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b/translated_images/yolo.a2648ec82ee8bb4ea27537677adb482fd4b733ca1705c561b6a24a85102dced5.sr.png deleted file mode 100644 index 95a8467d..00000000 Binary files a/translated_images/yolo.a2648ec82ee8bb4ea27537677adb482fd4b733ca1705c561b6a24a85102dced5.sr.png and /dev/null differ diff --git a/translations/bg/README.md b/translations/bg/README.md index 511a209c..5d3d025e 100644 --- a/translations/bg/README.md +++ b/translations/bg/README.md @@ -1,8 +1,8 @@ [Arabic](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgarian](./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](../ur/README.md) | [Vietnamese](../vi/README.md) > **Предпочитате да клонирате локално?** -> Това хранилище включва над 50 езикови превода, което значително увеличава размера на изтегляне. За да клонирате без преводи, използвайте sparse checkout: +> Това хранилище включва над 50 езикови превода, което значително увеличава размера на изтеглянето. За да клонирате без преводите, използвайте sparse checkout: > ```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' > ``` -> Това ви осигурява всичко необходимо за завършване на курса с много по-бързо изтегляне. +> Това ви дава всичко необходимо за завършване на курса с много по-бързо изтегляне. -**Ако желаете да бъдат поддържани допълнителни езици за превод, те са изброени [тук](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** +**Ако желаете поддръжка на допълнителни езици, те са изброени [тук](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** ## Присъединете се към общността [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) ## Какво ще научите -**[Мисловна карта на курса](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** +**[Ментална карта на курса](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** В тази учебна програма ще научите: -* Различни подходи към изкуствения интелект, включително „добрия стар“ символен подход с **Представяне на знания** и логическо разсъждение ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). -* **Невронни мрежи** и **Дълбоко обучение**, които стоят в основата на съвременния ИИ. Ще илюстрираме концепциите зад тези важни теми чрез код в две от най-популярните рамки - [TensorFlow](http://Tensorflow.org) и [PyTorch](http://pytorch.org). -* **Невронни архитектури** за работа с изображения и текст. Ще разгледаме съвременни модели, но може да сме малко по-ограничени в най-новите постижения. +* Различни подходи към изкуствения интелект, включително "добрия стар" символен подход с **Представяне на знания** и разсъждение ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). +* **Невронни мрежи** и **Дълбоко обучение**, които са в основата на съвременния ИИ. Ще илюстрираме концепциите зад тези важни теми с помощта на код в две от най-популярните рамки - [TensorFlow](http://Tensorflow.org) и [PyTorch](http://pytorch.org). +* **Невронни архитектури** за работа с изображения и текст. Ще обхванем по-нови модели, но може би леко да изоставаме по отношение на най-съвременните постижения. * По-малко популярни подходи в ИИ, като **Генетични алгоритми** и **Мултиагентни системи**. -Какво няма да покрием в тази учебна програма: +Какво няма да покриваме в тази учебна програма: > [Намерете всички допълнителни ресурси за този курс в нашата колекция Microsoft Learn](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) -* Бизнес случаи за използване на **ИИ в бизнеса**. Помислете да преминете учебната пътека [Въведение в ИИ за бизнес потребители](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) в Microsoft Learn или [Бизнес училище по ИИ](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), разработено в сътрудничество с [INSEAD](https://www.insead.edu/). -* **Класически машинно обучение**, което е добре описано в нашата [Учебна програма по машинно обучение за начинаещи](http://github.com/Microsoft/ML-for-Beginners). -* Практически приложения на ИИ, изградени с използване на **[Когнитивни услуги](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**. За това препоръчваме да започнете с модулите на Microsoft Learn за [визия](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [обработка на естествен език](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[Генеративен ИИ с Azure OpenAI Service](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** и други. -* Специфични **Облачни рамки за МL**, като [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) или [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). Помислете да използвате учебните пътеки [Създаване и управление на решения за машинно обучение с Azure Machine Learning](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) и [Създаване и управление на решения за машинно обучение с Azure Databricks](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum). -* **Разговорен ИИ** и **чат ботове**. Съществува отделна учебна пътека [Създаване на разговорни ИИ решения](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum) и можете също да се запознаете с [този блог пост](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) за повече подробности. -* **Дълбока математика** зад дълбокото обучение. За тази цел препоръчваме [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) от Иън Гудфелоу, Йошуа Бенджио и Аарон Кюрвил, която също е достъпна онлайн на [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/). +* Бизнес случаи за използване на **ИИ в бизнеса**. Помислете за вземане на учебния път [Въведение в ИИ за бизнес потребители](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) в Microsoft Learn или [Бизнес училище за ИИ](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), разработено в сътрудничество с [INSEAD](https://www.insead.edu/). +* **Класическо машинно обучение**, което е добре описано в нашата учебна програма [Машинно обучение за начинаещи](http://github.com/Microsoft/ML-for-Beginners). +* Практически AI приложения, изградени с помощта на **[Cognitive Services](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**. За това препоръчваме да започнете с модулите на Microsoft Learn за [визия](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [обработка на естествен език](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[Генеративен ИИ с Azure OpenAI Service](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** и други. +* Специфични ML **обръчни облачни рамки**, като [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) или [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). Помислете за използване на учебните пътища [Създаване и управление на решения за машинно обучение с Azure Machine Learning](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) и [Създаване и управление на решения за машинно обучение с Azure Databricks](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum). +* **Разговорен ИИ** и **чат ботове**. Има отделен учебен път [Създаване на решения за разговорен ИИ](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum), а също така можете да се обърнете към [този блог пост](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) за повече подробности. +* **Дълбока математика**, стояща зад дълбокото обучение. За това бихме препоръчали [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) от Ian Goodfellow, Yoshua Bengio и Aaron Courville, която е достъпна също онлайн на [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/). -За леко въведение в темите за _ИИ в облака_ може да разгледате учебната пътека [Започнете с изкуствен интелект в Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum). +За леко въведение в темите за _ИИ в облака_, може да обмислите учебния път [Започнете с изкуствен интелект в Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum). # Съдържание -| | Връзка към урока | PyTorch/Keras/TensorFlow | Лаборатория | +| | Връзка към урока | PyTorch/Keras/TensorFlow | Лаборатория | | :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | -| 0 | [Настройване на курса](./lessons/0-course-setup/setup.md) | [Настройте своята среда за разработка](./lessons/0-course-setup/how-to-run.md) | | +| 0 | [Настройка на курса](./lessons/0-course-setup/setup.md) | [Настройка на вашата среда за разработка](./lessons/0-course-setup/how-to-run.md) | | | I | [**Въведение в ИИ**](./lessons/1-Intro/README.md) | | | | 01 | [Въведение и история на ИИ](./lessons/1-Intro/README.md) | - | - | | II | **Символен ИИ** | | 02 | [Представяне на знания и експертни системи](./lessons/2-Symbolic/README.md) | [Експертни системи](./lessons/2-Symbolic/Animals.ipynb) / [Онтология](./lessons/2-Symbolic/FamilyOntology.ipynb) /[Граф на концепции](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | -| III | [**Въведение в невронните мрежи**](./lessons/3-NeuralNetworks/README.md) ||| -| 03 | [Перцептрон](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [Тетрадка](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [Лаборатория](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | -| 04 | [Многослоен перцептрон и създаване на собствена рамка](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [Тетрадка](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [Лаборатория](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | -| 05 | [Въведение в рамките (PyTorch/TensorFlow) и презаучаване](./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) | [Лаборатория](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | -| IV | [**Компютърно зрение**](./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)| [Открий Компютърно зрение в Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | -| 06 | [Въведение в компютърното зрение. OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [Тетрадка](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [Лаборатория](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | +| III | [**Въведение в Невронните мрежи**](./lessons/3-NeuralNetworks/README.md) ||| +| 03 | [Перцептрон](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [Бележник](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [Лаборатория](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | +| 04 | [Многослоен перцептрон и създаване на собствен фреймуърк](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [Бележник](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [Лаборатория](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | +| 05 | [Въведение във фреймуърковете (PyTorch/TensorFlow) и пренасищане](./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) | [Лаборатория](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | +| IV | [**Компютърно зрение**](./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)| [Изследвайте компютърното зрение в Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | +| 06 | [Въведение в компютърното зрение. OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [Бележник](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [Лаборатория](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | | 07 | [Конволюционни невронни мрежи](./lessons/4-ComputerVision/07-ConvNets/README.md) & [Архитектури на CNN](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [Лаборатория](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | -| 08 | [Предварително обучени мрежи и пренаучаване](./lessons/4-ComputerVision/08-TransferLearning/README.md) и [Трикове за обучение](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Лаборатория](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | -| 09 | [Автоенкодери и 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 | [Генеративни състезателни мрежи и трансфер на художествен стил](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | +| 08 | [Предварително обучени мрежи и трансферно обучение](./lessons/4-ComputerVision/08-TransferLearning/README.md) и [Трикове за обучение](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Лаборатория](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | +| 09 | [Автоенкодери и 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 | [Генеративни състезателни мрежи и пренос на артистичен стил](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | | 11 | [Обектно разпознаване](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [Лаборатория](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | | 12 | [Семантично сегментиране. 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 | [**Обработка на естествен език**](./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) | [Открий Обработка на естествен език в Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| +| V | [**Обработка на естествен език**](./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) | [Изследвайте обработката на естествен език в Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| | 13 | [Представяне на текст. 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 | [Семантични вградени представяния на думи. Word2Vec и 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 | [Моделиране на езика. Обучение на собствени вградени представяния](./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) | [Лаборатория](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | +| 14 | [Семантични вграждания на думи. Word2Vec и 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 | [Моделиране на езика. Обучение на собствени вграждания](./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) | [Лаборатория](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | | 16 | [Рекурентни невронни мрежи](./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 | [Генеративни рекурентни мрежи](./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) | [Лаборатория](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | | 18 | [Трансформъри. 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 | [Идентификация на именувани обекти](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [Лаборатория](./lessons/5-NLP/19-NER/lab/README.md) | -| 20 | [Големи езикови модели, програмиране с подсказки и задачи с малко примери](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | +| 19 | [Разпознаване на именовани обекти](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [Лаборатория](./lessons/5-NLP/19-NER/lab/README.md) | +| 20 | [Големи езикови модели, програмиране на заявки и задачи с малко примери](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | | VI | **Други AI техники** || | -| 21 | [Генетични алгоритми](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [Тетрадка](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | +| 21 | [Генетични алгоритми](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [Бележник](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | | 22 | [Дълбоко подсилващо обучение](./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) | [Лаборатория](./lessons/6-Other/22-DeepRL/lab/README.md) | -| 23 | [Мултиагентни системи](./lessons/6-Other/23-MultiagentSystems/README.md) | | | -| VII | **Етика на изкуствения интелект** | | | -| 24 | [Етика на AI и отговорен AI](./lessons/7-Ethics/README.md) | [Microsoft Learn: Принципи за отговорен AI](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | -| IX | **Допълнително** | | | -| 25 | [Мулти-модални мрежи, CLIP и VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [Тетрадка](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | +| 23 | [Многоагентни системи](./lessons/6-Other/23-MultiagentSystems/README.md) | | | +| VII | **Етика в AI** | | | +| 24 | [Етика в AI и отговорен AI](./lessons/7-Ethics/README.md) | [Microsoft Learn: Принципи на отговорния AI](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | +| IX | **Допълнителни материали** | | | +| 25 | [Мултимодални мрежи, CLIP и VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [Бележник](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | -## Всяко занятие съдържа +## Всяка лекция съдържа -* Предварителен материал за четене -* Изпълними Jupyter тетрадки, които често са специфични за рамката (**PyTorch** или **TensorFlow**). Изпълнимата тетрадка съдържа и много теоретичен материал, така че за да разберете темата, трябва да преминете поне една версия на тетрадката (PyTorch или TensorFlow). -* **Лаборатории**, налични за някои теми, които ви дават възможност да приложите наученото към конкретен проблем. -* Някои раздели съдържат връзки към модули на [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum), които покриват свързани теми. +* Материал за предварително четене +* Изпълними бележници Jupyter, които често са специфични за даден фреймуърк (**PyTorch** или **TensorFlow**). Изпълнимият бележник също съдържа много теоретичен материал, затова за да разберете темата, трябва да преминете поне през една от версиите на бележника (или PyTorch, или TensorFlow). +* **Лаборатории**, достъпни за някои теми, които ви дават възможност да опитате да приложите научения материал върху конкретен проблем. +* Някои раздели съдържат връзки към модули в [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum), които обхващат свързани теми. ## Започване ### 🎯 Нов в AI? Започнете тук! -Ако сте напълно нови в AI и искате бързи примери за работа, разгледайте нашите [**Примери за начинаещи**](./examples/README.md)! Те включват: +Ако сте напълно нови в AI и искате бързи, практични примери, разгледайте нашите [**Примери за начинаещи**](./examples/README.md)! Те включват: -- 🌟 **Здравей AI свят** - Вашата първа AI програма (разпознаване на шаблони) -- 🧠 **Проста невронна мрежа** - Създайте невронна мрежа от нулата +- 🌟 **Hello AI World** - Вашата първа AI програма (разпознаване на шаблони) +- 🧠 **Проста невронна мрежа** - Създаване на невронна мрежа от нулата - 🖼️ **Класификатор на изображения** - Класифицирайте изображения с подробни коментари -- 💬 **Текстов сентимент** - Анализиране на положителен / отрицателен текст +- 💬 **Настроение на текста** - Анализирайте положителен/отрицателен текст -Тези примери са създадени, за да ви помогнат да разберете концепциите на ИИ, преди да се потопите в пълната учебна програма. +Тези примери са създадени, за да ви помогнат да разберете концепциите на ИИ преди да се потопите в пълната учебна програма. -### 📚 Настройване на пълната учебна програма +### 📚 Настройка на пълната учебна програма -- Създадохме [урок за настройка](./lessons/0-course-setup/setup.md), за да ви помогнем с настройването на вашата среда за разработка. - За преподаватели също сме създали [урок за настройка на учебната програма](./lessons/0-course-setup/for-teachers.md)! -- Как да [стартирате кода във VSCode или Codepace](./lessons/0-course-setup/how-to-run.md) +- Създадохме [урок за настройка](./lessons/0-course-setup/setup.md), който да ви помогне с настройването на вашата среда за разработка. - За обучаващите сме създали и [урок за настройка на учебната програма](./lessons/0-course-setup/for-teachers.md)! +- Как да [стартирате кода във VSCode или Codespace](./lessons/0-course-setup/how-to-run.md) Следвайте тези стъпки: -Форкнете хранилището: Кликнете бутона "Fork" в горния десен ъгъл на тази страница. +Форкнете Репозитория: Кликнете на бутона „Fork“ в горния десен ъгъл на тази страница. -Клонирайте хранилището: `git clone https://github.com/microsoft/AI-For-Beginners.git` +Клонирайте Репозитория: `git clone https://github.com/microsoft/AI-For-Beginners.git` -Не забравяйте да звездирате (🌟) това хранилище, за да го намирате по-лесно по-късно. +Не забравяйте да дадете звезда (🌟) на това хранилище, за да го намерите по-лесно по-късно. -## Запознайте се с други учащи +## Запознайте се с други учащи се -Присъединете се към нашия [официален AI Discord сървър](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum), за да срещнете и създадете мрежа с други учащи, които следват този курс, и да получите подкрепа. +Присъединете се към нашия [официален AI Discord сървър](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum), за да срещнете и създадете мрежа с други учащи се, които посещават този курс, и да получите подкрепа. -Ако имате обратна връзка или въпроси по време на разработване, посетете нашия [Azure AI Foundry Developer Forum](https://aka.ms/foundry/forum) +Ако имате обратна връзка за продукта или въпроси докато изграждате, посетете нашия [Azure AI Foundry Developer Forum](https://aka.ms/foundry/forum) ## Тестове -> **Бележка за тестовете**: Всички тестове са в папката Quiz-app в etc\quiz-app или [онлайн тук](https://ff-quizzes.netlify.app/). Те са свързани от уроците; приложението за тестове може да се стартира локално или да се пусне в Azure; следвайте инструкциите в папката `quiz-app`. Постепенно се локализират. +> **Бележка за тестовете**: Всички тестове са в папката Quiz-app в etc\quiz-app, или [онлайн тук](https://ff-quizzes.netlify.app/) Те са свързани вътре в уроците, приложението за тестове може да се стартира локално или да се разположи в Azure; следвайте инструкциите в папката `quiz-app`. Постепенно се локализират. -## Търсим помощ +## Нужна помощ -Имате ли предложения или сте намерили правописни или кодови грешки? Създайте issue или pull request. +Имате ли предложения или сте открили правописни или кодови грешки? Създайте issue или pull request. ## Специални благодарности -* **✍️ Основен автор:** [Dmitry Soshnikov](http://soshnikov.com), доктор -* **🔥 Редактор:** [Jen Looper](https://twitter.com/jenlooper), доктор -* **🎨 Илюстратор на схеми:** [Tomomi Imura](https://twitter.com/girlie_mac) +* **✍️ Главен автор:** [Dmitry Soshnikov](http://soshnikov.com), PhD +* **🔥 Редактор:** [Jen Looper](https://twitter.com/jenlooper), PhD +* **🎨 Илюстратор на скици:** [Tomomi Imura](https://twitter.com/girlie_mac) * **✅ Създател на тестове:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) * **🙏 Основни сътрудници:** [Evgenii Pishchik](https://github.com/Pe4enIks) @@ -177,57 +176,57 @@ CO_OP_TRANSLATOR_METADATA: ### LangChain -[![LangChain4j за начинаещи](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 за начинаещи](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 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) --- -### Azure / Edge / MCP / Агенти -[![AZD за начинаещи](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 за начинаещи](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 за начинаещи](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 агенти за начинаещи](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) +### 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) --- -### Серия за генеративен AI -[![Генеративен AI за начинаещи](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) -[![Генеративен 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) -[![Генеративен 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) -[![Генеративен 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) +### Серия за генеративен ИИ +[![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) --- ### Основно обучение -[![ML за начинаещи](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) -[![Данни науки за начинаещи](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) -[![ИИ за начинаещи](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) -[![Киберсигурност за начинаещи](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) -[![Уеб разработка за начинаещи](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 за начинаещи](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 разработка за начинаещи](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) +[![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) --- ### Серия Copilot -[![Copilot за AI програмиране в екип](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 за 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 приключение](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 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) -## Получаване на помощ +## Как да получите помощ -Ако се затрудните или имате въпроси относно създаването на AI приложения, присъединете се към други учащи и опитни разработчици в дискусии по MCP. Това е подкрепяща общност, където въпросите са добре дошли и знанието се споделя свободно. +Ако срещнете трудности или имате въпроси за изграждането на ИИ приложения, присъединете се към други учащи се и опитни разработчици в обсъжданията за MCP. Това е подкрепяща общност, където въпросите са добре дошли и знанието се споделя свободно. [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -Ако имате обратна връзка за продукта или грешки по време на разработването, посетете: +Ако имате обратна връзка за продукта или грешки по време на работа, посетете: [![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) --- -**Отказ от отговорност**: -Този документ е преведен с помощта на AI преводаческа услуга [Co-op Translator](https://github.com/Azure/co-op-translator). Въпреки че се стремим към точност, моля, имайте предвид, че автоматизираните преводи могат да съдържат грешки или неточности. Оригиналният документ на неговия роден език трябва да се счита за авторитетен източник. За критична информация се препоръчва професионален човешки превод. Ние не носим отговорност за каквито и да е недоразумения или неправилни тълкувания, произтичащи от използването на този превод. +**Отказ от отговорност**: +Този документ е преведен с помощта на AI преводаческа услуга [Co-op Translator](https://github.com/Azure/co-op-translator). Въпреки че се стремим към точност, моля, имайте предвид, че автоматичните преводи могат да съдържат грешки или неточности. Оригиналният документ на езика, на който е написан, трябва да се счита за авторитетен източник. За критична информация се препоръчва професионален човешки превод. Ние не носим отговорност за каквито и да е недоразумения или неправилни тълкувания, възникнали в резултат на използването на този превод. \ No newline at end of file diff --git a/translations/bg/lessons/0-course-setup/how-to-run.md b/translations/bg/lessons/0-course-setup/how-to-run.md index bae878be..0831bee7 100644 --- a/translations/bg/lessons/0-course-setup/how-to-run.md +++ b/translations/bg/lessons/0-course-setup/how-to-run.md @@ -1,21 +1,21 @@ # Как да стартирате кода -Тази учебна програма съдържа множество изпълними примери и лабораторни упражнения, които вероятно ще искате да стартирате. За да направите това, трябва да имате възможност да изпълнявате Python код в Jupyter Notebooks, предоставени като част от тази учебна програма. Имате няколко опции за стартиране на кода: +Тази учебна програма съдържа много изпълними примери и лаборатории, които бихте искали да стартирате. За да направите това, ви е необходимо да можете да изпълнявате Python код в Jupyter бележници, предоставени като част от тази учебна програма. Имате няколко опции за стартиране на кода: ## Стартиране локално на вашия компютър -За да стартирате кода локално на вашия компютър, трябва да имате инсталирана някаква версия на Python. Лично препоръчвам да инсталирате **[miniconda](https://conda.io/en/latest/miniconda.html)** - това е сравнително лека инсталация, която поддържа `conda` пакетен мениджър за различни Python **виртуални среди**. +За да стартирате кода локално на вашия компютър, е необходима инсталация на Python. Една препоръка е да инсталирате **[miniconda](https://conda.io/en/latest/miniconda.html)** - това е сравнително лека инсталация, която поддържа пакетния мениджър `conda` за различни Python **виртуални среди**. -След като инсталирате miniconda, трябва да клонирате хранилището и да създадете виртуална среда, която да използвате за този курс: +След като инсталирате miniconda, клонирайте хранилището и създайте виртуална среда, която да се използва за този курс: ```bash git clone http://github.com/microsoft/ai-for-beginners @@ -24,17 +24,17 @@ conda env create --name ai4beg --file .devcontainer/environment.yml conda activate ai4beg ``` -### Използване на Visual Studio Code с Python Extension +### Използване на Visual Studio Code с Python разширение -Вероятно най-добрият начин да използвате учебната програма е да я отворите в [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) с [Python Extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste). +Тази учебна програма е най-добре да се използва, когато я отворите в [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) с [Python разширение](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste). -> **Забележка**: След като клонирате и отворите директорията във VS Code, програмата автоматично ще ви предложи да инсталирате Python разширения. Ще трябва също така да инсталирате miniconda, както е описано по-горе. +> **Бележка**: След като клонирате и отворите директорията във VS Code, той автоматично ще ви предложи да инсталирате Python разширения. Също така ще трябва да инсталирате miniconda, както е описано по-горе. -> **Забележка**: Ако VS Code ви предложи да отворите хранилището в контейнер, трябва да откажете, за да използвате локалната инсталация на Python. +> **Бележка**: Ако VS Code ви предложи да отворите хранилището в контейнер, трябва да откажете, за да използвате локалната Python инсталация. ### Използване на Jupyter в браузъра -Можете също така да използвате Jupyter среда директно от браузъра на вашия компютър. Всъщност, както класическият Jupyter, така и Jupyter Hub предоставят доста удобна среда за разработка с автоматично довършване, оцветяване на кода и др. +Можете също да използвате Jupyter среда от браузъра на вашия компютър. Класическият Jupyter и JupyterHub предоставят удобна развойна среда с автодовършване, оцветяване на кода и други. За да стартирате Jupyter локално, отидете в директорията на курса и изпълнете: @@ -45,32 +45,36 @@ jupyter notebook ```bash jupyterhub ``` -След това можете да навигирате до някой от `.ipynb` файловете, да ги отворите и да започнете работа. +След това можете да навигирате до някой от `.ipynb` файловете, да го отворите и да започнете работа. ### Стартиране в контейнер -Една алтернатива на инсталирането на Python е да стартирате кода в контейнер. Тъй като нашето хранилище съдържа специална папка `.devcontainer`, която указва как да се изгради контейнер за това хранилище, VS Code ще ви предложи да отворите кода в контейнер. Това ще изисква инсталиране на Docker и ще бъде по-сложно, така че препоръчваме този подход за по-опитни потребители. +Една алтернатива на инсталацията на Python е да стартирате кода в контейнер. Тъй като нашето хранилище предоставя специална папка `.devcontainer`, която дава инструкции за създаване на контейнер за това хранилище, VS Code предлага възможността да отворите кода в контейнер. Това изисква инсталация на Docker и е по-сложно, затова препоръчваме това на по-опитни потребители. ## Стартиране в облака -Ако не искате да инсталирате Python локално и имате достъп до някакви облачни ресурси, добра алтернатива е да стартирате кода в облака. Има няколко начина да направите