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b/translated_images/yolo.a2648ec82ee8bb4ea27537677adb482fd4b733ca1705c561b6a24a85102dced5.my.png deleted file mode 100644 index 98ce75f4..00000000 Binary files a/translated_images/yolo.a2648ec82ee8bb4ea27537677adb482fd4b733ca1705c561b6a24a85102dced5.my.png and /dev/null differ diff --git a/translated_images/yolo.a2648ec82ee8bb4ea27537677adb482fd4b733ca1705c561b6a24a85102dced5.sl.png b/translated_images/yolo.a2648ec82ee8bb4ea27537677adb482fd4b733ca1705c561b6a24a85102dced5.sl.png deleted file mode 100644 index 626440a2..00000000 Binary files a/translated_images/yolo.a2648ec82ee8bb4ea27537677adb482fd4b733ca1705c561b6a24a85102dced5.sl.png and /dev/null differ diff --git a/translations/hr/README.md b/translations/hr/README.md index 73a17b7d..d6b4ef01 100644 --- a/translations/hr/README.md +++ b/translations/hr/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](./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) +[arapski](../ar/README.md) | [bengalski](../bn/README.md) | [bugarski](../bg/README.md) | [birmanski (Myanmar)](../my/README.md) | [kineski (pojednostavljeni)](../zh/README.md) | [kineski (tradicionalni, Hong Kong)](../hk/README.md) | [kineski (tradicionalni, Macau)](../mo/README.md) | [kineski (tradicionalni, Tajvan)](../tw/README.md) | [hrvatski](./README.md) | [češki](../cs/README.md) | [danski](../da/README.md) | [nizozemski](../nl/README.md) | [estonski](../et/README.md) | [finski](../fi/README.md) | [francuski](../fr/README.md) | [njemački](../de/README.md) | [grčki](../el/README.md) | [hebrejski](../he/README.md) | [hindski](../hi/README.md) | [mađarski](../hu/README.md) | [indonezijski](../id/README.md) | [talijanski](../it/README.md) | [japanski](../ja/README.md) | [kanada](../kn/README.md) | [korejski](../ko/README.md) | [litavski](../lt/README.md) | [malajski](../ms/README.md) | [malajalamski](../ml/README.md) | [maratijski](../mr/README.md) | [nepalski](../ne/README.md) | [nigerijski pidgin](../pcm/README.md) | [norveški](../no/README.md) | [persijski (farsi)](../fa/README.md) | [poljski](../pl/README.md) | [portugalski (Brazil)](../br/README.md) | [portugalski (Portugal)](../pt/README.md) | [pandžapski (Gurmukhi)](../pa/README.md) | [rumunjski](../ro/README.md) | [ruski](../ru/README.md) | [srpski (ćirilica)](../sr/README.md) | [slovački](../sk/README.md) | [slovenski](../sl/README.md) | [španjolski](../es/README.md) | [svahili](../sw/README.md) | [švedski](../sv/README.md) | [tagalog (filipinski)](../tl/README.md) | [tamil](../ta/README.md) | [telugu](../te/README.md) | [tajlandski](../th/README.md) | [turski](../tr/README.md) | [ukrajinski](../uk/README.md) | [urdu](../ur/README.md) | [vijetnamski](../vi/README.md) -> **Radije klonirati lokalno?** +> **Želite li klonirati lokalno?** -> Ovaj repozitorij sadrži više od 50 prijevoda jezika što značajno povećava veličinu preuzimanja. Za kloniranje bez prijevoda, koristite sparse checkout: +> Ovo spremište uključuje više od 50 prijevoda jezika što znatno povećava veličinu preuzimanja. Za kloniranje bez prijevoda, koristite 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' > ``` -> Ovo vam daje sve što trebate za završetak tečaja uz znatno brže preuzimanje. +> Ovo vam daje sve što vam je potrebno za dovršetak tečaja znatno bržim preuzimanjem. -**Ako želite da dodatni jezici budu podržani, dostupni su na popisu [ovdje](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** +**Ako želite dodatne podržane jezike za prijevod, pogledajte [ovdje](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** ## Pridružite se zajednici [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) ## Što ćete naučiti -**[Mapa uma tečaja](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** +**[Mentalna mapa tečaja](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** -U ovom nastavnom planu naučit ćete: +U ovom kurikulumu naučit ćete: -* Različite pristupe umjetnoj inteligenciji, uključujući "dobri stari" simbolički pristup s **predstavljanjem znanja** i zaključivanjem ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). -* **Neuronske mreže** i **Duboko učenje**, koje su u središtu moderne UI. Koncepte iza ovih važnih tema ilustrirat ćemo pomoću koda u dva najpopularnija okvira - [TensorFlow](http://Tensorflow.org) i [PyTorch](http://pytorch.org). -* **Neuronske arhitekture** za obradu slika i teksta. Pokrit ćemo nedavne modele, ali možda ćemo biti nešto ograničeni u najnovijim dostignućima. -* Manje popularne pristupe UI, kao što su **Genetski algoritmi** i **sustavi s više agenata**. +* Različite pristupe umjetnoj inteligenciji, uključujući "dobri stari" simbolički pristup s **Reprezentacijom znanja** i zaključivanjem ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). +* **Neuronske mreže** i **duboko učenje**, koji su srž moderne UI. Objasnit ćemo koncepte iza ovih važnih tema koristeći kod u dva od najpopularnijih okvira - [TensorFlow](http://Tensorflow.org) i [PyTorch](http://pytorch.org). +* **Neuronske arhitekture** za rad sa slikama i tekstom. Pokrit ćemo nedavne modele, ali možda ćemo malo zaostajati za najnovijim dostignućima. +* Rjeđe popularne pristupe u UI, poput **genetskih algoritama** i **sustava s više agenata**. -Što nećemo pokriti u ovom nastavnom planu: +Što nećemo pokriti u ovom kurikulumu: -> [Pronađite sve dodatne resurse za ovaj tečaj u našoj Microsoft Learn kolekciji](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) +> [Pronađite sve dodatne resurse za ovaj tečaj u našoj kolekciji Microsoft Learn](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) -* Poslovne primjere korištenja **UI u poslovanju**. Razmotrite pohađanje [Uvod u UI za poslovne korisnike](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) na Microsoft Learn ili [AI Business School](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), razvijen u suradnji s [INSEAD](https://www.insead.edu/). -* **Klasično strojno učenje**, koje je dobro opisano u našem [Nastavnom planu za strojarstvo za početnike](http://github.com/Microsoft/ML-for-Beginners). -* Praktične AI primjene izgrađene koristeći **[Kognitivne usluge](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**. Za to preporučujemo da započnete s modulima Microsoft Learn o [vidu](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [obradi prirodnog jezika](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[Generativnoj UI s Azure OpenAI uslugom](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** i drugim. -* Specifične ML **cloud okvire**, poput [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) ili [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). Razmotrite korištenje nastavnih putova [Izgradite i upravljajte rješenjima strojnog učenja s Azure Machine Learning](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) i [Izgradite i upravljajte rješenjima strojnog učenja s Azure Databricks](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum). -* **Razgovorna UI** i **chat botovi**. Postoji poseban [nastavni put za stvaranje razgovornih UI rješenja](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum), a možete se također obratiti [ovom blogu](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) za više detalja. -* **Duboka matematika** iza dubokog učenja. Za to preporučujemo [Duboko učenje](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) autora Iana Goodfellowa, Yoshue Bengioa i Aarona Courvillea, koja je dostupna i online na [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/). +* Poslovne slučajeve korištenja **UI u poslovanju**. Razmislite o polaganju [Uvod u UI za poslovne korisnike](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) na Microsoft Learn, ili [AI Business School](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), razvijen u suradnji s [INSEAD](https://www.insead.edu/). +* **Klasično strojno učenje**, koje je dobro opisano u našem [Kurikulumu za strojno učenje za početnike](http://github.com/Microsoft/ML-for-Beginners). +* Praktične primjene UI izgrađene korištenjem **[Cognitive Services](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**. Za to preporučujemo da započnete s Microsoft Learn modulima za [viziju](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [obradu prirodnog jezika](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[generativnu umjetnu inteligenciju uz Azure OpenAI servis](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** i druge. +* Specifične okvire za ML u oblaku, poput [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) ili [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). Razmislite o korištenju tečajeva [Izgradite i upravljajte ML rješenjima uz Azure Machine Learning](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) i [Izgradite i upravljajte ML rješenjima s Azure Databricks](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum). +* **Conversational AI** i **chat botove**. Postoji zaseban tečaj [Kreirajte konverzacijske AI aplikacije](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum), a možete se referirati i na [ovaj blog post](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) za više detalja. +* **Duboku matematiku** iza dubokog učenja. Za to preporučujemo [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) autora Iana Goodfellowa, Yoshua Bengioa i Aarona Courvillea, koji je također dostupan online na [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/). -Za nježni uvod u teme _UI u oblaku_ možete razmotriti [Nastavni put: Početak rada s umjetnom inteligencijom na Azureu](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum). +Za lagani uvod u teme _AI u oblaku_ možete uzeti u obzir tečaj [Počnite s umjetnom inteligencijom na Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum). # Sadržaj -| | Poveznica na lekciju | PyTorch/Keras/TensorFlow | Laboratorij | -| :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | -| 0 | [Postavljanje tečaja](./lessons/0-course-setup/setup.md) | [Postavljanje razvojnog okruženja](./lessons/0-course-setup/how-to-run.md) | | -| I | [**Uvod u UI**](./lessons/1-Intro/README.md) | | | +| | Poveznica na lekciju | PyTorch/Keras/TensorFlow | Laboratorijske vježbe | +| :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------ | +| 0 | [Postavljanje tečaja](./lessons/0-course-setup/setup.md) | [Postavite svoje razvojno okruženje](./lessons/0-course-setup/how-to-run.md) | | +| I | [**Uvod u umjetnu inteligenciju**](./lessons/1-Intro/README.md) | | | | 01 | [Uvod i povijest UI](./lessons/1-Intro/README.md) | - | - | -| II | **Simbolička UI** | -| 02 | [Predstavljanje znanja i ekspertni sustavi](./lessons/2-Symbolic/README.md) | [Ekspertni sustavi](./lessons/2-Symbolic/Animals.ipynb) / [Ontologija](./lessons/2-Symbolic/FamilyOntology.ipynb) /[Graf koncepata](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | +| II | **Simbolička umjetna inteligencija** | +| 02 | [Reprezentacija znanja i ekspertni sustavi](./lessons/2-Symbolic/README.md) | [Ekspertni sustavi](./lessons/2-Symbolic/Animals.ipynb) / [Ontologija](./lessons/2-Symbolic/FamilyOntology.ipynb) /[Graf pojmova](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | | III | [**Uvod u neuronske mreže**](./lessons/3-NeuralNetworks/README.md) ||| | 03 | [Perceptron](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [Bilježnica](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [Laboratorij](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | -| 04 | [Višeslojni Perceptron i Izrada vlastitog okvira](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [Bilježnica](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [Laboratorij](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | -| 05 | [Uvod u Okvire (PyTorch/TensorFlow) i Prenaučavanje](./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) | [Laboratorij](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | -| IV | [**Računalni vid**](./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)| [Istražite računalni vid na Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | +| 04 | [Višeslojni perceptron i stvaranje vlastitog okvira](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [Bilježnica](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [Laboratorij](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | +| 05 | [Uvod u okvire (PyTorch/TensorFlow) i prekomjerno uklapanje](./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) | [Laboratorij](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | +| IV | [**Računalni vid**](./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)| [Istraži računalni vid na Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | | 06 | [Uvod u računalni vid. OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [Bilježnica](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [Laboratorij](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | -| 07 | [Konvolucijske neuronske mreže](./lessons/4-ComputerVision/07-ConvNets/README.md) & [CNN arhitekture](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [Laboratorij](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | -| 08 | [Prije-trenirane mreže i prijenosno učenje](./lessons/4-ComputerVision/08-TransferLearning/README.md) i [Trikovi treniranja](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Laboratorij](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | -| 09 | [Autoenkoderi i 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 | [Generativne natjecateljske mreže i prijenos umjetničkog stila](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | +| 07 | [Konvolucijske neuronske mreže](./lessons/4-ComputerVision/07-ConvNets/README.md) & [Arhitekture CNN-a](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [Laboratorij](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | +| 08 | [Unaprijed naučene mreže i transferno učenje](./lessons/4-ComputerVision/08-TransferLearning/README.md) i [Trikovi treniranja](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Laboratorij](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | +| 09 | [Autoenkoderi i VAE-ovi](./lessons/4-ComputerVision/09-Autoencoders/README.md) | [PyTorch](./lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb) | | +| 10 | [Generativne kontradiktorne mreže i prijenos umjetničkog stila](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | | 11 | [Detekcija objekata](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [Laboratorij](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | | 12 | [Semantička segmentacija. 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 | [**Obrada prirodnog jezika**](./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) | [Istražite obradu prirodnog jezika na Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| -| 13 | [Predstavljanje teksta. 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 | [Semantičke riječne ugrađenosti. Word2Vec i 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 | [Modeliranje jezika. Treniranje vlastitih ugradnji](./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) | [Laboratorij](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | +| V | [**Obrada prirodnog jezika**](./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) | [Istraži obradu prirodnog jezika na Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| +| 13 | [Predstavljanje teksta. 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 | [Semantičke vektorske reprezentacije riječi. Word2Vec i 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 | [Modeliranje jezika. Treniranje vlastitih ugrađenih modela](./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) | [Laboratorij](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | | 16 | [Rekurentne neuronske mreže](./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 | [Generativne rekurentne mreže](./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) | [Laboratorij](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | | 18 | [Transformeri. 