa6cceb.jpg) +![COCO](../../../../../translated_images/sw/coco-examples.71bc60380fa6cceb.webp) ## Vipimo vya Utambuzi wa Vitu @@ -50,7 +50,7 @@ Unaweza kukutana na seti zifuatazo za data kwa kazi hii: Wakati kwa uainishaji wa picha ni rahisi kupima jinsi algorithimu inavyofanya kazi, kwa utambuzi wa vitu tunahitaji kupima usahihi wa darasa, pamoja na usahihi wa eneo la boksi lililotabiriwa. Kwa hili la mwisho, tunatumia kipimo kinachoitwa **Muingiliano juu ya Muungano** (IoU), ambacho hupima jinsi maboksi mawili (au maeneo mawili yoyote) yanavyofanana. -![IoU](../../../../../translated_images/sw/iou_equation.9a4751d40fff4e11.png) +![IoU](../../../../../translated_images/sw/iou_equation.9a4751d40fff4e11.webp) > *Mchoro wa 2 kutoka [blogu hii bora kuhusu IoU](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ Kuna makundi mawili makuu ya algorithimu za utambuzi wa vitu: [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) hutumia [Utafutaji wa Kuchagua](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) kuzalisha muundo wa kihierarkia wa maeneo ya ROI, ambayo kisha hupitishwa kupitia viondoa sifa vya CNN na vianuai vya SVM ili kubaini darasa la kitu, na usawazishaji wa mstari ili kubaini m coordinates ya *maboksi ya mipaka*. [Karatasi Rasmi](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/sw/rcnn1.cae407020dfb1d1f.png) +![RCNN](../../../../../translated_images/sw/rcnn1.cae407020dfb1d1f.webp) > *Picha kutoka van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/sw/rcnn2.2d9530bb83516484.png) +![RCNN-1](../../../../../translated_images/sw/rcnn2.2d9530bb83516484.webp) > *Picha kutoka [blogu hii](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e) @@ -110,7 +110,7 @@ Kuna makundi mawili makuu ya algorithimu za utambuzi wa vitu: Mbinu hii ni sawa na R-CNN, lakini maeneo hufafanuliwa baada ya tabaka za convolution kutumika. -![FRCNN](../../../../../translated_images/sw/f-rcnn.3cda6d9bb4188875.png) +![FRCNN](../../../../../translated_images/sw/f-rcnn.3cda6d9bb4188875.webp) > Picha kutoka [Karatasi Rasmi](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ Mbinu hii ni sawa na R-CNN, lakini maeneo hufafanuliwa baada ya tabaka za convol Wazo kuu la mbinu hii ni kutumia mtandao wa neva kutabiri ROI - kinachoitwa *Mtandao wa Mapendekezo ya Maeneo*. [Karatasi](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/sw/faster-rcnn.8d46c099b87ef30a.png) +![FasterRCNN](../../../../../translated_images/sw/faster-rcnn.8d46c099b87ef30a.webp) > Picha kutoka [karatasi rasmi](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ Algorithimu hii ni ya haraka zaidi kuliko Faster R-CNN. Wazo kuu ni kama ifuatav 2. Sifa zinashughulikiwa na **Ramani ya Alama Inayozingatia Nafasi**. Kila kitu kutoka $C$ madarasa hugawanywa kwa maeneo $k\times k$, na tunafundisha kutabiri sehemu za vitu. 3. Kwa kila sehemu kutoka maeneo $k\times k$ mitandao yote hupiga kura kwa madarasa ya vitu, na darasa la kitu lenye kura nyingi zaidi huchaguliwa. -![r-fcn image](../../../../../translated_images/sw/r-fcn.13eb88158b99a3da.png) +![r-fcn image](../../../../../translated_images/sw/r-fcn.13eb88158b99a3da.webp) > Picha kutoka [karatasi rasmi](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO ni algorithimu ya wakati halisi ya kupita mara moja. Wazo kuu ni kama ifuat * Picha inagawanywa katika maeneo $S\times S$. * Kwa kila eneo, **CNN** inatabiri vitu $n$ vinavyowezekana, m coordinates ya *maboksi ya mipaka* na *uhakika*=*uwezekano* * IoU. - ![YOLO](../../../../../translated_images/sw/yolo.a2648ec82ee8bb4e.png) + ![YOLO](../../../../../translated_images/sw/yolo.a2648ec82ee8bb4e.webp) > Picha kutoka [karatasi rasmi](https://arxiv.org/abs/1506.02640) diff --git a/translations/sw/lessons/4-ComputerVision/README.md b/translations/sw/lessons/4-ComputerVision/README.md index 3fe1f7ac..1bf68b23 100644 --- a/translations/sw/lessons/4-ComputerVision/README.md +++ b/translations/sw/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Maono ya Kompyuta -![Muhtasari wa maudhui ya Maono ya Kompyuta katika mchoro](../../../../translated_images/sw/ai-computervision.6506ebebac3fbf76.png) +![Muhtasari wa maudhui ya Maono ya Kompyuta katika mchoro](../../../../translated_images/sw/ai-computervision.6506ebebac3fbf76.webp) Katika sehemu hii tutajifunza kuhusu: diff --git a/translations/sw/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/sw/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index 4fe7a4e1..ad30f25b 100644 --- a/translations/sw/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/sw/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**Bag of Words** (BoW) ni uwakilishi wa vector unaotumika sana katika mbinu za jadi. Kila neno linaunganishwa na faharasa ya vector, na kipengele cha vector kinaonyesha idadi ya mara neno fulani linavyotokea katika hati fulani.\n", "\n", - "![Picha inayoonyesha jinsi uwakilishi wa vector wa Bag of Words unavyohifadhiwa kwenye kumbukumbu.](../../../../../translated_images/sw/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![Picha inayoonyesha jinsi uwakilishi wa vector wa Bag of Words unavyohifadhiwa kwenye kumbukumbu.](../../../../../translated_images/sw/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Note**: Unaweza pia kufikiria BoW kama jumla ya vectors zote zilizowakilishwa kwa njia ya one-hot-encoding kwa maneno ya kibinafsi katika maandishi.\n", "\n", diff --git a/translations/sw/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/sw/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index cc7deace..724a7d3e 100644 --- a/translations/sw/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/sw/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**Bag-of-words** (BoW) ni uwakilishi wa vekta wa jadi ambao ni rahisi zaidi kueleweka. Kila neno linaunganishwa na faharasa ya vekta, na kipengele cha vekta kinaonyesha idadi ya mara neno fulani linavyotokea katika hati fulani.\n", "\n", - "![Picha inayoonyesha jinsi uwakilishi wa vekta wa bag-of-words unavyohifadhiwa kwenye kumbukumbu.](../../../../../translated_images/sw/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![Picha inayoonyesha jinsi uwakilishi wa vekta wa bag-of-words unavyohifadhiwa kwenye kumbukumbu.](../../../../../translated_images/sw/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Note**: Unaweza pia kufikiria BoW kama jumla ya vekta zote za one-hot-encoded kwa maneno binafsi katika maandishi.\n", "\n", diff --git a/translations/sw/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/sw/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 2ffcae34..2ddb2fe4 100644 --- a/translations/sw/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/sw/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "Kwa kutumia safu ya embedding kama safu ya kwanza katika mtandao wetu, tunaweza kubadilisha kutoka mfuko wa maneno (bag-of-words) kwenda kwenye mfano wa **embedding bag**, ambapo tunabadilisha kila neno katika maandishi yetu kuwa embedding inayolingana, kisha tunahesabu kazi fulani ya jumla juu ya embeddings zote hizo, kama vile `sum`, `average` au `max`.\n", "\n", - "![Picha inayoonyesha classifier ya embedding kwa maneno matano ya mfululizo.](../../../../../translated_images/sw/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![Picha inayoonyesha classifier ya embedding kwa maneno matano ya mfululizo.](../../../../../translated_images/sw/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "Mtandao wetu wa neva wa kuainisha utaanza na safu ya embedding, kisha safu ya jumlisho, na classifier ya linear juu yake:\n" ] @@ -176,7 +176,7 @@ "\n", "Katika usanifu wa awali, tulihitaji kuongeza urefu wa mfuatano wote ili kufanana na urefu mmoja kwa ajili ya kuingiza kwenye kundi dogo (minibatch). Hii si njia bora zaidi ya kuwakilisha mfuatano wa urefu tofauti - njia nyingine inaweza kuwa kutumia **offset** vector, ambayo itahifadhi nafasi za mfuatano wote uliowekwa kwenye vector moja kubwa.\n", "\n", - "![Picha inayoonyesha uwakilishi wa mfuatano wa offset](../../../../../translated_images/sw/offset-sequence-representation.eb73fcefb29b46ee.png)\n", + "![Picha inayoonyesha uwakilishi wa mfuatano wa offset](../../../../../translated_images/sw/offset-sequence-representation.eb73fcefb29b46ee.webp)\n", "\n", "> **Note**: Katika picha hapo juu, tunaonyesha mfuatano wa herufi, lakini katika mfano wetu tunafanya kazi na mfuatano wa maneno. Hata hivyo, kanuni ya jumla ya kuwakilisha mfuatano kwa kutumia offset vector inabaki ile ile.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW ni ya haraka, wakati skip-gram ni ya polepole, lakini inafanya kazi bora ya kuwakilisha maneno yasiyo ya kawaida.\n", "\n", - "![Picha inayoonyesha algorithimu za CBoW na Skip-Gram za kubadilisha maneno kuwa vekta.](../../../../../translated_images/sw/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![Picha inayoonyesha algorithimu za CBoW na Skip-Gram za kubadilisha maneno kuwa vekta.](../../../../../translated_images/sw/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Ili kujaribu kuingiza kwa Word2Vec iliyofundishwa awali kwenye seti ya data ya Google News, tunaweza kutumia maktaba ya **gensim**. Hapa chini tunapata maneno yanayofanana zaidi na 'neural'\n", "\n", diff --git a/translations/sw/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/sw/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index ea34fdc1..5967d534 100644 --- a/translations/sw/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/sw/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "Kwa kutumia safu ya embedding kama safu ya kwanza katika mtandao wetu, tunaweza kubadilisha kutoka bag-of-words kwenda kwenye modeli ya **embedding bag**, ambapo tunabadilisha kila neno katika maandishi yetu kuwa embedding inayolingana, kisha tunahesabu kazi fulani ya jumla juu ya embeddings hizo zote, kama `sum`, `average` au `max`.\n", "\n", - "![Picha inayoonyesha classifier ya embedding kwa maneno matano ya mfululizo.](../../../../../translated_images/sw/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![Picha inayoonyesha classifier ya embedding kwa maneno matano ya mfululizo.](../../../../../translated_images/sw/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "Mtandao wetu wa neural classifier unajumuisha safu zifuatazo:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW ni ya haraka, na ingawa skip-gram ni polepole, inafanya kazi bora ya kuwakilisha maneno yasiyo ya kawaida.\n", "\n", - "![Picha inayoonyesha algoriti za CBoW na Skip-Gram za kubadilisha maneno kuwa vekta.](../../../../../translated_images/sw/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![Picha inayoonyesha algoriti za CBoW na Skip-Gram za kubadilisha maneno kuwa vekta.](../../../../../translated_images/sw/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Ili kujaribu embedding ya Word2Vec iliyofundishwa awali kwenye seti ya data ya Google News, tunaweza kutumia maktaba ya **gensim**. Hapa chini tunapata maneno yanayofanana zaidi na 'neural'.\n", "\n", diff --git a/translations/sw/lessons/5-NLP/14-Embeddings/README.md b/translations/sw/lessons/5-NLP/14-Embeddings/README.md index 0c77f9d8..fb4be33f 100644 --- a/translations/sw/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/sw/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ Kwa hivyo, safu ya embedding itachukua neno kama ingizo, na kutoa vector ya mato Kwa kutumia safu ya embedding kama safu ya kwanza katika mtandao wetu wa classifier, tunaweza kubadilisha kutoka bag-of-words kwenda kwenye **embedding bag** model, ambapo tunabadilisha kila neno katika maandishi yetu kuwa embedding inayolingana, kisha tunahesabu kazi ya jumla juu ya embeddings hizo zote, kama vile `sum`, `average` au `max`. -![Picha inayoonyesha classifier ya embedding kwa maneno matano ya mfululizo.](../../../../../translated_images/sw/embedding-classifier-example.b77f021a7ee67eee.png) +![Picha inayoonyesha classifier ya embedding kwa maneno matano ya mfululizo.](../../../../../translated_images/sw/embedding-classifier-example.b77f021a7ee67eee.webp) > Picha na mwandishi @@ -40,7 +40,7 @@ Ili kufanya hivyo, tunahitaji kufundisha awali (pre-train) mfano wetu wa embeddi CBoW ni ya haraka, wakati skip-gram ni ya polepole, lakini inafanya kazi bora ya kuwakilisha maneno yasiyo ya kawaida. -![Picha inayoonyesha CBoW na Skip-Gram algorithms za kubadilisha maneno kuwa vectors.](../../../../../translated_images/sw/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![Picha inayoonyesha CBoW na Skip-Gram algorithms za kubadilisha maneno kuwa vectors.](../../../../../translated_images/sw/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Picha kutoka [karatasi hii](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/sw/lessons/5-NLP/15-LanguageModeling/README.md b/translations/sw/lessons/5-NLP/15-LanguageModeling/README.md index 5e1492d3..46360ec0 100644 --- a/translations/sw/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/sw/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ Katika mifano yetu ya awali, tulitumia uwakilishi wa semantiki uliokwisha fundis * **Mfuko Endelevu wa Maneno** (CBoW), ambapo tunatabiri tokeni ya katikati $W_0$ katika mlolongo wa tokeni $W_{-N}$, ..., $W_N$. * **Skip-gram**, ambapo tunatabiri seti ya tokeni za jirani {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} kutoka tokeni ya katikati $W_0$. -![picha kutoka karatasi kuhusu kubadilisha maneno kuwa vekta](../../../../../translated_images/sw/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![picha kutoka karatasi kuhusu kubadilisha maneno kuwa vekta](../../../../../translated_images/sw/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Picha kutoka [karatasi hii](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/sw/lessons/5-NLP/16-RNN/README.md b/translations/sw/lessons/5-NLP/16-RNN/README.md index df42ad78..f9aa6ad7 100644 --- a/translations/sw/lessons/5-NLP/16-RNN/README.md +++ b/translations/sw/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ Katika sehemu zilizopita, tumekuwa tukitumia uwakilishi wa kisemantiki wa maandi Ili kunasa maana ya mlolongo wa maandishi, tunahitaji kutumia usanifu mwingine wa mtandao wa neural, unaoitwa **mtandao wa neural unaojirudia**, au RNN. Katika RNN, tunapitisha sentensi yetu kupitia mtandao neno moja kwa wakati, na mtandao huzalisha hali fulani (**state**), ambayo tunapitisha tena kwenye mtandao pamoja na neno linalofuata. -![RNN](../../../../../translated_images/sw/rnn.27f5c29c53d727b5.png) +![RNN](../../../../../translated_images/sw/rnn.27f5c29c53d727b5.webp) > Picha na mwandishi @@ -61,7 +61,7 @@ Tumeelezea mitandao ya kujirudia inayofanya kazi kwa mwelekeo mmoja, kutoka mwan Mtandao wa kujirudia, iwe wa mwelekeo mmoja au wa mwelekeo mbili, hunasa mifumo fulani ndani ya mlolongo, na inaweza kuihifadhi kwenye vekta ya hali au kuipitisha kwenye matokeo. Kama ilivyo kwa mitandao ya convolutional, tunaweza kujenga safu nyingine ya kujirudia juu ya ile ya kwanza ili kunasa mifumo ya kiwango cha juu na kujenga kutoka kwa mifumo ya kiwango cha chini iliyotolewa na safu ya kwanza. Hii inatupeleka kwenye dhana ya **RNN ya tabaka nyingi** ambayo inajumuisha mitandao miwili au zaidi ya kujirudia, ambapo matokeo ya safu ya awali hupitishwa kwa safu inayofuata kama pembejeo. -![Picha inayoonyesha RNN ya tabaka nyingi ya kumbukumbu ya muda mrefu na mfupi](../../../../../translated_images/sw/multi-layer-lstm.dd975e29bb2a59fe.jpg) +![Picha inayoonyesha RNN ya tabaka nyingi ya kumbukumbu ya muda mrefu na mfupi](../../../../../translated_images/sw/multi-layer-lstm.dd975e29bb2a59fe.webp) *Picha kutoka [makala hii nzuri](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) ya Fernando López* diff --git a/translations/sw/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/sw/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index af085ef7..99bde02d 100644 --- a/translations/sw/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/sw/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "Mtandao wa kurudiarudia, wa mwelekeo mmoja au wa mwelekeo mbili, unakamata mifumo fulani ndani ya mlolongo, na inaweza kuihifadhi kwenye vekta ya hali au kuipitisha kwenye pato. Kama ilivyo kwa mitandao ya convolutional, tunaweza kujenga safu nyingine ya kurudiarudia juu ya safu ya kwanza ili kukamata mifumo ya kiwango cha juu, iliyojengwa kutoka kwa mifumo ya kiwango cha chini iliyotolewa na safu ya kwanza. Hii inatupeleka kwenye dhana ya **RNN ya tabaka nyingi**, ambayo inajumuisha mitandao miwili au zaidi ya kurudiarudia, ambapo pato la safu ya awali linapitishwa kwa safu inayofuata kama pembejeo.\n", "\n", - "![Picha inayoonyesha RNN ya tabaka nyingi ya kumbukumbu ya muda mrefu na mfupi](../../../../../translated_images/sw/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![Picha inayoonyesha RNN ya tabaka nyingi ya kumbukumbu ya muda mrefu na mfupi](../../../../../translated_images/sw/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*Picha kutoka [makala hii nzuri](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) ya Fernando López*\n", "\n", diff --git a/translations/sw/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/sw/lessons/5-NLP/16-RNN/RNNTF.ipynb index 5c0c5a8c..44b59310 100644 --- a/translations/sw/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/sw/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "Ili kukamata maana ya mlolongo wa maandishi, tutatumia usanifu wa mtandao wa neva unaoitwa **mtandao wa neva wa kurudia**, au RNN. Tunapotumia RNN, tunapitisha sentensi yetu kupitia mtandao tokeni moja kwa wakati, na mtandao huzalisha **hali fulani**, ambayo tunapitisha tena kwa mtandao pamoja na tokeni inayofuata.\n", "\n", - "![Picha inayoonyesha mfano wa uzalishaji wa mtandao wa neva wa kurudia.](../../../../../translated_images/sw/rnn.27f5c29c53d727b5.png)\n", + "![Picha inayoonyesha mfano wa uzalishaji wa mtandao wa neva wa kurudia.](../../../../../translated_images/sw/rnn.27f5c29c53d727b5.webp)\n", "\n", "Kwa kuzingatia mlolongo wa tokeni za ingizo $X_0,\\dots,X_n$, RNN huunda mlolongo wa vizuizi vya mtandao wa neva, na hufundisha mlolongo huu kutoka mwanzo hadi mwisho kwa kutumia backpropagation. Kila kizuizi cha mtandao huchukua jozi $(X_i,S_i)$ kama ingizo, na huzalisha $S_{i+1}$ kama matokeo. Hali ya mwisho $S_n$ au matokeo $Y_n$ huingia kwenye classifier ya mstari ili kutoa matokeo. Vizuizi vyote vya mtandao vinashiriki uzito sawa, na hufundishwa kutoka mwanzo hadi mwisho kwa kutumia mchakato mmoja wa backpropagation.\n", "\n", @@ -369,7 +369,7 @@ "\n", "Mitandao ya kurudia, iwe ya mwelekeo mmoja au mwelekeo mbili, huchukua mifumo ndani ya mlolongo, na kuihifadhi katika vekta za hali au kuzirudisha kama matokeo. Kama ilivyo kwa mitandao ya convolutional, tunaweza kujenga tabaka nyingine ya kurudia kufuatia ya kwanza ili kuchukua mifumo ya kiwango cha juu, iliyojengwa kutoka mifumo ya kiwango cha chini iliyotolewa na tabaka ya kwanza. Hii inatupeleka kwenye dhana ya **RNN ya tabaka nyingi**, ambayo inajumuisha mitandao miwili au zaidi ya kurudia, ambapo matokeo ya tabaka ya awali hupitishwa kwa tabaka inayofuata kama pembejeo.\n", "\n", - "![Picha inayoonyesha RNN ya Tabaka Nyingi ya kumbukumbu ya muda mrefu na mfupi](../../../../../translated_images/sw/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![Picha inayoonyesha RNN ya Tabaka Nyingi ya kumbukumbu ya muda mrefu na mfupi](../../../../../translated_images/sw/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*Picha kutoka [chapisho hili zuri](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) na Fernando López.*\n", "\n", diff --git a/translations/sw/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/sw/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index ffb11220..aedb6ddc 100644 --- a/translations/sw/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/sw/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "Njia tutakayotumia kufundisha RNN ili kuzalisha maandishi ni kama ifuatavyo. Kwenye kila hatua, tutachukua mlolongo wa herufi zenye urefu wa `nchars`, na kuiomba mtandao uzalishe herufi inayofuata kwa kila herufi ya ingizo:\n", "\n", - "![Picha inayoonyesha mfano wa RNN ikizalisha neno 'HELLO'.](../../../../../translated_images/sw/rnn-generate.56c54afb52f9781d.png)\n", + "![Picha inayoonyesha mfano wa RNN ikizalisha neno 'HELLO'.](../../../../../translated_images/sw/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "Kulingana na hali halisi, tunaweza pia kutaka kujumuisha baadhi ya herufi maalum, kama vile *mwisho-wa-mlolongo* ``. Katika hali yetu, tunataka tu kufundisha mtandao kwa ajili ya uzalishaji wa maandishi usio na mwisho, kwa hivyo tutarekebisha ukubwa wa kila mlolongo kuwa sawa na tokeni `nchars`. Kwa hivyo, kila mfano wa mafunzo utajumuisha viingizo `nchars` na matokeo `nchars` (ambayo ni mlolongo wa ingizo uliosogezwa herufi moja kushoto). Minibatch itajumuisha mifululizo kadhaa kama hiyo.\n", "\n", diff --git a/translations/sw/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/sw/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index abad4b87..b2da9ac9 100644 --- a/translations/sw/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/sw/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -111,7 +111,7 @@ "\n", "Njia tutakayotumia kufundisha RNN ili kutengeneza vichwa vya habari ni kama ifuatavyo. Kwenye kila hatua, tutachukua kichwa kimoja, ambacho kitaingizwa kwenye RNN, na kwa kila herufi ya ingizo tutaiomba mtandao kutengeneza herufi inayofuata ya matokeo:\n", "\n", - "![Picha inayoonyesha mfano wa kizazi cha RNN cha neno 'HELLO'.](../../../../../translated_images/sw/rnn-generate.56c54afb52f9781d.png)\n", + "![Picha inayoonyesha mfano wa kizazi cha RNN cha neno 'HELLO'.](../../../../../translated_images/sw/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "Kwa herufi ya mwisho ya mlolongo wetu, tutaiomba mtandao kutengeneza tokeni ``.\n", "\n", diff --git a/translations/sw/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/sw/lessons/5-NLP/17-GenerativeNetworks/README.md index 52100fa6..ab01f0c8 100644 --- a/translations/sw/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/sw/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ Katika usanifu wa RNN tuliojadili katika kitengo kilichopita, kila kitengo cha R Hii inaruhusu usanifu tofauti wa neural unaoonyeshwa kwenye picha hapa chini: -![Picha inayoonyesha mifumo ya kawaida ya mitandao ya neural ya kurudia.](../../../../../translated_images/sw/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Picha inayoonyesha mifumo ya kawaida ya mitandao ya neural ya kurudia.](../../../../../translated_images/sw/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > Picha kutoka kwa chapisho la blogu [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) na [Andrej Karpaty](http://karpathy.github.io/) @@ -32,7 +32,7 @@ Katika kitengo hiki, tutazingatia mifano rahisi ya kizazi inayotusaidia kuzalish Tutafundisha RNN hii kuzalisha maandishi hatua kwa hatua. Katika kila hatua, tutachukua mfululizo wa herufi za urefu `nchars`, na kuomba mtandao kuzalisha herufi inayofuata kwa kila herufi ya pembejeo: -![Picha inayoonyesha mfano wa kizazi cha RNN wa neno 'HELLO'.](../../../../../translated_images/sw/rnn-generate.56c54afb52f9781d.png) +![Picha inayoonyesha mfano wa kizazi cha RNN wa neno 'HELLO'.](../../../../../translated_images/sw/rnn-generate.56c54afb52f9781d.webp) Wakati wa kuzalisha maandishi (wakati wa utabiri), tunaanza na **msukumo fulani**, ambao unapitia seli za RNN ili kuzalisha hali yake ya kati, na kisha kutoka hali hii kizazi kinaanza. Tunazalisha herufi moja kwa wakati, na kupitisha hali na herufi iliyozalishwa kwa seli nyingine ya RNN ili kuzalisha inayofuata, hadi tutakapozalisha herufi za kutosha. diff --git a/translations/sw/lessons/5-NLP/18-Transformers/README.md b/translations/sw/lessons/5-NLP/18-Transformers/README.md index f5b2817e..f3cfe922 100644 --- a/translations/sw/lessons/5-NLP/18-Transformers/README.md +++ b/translations/sw/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ Kwa kutumia RNNs, mlolongo-kwa-mlolongo unatekelezwa na mitandao miwili ya kurud **Mbinu za Uangalizi** hutoa njia ya kupima athari ya muktadha wa kila vector ya pembejeo kwenye kila utabiri wa matokeo wa RNN. Njia ya kutekeleza hii ni kwa kuunda njia za mkato kati ya hali za kati za RNN ya pembejeo na RNN ya matokeo. Kwa njia hii, tunapozalisha alama ya matokeo yt, tutazingatia hali zote za siri za pembejeo hi, kwa kutumia viwango tofauti vya uzito αt,i. -![Picha inayoonyesha modeli ya encoder/decoder na safu ya uangalizi wa kuongeza](../../../../../translated_images/sw/encoder-decoder-attention.7a726296894fb567.png) +![Picha inayoonyesha modeli ya encoder/decoder na safu ya uangalizi wa kuongeza](../../../../../translated_images/sw/encoder-decoder-attention.7a726296894fb567.webp) > Modeli ya encoder-decoder na mbinu ya uangalizi wa kuongeza katika [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), iliyotajwa kutoka [blogu hii](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) Matriki ya uangalizi {αi,j} ingeonyesha kiwango ambacho maneno fulani ya pembejeo yanachangia katika uzalishaji wa neno fulani katika mlolongo wa matokeo. Hapa chini kuna mfano wa matriki kama hiyo: -![Picha inayoonyesha mpangilio wa mfano uliopatikana na RNNsearch-50, iliyochukuliwa kutoka Bahdanau - arviz.org](../../../../../translated_images/sw/bahdanau-fig3.09ba2d37f202a6af.png) +![Picha inayoonyesha mpangilio wa mfano uliopatikana na RNNsearch-50, iliyochukuliwa kutoka Bahdanau - arviz.org](../../../../../translated_images/sw/bahdanau-fig3.09ba2d37f202a6af.webp) > Mchoro kutoka [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) @@ -66,7 +66,7 @@ Matokeo tunayopata na embedding ya nafasi yanajumuisha tokeni asilia na nafasi y Kisha, tunahitaji kunasa mifumo fulani ndani ya mlolongo wetu. Ili kufanya hivyo, transformers hutumia mbinu ya **uangalizi wa kibinafsi**, ambayo kimsingi ni uangalizi unaotumika kwa mlolongo sawa kama pembejeo na matokeo. Kutumia uangalizi wa kibinafsi kunatuwezesha kuzingatia **muktadha** ndani ya sentensi, na kuona ni maneno gani yanayohusiana. Kwa mfano, inatuwezesha kuona ni maneno gani yanayorejelewa na marejeo, kama *it*, na pia kuzingatia muktadha: -![](../../../../../translated_images/sw/CoreferenceResolution.861924d6d384a7d6.png) +![](../../../../../translated_images/sw/CoreferenceResolution.861924d6d384a7d6.webp) > Picha kutoka [Blogu ya Google](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Kwa kuwa kila nafasi ya pembejeo inalinganishwa kwa uhuru na kila nafasi ya mato **BERT** (Bidirectional Encoder Representations from Transformers) ni mtandao mkubwa sana wa transformer wenye tabaka 12 kwa *BERT-base*, na 24 kwa *BERT-large*. Modeli hii kwanza inafundishwa awali kwenye hifadhidata kubwa ya maandishi (WikiPedia + vitabu) kwa kutumia mafunzo yasiyo ya kusimamiwa (kutabiri maneno yaliyofichwa katika sentensi). Wakati wa mafunzo ya awali, modeli inachukua viwango vikubwa vya uelewa wa lugha ambavyo vinaweza kutumika na hifadhidata nyingine kwa kutumia kurekebisha. Mchakato huu unaitwa **transfer learning**. -![picha kutoka http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/sw/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) +![picha kutoka http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/sw/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > Picha [chanzo](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/sw/lessons/5-NLP/18-Transformers/READMEtransformers.md b/translations/sw/lessons/5-NLP/18-Transformers/READMEtransformers.md index 52005c44..9e1ca89d 100644 --- a/translations/sw/lessons/5-NLP/18-Transformers/READMEtransformers.md +++ b/translations/sw/lessons/5-NLP/18-Transformers/READMEtransformers.md @@ -11,13 +11,13 @@ Med RNN:er implementeras sekvens-till-sekvens av två återkommande nätverk, d **Uppmärksamhetsmekanismer** ger ett sätt att vikta den kontextuella påverkan av varje ingångsvektor på varje utdataförutsägelse av RNN. Sättet det implementeras på är genom att skapa genvägar mellan mellanliggande tillstånd av ingångs-RNN och utgångs-RNN. På detta sätt, när vi genererar utdata symbol yt, kommer vi att ta hänsyn till alla ingångs dolda tillstånd hi, med olika viktkoefficienter αt,i. -![Bild som visar en kodare/avkodare-modell med ett additivt uppmärksamhetslager](../../../../../translated_images/sw/encoder-decoder-attention.7a726296894fb567.png) +![Bild som visar en kodare/avkodare-modell med ett additivt uppmärksamhetslager](../../../../../translated_images/sw/encoder-decoder-attention.7a726296894fb567.webp) > Kodare-avkodare-modell med additiv uppmärksamhetsmekanism i [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citerad från [denna bloggpost](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) Uppmärksamhetsmatrisen {αi,j} skulle representera graden av att vissa ingångsord spelar en roll i generationen av ett givet ord i utgångssekvensen. Nedan är ett exempel på en sådan matris: -![Bild som visar en exempeljustering som hittats av RNNsearch-50, tagen från Bahdanau - arviz.org](../../../../../translated_images/sw/bahdanau-fig3.09ba2d37f202a6af.png) +![Bild som visar en exempeljustering som hittats av RNNsearch-50, tagen från Bahdanau - arviz.org](../../../../../translated_images/sw/bahdanau-fig3.09ba2d37f202a6af.webp) > Figur från [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) @@ -57,7 +57,7 @@ Resultatet vi får med positionsinbäddning inbäddas både den ursprungliga tok Nästa steg är att fånga vissa mönster inom vår sekvens. För att göra detta använder transformatorer en **självuppmärksamhets**mekanism, som i grunden är uppmärksamhet tillämpad på samma sekvens som ingång och utgång. Tillämpning av självuppmärksamhet gör att vi kan ta hänsyn till **kontext** inom meningen och se vilka ord som är relaterade. Till exempel gör det att vi kan se vilka ord som hänvisas till av referenser, såsom *det*, och även ta kontexten i beaktande: -![](../../../../../translated_images/sw/CoreferenceResolution.861924d6d384a7d6.png) +![](../../../../../translated_images/sw/CoreferenceResolution.861924d6d384a7d6.webp) > Bild från [Google Blog](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -82,7 +82,7 @@ Eftersom varje ingångsposition mappas oberoende till varje utgångsposition kan **BERT** (Bidirectional Encoder Representations from Transformers) är ett mycket stort flerlagers transformatornätverk med 12 lager för *BERT-base*, och 24 för *BERT-large*. Modellen förtränas först på en stor korpus av textdata (WikiPedia + böcker) med hjälp av osupervised träning (förutsäga maskerade ord i en mening). Under förträningen absorberar modellen betydande nivåer av språkförståelse som sedan kan utnyttjas med andra dataset genom finjustering. Denna process kallas **överföringsinlärning**. -![Bild från http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/sw/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) +![Bild från http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/sw/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > Bild [källa](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/sw/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/sw/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index 826771d5..2c8afddb 100644 --- a/translations/sw/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/sw/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**Utaratibu wa Uangalizi** hutoa njia ya kupima athari ya muktadha wa kila vector ya pembejeo kwenye kila utabiri wa RNN. Hii inatekelezwa kwa kuunda njia za mkato kati ya hali za kati za RNN ya pembejeo na RNN ya matokeo. Kwa njia hii, tunapozalisha alama ya matokeo $y_t$, tutazingatia hali zote za siri za pembejeo $h_i$, kwa kutumia viwango tofauti vya uzito $\\alpha_{t,i}$.\n", "\n", - "![Picha inayoonyesha mfano wa encoder/decoder na safu ya uangalizi wa kuongeza](../../../../../translated_images/sw/encoder-decoder-attention.7a726296894fb567.png) \n", + "![Picha inayoonyesha mfano wa encoder/decoder na safu ya uangalizi wa kuongeza](../../../../../translated_images/sw/encoder-decoder-attention.7a726296894fb567.webp) \n", "*Mfano wa encoder-decoder na utaratibu wa uangalizi wa kuongeza katika [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), iliyonukuliwa kutoka [blogu hii](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Matriki ya uangalizi $\\{\\alpha_{i,j}\\}$ inawakilisha kiwango ambacho maneno fulani ya pembejeo yanachangia katika uzalishaji wa neno fulani katika mlolongo wa matokeo. Hapo chini kuna mfano wa matriki kama hiyo:\n", "\n", - "![Picha inayoonyesha mpangilio wa mfano uliopatikana na RNNsearch-50, kutoka Bahdanau - arviz.org](../../../../../translated_images/sw/bahdanau-fig3.09ba2d37f202a6af.png) \n", + "![Picha inayoonyesha mpangilio wa mfano uliopatikana na RNNsearch-50, kutoka Bahdanau - arviz.org](../../../../../translated_images/sw/bahdanau-fig3.09ba2d37f202a6af.webp) \n", "\n", "*Picha imetolewa kutoka [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Mchoro wa 3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) ni mtandao mkubwa sana wa transformer wenye tabaka 12 kwa *BERT-base*, na 24 kwa *BERT-large*. Mfano huu kwanza hufunzwa awali kwenye hifadhidata kubwa ya maandishi (WikiPedia + vitabu) kwa kutumia mafunzo yasiyo ya kusimamiwa (kutabiri maneno yaliyofichwa katika sentensi). Wakati wa mafunzo ya awali, mfano huu hujifunza kiwango kikubwa cha uelewa wa lugha ambacho kinaweza kutumika na hifadhidata nyingine kwa kutumia marekebisho madogo. Mchakato huu unaitwa **ujifunzaji wa uhamisho**.\n", "\n", - "![Picha kutoka http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/sw/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) \n", + "![Picha kutoka http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/sw/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) \n", "\n", "Kuna tofauti nyingi za usanifu wa Transformer ikiwa ni pamoja na BERT, DistilBERT, BigBird, OpenGPT3 na nyinginezo ambazo zinaweza kufanyiwa marekebisho madogo. [Paket ya HuggingFace](https://github.com/huggingface/) inatoa hifadhidata ya mafunzo kwa usanifu mwingi wa aina hii kwa kutumia PyTorch.\n", "\n", diff --git a/translations/sw/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/sw/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index fcfe2dff..5c6b185f 100644 --- a/translations/sw/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/sw/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**Mbinu za Uangalizi** hutoa njia ya kupima athari ya muktadha wa kila vector ya pembejeo kwenye kila utabiri wa matokeo wa RNN. Njia inavyotekelezwa ni kwa kuunda njia za mkato kati ya hali za kati za RNN ya pembejeo na RNN ya matokeo. Kwa njia hii, tunapozalisha alama ya matokeo $y_t$, tutazingatia hali zote zilizofichwa za pembejeo $h_i$, kwa kutumia viwango tofauti vya uzito $\\alpha_{t,i}$.\n", "\n", - "![Picha inayoonyesha mfano wa encoder/decoder na safu ya uangalizi wa kuongeza](../../../../../translated_images/sw/encoder-decoder-attention.7a726296894fb567.png)\n", + "![Picha inayoonyesha mfano wa encoder/decoder na safu ya uangalizi wa kuongeza](../../../../../translated_images/sw/encoder-decoder-attention.7a726296894fb567.webp)\n", "*Mfano wa encoder-decoder na mbinu ya uangalizi wa kuongeza katika [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), iliyotajwa kutoka [blogu hii](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Matriki ya uangalizi $\\{\\alpha_{i,j}\\}$ itaonyesha kiwango ambacho maneno fulani ya pembejeo yanachangia katika uzalishaji wa neno fulani katika mfuatano wa matokeo. Hapo chini kuna mfano wa matriki kama hiyo:\n", "\n", - "![Picha inayoonyesha mpangilio wa mfano uliopatikana na RNNsearch-50, kutoka Bahdanau - arviz.org](../../../../../translated_images/sw/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![Picha inayoonyesha mpangilio wa mfano uliopatikana na RNNsearch-50, kutoka Bahdanau - arviz.org](../../../../../translated_images/sw/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*Picha iliyotolewa kutoka [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3)*\n", "\n", @@ -231,7 +231,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) ni mtandao mkubwa sana wa tabaka nyingi wa transformer wenye tabaka 12 kwa *BERT-base*, na 24 kwa *BERT-large*. Modeli hii huanza kwa kufundishwa awali kwenye mkusanyiko mkubwa wa data ya maandishi (WikiPedia + vitabu) kwa kutumia mafunzo yasiyo ya usimamizi (kutabiri maneno yaliyofichwa katika sentensi). Wakati wa mafunzo ya awali, modeli hujifunza kiwango kikubwa cha uelewa wa lugha ambacho kinaweza kutumika na seti nyingine za data kwa kutumia kurekebisha mafunzo. Mchakato huu unaitwa **transfer learning**.\n", "\n", - "![picha kutoka http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/sw/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![picha kutoka http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/sw/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "Kuna aina nyingi za usanifu wa Transformer ikiwa ni pamoja na BERT, DistilBERT, BigBird, OpenGPT3 na nyinginezo ambazo zinaweza kurekebishwa. \n", "\n", diff --git a/translations/sw/lessons/5-NLP/19-NER/README.md b/translations/sw/lessons/5-NLP/19-NER/README.md index eb66bb2d..00bc442c 100644 --- a/translations/sw/lessons/5-NLP/19-NER/README.md +++ b/translations/sw/lessons/5-NLP/19-NER/README.md @@ -58,7 +58,7 @@ infant | O Kwa kuwa tunahitaji kujenga uhusiano wa moja kwa moja kati ya tokeni na madarasa, tunaweza kufundisha mfano wa mtandao wa neva wa **wengi-kwa-wengi** kutoka kwenye picha hii: -![Picha inayoonyesha mifumo ya kawaida ya mitandao ya neva ya kurudia.](../../../../../translated_images/sw/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Picha inayoonyesha mifumo ya kawaida ya mitandao ya neva ya kurudia.](../../../../../translated_images/sw/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > *Picha kutoka [blogu hii](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) na [Andrej Karpathy](http://karpathy.github.io/). Mifano ya uainishaji wa tokeni ya NER inahusiana na usanifu wa mtandao wa kulia kabisa kwenye picha hii.* diff --git a/translations/sw/lessons/5-NLP/README.md b/translations/sw/lessons/5-NLP/README.md index 15627514..885d1108 100644 --- a/translations/sw/lessons/5-NLP/README.md +++ b/translations/sw/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Usindikaji wa Lugha Asilia -![Muhtasari wa kazi za NLP katika mchoro](../../../../translated_images/sw/ai-nlp.b22dcb8ca4707cea.png) +![Muhtasari wa kazi za NLP katika mchoro](../../../../translated_images/sw/ai-nlp.b22dcb8ca4707cea.webp) Katika sehemu hii, tutazingatia kutumia Mitandao ya Neural kushughulikia kazi zinazohusiana na **Usindikaji wa Lugha Asilia (NLP)**. Kuna matatizo mengi ya NLP ambayo tunataka kompyuta iweze kuyatatua: diff --git a/translations/sw/lessons/6-Other/23-MultiagentSystems/README.md b/translations/sw/lessons/6-Other/23-MultiagentSystems/README.md index 2a90c775..fd1b0141 100644 --- a/translations/sw/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/sw/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ Unaweza kufungua mojawapo ya miundo, kwa mfano **Biology → Flocking** Baada ya kufungua mfano, unapelekwa kwenye skrini kuu ya NetLogo. Hapa kuna mfano wa mfano unaoelezea idadi ya mbwa mwitu na kondoo, kwa kuzingatia rasilimali finyu (nyasi). -![NetLogo Main Screen](../../../../../translated_images/sw/NetLogo-Main.32653711ec1a01b3.png) +![NetLogo Main Screen](../../../../../translated_images/sw/NetLogo-Main.32653711ec1a01b3.webp) > Picha ya skrini na Dmitry Soshnikov diff --git a/translations/sw/lessons/README.md b/translations/sw/lessons/README.md index 80fd660d..0f6b8659 100644 --- a/translations/sw/lessons/README.md +++ b/translations/sw/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Muhtasari -![Muhtasari katika mchoro](../../../translated_images/sw/ai-overview.0857791951d19500.png) +![Muhtasari katika mchoro](../../../translated_images/sw/ai-overview.0857791951d19500.webp) > Mchoro wa maandishi na [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/sw/lessons/X-Extras/X1-MultiModal/README.md b/translations/sw/lessons/X-Extras/X1-MultiModal/README.md index 3590d053..d8644ad4 100644 --- a/translations/sw/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/sw/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ Baada ya mafanikio ya mifano ya transformer katika kutatua kazi za NLP, usanifu Wazo kuu la CLIP ni kuwa na uwezo wa kulinganisha maelezo ya maandishi na picha na kuamua jinsi picha inavyolingana na maelezo hayo. -![CLIP Architecture](../../../../../translated_images/sw/clip-arch.b3dbf20b4e8ed8be.png) +![CLIP Architecture](../../../../../translated_images/sw/clip-arch.b3dbf20b4e8ed8be.webp) > *Picha kutoka [blogu hii](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ Mara tu mfano huu unapokuwa umefunzwa, tunaweza kuupa kundi la picha na kundi la Tuseme tunahitaji kuainisha picha kati ya, kwa mfano, paka, mbwa na binadamu. Katika hali hii, tunaweza kuupa mfano picha, na mfululizo wa maelezo ya maandishi: "*picha ya paka*", "*picha ya mbwa*", "*picha ya binadamu*". Katika vekta inayotokana ya uwezekano 3 tunahitaji tu kuchagua faharasa yenye thamani ya juu zaidi. -![CLIP for Image Classification](../../../../../translated_images/sw/clip-class.3af42ef0b2b19369.png) +![CLIP for Image Classification](../../../../../translated_images/sw/clip-class.3af42ef0b2b19369.webp) > *Picha kutoka [blogu hii](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ Jifunze zaidi kuhusu VQGAN kwenye tovuti ya [Taming Transformers](https://compvi Moja ya tofauti muhimu kati ya VQGAN na GAN ya jadi ni kwamba ya mwisho inaweza kuzalisha picha nzuri kutoka kwa vekta yoyote ya pembejeo, wakati VQGAN ina uwezekano wa kuzalisha picha ambayo haitakuwa thabiti. Kwa hivyo, tunahitaji kuongoza zaidi mchakato wa uundaji wa picha, na hilo linaweza kufanywa kwa kutumia CLIP. -![VQGAN+CLIP Architecture](../../../../../translated_images/sw/vqgan.5027fe05051dfa31.png) +![VQGAN+CLIP Architecture](../../../../../translated_images/sw/vqgan.5027fe05051dfa31.webp) Ili kuzalisha picha inayolingana na maelezo ya maandishi, tunaanza na vekta ya usimbaji wa nasibu ambayo inapitia VQGAN ili kuzalisha picha. Kisha CLIP hutumika kuzalisha kazi ya hasara inayonyesha jinsi picha inavyolingana na maelezo ya maandishi. Lengo basi ni kupunguza hasara hii, kwa kutumia kurudi nyuma ili kurekebisha vigezo vya vekta ya pembejeo. Maktaba nzuri inayotekeleza VQGAN+CLIP ni [Pixray](http://github.com/pixray/pixray) -![Picture produced by Pixray](../../../../../translated_images/sw/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Picture produced by pixray](../../../../../translated_images/sw/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Picture produced by Pixray](../../../../../translated_images/sw/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) +![Picture produced by Pixray](../../../../../translated_images/sw/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp) | ![Picture produced by pixray](../../../../../translated_images/sw/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp) | ![Picture produced by Pixray](../../../../../translated_images/sw/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp) ----|----|---- Picha iliyozalishwa kutoka maelezo *a closeup watercolor portrait of young male teacher of literature with a book* | Picha iliyozalishwa kutoka maelezo *a closeup oil portrait of young female teacher of computer science with a computer* | Picha iliyozalishwa kutoka maelezo *a closeup oil portrait of old male teacher of mathematics in front of blackboard* diff --git a/translations/ta/README.md b/translations/ta/README.md index 2945a4fe..a47c435c 100644 --- a/translations/ta/README.md +++ b/translations/ta/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # கலை நுண்ணறிவு ஆரம்பக் கற்கைத் திட்டம் -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ta/ai-overview.0857791951d19500.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ta/ai-overview.0857791951d19500.webp)| |:---:| | AI For Beginners - _சித்திரக்குறிப்பு [@girlie_mac](https://twitter.com/girlie_mac) மூலம்_ | diff --git a/translations/ta/lessons/1-Intro/README.md b/translations/ta/lessons/1-Intro/README.md index 16aa1fdf..9b368244 100644 --- a/translations/ta/lessons/1-Intro/README.md +++ b/translations/ta/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # AI அறிமுகம் -![AI அறிமுகத்தின் சுருக்கம் ஒரு டூடிலில்](../../../../translated_images/ta/ai-intro.bf28d1ac4235881c.png) +![AI அறிமுகத்தின் சுருக்கம் ஒரு டூடிலில்](../../../../translated_images/ta/ai-intro.bf28d1ac4235881c.webp) > [Tomomi Imura](https://twitter.com/girlie_mac) இன் ஸ்கெட்ச் நோட் @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: முதலில், கணினிகளை [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage) எண் கணக்கீடுகளை ஒரு நன்கு வரையறுக்கப்பட்ட செயல்முறை - ஒரு ஆல்கொரிதம் - மூலம் செயல்படுத்த உருவாக்கினார். 19ஆம் நூற்றாண்டில் முன்மாதிரியாக உருவாக்கப்பட்ட கணினி மாடலின் அடிப்படையில், நவீன கணினிகள் மிகவும் மேம்பட்டவை என்றாலும், கட்டுப்படுத்தப்பட்ட கணக்கீடுகளின் அடிப்படையில் செயல்படுகின்றன. எனவே, ஒரு குறிப்பிட்ட இலக்கை அடைய தேவையான செயல்முறைகளை நன்கு அறிந்தால், கணினியை ஒரு செயல்பாட்டிற்கு நிரலிட முடியும். -![ஒரு நபரின் புகைப்படம்](../../../../translated_images/ta/dsh_age.d212a30d4e54fb5f.png) +![ஒரு நபரின் புகைப்படம்](../../../../translated_images/ta/dsh_age.d212a30d4e54fb5f.webp) > [Vickie Soshnikova](http://twitter.com/vickievalerie) இன் புகைப்படம் @@ -45,7 +45,7 @@ CO_OP_TRANSLATOR_METADATA: **[Intelligence](https://en.wikipedia.org/wiki/Intelligence)** என்ற சொல்லைச் சிக்கலாகக் கருதும்போது, இந்த சொல்லுக்கு தெளிவான வரையறை இல்லை என்பது ஒரு பிரச்சனையாகும். அறிவு **மூலதன சிந்தனை** அல்லது **சுய விழிப்புணர்வு** உடன் தொடர்புடையது என்று ஒருவர் வாதிடலாம், ஆனால் அதை சரியாக வரையறுக்க முடியாது. -![ஒரு பூனையின் புகைப்படம்](../../../../translated_images/ta/photo-cat.8c8e8fb760ffe457.jpg) +![ஒரு பூனையின் புகைப்படம்](../../../../translated_images/ta/photo-cat.8c8e8fb760ffe457.webp) > [Amber Kipp](https://unsplash.com/@sadmax) இன் Unsplash புகைப்படம் [Photo](https://unsplash.com/photos/75715CVEJhI) @@ -97,13 +97,13 @@ AGI பற்றி பேசும்போது, ​​நாம் உண் > | ML பற்றி என்ன? | | > |--------------|-----------| -> | சில data அடிப்படையில் ஒரு பிரச்சனையைத் தீர்க்க computer learning அடிப்படையில் Artificial Intelligence இன் ஒரு பகுதி **Machine Learning** என்று அழைக்கப்படுகிறது. இந்த course இல் classical machine learning ஐ நாம் பரிசீலிக்க மாட்டோம் - [Machine Learning for Beginners](http://aka.ms/ml-beginners) curriculum ஐப் பார்க்கவும். | ![ML for Beginners](../../../../translated_images/ta/ml-for-beginners.9e4fed176fd5817d.png) | +> | சில data அடிப்படையில் ஒரு பிரச்சனையைத் தீர்க்க computer learning அடிப்படையில் Artificial Intelligence இன் ஒரு பகுதி **Machine Learning** என்று அழைக்கப்படுகிறது. இந்த course இல் classical machine learning ஐ நாம் பரிசீலிக்க மாட்டோம் - [Machine Learning for Beginners](http://aka.ms/ml-beginners) curriculum ஐப் பார்க்கவும். | ![ML for Beginners](../../../../translated_images/ta/ml-for-beginners.9e4fed176fd5817d.webp) | ## AI இன் சுருக்கமான வரலாறு Artificial Intelligence 20ஆம் நூற்றாண்டின் நடுப்பகுதியில் ஒரு துறையாக தொடங்கப்பட்டது. ஆரம்பத்தில், symbolic reasoning ஒரு முக்கியமான approach ஆக இருந்தது, மேலும் இது expert systems போன்ற சில முக்கியமான வெற்றிகளை உருவாக்கியது – குறிப்பிட்ட பிரச்சனை துறைகளில் ஒரு நிபுணராக செயல்படக்கூடிய computer programs. இருப்பினும், இந்த approach நன்றாக scale ஆகாது என்பது விரைவில் தெளிவாகியது. ஒரு நிபுணரிடமிருந்து அறிவை எடுப்பது, அதை கணினியில் பிரதிநிதித்துவப்படுத்துவது மற்றும் அந்த knowledgebase ஐ துல்லியமாக வைத்திருப்பது மிகவும் சிக்கலான task ஆகும், மேலும் பல சந்தர்ப்பங்களில் நடைமுறைக்கு மிகவும் செலவாகும். இது 1970களில் [AI Winter](https://en.wikipedia.org/wiki/AI_winter) என அழைக்கப்படும் நிலைக்கு வழிவகுத்தது. -AI வரலாற்றின் சுருக்கம் +AI வரலாற்றின் சுருக்கம் > [Dmitry Soshnikov](http://soshnikov.com) இன் படம் @@ -123,7 +123,7 @@ Artificial Intelligence 20ஆம் நூற்றாண்டின் நட * Cortana, Siri அல்லது Google Assistant போன்ற modern assistants அனைத்தும் hybrid systems ஆகும், அவை speech ஐ text ஆக மாற்ற neural networks ஐ பயன்படுத்துகின்றன மற்றும் நம் intent ஐ recognize செய்கின்றன, பின்னர் தேவையான actions ஐ செய்ய reasoning அல்லது explicit algorithms ஐ employ செய்கின்றன. * எதிர்காலத்தில், dialogue ஐ தானாகவே handle செய்ய ஒரு complete neural-based model ஐ எதிர்பார்க்கலாம். சமீபத்திய GPT மற்றும் [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) neural networks குடும்பம் இதில் சிறந்த வெற்றியை காட்டுகின்றன. -Turing Test இன் evolution +Turing Test இன் evolution > படம்: Dmitry Soshnikov, [புகைப்படம்](https://unsplash.com/photos/r8LmVbUKgns): [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto), Unsplash ## சமீபத்திய AI ஆராய்ச்சி diff --git a/translations/ta/lessons/2-Symbolic/Animals.ipynb b/translations/ta/lessons/2-Symbolic/Animals.ipynb index a7138574..5241a75f 100644 --- a/translations/ta/lessons/2-Symbolic/Animals.ipynb +++ b/translations/ta/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "இந்த எடுத்துக்காட்டில், சில உடல் பண்புகளின் அடிப்படையில் விலங்குகளை தீர்மானிக்க ஒரு எளிய அறிவு அடிப்படையிலான அமைப்பை செயல்படுத்துவோம். இந்த அமைப்பு கீழே உள்ள AND-OR மரத்தால் பிரதிநிதித்துவம் செய்யப்படுகிறது (இது முழு மரத்தின் ஒரு பகுதி மட்டுமே, மேலும் சில விதிகளை எளிதாக சேர்க்கலாம்):\n", "\n", - "![](../../../../translated_images/ta/AND-OR-Tree.5592d2c70187f283.png)\n" + "![](../../../../translated_images/ta/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { diff --git a/translations/ta/lessons/2-Symbolic/README.md b/translations/ta/lessons/2-Symbolic/README.md index e9f1d110..c5b0dc63 100644 --- a/translations/ta/lessons/2-Symbolic/README.md +++ b/translations/ta/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # அறிவு பிரதிநிதித்துவம் மற்றும் நிபுணர் அமைப்புகள் -![சின்னவியல் AI உள்ளடக்கத்தின் சுருக்கம்](../../../../translated_images/ta/ai-symbolic.715a30cb610411a6.png) +![சின்னவியல் AI உள்ளடக்கத்தின் சுருக்கம்](../../../../translated_images/ta/ai-symbolic.715a30cb610411a6.webp) > [Tomomi Imura](https://twitter.com/girlie_mac) அவர்களின் சின்னவியல் குறிப்பு @@ -35,13 +35,13 @@ AI-யின் ஆரம்ப காலங்களில், புத்த * **அறிவு** என்பது தகவலை நமது உலக மாதிரியில் ஒருங்கிணைப்பதாகும். உதாரணமாக, ஒரு கணினி என்ன என்பதை நாங்கள் கற்றுக்கொண்ட பிறகு, அது எப்படி செயல்படுகிறது, அதன் விலை எவ்வளவு, மற்றும் அது எதற்காக பயன்படுத்தப்படலாம் என்பதற்கான சில கருத்துக்கள் நமக்கு உருவாகின்றன. இந்த தொடர்புடைய கருத்துக்களின் வலை நமது அறிவை உருவாக்குகிறது. * **ஞானம்** என்பது உலகத்தைப் பற்றிய நமது புரிதலின் மேலும் ஒரு நிலையாகும், மேலும் இது *மெட்டா-அறிவை* பிரதிநிதித்துவப்படுத்துகிறது, உதாரணமாக, அறிவு எப்போது மற்றும் எப்படி பயன்படுத்தப்பட வேண்டும் என்பதற்கான கருத்து. - + *படம் [விக்கிப்பீடியாவில் இருந்து](https://commons.wikimedia.org/w/index.php?curid=37705247), Longlivetheux - சொந்த வேலை, CC BY-SA 4.0* அதனால், **அறிவு பிரதிநிதித்துவம்** என்ற பிரச்சினை என்பது கணினியில் தரவின் வடிவத்தில் அறிவை பிரதிநிதித்துவப்படுத்துவதற்கான சில பயனுள்ள வழிகளை கண்டுபிடிப்பதாகும், இதை தானாகவே பயன்படுத்த முடியும். இது ஒரு வரம்பாகக் காணப்படுகிறது: -![அறிவு பிரதிநிதித்துவ வரம்பு](../../../../translated_images/ta/knowledge-spectrum.b60df631852c0217.png) +![அறிவு பிரதிநிதித்துவ வரம்பு](../../../../translated_images/ta/knowledge-spectrum.b60df631852c0217.webp) > [Dmitry Soshnikov](http://soshnikov.com) அவர்களின் படங்கள் @@ -94,7 +94,7 @@ Block Syntax | Indent | | | சின்னவியல் AI-யின் ஆரம்ப வெற்றிகளில் ஒன்று **நிபுணர் அமைப்புகள்** - குறிப்பிட்ட பிரச்சினை துறையில் நிபுணராக செயல்பட வடிவமைக்கப்பட்ட கணினி அமைப்புகள். அவை **knowledge base** மற்றும் **inference engine** ஆகியவற்றின் அடிப்படையில் உருவாக்கப்பட்டன. -![மனித அமைப்பு](../../../../translated_images/ta/arch-human.5d4d35f1bba3ab1c.png) | ![அறிவு அடிப்படையிலான அமைப்பு](../../../../translated_images/ta/arch-kbs.3ec5c150b09fa8da.png) +![மனித அமைப்பு](../../../../translated_images/ta/arch-human.5d4d35f1bba3ab1c.webp) | ![அறிவு அடிப்படையிலான அமைப்பு](../../../../translated_images/ta/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ மனித நரம்பு அமைப்பின் எளிமையான அமைப்பு | அறிவு அடிப்படையிலான அமைப்பின் கட்டமைப்பு @@ -106,7 +106,7 @@ Block Syntax | Indent | | | உதாரணமாக, ஒரு விலங்கின் உடல் பண்புகளை அடிப்படையாகக் கொண்டு அதைத் தீர்மானிக்கும் பின்வரும் நிபுணர் அமைப்பைப் பார்ப்போம்: -![AND-OR Tree](../../../../translated_images/ta/AND-OR-Tree.5592d2c70187f283.png) +![AND-OR Tree](../../../../translated_images/ta/AND-OR-Tree.5592d2c70187f283.webp) > [Dmitry Soshnikov](http://soshnikov.com) அவர்களின் படங்கள் @@ -170,7 +170,7 @@ Semantic Web இல் முக்கியமான கருத்து **On Semantic Web இல், அனைத்து பிரதிநிதித்துவங்களும் triplets அடிப்படையில் அமைக்கப்பட்டுள்ளன. ஒவ்வொரு பொருளும் மற்றும் ஒவ்வொரு தொடர்பும் URI மூலம் தனித்துவமாக அடையாளம் காணப்படுகிறது. உதாரணமாக, இந்த AI Curriculum-ஐ Dmitry Soshnikov ஜனவரி 1, 2022 அன்று உருவாக்கியதாகக் கூற விரும்பினால், நாம் பயன்படுத்தக்கூடிய triplets இவை: - + ``` http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 13, 2007” @@ -181,7 +181,7 @@ http://github.com/microsoft/ai-for-beginners http://purl.org/dc/elements/1.1/cre சிக்கலான சூழலில், உருவாக்குநர்களின் பட்டியலை வரையறுக்க விரும்பினால், RDF-ல் வரையறுக்கப்பட்ட சில தரவமைப்புகளை பயன்படுத்தலாம். - + > மேலே உள்ள வரைபடங்கள் [Dmitry Soshnikov](http://soshnikov.com) மூலம். @@ -205,7 +205,7 @@ GROUP BY ?eyeColorLabel > ✅ உங்கள் சொந்த Ontology-களை உருவாக்க அல்லது ஏற்கனவே உள்ளவற்றைத் திறக்க முயற்சிக்க விரும்பினால், [Protégé](https://protege.stanford.edu/) என்ற ஒரு சிறந்த காட்சி Ontology தொகுப்பியைப் பயன்படுத்தலாம். இதைப் பதிவிறக்கவும் அல்லது ஆன்லைனில் பயன்படுத்தவும். - + *Romanov குடும்ப Ontology-யுடன் திறந்த Web Protégé தொகுப்பி. Dmitry Soshnikov மூலம் எடுத்த படக்காட்சி* diff --git a/translations/ta/lessons/3-NeuralNetworks/03-Perceptron/README.md b/translations/ta/lessons/3-NeuralNetworks/03-Perceptron/README.md index b89f95fe..7f0ab293 100644 --- a/translations/ta/lessons/3-NeuralNetworks/03-Perceptron/README.md +++ b/translations/ta/lessons/3-NeuralNetworks/03-Perceptron/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: | | | |--------------|-----------| -|ஃப்ராங்க் ரோசன்பிளாட் | மார்க் 1 பெர்செப்ட்ரான்| +|ஃப்ராங்க் ரோசன்பிளாட் | மார்க் 1 பெர்செப்ட்ரான்| > படங்கள் [விக்கிபீடியாவில் இருந்து](https://en.wikipedia.org/wiki/Perceptron) @@ -34,7 +34,7 @@ y(x) = f(wTx) இங்கு f என்பது ஒரு படி செயல்பாட்டு செயல்பாடு - + ## பெர்செப்ட்ரானை பயிற்சி செய்வது diff --git a/translations/ta/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/ta/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index 49d74130..f69880f5 100644 --- a/translations/ta/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/ta/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -373,7 +373,7 @@ "\n", "நாம் இரும நிலை வகைப்பாட்டுக்கான தரவுத்தொகுப்பை உருவாக்கியுள்ளோம். ஆனால், தொடக்கத்திலிருந்தே இதை பல நிலை வகைப்பாடாகக் கருதுவோம், இதனால் நமது குறியீட்டை எளிதாக பல நிலை வகைப்பாட்டுக்கு மாற்ற முடியும். இந்தச் சூழலில், நமது ஒரே அடுக்கு பர்செப்ட்ரான் பின்வரும் கட்டமைப்பைக் கொண்டிருக்கும்:\n", "\n", - "\n", + "\n", "\n", "நெட்வொர்க்கின் இரண்டு வெளியீடுகள் இரண்டு வகைகளை குறிக்கின்றன, மேலும் இரண்டு வெளியீடுகளின் மத்தியில் அதிக மதிப்பைக் கொண்ட வகை சரியான தீர்வாக இருக்கும்.\n", "\n", @@ -616,7 +616,7 @@ "\n", "## கணினி வரைபடம்\n", "\n", - "\n", + "\n", "\n", "இது வரை, நெட்வொர்க் பல்வேறு அடுக்குகளுக்கான வகுப்புகளை நாம் வரையறுத்துள்ளோம். அந்த அடுக்குகளின் அமைப்பை **கணினி வரைபடம்** எனக் குறிப்பிடலாம். இப்போது, கொடுக்கப்பட்ட பயிற்சி தரவுத்தொகுப்பிற்கான (அல்லது அதன் ஒரு பகுதிக்கான) loss ஐ கீழ்க்காணும் முறையில் கணக்கிடலாம்:\n" ] @@ -683,7 +683,7 @@ "source": [ "## பின்னோக்கி பரவல்\n", "\n", - "\n", + "\n", "\n", "$$\\def\\L{\\mathcal{L}}\\def\\zz#1#2{\\frac{\\partial#1}{\\partial#2}}\n", "\\begin{align}\n", @@ -1261,7 +1261,7 @@ "* குறைந்த பயிற்சி இழப்பு - மாதிரி பயிற்சி தரவுகளை நன்றாக அணுக முடியும், ஏனெனில் இது போதுமான வெளிப்பாட்டு சக்தி கொண்டது. \n", "* சரிபார்ப்பு இழப்பு (validation loss) பயிற்சி இழப்பை விட அதிகமாக இருக்கலாம் மற்றும் பயிற்சியின் போது அதிகரிக்க தொடங்கலாம் - இதன் காரணம் மாதிரி பயிற்சி புள்ளிகளை \"நினைவில் வைத்துக்கொள்கிறது\" மற்றும் \"மொத்தப் படத்தை\" இழக்கிறது.\n", "\n", - "![Overfitting](../../../../../translated_images/ta/overfit.a0bd57f717c15769.png)\n", + "![Overfitting](../../../../../translated_images/ta/overfit.a0bd57f717c15769.webp)\n", "\n", "> இந்த படத்தில், `x` என்பது பயிற்சி தரவுகளைக் குறிக்கிறது, `o` என்பது சரிபார்ப்பு தரவுகளைக் குறிக்கிறது. இடது பக்கம் - நேரியல் மாதிரி (ஒரு அடுக்கு), இது தரவுகளின் இயல்பை நன்றாக அணுகுகிறது. வலது பக்கம் - அதிகப் பயிற்சி பெற்ற மாதிரி, இது பயிற்சி தரவுகளை சரியாக அணுகுகிறது, ஆனால் பிற தரவுகளுடன் பொருந்துவதில் அர்த்தமற்றதாக மாறுகிறது (சரிபார்ப்பு பிழை மிகவும் அதிகமாக உள்ளது).\n" ] diff --git a/translations/ta/lessons/3-NeuralNetworks/04-OwnFramework/README.md b/translations/ta/lessons/3-NeuralNetworks/04-OwnFramework/README.md index d2cd77cd..8a18f418 100644 --- a/translations/ta/lessons/3-NeuralNetworks/04-OwnFramework/README.md +++ b/translations/ta/lessons/3-NeuralNetworks/04-OwnFramework/README.md @@ -65,7 +65,7 @@ CO_OP_TRANSLATOR_METADATA: இந்த வெளிப்பாடுகளின் இடது-மிகவும் பகுதி அனைத்தும் ஒரே மாதிரியாக இருப்பதால், இழப்பு செயல்பாட்டிலிருந்து "பின்சென்று" கணக்கீட்டு வரைபடத்தின் மூலம் வேறுபாடுகளை பயனுள்ளதாகக் கணக்கிட முடியும். எனவே, பன்மடங்கு அடுக்கப்பட்ட பர்செப்ட்ரானை பயிற்சி செய்யும் முறை **பின்செலுத்தல்** அல்லது 'backprop' என அழைக்கப்படுகிறது. -கணக்கீட்டு வரைபடம் +கணக்கீட்டு வரைபடம் > TODO: படம் மேற்கோள் diff --git a/translations/ta/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/ta/lessons/3-NeuralNetworks/05-Frameworks/README.md index db80a4cb..13547f1a 100644 --- a/translations/ta/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/ta/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Overfitting என்பது இயந்திர கற்றலில் கீழே உள்ள வரைபடங்களில் `x` மூலம் குறிக்கப்படும் 5 புள்ளிகளை அணுகும் சிக்கலான பிரச்சினையைப் பரிசீலிக்கவும்: -![linear](../../../../../translated_images/ta/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/ta/overfit2.131f5800ae10ca5e.jpg) +![linear](../../../../../translated_images/ta/overfit1.f24b71c6f652e59e.webp) | ![overfit](../../../../../translated_images/ta/overfit2.131f5800ae10ca5e.webp) -------------------------|-------------------------- **நேர்கோட்ட மாதிரி, 2 அளவுருக்கள்** | **நேர்மற்ற மாதிரி, 7 அளவுருக்கள்** பயிற்சி பிழை = 5.3 | பயிற்சி பிழை = 0 @@ -79,7 +79,7 @@ Overfitting என்பது இயந்திர கற்றலில் மேலே உள்ள வரைபடத்தில் காண்பது போல, பயிற்சி பிழை மிகவும் குறைவாகவும், சரிபார்ப்பு பிழை மிகவும் அதிகமாகவும் இருந்தால், அது overfitting என்பதை காட்டுகிறது. பொதுவாக பயிற்சியின் போது, பயிற்சி மற்றும் சரிபார்ப்பு பிழைகள் இரண்டும் குறையத் தொடங்கும், பின்னர் ஒரு கட்டத்தில் சரிபார்ப்பு பிழை குறையாமல் அதிகரிக்கத் தொடங்கலாம். இது overfitting-ஐக் காட்டும் ஒரு அறிகுறியாக இருக்கும், மேலும் இந்த கட்டத்தில் பயிற்சியை நிறுத்த வேண்டும் (அல்லது குறைந்தது மாதிரியின் ஒரு நகலை சேமிக்க வேண்டும்). -![overfitting](../../../../../translated_images/ta/Overfitting.408ad91cd90b4371.png) +![overfitting](../../../../../translated_images/ta/Overfitting.408ad91cd90b4371.webp) ## Overfitting-ஐ எப்படி தடுக்கலாம் diff --git a/translations/ta/lessons/3-NeuralNetworks/README.md b/translations/ta/lessons/3-NeuralNetworks/README.md index 5cb77d66..ac06d69d 100644 --- a/translations/ta/lessons/3-NeuralNetworks/README.md +++ b/translations/ta/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # நரம்பு வலையமைப்புகளுக்கான அறிமுகம் -![நரம்பு வலையமைப்புகளுக்கான உள்ளடக்கத்தின் சுருக்கம் ஒரு ஓவியத்தில்](../../../../translated_images/ta/ai-neuralnetworks.1c687ae40bc86e83.png) +![நரம்பு வலையமைப்புகளுக்கான உள்ளடக்கத்தின் சுருக்கம் ஒரு ஓவியத்தில்](../../../../translated_images/ta/ai-neuralnetworks.1c687ae40bc86e83.webp) அறிமுகத்தில் நாம் விவாதித்தபடி, நுண்ணறிவை அடைய ஒரு வழி **கணினி மாதிரி** அல்லது **கிரகித்தல் மூளை** உருவாக்குவது ஆகும். 20ஆம் நூற்றாண்டின் நடுப்பகுதியில் இருந்து, ஆராய்ச்சியாளர்கள் பல்வேறு கணித மாதிரிகளை முயற்சித்தனர், சமீபத்திய ஆண்டுகளில் இந்த திசை மிகுந்த வெற்றியை பெற்றது. மூளையின் இவ்வகை கணித மாதிரிகள் **நரம்பு வலையமைப்புகள்** என்று அழைக்கப்படுகின்றன. @@ -36,13 +36,13 @@ CO_OP_TRANSLATOR_METADATA: உயிரியல் அறிவியல் மூலம், நமது மூளை நரம்பு செல்களால் (நரம்புகள்) ஆனது என்பதை நாம் அறிகிறோம், அவற்றில் ஒவ்வொன்றும் பல "உள்ளீடுகள்" (டெண்ட்ரைட்கள்) மற்றும் ஒரு "வெளியீடு" (ஆக்சான்) கொண்டுள்ளது. டெண்ட்ரைட்கள் மற்றும் ஆக்சான்கள் மின்சார சிக்னல்களை நடத்த முடியும், மேலும் அவற்றுக்கிடையிலான இணைப்புகள் — சினாப்ஸ்கள் என்று அழைக்கப்படும் — மாறுபட்ட அளவிலான நடத்துதலைக் காட்ட முடியும், இது நரம்பு சுரப்பிகளால் கட்டுப்படுத்தப்படுகிறது. -![நரம்பு செலின் மாதிரி](../../../../translated_images/ta/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![நரம்பு செலின் மாதிரி](../../../../translated_images/ta/artneuron.1a5daa88d20ebe6f.png) +![நரம்பு செலின் மாதிரி](../../../../translated_images/ta/synapse-wikipedia.ed20a9e4726ea1c6.webp) | ![நரம்பு செலின் மாதிரி](../../../../translated_images/ta/artneuron.1a5daa88d20ebe6f.webp) ----|---- உயிரியல் நரம்பு செல் *([Wikipedia](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) இல் இருந்து படம்)* | கிரகித்தல் நரம்பு செல் *(ஆசிரியரின் படம்)* அதனால், ஒரு நரம்பு செலின் எளிய கணித மாதிரி பல உள்ளீடுகள் X1, ..., XN மற்றும் ஒரு வெளியீடு Y, மற்றும் ஒரு வரிசை எடைகள் W1, ..., WN கொண்டுள்ளது. வெளியீடு கீழ்க்கண்டவாறு கணக்கிடப்படுகிறது: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) இங்கு f என்பது சில கோட்பாட்டியல் **செயல்பாட்டு செயல்பாடு** ஆகும். diff --git a/translations/ta/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/ta/lessons/4-ComputerVision/06-IntroCV/README.md index f1feaba4..b1f95dea 100644 --- a/translations/ta/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/ta/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ OpenCV-ஐ வீடியோ ஃப்ரேம்களை ஃப்ரேம * **ப்ரெயில் புத்தகத்தின் புகைப்படத்தை முன்னோட்ட செயலாக்கம்**. ப்ரெயில் சின்னங்களை தனித்தனியாக பிரித்து, நரம்பியல் வலையமைப்பால் மேலும் வகைப்படுத்துவதற்கான முன்னோட்ட செயலாக்கம், தெளிவாக்கம், அம்ச கண்டறிதல், பர்ஸ்பெக்டிவ் மாற்றம் மற்றும் NumPy மாற்றங்களை எப்படி பயன்படுத்தலாம் என்பதை நாங்கள் கவனம் செலுத்துகிறோம். -![ப்ரெயில் படம்](../../../../../translated_images/ta/braille.341962ff76b1bd70.jpeg) | ![முன்னோட்ட செயலாக்கம் செய்யப்பட்ட ப்ரெயில் படம்](../../../../../translated_images/ta/braille-result.46530fea020b03c7.png) | ![ப்ரெயில் சின்னங்கள்](../../../../../translated_images/ta/braille-symbols.0159185ab69d5339.png) +![ப்ரெயில் படம்](../../../../../translated_images/ta/braille.341962ff76b1bd70.webp) | ![முன்னோட்ட செயலாக்கம் செய்யப்பட்ட ப்ரெயில் படம்](../../../../../translated_images/ta/braille-result.46530fea020b03c7.webp) | ![ப்ரெயில் சின்னங்கள்](../../../../../translated_images/ta/braille-symbols.0159185ab69d5339.webp) ----|-----|----- > படம் [OpenCV.ipynb](OpenCV.ipynb) இல் இருந்து * **ஃப்ரேம் வேறுபாட்டைப் பயன்படுத்தி வீடியோவில் இயக்கத்தை கண்டறிதல்**. கேமரா நிலையானதாக இருந்தால், கேமரா ஃபீடில் இருந்து ஃப்ரேம்கள் ஒருவருக்கொருவர் மிகவும் ஒத்ததாக இருக்க வேண்டும். ஃப்ரேம்கள் வரிசைகளாக பிரதிநிதித்துவம் செய்யப்படுவதால், இரண்டு தொடர்ச்சியான ஃப்ரேம்களுக்கு வரிசைகளை கழிப்பதன் மூலம் பிக்சல் வேறுபாட்டைப் பெறலாம், இது நிலையான ஃப்ரேம்களுக்கு குறைவாக இருக்கும், மற்றும் படத்தில் முக்கியமான இயக்கம் ஏற்பட்டால் அதிகமாக மாறும். -![வீடியோ ஃப்ரேம்கள் மற்றும் ஃப்ரேம் வேறுபாடுகளின் படம்](../../../../../translated_images/ta/frame-difference.706f805491a0883c.png) +![வீடியோ ஃப்ரேம்கள் மற்றும் ஃப்ரேம் வேறுபாடுகளின் படம்](../../../../../translated_images/ta/frame-difference.706f805491a0883c.webp) > படம் [OpenCV.ipynb](OpenCV.ipynb) இல் இருந்து @@ -89,7 +89,7 @@ OpenCV-ஐ வீடியோ ஃப்ரேம்களை ஃப்ரேம - **தனிமையான ஆப்டிகல் ஃப்ளோ** ஒவ்வொரு பிக்சலுக்கும் அது எங்கு நகர்கிறது என்பதை காட்டும் வெக்டர் புலத்தை கணக்கிடுகிறது. - **சிறிய ஆப்டிகல் ஃப்ளோ** படத்தில் சில தனித்துவமான அம்சங்களை (எ.கா. விளிமைகள்) அடிப்படையாகக் கொண்டு, ஃப்ரேம்-பை-ஃப்ரேம் அவற்றின் பாதையை உருவாக்குகிறது. -![ஆப்டிகல் ஃப்ளோ படம்](../../../../../translated_images/ta/optical.1f4a94464579a83a.png) +![ஆப்டிகல் ஃப்ளோ படம்](../../../../../translated_images/ta/optical.1f4a94464579a83a.webp) > படம் [OpenCV.ipynb](OpenCV.ipynb) இல் இருந்து @@ -115,7 +115,7 @@ AI நிகழ்ச்சியில் இருந்து [இந்த இந்த ஆய்வகத்தில், நீங்கள் எளிய சைகைகளுடன் ஒரு வீடியோ எடுப்பீர்கள், மற்றும் உங்கள் இலக்கு ஆப்டிகல் ஃப்ளோவைப் பயன்படுத்தி மேலே/கீழே/இடது/வலது இயக்கங்களை எடுக்க வேண்டும். -கை இயக்க ஃப்ரேம் +கை இயக்க ஃப்ரேம் --- diff --git a/translations/ta/lessons/4-ComputerVision/06-IntroCV/lab/MovementDetection.ipynb b/translations/ta/lessons/4-ComputerVision/06-IntroCV/lab/MovementDetection.ipynb index bad39a64..34a42e52 100644 --- a/translations/ta/lessons/4-ComputerVision/06-IntroCV/lab/MovementDetection.ipynb +++ b/translations/ta/lessons/4-ComputerVision/06-IntroCV/lab/MovementDetection.ipynb @@ -10,7 +10,7 @@ "\n", "[இந்த வீடியோவை](../../../../../../lessons/4-ComputerVision/06-IntroCV/lab/palm-movement.mp4) கவனியுங்கள், இதில் ஒரு நபரின் கையடக்கம் நிலையான பின்னணியில் இடது/வலது/மேலே/கீழே நகர்கிறது.\n", "\n", - "\"கையடக்க\n", + "\"கையடக்க\n", "\n", "**உங்கள் இலக்கு** Optical Flow ஐ பயன்படுத்தி, வீடியோவில் எந்த பகுதிகள் மேலே/கீழே/இடது/வலது இயக்கங்களை கொண்டுள்ளன என்பதை கண்டறிவதாக இருக்கும்.\n", "\n", diff --git a/translations/ta/lessons/4-ComputerVision/06-IntroCV/lab/README.md b/translations/ta/lessons/4-ComputerVision/06-IntroCV/lab/README.md index f59794a2..d218fd03 100644 --- a/translations/ta/lessons/4-ComputerVision/06-IntroCV/lab/README.md +++ b/translations/ta/lessons/4-ComputerVision/06-IntroCV/lab/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: [இந்த வீடியோவை](../../../../../../lessons/4-ComputerVision/06-IntroCV/lab/palm-movement.mp4) கவனியுங்கள், இதில் ஒரு நபரின் கைப்பிடி நிலையான பின்னணியில் இடது/வலது/மேலே/கீழே நகர்கிறது. -கை நகர்வு ஃப்ரேம் +கை நகர்வு ஃப்ரேம் **உங்கள் இலக்கு** ஆப்டிக்கல் ஃப்ளோவை பயன்படுத்தி, வீடியோவின் எந்த பகுதிகள் மேலே/கீழே/இடது/வலது இயக்கங்களை கொண்டுள்ளன என்பதை கண்டறிய வேண்டும். diff --git a/translations/ta/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/ta/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index d8da40d6..eacc079f 100644 --- a/translations/ta/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/ta/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 என்பது 2014 ஆம் ஆண்டில் ImageNet top-5 வகைப்படுத்தலில் 92.7% துல்லியத்தை அடைந்த ஒரு நெட்வொர்க் ஆகும். இதன் அடுக்குகளின் அமைப்பு பின்வருமாறு உள்ளது: -![ImageNet Layers](../../../../../translated_images/ta/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet Layers](../../../../../translated_images/ta/vgg-16-arch1.d901a5583b3a51ba.webp) நீங்கள் காணும் படி, VGG ஒரு பாரம்பரிய பyramிட் கட்டமைப்பை பின்பற்றுகிறது, இது கான்வல்யூஷன்-பூலிங் அடுக்குகளின் வரிசையாகும். -![ImageNet Pyramid](../../../../../translated_images/ta/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet Pyramid](../../../../../translated_images/ta/vgg-16-arch.64ff2137f50dd49f.webp) > [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) இல் இருந்து படம் @@ -25,7 +25,7 @@ VGG-16 என்பது 2014 ஆம் ஆண்டில் ImageNet top-5 ResNet என்பது 2015 ஆம் ஆண்டில் Microsoft Research மூலம் முன்மொழியப்பட்ட மாடல்களின் குடும்பமாகும். ResNet இன் முக்கிய யோசனை **மீதமுள்ள பிளாக்குகளை** பயன்படுத்துவது: - + > [இந்தக் கட்டுரையில்](https://arxiv.org/pdf/1512.03385.pdf) இருந்து படம் @@ -37,7 +37,7 @@ Identity pass-through ஐ பயன்படுத்துவதற்கான Google Inception கட்டமைப்பு இந்த யோசனையை மேலும் ஒரு படி முன்னேற்றுகிறது, மேலும் ஒவ்வொரு நெட்வொர்க் அடுக்கையும் பல்வேறு பாதைகளின் கலவையாக உருவாக்குகிறது: - + > [Researchgate](https://www.researchgate.net/figure/Inception-module-with-dimension-reductions-left-and-schema-for-Inception-ResNet-v1_fig2_355547454) இல் இருந்து படம் diff --git a/translations/ta/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/ta/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index 8adbff2e..3fa19761 100644 --- a/translations/ta/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/ta/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -260,7 +260,7 @@ "\n", "அதனால், ஒரு சாதாரண CNNல் பல கன்வல்யூஷன் அடுக்குகள் இருக்கும், அவற்றின் இடையே பூலிங் அடுக்குகள் இருக்கும், இது படத்தின் பரிமாணங்களை குறைக்க உதவுகிறது. மேலும், வடிகட்டிகளின் எண்ணிக்கையை அதிகரிக்க வேண்டும், ஏனெனில் வடிவங்கள் மேலும் மேம்பட்டவையாக மாறும்போது, நாம் தேட வேண்டிய சாத்தியமான சுவாரஸ்யமான இணைப்புகள் அதிகமாக இருக்கும்.\n", "\n", - "![பல கன்வல்யூஷன் அடுக்குகள் மற்றும் பூலிங் அடுக்குகளைக் கொண்ட ஒரு படத்தை காட்டும் படம்.](../../../../../translated_images/ta/cnn-pyramid.85915455759ef0ce.png)\n", + "![பல கன்வல்யூஷன் அடுக்குகள் மற்றும் பூலிங் அடுக்குகளைக் கொண்ட ஒரு படத்தை காட்டும் படம்.](../../../../../translated_images/ta/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "பரப்பளவின் பரிமாணங்கள் குறைவதாலும், அம்சங்கள்/வடிகட்டிகளின் பரிமாணங்கள் அதிகரிப்பதாலும், இந்த கட்டமைப்பை **பிரமிட் கட்டமைப்பு** என்று அழைக்கப்படுகிறது.\n" ] diff --git a/translations/ta/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/ta/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index 03579e24..dadb0382 100644 --- a/translations/ta/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/ta/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -112,7 +112,7 @@ "\n", "சாதாரண கணினி பார்வையில், படத்தில் பல வடிகட்டிகள் பயன்படுத்தப்பட்டு அம்சங்கள் உருவாக்கப்பட்டன, பின்னர் அவை இயந்திரக் கற்றல் الگوریتمால் வகைப்பாட்டாளரை உருவாக்க பயன்படுத்தப்பட்டன. இந்த வடிகட்டிகள் உண்மையில் சில விலங்குகளின் பார்வை அமைப்பில் உள்ள நரம்பு அமைப்புகளுக்கு ஒத்ததாக உள்ளன.\n", "\n", - "\n", + "\n", "\n", "ஆனால், ஆழமான கற்றலில், வகைப்பாட்டு பிரச்சினையை தீர்க்க **சிறந்த குவிகலன வடிகட்டிகளை** கற்றுக்கொள்ளும் நெட்வொர்க்குகளை உருவாக்குகிறோம். இதை செய்ய, **குவிகலன அடுக்குகளை** அறிமுகப்படுத்துகிறோம்.\n" ] @@ -358,7 +358,7 @@ "\n", "அதனால், ஒரு சாதாரண CNNயில் பல கன்வல்யூஷன் அடுக்குகள் இருக்கும், அவற்றின் இடையே பூலிங் அடுக்குகள் இருக்கும், இது படத்தின் பரிமாணங்களை குறைக்க உதவுகிறது. மேலும், வடிகட்டிகளின் எண்ணிக்கையை அதிகரிப்போம், ஏனெனில் வடிவங்கள் மேலும் மேம்பட்டவையாக மாறும்போது - நாம் தேட வேண்டிய சாத்தியமான சுவாரஸ்யமான இணைப்புகள் அதிகமாக இருக்கும்.\n", "\n", - "![பல கன்வல்யூஷன் அடுக்குகள் மற்றும் பூலிங் அடுக்குகளைக் காட்டும் ஒரு படம்.](../../../../../translated_images/ta/cnn-pyramid.85915455759ef0ce.png)\n", + "![பல கன்வல்யூஷன் அடுக்குகள் மற்றும் பூலிங் அடுக்குகளைக் காட்டும் ஒரு படம்.](../../../../../translated_images/ta/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "பரப்பளவின் பரிமாணங்கள் குறைவதால் மற்றும் அம்சங்கள்/வடிகட்டிகளின் பரிமாணங்கள் அதிகரிப்பதால், இந்த கட்டமைப்பை **பிரமிட் கட்டமைப்பு** என்று அழைக்கப்படுகிறது.\n" ] diff --git a/translations/ta/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/ta/lessons/4-ComputerVision/07-ConvNets/README.md index b647eccd..15f2582c 100644 --- a/translations/ta/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/ta/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,14 +17,14 @@ CO_OP_TRANSLATOR_METADATA: முறைகளை எடுக்க, **கன்வல்யூஷனல் ஃபில்டர்கள்** என்ற கருத்தை பயன்படுத்துவோம். நீங்கள் அறிந்தபடி, ஒரு படம் 2D-மாட்ரிக்ஸ் அல்லது நிற ஆழத்துடன் 3D-டென்சராக பிரதிநிதித்துவம் செய்யப்படுகிறது. ஒரு ஃபில்டரைப் பயன்படுத்துவது என்பது, சிறிய **ஃபில்டர் கர்னல்** மாட்ரிக்ஸை எடுத்து, மூலப்படத்தில் உள்ள ஒவ்வொரு பிக்சலுக்கும் அண்டை புள்ளிகளுடன் எடை செய்யப்பட்ட சராசரியை கணக்கிடுவதாகும். இதை ஒரு சிறிய சாளரம் முழு படத்திலும் நகர்ந்து, ஃபில்டர் கர்னல் மாட்ரிக்ஸில் உள்ள எடைகளுக்கு ஏற்ப அனைத்து பிக்சல்களையும் சராசரி செய்யும் முறையாகக் காணலாம். -![நெடுவரை விளிம்பு ஃபில்டர்](../../../../../translated_images/ta/filter-vert.b7148390ca0bc356.png) | ![கிடைமட்ட விளிம்பு ஃபில்டர்](../../../../../translated_images/ta/filter-horiz.59b80ed4feb946ef.png) +![நெடுவரை விளிம்பு ஃபில்டர்](../../../../../translated_images/ta/filter-vert.b7148390ca0bc356.webp) | ![கிடைமட்ட விளிம்பு ஃபில்டர்](../../../../../translated_images/ta/filter-horiz.59b80ed4feb946ef.webp) ----|---- > படம்: டிமிட்ரி சோஷ்னிகோவ் உதாரணமாக, MNIST எண்களுக்கு 3x3 நெடுவரை விளிம்பு மற்றும் கிடைமட்ட விளிம்பு ஃபில்டர்களை பயன்படுத்தினால், மூலப்படத்தில் உள்ள நெடுவரை மற்றும் கிடைமட்ட விளிம்புகள் உள்ள இடங்களில் முக்கியமான மதிப்புகளை (உதா. உயர் மதிப்புகள்) பெறலாம். எனவே, இந்த இரண்டு ஃபில்டர்களை விளிம்புகளை "தேட" பயன்படுத்தலாம். அதேபோல், பிற குறைந்த நிலை முறைகளைத் தேடுவதற்காக வெவ்வேறு ஃபில்டர்களை வடிவமைக்கலாம்: - + > [லியூங்-மாலிக் ஃபில்டர் வங்கி](https://www.robots.ox.ac.uk/~vgg/research/texclass/filters.html) படத்தை @@ -38,7 +38,7 @@ CNN-கள் செயல்படும் முறை பின்வரு * ஃபில்டர்கள் தானாக பயிற்சி பெறும் வகையில் நெட்வொர்க்கை வடிவமைக்க முடியும் * மூலப்படத்தில் மட்டுமல்லாமல், உயர் நிலை அம்சங்களில் முறைகளை கண்டுபிடிக்க இதே அணுகுமுறையைப் பயன்படுத்த முடியும். எனவே, CNN அம்ச எடுக்கும் செயல்பாடு குறைந்த நிலை பிக்சல் சேர்க்கைகளிலிருந்து தொடங்கி, படத்தின் பகுதிகளின் உயர் நிலை சேர்க்கை வரை அம்சங்களின் ஒரு அடுக்குக்கோபுரத்தில் செயல்படுகிறது. -![அடுக்குக்கோபுர அம்ச எடுக்கும்](../../../../../translated_images/ta/FeatureExtractionCNN.d9b456cbdae7cb64.png) +![அடுக்குக்கோபுர அம்ச எடுக்கும்](../../../../../translated_images/ta/FeatureExtractionCNN.d9b456cbdae7cb64.webp) > [ஹிஸ்லாப்-லின்ச்](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d) எழுதிய ஒரு ஆய்வுக் கட்டுரையிலிருந்து படம், [அவர்களின் ஆராய்ச்சி](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) அடிப்படையில் @@ -55,9 +55,9 @@ CNN-கள் செயல்படும் முறை பின்வரு உதாரணமாக, VGG-16 என்ற கட்டமைப்பின் கட்டமைப்பைப் பார்ப்போம், இது 2014 இல் ImageNet-இன் Top-5 வகைப்படுத்தலில் 92.7% துல்லியத்தை அடைந்தது: -![ImageNet லேயர்கள்](../../../../../translated_images/ta/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet லேயர்கள்](../../../../../translated_images/ta/vgg-16-arch1.d901a5583b3a51ba.webp) -![ImageNet பyramிட்](../../../../../translated_images/ta/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet பyramிட்](../../../../../translated_images/ta/vgg-16-arch.64ff2137f50dd49f.webp) > [ரிசர்ச்கேட்](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) மூலம் படம் diff --git a/translations/ta/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/ta/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 37e38fa5..640c5e5c 100644 --- a/translations/ta/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/ta/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ CO_OP_TRANSLATOR_METADATA: 37 வெவ்வேறு இனங்களுக்கான நாய்கள் மற்றும் பூனைகளின் படங்களை கொண்ட [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/) ஐ நாம் பயன்படுத்துவோம். -![நாம் கையாளும் தரவுத்தொகுப்பு](../../../../../../translated_images/ta/data.50b2a9d5484bdbf0.png) +![நாம் கையாளும் தரவுத்தொகுப்பு](../../../../../../translated_images/ta/data.50b2a9d5484bdbf0.webp) தரவுத்தொகுப்பைப் பதிவிறக்க, இந்தக் குறியீட்டு துண்டைப் பயன்படுத்தவும்: diff --git a/translations/ta/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/ta/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index fd3fd22b..fd10759f 100644 --- a/translations/ta/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/ta/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "சிறந்த பூனை உருவத்தை காட்சிப்படுத்த, நாம் ஒரு சீரற்ற சத்தம் கொண்ட படத்துடன் தொடங்குவோம், பின்னர் ஒரு நெட்வொர்க் பூனையை அடையாளம் காணும் வகையில் படத்தை சரிசெய்ய_gradient descent_ மேம்பாட்டு முறையை பயன்படுத்துவோம்.\n", "\n", - "![மேம்பாட்டு சுழற்சி](../../../../../translated_images/ta/ideal-cat-loop.999fbb8ff306e044.png)\n", + "![மேம்பாட்டு சுழற்சி](../../../../../translated_images/ta/ideal-cat-loop.999fbb8ff306e044.webp)\n", "\n", "இது தான் நமது தொடக்க படம்:\n" ] diff --git a/translations/ta/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/ta/lessons/4-ComputerVision/08-TransferLearning/README.md index db61c848..8c038dbb 100644 --- a/translations/ta/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/ta/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Keras மற்றும் PyTorch இரண்டிலும் பொது இங்கே VGG-16 நெட்வொர்க்கால் ஒரு பூனையின் படத்திலிருந்து எடுக்கப்பட்ட மாதிரி அம்சங்கள் உள்ளன: -![VGG-16 மூலம் எடுக்கப்பட்ட அம்சங்கள்](../../../../../translated_images/ta/features.6291f9c7ba3a0b95.png) +![VGG-16 மூலம் எடுக்கப்பட்ட அம்சங்கள்](../../../../../translated_images/ta/features.6291f9c7ba3a0b95.webp) ## பூனைகள் vs. நாய்கள் தரவுத்தொகுப்பு @@ -48,19 +48,19 @@ Keras மற்றும் PyTorch இரண்டிலும் பொது ஒரு அணுகுமுறை என்னவென்றால், ஒரு சீரற்ற படத்துடன் தொடங்குவது, பின்னர் **கிரேடியண்ட் டிசென்ட் ஆப்டிமைசேஷன்** தொழில்நுட்பத்தைப் பயன்படுத்தி அந்த படத்தை சரிசெய்வது, அதனால் நெட்வொர்க்கு அதை பூனையாக நினைக்கத் தொடங்குகிறது. -![படத்தை சரிசெய்யும் சுழற்சி](../../../../../translated_images/ta/ideal-cat-loop.999fbb8ff306e044.png) +![படத்தை சரிசெய்யும் சுழற்சி](../../../../../translated_images/ta/ideal-cat-loop.999fbb8ff306e044.webp) ஆனால், இதைச் செய்தால், சீரற்ற சத்தத்துடன் மிகவும் ஒத்ததாக ஏதாவது கிடைக்கும். இது *நெட்வொர்க்கு உள்ளீடு ஒரு பூனை என்று நினைக்க பல வழிகள் உள்ளன*, அதில் சில காட்சியளிப்பதில் அர்த்தமற்றவை. அந்த படங்கள் பூனைக்கு பொதுவான பல வடிவங்களை கொண்டிருந்தாலும், அவற்றை காட்சிப்படுத்துவதற்குத் தனித்துவமாக இருக்க வேண்டும் என்று கட்டாயப்படுத்த எதுவும் இல்லை. முடிவை மேம்படுத்த, **variation loss** என்று அழைக்கப்படும் மற்றொரு சொற்றொடரை இழப்புச் செயல்பாட்டில் சேர்க்கலாம். இது படத்தின் அடுத்தடுத்த பிக்சல்கள் எவ்வளவு ஒத்திருக்கின்றன என்பதை காட்டும் அளவீடு. variation loss ஐ குறைப்பது படத்தை மென்மையாக ஆக்குகிறது, மேலும் சத்தத்தை அகற்றுகிறது - இதனால் காட்சிப்படுத்துவதற்கு அழகான வடிவங்களை வெளிப்படுத்துகிறது. இங்கே பூனை மற்றும் ஜெப்ரா என்று அதிக சாத்தியத்துடன் வகைப்படுத்தப்படும் "சிறந்த" படங்களின் உதாரணம் உள்ளது: -![சிறந்த பூனை](../../../../../translated_images/ta/ideal-cat.203dd4597643d6b0.png) | ![சிறந்த ஜெப்ரா](../../../../../translated_images/ta/ideal-zebra.7f70e8b54ee15a7a.png) +![சிறந்த பூனை](../../../../../translated_images/ta/ideal-cat.203dd4597643d6b0.webp) | ![சிறந்த ஜெப்ரா](../../../../../translated_images/ta/ideal-zebra.7f70e8b54ee15a7a.webp) -----|----- *சிறந்த பூனை* | *சிறந்த ஜெப்ரா* இதே அணுகுமுறையை **எதிர்மறை தாக்குதல்** என்று அழைக்கப்படும் செயல்பாட்டை செய்ய பயன்படுத்தலாம். ஒரு நரம்பியல் நெட்வொர்க்கை ஏமாற்றி, ஒரு நாயை பூனையாக மாற்ற விரும்புகிறோம் என்று நினைத்தால், நாயின் படத்தை எடுத்து, அது நெட்வொர்க்கால் நாய் என்று அடையாளம் காணப்படுகிறது, பின்னர் அதை சிறிது மாற்றி, நெட்வொர்க்கு அதை பூனையாக வகைப்படுத்தும் வரை கிரேடியண்ட் டிசென்ட் ஆப்டிமைசேஷனைப் பயன்படுத்தலாம்: -![நாயின் படம்](../../../../../translated_images/ta/original-dog.8f68a67d2fe0911f.png) | ![பூனையாக வகைப்படுத்தப்படும் நாயின் படம்](../../../../../translated_images/ta/adversarial-dog.d9fc7773b0142b89.png) +![நாயின் படம்](../../../../../translated_images/ta/original-dog.8f68a67d2fe0911f.webp) | ![பூனையாக வகைப்படுத்தப்படும் நாயின் படம்](../../../../../translated_images/ta/adversarial-dog.d9fc7773b0142b89.webp) -----|----- *மூல நாயின் படம்* | *பூனையாக வகைப்படுத்தப்படும் நாயின் படம்* diff --git a/translations/ta/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/ta/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index b440c809..7ab9676d 100644 --- a/translations/ta/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/ta/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "ஆட்டோஎன்கோடரை அசல் படத்திலிருந்து அதிகமான தகவல்களைப் பிடிக்கவும், சரியான மீளுருவாக்கத்திற்காக பயிற்சி செய்யும் போது, நெட்வொர்க் உள்ளீட்டு படங்களின் சிறந்த **எம்பெடிங்** (embedding) ஐ கண்டறிந்து அதன் அர்த்தத்தைப் பிடிக்க முயற்சிக்கிறது.\n", "\n", - "![ஆட்டோஎன்கோடர் வரைபடம்](../../../../../translated_images/ta/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![ஆட்டோஎன்கோடர் வரைபடம்](../../../../../translated_images/ta/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "> படம் [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html) இலிருந்து\n", "\n", @@ -941,7 +941,7 @@ " * $N(\\mathrm{z\\_mean},e^{\\mathrm{z\\_log\\_sigma}})$ விநியோகத்திலிருந்து `sample(z_val in code)` எனும் ஒரு வெக்டரை எடுத்துக்கொள்கிறோம்\n", " * Decoder, `sample` ஐ உள்ளீட்டு வெக்டராகக் கொண்டு முதன்மை படத்தை மீண்டும் உருவாக்க முயலுகிறது\n", "\n", - " \n", + " \n", "\n", " > இந்த படத்தை [இந்த வலைப்பதிவில்](https://ijdykeman.github.io/ml/2016/12/21/cvae.html) Isaak Dykeman எழுதியுள்ளார்.\n" ] @@ -1264,7 +1264,7 @@ "\n", "இந்த அணுகுமுறையில் **மூன்று loss functions** உள்ளன: GAN-இன் generator loss, discriminator loss மற்றும் VAE-இன் reconstruction loss.\n", "\n", - " \n", + " \n", "\n", " > [இந்த வலைப்பதிவில்](https://blog.paperspace.com/adversarial-autoencoders-with-pytorch/) Felipe Ducau எழுதிய படத்தைப் பார்க்கவும்\n" ] diff --git a/translations/ta/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/ta/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index 4fbe737f..720a690c 100644 --- a/translations/ta/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/ta/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -21,7 +21,7 @@ "\n", "நாம் ஆட்டோஎன்கோடரை அசல் படத்திலிருந்து சரியான மீளுருவாக்கத்திற்காக அதிக தகவல்களை பிடிக்க பயிற்சி செய்யும் போது, நெட்வொர்க் உள்ளீட்டு படங்களின் அர்த்தத்தை பிடிக்க சிறந்த **எம்பெடிங்** ஐ கண்டுபிடிக்க முயல்கிறது.\n", "\n", - "![ஆட்டோஎன்கோடர் வரைபடம்](../../../../../translated_images/ta/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![ஆட்டோஎன்கோடர் வரைபடம்](../../../../../translated_images/ta/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "*படம் [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html) இலிருந்து*\n", "\n", @@ -888,7 +888,7 @@ " * $N(\\mathrm{z\\_mean},e^{\\mathrm{z\\_log\\_sigma}})$ விநியோகத்திலிருந்து ஒரு வெக்டரை `sample` என எடுக்கிறோம்\n", " * `sample` ஐ உள்ளீட்டு வெக்டராக பயன்படுத்தி, Decoder முதன்மை படத்தை மீண்டும் உருவாக்க முயற்சிக்கிறது\n", "\n", - " \n" + " \n" ] }, { diff --git a/translations/ta/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/ta/lessons/4-ComputerVision/09-Autoencoders/README.md index 5435ede0..4f98b0ce 100644 --- a/translations/ta/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/ta/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ CNNகளை பயிற்சி செய்யும்போது, ஒர ஆட்டோஎன்கோடரை அசல் படத்தின் தகவல்களை சரியாக மீண்டும் உருவாக்குவதற்காக அதிகமாக பிடிக்க பயிற்சி செய்யும் போது, நெட்வொர்க் சிறந்த **embedding** ஐ கண்டறிந்து, உள்ளீட்டு படங்களின் அர்த்தத்தை பிடிக்க முயற்சிக்கிறது. -![ஆட்டோஎன்கோடர் வரைபடம்](../../../../../translated_images/ta/autoencoder_schema.5e6fc9ad98a5eb61.jpg) +![ஆட்டோஎன்கோடர் வரைபடம்](../../../../../translated_images/ta/autoencoder_schema.5e6fc9ad98a5eb61.webp) > படம் [Keras வலைப்பதிவு](https://blog.keras.io/building-autoencoders-in-keras.html) மூலம் @@ -46,7 +46,7 @@ VAE என்பது latent அளவுருக்களின் *புள * N(zmean,exp(zlog\_sigma)) விநியோகத்திலிருந்து `sample` வெக்டரை எடுக்கிறோம் * டிகோடர் `sample` ஐ உள்ளீட்டு வெக்டராக பயன்படுத்தி அசல் படத்தை டிகோடு செய்ய முயற்சிக்கிறது - + > படம் [இந்த வலைப்பதிவு](https://ijdykeman.github.io/ml/2016/12/21/cvae.html) மூலம் Isaak Dykeman @@ -57,13 +57,13 @@ VAE என்பது latent அளவுருக்களின் *புள VAEs இன் ஒரு முக்கியமான நன்மை என்னவென்றால், புதிய படங்களை எளிதாக உருவாக்க முடியும், ஏனெனில் latent வெக்டர்களை எடுக்க வேண்டிய விநியோகத்தை நாங்கள் அறிந்திருக்கிறோம். உதாரணமாக, 2D latent வெக்டருடன் MNIST-ல் VAE ஐ பயிற்சி செய்தால், latent வெக்டரின் கூறுகளை மாறி வெவ்வேறு எண்களை பெறலாம்: -vaemnist +vaemnist > படம் [Dmitry Soshnikov](http://soshnikov.com) மூலம் latent அளவுரு இடத்தின் வெவ்வேறு பகுதிகளில் இருந்து latent வெக்டர்களை எடுக்க தொடங்கும்போது, படங்கள் ஒருவருக்கொருவர் கலக்க ஆரம்பிக்கின்றன என்பதை கவனிக்கவும். இந்த இடத்தை 2D-ல் காட்சிப்படுத்தவும் முடியும்: -vaemnist cluster +vaemnist cluster > படம் [Dmitry Soshnikov](http://soshnikov.com) மூலம் diff --git a/translations/ta/lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb b/translations/ta/lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb index 06202c6d..e6b87167 100644 --- a/translations/ta/lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb +++ b/translations/ta/lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb @@ -15,7 +15,7 @@ " * **ஜெனரேட்டர்** ஒரு சீரற்ற வெக்டரை எடுத்து, அதிலிருந்து ஒரு படத்தை உருவாக்க வேண்டும்\n", " * **டிஸ்கிரிமினேட்டர்** என்பது ஒரு நெட்வொர்க், இது மூலப்படம் (பயிற்சி தரவுத்தொகுப்பில் இருந்து) மற்றும் ஜெனரேட்டர் உருவாக்கிய படத்தை வேறுபடுத்த வேண்டும்.\n", "\n", - "\n" + "\n" ] }, { @@ -670,7 +670,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n", + "\n", "\n", "> படம் [இந்த பயிற்சியில்](https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html) இருந்து.\n" ] diff --git a/translations/ta/lessons/4-ComputerVision/10-GANs/GANTF.ipynb b/translations/ta/lessons/4-ComputerVision/10-GANs/GANTF.ipynb index 5585a352..d49aa67b 100644 --- a/translations/ta/lessons/4-ComputerVision/10-GANs/GANTF.ipynb +++ b/translations/ta/lessons/4-ComputerVision/10-GANs/GANTF.ipynb @@ -15,7 +15,7 @@ " * **ஜெனரேட்டர்** ஒரு சீரற்ற வெக்டரை எடுத்து, அதிலிருந்து ஒரு படத்தை உருவாக்க வேண்டும் \n", " * **டிஸ்கிரிமினேட்டர்** என்பது ஒரு நெட்வொர்க், இது அசல் படம் (பயிற்சி தரவுத்தொகுப்பிலிருந்து) மற்றும் ஜெனரேட்டர் உருவாக்கிய படத்தை வேறுபடுத்த வேண்டும்.\n", "\n", - "\n" + "\n" ] }, { diff --git a/translations/ta/lessons/4-ComputerVision/10-GANs/README.md b/translations/ta/lessons/4-ComputerVision/10-GANs/README.md index ccbecbdb..079065ff 100644 --- a/translations/ta/lessons/4-ComputerVision/10-GANs/README.md +++ b/translations/ta/lessons/4-ComputerVision/10-GANs/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: GAN-இன் முக்கிய யோசனை இரண்டு நரம்பியல் நெட்வொர்க்குகளை ஒன்றுக்கொன்று எதிராகப் பயிற்சி செய்யும் முறையாகும்: - + > படம்: [Dmitry Soshnikov](http://soshnikov.com) @@ -41,7 +41,7 @@ GAN-இன் முக்கிய யோசனை இரண்டு நரம > ✅ கான்வல்யூஷன் அடுக்கு ஒரு நேரியல் வடிகட்டி படத்தைச் சுற்றி செயல்படுவதால், டிகான்வல்யூஷன் அடிப்படையில் கான்வல்யூஷனுக்கு ஒத்ததாகும், மற்றும் அதே அடுக்கு தர்க்கத்தைப் பயன்படுத்தி செயல்படுத்தலாம். - + > படம்: [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/ta/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/ta/lessons/4-ComputerVision/11-ObjectDetection/README.md index bc70e5c0..4ddb1f3a 100644 --- a/translations/ta/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/ta/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: ## [முன்-வகுப்பு வினாடி வினா](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![பொருள் கண்டறிதல்](../../../../../translated_images/ta/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) +![பொருள் கண்டறிதல்](../../../../../translated_images/ta/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp) > படம் [YOLO v2 வலைத்தளத்திலிருந்து](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 2. ஒவ்வொரு பகுதியிலும் பட வகைப்படுத்தலை இயக்கவும். 3. போதுமான அளவு செயல்பாட்டை உருவாக்கும் பகுதிகள், குறிப்பிட்ட பொருளை கொண்டதாக கருதலாம். -![எளிய பொருள் கண்டறிதல்](../../../../../translated_images/ta/naive-detection.e7f1ba220ccd08c6.png) +![எளிய பொருள் கண்டறிதல்](../../../../../translated_images/ta/naive-detection.e7f1ba220ccd08c6.webp) > *படம் [பயிற்சி நோட்புக்](ObjectDetection-TF.ipynb) இலிருந்து* @@ -42,7 +42,7 @@ CO_OP_TRANSLATOR_METADATA: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 வகைகள் * [COCO](http://cocodataset.org/#home) - சூழலில் பொதுவான பொருட்கள். 80 வகைகள், அளவுரு பெட்டிகள் மற்றும் பிரிவுப் முகமூடிகள் -![COCO](../../../../../translated_images/ta/coco-examples.71bc60380fa6cceb.jpg) +![COCO](../../../../../translated_images/ta/coco-examples.71bc60380fa6cceb.webp) ## பொருள் கண்டறிதல் அளவுகோல்கள் @@ -50,7 +50,7 @@ CO_OP_TRANSLATOR_METADATA: பட வகைப்படுத்தலுக்கான செயல்திறனை அளவிடுவது எளிதானது, ஆனால் பொருள் கண்டறிதலுக்கான வகையின் சரியானதையும், அளவுரு பெட்டியின் துல்லியத்தையும் அளவிட வேண்டும். இதற்காக **இணைப்பு மற்றும் ஒன்றிணைவு** (IoU) என்ற அளவுகோலைப் பயன்படுத்துகிறோம், இது இரண்டு பெட்டிகள் (அல்லது இரண்டு பகுதி பகுதிகள்) எவ்வளவு நன்றாக ஒத்துப்போகின்றன என்பதை அளவிடுகிறது. -![IoU](../../../../../translated_images/ta/iou_equation.9a4751d40fff4e11.png) +![IoU](../../../../../translated_images/ta/iou_equation.9a4751d40fff4e11.webp) > *[இந்த சிறந்த வலைப்பதிவிலிருந்து](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/) IoU பற்றிய படங்கள்* @@ -97,11 +97,11 @@ IoU ஒரு குறிப்பிட்ட மதிப்புக்க [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) பயன்படுத்தி ROI பகுதிகளின் அடுக்கமைப்பை உருவாக்குகிறது, பின்னர் CNN அம்சங்களைப் பயன்படுத்தி SVM வகைப்படுத்திகளை இயக்கி பொருள் வகையைத் தீர்மானிக்கிறது, மற்றும் *அளவுரு பெட்டியின்* கோர்டினேட்டுகளை தீர்மானிக்க நேரியல் மீள்பார்வையை (linear regression) பயன்படுத்துகிறது. [அதிகாரப்பூர்வ ஆவணம்](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/ta/rcnn1.cae407020dfb1d1f.png) +![RCNN](../../../../../translated_images/ta/rcnn1.cae407020dfb1d1f.webp) > *படம் van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/ta/rcnn2.2d9530bb83516484.png) +![RCNN-1](../../../../../translated_images/ta/rcnn2.2d9530bb83516484.webp) > *படங்கள் [இந்த வலைப்பதிவிலிருந்து](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -109,7 +109,7 @@ IoU ஒரு குறிப்பிட்ட மதிப்புக்க இந்த அணுகுமுறை R-CNN போன்றது, ஆனால் பகுதிகள் குவியல் அடுக்குகள் (convolution layers) பயன்படுத்தப்பட்ட பிறகு வரையறுக்கப்படுகின்றன. -![FRCNN](../../../../../translated_images/ta/f-rcnn.3cda6d9bb4188875.png) +![FRCNN](../../../../../translated_images/ta/f-rcnn.3cda6d9bb4188875.webp) > படம் [அதிகாரப்பூர்வ ஆவணத்திலிருந்து](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -117,7 +117,7 @@ IoU ஒரு குறிப்பிட்ட மதிப்புக்க இந்த அணுகுமுறையின் முக்கியமான கருத்து, ROIs (Regions of Interests) கணிக்க நரம்பியல் நெட்வொர்க்கை (neural network) பயன்படுத்துவது - *Region Proposal Network* என அழைக்கப்படுகிறது. [ஆவணம்](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/ta/faster-rcnn.8d46c099b87ef30a.png) +![FasterRCNN](../../../../../translated_images/ta/faster-rcnn.8d46c099b87ef30a.webp) > படம் [அதிகாரப்பூர்வ ஆவணத்திலிருந்து](https://arxiv.org/pdf/1506.01497.pdf) @@ -129,7 +129,7 @@ IoU ஒரு குறிப்பிட்ட மதிப்புக்க 2. **Position-Sensitive Score Map** மூலம் அம்சங்கள் செயல்படுத்தப்படுகின்றன. $C$ வகைகளிலிருந்து ஒவ்வொரு பொருளும் $k\times k$ பகுதிகளால் பிரிக்கப்படுகிறது, மற்றும் பொருளின் பகுதிகளை கணிக்க பயிற்சி அளிக்கிறோம். 3. $k\times k$ பகுதிகளிலிருந்து ஒவ்வொரு பகுதியும் பொருள் வகைகளுக்கு வாக்களிக்கிறது, மற்றும் அதிகபட்ச வாக்குகளைப் பெறும் பொருள் வகை தேர்ந்தெடுக்கப்படுகிறது. -![r-fcn image](../../../../../translated_images/ta/r-fcn.13eb88158b99a3da.png) +![r-fcn image](../../../../../translated_images/ta/r-fcn.13eb88158b99a3da.webp) > படம் [அதிகாரப்பூர்வ ஆவணத்திலிருந்து](https://arxiv.org/abs/1605.06409) @@ -140,7 +140,7 @@ YOLO ஒரு நேரடி ஒரு முறை அல்காரித * படம் $S\times S$ பகுதிகளாகப் பிரிக்கப்படுகிறது. * ஒவ்வொரு பகுதிக்கும் **CNN** $n$ சாத்தியமான பொருட்கள், *அளவுரு பெட்டியின்* கோர்டினேட்டுகள் மற்றும் *நம்பகத்தன்மை*=*சாத்தியம்* * IoU கணிக்கிறது. - ![YOLO](../../../../../translated_images/ta/yolo.a2648ec82ee8bb4e.png) + ![YOLO](../../../../../translated_images/ta/yolo.a2648ec82ee8bb4e.webp) > படம் [அதிகாரப்பூர்வ ஆவணத்திலிருந்து](https://arxiv.org/abs/1506.02640) diff --git a/translations/ta/lessons/4-ComputerVision/12-Segmentation/README.md b/translations/ta/lessons/4-ComputerVision/12-Segmentation/README.md index 12b37fd1..9671a062 100644 --- a/translations/ta/lessons/4-ComputerVision/12-Segmentation/README.md +++ b/translations/ta/lessons/4-ComputerVision/12-Segmentation/README.md @@ -20,7 +20,7 @@ CO_OP_TRANSLATOR_METADATA: உதாரணமாக, instance segmentation-இல் இந்த ஆடுகள் வேறு பொருட்களாகக் கருதப்படும், ஆனால் semantic segmentation-இல் அனைத்து ஆடுகளும் ஒரே வகையாகக் கருதப்படும். - + > படம் [இந்த வலைப்பதிவில் இருந்து](https://nirmalamurali.medium.com/image-classification-vs-semantic-segmentation-vs-instance-segmentation-625c33a08d50) @@ -29,7 +29,7 @@ CO_OP_TRANSLATOR_METADATA: * **Encoder** உள்ளீடு படத்திலிருந்து அம்சங்களை எடுக்கிறது * **Decoder** அந்த அம்சங்களை **mask image**-ஆக மாற்றுகிறது, இதன் அளவு மற்றும் சேனல்கள் வகைகளின் எண்ணிக்கைக்கு இணையாக இருக்கும். - + > படம் [இந்த வெளியீட்டில் இருந்து](https://arxiv.org/pdf/2001.05566.pdf) @@ -43,7 +43,7 @@ CO_OP_TRANSLATOR_METADATA: > ✅ இந்த தொழில்நுட்பம் இந்த வகை மருத்துவ படங்களுக்கு மிகவும் பொருத்தமானது, ஆனால் மற்ற எந்த உண்மையான பயன்பாடுகளை நீங்கள் கற்பனை செய்ய முடியும்? -navi +navi > படம் PH2 Database-இல் இருந்து diff --git a/translations/ta/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb b/translations/ta/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb index d977b85f..e24527e4 100644 --- a/translations/ta/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb +++ b/translations/ta/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb @@ -17,7 +17,7 @@ "\n", "உதாரணமாக, instance segmentation-ல் 10 ஆடுகள் தனித்தனியான பொருட்களாகக் கருதப்படும், semantic segmentation-ல் அனைத்து ஆடுகளும் ஒரே வகையாகக் காணப்படும்.\n", "\n", - "\n", + "\n", "\n", "> [இந்த வலைப்பதிவில்](https://nirmalamurali.medium.com/image-classification-vs-semantic-segmentation-vs-instance-segmentation-625c33a08d50) இருந்து எடுத்த படம்\n", "\n", @@ -26,7 +26,7 @@ "* **Encoder** உள்ளீட்டு படத்திலிருந்து அம்சங்களை (features) எடுக்கிறது.\n", "* **Decoder** அந்த அம்சங்களை **முகமூடி படமாக** (mask image) மாற்றுகிறது, இது ஒரே அளவிலும் வகைகளின் எண்ணிக்கைக்கு இணையான சேனல்களுடன் இருக்கும்.\n", "\n", - "\n", + "\n", "\n", "> [இந்த வெளியீட்டில்](https://arxiv.org/pdf/2001.05566.pdf) இருந்து எடுத்த படம்\n" ] @@ -252,7 +252,7 @@ "\n", "எளிய என்கோடர்-டிகோடர் கட்டமைப்பை **SegNet** என்று அழைக்கப்படுகிறது. இது என்கோடரில் கான்வல்யூஷன்கள் மற்றும் பூலிங்களுடன் உள்ள நிலையான CNN-ஐ பயன்படுத்துகிறது, மேலும் டிகோடரில் கான்வல்யூஷன்கள் மற்றும் அப்சாம்ப்ளிங்களுடன் உள்ள டிகான்வல்யூஷன் CNN-ஐ பயன்படுத்துகிறது. பல அடுக்குகளைக் கொண்ட நெட்வொர்க்கை வெற்றிகரமாக பயிற்றுவிக்க பேட்ச் நார்மலைசேஷனை நம்புகிறது.\n", "\n", - "\n", + "\n", "\n", "> இந்த ஆய்வுக்கட்டுரையிலிருந்து படம்: Badrinarayanan, V., Kendall, A., & Cipolla, R. (2015). [SegNet: A deep convolutional\n", "encoder-decoder architecture for image segmentation](https://arxiv.org/pdf/1511.00561.pdf)\n" @@ -548,7 +548,7 @@ "\n", "இங்கே மிகவும் எளிய CNN கட்டமைப்பைப் பயன்படுத்துவோம், ஆனால் U-Net அம்சங்களைப் பெறுவதற்கான ResNet-50 போன்ற சிக்கலான encoder ஐயும் பயன்படுத்த முடியும்.\n", "\n", - "\n", + "\n", "\n", "> படத்தின் மூலமாக: Ronneberger, Olaf, Philipp Fischer, மற்றும் Thomas Brox. [U-Net: Convolutional networks for biomedical image segmentation.](https://arxiv.org/pdf/1505.04597.pdf)\n" ] diff --git a/translations/ta/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb b/translations/ta/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb index ab054b77..b07d0052 100644 --- a/translations/ta/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb +++ b/translations/ta/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb @@ -12,13 +12,13 @@ "\n", "உதாரணமாக, இன்ஸ்டன்ஸ் செக்மென்டேஷனில் பத்து கார்கள் **வேறு** பொருட்கள், செமாண்டிக் செக்மென்டேஷனில் **அனைத்து** கார்கள் ஒரு வகுப்பாக இருக்கும்.\n", "\n", - "\n", + "\n", "\n", "> படம் [இந்த வலைப்பதிவில்](https://nirmalamurali.medium.com/image-classification-vs-semantic-segmentation-vs-instance-segmentation-625c33a08d50) இருந்து\n", "\n", "கிட்டத்தட்ட அனைத்து கட்டமைப்புகளும் ஒரே அமைப்பைக் கொண்டுள்ளன. முதல் பகுதி **என்கோடர்**, இது உள்ளீட்டு படத்திலிருந்து அம்சங்களை எடுக்கிறது, இரண்டாவது பகுதி **டிகோடர்**, இது இந்த அம்சங்களை ஒரே உயரம் மற்றும் அகலத்துடன், மற்றும் சில சேனல்கள் கொண்ட படமாக மாற்றுகிறது, இது வகுப்புகளின் எண்ணிக்கைக்கு சமமாக இருக்கலாம்.\n", "\n", - "\n", + "\n", "\n", "> படம் [இந்த வெளியீட்டில்](https://arxiv.org/pdf/2001.05566.pdf) இருந்து\n" ] @@ -210,7 +210,7 @@ "\n", "எளிய என்கோடர் - டிகோடர் கட்டமைப்பு, என்கோடரில் கன்வல்யூஷன்கள், பூலிங்கள் மற்றும் டிகோடரில் கன்வல்யூஷன்கள், அப்சாம்பிளிங்கள் கொண்டது.\n", "\n", - "\n", + "\n", "\n", "* Badrinarayanan, V., Kendall, A., & Cipolla, R. (2015). [SegNet: A deep convolutional\n", "encoder-decoder architecture for image segmentation](https://arxiv.org/pdf/1511.00561.pdf)\n" @@ -602,7 +602,7 @@ "\n", "U-Net பொதுவாக அம்சங்களைப் பிரித்தெடுப்பதற்கான இயல்பான என்கோடரை கொண்டுள்ளது, உதாரணமாக resnet50.\n", "\n", - "\n", + "\n", "\n", "* Ronneberger, Olaf, Philipp Fischer, மற்றும் Thomas Brox. [U-Net: Convolutional networks for biomedical image segmentation.](https://arxiv.org/pdf/1505.04597.pdf)\n" ] diff --git a/translations/ta/lessons/4-ComputerVision/README.md b/translations/ta/lessons/4-ComputerVision/README.md index 449cf2d5..cbd58b85 100644 --- a/translations/ta/lessons/4-ComputerVision/README.md +++ b/translations/ta/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # கணினி பார்வை -![கணினி பார்வை உள்ளடக்கத்தின் சுருக்கம் ஒரு ஓவியத்தில்](../../../../translated_images/ta/ai-computervision.6506ebebac3fbf76.png) +![கணினி பார்வை உள்ளடக்கத்தின் சுருக்கம் ஒரு ஓவியத்தில்](../../../../translated_images/ta/ai-computervision.6506ebebac3fbf76.webp) இந்த பிரிவில் நாம் கற்றுக்கொள்ள போவது: diff --git a/translations/ta/lessons/5-NLP/13-TextRep/README.md b/translations/ta/lessons/5-NLP/13-TextRep/README.md index 4c3c8026..c9462592 100644 --- a/translations/ta/lessons/5-NLP/13-TextRep/README.md +++ b/translations/ta/lessons/5-NLP/13-TextRep/README.md @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: நடுநிலை மொழி செயலாக்க (NLP) பணிகளை நரம்பியல் வலையமைப்புகளுடன் தீர்க்க விரும்பினால், உரையை டென்சராக பிரதிநிதித்துவம் செய்ய ஒரு வழி தேவை. கணினிகள் ஏற்கனவே ASCII அல்லது UTF-8 போன்ற குறியீடுகளைப் பயன்படுத்தி உங்கள் திரையில் எழுத்துருக்களுக்கு வரைபடம் செய்யும் எண்களாக உரை எழுத்துக்களை பிரதிநிதித்துவம் செய்கின்றன. -ஒரு எழுத்தை ASCII மற்றும் பைனரி பிரதிநிதித்துவத்திற்கு வரைபடம் செய்யும் வரைபடத்தை காட்டும் படம் +ஒரு எழுத்தை ASCII மற்றும் பைனரி பிரதிநிதித்துவத்திற்கு வரைபடம் செய்யும் வரைபடத்தை காட்டும் படம் > [படத்தின் மூலதரவு](https://www.seobility.net/en/wiki/ASCII) @@ -48,7 +48,7 @@ CO_OP_TRANSLATOR_METADATA: உரை வகைப்படுத்தல் போன்ற பணிகளை தீர்க்கும்போது, ​​நாங்கள் ஒரு நிலையான அளவுள்ள வெக்டராக உரையை பிரதிநிதித்துவம் செய்ய வேண்டும், இது இறுதி அடர்த்தியான வகைப்படுத்தலுக்கான உள்ளீடாக பயன்படுத்தப்படும். அதைச் செய்யும் எளிய வழிகளில் ஒன்று, அனைத்து தனிப்பட்ட வார்த்தை பிரதிநிதித்துவங்களை இணைப்பது, உதாரணமாக, அவற்றைச் சேர்ப்பது. ஒவ்வொரு வார்த்தையின் ஒற்றை-சூடான குறியீட்டுகளைச் சேர்த்தால், உரையின் உள்ளே ஒவ்வொரு வார்த்தையும் எத்தனை முறை தோன்றுகிறது என்பதை காட்டும் அதிர்வெண் வெக்டராக முடிவடையும். உரையின் இப்படிப்பட்ட பிரதிநிதித்துவம் **bag of words** (BoW) என்று அழைக்கப்படுகிறது. - + > எழுத்தாளர் உருவாக்கிய படம் diff --git a/translations/ta/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/ta/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index 8297b105..0d39bbe0 100644 --- a/translations/ta/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/ta/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**Bag of Words** (BoW) வெக்டர் பிரதிநிதித்துவம் என்பது மிகவும் பரவலாக பயன்படுத்தப்படும் பாரம்பரிய வெக்டர் பிரதிநிதித்துவமாகும். ஒவ்வொரு சொல்லும் ஒரு வெக்டர் குறியீட்டுடன் இணைக்கப்பட்டிருக்கும், வெக்டர் கூறு குறிப்பிட்ட ஆவணத்தில் ஒரு சொல்லின் நிகழ்வுகளின் எண்ணிக்கையை கொண்டிருக்கும்.\n", "\n", - "![Bag of Words வெக்டர் பிரதிநிதித்துவம் நினைவகத்தில் எப்படி பிரதிநிதித்துவப்படுத்தப்படுகிறது என்பதை காட்டும் படம்.](../../../../../translated_images/ta/bag-of-words-example.606fc1738f1d7ba9.png)\n", + "![Bag of Words வெக்டர் பிரதிநிதித்துவம் நினைவகத்தில் எப்படி பிரதிநிதித்துவப்படுத்தப்படுகிறது என்பதை காட்டும் படம்.](../../../../../translated_images/ta/bag-of-words-example.606fc1738f1d7ba9.webp)\n", "\n", "> **Note**: BoW ஐ உரையில் உள்ள தனிப்பட்ட சொற்களுக்கான அனைத்து ஒரே-ஹாட்-கோடிடப்பட்ட வெக்டர்களின் கூட்டமாகவும் நீங்கள் சிந்திக்கலாம்.\n", "\n", diff --git a/translations/ta/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/ta/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index 4ef7ac85..78c43dfe 100644 --- a/translations/ta/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/ta/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**Bag-of-words** (BoW) வெக்டர் பிரதிநிதித்துவம் பாரம்பரிய வெக்டர் பிரதிநிதித்துவங்களில் மிகவும் எளிதாக புரிந்துகொள்ளக்கூடியது. ஒவ்வொரு சொல்லும் ஒரு வெக்டர் குறியீட்டுடன் இணைக்கப்பட்டிருக்கும், மேலும் ஒரு வெக்டர் கூறு ஒரு குறிப்பிட்ட ஆவணத்தில் ஒவ்வொரு சொல்லின் நிகழ்வுகளின் எண்ணிக்கையை கொண்டிருக்கும்.\n", "\n", - "![Bag-of-words வெக்டர் பிரதிநிதித்துவம் நினைவகத்தில் எப்படி பிரதிநிதித்துவப்படுத்தப்படுகிறது என்பதை காட்டும் படம்.](../../../../../translated_images/ta/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![Bag-of-words வெக்டர் பிரதிநிதித்துவம் நினைவகத்தில் எப்படி பிரதிநிதித்துவப்படுத்தப்படுகிறது என்பதை காட்டும் படம்.](../../../../../translated_images/ta/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Note**: BoW-ஐ உரையில் உள்ள தனிப்பட்ட சொற்களுக்கான அனைத்து ஒரே-ஹாட்-கோடிடப்பட்ட வெக்டர்களின் கூட்டமாகவும் நீங்கள் சிந்திக்கலாம்.\n", "\n", diff --git a/translations/ta/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/ta/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 3f77c6a5..513a0b8e 100644 --- a/translations/ta/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/ta/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "எங்கள் நெட்வொர்க்கில் முதல் லேயராக எம்பெடிங் லேயரை பயன்படுத்துவதன் மூலம், நாம் bag-of-words மாடலிலிருந்து **embedding bag** மாடலுக்கு மாற முடியும், இதில் முதலில் எங்கள் உரையில் உள்ள ஒவ்வொரு சொல்லையும் தொடர்புடைய எம்பெடிங்காக மாற்றி, பின்னர் அந்த எம்பெடிங்களிலிருந்து `sum`, `average` அல்லது `max` போன்ற ஒரு தொகுப்புக் செயல்பாட்டை கணக்கிடலாம்.\n", "\n", - "![ஐந்து வரிசை சொற்களுக்கான எம்பெடிங் வகைப்பாட்டாளரை காட்டும் படம்.](../../../../../translated_images/ta/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![ஐந்து வரிசை சொற்களுக்கான எம்பெடிங் வகைப்பாட்டாளரை காட்டும் படம்.](../../../../../translated_images/ta/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "எங்கள் வகைப்பாட்டாளர் நரம்பியல் நெட்வொர்க் எம்பெடிங் லேயருடன் தொடங்கும், பின்னர் தொகுப்பு லேயர், மற்றும் அதன் மேல் லினியர் வகைப்பாட்டாளர்:\n" ] @@ -176,7 +176,7 @@ "\n", "முந்தைய கட்டமைப்பில், மினிபேட்சில் பொருந்துவதற்காக அனைத்து வரிசைகளையும் ஒரே நீளத்திற்கு பதம் செய்ய வேண்டியிருந்தது. மாறுபட்ட நீள வரிசைகளை பிரதிநிதித்துவப்படுத்த இது மிகவும் திறமையான வழி அல்ல - மற்றொரு அணுகுமுறை **offset** வெக்டரைப் பயன்படுத்துவது, இது ஒரு பெரிய வெக்டரில் சேமிக்கப்பட்ட அனைத்து வரிசைகளின் இடைவெளிகளை வைத்திருக்கும்.\n", "\n", - "![Offset வரிசை பிரதிநிதித்துவத்தை காட்டும் படம்](../../../../../translated_images/ta/offset-sequence-representation.eb73fcefb29b46ee.png)\n", + "![Offset வரிசை பிரதிநிதித்துவத்தை காட்டும் படம்](../../../../../translated_images/ta/offset-sequence-representation.eb73fcefb29b46ee.webp)\n", "\n", "> **Note**: மேலே உள்ள படத்தில், நாம் எழுத்துக்களின் வரிசையை காட்டுகிறோம், ஆனால் எங்கள் எடுத்துக்காட்டில் நாம் சொற்களின் வரிசைகளுடன் வேலை செய்கிறோம். இருப்பினும், offset வெக்டருடன் வரிசைகளை பிரதிநிதித்துவப்படுத்தும் பொது கொள்கை மாறாமல் இருக்கும்.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW வேகமாக செயல்படுகிறது, ஆனால் ஸ்கிப்-கிராம் மெதுவாக செயல்படுகிறது, ஆனால் அரிதான வார்த்தைகளை பிரதிநிதித்துவப்படுத்த சிறப்பாக செயல்படுகிறது.\n", "\n", - "![வார்த்தைகளை வெக்டார்களாக மாற்ற CBoW மற்றும் ஸ்கிப்-கிராம் அல்காரிதங்களை காட்டும் படம்.](../../../../../translated_images/ta/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![வார்த்தைகளை வெக்டார்களாக மாற்ற CBoW மற்றும் ஸ்கிப்-கிராம் அல்காரிதங்களை காட்டும் படம்.](../../../../../translated_images/ta/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Google News தரவுத்தொகுப்பில் முன்கூட்டியே பயிற்சி செய்யப்பட்ட word2vec எம்பெடிங்குடன் பரிசோதிக்க, **gensim** நூலகத்தை பயன்படுத்தலாம். கீழே 'neural' என்ற வார்த்தைக்கு மிகவும் ஒத்த வார்த்தைகளை காணலாம்.\n", "\n", diff --git a/translations/ta/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/ta/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index 6d22003e..d9a830c7 100644 --- a/translations/ta/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/ta/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "எங்கள் நெட்வொர்க்கில் முதல் லேயராக எம்பெடிங் லேயரை பயன்படுத்துவதன் மூலம், **bag-of-words** மாடலிலிருந்து **embedding bag** மாடலுக்கு மாற முடியும். இதில், முதலில் எங்கள் உரையில் உள்ள ஒவ்வொரு வார்த்தையையும் அதற்கான எம்பெடிங்கில் மாற்றி, பின்னர் அந்த எம்பெடிங்குகளின் மீது `sum`, `average` அல்லது `max` போன்ற ஒரு தொகுப்புக் செயல்பாட்டை கணக்கிடலாம்.\n", "\n", - "![ஐந்து வரிசை வார்த்தைகளுக்கான எம்பெடிங் வகைப்பாட்டாளரை காட்டும் படம்.](../../../../../translated_images/ta/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![ஐந்து வரிசை வார்த்தைகளுக்கான எம்பெடிங் வகைப்பாட்டாளரை காட்டும் படம்.](../../../../../translated_images/ta/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "எங்கள் வகைப்பாட்டாளர் நரம்பியல் நெட்வொர்க்கில் பின்வரும் லேயர்கள் உள்ளன:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW வேகமாக செயல்படுகிறது, ஆனால் skip-gram மெதுவாக இருந்தாலும், அரிதான வார்த்தைகளை பிரதிநிதித்துவப்படுத்துவதில் சிறப்பாக செயல்படுகிறது.\n", "\n", - "![CBoW மற்றும் Skip-Gram அல்காரிதங்களை வார்த்தைகளை வெக்டார்களாக மாற்றும் முறையை காட்டும் படம்.](../../../../../translated_images/ta/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![CBoW மற்றும் Skip-Gram அல்காரிதங்களை வார்த்தைகளை வெக்டார்களாக மாற்றும் முறையை காட்டும் படம்.](../../../../../translated_images/ta/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Google News dataset-ல் முன்கூட்டியே பயிற்சி செய்யப்பட்ட Word2Vec embedding-ஐ பரிசோதிக்க, **gensim** நூலகத்தைப் பயன்படுத்தலாம். கீழே 'neural' என்ற வார்த்தைக்கு மிகவும் ஒத்த வார்த்தைகளை காணலாம்.\n", "\n", diff --git a/translations/ta/lessons/5-NLP/14-Embeddings/README.md b/translations/ta/lessons/5-NLP/14-Embeddings/README.md index b41fc3da..40e15b10 100644 --- a/translations/ta/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/ta/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ BoW அல்லது TF/IDF அடிப்படையில் வகைப எங்கள் வகைப்பாட்டாளர் நெட்வொர்க்கில் முதல் லேயராக எம்பெடிங் லேயரைப் பயன்படுத்துவதன் மூலம், BoW மாடலிலிருந்து **embedding bag** மாடலுக்கு மாறலாம், இதில் முதலில் எங்கள் உரையில் உள்ள ஒவ்வொரு வார்த்தையையும் தொடர்புடைய எம்பெடிங்காக மாற்றி, பின்னர் அந்த எம்பெடிங்குகளின் மீது `sum`, `average` அல்லது `max` போன்ற சில தொகுப்பு செயல்பாடுகளை கணக்கிடலாம். -![ஐந்து வரிசை வார்த்தைகளுக்கான எம்பெடிங் வகைப்பாட்டாளரை காட்டும் படம்.](../../../../../translated_images/ta/embedding-classifier-example.b77f021a7ee67eee.png) +![ஐந்து வரிசை வார்த்தைகளுக்கான எம்பெடிங் வகைப்பாட்டாளரை காட்டும் படம்.](../../../../../translated_images/ta/embedding-classifier-example.b77f021a7ee67eee.webp) > படத்தை உருவாக்கியவர் @@ -40,7 +40,7 @@ BoW அல்லது TF/IDF அடிப்படையில் வகைப CBoW வேகமாக செயல்படுகிறது, ஆனால் skip-gram மெதுவாக செயல்படுகிறது, ஆனால் அரிதான வார்த்தைகளை பிரதிநிதித்துவப்படுத்த சிறந்த வேலை செய்கிறது. -![வார்த்தைகளை வெக்டர்களாக மாற்ற CBoW மற்றும் Skip-Gram ஆல்கொரிதங்களை காட்டும் படம்.](../../../../../translated_images/ta/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![வார்த்தைகளை வெக்டர்களாக மாற்ற CBoW மற்றும் Skip-Gram ஆல்கொரிதங்களை காட்டும் படம்.](../../../../../translated_images/ta/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > [இந்த ஆவணத்தில்](https://arxiv.org/pdf/1301.3781.pdf) இருந்து படம் diff --git a/translations/ta/lessons/5-NLP/15-LanguageModeling/README.md b/translations/ta/lessons/5-NLP/15-LanguageModeling/README.md index 5b34b79b..6130b676 100644 --- a/translations/ta/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/ta/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ Word2Vec மற்றும் GloVe போன்ற அர்த்த அட * **கண்டினியூயஸ் பேக்-ஆஃப்-வேர்ட்ஸ்** (CBoW), இதில் நாம் ஒரு டோக்கன் வரிசையில் நடுவிலுள்ள டோக்கன் $W_0$ ஐ கணிக்கிறோம் $W_{-N}$, ..., $W_N$. * **ஸ்கிப்-கிராம்**, இதில் நாம் நடுவிலுள்ள டோக்கன் $W_0$ ஐ வைத்து அண்டை டோக்கன்களின் தொகுப்பை {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} கணிக்கிறோம். -![வார்த்தைகளை வெக்டர்களாக மாற்றும் ஆல்காரிதம்களின் உதாரணம்](../../../../../translated_images/ta/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![வார்த்தைகளை வெக்டர்களாக மாற்றும் ஆல்காரிதம்களின் உதாரணம்](../../../../../translated_images/ta/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > படம் [இந்த ஆராய்ச்சி ஆவணத்திலிருந்து](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/ta/lessons/5-NLP/16-RNN/README.md b/translations/ta/lessons/5-NLP/16-RNN/README.md index 09420c0a..7d271a5a 100644 --- a/translations/ta/lessons/5-NLP/16-RNN/README.md +++ b/translations/ta/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: உரையின் வரிசை அர்த்தத்தைப் பிடிக்க, **மீண்டும் நிகழும் நரம்பியல் வலை**, அல்லது RNN எனப்படும் மற்றொரு நரம்பியல் வலை கட்டமைப்பைப் பயன்படுத்த வேண்டும். RNN இல், நாங்கள் எங்கள் வாக்கியத்தை ஒவ்வொரு சின்னத்தையும் ஒரு நேரத்தில் வலையமைப்பில் அனுப்புகிறோம், மற்றும் வலையமைப்பு சில **நிலை** உருவாக்குகிறது, அதை அடுத்த சின்னத்துடன் மீண்டும் வலையமைப்பில் அனுப்புகிறோம். -![RNN](../../../../../translated_images/ta/rnn.27f5c29c53d727b5.png) +![RNN](../../../../../translated_images/ta/rnn.27f5c29c53d727b5.webp) > படத்தை உருவாக்கியவர் @@ -31,7 +31,7 @@ CO_OP_TRANSLATOR_METADATA: ஒரு எளிய RNN செல்லில் இரண்டு எடை மடிக்கோவைகள் உள்ளன: ஒன்று உள்ளீட்டு சின்னத்தை மாற்றுகிறது (W என்று அழைக்கலாம்), மற்றொன்று உள்ளீட்டு நிலையை மாற்றுகிறது (H). இந்த வழக்கில் வலையமைப்பின் வெளியீடு σ(W×Xi+H×Si-1+b) என கணக்கிடப்படுகிறது, இங்கு σ செயல்பாட்டைச் செயல்படுத்தும் செயல்பாடு மற்றும் b கூடுதல் பாகுபாடு. -RNN செல் அமைப்பு +RNN செல் அமைப்பு > படத்தை உருவாக்கியவர் @@ -61,7 +61,7 @@ LSTM வலையமைப்பு RNN போலவே அமைக்கப் ஒரு மீண்டும் நிகழும் வலையமைப்பு, ஒரு திசை அல்லது இருவழி, ஒரு வரிசையின் குறிப்பிட்ட முறைமைகளைப் பிடிக்கிறது, மற்றும் அவற்றை நிலை வெக்டரில் சேமிக்க அல்லது வெளியீட்டிற்கு அனுப்ப முடியும். குவியல்முறை வலையமைப்புகளின் வழக்கில், முதல் அடுக்கால் எடுக்கப்பட்ட குறைந்த நிலை முறைமைகளிலிருந்து கட்டமைக்க, முதல் அடுக்கால் எடுக்கப்பட்ட குறைந்த நிலை முறைமைகளைப் பிடிக்க மற்றொரு மீண்டும் நிகழும் அடுக்கை மேலே கட்டமைக்கலாம். இது **பல அடுக்கு RNN** என்ற கருத்துக்கு வழிவகுக்கிறது, இது இரண்டு அல்லது அதற்கு மேற்பட்ட மீண்டும் நிகழும் வலையமைப்புகளைக் கொண்டுள்ளது, இதில் முந்தைய அடுக்கின் வெளியீடு அடுத்த அடுக்கிற்கு உள்ளீடாக அனுப்பப்படுகிறது. -![பல அடுக்கு LSTM RNN](../../../../../translated_images/ta/multi-layer-lstm.dd975e29bb2a59fe.jpg) +![பல அடுக்கு LSTM RNN](../../../../../translated_images/ta/multi-layer-lstm.dd975e29bb2a59fe.webp) *Fernando López எழுதிய [இந்த அற்புதமான பதிவிலிருந்து](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) படம்* diff --git a/translations/ta/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/ta/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index d2b72678..07daa239 100644 --- a/translations/ta/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/ta/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -10,7 +10,7 @@ "\n", "உரையின் வரிசை அர்த்தத்தைப் பிடிக்க, **மீளும் நரம்பியல் வலை**, அல்லது RNN எனப்படும் மற்றொரு நரம்பியல் வலை கட்டமைப்பைப் பயன்படுத்த வேண்டும். RNN இல், நாங்கள் எங்கள் வாக்கியத்தை ஒவ்வொரு சின்னத்தையும் ஒரே நேரத்தில் வலையமைப்பில் கடத்துகிறோம், மற்றும் வலையமைப்பு சில **நிலை** உருவாக்குகிறது, அதை அடுத்த சின்னத்துடன் மீண்டும் வலையமைப்பில் கடத்துகிறோம்.