това: +Ако не искате да инсталирате Python локално и имате достъп до някакви облачни ресурси - добра алтернатива е да стартирате кода в облака. Има няколко начина да го направите: -* Използване на **[GitHub Codespaces](https://github.com/features/codespaces)**, което представлява виртуална среда, създадена за вас в GitHub, достъпна чрез браузърния интерфейс на VS Code. Ако имате достъп до Codespaces, просто кликнете върху бутона **Code** в хранилището, стартирайте Codespace и започнете работа за нула време. -* Използване на **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**. [Binder](https://mybinder.org) предоставя безплатни изчислителни ресурси в облака за хора като вас, които искат да тестват код в GitHub. Има бутон на началната страница за отваряне на хранилището в Binder - това бързо ще ви отведе до сайта на Binder, който ще изгради подлежащия контейнер и ще стартира Jupyter уеб интерфейс за вас безпроблемно. +* Използване на **[GitHub Codespaces](https://github.com/features/codespaces)**, които са виртуална среда, създадена за вас в GitHub, достъпна през браузърния интерфейс на VS Code. Ако имате достъп до Codespaces, просто натиснете бутона **Code** в хранилището, стартирайте codespace и започнете веднага. +* Използване на **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**. [Binder](https://mybinder.org) предлага безплатни изчислителни ресурси в облака, предоставени за хора като вас да изпробват код от GitHub. На началната страница има бутон за отваряне на хранилището в Binder - това бързо ще ви отведе до сайта на Binder, който ще създаде контейнер и ще стартира уеб интерфейс на Jupyter за вас безпроблемно. -> **Забележка**: За да се предотврати злоупотреба, Binder има блокиран достъп до някои уеб ресурси. Това може да попречи на работата на част от кода, който изтегля модели и/или набори от данни от публичния интернет. Може да се наложи да намерите алтернативни решения. Също така, изчислителните ресурси, предоставени от Binder, са доста базови, така че обучението ще бъде бавно, особено в по-късните и по-сложни уроци. +> **Бележка**: За да се предотврати злоупотреба, Binder има блокирани някои уеб ресурси. Това може да попречи на някои части от кода, които изтеглят модели и/или набори от данни от публичния интернет. Може да се наложи да намерите заобиколни решения. Освен това компютърните ресурси, предоставени от Binder, са сравнително базови, така че обучението ще бъде бавно, особено в по-късните, по-сложни уроци. ## Стартиране в облака с GPU -Някои от по-късните уроци в тази учебна програма ще се възползват значително от поддръжка на GPU, защото иначе обучението ще бъде изключително бавно. Има няколко опции, които можете да следвате, особено ако имате достъп до облака чрез [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) или чрез вашата институция: +Някои от по-късните уроци в тази учебна програма биха се възползвали значително от поддръжка на GPU. Обучението на модели например може да бъде много бавно без това. Имате няколко опции, особено ако имате достъп до облак чрез [Azure за студенти](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) или чрез вашата институция: -* Създайте [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) и се свържете с нея чрез Jupyter. След това можете да клонирате хранилището директно на машината и да започнете обучението. NC-серията виртуални машини поддържат GPU. +* Създайте [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) и се свържете с нея чрез Jupyter. Можете да клонирате хранилището директно на машината и да започнете обучение. NC-серията виртуални машини имат поддръжка на GPU. -> **Забележка**: Някои абонаменти, включително Azure for Students, не предоставят GPU поддръжка по подразбиране. Може да се наложи да заявите допълнителни GPU ядра чрез заявка за техническа поддръжка. +> **Бележка**: Някои абонаменти, включително Azure за студенти, не предоставят GPU поддръжка по подразбиране. Може да се наложи да поискате допълнителни GPU ядра чрез техническа поддръжка. -* Създайте [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) и след това използвайте функцията за Notebook там. [Това видео](https://azure-for-academics.github.io/quickstart/azureml-papers/) показва как да клонирате хранилище в Azure ML Notebook и да започнете работа. +* Създайте [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) и след това използвайте функцията Notebook там. [Това видео](https://azure-for-academics.github.io/quickstart/azureml-papers/) показва как да клонирате хранилище в Azure ML notebook и да го използвате. -Можете също така да използвате Google Colab, който предлага известна безплатна GPU поддръжка, и да качите Jupyter Notebooks там, за да ги изпълнявате една по една. +Можете също да използвате Google Colab, който предлага някаква безплатна GPU поддръжка, и да качите Jupyter бележници там, за да ги изпълните един по един. -**Отказ от отговорност**: -Този документ е преведен с помощта на AI услуга за превод [Co-op Translator](https://github.com/Azure/co-op-translator). Въпреки че се стремим към точност, моля, имайте предвид, че автоматизираните преводи може да съдържат грешки или неточности. Оригиналният документ на неговия роден език трябва да се счита за авторитетен източник. За критична информация се препоръчва професионален човешки превод. Ние не носим отговорност за недоразумения или погрешни интерпретации, произтичащи от използването на този превод. \ No newline at end of file +--- + + +**Отказ от отговорност**: +Този документ е преведен с помощта на AI преводаческа услуга [Co-op Translator](https://github.com/Azure/co-op-translator). Въпреки че се стремим към точност, моля, имайте предвид, че автоматизираните преводи могат да съдържат грешки или неточности. Оригиналният документ на неговия роден език трябва да се счита за авторитетен източник. За критична информация се препоръчва професионален човешки превод. Не носим отговорност за каквито и да е недоразумения или неправилни тълкувания, произтичащи от използването на този превод. + \ No newline at end of file diff --git a/translations/bg/lessons/2-Symbolic/Animals.ipynb b/translations/bg/lessons/2-Symbolic/Animals.ipynb index 5207b89e..16b37a09 100644 --- a/translations/bg/lessons/2-Symbolic/Animals.ipynb +++ b/translations/bg/lessons/2-Symbolic/Animals.ipynb @@ -6,25 +6,25 @@ "collapsed": true }, "source": [ - "# Изграждане на експертна система за животни\n", + "# Имплементиране на експертна система за животни\n", "\n", - "Пример от [AI for Beginners Curriculum](http://github.com/microsoft/ai-for-beginners).\n", + "Пример от [Учебна програма по изкуствен интелект за начинаещи](http://github.com/microsoft/ai-for-beginners).\n", "\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", + "В този пример ще имплементираме проста система, базирана на знания, за определяне на животно въз основа на някои физически характеристики. Системата може да се представи чрез следното AND-OR дърво (това е част от цялото дърво, можем лесно да добавим още правила):\n", "\n", - "![](../../../../translated_images/bg/AND-OR-Tree.5592d2c70187f283.webp)\n" + "![](../../../../../../translated_images/bg/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Наш собственен експертен системен шел с обратна логика\n", + "## Собствена експертна система с обратно извеждане\n", "\n", - "Нека се опитаме да дефинираме прост език за представяне на знания, базиран на продукционни правила. Ще използваме Python класове като ключови думи за дефиниране на правила. Ще има основно три типа класове:\n", + "Нека опитаме да дефинираме прост език за представяне на знания, базиран на продукционни правила. Ще използваме Python класове като ключови думи за дефиниране на правила. Съществено ще има 3 вида класове:\n", "* `Ask` представлява въпрос, който трябва да бъде зададен на потребителя. Той съдържа набора от възможни отговори.\n", - "* `If` представлява правило и е просто синтактична захар за съхраняване на съдържанието на правилото.\n", - "* `AND`/`OR` са класове за представяне на AND/OR разклонения на дървото. Те просто съхраняват списъка с аргументи вътре. За опростяване на кода, цялата функционалност е дефинирана в родителския клас `Content`.\n" + "* `If` представлява правило и е просто синтактичен захар за съхраняване на съдържанието на правилото\n", + "* `AND`/`OR` са класове за представяне на AND/OR клонове на дървото. Те просто съхраняват списъка с аргументи вътре. За опростяване на кода, цялата функционалност е дефинирана в родителския клас `Content`\n" ] }, { @@ -66,7 +66,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "В нашата система работната памет би съдържала списъка с **факти** като **атрибут-стойност двойки**. Базата знания може да бъде дефинирана като един голям речник, който свързва действия (нови факти, които трябва да бъдат добавени в работната памет) с условия, изразени като AND-OR изрази. Също така, някои факти могат да бъдат `Запитани`.\n" + "В нашата система, работната памет ще съдържа списъка с **факти** като **двойки атрибут-стойност**. Базата от знания може да се определи като един голям речник, който свързва действията (нови факти, които трябва да се вмъкнат в работната памет) с условия, изразени като AND-OR изрази. Също така, някои факти могат да бъдат `Ask`.\n" ] }, { @@ -99,13 +99,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "За да извършим обратното заключение, ще дефинираме клас `Knowledgebase`. Той ще съдържа:\n", - "* Работеща `памет` - речник, който свързва атрибути със стойности\n", - "* Правила на `Knowledgebase` във формат, както е определено по-горе\n", + "За да извършим обратно извод, ще дефинираме клас `Knowledgebase`. Той ще съдържа:\n", + "* Работна `memory` - речник, който свързва атрибути със стойности\n", + "* Правила на базата знания `rules` във формата, дефиниран по-горе\n", "\n", - "Двата основни метода са:\n", - "* `get` за получаване на стойността на атрибут, като се извършва заключение, ако е необходимо. Например, `get('color')` ще получи стойността на слот за цвят (ще попита, ако е необходимо, и ще съхрани стойността за по-късна употреба в работещата памет). Ако попитаме `get('color:blue')`, ще попита за цвят и след това ще върне стойност `y`/`n` в зависимост от цвета.\n", - "* `eval` извършва действителното заключение, т.е. преминава през AND/OR дървото, оценява подцели и т.н.\n" + "Два основни метода са:\n", + "* `get` за получаване на стойността на атрибут, като при необходимост извършва извод. Например, `get('color')` ще получи стойността на слот за цвят (ще попита ако е необходимо и ще запази стойността за бъдеща употреба в работната памет). Ако поискамe `get('color:blue')`, ще попита за цвят и след това ще върне стойност `y`/`n` в зависимост от цвета.\n", + "* `eval` извършва реалния извод, т.е. обхожда AND/OR дърво, оценява подцели и т.н.\n" ] }, { @@ -172,7 +172,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Сега нека дефинираме нашата база знания за животни и проведем консултацията. Имайте предвид, че това извикване ще ви задава въпроси. Можете да отговорите, като напишете `y`/`n` за въпроси с отговор да-не, или като посочите число (0..N) за въпроси с по-дълги отговори с множество опции.\n" + "Сега нека дефинираме нашата животинска база знания и да проведем консултацията. Обърнете внимание, че този разговор ще ви задава въпроси. Можете да отговаряте, като въвеждате `y`/`n` за въпроси с отговор да/не, или като посочите число (0..N) за въпроси с по-дълги множествени отговори.\n" ] }, { @@ -229,11 +229,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Използване на PyKnow за напредващо заключение\n", + "## Използване на Experta за напредващо извеждане\n", "\n", - "В следващия пример ще се опитаме да реализираме напредващо заключение, използвайки една от библиотеките за представяне на знания, [PyKnow](https://github.com/buguroo/pyknow/). **PyKnow** е библиотека за създаване на системи за напредващо заключение в Python, която е проектирана да бъде подобна на класическата стара система [CLIPS](http://www.clipsrules.net/index.html).\n", + "В следващия пример ще се опитаме да реализираме напредващо извеждане, използвайки една от библиотеките за представяне на знания, [Experta](https://github.com/nilp0inter/experta). **Experta** е библиотека за създаване на системи за напредващо извеждане на Python, която е проектирана да бъде подобна на класическата стара система [CLIPS](http://www.clipsrules.net/index.html).\n", "\n", - "Можехме също така да реализираме напредващо свързване сами без особени затруднения, но наивните реализации обикновено не са много ефективни. За по-ефективно съвпадение на правила се използва специален алгоритъм [Rete](https://en.wikipedia.org/wiki/Rete_algorithm).\n" + "Можехме също така да реализираме напредващо извеждане сами без много проблеми, но наивните реализации обикновено не са много ефективни. За по-ефективно съвпадение на правилата се използва специален алгоритъм [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": [ - "Ще дефинираме нашата система като клас, който наследява `KnowledgeEngine`. Всяко правило се дефинира чрез отделна функция с анотация `@Rule`, която указва кога правилото трябва да се изпълни. Вътре в правилото можем да добавяме нови факти, използвайки функцията `declare`, и добавянето на тези факти ще доведе до извикване на още правила от двигателя за напредващо заключение.\n" + "Ще дефинираме нашата система като клас, който наследява `KnowledgeEngine`. Всяко правило се дефинира чрез отделна функция с анотация `@Rule`, която указва кога правилото трябва да се изпълни. Вътре в правилото можем да добавим нови факти, използвайки функцията `declare`, и добавянето на тези факти ще доведе до извикване на още правила от напредналия инференчен механизъм.\n" ] }, { @@ -378,7 +377,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "След като сме дефинирали база от знания, попълваме работната памет с някои начални факти и след това извикваме метода `run()`, за да извършим изводите. Можете да видите като резултат, че нови изведени факти се добавят към работната памет, включително крайния факт за животното (ако сме задали всички начални факти правилно).\n" + "След като дефинираме база знания, попълваме работната си памет с някои първоначални факти и след това извикваме метода `run()`, за да извършим извод. Като резултат можете да видите, че нови изведени факти се добавят към работната памет, включително и крайния факт за животното (ако сме задали всички първоначални факти правилно).\n" ] }, { @@ -440,7 +439,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n---\n\n**Отказ от отговорност**: \nТози документ е преведен с помощта на AI услуга за превод [Co-op Translator](https://github.com/Azure/co-op-translator). Въпреки че се стремим към точност, моля, имайте предвид, че автоматизираните преводи може да съдържат грешки или неточности. Оригиналният документ на неговия роден език трябва да се счита за авторитетен източник. За критична информация се препоръчва професионален човешки превод. Ние не носим отговорност за недоразумения или погрешни интерпретации, произтичащи от използването на този превод.\n" + "---\n\n\n**Отказ от отговорност**:\nТози документ е преведен чрез AI преводаческа услуга [Co-op Translator](https://github.com/Azure/co-op-translator). Въпреки че се стремим към точност, моля, имайте предвид, че автоматизираните преводи могат да съдържат грешки или неточности. Оригиналният документ на първичния му език трябва да се счита за авторитетен източник. За критична информация се препоръчва професионален човешки превод. Ние не носим отговорност за каквито и да е неразбирателства или неправилни тълкувания, произтичащи от използването на този превод.\n\n" ] } ], @@ -467,8 +466,8 @@ "version": "3.11.2" }, "coopTranslator": { - "original_hash": "ab2bd97b0453415b89a469284609a8ce", - "translation_date": "2025-08-29T23:58:48+00:00", + "original_hash": "8ef43db4b9182239fd150a76bd494fdb", + "translation_date": "2026-01-16T05:22:00+00:00", "source_file": "lessons/2-Symbolic/Animals.ipynb", "language_code": "bg" } diff --git a/translations/bg/lessons/2-Symbolic/README.md b/translations/bg/lessons/2-Symbolic/README.md index 4448c223..396c6ddc 100644 --- a/translations/bg/lessons/2-Symbolic/README.md +++ b/translations/bg/lessons/2-Symbolic/README.md @@ -1,116 +1,116 @@ # Представяне на знания и експертни системи -![Обобщение на съдържанието за символен AI](../../../../translated_images/bg/ai-symbolic.715a30cb610411a6.webp) +![Обобщение на съдържанието за символичния ИИ](../../../../../../translated_images/bg/ai-symbolic.715a30cb610411a6.webp) -> Скица от [Tomomi Imura](https://twitter.com/girlie_mac) +> Скетчноут от [Tomomi Imura](https://twitter.com/girlie_mac) -Търсенето на изкуствен интелект се основава на стремежа към знания, за да се разбере света по начин, подобен на човешкия. Но как може да се постигне това? +Търсенето на изкуствен интелект се основава на търсенето на знания, за да разберем света по начин, подобен на човешкия. Но как можем да постигнем това? -## [Тест преди лекцията](https://ff-quizzes.netlify.app/en/ai/quiz/3) +## [Предварителен тест](https://ff-quizzes.netlify.app/en/ai/quiz/3) -В ранните дни на AI популярна беше подходът отгоре-надолу за създаване на интелигентни системи (обсъден в предишния урок). Идеята беше да се извлекат знанията от хората в машинно четим формат и след това да се използват за автоматично решаване на проблеми. Този подход се основаваше на две големи идеи: +В ранните дни на ИИ, подходът отгоре-надолу за създаване на интелигентни системи (обсъден в предишния урок) беше популярен. Идеята беше да се извлекат знания от хората в някаква машина разчетлива форма и след това да се използват за автоматично решаване на проблеми. Този подход се основаваше на две големи идеи: * Представяне на знания * Разсъждение ## Представяне на знания -Една от важните концепции в символния AI са **знанията**. Важно е да се разграничат знанията от *информация* или *данни*. Например, може да се каже, че книгите съдържат знания, защото човек може да ги изучава и да стане експерт. Въпреки това, това, което книгите съдържат, всъщност се нарича *данни*, а чрез четене на книги и интегриране на тези данни в нашия модел на света, ние ги превръщаме в знания. +Една от важните концепции в Символичния ИИ е **знанието**. Важно е да се разграничат знанията от *информация* или *данни*. Например, може да се каже, че книгите съдържат знания, защото човек може да учи от книги и да стане експерт. Въпреки това, това което книгите съдържат всъщност се нарича *данни*, и чрез четене на книги и интегриране на тези данни в нашия модел на света ние преобразуваме тези данни в знания. -> ✅ **Знанията** са нещо, което се съдържа в нашия ум и представлява нашето разбиране за света. Те се придобиват чрез активен процес на **учене**, който интегрира получената информация в нашия активен модел на света. +> ✅ **Знанието** е нещо, което съдържаме в главата си и представя нашето разбиране за света. То се получава чрез активен процес на **учене**, при който парчета информация, които получаваме, се интегрират в активния ни модел на света. -Често не дефинираме строго знанията, но ги свързваме с други свързани концепции, използвайки [пирамидата DIKW](https://en.wikipedia.org/wiki/DIKW_pyramid). Тя съдържа следните концепции: +Най-често не дефинираме строго знанието, а го съгласуваме с други свързани концепции, използвайки [пирамида DIKW](https://en.wikipedia.org/wiki/DIKW_pyramid). Тя съдържа следните понятия: -* **Данни** са нещо, представено на физически носител, като писмен текст или говорени думи. Данните съществуват независимо от човешките същества и могат да се предават между хора. -* **Информация** е начинът, по който интерпретираме данните в нашия ум. Например, когато чуем думата *компютър*, имаме някакво разбиране за това какво е. -* **Знания** са информация, интегрирана в нашия модел на света. Например, след като научим какво е компютър, започваме да имаме идеи за това как работи, колко струва и за какво може да се използва. Тази мрежа от взаимосвързани концепции формира нашите знания. -* **Мъдрост** е още едно ниво на нашето разбиране за света и представлява *мета-знания*, например някакво понятие за това как и кога трябва да се използват знанията. +* **Данни** са неща, представени във физически носители, като писмен текст или говорими думи. Данните съществуват независимо от хората и могат да се предават между тях. +* **Информация** е как интерпретираме данните в главата си. Например, когато чуем думата *компютър*, имаме някакво разбиране какво е това. +* **Знание** е информация, интегрирана в нашия модел на света. Например, след като научим какво е компютър, започваме да имаме някакви идеи за това как работи, колко струва и за какво може да се използва. Тази мрежа от взаимосвързани концепции формира нашето знание. +* **Мъдрост** е още едно ниво на нашето разбиране за света и представлява *метазнание*, например някакъв усет кога и как трябва да се използват знанията. - + -*Изображение [от Wikipedia](https://commons.wikimedia.org/w/index.php?curid=37705247), By Longlivetheux - Собствена работа, CC BY-SA 4.0* +*Изображение [от Wikipedia](https://commons.wikimedia.org/w/index.php?curid=37705247), от Longlivetheux - собствена работа, CC BY-SA 4.0* -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: +Така, проблемът с **представянето на знания** е да се намери ефективен начин за представяне на знания в компютър под формата на данни, за да станат автоматично използваеми. Това може да се разглежда като спектър: -![Спектър на представяне на знания](../../../../translated_images/bg/knowledge-spectrum.b60df631852c0217.webp) +![Спектър на представяне на знания](../../../../../../translated_images/bg/knowledge-spectrum.b60df631852c0217.webp) > Изображение от [Dmitry Soshnikov](http://soshnikov.com) -* Вляво са много прости типове представяне на знания, които могат ефективно да се използват от компютрите. Най-простото е алгоритмичното, когато знанията се представят чрез компютърна програма. Това обаче не е най-добрият начин за представяне на знания, защото не е гъвкав. Знанията в нашия ум често не са алгоритмични. -* Вдясно са представяния като естествен текст. Това е най-мощното, но не може да се използва за автоматично разсъждение. +* Вляво са много прости типове представяне на знания, които могат ефективно да се използват от компютрите. Най-простият е алгоритмичният, когато знанието се представя чрез компютърна програма. Това обаче не е най-добрият начин за представяне на знание, защото не е гъвкав. Знанията в главата ни често не са алгоритмични. +* Вдясно са представяния като естествения текст. Това е най-мощната форма, но не може да се използва за автоматично разсъждение. -> ✅ Помислете за момент как представяте знанията в ума си и ги превръщате в бележки. Има ли конкретен формат, който работи добре за вас, за да подпомогне запаметяването? +> ✅ Помислете за момент как представяте знания в главата си и как ги превръщате в бележки. Има ли формат, който ви помага по-добре да ги запомните? -## Класификация на компютърните представяния на знания +## Класификация на представянията на знания в компютри -Можем да класифицираме различните методи за представяне на знания в компютър в следните категории: +Можем да класифицираме различните методи за представяне на знания в компютри в следните категории: -* **Мрежови представяния** се основават на факта, че имаме мрежа от взаимосвързани концепции в нашия ум. Можем да се опитаме да възпроизведем същите мрежи като граф вътре в компютър - така наречената **семантична мрежа**. +* **Мрежови представяния** се базират на факта, че в главата ни имаме мрежа от взаимосвързани концепции. Можем да опитаме да пресъздадем същите мрежи като граф в компютър - така наречената **семантична мрежа**. -1. **Триплети обект-атрибут-стойност** или **двойки атрибут-стойност**. Тъй като графът може да бъде представен в компютър като списък от възли и ръбове, можем да представим семантична мрежа чрез списък от триплети, съдържащи обекти, атрибути и стойности. Например, изграждаме следните триплети за програмни езици: +1. **Обект-атрибут-стойност тройки** или **двойки атрибут-стойност**. Тъй като графът може да се представи в компютър като списък от възли и ръбове, можем да представим семантична мрежа чрез списък с тройки, съдържащи обекти, атрибути и стойности. Например, изграждаме следните тройки за програмни езици: Обект | Атрибут | Стойност --------|---------|--------- -Python | е | Нетипизиран език -Python | създаден от | Guido van Rossum -Python | синтаксис на блокове | отстъп -Нетипизиран език | няма | дефиниции на типове +------|---------|--------- +Python | е | Без видове +Python | изобретен от | Guido van Rossum +Python | синтаксис на блокове | отстъпи +Без видове | няма | дефиниции на типове -> ✅ Помислете как триплетите могат да се използват за представяне на други видове знания. +> ✅ Помислете как тройките могат да се използват за представяне на други типове знания. -2. **Йерархични представяния** подчертават факта, че често създаваме йерархия от обекти в нашия ум. Например, знаем, че канарчето е птица и всички птици имат крила. Също така имаме някаква представа за това какъв цвят обикновено е канарчето и каква е неговата скорост на полет. +2. **Йерархични представяния** акцентират върху факта, че често създаваме йерархия от обекти в главата си. Например знаем, че канарчето е птица, а всички птици имат крила. Имаме и представа за цвета на канарчето и скоростта, с която лети. - - **Представяне чрез рамки** се основава на представянето на всеки обект или клас обекти като **рамка**, която съдържа **слотове**. Слотовете имат възможни стойности по подразбиране, ограничения на стойностите или съхранени процедури, които могат да бъдат извикани за получаване на стойността на слот. Всички рамки формират йерархия, подобна на йерархията на обекти в езиците за програмиране, базирани на обекти. - - **Сценарии** са специален вид рамки, които представляват сложни ситуации, които могат да се развият във времето. + - **Рамковото представяне** се основава на представяне на всеки обект или клас обекти като **рамка**, която съдържа **слотове**. Слотовете имат възможни стойности по подразбиране, ограничения на стойностите или съхранени процедури, които могат да се извикат за получаване на стойност. Всички рамки образуват йерархия, подобна на обектна йерархия в обектно-ориентираните програмни езици. + - **Сценариите** са специален вид рамки, които представят сложни ситуации, развиващи се във времето. **Python** Слот | Стойност | Стойност по подразбиране | Интервал | ------|---------|--------------------------|----------| -Име | Python | | | -Е | Нетипизиран език | | | -Кейс на променливи | | CamelCase | | -Дължина на програма | | | 5-5000 реда | -Синтаксис на блокове | Отстъп | | | +-----|----------|---------------------------|----------| +Име | Python | | | +Дали е | Без видове | | | +Начин на изписване на променлива | | CamelCase | | +Дължина на програмата | | | 5-5000 реда | +Синтаксис на блокове | Отстъп | | | -3. **Процедурни представяния** се основават на представяне на знания чрез списък от действия, които могат да бъдат изпълнени при настъпване на определено условие. - - Правилата за производство са if-then изявления, които ни позволяват да правим заключения. Например, лекар може да има правило, което гласи, че **АКО** пациентът има висока температура **ИЛИ** високо ниво на C-реактивен протеин в кръвния тест **ТОГАВА** той има възпаление. След като срещнем едно от условията, можем да направим заключение за възпалението и след това да го използваме в по-нататъшно разсъждение. - - Алгоритмите могат да се считат за друга форма на процедурно представяне, въпреки че почти никога не се използват директно в системи, базирани на знания. +3. **Процедурни представяния** се основават на представяне на знания чрез списък от действия, които могат да се изпълнят при настъпване на определено условие. + - Правилата за продукция са if-then изрази, които ни позволяват да извеждаме заключения. Например, един лекар може да има правило, което гласи: **АКО** пациентът има висока температура **ИЛИ** висока концентрация на С-реактивен протеин в кръвта **ТОГАВА** има възпаление. След като се срещне едно от условията, можем да направим заключение за възпаление и да го използваме при по-нататъшни разсъждения. + - Алгоритмите могат да се разглеждат като друга форма на процедурно представяне, въпреки че почти никога не се използват директно в системи, базирани на знания. -4. **Логика** първоначално е предложена от Аристотел като начин за представяне на универсални човешки знания. - - Предикатната логика като математическа теория е твърде богата, за да бъде изчислима, затова обикновено се използва някакво нейно подмножество, като например клаузите на Хорн, използвани в Prolog. - - Описателната логика е семейство от логически системи, използвани за представяне и разсъждение за йерархии от обекти и разпределени представяния на знания като *семантична мрежа*. +4. **Логика** първоначално е предложена от Аристотел като начин за представяне на универсалните човешки знания. + - Предикатната логика като математическа теория е твърде богата, за да бъде изчислима, затова обикновено се използва някакво подмножество, като например Horn клаузите, използвани в Prolog. + - Описателната логика е семейство логически системи, използвани за представяне и разсъждение върху йерархии от обекти, разпределени представяния на знания като *семантичния уеб*. ## Експертни системи -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. +Един от ранните успехи на символичния ИИ бяха т.нар. **експертни системи** – компютърни системи, проектирани да действат като експерт в някаква ограничена предметна област. Те се базираха на **база знания**, извлечена от един или повече човешки експерти, и съдържаха **инференциален механизъм**, който извършваше разсъждения върху нея. -![Човешка архитектура](../../../../translated_images/bg/arch-human.5d4d35f1bba3ab1c.webp) | ![Архитектура на система, базирана на знания](../../../../translated_images/bg/arch-kbs.3ec5c150b09fa8da.webp) +![Човешка архитектура](../../../../../../translated_images/bg/arch-human.5d4d35f1bba3ab1c.webp) | ![Система базирана на знания](../../../../../../translated_images/bg/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ -Опростена структура на човешката нервна система | Архитектура на система, базирана на знания +Оптимизирана структура на човешката нервна система | Архитектура на система базирана на знания -Експертните системи са изградени подобно на системата за човешко разсъждение, която съдържа **краткосрочна памет** и **дългосрочна памет**. По подобен начин, в системите, базирани на знания, разграничаваме следните компоненти: +Експертните системи са изградени като човешката система за разсъждение, която съдържа **краткосрочна памет** и **дългосрочна памет**. По същия начин при системи базирани на знания разграничаваме следните компоненти: -* **Памет за проблема**: съдържа знанията за проблема, който се решава в момента, например температурата или кръвното налягане на пациент, дали има възпаление или не и т.