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 | [Prepoznavanje imenovanih entiteta](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [Laboratorij](./lessons/5-NLP/19-NER/lab/README.md) | -| 20 | [Veliki modeli jezika, programiranje promptova i few-shot zadaci](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | +| 20 | [Veliki jezični modeli, programiranje na zahtjev i zadaci s malo primjera](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | | VI | **Ostale AI tehnike** || | | 21 | [Genetski algoritmi](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [Bilježnica](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | | 22 | [Duboko učenje s pojačanjem](./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) | [Laboratorij](./lessons/6-Other/22-DeepRL/lab/README.md) | | 23 | [Sustavi s više agenata](./lessons/6-Other/23-MultiagentSystems/README.md) | | | | VII | **Etika umjetne inteligencije** | | | -| 24 | [Etika umjetne inteligencije i odgovorna AI](./lessons/7-Ethics/README.md) | [Microsoft Learn: Principi odgovorne AI](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | +| 24 | [Etika umjetne inteligencije i odgovorna AI](./lessons/7-Ethics/README.md) | [Microsoft Learn: Načela odgovorne AI](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | | IX | **Dodatno** | | | -| 25 | [Višemodalne mreže, CLIP i VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [Bilježnica](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | +| 25 | [Višeslojne mreže, CLIP i VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [Bilježnica](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | ## Svaka lekcija sadrži -* Materijal za prethodno čitanje -* Izvršne Jupyter bilježnice, često specifične za okvir (**PyTorch** ili **TensorFlow**). Izvršna bilježnica također sadrži mnogo teorijskog materijala, pa je za razumijevanje teme potrebno proći kroz barem jednu verziju bilježnice (bilo PyTorch ili TensorFlow). -* **Laboratorije** dostupne za neke teme, koje vam daju mogućnost da isprobate primjenu naučenog na određenom problemu. -* Neki dijelovi sadrže poveznice na module [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) koji pokrivaju povezane teme. +* Materijal za pred-čitanje +* Izvršljive Jupyter bilježnice, koje su često specifične za okvir (**PyTorch** ili **TensorFlow**). Izvršljiva bilježnica također sadrži mnogo teoretskog materijala, stoga da biste razumjeli temu potrebno je proći kroz barem jednu verziju bilježnice (bilo PyTorch ili TensorFlow). +* **Laboratorije** dostupne za neke teme, koje vam pružaju priliku da isprobate primjenu naučenog materijala na određenom problemu. +* Neki dijelovi sadrže poveznice na [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) module koji pokrivaju povezane teme. ## Početak -### 🎯 Novi ste u AI? Počnite ovdje! +### 🎯 Novi u AI? Počnite ovdje! -Ako ste potpuno novi u AI i želite brze, praktične primjere, pogledajte naše [**Primjere prilagođene početnicima**](./examples/README.md)! Oni uključuju: +Ako ste potpuno novi u umjetnoj inteligenciji i želite brze, praktične primjere, pogledajte naše [**Primjerima prilagođene početnicima**](./examples/README.md)! Oni uključuju: -- 🌟 **Pozdrav svijetu AI-a** - Vaš prvi AI program (prepoznavanje uzoraka) -- 🧠 **Jednostavna neuronska mreža** - Izgradite neuronsku mrežu od nule +- 🌟 **Pozdrav AI svijete** - Vaš prvi AI program (prepoznavanje uzoraka) +- 🧠 **Jednostavna neuronska mreža** - Izgradite neuronsku mrežu ispočetka - 🖼️ **Klasifikator slika** - Klasificirajte slike s detaljnim komentarima - 💬 **Sentiment teksta** - Analizirajte pozitivan/negativan tekst -Ovi primjeri su dizajnirani da vam pomognu razumjeti AI koncepte prije nego što prijeđete na cjelokupni kurikulum. +Ovi primjeri osmišljeni su kako bi vam pomogli razumjeti AI koncepte prije nego započnete s cjelokupnim kurikulumom. ### 📚 Postavljanje cjelokupnog kurikuluma -- Izradili smo [lekciju za postavljanje](./lessons/0-course-setup/setup.md) koja će vam pomoći s postavljanjem razvojne okoline. - Za nastavnike smo također izradili [lekciju za postavljanje kurikuluma](./lessons/0-course-setup/for-teachers.md)! -- Kako [pokrenuti kod u VSCode ili Codepace](./lessons/0-course-setup/how-to-run.md) +- Napravili smo [lekciju za postavljanje](./lessons/0-course-setup/setup.md) kako bismo vam pomogli pri postavljanju vašeg razvojog okruženja. - Za edukatore smo također kreirali [lekciju za postavljanje kurikuluma](./lessons/0-course-setup/for-teachers.md)! +- Kako [pokrenuti kod u VSCode-u ili Codespace-u](./lessons/0-course-setup/how-to-run.md) Slijedite ove korake: -Forkajte repozitorij: Kliknite na gumb "Fork" u gornjem desnom kutu ove stranice. +Forkajte repositorij: Kliknite na gumb "Fork" u gornjem desnom kutu ove stranice. -Klonirajte repozitorij: `git clone https://github.com/microsoft/AI-For-Beginners.git` +Klonirajte repositorij: `git clone https://github.com/microsoft/AI-For-Beginners.git` -Ne zaboravite označiti (🌟) ovaj repozitorij da biste ga lakše pronašli kasnije. +Ne zaboravite označiti (🌟) ovaj repozitorij kako biste ga lakše pronašli kasnije. -## Upoznajte druge učenike +## Upoznajte druge polaznike -Pridružite se našem [službenom AI Discord serveru](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) da upoznate i povežete se s drugim polaznicima ovog tečaja te dobijete podršku. +Pridružite se našem [službenom AI Discord serveru](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) kako biste upoznali i povezali se s ostalim polaznicima ovog tečaja i dobili podršku. -Ako imate povratne informacije ili pitanja tijekom izrade, posjetite naš [Azure AI Foundry Developer Forum](https://aka.ms/foundry/forum). +Ako imate povratne informacije o proizvodu ili pitanja tijekom izrade, posjetite naš [Azure AI Foundry Developer Forum](https://aka.ms/foundry/forum) ## Kvizovi -> **Napomena o kvizovima**: Svi kvizovi nalaze se u mapi Quiz-app u etc\quiz-app, ili [Online Ovdje](https://ff-quizzes.netlify.app/). Povezani su iz lekcija, aplikacija za kvizove može se pokrenuti lokalno ili postaviti na Azure; slijedite upute u mapi `quiz-app`. Postupno se lokaliziraju. +> **Napomena o kvizovima**: Svi kvizovi nalaze se u mapi Quiz-app u etc\quiz-app, ili [Online Ovdje](https://ff-quizzes.netlify.app/) Povezani su iz lekcija, kviz aplikacija se može pokrenuti lokalno ili implementirati na Azure; slijedite upute u mapi `quiz-app`. Postupno se lokaliziraju. -## Treba pomoć +## Tražimo pomoć -Imate li prijedloge ili ste pronašli pravopisne ili programske greške? Otvorite problem (issue) ili kreirajte zahtjev za promjenu (pull request). +Imate li prijedloge ili ste pronašli pravopisne ili kodne greške? Otvorite issue ili kreirajte pull request. -## Posebna zahvala +## Posebne zahvale * **✍️ Glavni autor:** [Dmitry Soshnikov](http://soshnikov.com), PhD * **🔥 Urednik:** [Jen Looper](https://twitter.com/jenlooper), PhD @@ -177,57 +176,57 @@ Naš tim proizvodi i druge kurikulume! Pogledajte: ### 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 za početnike](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 za početnike](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 / Agenti -[![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) +### Azure / Edge / MCP / Agent +[![AZD za početnike](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 za početnike](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 za početnike](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 agenti za početnike](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) --- - + ### Serija Generativnog AI -[![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) +[![Generativni AI za početnike](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) +[![Generativni 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) +[![Generativni 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) +[![Generativni 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) --- - + ### Osnovno učenje -[![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) +[![ML za početnike](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 za početnike](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 za početnike](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 za početnike](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 razvoj za početnike](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 za početnike](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 razvoj za početnike](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) --- - -### Serija Copilota -[![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) + +### Serija Copilot +[![Copilot za AI programsko uparivanje](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 za 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 avantura](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) ## Dobivanje pomoći -Ako zapnete ili imate pitanja u vezi izrade AI aplikacija, pridružite se kolegama polaznicima i iskusnim programerima u raspravama o MCP-u. To je poticajna zajednica gdje su pitanja dobrodošla, a znanje slobodno dijeljeno. +Ako zapnete ili imate pitanja o izradi AI aplikacija, pridružite se drugim polaznicima i iskusnim developerima u diskusijama o MCP-u. To je podržavajuća zajednica gdje su pitanja dobrodošla, a znanje se slobodno dijeli. [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -Ako imate povratne informacije o proizvodu ili probleme tijekom izrade posjetite: +Ako imate povratne informacije o proizvodu ili greške tijekom izrade, posjetite: [![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) --- -**Izjava o odricanju od odgovornosti**: -Ovaj je dokument preveden korištenjem AI usluge za prevođenje [Co-op Translator](https://github.com/Azure/co-op-translator). Iako nastojimo postići točnost, imajte na umu da automatizirani prijevodi mogu sadržavati pogreške ili netočnosti. Izvorni dokument na izvornom jeziku treba smatrati autoritativnim izvorom. Za kritične informacije preporučujemo profesionalni ljudski prijevod. Ne odgovaramo za bilo kakve nesporazume ili kriva tumačenja koja proizlaze iz korištenja ovog prijevoda. +**Izjava o odricanju odgovornosti**: +Ovaj dokument je preveden korištenjem AI prevoditeljskog servisa [Co-op Translator](https://github.com/Azure/co-op-translator). Iako nastojimo osigurati točnost, imajte na umu da automatski prijevodi mogu sadržavati pogreške ili netočnosti. Izvorni dokument na izvornom jeziku treba smatrati autoritativnim izvorom. Za važne informacije preporučuje se profesionalni ljudski prijevod. Ne snosimo odgovornost za bilo kakve nesporazume ili pogrešna tumačenja nastala uporabom ovog prijevoda. \ No newline at end of file diff --git a/translations/hr/lessons/0-course-setup/how-to-run.md b/translations/hr/lessons/0-course-setup/how-to-run.md index 5b78adf6..eacfec31 100644 --- a/translations/hr/lessons/0-course-setup/how-to-run.md +++ b/translations/hr/lessons/0-course-setup/how-to-run.md @@ -1,21 +1,21 @@ # Kako pokrenuti kod -Ovaj kurikulum sadrži mnogo primjera i laboratorijskih vježbi koje možete pokrenuti. Da biste to učinili, trebate mogućnost izvršavanja Python koda u Jupyter Notebooks koji su dio ovog kurikuluma. Postoji nekoliko opcija za pokretanje koda: +Ovaj nastavni plan sadrži mnogo izvršnih primjera i laboratorijskih vježbi koje želite pokrenuti. Da biste to učinili, morate imati mogućnost izvršavanja Python koda u Jupyter bilježnicama koje su dio ovog nastavnog plana. Imate nekoliko opcija za pokretanje koda: ## Pokretanje lokalno na vašem računalu -Za pokretanje koda lokalno na vašem računalu, trebate imati instaliranu neku verziju Pythona. Osobno preporučujem instalaciju **[miniconda](https://conda.io/en/latest/miniconda.html)** - to je prilično lagana instalacija koja podržava `conda` upravitelj paketa za različita Python **virtualna okruženja**. +Da biste pokrenuli kod lokalno na vašem računalu, potrebna je Python instalacija. Jedan od prijedloga je instalirati **[miniconda](https://conda.io/en/latest/miniconda.html)** - to je prilično lagana instalacija koja podržava upravitelja paketa `conda` za različita Python **virtualna okruženja**. -Nakon što instalirate miniconda, trebate klonirati repozitorij i stvoriti virtualno okruženje koje će se koristiti za ovaj tečaj: +Nakon što instalirate miniconda, klonirajte repozitorij i kreirajte virtualno okruženje koje će se koristiti za ovaj tečaj: ```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 ``` -### Korištenje Visual Studio Code-a s Python ekstenzijom +### Korištenje Visual Studio Code s Python ekstenzijom -Vjerojatno najbolji način za korištenje kurikuluma je otvaranje u [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) s [Python ekstenzijom](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste). +Ovaj nastavni plan najbolje je koristiti ako ga otvorite u [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) s [Python ekstenzijom](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste). -> **Napomena**: Kada klonirate i otvorite direktorij u VS Code-u, automatski će vam se predložiti instalacija Python ekstenzija. Također ćete morati instalirati miniconda kako je gore opisano. +> **Napomena**: Kada klonirate i otvorite direktorij u VS Code-u, automatski će vam predložiti instalaciju Python ekstenzija. Također ćete morati instalirati miniconda kako je prethodno opisano. > **Napomena**: Ako vam VS Code predloži ponovno otvaranje repozitorija u kontejneru, trebate to odbiti kako biste koristili lokalnu Python instalaciju. -### Korištenje Jupytera u pregledniku +### Korištenje Jupyter-a u pregledniku -Također možete koristiti Jupyter okruženje izravno iz preglednika na vašem računalu. Zapravo, i klasični Jupyter i Jupyter Hub pružaju prilično praktično razvojno okruženje s automatskim dovršavanjem, isticanjem koda itd. +Također možete koristiti Jupyter okruženje iz preglednika na vašem računalu. I klasični Jupyter i JupyterHub pružaju praktično razvojno okruženje s automatskim dovršavanjem, isticanjem koda itd. -Za pokretanje Jupytera lokalno, idite u direktorij tečaja i izvršite: +Za pokretanje Jupyter-a lokalno, idite u direktorij tečaja i pokrenite: ```bash jupyter notebook @@ -49,28 +49,32 @@ Zatim možete navigirati do bilo koje `.ipynb` datoteke, otvoriti je i početi r ### Pokretanje u kontejneru -Jedna alternativa Python instalaciji je pokretanje koda u kontejneru. Budući da naš repozitorij sadrži posebnu `.devcontainer` mapu koja daje upute za izgradnju kontejnera za ovaj repozitorij, VS Code će vam ponuditi ponovno otvaranje koda u kontejneru. Ovo će zahtijevati instalaciju Dockera, a također će biti složenije, pa ovo preporučujemo iskusnijim korisnicima. +Jedna alternativa Python instalaciji je pokretanje koda u kontejneru. Budući da naš repozitorij sadrži posebnu mapu `.devcontainer` koja daje upute kako izgraditi kontejner za ovaj repozitorij, VS Code nudi opciju ponovnog otvaranja koda u kontejneru. Za ovo je potrebna instalacija Dockera, a postupak može biti složeniji, pa to preporučujemo iskusnijim korisnicima. ## Pokretanje u oblaku -Ako ne želite instalirati Python lokalno i imate pristup nekim resursima u oblaku, dobra alternativa je pokretanje koda u oblaku. Postoji nekoliko načina kako to možete učiniti: +Ako ne želite instalirati Python lokalno, a imate pristup nekim resursima u oblaku - dobra alternativa je pokretanje koda u oblaku. Postoji nekoliko načina na koje to možete učiniti: -* Korištenje **[GitHub Codespaces](https://github.com/features/codespaces)**, što je virtualno okruženje stvoreno za vas na GitHubu, dostupno putem VS Code sučelja u pregledniku. Ako imate pristup Codespaces, možete jednostavno kliknuti gumb **Code** u repozitoriju, pokrenuti Codespace i započeti rad u trenu. -* Korištenje **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**. [Binder](https://mybinder.org) pruža besplatne računalne resurse u oblaku za ljude poput vas kako bi isprobali neki kod na GitHubu. Na početnoj stranici postoji gumb za otvaranje repozitorija u Binderu - ovo bi vas brzo trebalo odvesti na Binder stranicu, koja će izgraditi temeljni kontejner i besprijekorno pokrenuti Jupyter web sučelje. +* Korištenje **[GitHub Codespaces](https://github.com/features/codespaces)**, što je virtualno okruženje kreirano za vas na GitHubu, dostupno putem VS Code sučelja u pregledniku. Ako imate pristup Codespaces, samo kliknite na gumb **Code** u repozitoriju, pokrenite codespace i brzo počnite raditi. +* Korištenje **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**. [Binder](https://mybinder.org) nudi besplatne računalne resurse u oblaku za ljude poput vas da isprobaju kod s GitHuba. Na početnoj stranici postoji gumb za otvaranje repozitorija u Binderu - to će vas brzo dovesti na stranicu bindera, koji će izgraditi osnovni kontejner i pokrenuti Jupyter web sučelje bez prekida. -> **Napomena**: Kako bi se spriječila zloupotreba, Binder ima ograničen pristup nekim web resursima. Ovo može spriječiti rad nekog koda koji preuzima modele i/ili skupove podataka s javnog interneta. Možda ćete morati pronaći neka zaobilazna rješenja. Također, računalni resursi koje pruža Binder su prilično osnovni, pa će treniranje biti sporo, posebno u kasnijim, složenijim lekcijama. +> **Napomena**: Da bi spriječio zlonamjernu upotrebu, Binder ima blokiran pristup nekim mrežnim resursima. To može spriječiti rad nekog koda koji dohvaća modele i/ili skupove podataka s javnog Interneta. Možda ćete trebati pronaći neke zaobilazne načine. Također, računski resursi koje Binder pruža su prilično osnovni, pa će treniranje biti sporo, osobito u kasnijim, složenijim lekcijama. -## Pokretanje u oblaku s GPU-om +## Pokretanje u oblaku s GPU -Neke od kasnijih lekcija u ovom kurikulumu značajno bi koristile podršku za GPU, jer bi inače treniranje bilo bolno sporo. Postoji nekoliko opcija koje možete slijediti, posebno ako imate pristup oblaku putem [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) ili putem vaše institucije: +Neke od kasnijih lekcija u ovom nastavnom planu značajno bi imale koristi od podrške za GPU. Na primjer, treniranje modela može biti izuzetno sporo bez toga. Postoji nekoliko opcija koje možete pratiti, osobito ako imate pristup oblaku putem [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) ili preko vaše institucije: -* Stvorite [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) i povežite se s njom putem Jupytera. Zatim možete klonirati repozitorij izravno na stroj i započeti učenje. NC-serija VM-ova ima podršku za GPU. +* Kreirajte [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) i povežite se na nju putem Jupyter-a. Tada možete klonirati repozitorij direktno na tu mašinu i započeti učenje. NC-serija VM-ova ima podršku za GPU. -> **Napomena**: Neke pretplate, uključujući Azure for Students, ne pružaju GPU podršku odmah. Možda ćete morati zatražiti dodatne GPU jezgre putem zahtjeva za tehničku podršku. +> **Napomena**: Neki pretplatnički planovi, uključujući Azure for Students, ne pružaju GPU podršku odmah. Možda ćete morati zatražiti dodatne GPU jezgre tehničkim zahtjevom za podršku. -* Stvorite [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) i zatim koristite značajku Notebook tamo. [Ovaj video](https://azure-for-academics.github.io/quickstart/azureml-papers/) pokazuje kako klonirati repozitorij u Azure ML notebook i početi ga koristiti. +* Kreirajte [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) i zatim koristite značajku Notebooka tamo. [Ovaj video](https://azure-for-academics.github.io/quickstart/azureml-papers/) pokazuje kako klonirati repozitorij u Azure ML bilježnicu i početi ga koristiti. -Također možete koristiti Google Colab, koji dolazi s određenom besplatnom GPU podrškom, i tamo učitati Jupyter Notebooks kako biste ih izvršavali jedan po jedan. +Također možete koristiti Google Colab, koji dolazi s besplatnom podrškom za GPU, i tamo učitati Jupyter bilježnice za izvršavanje jednu po jednu. -**Odricanje od odgovornosti**: -Ovaj dokument je preveden pomoću AI usluge za prevođenje [Co-op Translator](https://github.com/Azure/co-op-translator). Iako nastojimo osigurati točnost, imajte na umu da automatski prijevodi mogu sadržavati pogreške ili netočnosti. Izvorni dokument na izvornom jeziku treba smatrati autoritativnim izvorom. Za ključne informacije preporučuje se profesionalni prijevod od strane čovjeka. Ne preuzimamo odgovornost za nesporazume ili pogrešna tumačenja koja mogu proizaći iz korištenja ovog prijevoda. \ No newline at end of file +--- + + +**Izjava o odricanju od odgovornosti**: +Ovaj je dokument preveden korištenjem AI usluge za prevođenje [Co-op Translator](https://github.com/Azure/co-op-translator). Iako težimo točnosti, imajte na umu da automatski prijevodi mogu sadržavati pogreške ili netočnosti. Izvorni dokument na izvornom jeziku treba smatrati autoritativnim izvorom. Za kritične informacije preporučuje se stručni ljudski prijevod. Nismo odgovorni za bilo kakva nesporazuma ili pogrešna tumačenja koja proizlaze iz korištenja ovog prijevoda. + \ No newline at end of file diff --git a/translations/hr/lessons/2-Symbolic/Animals.ipynb b/translations/hr/lessons/2-Symbolic/Animals.ipynb index c05c8f12..2596e4c3 100644 --- a/translations/hr/lessons/2-Symbolic/Animals.ipynb +++ b/translations/hr/lessons/2-Symbolic/Animals.ipynb @@ -6,25 +6,25 @@ "collapsed": true }, "source": [ - "# Implementacija stručnog sustava za životinje\n", + "# Implementacija sustava stručnjaka za životinje\n", "\n", "Primjer iz [AI for Beginners Curriculum](http://github.com/microsoft/ai-for-beginners).