\n", "\n", - "\"RNN\"\n", + "\"RNN\"\n", "\n", "உள்ளீட்டு டோக்கன்களின் வரிசை $X_0,\\dots,X_n$ கொடுக்கப்பட்டால், RNN நரம்பியல் வலைகள் வரிசையை உருவாக்குகிறது, மற்றும் இந்த வரிசையை முடிவு-to-end முறையில் பின்செலுத்தல் மூலம் பயிற்சி செய்கிறது. ஒவ்வொரு வலைகள் தொகுதியும் $(X_i,S_i)$ என்ற ஜோடியை உள்ளீடாக எடுத்து, $S_{i+1}$ என்ற முடிவை உருவாக்குகிறது. இறுதி நிலை $S_n$ அல்லது வெளியீடு $X_n$ ஒரு நேரியல் வகைப்பாட்டாளருக்குள் செலுத்தப்பட்டு முடிவை உருவாக்குகிறது. அனைத்து வலைகள் தொகுதிகளும் ஒரே எடைகளைப் பகிர்ந்து கொள்கின்றன, மற்றும் ஒரு பின்செலுத்தல் முறையில் முடிவு-to-end பயிற்சி செய்யப்படுகின்றன.\n", "\n", @@ -428,7 +428,7 @@ "\n", "ஒரு திசை அல்லது இரு திசை மீளச்சுழற்சி நெட்வொர்க்கு, ஒரு வரிசையின் குறிப்பிட்ட முறைமைகளை பிடித்து, அவற்றை state வெக்டரில் சேமிக்க அல்லது output-க்கு அனுப்ப முடியும். குவால்வோல்யூஷனல் நெட்வொர்க்குகளின் (convolutional networks) போல, முதல் அடுக்கால் எடுக்கப்பட்ட குறைந்த நிலை முறைமைகளிலிருந்து உயர் நிலை முறைமைகளை பிடிக்க, முதல் அடுக்கின் மேல் மற்றொரு மீளச்சுழற்சி அடுக்கை கட்டமைக்கலாம். இது **பல அடுக்கு RNN** என்ற கருத்துக்கு வழிவகுக்கிறது, இது இரண்டு அல்லது அதற்கு மேற்பட்ட மீளச்சுழற்சி நெட்வொர்க்குகளை கொண்டுள்ளது, இதில் முந்தைய அடுக்கின் output அடுத்த அடுக்கிற்கு input ஆக அனுப்பப்படுகிறது.\n", "\n", - "![பல அடுக்கு நீண்ட-குறுகிய-கால நினைவக RNN-ஐ காட்டும் படம்](../../../../../translated_images/ta/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![பல அடுக்கு நீண்ட-குறுகிய-கால நினைவக RNN-ஐ காட்டும் படம்](../../../../../translated_images/ta/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*Fernando López எழுதிய [இந்த அற்புதமான பதிவிலிருந்து](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) படம்*\n", "\n", diff --git a/translations/ta/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/ta/lessons/5-NLP/16-RNN/RNNTF.ipynb index 8eaa859d..1cb1526d 100644 --- a/translations/ta/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/ta/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "ஒரு உரை வரிசையின் அர்த்தத்தைப் பிடிக்க, **மீண்டும் நிகழும் நரம்பியல் வலை**, அல்லது RNN எனப்படும் நரம்பியல் வலை கட்டமைப்பைப் பயன்படுத்துவோம். RNN பயன்படுத்தும்போது, நாங்கள் எங்கள் வாக்கியத்தை வலைகளின் வழியாக ஒரு டோக்கன் ஒன்றாக அனுப்புகிறோம், மற்றும் வலைகள் சில **நிலை** உருவாக்குகிறது, அதை அடுத்த டோக்கனுடன் மீண்டும் வலைகளுக்கு அனுப்புகிறோம்.\n", "\n", - "![மீண்டும் நிகழும் நரம்பியல் வலை உருவாக்கத்தின் உதாரணத்தை காட்டும் படம்.](../../../../../translated_images/ta/rnn.27f5c29c53d727b5.png)\n", + "![மீண்டும் நிகழும் நரம்பியல் வலை உருவாக்கத்தின் உதாரணத்தை காட்டும் படம்.](../../../../../translated_images/ta/rnn.27f5c29c53d727b5.webp)\n", "\n", "$X_0,\\dots,X_n$ என்ற உள்ளீட்டு டோக்கன் வரிசையைத் தரும்போது, RNN நரம்பியல் வலைகள் வரிசையை உருவாக்குகிறது, மற்றும் இந்த வரிசையை முடிவுக்கு கொண்டு செல்ல ஒரு பின்செலுத்தல் செயல்பாட்டைப் பயன்படுத்தி பயிற்சி செய்கிறது. ஒவ்வொரு வலைகள் தொகுதியும் $(X_i,S_i)$ என்ற ஜோடியை உள்ளீடாக எடுத்து, $S_{i+1}$ என்ற முடிவை உருவாக்குகிறது. இறுதி நிலை $S_n$ அல்லது வெளியீடு $Y_n$ ஒரு நேரியல் வகைப்பாட்டாளருக்கு செல்கிறது முடிவை உருவாக்க. அனைத்து வலைகள் தொகுதிகளும் ஒரே எடைகளைப் பகிர்ந்து கொள்கின்றன, மற்றும் ஒரு பின்செலுத்தல் செயல்பாட்டைப் பயன்படுத்தி முடிவுக்கு பயிற்சி செய்யப்படுகின்றன.\n", "\n", @@ -371,7 +371,7 @@ "\n", "மீள்நோக்கு நெட்வொர்க்குகள், ஒருதிசை அல்லது இருதிசை, ஒரு வரிசையின் உள்ளமைப்புகளைப் பிடித்து, அவற்றை நிலை வெக்டர்களில் சேமிக்கின்றன அல்லது அவற்றை வெளியீடாக திருப்பி விடுகின்றன. குவால்வோல்யூஷன் நெட்வொர்க்குகளின் போல், முதல் அடுக்கால் எடுக்கப்பட்ட கீழ்நிலை அமைப்புகளிலிருந்து மேல்நிலை அமைப்புகளைப் பிடிக்க, முதல் அடுக்கைத் தொடர்ந்து மற்றொரு மீள்நோக்கு அடுக்கை உருவாக்கலாம். இது **பல அடுக்கு RNN** என்ற கருத்துக்கு வழிவகுக்கிறது, இது இரண்டு அல்லது அதற்கு மேற்பட்ட மீள்நோக்கு நெட்வொர்க்குகளை கொண்டுள்ளது, இதில் முந்தைய அடுக்கின் வெளியீடு அடுத்த அடுக்கிற்கு உள்ளீடாக அனுப்பப்படுகிறது.\n", "\n", - "![பல அடுக்கு நீண்ட-குறுகிய-கால நினைவக RNN-ஐ காட்டும் படம்](../../../../../translated_images/ta/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![பல அடுக்கு நீண்ட-குறுகிய-கால நினைவக RNN-ஐ காட்டும் படம்](../../../../../translated_images/ta/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*[Fernando López எழுதிய இந்த அற்புதமான பதிவிலிருந்து](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) படம்.*\n", "\n", diff --git a/translations/ta/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/ta/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index 80f6fcb2..e3d1ae5f 100644 --- a/translations/ta/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/ta/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "RNN-ஐ உரை உருவாக்க பயிற்சி செய்வது இதுவே. ஒவ்வொரு படியிலும், `nchars` நீளமான எழுத்துக்களின் வரிசையை எடுத்து, ஒவ்வொரு உள்ளீட்டு எழுத்துக்கான அடுத்த வெளியீட்டு எழுத்தை உருவாக்க வலியுறுத்துவோம்:\n", "\n", - "![RNN மூலம் 'HELLO' என்ற வார்த்தையை உருவாக்கும் உதாரணத்தை காட்டும் படம்.](../../../../../translated_images/ta/rnn-generate.56c54afb52f9781d.png)\n", + "![RNN மூலம் 'HELLO' என்ற வார்த்தையை உருவாக்கும் உதாரணத்தை காட்டும் படம்.](../../../../../translated_images/ta/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "உண்மையான சூழ்நிலைக்கு ஏற்ப, *end-of-sequence* `` போன்ற சில சிறப்பு எழுத்துக்களை சேர்க்க விரும்பலாம். எங்கள் நிலைமையில், முடிவில்லாத உரை உருவாக்கத்திற்காக நெட்வொர்க்கை பயிற்சி செய்ய விரும்புகிறோம், எனவே ஒவ்வொரு வரிசையின் அளவையும் `nchars` டோக்கன்களாக நிர்ணயிக்கிறோம். இதனால், ஒவ்வொரு பயிற்சி எடுத்துக்காட்டும் `nchars` உள்ளீடுகள் மற்றும் `nchars` வெளியீடுகளை (உள்ளீட்டு வரிசை ஒரு சின்னத்தை இடதுபுறம் நகர்த்தியது) கொண்டிருக்கும். மினிபேட்ச் பல இத்தகைய வரிசைகளைக் கொண்டிருக்கும்.\n", "\n", diff --git a/translations/ta/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/ta/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index 1aedf5db..3acae8c2 100644 --- a/translations/ta/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/ta/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -113,7 +113,7 @@ "\n", "செய்தி தலைப்புகளை உருவாக்க RNN-ஐ பயிற்சி செய்யும் முறை இதுவாகும். ஒவ்வொரு படியிலும், ஒரு தலைப்பை எடுத்து, அதை RNN-க்கு வழங்கி, ஒவ்வொரு உள்ளீட்டு எழுத்துக்காக, நெட்வொர்க்கை அடுத்த வெளியீட்டு எழுத்தை உருவாக்குமாறு கேட்கப்படும்:\n", "\n", - "![RNN மூலம் 'HELLO' என்ற வார்த்தையை உருவாக்கும் உதாரணத்தை காட்டும் படம்.](../../../../../translated_images/ta/rnn-generate.56c54afb52f9781d.png)\n", + "![RNN மூலம் 'HELLO' என்ற வார்த்தையை உருவாக்கும் உதாரணத்தை காட்டும் படம்.](../../../../../translated_images/ta/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "எங்கள் வரிசையின் கடைசி எழுத்துக்காக, நெட்வொர்க்கை `` டோக்கனை உருவாக்குமாறு கேட்போம்.\n", "\n", diff --git a/translations/ta/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/ta/lessons/5-NLP/17-GenerativeNetworks/README.md index 25913217..a564966b 100644 --- a/translations/ta/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/ta/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: இதனால், கீழே உள்ள படத்தில் காட்டப்பட்டுள்ள பல்வேறு நரம்பியல் கட்டமைப்புகள் உருவாகின்றன: -![பொதுவான மீளும் நரம்பியல் நெட்வொர்க் வடிவமைப்புகளை காட்டும் படம்.](../../../../../translated_images/ta/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![பொதுவான மீளும் நரம்பியல் நெட்வொர்க் வடிவமைப்புகளை காட்டும் படம்.](../../../../../translated_images/ta/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > [Andrej Karpaty](http://karpathy.github.io/) எழுதிய [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) என்ற வலைப்பதிவிலிருந்து படம் @@ -32,11 +32,11 @@ CO_OP_TRANSLATOR_METADATA: இந்த RNN-ஐ படி படியாக உரை உருவாக்க பயிற்சி செய்யலாம். ஒவ்வொரு படியிலும், `nchars` நீளத்திலான எழுத்துக்களின் வரிசையை எடுத்து, ஒவ்வொரு உள்ளீட்டு எழுத்துக்கான அடுத்த வெளியீட்டு எழுத்தை நெட்வொர்க்கிடம் கேட்போம்: -![RNN மூலம் 'HELLO' என்ற வார்த்தையை உருவாக்கும் உதாரணம் காட்டும் படம்.](../../../../../translated_images/ta/rnn-generate.56c54afb52f9781d.png) +![RNN மூலம் 'HELLO' என்ற வார்த்தையை உருவாக்கும் உதாரணம் காட்டும் படம்.](../../../../../translated_images/ta/rnn-generate.56c54afb52f9781d.webp) உரை உருவாக்கும் போது (தீர்மானத்தில்), சில **தூண்டுதல்** மூலம் தொடங்குவோம், இது RNN செல்களுக்குள் செலுத்தப்பட்டு அதன் இடைநிலை நிலையை உருவாக்கும், பின்னர் இந்த நிலையிலிருந்து உருவாக்கம் தொடங்கும். ஒவ்வொரு நேரத்திலும் ஒரு எழுத்தை உருவாக்கி, அந்த நிலை மற்றும் உருவாக்கப்பட்ட எழுத்தை மற்றொரு RNN செலுக்கு அனுப்பி அடுத்த எழுத்தை உருவாக்குவோம், தேவையான அளவு எழுத்துகளை உருவாக்கும் வரை. - + > எழுத்தாளர் உருவாக்கிய படம் diff --git a/translations/ta/lessons/5-NLP/18-Transformers/README.md b/translations/ta/lessons/5-NLP/18-Transformers/README.md index 1f9aea2e..b476c770 100644 --- a/translations/ta/lessons/5-NLP/18-Transformers/README.md +++ b/translations/ta/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ RNNகளுடன், வரிசை-முதல்-வரிசை இரண **கவன механизмங்கள்** RNN இன் ஒவ்வொரு வெளியீட்டு கணிப்பில் உள்ளீட்டு வெக்டரின் சூழலியல் தாக்கத்தை எடுக்கும் ஒரு வழியை வழங்குகின்றன. இது செயல்படுத்தப்படும் விதம், உள்ளீட்டு RNN மற்றும் வெளியீட்டு RNN இன் இடைநிலை நிலைகளுக்கு இடையே குறுக்குவழிகளை உருவாக்குவதன் மூலம். இந்த முறையில், yt வெளியீட்டு சின்னத்தை உருவாக்கும்போது, ​​hi உள்ளீட்டு மறைமாநிலங்களை, வெவ்வேறு எடை குணகங்கள் αt,i உடன் கணக்கில் எடுத்துக்கொள்வோம். -![என்கோடர்/டிகோடர் மாடல் மற்றும் சேர்க்கை கவன அடுக்கு](../../../../../translated_images/ta/encoder-decoder-attention.7a726296894fb567.png) +![என்கோடர்/டிகோடர் மாடல் மற்றும் சேர்க்கை கவன அடுக்கு](../../../../../translated_images/ta/encoder-decoder-attention.7a726296894fb567.webp) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) இல் சேர்க்கை கவன механизмம் கொண்ட என்கோடர்-டிகோடர் மாடல், [இந்த வலைப்பதிவு](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) இல் மேற்கோள். கவன அணி {αi,j} என்பது ஒரு வெளியீட்டு வரிசையில் ஒரு குறிப்பிட்ட வார்த்தையை உருவாக்குவதில் சில உள்ளீட்டு வார்த்தைகள் விளையாடும் அளவை பிரதிநிதித்துவப்படுத்தும். கீழே ஒரு அத்தகைய அணி எடுத்துக்காட்டாக உள்ளது: -![Bahdanau - arviz.org இல் இருந்து எடுத்த RNNsearch-50 மூலம் கண்டறியப்பட்ட மாதிரி ஒத்திசைவு](../../../../../translated_images/ta/bahdanau-fig3.09ba2d37f202a6af.png) +![Bahdanau - arviz.org இல் இருந்து எடுத்த RNNsearch-50 மூலம் கண்டறியப்பட்ட மாதிரி ஒத்திசைவு](../../../../../translated_images/ta/bahdanau-fig3.09ba2d37f202a6af.webp) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) இல் இருந்து எடுத்த படம் @@ -56,7 +56,7 @@ RNNகளுடன், வரிசை-முதல்-வரிசை இரண * டோக்கன் எம்பெடிங் போன்ற பயிற்சி செய்யக்கூடிய எம்பெடிங். இது இங்கு நாம் கருதும் அணுகுமுறை. டோக்கன்களுக்கும் அவற்றின் நிலைகளுக்கும் மேல் எம்பெடிங் அடுக்குகளைப் பயன்படுத்துகிறோம், இதனால் ஒரே பரிமாணங்களின் எம்பெடிங் வெக்டர்கள் கிடைக்கின்றன, பின்னர் அவற்றை ஒன்றாகச் சேர்க்கிறோம். * அசையாத நிலை குறியீட்டு செயல்பாடு, அசல் ஆவணத்தில் முன்மொழியப்பட்டது. - + > எழுத்தாளரின் படம் @@ -66,7 +66,7 @@ RNNகளுடன், வரிசை-முதல்-வரிசை இரண அடுத்ததாக, நமது வரிசையில் சில முறைபாடுகளைப் பிடிக்க வேண்டும். இதைச் செய்ய, டிரான்ஸ்ஃபார்மர்கள் **சுய-கவன механизмம்** பயன்படுத்துகின்றன, இது உள்ளீட்டு மற்றும் வெளியீட்டாக ஒரே வரிசையில் கவனம் செலுத்துவது ஆகும். சுய-கவனத்தைப் பயன்படுத்துவது வாக்கியத்தின் **சூழலத்தை** கணக்கில் எடுத்துக்கொள்ளவும், எந்த வார்த்தைகள் தொடர்புடையவை என்பதைப் பார்க்கவும் உதவுகிறது. உதாரணமாக, *it* போன்ற இணைப்புகள் எந்த வார்த்தைகளை குறிப்பிடுகின்றன என்பதைப் பார்க்கவும், சூழலத்தை கணக்கில் எடுத்துக்கொள்ளவும் உதவுகிறது: -![](../../../../../translated_images/ta/CoreferenceResolution.861924d6d384a7d6.png) +![](../../../../../translated_images/ta/CoreferenceResolution.861924d6d384a7d6.webp) > [Google வலைப்பதிவு](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) இல் இருந்து எடுத்த படம் @@ -91,7 +91,7 @@ RNNகளுடன், வரிசை-முதல்-வரிசை இரண **BERT** (Bidirectional Encoder Representations from Transformers) என்பது 12 அடுக்குகளைக் கொண்ட *BERT-base* மற்றும் 24 அடுக்குகளைக் கொண்ட *BERT-large* ஆகியவற்றுடன் மிகப்பெரிய பல அடுக்கு டிரான்ஸ்ஃபார்மர் நெட்வொர்க் ஆகும். மாடல் முதலில் ஒரு பெரிய உரை தரவுத்தொகுப்பில் (WikiPedia + புத்தகங்கள்) மேற்பயிற்சி செய்யப்படுகிறது, இது கண்காணிக்கப்படாத பயிற்சியைப் (ஒரு வாக்கியத்தில் மறைக்கப்பட்ட வார்த்தைகளை கணிக்க) பயன்படுத்துகிறது. மேற்பயிற்சியின் போது, ​​மாடல் முக்கியமான அளவிலான மொழி புரிதலை உறிஞ்சுகிறது, இது பிற தரவுத்தொகுப்புகளுடன் நன்றாக தகுந்து பயன்படுத்தப்படலாம். இந்த செயல்முறை **மாற்றம் கற்றல்** என்று அழைக்கப்படுகிறது. -![http://jalammar.github.io/illustrated-bert/ இல் இருந்து படம்](../../../../../translated_images/ta/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) +![http://jalammar.github.io/illustrated-bert/ இல் இருந்து படம்](../../../../../translated_images/ta/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > படம் [மூலம்](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/ta/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/ta/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index fdf9033c..fd4b6470 100644 --- a/translations/ta/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/ta/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**கவனிப்பு முறைமைகள்** RNN இன் ஒவ்வொரு வெளியீட்டு கணிப்பில் உள்ளீட்டு வெக்டரின் சூழலியல் தாக்கத்தை எடுக்கும் ஒரு வழியை வழங்குகின்றன. இது செயல்படுத்தப்படும் விதம் என்னவென்றால், உள்ளீட்டு RNN இன் இடைநிலை நிலைகள் மற்றும் வெளியீட்டு RNN இடையே குறுக்குவழிகளை உருவாக்குவதன் மூலம். இந்த முறையில், $y_t$ என்ற வெளியீட்டு சின்னத்தை உருவாக்கும்போது, ​​வித்தியாசமான எடை குணகங்கள் $\\alpha_{t,i}$ உடன் அனைத்து உள்ளீட்டு மறைமறைநிலை நிலைகள் $h_i$ ஐ கருத்தில் கொள்ளுவோம்.\n", "\n", - "![குறியாக்கி/குறியாக்கி மாடல் மற்றும் சேர்க்கை கவனிப்பு அடுக்கு](../../../../../translated_images/ta/encoder-decoder-attention.7a726296894fb567.png)\n", + "![குறியாக்கி/குறியாக்கி மாடல் மற்றும் சேர்க்கை கவனிப்பு அடுக்கு](../../../../../translated_images/ta/encoder-decoder-attention.7a726296894fb567.webp)\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) இல் சேர்க்கை கவனிப்பு முறைமையுடன் குறியாக்கி-குறியாக்கி மாடல், [இந்த வலைப்பதிவு](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) இடமிருந்து மேற்கோள்]*\n", "\n", "கவனிப்பு அணி $\\{\\alpha_{i,j}\\}$ என்பது ஒரு குறிப்பிட்ட உள்ளீட்டு சொற்கள் வெளியீட்டு வரிசையில் ஒரு குறிப்பிட்ட சொல்லை உருவாக்குவதில் விளையாடும் அளவை பிரதிநிதித்துவப்படுத்தும். கீழே அத்தகைய அணியின் உதாரணம் உள்ளது:\n", "\n", - "![Bahdanau - arviz.org இல் இருந்து எடுத்த RNNsearch-50 மூலம் கண்டுபிடிக்கப்பட்ட மாதAlignment](../../../../../translated_images/ta/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![Bahdanau - arviz.org இல் இருந்து எடுத்த RNNsearch-50 மூலம் கண்டுபிடிக்கப்பட்ட மாதAlignment](../../../../../translated_images/ta/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) இல் இருந்து எடுத்த படம்]*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) என்பது *BERT-base* க்கு 12 அடுக்குகள் மற்றும் *BERT-large* க்கு 24 அடுக்குகளுடன் மிகப்பெரிய பன்மடங்கு டிரான்ஸ்ஃபார்மர் நெட்வொர்க் ஆகும். இந்த மாடல் முதலில் பெரிய உர தரவுத்தொகுப்பில் (WikiPedia + புத்தகங்கள்) மேற்பயிற்சி செய்யப்படுகிறது, இது மேற்பார்வையற்ற பயிற்சியை (ஒரு வாக்கியத்தில் மறைக்கப்பட்ட சொற்களை கணிக்க) பயன்படுத்துகிறது. மேற்பயிற்சியின் போது, ​​மாடல் முக்கியமான அளவிலான மொழி புரிதலை உறிஞ்சுகிறது, இது பிற தரவுத்தொகுப்புகளுடன் நன்றாக அமைத்துக்கொள்ள முடியும். இந்த செயல்முறை **மாற்றக் கற்றல்** என்று அழைக்கப்படுகிறது.\n", "\n", - "![http://jalammar.github.io/illustrated-bert/ இல் இருந்து படம்](../../../../../translated_images/ta/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![http://jalammar.github.io/illustrated-bert/ இல் இருந்து படம்](../../../../../translated_images/ta/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "BERT, DistilBERT, BigBird, OpenGPT3 மற்றும் பலவற்றை fine-tune செய்யக்கூடிய டிரான்ஸ்ஃபார்மர் கட்டமைப்புகளின் பல மாறுபாடுகள் உள்ளன. [HuggingFace package](https://github.com/huggingface/) PyTorch உடன் இந்த கட்டமைப்புகளில் பலவற்றை பயிற்சி செய்ய ஒரு களஞ்சியத்தை வழங்குகிறது.\n", "\n", diff --git a/translations/ta/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/ta/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index 08a517ba..f58ac90b 100644 --- a/translations/ta/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/ta/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**கவனிப்பு முறைமைகள்** RNN இன் ஒவ்வொரு வெளியீட்டு கணிப்பில் உள்ளீட்டு வெக்டரின் சூழலியல் தாக்கத்தை எடுக்கும் ஒரு வழியை வழங்குகின்றன. இது செயல்படுத்தப்படும் விதம் என்னவென்றால், உள்ளீட்டு RNN இன் இடைநிலை நிலைகளுக்கும் வெளியீட்டு RNN க்கும் இடையில் குறுக்குவழிகளை உருவாக்குவதன் மூலம். இந்த முறையில், $y_t$ என்ற வெளியீட்டு சின்னத்தை உருவாக்கும்போது, ​​வித்தியாசமான எடை குணகங்கள் $\\alpha_{t,i}$ உடன் அனைத்து உள்ளீட்டு மறைமாநிலங்களை $h_i$ கணக்கில் எடுத்துக்கொள்வோம்.\n", "\n", - "![கூட்டல் கவனிப்பு அடுக்கு கொண்ட குறியாக்கி/குறியாக்கி மாடலைக் காட்டும் படம்](../../../../../translated_images/ta/encoder-decoder-attention.7a726296894fb567.png)\n", + "![கூட்டல் கவனிப்பு அடுக்கு கொண்ட குறியாக்கி/குறியாக்கி மாடலைக் காட்டும் படம்](../../../../../translated_images/ta/encoder-decoder-attention.7a726296894fb567.webp)\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) இல் உள்ள கூட்டல் கவனிப்பு முறைமையுடன் குறியாக்கி-குறியாக்கி மாடல், [இந்த வலைப்பதிவு](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) இடமிருந்து மேற்கோள்]*\n", "\n", "கவனிப்பு அட்டவணை $\\{\\alpha_{i,j}\\}$ ஒரு குறிப்பிட்ட உள்ளீட்டு சொற்கள் வெளியீட்டு வரிசையில் ஒரு கொடுக்கப்பட்ட சொல்லை உருவாக்குவதில் எந்த அளவுக்கு பங்கு வகிக்கின்றன என்பதை பிரதிநிதித்துவப்படுத்தும். கீழே அத்தகைய அட்டவணையின் உதாரணம் உள்ளது:\n", "\n", - "![Bahdanau - arviz.org இல் இருந்து எடுத்துக்கொள்ளப்பட்ட RNNsearch-50 கண்ட ஒரு மாதAlignment-ஐக் காட்டும் படம்](../../../../../translated_images/ta/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![Bahdanau - arviz.org இல் இருந்து எடுத்துக்கொள்ளப்பட்ட RNNsearch-50 கண்ட ஒரு மாதAlignment-ஐக் காட்டும் படம்](../../../../../translated_images/ta/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) இல் இருந்து எடுத்த படம்]*\n", "\n", @@ -92,7 +92,7 @@ "source": [ "இந்த அடுக்கு இரண்டு `Embedding` அடுக்குகளை கொண்டுள்ளது: டோக்கன்களை எம்பெடிங் செய்ய (முன்னதாக நாம் விவாதித்த முறையில்) மற்றும் டோக்கன் இடங்களை. டோக்கன் இடங்கள் `tf.range` பயன்படுத்தி 0 முதல் `maxlen` வரை இயற்கை எண்களின் வரிசையாக உருவாக்கப்படுகின்றன, பின்னர் எம்பெடிங் அடுக்கில் அனுப்பப்படுகின்றன. இரண்டு எம்பெடிங் வெக்டர்கள் சேர்க்கப்பட்டு, உள்ளீட்டின் இடமாற்ற எம்பெடிங் பிரதிநிதித்துவத்தை உருவாக்குகின்றன, இதன் வடிவம் `maxlen`$\\times$`embed_dim`.\n", "\n", - "\n", + "\n", "\n", "இப்போது, டிரான்ஸ்ஃபார்மர் பிளாக்கை செயல்படுத்துவோம். இது முன்பு வரையறுக்கப்பட்ட எம்பெடிங் அடுக்கின் வெளியீட்டை எடுக்கும்:\n" ] @@ -134,7 +134,7 @@ "\n", "இந்த அடுக்கின் வெளியீடு பின்னர் `Dense` நெட்வொர்க்கில் (எங்கள் வழக்கில் - இரண்டு அடுக்கு perceptron) அனுப்பப்படுகிறது, மற்றும் முடிவில் உள்ள வெளியீட்டுடன் சேர்க்கப்படுகிறது (மீண்டும் சீரமைக்கப்படுகிறது).\n", "\n", - "\n", + "\n", "\n", "இப்போது, முழுமையான transformer மாதிரியை வரையறுக்க தயாராக உள்ளோம்:\n" ] @@ -235,7 +235,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) என்பது மிகப்பெரிய, பல அடுக்கு மாற்றி நெட்வொர்க் ஆகும், இதில் *BERT-base* க்கு 12 அடுக்குகள் மற்றும் *BERT-large* க்கு 24 அடுக்குகள் உள்ளன. இந்த மாடல் முதலில் பெரிய உரை தரவுத்தொகுப்பில் (WikiPedia + புத்தகங்கள்) கண்காணிக்கப்படாத பயிற்சியை (ஒரு வாக்கியத்தில் மறைக்கப்பட்ட வார்த்தைகளை கணிக்க) பயன்படுத்தி முன்பயிற்சி செய்யப்படுகிறது. முன்பயிற்சியின் போது, மாடல் முக்கியமான மொழி புரிதலை உறிஞ்சுகிறது, இதை பிற தரவுத்தொகுப்புகளுடன் நன்றாகச் சீரமைத்து பயன்படுத்தலாம். இந்த செயல்முறை **மாற்றக் கற்றல்** என்று அழைக்கப்படுகிறது.\n", "\n", - "![http://jalammar.github.io/illustrated-bert/ இல் இருந்து படம்](../../../../../translated_images/ta/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![http://jalammar.github.io/illustrated-bert/ இல் இருந்து படம்](../../../../../translated_images/ta/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "BERT, DistilBERT, BigBird, OpenGPT3 மற்றும் பலவற்றைச் சேர்த்து நன்றாகச் சீரமைக்கக்கூடிய பல மாற்றி கட்டமைப்புகளின் மாறுபாடுகள் உள்ளன.\n", "\n", diff --git a/translations/ta/lessons/5-NLP/19-NER/README.md b/translations/ta/lessons/5-NLP/19-NER/README.md index 399d4599..4b7d32d9 100644 --- a/translations/ta/lessons/5-NLP/19-NER/README.md +++ b/translations/ta/lessons/5-NLP/19-NER/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: உங்கள் இயற்கை மொழி உரையாடல் சாட் பாட்டை உருவாக்க விரும்புகிறீர்கள் என்று நினைக்கவும், இது Amazon Alexa அல்லது Google Assistant போன்றது. புத்திசாலி சாட் பாட்டுகள் செயல்படுவது, பயனர் என்ன விரும்புகிறார் என்பதை *புரிந்து* கொள்ளும் வகையில் உள்ளீட்டு வாக்கியத்தில் உரை வகைப்படுத்தல் செய்வதன் மூலம். இந்த வகைப்படுத்தலின் முடிவு **நோக்கம்** என அழைக்கப்படுகிறது, இது சாட் பாட்டின் செயல்பாட்டை நிர்ணயிக்கிறது. -Bot NER +Bot NER > படத்தை உருவாக்கியவர் @@ -58,7 +58,7 @@ infant | O டோக்கன்கள் மற்றும் வகைகளுக்கு ஒரு-மற்றொரு தொடர்பை உருவாக்க வேண்டும் என்பதால், இந்த படத்திலிருந்து ஒரு சரியான **பல-மற்றொரு** நரம்பியல் வலை மாதிரியை உருவாக்கலாம்: -![சாதாரண மீள்நடப்ப நரம்பியல் வலைகள் பற்றிய படத்தை காட்டுகிறது.](../../../../../translated_images/ta/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![சாதாரண மீள்நடப்ப நரம்பியல் வலைகள் பற்றிய படத்தை காட்டுகிறது.](../../../../../translated_images/ta/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > *இந்த [வலைப்பதிவில்](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) இருந்து [Andrej Karpathy](http://karpathy.github.io/) எழுதிய படம். NER டோக்கன் வகைப்படுத்தல் மாதிரிகள் இந்த படத்தின் வலது பக்கம் உள்ள வலை معماريக்கு ஒத்ததாக உள்ளது.* diff --git a/translations/ta/lessons/5-NLP/README.md b/translations/ta/lessons/5-NLP/README.md index f276e41a..8b5d0568 100644 --- a/translations/ta/lessons/5-NLP/README.md +++ b/translations/ta/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # இயற்கை மொழி செயலாக்கம் -![NLP பணிகளின் சுருக்கம் ஒரு ஓவியத்தில்](../../../../translated_images/ta/ai-nlp.b22dcb8ca4707cea.png) +![NLP பணிகளின் சுருக்கம் ஒரு ஓவியத்தில்](../../../../translated_images/ta/ai-nlp.b22dcb8ca4707cea.webp) இந்த பிரிவில், **இயற்கை மொழி செயலாக்கம் (NLP)** தொடர்பான பணிகளை கையாள நரம்பியல் வலையமைப்புகளைப் பயன்படுத்துவதில் கவனம் செலுத்துவோம். கணினிகள் தீர்க்க வேண்டிய பல NLP பிரச்சினைகள் உள்ளன: diff --git a/translations/ta/lessons/6-Other/22-DeepRL/README.md b/translations/ta/lessons/6-Other/22-DeepRL/README.md index b6723cfa..8e08e695 100644 --- a/translations/ta/lessons/6-Other/22-DeepRL/README.md +++ b/translations/ta/lessons/6-Other/22-DeepRL/README.md @@ -34,7 +34,7 @@ RL க்கான ஒரு சிறந்த கருவி [OpenAI Gym](htt சமநிலையின் எளிமையான பதிப்பு **CartPole** பிரச்சினையாக அறியப்படுகிறது. CartPole உலகில், இடது அல்லது வலது நோக்கி நகரும் ஒரு கிடைமட்ட ஸ்லைடர் உள்ளது, மேலும் இலக்கு என்பது ஸ்லைடரின் மேல் ஒரு செங்குத்து தண்டை சமநிலையைப் பேணுவது. -ஒரு CartPole +ஒரு CartPole இந்த சூழலை உருவாக்கி பயன்படுத்த, Python கோடின் சில வரிகள் தேவை: diff --git a/translations/ta/lessons/6-Other/22-DeepRL/lab/README.md b/translations/ta/lessons/6-Other/22-DeepRL/lab/README.md index ceb3b79f..812724e1 100644 --- a/translations/ta/lessons/6-Other/22-DeepRL/lab/README.md +++ b/translations/ta/lessons/6-Other/22-DeepRL/lab/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: உங்கள் நோக்கம் OpenAI சூழலில் [Mountain Car](https://www.gymlibrary.ml/environments/classic_control/mountain_car/) ஐ கட்டுப்படுத்த RL முகவரை பயிற்சி செய்வதாகும். -Mountain Car +Mountain Car ## சூழல் diff --git a/translations/ta/lessons/6-Other/23-MultiagentSystems/README.md b/translations/ta/lessons/6-Other/23-MultiagentSystems/README.md index 32d06ece..60210064 100644 --- a/translations/ta/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/ta/lessons/6-Other/23-MultiagentSystems/README.md @@ -60,7 +60,7 @@ NetLogo ஐ [பதிவிறக்கம்](https://ccl.northwestern.edu/net NetLogo இன் சிறப்பம்சம், இது நீங்கள் முயற்சிக்கக்கூடிய செயல்படும் மாதிரிகளின் நூலகத்தை கொண்டுள்ளது. **File → Models Library** க்கு செல்லவும், மேலும் நீங்கள் தேர்ந்தெடுக்க பல வகைகளின் மாதிரிகள் உள்ளன. -NetLogo Models Library +NetLogo Models Library > NetLogo மாதிரிகள் நூலகத்தின் ஸ்கிரீன்ஷாட் - Dmitry Soshnikov @@ -70,7 +70,7 @@ NetLogo இன் சிறப்பம்சம், இது நீங்க மாதிரியைத் திறந்த பிறகு, நீங்கள் NetLogo இன் முக்கிய திரைக்கு கொண்டு செல்லப்படுகிறீர்கள். இங்கு முடிவான வளங்கள் (பசுமை) கொண்ட ஓநாய்கள் மற்றும் செம்மறியாடுகளின் மக்கள் தொகையை விவரிக்கும் மாதிரி உள்ளது. -![NetLogo Main Screen](../../../../../translated_images/ta/NetLogo-Main.32653711ec1a01b3.png) +![NetLogo Main Screen](../../../../../translated_images/ta/NetLogo-Main.32653711ec1a01b3.webp) > Dmitry Soshnikov இன் ஸ்கிரீன்ஷாட் diff --git a/translations/ta/lessons/README.md b/translations/ta/lessons/README.md index da9ea1c2..8a6266e7 100644 --- a/translations/ta/lessons/README.md +++ b/translations/ta/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # மேலோட்டம் -![ஒரு ஓவியத்தில் மேலோட்டம்](../../../translated_images/ta/ai-overview.0857791951d19500.png) +![ஒரு ஓவியத்தில் மேலோட்டம்](../../../translated_images/ta/ai-overview.0857791951d19500.webp) > ஸ்கெட்ச் குறிப்பு: [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/ta/lessons/X-Extras/X1-MultiModal/README.md b/translations/ta/lessons/X-Extras/X1-MultiModal/README.md index baa3f1e3..f0b174ed 100644 --- a/translations/ta/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/ta/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ NLP பணிகளைத் தீர்க்க டிரான்ஸ்ஃ CLIP இன் முக்கிய நோக்கம், உரை முன்மொழிவுகளை ஒரு படத்துடன் ஒப்பிட்டு, அந்த படம் முன்மொழிவுடன் எவ்வளவு பொருந்துகிறது என்பதைத் தீர்மானிக்க வேண்டும். -![CLIP கட்டமைப்பு](../../../../../translated_images/ta/clip-arch.b3dbf20b4e8ed8be.png) +![CLIP கட்டமைப்பு](../../../../../translated_images/ta/clip-arch.b3dbf20b4e8ed8be.webp) > *[இந்த வலைப்பதிவில்](https://openai.com/blog/clip/) இருந்து எடுத்த படம்* @@ -29,7 +29,7 @@ CLIP மாடல்/நூலகம் [OpenAI GitHub](https://github.com/opena நாம் படங்களை, உதாரணமாக, பூனைகள், நாய்கள் மற்றும் மனிதர்கள் ஆகியவற்றுக்கு இடையில் வகைப்படுத்த வேண்டும் எனக் கருதுக. இந்த நிலையில், மாடலுக்கு ஒரு படம் மற்றும் ஒரு தொடர் உரை முன்மொழிவுகளை கொடுக்கலாம்: "*ஒரு பூனையின் படம்*", "*ஒரு நாயின் படம்*", "*ஒரு மனிதனின் படம்*". 3 சாத்தியக்கூறுகளின் விளைவாக கிடைக்கும் வெக்டாரில், அதிக மதிப்புள்ள குறியீட்டை தேர்ந்தெடுக்க வேண்டும். -![பட வகைப்படுத்தலுக்கான CLIP](../../../../../translated_images/ta/clip-class.3af42ef0b2b19369.png) +![பட வகைப்படுத்தலுக்கான CLIP](../../../../../translated_images/ta/clip-class.3af42ef0b2b19369.webp) > *[இந்த வலைப்பதிவில்](https://openai.com/blog/clip/) இருந்து எடுத்த படம்* @@ -53,13 +53,13 @@ VQGAN பற்றி மேலும் அறிய [Taming Transformers](http VQGAN மற்றும் பாரம்பரிய GAN களுக்கிடையிலான முக்கியமான வேறுபாடுகளில் ஒன்று, GAN எந்த உள்ளீட்டு வெக்டாரிலிருந்தும் ஒரு நல்ல படத்தை உருவாக்க முடியும், ஆனால் VQGAN ஒருங்கிணைந்த படத்தை உருவாக்க முடியாமல் போகலாம். எனவே, பட உருவாக்க செயல்முறையை மேலும் வழிநடத்த வேண்டும், இது CLIP ஐப் பயன்படுத்தி செய்ய முடியும். -![VQGAN+CLIP கட்டமைப்பு](../../../../../translated_images/ta/vqgan.5027fe05051dfa31.png) +![VQGAN+CLIP கட்டமைப்பு](../../../../../translated_images/ta/vqgan.5027fe05051dfa31.webp) ஒரு உரை முன்மொழிவுக்கு பொருந்தும் படத்தை உருவாக்க, சில சீரற்ற குறியீட்டு வெக்டாருடன் தொடங்குகிறோம், இது VQGAN வழியாக அனுப்பப்பட்டு ஒரு படத்தை உருவாக்குகிறது. பின்னர் CLIP ஒரு இழப்புக் கோட்பாட்டை உருவாக்க பயன்படுத்தப்படுகிறது, இது படம் உரை முன்மொழிவுக்கு எவ்வளவு பொருந்துகிறது என்பதை காட்டுகிறது. பின்னர் இந்த இழப்பை குறைப்பதே நோக்கம், பின்னடைவு மூலம் உள்ளீட்டு வெக்டார் அளவுருக்களை சரிசெய்தல். VQGAN+CLIP ஐ செயல்படுத்தும் ஒரு சிறந்த நூலகம் [Pixray](http://github.com/pixray/pixray) -![Pixray மூலம் உருவாக்கப்பட்ட படம்](../../../../../translated_images/ta/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Pixray மூலம் உருவாக்கப்பட்ட படம்](../../../../../translated_images/ta/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Pixray மூலம் உருவாக்கப்பட்ட படம்](../../../../../translated_images/ta/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) +![Pixray மூலம் உருவாக்கப்பட்ட படம்](../../../../../translated_images/ta/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp) | ![Pixray மூலம் உருவாக்கப்பட்ட படம்](../../../../../translated_images/ta/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp) | ![Pixray மூலம் உருவாக்கப்பட்ட படம்](../../../../../translated_images/ta/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp) ----|----|---- முன்மொழிவு *ஒரு புத்தகத்துடன் இளம் ஆண் இலக்கிய ஆசிரியரின் நெருக்கமான நீர்வண்ண உருவப்படம்* | முன்மொழிவு *ஒரு கணினியுடன் இளம் பெண் கணினி அறிவியல் ஆசிரியரின் நெருக்கமான எண்ணெய் உருவப்படம்* | முன்மொழிவு *கரும்பலகையின் முன் முதிய ஆண் கணித ஆசிரியரின் நெருக்கமான எண்ணெய் உருவப்படம்* diff --git a/translations/te/README.md b/translations/te/README.md index eb7561f1..f91ba855 100644 --- a/translations/te/README.md +++ b/translations/te/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # ప్రారంభులకు ఆర్టిఫిషియల్ ఇంటెలిజెన్స్ - ఒక పాఠ్యాంశం -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/te/ai-overview.0857791951d19500.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/te/ai-overview.0857791951d19500.webp)| |:---:| | ప్రారంభులకు AI - _[ @girlie_mac](https://twitter.com/girlie_mac) వారి స్కెచ్ నోటు_ | diff --git a/translations/te/lessons/1-Intro/README.md b/translations/te/lessons/1-Intro/README.md index 8ee2d6dc..92cbcd2c 100644 --- a/translations/te/lessons/1-Intro/README.md +++ b/translations/te/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # AI పరిచయం -![AI పరిచయ విషయాల సారాంశం ఒక డ్రాయింగ్‌లో](../../../../translated_images/te/ai-intro.bf28d1ac4235881c.png) +![AI పరిచయ విషయాల సారాంశం ఒక డ్రాయింగ్‌లో](../../../../translated_images/te/ai-intro.bf28d1ac4235881c.webp) > స్కెచ్‌నోట్: [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: మూలంగా, కంప్యూటర్లు [చార్లెస్ బాబేజ్](https://en.wikipedia.org/wiki/Charles_Babbage) చేత సంఖ్యలపై ఒక సుస్పష్టమైన ప్రక్రియను అనుసరించి పనిచేయడానికి ఆవిష్కరించబడ్డాయి - ఒక అల్గోరిథం. ఆధునిక కంప్యూటర్లు, 19వ శతాబ్దంలో ప్రతిపాదించిన మోడల్ కంటే చాలా అభివృద్ధి చెందినప్పటికీ, ఇంకా నియంత్రిత గణనలను అనుసరిస్తాయి. కాబట్టి, లక్ష్యాన్ని సాధించడానికి అవసరమైన ఖచ్చితమైన దశలను మనం తెలుసుకుంటే, కంప్యూటర్‌ను ప్రోగ్రామ్ చేయడం సాధ్యం. -![వ్యక్తి ఫోటో](../../../../translated_images/te/dsh_age.d212a30d4e54fb5f.png) +![వ్యక్తి ఫోటో](../../../../translated_images/te/dsh_age.d212a30d4e54fb5f.webp) > ఫోటో: [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ CO_OP_TRANSLATOR_METADATA: **[మేధస్సు](https://en.wikipedia.org/wiki/Intelligence)** అనే పదం స్పష్టమైన నిర్వచనం లేకపోవడం ఒక సమస్య. మేధస్సు అనేది **సారాంశ ఆలోచన** లేదా **స్వీయ అవగాహన**కి సంబంధించినదని వాదించవచ్చు, కానీ దీన్ని సరైన రీతిలో నిర్వచించలేము. -![పిల్లి ఫోటో](../../../../translated_images/te/photo-cat.8c8e8fb760ffe457.jpg) +![పిల్లి ఫోటో](../../../../translated_images/te/photo-cat.8c8e8fb760ffe457.webp) > ఫోటో: [Amber Kipp](https://unsplash.com/@sadmax) నుండి Unsplash @@ -98,13 +98,13 @@ AGI గురించి మాట్లాడేటప్పుడు ని > | ML గురించి ఏమిటి? | | > |--------------|-----------| -> | డేటా ఆధారంగా సమస్య పరిష్కరించడానికి కంప్యూటర్ నేర్చుకునే కృత్రిమ మేధస్సు భాగం **మిషన్ లెర్నింగ్** అని పిలవబడుతుంది. ఈ కోర్సులో క్లాసికల్ మిషన్ లెర్నింగ్ చర్చించము - మీరు ప్రత్యేక [Machine Learning for Beginners](http://aka.ms/ml-beginners) పాఠ్యాంశాన్ని చూడండి. | ![ML for Beginners](../../../../translated_images/te/ml-for-beginners.9e4fed176fd5817d.png) | +> | డేటా ఆధారంగా సమస్య పరిష్కరించడానికి కంప్యూటర్ నేర్చుకునే కృత్రిమ మేధస్సు భాగం **మిషన్ లెర్నింగ్** అని పిలవబడుతుంది. ఈ కోర్సులో క్లాసికల్ మిషన్ లెర్నింగ్ చర్చించము - మీరు ప్రత్యేక [Machine Learning for Beginners](http://aka.ms/ml-beginners) పాఠ్యాంశాన్ని చూడండి. | ![ML for Beginners](../../../../translated_images/te/ml-for-beginners.9e4fed176fd5817d.webp) | ## AI చరిత్ర సంక్షిప్తంగా కృత్రిమ మేధస్సు 20వ శతాబ్ద మధ్యలో ఒక రంగంగా ప్రారంభమైంది. మొదట, సింబాలిక్ తర్కం ప్రాచుర్యం పొందింది, ఇది నిపుణుల వ్యవస్థలు వంటి విజయాలను తీసుకువచ్చింది – కొన్ని పరిమిత సమస్యల పరిధిలో నిపుణులుగా పనిచేసే కంప్యూటర్ ప్రోగ్రాములు. కానీ ఈ దృక్పథం విస్తరించలేదని స్పష్టమైంది. నిపుణుల నుండి జ్ఞానాన్ని సేకరించడం, కంప్యూటర్‌లో ప్రతినిధ్యం చేయడం, జ్ఞానాన్ని సరిగ్గా ఉంచడం చాలా క్లిష్టమైన పని మరియు చాలా ఖరీదైనది. ఇది 1970లలో [AI వింటర్](https://en.wikipedia.org/wiki/AI_winter)కి దారితీసింది. -AI చరిత్ర సంక్షిప్తం +AI చరిత్ర సంక్షిప్తం > చిత్రం: [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/te/lessons/2-Symbolic/Animals.ipynb b/translations/te/lessons/2-Symbolic/Animals.ipynb index 8ddc1ab5..04733068 100644 --- a/translations/te/lessons/2-Symbolic/Animals.ipynb +++ b/translations/te/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "ఈ నమూనాలో, కొన్ని శారీరక లక్షణాల ఆధారంగా జంతువును గుర్తించడానికి ఒక సులభమైన జ్ఞానాధారిత వ్యవస్థను అమలు చేస్తాము. ఈ వ్యవస్థను క్రింది AND-OR చెట్టు ద్వారా ప్రదర్శించవచ్చు (ఇది మొత్తం చెట్టు యొక్క ఒక భాగం, మేము సులభంగా మరిన్ని నియమాలను జోడించవచ్చు):\n", "\n", - "![](../../../../translated_images/te/AND-OR-Tree.5592d2c70187f283.png)\n" + "![](../../../../translated_images/te/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { diff --git a/translations/te/lessons/2-Symbolic/README.md b/translations/te/lessons/2-Symbolic/README.md index c125354d..ed10e722 100644 --- a/translations/te/lessons/2-Symbolic/README.md +++ b/translations/te/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # జ్ఞాన ప్రాతినిధ్యం మరియు నిపుణుల వ్యవస్థలు -![సాంబాలిక AI విషయాల సారాంశం](../../../../translated_images/te/ai-symbolic.715a30cb610411a6.png) +![సాంబాలిక AI విషయాల సారాంశం](../../../../translated_images/te/ai-symbolic.715a30cb610411a6.webp) > స్కెచ్ నోట్ [Tomomi Imura](https://twitter.com/girlie_mac) ద్వారా @@ -35,13 +35,13 @@ AI ప్రారంభ దశల్లో, తెలివైన వ్యవ * **జ్ఞానం** అనేది సమాచారాన్ని మన ప్రపంచ మోడల్‌లోకి సమీకరించడం. ఉదాహరణకు, కంప్యూటర్ అంటే ఏమిటి తెలుసుకున్న తర్వాత, అది ఎలా పనిచేస్తుంది, ధర ఎంత, దానిని ఏం కోసం ఉపయోగించవచ్చు అనే ఆలోచనలు కలుగుతాయి. ఈ సంబంధిత భావనల నెట్‌వర్క్ మన జ్ఞానాన్ని ఏర్పరుస్తుంది. * **ప్రజ్ఞ** అనేది మన ప్రపంచం గురించి మరొక స్థాయి అవగాహన, ఇది *మెటా-జ్ఞానం* ను సూచిస్తుంది, అంటే జ్ఞానాన్ని ఎప్పుడు మరియు ఎలా ఉపయోగించాలో తెలియజేస్తుంది. - + *చిత్రం [వికీపీడియా నుండి](https://commons.wikimedia.org/w/index.php?curid=37705247), Longlivetheux - స్వంత పని, CC BY-SA 4.0* కాబట్టి, **జ్ఞాన ప్రాతినిధ్యం** సమస్య అనేది కంప్యూటర్‌లో జ్ఞానాన్ని డేటా రూపంలో సమర్థవంతంగా ప్రాతినిధ్యం చేయడం, దాన్ని ఆటోమేటిక్‌గా ఉపయోగించుకునేలా చేయడం. దీన్ని ఒక స్పెక్ట్రమ్‌గా చూడవచ్చు: -![జ్ఞాన ప్రాతినిధ్యం స్పెక్ట్రమ్](../../../../translated_images/te/knowledge-spectrum.b60df631852c0217.png) +![జ్ఞాన ప్రాతినిధ్యం స్పెక్ట్రమ్](../../../../translated_images/te/knowledge-spectrum.b60df631852c0217.webp) > చిత్రం [Dmitry Soshnikov](http://soshnikov.com) ద్వారా @@ -94,7 +94,7 @@ Block Syntax | Indent | | | సాంబాలిక AI ప్రారంభ విజయాలలో ఒకటి **నిపుణుల వ్యవస్థలు** - కొన్ని పరిమిత సమస్యల పరిధిలో నిపుణులుగా వ్యవహరించే కంప్యూటర్ వ్యవస్థలు. ఇవి ఒక లేదా ఎక్కువ మానవ నిపుణుల నుండి తీసుకున్న **జ్ఞాన బేస్** ఆధారంగా ఉండి, దాని పై తర్కం చేసే **ఇన్ఫరెన్స్ ఇంజిన్** కలిగి ఉంటాయి. -![మానవ నిర్మాణం](../../../../translated_images/te/arch-human.5d4d35f1bba3ab1c.png) | ![జ్ఞాన ఆధారిత వ్యవస్థ](../../../../translated_images/te/arch-kbs.3ec5c150b09fa8da.png) +![మానవ నిర్మాణం](../../../../translated_images/te/arch-human.5d4d35f1bba3ab1c.webp) | ![జ్ఞాన ఆధారిత వ్యవస్థ](../../../../translated_images/te/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ మానవ న్యూరల్ సిస్టమ్ సరళీకృత నిర్మాణం | జ్ఞాన ఆధారిత వ్యవస్థ నిర్మాణం @@ -106,7 +106,7 @@ Block Syntax | Indent | | | ఉదాహరణకు, ఒక జంతువును దాని శారీరక లక్షణాల ఆధారంగా గుర్తించే నిపుణుల వ్యవస్థను పరిశీలిద్దాం: -![AND-OR చెట్టు](../../../../translated_images/te/AND-OR-Tree.5592d2c70187f283.png) +![AND-OR చెట్టు](../../../../translated_images/te/AND-OR-Tree.5592d2c70187f283.webp) > చిత్రం [Dmitry Soshnikov](http://soshnikov.com) ద్వారా @@ -168,7 +168,7 @@ THEN the animal is a carnivore సెమాంటిక్ వెబ్‌లో అన్ని ప్రాతినిధ్యాలు ట్రిప్లెట్లపై ఆధారపడి ఉంటాయి. ప్రతి వస్తువు మరియు ప్రతి సంబంధం ప్రత్యేకంగా URI ద్వారా గుర్తించబడతాయి. ఉదాహరణకు, ఈ AI పాఠ్యాంశం డిమిత్రి సోష్నికోవ్ 2022 జనవరి 1న అభివృద్ధి చేశారని చెప్పాలంటే, మనం ఉపయోగించగల ట్రిప్లెట్లు ఇవి: - + ``` http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 13, 2007” @@ -179,7 +179,7 @@ http://github.com/microsoft/ai-for-beginners http://purl.org/dc/elements/1.1/cre మరింత క్లిష్టమైన సందర్భంలో, సృష్టికర్తల జాబితాను నిర్వచించాలంటే, RDFలో నిర్వచించిన కొన్ని డేటా నిర్మాణాలను ఉపయోగించవచ్చు. - + > పై చిత్రాలు [డిమిత్రి సోష్నికోవ్](http://soshnikov.com) చేత రూపొందించబడ్డవి @@ -203,7 +203,7 @@ GROUP BY ?eyeColorLabel > ✅ మీ స్వంత ఆంటాలజీలు నిర్మించడానికి లేదా ఉన్న వాటిని తెరవడానికి, [Protégé](https://protege.stanford.edu/) అనే అద్భుతమైన విజువల్ ఆంటాలజీ ఎడిటర్ ఉంది. దాన్ని డౌన్లోడ్ చేసుకోండి లేదా ఆన్‌లైన్‌లో ఉపయోగించండి. - + *Web Protégé ఎడిటర్ రోమానోవ్ కుటుంబ ఆంటాలజీతో తెరవబడింది. స్క్రీన్‌షాట్ డిమిత్రి సోష్నికోవ్ చేత* diff --git a/translations/te/lessons/3-NeuralNetworks/03-Perceptron/README.md b/translations/te/lessons/3-NeuralNetworks/03-Perceptron/README.md index af7ce9d9..c513eede 100644 --- a/translations/te/lessons/3-NeuralNetworks/03-Perceptron/README.md +++ b/translations/te/lessons/3-NeuralNetworks/03-Perceptron/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: | | | |--------------|-----------| -|Frank Rosenblatt | The Mark 1 Perceptron| +|Frank Rosenblatt | The Mark 1 Perceptron| > చిత్రాలు [వికీపీడియా నుండి](https://en.wikipedia.org/wiki/Perceptron) @@ -34,7 +34,7 @@ y(x) = f(wTx) ఇక్కడ f అనేది స్టెప్ యాక్టివేషన్ ఫంక్షన్ - + ## పర్సెప్ట్రాన్ శిక్షణ diff --git a/translations/te/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/te/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index 0a579027..113c4da8 100644 --- a/translations/te/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/te/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -373,7 +373,7 @@ "\n", "మేము బైనరీ వర్గీకరణ సమస్య కోసం ఒక డేటాసెట్ సృష్టించాము. అయితే, మొదటినుండి దీన్ని బహుళ వర్గీకరణంగా పరిగణించుకుందాం, తద్వారా మనం తర్వాత సులభంగా మన కోడ్‌ను బహుళ వర్గీకరణకు మార్చుకోవచ్చు. ఈ సందర్భంలో, మన ఒక-పరత పర్సెప్ట్రాన్ క్రింది ఆర్కిటెక్చర్ కలిగి ఉంటుంది:\n", "\n", - "\n", + "\n", "\n", "నెట్‌వర్క్ యొక్క రెండు అవుట్పుట్లు రెండు వర్గాలకు సంబంధించినవి, మరియు రెండు అవుట్పుట్లలో అత్యధిక విలువ ఉన్న వర్గం సరైన పరిష్కారంగా పరిగణించబడుతుంది.\n", "\n", @@ -489,7 +489,7 @@ "\n", "మనం 2 కంటే ఎక్కువ తరగతులు ఉన్నప్పుడు, softmax వాటి అంతటా సంభావ్యతలను సరిచేస్తుంది. ఇక్కడ MNIST అంకెల వర్గీకరణ చేసే నెట్‌వర్క్ నిర్మాణం యొక్క చిత్రణ ఉంది:\n", "\n", - "![MNIST Classifier](../../../../../translated_images/te/Cross-Entropy-Loss.dc7ba633d2467ef3.png)\n" + "![MNIST Classifier](../../../../../translated_images/te/Cross-Entropy-Loss.dc7ba633d2467ef3.webp)\n" ] }, { @@ -620,7 +620,7 @@ "\n", "## గణనాత్మక గ్రాఫ్\n", "\n", - "\n", + "\n", "\n", "ఇప్పటి వరకు, మనం నెట్‌వర్క్ యొక్క వేర్వేరు లేయర్ల కోసం వేర్వేరు క్లాసులను నిర్వచించాము. ఆ లేయర్ల సమ్మేళనం **గణనాత్మక గ్రాఫ్**గా ప్రాతినిధ్యం వహించవచ్చు. ఇప్పుడు మనం ఇచ్చిన శిక్షణ డేటాసెట్ (లేదా దాని భాగం) కోసం లాస్‌ను క్రింది విధంగా లెక్కించవచ్చు:\n" ] @@ -687,7 +687,7 @@ "source": [ "## వెనుకకు ప్రేరణ\n", "\n", - "\n", + "\n", "\n", "$$\\def\\L{\\mathcal{L}}\\def\\zz#1#2{\\frac{\\partial#1}{\\partial#2}}\n", "\\begin{align}\n", @@ -712,7 +712,7 @@ "* ఇది నోడ్ $z$ లో మార్పులకు సరిపోతుంది: $\\Delta z = (\\partial\\mathcal{p}/\\partial z)\\Delta p$\n", "* ఈ లోపాన్ని తగ్గించడానికి, మనం పారామితులను తగిన విధంగా సర్దుబాటు చేయాలి: $\\Delta W = (\\partial\\mathcal{z}/\\partial W)\\Delta z$ (మరియు అదే విధంగా $b$ కోసం)\n", "\n", - "\n", + "\n", "\n", "ఈ ప్రక్రియ నెట్‌వర్క్ అవుట్‌పుట్ నుండి దాని పారామితుల వరకు లాస్ లోపాన్ని వెనుకకు పంపడం ప్రారంభిస్తుంది. అందుకే ఈ ప్రక్రియను **బ్యాక్ ప్రొపగేషన్** అంటారు.\n", "\n", @@ -1265,7 +1265,7 @@ "* తక్కువ శిక్షణ నష్టం - మోడల్ శిక్షణ డేటాను బాగా అంచనా వేయగలదు, ఎందుకంటే దానికి సరిపడా వ్యక్తీకరణ శక్తి ఉంది.\n", "* ధృవీకరణ నష్టం శిక్షణ నష్టంకంటే చాలా ఎక్కువగా ఉండవచ్చు మరియు శిక్షణ సమయంలో పెరుగుతూనే ఉండవచ్చు - ఇది మోడల్ శిక్షణ పాయింట్లను \"మరచిపోకుండా\" ఉంచడం వల్ల, \"మొత్తం దృశ్యం\" కోల్పోతుంది.\n", "\n", - "![Overfitting](../../../../../translated_images/te/overfit.a0bd57f717c15769.png)\n", + "![Overfitting](../../../../../translated_images/te/overfit.a0bd57f717c15769.webp)\n", "\n", "> ఈ చిత్రంలో, `x` శిక్షణ డేటాను సూచిస్తుంది, `o` - ధృవీకరణ డేటాను. ఎడమవైపు - రేఖీయ మోడల్ (ఒకే-పట్టా), ఇది డేటా స్వభావాన్ని బాగా అంచనా వేస్తుంది. కుడివైపు - ఓవర్‌ఫిట్టెడ్ మోడల్, మోడల్ శిక్షణ డేటాను పూర్తిగా అంచనా వేస్తుంది, కానీ ఇతర ఏ డేటాతోనూ అర్థం చేసుకోలేకపోతుంది (ధృవీకరణ లోపం చాలా ఎక్కువ).\n" ] diff --git a/translations/te/lessons/3-NeuralNetworks/04-OwnFramework/README.md b/translations/te/lessons/3-NeuralNetworks/04-OwnFramework/README.md index 028f49e0..6310a91f 100644 --- a/translations/te/lessons/3-NeuralNetworks/04-OwnFramework/README.md +++ b/translations/te/lessons/3-NeuralNetworks/04-OwnFramework/README.md @@ -65,7 +65,7 @@ CO_OP_TRANSLATOR_METADATA: గమనించండి, ఈ అన్ని వ్యక్తీకరణల ఎడమవైపు భాగం ఒకటే ఉంటుంది, కాబట్టి మనం లాస్ ఫంక్షన్ నుండి "వెనుకకు" కంప్యూటేషనల్ గ్రాఫ్ ద్వారా డెరివేటివ్స్‌ను సమర్థవంతంగా లెక్కించవచ్చు. అందువల్ల, మల్టీ-లేయర్డ్ పర్సెప్ట్రాన్ శిక్షణ పద్ధతిని **బ్యాక్‌ప్రొపగేషన్** లేదా 'బ్యాక్‌ప్రాప్' అంటారు. -compute graph +compute graph > TODO: చిత్రం మూలం diff --git a/translations/te/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/te/lessons/3-NeuralNetworks/05-Frameworks/README.md index 483f0555..b876c28a 100644 --- a/translations/te/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/te/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ CO_OP_TRANSLATOR_METADATA: క్రింది సమస్యను పరిగణించండి, 5 డాట్లను (గ్రాఫ్‌లలో `x`గా సూచించబడినవి) సన్నిహితంగా అంచనా వేయడం: -![linear](../../../../../translated_images/te/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/te/overfit2.131f5800ae10ca5e.jpg) +![linear](../../../../../translated_images/te/overfit1.f24b71c6f652e59e.webp) | ![overfit](../../../../../translated_images/te/overfit2.131f5800ae10ca5e.webp) -------------------------|-------------------------- **లీనియర్ మోడల్, 2 పారామీటర్లు** | **నాన్-లీనియర్ మోడల్, 7 పారామీటర్లు** శిక్షణ లోపం = 5.3 | శిక్షణ లోపం = 0 @@ -79,7 +79,7 @@ CO_OP_TRANSLATOR_METADATA: పై గ్రాఫ్ నుండి మీరు చూడగలిగినట్లుగా, ఓవర్‌ఫిట్టింగ్ చాలా తక్కువ శిక్షణ లోపం మరియు ఎక్కువ వాలిడేషన్ లోపం ద్వారా గుర్తించవచ్చు. సాధారణంగా శిక్షణ సమయంలో శిక్షణ మరియు వాలిడేషన్ లోపాలు రెండూ తగ్గడం ప్రారంభిస్తాయి, ఆపై ఏదో సమయంలో వాలిడేషన్ లోపం తగ్గడం ఆగి పెరుగుతుండవచ్చు. ఇది ఓవర్‌ఫిట్టింగ్ సంకేతం, మరియు ఆ సమయంలో శిక్షణను ఆపడం (లేదా కనీసం మోడల్ యొక్క స్నాప్‌షాట్ తీసుకోవడం) అవసరం. -![overfitting](../../../../../translated_images/te/Overfitting.408ad91cd90b4371.png) +![overfitting](../../../../../translated_images/te/Overfitting.408ad91cd90b4371.webp) ## ఓవర్‌ఫిట్టింగ్ ఎలా నివారించాలి diff --git a/translations/te/lessons/3-NeuralNetworks/README.md b/translations/te/lessons/3-NeuralNetworks/README.md index 81a67032..f9dd8179 100644 --- a/translations/te/lessons/3-NeuralNetworks/README.md +++ b/translations/te/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # న్యూరల్ నెట్‌వర్క్స్ పరిచయం -![Intro Neural Networks విషయాల సారాంశం డూడిల్‌లో](../../../../translated_images/te/ai-neuralnetworks.1c687ae40bc86e83.png) +![Intro Neural Networks విషయాల సారాంశం డూడిల్‌లో](../../../../translated_images/te/ai-neuralnetworks.1c687ae40bc86e83.webp) మనం పరిచయంలో చర్చించినట్లుగా, మేధస్సును సాధించడానికి ఒక మార్గం **కంప్యూటర్ మోడల్** లేదా **కృత్రిమ మెదడు**ను శిక్షణ ఇవ్వడం. 20వ శతాబ్దం మధ్య నుండి పరిశోధకులు వివిధ గణిత మోడల్స్ ప్రయత్నించారు, ఇటీవల సంవత్సరాలలో ఈ దిశ చాలా విజయవంతమైంది. మెదడుకు ఇలాంటి గణిత మోడల్స్‌ను **న్యూరల్ నెట్‌వర్క్స్** అంటారు. @@ -36,13 +36,13 @@ CO_OP_TRANSLATOR_METADATA: జీవశాస్త్రం ప్రకారం, మన మెదడు న్యూరల్ సెల్స్ (న్యూరాన్లు)తో కూడి ఉంటుంది, ప్రతి న్యూరాన్‌కు అనేక "ఇన్‌పుట్లు" (డెండ్రైట్స్) మరియు ఒకే "అవుట్‌పుట్" (ఆక్సాన్) ఉంటుంది. డెండ్రైట్స్ మరియు ఆక్సాన్లు విద్యుత్ సంకేతాలను ప్రసారం చేయగలవు, మరియు వాటి మధ్య కనెక్షన్లు — సైనాప్సెస్ అని పిలవబడతాయి — వివిధ స్థాయిలలో విద్యుత్ ప్రసరణ సామర్థ్యాన్ని చూపుతాయి, ఇవి న్యూరోట్రాన్స్‌మిటర్ల ద్వారా నియంత్రించబడతాయి. -![న్యూరాన్ మోడల్](../../../../translated_images/te/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![న్యూరాన్ మోడల్](../../../../translated_images/te/artneuron.1a5daa88d20ebe6f.png) +![న్యూరాన్ మోడల్](../../../../translated_images/te/synapse-wikipedia.ed20a9e4726ea1c6.webp) | ![న్యూరాన్ మోడల్](../../../../translated_images/te/artneuron.1a5daa88d20ebe6f.webp) ----|---- నిజమైన న్యూరాన్ *([చిత్రం](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) వికీపీడియా నుండి)* | కృత్రిమ న్యూరాన్ *(చిత్రం రచయిత ద్వారా)* కాబట్టి, న్యూరాన్ యొక్క సులభమైన గణిత మోడల్‌లో అనేక ఇన్‌పుట్లు X1, ..., XN, ఒక అవుట్‌పుట్ Y మరియు ఒక శ్రేణి వెయిట్లు W1, ..., WN ఉంటాయి. అవుట్‌పుట్ ఈ విధంగా లెక్కించబడుతుంది: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) ఇక్కడ f అనేది ఒక నాన్-లీనియర్ **యాక్టివేషన్ ఫంక్షన్**. diff --git a/translations/te/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/te/lessons/4-ComputerVision/06-IntroCV/README.md index f93fe235..810d02f9 100644 --- a/translations/te/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/te/lessons/4-ComputerVision/06-IntroCV/README.md @@ -75,14 +75,14 @@ OpenCV ను ఉపయోగించి వీడియోను ఫ్రే * **బ్రెయిల్ పుస్తకం ఫోటో ప్రీ-ప్రాసెసింగ్**. thresholding, ఫీచర్ డిటెక్షన్, perspective transformation మరియు NumPy మార్పులతో వ్యక్తిగత బ్రెయిల్ చిహ్నాలను వేరుచేసి, తరువాత న్యూరల్ నెట్‌వర్క్ ద్వారా వర్గీకరణ కోసం ఎలా సిద్ధం చేయాలో మనం దృష్టి సారిస్తాము. -![Braille Image](../../../../../translated_images/te/braille.341962ff76b1bd70.jpeg) | ![Braille Image Pre-processed](../../../../../translated_images/te/braille-result.46530fea020b03c7.png) | ![Braille Symbols](../../../../../translated_images/te/braille-symbols.0159185ab69d5339.png) +![Braille Image](../../../../../translated_images/te/braille.341962ff76b1bd70.webp) | ![Braille Image Pre-processed](../../../../../translated_images/te/braille-result.46530fea020b03c7.webp) | ![Braille Symbols](../../../../../translated_images/te/braille-symbols.0159185ab69d5339.webp) ----|-----|----- > చిత్రం [OpenCV.ipynb](OpenCV.ipynb) నుండి * **ఫ్రేమ్ తేడా ఉపయోగించి వీడియోలో కదలిక గుర్తించడం**. కెమెరా స్థిరంగా ఉంటే, కెమెరా ఫీడ్ నుండి ఫ్రేమ్‌లు ఒకదానితో ఒకటి చాలా సమానంగా ఉంటాయి. ఫ్రేమ్‌లు అర్రేలుగా ప్రాతినిధ్యం వహిస్తాయి, కాబట్టి రెండు వరుస ఫ్రేమ్‌ల అర్రేల మధ్య తేడాను తీసుకుంటే, స్థిరమైన ఫ్రేమ్‌లకు తేడా తక్కువగా ఉంటుంది, మరియు చిత్రంలో గణనీయమైన కదలిక ఉన్నప్పుడు ఎక్కువగా ఉంటుంది. -![Image of video frames and frame differences](../../../../../translated_images/te/frame-difference.706f805491a0883c.png) +![Image of video frames and frame differences](../../../../../translated_images/te/frame-difference.706f805491a0883c.webp) > చిత్రం [OpenCV.ipynb](OpenCV.ipynb) నుండి @@ -91,7 +91,7 @@ OpenCV ను ఉపయోగించి వీడియోను ఫ్రే - **Dense Optical Flow** ప్రతి పిక్సెల్ ఎక్కడికి కదులుతుందో చూపించే వెక్టర్ ఫీల్డ్‌ను లెక్కిస్తుంది - **Sparse Optical Flow** చిత్రంలోని కొన్ని ప్రత్యేక లక్షణాలను (ఉదా: అంచులు) తీసుకుని, వాటి ట్రాజెక్టరీని ఫ్రేమ్ నుండి ఫ్రేమ్ వరకు నిర్మిస్తుంది. -![Image of Optical Flow](../../../../../translated_images/te/optical.1f4a94464579a83a.png) +![Image of Optical Flow](../../../../../translated_images/te/optical.1f4a94464579a83a.webp) > చిత్రం [OpenCV.ipynb](OpenCV.ipynb) నుండి @@ -117,7 +117,7 @@ optical flow గురించి మరింత చదవండి [ఈ అ ఈ ల్యాబ్‌లో, మీరు సులభమైన జెస్తర్స్‌తో వీడియో తీసుకుంటారు, మరియు optical flow ఉపయోగించి పైకి/కిందకి/ఎడమ/కుడి కదలికలను వెలికి తీయడం మీ లక్ష్యం. -Palm Movement Frame +Palm Movement Frame --- diff --git a/translations/te/lessons/4-ComputerVision/06-IntroCV/lab/MovementDetection.ipynb b/translations/te/lessons/4-ComputerVision/06-IntroCV/lab/MovementDetection.ipynb index 3c08c5b0..97d4efb1 100644 --- a/translations/te/lessons/4-ComputerVision/06-IntroCV/lab/MovementDetection.ipynb +++ b/translations/te/lessons/4-ComputerVision/06-IntroCV/lab/MovementDetection.ipynb @@ -10,7 +10,7 @@ "\n", "ఒక వ్యక్తి చేతి తాళం స్థిరమైన నేపథ్యం మీద ఎడమ/కుడి/పై/కింద కదలుతున్న [ఈ వీడియో](../../../../../../lessons/4-ComputerVision/06-IntroCV/lab/palm-movement.mp4) ను పరిశీలించండి.\n", "\n", - "\"Palm\n", + "\"Palm\n", "\n", "**మీ లక్ష్యం** ఆప్టికల్ ఫ్లో ఉపయోగించి వీడియోలో ఎక్కడ ఎడమ/కుడి/పై/కింద కదలికలు ఉన్నాయో గుర్తించడం.\n", "\n", diff --git a/translations/te/lessons/4-ComputerVision/06-IntroCV/lab/README.md b/translations/te/lessons/4-ComputerVision/06-IntroCV/lab/README.md index d3525aa7..7a8a7c5c 100644 --- a/translations/te/lessons/4-ComputerVision/06-IntroCV/lab/README.md +++ b/translations/te/lessons/4-ComputerVision/06-IntroCV/lab/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: [ఈ వీడియో](../../../../../../lessons/4-ComputerVision/06-IntroCV/lab/palm-movement.mp4)ను పరిశీలించండి, ఇందులో ఒక వ్యక్తి చేతి తలుపు స్థిరమైన నేపథ్యంపై ఎడమ/కుడి/పై/కింద కదులుతుంది. -Palm Movement Frame +Palm Movement Frame **మీ లక్ష్యం** ఆప్టికల్ ఫ్లో ఉపయోగించి వీడియోలో ఎక్కడ ఎడమ/కుడి/పై/కింద కదలికలు ఉన్నాయో గుర్తించడం. diff --git a/translations/te/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/te/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 286936c3..59c1fc2b 100644 --- a/translations/te/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/te/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 అనేది 2014లో ImageNet టాప్-5 వర్గీకరణలో 92.7% ఖచ్చితత్వాన్ని సాధించిన నెట్‌వర్క్. దీని లేయర్ నిర్మాణం ఈ విధంగా ఉంది: -![ImageNet Layers](../../../../../translated_images/te/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet Layers](../../../../../translated_images/te/vgg-16-arch1.d901a5583b3a51ba.webp) మీరు చూడగలిగినట్లుగా, VGG సంప్రదాయమైన పిరమిడ్ నిర్మాణాన్ని అనుసరిస్తుంది, ఇది కన్‌వల్యూషన్-పూలింగ్ లేయర్ల శ్రేణి. -![ImageNet Pyramid](../../../../../translated_images/te/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet Pyramid](../../../../../translated_images/te/vgg-16-arch.64ff2137f50dd49f.webp) > చిత్రం [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) నుండి @@ -25,7 +25,7 @@ VGG-16 అనేది 2014లో ImageNet టాప్-5 వర్గీకర ResNet అనేది 2015లో Microsoft Research ప్రతిపాదించిన మోడల్స్ కుటుంబం. ResNet యొక్క ప్రధాన ఆలోచన **రెసిడ్యువల్ బ్లాక్స్** ఉపయోగించడం: - + > చిత్రం [ఈ పేపర్](https://arxiv.org/pdf/1512.03385.pdf) నుండి @@ -37,7 +37,7 @@ ResNet అనేది 2015లో Microsoft Research ప్రతిపాది Google Inception నిర్మాణం ఈ ఆలోచనను మరింత ముందుకు తీసుకెళ్తుంది, ప్రతి నెట్‌వర్క్ లేయర్‌ను అనేక మార్గాల సమ్మేళనంగా నిర్మిస్తుంది: - + > చిత్రం [Researchgate](https://www.researchgate.net/figure/Inception-module-with-dimension-reductions-left-and-schema-for-Inception-ResNet-v1_fig2_355547454) నుండి diff --git a/translations/te/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/te/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index 56e0ab90..e68bb123 100644 --- a/translations/te/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/te/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -260,7 +260,7 @@ "\n", "కాబట్టి, సాధారణ CNNలో అనేక కన్వల్యూషనల్ లేయర్లు ఉంటాయి, వాటి మధ్యలో పూలింగ్ లేయర్లు ఉంటాయి, ఇవి చిత్ర పరిమాణాలను తగ్గిస్తాయి. అలాగే ఫిల్టర్ల సంఖ్యను పెంచుతాము, ఎందుకంటే నమూనాలు మరింత అభివృద్ధి చెందుతున్నప్పుడు - మనం వెతకవలసిన ఆసక్తికరమైన సంయోజనాలు ఎక్కువగా ఉంటాయి.\n", "\n", - "![పూలింగ్ లేయర్లతో కూడిన అనేక కన్వల్యూషనల్ లేయర్లను చూపించే చిత్రం.](../../../../../translated_images/te/cnn-pyramid.85915455759ef0ce.png)\n", + "![పూలింగ్ లేయర్లతో కూడిన అనేక కన్వల్యూషనల్ లేయర్లను చూపించే చిత్రం.](../../../../../translated_images/te/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "స్థల పరిమాణాలు తగ్గడం మరియు ఫీచర్/ఫిల్టర్ల పరిమాణాలు పెరగడం వల్ల, ఈ నిర్మాణాన్ని **పిరమిడ్ నిర్మాణం** అని కూడా పిలుస్తారు.\n" ] diff --git a/translations/te/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/te/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index 3d7c5489..fd820952 100644 --- a/translations/te/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/te/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -112,7 +112,7 @@ "\n", "సాంప్రదాయ కంప్యూటర్ విజన్‌లో, చిత్రంపై అనేక ఫిల్టర్లు వర్తింపజేసి ఫీచర్లను ఉత్పత్తి చేస్తారు, ఆ ఫీచర్లు తరువాత మెషీన్ లెర్నింగ్ అల్గోరిథం ద్వారా క్లాసిఫయర్ నిర్మించడానికి ఉపయోగిస్తారు. ఆ ఫిల్టర్లు వాస్తవానికి కొన్ని జంతువుల విజన్ సిస్టమ్‌లో లభ్యమయ్యే న్యూరల్ నిర్మాణాలకు సమానంగా ఉంటాయి.\n", "\n", - "\n", + "\n", "\n", "కానీ, డీప్ లెర్నింగ్‌లో మేము క్లాసిఫికేషన్ సమస్యను పరిష్కరించడానికి ఉత్తమ కన్‌వల్యూషనల్ ఫిల్టర్లను **శిక్షణ పొందే** నెట్‌వర్క్‌లను నిర్మిస్తాము. దీని కోసం, మేము **కన్‌వల్యూషనల్ లేయర్లు**ను పరిచయం చేస్తాము.\n" ] @@ -358,7 +358,7 @@ "\n", "కాబట్టి, సాధారణ CNNలో అనేక కన్వల్యూషనల్ లేయర్లు ఉంటాయి, వాటి మధ్యలో పూలింగ్ లేయర్లు ఉంటాయి చిత్ర పరిమాణాలను తగ్గించడానికి. ఫిల్టర్ల సంఖ్యను కూడా పెంచుతాము, ఎందుకంటే నమూనాలు మరింత అభివృద్ధి చెందుతున్నప్పుడు - మనం వెతకవలసిన ఆసక్తికరమైన సంయోజనాలు ఎక్కువగా ఉంటాయి.\n", "\n", - "![పూలింగ్ లేయర్లతో కూడిన అనేక కన్వల్యూషనల్ లేయర్లను చూపించే చిత్రం.](../../../../../translated_images/te/cnn-pyramid.85915455759ef0ce.png)\n", + "![పూలింగ్ లేయర్లతో కూడిన అనేక కన్వల్యూషనల్ లేయర్లను చూపించే చిత్రం.](../../../../../translated_images/te/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "స్థల పరిమాణాలు తగ్గడం మరియు ఫీచర్/ఫిల్టర్ల పరిమాణాలు పెరగడం వలన, ఈ నిర్మాణాన్ని **పిరమిడ్ ఆర్కిటెక్చర్** అని కూడా పిలుస్తారు.\n" ] diff --git a/translations/te/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/te/lessons/4-ComputerVision/07-ConvNets/README.md index ad3c5444..3212b993 100644 --- a/translations/te/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/te/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,14 +17,14 @@ CO_OP_TRANSLATOR_METADATA: నమూనాలను తీసుకోవడానికి, మనం **కన్వల్యూషనల్ ఫిల్టర్స్** అనే భావనను ఉపయోగిస్తాము. మీరు తెలుసుకున్నట్లుగా, చిత్రం 2D-మ్యాట్రిక్స్ లేదా రంగు లోతుతో 3D-టెన్సర్ ద్వారా ప్రాతినిధ్యం వహిస్తుంది. ఫిల్టర్ వర్తింపజేయడం అంటే, మనం తక్కువ పరిమాణం గల **ఫిల్టర్ కర్నెల్** మ్యాట్రిక్స్ తీసుకుని, అసలు చిత్రంలోని ప్రతి పిక్సెల్ కోసం పక్కనున్న పాయింట్లతో బరువు గల సగటును లెక్కించడం. దీన్ని ఒక చిన్న విండో మొత్తం చిత్రంపై స్లయిడ్ అవుతూ, ఫిల్టర్ కర్నెల్ మ్యాట్రిక్స్ లోని బరువుల ప్రకారం అన్ని పిక్సెల్స్ సగటు తీసుకోవడం లాగా చూడవచ్చు. -![Vertical Edge Filter](../../../../../translated_images/te/filter-vert.b7148390ca0bc356.png) | ![Horizontal Edge Filter](../../../../../translated_images/te/filter-horiz.59b80ed4feb946ef.png) +![Vertical Edge Filter](../../../../../translated_images/te/filter-vert.b7148390ca0bc356.webp) | ![Horizontal Edge Filter](../../../../../translated_images/te/filter-horiz.59b80ed4feb946ef.webp) ----|---- > చిత్రాన్ని Dmitry Soshnikov అందించారు ఉదాహరణకు, మనం MNIST అంకెలపై 3x3 నిలువు అంచు మరియు ఆడవారపు అంచు ఫిల్టర్స్ వర్తింపజేస్తే, అసలు చిత్రంలో నిలువు మరియు ఆడవారపు అంచులు ఉన్న చోట హైలైట్స్ (ఉదా: ఎక్కువ విలువలు) పొందవచ్చు. కాబట్టి ఆ రెండు ఫిల్టర్స్ అంచులను "వెతకడానికి" ఉపయోగించవచ్చు. అలాగే, మనం ఇతర తక్కువ స్థాయి నమూనాలను వెతకడానికి వివిధ ఫిల్టర్స్ రూపొందించవచ్చు: - + > [Leung-Malik ఫిల్టర్ బ్యాంక్](https://www.robots.ox.ac.uk/~vgg/research/texclass/filters.html) చిత్రం @@ -38,7 +38,7 @@ CNNలు పనిచేసే విధానం ఈ క్రింది మ * ఫిల్టర్స్ స్వయంచాలకంగా శిక్షణ పొందేలా నెట్‌వర్క్‌ను రూపొందించవచ్చు * అసలు చిత్రంలో మాత్రమే కాకుండా, ఉన్నత స్థాయి లక్షణాలలో కూడా నమూనాలను కనుగొనడానికి అదే విధానాన్ని ఉపయోగించవచ్చు. కాబట్టి CNN లక్షణాల తీసుకోవడం తక్కువ స్థాయి పిక్సెల్ సంయోజనాల నుండి మొదలుకొని, చిత్ర భాగాల ఉన్నత స్థాయి సంయోజనాల వరకు లక్షణాల హైరార్కీపై పనిచేస్తుంది. -![Hierarchical Feature Extraction](../../../../../translated_images/te/FeatureExtractionCNN.d9b456cbdae7cb64.png) +![Hierarchical Feature Extraction](../../../../../translated_images/te/FeatureExtractionCNN.d9b456cbdae7cb64.webp) > [Hislop-Lynch పేపర్](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d) నుండి చిత్రం, వారి [గవేషణ](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) ఆధారంగా @@ -55,9 +55,9 @@ CNNలు పనిచేసే విధానం ఈ క్రింది మ ఉదాహరణకు, 2014లో ImageNet టాప్-5 వర్గీకరణలో 92.7% ఖచ్చితత్వం సాధించిన VGG-16 ఆర్కిటెక్చర్‌ను చూద్దాం: -![ImageNet Layers](../../../../../translated_images/te/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet Layers](../../../../../translated_images/te/vgg-16-arch1.d901a5583b3a51ba.webp) -![ImageNet Pyramid](../../../../../translated_images/te/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet Pyramid](../../../../../translated_images/te/vgg-16-arch.64ff2137f50dd49f.webp) > [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) నుండి చిత్రం diff --git a/translations/te/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/te/lessons/4-ComputerVision/07-ConvNets/lab/README.md index e8a6f70e..f47f385e 100644 --- a/translations/te/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/te/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ CO_OP_TRANSLATOR_METADATA: మేము [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/) ఉపయోగించబోతున్నాము, ఇది 37 వేర్వేరు జాతుల కుక్కలు మరియు పిల్లుల చిత్రాలను కలిగి ఉంది. -![మేము ఉపయోగించబోయే డేటాసెట్](../../../../../../translated_images/te/data.50b2a9d5484bdbf0.png) +![మేము ఉపయోగించబోయే డేటాసెట్](../../../../../../translated_images/te/data.50b2a9d5484bdbf0.webp) డేటాసెట్‌ను డౌన్లోడ్ చేసుకోవడానికి, ఈ కోడ్ స్నిపెట్ ఉపయోగించండి: diff --git a/translations/te/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/te/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index 162ebdcf..91149dce 100644 --- a/translations/te/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/te/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "సరైన పిల్లిని చూపించడానికి, మనం ఒక యాదృచ్ఛిక శబ్ద చిత్రం నుండి ప్రారంభించి, గ్రేడియంట్ డిసెంట్ ఆప్టిమైజేషన్ సాంకేతికతను ఉపయోగించి చిత్రాన్ని సర్దుబాటు చేస్తూ, నెట్‌వర్క్ ఒక పిల్లిని గుర్తించగలుగుతుంది.\n", "\n", - "![Optimization Loop](../../../../../translated_images/te/ideal-cat-loop.999fbb8ff306e044.png)\n", + "![Optimization Loop](../../../../../translated_images/te/ideal-cat-loop.999fbb8ff306e044.webp)\n", "\n", "ఇది మన ప్రారంభ చిత్రం:\n" ] diff --git a/translations/te/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/te/lessons/4-ComputerVision/08-TransferLearning/README.md index c7cb78ff..d31f58a3 100644 --- a/translations/te/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/te/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Keras మరియు PyTorch రెండింటిలోనూ సాధా ఇక్కడ VGG-16 నెట్‌వర్క్ ద్వారా పిల్లి చిత్రంలో నుండి తీసుకున్న కొన్ని ఫీచర్లు ఉన్నాయి: -![Features extracted by VGG-16](../../../../../translated_images/te/features.6291f9c7ba3a0b95.png) +![Features extracted by VGG-16](../../../../../translated_images/te/features.6291f9c7ba3a0b95.webp) ## పిల్లులు vs. కుక్కల డేటాసెట్ @@ -48,19 +48,19 @@ Keras మరియు PyTorch రెండింటిలోనూ సాధా మనము తీసుకోగల ఒక విధానం, యాదృచ్ఛిక చిత్రంతో ప్రారంభించి, ఆ చిత్రాన్ని **గ్రాడియెంట్ డిసెంట్ ఆప్టిమైజేషన్** సాంకేతికత ఉపయోగించి సవరించడం, తద్వారా నెట్‌వర్క్ ఆ చిత్రాన్ని పిల్లిగా భావించడం ప్రారంభిస్తుంది. -![Image Optimization Loop](../../../../../translated_images/te/ideal-cat-loop.999fbb8ff306e044.png) +![Image Optimization Loop](../../../../../translated_images/te/ideal-cat-loop.999fbb8ff306e044.webp) కానీ, ఇలాచేస్తే, మనం యాదృచ్ఛిక శబ్దం లాంటి దృశ్యాన్ని పొందుతాము. ఇది ఎందుకంటే *నెట్‌వర్క్ ఇన్‌పుట్ చిత్రాన్ని పిల్లిగా భావించడానికి అనేక మార్గాలు ఉన్నాయి*, వాటిలో కొన్ని దృశ్యంగా అర్థం కానివి కూడా ఉంటాయి. ఆ చిత్రాలు పిల్లికి సాధారణమైన చాలా నమూనాలను కలిగి ఉన్నప్పటికీ, అవి దృశ్యంగా ప్రత్యేకంగా ఉండాలని ఎటువంటి నియంత్రణ లేదు. ఫలితాన్ని మెరుగుపరచడానికి, మనం లాస్ ఫంక్షన్‌లో మరో పదాన్ని చేర్చవచ్చు, దీనిని **వేరియేషన్ లాస్** అంటారు. ఇది చిత్రంలోని పొరుగువారి పిక్సెల్స్ ఎంత సమానంగా ఉన్నాయో చూపే ప్రమాణం. వేరియేషన్ లాస్‌ను తగ్గించడం ద్వారా చిత్రం మృదువుగా మారుతుంది, శబ్దం తొలగిపోతుంది - తద్వారా మరింత ఆకర్షణీయమైన నమూనాలు బయటపడతాయి. ఇక్కడ పిల్లిగా మరియు జెబ్రాగా అధిక సంభావ్యతతో వర్గీకరించబడిన "ఆదర్శ" చిత్రాల ఉదాహరణ ఉంది: -![Ideal Cat](../../../../../translated_images/te/ideal-cat.203dd4597643d6b0.png) | ![Ideal Zebra](../../../../../translated_images/te/ideal-zebra.7f70e8b54ee15a7a.png) +![Ideal Cat](../../../../../translated_images/te/ideal-cat.203dd4597643d6b0.webp) | ![Ideal Zebra](../../../../../translated_images/te/ideal-zebra.7f70e8b54ee15a7a.webp) -----|----- *ఆదర్శ పిల్లి* | *ఆదర్శ జెబ్రా* ఇలాంటి విధానం న్యూరల్ నెట్‌వర్క్‌పై **ప్రత్యర్థి దాడులు** చేయడానికి కూడా ఉపయోగించవచ్చు. ఒక నెట్‌వర్క్‌ను మోసం చేసి కుక్కను పిల్లిలా చూపించాలనుకుంటే, కుక్క చిత్రాన్ని తీసుకుని, అది నెట్‌వర్క్ ద్వారా కుక్కగా గుర్తించబడినప్పుడు, గ్రాడియెంట్ డిసెంట్ ఆప్టిమైజేషన్ ఉపయోగించి కొంత సవరించి, నెట్‌వర్క్ దాన్ని పిల్లిగా వర్గీకరించడం ప్రారంభించే వరకు ప్రయత్నించవచ్చు: -![Picture of a Dog](../../../../../translated_images/te/original-dog.8f68a67d2fe0911f.png) | ![Picture of a dog classified as a cat](../../../../../translated_images/te/adversarial-dog.d9fc7773b0142b89.png) +![Picture of a Dog](../../../../../translated_images/te/original-dog.8f68a67d2fe0911f.webp) | ![Picture of a dog classified as a cat](../../../../../translated_images/te/adversarial-dog.d9fc7773b0142b89.webp) -----|----- *కుక్క యొక్క అసలు చిత్రం* | *పిల్లిగా వర్గీకరించబడిన కుక్క చిత్రం* diff --git a/translations/te/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/te/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index c2eadcf6..61211f96 100644 --- a/translations/te/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/te/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "మనం ఆటోఎంకోడర్‌ను అసలు చిత్రంలోని సమాచారాన్ని ఎక్కువగా పట్టుకోవడానికి శిక్షణ ఇస్తున్నందున, నెట్‌వర్క్ ఇన్‌పుట్ చిత్రాల అర్థాన్ని పట్టుకోవడానికి ఉత్తమమైన **ఎంబెడ్డింగ్**ను కనుగొనడానికి ప్రయత్నిస్తుంది.\n", "\n", - "![AutoEncoder Diagram](../../../../../translated_images/te/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![AutoEncoder Diagram](../../../../../translated_images/te/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "> చిత్రం [Keras బ్లాగ్](https://blog.keras.io/building-autoencoders-in-keras.html) నుండి\n", "\n", @@ -941,7 +941,7 @@ " * మేము $N(\\mathrm{z\\_mean},e^{\\mathrm{z\\_log\\_sigma}})$ పంపిణీ నుండి `sample(z_val in code)` అనే వెక్టర్‌ను నమూనా తీసుకుంటాము\n", " * డీకోడర్ ఆ `sample` ను ఇన్‌పుట్ వెక్టర్‌గా ఉపయోగించి అసలు చిత్రాన్ని పునర్నిర్మించడానికి ప్రయత్నిస్తుంది\n", "\n", - " \n", + " \n", "\n", " > ఇమేజ్ [ఈ బ్లాగ్ పోస్ట్](https://ijdykeman.github.io/ml/2016/12/21/cvae.html) నుండి, ఇసాక్ డైకెమన్ రచన\n" ] @@ -1264,7 +1264,7 @@ "\n", "ఈ విధానంలో మనకు **మూడు నష్టాల ఫంక్షన్లు** ఉంటాయి: GANల నుండి జనరేటర్ నష్టం, discriminator నష్టం మరియు VAE నుండి పునర్నిర్మాణ నష్టం.\n", "\n", - "\n", + "\n", "\n", "> చిత్రం [ఈ బ్లాగ్ పోస్ట్](https://blog.paperspace.com/adversarial-autoencoders-with-pytorch/) నుండి ఫెలిపే డుకౌ ద్వారా\n" ] diff --git a/translations/te/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/te/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index 127b4a1d..b527962d 100644 --- a/translations/te/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/te/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -21,7 +21,7 @@ "\n", "మనం ఆటోఎంకోడర్‌ను అసలు చిత్రంలోని సమాచారాన్ని ఎక్కువగా పట్టుకోవడానికి శిక్షణ ఇస్తున్నందున, నెట్‌వర్క్ ఇన్‌పుట్ చిత్రాల అర్థాన్ని పట్టుకోవడానికి ఉత్తమమైన **ఎంబెడ్డింగ్**ను కనుగొనడానికి ప్రయత్నిస్తుంది.\n", "\n", - "![AutoEncoder Diagram](../../../../../translated_images/te/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![AutoEncoder Diagram](../../../../../translated_images/te/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "*చిత్రం [Keras బ్లాగ్](https://blog.keras.io/building-autoencoders-in-keras.html) నుండి*\n", "\n", @@ -888,7 +888,7 @@ " * మేము $N(\\mathrm{z\\_mean},e^{\\mathrm{z\\_log\\_sigma}})$ పంపిణీ నుండి `sample` అనే వెక్టర్‌ను నమూనా తీసుకుంటాము\n", " * డీకోడర్ `sample`ని ఇన్‌పుట్ వెక్టర్‌గా ఉపయోగించి అసలు చిత్రాన్ని డీకోడ్ చేయడానికి ప్రయత్నిస్తుంది\n", "\n", - " \n" + " \n" ] }, { diff --git a/translations/te/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/te/lessons/4-ComputerVision/09-Autoencoders/README.md index 7c7fc4a5..cf4b5389 100644 --- a/translations/te/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/te/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ CNNలను శిక్షణ ఇచ్చేటప్పుడు, ఒక స మనం అసలు చిత్రంలోని సమాచారాన్ని ఎక్కువగా పట్టు కోవడానికి ఆటోఎంకోడర్‌ను శిక్షణ ఇస్తున్నందున, నెట్‌వర్క్ ఇన్‌పుట్ చిత్రాల అర్థాన్ని పట్టు కోవడానికి ఉత్తమ **ఎంబెడ్డింగ్**ను కనుగొనడానికి ప్రయత్నిస్తుంది. -![AutoEncoder Diagram](../../../../../translated_images/te/autoencoder_schema.5e6fc9ad98a5eb61.jpg) +![AutoEncoder Diagram](../../../../../translated_images/te/autoencoder_schema.5e6fc9ad98a5eb61.webp) > చిత్రం [Keras బ్లాగ్](https://blog.keras.io/building-autoencoders-in-keras.html) నుండి @@ -46,7 +46,7 @@ VAE అనేది లాటెంట్ పారామీటర్ల *సా * మనం N(zmean,exp(zlog_sigma)) పంపిణీ నుండి ఒక `sample` వెక్టర్‌ను సాంపిల్ చేస్తాము * డీకోడర్ `sample`ని ఇన్‌పుట్ వెక్టర్‌గా ఉపయోగించి అసలు చిత్రాన్ని పునఃసృష్టించడానికి ప్రయత్నిస్తుంది - + > చిత్రం [ఈ బ్లాగ్ పోస్ట్](https://ijdykeman.github.io/ml/2016/12/21/cvae.html) నుండి, ఇసాక్ డైకెమన్ @@ -57,13 +57,13 @@ VAE అనేది లాటెంట్ పారామీటర్ల *సా VAEల ముఖ్యమైన లాభం ఏమిటంటే, మనకు లాటెంట్ వెక్టర్లను ఎక్కడి నుండి సాంపిల్ చేయాలో తెలుసు కాబట్టి, కొత్త చిత్రాలను సులభంగా సృష్టించవచ్చు. ఉదాహరణకు, 2D లాటెంట్ వెక్టర్‌తో MNISTపై VAE శిక్షణ ఇస్తే, మనం లాటెంట్ వెక్టర్ భాగాలను మార్చి వేర్వేరు అంకెలను పొందవచ్చు: -vaemnist +vaemnist > చిత్రం [డ్మిత్రి సోష్నికోవ్](http://soshnikov.com) ద్వారా లాటెంట్ పారామీటర్ స్పేస్ యొక్క వేర్వేరు భాగాల నుండి లాటెంట్ వెక్టర్లను పొందడం ప్రారంభించినప్పుడు చిత్రాలు ఎలా కలిసిపోతున్నాయో గమనించండి. మనం ఈ స్పేస్‌ను 2Dలో కూడా దృశ్యీకరించవచ్చు: -vaemnist cluster +vaemnist cluster > చిత్రం [డ్మిత్రి సోష్నికోవ్](http://soshnikov.com) ద్వారా diff --git a/translations/te/lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb b/translations/te/lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb index bebba2d6..12e0ee97 100644 --- a/translations/te/lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb +++ b/translations/te/lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb @@ -15,7 +15,7 @@ "* **జనరేటర్** ఒక యాదృచ్ఛిక వెక్టర్ తీసుకుని, దానినుండి చిత్రం సృష్టించాలి\n", "* **డిస్క్రిమినేటర్** ఒక నెట్‌వర్క్, ఇది అసలు చిత్రం (శిక్షణ డేటాసెట్ నుండి) మరియు జనరేటర్ సృష్టించిన చిత్రాన్ని వేరుచేయగలగాలి.\n", "\n", - "\n" + "\n" ] }, { @@ -670,7 +670,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n", + "\n", "\n", "> చిత్రం [ఈ ట్యుటోరియల్](https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html) నుండి తీసుకోబడింది\n" ] diff --git a/translations/te/lessons/4-ComputerVision/10-GANs/GANTF.ipynb b/translations/te/lessons/4-ComputerVision/10-GANs/GANTF.ipynb index d2f0248b..d8555b3f 100644 --- a/translations/te/lessons/4-ComputerVision/10-GANs/GANTF.ipynb +++ b/translations/te/lessons/4-ComputerVision/10-GANs/GANTF.ipynb @@ -15,7 +15,7 @@ "* **జనరేటర్** ఒక యాదృచ్ఛిక వెక్టర్ తీసుకుని, దానినుండి చిత్రం సృష్టించాలి\n", "* **డిస్క్రిమినేటర్** ఒక నెట్‌వర్క్, ఇది అసలు చిత్రం (శిక్షణ డేటాసెట్ నుండి) మరియు జనరేటర్ సృష్టించిన చిత్రాన్ని వేరుచేయాలి.\n", "\n", - "\n" + "\n" ] }, { diff --git a/translations/te/lessons/4-ComputerVision/10-GANs/README.md b/translations/te/lessons/4-ComputerVision/10-GANs/README.md index a8b5673d..b2f71597 100644 --- a/translations/te/lessons/4-ComputerVision/10-GANs/README.md +++ b/translations/te/lessons/4-ComputerVision/10-GANs/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: GAN యొక్క ప్రధాన ఆలోచన రెండు న్యూరల్ నెట్‌వర్క్స్‌ను ఒకదానితో ఒకటి పోటీగా శిక్షణ ఇవ్వడం: - + > చిత్రం: [Dmitry Soshnikov](http://soshnikov.com) @@ -41,7 +41,7 @@ CNN డిస్క్రిమినేటర్ లో ఈ క్రింద > ✅ కన్వల్యూషన్ లేయర్ చిత్రం మీద లీనియర్ ఫిల్టర్ లాగా అమలు కావడంతో, డీకన్వల్యూషన్ కూడా కన్వల్యూషన్ లాగా ఉంటుంది మరియు అదే లేయర్ లాజిక్ ఉపయోగించి అమలు చేయవచ్చు. - + > చిత్రం: [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/te/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/te/lessons/4-ComputerVision/11-ObjectDetection/README.md index 3754bea6..f60b0020 100644 --- a/translations/te/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/te/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: ## [ప్రీ-లెక్చర్ క్విజ్](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Object Detection](../../../../../translated_images/te/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) +![Object Detection](../../../../../translated_images/te/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp) > చిత్రం [YOLO v2 వెబ్ సైట్](https://pjreddie.com/darknet/yolov2/) నుండి @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 2. ప్రతి టైల్స్‌పై ఇమేజ్ క్లాసిఫికేషన్ నడపండి. 3. తగినంతగా అధిక యాక్టివేషన్ ఉన్న టైల్స్ ఆ వస్తువు ఉన్నట్లు భావించవచ్చు. -![Naive Object Detection](../../../../../translated_images/te/naive-detection.e7f1ba220ccd08c6.png) +![Naive Object Detection](../../../../../translated_images/te/naive-detection.e7f1ba220ccd08c6.webp) > *చిత్రం [Exercise Notebook](ObjectDetection-TF.ipynb) నుండి* @@ -42,7 +42,7 @@ CO_OP_TRANSLATOR_METADATA: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 తరగతులు * [COCO](http://cocodataset.org/#home) - Common Objects in Context. 80 తరగతులు, బౌండింగ్ బాక్స్‌లు మరియు సెగ్మెంటేషన్ మాస్కులు -![COCO](../../../../../translated_images/te/coco-examples.71bc60380fa6cceb.jpg) +![COCO](../../../../../translated_images/te/coco-examples.71bc60380fa6cceb.webp) ## ఆబ్జెక్ట్ డిటెక్షన్ మెట్రిక్స్ @@ -50,7 +50,7 @@ CO_OP_TRANSLATOR_METADATA: ఇమేజ్ క్లాసిఫికేషన్‌లో అల్గోరిథం ఎంత బాగా పనిచేస్తుందో కొలవడం సులభం, కానీ ఆబ్జెక్ట్ డిటెక్షన్‌లో క్లాస్ సరైనదా మరియు అంచనా వేయబడిన బౌండింగ్ బాక్స్ స్థానం ఎంత ఖచ్చితమో రెండింటినీ కొలవాలి. రెండవదానికి, మనం **ఇంటర్సెక్షన్ ఓవర్ యూనియన్** (IoU) అనే ప్రమాణాన్ని ఉపయోగిస్తాము, ఇది రెండు బాక్స్‌లు (లేదా ఏదైనా రెండు ప్రాంతాలు) ఎంత overlap అవుతాయో కొలుస్తుంది. -![IoU](../../../../../translated_images/te/iou_equation.9a4751d40fff4e11.png) +![IoU](../../../../../translated_images/te/iou_equation.9a4751d40fff4e11.webp) > *చిత్రం [ఈ అద్భుతమైన IoU బ్లాగ్ పోస్ట్](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/) నుండి* @@ -97,11 +97,11 @@ $$ [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) ఉపయోగించి ROI ప్రాంతాల హైరార్కికల్ నిర్మాణాన్ని సృష్టిస్తుంది, వాటిని CNN ఫీచర్ ఎక్స్‌ట్రాక్టర్లు మరియు SVM-క్లాసిఫయర్లకు పంపించి వస్తువు తరగతిని నిర్ణయిస్తారు, మరియు లీనియర్ రెగ్రెషన్ ద్వారా *బౌండింగ్ బాక్స్* కోఆర్డినేట్లను అంచనా వేస్తారు. [అధికారిక పేపర్](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/te/rcnn1.cae407020dfb1d1f.png) +![RCNN](../../../../../translated_images/te/rcnn1.cae407020dfb1d1f.webp) > *చిత్రం van de Sande et al. ICCV’11 నుండి* -![RCNN-1](../../../../../translated_images/te/rcnn2.2d9530bb83516484.png) +![RCNN-1](../../../../../translated_images/te/rcnn2.2d9530bb83516484.webp) > *చిత్రాలు [ఈ బ్లాగ్](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e) నుండి* @@ -109,7 +109,7 @@ $$ ఈ విధానం R-CNNకి సమానంగా ఉంటుంది, కానీ ప్రాంతాలు కన్వల్యూషన్ లేయర్లు వరుసగా వర్తించిన తర్వాత నిర్వచించబడతాయి. -![FRCNN](../../../../../translated_images/te/f-rcnn.3cda6d9bb4188875.png) +![FRCNN](../../../../../translated_images/te/f-rcnn.3cda6d9bb4188875.webp) > చిత్రం [అధికారిక పేపర్](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 నుండి @@ -117,7 +117,7 @@ $$ ఈ విధానం ప్రధాన ఆలోచన ROIs అంచనా వేయడానికి న్యూరల్ నెట్‌వర్క్‌ను ఉపయోగించడం - దీనిని *Region Proposal Network* అంటారు. [పేపర్](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/te/faster-rcnn.8d46c099b87ef30a.png) +![FasterRCNN](../../../../../translated_images/te/faster-rcnn.8d46c099b87ef30a.webp) > చిత్రం [అధికారిక పేపర్](https://arxiv.org/pdf/1506.01497.pdf) నుండి @@ -129,7 +129,7 @@ $$ 2. ఫీచర్లు **Position-Sensitive Score Map** ద్వారా ప్రాసెస్ చేయబడతాయి. $C$ తరగతులలో ప్రతి వస్తువు $k\times k$ ప్రాంతాలుగా విభజించబడుతుంది, మరియు మనం వస్తువుల భాగాలను అంచనా వేయడానికి శిక్షణ పొందుతాము. 3. $k\times k$ ప్రాంతాల ప్రతి భాగం కోసం అన్ని నెట్‌వర్క్లు వస్తువు తరగతుల కోసం ఓటు వేస్తాయి, గరిష్ట ఓటు పొందిన వస్తువు తరగతి ఎంచుకోబడుతుంది. -![r-fcn image](../../../../../translated_images/te/r-fcn.13eb88158b99a3da.png) +![r-fcn image](../../../../../translated_images/te/r-fcn.13eb88158b99a3da.webp) > చిత్రం [అధికారిక పేపర్](https://arxiv.org/abs/1605.06409) నుండి @@ -140,7 +140,7 @@ YOLO ఒక రియల్‌టైమ్ ఒకసారి నడిపే * చిత్రాన్ని $S\times S$ ప్రాంతాలుగా విభజించడం * ప్రతి ప్రాంతం కోసం, **CNN** $n$ సాధ్యమైన వస్తువులు, *బౌండింగ్ బాక్స్* కోఆర్డినేట్లు మరియు *confidence* = *probability* * IoU అంచనా వేయడం. - ![YOLO](../../../../../translated_images/te/yolo.a2648ec82ee8bb4e.png) + ![YOLO](../../../../../translated_images/te/yolo.a2648ec82ee8bb4e.webp) > చిత్రం [అధికారిక పేపర్](https://arxiv.org/abs/1506.02640) నుండి diff --git a/translations/te/lessons/4-ComputerVision/12-Segmentation/README.md b/translations/te/lessons/4-ComputerVision/12-Segmentation/README.md index 835bb616..8db8d4fb 100644 --- a/translations/te/lessons/4-ComputerVision/12-Segmentation/README.md +++ b/translations/te/lessons/4-ComputerVision/12-Segmentation/README.md @@ -20,7 +20,7 @@ CO_OP_TRANSLATOR_METADATA: ఇన్స్టాన్స్ విభజనలో, ఈ గొర్రెలు వేర్వేరు ఆబ్జెక్టులు, కానీ సెమాంటిక్ విభజనలో అన్ని గొర్రెలు ఒకే వర్గంగా చూపబడతాయి. - + > చిత్రం [ఈ బ్లాగ్ పోస్ట్](https://nirmalamurali.medium.com/image-classification-vs-semantic-segmentation-vs-instance-segmentation-625c33a08d50) నుండి @@ -29,7 +29,7 @@ CO_OP_TRANSLATOR_METADATA: * **ఎంకోడర్** ఇన్‌పుట్ చిత్రంలో నుండి ఫీచర్లను తీసుకుంటుంది * **డీకోడర్** ఆ ఫీచర్లను **మాస్క్ చిత్రం**గా మార్చుతుంది, అదే పరిమాణం మరియు వర్గాల సంఖ్యకు సరిపోయే ఛానెల్‌లతో. - + > చిత్రం [ఈ ప్రచురణ](https://arxiv.org/pdf/2001.05566.pdf) నుండి @@ -43,7 +43,7 @@ CO_OP_TRANSLATOR_METADATA: > ✅ ఈ సాంకేతికత ఈ రకమైన వైద్య చిత్రీకరణకు చాలా అనుకూలంగా ఉంటుంది, కానీ మీరు మరే ఇతర వాస్తవ ప్రపంచ అనువర్తనాలను ఊహించగలరా? -navi +navi > చిత్రం PH2 డేటాబేస్ నుండి diff --git a/translations/te/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb b/translations/te/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb index 57447387..619279f8 100644 --- a/translations/te/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb +++ b/translations/te/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb @@ -17,7 +17,7 @@ "\n", "ఉదాహరణకు, ఇన్స్టాన్స్ విభజనలో 10 గొర్రెలు వేర్వేరు ఆబ్జెక్టులుగా ఉంటాయి, సెమాంటిక్ విభజనలో అన్ని గొర్రెలు ఒకే వర్గంగా సూచించబడతాయి.\n", "\n", - "\n", + "\n", "\n", "> చిత్రం [ఈ బ్లాగ్ పోస్ట్](https://nirmalamurali.medium.com/image-classification-vs-semantic-segmentation-vs-instance-segmentation-625c33a08d50) నుండి\n", "\n", @@ -26,7 +26,7 @@ "* **ఎంకోడర్** ఇన్‌పుట్ చిత్రంలో నుండి ఫీచర్లను తీసుకుంటుంది\n", "* **డీకోడర్** ఆ ఫీచర్లను **మాస్క్ చిత్రం**గా మార్చుతుంది, ఇది వర్గాల సంఖ్యకు అనుగుణంగా అదే పరిమాణం మరియు ఛానెల్‌ల సంఖ్య కలిగి ఉంటుంది.\n", "\n", - "\n", + "\n", "\n", "> చిత్రం [ఈ ప్రచురణ](https://arxiv.org/pdf/2001.05566.pdf) నుండి\n" ] @@ -252,7 +252,7 @@ "\n", "సరళమైన ఎన్‌కోడర్-డీకోడర్ ఆర్కిటెక్చర్‌ను **SegNet** అంటారు. ఇది ఎన్‌కోడర్‌లో కన్వల్యూషన్లు మరియు పూలింగ్‌లతో సాధారణ CNN ఉపయోగిస్తుంది, మరియు డీకోడర్‌లో కన్వల్యూషన్లు మరియు అప్‌సాంప్లింగ్‌లతో కూడిన డీకన్వల్యూషన్ CNN ఉపయోగిస్తుంది. ఇది బహుళ-పట్టాల నెట్‌వర్క్‌ను విజయవంతంగా శిక్షణ ఇవ్వడానికి బ్యాచ్ నార్మలైజేషన్‌పై ఆధారపడి ఉంటుంది.\n", "\n", - "\n", + "\n", "\n", "> ఈ చిత్రం ఈ పేపర్ నుండి: Badrinarayanan, V., Kendall, A., & Cipolla, R. (2015). [SegNet: A deep convolutional\n", "encoder-decoder architecture for image segmentation](https://arxiv.org/pdf/1511.00561.pdf)\n" @@ -548,7 +548,7 @@ "\n", "ఇక్కడ మేము చాలా సాదారణ CNN ఆర్కిటెక్చర్‌ను ఉపయోగిస్తాము, కానీ U-Net ఫీచర్ ఎక్స్‌ట్రాక్షన్ కోసం ResNet-50 వంటి మరింత సంక్లిష్ట ఎన్‌కోడర్‌ను కూడా ఉపయోగించవచ్చు.\n", "\n", - "\n", + "\n", "\n", "> పేపర్ నుండి చిత్రం: Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. [U-Net: Convolutional networks for biomedical image segmentation.](https://arxiv.org/pdf/1505.04597.pdf)\n" ] diff --git a/translations/te/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb b/translations/te/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb index fd48678a..02e4e774 100644 --- a/translations/te/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb +++ b/translations/te/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb @@ -12,13 +12,13 @@ "\n", "ఉదాహరణకు ఇన్స్టాన్స్ సెగ్మెంటేషన్‌లో పది కార్లు **వేరే** వస్తువులు, సేమాంటిక్ సెగ్మెంటేషన్‌లో **అన్ని** కార్లు ఒకే క్లాస్.\n", "\n", - "\n", + "\n", "\n", "> చిత్రం [ఈ బ్లాగ్ పోస్ట్](https://nirmalamurali.medium.com/image-classification-vs-semantic-segmentation-vs-instance-segmentation-625c33a08d50) నుండి\n", "\n", "దాదాపు అన్ని ఆర్కిటెక్చర్లు ఒకే నిర్మాణం కలిగి ఉంటాయి. మొదటి భాగం **ఎంకోడర్** ఇది ఇన్‌పుట్ చిత్రంలో నుండి ఫీచర్లను తీసుకుంటుంది, రెండవ భాగం **డీకోడర్** ఇది ఆ ఫీచర్లను అదే ఎత్తు, వెడల్పు మరియు కొన్ని ఛానెల్స్ సంఖ్యతో (క్లాసుల సంఖ్యకు సమానం కావచ్చు) ఉన్న చిత్రంగా మార్చుతుంది.\n", "\n", - "\n", + "\n", "\n", "> చిత్రం [ఈ ప్రచురణ](https://arxiv.org/pdf/2001.05566.pdf) నుండి\n" ] @@ -210,7 +210,7 @@ "\n", "సాదా ఎన్‌కోడర్ - డీకోడర్ నిర్మాణం, ఎన్‌కోడర్‌లో కన్వల్యూషన్లు, పూలింగ్‌లు మరియు డీకోడర్‌లో కన్వల్యూషన్లు, అప్‌సాంప్లింగ్‌లు ఉంటాయి.\n", "\n", - "\n", + "\n", "\n", "* Badrinarayanan, V., Kendall, A., & Cipolla, R. (2015). [SegNet: A deep convolutional\n", "encoder-decoder architecture for image segmentation](https://arxiv.org/pdf/1511.00561.pdf)\n" @@ -602,7 +602,7 @@ "\n", "U-Net సాధారణంగా ఫీచర్ ఎక్స్‌ట్రాక్షన్ కోసం డిఫాల్ట్ ఎంకోడర్ కలిగి ఉంటుంది, ఉదాహరణకు resnet50.\n", "\n", - "\n", + "\n", "\n", "* Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. [U-Net: Convolutional networks for biomedical image segmentation.](https://arxiv.org/pdf/1505.04597.pdf)\n" ] diff --git a/translations/te/lessons/4-ComputerVision/README.md b/translations/te/lessons/4-ComputerVision/README.md index 1b3751dd..26bbdd62 100644 --- a/translations/te/lessons/4-ComputerVision/README.md +++ b/translations/te/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # కంప్యూటర్ విజన్ -![డూడిల్‌లో కంప్యూటర్ విజన్ విషయాల సారాంశం](../../../../translated_images/te/ai-computervision.6506ebebac3fbf76.png) +![డూడిల్‌లో కంప్యూటర్ విజన్ విషయాల సారాంశం](../../../../translated_images/te/ai-computervision.6506ebebac3fbf76.webp) ఈ విభాగంలో మనం నేర్చుకోబోతున్నవి: diff --git a/translations/te/lessons/5-NLP/13-TextRep/README.md b/translations/te/lessons/5-NLP/13-TextRep/README.md index 5434d9ab..ec81e961 100644 --- a/translations/te/lessons/5-NLP/13-TextRep/README.md +++ b/translations/te/lessons/5-NLP/13-TextRep/README.md @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: నేచురల్ లాంగ్వేజ్ ప్రాసెసింగ్ (NLP) పనులను న్యూరల్ నెట్‌వర్క్‌లతో పరిష్కరించాలంటే, టెక్స్ట్‌ను టెన్సార్లుగా ప్రాతినిధ్యం వహించే విధానం అవసరం. కంప్యూటర్లు ఇప్పటికే ASCII లేదా UTF-8 వంటి ఎంకోడింగ్లను ఉపయోగించి స్క్రీన్‌పై ఫాంట్లకు మ్యాప్ అయ్యే సంఖ్యలుగా టెక్స్ట్ అక్షరాలను ప్రాతినిధ్యం వహిస్తాయి. -Image showing diagram mapping a character to an ASCII and binary representation +Image showing diagram mapping a character to an ASCII and binary representation > [చిత్ర మూలం](https://www.seobility.net/en/wiki/ASCII) @@ -48,7 +48,7 @@ CO_OP_TRANSLATOR_METADATA: టెక్స్ట్ వర్గీకరణ వంటి పనులను పరిష్కరించేటప్పుడు, మనం ఒక స్థిర పరిమాణం వెక్టర్‌తో టెక్స్ట్‌ను ప్రాతినిధ్యం చేయగలగాలి, దీన్ని తుది డెన్స్ క్లాసిఫయర్‌కు ఇన్పుట్‌గా ఉపయోగిస్తాము. దీని కోసం ఒక సరళమైన మార్గం, ప్రతి పద ప్రాతినిధ్యాలను కలిపి, ఉదాహరణకు వాటిని జోడించడం. ప్రతి పదం వన్-హాట్ ఎంకోడింగ్‌లను జోడిస్తే, మనకు పదాల సంభావ్యతలను చూపించే ఫ్రీక్వెన్సీ వెక్టర్ వస్తుంది, అంటే ప్రతి పదం టెక్స్ట్‌లో ఎన్ని సార్లు వస్తుందో. ఈ విధంగా టెక్స్ట్ ప్రాతినిధ్యం **బ్యాగ్ ఆఫ్ వర్డ్స్** (BoW) అని పిలవబడుతుంది. - + > రచయితచే చిత్రీకరణ diff --git a/translations/te/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/te/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index 42faf6e0..b6a4e754 100644 --- a/translations/te/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/te/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**పదాల సంచయం** (BoW) వెక్టర్ ప్రాతినిధ్యం అనేది అత్యంత సాధారణంగా ఉపయోగించే సాంప్రదాయ వెక్టర్ ప్రాతినిధ్యం. ప్రతి పదం ఒక వెక్టర్ సూచికకు అనుసంధానించబడుతుంది, వెక్టర్ అంశం ఆ పదం ఒక నిర్దిష్ట డాక్యుమెంట్‌లో ఎన్ని సార్లు వచ్చిందో చూపిస్తుంది.\n", "\n", - "![పదాల సంచయం వెక్టర్ ప్రాతినిధ్యం మెమరీలో ఎలా ప్రాతినిధ్యం పొందుతుందో చూపించే చిత్రం.](../../../../../translated_images/te/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![పదాల సంచయం వెక్టర్ ప్రాతినిధ్యం మెమరీలో ఎలా ప్రాతినిధ్యం పొందుతుందో చూపించే చిత్రం.](../../../../../translated_images/te/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **గమనిక**: BoWని టెక్స్ట్‌లోని ఒక్కొక్క పదానికి సంబంధించిన ఒక-హాట్-ఎన్‌కోడ్ చేసిన వెక్టర్ల మొత్తం అని కూడా భావించవచ్చు.\n", "\n", diff --git a/translations/te/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/te/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index c66ab68c..165d89f2 100644 --- a/translations/te/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/te/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**పదాల సంచయం** (BoW) వెక్టర్ ప్రాతినిధ్యం అనేది అతి సులభంగా అర్థం చేసుకునే సంప్రదాయ వెక్టర్ ప్రాతినిధ్యం. ప్రతి పదం ఒక వెక్టర్ సూచికతో అనుసంధానించబడుతుంది, మరియు ఒక వెక్టర్ అంశం ఒక నిర్దిష్ట డాక్యుమెంట్‌లో ప్రతి పదం ఎన్ని సార్లు వచ్చిందో చూపిస్తుంది.\n", "\n", - "![పదాల సంచయం వెక్టర్ ప్రాతినిధ్యం మెమరీలో ఎలా ప్రాతినిధ్యం పొందుతుందో చూపించే చిత్రం.](../../../../../translated_images/te/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![పదాల సంచయం వెక్టర్ ప్రాతినిధ్యం మెమరీలో ఎలా ప్రాతినిధ్యం పొందుతుందో చూపించే చిత్రం.](../../../../../translated_images/te/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **గమనిక**: BoWని టెక్స్ట్‌లోని ఒక్కొక్క పదానికి సంబంధించిన ఒక-హాట్-ఎన్‌కోడ్ చేసిన వెక్టర్ల మొత్తం అని కూడా భావించవచ్చు.\n", "\n", diff --git a/translations/te/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/te/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 765fbbe6..f873ab4e 100644 --- a/translations/te/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/te/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "మన నెట్‌వర్క్‌లో మొదటి లేయర్‌గా ఎంబెడ్డింగ్ లేయర్‌ను ఉపయోగించడం ద్వారా, మనం బాగ్-ఆఫ్-వర్డ్స్ నుండి **ఎంబెడ్డింగ్ బాగ్** మోడల్‌కు మారవచ్చు, ఇందులో మనం మొదట మన టెక్స్ట్‌లోని ప్రతి పదాన్ని సంబంధిత ఎంబెడ్డింగ్‌గా మార్చి, ఆ ఎంబెడ్డింగ్స్ మొత్తం మీద `sum`, `average` లేదా `max` వంటి ఏదైనా సమాహార ఫంక్షన్‌ను లెక్కిస్తాము.\n", "\n", - "![ఐదు వరుస పదాల కోసం ఎంబెడ్డింగ్ క్లాసిఫయర్‌ను చూపించే చిత్రం.](../../../../../translated_images/te/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![ఐదు వరుస పదాల కోసం ఎంబెడ్డింగ్ క్లాసిఫయర్‌ను చూపించే చిత్రం.](../../../../../translated_images/te/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "మన క్లాసిఫయర్ న్యూరల్ నెట్‌వర్క్ ఎంబెడ్డింగ్ లేయర్‌తో ప్రారంభమవుతుంది, ఆపై సమాహార లేయర్, మరియు దాని పై భాగంలో లీనియర్ క్లాసిఫయర్ ఉంటుంది:\n" ] @@ -176,7 +176,7 @@ "\n", "మునుపటి ఆర్కిటెక్చర్‌లో, మినీబ్యాచ్‌లోకి సరిపడేలా అన్ని సీక్వెన్స్‌లను ఒకే పొడవు చేయడానికి ప్యాడ్ చేయాల్సి ఉండేది. ఇది వేరియబుల్ పొడవు సీక్వెన్స్‌లను ప్రాతినిధ్యం చేయడానికి అత్యంత సమర్థవంతమైన విధానం కాదు - మరో విధానం అంటే **ఆఫ్సెట్** వెక్టర్ ఉపయోగించడం, ఇది ఒక పెద్ద వెక్టర్‌లో నిల్వ ఉన్న అన్ని సీక్వెన్స్‌ల ఆఫ్సెట్లను కలిగి ఉంటుంది.\n", "\n", - "![Image showing an offset sequence representation](../../../../../translated_images/te/offset-sequence-representation.eb73fcefb29b46ee.png)\n", + "![Image showing an offset sequence representation](../../../../../translated_images/te/offset-sequence-representation.eb73fcefb29b46ee.webp)\n", "\n", "> **Note**: పై చిత్రంలో, మనం అక్షరాల సీక్వెన్స్‌ను చూపిస్తున్నాము, కానీ మన ఉదాహరణలో మనం పదాల సీక్వెన్స్‌లతో పని చేస్తున్నాము. అయినప్పటికీ, ఆఫ్సెట్ వెక్టర్‌తో సీక్వెన్స్‌లను ప్రాతినిధ్యం చేయడం అనే సాధారణ సూత్రం అదే ఉంటుంది.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW వేగంగా ఉంటుంది, స్కిప్-గ్రామ్ మెల్లగా ఉంటుంది, కానీ అరుదైన పదాలను బాగా ప్రాతినిధ్యం చేస్తుంది.\n", "\n", - "![Image showing both CBoW and Skip-Gram algorithms to convert words to vectors.](../../../../../translated_images/te/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![Image showing both CBoW and Skip-Gram algorithms to convert words to vectors.](../../../../../translated_images/te/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Google News డేటాసెట్‌పై ప్రీ-ట్రెయిన్ చేసిన word2vec ఎంబెడ్డింగ్‌తో ప్రయోగం చేయడానికి, మనం **gensim** లైబ్రరీని ఉపయోగించవచ్చు. క్రింద 'neural' కు అత్యంత సమానమైన పదాలను కనుగొంటాము\n", "\n", diff --git a/translations/te/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/te/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index af2dc824..4ea567c3 100644 --- a/translations/te/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/te/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "మన నెట్‌వర్క్‌లో మొదటి లేయర్‌గా ఎంబెడ్డింగ్ లేయర్‌ను ఉపయోగించడం ద్వారా, మనం బాగ్-ఆఫ్-వర్డ్స్ నుండి **ఎంబెడ్డింగ్ బాగ్** మోడల్‌కు మారవచ్చు, ఇక్కడ మనం మొదట మన టెక్స్ట్‌లోని ప్రతి పదాన్ని సంబంధిత ఎంబెడ్డింగ్‌గా మార్చి, ఆ ఎంబెడ్డింగ్స్ మొత్తం మీద `sum`, `average` లేదా `max` వంటి ఏదైనా సమాహార ఫంక్షన్‌ను లెక్కిస్తాము.\n", "\n", - "![ఐదు వరుస పదాల కోసం ఎంబెడ్డింగ్ క్లాసిఫయర్ చూపిస్తున్న చిత్రం.](../../../../../translated_images/te/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![ఐదు వరుస పదాల కోసం ఎంబెడ్డింగ్ క్లాసిఫయర్ చూపిస్తున్న చిత్రం.](../../../../../translated_images/te/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "మన క్లాసిఫయర్ న్యూరల్ నెట్‌వర్క్ క్రింది లేయర్లతో కూడి ఉంటుంది:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW వేగంగా ఉంటుంది, స్కిప్-గ్రామ్ మందగించగా, అరుదైన పదాలను బాగా ప్రాతినిధ్యం చేయగలదు.\n", "\n", - "![పదాలను వెక్టర్లుగా మార్చడానికి CBoW మరియు స్కిప్-గ్రామ్ అల్గోరిథమ్స్ రెండింటినీ చూపించే చిత్రం.](../../../../../translated_images/te/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![పదాలను వెక్టర్లుగా మార్చడానికి CBoW మరియు స్కిప్-గ్రామ్ అల్గోరిథమ్స్ రెండింటినీ చూపించే చిత్రం.](../../../../../translated_images/te/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Google News డేటాసెట్‌పై ప్రీట్రెయిన్ చేసిన Word2Vec ఎంబెడ్డింగ్‌తో ప్రయోగం చేయడానికి, **gensim** లైబ్రరీని ఉపయోగించవచ్చు. క్రింద 'neural' కు అత్యంత సమానమైన పదాలను కనుగొంటాం.\n", "\n", diff --git a/translations/te/lessons/5-NLP/14-Embeddings/README.md b/translations/te/lessons/5-NLP/14-Embeddings/README.md index af94cecc..352bf392 100644 --- a/translations/te/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/te/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ BoW లేదా TF/IDF ఆధారంగా క్లాసిఫైయర్ మన క్లాసిఫైయర్ నెట్‌వర్క్‌లో మొదటి లేయర్‌గా ఎంబెడ్డింగ్ లేయర్ ఉపయోగించడం ద్వారా, మేము బాగ్-ఆఫ్-వర్డ్స్ నుండి **ఎంబెడ్డింగ్ బాగ్** మోడల్‌కు మారవచ్చు, ఇక్కడ మేము మొదట మన టెక్స్ట్‌లోని ప్రతి పదాన్ని సంబంధిత ఎంబెడ్డింగ్‌గా మార్చి, ఆ ఎంబెడ్డింగ్స్‌పై `sum`, `average` లేదా `max` వంటి ఏదైనా సమాహార ఫంక్షన్‌ను లెక్కిస్తాము. -![ఐదు వరుస పదాల కోసం ఎంబెడ్డింగ్ క్లాసిఫైయర్ చూపిస్తున్న చిత్రం.](../../../../../translated_images/te/embedding-classifier-example.b77f021a7ee67eee.png) +![ఐదు వరుస పదాల కోసం ఎంబెడ్డింగ్ క్లాసిఫైయర్ చూపిస్తున్న చిత్రం.](../../../../../translated_images/te/embedding-classifier-example.b77f021a7ee67eee.webp) > చిత్రాన్ని రచయిత అందించారు @@ -40,7 +40,7 @@ BoW లేదా TF/IDF ఆధారంగా క్లాసిఫైయర్ CBoW వేగంగా ఉంటుంది, స్కిప్-గ్రామ్ మందగిస్తుంది, కానీ అరుదైన పదాలను బాగా ప్రాతినిధ్యం చేస్తుంది. -![పదాలను వెక్టర్లుగా మార్చడానికి CBoW మరియు స్కిప్-గ్రామ్ అల్గోరిథమ్స్ చూపిస్తున్న చిత్రం.](../../../../../translated_images/te/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![పదాలను వెక్టర్లుగా మార్చడానికి CBoW మరియు స్కిప్-గ్రామ్ అల్గోరిథమ్స్ చూపిస్తున్న చిత్రం.](../../../../../translated_images/te/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > చిత్రం [ఈ పేపర్](https://arxiv.org/pdf/1301.3781.pdf) నుండి diff --git a/translations/te/lessons/5-NLP/15-LanguageModeling/README.md b/translations/te/lessons/5-NLP/15-LanguageModeling/README.md index 4f7a008b..d73e007b 100644 --- a/translations/te/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/te/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: * **కంటిన్యూయస్ బ్యాగ్-ఆఫ్-వర్డ్స్** (CBoW), ఇందులో టోకెన్ సీక్వెన్స్ $W_{-N}$, ..., $W_N$ లో మధ్య టోకెన్ $W_0$ ను అంచనా వేస్తాము. * **స్కిప్-గ్రామ్**, ఇందులో మధ్య టోకెన్ $W_0$ నుండి పొరుగువారైన టోకెన్ల సమూహం {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} ను అంచనా వేస్తాము. -![పదాలను వెక్టర్లుగా మార్చే అల్గోరిథమ్స్ పై పేపర్ నుండి చిత్రం](../../../../../translated_images/te/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![పదాలను వెక్టర్లుగా మార్చే అల్గోరిథమ్స్ పై పేపర్ నుండి చిత్రం](../../../../../translated_images/te/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > చిత్రం [ఈ పేపర్](https://arxiv.org/pdf/1301.3781.pdf) నుండి diff --git a/translations/te/lessons/5-NLP/16-RNN/README.md b/translations/te/lessons/5-NLP/16-RNN/README.md index 79a122ad..f08da264 100644 --- a/translations/te/lessons/5-NLP/16-RNN/README.md +++ b/translations/te/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: టెక్స్ట్ సీక్వెన్స్ అర్థాన్ని పట్టుకోవడానికి, మేము మరో న్యూరల్ నెట్‌వర్క్ ఆర్కిటెక్చర్ ఉపయోగించాలి, దీనిని **రికరెంట్ న్యూరల్ నెట్‌వర్క్** లేదా RNN అంటారు. RNNలో, మేము వాక్యాన్ని ఒక్కో చిహ్నం ద్వారా నెట్‌వర్క్‌లో పంపుతాము, మరియు నెట్‌వర్క్ కొన్ని **స్థితి**ని ఉత్పత్తి చేస్తుంది, ఆ స్థితిని తరువాతి చిహ్నంతో మళ్లీ నెట్‌వర్క్‌కు ఇస్తాము. -![RNN](../../../../../translated_images/te/rnn.27f5c29c53d727b5.png) +![RNN](../../../../../translated_images/te/rnn.27f5c29c53d727b5.webp) > చిత్రకారుడు @@ -31,7 +31,7 @@ CO_OP_TRANSLATOR_METADATA: సాదా RNN సెల్‌లో రెండు బరువు మ్యాట్రిక్స్‌లు ఉంటాయి: ఒకటి ఇన్‌పుట్ చిహ్నాన్ని మార్చుతుంది (దాన్ని W అంటాం), మరొకటి ఇన్‌పుట్ స్థితిని మార్చుతుంది (H). ఈ సందర్భంలో నెట్‌వర్క్ అవుట్‌పుట్ σ(W×Xi+H×Si-1+b) గా లెక్కించబడుతుంది, ఇక్కడ σ యాక్టివేషన్ ఫంక్షన్, b అదనపు బయాస్. -RNN Cell Anatomy +RNN Cell Anatomy > చిత్రకారుడు @@ -61,7 +61,7 @@ LSTM నెట్‌వర్క్ RNNకు సమానంగా ఏర్ప రికరెంట్ నెట్‌వర్క్, ఒక దిశలోనైనా లేదా బిడైరెక్షనల్‌గా ఉన్నా, సీక్వెన్స్‌లోని కొన్ని నమూనాలను పట్టుకుని, వాటిని స్థితి వెక్టర్‌లో నిల్వ చేయగలదు లేదా అవుట్‌పుట్‌గా పంపగలదు. కాంవల్యూషనల్ నెట్‌వర్క్స్ లాగా, మొదటి లేయర్ నుండి తీసుకున్న తక్కువ స్థాయి నమూనాలపై ఆధారపడి, మరొక రికరెంట్ లేయర్‌ను నిర్మించి, ఉన్నత స్థాయి నమూనాలను పట్టుకోవచ్చు. దీని ద్వారా **మల్టిలేయర్ RNN** అనే భావన వస్తుంది, ఇది రెండు లేదా అంతకంటే ఎక్కువ రికరెంట్ నెట్‌వర్క్స్ కలిగి ఉంటుంది, ఇక్కడ మునుపటి లేయర్ అవుట్‌పుట్ తదుపరి లేయర్ ఇన్‌పుట్‌గా పంపబడుతుంది. -![Image showing a Multilayer long-short-term-memory- RNN](../../../../../translated_images/te/multi-layer-lstm.dd975e29bb2a59fe.jpg) +![Image showing a Multilayer long-short-term-memory- RNN](../../../../../translated_images/te/multi-layer-lstm.dd975e29bb2a59fe.webp) *చిత్రం [ఈ అద్భుతమైన పోస్ట్](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) నుండి, ఫెర్నాండో లోపెజ్ రచన* diff --git a/translations/te/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/te/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index 8ab6d53a..8ff78445 100644 --- a/translations/te/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/te/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -10,7 +10,7 @@ "\n", "టెక్స్ట్ సీక్వెన్స్ అర్థాన్ని పట్టుకోవడానికి, మేము మరో న్యూరల్ నెట్‌వర్క్ ఆర్కిటెక్చర్‌ను ఉపయోగించాలి, దీనిని **రికరెంట్ న్యూరల్ నెట్‌వర్క్** లేదా RNN అంటారు. RNNలో, మేము మా వాక్యాన్ని ఒక్కో చిహ్నం ద్వారా నెట్‌వర్క్‌లో పంపుతాము, మరియు నెట్‌వర్క్ కొన్ని **స్థితి**ని ఉత్పత్తి చేస్తుంది, ఆ స్థితిని తరువాతి చిహ్నంతో మళ్లీ నెట్‌వర్క్‌కు పంపుతాము.\n", "\n", - "\"RNN\"\n", + "\"RNN\"\n", "\n", "ఇన్‌పుట్ టోకెన్ల సీక్వెన్స్ $X_0,\\dots,X_n$ ఇచ్చినప్పుడు, RNN న్యూరల్ నెట్‌వర్క్ బ్లాక్స్ సీక్వెన్స్‌ను సృష్టించి, బ్యాక్ ప్రొపగేషన్ ఉపయోగించి ఈ సీక్వెన్స్‌ను ఎండ్-టు-ఎండ్ శిక్షణ ఇస్తుంది. ప్రతి నెట్‌వర్క్ బ్లాక్ ఒక జంట $(X_i,S_i)$ని ఇన్‌పుట్‌గా తీసుకుని, $S_{i+1}$ని ఫలితంగా ఉత్పత్తి చేస్తుంది. తుది స్థితి $S_n$ లేదా అవుట్‌పుట్ $X_n$ లీనియర్ క్లాసిఫయర్‌లోకి వెళ్లి ఫలితాన్ని ఉత్పత్తి చేస్తుంది. అన్ని నెట్‌వర్క్ బ్లాక్స్ ఒకే బరువులను పంచుకుంటాయి, మరియు ఒకే బ్యాక్ ప్రొపగేషన్ పాస్ ద్వారా ఎండ్-టు-ఎండ్ శిక్షణ పొందుతాయి.\n", "\n", @@ -428,7 +428,7 @@ "\n", "రికరెంట్ నెట్‌వర్క్, ఒక దిశలోనైనా లేదా ద్విముఖి అయినా, ఒక సీక్వెన్స్‌లోని నిర్దిష్ట నమూనాలను పట్టుకుంటుంది, వాటిని స్టేట్ వెక్టర్‌లో నిల్వ చేయగలదు లేదా అవుట్‌పుట్‌గా పంపగలదు. కన్వల్యూషనల్ నెట్‌వర్క్ల లాగా, మొదటి లేయర్ ద్వారా తీసుకున్న తక్కువ స్థాయి నమూనాలపై ఆధారపడి ఉన్నత స్థాయి నమూనాలను పట్టుకోవడానికి మళ్ళీ మరో రికరెంట్ లేయర్‌ను నిర్మించవచ్చు. ఇది మనల్ని **బహుళస్థాయి RNN** అనే భావనకు తీసుకువెళ్తుంది, ఇది రెండు లేదా అంతకంటే ఎక్కువ రికరెంట్ నెట్‌వర్క్లతో కూడి ఉంటుంది, ఇక్కడ మునుపటి లేయర్ అవుట్‌పుట్‌ను తదుపరి లేయర్ ఇన్‌పుట్‌గా పంపుతారు.\n", "\n", - "![బహుళస్థాయి లాంగ్-షార్ట్-టర్మ్-మెమరీ RNNని చూపించే చిత్రం](../../../../../translated_images/te/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![బహుళస్థాయి లాంగ్-షార్ట్-టర్మ్-మెమరీ RNNని చూపించే చిత్రం](../../../../../translated_images/te/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*ఫెర్నాండో లోపెజ్ రాసిన [ఈ అద్భుతమైన పోస్ట్](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) నుండి చిత్రం*\n", "\n", diff --git a/translations/te/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/te/lessons/5-NLP/16-RNN/RNNTF.ipynb index 1da5dbcd..0ecb23f4 100644 --- a/translations/te/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/te/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "ఒక టెక్స్ట్ సీక్వెన్స్ అర్థాన్ని పట్టుకోవడానికి, మనం **పునరావృత న్యూరల్ నెట్‌వర్క్** లేదా RNN అనే న్యూరల్ నెట్‌వర్క్ ఆర్కిటెక్చర్‌ను ఉపయోగిస్తాము. RNN ఉపయోగించినప్పుడు, మన వాక్యాన్ని ఒక్కో టోకెన్‌గా నెట్‌వర్క్ ద్వారా పంపిస్తాము, మరియు నెట్‌వర్క్ కొన్ని **స్థితి**ని ఉత్పత్తి చేస్తుంది, ఆ స్థితిని తరువాత వచ్చే టోకెన్‌తో మళ్లీ నెట్‌వర్క్‌కు ఇస్తాము.\n", "\n", - "![పునరావృత న్యూరల్ నెట్‌వర్క్ జనరేషన్ ఉదాహరణ చూపిస్తున్న చిత్రం.](../../../../../translated_images/te/rnn.27f5c29c53d727b5.png)\n", + "![పునరావృత న్యూరల్ నెట్‌వర్క్ జనరేషన్ ఉదాహరణ చూపిస్తున్న చిత్రం.](../../../../../translated_images/te/rnn.27f5c29c53d727b5.webp)\n", "\n", "ఇన్‌పుట్ సీక్వెన్స్ టోకెన్లు $X_0,\\dots,X_n$ ఇచ్చినప్పుడు, RNN న్యూరల్ నెట్‌వర్క్ బ్లాక్స్ సీక్వెన్స్‌ను సృష్టిస్తుంది, మరియు ఈ సీక్వెన్స్‌ను బ్యాక్‌ప్రొపగేషన్ ద్వారా ఎండ్-టు-ఎండ్ శిక్షణ ఇస్తుంది. ప్రతి నెట్‌వర్క్ బ్లాక్ జంట $(X_i,S_i)$ని ఇన్‌పుట్‌గా తీసుకుని, $S_{i+1}$ని ఫలితంగా ఉత్పత్తి చేస్తుంది. చివరి స్థితి $S_n$ లేదా అవుట్‌పుట్ $Y_n$ లీనియర్ క్లాసిఫైయర్‌లోకి వెళ్లి ఫలితాన్ని ఉత్పత్తి చేస్తుంది. అన్ని నెట్‌వర్క్ బ్లాక్స్ ఒకే బరువులను పంచుకుంటాయి, మరియు ఒకే బ్యాక్‌ప్రొపగేషన్ పాస్‌తో ఎండ్-టు-ఎండ్ శిక్షణ పొందుతాయి.\n", "\n", @@ -371,7 +371,7 @@ "\n", "రికరెంట్ నెట్‌వర్క్లు, ఏదైనా ఒక దిశలోనైనా లేదా ద్విముఖి అయినా, సీక్వెన్స్‌లోని నమూనాలను పట్టుకుని వాటిని స్టేట్ వెక్టర్లుగా నిల్వ చేస్తాయి లేదా అవి అవుట్‌పుట్‌గా ఇస్తాయి. కాంవల్యూషనల్ నెట్‌వర్క్ల లాగా, మొదటి లేయర్ ద్వారా తీసుకున్న తక్కువ స్థాయి నమూనాల నుండి నిర్మించిన ఉన్నత స్థాయి నమూనాలను పట్టుకోవడానికి మరొక రికరెంట్ లేయర్‌ను మొదటి లేయర్ తర్వాత నిర్మించవచ్చు. దీని ద్వారా మనకు **బహుళస్థాయి RNN** అనే భావన వస్తుంది, ఇది రెండు లేదా అంతకంటే ఎక్కువ రికరెంట్ నెట్‌వర్క్లతో కూడి ఉంటుంది, ఇక్కడ మునుపటి లేయర్ అవుట్‌పుట్‌ను తదుపరి లేయర్ ఇన్‌పుట్‌గా పంపిస్తారు.\n", "\n", - "![బహుళస్థాయి లాంగ్-షార్ట్-టర్మ్-మెమరీ RNNని చూపించే చిత్రం](../../../../../translated_images/te/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![బహుళస్థాయి లాంగ్-షార్ట్-టర్మ్-మెమరీ RNNని చూపించే చిత్రం](../../../../../translated_images/te/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*ఫెర్నాండో లోపెజ్ రాసిన [ఈ అద్భుతమైన పోస్ట్](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) నుండి చిత్రం.*\n", "\n", diff --git a/translations/te/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/te/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index f299894d..188f10d4 100644 --- a/translations/te/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/te/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "RNN ను టెక్స్ట్ ఉత్పత్తి చేయడానికి శిక్షణ ఇవ్వడానికి మనం అనుసరించే విధానం ఈ విధంగా ఉంటుంది. ప్రతి దశలో, మనం `nchars` పొడవైన అక్షరాల సీక్వెన్స్ తీసుకుని, ప్రతి ఇన్‌పుట్ అక్షరానికి తదుపరి అవుట్‌పుట్ అక్షరాన్ని నెట్‌వర్క్ ఉత్పత్తి చేయమని అడుగుతాము:\n", "\n", - "!['HELLO' అనే పదం RNN ద్వారా ఉత్పత్తి చేయబడుతున్న ఉదాహరణను చూపించే చిత్రం.](../../../../../translated_images/te/rnn-generate.56c54afb52f9781d.png)\n", + "!['HELLO' అనే పదం RNN ద్వారా ఉత్పత్తి చేయబడుతున్న ఉదాహరణను చూపించే చిత్రం.](../../../../../translated_images/te/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "నిజమైన పరిస్థితులపై ఆధారపడి, మనం కొన్ని ప్రత్యేక అక్షరాలను కూడా చేర్చాలనుకోవచ్చు, ఉదాహరణకు *end-of-sequence* ``. మన సందర్భంలో, మనం నిరంతర టెక్స్ట్ ఉత్పత్తి కోసం నెట్‌వర్క్‌ను శిక్షణ ఇవ్వాలనుకుంటున్నాము, కాబట్టి ప్రతి సీక్వెన్స్ పరిమాణాన్ని `nchars` టోకెన్లకు సమానంగా స్థిరపరుస్తాము. ఫలితంగా, ప్రతి శిక్షణ ఉదాహరణలో `nchars` ఇన్‌పుట్లు మరియు `nchars` అవుట్‌పుట్లు ఉంటాయి (ఇవి ఇన్‌పుట్ సీక్వెన్స్‌ను ఒక అక్షరం ఎడమకు షిఫ్ట్ చేసినవి). మినీబ్యాచ్‌లో ఇలాంటి అనేక సీక్వెన్స్‌లు ఉంటాయి.\n", "\n", diff --git a/translations/te/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/te/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index a99eb1d3..685a1129 100644 --- a/translations/te/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/te/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -113,7 +113,7 @@ "\n", "మేము వార్తా శీర్షికలను సృష్టించడానికి RNN ను శిక్షణ ఇవ్వడానికి విధానం ఈ విధంగా ఉంటుంది. ప్రతి దశలో, ఒక శీర్షిక తీసుకుని దాన్ని RNN లో ఇన్పుట్ గా ఇస్తాము, మరియు ప్రతి ఇన్పుట్ అక్షరానికి తర్వాతి అవుట్పుట్ అక్షరాన్ని నెట్‌వర్క్ సృష్టించాలని అడుగుతాము:\n", "\n", - "!['HELLO' అనే పదం RNN ద్వారా సృష్టించబడుతున్న ఉదాహరణను చూపించే చిత్రం.](../../../../../translated_images/te/rnn-generate.56c54afb52f9781d.png)\n", + "!['HELLO' అనే పదం RNN ద్వారా సృష్టించబడుతున్న ఉదాహరణను చూపించే చిత్రం.](../../../../../translated_images/te/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "మన సీక్వెన్స్ చివరి అక్షరానికి, నెట్‌వర్క్ `` టోకెన్ సృష్టించాలని అడుగుతాము.\n", "\n", diff --git a/translations/te/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/te/lessons/5-NLP/17-GenerativeNetworks/README.md index 8613449a..a0b7eeb0 100644 --- a/translations/te/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/te/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: ఇది క్రింది చిత్రంలో చూపిన వివిధ న్యూరల్ ఆర్కిటెక్చర్లకు అవకాశం ఇస్తుంది: -![Image showing common recurrent neural network patterns.](../../../../../translated_images/te/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Image showing common recurrent neural network patterns.](../../../../../translated_images/te/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > చిత్రం బ్లాగ్ పోస్ట్ [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) నుండి [Andrej Karpaty](http://karpathy.github.io/) రచయిత @@ -32,11 +32,11 @@ CO_OP_TRANSLATOR_METADATA: మనం ఈ RNNను దశలవారీగా టెక్స్ట్ ఉత్పత్తి చేయడానికి శిక్షణ ఇస్తాము. ప్రతి దశలో, `nchars` పొడవు కలిగిన క్యారెక్టర్ల సీక్వెన్స్ తీసుకుని, ప్రతి ఇన్‌పుట్ క్యారెక్టర్ కోసం తదుపరి అవుట్‌పుట్ క్యారెక్టర్‌ను నెట్‌వర్క్ ఉత్పత్తి చేయమని అడుగుతాము: -![Image showing an example RNN generation of the word 'HELLO'.](../../../../../translated_images/te/rnn-generate.56c54afb52f9781d.png) +![Image showing an example RNN generation of the word 'HELLO'.](../../../../../translated_images/te/rnn-generate.56c54afb52f9781d.webp) టెక్స్ట్ ఉత్పత్తి (ఇన్ఫరెన్స్ సమయంలో) చేస్తున్నప్పుడు, మనం కొన్ని **ప్రాంప్ట్**తో ప్రారంభిస్తాము, ఇది RNN సెల్స్ ద్వారా మధ్యవర్తి స్టేట్‌ను ఉత్పత్తి చేయడానికి పంపబడుతుంది, ఆ తర్వాత ఆ స్టేట్ నుండి ఉత్పత్తి ప్రారంభమవుతుంది. ఒక్కో క్యారెక్టర్‌ను ఒకేసారి ఉత్పత్తి చేసి, ఆ స్టేట్ మరియు ఉత్పత్తి చేసిన క్యారెక్టర్‌ను మరొక RNN సెల్‌కు పంపించి తదుపరి క్యారెక్టర్‌ను ఉత్పత్తి చేస్తాము, అవసరమైనంత క్యారెక్టర్లు ఉత్పత్తి అయ్యేవరకు. - + > చిత్రం రచయిత diff --git a/translations/te/lessons/5-NLP/18-Transformers/README.md b/translations/te/lessons/5-NLP/18-Transformers/README.md index ca37d31e..3af87ac5 100644 --- a/translations/te/lessons/5-NLP/18-Transformers/README.md +++ b/translations/te/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ RNNలతో, సీక్వెన్స్-టు-సీక్వెన్స **అటెన్షన్ మెకానిజమ్స్** RNN యొక్క ప్రతి అవుట్‌పుట్ అంచనాపై ప్రతి ఇన్‌పుట్ వెక్టర్ యొక్క సందర్భాత్మక ప్రభావాన్ని తూగడానికి ఒక మార్గాన్ని అందిస్తాయి. ఇది అమలు చేయబడే విధానం ఇన్‌పుట్ RNN మరియు అవుట్‌పుట్ RNN మధ్య మధ్యవర్తి స్టేట్స్‌కు షార్ట్‌కట్స్ సృష్టించడం ద్వారా జరుగుతుంది. ఈ విధంగా, అవుట్‌పుట్ సింబల్ yt ఉత్పత్తి చేస్తున్నప్పుడు, మేము అన్ని ఇన్‌పుట్ హిడెన్ స్టేట్స్ hi ను వివిధ బరువు గుణకాలు αt,i తో పరిగణలోకి తీసుకుంటాము. -![ఎంకోడర్/డీకోడర్ మోడల్‌తో కూడిన యాడిటివ్ అటెన్షన్ లేయర్ చూపిస్తున్న చిత్రం](../../../../../translated_images/te/encoder-decoder-attention.7a726296894fb567.png) +![ఎంకోడర్/డీకోడర్ మోడల్‌తో కూడిన యాడిటివ్ అటెన్షన్ లేయర్ చూపిస్తున్న చిత్రం](../../../../../translated_images/te/encoder-decoder-attention.7a726296894fb567.webp) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) లోని యాడిటివ్ అటెన్షన్ మెకానిజం ఉన్న ఎంకోడర్-డీకోడర్ మోడల్, [ఈ బ్లాగ్ పోస్ట్](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) నుండి సైట్ చేయబడింది అటెన్షన్ మ్యాట్రిక్స్ {αi,j} ఒక నిర్దిష్ట అవుట్‌పుట్ పదం ఉత్పత్తిలో కొన్ని ఇన్‌పుట్ పదాలు ఎంత మేర పాత్ర పోషిస్తున్నాయో సూచిస్తుంది. క్రింద అలాంటి మ్యాట్రిక్స్ ఉదాహరణ ఉంది: -![RNNsearch-50 ద్వారా కనుగొనబడిన నమూనా అలైన్‌మెంట్ చూపిస్తున్న చిత్రం, Bahdanau నుండి తీసుకున్నది - arviz.org](../../../../../translated_images/te/bahdanau-fig3.09ba2d37f202a6af.png) +![RNNsearch-50 ద్వారా కనుగొనబడిన నమూనా అలైన్‌మెంట్ చూపిస్తున్న చిత్రం, Bahdanau నుండి తీసుకున్నది - arviz.org](../../../../../translated_images/te/bahdanau-fig3.09ba2d37f202a6af.webp) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) నుండి ఫిగర్ (ఫిగర్ 3) @@ -56,7 +56,7 @@ RNNలతో, సీక్వెన్స్-టు-సీక్వెన్స * ట్రైనబుల్ ఎంబెడ్డింగ్, టోకెన్ ఎంబెడ్డింగ్‌లకు సమానంగా. ఇక్కడ మేము ఈ విధానాన్ని పరిగణిస్తాము. టోకెన్లకు మరియు వాటి స్థానాలకు ఎంబెడ్డింగ్ లేయర్లు వర్తింపజేస్తాము, ఫలితంగా ఒకే పరిమాణాల ఎంబెడ్డింగ్ వెక్టర్లు వస్తాయి, వాటిని కలిపి ఉపయోగిస్తాము. * ఒరిజినల్ పేపర్‌లో ప్రతిపాదించిన స్థిరమైన పొజిషనల్ ఎంకోడింగ్ ఫంక్షన్. - + > రచయితచే రూపొందించిన చిత్రం @@ -66,7 +66,7 @@ RNNలతో, సీక్వెన్స్-టు-సీక్వెన్స తర్వాత, సీక్వెన్స్‌లోని కొన్ని ప్యాటర్న్స్‌ను పట్టుకోవాలి. దీని కోసం, ట్రాన్స్‌ఫార్మర్స్ **సెల్ఫ్-అటెన్షన్** మెకానిజం ఉపయోగిస్తాయి, ఇది ఇన్‌పుట్ మరియు అవుట్‌పుట్ ఒకే సీక్వెన్స్‌పై అటెన్షన్ వర్తింపజేయడం. సెల్ఫ్-అటెన్షన్ వాక్యంలో **సందర్భం** పరిగణలోకి తీసుకోవడానికి, మరియు ఏ పదాలు పరస్పరం సంబంధం ఉన్నాయో చూడటానికి సహాయపడుతుంది. ఉదాహరణకు, ఇది *it* వంటి కోరెఫరెన్సులు సూచించే పదాలను గుర్తించడంలో సహాయపడుతుంది, అలాగే సందర్భాన్ని పరిగణలోకి తీసుకుంటుంది: -![](../../../../../translated_images/te/CoreferenceResolution.861924d6d384a7d6.png) +![](../../../../../translated_images/te/CoreferenceResolution.861924d6d384a7d6.webp) > [Google బ్లాగ్](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) నుండి చిత్రం @@ -91,7 +91,7 @@ RNNలతో, సీక్వెన్స్-టు-సీక్వెన్స **BERT** (Bidirectional Encoder Representations from Transformers) అనేది చాలా పెద్ద బహుళ లేయర్ ట్రాన్స్‌ఫార్మర్ నెట్‌వర్క్, *BERT-base*కి 12 లేయర్లు, *BERT-large*కి 24 లేయర్లు ఉన్నాయి. ఈ మోడల్ మొదట పెద్ద టెక్స్ట్ డేటా కార్పస్ (వికీపీడియా + పుస్తకాలు) పై అన్‌సూపర్వైజ్డ్ ట్రైనింగ్ (వాక్యంలో మాస్క్ చేసిన పదాలను అంచనా వేయడం) ద్వారా ప్రీ-ట్రెయిన్ చేయబడుతుంది. ప్రీ-ట్రైనింగ్ సమయంలో మోడల్ భాషా అర్థం గణనీయంగా పెరుగుతుంది, దీన్ని తర్వాత ఇతర డేటాసెట్‌లతో ఫైన్-ట్యూనింగ్ ద్వారా ఉపయోగించవచ్చు. ఈ ప్రక్రియను **ట్రాన్స్‌ఫర్ లెర్నింగ్** అంటారు. -![http://jalammar.github.io/illustrated-bert/ నుండి చిత్రం](../../../../../translated_images/te/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) +![http://jalammar.github.io/illustrated-bert/ నుండి చిత్రం](../../../../../translated_images/te/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > చిత్రం [మూలం](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/te/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/te/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index 48a269bc..993e11c6 100644 --- a/translations/te/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/te/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**అటెన్షన్ మెకానిజమ్స్** RNN యొక్క ప్రతి అవుట్‌పుట్ అంచనాపై ప్రతి ఇన్‌పుట్ వెక్టర్ యొక్క సందర్భాత్మక ప్రభావాన్ని బరువు వేయడానికి ఒక మార్గాన్ని అందిస్తాయి. ఇది అమలు చేయబడే విధానం ఏమిటంటే, ఇన్‌పుట్ RNN యొక్క మధ్యస్థితుల మరియు అవుట్‌పుట్ RNN మధ్య షార్ట్‌కట్స్ సృష్టించడం. ఈ విధంగా, అవుట్‌పుట్ సింబల్ $y_t$ ఉత్పత్తి చేస్తున్నప్పుడు, అన్ని ఇన్‌పుట్ హిడెన్ స్టేట్స్ $h_i$ ను వివిధ బరువు గుణకాలు $\\alpha_{t,i}$ తో పరిగణలోకి తీసుకుంటాము.\n", "\n", - "![Image showing an encoder/decoder model with an additive attention layer](../../../../../translated_images/te/encoder-decoder-attention.7a726296894fb567.png)\n", + "![Image showing an encoder/decoder model with an additive attention layer](../../../../../translated_images/te/encoder-decoder-attention.7a726296894fb567.webp)\n", "*అడిటివ్ అటెన్షన్ మెకానిజం ఉన్న ఎంకోడర్-డీకోడర్ మోడల్ [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) నుండి, [ఈ బ్లాగ్ పోస్ట్](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) నుండి సేకరించబడింది*\n", "\n", "అటెన్షన్ మ్యాట్రిక్స్ $\\{\\alpha_{i,j}\\}$ ఒక నిర్దిష్ట అవుట్‌పుట్ పదం ఉత్పత్తిలో కొన్ని ఇన్‌పుట్ పదాలు ఎంత భాగం పోషిస్తున్నాయో సూచిస్తుంది. క్రింద అలాంటి మ్యాట్రిక్స్ ఉదాహరణ ఉంది:\n", "\n", - "![Image showing a sample alignment found by RNNsearch-50, taken from Bahdanau - arviz.org](../../../../../translated_images/te/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![Image showing a sample alignment found by RNNsearch-50, taken from Bahdanau - arviz.org](../../../../../translated_images/te/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) నుండి తీసుకున్న చిత్రం (ఫిగర్ 3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) అనేది చాలా పెద్ద బహుళ-లేయర్ ట్రాన్స్‌ఫార్మర్ నెట్‌వర్క్, *BERT-base* కోసం 12 లేయర్లు, *BERT-large* కోసం 24 లేయర్లు కలిగి ఉంటుంది. ఈ మోడల్ మొదట పెద్ద టెక్స్ట్ డేటా కార్పస్ (వికీపీడియా + పుస్తకాలు) పై అనుసూచిత శిక్షణ (unsupervised training) ద్వారా ప్రీ-ట్రెయిన్ చేయబడుతుంది (వాక్యంలో మాస్క్ చేసిన పదాలను అంచనా వేయడం). ప్రీ-ట్రెయినింగ్ సమయంలో మోడల్ భాషా అవగాహనను గణనీయంగా గ్రహిస్తుంది, దీన్ని తరువాత ఇతర డేటాసెట్లతో ఫైన్-ట్యూనింగ్ ద్వారా ఉపయోగించవచ్చు. ఈ ప్రక్రియను **ట్రాన్స్‌ఫర్ లెర్నింగ్** అంటారు.\n", "\n", - "![picture from http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/te/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![picture from http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/te/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "ట్రాన్స్‌ఫార్మర్ ఆర్కిటెక్చర్స్‌లో అనేక వేరియేషన్లు ఉన్నాయి, వాటిలో BERT, DistilBERT, BigBird, OpenGPT3 మరియు మరిన్ని ఉన్నాయి, వీటిని ఫైన్-ట్యూన్ చేయవచ్చు. [HuggingFace ప్యాకేజ్](https://github.com/huggingface/) PyTorchతో ఈ ఆర్కిటెక్చర్స్‌లో చాలా మోడల్స్ శిక్షణ కోసం రిపాజిటరీని అందిస్తుంది.\n", "\n", diff --git a/translations/te/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/te/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index ef24b6d9..a1f78496 100644 --- a/translations/te/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/te/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**అటెన్షన్ మెకానిజమ్స్** RNN యొక్క ప్రతి అవుట్‌పుట్ అంచనాపై ప్రతి ఇన్‌పుట్ వెక్టర్ యొక్క సందర్భాత్మక ప్రభావాన్ని బరువు వేయడానికి ఒక మార్గాన్ని అందిస్తాయి. ఇది అమలు చేయబడే విధానం ఏమిటంటే, ఇన్‌పుట్ RNN యొక్క మధ్యస్థితుల మరియు అవుట్‌పుట్ RNN మధ్య షార్ట్‌కట్స్ సృష్టించడం. ఈ విధంగా, అవుట్‌పుట్ సింబల్ $y_t$ ను ఉత్పత్తి చేస్తున్నప్పుడు, మేము అన్ని ఇన్‌పుట్ హిడెన్ స్టేట్స్ $h_i$ ను వివిధ బరువు గుణకాలు $\\alpha_{t,i}$ తో పరిగణలోకి తీసుకుంటాము.\n", "\n", - "![Image showing an encoder/decoder model with an additive attention layer](../../../../../translated_images/te/encoder-decoder-attention.7a726296894fb567.png)\n", + "![Image showing an encoder/decoder model with an additive attention layer](../../../../../translated_images/te/encoder-decoder-attention.7a726296894fb567.webp)\n", "*అడిటివ్ అటెన్షన్ మెకానిజం ఉన్న ఎంకోడర్-డీకోడర్ మోడల్ [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) నుండి, [ఈ బ్లాగ్ పోస్ట్](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) నుండి సైట్ చేయబడింది*\n", "\n", "అటెన్షన్ మ్యాట్రిక్స్ $\\{\\alpha_{i,j}\\}$ ఒక నిర్దిష్ట అవుట్‌పుట్ సీక్వెన్స్ పదం ఉత్పత్తిలో కొన్ని ఇన్‌పుట్ పదాలు ఎంత భాగం పోషిస్తున్నాయో సూచిస్తుంది. క్రింద అలాంటి మ్యాట్రిక్స్ ఉదాహరణ ఉంది:\n", "\n", - "![Image showing a sample alignment found by RNNsearch-50, taken from Bahdanau - arviz.org](../../../../../translated_images/te/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![Image showing a sample alignment found by RNNsearch-50, taken from Bahdanau - arviz.org](../../../../../translated_images/te/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) నుండి తీసుకున్న చిత్రం (ఫిగర్ 3)*\n", "\n", @@ -92,7 +92,7 @@ "source": [ "ఈ లేయర్ రెండు `Embedding` లేయర్లతో కూడి ఉంటుంది: టోకెన్లను ఎంబెడ్ చేయడానికి (ముందుగా చర్చించిన విధంగా) మరియు టోకెన్ స్థానాల కోసం. టోకెన్ స్థానాలు 0 నుండి `maxlen` వరకు సహజ సంఖ్యల శ్రేణిగా `tf.range` ఉపయోగించి సృష్టించబడతాయి, ఆపై ఎంబెడ్డింగ్ లేయర్ ద్వారా పంపబడతాయి. రెండు ఫలిత ఎంబెడ్డింగ్ వెక్టార్లు కలిపి, `maxlen`$\\times$`embed_dim` ఆకారంలో స్థానికంగా ఎంబెడ్ చేయబడిన ఇన్‌పుట్ ప్రాతినిధ్యాన్ని ఉత్పత్తి చేస్తాయి.\n", "\n", - "\n", + "\n", "\n", "ఇప్పుడు, ట్రాన్స్‌ఫార్మర్ బ్లాక్‌ను అమలు చేద్దాం. ఇది ముందుగా నిర్వచించిన ఎంబెడ్డింగ్ లేయర్ అవుట్‌పుట్‌ను తీసుకుంటుంది:\n" ] @@ -134,7 +134,7 @@ "\n", "ఈ లేయర్ అవుట్పుట్ తరువాత `Dense` నెట్‌వర్క్ (మన సందర్భంలో - రెండు లేయర్ పెర్సెప్ట్రాన్) ద్వారా పంపబడుతుంది, మరియు ఫలితం తుది అవుట్పుట్‌కు జోడించబడుతుంది (దీన్ని మళ్లీ సాధారణీకరణకు లోబెడతారు).\n", "\n", - "\n", + "\n", "\n", "ఇప్పుడు, పూర్తి ట్రాన్స్‌ఫార్మర్ మోడల్‌ను నిర్వచించడానికి మేము సిద్ధంగా ఉన్నాము:\n" ] @@ -235,7 +235,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) అనేది చాలా పెద్ద బహుళ పొరల ట్రాన్స్‌ఫార్మర్ నెట్‌వర్క్, *BERT-base* కోసం 12 పొరలు, *BERT-large* కోసం 24 పొరలు కలిగి ఉంటుంది. ఈ మోడల్ మొదట పెద్ద టెక్స్ట్ డేటా సేకరణ (వికీపీడియా + పుస్తకాలు) పై అనుసూచిత శిక్షణ (వాక్యంలో మాస్క్ చేసిన పదాలను అంచనా వేయడం) ద్వారా ప్రీ-ట్రెయిన్ చేయబడుతుంది. ప్రీ-ట్రెయినింగ్ సమయంలో మోడల్ భాషా అర్థం చేసుకోవడంలో గణనీయమైన స్థాయిని పొందుతుంది, దీన్ని తర్వాత ఇతర డేటాసెట్‌లతో ఫైన్ ట్యూనింగ్ ద్వారా ఉపయోగించవచ్చు. ఈ ప్రక్రియను **ట్రాన్స్‌ఫర్ లెర్నింగ్** అంటారు.\n", "\n", - "![picture from http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/te/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![picture from http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/te/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "BERT, DistilBERT, BigBird, OpenGPT3 మరియు మరిన్ని వంటి అనేక ట్రాన్స్‌ఫార్మర్ వేరియేషన్లు ఉన్నాయి, వీటిని ఫైన్ ట్యూన్ చేయవచ్చు.\n", "\n", diff --git a/translations/te/lessons/5-NLP/19-NER/README.md b/translations/te/lessons/5-NLP/19-NER/README.md index 74a2be10..228a93c2 100644 --- a/translations/te/lessons/5-NLP/19-NER/README.md +++ b/translations/te/lessons/5-NLP/19-NER/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: మీరు అమెజాన్ అలెక్సా లేదా గూగుల్ అసిస్టెంట్ లాంటి సహజ భాషా చాట్ బాట్‌ను అభివృద్ధి చేయాలనుకుంటున్నారని ఊహించుకోండి. తెలివైన చాట్ బాట్లు పనిచేసే విధానం ఏమిటంటే, వినియోగదారు ఏమి కోరుకుంటున్నాడో అర్థం చేసుకోవడం కోసం ఇన్‌పుట్ వాక్యంపై టెక్స్ట్ వర్గీకరణ చేస్తాయి. ఈ వర్గీకరణ ఫలితం **ఇంటెంట్** అని పిలవబడుతుంది, ఇది చాట్ బాట్ ఏం చేయాలో నిర్ణయిస్తుంది. -Bot NER +Bot NER > చిత్రాన్ని రచయిత అందించారు @@ -58,7 +58,7 @@ infant | O టోకెన్లు మరియు తరగతుల మధ్య ఒకటి-కోటి అనుసంధానం అవసరం కాబట్టి, ఈ చిత్రంలో చూపినట్లుగా ఒక కుడి వైపు **బహుళ-కు-బహుళ** న్యూరల్ నెట్‌వర్క్ మోడల్‌ను శిక్షణ ఇవ్వవచ్చు: -![Image showing common recurrent neural network patterns.](../../../../../translated_images/te/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Image showing common recurrent neural network patterns.](../../../../../translated_images/te/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > *చిత్రం [ఈ బ్లాగ్ పోస్ట్](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) నుండి, రచయిత [అండ్రేజ్ కార్పతి](http://karpathy.github.io/). NER టోకెన్ వర్గీకరణ మోడల్స్ ఈ చిత్రంలో కుడి వైపు నెట్‌వర్క్ నిర్మాణానికి సరిపోతాయి.