н. Тези знания също се наричат **статични знания**, защото съдържат моментна снимка на това, което знаем за проблема - така нареченото *състояние на проблема*. -* **База знания**: представлява дългосрочни знания за проблемната област. Те се извличат ръчно от човешки експерти и не се променят от консултация до консултация. Тъй като позволяват навигация от едно състояние на проблема към друго, те също се наричат **динамични знания**. -* **Инференционен двигател**: организира целия процес на търсене в пространството на състоянията на проблема, задава въпроси на потребителя, когато е необходимо. Той също така е отговорен за намирането на правилните правила, които да се приложат към всяко състояние. +* **Памет на проблема**: съдържа знания за проблема, който в момента се решава, например температурата или кръвното налягане на пациент, дали има възпаление и др. Това знание се нарича и **статично знание**, защото представлява снимка на това, което в момента знаем за проблема – т.нар. *състояние на проблема*. +* **База знания**: представлява дългосрочните знания за предметната област. Извлича се ръчно от човешки експерти и не се променя от консултация на консултация. Тъй като позволява навигация от едно състояние на проблема до друго, тя се нарича и **динамично знание**. +* **Инференциален механизъм**: оркестрира целия процес на търсене в пространството на състоянията на проблема, задава въпроси на потребителя когато е необходимо. Отговаря за намирането на правилата, които да се прилагат за всяко състояние. -Като пример, нека разгледаме следната експертна система за определяне на животно въз основа на неговите физически характеристики: +Като пример нека разгледаме следната експертна система за определяне на животно по неговите физически характеристики: -![AND-OR дърво](../../../../translated_images/bg/AND-OR-Tree.5592d2c70187f283.webp) +![AND-OR дърво](../../../../../../translated_images/bg/AND-OR-Tree.5592d2c70187f283.webp) > Изображение от [Dmitry Soshnikov](http://soshnikov.com) -Тази диаграма се нарича **AND-OR дърво** и е графично представяне на набор от правила за производство. Рисуването на дърво е полезно в началото на извличането на знания от експерта. За представяне на знанията вътре в компютъра е по-удобно да се използват правила: +Тази диаграма се нарича **AND-OR дърво** и е графично представяне на набор от производствени правила. Чертането на дървото е полезно в началото при извличането на знания от експерта. За да представим знанията в компютъра, е по-удобно да използваме правила: ``` IF the animal eats meat @@ -120,76 +120,79 @@ OR (animal has sharp teeth ) THEN the animal is a carnivore ``` + +Можете да забележите, че всяко условие в лявата страна на правилото и действието са по същество обект-атрибут-стойност (OAV) тройки. **Работната памет** съдържа множеството от OAV тройки, които съответстват на проблема, който в момента се решава. **Двигател на правилата** търси правила, чиито условия са изпълнени, и ги прилага, добавяйки още една тройка в работната памет. -Можете да забележите, че всяко условие от лявата страна на правилото и действието всъщност са триплети обект-атрибут-стойност (OAV). **Работната памет** съдържа набора от OAV триплети, които съответстват на проблема, който се решава в момента. **Двигателят за правила** търси правила, за които условието е изпълнено, и ги прилага, добавяйки нов триплет към работната памет. +> ✅ Напишете ваше собствено AND-OR дърво по тема, която ви е интересна! -> ✅ Напишете свое собствено AND-OR дърво по тема, която ви харесва! +### Фордуърд срещу беквард (напредни срещу назад) извод -### Напред срещу назад разсъждение +Процесът, описан по-горе, се нарича **фордуърд извод**. Той започва с някакви начални данни за проблема, налични в работната памет, и изпълнява следния цикъл на разсъждение: -Процесът, описан по-горе, се нарича **напред разсъждение**. Той започва с някакви начални данни за проблема, налични в работната памет, и след това изпълнява следния цикъл на разсъждение: +1. Ако целевият атрибут присъства в работната памет – спри и дай резултата +2. Търси всички правила, чиито условие е в момента изпълнено – получава се **конфликтен набор** от правила. +3. Извършва се **решаване на конфликти** – избира се едно правило, което ще се изпълни този ход. Могат да се използват различни стратегии за решаване на конфликти: + - Избери първото приложимо правило в базата знания + - Избери случайно правило + - Избери *по-конкретно* правило, т.е. това, което удовлетворява най-много условия в "лявата страна" (LHS) +4. Прилагай избраното правило и вмъкни ново парче знание в състоянието на проблема +5. Повтори от стъпка 1. -1. Ако целевият атрибут е наличен в работната памет - спрете и дайте резултата -2. Потърсете всички правила, чието условие е изпълнено в момента - получете **конфликтен набор** от правила. -3. Извършете **разрешаване на конфликти** - изберете едно правило, което ще бъде изпълнено на тази стъпка. Могат да се използват различни стратегии за разрешаване на конфликти: - - Изберете първото приложимо правило в базата знания - - Изберете случайно правило - - Изберете *по-специфично* правило, т.е. това, което отговаря на най-много условия в "лявата страна" (LHS) -4. Приложете избраното правило и добавете ново парче знание към състоянието на проблема -5. Повторете от стъпка 1. +Въпреки това, в някои случаи може да искаме да започнем с празни знания за проблема и да задаваме въпроси, които да ни помогнат да стигнем до заключение. Например, при медицинска диагноза, обикновено не правим всички медицински анализи предварително, а ги правим когато е необходима извод. -Въпреки това, в някои случаи може да искаме да започнем с празни знания за проблема и да задаваме въпроси, които ще ни помогнат да стигнем до заключението. Например, при медицинска диагностика обикновено не извършваме всички медицински анализи предварително, преди да започнем диагностицирането на пациента. По-скоро искаме да извършваме анализи, когато трябва да се вземе решение. +Този процес може да се моделира чрез **беквард извод**. Той се движи от **целта** – атрибутът, който искаме да намерим: -Този процес може да се моделира чрез **назад разсъждение**. То се ръководи от **целта** - стойността на атрибута, която търсим: +1. Избери всички правила, които могат да ни дадат стойност за целта (т.е. с целта в дясната страна (RHS)) – получава се конфликтен набор +2. Ако няма правила за този атрибут, или има правило, което казва, че трябва да попитаме потребителя – питай, в противен случай: +3. Използвай стратегия за решаване на конфликти, за да избереш едно правило, което ще използваш като *хипотеза* – ще се опитаме да я докажем +4. Рекурзивно повтори процеса за всички атрибути в лявата страна (LHS) на правилото, опитвайки се да ги докажеш като цели +5. Ако по някое време процесът се провали – използвай друго правило на стъпка 3. -1. Изберете всички правила, които могат да ни дадат стойността на целта (т.е. с целта в RHS ("дясната страна")) - конфликтен набор -1. Ако няма правила за този атрибут или има правило, което казва, че трябва да попитаме потребителя за стойността - попитайте за нея, в противен случай: -1. Използвайте стратегия за разрешаване на конфликти, за да изберете едно правило, което ще използваме като *хипотеза* - ще се опитаме да го докажем -1. Рекурсивно повторете процеса за всички атрибути в LHS на правилото, опитвайки се да ги докажем като цели -1. Ако процесът се провали на някакъв етап - използвайте друго правило на стъпка 3. - -> ✅ В кои ситуации напред разсъждението е по-подходящо? А какво да кажем за назад разсъждението? +> ✅ При какви ситуации фордуърд извод е по-подходящ? А какво за беквард извод? ### Имплементиране на експертни системи -Експертните системи могат да бъдат имплементирани с различни инструменти: +Експертните системи могат да се имплементират с различни инструменти: -* Програмиране директно в някакъв език за програмиране от високо ниво. Това не е най-добрата идея, защото основното предимство на система, базирана на знания, е че знанията са отделени от разсъждението и потенциално експертът в проблемната област трябва да може да пише правила, без да разбира детайлите на процеса на разсъждение. -* Използване на **обвивка за експертни системи**, т.е. система, специално проектирана да бъде попълнена със знания, използвайки някакъв език за представяне на знания. +* Програмиране директно на някой високо ниво език. Това обаче не е най-добрата идея, защото основното предимство на система базирана на знания е, че знанието е отделено от изводите, и потенциално експерт от предметната област трябва да може да пише правила без да разбира детайлите на процеса на извод. +* Използване на **шкаф за експертни системи**, т.е. система, специално проектирана да се попълва с знания чрез някакъв език за представяне на знания. -## ✍️ Упражнение: Разсъждение за животни +## ✍️ Упражнение: Извод за животно -Вижте [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) за пример за имплементиране на експертна система с напред и назад разсъждение. +Вижте [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) за пример за имплементиране на експертна система с фордуърд и беквард извод. -> **Забележка**: Този пример е доста прост и само дава представа за това как изглежда една експертна система. След като започнете да създавате такава система, ще забележите някакво *интелигентно* поведение от нея едва когато достигнете определен брой правила, около 200+. В даден момент правилата стават твърде сложни, за да се държат всички в ума, и тогава може да започнете да се чудите защо системата взема определени решения. Въпреки това, важна характеристика на системите, базирани на знания, е че винаги можете *да обясните* точно как е взето всяко от решенията. +> **Забележка**: Този пример е доста опростен и дава само представа как изглежда една експертна система. Когато започнете да създавате такава система, ще забележите *интелигентно* поведение едва когато достигнете определен брой правила, около 200+. В един момент правилата стават твърде сложни, за да се помнят всички, и тогава може да се зачудите защо системата взима определени решения. Важно качество на системите базирани на знания е, че винаги можете да *обясните* точно как и защо е било взето някое решение. -## Онтологии и семантична мрежа +## Онтологии и семантичния уеб -В края на 20-ти век имаше инициатива за използване на представяне на знания за анотиране на интернет ресурси, така че да бъде възможно да се намерят ресурси, -- Семейство от XML-базирани езици за описание на знания: RDF (Resource Description Framework), RDFS (RDF Schema), OWL (Ontology Web Language). +В края на 20-ти век имаше инициатива за използване на представяне на знания за анотации на интернет ресурси, така че да бъде възможно да се намират ресурси с много специфични заявки. Това движение се наричаше **Семантичен уеб** и се основаваше на няколко концепции: -Основна концепция в Семантичната мрежа е концепцията за **Онтология**. Тя се отнася до явна спецификация на проблемна област, използвайки някаква формална представителна система за знания. Най-простата онтология може да бъде просто йерархия от обекти в дадена проблемна област, но по-сложните онтологии включват правила, които могат да се използват за извеждане на заключения. +- Специално представяне на знания основано на **[описателна логика](https://en.wikipedia.org/wiki/Description_logic)** (DL). То е подобно на рамковото представяне на знания, защото изгражда йерархия от обекти със свойства, но има формална логическа семантика и извод. Съществува цял род DL, които балансират между експресивност и алгоритмична сложност на извода. +- Разпределено представяне на знания, където всички концепции се представят чрез глобален URI идентификатор, което прави възможно създаването на йерархии от знания, обхващащи целия интернет. +- Семейство XML-базирани езици за описание на знания: RDF (Resource Description Framework), RDFS (RDF Schema), OWL (Ontology Web Language). -В семантичната мрежа всички представяния се базират на триплети. Всеки обект и всяка връзка са уникално идентифицирани чрез URI. Например, ако искаме да заявим факта, че този AI Curriculum е разработен от Дмитрий Сошников на 1 януари 2022 г., ето триплетите, които можем да използваме: +Основна концепция в Семантичния уеб е концепцията за **Онтология**. Тя се отнася до явна спецификация на проблемна област, използваща някакво формално представяне на знания. Най-простата онтология може да бъде просто йерархия от обекти в проблемната област, но по-сложните онтологии ще включват правила, които могат да се използват за извеждане. - +В семантичния уеб всички представяния са базирани на тройки. Всеки обект и всяко отношение са уникално идентифицирани чрез URI. Например, ако искаме да заявим факта, че тази AI учебна програма е разработена от Дмитрий Сошников на 1 януари 2022 г. - ето тройките, които можем да използваме: + + ``` -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 ``` -> ✅ Тук `http://www.example.com/terms/creation-date` и `http://purl.org/dc/elements/1.1/creator` са добре познати и универсално приети URI за изразяване на концепциите *създател* и *дата на създаване*. +> ✅ Тук `http://www.example.com/terms/creation-date` и `http://purl.org/dc/elements/1.1/creator` са някои добре известни и универсално приети URI за изразяване на концепциите *създател* и *дата на създаване*. -В по-сложен случай, ако искаме да дефинираме списък от създатели, можем да използваме някои структури от данни, дефинирани в RDF. +В по-сложен случай, ако искаме да дефинираме списък със създатели, можем да използваме някои структури от данни, дефинирани в RDF. - + -> Диаграмите по-горе са от [Дмитрий Сошников](http://soshnikov.com) +> Диаграми по-горе от [Dmitry Soshnikov](http://soshnikov.com) -Напредъкът в изграждането на Семантичната мрежа беше донякъде забавен от успеха на търсачките и техниките за обработка на естествен език, които позволяват извличане на структурирани данни от текст. Въпреки това, в някои области все още има значителни усилия за поддържане на онтологии и бази знания. Няколко проекта, които си струва да се отбележат: +Напредъкът в изграждането на Семантичния уеб беше донякъде забавен от успеха на търсачките и техниките за обработка на естествен език, които позволяват извличане на структурирани данни от текст. Въпреки това, в някои области все още се полагат значителни усилия за поддържане на онтологии и бази от знания. Няколко проекта, които заслужават внимание: -* [WikiData](https://wikidata.org/) е колекция от машинно четими бази знания, свързани с Wikipedia. Повечето от данните са извлечени от *InfoBoxes* на Wikipedia, части от структурирано съдържание в страниците на Wikipedia. Можете да [запитвате](https://query.wikidata.org/) WikiData в SPARQL, специален език за заявки за Семантичната мрежа. Ето примерна заявка, която показва най-популярните цветове на очите сред хората: +* [WikiData](https://wikidata.org/) е колекция от машинно-прочетени бази от знания, свързани с Уикипедия. Повечето от данните се добиват от *InfoBoxes* в Уикипедия, парчета структурирано съдържание вътре в страници от Уикипедия. Можете да [заявявате](https://query.wikidata.org/) wikidata чрез SPARQL, специален език за заявки за Семантичния уеб. Ето примерна заявка, която показва най-популярните цветове на очите сред хората: ```sparql #defaultView:BubbleChart @@ -205,45 +208,49 @@ GROUP BY ?eyeColorLabel * [DBpedia](https://www.dbpedia.org/) е друг проект, подобен на WikiData. -> ✅ Ако искате да експериментирате с изграждането на собствени онтологии или с отварянето на съществуващи, има страхотен визуален редактор за онтологии, наречен [Protégé](https://protege.stanford.edu/). Изтеглете го или го използвайте онлайн. +> ✅ Ако искате да експериментирате с изграждане на свои собствени онтологии или да отворите вече съществуващи, има чудесен визуален редактор на онтологии, наречен [Protégé](https://protege.stanford.edu/). Изтеглете го или го използвайте онлайн. - + -*Уеб редакторът Protégé, отворен с онтологията на семейство Романов. Снимка от Дмитрий Сошников* +*Web Protégé редактор отворен с онтологията на фамилията Романови. Скриншот от Дмитрий Сошников* ## ✍️ Упражнение: Онтология на семейство -Вижте [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) за пример за използване на техники от Семантичната мрежа за разсъждение върху семейни връзки. Ще вземем родословно дърво, представено в общия формат GEDCOM, и онтология на семейните връзки, за да изградим граф на всички семейни връзки за даден набор от индивиди. +Вижте [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) за пример за използване на техники от Семантичния уеб за разсъждения върху семейни отношения. Ще вземем семейно дърво, представено в общ формат GEDCOM, и онтология на семейните отношения, за да изградим граф на всички семейни отношения за даден набор от индивиди. ## Microsoft Concept Graph -В повечето случаи онтологиите се създават внимателно на ръка. Въпреки това, също така е възможно да се **извличат** онтологии от неструктурирани данни, например от текстове на естествен език. +В повечето случаи онтологиите се създават внимателно на ръка. Възможно е обаче и да се **добиват** онтологии от неструктурирани данни, например от текстове на естествен език. -Един такъв опит е направен от Microsoft Research и е довел до [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste). +Такова усилие беше предприето от Microsoft Research и доведе до [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste). -Това е голяма колекция от обекти, групирани заедно чрез наследствена връзка `is-a`. Тя позволява отговаряне на въпроси като "Какво е Microsoft?" - отговорът може да бъде нещо като "компания с вероятност 0.87 и марка с вероятност 0.75". +Това е голяма колекция от обекти, групирани заедно с използване на наследствената връзка `is-a`. Тя позволява отговарянето на въпроси като „Какво е Microsoft?“ - отговорът е нещо като „компания с вероятност 0.87 и бранд с вероятност 0.75“. -Графът е достъпен или като REST API, или като голям текстов файл за изтегляне, който изброява всички двойки обекти. +Графът е достъпен или като REST API, или като голям сваляем текстов файл, който изброява всички двойки обекти. ## ✍️ Упражнение: Граф на концепции -Опитайте [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) тетрадката, за да видите как можем да използваме Microsoft Concept Graph за групиране на новинарски статии в няколко категории. +Опитайте [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb), за да видите как можем да използваме Microsoft Concept Graph, за да групираме новинарски статии в няколко категории. ## Заключение -Днес AI често се счита за синоним на *Машинно обучение* или *Невронни мрежи*. Въпреки това, човекът също демонстрира явни разсъждения, което е нещо, което в момента не се обработва от невронните мрежи. В реални проекти явните разсъждения все още се използват за изпълнение на задачи, които изискват обяснения или способност за контролирано модифициране на поведението на системата. +В наши дни AI често се смята за синоним на *Машинно обучение* или *Невронни мрежи*. Обаче човекът също проявява явни разсъждения, които в момента не се обработват от невронните мрежи. В реални проекти явните разсъждения все още се използват за изпълнението на задачи, които изискват обяснения или способността да се променя поведението на системата по контролиран начин. ## 🚀 Предизвикателство -В тетрадката Family Ontology, свързана с този урок, има възможност за експериментиране с други семейни връзки. Опитайте да откриете нови връзки между хората в родословното дърво. +В ноутбука Family Ontology, свързан с този урок, има възможност да експериментирате с други семейни връзки. Опитайте се да откриете нови връзки между хора в семейното дърво. -## [Тест след лекцията](https://ff-quizzes.netlify.app/en/ai/quiz/4) +## [Квиз след лекцията](https://ff-quizzes.netlify.app/en/ai/quiz/4) -## Преглед и самостоятелно обучение +## Преглед и самостоятелно учене -Направете проучване в интернет, за да откриете области, в които хората са се опитвали да количествено измерят и кодифицират знания. Разгледайте таксономията на Блум и се върнете назад в историята, за да научите как хората са се опитвали да разберат света около тях. Изследвайте работата на Линей за създаване на таксономия на организмите и наблюдавайте начина, по който Дмитрий Менделеев е създал начин за описание и групиране на химичните елементи. Какви други интересни примери можете да намерите? +Направете проучване в интернет, за да откриете области, в които хората са се опитвали да количествено оценят и кодифицират знания. Разгледайте таксономията на Блум и се върнете назад в историята, за да научите как хората са се опитвали да разбират своя свят. Изследвайте работата на Линей, създал таксономия на организмите, и наблюдавайте начина, по който Дмитрий Менделеев е създал начин за описание и групиране на химичните елементи. Какви други интересни примери можете да намерите? -**Задача**: [Създайте онтология](assignment.md) +**Задача**: [Създаване на онтология](assignment.md) --- + +**Декларация за отказ от отговорност**: +Този документ е преведен с помощта на AI преводаческа услуга [Co-op Translator](https://github.com/Azure/co-op-translator). Въпреки че се стремим към точност, имайте предвид, че автоматизираните преводи могат да съдържат грешки или неточности. Оригиналният документ на неговия роден език трябва да се счита за авторитетен източник. За критична информация се препоръчва професионален превод от човек. Ние не носим отговорност за каквито и да е недоразумения или неправилни тълкувания, произтичащи от използването на този превод. + \ No newline at end of file diff --git a/translations/ro/README.md b/translations/ro/README.md index c28a5371..41bd991b 100644 --- a/translations/ro/README.md +++ b/translations/ro/README.md @@ -1,8 +1,8 @@ -**Dacă dorești să adaugi suport pentru alte limbi de traducere, acestea sunt listate [aici](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** +**Dacă dorești să susții suportul pentru limbi adiționale, acestea sunt listate [aici](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** ## Alătură-te Comunității [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) ## Ce vei învăța -**[Hartă mentală a cursului](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** +**[Harta mentală a cursului](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** În acest curriculum vei învăța: -* Diferite abordări ale Inteligenței Artificiale, inclusiv abordarea simbolică „buna și veche” cu **Reprezentarea Cunoștințelor** și raționamentul ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). -* **Rețele Neuronale** și **Învățare Profundă**, care stau la baza IA moderne. Vom ilustra conceptele din spatele acestor subiecte importante folosind cod în două dintre cele mai populare cadre - [TensorFlow](http://Tensorflow.org) și [PyTorch](http://pytorch.org). -* **Arhitecturi Neuronale** pentru a lucra cu imagini și text. Vom acoperi modele recente, deși poate fi ușor lipsit de cele mai noi tehnologii de ultimă generație. -* Abordări mai puțin populare ale IA, cum ar fi **Algoritmi Genetici** și **Sisteme Multi-Agent**. +* Diferite abordări ale Inteligenței Artificiale, inclusiv abordarea „bună și veche” simbolică cu **Reprezentarea Cunoașterii** și raționamentul ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). +* **Rețele Neuronale** și **Învățare Profundă**, care sunt în centrul IA moderne. Vom ilustra conceptele din spatele acestor subiecte importante folosind cod în două dintre cele mai populare framework-uri - [TensorFlow](http://Tensorflow.org) și [PyTorch](http://pytorch.org). +* **Arhitecturi Neuronale** pentru lucrul cu imagini și text. Vom acoperi modele recente, dar ar putea fi puțin lipsit de cele mai noi tehnologii. +* Abordări IA mai puțin populare, cum ar fi **Algoritmi Genetici** și **Sisteme Multi-Agent**. Ce nu vom acoperi în acest curriculum: > [Găsește toate resursele suplimentare pentru acest curs în colecția noastră Microsoft Learn](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) -* Cazuri de afaceri folosind **IA în business**. Poți lua în considerare traseul de învățare [Introducere în IA pentru utilizatori de business](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) pe Microsoft Learn sau [Școala de business AI](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), dezvoltată în colaborare cu [INSEAD](https://www.insead.edu/). -* **Machine Learning clasic**, bine descris în curriculumul nostru [Machine Learning pentru Începători](http://github.com/Microsoft/ML-for-Beginners). -* Aplicații practice IA construite folosind **[Servicii Cognitive](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**. Pentru acestea, recomandăm să începi cu modulele Microsoft Learn pentru [vedere](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [procesarea limbajului natural](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[IA Generativă cu Azure OpenAI Service](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** și altele. -* **Framework-uri ML specifice Cloud-ului**, precum [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) sau [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). Ia în considerare traseele de învățare [Construiește și operează soluții de machine learning cu Azure Machine Learning](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) și [Construiește și operează soluții de machine learning cu Azure Databricks](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum). -* **Inteligență Artificială Conversațională** și **Chat Bots**. Există un traseu separat de învățare, [Creează soluții AI conversaționale](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum), și poți, de asemenea, să consulți [acest blog](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) pentru mai multe detalii. -* **Matematică avansată** din spatele învățării profunde. Pentru aceasta recomandăm [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) de Ian Goodfellow, Yoshua Bengio și Aaron Courville, disponibil și online la [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/). +* Cazuri de utilizare a **IA în Afaceri**. Ia în considerare să urmezi calea de învățare [Introducere în IA pentru utilizatorii de business](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) pe Microsoft Learn, sau [Școala de Afaceri AI](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), dezvoltată în cooperare cu [INSEAD](https://www.insead.edu/). +* **Învățarea Automatizată Clasică**, care este bine descrisă în [Curriculum-ul nostru Machine Learning pentru Începători](http://github.com/Microsoft/ML-for-Beginners). +* Aplicații practice de IA construite folosind **[Servicii Cognitive](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**. Pentru acestea, recomandăm să începi cu modulele Microsoft Learn pentru [viziune](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [procesarea limbajului natural](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[IA Generativă cu Azure OpenAI Service](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** și altele. +* **Framework-uri Cloud specifice ML**, cum ar fi [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), sau [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). Ia în considerare să folosești căile de învățare [Build and operate machine learning solutions with Azure Machine Learning](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) și [Build and Operate Machine Learning Solutions with Azure Databricks](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum). +* **IA Conversațională** și **Chat Bots**. Există o cale de învățare separată [Create conversational AI solutions](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum), iar tu poți de asemenea consulta [acest articol de blog](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) pentru mai multe detalii. +* **Matematică profundă** din spatele învățării profunde. Pentru aceasta recomandăm [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) de Ian Goodfellow, Yoshua Bengio și Aaron Courville, care este disponibil și online la [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/). -Pentru o introducere blândă în subiectele _AI în Cloud_, poți lua în considerare traseul de învățare [Începe cu inteligența artificială pe Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum). +Pentru o introducere blândă în subiectele _IA în Cloud_ poți lua în considerare urmarea căii de învățare [Începe cu Inteligența Artificială pe Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum). # Conținut | | Link Lecție | PyTorch/Keras/TensorFlow | Laborator | | :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | -| 0 | [Configurare Curs](./lessons/0-course-setup/setup.md) | [Configurează-ți mediul de dezvoltare](./lessons/0-course-setup/how-to-run.md) | | +| 0 | [Configurarea Cursului](./lessons/0-course-setup/setup.md) | [Configurează-ți Mediul de Dezvoltare](./lessons/0-course-setup/how-to-run.md) | | | I | [**Introducere în IA**](./lessons/1-Intro/README.md) | | | -| 01 | [Introducere și Istorie a IA](./lessons/1-Intro/README.md) | - | - | +| 01 | [Introducere și Istoria IA](./lessons/1-Intro/README.md) | - | - | | II | **IA Simbolică** | -| 02 | [Reprezentarea cunoștințelor și Sisteme Expert](./lessons/2-Symbolic/README.md) | [Sisteme Expert](./lessons/2-Symbolic/Animals.ipynb) / [Ontologie](./lessons/2-Symbolic/FamilyOntology.ipynb) /[Graf de concepte](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | +| 02 | [Reprezentarea Cunoștințelor și Sisteme Expert](./lessons/2-Symbolic/README.md) | [Sisteme Expert](./lessons/2-Symbolic/Animals.ipynb) / [Ontologie](./lessons/2-Symbolic/FamilyOntology.ipynb) /[Grafic de Concepte](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | | III | [**Introducere în Rețele Neuronale**](./lessons/3-NeuralNetworks/README.md) ||| | 03 | [Perceptron](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [Notebook](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [Lab](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | -| 04 | [Perceptron Multi-Stratificat și Crearea propriului nostru Framework](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [Notebook](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [Lab](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | -| 05 | [Introducere în Framework-uri (PyTorch/TensorFlow) și Supraînvățare](./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) | [Lab](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | -| IV | [**Viziune pe Calculator**](./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)| [Explorează Viziunea pe Calculator pe Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | -| 06 | [Introducere în Viziunea pe Calculator. OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [Notebook](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [Lab](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | +| 04 | [Perceptron multi-stratificat și crearea propriului nostru framework](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [Notebook](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [Lab](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | +| 05 | [Introducere în Framework-uri (PyTorch/TensorFlow) și Suprapotrivire](./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) | [Lab](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | +| IV | [**Viziune Computațională**](./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)| [Explorați Viziunea Computațională pe Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | +| 06 | [Introducere în Viziune Computațională. OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [Notebook](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [Lab](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | | 07 | [Rețele Neuronale Convoluționale](./lessons/4-ComputerVision/07-ConvNets/README.md) & [Arhitecturi CNN](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [Lab](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | -| 08 | [Rețele Pre-antrenate și Învățare prin Transfer](./lessons/4-ComputerVision/08-TransferLearning/README.md) și [Trucuri de Antrenament](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Lab](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | -| 09 | [Autoencodere și 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 | [Rețele Generative Adversariale & Transfer de Stil Artistic](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | +| 08 | [Rețele Pre-antrenate și Învățare Transfer](./lessons/4-ComputerVision/08-TransferLearning/README.md) și [Trucuri de Antrenament](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Lab](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | +| 09 | [Autoencodere și VAE-uri](./lessons/4-ComputerVision/09-Autoencoders/README.md) | [PyTorch](./lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb) | | +| 10 | [Rețele Generative Adversariale & Transfer Stil Artistic](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | | 11 | [Detecția Obiectelor](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [Lab](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | | 12 | [Segmentare Semantică. 