\n", "\n", - "U ovom primjeru implementirat ćemo jednostavan sustav temeljen na znanju za određivanje životinje na temelju nekih fizičkih karakteristika. Sustav se može prikazati sljedećim AND-OR stablom (ovo je dio cijelog stabla, lako možemo dodati još pravila):\n", + "U ovom primjeru implementirat ćemo jednostavan sustav utemeljen na znanju za određivanje životinje na temelju nekih fizičkih karakteristika. Sustav se može prikazati sljedećim AND-OR stablom (ovo je dio cijelog stabla, lako možemo dodati još pravila):\n", "\n", - "![](../../../../translated_images/hr/AND-OR-Tree.5592d2c70187f283.webp)\n" + "![](../../../../../../translated_images/hr/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Naša vlastita ljuska za ekspertne sustave s unatrag zaključivanjem\n", + "## Naš vlastiti stručni sustav s reverznom inferencijom\n", "\n", - "Pokušajmo definirati jednostavan jezik za reprezentaciju znanja temeljen na produkcijskim pravilima. Koristit ćemo Python klase kao ključne riječi za definiranje pravila. Postojat će tri osnovne vrste klasa:\n", + "Pokušajmo definirati jednostavan jezik za predstavljanje znanja temeljen na produkcijskim pravilima. Koristit ćemo Python klase kao ključne riječi za definiranje pravila. Postoje u osnovi 3 vrste klasa:\n", "* `Ask` predstavlja pitanje koje treba postaviti korisniku. Sadrži skup mogućih odgovora.\n", - "* `If` predstavlja pravilo i samo je sintaktički šećer za pohranu sadržaja pravila.\n", - "* `AND`/`OR` su klase za predstavljanje AND/OR grana stabla. One samo pohranjuju popis argumenata unutar sebe. Kako bismo pojednostavili kod, sva funkcionalnost je definirana u roditeljskoj klasi `Content`.\n" + "* `If` predstavlja pravilo i samo je sintaktički šećer za pohranjivanje sadržaja pravila\n", + "* `AND`/`OR` su klase za predstavljanje AND/OR grana stabla. One samo pohranjuju popis argumenata unutar sebe. Za pojednostavljenje koda, sva funkcionalnost definirana je u nadređenoj klasi `Content`\n" ] }, { @@ -66,7 +66,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "U našem sustavu, radna memorija bi sadržavala popis **činjenica** kao **parova atribut-vrijednost**. Baza znanja može se definirati kao jedan veliki rječnik koji povezuje radnje (nove činjenice koje bi trebale biti umetnute u radnu memoriju) s uvjetima, izraženima kao AND-OR izrazi. Također, neke činjenice mogu biti `Pitaj`-ane.\n" + "U našem sustavu, radna memorija bi sadržavala popis **činjenica** kao **parova atribut-vrijednost**. Baza znanja može se definirati kao jedan veliki rječnik koji povezuje akcije (nove činjenice koje bi trebale biti umetnute u radnu memoriju) s uvjetima, izraženim kao AND-OR izrazi. Također, neke se činjenice mogu `Ask`-irati.\n" ] }, { @@ -99,13 +99,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Za izvođenje unatrag zaključivanja, definirat ćemo klasu `Knowledgebase`. Ona će sadržavati:\n", - "* Radnu `memoriju` - rječnik koji mapira atribute na vrijednosti\n", - "* Pravila `Knowledgebase` u formatu definiranom gore\n", + "Za izvođenje unatrag zaključivanja definirat ćemo klasu `Knowledgebase`. Ona će sadržavati:\n", + "* Radnu `memory` - rječnik koji preslikava atribute u vrijednosti\n", + "* Pravila Knowledgebase u formatu definiranom gore\n", "\n", "Dvije glavne metode su:\n", - "* `get` za dobivanje vrijednosti atributa, uz izvođenje zaključivanja ako je potrebno. Na primjer, `get('color')` bi dobio vrijednost za atribut boje (pitati će ako je potrebno i pohraniti vrijednost za kasniju upotrebu u radnoj memoriji). Ako pitamo `get('color:blue')`, tražit će boju, a zatim vratiti vrijednost `y`/`n` ovisno o boji.\n", - "* `eval` izvodi stvarno zaključivanje, tj. prolazi kroz AND/OR stablo, procjenjuje podciljeve itd.\n" + "* `get` za dobivanje vrijednosti atributa, izvodeći zaključivanje ako je potrebno. Na primjer, `get('color')` dobiva vrijednost za atribut boje (pitat će ako je potrebno i spremiti vrijednost za kasniju upotrebu u radnoj memoriji). Ako pitamo `get('color:blue')`, pitat će za boju, a zatim vratiti vrijednost `y`/`n` ovisno o boji.\n", + "* `eval` izvodi stvarno zaključivanje, tj. prolazi kroz AND/OR stablo, evaluira podciljeve i slično.\n" ] }, { @@ -172,7 +172,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Sada definirajmo našu bazu znanja o životinjama i obavimo konzultaciju. Imajte na umu da će vas ovaj poziv postavljati pitanja. Možete odgovoriti upisivanjem `y`/`n` za pitanja s odgovorima da-ne, ili navođenjem broja (0..N) za pitanja s duljim odgovorima s više izbora.\n" + "Sada definirajmo našu bazu znanja o životinjama i izvršimo konzultaciju. Imajte na umu da će vas ovaj poziv postavljati pitanja. Možete odgovoriti upisivanjem `y`/`n` za pitanja s odgovorom da/ne, ili navođenjem broja (0..N) za pitanja s višestrukim odgovorima.\n" ] }, { @@ -229,11 +229,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Korištenje PyKnow za naprijedno zaključivanje\n", + "## Korištenje Experte za unaprijednu inferenciju\n", "\n", - "U sljedećem primjeru pokušat ćemo implementirati naprijedno zaključivanje koristeći jednu od biblioteka za reprezentaciju znanja, [PyKnow](https://github.com/buguroo/pyknow/). **PyKnow** je biblioteka za kreiranje sustava naprijednog zaključivanja u Pythonu, koja je dizajnirana da bude slična klasičnom starom sustavu [CLIPS](http://www.clipsrules.net/index.html).\n", + "U sljedećem ćemo primjeru pokušati implementirati unaprijednu inferenciju korištenjem jedne od biblioteka za predstavljanje znanja, [Experta](https://github.com/nilp0inter/experta). **Experta** je biblioteka za stvaranje sustava unaprijednog zaključivanja u Pythonu, koja je dizajnirana da bude slična klasičnom starom sustavu [CLIPS](http://www.clipsrules.net/index.html).\n", "\n", - "Također smo mogli sami implementirati naprijedno povezivanje bez većih problema, ali naivne implementacije obično nisu vrlo učinkovite. Za učinkovitije podudaranje pravila koristi se poseban algoritam [Rete](https://en.wikipedia.org/wiki/Rete_algorithm).\n" + "Također bismo mogli implementirati unaprijedno lančanje sami bez mnogo problema, ali naivne implementacije obično nisu vrlo učinkovite. Za učinkovitije podudaranje pravila koristi se poseban algoritam [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": [ - "Definirat ćemo naš sustav kao klasu koja nasljeđuje `KnowledgeEngine`. Svako pravilo definirano je zasebnom funkcijom s anotacijom `@Rule`, koja specificira kada bi se pravilo trebalo aktivirati. Unutar pravila možemo dodavati nove činjenice koristeći funkciju `declare`, a dodavanje tih činjenica rezultirat će pozivanjem dodatnih pravila od strane stroja za zaključivanje unaprijed.\n" + "Definirat ćemo naš sustav kao klasu koja nasljeđuje `KnowledgeEngine`. Svako pravilo definirano je zasebnom funkcijom s anotacijom `@Rule`, koja specificira kada pravilo treba biti aktivirano. Unutar pravila možemo dodavati nove činjenice pomoću funkcije `declare`, a dodavanje tih činjenica rezultirat će pozivanjem još nekih pravila od strane motora unaprijednog zaključivanja.\n" ] }, { @@ -378,7 +377,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Nakon što definiramo bazu znanja, popunjavamo radnu memoriju s nekim početnim činjenicama, a zatim pozivamo metodu `run()` za izvođenje zaključivanja. Kao rezultat možete vidjeti da se nove izvedene činjenice dodaju u radnu memoriju, uključujući konačnu činjenicu o životinji (ako smo ispravno postavili sve početne činjenice).\n" + "Kada definiramo bazu znanja, popunjavamo našu radnu memoriju s nekim početnim činjenicama, a zatim pozivamo metodu `run()` za izvođenje zaključivanja. Kao rezultat možete vidjeti da se nove zaključene činjenice dodaju u radnu memoriju, uključujući i završnu činjenicu o životinji (ako smo pravilno postavili sve početne činjenice).\n" ] }, { @@ -440,7 +439,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n---\n\n**Odricanje od odgovornosti**: \nOvaj dokument je preveden korištenjem AI usluge za prevođenje [Co-op Translator](https://github.com/Azure/co-op-translator). Iako nastojimo osigurati točnost, imajte na umu da automatski prijevodi mogu sadržavati pogreške ili netočnosti. Izvorni dokument na izvornom jeziku treba smatrati mjerodavnim izvorom. Za ključne informacije preporučuje se profesionalni prijevod od strane stručnjaka. Ne preuzimamo odgovornost za bilo kakva nesporazuma ili pogrešna tumačenja koja mogu proizaći iz korištenja ovog prijevoda.\n" + "---\n\n\n**Odricanje od odgovornosti**: \nOvaj dokument preveden je korištenjem AI usluge prevođenja [Co-op Translator](https://github.com/Azure/co-op-translator). Iako nastojimo postići točnost, imajte na umu da automatski prijevodi mogu sadržavati pogreške ili netočnosti. Izvorni dokument na izvornom jeziku treba smatrati autoritativnim izvorom. Za ključne informacije preporuča se profesionalan prijevod od strane ljudskog prevoditelja. Nismo odgovorni za bilo kakve nesporazume ili pogrešna tumačenja koja proizlaze iz korištenja ovog prijevoda.\n\n" ] } ], @@ -467,8 +466,8 @@ "version": "3.11.2" }, "coopTranslator": { - "original_hash": "ab2bd97b0453415b89a469284609a8ce", - "translation_date": "2025-08-30T07:41:52+00:00", + "original_hash": "8ef43db4b9182239fd150a76bd494fdb", + "translation_date": "2026-01-16T05:59:32+00:00", "source_file": "lessons/2-Symbolic/Animals.ipynb", "language_code": "hr" } diff --git a/translations/hr/lessons/2-Symbolic/README.md b/translations/hr/lessons/2-Symbolic/README.md index a4362088..9cd4dbc3 100644 --- a/translations/hr/lessons/2-Symbolic/README.md +++ b/translations/hr/lessons/2-Symbolic/README.md @@ -1,116 +1,116 @@ -# Predstavljanje znanja i ekspertni sustavi +# Predstavljanje znanja i stručni sustavi -![Sažetak sadržaja o simboličkoj umjetnoj inteligenciji](../../../../translated_images/hr/ai-symbolic.715a30cb610411a6.webp) +![Sažetak simboličke umjetne inteligencije](../../../../../../translated_images/hr/ai-symbolic.715a30cb610411a6.webp) -> Sketchnote autorice [Tomomi Imura](https://twitter.com/girlie_mac) +> Sketchnote autora [Tomomi Imura](https://twitter.com/girlie_mac) -Potraga za umjetnom inteligencijom temelji se na traženju znanja kako bi se svijet razumio na način sličan ljudskom. No, kako to postići? +Potraga za umjetnom inteligencijom temelji se na traženju znanja kako bi se svijet shvatio slično kao što to rade ljudi. Ali kako to učiniti? ## [Kviz prije predavanja](https://ff-quizzes.netlify.app/en/ai/quiz/3) -U ranim danima razvoja umjetne inteligencije bio je popularan pristup "odozgo prema dolje" za stvaranje inteligentnih sustava (o kojem smo govorili u prethodnoj lekciji). Ideja je bila izvući znanje od ljudi u oblik koji računalo može čitati, a zatim ga koristiti za automatsko rješavanje problema. Ovaj pristup temeljio se na dva ključna koncepta: +U ranim danima AI, popularan je bio pristup odozgo prema dolje za stvaranje inteligentnih sustava (objašnjen u prethodnoj lekciji). Ideja je bila izdvojiti znanje od ljudi u neki oblik razumljiv stroju, a zatim ga automatski koristiti za rješavanje problema. Ovaj pristup temelji se na dvije velike ideje: * Predstavljanje znanja * Zaključivanje ## Predstavljanje znanja -Jedan od važnih pojmova u simboličkoj umjetnoj inteligenciji je **znanje**. Važno je razlikovati znanje od *informacija* ili *podataka*. Na primjer, može se reći da knjige sadrže znanje jer se iz njih može učiti i postati stručnjak. Međutim, ono što knjige zapravo sadrže naziva se *podaci*, a čitanjem knjiga i integriranjem tih podataka u naš model svijeta pretvaramo podatke u znanje. +Jedan od važnih pojmova u simboličkoj umjetnoj inteligenciji je **znanje**. Važno je razlikovati znanje od *informacija* ili *podataka*. Na primjer, može se reći da knjige sadrže znanje jer proučavanjem knjiga postajemo stručnjaci. Međutim, ono što knjige sadrže zapravo su *podatci*, a čitanjem knjiga i integriranjem tih podataka u naš svjetonazor pretvaramo te podatke u znanje. -> ✅ **Znanje** je ono što se nalazi u našoj glavi i predstavlja naše razumijevanje svijeta. Ono se stječe aktivnim procesom **učenja**, koji integrira primljene informacije u naš aktivni model svijeta. +> ✅ **Znanje** je nešto što se nalazi u našem umu i predstavlja naše razumijevanje svijeta. Dobiva se aktivnim procesom **učenja**, koji integrira dijelove informacija koje primamo u naš aktivni model svijeta. -Najčešće ne definiramo strogo znanje, već ga povezujemo s drugim srodnim pojmovima koristeći [DIKW piramidu](https://en.wikipedia.org/wiki/DIKW_pyramid). Ona uključuje sljedeće pojmove: +Najčešće ne definiramo strogo znanje, već ga usklađujemo s drugim povezanim pojmovima pomoću [DIKW piramide](https://en.wikipedia.org/wiki/DIKW_pyramid). Sadrži sljedeće pojmove: -* **Podaci** su nešto što je predstavljeno na fizičkom mediju, poput pisanog teksta ili izgovorenih riječi. Podaci postoje neovisno o ljudima i mogu se prenositi među njima. -* **Informacije** su način na koji interpretiramo podatke u svojoj glavi. Na primjer, kada čujemo riječ *računalo*, imamo određeno razumijevanje što to znači. -* **Znanje** je informacija integrirana u naš model svijeta. Na primjer, kada naučimo što je računalo, počinjemo imati ideje o tome kako radi, koliko košta i za što se može koristiti. Ova mreža međusobno povezanih pojmova čini naše znanje. -* **Mudrost** je još jedna razina našeg razumijevanja svijeta i predstavlja *meta-znanje*, tj. neku ideju o tome kako i kada koristiti znanje. +* **Podaci** su nešto predstavljeno na fizičkim medijima, poput pisanog teksta ili izgovorenih riječi. Podaci postoje neovisno o ljudima i mogu se prenositi među ljudima. +* **Informacije** su način na koji tumačimo podatke u svojoj glavi. Na primjer, kad čujemo riječ *računalo*, imamo neko razumijevanje što to jest. +* **Znanje** je informacije integrirane u naš svjetonazor. Na primjer, nakon što naučimo što je računalo, počinjemo imati ideje o tome kako radi, koliko košta i za što se može koristiti. Ta mreža međusobno povezanih pojmova čini naše znanje. +* **Mudrost** je još jedna razina našeg razumijevanja svijeta i predstavlja *meta-znanje*, npr. neku ideju o tome kako i kada treba koristiti znanje. - + -*Slika [s Wikipedije](https://commons.wikimedia.org/w/index.php?curid=37705247), Autor: Longlivetheux - Vlastito djelo, CC BY-SA 4.0* +*Slika [s Wikipedije](https://commons.wikimedia.org/w/index.php?curid=37705247), autor Longlivetheux - vlastiti rad, CC BY-SA 4.0* -Dakle, problem **predstavljanja znanja** je pronaći učinkovit način za predstavljanje znanja unutar računala u obliku podataka kako bi se ono moglo automatski koristiti. To se može promatrati kao spektar: +Dakle, problem **predstavljanja znanja** je pronaći neki učinkovit način predstavljanja znanja unutar računala u obliku podataka kako bi bilo automatski upotrebljivo. To se može promatrati kao spektar: -![Spektar predstavljanja znanja](../../../../translated_images/hr/knowledge-spectrum.b60df631852c0217.webp) +![Spektar predstavljanja znanja](../../../../../../translated_images/hr/knowledge-spectrum.b60df631852c0217.webp) > Slika autora [Dmitry Soshnikov](http://soshnikov.com) -* Na lijevoj strani nalaze se vrlo jednostavni tipovi predstavljanja znanja koji se mogu učinkovito koristiti u računalima. Najjednostavniji je algoritamski, gdje je znanje predstavljeno računalnim programom. Međutim, to nije najbolji način za predstavljanje znanja jer nije fleksibilan. Znanje u našoj glavi često nije algoritamsko. -* Na desnoj strani nalaze se reprezentacije poput prirodnog teksta. To je najmoćnije, ali se ne može koristiti za automatsko zaključivanje. +* S lijeve strane postoje vrlo jednostavni tipovi predstavljanja znanja koje računala mogu učinkovito koristiti. Najjednostavniji je algoritamski, kada se znanje predstavlja računalnim programom. To, međutim, nije najbolji način predstavljanja znanja jer nije fleksibilan. Znanje u našim umovima često nije algoritamsko. +* S desne strane su prikazi poput prirodnog teksta. To je najsnažniji oblik, no ne može se koristiti za automatsko zaključivanje. -> ✅ Razmislite na trenutak o tome kako predstavljate znanje u svojoj glavi i pretvarate ga u bilješke. Postoji li određeni format koji vam pomaže u zadržavanju informacija? +> ✅ Razmislite na trenutak kako predstavljate znanje u svojoj glavi i kako ga pretvarate u bilješke. Postoji li neki poseban format koji vam pomaže u zadržavanju znanja? -## Klasifikacija računalnih metoda predstavljanja znanja +## Klasicifikacija računalnih načina predstavljanja znanja -Različite metode predstavljanja znanja u računalima možemo klasificirati u sljedeće kategorije: +Možemo klasificirati različite metode računalnog predstavljanja znanja u sljedeće kategorije: -* **Mrežne reprezentacije** temelje se na činjenici da imamo mrežu međusobno povezanih pojmova u svojoj glavi. Možemo pokušati reproducirati iste mreže kao graf unutar računala - tzv. **semantička mreža**. +* **Mrežna predstava** temelji se na činjenici da u glavi imamo mrežu međusobno povezanih pojmova. Možemo pokušati reproducirati iste mreže kao graf unutar računala - tzv. **semantičku mrežu**. -1. **Objekt-atribut-vrijednost trojke** ili **parovi atribut-vrijednost**. Budući da se graf može predstaviti unutar računala kao popis čvorova i bridova, semantičku mrežu možemo predstaviti popisom trojki koje sadrže objekte, atribute i vrijednosti. Na