* diff --git a/translations/te/lessons/5-NLP/README.md b/translations/te/lessons/5-NLP/README.md index 3b6bb721..991a5cb5 100644 --- a/translations/te/lessons/5-NLP/README.md +++ b/translations/te/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # సహజ భాషా ప్రాసెసింగ్ -![NLP పనుల సారాంశం ఒక డ్రాయింగ్‌లో](../../../../translated_images/te/ai-nlp.b22dcb8ca4707cea.png) +![NLP పనుల సారాంశం ఒక డ్రాయింగ్‌లో](../../../../translated_images/te/ai-nlp.b22dcb8ca4707cea.webp) ఈ విభాగంలో, మనం సహజ భాషా ప్రాసెసింగ్ (NLP) సంబంధిత పనులను నిర్వహించడానికి న్యూరల్ నెట్‌వర్క్‌లను ఉపయోగించడంపై దృష్టి పెట్టబోతున్నాము. కంప్యూటర్లు పరిష్కరించగలిగే అనేక NLP సమస్యలు ఉన్నాయి: diff --git a/translations/te/lessons/6-Other/22-DeepRL/README.md b/translations/te/lessons/6-Other/22-DeepRL/README.md index 819bcb75..24a02fb7 100644 --- a/translations/te/lessons/6-Other/22-DeepRL/README.md +++ b/translations/te/lessons/6-Other/22-DeepRL/README.md @@ -34,7 +34,7 @@ RL కోసం అద్భుతమైన సాధనం [OpenAI Gym](https:/ సరళీకృత బలాన్సింగ్ వెర్షన్‌ను **కార్ట్‌పోల్** సమస్యగా పిలుస్తారు. కార్ట్‌పోల్ ప్రపంచంలో, మనకు ఎడమ లేదా కుడి వైపు కదలగల ఒక హారిజాంటల్ స్లైడర్ ఉంటుంది, మరియు స్లైడర్ పై ఒక నిలువు కంబళిని సమతుల్యం చేయడం లక్ష్యం. -a cartpole +a cartpole ఈ పరిసరాన్ని సృష్టించి ఉపయోగించడానికి, మనకు కొన్ని పాథాన్ కోడ్ లైన్లు అవసరం: diff --git a/translations/te/lessons/6-Other/22-DeepRL/lab/README.md b/translations/te/lessons/6-Other/22-DeepRL/lab/README.md index 7f45e8b6..7c01d54a 100644 --- a/translations/te/lessons/6-Other/22-DeepRL/lab/README.md +++ b/translations/te/lessons/6-Other/22-DeepRL/lab/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: OpenAI పరిసరంలో [Mountain Car](https://www.gymlibrary.ml/environments/classic_control/mountain_car/) ను నియంత్రించడానికి RL ఏజెంట్‌ను శిక్షణ ఇవ్వడం మీ లక్ష్యం. -Mountain Car +Mountain Car ## పరిసరము diff --git a/translations/te/lessons/6-Other/23-MultiagentSystems/README.md b/translations/te/lessons/6-Other/23-MultiagentSystems/README.md index cc1b36b9..d22ba1bc 100644 --- a/translations/te/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/te/lessons/6-Other/23-MultiagentSystems/README.md @@ -61,7 +61,7 @@ ask turtles [ నెట్‌లాగోలో ఉన్న గొప్ప విషయం ఏమిటంటే, మీరు ప్రయత్నించగల పని చేసే మోడల్స్ లైబ్రరీ ఉంది. **File → Models Library**కి వెళ్లండి, మీరు ఎన్నుకోవడానికి అనేక మోడల్స్ వర్గాలు ఉంటాయి. -NetLogo Models Library +NetLogo Models Library > మోడల్స్ లైబ్రరీ స్క్రీన్‌షాట్ - Dmitry Soshnikov @@ -71,7 +71,7 @@ ask turtles [ మోడల్ తెరిచిన తర్వాత, మీరు ప్రధాన నెట్‌లాగో స్క్రీన్‌కు తీసుకువెళ్ళబడతారు. ఇక్కడ ఒక నమూనా మోడల్ ఉంది, ఇది పరిమిత వనరులు (గడ్డి) ఉన్నప్పుడు నక్కలు మరియు గొర్రెల జనాభాను వివరించేది. -![NetLogo Main Screen](../../../../../translated_images/te/NetLogo-Main.32653711ec1a01b3.png) +![NetLogo Main Screen](../../../../../translated_images/te/NetLogo-Main.32653711ec1a01b3.webp) > స్క్రీన్‌షాట్ - Dmitry Soshnikov diff --git a/translations/te/lessons/README.md b/translations/te/lessons/README.md index 9493ad30..1be0ba86 100644 --- a/translations/te/lessons/README.md +++ b/translations/te/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # అవలోకనం -![డూడుల్‌లో అవలోకనం](../../../translated_images/te/ai-overview.0857791951d19500.png) +![డూడుల్‌లో అవలోకనం](../../../translated_images/te/ai-overview.0857791951d19500.webp) > స్కెచ్‌నోట్: [టోమోమీ ఇమురా](https://twitter.com/girlie_mac) diff --git a/translations/te/lessons/X-Extras/X1-MultiModal/README.md b/translations/te/lessons/X-Extras/X1-MultiModal/README.md index f54476d5..614ca275 100644 --- a/translations/te/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/te/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ NLP పనులను పరిష్కరించడంలో ట్రా CLIP యొక్క ప్రధాన ఆలోచన ఏమిటంటే, టెక్స్ట్ ప్రాంప్ట్‌లను ఒక చిత్రంతో పోల్చి, ఆ చిత్రం ప్రాంప్ట్‌కు ఎంతగా సరిపోతుందో నిర్ణయించగలగడం. -![CLIP Architecture](../../../../../translated_images/te/clip-arch.b3dbf20b4e8ed8be.png) +![CLIP Architecture](../../../../../translated_images/te/clip-arch.b3dbf20b4e8ed8be.webp) > *చిత్రం [ఈ బ్లాగ్ పోస్ట్](https://openai.com/blog/clip/) నుండి* @@ -29,7 +29,7 @@ CLIP మోడల్/లైబ్రరీ [OpenAI GitHub](https://github.com/op ఉదాహరణకు, పిల్లులు, కుక్కలు మరియు మనుషుల మధ్య చిత్రాలను వర్గీకరించాలి అనుకుందాం. ఈ సందర్భంలో, మోడల్‌కు ఒక చిత్రం మరియు టెక్స్ట్ ప్రాంప్ట్‌ల సిరీస్ ఇవ్వవచ్చు: "*పిల్లి యొక్క చిత్రం*", "*కుక్క యొక్క చిత్రం*", "*మనిషి యొక్క చిత్రం*". 3 ప్రాబబిలిటీల వెక్టర్‌లో అత్యధిక విలువ ఉన్న సూచికను ఎంచుకోవడం సరిపోతుంది. -![CLIP for Image Classification](../../../../../translated_images/te/clip-class.3af42ef0b2b19369.png) +![CLIP for Image Classification](../../../../../translated_images/te/clip-class.3af42ef0b2b19369.webp) > *చిత్రం [ఈ బ్లాగ్ పోస్ట్](https://openai.com/blog/clip/) నుండి* @@ -53,13 +53,13 @@ VQGAN గురించి మరింత తెలుసుకోడాని VQGAN మరియు సాంప్రదాయ GAN మధ్య ముఖ్య తేడా ఏమిటంటే, సాంప్రదాయ GAN ఏ ఇన్‌పుట్ వెక్టర్ నుండి సరైన చిత్రం ఉత్పత్తి చేయగలదు, కానీ VQGAN కొన్నిసార్లు సారూప్యమైన చిత్రం కాకపోవచ్చు. అందుకే, చిత్ర సృష్టి ప్రక్రియను మరింత మార్గనిర్దేశం చేయాలి, దీని కోసం CLIP ఉపయోగించవచ్చు. -![VQGAN+CLIP Architecture](../../../../../translated_images/te/vqgan.5027fe05051dfa31.png) +![VQGAN+CLIP Architecture](../../../../../translated_images/te/vqgan.5027fe05051dfa31.webp) టెక్స్ట్ ప్రాంప్ట్‌కు అనుగుణంగా చిత్రం సృష్టించడానికి, మొదట రాండమ్ ఎంకోడింగ్ వెక్టర్ తీసుకుని దాన్ని VQGAN ద్వారా చిత్రంగా మార్చుతారు. ఆ తర్వాత CLIP ఉపయోగించి ఆ చిత్రం టెక్స్ట్ ప్రాంప్ట్‌కు ఎంతగా సరిపోతుందో చూపించే లాస్ ఫంక్షన్ తయారుచేస్తారు. ఆ లాస్‌ను తగ్గించడం లక్ష్యం, బ్యాక్ ప్రొపగేషన్ ద్వారా ఇన్‌పుట్ వెక్టర్ పరామితులను సర్దుబాటు చేస్తారు. VQGAN+CLIP ను అమలు చేసే గొప్ప లైబ్రరీ [Pixray](http://github.com/pixray/pixray) -![Picture produced by Pixray](../../../../../translated_images/te/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Picture produced by pixray](../../../../../translated_images/te/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Picture produced by Pixray](../../../../../translated_images/te/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) +![Picture produced by Pixray](../../../../../translated_images/te/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp) | ![Picture produced by pixray](../../../../../translated_images/te/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp) | ![Picture produced by Pixray](../../../../../translated_images/te/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp) ----|----|---- *పుస్తకం తో యువ సాహిత్య ఉపాధ్యాయుడి watercolor సమీప చిత్రము* | *కంప్యూటర్ తో యువ కంప్యూటర్ సైన్స్ ఉపాధ్యాయురాలి oil సమీప చిత్రము* | *బ్లాక్‌బోర్డ్ ముందు వృద్ధ గణితం ఉపాధ్యాయుడి oil సమీప చిత్రము* diff --git a/translations/th/README.md b/translations/th/README.md index 0310f178..f29c0489 100644 --- a/translations/th/README.md +++ b/translations/th/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # ปัญญาประดิษฐ์สำหรับผู้เริ่มต้น - หลักสูตร -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/th/ai-overview.0857791951d19500.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/th/ai-overview.0857791951d19500.webp)| |:---:| | AI For Beginners - _สเก็ตช์โน้ตโดย [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/th/lessons/1-Intro/README.md b/translations/th/lessons/1-Intro/README.md index 5446df63..4462363d 100644 --- a/translations/th/lessons/1-Intro/README.md +++ b/translations/th/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # การแนะนำเกี่ยวกับ AI -![สรุปเนื้อหาเกี่ยวกับการแนะนำ AI ในรูปแบบภาพวาด](../../../../translated_images/th/ai-intro.bf28d1ac4235881c.png) +![สรุปเนื้อหาเกี่ยวกับการแนะนำ AI ในรูปแบบภาพวาด](../../../../translated_images/th/ai-intro.bf28d1ac4235881c.webp) > ภาพวาดโดย [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: เดิมทีคอมพิวเตอร์ถูกคิดค้นโดย [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage) เพื่อทำงานกับตัวเลขตามกระบวนการที่กำหนดไว้อย่างชัดเจน - หรือที่เรียกว่าอัลกอริทึม คอมพิวเตอร์สมัยใหม่ แม้ว่าจะมีความก้าวหน้ามากกว่ารุ่นต้นแบบในศตวรรษที่ 19 แต่ก็ยังคงยึดแนวคิดเดิมเกี่ยวกับการคำนวณที่ควบคุมได้ ดังนั้นจึงเป็นไปได้ที่จะเขียนโปรแกรมให้คอมพิวเตอร์ทำบางสิ่งได้ หากเรารู้ลำดับขั้นตอนที่แน่นอนที่เราต้องทำเพื่อให้บรรลุเป้าหมาย -![ภาพถ่ายของบุคคล](../../../../translated_images/th/dsh_age.d212a30d4e54fb5f.png) +![ภาพถ่ายของบุคคล](../../../../translated_images/th/dsh_age.d212a30d4e54fb5f.webp) > ภาพถ่ายโดย [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ AI แบบอ่อนมีความเชี่ยวชาญสูง หนึ่งในปัญหาเมื่อพูดถึงคำว่า **[ความฉลาด](https://en.wikipedia.org/wiki/Intelligence)** คือไม่มีคำจำกัดความที่ชัดเจนของคำนี้ บางคนอาจโต้แย้งว่าความฉลาดเชื่อมโยงกับ **การคิดเชิงนามธรรม** หรือ **การรับรู้ตัวเอง** แต่เราไม่สามารถกำหนดมันได้อย่างเหมาะสม -![ภาพถ่ายของแมว](../../../../translated_images/th/photo-cat.8c8e8fb760ffe457.jpg) +![ภาพถ่ายของแมว](../../../../translated_images/th/photo-cat.8c8e8fb760ffe457.webp) > [ภาพถ่าย](https://unsplash.com/photos/75715CVEJhI) โดย [Amber Kipp](https://unsplash.com/@sadmax) จาก Unsplash @@ -98,13 +98,13 @@ AI แบบอ่อนมีความเชี่ยวชาญสูง > | แล้ว ML ล่ะ? | | > |--------------|-----------| -> | ส่วนหนึ่งของปัญญาประดิษฐ์ที่อิงกับการเรียนรู้ของคอมพิวเตอร์ในการแก้ปัญหาตามข้อมูลบางอย่างเรียกว่า **Machine Learning** เราจะไม่พิจารณาการเรียนรู้ของเครื่องแบบคลาสสิกในหลักสูตรนี้ - เราแนะนำให้คุณดูหลักสูตร [Machine Learning for Beginners](http://aka.ms/ml-beginners) แยกต่างหาก | ![ML for Beginners](../../../../translated_images/th/ml-for-beginners.9e4fed176fd5817d.png) | +> | ส่วนหนึ่งของปัญญาประดิษฐ์ที่อิงกับการเรียนรู้ของคอมพิวเตอร์ในการแก้ปัญหาตามข้อมูลบางอย่างเรียกว่า **Machine Learning** เราจะไม่พิจารณาการเรียนรู้ของเครื่องแบบคลาสสิกในหลักสูตรนี้ - เราแนะนำให้คุณดูหลักสูตร [Machine Learning for Beginners](http://aka.ms/ml-beginners) แยกต่างหาก | ![ML for Beginners](../../../../translated_images/th/ml-for-beginners.9e4fed176fd5817d.webp) | ## ประวัติย่อของ AI ปัญญาประดิษฐ์เริ่มต้นเป็นสาขาในช่วงกลางศตวรรษที่ 20 ในช่วงแรก วิธีการให้เหตุผลเชิงสัญลักษณ์เป็นวิธีที่แพร่หลาย และนำไปสู่ความสำเร็จที่สำคัญหลายประการ เช่น ระบบผู้เชี่ยวชาญ – โปรแกรมคอมพิวเตอร์ที่สามารถทำหน้าที่เป็นผู้เชี่ยวชาญในบางโดเมนปัญหาที่จำกัด อย่างไรก็ตาม ไม่นานก็ชัดเจนว่าวิธีการดังกล่าวไม่สามารถขยายขอบเขตได้ดี การดึงความรู้จากผู้เชี่ยวชาญ การแสดงในคอมพิวเตอร์ และการรักษาฐานความรู้ให้ถูกต้องกลายเป็นงานที่ซับซ้อนมาก และมีค่าใช้จ่ายสูงเกินไปที่จะนำไปใช้ในหลายกรณี สิ่งนี้นำไปสู่สิ่งที่เรียกว่า [AI Winter](https://en.wikipedia.org/wiki/AI_winter) ในทศวรรษ 1970 -ประวัติย่อของ AI +ประวัติย่อของ AI > ภาพโดย [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/th/lessons/2-Symbolic/Animals.ipynb b/translations/th/lessons/2-Symbolic/Animals.ipynb index fe65f971..7a2344e9 100644 --- a/translations/th/lessons/2-Symbolic/Animals.ipynb +++ b/translations/th/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "ในตัวอย่างนี้ เราจะสร้างระบบที่ใช้ความรู้เพื่อระบุชนิดของสัตว์โดยอิงจากลักษณะทางกายภาพบางประการ ระบบนี้สามารถแสดงผลได้ในรูปแบบต้นไม้ AND-OR (นี่เป็นเพียงส่วนหนึ่งของต้นไม้ทั้งหมด เราสามารถเพิ่มกฎเพิ่มเติมได้อย่างง่ายดาย):\n", "\n", - "![](../../../../translated_images/th/AND-OR-Tree.5592d2c70187f283.png)\n" + "![](../../../../translated_images/th/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { diff --git a/translations/th/lessons/2-Symbolic/README.md b/translations/th/lessons/2-Symbolic/README.md index bc8e5ad9..4395f60d 100644 --- a/translations/th/lessons/2-Symbolic/README.md +++ b/translations/th/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # การแทนความรู้และระบบผู้เชี่ยวชาญ -![สรุปเนื้อหา Symbolic AI](../../../../translated_images/th/ai-symbolic.715a30cb610411a6.png) +![สรุปเนื้อหา Symbolic AI](../../../../translated_images/th/ai-symbolic.715a30cb610411a6.webp) > ภาพสเก็ตโน้ตโดย [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ CO_OP_TRANSLATOR_METADATA: ดังนั้น ปัญหาของ **การแทนความรู้** คือการหาวิธีที่มีประสิทธิภาพในการแทนความรู้ภายในคอมพิวเตอร์ในรูปแบบของข้อมูล เพื่อให้สามารถใช้งานได้โดยอัตโนมัติ สิ่งนี้สามารถมองได้ว่าเป็นสเปกตรัม: -![สเปกตรัมการแทนความรู้](../../../../translated_images/th/knowledge-spectrum.b60df631852c0217.png) +![สเปกตรัมการแทนความรู้](../../../../translated_images/th/knowledge-spectrum.b60df631852c0217.webp) > ภาพโดย [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Block Syntax | Indent | | | หนึ่งในความสำเร็จแรกๆ ของ Symbolic AI คือ **ระบบผู้เชี่ยวชาญ (Expert Systems)** - ระบบคอมพิวเตอร์ที่ถูกออกแบบมาให้ทำหน้าที่เป็นผู้เชี่ยวชาญในโดเมนปัญหาที่จำกัด ระบบเหล่านี้มีพื้นฐานมาจาก **ฐานความรู้ (Knowledge Base)** ที่ดึงมาจากผู้เชี่ยวชาญมนุษย์ และมี **เครื่องมืออนุมาน (Inference Engine)** ที่ทำการให้เหตุผลบนฐานความรู้นั้น -![โครงสร้างระบบประสาทมนุษย์](../../../../translated_images/th/arch-human.5d4d35f1bba3ab1c.png) | ![โครงสร้างระบบที่ใช้ความรู้](../../../../translated_images/th/arch-kbs.3ec5c150b09fa8da.png) +![โครงสร้างระบบประสาทมนุษย์](../../../../translated_images/th/arch-human.5d4d35f1bba3ab1c.webp) | ![โครงสร้างระบบที่ใช้ความรู้](../../../../translated_images/th/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ โครงสร้างระบบประสาทมนุษย์แบบง่าย | โครงสร้างของระบบที่ใช้ความรู้ @@ -106,7 +106,7 @@ Block Syntax | Indent | | | ตัวอย่างเช่น ลองพิจารณาระบบผู้เชี่ยวชาญที่ใช้ในการระบุสัตว์ตามลักษณะทางกายภาพ: -![ต้นไม้ AND-OR](../../../../translated_images/th/AND-OR-Tree.5592d2c70187f283.png) +![ต้นไม้ AND-OR](../../../../translated_images/th/AND-OR-Tree.5592d2c70187f283.webp) > ภาพโดย [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/th/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/th/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index 3944f989..8d36178e 100644 --- a/translations/th/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/th/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -1261,7 +1261,7 @@ "* ค่าความสูญเสียในการฝึกต่ำ - โมเดลสามารถประมาณค่าข้อมูลการฝึกได้ดี เพราะมีพลังในการแสดงออกเพียงพอ\n", "* ค่าความสูญเสียในการตรวจสอบอาจสูงกว่าค่าความสูญเสียในการฝึกมาก และอาจเริ่มเพิ่มขึ้นระหว่างการฝึก - เนื่องจากโมเดล \"จดจำ\" จุดข้อมูลการฝึก และสูญเสีย \"ภาพรวม\"\n", "\n", - "![Overfitting](../../../../../translated_images/th/overfit.a0bd57f717c15769.png)\n", + "![Overfitting](../../../../../translated_images/th/overfit.a0bd57f717c15769.webp)\n", "\n", "> ในภาพนี้ `x` แทนข้อมูลการฝึก, `o` แทนข้อมูลการตรวจสอบ ซ้าย - โมเดลเชิงเส้น (ชั้นเดียว) ซึ่งประมาณค่าลักษณะของข้อมูลได้ค่อนข้างดี ขวา - โมเดลที่เกิด overfitting ซึ่งสามารถประมาณค่าข้อมูลการฝึกได้อย่างสมบูรณ์แบบ แต่ไม่สามารถใช้งานได้กับข้อมูลอื่น (ค่าความผิดพลาดในการตรวจสอบสูงมาก)\n" ] diff --git a/translations/th/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/th/lessons/3-NeuralNetworks/05-Frameworks/README.md index f67e2ed9..61e65c2a 100644 --- a/translations/th/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/th/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Overfitting เป็นแนวคิดที่สำคัญมากใ ลองพิจารณาปัญหาการประมาณค่าจุด 5 จุด (แสดงด้วย `x` ในกราฟด้านล่าง): -![linear](../../../../../translated_images/th/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/th/overfit2.131f5800ae10ca5e.jpg) +![linear](../../../../../translated_images/th/overfit1.f24b71c6f652e59e.webp) | ![overfit](../../../../../translated_images/th/overfit2.131f5800ae10ca5e.webp) -------------------------|-------------------------- **โมเดลเชิงเส้น, 2 พารามิเตอร์** | **โมเดลไม่เชิงเส้น, 7 พารามิเตอร์** Training error = 5.3 | Training error = 0 @@ -79,7 +79,7 @@ Validation error = 5.1 | Validation error = 20 จากกราฟด้านบน เราสามารถตรวจจับ overfitting ได้จาก training error ที่ต่ำมาก และ validation error ที่สูง โดยปกติระหว่างการฝึก เราจะเห็นทั้ง training และ validation error ลดลง แต่ในบางจุด validation error อาจหยุดลดลงและเริ่มเพิ่มขึ้น นี่เป็นสัญญาณของ overfitting และเป็นตัวบ่งชี้ว่าเราควรหยุดการฝึกในจุดนี้ (หรืออย่างน้อยควรบันทึกสถานะของโมเดล) -![overfitting](../../../../../translated_images/th/Overfitting.408ad91cd90b4371.png) +![overfitting](../../../../../translated_images/th/Overfitting.408ad91cd90b4371.webp) ## วิธีป้องกัน overfitting diff --git a/translations/th/lessons/3-NeuralNetworks/README.md b/translations/th/lessons/3-NeuralNetworks/README.md index 4b663cd9..d61efac0 100644 --- a/translations/th/lessons/3-NeuralNetworks/README.md +++ b/translations/th/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # แนะนำเกี่ยวกับเครือข่ายประสาทเทียม -![สรุปเนื้อหาเกี่ยวกับเครือข่ายประสาทเทียมในรูปวาด](../../../../translated_images/th/ai-neuralnetworks.1c687ae40bc86e83.png) +![สรุปเนื้อหาเกี่ยวกับเครือข่ายประสาทเทียมในรูปวาด](../../../../translated_images/th/ai-neuralnetworks.1c687ae40bc86e83.webp) ตามที่เราได้พูดถึงในบทนำ หนึ่งในวิธีที่จะสร้างความฉลาดคือการฝึก **โมเดลคอมพิวเตอร์** หรือ **สมองเทียม** ตั้งแต่กลางศตวรรษที่ 20 นักวิจัยได้ลองใช้โมเดลทางคณิตศาสตร์ต่าง ๆ จนกระทั่งในช่วงไม่กี่ปีที่ผ่านมาแนวทางนี้ได้พิสูจน์แล้วว่าประสบความสำเร็จอย่างมาก โมเดลทางคณิตศาสตร์ของสมองเหล่านี้เรียกว่า **เครือข่ายประสาทเทียม** @@ -36,13 +36,13 @@ CO_OP_TRANSLATOR_METADATA: จากชีววิทยา เราทราบว่าสมองของเราประกอบด้วยเซลล์ประสาท (neurons) ซึ่งแต่ละเซลล์มี "อินพุต" หลายตัว (dendrites) และ "เอาต์พุต" หนึ่งตัว (axon) ทั้ง dendrites และ axons สามารถนำสัญญาณไฟฟ้าได้ และการเชื่อมต่อระหว่างพวกมัน — ที่เรียกว่า synapses — สามารถแสดงระดับการนำไฟฟ้าที่แตกต่างกัน ซึ่งถูกควบคุมโดยสารสื่อประสาท -![โมเดลของเซลล์ประสาท](../../../../translated_images/th/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![โมเดลของเซลล์ประสาท](../../../../translated_images/th/artneuron.1a5daa88d20ebe6f.png) +![โมเดลของเซลล์ประสาท](../../../../translated_images/th/synapse-wikipedia.ed20a9e4726ea1c6.webp) | ![โมเดลของเซลล์ประสาท](../../../../translated_images/th/artneuron.1a5daa88d20ebe6f.webp) ----|---- เซลล์ประสาทจริง *([ภาพ](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) จาก Wikipedia)* | เซลล์ประสาทเทียม *(ภาพโดยผู้เขียน)* ดังนั้น โมเดลทางคณิตศาสตร์ที่ง่ายที่สุดของเซลล์ประสาทจะมีอินพุตหลายตัว X1, ..., XN และเอาต์พุต Y พร้อมกับชุดของน้ำหนัก W1, ..., WN เอาต์พุตจะถูกคำนวณเป็น: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) โดยที่ f คือ **ฟังก์ชันการกระตุ้น** ที่ไม่เป็นเชิงเส้น diff --git a/translations/th/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/th/lessons/4-ComputerVision/06-IntroCV/README.md index e84d4685..a984596b 100644 --- a/translations/th/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/th/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) * **การประมวลผลภาพถ่ายของหนังสือเบรลล์** เรามุ่งเน้นที่การใช้ thresholding, การตรวจจับคุณลักษณะ, การแปลงมุมมอง และการปรับอาร์เรย์ NumPy เพื่อแยกสัญลักษณ์เบรลล์แต่ละตัวสำหรับการจำแนกผลลัพธ์โดยเครือข่ายประสาทเทียม -![Braille Image](../../../../../translated_images/th/braille.341962ff76b1bd70.jpeg) | ![Braille Image Pre-processed](../../../../../translated_images/th/braille-result.46530fea020b03c7.png) | ![Braille Symbols](../../../../../translated_images/th/braille-symbols.0159185ab69d5339.png) +![Braille Image](../../../../../translated_images/th/braille.341962ff76b1bd70.webp) | ![Braille Image Pre-processed](../../../../../translated_images/th/braille-result.46530fea020b03c7.webp) | ![Braille Symbols](../../../../../translated_images/th/braille-symbols.0159185ab69d5339.webp) ----|-----|----- > ภาพจาก [OpenCV.ipynb](OpenCV.ipynb) * **การตรวจจับการเคลื่อนไหวในวิดีโอโดยใช้ความแตกต่างของเฟรม** หากกล้องอยู่ในตำแหน่งคงที่ เฟรมจากกล้องควรมีความคล้ายคลึงกันมาก โดยเฟรมที่แสดงผลเป็นอาร์เรย์ เพียงแค่ลบอาร์เรย์ของเฟรมสองเฟรมที่ต่อเนื่องกัน คุณจะได้ความแตกต่างของพิกเซล ซึ่งควรต่ำสำหรับเฟรมที่นิ่ง และสูงขึ้นเมื่อมีการเคลื่อนไหวในภาพ -![Image of video frames and frame differences](../../../../../translated_images/th/frame-difference.706f805491a0883c.png) +![Image of video frames and frame differences](../../../../../translated_images/th/frame-difference.706f805491a0883c.webp) > ภาพจาก [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) - **Dense Optical Flow** คำนวณสนามเวกเตอร์ที่แสดงว่าพิกเซลแต่ละตัวเคลื่อนที่ไปที่ใด - **Sparse Optical Flow** ใช้คุณลักษณะเด่นในภาพ (เช่น ขอบ) และสร้างเส้นทางการเคลื่อนที่ของมันจากเฟรมหนึ่งไปยังอีกเฟรมหนึ่ง -![Image of Optical Flow](../../../../../translated_images/th/optical.1f4a94464579a83a.png) +![Image of Optical Flow](../../../../../translated_images/th/optical.1f4a94464579a83a.webp) > ภาพจาก [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/th/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/th/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 1a7a5dd7..b9a8673b 100644 --- a/translations/th/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/th/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 เป็นเครือข่ายที่ทำคะแนนความแม่นยำได้ถึง 92.7% ในการจัดประเภท ImageNet top-5 ในปี 2014 โดยมีโครงสร้างเลเยอร์ดังนี้: -![ImageNet Layers](../../../../../translated_images/th/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet Layers](../../../../../translated_images/th/vgg-16-arch1.d901a5583b3a51ba.webp) ดังที่คุณเห็น VGG ใช้สถาปัตยกรรมแบบพีระมิดดั้งเดิม ซึ่งเป็นลำดับของเลเยอร์คอนโวลูชันและพูลลิ่ง -![ImageNet Pyramid](../../../../../translated_images/th/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet Pyramid](../../../../../translated_images/th/vgg-16-arch.64ff2137f50dd49f.webp) > ภาพจาก [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/th/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/th/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index 050cefae..65521faa 100644 --- a/translations/th/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/th/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -262,7 +262,7 @@ "\n", "ดังนั้น ใน CNN ทั่วไปจะมีเลเยอร์คอนโวลูชันหลายชั้น โดยมีเลเยอร์การลดขนาดอยู่ระหว่างเพื่อช่วยลดมิติของภาพ นอกจากนี้เรายังเพิ่มจำนวนตัวกรอง เพราะเมื่อรูปแบบมีความซับซ้อนมากขึ้น จะมีการผสมผสานที่น่าสนใจมากขึ้นที่เราต้องค้นหา\n", "\n", - "![ภาพแสดงเลเยอร์คอนโวลูชันหลายชั้นพร้อมเลเยอร์การลดขนาด.](../../../../../translated_images/th/cnn-pyramid.85915455759ef0ce.png)\n", + "![ภาพแสดงเลเยอร์คอนโวลูชันหลายชั้นพร้อมเลเยอร์การลดขนาด.](../../../../../translated_images/th/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "เนื่องจากการลดมิติของพื้นที่และการเพิ่มมิติของฟีเจอร์/ตัวกรอง สถาปัตยกรรมนี้จึงถูกเรียกว่า **pyramid architecture**\n" ] diff --git a/translations/th/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/th/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index ba302360..39546ab2 100644 --- a/translations/th/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/th/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -361,7 +361,7 @@ "\n", "ดังนั้น ใน CNN ทั่วไปจะมีเลเยอร์คอนโวลูชันหลายชั้น โดยมีเลเยอร์การทำพูลลิ่งแทรกอยู่ระหว่างเพื่อช่วยลดมิติของภาพ นอกจากนี้ เรายังเพิ่มจำนวนฟิลเตอร์ เพราะเมื่อรูปแบบมีความซับซ้อนมากขึ้น ก็จะมีการผสมผสานที่น่าสนใจมากขึ้นที่เราต้องค้นหา\n", "\n", - "![ภาพแสดงเลเยอร์คอนโวลูชันหลายชั้นพร้อมเลเยอร์การทำพูลลิ่ง](../../../../../translated_images/th/cnn-pyramid.85915455759ef0ce.png)\n", + "![ภาพแสดงเลเยอร์คอนโวลูชันหลายชั้นพร้อมเลเยอร์การทำพูลลิ่ง](../../../../../translated_images/th/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "เนื่องจากการลดมิติของพื้นที่และการเพิ่มมิติของฟีเจอร์/ฟิลเตอร์ สถาปัตยกรรมนี้จึงถูกเรียกว่า **pyramid architecture**\n" ] diff --git a/translations/th/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/th/lessons/4-ComputerVision/07-ConvNets/README.md index bafb519a..de00e9e7 100644 --- a/translations/th/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/th/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: เพื่อดึงรูปแบบเหล่านี้ออกมา เราจะใช้แนวคิดของ **ฟิลเตอร์คอนโวลูชัน** อย่างที่คุณทราบ ภาพถูกแสดงในรูปแบบเมทริกซ์ 2 มิติ หรือเทนเซอร์ 3 มิติที่มีความลึกของสี การใช้ฟิลเตอร์หมายถึงการนำเมทริกซ์ **ฟิลเตอร์เคอร์เนล** ขนาดเล็กมาใช้ และสำหรับแต่ละพิกเซลในภาพต้นฉบับ เราจะคำนวณค่าเฉลี่ยถ่วงน้ำหนักกับจุดที่อยู่ใกล้เคียง เราสามารถมองสิ่งนี้เหมือนหน้าต่างเล็ก ๆ ที่เลื่อนผ่านภาพทั้งหมด และเฉลี่ยค่าพิกเซลทั้งหมดตามน้ำหนักในเมทริกซ์ฟิลเตอร์เคอร์เนล -![ฟิลเตอร์ขอบแนวตั้ง](../../../../../translated_images/th/filter-vert.b7148390ca0bc356.png) | ![ฟิลเตอร์ขอบแนวนอน](../../../../../translated_images/th/filter-horiz.59b80ed4feb946ef.png) +![ฟิลเตอร์ขอบแนวตั้ง](../../../../../translated_images/th/filter-vert.b7148390ca0bc356.webp) | ![ฟิลเตอร์ขอบแนวนอน](../../../../../translated_images/th/filter-horiz.59b80ed4feb946ef.webp) ----|---- > ภาพโดย Dmitry Soshnikov @@ -38,7 +38,7 @@ CO_OP_TRANSLATOR_METADATA: * เราสามารถออกแบบเครือข่ายในลักษณะที่ฟิลเตอร์ถูกฝึกฝนโดยอัตโนมัติ * เราสามารถใช้วิธีเดียวกันนี้เพื่อค้นหารูปแบบในคุณลักษณะระดับสูง ไม่ใช่แค่ในภาพต้นฉบับ ดังนั้นการดึงคุณลักษณะของ CNN จึงทำงานในลำดับชั้นของคุณลักษณะ โดยเริ่มจากการผสมผสานพิกเซลระดับต่ำไปจนถึงการผสมผสานระดับสูงของส่วนต่าง ๆ ของภาพ -![การดึงคุณลักษณะแบบลำดับชั้น](../../../../../translated_images/th/FeatureExtractionCNN.d9b456cbdae7cb64.png) +![การดึงคุณลักษณะแบบลำดับชั้น](../../../../../translated_images/th/FeatureExtractionCNN.d9b456cbdae7cb64.webp) > ภาพจาก [งานวิจัยโดย Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d) ซึ่งอ้างอิงจาก [งานวิจัยของพวกเขา](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ CNN ส่วนใหญ่ที่ใช้สำหรับการปร ตัวอย่างเช่น ลองดูสถาปัตยกรรมของ VGG-16 ซึ่งเป็นเครือข่ายที่ทำได้ถึงความแม่นยำ 92.7% ในการจัดอันดับ 5 อันดับแรกของ ImageNet ในปี 2014: -![ชั้นของ ImageNet](../../../../../translated_images/th/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ชั้นของ ImageNet](../../../../../translated_images/th/vgg-16-arch1.d901a5583b3a51ba.webp) -![พีระมิดของ ImageNet](../../../../../translated_images/th/vgg-16-arch.64ff2137f50dd49f.jpg) +![พีระมิดของ ImageNet](../../../../../translated_images/th/vgg-16-arch.64ff2137f50dd49f.webp) > ภาพจาก [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/th/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/th/lessons/4-ComputerVision/07-ConvNets/lab/README.md index f9b2eb2a..9d922c2e 100644 --- a/translations/th/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/th/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ CO_OP_TRANSLATOR_METADATA: เราจะใช้ [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/) ซึ่งมีภาพของสุนัขและแมวจาก 37 สายพันธุ์ที่แตกต่างกัน -![ชุดข้อมูลที่เราจะใช้](../../../../../../translated_images/th/data.50b2a9d5484bdbf0.png) +![ชุดข้อมูลที่เราจะใช้](../../../../../../translated_images/th/data.50b2a9d5484bdbf0.webp) เพื่อดาวน์โหลดชุดข้อมูล ให้ใช้โค้ดนี้: diff --git a/translations/th/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/th/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index e7634709..f6e27337 100644 --- a/translations/th/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/th/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "เพื่อสร้างภาพแมวที่สมบูรณ์แบบ เราจะเริ่มต้นด้วยภาพที่เป็นสัญญาณรบกวนแบบสุ่ม และจะใช้เทคนิคการปรับให้เหมาะสมด้วยการไล่ระดับ (gradient descent) เพื่อปรับภาพให้เครือข่ายสามารถจดจำว่าเป็นแมวได้\n", "\n", - "![วงจรการปรับให้เหมาะสม](../../../../../translated_images/th/ideal-cat-loop.999fbb8ff306e044.png)\n", + "![วงจรการปรับให้เหมาะสม](../../../../../translated_images/th/ideal-cat-loop.999fbb8ff306e044.webp)\n", "\n", "นี่คือภาพเริ่มต้นของเรา:\n" ] diff --git a/translations/th/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/th/lessons/4-ComputerVision/08-TransferLearning/README.md index ed541f92..7fcc1825 100644 --- a/translations/th/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/th/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ CO_OP_TRANSLATOR_METADATA: ตัวอย่างคุณลักษณะที่ดึงออกมาจากภาพแมวโดยเครือข่าย VGG-16: -![คุณลักษณะที่ดึงออกมาโดย VGG-16](../../../../../translated_images/th/features.6291f9c7ba3a0b95.png) +![คุณลักษณะที่ดึงออกมาโดย VGG-16](../../../../../translated_images/th/features.6291f9c7ba3a0b95.webp) ## ชุดข้อมูลแมวและสุนัข @@ -48,19 +48,19 @@ CO_OP_TRANSLATOR_METADATA: วิธีหนึ่งที่เราสามารถทำได้คือเริ่มต้นด้วยภาพสุ่ม และพยายามใช้เทคนิค **การปรับแต่งด้วยการลดเกรเดียนต์** เพื่อปรับภาพนั้นให้เครือข่ายเริ่มคิดว่ามันคือแมว -![วงจรการปรับแต่งภาพ](../../../../../translated_images/th/ideal-cat-loop.999fbb8ff306e044.png) +![วงจรการปรับแต่งภาพ](../../../../../translated_images/th/ideal-cat-loop.999fbb8ff306e044.webp) อย่างไรก็ตาม หากเราทำเช่นนี้ เราจะได้สิ่งที่คล้ายกับสัญญาณรบกวนแบบสุ่ม นั่นเป็นเพราะ *มีหลายวิธีที่จะทำให้เครือข่ายคิดว่าภาพอินพุตคือแมว* รวมถึงบางวิธีที่ไม่มีความหมายในเชิงภาพ แม้ว่าภาพเหล่านั้นจะมีรูปแบบที่เป็นลักษณะเฉพาะของแมว แต่ไม่มีอะไรบังคับให้มันดูโดดเด่นในเชิงภาพ เพื่อปรับปรุงผลลัพธ์ เราสามารถเพิ่มคำศัพท์อีกคำหนึ่งในฟังก์ชันการสูญเสีย ซึ่งเรียกว่า **การสูญเสียความแปรปรวน** เป็นเมตริกที่แสดงให้เห็นว่าพิกเซลที่อยู่ใกล้เคียงในภาพมีความคล้ายคลึงกันเพียงใด การลดการสูญเสียความแปรปรวนทำให้ภาพเรียบขึ้นและกำจัดสัญญาณรบกวน - เผยให้เห็นรูปแบบที่น่าดึงดูดในเชิงภาพมากขึ้น นี่คือตัวอย่างของภาพ "ในอุดมคติ" ที่ถูกจัดประเภทว่าเป็นแมวและม้าลายด้วยความน่าจะเป็นสูง: -![แมวในอุดมคติ](../../../../../translated_images/th/ideal-cat.203dd4597643d6b0.png) | ![ม้าลายในอุดมคติ](../../../../../translated_images/th/ideal-zebra.7f70e8b54ee15a7a.png) +![แมวในอุดมคติ](../../../../../translated_images/th/ideal-cat.203dd4597643d6b0.webp) | ![ม้าลายในอุดมคติ](../../../../../translated_images/th/ideal-zebra.7f70e8b54ee15a7a.webp) -----|----- *แมวในอุดมคติ* | *ม้าลายในอุดมคติ* วิธีการที่คล้ายกันสามารถใช้เพื่อทำการโจมตีแบบ **adversarial** บนเครือข่ายประสาท สมมติว่าเราต้องการหลอกเครือข่ายประสาทและทำให้สุนัขดูเหมือนแมว หากเราใช้ภาพของสุนัขที่เครือข่ายจำแนกได้ว่าเป็นสุนัข เราสามารถปรับแต่งมันเล็กน้อยโดยใช้การลดเกรเดียนต์จนกว่าเครือข่ายจะเริ่มจำแนกมันว่าเป็นแมว: -![ภาพของสุนัข](../../../../../translated_images/th/original-dog.8f68a67d2fe0911f.png) | ![ภาพของสุนัขที่ถูกจัดประเภทว่าเป็นแมว](../../../../../translated_images/th/adversarial-dog.d9fc7773b0142b89.png) +![ภาพของสุนัข](../../../../../translated_images/th/original-dog.8f68a67d2fe0911f.webp) | ![ภาพของสุนัขที่ถูกจัดประเภทว่าเป็นแมว](../../../../../translated_images/th/adversarial-dog.d9fc7773b0142b89.webp) -----|----- *ภาพต้นฉบับของสุนัข* | *ภาพของสุนัขที่ถูกจัดประเภทว่าเป็นแมว* diff --git a/translations/th/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/th/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index 0e3dc24f..c9792131 100644 --- a/translations/th/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/th/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "เนื่องจากเรากำลังฝึก autoencoder เพื่อจับข้อมูลจากภาพต้นฉบับให้ได้มากที่สุดเพื่อการสร้างใหม่ที่แม่นยำ เครือข่ายจึงพยายามค้นหา **embedding** ที่ดีที่สุดของภาพนำเข้าเพื่อจับความหมาย\n", "\n", - "![AutoEncoder Diagram](../../../../../translated_images/th/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![AutoEncoder Diagram](../../../../../translated_images/th/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "> ภาพจาก [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", diff --git a/translations/th/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/th/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index ad6bab17..64d10ec7 100644 --- a/translations/th/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/th/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "เนื่องจากเรากำลังฝึก autoencoder เพื่อจับข้อมูลจากภาพต้นฉบับให้ได้มากที่สุดเพื่อการสร้างใหม่ที่แม่นยำ เครือข่ายจึงพยายามค้นหา **embedding** ที่ดีที่สุดของภาพนำเข้าเพื่อจับความหมายของภาพ\n", "\n", - "![AutoEncoder Diagram](../../../../../translated_images/th/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![AutoEncoder Diagram](../../../../../translated_images/th/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "*ภาพจาก [บล็อก Keras](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", diff --git a/translations/th/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/th/lessons/4-ComputerVision/09-Autoencoders/README.md index ceb22d0f..7cd467ce 100644 --- a/translations/th/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/th/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: เนื่องจากเรากำลังฝึกออโตเอนโคเดอร์เพื่อจับข้อมูลจากภาพต้นฉบับให้ได้มากที่สุดเพื่อการสร้างใหม่ที่แม่นยำ เครือข่ายจึงพยายามค้นหา **การฝังตัว** ที่ดีที่สุดของภาพอินพุตเพื่อจับความหมายของภาพ -![AutoEncoder Diagram](../../../../../translated_images/th/autoencoder_schema.5e6fc9ad98a5eb61.jpg) +![AutoEncoder Diagram](../../../../../translated_images/th/autoencoder_schema.5e6fc9ad98a5eb61.webp) > ภาพจาก [บล็อก Keras](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/th/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/th/lessons/4-ComputerVision/11-ObjectDetection/README.md index 1e378196..b09f6c06 100644 --- a/translations/th/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/th/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: ## [แบบทดสอบก่อนเรียน](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![การตรวจจับวัตถุ](../../../../../translated_images/th/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) +![การตรวจจับวัตถุ](../../../../../translated_images/th/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp) > ภาพจาก [เว็บไซต์ YOLO v2](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 2. ใช้การจำแนกภาพในแต่ละส่วน 3. ส่วนที่มีการกระตุ้นสูงพอสมควรสามารถพิจารณาได้ว่ามีวัตถุที่เราต้องการอยู่ -![การตรวจจับวัตถุแบบพื้นฐาน](../../../../../translated_images/th/naive-detection.e7f1ba220ccd08c6.png) +![การตรวจจับวัตถุแบบพื้นฐาน](../../../../../translated_images/th/naive-detection.e7f1ba220ccd08c6.webp) > *ภาพจาก [สมุดบันทึกการฝึกฝน](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ CO_OP_TRANSLATOR_METADATA: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 คลาส * [COCO](http://cocodataset.org/#home) - Common Objects in Context. มี 80 คลาส, กรอบวัตถุ และหน้ากากการแบ่งส่วน -![COCO](../../../../../translated_images/th/coco-examples.71bc60380fa6cceb.jpg) +![COCO](../../../../../translated_images/th/coco-examples.71bc60380fa6cceb.webp) ## ตัวชี้วัดสำหรับการตรวจจับวัตถุ @@ -50,7 +50,7 @@ CO_OP_TRANSLATOR_METADATA: ในขณะที่การจำแนกภาพสามารถวัดผลได้ง่ายว่าประสิทธิภาพของอัลกอริทึมเป็นอย่างไร สำหรับการตรวจจับวัตถุ เราต้องวัดทั้งความถูกต้องของคลาส และความแม่นยำของตำแหน่งกรอบวัตถุที่คาดการณ์ได้ สำหรับอย่างหลัง เราใช้ตัวชี้วัดที่เรียกว่า **Intersection over Union** (IoU) ซึ่งวัดว่าพื้นที่สองส่วน (หรือพื้นที่ใดๆ) ซ้อนทับกันได้ดีเพียงใด -![IoU](../../../../../translated_images/th/iou_equation.9a4751d40fff4e11.png) +![IoU](../../../../../translated_images/th/iou_equation.9a4751d40fff4e11.webp) > *รูปที่ 2 จาก [บทความที่ยอดเยี่ยมเกี่ยวกับ IoU](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ $$ [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) ใช้ [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) เพื่อสร้างโครงสร้างลำดับชั้นของ ROI ซึ่งจะถูกส่งผ่าน CNN เพื่อดึงคุณลักษณะ และใช้ SVM-classifiers เพื่อกำหนดคลาสของวัตถุ และการถดถอยเชิงเส้นเพื่อกำหนดพิกัดของ *กรอบวัตถุ* [เอกสารอย่างเป็นทางการ](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/th/rcnn1.cae407020dfb1d1f.png) +![RCNN](../../../../../translated_images/th/rcnn1.cae407020dfb1d1f.webp) > *ภาพจาก van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/th/rcnn2.2d9530bb83516484.png) +![RCNN-1](../../../../../translated_images/th/rcnn2.2d9530bb83516484.webp) > *ภาพจาก [บทความนี้](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -110,7 +110,7 @@ $$ วิธีนี้คล้ายกับ R-CNN แต่ ROI ถูกกำหนดหลังจากที่เลเยอร์คอนโวลูชันถูกประยุกต์ใช้แล้ว -![FRCNN](../../../../../translated_images/th/f-rcnn.3cda6d9bb4188875.png) +![FRCNN](../../../../../translated_images/th/f-rcnn.3cda6d9bb4188875.webp) > ภาพจาก [เอกสารอย่างเป็นทางการ](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ $$ แนวคิดหลักของวิธีนี้คือการใช้เครือข่ายประสาทเพื่อทำนาย ROI ที่เรียกว่า *Region Proposal Network* [เอกสาร](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/th/faster-rcnn.8d46c099b87ef30a.png) +![FasterRCNN](../../../../../translated_images/th/faster-rcnn.8d46c099b87ef30a.webp) > ภาพจาก [เอกสารอย่างเป็นทางการ](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ $$ 2. คุณลักษณะถูกประมวลผลโดย **Position-Sensitive Score Map** วัตถุแต่ละตัวจาก $C$ คลาสถูกแบ่งออกเป็น $k\times k$ พื้นที่ และเราฝึกให้ทำนายส่วนต่างๆ ของวัตถุ 3. สำหรับแต่ละส่วนจาก $k\times k$ พื้นที่ เครือข่ายทั้งหมดจะโหวตให้คลาสของวัตถุ และคลาสที่มีคะแนนโหวตสูงสุดจะถูกเลือก -![r-fcn image](../../../../../translated_images/th/r-fcn.13eb88158b99a3da.png) +![r-fcn image](../../../../../translated_images/th/r-fcn.13eb88158b99a3da.webp) > ภาพจาก [เอกสารอย่างเป็นทางการ](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO เป็นอัลกอริทึมแบบเรียลไทม * ภาพถูกแบ่งออกเป็น $S\times S$ พื้นที่ * สำหรับแต่ละพื้นที่ **CNN** ทำนายวัตถุ $n$ ชนิด, พิกัด *กรอบวัตถุ* และ *ความมั่นใจ*=*ความน่าจะเป็น* * IoU - ![YOLO](../../../../../translated_images/th/yolo.a2648ec82ee8bb4e.png) + ![YOLO](../../../../../translated_images/th/yolo.a2648ec82ee8bb4e.webp) > ภาพจาก [เอกสารอย่างเป็นทางการ](https://arxiv.org/abs/1506.02640) diff --git a/translations/th/lessons/4-ComputerVision/README.md b/translations/th/lessons/4-ComputerVision/README.md index 256c52f8..de38f2d9 100644 --- a/translations/th/lessons/4-ComputerVision/README.md +++ b/translations/th/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # การมองเห็นของคอมพิวเตอร์ -![สรุปเนื้อหาเกี่ยวกับการมองเห็นของคอมพิวเตอร์ในรูปวาด](../../../../translated_images/th/ai-computervision.6506ebebac3fbf76.png) +![สรุปเนื้อหาเกี่ยวกับการมองเห็นของคอมพิวเตอร์ในรูปวาด](../../../../translated_images/th/ai-computervision.6506ebebac3fbf76.webp) ในส่วนนี้เราจะเรียนรู้เกี่ยวกับ: diff --git a/translations/th/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/th/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index 3ccee307..ef946d96 100644 --- a/translations/th/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/th/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**Bag of Words** (BoW) เป็นการแสดงข้อความในรูปแบบเวกเตอร์ที่ใช้กันอย่างแพร่หลายที่สุดในวิธีการแบบดั้งเดิม โดยแต่ละคำจะถูกเชื่อมโยงกับดัชนีในเวกเตอร์ และแต่ละองค์ประกอบในเวกเตอร์จะแสดงจำนวนครั้งที่คำปรากฏในเอกสารที่กำหนด\n", "\n", - "![ภาพแสดงการแสดงข้อความแบบ Bag of Words ในหน่วยความจำ](../../../../../translated_images/th/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![ภาพแสดงการแสดงข้อความแบบ Bag of Words ในหน่วยความจำ](../../../../../translated_images/th/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Note**: คุณสามารถมองว่า BoW เป็นผลรวมของเวกเตอร์แบบ one-hot-encoded ของคำแต่ละคำในข้อความได้เช่นกัน\n", "\n", diff --git a/translations/th/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/th/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index 66d8e789..f7fe0a07 100644 --- a/translations/th/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/th/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**Bag-of-words** (BoW) เป็นการแสดงข้อความในรูปแบบเวกเตอร์ที่เข้าใจได้ง่ายที่สุดในบรรดาการแสดงข้อความแบบเวกเตอร์แบบดั้งเดิม โดยแต่ละคำจะถูกเชื่อมโยงกับดัชนีในเวกเตอร์ และแต่ละองค์ประกอบในเวกเตอร์จะบอกจำนวนครั้งที่คำแต่ละคำปรากฏในเอกสารที่กำหนด\n", "\n", - "![ภาพแสดงการแสดงข้อความแบบ bag-of-words ในหน่วยความจำ](../../../../../translated_images/th/bag-of-words-example.606fc1738f1d7ba9.png)\n", + "![ภาพแสดงการแสดงข้อความแบบ bag-of-words ในหน่วยความจำ](../../../../../translated_images/th/bag-of-words-example.606fc1738f1d7ba9.webp)\n", "\n", "> **Note**: คุณสามารถคิดถึง BoW ว่าเป็นผลรวมของเวกเตอร์แบบ one-hot-encoded ของคำแต่ละคำในข้อความก็ได้เช่นกัน\n", "\n", diff --git a/translations/th/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/th/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 503753ac..7d329330 100644 --- a/translations/th/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/th/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "โดยการใช้ชั้นการฝังข้อมูลเป็นชั้นแรกในเครือข่ายของเรา เราสามารถเปลี่ยนจากโมเดล bag-of-words ไปเป็นโมเดล **embedding bag** ซึ่งเราจะเปลี่ยนคำแต่ละคำในข้อความของเราให้เป็นการฝังข้อมูลที่สอดคล้องกัน และจากนั้นคำนวณฟังก์ชันรวมบางอย่างจากการฝังข้อมูลเหล่านั้น เช่น `sum`, `average` หรือ `max`\n", "\n", - "![ภาพแสดงตัวอย่างตัวจำแนกที่ใช้การฝังข้อมูลสำหรับคำในลำดับห้าคำ](../../../../../translated_images/th/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![ภาพแสดงตัวอย่างตัวจำแนกที่ใช้การฝังข้อมูลสำหรับคำในลำดับห้าคำ](../../../../../translated_images/th/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "เครือข่ายประสาทเทียมของเราจะเริ่มต้นด้วยชั้นการฝังข้อมูล ตามด้วยชั้นการรวม และตัวจำแนกเชิงเส้นที่อยู่ด้านบน\n" ] @@ -176,7 +176,7 @@ "\n", "ในสถาปัตยกรรมก่อนหน้านี้ เราจำเป็นต้องเติมข้อมูลในทุกลำดับให้มีความยาวเท่ากันเพื่อให้สามารถใส่ลงในชุดข้อมูลย่อยได้ วิธีนี้ไม่ใช่วิธีที่มีประสิทธิภาพที่สุดในการแสดงผลลำดับที่มีความยาวแปรผัน - อีกวิธีหนึ่งคือการใช้ **เวกเตอร์ออฟเซ็ต** ซึ่งจะเก็บค่าตำแหน่งเริ่มต้นของลำดับทั้งหมดที่ถูกจัดเก็บในเวกเตอร์ขนาดใหญ่หนึ่งตัว\n", "\n", - "![ภาพแสดงการแสดงผลลำดับแบบออฟเซ็ต](../../../../../translated_images/th/offset-sequence-representation.eb73fcefb29b46ee.png)\n", + "![ภาพแสดงการแสดงผลลำดับแบบออฟเซ็ต](../../../../../translated_images/th/offset-sequence-representation.eb73fcefb29b46ee.webp)\n", "\n", "> **Note**: ในภาพด้านบน เราแสดงลำดับของตัวอักษร แต่ในตัวอย่างของเรา เรากำลังทำงานกับลำดับของคำ อย่างไรก็ตาม หลักการทั่วไปในการแสดงผลลำดับด้วยเวกเตอร์ออฟเซ็ตยังคงเหมือนเดิม\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW ทำงานได้เร็วกว่า ในขณะที่ skip-gram ช้ากว่า แต่สามารถแสดงคำที่พบได้น้อยได้ดีกว่า\n", "\n", - "![ภาพแสดงอัลกอริทึม CBoW และ Skip-Gram สำหรับการแปลงคำเป็นเวกเตอร์](../../../../../translated_images/th/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![ภาพแสดงอัลกอริทึม CBoW และ Skip-Gram สำหรับการแปลงคำเป็นเวกเตอร์](../../../../../translated_images/th/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "เพื่อทดลองใช้การฝัง Word2Vec ที่ได้รับการฝึกฝนล่วงหน้าบนชุดข้อมูล Google News เราสามารถใช้ไลบรารี **gensim** ด้านล่างนี้คือตัวอย่างการค้นหาคำที่คล้ายกับ 'neural' มากที่สุด\n", "\n", diff --git a/translations/th/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/th/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index 8a4f7b51..8043eda7 100644 --- a/translations/th/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/th/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "เมื่อใช้ embedding layer เป็นเลเยอร์แรกในเครือข่ายของเรา เราสามารถเปลี่ยนจาก bag-of-words ไปเป็นโมเดล **embedding bag** โดยที่เราจะแปลงแต่ละคำในข้อความของเราให้เป็น embedding ที่สอดคล้องกัน และคำนวณฟังก์ชันรวมบางอย่างจาก embeddings เหล่านั้น เช่น `sum`, `average` หรือ `max`\n", "\n", - "![ภาพแสดงตัวอย่าง embedding classifier สำหรับคำในลำดับห้าคำ](../../../../../translated_images/th/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![ภาพแสดงตัวอย่าง embedding classifier สำหรับคำในลำดับห้าคำ](../../../../../translated_images/th/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "เครือข่ายประสาทสำหรับการจำแนกของเราประกอบด้วยเลเยอร์ดังต่อไปนี้:\n", "\n", @@ -281,7 +281,7 @@ "\n", "CBoW ทำงานได้เร็วกว่า ในขณะที่ skip-gram แม้จะช้ากว่า แต่สามารถแสดงคำที่พบได้น้อยได้ดีกว่า\n", "\n", - "![ภาพแสดงอัลกอริธึม CBoW และ Skip-Gram สำหรับแปลงคำเป็นเวกเตอร์](../../../../../translated_images/th/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![ภาพแสดงอัลกอริธึม CBoW และ Skip-Gram สำหรับแปลงคำเป็นเวกเตอร์](../../../../../translated_images/th/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "เพื่อทดลองใช้การฝัง Word2Vec ที่ฝึกไว้ล่วงหน้าด้วยชุดข้อมูล Google News เราสามารถใช้ไลบรารี **gensim** ด้านล่างนี้เป็นตัวอย่างการค้นหาคำที่คล้ายกับ 'neural' มากที่สุด\n", "\n", diff --git a/translations/th/lessons/5-NLP/14-Embeddings/README.md b/translations/th/lessons/5-NLP/14-Embeddings/README.md index ab62698e..37473f7c 100644 --- a/translations/th/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/th/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: โดยการใช้เลเยอร์การฝังข้อมูลเป็นเลเยอร์แรกในเครือข่ายจำแนกประเภทของเรา เราสามารถเปลี่ยนจากโมเดลถุงคำไปเป็น **embedding bag** ซึ่งเราจะเปลี่ยนคำแต่ละคำในข้อความของเราให้เป็นการฝังข้อมูลที่สอดคล้องกัน และคำนวณฟังก์ชันรวมบางอย่างจากการฝังข้อมูลเหล่านั้น เช่น `sum`, `average` หรือ `max` -![ภาพแสดงตัวอย่างการจำแนกประเภทด้วยการฝังข้อมูลสำหรับคำในลำดับห้าคำ](../../../../../translated_images/th/embedding-classifier-example.b77f021a7ee67eee.png) +![ภาพแสดงตัวอย่างการจำแนกประเภทด้วยการฝังข้อมูลสำหรับคำในลำดับห้าคำ](../../../../../translated_images/th/embedding-classifier-example.b77f021a7ee67eee.webp) > ภาพโดยผู้เขียน @@ -40,7 +40,7 @@ CO_OP_TRANSLATOR_METADATA: CBoW ทำงานได้เร็วกว่า ในขณะที่ skip-gram ทำงานช้ากว่า แต่สามารถแสดงคำที่ไม่ค่อยปรากฏได้ดีกว่า -![ภาพแสดงทั้งอัลกอริทึม CBoW และ Skip-Gram ในการแปลงคำเป็นเวกเตอร์](../../../../../translated_images/th/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![ภาพแสดงทั้งอัลกอริทึม CBoW และ Skip-Gram ในการแปลงคำเป็นเวกเตอร์](../../../../../translated_images/th/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > ภาพจาก [เอกสารนี้](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/th/lessons/5-NLP/15-LanguageModeling/README.md b/translations/th/lessons/5-NLP/15-LanguageModeling/README.md index c136835c..d62d41cb 100644 --- a/translations/th/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/th/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: * **Continuous Bag-of-Words** (CBoW) โดยการทำนายโทเค็นตรงกลาง $W_0$ ในลำดับโทเค็น $W_{-N}$, ..., $W_N$ * **Skip-gram** โดยการทำนายชุดโทเค็นที่อยู่ใกล้เคียง {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} จากโทเค็นตรงกลาง $W_0$ -![ภาพจากงานวิจัยเกี่ยวกับการแปลงคำเป็นเวกเตอร์](../../../../../translated_images/th/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![ภาพจากงานวิจัยเกี่ยวกับการแปลงคำเป็นเวกเตอร์](../../../../../translated_images/th/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > ภาพจาก [งานวิจัยนี้](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/th/lessons/5-NLP/16-RNN/README.md b/translations/th/lessons/5-NLP/16-RNN/README.md index 9147b1ff..ff23ecb9 100644 --- a/translations/th/lessons/5-NLP/16-RNN/README.md +++ b/translations/th/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: เพื่อจับความหมายของลำดับข้อความ เราจำเป็นต้องใช้สถาปัตยกรรมเครือข่ายประสาทอีกแบบหนึ่ง ซึ่งเรียกว่า **เครือข่ายประสาทแบบวนซ้ำ** หรือ RNN ใน RNN เราจะส่งประโยคผ่านเครือข่ายทีละสัญลักษณ์ และเครือข่ายจะสร้าง **สถานะ** ซึ่งเราจะส่งกลับเข้าเครือข่ายพร้อมกับสัญลักษณ์ถัดไป -![RNN](../../../../../translated_images/th/rnn.27f5c29c53d727b5.png) +![RNN](../../../../../translated_images/th/rnn.27f5c29c53d727b5.webp) > ภาพโดยผู้เขียน @@ -61,7 +61,7 @@ CO_OP_TRANSLATOR_METADATA: เครือข่ายแบบวนซ้ำ ไม่ว่าจะเป็นแบบทิศทางเดียวหรือสองทิศทาง จะจับรูปแบบบางอย่างภายในลำดับ และสามารถเก็บรูปแบบเหล่านั้นไว้ในเวกเตอร์สถานะหรือส่งผ่านไปยังผลลัพธ์ เช่นเดียวกับเครือข่ายแบบ convolutional เราสามารถสร้างเลเยอร์แบบวนซ้ำอีกเลเยอร์หนึ่งบนเลเยอร์แรกเพื่อจับรูปแบบระดับสูงและสร้างจากรูปแบบระดับต่ำที่ถูกดึงออกโดยเลเยอร์แรก สิ่งนี้นำเราไปสู่แนวคิดของ **RNN หลายเลเยอร์** ซึ่งประกอบด้วยเครือข่ายแบบวนซ้ำสองหรือมากกว่า โดยที่ผลลัพธ์ของเลเยอร์ก่อนหน้าจะถูกส่งไปยังเลเยอร์ถัดไปเป็นอินพุต -![ภาพแสดง RNN แบบหลายเลเยอร์ LSTM](../../../../../translated_images/th/multi-layer-lstm.dd975e29bb2a59fe.jpg) +![ภาพแสดง RNN แบบหลายเลเยอร์ LSTM](../../../../../translated_images/th/multi-layer-lstm.dd975e29bb2a59fe.webp) *ภาพจาก [โพสต์ที่ยอดเยี่ยมนี้](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) โดย Fernando López* diff --git a/translations/th/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/th/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index b49cca48..ce18ab1f 100644 --- a/translations/th/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/th/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -420,7 +420,7 @@ "\n", "เครือข่ายแบบวนซ้ำ ไม่ว่าจะเป็นแบบทิศทางเดียวหรือสองทิศทาง จะจับรูปแบบบางอย่างภายในลำดับ และสามารถเก็บรูปแบบเหล่านั้นไว้ในเวกเตอร์สถานะหรือส่งต่อไปยังผลลัพธ์ เช่นเดียวกับเครือข่ายแบบคอนโวลูชัน (convolutional networks) เราสามารถสร้างเลเยอร์แบบวนซ้ำอีกชั้นหนึ่งบนเลเยอร์แรกเพื่อจับรูปแบบในระดับที่สูงขึ้น ซึ่งสร้างขึ้นจากรูปแบบระดับต่ำที่เลเยอร์แรกสกัดออกมา สิ่งนี้นำเราไปสู่แนวคิดของ **multi-layer RNN** ซึ่งประกอบด้วยเครือข่ายแบบวนซ้ำสองชั้นหรือมากกว่า โดยที่ผลลัพธ์ของเลเยอร์ก่อนหน้าจะถูกส่งไปยังเลเยอร์ถัดไปเป็นอินพุต\n", "\n", - "![ภาพแสดง Multilayer long-short-term-memory- RNN](../../../../../translated_images/th/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![ภาพแสดง Multilayer long-short-term-memory- RNN](../../../../../translated_images/th/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*ภาพจาก [บทความที่ยอดเยี่ยมนี้](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) โดย Fernando López*\n", "\n", diff --git a/translations/th/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/th/lessons/5-NLP/16-RNN/RNNTF.ipynb index ebc63664..4e7d5471 100644 --- a/translations/th/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/th/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "เพื่อจับความหมายของลำดับข้อความ เราจะใช้สถาปัตยกรรมเครือข่ายประสาทเทียมที่เรียกว่า **เครือข่ายประสาทเทียมแบบวนซ้ำ** หรือ RNN เมื่อใช้ RNN เราจะส่งประโยคของเราผ่านเครือข่ายทีละโทเค็น และเครือข่ายจะสร้าง **สถานะ** ซึ่งเราจะส่งกลับเข้าไปในเครือข่ายอีกครั้งพร้อมกับโทเค็นถัดไป\n", "\n", - "![ภาพแสดงตัวอย่างการสร้างเครือข่ายประสาทเทียมแบบวนซ้ำ](../../../../../translated_images/th/rnn.27f5c29c53d727b5.png)\n", + "![ภาพแสดงตัวอย่างการสร้างเครือข่ายประสาทเทียมแบบวนซ้ำ](../../../../../translated_images/th/rnn.27f5c29c53d727b5.webp)\n", "\n", "เมื่อได้รับลำดับโทเค็นอินพุต $X_0,\\dots,X_n$ RNN จะสร้างลำดับของบล็อกเครือข่ายประสาทเทียม และฝึกฝนลำดับนี้แบบ end-to-end โดยใช้ backpropagation แต่ละบล็อกเครือข่ายจะรับคู่ $(X_i,S_i)$ เป็นอินพุต และสร้าง $S_{i+1}$ เป็นผลลัพธ์ สถานะสุดท้าย $S_n$ หรือผลลัพธ์ $Y_n$ จะถูกส่งไปยังตัวจำแนกเชิงเส้นเพื่อสร้างผลลัพธ์ บล็อกเครือข่ายทั้งหมดใช้เวทเดียวกัน และถูกฝึกแบบ end-to-end โดยใช้การ backpropagation เพียงครั้งเดียว\n", "\n", @@ -369,7 +369,7 @@ "\n", "เครือข่ายประสาทแบบวนซ้ำ ไม่ว่าจะเป็นแบบทิศทางเดียวหรือสองทิศทาง จะจับรูปแบบภายในลำดับและเก็บไว้ในเวกเตอร์สถานะ (state vectors) หรือส่งคืนเป็นผลลัพธ์ เช่นเดียวกับเครือข่ายแบบคอนโวลูชัน (convolutional networks) เราสามารถสร้างชั้น recurrent อีกชั้นหนึ่งตามหลังชั้นแรกเพื่อจับรูปแบบในระดับที่สูงขึ้น ซึ่งสร้างขึ้นจากรูปแบบระดับต่ำที่ชั้นแรกดึงออกมาได้ สิ่งนี้นำเราไปสู่แนวคิดของ **multi-layer RNN** ซึ่งประกอบด้วยเครือข่ายประสาทแบบวนซ้ำสองชั้นหรือมากกว่า โดยที่ผลลัพธ์ของชั้นก่อนหน้าจะถูกส่งไปยังชั้นถัดไปเป็นข้อมูลนำเข้า\n", "\n", - "![ภาพแสดง Multilayer long-short-term-memory- RNN](../../../../../translated_images/th/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![ภาพแสดง Multilayer long-short-term-memory- RNN](../../../../../translated_images/th/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*ภาพจาก [บทความที่ยอดเยี่ยมนี้](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) โดย Fernando López*\n", "\n", diff --git a/translations/th/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/th/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index 81de74b7..2e45cb43 100644 --- a/translations/th/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/th/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "วิธีที่เราจะฝึก RNN เพื่อสร้างข้อความมีดังนี้ ในแต่ละขั้นตอน เราจะนำลำดับของตัวอักษรที่มีความยาว `nchars` และให้เครือข่ายสร้างตัวอักษรถัดไปสำหรับแต่ละตัวอักษรในลำดับอินพุต:\n", "\n", - "![ภาพแสดงตัวอย่างการสร้างคำว่า 'HELLO' ด้วย RNN](../../../../../translated_images/th/rnn-generate.56c54afb52f9781d.png)\n", + "![ภาพแสดงตัวอย่างการสร้างคำว่า 'HELLO' ด้วย RNN](../../../../../translated_images/th/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "ขึ้นอยู่กับสถานการณ์จริง เราอาจต้องการเพิ่มตัวอักษรพิเศษบางตัว เช่น *end-of-sequence* `` ในกรณีของเรา เราต้องการฝึกเครือข่ายเพื่อสร้างข้อความแบบไม่มีที่สิ้นสุด ดังนั้นเราจะกำหนดขนาดของแต่ละลำดับให้เท่ากับโทเค็น `nchars` ดังนั้น ตัวอย่างการฝึกแต่ละตัวจะประกอบด้วยอินพุต `nchars` และเอาต์พุต `nchars` (ซึ่งเป็นลำดับอินพุตที่เลื่อนหนึ่งสัญลักษณ์ไปทางซ้าย) Minibatch จะประกอบด้วยลำดับดังกล่าวหลายชุด\n", "\n", diff --git a/translations/th/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/th/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index d88968be..37c5e4cd 100644 --- a/translations/th/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/th/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -111,7 +111,7 @@ "\n", "วิธีที่เราจะฝึก RNN เพื่อสร้างหัวข้อข่าวมีดังนี้ ในแต่ละขั้นตอน เราจะนำหัวข้อข่าวหนึ่งหัวข้อมาใส่ใน RNN และสำหรับแต่ละตัวอักษรที่ป้อนเข้าไป เราจะให้เครือข่ายสร้างตัวอักษรถัดไป:\n", "\n", - "![ภาพแสดงตัวอย่างการสร้างคำว่า 'HELLO' ด้วย RNN](../../../../../translated_images/th/rnn-generate.56c54afb52f9781d.png)\n", + "![ภาพแสดงตัวอย่างการสร้างคำว่า 'HELLO' ด้วย RNN](../../../../../translated_images/th/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "สำหรับตัวอักษรสุดท้ายของลำดับ เราจะให้เครือข่ายสร้างโทเค็น `` \n", "\n", diff --git a/translations/th/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/th/lessons/5-NLP/17-GenerativeNetworks/README.md index 6c0c8c7f..76d5c4f5 100644 --- a/translations/th/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/th/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ Recurrent Neural Networks (RNNs) และรูปแบบเซลล์ท สิ่งนี้นำไปสู่สถาปัตยกรรมเครือข่ายประสาทที่แตกต่างกัน ซึ่งแสดงในภาพด้านล่าง: -![ภาพแสดงรูปแบบเครือข่ายประสาทแบบวนซ้ำทั่วไป](../../../../../translated_images/th/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![ภาพแสดงรูปแบบเครือข่ายประสาทแบบวนซ้ำทั่วไป](../../../../../translated_images/th/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > ภาพจากบล็อกโพสต์ [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) โดย [Andrej Karpaty](http://karpathy.github.io/) @@ -32,7 +32,7 @@ Recurrent Neural Networks (RNNs) และรูปแบบเซลล์ท เราจะฝึก RNN นี้เพื่อสร้างข้อความทีละขั้นตอน ในแต่ละขั้นตอน เราจะใช้ลำดับตัวอักษรที่มีความยาว `nchars` และให้เครือข่ายสร้างตัวอักษรถัดไปสำหรับแต่ละตัวอักษรในข้อมูลเข้า: -![ภาพแสดงตัวอย่างการสร้างคำ 'HELLO' โดย RNN](../../../../../translated_images/th/rnn-generate.56c54afb52f9781d.png) +![ภาพแสดงตัวอย่างการสร้างคำ 'HELLO' โดย RNN](../../../../../translated_images/th/rnn-generate.56c54afb52f9781d.webp) เมื่อสร้างข้อความ (ในระหว่างการอนุมาน) เราจะเริ่มต้นด้วย **คำเริ่มต้น** ซึ่งจะถูกส่งผ่านเซลล์ RNN เพื่อสร้างสถานะกลาง และจากสถานะนี้การสร้างข้อความจะเริ่มต้น เราจะสร้างตัวอักษรทีละตัว และส่งสถานะและตัวอักษรที่สร้างไปยังเซลล์ RNN ตัวถัดไปเพื่อสร้างตัวอักษรถัดไป จนกว่าจะสร้างข้อความครบตามที่ต้องการ diff --git a/translations/th/lessons/5-NLP/18-Transformers/README.md b/translations/th/lessons/5-NLP/18-Transformers/README.md index 1110df67..4badae9f 100644 --- a/translations/th/lessons/5-NLP/18-Transformers/README.md +++ b/translations/th/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ CO_OP_TRANSLATOR_METADATA: **กลไก Attention** ให้วิธีการในการถ่วงน้ำหนักผลกระทบเชิงบริบทของแต่ละเวกเตอร์ข้อมูลเข้าในแต่ละการทำนายผลลัพธ์ของ RNN วิธีการนี้ถูกดำเนินการโดยการสร้างทางลัดระหว่างสถานะกลางของ RNN ข้อมูลเข้าและ RNN ข้อมูลออก ด้วยวิธีนี้ เมื่อสร้างสัญลักษณ์ผลลัพธ์ yt เราจะพิจารณาสถานะ hidden ทั้งหมด hi ของข้อมูลเข้า โดยมีค่าสัมประสิทธิ์น้ำหนักที่แตกต่างกัน αt,i -![ภาพแสดงโมเดล encoder/decoder พร้อมชั้น attention แบบ additive](../../../../../translated_images/th/encoder-decoder-attention.7a726296894fb567.png) +![ภาพแสดงโมเดล encoder/decoder พร้อมชั้น attention แบบ additive](../../../../../translated_images/th/encoder-decoder-attention.7a726296894fb567.webp) > โมเดล encoder-decoder พร้อมกลไก attention แบบ additive ใน [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) อ้างอิงจาก [บล็อกโพสต์นี้](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) เมทริกซ์ attention {αi,j} จะเป็นตัวแทนระดับที่คำบางคำในข้อมูลเข้ามีบทบาทในการสร้างคำที่กำหนดในลำดับผลลัพธ์ ด้านล่างเป็นตัวอย่างของเมทริกซ์ดังกล่าว: -![ภาพแสดงการจัดแนวตัวอย่างที่พบโดย RNNsearch-50 จาก Bahdanau - arviz.org](../../../../../translated_images/th/bahdanau-fig3.09ba2d37f202a6af.png) +![ภาพแสดงการจัดแนวตัวอย่างที่พบโดย RNNsearch-50 จาก Bahdanau - arviz.org](../../../../../translated_images/th/bahdanau-fig3.09ba2d37f202a6af.webp) > ภาพจาก [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) @@ -66,7 +66,7 @@ CO_OP_TRANSLATOR_METADATA: ต่อไป เราจำเป็นต้องจับรูปแบบบางอย่างในลำดับของเรา เพื่อทำสิ่งนี้ Transformers ใช้กลไก **self-attention** ซึ่งเป็น Attention ที่นำไปใช้กับลำดับเดียวกันทั้งข้อมูลเข้าและข้อมูลออก การใช้ self-attention ช่วยให้เราพิจารณา **บริบท** ภายในประโยค และดูว่าคำใดมีความสัมพันธ์กัน ตัวอย่างเช่น มันช่วยให้เราเห็นว่าคำใดถูกอ้างถึงโดยคำสรรพนาม เช่น *it* และยังพิจารณาบริบทด้วย: -![](../../../../../translated_images/th/CoreferenceResolution.861924d6d384a7d6.png) +![](../../../../../translated_images/th/CoreferenceResolution.861924d6d384a7d6.webp) > ภาพจาก [บล็อกของ Google](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Encoder-decoder attention มีความคล้ายคลึงกับ **BERT** (Bidirectional Encoder Representations from Transformers) เป็นเครือข่าย Transformer ขนาดใหญ่มากที่มีหลายชั้น โดยมี 12 ชั้นสำหรับ *BERT-base* และ 24 ชั้นสำหรับ *BERT-large* โมเดลนี้ถูกฝึกเบื้องต้นด้วยชุดข้อมูลข้อความขนาดใหญ่ (WikiPedia + หนังสือ) โดยใช้การฝึกแบบไม่ต้องมีการกำกับดูแล (การทำนายคำที่ถูกปิดบังในประโยค) ในระหว่างการฝึกเบื้องต้น โมเดลจะดูดซับความเข้าใจภาษาระดับสูง ซึ่งสามารถนำไปใช้กับชุดข้อมูลอื่นๆ ผ่านการปรับแต่งเพิ่มเติม กระบวนการนี้เรียกว่า **transfer learning** -![ภาพจาก http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/th/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) +![ภาพจาก http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/th/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > ภาพ [แหล่งที่มา](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/th/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/th/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index efa5ad34..db53e2e1 100644 --- a/translations/th/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/th/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**กลไก Attention** เป็นวิธีการที่ช่วยให้น้ำหนักของผลกระทบเชิงบริบทของแต่ละเวกเตอร์นำเข้าต่อการทำนายผลลัพธ์ของ RNN แตกต่างกัน วิธีการนี้ถูกนำมาใช้โดยการสร้างทางลัดระหว่างสถานะกลางของ RNN นำเข้าและ RNN ผลลัพธ์ ด้วยวิธีนี้ เมื่อสร้างสัญลักษณ์ผลลัพธ์ $y_t$ เราจะพิจารณา hidden states นำเข้าทั้งหมด $h_i$ โดยมีค่าสัมประสิทธิ์น้ำหนักที่แตกต่างกัน $\\alpha_{t,i}$\n", "\n", - "![ภาพแสดงโมเดล encoder/decoder พร้อมชั้น attention แบบ additive](../../../../../translated_images/th/encoder-decoder-attention.7a726296894fb567.png)\n", + "![ภาพแสดงโมเดล encoder/decoder พร้อมชั้น attention แบบ additive](../../../../../translated_images/th/encoder-decoder-attention.7a726296894fb567.webp)\n", "*โมเดล encoder-decoder พร้อมกลไก attention แบบ additive จาก [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) อ้างอิงจาก [บล็อกโพสต์นี้](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "เมทริกซ์ Attention $\\{\\alpha_{i,j}\\}$ แสดงระดับที่คำบางคำในข้อมูลนำเข้ามีบทบาทในการสร้างคำในลำดับผลลัพธ์ ตัวอย่างของเมทริกซ์ดังกล่าวแสดงอยู่ด้านล่าง:\n", "\n", - "![ภาพแสดงตัวอย่างการจัดแนวที่พบโดย RNNsearch-50 จาก Bahdanau - arviz.org](../../../../../translated_images/th/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![ภาพแสดงตัวอย่างการจัดแนวที่พบโดย RNNsearch-50 จาก Bahdanau - arviz.org](../../../../../translated_images/th/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*ภาพจาก [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) เป็นเครือข่าย Transformer ขนาดใหญ่มากที่มีหลายชั้น โดย *BERT-base* มี 12 ชั้น และ *BERT-large* มี 24 ชั้น โมเดลนี้ถูก pre-trained บนชุดข้อมูลข้อความขนาดใหญ่ (WikiPedia + หนังสือ) โดยใช้การฝึกแบบ unsupervised (การทำนายคำที่ถูก mask ในประโยค) ในระหว่างการ pre-training โมเดลจะเรียนรู้ความเข้าใจภาษาระดับสูง ซึ่งสามารถนำไปใช้กับชุดข้อมูลอื่น ๆ ผ่านการ fine tuning กระบวนการนี้เรียกว่า **transfer learning**\n", "\n", - "![ภาพจาก http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/th/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![ภาพจาก http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/th/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "มีสถาปัตยกรรม Transformer หลากหลายรูปแบบ เช่น BERT, DistilBERT, BigBird, OpenGPT3 และอื่น ๆ ที่สามารถนำไป fine tune ได้ [HuggingFace package](https://github.com/huggingface/) มี repository สำหรับการฝึกสถาปัตยกรรมเหล่านี้หลายตัวด้วย PyTorch\n", "\n", diff --git a/translations/th/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/th/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index a60def39..c212bace 100644 --- a/translations/th/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/th/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**กลไก Attention** เป็นวิธีการที่ช่วยให้น้ำหนักความสำคัญของแต่ละเวกเตอร์ข้อมูลเข้ามีผลต่อการทำนายผลลัพธ์ของ RNN โดยวิธีการนี้จะสร้างทางลัดระหว่างสถานะกลางของ RNN ข้อมูลเข้าและ RNN ข้อมูลออก ในลักษณะนี้ เมื่อสร้างสัญลักษณ์ผลลัพธ์ $y_t$ เราจะพิจารณาสถานะซ่อนของข้อมูลเข้าทั้งหมด $h_i$ โดยมีค่าสัมประสิทธิ์น้ำหนักที่แตกต่างกัน $\\alpha_{t,i}$\n", "\n", - "![ภาพแสดงโมเดล encoder/decoder พร้อมชั้น Attention แบบ additive](../../../../../translated_images/th/encoder-decoder-attention.7a726296894fb567.png)\n", + "![ภาพแสดงโมเดล encoder/decoder พร้อมชั้น Attention แบบ additive](../../../../../translated_images/th/encoder-decoder-attention.7a726296894fb567.webp)\n", "*โมเดล encoder-decoder พร้อมกลไก Attention แบบ additive จาก [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) อ้างอิงจาก [บล็อกโพสต์นี้](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "เมทริกซ์ Attention $\\{\\alpha_{i,j}\\}$ จะเป็นตัวแทนระดับที่คำบางคำในข้อมูลเข้ามีบทบาทในการสร้างคำในลำดับผลลัพธ์ ตัวอย่างของเมทริกซ์ดังกล่าวแสดงอยู่ด้านล่าง:\n", "\n", - "![ภาพแสดงตัวอย่างการจัดแนวที่พบโดย RNNsearch-50 จาก Bahdanau - arviz.org](../../../../../translated_images/th/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![ภาพแสดงตัวอย่างการจัดแนวที่พบโดย RNNsearch-50 จาก Bahdanau - arviz.org](../../../../../translated_images/th/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*ภาพจาก [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3)*\n", "\n", @@ -225,7 +225,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) เป็นเครือข่ายทรานส์ฟอร์เมอร์ขนาดใหญ่มากที่มีหลายชั้น โดย *BERT-base* มี 12 ชั้น และ *BERT-large* มี 24 ชั้น โมเดลนี้ถูกฝึกเบื้องต้นด้วยชุดข้อมูลข้อความขนาดใหญ่ (WikiPedia + หนังสือ) โดยใช้การฝึกแบบไม่มีการกำกับดูแล (การทำนายคำที่ถูกปิดบังในประโยค) ในระหว่างการฝึกเบื้องต้น โมเดลจะเรียนรู้ความเข้าใจในภาษาระดับสูง ซึ่งสามารถนำไปใช้กับชุดข้อมูลอื่น ๆ ได้โดยการปรับแต่งเพิ่มเติม กระบวนการนี้เรียกว่า **การเรียนรู้แบบถ่ายโอน** \n", "\n", - "![ภาพจาก http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/th/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![ภาพจาก http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/th/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "มีสถาปัตยกรรม Transformer หลากหลายรูปแบบ เช่น BERT, DistilBERT, BigBird, OpenGPT3 และอื่น ๆ ที่สามารถปรับแต่งเพิ่มเติมได้ \n", "\n", diff --git a/translations/th/lessons/5-NLP/19-NER/README.md b/translations/th/lessons/5-NLP/19-NER/README.md index 87c472ad..9e71cea5 100644 --- a/translations/th/lessons/5-NLP/19-NER/README.md +++ b/translations/th/lessons/5-NLP/19-NER/README.md @@ -58,7 +58,7 @@ infant | O เนื่องจากเราต้องสร้างความสัมพันธ์แบบหนึ่งต่อหนึ่งระหว่างโทเค็นและคลาส เราสามารถฝึกโมเดลเครือข่ายประสาทเทียมแบบ **many-to-many** ที่เหมาะสมจากภาพนี้: -![ภาพแสดงรูปแบบเครือข่ายประสาทเทียมแบบ recurrent ทั่วไป](../../../../../translated_images/th/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![ภาพแสดงรูปแบบเครือข่ายประสาทเทียมแบบ recurrent ทั่วไป](../../../../../translated_images/th/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > *ภาพจาก [บล็อกโพสต์นี้](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) โดย [Andrej Karpathy](http://karpathy.github.io/) โมเดลการจัดประเภทโทเค็น NER สอดคล้องกับสถาปัตยกรรมเครือข่ายที่อยู่ทางขวาสุดในภาพนี้* diff --git a/translations/th/lessons/5-NLP/README.md b/translations/th/lessons/5-NLP/README.md index cb38d8f8..f3df57bd 100644 --- a/translations/th/lessons/5-NLP/README.md +++ b/translations/th/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # การประมวลผลภาษาธรรมชาติ -![ภาพสรุปงาน NLP ในรูปวาด](../../../../translated_images/th/ai-nlp.b22dcb8ca4707cea.png) +![ภาพสรุปงาน NLP ในรูปวาด](../../../../translated_images/th/ai-nlp.b22dcb8ca4707cea.webp) ในส่วนนี้ เราจะมุ่งเน้นการใช้เครือข่ายประสาทเทียม (Neural Networks) เพื่อจัดการกับงานที่เกี่ยวข้องกับ **การประมวลผลภาษาธรรมชาติ (Natural Language Processing - NLP)** มีปัญหาหลายประเภทใน NLP ที่เราต้องการให้คอมพิวเตอร์สามารถแก้ไขได้: diff --git a/translations/th/lessons/6-Other/23-MultiagentSystems/README.md b/translations/th/lessons/6-Other/23-MultiagentSystems/README.md index 0382fcdb..9a91952b 100644 --- a/translations/th/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/th/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ ask turtles [ หลังจากเปิดโมเดล คุณจะเข้าสู่หน้าจอหลักของ NetLogo นี่คือตัวอย่างโมเดลที่อธิบายประชากรของหมาป่าและแกะ โดยมีทรัพยากรจำกัด (หญ้า) -![NetLogo Main Screen](../../../../../translated_images/th/NetLogo-Main.32653711ec1a01b3.png) +![NetLogo Main Screen](../../../../../translated_images/th/NetLogo-Main.32653711ec1a01b3.webp) > ภาพหน้าจอโดย Dmitry Soshnikov diff --git a/translations/th/lessons/README.md b/translations/th/lessons/README.md index 417d0e2a..ab7801e3 100644 --- a/translations/th/lessons/README.md +++ b/translations/th/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # ภาพรวม -![ภาพรวมในรูปวาด](../../../translated_images/th/ai-overview.0857791951d19500.png) +![ภาพรวมในรูปวาด](../../../translated_images/th/ai-overview.0857791951d19500.webp) > ภาพวาดโดย [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/th/lessons/X-Extras/X1-MultiModal/README.md b/translations/th/lessons/X-Extras/X1-MultiModal/README.md index 4cd60dbe..22a6f079 100644 --- a/translations/th/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/th/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: แนวคิดหลักของ CLIP คือการเปรียบเทียบข้อความ (text prompts) กับภาพ และประเมินว่าภาพนั้นสอดคล้องกับข้อความมากน้อยเพียงใด -![สถาปัตยกรรม CLIP](../../../../../translated_images/th/clip-arch.b3dbf20b4e8ed8be.png) +![สถาปัตยกรรม CLIP](../../../../../translated_images/th/clip-arch.b3dbf20b4e8ed8be.webp) > *ภาพจาก [บทความนี้](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ CO_OP_TRANSLATOR_METADATA: สมมติว่าเราต้องการจำแนกภาพระหว่างแมว สุนัข และมนุษย์ ในกรณีนี้ เราสามารถให้โมเดลรับภาพและข้อความ เช่น "*ภาพของแมว*", "*ภาพของสุนัข*", "*ภาพของมนุษย์*" ในเวกเตอร์ผลลัพธ์ที่มีความน่าจะเป็น 3 ค่า เราเพียงแค่เลือกดัชนีที่มีค่ามากที่สุด -![CLIP สำหรับการจำแนกภาพ](../../../../../translated_images/th/clip-class.3af42ef0b2b19369.png) +![CLIP สำหรับการจำแนกภาพ](../../../../../translated_images/th/clip-class.3af42ef0b2b19369.webp) > *ภาพจาก [บทความนี้](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ CLIP ยังสามารถใช้สำหรับ **การสร้ ความแตกต่างสำคัญระหว่าง VQGAN และ GAN ทั่วไปคือ GAN สามารถสร้างภาพที่ดีจากเวกเตอร์อินพุตใด ๆ ได้ แต่ VQGAN อาจสร้างภาพที่ไม่สอดคล้องกัน ดังนั้นเราจำเป็นต้องมีการชี้นำเพิ่มเติมในกระบวนการสร้างภาพ ซึ่งสามารถทำได้โดยใช้ CLIP -![สถาปัตยกรรม VQGAN+CLIP](../../../../../translated_images/th/vqgan.5027fe05051dfa31.png) +![สถาปัตยกรรม VQGAN+CLIP](../../../../../translated_images/th/vqgan.5027fe05051dfa31.webp) ในการสร้างภาพที่สอดคล้องกับข้อความ เราเริ่มต้นด้วยเวกเตอร์การเข้ารหัสแบบสุ่มที่ถูกส่งผ่าน VQGAN เพื่อสร้างภาพ จากนั้นใช้ CLIP เพื่อสร้างฟังก์ชันการสูญเสียที่แสดงว่าภาพสอดคล้องกับข้อความมากน้อยเพียงใด เป้าหมายคือการลดค่าฟังก์ชันการสูญเสียนี้โดยใช้การถ่ายทอดย้อนกลับ (back propagation) เพื่อปรับพารามิเตอร์ของเวกเตอร์อินพุต ไลบรารีที่ยอดเยี่ยมที่นำ VQGAN+CLIP มาใช้งานคือ [Pixray](http://github.com/pixray/pixray) -![ภาพที่สร้างโดย Pixray](../../../../../translated_images/th/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![ภาพที่สร้างโดย Pixray](../../../../../translated_images/th/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![ภาพที่สร้างโดย Pixray](../../../../../translated_images/th/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) +![ภาพที่สร้างโดย Pixray](../../../../../translated_images/th/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp) | ![ภาพที่สร้างโดย Pixray](../../../../../translated_images/th/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp) | ![ภาพที่สร้างโดย Pixray](../../../../../translated_images/th/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp) ----|----|---- ภาพที่สร้างจากข้อความ *ภาพเหมือนสีน้ำของครูหนุ่มสอนวรรณกรรมพร้อมหนังสือ* | ภาพที่สร้างจากข้อความ *ภาพเหมือนสีน้ำมันของครูสาวสอนวิทยาการคอมพิวเตอร์พร้อมคอมพิวเตอร์* | ภาพที่สร้างจากข้อความ *ภาพเหมือนสีน้ำมันของครูชายสูงวัยสอนคณิตศาสตร์หน้ากระดานดำ* diff --git a/translations/tl/README.md b/translations/tl/README.md index 8b3a4dd5..dfe3a264 100644 --- a/translations/tl/README.md +++ b/translations/tl/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # Artificial Intelligence for Beginners - Isang Kurikulum -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/tl/ai-overview.0857791951d19500.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/tl/ai-overview.0857791951d19500.webp)| |:---:| | AI For Beginners - _Sketchnote ni [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/tl/lessons/1-Intro/README.md b/translations/tl/lessons/1-Intro/README.md index b1f73ba4..b7aa88fc 100644 --- a/translations/tl/lessons/1-Intro/README.md +++ b/translations/tl/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Panimula sa AI -![Buod ng nilalaman ng Panimula sa AI sa isang doodle](../../../../translated_images/tl/ai-intro.bf28d1ac4235881c.png) +![Buod ng nilalaman ng Panimula sa AI sa isang doodle](../../../../translated_images/tl/ai-intro.bf28d1ac4235881c.webp) > Sketchnote ni [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Noong una, ang mga computer ay naimbento ni [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage) upang mag-operate sa mga numero gamit ang isang malinaw na proseso - isang algorithm. Ang mga modernong computer, kahit na mas advanced kaysa sa orihinal na modelo noong ika-19 na siglo, ay sumusunod pa rin sa parehong ideya ng kontroladong pagkalkula. Kaya't posible na i-program ang isang computer upang gawin ang isang bagay kung alam natin ang eksaktong pagkakasunod-sunod ng mga hakbang na kailangan upang makamit ang layunin. -![Larawan ng isang tao](../../../../translated_images/tl/dsh_age.d212a30d4e54fb5f.png) +![Larawan ng isang tao](../../../../translated_images/tl/dsh_age.d212a30d4e54fb5f.webp) > Larawan ni [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ Para sa karagdagang impormasyon, tingnan ang **[Artificial General Intelligence] Isa sa mga problema sa pagharap sa terminong **[Katalinuhan](https://en.wikipedia.org/wiki/Intelligence)** ay walang malinaw na depinisyon ng terminong ito. Maaaring sabihin ng iba na ang katalinuhan ay konektado sa **abstraktong pag-iisip**, o sa **sariling kamalayan**, ngunit hindi natin ito maipaliwanag nang maayos. -![Larawan ng Pusa](../../../../translated_images/tl/photo-cat.8c8e8fb760ffe457.jpg) +![Larawan ng Pusa](../../../../translated_images/tl/photo-cat.8c8e8fb760ffe457.webp) > [Larawan](https://unsplash.com/photos/75715CVEJhI) ni [Amber Kipp](https://unsplash.com/@sadmax) mula sa Unsplash @@ -98,13 +98,13 @@ Sa kabilang banda, maaari nating subukang i-modelo ang pinakasimpleng elemento s > | Paano naman ang ML? | | > |--------------|-----------| -> | Ang bahagi ng Artificial Intelligence na batay sa pag-aaral ng computer upang lutasin ang problema batay sa ilang data ay tinatawag na **Machine Learning**. Hindi natin tatalakayin ang klasikong machine learning sa kursong ito - tinutukoy namin kayo sa hiwalay na [Machine Learning for Beginners](http://aka.ms/ml-beginners) na kurikulum. | ![ML for Beginners](../../../../translated_images/tl/ml-for-beginners.9e4fed176fd5817d.png) | +> | Ang bahagi ng Artificial Intelligence na batay sa pag-aaral ng computer upang lutasin ang problema batay sa ilang data ay tinatawag na **Machine Learning**. Hindi natin tatalakayin ang klasikong machine learning sa kursong ito - tinutukoy namin kayo sa hiwalay na [Machine Learning for Beginners](http://aka.ms/ml-beginners) na kurikulum. | ![ML for Beginners](../../../../translated_images/tl/ml-for-beginners.9e4fed176fd5817d.webp) | ## Maikling Kasaysayan ng AI Ang Artificial Intelligence ay nagsimula bilang isang larangan noong kalagitnaan ng ikadalawampung siglo. Sa simula, ang symbolic reasoning ay isang pangunahing diskarte, at nagresulta ito sa ilang mahahalagang tagumpay, tulad ng mga expert systems – mga programang computer na kayang kumilos bilang eksperto sa ilang limitadong larangan ng problema. Gayunpaman, kalaunan ay naging malinaw na ang ganitong diskarte ay hindi masyadong epektibo. Ang pagkuha ng kaalaman mula sa isang eksperto, pagrepresenta nito sa computer, at pagpapanatili ng kaalaman na tumpak ay naging napakakomplikado at masyadong mahal upang maging praktikal sa maraming kaso. Ito ang nagresulta sa tinatawag na [AI Winter](https://en.wikipedia.org/wiki/AI_winter) noong dekada 1970. -Maikling Kasaysayan ng AI +Maikling Kasaysayan ng AI > Larawan ni [Dmitry Soshnikov](http://soshnikov.com) @@ -124,7 +124,7 @@ Katulad nito, makikita natin kung paano nagbago ang diskarte sa paglikha ng “m * Ang mga modernong assistant, tulad ng Cortana, Siri o Google Assistant ay lahat ng hybrid systems na gumagamit ng Neural networks upang i-convert ang pagsasalita sa teksto at kilalanin ang ating intensyon, at pagkatapos ay gumamit ng ilang pangangatwiran o tahasang algorithm upang maisagawa ang mga kinakailangang aksyon. * Sa hinaharap, maaari nating asahan ang isang kumpletong neural-based na modelo upang pangasiwaan ang pag-uusap nang mag-isa. Ang kamakailang GPT at [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) na pamilya ng neural networks ay nagpapakita ng mahusay na tagumpay sa larangang ito. -ebolusyon ng Turing test +ebolusyon ng Turing test > Larawan ni Dmitry Soshnikov, [larawan](https://unsplash.com/photos/r8LmVbUKgns) ni [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto), Unsplash ## Kamakailang Pananaliksik sa AI diff --git a/translations/tl/lessons/2-Symbolic/Animals.ipynb b/translations/tl/lessons/2-Symbolic/Animals.ipynb index fd4b688e..e132a86b 100644 --- a/translations/tl/lessons/2-Symbolic/Animals.ipynb +++ b/translations/tl/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "Sa halimbawang ito, magpapatupad tayo ng isang simpleng sistema na batay sa kaalaman upang matukoy ang isang hayop batay sa ilang pisikal na katangian. Ang sistema ay maaaring i-representa gamit ang sumusunod na AND-OR tree (ito ay bahagi lamang ng buong puno, madali nating maidaragdag ang iba pang mga patakaran):\n", "\n", - "![](../../../../translated_images/tl/AND-OR-Tree.5592d2c70187f283.png)\n" + "![](../../../../translated_images/tl/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { diff --git a/translations/tl/lessons/2-Symbolic/README.md b/translations/tl/lessons/2-Symbolic/README.md index b5f6679a..d713b569 100644 --- a/translations/tl/lessons/2-Symbolic/README.md +++ b/translations/tl/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Representasyon ng Kaalaman at Mga Ekspertong Sistema -![Buod ng Nilalaman ng Symbolic AI](../../../../translated_images/tl/ai-symbolic.715a30cb610411a6.png) +![Buod ng Nilalaman ng Symbolic AI](../../../../translated_images/tl/ai-symbolic.715a30cb610411a6.webp) > Sketchnote ni [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ Kadalasan, hindi natin mahigpit na tinutukoy ang kaalaman, ngunit iniuugnay nati Kaya, ang problema ng **representasyon ng kaalaman** ay ang paghahanap ng epektibong paraan upang kumatawan sa kaalaman sa loob ng isang kompyuter sa anyo ng data, upang magamit ito nang awtomatiko. Ito ay maaaring makita bilang isang spectrum: -![Spectrum ng Representasyon ng Kaalaman](../../../../translated_images/tl/knowledge-spectrum.b60df631852c0217.png) +![Spectrum ng Representasyon ng Kaalaman](../../../../translated_images/tl/knowledge-spectrum.b60df631852c0217.webp) > Larawan ni [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Block Syntax | Indent | | | Isa sa mga maagang tagumpay ng symbolic AI ay ang tinatawag na **mga ekspertong sistema** - mga sistema ng kompyuter na idinisenyo upang kumilos bilang isang eksperto sa ilang limitadong domain ng problema. Ang mga ito ay nakabatay sa isang **knowledge base** na kinuha mula sa isa o higit pang mga eksperto, at naglalaman ng isang **inference engine** na gumagawa ng pangangatwiran sa ibabaw nito. -![Arkitektura ng Tao](../../../../translated_images/tl/arch-human.5d4d35f1bba3ab1c.png) | ![Sistema na Batay sa Kaalaman](../../../../translated_images/tl/arch-kbs.3ec5c150b09fa8da.png) +![Arkitektura ng Tao](../../../../translated_images/tl/arch-human.5d4d35f1bba3ab1c.webp) | ![Sistema na Batay sa Kaalaman](../../../../translated_images/tl/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ Pinadaling istruktura ng neural system ng tao | Arkitektura ng sistema na batay sa kaalaman @@ -106,7 +106,7 @@ Ang mga ekspertong sistema ay binuo tulad ng sistema ng pangangatwiran ng tao, n Bilang halimbawa, isaalang-alang natin ang sumusunod na ekspertong sistema ng pagtukoy ng isang hayop batay sa mga pisikal na katangian nito: -![AND-OR Tree](../../../../translated_images/tl/AND-OR-Tree.5592d2c70187f283.png) +![AND-OR Tree](../../../../translated_images/tl/AND-OR-Tree.5592d2c70187f283.webp) > Larawan ni [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/tl/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/tl/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index 4c5ea0f7..584ef566 100644 --- a/translations/tl/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/tl/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -1250,7 +1250,7 @@ "* Mababa ang training loss - magaling ang model sa pag-approximate ng training data dahil sapat ang expressive power nito.\n", "* Ang validation loss ay maaaring mas mataas kaysa sa training loss at maaaring magsimulang tumaas habang nagte-training - ito ay dahil \"inaalala\" ng model ang mga training points, at nawawala ang \"kabuuang larawan.\"\n", "\n", - "![Overfitting](../../../../../translated_images/tl/overfit.a0bd57f717c15769.png)\n", + "![Overfitting](../../../../../translated_images/tl/overfit.a0bd57f717c15769.webp)\n", "\n", "> Sa larawang ito, ang `x` ay kumakatawan sa training data, at ang `o` ay validation data. Kaliwa - linear model (one-layer), maayos nitong na-aapproximate ang likas na katangian ng data. Kanan - overfitted model, perpektong na-aapproximate ng model ang training data, pero nawawala ang saysay nito sa ibang data (napakataas ng validation error).\n" ] diff --git a/translations/tl/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/tl/lessons/3-NeuralNetworks/05-Frameworks/README.md index 9a3a07f2..ca2b586c 100644 --- a/translations/tl/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/tl/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Ang overfitting ay isang napakahalagang konsepto sa machine learning, at napakah Isaalang-alang ang sumusunod na problema ng pag-aapproximate sa 5 puntos (na kinakatawan ng `x` sa mga graph sa ibaba): -![linear](../../../../../translated_images/tl/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/tl/overfit2.131f5800ae10ca5e.jpg) +![linear](../../../../../translated_images/tl/overfit1.f24b71c6f652e59e.webp) | ![overfit](../../../../../translated_images/tl/overfit2.131f5800ae10ca5e.webp) -------------------------|-------------------------- **Linear model, 2 parameters** | **Non-linear model, 7 parameters** Training error = 5.3 | Training error = 0 @@ -79,7 +79,7 @@ Napakahalaga na mahanap ang tamang balanse sa pagitan ng dami ng parameters ng m Tulad ng makikita mula sa graph sa itaas, ang overfitting ay maaaring matukoy sa pamamagitan ng napakababang training error, at mataas na validation error. Karaniwan sa panahon ng training, makikita natin ang parehong training at validation errors na nagsisimulang bumaba, at pagkatapos ay sa isang punto maaaring tumigil ang validation error sa pagbaba at magsimulang tumaas. Ito ang magiging senyales ng overfitting, at indikasyon na dapat nating itigil ang training sa puntong ito (o kahit papaano gumawa ng snapshot ng model). -![overfitting](../../../../../translated_images/tl/Overfitting.408ad91cd90b4371.png) +![overfitting](../../../../../translated_images/tl/Overfitting.408ad91cd90b4371.webp) ## Paano maiwasan ang overfitting diff --git a/translations/tl/lessons/3-NeuralNetworks/README.md b/translations/tl/lessons/3-NeuralNetworks/README.md index 8e786b96..a63cb08d 100644 --- a/translations/tl/lessons/3-NeuralNetworks/README.md +++ b/translations/tl/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Panimula sa Neural Networks -![Buod ng nilalaman ng Intro Neural Networks sa isang doodle](../../../../translated_images/tl/ai-neuralnetworks.1c687ae40bc86e83.png) +![Buod ng nilalaman ng Intro Neural Networks sa isang doodle](../../../../translated_images/tl/ai-neuralnetworks.1c687ae40bc86e83.webp) Tulad ng tinalakay natin sa panimula, isa sa mga paraan upang makamit ang katalinuhan ay ang pagsasanay ng isang **modelo ng computer** o isang **artipisyal na utak**. Simula noong kalagitnaan ng ika-20 siglo, sinubukan ng mga mananaliksik ang iba't ibang mga matematikal na modelo, hanggang sa mga nakaraang taon kung saan ang direksyong ito ay napatunayang napaka-epektibo. Ang ganitong mga matematikal na modelo ng utak ay tinatawag na **neural networks**. @@ -36,13 +36,13 @@ Sa kurikulum na ito, magtutuon lamang tayo sa mga modelo ng neural network. Mula sa biology, alam natin na ang ating utak ay binubuo ng mga neural cells (neurons), bawat isa ay may maraming "inputs" (dendrites) at isang "output" (axon). Parehong dendrites at axons ay maaaring magdala ng mga signal na elektrikal, at ang mga koneksyon sa pagitan nila — na kilala bilang synapses — ay maaaring magpakita ng iba't ibang antas ng conductivity, na kinokontrol ng mga neurotransmitters. -![Modelo ng Isang Neuron](../../../../translated_images/tl/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![Modelo ng Isang Neuron](../../../../translated_images/tl/artneuron.1a5daa88d20ebe6f.png) +![Modelo ng Isang Neuron](../../../../translated_images/tl/synapse-wikipedia.ed20a9e4726ea1c6.webp) | ![Modelo ng Isang Neuron](../../../../translated_images/tl/artneuron.1a5daa88d20ebe6f.webp) ----|---- Tunay na Neuron *([Larawan](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) mula sa Wikipedia)* | Artipisyal na Neuron *(Larawan ng May-akda)* Kaya, ang pinakasimpleng matematikal na modelo ng isang neuron ay naglalaman ng ilang inputs X1, ..., XN at isang output Y, at isang serye ng mga weights W1, ..., WN. Ang output ay kinakalkula bilang: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) kung saan ang f ay isang non-linear na **activation function**. diff --git a/translations/tl/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/tl/lessons/4-ComputerVision/06-IntroCV/README.md index 6b66a11a..3f090226 100644 --- a/translations/tl/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/tl/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ Sa aming [OpenCV Notebook](OpenCV.ipynb), nagbibigay kami ng ilang mga halimbawa * **Pre-processing ng larawan ng isang Braille book**. Nakatuon kami sa kung paano namin magagamit ang thresholding, feature detection, perspective transformation, at NumPy manipulations upang paghiwalayin ang mga indibidwal na Braille symbols para sa karagdagang classification ng isang neural network. -![Braille Image](../../../../../translated_images/tl/braille.341962ff76b1bd70.jpeg) | ![Braille Image Pre-processed](../../../../../translated_images/tl/braille-result.46530fea020b03c7.png) | ![Braille Symbols](../../../../../translated_images/tl/braille-symbols.0159185ab69d5339.png) +![Braille Image](../../../../../translated_images/tl/braille.341962ff76b1bd70.webp) | ![Braille Image Pre-processed](../../../../../translated_images/tl/braille-result.46530fea020b03c7.webp) | ![Braille Symbols](../../../../../translated_images/tl/braille-symbols.0159185ab69d5339.webp) ----|-----|----- > Imahe mula sa [OpenCV.ipynb](OpenCV.ipynb) * **Pagtuklas ng galaw sa video gamit ang frame difference**. Kung ang camera ay nakapirmi, ang mga frame mula sa camera feed ay dapat medyo magkatulad sa isa't isa. Dahil ang mga frame ay kinakatawan bilang arrays, sa pamamagitan lamang ng pagbabawas ng mga arrays para sa dalawang magkasunod na frame ay makakakuha tayo ng pixel difference, na dapat mababa para sa static frames, at magiging mas mataas kapag may makabuluhang galaw sa imahe. -![Image of video frames and frame differences](../../../../../translated_images/tl/frame-difference.706f805491a0883c.png) +![Image of video frames and frame differences](../../../../../translated_images/tl/frame-difference.706f805491a0883c.webp) > Imahe mula sa [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ Sa aming [OpenCV Notebook](OpenCV.ipynb), nagbibigay kami ng ilang mga halimbawa - **Dense Optical Flow** ay kinakalkula ang vector field na nagpapakita kung saan gumagalaw ang bawat pixel - **Sparse Optical Flow** ay batay sa pagkuha ng ilang natatanging features sa imahe (hal. edges), at pagbuo ng kanilang trajectory mula frame to frame. -![Image of Optical Flow](../../../../../translated_images/tl/optical.1f4a94464579a83a.png) +![Image of Optical Flow](../../../../../translated_images/tl/optical.1f4a94464579a83a.webp) > Imahe mula sa [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/tl/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/tl/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index f1093dd7..fb782369 100644 --- a/translations/tl/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/tl/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: Ang VGG-16 ay isang network na nakamit ang 92.7% na katumpakan sa ImageNet top-5 classification noong 2014. Ito ay may ganitong istruktura ng mga layer: -![ImageNet Layers](../../../../../translated_images/tl/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet Layers](../../../../../translated_images/tl/vgg-16-arch1.d901a5583b3a51ba.webp) Tulad ng nakikita mo, sinusunod ng VGG ang tradisyunal na pyramid architecture, na isang sunod-sunod na convolution-pooling layers. -![ImageNet Pyramid](../../../../../translated_images/tl/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet Pyramid](../../../../../translated_images/tl/vgg-16-arch.64ff2137f50dd49f.webp) > Larawan mula sa [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/tl/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/tl/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index cdbd50b2..a0669e46 100644 --- a/translations/tl/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/tl/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -260,7 +260,7 @@ "\n", "Kaya, sa isang tipikal na CNN, magkakaroon ng ilang convolutional layers, na may pooling layers sa pagitan ng mga ito upang bawasan ang sukat ng imahe. Dagdag pa rito, pinapataas natin ang bilang ng mga filter, dahil habang nagiging mas advanced ang mga pattern - mas maraming posibleng kombinasyon na kailangang hanapin.\n", "\n", - "![Isang larawan na nagpapakita ng ilang convolutional layers na may pooling layers.](../../../../../translated_images/tl/cnn-pyramid.85915455759ef0ce.png)\n", + "![Isang larawan na nagpapakita ng ilang convolutional layers na may pooling layers.](../../../../../translated_images/tl/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "Dahil sa pagbawas ng spatial dimensions at pagtaas ng feature/filter dimensions, ang arkitekturang ito ay tinatawag ding **pyramid architecture**.\n" ] diff --git a/translations/tl/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/tl/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index 6d36dd45..cc7e89dd 100644 --- a/translations/tl/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/tl/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -358,7 +358,7 @@ "\n", "Kaya, sa isang tipikal na CNN, magkakaroon ng ilang convolutional layer, na may mga pooling layer sa pagitan ng mga ito upang mabawasan ang dimensyon ng imahe. Dagdag pa rito, pinapataas natin ang bilang ng mga filter, dahil habang nagiging mas kumplikado ang mga pattern, mas maraming posibleng interesanteng kombinasyon ang kailangang hanapin.\n", "\n", - "![Isang larawan na nagpapakita ng ilang convolutional layer na may pooling layer.](../../../../../translated_images/tl/cnn-pyramid.85915455759ef0ce.png)\n", + "![Isang larawan na nagpapakita ng ilang convolutional layer na may pooling layer.](../../../../../translated_images/tl/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "Dahil sa pagbawas ng spatial na dimensyon at pagtaas ng feature/filter na dimensyon, ang arkitekturang ito ay tinatawag ding **pyramid architecture**.\n" ] diff --git a/translations/tl/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/tl/lessons/4-ComputerVision/07-ConvNets/README.md index 8b3c16f8..bada001d 100644 --- a/translations/tl/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/tl/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ Sa totoong buhay, gusto nating makilala ang mga bagay sa isang larawan kahit saa Upang makuha ang mga pattern, gagamit tayo ng konsepto ng **convolutional filters**. Tulad ng alam mo, ang isang imahe ay kinakatawan ng isang 2D-matrix, o isang 3D-tensor na may color depth. Ang pag-aapply ng filter ay nangangahulugan na kukuha tayo ng medyo maliit na **filter kernel** matrix, at para sa bawat pixel sa orihinal na imahe, kinakalkula natin ang weighted average kasama ang mga kalapit na puntos. Maaari nating tingnan ito bilang isang maliit na bintana na gumagalaw sa buong imahe, at ina-average ang lahat ng pixels ayon sa mga weights sa filter kernel matrix. -![Vertical Edge Filter](../../../../../translated_images/tl/filter-vert.b7148390ca0bc356.png) | ![Horizontal Edge Filter](../../../../../translated_images/tl/filter-horiz.59b80ed4feb946ef.png) +![Vertical Edge Filter](../../../../../translated_images/tl/filter-vert.b7148390ca0bc356.webp) | ![Horizontal Edge Filter](../../../../../translated_images/tl/filter-horiz.59b80ed4feb946ef.webp) ----|---- > Larawan ni Dmitry Soshnikov @@ -38,7 +38,7 @@ Ang paraan ng paggana ng CNN ay batay sa mga sumusunod na mahalagang ideya: * Maaari nating i-disensyo ang network sa paraang ang filters ay matututo nang awtomatiko * Maaari nating gamitin ang parehong paraan upang hanapin ang mga pattern sa high-level features, hindi lamang sa orihinal na imahe. Kaya ang feature extraction ng CNN ay gumagana sa isang hierarchy ng features, simula sa low-level pixel combinations, hanggang sa mas mataas na level na kombinasyon ng mga bahagi ng larawan. -![Hierarchical Feature Extraction](../../../../../translated_images/tl/FeatureExtractionCNN.d9b456cbdae7cb64.png) +![Hierarchical Feature Extraction](../../../../../translated_images/tl/FeatureExtractionCNN.d9b456cbdae7cb64.webp) > Larawan mula sa [isang papel ni Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), batay sa [kanilang pananaliksik](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ Karamihan sa mga CNN na ginagamit para sa pagproseso ng imahe ay sumusunod sa ti Halimbawa, tingnan natin ang arkitektura ng VGG-16, isang network na nakamit ang 92.7% accuracy sa ImageNet's top-5 classification noong 2014: -![ImageNet Layers](../../../../../translated_images/tl/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet Layers](../../../../../translated_images/tl/vgg-16-arch1.d901a5583b3a51ba.webp) -![ImageNet Pyramid](../../../../../translated_images/tl/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet Pyramid](../../../../../translated_images/tl/vgg-16-arch.64ff2137f50dd49f.webp) > Larawan mula sa [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/tl/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/tl/lessons/4-ComputerVision/07-ConvNets/lab/README.md index cb273ec3..f1ae7865 100644 --- a/translations/tl/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/tl/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ Kailangan mong sanayin ang isang convolutional neural network upang uriin ang ib Gagamitin natin ang [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/), na naglalaman ng mga larawan ng 37 iba't ibang lahi ng aso at pusa. -![Dataset na gagamitin natin](../../../../../../translated_images/tl/data.50b2a9d5484bdbf0.png) +![Dataset na gagamitin natin](../../../../../../translated_images/tl/data.50b2a9d5484bdbf0.webp) Upang i-download ang dataset, gamitin ang code snippet na ito: diff --git a/translations/tl/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/tl/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index ffddfbd8..ad9d511d 100644 --- a/translations/tl/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/tl/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "Upang maipakita ang ideal na pusa, magsisimula tayo sa isang random na noise image, at susubukan nating gamitin ang gradient descent optimization technique upang ayusin ang imahe para makilala ng network ang isang pusa.\n", "\n", - "![Optimization Loop](../../../../../translated_images/tl/ideal-cat-loop.999fbb8ff306e044.png)\n", + "![Optimization Loop](../../../../../translated_images/tl/ideal-cat-loop.999fbb8ff306e044.webp)\n", "\n", "Narito ang ating panimulang imahe:\n" ] diff --git a/translations/tl/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/tl/lessons/4-ComputerVision/08-TransferLearning/README.md index 249ab3a4..4a2b3dc0 100644 --- a/translations/tl/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/tl/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Parehong Keras at PyTorch ay may mga function upang madaling ma-load ang pre-tra Narito ang mga sample features na na-extract mula sa larawan ng isang pusa gamit ang VGG-16 network: -![Features extracted by VGG-16](../../../../../translated_images/tl/features.6291f9c7ba3a0b95.png) +![Features extracted by VGG-16](../../../../../translated_images/tl/features.6291f9c7ba3a0b95.webp) ## Dataset ng Cats vs. Dogs @@ -48,19 +48,19 @@ Ang pre-trained neural network ay naglalaman ng iba't ibang patterns sa loob ng Isang approach na maaari nating gawin ay magsimula sa isang random na imahe, at pagkatapos ay subukang gamitin ang **gradient descent optimization** technique upang i-adjust ang imahe na iyon sa paraang magsisimula ang network na isipin na ito ay isang pusa. -![Image Optimization Loop](../../../../../translated_images/tl/ideal-cat-loop.999fbb8ff306e044.png) +![Image Optimization Loop](../../../../../translated_images/tl/ideal-cat-loop.999fbb8ff306e044.webp) Gayunpaman, kung gagawin natin ito, makakakuha tayo ng isang bagay na halos katulad ng random noise. Ito ay dahil *maraming paraan upang mag-isip ang network na ang input image ay isang pusa*, kabilang ang ilan na hindi makatuwiran sa visual. Bagama't ang mga imahe ay naglalaman ng maraming patterns na tipikal para sa isang pusa, walang anumang constraint upang maging visually distinctive ang mga ito. Upang mapabuti ang resulta, maaari tayong magdagdag ng isa pang term sa loss function, na tinatawag na **variation loss**. Ito ay isang metric na nagpapakita kung gaano kahawig ang mga magkatabing pixels ng imahe. Ang pag-minimize ng variation loss ay nagpapakinis sa imahe, at nag-aalis ng noise - kaya mas naipapakita ang mas visually appealing patterns. Narito ang halimbawa ng mga "ideal" na imahe, na na-classify bilang pusa at zebra na may mataas na probability: -![Ideal Cat](../../../../../translated_images/tl/ideal-cat.203dd4597643d6b0.png) | ![Ideal Zebra](../../../../../translated_images/tl/ideal-zebra.7f70e8b54ee15a7a.png) +![Ideal Cat](../../../../../translated_images/tl/ideal-cat.203dd4597643d6b0.webp) | ![Ideal Zebra](../../../../../translated_images/tl/ideal-zebra.7f70e8b54ee15a7a.webp) -----|----- *Ideal Cat* | *Ideal Zebra* Ang parehong approach ay maaaring gamitin upang magsagawa ng tinatawag na **adversarial attacks** sa isang neural network. Halimbawa, gusto nating linlangin ang isang neural network at gawing mukhang pusa ang isang aso. Kung kukunin natin ang imahe ng aso, na kinikilala ng network bilang aso, maaari natin itong i-tweak nang kaunti gamit ang gradient descent optimization, hanggang sa magsimulang i-classify ito ng network bilang pusa: -![Picture of a Dog](../../../../../translated_images/tl/original-dog.8f68a67d2fe0911f.png) | ![Picture of a dog classified as a cat](../../../../../translated_images/tl/adversarial-dog.d9fc7773b0142b89.png) +![Picture of a Dog](../../../../../translated_images/tl/original-dog.8f68a67d2fe0911f.webp) | ![Picture of a dog classified as a cat](../../../../../translated_images/tl/adversarial-dog.d9fc7773b0142b89.webp) -----|----- *Orihinal na larawan ng aso* | *Larawan ng aso na na-classify bilang pusa* diff --git a/translations/tl/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/tl/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index 8fc240bc..a678b7fd 100644 --- a/translations/tl/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/tl/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "Dahil sinasanay natin ang autoencoder upang makuha ang pinakamaraming impormasyon mula sa orihinal na larawan para sa tumpak na reconstruction, sinusubukan ng network na hanapin ang pinakamahusay na **embedding** ng input images upang makuha ang kahulugan nito.\n", "\n", - "![AutoEncoder Diagram](../../../../../translated_images/tl/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![AutoEncoder Diagram](../../../../../translated_images/tl/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "> Larawan mula sa [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", diff --git a/translations/tl/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/tl/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index d5ee5530..3131f8d3 100644 --- a/translations/tl/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/tl/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "Dahil sinasanay natin ang autoencoder upang makuha ang pinakamaraming impormasyon mula sa orihinal na larawan para sa tumpak na reconstruction, sinusubukan ng network na hanapin ang pinakamahusay na **embedding** ng input images upang makuha ang kahulugan nito.\n", "\n", - "![AutoEncoder Diagram](../../../../../translated_images/tl/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![AutoEncoder Diagram](../../../../../translated_images/tl/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "*Larawan mula sa [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", diff --git a/translations/tl/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/tl/lessons/4-ComputerVision/09-Autoencoders/README.md index 886a09ac..a6ab2b81 100644 --- a/translations/tl/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/tl/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ Gayunpaman, maaaring gusto nating gamitin ang raw (unlabeled) na data para sa pa Dahil tina-train natin ang autoencoder upang makuha ang pinakamaraming impormasyon mula sa orihinal na imahe para sa tumpak na reconstruction, sinusubukan ng network na hanapin ang pinakamahusay na **embedding** ng input images upang makuha ang kahulugan nito. -![AutoEncoder Diagram](../../../../../translated_images/tl/autoencoder_schema.5e6fc9ad98a5eb61.jpg) +![AutoEncoder Diagram](../../../../../translated_images/tl/autoencoder_schema.5e6fc9ad98a5eb61.webp) > Imahe mula sa [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/tl/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/tl/lessons/4-ComputerVision/11-ObjectDetection/README.md index c708e534..c3c4badd 100644 --- a/translations/tl/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/tl/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ Ang mga modelo ng image classification na ating tinalakay hanggang ngayon ay tum ## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Pag-detect ng Objekto](../../../../../translated_images/tl/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) +![Pag-detect ng Objekto](../../../../../translated_images/tl/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp) > Larawan mula sa [YOLO v2 web site](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ Kung nais nating hanapin ang isang pusa sa isang larawan, ang isang napakasimple 2. Patakbuhin ang image classification sa bawat tile. 3. Ang mga tile na may sapat na mataas na activation ay maaaring ituring na naglalaman ng hinahanap na objekto. -![Simpleng Pag-detect ng Objekto](../../../../../translated_images/tl/naive-detection.e7f1ba220ccd08c6.png) +![Simpleng Pag-detect ng Objekto](../../../../../translated_images/tl/naive-detection.e7f1ba220ccd08c6.webp) > *Larawan mula sa [Exercise Notebook](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ Maaaring makatagpo ka ng mga sumusunod na dataset para sa gawaing ito: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 klase * [COCO](http://cocodataset.org/#home) - Common Objects in Context. 80 klase, bounding boxes, at segmentation masks -![COCO](../../../../../translated_images/tl/coco-examples.71bc60380fa6cceb.jpg) +![COCO](../../../../../translated_images/tl/coco-examples.71bc60380fa6cceb.webp) ## Mga Sukatan para sa Pag-detect ng Objekto @@ -50,7 +50,7 @@ Maaaring makatagpo ka ng mga sumusunod na dataset para sa gawaing ito: Habang madali ang pagsukat ng performance ng algorithm sa image classification, sa pag-detect ng objekto kailangan nating sukatin ang tamang klase pati na rin ang eksaktong lokasyon ng inferred bounding box. Para sa huli, ginagamit natin ang tinatawag na **Intersection over Union** (IoU), na sumusukat kung gaano kahusay ang overlap ng dalawang kahon (o dalawang arbitrary na lugar). -![IoU](../../../../../translated_images/tl/iou_equation.9a4751d40fff4e11.png) +![IoU](../../../../../translated_images/tl/iou_equation.9a4751d40fff4e11.webp) > *Figure 2 mula sa [napakagandang blog post na ito tungkol sa IoU](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ May dalawang malawak na klase ng mga algorithm sa pag-detect ng objekto: Ang [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) ay gumagamit ng [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) upang makabuo ng hierarchical structure ng mga ROI region, na pagkatapos ay ipinapasa sa CNN feature extractors at SVM-classifiers upang matukoy ang klase ng objekto, at linear regression upang matukoy ang mga coordinate ng *bounding box*. [Opisyal na Papel](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/tl/rcnn1.cae407020dfb1d1f.png) +![RCNN](../../../../../translated_images/tl/rcnn1.cae407020dfb1d1f.webp) > *Larawan mula kay van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/tl/rcnn2.2d9530bb83516484.png) +![RCNN-1](../../../../../translated_images/tl/rcnn2.2d9530bb83516484.webp) > *Mga larawan mula sa [blog na ito](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e) @@ -110,7 +110,7 @@ Ang [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) ay gu Ang pamamaraang ito ay katulad ng R-CNN, ngunit ang mga region ay tinutukoy pagkatapos ma-apply ang convolution layers. -![FRCNN](../../../../../translated_images/tl/f-rcnn.3cda6d9bb4188875.png) +![FRCNN](../../../../../translated_images/tl/f-rcnn.3cda6d9bb4188875.webp) > Larawan mula sa [Opisyal na Papel](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ Ang pamamaraang ito ay katulad ng R-CNN, ngunit ang mga region ay tinutukoy pagk Ang pangunahing ideya ng pamamaraang ito ay ang paggamit ng neural network upang mahulaan ang mga ROI - tinatawag na *Region Proposal Network*. [Papel](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/tl/faster-rcnn.8d46c099b87ef30a.png) +![FasterRCNN](../../../../../translated_images/tl/faster-rcnn.8d46c099b87ef30a.webp) > Larawan mula sa [opisyal na papel](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ Ang algorithm na ito ay mas mabilis pa kaysa sa Faster R-CNN. Ang pangunahing id 1. Ang mga feature ay pinoproseso ng **Position-Sensitive Score Map**. Ang bawat objekto mula sa $C$ klase ay hinahati sa $k\times k$ na mga region, at sinasanay upang mahulaan ang mga bahagi ng mga objekto. 1. Para sa bawat bahagi mula sa $k\times k$ na mga region, lahat ng network ay bumoboto para sa klase ng objekto, at ang klase ng objekto na may pinakamataas na boto ang pinipili. -![r-fcn image](../../../../../translated_images/tl/r-fcn.13eb88158b99a3da.png) +![r-fcn image](../../../../../translated_images/tl/r-fcn.13eb88158b99a3da.webp) > Larawan mula sa [opisyal na papel](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ Ang YOLO ay isang realtime one-pass algorithm. Ang pangunahing ideya ay ang sumu * Ang larawan ay hinahati sa $S\times S$ na mga region. * Para sa bawat region, **CNN** ay hinuhulaan ang $n$ posibleng mga objekto, *bounding box* coordinates, at *confidence*=*probability* * IoU. - ![YOLO](../../../../../translated_images/tl/yolo.a2648ec82ee8bb4e.png) + ![YOLO](../../../../../translated_images/tl/yolo.a2648ec82ee8bb4e.webp) > Larawan mula sa [opisyal na papel](https://arxiv.org/abs/1506.02640) diff --git a/translations/tl/lessons/4-ComputerVision/README.md b/translations/tl/lessons/4-ComputerVision/README.md index 7f4dd5ed..117c6ef5 100644 --- a/translations/tl/lessons/4-ComputerVision/README.md +++ b/translations/tl/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Computer Vision -![Buod ng nilalaman ng Computer Vision sa isang doodle](../../../../translated_images/tl/ai-computervision.6506ebebac3fbf76.png) +![Buod ng nilalaman ng Computer Vision sa isang doodle](../../../../translated_images/tl/ai-computervision.6506ebebac3fbf76.webp) Sa seksyong ito, matututo tayo tungkol sa: diff --git a/translations/tl/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/tl/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index 173e0db5..5e1ee852 100644 --- a/translations/tl/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/tl/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "Ang **Bag of Words** (BoW) na representasyon ng vector ay ang pinakakaraniwang ginagamit na tradisyunal na representasyon ng vector. Ang bawat salita ay naka-link sa isang index ng vector, at ang elemento ng vector ay naglalaman ng bilang ng paglitaw ng isang salita sa isang partikular na dokumento.\n", "\n", - "![Larawan na nagpapakita kung paano kinakatawan ang bag of words na representasyon ng vector sa memorya.](../../../../../translated_images/tl/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![Larawan na nagpapakita kung paano kinakatawan ang bag of words na representasyon ng vector sa memorya.](../../../../../translated_images/tl/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Note**: Maaari mo ring isipin ang BoW bilang kabuuan ng lahat ng one-hot-encoded na mga vector para sa bawat indibidwal na salita sa teksto.\n", "\n", diff --git a/translations/tl/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/tl/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index c60628e5..f390b13a 100644 --- a/translations/tl/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/tl/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "Ang **Bag-of-words** (BoW) na representasyon ng vector ay ang pinakasimple at madaling maunawaan na tradisyunal na representasyon ng vector. Ang bawat salita ay nauugnay sa isang index ng vector, at ang isang elemento ng vector ay naglalaman ng bilang ng paglitaw ng bawat salita sa isang partikular na dokumento.\n", "\n", - "![Larawan na nagpapakita kung paano kinakatawan ang bag-of-words vector sa memorya.](../../../../../translated_images/tl/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![Larawan na nagpapakita kung paano kinakatawan ang bag-of-words vector sa memorya.](../../../../../translated_images/tl/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Note**: Maaari mo ring isipin ang BoW bilang kabuuan ng lahat ng one-hot-encoded vectors para sa bawat indibidwal na salita sa teksto.\n", "\n", diff --git a/translations/tl/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/tl/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 7a93dd84..151c7c66 100644 --- a/translations/tl/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/tl/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "Sa paggamit ng embedding layer bilang unang layer sa ating network, maaari tayong lumipat mula sa bag-of-words patungo sa **embedding bag** model, kung saan una nating kino-convert ang bawat salita sa ating teksto sa kaukulang embedding, at pagkatapos ay kinakalkula ang isang aggregate function sa lahat ng mga embeddings na iyon, tulad ng `sum`, `average`, o `max`.\n", "\n", - "![Larawan na nagpapakita ng isang embedding classifier para sa limang sequence na salita.](../../../../../translated_images/tl/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![Larawan na nagpapakita ng isang embedding classifier para sa limang sequence na salita.](../../../../../translated_images/tl/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "Ang ating classifier neural network ay magsisimula sa embedding layer, pagkatapos ay aggregation layer, at linear classifier sa ibabaw nito:\n" ] @@ -176,7 +176,7 @@ "\n", "Sa nakaraang arkitektura, kinakailangan nating i-pad ang lahat ng sequence upang magkapareho ang haba para magkasya ang mga ito sa isang minibatch. Hindi ito ang pinakaepektibong paraan upang i-representa ang mga sequence na may iba't ibang haba - isang alternatibong paraan ay ang paggamit ng **offset** vector, na maglalaman ng mga offset ng lahat ng sequence na nakaimbak sa isang malaking vector.