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 | [**Procesarea Limbajului Natural**](./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) | [Explorează Procesarea Limbajului Natural pe Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| +| V | [**Procesarea Limbajului Natural**](./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) | [Explorați Procesarea Limbajului Natural pe Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| | 13 | [Reprezentarea Textului. 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 | [Încapsulări semantice ale cuvintelor. Word2Vec și 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 | [Modelarea Limbajului. Antrenarea propriilor încorporări](./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) | [Lab](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | -| 16 | [Rețele Neuronale Recurente](./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 | [Rețele Recurente Generative](./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) | [Lab](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | +| 16 | [Rețele Neuronale Recurrente](./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 | [Rețele Generative Recurente](./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) | [Lab](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | | 18 | [Transformere. 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 | [Recunoașterea Entităților Nume](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [Lab](./lessons/5-NLP/19-NER/lab/README.md) | -| 20 | [Modele Mari de Limbaj, Programarea Prompturilor și Task-uri Few-Shot](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | +| 19 | [Recunoașterea Entităților Numeite](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [Lab](./lessons/5-NLP/19-NER/lab/README.md) | +| 20 | [Modele Mari de Limbaj, Programarea Prompt-urilor și Sarcini Few-Shot](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | | VI | **Alte Tehnici AI** || | | 21 | [Algoritmi Genetici](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [Notebook](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | -| 22 | [Învățare prin Recompensă Profundă](./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) | [Lab](./lessons/6-Other/22-DeepRL/lab/README.md) | -| 23 | [Sisteme Multi-Agent](./lessons/6-Other/23-MultiagentSystems/README.md) | | | +| 22 | [Învățare Profundă prin Recompensă](./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) | [Lab](./lessons/6-Other/22-DeepRL/lab/README.md) | +| 23 | [Sisteme Multi-Agente](./lessons/6-Other/23-MultiagentSystems/README.md) | | | | VII | **Etica AI** | | | -| 24 | [Etica AI și AI Responsabil](./lessons/7-Ethics/README.md) | [Microsoft Learn: Principii AI Responsabil](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | +| 24 | [Etica AI și AI Responsabil](./lessons/7-Ethics/README.md) | [Microsoft Learn: Principiile AI Responsabil](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | | IX | **Extra** | | | | 25 | [Rețele Multi-Modal, CLIP și VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [Notebook](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | ## Fiecare lecție conține * Material de pre-lectură -* Jupyter Notebooks executabile, care sunt adesea specifice unui framework (**PyTorch** sau **TensorFlow**). Notebook-ul executabil conține și mult material teoretic, astfel încât pentru a înțelege tema trebuie să parcurgeți cel puțin o versiune a notebook-ului (fie PyTorch, fie TensorFlow). -* **Laboratoare** disponibile pentru unele subiecte, care vă oferă oportunitatea de a încerca aplicarea materialului învățat la o problemă specifică. +* Caiete Jupyter executabile, care sunt adesea specifice framework-ului (**PyTorch** sau **TensorFlow**). Caietul executabil conține și mult material teoretic, deci pentru a înțelege subiectul trebuie să parcurgeți cel puțin o versiune a caietului (fie PyTorch sau TensorFlow). +* **Laboratoare** disponibile pentru unele subiecte, care vă oferă oportunitatea de a încerca aplicarea materialului învățat pe o problemă specifică. * Unele secțiuni conțin link-uri către module [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) care acoperă subiecte conexe. ## Începeți -### 🎯 Nou în AI? Începeți aici! +### 🎯 Nou în domeniul AI? Începeți aici! -Dacă sunteți complet nou în AI și doriți exemple rapide, practice, consultați [**Exemplele prietenoase pentru începători**](./examples/README.md)! Acestea includ: +Dacă sunteți complet nou în AI și vreți exemple rapide, practice, consultați [**Exemple Prietenoase pentru Începători**](./examples/README.md)! Acestea includ: -- 🌟 **Salut Lume AI** - Primul tău program AI (recunoașterea patternurilor) -- 🧠 **Rețea Neuronală Simplă** - Construiește o rețea neuronală de la zero -- 🖼️ **Clasificator de Imagini** - Clasifică imagini cu comentarii detaliate -- 💬 **Sentimentul Textului** - Analiza textului pozitiv/negativ +- 🌟 **Salut, Lume AI** - Primul dvs. program AI (recunoaștere de tipare) +- 🧠 **Rețea Neurală Simplă** - Construiți o rețea neurală de la zero +- 🖼️ **Clasificator de Imagini** - Clasificați imagini cu comentarii detaliate +- 💬 **Sentimentul textului** - Analizează text pozitiv/negativ -Aceste exemple sunt concepute pentru a vă ajuta să înțelegeți conceptele AI înainte de a vă adânci în curriculumul complet. +Aceste exemple sunt concepute pentru a vă ajuta să înțelegeți conceptele AI înainte de a explora întregul curriculum. -### 📚 Configurarea Curriculumului Complet +### 📚 Configurarea întregului curriculum -- Am creat o [lecție de configurare](./lessons/0-course-setup/setup.md) pentru a vă ajuta cu configurarea mediului de dezvoltare. -- Pentru educatori, am creat și o [lecție de configurare a curriculumului](./lessons/0-course-setup/for-teachers.md)! -- Cum să [rulați codul în VSCode sau Codepace](./lessons/0-course-setup/how-to-run.md) +- Am creat o [lecție de configurare](./lessons/0-course-setup/setup.md) pentru a vă ajuta cu configurarea mediului de dezvoltare. - Pentru educatori, am creat și o [lecție de configurare a curriculumului](./lessons/0-course-setup/for-teachers.md)! +- Cum să [rulați codul în VSCode sau în Codespace](./lessons/0-course-setup/how-to-run.md) -Urmați acești pași: +Urmăriți acești pași: -Fork-uiți Repository-ul: Faceți clic pe butonul „Fork” din colțul din dreapta sus al acestei pagini. +Fork la Repository: Faceți clic pe butonul „Fork” din colțul din dreapta sus al acestei pagini. Clonați Repository-ul: `git clone https://github.com/microsoft/AI-For-Beginners.git` -Nu uitați să dați stea (🌟) acestui repo pentru a-l găsi mai ușor ulterior. +Nu uitați să acordați stea (🌟) acestui repo pentru a-l găsi mai ușor mai târziu. -## Cunoașteți alți Începători +## Cunoașteți alți cursanți -Alăturați-vă [serverului oficial AI Discord](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) pentru a întâlni și a face networking cu alți cursanți ce urmează acest curs și pentru a primi suport. +Alăturați-vă [serverului oficial AI Discord](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) pentru a întâlni și a interacționa cu alți cursanți care urmează acest curs și pentru a primi suport. -Dacă aveți feedback despre produs sau întrebări în timpul dezvoltării, vizitați [Azure AI Foundry Developer Forum](https://aka.ms/foundry/forum) +Dacă aveți feedback despre produs sau întrebări în timpul construirii, vizitați [Azure AI Foundry Developer Forum](https://aka.ms/foundry/forum) -## Teste +## Chestionare -> **O notă despre teste**: Toate testele sunt conținute în folderul Quiz-app în etc\quiz-app, sau [Online Aici](https://ff-quizzes.netlify.app/). Sunt legate din cadrul lecțiilor, aplicația pentru teste poate fi rulată local sau implementată pe Azure; urmați instrucțiunile din folderul `quiz-app`. Ele sunt gradual localizate. +> **O notă despre chestionare**: Toate chestionarele se află în folderul Quiz-app din etc\quiz-app, sau [Online aici](https://ff-quizzes.netlify.app/) Sunt legate din lecții, aplicația de chestionar poate fi rulată local sau implementată în Azure; urmați instrucțiunile din folder `quiz-app`. Ele sunt treptat localizate. -## Ajutor Cerut +## Ajutor căutat -Aveți sugestii sau ați găsit erori de ortografie sau cod? Ridicați un issue sau creați un pull request. +Aveți sugestii sau ați găsit greșeli de ortografie ori erori de cod? Deschideți un issue sau creați un pull request. -## Mulțumiri Speciale +## Mulțumiri speciale -* **✍️ Autor Principal:** [Dmitry Soshnikov](http://soshnikov.com), PhD -* **🔥 Editor:** [Jen Looper](https://twitter.com/jenlooper), PhD -* **🎨 Ilustrator Sketchnote:** [Tomomi Imura](https://twitter.com/girlie_mac) -* **✅ Creator Teste:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) -* **🙏 Contribuitori de Bază:** [Evgenii Pishchik](https://github.com/Pe4enIks) +* **✍️ Autor principal:** [Dmitry Soshnikov](http://soshnikov.com), PhD +* **🔥 Editor:** [Jen Looper](https://twitter.com/jenlooper), PhD +* **🎨 Ilustrator schițe:** [Tomomi Imura](https://twitter.com/girlie_mac) +* **✅ Creatoare chestionare:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) +* **🙏 Contributori de bază:** [Evgenii Pishchik](https://github.com/Pe4enIks) -## Alte Curricula +## Alte curriculum-uri -Echipa noastră produce și alte curricula! Verificați: +Echipa noastră produce și alte curriculum-uri! Verificați: ### 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 pentru începători](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 pentru începători](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 / Agenți -[![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 pentru începători](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 pentru începători](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 pentru începători](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) +[![Agenți AI pentru începători](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) --- - -### Seria AI Generativă -[![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) + +### Seria AI Generativ +[![AI generativ pentru începători](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) +[![AI generativ (.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) +[![AI generativ (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) +[![AI generativ (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) --- - -### Învățare de Bază -[![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) + +### Învățare de bază +[![ML pentru începători](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) +[![Știința datelor pentru începători](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 pentru începători](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) +[![Securitate cibernetică pentru începători](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) +[![Dezvoltare Web pentru începători](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 pentru începători](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) +[![Dezvoltare XR pentru începători](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) --- - + ### Seria Copilot -[![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 pentru programare asistată AI](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 pentru 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) +[![Aventuri Copilot](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) -## Obținerea Ajutorului +## Obțineți ajutor -Dacă sunteți blocat sau aveți întrebări despre construirea aplicațiilor AI, alăturați-vă altor cursanți și dezvoltatori experimentați în discuții despre MCP. Este o comunitate suportivă unde întrebările sunt binevenite și cunoștințele sunt împărtășite liber. +Dacă vă blocați sau aveți întrebări despre construirea aplicațiilor AI. Alăturați-vă altor cursanți și dezvoltatori experimentați în discuții despre MCP. Este o comunitate suportivă unde întrebările sunt binevenite și cunoștințele sunt împărtășite liber. [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -Dacă aveți feedback despre produs sau erori în timpul dezvoltării, vizitați: +Dacă aveți feedback despre produs sau erori în timpul construirii, vizitați: [![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) --- -**Declinare a responsabilității**: -Acest document a fost tradus folosind serviciul de traducere AI [Co-op Translator](https://github.com/Azure/co-op-translator). Deși ne străduim pentru acuratețe, vă rugăm să rețineți că traducerile automate pot conține erori sau inexactități. Documentul original, în limba sa nativă, trebuie considerat sursa autoritară. Pentru informații critice, se recomandă traducerea profesională realizată de un specialist uman. Nu ne asumăm responsabilitatea pentru eventualele neînțelegeri sau interpretări greșite care pot apărea ca urmare a utilizării acestei traduceri. +**Declinare de responsabilitate**: +Acest document a fost tradus folosind serviciul de traducere AI [Co-op Translator](https://github.com/Azure/co-op-translator). Deși ne străduim pentru acuratețe, vă rugăm să aveți în vedere că traducerile automate pot conține erori sau inexactități. Documentul original, în limba sa nativă, trebuie considerat sursa autorizată. Pentru informații critice, se recomandă traducerea profesională efectuată de un specialist uman. Nu ne asumăm responsabilitatea pentru eventualele neînțelegeri sau interpretări greșite rezultate din utilizarea acestei traduceri. \ No newline at end of file diff --git a/translations/ro/lessons/0-course-setup/how-to-run.md b/translations/ro/lessons/0-course-setup/how-to-run.md index 61ad341a..c3b059f1 100644 --- a/translations/ro/lessons/0-course-setup/how-to-run.md +++ b/translations/ro/lessons/0-course-setup/how-to-run.md @@ -1,21 +1,21 @@ -# Cum să rulezi codul +# Cum să Rulați Codul -Acest curs conține multe exemple executabile și laboratoare pe care vei dori să le rulezi. Pentru a face acest lucru, ai nevoie de posibilitatea de a executa cod Python în Jupyter Notebooks, care sunt furnizate ca parte a acestui curs. Ai mai multe opțiuni pentru a rula codul: +Acest curriculum conține multe exemple și laboratoare executabile pe care veți dori să le rulați. Pentru a face acest lucru, aveți nevoie de posibilitatea de a executa cod Python în Jupyter Notebooks furnizate ca parte a acestui curriculum. Aveți mai multe opțiuni pentru a rula codul: -## Rulează local pe computerul tău +## Rulare locală pe calculatorul dvs. -Pentru a rula codul local pe computerul tău, trebuie să ai instalată o versiune de Python. Personal, recomand instalarea **[miniconda](https://conda.io/en/latest/miniconda.html)** - este o instalare destul de ușoară care suportă managerul de pachete `conda` pentru diferite **medii virtuale** Python. +Pentru a rula codul local pe calculatorul dvs., este necesară o instalare Python. O recomandare este să instalați **[miniconda](https://conda.io/en/latest/miniconda.html)** - este o instalare relativ ușoară care suportă managerul de pachete `conda` pentru diferite **medii virtuale** Python. -După ce instalezi miniconda, trebuie să clonezi repository-ul și să creezi un mediu virtual care va fi folosit pentru acest curs: +După ce instalați miniconda, clonați depozitul și creați un mediu virtual care să fie folosit pentru acest curs: ```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 ``` -### Utilizarea Visual Studio Code cu extensia Python +### Utilizarea Visual Studio Code cu Extensia Python -Probabil cea mai bună metodă de a folosi acest curs este să-l deschizi în [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) cu [extensia Python](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste). +Acest curriculum este cel mai bine folosit atunci când îl deschideți în [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) cu [Extensia Python](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste). -> **Note**: După ce clonezi și deschizi directorul în VS Code, acesta îți va sugera automat să instalezi extensiile Python. De asemenea, va trebui să instalezi miniconda, așa cum este descris mai sus. +> **Notă**: Odată ce clonați și deschideți directorul în VS Code, acesta vă va sugera automat să instalați extensiile pentru Python. Va trebui, de asemenea, să instalați miniconda așa cum este descris mai sus. -> **Note**: Dacă VS Code îți sugerează să redeschizi repository-ul într-un container, trebuie să refuzi această opțiune pentru a folosi instalarea locală de Python. +> **Notă**: Dacă VS Code vă sugerează să redeschideți depozitul într-un container, ar trebui să refuzați această opțiune pentru a folosi instalarea locală Python. -### Utilizarea Jupyter în browser +### Utilizarea Jupyter în Browser -Poți folosi și mediul Jupyter direct din browser pe computerul tău. De fapt, atât Jupyter clasic, cât și Jupyter Hub oferă un mediu de dezvoltare destul de convenabil, cu completare automată, evidențiere a codului etc. +Puteți, de asemenea, să folosiți un mediu Jupyter din browser pe propriul calculator. Atât Jupyter clasic, cât și JupyterHub oferă un mediu convenabil de dezvoltare cu completare automată, evidențierea codului, etc. -Pentru a porni Jupyter local, mergi în directorul cursului și execută: +Pentru a porni Jupyter local, navigați în directorul cursului și executați: ```bash jupyter notebook -``` -sau +``` +sau ```bash jupyterhub -``` -Apoi poți naviga la orice fișier `.ipynb`, să-l deschizi și să începi să lucrezi. +``` +Apoi puteți naviga la oricare din fișierele `.ipynb`, să le deschideți și să începeți să lucrați. -### Rularea într-un container +### Rulare în container -O alternativă la instalarea Python ar fi rularea codului într-un container. Deoarece repository-ul nostru conține un folder special `.devcontainer` care indică cum să construiești un container pentru acest repository, VS Code îți va oferi opțiunea de a redeschide codul într-un container. Acest lucru va necesita instalarea Docker și va fi mai complex, așa că recomandăm această opțiune utilizatorilor mai experimentați. +O alternativă la instalarea Python ar fi să rulați codul într-un container. Deoarece depozitul nostru oferă un folder special `.devcontainer` care indică cum să construiți un container pentru acest repo, VS Code oferă oportunitatea de a redeschide codul în container. Aceasta va necesita instalarea Docker și ar fi mai complexă, așadar recomandăm acest lucru utilizatorilor mai experimentați. -## Rularea în cloud +## Rulare în Cloud -Dacă nu dorești să instalezi Python local și ai acces la resurse cloud, o alternativă bună ar fi să rulezi codul în cloud. Există mai multe modalități de a face acest lucru: +Dacă nu doriți să instalați Python local și aveți acces la unele resurse cloud – o alternativă bună este să rulați codul în cloud. Există mai multe moduri în care puteți face asta: -* Utilizând **[GitHub Codespaces](https://github.com/features/codespaces)**, care este un mediu virtual creat pentru tine pe GitHub, accesibil prin interfața browserului VS Code. Dacă ai acces la Codespaces, poți pur și simplu să dai click pe butonul **Code** din repository, să pornești un codespace și să începi să lucrezi imediat. -* Utilizând **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**. [Binder](https://mybinder.org) oferă resurse de calcul gratuite în cloud pentru a testa codul de pe GitHub. Există un buton pe pagina principală pentru a deschide repository-ul în Binder - acest lucru te va duce rapid pe site-ul Binder, care va construi containerul subiacente și va porni interfața web Jupyter pentru tine fără probleme. +* Utilizând **[GitHub Codespaces](https://github.com/features/codespaces)**, care este un mediu virtual creat pentru dvs. pe GitHub, accesibil printr-o interfață VS Code în browser. Dacă aveți acces la Codespaces, puteți face pur și simplu clic pe butonul **Code** din repo, începeți un codespace și porniți rapid. +* Utilizând **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**. [Binder](https://mybinder.org) oferă resurse de calcul gratuite furnizate în cloud pentru persoane ca dvs. să testeze cod de pe GitHub. Există un buton pe pagina principală pentru a deschide depozitul în Binder – asta vă va duce rapid pe site-ul Binder, care va construi un container de bază și va porni o interfață web Jupyter pentru dvs. fără probleme. -> **Note**: Pentru a preveni utilizarea abuzivă, Binder are accesul la unele resurse web blocat. Acest lucru poate împiedica funcționarea unor coduri care descarcă modele și/sau seturi de date de pe Internet. Este posibil să fie nevoie să găsești soluții alternative. De asemenea, resursele de calcul oferite de Binder sunt destul de limitate, așa că antrenarea va fi lentă, mai ales în lecțiile mai complexe. +> **Notă**: Pentru a preveni utilizarea neadecvată, Binder are acces blocat la unele resurse web. Acest lucru poate împiedica funcționarea unor coduri care descarcă modele și/sau seturi de date de pe Internetul public. Va trebui să găsiți unele soluții alternative. În plus, resursele de calcul oferite de Binder sunt destul de simple, deci antrenamentul va fi lent, mai ales în lecțiile mai complexe ulterioare. -## Rularea în cloud cu GPU +## Rulare în Cloud cu GPU -Unele dintre lecțiile mai avansate din acest curs ar beneficia foarte mult de suportul GPU, deoarece altfel antrenarea va fi extrem de lentă. Există câteva opțiuni pe care le poți urma, mai ales dacă ai acces la cloud fie prin [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste), fie prin instituția ta: +Unele dintre lecțiile ulterioare din acest curriculum ar beneficia foarte mult de suport GPU. Antrenarea modelului, de exemplu, poate fi dureros de lentă altfel. Există câteva opțiuni pe care le puteți urma, mai ales dacă aveți acces la cloud prin [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) sau prin instituția dvs.: -* Creează o [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) și conectează-te la ea prin Jupyter. Poți clona repository-ul direct pe mașină și să începi să înveți. Mașinile virtuale din seria NC au suport pentru GPU. +* Creați [Mașină Virtuală pentru Data Science](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) și conectați-vă la ea prin Jupyter. Puteți apoi să clonați repo direct pe mașină și să începeți să învățați. Mașinile virtuale din seria NC au suport GPU. -> **Note**: Unele abonamente, inclusiv Azure for Students, nu oferă suport pentru GPU implicit. Este posibil să fie nevoie să soliciți resurse GPU suplimentare printr-o cerere de suport tehnic. +> **Notă**: Unele abonamente, inclusiv Azure for Students, nu oferă suport GPU din start. Este posibil să trebuiască să solicitați nuclee GPU suplimentare printr-o cerere de asistență tehnică. -* Creează un [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) și folosește funcția Notebook de acolo. [Acest video](https://azure-for-academics.github.io/quickstart/azureml-papers/) arată cum să clonezi un repository în notebook-ul Azure ML și să începi să-l folosești. +* Creați [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) și apoi folosiți funcția Notebook acolo. [Acest video](https://azure-for-academics.github.io/quickstart/azureml-papers/) arată cum să clonați un depozit într-un notebook Azure ML și să începeți să îl folosiți. -De asemenea, poți folosi Google Colab, care oferă suport gratuit pentru GPU, și să încarci Jupyter Notebooks acolo pentru a le executa pe rând. +De asemenea, puteți folosi Google Colab, care vine cu suport GPU gratuit și puteți încărca acolo Jupyter Notebooks pentru a le executa unul câte unul. -**Declinare de responsabilitate**: -Acest document a fost tradus folosind serviciul de traducere AI [Co-op Translator](https://github.com/Azure/co-op-translator). Deși ne străduim să asigurăm acuratețea, vă rugăm să fiți conștienți că traducerile automate pot conține erori sau inexactități. Documentul original în limba sa natală ar trebui considerat sursa autoritară. Pentru informații critice, se recomandă traducerea profesională realizată de un specialist uman. Nu ne asumăm responsabilitatea pentru eventualele neînțelegeri sau interpretări greșite care pot apărea din utilizarea acestei traduceri. \ No newline at end of file +--- + + +**Declinarea responsabilității**: +Acest document a fost tradus folosind serviciul de traducere AI [Co-op Translator](https://github.com/Azure/co-op-translator). În timp ce ne străduim pentru acuratețe, vă rugăm să rețineți că traducerile automate pot conține erori sau inexactități. Documentul original în limba sa nativă trebuie considerat sursa autorizată. Pentru informații critice, se recomandă traducerea profesională realizată de un specialist. Nu ne asumăm nicio responsabilitate pentru eventualele neînțelegeri sau interpretări greșite rezultate din utilizarea acestei traduceri. + \ No newline at end of file diff --git a/translations/ro/lessons/2-Symbolic/Animals.ipynb b/translations/ro/lessons/2-Symbolic/Animals.ipynb index 42c6bc2e..ddb4ed73 100644 --- a/translations/ro/lessons/2-Symbolic/Animals.ipynb +++ b/translations/ro/lessons/2-Symbolic/Animals.ipynb @@ -8,23 +8,23 @@ "source": [ "# Implementarea unui Sistem Expert pentru Animale\n", "\n", - "Un exemplu din [AI for Beginners Curriculum](http://github.com/microsoft/ai-for-beginners).\n", + "Un exemplu din [Curriculum AI pentru Începători](http://github.com/microsoft/ai-for-beginners).\n", "\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", + "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 (aceasta este o parte a întregului arbore, putem adăuga cu ușurință mai multe reguli):\n", "\n", - "![](../../../../translated_images/ro/AND-OR-Tree.5592d2c70187f283.webp)\n" + "![](../../../../../../translated_images/ro/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Propriul nostru shell pentru sisteme expert cu inferență inversă\n", + "## Propria noastră interfață expert pentru sisteme cu inferență inversă\n", "\n", - "Să încercăm să definim un limbaj simplu pentru reprezentarea cunoștințelor bazat pe reguli de producție. Vom folosi clase Python ca cuvinte cheie pentru a defini regulile. Vor exista, în esență, 3 tipuri de clase:\n", - "* `Ask` reprezintă o întrebare care trebuie adresată utilizatorului. Conține setul de răspunsuri posibile.\n", - "* `If` reprezintă o regulă și este doar o sintaxă simplificată pentru a stoca conținutul regulii.\n", - "* `AND`/`OR` sunt clase care reprezintă ramuri AND/OR ale arborelui. Ele doar stochează lista de argumente în interior. Pentru a simplifica codul, toată funcționalitatea este definită în clasa părinte `Content`.\n" + "Să încercăm să definim un limbaj simplu pentru reprezentarea cunoștințelor bazat pe reguli de producție. Vom folosi clase Python ca și cuvinte cheie pentru a defini regulile. Ar exista în esență 3 tipuri de clase:\n", + "* `Ask` reprezintă o întrebare care trebuie adresată utilizatorului. Conține setul de posibile răspunsuri.\n", + "* `If` reprezintă o regulă și este doar un zahăr sintactic pentru a stoca conținutul regulii.\n", + "* `AND`/`OR` sunt clase pentru a reprezenta ramurile AND/OR ale arborelui. Acestea stochează doar lista de argumente în interior. Pentru a simplifica codul, toată funcționalitatea este definită în clasa părinte `Content`\n" ] }, { @@ -66,7 +66,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "În sistemul nostru, memoria de lucru ar conține lista de **fapte** ca **perechi atribut-valoare**. Baza de cunoștințe poate fi definită ca un dicționar mare care asociază acțiuni (fapte noi care ar trebui inserate în memoria de lucru) cu condiții, exprimate ca expresii AND-OR. De asemenea, unele fapte pot fi `Întrebate`.\n" + "În sistemul nostru, memoria de lucru ar conține lista de **fapte** ca **perechi atribut-valoare**. Baza de cunoștințe poate fi definită ca un singur dicționar mare care asociază acțiunile (noi fapte ce trebuie inserate în memoria de lucru) cu condiții, exprimate ca expresii AND-OR. De asemenea, unele fapte pot fi `Ask`-uite.