primjer, možemo izgraditi sljedeće trojke o programskim jezicima: +1. **Triade objekt-atribut-vrijednost** ili **parovi atribut-vrijednost**. Kako se graf može predstaviti unutar računala kao popis čvorova i bridova, možemo predstaviti semantičku mrežu kao popis troki koje sadrže objekte, atribute i vrijednosti. Na primjer, možemo napraviti sljedeće trojke o programskim jezicima: Objekt | Atribut | Vrijednost --------|---------|---------- -Python | je | Ne-tipizirani jezik +-------|---------|----------- +Python | je | Netyperani jezik Python | izumio | Guido van Rossum Python | sintaksa bloka | uvlačenje -Ne-tipizirani jezik | nema | definicije tipova +Netyperani jezik | nema | definicije tipova > ✅ Razmislite kako se trojke mogu koristiti za predstavljanje drugih vrsta znanja. -2. **Hijerarhijske reprezentacije** naglašavaju činjenicu da često stvaramo hijerarhiju objekata u svojoj glavi. Na primjer, znamo da je kanarinac ptica i da sve ptice imaju krila. Također imamo neku ideju o tome koje je boje kanarinac obično i koja je njegova brzina leta. +2. **Hijerarhijske predstave** naglašavaju činjenicu da često u glavi stvaramo hijerarhiju objekata. Na primjer, znamo da je kanarinac ptica, a sve ptice imaju krila. Također imamo neku ideju o boji kanarinca i brzini leta. - - **Reprezentacija okvira** temelji se na predstavljanju svakog objekta ili klase objekata kao **okvira** koji sadrži **utore**. Utori imaju moguće zadane vrijednosti, ograničenja vrijednosti ili pohranjene procedure koje se mogu pozvati za dobivanje vrijednosti utora. Svi okviri tvore hijerarhiju sličnu hijerarhiji objekata u objektno-orijentiranim programskim jezicima. - - **Scenariji** su posebna vrsta okvira koji predstavljaju složene situacije koje se mogu razvijati tijekom vremena. + - **Predstava okvira** temelji se na predstavljanju svakog objekta ili klase objekata kao **okvira** koji sadrži **slotove**. Slotovi imaju moguće zadane vrijednosti, ograničenja vrijednosti ili pohranjene procedure koje se pozivaju za dobivanje vrijednosti slota. Svi okviri formiraju hijerarhiju slično kao hijerarhija objekata u objektno orijentiranim programskim jezicima. + - **Scenariji** su posebna vrsta okvira koji predstavljaju složene situacije koje se mogu razviti u vremenu. **Python** -Utor | Vrijednost | Zadana vrijednost | Interval | ------|-----------|-------------------|----------| -Ime | Python | | | -Je | Ne-tipizirani jezik | | | -Varijabla | | CamelCase | | +Slot | Vrijednost | Zadana vrijednost | Interval +-----|------------|-------------------|--------- +Name | Python | | | +Is-A | Netyperani jezik | | | +Variable Case | | CamelCase | | Duljina programa | | | 5-5000 linija | -Sintaksa bloka | Uvlačenje | | | +Sintaksa bloka | Indent | | | -3. **Proceduralne reprezentacije** temelje se na predstavljanju znanja popisom radnji koje se mogu izvršiti kada se dogodi određeni uvjet. - - Produkcijska pravila su if-then izjave koje nam omogućuju donošenje zaključaka. Na primjer, liječnik može imati pravilo koje kaže da **AKO** pacijent ima visoku temperaturu **ILI** visoku razinu C-reaktivnog proteina u krvnom testu **ONDA** ima upalu. Kada naiđemo na jedan od uvjeta, možemo donijeti zaključak o upali, a zatim ga koristiti u daljnjem zaključivanju. - - Algoritmi se mogu smatrati drugom vrstom proceduralne reprezentacije, iako se gotovo nikada ne koriste izravno u sustavima temeljenim na znanju. +3. **Proceduralne predstave** temelje se na predstavljanju znanja kao popisa akcija koje se mogu izvršiti kada nastupi određeni uvjet. + - Produkcijska pravila su if-then izrazi koji nam omogućuju donošenje zaključaka. Na primjer, liječnik može imati pravilo koje kaže da **AKO** pacijent ima visoku temperaturu **ILI** visok nivo C-reaktivnog proteina u krvnom testu **ONDA** ima upalu. Kada naiđemo na jedan od uvjeta, možemo donijeti zaključak o upali i koristiti ga u daljnjem zaključivanju. + - Algoritmi se mogu smatrati oblikom proceduralne predodžbe, iako se gotovo nikad ne koriste izravno u sustavima temeljenim na znanju. 4. **Logika** je izvorno predložena od strane Aristotela kao način predstavljanja univerzalnog ljudskog znanja. - - Predikatna logika kao matematička teorija je prebogata da bi bila računalno izvediva, stoga se obično koristi neki njezin podskup, poput Hornovih klauzula koje se koriste u Prologu. - - Deskriptivna logika je obitelj logičkih sustava koja se koristi za predstavljanje i zaključivanje o hijerarhijama objekata u distribuiranim reprezentacijama znanja poput *semantičkog weba*. + - Predikatna logika kao matematička teorija je prebogata da bi bila računski dostižna, stoga se obično koristi neki njezin podskup, poput Hornovih klauzula korištenih u Prologu. + - Deskriptivna logika je skup logičkih sustava koji se koriste za predstavljanje i zaključivanje o hijerarhijama objekata u distribuiranim predstavama znanja kao što je *semantički web*. -## Ekspertni sustavi +## Stručni sustavi -Jedan od ranih uspjeha simboličke umjetne inteligencije bili su tzv. **ekspertni sustavi** - računalni sustavi dizajnirani da djeluju kao stručnjaci u nekom ograničenom problematičnom području. Temeljili su se na **bazi znanja** izvučenoj od jednog ili više ljudskih stručnjaka i sadržavali su **mehanizam zaključivanja** koji je provodio zaključivanje na temelju te baze. +Jedan od ranih uspjeha simboličke AI bili su tzv. **stručni sustavi** - računalni sustavi dizajnirani da djeluju kao stručnjak u nekom ograničenom području problema. Temeljili su se na **bazi znanja** izvučenoj od jednog ili više ljudskih stručnjaka i sadržavali su **zaključni stroj** koji je vršio zaključivanje na temelju nje. -![Ljudska arhitektura](../../../../translated_images/hr/arch-human.5d4d35f1bba3ab1c.webp) | ![Arhitektura sustava temeljenog na znanju](../../../../translated_images/hr/arch-kbs.3ec5c150b09fa8da.webp) +![Ljudska arhitektura](../../../../../../translated_images/hr/arch-human.5d4d35f1bba3ab1c.webp) | ![Sustav temeljen na znanju](../../../../../../translated_images/hr/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ Pojednostavljena struktura ljudskog živčanog sustava | Arhitektura sustava temeljenog na znanju -Ekspertni sustavi izgrađeni su poput ljudskog sustava zaključivanja, koji sadrži **kratkoročnu memoriju** i **dugoročnu memoriju**. Slično tome, u sustavima temeljenim na znanju razlikujemo sljedeće komponente: +Stručni sustavi se grade poput ljudskog sustava zaključivanja, koji sadrži **kratkoročnu memoriju** i **dugoročnu memoriju**. Slično tome u sustavima temeljenim na znanju razlikujemo sljedeće komponente: -* **Memorija problema**: sadrži znanje o problemu koji se trenutno rješava, tj. temperaturu ili krvni tlak pacijenta, ima li upalu ili ne itd. Ovo znanje se također naziva **statističko znanje**, jer sadrži trenutni prikaz onoga što trenutno znamo o problemu - tzv. *stanje problema*. -* **Baza znanja**: predstavlja dugoročno znanje o problematičnom području. Ručno se izvlači od ljudskih stručnjaka i ne mijenja se od konzultacije do konzultacije. Budući da nam omogućuje navigaciju od jednog stanja problema do drugog, također se naziva **dinamičko znanje**. -* **Mehanizam zaključivanja**: orkestrira cijeli proces pretraživanja u prostoru stanja problema, postavljajući pitanja korisniku kada je to potrebno. Također je odgovoran za pronalaženje pravih pravila koja se primjenjuju na svako stanje. +* **Memorija problema**: sadrži znanje o problemu koji se trenutačno rješava, npr. tjelesnu temperaturu ili krvni tlak pacijenta, ima li upalu ili ne itd. Ovo znanje se naziva i **statističko znanje**, jer sadrži trenutni prikaz onoga što trenutno znamo o problemu - tzv. *stanje problema*. +* **Baza znanja**: predstavlja dugoročno znanje o nekom području problema. Izvučena je ručno od ljudskih stručnjaka i ne mijenja se od konzultacije do konzultacije. Budući da nam omogućuje navigaciju iz jednog stanja problema u drugo, također se naziva **dinamičko znanje**. +* **Zaključni stroj**: orkestrira cijeli proces pretraživanja prostora stanja problema, postavljajući korisniku pitanja kad je potrebno. Također je odgovoran za pronalaženje pravila koja će se primijeniti u svakom stanju. -Kao primjer, razmotrimo sljedeći ekspertni sustav za određivanje životinje na temelju njezinih fizičkih karakteristika: +Kao primjer, razmotrimo sljedeći stručni sustav za određivanje životinje na temelju fizičkih karakteristika: -![AND-OR stablo](../../../../translated_images/hr/AND-OR-Tree.5592d2c70187f283.webp) +![AND-ILI stablo](../../../../../../translated_images/hr/AND-OR-Tree.5592d2c70187f283.webp) > Slika autora [Dmitry Soshnikov](http://soshnikov.com) -Ovaj dijagram naziva se **AND-OR stablo**, i to je grafički prikaz skupa produkcijskih pravila. Crtanje stabla korisno je na početku izvlačenja znanja od stručnjaka. Za predstavljanje znanja unutar računala prikladnije je koristiti pravila: +Ovaj dijagram naziva se **AND-ILI stablo** i grafički je prikaz skupa produkcijskih pravila. Crtanje stabla korisno je na početku izvlačenja znanja od stručnjaka. Za predstavljanje znanja unutar računala prikladnije je koristiti pravila: ``` IF the animal eats meat @@ -121,78 +121,78 @@ OR (animal has sharp teeth THEN the animal is a carnivore ``` -Možete primijetiti da svaki uvjet na lijevoj strani pravila i radnja zapravo predstavljaju trojke objekt-atribut-vrijednost (OAV). **Radna memorija** sadrži skup OAV trojki koje odgovaraju problemu koji se trenutno rješava. **Mehanizam pravila** traži pravila za koja je uvjet zadovoljen i primjenjuje ih, dodajući novu trojku u radnu memoriju. +Primijetit ćete da su svi uvjeti na lijevoj strani pravila i akcija u biti trojke objekt-atribut-vrijednost (OAV). **Radna memorija** sadrži skup OAV trojki koje odgovaraju trenutačnom rješavanju problema. **Stroj za pravila** traži pravila za koja je uvjet zadovoljen i primjenjuje ih, dodajući novu trojku u radnu memoriju. -> ✅ Nacrtajte vlastito AND-OR stablo na temu koja vas zanima! +> ✅ Napišite svoje AND-ILI stablo na temu koja vam se sviđa! -### Naprijed vs. unatrag zaključivanje +### Zaključivanje unaprijed naspram unatrag -Proces opisan gore naziva se **naprijed zaključivanje**. Počinje s nekim početnim podacima o problemu dostupnim u radnoj memoriji, a zatim izvršava sljedeću petlju zaključivanja: +Ovo gore opisani proces naziva se **zaključivanje unaprijed**. Počinje s početnim podacima o problemu dostupnim u radnoj memoriji, a zatim izvršava sljedeću petlju zaključivanja: -1. Ako je ciljni atribut prisutan u radnoj memoriji - zaustavi se i daj rezultat -2. Potraži sva pravila čiji je uvjet trenutno zadovoljen - dobiva se **skup konflikata** pravila. -3. Izvrši **rješavanje konflikata** - odaberi jedno pravilo koje će se izvršiti u ovom koraku. Mogu postojati različite strategije rješavanja konflikata: +1. Ako je ciljna značajka prisutna u radnoj memoriji - zaustavi se i da rezultat +2. Pronađi sva pravila čiji je uvjet trenutno zadovoljen - dobiva se **skup sukoba** pravila. +3. Izvrši **rješavanje sukoba** - odaberi jedno pravilo koje će se izvršiti u ovom koraku. Mogu postojati različite strategije rješavanja sukoba: - Odaberi prvo primjenjivo pravilo u bazi znanja - Odaberi nasumično pravilo - Odaberi *specifičnije* pravilo, tj. ono koje zadovoljava najviše uvjeta na "lijevoj strani" (LHS) -4. Primijeni odabrano pravilo i dodaj novi dio znanja u stanje problema +4. Primijeni odabrano pravilo i ubaci novi dio znanja u stanje problema 5. Ponovi od koraka 1. -Međutim, u nekim slučajevima možda želimo započeti s praznim znanjem o problemu i postavljati pitanja koja će nam pomoći da dođemo do zaključka. Na primjer, prilikom medicinske dijagnoze obično ne provodimo sve medicinske analize unaprijed prije nego što počnemo dijagnosticirati pacijenta. Radije želimo provesti analize kada je potrebno donijeti odluku. +Međutim, u nekim slučajevima želimo započeti s praznim znanjem o problemu i postavljati pitanja koja će nam pomoći da dođemo do zaključka. Na primjer, kod medicinske dijagnoze obično ne radimo sve medicinske analize unaprijed prije dijagnosticiranja pacijenta. Radije želimo napraviti analize kad treba donijeti odluku. -Ovaj proces može se modelirati pomoću **unatrag zaključivanja**. Ono je vođeno **ciljem** - vrijednošću atributa koju pokušavamo pronaći: +Ovaj se proces može modelirati pomoću **zaključivanja unatrag**. Njime upravlja **cilj** - vrijednost svojstva koju želimo pronaći: -1. Odaberi sva pravila koja mogu dati vrijednost cilja (tj. s ciljem na RHS ("desnoj strani")) - skup konflikata -1. Ako ne postoje pravila za ovaj atribut ili postoji pravilo koje kaže da bismo trebali pitati korisnika za vrijednost - pitaj za nju, inače: -1. Koristi strategiju rješavanja konflikata za odabir jednog pravila koje ćemo koristiti kao *hipotezu* - pokušat ćemo je dokazati -1. Rekurzivno ponovi proces za sve atribute na LHS pravila, pokušavajući ih dokazati kao ciljeve -1. Ako proces u bilo kojem trenutku ne uspije - koristi drugo pravilo u koraku 3. +1. Odaberi sva pravila koja nam mogu dati vrijednost cilja (tj. s ciljem na desnoj strani [RHS]) - skup sukoba +1. Ako nema pravila za ovaj atribut ili postoji pravilo koje kaže da trebamo pitati korisnika za vrijednost - pitaj, inače: +1. Koristi strategiju rješavanja sukoba da odabereš pravilo koje će se koristiti kao *hipoteza* - pokušat ćemo ga dokazati +1. Rekurzivno ponavljaj postupak za sve atribute s lijeve strane (LHS) pravila, pokušavajući ih dokazati kao ciljeve +1. Ako proces u bilo kojem trenutku ne uspije - koristi drugo pravilo iz koraka 3. -> ✅ U kojim situacijama je naprijed zaključivanje prikladnije? A što je s unatrag zaključivanjem? +> ✅ U kojim situacijama je pogodnije upotrijebiti zaključivanje unaprijed? Kako je s zaključivanjem unatrag? -### Implementacija ekspertnih sustava +### Implementacija stručnih sustava -Ekspertni sustavi mogu se implementirati koristeći različite alate: +Stručni se sustavi mogu implementirati koristeći različite alate: -* Programiranjem izravno u nekom visoko razinom programskom jeziku. Ovo nije najbolja ideja jer je glavna prednost sustava temeljenog na znanju to što je znanje odvojeno od zaključivanja, a potencijalno bi stručnjak za problematično područje trebao moći pisati pravila bez razumijevanja detalja procesa zaključivanja. -* Korištenjem **ljuske ekspertnih sustava**, tj. sustava posebno dizajniranog za popunjavanje znanjem koristeći neki jezik za predstavljanje znanja. +* Izravnim programiranjem u nekom visoko razinom programskom jeziku. To nije najbolja ideja jer je glavna prednost sustava temeljenog na znanju to što je znanje odvojeno od zaključivanja, pa stručnjak za problem domene potencijalno može pisati pravila bez razumijevanja detalja postupka zaključivanja. +* Korištenjem **stručnih ljuski**, tj. sustava specijalno dizajniranih da budu popunjavani znanjem koristeći neki jezik za predstavljanje znanja. ## ✍️ Vježba: Zaključivanje o životinjama -Pogledajte [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) za primjer implementacije ekspertnih sustava s naprijed i unatrag zaključivanjem. +Pogledajte [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) kao primjer implementacije stručnog sustava s zaključivanjem unaprijed i unatrag. -> **Napomena**: Ovaj primjer je prilično jednostavan i samo daje ideju kako izgleda ekspertni sustav. Kada počnete stvarati takav sustav, primijetit ćete neku *inteligentnu* ponašanje tek kada dosegnete određeni broj pravila, oko 200+. U nekom trenutku pravila postaju previše složena da biste ih sve držali u glavi, i tada se možete zapitati zašto sustav donosi određene odluke. Međutim, važna karakteristika sustava temeljenih na znanju je da uvijek možete *objasniti* točno kako je donesena bilo koja odluka. +> **Napomena**: Ovaj primjer je prilično jednostavan i daje samo ideju kako izgleda stručni sustav. Kad počnete izrađivati takav sustav, primijetit ćete *inteligentno* ponašanje tek kad se dosegne određen broj pravila, otprilike 200+. U nekom trenutku pravila postaju previše složena da bismo ih sve držali na umu, i tada se možete zapitati zašto sustav donosi određene odluke. Ipak, važna karakteristika sustava temeljenih na znanju jest da uvijek možete *objasniti* kako je koja odluka donesena. ## Ontologije i semantički web -Krajem 20. stoljeća postojala je inicijativa za korištenje predstavljanja znanja za označavanje internetskih resursa kako bi bilo moguće pronaći resurse koji odgovaraju vrlo specifičnim upitima. Ovaj pokret nazvan je **Semantički web**, i oslanjao se na nekoliko koncepata: +Krajem 20. stoljeća postojala je inicijativa za korištenje predstavljanja znanja za označavanje internetskih resursa, kako bi se moglo pronalaziti resurse koji odgovaraju vrlo specifičnim upitima. Taj je pokret nazvan **Semantički web** i oslanjao se na nekoliko koncepata: -- Posebno predstavljanje znanja temeljeno na **[deskriptivnoj logici](https://en.wikipedia.org/wiki/Description_logic)** (DL). Slično je predstavljanju znanja okvirom jer gradi hijerarhiju objekata s svojstvima, ali ima formalnu logičku semantiku i zaključivanje. Postoji cijela obitelj DL-a koja balansira između izražajnosti i algoritamske složenosti zaključivanja. -- Distribuirano predstavljanje znanja, gdje su svi pojmovi predstavljeni globalnim URI identifikatorom, što omogućuje stvaranje hijerarhija znanja koje se protežu internetom. -- Obitelj XML jezika za opisivanje znanja: RDF (Resource Description Framework), RDFS (RDF Schema), OWL (Ontology Web Language). +- Posebno predstavljanje znanja temeljeno na **[deskriptivnoj logici](https://en.wikipedia.org/wiki/Description_logic)** (DL). Slično je predstavljanju znanja u okviru jer gradi hijerarhiju objekata sa svojstvima, ali ima formalnu logičku semantiku i zaključivanje. Postoji cijela skupina DL sustava koji balansiraju između izražajnosti i algoritamske složenosti zaključivanja. +- Distribuirano predstavljanje znanja, gdje su svi pojmovi predstavljeni globalnim URI identifikatorom, što omogućuje stvaranje hijerarhija znanja koje se šire internetom. +- Obitelj XML-basiranih jezika za opis znanja: RDF (Resource Description Framework), RDFS (RDF Schema), OWL (Ontology Web Language). -Ključni koncept u Semantičkom webu je koncept **Ontologije**. Ontologija se odnosi na eksplicitnu