\n", "\n", - "![Larawan na nagpapakita ng offset sequence representation](../../../../../translated_images/tl/offset-sequence-representation.eb73fcefb29b46ee.png)\n", + "![Larawan na nagpapakita ng offset sequence representation](../../../../../translated_images/tl/offset-sequence-representation.eb73fcefb29b46ee.webp)\n", "\n", "> **Note**: Sa larawan sa itaas, ipinapakita namin ang isang sequence ng mga karakter, ngunit sa ating halimbawa, nagtatrabaho tayo sa mga sequence ng salita. Gayunpaman, ang pangkalahatang prinsipyo ng pagre-representa ng mga sequence gamit ang offset vector ay nananatiling pareho.\n", "\n", @@ -311,7 +311,7 @@ "\n", "Mas mabilis ang CBoW, habang ang skip-gram ay mas mabagal, ngunit mas mahusay sa pag-representa ng mga bihirang salita.\n", "\n", - "![Larawan na nagpapakita ng parehong CBoW at Skip-Gram na mga algorithm para i-convert ang mga salita sa mga vector.](../../../../../translated_images/tl/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![Larawan na nagpapakita ng parehong CBoW at Skip-Gram na mga algorithm para i-convert ang mga salita sa mga vector.](../../../../../translated_images/tl/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Upang mag-eksperimento gamit ang word2vec embedding na pre-trained sa Google News dataset, maaari nating gamitin ang **gensim** library. Sa ibaba, makikita natin ang mga salitang pinakamalapit sa 'neural'.\n", "\n", diff --git a/translations/tl/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/tl/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index a0e080ca..8969825b 100644 --- a/translations/tl/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/tl/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "Sa pamamagitan ng paggamit ng embedding layer bilang unang layer sa ating network, maaari tayong lumipat mula sa bag-of-words patungo sa isang **embedding bag** model, kung saan una nating kino-convert ang bawat salita sa ating teksto sa kaukulang embedding, at pagkatapos ay kinakalkula ang isang aggregate function sa lahat ng mga embeddings na iyon, tulad ng `sum`, `average`, o `max`.\n", "\n", - "![Larawan na nagpapakita ng isang embedding classifier para sa limang sequence na salita.](../../../../../translated_images/tl/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![Larawan na nagpapakita ng isang embedding classifier para sa limang sequence na salita.](../../../../../translated_images/tl/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "Ang ating classifier neural network ay binubuo ng mga sumusunod na layer:\n", "\n", @@ -283,7 +283,7 @@ "\n", "Mas mabilis ang CBoW, ngunit habang mas mabagal ang skip-gram, mas mahusay ito sa pagrepresenta ng mga bihirang salita.\n", "\n", - "![Larawan na nagpapakita ng parehong CBoW at Skip-Gram na mga algorithm para i-convert ang mga salita sa vectors.](../../../../../translated_images/tl/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![Larawan na nagpapakita ng parehong CBoW at Skip-Gram na mga algorithm para i-convert ang mga salita sa vectors.](../../../../../translated_images/tl/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Upang mag-eksperimento gamit ang Word2Vec embedding na pre-trained sa Google News dataset, maaari nating gamitin ang **gensim** library. Sa ibaba, makikita natin ang mga salitang pinakamalapit sa 'neural'.\n", "\n", diff --git a/translations/tl/lessons/5-NLP/14-Embeddings/README.md b/translations/tl/lessons/5-NLP/14-Embeddings/README.md index a6c84c8a..0ca8beca 100644 --- a/translations/tl/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/tl/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ Ang embedding layer ay kukuha ng isang salita bilang input, at magbibigay ng out Sa paggamit ng embedding layer bilang unang layer sa ating classifier network, maaari tayong lumipat mula sa bag-of-words patungo sa **embedding bag** model, kung saan una nating kino-convert ang bawat salita sa ating teksto sa kaukulang embedding, at pagkatapos ay kinakalkula ang ilang aggregate function sa lahat ng mga embedding na iyon, tulad ng `sum`, `average`, o `max`. -![Larawan na nagpapakita ng isang embedding classifier para sa limang sequence words.](../../../../../translated_images/tl/embedding-classifier-example.b77f021a7ee67eee.png) +![Larawan na nagpapakita ng isang embedding classifier para sa limang sequence words.](../../../../../translated_images/tl/embedding-classifier-example.b77f021a7ee67eee.webp) > Larawan mula sa may-akda @@ -40,7 +40,7 @@ Upang magawa ito, kailangan nating i-pre-train ang ating embedding model sa isan Mas mabilis ang CBoW, habang ang skip-gram ay mas mabagal, ngunit mas mahusay sa pagrepresenta ng mga bihirang salita. -![Larawan na nagpapakita ng parehong CBoW at Skip-Gram algorithms upang i-convert ang mga salita sa vectors.](../../../../../translated_images/tl/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![Larawan na nagpapakita ng parehong CBoW at Skip-Gram algorithms upang i-convert ang mga salita sa vectors.](../../../../../translated_images/tl/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Larawan mula sa [papel na ito](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/tl/lessons/5-NLP/15-LanguageModeling/README.md b/translations/tl/lessons/5-NLP/15-LanguageModeling/README.md index da18401b..4c5e370b 100644 --- a/translations/tl/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/tl/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ Sa mga naunang halimbawa, gumamit tayo ng mga pre-trained na semantic embeddings * **Continuous Bag-of-Words** (CBoW), kung saan hinuhulaan natin ang gitnang token $W_0$ sa isang hanay ng mga token $W_{-N}$, ..., $W_N$. * **Skip-gram**, kung saan hinuhulaan natin ang isang hanay ng mga kalapit na token {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} mula sa gitnang token $W_0$. -![larawan mula sa papel tungkol sa pag-convert ng mga salita sa vectors](../../../../../translated_images/tl/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![larawan mula sa papel tungkol sa pag-convert ng mga salita sa vectors](../../../../../translated_images/tl/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Larawan mula sa [papel na ito](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/tl/lessons/5-NLP/16-RNN/README.md b/translations/tl/lessons/5-NLP/16-RNN/README.md index ba664820..25ffaec5 100644 --- a/translations/tl/lessons/5-NLP/16-RNN/README.md +++ b/translations/tl/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ Sa mga nakaraang seksyon, gumamit tayo ng mas mayamang semantic na representasyo Upang makuha ang kahulugan ng pagkakasunod-sunod ng teksto, kailangan nating gumamit ng ibang arkitektura ng neural network, na tinatawag na **recurrent neural network**, o RNN. Sa RNN, ipinapasa natin ang ating pangungusap sa network nang paisa-isang simbolo, at ang network ay gumagawa ng isang **estado**, na pagkatapos ay ipinapasa natin muli sa network kasama ang susunod na simbolo. -![RNN](../../../../../translated_images/tl/rnn.27f5c29c53d727b5.png) +![RNN](../../../../../translated_images/tl/rnn.27f5c29c53d727b5.webp) > Larawan mula sa may-akda @@ -61,7 +61,7 @@ Napag-usapan natin ang mga recurrent networks na gumagana sa isang direksyon, mu Ang isang Recurrent network, alinman sa one-directional o bidirectional, ay kumukuha ng ilang patterns sa loob ng isang sequence, at maaaring i-store ang mga ito sa isang state vector o ipasa sa output. Tulad ng convolutional networks, maaari tayong bumuo ng isa pang recurrent layer sa ibabaw ng una upang makuha ang mas mataas na level na patterns at bumuo mula sa low-level patterns na nakuha ng unang layer. Ito ay humahantong sa konsepto ng isang **multi-layer RNN** na binubuo ng dalawa o higit pang recurrent networks, kung saan ang output ng nakaraang layer ay ipinapasa sa susunod na layer bilang input. -![Image showing a Multilayer long-short-term-memory- RNN](../../../../../translated_images/tl/multi-layer-lstm.dd975e29bb2a59fe.jpg) +![Image showing a Multilayer long-short-term-memory- RNN](../../../../../translated_images/tl/multi-layer-lstm.dd975e29bb2a59fe.webp) *Larawan mula sa [napakagandang post na ito](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) ni Fernando López* diff --git a/translations/tl/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/tl/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index ffed0adf..d03d4d3d 100644 --- a/translations/tl/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/tl/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "Ang recurrent network, one-directional man o bidirectional, ay nakakakuha ng ilang mga pattern sa loob ng isang sequence, at maaaring itago ang mga ito sa state vector o ipasa sa output. Tulad ng convolutional networks, maaari tayong magtayo ng isa pang recurrent layer sa ibabaw ng una upang makuha ang mas mataas na antas ng mga pattern, na binuo mula sa mga low-level pattern na nakuha ng unang layer. Ito ang nagdadala sa atin sa konsepto ng **multi-layer RNN**, na binubuo ng dalawa o higit pang recurrent networks, kung saan ang output ng nakaraang layer ay ipinapasa sa susunod na layer bilang input.\n", "\n", - "![Larawan na nagpapakita ng Multilayer long-short-term-memory- RNN](../../../../../translated_images/tl/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![Larawan na nagpapakita ng Multilayer long-short-term-memory- RNN](../../../../../translated_images/tl/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*Larawan mula sa [napakagandang post na ito](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) ni Fernando López*\n", "\n", diff --git a/translations/tl/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/tl/lessons/5-NLP/16-RNN/RNNTF.ipynb index 74964141..8ced811d 100644 --- a/translations/tl/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/tl/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "Upang makuha ang kahulugan ng isang sunod-sunod na teksto, gagamit tayo ng arkitektura ng neural network na tinatawag na **recurrent neural network**, o RNN. Kapag gumagamit ng RNN, ipinapasa natin ang ating pangungusap sa network nang paisa-isang token, at ang network ay gumagawa ng isang **estado**, na pagkatapos ay ipinapasa muli sa network kasama ang susunod na token.\n", "\n", - "![Larawan na nagpapakita ng halimbawa ng pagbuo ng recurrent neural network.](../../../../../translated_images/tl/rnn.27f5c29c53d727b5.png)\n", + "![Larawan na nagpapakita ng halimbawa ng pagbuo ng recurrent neural network.](../../../../../translated_images/tl/rnn.27f5c29c53d727b5.webp)\n", "\n", "Sa ibinigay na input sequence ng mga token $X_0,\\dots,X_n$, ang RNN ay lumilikha ng isang sunod-sunod na mga neural network block, at sinasanay ang sequence na ito mula simula hanggang dulo gamit ang backpropagation. Ang bawat network block ay tumatanggap ng pares $(X_i,S_i)$ bilang input, at gumagawa ng $S_{i+1}$ bilang resulta. Ang huling estado $S_n$ o output $Y_n$ ay ipinapasa sa isang linear classifier upang makabuo ng resulta. Ang lahat ng network block ay may parehong weights, at sinasanay mula simula hanggang dulo gamit ang isang backpropagation pass.\n", "\n", @@ -369,7 +369,7 @@ "\n", "Ang mga recurrent network, unidirectional man o bidirectional, ay kumukuha ng mga pattern sa loob ng isang sequence, at iniimbak ang mga ito sa state vectors o ibinabalik bilang output. Tulad ng convolutional networks, maaari tayong magtayo ng isa pang recurrent layer kasunod ng una upang makuha ang mas mataas na antas ng mga pattern, na binuo mula sa mas mababang antas ng mga pattern na nakuha ng unang layer. Ito ang nagdadala sa atin sa konsepto ng isang **multi-layer RNN**, na binubuo ng dalawa o higit pang recurrent networks, kung saan ang output ng nakaraang layer ay ipinapasa sa susunod na layer bilang input.\n", "\n", - "![Larawan na nagpapakita ng isang Multilayer long-short-term-memory- RNN](../../../../../translated_images/tl/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![Larawan na nagpapakita ng isang Multilayer long-short-term-memory- RNN](../../../../../translated_images/tl/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*Larawan mula sa [napakagandang post na ito](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) ni Fernando López.*\n", "\n", diff --git a/translations/tl/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/tl/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index 91653a59..382f4852 100644 --- a/translations/tl/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/tl/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "Ang paraan ng pagsasanay natin sa RNN upang makabuo ng teksto ay ang sumusunod. Sa bawat hakbang, kukuha tayo ng isang sequence ng mga karakter na may haba na `nchars`, at hihilingin sa network na bumuo ng susunod na output na karakter para sa bawat input na karakter:\n", "\n", - "![Larawan na nagpapakita ng halimbawa ng RNN na bumubuo ng salitang 'HELLO'.](../../../../../translated_images/tl/rnn-generate.56c54afb52f9781d.png)\n", + "![Larawan na nagpapakita ng halimbawa ng RNN na bumubuo ng salitang 'HELLO'.](../../../../../translated_images/tl/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "Depende sa aktwal na sitwasyon, maaaring gusto rin nating isama ang ilang espesyal na karakter, tulad ng *end-of-sequence* ``. Sa ating kaso, nais lamang nating sanayin ang network para sa walang katapusang pagbuo ng teksto, kaya't itatakda natin ang laki ng bawat sequence na maging katumbas ng `nchars` na mga token. Dahil dito, ang bawat halimbawa ng pagsasanay ay binubuo ng `nchars` na mga input at `nchars` na mga output (na siyang input sequence na inilipat ng isang simbolo sa kaliwa). Ang minibatch ay binubuo ng ilang ganitong mga sequence.\n", "\n", diff --git a/translations/tl/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/tl/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index a4090f3e..591ce113 100644 --- a/translations/tl/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/tl/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -111,7 +111,7 @@ "\n", "Ang paraan ng pagsasanay natin sa RNN upang lumikha ng mga pamagat ng balita ay ganito. Sa bawat hakbang, kukuha tayo ng isang pamagat, na ipapasok sa isang RNN, at para sa bawat input na karakter, hihilingin natin sa network na lumikha ng susunod na output na karakter:\n", "\n", - "![Larawan na nagpapakita ng halimbawa ng RNN na lumilikha ng salitang 'HELLO'.](../../../../../translated_images/tl/rnn-generate.56c54afb52f9781d.png)\n", + "![Larawan na nagpapakita ng halimbawa ng RNN na lumilikha ng salitang 'HELLO'.](../../../../../translated_images/tl/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "Para sa huling karakter ng ating sequence, hihilingin natin sa network na lumikha ng `` token.\n", "\n", diff --git a/translations/tl/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/tl/lessons/5-NLP/17-GenerativeNetworks/README.md index bfd2c686..24c3c8bd 100644 --- a/translations/tl/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/tl/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ Sa RNN architecture na tinalakay natin sa nakaraang unit, bawat RNN unit ay guma Ito ay nagbibigay-daan para sa iba't ibang neural architectures na ipinapakita sa larawan sa ibaba: -![Larawan na nagpapakita ng mga karaniwang pattern ng recurrent neural network.](../../../../../translated_images/tl/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Larawan na nagpapakita ng mga karaniwang pattern ng recurrent neural network.](../../../../../translated_images/tl/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > Larawan mula sa blog post [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) ni [Andrej Karpaty](http://karpathy.github.io/) @@ -32,7 +32,7 @@ Sa unit na ito, magpo-focus tayo sa simpleng generative models na tumutulong sa Sasanayin natin ang RNN na ito upang mag-generate ng teksto hakbang-hakbang. Sa bawat hakbang, kukuha tayo ng isang sequence ng mga character na may haba na `nchars`, at hihilingin sa network na mag-generate ng susunod na output character para sa bawat input character: -![Larawan na nagpapakita ng halimbawa ng RNN generation ng salitang 'HELLO'.](../../../../../translated_images/tl/rnn-generate.56c54afb52f9781d.png) +![Larawan na nagpapakita ng halimbawa ng RNN generation ng salitang 'HELLO'.](../../../../../translated_images/tl/rnn-generate.56c54afb52f9781d.webp) Kapag nag-generate ng teksto (sa panahon ng inference), magsisimula tayo sa isang **prompt**, na ipapasa sa RNN cells upang mag-generate ng intermediate state nito, at pagkatapos mula sa state na ito magsisimula ang generation. Mag-generate tayo ng isang character sa bawat pagkakataon, at ipapasa ang state at ang generated character sa isa pang RNN cell upang mag-generate ng susunod, hanggang sa makabuo tayo ng sapat na mga character. diff --git a/translations/tl/lessons/5-NLP/18-Transformers/README.md b/translations/tl/lessons/5-NLP/18-Transformers/README.md index 3c22b910..97c37f4e 100644 --- a/translations/tl/lessons/5-NLP/18-Transformers/README.md +++ b/translations/tl/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ Sa RNNs, ang sequence-to-sequence ay ipinatutupad gamit ang dalawang recurrent n Ang **Attention Mechanisms** ay nagbibigay ng paraan upang timbangin ang contextual na epekto ng bawat input vector sa bawat output prediction ng RNN. Ang paraan ng pagpapatupad nito ay sa pamamagitan ng paglikha ng mga shortcut sa pagitan ng mga intermediate states ng input RNN at output RNN. Sa ganitong paraan, kapag gumagawa ng output symbol yt, isasaalang-alang natin ang lahat ng input hidden states hi, na may iba't ibang weight coefficients αt,i. -![Larawan na nagpapakita ng encoder/decoder model na may additive attention layer](../../../../../translated_images/tl/encoder-decoder-attention.7a726296894fb567.png) +![Larawan na nagpapakita ng encoder/decoder model na may additive attention layer](../../../../../translated_images/tl/encoder-decoder-attention.7a726296894fb567.webp) > Ang encoder-decoder model na may additive attention mechanism sa [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), mula sa [blog post na ito](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) Ang attention matrix {αi,j} ay kumakatawan sa antas kung paano ang ilang input words ay may papel sa pagbuo ng isang partikular na salita sa output sequence. Narito ang isang halimbawa ng ganitong matrix: -![Larawan na nagpapakita ng sample alignment na natagpuan ng RNNsearch-50, mula sa Bahdanau - arviz.org](../../../../../translated_images/tl/bahdanau-fig3.09ba2d37f202a6af.png) +![Larawan na nagpapakita ng sample alignment na natagpuan ng RNNsearch-50, mula sa Bahdanau - arviz.org](../../../../../translated_images/tl/bahdanau-fig3.09ba2d37f202a6af.webp) > Larawan mula sa [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) @@ -66,7 +66,7 @@ Ang resulta na nakukuha natin sa positional embedding ay nag-e-embed sa parehong Susunod, kailangan nating makuha ang ilang mga pattern sa loob ng ating sequence. Upang gawin ito, gumagamit ang transformers ng **self-attention** mechanism, na mahalagang atensyon na inilapat sa parehong sequence bilang input at output. Ang pag-aapply ng self-attention ay nagbibigay-daan sa atin na isaalang-alang ang **context** sa loob ng pangungusap, at makita kung aling mga salita ang magkakaugnay. Halimbawa, pinapayagan tayo nitong makita kung aling mga salita ang tinutukoy ng mga coreferences, tulad ng *it*, at isaalang-alang din ang konteksto: -![](../../../../../translated_images/tl/CoreferenceResolution.861924d6d384a7d6.png) +![](../../../../../translated_images/tl/CoreferenceResolution.861924d6d384a7d6.webp) > Larawan mula sa [Google Blog](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Dahil ang bawat input position ay na-map nang independyente sa bawat output posi Ang **BERT** (Bidirectional Encoder Representations from Transformers) ay isang napakalaking multi-layer transformer network na may 12 layers para sa *BERT-base*, at 24 para sa *BERT-large*. Ang modelo ay unang pre-trained sa isang malaking corpus ng text data (WikiPedia + mga libro) gamit ang unsupervised training (pagpredikta ng mga masked words sa isang pangungusap). Sa panahon ng pre-training, ang modelo ay sumisipsip ng makabuluhang antas ng pag-unawa sa wika na maaaring magamit sa iba pang mga dataset gamit ang fine tuning. Ang prosesong ito ay tinatawag na **transfer learning**. -![larawan mula sa http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/tl/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) +![larawan mula sa http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/tl/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > Larawan [source](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/tl/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/tl/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index c184c644..f69cc28c 100644 --- a/translations/tl/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/tl/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "Ang **Mekanismo ng Atensyon** ay nagbibigay ng paraan upang timbangin ang kontekstwal na epekto ng bawat input vector sa bawat output prediction ng RNN. Ang paraan ng pagpapatupad nito ay sa pamamagitan ng paglikha ng mga shortcut sa pagitan ng mga intermediate state ng input RNN at output RNN. Sa ganitong paraan, kapag gumagawa ng output symbol $y_t$, isasaalang-alang natin ang lahat ng input hidden states $h_i$, na may iba't ibang timbang na coefficients $\\alpha_{t,i}$.\n", "\n", - "![Larawan na nagpapakita ng encoder/decoder model na may additive attention layer](../../../../../translated_images/tl/encoder-decoder-attention.7a726296894fb567.png) \n", + "![Larawan na nagpapakita ng encoder/decoder model na may additive attention layer](../../../../../translated_images/tl/encoder-decoder-attention.7a726296894fb567.webp) \n", "*Ang encoder-decoder model na may additive attention mechanism mula sa [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), na binanggit mula sa [blog post na ito](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Ang attention matrix $\\{\\alpha_{i,j}\\}$ ay kumakatawan sa antas kung saan ang ilang input na salita ay may papel sa pagbuo ng isang partikular na salita sa output sequence. Narito ang halimbawa ng ganitong matrix:\n", "\n", - "![Larawan na nagpapakita ng sample alignment na natagpuan ng RNNsearch-50, kinuha mula sa Bahdanau - arviz.org](../../../../../translated_images/tl/bahdanau-fig3.09ba2d37f202a6af.png) \n", + "![Larawan na nagpapakita ng sample alignment na natagpuan ng RNNsearch-50, kinuha mula sa Bahdanau - arviz.org](../../../../../translated_images/tl/bahdanau-fig3.09ba2d37f202a6af.webp) \n", "\n", "*Larawan mula sa [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "Ang **BERT** (Bidirectional Encoder Representations from Transformers) ay isang napakalaking multi-layer transformer network na may 12 layer para sa *BERT-base*, at 24 para sa *BERT-large*. Ang modelo ay unang pre-trained sa malaking corpus ng text data (WikiPedia + mga libro) gamit ang unsupervised training (pagpapredikta ng mga masked na salita sa isang pangungusap). Sa panahon ng pre-training, ang modelo ay sumisipsip ng makabuluhang antas ng pag-unawa sa wika na maaaring magamit sa iba pang mga dataset gamit ang fine tuning. Ang prosesong ito ay tinatawag na **transfer learning**.\n", "\n", - "![Larawan mula sa http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/tl/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) \n", + "![Larawan mula sa http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/tl/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) \n", "\n", "Maraming mga bersyon ng Transformer architectures kabilang ang BERT, DistilBERT, BigBird, OpenGPT3, at iba pa na maaaring i-fine tune. Ang [HuggingFace package](https://github.com/huggingface/) ay nagbibigay ng repository para sa pag-train ng marami sa mga arkitekturang ito gamit ang PyTorch.\n", "\n", diff --git a/translations/tl/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/tl/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index 75e61567..7f1737a9 100644 --- a/translations/tl/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/tl/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "Ang **Mekanismo ng Atensyon** ay nagbibigay ng paraan upang timbangin ang kontekstwal na epekto ng bawat input vector sa bawat output prediction ng RNN. Ang paraan ng pagpapatupad nito ay sa pamamagitan ng paglikha ng mga shortcut sa pagitan ng mga intermediate state ng input RNN at output RNN. Sa ganitong paraan, kapag gumagawa ng output symbol $y_t$, isasaalang-alang natin ang lahat ng input hidden states $h_i$, na may iba't ibang weight coefficients $\\alpha_{t,i}$. \n", "\n", - "![Larawan na nagpapakita ng encoder/decoder model na may additive attention layer](../../../../../translated_images/tl/encoder-decoder-attention.7a726296894fb567.png)\n", + "![Larawan na nagpapakita ng encoder/decoder model na may additive attention layer](../../../../../translated_images/tl/encoder-decoder-attention.7a726296894fb567.webp)\n", "*Ang encoder-decoder model na may additive attention mechanism mula sa [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), na binanggit mula sa [blog post na ito](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Ang attention matrix $\\{\\alpha_{i,j}\\}$ ay kumakatawan sa antas kung saan ang ilang input na salita ay may papel sa pagbuo ng isang partikular na salita sa output sequence. Narito ang halimbawa ng ganitong matrix:\n", "\n", - "![Larawan na nagpapakita ng sample alignment na natagpuan ng RNNsearch-50, kinuha mula sa Bahdanau - arviz.org](../../../../../translated_images/tl/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![Larawan na nagpapakita ng sample alignment na natagpuan ng RNNsearch-50, kinuha mula sa Bahdanau - arviz.org](../../../../../translated_images/tl/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*Larawan mula sa [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3)*\n", "\n", @@ -223,7 +223,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) ay isang napakalaking multi-layer transformer network na may 12 layers para sa *BERT-base*, at 24 para sa *BERT-large*. Ang modelo ay unang sinanay gamit ang malaking corpus ng text data (WikiPedia + mga libro) gamit ang unsupervised training (pagpapredikta ng mga nakatagong salita sa isang pangungusap). Sa panahon ng pre-training, ang modelo ay nakakapulot ng mataas na antas ng pag-unawa sa wika na maaaring magamit sa iba pang mga dataset sa pamamagitan ng fine tuning. Ang prosesong ito ay tinatawag na **transfer learning**.\n", "\n", - "![larawan mula sa http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/tl/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![larawan mula sa http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/tl/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "Maraming mga bersyon ng Transformer architectures kabilang ang BERT, DistilBERT, BigBird, OpenGPT3, at iba pa na maaaring i-fine tune.\n", "\n", diff --git a/translations/tl/lessons/5-NLP/19-NER/README.md b/translations/tl/lessons/5-NLP/19-NER/README.md index 28485802..2f8f4f6c 100644 --- a/translations/tl/lessons/5-NLP/19-NER/README.md +++ b/translations/tl/lessons/5-NLP/19-NER/README.md @@ -58,7 +58,7 @@ infant | O Dahil kailangan nating bumuo ng one-to-one na kaugnayan sa pagitan ng mga token at klase, maaari tayong mag-train ng tamang **many-to-many** neural network model mula sa larawang ito: -![Image showing common recurrent neural network patterns.](../../../../../translated_images/tl/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Image showing common recurrent neural network patterns.](../../../../../translated_images/tl/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > *Larawan mula sa [blog post na ito](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) ni [Andrej Karpathy](http://karpathy.github.io/). Ang mga NER token classification models ay tumutugma sa pinakakanan na network architecture sa larawang ito.* diff --git a/translations/tl/lessons/5-NLP/README.md b/translations/tl/lessons/5-NLP/README.md index 891d4007..420154f6 100644 --- a/translations/tl/lessons/5-NLP/README.md +++ b/translations/tl/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Natural Language Processing -![Buod ng mga gawain sa NLP sa isang doodle](../../../../translated_images/tl/ai-nlp.b22dcb8ca4707cea.png) +![Buod ng mga gawain sa NLP sa isang doodle](../../../../translated_images/tl/ai-nlp.b22dcb8ca4707cea.webp) Sa seksyong ito, magtutuon tayo sa paggamit ng Neural Networks upang harapin ang mga gawain na may kaugnayan sa **Natural Language Processing (NLP)**. Maraming mga problema sa NLP na nais nating masolusyunan ng mga computer: diff --git a/translations/tl/lessons/6-Other/23-MultiagentSystems/README.md b/translations/tl/lessons/6-Other/23-MultiagentSystems/README.md index fc98eeac..a239669a 100644 --- a/translations/tl/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/tl/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ Maaari mong buksan ang isa sa mga modelo, halimbawa **Biology → Flock Pagkatapos buksan ang modelo, dadalhin ka sa pangunahing screen ng NetLogo. Narito ang isang sample na modelo na naglalarawan sa populasyon ng mga lobo at tupa, na may limitadong resources (damo). -![NetLogo Main Screen](../../../../../translated_images/tl/NetLogo-Main.32653711ec1a01b3.png) +![NetLogo Main Screen](../../../../../translated_images/tl/NetLogo-Main.32653711ec1a01b3.webp) > Screenshot ni Dmitry Soshnikov diff --git a/translations/tl/lessons/README.md b/translations/tl/lessons/README.md index d1d83c0d..3918e50d 100644 --- a/translations/tl/lessons/README.md +++ b/translations/tl/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Pangkalahatang-ideya -![Pangkalahatang-ideya sa isang doodle](../../../translated_images/tl/ai-overview.0857791951d19500.png) +![Pangkalahatang-ideya sa isang doodle](../../../translated_images/tl/ai-overview.0857791951d19500.webp) > Sketchnote ni [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/tl/lessons/X-Extras/X1-MultiModal/README.md b/translations/tl/lessons/X-Extras/X1-MultiModal/README.md index 4529b6b5..503e432f 100644 --- a/translations/tl/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/tl/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ Matapos ang tagumpay ng mga transformer model sa paglutas ng mga gawain sa NLP, Ang pangunahing ideya ng CLIP ay ang kakayahang ihambing ang mga text prompt sa isang imahe at tukuyin kung gaano kahusay na tumutugma ang imahe sa prompt. -![CLIP Architecture](../../../../../translated_images/tl/clip-arch.b3dbf20b4e8ed8be.png) +![CLIP Architecture](../../../../../translated_images/tl/clip-arch.b3dbf20b4e8ed8be.webp) > *Larawan mula sa [blog post na ito](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ Kapag ang modelong ito ay na-pretrain na, maaari nating bigyan ito ng batch ng m Halimbawa, kailangan nating i-classify ang mga imahe sa pagitan ng, sabihin nating, pusa, aso, at tao. Sa kasong ito, maaari nating bigyan ang modelo ng isang imahe, at isang serye ng mga text prompt: "*isang larawan ng pusa*", "*isang larawan ng aso*", "*isang larawan ng tao*". Sa resultang vector ng 3 probabilidad, pipiliin lang natin ang index na may pinakamataas na halaga. -![CLIP for Image Classification](../../../../../translated_images/tl/clip-class.3af42ef0b2b19369.png) +![CLIP for Image Classification](../../../../../translated_images/tl/clip-class.3af42ef0b2b19369.webp) > *Larawan mula sa [blog post na ito](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ Alamin ang higit pa tungkol sa VQGAN sa [Taming Transformers](https://compvis.gi Isa sa mga mahalagang pagkakaiba ng VQGAN sa tradisyunal na GAN ay ang huli ay kayang gumawa ng disenteng imahe mula sa anumang input vector, habang ang VQGAN ay malamang na makagawa ng imahe na hindi coherent. Kaya, kailangan nating gabayan pa ang proseso ng paggawa ng imahe, at magagawa ito gamit ang CLIP. -![VQGAN+CLIP Architecture](../../../../../translated_images/tl/vqgan.5027fe05051dfa31.png) +![VQGAN+CLIP Architecture](../../../../../translated_images/tl/vqgan.5027fe05051dfa31.webp) Upang makabuo ng isang imahe na tumutugma sa isang text prompt, nagsisimula tayo sa isang random encoding vector na ipinapasa sa VQGAN upang makabuo ng isang imahe. Pagkatapos, ginagamit ang CLIP upang makabuo ng isang loss function na nagpapakita kung gaano kahusay na tumutugma ang imahe sa text prompt. Ang layunin ay i-minimize ang loss na ito, gamit ang back propagation upang ayusin ang mga parameter ng input vector. Isang mahusay na library na nagpapatupad ng VQGAN+CLIP ay ang [Pixray](http://github.com/pixray/pixray). -![Larawang ginawa ng Pixray](../../../../../translated_images/tl/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Larawang ginawa ng Pixray](../../../../../translated_images/tl/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Larawang ginawa ng Pixray](../../../../../translated_images/tl/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) +![Larawang ginawa ng Pixray](../../../../../translated_images/tl/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp) | ![Larawang ginawa ng Pixray](../../../../../translated_images/tl/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp) | ![Larawang ginawa ng Pixray](../../../../../translated_images/tl/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp) ----|----|---- Larawang ginawa mula sa prompt *isang closeup watercolor portrait ng batang lalaking guro ng panitikan na may hawak na libro* | Larawang ginawa mula sa prompt *isang closeup oil portrait ng batang babaeng guro ng computer science na may hawak na computer* | Larawang ginawa mula sa prompt *isang closeup oil portrait ng matandang lalaking guro ng matematika sa harap ng blackboard* diff --git a/translations/tr/README.md b/translations/tr/README.md index 77946de3..2fdb8171 100644 --- a/translations/tr/README.md +++ b/translations/tr/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # Yeni Başlayanlar İçin Yapay Zeka - Bir Müfredat -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/tr/ai-overview.0857791951d19500.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/tr/ai-overview.0857791951d19500.webp)| |:---:| | Yeni Başlayanlar İçin Yapay Zeka - _[@girlie_mac](https://twitter.com/girlie_mac) tarafından Sketchnote_ | diff --git a/translations/tr/lessons/1-Intro/README.md b/translations/tr/lessons/1-Intro/README.md index f1ae656f..c4e28118 100644 --- a/translations/tr/lessons/1-Intro/README.md +++ b/translations/tr/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Yapay Zekaya Giriş -![Yapay Zeka içeriğinin özetini gösteren bir çizim](../../../../translated_images/tr/ai-intro.bf28d1ac4235881c.png) +![Yapay Zeka içeriğinin özetini gösteren bir çizim](../../../../translated_images/tr/ai-intro.bf28d1ac4235881c.webp) > Çizim: [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Başlangıçta, bilgisayarlar [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage) tarafından iyi tanımlanmış bir prosedürü - bir algoritmayı - takip ederek sayılar üzerinde işlem yapmak için icat edilmiştir. Modern bilgisayarlar, 19. yüzyılda önerilen orijinal modelden çok daha gelişmiş olmasına rağmen, hala kontrollü hesaplama fikrini takip etmektedir. Bu nedenle, bir hedefe ulaşmak için gereken adımların tam sırasını bildiğimiz sürece bir bilgisayarı bir şey yapması için programlamak mümkündür. -![Bir kişinin fotoğrafı](../../../../translated_images/tr/dsh_age.d212a30d4e54fb5f.png) +![Bir kişinin fotoğrafı](../../../../translated_images/tr/dsh_age.d212a30d4e54fb5f.webp) > Fotoğraf: [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ Daha fazla bilgi için **[Yapay Genel Zeka](https://en.wikipedia.org/wiki/Artifi **[Zeka](https://en.wikipedia.org/wiki/Intelligence)** terimiyle uğraşırken karşılaşılan sorunlardan biri, bu terimin net bir tanımının olmamasıdır. Zekanın **soyut düşünce** veya **öz farkındalık** ile bağlantılı olduğunu iddia edebilirsiniz, ancak bunu doğru bir şekilde tanımlayamayız. -![Bir kedinin fotoğrafı](../../../../translated_images/tr/photo-cat.8c8e8fb760ffe457.jpg) +![Bir kedinin fotoğrafı](../../../../translated_images/tr/photo-cat.8c8e8fb760ffe457.webp) > [Fotoğraf](https://unsplash.com/photos/75715CVEJhI): [Amber Kipp](https://unsplash.com/@sadmax) tarafından Unsplash'tan alınmıştır. @@ -98,13 +98,13 @@ Alternatif olarak, beynimizdeki en basit öğeleri – bir nöronu – modelleme > | Peki ya ML? | | > |--------------|-----------| -> | Bazı verilere dayanarak bir problemi çözmeyi öğrenen bilgisayar temelli yapay zeka kısmına **Makine Öğrenimi** denir. Bu kursta klasik makine öğrenimini ele almayacağız - sizi ayrı bir [Makine Öğrenimi için Başlangıç](http://aka.ms/ml-beginners) müfredatına yönlendiriyoruz. | ![Başlangıç için ML](../../../../translated_images/tr/ml-for-beginners.9e4fed176fd5817d.png) | +> | Bazı verilere dayanarak bir problemi çözmeyi öğrenen bilgisayar temelli yapay zeka kısmına **Makine Öğrenimi** denir. Bu kursta klasik makine öğrenimini ele almayacağız - sizi ayrı bir [Makine Öğrenimi için Başlangıç](http://aka.ms/ml-beginners) müfredatına yönlendiriyoruz. | ![Başlangıç için ML](../../../../translated_images/tr/ml-for-beginners.9e4fed176fd5817d.webp) | ## Yapay Zekanın Kısa Tarihi Yapay Zeka, yirminci yüzyılın ortalarında bir alan olarak başladı. Başlangıçta, sembolik akıl yürütme yaygın bir yaklaşımdı ve uzman sistemler gibi bazı sınırlı problem alanlarında uzman gibi davranabilen bilgisayar programları gibi önemli başarılara yol açtı. Ancak, bu yaklaşımın iyi ölçeklenmediği kısa sürede anlaşıldı. Bir uzmandan bilgi çıkarmak, bunu bir bilgisayarda temsil etmek ve bilgi tabanını doğru tutmak çok karmaşık bir görev ve birçok durumda pratik olmaktan çok pahalı olduğu ortaya çıktı. Bu, 1970'lerde [AI Kışı](https://en.wikipedia.org/wiki/AI_winter) olarak adlandırılan döneme yol açtı. -Yapay Zekanın Kısa Tarihi +Yapay Zekanın Kısa Tarihi > Görsel: [Dmitry Soshnikov](http://soshnikov.com) @@ -124,7 +124,7 @@ Benzer şekilde, "konuşan programlar" (Turing testini geçebilecek türden) olu * Cortana, Siri veya Google Asistan gibi modern asistanlar, konuşmayı metne dönüştürmek ve niyetimizi tanımak için Sinir ağlarını kullanan ve ardından gerekli eylemleri gerçekleştirmek için bazı akıl yürütme veya açık algoritmalar kullanan hibrit sistemlerdir. * Gelecekte, diyaloğu tamamen kendi başına yönetebilecek tam bir sinir temelli model bekleyebiliriz. Son zamanlardaki GPT ve [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) sinir ağı ailesi bu konuda büyük başarılar göstermektedir. -Turing testinin evrimi +Turing testinin evrimi > Görsel Dmitry Soshnikov tarafından, [fotoğraf](https://unsplash.com/photos/r8LmVbUKgns) [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto) tarafından, Unsplash ## Son Dönem AI Araştırmaları diff --git a/translations/tr/lessons/2-Symbolic/Animals.ipynb b/translations/tr/lessons/2-Symbolic/Animals.ipynb index ae609641..09836686 100644 --- a/translations/tr/lessons/2-Symbolic/Animals.ipynb +++ b/translations/tr/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "Bu örnekte, bazı fiziksel özelliklere dayanarak bir hayvanı belirlemek için basit bir bilgi tabanlı sistem uygulayacağız. Sistem aşağıdaki AND-OR ağacı ile temsil edilebilir (bu, ağacın bir kısmıdır, kolayca daha fazla kural ekleyebiliriz):\n", "\n", - "![](../../../../translated_images/tr/AND-OR-Tree.5592d2c70187f283.png)\n" + "![](../../../../translated_images/tr/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { diff --git a/translations/tr/lessons/2-Symbolic/README.md b/translations/tr/lessons/2-Symbolic/README.md index 8b345f40..5eebea40 100644 --- a/translations/tr/lessons/2-Symbolic/README.md +++ b/translations/tr/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Bilgi Temsili ve Uzman Sistemler -![Sembolik AI içeriği özeti](../../../../translated_images/tr/ai-symbolic.715a30cb610411a6.png) +![Sembolik AI içeriği özeti](../../../../translated_images/tr/ai-symbolic.715a30cb610411a6.webp) > Sketchnote: [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ Sembolik AI'deki önemli kavramlardan biri **bilgi**dir. Bilgiyi *bilgi* veya *v Bu nedenle, **bilgi temsili** problemi, bilgiyi bir bilgisayar içinde veri şeklinde etkili bir şekilde temsil etmenin bir yolunu bulmaktır, böylece otomatik olarak kullanılabilir hale gelir. Bu bir spektrum olarak görülebilir: -![Bilgi temsili spektrumu](../../../../translated_images/tr/knowledge-spectrum.b60df631852c0217.png) +![Bilgi temsili spektrumu](../../../../translated_images/tr/knowledge-spectrum.b60df631852c0217.webp) > Resim: [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Blok Sözdizimi | Girinti | | | Sembolik AI'nın erken başarılarından biri, **uzman sistemler** olarak adlandırılan sistemlerdi - belirli bir problem alanında uzman gibi davranmak üzere tasarlanmış bilgisayar sistemleri. Bu sistemler, bir veya daha fazla insan uzmandan çıkarılan bir **bilgi tabanı**na dayanıyordu ve bunun üzerinde akıl yürütme yapan bir **çıkarım motoru** içeriyordu. -![İnsan Mimarisi](../../../../translated_images/tr/arch-human.5d4d35f1bba3ab1c.png) | ![Bilgi Tabanlı Sistem](../../../../translated_images/tr/arch-kbs.3ec5c150b09fa8da.png) +![İnsan Mimarisi](../../../../translated_images/tr/arch-human.5d4d35f1bba3ab1c.webp) | ![Bilgi Tabanlı Sistem](../../../../translated_images/tr/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ İnsan sinir sisteminin basitleştirilmiş yapısı | Bilgi tabanlı sistemin mimarisi @@ -106,7 +106,7 @@ Uzman sistemler, insan akıl yürütme sistemine benzer şekilde inşa edilir, b Örneğin, fiziksel özelliklere dayanarak bir hayvanı belirleyen bir uzman sistemini ele alalım: -![AND-OR Ağacı](../../../../translated_images/tr/AND-OR-Tree.5592d2c70187f283.png) +![AND-OR Ağacı](../../../../translated_images/tr/AND-OR-Tree.5592d2c70187f283.webp) > Resim: [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/tr/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/tr/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index cc205647..c8ac412c 100644 --- a/translations/tr/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/tr/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -1255,7 +1255,7 @@ "* Düşük eğitim kaybı - model, yeterli ifade gücüne sahip olduğu için eğitim verisini iyi bir şekilde tahmin edebilir.\n", "* Doğrulama kaybı, eğitim kaybından çok daha yüksek olabilir ve eğitim sırasında artmaya başlayabilir - bunun nedeni modelin eğitim noktalarını \"ezberlemesi\" ve \"genel resmi\" kaybetmesidir.\n", "\n", - "![Aşırı Öğrenme](../../../../../translated_images/tr/overfit.a0bd57f717c15769.png)\n", + "![Aşırı Öğrenme](../../../../../translated_images/tr/overfit.a0bd57f717c15769.webp)\n", "\n", "> Bu resimde, `x` eğitim verisini, `o` ise doğrulama verisini temsil eder. Sol tarafta - doğrusal model (tek katmanlı), verinin doğasını oldukça iyi tahmin eder. Sağ tarafta - aşırı öğrenmiş model, eğitim verisini mükemmel bir şekilde tahmin eder, ancak diğer herhangi bir veriyle anlam ifade etmeyi bırakır (doğrulama hatası çok yüksektir).\n" ] diff --git a/translations/tr/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/tr/lessons/3-NeuralNetworks/05-Frameworks/README.md index 49919946..5a55f7e8 100644 --- a/translations/tr/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/tr/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Aşırı öğrenme, makine öğreniminde son derece önemli bir kavramdır ve do Aşağıdaki 5 noktayı (grafiklerde `x` ile gösterilen) yaklaşık olarak tahmin etme problemini düşünün: -![linear](../../../../../translated_images/tr/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/tr/overfit2.131f5800ae10ca5e.jpg) +![linear](../../../../../translated_images/tr/overfit1.f24b71c6f652e59e.webp) | ![overfit](../../../../../translated_images/tr/overfit2.131f5800ae10ca5e.webp) -------------------------|-------------------------- **Doğrusal model, 2 parametre** | **Doğrusal olmayan model, 7 parametre** Eğitim hatası = 5.3 | Eğitim hatası = 0 @@ -79,7 +79,7 @@ Modelin zenginliği (parametre sayısı) ile eğitim örneklerinin sayısı aras Yukarıdaki grafikten görebileceğiniz gibi, aşırı öğrenme çok düşük bir eğitim hatası ve yüksek bir doğrulama hatası ile tespit edilebilir. Normalde eğitim sırasında hem eğitim hem de doğrulama hatalarının azalmaya başladığını görürüz, ancak bir noktada doğrulama hatası azalmayı durdurabilir ve artmaya başlayabilir. Bu, aşırı öğrenmenin bir işareti ve eğitimi muhtemelen bu noktada durdurmamız gerektiğinin (veya en azından modelin bir anlık görüntüsünü almamız gerektiğinin) göstergesidir. -![overfitting](../../../../../translated_images/tr/Overfitting.408ad91cd90b4371.png) +![overfitting](../../../../../translated_images/tr/Overfitting.408ad91cd90b4371.webp) ## Aşırı Öğrenme Nasıl Önlenir? diff --git a/translations/tr/lessons/3-NeuralNetworks/README.md b/translations/tr/lessons/3-NeuralNetworks/README.md index ab29ae66..af243e60 100644 --- a/translations/tr/lessons/3-NeuralNetworks/README.md +++ b/translations/tr/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Sinir Ağlarına Giriş -![Sinir Ağlarına Giriş içeriğinin özetini gösteren bir çizim](../../../../translated_images/tr/ai-neuralnetworks.1c687ae40bc86e83.png) +![Sinir Ağlarına Giriş içeriğinin özetini gösteren bir çizim](../../../../translated_images/tr/ai-neuralnetworks.1c687ae40bc86e83.webp) Giriş bölümünde tartıştığımız gibi, zekaya ulaşmanın yollarından biri bir **bilgisayar modeli** veya bir **yapay beyin** eğitmekten geçer. 20. yüzyılın ortalarından itibaren araştırmacılar farklı matematiksel modeller denediler ve son yıllarda bu yaklaşım büyük ölçüde başarılı oldu. Beynin bu tür matematiksel modellerine **sinir ağları** denir. @@ -36,13 +36,13 @@ Bu müfredatta yalnızca sinir ağı modellerine odaklanacağız. Biyolojiden biliyoruz ki beynimiz, her biri birden fazla "girişe" (dendritler) ve tek bir "çıkışa" (akson) sahip olan sinir hücrelerinden (nöronlar) oluşur. Hem dendritler hem de aksonlar elektrik sinyalleri iletebilir ve aralarındaki bağlantılar — sinapslar olarak bilinir — iletkenlik derecelerini değiştirebilir. Bu iletkenlik, nörotransmitterler tarafından düzenlenir. -![Bir Nöron Modeli](../../../../translated_images/tr/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![Bir Nöron Modeli](../../../../translated_images/tr/artneuron.1a5daa88d20ebe6f.png) +![Bir Nöron Modeli](../../../../translated_images/tr/synapse-wikipedia.ed20a9e4726ea1c6.webp) | ![Bir Nöron Modeli](../../../../translated_images/tr/artneuron.1a5daa88d20ebe6f.webp) ----|---- Gerçek Nöron *([Resim](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) Wikipedia'dan)* | Yapay Nöron *(Yazarın Görseli)* Dolayısıyla, bir nöronun en basit matematiksel modeli birkaç giriş X1, ..., XN ve bir çıkış Y ile bir dizi ağırlık W1, ..., WN içerir. Çıkış şu şekilde hesaplanır: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) burada f, bazı doğrusal olmayan **aktivasyon fonksiyonudur**. diff --git a/translations/tr/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/tr/lessons/4-ComputerVision/06-IntroCV/README.md index 2c12f497..cb0982b4 100644 --- a/translations/tr/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/tr/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ Bir görüntüyü sinir ağına beslemeden önce, birkaç ön işleme adımı uy * **Braille kitabının bir fotoğrafını ön işleme**. Eşikleme, özellik tespiti, perspektif dönüşümü ve NumPy manipülasyonlarını kullanarak bireysel Braille sembollerini bir sinir ağı tarafından daha fazla sınıflandırma için ayırmaya odaklanıyoruz. -![Braille Görüntüsü](../../../../../translated_images/tr/braille.341962ff76b1bd70.jpeg) | ![Braille Görüntüsü Ön İşlenmiş](../../../../../translated_images/tr/braille-result.46530fea020b03c7.png) | ![Braille Sembolleri](../../../../../translated_images/tr/braille-symbols.0159185ab69d5339.png) +![Braille Görüntüsü](../../../../../translated_images/tr/braille.341962ff76b1bd70.webp) | ![Braille Görüntüsü Ön İşlenmiş](../../../../../translated_images/tr/braille-result.46530fea020b03c7.webp) | ![Braille Sembolleri](../../../../../translated_images/tr/braille-symbols.0159185ab69d5339.webp) ----|-----|----- > Görüntü [OpenCV.ipynb](OpenCV.ipynb) dosyasından alınmıştır. * **Video içinde hareketi kare farkı kullanarak tespit etme**. Kamera sabit ise, kamera akışından gelen kareler birbirine oldukça benzer olmalıdır. Kareler diziler olarak temsil edildiğinden, iki ardışık kare için bu dizileri çıkararak piksel farkını elde edebiliriz; bu fark statik kareler için düşük olmalı ve görüntüde önemli bir hareket olduğunda artmalıdır. -![Video kareleri ve kare farkları görüntüsü](../../../../../translated_images/tr/frame-difference.706f805491a0883c.png) +![Video kareleri ve kare farkları görüntüsü](../../../../../translated_images/tr/frame-difference.706f805491a0883c.webp) > Görüntü [OpenCV.ipynb](OpenCV.ipynb) dosyasından alınmıştır. @@ -89,7 +89,7 @@ Bir görüntüyü sinir ağına beslemeden önce, birkaç ön işleme adımı uy - **Yoğun Optik Akış**, her pikselin nereye hareket ettiğini gösteren vektör alanını hesaplar. - **Seyrek Optik Akış**, görüntüdeki bazı belirgin özellikleri (örneğin kenarları) alır ve bunların kareden kareye olan hareket yolunu oluşturur. -![Optik Akış Görüntüsü](../../../../../translated_images/tr/optical.1f4a94464579a83a.png) +![Optik Akış Görüntüsü](../../../../../translated_images/tr/optical.1f4a94464579a83a.webp) > Görüntü [OpenCV.ipynb](OpenCV.ipynb) dosyasından alınmıştır. diff --git a/translations/tr/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/tr/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 19ab2fe0..5c58b41d 100644 --- a/translations/tr/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/tr/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16, 2014 yılında ImageNet top-5 sınıflandırmasında %92.7 doğruluk elde eden bir ağdır. Katman yapısı şu şekildedir: -![ImageNet Katmanları](../../../../../translated_images/tr/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet Katmanları](../../../../../translated_images/tr/vgg-16-arch1.d901a5583b3a51ba.webp) Gördüğünüz gibi, VGG geleneksel bir piramit mimarisini takip eder; bu, bir dizi evrişim-havuzlama katmanıdır. -![ImageNet Piramidi](../../../../../translated_images/tr/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet Piramidi](../../../../../translated_images/tr/vgg-16-arch.64ff2137f50dd49f.webp) > Görsel [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) kaynağından alınmıştır. diff --git a/translations/tr/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/tr/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index 2e32b4ac..c074dd44 100644 --- a/translations/tr/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/tr/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -262,7 +262,7 @@ "\n", "Bu nedenle, tipik bir CNN'de birkaç evrişim katmanı bulunur ve bunların arasında görüntünün boyutlarını azaltmak için havuzlama katmanları yer alır. Ayrıca filtre sayısını artırırız, çünkü desenler daha karmaşık hale geldikçe - dikkate alınması gereken daha fazla ilginç kombinasyon ortaya çıkar.\n", "\n", - "![Havuzlama katmanları ile birkaç evrişim katmanını gösteren bir görüntü.](../../../../../translated_images/tr/cnn-pyramid.85915455759ef0ce.png)\n", + "![Havuzlama katmanları ile birkaç evrişim katmanını gösteren bir görüntü.](../../../../../translated_images/tr/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "Mekansal boyutların azalması ve özellik/filtre boyutlarının artması nedeniyle, bu mimariye aynı zamanda **piramit mimarisi** denir.\n" ] diff --git a/translations/tr/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/tr/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index 5b7a6c7d..a24906c4 100644 --- a/translations/tr/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/tr/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -361,7 +361,7 @@ "\n", "Bu nedenle, tipik bir CNN'de birkaç evrişim katmanı bulunur ve bunların arasında görüntünün boyutlarını küçültmek için havuzlama katmanları yer alır. Ayrıca, desenler daha karmaşık hale geldikçe, aramamız gereken daha fazla ilginç kombinasyon olduğu için filtre sayısını artırırız.\n", "\n", - "![Birden fazla evrişim katmanını ve havuzlama katmanlarını gösteren bir görüntü.](../../../../../translated_images/tr/cnn-pyramid.85915455759ef0ce.png)\n", + "![Birden fazla evrişim katmanını ve havuzlama katmanlarını gösteren bir görüntü.](../../../../../translated_images/tr/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "Mekansal boyutların azalması ve özellik/filtre boyutlarının artması nedeniyle, bu mimariye **piramit mimarisi** de denir.\n" ] diff --git a/translations/tr/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/tr/lessons/4-ComputerVision/07-ConvNets/README.md index 9ecd04e1..11c7fbbf 100644 --- a/translations/tr/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/tr/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ Gerçek hayatta, bir görüntüdeki nesneleri tam olarak nerede olduklarına bak Desenleri çıkarmak için **evrişimsel filtreler** kavramını kullanacağız. Bildiğiniz gibi, bir görüntü 2D bir matris veya renk derinliği olan bir 3D tensör olarak temsil edilir. Bir filtre uygulamak, nispeten küçük bir **filtre çekirdeği** matrisini alıp, orijinal görüntüdeki her bir piksel için komşu noktalarla ağırlıklı ortalamayı hesaplamak anlamına gelir. Bunu, filtre çekirdeği matrisindeki ağırlıklara göre tüm pikselleri ortalayan küçük bir pencerenin tüm görüntü üzerinde kayması gibi düşünebiliriz. -![Dikey Kenar Filtresi](../../../../../translated_images/tr/filter-vert.b7148390ca0bc356.png) | ![Yatay Kenar Filtresi](../../../../../translated_images/tr/filter-horiz.59b80ed4feb946ef.png) +![Dikey Kenar Filtresi](../../../../../translated_images/tr/filter-vert.b7148390ca0bc356.webp) | ![Yatay Kenar Filtresi](../../../../../translated_images/tr/filter-horiz.59b80ed4feb946ef.webp) ----|---- > Görsel: Dmitry Soshnikov @@ -38,7 +38,7 @@ CNN'lerin çalışma şekli şu önemli fikirlere dayanır: * Ağı, filtrelerin otomatik olarak eğitileceği şekilde tasarlayabiliriz. * Aynı yaklaşımı yalnızca orijinal görüntüde değil, yüksek seviyeli özelliklerdeki desenleri bulmak için de kullanabiliriz. Böylece, CNN özellik çıkarımı, düşük seviyeli piksel kombinasyonlarından başlayarak, görüntü parçalarının daha yüksek seviyeli kombinasyonlarına kadar bir özellik hiyerarşisi üzerinde çalışır. -![Hiyerarşik Özellik Çıkarımı](../../../../../translated_images/tr/FeatureExtractionCNN.d9b456cbdae7cb64.png) +![Hiyerarşik Özellik Çıkarımı](../../../../../translated_images/tr/FeatureExtractionCNN.d9b456cbdae7cb64.webp) > Görsel: [Hislop-Lynch'in bir makalesinden](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), [araştırmalarına dayanarak](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ Görüntü işleme için kullanılan çoğu CNN, sözde piramit mimarisini takip Örneğin, 2014 yılında ImageNet'in ilk 5 sınıflandırmasında %92.7 doğruluk elde eden VGG-16 ağının mimarisine bakalım: -![ImageNet Katmanları](../../../../../translated_images/tr/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet Katmanları](../../../../../translated_images/tr/vgg-16-arch1.d901a5583b3a51ba.webp) -![ImageNet Piramidi](../../../../../translated_images/tr/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet Piramidi](../../../../../translated_images/tr/vgg-16-arch.64ff2137f50dd49f.webp) > Görsel: [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/tr/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/tr/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 35f8409e..10d0ace8 100644 --- a/translations/tr/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/tr/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ Bir evcil hayvan yuvası için tüm evcil hayvanları kataloglamak amacıyla bir [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/) veri setini kullanacağız. Bu veri seti, 37 farklı köpek ve kedi cinsine ait görüntüler içerir. -![Çalışacağımız veri seti](../../../../../../translated_images/tr/data.50b2a9d5484bdbf0.png) +![Çalışacağımız veri seti](../../../../../../translated_images/tr/data.50b2a9d5484bdbf0.webp) Veri setini indirmek için şu kod parçacığını kullanın: diff --git a/translations/tr/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/tr/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index ce0fdc0b..cd854c9a 100644 --- a/translations/tr/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/tr/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "İdeal kediyi görselleştirmek için rastgele bir gürültü görüntüsüyle başlayacağız ve bir ağın kediyi tanımasını sağlamak için görüntüyü ayarlamak amacıyla gradyan iniş optimizasyon tekniğini kullanmaya çalışacağız.\n", "\n", - "![Optimizasyon Döngüsü](../../../../../translated_images/tr/ideal-cat-loop.999fbb8ff306e044.png)\n", + "![Optimizasyon Döngüsü](../../../../../translated_images/tr/ideal-cat-loop.999fbb8ff306e044.webp)\n", "\n", "İşte başlangıç görüntümüz:\n" ] diff --git a/translations/tr/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/tr/lessons/4-ComputerVision/08-TransferLearning/README.md index cb0a7176..6ed0428a 100644 --- a/translations/tr/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/tr/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Hem Keras hem de PyTorch, yaygın mimariler için önceden eğitilmiş sinir ağ İşte VGG-16 ağı tarafından bir kedi resminden çıkarılan örnek özellikler: -![VGG-16 tarafından çıkarılan özellikler](../../../../../translated_images/tr/features.6291f9c7ba3a0b95.png) +![VGG-16 tarafından çıkarılan özellikler](../../../../../translated_images/tr/features.6291f9c7ba3a0b95.webp) ## Kediler ve Köpekler Veri Kümesi @@ -48,19 +48,19 @@ Transfer öğrenimini ilgili not defterlerinde nasıl çalıştığını göreli Alabileceğimiz bir yaklaşım, rastgele bir görüntüyle başlamak ve ardından **gradyan iniş optimizasyonu** tekniğini kullanarak bu görüntüyü ağın bir kedi olduğunu düşünmesini sağlayacak şekilde ayarlamaktır. -![Görüntü Optimizasyon Döngüsü](../../../../../translated_images/tr/ideal-cat-loop.999fbb8ff306e044.png) +![Görüntü Optimizasyon Döngüsü](../../../../../translated_images/tr/ideal-cat-loop.999fbb8ff306e044.webp) Ancak bunu yaparsak, rastgele bir gürültüye çok benzeyen bir şey elde ederiz. Bunun nedeni, *ağın giriş görüntüsünü bir kedi olarak düşünmesini sağlamanın birçok yolu olmasıdır*, bunların bazıları görsel olarak mantıklı değildir. Bu görüntüler kediye özgü birçok desen içerirken, görsel olarak ayırt edici olmalarını sağlayacak bir kısıtlama yoktur. Sonucu iyileştirmek için kayıp fonksiyonuna **varyasyon kaybı** adı verilen başka bir terim ekleyebiliriz. Bu, görüntünün komşu piksellerinin ne kadar benzer olduğunu gösteren bir metriktir. Varyasyon kaybını minimize etmek, görüntüyü daha düzgün hale getirir ve gürültüyü ortadan kaldırır - böylece daha görsel olarak çekici desenler ortaya çıkar. İşte yüksek olasılıkla kedi ve zebra olarak sınıflandırılan bu "ideal" görüntülere bir örnek: -![İdeal Kedi](../../../../../translated_images/tr/ideal-cat.203dd4597643d6b0.png) | ![İdeal Zebra](../../../../../translated_images/tr/ideal-zebra.7f70e8b54ee15a7a.png) +![İdeal Kedi](../../../../../translated_images/tr/ideal-cat.203dd4597643d6b0.webp) | ![İdeal Zebra](../../../../../translated_images/tr/ideal-zebra.7f70e8b54ee15a7a.webp) -----|----- *İdeal Kedi* | *İdeal Zebra* Benzer bir yaklaşım, sinir ağına karşı **adversaryal saldırılar** gerçekleştirmek için kullanılabilir. Diyelim ki bir sinir ağını kandırmak ve bir köpeği kedi gibi göstermek istiyoruz. Eğer ağ tarafından köpek olarak tanınan bir köpek görüntüsü alırsak, bunu biraz ayarlayarak gradyan iniş optimizasyonu kullanabiliriz, ta ki ağ bunu kedi olarak sınıflandırana kadar: -![Köpek Resmi](../../../../../translated_images/tr/original-dog.8f68a67d2fe0911f.png) | ![Kedi olarak sınıflandırılan köpek resmi](../../../../../translated_images/tr/adversarial-dog.d9fc7773b0142b89.png) +![Köpek Resmi](../../../../../translated_images/tr/original-dog.8f68a67d2fe0911f.webp) | ![Kedi olarak sınıflandırılan köpek resmi](../../../../../translated_images/tr/adversarial-dog.d9fc7773b0142b89.webp) -----|----- *Orijinal köpek resmi* | *Kedi olarak sınıflandırılan köpek resmi* diff --git a/translations/tr/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/tr/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index b64d6ad9..2c82f3fc 100644 --- a/translations/tr/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/tr/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "Otoenkoderi, orijinal görüntüden mümkün olduğunca fazla bilgiyi doğru bir şekilde yeniden oluşturmak için yakalamaya çalışacak şekilde eğittiğimizden, ağ, giriş görüntülerinin anlamını yakalamak için en iyi **gömülü temsili (embedding)** bulmaya çalışır.\n", "\n", - "![AutoEncoder Şeması](../../../../../translated_images/tr/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![AutoEncoder Şeması](../../../../../translated_images/tr/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "> Görsel [Keras blogu](https://blog.keras.io/building-autoencoders-in-keras.html)'ndan alınmıştır.\n", "\n", diff --git a/translations/tr/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/tr/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index e4590d14..e119ca91 100644 --- a/translations/tr/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/tr/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "Otoenkoderi, orijinal görüntüden mümkün olduğunca fazla bilgi yakalamak ve doğru bir şekilde yeniden oluşturmak için eğittiğimizden, ağ giriş görüntülerinin anlamını yakalamak için en iyi **gömülü temsili** bulmaya çalışır.\n", "\n", - "![Otoenkoder Diyagramı](../../../../../translated_images/tr/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![Otoenkoder Diyagramı](../../../../../translated_images/tr/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "*[Keras blogundan](https://blog.keras.io/building-autoencoders-in-keras.html) alınmış görüntü*\n", "\n", diff --git a/translations/tr/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/tr/lessons/4-ComputerVision/09-Autoencoders/README.md index c186c663..6bab3cc1 100644 --- a/translations/tr/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/tr/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ Ancak, CNN özellik çıkarıcılarını eğitmek için ham (etiketlenmemiş) ve Otomatik kodlayıcıyı, orijinal görüntüden mümkün olduğunca fazla bilgi yakalamak ve doğru bir şekilde yeniden oluşturmak için eğittiğimizden, ağ en iyi **gömülü temsili** bulmaya çalışır. -![Otomatik Kodlayıcı Şeması](../../../../../translated_images/tr/autoencoder_schema.5e6fc9ad98a5eb61.jpg) +![Otomatik Kodlayıcı Şeması](../../../../../translated_images/tr/autoencoder_schema.5e6fc9ad98a5eb61.webp) > Görsel [Keras blogundan](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/tr/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/tr/lessons/4-ComputerVision/11-ObjectDetection/README.md index a3e937ee..d7f4a0ef 100644 --- a/translations/tr/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/tr/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ Bugüne kadar ele aldığımız görüntü sınıflandırma modelleri, bir gör ## [Ders Öncesi Test](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Nesne Tespiti](../../../../../translated_images/tr/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) +![Nesne Tespiti](../../../../../translated_images/tr/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp) > Görsel [YOLO v2 web sitesi](https://pjreddie.com/darknet/yolov2/) üzerinden alınmıştır. @@ -25,7 +25,7 @@ Bir resimde bir kediyi bulmak istediğimizi varsayalım, nesne tespiti için ço 2. Her bir karede görüntü sınıflandırma işlemi gerçekleştirin. 3. Yeterince yüksek aktivasyon veren kareler, ilgili nesneyi içeriyor olarak kabul edilebilir. -![Naif Nesne Tespiti](../../../../../translated_images/tr/naive-detection.e7f1ba220ccd08c6.png) +![Naif Nesne Tespiti](../../../../../translated_images/tr/naive-detection.e7f1ba220ccd08c6.webp) > *Görsel [Egzersiz Defteri](ObjectDetection-TF.ipynb) üzerinden alınmıştır.* @@ -42,7 +42,7 @@ Bu görev için aşağıdaki veri setleriyle karşılaşabilirsiniz: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 sınıf * [COCO](http://cocodataset.org/#home) - Bağlamdaki Yaygın Nesneler. 80 sınıf, sınır kutuları ve segmentasyon maskeleri -![COCO](../../../../../translated_images/tr/coco-examples.71bc60380fa6cceb.jpg) +![COCO](../../../../../translated_images/tr/coco-examples.71bc60380fa6cceb.webp) ## Nesne Tespiti Metrikleri @@ -50,7 +50,7 @@ Bu görev için aşağıdaki veri setleriyle karşılaşabilirsiniz: Görüntü sınıflandırma için algoritmanın ne kadar iyi performans gösterdiğini ölçmek kolaydır, ancak nesne tespiti için hem sınıfın doğruluğunu hem de tahmin edilen sınır kutusu konumunun hassasiyetini ölçmemiz gerekir. İkincisi için, **Kesişim Bölü Birleşim** (IoU) adı verilen bir ölçüm kullanırız, bu iki kutunun (veya iki rastgele alanın) ne kadar iyi örtüştüğünü ölçer. -![IoU](../../../../../translated_images/tr/iou_equation.9a4751d40fff4e11.png) +![IoU](../../../../../translated_images/tr/iou_equation.9a4751d40fff4e11.webp) > *[Bu harika IoU blog yazısından](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/) alınan Şekil 2.* @@ -98,11 +98,11 @@ Nesne tespiti algoritmaları iki geniş sınıfa ayrılır: [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf), ROI bölgelerinin hiyerarşik yapısını oluşturmak için [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) kullanır. Bu bölgeler daha sonra CNN özellik çıkarıcıları ve SVM sınıflandırıcıları aracılığıyla nesne sınıfını belirlemek ve *sınır kutusu* koordinatlarını belirlemek için doğrusal regresyon ile işlenir. [Resmi Makale](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/tr/rcnn1.cae407020dfb1d1f.png) +![RCNN](../../../../../translated_images/tr/rcnn1.cae407020dfb1d1f.webp) > *Görsel van de Sande ve ark. ICCV’11'dan alınmıştır.* -![RCNN-1](../../../../../translated_images/tr/rcnn2.2d9530bb83516484.png) +![RCNN-1](../../../../../translated_images/tr/rcnn2.2d9530bb83516484.webp) > *Görseller [bu blogdan](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e) alınmıştır.* @@ -110,7 +110,7 @@ Nesne tespiti algoritmaları iki geniş sınıfa ayrılır: Bu yaklaşım R-CNN'e benzer, ancak bölgeler konvolüsyon katmanları uygulandıktan sonra tanımlanır. -![FRCNN](../../../../../translated_images/tr/f-rcnn.3cda6d9bb4188875.png) +![FRCNN](../../../../../translated_images/tr/f-rcnn.3cda6d9bb4188875.webp) > Görsel [Resmi Makale](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 üzerinden alınmıştır. @@ -118,7 +118,7 @@ Bu yaklaşım R-CNN'e benzer, ancak bölgeler konvolüsyon katmanları uyguland Bu yaklaşımın ana fikri, ROI'leri tahmin etmek için sinir ağı kullanmaktır - *Bölge Öneri Ağı* olarak adlandırılır. [Makale](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/tr/faster-rcnn.8d46c099b87ef30a.png) +![FasterRCNN](../../../../../translated_images/tr/faster-rcnn.8d46c099b87ef30a.webp) > Görsel [Resmi Makale](https://arxiv.org/pdf/1506.01497.pdf) üzerinden alınmıştır. @@ -130,7 +130,7 @@ Bu algoritma, Daha Hızlı R-CNN'den bile daha hızlıdır. Ana fikir şu şekil 2. Özellikler **Pozisyon-Duyarlı Skor Haritası** tarafından işlenir. $C$ sınıflarından her bir nesne $k\times k$ bölgelere ayrılır ve nesne parçalarını tahmin etmek için eğitim yapılır. 3. $k\times k$ bölgelerden her bir parça için tüm ağlar nesne sınıfları için oy kullanır ve maksimum oyu alan nesne sınıfı seçilir. -![r-fcn image](../../../../../translated_images/tr/r-fcn.13eb88158b99a3da.png) +![r-fcn image](../../../../../translated_images/tr/r-fcn.13eb88158b99a3da.webp) > Görsel [Resmi Makale](https://arxiv.org/abs/1605.06409) üzerinden alınmıştır. @@ -141,7 +141,7 @@ YOLO, gerçek zamanlı tek geçişli bir algoritmadır. Ana fikir şu şekildedi * Görüntü $S\times S$ bölgelere ayrılır. * Her bölge için **CNN**, $n$ olası nesneleri, *sınır kutusu* koordinatlarını ve *güven* = *olasılık* * IoU tahmin eder. - ![YOLO](../../../../../translated_images/tr/yolo.a2648ec82ee8bb4e.png) + ![YOLO](../../../../../translated_images/tr/yolo.a2648ec82ee8bb4e.webp) > Görsel [Resmi Makale](https://arxiv.org/abs/1506.02640) üzerinden alınmıştır. diff --git a/translations/tr/lessons/4-ComputerVision/README.md b/translations/tr/lessons/4-ComputerVision/README.md index 03270ae7..c783514e 100644 --- a/translations/tr/lessons/4-ComputerVision/README.md +++ b/translations/tr/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Bilgisayarlı Görü -![Bilgisayarlı Görü içeriğinin bir çizim özeti](../../../../translated_images/tr/ai-computervision.6506ebebac3fbf76.png) +![Bilgisayarlı Görü içeriğinin bir çizim özeti](../../../../translated_images/tr/ai-computervision.6506ebebac3fbf76.webp) Bu bölümde şunları öğreneceğiz: diff --git a/translations/tr/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/tr/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index 4dd935a4..013f212a 100644 --- a/translations/tr/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/tr/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**Kelime Torbası** (BoW) vektör temsili, en yaygın kullanılan geleneksel vektör temsilidir. Her kelime bir vektör indeksine bağlanır ve vektör elemanı, bir belgedeki bir kelimenin kaç kez geçtiğini içerir.\n", "\n", - "![Kelime torbası vektör temsilinin bellekte nasıl temsil edildiğini gösteren bir görsel.](../../../../../translated_images/tr/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![Kelime torbası vektör temsilinin bellekte nasıl temsil edildiğini gösteren bir görsel.](../../../../../translated_images/tr/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Not**: BoW'yu, metindeki bireysel kelimeler için tekil olarak bir-hot kodlanmış vektörlerin toplamı olarak da düşünebilirsiniz.\n", "\n", diff --git a/translations/tr/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/tr/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index 6a6f3dc4..f78f7766 100644 --- a/translations/tr/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/tr/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**Bag-of-words** (BoW) vektör temsili, anlaması en basit geleneksel vektör temsilidir. Her kelime bir vektör indeksine bağlanır ve bir vektör elemanı, belirli bir belgede her kelimenin kaç kez geçtiğini içerir.\n", "\n", - "![Bag-of-words vektör temsilinin bellekte nasıl temsil edildiğini gösteren bir görsel.](../../../../../translated_images/tr/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![Bag-of-words vektör temsilinin bellekte nasıl temsil edildiğini gösteren bir görsel.](../../../../../translated_images/tr/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Not**: BoW'yu, metindeki bireysel kelimeler için tekil olarak birleştirilmiş tüm one-hot-encoded vektörlerin toplamı olarak da düşünebilirsiniz.\n", "\n", diff --git a/translations/tr/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/tr/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 957edf30..969c8e58 100644 --- a/translations/tr/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/tr/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "Ağımızda ilk katman olarak gömme katmanını kullanarak, kelime torbasından **gömme torbası** modeline geçebiliriz. Bu modelde, metnimizdeki her kelimeyi ilgili gömmeye dönüştürürüz ve ardından bu gömmeler üzerinde `sum`, `average` veya `max` gibi bir toplama fonksiyonu hesaplarız.\n", "\n", - "![Beş sıralı kelime için bir gömme sınıflandırıcıyı gösteren görsel.](../../../../../translated_images/tr/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![Beş sıralı kelime için bir gömme sınıflandırıcıyı gösteren görsel.](../../../../../translated_images/tr/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "Sınıflandırıcı sinir ağımız, gömme katmanı ile başlayacak, ardından toplama katmanı ve en üstte doğrusal bir sınıflandırıcı ile devam edecektir:\n" ] @@ -176,7 +176,7 @@ "\n", "Önceki mimaride, dizileri bir minibatch'e sığdırmak için hepsini aynı uzunlukta doldurmamız gerekiyordu. Bu, değişken uzunluklu dizileri temsil etmenin en verimli yolu değildir - başka bir yaklaşım, tüm dizilerin bir büyük vektördeki offsetlerini tutan bir **offset** vektörü kullanmak olabilir.\n", "\n", - "![Offset dizi temsilini gösteren bir görüntü](../../../../../translated_images/tr/offset-sequence-representation.eb73fcefb29b46ee.png)\n", + "![Offset dizi temsilini gösteren bir görüntü](../../../../../translated_images/tr/offset-sequence-representation.eb73fcefb29b46ee.webp)\n", "\n", "> **Not**: Yukarıdaki resimde karakter dizisi gösteriyoruz, ancak örneğimizde kelime dizileriyle çalışıyoruz. Ancak, dizileri offset vektörüyle temsil etme genel prensibi aynı kalır.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW daha hızlıdır, ancak atlama-gramı daha yavaş olmasına rağmen nadir kelimeleri temsil etmede daha iyi bir iş çıkarır.\n", "\n", - "![Kelimeyi vektörlere dönüştürmek için hem CBoW hem de Skip-Gram algoritmalarını gösteren bir görüntü.](../../../../../translated_images/tr/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![Kelimeyi vektörlere dönüştürmek için hem CBoW hem de Skip-Gram algoritmalarını gösteren bir görüntü.](../../../../../translated_images/tr/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Google News veri seti üzerinde önceden eğitilmiş word2vec gömüsünü denemek için **gensim** kütüphanesini kullanabiliriz. Aşağıda 'neural' kelimesine en benzer kelimeleri buluyoruz.\n", "\n", diff --git a/translations/tr/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/tr/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index 2b2b4559..5e165dda 100644 --- a/translations/tr/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/tr/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "Ağımızdaki ilk katman olarak bir gömme katmanı kullanarak, kelime torbasından **gömme torbası** modeline geçebiliriz. Bu modelde, önce metnimizdeki her kelimeyi karşılık gelen gömmeye dönüştürürüz ve ardından bu gömmelerin tümü üzerinde `sum`, `average` veya `max` gibi bir toplama fonksiyonu hesaplarız.\n", "\n", - "![Beş sıralı kelime için bir gömme sınıflandırıcısını gösteren görsel.](../../../../../translated_images/tr/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![Beş sıralı kelime için bir gömme sınıflandırıcısını gösteren görsel.](../../../../../translated_images/tr/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "Sınıflandırıcı sinir ağımız şu katmanlardan oluşur:\n", "\n", @@ -281,7 +281,7 @@ "\n", "CBoW daha hızlıdır, ancak skip-gram daha yavaş olmasına rağmen nadir kelimeleri temsil etmede daha başarılıdır.\n", "\n", - "![Kelimeyi vektörlere dönüştürmek için hem CBoW hem de Skip-Gram algoritmalarını gösteren bir görsel.](../../../../../translated_images/tr/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![Kelimeyi vektörlere dönüştürmek için hem CBoW hem de Skip-Gram algoritmalarını gösteren bir görsel.](../../../../../translated_images/tr/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Google News veri seti üzerinde önceden eğitilmiş Word2Vec gömüsünü denemek için **gensim** kütüphanesini kullanabiliriz. Aşağıda 'neural' kelimesine en benzer kelimeleri buluyoruz.\n", "\n", diff --git a/translations/tr/lessons/5-NLP/14-Embeddings/README.md b/translations/tr/lessons/5-NLP/14-Embeddings/README.md index 5b309b39..108c9469 100644 --- a/translations/tr/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/tr/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ Bu nedenle, gömülü temsil katmanı bir kelimeyi giriş olarak alır ve belirl Sınıflandırıcı ağımızda ilk katman olarak bir gömülü temsil katmanı kullanarak, kelime torbasından **gömülü torba** modeline geçebiliriz. Bu modelde, önce metnimizdeki her kelimeyi ilgili gömülü temsile dönüştürürüz ve ardından bu gömülü temsillerin tümü üzerinde `sum`, `average` veya `max` gibi bir toplama fonksiyonu hesaplarız. -![Beş kelimelik bir dizinin gömülü temsil sınıflandırıcısını gösteren görsel.](../../../../../translated_images/tr/embedding-classifier-example.b77f021a7ee67eee.png) +![Beş kelimelik bir dizinin gömülü temsil sınıflandırıcısını gösteren görsel.](../../../../../translated_images/tr/embedding-classifier-example.b77f021a7ee67eee.webp) > Görsel yazar tarafından oluşturulmuştur @@ -40,7 +40,7 @@ Bunu yapmak için, gömülü temsil modelimizi büyük bir metin koleksiyonu üz CBoW daha hızlıdır, ancak atlama-gram daha yavaş olmasına rağmen nadir kelimeleri temsil etmede daha iyi bir iş çıkarır. -![Kelimeyi vektöre dönüştürmek için kullanılan CBoW ve Skip-Gram algoritmalarını gösteren görsel.](../../../../../translated_images/tr/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![Kelimeyi vektöre dönüştürmek için kullanılan CBoW ve Skip-Gram algoritmalarını gösteren görsel.](../../../../../translated_images/tr/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Görsel [bu makaleden](https://arxiv.org/pdf/1301.3781.pdf) alınmıştır diff --git a/translations/tr/lessons/5-NLP/15-LanguageModeling/README.md b/translations/tr/lessons/5-NLP/15-LanguageModeling/README.md index a2ed3db7..9ebbd624 100644 --- a/translations/tr/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/tr/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ Dil modellemesinin temel fikri, modelleri etiketlenmemiş veri kümeleri üzerin * **Continuous Bag-of-Words** (CBoW), burada bir token dizisindeki $W_{-N}$, ..., $W_N$ arasında ortadaki token $W_0$'ı tahmin ederiz. * **Skip-gram**, burada ortadaki token $W_0$'dan komşu tokenlerin bir setini {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} tahmin ederiz. -![Kelimeyi vektöre dönüştürme algoritmalarına dair makaleden görsel](../../../../../translated_images/tr/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![Kelimeyi vektöre dönüştürme algoritmalarına dair makaleden görsel](../../../../../translated_images/tr/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Görsel [bu makaleden](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/tr/lessons/5-NLP/16-RNN/README.md b/translations/tr/lessons/5-NLP/16-RNN/README.md index 86093636..519b19f9 100644 --- a/translations/tr/lessons/5-NLP/16-RNN/README.md +++ b/translations/tr/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: Metin dizisinin anlamını yakalamak için, **tekrarlayan sinir ağı** veya RNN adı verilen başka bir sinir ağı mimarisi kullanmamız gerekir. RNN'de, cümlemizi ağdan bir sembol biriminde geçiririz ve ağ bir **durum** üretir, bu durumu bir sonraki sembolle birlikte tekrar ağa geçiririz. -![RNN](../../../../../translated_images/tr/rnn.27f5c29c53d727b5.png) +![RNN](../../../../../translated_images/tr/rnn.27f5c29c53d727b5.webp) > Görsel yazar tarafından oluşturulmuştur @@ -61,7 +61,7 @@ Durum C'nin bileşenleri, açılıp kapatılabilen bayraklar olarak düşünüle Tek yönlü veya çift yönlü bir tekrarlayan ağ, bir dizideki belirli kalıpları yakalar ve bunları bir durum vektörüne depolayabilir veya çıktıya aktarabilir. Konvolüsyonel ağlarda olduğu gibi, ilk katman tarafından çıkarılan düşük seviyeli kalıplardan daha yüksek seviyeli kalıpları yakalamak ve inşa etmek için ilk katmanın üzerine başka bir tekrarlayan katman inşa edebiliriz. Bu bizi, önceki katmanın çıktısının bir sonraki katmana giriş olarak geçtiği iki veya daha fazla tekrarlayan ağdan oluşan bir **çok katmanlı RNN** kavramına götürür. -![Çok katmanlı uzun kısa süreli bellek RNN'yi gösteren görsel](../../../../../translated_images/tr/multi-layer-lstm.dd975e29bb2a59fe.jpg) +![Çok katmanlı uzun kısa süreli bellek RNN'yi gösteren görsel](../../../../../translated_images/tr/multi-layer-lstm.dd975e29bb2a59fe.webp) *[Bu harika yazıdan](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) Fernando López tarafından alınmıştır.* diff --git a/translations/tr/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/tr/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index 5e225acc..81f4b9f3 100644 --- a/translations/tr/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/tr/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "Tekrarlayan ağlar, tek yönlü veya çift yönlü olsun, bir dizideki belirli desenleri yakalar ve bunları durum vektörüne kaydedebilir veya çıktıya aktarabilir. Konvolüsyonel ağlarda olduğu gibi, birinci katman tarafından çıkarılan düşük seviyeli desenlerden daha yüksek seviyeli desenleri yakalamak için birinci katmanın üzerine başka bir tekrarlayan katman inşa edebiliriz. Bu bizi, bir önceki katmanın çıktısının bir sonraki katmana giriş olarak geçtiği, iki veya daha fazla tekrarlayan ağdan oluşan **çok katmanlı RNN** kavramına götürür.\n", "\n", - "![Çok Katmanlı Uzun-Kısa Süreli Bellek RNN'yi gösteren bir görsel](../../../../../translated_images/tr/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![Çok Katmanlı Uzun-Kısa Süreli Bellek RNN'yi gösteren bir görsel](../../../../../translated_images/tr/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*Fernando López'in [bu harika yazısından](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) alınmış bir görsel*\n", "\n", diff --git a/translations/tr/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/tr/lessons/5-NLP/16-RNN/RNNTF.ipynb index b2015fc4..155acef2 100644 --- a/translations/tr/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/tr/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "Bir metin dizisinin anlamını yakalamak için, **tekrarlayan sinir ağı** veya RNN adı verilen bir sinir ağı mimarisi kullanacağız. RNN kullanırken, cümlemizi ağı birer birer token olarak geçiririz ve ağ bir **durum** üretir, bu durumu bir sonraki token ile birlikte tekrar ağa iletiriz.\n", "\n", - "![Tekrarlayan sinir ağı üretimine dair bir örnek gösteren görsel.](../../../../../translated_images/tr/rnn.27f5c29c53d727b5.png)\n", + "![Tekrarlayan sinir ağı üretimine dair bir örnek gösteren görsel.](../../../../../translated_images/tr/rnn.27f5c29c53d727b5.webp)\n", "\n", "Token giriş dizisi $X_0,\\dots,X_n$ verildiğinde, RNN bir sinir ağı blokları dizisi oluşturur ve bu diziyi baştan sona geri yayılım kullanarak eğitir. Her ağ bloğu $(X_i,S_i)$ çiftini giriş olarak alır ve sonuç olarak $S_{i+1}$ üretir. Son durum $S_n$ veya çıktı $Y_n$, sonucu üretmek için doğrusal bir sınıflandırıcıya gider. Tüm ağ blokları aynı ağırlıkları paylaşır ve tek bir geri yayılım geçişiyle baştan sona eğitilir.\n", "\n", @@ -369,7 +369,7 @@ "\n", "Tek yönlü veya çift yönlü tekrarlayan ağlar, bir dizideki desenleri yakalar ve bunları durum vektörlerine kaydeder veya çıktı olarak döndürür. Konvolüsyonel ağlarda olduğu gibi, birinci katman tarafından çıkarılan alt düzey desenlerden daha yüksek düzey desenleri yakalamak için birinci katmanın ardından başka bir tekrarlayan katman ekleyebiliriz. Bu bizi **çok katmanlı RNN** kavramına götürür; bu, iki veya daha fazla tekrarlayan ağdan oluşur ve önceki katmanın çıktısı bir sonraki katmana giriş olarak aktarılır.\n", "\n", - "![Çok katmanlı uzun-kısa süreli bellek RNN'yi gösteren bir resim](../../../../../translated_images/tr/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![Çok katmanlı uzun-kısa süreli bellek RNN'yi gösteren bir resim](../../../../../translated_images/tr/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*Fernando López'in [bu harika yazısından](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) alınmış bir resim.*\n", "\n", diff --git a/translations/tr/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/tr/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index 5f7f904b..e2f3ff2c 100644 --- a/translations/tr/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/tr/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "RNN'yi metin üretmek üzere eğitme yöntemimiz şu şekilde olacak. Her adımda, `nchars` uzunluğunda bir karakter dizisi alacağız ve ağdan her bir giriş karakteri için bir sonraki çıkış karakterini üretmesini isteyeceğiz:\n", "\n", - "![RNN'nin 'HELLO' kelimesini üretme örneğini gösteren bir görsel.](../../../../../translated_images/tr/rnn-generate.56c54afb52f9781d.png)\n", + "![RNN'nin 'HELLO' kelimesini üretme örneğini gösteren bir görsel.](../../../../../translated_images/tr/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "Gerçek senaryoya bağlı olarak, *dizinin sonu* `` gibi bazı özel karakterleri de dahil etmek isteyebiliriz. Ancak bizim durumumuzda, ağı sonsuz metin üretimi için eğitmek istiyoruz, bu nedenle her bir dizinin boyutunu `nchars` tokenine eşit olarak sabitleyeceğiz. Sonuç olarak, her bir eğitim örneği `nchars` giriş ve `nchars` çıkıştan (giriş dizisinin bir sembol sola kaydırılmış hali) oluşacak. Minibatch, bu tür birkaç diziden oluşacak.\n", "\n", diff --git a/translations/tr/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/tr/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index 5b7c47d7..4aee94b6 100644 --- a/translations/tr/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/tr/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -113,7 +113,7 @@ "\n", "RNN'i haber başlıkları üretmek için şu şekilde eğiteceğiz. Her adımda bir başlık alacağız, bu başlık bir RNN'e verilecek ve her bir giriş karakteri için ağdan bir sonraki çıkış karakterini üretmesini isteyeceğiz:\n", "\n", - "!['HELLO' kelimesinin bir RNN tarafından nasıl üretildiğini gösteren bir örnek görüntü.](../../../../../translated_images/tr/rnn-generate.56c54afb52f9781d.png)\n", + "!['HELLO' kelimesinin bir RNN tarafından nasıl üretildiğini gösteren bir örnek görüntü.](../../../../../translated_images/tr/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "Dizimizin son karakteri için ağdan `` tokenini üretmesini isteyeceğiz.\n", "\n", diff --git a/translations/tr/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/tr/lessons/5-NLP/17-GenerativeNetworks/README.md index 0c5236ec..d3a0f143 100644 --- a/translations/tr/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/tr/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ Tekrarlayan Sinir Ağları (RNN'ler) ve Uzun Kısa Süreli Bellek Hücreleri (LS Bu, aşağıdaki resimde gösterilen farklı sinir ağı mimarilerini mümkün kılar: -![Yaygın tekrarlayan sinir ağı desenlerini gösteren bir resim.](../../../../../translated_images/tr/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Yaygın tekrarlayan sinir ağı desenlerini gösteren bir resim.](../../../../../translated_images/tr/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > Resim, [Andrej Karpaty](http://karpathy.github.io/) tarafından yazılan [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) blog yazısından alınmıştır. @@ -32,7 +32,7 @@ Bu birimde, metin üretmemize yardımcı olan basit üretici modeller üzerine o Bu RNN'yi adım adım metin üretmek için eğiteceğiz. Her adımda, `nchars` uzunluğunda bir karakter dizisi alacağız ve ağdan her giriş karakteri için bir sonraki çıktı karakterini üretmesini isteyeceğiz: -![RNN'nin 'HELLO' kelimesini üretme örneğini gösteren bir resim.](../../../../../translated_images/tr/rnn-generate.56c54afb52f9781d.png) +![RNN'nin 'HELLO' kelimesini üretme örneğini gösteren bir resim.](../../../../../translated_images/tr/rnn-generate.56c54afb52f9781d.webp) Metin üretirken (çıkarsama sırasında), bazı **başlangıç** verileriyle başlarız. Bu veri RNN hücrelerinden geçirilerek ara durumu oluşturur ve ardından üretim başlar. Her seferinde bir karakter üretiriz ve durumu ve üretilen karakteri bir sonraki karakteri üretmek için başka bir RNN hücresine geçiririz. Bu işlem yeterli sayıda karakter üretilene kadar devam eder. diff --git a/translations/tr/lessons/5-NLP/18-Transformers/README.md b/translations/tr/lessons/5-NLP/18-Transformers/README.md index e7ac6db7..9ba0e555 100644 --- a/translations/tr/lessons/5-NLP/18-Transformers/README.md +++ b/translations/tr/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ RNN'lerle diziden-diziye yaklaşımı, iki tekrarlayan ağ tarafından uygulanı **Dikkat Mekanizmaları**, RNN'nin her bir çıktı tahmininde her bir giriş vektörünün bağlamsal etkisini ağırlıklandırmanın bir yolunu sağlar. Bu, giriş RNN'nin ara durumları ile çıkış RNN arasında kısayollar oluşturarak uygulanır. Bu şekilde, çıktı sembolü yt'yi oluştururken, farklı ağırlık katsayıları αt,i ile tüm giriş gizli durumlarını hi dikkate alırız. -![Eklenecek bir dikkat katmanı ile encoder/decoder modelini gösteren görsel](../../../../../translated_images/tr/encoder-decoder-attention.7a726296894fb567.png) +![Eklenecek bir dikkat katmanı ile encoder/decoder modelini gösteren görsel](../../../../../translated_images/tr/encoder-decoder-attention.7a726296894fb567.webp) > [Bahdanau ve diğerleri, 2015](https://arxiv.org/pdf/1409.0473.pdf) tarafından önerilen eklemeli dikkat mekanizması ile encoder-decoder modeli, [bu blog yazısından](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) alıntılanmıştır. Dikkat matrisi {αi,j} belirli giriş kelimelerinin çıktı dizisindeki bir kelimenin oluşturulmasında oynadığı rolü temsil eder. Aşağıda böyle bir matrisin örneği verilmiştir: -![Bahdanau - arviz.org'dan alınan örnek hizalamayı gösteren görsel](../../../../../translated_images/tr/bahdanau-fig3.09ba2d37f202a6af.png) +![Bahdanau - arviz.org'dan alınan örnek hizalamayı gösteren görsel](../../../../../translated_images/tr/bahdanau-fig3.09ba2d37f202a6af.webp) > [Bahdanau ve diğerleri, 2015](https://arxiv.org/pdf/1409.0473.pdf) (Şekil 3) tarafından önerilen şekil. @@ -66,7 +66,7 @@ Pozisyonel gömme ile elde ettiğimiz sonuç, hem orijinal tokenı hem de dizide Sonraki adımda, dizimizdeki bazı desenleri yakalamamız gerekir. Bunu yapmak için transformerlar **kendine dikkat** mekanizmasını kullanır; bu, giriş ve çıkış olarak aynı diziye uygulanan dikkattir. Kendine dikkat uygulamak, cümle içindeki **bağlamı** dikkate almamızı ve hangi kelimelerin birbirleriyle ilişkili olduğunu görmemizi sağlar. Örneğin, *it* gibi zamirlerin hangi kelimelere atıfta bulunduğunu görmemizi ve bağlamı dikkate almamızı sağlar: -![](../../../../../translated_images/tr/CoreferenceResolution.861924d6d384a7d6.png) +![](../../../../../translated_images/tr/CoreferenceResolution.861924d6d384a7d6.webp) > [Google Blogundan](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) alınan görsel. @@ -91,7 +91,7 @@ Her giriş pozisyonu bağımsız olarak her çıkış pozisyonuna eşlendiğinde **BERT** (Bidirectional Encoder Representations from Transformers), *BERT-base* için 12 katman ve *BERT-large* için 24 katman içeren çok büyük bir çok katmanlı transformer ağıdır. Model, büyük bir metin veri kümesi (WikiPedia + kitaplar) üzerinde denetimsiz eğitim (bir cümledeki maskelenmiş kelimeleri tahmin etme) kullanılarak önceden eğitilir. Ön eğitim sırasında model, dil anlayışının önemli seviyelerini emer ve bu daha sonra diğer veri kümeleriyle ince ayar yapılarak kullanılabilir. Bu sürece **transfer öğrenme** denir. -![http://jalammar.github.io/illustrated-bert/ adresinden alınan görsel](../../../../../translated_images/tr/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) +![http://jalammar.github.io/illustrated-bert/ adresinden alınan görsel](../../../../../translated_images/tr/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > Görsel [kaynağı](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/tr/lessons/5-NLP/18-Transformers/READMEtransformers.md b/translations/tr/lessons/5-NLP/18-Transformers/READMEtransformers.md index 39e4bb58..ac204c85 100644 --- a/translations/tr/lessons/5-NLP/18-Transformers/READMEtransformers.md +++ b/translations/tr/lessons/5-NLP/18-Transformers/READMEtransformers.md @@ -11,13 +11,13 @@ RNN'lerle, dizi-dizi işlemi, bir giriş dizisini gizli bir duruma dönüştüre **Dikkat Mekanizmaları**, RNN'nin her bir çıktı tahmini üzerindeki her giriş vektörünün bağlamsal etkisini ağırlıklandırmanın bir yolunu sunar. Bu, giriş RNN'sinin ara durumları ile çıktı RNN'si arasında kısayollar oluşturarak uygulanır. Bu şekilde, çıktı sembolü yt üretilirken, farklı ağırlık katsayıları αt,i ile tüm giriş gizli durumları hi dikkate alınacaktır. -![Eklemeli dikkat katmanına sahip bir kodlayıcı/çözücü modelini gösteren resim](../../../../../translated_images/tr/encoder-decoder-attention.7a726296894fb567.png) +![Eklemeli dikkat katmanına sahip bir kodlayıcı/çözücü modelini gösteren resim](../../../../../translated_images/tr/encoder-decoder-attention.7a726296894fb567.webp) > Eklemeli dikkat mekanizmasına sahip kodlayıcı-çözücü modeli [Bahdanau ve diğerleri, 2015](https://arxiv.org/pdf/1409.0473.pdf) tarafından, [bu blog yazısından](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) alıntıdır. Dikkat matris {αi,j} belirli giriş kelimelerinin bir çıktı dizisindeki belirli bir kelimenin üretilmesindeki rolünü temsil eder. Aşağıda böyle bir matrisin örneği verilmiştir: -![Bahdanau - arviz.org'dan alınan RNNsearch-50 tarafından bulunan örnek hizalamayı gösteren resim](../../../../../translated_images/tr/bahdanau-fig3.09ba2d37f202a6af.png) +![Bahdanau - arviz.org'dan alınan RNNsearch-50 tarafından bulunan örnek hizalamayı gösteren resim](../../../../../translated_images/tr/bahdanau-fig3.09ba2d37f202a6af.webp) > [Bahdanau ve diğerleri, 2015](https://arxiv.org/pdf/1409.0473.pdf) (Şekil 3) @@ -57,7 +57,7 @@ Konumsal gömme ile elde ettiğimiz sonuç, hem orijinal tokenı hem de dizideki Sonraki adım, dizimizdeki bazı desenleri yakalamaktır. Bunu yapmak için, transformerlar **kendine dikkat** mekanizmasını kullanır; bu, esasen aynı diziyi girdi ve çıktı olarak uygulanan dikkattir. Kendine dikkati uygulamak, cümle içindeki **bağlamı** dikkate almamızı sağlar ve hangi kelimelerin birbiriyle ilişkili olduğunu görmemize olanak tanır. Örneğin, hangi kelimelerin *o* gibi referanslarla ifade edildiğini görmemizi sağlar ve ayrıca bağlamı dikkate alır: -![](../../../../../translated_images/tr/CoreferenceResolution.861924d6d384a7d6.png) +![](../../../../../translated_images/tr/CoreferenceResolution.861924d6d384a7d6.webp) > Resim [Google Blogu](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) kaynaklı @@ -82,7 +82,7 @@ Her bir giriş konumu bağımsız olarak her bir çıkış konumuna eşlendiğin **BERT** (Transformerlardan İkili Kodlayıcı Temsilleri), *BERT-base* için 12 katman ve *BERT-large* için 24 katman içeren çok büyük bir çok katmanlı transformer ağıdır. Model, öncelikle geniş bir metin veri kümesi (WikiPedia + kitaplar) üzerinde denetimsiz eğitim (bir cümlede maskelenmiş kelimeleri tahmin etme) kullanılarak önceden eğitilir. Ön eğitim sırasında model, daha sonra ince ayar ile diğer veri kümeleri ile kullanılabilecek önemli düzeyde dil anlayışı kazanır. Bu süreç **aktarım öğrenimi** olarak adlandırılır. -![http://jalammar.github.io/illustrated-bert/ adresinden bir resim](../../../../../translated_images/tr/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) +![http://jalammar.github.io/illustrated-bert/ adresinden bir resim](../../../../../translated_images/tr/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > Resim [kaynak](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/tr/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/tr/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index 328c2bcb..abfdbb0a 100644 --- a/translations/tr/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/tr/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**Dikkat Mekanizmaları**, her bir giriş vektörünün RNN'nin her bir çıktı tahmini üzerindeki bağlamsal etkisini ağırlıklandırmanın bir yolunu sağlar. Bu, giriş RNN'nin ara durumları ile çıkış RNN arasında kısayollar oluşturarak uygulanır. Bu şekilde, çıktı sembolü $y_t$ oluşturulurken, farklı ağırlık katsayıları $\\alpha_{t,i}$ ile tüm giriş gizli durumlarını $h_i$ dikkate alırız.\n", "\n", - "![Eklenecek bir dikkat katmanı ile encoder/decoder modelini gösteren görsel](../../../../../translated_images/tr/encoder-decoder-attention.7a726296894fb567.png)\n", + "![Eklenecek bir dikkat katmanı ile encoder/decoder modelini gösteren görsel](../../../../../translated_images/tr/encoder-decoder-attention.7a726296894fb567.webp)\n", "*[Bahdanau ve ark., 2015](https://arxiv.org/pdf/1409.0473.pdf) çalışmasından alınan ve [bu blog yazısından](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) alıntılanan eklemeli dikkat mekanizmalı encoder-decoder modeli]*\n", "\n", "Dikkat matrisi $\\{\\alpha_{i,j}\\}$, belirli giriş kelimelerinin çıktı dizisindeki bir kelimenin oluşturulmasında oynadığı rol derecesini temsil eder. Aşağıda böyle bir matrisin örneği verilmiştir:\n", "\n", - "![Bahdanau - arviz.org'dan alınan RNNsearch-50 tarafından bulunan örnek bir hizalamayı gösteren görsel](../../../../../translated_images/tr/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![Bahdanau - arviz.org'dan alınan RNNsearch-50 tarafından bulunan örnek bir hizalamayı gösteren görsel](../../../../../translated_images/tr/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*[Bahdanau ve ark., 2015](https://arxiv.org/pdf/1409.0473.pdf) çalışmasından alınan (Şekil 3)]*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers), *BERT-base* için 12 katman ve *BERT-large* için 24 katman içeren çok büyük bir çok katmanlı transformer ağıdır. Model, büyük bir metin veri kümesi (WikiPedia + kitaplar) üzerinde denetimsiz eğitim (bir cümledeki maskelenmiş kelimeleri tahmin etme) kullanılarak önce önceden eğitilir. Önceden eğitim sırasında model, önemli bir dil anlayışı seviyesini emer ve bu daha sonra diğer veri kümeleriyle ince ayar yapılarak kullanılabilir. Bu sürece **transfer öğrenme** denir.\n", "\n", - "![http://jalammar.github.io/illustrated-bert/ adresinden alınan görsel](../../../../../translated_images/tr/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![http://jalammar.github.io/illustrated-bert/ adresinden alınan görsel](../../../../../translated_images/tr/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "Transformer mimarilerinin BERT, DistilBERT, BigBird, OpenGPT3 ve daha fazlası gibi birçok varyasyonu vardır ve bunlar ince ayar yapılabilir. [HuggingFace paketi](https://github.com/huggingface/) PyTorch ile bu mimarilerin birçoğunu eğitmek için bir depo sağlar.\n", "\n", diff --git a/translations/tr/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/tr/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index a8701641..e14873ad 100644 --- a/translations/tr/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/tr/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**Dikkat Mekanizmaları**, her bir giriş vektörünün RNN'nin her bir çıktı tahmini üzerindeki bağlamsal etkisini ağırlıklandırmanın bir yolunu sunar. Bu, giriş RNN'sinin ara durumları ile çıkış RNN'si arasında kısayollar oluşturarak uygulanır. Bu şekilde, $y_t$ çıktı sembolünü üretirken, farklı ağırlık katsayıları $\\alpha_{t,i}$ ile tüm giriş gizli durumlarını $h_i$ dikkate alırız.\n", "\n", - "![Eklendiği bir dikkat katmanına sahip kodlayıcı/çözücü modelini gösteren görsel](../../../../../translated_images/tr/encoder-decoder-attention.7a726296894fb567.png)\n", + "![Eklendiği bir dikkat katmanına sahip kodlayıcı/çözücü modelini gösteren görsel](../../../../../translated_images/tr/encoder-decoder-attention.7a726296894fb567.webp)\n", "*[Bahdanau ve diğerleri, 2015](https://arxiv.org/pdf/1409.0473.pdf)'teki toplamsal dikkat mekanizmasına sahip kodlayıcı-çözücü modeli, [bu blog yazısından](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) alıntılanmıştır.*\n", "\n", "Dikkat matrisi $\\{\\alpha_{i,j}\\}$, belirli giriş kelimelerinin çıktı dizisindeki bir kelimenin oluşturulmasında ne derece etkili olduğunu temsil eder. Aşağıda böyle bir matrisin örneği verilmiştir:\n", "\n", - "![RNNsearch-50 tarafından bulunan bir hizalamayı gösteren görsel, Bahdanau - arviz.org'dan alınmıştır](../../../../../translated_images/tr/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![RNNsearch-50 tarafından bulunan bir hizalamayı gösteren görsel, Bahdanau - arviz.org'dan alınmıştır](../../../../../translated_images/tr/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*[Bahdanau ve diğerleri, 2015](https://arxiv.org/pdf/1409.0473.pdf)'ten alınan şekil (Şekil 3)*\n", "\n", @@ -225,7 +225,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers), *BERT-base* için 12 katman ve *BERT-large* için 24 katman içeren çok büyük bir çok katmanlı transformer ağıdır. Model, büyük bir metin veri kümesi (Vikipedi + kitaplar) üzerinde denetimsiz eğitim (bir cümledeki maskelenmiş kelimeleri tahmin etme) kullanılarak önce önceden eğitilir. Ön eğitim sırasında model, önemli bir dil anlama seviyesini öğrenir ve bu bilgi, diğer veri kümeleriyle ince ayar yapılarak kullanılabilir. Bu sürece **transfer öğrenimi** denir.\n", "\n", - "![http://jalammar.github.io/illustrated-bert/ adresinden alınan görsel](../../../../../translated_images/tr/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![http://jalammar.github.io/illustrated-bert/ adresinden alınan görsel](../../../../../translated_images/tr/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "BERT, DistilBERT, BigBird, OpenGPT3 ve daha fazlası gibi ince ayar yapılabilen birçok Transformer mimarisi varyasyonu bulunmaktadır.\n", "\n", diff --git a/translations/tr/lessons/5-NLP/19-NER/README.md b/translations/tr/lessons/5-NLP/19-NER/README.md index d53618bc..fc0b7cf3 100644 --- a/translations/tr/lessons/5-NLP/19-NER/README.md +++ b/translations/tr/lessons/5-NLP/19-NER/README.md @@ -57,7 +57,7 @@ bebekte | O Tokenler ve sınıflar arasında birebir bir ilişki kurmamız gerektiğinden, bu resimden **çoktan-çoka** bir sinir ağı modeli eğitebiliriz: -![Yaygın tekrarlayan sinir ağı desenlerini gösteren bir görsel.](../../../../../translated_images/tr/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Yaygın tekrarlayan sinir ağı desenlerini gösteren bir görsel.](../../../../../translated_images/tr/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > *Görsel, [Andrej Karpathy](http://karpathy.github.io/) tarafından yazılmış [bu blog yazısından](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) alınmıştır. NER token sınıflandırma modelleri, bu resimdeki en sağdaki ağ mimarisine karşılık gelir.* diff --git a/translations/tr/lessons/5-NLP/README.md b/translations/tr/lessons/5-NLP/README.md index 34cbbf98..5f632551 100644 --- a/translations/tr/lessons/5-NLP/README.md +++ b/translations/tr/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Doğal Dil İşleme -![NLP görevlerinin bir çizimi](../../../../translated_images/tr/ai-nlp.b22dcb8ca4707cea.png) +![NLP görevlerinin bir çizimi](../../../../translated_images/tr/ai-nlp.b22dcb8ca4707cea.webp) Bu bölümde, **Doğal Dil İşleme (NLP)** ile ilgili görevleri çözmek için Sinir Ağlarını kullanmaya odaklanacağız. Bilgisayarların çözmesini istediğimiz birçok NLP problemi bulunmaktadır: diff --git a/translations/tr/lessons/6-Other/23-MultiagentSystems/README.md b/translations/tr/lessons/6-Other/23-MultiagentSystems/README.md index 4e48d2ae..e7910216 100644 --- a/translations/tr/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/tr/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ Bir modeli açabilirsiniz, örneğin **Biology → Flocking**. Modeli açtıktan sonra, ana NetLogo ekranına yönlendirilirsiniz. İşte sınırlı kaynaklar (ot) göz önüne alındığında kurtlar ve koyunların popülasyonunu açıklayan örnek bir model. -![NetLogo Ana Ekran](../../../../../translated_images/tr/NetLogo-Main.32653711ec1a01b3.png) +![NetLogo Ana Ekran](../../../../../translated_images/tr/NetLogo-Main.32653711ec1a01b3.webp) > Dmitry Soshnikov tarafından ekran görüntüsü diff --git a/translations/tr/lessons/README.md b/translations/tr/lessons/README.md index 94709979..fede0813 100644 --- a/translations/tr/lessons/README.md +++ b/translations/tr/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Genel Bakış -![Bir çizimde genel bakış](../../../translated_images/tr/ai-overview.0857791951d19500.png) +![Bir çizimde genel bakış](../../../translated_images/tr/ai-overview.0857791951d19500.webp) > Çizim notu: [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/tr/lessons/X-Extras/X1-MultiModal/README.md b/translations/tr/lessons/X-Extras/X1-MultiModal/README.md index 88023847..f963df76 100644 --- a/translations/tr/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/tr/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ Transformer modellerinin NLP görevlerini çözmedeki başarısından sonra, ayn CLIP'in temel fikri, metin istemlerini bir görüntüyle karşılaştırabilmek ve görüntünün istemle ne kadar iyi eşleştiğini belirlemektir. -![CLIP Mimari](../../../../../translated_images/tr/clip-arch.b3dbf20b4e8ed8be.png) +![CLIP Mimari](../../../../../translated_images/tr/clip-arch.b3dbf20b4e8ed8be.webp) > *Resim [bu blog yazısından](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ Bu model önceden eğitildikten sonra, bir grup görüntü ve bir grup metin ist Diyelim ki görüntüleri kediler, köpekler ve insanlar arasında sınıflandırmamız gerekiyor. Bu durumda, modele bir görüntü ve bir dizi metin istemi verebiliriz: "*bir kedi resmi*", "*bir köpek resmi*", "*bir insan resmi*". Sonuçta elde edilen 3 olasılık vektöründe en yüksek değere sahip indeksi seçmemiz yeterlidir. -![CLIP ile Görüntü Sınıflandırma](../../../../../translated_images/tr/clip-class.3af42ef0b2b19369.png) +![CLIP ile Görüntü Sınıflandırma](../../../../../translated_images/tr/clip-class.3af42ef0b2b19369.webp) > *Resim [bu blog yazısından](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ VQGAN hakkında daha fazla bilgi edinmek için [Taming Transformers](https://com VQGAN ile geleneksel GAN arasındaki önemli farklardan biri, geleneksel GAN'ın herhangi bir giriş vektöründen düzgün bir görüntü üretebilmesi, ancak VQGAN'ın tutarlı bir görüntü üretme olasılığının düşük olmasıdır. Bu nedenle, görüntü oluşturma sürecini daha fazla yönlendirmemiz gerekir ve bu CLIP kullanılarak yapılabilir. -![VQGAN+CLIP Mimari](../../../../../translated_images/tr/vqgan.5027fe05051dfa31.png) +![VQGAN+CLIP Mimari](../../../../../translated_images/tr/vqgan.5027fe05051dfa31.webp) Bir metin istemine karşılık gelen bir görüntü oluşturmak için, rastgele bir kodlama vektörüyle başlarız ve bu vektör VQGAN'dan geçirilerek bir görüntü oluşturulur. Daha sonra CLIP, görüntünün metin istemine ne kadar iyi uyduğunu gösteren bir kayıp fonksiyonu üretmek için kullanılır. Amaç, bu kaybı minimize etmek ve geri yayılım kullanarak giriş vektör parametrelerini ayarlamaktır. VQGAN+CLIP'i uygulayan harika bir kütüphane [Pixray](http://github.com/pixray/pixray)'dir. -![Pixray tarafından üretilen resim](../../../../../translated_images/tr/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Pixray tarafından üretilen resim](../../../../../translated_images/tr/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Pixray tarafından üretilen resim](../../../../../translated_images/tr/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) +![Pixray tarafından üretilen resim](../../../../../translated_images/tr/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp) | ![Pixray tarafından üretilen resim](../../../../../translated_images/tr/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp) | ![Pixray tarafından üretilen resim](../../../../../translated_images/tr/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp) ----|----|---- *Edebiyat öğretmeni olan genç bir erkeğin kitapla yakın çekim suluboya portresi* isteminden üretilen resim | *Bilgisayar bilimi öğretmeni olan genç bir kadının bilgisayarla yakın çekim yağlı boya portresi* isteminden üretilen resim | *Matematik öğretmeni olan yaşlı bir erkeğin kara tahta önünde yakın çekim yağlı boya portresi* isteminden üretilen resim diff --git a/translations/tw/README.md b/translations/tw/README.md index 04553fc8..3e6f3280 100644 --- a/translations/tw/README.md +++ b/translations/tw/README.md @@ -1,8 +1,8 @@ -[阿拉伯文](../ar/README.md) | [孟加拉文](../bn/README.md) | [保加利亞文](../bg/README.md) | [緬甸語](../my/README.md) | [中文（簡體）](../zh/README.md) | [中文（繁體，香港）](../hk/README.md) | [中文（繁體，澳門）](../mo/README.md) | [中文（繁體，台灣）](./README.md) | [克羅埃西亞文](../hr/README.md) | [捷克文](../cs/README.md) | [丹麥文](../da/README.md) | [荷蘭文](../nl/README.md) | 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[Marathi](../mr/README.md) | [Nepali](../ne/README.md) | [Nigerian Pidgin](../pcm/README.md) | [Norwegian](../no/README.md) | [Persian (Farsi)](../fa/README.md) | [Polish](../pl/README.md) | [Portuguese (Brazil)](../br/README.md) | [Portuguese (Portugal)](../pt/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Romanian](../ro/README.md) | [Russian](../ru/README.md) | [Serbian (Cyrillic)](../sr/README.md) | [Slovak](../sk/README.md) | [Slovenian](../sl/README.md) | [Spanish](../es/README.md) | [Swahili](../sw/README.md) | [Swedish](../sv/README.md) | [Tagalog (Filipino)](../tl/README.md) | [Tamil](../ta/README.md) | [Telugu](../te/README.md) | [Thai](../th/README.md) | [Turkish](../tr/README.md) | [Ukrainian](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamese](../vi/README.md) -> **想要本地克隆？** +> **偏好在本地端 Clone？** -> 本 repo 包含 50 多種語言翻譯，會大幅增加下載大小。若想跳過翻譯資料可使用 sparse checkout： +> 本儲存庫包含 50 多種語言的翻譯，因此下載大小會顯著增加。若要不包含翻譯內容地下載，請使用稀疏檢出功能： > ```bash > git clone --filter=blob:none --sparse https://github.com/microsoft/AI-For-Beginners.git > cd AI-For-Beginners > git sparse-checkout set --no-cone '/*' '!translations' '!translated_images' > ``` -> 這樣可更快下載，且擁有完成課程所需的一切。 +> 這樣您可以快速下載完成課程所需的所有內容。 -**如需支援其它翻譯語言，請見[此列表](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** +**如果您希望支援其他翻譯語言，請參考 [此處](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md) 列出的語言。** ## 加入社群 [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) @@ -56,118 +56,118 @@ CO_OP_TRANSLATOR_METADATA: **[課程心智圖](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** -在此課程中，你將學習： +在本課程中，你將學習： -* 人工智慧的不同方法，包括「老派的」符號式方法，具備**知識表示**和推理能力（[GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)）。 -* **神經網路**與**深度學習**，現代 AI 的核心。我們會用兩個熱門框架——[TensorFlow](http://Tensorflow.org) 與 [PyTorch](http://pytorch.org) 的程式碼來說明這些重要概念。 -* 處理影像與文本的**神經架構**。我們將涵蓋近年的模型，但可能較缺乏最新最先進的。 -* 較少流行的 AI 方法，如**遺傳算法**及**多代理系統**。 +* 不同的人工智慧方法，包括使用 **知識表達** 和推理的「經典舊派」符號主義方法 ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence))。 +* 作為現代 AI 核心的 **神經網路** 與 **深度學習**。我們將使用兩個最受歡迎框架 —— [TensorFlow](http://Tensorflow.org) 和 [PyTorch](http://pytorch.org) 的程式碼範例，來說明這些重要主題背後的概念。 +* 運用於圖像與文本處理的 **神經架構**。將涵蓋近期模型，但可能未包含最新的尖端技術。 +* 較不普及的 AI 方法，例如 **基因演算法** 與 **多代理系統**。 -本課程不包含： +本課程不涵蓋： -> [課程所有附加資源均在 Microsoft Learn 聚合頁面](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) +> [在我們的 Microsoft Learn 系列中，找到本課程的所有額外資源](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) -* **商業領域的 AI 應用**案例。建議可參考微軟學習平台的 [面向商業用戶的 AI 入門](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) 課程，或與 [INSEAD](https://www.insead.edu/) 合作開發的 [AI 商業學校](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum)。 -* **經典機器學習**，我們的 [機器學習初學者課程](http://github.com/Microsoft/ML-for-Beginners) 有詳細介紹。 -* 以 **[認知服務](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)** 實作的應用。建議從 Microsoft Learn 上的 [視覺](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum)、[自然語言處理](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum)、**[Azure OpenAI 服務的生成式 AI](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** 等模組開始。 -* 特定的機器學習**雲端框架**，如 [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-bethanycheum)、[Microsoft Fabric](https://learn.microsoft.com/en-us/training/paths/get-started-fabric/?WT.mc_id=academic-77998-bethanycheum)、或 [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum)。可參考 [使用 Azure Machine Learning 建置及操作機器學習方案](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) 和 [使用 Azure Databricks 建置及操作機器學習方案](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum) 課程。 -* **會話式 AI**與**聊天機器人**。有專門的 [建立會話式 AI 方案](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum) 課程，亦可參考[此篇部落格](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/)。 -* 深度學習背後的**深奧數學**。建議閱讀 Ian Goodfellow、Yoshua Bengio 和 Aaron Courville 所著的 [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618)，在線版亦可於 [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/) 查閱。 +* **AI 在商業**中的案例。建議參考 Microsoft Learn 的 [商業用戶 AI 入門](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) 課程路徑，或與 [INSEAD](https://www.insead.edu/) 合作開發的 [AI 商業學院](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum)。 +* **經典機器學習**，詳細說明請參考我們的 [初學者機器學習課程](http://github.com/Microsoft/ML-for-Beginners)。 +* 使用 **[認知服務](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)** 架設的實務 AI 應用。建議從 Microsoft Learn 的 [視覺](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum)、[自然語言處理](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum)、**[Generative AI with Azure OpenAI Service](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** 等模組開始。 +* 具體的 ML **雲端框架**，如 [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-bethanycheum)、[Microsoft Fabric](https://learn.microsoft.com/en-us/training/paths/get-started-fabric/?WT.mc_id=academic-77998-bethanycheum) 或 [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum)。可參考[使用 Azure Machine Learning 建構與營運機器學習解決方案](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum)及[使用 Azure Databricks 建構與營運機器學習解決方案](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum)課程路徑。 +* **對話式 AI** 與 **聊天機器人**，另有 [建立對話式 AI 解決方案](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum) 專門課程，且可參考[此部落格文章](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/)詳細了解。 +* 深入的 **深度學習數學理論**。推薦閱讀 Ian Goodfellow、Yoshua Bengio 與 Aaron Courville 所著的 [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618)，線上版也可在 [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/) 瀏覽。 -若想輕鬆入門 _雲端 AI_ 主題，建議學習 [在 Azure 上開始人工智慧](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum) 路徑。 +欲輕鬆入門 _雲端 AI_ 可考慮學習 [Azure 人工智慧入門](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum) 路徑。 -# 內容目錄 +# 內容 -| | 課程連結 | PyTorch/Keras/TensorFlow | 實驗室 | -| :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | -------------------------------------------------------------- | -| 0 | [課程設定](./lessons/0-course-setup/setup.md) | [設定你的開發環境](./lessons/0-course-setup/how-to-run.md) | | -| I | [**AI 簡介**](./lessons/1-Intro/README.md) | | | -| 01 | [人工智慧介紹與歷史](./lessons/1-Intro/README.md) | - | - | -| II | **符號式人工智慧** | -| 02 | [知識表示與專家系統](./lessons/2-Symbolic/README.md) | [專家系統](./lessons/2-Symbolic/Animals.ipynb) / [本體論](./lessons/2-Symbolic/FamilyOntology.ipynb) /[概念圖](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | -| III | [**神經網路簡介**](./lessons/3-NeuralNetworks/README.md) ||| -| 03 | [感知機](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [筆記本](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [實驗室](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | -| 04 | [多層感知機與建立我們自己的框架](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [筆記本](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [實驗室](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | -| 05 | [框架介紹（PyTorch/TensorFlow）及過擬合](./lessons/3-NeuralNetworks/05-Frameworks/README.md) | [PyTorch](./lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb) / [Keras](./lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [實驗室](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | +| | 課程連結 | PyTorch/Keras/TensorFlow | 實驗室 | +| :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | +| 0 | [課程設置](./lessons/0-course-setup/setup.md) | [開發環境設定說明](./lessons/0-course-setup/how-to-run.md) | | +| I | [**人工智慧導論**](./lessons/1-Intro/README.md) | | | +| 01 | [人工智慧簡介與歷史](./lessons/1-Intro/README.md) | - | - | +| II | **符號主義 AI** | +| 02 | [知識表示與專家系統](./lessons/2-Symbolic/README.md) | [專家系統](./lessons/2-Symbolic/Animals.ipynb) / [本體論](./lessons/2-Symbolic/FamilyOntology.ipynb) /[概念圖](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | +| III | [**神經網路導論**](./lessons/3-NeuralNetworks/README.md) ||| +| 03 | [感知器](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [筆記本](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [實驗室](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | +| 04 | [多層感知器與創建我們自己的框架](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [筆記本](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [實驗室](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | +| 05 | [框架介紹 (PyTorch/TensorFlow) 與過擬合](./lessons/3-NeuralNetworks/05-Frameworks/README.md) | [PyTorch](./lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb) / [Keras](./lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [實驗室](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | | IV | [**電腦視覺**](./lessons/4-ComputerVision/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-77998-cacaste) / [TensorFlow](https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-77998-cacaste)| [在 Microsoft Azure 探索電腦視覺](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | -| 06 | [電腦視覺介紹。OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [筆記本](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [實驗室](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | -| 07 | [卷積神經網絡](./lessons/4-ComputerVision/07-ConvNets/README.md) & [CNN 架構](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [實驗室](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | -| 08 | [預訓練網絡與遷移學習](./lessons/4-ComputerVision/08-TransferLearning/README.md) 及 [訓練技巧](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [實驗室](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | -| 09 | [自編碼器與變分自編碼器](./lessons/4-ComputerVision/09-Autoencoders/README.md) | [PyTorch](./lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb) | | -| 10 | [生成對抗網絡與藝術風格轉換](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | -| 11 | [物件檢測](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [實驗室](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | -| 12 | [語義分割。U-Net](./lessons/4-ComputerVision/12-Segmentation/README.md) | [PyTorch](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb) | | +| 06 | [電腦視覺入門. OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [筆記本](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [實驗室](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | +| 07 | [卷積神經網路](./lessons/4-ComputerVision/07-ConvNets/README.md) & [CNN 架構](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [實驗室](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | +| 08 | [預訓練網絡和遷移學習](./lessons/4-ComputerVision/08-TransferLearning/README.md) 及 [訓練技巧](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [實驗室](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | +| 09 | [自編碼器與變分自編碼器 (VAE)](./lessons/4-ComputerVision/09-Autoencoders/README.md) | [PyTorch](./lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb) | | +| 10 | [生成對抗網絡 & 藝術風格轉換](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | +| 11 | [物體偵測](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [實驗室](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | +| 12 | [語義分割. U-Net](./lessons/4-ComputerVision/12-Segmentation/README.md) | [PyTorch](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb) | | | V | [**自然語言處理**](./lessons/5-NLP/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-77998-cacaste) /[TensorFlow](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-77998-cacaste) | [在 Microsoft Azure 探索自然語言處理](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| -| 13 | [文字表示法。詞袋模型/TF-IDF](./lessons/5-NLP/13-TextRep/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb) | | -| 14 | [語義字嵌入。Word2Vec 與 GloVe](./lessons/5-NLP/14-Embeddings/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb) | | -| 15 | [語言建模。訓練你自己的字嵌入](./lessons/5-NLP/15-LanguageModeling/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-PyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-TF.ipynb) | [實驗室](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | -| 16 | [循環神經網絡](./lessons/5-NLP/16-RNN/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNTF.ipynb) | | -| 17 | [生成式循環網絡](./lessons/5-NLP/17-GenerativeNetworks/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb) | [實驗室](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | -| 18 | [Transformer。BERT。](./lessons/5-NLP/18-Transformers/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb) /[TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersTF.ipynb) | | +| 13 | [文本表示法. 词袋模型/Bow 及 TF-IDF](./lessons/5-NLP/13-TextRep/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb) | | +| 14 | [語義詞嵌入. Word2Vec 與 GloVe](./lessons/5-NLP/14-Embeddings/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb) | | +| 15 | [語言建模. 訓練您自己的嵌入](./lessons/5-NLP/15-LanguageModeling/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-PyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-TF.ipynb) | [實驗室](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | +| 16 | [循環神經網路](./lessons/5-NLP/16-RNN/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNTF.ipynb) | | +| 17 | [生成循環網絡](./lessons/5-NLP/17-GenerativeNetworks/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb) | [實驗室](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | +| 18 | [Transformer. BERT.](./lessons/5-NLP/18-Transformers/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb) /[TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersTF.ipynb) | | | 19 | [命名實體識別](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [實驗室](./lessons/5-NLP/19-NER/lab/README.md) | -| 20 | [大型語言模型、提示編程與少樣本任務](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | +| 20 | [大型語言模型、提示程式設計與少量示例任務](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | | VI | **其他 AI 技術** || | | 21 | [遺傳演算法](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [筆記本](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | | 22 | [深度強化學習](./lessons/6-Other/22-DeepRL/README.md) | [PyTorch](./lessons/6-Other/22-DeepRL/CartPole-RL-PyTorch.ipynb) /[TensorFlow](./lessons/6-Other/22-DeepRL/CartPole-RL-TF.ipynb) | [實驗室](./lessons/6-Other/22-DeepRL/lab/README.md) | -| 23 | [多代理系統](./lessons/6-Other/23-MultiagentSystems/README.md) | | | +| 23 | [多智能體系統](./lessons/6-Other/23-MultiagentSystems/README.md) | | | | VII | **AI 倫理** | | | -| 24 | [AI 倫理與負責任的 AI](./lessons/7-Ethics/README.md) | [Microsoft Learn: 負責任的 AI 原則](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | +| 24 | [AI 倫理與負責任的人工智慧](./lessons/7-Ethics/README.md) | [Microsoft Learn: 負責任的 AI 原則](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | | IX | **額外資源** | | | | 25 | [多模態網絡、CLIP 與 VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [筆記本](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | -## 每課程包含 +## 每堂課包含 -* 預習材料 -* 可執行的 Jupyter 筆記本，通常針對特定框架（**PyTorch** 或 **TensorFlow**）。可執行筆記本亦包含大量理論材料，因此要理解主題，至少需要學習一種版本的筆記本（PyTorch 或 TensorFlow）。 -* 部分主題提供 **實驗室**，讓你有機會嘗試將學習的內容應用於具體問題。 -* 部分章節包含連結到涵蓋相關主題的 [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) 模組。 +* 預習資料 +* 可執行的 Jupyter 筆記本，通常針對特定框架（**PyTorch** 或 **TensorFlow**）。該筆記本也包含大量理論內容，因此要理解主題，您至少需要完成其中一種版本的筆記本（PyTorch 或 TensorFlow）。 +* 有些主題提供**實驗室**，讓您有機會嘗試將所學應用於特定問題。 +* 部分章節含有鏈接至涵蓋相關主題的 [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) 模組。 -## 開始上路 +## 開始前 -### 🎯 AI 新手？從這裡開始！ +### 🎯 AI 新手？ 從這裡開始！ -如果你是 AI 完全新手，想快速動手實作範例，請查看我們的 [**初學者友善範例**](./examples/README.md)！這些範例包括： +如果您是 AI 完全新手，想要快速、實作範例，請查看我們的 [**初學者友善範例**](./examples/README.md)！這些範例包括： -- 🌟 **Hello AI World** - 你的第一個 AI 程式（模式識別） -- 🧠 **簡單神經網絡** - 從零開始建立神經網絡 -- 🖼️ **影像分類器** - 詳細註解的影像分類器 +- 🌟 **哈囉 AI 世界** - 您的第一個 AI 程式（模式識別） +- 🧠 **簡易神經網路** - 從零建造一個神經網路 +- 🖼️ **圖像分類器** - 帶詳細註解的圖像分類器 - 💬 **文字情感分析** - 分析正面/負面文字 這些範例旨在幫助您在深入完整課程之前理解 AI 概念。 -### 📚 完整課程設置 +### 📚 完整課程設定 -- 我們創建了[設置課程](./lessons/0-course-setup/setup.md)來協助您設置開發環境。 - 對於教育者，我們也創建了[課程設置課程](./lessons/0-course-setup/for-teachers.md)！ -- 如何[在 VSCode 或 Codepace 中執行程式碼](./lessons/0-course-setup/how-to-run.md) +- 我們已建立一個[設定課程](./lessons/0-course-setup/setup.md)來協助您設定開發環境。 - 對教育工作者，我們也建立了[課程設定教學](./lessons/0-course-setup/for-teachers.md)！ +- 如何在 VSCode 或 Codespace 中[執行程式碼](./lessons/0-course-setup/how-to-run.md) -請遵循這些步驟： +請依照以下步驟操作： -從倉庫分叉：點擊此頁面右上角的「Fork」按鈕。 +分支此倉儲：點擊此頁右上角的「Fork」按鈕。 -克隆倉庫：`git clone https://github.com/microsoft/AI-For-Beginners.git` +複製倉儲：`git clone https://github.com/microsoft/AI-For-Beginners.git` -別忘了點星標 (🌟)，以便日後更容易找到此倉庫。 +別忘了給這個 repo 點星 (🌟)，以便日後更容易找到。 ## 認識其他學習者 -加入我們的[官方 AI Discord 伺服器](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum)，與學習本課程的其他學習者交流並獲得支援。 +加入我們的[官方 AI Discord 伺服器](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum)，與其他修讀本課程的學習者交流並獲得支援。 -如果您在構建過程中有產品反饋或問題，請訪問我們的[Azure AI Foundry 開發者論壇](https://aka.ms/foundry/forum) +如果在開發過程中有產品反饋或問題，請造訪我們的[Azure AI Foundry 開發者論壇](https://aka.ms/foundry/forum) -## 測驗 +## 小測驗 -> **關於測驗的說明**：所有測驗都包含在 etc\quiz-app 的 Quiz-app 資料夾中，或可[在線上使用](https://ff-quizzes.netlify.app/)。它們在課程中都有連結，測驗應用程式可以本地運行或部署到 Azure；請遵循 `quiz-app` 資料夾中的說明。目前正在逐步本地化。 +> **關於小測驗的提醒**：所有小測驗都在 etc\quiz-app 的 Quiz-app 資料夾中，或於[線上查看](https://ff-quizzes.netlify.app/)。它們從課程內容中鏈接，測驗應用程式可以在本地執行或部署到 Azure；請依照 `quiz-app` 資料夾中的說明操作。這些內容正在逐步本地化中。 -## 徵求協助 +## 需要協助 -您有建議或發現拼寫或程式碼錯誤嗎？請提出問題或建立拉取請求。 +您有建議或發現拼字或程式碼錯誤嗎？請提出議題或建立拉取請求。 ## 特別感謝 * **✍️ 主要作者：** [Dmitry Soshnikov](http://soshnikov.com), PhD * **🔥 編輯：** [Jen Looper](https://twitter.com/jenlooper), PhD -* **🎨 筆記插畫家：** [Tomomi Imura](https://twitter.com/girlie_mac) -* **✅ 測驗創作者：** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) +* **🎨 筆記插畫師：** [Tomomi Imura](https://twitter.com/girlie_mac) +* **✅ 小測驗創作者：** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) * **🙏 核心貢獻者：** [Evgenii Pishchik](https://github.com/Pe4enIks) ## 其他課程 @@ -216,11 +216,11 @@ CO_OP_TRANSLATOR_METADATA: ## 尋求協助 -如果您遇到卡關或對構建 AI 應用有任何疑問，歡迎加入學習者及經驗豐富的開發者討論 MCP。這是一個支持性強的社群，歡迎提問並自由分享知識。 +如果你遇到困難或者對開發 AI 應用有任何疑問，加入學習者與資深開發者的 MCP 討論群組吧。這是個支持性社群，歡迎提問並無私分享知識。 [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -如果您在構建過程中有產品反饋或錯誤，請訪問： +如果在開發過程中有產品反饋或錯誤，請造訪： [![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) @@ -228,5 +228,5 @@ CO_OP_TRANSLATOR_METADATA: **免責聲明**： -本文件係使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。雖然我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。文件的原始語言版本應視為權威來源。對於關鍵資訊，建議採用專業人工翻譯。本公司不對因使用本翻譯而產生的任何誤解或誤譯負責。 +本文件使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。雖然我們致力於翻譯的準確性，但請注意自動翻譯可能包含錯誤或不準確之處。原始文件的母語版本應視為權威來源。對於重要資訊，建議使用專業人工翻譯。對於因使用本翻譯而產生的任何誤解或錯誤詮釋，我們不承擔任何責任。 \ No newline at end of file diff --git a/translations/tw/lessons/0-course-setup/how-to-run.md b/translations/tw/lessons/0-course-setup/how-to-run.md index 9ffbfa85..db35d969 100644 --- a/translations/tw/lessons/0-course-setup/how-to-run.md +++ b/translations/tw/lessons/0-course-setup/how-to-run.md @@ -1,21 +1,21 @@ # 如何執行程式碼 -這份課程包含許多可執行的範例和實驗，您可能會希望執行它們。為了做到這一點，您需要能夠在課程提供的 Jupyter Notebook 中執行 Python 程式碼。以下是幾種執行程式碼的選項： +本課程包含許多可執行的範例和實驗，你會想要運行這些範例。為此，你需要具備在本課程所提供的 Jupyter 筆記本中執行 Python 程式碼的能力。你有幾種選擇可以執行程式碼： -## 在本地電腦上執行 +## 在你的電腦上本地執行 -若要在本地電腦上執行程式碼，您需要安裝某個版本的 Python。我個人推薦安裝 **[miniconda](https://conda.io/en/latest/miniconda.html)** —— 它是一個輕量級的安裝包，支援 `conda` 套件管理器，用於建立不同的 Python **虛擬環境**。 +若要在你的電腦本地運行程式碼，需要安裝 Python。一個建議是安裝 **[miniconda](https://conda.io/en/latest/miniconda.html)** — 它是一個相當輕量的安裝，支援 `conda` 套件管理工具來管理不同的 Python **虛擬環境**。 -安裝 miniconda 後，您需要克隆此課程的存儲庫並建立一個虛擬環境來使用： +安裝 miniconda 之後，複製本儲存庫並建立一個虛擬環境來使用這門課程： ```bash git clone http://github.com/microsoft/ai-for-beginners @@ -24,19 +24,19 @@ conda env create --name ai4beg --file .devcontainer/environment.yml conda activate ai4beg ``` -### 使用 Visual Studio Code 和 Python 擴展 +### 使用帶有 Python 擴充功能的 Visual Studio Code -使用此課程的最佳方式可能是透過 [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) 搭配 [Python 擴展](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste) 開啟課程。 +本課程在使用 [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) 並安裝 [Python 擴充功能](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste) 時效果最佳。 -> **注意**：當您克隆並在 VS Code 中開啟目錄時，它會自動建議您安裝 Python 擴展。您還需要按照上述步驟安裝 miniconda。 +> **注意**：一旦你複製儲存庫並在 VS Code 中打開目錄，它會自動建議你安裝 Python 擴充功能。你還需要按上述說明安裝 miniconda。 -> **注意**：如果 VS Code 建議您在容器中重新開啟存儲庫，請拒絕此建議以使用本地的 Python 安裝。 +> **注意**：如果 VS Code 建議你在容器中重新打開儲存庫，你應該拒絕此建議以使用本地的 Python 安裝。 ### 在瀏覽器中使用 Jupyter -您也可以直接在瀏覽器中使用 Jupyter 環境。事實上，無論是傳統的 Jupyter 還是 Jupyter Hub，都提供了相當方便的開發環境，包括自動完成、程式碼高亮等功能。 +你也可以在自己的電腦瀏覽器中使用 Jupyter 環境。傳統的 Jupyter 和 JupyterHub 都提供方便的開發環境，具備自動完成、程式碼高亮等功能。 -若要在本地啟動 Jupyter，請進入課程目錄並執行以下指令： +要在本地啟動 Jupyter，請前往課程目錄，並執行： ```bash jupyter notebook @@ -45,33 +45,36 @@ jupyter notebook ```bash jupyterhub ``` -接著，您可以瀏覽任何 `.ipynb` 文件，開啟並開始工作。 +然後你可以瀏覽任何 `.ipynb` 檔案，打開它們並開始工作。 ### 在容器中執行 -另一種替代 Python 安裝的方式是使用容器執行程式碼。由於我們的存儲庫包含特殊的 `.devcontainer` 資料夾，指示如何為此存儲庫建立容器，VS Code 會建議您在容器中重新開啟程式碼。這需要安裝 Docker，並且操作較為複雜，因此我們建議有經驗的使用者採用此方式。 +另一個替代 Python 安裝的選項是直接在容器中執行程式碼。由於我們的儲存庫提供了一個特殊的 `.devcontainer` 資料夾，指示如何為本儲存庫建構容器，VS Code 提供重新在容器中打開程式碼的功能。這需要安裝 Docker，流程也會較複雜，因此建議較有經驗的使用者採用此方案。 -## 在雲端執行 +## 在雲端運行 -如果您不想在本地安裝 Python，並且擁有一些雲端資源，另一個不錯的選擇是在雲端執行程式碼。以下是幾種方式： +如果你不想在本地安裝 Python，並且有雲端資源可用，另一個好選擇是直接在雲端執行程式碼。有幾種方式可以做到： -* 使用 **[GitHub Codespaces](https://github.com/features/codespaces)**，這是一個在 GitHub 上為您建立的虛擬環境，可透過 VS Code 的瀏覽器介面存取。如果您有 Codespaces 的使用權限，只需點擊存儲庫中的 **Code** 按鈕，啟動一個 Codespace，即可快速開始使用。 +* 使用 **[GitHub Codespaces](https://github.com/features/codespaces)**，這是在 GitHub 上為你建立的虛擬環境，可經由 VS Code 瀏覽器介面存取。如果你有 Codespaces 的權限，只需點擊儲存庫中的 **Code** 按鈕，啟動 codespace，馬上即可開始執行。 +* 使用 **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**。[Binder](https://mybinder.org) 提供免費的雲端運算資源，讓你可以測試 GitHub 上的程式碼。首頁有一個按鈕可直接開啟儲存庫於 Binder — 這會快速引導你到 Binder 網站，自動建構底層容器並啟動 Jupyter 網頁介面。 -* 使用 **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**。 [Binder](https://mybinder.org) 是一個免費的雲端計算資源，供像您這樣的使用者在 GitHub 上測試程式碼。首頁上有一個按鈕可以在 Binder 中開啟存儲庫——這會快速將您帶到 Binder 網站，並自動建立底層容器，無縫啟動 Jupyter 網頁介面。 +> **注意**：為防止濫用，Binder 對部分網路資源進行封鎖，這可能導致部分從公共網路下載模型和/或資料集的程式碼無法正常運作，你可能需找尋替代方案。此外，Binder 提供的運算資源相當基礎，訓練過程會較慢，尤其是後期更複雜的課程。 -> **注意**：為了防止濫用，Binder 對某些網路資源的存取進行了限制。這可能會導致某些程式碼無法正常運行，特別是那些需要從公共網路下載模型或數據集的程式碼。您可能需要尋找一些替代方案。此外，Binder 提供的計算資源相對基本，因此在後期更複雜的課程中，訓練速度可能會很慢。 +## 使用 GPU 雲端運行 -## 在雲端使用 GPU 執行 +在本課程後期，有些課程會非常受惠於 GPU 支援。例如，模型訓練否則會非常緩慢。以下是一些選項，特別是如果你透過 [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) 或機構有雲端存取權： -課程中的某些後期課程會因 GPU 支援而受益匪淺，否則訓練速度可能會非常緩慢。如果您透過 [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) 或您的機構擁有雲端資源，以下是幾種選擇： +* 建立 [資料科學虛擬機](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) 並透過 Jupyter 連線。你可以直接在機器上複製儲存庫並開始學習。NC 系列虛擬機支援 GPU。 -* 建立 [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste)，並透過 Jupyter 連接到該虛擬機器。您可以直接在機器上克隆存儲庫並開始學習。NC 系列虛擬機器支援 GPU。 +> **注意**：部分訂閱，包括 Azure for Students，預設不包含 GPU 支援。你可能需要透過技術支援申請額外的 GPU 核心。 -> **注意**：某些訂閱（包括 Azure for Students）並未預設提供 GPU 支援。您可能需要透過技術支援請求額外的 GPU 核心。 +* 建立 [Azure 機器學習工作區](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste)，然後使用其中的 Notebook 功能。[此影片](https://azure-for-academics.github.io/quickstart/azureml-papers/) 示範如何將儲存庫克隆到 Azure ML 筆記本並開始使用。 -* 建立 [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste)，然後使用其中的 Notebook 功能。[這段影片](https://azure-for-academics.github.io/quickstart/azureml-papers/) 介紹了如何將存儲庫克隆到 Azure ML Notebook 並開始使用。 +你也可以使用 Google Colab，該服務附帶部分免費的 GPU 支援，並上傳 Jupyter 筆記本，一筆一筆地執行。 -您也可以使用 Google Colab，它提供一些免費的 GPU 支援，並將 Jupyter Notebook 上傳到 Colab 中逐一執行。 +--- + **免責聲明**： -本文件使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。應以原文文件作為權威來源。對於關鍵資訊，建議尋求專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解釋概不負責。 \ No newline at end of file +本文件係使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。雖然我們努力追求準確性，但請注意，自動翻譯可能包含錯誤或不精確之處。原始文件的母語版本應視為權威資料來源。對於重要資訊，建議採用專業人工翻譯。我們對因使用本翻譯所引起的任何誤解或誤譯不承擔任何責任。 + \ No newline at end of file diff --git a/translations/tw/lessons/1-Intro/README.md b/translations/tw/lessons/1-Intro/README.md index 122303c8..1f61e8c2 100644 --- a/translations/tw/lessons/1-Intro/README.md +++ b/translations/tw/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 人工智慧簡介 -![人工智慧內容摘要的手繪圖](../../../../translated_images/tw/ai-intro.bf28d1ac4235881c.png) +![人工智慧內容摘要的手繪圖](../../../../translated_images/tw/ai-intro.bf28d1ac4235881c.webp) > 手繪筆記由 [Tomomi Imura](https://twitter.com/girlie_mac) 提供 @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: 最初，電腦由 [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage) 發明，用來根據明確定義的程序（演算法）操作數字。現代電腦雖然比19世紀提出的原型複雜得多，但仍然遵循受控計算的理念。因此，只要我們知道達成目標所需的精確步驟，就可以編程讓電腦完成某些事情。 -![一個人的照片](../../../../translated_images/tw/dsh_age.d212a30d4e54fb5f.png) +![一個人的照片](../../../../translated_images/tw/dsh_age.d212a30d4e54fb5f.webp) > 照片由 [Vickie Soshnikova](http://twitter.com/vickievalerie) 提供 @@ -46,7 +46,7 @@ CO_OP_TRANSLATOR_METADATA: 在討論**[智能](https://en.wikipedia.org/wiki/Intelligence)**這個術語時，其中一個問題是我們對此並沒有明確的定義。有人可能認為智能與**抽象思維**或**自我意識**相關，但我們無法準確定義它。 -![貓的照片](../../../../translated_images/tw/photo-cat.8c8e8fb760ffe457.jpg) +![貓的照片](../../../../translated_images/tw/photo-cat.8c8e8fb760ffe457.webp) > [照片](https://unsplash.com/photos/75715CVEJhI)由 [Amber Kipp](https://unsplash.com/@sadmax) 提供，來自 Unsplash @@ -98,13 +98,13 @@ CO_OP_TRANSLATOR_METADATA: > | 那機器學習呢？ | | > |--------------|-----------| -> | 基於電腦通過某些數據學習解決問題的人工智慧部分稱為**機器學習**。我們不會在本課程中考慮傳統機器學習——我們推薦您參考單獨的 [機器學習初學者課程](http://aka.ms/ml-beginners)。 | ![機器學習初學者課程](../../../../translated_images/tw/ml-for-beginners.9e4fed176fd5817d.png) | +> | 基於電腦通過某些數據學習解決問題的人工智慧部分稱為**機器學習**。我們不會在本課程中考慮傳統機器學習——我們推薦您參考單獨的 [機器學習初學者課程](http://aka.ms/ml-beginners)。 | ![機器學習初學者課程](../../../../translated_images/tw/ml-for-beginners.9e4fed176fd5817d.webp) | ## 人工智慧的簡史 人工智慧作為一個領域始於20世紀中期。最初，符號推理是主要方法，並取得了一些重要成功，例如專家系統——能夠在某些有限問題領域中充當專家的電腦程序。然而，很快就發現這種方法的可擴展性不佳。從專家中提取知識、在電腦中表示知識並保持知識庫的準確性，事實證明是一項非常複雜且在許多情況下成本過高的任務。這導致了1970年代的所謂[人工智慧寒冬](https://en.wikipedia.org/wiki/AI_winter)。 -人工智慧簡史 +人工智慧簡史 > 圖片由 [Dmitry Soshnikov](http://soshnikov.com) 提供 @@ -124,7 +124,7 @@ CO_OP_TRANSLATOR_METADATA: * 現代助理，例如 Cortana、Siri 或 Google Assistant，都是混合系統，使用神經網路將語音轉換為文本並識別我們的意圖，然後採用一些推理或明確算法執行所需操作。 * 未來，我們可能期待一個完全基於神經網路的模型能夠自行處理對話。最近的 GPT 和 [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) 神經網路系列顯示了巨大的成功。 -圖靈測試的演變 +圖靈測試的演變 > 圖片由 Dmitry Soshnikov 提供，[照片](https://unsplash.com/photos/r8LmVbUKgns) 由 [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto) 提供，Unsplash ## 最近的人工智慧研究 diff --git a/translations/tw/lessons/2-Symbolic/Animals.ipynb b/translations/tw/lessons/2-Symbolic/Animals.ipynb index 575f9c08..32407e00 100644 --- a/translations/tw/lessons/2-Symbolic/Animals.ipynb +++ b/translations/tw/lessons/2-Symbolic/Animals.ipynb @@ -6,25 +6,25 @@ "collapsed": true }, "source": [ - "# 實現動物專家系統\n", + "# 實作一個動物專家系統\n", "\n", - "範例來源：[AI for Beginners Curriculum](http://github.com/microsoft/ai-for-beginners)。\n", + "一個來自 [AI for Beginners Curriculum](http://github.com/microsoft/ai-for-beginners) 的範例。\n", "\n", - "在此範例中，我們將實現一個簡單的基於知識的系統，根據一些外部特徵來判斷動物。該系統可以用以下的 AND-OR 樹來表示（這只是整個樹的一部分，我們可以輕鬆地添加更多規則）：\n", + "在此範例中，我們將實作一個簡單的知識基礎系統，根據一些物理特徵來判斷動物。系統可以用以下的 AND-OR 樹來表示（這是整個樹的一部分，我們可以輕鬆地添加更多規則）：\n", "\n", - "![](../../../../lessons/2-Symbolic/images/AND-OR-Tree.png)\n" + "![](../../../../../../translated_images/tw/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## 我們自己的專家系統殼層與反向推理\n", + "## 我們自己的專家系統外殼與逆向推理\n", "\n", - "讓我們嘗試基於生成規則定義一種簡單的知識表示語言。我們將使用 Python 類作為關鍵字來定義規則。基本上會有三種類型的類別：\n", + "讓我們嘗試定義一個基於產生規則的簡單知識表示語言。我們將使用 Python 類別作為關鍵字來定義規則。基本上會有 3 種主要的類別：\n", "* `Ask` 代表需要向使用者提問的問題。它包含可能的答案集合。\n", - "* `If` 代表一條規則，它只是用來存儲規則內容的語法糖。\n", - "* `AND`/`OR` 是用來表示樹的 AND/OR 分支的類別。它們僅存儲內部的參數列表。為了簡化程式碼，所有功能都定義在父類別 `Content` 中。\n" + "* `If` 代表一條規則，它只是用語法糖來儲存規則的內容。\n", + "* `AND`/`OR` 是用來表示樹狀結構中 AND/OR 分支的類別。它們只儲存內部的引數列表。為了簡化程式碼，所有功能皆定義在父類別 `Content` 裡。\n" ] }, { @@ -66,7 +66,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "在我們的系統中，工作記憶將包含作為**屬性-值對**的**事實**列表。知識庫可以定義為一個大的字典，將行動（應插入工作記憶中的新事實）映射到條件，這些條件以 AND-OR 表達式表示。此外，一些事實可以被`詢問`。\n" + "在我們的系統中，工作記憶會包含作為**屬性-值對**的**事實**列表。知識庫可以定義為一個大型字典，將動作（應該插入工作記憶的新事實）對應到以 AND-OR 表達式表示的條件。此外，有些事實可以被`Ask`詢問。\n" ] }, { @@ -100,12 +100,12 @@ "metadata": {}, "source": [ "為了執行反向推理，我們將定義 `Knowledgebase` 類別。它將包含：\n", - "* 工作中的 `memory` - 一個將屬性映射到值的字典\n", - "* 知識庫中的 `rules` - 以上述定義的格式表示\n", + "* 工作記憶體 `memory` - 一個將屬性映射到值的字典\n", + "* 知識庫 `rules`，格式如上所述定義\n", "\n", - "兩個主要的方法是：\n", - "* `get` 用於獲取屬性的值，必要時執行推理。例如，`get('color')` 將獲取顏色槽的值（如果需要，會詢問並將值存儲在工作記憶中以供後續使用）。如果我們詢問 `get('color:blue')`，它將詢問顏色，然後根據顏色返回 `y`/`n` 值。\n", - "* `eval` 執行實際的推理，即遍歷 AND/OR 樹，評估子目標等。\n" + "兩個主要方法是：\n", + "* `get` 用於取得屬性的值，必要時執行推理。例如，`get('color')` 將取得一個顏色欄位的值（必要時會詢問，並將值存入工作記憶體以便日後使用）。如果我們詢問 `get('color:blue')`，它會詢問顏色，然後根據顏色回傳 `y`/`n` 值。\n", + "* `eval` 執行實際推理，即遍歷 AND/OR 樹，評估子任務，等等。\n" ] }, { @@ -172,7 +172,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "現在讓我們定義我們的動物知識庫並進行諮詢。請注意，此操作將向您提問。您可以通過輸入 `y`/`n` 來回答是非問題，或者通過指定數字（0..N）來回答具有較多選擇的問題。\n" + "現在讓我們定義我們的動物知識庫並進行諮詢。請注意，此呼叫將會問您問題。您可以對是非題輸入 `y`/`n` 作答，或者對較長的多選題指定數字（0..N）進行回答。\n" ] }, { @@ -229,11 +229,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 使用 PyKnow 進行前向推理\n", + "## 使用 Experta 進行正向推論\n", "\n", - "在接下來的範例中，我們將嘗試使用一個知識表示的函式庫 [PyKnow](https://github.com/buguroo/pyknow/) 來實現前向推理。**PyKnow** 是一個用於在 Python 中建立前向推理系統的函式庫，其設計類似於經典的舊系統 [CLIPS](http://www.clipsrules.net/index.html)。\n", + "在下一個範例中，我們將嘗試使用其中一個知識表示庫 [Experta](https://github.com/nilp0inter/experta) 來實作正向推論。**Experta** 是一個用於在 Python 中建立正向推論系統的庫，其設計類似於經典的舊系統 [CLIPS](http://www.clipsrules.net/index.html)。\n", "\n", - "我們也可以自己實現前向鏈結，這並不會有太大的困難，但簡單的實現通常效率不高。為了更有效地進行規則匹配，會使用一種特殊的演算法 [Rete](https://en.wikipedia.org/wiki/Rete_algorithm)。\n" + "我們當然也可以自己實作正向鏈結，但天真的實作通常效率不高。為了更有效率的規則匹配，使用了一種特殊的演算法 [Rete](https://en.wikipedia.org/wiki/Rete_algorithm)。\n" ] }, { @@ -247,32 +247,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "Collecting git+https://github.com/buguroo/pyknow/\n", - " Cloning https://github.com/buguroo/pyknow/ to /tmp/pip-req-build-3cqeulyl\n", - " Running command git clone --filter=blob:none --quiet https://github.com/buguroo/pyknow/ /tmp/pip-req-build-3cqeulyl\n", - " Resolved https://github.com/buguroo/pyknow/ to commit 48818336f2e9a126f1964f2d8dc22d37ff800fe8\n", - " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hCollecting frozendict==1.2\n", - " Using cached frozendict-1.2.tar.gz (2.6 kB)\n", - " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hCollecting schema==0.6.7\n", - " Using cached schema-0.6.7-py2.py3-none-any.whl (14 kB)\n", - "Building wheels for collected packages: pyknow, frozendict\n", - " Building wheel for pyknow (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for pyknow: filename=pyknow-1.7.0-py3-none-any.whl size=34228 sha256=b7de5b09292c4007667c72f69b98d5a1b5f7324ff15f9dd8e077c3d5f7aade42\n", - " Stored in directory: /tmp/pip-ephem-wheel-cache-k7jpave7/wheels/81/1a/d3/f6c15dbe1955598a37755215f2a10449e7418500d7bd4b9508\n", - " Building wheel for frozendict (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for frozendict: filename=frozendict-1.2-py3-none-any.whl size=3148 sha256=2863d55c240d2409cddf05ccfe600591f8478681549fc97555c47c90dc6bb160\n", - " Stored in directory: /home/rg/.cache/pip/wheels/49/ac/f8/cb8120244e710bdb479c86198b03c7b08c3c2d3d2bf448fd6e\n", - "Successfully built pyknow frozendict\n", - "Installing collected packages: schema, frozendict, pyknow\n", - "Successfully installed frozendict-1.2 pyknow-1.7.0 schema-0.6.7\n" + "Collecting git+https://github.com/nilp0inter/experta\n", + " Cloning https://github.com/nilp0inter/experta to /tmp/pip-req-build-7qurtwk3\n", + " Running command git clone --filter=blob:none --quiet https://github.com/nilp0inter/experta /tmp/pip-req-build-7qurtwk3\n", + " Resolved https://github.com/nilp0inter/experta to commit c6d5834b123861f5ae09e7d07027dc98bec58741\n", + " Installing build dependencies ... \u001b[?25ldone\n", + "\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n", + "\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25hRequirement already satisfied: frozendict~=2.4.6 in /opt/conda/envs/ai4beg/lib/python3.12/site-packages (from experta==1.9.5.dev1) (2.4.7)\n", + "Collecting schema~=0.6.7 (from experta==1.9.5.dev1)\n", + " Downloading schema-0.6.8-py2.py3-none-any.whl.metadata (14 kB)\n", + "Downloading schema-0.6.8-py2.py3-none-any.whl (14 kB)\n", + "Building wheels for collected packages: experta\n", + " Building wheel for experta (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25h Created wheel for experta: filename=experta-1.9.5.dev1-py3-none-any.whl size=34804 sha256=888c459512a5e713f4b674caa9a0f96cfdf07ec0d6eb56cc318ce0653d218014\n", + " Stored in directory: /tmp/pip-ephem-wheel-cache-1eeii9zy/wheels/3d/e8/bb/22d7956359603fa8dd679aa09f5b8efb3f29991c3986fdc787\n", + "Successfully built experta\n", + "Installing collected packages: schema, experta\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2/2\u001b[0m [experta]\n", + "\u001b[1A\u001b[2KSuccessfully installed experta-1.9.5.dev1 schema-0.6.8\n" ] } ], "source": [ "import sys\n", - "!{sys.executable} -m pip install git+https://github.com/buguroo/pyknow/" + "!{sys.executable} -m pip install git+https://github.com/nilp0inter/experta" ] }, { @@ -283,15 +282,15 @@ }, "outputs": [], "source": [ - "from pyknow import *\n", - "#import pyknow" + "from experta import *\n", + "#import experta" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "我們將把系統定義為一個繼承 `KnowledgeEngine` 的類別。每條規則由一個帶有 `@Rule` 註解的單獨函數定義，該註解指定了規則應該觸發的時機。在規則內部，我們可以使用 `declare` 函數添加新的事實，添加這些事實將導致前向推理引擎調用更多的規則。\n" + "我們將系統定義為繼承自 `KnowledgeEngine` 的類別。每個規則由帶有 `@Rule` 註解的單獨函式定義，該註解指定規則何時觸發。在規則內部，我們可以使用 `declare` 函式新增新的事實，新增這些事實將導致前向推理引擎調用更多規則。\n" ] }, { @@ -378,7 +377,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "一旦我們定義了一個知識庫，我們會用一些初始事實填充工作記憶，然後調用 `run()` 方法來執行推理。結果你可以看到新的推導事實被添加到工作記憶中，包括關於動物的最終事實（如果我們正確設置了所有初始事實）。\n" + "一旦我們定義了知識庫，我們會將一些初始事實填入工作記憶，然後呼叫 `run()` 方法來執行推理。你可以看到作為結果，新的推論事實會被加入到工作記憶中，包括關於動物的最終事實（如果我們正確設定了所有初始事實）。\n" ] }, { @@ -440,7 +439,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n---\n\n**免責聲明**： \n本文件已使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。儘管我們努力確保翻譯的準確性，但請注意，自動翻譯可能包含錯誤或不準確之處。原始文件的母語版本應被視為權威來源。對於關鍵信息，建議使用專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解釋不承擔責任。\n" + "---\n\n\n**免責聲明**：\n本文件係使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。雖然我們致力於確保準確性，但請注意，機器翻譯可能存在錯誤或不準確之處。原始文件的母語版本應視為權威來源。對於重要資訊，建議採用專業人工翻譯。我們不對因使用本翻譯所產生的任何誤解或誤譯承擔責任。\n\n" ] } ], @@ -467,8 +466,8 @@ "version": "3.11.2" }, "coopTranslator": { - "original_hash": "ab2bd97b0453415b89a469284609a8ce", - "translation_date": "2025-08-31T10:07:14+00:00", + "original_hash": "8ef43db4b9182239fd150a76bd494fdb", + "translation_date": "2026-01-15T11:44:31+00:00", "source_file": "lessons/2-Symbolic/Animals.ipynb", "language_code": "tw" } diff --git a/translations/tw/lessons/2-Symbolic/README.md b/translations/tw/lessons/2-Symbolic/README.md index eb8b5ef7..593ef4d4 100644 --- a/translations/tw/lessons/2-Symbolic/README.md +++ b/translations/tw/lessons/2-Symbolic/README.md @@ -1,116 +1,116 @@ # 知識表示與專家系統 -![符號 AI 內容摘要](../../../../translated_images/tw/ai-symbolic.715a30cb610411a6.png) +![Symbolic AI 內容摘要](../../../../../../translated_images/tw/ai-symbolic.715a30cb610411a6.webp) -> Sketchnote by [Tomomi Imura](https://twitter.com/girlie_mac) +> 筆記作者：[Tomomi Imura](https://twitter.com/girlie_mac) -人工智慧的追求基於尋找知識，試圖像人類一樣理解世界。但要如何實現這一目標呢？ +人工智慧的追求是基於對知識的探索，以類似人類理解世界的方式來解釋世界。但你該如何達成這個目標呢？ ## [課前測驗](https://ff-quizzes.netlify.app/en/ai/quiz/3) -在人工智慧的早期，採用自上而下的方法來創建智能系統（在上一課中討論過）非常流行。這種方法的核心思想是將人類的知識提取成機器可讀的形式，然後用它來自動解決問題。這種方法基於兩個重要概念： +在 AI 的早期階段，自頂向下創建智能系統的方法（在前一課討論過）非常受歡迎。這個想法是將人類的知識提取成機器可讀的形式，然後用它來自動解決問題。這種方法基於兩大核心理念： * 知識表示 * 推理 ## 知識表示 -符號 AI 的一個重要概念是**知識**。需要將知識與*信息*或*數據*區分開。例如，我們可以說書籍包含知識，因為人們可以通過學習書籍成為專家。然而，書籍實際上包含的是*數據*，而我們通過閱讀書籍並將這些數據整合到我們的世界模型中，將數據轉化為知識。 +符號 AI 中一個重要的概念是**知識**。區分知識和*資訊*或*數據*非常重要。例如，可以說書籍包含知識，因為我們通過學習書籍可以成為專家。然而，書中實際包含的是所謂的*數據*，透過閱讀書籍並將這些數據整合到我們的世界模型中，我們將數據轉變為知識。 -> ✅ **知識**是我們頭腦中所包含的內容，代表我們對世界的理解。它是通過主動的**學習**過程獲得的，這個過程將我們接收到的信息片段整合到我們的世界模型中。 +> ✅ **知識** 是指存在於我們腦海中，代表我們對世界理解的東西。它是透過一個主動的**學習**過程獲得，將我們收到的資訊片段整合進我們的活躍世界模型中。 -通常，我們不會嚴格定義知識，而是通過 [DIKW 金字塔](https://en.wikipedia.org/wiki/DIKW_pyramid)將其與其他相關概念對齊。金字塔包含以下概念： +通常，我們不會嚴格定義知識，而是透過[DIKW 金字塔](https://en.wikipedia.org/wiki/DIKW_pyramid)將其與其他相關概念對齊。包含以下概念： -* **數據**是以物理媒介表示的內容，例如書面文字或口頭語言。數據獨立於人類存在，可以在人與人之間傳遞。 -* **信息**是我們在頭腦中對數據的解釋。例如，當我們聽到“電腦”這個詞時，我們對它有一定的理解。 -* **知識**是信息被整合到我們的世界模型中。例如，一旦我們學會了什麼是電腦，我們就開始對它的工作原理、價格以及用途有一些想法。這些相互關聯的概念網絡構成了我們的知識。 -* **智慧**是我們對世界理解的更高層次，代表著*元知識*，例如關於如何以及何時使用知識的概念。 +* **數據（Data）** 是指表現在物理媒介上的東西，如書面文字或口語。數據獨立於人類存在，且可在不同人間傳遞。 +* **資訊（Information）** 是我們對數據的解讀。例如，當我們聽到「電腦」這個字時，就會對它有所理解。 +* **知識（Knowledge）** 是將資訊整合到我們的世界模型中。例如，一旦學習到電腦是什麼，我們就開始瞭解它如何運作、價格多少、用途為何。這種相互關聯的概念網絡形成了我們的知識。 +* **智慧（Wisdom）** 是我們對世界理解的更高層次，代表*元知識*，例如有關知識應用的時機與方式的概念。 - + -*圖片來源：[維基百科](https://commons.wikimedia.org/w/index.php?curid=37705247)，作者 Longlivetheux - 自製作品，CC BY-SA 4.0* +*圖片來源 [維基百科](https://commons.wikimedia.org/w/index.php?curid=37705247)，作者 Longlivetheux 自作，CC BY-SA 4.0* -因此，**知識表示**的問題是找到某種有效的方法，將知識以數據的形式表示在計算機中，使其能夠自動使用。這可以看作是一個光譜： +因此，**知識表示** 問題是尋找一些有效的方法來以資料形式在電腦內代表知識，使其可被自動使用。這可視為一個光譜： -![知識表示光譜](../../../../translated_images/tw/knowledge-spectrum.b60df631852c0217.png) +![知識表示光譜](../../../../../../translated_images/tw/knowledge-spectrum.b60df631852c0217.webp) -> 圖片來源：[Dmitry Soshnikov](http://soshnikov.com) +> 圖片來源 [Dmitry Soshnikov](http://soshnikov.com) -* 在左側，有非常簡單的知識表示類型，可以被計算機有效使用。最簡單的是算法式，當知識以計算機程序的形式表示時。然而，這並不是表示知識的最佳方式，因為它不夠靈活。我們頭腦中的知識通常是非算法的。 -* 在右側，有像自然文本這樣的表示方式。它是最強大的，但無法用於自動推理。 +* 左邊是電腦可有效使用的非常簡單的知識表示類型。最簡單的是算法式表示，當知識以電腦程式形式存在。然而，這並非最佳的知識表示方式，因為它不具彈性。腦中知識通常是非算法的。 +* 右邊則是自然文字等表示，它功能最強大，但無法用於自動推理。 -> ✅ 想一想你如何在頭腦中表示知識並將其轉化為筆記。是否有某種格式能幫助你更好地記住？ +> ✅ 花一分鐘想想你如何在腦海中表達知識，並將它轉換成筆記。是否有特定格式有助於記憶保留？ -## 計算機知識表示的分類 +## 電腦知識表示的分類 -我們可以將不同的計算機知識表示方法分為以下幾類： +我們可以將不同的電腦知識表示方法分為以下類別： -* **網絡表示**基於我們頭腦中有一個相互關聯的概念網絡。我們可以嘗試在計算機中以圖的形式重現這些網絡——即所謂的**語義網絡**。 +* **網絡表示** 基於我們腦中存在互相關聯的概念網絡。可嘗試在電腦中以圖形方式重現這些網絡，也就是所謂的**語意網絡**。 -1. **對象-屬性-值三元組**或**屬性-值對**。由於圖可以在計算機中表示為節點和邊的列表，我們可以通過三元組列表來表示語義網絡，包含對象、屬性和值。例如，我們可以建立以下關於程式語言的三元組： +1. **物件-屬性-值三元組** 或 **屬性-值對**。由於電腦中圖形可用節點與邊的列表來表示，於是可用物件、屬性和值組成的三元組來表現語意網絡。例如關於程式語言，可建立以下三元組： -對象 | 屬性 | 值 ------|------|----- -Python | 是 | 無類型語言 +物件 | 屬性 | 值 +-------|-----------|------ +Python | 是 | 無型別程式語言 Python | 發明者 | Guido van Rossum Python | 區塊語法 | 縮排 -無類型語言 | 沒有 | 類型定義 +無型別程式語言 | 不具備 | 型別定義 -> ✅ 想一想三元組如何用於表示其他類型的知識。 +> ✅ 想想三元組如何用來表示其他類型的知識。 -2. **層次表示**強調我們通常在頭腦中創建對象的層次結構。例如，我們知道金絲雀是一種鳥類，而所有鳥類都有翅膀。我們也知道金絲雀通常是什麼顏色，以及它們的飛行速度。 +2. **階層表示** 強調我們常在腦中建立物件階層。例如，我們知道金絲雀是鳥類，鳥類都有翅膀，且了解金絲雀通常的顏色和飛行速度。 - - **框架表示**基於將每個對象或對象類表示為**框架**，框架包含**槽**。槽可以有可能的默認值、值限制或存儲的程序，這些程序可以被調用以獲得槽的值。所有框架形成一個層次結構，類似於面向對象程式語言中的對象層次結構。 - - **場景**是表示隨時間展開的複雜情境的一種特殊框架。 + - **框架表示（Frame representation）** 以每個物件或物件類別表示為一個**框架**，內含**插槽（slots）**。插槽可能有預設值、值限制，或可呼叫的程序來獲取插槽值。所有框架形成類似物件導向程式語言中的物件階層。 + - **場景（Scenarios）** 是一種特殊的框架，用來表示可能隨時間展開的複雜情境。 **Python** -槽 | 值 | 默認值 | 範圍 | -----|----|--------|------ +插槽 | 值 | 預設值 | 範圍 | +-----|-------|---------------|----------| 名稱 | Python | | | -是 | 無類型語言 | | | -變數命名方式 | | 駝峰式命名 | | +是屬於 | 無型別程式語言 | | | +變數命名格式 | | CamelCase | | 程式長度 | | | 5-5000 行 | 區塊語法 | 縮排 | | | -3. **程序表示**基於通過一系列動作來表示知識，當某些條件發生時可以執行。 - - 產生規則是 if-then 語句，允許我們得出結論。例如，醫生可以有一條規則說**如果**患者有高燒**或**血液檢測中 C 反應蛋白水平高**那麼**他有炎症。一旦遇到其中一個條件，我們可以得出關於炎症的結論，然後在進一步推理中使用它。 - - 算法可以被認為是另一種程序表示形式，儘管它們幾乎從未直接用於基於知識的系統。 +3. **程序式表示** 基於以一組動作表示知識，於特定條件下執行這些動作。 + - 生產規則是 if-then 陳述句，可用來推理結論。例如，醫生可能有規則：**如果** 病人體溫高 **或** 血液檢查中的 C 反應蛋白指數高 **那麼** 他有發炎。一旦遇到條件，即可推論發炎，並用於後續推理。 + - 演算法也可視為另一種程序式表示，儘管在基於知識的系統中幾乎不直接使用。 -4. **邏輯**最初由亞里士多德提出，作為表示普遍人類知識的一種方式。 - - 謂詞邏輯作為數學理論過於豐富而無法計算，因此通常使用它的一些子集，例如 Prolog 中使用的 Horn 子句。 - - 描述邏輯是一系列邏輯系統，用於表示和推理對象層次結構以及分佈式知識表示，例如*語義網*。 +4. **邏輯** 起始於亞里斯多德提出作為表達普遍人類知識的方法。 + - 謂詞邏輯作為數學理論過於豐富，無法完全計算，因此通常使用某些子集，如 Prolog 中的 Horn 子句。 + - 描述邏輯是一系列用於表示與推論階層物件及分散式知識表示（如*語意網*）的邏輯系統。 ## 專家系統 -符號 AI 的早期成功之一是所謂的**專家系統**——設計用於在某些有限問題領域中充當專家的計算機系統。它們基於從一位或多位人類專家提取的**知識庫**，並包含一個在其上進行推理的**推理引擎**。 +符號 AI 的早期成功之一是所謂的**專家系統** — 設計成在有限問題領域中，充當專家的電腦系統。它們基於由一人或多位專家提取的**知識庫**，並含有執行推理的**推理引擎**。 -![人類架構](../../../../translated_images/tw/arch-human.5d4d35f1bba3ab1c.png) | ![基於知識的系統架構](../../../../translated_images/tw/arch-kbs.3ec5c150b09fa8da.png) -----------------------------------|---------------------------------------- -人類神經系統的簡化結構 | 基於知識的系統架構 +![人類架構](../../../../../../translated_images/tw/arch-human.5d4d35f1bba3ab1c.webp) | ![知識基礎系統](../../../../../../translated_images/tw/arch-kbs.3ec5c150b09fa8da.webp) +---------------------------------------------|------------------------------------------------ +人類神經系統簡化結構 | 知識基礎系統架構 -專家系統的構建類似於人類的推理系統，該系統包含**短期記憶**和**長期記憶**。同樣，在基於知識的系統中，我們區分以下組件： +專家系統的構造類似人類推理系統，包含**短期記憶**和**長期記憶**。類似地，知識基礎系統區分以下元件： -* **問題記憶**：包含當前正在解決的問題的知識，例如患者的體溫或血壓、是否有炎症等。這些知識也被稱為**靜態知識**，因為它包含了我們目前對問題的了解的快照——即所謂的*問題狀態*。 -* **知識庫**：表示關於問題領域的長期知識。它是從人類專家手動提取的，並且不會因諮詢而改變。由於它使我們能夠從一個問題狀態導航到另一個問題狀態，它也被稱為**動態知識**。 -* **推理引擎**：負責協調在問題狀態空間中的搜索過程，必要時向用戶提問。它還負責找到適合每個狀態的規則。 +* **問題記憶**：包含當前正解決問題的知識，如患者的體溫或血壓，是否有發炎等。此知識稱為**靜態知識**，因為它記錄目前問題的快照，也稱為*問題狀態*。 +* **知識庫**：代表問題領域的長期知識。手動從人類專家提取，不會因諮詢而變動。它讓我們能從一個問題狀態轉到另一個，也稱為**動態知識**。 +* **推理引擎**：協調整個問題狀態空間中的搜尋過程，必要時向使用者提問，也負責找出適用於每個問題狀態的規則。 -例如，讓我們考慮以下基於動物物理特徵的專家系統： +以下為根據動物物理特徵判斷動物的專家系統示例： -![AND-OR 樹](../../../../translated_images/tw/AND-OR-Tree.5592d2c70187f283.png) +![AND-OR 樹](../../../../../../translated_images/tw/AND-OR-Tree.5592d2c70187f283.webp) -> 圖片來源：[Dmitry Soshnikov](http://soshnikov.com) +> 圖片來源 [Dmitry Soshnikov](http://soshnikov.com) -此圖表稱為**AND-OR 樹**，它是產生規則集的圖形表示。在提取專家知識的初期，繪製樹是有用的。要在計算機中表示知識，使用規則會更方便： +此圖稱為**AND-OR 樹**，是生產規則集的圖形表示。繪製樹狀圖有助於初期從專家處提取知識。在電腦中表示知識則較方便用規則表示： ``` IF the animal eats meat @@ -121,78 +121,78 @@ OR (animal has sharp teeth THEN the animal is a carnivore ``` -你會注意到規則左側的每個條件和動作本質上都是對象-屬性-值（OAV）三元組。**工作記憶**包含與當前正在解決的問題相關的 OAV 三元組集。**規則引擎**尋找條件滿足的規則並應用它們，將另一個三元組添加到工作記憶中。 +你可以發現，規則左側條件與動作本質上是物件-屬性-值 (OAV) 三元組。**工作記憶** 包含當前正解決問題的 OAV 三元組集合。**規則引擎** 尋找符合條件的規則並執行，向工作記憶新增三元組。 -> ✅ 試著畫出你喜歡的主題的 AND-OR 樹！ +> ✅ 嘗試寫一個你喜歡主題的 AND-OR 樹！ -### 前向推理與後向推理 +### 前向推理 vs. 後向推理 -上述過程稱為**前向推理**。它從工作記憶中可用的初始問題數據開始，然後執行以下推理循環： +上述過程稱為**前向推理**。它從工作記憶中已知的初始資料開始，然後執行以下推理循環： -1. 如果目標屬性存在於工作記憶中——停止並給出結果 -2. 查找所有條件當前滿足的規則——獲得**衝突集**規則。 -3. 執行**衝突解決**——選擇一條將在此步驟中執行的規則。可能有不同的衝突解決策略： +1. 若目標屬性已存在於工作記憶中，停止並返回結果。 +2. 找出所有條件當前成立的規則，得到**衝突集**。 +3. 執行**衝突解決** — 選擇本步執行的規則。衝突解決策略包括： - 選擇知識庫中第一個適用的規則 - 隨機選擇一條規則 - - 選擇*更具體*的規則，即滿足左側（LHS）最多條件的規則 -4. 應用選定的規則並將新知識插入問題狀態 -5. 從第 1 步重複。 + - 選擇*更具體*的規則，即符合條件（左側）最多的規則 +4. 執行選中規則，將新知識插入問題狀態中。 +5. 從步驟 1 重複。 -然而，在某些情況下，我們可能希望從對問題的空白知識開始，並通過提問來幫助我們得出結論。例如，在進行醫學診斷時，我們通常不會在診斷患者之前提前進行所有醫學分析。我們更希望在需要做出決定時進行分析。 +但有些情況下，我們希望從空白知識開始，透過提問來導致結論。例如醫療診斷通常不會事先做完所有檢驗，而是根據診斷需要才決定檢驗項目。 -此過程可以使用**後向推理**建模。它由**目標**驅動——即我們希望找到的屬性值： +該過程可用**後向推理** 模型。它是由**目標**驅動 — 我們想找到的屬性值： -1. 選擇所有可以給出目標值的規則（即目標在右側（RHS））——衝突集 -1. 如果該屬性沒有規則，或者有規則表明我們應該向用戶詢問該值——詢問該值，否則： -1. 使用衝突解決策略選擇一條規則作為*假設*——我們將嘗試證明它 -1. 對規則左側的所有屬性重複該過程，嘗試將它們作為目標證明 -1. 如果過程在任何時候失敗——在第 3 步使用另一條規則。 +1. 選擇所有能得出目標值的規則 (目標在右側) — 衝突集 +2. 若找不到相關規則，或有規則指示需直接詢問使用者，則詢問；否則： +3. 透過衝突解決策略選一條作為*假設*，嘗試證實它 +4. 遞迴處理規則左側所有屬性，嘗試證明它們為目標 +5. 若任何時刻失敗，返回步驟 3 換用其他規則。 -> ✅ 在哪些情況下前向推理更合適？後向推理又適合哪些情況？ +> ✅ 何種情況適合使用前向推理？又何時適合後向推理？ -### 專家系統的實現 +### 專家系統的實作 -專家系統可以使用不同的工具來實現： +專家系統可以用不同工具實作： -* 直接使用某些高級程式語言進行編程。這不是最好的方法，因為基於知識的系統的主要優勢是知識與推理分離，並且問題領域的專家應該能夠在不理解推理過程細節的情況下編寫規則。 -* 使用**專家系統外殼**，即專門設計用於使用某種知識表示語言填充知識的系統。 +* 使用高階程式語言直接編寫。這並非最佳選擇，因為知識基礎系統的主要優勢是知識與推理分離，並且理想狀況下，問題領域專家應能不必瞭解推理細節而撰寫規則。 +* 使用**專家系統外殼**，即專為輸入知識而設計，並以某種知識表示語言編寫的系統。 ## ✍️ 練習：動物推理 -請參考 [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb)，了解如何實現前向和後向推理的專家系統。 +請參見 [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) 了解前向與後向推理專家系統的範例實作。 -> **注意**：此示例相對簡單，只能提供專家系統的基本概念。一旦你開始創建這樣的系統，只有當規則數量達到一定程度（大約 200+）時，你才會注意到系統的一些*智能*行為。在某些時候，規則變得過於複雜，難以全部記住，此時你可能會開始思考為什麼系統會做出某些決定。然而，基於知識的系統的一個重要特徵是你始終可以*解釋*每個決定是如何做出的。 +> **註**：此範例相當簡單，只是展示專家系統的概念。當你開始建立這類系統，至少達到約 200 條規則以上時，系統才會展現某種*智能*行為。某個階段，規則變得過於複雜而難以完全記憶，屆時你可能會想知道系統為什麼做出特定決策。不過，知識基礎系統重要特點是可以隨時*解釋*決策的產生過程。 -## 本體論與語義網 +## 本體論與語意網 -20 世紀末，有一項倡議使用知識表示來註釋互聯網資源，以便能夠找到符合非常特定查詢的資源。這一運動被稱為**語義網**，它依賴於以下幾個概念： +20 世紀末，出現一個倡議，使用知識表示為網際網路資源做標註，以便能找到非常具體查詢對應的資源。這個運動稱為**語意網**，涵蓋以下概念： -- 基於**[描述邏輯](https://en.wikipedia.org/wiki/Description_logic)**（DL）的特殊知識表示。它類似於框架知識表示，因為它構建了具有屬性的對象層次結構，但它具有正式的邏輯語義和推理。描述邏輯有一整個家族，平衡了表達能力與推理的算法複雜性。 -- 分佈式知識表示，其中所有概念都由全局 URI 標識符表示，使得可以創建跨越互聯網的知識層次結構。 -- 一系列基於 XML 的知識描述語言：RDF（資源描述框架）、RDFS（RDF Schema）、OWL（網絡本體語言）。 +- 一種基於**[描述邏輯](https://en.wikipedia.org/wiki/Description_logic)**（DL）的特殊知識表示。它類似框架知識表示，建構具有屬性的物件階層，但具備形式邏輯語義及推理功能。描述邏輯有整個系列，用以權衡表達力與推理演算法的複雜度。 +- 分散式知識表示，所有概念都用全球唯一的 URI 識別符號表示，使得可以建立跨網際網路的知識階層。 +- 一族基於 XML 的知識描述語言：RDF（資源描述框架）、RDFS（RDF 架構）、OWL（本體網路語言）。 -語義網中的核心概念是**本體**。它指的是使用某種形式化知識表示對問題領域進行明確的規範。最簡單的本體可能僅僅是問題領域中的對象層次結構，但更複雜的本體會包含可用於推理的規則。 +語義網的一個核心概念是**本體（Ontology）**。指的是使用某種形式化知識表示對問題領域進行明確規範。最簡單的本體只是一個問題領域中的物件階層，但更複雜的本體會包含可用於推理的規則。 -在語義網中，所有表示都基於三元組。每個對象和每個關係都由 URI 唯一標識。例如，如果我們想表達這份 AI 課程是由 Dmitry Soshnikov 在 2022 年 1 月 1 日開發的——以下是我們可以使用的三元組： +在語義網中，所有表示均基於三元組。每個物件和每個關係均由 URI 唯一標識。例如，如果我們想要陳述這個 AI 課程是由 Dmitry Soshnikov 於 2022 年 1 月 1 日開發的事實——我們可以使用以下三元組： - + ``` -http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 13, 2007” +http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 1, 2022” http://github.com/microsoft/ai-for-beginners http://purl.org/dc/elements/1.1/creator http://soshnikov.com ``` -> ✅ 這裡的 `http://www.example.com/terms/creation-date` 和 `http://purl.org/dc/elements/1.1/creator` 是一些公認且通用的 URI，用於表達*創建者*和*創建日期*的概念。 +> ✅ 此處的 `http://www.example.com/terms/creation-date` 和 `http://purl.org/dc/elements/1.1/creator` 是表達*創建者*和*創建日期*概念的一些知名且被普遍接受的 URI。 -在更複雜的情況下，如果我們想定義一個創建者列表，可以使用 RDF 中定義的一些數據結構。 +在較複雜的情況下，如果我們想定義一個創建者列表，可以使用 RDF 中定義的一些資料結構。 - + -> 上述圖表由 [Dmitry Soshnikov](http://soshnikov.com) 提供 +> 上述圖由 [Dmitry Soshnikov](http://soshnikov.com) 製作 -語義網的發展因搜索引擎和自然語言處理技術的成功而有所放緩，這些技術能夠從文本中提取結構化數據。然而，在某些領域仍然有重要的努力來維護本體和知識庫。以下是幾個值得注意的項目： +語義網的建設進展在某種程度上被搜尋引擎和自然語言處理技術的成功所放慢，這些技術允許從文本中抽取結構化資料。然而，在某些領域中，仍有大量努力用於維護本體和知識庫。值得注意的幾個專案： -* [WikiData](https://wikidata.org/) 是與 Wikipedia 相關的機器可讀知識庫集合。大部分數據是從 Wikipedia 的*信息框*中挖掘出來的，這些信息框是 Wikipedia 頁面中的結構化內容片段。你可以使用 SPARQL（一種語義網的特殊查詢語言）[查詢](https://query.wikidata.org/) WikiData。以下是一個示例查詢，顯示人類中最常見的眼睛顏色： +* [WikiData](https://wikidata.org/) 是一個機器可讀的知識庫集合，與維基百科相關聯。大多數資料來源於維基百科的 *InfoBoxes*，即維基百科頁面內的結構化內容片段。你可以用 SPARQL（一種語義網專用查詢語言）[查詢](https://query.wikidata.org/) wikidata。這裡是一個顯示人類最常見眼睛顏色的範例查詢： ```sparql #defaultView:BubbleChart @@ -206,47 +206,52 @@ WHERE GROUP BY ?eyeColorLabel ``` -* [DBpedia](https://www.dbpedia.org/) 是另一個類似於 WikiData 的項目。 +* [DBpedia](https://www.dbpedia.org/) 是另一個類似 WikiData 的努力。 -> ✅ 如果你想嘗試構建自己的本體，或打開現有的本體，有一個很棒的可視化本體編輯器叫 [Protégé](https://protege.stanford.edu/)。你可以下載它，或者在線使用。 +> ✅ 如果你想嘗試建立自己的本體，或者開啟現有的本體，可以使用一個很棒的視覺化本體編輯器 [Protégé](https://protege.stanford.edu/)。下載它或線上使用。 - + -*Web Protégé 編輯器打開了 Romanov 家族本體。截圖由 Dmitry Soshnikov 提供* +*Web Protégé 編輯器開啟羅曼諾夫家族本體。截圖由 Dmitry Soshnikov 提供* ## ✍️ 練習：家族本體 -請參考 [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb)，了解如何使用語義網技術推理家族關係。我們將使用常見的 GEDCOM 格式表示的家族樹和家族關係的本體，為給定的一組個體構建所有家族關係的圖。 -## 微軟概念圖 +請參閱 [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) 範例，演示如何使用語義網技術推理家庭關係。我們將採用以常見 GEDCOM 格式表示的家譜和一個家庭關係本體，為給定成員集建立所有家庭關係的圖譜。 -在大多數情況下，本體是由人工仔細創建的。然而，也可以從非結構化數據中**挖掘**本體，例如從自然語言文本中。 +## Microsoft 概念圖譜 -微軟研究院曾進行過這樣的嘗試，並推出了 [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste)。 +在大多數情況下，本體是透過人工仔細建立的。然而，也可以從非結構化資料中**挖掘**本體，例如從自然語言文本中。 -這是一個使用 `is-a` 繼承關係將實體分組的大型集合。它可以回答像「微軟是什麼？」這樣的問題——答案可能是「一家公司，概率為 0.87；一個品牌，概率為 0.75」。 +其中一次嘗試由微軟研究院進行，結果是 [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste)。 -該圖可以通過 REST API 獲取，也可以作為一個大型可下載的文本文件，其中列出了所有的實體對。 +它是使用 `is-a` 繼承關係聚合在一起的大量實體集合。允許回答「微軟是什麼？」這樣的問題——答案可能是「87% 機率是一家公司，75% 機率是一個品牌」。 -## ✍️ 練習：概念圖 +該圖譜可透過 REST API 使用，或作為一個大型可下載的文本檔列出所有實體對。 -試試 [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) 筆記本，看看我們如何使用 Microsoft Concept Graph 將新聞文章分組到幾個類別中。 +## ✍️ 練習：概念圖譜 + +試試 [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) 筆記本，看看如何使用 Microsoft 概念圖譜將新聞文章分組為幾個類別。 ## 結論 -如今，AI 通常被認為是*機器學習*或*神經網絡*的代名詞。然而，人類也表現出明確的推理能力，而這是神經網絡目前無法處理的。在實際項目中，明確的推理仍然被用來執行需要解釋或能夠以可控方式修改系統行為的任務。 +當前，AI 常被視為*機器學習*或*神經網路*的代名詞。然而，人類也展示出顯式推理，這是當前神經網絡尚未處理的東西。在實際專案中，顯式推理仍用於執行需要解釋或能以受控方式調整系統行為的任務。 ## 🚀 挑戰 -在與本課程相關的家族本體筆記本中，有機會嘗試其他家族關係。試著發現家族樹中人物之間的新聯繫。 +在本課程相關的家族本體筆記本中，提供了嘗試其他家庭關係的機會。試著發現家譜中人物之間的新連結。 -## [課後測驗](https://ff-quizzes.netlify.app/en/ai/quiz/4) +## [課後小測驗](https://ff-quizzes.netlify.app/en/ai/quiz/4) -## 回顧與自學 +## 複習與自學 -在互聯網上進行一些研究，探索人類試圖量化和編碼知識的領域。了解布魯姆的教育目標分類法，回顧歷史，了解人類如何試圖理解世界。研究林奈斯的工作，看看他如何創建生物分類法，並觀察德米特里·門捷列夫如何創建化學元素的描述和分組方式。你還能找到哪些有趣的例子？ +在網路上搜尋人類試圖量化與編碼知識的領域。查看布魯姆分類法，並回顧歷史，瞭解人類如何嘗試理解世界。探索林奈製作生物分類法的工作，並觀察德米特里·孟德爾葉夫如何創造化學元素的描述與分組方式。還能找到什麼其他有趣的例子？ -**作業**：[構建一個本體](assignment.md) +**作業**: [建立本體](assignment.md) --- + +**免責聲明**： +本文件係使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 翻譯而成。雖然我們致力於確保準確性，但請注意，自動翻譯可能包含錯誤或不準確之處。原始文件之母語版本應視為權威來源。對於重要資訊，建議尋求專業人工翻譯。我們不對因使用本翻譯而產生之任何誤解或誤釋負責。 + \ No newline at end of file diff --git a/translations/tw/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/tw/lessons/3-NeuralNetworks/05-Frameworks/README.md index cf8df536..3f0efd33 100644 --- a/translations/tw/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/tw/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ CO_OP_TRANSLATOR_METADATA: 考慮以下近似 5 個點的問題（圖中的 `x` 表示點）： -![線性模型](../../../../../translated_images/tw/overfit1.f24b71c6f652e59e.jpg) | ![過擬合模型](../../../../../translated_images/tw/overfit2.131f5800ae10ca5e.jpg) +![線性模型](../../../../../translated_images/tw/overfit1.f24b71c6f652e59e.webp) | ![過擬合模型](../../../../../translated_images/tw/overfit2.131f5800ae10ca5e.webp) -------------------------|-------------------------- **線性模型，2 個參數** | **非線性模型，7 個參數** 訓練誤差 = 5.3 | 訓練誤差 = 0 @@ -79,7 +79,7 @@ CO_OP_TRANSLATOR_METADATA: 從上圖可以看出，過擬合可以通過非常低的訓練誤差和非常高的驗證誤差來檢測。通常在訓練過程中，我們會看到訓練誤差和驗證誤差都開始下降，但在某個時候，驗證誤差可能停止下降並開始上升。這將是過擬合的跡象，表明我們應該停止訓練（或者至少保存模型的快照）。 -![過擬合圖示](../../../../../translated_images/tw/Overfitting.408ad91cd90b4371.png) +![過擬合圖示](../../../../../translated_images/tw/Overfitting.408ad91cd90b4371.webp) ## 如何防止過擬合 diff --git a/translations/tw/lessons/3-NeuralNetworks/README.md b/translations/tw/lessons/3-NeuralNetworks/README.md index e6ee11ca..b863925f 100644 --- a/translations/tw/lessons/3-NeuralNetworks/README.md +++ b/translations/tw/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 神經網路簡介 -![神經網路內容摘要的手繪圖](../../../../translated_images/tw/ai-neuralnetworks.1c687ae40bc86e83.png) +![神經網路內容摘要的手繪圖](../../../../translated_images/tw/ai-neuralnetworks.1c687ae40bc86e83.webp) 如我們在介紹中所討論的，實現智能的一種方法是訓練一個**計算機模型**或**人工大腦**。自20世紀中期以來，研究人員嘗試了不同的數學模型，直到最近這一方向證明非常成功。這些模仿大腦的數學模型被稱為**神經網路**。 @@ -36,13 +36,13 @@ CO_OP_TRANSLATOR_METADATA: 從生物學中，我們知道大腦由神經細胞（神經元）組成，每個神經元有多個“輸入”（樹突）和一個“輸出”（軸突）。樹突和軸突都能傳導電信號，而它們之間的連接——稱為突觸——可以表現出不同程度的導電性，這些導電性由神經遞質調節。 -![神經元模型](../../../../translated_images/tw/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![神經元模型](../../../../translated_images/tw/artneuron.1a5daa88d20ebe6f.png) +![神經元模型](../../../../translated_images/tw/synapse-wikipedia.ed20a9e4726ea1c6.webp) | ![神經元模型](../../../../translated_images/tw/artneuron.1a5daa88d20ebe6f.webp) ----|---- 真實神經元 *（[圖片](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) 來自維基百科）* | 人工神經元 *（作者提供圖片）* 因此，神經元的最簡單數學模型包含幾個輸入 X1, ..., XN 和一個輸出 Y，以及一系列權重 W1, ..., WN。輸出計算公式為： -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) 其中 f 是某種非線性的**激活函數**。 diff --git a/translations/tw/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/tw/lessons/4-ComputerVision/06-IntroCV/README.md index 57638d82..d8bee3d1 100644 --- a/translations/tw/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/tw/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) * **預處理盲文書的照片**。我們專注於如何使用閾值處理、特徵檢測、透視變換和 NumPy 操作來分離單個盲文符號，以便進一步由神經網路進行分類。 -![盲文影像](../../../../../translated_images/tw/braille.341962ff76b1bd70.jpeg) | ![盲文影像預處理結果](../../../../../translated_images/tw/braille-result.46530fea020b03c7.png) | ![盲文符號](../../../../../translated_images/tw/braille-symbols.0159185ab69d5339.png) +![盲文影像](../../../../../translated_images/tw/braille.341962ff76b1bd70.webp) | ![盲文影像預處理結果](../../../../../translated_images/tw/braille-result.46530fea020b03c7.webp) | ![盲文符號](../../../../../translated_images/tw/braille-symbols.0159185ab69d5339.webp) ----|-----|----- > 圖片來自 [OpenCV.ipynb](OpenCV.ipynb) * **使用幀差檢測影片中的運動**。如果相機是固定的，那麼相機畫面中的幀應該彼此非常相似。由於幀被表示為陣列，只需對兩個連續幀的陣列進行相減，我們就能得到像素差異，靜態幀的差異應該很低，而當影像中有顯著運動時，差異會變高。 -![影片幀和幀差的影像](../../../../../translated_images/tw/frame-difference.706f805491a0883c.png) +![影片幀和幀差的影像](../../../../../translated_images/tw/frame-difference.706f805491a0883c.webp) > 圖片來自 [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) - **密集光流** 計算每個像素的移動向量場 - **稀疏光流** 基於影像中的一些顯著特徵（例如邊緣），並從幀到幀構建它們的軌跡。 -![光流影像](../../../../../translated_images/tw/optical.1f4a94464579a83a.png) +![光流影像](../../../../../translated_images/tw/optical.1f4a94464579a83a.webp) > 圖片來自 [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/tw/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/tw/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 24eac3bc..9f045a74 100644 --- a/translations/tw/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/tw/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 是一個在 2014 年 ImageNet top-5 分類中達到 92.7% 準確率的網路。它的層結構如下： -![ImageNet 層結構](../../../../../translated_images/tw/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet 層結構](../../../../../translated_images/tw/vgg-16-arch1.d901a5583b3a51ba.webp) 如圖所示，VGG 採用了傳統的金字塔架構，也就是一系列的卷積-池化層。 -![ImageNet 金字塔](../../../../../translated_images/tw/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet 金字塔](../../../../../translated_images/tw/vgg-16-arch.64ff2137f50dd49f.webp) > 圖片來源：[Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/tw/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/tw/lessons/4-ComputerVision/07-ConvNets/README.md index 3645ff90..6ad4d03e 100644 --- a/translations/tw/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/tw/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: 為了提取模式，我們將使用**卷積濾波器**的概念。正如你所知，圖像是由一個二維矩陣或具有顏色深度的三維張量表示的。應用濾波器意味著我們取一個相對較小的**濾波器核**矩陣，並對原始圖像中的每個像素與其鄰近點進行加權平均。我們可以將其視為一個小窗口滑過整個圖像，並根據濾波器核矩陣中的權重對所有像素進行平均。 -![垂直邊緣濾波器](../../../../../translated_images/tw/filter-vert.b7148390ca0bc356.png) | ![水平邊緣濾波器](../../../../../translated_images/tw/filter-horiz.59b80ed4feb946ef.png) +![垂直邊緣濾波器](../../../../../translated_images/tw/filter-vert.b7148390ca0bc356.webp) | ![水平邊緣濾波器](../../../../../translated_images/tw/filter-horiz.59b80ed4feb946ef.webp) ----|---- > 圖片來源：Dmitry Soshnikov @@ -38,7 +38,7 @@ CNN 的工作方式基於以下重要思想： * 我們可以設計網絡，使濾波器能夠自動訓練 * 我們可以使用相同的方法來在高層次特徵中找到模式，而不僅僅是在原始圖像中。因此，CNN 的特徵提取在特徵的層次結構中工作，從低層次的像素組合開始，到更高層次的圖像部分組合。 -![層次特徵提取](../../../../../translated_images/tw/FeatureExtractionCNN.d9b456cbdae7cb64.png) +![層次特徵提取](../../../../../translated_images/tw/FeatureExtractionCNN.d9b456cbdae7cb64.webp) > 圖片來源：[Hislop-Lynch 的論文](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d)，基於[他們的研究](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ CNN 的工作方式基於以下重要思想： 例如，讓我們看看 VGG-16 的架構，這是一個在 2014 年 ImageNet 的 top-5 分類中達到 92.7% 準確率的網絡： -![ImageNet 層](../../../../../translated_images/tw/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet 層](../../../../../translated_images/tw/vgg-16-arch1.d901a5583b3a51ba.webp) -![ImageNet 金字塔](../../../../../translated_images/tw/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet 金字塔](../../../../../translated_images/tw/vgg-16-arch.64ff2137f50dd49f.webp) > 圖片來源：[Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/tw/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/tw/lessons/4-ComputerVision/07-ConvNets/lab/README.md index eac3d47b..50b9a447 100644 --- a/translations/tw/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/tw/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ CO_OP_TRANSLATOR_METADATA: 我們將使用 [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/)，該數據集包含 37 種不同品種的狗和貓的圖片。 -![我們將處理的數據集](../../../../../../translated_images/tw/data.50b2a9d5484bdbf0.png) +![我們將處理的數據集](../../../../../../translated_images/tw/data.50b2a9d5484bdbf0.webp) 要下載數據集，請使用以下程式碼片段： diff --git a/translations/tw/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/tw/lessons/4-ComputerVision/08-TransferLearning/README.md index c683e853..5711ed83 100644 --- a/translations/tw/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/tw/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Keras 和 PyTorch 都包含了方便的函數，可以輕鬆加載一些常見 以下是 VGG-16 網路從一張貓的圖片中提取的特徵示例： -![VGG-16 提取的特徵](../../../../../translated_images/tw/features.6291f9c7ba3a0b95.png) +![VGG-16 提取的特徵](../../../../../translated_images/tw/features.6291f9c7ba3a0b95.webp) ## 貓與狗數據集 @@ -48,19 +48,19 @@ Keras 和 PyTorch 都包含了方便的函數，可以輕鬆加載一些常見 我們可以採取的一種方法是從一張隨機圖像開始，然後嘗試使用**梯度下降優化**技術調整該圖像，使得網路認為它是一隻貓。 -![圖像優化循環](../../../../../translated_images/tw/ideal-cat-loop.999fbb8ff306e044.png) +![圖像優化循環](../../../../../translated_images/tw/ideal-cat-loop.999fbb8ff306e044.webp) 然而，如果我們這樣做，我們會得到一些非常接近隨機噪聲的東西。這是因為*有很多方法可以讓網路認為輸入圖像是一隻貓*，其中一些方法在視覺上並不合理。雖然這些圖像包含了許多典型於貓的模式，但並沒有任何約束使它們在視覺上具有辨識度。 為了改善結果，我們可以在損失函數中添加另一個項，稱為**變異損失**。這是一種衡量圖像中相鄰像素相似程度的指標。最小化變異損失可以使圖像更平滑，並消除噪聲——從而揭示出更具視覺吸引力的模式。以下是一些這樣的「理想」圖像的例子，它們被高概率地分類為貓和斑馬： -![理想貓](../../../../../translated_images/tw/ideal-cat.203dd4597643d6b0.png) | ![理想斑馬](../../../../../translated_images/tw/ideal-zebra.7f70e8b54ee15a7a.png) +![理想貓](../../../../../translated_images/tw/ideal-cat.203dd4597643d6b0.webp) | ![理想斑馬](../../../../../translated_images/tw/ideal-zebra.7f70e8b54ee15a7a.webp) -----|----- *理想貓* | *理想斑馬* 類似的方法可以用來對神經網路進行所謂的**對抗性攻擊**。假設我們想要欺騙神經網路，讓一隻狗看起來像一隻貓。如果我們拿一張狗的圖片，該圖片被網路識別為狗，然後稍微調整它，使用梯度下降優化，直到網路開始將其分類為貓： -![狗的圖片](../../../../../translated_images/tw/original-dog.8f68a67d2fe0911f.png) | ![被分類為貓的狗圖片](../../../../../translated_images/tw/adversarial-dog.d9fc7773b0142b89.png) +![狗的圖片](../../../../../translated_images/tw/original-dog.8f68a67d2fe0911f.webp) | ![被分類為貓的狗圖片](../../../../../translated_images/tw/adversarial-dog.d9fc7773b0142b89.webp) -----|----- *原始狗圖片* | *被分類為貓的狗圖片* diff --git a/translations/tw/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/tw/lessons/4-ComputerVision/09-Autoencoders/README.md index af35c403..98fbd94c 100644 --- a/translations/tw/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/tw/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: 由於我們訓練自動編碼器以捕捉原始圖像中的盡可能多的信息以進行準確重建，網絡會嘗試找到最佳的**嵌入**來捕捉輸入圖像的含義。 -![自動編碼器示意圖](../../../../../translated_images/tw/autoencoder_schema.5e6fc9ad98a5eb61.jpg) +![自動編碼器示意圖](../../../../../translated_images/tw/autoencoder_schema.5e6fc9ad98a5eb61.webp) > 圖片來源：[Keras 博客](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/tw/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/tw/lessons/4-ComputerVision/11-ObjectDetection/README.md index 76ec084f..fbbf96d0 100644 --- a/translations/tw/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/tw/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: ## [課前測驗](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![物件偵測](../../../../../translated_images/tw/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) +![物件偵測](../../../../../translated_images/tw/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp) > 圖片來源：[YOLO v2 網站](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 2. 對每個區塊進行影像分類。 3. 對於分類結果有足夠高信心的區塊，可以認為包含目標物件。 -![簡單物件偵測](../../../../../translated_images/tw/naive-detection.e7f1ba220ccd08c6.png) +![簡單物件偵測](../../../../../translated_images/tw/naive-detection.e7f1ba220ccd08c6.webp) > *圖片來源：[練習筆記本](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ CO_OP_TRANSLATOR_METADATA: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 包含 20 個類別 * [COCO](http://cocodataset.org/#home) - 常見物件的上下文。包含 80 個類別、邊界框和分割遮罩 -![COCO](../../../../../translated_images/tw/coco-examples.71bc60380fa6cceb.jpg) +![COCO](../../../../../translated_images/tw/coco-examples.71bc60380fa6cceb.webp) ## 物件偵測的評估指標 @@ -50,7 +50,7 @@ CO_OP_TRANSLATOR_METADATA: 在影像分類中，衡量算法表現相對簡單；但在物件偵測中，我們需要同時衡量類別的正確性以及推測邊界框位置的精確性。對於後者，我們使用所謂的**交集比聯集** (IoU)，它衡量兩個框（或任意兩個區域）的重疊程度。 -![IoU](../../../../../translated_images/tw/iou_equation.9a4751d40fff4e11.png) +![IoU](../../../../../translated_images/tw/iou_equation.9a4751d40fff4e11.webp) > *圖片來源：[這篇優秀的 IoU 部落格文章](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ $$ [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) 使用[選擇性搜索](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf)生成 ROI 區域的層次結構，然後通過 CNN 特徵提取器和 SVM 分類器來確定物件類別，並通過線性回歸確定*邊界框*座標。[官方論文](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/tw/rcnn1.cae407020dfb1d1f.png) +![RCNN](../../../../../translated_images/tw/rcnn1.cae407020dfb1d1f.webp) > *圖片來源：van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/tw/rcnn2.2d9530bb83516484.png) +![RCNN-1](../../../../../translated_images/tw/rcnn2.2d9530bb83516484.webp) > *圖片來源：[這篇部落格](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -110,7 +110,7 @@ $$ 這種方法與 R-CNN 類似，但區域是在卷積層應用後定義的。 -![FRCNN](../../../../../translated_images/tw/f-rcnn.3cda6d9bb4188875.png) +![FRCNN](../../../../../translated_images/tw/f-rcnn.3cda6d9bb4188875.webp) > 圖片來源：[官方論文](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf)，[arXiv](https://arxiv.org/pdf/1504.08083.pdf)，2015 @@ -118,7 +118,7 @@ $$ 這種方法的主要思想是使用神經網路來預測 ROI，即所謂的*區域提案網路* (Region Proposal Network)。[論文](https://arxiv.org/pdf/1506.01497.pdf)，2016 -![FasterRCNN](../../../../../translated_images/tw/faster-rcnn.8d46c099b87ef30a.png) +![FasterRCNN](../../../../../translated_images/tw/faster-rcnn.8d46c099b87ef30a.webp) > 圖片來源：[官方論文](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ $$ 2. 特徵通過**位置敏感分數圖** (Position-Sensitive Score Map) 處理。每個類別 $C$ 的物件被分成 $k\times k$ 區域，我們訓練模型來預測物件的部分。 3. 對於 $k\times k$ 區域中的每個部分，所有網路對物件類別進行投票，選擇投票最多的物件類別。 -![r-fcn 圖片](../../../../../translated_images/tw/r-fcn.13eb88158b99a3da.png) +![r-fcn 圖片](../../../../../translated_images/tw/r-fcn.13eb88158b99a3da.webp) > 圖片來源：[官方論文](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO 是一種即時的一次通過算法。主要思想如下： * 將圖片分成 $S\times S$ 區域。 * 對每個區域，**CNN** 預測 $n$ 個可能的物件、*邊界框*座標以及*信心值*=*概率* * IoU。 - ![YOLO](../../../../../translated_images/tw/yolo.a2648ec82ee8bb4e.png) + ![YOLO](../../../../../translated_images/tw/yolo.a2648ec82ee8bb4e.webp) > 圖片來源：[官方論文](https://arxiv.org/abs/1506.02640) diff --git a/translations/tw/lessons/4-ComputerVision/README.md b/translations/tw/lessons/4-ComputerVision/README.md index 800b4e3c..0e954f53 100644 --- a/translations/tw/lessons/4-ComputerVision/README.md +++ b/translations/tw/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 電腦視覺 -![電腦視覺內容摘要手繪圖](../../../../translated_images/tw/ai-computervision.6506ebebac3fbf76.png) +![電腦視覺內容摘要手繪圖](../../../../translated_images/tw/ai-computervision.6506ebebac3fbf76.webp) 在本節中，我們將學習以下內容： diff --git a/translations/tw/lessons/5-NLP/14-Embeddings/README.md b/translations/tw/lessons/5-NLP/14-Embeddings/README.md index 9cd78c98..e5bb69db 100644 --- a/translations/tw/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/tw/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: 通過在分類器網絡中使用嵌入層作為第一層，我們可以從詞袋模型切換到 **嵌入袋** 模型。在嵌入袋模型中，我們首先將文本中的每個詞轉換為相應的嵌入，然後對所有嵌入計算某種聚合函數，例如 `sum`、`average` 或 `max`。 -![展示五個序列詞的嵌入分類器的圖片。](../../../../../translated_images/tw/embedding-classifier-example.b77f021a7ee67eee.png) +![展示五個序列詞的嵌入分類器的圖片。](../../../../../translated_images/tw/embedding-classifier-example.b77f021a7ee67eee.webp) > 圖片由作者提供 @@ -40,7 +40,7 @@ CO_OP_TRANSLATOR_METADATA: CBoW 訓練速度更快，而 Skip-Gram 雖然較慢，但在表示不常見詞方面效果更好。 -![展示 CBoW 和 Skip-Gram 算法如何將詞轉換為向量的圖片。](../../../../../translated_images/tw/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![展示 CBoW 和 Skip-Gram 算法如何將詞轉換為向量的圖片。](../../../../../translated_images/tw/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > 圖片來源：[這篇論文](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/tw/lessons/5-NLP/15-LanguageModeling/README.md b/translations/tw/lessons/5-NLP/15-LanguageModeling/README.md index 30e5cbd8..20d70706 100644 --- a/translations/tw/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/tw/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: * **連續詞袋模型** (CBoW)，在詞元序列 $W_{-N}$, ..., $W_N$ 中預測中間的詞元 $W_0$。 * **Skip-gram**，通過中間詞元 $W_0$ 預測一組相鄰的詞元 {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$}。 -![來自論文的將詞轉換為向量的算法示例](../../../../../translated_images/tw/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![來自論文的將詞轉換為向量的算法示例](../../../../../translated_images/tw/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > 圖片來源：[這篇論文](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/tw/lessons/5-NLP/16-RNN/README.md b/translations/tw/lessons/5-NLP/16-RNN/README.md index 9da74156..7cf5b6f9 100644 --- a/translations/tw/lessons/5-NLP/16-RNN/README.md +++ b/translations/tw/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: 為了捕捉文本序列的意義，我們需要使用另一種神經網絡架構，稱為**循環神經網絡**（Recurrent Neural Network，簡稱 RNN）。在 RNN 中，我們將句子逐個符號地傳遞給網絡，網絡會生成某種**狀態**，然後將該狀態與下一個符號一起再次傳遞給網絡。 -![RNN](../../../../../translated_images/tw/rnn.27f5c29c53d727b5.png) +![RNN](../../../../../translated_images/tw/rnn.27f5c29c53d727b5.webp) > 圖片由作者提供 @@ -61,7 +61,7 @@ LSTM 網絡的組織方式與 RNN 類似，但有兩個狀態會從層到層傳 循環網絡（無論是單向還是雙向）能夠捕捉序列中的某些模式，並將它們存儲到狀態向量中或傳遞到輸出中。與卷積網絡類似，我們可以在第一層之上構建另一個循環層，以捕捉更高層次的模式，並基於第一層提取的低層次模式進行構建。這引出了**多層 RNN** 的概念，它由兩個或更多循環網絡組成，其中前一層的輸出作為輸入傳遞到下一層。 -![顯示多層長短期記憶 RNN 的圖片](../../../../../translated_images/tw/multi-layer-lstm.dd975e29bb2a59fe.jpg) +![顯示多層長短期記憶 RNN 的圖片](../../../../../translated_images/tw/multi-layer-lstm.dd975e29bb2a59fe.webp) *圖片來自 Fernando López 的[這篇精彩文章](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3)* diff --git a/translations/tw/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/tw/lessons/5-NLP/17-GenerativeNetworks/README.md index 78483775..c35412cf 100644 --- a/translations/tw/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/tw/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: 這使得不同的神經網絡架構成為可能，如下圖所示： -![展示常見循環神經網絡模式的圖片。](../../../../../translated_images/tw/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![展示常見循環神經網絡模式的圖片。](../../../../../translated_images/tw/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > 圖片來源：[Andrej Karpaty](http://karpathy.github.io/) 的博客文章 [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) @@ -32,7 +32,7 @@ CO_OP_TRANSLATOR_METADATA: 我們將訓練這個 RNN 逐步生成文本。在每一步中，我們將取一個長度為 `nchars` 的字符序列，並要求網絡為每個輸入字符生成下一個輸出字符： -![展示 RNN 生成單詞 'HELLO' 的示例圖片。](../../../../../translated_images/tw/rnn-generate.56c54afb52f9781d.png) +![展示 RNN 生成單詞 'HELLO' 的示例圖片。](../../../../../translated_images/tw/rnn-generate.56c54afb52f9781d.webp) 在生成文本（推理過程中），我們從某個**提示**開始，將其通過 RNN 單元生成中間狀態，然後從該狀態開始生成。我們一次生成一個字符，並將狀態和生成的字符傳遞給另一個 RNN 單元以生成下一個字符，直到生成足夠的字符。 diff --git a/translations/tw/lessons/5-NLP/18-Transformers/README.md b/translations/tw/lessons/5-NLP/18-Transformers/README.md index c84ccee7..7356902e 100644 --- a/translations/tw/lessons/5-NLP/18-Transformers/README.md +++ b/translations/tw/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ CO_OP_TRANSLATOR_METADATA: **注意機制**提供了一種方法，能夠對每個輸入向量對RNN輸出預測的上下文影響進行加權。其實現方式是通過在輸入RNN的中間狀態和輸出RNN之間建立捷徑。在生成輸出符號yt時，我們會考慮所有輸入隱藏狀態hi，並賦予不同的權重係數αt,i。 -![顯示具有加性注意層的編碼器/解碼器模型的圖片](../../../../../translated_images/tw/encoder-decoder-attention.7a726296894fb567.png) +![顯示具有加性注意層的編碼器/解碼器模型的圖片](../../../../../translated_images/tw/encoder-decoder-attention.7a726296894fb567.webp) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf)中的加性注意機制編碼器-解碼器模型，引用自[這篇博客文章](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) 注意矩陣{αi,j}表示某些輸入詞在生成輸出序列中的某個詞時所起的作用程度。以下是一個這樣的矩陣示例： -![顯示由RNNsearch-50找到的樣本對齊的圖片，取自Bahdanau - arviz.org](../../../../../translated_images/tw/bahdanau-fig3.09ba2d37f202a6af.png) +![顯示由RNNsearch-50找到的樣本對齊的圖片，取自Bahdanau - arviz.org](../../../../../translated_images/tw/bahdanau-fig3.09ba2d37f202a6af.webp) > 圖片來自[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf)（圖3） @@ -66,7 +66,7 @@ Transformer的主要理念之一是避免RNN的序列特性，並創建一個在 接下來，我們需要捕捉序列中的一些模式。為此，Transformer使用了**自注意機制**，這本質上是將注意機制應用於相同的輸入和輸出序列。應用自注意機制使我們能夠考慮句子中的**上下文**，並查看哪些詞是相互關聯的。例如，它使我們能夠看到哪些詞是由指代詞（如*它*）指代的，並且能夠考慮上下文： -![](../../../../../translated_images/tw/CoreferenceResolution.861924d6d384a7d6.png) +![](../../../../../translated_images/tw/CoreferenceResolution.861924d6d384a7d6.webp) > 圖片來自[Google的博客](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Transformer的主要理念之一是避免RNN的序列特性，並創建一個在 **BERT**（Bidirectional Encoder Representations from Transformers）是一個非常大的多層Transformer網絡，*BERT-base*有12層，*BERT-large*有24層。該模型首先在大規模文本數據（維基百科+書籍）上進行無監督訓練（預測句子中的被遮蔽詞）。在預訓練過程中，模型吸收了大量的語言理解能力，這些能力可以通過微調其他數據集來利用。這個過程稱為**遷移學習**。 -![圖片來自http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/tw/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) +![圖片來自http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/tw/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > 圖片[來源](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/tw/lessons/5-NLP/19-NER/README.md b/translations/tw/lessons/5-NLP/19-NER/README.md index fc19be86..7f02ce0f 100644 --- a/translations/tw/lessons/5-NLP/19-NER/README.md +++ b/translations/tw/lessons/5-NLP/19-NER/README.md @@ -58,7 +58,7 @@ NER 模型本質上是 **標記分類模型**，因為對於每個輸入的標 由於我們需要在標記和類別之間建立一對一的對應關係，我們可以從這張圖中訓練一個右側的 **多對多** 神經網絡模型： -![顯示常見循環神經網絡模式的圖片。](../../../../../translated_images/tw/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![顯示常見循環神經網絡模式的圖片。](../../../../../translated_images/tw/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > *圖片來自 [這篇部落格文章](http://karpathy.github.io/2015/05/21/rnn-effectiveness/)，作者為 [Andrej Karpathy](http://karpathy.github.io/)。NER 標記分類模型對應於圖片中最右側的網絡架構。* diff --git a/translations/tw/lessons/5-NLP/README.md b/translations/tw/lessons/5-NLP/README.md index 21a2e7f4..725aacc0 100644 --- a/translations/tw/lessons/5-NLP/README.md +++ b/translations/tw/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 自然語言處理 -![NLP任務摘要手繪圖](../../../../translated_images/tw/ai-nlp.b22dcb8ca4707cea.png) +![NLP任務摘要手繪圖](../../../../translated_images/tw/ai-nlp.b22dcb8ca4707cea.webp) 在本章節中，我們將專注於使用神經網絡來處理與**自然語言處理 (NLP)**相關的任務。我們希望電腦能夠解決許多NLP問題： diff --git a/translations/tw/lessons/6-Other/23-MultiagentSystems/README.md b/translations/tw/lessons/6-Other/23-MultiagentSystems/README.md index 4dfa6c48..5d1a41ef 100644 --- a/translations/tw/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/tw/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ NetLogo的一大優點是它包含一個可供試用的工作模型庫。進入* 打開模型後，你會進入NetLogo的主界面。以下是一個描述狼和羊的種群模型，給定有限資源（草地）。 -![NetLogo主界面](../../../../../translated_images/tw/NetLogo-Main.32653711ec1a01b3.png) +![NetLogo主界面](../../../../../translated_images/tw/NetLogo-Main.32653711ec1a01b3.webp) > Dmitry Soshnikov提供的截圖 diff --git a/translations/tw/lessons/README.md b/translations/tw/lessons/README.md index 0fba1541..7e5e0e55 100644 --- a/translations/tw/lessons/README.md +++ b/translations/tw/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 概覽 -![概覽手繪圖](../../../translated_images/tw/ai-overview.0857791951d19500.png) +![概覽手繪圖](../../../translated_images/tw/ai-overview.0857791951d19500.webp) > 手繪筆記由 [Tomomi Imura](https://twitter.com/girlie_mac) 提供 diff --git a/translations/tw/lessons/X-Extras/X1-MultiModal/README.md b/translations/tw/lessons/X-Extras/X1-MultiModal/README.md index 6d6908c4..6ddc6450 100644 --- a/translations/tw/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/tw/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: CLIP 的主要理念是能夠比較文本提示與圖像，並確定圖像與提示的匹配程度。 -![CLIP 架構](../../../../../translated_images/tw/clip-arch.b3dbf20b4e8ed8be.png) +![CLIP 架構](../../../../../translated_images/tw/clip-arch.b3dbf20b4e8ed8be.webp) > *圖片來自[這篇博客文章](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ CLIP 模型/庫可以從 [OpenAI GitHub](https://github.com/openai/CLIP) 獲取 假設我們需要將圖像分類為貓、狗和人類。在這種情況下，我們可以給模型一張圖像，以及一系列文本提示：“*一張貓的圖片*”、“*一張狗的圖片*”、“*一張人類的圖片*”。在結果的 3 個概率向量中，我們只需選擇值最高的索引。 -![CLIP 用於圖像分類](../../../../../translated_images/tw/clip-class.3af42ef0b2b19369.png) +![CLIP 用於圖像分類](../../../../../translated_images/tw/clip-class.3af42ef0b2b19369.webp) > *圖片來自[這篇博客文章](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ VQGAN 與普通 [GAN](../../4-ComputerVision/10-GANs/README.md) 的主要區別 VQGAN 與傳統 GAN 的一個重要區別在於，後者可以從任何輸入向量生成一張像樣的圖像，而 VQGAN 可能生成一張不連貫的圖像。因此，我們需要進一步引導圖像創建過程，而這可以通過 CLIP 完成。 -![VQGAN+CLIP 架構](../../../../../translated_images/tw/vqgan.5027fe05051dfa31.png) +![VQGAN+CLIP 架構](../../../../../translated_images/tw/vqgan.5027fe05051dfa31.webp) 為了生成與文本提示相對應的圖像，我們從一些隨機編碼向量開始，將其傳遞給 VQGAN 以生成圖像。然後使用 CLIP 生成一個損失函數，該函數顯示圖像與文本提示的匹配程度。目標是最小化這個損失，通過反向傳播調整輸入向量參數。 一個實現 VQGAN+CLIP 的優秀庫是 [Pixray](http://github.com/pixray/pixray) -![由 Pixray 生成的圖片](../../../../../translated_images/tw/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![由 Pixray 生成的圖片](../../../../../translated_images/tw/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![由 Pixray 生成的圖片](../../../../../translated_images/tw/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) +![由 Pixray 生成的圖片](../../../../../translated_images/tw/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp) | ![由 Pixray 生成的圖片](../../../../../translated_images/tw/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp) | ![由 Pixray 生成的圖片](../../../../../translated_images/tw/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp) ----|----|---- 由提示 *一幅年輕男性文學教師手持書本的水彩特寫肖像* 生成的圖片 | 由提示 *一幅年輕女性計算機科學教師手持電腦的油畫特寫肖像* 生成的圖片 | 由提示 *一幅年長男性數學教師站在黑板前的油畫特寫肖像* 生成的圖片 diff --git a/translations/uk/README.md b/translations/uk/README.md index 89222a4a..b4e1e321 100644 --- a/translations/uk/README.md +++ b/translations/uk/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # Штучний Інтелект для Початківців - Учбова Програма -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/uk/ai-overview.0857791951d19500.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/uk/ai-overview.0857791951d19500.webp)| |:---:| | AI For Beginners - _Скетчнот від [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/uk/lessons/1-Intro/README.md b/translations/uk/lessons/1-Intro/README.md index b64c9f64..2b4ea953 100644 --- a/translations/uk/lessons/1-Intro/README.md +++ b/translations/uk/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Вступ до штучного інтелекту -![Резюме змісту вступу до ШІ у вигляді малюнка](../../../../translated_images/uk/ai-intro.bf28d1ac4235881c.png) +![Резюме змісту вступу до ШІ у вигляді малюнка](../../../../translated_images/uk/ai-intro.bf28d1ac4235881c.webp) > Малюнок від [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Спочатку комп’ютери були винайдені [Чарльзом Беббіджем](https://en.wikipedia.org/wiki/Charles_Babbage) для роботи з числами за чітко визначеною процедурою — алгоритмом. Сучасні комп’ютери, хоча й значно більш розвинені, ніж оригінальна модель, запропонована у 19 столітті, все ще дотримуються тієї ж ідеї контрольованих обчислень. Таким чином, можна запрограмувати комп’ютер виконувати щось, якщо ми знаємо точну послідовність кроків, необхідних для досягнення мети. -![Фото людини](../../../../translated_images/uk/dsh_age.d212a30d4e54fb5f.png) +![Фото людини](../../../../translated_images/uk/dsh_age.d212a30d4e54fb5f.webp) > Фото від [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ CO_OP_TRANSLATOR_METADATA: Однією з проблем при роботі з терміном **[Інтелект](https://en.wikipedia.org/wiki/Intelligence)** є те, що немає чіткого визначення цього терміну. Можна стверджувати, що інтелект пов’язаний з **абстрактним мисленням** або **самосвідомістю**, але ми не можемо належним чином його визначити. -![Фото кота](../../../../translated_images/uk/photo-cat.8c8e8fb760ffe457.jpg) +![Фото кота](../../../../translated_images/uk/photo-cat.8c8e8fb760ffe457.webp) > [Фото](https://unsplash.com/photos/75715CVEJhI) від [Amber Kipp](https://unsplash.com/@sadmax) з Unsplash @@ -98,13 +98,13 @@ CO_OP_TRANSLATOR_METADATA: > | А як щодо ML? | | > |--------------|-----------| -> | Частина штучного інтелекту, яка базується на навчанні комп’ютера вирішувати проблему на основі деяких даних, називається **Машинним навчанням**. Ми не будемо розглядати класичне машинне навчання в цьому курсі — ми рекомендуємо вам окремий навчальний курс [Машинне навчання для початківців](http://aka.ms/ml-beginners). | ![ML для початківців](../../../../translated_images/uk/ml-for-beginners.9e4fed176fd5817d.png) | +> | Частина штучного інтелекту, яка базується на навчанні комп’ютера вирішувати проблему на основі деяких даних, називається **Машинним навчанням**. Ми не будемо розглядати класичне машинне навчання в цьому курсі — ми рекомендуємо вам окремий навчальний курс [Машинне навчання для початківців](http://aka.ms/ml-beginners). | ![ML для початківців](../../../../translated_images/uk/ml-for-beginners.9e4fed176fd5817d.webp) | ## Коротка історія ШІ Штучний інтелект як галузь був започаткований у середині двадцятого століття. Спочатку символічне міркування було переважним підходом, і це призвело до низки важливих успіхів, таких як експертні системи — комп’ютерні програми, які могли діяти як експерт у деяких обмежених проблемних областях. Однак незабаром стало зрозуміло, що такий підхід погано масштабується. Вилучення знань від експерта, представлення їх у комп’ютері та підтримка цієї бази знань у актуальному стані виявляється дуже складним завданням і занадто дорогим для практичного використання в багатьох випадках. Це призвело до так званої [Зими ШІ](https://en.wikipedia.org/wiki/AI_winter) у 1970-х роках. -Коротка історія ШІ +Коротка історія ШІ > Зображення від [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/uk/lessons/2-Symbolic/Animals.ipynb b/translations/uk/lessons/2-Symbolic/Animals.ipynb index f6693b82..cf584026 100644 --- a/translations/uk/lessons/2-Symbolic/Animals.ipynb +++ b/translations/uk/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "У цьому прикладі ми реалізуємо просту систему на основі знань для визначення тварини за деякими фізичними характеристиками. Система може бути представлена наступним деревом AND-OR (це частина всього дерева, ми легко можемо додати ще кілька правил):\n", "\n", - "![](../../../../translated_images/uk/AND-OR-Tree.5592d2c70187f283.png)\n" + "![](../../../../translated_images/uk/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { diff --git a/translations/uk/lessons/2-Symbolic/README.md b/translations/uk/lessons/2-Symbolic/README.md index 9ba0bfd2..e2ce8685 100644 --- a/translations/uk/lessons/2-Symbolic/README.md +++ b/translations/uk/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Представлення знань та експертні системи -![Резюме змісту символічного AI](../../../../translated_images/uk/ai-symbolic.715a30cb610411a6.png) +![Резюме змісту символічного AI](../../../../translated_images/uk/ai-symbolic.715a30cb610411a6.webp) > Скетчноут від [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ CO_OP_TRANSLATOR_METADATA: Таким чином, проблема **представлення знань** полягає у пошуку ефективного способу представлення знань у комп'ютері у формі даних, щоб зробити їх автоматично придатними для використання. Це можна розглядати як спектр: -![Спектр представлення знань](../../../../translated_images/uk/knowledge-spectrum.b60df631852c0217.png) +![Спектр представлення знань](../../../../translated_images/uk/knowledge-spectrum.b60df631852c0217.webp) > Зображення від [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Python | синтаксис блоку | відступи Одним із ранніх успіхів символічного AI були так звані **експертні системи** — комп'ютерні системи, які були розроблені для того, щоб діяти як експерт у певній обмеженій області задач. Вони базувалися на **базі знань**, отриманій від одного або кількох людських експертів, і містили **мотор виведення**, який виконував певні міркування на її основі. -![Архітектура людини](../../../../translated_images/uk/arch-human.5d4d35f1bba3ab1c.png) | ![Система на основі знань](../../../../translated_images/uk/arch-kbs.3ec5c150b09fa8da.png) +![Архітектура людини](../../../../translated_images/uk/arch-human.5d4d35f1bba3ab1c.webp) | ![Система на основі знань](../../../../translated_images/uk/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ Спрощена структура людської нервової системи | Архітектура системи на основі знань @@ -106,7 +106,7 @@ Python | синтаксис блоку | відступи Наприклад, розглянемо наступну експертну систему для визначення тварини на основі її фізичних характеристик: -![AND-OR дерево](../../../../translated_images/uk/AND-OR-Tree.5592d2c70187f283.png) +![AND-OR дерево](../../../../translated_images/uk/AND-OR-Tree.5592d2c70187f283.webp) > Зображення від [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/uk/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/uk/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index 99b0f7c0..eac7df37 100644 --- a/translations/uk/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/uk/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -1252,7 +1252,7 @@ "* Низька втрата на навчанні — модель добре наближає навчальні дані, оскільки має достатню виразну потужність.\n", "* Втрата на валідації може бути значно вищою за втрату на навчанні і може почати зростати під час навчання — це відбувається тому, що модель \"запам’ятовує\" навчальні точки і втрачає \"загальну картину\".\n", "\n", - "![Перенавчання](../../../../../translated_images/uk/overfit.a0bd57f717c15769.png)\n", + "![Перенавчання](../../../../../translated_images/uk/overfit.a0bd57f717c15769.webp)\n", "\n", "> На цьому зображенні `x` позначає навчальні дані, `o` — валідаційні дані. Ліворуч — лінійна модель (одношарова), вона досить добре наближає природу даних. Праворуч — перенавчена модель, яка ідеально наближає навчальні дані, але перестає бути корисною для будь-яких інших даних (помилка на валідації дуже висока).\n" ] diff --git a/translations/uk/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/uk/lessons/3-NeuralNetworks/05-Frameworks/README.md index 24722722..61330b16 100644 --- a/translations/uk/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/uk/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ CO_OP_TRANSLATOR_METADATA: Розглянемо наступну задачу апроксимації 5 точок (представлених як `x` на графіках нижче): -![linear](../../../../../translated_images/uk/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/uk/overfit2.131f5800ae10ca5e.jpg) +![linear](../../../../../translated_images/uk/overfit1.f24b71c6f652e59e.webp) | ![overfit](../../../../../translated_images/uk/overfit2.131f5800ae10ca5e.webp) -------------------------|-------------------------- **Лінійна модель, 2 параметри** | **Нелінійна модель, 7 параметрів** Помилка навчання = 5.3 | Помилка навчання = 0 @@ -79,7 +79,7 @@ CO_OP_TRANSLATOR_METADATA: Як видно з графіка вище, перенавчання можна виявити за дуже низькою помилкою навчання і високою помилкою валідації. Зазвичай під час навчання ми бачимо, як помилки навчання і валідації починають зменшуватися, а потім у певний момент помилка валідації може перестати зменшуватися і почати зростати. Це буде ознакою перенавчання і сигналом, що, ймовірно, слід припинити навчання (або принаймні зробити знімок моделі). -![overfitting](../../../../../translated_images/uk/Overfitting.408ad91cd90b4371.png) +![overfitting](../../../../../translated_images/uk/Overfitting.408ad91cd90b4371.webp) ## Як запобігти перенавчанню diff --git a/translations/uk/lessons/3-NeuralNetworks/README.md b/translations/uk/lessons/3-NeuralNetworks/README.md index bdcbedac..0bc9773d 100644 --- a/translations/uk/lessons/3-NeuralNetworks/README.md +++ b/translations/uk/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Вступ до нейронних мереж -![Резюме змісту "Вступ до нейронних мереж" у вигляді малюнка](../../../../translated_images/uk/ai-neuralnetworks.1c687ae40bc86e83.png) +![Резюме змісту "Вступ до нейронних мереж" у вигляді малюнка](../../../../translated_images/uk/ai-neuralnetworks.1c687ae40bc86e83.webp) Як ми обговорювали у вступі, одним із способів досягнення інтелекту є навчання **комп'ютерної моделі** або **штучного мозку**. З середини 20-го століття дослідники пробували різні математичні моделі, і лише в останні роки цей напрямок став надзвичайно успішним. Такі математичні моделі мозку називаються **нейронними мережами**. @@ -36,13 +36,13 @@ CO_OP_TRANSLATOR_METADATA: З біології ми знаємо, що наш мозок складається з нейронних клітин (нейронів), кожна з яких має кілька "входів" (дендрити) і один "вихід" (аксон). Як дендрити, так і аксони можуть проводити електричні сигнали, а зв’язки між ними — відомі як синапси — можуть мати різний ступінь провідності, який регулюється нейромедіаторами. -![Модель нейрона](../../../../translated_images/uk/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![Модель нейрона](../../../../translated_images/uk/artneuron.1a5daa88d20ebe6f.png) +![Модель нейрона](../../../../translated_images/uk/synapse-wikipedia.ed20a9e4726ea1c6.webp) | ![Модель нейрона](../../../../translated_images/uk/artneuron.1a5daa88d20ebe6f.webp) ----|---- Реальний нейрон *([Зображення](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) з Вікіпедії)* | Штучний нейрон *(Зображення автора)* Таким чином, найпростіша математична модель нейрона містить кілька входів X1, ..., XN і один вихід Y, а також набір ваг W1, ..., WN. Вихід обчислюється як: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) де f — це деяка нелінійна **функція активації**. diff --git a/translations/uk/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/uk/lessons/4-ComputerVision/06-IntroCV/README.md index 27659b08..93858c03 100644 --- a/translations/uk/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/uk/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) * **Попередня обробка фотографії книги шрифтом Брайля**. Ми зосереджуємося на тому, як можна використовувати порогову обробку, виявлення особливостей, перспективне перетворення та маніпуляції з NumPy для відокремлення окремих символів шрифту Брайля для подальшої класифікації нейронною мережею. -![Зображення шрифту Брайля](../../../../../translated_images/uk/braille.341962ff76b1bd70.jpeg) | ![Попередньо оброблене зображення шрифту Брайля](../../../../../translated_images/uk/braille-result.46530fea020b03c7.png) | ![Символи шрифту Брайля](../../../../../translated_images/uk/braille-symbols.0159185ab69d5339.png) +![Зображення шрифту Брайля](../../../../../translated_images/uk/braille.341962ff76b1bd70.webp) | ![Попередньо оброблене зображення шрифту Брайля](../../../../../translated_images/uk/braille-result.46530fea020b03c7.webp) | ![Символи шрифту Брайля](../../../../../translated_images/uk/braille-symbols.0159185ab69d5339.webp) ----|-----|----- > Зображення з [OpenCV.ipynb](OpenCV.ipynb) * **Виявлення руху у відео за допомогою різниці кадрів**. Якщо камера нерухома, то кадри з її потоку мають бути досить схожими один на одного. Оскільки кадри представлені у вигляді масивів, просто віднімаючи ці масиви для двох послідовних кадрів, ми отримаємо різницю пікселів, яка має бути низькою для статичних кадрів і ставати вищою, коли в зображенні є значний рух. -![Зображення кадрів відео та різниці кадрів](../../../../../translated_images/uk/frame-difference.706f805491a0883c.png) +![Зображення кадрів відео та різниці кадрів](../../../../../translated_images/uk/frame-difference.706f805491a0883c.webp) > Зображення з [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) - **Щільний оптичний потік** обчислює векторне поле, яке показує, куди рухається кожен піксель. - **Рідкісний оптичний потік** базується на виборі деяких характерних особливостей на зображенні (наприклад, країв) і побудові їх траєкторії від кадру до кадру. -![Зображення оптичного потоку](../../../../../translated_images/uk/optical.1f4a94464579a83a.png) +![Зображення оптичного потоку](../../../../../translated_images/uk/optical.1f4a94464579a83a.webp) > Зображення з [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/uk/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/uk/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 1785ed59..81a6ae01 100644 --- a/translations/uk/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/uk/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 — це мережа, яка досягла точності 92.7% у класифікації ImageNet top-5 у 2014 році. Вона має наступну структуру шарів: -![ImageNet Layers](../../../../../translated_images/uk/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet Layers](../../../../../translated_images/uk/vgg-16-arch1.d901a5583b3a51ba.webp) Як видно, VGG слідує традиційній пірамідальній архітектурі, яка є послідовністю шарів згортки та пулінгу. -![ImageNet Pyramid](../../../../../translated_images/uk/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet Pyramid](../../../../../translated_images/uk/vgg-16-arch.64ff2137f50dd49f.webp) > Зображення з [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/uk/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/uk/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index b76c903b..a21e4657 100644 --- a/translations/uk/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/uk/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -260,7 +260,7 @@ "\n", "Таким чином, у типовій CNN буде кілька згорткових шарів, між якими розташовані шари пулінгу для зменшення розмірів зображення. Ми також збільшуватимемо кількість фільтрів, оскільки, коли шаблони стають складнішими, з'являється більше можливих цікавих комбінацій, які потрібно шукати.\n", "\n", - "![Зображення, що показує кілька згорткових шарів із шарами пулінгу.](../../../../../translated_images/uk/cnn-pyramid.85915455759ef0ce.png)\n", + "![Зображення, що показує кілька згорткових шарів із шарами пулінгу.](../../../../../translated_images/uk/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "Через зменшення просторових розмірів і збільшення розмірів ознак/фільтрів цю архітектуру також називають **пірамідальною архітектурою**.\n" ] diff --git a/translations/uk/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/uk/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index fa4cd729..7dbdc186 100644 --- a/translations/uk/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/uk/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -358,7 +358,7 @@ "\n", "Таким чином, у типовій CNN буде кілька згорткових шарів, між якими розташовані шари пулінгу для зменшення розмірів зображення. Ми також збільшимо кількість фільтрів, оскільки, коли шаблони стають більш складними, з'являється більше можливих цікавих комбінацій, які потрібно шукати.\n", "\n", - "![Зображення, що показує кілька згорткових шарів із шарами пулінгу.](../../../../../translated_images/uk/cnn-pyramid.85915455759ef0ce.png)\n", + "![Зображення, що показує кілька згорткових шарів із шарами пулінгу.](../../../../../translated_images/uk/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "Через зменшення просторових розмірів і збільшення розмірів ознак/фільтрів ця архітектура також називається **пірамідальною архітектурою**.\n" ] diff --git a/translations/uk/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/uk/lessons/4-ComputerVision/07-ConvNets/README.md index 230ec0a1..e259259d 100644 --- a/translations/uk/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/uk/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: Для вилучення шаблонів ми будемо використовувати поняття **конволюційних фільтрів**. Як вам відомо, зображення представляється у вигляді 2D-матриці або 3D-тензора з глибиною кольору. Застосування фільтра означає, що ми беремо відносно невелику матрицю **ядра фільтра**, і для кожного пікселя в оригінальному зображенні обчислюємо зважене середнє з сусідніми точками. Це можна уявити як невелике вікно, яке ковзає по всьому зображенню, усереднюючи всі пікселі відповідно до ваг у матриці ядра фільтра. -![Фільтр вертикальних країв](../../../../../translated_images/uk/filter-vert.b7148390ca0bc356.png) | ![Фільтр горизонтальних країв](../../../../../translated_images/uk/filter-horiz.59b80ed4feb946ef.png) +![Фільтр вертикальних країв](../../../../../translated_images/uk/filter-vert.b7148390ca0bc356.webp) | ![Фільтр горизонтальних країв](../../../../../translated_images/uk/filter-horiz.59b80ed4feb946ef.webp) ----|---- > Зображення Дмитра Сошникова @@ -38,7 +38,7 @@ CO_OP_TRANSLATOR_METADATA: * Ми можемо спроєктувати мережу таким чином, щоб фільтри навчалися автоматично * Ми можемо використовувати той самий підхід для пошуку шаблонів у високорівневих ознаках, а не лише в оригінальному зображенні. Таким чином, вилучення ознак у CNN працює на ієрархії ознак, починаючи з низькорівневих комбінацій пікселів і до високорівневих комбінацій частин зображення. -![Ієрархічне вилучення ознак](../../../../../translated_images/uk/FeatureExtractionCNN.d9b456cbdae7cb64.png) +![Ієрархічне вилучення ознак](../../../../../translated_images/uk/FeatureExtractionCNN.d9b456cbdae7cb64.webp) > Зображення з [статті Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), засноване на [їхньому дослідженні](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ CO_OP_TRANSLATOR_METADATA: Наприклад, давайте розглянемо архітектуру VGG-16, мережі, яка досягла 92.7% точності в топ-5 класифікації ImageNet у 2014 році: -![Шари ImageNet](../../../../../translated_images/uk/vgg-16-arch1.d901a5583b3a51ba.jpg) +![Шари ImageNet](../../../../../translated_images/uk/vgg-16-arch1.d901a5583b3a51ba.webp) -![Піраміда ImageNet](../../../../../translated_images/uk/vgg-16-arch.64ff2137f50dd49f.jpg) +![Піраміда ImageNet](../../../../../translated_images/uk/vgg-16-arch.64ff2137f50dd49f.webp) > Зображення з [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/uk/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/uk/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 16979aa6..ea9c7f99 100644 --- a/translations/uk/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/uk/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ CO_OP_TRANSLATOR_METADATA: Ми будемо використовувати [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/), який містить зображення 37 різних порід собак і котів. -![Набір даних, з яким ми будемо працювати](../../../../../../translated_images/uk/data.50b2a9d5484bdbf0.png) +![Набір даних, з яким ми будемо працювати](../../../../../../translated_images/uk/data.50b2a9d5484bdbf0.webp) Щоб завантажити набір даних, скористайтеся цим фрагментом коду: diff --git a/translations/uk/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/uk/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index 6f5c3540..20c9aae4 100644 --- a/translations/uk/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/uk/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "Щоб уявити ідеального кота, ми почнемо з випадкового шумового зображення і спробуємо використати метод оптимізації градієнтного спуску, щоб змінити зображення так, щоб мережа розпізнала кота.\n", "\n", - "![Цикл оптимізації](../../../../../translated_images/uk/ideal-cat-loop.999fbb8ff306e044.png)\n", + "![Цикл оптимізації](../../../../../translated_images/uk/ideal-cat-loop.999fbb8ff306e044.webp)\n", "\n", "Ось наше початкове зображення:\n" ] diff --git a/translations/uk/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/uk/lessons/4-ComputerVision/08-TransferLearning/README.md index d7f381ba..31e55904 100644 --- a/translations/uk/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/uk/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ CO_OP_TRANSLATOR_METADATA: Ось приклади ознак, вилучених із зображення кота мережею VGG-16: -![Ознаки, вилучені мережею VGG-16](../../../../../translated_images/uk/features.6291f9c7ba3a0b95.png) +![Ознаки, вилучені мережею VGG-16](../../../../../translated_images/uk/features.6291f9c7ba3a0b95.webp) ## Набір даних "Коти проти собак" @@ -48,19 +48,19 @@ CO_OP_TRANSLATOR_METADATA: Один із підходів, який ми можемо використати, — це почати з випадкового зображення, а потім спробувати використати техніку **оптимізації градієнтного спуску**, щоб змінити це зображення таким чином, щоб мережа почала думати, що це кіт. -![Цикл оптимізації зображення](../../../../../translated_images/uk/ideal-cat-loop.999fbb8ff306e044.png) +![Цикл оптимізації зображення](../../../../../translated_images/uk/ideal-cat-loop.999fbb8ff306e044.webp) Однак, якщо ми це зробимо, ми отримаємо щось дуже схоже на випадковий шум. Це тому, що *існує багато способів змусити мережу думати, що вхідне зображення — це кіт*, включаючи ті, які не мають сенсу візуально. Хоча ці зображення містять багато шаблонів, характерних для кота, немає нічого, що обмежувало б їх бути візуально виразними. Щоб покращити результат, ми можемо додати ще один термін до функції втрат, який називається **варіаційна втрата**. Це метрика, яка показує, наскільки схожі сусідні пікселі зображення. Мінімізуючи варіаційну втрату, ми робимо зображення більш гладким і позбавляємося шуму — таким чином розкриваючи більш привабливі візуальні шаблони. Ось приклад таких "ідеальних" зображень, які класифікуються як кіт і як зебра з високою ймовірністю: -![Ідеальний кіт](../../../../../translated_images/uk/ideal-cat.203dd4597643d6b0.png) | ![Ідеальна зебра](../../../../../translated_images/uk/ideal-zebra.7f70e8b54ee15a7a.png) +![Ідеальний кіт](../../../../../translated_images/uk/ideal-cat.203dd4597643d6b0.webp) | ![Ідеальна зебра](../../../../../translated_images/uk/ideal-zebra.7f70e8b54ee15a7a.webp) -----|----- *Ідеальний кіт* | *Ідеальна зебра* Схожий підхід можна використати для виконання так званих **атак на нейронну мережу**. Припустимо, ми хочемо обдурити нейронну мережу і змусити собаку виглядати як кіт. Якщо ми візьмемо зображення собаки, яке мережа розпізнає як собаку, ми можемо трохи змінити його за допомогою оптимізації градієнтного спуску, поки мережа не почне класифікувати його як кота: -![Зображення собаки](../../../../../translated_images/uk/original-dog.8f68a67d2fe0911f.png) | ![Зображення собаки, класифіковане як кіт](../../../../../translated_images/uk/adversarial-dog.d9fc7773b0142b89.png) +![Зображення собаки](../../../../../translated_images/uk/original-dog.8f68a67d2fe0911f.webp) | ![Зображення собаки, класифіковане як кіт](../../../../../translated_images/uk/adversarial-dog.d9fc7773b0142b89.webp) -----|----- *Оригінальне зображення собаки* | *Зображення собаки, класифіковане як кіт* diff --git a/translations/uk/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/uk/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index 62aa241d..7574ead0 100644 --- a/translations/uk/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/uk/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "Оскільки ми тренуємо автоенкодер, щоб захопити якомога більше інформації з оригінального зображення для точного відновлення, мережа намагається знайти найкраще **вбудовування** вхідних зображень, щоб передати їхній зміст.\n", "\n", - "![Схема Автоенкодера](../../../../../translated_images/uk/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![Схема Автоенкодера](../../../../../translated_images/uk/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "> Зображення з [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", diff --git a/translations/uk/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/uk/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index 048f97f4..71440259 100644 --- a/translations/uk/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/uk/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "Оскільки ми тренуємо автоенкодер, щоб захопити якомога більше інформації з оригінального зображення для точного відновлення, мережа намагається знайти найкраще **вбудовування** вхідних зображень, щоб передати їхній зміст.\n", "\n", - "![Схема Автоенкодера](../../../../../translated_images/uk/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![Схема Автоенкодера](../../../../../translated_images/uk/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "*Зображення з [блогу Keras](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", diff --git a/translations/uk/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/uk/lessons/4-ComputerVision/09-Autoencoders/README.md index ded38e7c..77f98978 100644 --- a/translations/uk/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/uk/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Оскільки ми навчаємо автоенкодер захоплювати якомога більше інформації з оригінального зображення для точного відновлення, мережа намагається знайти найкраще **вбудовування** вхідних зображень, щоб передати їх зміст. -![Схема автоенкодера](../../../../../translated_images/uk/autoencoder_schema.5e6fc9ad98a5eb61.jpg) +![Схема автоенкодера](../../../../../translated_images/uk/autoencoder_schema.5e6fc9ad98a5eb61.webp) > Зображення з [блогу Keras](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/uk/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/uk/lessons/4-ComputerVision/11-ObjectDetection/README.md index 8f182185..4c012c3b 100644 --- a/translations/uk/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/uk/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: ## [Квіз перед лекцією](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Виявлення об'єктів](../../../../../translated_images/uk/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) +![Виявлення об'єктів](../../../../../translated_images/uk/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp) > Зображення з [веб-сайту YOLO v2](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 2. Запустити класифікацію зображень для кожної плитки. 3. Ті плитки, які дають достатньо високу активацію, можна вважати такими, що містять потрібний об'єкт. -![Наївне виявлення об'єктів](../../../../../translated_images/uk/naive-detection.e7f1ba220ccd08c6.png) +![Наївне виявлення об'єктів](../../../../../translated_images/uk/naive-detection.e7f1ba220ccd08c6.webp) > *Зображення з [зошита вправ](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ CO_OP_TRANSLATOR_METADATA: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) — 20 класів * [COCO](http://cocodataset.org/#home) — Загальні об'єкти в контексті. 80 класів, межові рамки та маски сегментації -![COCO](../../../../../translated_images/uk/coco-examples.71bc60380fa6cceb.jpg) +![COCO](../../../../../translated_images/uk/coco-examples.71bc60380fa6cceb.webp) ## Метрики для виявлення об'єктів @@ -50,7 +50,7 @@ CO_OP_TRANSLATOR_METADATA: Для класифікації зображень легко виміряти, наскільки добре працює алгоритм, але для виявлення об'єктів потрібно оцінити як правильність класу, так і точність визначення розташування межової рамки. Для останнього використовується так звана **Перетин над об'єднанням** (IoU), яка вимірює, наскільки добре дві рамки (або дві довільні області) перекриваються. -![IoU](../../../../../translated_images/uk/iou_equation.9a4751d40fff4e11.png) +![IoU](../../../../../translated_images/uk/iou_equation.9a4751d40fff4e11.webp) > *Рисунок 2 з [цієї чудової статті про IoU](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ $$ [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) використовує [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) для створення ієрархічної структури регіонів ROI, які потім проходять через CNN для вилучення ознак і SVM-класифікатори для визначення класу об'єкта, а також лінійну регресію для визначення координат *межової рамки*. [Офіційна стаття](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/uk/rcnn1.cae407020dfb1d1f.png) +![RCNN](../../../../../translated_images/uk/rcnn1.cae407020dfb1d1f.webp) > *Зображення з van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/uk/rcnn2.2d9530bb83516484.png) +![RCNN-1](../../../../../translated_images/uk/rcnn2.2d9530bb83516484.webp) > *Зображення з [цієї статті](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -110,7 +110,7 @@ $$ Цей підхід схожий на R-CNN, але регіони визначаються після застосування шарів згортки. -![FRCNN](../../../../../translated_images/uk/f-rcnn.3cda6d9bb4188875.png) +![FRCNN](../../../../../translated_images/uk/f-rcnn.3cda6d9bb4188875.webp) > Зображення з [офіційної статті](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ $$ Основна ідея цього підходу полягає у використанні нейронної мережі для прогнозування ROI — так званої *мережі пропозицій регіонів*. [Стаття](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/uk/faster-rcnn.8d46c099b87ef30a.png) +![FasterRCNN](../../../../../translated_images/uk/faster-rcnn.8d46c099b87ef30a.webp) > Зображення з [офіційної статті](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ $$ 2. Ознаки обробляються **картою оцінок, чутливою до позиції**. Кожен об'єкт із $C$ класів ділиться на $k\times k$ регіонів, і ми навчаємося прогнозувати частини об'єктів. 3. Для кожної частини з $k\times k$ регіонів усі мережі голосують за класи об'єктів, і вибирається клас об'єкта з максимальним голосом. -![r-fcn image](../../../../../translated_images/uk/r-fcn.13eb88158b99a3da.png) +![r-fcn image](../../../../../translated_images/uk/r-fcn.13eb88158b99a3da.webp) > Зображення з [офіційної статті](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO — це алгоритм реального часу з одним про * Зображення ділиться на $S\times S$ регіони. * Для кожного регіону **CNN** прогнозує $n$ можливих об'єктів, координати *межової рамки* та *довіру*=*ймовірність* * IoU. - ![YOLO](../../../../../translated_images/uk/yolo.a2648ec82ee8bb4e.png) + ![YOLO](../../../../../translated_images/uk/yolo.a2648ec82ee8bb4e.webp) > Зображення з [офіційної статті](https://arxiv.org/abs/1506.02640) diff --git a/translations/uk/lessons/4-ComputerVision/README.md b/translations/uk/lessons/4-ComputerVision/README.md index 3369ace9..de9437e5 100644 --- a/translations/uk/lessons/4-ComputerVision/README.md +++ b/translations/uk/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Комп'ютерний зір -![Резюме матеріалів про комп'ютерний зір у вигляді малюнка](../../../../translated_images/uk/ai-computervision.6506ebebac3fbf76.png) +![Резюме матеріалів про комп'ютерний зір у вигляді малюнка](../../../../translated_images/uk/ai-computervision.6506ebebac3fbf76.webp) У цьому розділі ми дізнаємося про: diff --git a/translations/uk/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/uk/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index fca08285..c80665aa 100644 --- a/translations/uk/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/uk/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**Мішок слів** (BoW) — це найпоширеніше традиційне представлення векторів. Кожне слово пов’язане з індексом вектора, а елемент вектора містить кількість появ слова в даному документі.\n", "\n", - "![Зображення, яке показує, як представлення вектора \"Мішок слів\" зберігається в пам’яті.](../../../../../translated_images/uk/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![Зображення, яке показує, як представлення вектора \"Мішок слів\" зберігається в пам’яті.](../../../../../translated_images/uk/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Note**: Ви також можете уявити BoW як суму всіх векторів з одним активним елементом для окремих слів у тексті.\n", "\n", diff --git a/translations/uk/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/uk/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index 8aad0ccf..fcce5083 100644 --- a/translations/uk/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/uk/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**Мішок слів** (BoW) — це найпростіше для розуміння традиційне представлення векторів. Кожне слово пов'язане з індексом вектора, а елемент вектора містить кількість появ кожного слова в даному документі.\n", "\n", - "![Зображення, яке показує, як представлення вектора \"Мішок слів\" зберігається в пам'яті.](../../../../../translated_images/uk/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![Зображення, яке показує, як представлення вектора \"Мішок слів\" зберігається в пам'яті.](../../../../../translated_images/uk/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Note**: Ви також можете думати про BoW як про суму всіх векторів з одним активним елементом (one-hot-encoded) для окремих слів у тексті.\n", "\n", diff --git a/translations/uk/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/uk/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 9e6bf57a..f244f041 100644 --- a/translations/uk/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/uk/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "Використовуючи шар вбудовування як перший шар у нашій мережі, ми можемо перейти від моделі \"мішок слів\" до моделі **мішок вбудовувань**, де спочатку кожне слово в тексті перетворюється на відповідне вбудовування, а потім обчислюється певна агрегатна функція для всіх цих вбудовувань, наприклад `sum`, `average` або `max`.\n", "\n", - "![Зображення, що показує класифікатор вбудовувань для п’яти слів у послідовності.](../../../../../translated_images/uk/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![Зображення, що показує класифікатор вбудовувань для п’яти слів у послідовності.](../../../../../translated_images/uk/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "Наша нейронна мережа класифікатора починатиметься з шару вбудовування, потім шару агрегування, а зверху — лінійного класифікатора:\n" ] @@ -176,7 +176,7 @@ "\n", "У попередній архітектурі нам потрібно було доповнювати всі послідовності до однакової довжини, щоб вони відповідали розміру мініпакету. Це не найефективніший спосіб представлення послідовностей змінної довжини — інший підхід полягає у використанні **вектора зсувів**, який містить зсуви всіх послідовностей, збережених в одному великому векторі.\n", "\n", - "![Зображення, що показує представлення послідовності за допомогою зсувів](../../../../../translated_images/uk/offset-sequence-representation.eb73fcefb29b46ee.png)\n", + "![Зображення, що показує представлення послідовності за допомогою зсувів](../../../../../translated_images/uk/offset-sequence-representation.eb73fcefb29b46ee.webp)\n", "\n", "> **Примітка**: На зображенні вище показано послідовність символів, але в нашому прикладі ми працюємо з послідовностями слів. Однак загальний принцип представлення послідовностей за допомогою вектора зсувів залишається тим самим.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW працює швидше, тоді як skip-gram повільніший, але краще справляється з представленням рідковживаних слів.\n", "\n", - "![Зображення, що демонструє алгоритми CBoW та Skip-Gram для перетворення слів у вектори.](../../../../../translated_images/uk/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![Зображення, що демонструє алгоритми CBoW та Skip-Gram для перетворення слів у вектори.](../../../../../translated_images/uk/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Щоб експериментувати з вбудовуванням word2vec, попередньо навченим на наборі даних Google News, ми можемо використовувати бібліотеку **gensim**. Нижче наведено приклад пошуку слів, найбільш схожих на 'neural'.\n", "\n", diff --git a/translations/uk/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/uk/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index 101f4e6c..20bb55f8 100644 --- a/translations/uk/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/uk/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "Використовуючи шар вбудовування як перший шар у нашій мережі, ми можемо перейти від моделі \"мішка слів\" до моделі **мішка вбудовувань**, де спочатку кожне слово в тексті перетворюється на відповідне вбудовування, а потім обчислюється певна агрегатна функція для всіх цих вбудовувань, наприклад, `sum`, `average` або `max`.\n", "\n", - "![Зображення, що показує класифікатор з вбудовуванням для п’яти послідовних слів.](../../../../../translated_images/uk/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![Зображення, що показує класифікатор з вбудовуванням для п’яти послідовних слів.](../../../../../translated_images/uk/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "Наша нейронна мережа класифікатора складається з таких шарів:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW працює швидше, тоді як скіп-грам повільніший, але краще представляє рідковживані слова.\n", "\n", - "![Зображення, що показує алгоритми CBoW і Skip-Gram для перетворення слів у вектори.](../../../../../translated_images/uk/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![Зображення, що показує алгоритми CBoW і Skip-Gram для перетворення слів у вектори.](../../../../../translated_images/uk/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Щоб експериментувати з вбудовуванням Word2Vec, попередньо навченим на наборі даних Google News, ми можемо використовувати бібліотеку **gensim**. Нижче наведено приклад пошуку слів, найбільш схожих до 'neural'.