\n" ] }, { @@ -99,13 +99,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Pentru a realiza inferența inversă, vom defini clasa `Knowledgebase`. Aceasta va conține:\n", - "* `Memoria` de lucru - un dicționar care mapă atributele la valori\n", - "* `Regulile` bazei de cunoștințe în formatul definit mai sus\n", + "Pentru a efectua inferența inversă, vom defini clasa `Knowledgebase`. Aceasta va conține:\n", + "* Memoria de lucru `memory` - un dicționar care asociază atributele cu valorile\n", + "* Regulile din baza de cunoștințe `rules` în formatul definit mai sus\n", "\n", "Două metode principale sunt:\n", - "* `get` pentru a obține valoarea unui atribut, efectuând inferența dacă este necesar. De exemplu, `get('color')` ar obține valoarea unui slot de culoare (va întreba dacă este necesar și va stoca valoarea pentru utilizare ulterioară în memoria de lucru). Dacă cerem `get('color:blue')`, va întreba pentru o culoare și apoi va returna valoarea `y`/`n` în funcție de culoare.\n", - "* `eval` efectuează inferența propriu-zisă, adică parcurge arborele AND/OR, evaluează sub-obiectivele etc.\n" + "* `get` pentru a obține valoarea unui atribut, efectuând inferența dacă este necesar. De exemplu, `get('color')` ar obține valoarea unui slot de culoare (va întreba dacă este necesar și va stoca valoarea pentru utilizări ulterioare în memoria de lucru). Dacă întrebăm `get('color:blue')`, va întreba pentru o culoare și apoi va returna valoarea `y`/`n` în funcție de culoare.\n", + "* `eval` realizează inferența propriu-zisă, adică parcurge arborele AND/OR, evaluează sub-obiectivele etc.\n" ] }, { @@ -172,7 +172,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Acum să definim baza noastră de cunoștințe despre animale și să efectuăm consultarea. Rețineți că acest apel vă va pune întrebări. Puteți răspunde tastând `y`/`n` pentru întrebările de tip da-nu sau specificând un număr (0..N) pentru întrebările cu răspunsuri multiple mai lungi.\n" + "Acum să definim baza noastră de cunoștințe despre animale și să efectuăm consultația. Rețineți că acest apel vă va pune întrebări. Puteți răspunde tastând `y`/`n` pentru întrebări de tip da-nu, sau specificând un număr (0..N) pentru întrebări cu răspunsuri multiple mai lungi.\n" ] }, { @@ -229,11 +229,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Utilizarea PyKnow pentru inferență înainte\n", + "## Folosirea Experta pentru Inferență Directă\n", "\n", - "În următorul exemplu, vom încerca să implementăm inferența înainte folosind una dintre bibliotecile pentru reprezentarea cunoștințelor, [PyKnow](https://github.com/buguroo/pyknow/). **PyKnow** este o bibliotecă pentru crearea sistemelor de inferență înainte în Python, concepută să fie similară cu vechiul sistem clasic [CLIPS](http://www.clipsrules.net/index.html).\n", + "În următorul exemplu, vom încerca să implementăm inferența directă folosind una dintre bibliotecile pentru reprezentarea cunoștințelor, [Experta](https://github.com/nilp0inter/experta). **Experta** este o bibliotecă pentru crearea de sisteme de inferență directă în Python, concepută să fie similară cu sistemul clasic vechi [CLIPS](http://www.clipsrules.net/index.html).\n", "\n", - "Am fi putut implementa și noi lanțul înainte fără prea multe probleme, dar implementările naive nu sunt, de obicei, foarte eficiente. Pentru o potrivire mai eficientă a regulilor, se folosește un algoritm special, [Rete](https://en.wikipedia.org/wiki/Rete_algorithm).\n" + "Am fi putut implementa și noi lanțuirea înainte fără multe probleme, dar implementările naive de obicei nu sunt foarte eficiente. Pentru potrivirea mai eficientă a regulilor se folosește un algoritm special [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": [ - "Vom defini sistemul nostru ca o clasă care extinde `KnowledgeEngine`. Fiecare regulă este definită printr-o funcție separată cu adnotarea `@Rule`, care specifică momentul în care regula ar trebui să fie declanșată. În interiorul regulii, putem adăuga noi fapte folosind funcția `declare`, iar adăugarea acestor fapte va duce la apelarea unor reguli suplimentare de către motorul de inferență înainte.\n" + "Vom defini sistemul nostru ca o clasă care derivă din `KnowledgeEngine`. Fiecare regulă este definită printr-o funcție separată cu adnotarea `@Rule`, care specifică când ar trebui să se declanșeze regula. În interiorul regulii, putem adăuga noi fapte folosind funcția `declare`, iar adăugarea acestor fapte va determina apelarea unor reguli suplimentare de către motorul de inferență înainte.\n" ] }, { @@ -378,7 +377,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Odată ce am definit o bază de cunoștințe, populăm memoria noastră de lucru cu câteva fapte inițiale și apoi apelăm metoda `run()` pentru a efectua inferența. Puteți observa ca rezultat că noi fapte deduse sunt adăugate în memoria de lucru, inclusiv faptul final despre animal (dacă am configurat corect toate faptele inițiale).\n" + "Odată ce am definit o bază de cunoștințe, populăm memoria de lucru cu câteva fapte inițiale, apoi apelăm metoda `run()` pentru a efectua inferența. Putem observa ca rezultat că noi fapte deduse sunt adăugate în memoria de lucru, inclusiv faptul final despre animal (dacă am configurat toate faptele inițiale corect).\n" ] }, { @@ -440,7 +439,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n---\n\n**Declinare de responsabilitate**: \nAcest document a fost tradus folosind serviciul de traducere AI [Co-op Translator](https://github.com/Azure/co-op-translator). Deși ne străduim să asigurăm acuratețea, vă rugăm să rețineți că traducerile automate pot conține erori sau inexactități. Documentul original în limba sa natală ar trebui considerat sursa autoritară. Pentru informații critice, se recomandă traducerea profesională realizată de un specialist uman. Nu ne asumăm responsabilitatea pentru eventualele neînțelegeri sau interpretări greșite care pot apărea din utilizarea acestei traduceri.\n" + "---\n\n\n**Declinare de responsabilitate**: \nAcest document a fost tradus folosind serviciul de traducere AI [Co-op Translator](https://github.com/Azure/co-op-translator). Deși depunem eforturi pentru a asigura acuratețea, vă rugăm să țineți cont că traducerile automate pot conține erori sau inexactități. Documentul original în limba sa nativă trebuie considerat sursa autoritară. Pentru informații critice, se recomandă traducerea profesională realizată de un specialist uman. Nu ne asumăm răspunderea pentru eventuale neînțelegeri sau interpretări greșite rezultate din utilizarea acestei traduceri.\n\n" ] } ], @@ -467,8 +466,8 @@ "version": "3.11.2" }, "coopTranslator": { - "original_hash": "ab2bd97b0453415b89a469284609a8ce", - "translation_date": "2025-08-29T23:58:23+00:00", + "original_hash": "8ef43db4b9182239fd150a76bd494fdb", + "translation_date": "2026-01-16T05:21:34+00:00", "source_file": "lessons/2-Symbolic/Animals.ipynb", "language_code": "ro" } diff --git a/translations/ro/lessons/2-Symbolic/README.md b/translations/ro/lessons/2-Symbolic/README.md index 7bf9f0ab..87f553f0 100644 --- a/translations/ro/lessons/2-Symbolic/README.md +++ b/translations/ro/lessons/2-Symbolic/README.md @@ -1,116 +1,116 @@ -# Reprezentarea Cunoașterii și Sisteme Expert +# Reprezentarea Cunoașterii și Sistemele Expert -![Rezumat al conținutului AI simbolic](../../../../translated_images/ro/ai-symbolic.715a30cb610411a6.webp) +![Rezumat al conținutului AI simbolic](../../../../../../translated_images/ro/ai-symbolic.715a30cb610411a6.webp) -> Sketchnote de [Tomomi Imura](https://twitter.com/girlie_mac) +> Schiță realizată de [Tomomi Imura](https://twitter.com/girlie_mac) -Căutarea inteligenței artificiale se bazează pe dorința de a înțelege lumea, similar modului în care o fac oamenii. Dar cum putem realiza acest lucru? +Căutarea inteligenței artificiale se bazează pe o căutare a cunoașterii, pentru a înțelege lumea în mod similar cu modul în care o fac oamenii. Dar cum poți face acest lucru? -## [Chestionar înainte de lecție](https://ff-quizzes.netlify.app/en/ai/quiz/3) +## [Chestionar înaintea prelegerii](https://ff-quizzes.netlify.app/en/ai/quiz/3) -În primele zile ale AI, abordarea de sus în jos pentru crearea sistemelor inteligente (discutată în lecția anterioară) era populară. Ideea era să extragem cunoașterea de la oameni într-o formă care poate fi citită de mașini și apoi să o folosim pentru a rezolva automat probleme. Această abordare se baza pe două idei mari: +În primele zile ale IA, abordarea top-down pentru crearea sistemelor inteligente (discutată în lecția anterioară) era populară. Ideea era de a extrage cunoașterea de la oameni într-o formă lizibilă de către mașini, apoi de a o folosi pentru a rezolva automat probleme. Această abordare se baza pe două idei principale: * Reprezentarea Cunoașterii * Raționamentul ## Reprezentarea Cunoașterii -Unul dintre conceptele importante în AI simbolic este **cunoașterea**. Este esențial să diferențiem cunoașterea de *informație* sau *date*. De exemplu, putem spune că cărțile conțin cunoaștere, deoarece putem studia cărțile și deveni experți. Totuși, ceea ce conțin cărțile se numește de fapt *date*, iar prin citirea cărților și integrarea acestor date în modelul nostru al lumii, transformăm datele în cunoaștere. +Unul dintre conceptele importante din AI simbolic este **cunoașterea**. Este important să diferențiem cunoașterea de *informație* sau *date*. De exemplu, se poate spune că cărțile conțin cunoaștere, pentru că le poți studia și deveni expert. Totuși, ceea ce conțin cărțile se numește de fapt *date*, iar prin citirea cărților și integrarea acestor date în modelul nostru al lumii, le transformăm în cunoaștere. -> ✅ **Cunoașterea** este ceva ce se află în mintea noastră și reprezintă înțelegerea noastră asupra lumii. Este obținută printr-un proces activ de **învățare**, care integrează bucăți de informație pe care le primim în modelul nostru activ al lumii. +> ✅ **Cunoașterea** este ceva ce este conținut în mintea noastră și reprezintă înțelegerea noastră asupra lumii. Este obținută printr-un proces activ de **învățare**, care integrează bucăți de informație primite în modelul nostru activ al lumii. -De cele mai multe ori, nu definim strict cunoașterea, ci o aliniem cu alte concepte conexe folosind [Piramida DIKW](https://en.wikipedia.org/wiki/DIKW_pyramid). Aceasta conține următoarele concepte: +De cele mai multe ori, nu definim strict cunoașterea, ci o aliniem cu alte concepte înrudite folosind [Pirâmida DIKW](https://en.wikipedia.org/wiki/DIKW_pyramid). Aceasta conține următoarele concepte: -* **Datele** sunt reprezentate pe suporturi fizice, cum ar fi textul scris sau cuvintele rostite. Datele există independent de ființele umane și pot fi transmise între oameni. -* **Informația** este modul în care interpretăm datele în mintea noastră. De exemplu, când auzim cuvântul *computer*, avem o anumită înțelegere despre ce este. -* **Cunoașterea** este informația integrată în modelul nostru al lumii. De exemplu, odată ce învățăm ce este un computer, începem să avem idei despre cum funcționează, cât costă și la ce poate fi folosit. Această rețea de concepte interconectate formează cunoașterea noastră. -* **Înțelepciunea** este un nivel superior al înțelegerii noastre asupra lumii și reprezintă *meta-cunoaștere*, adică o idee despre cum și când ar trebui folosită cunoașterea. +* **Date** sunt ceva reprezentat în medii fizice, cum ar fi text scris sau cuvinte vorbite. Datele există independent de ființele umane și pot fi transmise între oameni. +* **Informație** este modul în care interpretăm datele în mintea noastră. De exemplu, când auzim cuvântul *computer*, avem o oarecare înțelegere despre ce este. +* **Cunoaștere** este informația integrată în modelul nostru al lumii. De exemplu, odată ce învățăm ce este un computer, începem să avem unele idei despre cum funcționează, cât costă și la ce poate fi folosit. Această rețea de concepte interconectate formează cunoașterea noastră. +* **Înțelepciune** este un nivel suplimentar al înțelegerii noastre asupra lumii, și reprezintă *meta-cunoaștere*, adică o noțiune despre cum și când trebuie folosită cunoașterea. - + -*Imagine [din Wikipedia](https://commons.wikimedia.org/w/index.php?curid=37705247), By Longlivetheux - Own work, CC BY-SA 4.0* +*Imagine [de pe Wikipedia](https://commons.wikimedia.org/w/index.php?curid=37705247), De Longlivetheux - Lucrare proprie, CC BY-SA 4.0* -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: +Astfel, problema **reprezentării cunoașterii** este să găsim o modalitate eficace de a reprezenta cunoașterea în interiorul unui calculator sub formă de date, pentru a o face utilizabilă automat. Aceasta poate fi văzută ca un spectru: -![Spectrul reprezentării cunoașterii](../../../../translated_images/ro/knowledge-spectrum.b60df631852c0217.webp) +![Spectru al reprezentării cunoașterii](../../../../../../translated_images/ro/knowledge-spectrum.b60df631852c0217.webp) > Imagine de [Dmitry Soshnikov](http://soshnikov.com) -* În partea stângă, există tipuri foarte simple de reprezentări ale cunoașterii care pot fi utilizate eficient de computere. Cea mai simplă este cea algoritmică, când cunoașterea este reprezentată printr-un program de computer. Totuși, aceasta nu este cea mai bună modalitate de a reprezenta cunoașterea, deoarece nu este flexibilă. Cunoașterea din mintea noastră este adesea non-algoritmică. -* În partea dreaptă, există reprezentări precum textul natural. Este cea mai puternică, dar nu poate fi utilizată pentru raționament automat. +* În stânga se află tipuri foarte simple de reprezentări ale cunoașterii care pot fi folosite eficient de computere. Cea mai simplă este cea algoritmică, când cunoașterea este reprezentată printr-un program de calculator. Totuși, aceasta nu este cea mai bună metodă pentru reprezentarea cunoașterii, deoarece nu este flexibilă. Cunoașterea din mintea noastră este adesea non-algoritmică. +* În dreapta sunt reprezentări precum textul natural. Este cea mai puternică, dar nu poate fi folosită pentru raționament automat. -> ✅ Gândește-te un minut la modul în care reprezinți cunoașterea în mintea ta și o transformi în notițe. Există un format anume care funcționează bine pentru tine pentru a ajuta la reținere? +> ✅ Gândește-te un moment cum reprezinți cunoașterea în mintea ta și o convertești în notițe. Există un format anume care funcționează bine pentru tine pentru a ajuta la reținere? -## Clasificarea Reprezentărilor Cunoașterii în Computere +## Clasificarea Reprezentărilor Cunoașterii în Calculatoare -Putem clasifica diferite metode de reprezentare a cunoașterii în computere în următoarele categorii: +Putem clasifica diferitele metode de reprezentare a cunoașterii în calculator în următoarele categorii: -* **Reprezentări de rețea** se bazează pe faptul că avem o rețea de concepte interconectate în mintea noastră. Putem încerca să reproducem aceleași rețele ca un grafic într-un computer - o așa-numită **rețea semantică**. +* **Reprezentări în rețea** se bazează pe faptul că avem o rețea de concepte interconectate în minte. Putem încerca să reproducem aceleași rețele ca un graf în interiorul unui calculator - un așa-numit **network semantic**. -1. **Triplete Obiect-Caracteristică-Valoare** sau **perechi caracteristică-valoare**. Deoarece un grafic poate fi reprezentat într-un computer ca o listă de noduri și muchii, putem reprezenta o rețea semantică printr-o listă de triplete, conținând obiecte, caracteristici și valori. De exemplu, construim următoarele triplete despre limbajele de programare: +1. **Triplete Obiect-Atribut-Valoare** sau **perechi atribut-valoare**. Deoarece un graf poate fi reprezentat în interiorul unui calculator ca o listă de noduri și muchii, putem reprezenta o rețea semantică printr-o listă de triplete, care conțin obiecte, atribute și valori. De exemplu, construim următoarele triplete despre limbaje de programare: -Obiect | Caracteristică | Valoare --------|----------------|------ -Python | este | Limbaj-Netipat +Obiect | Atribut | Valoare +-------|-----------|------ +Python | este | Limbaj-Nespecificat Python | inventat-de | Guido van Rossum Python | sintaxă-bloc | indentare -Limbaj-Netipat | nu are | definiții de tip +Limbaj-Nespecificat | nu are | definiții de tip -> ✅ Gândește-te cum pot fi utilizate tripletele pentru a reprezenta alte tipuri de cunoaștere. +> ✅ Gândește-te cum pot fi folosite tripletele pentru a reprezenta alte tipuri de cunoaștere. -2. **Reprezentări ierarhice** subliniază faptul că adesea creăm o ierarhie de obiecte în mintea noastră. De exemplu, știm că canarul este o pasăre, iar toate păsările au aripi. De asemenea, avem o idee despre ce culoare are de obicei un canar și care este viteza lui de zbor. +2. **Reprezentări ierarhice** evidențiază faptul că deseori creăm o ierarhie de obiecte în mintea noastră. De exemplu, știm că canarul este o pasăre, iar toate păsările au aripi. Avem și o idee despre ce culoare are de obicei un canar și care este viteza sa de zbor. - - **Reprezentarea prin cadre** se bazează pe reprezentarea fiecărui obiect sau clasă de obiecte ca un **cadru** care conține **sloturi**. Sloturile au valori implicite posibile, restricții de valoare sau proceduri stocate care pot fi apelate pentru a obține valoarea unui slot. Toate cadrele formează o ierarhie similară cu ierarhia de obiecte din limbajele de programare orientate pe obiecte. - - **Scenariile** sunt un tip special de cadre care reprezintă situații complexe care se pot desfășura în timp. + - **Reprezentarea pe cadre** se bazează pe reprezentarea fiecărui obiect sau clasă de obiecte ca un **cadru** care conține **sloturi**. Sloturile au valori implicite posibile, restricții de valori sau proceduri stocate care pot fi apelate pentru a obține valoarea unui slot. Toate cadrele formează o ierarhie similară cu ierarhia de obiecte din limbajele de programare orientate pe obiecte. + - **Scenariile** sunt un tip special de cadre care reprezintă situații complexe ce se pot derula în timp. **Python** Slot | Valoare | Valoare implicită | Interval | ------|--------|-------------------|----------| +-----|---------|-------------------|----------| Nume | Python | | | -Este-Un | Limbaj-Netipat | | | +Este-un | Limbaj-Nespecificat | | | Caz Variabilă | | CamelCase | | Lungime Program | | | 5-5000 linii | -Sintaxă Bloc | Indentare | | | +Sintaxă-Bloc | Indentare | | | -3. **Reprezentări procedurale** se bazează pe reprezentarea cunoașterii printr-o listă de acțiuni care pot fi executate atunci când apare o anumită condiție. - - Regulile de producție sunt declarații de tip dacă-atunci care ne permit să tragem concluzii. De exemplu, un medic poate avea o regulă care spune că **DACĂ** un pacient are febră mare **SAU** un nivel ridicat de proteină C-reactivă în testul de sânge **ATUNCI** are o inflamație. Odată ce întâlnim una dintre condiții, putem trage o concluzie despre inflamație și apoi o putem folosi în raționamente ulterioare. - - Algoritmii pot fi considerați o altă formă de reprezentare procedurală, deși aproape niciodată nu sunt utilizați direct în sistemele bazate pe cunoaștere. +3. **Reprezentări procedurale** se bazează pe reprezentarea cunoașterii printr-o listă de acțiuni ce pot fi executate când apare o anumită condiție. + - Regulile de producție sunt afirmații dacă-atunci care ne permit să tragem concluzii. De exemplu, un doctor poate avea o regulă care spune că **DACĂ** un pacient are febră mare **SAU** nivel ridicat de proteina C-reactivă în testul de sânge **ATUNCI** are o inflamație. Odată ce întâlnim una dintre condiții, putem trage o concluzie despre inflamație și apoi o putem folosi în raționament ulterior. + - Algoritmii pot fi considerați o altă formă de reprezentare procedurală, deși aproape niciodată nu sunt folosiți direct în sistemele bazate pe cunoaștere. -4. **Logica** a fost propusă inițial de Aristotel ca o modalitate de a reprezenta cunoașterea universală umană. - - Logica predicatelor ca teorie matematică este prea bogată pentru a fi calculabilă, de aceea se folosește de obicei un subset al acesteia, cum ar fi clauzele Horn utilizate în Prolog. - - Logica descriptivă este o familie de sisteme logice utilizate pentru a reprezenta și raționa despre ierarhiile de obiecte și reprezentările distribuite ale cunoașterii, cum ar fi *web-ul semantic*. +4. **Logica** a fost propusă inițial de Aristotel ca o metodă de a reprezenta cunoașterea universală umană. + - Logica predicatelor, ca teorie matematică, este prea bogată pentru a fi calculabilă, prin urmare se folosește de obicei un subset al ei, cum ar fi clauzele Horn folosite în Prolog. + - Logica descriptivă este o familie de sisteme logice folosite pentru a reprezenta și raționa despre ierarhii de obiecte și reprezentări distribuite ale cunoașterii cum ar fi *web-ul semantic*. ## Sisteme Expert -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. +Unul dintre succesele timpurii ale AI simbolic au fost aşa-numitele **sisteme expert** - sisteme de calculator proiectate să acționeze ca experți într-un domeniu de problemă limitat. Acestea se bazau pe o **bază de cunoștințe** extrasă de la unul sau mai mulți experți umani și conțineau un **motor de inferență** care făcea raționamente pe baza acesteia. -![Arhitectura umană](../../../../translated_images/ro/arch-human.5d4d35f1bba3ab1c.webp) | ![Sistem bazat pe cunoaștere](../../../../translated_images/ro/arch-kbs.3ec5c150b09fa8da.webp) +![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 +Structură simplificată a unui sistem neural uman | Arhitectura unui sistem bazat pe cunoaștere -Sistemele expert sunt construite similar cu sistemul de raționament uman, care conține **memorie pe termen scurt** și **memorie pe termen lung**. În mod similar, în sistemele bazate pe cunoaștere distingem următoarele componente: +Sistemele expert sunt construite asemeni sistemului de raționament uman, care conține **memorie pe termen scurt** și **memorie pe termen lung**. În mod similar, în sistemele bazate pe cunoaștere distingem următoarele componente: -* **Memoria problemei**: conține cunoașterea despre problema care este în prezent rezolvată, de exemplu temperatura sau tensiunea arterială a unui pacient, dacă are inflamație sau nu etc. Această cunoaștere este numită și **cunoaștere statică**, deoarece conține o imagine a ceea ce știm în prezent despre problemă - așa-numitul *stare a problemei*. -* **Baza de cunoaștere**: reprezintă cunoașterea pe termen lung despre un domeniu de probleme. Este extrasă manual de la experți umani și nu se schimbă de la o consultație la alta. Deoarece ne permite să navigăm de la o stare a problemei la alta, este numită și **cunoaștere dinamică**. -* **Motorul de inferență**: orchestrează întregul proces de căutare în spațiul stării problemei, punând întrebări utilizatorului atunci când este necesar. Este, de asemenea, responsabil pentru găsirea regulilor potrivite care trebuie aplicate fiecărei stări. +* **Memoria problemei**: conține cunoașterea legată de problema care este rezolvată în prezent, de exemplu temperatura sau tensiunea arterială a unui pacient, dacă are inflamație sau nu, etc. Această cunoaștere este numită și **cunoaștere statică**, deoarece conține un instantaneu a ceea ce știm momentan despre problemă - așa-numita *stare a problemei*. +* **Baza de cunoștințe**: reprezintă cunoașterea pe termen lung despre un domeniu de problemă. Este extrasă manual de la experți umani și nu se schimbă de la o consultație la alta. Pentru că ne permite să navigăm de la o stare a problemei la alta, este denumită și **cunoaștere dinamică**. +* **Motorul de inferență**: orchestrează întreg procesul de căutare în spațiul stărilor problemei, punând întrebări utilizatorului atunci când este necesar. Este responsabil și pentru găsirea regulilor potrivite pentru fiecare stare. -Ca exemplu, să luăm în considerare următorul sistem expert de determinare a unui animal pe baza caracteristicilor sale fizice: +Ca exemplu, să considerăm următorul sistem expert pentru determinarea unui animal bazat pe caracteristicile sale fizice: -![Arbore AND-OR](../../../../translated_images/ro/AND-OR-Tree.5592d2c70187f283.webp) +![Arbore AND-OR](../../../../../../translated_images/ro/AND-OR-Tree.5592d2c70187f283.webp) > Imagine de [Dmitry Soshnikov](http://soshnikov.com) -Acest diagramă se numește **arbore AND-OR** și este o reprezentare grafică a unui set de reguli de producție. Desenarea unui arbore este utilă la începutul procesului de extragere a cunoașterii de la expert. Pentru a reprezenta cunoașterea în computer, este mai convenabil să folosim reguli: +Acest diagram este numit **arbore AND-OR**, și este o reprezentare grafică a unui set de reguli de producție. Desenarea unui arbore este utilă la începutul extragerii cunoașterii de la expert. Pentru a reprezenta cunoașterea în interiorul calculatorului este mai convenabil să folosim reguli: ``` IF the animal eats meat @@ -121,78 +121,78 @@ OR (animal has sharp teeth THEN the animal is a carnivore ``` -Poți observa că fiecare condiție din partea stângă a regulii și acțiunea sunt, în esență, triplete Obiect-Caracteristică-Valoare (OAV). **Memoria de lucru** conține setul de triplete OAV care corespund problemei care este în prezent rezolvată. Un **motor de reguli** caută reguli pentru care o condiție este satisfăcută și le aplică, adăugând un alt triplet în memoria de lucru. +Poți observa că fiecare condiție pe partea stângă a regulii și acțiunea sunt în esență triplete obiect-atribut-valoare (OAV). **Memoria de lucru** conține setul de triplete OAV care corespund problemei ce se rezolvă în prezent. Un **motor de reguli** caută reguli pentru care o condiție este satisfăcută și le aplică, adăugând un alt triplet în memoria de lucru. -> ✅ Scrie propriul tău arbore AND-OR pe un subiect care îți place! +> ✅ Scrie-ți propriul arbore AND-OR pe un subiect care îți place! -### Inferență înainte vs. Inferență înapoi +### Inferență înainte vs. inferență înapoi -Procesul descris mai sus se numește **inferență înainte**. Acesta începe cu unele date inițiale despre problemă disponibile în memoria de lucru și apoi execută următorul ciclu de raționament: +Procesul descris mai sus se numește **inferență înainte**. Pornește de la unele date inițiale despre problema disponibile în memoria de lucru, apoi execută următorul ciclu de raționament: -1. Dacă atributul țintă este prezent în memoria de lucru - oprește-te și oferă rezultatul -2. Caută toate regulile ale căror condiții sunt în prezent satisfăcute - obține **setul de conflict** de reguli. -3. Realizează **rezolvarea conflictului** - selectează o regulă care va fi executată în acest pas. Pot exista diferite strategii de rezolvare a conflictului: - - Selectează prima regulă aplicabilă din baza de cunoaștere +1. Dacă atributul țintă este prezent în memoria de lucru - oprește și dă rezultatul +2. Caută toate regulile a căror condiție este în prezent satisfăcută - obține **setul de conflicte** al regulilor. +3. Efectuează **rezolvarea conflictelor** - selectează o regulă care va fi executată în acest pas. Există diferite strategii de rezolvare a conflictului: + - Selectează prima regulă aplicabilă în baza de cunoștințe - Selectează o regulă aleatorie - - Selectează o regulă *mai specifică*, adică cea care îndeplinește cele mai multe condiții în partea "stângă" (LHS) + - Selectează o regulă *mai specifică*, adică care satisface cele mai multe condiții din partea stângă (LHS) 4. Aplică regula selectată și inserează o nouă bucată de cunoaștere în starea problemei 5. Repetă de la pasul 1. -Totuși, în unele cazuri, s-ar putea să dorim să începem cu o cunoaștere goală despre problemă și să punem întrebări care ne vor ajuta să ajungem la concluzie. De exemplu, atunci când facem un diagnostic medical, de obicei nu realizăm toate analizele medicale în avans înainte de a începe diagnosticarea pacientului. Mai degrabă, dorim să realizăm analizele atunci când trebuie luată o decizie. +Totuși, în unele cazuri poate dorim să începem cu cunoaștere vidă despre problemă și să punem întrebări care ne vor ajuta să ajungem la concluzie. De exemplu, când facem diagnostic medical, de obicei nu efectuăm toate analizele medicale dinainte înainte de a începe diagnosticarea pacientului. Mai degrabă vrem să efectuăm analizele când trebuie să luăm o decizie. -Acest proces poate fi modelat folosind **inferența înapoi**. Este condus de **scop** - valoarea atributului pe care încercăm să o găsim: +Acest proces poate fi modelat folosind **inferența înapoi**. Este condus de **obiectiv** - valoarea atributului pe care încercăm să o găsim: -1. Selectează toate regulile care pot oferi valoarea unui scop (adică cu scopul în partea dreaptă ("right-hand-side")) - un set de conflict -1. Dacă nu există reguli pentru acest atribut sau există o regulă care spune că ar trebui să cerem valoarea de la utilizator - cere-o, altfel: -1. Folosește strategia de rezolvare a conflictului pentru a selecta o regulă pe care o vom folosi ca *ipoteză* - vom încerca să o demonstrăm -1. Recurent, repetă procesul pentru toate atributele din partea stângă a regulii, încercând să le demonstrezi ca scopuri +1. Selectează toate regulile care ne pot oferi valoarea unui obiectiv (adică cu obiectivul pe partea dreaptă (RHS) a regulii) - un set de conflicte +1. Dacă nu există reguli pentru acest atribut sau există o regulă care spune că trebuie să cerem valoarea de la utilizator - întreabă valoarea, altfel: +1. Folosește strategia de rezolvare a conflictului pentru a selecta o regulă pe care o vom folosi ca *ipoteză* - o vom încerca să o demonstrăm +1. Repetă recurent procesul pentru toți atributele din partea stângă a regulii (LHS), încercând să le dovedim ca obiective 1. Dacă în orice moment procesul eșuează - folosește o altă regulă la pasul 3. -> ✅ În ce situații este mai potrivită inferența înainte? Dar inferența înapoi? +> ✅ În ce situații este inferența înainte mai potrivită? Dar inferența înapoi? ### Implementarea Sistemelor Expert -Sistemele expert pot fi implementate folosind diferite instrumente: +Sistemele expert pot fi implementate folosind diverse unelte: -* Programarea lor directă într-un limbaj de programare de nivel înalt. Aceasta nu este cea mai bună idee, deoarece principalul avantaj al unui sistem bazat pe cunoaștere este că cunoașterea este separată de inferență, iar un expert în domeniul problemei ar trebui să poată scrie reguli fără a înțelege detaliile procesului de inferență. -* Utilizarea unui **shell pentru sisteme expert**, adică un sistem conceput special pentru a fi populat cu cunoaștere folosind un limbaj de reprezentare a cunoașterii. +* Programarea