specifikaciju domene problema koristeći formalnu reprezentaciju znanja. Najjednostavnija ontologija može biti samo hijerarhija objekata u domeni problema, dok složenije ontologije uključuju pravila koja se mogu koristiti za zaključivanje. +Ključni koncept u Semantičkom webu je koncept **Ontologije**. Odnosi se na eksplicitnu specifikaciju domena problema koristeći neki formalni prikaz znanja. Najjednostavnija ontologija može biti samo hijerarhija objekata u domeni problema, ali složenije ontologije uključuju pravila koja se mogu koristiti za zaključivanje. -U semantičkom webu, sve reprezentacije temelje se na trojkama. Svaki objekt i svaka relacija jedinstveno su identificirani URI-jem. Na primjer, ako želimo navesti činjenicu da je ovaj AI kurikulum razvio Dmitry Soshnikov 1. siječnja 2022. - evo trojki koje možemo koristiti: +U semantičkom webu, svi prikazi temeljeni su na trojkama. Svaki objekt i svaki odnos jedinstveno su identificirani URI-jem. Na primjer, ako želimo navesti činjenicu da je ovaj AI Kurikulum razvijen od strane Dmitryja Soshnikova 1. siječnja 2022. - evo trojki koje možemo koristiti: - + ``` -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 ``` -> ✅ Ovdje su `http://www.example.com/terms/creation-date` i `http://purl.org/dc/elements/1.1/creator` dobro poznati i univerzalno prihvaćeni URI-ji za izražavanje pojmova *autor* i *datum kreiranja*. +> ✅ Ovdje su `http://www.example.com/terms/creation-date` i `http://purl.org/dc/elements/1.1/creator` neki dobro poznati i univerzalno prihvaćeni URI-ji za izražavanje pojmova *stvoritelja* i *datuma stvaranja*. -U složenijem slučaju, ako želimo definirati popis autora, možemo koristiti neke podatkovne strukture definirane u RDF-u. +U složenijem slučaju, ako želimo definirati popis stvoritelja, možemo koristiti neke podatkovne strukture definirane u RDF-u. - + -> Gornje dijagrame izradio [Dmitry Soshnikov](http://soshnikov.com) +> Dijagrami iznad autora su [Dmitry Soshnikov](http://soshnikov.com) -Napredak u izgradnji Semantičkog weba donekle je usporen uspjehom tražilica i tehnika obrade prirodnog jezika, koje omogućuju izdvajanje strukturiranih podataka iz teksta. Međutim, u nekim područjima i dalje postoje značajni napori za održavanje ontologija i baza znanja. Nekoliko projekata vrijednih spomena: +Napredak izgradnje Semantičkog weba donekle je usporen uspjehom tražilica i tehnika za obradu prirodnog jezika, koje omogućuju izdvajanje strukturiranih podataka iz teksta. Međutim, u nekim područjima i dalje postoje značajni napori za održavanje ontologija i baza znanja. Neki projekti vrijedni spomena: -* [WikiData](https://wikidata.org/) je zbirka strojno čitljivih baza znanja povezanih s Wikipedijom. Većina podataka izvučena je iz Wikipedia *InfoBoxova*, dijelova strukturiranog sadržaja unutar stranica Wikipedije. WikiData možete [upitavati](https://query.wikidata.org/) pomoću SPARQL-a, posebnog jezika upita za Semantički web. Evo primjera upita koji prikazuje najpopularnije boje očiju među ljudima: +* [WikiData](https://wikidata.org/) je zbirka strojno čitljivih baza podataka povezanim s Wikipedijom. Većina podataka iskopana je iz Wikipedia *InfoBoxova*, dijelova strukturiranog sadržaja unutar stranica Wikipedije. Možete [upitima](https://query.wikidata.org/) koristiti wikidata u SPARQL-u, posebnom upitnom jeziku za Semantički Web. Evo primjera upita koji prikazuje najpopularnije boje očiju među ljudima: ```sparql #defaultView:BubbleChart @@ -206,13 +206,13 @@ WHERE GROUP BY ?eyeColorLabel ``` -* [DBpedia](https://www.dbpedia.org/) je još jedan projekt sličan WikiData. +* [DBpedia](https://www.dbpedia.org/) je još jedan sličan pokušaj kao WikiData. -> ✅ Ako želite eksperimentirati s izradom vlastitih ontologija ili otvaranjem postojećih, postoji odličan vizualni uređivač ontologija nazvan [Protégé](https://protege.stanford.edu/). Preuzmite ga ili koristite online. +> ✅ Ako želite eksperimentirati s izgradnjom vlastitih ontologija ili otvaranjem postojećih, postoji izvrstan vizualni uređivač ontologija nazvan [Protégé](https://protege.stanford.edu/). Preuzmite ga ili koristite online. - + -*Web Protégé uređivač otvoren s ontologijom obitelji Romanov. Snimka zaslona Dmitry Soshnikov* +*Web Protégé uređivač otvoren s ontologijom obitelji Romanov. Screenshot Dmitryja Soshnikova* ## ✍️ Vježba: Ontologija obitelji @@ -220,33 +220,37 @@ Pogledajte [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/b ## Microsoft Concept Graph -U većini slučajeva, ontologije se pažljivo izrađuju ručno. Međutim, također je moguće **izvući** ontologije iz nestrukturiranih podataka, na primjer, iz tekstova prirodnog jezika. +U većini slučajeva, ontologije se pažljivo izrađuju ručno. Međutim, moguće je i **iskopavati** ontologije iz nestrukturiranih podataka, na primjer, iz tekstova prirodnog jezika. -Jedan takav pokušaj napravljen je od strane Microsoft Researcha, što je rezultiralo [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste). +Jedan takav pokušaj napravio je Microsoft Research, što je rezultiralo [Microsoft Concept Graphom](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste). -To je velika zbirka entiteta grupiranih pomoću `is-a` nasljednog odnosa. Omogućuje odgovaranje na pitanja poput "Što je Microsoft?" - odgovor bi mogao biti nešto poput "tvrtka s vjerojatnošću 0.87, i brend s vjerojatnošću 0.75". +Radi se o velikoj zbirci entiteta grupiranih pomoću `is-a` nasljednih odnosa. Omogućuje odgovore na pitanja poput "Što je Microsoft?" – a odgovor bi bio nešto poput "tvrtka s vjerojatnošću 0.87, i brend s vjerojatnošću 0.75". -Graf je dostupan ili kao REST API, ili kao veliki tekstualni dokument za preuzimanje koji navodi sve parove entiteta. +Graf je dostupan kao REST API ili kao velika datoteka za preuzimanje koja sadrži popis svih parova entiteta. -## ✍️ Vježba: Graf koncepta +## ✍️ Vježba: Graf koncepata -Isprobajte bilježnicu [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) kako biste vidjeli kako možemo koristiti Microsoft Concept Graph za grupiranje vijesti u nekoliko kategorija. +Isprobajte bilježnicu [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) da vidite kako možemo koristiti Microsoft Concept Graph za grupiranje vijesti u nekoliko kategorija. ## Zaključak -Danas se AI često smatra sinonimom za *strojno učenje* ili *neuronske mreže*. Međutim, ljudska bića također pokazuju eksplicitno zaključivanje, što je nešto što trenutno nije obuhvaćeno neuronskim mrežama. U stvarnim projektima, eksplicitno zaključivanje i dalje se koristi za obavljanje zadataka koji zahtijevaju objašnjenja ili mogućnost kontrolirane promjene ponašanja sustava. +Danas se AI često smatra sinonimom za *strojno učenje* ili *neuronske mreže*. Međutim, ljudsko biće također pokazuje eksplicitno zaključivanje, što trenutačno nije pokriveno neuronskim mrežama. U stvarnim projektima, eksplicitno zaključivanje još se koristi za obavljanje zadataka koji zahtijevaju objašnjenja ili mogućnost kontroliranog mijenjanja ponašanja sustava. ## 🚀 Izazov -U bilježnici Ontologija obitelji povezanoj s ovom lekcijom, postoji prilika za eksperimentiranje s drugim obiteljskim odnosima. Pokušajte otkriti nove veze između ljudi u obiteljskom stablu. +U bilježnici Ontologija obitelji povezanoj s ovim predavanjem postoji mogućnost eksperimentiranja s ostalim obiteljskim odnosima. Pokušajte otkriti nove veze između ljudi u obiteljskom stablu. -## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ai/quiz/4) +## [Kviz nakon predavanja](https://ff-quizzes.netlify.app/en/ai/quiz/4) -## Pregled i samostalno učenje +## Pregled & Samostalan rad -Istražite na internetu područja u kojima su ljudi pokušali kvantificirati i kodificirati znanje. Pogledajte Bloomovu taksonomiju i vratite se u povijest kako biste saznali kako su ljudi pokušavali razumjeti svoj svijet. Istražite rad Linnaeusa na stvaranju taksonomije organizama i promatrajte način na koji je Dmitri Mendeleev stvorio način za opisivanje i grupiranje kemijskih elemenata. Koje druge zanimljive primjere možete pronaći? +Istražite na internetu područja gdje su ljudi pokušali kvantificirati i kodificirati znanje. Pogledajte Bloomovu taksonomiju i vratite se u povijest kako biste naučili kako su ljudi pokušavali razumjeti svoj svijet. Istražite rad Linneusa na stvaranju taksonomije organizama i promatrajte način na koji je Dmitrij Mendelejev stvorio način da se kemijski elementi opisuju i grupiraju. Koje druge zanimljive primjere možete pronaći? **Zadatak**: [Izradite ontologiju](assignment.md) --- + +**Odricanje od odgovornosti**: +Ovaj dokument je preveden korištenjem AI prevoditeljskog servisa [Co-op Translator](https://github.com/Azure/co-op-translator). Iako nastojimo osigurati točnost, imajte na umu da automatski prijevodi mogu sadržavati pogreške ili netočnosti. Izvorni dokument na izvornom jeziku treba se smatrati autoritativnim izvorom. Za kritične informacije preporučuje se profesionalni ljudski prijevod. Nismo odgovorni za bilo kakve nesporazume ili pogrešna tumačenja koja proizlaze iz korištenja ovog prijevoda. + \ No newline at end of file diff --git a/translations/my/README.md b/translations/my/README.md index 6d06b17e..160cb26d 100644 --- a/translations/my/README.md +++ b/translations/my/README.md @@ -1,8 +1,8 @@ -[အာရပ်](../ar/README.md) | [ဘင်္ဂလီ](../bn/README.md) | [ဘူလ်ဂေးရီးယား](../bg/README.md) | [မြန်မာ](./README.md) | [တရုတ် (ရိုးရိုးအဆင့်)](../zh/README.md) | [တရုတ် (စန္ဒာခေါင်, ရိုးရာ)](../hk/README.md) | [တရုတ် (မကာအို, ရိုးရာ)](../mo/README.md) | [တရုတ် (ထိုင်ဝမ်, ရိုးရာ)](../tw/README.md) | [ခရိုအေးရှား](../hr/README.md) | [ချက်](../cs/README.md) | [ဒိန်းမတ်](../da/README.md) | [ဒတ်ချ်](../nl/README.md) | [အက်စတိုနီးယား](../et/README.md) | [ဖင်လန်](../fi/README.md) | [ပြင်သစ်](../fr/README.md) | [ဂျာမန်](../de/README.md) | [ဂရိ](../el/README.md) | [ဟီဘရူး](../he/README.md) | [ဟိန္ဒီ](../hi/README.md) | [ဟန်ဂေရီး](../hu/README.md) | [အင်ဒိုနီးရှား](../id/README.md) | [အီတလီ](../it/README.md) | [ဂျပန်](../ja/README.md) | [ကန္ဏဒါ](../kn/README.md) | [ကိုးရီးယား](../ko/README.md) | [လစ်သူအေးနီးယား](../lt/README.md) | [မလေး](../ms/README.md) | [မလေလာမ်](../ml/README.md) | [မာရသီ](../mr/README.md) | [နီပေါ](../ne/README.md) | [ไนဂျီးရီးယား ပစ်ဂျင်](../pcm/README.md) | [နော်ဝေ](../no/README.md) | [ဖာစီ (ပါ့ရှန်)](../fa/README.md) | [ပိုလန်](../pl/README.md) | [ပေါ်ချဆီ (ဘရာဇီး)\](../br/README.md) | [ပိုချချီ (ပိုတူဂီ)\](../pt/README.md) | [ပန်ဂျာဘီ (ဂူမြူခီ)](../pa/README.md) | [ရိုမေးနီးယား](../ro/README.md) | [ရုရှား](../ru/README.md) | [ဆာဘီးယား (စီရီလစ်)\](../sr/README.md) | [ဆိုဗီးယား](../sk/README.md) | [စလိုဗေးနီးယား](../sl/README.md) | [စပိန်](../es/README.md) | [ဆွာဟီလီ](../sw/README.md) | [ဆွီဒင်](../sv/README.md) | [တာဂလို (ဖိလစ်ပိုင်)](../tl/README.md) | [တမီးလ်](../ta/README.md) | [တယ်လူဂူ](../te/README.md) | [ထိုင်း](../th/README.md) | [တူရ်ကီ](../tr/README.md) | [ယူနိုခရိုင်း](../uk/README.md) | [ဥရူး](../ur/README.md) | [ဗီယက်နမ်](../vi/README.md) +[အာရပ်](../ar/README.md) | [ဘင်္ဂါလီ](../bn/README.md) | [ဘူလ်ဂေးရီးယား](../bg/README.md) | [မြန်မာ (မြန်မာ)](./README.md) | [တရုတ် (ရိုးရိုး)](../zh/README.md) | [တရုတ် (ရိုးရိုး, ဟောင်ကောင်)](../hk/README.md) | [တရုတ် (ရိုးရိုး, မာစူ)](../mo/README.md) | [တရုတ် (ရိုးရိုး, တိုင်ဝမ်)](../tw/README.md) | [ခရိုက်ရှားဒီယား](../hr/README.md) | [ချက်](../cs/README.md) | [ဒိန်းမတ်](../da/README.md) | [ဒတ်ချ်](../nl/README.md) | [အက်စ်တိုးနီးယား](../et/README.md) | [ဖင်နစ်](../fi/README.md) | [ပြင်သစ်](../fr/README.md) | [ဂျာမာန်](../de/README.md) | [ဂရိ](../el/README.md) | [ဟီဘရူး](../he/README.md) | [ဟင်းဒီ](../hi/README.md) | [ဟန်ဂေရီးယား](../hu/README.md) | [အင်ဒိုနီးရှား](../id/README.md) | [အီတလီ](../it/README.md) | [ဂျပန်](../ja/README.md) | [ကနေဒါ](../kn/README.md) | [ကိုရီးယား](../ko/README.md) | [လစ္သူနီးယား](../lt/README.md) | [မလေး](../ms/README.md) | [မလေးလာမ်](../ml/README.md) | [မာရသီ](../mr/README.md) | [နီပေါလီ](../ne/README.md) | [နိုင်ဂျီးရီးယား ပစ်ဂင်](../pcm/README.md) | [နော်ဝေ](../no/README.md) | [ပါရှန် (ဖာဆီ)](../fa/README.md) | [ပိုလန်](../pl/README.md) | [ပေါ်တူဂီ (ဘရဇီးလ်)](../br/README.md) | [ပေါ်တူဂီ (ပေါ်ချီဂျီ)](../pt/README.md) | [ပန်ဇာဘီ (ဂျာမူခီ)](../pa/README.md) | [ရိုမေးနီးယား](../ro/README.md) | [ရုရှား](../ru/README.md) | [ဆားဘီးယား (စာရိုလစ်လစ်)](../sr/README.md) | [စလိုဗက်](../sk/README.md) | [စလိုဗေးနီးယား](../sl/README.md) | [စပိန်](../es/README.md) | [ဆွာဟီလီ](../sw/README.md) | [ဆွီဒင်](../sv/README.md) | [တာဂလိုဂ် (ဖိလစ်ပိုင်)](../tl/README.md) | [တမီးလ်](../ta/README.md) | [တယ်လူဂူ](../te/README.md) | [ထိုင်း](../th/README.md) | [တူရ်ကီ](../tr/README.md) | [ယူကရိန်း](../uk/README.md) | [ဥာဒူ](../ur/README.md) | [ဗီယက်နမ်](../vi/README.md) -> **ဒေသတွင်း ကလိန်း ထုတ်ယူချင်ပါသလား?** +> **ဒေသတွင်းမှာ Clone လုပ်ချင်ပါသလား?** -> ဤသိုလှောင်ရာတွင် ဘာသာစကား ၅၀ ကျော်ဖြင့် ဘာသာပြန်ထားပြီး ဒေါင်းလုပ်အရွယ်အစားအပြားဏ်တိုးပွားစေသည်။ ဘာသာပြန် မပါဘဲ ချိတ်ဆွဲရန် sparse checkout ကို အသုံးပြုပါ: +> ဤ repository တွင် ဘာသာစကား ၅၀ ကျော်သော ဘာသာပြန်ချက်များပါရှိပြီး ဒါကြောင့် ဒေါင်းလုတ်အရွယ်အစား အများကြီးတိုးပွားပါသည်။ ဘာသာပြန်ချက် မပါမှုဖြင့် clone လုပ်လိုပါက sparse checkout ကို အသုံးပြုပါ: > ```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' > ``` -> ၎င်းသည် သင့်သင်တန်းကို အလွန်မြန်ဆန်စွာ ဒေါင်းလုပ်ပြီး အားလုံးလိုအပ်သော အရာများဖြင့် ပြည့်စုံစေသည်။ +> ဒါက သင်အတန်းလေ့လာပြီးမြန်ဆန်စွာ ဒေါင်းလုတ်လုပ်နိုင်မှာ ဖြစ်ပါတယ်။ -**ထို့အပြင် ထပ်မံအသုံးပြုလိုသော ဘာသာစကားများကို [ဒီနေရာမှာ](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md) တွေ့နိုင်ပါသည်။** +**ထပ်မံဘာသာပြန်စကားများ ထောက်ပံ့လိုပါက [ဒီနေရာ](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md) တွင်စာရင်းပြထားပါသည်။** -## အသိုင်းအဝိုင်းသို့ လက်တွဲပါ +## အသိုင်းအဝိုင်းတွင် ပါဝင်ပါ [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -## သင်တို့ သင်ယူမည့်အရာများ +## သင်တတ်မည့်အရာများ -**[သင်တန်း၏ စိတ်ကူးပြဇယား](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** +**[သင်တန်း၏ စိတ်ကူးမြေပုံ](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** -ဤသင်ရိုးညွှန်းတမ်းတွင် သင်သည် သင်ယူမည်မှာ - +ဤသင်ရိုးညွှန်းတမ်းတွင် သင်သည် လေ့လာမည်မှာ – -* Artificial Intelligence ၏ မတူကွဲပြားသည့် နည်းလမ်းများ၊ "ဟောင်းရွှင်" သင်္ကေတနည်းလမ်းအပါအဝင် **အသိအမှတ်ပြုမှု** နှင့် အနက်လိုက်လေ့လာခြင်း ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence))။ -* နေရာလတ်တလော AI ၏ အခြေခံဖြစ်သည့် **နဲ့ရယ်ကွန်ရက်များ** နှင့် **နက်ရှိုင်းလေ့လာမှု** ။ ဤအရေးကြီးသော မှတ်ချက်များကို လူကြိုက်များသော ကွန်ယက်ဖွဲ့စည်းမှုများ - [TensorFlow](http://Tensorflow.org) နှင့် [PyTorch](http://pytorch.org) ကိုအသုံးပြုပြီး ကုဒ်ဖြင့် ပုံပြင်ဖြစ်စေမည်။ -* ဓာတ်ပုံများနှင့် စာသားများဆိုင်ရာလုပ်ငန်းများအတွက် **နဲ့ရယ်ပုံစံများ**။ နောက်ဆုံးပေါ် မော်ဒယ်များကို ဖုံးလွှမ်းမည်သို့ပင် ဖြစ်သည်။ -* နည်းခဏလူမသိသော AI နည်းလမ်းများ - **ဂျီနက်တစ် အယ်ဂိုရစ်သမ်များ** နှင့် **ပရိုဂရမ်အေဂျင့် စနစ်များ**။ +* အတုယူမှုအင်တဲလီဂျင့် (AI) ဆိုင်ရာ မတူကွဲပြားသောနည်းလမ်းများ၊ ပုံမှန်ရိုးရာ "အဟောင်း" သင်္ကေတနည်းလမ်းနှင့် **အကြောင်းအရာ ဖော်ပြချက်** နဲ့ သဘောထားခြင်း ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)) ပါဝင်သည်။ +* ခေတ်မီ AI ၏ အဓိကဖြစ်သော **နာယူးရယ်ကွန်ရက်များ** နှင့် **နက်ကြီးသင်ယူခြင်း**။ မူလတန်းအတွက် များသောအားဖြင့် အဓိက သဘောတရားများကို ပရိုဂရမ်ကို အသုံးပြု၍ ထုတ်ပြသမယ် – နာမည်ကြီးသော Framework နှစ်ခု ဖြစ်သည့် [TensorFlow](http://Tensorflow.org) နှင့် [PyTorch](http://pytorch.org) တွင်ဖြစ်သည်။ +* ပုံရိပ်နှင့် စာသားများကို ကိုင်တွယ်သုံးစွဲနိုင်ဖို့ **နာယူးရယ်ဖွဲ့စည်းမှုများ**။ နောက်ဆုံးပေါ်မော်ဒယ်များကို ပါဝင်ကာ ပြီးမြောက်မှု လောက်မရှိသော်လည်း ပါဝင်သည်။ +* နည်းနည်း နာမည်ကျော် မဟုတ်သော AI နည်းလမ်းများကဲ့သို့ **ဂျီနက်တစ် အယ်လ်ဂိုရစ်သမ်များ** နှင့် **Multi-Agent Systems** လည်း ပါဝင်သည်။ -ဤသင်ရိုးညွှန်းတမ်းတွင် ဖုံးလွှမ်းမည့်အရာ များ - +ဤသင်ရိုးညွှန်းတမ်းတွင် မပါဝင်သည့် အရာများက -> [ဤသင်တန်းအတွက် ထပ်မံအရင်းအမြစ်များအားလုံးကို ကျွန်ုပ်တို့့ Microsoft Learn စုစည်းမှုမှာ ရှာဖွေပါ](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) +> [ဤသင်တန်း၏ အပိုဆောင်း အရင်းအမြစ်များအား Microsoft Learn စုစည်းမှုတွင် ရှာဖွေပါ](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) -* **စီးပွားရေးတွင် AI အသုံးပြုမှု** အကြောင်းစီးပွားရေး အခြေအနေများ။ Microsoft Learn ပေါ်ရှိ [စီးပွားရေး စနစ်အသုံးပြုသူများအတွက် AI နိဒါန်း](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) သင်တန်းလမ်းကြောင်း သို့မဟုတ် [AI စီးပွားရေးကျောင်း](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum) ကို [INSEAD](https://www.insead.edu/) နှင့် ပူးပေါင်းထားပါသည်။ -* ကျွန်ုပ်တို့၏ [စက်မှုသိပ္ပံအခြေခံ သင်ရိုး](http://github.com/Microsoft/ML-for-Beginners) တွင် လေ့လာထားသော **စမတ်စက်မှုသိပ္ပံ**။ -* **[Cognitive Services](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)** ဖြင့် စက်မှုသိပ္ပံ အချက်အလက် လက်တွေ့အသုံးချမှုများ။ ဤအတွက် Microsoft Learn ၏ [မြင်ကွင်း](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum) ၊ [သဘာဝဘာသာစကား အသုံးပြုမှု](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum) နှင့် **[Azure OpenAI Service ဖြင့် စက်မှုသိပ္ပံ ဖန်တီးမှု](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** တို့ကို 推荐 လုပ်ပါသည်။ -* နည်းပေါင်းတစ်ခုတည်းသော ML **တိကျသော Cloud Frameworks**, ဥပမာ [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), သို့မဟုတ် [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum) ကို အသုံးပြုသူများ ချိတ်ဆက်ရန် [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) နှင့် [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) သင်တန်းလမ်းကြောင်းများကို သင်ယူရန် အကြံပြုသည်။ -* **စကားပြော စက်မှုသိပ္ပံ** နှင့် **စကားပြော BOT များ**။ အထူးသီးသန့် [စကားပြောစက်မှုသိပ္ပံ ဖြေရှင်းချက်များ ဖန်တီးခြင်း](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum) သင်တန်းလမ်းကြောင်းရှိပြီး၊ အသေးစိတ်အတွက် [ဤ ဘလော့ဂ်ပိုစ့်](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) ကိုလည်း ဖတ်ရှုနိုင်ပါသည်။ -* နက်ရှိုင်းလေ့လာမှု၏ အခြေခံသင်္ချာရေး **နက်ရှိုင်းသင်္ချာ**။ ဤအတွက် Ian Goodfellow, Yoshua Bengio နှင့် Aaron Courville အားရေးသားသော [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) စာအုပ်ကို အကြံပြုပြီး ဘာသာပြန်ထားသော [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/) မှာလည်း ရနိုင်သည်။ +* **AI ကို စီးပွားရေးထဲတွင် အသုံးပြုခြင်း** စီးပွားရေးဆိုင်ရာကိစ္စများ။ Microsoft Learn မှ [စီးပွားရေးအသုံးပြုသူများအတွက် AI မိတ်ဆက်](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) သင်ကြားမှုလမ်းကြောင်း သို့မဟုတ် [AI စီးပွားရေးကျောင်း](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum) ကို [INSEAD](https://www.insead.edu/) နှင့် ပူးပေါင်းဖန်တီးထားသည်။ +* ပထမအဆင့် မော်ကွန်းသင်ယူမှု **စတင်ခြင်း** အား