\n", "\n", diff --git a/translations/uk/lessons/5-NLP/14-Embeddings/README.md b/translations/uk/lessons/5-NLP/14-Embeddings/README.md index dc1f4c4d..a032d5c1 100644 --- a/translations/uk/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/uk/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Використовуючи шар вбудовування як перший шар у нашій мережі класифікатора, ми можемо перейти від моделі "мішка слів" до моделі **мішка вбудовувань**, де спочатку кожне слово в нашому тексті перетворюється у відповідне вбудовування, а потім обчислюється певна агрегатна функція над усіма цими вбудовуваннями, така як `sum`, `average` або `max`. -![Зображення, що показує класифікатор на основі вбудовувань для п'яти слів у послідовності.](../../../../../translated_images/uk/embedding-classifier-example.b77f021a7ee67eee.png) +![Зображення, що показує класифікатор на основі вбудовувань для п'яти слів у послідовності.](../../../../../translated_images/uk/embedding-classifier-example.b77f021a7ee67eee.webp) > Зображення автора @@ -40,7 +40,7 @@ CO_OP_TRANSLATOR_METADATA: CBoW працює швидше, тоді як скіп-грам повільніший, але краще представляє рідковживані слова. -![Зображення, що показує алгоритми CBoW і Skip-Gram для перетворення слів у вектори.](../../../../../translated_images/uk/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![Зображення, що показує алгоритми CBoW і Skip-Gram для перетворення слів у вектори.](../../../../../translated_images/uk/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Зображення з [цієї статті](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/uk/lessons/5-NLP/15-LanguageModeling/README.md b/translations/uk/lessons/5-NLP/15-LanguageModeling/README.md index b1c8b563..0c471fd9 100644 --- a/translations/uk/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/uk/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: * **Безперервний мішок слів** (CBoW), коли ми передбачаємо середній токен $W_0$ у послідовності токенів $W_{-N}$, ..., $W_N$. * **Skip-gram**, де ми передбачаємо набір сусідніх токенів {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} на основі середнього токена $W_0$. -![зображення з наукової статті про перетворення слів у вектори](../../../../../translated_images/uk/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![зображення з наукової статті про перетворення слів у вектори](../../../../../translated_images/uk/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Зображення з [цієї статті](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/uk/lessons/5-NLP/16-RNN/README.md b/translations/uk/lessons/5-NLP/16-RNN/README.md index db9e2dd2..f77fe0a4 100644 --- a/translations/uk/lessons/5-NLP/16-RNN/README.md +++ b/translations/uk/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: Щоб захопити значення текстової послідовності, нам потрібно використовувати іншу архітектуру нейронної мережі, яка називається **рекурентною нейронною мережею** або RNN. У RNN ми пропускаємо речення через мережу по одному символу за раз, і мережа створює певний **стан**, який ми потім передаємо назад у мережу разом із наступним символом. -![RNN](../../../../../translated_images/uk/rnn.27f5c29c53d727b5.png) +![RNN](../../../../../translated_images/uk/rnn.27f5c29c53d727b5.webp) > Зображення автора @@ -61,7 +61,7 @@ CO_OP_TRANSLATOR_METADATA: Рекурентна мережа, одностороння чи двонаправлена, захоплює певні шаблони в послідовності та може зберігати їх у векторі стану або передавати у вихід. Як і у випадку з згортковими мережами, ми можемо побудувати ще один рекурентний шар поверх першого, щоб захопити шаблони вищого рівня та побудувати з шаблонів нижчого рівня, які витягнув перший шар. Це приводить нас до поняття **багатошарової RNN**, яка складається з двох або більше рекурентних мереж, де вихід попереднього шару передається наступному шару як вхід. -![Зображення, що показує багатошарову довготривалу короткочасну пам'ять RNN](../../../../../translated_images/uk/multi-layer-lstm.dd975e29bb2a59fe.jpg) +![Зображення, що показує багатошарову довготривалу короткочасну пам'ять RNN](../../../../../translated_images/uk/multi-layer-lstm.dd975e29bb2a59fe.webp) *Зображення з [цієї чудової статті](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) Фернандо Лопеса* diff --git a/translations/uk/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/uk/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index 10ce7e78..a892b0e8 100644 --- a/translations/uk/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/uk/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "Рекурентна мережа, одностороння чи двонаправлена, захоплює певні шаблони в межах послідовності та може зберігати їх у векторі стану або передавати у вихідні дані. Як і у випадку з згортковими мережами, ми можемо побудувати ще один рекурентний шар поверх першого, щоб захоплювати шаблони вищого рівня, створені з шаблонів нижчого рівня, які витягнув перший шар. Це приводить нас до поняття **багатошарової RNN**, яка складається з двох або більше рекурентних мереж, де вихід попереднього шару передається до наступного шару як вхідні дані.\n", "\n", - "![Зображення багатошарової довготривалої пам’яті RNN](../../../../../translated_images/uk/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![Зображення багатошарової довготривалої пам’яті RNN](../../../../../translated_images/uk/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*Зображення з [цієї чудової статті](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) Фернандо Лопеса*\n", "\n", diff --git a/translations/uk/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/uk/lessons/5-NLP/16-RNN/RNNTF.ipynb index 5a53dd3b..e83be25d 100644 --- a/translations/uk/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/uk/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "Щоб захопити значення послідовності тексту, ми використаємо архітектуру нейронної мережі, яка називається **рекурентна нейронна мережа** (RNN). Використовуючи RNN, ми пропускаємо наше речення через мережу по одному токену за раз, і мережа генерує певний **стан**, який ми потім передаємо в мережу разом із наступним токеном.\n", "\n", - "![Зображення, що показує приклад генерації рекурентної нейронної мережі.](../../../../../translated_images/uk/rnn.27f5c29c53d727b5.png)\n", + "![Зображення, що показує приклад генерації рекурентної нейронної мережі.](../../../../../translated_images/uk/rnn.27f5c29c53d727b5.webp)\n", "\n", "З огляду на вхідну послідовність токенів $X_0,\\dots,X_n$, RNN створює послідовність блоків нейронної мережі та навчає цю послідовність від початку до кінця за допомогою зворотного поширення. Кожен блок мережі приймає пару $(X_i,S_i)$ як вхід і генерує $S_{i+1}$ як результат. Кінцевий стан $S_n$ або вихід $Y_n$ передається в лінійний класифікатор для отримання результату. Усі блоки мережі мають однакові ваги та навчаються від початку до кінця за допомогою одного проходу зворотного поширення.\n", "\n", @@ -369,7 +369,7 @@ "\n", "Рекурентні мережі, однонаправлені чи двонаправлені, захоплюють шаблони в межах послідовності та зберігають їх у векторі станів або повертають як вихідні дані. Як і у випадку з згортковими мережами, ми можемо створити ще один рекурентний шар після першого, щоб захопити шаблони вищого рівня, побудовані з шаблонів нижчого рівня, які витягує перший шар. Це приводить нас до поняття **багатошарової RNN**, яка складається з двох або більше рекурентних мереж, де вихід попереднього шару передається наступному шару як вхідні дані.\n", "\n", - "![Зображення багатошарової довготривалої пам'яті RNN](../../../../../translated_images/uk/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![Зображення багатошарової довготривалої пам'яті RNN](../../../../../translated_images/uk/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*Зображення з [цієї чудової статті](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) Фернандо Лопеса.*\n", "\n", diff --git a/translations/uk/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/uk/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index 3da3b34f..40efa301 100644 --- a/translations/uk/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/uk/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "Метод, за яким ми будемо навчати RNN для генерації тексту, виглядає наступним чином. На кожному кроці ми беремо послідовність символів довжиною `nchars` і просимо мережу згенерувати наступний вихідний символ для кожного вхідного символу:\n", "\n", - "![Зображення, що показує приклад генерації слова 'HELLO' за допомогою RNN.](../../../../../translated_images/uk/rnn-generate.56c54afb52f9781d.png)\n", + "![Зображення, що показує приклад генерації слова 'HELLO' за допомогою RNN.](../../../../../translated_images/uk/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "Залежно від конкретного сценарію, ми також можемо захотіти включити деякі спеціальні символи, такі як *кінець послідовності* ``. У нашому випадку ми просто хочемо навчити мережу генерувати нескінченний текст, тому ми зафіксуємо розмір кожної послідовності, щоб він дорівнював `nchars` токенам. Таким чином, кожен навчальний приклад складатиметься з `nchars` входів і `nchars` виходів (які є вхідною послідовністю, зміщеною на один символ вліво). Мініпакет складатиметься з кількох таких послідовностей.\n", "\n", diff --git a/translations/uk/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/uk/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index 6db2090f..66412f64 100644 --- a/translations/uk/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/uk/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -111,7 +111,7 @@ "\n", "Ось як ми будемо навчати RNN створювати заголовки новин. На кожному кроці ми беремо один заголовок, який подається в RNN, і для кожного вхідного символу ми просимо мережу згенерувати наступний вихідний символ:\n", "\n", - "![Зображення, що демонструє приклад генерації слова 'HELLO' за допомогою RNN.](../../../../../translated_images/uk/rnn-generate.56c54afb52f9781d.png)\n", + "![Зображення, що демонструє приклад генерації слова 'HELLO' за допомогою RNN.](../../../../../translated_images/uk/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "Для останнього символу нашої послідовності ми попросимо мережу згенерувати токен ``.\n", "\n", diff --git a/translations/uk/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/uk/lessons/5-NLP/17-GenerativeNetworks/README.md index ec808fb0..09e40c3d 100644 --- a/translations/uk/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/uk/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Це дозволяє створювати різні нейронні архітектури, які показані на зображенні нижче: -![Зображення, що показує поширені шаблони рекурентних нейронних мереж.](../../../../../translated_images/uk/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Зображення, що показує поширені шаблони рекурентних нейронних мереж.](../../../../../translated_images/uk/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > Зображення з блогу [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) авторства [Андрея Карпаті](http://karpathy.github.io/) @@ -32,7 +32,7 @@ CO_OP_TRANSLATOR_METADATA: Ми навчимо цю RNN генерувати текст крок за кроком. На кожному кроці ми братимемо послідовність символів довжиною `nchars` і проситимемо мережу створити наступний вихідний символ для кожного вхідного символу: -![Зображення, що показує приклад генерації слова 'HELLO' за допомогою RNN.](../../../../../translated_images/uk/rnn-generate.56c54afb52f9781d.png) +![Зображення, що показує приклад генерації слова 'HELLO' за допомогою RNN.](../../../../../translated_images/uk/rnn-generate.56c54afb52f9781d.webp) Під час генерації тексту (під час інференсу) ми починаємо з деякого **запиту**, який передається через блоки RNN для створення його проміжного стану, а потім з цього стану починається генерація. Ми генеруємо один символ за раз і передаємо стан та створений символ до іншого блоку RNN для генерації наступного, поки не буде створено достатню кількість символів. diff --git a/translations/uk/lessons/5-NLP/18-Transformers/README.md b/translations/uk/lessons/5-NLP/18-Transformers/README.md index ebfb7f6b..27bbe66e 100644 --- a/translations/uk/lessons/5-NLP/18-Transformers/README.md +++ b/translations/uk/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ CO_OP_TRANSLATOR_METADATA: **Механізми уваги** забезпечують спосіб зважування контекстуального впливу кожного вхідного вектора на кожне передбачення виходу RNN. Це реалізується шляхом створення "коротких шляхів" між проміжними станами вхідної RNN та вихідної RNN. Таким чином, при генерації вихідного символу yt, ми враховуємо всі приховані стани hi вхідної мережі з різними коефіцієнтами ваги αt,i. -![Зображення моделі кодер/декодер з додатковим шаром уваги](../../../../../translated_images/uk/encoder-decoder-attention.7a726296894fb567.png) +![Зображення моделі кодер/декодер з додатковим шаром уваги](../../../../../translated_images/uk/encoder-decoder-attention.7a726296894fb567.webp) > Модель кодер-декодер з механізмом додаткової уваги у [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), цитовано з [цього блогу](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) Матриця уваги {αi,j} представляє ступінь, до якого певні слова у вході впливають на генерацію конкретного слова у вихідній послідовності. Нижче наведено приклад такої матриці: -![Зображення прикладу вирівнювання, знайденого RNNsearch-50, взято з Bahdanau - arviz.org](../../../../../translated_images/uk/bahdanau-fig3.09ba2d37f202a6af.png) +![Зображення прикладу вирівнювання, знайденого RNNsearch-50, взято з Bahdanau - arviz.org](../../../../../translated_images/uk/bahdanau-fig3.09ba2d37f202a6af.webp) > Рисунок з [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Рис.3) @@ -66,7 +66,7 @@ CO_OP_TRANSLATOR_METADATA: Далі нам потрібно захопити певні шаблони в нашій послідовності. Для цього трансформери використовують механізм **самоуваги**, який, по суті, є увагою, застосованою до тієї ж послідовності як до входу, так і до виходу. Застосування самоуваги дозволяє враховувати **контекст** у реченні та бачити, які слова взаємопов'язані. Наприклад, це дозволяє бачити, до яких слів відносяться кореференції, такі як *це*, а також враховувати контекст: -![](../../../../../translated_images/uk/CoreferenceResolution.861924d6d384a7d6.png) +![](../../../../../translated_images/uk/CoreferenceResolution.861924d6d384a7d6.webp) > Зображення з [блогу Google](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ CO_OP_TRANSLATOR_METADATA: **BERT** (Bidirectional Encoder Representations from Transformers) — це дуже велика багатошарова трансформерна мережа з 12 шарами для *BERT-base* і 24 для *BERT-large*. Модель спочатку проходить попереднє навчання на великому корпусі текстових даних (WikiPedia + книги) за допомогою ненаглядуваного навчання (передбачення замаскованих слів у реченні). Під час попереднього навчання модель засвоює значний рівень розуміння мови, який потім можна використовувати з іншими наборами даних за допомогою тонкого налаштування. Цей процес називається **трансферним навчанням**. -![зображення з http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/uk/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) +![зображення з http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/uk/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > Зображення [джерело](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/uk/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/uk/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index 258dce82..a6821412 100644 --- a/translations/uk/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/uk/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**Механізми уваги** забезпечують спосіб зважування контекстуального впливу кожного вхідного вектора на кожне передбачення виходу RNN. Це реалізується шляхом створення \"коротких шляхів\" між проміжними станами вхідної RNN та вихідної RNN. Таким чином, при генерації вихідного символу $y_t$, ми враховуємо всі приховані стани входу $h_i$ з різними ваговими коефіцієнтами $\\alpha_{t,i}$.\n", "\n", - "![Зображення моделі енкодер/декодер з додатковим шаром уваги](../../../../../translated_images/uk/encoder-decoder-attention.7a726296894fb567.png)\n", + "![Зображення моделі енкодер/декодер з додатковим шаром уваги](../../../../../translated_images/uk/encoder-decoder-attention.7a726296894fb567.webp)\n", "*Модель енкодер-декодер з механізмом додаткової уваги у [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), цитовано з [цього блогу](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Матриця уваги $\\{\\alpha_{i,j}\\}$ представляє ступінь, до якого певні вхідні слова впливають на генерацію конкретного слова у вихідній послідовності. Нижче наведено приклад такої матриці:\n", "\n", - "![Зображення прикладу вирівнювання, знайденого RNNsearch-50, взято з Bahdanau - arviz.org](../../../../../translated_images/uk/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![Зображення прикладу вирівнювання, знайденого RNNsearch-50, взято з Bahdanau - arviz.org](../../../../../translated_images/uk/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*Зображення взято з [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Рис.3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) — це дуже велика багатошарова трансформерна мережа з 12 шарами для *BERT-base* і 24 для *BERT-large*. Модель спочатку проходить попереднє навчання на великому корпусі текстових даних (WikiPedia + книги) за допомогою неконтрольованого навчання (передбачення замаскованих слів у реченні). Під час попереднього навчання модель засвоює значний рівень розуміння мови, який потім можна використовувати з іншими наборами даних за допомогою тонкого налаштування. Цей процес називається **трансферним навчанням**.\n", "\n", - "![Зображення з http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/uk/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![Зображення з http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/uk/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "Існує багато варіацій архітектур трансформерів, включаючи BERT, DistilBERT, BigBird, OpenGPT3 та інші, які можна налаштовувати. Пакет [HuggingFace](https://github.com/huggingface/) надає репозиторій для навчання багатьох із цих архітектур за допомогою PyTorch.\n", "\n", diff --git a/translations/uk/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/uk/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index 22cec0b9..fd4cdc56 100644 --- a/translations/uk/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/uk/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**Механізми уваги** забезпечують спосіб зважування контекстуального впливу кожного вхідного вектора на кожне передбачення виходу RNN. Це реалізується шляхом створення \"ярликів\" між проміжними станами вхідної RNN та вихідної RNN. Таким чином, при генерації вихідного символу $y_t$ ми враховуємо всі приховані стани входу $h_i$ з різними ваговими коефіцієнтами $\\alpha_{t,i}$.\n", "\n", - "![Зображення моделі енкодер/декодер з додатковим шаром уваги](../../../../../translated_images/uk/encoder-decoder-attention.7a726296894fb567.png)\n", + "![Зображення моделі енкодер/декодер з додатковим шаром уваги](../../../../../translated_images/uk/encoder-decoder-attention.7a726296894fb567.webp)\n", "*Модель енкодер-декодер з механізмом додаткової уваги у [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), цитовано з [цього блогу](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Матриця уваги $\\{\\alpha_{i,j}\\}$ представляє ступінь, до якого певні вхідні слова впливають на генерацію конкретного слова у вихідній послідовності. Нижче наведено приклад такої матриці:\n", "\n", - "![Зображення прикладу вирівнювання, знайденого RNNsearch-50, взято з Bahdanau - arviz.org](../../../../../translated_images/uk/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![Зображення прикладу вирівнювання, знайденого RNNsearch-50, взято з Bahdanau - arviz.org](../../../../../translated_images/uk/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*Зображення взято з [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Рис.3)*\n", "\n", @@ -229,7 +229,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) — це велика багатошарова трансформерна мережа з 12 шарами для *BERT-base* і 24 для *BERT-large*. Модель спочатку проходить попереднє навчання на великому корпусі текстових даних (WikiPedia + книги) за допомогою неконтрольованого навчання (прогнозування замаскованих слів у реченні). Під час попереднього навчання модель засвоює значний рівень розуміння мови, який потім можна використовувати з іншими наборами даних за допомогою тонкого налаштування. Цей процес називається **трансферним навчанням**.\n", "\n", - "![зображення з http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/uk/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![зображення з http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/uk/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "Існує багато варіацій архітектур трансформерів, включаючи BERT, DistilBERT, BigBird, OpenGPT3 та інші, які можна налаштовувати.\n", "\n", diff --git a/translations/uk/lessons/5-NLP/19-NER/README.md b/translations/uk/lessons/5-NLP/19-NER/README.md index 9ed44b27..6ea47f93 100644 --- a/translations/uk/lessons/5-NLP/19-NER/README.md +++ b/translations/uk/lessons/5-NLP/19-NER/README.md @@ -58,7 +58,7 @@ infant | O Оскільки нам потрібно створити однозначну відповідність між токенами та класами, ми можемо навчити правосторонню **багатозначну** модель нейронної мережі з цієї схеми: -![Зображення, що показує загальні шаблони рекурентних нейронних мереж.](../../../../../translated_images/uk/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Зображення, що показує загальні шаблони рекурентних нейронних мереж.](../../../../../translated_images/uk/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > *Зображення з [цього блогу](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) від [Андрія Карпатія](http://karpathy.github.io/). Моделі класифікації токенів для NER відповідають правосторонній архітектурі мережі на цьому зображенні.* diff --git a/translations/uk/lessons/5-NLP/README.md b/translations/uk/lessons/5-NLP/README.md index 32df8ec0..ba099fff 100644 --- a/translations/uk/lessons/5-NLP/README.md +++ b/translations/uk/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Обробка природної мови -![Резюме задач NLP у вигляді малюнка](../../../../translated_images/uk/ai-nlp.b22dcb8ca4707cea.png) +![Резюме задач NLP у вигляді малюнка](../../../../translated_images/uk/ai-nlp.b22dcb8ca4707cea.webp) У цьому розділі ми зосередимося на використанні нейронних мереж для вирішення задач, пов'язаних із **обробкою природної мови (NLP)**. Існує багато проблем NLP, які ми хочемо, щоб комп'ютери могли вирішувати: diff --git a/translations/uk/lessons/6-Other/23-MultiagentSystems/README.md b/translations/uk/lessons/6-Other/23-MultiagentSystems/README.md index 2cc7fd61..a2260c23 100644 --- a/translations/uk/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/uk/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ ask turtles [ Після відкриття моделі ви потрапляєте на головний екран NetLogo. Ось приклад моделі, яка описує популяцію вовків і овець за умови обмежених ресурсів (трави). -![Головний екран NetLogo](../../../../../translated_images/uk/NetLogo-Main.32653711ec1a01b3.png) +![Головний екран NetLogo](../../../../../translated_images/uk/NetLogo-Main.32653711ec1a01b3.webp) > Знімок екрана від Дмитра Сошникова diff --git a/translations/uk/lessons/README.md b/translations/uk/lessons/README.md index 16fbb7bd..438a2de2 100644 --- a/translations/uk/lessons/README.md +++ b/translations/uk/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Огляд -![Огляд у вигляді замальовки](../../../translated_images/uk/ai-overview.0857791951d19500.png) +![Огляд у вигляді замальовки](../../../translated_images/uk/ai-overview.0857791951d19500.webp) > Замальовка від [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/uk/lessons/X-Extras/X1-MultiModal/README.md b/translations/uk/lessons/X-Extras/X1-MultiModal/README.md index 6088b60e..534aed10 100644 --- a/translations/uk/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/uk/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: Основна ідея CLIP полягає в тому, щоб порівнювати текстові запити із зображеннями та визначати, наскільки добре зображення відповідає запиту. -![Архітектура CLIP](../../../../../translated_images/uk/clip-arch.b3dbf20b4e8ed8be.png) +![Архітектура CLIP](../../../../../translated_images/uk/clip-arch.b3dbf20b4e8ed8be.webp) > *Зображення з [цього блогу](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ CO_OP_TRANSLATOR_METADATA: Припустимо, нам потрібно класифікувати зображення, наприклад, між котами, собаками та людьми. У цьому випадку ми можемо надати моделі зображення та серію текстових запитів: "*зображення кота*", "*зображення собаки*", "*зображення людини*". У результатуючому векторі з 3 ймовірностей нам потрібно вибрати індекс із найвищим значенням. -![CLIP для класифікації зображень](../../../../../translated_images/uk/clip-class.3af42ef0b2b19369.png) +![CLIP для класифікації зображень](../../../../../translated_images/uk/clip-class.3af42ef0b2b19369.webp) > *Зображення з [цього блогу](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ CLIP також можна використовувати для **генера Однією з важливих відмінностей між VQGAN і традиційним GAN є те, що останній може створити пристойне зображення з будь-якого вхідного вектора, тоді як VQGAN, ймовірно, створить зображення, яке не буде узгодженим. Тому нам потрібно додатково керувати процесом створення зображення, і це можна зробити за допомогою CLIP. -![Архітектура VQGAN+CLIP](../../../../../translated_images/uk/vqgan.5027fe05051dfa31.png) +![Архітектура VQGAN+CLIP](../../../../../translated_images/uk/vqgan.5027fe05051dfa31.webp) Щоб створити зображення, яке відповідає текстовому запиту, ми починаємо з випадкового вектора кодування, який передається через VQGAN для створення зображення. Потім CLIP використовується для створення функції втрат, яка показує, наскільки добре зображення відповідає текстовому запиту. Мета полягає в тому, щоб мінімізувати цю втрату, використовуючи зворотне поширення для коригування параметрів вхідного вектора. Чудова бібліотека, яка реалізує VQGAN+CLIP, — це [Pixray](http://github.com/pixray/pixray). -![Зображення, створене Pixray](../../../../../translated_images/uk/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Зображення, створене Pixray](../../../../../translated_images/uk/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Зображення, створене Pixray](../../../../../translated_images/uk/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) +![Зображення, створене Pixray](../../../../../translated_images/uk/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp) | ![Зображення, створене Pixray](../../../../../translated_images/uk/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp) | ![Зображення, створене Pixray](../../../../../translated_images/uk/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp) ----|----|---- Зображення, створене за запитом *акварельний портрет молодого чоловіка-вчителя літератури з книгою* | Зображення, створене за запитом *масляний портрет молодої жінки-вчителя інформатики з комп'ютером* | Зображення, створене за запитом *масляний портрет старого чоловіка-вчителя математики перед дошкою* diff --git a/translations/ur/README.md b/translations/ur/README.md index ea0e68af..286c8f08 100644 --- a/translations/ur/README.md +++ b/translations/ur/README.md @@ -1,8 +1,8 @@ [Arabic](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgarian](../bg/README.md) | [Burmese (Myanmar)](../my/README.md) | [Chinese (Simplified)](../zh/README.md) | [Chinese (Traditional, Hong Kong)](../hk/README.md) | [Chinese (Traditional, Macau)](../mo/README.md) | [Chinese (Traditional, Taiwan)](../tw/README.md) | [Croatian](../hr/README.md) | [Czech](../cs/README.md) | [Danish](../da/README.md) | [Dutch](../nl/README.md) | [Estonian](../et/README.md) | [Finnish](../fi/README.md) | [French](../fr/README.md) | [German](../de/README.md) | [Greek](../el/README.md) | [Hebrew](../he/README.md) | [Hindi](../hi/README.md) | [Hungarian](../hu/README.md) | [Indonesian](../id/README.md) | [Italian](../it/README.md) | [Japanese](../ja/README.md) | [Kannada](../kn/README.md) | [Korean](../ko/README.md) | [Lithuanian](../lt/README.md) | [Malay](../ms/README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Nepali](../ne/README.md) | [Nigerian Pidgin](../pcm/README.md) | [Norwegian](../no/README.md) | [Persian (Farsi)](../fa/README.md) | [Polish](../pl/README.md) | [Portuguese (Brazil)](../br/README.md) | [Portuguese (Portugal)](../pt/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Romanian](../ro/README.md) | [Russian](../ru/README.md) | [Serbian (Cyrillic)](../sr/README.md) | [Slovak](../sk/README.md) | [Slovenian](../sl/README.md) | [Spanish](../es/README.md) | [Swahili](../sw/README.md) | [Swedish](../sv/README.md) | [Tagalog (Filipino)](../tl/README.md) | [Tamil](../ta/README.md) | [Telugu](../te/README.md) | [Thai](../th/README.md) | [Turkish](../tr/README.md) | [Ukrainian](../uk/README.md) | [Urdu](./README.md) | [Vietnamese](../vi/README.md) -> **مقامی طور پر کلون کرنا پسند کریں گے؟** +> **ترجیح دیتے ہیں کہ مقامی طور پر کلون کریں؟** -> یہ ذخیرہ 50+ زبانوں کے تراجم شامل کرتا ہے جو ڈاؤن لوڈ کے حجم میں نمایاں اضافہ کرتے ہیں۔ بغیر تراجم کے کلون کرنے کے لیے، sparse checkout استعمال کریں: +> اس مخزن میں 50+ زبانوں کے ترجمے شامل ہیں جو ڈاؤنلوڈ سائز میں نمایاں اضافہ کرتے ہیں۔ بغیر ترجموں کے کلون کرنے کے لیے sparse checkout استعمال کریں: > ```bash > git clone --filter=blob:none --sparse https://github.com/microsoft/AI-For-Beginners.git > cd AI-For-Beginners > git sparse-checkout set --no-cone '/*' '!translations' '!translated_images' > ``` -> اس سے آپ کو کورس مکمل کرنے کے لیے درکار ہر چیز مل جائے گی اور ڈاؤن لوڈ بھی بہت تیز ہو جائے گا۔ +> یہ آپ کو کورس مکمل کرنے کے لیے ہر چیز مہیا کرتا ہے اور ڈاؤنلوڈ بہت تیز ہوتا ہے۔ -**اگر آپ چاہیں کہ اضافی ترجمہ زبانیں شامل کی جائیں تو وہ یہاں [listed](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md) دستیاب ہیں۔** +**اگر آپ اضافی ترجمہ زبانوں کی حمایت چاہتے ہیں تو انہیں [یہاں](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md) دیکھیں** ## کمیونٹی میں شامل ہوں [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) @@ -56,123 +56,123 @@ CO_OP_TRANSLATOR_METADATA: **[کورس کا مائنڈ میپ](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** -اس نصاب میں آپ سیکھیں گے: +اس نصاب میں، آپ سیکھیں گے: -* مصنوعی ذہانت کے مختلف طریقے، جن میں "پرانی اچھی" علامتی طریقہ کار شامل ہے جس میں **علم کی نمائندگی** اور دلیل شامل ہے ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence))۔ -* **نیورل نیٹ ورکس** اور **گہری تعلیم**، جو جدید AI کا مرکزی حصہ ہیں۔ ہم ان اہم موضوعات کے پیچھے کے تصورات کو دو مشہور فریم ورکس — [TensorFlow](http://Tensorflow.org) اور [PyTorch](http://pytorch.org) — میں کوڈ کے ذریعے واضح کریں گے۔ -* تصویروں اور متون کے ساتھ کام کرنے کے لیے **نیورل آرکیٹیکچرز**۔ ہم حالیہ ماڈلز کا احاطہ کریں گے مگر جدید ترین کی سطح پر کچھ کمی ہو سکتی ہے۔ -* کم مقبول AI طریقے، جیسے **جینیاتی الگورتھمز** اور **متعدد ایجنٹ سسٹمز**۔ +* مصنوعی ذہانت کے مختلف طریقے، بشمول "پرانی اچھی" علامتی طریقہ کار جس میں **علم کی نمائندگی** اور استدلال شامل ہے ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence))۔ +* **نیورل نیٹ ورکس** اور **ڈیپ لرننگ**، جو جدید AI کی بنیاد ہیں۔ ہم ان اہم موضوعات کے پیچھے کے تصورات کو کوڈ کی مدد سے دو مقبول فریم ورکس - [TensorFlow](http://Tensorflow.org) اور [PyTorch](http://pytorch.org) میں سمجھائیں گے۔ +* تصاویر اور متن کے ساتھ کام کرنے کے لیے **نیورل آرکیٹیکچر**۔ ہم حالیہ ماڈلز کو کور کریں گے، لیکن جدید ترین حالات میں کچھ کمی ہو سکتی ہے۔ +* کم مشہور AI طریقے، جیسے کہ **جینیٹک الگوردمز** اور **کثیر ایجنٹ سسٹمز**۔ -اس نصاب میں ہم کیا شامل نہیں کریں گے: +اس نصاب میں ہم جو چیزیں شامل نہیں کریں گے: -> [اس کورس کے لیے تمام اضافی وسائل ہماری Microsoft Learn کلیکشن میں تلاش کریں](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) +> [ہمارے Microsoft Learn مجموعہ میں اس کورس کے تمام اضافی ذرائع دریافت کریں](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) -* کاروبار میں **AI کے استعمال** کے کاروباری کیسز۔ اس کے لیے آپ Microsoft Learn پر [Introduction to AI for business users](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) لرننگ پاتھ یا [AI Business School](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum) استعمال کر سکتے ہیں، جو [INSEAD](https://www.insead.edu/) کے تعاون سے تیار کیا گیا ہے۔ -* **کلاسیکی مشین لرننگ**، جو ہمارے [Machine Learning for Beginners Curriculum](http://github.com/Microsoft/ML-for-Beginners) میں اچھی طرح بیان کی گئی ہے۔ -* عملی AI ایپلیکیشنز جو **[Cognitive Services](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)** کے استعمال سے بنائی گئی ہوں۔ اس کے لیے ہم تجویز کرتے ہیں کہ آپ Microsoft Learn کے ماڈیولز جیسے [vision](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum)، [natural language processing](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum)، **[Generative AI with Azure OpenAI Service](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** وغیرہ سے شروع کریں۔ -* مخصوص ML **کلاؤڈ فریم ورکس**، جیسے [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-bethanycheum)، [Microsoft 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پوسٹ](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) بھی دیکھ سکتے ہیں۔ -* گہری تعلیم کے پیچھے کا **گہرا ریاضی**۔ اس کے لیے ہم Ian Goodfellow, Yoshua Bengio اور Aaron Courville کی کتاب [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) کی سفارش کرتے ہیں، جو آن لائن بھی دستیاب ہے: [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/)۔ +* **کاروبار میں AI** کے استعمال کے کاروباری کیسز۔ Microsoft Learn پر [کاروباری صارفین کے لیے AI تعارف](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) یا [AI بزنس اسکول](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum)، جو [INSEAD](https://www.insead.edu/) کے تعاون سے تیار کیا گیا ہے، اختیار کرنے پر غور کریں۔ +* **کلاسی میشن لرننگ**، جو ہمارے [نو آموزوں کے لیے مشین لرننگ نصاب](http://github.com/Microsoft/ML-for-Beginners) میں اچھی طرح بیان کی گئی ہے۔ +* **[کگنیٹیو سروسز](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)** کے استعمال سے بنائے گئے عملی AI ایپلیکیشنز۔ اس کے لیے، ہم Microsoft Learn پر [ویژن](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum)، [قدرتی زبان کی پروسیسنگ](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum)، **[Azure OpenAI سروس کے ساتھ جنریٹیو AI](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** اور دیگر ماڈیولز سے آغاز کرنے کی سفارش کرتے ہیں۔ +* مخصوص ML **کلاؤڈ فریم ورکس**، جیسے کہ [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-bethanycheum)، [Microsoft Fabric](https://learn.microsoft.com/en-us/training/paths/get-started-fabric/?WT.mc_id=academic-77998-bethanycheum)، یا [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum)۔ [Azure Machine Learning کے ساتھ مشین لرننگ حل تیار اور چلائیں](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) اور [Azure Databricks کے ساتھ مشین لرننگ حل بنائیں اور چلائیں](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum) لرننگ راستے استعمال کرنے پر غور کریں۔ +* **مکالماتی AI** اور **چیٹ بوٹس**۔ ایک علیحدہ [مکالماتی AI حل تیار کریں](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum) لرننگ راستہ موجود ہے، اور مزید تفصیل کے لیے آپ [اس بلاگ پوسٹ](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) کا حوالہ بھی دے سکتے ہیں۔ +* ڈیپ لرننگ کے پیچھے کی **گہری ریاضی**۔ اس کے لیے، ہم [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) کتاب کی سفارش کرتے ہیں جو ایان گڈ فیلو، یوشوا بنجییو، اور ایرون کورویل کی لکھی ہوئی ہے، جو آن لائن بھی دستیاب ہے: [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/)۔ -اگر آپ _کلاؤڈ میں AI_ کے موضوعات کا نرمی سے تعارف چاہتے ہیں تو آپ [Get started with artificial intelligence on Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum) لرننگ پاتھ پر غور کر سکتے ہیں۔ +_کلاؤڈ میں AI_ کے موضوعات کے لیے نرمی سے تعارف کے لیے آپ [Azure پر مصنوعی ذہانت کے ساتھ شروع کریں](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum) لرننگ راستہ اختیار کر سکتے ہیں۔ # مواد -| | سبق کا لنک | PyTorch/Keras/TensorFlow | تجربہ گاہ | +| | سبق کا لنک | PyTorch/Keras/TensorFlow | لیب | | :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | -| 0 | [کورس سیٹ اپ](./lessons/0-course-setup/setup.md) | [اپنا ڈیولپمنٹ ماحول ترتیب دیں](./lessons/0-course-setup/how-to-run.md) | | +| 0 | [کورس سیٹ اپ](./lessons/0-course-setup/setup.md) | [اپنا ڈیولپمنٹ ماحول سیٹ اپ کریں](./lessons/0-course-setup/how-to-run.md) | | | I | [**مصنوعی ذہانت کا تعارف**](./lessons/1-Intro/README.md) | | | | 01 | [مصنوعی ذہانت کا تعارف اور تاریخ](./lessons/1-Intro/README.md) | - | - | | II | **علامتی AI** | -| 02 | [علم کی نمائندگی اور ماہر نظام](./lessons/2-Symbolic/README.md) | [ماہر نظام](./lessons/2-Symbolic/Animals.ipynb) / [آنتولوجی](./lessons/2-Symbolic/FamilyOntology.ipynb) /[تصوری گراف](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | +| 02 | [علم کی نمائندگی اور ماہر نظام](./lessons/2-Symbolic/README.md) | [ماہر نظام](./lessons/2-Symbolic/Animals.ipynb) / [وجودیات کا خاکہ](./lessons/2-Symbolic/FamilyOntology.ipynb) /[تصوری گراف](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | | III | [**نیورل نیٹ ورکس کا تعارف**](./lessons/3-NeuralNetworks/README.md) ||| -| 03 | [پرسپٹرون](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [نوٹ بک](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [لیب](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | -| 04 | [ملٹی لئیرڈ پرسپٹرون اور ہمارا اپنا فریم ورک بنانا](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [نوٹ بک](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [لیب](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | -| 05 | [فریم ورکس کا تعارف (PyTorch/TensorFlow) اور اوورفٹنگ](./lessons/3-NeuralNetworks/05-Frameworks/README.md) | [پائی ٹورچ](./lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb) / [کیرس](./lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb) / [ٹینسر فلو](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [لیب](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | -| IV | [**کمپیوٹر وژن**](./lessons/4-ComputerVision/README.md) | [پائی ٹورچ](https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-77998-cacaste) / [ٹینسر فلو](https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-77998-cacaste)| [مائیکروسافٹ ایژر پر کمپیوٹر وژن دریافت کریں](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | +| 03 | [پرسیپٹرون](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [نوٹ بک](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [لیب](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | +| 04 | [کثیر تہہ پرسیپٹرون اور اپنا فریم ورک بنانا](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [نوٹ بک](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [لیب](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | +| 05 | [فریم ورکس کا تعارف (پائی ٹورچ/ٹینسر فلو) اور اوور فٹنگ](./lessons/3-NeuralNetworks/05-Frameworks/README.md) | [پائی ٹورچ](./lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb) / [کیرس](./lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb) / [ٹینسر فلو](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [لیب](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | +| IV | [**کمپیوٹر وژن**](./lessons/4-ComputerVision/README.md) | [پائی ٹورچ](https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-77998-cacaste) / [ٹینسر فلو](https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-77998-cacaste)| [مائیکروسافٹ ایزور پر کمپیوٹر وژن دریافت کریں](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | | 06 | [کمپیوٹر وژن کا تعارف۔ اوپن سی وی](./lessons/4-ComputerVision/06-IntroCV/README.md) | [نوٹ بک](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [لیب](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | -| 07 | [کنولوشنل نیورل نیٹ ورکس](./lessons/4-ComputerVision/07-ConvNets/README.md) & [CNN فن تعمیر](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [پائی ٹورچ](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[ٹینسر فلو](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [لیب](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | -| 08 | [پری ٹرینڈ نیٹ ورکس اور ٹرانسفر لرننگ](./lessons/4-ComputerVision/08-TransferLearning/README.md) اور [ٹریننگ چالاکیاں](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [پائی ٹورچ](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [ٹینسر فلو](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [لیب](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | -| 09 | [آٹو انکوڈرز اور وی اے ایز](./lessons/4-ComputerVision/09-Autoencoders/README.md) | [پائی ٹورچ](./lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [ٹینسر فلو](./lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb) | | -| 10 | [جنریٹیو ایڈورسیریل نیٹ ورکس اور آرٹسٹک اسٹائل ٹرانسفر](./lessons/4-ComputerVision/10-GANs/README.md) | [پائی ٹورچ](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [ٹینسر فلو](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | -| 11 | [آبجیکٹ ڈیٹیکشن](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [ٹینسر فلو](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [لیب](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | -| 12 | [سیمانٹک سیگمنٹیشن۔ یو-نیٹ](./lessons/4-ComputerVision/12-Segmentation/README.md) | [پائی ٹورچ](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb) / [ٹینسر فلو](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb) | | -| V | [**قدرتی زبان کی پروسیسنگ**](./lessons/5-NLP/README.md) | [پائی ٹورچ](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-77998-cacaste) /[ٹینسر فلو](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-77998-cacaste) | [مائیکروسافٹ ایژر پر قدرتی زبان کی پروسیسنگ دریافت کریں](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| -| 13 | [متن کی نمائندگی۔ باؤ/ٹی ایف-آئی ڈی ایف](./lessons/5-NLP/13-TextRep/README.md) | [پائی ٹورچ](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb) / [ٹینسر فلو](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb) | | -| 14 | [سیمانٹک ورڈ ایمبیڈنگز۔ ورڈ2ویک اور گلوو](./lessons/5-NLP/14-Embeddings/README.md) | [پائی ٹورچ](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb) / [ٹینسر فلو](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb) | | -| 15 | [زبان کی ماڈلنگ۔ اپنی امبیڈنگز ٹرین کریں](./lessons/5-NLP/15-LanguageModeling/README.md) | [پائی ٹورچ](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-PyTorch.ipynb) / [ٹینسر فلو](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-TF.ipynb) | [لیب](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | +| 07 | [کنولوشنل نیورل نیٹ ورکس](./lessons/4-ComputerVision/07-ConvNets/README.md) & [CNN کی ساختیں](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [پائی ٹورچ](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[ٹینسر فلو](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [لیب](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | +| 08 | [پری ٹرینڈ نیٹ ورکس اور ٹرانسفر لرننگ](./lessons/4-ComputerVision/08-TransferLearning/README.md) اور [ٹریننگ ٹرکس](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [پائی ٹورچ](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [ٹینسر فلو](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [لیب](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | +| 09 | [آٹواینکوڈر اور وی اے ای](./lessons/4-ComputerVision/09-Autoencoders/README.md) | [پائی ٹورچ](./lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [ٹینسر فلو](./lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb) | | +| 10 | [جنریٹیو ایڈورسریل نیٹ ورکس اور آرٹسٹک اسٹائل ٹرانسفر](./lessons/4-ComputerVision/10-GANs/README.md) | [پائی ٹورچ](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [ٹینسر فلو](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | +| 11 | [آبجیکٹ کا پتہ لگانا](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [ٹینسر فلو](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [لیب](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | +| 12 | [سیمینٹک سیگمنٹیشن۔ یو-نیٹ](./lessons/4-ComputerVision/12-Segmentation/README.md) | [پائی ٹورچ](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb) / [ٹینسر فلو](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb) | | +| V | [**قدرتی زبان کی پروسیسنگ**](./lessons/5-NLP/README.md) | [پائی ٹورچ](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-77998-cacaste) /[ٹینسر فلو](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-77998-cacaste) | [مائیکروسافٹ ایزور پر قدرتی زبان کی پروسیسنگ دریافت کریں](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| +| 13 | [متن کی نمائندگی۔ بو/ٹی ایف-آئی ڈی ایف](./lessons/5-NLP/13-TextRep/README.md) | [پائی ٹورچ](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb) / [ٹینسر فلو](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb) | | +| 14 | [سیمینٹک لفظی ایمبیڈنگ۔ ورڈ ٹو ویک اور گلوو](./lessons/5-NLP/14-Embeddings/README.md) | [پائی ٹورچ](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb) / [ٹینسر فلو](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb) | | +| 15 | [زبان ماڈلنگ۔ اپنی ایمبیڈنگ ٹرین کرنا](./lessons/5-NLP/15-LanguageModeling/README.md) | [پائی ٹورچ](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-PyTorch.ipynb) / [ٹینسر فلو](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-TF.ipynb) | [لیب](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | | 16 | [ریکرنٹ نیورل نیٹ ورکس](./lessons/5-NLP/16-RNN/README.md) | [پائی ٹورچ](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb) / [ٹینسر فلو](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNTF.ipynb) | | | 17 | [جنریٹیو ریکرنٹ نیٹ ورکس](./lessons/5-NLP/17-GenerativeNetworks/README.md) | [پائی ٹورچ](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb) / [ٹینسر فلو](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb) | [لیب](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | | 18 | [ٹرانسفارمرز۔ برٹ۔](./lessons/5-NLP/18-Transformers/README.md) | [پائی ٹورچ](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb) /[ٹینسر فلو](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersTF.ipynb) | | -| 19 | [نامزد اکائی کی پہچان](./lessons/5-NLP/19-NER/README.md) | [ٹینسر فلو](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [لیب](./lessons/5-NLP/19-NER/lab/README.md) | -| 20 | [بڑے زبان کے ماڈلز، پرامپٹ پروگرامنگ اور چند شاٹس کے کام](./lessons/5-NLP/20-LangModels/README.md) | [پائی ٹورچ](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | +| 19 | [نامزد ادارے کی شناخت](./lessons/5-NLP/19-NER/README.md) | [ٹینسر فلو](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [لیب](./lessons/5-NLP/19-NER/lab/README.md) | +| 20 | [بڑے زبان کے ماڈل، پرامپٹ پروگرامنگ اور تھوڑے سے شاٹ کے کام](./lessons/5-NLP/20-LangModels/README.md) | [پائی ٹورچ](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | | VI | **دیگر AI تکنیکیں** || | | 21 | [جینیاتی الگورتھمز](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [نوٹ بک](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | -| 22 | [گہری تقویتی تعلیم](./lessons/6-Other/22-DeepRL/README.md) | [پائی ٹورچ](./lessons/6-Other/22-DeepRL/CartPole-RL-PyTorch.ipynb) /[ٹینسر فلو](./lessons/6-Other/22-DeepRL/CartPole-RL-TF.ipynb) | [لیب](./lessons/6-Other/22-DeepRL/lab/README.md) | -| 23 | [کئی ایجنٹ سسٹمز](./lessons/6-Other/23-MultiagentSystems/README.md) | | | +| 22 | [گہری ری انفورسمنٹ لرننگ](./lessons/6-Other/22-DeepRL/README.md) | [پائی ٹورچ](./lessons/6-Other/22-DeepRL/CartPole-RL-PyTorch.ipynb) /[ٹینسر فلو](./lessons/6-Other/22-DeepRL/CartPole-RL-TF.ipynb) | [لیب](./lessons/6-Other/22-DeepRL/lab/README.md) | +| 23 | [کثیر ایجنٹ سسٹمز](./lessons/6-Other/23-MultiagentSystems/README.md) | | | | VII | **AI اخلاقیات** | | | -| 24 | [AI اخلاقیات اور ذمہ دار AI](./lessons/7-Ethics/README.md) | [مائیکروسافٹ لرن: ذمہ دار AI اصول](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | +| 24 | [AI اخلاقیات اور ذمہ دار AI](./lessons/7-Ethics/README.md) | [مائیکروسافٹ لرن: ذمہ دار AI کے اصول](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | | IX | **اضافی مواد** | | | -| 25 | [کثیر الوضع نیٹ ورکس، کلپ اور وی کیو جی اے این](./lessons/X-Extras/X1-MultiModal/README.md) | [نوٹ بک](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | +| 25 | [کثیرالوجی نیٹ ورکس، کلپ اور VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [نوٹ بک](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | ## ہر سبق میں شامل ہے -* قبل از مطالعہ مواد -* قابل عمل جوپیٹر نوٹ بکس، جو اکثر مخصوص فریم ورک (**پائی ٹورچ** یا **ٹینسر فلو**) کے لیے ہوتے ہیں۔ قابل عمل نوٹ بک میں بہت سا نظریاتی مواد بھی ہوتا ہے، اس لیے موضوع کو سمجھنے کے لیے آپ کو کم از کم نوٹ بک کا ایک ورژن دیکھنا ضروری ہے (چاہے وہ پائی ٹورچ ہو یا ٹینسر فلو)۔ -* کچھ موضوعات کے لیے **لیبز** دستیاب ہیں، جو آپ کو سیکھی ہوئی مواد کو کسی مخصوص مسئلے پر آزمانے کا موقع دیتے ہیں۔ -* کچھ حصوں میں متعلقہ موضوعات کا احاطہ کرنے والے [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) ماڈیول کے لنکس شامل ہیں۔ +* پیشگی پڑھنے کا مواد +* قابل عمل جیوپیٹر نوٹ بکس، جو اکثر فریم ورک (**پائی ٹورچ** یا **ٹینسر فلو**) کے مخصوص ہوتے ہیں۔ قابل عمل نوٹ بک میں بہت سا نظریاتی مواد بھی شامل ہے، اس لیے موضوع کو سمجھنے کے لیے آپ کو کم از کم ایک ورژن (یا پائی ٹورچ یا ٹینسر فلو) دیکھنا ضروری ہے۔ +* کچھ موضوعات کے لیے **لیب** دستیاب ہیں، جو آپ کو سیکھے ہوئے مواد کو کسی خاص مسئلے پر آزمانے کا موقع دیتی ہیں۔ +* کچھ سیکشنز میں [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) کے لنکس شامل ہیں جو متعلقہ موضوعات کا احاطہ کرتے ہیں۔ -## شروع کریں +## شروع کرنا -### 🎯 AI میں نیا ہیں؟ یہاں سے شروع کریں! +### 🎯 AI میں نئے ہیں؟ یہاں سے شروع کریں! -اگر آپ مکمل طور پر AI میں نئے ہیں اور فوری، عملی مثالیں چاہتے ہیں، تو ہمارے [**ابتدائی دوستانہ مثالیں**](./examples/README.md) دیکھیں! ان میں شامل ہیں: +اگر آپ بالکل نئے ہیں اور فوری، عملی مثالیں چاہتے ہیں تو ہمارے [**ابتدائی دوستانہ مثالیں**](./examples/README.md) دیکھیں! ان میں شامل ہیں: - 🌟 **ہیلو AI ورلڈ** - آپ کا پہلا AI پروگرام (پیٹرن کی پہچان) -- 🧠 **سادہ نیورل نیٹ ورک** - نیورل نیٹ ورک شروع سے بنائیں -- 🖼️ **تصویری درجہ بندی کرنے والا** - تصاویر کی درجہ بندی تفصیلی تبصروں کے ساتھ -- 💬 **متن کا جذباتی تجزیہ** - مثبت/منفی متن کا تجزیہ کریں +- 🧠 **سادہ نیورل نیٹ ورک** - ایک نیورل نیٹ ورک خود بنائیں +- 🖼️ **امیج کلاسیفائر** - تفصیلی تبصروں کے ساتھ تصاویر کی درجہ بندی کریں +- 💬 **متن کے جذبات** - مثبت/منفی متن کا تجزیہ کریں -یہ مثالیں آپ کو مکمل نصاب میں شامل ہونے سے پہلے AI تصورات کو سمجھنے میں مدد دینے کے لیے تیار کی گئی ہیں۔ +یہ مثالیں آپ کو مکمل نصاب میں غوطہ لگانے سے پہلے AI تصورات کو سمجھنے میں مدد کے لیے ڈیزائن کی گئی ہیں۔ ### 📚 مکمل نصاب کی ترتیب -- ہم نے آپ کے ترقیاتی ماحول کو سیٹ اپ کرنے میں مدد کے لیے ایک [سیٹ اپ سبق](./lessons/0-course-setup/setup.md) بنایا ہے۔ - اساتذہ کے لیے بھی ہم نے ایک [نصاب سیٹ اپ سبق](./lessons/0-course-setup/for-teachers.md) بنایا ہے! -- VSCode یا Codepace میں [کوڈ کیسے چلائیں](./lessons/0-course-setup/how-to-run.md) +- ہم نے آپ کے ڈیولپمنٹ ماحول کے قیام میں مدد کے لیے ایک [ترتیب سبق](./lessons/0-course-setup/setup.md) تیار کیا ہے۔ - اساتذہ کے لیے، ہم نے آپ کے لیے بھی ایک [نصاب کی ترتیب سبق](./lessons/0-course-setup/for-teachers.md) بنایا ہے! +- VSCode یا Codespace میں کوڈ کو [چالان کرنے کا طریقہ](./lessons/0-course-setup/how-to-run.md) یہ اقدامات کریں: -ریزپوزٹری کو فورک کریں: اس صفحہ کے اوپری دائیں کونے میں "Fork" بٹن پر کلک کریں۔ +ریپوزیٹری کو فورک کریں: اس صفحے کے اوپر دائیں جانب "Fork" بٹن پر کلک کریں۔ -ریزپوزٹری کلون کریں: `git clone https://github.com/microsoft/AI-For-Beginners.git` +ریپوزیٹری کو کلون کریں: `git clone https://github.com/microsoft/AI-For-Beginners.git` -اس رپو کو بعد میں آسانی سے تلاش کرنے کے لیے اس پر ستارہ (🌟) لگانا نہ بھولیں۔ +اس ریپو کو ⭐ ستارہ لگانا نہ بھولیں تاکہ بعد میں اسے آسانی سے تلاش کیا جا سکے۔ -## دوسرے سیکھنے والوں سے ملیں +## دیگر سیکھنے والوں سے ملاقات کریں -دوسرے سیکھنے والوں سے ملاقات اور رابطہ کے لیے ہمارے [سرکاری AI Discord سرور](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) میں شامل ہوں جو یہ کورس کر رہے ہیں اور مدد حاصل کریں۔ +ہمارے [سرکاری AI Discord سرور](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) میں شامل ہوں تاکہ اس کورس میں شامل دوسرے سیکھنے والوں سے ملاقات اور نیٹ ورکنگ کریں اور مدد حاصل کریں۔ -اگر آپ کے پاس پروڈکٹ فیڈبیک یا سوالات ہیں تو ہمارے [Azure AI Foundry Developer Forum](https://aka.ms/foundry/forum) پر جائیں۔ +اگر آپ کو مصنوعات کے بارے میں فیڈبیک یا سوالات ہیں تو ہمارے [Azure AI Foundry Developer Forum](https://aka.ms/foundry/forum) پر تشریف لائیں۔ ## کوئزز -> **کوئزز کے بارے میں نوٹ**: تمام کوئزز Quiz-app فولڈر میں etc\quiz-app میں موجود ہیں، یا [آن لائن یہاں](https://ff-quizzes.netlify.app/) یہ اسباق میں لنک کیے گئے ہیں۔ کوئز ایپ کو لوکل چلایا جا سکتا ہے یا Azure پر ڈیپلائے کیا جا سکتا ہے؛ `quiz-app` فولڈر میں ہدایات پر عمل کریں۔ انہیں آہستہ آہستہ مقامی زبانوں میں ڈھالا جا رہا ہے۔ +> **کوئزز کے بارے میں ایک نوٹ**: تمام کوئزز Quiz-app فولڈر میں etc\quiz-app میں شامل ہیں، یا [آن لائن یہاں](https://ff-quizzes.netlify.app/)۔ یہ اسباق میں لنک کیے گئے ہیں۔ Quiz app کو مقامی طور پر چلایا جا سکتا ہے یا Azure پر تعینات کیا جا سکتا ہے؛ `quiz-app` فولڈر میں ہدایات پر عمل کریں۔ انہیں تدریجی طور پر مقامی زبانوں میں ڈھالا جا رہا ہے۔ -## مدد درکار ہے +## مدد چاہیے -کیا آپ کے پاس تجاویز ہیں یا کوئی املا یا کوڈ کی غلطیاں ملی ہیں؟ مسئلہ اٹھائیں یا پل ریکویسٹ بنائیں۔ +کیا آپ کے پاس تجاویز ہیں یا آپ نے املا یا کوڈ کی غلطیاں پائی ہیں؟ ایک مسئلہ اٹھائیں یا پل ریکویسٹ بنائیں۔ ## خصوصی شکریہ -* **✍️ بنیادی مصنف:** [Dmitry Soshnikov](http://soshnikov.com)، پی ایچ ڈی -* **🔥 ایڈیٹر:** [Jen Looper](https://twitter.com/jenlooper)، پی ایچ ڈی -* **🎨 سکیچ نوٹ مصور:** [Tomomi Imura](https://twitter.com/girlie_mac) -* **✅ کوئز بنانے والا:** [Lateefah Bello](https://github.com/CinnamonXI)، [MLSA](https://studentambassadors.microsoft.com/) -* **🙏 بنیادی شراکت دار:** [Evgenii Pishchik](https://github.com/Pe4enIks) +* **✍️ اصل مصنف:** [Dmitry Soshnikov](http://soshnikov.com), PhD +* **🔥 ایڈیٹر:** [Jen Looper](https://twitter.com/jenlooper), PhD +* **🎨 سکیچنوٹ مصور:** [Tomomi Imura](https://twitter.com/girlie_mac) +* **✅ کوئز تخلیق کنندہ:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) +* **🙏 کلیدی شراکت دار:** [Evgenii Pishchik](https://github.com/Pe4enIks) ## دیگر نصاب -ہماری ٹیم دوسرے نصاب بھی تیار کرتی ہے! دیکھیں: +ہماری ٹیم دیگر نصاب بھی تیار کرتی ہے! دیکھیں: ### لینگ چین @@ -188,15 +188,15 @@ CO_OP_TRANSLATOR_METADATA: [![AI Agents for Beginners](https://img.shields.io/badge/AI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst) --- - -### جنرٹیو AI سیریز + +### جنریٹیو AI سیریز [![Generative AI for Beginners](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst) [![Generative AI (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst) [![Generative AI (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst) [![Generative AI (JavaScript)](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst) --- - + ### بنیادی تعلیم [![ML for Beginners](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst) [![Data Science for Beginners](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst) @@ -207,8 +207,8 @@ CO_OP_TRANSLATOR_METADATA: [![XR Development for Beginners](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst) --- - -### کوپائلٹ سیریز + +### کو پائلٹ سیریز [![Copilot for AI Paired Programming](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst) [![Copilot for C#/.NET](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst) [![Copilot Adventure](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst) @@ -216,17 +216,17 @@ CO_OP_TRANSLATOR_METADATA: ## مدد حاصل کرنا -اگر آپ پھنس جائیں یا AI ایپس بنانے کے دوران کوئی سوال ہو۔ MCP پر ساتھی سیکھنے والوں اور تجربہ کار ڈیولپرز کے ساتھ بحث میں شامل ہوں۔ یہ ایک معاون کمیونٹی ہے جہاں سوالات کا خیرمقدم کیا جاتا ہے اور علم آزادانہ طور پر بانٹا جاتا ہے۔ +اگر آپ پھنس جائیں یا AI ایپ بنانے کے بارے میں کوئی سوال ہو۔ MCP پر دیگر سیکھنے والوں اور تجربہ کار ڈیولپرز کے ساتھ بحث میں شامل ہوں۔ یہ ایک معاون کمیونٹی ہے جہاں سوالات کا خیرمقدم کیا جاتا ہے اور علم بلا روک ٹوک شئیر کیا جاتا ہے۔ [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -اگر آپ کے پاس پروڈکٹ فیڈبیک یا تعمیر کے دوران غلطیاں ہوں تو ملاحظہ کریں: +اگر آپ کو مصنوعات کے بارے میں فیڈبیک یا غلطیاں ملیں تو براہ کرم یہاں جائیں: [![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) --- -**دستخطی دستبرداری**: -یہ دستاویز AI ترجمہ سروس [Co-op Translator](https://github.com/Azure/co-op-translator) کے ذریعے ترجمہ کی گئی ہے۔ اگرچہ ہم درستگی کی کوشش کرتے ہیں، براہ کرم یہ ذہن میں رکھیں کہ خودکار تراجم میں غلطیاں یا عدم درستیاں ہو سکتی ہیں۔ اصل دستاویز اپنی مادری زبان میں معتبر ماخذ سمجھی جانی چاہیے۔ اہم معلومات کے لیے پیشہ ور انسانی ترجمہ تجویز کیا جاتا ہے۔ اس ترجمے کے استعمال سے پیدا ہونے والی کسی بھی غلط فہمی یا غلط تشریح کی ذمہ داری ہم پر نہیں ہوگی۔ +**دستبرداری**: +یہ دستاویز AI ترجمہ خدمات [Co-op Translator](https://github.com/Azure/co-op-translator) کے ذریعے ترجمہ کی گئی ہے۔ ہم درستگی کی کوشش کرتے ہیں، تاہم براہ کرم نوٹ کریں کہ خودکار ترجموں میں غلطیاں یا کوتاہیاں ہو سکتی ہیں۔ اصل دستاویز اپنی مادری زبان میں معتبر ماخذ سمجھی جانی چاہیے۔ اہم معلومات کے لیے پیشہ ور انسانی ترجمہ تجویز کیا جاتا ہے۔ ہم اس ترجمے کے استعمال سے پیدا ہونے والی کسی بھی غلط فہمی یا غلط تشریح کے ذمہ دار نہیں ہیں۔ \ No newline at end of file diff --git a/translations/ur/lessons/0-course-setup/how-to-run.md b/translations/ur/lessons/0-course-setup/how-to-run.md index fd5e64e7..f1b08829 100644 --- a/translations/ur/lessons/0-course-setup/how-to-run.md +++ b/translations/ur/lessons/0-course-setup/how-to-run.md @@ -1,21 +1,21 @@ -# کوڈ چلانے کا طریقہ +# کوڈ کیسے چلائیں -یہ نصاب بہت سے قابل عمل مثالوں اور تجربہ گاہوں پر مشتمل ہے جنہیں آپ چلانا چاہیں گے۔ ایسا کرنے کے لیے، آپ کو اس نصاب کے حصے کے طور پر فراہم کردہ Jupyter Notebooks میں Python کوڈ چلانے کی صلاحیت کی ضرورت ہوگی۔ کوڈ چلانے کے لیے آپ کے پاس کئی اختیارات ہیں: +اس نصاب میں بہت سے قابل عمل مثالیں اور لیب شامل ہیں جنہیں آپ چلانا چاہیں گے۔ ایسا کرنے کے لیے، آپ کو اس نصاب کے حصے کے طور پر فراہم کردہ Jupyter نوٹ بکس میں Python کوڈ چلانے کی صلاحیت کی ضرورت ہے۔ کوڈ چلانے کے کئی اختیارات موجود ہیں: -## اپنے کمپیوٹر پر مقامی طور پر چلائیں +## اپنے کمپیوٹر پر مقامی طور پر چلانا -کوڈ کو اپنے کمپیوٹر پر مقامی طور پر چلانے کے لیے، آپ کو Python کا کوئی ورژن انسٹال کرنا ہوگا۔ میں ذاتی طور پر **[miniconda](https://conda.io/en/latest/miniconda.html)** انسٹال کرنے کی تجویز دیتا ہوں - یہ ایک ہلکی پھلکی انسٹالیشن ہے جو مختلف Python **ورچوئل ماحول** کے لیے `conda` پیکیج مینیجر کو سپورٹ کرتی ہے۔ +اپنے کمپیوٹر پر کوڈ چلانے کے لیے، Python کی تنصیب ضروری ہے۔ ایک تجویز یہ ہے کہ **[miniconda](https://conda.io/en/latest/miniconda.html)** انسٹال کریں - یہ ایک نسبتاً ہلکی تنصیب ہے جو مختلف Python **ورچوئل ماحولیات** کے لیے `conda` پیکج مینیجر کی حمایت کرتی ہے۔ -Miniconda انسٹال کرنے کے بعد، آپ کو ریپوزٹری کلون کرنی ہوگی اور اس کورس کے لیے ایک ورچوئل ماحول بنانا ہوگا: +miniconda انسٹال کرنے کے بعد، ریپوزٹری کلون کریں اور اس کورس کے لیے استعمال ہونے والا ورچوئل ماحول بنائیں: ```bash git clone http://github.com/microsoft/ai-for-beginners @@ -24,19 +24,19 @@ conda env create --name ai4beg --file .devcontainer/environment.yml conda activate ai4beg ``` -### Python ایکسٹینشن کے ساتھ Visual Studio Code استعمال کرنا +### Visual Studio Code میں Python Extension کے ساتھ استعمال -نصاب کو استعمال کرنے کا شاید سب سے بہترین طریقہ یہ ہے کہ اسے [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) میں [Python Extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste) کے ساتھ کھولا جائے۔ +یہ نصاب سب سے بہتر اس وقت استعمال ہوتا ہے جب اسے [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) میں [Python Extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste) کے ساتھ کھولا جائے۔ -> **نوٹ**: جب آپ ریپوزٹری کو کلون کریں اور اسے VS Code میں کھولیں، تو یہ خودکار طور پر آپ کو Python ایکسٹینشنز انسٹال کرنے کی تجویز دے گا۔ آپ کو اوپر بیان کردہ طریقے سے Miniconda بھی انسٹال کرنا ہوگا۔ +> **نوٹ**: جب آپ ریپوزٹری کلون کر کے اسے VS Code میں کھولیں گے، تو یہ خود بخود Python کی توسیعات انسٹال کرنے کی تجویز دے گا۔ آپ کو منیکونڈا بھی انسٹال کرنا ہوگا جیسا کہ اوپر بیان کیا گیا ہے۔ -> **نوٹ**: اگر VS Code آپ کو ریپوزٹری کو کنٹینر میں دوبارہ کھولنے کی تجویز دے تو آپ کو اسے مسترد کرنا ہوگا تاکہ مقامی Python انسٹالیشن استعمال کی جا سکے۔ +> **نوٹ**: اگر VS Code آپ کو ریپوزٹری کو کنٹینر میں دوبارہ کھولنے کا مشورہ دے تو آپ کو یہ رد کر دینا چاہیے تاکہ مقامی Python تنصیب استعمال کی جا سکے۔ ### براؤزر میں Jupyter استعمال کرنا -آپ اپنے کمپیوٹر پر براؤزر سے Jupyter ماحول بھی استعمال کر سکتے ہیں۔ درحقیقت، کلاسیکل Jupyter اور Jupyter Hub دونوں آٹو کمپلیشن، کوڈ ہائی لائٹنگ وغیرہ کے ساتھ کافی آسان ترقیاتی ماحول فراہم کرتے ہیں۔ +آپ اپنے کمپیوٹر کے براؤزر سے Jupyter ماحول بھی استعمال کر سکتے ہیں۔ کلاسیکی Jupyter اور JupyterHub دونوں آٹو مکمل کرنے، کوڈ ہائلائٹنگ، وغیرہ کے ساتھ سہولت بخش ترقیاتی ماحول فراہم کرتے ہیں۔ -Jupyter کو مقامی طور پر شروع کرنے کے لیے، کورس کی ڈائریکٹری پر جائیں اور درج ذیل کمانڈ چلائیں: +مقامی Jupyter شروع کرنے کے لیے، کورس کی ڈائریکٹری میں جائیں، اور یہ کمانڈ چلائیں: ```bash jupyter notebook @@ -45,32 +45,36 @@ jupyter notebook ```bash jupyterhub ``` -اس کے بعد آپ کسی بھی `.ipynb` فائل پر جا سکتے ہیں، اسے کھول سکتے ہیں اور کام شروع کر سکتے ہیں۔ +پھر آپ کسی بھی `.ipynb` فائل پر جا سکتے ہیں، انہیں کھول سکتے ہیں اور کام شروع کر سکتے ہیں۔ ### کنٹینر میں چلانا -Python انسٹالیشن کا ایک متبادل یہ ہوگا کہ کوڈ کو کنٹینر میں چلایا جائے۔ چونکہ ہماری ریپوزٹری میں ایک خاص `.devcontainer` فولڈر موجود ہے جو اس ریپوزٹری کے لیے کنٹینر بنانے کی ہدایات دیتا ہے، VS Code آپ کو کوڈ کو کنٹینر میں دوبارہ کھولنے کی پیشکش کرے گا۔ اس کے لیے Docker انسٹالیشن کی ضرورت ہوگی، اور یہ زیادہ پیچیدہ ہوگا، لہذا ہم اسے زیادہ تجربہ کار صارفین کے لیے تجویز کرتے ہیں۔ +Python کی تنصیب کا ایک متبادل کنٹینر میں کوڈ چلانا ہے۔ چونکہ ہماری ریپوزٹری میں ایک خاص `.devcontainer` فولڈر موجود ہے جو اس ریپو کے لیے کنٹینر بنانے کی ہدایت دیتا ہے، VS Code کوڈ کو کنٹینر میں دوبارہ کھولنے کا موقع فراہم کرتا ہے۔ اس کے لیے Docker کی تنصیب ضروری ہوگی اور یہ زیادہ پیچیدہ ہو گا، اس لیے ہم اسے زیادہ تجربہ کار صارفین کے لیے تجویز کرتے ہیں۔ ## کلاؤڈ میں چلانا -اگر آپ Python کو مقامی طور پر انسٹال نہیں کرنا چاہتے اور آپ کے پاس کچھ کلاؤڈ وسائل تک رسائی ہے - تو ایک اچھا متبادل یہ ہوگا کہ کوڈ کو کلاؤڈ میں چلایا جائے۔ آپ کے پاس ایسا کرنے کے کئی طریقے ہیں: +اگر آپ Python مقامی طور پر انسٹال نہیں کرنا چاہتے، اور آپ کے پاس کچھ کلاؤڈ وسائل تک رسائی ہے تو ایک اچھا متبادل کوڈ کلاؤڈ میں چلانا ہے۔ آپ یہ کئی طریقوں سے کر سکتے ہیں: -* **[GitHub Codespaces](https://github.com/features/codespaces)** استعمال کرتے ہوئے، جو GitHub پر آپ کے لیے ایک ورچوئل ماحول بناتا ہے، جو VS Code براؤزر انٹرفیس کے ذریعے قابل رسائی ہے۔ اگر آپ کو Codespaces تک رسائی حاصل ہے، تو آپ ریپوزٹری میں **Code** بٹن پر کلک کر سکتے ہیں، Codespace شروع کر سکتے ہیں، اور فوراً کام شروع کر سکتے ہیں۔ -* **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)** استعمال کرتے ہوئے۔ [Binder](https://mybinder.org) کلاؤڈ میں مفت کمپیوٹنگ وسائل فراہم کرتا ہے تاکہ آپ GitHub پر کچھ کوڈ آزما سکیں۔ ریپوزٹری کے فرنٹ پیج پر ایک بٹن موجود ہے جو آپ کو Binder میں ریپوزٹری کھولنے کی اجازت دیتا ہے - یہ آپ کو جلدی سے Binder سائٹ پر لے جائے گا، جو بنیادی کنٹینر بنائے گا اور Jupyter ویب انٹرفیس کو بغیر کسی رکاوٹ کے شروع کرے گا۔ +* **[GitHub Codespaces](https://github.com/features/codespaces)** استعمال کرنا، جو GitHub پر آپ کے لیے ایک ورچوئل ماحول تخلیق کرتا ہے، جو VS Code کے براؤزر انٹرفیس کے ذریعے قابل رسائی ہے۔ اگر آپ کے پاس Codespaces تک رسائی ہے، تو آپ صرف ریپو میں **Code** بٹن پر کلک کریں، codespace شروع کریں، اور فوراً کام شروع کر دیں۔ +* **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)** استعمال کرنا۔ [Binder](https://mybinder.org) مفت کلاؤڈ وسائل فراہم کرتا ہے تاکہ آپ GitHub پر کچھ کوڈ آزما سکیں۔ فرنٹ پیج پر ایک بٹن ہے جو ریپوزٹری کو Binder میں کھولتا ہے - یہ آپ کو فوری طور پر binder سائٹ پر لے جائے گا، جو پس منظر میں ایک کنٹینر بنائے گا اور بغیر کسی رکاوٹ کے Jupyter ویب انٹرفیس شروع کرے گا۔ -> **نوٹ**: غلط استعمال کو روکنے کے لیے، Binder کو کچھ ویب وسائل تک رسائی محدود ہے۔ یہ کچھ کوڈ کو کام کرنے سے روک سکتا ہے جو ماڈلز اور/یا ڈیٹا سیٹس کو عوامی انٹرنیٹ سے حاصل کرتا ہے۔ آپ کو کچھ متبادل تلاش کرنے کی ضرورت ہو سکتی ہے۔ نیز، Binder کے ذریعے فراہم کردہ کمپیوٹ وسائل کافی بنیادی ہیں، لہذا تربیت خاص طور پر بعد کے زیادہ پیچیدہ اسباق میں سست ہوگی۔ +> **نوٹ**: بدسلوکی کو روکنے کے لیے، Binder کی کچھ ویب وسائل تک رسائی محدود ہے۔ اس سے کچھ کوڈ کے کام کرنے میں رکاوٹ آ سکتی ہے جو ماڈلز اور/یا ڈیٹاسیٹس عوامی انٹرنیٹ سے حاصل کرتے ہیں۔ آپ کو کچھ حل تلاش کرنے کی ضرورت پڑ سکتی ہے۔ نیز، Binder کے فراہم کردہ کمپیوٹ وسائل بنیادی نوعیت کے ہیں، اس لیے تربیت آہستہ ہوگی، خاص طور پر بعد کے مزید پیچیدہ اسباق میں۔ -## کلاؤڈ میں GPU کے ساتھ چلانا +## GPU کے ساتھ کلاؤڈ میں چلانا -اس نصاب کے کچھ بعد کے اسباق GPU سپورٹ سے بہت فائدہ اٹھائیں گے، کیونکہ بصورت دیگر تربیت انتہائی سست ہوگی۔ آپ کے پاس کچھ اختیارات ہیں، خاص طور پر اگر آپ کو کلاؤڈ تک رسائی حاصل ہے، چاہے وہ [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) کے ذریعے ہو یا آپ کے ادارے کے ذریعے: +اس نصاب کے کچھ بعد کے اسباق GPU سپورٹ سے بہت فائدہ اٹھائیں گے۔ مثلاً ماڈل کی تربیت بصورت دیگر بہت سست ہو سکتی ہے۔ آپ چند اختیارات اپنائیں، خاص طور پر اگر آپ کے پاس کلاؤڈ تک رسائی ہو، چاہے وہ [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) کے ذریعے ہو، یا آپ کے ادارے کے ذریعہ: -* [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) بنائیں اور Jupyter کے ذریعے اس سے جڑیں۔ آپ پھر ریپوزٹری کو براہ راست مشین پر کلون کر سکتے ہیں اور سیکھنا شروع کر سکتے ہیں۔ NC-series VMs میں GPU سپورٹ موجود ہے۔ +* [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) بنائیں اور Jupyter کے ذریعے اس سے جڑیں۔ پھر آپ مشین پر ریپو کلون کر کے تعلیم شروع کر سکتے ہیں۔ NC-سیریز VM میں GPU سپورٹ موجود ہے۔ -> **نوٹ**: کچھ سبسکرپشنز، بشمول Azure for Students، GPU سپورٹ کو فوری طور پر فراہم نہیں کرتے۔ آپ کو تکنیکی سپورٹ درخواست کے ذریعے اضافی GPU کورز کی درخواست کرنے کی ضرورت ہو سکتی ہے۔ +> **نوٹ**: کچھ سبسکرپشنز، بشمول Azure for Students، ڈیفالٹ طور پر GPU سپورٹ فراہم نہیں کرتے۔ آپ کو تکنیکی مدد کی درخواست کے ذریعے اضافی GPU کورز کے لیے درخواست دینی پڑ سکتی ہے۔ -* [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) بنائیں اور وہاں نوٹ بک فیچر استعمال کریں۔ [یہ ویڈیو](https://azure-for-academics.github.io/quickstart/azureml-papers/) دکھاتی ہے کہ Azure ML نوٹ بک میں ریپوزٹری کو کیسے کلون کریں اور اسے استعمال کرنا شروع کریں۔ +* [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) بنائیں اور وہاں نوٹ بک فیچر استعمال کریں۔ [یہ ویڈیو](https://azure-for-academics.github.io/quickstart/azureml-papers/) دکھاتی ہے کہ Azure ML نوٹ بک میں ریپوزٹری کیسے کلون کی جاتی ہے اور اسے کیسے استعمال کیا جاتا ہے۔ -آپ Google Colab بھی استعمال کر سکتے ہیں، جو کچھ مفت GPU سپورٹ کے ساتھ آتا ہے، اور Jupyter Notebooks کو وہاں اپلوڈ کر کے انہیں ایک ایک کر کے چلائیں۔ +آپ Google Colab بھی استعمال کر سکتے ہیں، جو کچھ مفت GPU سپورٹ کے ساتھ آتا ہے، اور وہاں Jupyter نوٹ بکس اپلوڈ کر کے انہیں ایک ایک کر کے چلا سکتے ہیں۔ -**ڈسکلیمر**: -یہ دستاویز AI ترجمہ سروس [Co-op Translator](https://github.com/Azure/co-op-translator) کا استعمال کرتے ہوئے ترجمہ کی گئی ہے۔ ہم درستگی کے لیے کوشش کرتے ہیں، لیکن براہ کرم آگاہ رہیں کہ خودکار ترجمے میں غلطیاں یا عدم درستگی ہو سکتی ہیں۔ اصل دستاویز، جو اس کی مقامی زبان میں ہے، کو مستند ذریعہ سمجھا جانا چاہیے۔ اہم معلومات کے لیے، پیشہ ور انسانی ترجمہ کی سفارش کی جاتی ہے۔ اس ترجمے کے استعمال سے پیدا ہونے والی کسی بھی غلط فہمی یا غلط تشریح کے لیے ہم ذمہ دار نہیں ہیں۔ \ No newline at end of file +--- + + +**انتباہ**: +یہ دستاویز AI ترجمہ سروس [Co-op Translator](https://github.com/Azure/co-op-translator) کے ذریعے ترجمہ کی گئی ہے۔ اگرچہ ہم درستگی کی کوشش کرتے ہیں، براہ کرم اس بات سے آگاہ رہیں کہ خودکار ترجمے میں غلطیاں یا عدم مطابقت ہو سکتی ہے۔ اصل دستاویز اپنی مادری زبان میں ماخذ کا معتبر ذریعہ سمجھا جانا چاہیے۔ اہم معلومات کے لیے پیشہ ور انسانی ترجمہ کی سفارش کی جاتی ہے۔ ہم اس ترجمے کے استعمال سے پیدا ہونے والی کسی بھی غلط فہمی یا غلط تشریح کے ذمہ دار نہیں ہیں۔ + \ No newline at end of file diff --git a/translations/ur/lessons/1-Intro/README.md b/translations/ur/lessons/1-Intro/README.md index a612bbda..e18762d5 100644 --- a/translations/ur/lessons/1-Intro/README.md +++ b/translations/ur/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # مصنوعی ذہانت کا تعارف -![مصنوعی ذہانت کے تعارف کا خلاصہ ایک خاکے میں](../../../../translated_images/ur/ai-intro.bf28d1ac4235881c.png) +![مصنوعی ذہانت کے تعارف کا خلاصہ ایک خاکے میں](../../../../translated_images/ur/ai-intro.bf28d1ac4235881c.webp) > خاکہ: [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: ابتدائی طور پر، کمپیوٹرز کو [چارلس بیبج](https://en.wikipedia.org/wiki/Charles_Babbage) نے اعداد پر ایک واضح طریقہ کار کے تحت کام کرنے کے لیے ایجاد کیا تھا - ایک الگورتھم۔ جدید کمپیوٹرز، اگرچہ انیسویں صدی میں پیش کیے گئے اصل ماڈل سے کہیں زیادہ ترقی یافتہ ہیں، پھر بھی کنٹرول شدہ حسابات کے اسی خیال پر عمل کرتے ہیں۔ اس لیے، اگر ہمیں وہ درست مراحل معلوم ہوں جو کسی مقصد کو حاصل کرنے کے لیے ضروری ہیں، تو کمپیوٹر کو پروگرام کرنا ممکن ہے۔ -![ایک شخص کی تصویر](../../../../translated_images/ur/dsh_age.d212a30d4e54fb5f.png) +![ایک شخص کی تصویر](../../../../translated_images/ur/dsh_age.d212a30d4e54fb5f.webp) > تصویر: [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ CO_OP_TRANSLATOR_METADATA: **[ذہانت](https://en.wikipedia.org/wiki/Intelligence)** کی اصطلاح سے نمٹنے میں ایک مسئلہ یہ ہے کہ اس کی کوئی واضح تعریف نہیں ہے۔ کوئی یہ دلیل دے سکتا ہے کہ ذہانت کا تعلق **تجریدی سوچ** یا **خود آگاہی** سے ہے، لیکن ہم اسے مناسب طریقے سے بیان نہیں کر سکتے۔ -![بلی کی تصویر](../../../../translated_images/ur/photo-cat.8c8e8fb760ffe457.jpg) +![بلی کی تصویر](../../../../translated_images/ur/photo-cat.8c8e8fb760ffe457.webp) > [تصویر](https://unsplash.com/photos/75715CVEJhI) از [Amber Kipp](https://unsplash.com/@sadmax) Unsplash سے @@ -98,13 +98,13 @@ CO_OP_TRANSLATOR_METADATA: > | مشین لرننگ کے بارے میں کیا خیال ہے؟ | | > |--------------|-----------| -> | مصنوعی ذہانت کا وہ حصہ جو کمپیوٹر کو کچھ ڈیٹا کی بنیاد پر مسئلہ حل کرنے کے لیے سیکھنے پر مبنی ہے، اسے **مشین لرننگ** کہا جاتا ہے۔ ہم اس کورس میں کلاسیکل مشین لرننگ پر غور نہیں کریں گے - ہم آپ کو ایک علیحدہ [مشین لرننگ کے ابتدائی کورس](http://aka.ms/ml-beginners) کی طرف رجوع کرتے ہیں۔ | ![مشین لرننگ کے ابتدائی کورس](../../../../translated_images/ur/ml-for-beginners.9e4fed176fd5817d.png) | +> | مصنوعی ذہانت کا وہ حصہ جو کمپیوٹر کو کچھ ڈیٹا کی بنیاد پر مسئلہ حل کرنے کے لیے سیکھنے پر مبنی ہے، اسے **مشین لرننگ** کہا جاتا ہے۔ ہم اس کورس میں کلاسیکل مشین لرننگ پر غور نہیں کریں گے - ہم آپ کو ایک علیحدہ [مشین لرننگ کے ابتدائی کورس](http://aka.ms/ml-beginners) کی طرف رجوع کرتے ہیں۔ | ![مشین لرننگ کے ابتدائی کورس](../../../../translated_images/ur/ml-for-beginners.9e4fed176fd5817d.webp) | ## مصنوعی ذہانت کی مختصر تاریخ مصنوعی ذہانت کو بیسویں صدی کے وسط میں ایک شعبے کے طور پر شروع کیا گیا۔ ابتدا میں، علامتی استدلال ایک غالب طریقہ تھا، اور اس نے کئی اہم کامیابیاں حاصل کیں، جیسے ماہر نظام – کمپیوٹر پروگرامز جو محدود مسئلہ کے شعبوں میں ماہر کے طور پر کام کر سکتے تھے۔ تاہم، جلد ہی یہ واضح ہو گیا کہ یہ طریقہ اچھی طرح سے توسیع پذیر نہیں ہے۔ ماہر سے علم نکالنا، اسے کمپیوٹر میں پیش کرنا، اور اس علم کے ذخیرے کو درست رکھنا ایک بہت پیچیدہ کام ثابت ہوا، اور بہت سے معاملات میں عملی طور پر بہت مہنگا۔ اس نے 1970 کی دہائی میں نام نہاد [AI سردی](https://en.wikipedia.org/wiki/AI_winter) کو جنم دیا۔ -مصنوعی ذہانت کی مختصر تاریخ +مصنوعی ذہانت کی مختصر تاریخ > تصویر: [Dmitry Soshnikov](http://soshnikov.com) @@ -124,7 +124,7 @@ CO_OP_TRANSLATOR_METADATA: * جدید اسسٹنٹس، جیسے Cortana، Siri یا Google Assistant، سب ہائبرڈ سسٹمز ہیں جو نیورل نیٹ ورکس کا استعمال کرتے ہیں تاکہ تقریر کو متن میں تبدیل کریں اور ہمارے ارادے کو پہچانیں، اور پھر کچھ استدلال یا واضح الگورتھمز کو مطلوبہ اعمال انجام دینے کے لیے استعمال کریں۔ * مستقبل میں، ہم توقع کر سکتے ہیں کہ ایک مکمل نیورل پر مبنی ماڈل خود ہی مکالمے کو سنبھالے گا۔ حالیہ GPT اور [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) نیورل نیٹ ورکس کے خاندان اس میں بڑی کامیابی دکھا رہے ہیں۔ -ٹورنگ ٹیسٹ کا ارتقاء +ٹورنگ ٹیسٹ کا ارتقاء > تصویر از دمتری سوشنیکوف، [تصویر](https://unsplash.com/photos/r8LmVbUKgns) از [مارینا ابروسیمووا](https://unsplash.com/@abrosimova_marina_foto)، انسپلیش ## حالیہ AI تحقیق diff --git a/translations/ur/lessons/2-Symbolic/Animals.ipynb b/translations/ur/lessons/2-Symbolic/Animals.ipynb index 77b9a186..da254e90 100644 --- a/translations/ur/lessons/2-Symbolic/Animals.ipynb +++ b/translations/ur/lessons/2-Symbolic/Animals.ipynb @@ -6,25 +6,25 @@ "collapsed": true }, "source": [ - "# جانوروں کے ماہر نظام کا نفاذ\n", + "# حیوانات کے ماہر نظام کا نفاذ\n", "\n", "[AI for Beginners Curriculum](http://github.com/microsoft/ai-for-beginners) سے ایک مثال۔\n", "\n", - "اس نمونے میں، ہم ایک سادہ علم پر مبنی نظام نافذ کریں گے جو کچھ جسمانی خصوصیات کی بنیاد پر جانور کا تعین کرے گا۔ اس نظام کو درج ذیل AND-OR درخت کے ذریعے ظاہر کیا جا سکتا ہے (یہ پورے درخت کا ایک حصہ ہے، ہم آسانی سے مزید قواعد شامل کر سکتے ہیں):\n", + "اس نمونہ میں، ہم ایک سادہ علم پر مبنی نظام نافذ کریں گے تاکہ کچھ جسمانی خصوصیات کی بنیاد پر ایک جانور کا تعین کیا جا سکے۔ نظام کو مندرجہ ذیل AND-OR درخت کے ذریعے ظاہر کیا جا سکتا ہے (یہ پورے درخت کا ایک حصہ ہے، ہم آسانی سے مزید قواعد شامل کر سکتے ہیں):\n", "\n", - "![](../../../../translated_images/ur/AND-OR-Tree.5592d2c70187f283.png)\n" + "![](../../../../../../translated_images/ur/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## ہمارا اپنا ماہر نظام شیل بیکورڈ انفرنس کے ساتھ\n", + "## ہمارا اپنا ماہر نظام شیل مع الٹی استدلال کے ساتھ\n", "\n", - "آئیے پیداوار کے اصولوں پر مبنی علم کی نمائندگی کے لیے ایک سادہ زبان کی تعریف کرنے کی کوشش کریں۔ ہم قواعد کی تعریف کے لیے Python کلاسز کو بطور کلیدی الفاظ استعمال کریں گے۔ بنیادی طور پر تین قسم کی کلاسز ہوں گی:\n", - "* `Ask` ایک سوال کی نمائندگی کرتا ہے جو صارف سے پوچھا جانا ضروری ہے۔ اس میں ممکنہ جوابات کا سیٹ شامل ہوتا ہے۔\n", - "* `If` ایک اصول کی نمائندگی کرتا ہے، اور یہ صرف اصول کے مواد کو ذخیرہ کرنے کے لیے ایک نحوی سہولت ہے۔\n", - "* `AND`/`OR` درخت کی AND/OR شاخوں کی نمائندگی کرنے کے لیے کلاسز ہیں۔ یہ صرف دلائل کی فہرست کو اندر ذخیرہ کرتے ہیں۔ کوڈ کو آسان بنانے کے لیے، تمام فعالیت والدین کلاس `Content` میں بیان کی گئی ہے۔\n" + "آئیے ایک آسان زبان کی تعریف کرنے کی کوشش کرتے ہیں جو پیداواری قواعد کی بنیاد پر علم کی نمائندگی کے لیے ہو۔ ہم قواعد کی تعریف کے لیے Python کلاسز کو کلیدی الفاظ کے طور پر استعمال کریں گے۔ بنیادی طور پر تین اقسام کی کلاسز ہوں گی:\n", + "* `Ask` ایک سوال کی نمائندگی کرتا ہے جو صارف سے پوچھنا ضروری ہوتا ہے۔ اس میں ممکنہ جوابات کا مجموعہ ہوتا ہے۔\n", + "* `If` ایک قاعدے کی نمائندگی کرتا ہے، اور یہ قاعدے کے مواد کو ذخیرہ کرنے کے لیے صرف ایک نحوی سہولت ہے۔\n", + "* `AND`/`OR` کلاسز درخت کی AND/OR شاخوں کی نمائندگی کے لیے ہیں۔ وہ بس اندر دلائل کی فہرست کو ذخیرہ کرتے ہیں۔ کوڈ کو آسان بنانے کے لیے، تمام فعالیت والد کلاس `Content` میں تعریف کی گئی ہے۔\n" ] }, { @@ -66,7 +66,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "ہمارے نظام میں، کام کرنے والی میموری میں **حقائق** کی فہرست **صفات-قدر جوڑوں** کے طور پر شامل ہوگی۔ علم کا ذخیرہ ایک بڑا لغت کے طور پر بیان کیا جا سکتا ہے جو اعمال (نئے حقائق جو کام کرنے والی میموری میں شامل کیے جانے چاہئیں) کو شرائط کے ساتھ نقشہ کرتا ہے، جو AND-OR اظہارات کے طور پر ظاہر کیے جاتے ہیں۔ نیز، کچھ حقائق کو `پوچھا` جا سکتا ہے۔\n" + "ہمارے نظام میں، ورکنگ میموری میں **حقائق** کی فہرست **خصوصیت-قدر جوڑے** کے طور پر موجود ہوگی۔ علم کا ذخیرہ ایک بڑی لغت کے طور پر تعریف کیا جا سکتا ہے جو اعمال (نئے حقائق جو ورکنگ میموری میں شامل کیے جانے چاہئیں) کو شرائط کے ساتھ جوڑتا ہے، جو AND-OR اظہار کے طور پر ظاہر کی جاتی ہیں۔ نیز، کچھ حقائق کو `Ask` کیا جا سکتا ہے۔\n" ] }, { @@ -99,13 +99,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "پچھلی استدلالی عمل کو انجام دینے کے لیے، ہم `Knowledgebase` کلاس کو ڈیفائن کریں گے۔ یہ درج ذیل پر مشتمل ہوگی:\n", - "* کام کرنے والی `memory` - ایک ڈکشنری جو خصوصیات کو ان کی قدروں کے ساتھ جوڑتی ہے\n", - "* Knowledgebase کے `rules` - اوپر بیان کردہ فارمیٹ میں\n", + "پیچھے کی طرف استدلال کرنے کے لیے، ہم `Knowledgebase` کلاس کی تعریف کریں گے۔ اس میں شامل ہوں گے: \n", + "* کام کرنے کی `memory` - ایک لغت جو خصوصیات کو اقدار سے میپ کرتی ہے \n", + "* Knowledgebase کے `rules` اوپر دیے گئے فارمیٹ میں \n", "\n", - "دو اہم طریقے یہ ہیں:\n", - "* `get` کسی خصوصیت کی قدر حاصل کرنے کے لیے، اگر ضروری ہو تو استدلالی عمل انجام دے گا۔ مثال کے طور پر، `get('color')` رنگ کے سلاٹ کی قدر حاصل کرے گا (یہ اگر ضروری ہو تو پوچھے گا، اور بعد میں استعمال کے لیے کام کرنے والی میموری میں قدر محفوظ کرے گا)۔ اگر ہم `get('color:blue')` پوچھیں، تو یہ رنگ کے بارے میں پوچھے گا، اور پھر رنگ کے مطابق `y`/`n` قدر واپس کرے گا۔\n", - "* `eval` اصل استدلالی عمل انجام دیتا ہے، یعنی AND/OR درخت کو عبور کرتا ہے، ذیلی مقاصد کا جائزہ لیتا ہے، وغیرہ۔\n" + "دو اہم طریقے ہیں: \n", + "* `get` خاصیت کی قدر حاصل کرنے کے لیے، ضرورت پڑنے پر استدلال کرتے ہوئے۔ مثال کے طور پر، `get('color')` رنگ کے سلاٹ کی قدر حاصل کرے گا (اگر ضروری ہوا تو پوچھے گا، اور بعد میں استعمال کے لیے کام کرنے والی میموری میں قدر محفوظ کرے گا)۔ اگر ہم پوچھیں `get('color:blue')`، تو یہ رنگ کے بارے میں پوچھے گا، اور پھر رنگ کی بنیاد پر `y`/`n` قدر واپس کرے گا۔ \n", + "* `eval` اصل استدلال انجام دیتا ہے، یعنی AND/OR درخت کو عبور کرتا ہے، ذیلی اہداف کا اندازہ لگاتا ہے، وغیرہ۔\n" ] }, { @@ -172,7 +172,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "اب آئیے ہم اپنے جانوروں کے علم کے ذخیرے کو متعین کریں اور مشاورت انجام دیں۔ نوٹ کریں کہ یہ کال آپ سے سوالات پوچھے گی۔ آپ `y`/`n` کے ذریعے ہاں-نہیں کے سوالات کے جواب دے سکتے ہیں، یا طویل متعدد انتخابی جوابات والے سوالات کے لیے نمبر (0..N) بتا سکتے ہیں۔\n" + "اب آئیے اپنے حیوانات کے علم کے ذخیرے کی تعریف کریں اور مشاورت انجام دیں۔ نوٹ کریں کہ یہ کال آپ سے سوالات کرے گی۔ آپ ہاں-نہ کے سوالات کے لیے `y`/`n` ٹائپ کرکے جواب دے سکتے ہیں، یا طویل متعدد انتخابی جوابات والے سوالات کے لیے نمبر (0..N) دے کر جواب دے سکتے ہیں۔\n" ] }, { @@ -229,11 +229,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## PyKnow کا استعمال کرتے ہوئے فارورڈ انفرنس\n", + "## فارورڈ انفرنس کے لیے Experta کا استعمال\n", "\n", - "اگلی مثال میں، ہم علم کی نمائندگی کے لیے ایک لائبریری [PyKnow](https://github.com/buguroo/pyknow/) کا استعمال کرتے ہوئے فارورڈ انفرنس کو نافذ کرنے کی کوشش کریں گے۔ **PyKnow** ایک لائبریری ہے جو Python میں فارورڈ انفرنس سسٹمز بنانے کے لیے استعمال ہوتی ہے، اور یہ کلاسیکی پرانے سسٹم [CLIPS](http://www.clipsrules.net/index.html) سے مشابہت رکھنے کے لیے ڈیزائن کی گئی ہے۔\n", + "اگلے مثال میں، ہم علم کی نمائندگی کی لائبریریز میں سے ایک، [Experta](https://github.com/nilp0inter/experta) کے ذریعے فارورڈ انفرنس کو نافذ کرنے کی کوشش کریں گے۔ **Experta** ایک لائبریری ہے جو Python میں فارورڈ انفرنس سسٹمز بنانے کے لیے استعمال ہوتی ہے، جو کلاسیکی پرانے سسٹم [CLIPS](http://www.clipsrules.net/index.html) کے مماثل ڈیزائن کی گئی ہے۔\n", "\n", - "ہم خود بھی فارورڈ چیننگ کو بغیر کسی بڑی مشکل کے نافذ کر سکتے تھے، لیکن سادہ طریقے عام طور پر زیادہ مؤثر نہیں ہوتے۔ زیادہ مؤثر اصولوں کے ملاپ کے لیے ایک خاص الگورتھم [Rete](https://en.wikipedia.org/wiki/Rete_algorithm) استعمال کیا جاتا ہے۔\n" + "ہم اپنے طور پر بھی فارورڈ چیننگ کو بغیر کسی بڑی مسئلے کے نافذ کر سکتے تھے، لیکن سادہ نفاذ عموماً بہت زیادہ مؤثر نہیں ہوتے۔ زیادہ مؤثر اصول میچنگ کے لیے ایک خاص الگورتھم [Rete](https://en.wikipedia.org/wiki/Rete_algorithm) استعمال کیا جاتا ہے۔\n" ] }, { @@ -247,32 +247,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "Collecting git+https://github.com/buguroo/pyknow/\n", - " Cloning https://github.com/buguroo/pyknow/ to /tmp/pip-req-build-3cqeulyl\n", - " Running command git clone --filter=blob:none --quiet https://github.com/buguroo/pyknow/ /tmp/pip-req-build-3cqeulyl\n", - " Resolved https://github.com/buguroo/pyknow/ to commit 48818336f2e9a126f1964f2d8dc22d37ff800fe8\n", - " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hCollecting frozendict==1.2\n", - " Using cached frozendict-1.2.tar.gz (2.6 kB)\n", - " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hCollecting schema==0.6.7\n", - " Using cached schema-0.6.7-py2.py3-none-any.whl (14 kB)\n", - "Building wheels for collected packages: pyknow, frozendict\n", - " Building wheel for pyknow (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for pyknow: filename=pyknow-1.7.0-py3-none-any.whl size=34228 sha256=b7de5b09292c4007667c72f69b98d5a1b5f7324ff15f9dd8e077c3d5f7aade42\n", - " Stored in directory: /tmp/pip-ephem-wheel-cache-k7jpave7/wheels/81/1a/d3/f6c15dbe1955598a37755215f2a10449e7418500d7bd4b9508\n", - " Building wheel for frozendict (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for frozendict: filename=frozendict-1.2-py3-none-any.whl size=3148 sha256=2863d55c240d2409cddf05ccfe600591f8478681549fc97555c47c90dc6bb160\n", - " Stored in directory: /home/rg/.cache/pip/wheels/49/ac/f8/cb8120244e710bdb479c86198b03c7b08c3c2d3d2bf448fd6e\n", - "Successfully built pyknow frozendict\n", - "Installing collected packages: schema, frozendict, pyknow\n", - "Successfully installed frozendict-1.2 pyknow-1.7.0 schema-0.6.7\n" + "Collecting git+https://github.com/nilp0inter/experta\n", + " Cloning https://github.com/nilp0inter/experta to /tmp/pip-req-build-7qurtwk3\n", + " Running command git clone --filter=blob:none --quiet https://github.com/nilp0inter/experta /tmp/pip-req-build-7qurtwk3\n", + " Resolved https://github.com/nilp0inter/experta to commit c6d5834b123861f5ae09e7d07027dc98bec58741\n", + " Installing build dependencies ... \u001b[?25ldone\n", + "\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n", + "\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25hRequirement already satisfied: frozendict~=2.4.6 in /opt/conda/envs/ai4beg/lib/python3.12/site-packages (from experta==1.9.5.dev1) (2.4.7)\n", + "Collecting schema~=0.6.7 (from experta==1.9.5.dev1)\n", + " Downloading schema-0.6.8-py2.py3-none-any.whl.metadata (14 kB)\n", + "Downloading schema-0.6.8-py2.py3-none-any.whl (14 kB)\n", + "Building wheels for collected packages: experta\n", + " Building wheel for experta (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25h Created wheel for experta: filename=experta-1.9.5.dev1-py3-none-any.whl size=34804 sha256=888c459512a5e713f4b674caa9a0f96cfdf07ec0d6eb56cc318ce0653d218014\n", + " Stored in directory: /tmp/pip-ephem-wheel-cache-1eeii9zy/wheels/3d/e8/bb/22d7956359603fa8dd679aa09f5b8efb3f29991c3986fdc787\n", + "Successfully built experta\n", + "Installing collected packages: schema, experta\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2/2\u001b[0m [experta]\n", + "\u001b[1A\u001b[2KSuccessfully installed experta-1.9.5.dev1 schema-0.6.8\n" ] } ], "source": [ "import sys\n", - "!{sys.executable} -m pip install git+https://github.com/buguroo/pyknow/" + "!{sys.executable} -m pip install git+https://github.com/nilp0inter/experta" ] }, { @@ -283,15 +282,15 @@ }, "outputs": [], "source": [ - "from pyknow import *\n", - "#import pyknow" + "from experta import *\n", + "#import experta" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "ہم اپنے نظام کو ایک کلاس کے طور پر تعریف کریں گے جو `KnowledgeEngine` کو سب کلاس کرے گی۔ ہر اصول کو ایک الگ فنکشن کے ذریعے تعریف کیا جاتا ہے جس میں `@Rule` تشریح ہوتی ہے، جو یہ بتاتی ہے کہ اصول کب فعال ہونا چاہیے۔ اصول کے اندر، ہم `declare` فنکشن کا استعمال کرتے ہوئے نئے حقائق شامل کر سکتے ہیں، اور ان حقائق کو شامل کرنے سے مزید اصولوں کو فارورڈ انفرنس انجن کے ذریعے فعال کیا جائے گا۔\n" + "ہم اپنے نظام کو ایک کلاس کے طور پر تعریف کریں گے جو `KnowledgeEngine` کی ذیلی کلاس ہو۔ ہر قاعدہ ایک الگ فنکشن کے ذریعے تعریف کیا جاتا ہے جس پر `@Rule` تشریح ہوتی ہے، جو بتاتی ہے کہ یہ قاعدہ کب فعال ہونا چاہیے۔ قاعدے کے اندر، ہم `declare` فنکشن کا استعمال کرتے ہوئے نئے حقائق شامل کر سکتے ہیں، اور ان حقائق کو شامل کرنے سے آگے کے استدلال کے انجن کے ذریعے مزید قواعد کو بلایا جائے گا۔\n" ] }, { @@ -378,7 +377,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "ایک بار جب ہم نے نالج بیس کی تعریف کر لی، تو ہم اپنی ورکنگ میموری کو کچھ ابتدائی حقائق کے ساتھ بھر دیتے ہیں، اور پھر `run()` میتھڈ کو کال کرتے ہیں تاکہ استنتاج انجام دیا جا سکے۔ آپ نتیجے کے طور پر دیکھ سکتے ہیں کہ نئے استنتاج شدہ حقائق ورکنگ میموری میں شامل کیے جاتے ہیں، بشمول جانور کے بارے میں آخری حقیقت (اگر ہم نے تمام ابتدائی حقائق کو درست طریقے سے ترتیب دیا ہو)۔\n" + "جب ہم نے ایک علم کا ذخیرہ متعین کر لیا، ہم اپنی ورکنگ میموری کو کچھ ابتدائی حقائق سے بھر دیتے ہیں، اور پھر استدلال انجام دینے کے لیے `run()` طریقہ کو کال کرتے ہیں۔ آپ دیکھ سکتے ہیں کہ نتیجے کے طور پر نئی استنباط شدہ حقائق ورکنگ میموری میں شامل ہو جاتی ہیں، جن میں جانور کے بارے میں آخری حقیقت بھی شامل ہے (اگر ہم تمام ابتدائی حقائق کو درست طریقے سے سیٹ کریں)۔\n" ] }, { @@ -440,7 +439,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n---\n\n**ڈسکلیمر**: \nیہ دستاویز AI ترجمہ سروس [Co-op Translator](https://github.com/Azure/co-op-translator) کا استعمال کرتے ہوئے ترجمہ کی گئی ہے۔ ہم درستگی کے لیے کوشش کرتے ہیں، لیکن براہ کرم آگاہ رہیں کہ خودکار ترجمے میں غلطیاں یا غیر درستیاں ہو سکتی ہیں۔ اصل دستاویز کو اس کی اصل زبان میں مستند ذریعہ سمجھا جانا چاہیے۔ اہم معلومات کے لیے، پیشہ ور انسانی ترجمہ کی سفارش کی جاتی ہے۔ ہم اس ترجمے کے استعمال سے پیدا ہونے والی کسی بھی غلط فہمی یا غلط تشریح کے ذمہ دار نہیں ہیں۔\n" + "---\n\n\n**ڈسکلیمر**:\nیہ دستاویز AI ترجمہ سروس [Co-op Translator](https://github.com/Azure/co-op-translator) کے ذریعے ترجمہ کی گئی ہے۔ اگرچہ ہم درستگی کے لئے کوشاں ہیں، براہ کرم اس بات سے آگاہ رہیں کہ خودکار تراجم میں غلطیاں یا نقصانات ہو سکتے ہیں۔ اصل دستاویز اپنی مادری زبان میں ہی قابل اعتبار ذریعہ سمجھی جانی چاہیے۔ اہم معلومات کے لیے پیشہ ور انسانی ترجمہ کی سفارش کی جاتی ہے۔ اس ترجمے کے استعمال سے پیدا ہونے والے کسی بھی غلط فہمی یا غلط تشریح کے لیے ہم ذمہ دار نہیں ہیں۔\n\n" ] } ], @@ -467,8 +466,8 @@ "version": "3.11.2" }, "coopTranslator": { - "original_hash": "ab2bd97b0453415b89a469284609a8ce", - "translation_date": "2025-08-28T03:39:57+00:00", + "original_hash": "8ef43db4b9182239fd150a76bd494fdb", + "translation_date": "2026-01-15T11:42:55+00:00", "source_file": "lessons/2-Symbolic/Animals.ipynb", "language_code": "ur" } diff --git a/translations/ur/lessons/2-Symbolic/README.md b/translations/ur/lessons/2-Symbolic/README.md index 5a80b0e0..f7e0ca33 100644 --- a/translations/ur/lessons/2-Symbolic/README.md +++ b/translations/ur/lessons/2-Symbolic/README.md @@ -1,116 +1,116 @@ # علم کی نمائندگی اور ماہر نظام -![سمبولک AI کا خلاصہ](../../../../translated_images/ur/ai-symbolic.715a30cb610411a6.png) +![علامتی AI کے مواد کا خلاصہ](../../../../../../translated_images/ur/ai-symbolic.715a30cb610411a6.webp) -> اسکیچ نوٹ از [Tomomi Imura](https://twitter.com/girlie_mac) +> اسکیچنوٹ بذریعہ [تومومی ایمورا](https://twitter.com/girlie_mac) -مصنوعی ذہانت کی تلاش علم کی تلاش پر مبنی ہے، تاکہ دنیا کو انسانوں کی طرح سمجھا جا سکے۔ لیکن یہ کیسے ممکن ہے؟ +مصنوعی ذہانت کی تلاش علم کی تلاش پر مبنی ہے، تاکہ دنیا کو ویسا ہی سمجھا جائے جس طرح انسان سمجھتے ہیں۔ لیکن آپ یہ کیسے کر سکتے ہیں؟ ## [لیکچر سے پہلے کا کوئز](https://ff-quizzes.netlify.app/en/ai/quiz/3) -AI کے ابتدائی دنوں میں، ذہین نظام بنانے کے لیے اوپر سے نیچے تک کا طریقہ (پچھلے سبق میں بیان کیا گیا) مقبول تھا۔ اس کا مقصد لوگوں سے علم کو مشین کے قابل پڑھنے کے قابل شکل میں نکالنا اور پھر اسے خودکار طریقے سے مسائل حل کرنے کے لیے استعمال کرنا تھا۔ یہ طریقہ دو بڑے خیالات پر مبنی تھا: +AI کے ابتدائی دنوں میں، ذہین نظام بنانے کا اوپر سے نیچے کا طریقہ (جو پچھلے سبق میں زیر بحث آیا) مقبول تھا۔ خیال یہ تھا کہ لوگوں سے علم نکال کر اسے کسی کمپیوٹر قابل پڑھائی شکل میں ڈالا جائے، اور پھر خودکار طور پر مسائل حل کیے جائیں۔ یہ طریقہ دو بڑے خیالوں پر مبنی تھا: * علم کی نمائندگی * استدلال ## علم کی نمائندگی -سمبولک AI کے اہم تصورات میں سے ایک **علم** ہے۔ علم کو *معلومات* یا *ڈیٹا* سے الگ کرنا ضروری ہے۔ مثال کے طور پر، کہا جا سکتا ہے کہ کتابیں علم پر مشتمل ہیں، کیونکہ کتابوں کا مطالعہ کر کے کوئی ماہر بن سکتا ہے۔ تاہم، کتابوں میں جو کچھ ہوتا ہے اسے دراصل *ڈیٹا* کہا جاتا ہے، اور کتابیں پڑھ کر اور اس ڈیٹا کو اپنی دنیا کے ماڈل میں ضم کر کے ہم اس ڈیٹا کو علم میں تبدیل کرتے ہیں۔ +علامتی AI کے اہم تصورات میں سے ایک **علم** ہے۔ علم کو *معلومات* یا *ڈیٹا* سے فرق کرنا ضروری ہے۔ مثال کے طور پر، کہا جا سکتا ہے کہ کتابوں میں علم ہوتا ہے، کیونکہ کتابوں کا مطالعہ کر کے کوئی ماہر بن سکتا ہے۔ تاہم، کتابوں میں جو چیز ہوتی ہے اسے درحقیقت *ڈیٹا* کہا جاتا ہے، اور کتابوں کو پڑھ کر اور اس ڈیٹا کو اپنے عالمی ماڈل میں ضم کر کے ہم اس ڈیٹا کو علم میں تبدیل کرتے ہیں۔ -> ✅ **علم** وہ چیز ہے جو ہمارے دماغ میں موجود ہوتی ہے اور دنیا کے بارے میں ہماری سمجھ کی نمائندگی کرتی ہے۔ یہ ایک فعال **سیکھنے** کے عمل کے ذریعے حاصل کیا جاتا ہے، جو ہمیں موصول ہونے والی معلومات کے ٹکڑوں کو ہماری دنیا کے فعال ماڈل میں ضم کرتا ہے۔ +> ✅ **علم** وہ چیز ہے جو ہمارے ذہن میں ہوتی ہے اور ہماری دنیا کی سمجھ کی نمائندگی کرتی ہے۔ یہ ایک فعال **سیکھنے** کے عمل کے ذریعے حاصل کیا جاتا ہے، جو ہمیں موصول ہونے والی معلومات کے ٹکڑوں کو ہماری متحرک عالمی ماڈل میں ضم کرتا ہے۔ -اکثر اوقات، ہم علم کی سختی سے وضاحت نہیں کرتے، بلکہ ہم اسے دوسرے متعلقہ تصورات کے ساتھ [DIKW Pyramid](https://en.wikipedia.org/wiki/DIKW_pyramid) کے ذریعے ہم آہنگ کرتے ہیں۔ اس میں درج ذیل تصورات شامل ہیں: +اکثر اوقات، ہم علم کو سختی سے تعریف نہیں کرتے، بلکہ اسے دیگر متعلقہ تصورات کے ساتھ [DIKW پیرامڈ](https://en.wikipedia.org/wiki/DIKW_pyramid) کے ذریعے ہم آہنگ کرتے ہیں۔ اس میں درج ذیل تصورات شامل ہیں: -* **ڈیٹا** وہ چیز ہے جو جسمانی میڈیا میں ظاہر ہوتی ہے، جیسے کہ تحریری متن یا بولے گئے الفاظ۔ ڈیٹا انسانوں سے آزاد ہوتا ہے اور لوگوں کے درمیان منتقل کیا جا سکتا ہے۔ -* **معلومات** وہ ہے جس طرح ہم اپنے دماغ میں ڈیٹا کی تشریح کرتے ہیں۔ مثال کے طور پر، جب ہم لفظ *کمپیوٹر* سنتے ہیں، تو ہمیں اس کے بارے میں کچھ سمجھ آتی ہے۔ -* **علم** وہ معلومات ہے جو ہمارے دنیا کے ماڈل میں ضم ہو جاتی ہے۔ مثال کے طور پر، ایک بار جب ہم سیکھ لیتے ہیں کہ کمپیوٹر کیا ہے، تو ہمیں اس کے کام کرنے کے طریقے، اس کی قیمت، اور اس کے استعمال کے بارے میں کچھ خیالات ہونے لگتے ہیں۔ یہ باہم جڑے ہوئے تصورات کا نیٹ ورک ہمارا علم بناتا ہے۔ -* **دانائی** ہماری دنیا کی سمجھ کا ایک اور سطح ہے، اور یہ *میٹا-علم* کی نمائندگی کرتی ہے، جیسے کہ یہ جاننا کہ علم کو کب اور کیسے استعمال کرنا چاہیے۔ +* **ڈیٹا** وہ چیز ہے جو مادی میڈیا میں ظاہر ہوتی ہے، جیسے لکھا ہوا متن یا بولا ہوا لفظ۔ ڈیٹا انسانوں سے آزاد وجود رکھتا ہے اور لوگوں کے درمیان منتقل ہو سکتا ہے۔ +* **معلومات** وہ ہے جو ہم اپنے دماغ میں ڈیٹا کی تعبیر کرتے ہیں۔ مثال کے طور پر، جب ہم لفظ *کمپیوٹر* سنتے ہیں، تو ہمارے ذہن میں اس کا کچھ مطلب آتا ہے۔ +* **علم** وہ معلومات ہے جو ہمارے عالمی ماڈل میں شدت سے شامل ہوتی ہے۔ مثال کے طور پر، جب ہم سیکھتے ہیں کہ کمپیوٹر کیا ہے، تو ہم اس کے کام کرنے کے طریقے، اس کی قیمت، اور اس کے استعمال کے بارے میں کچھ خیالات پیدا کرتے ہیں۔ یہ باہم جڑے ہوئے تصورات کا جال ہمارا علم بناتے ہیں۔ +* **حکمت** ہماری دنیا کی سمجھ کا ایک اور درجہ ہے، اور یہ *میٹا-علم* کی نمائندگی کرتی ہے، جیسے کہ علم کو کب اور کیسے استعمال کرنا چاہیے۔ - + -*تصویر [ویکیپیڈیا سے](https://commons.wikimedia.org/w/index.php?curid=37705247)، از Longlivetheux - Own work, CC BY-SA 4.0* +*تصویر [ویکیپیڈیا سے](https://commons.wikimedia.org/w/index.php?curid=37705247)، بذریعہ Longlivetheux - اپنی تخلیق، CC BY-SA 4.0* -لہٰذا، **علم کی نمائندگی** کا مسئلہ یہ ہے کہ کمپیوٹر کے اندر علم کو ڈیٹا کی شکل میں مؤثر طریقے سے کیسے پیش کیا جائے، تاکہ اسے خودکار طور پر استعمال کیا جا سکے۔ اسے ایک اسپیکٹرم کے طور پر دیکھا جا سکتا ہے: +لہٰذا، **علم کی نمائندگی** کا مسئلہ یہ ہے کہ علم کو کمپیوٹر کے اندر ڈیٹا کی شکل میں مؤثر طریقے سے قابل استعمال بنانے کا کوئی طریقہ تلاش کیا جائے۔ اسے ایک پھیلاؤ کے طور پر دیکھا جا سکتا ہے: -![علم کی نمائندگی کا اسپیکٹرم](../../../../translated_images/ur/knowledge-spectrum.b60df631852c0217.png) +![علم کی نمائندگی کا پھیلاؤ](../../../../../../translated_images/ur/knowledge-spectrum.b60df631852c0217.webp) -> تصویر از [Dmitry Soshnikov](http://soshnikov.com) +> تصویر بذریعہ [دمتری سوشنیکوف](http://soshnikov.com) -* بائیں جانب، علم کی نمائندگی کی بہت سادہ اقسام ہیں جو کمپیوٹرز کے ذریعے مؤثر طریقے سے استعمال کی جا سکتی ہیں۔ سب سے سادہ قسم الگوریتھمک ہے، جہاں علم کو کمپیوٹر پروگرام کے ذریعے پیش کیا جاتا ہے۔ تاہم، یہ علم کی نمائندگی کا بہترین طریقہ نہیں ہے، کیونکہ یہ لچکدار نہیں ہے۔ ہمارے دماغ میں موجود علم اکثر غیر الگوریتھمک ہوتا ہے۔ -* دائیں جانب، قدرتی متن جیسی نمائندگیاں ہیں۔ یہ سب سے زیادہ طاقتور ہے، لیکن خودکار استدلال کے لیے استعمال نہیں کی جا سکتی۔ +* بائیں جانب، بہت سادہ اقسام کی علم کی نمائندگی ہیں جو کمپیوٹروں کے ذریعے مؤثر طریقے سے استعمال کی جا سکتی ہیں۔ سب سے آسان الگورتھمک ہوتی ہے، جب علم کمپیوٹر پروگرام کے ذریعے ظاہر کیا جاتا ہے۔ تاہم، یہ علم کی بہترین نمائندگی نہیں ہے، کیونکہ یہ لچکدار نہیں ہوتی۔ ہمارے ذہن میں موجود علم اکثر غیر الگورتھمک ہوتا ہے۔ +* دائیں جانب، ایسی نمائندگیاں ہیں جیسے قدرتی متن۔ یہ سب سے زیادہ طاقتور ہے، لیکن خودکار استدلال کے لیے استعمال نہیں ہو سکتی۔ -> ✅ ایک منٹ کے لیے سوچیں کہ آپ اپنے دماغ میں علم کو کیسے پیش کرتے ہیں اور اسے نوٹس میں کیسے تبدیل کرتے ہیں۔ کیا کوئی خاص فارمیٹ ہے جو آپ کے لیے یادداشت میں مددگار ثابت ہوتا ہے؟ +> ✅ ایک لمحے کے لیے سوچیں کہ آپ اپنے ذہن میں علم کو کیسے ظاہر کرتے ہیں اور اسے نوٹس میں تبدیل کرتے ہیں۔ کیا کوئی مخصوص فارمیٹ ہے جو یادداشت میں مدد دیتا ہے؟ -## کمپیوٹر علم کی نمائندگی کی درجہ بندی +## کمپیوٹر علم کی نمائندگی کی اقسام -ہم کمپیوٹر علم کی نمائندگی کے مختلف طریقوں کو درج ذیل زمروں میں درجہ بندی کر سکتے ہیں: +ہم مختلف کمپیوٹر علم نمائندگی کے طریقوں کو درج ذیل اقسام میں تقسیم کر سکتے ہیں: -* **نیٹ ورک نمائندگی** اس حقیقت پر مبنی ہیں کہ ہمارے دماغ میں باہم جڑے ہوئے تصورات کا ایک نیٹ ورک موجود ہے۔ ہم کمپیوٹر کے اندر ایک گراف کے طور پر وہی نیٹ ورک دوبارہ بنانے کی کوشش کر سکتے ہیں - جسے **سیمینٹک نیٹ ورک** کہا جاتا ہے۔ +* **نیٹ ورک نمائندگیاں** اس حقیقت پر مبنی ہیں کہ ہمارے ذہن میں باہم جڑے ہوئے تصورات کا نیٹ ورک ہوتا ہے۔ ہم کمپیوٹر کے اندر اسی نیٹ ورک کو ایک گراف کی شکل میں دوبارہ بنا سکتے ہیں - جسے **سیمنٹک نیٹ ورک** کہتے ہیں۔ -1. **آبجیکٹ-ایٹریبیوٹ-ویلیو ٹرپلٹس** یا **ایٹریبیوٹ-ویلیو جوڑے**۔ چونکہ گراف کو کمپیوٹر کے اندر نوڈز اور ایجز کی فہرست کے طور پر پیش کیا جا سکتا ہے، ہم سیمینٹک نیٹ ورک کو ٹرپلٹس کی فہرست کے ذریعے پیش کر سکتے ہیں، جس میں اشیاء، صفات، اور اقدار شامل ہیں۔ مثال کے طور پر، ہم پروگرامنگ زبانوں کے بارے میں درج ذیل ٹرپلٹس بنا سکتے ہیں: +1. **آبجیکٹ-اتریبیوٹ-ویلیو ٹرپلٹس** یا **اتریبیوٹ-ویلیو جوڑے**۔ چونکہ گراف کو کمپیوٹر میں نوڈز اور ایجز کی فہرست کے طور پر ظاہر کیا جا سکتا ہے، ہم سیمنٹک نیٹ ورک کو ٹرپلٹس کی فہرست کے ذریعے بیان کر سکتے ہیں، جو آبجیکٹس، صفات، اور قدروں پر مشتمل ہو۔ مثال کے طور پر، ہم درج ذیل ٹرپلٹس پروگرامنگ زبانوں کے بارے میں بناتے ہیں: Object | Attribute | Value -------|-----------|------ -Python | is | Untyped-Language -Python | invented-by | Guido van Rossum -Python | block-syntax | indentation -Untyped-Language | doesn't have | type definitions +Python | ہے | غیر ٹائپڈ زبان +Python | ایجاد کنندہ | گوئڈو وان روسم +Python | بلاک نحو | وقفہ یا انڈینٹیشن +غیر ٹائپڈ زبان | نہیں رکھتی | قسم کی تعریفیں -> ✅ سوچیں کہ ٹرپلٹس کو علم کی دیگر اقسام کی نمائندگی کے لیے کیسے استعمال کیا جا سکتا ہے۔ +> ✅ سوچیں کہ ٹرپلٹس کو دیگر اقسام کے علم کی نمائندگی کے لیے کیسے استعمال کیا جا سکتا ہے۔ -2. **درجہ بندی کی نمائندگی** اس بات پر زور دیتی ہے کہ ہم اکثر اپنے دماغ میں اشیاء کی ایک درجہ بندی بناتے ہیں۔ مثال کے طور پر، ہم جانتے ہیں کہ کینری ایک پرندہ ہے، اور تمام پرندوں کے پر ہوتے ہیں۔ ہمیں یہ بھی اندازہ ہوتا ہے کہ کینری کا رنگ عام طور پر کیا ہوتا ہے، اور ان کی پرواز کی رفتار کیا ہوتی ہے۔ +2. **درجہ بندی نمائندگیاں** اس بات پر زور دیتی ہیں کہ ہم اکثر اپنے ذہن میں اشیاء کی درجہ بندی کرتے ہیں۔ مثال کے طور پر، ہم جانتے ہیں کہ کناری ایک پرندہ ہے، اور تمام پرندوں کے پر ہوتے ہیں۔ ہمارے پاس یہ بھی اندازہ ہوتا ہے کہ کناری کا رنگ عام طور پر کیا ہوتا ہے، اور اس کی پرواز کی رفتار کیا ہے۔ - - **فریم نمائندگی** ہر شے یا اشیاء کے طبقے کو ایک **فریم** کے طور پر پیش کرنے پر مبنی ہے، جس میں **سلاٹس** شامل ہیں۔ سلاٹس میں ممکنہ ڈیفالٹ اقدار، قدر کی پابندیاں، یا محفوظ شدہ طریقہ کار ہو سکتے ہیں جو سلاٹ کی قدر حاصل کرنے کے لیے بلائے جا سکتے ہیں۔ تمام فریمز ایک درجہ بندی بناتے ہیں جو آبجیکٹ اورینٹڈ پروگرامنگ زبانوں میں آبجیکٹ درجہ بندی سے مشابہت رکھتی ہے۔ - - **مناظر** فریمز کی ایک خاص قسم ہیں جو پیچیدہ حالات کی نمائندگی کرتے ہیں جو وقت کے ساتھ کھل سکتے ہیں۔ + - **فریم نمائندگی** ہر آبجیکٹ یا آبجیکٹ کی قسم کو ایک **فریم** کے طور پر ظاہر کرنے پر مبنی ہے، جس میں **سلاٹ** ہوتے ہیں۔ سلاٹس کے ممکنہ ڈیفالٹ ویلیوز، ویلیو کی پابندیاں، یا اسٹور کیے گئے پروسیجرز ہو سکتے ہیں جنہیں کال کیا جا سکتا ہے تاکہ سلاٹ کی ویلیو حاصل ہو۔ تمام فریم آبجیکٹ کی درجہ بندی کی طرح ایک ہائیرارکی بناتے ہیں، جیسا کہ آبجیکٹ اورینٹڈ پروگرامنگ زبانوں میں ہوتا ہے۔ + - **مناظر** ایسی خاص قسم کے فریم ہوتے ہیں جو پیچیدہ حالات کی نمائندگی کرتے ہیں جو وقت کے ساتھ بدل سکتے ہیں۔ -**Python** +**پائتھن** Slot | Value | Default value | Interval | -----|-------|---------------|----------| -Name | Python | | | -Is-A | Untyped-Language | | | -Variable Case | | CamelCase | | -Program Length | | | 5-5000 lines | -Block Syntax | Indent | | | +نام | Python | | | +ایسا ہے | غیر ٹائپڈ زبان | | | +ویری ایبل کیس | | CamelCase | | +پروگرام کی لمبائی | | | ۵-۵۰۰۰ لائنیں | +بلاک نحو | انڈینٹ | | | -3. **عملی نمائندگی** اس بات پر مبنی ہیں کہ علم کو اعمال کی ایک فہرست کے ذریعے پیش کیا جائے جو کسی خاص حالت میں عمل میں لائی جا سکتی ہیں۔ - - پروڈکشن رولز وہ if-then بیانات ہیں جو ہمیں نتائج اخذ کرنے کی اجازت دیتے ہیں۔ مثال کے طور پر، ایک ڈاکٹر کے پاس ایک قاعدہ ہو سکتا ہے جو کہتا ہے کہ **اگر** مریض کو تیز بخار ہو **یا** خون کے ٹیسٹ میں سی-ری ایکٹیو پروٹین کی سطح زیادہ ہو **تو** اسے سوزش ہے۔ جب ہم ان میں سے کسی ایک حالت کا سامنا کرتے ہیں، تو ہم سوزش کے بارے میں نتیجہ اخذ کر سکتے ہیں، اور پھر اسے مزید استدلال میں استعمال کر سکتے ہیں۔ - - الگوریتھمز کو عملی نمائندگی کی ایک اور شکل سمجھا جا سکتا ہے، حالانکہ انہیں علم پر مبنی نظاموں میں شاذ و نادر ہی براہ راست استعمال کیا جاتا ہے۔ +3. **طریقہ کار کی نمائندگی** علم کو کارروائیوں کی فہرست کے ذریعے ظاہر کرتی ہے جو کسی شرط کی موجودگی پر انجام دی جا سکتی ہیں۔ + - پروڈکشن رولز اگر-تو کے قواعد ہوتے ہیں جو ہمیں نتائج اخذ کرنے دیتے ہیں۔ مثال کے طور پر، ایک ڈاکٹر کا قاعدہ ہو سکتا ہے کہ **اگر** مریض کو تیز بخار ہو **یا** خون کے ٹیسٹ میں C-ری ایکٹیو پروٹین کی سطح زیادہ ہو **تو** اسے سوزش ہے۔ جب ہم ان شرائط میں سے کسی ایک کا سامنا کریں، تو ہم سوزش کا نتیجہ اخذ کر سکتے ہیں، اور پھر اس کا استعمال مزید استدلال میں کرتے ہیں۔ + - الگورتھمز کو طریقہ کار کی نمائندگی کی ایک اور شکل سمجھا جا سکتا ہے، حالانکہ وہ علم کے نظاموں میں عام طور پر براہِ راست استعمال نہیں ہوتے۔ -4. **منطق** کو اصل میں ارسطو نے انسانی علم کی عالمگیر نمائندگی کے طور پر تجویز کیا تھا۔ - - پریڈی کیٹ منطق ایک ریاضیاتی نظریہ کے طور پر بہت زیادہ پیچیدہ ہے، اس لیے عام طور پر اس کا کوئی سب سیٹ استعمال کیا جاتا ہے، جیسے کہ Prolog میں استعمال ہونے والے ہارن کلاز۔ - - وضاحتی منطق منطقی نظاموں کا ایک خاندان ہے جو اشیاء کی درجہ بندی اور تقسیم شدہ علم کی نمائندگی جیسے *سیمینٹک ویب* کے بارے میں استدلال کرنے کے لیے استعمال ہوتا ہے۔ +4. **منطق** ابتدا میں ارسطو نے اسے عالمی انسانی علم کی نمائندگی کا ذریعہ تجویز کیا تھا۔ + - پریڈیکیٹ لاجک بطور ریاضیاتی نظریہ بہت وسیع ہے کہ کمپیوٹ کیا جا سکے، لہٰذا اس کا کچھ ذیلی حصہ عمومًا استعمال ہوتا ہے، جیسے Prolog میں ہارن کلاز۔ + - ڈسکرپٹو لاجک ایسی منطقی نظاموں کا خاندان ہے جو اشیاء کی درجہ بندی اور تقسیم شدہ علم کی نمائندگی اور استدلال کے لیے استعمال ہوتے ہیں، جیسے کہ *سیمنٹک ویب*۔ ## ماہر نظام -سمبولک AI کی ابتدائی کامیابیوں میں سے ایک **ماہر نظام** تھے - کمپیوٹر سسٹمز جو کسی محدود مسئلہ کے دائرہ کار میں ماہر کے طور پر کام کرنے کے لیے ڈیزائن کیے گئے تھے۔ یہ ایک **علمی بنیاد** پر مبنی تھے جو ایک یا زیادہ انسانی ماہرین سے نکالا گیا تھا، اور ان میں ایک **استدلالی انجن** شامل تھا جو اس پر کچھ استدلال کرتا تھا۔ +علامتی AI کی ابتدائی کامیابیوں میں سے ایک ایسے کمپیوٹر نظام تھے جنہیں **ماہر نظام** کہا جاتا ہے — کمپیوٹر نظام جو کسی محدود مسئلہ کے میدان میں ماہر کی طرح کام کرنے کے لیے بنائے گئے تھے۔ یہ ایک **علمی بنیاد** پر مبنی ہوتے تھے جو ایک یا زیادہ انسانی ماہرین سے نکالا جاتا تھا، اور اس میں ایک **استدلال انجن** ہوتا تھا جو اس پر کچھ استدلال کرتا تھا۔ -![انسانی نظام کی ساخت](../../../../translated_images/ur/arch-human.5d4d35f1bba3ab1c.png) | ![علم پر مبنی نظام کی ساخت](../../../../translated_images/ur/arch-kbs.3ec5c150b09fa8da.png) +![انسانی ساخت](../../../../../../translated_images/ur/arch-human.5d4d35f1bba3ab1c.webp) | ![علمی نظام کی ساخت](../../../../../../translated_images/ur/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ -انسانی اعصابی نظام کی سادہ ساخت | علم پر مبنی نظام کی ساخت +انسانی عصبی نظام کی سادہ ساخت | علمی نظام کی ساخت -ماہر نظام انسانی استدلالی نظام کی طرح بنائے گئے ہیں، جس میں **مختصر مدتی یادداشت** اور **طویل مدتی یادداشت** شامل ہیں۔ اسی طرح، علم پر مبنی نظاموں میں ہم درج ذیل اجزاء کو الگ کرتے ہیں: +ماہر نظام کو انسانی استدلالی نظام کی طرح بنایا جاتا ہے، جس میں **مختصر مدت حافظہ** اور **طویل مدت حافظہ** ہوتا ہے۔ اسی طرح، علمی نظام میں درج ذیل اجزاء کی تمیز کی جاتی ہے: -* **مسئلہ کی یادداشت**: اس میں اس مسئلے کے بارے میں علم شامل ہوتا ہے جو اس وقت حل کیا جا رہا ہے، جیسے کہ مریض کا درجہ حرارت یا بلڈ پریشر، آیا اسے سوزش ہے یا نہیں، وغیرہ۔ اس علم کو **جامد علم** بھی کہا جاتا ہے، کیونکہ اس میں وہ چیز شامل ہوتی ہے جو ہم اس وقت مسئلے کے بارے میں جانتے ہیں - جسے *مسئلہ کی حالت* کہا جاتا ہے۔ -* **علمی بنیاد**: یہ کسی مسئلہ کے دائرہ کار کے بارے میں طویل مدتی علم کی نمائندگی کرتی ہے۔ یہ انسانی ماہرین سے دستی طور پر نکالا جاتا ہے، اور مشاورت سے مشاورت تک تبدیل نہیں ہوتا۔ چونکہ یہ ہمیں ایک مسئلہ کی حالت سے دوسرے میں نیویگیٹ کرنے کی اجازت دیتا ہے، اسے **متحرک علم** بھی کہا جاتا ہے۔ -* **استدلالی انجن**: یہ مسئلہ کی حالت کی جگہ میں تلاش کے پورے عمل کو منظم کرتا ہے، جب ضروری ہو تو صارف سے سوالات پوچھتا ہے۔ یہ ہر حالت پر لاگو ہونے والے صحیح قواعد تلاش کرنے کا بھی ذمہ دار ہے۔ +* **مسئلہ حافظہ**: اس میں مسئلہ کے متعلق علم ہوتا ہے جو اس وقت حل ہو رہا ہوتا ہے، مثلاً کسی مریض کا درجہ حرارت یا بلڈ پریشر، کیا اسے سوزش ہے یا نہیں، وغیرہ۔ اسے **جامد علم** بھی کہا جاتا ہے، کیونکہ یہ مسئلہ کی موجودہ حالت کا عکس ہوتا ہے — جسے *مسئلہ کا حال* کہتے ہیں۔ +* **علمی بنیاد**: مسئلہ کے میدان کے بارے میں طویل مدت کا علم ظاہر کرتی ہے۔ یہ انسانی ماہرین سے دستی طور پر نکالا جاتا ہے، اور مشورے کے دوران تبدیل نہیں ہوتا۔ چونکہ یہ ایک مسئلہ کی حالت سے دوسری پر جانے کی اجازت دیتی ہے، اسے **متحرک علم** بھی کہا جاتا ہے۔ +* **استدلال انجن**: پورے مسئلہ کی حالت کی تلاش کے عمل کو منظم کرتا ہے، جب ضرورت ہو صارف سے سوالات پوچھتا ہے۔ یہ ہر حالت پر لاگو ہونے والے درست قواعد تلاش کرنے کا ذمہ دار بھی ہے۔ -ایک مثال کے طور پر، آئیے ایک ماہر نظام پر غور کریں جو کسی جانور کا تعین اس کی جسمانی خصوصیات کی بنیاد پر کرتا ہے: +مثال کے طور پر، آئیے ایک ماہر نظام پر غور کریں جو کسی جانور کو اس کی جسمانی خصوصیات کی بنیاد پر شناخت کرتا ہے: -![AND-OR درخت](../../../../translated_images/ur/AND-OR-Tree.5592d2c70187f283.png) +![اور-اور درخت](../../../../../../translated_images/ur/AND-OR-Tree.5592d2c70187f283.webp) -> تصویر از [Dmitry Soshnikov](http://soshnikov.com) +> تصویر بذریعہ [دمتری سوشنیکوف](http://soshnikov.com) -اس ڈایاگرام کو **AND-OR درخت** کہا جاتا ہے، اور یہ پروڈکشن رولز کے ایک سیٹ کی گرافیکل نمائندگی ہے۔ درخت بنانا ماہر سے علم نکالنے کے آغاز میں مفید ہے۔ کمپیوٹر کے اندر علم کی نمائندگی کے لیے قواعد کا استعمال زیادہ آسان ہے: +اس خاکے کو **اور-یا درخت** کہا جاتا ہے، اور یہ پروڈکشن قواعد کے مجموعہ کی گرافیکل نمائندگی ہے۔ ماہر سے علم حاصل کرنے کے عمل کے آغاز میں درخت بنانا مفید ہوتا ہے۔ کمپیوٹر کے اندر علم کی نمائندگی کے لیے قوانین کا استعمال زیادہ مناسب ہے: ``` IF the animal eats meat @@ -121,78 +121,78 @@ OR (animal has sharp teeth THEN the animal is a carnivore ``` -آپ دیکھ سکتے ہیں کہ قاعدے کے بائیں جانب کی حالت اور عمل بنیادی طور پر آبجیکٹ-ایٹریبیوٹ-ویلیو (OAV) ٹرپلٹس ہیں۔ **ورکنگ میموری** ان OAV ٹرپلٹس کے سیٹ پر مشتمل ہے جو اس وقت حل کیے جا رہے مسئلے سے مطابقت رکھتے ہیں۔ ایک **رولز انجن** ان قواعد کو تلاش کرتا ہے جن کی حالت پوری ہو رہی ہو اور انہیں لاگو کرتا ہے، ورکنگ میموری میں ایک اور ٹرپلٹ شامل کرتا ہے۔ +آپ دیکھ سکتے ہیں کہ ہر شرط جو قواعد کے بائیں طرف ہے اور عمل درحقیقت آبجیکٹ-اتریبیوٹ-ویلیو (OAV) ٹرپلٹس ہوتے ہیں۔ **کام کرنے والا حافظہ** OAV ٹرپلٹس کے سیٹ پر مشتمل ہوتا ہے جو اس مسئلہ سے متعلق ہوتا ہے جو فی الحال حل ہو رہا ہوتا ہے۔ **قواعد کا انجن** ایسے قواعد کے لیے تلاش کرتا ہے جن کی شرط پوری ہو گئی ہو اور انہیں نافذ کرتا ہے، نئے ٹرپلٹس کو کام کرنے والے حافظے میں شامل کرتے ہوئے۔ -> ✅ اپنے پسندیدہ موضوع پر اپنا AND-OR درخت بنائیں! +> ✅ اپنی پسند کے موضوع پر اپنا اور-یا درخت بنائیں! -### فارورڈ بمقابلہ بیکورڈ استدلال +### آگے کے استدلال بمقابلہ پیچھے کے استدلال -اوپر بیان کردہ عمل کو **فارورڈ استدلال** کہا جاتا ہے۔ یہ مسئلے کے بارے میں ورکنگ میموری میں دستیاب کچھ ابتدائی ڈیٹا سے شروع ہوتا ہے، اور پھر درج ذیل استدلالی لوپ کو انجام دیتا ہے: +مندرجہ بالا عمل کو **آگے کی استدلال** کہا جاتا ہے۔ یہ مسئلہ کے بارے میں ابتدائی ڈیٹا سے شروع ہوتا ہے جو کام کرنے والے حافظے میں موجود ہوتا ہے، اور پھر درج ذیل استدلال کے چکر کو انجام دیتا ہے: -1. اگر ہدف کی صفت ورکنگ میموری میں موجود ہو - رک جائیں اور نتیجہ دیں۔ -2. ان تمام قواعد کو تلاش کریں جن کی حالت اس وقت پوری ہو رہی ہو - قواعد کے **تنازعہ سیٹ** کو حاصل کریں۔ -3. **تنازعہ حل** انجام دیں - ایک قاعدہ منتخب کریں جو اس مرحلے پر نافذ کیا جائے گا۔ مختلف تنازعہ حل کی حکمت عملی ہو سکتی ہیں: - - علمی بنیاد میں پہلا قابل اطلاق قاعدہ منتخب کریں۔ - - ایک تصادفی قاعدہ منتخب کریں۔ - - ایک *زیادہ مخصوص* قاعدہ منتخب کریں، یعنی وہ جو "بائیں جانب" (LHS) میں سب سے زیادہ حالات کو پورا کرتا ہو۔ -4. منتخب قاعدہ لاگو کریں اور مسئلہ کی حالت میں نیا علم شامل کریں۔ -5. مرحلہ 1 سے دوبارہ شروع کریں۔ +1. اگر مطلوبہ وصف کام کرنے والے حافظے میں موجود ہو — رکیں اور نتیجہ دیں +2. تمام ایسے قواعد تلاش کریں جن کی شرط اس وقت پوری ہو — **تنازعہ کے قواعد** حاصل کریں۔ +3. **تنازعہ کے حل** کریں — ایک قاعدہ منتخب کریں جسے اس مرحلے پر نافذ کیا جائے گا۔ مختلف تنازعہ حل کی حکمت عملیاں ہو سکتی ہیں: + - علمی بنیاد میں پہلے قابل اطلاق قاعدے کا انتخاب کریں + - کوئی بھی قاعدہ بخت آزما منتخب کریں + - *زیادہ مخصوص* قاعدہ منتخب کریں، یعنی وہ جو "بائیں طرف" (LHS) میں زیادہ شرائط پوری کرتا ہو۔ +4. منتخب کردہ قاعدہ نافذ کریں اور مسئلہ کی حالت میں نیا علم شامل کریں +5. قدم 1 سے دوبارہ آغاز کریں۔ -تاہم، بعض صورتوں میں ہم مسئلے کے بارے میں خالی علم سے شروع کرنا چاہتے ہیں، اور ایسے سوالات پوچھنا چاہتے ہیں جو ہمیں نتیجہ تک پہنچنے میں مدد دیں۔ مثال کے طور پر، جب طبی تشخیص کی بات آتی ہے، تو ہم عام طور پر مریض کی تشخیص شروع کرنے سے پہلے تمام طبی تجزیے نہیں کرتے۔ ہم اس وقت تجزیے کرنا چاہتے ہیں جب کوئی فیصلہ کرنا ہو۔ +تاہم، بعض حالات میں ہم مسئلے کے بارے میں خالی علم سے شروع کرنا چاہتے ہیں، اور ایسے سوالات پوچھتے ہیں جو نتیجہ تک پہنچنے میں مدد دیں۔ مثال کے طور پر، طبی تشخیص کرتے وقت، ہم عموماً پیشگی تمام میڈیکل تجزیے نہیں کرتے۔ ہم تجزیے تب کرتے ہیں جب فیصلہ کرنا ہو۔ -اس عمل کو **بیکورڈ استدلال** کے ذریعے ماڈل کیا جا سکتا ہے۔ یہ **ہدف** کے ذریعے چلایا جاتا ہے - وہ صفت جس کی قدر ہم تلاش کرنا چاہتے ہیں: +اس عمل کو **پیچھے کی استدلال** کے ذریعے ماڈل کیا جا سکتا ہے۔ یہ **ہدف** کے تحت چلتا ہے — جس وصف کی قدر ہم تلاش کرنا چاہتے ہیں: -1. ان تمام قواعد کو منتخب کریں جو ہمیں ہدف کی قدر دے سکتے ہیں (یعنی ہدف RHS ("دائیں جانب") پر ہو) - ایک تنازعہ سیٹ۔ -1. اگر اس صفت کے لیے کوئی قاعدہ موجود نہ ہو، یا کوئی قاعدہ ہو جو کہتا ہو کہ ہمیں صارف سے قدر پوچھنی چاہیے - اس سے پوچھیں، ورنہ: -1. تنازعہ حل کی حکمت عملی کا استعمال کرتے ہوئے ایک قاعدہ منتخب کریں جسے ہم *مفروضہ* کے طور پر استعمال کریں گے - ہم اسے ثابت کرنے کی کوشش کریں گے۔ -1. قاعدے کے LHS میں موجود تمام صفات کے لیے عمل کو بار بار دہرائیں، انہیں اہداف کے طور پر ثابت کرنے کی کوشش کریں۔ -1. اگر کسی بھی وقت عمل ناکام ہو جائے - مرحلہ 3 پر دوسرا قاعدہ استعمال کریں۔ +1. تمام وہ قواعد منتخب کریں جو ہمیں ہدف کی قدر دے سکیں (یعنی جن میں ہدف "دائیں طرف" (RHS) میں ہو) — تنازعہ کا سیٹ +2. اگر اس وصف کے لیے کوئی قواعد موجود نہیں، یا ایسا قاعدہ ہو جو کہتا ہو کہ ہمیں صارف سے قدر پوچھنی چاہیے — پوچھیں، ورنہ: +3. تنازعہ حل کی حکمت عملی استعمال کر کے ایک قاعدہ منتخب کریں جسے ہم *مفروضہ* سمجھ کر ثابت کرنے کی کوشش کریں گے +4. قاعدہ کے LHS میں موجود تمام صفات کے لیے اس عمل کو دہرائیں، انہیں ہدف بنا کر ثابت کرنے کی کوشش کریں +5. اگر کسی بھی مقام پر عمل ناکام ہو جائے — قدم 3 میں دوسرا قاعدہ استعمال کریں۔ -> ✅ کن حالات میں فارورڈ استدلال زیادہ مناسب ہے؟ اور بیکورڈ استدلال کب بہتر ہے؟ +> ✅ کن حالات میں آگے کی استدلال زیادہ مناسب ہے؟ اور پیچھے کی استدلال کے بارے میں کیا خیال ہے؟ -### ماہر نظاموں کا نفاذ +### ماہر نظام کی عمل درآمد -ماہر نظام مختلف ٹولز کا استعمال کرتے ہوئے نافذ کیے جا سکتے ہیں: +ماہر نظام مختلف اوزار استعمال کر کے بنائے جا سکتے ہیں: -* انہیں کسی اعلیٰ سطحی پروگرامنگ زبان میں براہ راست پروگرام کرنا۔ یہ بہترین خیال نہیں ہے، کیونکہ علم پر مبنی نظام کا بنیادی فائدہ یہ ہے کہ علم استدلال سے الگ ہوتا ہے، اور ممکنہ طور پر مسئلہ کے دائرہ کار کے ماہر کو قواعد لکھنے کے قابل ہونا چاہیے بغیر استدلالی عمل کی تفصیلات کو سمجھے۔ -* **ماہر نظام شیل** کا استعمال، یعنی ایک ایسا نظام جو خاص طور پر کسی علم کی نمائندگی کی زبان کا استعمال کرتے ہوئے علم سے آباد ہونے کے لیے ڈیزائن کیا گیا ہو۔ +* انہیں براہِ راست کسی اعلیٰ سطح کی پروگرامنگ زبان میں پروگرام کرنا۔ یہ مناسب نہیں کیونکہ علم پر مبنی نظام کا سب سے بڑا فائدہ یہ ہے کہ علم استدلال سے الگ ہوتا ہے، اور ممکنہ طور پر مسئلہ کے ماہر کو استدلال کی تفصیلات سمجھے بغیر قواعد لکھنے کا موقع ملتا ہے۔ +* **ماہر نظام شیل** کا استعمال، یعنی ایسا نظام خاص طور پر ڈیزائن کیا گیا ہو جو علم نمائندگی کی زبان کے ذریعے علم سے بھر دیا جا سکے۔ -## ✍️ مشق: جانوروں کا استدلال +## ✍️ مشق: جانور کی استدلال -[Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) دیکھیں، جو فارورڈ اور بیکورڈ استدلال ماہر نظام کو نافذ کرنے کی ایک مثال ہے۔ +آگے اور پیچھے کی استدلال ماہر نظام کی مثال کے لیے [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) دیکھیں۔ -> **نوٹ**: یہ مثال کافی سادہ ہے، اور صرف یہ بتاتی ہے کہ ماہر نظام کیسا نظر آتا ہے۔ جب آپ ایسا نظام بنانا شروع کریں گے، تو آپ کو اس میں کچھ *ذہین* رویہ اس وقت نظر آئے گا جب آپ 200+ قواعد تک پہنچ جائیں گے۔ ایک وقت ایسا آئے گا جب قواعد اتنے پیچیدہ ہو جائیں گے کہ آپ ان سب کو ذہن میں نہیں رکھ سکیں گے، اور اس وقت آپ سوچ سکتے ہیں کہ نظام نے کچھ فیصلے کیوں کیے۔ تاہم، علم پر مبنی نظاموں کی ایک اہم خصوصیت یہ ہے کہ آپ ہمیشہ *وضاحت* کر سکتے ہیں کہ کسی بھی فیصلے کو کیسے بنایا گیا۔ +> **نوٹ**: یہ مثال سادہ ہے، اور صرف یہ سمجھانے کے لیے ہے کہ ماہر نظام کیسا ہوتا ہے۔ جب آپ اس طرح کا نظام بنانا شروع کریں گے، تو آپ کو صرف کچھ *ذہین* رویہ نظر آئے گا جب آپ کی قواعد کی تعداد تقریباً ۲۰۰+ ہو جائے۔ کسی وقت قواعد اتنے پیچیدہ ہو جاتے ہیں کہ تمام کو یاد رکھنا مشکل ہو جاتا ہے، اور آپ سوچنے لگتے ہیں کہ نظام کچھ فیصلے کیوں کر رہا ہے۔ تاہم، علم پر مبنی نظام کی اہم بات یہ ہے کہ آپ ہمیشہ *بالکل بتا سکتے ہیں* کہ کسی بھی فیصلے تک کیسے پہنچا گیا۔ -## اونٹولوجیز اور سیمینٹک ویب +## انتولوجیز اور سیمنٹک ویب -20ویں صدی کے آخر میں ایک پہل کی گئی کہ علم کی نمائندگی کو انٹرنیٹ وسائل کو تشریح کرنے کے لیے استعمال کیا جائے، تاکہ یہ ممکن ہو کہ بہت مخصوص سوالات کے مطابق وسائل تلاش کیے جا سکیں۔ اس تحریک کو **سیمینٹک ویب** کہا گیا، اور یہ کئی تصورات پر مبنی تھی: +۲۰ویں صدی کے آخر میں، انٹرنیٹ کے وسائل کو نوٹ کرنے کے لیے علم کی نمائندگی استعمال کرنے کی ایک تحریک ہوئی، تاکہ مخصوص سوالات کے جواب میں وسائل تلاش کیے جا سکیں۔ اس کوشش کو **سیمنٹک ویب** کہا جاتا ہے، اور یہ چند تصورات پر مبنی تھی: -- **[وضاحتی منطق](https://en.wikipedia.org/wiki/Description_logic)** (DL) پر مبنی ایک خاص علم کی نمائندگی۔ یہ فریم علم کی نمائندگی سے مشابہت رکھتی ہے، کیونکہ یہ اشیاء کی خصوصیات کے ساتھ ایک درجہ بندی بناتی ہے، لیکن اس میں رسمی منطقی معنویت اور استدلال ہوتا ہے۔ DLs کا ایک پورا خاندان ہے جو اظہاریت اور استدلال کی الگورتھمک پیچیدگی کے درمیان توازن رکھتا ہے۔ -- تقسیم شدہ علم کی نمائندگی، جہاں تمام تصورات کو ایک عالمی URI شناخت کنندہ کے ذریعے پیش کیا جاتا ہے، جس سے انٹرنیٹ پر علم کی درجہ بندی بنانا ممکن ہوتا ہے۔ -- XML پر مبنی زبانوں کا ایک خاندان جو علم کی وضاحت کے لیے استعمال ہوتا ہے: RDF (Resource Description Framework)، RDFS (RDF Schema)، OWL (Ontology Web Language)۔ +- علم کی ایک خاص نمائندگی جو **[ڈسکرپشن لاجکس](https://en.wikipedia.org/wiki/Description_logic)** (DL) پر مبنی ہے۔ یہ فریم علم کی نمائندگی سے مشابہت رکھتی ہے، کیونکہ اس میں اشیاء کی درجہ بندی اور خصوصیات شامل ہوتی ہیں، لیکن اس کا رسمی منطقی مفہوم اور استدلال ہوتا ہے۔ DLs کا ایک گروپ ہے جو اظہاریت اور استدلال کی الگورتھمک پیچیدگی کے درمیان توازن رکھتا ہے۔ +- تقسیم شدہ علم کی نمائندگی، جہاں تمام تصورات کو ایک عالمی URI شناخت کے ذریعے ظاہر کیا جاتا ہے، جس سے انٹرنیٹ پر وسیع علم کی درجہ بندی بنانا ممکن ہوتا ہے۔ +- ایک XML پر مبنی زبانوں کا خاندان علم کی وضاحت کے لیے: RDF (Resource Description Framework)، RDFS (RDF Schema)، OWL (Ontology Web Language)۔ -سمینٹک ویب میں ایک بنیادی تصور **Ontology** کا ہے۔ یہ کسی مسئلے کے دائرہ کار کی واضح وضاحت کو ظاہر کرتا ہے، جو کسی رسمی علم کی نمائندگی کے ذریعے کی جاتی ہے۔ سب سے سادہ ontology صرف مسئلے کے دائرہ کار میں اشیاء کی ایک درجہ بندی ہو سکتی ہے، لیکن زیادہ پیچیدہ ontologies میں ایسے قواعد شامل ہوں گے جو استنباط کے لیے استعمال کیے جا سکتے ہیں۔ +سیمانٹک ویب کا ایک بنیادی تصور **Ontology** ہے۔ اس سے مراد کسی مسئلے کے دائرہ کار کی واضح وضاحت ہوتی ہے جو کسی رسمی علم کی نمائندگی کے استعمال سے کی جاتی ہے۔ سب سے سادہ آنٹولوجی مسئلہ کے دائرہ کار میں اشیاء کا ہائرارکی ہو سکتی ہے، لیکن زیادہ پیچیدہ آنٹولوجیز میں قواعد شامل ہوتے ہیں جو استنتاج کے لیے استعمال کیے جا سکتے ہیں۔ -سمینٹک ویب میں تمام نمائندگیاں triplets پر مبنی ہوتی ہیں۔ ہر شے اور ہر تعلق کو URI کے ذریعے منفرد طور پر شناخت کیا جاتا ہے۔ مثال کے طور پر، اگر ہم یہ بیان کرنا چاہیں کہ یہ AI Curriculum دمتری سوشنیکوف نے 1 جنوری 2022 کو تیار کیا ہے - تو ہم ان triplets کا استعمال کر سکتے ہیں: +سیمانٹک ویب میں، تمام نمائندگیاں ٹرپلٹس (triplets) پر مبنی ہوتی ہیں۔ ہر شے اور ہر تعلق کو منفرد طور پر URI سے شناخت کیا جاتا ہے۔ مثال کے طور پر، اگر ہم یہ بیان کرنا چاہیں کہ یہ AI نصاب Dmitry Soshnikov نے 1 جنوری، 2022 کو تیار کیا ہے - تو یہاں استعمال کیے جانے والے ٹرپلٹس ہیں: - + ``` -http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 13, 2007” +http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 1, 2022” http://github.com/microsoft/ai-for-beginners http://purl.org/dc/elements/1.1/creator http://soshnikov.com ``` > ✅ یہاں `http://www.example.com/terms/creation-date` اور `http://purl.org/dc/elements/1.1/creator` کچھ معروف اور عالمی سطح پر قبول شدہ URIs ہیں جو *creator* اور *creation date* کے تصورات کو ظاہر کرتے ہیں۔ -زیادہ پیچیدہ صورت میں، اگر ہم تخلیق کاروں کی فہرست کو بیان کرنا چاہیں، تو ہم RDF میں بیان کردہ کچھ ڈیٹا ڈھانچے استعمال کر سکتے ہیں۔ +ایک زیادہ پیچیدہ صورت میں، اگر ہم تخلیق کاروں کی فہرست تعریف کرنا چاہیں تو ہم RDF میں تعریف کردہ کچھ ڈیٹا سٹرکچرز استعمال کر سکتے ہیں۔ - + -> اوپر کے ڈایاگرامز [دمتری سوشنیکوف](http://soshnikov.com) کے ذریعے۔ +> اوپر کے خاکے [Dmitry Soshnikov](http://soshnikov.com) کے ہیں -سمینٹک ویب کی تعمیر کی پیش رفت کو سرچ انجنز اور قدرتی زبان کی پروسیسنگ تکنیکوں کی کامیابی نے کسی حد تک سست کر دیا، جو متن سے منظم ڈیٹا نکالنے کی اجازت دیتی ہیں۔ تاہم، کچھ شعبوں میں اب بھی ontologies اور knowledge bases کو برقرار رکھنے کے لیے اہم کوششیں کی جا رہی ہیں۔ چند قابل ذکر منصوبے: +سیمانٹک ویب کی تعمیر کی پیش رفت کچھ حد تک سرچ انجنز اور قدرتی زبان کی پروسیسنگ تکنیکس کی کامیابی کی وجہ سے سست پڑ گئی، جو متن سے منظم ڈیٹا نکالنے کی اجازت دیتی ہیں۔ تاہم، کچھ شعبوں میں اب بھی آنٹولوجیز اور علم کی بنیادوں کو برقرار رکھنے کی خاطر قابل ذکر کوششیں جاری ہیں۔ چند قابل ذکر پروجیکٹس: -* [WikiData](https://wikidata.org/) مشین ریڈ ایبل knowledge bases کا مجموعہ ہے جو Wikipedia سے منسلک ہے۔ زیادہ تر ڈیٹا Wikipedia *InfoBoxes* سے نکالا جاتا ہے، جو Wikipedia صفحات کے اندر منظم مواد کے ٹکڑے ہیں۔ آپ [query](https://query.wikidata.org/) wikidata کو SPARQL میں کر سکتے ہیں، جو سمینٹک ویب کے لیے ایک خاص query زبان ہے۔ یہاں ایک نمونہ query ہے جو انسانوں میں سب سے زیادہ مقبول آنکھوں کے رنگ دکھاتا ہے: +* [WikiData](https://wikidata.org/) مشین قابلِ پڑھائی علم کی بنیادوں کا مجموعہ ہے جو ویکیپیڈیا سے منسلک ہے۔ زیادہ تر ڈیٹا ویکیپیڈیا *InfoBoxes* سے نکالا جاتا ہے، جو ویکیپیڈیا صفحات کے اندر منظم مواد کے ٹکڑے ہیں۔ آپ [query](https://query.wikidata.org/) میں SPARQL، جو سیمانٹک ویب کے لیے ایک خاص کوئری زبان ہے، استعمال کر کے ویک ڈیٹا کو تلاش کر سکتے ہیں۔ یہاں ایک نمونہ کوئری ہے جو انسانوں میں سب سے زیادہ مقبول آنکھوں کے رنگ دکھاتی ہے: ```sparql #defaultView:BubbleChart @@ -206,47 +206,51 @@ WHERE GROUP BY ?eyeColorLabel ``` -* [DBpedia](https://www.dbpedia.org/) WikiData جیسی ایک اور کوشش ہے۔ +* [DBpedia](https://www.dbpedia.org/) ویکیڈیٹا کے مشابہ ایک اور کوشش ہے۔ -> ✅ اگر آپ اپنی ontologies بنانے یا موجودہ کو کھولنے کے ساتھ تجربہ کرنا چاہتے ہیں، تو ایک بہترین بصری ontology ایڈیٹر [Protégé](https://protege.stanford.edu/) ہے۔ اسے ڈاؤن لوڈ کریں، یا آن لائن استعمال کریں۔ +> ✅ اگر آپ اپنی خود کی آنٹولوجیز بنانے یا موجودہ آنٹولوجیز کو کھولنے کا تجربہ کرنا چاہتے ہیں، تو ایک بہترین بصری آنٹولوجی ایڈیٹر ہے [Protégé](https://protege.stanford.edu/)۔ اسے ڈاؤن لوڈ کریں، یا آن لائن استعمال کریں۔ - + -*Web Protégé ایڈیٹر Romanov Family ontology کے ساتھ کھلا ہوا۔ اسکرین شاٹ دمتری سوشنیکوف کے ذریعے* +*ویب پروٹیج ایڈیٹر رومانوف خاندان کی آنٹولوجی کے ساتھ کھلا ہوا۔ سکرین شاٹ Dmitry Soshnikov کی طرف سے* -## ✍️ مشق: ایک فیملی Ontology +## ✍️ مشق: خاندان کی آنٹولوجی -[FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) دیکھیں، جو سمینٹک ویب تکنیکوں کا استعمال کرتے ہوئے خاندانی تعلقات کے بارے میں استدلال کرنے کی مثال فراہم کرتا ہے۔ ہم ایک فیملی ٹری کو عام GEDCOM فارمیٹ میں اور خاندانی تعلقات کی ontology کو لے کر دیے گئے افراد کے سیٹ کے لیے تمام خاندانی تعلقات کا گراف بنائیں گے۔ +دیکھیں [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) ایک مثال کے لیے کہ کیسے سیمانٹک ویب تکنیکس کو خاندان کے تعلقات پر غور کرنے کے لیے استعمال کیا جا سکتا ہے۔ ہم GEDCOM فارمیٹ میں نمائندہ خاندان کا درخت اور خاندان کے تعلقات کی آنٹولوجی لیں گے اور مخصوص افراد کے لیے تمام خاندان کے تعلقات کا ایک گراف بنائیں گے۔ -## Microsoft Concept Graph +## مائیکروسافٹ کانسپٹ گراف -زیادہ تر معاملات میں، ontologies کو احتیاط سے ہاتھ سے تیار کیا جاتا ہے۔ تاہم، یہ بھی ممکن ہے کہ **mine** ontologies کو غیر منظم ڈیٹا سے نکالا جائے، مثال کے طور پر، قدرتی زبان کے متن سے۔ +زیادہ تر معاملات میں، آنٹولوجیز احتیاط سے ہاتھ سے بنائی جاتی ہیں۔ تاہم، ان اسٹرکچرڈ ڈیٹا سے آنٹولوجیز **مین** کرنا بھی ممکن ہے، مثلاً قدرتی زبان کے متون سے۔ -ایسا ہی ایک تجربہ Microsoft Research نے کیا، جس کے نتیجے میں [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste) سامنے آیا۔ +ایسی ایک کوشش مائیکروسافٹ ریسرچ نے کی، اور اس کا نتیجہ [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste) نکلا۔ -یہ entities کا ایک بڑا مجموعہ ہے جو `is-a` inheritance تعلق کے ذریعے گروپ کیا گیا ہے۔ یہ سوالات کے جواب دینے کی اجازت دیتا ہے جیسے "Microsoft کیا ہے؟" - جواب کچھ اس طرح ہوگا "ایک کمپنی، امکان 0.87 کے ساتھ، اور ایک برانڈ، امکان 0.75 کے ساتھ"۔ +یہ کثیر تعداد میں اشیاء کا مجموعہ ہے جو `is-a` موروثی تعلق کے ذریعے گروپ بند کیا گیا ہے۔ یہ ایسے سوالات کے جوابات فراہم کرتا ہے جیسے "مائیکروسافٹ کیا ہے؟" - جواب کچھ یوں ہو گا "ایک کمپنی احتمال 0.87 کے ساتھ، اور ایک برانڈ احتمال 0.75 کے ساتھ"۔ -Graph REST API کے طور پر دستیاب ہے، یا ایک بڑے ڈاؤن لوڈ کے قابل ٹیکسٹ فائل کے طور پر جو تمام entity pairs کو فہرست میں شامل کرتا ہے۔ +یہ گراف REST API اور ایک بڑی ڈاؤن لوڈ کرنے والی ٹیکسٹ فائل کی صورت میں دستیاب ہے جس میں تمام اشیاء کے جوڑے درج ہیں۔ -## ✍️ مشق: ایک Concept Graph +## ✍️ مشق: کانسپٹ گراف -[MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) نوٹ بک آزمائیں تاکہ یہ دیکھ سکیں کہ ہم Microsoft Concept Graph کا استعمال کرتے ہوئے نیوز آرٹیکلز کو کئی زمروں میں کیسے گروپ کر سکتے ہیں۔ +[MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) نوٹ بک آزمائیں کہ کیسے ہم مائیکروسافٹ کانسپٹ گراف کو خبری مضامین کو کئی زمروں میں گروپ کرنے کے لیے استعمال کر سکتے ہیں۔ ## نتیجہ -آج کل، AI کو اکثر *Machine Learning* یا *Neural Networks* کا مترادف سمجھا جاتا ہے۔ تاہم، ایک انسان بھی واضح استدلال کا مظاہرہ کرتا ہے، جو کچھ ایسا ہے جو فی الحال neural networks کے ذریعے نہیں سنبھالا جا رہا۔ حقیقی دنیا کے منصوبوں میں، واضح استدلال اب بھی ان کاموں کو انجام دینے کے لیے استعمال کیا جاتا ہے جن کے لیے وضاحت کی ضرورت ہوتی ہے، یا نظام کے رویے کو کنٹرول شدہ طریقے سے تبدیل کرنے کی صلاحیت۔ +آج کل، AI کو اکثر *مشین لرننگ* یا *نیورل نیٹ ورکس* کا مترادف سمجھا جاتا ہے۔ تاہم، ایک انسان واضح استدلال بھی دکھاتا ہے، جو فی الحال نیورل نیٹ ورکس کے ذریعہ نہیں سنبھالا جاتا۔ حقیقی دنیا کے پروجیکٹس میں، واضح استدلال ابھی بھی ان کاموں کے لیے استعمال ہوتا ہے جنہیں وضاحتیں درکار ہوتی ہیں، یا نظام کے رویے کو ایک کنٹرول شدہ طریقے سے تبدیل کرنے کی ضرورت ہوتی ہے۔ ## 🚀 چیلنج -اس سبق سے منسلک Family Ontology نوٹ بک میں، دیگر خاندانی تعلقات کے ساتھ تجربہ کرنے کا موقع موجود ہے۔ فیملی ٹری میں لوگوں کے درمیان نئے تعلقات دریافت کرنے کی کوشش کریں۔ +اس سبق کے ساتھ ہونی والی فیملی آنٹولوجی نوٹ بک میں، خاندان کے دیگر تعلقات کے تجربے کا موقع موجود ہے۔ خاندان کے درخت میں لوگوں کے نئے تعلقات دریافت کرنے کی کوشش کریں۔ -## [لیکچر کے بعد کا کوئز](https://ff-quizzes.netlify.app/en/ai/quiz/4) +## [لیکچر کے بعد کوئز](https://ff-quizzes.netlify.app/en/ai/quiz/4) ## جائزہ اور خود مطالعہ -انٹرنیٹ پر تحقیق کریں تاکہ ان شعبوں کو دریافت کریں جہاں انسانوں نے علم کو مقدار میں تبدیل کرنے اور کوڈفائی کرنے کی کوشش کی ہے۔ Bloom's Taxonomy پر نظر ڈالیں، اور تاریخ میں واپس جائیں تاکہ یہ جان سکیں کہ انسانوں نے اپنی دنیا کو سمجھنے کی کوشش کیسے کی۔ Linnaeus کے کام کو دریافت کریں تاکہ جانداروں کی درجہ بندی بنائی جا سکے، اور دیکھیں کہ Dmitri Mendeleev نے کیمیائی عناصر کو بیان کرنے اور گروپ کرنے کا طریقہ کیسے بنایا۔ آپ کون سے دیگر دلچسپ مثالیں تلاش کر سکتے ہیں؟ +انٹرنیٹ پر تحقیق کریں تاکہ ایسی جگہوں کا پتہ لگا سکیں جہاں انسانوں نے علم کی مقدار کو ناپنے اور ضابطہ بندی کرنے کی کوشش کی ہے۔ Bloom کی ٹیکسونومی پر نظر ڈالیں، اور تاریخ میں واپس جائیں کہ انسانوں نے کیسے اپنی دنیا کو سمجھنے کی کوشش کی۔ Linnaeus کے کام کو دریافت کریں جس نے جانداروں کی ٹیکسونومی بنائی، اور مِندلیف کے طریقے کو مشاہدہ کریں جس سے کیمیائی عناصر کی وضاحت اور گروہ بندی ممکن ہوئی۔ آپ کو اور کون سے دلچسپ مثالیں مل سکتی ہیں؟ -**اسائنمنٹ**: [ایک Ontology بنائیں](assignment.md) +**کام**: [ایک آنٹولوجی بنائیں](assignment.md) --- + +**دستخطی بیان**: +یہ دستاویز AI ترجمہ خدمت [Co-op Translator](https://github.com/Azure/co-op-translator) کے ذریعے ترجمہ کی گئی ہے۔ اگرچہ ہم درستگی کی پوری کوشش کرتے ہیں، براہ کرم اس بات سے آگاہ رہیں کہ خودکار ترجمے میں غلطیاں یا نقائص ہو سکتے ہیں۔ اصل دستاویز اپنی مادری زبان میں ہی معتبر ماخذ سمجھی جائے گی۔ اہم معلومات کے لئے پیشہ ورانہ انسانی ترجمہ تجویز کیا جاتا ہے۔ اس ترجمے کے استعمال سے پیدا ہونے والی کسی بھی غلط فہمی یا غلط تشریح کی ذمہ داری ہم پر نہیں عائد ہوگی۔ + \ No newline at end of file diff --git a/translations/ur/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/ur/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index 912c7daf..deaa5b48 100644 --- a/translations/ur/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/ur/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -1251,7 +1251,7 @@ "* کم تربیتی نقصان - ماڈل تربیتی ڈیٹا کو اچھی طرح سمجھ سکتا ہے، کیونکہ اس میں کافی اظہاری طاقت ہوتی ہے۔\n", "* ویلیڈیشن نقصان تربیتی نقصان سے کہیں زیادہ ہو سکتا ہے اور تربیت کے دوران بڑھنا شروع کر سکتا ہے - اس کی وجہ یہ ہے کہ ماڈل تربیتی پوائنٹس کو \"یاد\" کر لیتا ہے اور \"مجموعی تصویر\" کھو دیتا ہے۔\n", "\n", - "![اوورفٹنگ](../../../../../translated_images/ur/overfit.a0bd57f717c15769.png)\n", + "![اوورفٹنگ](../../../../../translated_images/ur/overfit.a0bd57f717c15769.webp)\n", "\n", "> اس تصویر میں، `x` تربیتی ڈیٹا کو ظاہر کرتا ہے، `o` - ویلیڈیشن ڈیٹا۔ بائیں طرف - لکیری ماڈل (ایک پرت)، یہ ڈیٹا کی نوعیت کو کافی حد تک اچھی طرح سمجھتا ہے۔ دائیں طرف - اوورفٹنگ ماڈل، ماڈل تربیتی ڈیٹا کو بالکل درست سمجھتا ہے، لیکن کسی دوسرے ڈیٹا کے ساتھ معنی کھو دیتا ہے (ویلیڈیشن نقصان بہت زیادہ ہے)\n" ] diff --git a/translations/ur/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/ur/lessons/3-NeuralNetworks/05-Frameworks/README.md index 207b9df2..e42d6d23 100644 --- a/translations/ur/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/ur/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ High-level API| [Keras](IntroKeras.ipynb) | *PyTorch Lightning* ذیل میں 5 نقاط کو اپروکسیمیٹ کرنے کے مسئلے پر غور کریں (گراف میں `x` کے ذریعے ظاہر کیے گئے): -![linear](../../../../../translated_images/ur/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/ur/overfit2.131f5800ae10ca5e.jpg) +![linear](../../../../../translated_images/ur/overfit1.f24b71c6f652e59e.webp) | ![overfit](../../../../../translated_images/ur/overfit2.131f5800ae10ca5e.webp) -------------------------|-------------------------- **لینیئر ماڈل، 2 پیرامیٹرز** | **نان-لینیئر ماڈل، 7 پیرامیٹرز** ٹریننگ ایرر = 5.3 | ٹریننگ ایرر = 0 @@ -79,7 +79,7 @@ High-level API| [Keras](IntroKeras.ipynb) | *PyTorch Lightning* جیسا کہ آپ اوپر گراف سے دیکھ سکتے ہیں، اوورفٹنگ کا پتہ بہت کم ٹریننگ ایرر اور بہت زیادہ ویلیڈیشن ایرر سے لگایا جا سکتا ہے۔ عام طور پر تربیت کے دوران ہم دیکھیں گے کہ ٹریننگ اور ویلیڈیشن ایرر دونوں کم ہونا شروع ہو جاتے ہیں، اور پھر کسی وقت ویلیڈیشن ایرر کم ہونا بند کر سکتا ہے اور بڑھنا شروع کر سکتا ہے۔ یہ اوورفٹنگ کی علامت ہوگی، اور اس بات کا اشارہ کہ ہمیں شاید اس وقت تربیت روک دینی چاہیے (یا کم از کم ماڈل کا اسنیپ شاٹ لینا چاہیے)۔ -![overfitting](../../../../../translated_images/ur/Overfitting.408ad91cd90b4371.png) +![overfitting](../../../../../translated_images/ur/Overfitting.408ad91cd90b4371.webp) ## اوورفٹنگ کو کیسے روکا جائے؟ diff --git a/translations/ur/lessons/3-NeuralNetworks/README.md b/translations/ur/lessons/3-NeuralNetworks/README.md index 78660272..3a91fe43 100644 --- a/translations/ur/lessons/3-NeuralNetworks/README.md +++ b/translations/ur/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # نیورل نیٹ ورکس کا تعارف -![نیورل نیٹ ورکس کے تعارف کا خلاصہ ایک خاکے میں](../../../../translated_images/ur/ai-neuralnetworks.1c687ae40bc86e83.png) +![نیورل نیٹ ورکس کے تعارف کا خلاصہ ایک خاکے میں](../../../../translated_images/ur/ai-neuralnetworks.1c687ae40bc86e83.webp) جیسا کہ ہم نے تعارف میں بات کی تھی، ذہانت حاصل کرنے کے طریقوں میں سے ایک یہ ہے کہ ایک **کمپیوٹر ماڈل** یا **مصنوعی دماغ** کو تربیت دی جائے۔ بیسویں صدی کے وسط سے، محققین نے مختلف ریاضیاتی ماڈلز آزمائے، اور حالیہ برسوں میں یہ سمت بہت کامیاب ثابت ہوئی۔ دماغ کے ان ریاضیاتی ماڈلز کو **نیورل نیٹ ورکس** کہا جاتا ہے۔ @@ -36,13 +36,13 @@ CO_OP_TRANSLATOR_METADATA: حیاتیات سے، ہم جانتے ہیں کہ ہمارا دماغ نیورل سیلز (نیورونز) پر مشتمل ہوتا ہے، جن میں سے ہر ایک کے پاس متعدد "ان پٹس" (ڈینڈریٹس) اور ایک "آؤٹ پٹ" (ایکسون) ہوتا ہے۔ ڈینڈریٹس اور ایکسون دونوں برقی سگنلز منتقل کر سکتے ہیں، اور ان کے درمیان کنکشنز — جنہیں سیناپسز کہا جاتا ہے — مختلف درجات کی کنڈکٹویٹی ظاہر کر سکتے ہیں، جو نیوروٹرانسمیٹرز کے ذریعے منظم کی جاتی ہیں۔ -![نیورون کا ماڈل](../../../../translated_images/ur/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![نیورون کا ماڈل](../../../../translated_images/ur/artneuron.1a5daa88d20ebe6f.png) +![نیورون کا ماڈل](../../../../translated_images/ur/synapse-wikipedia.ed20a9e4726ea1c6.webp) | ![نیورون کا ماڈل](../../../../translated_images/ur/artneuron.1a5daa88d20ebe6f.webp) ----|---- حقیقی نیورون *([تصویر](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) ویکیپیڈیا سے)* | مصنوعی نیورون *(تصویر مصنف کی طرف سے)* لہٰذا، نیورون کا سب سے سادہ ریاضیاتی ماڈل کئی ان پٹس X1, ..., XN اور ایک آؤٹ پٹ Y پر مشتمل ہوتا ہے، اور ایک سلسلہ وزن W1, ..., WN۔ آؤٹ پٹ کا حساب درج ذیل طریقے سے کیا جاتا ہے: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) جہاں f کوئی غیر خطی **ایکٹیویشن فنکشن** ہے۔ diff --git a/translations/ur/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/ur/lessons/4-ComputerVision/06-IntroCV/README.md index c845a761..a886445e 100644 --- a/translations/ur/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/ur/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) * **بریل کتاب کی تصویر کی پری پروسیسنگ**۔ ہم اس پر توجہ دیتے ہیں کہ کس طرح تھریشولڈنگ، فیچر ڈیٹیکشن، پرسپیکٹیو تبدیلی اور NumPy تبدیلیوں کا استعمال کرکے بریل کے انفرادی علامات کو الگ کیا جا سکتا ہے تاکہ نیورل نیٹ ورک کے ذریعے مزید درجہ بندی کی جا سکے۔ -![بریل تصویر](../../../../../translated_images/ur/braille.341962ff76b1bd70.jpeg) | ![بریل تصویر پری پروسیسڈ](../../../../../translated_images/ur/braille-result.46530fea020b03c7.png) | ![بریل علامات](../../../../../translated_images/ur/braille-symbols.0159185ab69d5339.png) +![بریل تصویر](../../../../../translated_images/ur/braille.341962ff76b1bd70.webp) | ![بریل تصویر پری پروسیسڈ](../../../../../translated_images/ur/braille-result.46530fea020b03c7.webp) | ![بریل علامات](../../../../../translated_images/ur/braille-symbols.0159185ab69d5339.webp) ----|-----|----- > تصویر [OpenCV.ipynb](OpenCV.ipynb) سے * **ویڈیو میں حرکت کا پتہ لگانا فریم فرق کے ذریعے**۔ اگر کیمرہ فکسڈ ہے، تو کیمرہ فیڈ کے فریمز ایک دوسرے سے کافی حد تک مشابہت رکھتے ہیں۔ چونکہ فریمز arrays کے طور پر ظاہر کیے جاتے ہیں، صرف ان arrays کو دو مسلسل فریمز کے لیے گھٹانے سے ہمیں پکسل فرق ملے گا، جو جامد فریمز کے لیے کم ہونا چاہیے، اور تصویر میں نمایاں حرکت ہونے پر زیادہ ہو جائے گا۔ -![ویڈیو فریمز اور فریم فرق کی تصویر](../../../../../translated_images/ur/frame-difference.706f805491a0883c.png) +![ویڈیو فریمز اور فریم فرق کی تصویر](../../../../../translated_images/ur/frame-difference.706f805491a0883c.webp) > تصویر [OpenCV.ipynb](OpenCV.ipynb) سے @@ -89,7 +89,7 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) - **ڈینس آپٹیکل فلو** وہ ویکٹر فیلڈ بناتا ہے جو ہر پکسل کے لیے دکھاتا ہے کہ وہ کہاں حرکت کر رہا ہے۔ - **اسپارس آپٹیکل فلو** تصویر میں کچھ نمایاں خصوصیات (جیسے کنارے) لیتا ہے اور ان کی فریم سے فریم تک حرکت کی راہ بناتا ہے۔ -![آپٹیکل فلو کی تصویر](../../../../../translated_images/ur/optical.1f4a94464579a83a.png) +![آپٹیکل فلو کی تصویر](../../../../../translated_images/ur/optical.1f4a94464579a83a.webp) > تصویر [OpenCV.ipynb](OpenCV.ipynb) سے diff --git a/translations/ur/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/ur/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 013a43ff..dcccecc0 100644 --- a/translations/ur/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/ur/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 ایک نیٹ ورک ہے جس نے 2014 میں ImageNet کے ٹاپ-5 کلاسیفیکیشن میں 92.7% درستگی حاصل کی۔ اس کی درج ذیل لیئر ساخت ہے: -![ImageNet Layers](../../../../../translated_images/ur/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet Layers](../../../../../translated_images/ur/vgg-16-arch1.d901a5583b3a51ba.webp) جیسا کہ آپ دیکھ سکتے ہیں، VGG ایک روایتی پیرامڈ آرکیٹیکچر کی پیروی کرتا ہے، جو کہ کنوولوشن-پولنگ لیئرز کی ترتیب ہے۔ -![ImageNet Pyramid](../../../../../translated_images/ur/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet Pyramid](../../../../../translated_images/ur/vgg-16-arch.64ff2137f50dd49f.webp) > تصویر [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) سے لی گئی ہے۔ diff --git a/translations/ur/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/ur/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index e0ff7e63..4906c0c0 100644 --- a/translations/ur/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/ur/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -260,7 +260,7 @@ "\n", "اس طرح، ایک عام CNN میں کئی کنولوشنل لیئرز ہوں گی، جن کے درمیان پولنگ لیئرز ہوں گی تاکہ تصویر کے ڈائمینشنز کو کم کیا جا سکے۔ ہم فلٹرز کی تعداد بھی بڑھائیں گے، کیونکہ جیسے جیسے نمونے زیادہ پیچیدہ ہوتے جاتے ہیں - ہمیں زیادہ ممکنہ دلچسپ امتزاجات کو تلاش کرنے کی ضرورت ہوتی ہے۔\n", "\n", - "![ایک تصویر جو کئی کنولوشنل لیئرز کو پولنگ لیئرز کے ساتھ دکھا رہی ہے۔](../../../../../translated_images/ur/cnn-pyramid.85915455759ef0ce.png)\n", + "![ایک تصویر جو کئی کنولوشنل لیئرز کو پولنگ لیئرز کے ساتھ دکھا رہی ہے۔](../../../../../translated_images/ur/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "اسپیشل ڈائمینشنز کو کم کرنے اور فیچر/فلٹرز ڈائمینشنز کو بڑھانے کی وجہ سے، اس آرکیٹیکچر کو **پیرامیڈ آرکیٹیکچر** بھی کہا جاتا ہے۔\n" ] diff --git a/translations/ur/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/ur/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index 33956811..5186b68e 100644 --- a/translations/ur/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/ur/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -361,7 +361,7 @@ "\n", "اس طرح، ایک عام CNN میں کئی کنوولوشنل لیئرز ہوتی ہیں، جن کے درمیان پولنگ لیئرز ہوتی ہیں تاکہ تصویر کے ڈائمینشنز کو کم کیا جا سکے۔ ہم فلٹرز کی تعداد بھی بڑھاتے ہیں، کیونکہ جیسے جیسے پیٹرنز زیادہ پیچیدہ ہوتے جاتے ہیں - ہمیں زیادہ ممکنہ دلچسپ امتزاجات کو تلاش کرنے کی ضرورت ہوتی ہے۔\n", "\n", - "![کئی کنوولوشنل لیئرز کو پولنگ لیئرز کے ساتھ دکھانے والی ایک تصویر۔](../../../../../translated_images/ur/cnn-pyramid.85915455759ef0ce.png)\n", + "![کئی کنوولوشنل لیئرز کو پولنگ لیئرز کے ساتھ دکھانے والی ایک تصویر۔](../../../../../translated_images/ur/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "اسپیشل ڈائمینشنز کو کم کرنے اور فیچر/فلٹرز کے ڈائمینشنز کو بڑھانے کی وجہ سے، اس آرکیٹیکچر کو **پیرامیڈ آرکیٹیکچر** بھی کہا جاتا ہے۔\n" ] diff --git a/translations/ur/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/ur/lessons/4-ComputerVision/07-ConvNets/README.md index cbcf51f6..b6ce3263 100644 --- a/translations/ur/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/ur/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: پیٹرنز نکالنے کے لیے، ہم **کنولوشنل فلٹرز** کا تصور استعمال کریں گے۔ جیسا کہ آپ جانتے ہیں، ایک تصویر کو 2D-میٹرکس یا رنگ کی گہرائی کے ساتھ 3D-ٹینسر کے طور پر ظاہر کیا جاتا ہے۔ فلٹر لگانے کا مطلب یہ ہے کہ ہم ایک نسبتاً چھوٹا **فلٹر کرنل** میٹرکس لیتے ہیں، اور اصل تصویر کے ہر پکسل کے لیے ہم پڑوسی پوائنٹس کے ساتھ وزنی اوسط کا حساب لگاتے ہیں۔ ہم اسے اس طرح دیکھ سکتے ہیں جیسے ایک چھوٹی ونڈو پوری تصویر پر سلائیڈ کر رہی ہو، اور فلٹر کرنل میٹرکس میں وزن کے مطابق تمام پکسلز کو اوسط کر رہی ہو۔ -![عمودی کنارے فلٹر](../../../../../translated_images/ur/filter-vert.b7148390ca0bc356.png) | ![افقی کنارے فلٹر](../../../../../translated_images/ur/filter-horiz.59b80ed4feb946ef.png) +![عمودی کنارے فلٹر](../../../../../translated_images/ur/filter-vert.b7148390ca0bc356.webp) | ![افقی کنارے فلٹر](../../../../../translated_images/ur/filter-horiz.59b80ed4feb946ef.webp) ----|---- > تصویر: دمتری سوشنیکوف @@ -38,7 +38,7 @@ CNN کے کام کرنے کا طریقہ درج ذیل اہم خیالات پر * ہم نیٹ ورک کو اس طرح ڈیزائن کر سکتے ہیں کہ فلٹرز خود بخود تربیت حاصل کریں * ہم اسی طریقے کو اعلی سطحی خصوصیات میں پیٹرنز تلاش کرنے کے لیے استعمال کر سکتے ہیں، نہ صرف اصل تصویر میں۔ اس طرح CNN خصوصیات نکالنے کا کام خصوصیات کی ایک درجہ بندی پر کرتا ہے، جو کم سطحی پکسل امتزاج سے شروع ہو کر تصویر کے حصوں کے اعلی سطحی امتزاج تک جاتا ہے۔ -![درجہ بندی خصوصیات نکالنا](../../../../../translated_images/ur/FeatureExtractionCNN.d9b456cbdae7cb64.png) +![درجہ بندی خصوصیات نکالنا](../../../../../translated_images/ur/FeatureExtractionCNN.d9b456cbdae7cb64.webp) > تصویر: [ہسلپ-لنچ کے مقالے](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d) سے، [ان کی تحقیق](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) پر مبنی @@ -55,9 +55,9 @@ CNN کے کام کرنے کا طریقہ درج ذیل اہم خیالات پر ایک مثال کے طور پر، آئیے VGG-16 کی آرکیٹیکچر کو دیکھیں، ایک نیٹ ورک جس نے 2014 میں ImageNet کے ٹاپ-5 درجہ بندی میں 92.7% درستگی حاصل کی: -![ImageNet لیئرز](../../../../../translated_images/ur/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet لیئرز](../../../../../translated_images/ur/vgg-16-arch1.d901a5583b3a51ba.webp) -![ImageNet پیرامڈ](../../../../../translated_images/ur/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet پیرامڈ](../../../../../translated_images/ur/vgg-16-arch.64ff2137f50dd49f.webp) > تصویر: [ریسرچ گیٹ](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) سے diff --git a/translations/ur/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/ur/lessons/4-ComputerVision/07-ConvNets/lab/README.md index b706e287..86f9db81 100644 --- a/translations/ur/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/ur/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ CO_OP_TRANSLATOR_METADATA: ہم [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/) استعمال کریں گے، جس میں کتوں اور بلیوں کی 37 مختلف نسلوں کی تصاویر شامل ہیں۔ -![ہم جس ڈیٹا سیٹ سے نمٹیں گے](../../../../../../translated_images/ur/data.50b2a9d5484bdbf0.png) +![ہم جس ڈیٹا سیٹ سے نمٹیں گے](../../../../../../translated_images/ur/data.50b2a9d5484bdbf0.webp) ڈیٹا سیٹ ڈاؤن لوڈ کرنے کے لیے، یہ کوڈ استعمال کریں: diff --git a/translations/ur/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/ur/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index 1590c359..7651bbde 100644 --- a/translations/ur/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/ur/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "مثالی بلی کو دیکھنے کے لیے، ہم ایک بے ترتیب شور والی تصویر سے آغاز کریں گے، اور پھر گریڈینٹ ڈیسینٹ آپٹیمائزیشن تکنیک کا استعمال کرتے ہوئے تصویر کو اس طرح ایڈجسٹ کریں گے کہ نیٹ ورک بلی کو پہچان سکے۔\n", "\n", - "![آپٹیمائزیشن لوپ](../../../../../translated_images/ur/ideal-cat-loop.999fbb8ff306e044.png)\n", + "![آپٹیمائزیشن لوپ](../../../../../translated_images/ur/ideal-cat-loop.999fbb8ff306e044.webp)\n", "\n", "یہ ہے ہماری ابتدائی تصویر:\n" ] diff --git a/translations/ur/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/ur/lessons/4-ComputerVision/08-TransferLearning/README.md index 438da23a..029925a0 100644 --- a/translations/ur/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/ur/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Keras اور PyTorch دونوں میں کچھ عام آرکیٹیکچرز کے یہاں VGG-16 نیٹ ورک کے ذریعے بلی کی تصویر سے نکالے گئے نمونہ فیچرز ہیں: -![VGG-16 کے ذریعے نکالے گئے فیچرز](../../../../../translated_images/ur/features.6291f9c7ba3a0b95.png) +![VGG-16 کے ذریعے نکالے گئے فیچرز](../../../../../translated_images/ur/features.6291f9c7ba3a0b95.webp) ## بلیوں اور کتوں کا ڈیٹا سیٹ @@ -48,19 +48,19 @@ Keras اور PyTorch دونوں میں کچھ عام آرکیٹیکچرز کے ایک طریقہ یہ ہے کہ ہم ایک بے ترتیب تصویر سے شروع کریں، اور پھر **گریڈینٹ ڈیسینٹ آپٹیمائزیشن** تکنیک کا استعمال کرتے ہوئے اس تصویر کو اس طرح ایڈجسٹ کریں کہ نیٹ ورک یہ سوچنا شروع کر دے کہ یہ بلی ہے۔ -![امیج آپٹیمائزیشن لوپ](../../../../../translated_images/ur/ideal-cat-loop.999fbb8ff306e044.png) +![امیج آپٹیمائزیشن لوپ](../../../../../translated_images/ur/ideal-cat-loop.999fbb8ff306e044.webp) تاہم، اگر ہم ایسا کریں، تو ہمیں کچھ ایسا ملے گا جو بے ترتیب شور کے بہت قریب ہوگا۔ اس کی وجہ یہ ہے کہ *نیٹ ورک کو یہ سوچنے کے لیے کہ ان پٹ تصویر بلی ہے، بہت سے طریقے ہیں*، جن میں کچھ بصری طور پر معنی خیز نہیں ہیں۔ اگرچہ ان تصاویر میں بلی کے لیے عام پیٹرنز کی بہتات ہوتی ہے، لیکن انہیں بصری طور پر ممتاز ہونے کے لیے کچھ بھی مجبور نہیں کرتا۔ نتیجہ بہتر بنانے کے لیے، ہم نقصان کے فنکشن میں ایک اور اصطلاح شامل کر سکتے ہیں، جسے **ویریئشن لاس** کہا جاتا ہے۔ یہ ایک میٹرک ہے جو دکھاتا ہے کہ تصویر کے پڑوسی پکسلز کتنے مماثل ہیں۔ ویریئشن لاس کو کم کرنے سے تصویر ہموار ہو جاتی ہے، اور شور ختم ہو جاتا ہے - اس طرح زیادہ بصری طور پر دلکش پیٹرنز ظاہر ہوتے ہیں۔ یہاں ایسے "مثالی" تصاویر کی مثالیں ہیں، جو بلی اور زیبرا کے طور پر اعلیٰ امکان کے ساتھ درجہ بندی کی گئی ہیں: -![مثالی بلی](../../../../../translated_images/ur/ideal-cat.203dd4597643d6b0.png) | ![مثالی زیبرا](../../../../../translated_images/ur/ideal-zebra.7f70e8b54ee15a7a.png) +![مثالی بلی](../../../../../translated_images/ur/ideal-cat.203dd4597643d6b0.webp) | ![مثالی زیبرا](../../../../../translated_images/ur/ideal-zebra.7f70e8b54ee15a7a.webp) -----|----- *مثالی بلی* | *مثالی زیبرا* اسی طرح کا طریقہ نیورل نیٹ ورک پر **ایڈورسریل حملے** کرنے کے لیے استعمال کیا جا سکتا ہے۔ فرض کریں کہ ہم نیورل نیٹ ورک کو دھوکہ دینا چاہتے ہیں اور کتے کو بلی کی طرح دکھانا چاہتے ہیں۔ اگر ہم کتے کی تصویر لیں، جسے نیٹ ورک کتے کے طور پر پہچانتا ہے، تو ہم اسے تھوڑا سا ایڈجسٹ کر سکتے ہیں گریڈینٹ ڈیسینٹ آپٹیمائزیشن کا استعمال کرتے ہوئے، جب تک کہ نیٹ ورک اسے بلی کے طور پر درجہ بندی کرنا شروع نہ کر دے: -![کتے کی تصویر](../../../../../translated_images/ur/original-dog.8f68a67d2fe0911f.png) | ![بلی کے طور پر درجہ بندی کی گئی کتے کی تصویر](../../../../../translated_images/ur/adversarial-dog.d9fc7773b0142b89.png) +![کتے کی تصویر](../../../../../translated_images/ur/original-dog.8f68a67d2fe0911f.webp) | ![بلی کے طور پر درجہ بندی کی گئی کتے کی تصویر](../../../../../translated_images/ur/adversarial-dog.d9fc7773b0142b89.webp) -----|----- *کتے کی اصل تصویر* | *بلی کے طور پر درجہ بندی کی گئی کتے کی تصویر* diff --git a/translations/ur/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/ur/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index 191df249..74889e8e 100644 --- a/translations/ur/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/ur/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "چونکہ ہم آٹو اینکوڈر کو اس طرح تربیت دے رہے ہیں کہ وہ اصل تصویر سے زیادہ سے زیادہ معلومات حاصل کرے تاکہ درست دوبارہ تشکیل ممکن ہو، نیٹ ورک ان پٹ تصاویر کے بہترین **ایمبیڈنگ** تلاش کرنے کی کوشش کرتا ہے تاکہ ان کا مطلب واضح ہو۔\n", "\n", - "![آٹو اینکوڈر ڈایاگرام](../../../../../translated_images/ur/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![آٹو اینکوڈر ڈایاگرام](../../../../../translated_images/ur/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "> تصویر [Keras بلاگ](https://blog.keras.io/building-autoencoders-in-keras.html) سے لی گئی ہے\n", "\n", diff --git a/translations/ur/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/ur/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index 8845345a..9d0e66d9 100644 --- a/translations/ur/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/ur/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -21,7 +21,7 @@ "\n", "چونکہ ہم آٹو اینکوڈر کو تربیت دے رہے ہیں تاکہ اصل تصویر سے زیادہ سے زیادہ معلومات حاصل کی جا سکیں تاکہ درست دوبارہ تعمیر ممکن ہو، نیٹ ورک ان پٹ تصاویر کی بہترین **ایمبیڈنگ** تلاش کرنے کی کوشش کرتا ہے تاکہ ان کا مطلب سمجھا جا سکے۔\n", "\n", - "![آٹو اینکوڈر ڈایاگرام](../../../../../translated_images/ur/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![آٹو اینکوڈر ڈایاگرام](../../../../../translated_images/ur/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "*تصویر [Keras بلاگ](https://blog.keras.io/building-autoencoders-in-keras.html) سے لی گئی ہے*\n", "\n", diff --git a/translations/ur/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/ur/lessons/4-ComputerVision/09-Autoencoders/README.md index 3c141e36..30e0ee41 100644 --- a/translations/ur/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/ur/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: چونکہ ہم آٹو اینکوڈر کو اصل تصویر سے زیادہ سے زیادہ معلومات حاصل کرنے کے لیے تربیت دے رہے ہیں تاکہ درست تعمیر نو ہو سکے، نیٹ ورک ان پٹ تصاویر کی بہترین **ایمبیڈنگ** تلاش کرنے کی کوشش کرتا ہے تاکہ معنی کو حاصل کیا جا سکے۔ -![آٹو اینکوڈر ڈایاگرام](../../../../../translated_images/ur/autoencoder_schema.5e6fc9ad98a5eb61.jpg) +![آٹو اینکوڈر ڈایاگرام](../../../../../translated_images/ur/autoencoder_schema.5e6fc9ad98a5eb61.webp) > تصویر [Keras بلاگ](https://blog.keras.io/building-autoencoders-in-keras.html) سے لی گئی ہے diff --git a/translations/ur/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/ur/lessons/4-ComputerVision/11-ObjectDetection/README.md index dc85fb8e..70a78eea 100644 --- a/translations/ur/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/ur/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: ## [لیکچر سے پہلے کا کوئز](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![آبجیکٹ ڈیٹیکشن](../../../../../translated_images/ur/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) +![آبجیکٹ ڈیٹیکشن](../../../../../translated_images/ur/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp) > تصویر [YOLO v2 ویب سائٹ](https://pjreddie.com/darknet/yolov2/) سے لی گئی ہے۔ @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 2. ہر حصے پر امیج کلاسیفیکیشن چلائیں۔ 3. وہ حصے جن میں کافی زیادہ ایکٹیویشن ہو، انہیں مطلوبہ آبجیکٹ کے حامل سمجھا جا سکتا ہے۔ -![سادہ آبجیکٹ ڈیٹیکشن](../../../../../translated_images/ur/naive-detection.e7f1ba220ccd08c6.png) +![سادہ آبجیکٹ ڈیٹیکشن](../../../../../translated_images/ur/naive-detection.e7f1ba220ccd08c6.webp) > *تصویر [ایکسسرسائز نوٹ بک](ObjectDetection-TF.ipynb) سے لی گئی ہے۔* @@ -42,7 +42,7 @@ CO_OP_TRANSLATOR_METADATA: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 کلاسز * [COCO](http://cocodataset.org/#home) - عام اشیاء کے سیاق و سباق میں۔ 80 کلاسز، باؤنڈنگ باکسز اور سیگمنٹیشن ماسکس -![COCO](../../../../../translated_images/ur/coco-examples.71bc60380fa6cceb.jpg) +![COCO](../../../../../translated_images/ur/coco-examples.71bc60380fa6cceb.webp) ## آبجیکٹ ڈیٹیکشن میٹرکس @@ -50,7 +50,7 @@ CO_OP_TRANSLATOR_METADATA: امیج کلاسیفیکیشن کے لیے یہ آسان ہے کہ ہم الگورتھم کی کارکردگی کو ماپ سکیں، لیکن آبجیکٹ ڈیٹیکشن کے لیے ہمیں کلاس کی درستگی کے ساتھ ساتھ باؤنڈنگ باکس کی پیش گوئی کی درستگی کو بھی ماپنا ہوتا ہے۔ اس کے لیے ہم **انٹرسیکشن اوور یونین** (IoU) استعمال کرتے ہیں، جو دو باکسز (یا دو کسی بھی علاقے) کے اوورلیپ کو ماپتا ہے۔ -![IoU](../../../../../translated_images/ur/iou_equation.9a4751d40fff4e11.png) +![IoU](../../../../../translated_images/ur/iou_equation.9a4751d40fff4e11.webp) > *تصویر 2 [اس بہترین بلاگ پوسٹ](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/) سے لی گئی ہے۔* @@ -98,11 +98,11 @@ $$ [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) [سیلیکٹیو سرچ](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) استعمال کرتا ہے تاکہ ROI ریجنز کی ہائیرارکل ساخت بنائی جا سکے، جنہیں پھر CNN فیچر ایکسٹریکٹرز اور SVM کلاسیفائرز کے ذریعے آبجیکٹ کلاس کا تعین کرنے کے لیے پاس کیا جاتا ہے، اور *باؤنڈنگ باکس* کے کوآرڈینیٹس کا تعین کرنے کے لیے لینیئر ریگریشن استعمال کی جاتی ہے۔ [آفیشل پیپر](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/ur/rcnn1.cae407020dfb1d1f.png) +![RCNN](../../../../../translated_images/ur/rcnn1.cae407020dfb1d1f.webp) > *تصویر van de Sande et al. ICCV’11 سے لی گئی ہے۔* -![RCNN-1](../../../../../translated_images/ur/rcnn2.2d9530bb83516484.png) +![RCNN-1](../../../../../translated_images/ur/rcnn2.2d9530bb83516484.webp) > *تصاویر [اس بلاگ](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e) سے لی گئی ہیں۔* @@ -110,7 +110,7 @@ $$ یہ طریقہ R-CNN سے ملتا جلتا ہے، لیکن ریجنز کنوولوشن لیئرز کے بعد ڈیفائن کیے جاتے ہیں۔ -![FRCNN](../../../../../translated_images/ur/f-rcnn.3cda6d9bb4188875.png) +![FRCNN](../../../../../translated_images/ur/f-rcnn.3cda6d9bb4188875.webp) > تصویر [آفیشل پیپر](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf)، [arXiv](https://arxiv.org/pdf/1504.08083.pdf)، 2015 سے لی گئی ہے۔ @@ -118,7 +118,7 @@ $$ اس طریقے کا بنیادی خیال یہ ہے کہ ریجنز کی پیش گوئی کے لیے نیورل نیٹ ورک استعمال کیا جائے - جسے *ریجن پروپوزل نیٹ ورک* کہا جاتا ہے۔ [پیپر](https://arxiv.org/pdf/1506.01497.pdf)، 2016 -![FasterRCNN](../../../../../translated_images/ur/faster-rcnn.8d46c099b87ef30a.png) +![FasterRCNN](../../../../../translated_images/ur/faster-rcnn.8d46c099b87ef30a.webp) > تصویر [آفیشل پیپر](https://arxiv.org/pdf/1506.01497.pdf) سے لی گئی ہے۔ @@ -130,7 +130,7 @@ $$ 1. فیچرز **پوزیشن سینسیٹو اسکور میپ** کے ذریعے پروسیس کیے جاتے ہیں۔ $C$ کلاسز کے ہر آبجیکٹ کو $k\times k$ ریجنز میں تقسیم کیا جاتا ہے، اور ہم آبجیکٹس کے حصے پیش گوئی کرنے کی تربیت کرتے ہیں۔ 1. $k\times k$ ریجنز کے ہر حصے کے لیے تمام نیٹ ورکس آبجیکٹ کلاسز کے لیے ووٹ دیتے ہیں، اور زیادہ سے زیادہ ووٹ کے ساتھ آبجیکٹ کلاس منتخب کی جاتی ہے۔ -![r-fcn image](../../../../../translated_images/ur/r-fcn.13eb88158b99a3da.png) +![r-fcn image](../../../../../translated_images/ur/r-fcn.13eb88158b99a3da.webp) > تصویر [آفیشل پیپر](https://arxiv.org/abs/1605.06409) سے لی گئی ہے۔ @@ -141,7 +141,7 @@ YOLO ایک ریئل ٹائم ون پاس الگورتھم ہے۔ بنیادی * تصویر کو $S\times S$ ریجنز میں تقسیم کیا جاتا ہے۔ * ہر ریجن کے لیے، **CNN** $n$ ممکنہ آبجیکٹس، *باؤنڈنگ باکس* کے کوآرڈینیٹس اور *کانفیڈنس*=*پروببلیٹی* * IoU کی پیش گوئی کرتا ہے۔ - ![YOLO](../../../../../translated_images/ur/yolo.a2648ec82ee8bb4e.png) + ![YOLO](../../../../../translated_images/ur/yolo.a2648ec82ee8bb4e.webp) > تصویر [آفیشل پیپر](https://arxiv.org/abs/1506.02640) سے لی گئی ہے۔ diff --git a/translations/ur/lessons/4-ComputerVision/README.md b/translations/ur/lessons/4-ComputerVision/README.md index a31b9d0b..642482c3 100644 --- a/translations/ur/lessons/4-ComputerVision/README.md +++ b/translations/ur/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # کمپیوٹر وژن -![کمپیوٹر وژن کے مواد کا خلاصہ ایک خاکے میں](../../../../translated_images/ur/ai-computervision.6506ebebac3fbf76.png) +![کمپیوٹر وژن کے مواد کا خلاصہ ایک خاکے میں](../../../../translated_images/ur/ai-computervision.6506ebebac3fbf76.webp) اس سیکشن میں ہم سیکھیں گے: diff --git a/translations/ur/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/ur/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index d1fc87b2..4b635760 100644 --- a/translations/ur/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/ur/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**الفاظ کے تھیلے** (BoW) ویکٹر کی نمائندگی سب سے زیادہ استعمال ہونے والی روایتی ویکٹر نمائندگی ہے۔ ہر لفظ کو ایک ویکٹر انڈیکس سے جوڑا جاتا ہے، اور ویکٹر عنصر کسی دیے گئے دستاویز میں کسی لفظ کے وقوعات کی تعداد کو ظاہر کرتا ہے۔\n", "\n", - "![تصویر جو دکھاتی ہے کہ الفاظ کے تھیلے کی ویکٹر نمائندگی میموری میں کیسے ظاہر کی جاتی ہے۔](../../../../../translated_images/ur/bag-of-words-example.606fc1738f1d7ba9.png)\n", + "![تصویر جو دکھاتی ہے کہ الفاظ کے تھیلے کی ویکٹر نمائندگی میموری میں کیسے ظاہر کی جاتی ہے۔](../../../../../translated_images/ur/bag-of-words-example.606fc1738f1d7ba9.webp)\n", "\n", "> **نوٹ**: آپ BoW کو متن میں انفرادی الفاظ کے لیے تمام ایک-ہاٹ-انکوڈڈ ویکٹرز کے مجموعے کے طور پر بھی سوچ سکتے ہیں۔\n", "\n", diff --git a/translations/ur/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/ur/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index 8e8c2783..7416ca66 100644 --- a/translations/ur/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/ur/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**الفاظ کے تھیلے** (BoW) ویکٹر کی نمائندگی سب سے آسان اور روایتی ویکٹر کی نمائندگی ہے۔ ہر لفظ کو ایک ویکٹر انڈیکس سے جوڑا جاتا ہے، اور ویکٹر عنصر کسی دیے گئے دستاویز میں ہر لفظ کے وقوعات کی تعداد کو ظاہر کرتا ہے۔\n", "\n", - "![تصویر جو دکھاتی ہے کہ الفاظ کے تھیلے کی ویکٹر نمائندگی میموری میں کیسے ظاہر ہوتی ہے۔](../../../../../translated_images/ur/bag-of-words-example.606fc1738f1d7ba9.png)\n", + "![تصویر جو دکھاتی ہے کہ الفاظ کے تھیلے کی ویکٹر نمائندگی میموری میں کیسے ظاہر ہوتی ہے۔](../../../../../translated_images/ur/bag-of-words-example.606fc1738f1d7ba9.webp)\n", "\n", "> **نوٹ**: آپ BoW کو متن میں انفرادی الفاظ کے لیے تمام ایک-ہاٹ-انکوڈڈ ویکٹرز کے مجموعے کے طور پر بھی سوچ سکتے ہیں۔\n", "\n", diff --git a/translations/ur/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/ur/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index fb2f32ae..7f422ac6 100644 --- a/translations/ur/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/ur/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "اپنے نیٹ ورک میں ایمبیڈنگ لیئر کو پہلی لیئر کے طور پر استعمال کرتے ہوئے، ہم بیگ-آف-ورڈز ماڈل سے **ایمبیڈنگ بیگ** ماڈل میں تبدیل ہو سکتے ہیں، جہاں ہم پہلے اپنے متن کے ہر لفظ کو متعلقہ ایمبیڈنگ میں تبدیل کرتے ہیں، اور پھر ان تمام ایمبیڈنگز پر کوئی مجموعی فنکشن جیسے `sum`, `average` یا `max` کا حساب لگاتے ہیں۔\n", "\n", - "![پانچ سلسلہ وار الفاظ کے لیے ایمبیڈنگ کلاسیفائر دکھانے والی تصویر۔](../../../../../translated_images/ur/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![پانچ سلسلہ وار الفاظ کے لیے ایمبیڈنگ کلاسیفائر دکھانے والی تصویر۔](../../../../../translated_images/ur/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "ہمارا کلاسیفائر نیورل نیٹ ورک ایمبیڈنگ لیئر سے شروع ہوگا، پھر ایگریگیشن لیئر، اور اس کے اوپر ایک لینیئر کلاسیفائر ہوگا:\n" ] @@ -176,7 +176,7 @@ "\n", "پچھلی آرکیٹیکچر میں، ہمیں تمام ترتیبوں کو ایک ہی لمبائی تک بڑھانا پڑتا تھا تاکہ انہیں ایک منی بیچ میں فٹ کیا جا سکے۔ یہ مختلف لمبائی کی ترتیبوں کو ظاہر کرنے کا سب سے مؤثر طریقہ نہیں ہے - ایک اور طریقہ یہ ہو سکتا ہے کہ **آفسیٹ** ویکٹر استعمال کیا جائے، جو ایک بڑے ویکٹر میں محفوظ تمام ترتیبوں کے آفسیٹس کو رکھے۔\n", "\n", - "![آفسیٹ ترتیب کی نمائندگی دکھانے والی تصویر](../../../../../translated_images/ur/offset-sequence-representation.eb73fcefb29b46ee.png)\n", + "![آفسیٹ ترتیب کی نمائندگی دکھانے والی تصویر](../../../../../translated_images/ur/offset-sequence-representation.eb73fcefb29b46ee.webp)\n", "\n", "> **نوٹ**: اوپر دی گئی تصویر میں، ہم حروف کی ترتیب دکھا رہے ہیں، لیکن ہمارے مثال میں ہم الفاظ کی ترتیب کے ساتھ کام کر رہے ہیں۔ تاہم، آفسیٹ ویکٹر کے ساتھ ترتیبوں کی نمائندگی کا عمومی اصول وہی رہتا ہے۔\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW تیز ہے، جبکہ اسکیپ-گرام سست ہے، لیکن کم استعمال ہونے والے الفاظ کی بہتر نمائندگی کرتا ہے۔\n", "\n", - "![CBoW اور اسکیپ-گرام الگورتھمز کو الفاظ کو ویکٹرز میں تبدیل کرنے کے لیے دکھانے والی تصویر۔](../../../../../translated_images/ur/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![CBoW اور اسکیپ-گرام الگورتھمز کو الفاظ کو ویکٹرز میں تبدیل کرنے کے لیے دکھانے والی تصویر۔](../../../../../translated_images/ur/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "گوگل نیوز ڈیٹاسیٹ پر پہلے سے ٹرین کیے گئے ورڈ2ویک ایمبیڈنگ کے ساتھ تجربہ کرنے کے لیے، ہم **gensim** لائبریری استعمال کر سکتے ہیں۔ نیچے ہم 'neural' کے سب سے زیادہ مشابہ الفاظ تلاش کرتے ہیں۔\n", "\n", diff --git a/translations/ur/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/ur/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index 9f653491..297e7a52 100644 --- a/translations/ur/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/ur/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "جب ہم اپنے نیٹ ورک میں پہلی لیئر کے طور پر ایمبیڈنگ لیئر کا استعمال کرتے ہیں، تو ہم بیگ-آف-ورڈز ماڈل سے **ایمبیڈنگ بیگ** ماڈل میں منتقل ہو سکتے ہیں، جہاں ہم پہلے اپنے متن کے ہر لفظ کو اس کے متعلقہ ایمبیڈنگ میں تبدیل کرتے ہیں، اور پھر ان تمام ایمبیڈنگز پر کوئی مجموعی فنکشن جیسے کہ `sum`، `average` یا `max` کا حساب لگاتے ہیں۔\n", "\n", - "![پانچ سیکوئنس الفاظ کے لیے ایمبیڈنگ کلاسیفائر کی تصویر۔](../../../../../translated_images/ur/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![پانچ سیکوئنس الفاظ کے لیے ایمبیڈنگ کلاسیفائر کی تصویر۔](../../../../../translated_images/ur/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "ہمارے کلاسیفائر نیورل نیٹ ورک میں درج ذیل لیئرز شامل ہیں:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW تیز ہے، جبکہ اسکیپ-گرام سست ہے، لیکن یہ کم استعمال ہونے والے الفاظ کی بہتر نمائندگی کرتا ہے۔\n", "\n", - "![تصویر جو CBoW اور اسکیپ-گرام الگورتھمز کو الفاظ کو ویکٹرز میں تبدیل کرنے کے لیے دکھا رہی ہے۔](../../../../../translated_images/ur/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![تصویر جو CBoW اور اسکیپ-گرام الگورتھمز کو الفاظ کو ویکٹرز میں تبدیل کرنے کے لیے دکھا رہی ہے۔](../../../../../translated_images/ur/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "گوگل نیوز ڈیٹاسیٹ پر پری ٹرینڈ Word2Vec ایمبیڈنگ کے ساتھ تجربہ کرنے کے لیے، ہم **gensim** لائبریری استعمال کر سکتے ہیں۔ نیچے ہم 'neural' کے سب سے مشابہ الفاظ تلاش کرتے ہیں۔\n", "\n", diff --git a/translations/ur/lessons/5-NLP/14-Embeddings/README.md b/translations/ur/lessons/5-NLP/14-Embeddings/README.md index fb87cc23..3bc4e7ba 100644 --- a/translations/ur/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/ur/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: ایمبیڈنگ لیئر کو ہمارے کلاسیفائر نیٹ ورک کی پہلی لیئر کے طور پر استعمال کرکے، ہم بیگ آف ورڈز ماڈل سے **ایمبیڈنگ بیگ** ماڈل میں تبدیل ہو سکتے ہیں، جہاں ہم پہلے اپنے متن کے ہر لفظ کو متعلقہ ایمبیڈنگ میں تبدیل کرتے ہیں، اور پھر ان تمام ایمبیڈنگز پر کوئی مجموعی فنکشن جیسے `sum`، `average` یا `max` کا حساب لگاتے ہیں۔ -![پانچ سیکوئنس الفاظ کے لیے ایک ایمبیڈنگ کلاسیفائر کی تصویر۔](../../../../../translated_images/ur/embedding-classifier-example.b77f021a7ee67eee.png) +![پانچ سیکوئنس الفاظ کے لیے ایک ایمبیڈنگ کلاسیفائر کی تصویر۔](../../../../../translated_images/ur/embedding-classifier-example.b77f021a7ee67eee.webp) > تصویر مصنف کی جانب سے @@ -40,7 +40,7 @@ CO_OP_TRANSLATOR_METADATA: CBoW تیز ہے، جبکہ اسکیپ-گرام سست ہے، لیکن کم استعمال ہونے والے الفاظ کی بہتر نمائندگی کرتا ہے۔ -![CBoW اور اسکیپ-گرام الگورتھمز کی تصویر جو الفاظ کو ویکٹرز میں تبدیل کرتے ہیں۔](../../../../../translated_images/ur/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![CBoW اور اسکیپ-گرام الگورتھمز کی تصویر جو الفاظ کو ویکٹرز میں تبدیل کرتے ہیں۔](../../../../../translated_images/ur/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > تصویر [اس مقالے](https://arxiv.org/pdf/1301.3781.pdf) سے diff --git a/translations/ur/lessons/5-NLP/15-LanguageModeling/README.md b/translations/ur/lessons/5-NLP/15-LanguageModeling/README.md index 299909a8..0a115365 100644 --- a/translations/ur/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/ur/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: * **Continuous Bag-of-Words** (CBoW)، جہاں ہم ٹوکن سیکوئنس $W_{-N}$, ..., $W_N$ میں درمیانی ٹوکن $W_0$ کی پیش گوئی کرتے ہیں۔ * **Skip-gram**، جہاں ہم درمیانی ٹوکن $W_0$ سے پڑوسی ٹوکنز {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} کی پیش گوئی کرتے ہیں۔ -![الفاظ کو ویکٹرز میں تبدیل کرنے کے لیے پیپر سے تصویر](../../../../../translated_images/ur/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![الفاظ کو ویکٹرز میں تبدیل کرنے کے لیے پیپر سے تصویر](../../../../../translated_images/ur/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > تصویر [اس پیپر](https://arxiv.org/pdf/1301.3781.pdf) سے لی گئی ہے۔ diff --git a/translations/ur/lessons/5-NLP/16-RNN/README.md b/translations/ur/lessons/5-NLP/16-RNN/README.md index 1732e87b..8a2b810f 100644 --- a/translations/ur/lessons/5-NLP/16-RNN/README.md +++ b/translations/ur/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: متن کی ترتیب کے معنی کو سمجھنے کے لیے، ہمیں ایک اور نیورل نیٹ ورک آرکیٹیکچر استعمال کرنے کی ضرورت ہے، جسے **ری کرنٹ نیورل نیٹ ورک** یا RNN کہا جاتا ہے۔ RNN میں، ہم اپنے جملے کو نیٹ ورک کے ذریعے ایک وقت میں ایک علامت کے ساتھ گزارتے ہیں، اور نیٹ ورک کچھ **حالت** پیدا کرتا ہے، جسے ہم اگلی علامت کے ساتھ دوبارہ نیٹ ورک میں پاس کرتے ہیں۔ -![RNN](../../../../../translated_images/ur/rnn.27f5c29c53d727b5.png) +![RNN](../../../../../translated_images/ur/rnn.27f5c29c53d727b5.webp) > تصویر مصنف کی جانب سے @@ -61,7 +61,7 @@ LSTM نیٹ ورک RNN کی طرح منظم ہے، لیکن یہاں دو حال ایک ری کرنٹ نیٹ ورک، چاہے وہ ایک سمت میں ہو یا بائی ڈائریکشنل، ترتیب کے اندر کچھ پیٹرنز کو پکڑتا ہے، اور انہیں حالت ویکٹر میں ذخیرہ کر سکتا ہے یا آؤٹ پٹ میں منتقل کر سکتا ہے۔ جیسا کہ کنوولوشنل نیٹ ورکس کے ساتھ، ہم پہلے والے پر ایک اور ری کرنٹ لیئر بنا سکتے ہیں تاکہ اعلیٰ سطح کے پیٹرنز کو پکڑ سکیں اور پہلی لیئر کے ذریعے نکالے گئے کم سطح کے پیٹرنز سے تعمیر کریں۔ یہ ہمیں **ملٹی لیئر RNN** کے تصور کی طرف لے جاتا ہے، جو دو یا زیادہ ری کرنٹ نیٹ ورکس پر مشتمل ہوتا ہے، جہاں پچھلی لیئر کا آؤٹ پٹ اگلی لیئر میں ان پٹ کے طور پر پاس کیا جاتا ہے۔ -![ملٹی لیئر LSTM RNN کی تصویر](../../../../../translated_images/ur/multi-layer-lstm.dd975e29bb2a59fe.jpg) +![ملٹی لیئر LSTM RNN کی تصویر](../../../../../translated_images/ur/multi-layer-lstm.dd975e29bb2a59fe.webp) *تصویر [اس شاندار پوسٹ](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) فرنینڈو لوپیز کی جانب سے* diff --git a/translations/ur/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/ur/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index 94f43476..67ceaed7 100644 --- a/translations/ur/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/ur/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "Recurrent نیٹ ورک، چاہے وہ ایک طرفہ ہو یا دو طرفہ، ترتیب کے اندر کچھ خاص patterns کو پکڑتا ہے اور انہیں state ویکٹر میں محفوظ کر سکتا ہے یا آؤٹ پٹ میں منتقل کر سکتا ہے۔ جیسے convolutional نیٹ ورکس کے ساتھ، ہم پہلے layer کے اوپر ایک اور recurrent layer بنا سکتے ہیں تاکہ اعلیٰ سطح کے patterns کو پکڑا جا سکے، جو پہلے layer کے ذریعے نکالے گئے نچلی سطح کے patterns سے بنے ہوں۔ یہ ہمیں **کثیر پرت RNN** کے تصور تک لے جاتا ہے، جو دو یا زیادہ recurrent نیٹ ورکس پر مشتمل ہوتا ہے، جہاں پچھلے layer کا آؤٹ پٹ اگلے layer کو ان پٹ کے طور پر دیا جاتا ہے۔\n", "\n", - "![کثیر پرت long-short-term-memory- RNN کی تصویر](../../../../../translated_images/ur/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![کثیر پرت long-short-term-memory- RNN کی تصویر](../../../../../translated_images/ur/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*یہ تصویر [اس شاندار پوسٹ](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) سے لی گئی ہے، جو فرنینڈو لوپیز نے لکھی ہے۔*\n", "\n", diff --git a/translations/ur/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/ur/lessons/5-NLP/16-RNN/RNNTF.ipynb index f60f4ece..0b0f7d53 100644 --- a/translations/ur/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/ur/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "متن کے سلسلے کے معنی کو سمجھنے کے لیے، ہم ایک اعصابی نیٹ ورک آرکیٹیکچر استعمال کریں گے جسے **ریکرنٹ نیورل نیٹ ورک** یا RNN کہا جاتا ہے۔ RNN استعمال کرتے وقت، ہم اپنے جملے کو نیٹ ورک کے ذریعے ایک وقت میں ایک ٹوکن پاس کرتے ہیں، اور نیٹ ورک کچھ **حالت** پیدا کرتا ہے، جسے ہم اگلے ٹوکن کے ساتھ دوبارہ نیٹ ورک میں پاس کرتے ہیں۔\n", "\n", - "![ریکرنٹ نیورل نیٹ ورک کی تخلیق کی ایک مثال دکھانے والی تصویر۔](../../../../../translated_images/ur/rnn.27f5c29c53d727b5.png)\n", + "![ریکرنٹ نیورل نیٹ ورک کی تخلیق کی ایک مثال دکھانے والی تصویر۔](../../../../../translated_images/ur/rnn.27f5c29c53d727b5.webp)\n", "\n", "دیے گئے ان پٹ ٹوکنز کے سلسلے $X_0,\\dots,X_n$ کے لیے، RNN اعصابی نیٹ ورک کے بلاکس کا ایک سلسلہ تخلیق کرتا ہے، اور اس سلسلے کو بیک پروپیگیشن کے ذریعے اختتام سے اختتام تک تربیت دیتا ہے۔ ہر نیٹ ورک بلاک ایک جوڑی $(X_i,S_i)$ کو ان پٹ کے طور پر لیتا ہے، اور نتیجے میں $S_{i+1}$ پیدا کرتا ہے۔ آخری حالت $S_n$ یا آؤٹ پٹ $Y_n$ کو ایک لکیری کلاسیفائر میں بھیجا جاتا ہے تاکہ نتیجہ پیدا کیا جا سکے۔ تمام نیٹ ورک بلاکس ایک جیسے وزن کا اشتراک کرتے ہیں، اور ایک بیک پروپیگیشن پاس کے ذریعے اختتام سے اختتام تک تربیت دی جاتی ہے۔\n", "\n", @@ -371,7 +371,7 @@ "\n", "ری کرنٹ نیٹ ورکس، چاہے یک طرفہ ہوں یا دو طرفہ، ترتیب کے اندر موجود پیٹرنز کو پکڑتے ہیں، اور انہیں اسٹیٹ ویکٹرز میں محفوظ کرتے ہیں یا آؤٹ پٹ کے طور پر واپس کرتے ہیں۔ جیسے کنوولوشنل نیٹ ورکس میں، ہم پہلے لیئر کے بعد ایک اور ری کرنٹ لیئر بنا سکتے ہیں تاکہ اعلیٰ سطح کے پیٹرنز کو پکڑا جا سکے، جو پہلے لیئر کے ذریعے نکالے گئے نچلے سطح کے پیٹرنز سے بنے ہوں۔ یہ ہمیں **کثیر پرت RNN** کے تصور تک لے جاتا ہے، جو دو یا زیادہ ری کرنٹ نیٹ ورکس پر مشتمل ہوتا ہے، جہاں پچھلی لیئر کا آؤٹ پٹ اگلی لیئر کو ان پٹ کے طور پر دیا جاتا ہے۔\n", "\n", - "![تصویر جو ایک کثیر پرت لمبی-مختصر-مدتی-میموری RNN دکھا رہی ہے](../../../../../translated_images/ur/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![تصویر جو ایک کثیر پرت لمبی-مختصر-مدتی-میموری RNN دکھا رہی ہے](../../../../../translated_images/ur/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*تصویر [اس شاندار پوسٹ](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) سے لی گئی ہے، جو فرنینڈو لوپیز نے لکھی ہے۔*\n", "\n", diff --git a/translations/ur/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/ur/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index ffad6508..d1e8f034 100644 --- a/translations/ur/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/ur/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "ہم RNN کو ٹیکسٹ جنریٹ کرنے کے لیے اس طرح تربیت دیں گے۔ ہر قدم پر، ہم `nchars` لمبائی کے کرداروں کی ایک ترتیب لیں گے، اور نیٹ ورک سے کہیں گے کہ ہر ان پٹ کردار کے لیے اگلا آؤٹ پٹ کردار پیدا کرے:\n", "\n", - "![تصویر میں RNN کے ذریعے 'HELLO' لفظ کی جنریشن کا ایک مثال دکھایا گیا ہے۔](../../../../../translated_images/ur/rnn-generate.56c54afb52f9781d.png)\n", + "![تصویر میں RNN کے ذریعے 'HELLO' لفظ کی جنریشن کا ایک مثال دکھایا گیا ہے۔](../../../../../translated_images/ur/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "حقیقی منظرنامے کے مطابق، ہم کچھ خاص کرداروں کو بھی شامل کرنا چاہ سکتے ہیں، جیسے *end-of-sequence* ``۔ ہمارے معاملے میں، ہم صرف نیٹ ورک کو لامتناہی ٹیکسٹ جنریشن کے لیے تربیت دینا چاہتے ہیں، اس لیے ہم ہر ترتیب کا سائز `nchars` ٹوکنز کے برابر مقرر کریں گے۔ نتیجتاً، ہر تربیتی مثال `nchars` ان پٹس اور `nchars` آؤٹ پٹس پر مشتمل ہوگی (جو ان پٹ ترتیب کو ایک علامت بائیں طرف منتقل کرنے سے حاصل ہوں گے)۔ منی بیچ کئی ایسی ترتیبوں پر مشتمل ہوگا۔\n", "\n", diff --git a/translations/ur/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/ur/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index 32ee54c0..82c5dd59 100644 --- a/translations/ur/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/ur/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -113,7 +113,7 @@ "\n", "ہم RNN کو خبروں کے عنوانات بنانے کے لیے اس طرح تربیت دیں گے۔ ہر مرحلے پر، ہم ایک عنوان لیں گے، جسے RNN میں فیڈ کیا جائے گا، اور ہر ان پٹ کردار کے لیے ہم نیٹ ورک سے اگلا آؤٹ پٹ کردار پیدا کرنے کو کہیں گے:\n", "\n", - "![تصویر جو 'HELLO' لفظ کے RNN جنریشن کی مثال دکھا رہی ہے۔](../../../../../translated_images/ur/rnn-generate.56c54afb52f9781d.png)\n", + "![تصویر جو 'HELLO' لفظ کے RNN جنریشن کی مثال دکھا رہی ہے۔](../../../../../translated_images/ur/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "ہمارے سلسلے کے آخری کردار کے لیے، ہم نیٹ ورک سے `` ٹوکن پیدا کرنے کو کہیں گے۔\n", "\n", diff --git a/translations/ur/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/ur/lessons/5-NLP/17-GenerativeNetworks/README.md index 2244b20e..c43e4df0 100644 --- a/translations/ur/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/ur/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: یہ مختلف نیورل آرکیٹیکچرز کی اجازت دیتا ہے، جیسا کہ نیچے دی گئی تصویر میں دکھایا گیا ہے: -![تصویر جو عام ریکرنٹ نیورل نیٹ ورک کے پیٹرنز دکھا رہی ہے۔](../../../../../translated_images/ur/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![تصویر جو عام ریکرنٹ نیورل نیٹ ورک کے پیٹرنز دکھا رہی ہے۔](../../../../../translated_images/ur/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > تصویر بلاگ پوسٹ [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) سے لی گئی ہے، از [Andrej Karpaty](http://karpathy.github.io/) @@ -32,7 +32,7 @@ CO_OP_TRANSLATOR_METADATA: ہم اس RNN کو متن قدم بہ قدم تخلیق کرنے کے لیے تربیت دیں گے۔ ہر قدم پر، ہم `nchars` کی لمبائی کے کریکٹرز کا ایک سیکوئنس لیں گے، اور نیٹ ورک سے ہر ان پٹ کریکٹر کے لیے اگلا آؤٹ پٹ کریکٹر پیدا کرنے کو کہیں گے: -![تصویر جو 'HELLO' لفظ کے RNN تخلیق کی مثال دکھا رہی ہے۔](../../../../../translated_images/ur/rnn-generate.56c54afb52f9781d.png) +![تصویر جو 'HELLO' لفظ کے RNN تخلیق کی مثال دکھا رہی ہے۔](../../../../../translated_images/ur/rnn-generate.56c54afb52f9781d.webp) جب متن تخلیق کرتے ہیں (انفرنس کے دوران)، ہم کچھ **پرومپٹ** کے ساتھ شروع کرتے ہیں، جسے RNN سیلز کے ذریعے اس کی درمیانی حالت پیدا کرنے کے لیے پاس کیا جاتا ہے، اور پھر اس حالت سے تخلیق شروع ہوتی ہے۔ ہم ایک وقت میں ایک کریکٹر تخلیق کرتے ہیں، اور حالت اور تخلیق شدہ کریکٹر کو اگلے کریکٹر تخلیق کرنے کے لیے دوسرے RNN سیل کو پاس کرتے ہیں، جب تک کہ ہم کافی کریکٹرز تخلیق نہ کر لیں۔ diff --git a/translations/ur/lessons/5-NLP/18-Transformers/README.md b/translations/ur/lessons/5-NLP/18-Transformers/README.md index 578438f4..a6b41a14 100644 --- a/translations/ur/lessons/5-NLP/18-Transformers/README.md +++ b/translations/ur/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ RNNs کے ساتھ، sequence-to-sequence دو recurrent نیٹ ورکس کے ذ **توجہ کے طریقہ کار** RNN کے ہر آؤٹ پٹ پیش گوئی پر ہر ان پٹ ویکٹر کے سیاق و سباق کے اثر کو وزن دینے کا ایک ذریعہ فراہم کرتے ہیں۔ اس کو نافذ کرنے کا طریقہ یہ ہے کہ ان پٹ RNN کی درمیانی حالتوں اور آؤٹ پٹ RNN کے درمیان شارٹ کٹس بنائے جائیں۔ اس طرح، جب آؤٹ پٹ علامت yt پیدا کی جا رہی ہو، ہم تمام ان پٹ hidden states hi کو مختلف وزن کے coefficients αt,i کے ساتھ مدنظر رکھیں گے۔ -![تصویر جو encoder/decoder ماڈل کو additive attention layer کے ساتھ دکھا رہی ہے](../../../../../translated_images/ur/encoder-decoder-attention.7a726296894fb567.png) +![تصویر جو encoder/decoder ماڈل کو additive attention layer کے ساتھ دکھا رہی ہے](../../../../../translated_images/ur/encoder-decoder-attention.7a726296894fb567.webp) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) میں additive attention mechanism کے ساتھ encoder-decoder ماڈل، [اس بلاگ پوسٹ](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) سے لیا گیا۔ توجہ میٹرکس {αi,j} اس حد کو ظاہر کرے گی کہ ان پٹ سیکوئنس میں موجود مخصوص الفاظ آؤٹ پٹ سیکوئنس میں دیے گئے لفظ کی تخلیق میں کتنا کردار ادا کرتے ہیں۔ نیچے ایک مثال دی گئی ہے: -![تصویر جو RNNsearch-50 کے ذریعے پائی گئی ایک نمونہ alignment کو دکھا رہی ہے، Bahdanau - arviz.org سے لی گئی](../../../../../translated_images/ur/bahdanau-fig3.09ba2d37f202a6af.png) +![تصویر جو RNNsearch-50 کے ذریعے پائی گئی ایک نمونہ alignment کو دکھا رہی ہے، Bahdanau - arviz.org سے لی گئی](../../../../../translated_images/ur/bahdanau-fig3.09ba2d37f202a6af.webp) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) سے تصویر (Fig.3) @@ -66,7 +66,7 @@ positional encoding کا خیال درج ذیل ہے: اگلا، ہمیں اپنی سیکوئنس کے اندر کچھ patterns کو capture کرنے کی ضرورت ہے۔ ایسا کرنے کے لیے، ٹرانسفارمرز **self-attention** mechanism استعمال کرتے ہیں، جو بنیادی طور پر وہی توجہ ہے جو ان پٹ اور آؤٹ پٹ کے طور پر ایک ہی سیکوئنس پر لاگو ہوتی ہے۔ self-attention کو لاگو کرنے سے ہمیں جملے کے اندر **context** کو مدنظر رکھنے کی اجازت ملتی ہے، اور یہ دیکھنے کی اجازت ملتی ہے کہ کون سے الفاظ آپس میں جڑے ہوئے ہیں۔ مثال کے طور پر، یہ ہمیں یہ دیکھنے کی اجازت دیتا ہے کہ کون سے الفاظ coreferences جیسے *it* کے ذریعے حوالہ دیے گئے ہیں، اور سیاق و سباق کو بھی مدنظر رکھتا ہے: -![](../../../../../translated_images/ur/CoreferenceResolution.861924d6d384a7d6.png) +![](../../../../../translated_images/ur/CoreferenceResolution.861924d6d384a7d6.webp) > [گوگل بلاگ](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) سے تصویر @@ -91,7 +91,7 @@ Encoder-decoder attention RNNs میں استعمال ہونے والے توجہ **BERT** (Bidirectional Encoder Representations from Transformers) ایک بہت بڑا multi-layer transformer نیٹ ورک ہے جس میں *BERT-base* کے لیے 12 layers ہیں، اور *BERT-large* کے لیے 24 layers ہیں۔ ماڈل کو پہلے ایک بڑے text data corpus (WikiPedia + books) پر unsupervised training (جملے میں masked words کی پیش گوئی) کا استعمال کرتے ہوئے pre-train کیا جاتا ہے۔ pre-training کے دوران ماڈل زبان کی سمجھ کے اہم سطحوں کو جذب کرتا ہے، جنہیں پھر دیگر datasets کے ساتھ fine tuning کے ذریعے استعمال کیا جا سکتا ہے۔ اس عمل کو **transfer learning** کہا جاتا ہے۔ -![تصویر http://jalammar.github.io/illustrated-bert/ سے](../../../../../translated_images/ur/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) +![تصویر http://jalammar.github.io/illustrated-bert/ سے](../../../../../translated_images/ur/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > تصویر [ماخذ](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/ur/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/ur/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index a0761aca..0ee62060 100644 --- a/translations/ur/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/ur/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**توجہ کے میکانزم** ہر ان پٹ ویکٹر کے سیاق و سباق کے اثر کو ہر آؤٹ پٹ پیش گوئی پر وزن دینے کا ایک ذریعہ فراہم کرتے ہیں۔ اس کو نافذ کرنے کا طریقہ یہ ہے کہ ان پٹ RNN کی درمیانی حالتوں اور آؤٹ پٹ RNN کے درمیان شارٹ کٹس بنائے جائیں۔ اس طرح، جب آؤٹ پٹ علامت $y_t$ پیدا کی جا رہی ہو، تو ہم تمام ان پٹ چھپی ہوئی حالتوں $h_i$ کو مختلف وزن کے گتانک $\\alpha_{t,i}$ کے ساتھ مدنظر رکھیں گے۔\n", "\n", - "![ایک انکوڈر/ڈیکوڈر ماڈل کی تصویر جس میں ایک اضافی توجہ کی تہہ شامل ہے](../../../../../translated_images/ur/encoder-decoder-attention.7a726296894fb567.png)\n", + "![ایک انکوڈر/ڈیکوڈر ماڈل کی تصویر جس میں ایک اضافی توجہ کی تہہ شامل ہے](../../../../../translated_images/ur/encoder-decoder-attention.7a726296894fb567.webp)\n", "*انکوڈر-ڈیکوڈر ماڈل اضافی توجہ کے میکانزم کے ساتھ [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) میں، [اس بلاگ پوسٹ](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) سے لیا گیا*\n", "\n", "توجہ میٹرکس $\\{\\alpha_{i,j}\\}$ اس حد کو ظاہر کرے گی کہ کس حد تک مخصوص ان پٹ الفاظ آؤٹ پٹ ترتیب میں دیے گئے لفظ کی تخلیق میں کردار ادا کرتے ہیں۔ نیچے ایک ایسی میٹرکس کی مثال دی گئی ہے:\n", "\n", - "![RNNsearch-50 کے ذریعے پائی گئی ایک نمونہ ترتیب کی تصویر، Bahdanau - arviz.org سے لی گئی](../../../../../translated_images/ur/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![RNNsearch-50 کے ذریعے پائی گئی ایک نمونہ ترتیب کی تصویر، Bahdanau - arviz.org سے لی گئی](../../../../../translated_images/ur/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (شکل 3) سے لی گئی تصویر*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) ایک بہت بڑا ملٹی لیئر ٹرانسفارمر نیٹ ورک ہے جس میں *BERT-base* کے لیے 12 تہیں، اور *BERT-large* کے لیے 24 تہیں ہیں۔ ماڈل کو پہلے بڑے متن کے ڈیٹا کارپس (ویکیپیڈیا + کتابیں) پر غیر نگرانی شدہ تربیت (ایک جملے میں ماسک کیے گئے الفاظ کی پیش گوئی) کا استعمال کرتے ہوئے پری ٹرین کیا جاتا ہے۔ پری ٹریننگ کے دوران ماڈل زبان کی سمجھ کا ایک اہم سطح جذب کرتا ہے، جسے پھر دیگر ڈیٹاسیٹس کے ساتھ فائن ٹیوننگ کے ذریعے استعمال کیا جا سکتا ہے۔ اس عمل کو **ٹرانسفر لرننگ** کہا جاتا ہے۔\n", "\n", - "![تصویر http://jalammar.github.io/illustrated-bert/ سے](../../../../../translated_images/ur/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![تصویر http://jalammar.github.io/illustrated-bert/ سے](../../../../../translated_images/ur/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "ٹرانسفارمر آرکیٹیکچرز کی بہت سی مختلف حالتیں ہیں، جن میں BERT، DistilBERT، BigBird، OpenGPT3 اور مزید شامل ہیں، جنہیں فائن ٹیون کیا جا سکتا ہے۔ [HuggingFace پیکیج](https://github.com/huggingface/) ان آرکیٹیکچرز میں سے بہت سے کو PyTorch کے ساتھ تربیت دینے کے لیے ایک ریپوزٹری فراہم کرتا ہے۔\n", "\n", diff --git a/translations/ur/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/ur/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index 3d7caaa3..61b9a250 100644 --- a/translations/ur/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/ur/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**توجہ کے میکانزم** ہر ان پٹ ویکٹر کے سیاق و سباق کے اثر کو RNN کے ہر آؤٹ پٹ پیش گوئی پر وزن دینے کا ایک ذریعہ فراہم کرتے ہیں۔ اس کو نافذ کرنے کا طریقہ یہ ہے کہ ان پٹ RNN کی درمیانی حالتوں اور آؤٹ پٹ RNN کے درمیان شارٹ کٹس بنائے جائیں۔ اس طرح، جب آؤٹ پٹ علامت $y_t$ پیدا کی جا رہی ہو، تو ہم تمام ان پٹ چھپی ہوئی حالتوں $h_i$ کو مختلف وزن کے گتانک $\\alpha_{t,i}$ کے ساتھ مدنظر رکھیں گے۔\n", "\n", - "![ایک انکوڈر/ڈیکوڈر ماڈل کی تصویر جس میں ایک اضافی توجہ کی تہہ شامل ہے](../../../../../translated_images/ur/encoder-decoder-attention.7a726296894fb567.png)\n", + "![ایک انکوڈر/ڈیکوڈر ماڈل کی تصویر جس میں ایک اضافی توجہ کی تہہ شامل ہے](../../../../../translated_images/ur/encoder-decoder-attention.7a726296894fb567.webp)\n", "*انکوڈر-ڈیکوڈر ماڈل اضافی توجہ کے میکانزم کے ساتھ [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) سے لیا گیا، [اس بلاگ پوسٹ](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) سے حوالہ شدہ*\n", "\n", "توجہ میٹرکس $\\{\\alpha_{i,j}\\}$ اس حد کو ظاہر کرے گی کہ ان پٹ ترتیب کے کون سے الفاظ آؤٹ پٹ ترتیب میں دیے گئے لفظ کی تخلیق میں کردار ادا کرتے ہیں۔ نیچے ایک ایسی میٹرکس کی مثال دی گئی ہے:\n", "\n", - "![ایک نمونہ ترتیب کی تصویر جو RNNsearch-50 کے ذریعے پائی گئی، Bahdanau - arviz.org سے لی گئی](../../../../../translated_images/ur/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![ایک نمونہ ترتیب کی تصویر جو RNNsearch-50 کے ذریعے پائی گئی، Bahdanau - arviz.org سے لی گئی](../../../../../translated_images/ur/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (شکل 3) سے لی گئی تصویر*\n", "\n", @@ -225,7 +225,7 @@ "\n", "**بی ای آر ٹی** (Bidirectional Encoder Representations from Transformers) ایک بہت بڑا ملٹی لیئر ٹرانسفارمر نیٹ ورک ہے جس میں *BERT-base* کے لیے 12 لیئرز اور *BERT-large* کے لیے 24 لیئرز ہیں۔ یہ ماڈل پہلے بڑی مقدار میں ٹیکسٹ ڈیٹا (ویکیپیڈیا + کتابیں) پر غیر نگرانی شدہ تربیت کے ذریعے (جملے میں ماسک کیے گئے الفاظ کی پیش گوئی کرتے ہوئے) تربیت یافتہ ہوتا ہے۔ تربیت کے دوران ماڈل زبان کو سمجھنے کی ایک اہم سطح حاصل کرتا ہے، جسے پھر دیگر ڈیٹاسیٹس کے ساتھ فائن ٹیوننگ کے ذریعے استعمال کیا جا سکتا ہے۔ اس عمل کو **ٹرانسفر لرننگ** کہا جاتا ہے۔\n", "\n", - "![تصویر http://jalammar.github.io/illustrated-bert/ سے](../../../../../translated_images/ur/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![تصویر http://jalammar.github.io/illustrated-bert/ سے](../../../../../translated_images/ur/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "ٹرانسفارمر آرکیٹیکچرز کی کئی اقسام ہیں، جن میں بی ای آر ٹی، ڈسٹل بی ای آر ٹی، بگ برڈ، اوپن جی پی ٹی 3 اور مزید شامل ہیں، جنہیں فائن ٹیون کیا جا سکتا ہے۔\n", "\n", diff --git a/translations/ur/lessons/5-NLP/19-NER/README.md b/translations/ur/lessons/5-NLP/19-NER/README.md index b77ef2ce..b91afdc2 100644 --- a/translations/ur/lessons/5-NLP/19-NER/README.md +++ b/translations/ur/lessons/5-NLP/19-NER/README.md @@ -58,7 +58,7 @@ infant | O چونکہ ہمیں ٹوکنز اور کلاسز کے درمیان ایک سے ایک مطابقت پیدا کرنی ہوتی ہے، ہم اس تصویر سے ایک دائیں طرف **کئی سے کئی** نیورل نیٹ ورک ماڈل بنا سکتے ہیں: -![تصویر جو عام recurrent neural network patterns کو دکھاتی ہے۔](../../../../../translated_images/ur/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![تصویر جو عام recurrent neural network patterns کو دکھاتی ہے۔](../../../../../translated_images/ur/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > *تصویر [اس بلاگ پوسٹ](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) سے لی گئی ہے، جو [Andrej Karpathy](http://karpathy.github.io/) کی جانب سے ہے۔ NER ٹوکن درجہ بندی ماڈلز اس تصویر میں دائیں طرف کے نیٹ ورک آرکیٹیکچر سے مطابقت رکھتے ہیں۔* diff --git a/translations/ur/lessons/5-NLP/README.md b/translations/ur/lessons/5-NLP/README.md index fe178056..e4296375 100644 --- a/translations/ur/lessons/5-NLP/README.md +++ b/translations/ur/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # قدرتی زبان کی پروسیسنگ -![NLP کے کاموں کا خلاصہ ایک خاکے میں](../../../../translated_images/ur/ai-nlp.b22dcb8ca4707cea.png) +![NLP کے کاموں کا خلاصہ ایک خاکے میں](../../../../translated_images/ur/ai-nlp.b22dcb8ca4707cea.webp) اس حصے میں، ہم نیورل نیٹ ورکس کا استعمال کرتے ہوئے **قدرتی زبان کی پروسیسنگ (NLP)** سے متعلق کاموں کو حل کرنے پر توجہ مرکوز کریں گے۔ NLP کے کئی مسائل ہیں جنہیں ہم چاہتے ہیں کہ کمپیوٹر حل کر سکیں: diff --git a/translations/ur/lessons/6-Other/23-MultiagentSystems/README.md b/translations/ur/lessons/6-Other/23-MultiagentSystems/README.md index bf24347c..725bdd94 100644 --- a/translations/ur/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/ur/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ ask turtles [ ماڈل کھولنے کے بعد، آپ کو نیٹ لوگو کے مرکزی اسکرین پر لے جایا جاتا ہے۔ یہاں ایک نمونہ ماڈل ہے جو بھیڑیوں اور بھیڑوں کی آبادی کو محدود وسائل (گھاس) کے ساتھ بیان کرتا ہے۔ -![NetLogo Main Screen](../../../../../translated_images/ur/NetLogo-Main.32653711ec1a01b3.png) +![NetLogo Main Screen](../../../../../translated_images/ur/NetLogo-Main.32653711ec1a01b3.webp) > دمتری سوشنیکوف کے ذریعہ اسکرین شاٹ diff --git a/translations/ur/lessons/README.md b/translations/ur/lessons/README.md index cc20d6b9..b5c80571 100644 --- a/translations/ur/lessons/README.md +++ b/translations/ur/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # جائزہ -![ڈوڈل میں جائزہ](../../../translated_images/ur/ai-overview.0857791951d19500.png) +![ڈوڈل میں جائزہ](../../../translated_images/ur/ai-overview.0857791951d19500.webp) > اسکیچ نوٹ از [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/ur/lessons/X-Extras/X1-MultiModal/README.md b/translations/ur/lessons/X-Extras/X1-MultiModal/README.md index b347c90a..281fd060 100644 --- a/translations/ur/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/ur/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: CLIP کا بنیادی خیال یہ ہے کہ ٹیکسٹ پرامپٹس کو کسی تصویر کے ساتھ موازنہ کیا جا سکے اور یہ معلوم کیا جا سکے کہ تصویر پرامپٹ سے کتنی مطابقت رکھتی ہے۔ -![CLIP آرکیٹیکچر](../../../../../translated_images/ur/clip-arch.b3dbf20b4e8ed8be.png) +![CLIP آرکیٹیکچر](../../../../../translated_images/ur/clip-arch.b3dbf20b4e8ed8be.webp) > *تصویر [اس بلاگ پوسٹ](https://openai.com/blog/clip/) سے لی گئی ہے* @@ -31,7 +31,7 @@ CLIP ماڈل/لائبریری [OpenAI GitHub](https://github.com/openai/CLIP) فرض کریں ہمیں تصاویر کو بلیوں، کتوں اور انسانوں کے درمیان کلاسیفائی کرنا ہے۔ اس صورت میں، ہم ماڈل کو ایک تصویر اور ٹیکسٹ پرامپٹس کی ایک سیریز دیتے ہیں: "*بلی کی تصویر*", "*کتے کی تصویر*", "*انسان کی تصویر*۔" نتیجے میں ملنے والے 3 پروبیبلیٹیز کے ویکٹر میں ہمیں صرف سب سے زیادہ ویلیو والے انڈیکس کو منتخب کرنا ہوگا۔ -![امیج کلاسیفکیشن کے لیے CLIP](../../../../../translated_images/ur/clip-class.3af42ef0b2b19369.png) +![امیج کلاسیفکیشن کے لیے CLIP](../../../../../translated_images/ur/clip-class.3af42ef0b2b19369.webp) > *تصویر [اس بلاگ پوسٹ](https://openai.com/blog/clip/) سے لی گئی ہے* @@ -55,13 +55,13 @@ VQGAN کے بارے میں مزید جاننے کے لیے [Taming Transformers] VQGAN اور روایتی GAN کے درمیان ایک اہم فرق یہ ہے کہ روایتی GAN کسی بھی ان پٹ ویکٹر سے ایک معقول تصویر بنا سکتا ہے، جبکہ VQGAN ممکنہ طور پر ایک غیر مربوط تصویر بنا سکتا ہے۔ لہذا، ہمیں امیج کریشن کے عمل کو مزید گائیڈ کرنے کی ضرورت ہوتی ہے، اور یہ CLIP کے ذریعے کیا جا سکتا ہے۔ -![VQGAN+CLIP آرکیٹیکچر](../../../../../translated_images/ur/vqgan.5027fe05051dfa31.png) +![VQGAN+CLIP آرکیٹیکچر](../../../../../translated_images/ur/vqgan.5027fe05051dfa31.webp) کسی ٹیکسٹ پرامپٹ سے مطابقت رکھنے والی تصویر بنانے کے لیے، ہم کسی رینڈم انکوڈنگ ویکٹر سے شروع کرتے ہیں، جو VQGAN کے ذریعے ایک تصویر تیار کرتا ہے۔ پھر CLIP ایک لاس فنکشن تیار کرتا ہے جو یہ ظاہر کرتا ہے کہ تصویر ٹیکسٹ پرامپٹ سے کتنی مطابقت رکھتی ہے۔ اس کے بعد مقصد یہ ہوتا ہے کہ اس لاس کو کم سے کم کیا جائے، بیک پروپیگیشن کے ذریعے ان پٹ ویکٹر کے پیرامیٹرز کو ایڈجسٹ کرتے ہوئے۔ VQGAN+CLIP کو نافذ کرنے والی ایک بہترین لائبریری [Pixray](http://github.com/pixray/pixray) ہے۔ -![Pixray کے ذریعے تیار کردہ تصویر](../../../../../translated_images/ur/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Pixray کے ذریعے تیار کردہ تصویر](../../../../../translated_images/ur/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Pixray کے ذریعے تیار کردہ تصویر](../../../../../translated_images/ur/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) +![Pixray کے ذریعے تیار کردہ تصویر](../../../../../translated_images/ur/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp) | ![Pixray کے ذریعے تیار کردہ تصویر](../../../../../translated_images/ur/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp) | ![Pixray کے ذریعے تیار کردہ تصویر](../../../../../translated_images/ur/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp) ----|----|---- پرامپٹ سے تیار کردہ تصویر *ایک نوجوان مرد ادب کے استاد کی کتاب کے ساتھ واٹر کلر پورٹریٹ* | پرامپٹ سے تیار کردہ تصویر *ایک نوجوان خاتون کمپیوٹر سائنس کی استاد کا آئل پورٹریٹ کمپیوٹر کے ساتھ* | پرامپٹ سے تیار کردہ تصویر *ایک بوڑھے مرد ریاضی کے استاد کا آئل پورٹریٹ بلیک بورڈ کے سامنے* diff --git a/translations/vi/README.md b/translations/vi/README.md index fbb9b087..36a9cee7 100644 --- a/translations/vi/README.md +++ b/translations/vi/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # Trí Tuệ Nhân Tạo Cho Người Mới Bắt Đầu - Một Chương Trình Học -|![Sketchnote bởi @girlie_mac https://twitter.com/girlie_mac](../../translated_images/vi/ai-overview.0857791951d19500.png)| +|![Sketchnote bởi @girlie_mac https://twitter.com/girlie_mac](../../translated_images/vi/ai-overview.0857791951d19500.webp)| |:---:| | Trí Tuệ Nhân Tạo Cho Người Mới Bắt Đầu - _Sketchnote bởi [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/vi/lessons/1-Intro/README.md b/translations/vi/lessons/1-Intro/README.md index b498e9ea..10ecea15 100644 --- a/translations/vi/lessons/1-Intro/README.md +++ b/translations/vi/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Giới thiệu về AI -![Tóm tắt nội dung Giới thiệu về AI trong một hình vẽ](../../../../translated_images/vi/ai-intro.bf28d1ac4235881c.png) +![Tóm tắt nội dung Giới thiệu về AI trong một hình vẽ](../../../../translated_images/vi/ai-intro.bf28d1ac4235881c.webp) > Hình vẽ bởi [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Ban đầu, máy tính được phát minh bởi [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage) để xử lý các con số theo một quy trình được định nghĩa rõ ràng - một thuật toán. Máy tính hiện đại, mặc dù tiên tiến hơn rất nhiều so với mô hình ban đầu được đề xuất vào thế kỷ 19, vẫn tuân theo ý tưởng về các tính toán có kiểm soát. Do đó, có thể lập trình một máy tính để thực hiện một việc gì đó nếu chúng ta biết chính xác chuỗi các bước cần thực hiện để đạt được mục tiêu. -![Ảnh một người](../../../../translated_images/vi/dsh_age.d212a30d4e54fb5f.png) +![Ảnh một người](../../../../translated_images/vi/dsh_age.d212a30d4e54fb5f.webp) > Ảnh bởi [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ AI yếu rất chuyên biệt và không sở hữu khả năng nhận thức gi Một trong những vấn đề khi xử lý thuật ngữ **[Trí thông minh](https://en.wikipedia.org/wiki/Intelligence)** là không có định nghĩa rõ ràng cho thuật ngữ này. Một số người có thể cho rằng trí thông minh liên quan đến **tư duy trừu tượng**, hoặc đến **tự nhận thức**, nhưng chúng ta không thể định nghĩa nó một cách chính xác. -![Ảnh một con mèo](../../../../translated_images/vi/photo-cat.8c8e8fb760ffe457.jpg) +![Ảnh một con mèo](../../../../translated_images/vi/photo-cat.8c8e8fb760ffe457.webp) > [Ảnh](https://unsplash.com/photos/75715CVEJhI) bởi [Amber Kipp](https://unsplash.com/@sadmax) từ Unsplash @@ -98,13 +98,13 @@ Ngược lại, chúng ta có thể cố gắng mô phỏng các yếu tố đơ > | Còn về ML thì sao? | | > |--------------|-----------| -> | Một phần của Trí tuệ Nhân tạo dựa trên việc máy tính học cách giải quyết vấn đề dựa trên một số dữ liệu được gọi là **Học máy**. Chúng ta sẽ không xem xét học máy cổ điển trong khóa học này - chúng tôi giới thiệu bạn đến chương trình học riêng [Học máy cho người mới bắt đầu](http://aka.ms/ml-beginners). | ![Học máy cho người mới bắt đầu](../../../../translated_images/vi/ml-for-beginners.9e4fed176fd5817d.png) | +> | Một phần của Trí tuệ Nhân tạo dựa trên việc máy tính học cách giải quyết vấn đề dựa trên một số dữ liệu được gọi là **Học máy**. Chúng ta sẽ không xem xét học máy cổ điển trong khóa học này - chúng tôi giới thiệu bạn đến chương trình học riêng [Học máy cho người mới bắt đầu](http://aka.ms/ml-beginners). | ![Học máy cho người mới bắt đầu](../../../../translated_images/vi/ml-for-beginners.9e4fed176fd5817d.webp) | ## Lịch sử ngắn gọn về AI Trí tuệ Nhân tạo được bắt đầu như một lĩnh vực vào giữa thế kỷ 20. Ban đầu, lý luận biểu tượng là cách tiếp cận phổ biến, và nó đã dẫn đến một số thành công quan trọng, chẳng hạn như các hệ thống chuyên gia – các chương trình máy tính có thể hoạt động như một chuyên gia trong một số lĩnh vực vấn đề hạn chế. Tuy nhiên, sớm nhận ra rằng cách tiếp cận này không mở rộng tốt. Việc trích xuất kiến thức từ một chuyên gia, biểu diễn nó trong máy tính, và giữ cho cơ sở kiến thức đó chính xác hóa ra là một nhiệm vụ rất phức tạp, và quá tốn kém để thực tế trong nhiều trường hợp. Điều này dẫn đến cái gọi là [Mùa đông AI](https://en.wikipedia.org/wiki/AI_winter) vào những năm 1970. -Lịch sử ngắn gọn về AI +Lịch sử ngắn gọn về AI > Hình ảnh bởi [Dmitry Soshnikov](http://soshnikov.com) @@ -124,7 +124,7 @@ Tương tự, chúng ta có thể thấy cách tiếp cận đối với việc * Các trợ lý hiện đại, chẳng hạn như Cortana, Siri hoặc Google Assistant đều là các hệ thống lai sử dụng Mạng nơ-ron để chuyển đổi giọng nói thành văn bản và nhận ra ý định của chúng ta, và sau đó sử dụng một số lý luận hoặc thuật toán rõ ràng để thực hiện các hành động cần thiết. * Trong tương lai, chúng ta có thể mong đợi một mô hình hoàn toàn dựa trên mạng nơ-ron để xử lý đối thoại một cách độc lập. Các mạng nơ-ron GPT gần đây và [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) cho thấy thành công lớn trong lĩnh vực này. -sự tiến hóa của bài kiểm tra Turing +sự tiến hóa của bài kiểm tra Turing > Hình ảnh của Dmitry Soshnikov, [ảnh](https://unsplash.com/photos/r8LmVbUKgns) bởi [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto), Unsplash ## Nghiên cứu AI gần đây diff --git a/translations/vi/lessons/2-Symbolic/Animals.ipynb b/translations/vi/lessons/2-Symbolic/Animals.ipynb index 9a5f1342..1d8097bb 100644 --- a/translations/vi/lessons/2-Symbolic/Animals.ipynb +++ b/translations/vi/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "Trong ví dụ này, chúng ta sẽ triển khai một hệ thống dựa trên kiến thức đơn giản để xác định một loài động vật dựa trên một số đặc điểm vật lý. Hệ thống có thể được biểu diễn bằng cây AND-OR sau đây (đây chỉ là một phần của toàn bộ cây, chúng ta có thể dễ dàng thêm một số quy tắc khác):\n", "\n", - "![](../../../../translated_images/vi/AND-OR-Tree.5592d2c70187f283.png)\n" + "![](../../../../translated_images/vi/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { diff --git a/translations/vi/lessons/2-Symbolic/README.md b/translations/vi/lessons/2-Symbolic/README.md index 6d10451f..214c8939 100644 --- a/translations/vi/lessons/2-Symbolic/README.md +++ b/translations/vi/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Đại diện Tri thức và Hệ thống Chuyên gia -![Tóm tắt nội dung AI biểu tượng](../../../../translated_images/vi/ai-symbolic.715a30cb610411a6.png) +![Tóm tắt nội dung AI biểu tượng](../../../../translated_images/vi/ai-symbolic.715a30cb610411a6.webp) > Sketchnote bởi [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ Thông thường, chúng ta không định nghĩa tri thức một cách nghiêm Do đó, vấn đề của **đại diện tri thức** là tìm một cách hiệu quả để biểu diễn tri thức bên trong máy tính dưới dạng dữ liệu, để có thể sử dụng tự động. Điều này có thể được xem như một phổ: -![Phổ đại diện tri thức](../../../../translated_images/vi/knowledge-spectrum.b60df631852c0217.png) +![Phổ đại diện tri thức](../../../../translated_images/vi/knowledge-spectrum.b60df631852c0217.webp) > Hình ảnh bởi [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Cú pháp khối | Thụt lề | | | Một trong những thành công ban đầu của AI biểu tượng là các **hệ thống chuyên gia** - các hệ thống máy tính được thiết kế để hoạt động như một chuyên gia trong một lĩnh vực vấn đề hạn chế. Chúng dựa trên một **cơ sở tri thức** được trích xuất từ một hoặc nhiều chuyên gia con người, và chứa một **động cơ suy luận** thực hiện một số lý luận dựa trên cơ sở đó. -![Kiến trúc con người](../../../../translated_images/vi/arch-human.5d4d35f1bba3ab1c.png) | ![Hệ thống dựa trên tri thức](../../../../translated_images/vi/arch-kbs.3ec5c150b09fa8da.png) +![Kiến trúc con người](../../../../translated_images/vi/arch-human.5d4d35f1bba3ab1c.webp) | ![Hệ thống dựa trên tri thức](../../../../translated_images/vi/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ Cấu trúc đơn giản của hệ thần kinh con người | Kiến trúc của hệ thống dựa trên tri thức @@ -106,7 +106,7 @@ Hệ thống chuyên gia được xây dựng giống như hệ thống lý lu Ví dụ, hãy xem xét hệ thống chuyên gia sau đây để xác định một loài động vật dựa trên các đặc điểm vật lý của nó: -![Cây AND-OR](../../../../translated_images/vi/AND-OR-Tree.5592d2c70187f283.png) +![Cây AND-OR](../../../../translated_images/vi/AND-OR-Tree.5592d2c70187f283.webp) > Hình ảnh bởi [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/vi/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/vi/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index 98bc029c..489091a6 100644 --- a/translations/vi/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/vi/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -1255,7 +1255,7 @@ "* Lỗi huấn luyện thấp - mô hình có thể xấp xỉ dữ liệu huấn luyện tốt vì nó có đủ khả năng biểu đạt.\n", "* Lỗi xác thực có thể cao hơn nhiều so với lỗi huấn luyện và có thể bắt đầu tăng trong quá trình huấn luyện - điều này xảy ra vì mô hình \"ghi nhớ\" các điểm dữ liệu huấn luyện và mất đi \"bức tranh tổng thể\".\n", "\n", - "![Overfitting](../../../../../translated_images/vi/overfit.a0bd57f717c15769.png)\n", + "![Overfitting](../../../../../translated_images/vi/overfit.a0bd57f717c15769.webp)\n", "\n", "> Trong hình này, `x` đại diện cho dữ liệu huấn luyện, `o` - dữ liệu xác thực. Bên trái - mô hình tuyến tính (một lớp), nó xấp xỉ bản chất của dữ liệu khá tốt. Bên phải - mô hình bị quá khớp, mô hình xấp xỉ dữ liệu huấn luyện hoàn hảo, nhưng không còn ý nghĩa với bất kỳ dữ liệu nào khác (lỗi xác thực rất cao).\n" ] diff --git a/translations/vi/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/vi/lessons/3-NeuralNetworks/05-Frameworks/README.md index a9978948..4ee7ab9f 100644 --- a/translations/vi/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/vi/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Overfitting là một khái niệm cực kỳ quan trọng trong học máy, và Hãy xem xét vấn đề sau đây về việc xấp xỉ 5 điểm (được biểu diễn bằng `x` trên các đồ thị dưới đây): -![linear](../../../../../translated_images/vi/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/vi/overfit2.131f5800ae10ca5e.jpg) +![linear](../../../../../translated_images/vi/overfit1.f24b71c6f652e59e.webp) | ![overfit](../../../../../translated_images/vi/overfit2.131f5800ae10ca5e.webp) -------------------------|-------------------------- **Mô hình tuyến tính, 2 tham số** | **Mô hình phi tuyến, 7 tham số** Lỗi huấn luyện = 5.3 | Lỗi huấn luyện = 0 @@ -79,7 +79,7 @@ Lỗi kiểm định = 5.1 | Lỗi kiểm định = 20 Như bạn có thể thấy từ đồ thị trên, overfitting có thể được phát hiện bằng lỗi huấn luyện rất thấp và lỗi kiểm định rất cao. Thông thường trong quá trình huấn luyện, chúng ta sẽ thấy cả lỗi huấn luyện và lỗi kiểm định bắt đầu giảm, và sau đó tại một thời điểm nào đó lỗi kiểm định có thể ngừng giảm và bắt đầu tăng. Đây sẽ là dấu hiệu của overfitting, và là chỉ báo rằng chúng ta nên dừng huấn luyện tại thời điểm này (hoặc ít nhất là lưu lại trạng thái của mô hình). -![overfitting](../../../../../translated_images/vi/Overfitting.408ad91cd90b4371.png) +![overfitting](../../../../../translated_images/vi/Overfitting.408ad91cd90b4371.webp) ## Cách ngăn chặn overfitting diff --git a/translations/vi/lessons/3-NeuralNetworks/README.md b/translations/vi/lessons/3-NeuralNetworks/README.md index c7ed17d8..a7f0e609 100644 --- a/translations/vi/lessons/3-NeuralNetworks/README.md +++ b/translations/vi/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Giới thiệu về Mạng Nơ-ron -![Tóm tắt nội dung Giới thiệu Mạng Nơ-ron trong một hình vẽ](../../../../translated_images/vi/ai-neuralnetworks.1c687ae40bc86e83.png) +![Tóm tắt nội dung Giới thiệu Mạng Nơ-ron trong một hình vẽ](../../../../translated_images/vi/ai-neuralnetworks.1c687ae40bc86e83.webp) Như chúng ta đã thảo luận trong phần giới thiệu, một trong những cách để đạt được trí tuệ là huấn luyện một **mô hình máy tính** hoặc một **bộ não nhân tạo**. Từ giữa thế kỷ 20, các nhà nghiên cứu đã thử nghiệm nhiều mô hình toán học khác nhau, cho đến những năm gần đây, hướng đi này đã chứng minh được sự thành công vượt bậc. Những mô hình toán học của bộ não này được gọi là **mạng nơ-ron**. @@ -36,13 +36,13 @@ Trong chương trình này, chúng ta sẽ chỉ tập trung vào các mô hình Từ sinh học, chúng ta biết rằng bộ não của chúng ta bao gồm các tế bào thần kinh (nơ-ron), mỗi tế bào có nhiều "đầu vào" (dendrite) và một "đầu ra" (axon). Cả dendrite và axon đều có thể dẫn truyền tín hiệu điện, và các kết nối giữa chúng — được gọi là synapse — có thể thể hiện các mức độ dẫn truyền khác nhau, được điều chỉnh bởi các chất dẫn truyền thần kinh. -![Mô hình của một Nơ-ron](../../../../translated_images/vi/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![Mô hình của một Nơ-ron](../../../../translated_images/vi/artneuron.1a5daa88d20ebe6f.png) +![Mô hình của một Nơ-ron](../../../../translated_images/vi/synapse-wikipedia.ed20a9e4726ea1c6.webp) | ![Mô hình của một Nơ-ron](../../../../translated_images/vi/artneuron.1a5daa88d20ebe6f.webp) ----|---- Nơ-ron Thực *([Hình ảnh](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) từ Wikipedia)* | Nơ-ron Nhân Tạo *(Hình ảnh của Tác giả)* Do đó, mô hình toán học đơn giản nhất của một nơ-ron bao gồm nhiều đầu vào X1, ..., XN và một đầu ra Y, cùng một loạt các trọng số W1, ..., WN. Đầu ra được tính toán như sau: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) trong đó f là một **hàm kích hoạt** phi tuyến. diff --git a/translations/vi/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/vi/lessons/4-ComputerVision/06-IntroCV/README.md index 153ba00a..ae9491e9 100644 --- a/translations/vi/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/vi/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ Trong [OpenCV Notebook](OpenCV.ipynb), chúng tôi đưa ra một số ví dụ * **Tiền xử lý một bức ảnh của sách chữ Braille**. Chúng tôi tập trung vào cách sử dụng ngưỡng, phát hiện đặc điểm, biến đổi phối cảnh và thao tác NumPy để tách các ký hiệu Braille riêng lẻ để phân loại thêm bằng mạng nơ-ron. -![Hình ảnh Braille](../../../../../translated_images/vi/braille.341962ff76b1bd70.jpeg) | ![Hình ảnh Braille đã tiền xử lý](../../../../../translated_images/vi/braille-result.46530fea020b03c7.png) | ![Ký hiệu Braille](../../../../../translated_images/vi/braille-symbols.0159185ab69d5339.png) +![Hình ảnh Braille](../../../../../translated_images/vi/braille.341962ff76b1bd70.webp) | ![Hình ảnh Braille đã tiền xử lý](../../../../../translated_images/vi/braille-result.46530fea020b03c7.webp) | ![Ký hiệu Braille](../../../../../translated_images/vi/braille-symbols.0159185ab69d5339.webp) ----|-----|----- > Hình ảnh từ [OpenCV.ipynb](OpenCV.ipynb) * **Phát hiện chuyển động trong video bằng sự khác biệt giữa các khung hình**. Nếu camera cố định, thì các khung hình từ luồng camera sẽ khá giống nhau. Vì các khung hình được biểu diễn dưới dạng mảng, chỉ cần trừ các mảng của hai khung hình liên tiếp, chúng ta sẽ nhận được sự khác biệt pixel, điều này sẽ thấp đối với các khung hình tĩnh và trở nên cao hơn khi có chuyển động đáng kể trong hình ảnh. -![Hình ảnh các khung hình video và sự khác biệt giữa các khung hình](../../../../../translated_images/vi/frame-difference.706f805491a0883c.png) +![Hình ảnh các khung hình video và sự khác biệt giữa các khung hình](../../../../../translated_images/vi/frame-difference.706f805491a0883c.webp) > Hình ảnh từ [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ Trong [OpenCV Notebook](OpenCV.ipynb), chúng tôi đưa ra một số ví dụ - **Dòng Quang học Dày đặc** tính toán trường vector cho thấy mỗi pixel đang di chuyển đến đâu. - **Dòng Quang học Thưa** dựa trên việc lấy một số đặc điểm nổi bật trong hình ảnh (ví dụ: các cạnh) và xây dựng quỹ đạo của chúng từ khung hình này sang khung hình khác. -![Hình ảnh Dòng Quang học](../../../../../translated_images/vi/optical.1f4a94464579a83a.png) +![Hình ảnh Dòng Quang học](../../../../../translated_images/vi/optical.1f4a94464579a83a.webp) > Hình ảnh từ [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/vi/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/vi/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 195abdde..54ad820e 100644 --- a/translations/vi/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/vi/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 là một mạng đạt độ chính xác 92.7% trong phân loại top-5 của ImageNet vào năm 2014. Nó có cấu trúc các lớp như sau: -![ImageNet Layers](../../../../../translated_images/vi/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet Layers](../../../../../translated_images/vi/vgg-16-arch1.d901a5583b3a51ba.webp) Như bạn có thể thấy, VGG tuân theo kiến trúc hình kim tự tháp truyền thống, bao gồm một chuỗi các lớp tích chập và lớp pooling. -![ImageNet Pyramid](../../../../../translated_images/vi/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet Pyramid](../../../../../translated_images/vi/vgg-16-arch.64ff2137f50dd49f.webp) > Hình ảnh từ [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/vi/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/vi/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index 204f9036..5adf9b12 100644 --- a/translations/vi/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/vi/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -260,7 +260,7 @@ "\n", "Do đó, trong một CNN điển hình sẽ có một số lớp tích chập, với các lớp pooling xen kẽ giữa chúng để giảm kích thước của hình ảnh. Chúng ta cũng sẽ tăng số lượng bộ lọc, bởi vì khi các mẫu trở nên phức tạp hơn - sẽ có nhiều tổ hợp thú vị hơn mà chúng ta cần tìm kiếm.\n", "\n", - "![Hình ảnh minh họa một số lớp tích chập với các lớp pooling.](../../../../../translated_images/vi/cnn-pyramid.85915455759ef0ce.png)\n", + "![Hình ảnh minh họa một số lớp tích chập với các lớp pooling.](../../../../../translated_images/vi/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "Do kích thước không gian giảm dần và kích thước đặc trưng/bộ lọc tăng dần, kiến trúc này cũng được gọi là **kiến trúc hình chóp**.\n" ] diff --git a/translations/vi/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/vi/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index 7cfa43d0..1dd58f6f 100644 --- a/translations/vi/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/vi/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -358,7 +358,7 @@ "\n", "Do đó, trong một CNN điển hình sẽ có một số lớp tích chập, với các lớp pooling xen kẽ để giảm kích thước của hình ảnh. Chúng ta cũng sẽ tăng số lượng bộ lọc, bởi vì khi các mẫu trở nên phức tạp hơn - có nhiều tổ hợp thú vị hơn mà chúng ta cần tìm kiếm.\n", "\n", - "![Hình ảnh minh họa một số lớp tích chập với các lớp pooling.](../../../../../translated_images/vi/cnn-pyramid.85915455759ef0ce.png)\n", + "![Hình ảnh minh họa một số lớp tích chập với các lớp pooling.](../../../../../translated_images/vi/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "Do kích thước không gian giảm và kích thước đặc trưng/bộ lọc tăng, kiến trúc này cũng được gọi là **kiến trúc hình kim tự tháp**.\n" ] diff --git a/translations/vi/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/vi/lessons/4-ComputerVision/07-ConvNets/README.md index 7c3b7016..c708605b 100644 --- a/translations/vi/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/vi/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ Trong thực tế, chúng ta muốn có khả năng nhận diện các đối t Để trích xuất các mẫu, chúng ta sẽ sử dụng khái niệm **bộ lọc tích chập**. Như bạn đã biết, một hình ảnh được biểu diễn bằng một ma trận 2D, hoặc một tensor 3D với độ sâu màu. Việc áp dụng một bộ lọc có nghĩa là chúng ta lấy một ma trận **hạt nhân bộ lọc** tương đối nhỏ, và đối với mỗi điểm ảnh trong hình ảnh gốc, chúng ta tính trung bình có trọng số với các điểm lân cận. Chúng ta có thể hình dung điều này như một cửa sổ nhỏ trượt qua toàn bộ hình ảnh, và tính trung bình tất cả các điểm ảnh theo các trọng số trong ma trận hạt nhân bộ lọc. -![Bộ lọc cạnh dọc](../../../../../translated_images/vi/filter-vert.b7148390ca0bc356.png) | ![Bộ lọc cạnh ngang](../../../../../translated_images/vi/filter-horiz.59b80ed4feb946ef.png) +![Bộ lọc cạnh dọc](../../../../../translated_images/vi/filter-vert.b7148390ca0bc356.webp) | ![Bộ lọc cạnh ngang](../../../../../translated_images/vi/filter-horiz.59b80ed4feb946ef.webp) ----|---- > Hình ảnh của Dmitry Soshnikov @@ -38,7 +38,7 @@ Cách hoạt động của CNN dựa trên các ý tưởng quan trọng sau: * Chúng ta có thể thiết kế mạng theo cách mà các bộ lọc được huấn luyện tự động * Chúng ta có thể sử dụng cùng một phương pháp để tìm các mẫu trong các đặc trưng cấp cao, không chỉ trong hình ảnh gốc. Do đó, việc trích xuất đặc trưng của CNN hoạt động trên một hệ thống phân cấp các đặc trưng, bắt đầu từ các tổ hợp điểm ảnh cấp thấp, cho đến các tổ hợp cấp cao hơn của các phần trong hình ảnh. -![Trích xuất đặc trưng phân cấp](../../../../../translated_images/vi/FeatureExtractionCNN.d9b456cbdae7cb64.png) +![Trích xuất đặc trưng phân cấp](../../../../../translated_images/vi/FeatureExtractionCNN.d9b456cbdae7cb64.webp) > Hình ảnh từ [một bài báo của Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), dựa trên [nghiên cứu của họ](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ Hầu hết các CNN được sử dụng để xử lý hình ảnh đều tuâ Ví dụ, hãy xem kiến trúc của VGG-16, một mạng đạt được độ chính xác 92.7% trong phân loại top-5 của ImageNet vào năm 2014: -![Các lớp của ImageNet](../../../../../translated_images/vi/vgg-16-arch1.d901a5583b3a51ba.jpg) +![Các lớp của ImageNet](../../../../../translated_images/vi/vgg-16-arch1.d901a5583b3a51ba.webp) -![Kim tự tháp của ImageNet](../../../../../translated_images/vi/vgg-16-arch.64ff2137f50dd49f.jpg) +![Kim tự tháp của ImageNet](../../../../../translated_images/vi/vgg-16-arch.64ff2137f50dd49f.webp) > Hình ảnh từ [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/vi/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/vi/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 56de1069..175a5204 100644 --- a/translations/vi/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/vi/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ Bạn cần huấn luyện một mạng nơ-ron tích chập để phân loại Chúng ta sẽ sử dụng [Bộ Dữ Liệu Thú Cưng Oxford-IIIT](https://www.robots.ox.ac.uk/~vgg/data/pets/), bộ dữ liệu này chứa hình ảnh của 37 giống loài chó và mèo khác nhau. -![Bộ dữ liệu chúng ta sẽ làm việc](../../../../../../translated_images/vi/data.50b2a9d5484bdbf0.png) +![Bộ dữ liệu chúng ta sẽ làm việc](../../../../../../translated_images/vi/data.50b2a9d5484bdbf0.webp) Để tải bộ dữ liệu, sử dụng đoạn mã sau: diff --git a/translations/vi/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/vi/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index 810b483a..3ef41f34 100644 --- a/translations/vi/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/vi/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "Để hình dung con mèo lý tưởng, chúng ta sẽ bắt đầu với một hình ảnh nhiễu ngẫu nhiên và cố gắng sử dụng kỹ thuật tối ưu hóa gradient descent để điều chỉnh hình ảnh sao cho mạng nhận diện được một con mèo.\n", "\n", - "![Vòng lặp tối ưu hóa](../../../../../translated_images/vi/ideal-cat-loop.999fbb8ff306e044.png)\n", + "![Vòng lặp tối ưu hóa](../../../../../translated_images/vi/ideal-cat-loop.999fbb8ff306e044.webp)\n", "\n", "Đây là hình ảnh ban đầu của chúng ta:\n" ] diff --git a/translations/vi/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/vi/lessons/4-ComputerVision/08-TransferLearning/README.md index 0be43513..2d59a6a2 100644 --- a/translations/vi/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/vi/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Cả Keras và PyTorch đều có các hàm để dễ dàng tải trọng số Dưới đây là các đặc điểm mẫu được trích xuất từ một bức ảnh của một con mèo bởi mạng VGG-16: -![Các đặc điểm được trích xuất bởi VGG-16](../../../../../translated_images/vi/features.6291f9c7ba3a0b95.png) +![Các đặc điểm được trích xuất bởi VGG-16](../../../../../translated_images/vi/features.6291f9c7ba3a0b95.webp) ## Tập Dữ Liệu Mèo và Chó @@ -48,19 +48,19 @@ Mạng nơ-ron đã được huấn luyện sẵn chứa các mẫu khác nhau b Một cách tiếp cận mà chúng ta có thể thực hiện là bắt đầu với một hình ảnh ngẫu nhiên, sau đó cố gắng sử dụng kỹ thuật **tối ưu hóa gradient descent** để điều chỉnh hình ảnh đó sao cho mạng bắt đầu nghĩ rằng đó là một con mèo. -![Vòng lặp tối ưu hóa hình ảnh](../../../../../translated_images/vi/ideal-cat-loop.999fbb8ff306e044.png) +![Vòng lặp tối ưu hóa hình ảnh](../../../../../translated_images/vi/ideal-cat-loop.999fbb8ff306e044.webp) Tuy nhiên, nếu chúng ta làm điều này, chúng ta sẽ nhận được một thứ rất giống với nhiễu ngẫu nhiên. Điều này là do *có nhiều cách để khiến mạng nghĩ rằng hình ảnh đầu vào là một con mèo*, bao gồm một số cách không có ý nghĩa về mặt thị giác. Mặc dù những hình ảnh này chứa nhiều mẫu đặc trưng cho một con mèo, nhưng không có gì ràng buộc chúng phải rõ ràng về mặt thị giác. Để cải thiện kết quả, chúng ta có thể thêm một thuật ngữ khác vào hàm mất mát, được gọi là **mất mát biến đổi**. Đây là một chỉ số cho thấy mức độ tương đồng giữa các pixel lân cận của hình ảnh. Việc giảm thiểu mất mát biến đổi làm cho hình ảnh mượt mà hơn và loại bỏ nhiễu - từ đó làm lộ ra các mẫu hấp dẫn hơn về mặt thị giác. Dưới đây là ví dụ về các hình ảnh "lý tưởng" như vậy, được phân loại là mèo và ngựa vằn với xác suất cao: -![Mèo Lý Tưởng](../../../../../translated_images/vi/ideal-cat.203dd4597643d6b0.png) | ![Ngựa Vằn Lý Tưởng](../../../../../translated_images/vi/ideal-zebra.7f70e8b54ee15a7a.png) +![Mèo Lý Tưởng](../../../../../translated_images/vi/ideal-cat.203dd4597643d6b0.webp) | ![Ngựa Vằn Lý Tưởng](../../../../../translated_images/vi/ideal-zebra.7f70e8b54ee15a7a.webp) -----|----- *Mèo Lý Tưởng* | *Ngựa Vằn Lý Tưởng* Cách tiếp cận tương tự có thể được sử dụng để thực hiện cái gọi là **tấn công đối kháng** trên mạng nơ-ron. Giả sử chúng ta muốn đánh lừa một mạng nơ-ron và làm cho một con chó trông giống như một con mèo. Nếu chúng ta lấy hình ảnh của một con chó, được mạng nhận diện là một con chó, chúng ta có thể điều chỉnh nó một chút bằng cách sử dụng tối ưu hóa gradient descent, cho đến khi mạng bắt đầu phân loại nó là một con mèo: -![Hình ảnh của một con chó](../../../../../translated_images/vi/original-dog.8f68a67d2fe0911f.png) | ![Hình ảnh của một con chó được phân loại là mèo](../../../../../translated_images/vi/adversarial-dog.d9fc7773b0142b89.png) +![Hình ảnh của một con chó](../../../../../translated_images/vi/original-dog.8f68a67d2fe0911f.webp) | ![Hình ảnh của một con chó được phân loại là mèo](../../../../../translated_images/vi/adversarial-dog.d9fc7773b0142b89.webp) -----|----- *Hình ảnh gốc của một con chó* | *Hình ảnh của một con chó được phân loại là mèo* diff --git a/translations/vi/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/vi/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index ff33c190..36ab3663 100644 --- a/translations/vi/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/vi/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "Vì chúng ta đang huấn luyện autoencoder để nắm bắt càng nhiều thông tin từ hình ảnh gốc càng tốt nhằm tái tạo chính xác, mạng sẽ cố gắng tìm **biểu diễn** tốt nhất của hình ảnh đầu vào để nắm bắt ý nghĩa.\n", "\n", - "![Sơ đồ AutoEncoder](../../../../../translated_images/vi/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![Sơ đồ AutoEncoder](../../../../../translated_images/vi/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "> Hình ảnh từ [blog Keras](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", diff --git a/translations/vi/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/vi/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index da5d9cc3..5c1ab3b1 100644 --- a/translations/vi/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/vi/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "Vì chúng ta đang huấn luyện autoencoder để nắm bắt càng nhiều thông tin từ hình ảnh gốc càng tốt nhằm tái tạo chính xác, mạng sẽ cố gắng tìm **embedding** tốt nhất của các hình ảnh đầu vào để nắm bắt ý nghĩa.\n", "\n", - "![Sơ đồ AutoEncoder](../../../../../translated_images/vi/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", + "![Sơ đồ AutoEncoder](../../../../../translated_images/vi/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "*Hình ảnh từ [blog Keras](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", diff --git a/translations/vi/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/vi/lessons/4-ComputerVision/09-Autoencoders/README.md index fe31eeb0..884c8c3f 100644 --- a/translations/vi/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/vi/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ Tuy nhiên, chúng ta có thể muốn sử dụng dữ liệu thô (không đư Vì chúng ta đang huấn luyện autoencoder để nắm bắt càng nhiều thông tin từ hình ảnh gốc càng tốt nhằm tái tạo chính xác, mạng cố gắng tìm **embedding** tốt nhất của hình ảnh đầu vào để nắm bắt ý nghĩa. -![Sơ đồ AutoEncoder](../../../../../translated_images/vi/autoencoder_schema.5e6fc9ad98a5eb61.jpg) +![Sơ đồ AutoEncoder](../../../../../translated_images/vi/autoencoder_schema.5e6fc9ad98a5eb61.webp) > Hình ảnh từ [blog Keras](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/vi/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/vi/lessons/4-ComputerVision/11-ObjectDetection/README.md index d6804f28..a841aecc 100644 --- a/translations/vi/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/vi/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ Các mô hình phân loại hình ảnh mà chúng ta đã làm việc trước ## [Câu hỏi trước bài giảng](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Phát Hiện Đối Tượng](../../../../../translated_images/vi/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) +![Phát Hiện Đối Tượng](../../../../../translated_images/vi/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp) > Hình ảnh từ [trang web YOLO v2](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ Giả sử chúng ta muốn tìm một con mèo trong một bức ảnh, một c 2. Chạy phân loại hình ảnh trên từng ô. 3. Những ô có kết quả kích hoạt đủ cao có thể được coi là chứa đối tượng cần tìm. -![Phát Hiện Đối Tượng Đơn Giản](../../../../../translated_images/vi/naive-detection.e7f1ba220ccd08c6.png) +![Phát Hiện Đối Tượng Đơn Giản](../../../../../translated_images/vi/naive-detection.e7f1ba220ccd08c6.webp) > *Hình ảnh từ [Notebook Bài Tập](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ Bạn có thể gặp các tập dữ liệu sau cho nhiệm vụ này: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 lớp * [COCO](http://cocodataset.org/#home) - Các Đối Tượng Thông Thường Trong Ngữ Cảnh. 80 lớp, hộp bao và mặt nạ phân đoạn -![COCO](../../../../../translated_images/vi/coco-examples.71bc60380fa6cceb.jpg) +![COCO](../../../../../translated_images/vi/coco-examples.71bc60380fa6cceb.webp) ## Các Chỉ Số Đánh Giá Phát Hiện Đối Tượng @@ -50,7 +50,7 @@ Bạn có thể gặp các tập dữ liệu sau cho nhiệm vụ này: Trong khi đối với phân loại hình ảnh, việc đo lường hiệu suất của thuật toán khá dễ dàng, thì đối với phát hiện đối tượng, chúng ta cần đo lường cả độ chính xác của lớp, cũng như độ chính xác của vị trí hộp bao được suy ra. Đối với yếu tố sau, chúng ta sử dụng chỉ số **Intersection over Union** (IoU), đo lường mức độ chồng lấp giữa hai hộp (hoặc hai khu vực bất kỳ). -![IoU](../../../../../translated_images/vi/iou_equation.9a4751d40fff4e11.png) +![IoU](../../../../../translated_images/vi/iou_equation.9a4751d40fff4e11.webp) > *Hình 2 từ [bài viết blog xuất sắc về IoU này](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ Có hai loại thuật toán phát hiện đối tượng chính: [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) sử dụng [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) để tạo ra cấu trúc phân cấp của các vùng ROI, sau đó được đưa qua các bộ trích xuất đặc trưng CNN và các bộ phân loại SVM để xác định lớp đối tượng, và hồi quy tuyến tính để xác định tọa độ *hộp bao*. [Bài báo chính thức](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/vi/rcnn1.cae407020dfb1d1f.png) +![RCNN](../../../../../translated_images/vi/rcnn1.cae407020dfb1d1f.webp) > *Hình ảnh từ van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/vi/rcnn2.2d9530bb83516484.png) +![RCNN-1](../../../../../translated_images/vi/rcnn2.2d9530bb83516484.webp) > *Hình ảnh từ [bài blog này](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -110,7 +110,7 @@ Có hai loại thuật toán phát hiện đối tượng chính: Phương pháp này tương tự như R-CNN, nhưng các vùng được xác định sau khi các lớp tích chập đã được áp dụng. -![FRCNN](../../../../../translated_images/vi/f-rcnn.3cda6d9bb4188875.png) +![FRCNN](../../../../../translated_images/vi/f-rcnn.3cda6d9bb4188875.webp) > Hình ảnh từ [Bài báo chính thức](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ Phương pháp này tương tự như R-CNN, nhưng các vùng được xác đ Ý tưởng chính của phương pháp này là sử dụng mạng nơ-ron để dự đoán các ROI - được gọi là *Mạng Đề Xuất Vùng* (Region Proposal Network). [Bài báo](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/vi/faster-rcnn.8d46c099b87ef30a.png) +![FasterRCNN](../../../../../translated_images/vi/faster-rcnn.8d46c099b87ef30a.webp) > Hình ảnh từ [bài báo chính thức](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ Thuật toán này thậm chí còn nhanh hơn Faster R-CNN. Ý tưởng chính 2. Các đặc trưng được xử lý bởi **Bản Đồ Điểm Nhạy Cảm Vị Trí**. Mỗi đối tượng từ $C$ lớp được chia thành các vùng $k\times k$, và chúng ta huấn luyện để dự đoán các phần của đối tượng. 3. Đối với mỗi phần từ các vùng $k\times k$, tất cả các mạng bỏ phiếu cho các lớp đối tượng, và lớp đối tượng có số phiếu cao nhất được chọn. -![r-fcn image](../../../../../translated_images/vi/r-fcn.13eb88158b99a3da.png) +![r-fcn image](../../../../../translated_images/vi/r-fcn.13eb88158b99a3da.webp) > Hình ảnh từ [bài báo chính thức](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO là một thuật toán một lần duy nhất thời gian thực. Ý tư * Hình ảnh được chia thành các vùng $S\times S$. * Đối với mỗi vùng, **CNN** dự đoán $n$ đối tượng có thể, tọa độ *hộp bao* và *độ tin cậy* = *xác suất* * IoU. - ![YOLO](../../../../../translated_images/vi/yolo.a2648ec82ee8bb4e.png) + ![YOLO](../../../../../translated_images/vi/yolo.a2648ec82ee8bb4e.webp) > Hình ảnh từ [bài báo chính thức](https://arxiv.org/abs/1506.02640) diff --git a/translations/vi/lessons/4-ComputerVision/README.md b/translations/vi/lessons/4-ComputerVision/README.md index 99e3cfdd..b3681741 100644 --- a/translations/vi/lessons/4-ComputerVision/README.md +++ b/translations/vi/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Thị giác Máy tính -![Tóm tắt nội dung Thị giác Máy tính dưới dạng hình vẽ](../../../../translated_images/vi/ai-computervision.6506ebebac3fbf76.png) +![Tóm tắt nội dung Thị giác Máy tính dưới dạng hình vẽ](../../../../translated_images/vi/ai-computervision.6506ebebac3fbf76.webp) Trong phần này, chúng ta sẽ tìm hiểu về: diff --git a/translations/vi/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/vi/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index 115f03c1..c6c03e8c 100644 --- a/translations/vi/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/vi/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**Bag of Words** (BoW) là cách biểu diễn vector truyền thống được sử dụng phổ biến nhất. Mỗi từ được liên kết với một chỉ số vector, phần tử trong vector chứa số lần xuất hiện của từ đó trong một tài liệu cụ thể.\n", "\n", - "![Hình ảnh minh họa cách biểu diễn vector Bag of Words trong bộ nhớ.](../../../../../translated_images/vi/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![Hình ảnh minh họa cách biểu diễn vector Bag of Words trong bộ nhớ.](../../../../../translated_images/vi/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Note**: Bạn cũng có thể nghĩ về BoW như tổng của tất cả các vector mã hóa một-hot cho từng từ riêng lẻ trong văn bản.\n", "\n", diff --git a/translations/vi/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/vi/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index 9c280a7b..da503761 100644 --- a/translations/vi/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/vi/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "Biểu diễn vector **Bag-of-words** (BoW) là cách biểu diễn vector truyền thống đơn giản nhất để hiểu. Mỗi từ được liên kết với một chỉ số vector, và một phần tử trong vector chứa số lần xuất hiện của mỗi từ trong một tài liệu cụ thể.\n", "\n", - "![Hình ảnh minh họa cách biểu diễn vector bag-of-words trong bộ nhớ.](../../../../../translated_images/vi/bag-of-words-example.606fc1738f1d7ba9.png) \n", + "![Hình ảnh minh họa cách biểu diễn vector bag-of-words trong bộ nhớ.](../../../../../translated_images/vi/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Note**: Bạn cũng có thể nghĩ về BoW như tổng của tất cả các vector mã hóa một-hot cho từng từ trong văn bản.\n", "\n", diff --git a/translations/vi/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/vi/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index d22d5fb7..29552db3 100644 --- a/translations/vi/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/vi/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "Bằng cách sử dụng lớp nhúng làm lớp đầu tiên trong mạng của chúng ta, chúng ta có thể chuyển từ mô hình túi từ (**bag-of-words**) sang mô hình túi nhúng (**embedding bag**), nơi chúng ta đầu tiên chuyển đổi mỗi từ trong văn bản thành nhúng tương ứng, sau đó tính toán một hàm tổng hợp nào đó trên tất cả các nhúng đó, chẳng hạn như `sum`, `average` hoặc `max`.\n", "\n", - "![Hình ảnh minh họa một bộ phân loại nhúng cho năm từ trong chuỗi.](../../../../../translated_images/vi/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![Hình ảnh minh họa một bộ phân loại nhúng cho năm từ trong chuỗi.](../../../../../translated_images/vi/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "Mạng neural phân loại của chúng ta sẽ bắt đầu với lớp nhúng, sau đó là lớp tổng hợp, và cuối cùng là bộ phân loại tuyến tính ở trên cùng:\n" ] @@ -176,7 +176,7 @@ "\n", "Trong kiến trúc trước, chúng ta cần đệm tất cả các chuỗi để có cùng độ dài nhằm đưa chúng vào một minibatch. Đây không phải là cách hiệu quả nhất để biểu diễn các chuỗi có độ dài biến đổi - một cách tiếp cận khác là sử dụng **vector offset**, chứa các điểm bắt đầu của tất cả các chuỗi được lưu trữ trong một vector lớn.\n", "\n", - "![Hình ảnh minh họa biểu diễn chuỗi bằng offset](../../../../../translated_images/vi/offset-sequence-representation.eb73fcefb29b46ee.png)\n", + "![Hình ảnh minh họa biểu diễn chuỗi bằng offset](../../../../../translated_images/vi/offset-sequence-representation.eb73fcefb29b46ee.webp)\n", "\n", "> **Note**: Trong hình trên, chúng ta minh họa một chuỗi ký tự, nhưng trong ví dụ của chúng ta, chúng ta đang làm việc với các chuỗi từ. Tuy nhiên, nguyên tắc chung của việc biểu diễn chuỗi bằng vector offset vẫn giữ nguyên.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW nhanh hơn, trong khi skip-gram chậm hơn nhưng làm tốt hơn trong việc biểu diễn các từ ít xuất hiện.\n", "\n", - "![Hình ảnh minh họa cả hai thuật toán CBoW và Skip-Gram để chuyển đổi từ thành vector.](../../../../../translated_images/vi/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![Hình ảnh minh họa cả hai thuật toán CBoW và Skip-Gram để chuyển đổi từ thành vector.](../../../../../translated_images/vi/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Để thử nghiệm với nhúng word2vec được tiền huấn luyện trên tập dữ liệu Google News, chúng ta có thể sử dụng thư viện **gensim**. Dưới đây là cách tìm các từ giống nhất với 'neural'\n", "\n", diff --git a/translations/vi/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/vi/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index d8b7a6c9..85da9846 100644 --- a/translations/vi/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/vi/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "Bằng cách sử dụng lớp embedding làm lớp đầu tiên trong mạng của chúng ta, chúng ta có thể chuyển từ mô hình bag-of-words sang mô hình **embedding bag**, nơi chúng ta đầu tiên chuyển đổi mỗi từ trong văn bản thành embedding tương ứng, sau đó tính toán một số hàm tổng hợp trên tất cả các embedding đó, chẳng hạn như `sum`, `average` hoặc `max`.