lor directă într-un limbaj de programare de nivel înalt. Nu este o idee prea bună, deoarece avantajul principal al unui sistem bazat pe cunoaștere este că cunoașterea este separată de inferență, iar potențial un expert în domeniu ar trebui să poată scrie reguli fără a înțelege detaliile procesului de inferență. +* Folosirea unui **shell pentru sistem expert**, adică un sistem special conceput pentru a fi populat cu cunoaștere folosind un limbaj de reprezentare a cunoașterii. -## ✍️ Exercițiu: Inferența Animalelor +## ✍️ Exercițiu: Inferența animalelor -Vezi [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) pentru un exemplu de implementare a unui sistem expert de inferență înainte și înapoi. +Vezi [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) pentru un exemplu de implementare a unui sistem expert cu inferență înainte și înapoi. -> **Notă**: Acest exemplu este destul de simplu și oferă doar o idee despre cum arată un sistem expert. Odată ce începi să creezi un astfel de sistem, vei observa un comportament *inteligent* doar atunci când ajungi la un anumit număr de reguli, în jur de 200+. La un moment dat, regulile devin prea complexe pentru a le ține pe toate în minte, iar în acel moment s-ar putea să te întrebi de ce sistemul ia anumite decizii. Totuși, caracteristica importantă a sistemelor bazate pe cunoaștere este că poți întotdeauna *explica* exact cum a fost luată orice decizie. +> **Notă**: Acest exemplu este destul de simplu și doar oferă ideea cum arată un sistem expert. Odată ce începi să creezi un astfel de sistem, vei observa *comportament inteligent* doar când ajungi la un anumit număr de reguli, în jur de 200+. La un moment dat, regulile devin prea complexe ca să le ții toate în minte, iar în acel punct te vei întreba de ce un sistem ia anumite decizii. Totuși, caracteristica importantă a sistemelor bazate pe cunoaștere este că poți întotdeauna să *explici* exact cum a fost luată oricare decizie. ## Ontologii și Web-ul Semantic -La sfârșitul secolului XX a existat o inițiativă de a folosi reprezentarea cunoașterii pentru a adnota resursele de pe Internet, astfel încât să fie posibil să găsim resurse care corespund unor interogări foarte specifice. Această mișcare a fost numită **Web Semantic** și s-a bazat pe mai multe concepte: +La sfârșitul secolului XX a existat o inițiativă de a folosi reprezentarea cunoașterii pentru a adnota resursele de pe Internet, astfel încât să fie posibilă găsirea de resurse care corespund unor căutări foarte specifice. Această mișcare a fost numită **Web Semantic**, și s-a bazat pe mai multe concepte: -- O reprezentare specială a cunoașterii bazată pe **[logici descriptive](https://en.wikipedia.org/wiki/Description_logic)** (DL). Este similară cu reprezentarea cunoașterii prin cadre, deoarece construiește o ierarhie de obiecte cu proprietăți, dar are semantică logică formală și inferență. Există o întreagă familie de DL-uri care echilibrează între expresivitate și complexitatea algoritmică a inferenței. -- Reprezentarea distribuită a cunoașterii, unde toate conceptele sunt reprezentate printr-un identificator global URI, făcând posibilă crearea ierarhiilor de cunoaștere care se extind pe internet. +- O reprezentare specială a cunoașterii bazată pe **[logici descriptive](https://en.wikipedia.org/wiki/Description_logic)** (DL). Este similară cu reprezentarea pe cadre, pentru că construiește o ierarhie de obiecte cu proprietăți, dar are semantică formală logică și inferență. Există o familie întreagă de DL-uri care echilibrează între expresivitate și complexitatea algoritmică a inferenței. +- Reprezentare distribuită a cunoașterii, unde toate conceptele sunt reprezentate printr-un identificator global URI, făcând posibilă crearea de ierarhii de cunoștințe care să acopere Internetul. - O familie de limbaje bazate pe XML pentru descrierea cunoștințelor: RDF (Resource Description Framework), RDFS (RDF Schema), OWL (Ontology Web Language). -Un concept central în Web-ul Semantic este conceptul de **Ontologie**. Acesta se referă la o specificație explicită a unui domeniu de problemă utilizând o reprezentare formală a cunoștințelor. Cea mai simplă ontologie poate fi doar o ierarhie de obiecte dintr-un domeniu de problemă, dar ontologiile mai complexe vor include reguli care pot fi utilizate pentru inferență. +Un concept fundamental în Web-ul Semantic este conceptul de **Ontologie**. Se referă la o specificare explicită a unui domeniu de problemă folosind o reprezentare formală a cunoștințelor. Cea mai simplă ontologie poate fi doar o ierarhie de obiecte în domeniul problemei, dar ontologiile mai complexe vor include reguli care pot fi folosite pentru inferență. -În Web-ul Semantic, toate reprezentările se bazează pe triplete. Fiecare obiect și fiecare relație sunt identificate în mod unic prin URI. De exemplu, dacă dorim să afirmăm faptul că acest Curriculum AI a fost dezvoltat de Dmitry Soshnikov pe 1 ianuarie 2022 - iată tripletele pe care le putem folosi: +În web-ul semantic, toate reprezentările se bazează pe triplete. Fiecare obiect și fiecare relație sunt identificate în mod unic prin URI. De exemplu, dacă vrem să afirmăm faptul că acest Curriculum AI a fost dezvoltat de Dmitry Soshnikov pe 1 ianuarie 2022 - iată tripletele pe care le putem folosi: - + ``` -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 ``` -> ✅ Aici `http://www.example.com/terms/creation-date` și `http://purl.org/dc/elements/1.1/creator` sunt câteva URI bine cunoscute și universal acceptate pentru a exprima conceptele de *creator* și *data creării*. +> ✅ Aici `http://www.example.com/terms/creation-date` și `http://purl.org/dc/elements/1.1/creator` sunt niște URI-uri bine cunoscute și universal acceptate pentru a exprima conceptele de *creator* și *data creației*. Într-un caz mai complex, dacă dorim să definim o listă de creatori, putem folosi unele structuri de date definite în RDF. - + -> Diagramele de mai sus de [Dmitry Soshnikov](http://soshnikov.com) +> Diagramele de mai sus sunt realizate de [Dmitry Soshnikov](http://soshnikov.com) -Progresul construirii Web-ului Semantic a fost într-o oarecare măsură încetinit de succesul motoarelor de căutare și al tehnicilor de procesare a limbajului natural, care permit extragerea datelor structurate din text. Cu toate acestea, în unele domenii există încă eforturi semnificative pentru a menține ontologii și baze de cunoștințe. Câteva proiecte notabile: +Progresul construirii Web-ului Semantic a fost cumva încetinit de succesul motoarelor de căutare și al tehnicilor de procesare a limbajului natural, care permit extragerea datelor structurate din text. Totuși, în unele domenii există încă eforturi semnificative pentru întreținerea ontologiilor și bazelor de cunoștințe. Câteva proiecte demne de menționat: -* [WikiData](https://wikidata.org/) este o colecție de baze de cunoștințe lizibile de mașini asociate cu Wikipedia. Majoritatea datelor sunt extrase din *InfoBox-uri* Wikipedia, bucăți de conținut structurat din paginile Wikipedia. Puteți [interoga](https://query.wikidata.org/) WikiData în SPARQL, un limbaj special de interogare pentru Web-ul Semantic. Iată un exemplu de interogare care afișează cele mai populare culori ale ochilor la oameni: +* [WikiData](https://wikidata.org/) este o colecție de baze de cunoștințe machine-readable asociate cu Wikipedia. Majoritatea datelor sunt extrase din *InfoBox-urile* Wikipedia, bucăți de conținut structurat din paginile Wikipedia. Poți [interoga](https://query.wikidata.org/) Wikidata folosind SPARQL, un limbaj special de interogare pentru Web-ul Semantic. Iată o interogare exemplu care afișează cele mai populare culori ale ochilor printre oameni: ```sparql #defaultView:BubbleChart @@ -208,45 +208,49 @@ GROUP BY ?eyeColorLabel * [DBpedia](https://www.dbpedia.org/) este un alt efort similar cu WikiData. -> ✅ Dacă doriți să experimentați cu construirea propriilor ontologii sau deschiderea unora existente, există un editor vizual excelent de ontologii numit [Protégé](https://protege.stanford.edu/). Descărcați-l sau folosiți-l online. +> ✅ Dacă vrei să experimentezi construind propriile ontologii sau să deschizi ontologii existente, există un editor vizual excelent de ontologii numit [Protégé](https://protege.stanford.edu/). Descarcă-l sau folosește-l online. - + *Editorul Web Protégé deschis cu ontologia Familiei Romanov. Captură de ecran de Dmitry Soshnikov* ## ✍️ Exercițiu: O Ontologie de Familie -Consultați [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) pentru un exemplu de utilizare a tehnicilor Web-ului Semantic pentru a raționa despre relațiile de familie. Vom lua un arbore genealogic reprezentat în formatul comun GEDCOM și o ontologie a relațiilor de familie și vom construi un grafic al tuturor relațiilor de familie pentru un set dat de indivizi. +Vezi [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) pentru un exemplu de utilizare a tehnicilor Web Semantic pentru a raționa despre relațiile de familie. Vom lua un arbore genealogic reprezentat în formatul obișnuit GEDCOM și o ontologie a relațiilor de familie și vom construi un graf al tuturor relațiilor de familie pentru un set dat de indivizi. ## Microsoft Concept Graph -În cele mai multe cazuri, ontologiile sunt create cu atenție manual. Cu toate acestea, este posibil și să **extragem** ontologii din date nestructurate, de exemplu, din texte în limbaj natural. +În cele mai multe cazuri, ontologiile sunt create cu grijă manual. Totuși, este posibil să **extragem** ontologii din date nestructurate, de exemplu, din texte în limbaj natural. -O astfel de încercare a fost realizată de Microsoft Research și a dus la [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste). +Un astfel de demers a fost făcut de Microsoft Research și a dus la [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste). -Este o colecție mare de entități grupate împreună folosind relația de moștenire `is-a`. Permite răspunsuri la întrebări precum "Ce este Microsoft?" - răspunsul fiind ceva de genul "o companie cu probabilitatea 0.87 și un brand cu probabilitatea 0.75". +Este o colecție vastă de entități grupate folosind relația de moștenire `is-a`. Permite răspunsuri la întrebări de genul "Ce este Microsoft?" - răspunsul fiind ceva de genul "o companie cu o probabilitate de 0.87, și un brand cu o probabilitate de 0.75". -Graficul este disponibil fie ca REST API, fie ca un fișier text mare descărcabil care listează toate perechile de entități. +Graficul este disponibil fie ca REST API, fie ca un fișier de text mare ce poate fi descărcat și care listează toate perechile de entități. ## ✍️ Exercițiu: Un Grafic de Concepte -Încercați notebook-ul [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) pentru a vedea cum putem folosi Microsoft Concept Graph pentru a grupa articole de știri în mai multe categorii. +Încearcă notebook-ul [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) pentru a vedea cum putem folosi Microsoft Concept Graph pentru a grupa articole de știri în mai multe categorii. ## Concluzie -În zilele noastre, AI este adesea considerată sinonimă cu *Machine Learning* sau *Rețele Neuronale*. Cu toate acestea, o ființă umană manifestă și raționament explicit, ceva ce rețelele neuronale nu gestionează în prezent. În proiectele din lumea reală, raționamentul explicit este încă utilizat pentru a îndeplini sarcini care necesită explicații sau capacitatea de a modifica comportamentul sistemului într-un mod controlat. +În zilele noastre, AI este adesea considerată un sinonim pentru *Învățare Automată* sau *Rețele Neuronale*. Cu toate acestea, un om manifestă și raționament explicit, ceea ce în prezent nu este tratat de rețelele neuronale. În proiectele reale, raționamentul explicit este încă folosit pentru a realiza sarcini care necesită explicații sau pentru a putea modifica comportamentul sistemului într-un mod controlat. ## 🚀 Provocare -În notebook-ul Family Ontology asociat acestei lecții, există oportunitatea de a experimenta cu alte relații de familie. Încercați să descoperiți noi conexiuni între persoanele din arborele genealogic. +În notebook-ul Ontologia de Familie asociat acestei lecții există oportunitatea de a experimenta cu alte relații de familie. Încearcă să descoperi conexiuni noi între oamenii din arborele genealogic. ## [Quiz post-lectură](https://ff-quizzes.netlify.app/en/ai/quiz/4) -## Recapitulare & Studiu Individual +## Recapitulare și Auto-studiu -Faceți cercetări pe internet pentru a descoperi domenii în care oamenii au încercat să cuantifice și să codifice cunoștințele. Aruncați o privire asupra Taxonomiei lui Bloom și mergeți înapoi în istorie pentru a învăța cum oamenii au încercat să înțeleagă lumea lor. Explorați munca lui Linnaeus pentru a crea o taxonomie a organismelor și observați modul în care Dmitri Mendeleev a creat o modalitate de descriere și grupare a elementelor chimice. Ce alte exemple interesante puteți găsi? +Caută pe internet pentru a descoperi domeniile în care oamenii au încercat să cuantifice și să codifice cunoștințele. Aruncă o privire asupra Taxonomiei lui Bloom și întoarce-te în istorie pentru a înțelege cum au încercat oamenii să-și dea seama de lumea lor. Explorează lucrările lui Linnaeus pentru crearea unei taxonomii a organismelor și observă modul în care Dmitri Mendeleev a creat o metodă pentru descrierea și gruparea elementelor chimice. Ce alte exemple interesante poți găsi? -**Temă**: [Construiește o Ontologie](assignment.md) +**Tema**: [Construiește o Ontologie](assignment.md) --- + +**Declinare de responsabilitate**: +Acest document a fost tradus folosind serviciul de traducere AI [Co-op Translator](https://github.com/Azure/co-op-translator). Deși ne străduim pentru acuratețe, vă rugăm să rețineți că traducerile automate pot conține erori sau inexactități. Documentul original, în limba sa nativă, trebuie considerat sursa autorizată. Pentru informații critice, se recomandă traducerea profesională realizată de traducători umani. Nu ne asumăm răspunderea pentru orice neînțelegeri sau interpretări greșite rezultate din utilizarea acestei traduceri. + \ No newline at end of file diff --git a/translations/sr/README.md b/translations/sr/README.md index cbc2be8b..a211d379 100644 --- a/translations/sr/README.md +++ b/translations/sr/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)](./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) -> **Препоручујемо локално клонирање?** +> **Више волите да клонирате локално?** -> Овај репозиторијум укључује преко 50 превода што значајно повећава величину преузимања. За клонирање без превода, користите sparse checkout: +> Овај репозиторијум укључује преко 50 превода што значајно повећава величину преузимања. Да бисте клонирали без превода, користите sparse checkout: > ```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' > ``` -> Ово вам даје све што вам је потребно за завршетак курса са знатно бржим преузимањем. +> Ово вам даје све што вам је потребно за завршавање курса са много бржим преузимањем. -**Ако желите да додате подршку за више језика превода, они су наведени [овде](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** +**Ако желите да додатни језици буду подржани, они су наведени [овде](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** ## Придружите се заједници [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) ## Шта ћете научити -**[Мапа ума курса](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** +**[Ментална мапа курса](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** -У овом наставном програму ћете научити: +У овом наставном плану ћете научити: -* Различите приступе вештачкој интелигенцији, укључујући „стару добру“ симболичку методу са **представљањем знања** и размишљањем ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). -* **Невронске мреже** и **дубоко учење**, које су у основи модерне AI. Илустроваћемо концепте иза ових важних тема користећи код у два од најпопуларнијих фрејмворка - [TensorFlow](http://Tensorflow.org) и [PyTorch](http://pytorch.org). -* **Невронске архитектуре** за рад са сликама и текстом. Покрићемо најновије моделе, али можда ћемо мало заостати у најсавременијим достигнућима. -* Мање популарне AI приступе, као што су **генетски алгоритми** и **мултиагентски системи**. +* Различите приступе Вештачкој интелигенцији, укључујући "добар стари" симболички приступ са **Представљањем знања** и резоновањем ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). +* **Нервне мреже** и **Дубоко учење**, које су у срцу модерне AI. Илустроваћемо концепте иза ових важних тема користећи код у два од најпопуларнијих фрејмворка - [TensorFlow](http://Tensorflow.org) и [PyTorch](http://pytorch.org). +* **Нервне архитектуре** за рад са сликама и текстом. Покрићемо новије моделе али можда ћемо мало заостати за најмодернијим стањем. +* Мање популарне AI приступе, као што су **Генетски алгоритми** и **Мулти-агентски системи**. -Шта нећемо обрадити у овом наставном програму: +Шта нећемо покрити у овом наставном плану: > [Пронађите све додатне ресурсе за овај курс у нашој Microsoft Learn колекцији](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) -* Пословне случајеве коришћења **AI у бизнису**. Размотрите похађање учећег пута [Увод у AI за пословне кориснике](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) на Microsoft Learn, или [AI Бизнис школу](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), развијену у сарадњи са [INSEAD](https://www.insead.edu/). -* **Класично машинско учење**, које је добро описано у нашем [Наставном програму за машинско учење за почетнике](http://github.com/Microsoft/ML-for-Beginners). -* Практичне AI примене изграђене коришћењем **[Cognitive Services](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**. За ово препоручујемо да почнете са Microsoft Learn модулима за [вид](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [обраду природног језика](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[генеративни AI са Azure OpenAI сервисом](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** и друге. -* Специфичне ML **Cloud Frameworks**, као што су [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), или [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). Размотрите коришћење учећих путања [Build and operate machine learning solutions with Azure Machine Learning](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) и [Build and Operate Machine Learning Solutions with Azure Databricks](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum). -* **Разговорни AI** и **Чет ботове**. Постоји посебан учећи пут [Create conversational AI solutions](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum), и можете се такође обратити [овом блогу](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) за детаљније информације. -* **Дубока математика** иза дубоког учења. За ово препоручујемо књигу [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) аутора Иана Гудфелоу, Јошуа Бенгија и Аарона Курвила, која је такође доступна онлајн на [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/). +* Пословне случајеве коришћења **AI у пословању**. Размотрите узимање [Увода у AI за пословне кориснике](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) на Microsoft Learn, или [AI Пословне школе](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), развијене у сарадњи са [INSEAD](https://www.insead.edu/). +* **Класично машинско учење**, које је добро описано у нашем [Плану учења Машинско учење за почетнике](http://github.com/Microsoft/ML-for-Beginners). +* Практичне AI апликације изграђене коришћењем **[Когнитивних услуга](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**. За ово препоручујемо да почнете са Microsoft Learn модулима за [видио](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [природну обраду језика](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[Генеративни AI са Azure OpenAI сервисом](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** и друге. +* Специфичне ML **Cloud фрејмворке**, као што су [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), или [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). Размотрите коришћење [Изградње и управљања машинским учењем са Azure Machine Learning](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) и [Изградње и управљања машинским учењем са Azure Databricks](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum) планова учења. +* **Разговорни AI** и **Чет ботове**. Постоји посебан [Креирај разговорна AI решења](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum) план учења, а можете погледати и [овaj блог пост](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) за више детаља. +* **Дубока математика** иза дубоког учења. За ово препоручујемо [Дубоко учење](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) аутора Иана Гудфелоу, Јошуа Бенгио и Аарона Коурвила, која је доступна и онлајн на [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/). -За благи увод у теме _AI у облаку_ можете размотрити похађање учећег пута [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). +За благи увод у теме _AI у облаку_ можете размотрити [Почните са вештачком интелигенцијом на Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum) план учења. # Садржај -| | Веза ка лекцији | PyTorch/Keras/TensorFlow | Лабораторија | -| :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | -| 0 | [Подешавање курса](./lessons/0-course-setup/setup.md) | [Подесите ваше развојно окружење](./lessons/0-course-setup/how-to-run.md) | | -| I | [**Увод у AI**](./lessons/1-Intro/README.md) | | | -| 01 | [Увод и историја AI](./lessons/1-Intro/README.md) | - | - | -| II | **Симболички AI** | -| 02 | [Представљање знања и експертски системи](./lessons/2-Symbolic/README.md) | [Експертски системи](./lessons/2-Symbolic/Animals.ipynb) / [Онтологија](./lessons/2-Symbolic/FamilyOntology.ipynb) /[Граф концепата](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | -| III | [**Увод у неуронске мреже**](./lessons/3-NeuralNetworks/README.md) ||| +| | Лекција | PyTorch/Keras/TensorFlow | Лабораторијски задатак | +| :-: | :--------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | -------------------------------------------------------- | +| 0 | [Подешавање курса](./lessons/0-course-setup/setup.md) | [Подеси свој развојни амбијент](./lessons/0-course-setup/how-to-run.md) | | +| I | [**Увод у AI**](./lessons/1-Intro/README.md) | | | +| 01 | [Увод и историја AI](./lessons/1-Intro/README.md) | - | - | +| II | **Симболичка AI** | +| 02 | [Представљање знања и експертски системи](./lessons/2-Symbolic/README.md) | [Експертски системи](./lessons/2-Symbolic/Animals.ipynb) / [Онтологија](./lessons/2-Symbolic/FamilyOntology.ipynb) /[Граф концепата](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | +| III | [**Увод у нервне мреже**](./lessons/3-NeuralNetworks/README.md) | | | | 03 | [Перцептрон](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [Бележница](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [Лаб](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | -| 04 | [Вишеслојни перцептрон и креирање сопственог оквира](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [Бележница](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [Лаб](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | -| 05 | [Увод у оквире (PyTorch/TensorFlow) и пренамеравање](./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) | [Лаб](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | -| IV | [**Рачунарски вид**](./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)| [Истражите рачунарски вид на Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | -| 06 | [Увод у рачунарски вид. OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [Бележница](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [Лаб](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | -| 07 | [Конволуционе неуронске мреже](./lessons/4-ComputerVision/07-ConvNets/README.md) & [CNN архетиктуре](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [Лаб](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | -| 08 | [Унапред обучене мреже и трансфер учење](./lessons/4-ComputerVision/08-TransferLearning/README.md) и [Трикси тренинга](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Лаб](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | -| 09 | [Аутокодери и 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 | [Генеративне противничке мреже и пренос уметничког стила](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | +| 04 | [Вишеслојни перцептрон и креирање нашег оквира](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [Бележница](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [Лаб](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | +| 05 | [Увод у оквире (PyTorch/TensorFlow) и пренаглашавање](./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) | [Лаб](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | +| IV | [**Компјутерски вид**](./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)| [Истражи Компјутерски вид на Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | +| 06 | [Увод у компјутерски вид. OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [Бележница](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [Лаб](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | +| 07 | [Конволуционе неуронске мреже](./lessons/4-ComputerVision/07-ConvNets/README.md) & [CNN архитектуре](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [Лаб](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | +| 08 | [Претрениране мреже и преносно учење](./lessons/4-ComputerVision/08-TransferLearning/README.md) и [Тренинг трикови](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Лаб](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | +| 09 | [Аутоенкодери и VAE-ови](./lessons/4-ComputerVision/09-Autoencoders/README.md) | [PyTorch](./lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb) | | +| 10 | [Генеративне принципе мреже и пренос уметничког стила](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | | 11 | [Детекција објеката](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [Лаб](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | | 12 | [Семантичка сегментација. 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 | [**Обрада природног језика**](./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) | [Истражите обраду природног језика на Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| -| 13 | [Презентација текста. 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 | [Семантичке речне уградње. Word2Vec и 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 | [Језичко моделирање. Тренирање сопствених уградњи](./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) | [Лаб](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | +| V | [**Обрада природног језика**](./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) | [Истражи Обраду природног језика на Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| +| 13 | [Представљање текста. 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 | [Семантички уградљиви вектори речи. Word2Vec и 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 | [Моделирање језика. Тренирање сопствених уградњи](./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) | [Лаб](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | | 16 | [Рекурентне неуронске мреже](./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 | [Генеративне рекурентне мреже](./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) | [Лаб](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | | 18 | [Трансформери. 