အောက်ပါ [Machine Learning for Beginners Curriculum](http://github.com/Microsoft/ML-for-Beginners) တွင် ပြည့်စုံစွာ ဖော်ပြထားသည်။ +* **[Cognitive Services](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)** အသုံးပြု၍ တည်ဆောက်ထားသော လက်တွေ့ AI လျှောက်လွှာများ။ ဤအတွက် Microsoft Learn တွင် [မြင်ကွင်း](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [သဘာဝဘာသာစကား သီးခြားစီစစ်ခြင်း](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[Azure OpenAI ဝန်ဆောင်မှုဖြင့် စိတ်မဖြစ်သော AI ဖန်တီးခြင်း](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** နှင့် အခြား ဒါမှမဟုတ် မော်ဂျူးများကို စတင်လေ့လာရန် အကြံပြုပါသည်။ +* အထူးပြု ML **Cloud Frameworks** များကဲ့သို့ [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), သို့မဟုတ် [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum) တို့ပါဝင်သည်။ [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) နှင့် [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) သင်ကြားပေးမှု လမ်းကြောင်းများကို အသုံးပြုဖို့ တိုက်တွန်းပါသည်။ +* **စကားပြော AI** နှင့် **စကားပြော ဘော့များ**။ ပါဝင်သင်ကြားမှု ပြုသည့် [Create conversational AI solutions](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum) ကိုသင်ယူနိုင်ပြီး၊ အသေးစိတ်အတွက် [ဒီဘလော့ဂ်စာတမ်း](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) ကိုလည်း အကြံပြုပါသည်။ +* နက်ကြီးသင်ယူမှုအတွက် ကျယ်ပြန့်သော **ဂဏန်းဆိုင်ရာ သင်္ချာများ**။ ဤအတွက် Ian Goodfellow, Yoshua Bengio နှင့် Aaron Courville ၏ [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) စာအုပ်ကို အကြံပြုလို 있으며၊ အွန်လိုင်းတွင် [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/) မှလည်း ရနိုင်ပါသည်။ -_Cloud AI_ အရာများကို ကိုင်တွယ်မည့် ဝင်ရိုးလမ်းအတွက် [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) သင်တန်းလမ်းကြောင်းကို သတိပြုစဉ်းစားပါ။ +_cloud_ အတွင်းရှိ _AI_ အကြောင်းအရာများကို မျက်လုံးပိတ်ကာ အနည်းငယ် မိတ်ဆက်ကြည့်လိုလျှင် [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) သင်ကြားမှုလမ်းကြောင်းကို ဆင်ခြင်ကြည့်ရှုနိုင်ပါသည်။ -# အကြောင်းအရာ +# အကြောင်းအရာများ -| | သင်ခန်းစာလင့်ခ် | PyTorch/Keras/TensorFlow | သုခသုံးခန်း | +| | သင်ခန်းစာ လင့်ခ် | PyTorch/Keras/TensorFlow | ဂုဏ်ရည်ခန်း | | :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | -| 0 | [သင်တန်း စတင်ပြင်ဆင်မှု](./lessons/0-course-setup/setup.md) | [ဖွံ့ဖြိုးတိုးတက်ရေးပတ်ဝန်းကျင်ကို ပြင်ဆင်ခြင်း](./lessons/0-course-setup/how-to-run.md) | | -| I | [**AI နိဒါန်း**](./lessons/1-Intro/README.md) | | | -| 01 | [AI ၏ နိဒါန်းနှင့် သမိုင်းကြောင်း](./lessons/1-Intro/README.md) | - | - | -| II | **သင်္ကေတစနစ် AI** | -| 02 | [အသိအမှတ်ပြုမှုနှင့် တက်ကြွသူစနစ်များ](./lessons/2-Symbolic/README.md) | [တက်ကြွသူစနစ်များ](./lessons/2-Symbolic/Animals.ipynb) / [Ontology](./lessons/2-Symbolic/FamilyOntology.ipynb) /[Concept Graph](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | -| III | [**နဲ့ရယ်ကွန်ရက်များ နိဒါန်း**](./lessons/3-NeuralNetworks/README.md) ||| +| 0 | [သင်တန်း စတင်ခြင်း](./lessons/0-course-setup/setup.md) | [သင့် ဖွံ့ဖြိုးတိုးတက်မှု ပတ်ဝန်းကျင် စတင်ဆောင်ရွက်ခြင်း](./lessons/0-course-setup/how-to-run.md) | | +| I | [**AI မိတ်ဆက်**](./lessons/1-Intro/README.md) | | | +| 01 | [AI ၏ မိတ်ဆက်နှင့် သမိုင်း](./lessons/1-Intro/README.md) | - | - | +| II | **သင်္ကေတ AI** | +| 02 | [အကြောင်းအရာ ဖော်ပြခြင်းနှင့် ကျွမ်းကျင်သူ စနစ်များ](./lessons/2-Symbolic/README.md) | [ကျွမ်းကျင်သူ စနစ်များ](./lessons/2-Symbolic/Animals.ipynb) / [အောင့်တောလီ](./lessons/2-Symbolic/FamilyOntology.ipynb) /[အယူအဆဇယား](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | +| III | [**နာယူးရယ်ကွန်ရက် မိတ်ဆက်**](./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 | [Multi-Layered Perceptron and Creating our own 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 | [Intro to Frameworks (PyTorch/TensorFlow) and Overfitting](./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 | [**ကွန်ပျူတာမြင်ကွင်း**](./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)| [Explore Computer Vision on Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | -| 06 | [ကွန်ပျူတာမြင်ကွင်းအတွက် နိဒါန်း။ 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) | +| IV | [**ကွန်ပျူတာဗွေရှင်း**](./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)| [Explore Computer Vision on Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | +| 06 | [ကွန်ပျူတာဗွေရှင်းနဲ့နိဒါန်း။ 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 | [Convolutional Neural Networks](./lessons/4-ComputerVision/07-ConvNets/README.md) & [CNN Architectures](./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 | [Pre-trained Networks and Transfer Learning](./lessons/4-ComputerVision/08-TransferLearning/README.md) and [Training Tricks](./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 | [Autoencoders and 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 | [Generative Adversarial Networks & Artistic Style Transfer](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | | 11 | [Object Detection](./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 | [Semantic Segmentation. 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 | [**ဘာသာစကားသဘာဝ ဖြေရှင်းခြင်း**](./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) | [Explore Natural Language Processing on Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| -| 13 | [စာသား ကိုယ်စားပြုခြင်း။ 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 | [Semantic word embeddings. Word2Vec and 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 | [ဘာသာစကား မော်ဒယ်ဖန်တီးခြင်း။ ကိုယ်ပိုင် embeddings သင်ကြားခြင်း](./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) | +| V | [**သဘာဝဘာသာစကားကုမန့်ဆုံးခြင်း**](./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) | [Explore Natural Language Processing on Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| +| 13 | [စာသားဖော်ပြချက်။ 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 | [အဓိပ္ပါယ်အရ စကားလုံးထိုးသွင်းပါတယ်။ Word2Vec နဲ့ 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 | [ဘာသာစကားပုံစံ။ ကိုယ်ပိုင်အပ်ဒိတ်ထိုးသွင်းခြင်းလေ့ကျင့်ခြင်း](./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 | [Recurrent Neural Networks](./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 | [Generative Recurrent Networks](./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 | [Transformers. BERT.](./lessons/5-NLP/18-Transformers/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb) /[TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersTF.ipynb) | | -| 19 | [Named Entity Recognition](./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 | [ကြီးမားသော ဘာသာစကား မော်ဒယ်များ၊ Prompt Programming နှင့် Few-Shot Tasks](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | -| VI | **အခြား AI နည်းလမ်းများ** || | -| 21 | [Genetic Algorithms](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [Notebook](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | -| 22 | [နက်ရှိုင်းသော Reinforcement Learning](./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 | [Multi-Agent Systems](./lessons/6-Other/23-MultiagentSystems/README.md) | | | -| VII | **AI ဓေသန** | | | -| 24 | [AI Ethics and Responsible AI](./lessons/7-Ethics/README.md) | [Microsoft Learn: Responsible AI Principles](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | +| 19 | [အမည်ရှိနယ်မြေအသိအမှတ်ပြုခြင်း](./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 | [ကြီးမားသောဘာသာစကားပုံစံများ၊ ကမ်းလှမ်းချက်ပရိုဂရမ်းမင်းနှင့် အနည်းငယ်-သွားတာလှုပ်ရှားမှုများ](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | +| VI | **အခြား AI နည်းပညာများ** || | +| 21 | [ဂျင်နက်တစ် အယ်လ်ဂိုရီသမ်များ](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [Notebook](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | +| 22 | [နက်ရှိုင်းသော အားပေးသင်ကြားမှု လေ့လာမှု](./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 | [အများပြည်သူအေဂျင့်စနစ်များ](./lessons/6-Other/23-MultiagentSystems/README.md) | | | +| VII | **AI သမာဓိ** | | | +| 24 | [AI သမာဓိနှင့် တာဝန်ခံ AI](./lessons/7-Ethics/README.md) | [Microsoft Learn: Responsible AI Principles](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | | IX | **အပိုဆောင်းများ** | | | -| 25 | [Multi-Modal Networks, CLIP and VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [Notebook](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | +| 25 | [မူလတန်းစုံကွန်ရက်များ၊ CLIP နှင့် VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [Notebook](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | -## တစ်ခုချင်းသင်ခန်းစာများတွင် ပါဝင်သော အကြောင်းအရာများ +## သင်ခန်းစာတိုင်းတွင်ပါဝင်သောအရာများ -* ကြိုတင်ဖတ်ရှုရန် ပစ္စည်းများ -* ဆောင်ရွက်နိုင်သော Jupyter Notebooks များ၊ အများအားဖြင့် framework (**PyTorch** သို့မဟုတ် **TensorFlow**) အတွက် သီးသန့်ဖြစ်သည်။ ဆောင်ရွက်နိုင်သော notebook တွင် နိုင်ငံတော်သိပ္ပံဆိုင်ရာ ပစ္စည်းများလည်းများလှလွန်းသဖြင့် ခေါင်းစဉ်ကိုနားလည်ရန် အနည်းဆုံး notebook တစ်ခု (PyTorch သို့မဟုတ် TensorFlow) ကို ကြည့်ရပါမည်။ -* သင်ယူမှု အခွင့်အလမ်းကို ပို့ချခြင်းဖြင့် သင်ယူထားသော ပစ္စည်းကို သက်ဆိုင်သော ပြဿနာတွင် လက်တွေ့အသုံးပြုကြည့်ရန် အခွင့်အရေး ပေးသော **Labs** များ။ -* အချို့အပိုင်းများတွင် သက်ဆိုင်သော ခေါင်းစဉ်များကို ဖုံးကွယ်ထားသည့် [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) မော်ဂျူးများသို့ လင့်ခ်များ ပါဝင်သည်။ +* မင်္ဂလာဆောင်စာအုပ်ပုံစံစာမျက်နှာများ +* စနစ်အသီးသီးအတွက် အထူးသီးသန့်ဖြစ်သော Jupyter Notebooks များ (**PyTorch** သို့မဟုတ် **TensorFlow** ဖြစ်ကြောင်း) ပါဝင်သည်။ ကိုယ့်ဘာသာ အကြောင်းအရာကိုနားလည်ရန်အတွက် နောက်ထပ်အနည်းဆုံးတစ်ခုခု Notebook (PyTorch သို့မဟုတ် TensorFlow) ကို ဖတ်ပါ။ +* အချို့သောခေါင်းစဉ်များအတွက် **Labs** များ ရရှိနိုင်ပြီး သင်လေ့လာထားသော အကြောင်းအရာများကို အကောင်အထည်ဖော်ရန်အခွင့်အရေးပေးသည်။ +* အချို့သောအပိုင်းများတွင် သက်ဆိုင်ရာခေါင်းစဉ်များကို ဖုံးကွယ်ထားသည့် [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) modules များသို့ ချိတ်ဆက်ထားသည်။ -## စတင်သင်ယူခြင်း +## စတင်လုပ်ဆောင်ခြင်း -### 🎯 AI အသစ်လား? ဒီမှာ စပြီး သင်ယူပါ! +### 🎯 AI အသစ်လား? ဒီမှာစပါ! -AI အသစ်စ completelyပြီး လက်တွေ့ လက်ခံနိုင်သော ဥပမာများ လိုချင်ပါက ကျွန်ုပ်တို့၏ [**အသစ်စလစ်လက်တွေ့ ဥပမာများ**](./examples/README.md) ကိုကြည့်ပါ! ဤမှာ ပါဝင်သော ဥပမာများမှာ - +သင် AI ကိုမပြည့်စုံသော အသစ်တစ်ယောက်ဖြစ်ပြီး လွယ်ကူမြန်ဆန်သော လက်တွေ့နမူနာများလိုလျှင် ကျွန်ုပ်တို့ရဲ့ [**စတင်ရန်အဆင်ပြေသောနမူနာများ**](./examples/README.md) ကို ကြည့်ပါ! ၎င်းတွင်ပါဝင်သည်မှာ- -- 🌟 **Hello AI World** - သင့်ရဲ့ ပထမဆုံး AI ပရိုဂရမ် (နမူနာသိရှိခြင်း) -- 🧠 **ရိုးရှင်းသော Neural Network** - စကျပ်မှ neural network တည်ဆောက်ခြင်း -- 🖼️ **ပုံခွဲခြားသူ** - အသေးစိတ်မှတ်ချက်များပါသော ပုံများ ခွဲခြားခြင်း -- 💬 **စာသားခံစားချက်** - အကောင်းမြင်/မကောင်းမြင် စာသားကို ခွဲခြားစစ်ဆေးခြင်း +- 🌟 **မင်္ဂလာပါ AI ကမ္ဘာ** - သင့်ရဲ့ ပထမဆုံး AI ပရိုဂရမ်း (ပုံစံသိရှိခြင်း) +- 🧠 **ရိုးရှင်းသောနည်းလမ်း Networks** - စတင်ဖန်တီးသော နယူးရယ်နက်ဝက် +- 🖼️ **ပုံရိပ်စာတန်းသတ်မှတ်စက်** - အသေးစိတ် မှတ်ချက်များဖြင့် ပုံရိပ်များ သတ်မှတ်ခြင်း +- 💬 **စာသားခံစားချက်** - ဂရုတစိုက်/မဂြိုဟ် စာသားကိုစစ်ဆေးပါ -ဤဥပမာများကို သင်သည် AI အကြောင်းအရာများကို နားလည်ရန် အထောက်အကူပြုရန် ဒီတန်းအစီအစဉ်အပြည့်အစုံနောက်ကျ ဖတ်ရှုခင် ပြုလုပ်ထားသည်။ +ဤဥပမာများသည် သင်အား AI အတွေးအခေါ်များကို နားလည်စေရန်နှင့် အပြည့်အစုံ သင်ရိုးအစီအစဉ်ထဲ ဝင်ရောက်ရန်အတွက် အထောက်အကူပြုရန် ရည်ရွယ်ပါသည်။ -### 📚 အပြည့်အစုံ သင်ခန်းစာ စီစဉ်ခြင်း +### 📚 အပြည့်အစုံ သင်ရိုးအစီအစဉ် တပ်ဆင်ခြင်း -- သင်၏ အဖွဲ့အစည်းဖွံ့ဖြိုးရေးပတ်ဝန်းကျင်ကို စတင်တည်ဆောက်ရန် အထောက်အကူပြုရန် [setup lesson](./lessons/0-course-setup/setup.md) တစ်ခု ပြုလုပ်ထားသည်။ - မူမမှီပညာရှင်များအတွက်လည်း [curricula setup lesson](./lessons/0-course-setup/for-teachers.md) တစ်ခု ရေးသားပေးထားသည်။ -- VSCode သို့မဟုတ် Codepace တွင် [Code ကိုမည်သို့ ပြေးပေးရမည်](./lessons/0-course-setup/how-to-run.md) ဆိုသည်ကို မျှဝေထားသည်။ +- သင်၏ ဖွံ့ဖြိုးရေး ပတ်ဝန်းကျင်ကို တပ်ဆင်ရာတွင် ကူညီရန် အတွက် [တပ်ဆင်ခြင်းသင်ခန်းစာ](./lessons/0-course-setup/setup.md) တစ်ခု ပြုလုပ်ထားပါသည်။ - ပညာသင်ကြားသူများအတွက်လည်း သင့်အတွက် [သင်ရိုးစီစဉ်ခြင်း သင်ခန်းစာ](./lessons/0-course-setup/for-teachers.md) တစ်ခု ပြုလုပ်ထားပါသည်။ +- VSCode သို့မဟုတ် Codespace တွင် [ကုဒ်မှတ်တမ်းများကို အကောင်အထည်ဖော်နည်း](./lessons/0-course-setup/how-to-run.md) -အောက်ပါ အဆင့်များကို လိုက်နာပါ- +အောက်ပါအဆင့်များကို လိုက်နာပါ။ -Repositories ကို Fork လုပ်ပါ- ဒီစာမျက်နှာအပေါ်ညာဘက် ထောင့်ရှိ "Fork" ခလုတ်ကိုနှိပ်ပါ။ +Repository ကို Fork လုပ်ပါ- ဤ စာမျက်နှာ၏ ညာဘက်ထိပ်ရှိ "Fork" ခလုတ်ကို နှိပ်ပါ။ -Repositories ကို Clone လုပ်ပါ- `git clone https://github.com/microsoft/AI-For-Beginners.git` +Repository ကို Clone လုပ်ပါ- `git clone https://github.com/microsoft/AI-For-Beginners.git` -နောက်မှ ရှာဖွေရာတွင် လွယ်ကူစေရန် ဤ repo ကို အမြဲ (🌟) အဖြစ် မှတ်သားထားဖို့ မမေ့နှိပ်နှုတ်ပါနှင့်။ +နောက်မှရှာဖွေရန်အဆင်ပြေသည့်အတွက် ဤ repo ကို စတား (🌟) ခြင်းမမေ့ပါနှင့်။ -## အခြား ပညာသင်များနှင့် တွေ့ဆုံခြင်း +## အခြား သင်ယူသူများနှင့် တွေ့ဆုံခြင်း -ဒီသင်တန်းကို လေ့လာနေသော အခြား သင်ယူသူများနှင့် တွေ့ဆုံဆက်ဆံရန် နှင့် အထောက်အပံ့ရယူရန်အတွက် ကျွန်ုပ်တို့၏ [တရားဝင် AI Discord ဆာဗာ](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) ကို ဝင်ရောက်ပါ။ +ဤ သင်တန်းကို လေ့လာနေသူများနှင့် တွေ့ဆုံ၍ ပူးပေါင်းဆွေးနွေးနိုင်ရန်အတွက် ကျွန်ုပ်တို့၏ [တရားဝင် AI Discord ဆာဗာ](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) တက်ရောက်ပါ။ -ထုတ်ကုန်ဆိုင်ရာ ထောက်ပြချက်များ သို့မဟုတ် မေးခွန်းများရှိပါက ထိထိရောက်ရောက် တည်ဆောက်ပါက ကျွန်ုပ်တို့၏ [Azure AI Foundry Developer Forum](https://aka.ms/foundry/forum) သို့ ဝင်ကြည့်နိုင်ပါသည်။ +ထုတ်ကုန်ဆိုင်ရာ အကြံပြုချက်များ သို့မဟုတ် မေးခွန်းများ ရှိပါက ကျွန်ုပ်တို့၏ [Azure AI Foundry Developer Forum](https://aka.ms/foundry/forum) မှာ လည်းလာဆွေးနွေးနိုင်ပါသည်။ -## စစ်ဆေးမေးခွန်းများ +## စမ်းသပ်မေးခွန်းများ -> **စစ်ဆေးမေးခွန်းများအကြောင်း မှတ်ချက်**: စစ်ဆေးမေးခွန်းများအားလုံးကို quiz-app ဖိုဒါ etc\quiz-app တွင် ထည့်သွင်းထားပြီး၊ သို့မဟုတ် [အွန်လိုင်းတွင် ဒီမှာ](https://ff-quizzes.netlify.app/) ရနိုင်သည်။ ၎င်းများကို သင်ခန်းစာများမှ တစ်ဆင့် ချိတ်ဆက်ထားပြီး quiz app ကို မိမိစက်တွင် ပြေးနိုင် သို့မဟုတ် Azure တွင် တပ်ဆင်နိုင်သည်။ `quiz-app` ဖိုဒါအတွင်း ညွှန်ကြားချက်များကို လိုက်နာပါ။ မူလတန်းဖြင့် ဘာသာပြန်ခြင်း လုပ်ဆောင်နေပါသည်။ +> **စမ်းသပ်မေးခွန်းများအကြောင်း မှတ်ချက်**: စမ်းသပ်မေးခွန်းအားလုံးကို Quiz-app ဖိုလ်ဒါတွင်း etc\quiz-app မှာပါရှိပြီး [ဤနေရာတွင် အွန်လိုင်း](https://ff-quizzes.netlify.app/) မှာလည်း ကြည့်ရှုနိုင်ပါသည်။ စမ်းသပ်မေးခွန်း app ကို ဒေသတွင်းတွင် အကောင်အထည်ဖော်နိုင်ပြီး Azure သို့ deployment လုပ်နိုင်ပါသည်။ `quiz-app` ဖိုလ်ဒါအတွင်း လမ်းညွှန်ချက်များကို လိုက်နာပါ။ အစီအစဉ်များကို တဖြည်းဖြည်း ဘာသာပြန်ဆောင်ရွက်နေပါသည်။ -## ကူညီလိုသူများ +## ကူညီလိုသူ -အကြံပြုချက်များ သို့မဟုတ် စာလုံးဇယားမှားယွင်းခြင်း သို့မဟုတ် ကုတ်မှားများ တွေ့ရှိပါသလား။ မေးခွန်းတစ်ခု တင်ပါ သို့မဟုတ် pull request တစ်ခု ဖန်တီးပေးပါ။ +အကြံပြုချက်များရှိပါသလား၊ လက်လွတ်စကားလုံးများ သို့မဟုတ် ကုဒ်အမှားများကို တွေ့ရှိပါသလား? ပြဿနာတင်ပါ သို့မဟုတ် pull request တင်ပါ။ -## အထူးပင်ကျေးဇူးတင်စကား +## အထူးကျေးဇူးတင်၏ -* **✍️ အဓိကစာရေးသူ:** [Dmitry Soshnikov](http://soshnikov.com), PhD -* **🔥 စာတည်းဖြတ်သူ:** [Jen Looper](https://twitter.com/jenlooper), PhD -* **🎨 Sketchnote ရေးဆွဲသူ:** [Tomomi Imura](https://twitter.com/girlie_mac) -* **✅ စစ်ဆေးမေးခွန်း ဖန်တီးသူ:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) -* **🙏 အဓိက အကျိုးရှိသူများ:** [Evgenii Pishchik](https://github.com/Pe4enIks) +* **✍️ အဓိကရေးသားသူ**: [Dmitry Soshnikov](http://soshnikov.com), PhD +* **🔥 တည်းဖြတ်သူ**: [Jen Looper](https://twitter.com/jenlooper), PhD +* **🎨 အမှတ်အသားပုံဆွဲသူ**: [Tomomi Imura](https://twitter.com/girlie_mac) +* **✅ စမ်းသပ်မေးခွန်း ဖန်တီးသူ**: [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) +* **🙏 အဓိက ပံ့ပိုးသူများ**: [Evgenii Pishchik](https://github.com/Pe4enIks) -## အခြား သင်တန်းအစီအစဉ်များ +## အခြား သင်ရိုးအစီအစဉ်များ -ကျွန်ုပ်တို့အဖွဲ့မှ အခြားသင်တန်းအစီအစဉ်များလည်း ထုတ်လုပ်ပါသည်! ကြည့်ပါ- +ကျွန်ုပ်တို့၏အဖွဲ့သည် အခြား သင်ရိုးအစီအစဉ်များကို လည်း ထုတ်လုပ်ပါသည်။ ကြည့်ရှုပါ။ ### LangChain @@ -214,19 +214,19 @@ Repositories ကို Clone လုပ်ပါ- `git clone https://github.com/m [![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) -## ကူညီမှု ရရှိရန် +## ကူညီမှု ရယူခြင်း -AI apps ဖန်တီးရာတွင် အတားအဆီးများ ဖြစ်ပါက သို့မဟုတ် မေးခွန်းများရှိပါက MCP နှင့်ပတ်သက်သော ဆွေးနွေးပွဲများတွင် အတူတကွ သင်ယူနေသူများနှင့် အတွေ့အကြုံရှိ ဒီဗေလိုပါများအဖြစ် ဝင်ရောက်ပါ။ ဤကူညီပံ့ပိုးမှုကောင်းသော အသိုင်းအဝိုင်းတွင် မေးခွန်းများကို လက်ခံရာ Knowledge များ ထပ်မံမျှဝေရန် ရှိသည်။ +AI apps ဖန်တီးရာတွင် ပိတ်ဆို့မှုရှိပါက သို့မဟုတ် မေးခွန်းများရှိပါက MCP အကြောင်း ဆွေးနွေးရန်အတွက် အခြား သင်ယူသူများနှင့် အတွေ့အကြုံရှိ ဖွံ့ဖြိုးသူများနှင့် ပူးပေါင်းဆွေးနွေးနိုင်ပါသည်။ ဒီသည် မေးခွန်းများကို ကြိုဆိုပြီး အသိပညာကို လွတ်လပ်စွာ မျှဝေနိုင်သော ပံ့ပိုးကူညီမှုရှိသော အသိုင်းအဝိုင်းဖြစ်ပါသည်။ [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -ထုတ်ကုန်ဆိုင်ရာ အကြံပြုချက်များ သို့မဟုတ် error များရှိပါက အောက်ပါနေရာသို့ ဝင်ကြည့်ပါ- +ထုတ်ကုန်ဆိုင်ရာ တုံ့ပြန်ချက်များ သို့မဟုတ် ပြဿနာများ ရှိပါက လည်းလေ့လာရန်: [![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) --- -**အချက်ဖော်ကြားချက်** -ဤစာရွက်စာတမ်းကို AI ဘာသာပြန်ဝန်ဆောင်မှု [Co-op Translator](https://github.com/Azure/co-op-translator) မှ တပ်ဆင်ပြီး ဘာသာပြန်ထားပါသည်။ တိကျမှုအတွက် အထူးကြိုးပမ်းမှုရှိသော်လည်း၊ စက်ယန္တရားဘာသာပြန်ခြင်းဖြင့် အမှားများ သို့မဟုတ် မှားယွင်းချက်များ ရှိနိုင်ကြောင်း သတိပြုပါ။ မူလစာရွက်စာတမ်းသည် သူ၏ မူလဘာသာဖြင့် တရားဝင်အချက်အလက် အနေဖြင့် ယူဆရန် ဖြစ်ပါသည်။ အရေးကြီးသော အချက်အလက်များအတွက် လူကြီးမင်းသည် ပရော်ဖက်ရှင်နယ်လူသားဘာသာပြန်ကို အသုံးပြုရန် အကြံပြုပါသည်။ ဤဘာသာပြန်ခြင်းမှဖြစ်ပေါ်လာသော သဘောမတူချက်များ သို့မဟုတ် မှားယွင်းစကားများအတွက် ကျွန်ုပ်တို့ မည်သည့် တာဝန်မှ မယူဆောင်ပါ။ +**အကြောင်းကြားချက်** +ဤစာတမ်းကို AI ဘာသာပြန်ဝန်ဆောင်မှုဖြစ်သော [Co-op Translator](https://github.com/Azure/co-op-translator) အသုံးပြု၍ ဘာသာပြန်ထားပါသည်။ ကျွန်ုပ်တို့သည် မှန်ကန်မှုအတွက် ကြိုးစားတတ်သော်လည်း၊ အလိုအလျှောက် ဘာသာပြန်ခြင်းသည် အမှားများ သို့မဟုတ် မှားယွင်းမှုများ ပါရှိနိုင်ကြောင်း ကျေးဇူးပြု၍ သိရှိထားပေးပါရန် တိုက်တွန်းအပ်ပါသည်။ ဆက်စပ်ဘာသာစကားဖြင့် မူရင်းစာတမ်းကို စွဲဆိုနိုင်သော အတည်ပြုရရှိသော အရင်းအမြစ်အဖြစ် သတ်မှတ်စဉ်းစားသင့်ပါသည်။ အရေးကြီးသော သတင်းအချက်အလက်များအတွက်တော့ ပရော်ဖက်ရှင်နယ် လူသားဘာသာပြန်ခြင်းကို အကြံပြုအပ်ပါသည်။ ဤ ဘာသာပြန်ချက်ကို အသုံးပြုရာမှ ဖြစ်ပေါ်နိုင်သော နားမလည်မှုများ သို့မဟုတ် မှားယွင်းဖတ်ရှုမှုများအတွက် ကျွန်ုပ်တို့ အာမခံချက် မရှိပါ။ \ No newline at end of file diff --git a/translations/my/lessons/0-course-setup/how-to-run.md b/translations/my/lessons/0-course-setup/how-to-run.md index 5da6ff36..07b2f5c4 100644 --- a/translations/my/lessons/0-course-setup/how-to-run.md +++ b/translations/my/lessons/0-course-setup/how-to-run.md @@ -1,21 +1,21 @@ -# ကုဒ်ကို အလုပ်လုပ်စေခြင်း +# နည်းလမ်း အတိုင်း ကုတ်ကို လည်ပတ်ရန် -ဒီသင်ခန်းစာမှာ သင်လုပ်ဆောင်လိုတဲ့ အလုပ်လုပ်နိုင်တဲ့ နမူနာများနဲ့ လက်တွေ့လေ့ကျင့်ခန်းများ ပါဝင်ပါတယ်။ ဒါကို အလုပ်လုပ်စေဖို့အတွက် Python