\n", "\n", - "![Hình ảnh minh họa một bộ phân loại embedding cho năm từ trong chuỗi.](../../../../../translated_images/vi/embedding-classifier-example.b77f021a7ee67eee.png)\n", + "![Hình ảnh minh họa một bộ phân loại embedding cho năm từ trong chuỗi.](../../../../../translated_images/vi/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "Mạng neural phân loại của chúng ta bao gồm các lớp sau:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW nhanh hơn, trong khi skip-gram chậm hơn nhưng lại làm tốt hơn trong việc biểu diễn các từ ít xuất hiện.\n", "\n", - "![Hình minh họa cả hai thuật toán CBoW và Skip-Gram để chuyển đổi từ thành vector.](../../../../../translated_images/vi/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", + "![Hình minh họa cả hai thuật toán CBoW và Skip-Gram để chuyển đổi từ thành vector.](../../../../../translated_images/vi/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Để thử nghiệm với nhúng Word2Vec được tiền huấn luyện trên tập dữ liệu Google News, chúng ta có thể sử dụng thư viện **gensim**. Dưới đây là cách tìm các từ giống nhất với 'neural'.\n", "\n", diff --git a/translations/vi/lessons/5-NLP/14-Embeddings/README.md b/translations/vi/lessons/5-NLP/14-Embeddings/README.md index 5e50f550..a75171a1 100644 --- a/translations/vi/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/vi/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ Vì vậy, lớp nhúng sẽ nhận một từ làm đầu vào và tạo ra m Bằng cách sử dụng lớp nhúng làm lớp đầu tiên trong mạng phân loại của chúng ta, chúng ta có thể chuyển từ mô hình túi từ sang mô hình **túi nhúng**, nơi chúng ta đầu tiên chuyển đổi mỗi từ trong văn bản của mình thành nhúng tương ứng, và sau đó tính toán một số hàm tổng hợp trên tất cả các nhúng đó, chẳng hạn như `sum`, `average` hoặc `max`. -![Hình ảnh minh họa một bộ phân loại nhúng cho năm từ trong chuỗi.](../../../../../translated_images/vi/embedding-classifier-example.b77f021a7ee67eee.png) +![Hình ảnh minh họa một bộ phân loại nhúng cho năm từ trong chuỗi.](../../../../../translated_images/vi/embedding-classifier-example.b77f021a7ee67eee.webp) > Hình ảnh của tác giả @@ -40,7 +40,7 @@ Mặc dù lớp nhúng đã học cách ánh xạ các từ sang biểu diễn v CBoW nhanh hơn, trong khi skip-gram chậm hơn nhưng làm tốt hơn trong việc biểu diễn các từ ít xuất hiện. -![Hình ảnh minh họa cả hai thuật toán CBoW và Skip-Gram để chuyển đổi từ thành vector.](../../../../../translated_images/vi/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![Hình ảnh minh họa cả hai thuật toán CBoW và Skip-Gram để chuyển đổi từ thành vector.](../../../../../translated_images/vi/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Hình ảnh từ [bài báo này](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/vi/lessons/5-NLP/15-LanguageModeling/README.md b/translations/vi/lessons/5-NLP/15-LanguageModeling/README.md index e2a3e6b4..376da40d 100644 --- a/translations/vi/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/vi/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ Trong các ví dụ trước, chúng ta đã sử dụng các biểu diễn ng * **Continuous Bag-of-Words** (CBoW), khi chúng ta dự đoán token ở giữa $W_0$ trong một chuỗi token $W_{-N}$, ..., $W_N$. * **Skip-gram**, nơi chúng ta dự đoán một tập hợp các token lân cận {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} từ token ở giữa $W_0$. -![hình ảnh từ bài báo về chuyển đổi từ thành vector](../../../../../translated_images/vi/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![hình ảnh từ bài báo về chuyển đổi từ thành vector](../../../../../translated_images/vi/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Hình ảnh từ [bài báo này](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/vi/lessons/5-NLP/16-RNN/README.md b/translations/vi/lessons/5-NLP/16-RNN/README.md index 2f18e530..66176d28 100644 --- a/translations/vi/lessons/5-NLP/16-RNN/README.md +++ b/translations/vi/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ Trong các phần trước, chúng ta đã sử dụng các biểu diễn ngữ Để nắm bắt ý nghĩa của chuỗi văn bản, chúng ta cần sử dụng một kiến trúc mạng nơ-ron khác, được gọi là **mạng nơ-ron tái phục hồi**, hay RNN. Trong RNN, chúng ta đưa câu qua mạng từng ký hiệu một, và mạng sẽ tạo ra một **trạng thái**, sau đó chúng ta đưa trạng thái này vào mạng cùng với ký hiệu tiếp theo. -![RNN](../../../../../translated_images/vi/rnn.27f5c29c53d727b5.png) +![RNN](../../../../../translated_images/vi/rnn.27f5c29c53d727b5.webp) > Hình ảnh của tác giả @@ -61,7 +61,7 @@ Chúng ta đã thảo luận về các mạng tái phục hồi hoạt động t Một mạng tái phục hồi, dù là một chiều hay hai chiều, nắm bắt các mẫu nhất định trong một chuỗi và có thể lưu trữ chúng vào một vector trạng thái hoặc truyền vào đầu ra. Tương tự như các mạng tích chập, chúng ta có thể xây dựng một lớp tái phục hồi khác trên lớp đầu tiên để nắm bắt các mẫu cấp cao hơn và xây dựng từ các mẫu cấp thấp được trích xuất bởi lớp đầu tiên. Điều này dẫn đến khái niệm về một **RNN nhiều lớp**, bao gồm hai hoặc nhiều mạng tái phục hồi, trong đó đầu ra của lớp trước được truyền vào lớp tiếp theo làm đầu vào. -![Hình ảnh minh họa một RNN LSTM nhiều lớp](../../../../../translated_images/vi/multi-layer-lstm.dd975e29bb2a59fe.jpg) +![Hình ảnh minh họa một RNN LSTM nhiều lớp](../../../../../translated_images/vi/multi-layer-lstm.dd975e29bb2a59fe.webp) *Hình ảnh từ [bài viết tuyệt vời này](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) của Fernando López* diff --git a/translations/vi/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/vi/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index bc149176..104681c0 100644 --- a/translations/vi/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/vi/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "Mạng hồi quy, dù một chiều hay hai chiều, đều nắm bắt các mẫu nhất định trong một chuỗi và có thể lưu trữ chúng vào vector trạng thái hoặc truyền vào đầu ra. Tương tự như mạng tích chập, chúng ta có thể xây dựng một lớp hồi quy khác trên lớp đầu tiên để nắm bắt các mẫu cấp cao hơn, được xây dựng từ các mẫu cấp thấp do lớp đầu tiên trích xuất. Điều này dẫn đến khái niệm **RNN nhiều lớp**, bao gồm hai hoặc nhiều mạng hồi quy, trong đó đầu ra của lớp trước được truyền vào lớp tiếp theo làm đầu vào.\n", "\n", - "![Hình ảnh minh họa một mạng RNN LSTM nhiều lớp](../../../../../translated_images/vi/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![Hình ảnh minh họa một mạng RNN LSTM nhiều lớp](../../../../../translated_images/vi/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*Hình ảnh từ [bài viết tuyệt vời này](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) của Fernando López*\n", "\n", diff --git a/translations/vi/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/vi/lessons/5-NLP/16-RNN/RNNTF.ipynb index a2d095da..c0075898 100644 --- a/translations/vi/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/vi/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "Để nắm bắt ý nghĩa của một chuỗi văn bản, chúng ta sẽ sử dụng một kiến trúc mạng nơ-ron gọi là **mạng nơ-ron hồi quy**, hay RNN. Khi sử dụng RNN, chúng ta truyền câu qua mạng từng token một, và mạng tạo ra một **trạng thái**, sau đó chúng ta truyền trạng thái này vào mạng cùng với token tiếp theo.\n", "\n", - "![Hình minh họa một ví dụ về quá trình tạo mạng nơ-ron hồi quy.](../../../../../translated_images/vi/rnn.27f5c29c53d727b5.png)\n", + "![Hình minh họa một ví dụ về quá trình tạo mạng nơ-ron hồi quy.](../../../../../translated_images/vi/rnn.27f5c29c53d727b5.webp)\n", "\n", "Với chuỗi đầu vào các token $X_0,\\dots,X_n$, RNN tạo ra một chuỗi các khối mạng nơ-ron và huấn luyện chuỗi này từ đầu đến cuối bằng cách sử dụng lan truyền ngược. Mỗi khối mạng nhận một cặp $(X_i,S_i)$ làm đầu vào và tạo ra $S_{i+1}$ làm kết quả. Trạng thái cuối cùng $S_n$ hoặc đầu ra $Y_n$ được đưa vào một bộ phân loại tuyến tính để tạo ra kết quả. Tất cả các khối mạng đều chia sẻ cùng một trọng số và được huấn luyện từ đầu đến cuối bằng một lần lan truyền ngược.\n", "\n", @@ -369,7 +369,7 @@ "\n", "Mạng hồi quy, dù là một chiều hay hai chiều, đều nắm bắt các mẫu trong một chuỗi và lưu trữ chúng vào các vector trạng thái hoặc trả về chúng dưới dạng đầu ra. Tương tự như mạng tích chập, chúng ta có thể xây dựng một lớp hồi quy khác sau lớp đầu tiên để nắm bắt các mẫu cấp cao hơn, được xây dựng từ các mẫu cấp thấp hơn mà lớp đầu tiên đã trích xuất. Điều này dẫn đến khái niệm về **RNN nhiều lớp**, bao gồm hai hoặc nhiều mạng hồi quy, trong đó đầu ra của lớp trước được truyền vào lớp tiếp theo dưới dạng đầu vào.\n", "\n", - "![Hình ảnh minh họa một RNN LSTM nhiều lớp](../../../../../translated_images/vi/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", + "![Hình ảnh minh họa một RNN LSTM nhiều lớp](../../../../../translated_images/vi/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*Hình ảnh từ [bài viết tuyệt vời này](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) của Fernando López.*\n", "\n", diff --git a/translations/vi/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/vi/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index 26c23cfd..8644c3a9 100644 --- a/translations/vi/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/vi/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "Cách chúng ta sẽ huấn luyện RNN để tạo văn bản như sau. Ở mỗi bước, chúng ta sẽ lấy một chuỗi ký tự có độ dài `nchars`, và yêu cầu mạng tạo ra ký tự đầu ra tiếp theo cho mỗi ký tự đầu vào:\n", "\n", - "![Hình minh họa ví dụ RNN tạo ra từ 'HELLO'.](../../../../../translated_images/vi/rnn-generate.56c54afb52f9781d.png)\n", + "![Hình minh họa ví dụ RNN tạo ra từ 'HELLO'.](../../../../../translated_images/vi/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "Tùy thuộc vào tình huống thực tế, chúng ta cũng có thể muốn bao gồm một số ký tự đặc biệt, chẳng hạn như *kết thúc chuỗi* ``. Trong trường hợp của chúng ta, mục tiêu là huấn luyện mạng để tạo văn bản liên tục, vì vậy chúng ta sẽ cố định kích thước của mỗi chuỗi bằng số lượng token `nchars`. Do đó, mỗi ví dụ huấn luyện sẽ bao gồm `nchars` đầu vào và `nchars` đầu ra (là chuỗi đầu vào được dịch sang trái một ký tự). Minibatch sẽ bao gồm một số chuỗi như vậy.\n", "\n", diff --git a/translations/vi/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/vi/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index 6737c8ee..ede1eab0 100644 --- a/translations/vi/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/vi/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -111,7 +111,7 @@ "\n", "Cách chúng ta sẽ huấn luyện RNN để tạo tiêu đề tin tức như sau. Ở mỗi bước, chúng ta sẽ lấy một tiêu đề, tiêu đề này sẽ được đưa vào RNN, và với mỗi ký tự đầu vào, chúng ta sẽ yêu cầu mạng tạo ra ký tự đầu ra tiếp theo:\n", "\n", - "![Hình ảnh minh họa việc RNN tạo ra từ 'HELLO'.](../../../../../translated_images/vi/rnn-generate.56c54afb52f9781d.png)\n", + "![Hình ảnh minh họa việc RNN tạo ra từ 'HELLO'.](../../../../../translated_images/vi/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "Đối với ký tự cuối cùng của chuỗi, chúng ta sẽ yêu cầu mạng tạo ra token ``.\n", "\n", diff --git a/translations/vi/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/vi/lessons/5-NLP/17-GenerativeNetworks/README.md index 3dd5e9ad..8a6be17b 100644 --- a/translations/vi/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/vi/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ Trong kiến trúc RNN mà chúng ta đã thảo luận trong bài trước, m Điều này cho phép các kiến trúc nơ-ron khác nhau như được hiển thị trong hình dưới đây: -![Hình ảnh hiển thị các mẫu mạng nơ-ron hồi quy phổ biến.](../../../../../translated_images/vi/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Hình ảnh hiển thị các mẫu mạng nơ-ron hồi quy phổ biến.](../../../../../translated_images/vi/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > Hình ảnh từ bài viết blog [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) của [Andrej Karpaty](http://karpathy.github.io/) @@ -32,7 +32,7 @@ Trong bài này, chúng ta sẽ tập trung vào các mô hình tạo sinh đơn Chúng ta sẽ huấn luyện RNN này để tạo văn bản từng bước. Ở mỗi bước, chúng ta sẽ lấy một chuỗi ký tự có độ dài `nchars`, và yêu cầu mạng tạo ra ký tự đầu ra tiếp theo cho mỗi ký tự đầu vào: -![Hình ảnh minh họa RNN tạo ra từ 'HELLO'.](../../../../../translated_images/vi/rnn-generate.56c54afb52f9781d.png) +![Hình ảnh minh họa RNN tạo ra từ 'HELLO'.](../../../../../translated_images/vi/rnn-generate.56c54afb52f9781d.webp) Khi tạo văn bản (trong quá trình suy luận), chúng ta bắt đầu với một **gợi ý**, được truyền qua các tế bào RNN để tạo trạng thái trung gian của nó, và sau đó từ trạng thái này bắt đầu quá trình tạo. Chúng ta tạo từng ký tự một, và truyền trạng thái cùng ký tự vừa tạo vào một tế bào RNN khác để tạo ký tự tiếp theo, cho đến khi tạo đủ số ký tự. diff --git a/translations/vi/lessons/5-NLP/18-Transformers/README.md b/translations/vi/lessons/5-NLP/18-Transformers/README.md index 16e2a0b5..4b7d9536 100644 --- a/translations/vi/lessons/5-NLP/18-Transformers/README.md +++ b/translations/vi/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ Với RNNs, sequence-to-sequence được thực hiện bởi hai mạng hồi q **Cơ chế Attention** cung cấp một cách để cân nhắc tác động ngữ cảnh của từng vector đầu vào lên từng dự đoán đầu ra của RNN. Cách nó được thực hiện là tạo các đường tắt giữa các trạng thái trung gian của RNN đầu vào và RNN đầu ra. Theo cách này, khi tạo ra ký hiệu đầu ra yt, chúng ta sẽ xem xét tất cả các trạng thái ẩn đầu vào hi, với các hệ số trọng số khác nhau αt,i. -![Hình ảnh mô tả mô hình encoder/decoder với lớp attention cộng](../../../../../translated_images/vi/encoder-decoder-attention.7a726296894fb567.png) +![Hình ảnh mô tả mô hình encoder/decoder với lớp attention cộng](../../../../../translated_images/vi/encoder-decoder-attention.7a726296894fb567.webp) > Mô hình encoder-decoder với cơ chế attention cộng trong [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), trích dẫn từ [bài viết blog này](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) Ma trận attention {αi,j} sẽ biểu thị mức độ mà các từ đầu vào nhất định ảnh hưởng đến việc tạo ra một từ cụ thể trong chuỗi đầu ra. Dưới đây là một ví dụ về ma trận như vậy: -![Hình ảnh hiển thị một mẫu căn chỉnh được tìm thấy bởi RNNsearch-50, lấy từ Bahdanau - arviz.org](../../../../../translated_images/vi/bahdanau-fig3.09ba2d37f202a6af.png) +![Hình ảnh hiển thị một mẫu căn chỉnh được tìm thấy bởi RNNsearch-50, lấy từ Bahdanau - arviz.org](../../../../../translated_images/vi/bahdanau-fig3.09ba2d37f202a6af.webp) > Hình từ [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Hình 3) @@ -66,7 +66,7 @@ Kết quả mà chúng ta nhận được với nhúng vị trí là nhúng cả Tiếp theo, chúng ta cần nắm bắt một số mẫu trong chuỗi của mình. Để làm điều này, transformers sử dụng cơ chế **self-attention**, về cơ bản là attention được áp dụng cho cùng một chuỗi làm đầu vào và đầu ra. Việc áp dụng self-attention cho phép chúng ta xem xét **ngữ cảnh** trong câu và xem các từ nào có liên quan đến nhau. Ví dụ, nó cho phép chúng ta thấy các từ nào được tham chiếu bởi các đại từ như *it*, và cũng xem xét ngữ cảnh: -![](../../../../../translated_images/vi/CoreferenceResolution.861924d6d384a7d6.png) +![](../../../../../translated_images/vi/CoreferenceResolution.861924d6d384a7d6.webp) > Hình ảnh từ [Blog của Google](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Vì mỗi vị trí đầu vào được ánh xạ độc lập đến mỗi v **BERT** (Bidirectional Encoder Representations from Transformers) là một mạng transformer nhiều lớp rất lớn với 12 lớp cho *BERT-base*, và 24 lớp cho *BERT-large*. Mô hình này được huấn luyện trước trên một tập dữ liệu văn bản lớn (WikiPedia + sách) bằng cách huấn luyện không giám sát (dự đoán các từ bị che trong câu). Trong quá trình huấn luyện trước, mô hình hấp thụ mức độ hiểu biết ngôn ngữ đáng kể, sau đó có thể được tận dụng với các tập dữ liệu khác bằng cách tinh chỉnh. Quá trình này được gọi là **học chuyển giao**. -![Hình ảnh từ http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/vi/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) +![Hình ảnh từ http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/vi/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > Hình ảnh [nguồn](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/vi/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/vi/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index 4ae6ada5..94529658 100644 --- a/translations/vi/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/vi/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**Cơ chế Attention** cung cấp một cách để cân nhắc mức độ ảnh hưởng ngữ cảnh của từng vector đầu vào lên từng dự đoán đầu ra của RNN. Cách triển khai là tạo các đường tắt giữa các trạng thái trung gian của RNN đầu vào và RNN đầu ra. Theo cách này, khi tạo ra ký hiệu đầu ra $y_t$, chúng ta sẽ xem xét tất cả các trạng thái ẩn đầu vào $h_i$, với các hệ số trọng số khác nhau $\\alpha_{t,i}$.\n", "\n", - "![Hình ảnh mô hình encoder/decoder với lớp attention cộng tính](../../../../../translated_images/vi/encoder-decoder-attention.7a726296894fb567.png)\n", + "![Hình ảnh mô hình encoder/decoder với lớp attention cộng tính](../../../../../translated_images/vi/encoder-decoder-attention.7a726296894fb567.webp)\n", "*Mô hình encoder-decoder với cơ chế attention cộng tính trong [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), được trích dẫn từ [bài viết blog này](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Ma trận Attention $\\{\\alpha_{i,j}\\}$ sẽ đại diện cho mức độ mà các từ đầu vào cụ thể ảnh hưởng đến việc tạo ra một từ nhất định trong chuỗi đầu ra. Dưới đây là ví dụ về một ma trận như vậy:\n", "\n", - "![Hình ảnh minh họa sự liên kết mẫu được tìm thấy bởi RNNsearch-50, lấy từ Bahdanau - arviz.org](../../../../../translated_images/vi/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![Hình ảnh minh họa sự liên kết mẫu được tìm thấy bởi RNNsearch-50, lấy từ Bahdanau - arviz.org](../../../../../translated_images/vi/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*Hình ảnh được lấy từ [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Hình 3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) là một mạng Transformer đa lớp rất lớn với 12 lớp cho *BERT-base*, và 24 lớp cho *BERT-large*. Mô hình này được huấn luyện trước trên một tập dữ liệu văn bản lớn (WikiPedia + sách) bằng cách huấn luyện không giám sát (dự đoán các từ bị che trong một câu). Trong quá trình huấn luyện trước, mô hình hấp thụ một mức độ hiểu biết ngôn ngữ đáng kể, sau đó có thể được tận dụng với các tập dữ liệu khác thông qua việc tinh chỉnh. Quá trình này được gọi là **học chuyển giao** (transfer learning).\n", "\n", - "![Hình ảnh từ http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/vi/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![Hình ảnh từ http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/vi/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "Có nhiều biến thể của kiến trúc Transformer bao gồm BERT, DistilBERT, BigBird, OpenGPT3 và nhiều hơn nữa có thể được tinh chỉnh. Gói [HuggingFace](https://github.com/huggingface/) cung cấp kho lưu trữ để huấn luyện nhiều kiến trúc này với PyTorch.\n", "\n", diff --git a/translations/vi/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/vi/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index 28528eca..8781391d 100644 --- a/translations/vi/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/vi/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**Cơ chế Attention** cung cấp một phương pháp để gán trọng số cho tác động ngữ cảnh của từng vector đầu vào lên từng dự đoán đầu ra của RNN. Cách triển khai là tạo các đường tắt giữa các trạng thái trung gian của RNN đầu vào và RNN đầu ra. Theo cách này, khi tạo ra ký hiệu đầu ra $y_t$, chúng ta sẽ xem xét tất cả các trạng thái ẩn đầu vào $h_i$, với các hệ số trọng số khác nhau $\\alpha_{t,i}$.\n", "\n", - "![Hình ảnh mô tả mô hình encoder/decoder với lớp attention cộng tính](../../../../../translated_images/vi/encoder-decoder-attention.7a726296894fb567.png)\n", + "![Hình ảnh mô tả mô hình encoder/decoder với lớp attention cộng tính](../../../../../translated_images/vi/encoder-decoder-attention.7a726296894fb567.webp)\n", "*Mô hình encoder-decoder với cơ chế attention cộng tính trong [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), được trích dẫn từ [bài viết blog này](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Ma trận Attention $\\{\\alpha_{i,j}\\}$ sẽ biểu thị mức độ mà các từ đầu vào cụ thể ảnh hưởng đến việc tạo ra một từ nhất định trong chuỗi đầu ra. Dưới đây là ví dụ về một ma trận như vậy:\n", "\n", - "![Hình ảnh minh họa một mẫu căn chỉnh được tìm thấy bởi RNNsearch-50, lấy từ Bahdanau - arviz.org](../../../../../translated_images/vi/bahdanau-fig3.09ba2d37f202a6af.png)\n", + "![Hình ảnh minh họa một mẫu căn chỉnh được tìm thấy bởi RNNsearch-50, lấy từ Bahdanau - arviz.org](../../../../../translated_images/vi/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*Hình ảnh được lấy từ [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Hình 3)*\n", "\n", @@ -231,7 +231,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) là một mạng transformer nhiều lớp rất lớn với 12 lớp cho *BERT-base*, và 24 lớp cho *BERT-large*. Mô hình này được huấn luyện trước trên một tập dữ liệu văn bản lớn (WikiPedia + sách) bằng cách sử dụng phương pháp huấn luyện không giám sát (dự đoán các từ bị che trong câu). Trong quá trình huấn luyện trước, mô hình hấp thụ một mức độ hiểu biết ngôn ngữ đáng kể, sau đó có thể được tận dụng với các tập dữ liệu khác thông qua việc tinh chỉnh. Quá trình này được gọi là **học chuyển giao**.\n", "\n", - "![hình ảnh từ http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/vi/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", + "![hình ảnh từ http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/vi/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "Có nhiều biến thể của kiến trúc Transformer bao gồm BERT, DistilBERT, BigBird, OpenGPT3 và nhiều hơn nữa có thể được tinh chỉnh.\n", "\n", diff --git a/translations/vi/lessons/5-NLP/19-NER/README.md b/translations/vi/lessons/5-NLP/19-NER/README.md index fc653628..151a13f8 100644 --- a/translations/vi/lessons/5-NLP/19-NER/README.md +++ b/translations/vi/lessons/5-NLP/19-NER/README.md @@ -58,7 +58,7 @@ infant | O Vì chúng ta cần xây dựng một sự tương ứng một-một giữa các token và các lớp, chúng ta có thể huấn luyện một mô hình mạng nơ-ron **nhiều-đến-nhiều** từ hình ảnh này: -![Hình ảnh hiển thị các mẫu mạng nơ-ron hồi quy phổ biến.](../../../../../translated_images/vi/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![Hình ảnh hiển thị các mẫu mạng nơ-ron hồi quy phổ biến.](../../../../../translated_images/vi/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > *Hình ảnh từ [bài viết blog này](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) của [Andrej Karpathy](http://karpathy.github.io/). Các mô hình phân loại token NER tương ứng với kiến trúc mạng ở phía bên phải của hình ảnh này.* diff --git a/translations/vi/lessons/5-NLP/README.md b/translations/vi/lessons/5-NLP/README.md index 848dec6b..aef06315 100644 --- a/translations/vi/lessons/5-NLP/README.md +++ b/translations/vi/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Xử lý Ngôn ngữ Tự nhiên -![Tóm tắt các nhiệm vụ NLP trong một hình vẽ](../../../../translated_images/vi/ai-nlp.b22dcb8ca4707cea.png) +![Tóm tắt các nhiệm vụ NLP trong một hình vẽ](../../../../translated_images/vi/ai-nlp.b22dcb8ca4707cea.webp) Trong phần này, chúng ta sẽ tập trung vào việc sử dụng Mạng Nơ-ron để xử lý các nhiệm vụ liên quan đến **Xử lý Ngôn ngữ Tự nhiên (NLP)**. Có rất nhiều vấn đề NLP mà chúng ta muốn máy tính có thể giải quyết: diff --git a/translations/vi/lessons/6-Other/23-MultiagentSystems/README.md b/translations/vi/lessons/6-Other/23-MultiagentSystems/README.md index 0c744bd3..86a267b4 100644 --- a/translations/vi/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/vi/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ Bạn có thể mở một trong các mô hình, ví dụ **Biology → Sau khi mở mô hình, bạn sẽ được đưa đến màn hình chính của NetLogo. Đây là một mô hình mẫu mô tả dân số của sói và cừu, với các tài nguyên hữu hạn (cỏ). -![Màn hình chính NetLogo](../../../../../translated_images/vi/NetLogo-Main.32653711ec1a01b3.png) +![Màn hình chính NetLogo](../../../../../translated_images/vi/NetLogo-Main.32653711ec1a01b3.webp) > Ảnh chụp màn hình của Dmitry Soshnikov diff --git a/translations/vi/lessons/README.md b/translations/vi/lessons/README.md index 5f866928..74a2dbdd 100644 --- a/translations/vi/lessons/README.md +++ b/translations/vi/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Tổng quan -![Tổng quan trong một hình vẽ minh họa](../../../translated_images/vi/ai-overview.0857791951d19500.png) +![Tổng quan trong một hình vẽ minh họa](../../../translated_images/vi/ai-overview.0857791951d19500.webp) > Hình minh họa bởi [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/vi/lessons/X-Extras/X1-MultiModal/README.md b/translations/vi/lessons/X-Extras/X1-MultiModal/README.md index 173b6e04..e95ef145 100644 --- a/translations/vi/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/vi/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ Sau thành công của các mô hình transformer trong việc giải quyết c Ý tưởng chính của CLIP là có thể so sánh các gợi ý văn bản với một hình ảnh và xác định mức độ phù hợp của hình ảnh với gợi ý đó. -![Kiến trúc CLIP](../../../../../translated_images/vi/clip-arch.b3dbf20b4e8ed8be.png) +![Kiến trúc CLIP](../../../../../translated_images/vi/clip-arch.b3dbf20b4e8ed8be.webp) > *Hình ảnh từ [bài viết blog này](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ Khi mô hình này đã được huấn luyện trước, chúng ta có thể cu Giả sử chúng ta cần phân loại hình ảnh giữa, ví dụ, mèo, chó và con người. Trong trường hợp này, chúng ta có thể cung cấp cho mô hình một hình ảnh và một loạt các gợi ý văn bản: "*một bức ảnh của một con mèo*", "*một bức ảnh của một con chó*", "*một bức ảnh của một con người*". Trong vector kết quả gồm 3 xác suất, chúng ta chỉ cần chọn chỉ số có giá trị cao nhất. -![CLIP cho Phân loại Hình ảnh](../../../../../translated_images/vi/clip-class.3af42ef0b2b19369.png) +![CLIP cho Phân loại Hình ảnh](../../../../../translated_images/vi/clip-class.3af42ef0b2b19369.webp) > *Hình ảnh từ [bài viết blog này](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ Tìm hiểu thêm về VQGAN tại trang web [Taming Transformers](https://compv Một trong những khác biệt quan trọng giữa VQGAN và GAN truyền thống là GAN truyền thống có thể tạo ra một hình ảnh khá tốt từ bất kỳ vector đầu vào nào, trong khi VQGAN có thể tạo ra một hình ảnh không nhất quán. Do đó, chúng ta cần hướng dẫn thêm quá trình tạo hình ảnh, và điều này có thể được thực hiện bằng cách sử dụng CLIP. -![Kiến trúc VQGAN+CLIP](../../../../../translated_images/vi/vqgan.5027fe05051dfa31.png) +![Kiến trúc VQGAN+CLIP](../../../../../translated_images/vi/vqgan.5027fe05051dfa31.webp) Để tạo ra một hình ảnh tương ứng với một gợi ý văn bản, chúng ta bắt đầu với một vector mã hóa ngẫu nhiên được đưa qua VQGAN để tạo ra một hình ảnh. Sau đó, CLIP được sử dụng để tạo ra một hàm mất mát cho biết mức độ phù hợp của hình ảnh với gợi ý văn bản. Mục tiêu sau đó là giảm thiểu hàm mất mát này, sử dụng lan truyền ngược để điều chỉnh các tham số vector đầu vào. Một thư viện tuyệt vời triển khai VQGAN+CLIP là [Pixray](http://github.com/pixray/pixray) -![Hình ảnh được tạo bởi Pixray](../../../../../translated_images/vi/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Hình ảnh được tạo bởi Pixray](../../../../../translated_images/vi/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Hình ảnh được tạo bởi Pixray](../../../../../translated_images/vi/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) +![Hình ảnh được tạo bởi Pixray](../../../../../translated_images/vi/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp) | ![Hình ảnh được tạo bởi Pixray](../../../../../translated_images/vi/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp) | ![Hình ảnh được tạo bởi Pixray](../../../../../translated_images/vi/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp) ----|----|---- Hình ảnh được tạo từ gợi ý *một bức chân dung cận cảnh bằng màu nước của một giáo viên văn học trẻ tuổi với một cuốn sách* | Hình ảnh được tạo từ gợi ý *một bức chân dung cận cảnh bằng sơn dầu của một giáo viên khoa học máy tính trẻ tuổi với một chiếc máy tính* | Hình ảnh được tạo từ gợi ý *một bức chân dung cận cảnh bằng sơn dầu của một giáo viên toán học lớn tuổi trước bảng đen* diff --git a/translations/zh/README.md b/translations/zh/README.md index e4d7c8d5..d5fc7216 100644 --- a/translations/zh/README.md +++ b/translations/zh/README.md @@ -1,8 +1,8 @@ -[阿拉伯语](../ar/README.md) | [孟加拉语](../bn/README.md) | [保加利亚语](../bg/README.md) | [缅甸语 (Myanmar)](../my/README.md) | [中文 (简体)](./README.md) | [中文 (繁体，香港)](../hk/README.md) | [中文 (繁体，澳门)](../mo/README.md) | [中文 (繁体，台湾)](../tw/README.md) | [克罗地亚语](../hr/README.md) | [捷克语](../cs/README.md) | [丹麦语](../da/README.md) | [荷兰语](../nl/README.md) | [爱沙尼亚语](../et/README.md) | [芬兰语](../fi/README.md) | [法语](../fr/README.md) | [德语](../de/README.md) | [希腊语](../el/README.md) | [希伯来语](../he/README.md) | [印地语](../hi/README.md) | [匈牙利语](../hu/README.md) | [印度尼西亚语](../id/README.md) | [意大利语](../it/README.md) | [日语](../ja/README.md) | [卡纳达语](../kn/README.md) | [韩语](../ko/README.md) | [立陶宛语](../lt/README.md) | [马来语](../ms/README.md) | [马拉雅拉姆语](../ml/README.md) | [马拉地语](../mr/README.md) | [尼泊尔语](../ne/README.md) | [尼日利亚皮钦语](../pcm/README.md) | [挪威语](../no/README.md) | [波斯语 (法尔西语)](../fa/README.md) | [波兰语](../pl/README.md) | [葡萄牙语 (巴西)](../br/README.md) | [葡萄牙语 (葡萄牙)](../pt/README.md) | [旁遮普语 (Gurmukhi)](../pa/README.md) | [罗马尼亚语](../ro/README.md) | [俄语](../ru/README.md) | [塞尔维亚语 (西里尔字母)](../sr/README.md) | [斯洛伐克语](../sk/README.md) | [斯洛文尼亚语](../sl/README.md) | [西班牙语](../es/README.md) | [斯瓦希里语](../sw/README.md) | [瑞典语](../sv/README.md) | [他加禄语 (菲律宾语)](../tl/README.md) | [泰米尔语](../ta/README.md) | [泰卢固语](../te/README.md) | [泰语](../th/README.md) | [土耳其语](../tr/README.md) | [乌克兰语](../uk/README.md) | [乌尔都语](../ur/README.md) | [越南语](../vi/README.md) +[阿拉伯语](../ar/README.md) | [孟加拉语](../bn/README.md) | [保加利亚语](../bg/README.md) | [缅甸语](../my/README.md) | [中文（简体）](./README.md) | [中文（繁体，香港）](../hk/README.md) | [中文（繁体，澳门）](../mo/README.md) | [中文（繁体，台湾）](../tw/README.md) | [克罗地亚语](../hr/README.md) | [捷克语](../cs/README.md) | [丹麦语](../da/README.md) | [荷兰语](../nl/README.md) | [爱沙尼亚语](../et/README.md) | [芬兰语](../fi/README.md) | [法语](../fr/README.md) | [德语](../de/README.md) | [希腊语](../el/README.md) | [希伯来语](../he/README.md) | [印地语](../hi/README.md) | [匈牙利语](../hu/README.md) | [印尼语](../id/README.md) | [意大利语](../it/README.md) | [日语](../ja/README.md) | [卡纳达语](../kn/README.md) | [韩语](../ko/README.md) | [立陶宛语](../lt/README.md) | [马来语](../ms/README.md) | [马拉雅拉姆语](../ml/README.md) | [马拉地语](../mr/README.md) | [尼泊尔语](../ne/README.md) | [尼日利亚皮钦语](../pcm/README.md) | [挪威语](../no/README.md) | [波斯语（法尔西语）](../fa/README.md) | [波兰语](../pl/README.md) | [葡萄牙语（巴西）](../br/README.md) | [葡萄牙语（葡萄牙）](../pt/README.md) | [旁遮普语（古鲁姆奇）](../pa/README.md) | [罗马尼亚语](../ro/README.md) | [俄语](../ru/README.md) | [塞尔维亚语（西里尔字母）](../sr/README.md) | [斯洛伐克语](../sk/README.md) | [斯洛文尼亚语](../sl/README.md) | [西班牙语](../es/README.md) | [斯瓦希里语](../sw/README.md) | [瑞典语](../sv/README.md) | [他加禄语（菲律宾语）](../tl/README.md) | [泰米尔语](../ta/README.md) | [泰卢固语](../te/README.md) | [泰语](../th/README.md) | [土耳其语](../tr/README.md) | [乌克兰语](../uk/README.md) | [乌尔都语](../ur/README.md) | [越南语](../vi/README.md) -> **想要本地克隆？** +> **倾向于本地克隆？** -> 本仓库包含50多种语言的翻译，显著增加了下载大小。要在不下载翻译的情况下克隆，请使用稀疏检出: +> 本仓库包含50多种语言翻译，显著增加了下载大小。若想克隆时不包含翻译文件，请使用稀疏检出： > ```bash > git clone --filter=blob:none --sparse https://github.com/microsoft/AI-For-Beginners.git > cd AI-For-Beginners > git sparse-checkout set --no-cone '/*' '!translations' '!translated_images' > ``` -> 这样您就能快速获得完成课程所需的全部内容。 +> 这样您可以快速下载完成课程所需的所有内容。 -**如果您希望支持更多翻译语言，请查看[这里](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** +**如果您希望支持其他翻译语言，支持的语言列表请查看[这里](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** ## 加入社区 [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -## 你将学习到的内容 +## 你将学到什么 **[课程思维导图](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** 在本课程中，你将学习： -* 人工智能的不同方法，包括使用**知识表示**和推理的“老派”符号方法（[GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)）。 -* **神经网络**和**深度学习**，它们是现代人工智能的核心。我们将通过两种最流行的框架——[TensorFlow](http://Tensorflow.org)和[PyTorch](http://pytorch.org)中的代码讲解这些重要主题背后的概念。 -* 处理图像和文本的**神经架构**。我们会介绍最新模型，但可能稍显缺乏前沿内容。 -* 不太常见的AI方法，如**遗传算法**和**多智能体系统**。 +* 人工智能的不同方法，包括具有**知识表示**和推理的“传统”符号方法（[GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)）。 +* 现代AI核心的**神经网络**和**深度学习**。我们将通过两大流行框架——[TensorFlow](http://Tensorflow.org)和[PyTorch](http://pytorch.org)——中的代码示例解释这些重要主题背后的概念。 +* 用于图像和文本处理的**神经网络架构**。我们将介绍近期模型，但可能未涵盖最前沿的技术。 +* 较少流行的AI方法，如**遗传算法**和**多智能体系统**。 -本课程不会涵盖： +本课程未涵盖内容： -> [在我们的Microsoft Learn合集里找到本课程的全部额外资源](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) +> [在我们的Microsoft Learn合集里查找本课程的所有额外资源](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) -* 使用**AI进行商业应用**的案例。建议参加Microsoft Learn上的[面向业务用户的AI入门](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum)学习路径，或与[INSEAD](https://www.insead.edu/)合作开发的[AI商业学院](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum)。 -* **传统机器学习**，我们在[机器学习初学者课程](http://github.com/Microsoft/ML-for-Beginners)中已有详尽描述。 -* 使用**[认知服务](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**构建的实际AI应用。对此，建议从微软学习的[视觉](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum)、[自然语言处理](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum)、**[使用Azure OpenAI服务的生成式AI](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)**等模块开始。 -* 特定的机器学习**云框架**，如[Azure机器学习](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-bethanycheum)、[Microsoft Fabric](https://learn.microsoft.com/en-us/training/paths/get-started-fabric/?WT.mc_id=academic-77998-bethanycheum) 或 [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum)。建议使用[使用Azure机器学习构建和操作机器学习解决方案](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum)及[使用Azure Databricks构建和操作机器学习解决方案](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum)学习路径。 -* **对话式AI**及**聊天机器人**。有独立的[创建对话式AI解决方案](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum)学习路径，也可参考[这篇博客](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/)了解更多细节。 -* 深度学习背后的**深层数学**。对此我们推荐 Ian Goodfellow、Yoshua Bengio 和 Aaron Courville 合著的 [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) 教科书，在线版本见 [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/)。 +* **AI在商业中的应用案例**。建议学习微软Learn上的[面向商业用户的AI入门](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum)路线上，或与[INSEAD](https://www.insead.edu/)合作开发的[AI商业学院](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum)。 +* 经典的**机器学习**，详见我们的[机器学习初学者课程](http://github.com/Microsoft/ML-for-Beginners)。 +* 使用**[认知服务](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**构建的实际AI应用。建议先学习微软Learn中的视觉、[自然语言处理](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum)、**[Azure OpenAI服务的生成式AI](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)**等模块。 +* 特定的机器学习**云框架**，如[Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-bethanycheum)、[Microsoft Fabric](https://learn.microsoft.com/en-us/training/paths/get-started-fabric/?WT.mc_id=academic-77998-bethanycheum)或[Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum)。建议学习[使用Azure机器学习构建和运营机器学习解决方案](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum)和[使用Azure Databricks构建和运营机器学习解决方案](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum)学习路线。 +* **对话式AI**和**聊天机器人**。另有单独的[创建对话式AI解决方案](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum)学习路径，也可参考[此博客文章](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/)了解详情。 +* 深入的深度学习数学理论。推荐阅读Ian Goodfellow、Yoshua Bengio和Aaron Courville合著的《[深度学习](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618)》，该书也在线提供[https://www.deeplearningbook.org/](https://www.deeplearningbook.org/)。 -若想温和入门云端的_AI_相关主题，可考虑参加[Azure人工智能入门](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum)学习路径。 +如果想平滑入门云端人工智能相关主题，可以考虑学习[Azure上人工智能入门](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum)学习路线。 # 内容 | | 课程链接 | PyTorch/Keras/TensorFlow | 实验 | | :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | | 0 | [课程设置](./lessons/0-course-setup/setup.md) | [设置你的开发环境](./lessons/0-course-setup/how-to-run.md) | | -| I | [**人工智能简介**](./lessons/1-Intro/README.md) | | | -| 01 | [人工智能的介绍与历史](./lessons/1-Intro/README.md) | - | - | +| I | [**人工智能导论**](./lessons/1-Intro/README.md) | | | +| 01 | [人工智能简介和历史](./lessons/1-Intro/README.md) | - | - | | II | **符号人工智能** | -| 02 | [知识表示与专家系统](./lessons/2-Symbolic/README.md) | [专家系统](./lessons/2-Symbolic/Animals.ipynb) / [本体论](./lessons/2-Symbolic/FamilyOntology.ipynb) /[概念图](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | -| III | [**神经网络简介**](./lessons/3-NeuralNetworks/README.md) ||| -| 03 | [感知机](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [笔记本](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [实验](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | -| 04 | [多层感知机及创建我们自己的框架](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [笔记本](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [实验](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | -| 05 | [框架介绍（PyTorch/TensorFlow）及过拟合](./lessons/3-NeuralNetworks/05-Frameworks/README.md) | [PyTorch](./lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb) / [Keras](./lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [实验](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | -| IV | [**计算机视觉**](./lessons/4-ComputerVision/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-77998-cacaste) / [TensorFlow](https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-77998-cacaste)| [在 Microsoft Azure 上探索计算机视觉](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | +| 02 | [知识表示与专家系统](./lessons/2-Symbolic/README.md) | [专家系统](./lessons/2-Symbolic/Animals.ipynb) / [本体](./lessons/2-Symbolic/FamilyOntology.ipynb) / [概念图](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | +| III | [**神经网络导论**](./lessons/3-NeuralNetworks/README.md) ||| +| 03 | [感知器](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [笔记本](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [实验](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | +| 04 | [多层感知器与自创框架](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [笔记本](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [实验](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | +| 05 | [框架简介（PyTorch/TensorFlow）与过拟合](./lessons/3-NeuralNetworks/05-Frameworks/README.md) | [PyTorch](./lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb) / [Keras](./lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [实验](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | +| IV | [**计算机视觉**](./lessons/4-ComputerVision/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-77998-cacaste) / [TensorFlow](https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-77998-cacaste)| [在Microsoft Azure中探索计算机视觉](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | | 06 | [计算机视觉简介。OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [笔记本](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [实验](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | -| 07 | [卷积神经网络](./lessons/4-ComputerVision/07-ConvNets/README.md) & [CNN 结构](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [实验](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | -| 08 | [预训练网络和迁移学习](./lessons/4-ComputerVision/08-TransferLearning/README.md) 和 [训练技巧](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [实验](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | -| 09 | [自编码器和变分自编码器(VAE)](./lessons/4-ComputerVision/09-Autoencoders/README.md) | [PyTorch](./lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb) | | -| 10 | [生成对抗网络 和 艺术风格迁移](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | +| 07 | [卷积神经网络](./lessons/4-ComputerVision/07-ConvNets/README.md) & [CNN架构](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [实验](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | +| 08 | [预训练网络与迁移学习](./lessons/4-ComputerVision/08-TransferLearning/README.md) 和 [训练技巧](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [实验](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | +| 09 | [自动编码器与变分自动编码器(VAEs)](./lessons/4-ComputerVision/09-Autoencoders/README.md) | [PyTorch](./lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb) | | +| 10 | [生成对抗网络 & 艺术风格迁移](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | | 11 | [目标检测](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [实验](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | | 12 | [语义分割。U-Net](./lessons/4-ComputerVision/12-Segmentation/README.md) | [PyTorch](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb) | | -| V | [**自然语言处理**](./lessons/5-NLP/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-77998-cacaste) /[TensorFlow](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-77998-cacaste) | [在 Microsoft Azure 上探索自然语言处理](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| -| 13 | [文本表示。词袋模型/Bow/TF-IDF](./lessons/5-NLP/13-TextRep/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb) | | -| 14 | [语义词嵌入。Word2Vec 和 GloVe](./lessons/5-NLP/14-Embeddings/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb) | | -| 15 | [语言建模。训练你自己的嵌入](./lessons/5-NLP/15-LanguageModeling/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-PyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-TF.ipynb) | [实验](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | -| 16 | [循环神经网络](./lessons/5-NLP/16-RNN/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNTF.ipynb) | | -| 17 | [生成循环网络](./lessons/5-NLP/17-GenerativeNetworks/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb) | [实验](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | -| 18 | [Transformers。BERT。](./lessons/5-NLP/18-Transformers/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb) /[TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersTF.ipynb) | | +| V | [**自然语言处理**](./lessons/5-NLP/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-77998-cacaste) /[TensorFlow](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-77998-cacaste) | [在Microsoft Azure中探索自然语言处理](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| +| 13 | [文本表示。词袋模型(Bow)/TF-IDF](./lessons/5-NLP/13-TextRep/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb) | | +| 14 | [语义词嵌入。Word2Vec和GloVe](./lessons/5-NLP/14-Embeddings/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb) | | +| 15 | [语言模型。训练你自己的词嵌入](./lessons/5-NLP/15-LanguageModeling/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-PyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-TF.ipynb) | [实验](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | +| 16 | [循环神经网络(RNN)](./lessons/5-NLP/16-RNN/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNTF.ipynb) | | +| 17 | [生成型循环网络](./lessons/5-NLP/17-GenerativeNetworks/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb) | [实验](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | +| 18 | [变换器（Transformers）。BERT.](./lessons/5-NLP/18-Transformers/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb) /[TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersTF.ipynb) | | | 19 | [命名实体识别](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [实验](./lessons/5-NLP/19-NER/lab/README.md) | -| 20 | [大型语言模型，提示编程和少样本任务](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | -| VI | **其他 AI 技术** || | +| 20 | [大型语言模型，提示编程与少样本任务](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | +| VI | **其他人工智能技术** || | | 21 | [遗传算法](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [笔记本](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | | 22 | [深度强化学习](./lessons/6-Other/22-DeepRL/README.md) | [PyTorch](./lessons/6-Other/22-DeepRL/CartPole-RL-PyTorch.ipynb) /[TensorFlow](./lessons/6-Other/22-DeepRL/CartPole-RL-TF.ipynb) | [实验](./lessons/6-Other/22-DeepRL/lab/README.md) | | 23 | [多智能体系统](./lessons/6-Other/23-MultiagentSystems/README.md) | | | | VII | **人工智能伦理** | | | -| 24 | [人工智能伦理与负责任的 AI](./lessons/7-Ethics/README.md) | [Microsoft Learn：负责任的 AI 原则](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | -| IX | **额外内容** | | | -| 25 | [多模态网络，CLIP 和 VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [笔记本](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | +| 24 | [人工智能伦理与负责任的人工智能](./lessons/7-Ethics/README.md) | [Microsoft Learn：责任人工智能原则](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | +| IX | **附加内容** | | | +| 25 | [多模态网络，CLIP和VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [笔记本](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | ## 每节课包含 -* 预备阅读材料 -* 可执行的 Jupyter 笔记本，通常针对特定框架（**PyTorch** 或 **TensorFlow**）。可执行笔记本中也包含大量理论内容，因此要理解主题，您需要至少通读其中一个版本的笔记本（PyTorch 或 TensorFlow）。 -* 部分主题提供**实验室**，让您有机会尝试将所学应用于具体问题。 -* 一些章节包含链接至涵盖相关主题的 [**微软学习平台 MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) 模块。 +* 预习材料 +* 可执行的Jupyter笔记本，通常针对特定框架（**PyTorch** 或 **TensorFlow**）。可执行笔记本中还包含大量理论材料，因此为了理解主题，你需要至少完成其中一个版本的笔记本（PyTorch或TensorFlow）。 +* 一些主题还有**实验**，让你有机会将所学知识应用到具体问题中。 +* 某些章节包含链接到覆盖相关主题的[**微软学习**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)模块。 -## 快速开始 +## 入门指南 -### 🎯 AI 新手？从这里开始！ +### 🎯 AI新手？从这里开始！ -如果您是 AI 完全新手，想看快速的实操示例，请查看我们的 [**面向初学者的示例**](./examples/README.md)！这些示例包括： +如果你是AI完全新手，想要快速上手实用示例，请查看我们的[**新手友好示例**](./examples/README.md)！其中包括： -- 🌟 **你好 AI 世界** - 您的第一个 AI 程序（模式识别） +- 🌟 **你好 AI 世界** - 你的第一个AI程序（模式识别） - 🧠 **简单神经网络** - 从零构建神经网络 - 🖼️ **图像分类器** - 带详细注释的图像分类器 -- 💬 **文本情感分析** - 分析文本的积极/消极情绪 +- 💬 **文本情感** - 分析正面/负面文本 这些示例旨在帮助你在深入完整课程之前理解 AI 概念。 ### 📚 完整课程设置 -- 我们创建了一个[设置课程](./lessons/0-course-setup/setup.md)，帮助你配置开发环境。- 对于教育工作者，我们也创建了一个[课程设置课](./lessons/0-course-setup/for-teachers.md)！ -- 如何[在 VSCode 或 Codepace 中运行代码](./lessons/0-course-setup/how-to-run.md) +- 我们创建了一个[设置课程](./lessons/0-course-setup/setup.md)来帮助你设置开发环境。- 对于教育者，我们也创建了一个[课程设置课程](./lessons/0-course-setup/for-teachers.md)！ +- 如何在 VSCode 或 Codespace 中[运行代码](./lessons/0-course-setup/how-to-run.md) -按照以下步骤操作： +请按以下步骤操作： -Fork 仓库：点击本页右上角的“Fork”按钮。 +分叉仓库：点击本页右上角的“Fork”按钮。 克隆仓库：`git clone https://github.com/microsoft/AI-For-Beginners.git` -别忘了给这个仓库加星(🌟)，方便以后查找。 +别忘了给此仓库加星（🌟），这样以后更容易找到。 -## 结识其他学习者 +## 认识其他学习者 -加入我们的[官方 AI Discord 服务器](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum)，结识并与其他参加此课程的学习者交流并获得支持。 +加入我们的[官方 AI Discord 服务器](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum)，与正在参加此课程的其他学习者交流并获得支持。 -如果你在构建过程中有产品反馈或问题，请访问我们的[Azure AI Foundry 开发者论坛](https://aka.ms/foundry/forum) +如果你在构建过程中有产品反馈或疑问，请访问我们的[Azure AI Foundry 开发者论坛](https://aka.ms/foundry/forum) -## 小测验 +## 测验 -> **关于小测验的说明**：所有小测验都包含在etc\quiz-app中的 Quiz-app 文件夹内，或者[在线版](https://ff-quizzes.netlify.app/)。它们在课程内有链接，Quiz 应用程序可本地运行或部署到 Azure；请按照`quiz-app`文件夹中的说明操作。它们正在逐步实现本地化。 +> **关于测验的说明**：所有测验均包含在 etc\quiz-app 文件夹下的 Quiz-app 目录中，或可[在线访问](https://ff-quizzes.netlify.app/) 。它们链接在课程内容内，测验应用可以本地运行或部署到 Azure；请参阅 `quiz-app` 文件夹内的说明。测验内容正在逐步本地化中。 ## 需要帮助 -你有建议或者发现拼写或代码错误吗？请提出 issue 或创建 pull request。 +你有建议或发现拼写或代码错误吗？请提出 issue 或创建拉取请求。 ## 特别感谢 -* **✍️ 主要作者:** [Dmitry Soshnikov](http://soshnikov.com), 博士 -* **🔥 编辑:** [Jen Looper](https://twitter.com/jenlooper), 博士 -* **🎨 速记插画师:** [Tomomi Imura](https://twitter.com/girlie_mac) -* **✅ 小测验制作:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) -* **🙏 核心贡献者:** [Evgenii Pishchik](https://github.com/Pe4enIks) +* **✍️ 主要作者：** [Dmitry Soshnikov](http://soshnikov.com)，博士 +* **🔥 编辑：** [Jen Looper](https://twitter.com/jenlooper)，博士 +* **🎨 速写插画师：** [Tomomi Imura](https://twitter.com/girlie_mac) +* **✅ 测验创建者：** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) +* **🙏 核心贡献者：** [Evgenii Pishchik](https://github.com/Pe4enIks) ## 其他课程 -我们的团队还制作了其他课程！查看： +我们的团队还制作了其他课程！请查看： ### LangChain -[![LangChain4j for Beginners](https://img.shields.io/badge/LangChain4j%20for%20Beginners-22C55E?style=for-the-badge&&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchain4j-for-beginners) -[![LangChain.js for Beginners](https://img.shields.io/badge/LangChain.js%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchainjs-for-beginners?WT.mc_id=m365-94501-dwahlin) +[![LangChain4j 入门](https://img.shields.io/badge/LangChain4j%20for%20Beginners-22C55E?style=for-the-badge&&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchain4j-for-beginners) +[![LangChain.js 入门](https://img.shields.io/badge/LangChain.js%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchainjs-for-beginners?WT.mc_id=m365-94501-dwahlin) --- ### Azure / Edge / MCP / Agents -[![AZD for Beginners](https://img.shields.io/badge/AZD%20for%20Beginners-0078D4?style=for-the-badge&labelColor=E5E7EB&color=0078D4)](https://github.com/microsoft/AZD-for-beginners?WT.mc_id=academic-105485-koreyst) -[![Edge AI for Beginners](https://img.shields.io/badge/Edge%20AI%20for%20Beginners-00B8E4?style=for-the-badge&labelColor=E5E7EB&color=00B8E4)](https://github.com/microsoft/edgeai-for-beginners?WT.mc_id=academic-105485-koreyst) -[![MCP for Beginners](https://img.shields.io/badge/MCP%20for%20Beginners-009688?style=for-the-badge&labelColor=E5E7EB&color=009688)](https://github.com/microsoft/mcp-for-beginners?WT.mc_id=academic-105485-koreyst) -[![AI Agents for Beginners](https://img.shields.io/badge/AI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst) +[![AZD 入门](https://img.shields.io/badge/AZD%20for%20Beginners-0078D4?style=for-the-badge&labelColor=E5E7EB&color=0078D4)](https://github.com/microsoft/AZD-for-beginners?WT.mc_id=academic-105485-koreyst) +[![Edge AI 入门](https://img.shields.io/badge/Edge%20AI%20for%20Beginners-00B8E4?style=for-the-badge&labelColor=E5E7EB&color=00B8E4)](https://github.com/microsoft/edgeai-for-beginners?WT.mc_id=academic-105485-koreyst) +[![MCP 入门](https://img.shields.io/badge/MCP%20for%20Beginners-009688?style=for-the-badge&labelColor=E5E7EB&color=009688)](https://github.com/microsoft/mcp-for-beginners?WT.mc_id=academic-105485-koreyst) +[![AI Agents 入门](https://img.shields.io/badge/AI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst) --- ### 生成式 AI 系列 -[![Generative AI for Beginners](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst) -[![Generative AI (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst) -[![Generative AI (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst) -[![Generative AI (JavaScript)](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst) +[![生成式 AI 入门](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst) +[![生成式 AI (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst) +[![生成式 AI (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst) +[![生成式 AI (JavaScript)](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst) --- ### 核心学习 -[![ML for Beginners](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst) -[![Data Science for Beginners](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst) -[![AI for Beginners](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst) -[![Cybersecurity for Beginners](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung) -[![Web Dev for Beginners](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst) -[![IoT for Beginners](https://img.shields.io/badge/IoT%20for%20Beginners-14B8A6?style=for-the-badge&labelColor=E5E7EB&color=14B8A6)](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst) -[![XR Development for Beginners](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst) +[![机器学习入门](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst) +[![数据科学入门](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst) +[![人工智能入门](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst) +[![网络安全入门](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung) +[![网页开发入门](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst) +[![物联网入门](https://img.shields.io/badge/IoT%20for%20Beginners-14B8A6?style=for-the-badge&labelColor=E5E7EB&color=14B8A6)](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst) +[![XR 开发入门](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst) --- ### Copilot 系列 -[![Copilot for AI Paired Programming](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst) -[![Copilot for C#/.NET](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst) -[![Copilot Adventure](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst) +[![Copilot AI 配对编程](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst) +[![C#/.NET Copilot](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst) +[![Copilot 冒险](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst) ## 获取帮助 -如果你遇到困难或对构建 AI 应用有任何问题。加入其他学习者和经验开发者的讨论，参与 MCP 社区。这是一个支持性的社区，欢迎提问并自由分享知识。 +如果你遇到困难或在构建 AI 应用时有任何问题。加入其他学习者和经验丰富的开发者的讨论社区，共同讨论 MCP。这里是一个支持性的社区，欢迎提问并自由分享知识。 [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -如果在构建过程中有产品反馈或错误，请访问： +如果你在构建过程中有产品反馈或遇到错误，请访问： [![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) --- -**免责声明**： -本文件使用人工智能翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。尽管我们力求准确，但请注意自动翻译可能存在错误或不准确之处。原文文件的原始语言版本应被视为权威来源。对于关键信息，建议使用专业人工翻译。对于因使用本翻译而产生的任何误解或错误解释，我们不承担任何责任。 +**免责声明**： +本文件通过AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)翻译而成。尽管我们力求准确，但请注意，自动翻译可能存在错误或不准确之处。原文应被视为权威来源。对于关键信息，建议采用专业人工翻译。我们不对因使用本翻译而产生的任何误解或误读承担责任。 \ No newline at end of file diff --git a/translations/zh/lessons/0-course-setup/how-to-run.md b/translations/zh/lessons/0-course-setup/how-to-run.md index 831020ef..55792ee9 100644 --- a/translations/zh/lessons/0-course-setup/how-to-run.md +++ b/translations/zh/lessons/0-course-setup/how-to-run.md @@ -1,21 +1,21 @@ # 如何运行代码 -本课程包含许多可执行的示例和实验室，您可能希望运行这些代码。为此，您需要能够在本课程提供的 Jupyter Notebooks 中执行 Python 代码。以下是几种运行代码的方式： +本课程包含许多可执行的示例和实验，您可能希望运行这些内容。为此，您需要能够在本课程提供的 Jupyter 笔记本中执行 Python 代码。运行代码时，您有以下几种选择： -## 在本地计算机上运行 +## 在您电脑上本地运行 -要在本地计算机上运行代码，您需要安装某个版本的 Python。我个人推荐安装 **[miniconda](https://conda.io/en/latest/miniconda.html)**——这是一种轻量级的安装方式，支持用于不同 Python **虚拟环境**的 `conda` 包管理器。 +要在本地电脑上运行代码，您需要安装 Python。推荐安装 **[miniconda](https://conda.io/en/latest/miniconda.html)** ——这是一种相对轻量级的安装方式，支持不同 Python **虚拟环境** 的 `conda` 包管理器。 -安装 miniconda 后，您需要克隆代码库并创建一个用于本课程的虚拟环境： +安装 miniconda 后，克隆本仓库并创建一个虚拟环境来用于本课程： ```bash git clone http://github.com/microsoft/ai-for-beginners @@ -24,53 +24,57 @@ conda env create --name ai4beg --file .devcontainer/environment.yml conda activate ai4beg ``` -### 使用带有 Python 扩展的 Visual Studio Code +### 使用带 Python 扩展的 Visual Studio Code -使用本课程的最佳方式可能是通过 [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) 和 [Python 扩展](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste) 打开它。 +本课程建议使用带有 [Python 扩展](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste) 的 [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) 打开学习。 -> **注意**：克隆并在 VS Code 中打开目录后，系统会自动建议您安装 Python 扩展。您还需要按照上述说明安装 miniconda。 +> **注意**：克隆并在 VS Code 中打开目录后，它会自动建议您安装 Python 扩展。同时您需要按照上述说明安装 miniconda。 -> **注意**：如果 VS Code 建议您在容器中重新打开代码库，请拒绝此操作以使用本地 Python 安装。 +> **注意**：如果 VS Code 建议您在容器中重新打开仓库，您应拒绝此操作以使用本地 Python 安装。 -### 在浏览器中使用 Jupyter +### 使用浏览器中的 Jupyter -您还可以直接在自己的计算机浏览器中使用 Jupyter 环境。实际上，经典 Jupyter 和 Jupyter Hub 都提供了非常方便的开发环境，包括自动补全、代码高亮等功能。 +您也可以在本地电脑的浏览器中使用 Jupyter 环境。无论是经典 Jupyter 还是 JupyterHub，都提供了自动补全、代码高亮等便利的开发环境。 -要在本地启动 Jupyter，请进入课程目录并执行以下命令： +要在本地启动 Jupyter，进入课程目录，然后执行： ```bash jupyter notebook -``` -或 +``` +或 ```bash jupyterhub -``` -然后，您可以导航到任意 `.ipynb` 文件，打开并开始工作。 +``` +然后您可以导航到任何 `.ipynb` 文件，打开并开始操作。 ### 在容器中运行 -Python 安装的一个替代方案是使用容器运行代码。由于我们的代码库包含一个特殊的 `.devcontainer` 文件夹，指示如何为此代码库构建容器，VS Code 会提示您在容器中重新打开代码。这需要安装 Docker，并且操作会更复杂，因此我们建议更有经验的用户使用此方法。 +另一种替代 Python 安装的方法是运行容器。由于本仓库提供了专门的 `.devcontainer` 文件夹，指导如何为本仓库构建容器，VS Code 支持重新在容器中打开代码。这需要安装 Docker，并且稍微复杂些，因此建议更有经验的用户使用。 ## 在云端运行 -如果您不想在本地安装 Python，并且可以访问一些云资源，那么在云端运行代码是一个不错的选择。以下是几种方法： +如果您不想在本地安装 Python，并且可以访问一些云资源，另一个不错的选择是云端运行代码。您可以通过以下几种方式实现： -* 使用 **[GitHub Codespaces](https://github.com/features/codespaces)**，这是 GitHub 为您创建的虚拟环境，可通过 VS Code 的浏览器界面访问。如果您有 Codespaces 的访问权限，只需点击代码库中的 **Code** 按钮，启动一个 codespace，即可快速开始运行。 -* 使用 **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**。 [Binder](https://mybinder.org) 是为像您这样的用户提供的免费云计算资源，用于测试 GitHub 上的代码。在代码库首页有一个按钮可以在 Binder 中打开代码库——这会快速将您带到 Binder 网站，自动构建底层容器并无缝启动 Jupyter 网页界面。 +* 使用 **[GitHub Codespaces](https://github.com/features/codespaces)**，这是 GitHub 为您创建的虚拟环境，可以通过 VS Code 浏览器界面访问。如果您有 Codespaces 权限，只需点击仓库中的 **Code** 按钮，启动 codespace，马上即可运行。 +* 使用 **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**。[Binder](https://mybinder.org) 提供免费的云计算资源，方便用户在线测试 GitHub 上的代码。主页上有按钮可一键打开仓库到 Binder——您将进入 Binder 网站，它会构建基础容器，并无缝启动 Jupyter 网页界面。 -> **注意**：为防止滥用，Binder 对某些网络资源的访问进行了限制。这可能会导致某些代码无法运行，例如从公共互联网获取模型或数据集的代码。您可能需要找到一些替代方法。此外，Binder 提供的计算资源相对基础，因此在后续更复杂的课程中，训练速度会很慢。 +> **注意**：为防止滥用，Binder 屏蔽了部分网络资源访问。这可能导致部分依赖于互联网获取模型和/或数据集的代码无法运行，您可能需要寻找变通方案。此外，Binder 提供的计算资源较为基础，因此训练速度较慢，特别是在后期较复杂的课程中。 -## 在云端使用 GPU 运行 +## 在带 GPU 的云端运行 -本课程的一些后续课程如果有 GPU 支持会受益匪浅，否则训练速度会非常慢。如果您可以通过 [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) 或您的机构访问云资源，可以选择以下几种方式： +本课程后期一些课程如果支持 GPU 会大大加快速度。例如，模型训练在没有 GPU 的情况下会非常缓慢。您可以考虑以下几种方案，尤其是如果您通过 [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) 或您的学校具备云资源访问权限： -* 创建 [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste)，并通过 Jupyter 连接到它。然后，您可以直接将代码库克隆到虚拟机上并开始学习。NC 系列虚拟机支持 GPU。 +* 创建 [数据科学虚拟机](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste)，并通过 Jupyter 连接。您可以直接在虚拟机上克隆仓库，开始学习。NC 系列虚拟机支持 GPU。 -> **注意**：某些订阅（包括 Azure for Students）默认不提供 GPU 支持。您可能需要通过技术支持请求额外的 GPU 核心。 +> **注意**：部分订阅，包括 Azure for Students，开箱即用并不支持 GPU，您可能需要通过技术支持请求申请额外 GPU 核心。 -* 创建 [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste)，然后使用其中的 Notebook 功能。[此视频](https://azure-for-academics.github.io/quickstart/azureml-papers/) 展示了如何将代码库克隆到 Azure ML Notebook 并开始使用。 +* 创建 [Azure 机器学习工作区](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste)，并使用其中的 Notebook 功能。[此视频](https://azure-for-academics.github.io/quickstart/azureml-papers/) 展示了如何在 Azure ML Notebook 中克隆仓库并开始使用。 -您还可以使用 Google Colab，它提供一些免费的 GPU 支持，并将 Jupyter Notebooks 上传到其中逐一执行。 +您还可以使用 Google Colab，其免费提供部分 GPU 支持，并上传 Jupyter 笔记本逐个执行。 +--- + + **免责声明**： -本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档为权威来源。对于关键信息，建议使用专业人工翻译。因使用本翻译而引起的任何误解或误读，我们概不负责。 \ No newline at end of file +本文档使用 AI 翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 翻译而成。尽管我们力求准确，但请注意，自动翻译可能包含错误或不准确之处。原始文档的母语版本应被视为权威来源。对于重要信息，建议采用专业人工翻译。我们不对因使用此翻译所引起的任何误解或错误解读承担责任。 + \ No newline at end of file diff --git a/translations/zh/lessons/1-Intro/README.md b/translations/zh/lessons/1-Intro/README.md index f9740728..241337e1 100644 --- a/translations/zh/lessons/1-Intro/README.md +++ b/translations/zh/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 人工智能简介 -![人工智能内容简介的涂鸦](../../../../translated_images/zh/ai-intro.bf28d1ac4235881c.png) +![人工智能内容简介的涂鸦](../../../../translated_images/zh/ai-intro.bf28d1ac4235881c.webp) > 涂鸦作者：[Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: 最初，计算机是由[查尔斯·巴贝奇](https://en.wikipedia.org/wiki/Charles_Babbage)发明的，用于按照明确的程序（算法）对数字进行操作。尽管现代计算机比19世纪提出的原始模型先进得多，但它们仍然遵循受控计算的基本理念。因此，如果我们知道实现目标所需的确切步骤序列，就可以编程让计算机完成某项任务。 -![一个人的照片](../../../../translated_images/zh/dsh_age.d212a30d4e54fb5f.png) +![一个人的照片](../../../../translated_images/zh/dsh_age.d212a30d4e54fb5f.webp) > 照片由 [Vickie Soshnikova](http://twitter.com/vickievalerie) 提供 @@ -46,7 +46,7 @@ CO_OP_TRANSLATOR_METADATA: 在讨论**[智能](https://en.wikipedia.org/wiki/Intelligence)**这个术语时，一个问题是我们并没有对其明确的定义。有人认为智能与**抽象思维**或**自我意识**相关，但我们无法准确定义它。 -![一只猫的照片](../../../../translated_images/zh/photo-cat.8c8e8fb760ffe457.jpg) +![一只猫的照片](../../../../translated_images/zh/photo-cat.8c8e8fb760ffe457.webp) > [照片](https://unsplash.com/photos/75715CVEJhI)由 [Amber Kipp](https://unsplash.com/@sadmax) 提供，来自Unsplash @@ -98,13 +98,13 @@ CO_OP_TRANSLATOR_METADATA: > | 那机器学习呢？ | | > |--------------|-----------| -> | 人工智能的一部分是基于计算机通过一些数据学习解决问题的，这被称为**机器学习**。我们不会在本课程中讨论经典的机器学习——请参考单独的[机器学习初学者课程](http://aka.ms/ml-beginners)。 | ![机器学习初学者](../../../../translated_images/zh/ml-for-beginners.9e4fed176fd5817d.png) | +> | 人工智能的一部分是基于计算机通过一些数据学习解决问题的，这被称为**机器学习**。我们不会在本课程中讨论经典的机器学习——请参考单独的[机器学习初学者课程](http://aka.ms/ml-beginners)。 | ![机器学习初学者](../../../../translated_images/zh/ml-for-beginners.9e4fed176fd5817d.webp) | ## 人工智能简史 人工智能作为一个领域始于20世纪中期。最初，符号推理是一种流行的方法，并取得了一些重要的成功，例如专家系统——能够在某些有限问题领域中充当专家的计算机程序。然而，很快就发现这种方法并不具有良好的扩展性。从专家那里提取知识、在计算机中表示这些知识并保持知识库的准确性，结果证明是一项非常复杂且在许多情况下成本过高的任务。这导致了20世纪70年代所谓的[人工智能寒冬](https://en.wikipedia.org/wiki/AI_winter)。 -人工智能简史 +人工智能简史 > 图片由 [Dmitry Soshnikov](http://soshnikov.com) 提供 @@ -124,7 +124,7 @@ CO_OP_TRANSLATOR_METADATA: * 现代助手，如Cortana、Siri或Google Assistant，都是混合系统，使用神经网络将语音转换为文本并识别我们的意图，然后采用一些推理或明确的算法来执行所需的操作。 * 未来，我们可能会期待一个完全基于神经网络的模型能够自行处理对话。最近的GPT和[Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft)系列神经网络在这方面表现出色。 -图灵测试的演变 +图灵测试的演变 > 图片由 Dmitry Soshnikov 提供，[照片](https://unsplash.com/photos/r8LmVbUKgns)由 [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto) 提供，来源于 Unsplash ## 最近的人工智能研究 diff --git a/translations/zh/lessons/2-Symbolic/Animals.ipynb b/translations/zh/lessons/2-Symbolic/Animals.ipynb index bbec2cd4..b2aab4de 100644 --- a/translations/zh/lessons/2-Symbolic/Animals.ipynb +++ b/translations/zh/lessons/2-Symbolic/Animals.ipynb @@ -6,25 +6,25 @@ "collapsed": true }, "source": [ - "# 实现一个动物专家系统\n", + "# 实现动物专家系统\n", "\n", "来自 [AI for Beginners Curriculum](http://github.com/microsoft/ai-for-beginners) 的示例。\n", "\n", - "在这个示例中，我们将实现一个简单的基于知识的系统，通过一些物理特征来判断动物。该系统可以用以下的 AND-OR 树表示（这是整个树的一部分，我们可以轻松添加更多规则）：\n", + "在此示例中，我们将实现一个简单的基于知识的系统，根据一些物理特征来确定动物。该系统可以用以下 AND-OR 树表示（这是整个树的一部分，我们可以很容易地添加更多规则）：\n", "\n", - "![](../../../../lessons/2-Symbolic/images/AND-OR-Tree.png)\n" + "![](../../../../../../translated_images/zh/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## 我们自己的基于反向推理的专家系统外壳\n", + "## 我们自己的带有逆向推理的专家系统外壳\n", "\n", - "让我们尝试基于产生式规则定义一种简单的知识表示语言。我们将使用 Python 类作为关键字来定义规则。主要会有三种类型的类：\n", - "* `Ask` 表示需要向用户提问的问题。它包含一组可能的答案。\n", - "* `If` 表示一条规则，它只是用于存储规则内容的语法糖。\n", - "* `AND`/`OR` 是用于表示树的 AND/OR 分支的类。它们仅存储内部的参数列表。为了简化代码，所有功能都定义在父类 `Content` 中。\n" + "让我们尝试定义一个基于产生规则的简单知识表示语言。我们将使用 Python 类作为关键字来定义规则。基本上将有三种类型的类：\n", + "* `Ask` 表示需要向用户提出的问题。它包含可能的答案集合。\n", + "* `If` 表示一个规则，它只是存储规则内容的语法糖\n", + "* `AND`/`OR` 是表示树的 AND/OR 分支的类。它们只存储内部的参数列表。为简化代码，所有功能都定义在父类 `Content` 中。\n" ] }, { @@ -66,7 +66,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "在我们的系统中，工作记忆将包含作为**属性-值对**的**事实**列表。知识库可以定义为一个大的字典，将动作（应插入工作记忆的新事实）映射到条件，这些条件以AND-OR表达式表示。此外，一些事实可以被`询问`。\n" + "在我们的系统中，工作记忆将包含作为**属性-值对**的**事实**列表。知识库可以定义为一个大型字典，将动作（应插入工作记忆的新事实）映射到以与或表达式表示的条件。此外，某些事实可以被`Ask`。\n" ] }, { @@ -99,13 +99,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "为了进行反向推理，我们将定义一个 `Knowledgebase` 类。它将包含以下内容：\n", - "* 工作的 `memory` - 一个将属性映射到值的字典\n", - "* 知识库的 `rules`，格式如上所述\n", + "为了执行向后推理，我们将定义 `Knowledgebase` 类。它将包含：\n", + "* 工作的 `memory` —— 一个将属性映射到值的字典\n", + "* 知识库中的 `rules`，格式如上文所定义\n", "\n", "两个主要方法是：\n", - "* `get` 用于获取某个属性的值，如果需要会执行推理。例如，`get('color')` 将获取颜色槽的值（如果需要会询问，并将值存储在工作内存中以供后续使用）。如果我们询问 `get('color:blue')`，它会询问颜色，然后根据颜色返回 `y`/`n` 值。\n", - "* `eval` 执行实际的推理，即遍历 AND/OR 树，评估子目标等。\n" + "* `get` 用于获取属性的值，必要时执行推理。例如，`get('color')` 会获取颜色槽的值（如有必要会询问，并将值存储以备后续使用在工作内存中）。如果我们调用 `get('color:blue')`，它会询问颜色，然后根据颜色返回 `y`/`n` 值。\n", + "* `eval` 执行实际推理，即遍历 AND/OR 树，评估子目标等。\n" ] }, { @@ -172,7 +172,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "现在让我们定义我们的动物知识库并进行咨询。请注意，此操作会向您提问。您可以通过输入 `y`/`n` 来回答是非问题，或者通过指定数字（0..N）来回答有多个选项的问题。\n" + "现在让我们定义我们的动物知识库并进行咨询。请注意，此调用会向您提问。您可以通过输入 `y`/`n` 来回答是非题，或者通过指定数字（0..N）来回答具有多个选项的题目。\n" ] }, { @@ -229,11 +229,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 使用 PyKnow 进行前向推理\n", + "## 使用 Experta 进行正向推理\n", "\n", - "在下一个示例中，我们将尝试使用一个知识表示库 [PyKnow](https://github.com/buguroo/pyknow/) 来实现前向推理。**PyKnow** 是一个用于在 Python 中创建前向推理系统的库，其设计类似于经典的旧系统 [CLIPS](http://www.clipsrules.net/index.html)。\n", + "在下一个示例中，我们将尝试使用用于知识表示的库之一 [Experta](https://github.com/nilp0inter/experta) 实现正向推理。**Experta** 是一个用于在 Python 中创建正向推理系统的库，其设计类似于经典的旧系统 [CLIPS](http://www.clipsrules.net/index.html)。\n", "\n", - "我们本可以自己实现前向链推理，且不会遇到太多问题，但简单的实现通常效率不高。为了更高效地进行规则匹配，使用了一种特殊的算法 [Rete](https://en.wikipedia.org/wiki/Rete_algorithm)。\n" + "我们本来也可以自己实现正向链式推理而不会有太大问题，但简单的实现通常效率不高。为了更有效地进行规则匹配，使用了一种特殊算法 [Rete](https://en.wikipedia.org/wiki/Rete_algorithm)。\n" ] }, { @@ -247,32 +247,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "Collecting git+https://github.com/buguroo/pyknow/\n", - " Cloning https://github.com/buguroo/pyknow/ to /tmp/pip-req-build-3cqeulyl\n", - " Running command git clone --filter=blob:none --quiet https://github.com/buguroo/pyknow/ /tmp/pip-req-build-3cqeulyl\n", - " Resolved https://github.com/buguroo/pyknow/ to commit 48818336f2e9a126f1964f2d8dc22d37ff800fe8\n", - " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hCollecting frozendict==1.2\n", - " Using cached frozendict-1.2.tar.gz (2.6 kB)\n", - " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hCollecting schema==0.6.7\n", - " Using cached schema-0.6.7-py2.py3-none-any.whl (14 kB)\n", - "Building wheels for collected packages: pyknow, frozendict\n", - " Building wheel for pyknow (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for pyknow: filename=pyknow-1.7.0-py3-none-any.whl size=34228 sha256=b7de5b09292c4007667c72f69b98d5a1b5f7324ff15f9dd8e077c3d5f7aade42\n", - " Stored in directory: /tmp/pip-ephem-wheel-cache-k7jpave7/wheels/81/1a/d3/f6c15dbe1955598a37755215f2a10449e7418500d7bd4b9508\n", - " Building wheel for frozendict (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for frozendict: filename=frozendict-1.2-py3-none-any.whl size=3148 sha256=2863d55c240d2409cddf05ccfe600591f8478681549fc97555c47c90dc6bb160\n", - " Stored in directory: /home/rg/.cache/pip/wheels/49/ac/f8/cb8120244e710bdb479c86198b03c7b08c3c2d3d2bf448fd6e\n", - "Successfully built pyknow frozendict\n", - "Installing collected packages: schema, frozendict, pyknow\n", - "Successfully installed frozendict-1.2 pyknow-1.7.0 schema-0.6.7\n" + "Collecting git+https://github.com/nilp0inter/experta\n", + " Cloning https://github.com/nilp0inter/experta to /tmp/pip-req-build-7qurtwk3\n", + " Running command git clone --filter=blob:none --quiet https://github.com/nilp0inter/experta /tmp/pip-req-build-7qurtwk3\n", + " Resolved https://github.com/nilp0inter/experta to commit c6d5834b123861f5ae09e7d07027dc98bec58741\n", + " Installing build dependencies ... \u001b[?25ldone\n", + "\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n", + "\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25hRequirement already satisfied: frozendict~=2.4.6 in /opt/conda/envs/ai4beg/lib/python3.12/site-packages (from experta==1.9.5.dev1) (2.4.7)\n", + "Collecting schema~=0.6.7 (from experta==1.9.5.dev1)\n", + " Downloading schema-0.6.8-py2.py3-none-any.whl.metadata (14 kB)\n", + "Downloading schema-0.6.8-py2.py3-none-any.whl (14 kB)\n", + "Building wheels for collected packages: experta\n", + " Building wheel for experta (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25h Created wheel for experta: filename=experta-1.9.5.dev1-py3-none-any.whl size=34804 sha256=888c459512a5e713f4b674caa9a0f96cfdf07ec0d6eb56cc318ce0653d218014\n", + " Stored in directory: /tmp/pip-ephem-wheel-cache-1eeii9zy/wheels/3d/e8/bb/22d7956359603fa8dd679aa09f5b8efb3f29991c3986fdc787\n", + "Successfully built experta\n", + "Installing collected packages: schema, experta\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2/2\u001b[0m [experta]\n", + "\u001b[1A\u001b[2KSuccessfully installed experta-1.9.5.dev1 schema-0.6.8\n" ] } ], "source": [ "import sys\n", - "!{sys.executable} -m pip install git+https://github.com/buguroo/pyknow/" + "!{sys.executable} -m pip install git+https://github.com/nilp0inter/experta" ] }, { @@ -283,15 +282,15 @@ }, "outputs": [], "source": [ - "from pyknow import *\n", - "#import pyknow" + "from experta import *\n", + "#import experta" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "我们将把我们的系统定义为一个继承自 `KnowledgeEngine` 的类。每条规则由一个带有 `@Rule` 注解的单独函数定义，该注解指定规则何时触发。在规则内部，我们可以使用 `declare` 函数添加新事实，添加这些事实将导致前向推理引擎调用更多规则。\n" + "我们将把系统定义为一个继承自 `KnowledgeEngine` 的类。每条规则由一个带有 `@Rule` 注解的单独函数定义，该注解指定规则何时触发。在规则内部，我们可以使用 `declare` 函数添加新的事实，添加这些事实将导致前向推理引擎调用更多规则。\n" ] }, { @@ -378,7 +377,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "一旦我们定义了一个知识库，我们会用一些初始事实填充工作内存，然后调用`run()`方法来执行推理。结果可以看到新的推导事实被添加到工作内存中，包括关于动物的最终事实（如果我们正确设置了所有初始事实）。\n" + "一旦我们定义了知识库，我们就用一些初始事实来填充工作内存，然后调用 `run()` 方法来执行推理。结果你可以看到，新的推断事实被添加到工作内存中，包括关于动物的最终事实（如果我们正确设置了所有初始事实）。\n" ] }, { @@ -440,7 +439,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n---\n\n**免责声明**： \n本文档使用AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。原始语言的文档应被视为权威来源。对于关键信息，建议使用专业人工翻译。我们不对因使用此翻译而产生的任何误解或误读承担责任。\n" + "---\n\n\n**免责声明**： \n本文件已使用人工智能翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。尽管我们力求准确，但请注意，自动翻译可能存在错误或不准确之处。请以原始语言的原文件为权威来源。对于重要信息，建议采用专业人工翻译。我们不对因使用本翻译而产生的任何误解或误释承担责任。\n\n" ] } ], @@ -467,8 +466,8 @@ "version": "3.11.2" }, "coopTranslator": { - "original_hash": "ab2bd97b0453415b89a469284609a8ce", - "translation_date": "2025-08-31T10:07:39+00:00", + "original_hash": "8ef43db4b9182239fd150a76bd494fdb", + "translation_date": "2026-01-15T11:43:18+00:00", "source_file": "lessons/2-Symbolic/Animals.ipynb", "language_code": "zh" } diff --git a/translations/zh/lessons/2-Symbolic/README.md b/translations/zh/lessons/2-Symbolic/README.md index db01d372..c5d769af 100644 --- a/translations/zh/lessons/2-Symbolic/README.md +++ b/translations/zh/lessons/2-Symbolic/README.md @@ -1,116 +1,116 @@ # 知识表示与专家系统 -![符号 AI 内容摘要](../../../../translated_images/zh/ai-symbolic.715a30cb610411a6.png) +![符号人工智能内容摘要](../../../../../../translated_images/zh/ai-symbolic.715a30cb610411a6.webp) -> [Tomomi Imura](https://twitter.com/girlie_mac) 的手绘笔记 +> 草图由 [Tomomi Imura](https://twitter.com/girlie_mac) 绘制 -人工智能的探索基于对知识的追求，试图像人类一样理解世界。但如何实现这一目标呢？ +人工智能的追求是基于对知识的探索，以类似人类的方式理解世界。但你该如何实现这一点呢？ ## [课前测验](https://ff-quizzes.netlify.app/en/ai/quiz/3) -在人工智能的早期，创建智能系统的自上而下方法（在上一课中讨论过）非常流行。这种方法的核心思想是将人类的知识提取为机器可读的形式，然后利用这些知识自动解决问题。这种方法基于两个重要的理念： +在人工智能的早期阶段，自顶向下创建智能系统的方法（在上一课中讨论）很流行。其想法是从人类身上提取知识，转换成机器可读的形式，然后用它来自动解决问题。这种方法基于两个重要理念： * 知识表示 * 推理 ## 知识表示 -符号 AI 中一个重要的概念是**知识**。需要将知识与*信息*或*数据*区分开。例如，人们可以说书籍包含知识，因为通过学习书籍可以成为专家。然而，书籍中实际包含的是*数据*，通过阅读书籍并将这些数据整合到我们的世界模型中，我们将数据转化为知识。 +符号人工智能中的一个重要概念是**知识**。必须区分知识与*信息*或*数据*。例如，可以说书中包含知识，因为学习书籍可以成为专家。然而，书中实际上包含的是*数据*，通过阅读书籍并将这些数据整合进我们的世界模型，我们把数据转化为知识。 -> ✅ **知识**是我们头脑中包含的内容，代表我们对世界的理解。它通过主动的**学习**过程获得，将我们接收到的信息片段整合到我们对世界的活动模型中。 +> ✅ **知识**是我们头脑中包含的东西，代表我们对世界的理解。它是通过主动的**学习**过程获得的，将我们接收到的信息片段整合进我们的活动世界模型。 -通常，我们不会严格定义知识，而是通过 [DIKW 金字塔](https://en.wikipedia.org/wiki/DIKW_pyramid)将其与其他相关概念对齐。金字塔包含以下概念： +通常，我们不会严格定义知识，而是通过[DIKW金字塔](https://en.wikipedia.org/wiki/DIKW_pyramid)将其与其他相关概念对齐。它包含以下概念： -* **数据**是以物理媒介表示的内容，例如书面文字或口头语言。数据独立于人类存在，可以在人与人之间传递。 -* **信息**是我们在头脑中对数据的解释。例如，当我们听到“计算机”这个词时，我们对它有一定的理解。 -* **知识**是信息被整合到我们的世界模型中。例如，一旦我们了解了什么是计算机，我们就开始对它的工作原理、价格以及用途有一些想法。这些相互关联的概念网络构成了我们的知识。 -* **智慧**是我们对世界理解的更高层次，代表着*元知识*，例如关于如何以及何时使用知识的概念。 +* **数据**是以物理媒介表示的事物，如书面文字或口语。数据独立于人类存在，可以在人与人之间传递。 +* **信息**是我们头脑中对数据的解释。例如，当我们听到“计算机”这个词时，我们对它有一定理解。 +* **知识**是信息被整合进我们的世界模型。例如，一旦我们了解计算机是什么，我们便开始对它的工作原理、价格及用途有一些想法。这些相互关联的概念网络构成了我们的知识。 +* **智慧**是我们对世界理解的更高层次，代表*元知识*，例如关于知识何时及如何使用的某种认知。 - + -*图片来源：[维基百科](https://commons.wikimedia.org/w/index.php?curid=37705247)，作者 Longlivetheux - 自制作品，CC BY-SA 4.0* +*图片来自[维基百科](https://commons.wikimedia.org/w/index.php?curid=37705247)，作者Longlivetheux，CC BY-SA 4.0* -因此，**知识表示**的问题是找到一种有效的方法，将知识以数据的形式表示在计算机中，使其能够自动使用。这可以看作是一个光谱： +因此，**知识表示**的问题是找到一种有效方式，将知识以数据形式表示于计算机内部，使其能被自动使用。这可以看作一个光谱： -![知识表示光谱](../../../../translated_images/zh/knowledge-spectrum.b60df631852c0217.png) +![知识表示光谱](../../../../../../translated_images/zh/knowledge-spectrum.b60df631852c0217.webp) -> 图片来源：[Dmitry Soshnikov](http://soshnikov.com) +> 图片来自 [Dmitry Soshnikov](http://soshnikov.com) -* 左侧是非常简单的知识表示类型，可以被计算机有效使用。最简单的是算法式表示，即通过计算机程序表示知识。然而，这并不是表示知识的最佳方式，因为它缺乏灵活性。我们头脑中的知识通常是非算法化的。 -* 右侧是自然语言文本等表示方式。这种方式最强大，但无法用于自动推理。 +* 在左侧，是计算机能够有效使用的非常简单的知识表示类型。最简单的是算法式，知识由计算机程序表示。然而，这不是最佳的知识表示方式，因为它不够灵活。我们头脑中的知识往往是非算法的。 +* 在右侧，是自然文本这类表示手段。它最强大，但无法用于自动推理。 -> ✅ 想一想你是如何在头脑中表示知识并将其转化为笔记的。是否有某种格式能帮助你更好地记忆？ +> ✅ 花一分钟思考你是如何在头脑中表示知识并转化为笔记的。有哪种格式对你来说特别有效，有助于记忆？ -## 计算机知识表示的分类 +## 计算机知识表示分类 我们可以将不同的计算机知识表示方法分为以下几类： -* **网络表示**基于我们头脑中存在相互关联的概念网络。我们可以尝试在计算机中以图的形式再现这些网络——即所谓的**语义网络**。 +* **网络表示**基于我们头脑中存在一个相互关联的概念网络。我们可以尝试在计算机中以图的形式重现同样的网络——所谓的**语义网络**。 -1. **对象-属性-值三元组**或**属性-值对**。由于图可以在计算机中表示为节点和边的列表，我们可以通过三元组列表表示语义网络，其中包含对象、属性和值。例如，我们可以构建以下关于编程语言的三元组： +1. **对象-属性-值三元组**或**属性-值对**。由于图可以被表示为计算机中的节点和边列表，我们可以用一个三元组列表表示语义网络，三元组包含对象、属性和值。例如，我们构建以下关于编程语言的三元组： 对象 | 属性 | 值 ------|------|----- -Python | 是 | 无类型语言 -Python | 发明者 | Guido van Rossum -Python | 块语法 | 缩进 -无类型语言 | 没有 | 类型定义 +-------|-----------|------ +Python | is | Untyped-Language +Python | invented-by | Guido van Rossum +Python | block-syntax | indentation +Untyped-Language | doesn't have | type definitions -> ✅ 想一想三元组如何用于表示其他类型的知识。 +> ✅ 思考三元组如何用来表示其他类型的知识。 -2. **层次表示**强调我们通常在头脑中创建对象的层次结构。例如，我们知道金丝雀是一种鸟类，而所有鸟类都有翅膀。我们还知道金丝雀通常是什么颜色，以及它们的飞行速度。 +2. **层次表示**强调我们头脑中常常构建一个对象层次。例如，我们知道金丝雀是鸟，而所有鸟都有翅膀。我们还对金丝雀通常的颜色和飞行速度有所了解。 - - **框架表示**基于将每个对象或对象类别表示为一个**框架**，框架包含**槽**。槽可以有默认值、值限制或存储过程，这些过程可以被调用以获取槽的值。所有框架形成一个类似于面向对象编程语言中的对象层次结构。 - - **场景**是表示复杂情境的特殊框架，这些情境可以随着时间展开。 + - **框架表示**基于将每个对象或对象类表示为包含**槽位**的**框架**。槽位有可能的默认值、值的限制或可以调用的存储过程以获得槽位的值。所有框架形成一个类似面向对象编程语言中的对象层次。 + - **场景**是一种特殊的框架，表示可以随时间展开的复杂情境。 **Python** -槽 | 值 | 默认值 | 区间 ----|----|--------|----- -名称 | Python | | -是 | 无类型语言 | | -变量命名 | | 驼峰命名 | -程序长度 | | | 5-5000 行 -块语法 | 缩进 | | +槽位 | 值 | 默认值 | 范围 | +-----|-------|---------------|----------| +名称 | Python | | | +类别 | Untyped-Language | | | +变量大小写 | | CamelCase | | +程序长度 | | | 5-5000 行 | +块语法 | 缩进 | | | -3. **过程表示**基于通过一系列动作表示知识，这些动作可以在某些条件发生时执行。 - - 生产规则是允许我们得出结论的 if-then 语句。例如，医生可以有一个规则：**如果**患者发高烧**或**血液检测中 C 反应蛋白水平高**那么**他有炎症。一旦我们遇到其中一个条件，就可以得出关于炎症的结论，然后在进一步推理中使用它。 - - 算法可以被认为是另一种过程表示形式，尽管它们几乎从未直接用于基于知识的系统。 +3. **过程表示**基于通过一系列条件发生时可执行的动作列表来表示知识。 + - 产生规则是允许我们得出结论的 if-then 语句。例如，医生可能有规则说**如果**病人有高烧**或**血液检测中C-反应蛋白含量高**，那么病人有炎症。一旦遇到其中一个条件，我们就可以得出炎症结论，并在进一步推理中使用。 + - 算法可以被认为是过程表示的另一种形式，尽管它们几乎从不直接用于基于知识的系统。 -4. **逻辑**最初由亚里士多德提出，作为一种表示人类普遍知识的方法。 - - 谓词逻辑作为一种数学理论过于复杂而无法计算，因此通常使用它的某些子集，例如 Prolog 中使用的 Horn 子句。 - - 描述逻辑是一组逻辑系统，用于表示和推理对象层次结构以及分布式知识表示，例如*语义网*。 +4. **逻辑**最初由亚里士多德提出，作为表示普遍人类知识的方法。 + - 谓词逻辑作为数学理论过于丰富，无法计算，因此通常使用其某个子集，如在Prolog中使用的Horn子句。 + - 描述逻辑是一族逻辑系统，用于表示和推理对象层次及分布式知识表示，如*语义网*。 ## 专家系统 -符号 AI 的早期成功之一是所谓的**专家系统**——设计为在某些有限问题领域中充当专家的计算机系统。它们基于从一个或多个领域专家提取的**知识库**，并包含一个在其之上执行推理的**推理引擎**。 +符号人工智能的早期成功之一是所谓的**专家系统**——设计用来在有限问题领域充当专家的计算机系统。它们基于从一个或多个专家处提取的**知识库**，并包含一个在其上执行推理的**推理引擎**。 -![人类架构](../../../../translated_images/zh/arch-human.5d4d35f1bba3ab1c.png) | ![基于知识的系统架构](../../../../translated_images/zh/arch-kbs.3ec5c150b09fa8da.png) -----------------------------------|---------------------------------------- -人类神经系统的简化结构 | 基于知识的系统的架构 +![人类结构](../../../../../../translated_images/zh/arch-human.5d4d35f1bba3ab1c.webp) | ![基于知识的系统](../../../../../../translated_images/zh/arch-kbs.3ec5c150b09fa8da.webp) +---------------------------------------------|------------------------------------------------ +人类神经系统简化结构 | 基于知识的系统架构 -专家系统的构建类似于人类的推理系统，其中包含**短期记忆**和**长期记忆**。同样，在基于知识的系统中，我们区分以下组件： +专家系统构建类似人类推理系统，包含**短期记忆**和**长期记忆**。类似地，在基于知识的系统中我们区分以下组成部分： -* **问题记忆**：包含当前正在解决的问题的知识，例如患者的体温或血压、是否有炎症等。这些知识也称为**静态知识**，因为它包含我们当前对问题的了解的快照——即所谓的*问题状态*。 -* **知识库**：表示关于问题领域的长期知识。它是从领域专家手动提取的，并且不会因咨询而改变。由于它允许我们从一个问题状态导航到另一个问题状态，也被称为**动态知识**。 -* **推理引擎**：协调整个搜索问题状态空间的过程，在必要时向用户提问。它还负责为每个状态找到适用的规则。 +* **问题记忆**：包含当前正在解决问题的知识，即患者的体温或血压、是否有炎症等。这也称为**静态知识**，因为它包含我们当前对问题的快照——所谓的*问题状态*。 +* **知识库**：代表问题领域的长期知识。它从人类专家手动提取，在每次咨询间不变。由于它允许我们从一个问题状态导航到另一个状态，也称为**动态知识**。 +* **推理引擎**：协调整个在问题状态空间的搜索过程，必要时向用户提问。它还负责找到适用于每个状态的规则。 -例如，让我们考虑以下基于动物物理特征的专家系统： +例如，考虑一个基于动物物理特征决定动物种类的专家系统： -![AND-OR 树](../../../../translated_images/zh/AND-OR-Tree.5592d2c70187f283.png) +![与-或树](../../../../../../translated_images/zh/AND-OR-Tree.5592d2c70187f283.webp) -> 图片来源：[Dmitry Soshnikov](http://soshnikov.com) +> 图片来自 [Dmitry Soshnikov](http://soshnikov.com) -此图称为**AND-OR 树**，它是生产规则集的图形表示。在从专家提取知识的初期，绘制树图非常有用。为了在计算机中表示知识，使用规则会更方便： +该图称为**与-或树**，是产生规则集合的图形表示。绘制树在提取专家知识初期十分有用。为了在计算机内表示知识，更方便用规则表示： ``` IF the animal eats meat @@ -121,78 +121,78 @@ OR (animal has sharp teeth THEN the animal is a carnivore ``` -你会注意到规则左侧的每个条件和动作本质上都是对象-属性-值（OAV）三元组。**工作记忆**包含与当前正在解决的问题相关的 OAV 三元组集合。**规则引擎**寻找满足条件的规则并应用它们，将新的三元组添加到工作记忆中。 +你可以注意到，规则左侧的每一个条件和动作本质上是对象-属性-值（OAV）三元组。**工作内存**包含与当前解决问题相关的OAV三元组集合。**规则引擎**寻找满足条件的规则并应用它们，向工作内存添加新的三元组。 -> ✅ 画一个你喜欢主题的 AND-OR 树！ +> ✅ 自己绘制一个你喜欢主题的与-或树吧！ ### 前向推理与后向推理 -上述过程称为**前向推理**。它从工作记忆中关于问题的初始数据开始，然后执行以下推理循环： +上述过程称为**前向推理**。它从工作内存中关于问题的初始数据开始，然后执行以下推理循环： -1. 如果目标属性存在于工作记忆中——停止并给出结果 -2. 查找所有条件当前满足的规则——获得**冲突集**规则。 -3. 执行**冲突解决**——选择一个将在此步骤中执行的规则。可能有不同的冲突解决策略： - - 选择知识库中第一个适用的规则 - - 随机选择一个规则 - - 选择*更具体*的规则，即满足“左侧”（LHS）最多条件的规则 -4. 应用选定规则并将新的知识片段插入问题状态 -5. 从第 1 步重复。 +1. 如果目标属性已在工作内存中——停止并给出结果 +2. 找出所有条件当前满足的规则——获得**冲突集**规则 +3. 执行**冲突解决**——选出本步将执行的一条规则。冲突解决有不同策略： + - 选择知识库中第一个适用规则 + - 随机选择规则 + - 选择*更具体*规则，即“左侧”（LHS）条件满足最多的规则 +4. 应用选中规则，向问题状态插入新知识 +5. 从步骤1重复 -然而，在某些情况下，我们可能希望从对问题的空白知识开始，并通过提问来帮助我们得出结论。例如，在进行医疗诊断时，我们通常不会在开始诊断患者之前提前进行所有医疗分析。我们更希望在需要做出决定时进行分析。 +但在某些情况下，我们可能希望以对问题一无所知开始，提出帮助得出结论的问题。例如，在医学诊断中，一般不会先做完所有检查再诊断患者，而是在需要决策时做检查。 -这种过程可以通过**后向推理**建模。它由**目标**驱动——即我们试图找到的属性值： +该过程可用**后向推理**建模。它由**目标**驱动——我们想找到的属性值： -1. 选择所有可以给出目标值的规则（即目标在规则的 RHS（右侧））——冲突集 -1. 如果没有关于此属性的规则，或者有规则表明我们应该向用户询问值——询问用户，否则： -1. 使用冲突解决策略选择一个规则作为*假设*——我们将尝试证明它 -1. 递归地对规则 LHS 中的所有属性重复此过程，尝试将它们作为目标证明 -1. 如果过程在任何时候失败——在第 3 步使用另一个规则。 +1. 选择所有能给出目标值的规则（即目标在右侧，RHS）——冲突集 +2. 如果该属性无规则或有规则指出需向用户询问值——则询问，否则： +3. 使用冲突解决策略选择一条将用作*假设*的规则——尝试证明该规则 +4. 递归地对规则左侧所有属性重复该过程，尝试将它们视为目标证明 +5. 如果任何步骤失败——回到步骤3选用另一规则 -> ✅ 在哪些情况下前向推理更合适？后向推理又适用于哪些情况？ +> ✅ 何种情形下前向推理更合适？后向推理呢？ ### 实现专家系统 -专家系统可以使用不同的工具实现： +专家系统可使用不同工具实现： -* 直接使用某种高级编程语言编写。这不是最好的方法，因为基于知识的系统的主要优势在于知识与推理分离，并且领域专家应该能够在不了解推理过程细节的情况下编写规则。 -* 使用**专家系统外壳**，即专门设计用于通过某种知识表示语言填充知识的系统。 +* 直接用某高级编程语言编写。通常不是最佳选择，因为知识库系统的主要优势是知识和推理分离，领域专家理应能编写规则而不必懂推理细节。 +* 使用**专家系统壳**，即专门设计用于通过某些知识表示语言填充知识的系统。 ## ✍️ 练习：动物推理 -参见 [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb)，了解实现前向和后向推理专家系统的示例。 +参见 [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) ，示例实现了前向和后向推理专家系统。 -> **注意**：此示例相对简单，仅展示专家系统的基本概念。一旦你开始创建这样的系统，只有当规则数量达到一定规模（约 200+）时，你才会注意到系统表现出某种*智能*行为。在某些时候，规则变得过于复杂，难以全部记住，此时你可能会开始思考为什么系统会做出某些决策。然而，基于知识的系统的重要特性是你始终可以*解释*每个决策是如何做出的。 +> **备注**：本例较为简单，仅展示专家系统基本样貌。只有当规则数量达到一定量（约200条以上）时，系统才会表现出某种*智能*行为。规则过多时，难以一一记住，此时你可能开始怀疑系统为何做出某些决策。但基于知识系统的关键特点是你总能*解释*任何决策是如何作出的。 -## 本体与语义网 +## 本体论与语义网 -20 世纪末，有一个倡议使用知识表示来标注互联网资源，以便能够找到与非常具体查询相对应的资源。这一运动被称为**语义网**，它依赖于几个概念： +20世纪末，曾有一项倡议利用知识表示对互联网资源进行注释，以便能够找到符合非常具体查询的资源。该倡议称为**语义网**，依赖以下几个概念： -- 基于**[描述逻辑](https://en.wikipedia.org/wiki/Description_logic)**（DL）的特殊知识表示。它类似于框架知识表示，因为它构建了一个具有属性的对象层次结构，但它具有形式逻辑语义和推理。描述逻辑有一个家族，它在表达能力和推理的算法复杂性之间取得平衡。 -- 分布式知识表示，其中所有概念都由全局 URI 标识符表示，使得能够创建跨互联网的知识层次结构。 -- 一组基于XML的知识描述语言：RDF（资源描述框架）、RDFS（RDF Schema）、OWL（Web本体语言）。 +- 基于**[描述逻辑](https://en.wikipedia.org/wiki/Description_logic)**（DL）的一种特殊知识表示。它类似框架知识表示，因为它建立含属性对象的层次，但具有形式逻辑语义和推理。存在一整个DL家族，在表达能力与推理算法复杂度之间进行平衡。 +- 分布式知识表示，其中所有概念由全局URI标识符表示，能够创建跨互联网的知识层次。 +- 一个基于 XML 的知识描述语言家族：RDF（资源描述框架）、RDFS（RDF 词汇表）、OWL（本体网络语言）。 -语义网的核心概念是**本体**。它指的是使用某种形式化知识表示对问题领域进行明确的规范。最简单的本体可以是问题领域中的对象层次结构，但更复杂的本体会包含可用于推理的规则。 +语义网的核心概念之一是**本体**。它指的是使用某种形式化知识表示对问题领域的明确说明。最简单的本体可以只是问题领域中对象的层次结构，但更复杂的本体会包含可用于推理的规则。 -在语义网中，所有表示都基于三元组。每个对象和每个关系都通过URI唯一标识。例如，如果我们想表达这个AI课程是由Dmitry Soshnikov在2022年1月1日开发的事实，可以使用以下三元组： +在语义网中，所有表示均基于三元组。每个对象和每个关系都由 URI 唯一标识。例如，如果我们想陈述这个 AI 课程是由 Dmitry Soshnikov 于 2022 年 1 月 1 日开发的事实——我们可以使用以下三元组： - + ``` -http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 13, 2007” +http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 1, 2022” http://github.com/microsoft/ai-for-beginners http://purl.org/dc/elements/1.1/creator http://soshnikov.com ``` -> ✅ 这里的 `http://www.example.com/terms/creation-date` 和 `http://purl.org/dc/elements/1.1/creator` 是一些公认的、通用的URI，用于表达*创建者*和*创建日期*的概念。 +> ✅ 这里 `http://www.example.com/terms/creation-date` 和 `http://purl.org/dc/elements/1.1/creator` 是表达*创建者*和*创建日期*概念的一些广为人知且被普遍接受的 URI。 -在更复杂的情况下，如果我们想定义一个创建者列表，可以使用RDF中定义的一些数据结构。 +在更复杂的情况下，如果我们想定义一个创建者列表，可以使用 RDF 中定义的一些数据结构。 - + -> 上图由 [Dmitry Soshnikov](http://soshnikov.com) 提供 +> 上图由 [Dmitry Soshnikov](http://soshnikov.com) 绘制 -语义网的发展因搜索引擎和自然语言处理技术的成功而有所放缓，这些技术能够从文本中提取结构化数据。然而，在某些领域仍有显著努力来维护本体和知识库。以下是几个值得注意的项目： +语义网建设的进展在某种程度上被搜索引擎和自然语言处理技术的发展减缓了，这些技术可以从文本中提取结构化数据。然而，在某些领域，仍然有大量工作致力于维护本体和知识库。一些值得关注的项目： -* [WikiData](https://wikidata.org/) 是与维基百科相关的机器可读知识库集合。大部分数据来自维基百科的*信息框*，即维基百科页面中的结构化内容片段。你可以使用SPARQL（一种语义网的特殊查询语言）[查询](https://query.wikidata.org/)WikiData。以下是一个示例查询，显示人类中最常见的眼睛颜色： +* [WikiData](https://wikidata.org/) 是一个与维基百科关联的机器可读知识库集合。大部分数据来自维基百科的 *信息框*，即维基百科页面内的结构化内容。你可以用专门为语义网设计的查询语言 SPARQL 来[查询](https://query.wikidata.org/) wikidata。这里是一个示例查询，显示人类中最常见的眼睛颜色： ```sparql #defaultView:BubbleChart @@ -206,47 +206,52 @@ WHERE GROUP BY ?eyeColorLabel ``` -* [DBpedia](https://www.dbpedia.org/) 是另一个类似于WikiData的项目。 +* [DBpedia](https://www.dbpedia.org/) 是另一个类似 WikiData 的项目。 -> ✅ 如果你想尝试构建自己的本体，或打开现有的本体，有一个很棒的可视化本体编辑器叫 [Protégé](https://protege.stanford.edu/)。你可以下载它，或者在线使用。 +> ✅ 如果你想试验构建自己的本体，或者打开已有本体，有一个很棒的图形化本体编辑器叫做 [Protégé](https://protege.stanford.edu/)。下载它，或者在线使用。 - + -*Web Protégé编辑器打开了Romanov家族本体。截图由Dmitry Soshnikov提供* +*Web Protégé 编辑器打开了罗曼诺夫家族的本体。截屏来自 Dmitry Soshnikov* -## ✍️ 练习：家庭本体 +## ✍️ 练习：家族本体 -参见 [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb)，了解如何使用语义网技术推理家庭关系。我们将使用常见的GEDCOM格式表示的家谱和家庭关系本体，为给定的一组个人构建所有家庭关系的图。 -## Microsoft概念图 +参见 [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb)，该示例演示了如何使用语义网技术对家庭关系进行推理。我们将使用常见的 GEDCOM 格式表示的家谱和家族关系本体，构建给定个体集合的所有家族关系图。 -在大多数情况下，本体是由人工精心创建的。然而，也可以从非结构化数据中**挖掘**本体，例如从自然语言文本中。 +## 微软概念图 -微软研究院曾进行过这样的尝试，结果是 [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste)。 +大多数情况下，本体都是精心手工创建的。然而，也可以从非结构化数据中**挖掘**本体，例如来自自然语言文本。 -这是一个使用`is-a`继承关系将实体分组的大型集合。它可以回答诸如“微软是什么？”这样的问题——答案可能是“一个公司，概率为0.87；一个品牌，概率为0.75”。 +微软研究院曾做过这样的尝试，产出了[微软概念图](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste)。 -该图既可以通过REST API访问，也可以作为一个大型可下载的文本文件，列出所有实体对。 +它是一个以 `is-a` 继承关系分组的大型实体集合。它可以回答诸如“微软是什么？”的问题——答案类似于“微软是一家公司，概率为 0.87，同时也是一个品牌，概率为 0.75”。 + +该图谱既可通过 REST API 访问，也可以作为一个大型可下载文本文件列出所有实体对。 ## ✍️ 练习：概念图 -尝试 [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) 笔记本，了解如何使用Microsoft概念图将新闻文章分组到几个类别中。 +尝试 [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) 笔记本，看看我们如何使用微软概念图将新闻文章分到几个类别中。 ## 结论 -如今，AI通常被认为是*机器学习*或*神经网络*的代名词。然而，人类也表现出显式推理，这是一种目前神经网络无法处理的能力。在实际项目中，显式推理仍然用于执行需要解释或能够以受控方式修改系统行为的任务。 +如今，人工智能常被认为是*机器学习*或*神经网络*的同义词。然而，人类也表现出明确的推理能力，而这正是当前神经网络尚未处理的内容。在实际项目中，明确推理仍然用于执行需要解释或能控制修改系统行为的任务。 ## 🚀 挑战 -在与本课相关的家庭本体笔记本中，有机会尝试其他家庭关系。尝试发现家谱中人物之间的新连接。 +本节课对应的家族本体笔记本中，提供了试验其他家族关系的机会。尝试发现家谱中人们之间的新联系。 ## [课后测验](https://ff-quizzes.netlify.app/en/ai/quiz/4) -## 回顾与自学 +## 复习与自学 -在互联网上进行一些研究，发现人类试图量化和编码知识的领域。了解布鲁姆分类法，并回顾历史，学习人类如何试图理解他们的世界。探索林奈斯的工作，了解他如何创建生物分类法，并观察德米特里·门捷列夫如何为化学元素创建描述和分组的方法。你还能找到哪些有趣的例子？ +在网上做些研究，了解人类尝试量化和编码知识的领域。看看布鲁姆的认知分类法，回顾人类历史上如何试图理解他们的世界。探索林奈创建生物分类法的工作，观察门捷列夫如何创建化学元素的描述和分类方法。你还能发现哪些有趣的例子？ -**作业**: [构建一个本体](assignment.md) +**作业**: [构建本体](assignment.md) --- + +**免责声明**： +本文件已使用AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。虽然我们努力保证准确性，但请注意自动翻译可能存在错误或不准确之处。原始文件的母语版本应被视为权威来源。对于重要信息，建议采用专业人工翻译。我们不对因使用本翻译而产生的任何误解或误释承担责任。 + \ No newline at end of file diff --git a/translations/zh/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/zh/lessons/3-NeuralNetworks/05-Frameworks/README.md index 87d9eefd..be2d2c16 100644 --- a/translations/zh/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/zh/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ CO_OP_TRANSLATOR_METADATA: 考虑以下拟合 5 个点的问题（图中的 `x` 表示点）： -![线性模型](../../../../../translated_images/zh/overfit1.f24b71c6f652e59e.jpg) | ![过拟合模型](../../../../../translated_images/zh/overfit2.131f5800ae10ca5e.jpg) +![线性模型](../../../../../translated_images/zh/overfit1.f24b71c6f652e59e.webp) | ![过拟合模型](../../../../../translated_images/zh/overfit2.131f5800ae10ca5e.webp) -------------------------|-------------------------- **线性模型，2 个参数** | **非线性模型，7 个参数** 训练误差 = 5.3 | 训练误差 = 0 @@ -79,7 +79,7 @@ CO_OP_TRANSLATOR_METADATA: 从上图可以看出，过拟合可以通过非常低的训练误差和非常高的验证误差来检测。通常在训练过程中，我们会看到训练误差和验证误差都开始下降，但在某个点之后，验证误差可能停止下降并开始上升。这是过拟合的信号，表明我们可能应该停止训练（或者至少保存模型的快照）。 -![过拟合](../../../../../translated_images/zh/Overfitting.408ad91cd90b4371.png) +![过拟合](../../../../../translated_images/zh/Overfitting.408ad91cd90b4371.webp) ## 如何防止过拟合 diff --git a/translations/zh/lessons/3-NeuralNetworks/README.md b/translations/zh/lessons/3-NeuralNetworks/README.md index 401512d4..143f4176 100644 --- a/translations/zh/lessons/3-NeuralNetworks/README.md +++ b/translations/zh/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 神经网络简介 -![神经网络内容总结涂鸦](../../../../translated_images/zh/ai-neuralnetworks.1c687ae40bc86e83.png) +![神经网络内容总结涂鸦](../../../../translated_images/zh/ai-neuralnetworks.1c687ae40bc86e83.webp) 正如我们在介绍中讨论的那样，实现智能的一种方法是训练一个**计算机模型**或一个**人工大脑**。自20世纪中期以来，研究人员尝试了各种数学模型，直到近年来，这一方向取得了巨大的成功。这些大脑的数学模型被称为**神经网络**。 @@ -36,13 +36,13 @@ CO_OP_TRANSLATOR_METADATA: 从生物学中我们知道，大脑由神经细胞（神经元）组成，每个神经元有多个“输入”（树突）和一个“输出”（轴突）。树突和轴突都可以传导电信号，它们之间的连接——称为突触——可以表现出不同程度的导电性，这种导电性由神经递质调节。 -![神经元模型](../../../../translated_images/zh/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![神经元模型](../../../../translated_images/zh/artneuron.1a5daa88d20ebe6f.png) +![神经元模型](../../../../translated_images/zh/synapse-wikipedia.ed20a9e4726ea1c6.webp) | ![神经元模型](../../../../translated_images/zh/artneuron.1a5daa88d20ebe6f.webp) ----|---- 真实神经元 *（[图片](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg)来自维基百科）* | 人工神经元 *（作者提供图片）* 因此，神经元的最简单数学模型包含若干输入 X1, ..., XN 和一个输出 Y，以及一系列权重 W1, ..., WN。输出的计算公式为： -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) 其中 f 是某种非线性的**激活函数**。 diff --git a/translations/zh/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/zh/lessons/4-ComputerVision/06-IntroCV/README.md index dd3d8b74..aff8320c 100644 --- a/translations/zh/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/zh/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) * **对盲文书籍的照片进行预处理**。我们重点介绍如何使用阈值处理、特征检测、透视变换和NumPy操作来分离单个盲文符号，以便神经网络进一步分类。 -![盲文图像](../../../../../translated_images/zh/braille.341962ff76b1bd70.jpeg) | ![盲文图像预处理结果](../../../../../translated_images/zh/braille-result.46530fea020b03c7.png) | ![盲文符号](../../../../../translated_images/zh/braille-symbols.0159185ab69d5339.png) +![盲文图像](../../../../../translated_images/zh/braille.341962ff76b1bd70.webp) | ![盲文图像预处理结果](../../../../../translated_images/zh/braille-result.46530fea020b03c7.webp) | ![盲文符号](../../../../../translated_images/zh/braille-symbols.0159185ab69d5339.webp) ----|-----|----- > 图片来源：[OpenCV.ipynb](OpenCV.ipynb) * **使用帧差检测视频中的运动**。如果摄像机是固定的，那么摄像机画面中的帧应该彼此非常相似。由于帧被表示为数组，只需对两个连续帧的数组进行减法运算，就可以得到像素差异，对于静态帧来说差异应该很小，而当图像中有显著运动时差异会变大。 -![视频帧和帧差异图像](../../../../../translated_images/zh/frame-difference.706f805491a0883c.png) +![视频帧和帧差异图像](../../../../../translated_images/zh/frame-difference.706f805491a0883c.webp) > 图片来源：[OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) - **密集光流**计算每个像素的运动向量场。 - **稀疏光流**基于图像中的一些显著特征（例如边缘），并从帧到帧构建它们的轨迹。 -![光流图像](../../../../../translated_images/zh/optical.1f4a94464579a83a.png) +![光流图像](../../../../../translated_images/zh/optical.1f4a94464579a83a.webp) > 图片来源：[OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/zh/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/zh/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 8b7d7a1a..5a1dc757 100644 --- a/translations/zh/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/zh/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16是一种网络，在2014年的ImageNet top-5分类中达到了92.7%的准确率。它的层结构如下： -![ImageNet Layers](../../../../../translated_images/zh/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet Layers](../../../../../translated_images/zh/vgg-16-arch1.d901a5583b3a51ba.webp) 如图所示，VGG采用了传统的金字塔架构，即一系列卷积-池化层的组合。 -![ImageNet Pyramid](../../../../../translated_images/zh/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet Pyramid](../../../../../translated_images/zh/vgg-16-arch.64ff2137f50dd49f.webp) > 图片来源：[Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/zh/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/zh/lessons/4-ComputerVision/07-ConvNets/README.md index f7629b67..1bf375f6 100644 --- a/translations/zh/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/zh/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: 为了提取模式，我们将使用**卷积滤波器**的概念。正如你所知，图像可以用二维矩阵或带有颜色深度的三维张量来表示。应用滤波器意味着我们使用一个相对较小的**滤波核**矩阵，并对原始图像中的每个像素与其邻近点进行加权平均计算。我们可以将其视为一个小窗口在整个图像上滑动，并根据滤波核矩阵中的权重对所有像素进行平均。 -![垂直边缘滤波器](../../../../../translated_images/zh/filter-vert.b7148390ca0bc356.png) | ![水平边缘滤波器](../../../../../translated_images/zh/filter-horiz.59b80ed4feb946ef.png) +![垂直边缘滤波器](../../../../../translated_images/zh/filter-vert.b7148390ca0bc356.webp) | ![水平边缘滤波器](../../../../../translated_images/zh/filter-horiz.59b80ed4feb946ef.webp) ----|---- > 图片来源：Dmitry Soshnikov @@ -38,7 +38,7 @@ CNN 的工作方式基于以下重要思想： * 我们可以设计网络，使滤波器能够自动训练 * 我们可以使用相同的方法来发现高级特征中的模式，而不仅仅是原始图像中的模式。因此，CNN 的特征提取在特征的层次结构中工作，从低级像素组合开始，到更高级的图像部分组合。 -![层次特征提取](../../../../../translated_images/zh/FeatureExtractionCNN.d9b456cbdae7cb64.png) +![层次特征提取](../../../../../translated_images/zh/FeatureExtractionCNN.d9b456cbdae7cb64.webp) > 图片来源：[Hislop-Lynch 的论文](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d)，基于[他们的研究](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ CNN 的工作方式基于以下重要思想： 例如，让我们看看 VGG-16 的架构，这是一种在 2014 年 ImageNet 的 top-5 分类中实现了 92.7% 准确率的网络： -![ImageNet 层次结构](../../../../../translated_images/zh/vgg-16-arch1.d901a5583b3a51ba.jpg) +![ImageNet 层次结构](../../../../../translated_images/zh/vgg-16-arch1.d901a5583b3a51ba.webp) -![ImageNet 金字塔](../../../../../translated_images/zh/vgg-16-arch.64ff2137f50dd49f.jpg) +![ImageNet 金字塔](../../../../../translated_images/zh/vgg-16-arch.64ff2137f50dd49f.webp) > 图片来源：[Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/zh/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/zh/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 174d0287..3e5e8968 100644 --- a/translations/zh/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/zh/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ CO_OP_TRANSLATOR_METADATA: 我们将使用 [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/)，该数据集包含37种不同品种的猫和狗的图像。 -![我们将处理的数据集](../../../../../../translated_images/zh/data.50b2a9d5484bdbf0.png) +![我们将处理的数据集](../../../../../../translated_images/zh/data.50b2a9d5484bdbf0.webp) 要下载数据集，请使用以下代码片段： diff --git a/translations/zh/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/zh/lessons/4-ComputerVision/08-TransferLearning/README.md index f31e6a14..ba35a033 100644 --- a/translations/zh/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/zh/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Keras 和 PyTorch 都包含函数，可以轻松加载一些常见架构的预 以下是 VGG-16 网络从一张猫的图片中提取的示例特征： -![VGG-16 提取的特征](../../../../../translated_images/zh/features.6291f9c7ba3a0b95.png) +![VGG-16 提取的特征](../../../../../translated_images/zh/features.6291f9c7ba3a0b95.webp) ## 猫与狗数据集 @@ -48,19 +48,19 @@ Keras 和 PyTorch 都包含函数，可以轻松加载一些常见架构的预 我们可以采取的一种方法是从一个随机图像开始，然后尝试使用**梯度下降优化**技术调整该图像，使网络认为它是一只猫。 -![图像优化循环](../../../../../translated_images/zh/ideal-cat-loop.999fbb8ff306e044.png) +![图像优化循环](../../../../../translated_images/zh/ideal-cat-loop.999fbb8ff306e044.webp) 然而，如果我们这样做，我们会得到一些非常类似于随机噪声的东西。这是因为*有很多方法可以让网络认为输入图像是一只猫*，包括一些在视觉上没有意义的方式。虽然这些图像包含了许多典型的猫的模式，但没有任何约束使它们在视觉上具有辨识度。 为了改善结果，我们可以在损失函数中添加另一个项，称为**变化损失**。它是一种度量，显示图像中相邻像素的相似程度。最小化变化损失可以使图像更平滑，并消除噪声，从而揭示更具视觉吸引力的模式。以下是一些这样的“理想”图像示例，它们被高概率分类为猫和斑马： -![理想猫](../../../../../translated_images/zh/ideal-cat.203dd4597643d6b0.png) | ![理想斑马](../../../../../translated_images/zh/ideal-zebra.7f70e8b54ee15a7a.png) +![理想猫](../../../../../translated_images/zh/ideal-cat.203dd4597643d6b0.webp) | ![理想斑马](../../../../../translated_images/zh/ideal-zebra.7f70e8b54ee15a7a.webp) -----|----- *理想猫* | *理想斑马* 类似的方法可以用来对神经网络进行所谓的**对抗性攻击**。假设我们想欺骗一个神经网络，让一只狗看起来像一只猫。如果我们拿一张被网络识别为狗的狗的图片，然后稍微调整它，直到网络开始将其分类为猫： -![狗的图片](../../../../../translated_images/zh/original-dog.8f68a67d2fe0911f.png) | ![被分类为猫的狗的图片](../../../../../translated_images/zh/adversarial-dog.d9fc7773b0142b89.png) +![狗的图片](../../../../../translated_images/zh/original-dog.8f68a67d2fe0911f.webp) | ![被分类为猫的狗的图片](../../../../../translated_images/zh/adversarial-dog.d9fc7773b0142b89.webp) -----|----- *狗的原始图片* | *被分类为猫的狗的图片* diff --git a/translations/zh/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/zh/lessons/4-ComputerVision/09-Autoencoders/README.md index 42a90876..2617041d 100644 --- a/translations/zh/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/zh/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: 由于我们训练自动编码器的目标是尽可能捕捉原始图像的信息以实现准确的重建，网络会尝试找到输入图像的最佳**嵌入**以捕捉其意义。 -![自动编码器示意图](../../../../../translated_images/zh/autoencoder_schema.5e6fc9ad98a5eb61.jpg) +![自动编码器示意图](../../../../../translated_images/zh/autoencoder_schema.5e6fc9ad98a5eb61.webp) > 图片来源：[Keras 博客](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/zh/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/zh/lessons/4-ComputerVision/11-ObjectDetection/README.md index 384941ec..6110dc9c 100644 --- a/translations/zh/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/zh/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: ## [课前测验](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![目标检测](../../../../../translated_images/zh/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) +![目标检测](../../../../../translated_images/zh/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp) > 图片来源：[YOLO v2 网站](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 2. 对每个小块进行图像分类。 3. 对于分类结果激活值足够高的小块，可以认为其中包含目标物体。 -![简单目标检测](../../../../../translated_images/zh/naive-detection.e7f1ba220ccd08c6.png) +![简单目标检测](../../../../../translated_images/zh/naive-detection.e7f1ba220ccd08c6.webp) > *图片来源：[练习笔记本](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ CO_OP_TRANSLATOR_METADATA: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 包含20个类别 * [COCO](http://cocodataset.org/#home) - 常见物体上下文数据集。包含80个类别、边界框和分割掩码 -![COCO](../../../../../translated_images/zh/coco-examples.71bc60380fa6cceb.jpg) +![COCO](../../../../../translated_images/zh/coco-examples.71bc60380fa6cceb.webp) ## 目标检测评估指标 @@ -50,7 +50,7 @@ CO_OP_TRANSLATOR_METADATA: 对于图像分类来说，评估算法性能相对简单；但对于目标检测，我们需要同时评估类别的正确性以及推断出的边界框位置的精确性。后者通常使用**交并比**（IoU）来衡量两个框（或任意两个区域）的重叠程度。 -![IoU](../../../../../translated_images/zh/iou_equation.9a4751d40fff4e11.png) +![IoU](../../../../../translated_images/zh/iou_equation.9a4751d40fff4e11.webp) > *图片来源：[这篇关于IoU的优秀博客文章](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ $$ [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf)使用[选择性搜索](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf)生成ROI区域的层次结构，然后通过CNN特征提取器和SVM分类器确定物体类别，并通过线性回归确定*边界框*坐标。[官方论文](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/zh/rcnn1.cae407020dfb1d1f.png) +![RCNN](../../../../../translated_images/zh/rcnn1.cae407020dfb1d1f.webp) > *图片来源：van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/zh/rcnn2.2d9530bb83516484.png) +![RCNN-1](../../../../../translated_images/zh/rcnn2.2d9530bb83516484.webp) > *图片来源：[这篇博客](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -110,7 +110,7 @@ $$ 这种方法与R-CNN类似，但区域是在应用卷积层之后定义的。 -![FRCNN](../../../../../translated_images/zh/f-rcnn.3cda6d9bb4188875.png) +![FRCNN](../../../../../translated_images/zh/f-rcnn.3cda6d9bb4188875.webp) > 图片来源：[官方论文](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf)，[arXiv](https://arxiv.org/pdf/1504.08083.pdf)，2015 @@ -118,7 +118,7 @@ $$ 这种方法的核心思想是使用神经网络预测ROI——即所谓的*区域提议网络*。[论文](https://arxiv.org/pdf/1506.01497.pdf)，2016 -![FasterRCNN](../../../../../translated_images/zh/faster-rcnn.8d46c099b87ef30a.png) +![FasterRCNN](../../../../../translated_images/zh/faster-rcnn.8d46c099b87ef30a.webp) > 图片来源：[官方论文](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ $$ 2. 特征通过**位置敏感得分图**处理。每个类别$C$的物体被划分为$k\times k$区域，并训练预测物体的各部分。 3. 对于$k\times k$区域中的每个部分，所有网络对物体类别进行投票，选择投票最多的类别。 -![r-fcn image](../../../../../translated_images/zh/r-fcn.13eb88158b99a3da.png) +![r-fcn image](../../../../../translated_images/zh/r-fcn.13eb88158b99a3da.webp) > 图片来源：[官方论文](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO是一种实时单次检测算法。其核心思想如下： * 将图像划分为$S\times S$区域。 * 对每个区域，**CNN**预测$n$个可能的物体、*边界框*坐标和*置信度*=*概率* * IoU。 - ![YOLO](../../../../../translated_images/zh/yolo.a2648ec82ee8bb4e.png) + ![YOLO](../../../../../translated_images/zh/yolo.a2648ec82ee8bb4e.webp) > 图片来源：[官方论文](https://arxiv.org/abs/1506.02640) diff --git a/translations/zh/lessons/4-ComputerVision/README.md b/translations/zh/lessons/4-ComputerVision/README.md index c5695ad0..f559fc76 100644 --- a/translations/zh/lessons/4-ComputerVision/README.md +++ b/translations/zh/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 计算机视觉 -![计算机视觉内容总结涂鸦](../../../../translated_images/zh/ai-computervision.6506ebebac3fbf76.png) +![计算机视觉内容总结涂鸦](../../../../translated_images/zh/ai-computervision.6506ebebac3fbf76.webp) 在本节中，我们将学习以下内容： diff --git a/translations/zh/lessons/5-NLP/14-Embeddings/README.md b/translations/zh/lessons/5-NLP/14-Embeddings/README.md index d56ecc00..666ecd7a 100644 --- a/translations/zh/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/zh/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: 通过在分类器网络中使用嵌入层作为第一层，我们可以从词袋模型切换到 **嵌入袋** 模型。在嵌入袋模型中，我们首先将文本中的每个单词转换为对应的嵌入，然后对所有这些嵌入计算某种聚合函数，例如 `sum`、`average` 或 `max`。 -![展示五个序列单词的嵌入分类器的图片。](../../../../../translated_images/zh/embedding-classifier-example.b77f021a7ee67eee.png) +![展示五个序列单词的嵌入分类器的图片。](../../../../../translated_images/zh/embedding-classifier-example.b77f021a7ee67eee.webp) > 图片由作者提供 @@ -40,7 +40,7 @@ CO_OP_TRANSLATOR_METADATA: CBoW 速度更快，而 Skip-Gram 虽然较慢，但在表示不常见单词方面表现更好。 -![展示 CBoW 和 Skip-Gram 算法将单词转换为向量的图片。](../../../../../translated_images/zh/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![展示 CBoW 和 Skip-Gram 算法将单词转换为向量的图片。](../../../../../translated_images/zh/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > 图片来源于 [这篇论文](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/zh/lessons/5-NLP/15-LanguageModeling/README.md b/translations/zh/lessons/5-NLP/15-LanguageModeling/README.md index 7786ce46..aec20b2d 100644 --- a/translations/zh/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/zh/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: * **连续词袋模型** (CBoW)，通过预测标记序列 $W_{-N}$, ..., $W_N$ 中的中间标记 $W_0$。 * **Skip-gram**，通过中间标记 $W_0$ 来预测一组邻近标记 {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$}。 -![论文中关于将单词转换为向量的算法示例](../../../../../translated_images/zh/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) +![论文中关于将单词转换为向量的算法示例](../../../../../translated_images/zh/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > 图片来源于[这篇论文](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/zh/lessons/5-NLP/16-RNN/README.md b/translations/zh/lessons/5-NLP/16-RNN/README.md index d2e5e5ea..903d1eb0 100644 --- a/translations/zh/lessons/5-NLP/16-RNN/README.md +++ b/translations/zh/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: 为了捕捉文本序列的意义，我们需要使用另一种神经网络架构，称为**循环神经网络**（Recurrent Neural Network，RNN）。在 RNN 中，我们将句子逐个符号输入网络，网络会生成某种**状态**，然后将该状态与下一个符号一起再次输入网络。 -![RNN](../../../../../translated_images/zh/rnn.27f5c29c53d727b5.png) +![RNN](../../../../../translated_images/zh/rnn.27f5c29c53d727b5.webp) > 图片由作者提供 @@ -61,7 +61,7 @@ LSTM 网络的组织方式与 RNN 类似，但有两个状态会从层到层传 循环网络，无论是单向还是双向，都能捕捉序列中的某些模式，并将其存储到状态向量中或传递到输出中。与卷积网络类似，我们可以在第一层之上构建另一层循环网络，以捕捉更高级的模式，并从第一层提取的低级模式中构建。这引出了**多层 RNN** 的概念，它由两个或更多循环网络组成，其中前一层的输出作为输入传递到下一层。 -![显示多层长短时记忆 RNN 的图片](../../../../../translated_images/zh/multi-layer-lstm.dd975e29bb2a59fe.jpg) +![显示多层长短时记忆 RNN 的图片](../../../../../translated_images/zh/multi-layer-lstm.dd975e29bb2a59fe.webp) *图片来自 Fernando López 的[这篇精彩文章](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3)* diff --git a/translations/zh/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/zh/lessons/5-NLP/17-GenerativeNetworks/README.md index 9ef1d2c3..6c6e2b2d 100644 --- a/translations/zh/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/zh/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: 这使得可以构建不同的神经网络架构，如下图所示： -![展示常见循环神经网络模式的图片。](../../../../../translated_images/zh/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![展示常见循环神经网络模式的图片。](../../../../../translated_images/zh/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > 图片来源于 [Andrej Karpaty](http://karpathy.github.io/) 的博客文章 [循环神经网络的非凡有效性](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) @@ -32,7 +32,7 @@ CO_OP_TRANSLATOR_METADATA: 我们将训练这个 RNN 逐步生成文本。在每一步中，我们将取长度为 `nchars` 的字符序列，并要求网络为每个输入字符生成下一个输出字符： -![展示 RNN 生成单词 'HELLO' 的示例图片。](../../../../../translated_images/zh/rnn-generate.56c54afb52f9781d.png) +![展示 RNN 生成单词 'HELLO' 的示例图片。](../../../../../translated_images/zh/rnn-generate.56c54afb52f9781d.webp) 在生成文本（推理阶段）时，我们从某个**提示**开始，将其传递给 RNN 单元以生成中间状态，然后从该状态开始生成。我们一次生成一个字符，并将状态和生成的字符传递给另一个 RNN 单元以生成下一个字符，直到生成足够的字符。 diff --git a/translations/zh/lessons/5-NLP/18-Transformers/README.md b/translations/zh/lessons/5-NLP/18-Transformers/README.md index be8f3a7f..cfa242f1 100644 --- a/translations/zh/lessons/5-NLP/18-Transformers/README.md +++ b/translations/zh/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ CO_OP_TRANSLATOR_METADATA: **注意机制**提供了一种方法，可以对每个输入向量对RNN输出预测的上下文影响进行加权。其实现方式是创建输入RNN的中间状态与输出RNN之间的快捷路径。这样，在生成输出符号yt时，我们会考虑所有输入隐藏状态hi，并赋予不同的权重系数αt,i。 -![显示带有加性注意层的编码器/解码器模型的图像](../../../../../translated_images/zh/encoder-decoder-attention.7a726296894fb567.png) +![显示带有加性注意层的编码器/解码器模型的图像](../../../../../translated_images/zh/encoder-decoder-attention.7a726296894fb567.webp) > [Bahdanau等人，2015](https://arxiv.org/pdf/1409.0473.pdf)中的加性注意机制编码器-解码器模型，图片来源于[这篇博客](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) 注意矩阵{αi,j}表示某些输入词在生成输出序列中某个词时的影响程度。下面是一个这样的矩阵示例： -![显示由RNNsearch-50找到的样本对齐的图像，取自Bahdanau - arviz.org](../../../../../translated_images/zh/bahdanau-fig3.09ba2d37f202a6af.png) +![显示由RNNsearch-50找到的样本对齐的图像，取自Bahdanau - arviz.org](../../../../../translated_images/zh/bahdanau-fig3.09ba2d37f202a6af.webp) > 图片来自[Bahdanau等人，2015](https://arxiv.org/pdf/1409.0473.pdf)（图3） @@ -66,7 +66,7 @@ Transformer的核心思想之一是避免RNN的顺序处理特性，并创建一 接下来，我们需要捕捉序列中的一些模式。为此，Transformer使用了**自注意机制**，即将注意机制同时应用于输入和输出的同一序列。应用自注意机制使我们能够考虑句子中的**上下文**，并查看哪些词是相互关联的。例如，它可以帮助我们识别代词*it*所指代的词，并考虑上下文： -![](../../../../../translated_images/zh/CoreferenceResolution.861924d6d384a7d6.png) +![](../../../../../translated_images/zh/CoreferenceResolution.861924d6d384a7d6.webp) > 图片来源于[谷歌博客](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Transformer的核心思想之一是避免RNN的顺序处理特性，并创建一 **BERT**（Bidirectional Encoder Representations from Transformers，双向编码器表示）是一个非常大的多层Transformer网络，*BERT-base*有12层，*BERT-large*有24层。模型首先在大规模文本数据（维基百科+书籍）上进行无监督训练（预测句子中的被遮蔽词）。在预训练过程中，模型吸收了大量的语言理解能力，这些能力可以通过微调其他数据集来利用。这一过程称为**迁移学习**。 -![图片来源于http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/zh/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) +![图片来源于http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/zh/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > 图片[来源](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/zh/lessons/5-NLP/18-Transformers/READMEtransformers.md b/translations/zh/lessons/5-NLP/18-Transformers/READMEtransformers.md index 9da91135..dcc9e593 100644 --- a/translations/zh/lessons/5-NLP/18-Transformers/READMEtransformers.md +++ b/translations/zh/lessons/5-NLP/18-Transformers/READMEtransformers.md @@ -11,13 +11,13 @@ **注意力机制**提供了一种对每个输入向量在RNN每个输出预测中的上下文影响进行加权的方法。它的实现方式是创建输入RNN和输出RNN之间的中间状态的快捷方式。通过这种方式，在生成输出符号 yt 时，我们将考虑所有输入隐藏状态 hi，并使用不同的权重系数 αt,i。 -![展示带有加性注意力层的编码器/解码器模型的图像](../../../../../translated_images/zh/encoder-decoder-attention.7a726296894fb567.png) +![展示带有加性注意力层的编码器/解码器模型的图像](../../../../../translated_images/zh/encoder-decoder-attention.7a726296894fb567.webp) > 该编码器-解码器模型与加性注意力机制见于 [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf)，引用自 [这篇博客文章](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) 注意力矩阵 {αi,j} 表示某些输入单词在生成输出序列中给定单词的程度。以下是这样一个矩阵的示例： -![展示由RNNsearch-50找到的示例对齐的图像，取自Bahdanau - arviz.org](../../../../../translated_images/zh/bahdanau-fig3.09ba2d37f202a6af.png) +![展示由RNNsearch-50找到的示例对齐的图像，取自Bahdanau - arviz.org](../../../../../translated_images/zh/bahdanau-fig3.09ba2d37f202a6af.webp) > 图自 [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (图3) @@ -57,7 +57,7 @@ 接下来，我们需要捕捉序列中的一些模式。为此，变换器使用**自注意力**机制，这本质上是对同一序列应用的注意力。应用自注意力使我们能够考虑句子中的**上下文**，并查看哪些单词是相互关联的。例如，它使我们能够看到哪些单词是由指代词（如 *它*）引用的，并且还考虑上下文： -![](../../../../../translated_images/zh/CoreferenceResolution.861924d6d384a7d6.png) +![](../../../../../translated_images/zh/CoreferenceResolution.861924d6d384a7d6.webp) > 图自 [Google博客](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -82,7 +82,7 @@ **BERT**（来自变换器的双向编码器表示）是一个非常大的多层变换器网络，其中 *BERT-base* 有12层，*BERT-large* 有24层。该模型首先在一个大型文本数据集（维基百科 + 书籍）上进行预训练，采用无监督训练（预测句子中被屏蔽的单词）。在预训练期间，模型吸收了显著的语言理解能力，这可以通过微调与其他数据集结合使用。这个过程被称为**迁移学习**。 -![图片来自 http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/zh/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) +![图片来自 http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/zh/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > 图片 [来源](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/zh/lessons/5-NLP/19-NER/README.md b/translations/zh/lessons/5-NLP/19-NER/README.md index 6e9f0379..5dc723d3 100644 --- a/translations/zh/lessons/5-NLP/19-NER/README.md +++ b/translations/zh/lessons/5-NLP/19-NER/README.md @@ -58,7 +58,7 @@ NER模型本质上是**标记分类模型**，因为对于每个输入标记， 由于我们需要在标记和类别之间建立一一对应关系，我们可以从下图中训练一个最右侧的**多对多**神经网络模型： -![展示常见循环神经网络模式的图片。](../../../../../translated_images/zh/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) +![展示常见循环神经网络模式的图片。](../../../../../translated_images/zh/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > *图片来自[这篇博客文章](http://karpathy.github.io/2015/05/21/rnn-effectiveness/)作者为[Andrej Karpathy](http://karpathy.github.io/)。NER标记分类模型对应于此图片中最右侧的网络架构。* diff --git a/translations/zh/lessons/5-NLP/README.md b/translations/zh/lessons/5-NLP/README.md index 40be79d3..58f745e0 100644 --- a/translations/zh/lessons/5-NLP/README.md +++ b/translations/zh/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 自然语言处理 -![NLP任务总结图](../../../../translated_images/zh/ai-nlp.b22dcb8ca4707cea.png) +![NLP任务总结图](../../../../translated_images/zh/ai-nlp.b22dcb8ca4707cea.webp) 在本节中，我们将重点使用神经网络来处理与**自然语言处理 (NLP)**相关的任务。我们希望计算机能够解决许多NLP问题： diff --git a/translations/zh/lessons/6-Other/23-MultiagentSystems/README.md b/translations/zh/lessons/6-Other/23-MultiagentSystems/README.md index 9284de18..771ee192 100644 --- a/translations/zh/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/zh/lessons/6-Other/23-MultiagentSystems/README.md @@ -71,7 +71,7 @@ NetLogo的一个优点是它包含一个可供尝试的工作模型库。进入* 打开模型后，你会进入NetLogo的主界面。以下是一个描述狼和羊种群的示例模型，考虑到有限资源（草地）。 -![NetLogo主界面](../../../../../translated_images/zh/NetLogo-Main.32653711ec1a01b3.png) +![NetLogo主界面](../../../../../translated_images/zh/NetLogo-Main.32653711ec1a01b3.webp) > Dmitry Soshnikov提供的截图 diff --git a/translations/zh/lessons/README.md b/translations/zh/lessons/README.md index e03fe13f..8dd52219 100644 --- a/translations/zh/lessons/README.md +++ b/translations/zh/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 概述 -![概述的手绘图](../../../translated_images/zh/ai-overview.0857791951d19500.png) +![概述的手绘图](../../../translated_images/zh/ai-overview.0857791951d19500.webp) > 手绘笔记由 [Tomomi Imura](https://twitter.com/girlie_mac) 提供 diff --git a/translations/zh/lessons/X-Extras/X1-MultiModal/README.md b/translations/zh/lessons/X-Extras/X1-MultiModal/README.md index a4e5e4ea..8567ac3e 100644 --- a/translations/zh/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/zh/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: CLIP的核心思想是能够比较文本提示与图像，并确定图像与提示的匹配程度。 -![CLIP架构](../../../../../translated_images/zh/clip-arch.b3dbf20b4e8ed8be.png) +![CLIP架构](../../../../../translated_images/zh/clip-arch.b3dbf20b4e8ed8be.webp) > *图片来源于[这篇博客](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ CLIP模型/库可以从[OpenAI GitHub](https://github.com/openai/CLIP)获取。 假设我们需要将图像分类为猫、狗和人类。在这种情况下，我们可以给模型一个图像，以及一系列文本提示：“*一张猫的图片*”、“*一张狗的图片*”、“*一张人类的图片*”。在结果的3个概率向量中，我们只需选择值最高的索引。 -![CLIP用于图像分类](../../../../../translated_images/zh/clip-class.3af42ef0b2b19369.png) +![CLIP用于图像分类](../../../../../translated_images/zh/clip-class.3af42ef0b2b19369.webp) > *图片来源于[这篇博客](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ VQGAN与普通[GAN](../../4-ComputerVision/10-GANs/README.md)的主要区别在 VQGAN与传统GAN的一个重要区别在于，后者可以从任何输入向量生成一个不错的图像，而VQGAN可能生成一个不连贯的图像。因此，我们需要进一步引导图像创建过程，这可以通过CLIP来实现。 -![VQGAN+CLIP架构](../../../../../translated_images/zh/vqgan.5027fe05051dfa31.png) +![VQGAN+CLIP架构](../../../../../translated_images/zh/vqgan.5027fe05051dfa31.webp) 为了生成与文本提示相对应的图像，我们从一些随机编码向量开始，将其传递给VQGAN以生成图像。然后使用CLIP生成一个损失函数，显示图像与文本提示的匹配程度。目标是通过反向传播调整输入向量参数以最小化该损失。 一个实现VQGAN+CLIP的优秀库是[Pixray](http://github.com/pixray/pixray)。 -![Pixray生成的图片](../../../../../translated_images/zh/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Pixray生成的图片](../../../../../translated_images/zh/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Pixray生成的图片](../../../../../translated_images/zh/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) +![Pixray生成的图片](../../../../../translated_images/zh/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp) | ![Pixray生成的图片](../../../../../translated_images/zh/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp) | ![Pixray生成的图片](../../../../../translated_images/zh/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp) ----|----|---- 从提示*一张年轻男性文学教师拿着书的水彩特写肖像*生成的图片 | 从提示*一张年轻女性计算机科学教师拿着电脑的油画特写肖像*生成的图片 | 从提示*一张老年男性数学教师站在黑板前的油画特写肖像*生成的图片