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 | [Препознавање именованих ентитета](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [Лаб](./lessons/5-NLP/19-NER/lab/README.md) | -| 20 | [Велики језички модели, програмирање упита и задатци са мало примера](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | -| VI | **Друге AI технике** || | +| 20 | [Велики језички модели, програмирање упита и Few-Shot задаци](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | +| VI | **Остале AI технике** || | | 21 | [Генетски алгоритми](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [Бележница](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | -| 22 | [Дубоко учење појачањем](./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) | [Лаб](./lessons/6-Other/22-DeepRL/lab/README.md) | -| 23 | [Системи са више агената](./lessons/6-Other/23-MultiagentSystems/README.md) | | | -| VII | **Етика у вези са AI** | | | -| 24 | [Етика везана за AI и одговоран AI](./lessons/7-Ethics/README.md) | [Microsoft Learn: Принципи одговорног AI](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | +| 22 | [Дубоко учење појачања](./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) | [Лаб](./lessons/6-Other/22-DeepRL/lab/README.md) | +| 23 | [Вишеразински системи](./lessons/6-Other/23-MultiagentSystems/README.md) | | | +| VII | **AI етика** | | | +| 24 | [Етика AI и одговорни AI](./lessons/7-Ethics/README.md) | [Microsoft Learn: Начела одговорног AI-а](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | | IX | **Додатно** | | | | 25 | [Вишемодалне мреже, CLIP и VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [Бележница](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | -## Свака лекција садржи +## Ће урок садржи * Материјал за претходно читање -* Извршиве Јупитер бележнице, које су често специфичне за оквир (**PyTorch** или **TensorFlow**). Извршива бележница такође садржи велики број теоријских материјала, па да бисте разумели тему потребно је да прођете кроз најмање једну верзију бележнице (или PyTorch или TensorFlow). -* **Лабораторије** доступне за неке теме, које вам пружају могућност да примените научени материјал на конкретан проблем. -* Неки одељци садрже линкове ка [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) модулима који обрађују сродне теме. +* Извршне Jupyter бележнице, које су често специфичне за оквир (**PyTorch** или **TensorFlow**). Извршна бележница такође садржи много теоријског материјала, па да бисте разумели тему потребно је да прођете кроз бар једну верзију бележнице (било PyTorch или TensorFlow). +* **Лабораторије** доступне за неке теме, које вам омогућавају да пробате применити научени материјал на конкретан проблем. +* Неке секције садрже линкове ка [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) модулима који покривају сродне теме. -## Започињање +## Почетак -### 🎯 Нови у AI? Почните овде! +### 🎯 Нов у AI? Почни овде! -Ако сте потпуно нови у AI и желите брзе, практичне примере, погледајте наше [**Примере прилагођене почетницима**](./examples/README.md)! Ово укључује: +Ако си потпуно нов у AI и желиш брзе, практичне примере, погледај наше [**Пријатељске примере за почетнике**](./examples/README.md)! Ово укључује: -- 🌟 **Здраво AI свет** - Ваш први AI програм (препознавање образаца) -- 🧠 **Једноставна неуронска мрежа** - Направите неуронску мрежу од почетка -- 🖼️ **Класификатор слика** - Класификујте слике са детаљним коментарима -- 💬 **Сентимент текста** - Анализирајте позитиван/негативан текст +- 🌟 **Здраво AI света** - Твој први AI програм (препознавање образаца) +- 🧠 **Једноставна неуронска мрежа** - Направи неуронску мрежу од нуле +- 🖼️ **Класификатор слика** - Класификуј слике са детаљним коментарима +- 💬 **Сентимент текста** - Анализа позитивног/негативног текста -Ови примери су дизајнирани да вам помогну да разумете AI концепте пре него што зароните у цео курс. +Ови примери су осмишљени да вам помогну да разумете концепте вештачке интелигенције пре него што зароните у цео курикулум. -### 📚 Постављање целог курса +### 📚 Подешавање пуног курикулума -- Креирали смо [постављање лекције](./lessons/0-course-setup/setup.md) да вам помогнемо са подешавањем развојног окружења. - За предаваче, такође смо креирали [постављање наставног плана](./lessons/0-course-setup/for-teachers.md)! -- Како да [пустите код у VSCode или Codepace](./lessons/0-course-setup/how-to-run.md) +- Направили смо [лекцију за подешавање](./lessons/0-course-setup/setup.md) да вам помогнемо са подешавањем развојног окружења. - За наставнике, направили смо и [лекцију за подешавање курикулума](./lessons/0-course-setup/for-teachers.md)! +- Како да [покренете код у VSCode или Codespace](./lessons/0-course-setup/how-to-run.md) Пратите ове кораке: -Форк-ујте Репозиторијум: Кликните на дугме "Fork" у горњем десном углу ове странице. +Направите форк репозиторијума: Кликните на дугме „Fork“ у горњем десном углу ове странице. -Клонирајте Репозиторијум: `git clone https://github.com/microsoft/AI-For-Beginners.git` +Клонирајте репозиторијум: `git clone https://github.com/microsoft/AI-For-Beginners.git` -Не заборавите да ставите звездицу (🌟) на овај репозиторијум да бисте га касније лакше пронашли. +Не заборавите да ставите звездицу (🌟) овом репозиторијуму да бисте га касније лакше пронашли. ## Упознајте друге учеснике -Придружите се нашем [службеном AI Discord серверу](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) да бисте упознали и умрежили се са другим учесницима курса и добили подршку. +Придружите се нашем [званичном AI Discord серверу](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) да упознате и умрежите се са другим учесницима овог курса и добијете подршку. -Ако имате повратну информацију о производу или питања током прављења апликација, посетите наш [Azure AI Foundry Developer Forum](https://aka.ms/foundry/forum) +Ако имате повратне информације о производу или питања током израде посетите наш [Azure AI Foundry Developer Forum](https://aka.ms/foundry/forum) ## Квизови -> **Напомена о квизовима**: Сви квизови се налазе у Quiz-app фолдеру у etc\quiz-app, или [онлајн овде](https://ff-quizzes.netlify.app/) Они су повезани изнутра у лекцијама, квиз апликација се може покренути локално или поставити на Azure; пратите упутства у фолдеру `quiz-app`. Квизови се постепено локализују. +> **Напомена о квизовима**: Сви квизови су садржани у фасцикли Quiz-app у etc\quiz-app, или [онлајн овде](https://ff-quizzes.netlify.app/) Они су повезани изнутра у лекцијама, квиз апликација може да се покрене локално или да се инсталира на Azure; пратите упутства у фасцикли `quiz-app`. Полако се лоцализују. ## Тражи се помоћ -Имате ли предлоге или сте пронашли правописне или код грешке? Отворите issue или креирајте pull request. +Имате ли предлоге или сте пронашли правописне или код грешке? Отворите issue или направите pull request. ## Посебне захвалности -* **✍️ Главни аутор:** [Dmitry Soshnikov](http://soshnikov.com), доктор наука -* **🔥 Редактор:** [Jen Looper](https://twitter.com/jenlooper), доктор наука -* **🎨 Илустратор скичнота:** [Tomomi Imura](https://twitter.com/girlie_mac) -* **✅ Креатор квизова:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) -* **🙏 Језгрови сарадници:** [Evgenii Pishchik](https://github.com/Pe4enIks) +* **✍️ Главни аутор:** [Дмитриј Сошников](http://soshnikov.com), доктор наука +* **🔥 Уредник:** [Џен Лупер](https://twitter.com/jenlooper), доктор наука +* **🎨 Илустратор скичнота:** [Томоми Имура](https://twitter.com/girlie_mac) +* **✅ Креатор квизова:** [Латифа Белло](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) +* **🙏 Основни сарадници:** [Евгениј Пишчик](https://github.com/Pe4enIks) ## Остали курикулуми @@ -190,7 +189,7 @@ CO_OP_TRANSLATOR_METADATA: --- -### Серия генеративног AI +### Generative AI Series [![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) @@ -198,7 +197,7 @@ CO_OP_TRANSLATOR_METADATA: --- -### Основно учење +### Core Learning [![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) @@ -209,25 +208,25 @@ CO_OP_TRANSLATOR_METADATA: --- -### Серия Copilot +### Copilot Series [![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) -## Добијање помоћи +## Како добити помоћ -Ако запнете или имате било каквих питања о креирању AI апликација. Придружите се другим учесницима и искусним програмерима у дискусијама о MCP-у. То је подржавајућа заједница где су питања добродошла и знање се слободно дели. +Ако сте заглављени или имате било каквих питања о изради AI апликација. Придружите се осталим ученицима и искусним програмерима у дискусијама о MCP. То је подржавајућа заједница у којој су питања добродошла и знање се слободно дели. [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -Ако имате повратне информације о производу или грешке током прављења посетите: +Ако имате повратне информације о производу или грешке током израде посетите: [![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) --- -**Одрицање од одговорности**: -Овај документ је преведен коришћењем услуге за машински превод [Co-op Translator](https://github.com/Azure/co-op-translator). Иако се трудимо да превод буде тачан, имајте у виду да аутоматизовани преводи могу садржати грешке или нетачности. Оригинални документ на његовом матерњем језику треба сматрати ауторитетним извором. За критичне информације препоручује се стручни људски превод. Нисмо одговорни за било какве неспоразуме или неправилна тумачења настала употребом овог превода. +**Одрицање одговорности**: +Овај документ је преведен помоћу AI услуге за превођење [Co-op Translator](https://github.com/Azure/co-op-translator). Иако тежимо прецизности, молимо да имате у виду да аутоматски преводи могу садржати грешке или нетачности. Оригинални документ на његовом изворном језику треба сматрати ауторитетним извором. За критичне информације препоручује се професионални људски превод. Нисмо одговорни за било какве неспоразуме или погрешна тумачења која произилазе из коришћења овог превода. \ No newline at end of file diff --git a/translations/sr/lessons/0-course-setup/how-to-run.md b/translations/sr/lessons/0-course-setup/how-to-run.md index a766b5a4..a1171018 100644 --- a/translations/sr/lessons/0-course-setup/how-to-run.md +++ b/translations/sr/lessons/0-course-setup/how-to-run.md @@ -1,21 +1,21 @@ # Како покренути код -Овај курс садржи много извршивих примера и лабораторија које ћете желети да покренете. Да бисте то урадили, потребно је да имате могућност извршавања Python кода у Jupyter бележницама које су део овог курса. Постоји неколико опција за покретање кода: +Овај програм садржи много извршних примера и лабораторија које ћете желети да покренете. Да бисте то урадили, потребна вам је могућност извршавања Питхон кода у Јупитер свескама које су део овог програма. Имате неколико опција за покретање кода: -## Покретање локално на вашем рачунару +## Покрени локално на вашем рачунару -Да бисте покренули код локално на вашем рачунару, потребно је да имате инсталирану неку верзију Python-а. Лично препоручујем инсталацију **[miniconda](https://conda.io/en/latest/miniconda.html)** - то је прилично лагана инсталација која подржава `conda` менаџер пакета за различита Python **виртуелна окружења**. +Да бисте покренули код локално на вашем рачунару, потребна је инсталација Питхона. Један од предлога је да инсталирате **[miniconda](https://conda.io/en/latest/miniconda.html)** – то је прилично лагана инсталација која подржава `conda` менаџер пакета за различита Питхон **виртуелна окружења**. -Након што инсталирате miniconda, потребно је да клонирате репозиторијум и креирате виртуелно окружење које ће се користити за овај курс: +Након што инсталирате miniconda, клонирајте репозиторијум и креирајте виртуелно окружење које ће се користити за овај курс: ```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 ``` -### Коришћење Visual Studio Code-а са Python екстензијом +### Коришћење Visual Studio Code са Python продужетком -Вероватно најбољи начин за коришћење курса је да га отворите у [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) са [Python екстензијом](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste). +Овај програм је најбоље користити када га отворите у [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) са [Python продужетком](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste). -> **Напомена**: Када клонирате и отворите директоријум у VS Code-у, аутоматски ће вам бити предложено да инсталирате Python екстензије. Такође ћете морати да инсталирате miniconda као што је горе описано. +> **Напомена**: Када клонирате и отворите фасциклу у VS Code, он ће вам аутоматски предложити да инсталирате Python продужетке. Такође ћете морати да инсталирате miniconda као што је описано изнад. -> **Напомена**: Ако вам VS Code предложи да поново отворите репозиторијум у контејнеру, потребно је да одбијете ову опцију како бисте користили локалну Python инсталацију. +> **Напомена**: Ако вам VS Code предложи да поново отворите репозиторијум у контејнеру, требало би да одбијете то да бисте користили локалну Python инсталацију. ### Коришћење Jupyter-а у прегледачу -Такође можете користити Jupyter окружење директно из прегледача на вашем рачунару. У ствари, и класични Jupyter и Jupyter Hub пружају прилично згодно окружење за развој са аутоматским довршавањем, истицањем кода итд. +Такође можете користити Jupyter окружење у прегледачу на свом рачунару. И класични Jupyter и JupyterHub пружају практично развојно окружење са ауто-комплетирањем, истицањем кода итд. -Да бисте покренули Jupyter локално, идите у директоријум курса и извршите: +За покретање Jupyter-а локално, идите у фасциклу курса и извршите: ```bash jupyter notebook @@ -45,32 +45,36 @@ jupyter notebook ```bash jupyterhub ``` -Затим можете отићи до било које `.ipynb` датотеке, отворити је и почети са радом. +Затим можете навигацијом отворити било који од `.ipynb` фајлова и почети да радите. ### Покретање у контејнеру -Једна алтернатива Python инсталацији је покретање кода у контејнеру. Пошто наш репозиторијум садржи посебан `.devcontainer` фолдер који даје упутства како да се изгради контејнер за овај репозиторијум, VS Code ће вам понудити да поново отворите код у контејнеру. Ово ће захтевати инсталацију Docker-а и биће сложеније, па ово препоручујемо искуснијим корисницима. +Једна алтернатива инсталацији Python-а је покретање кода у контејнеру. Пошто наш репозиторијум садржи посебну `.devcontainer` фасциклу која указује како се гради контејнер за овај репо, VS Code пружа могућност да поново отворите код у контејнеру. Ово захтева инсталацију Docker-а и је сложеније, па то препоручујемо корисницима са више искуства. ## Покретање у облаку -Ако не желите да инсталирате Python локално и имате приступ неким ресурсима у облаку, добра алтернатива је покретање кода у облаку. Постоји неколико начина да то урадите: +Ако не желите локалну инсталацију Python-а, а имате приступ неким облачним ресурсима – добра алтернатива је покретање кода у облаку. Постоји неколико начина да то урадите: -* Коришћење **[GitHub Codespaces](https://github.com/features/codespaces)**, што је виртуелно окружење креирано за вас на GitHub-у, доступно преко VS Code интерфејса у прегледачу. Ако имате приступ Codespaces-у, можете само кликнути на дугме **Code** у репозиторијуму, покренути Codespace и почети са радом у кратком року. -* Коришћење **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**. [Binder](https://mybinder.org) је бесплатан рачунарски ресурс у облаку који омогућава људима да тестирају код са GitHub-а. На почетној страници постоји дугме за отварање репозиторијума у Binder-у - ово би вас требало брзо одвести на Binder сајт, који ће изградити основни контејнер и покренути Jupyter веб интерфејс за вас без проблема. +* Коришћење **[GitHub Codespaces](https://github.com/features/codespaces)**, који је виртуелно окружење направљено за вас на GitHub-у, доступно преко VS Code интерфејса у прегледачу. Ако имате приступ Codespaces, можете само кликнути на дугме **Code** у репозиторијуму, покренути codespace и почети одмах. +* Коришћење **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**. [Binder](https://mybinder.org) нуди бесплатне рачунарске ресурсе у облаку, тако да људи попут вас могу тестирати неки код са GitHub-а. На почетној страници постоји дугме за отварање репозиторијума у Binder-у – ово вас брзо води на сајт Binder-а, који ће изградити основни контејнер и покренути вам Jupyter веб интерфејс глатко. -> **Напомена**: Да би се спречила злоупотреба, Binder има блокиран приступ неким веб ресурсима. Ово може спречити рад неког кода који преузима моделе и/или скупове података са јавног интернета. Можда ћете морати да пронађете нека алтернативна решења. Такође, рачунарски ресурси које Binder пружа су прилично основни, па ће обука бити спора, посебно у каснијим, сложенијим лекцијама. +> **Напомена**: Да би се спречила злоупотреба, Binder има приступ блокиран за неке веб ресурсе. Ово може спречити рад дела кода који преузима моделе и/или скупове података са јавног интернета. Можда ће вам бити потребна нека заобилазна решења. Такође, ресурси за рачунање које пружа Binder су прилично основни, па ће тренирање бити споро, нарочито у каснијим, сложенијим лекцијама. -## Покретање у облаку са GPU подршком +## Покретање у облаку са GPU -Неке од каснијих лекција у овом курсу би значајно користиле GPU подршку, јер би иначе обука била веома спора. Постоји неколико опција које можете размотрити, посебно ако имате приступ облаку преко [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) или преко ваше институције: +Неколико каснијих лекција у овом програму веома би имало користи од подршке за GPU. Тренирање модела, на пример, може бити веома споро без тога. Постоји неколико опција које можете пратити, нарочито ако имате приступ облаку преко [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) или преко ваше институције: -* Креирајте [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) и повежите се са њом преко Jupyter-а. Затим можете клонирати репозиторијум директно на машину и почети са учењем. NC-серије виртуелних машина имају GPU подршку. +* Креирајте [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) и повежите се на њу преко Jupyter-а. Потом можете клонирати репо право на ту машину и почети са учењем. NC-series виртуелне машине имају подршку за GPU. -> **Напомена**: Неке претплате, укључујући Azure for Students, не пружају GPU подршку одмах. Можда ћете морати да затражите додатне GPU језгре преко техничке подршке. +> **Напомена**: Неки претплатни планови, укључујући Azure for Students, не пружају GPU подршку одмах. Можда ћете морати да затражите додатне GPU језгре путем техничке подршке. -* Креирајте [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) и затим користите функцију Notebook тамо. [Овај видео](https://azure-for-academics.github.io/quickstart/azureml-papers/) показује како да клонирате репозиторијум у Azure ML бележницу и почнете са коришћењем. +* Креирајте [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) и затим користите тамо функцију Notebook-а. [Овај видео](https://azure-for-academics.github.io/quickstart/azureml-papers/) показује како да клонирате репозиторијум у Azure ML notebook и почнете да га користите. -Такође можете користити Google Colab, који долази са одређеном бесплатном GPU подршком, и отпремити Jupyter бележнице тамо како бисте их извршавали једну по једну. +Такође можете користити Google Colab, који долази са неком бесплатном подршком за GPU, и у њега отпремити Jupyter свеске да их извршавате један по један. -**Одрицање од одговорности**: -Овај документ је преведен коришћењем услуге за превођење помоћу вештачке интелигенције [Co-op Translator](https://github.com/Azure/co-op-translator). Иако настојимо да обезбедимо тачност, молимо вас да имате у виду да аутоматски преводи могу садржати грешке или нетачности. Оригинални документ на његовом изворном језику треба сматрати меродавним извором. За критичне информације препоручује се професионални превод од стране људи. Не преузимамо одговорност за било каква погрешна тумачења или неспоразуме који могу настати услед коришћења овог превода. \ No newline at end of file +--- + + +**Изјава о одрицању одговорности**: +Овај документ је преведен коришћењем AI преводилачке услуге [Co-op Translator](https://github.com/Azure/co-op-translator). Док се трудимо да превод буде што прецизнији, молимо вас да имате у виду да аутоматизовани преводи могу садржати грешке или нетачности. Оригинални документ на његовом изворном језику треба сматрати ауторитетним извором. За критичне информације препоручује се професионални превод од стране људског стручњака. Нисмо одговорни за било каква неспоразумевања или погрешне тумачења настале коришћењем овог превода. + \ No newline at end of file diff --git a/translations/sr/lessons/2-Symbolic/Animals.ipynb b/translations/sr/lessons/2-Symbolic/Animals.ipynb index dc8df4c9..c287351e 100644 --- a/translations/sr/lessons/2-Symbolic/Animals.ipynb +++ b/translations/sr/lessons/2-Symbolic/Animals.ipynb @@ -10,21 +10,21 @@ "\n", "Пример из [AI for Beginners Curriculum](http://github.com/microsoft/ai-for-beginners).\n", "\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", + "У овом примеру ћемо имплементирати 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 неких правила):\n", "\n", - "![](../../../../translated_images/sr/AND-OR-Tree.5592d2c70187f283.webp)\n" + "![](../../../../../../translated_images/sr/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Наш сопствени систем експертске љуске са уназадним закључивањем\n", + "## Наш сопствени експертски систем шкољка са инверзним закључивањем\n", "\n", - "Хајде да дефинишемо једноставан језик за представљање знања заснован на продукционим правилима. Користићемо Python класе као кључне речи за дефинисање правила. У суштини, постојаће три типа класа:\n", + "Хајде да покушамо да дефинишемо једноставан језик за представљање знања заснован на продукционим правилима. Користићемо Python класе као кључне речи за дефинисање правила. У суштини, биће 3 типа класа:\n", "* `Ask` представља питање које треба поставити кориснику. Садржи скуп могућих одговора.\n", - "* `If` представља правило и само је синтаксни шећер за чување садржаја правила.\n", - "* `AND`/`OR` су класе за представљање AND/OR грана стабла. Оне само чувају листу аргумената унутар себе. Да бисмо поједноставили код, сва функционалност је дефинисана у родитељској класи `Content`.\n" + "* `If` представља правило, и то је само синтаксички шећер за чување садржаја правила\n", + "* `AND`/`OR` су класе које представљају AND/OR гране стабла. Оне само чувају листу аргумената у себи. Да бисмо поједноставили код, сва функционалност је дефинисана у родитељској класи `Content`\n" ] }, { @@ -66,7 +66,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "У нашем систему, радна меморија би садржала списак **чињеница** као **парова атрибут-вредност**. База знања може бити дефинисана као један велики речник који повезује акције (нове чињенице које треба унети у радну меморију) са условима, израженим као AND-OR изрази. Такође, неке чињенице могу бити `питане`.\n" + "У нашем систему, радна меморија би садржала листу **чињеница** као **парова атрибут-вредност**. Базу знања можемо дефинисати као један велики речник који мапира акције (нове чињенице које треба убацити у радну меморију) на услове, изражене као AND-OR изрази. Такође, неке чињенице се могу `Питати`.\n" ] }, { @@ -99,13 +99,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Да бисмо извршили уназадну инференцију, дефинисаћемо класу `Knowledgebase`. Она ће садржати:\n", - "* Радну `меморију` - речник који мапира атрибуте на вредности\n", - "* `Правила` базе знања у формату као што је горе дефинисано\n", + "Да бисмо извршили обрнути закључак, дефинисаћемо класу `Knowledgebase`. Она ће садржати:\n", + "* Радну `memory` - речник који мапира атрибуте на вредности\n", + "* Правила базе знања `rules` у формату као што је горе дефинисано\n", "\n", - "Два главна метода су:\n", - "* `get` за добијање вредности атрибута, уз извршавање инференције ако је потребно. На пример, `get('color')` би добио вредност за слот боје (питаће ако је потребно и сачувати вредност за каснију употребу у радној меморији). Ако питамо `get('color:blue')`, питаће за боју, а затим вратити вредност `y`/`n` у зависности од боје.\n", - "* `eval` извршава стварну инференцију, односно прелази кроз AND/OR стабло, евалуира под-циљеве, итд.\n" + "Две главне методе су:\n", + "* `get` за добијање вредности атрибута, извођење инференције ако је потребно. На пример, `get('color')` би добило вредност за поље боје (питаће ако је потребно, и сачуваће ту вредност за каснију употребу у радној меморији). Ако питамо `get('color:blue')`, питаће за боју, а затим вратити вредност `y`/`n` у зависности од боје.\n", + "* `eval` извршава саму инференцију, тј. прелази кроз AND/OR стабло, процењује под-циљеве, итд.\n" ] }, { @@ -172,7 +172,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Сада хајде да дефинишемо нашу базу знања о животињама и обавимо консултацију. Имајте на уму да ће вас овај позив постављати питања. Можете одговорити тако што ћете укуцати `y`/`n` за питања са одговорима да-не, или тако што ћете навести број (0..N) за питања са дужим одговорима са више опција.\n" + "Хајде сада да дефинишемо нашу базу знања о животињама и обавимо консултацију. Имајте у виду да ће вам овај позив постављати питања. Можете одговарати тако што ћете уносити `y`/`n` за питања на која се одговара да/не, или тако што ћете назначити број (0..N) за питања са дужим избором одговора.\n" ] }, { @@ -229,11 +229,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Коришћење PyKnow за напредно закључивање\n", + "## Коришћење Experta за унапредно извођење\n", "\n", - "У следећем примеру, покушаћемо да имплементирамо напредно закључивање користећи једну од библиотека за представљање знања, [PyKnow](https://github.com/buguroo/pyknow/). **PyKnow** је библиотека за креирање система за напредно закључивање у Python-у, која је дизајнирана да буде слична класичном старом систему [CLIPS](http://www.clipsrules.net/index.html).\n", + "У следећем примеру ћемо покушати да имплементирамо унапредно извођење користећи једну од библиотека за представљање знања, [Experta](https://github.com/nilp0inter/experta). **Experta** је библиотека за креирање система унапредног извођења у Питону, која је дизајнирана да буде слична класичном старом систему [CLIPS](http://www.clipsrules.net/index.html).\n", "\n", - "Могли бисмо и сами да имплементирамо напредно повезивање без већих проблема, али наивне имплементације обично нису веома ефикасне. За ефикасније подударање правила користи се посебан алгоритам [Rete](https://en.wikipedia.org/wiki/Rete_algorithm).\n" + "Такође бисмо могли да имплементирамо унапредно повезивање сами без већих проблема, али наивне имплементације обично нису веома ефикасне. За ефикасније поклапање правила користи се посебан алгоритам [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": [ - "Наш систем ћемо дефинисати као класу која наследи `KnowledgeEngine`. Свака правила су дефинисана посебном функцијом са `@Rule` анотацијом, која одређује када правило треба да се активира. Унутар правила можемо додати нове чињенице користећи функцију `declare`, а додавање тих чињеница ће довести до тога да нека друга правила буду позвана од стране мотора за напредно закључивање.\n" + "Систем ћемо дефинисати као класу која наслеђује `KnowledgeEngine`. Свака правила је дефинисана као посебна функција са анотацијом `@Rule`, која одређује када правило треба да се активира. Унутар правила можемо додати нове чињенице користећи функцију `declare`, а додавање тих чињеница ће резултирати позивом још неких правила од стране механизма за унапредну инференцу.\n" ] }, { @@ -378,7 +377,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Једном када дефинишемо базу знања, попуњавамо радну меморију почетним чињеницама, а затим позивамо метод `run()` да изврши закључивање. Као резултат можете видети да су нове изведене чињенице додате у радну меморију, укључујући и коначну чињеницу о животињи (ако смо исправно поставили све почетне чињенице).\n" + "Једном када дефинишемо базу знања, попуњавамо нашу радну меморију са неким почетним чињеницама, а затим позивамо `run()` методу да изврши закључивање. Као резултат, можете видети да се нове изведене чињенице додају у радну меморију, укључујући коначну чињеницу о животињи (ако смо правилно поставили све почетне чињенице).\n" ] }, { @@ -440,7 +439,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n---\n\n**Одрицање од одговорности**: \nОвај документ је преведен коришћењем услуге за превођење помоћу вештачке интелигенције [Co-op Translator](https://github.com/Azure/co-op-translator). Иако се трудимо да обезбедимо тачност, молимо вас да имате у виду да аутоматски преводи могу садржати грешке или нетачности. Оригинални документ на његовом изворном језику треба сматрати ауторитативним извором. За критичне информације препоручује се професионални превод од стране људи. Не преузимамо одговорност за било каква погрешна тумачења или неспоразуме који могу настати услед коришћења овог превода.\n" + "---\n\n\n**Одрицање од одговорности**: \nОвај документ је преведен помоћу АИ услуге за превођење [Co-op Translator](https://github.com/Azure/co-op-translator). Иако дајемо све од себе да буде прецизно, имајте у виду да аутоматизовани преводи могу садржати грешке или нетачности. Оригинални документ на његовом изворном језику треба сматрати ауторитетом. За критичне информације препоручује се професионални људски превод. Није нам могуће преузети одговорност за било каква неразумевања или погрешне интерпретације настале употребом овог превода.