ကုဒ်ကို Jupyter Notebooks မှာ အလုပ်လုပ်နိုင်ဖို့ လိုအပ်ပါတယ်။ သင့်မှာ ကုဒ်ကို အလုပ်လုပ်စေဖို့ အနည်းဆုံး အောက်ပါနည်းလမ်းများ ရှိပါတယ်: +ဒီ သင်ခန်းစာဟာ အလုပ်လုပ်နိုင်တဲ့ ဥပမာတွေ နဲ့ လက်တွေ့လေ့ကျင့်ခန်းတွေ များစွာ ပါဝင်ပြီး သင် လည်ပတ်ချင်မယ်။ ဒါကို လုပ်ဖို့ သင် လိုအပ်တာက ဒီသင်ခန်းစာ၏ အစိတ်အပိုင်းတစ်ခုအနေဖြင့် ပေးထားတဲ့ Jupyter Notebooks ထဲမှာ Python ကုတ်ကို လည်ပတ်နိုင်စွမ်းရှိဖို့ ဖြစ်ပါတယ်။ ကုတ်ကို လည်ပတ်ဖို့ အနည်းငယ် ရွေးချယ်စရာရှိပါတယ်- -## သင့်ကွန်ပျူတာမှာ တိုက်ရိုက် အလုပ်လုပ်စေခြင်း +## သင့် ကွန်ပျူတာပေါ်မှာ ဒေသခံ လည်ပတ်ရန် -သင့်ကွန်ပျူတာမှာ တိုက်ရိုက် အလုပ်လုပ်စေဖို့ Python ရဲ့ အချို့သော version တစ်ခုခုကို ထည့်သွင်းထားဖို့ လိုအပ်ပါတယ်။ **[miniconda](https://conda.io/en/latest/miniconda.html)** ကို ထည့်သွင်းဖို့ အကြံပြုပါတယ် - ဒါဟာ `conda` package manager ကို အသုံးပြုနိုင်တဲ့ Python **virtual environments** အတွက် အလွန်ပေါ့ပါးတဲ့ installation ဖြစ်ပါတယ်။ +သင့် ကွန်ပျူတာပေါ်မှာ ဒေသခံ လည်ပတ်ဖို့ Python တပ်ဆင်ထားဖို့ လိုအပ်ပါတယ်။ တစ်ခုအတွဲအနေနဲ့ အကြံပြုတာက **[miniconda](https://conda.io/en/latest/miniconda.html)** ကို တပ်ဆင်ဖို့ ဖြစ်ပြီး ဒါဟာ `conda` package manager ကို ထောက်ပံ့တဲ့ ပိုလွယ်ကူတဲ့ Python **virtual environment** များအတွက် တပ်ဆင်ဖို့ တစ်ခုလေးပါ။ -miniconda ကို ထည့်သွင်းပြီးနောက် သင့်မှာ repository ကို clone လုပ်ပြီး ဒီသင်ခန်းစာအတွက် အသုံးပြုမယ့် virtual environment ကို ဖန်တီးဖို့ လိုအပ်ပါတယ်: +miniconda တပ်ဆင်ပြီးပါက repository ကို clone ဆွဲပြီး ဒီသင်တန်းအတွက် အသုံးပြုမဲ့ virtual environment တစ်ခု ဖန်တီးပါ- ```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 ``` -### Visual Studio Code နဲ့ Python Extension ကို အသုံးပြုခြင်း +### Visual Studio Code ကို Python Extension နဲ့ အသုံးပြုခြင်း -ဒီသင်ခန်းစာကို အသုံးပြုဖို့ အကောင်းဆုံးနည်းလမ်းက [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) ကို [Python Extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste) နဲ့ ဖွင့်ခြင်းဖြစ်ပါတယ်။ +ဒီ သင်ခန်းစာကို အသုံးပြုရာမှာ [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) ကို [Python Extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste) နဲ့ဖွင့်၍ သုံးလို့ အကောင်းဆုံးပါ။ -> **Note**: သင် repository ကို clone လုပ်ပြီး VS Code မှာ directory ကို ဖွင့်တဲ့အခါ Python extensions ကို ထည့်သွင်းဖို့ အလိုအလျောက် အကြံပြုပါလိမ့်မယ်။ Miniconda ကို အထက်မှာ ဖော်ပြထားတဲ့အတိုင်း ထည့်သွင်းဖို့လည်း လိုအပ်ပါတယ်။ +> **မှတ်ချက်**: VS Code ထဲမှာ directory ကို clone ဆွဲပြီးဖွင့်လိုက်တာနဲ့ Python extensions ကို အလိုအလျောက် တပ်ဆင်ဖို့ ပေးရှုံ့ပေးမှာ ဖြစ်ပါတယ်။ အထက်မှာ ဖော်ပြထားတဲ့အတိုင်း miniconda ကိုလည်း တပ်ဆင်ထားဖို့ လိုအပ်မှာ ဖြစ်ပါတယ်။ -> **Note**: VS Code က repository ကို container မှာ ပြန်ဖွင့်ဖို့ အကြံပြုလျှင်၊ local Python installation ကို အသုံးပြုဖို့ container ကို ဖွင့်ခြင်းကို ငြင်းပယ်ဖို့ လိုအပ်ပါတယ်။ +> **မှတ်ချက်**: VS Code က container အတွင်းမှာ repo ကို ပြန်ဖွင့်ဖို့ 제안လာရင် ဒေသခံ Python တပ်ဆင်မှုကို အသုံးပြုဖို့ ထို ပြန်ဖွင့်ခြင်းကို ငြင်းဆန်သင့်ပါတယ်။ -### Browser မှာ Jupyter ကို အသုံးပြုခြင်း +### Browser တွင် Jupyter အသုံးပြုခြင်း -သင့်ကွန်ပျူတာမှာ Browser မှာ Jupyter environment ကို အသုံးပြုနိုင်ပါတယ်။ အမှန်အားဖြင့် classical Jupyter နဲ့ Jupyter Hub နှစ်ခုလုံးမှာ auto-completion, code highlighting စတဲ့ အဆင်ပြေတဲ့ development environment တွေ ရှိပါတယ်။ +သင့် ရှိရာ ကွန်ပျူတာမှာ browser မှတစ်ဆင့် Jupyter ပတ်ဝန်းကျင်ကိုလည်း အသုံးပြုနိုင်ပါတယ်။ classical Jupyter နဲ့ JupyterHub နှစ်ခုလုံးမှာ auto-completion, ကုတ်အလင်းပေးခြင်း စသည်တို့ ပါဝင်တဲ့ သက်တောင့်သက်သာ ဖွံ့ဖြိုးမှုပတ်ဝန်းကျင်ကို ရရှိစေပါတယ်။ -Jupyter ကို locally စတင်ဖို့ သင့်သင်ခန်းစာ directory ကို သွားပြီး အောက်ပါ command ကို အလုပ်လုပ်စေပါ: +ဒေသခံ Jupyter ကို စတင်ရန် သင်တန်း folder ကိုသွားပြီး အောက်ပါအတိုင်း လည်ပတ်ပါ- ```bash jupyter notebook -``` -or +``` +or ```bash jupyterhub -``` -ပြီးရင် `.ipynb` ဖိုင်တွေကို သွားပြီး ဖွင့်ပြီး အလုပ်လုပ်နိုင်ပါပြီ။ +``` +ပြီးရင် `.ipynb` ဖိုင် ဘယ်ဟာမဆို သွားရှာဖွေ၊ ဖွင့်ပြီး အလုပ်ဆောင်နိုင်ပါပြီ။ -### Container မှာ အလုပ်လုပ်စေခြင်း +### Container မှာ လည်ပတ်ခြင်း -Python installation ကို အစားထိုးတဲ့ နည်းလမ်းတစ်ခုက container မှာ အလုပ်လုပ်စေခြင်းဖြစ်ပါတယ်။ ကျွန်တော်တို့ရဲ့ repository မှာ `.devcontainer` folder ပါဝင်ပြီး ဒီ repo အတွက် container ကို ဘယ်လို build လုပ်မလဲဆိုတာကို သတ်မှတ်ထားပါတယ်။ ဒါကြောင့် VS Code က code ကို container မှာ ပြန်ဖွင့်ဖို့ အကြံပြုပါလိမ့်မယ်။ ဒါကို အသုံးပြုဖို့ Docker installation လိုအပ်ပြီး အနည်းငယ် ပိုရှုပ်ထွေးပါတယ်၊ ဒါကြောင့် အတွေ့အကြုံရှိတဲ့ အသုံးပြုသူများအတွက် အကြံပြုပါတယ်။ +Python တပ်ဆင်မထားဘဲ container ထဲမှာ ကုတ်တွေ လည်ပတ်လိုက်ရင်နည်းလမ်းတစ်ခုရှိပါတယ်။ ကျွန်တော်တို့ရဲ့ repo ထဲမှာ `.devcontainer` ဆိုတဲ့ folder က container ဖန်တီးနည်းကို ဖော်ပြပေးတာကြောင့် VS Code က container မှာ repo ကို ပြန်ဖွင့်ဖို့ အခွင့်အလမ်းပေးပါတယ်။ ဒီအတွက် Docker ကို တပ်ဆင်ထားရမှာဖြစ်ပြီး ပိုကြမ်းတမ်းရှုပ်ထွေးတဲ့ နယ်ပယ်ဖြစ်လို့ နည်းပညာပိုင်းမှာ ကျွမ်းကျင်သူတွေအတွက်သာ အသုံးပြုဖို့ အကြံပြုပါတယ်။ -## Cloud မှာ အလုပ်လုပ်စေခြင်း +## Cloud မှာ လည်ပတ်ခြင်း -Python ကို locally ထည့်သွင်းချင်မရင်၊ Cloud resources ရှိရင် Cloud မှာ အလုပ်လုပ်စေဖို့ ကောင်းတဲ့ နည်းလမ်းတစ်ခုလည်း ရှိပါတယ်။ အောက်ပါနည်းလမ်းများကို အသုံးပြုနိုင်ပါတယ်: +Python ကို ဒေသခံမှာ တပ်ဆင်ချင် မထားဘူး၊ cloud資源အသုံးပြုခွင့်ရှိရင် cloud မှာ ကုတ်ကို လည်ပတ်အသုံးပြုဖို့ ကောင်းတဲ့ နည်းလမ်းများ ရှိပါတယ်- -* **[GitHub Codespaces](https://github.com/features/codespaces)** ကို အသုံးပြုခြင်း။ ဒါဟာ GitHub မှာ သင့်အတွက် ဖန်တီးထားတဲ့ virtual environment ဖြစ်ပြီး VS Code browser interface မှတစ်ဆင့် အသုံးပြုနိုင်ပါတယ်။ Codespaces ရှိရင် repo မှာ **Code** button ကို click လုပ်ပြီး codespace ကို စတင်နိုင်ပါတယ်၊ အချိန်မရွေး အလုပ်လုပ်နိုင်ပါပြီ။ -* **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)** ကို အသုံးပြုခြင်း။ [Binder](https://mybinder.org) ဟာ GitHub မှာ code တွေကို စမ်းသပ်ဖို့ Cloud မှာ အခမဲ့ computing resources ပေးတဲ့ နည်းလမ်းဖြစ်ပါတယ်။ Repository ရဲ့ front page မှာ Binder ကို ဖွင့်ဖို့ button ရှိပါတယ် - ဒါဟာ binder site ကို အလွယ်တကူ ပို့ပြီး underlying container ကို build လုပ်ပြီး Jupyter web interface ကို seamless အဖြစ် စတင်ပေးပါလိမ့်မယ်။ +* **[GitHub Codespaces](https://github.com/features/codespaces)** ကို အသုံးပြုခြင်း။ GitHub မှာ သင့်အတွက် virtual environment ဖန်တီးပေးပြီး VS Code browser များဖြင့် ရရှိနိုင်ပါတယ်။ Codespaces အသုံးပြုခွင့် ရှိရင် repo မှာ **Code** ခလုတ်ကို နှိပ်ပြီး codespace စတင်ပြီး အခက်အခဲမရှိတတ်နိုင်ပြီ။ +* **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)** ကို အသုံးပြုခြင်း။ [Binder](https://mybinder.org) က GitHub မှာရှိတဲ့ ကုတ်တွေကို စမ်းသပ်ဖို့အတွက် cloud မှကွန်ပျူတာ အရင်းအမြစ်များကို အခမဲ့ ပေးအပ်ပါတယ်။ repo ကို Binder မှာဖွင့်ဖို့ front page မှာ ခလုတ်တစ်ခုရှိပြီး ၎င်းက သင့်အတွက် container တစ်ခုပြုလုပ်ပြီး Jupyter web interface ကို စတင်ပေးပါလိမ့်မယ်။ -> **Note**: Binder ဟာ အချို့ web resources ကို အသုံးချမှုကို ကန့်သတ်ထားပါတယ်။ ဒါဟာ public Internet မှာ models နဲ့ datasets တွေကို fetch လုပ်တဲ့ code တွေ အလုပ်မလုပ်နိုင်စေပါလိမ့်မယ်။ Workarounds တွေ ရှာဖွေရန် လိုအပ်နိုင်ပါတယ်။ Binder မှာ ပေးထားတဲ့ compute resources တွေက အခြေခံဖြစ်ပြီး training အလွန်နှေးပါလိမ့်မယ်၊ အထူးသဖြင့် နောက်ပိုင်းမှာ ရှုပ်ထွေးတဲ့ သင်ခန်းစာတွေမှာ။ +> **မှတ်ချက်**: မမှန်ကန်စွာ အသုံးပြုမှု တားမြစ်ဖို့ Binder ကတချို့ web resources များကို ပိတ်ထားပါတယ်။ ထို့ကြောင့် မော်ဒယ်များ သို့မဟုတ် ဒေတာအစုံများကို အွန်လိုင်းမှ ယူလာတဲ့ ကုတ်တချို့ ဘာလုပ်မရဘူး ဖြစ်နိုင်ပါတယ်။ အလုပ်ဖြေရှင်းနည်းခြင်းတွေလည်း လိုအပ်နိုင်ပါတယ်။ ထို့အပြင် Binder ပေးတဲ့ ကွန်ပျူတာ အရင်းအမြစ်တွေ အခြေခံအဆင့်ဖြစ်လို့ သင်ကြားမှု မြန်မရနိုင်ပါ၊ အထူးသဖြင့် နောက်ပိုင်းမှာ ပိုခက်ခဲမယ့် သင်ခန်းစာများမှာပြသနာဖြစ်နိုင်ပါတယ်။ -## GPU ပါဝင်တဲ့ Cloud မှာ အလုပ်လုပ်စေခြင်း +## GPU ပါ Cloud မှာ လည်ပတ်ခြင်း -ဒီသင်ခန်းစာရဲ့ နောက်ပိုင်းမှာ GPU support ရှိရင် training အလွန်မြန်နိုင်ပါတယ်၊ မဟုတ်ရင် training အလွန်နှေးပါလိမ့်မယ်။ သင့်မှာ Cloud ကို [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) သို့မဟုတ် သင့်အဖွဲ့အစည်းမှတစ်ဆင့် အသုံးပြုနိုင်ရင် အောက်ပါနည်းလမ်းများကို လိုက်နာနိုင်ပါတယ်: +ဒီသင်ခန်းစာအတွင်း နောက်ပိုင်းသင်ခန်းစာအချို့မှာ GPU ပံ့ပိုးမှုက အလွန် အကျိုးရှိပါလိမ့်မယ်။ မော်ဒယ် သင်ကြားမှုကို ပိုမြန်စေမှာ ဖြစ်ပြီး မရှိမဖြစ်လိုအပ်ပါတယ်။ [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) သို့မဟုတ် သင့်တက္ကသိုလ်မှတဆင့် cloud ရရှိသူတွေ အတွက် အောက်ပါနည်းလမ်းများ ရှိပါတယ်- -* [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) ကို ဖန်တီးပြီး Jupyter မှတစ်ဆင့် ချိတ်ဆက်ပါ။ Repo ကို machine မှာ တိုက်ရိုက် clone လုပ်ပြီး သင့်သင်ခန်းစာကို စတင်နိုင်ပါတယ်။ NC-series VMs တွေမှာ GPU support ရှိပါတယ်။ +* [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) တစ်ခု ဖန်တီးပြီး Jupyter မှတစ်ဆင့် ချိတ်ဆက်ပြီး အသုံးပြုနိုင်ပါတယ်။ repo ကို တိုက်ရိုက် machine မှာ clone ဆွဲပြီး စတင်လေ့လာနိုင်ပါတယ်။ NC-series VMs တွေဟာ GPU ပါဝင်ပါတယ်။ -> **Note**: Azure for Students အပါအဝင် subscription အချို့မှာ GPU support ကို အလိုအလျောက် မပေးထားပါဘူး။ GPU cores အပိုတောင်းဖို့ technical support request တင်ဖို့ လိုအပ်နိုင်ပါတယ်။ +> **မှတ်ချက်**: Azure for Students စသည့် subscription တချို့ဟာ ပုံမှန်အတိုင်း GPU ပံ့ပိုးမှု မပေးပါ၊ technical support ကနေ GPU cores ပိုများအောင် တောင်းဆိုရပါမယ်။ -* [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) ကို ဖန်တီးပြီး Notebook feature ကို အသုံးပြုပါ။ [ဒီဗီဒီယို](https://azure-for-academics.github.io/quickstart/azureml-papers/) မှာ Azure ML notebook မှာ repository ကို clone လုပ်ပြီး အသုံးပြုပုံကို ပြထားပါတယ်။ +* [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) တစ်ခု ဖန်တီးပြီး Notebook feature ကို အသုံးပြုနိုင်ပါတယ်။ [ဤဗီဒီယို](https://azure-for-academics.github.io/quickstart/azureml-papers/) သည် Azure ML Notebook တွင် repo တစ်ခုကို clone ဆွဲပြီး အသုံးပြုနည်းကို ပြသထားပါတယ်။ -Google Colab ကိုလည်း အသုံးပြုနိုင်ပြီး အခမဲ့ GPU support ပါဝင်ပါတယ်၊ Jupyter Notebooks တွေကို upload လုပ်ပြီး တစ်ခုချင်းစီကို အလုပ်လုပ်စေပါ။ +Google Colab ကိုလည်း အသုံးပြုနိုင်ပြီး အခမဲ့ GPU ပံ့ပိုးမှု နဲ့ Jupyter Notebooks တွေကို တစ်ခုချင်းစီ တင်ပြီး လည်ပတ်နိုင်ပါတယ်။ -**အကြောင်းကြားချက်**: -ဤစာရွက်စာတမ်းကို AI ဘာသာပြန်ဝန်ဆောင်မှု [Co-op Translator](https://github.com/Azure/co-op-translator) ကို အသုံးပြု၍ ဘာသာပြန်ထားပါသည်။ ကျွန်ုပ်တို့သည် တိကျမှုအတွက် ကြိုးစားနေသော်လည်း၊ အလိုအလျောက် ဘာသာပြန်ခြင်းတွင် အမှားများ သို့မဟုတ် မတိကျမှုများ ပါဝင်နိုင်သည်ကို သတိပြုပါ။ မူရင်းဘာသာစကားဖြင့် ရေးသားထားသော စာရွက်စာတမ်းကို အာဏာတရ အရင်းအမြစ်အဖြစ် ရှုလေ့လာသင့်ပါသည်။ အရေးကြီးသော အချက်အလက်များအတွက် လူ့ဘာသာပြန်ပညာရှင်များမှ ပရော်ဖက်ရှင်နယ် ဘာသာပြန်ခြင်းကို အကြံပြုပါသည်။ ဤဘာသာပြန်ကို အသုံးပြုခြင်းမှ ဖြစ်ပေါ်လာသော အလွဲအလွတ်များ သို့မဟုတ် အနားယူမှားမှုများအတွက် ကျွန်ုပ်တို့သည် တာဝန်မယူပါ။ \ No newline at end of file +--- + + +**အကြောင်းကြားချက်**: +ဤစာတမ်းကို AI ဘာသာပြန်ဝန်ဆောင်မှု [Co-op Translator](https://github.com/Azure/co-op-translator) အသုံးပြု၍ ဘာသာပြန်ထားပါသည်။ မှန်ကန်မှုအတွက် ကြိုးစားပေမယ့် စက်မြန်မာဘာသာပြန်ချက်များတွင် အမှားများ သို့မဟုတ် မမှန်ကန်မှုများ ပါဝင်နိုင်ကြောင်း သတိပြုပါရန် အပ်ပါသည်။ မူရင်းစာတမ်းကို မိခင်ဘာသာဖြင့်သာ ထောက်ခံချက်အဖြစ်ယူဆသင့်ပါသည်။ အရေးကြီးသည့် သတင်းအချက်အလက်များအတွက်တော့ တတ်ကျွမ်းသော လူ့ဘာသာပြန်သူများ၏ ဘာသာပြန်ခြင်းကို အကြံပြုပါသည်။ ဤဘာသာပြန်ချက်ကို အသုံးပြုမှုမှဖြစ်ပေါ်လာသော လွဲမှား သို့မဟုတ် နားလည်မှုမှားများအတွက် ကျွန်ုပ်တို့ မည်သည့် တာဝန်မရှိပါ။ + \ No newline at end of file diff --git a/translations/my/lessons/2-Symbolic/Animals.ipynb b/translations/my/lessons/2-Symbolic/Animals.ipynb index e0fbde1c..ba48d6a1 100644 --- a/translations/my/lessons/2-Symbolic/Animals.ipynb +++ b/translations/my/lessons/2-Symbolic/Animals.ipynb @@ -6,25 +6,25 @@ "collapsed": true }, "source": [ - "# တိရစ္ဆာန် အတတ်ပညာ စနစ် တည်ဆောက်ခြင်း\n", + "# တိရစ္ဆာန်ကျွမ်းကျင်စနစ်တစ်ခုကို အကောင်အထည်ဖော်ခြင်း\n", "\n", - "[AI for Beginners Curriculum](http://github.com/microsoft/ai-for-beginners) မှ နမူနာတစ်ခု။\n", + "[AI for Beginners Curriculum](http://github.com/microsoft/ai-for-beginners) မှ ဥပမာတစ်ခု။\n", "\n", - "ဒီနမူနာမှာတော့ ရုပ်ပိုင်းဆိုင်ရာ အင်္ဂါရပ်အချို့အပေါ် မူတည်ပြီး တိရစ္ဆာန်ကို သတ်မှတ်နိုင်ရန် ရိုးရှင်းတဲ့ အသိပညာအခြေပြု စနစ်တစ်ခုကို တည်ဆောက်သွားမှာ ဖြစ်ပါတယ်။ ဒီစနစ်ကို အောက်ပါ AND-OR သစ်ပင်ဖြင့် ကိုယ်စားပြုနိုင်ပါတယ် (ဒီဟာက သစ်ပင်တစ်ခုလုံးရဲ့ အစိတ်အပိုင်းတစ်ခုသာဖြစ်ပြီး နောက်ထပ် စည်းမျဉ်းများကို လွယ်ကူစွာ ထပ်ထည့်နိုင်ပါတယ်)။\n", + "ဒီနမူနာမှာ၊ တိရစ္ဆာန်တစ်ကောင်ကို ရုပ်ပိုင်းဆိုင်ရာ လက္ခဏာအခြေခံပြီး သတ်မှတ်ပေးနိုင်ရန် အခြေခံသိပ္ပံကိရိယာစနစ်တစ်ခုကို တည်ဆောက်မှာဖြစ်ပါတယ်။ ဤစနစ်ကို အောက်ပါ AND-OR ဉပမှတ်ပင် (tree) ဖြင့် ကိုယ်စားပြုနိုင်ပါတယ် (ဒါဟာ အစိတ်အပိုင်းတစ်ခုသာဖြစ်ပြီး ကောင်းကောင်းစွာ ဥပဒေများကို ပေါင်းထည့်နိုင်ပါတယ်)။\n", "\n", - "![](../../../../translated_images/my/AND-OR-Tree.5592d2c70187f283.webp)\n" + "![](../../../../../../translated_images/my/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## ကျွန်ုပ်တို့၏ အတတ်ပညာစနစ် shell ကို နောက်ပြန်သုံးသပ်မှုဖြင့်\n", + "## ကျွန်ုပ်တို့၏ ကိုယ်ပိုင် ကျွမ်းကျင်သူ စနစ် shell နှင့် နောက်ကျော ခေါ်ဆောင်ခြင်း\n", "\n", - "ပုံစံစည်းမျဉ်းများအပေါ် အခြေခံပြီး အသိပညာကို ကိုယ်စားပြုရန်အတွက် ရိုးရှင်းသော ဘာသာစကားတစ်ခုကို သတ်မှတ်ကြည့်ရအောင်။ ကျွန်ုပ်တို့သည် စည်းမျဉ်းများကို သတ်မှတ်ရန်အတွက် Python class များကို keyword အဖြစ် အသုံးပြုမည်။ အဓိကအားဖြင့် အတန်း (classes) ၃ မျိုးရှိမည်ဖြစ်သည်။\n", - "* `Ask` သည် အသုံးပြုသူကို မေးရမည့် မေးခွန်းကို ကိုယ်စားပြုသည်။ ၎င်းတွင် အဖြေများ၏ အစုအဖွဲ့ကို ပါဝင်သည်။\n", - "* `If` သည် စည်းမျဉ်းကို ကိုယ်စားပြုသည်၊ ၎င်းသည် စည်းမျဉ်း၏ အကြောင်းအရာကို သိမ်းဆည်းရန် syntactic sugar ဖြစ်သည်။\n", - "* `AND`/`OR` သည် အပင်၏ AND/OR ချိတ်ဆက်မှုများကို ကိုယ်စားပြုရန်အတွက် classes ဖြစ်သည်။ ၎င်းတို့သည် အတွင်းရှိ arguments များ၏ စာရင်းကိုသာ သိမ်းဆည်းသည်။ ကုဒ်ကို ရိုးရှင်းစေရန် အလုပ်ဆောင်မှုအားလုံးကို မိဘ class `Content` တွင် သတ်မှတ်ထားသည်။\n" + "ထုတ်လုပ်မှု စည်းကမ်းများအပေါ် မူတည်၍ အသိပညာ ကိုယ်စားပြုရန် လွယ်ကူသော ဘာသာစကားကို သတ်မှတ်ကြည့်ရအောင်။ စည်းကမ်းများ သတ်မှတ်ရန် မူလတန်းလက္ခဏာများအဖြစ် Python class များကို အသုံးပြုမည်။ အဓိကအားဖြင့် class ၃ မျိုး ရှိပါမည်။\n", + "* `Ask` သည် အသုံးပြုသူထံ မေးမြန်းရန် လိုအပ်သော မေးခွန်းကို ကိုယ်စားပြုသည်။ ၎င်းတွင် ဖြေကြောင်းများ စုဆောင်းထားသည်။\n", + "* `If` သည် စည်းကမ်းတစ်ခုကို ကိုယ်စားပြုသည်၊ ၎င်းသည် စည်းကမ်း၏ အကြောင်းအရာကို သိမ်းဆည်းရန် စာလုံးပေါင်း ဂုဏ်သတ္တိဖြစ်သည်။\n", + "* `AND`/`OR` သည် သစ်ပင်၏ AND/OR ရင်းနှီးခွဲများကို ကိုယ်စားပြုသော class များဖြစ်သည်။ ၎င်းတို့သည် အတွင်းရှိ အရေအတွက်စာရင်းကို သိမ်းဆည်းသည်။ ကုဒ်ကို လွယ်ကူစေရန် အားလုံးသော လုပ်ဆောင်ချက်များကို မိခင် class ဖြစ်သော `Content` တွင် သတ်မှတ်ထားသည်။\n" ] }, { @@ -66,7 +66,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "ကျွန်ုပ်တို့၏စနစ်တွင်၊ အလုပ်လုပ်နေသောမှတ်ဉာဏ်သည် **attribute-value pairs** အဖြစ် **အချက်အလက်များ** စာရင်းကို ပါဝင်မည်ဖြစ်သည်။ အသိပညာအခြေခံကို AND-OR အထူးပြုချက်များအဖြစ် ဖော်ပြထားသော အခြေအနေများသို့ လုပ်ဆောင်မှုများ (အလုပ်လုပ်နေသောမှတ်ဉာဏ်ထဲသို့ ထည့်သွင်းသင့်သော အချက်အလက်အသစ်များ) ကို မြေပုံဆွဲပေးသည့် dictionary တစ်ခုအဖြစ် သတ်မှတ်နိုင်သည်။ ထို့အပြင်၊ အချက်အလက်အချို့ကို `Ask`-ed ပြုလုပ်နိုင်သည်။\n" + "ကျွန်ုပ်တို့၏ စနစ်တွင်၊ အလုပ်လုပ်စဉ်မှတ်ဉာဏ်သည် **အချက်အလက်များ** ကို **ကြောင့်အတန်းတန်ဖိုးစုံ** အဖြစ် ပါဝင်မည်ဖြစ်သည်။ သိပ္ပံရင်းမြစ်ကို များသောကြီးသော အဘိဓာန်တစ်ခုအဖြစ် သတ်မှတ်နိုင်ပြီး၊ အဲဒီအဘိဓာန်တွင် လုပ်ဆောင်ချက်များ (အလုပ်လုပ်စဉ်မှတ်ဉာဏ်သို့ အသစ်ထည့်သွင်းသင့်သော အချက်အလက်များ) ကို အခြေအနေများနှင့် တွဲဖက်ပြီး AND-OR ဖော်ပြချက်များအဖြစ် ဖေါ်ပြသည်။ ထို့အပြင် အချို့အချက်အလက်များကို `Ask` လုပ်နိုင်ပါသည်။\n" ] }, { @@ -99,15 +99,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "နောက်ပြန်သုံးသပ်မှုလုပ်ဆောင်ရန် `Knowledgebase` class ကိုသတ်မှတ်မည်။ ၎င်းတွင်ပါဝင်မည်မှာ:\n", + "နောက်ပြန်အဓိပ္ပာယ်ဖော်ထုတ်မှုကိုဆောင်ရွက်ရန်အတွက် `Knowledgebase` အတန်းကို သတ်မှတ်ပါမည်။ ၎င်းတွင်ပါဝင်မည့်အရာများမှာ-\n", + "* ဇာတ်ကြောင်းအလုပ်လုပ်သော `memory` - attribute များကိုတန်ဖိုးများနှင့်တိုက်ဆိုင်စေသော dictionary တစ်ခု\n", + "* Knowledgebase တွင် `rules` ကိုအထက်ဖော်ပြထားသည့် ပုံစံဖြင့်ပါဝင်သည်\n", "\n", - "* `memory` - attribute များနှင့် value များကို mapping လုပ်ထားသော dictionary\n", - "* Knowledgebase `rules` - အထက်တွင်ဖော်ပြထားသည့်ပုံစံအတိုင်း\n", - "\n", - "အဓိက method နှစ်ခုမှာ:\n", - "\n", - "* `get` - attribute တစ်ခု၏ value ကိုရယူရန်၊ လိုအပ်ပါက inference လုပ်ဆောင်မည်။ ဥပမာအားဖြင့် `get('color')` သည် color slot ၏ value ကိုရယူမည် (လိုအပ်ပါကမေးမြန်းပြီး၊ နောက်ပိုင်းအသုံးပြုရန် working memory တွင် value ကိုသိမ်းဆည်းမည်)။ `get('color:blue')` ကိုမေးမြန်းပါက color ကိုမေးမြန်းပြီး၊ color အပေါ်မူတည်၍ `y`/`n` value ကိုပြန်ပေးမည်။\n", - "* `eval` - အမှန်တကယ်သော inference ကိုလုပ်ဆောင်မည်၊ AND/OR tree ကိုဖြတ်သန်းပြီး sub-goals များကိုအကဲဖြတ်မည်။\n" + "အဓိကနည်းလမ်းနှစ်ခုမှာ-\n", + "* `get` သည် attribute ၏တန်ဖိုးကို ရရှိစေပြီး လိုအပ်လျှင် အဓိပ္ပာယ်ဖော်ထုတ်မှုကိုလုပ်ဆောင်သည်။ ဥပမာအားဖြင့်၊ `get('color')` သည် အရောင်ဆိုင်ရာ slot ၏တန်ဖိုးကို ရယူမည် (လိုအပ်ပါက မေးမြန်းပြီး နောက်ပိုင်းအသုံးပြုမှုအတွက် working memory တွင် သိမ်းဆည်းမည်)။ `get('color:blue')` ကို မေးမြန်းပါက အရောင်တစ်ခုကို မေးမြန်းပြီး၊ အရောင်အရ `y`/`n` တန်ဖိုးကို ပြန်လည်ပေးပါမည်။\n", + "* `eval` သည် လက်တွေ့အဓိပ္ပာယ်ဖော်ထုတ်မှုကို လုပ်ဆောင်သည်၊ ၎င်းတွင် AND/OR သစ်ပင်ကို ဖြတ်သန်းခြင်း၊ ညွှန်ကြားချက်အောက်ဆုံးများကို တန်ဖိုးသတ်မှတ်ခြင်း စသည်တို့ပါဝင်သည်။\n" ] }, { @@ -174,7 +172,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "အခုတော့ ကျွန်ုပ်တို့၏ တိရစ္ဆာန် အသိပညာအခြေခံကို သတ်မှတ်ပြီး အကြံပေးမှုကို ဆောင်ရွက်ကြမည်။ ဤခေါ်ဆိုမှုသည် သင့်ကို မေးခွန်းများ မေးမည်ဖြစ်သည်။ ဟုတ်/မဟုတ် မေးခွန်းများအတွက် `y`/`n` ကို ရိုက်ထည့်ခြင်းဖြင့် သင် ဖြေနိုင်ပြီး၊ ရွေးချယ်စရာများ မေးခွန်းများအတွက် (0..N) ကို သတ်မှတ်ခြင်းဖြင့် ဖြေနိုင်သည်။\n" + "ယခုကျွန်ုပ်တို့၏တိရစ္ဆာန်အချက်အလက်အခြေခံကိုသတ်မှတ်ပြီးအကြံပေးမှုကိုပြုလုပ်ကြမည်။ ဤခေါ်ဆိုမှုသည်သင့်အားမေးခွန်းများမေးမည်ကိုသတိပြုပါ။ ဖြေကြားရာတွင် ဟုတ်/မဟုတ်မေးခွန်းများအတွက် `y`/`n` ဟုရိုက်ထည့်နိုင်ပြီး၊ ရွေးချယ်မှုအများအပြားပါရှိသောမေးခွန်းများအတွက် ၀..N အထိနံပါတ်များဖြင့်ဖြေဆိုနိုင်ပါသည်။\n" ] }, { @@ -231,11 +229,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## PyKnow ကို အသုံးပြု၍ Forward Inference ပြုလုပ်ခြင်း\n", + "## အရှေ့တန်း မှတ်ချက်အတွက် Experta ကို အသုံးပြုခြင်း\n", "\n", - "နောက်ထပ် ဥပမာတွင် ကျွန်ုပ်တို့သည် သိမြင်မှုကို ကိုယ်စားပြုရန်အတွက် အသုံးပြုနိုင်သော စာကြောင်းများထဲမှ တစ်ခုဖြစ်သည့် [PyKnow](https://github.com/buguroo/pyknow/) ကို အသုံးပြု၍ forward inference ကို အကောင်အထည်ဖော်ကြည့်မည်ဖြစ်သည်။ **PyKnow** သည် Python တွင် forward inference system များ ဖန်တီးရန်အတွက် အသုံးပြုနိုင်သော စာကြောင်းတစ်ခုဖြစ်ပြီး၊ ရှေးဟောင်းစနစ် [CLIPS](http://www.clipsrules.net/index.html) နှင့် ဆင်တူစေရန် ဒီဇိုင်းထုတ်ထားသည်။\n", + "နောက်ထပ် ဥပမာတွင် ကျွန်ုပ်တို့ သဘောတရား ကိုယ်စားပြုမှု လိုင်းဘရယ်ရီများထဲမှ တစ်ခုဖြစ်သော [Experta](https://github.com/nilp0inter/experta) ကို အသုံးပြု၍ အရှေ့တန်း မှတ်ချက်ကို အကောင်အထည် ဖော်ကြမည်။ **Experta** သည် Python တွင် အရှေ့တန်း မှတ်ချက် စနစ်များကို ဖန်တီးရန် အသုံးပြုသော လိုင်းဘရယ်ရီဖြစ်ပြီး၊ ရိုးရာ အဟောင်း စနစ် [CLIPS](http://www.clipsrules.net/index.html) နှင့် ဆင်တူအောင် ဒီဇိုင်းပြုထားသည်။ \n", "\n", - "ကျွန်ုပ်တို့သည် forward chaining ကို ကိုယ်တိုင်လည်း အလွယ်တကူ အကောင်အထည်ဖော်နိုင်မည်ဖြစ်သော်လည်း၊ ရိုးရိုးရှင်းရှင်း