\n\n" ] } ], @@ -467,8 +466,8 @@ "version": "3.11.2" }, "coopTranslator": { - "original_hash": "ab2bd97b0453415b89a469284609a8ce", - "translation_date": "2025-08-29T23:59:15+00:00", + "original_hash": "8ef43db4b9182239fd150a76bd494fdb", + "translation_date": "2026-01-16T05:22:28+00:00", "source_file": "lessons/2-Symbolic/Animals.ipynb", "language_code": "sr" } diff --git a/translations/sr/lessons/2-Symbolic/README.md b/translations/sr/lessons/2-Symbolic/README.md index e2e444fd..c02527fd 100644 --- a/translations/sr/lessons/2-Symbolic/README.md +++ b/translations/sr/lessons/2-Symbolic/README.md @@ -1,116 +1,116 @@ -# Представљање знања и експертски системи +# Репрезентација Знања и Експертски Системи -![Резиме садржаја о симболичкој вештачкој интелигенцији](../../../../translated_images/sr/ai-symbolic.715a30cb610411a6.webp) +![Сажетак симболичке вештачке интелигенције](../../../../../../translated_images/sr/ai-symbolic.715a30cb610411a6.webp) -> Скетч од [Томоми Имура](https://twitter.com/girlie_mac) +> Скетчнот од [Tomomi Imura](https://twitter.com/girlie_mac) -Потрага за вештачком интелигенцијом заснива се на трагању за знањем, како би се свет разумео на начин сличан људском. Али како то постићи? +Потрага за вештачком интелигенцијом заснива се на претраживању знања, да се свет разуме слично као што људи разумеју. Али како то можете урадити? ## [Квиз пре предавања](https://ff-quizzes.netlify.app/en/ai/quiz/3) -У раним данима развоја вештачке интелигенције, популаран је био приступ "одозго надоле" у креирању интелигентних система (о чему је било речи у претходном часу). Идеја је била да се знање извуче од људи у неки машински читљив облик, а затим искористи за аутоматско решавање проблема. Овај приступ се заснивао на две велике идеје: +У раним данима вештачке интелигенције, приступ од горе ка доле за креирање интелигентних система (који је разматран у претходној лекцији) био је популаран. Идеја је била да се знање извуће од људи у неки облик читљив машини, а затим да се користи за аутоматско решавање проблема. Овај приступ заснован је на две велике идеје: -* Представљање знања +* Репрезентација знања * Закључивање -## Представљање знања +## Репрезентација знања -Један од важних концепата у симболичкој вештачкој интелигенцији је **знање**. Важно је разликовати знање од *информација* или *података*. На пример, може се рећи да књиге садрже знање, јер се њиховим проучавањем може постати стручњак. Међутим, оно што књиге заправо садрже назива се *подаци*, а читањем књига и интегрисањем тих података у наш модел света, претварамо те податке у знање. +Један од важних појмова у Симболичкој вештачкој интелигенцији је **знање**. Важно је направити разлику између знања и *информације* или *подака*. На пример, може се рећи да књиге садрже знање, јер их можете проучавати и постати експерт. Међутим, оно што књиге садрже заправо се зове *подаци*, и читањем књига и интеграцијом ових података у наш модел света претварамо ове податке у знање. -> ✅ **Знање** је нешто што се налази у нашој глави и представља наше разумевање света. Оно се стиче активним процесом **учења**, који интегрише делове информација које добијамо у наш активни модел света. +> ✅ **Знање** је нешто што се налази у нашој глави и представља наше разумевање света. Добија се активним процесом **учења**, који интегрише делове информација које добијамо у наш активни модел света. -Најчешће не дефинишемо строго знање, већ га повезујемо са другим сродним концептима користећи [DIKW пирамиду](https://en.wikipedia.org/wiki/DIKW_pyramid). Она садржи следеће концепте: +Најчешће не дефинишемо знање строго, али га повезујемо са другим сродним појмовима коришћењем [DIKW пирамиде](https://en.wikipedia.org/wiki/DIKW_pyramid). Она садржи следеће појмове: -* **Подаци** су нешто што је представљено на физичким медијима, као што су писани текст или изговорене речи. Подаци постоје независно од људи и могу се преносити између њих. -* **Информације** су начин на који тумачимо податке у својој глави. На пример, када чујемо реч *рачунар*, имамо неко разумевање шта је то. -* **Знање** је информација интегрисана у наш модел света. На пример, када научимо шта је рачунар, почињемо да имамо идеје о томе како функционише, колико кошта и за шта се може користити. Ова мрежа међусобно повезаних концепата чини наше знање. -* **Мудрост** је још један ниво нашег разумевања света и представља *мета-знање*, односно неку представу о томе како и када треба користити знање. +* **Подаци** су нешто што је представљено у физичком медијуму, као што је писани текст или изговорене речи. Подаци постоје независно од људи и могу бити преношени између људи. +* **Информација** је како тумачимо податке у својој глави. На пример, када чујемо реч *рачунар*, имамо неко разумевање шта је то. +* **Знање** је информација која је интегрисана у наш модел света. На пример, када сазнамо шта је рачунар, почињемо имати неке идеје о томе како ради, колико кошта и за шта се може користити. Та мрежа међусобно повезаних појмова чини наше знање. +* **Мудрост** је још један ниво нашег разумевања света, и представља *метазнање*, нпр. неку представу о томе како и када знање треба користити. - + -*Слика [са Википедије](https://commons.wikimedia.org/w/index.php?curid=37705247), Аутор Longlivetheux - Лично дело, CC BY-SA 4.0* +*Слика [са Википедије](https://commons.wikimedia.org/w/index.php?curid=37705247), Аутор Longlivetheux - властито дело, CC BY-SA 4.0* -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: +Дакле, проблем **репрезентације знања** је како наћи неки ефикасан начин да се знање представи у рачунару у облику података, да би могло да се користи аутоматски. Ово се може посматрати као спектар: -![Спектар представљања знања](../../../../translated_images/sr/knowledge-spectrum.b60df631852c0217.webp) +![Спектар репрезентације знања](../../../../../../translated_images/sr/knowledge-spectrum.b60df631852c0217.webp) -> Слика од [Дмитрија Сошњикова](http://soshnikov.com) +> Слика од [Dmitry Soshnikov](http://soshnikov.com) -* Са леве стране налазе се веома једноставни типови представљања знања који се могу ефикасно користити у рачунарима. Најједноставнији је алгоритамски, када је знање представљено рачунарским програмом. Међутим, ово није најбољи начин за представљање знања, јер није флексибилан. Знање у нашој глави често није алгоритамско. -* Са десне стране налазе се представљања као што је природни текст. То је најмоћније, али се не може користити за аутоматско закључивање. +* Са леве стране се налазе веома једноставни типови репрезентације знања који се могу ефикасно користити у рачунарима. Најједноставнији је алгоритамски, када је знање представљено програмом. Међутим, ово није најбољи начин за представљање знања јер није флексибилан. Знање у нашој глави често није алгоритамско. +* Са десне стране се налазе репрезентације као што је природни текст. Он је најмоћнији, али се не може користити за аутоматско закључивање. -> ✅ Размислите на тренутак како представљате знање у својој глави и претварате га у белешке. Да ли постоји одређени формат који вам помаже у памћењу? +> ✅ Размислите минуту о томе како представљате знање у својој глави и претварате га у белешке. Постоји ли посебан формат који вам добро помаже у памћењу? -## Класификација рачунарских представљања знања +## Класификација рачунарских репрезентација знања -Различите методе представљања знања у рачунарима можемо класификовати у следеће категорије: +Различите рачунарске методе репрезентације знања можемо класификовати у следеће категорије: -* **Мрежна представљања** заснивају се на чињеници да у својој глави имамо мрежу међусобно повезаних концепата. Можемо покушати да исте мреже репродукујемо као граф унутар рачунара - такозвану **семантичку мрежу**. +* **Мрежне репрезентације** заснивају се на чињеници да у нашој глави имамо мрежу међусобно повезаних појмова. Можемо покушати да исте мреже урадимо као граф у рачунару – такозвана **семантичка мрежа**. -1. **Триплети објекат-атрибут-вредност** или **парови атрибут-вредност**. Пошто се граф може представити у рачунару као листа чворова и ивица, семантичка мрежа се може представити листом триплета који садрже објекте, атрибуте и вредности. На пример, можемо изградити следеће триплете о програмским језицима: +1. **Триплети Објекат-Атрибут-Вредност** или **парови атрибут-вредност**. Пошто се граф може представити у рачунару као листа чворова и ивица, можемо представити семантичку мрежу листом триплета која садрже објекте, атрибуте и вредности. На пример, градимо следеће триплете о програмским језицима: -Објекат | Атрибут | Вредност --------|---------|--------- -Python | је | Језик без типова -Python | изумео | Гвидо ван Росум -Python | блок-синтакса | увлачење -Језик без типова | нема | дефиниције типова +Објекат | Атрибут | Вредност +-------|-----------|------ +Python | је | Нетипизирани-Језик +Python | измислио | Guido van Rossum +Python | синтакса-блока | увученост +Нетипизирани-Језик | нема | дефиниције типа > ✅ Размислите како се триплети могу користити за представљање других врста знања. -2. **Хијерархијска представљања** наглашавају чињеницу да често у својој глави стварамо хијерархију објеката. На пример, знамо да је канаринац птица и да све птице имају крила. Такође имамо неку представу о томе које је боје канаринац и колика је његова брзина лета. +2. **Хијерархијске репрезентације** наглашавају чињеницу да често креирамо хијерархију објеката у нашој глави. На пример, знамо да је канаринац птица, и свака птица има крила. Такође имамо идеју о боји уобичајеног канаринца и брзини лета. - - **Представљање оквирима** заснива се на представљању сваког објекта или класе објеката као **оквира** који садржи **слотове**. Слотови могу имати подразумеване вредности, ограничења вредности или процедуре које се могу позвати да би се добила вредност слота. Сви оквири формирају хијерархију сличну хијерархији објеката у објектно-оријентисаним програмским језицима. - - **Сценарији** су посебна врста оквира који представљају сложене ситуације које се могу одвијати у времену. + - **Репрезентација у облику фрејмова** заснива се на представљању сваког објекта или класе објеката као **фрејма** који садржи **слотове**. Слотови могу имати подразумеване вредности, ограничења вредности или сачуване процедуре које можемо позвати да добијемо вредност слота. Сви фрејмови формирају хијерархију сличну објектној хијерархији у објектно оријентисаним програмским језицима. + - **Сценарији** су посебна врста фрејмова која представљају сложене ситуације које се могу одвијати током времена. **Python** -Слот | Вредност | Подразумевана вредност | Интервал ------|---------|-------------------------|--------- -Име | Python | | -Је | Језик без типова | | -Карактер променљиве | | CamelCase | -Дужина програма | | | 5-5000 линија -Блок-синтакса | Увлачење | | +Слот | Вредност | Подразумевана вредност | Интервал | +-----|----------|------------------------|----------| +Име | Python | | | +Је-Тип | Нетипизирани-Језик | | | +Величина слова| | CamelCase | | +Дужина програма| | | 5-5000 линија | +Синтакса Блока | Увученост | | | -3. **Процедурална представљања** заснивају се на представљању знања листом акција које се могу извршити када се догоди одређени услов. - - Правила продукције су if-then изјаве које нам омогућавају да доносимо закључке. На пример, доктор може имати правило које каже да **АКО** пацијент има високу температуру **ИЛИ** висок ниво Ц-реактивног протеина у крвној анализи **ОНДА** има упалу. Када наиђемо на један од услова, можемо закључити да постоји упала и затим то користити у даљем закључивању. - - Алгоритми се могу сматрати другом формом процедуралног представљања, иако се скоро никада не користе директно у системима заснованим на знању. +3. **Процедурне репрезентације** заснивају се на представљању знања као листе радњи које се могу извршити када се испуни одређени услов. + - Правила продукције су ако-онда изјаве које нам омогућавају доношење закључака. На пример, доктор може имати правило да **АКО** пацијент има високу температуру **ИЛИ** високе нивое Ц-реактивног протеина у крвном тесту **ОНДА** има упалу. Када наиђемо на један од услова, можемо закључити о упали, и затим то користити у даљем закључивању. + - Алгоритми се могу сматрати другим обликом процедуралне репрезентације, иако се скоро никада не користе директно у системима заснованим на знању. -4. **Логика** је првобитно предложена од стране Аристотела као начин за представљање универзалног људског знања. - - Предикатска логика као математичка теорија је превише богата да би била рачунарски обрадљива, па се обично користи неки њен подскуп, као што су Хорнове клаузе које се користе у Prolog-у. - - Дескриптивна логика је породица логичких система која се користи за представљање и закључивање о хијерархијама објеката у дистрибуираним представљањима знања као што је *семантички веб*. +4. **Логика** је првобитно предложена од стране Аристотела као начин представљања универзалног људског знања. + - Предикатна логика као математичка теорија је прегушћа да би била компјутабилна, па се обично користи неки подскуп, као што су Horn клаузуле коришћене у Prolog-у. + - Дескриптивна логика је породични систем логичких система који се користе за представљање и закључивање о хијерархијама објеката, дистрибуираним репрезентацијама знања као што је *семантички веб*. ## Експертски системи -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. +Један од раних успеха симболичке вештачке интелигенције били су такозвани **експертски системи** – рачунарски системи дизајнирани да делују као експерт у некој ограниченој проблемској области. Они су засновани на **бази знања** коју су извели један или више људских експерата, и садрже **инференцијски мотор** који изводи закључивања на њој. -![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 +![Архитектура човека](../../../../../../translated_images/sr/arch-human.5d4d35f1bba3ab1c.webp) | ![Систем заснован на знању](../../../../../../translated_images/sr/arch-kbs.3ec5c150b09fa8da.webp) +---------------------------------------------|------------------------------------------------ +Поједностављена структура људског неуралног система | Архитектура система заснованог на знању -Експертски системи су изграђени слично људском систему закључивања, који садржи **краткорочно памћење** и **дугорочно памћење**. Слично томе, у системима заснованим на знању разликујемо следеће компоненте: +Експертски системи су изграђени по узору на људски систем резоновања, који садржи **краткорочну меморију** и **дугорочну меморију**. Слично томе, у системима заснованим на знању разликујемо следеће компоненте: -* **Проблемска меморија**: садржи знање о проблему који се тренутно решава, нпр. температуру или крвни притисак пацијента, да ли има упалу или не. Ово знање се такође назива **статичко знање**, јер садржи снимак онога што тренутно знамо о проблему - такозвано *стање проблема*. -* **База знања**: представља дугорочно знање о области проблема. Оно се ручно извлачи од људских стручњака и не мења се од једне консултације до друге. Пошто нам омогућава да се крећемо од једног стања проблема до другог, назива се и **динамичко знање**. -* **Инференцијски механизам**: оркестрира цео процес претраге у простору стања проблема, постављајући питања кориснику када је то потребно. Такође је одговоран за проналажење правих правила која ће се применити у сваком стању. +* **Меморија проблема**: садржи знање о проблему који се тренутно решава, нпр. температура или крвни притисак пацијента, да ли има упалу или не итд. Ово знање назива се и **статичким знањем**, јер садржи снимак онога што тренутно знамо о проблему – такозвани *статус проблема*. +* **База знања**: представља дугорочно знање о проблемској области. Ручно је изведена од људских експерата, и не мења се од консултације до консултације. Пошто нам омогућава навигацију од једног статуса проблема до другог, назива се и **динамичким знањем**. +* **Инференцијски мотор**: координише цео процес претраживања у простору статуса проблема, поставља питања кориснику када је потребно. Такође је одговоран за проналажење правих правила која ће се применити за сваки статус. -Као пример, размотримо следећи експертски систем за одређивање животиње на основу њених физичких карактеристика: +Као пример, размотримо следећи експертски систем за одређивање животиње на основу њених физичких особина: -![AND-OR стабло](../../../../translated_images/sr/AND-OR-Tree.5592d2c70187f283.webp) +![AND-OR стабло](../../../../../../translated_images/sr/AND-OR-Tree.5592d2c70187f283.webp) -> Слика од [Дмитрија Сошњикова](http://soshnikov.com) +> Слика од [Dmitry Soshnikov](http://soshnikov.com) -Овај дијаграм се назива **AND-OR стабло**, и представља графички приказ скупа правила продукције. Цртање стабла је корисно на почетку извлачења знања од стручњака. За представљање знања унутар рачунара погодније је користити правила: +Овај дијаграм се зове **AND-OR стабло**, и графички представља скуп правила продукције. Цртање стабла је корисно на почетку издвајања знања од експерта. За представљање знања у рачунару згодније је користити правила: ``` IF the animal eats meat @@ -120,72 +120,79 @@ OR (animal has sharp teeth ) THEN the animal is a carnivore ``` - -Можете приметити да сваки услов на левој страни правила и акција суштински представљају триплете објекат-атрибут-вредност (OAV). **Радна меморија** садржи скуп OAV триплета који одговарају проблему који се тренутно решава. **Механизам правила** тражи правила чији је услов задовољен и примењује их, додајући нови триплет у радну меморију. -> ✅ Направите своје AND-OR стабло на тему која вас занима! +Можете приметити да је сваки услов на левој страни правила и акција у суштини триплет објекат-атрибут-вредност (ОАВ). **Радна меморија** садржи скуп ОАВ триплета који одговарају тренутно решаваном проблему. **Мотор правила** тражи правила за која је услов испуњен и примењује их, додајући још један триплет у радну меморију. -### Напредно и уназадно закључивање +> ✅ Направите своје AND-OR стабло на тему која вас интересује! -Процес описан изнад назива се **напредно закључивање**. Он почиње са неким почетним подацима о проблему доступним у радној меморији, а затим извршава следећу петљу закључивања: +### Напредно и назадно закључивање -1. Ако је циљни атрибут присутан у радној меморији - зауставите се и дајте резултат -2. Потражите сва правила чији је услов тренутно задовољен - добијте **конфликтни скуп** правила. -3. Извршите **разрешење конфликта** - изаберите једно правило које ће бити извршено у овом кораку. Могу постојати различите стратегије за разрешење конфликта: - - Изаберите прво применљиво правило у бази знања - - Изаберите насумично правило - - Изаберите *специфичније* правило, тј. оно које задовољава највише услова на "левој страни" (LHS) -4. Примените изабрано правило и унесите нови део знања у стање проблема -5. Поновите од корака 1. +Процес описан горе зове се **напредно закључивање**. Започиње неким почетним подацима о проблему које су доступне у радној меморији, а затим изводи следећу петљу резоновања: -Међутим, у неким случајевима можемо желети да почнемо са празним знањем о проблему и постављамо питања која ће нам помоћи да дођемо до закључка. На пример, приликом медицинске дијагнозе, обично не извршавамо све медицинске анализе унапред пре него што почнемо са дијагностиковањем пацијента. Радије желимо да извршимо анализе када је потребно донети одлуку. +1. Ако је циљни атрибут присутан у радној меморији – заустави се и дај резултат +2. Потражи сва правила чији је услов тренутно испуњен – добиј сет правила у конфликту. +3. Изврши **решавање конфликта** – изабери једно правило које ће се извршити у овом кораку. Постоје различите стратегије решавања конфликта: + - Изабери прво правило која важи у бази знања + - Изабери случајно правило + - Изабери *специфичније* правило, тј. оно које испуњава највише услова у "левој страни" (LHS) +4. Примени изабрано правило и убаци нови део знања у статус проблема +5. Понови од корака 1. -Овај процес се може моделирати коришћењем **уназадног закључивања**. Оно је вођено **циљем** - вредношћу атрибута коју желимо да пронађемо: +Међутим, у неким случајевима желимо да започнемо са празним знањем о проблему и постављамо питања која ће нам помоћи да дођемо до закључка. На пример, када радимо медицнску дијагнозу, обично не радимо све медицинске анализе унапред пре почетка дијагнозе пацијента. Радије желимо радити анализе кад треба донети одлуку. -1. Изаберите сва правила која могу дати вредност циља (тј. са циљем на RHS ("десној страни")) - конфликтни скуп -1. Ако не постоје правила за овај атрибут, или постоји правило које каже да треба питати корисника за вредност - питајте, у супротном: -1. Користите стратегију разрешења конфликта да изаберете једно правило које ћемо користити као *хипотезу* - покушаћемо да је докажемо -1. Рекурзивно поновите процес за све атрибуте на LHS правила, покушавајући да их докажете као циљеве -1. Ако процес у било ком тренутку не успе - користите друго правило у кораку 3. +Овај процес може бити моделован коришћењем **назадног закључивања**. Њиме управља **циљ** – вредност атрибута коју желимо пронаћи: -> ✅ У којим ситуацијама је напредно закључивање прикладније? А уназадно закључивање? +1. Изабери сва правила која могу дати вредност циљу (нпр. са циљем на десној страни ("right-hand-side")) – сет у конфликту +1. Ако нема правила за овај атрибут или постоји правило које каже да треба питати корисника – питај, у супротном: +1. Користи стратегију решавања конфликта да изабереш једно правило које ћеш користити као *хипотезу* – покушаћемо да је докажемо +1. Понављај рекурзивно процес за све атрибуте у LHS правила, покушавајући да их докажеш као циљеве +1. Ако процес не успе у неком тренутку – користи друго правило на кораку 3. -### Имплементација експертских система +> ✅ У којим ситуацијама је напредно закључивање прикладније? А како је са назадним закључивањем? -Експертски системи се могу имплементирати коришћењем различитих алата: +### Имплементација Експертских Система -* Програмирањем директно у неком програмском језику високог нивоа. Ово није најбоља идеја, јер је главна предност система заснованог на знању то што је знање одвојено од закључивања, и потенцијално стручњак за проблемску област треба да буде у могућности да пише правила без разумевања детаља процеса закључивања. -* Коришћењем **оквира за експертске системе**, тј. система специјално дизајнираног да се попуни знањем коришћењем неког језика за представљање знања. +Експертски системи могу бити имплементирани коришћењем различитих алата: -## ✍️ Вежба: Инференција о животињама +* Програмирањем директно у неком високо-нивоу програмском језику. Ово није најбоља идеја, јер је главна предност система заснованих на знању да је знање издвојено од закључивања, и потенцијално експерт из проблемске области треба да може писати правила без разумевања детаља закључивања +* Коришћењем **шкољке експертског система**, тј. система посебно дизајнираног да се пуни знањем користећи неки језик за репрезентацију знања. -Погледајте [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) за пример имплементације експертског система са напредним и уназадним закључивањем. +## ✍️ Вежба: Закључивање о животињама -> **Напомена**: Овај пример је прилично једноставан и само даје идеју како изгледа експертски систем. Када почнете да креирате такав систем, приметићете *интелигентно* понашање -- Породица XML-базираних језика за описивање знања: RDF (Resource Description Framework), RDFS (RDF Schema), OWL (Ontology Web Language). +Погледајте [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) за пример имплементације експертског система са напредним и назадним закључивањем. -Основни концепт у Семантичком вебу је концепт **Онтологије**. Онтологија се односи на експлицитну спецификацију домена проблема користећи неку формалну репрезентацију знања. Најједноставнија онтологија може бити само хијерархија објеката у домену проблема, али сложеније онтологије укључују правила која се могу користити за закључивање. +> **Напомена**: Овај пример је прилично једноставан и само представља идеју како експертски систем изгледа. Када почнете да креирате такав систем, тек када достигнете одређени број правила, око 200+, почињете примећивати неко *интелигентно* понашање система. У неком тренутку, правила постају превише сложена да се све упамте, и у том тренутку можете почети да се питате зашто систем доноси одређене одлуке. Међутим, важно својство система заснованих на знању је да увек можете *објаснити* тачно како су одлуке донете. -У семантичком вебу, све репрезентације се заснивају на триплетима. Сваки објекат и свака релација су јединствено идентификовани URI-јем. На пример, ако желимо да наведемо чињеницу да је овај AI Curriculum развио Дмитриј Сошњиков 1. јануара 2022. године, ево триплета које можемо користити: +## Онтологије и Семантички Веб - +Крајем 20. века постојала је иницијатива да се репрезентација знања користи за означавање интернет ресурса, тако да буде могуће проналажење ресурса који одговарају врло специфичним упитима. Та иницијатива се звала **Семантички Веб**, и заснивала се на неколико појмова: + +- Посебна репрезентација знања заснована на **[дескриптивној логици](https://en.wikipedia.org/wiki/Description_logic)** (DL). Слична је репрезентацији у облику фрејмова, јер гради хијерархију објеката са својствима, али има формалну логичку семантику и могућност закључивања. Постоји цела породица DL система који балансирају између експресивности и алгоритамске сложености закључивања. +- Дистрибуирана репрезентација знања, где су сви појмови представљени глобалним URI идентификатором, што омогућава креирање хијерархија знања које се простиру по интернету. +- Породица XML-базираног језика за опис знања: RDF (Resource Description Framework), RDFS (RDF Schema), OWL (Ontology Web Language). + +Кључни појам у Семантичком Вебу је појам **Онтологије**. То се односи на експлицитну спецификацију домена проблема помоћу неке формалне репрезентације знања. Најједноставнија онтологија може бити само хијерархија објеката у домену проблема, али сложеније онтологије укључују правила која се могу користити за извођење закључака. + +У семантичком вебу, све репрезентације базиране су на триплетима. Сваки објекат и свака релација су једнозначно идентификовани URI-јем. На пример, ако желимо да наведемо чињеницу да је овај AI Курс развио Дмитриј Сошников 1. јануара 2022. године - ево триплета које можемо користити: + + ``` -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 ``` -> ✅ Овде су `http://www.example.com/terms/creation-date` и `http://purl.org/dc/elements/1.1/creator` неки добро познати и универзално прихваћени URI-ји за изражавање концепата *креатора* и *датума креирања*. +> ✅ Овде су `http://www.example.com/terms/creation-date` и `http://purl.org/dc/elements/1.1/creator` неки добро познати и универзално прихваћени URI-ји за изражавање појмова *творца* и *датума стварања*. -У сложенијем случају, ако желимо да дефинишемо листу креатора, можемо користити неке структуре података дефинисане у RDF-у. +У сложенијем случају, ако желимо дефинисати списак твораца, можемо користити неке структуре података дефинисане у RDF-у. - + -> Дијаграми изнад од [Дмитрија Сошњикова](http://soshnikov.com) +> Дијаграми изнад, аутора [Дмитриј Сошников](http://soshnikov.com) -Напредак у изградњи Семантичког веба је донекле успорен успехом претраживача и техника обраде природног језика, које омогућавају извлачење структурираних података из текста. Међутим, у неким областима и даље постоје значајни напори за одржавање онтологија и база знања. Неколико пројеката вредних пажње: +Напредак у изградњи Семантичког Веба је на неки начин био успорен успехом претраживача и техника обраде природног језика које омогућавају извлачење структурираних података из текста. Међутим, у неким областима и даље постоје значајни напори за одржавање онтологија и база знања. Неки пројекти вредни помена су: -* [WikiData](https://wikidata.org/) је збирка машински читљивих база знања повезаних са Википедијом. Већина података је изведена из Википедијиних *InfoBox*-ова, делова структурисаног садржаја унутар страница Википедије. Можете [упитати](https://query.wikidata.org/) WikiData у SPARQL-у, посебном језику упита за Семантички веб. Ево примера упита који приказује најпопуларније боје очију међу људима: +* [WikiData](https://wikidata.org/) је збирка машински читљивих база знања повезаних са Википедијом. Већина података се вађе из Википедијиних *ИнфоБокова*, делова структурираног садржаја у страницама Википедије. Можете [упитати](https://query.wikidata.org/) викидати помоћу SPARQL, посебног језика за упите за Семантички Веб. Ево примера упита који приказује најпопуларније боје очију међу људима: ```sparql #defaultView:BubbleChart @@ -199,47 +206,51 @@ WHERE GROUP BY ?eyeColorLabel ``` -* [DBpedia](https://www.dbpedia.org/) је још један пројекат сличан WikiData. +* [DBpedia](https://www.dbpedia.org/) је још један напор сличан WikiData. -> ✅ Ако желите да експериментишете са изградњом сопствених онтологија или отварањем постојећих, постоји одличан визуелни уређивач онтологија зван [Protégé](https://protege.stanford.edu/). Преузмите га или га користите онлајн. +> ✅ Ако желите да експериментишете са израдом својих онтологија или да отварате постојеће, постоји одличан визуелни едитор онтологија под називом [Protégé](https://protege.stanford.edu/). Преузмите га или користите онлайн. - + -*Web Protégé уређивач отворен са онтологијом породице Романов. Снимак екрана од Дмитрија Сошњикова* +*Web Protégé едитор отворен са онтологијом породице Романов. Скриншот Дмитриј Сошников* ## ✍️ Вежба: Онтологија породице -Погледајте [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) за пример коришћења техника Семантичког веба за закључивање о породичним односима. Узимамо породично стабло представљено у уобичајеном GEDCOM формату и онтологију породичних односа и градимо графикон свих породичних односа за дати скуп појединаца. +Погледајте [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) за пример коришћења техника Семантичког Веба за закључивање о породичним везама. Узмемо породично стабло представљено у уобичајеном GEDCOM формату и онтологију породичних односа и изградићемо граф свих породичних веза за дат скуп појединаца. ## Microsoft Concept Graph -У већини случајева, онтологије се пажљиво креирају ручно. Међутим, могуће је и **извлачити** онтологије из неструктурираних података, на пример, из текстова на природном језику. +У већини случајева, онтологије се пажљиво израђују ручно. Међутим, могуће је и **вађење** онтологија из неструктурираних података, на пример из текстова природног језика. -Један такав покушај направљен је од стране Microsoft Research-а, и резултирао је [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste). +Један такав покушај урадио је Microsoft Research, који је резултовао у [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste). -То је велика збирка ентитета груписаних заједно користећи `is-a` релацију наслеђивања. Омогућава одговарање на питања попут "Шта је Microsoft?" - одговор би био нешто попут "компанија са вероватноћом 0.87, и бренд са вероватноћом 0.75". +То је велика колекција ентитета груписаних заједно коришћењем односа наследства `is-a`. Омогућава одговоре на питања као што је "Шта је Microsoft?" - одговор је нешто попут "компанија са вероватноћом 0.87, и бренд са вероватноћом 0.75". -Графикон је доступан или као REST API, или као велики текстуални фајл који наводи све парове ентитета. +Граф је доступан или као REST API, или као велика преузимајућа текстуална датотека која садржи све парове ентитета. -## ✍️ Вежба: Графикон концепата +## ✍️ Вежба: Концептуални граф -Испробајте [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) нотебук да видите како можемо користити Microsoft Concept Graph за груписање новинских чланака у неколико категорија. +Испробајте [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) нотебоок да видите како можемо користити Microsoft Concept Graph за груписање новинских чланака у неколико категорија. ## Закључак -Данас се AI често сматра синонимом за *машинско учење* или *неуронске мреже*. Међутим, људско биће такође показује експлицитно резоновање, што је нешто што тренутно не обрађују неуронске мреже. У стварним пројектима, експлицитно резоновање се и даље користи за обављање задатака који захтевају објашњења или могућност контролисане модификације понашања система. +Данас се AI често сматра синонимом за *машинско учење* или *неуронске мреже*. Међутим, човек такође показује експлицитно расуђивање, што тренутно не решавају неуронске мреже. У стварним пројектима, експлицитно расуђивање се и даље користи за извршавање задатака који захтевају објашњења или могућност контролисане измене понашања система. ## 🚀 Изазов -У нотебуку Онтологија породице повезаном са овом лекцијом, постоји прилика да експериментишете са другим породичним односима. Покушајте да откријете нове везе између људи у породичном стаблу. +У нотебоок-у Онтологија породице везаном за ову лекцију постоји могућност експериментисања са другим породичним везама. Покушајте да откријете нове везе између људи у породичном стаблу. ## [Квиз након предавања](https://ff-quizzes.netlify.app/en/ai/quiz/4) -## Преглед и самостално учење +## Ревизија и самостално учење -Истражите на интернету области у којима су људи покушали да квантификују и кодирају знање. Погледајте Блумову таксономију и вратите се у историју да бисте научили како су људи покушавали да схвате свој свет. Истражите рад Линеауса на креирању таксономије организама и посматрајте начин на који је Дмитриј Мендељејев створио начин за описивање и груписање хемијских елемената. Које друге занимљиве примере можете пронаћи? +Истражите на интернету области у којима су људи покушавали да квантитују и кодирају знање. Погледајте Блумову Таксономију и вратите се у прошлост да бисте видели како су људи покушавали да схвате свој свет. Истражите рад Линеауса на креирању таксономије организама и посматрајте како је Дмитриј Мендељејев створио начин за описивање и груписање хемијских елемената. Које друге занимљиве примере можете пронаћи? **Задатак**: [Изградите онтологију](assignment.md) --- + +**Одрицање од одговорности**: +Овај документ је преведен помоћу АИ услуге за превођење [Co-op Translator](https://github.com/Azure/co-op-translator). Иако тежимо тачности, молимо имајте у виду да аутоматизовани преводи могу садржати грешке или нетачности. Оригинални документ на његовом изворном језику треба сматрати ауторитетним извором. За критичне информације препоручује се професионални људски превод. Не можемо бити одговорни за било каква неспоразума или погрешна тумачења која произилазе из коришћења овог превода. + \ No newline at end of file