အကောင်အထည်ဖော်မှုများသည် အများအားဖြင့် ထိရောက်မှုမရှိလှပါ။ စည်းမျဉ်းများကို ထိရောက်စွာ ကိုက်ညီစေရန်အတွက် အထူး algorithm တစ်ခုဖြစ်သည့် [Rete](https://en.wikipedia.org/wiki/Rete_algorithm) ကို အသုံးပြုသည်။\n" + "ကျွန်ုပ်တို့ကိုယ်တိုင် အရှေ့တန်း အချိတ်ကို များများ ပြုလုပ်ဖို့လည်း ရှိပေမယ့်၊ ရိုးရှင်းစွာ ဖော်ဆောင်ခြင်းမှာ မလှပသလောက် ထိရောက်မှုမရှိသေးပါ။ ထို့ကြောင့် ပိုထိရောက်သော စည်းကမ်းကို ကိုက်ညီစေရန် အထူးအယ်လ်ဂိုရီသမ် [Rete](https://en.wikipedia.org/wiki/Rete_algorithm) ကို အသုံးပြုကြသည်။\n" ] }, { @@ -249,32 +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" ] }, { @@ -285,15 +282,15 @@ }, "outputs": [], "source": [ - "from pyknow import *\n", - "#import pyknow" + "from experta import *\n", + "#import experta" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "ကျွန်ုပ်တို့၏စနစ်ကို `KnowledgeEngine` ကိုအခြေခံသော class အဖြစ်သတ်မှတ်ပါမည်။ Rule တစ်ခုစီကို `@Rule` အမှတ်အသားဖြင့်သတ်မှတ်ထားသော function တစ်ခုစီဖြင့်သတ်မှတ်ပါမည်၊ အဲဒီ Rule သည်မည်သည့်အချိန်တွင်အလုပ်လုပ်သင့်သည်ကိုသတ်မှတ်ပေးပါသည်။ Rule အတွင်းတွင် `declare` function ကိုအသုံးပြု၍ fact အသစ်များထည့်သွင်းနိုင်ပြီး၊ အဲဒီ fact များကိုထည့်သွင်းခြင်းဖြင့် forward inference engine မှတစ်ဆင့် Rule များအပိုဆောင်းခေါ်ယူခြင်းဖြစ်ပေါ်စေပါမည်။\n" + "ကျွန်ုပ်တို့၏စနစ်ကို `KnowledgeEngine` ကို subclass ချထားသည့် class အဖြစ် သတ်မှတ်ပါမည်။ စည်းမျဉ်းတစ်ခုချင်းစီကို `@Rule` အမှတ်အသားဖြင့် သတ်မှတ်ထားသော function တစ်ခုဖြစ်ပြီး၊ ၎င်းသည် စည်းမျဉ်းသည် မည်သောအချိန်တွင် တက်ကြွရမည်ဆိုသည်ကို ဖော်ပြသည်။ စည်းမျဉ်းအတွင်းတွင် `declare` function ကို အသုံးပြု၍ အချက်အလက်အသစ်များ ထည့်သွင်းနိုင်ပြီး၊ ထိုအချက်အလက်များ ထည့်သွင်းခြင်းကြောင့် ပြောင်းလဲမှုတိုးလာသည့် စည်းမျဉ်းများ ရှေ့ဆက် inference engine ကဖုန်းခေါ်ခံရမည်ဖြစ်သည်။\n" ] }, { @@ -380,7 +377,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "ကျွန်ုပ်တို့သည် အသိပညာအခြေခံကို သတ်မှတ်ပြီးပါက အစောပိုင်းအချက်အလက်များဖြင့် အလုပ်လုပ်မိသားစုကို ဖြည့်ဆည်းပြီးနောက် `run()` နည်းလမ်းကို ခေါ်ဆို၍ သုံးသပ်မှုကို ဆောင်ရွက်ပါသည်။ အကျိုးအဖြစ် အသစ်ထွက်ရှိသော အချက်အလက်များကို အလုပ်လုပ်မိသားစုတွင် ထည့်သွင်းထားသည်ကို တွေ့နိုင်ပြီး (အစောပိုင်းအချက်အလက်များကို မှန်ကန်စွာ သတ်မှတ်ထားပါက) တိရစ္ဆာန်နှင့်ဆိုင်သော နောက်ဆုံးအချက်အလက်ကိုလည်း ပါဝင်သည်။\n" + "ကျွန်ုပ်တို့သည် knowledgebase ကို သတ်မှတ်ပြီးနောက်၊ စတင်သော အချက်အလက်များအား ကျွန်ုပ်တို့၏ အလုပ်လုပ်စဉ်မှတ်ဉာဏ်တွင် ထည့်သွင်းပြီးနောက် `run()` method ကို ခေါ်ဆိုကာ သုံးသပ်ချက်များ ပြုလုပ်ပါသည်။ အဖြစ်အပျက်အနေဖြင့် မှန်မှန်ကန်ကန် စတင်အချက်အလက်များအားလုံးကို တပ်ဆင်ထားခဲ့ပါက ဆုည့္ကိန်းသစ်များသည် အလုပ်လုပ်စဉ်မှတ်ဉာဏ်ထဲသို့ ထပ်မံထည့်သွင်းခြင်းဖြစ်ပြီး သတ္တဝါအကြောင်း နောက်ဆုံးအချက်အလက်ပါ ပါဝင်သည်ကို တွေ့နိုင်ပါသည်။\n" ] }, { @@ -442,7 +439,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n---\n\n**အကြောင်းကြားချက်**: \nဤစာရွက်စာတမ်းကို AI ဘာသာပြန်ဝန်ဆောင်မှု [Co-op Translator](https://github.com/Azure/co-op-translator) ကို အသုံးပြု၍ ဘာသာပြန်ထားပါသည်။ ကျွန်ုပ်တို့သည် တိကျမှုအတွက် ကြိုးစားနေသော်လည်း၊ အလိုအလျောက် ဘာသာပြန်မှုများတွင် အမှားများ သို့မဟုတ် မမှန်ကန်မှုများ ပါဝင်နိုင်သည်ကို သတိပြုပါ။ မူရင်းဘာသာစကားဖြင့် ရေးသားထားသော စာရွက်စာတမ်းကို အာဏာတရ အရင်းအမြစ်အဖြစ် ရှုလေ့လာသင့်ပါသည်။ အရေးကြီးသော အချက်အလက်များအတွက် လူက ဘာသာပြန်မှု ဝန်ဆောင်မှုကို အကြံပြုပါသည်။ ဤဘာသာပြန်မှုကို အသုံးပြုခြင်းမှ ဖြစ်ပေါ်လာသော အလွဲအလွတ်များ သို့မဟုတ် အနားလွဲမှုများအတွက် ကျွန်ုပ်တို့သည် တာဝန်မယူပါ။\n" + "---\n\n\n**ညွှန်ကြားချက်** \nဤစာရွက်စာတမ်းကို AI ဘာသာပြန်ဝန်ဆောင်မှုဖြစ်သည့် [Co-op Translator](https://github.com/Azure/co-op-translator) ဖြင့် ဘာသာပြန်ထားပါသည်။ ကျွန်ုပ်တို့သည် မှန်ကန်မှုအတွက် ကြိုးပမ်းထားပေမယ့်၊ အလိုအလျောက် ဘာသာပြန်ခြင်းများတွင် အမှားများ သို့မဟုတ် မှားယွင်းမှုများ ရှိနိုင်ကြောင်း သတိပြုပါရန်။ ဇာတိဘာသာစကားဖြင့် ရေးသားထားသော မူရင်းစာရွက်စာတမ်းကို တရားဝင်အချက်အလက်အရင်းအမြစ်အဖြစ် သတ်မှတ်ရမည်ဖြစ်သည်။ အရေးကြီးသောအချက်အလက်များအတွက် သမားရိုးကျလူသား ဘာသာပြန်ခြင်းကို အကြံပြုပါသည်။ ဤဘာသာပြန်ချက်ကို အသုံးပြုရာမှ ဖြစ်ပေါ်နိုင်သည့် မဖြစ်မနေနားလည်မှုများ သို့မဟုတ် မှားယွင်းဖတ်ရှုမှုများအတွက် ကျွန်ုပ်တို့သည် တာဝန်မယူပါ။\n\n" ] } ], @@ -469,8 +466,8 @@ "version": "3.11.2" }, "coopTranslator": { - "original_hash": "ab2bd97b0453415b89a469284609a8ce", - "translation_date": "2025-08-30T09:42:22+00:00", + "original_hash": "8ef43db4b9182239fd150a76bd494fdb", + "translation_date": "2026-01-16T06:00:36+00:00", "source_file": "lessons/2-Symbolic/Animals.ipynb", "language_code": "my" } diff --git a/translations/my/lessons/2-Symbolic/README.md b/translations/my/lessons/2-Symbolic/README.md index 2b1dd2b9..00ff8ab7 100644 --- a/translations/my/lessons/2-Symbolic/README.md +++ b/translations/my/lessons/2-Symbolic/README.md @@ -1,116 +1,117 @@ -# Knowledge Representation and Expert Systems +# အသိပညာ ကိုယ်စားပြုခြင်းနှင့် ကျွမ်းကျင်သူ စနစ်များ -![Summary of Symbolic AI content](../../../../translated_images/my/ai-symbolic.715a30cb610411a6.webp) +![Summary of Symbolic AI content](../../../../../../translated_images/my/ai-symbolic.715a30cb610411a6.webp) -> Sketchnote by [Tomomi Imura](https://twitter.com/girlie_mac) +> Sketchnote ကို [Tomomi Imura](https://twitter.com/girlie_mac) က ဖန်တီးထားသည်။ -အတုအကျင့်တုရရှိရန် လူသားများကဲ့သို့ ကမ္ဘာကြီးကို နားလည်နိုင်ရန် အသိပညာကို ရှာဖွေခြင်းအပေါ် အခြေခံထားသည်။ ဒါပေမယ့် ဒါကို ဘယ်လိုလုပ်နိုင်မလဲ? +အတုအစား စွမ်းဆောင်ရည် ရရှိစေနိုင်ရန် လေ့လာသင်ယူမှုမှာ လူတို့ကဲ့သို့ ကမ္ဘာကို နားလည်နိုင်ရေးအတွက် အသိပညာကို ရှာဖွေခြင်းဖြစ်သည်။ သို့သော် ဤအရာကို မည်သို့ဆောင်ရွက်နိုင်မည်နည်း? -## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ai/quiz/3) +## [မည်သည့်အခန်းမဆို မတိုင်မီ စစ်ဆေးမှု](https://ff-quizzes.netlify.app/en/ai/quiz/3) -AI ရဲ့အစောပိုင်းကာလတွေမှာ အတုအကျင့်တုစနစ်တွေကို ဖန်တီးဖို့အတွက် အပေါ်မှအောက်သို့သွားတဲ့နည်းလမ်း (ယခင်သင်ခန်းစာမှာ ဆွေးနွေးခဲ့တဲ့) အလွန်လူကြိုက်များခဲ့တယ်။ ဒီအယူအဆက လူတွေကနေ အသိပညာကို စက်နည်းနည်းဖြင့် ဖော်ပြနိုင်တဲ့ပုံစံတစ်ခုအဖြစ် ထုတ်ယူပြီး ပြဿနာတွေကို အလိုအလျောက် ဖြေရှင်းဖို့ အသုံးပြုတာဖြစ်တယ်။ ဒီနည်းလမ်းက အဓိကအယူအဆနှစ်ခုအပေါ် အခြေခံထားပါတယ်- +AI ရဲ့အစောပိုင်းကာလများတွင် ၎င်း၏အလုပ်လိုက် နည်းလမ်း (ယခင်သင်ခန်းစာတွင် ဆွေးနွေးခဲ့သည့်) လေ့လာရေး နည်းလမ်း ကျယ်ကျယ်ပြန့်ပြန့် အသုံးများခဲ့သည်။ ယင်း၏အကြံအတွေးမှာ လူများထံမှ အသိပညာကို မှန်ကန်စွာ ပုံဖော်၍ စက်က စီမံခန့်ခွဲနိုင်သော ပုံစံသို့ ပြောင်းလဲ၍ မူမမှန်သော ပြဿနာများကိုအလိုအလျောက် ဖြေရှင်းနိုင်စေရန်ဖြစ်သည်။ ယင်းနည်းလမ်းတွင် အကြီးမားဆုံး အယူအဆ နှစ်ခု ပါဝင်သည် - -* Knowledge Representation (အသိပညာကိုဖော်ပြခြင်း) -* Reasoning (အကြောင်းအရာဆင်ခြင်း) +* အသိပညာ ကိုယ်စားပြုခြင်း +* ထုတ်ဖော်စဉ်းစားမှု -## Knowledge Representation +## အသိပညာ ကိုယ်စားပြုခြင်း -Symbolic AI ရဲ့ အရေးကြီးတဲ့အယူအဆတစ်ခုက **အသိပညာ** ဖြစ်ပါတယ်။ အသိပညာကို *အချက်အလက်* သို့မဟုတ် *ဒေတာ* နဲ့ ကွဲပြားစွာ သတ်မှတ်ဖို့ အရေးကြီးပါတယ်။ ဥပမာအားဖြင့် စာအုပ်တွေက အသိပညာကို ပါဝင်တယ်လို့ ပြောနိုင်တယ်၊ အကြောင်းက စာအုပ်တွေကို လေ့လာပြီး ကျွမ်းကျင်သူဖြစ်နိုင်တယ်။ ဒါပေမယ့် စာအုပ်တွေမှာ ပါဝင်တာက *ဒေတာ* လို့ခေါ်ပြီး စာအုပ်တွေကို ဖတ်ပြီး ဒီဒေတာကို ကမ္ဘာကြီးရဲ့ မော်ဒယ်ထဲမှာ ပေါင်းစည်းခြင်းအားဖြင့် ဒေတာကို အသိပညာအဖြစ် ပြောင်းလဲပါတယ်။ +Symbolic AI တွင် အရေးကြီးသော အယူအဆတစ်ခုမှာ **အသိပညာ** ဖြစ်သည်။ ဒီအသိပညာကို *အချက်အလက်* သို့မဟုတ် *ဒေတာ* ထက် ကွာခြားစွာ ထားရမည်ဖြစ်သည်။ ဥပမာအားဖြင့်၊ စာအုပ်များမှာ အသိပညာကို ထည့်ထားသည်ဟု ပြောနိုင်သည်၊ အကြောင်းမှာ စာအုပ်များကို လေ့လာခြင်းနှင့် ကျွမ်းကျင်သူဖြစ်လာနိုင်သည်။ သို့သော် စာအုပ်ထဲတွင် ရှိနေသည့် အရာသည် *ဒေတာ* ဟု မှတ်ယူရမည် ဖြစ်၍ စာအုပ်များကို ဖတ်ခြင်းဖြင့် ထိုဒေတာကို ကမ္ဘာ့မော်ဒယ်သို့ ပေါင်းစည်းခြင်းက ဒီဒေတာကို အသိပညာသို့ ပြောင်းလဲပေးသည်။ -> ✅ **အသိပညာ** ဆိုတာ ကျွန်တော်တို့ရဲ့ ဦးနှောက်ထဲမှာ ပါဝင်ပြီး ကမ္ဘာကြီးကို နားလည်မှုကို ကိုယ်စားပြုပါတယ်။ ဒါကို **လေ့လာမှု** လုပ်ငန်းစဉ်တစ်ခုအားဖြင့် ရရှိပြီး ကျွန်တော်တို့ရဲ့ ကမ္ဘာကြီးရဲ့ မော်ဒယ်ထဲမှာ ရရှိတဲ့ အချက်အလက်တွေကို ပေါင်းစည်းခြင်းဖြင့် ရရှိပါတယ်။ +> ✅ **အသိပညာ** ဆိုသည်မှာ ကျွန်ုပ်တို့၏ ဦးနှောက် ထဲတွင်ပါဝင်ပြီး ကမ္ဘာအပေါ် ကျွန်ုပ်တို့၏ နားလည်မှုကို ကိုယ်စားပြုသည်။ ၎င်းမှာ ကျွန်ုပ်တို့ထံ ရှိသော အချက်အလက်စုစည်းမှုများကို ပေါင်းစည်းထားသည့် တက်ကြွ လေ့လာမှု ဖြစ်စဉ်ဖြစ်သည်။ -အများအားဖြင့် အသိပညာကို တိတိကျကျ သတ်မှတ်မထားပေမယ့် [DIKW Pyramid](https://en.wikipedia.org/wiki/DIKW_pyramid) ကို အသုံးပြုပြီး အခြားဆက်စပ်အယူအဆတွေနဲ့ ကိုက်ညီစေပါတယ်။ ဒီမှာ အောက်ပါအယူအဆတွေ ပါဝင်ပါတယ်- +ပုံမှန်အားဖြင့် ကျွန်ုပ်တို့ သေချာစွာ အသိပညာအား သတ်မှတ်ခြင်း မပြုဘဲ [DIKW Pyramid](https://en.wikipedia.org/wiki/DIKW_pyramid) အား အသုံးပြု၍ ပတ်သက်သော အယူအဆများနှင့် သွယ်ဝိုက်စေနိုင်သည်။ ထိုအတွင်း ပါဝင်သော အယူအဆများမှာ - -* **ဒေတာ** ဆိုတာ ရုပ်ပိုင်းဆိုင်ရာမီဒီယာမှာ ဖော်ပြထားတဲ့ အရာဖြစ်ပြီး စာသားရေးခြင်း သို့မဟုတ် စကားပြောခြင်းလိုမျိုးဖြစ်ပါတယ်။ ဒေတာက လူသားတွေကို မူတည်မထားဘဲ အပြန်အလှန် ပေးပို့နိုင်ပါတယ်။ -* **အချက်အလက်** ဆိုတာ ကျွန်တော်တို့ရဲ့ ဦးနှောက်ထဲမှာ ဒေတာကို အဓိပ္ပါယ်ဖွင့်ခြင်းဖြစ်ပါတယ်။ ဥပမာအားဖြင့် *ကွန်ပျူတာ* ဆိုတဲ့ စကားလုံးကို ကြားရင် ကျွန်တော်တို့မှာ အဲဒီအရာက ဘာလဲဆိုတာ နားလည်မှုတစ်ခု ရှိပါတယ်။ -* **အသိပညာ** ဆိုတာ အချက်အလက်ကို ကျွန်တော်တို့ရဲ့ ကမ္ဘာကြီးရဲ့ မော်ဒယ်ထဲမှာ ပေါင်းစည်းခြင်းဖြစ်ပါတယ်။ ဥပမာအားဖြင့် ကွန်ပျူတာက ဘာလဲဆိုတာ လေ့လာပြီးရင် အဲဒါ ဘယ်လိုအလုပ်လုပ်လဲ၊ ဘယ်လောက်ကုန်ကျလဲ၊ ဘာအတွက် အသုံးပြုနိုင်လဲဆိုတာ အကြောင်းအရာတွေကို သိလာပါတယ်။ ဒီဆက်စပ်အကြောင်းအရာတွေက ကျွန်တော်တို့ရဲ့ အသိပညာကို ဖွဲ့စည်းပါတယ်။ -* **ပညာ** ဆိုတာ ကမ္ဘာကြီးကို နားလည်မှုရဲ့ တစ်ဆင့်ပိုမိုမြင့်မားတဲ့အဆင့်ဖြစ်ပြီး *meta-knowledge* ကို ကိုယ်စားပြုပါတယ်၊ ဥပမာအားဖြင့် အသိပညာကို ဘယ်လို သုံးရမလဲ၊ ဘယ်အချိန်မှာ သုံးရမလဲဆိုတဲ့ အယူအဆတွေပါဝင်ပါတယ်။ +* **ဒေတာ** ဆိုသည်မှာ ရေးသားထားသော စာသား သို့မဟုတ် ပြောဆိုထားသော စကားလုံးများကဲ့သို့ စာသားပုံစံဖြင့် ဖော်ပြထားသောပစ္စည်းဖြစ်သည်။ ဒေတာမှာ လူသားများတွင် မပိုင်ဆိုင်ပဲ တစ်ဦးနှင့်တစ်ဦး ပြန်လည်လွှဲပြောင်းနိုင်သည်။ +* **အချက်အလက်** ဆိုသည်မှာ ကျွန်ုပ်တို့၏ ဦးနှောက်တွင် ဒေတာကို ဘယ်လိုအဓိပ္ပါယ်သတ်မှတ်သည့် ပုံစံဖြစ်သည်။ ဥပမာအားဖြင့် *ကွန်ပျူတာ* ဟု ကြားရင် ၎င်းသည် မည်သည့်အရာဖြစ်ကြောင်း နားလည်မှု ရှိသည်။ +* **အသိပညာ** ဆိုသည်မှာ တစ်ခုခုအချက်အလက်ကို ကမ္ဘာ့မော်ဒယ်ထဲ ပေါင်းစည်းထားခြင်းဖြစ်သည်။ ဥပမာအားဖြင့် ကွန်ပျူတာ ဆိုတာ ဘာလဲဆိုတာကို လေ့လာပြီးနောက်၎င်းသည် မည်သို့ လုပ်ဆောင်သည်၊ ကျသင့်ငွေဘယ်လောက်ရှိကြောင်း၊ မည်သို့ အသုံးပြုနိုင်သည်ဆိုသည့် အကြံကို သဘောပေါက်လာသည်။ ၎င်း။ - +* **အမြတ်ပညာ (Wisdom)** သည် ကမ္ဘာကို နားလည်မှု့ အဆင့်မြင့်တစ်ခုဖြစ်ပြီး *meta-knowledge* ဖြစ်ပြီး အသိပညာကို မည်သည့်အချိန်တွင် ဟီးပြီး မည်သည့်နည်းလမ်းဖြင့် အသုံးပြုရမည်ကို ကိုယ်စားပြုသည်။ -*Image [from Wikipedia](https://commons.wikimedia.org/w/index.php?curid=37705247), By Longlivetheux - Own work, CC BY-SA 4.0* + -ဒါကြောင့် **အသိပညာကိုဖော်ပြခြင်း** ဆိုတာ စက်ထဲမှာ ဒေတာအဖြစ် အသိပညာကို ထိရောက်စွာ ဖော်ပြနိုင်တဲ့ နည်းလမ်းတစ်ခုကို ရှာဖွေဖို့ ပြဿနာဖြစ်ပါတယ်၊ အလိုအလျောက် အသုံးပြုနိုင်စေဖို့ပါ။ ဒါကို အောက်ပါအတိုင်း စက်ရုပ်နည်းလမ်းအဖြစ် မြင်နိုင်ပါတယ်- +*ပုံ [Wikipedia မှ](https://commons.wikimedia.org/w/index.php?curid=37705247), Longlivetheux - အပါတ်သားကိုယ်တိုင်၊ CC BY-SA 4.0* -![Knowledge representation spectrum](../../../../translated_images/my/knowledge-spectrum.b60df631852c0217.webp) +ထို့ကြောင့် **အသိပညာ ကိုယ်စားပြုခြင်း** ပြဿနာမှာ ကွန်ပျူတာထဲတွင် ဒေတာ ပုံစံဖြင့် အသိပညာကို ထိရောက်စွာ ကိုယ်စားပြုနိုင်ရန် နည်းလမ်းတစ်ခု ရှာဖွေခြင်း ဖြစ်သည်။ ၎င်းကို အောက်ပါအတိုင်း အမျိုးအစားများဖြင့် ဖော်ပြနိုင်သည် - -> Image by [Dmitry Soshnikov](http://soshnikov.com) +![Knowledge representation spectrum](../../../../../../translated_images/my/knowledge-spectrum.b60df631852c0217.webp) -* ဘယ်ဘက်မှာတော့ စက်ရုပ်တွေက ထိရောက်စွာ အသုံးပြုနိုင်တဲ့ အသိပညာကို ဖော်ပြတဲ့ အလွန်ရိုးရှင်းတဲ့ပုံစံတွေ ရှိပါတယ်။ အလွန်ရိုးရှင်းတဲ့ပုံစံက algorithmic ဖြစ်ပြီး အသိပညာကို ကွန်ပျူတာပရိုဂရမ်အဖြစ် ဖော်ပြပါတယ်။ ဒါပေမယ့် ဒါက အသိပညာကို ဖော်ပြဖို့ အကောင်းဆုံးနည်းလမ်းမဟုတ်ပါဘူး၊ အကြောင်းက ဒါက အလွန်တင်းကြပ်ပါတယ်။ ကျွန်တော်တို့ရဲ့ ဦးနှောက်ထဲမှာ ရှိတဲ့ အသိပညာက algorithmic မဟုတ်ပါဘူး။ -* ညာဘက်မှာတော့ သဘာဝစာသားလိုမျိုး ဖော်ပြချက်တွေ ရှိပါတယ်။ ဒါက အလွန်အစွမ်းထက်ပေမယ့် အလိုအလျောက် reasoning အတွက် အသုံးမပြုနိုင်ပါဘူး။ +> ပုံကို [Dmitry Soshnikov](http://soshnikov.com) ဖန်တီးသည် -> ✅ သင့်ရဲ့ ဦးနှောက်ထဲမှာ အသိပညာကို ဘယ်လို ဖော်ပြပြီး မှတ်စုအဖြစ် ပြောင်းလဲတယ်ဆိုတာ တစ်မိနစ်လောက် စဉ်းစားပါ။ retention ကို အကောင်းဆုံး အထောက်အကူဖြစ်စေတဲ့ ပုံစံတစ်ခု ရှိပါသလား? +* ဘောင်ဘက်ဘယ် (left) တွင် ကွန်ပျူတာများက ထိရောက်စွာ အသုံးပြုနိုင်သော အသိပညာ ကိုယ်စားပြုမှု အလွန်ရိုးရှင်းသော အမျိုးအစားတွေ ရှိသည်။ အခြေခံဆုံးမှာ အယ်လဂိုရီသမ်ဖြစ်ပြီး ကွန်ပျူတာပရိုဂရမ်းဖြင့် ကိုယ်စားပြုသည်။ သို့သော် ၎င်းက အသိပညာကို ကိုယ်စားပြုရာ၌ မလွယ်ကူပေ။ ဦးနှောက်ထဲရှိ အသိပညာသည် မအယ်လဂိုရီသမ်ဖြစ်ပုံပိုဆိုင်သည်။ +* ဘောင်ဘက်ညာ (right) တွင် သဘာဝစာသားမှ ပုံစံများ စုစည်းထားသည်။ ၎င်းသည် အင်မတန် အင်အားကြီးပေမယ့် အလိုအလျောက် ထုတ်ဖော်စဉ်းစားမှုတွင် အသုံးမပြုနိုင်။ -## Classifying Computer Knowledge Representations +> ✅ သင့်ဦးနှောက်အတွင်း အသိပညာကို မည်သို့ ကိုယ်စားပြုထားသည်နှင့် မှတ်ချက်များသို့ ပြောင်းလဲမှုအတွက် ဤပုံစံများထဲတွင် ထူးထူးခြားခြား ဖြစ်သောပုံစံတစ်ခု ရှိပါသလား? -ကွန်ပျူတာအသိပညာကို ဖော်ပြတဲ့ နည်းလမ်းတွေကို အောက်ပါအမျိုးအစားတွေမှာ ခွဲခြားနိုင်ပါတယ်- +## ကွန်ပျူတာ အသိပညာ ကိုယ်စားပြုမှု အမျိုးအစား ခွဲခြားခြင်း -* **Network representations** ဆိုတာ ကျွန်တော်တို့ရဲ့ ဦးနှောက်ထဲမှာ ဆက်စပ်အကြောင်းအရာတွေ ရှိတဲ့ network ကို အခြေခံထားပါတယ်။ ဒီ network တွေကို ကွန်ပျူတာထဲမှာ graph အဖြစ် ပြန်ဖန်တီးနိုင်ပါတယ် - **semantic network** လို့ခေါ်ပါတယ်။ +ကွန်ပျူတာ အသိပညာ ကိုယ်စားပြုမှု နည်းလမ်းအမျိုးမျိုးကို အောက်ပါအမျိုးအစားများဖြင့် ခွဲခြားနိုင်သည်- -1. **Object-Attribute-Value triplets** သို့မဟုတ် **attribute-value pairs**. Graph ကို ကွန်ပျူတာထဲမှာ nodes နဲ့ edges ရဲ့ စာရင်းအဖြစ် ဖော်ပြနိုင်တဲ့အတွက် semantic network ကို objects, attributes, နဲ့ values ပါဝင်တဲ့ triplets စာရင်းအဖြစ် ဖော်ပြနိုင်ပါတယ်။ ဥပမာအားဖြင့် programming languages အကြောင်း triplets တွေကို ဖန်တီးနိုင်ပါတယ်- +* **ကွန်ရက် ကိုယ်စားပြုမှုများ** ဆိုသည်မှာ ဦးနှောက်ထဲရှိ အကြောင်းအရာများကွန်ရက်ကို အခြေခံ၍ ဖြစ်သည်။ ပုံမှန်အတိုင်း ကွန်ပျူတာအတွင်း ဂရပ် (graph) အဖြစ် ပြန်ဖော်နိုင်ပြီး၊ ဒါကို **semantic network** လို့ ခေါ်သည်။ + +1. **Object-Attribute-Value (OAV) သုံးယူနစ်များ** သို့မဟုတ် **attribute-value စုံများ**။ ဂရပ်မှာ ကွန်ပျူတာအတွင်း များစွာရှိသော နုတ်များနှင့် အဆက်အစပ်များ အဖြစ် ဖော်ပြထားနိုင်မှုကြောင့် semantic network ကို triplets စာရင်းဖြစ်စေ ဖြင့် ကိုယ်စားပြုနိုင်သည်။ ဥပမာ အောက်ပါ programming language များနှင့် ဆက်စပ် သုံးယူနစ်များ ရှိသည် - Object | Attribute | Value -------|-----------|------ -Python | is | Untyped-Language -Python | invented-by | Guido van Rossum -Python | block-syntax | indentation -Untyped-Language | doesn't have | type definitions +Python | ပုံသွင်း | အမျိုးမသတ်မှတ်ထားသော ဘာသာစကား +Python | တီထွင်သူ | Guido van Rossum +Python | Block သဘောအခြေအနေ | Indentation +အမျိုးမသတ်မှတ်ထားသော ဘာသာစကား | မပါဝင် | type definitions -> ✅ Triplets တွေကို အသိပညာအခြားအမျိုးအစားတွေကို ဖော်ပြဖို့ ဘယ်လို အသုံးပြုနိုင်မလဲဆိုတာ စဉ်းစားပါ။ +> ✅ သုံးယူနစ်များကို အသုံးပြုပြီး အခြားအသိပညာအမျိုးမျိုးကို မည်သို့ ကိုယ်စားပြုနိုင်သနည်း လေ့လာကြည့်ပါ။ -2. **Hierarchical representations** ဆိုတာ ကျွန်တော်တို့ရဲ့ ဦးနှောက်ထဲမှာ objects တွေကို hierarchy အဖြစ် ဖန်တီးတယ်ဆိုတဲ့ အချက်ကို အလေးပေးပါတယ်။ ဥပမာအားဖြင့် canary က ငှက်တစ်မျိုးဖြစ်ပြီး ငှက်အားလုံးမှာ တောင်ပံရှိတယ်ဆိုတာ ကျွန်တော်တို့သိပါတယ်။ ကျွန်တော်တို့မှာ canary ရဲ့ အရောင်၊ ပျံသန်းမှုအမြန်နှုန်းအကြောင်း အယူအဆတွေ ရှိပါတယ်။ - - - **Frame representation** ဆိုတာ object တစ်ခု သို့မဟုတ် object အမျိုးအစားတစ်ခုကို **frame** အဖြစ် ဖော်ပြပြီး **slots** ပါဝင်ပါတယ်။ Slots တွေမှာ default values, value restrictions, သို့မဟုတ် value ကို ရရှိဖို့ stored procedures တွေ ပါဝင်နိုင်ပါတယ်။ Frame အားလုံးက object hierarchy နဲ့ တူတဲ့ hierarchy ကို ဖွဲ့စည်းပါတယ်။ - - **Scenarios** ဆိုတာ အချိန်အတွင်း ဖွင့်လှစ်နိုင်တဲ့ အခွအေနေတွေကို ကိုယ်စားပြုတဲ့ frame အမျိုးအစားတစ်ခုဖြစ်ပါတယ်။ +2. **အဆင့်လိုက် ကိုယ်စားပြုမှုများ** ဆိုသည်မှာ ဦးနှောက်ထဲမှာ အရာဝတ္ထုများ၏ အဆင့်လိုက် စဉ်ဆက်ဖြစ်ပေါ်မှု့ကို တောက်ပြောင်စွာ ဖော်ပြခြင်းဖြစ်သည်။ ဥပမာ၊ canary သည် ငှက်တစ်မျိုးဖြစ်ပြီး ငှက်တို့မှာပင်ပိုင်း ရှိကြောင်းနှင့် canary သည် ဘယ်အရောင်များနှင့် ဘယ်လို ကပ်ပျံကွာမြန်သော အတိုင်းသည်။ + - **Frame ကိုယ်စားပြုမှု** သည် အရာဝတ္ထုတစ်ခုနှင့် သို့မဟုတ် အရာဝတ္ထုအုပ်စုတစ်ခုအား **frame** အဖြစ် ကိုယ်စားပြုပြီး ထို frame တွင် **slots** ပါဝင်သည်။ Slot များတွင် နမူနာတန်ဖိုးများ၊ တန်ဖိုးကန့်သတ်ချက်များ သို့မဟုတ် တန်ဖိုးကိုရယူနိုင်ရန် ဖြောင့်ဖြူးထားသော လုပ်ထုံးလုပ်နည်းများ ပါဝင်သည်။ ၎င်း frame များအားလုံးသည် အရာဝတ္ထုအုပ်စု အဆင့်လိုက်အတိုင်း ဆက်သွယ်ထားသည်။ + - **Scenario များ** သည် အချိန်ကြာမြင့်သည့် ရုပ်သိမ်းခက်ခဲသော အခြေအနေတွေကို ကိုယ်စားပြုသည့် frames အထူးအမျိုးအစားဖြစ်သည်။ **Python** Slot | Value | Default value | Interval | -----|-------|---------------|----------| -Name | Python | | | -Is-A | Untyped-Language | | | -Variable Case | | CamelCase | | -Program Length | | | 5-5000 lines | -Block Syntax | Indent | | | +အမည် | Python | | | +အမျိုးအစား | အမျိုးမသတ်မှတ်ထားသော ဘာသာစကား | | | +အပြောင်းအလဲ စံအမည် | | CamelCase | | +ပရိုဂရမ် အရှည် | | | ၅-၅၀၀၀ ဖတ်ရှုလိုင်း | +Block သဘောစနစ် | Indent | | | -3. **Procedural representations** ဆိုတာ အခြေအနေတစ်ခု ဖြစ်ပေါ်တဲ့အခါ အကောင်အထည်ဖော်နိုင်တဲ့ လုပ်ဆောင်မှုစာရင်းတစ်ခုအားဖြင့် အသိပညာကို ဖော်ပြခြင်းဖြစ်ပါတယ်။ - - Production rules ဆိုတာ if-then စကားပြောချက်တွေဖြစ်ပြီး ကျွန်တော်တို့ကို အကြောင်းအရာဆင်နိုင်စေပါတယ်။ ဥပမာအားဖြင့် ဆရာဝန်တစ်ဦးမှာ **IF** လူနာမှာ အဖျားအလွန်မြင့် **OR** သွေးစမ်းသပ်မှုမှာ C-reactive protein အဆင့်မြင့် **THEN** သူမှာ အရောင်ရောင်ခြင်းရှိတယ်ဆိုတဲ့ rule ရှိနိုင်ပါတယ်။ အခြေအနေတစ်ခုကို တွေ့ရင် အရောင်ရောင်ခြင်းအကြောင်းအရာကို ဆင်နိုင်ပြီး နောက်ထပ် reasoning အတွက် အသုံးပြုနိုင်ပါတယ်။ - - Algorithms တွေကို knowledge-based systems တွေမှာ တိုက်ရိုက် အသုံးမပြုသော်လည်း procedural representation အမျိုးအစားတစ်ခုအဖြစ် သတ်မှတ်နိုင်ပါတယ်။ +3. **လုပ်ထုံးလုပ်နည်း ကိုယ်စားပြုမှုများ** ဆိုသည်မှာ အခြေအနေတစ်ခု ဖြစ်လာသောအခါ အချက်အလက်များကို လုပ်ဆောင်ရန် လုပ်ငန်းများ စာရင်းအဖြစ် ကိုယ်စားပြုခြင်း ဖြစ်သည်။ + - ထုတ်လုပ်မှုဆိုင်ရာ စည်းမျဉ်းများမှာ if-then ပုံစံဖြင့်ဖြစ်ပြီး ဆုံးဖြတ်ချက်ရရန် အခြေအနေများကို ဖော်ပြသည်။ ဥပမာ ဆရာဝန်တစ်ယောက်မှာ If မိမိရဲ့ လူနာတွင် အပူချိန်မြင့်သော စောင့်ကြည့်မှုရှိပါက **သို့မဟုတ်** C-reactive protein အဆင့်မြင့်ကဲ့သို့ တွေ့ရှိပါက ** THEN** ရောင်ရမ်းမှု တက်နေကြောင်း သတ်မှတ်ထားသည်။ ယင်း ဥပဒေများနှင့် သက်ဆိုင်သော အခြေအနေများတစ်ခုခု တွေ့ရှိသည်နှင့်အမျှ ဆုံးဖြတ်ချက်ရရန် အသုံးပြုသည်။ + - Algorithm များကို procedural representation ၏ တစ်မျိုးအဖြစ် သတ်မှတ်နိုင်သော်လည်း သိပ္ပံဆိုင်ရာစနစ်များတွင် တိုက်ရိုက် မသုံးသော်လည်း ဖြစ်နိုင်သည်။ -4. **Logic** ကို Aristotle က လူသားရဲ့ အထူးသဖြင့် universal အသိပညာကို ဖော်ပြဖို့ နည်းလမ်းတစ်ခုအဖြစ် အစဉ်အလာအဖြစ် အကြံပြုခဲ့ပါတယ်။ - - Predicate Logic ကို mathematical theory အဖြစ် သတ်မှတ်နိုင်ပေမယ့် computable မဖြစ်နိုင်လို့ Prolog မှာ အသုံးပြုတဲ့ Horn clauses လိုမျိုး subset တစ်ခုကို အသုံးပြုပါတယ်။ - - Descriptive Logic ဆိုတာ object hierarchy တွေကို ဖော်ပြပြီး semantic web လိုမျိုး knowledge representations တွေကို reasoning လုပ်ဖို့ အသုံးပြုတဲ့ logical systems မျိုးရိုးတစ်ခုဖြစ်ပါတယ်။ +4. **Logic** ကို အစပိုင်းတွင် Aristotle မှ လူသားအသိပညာတရားလုံးကို ကိုယ်စားပြုရန် အကြံပေးခဲ့သည်။ -## Expert Systems + - Predicate Logic သည် သင်္ချာဆိုင်ရာ ပညာရပ်တစ်ခုသို့မဟုတ် မှတ်ချက်တွေပါဝင်သည့် သဘောတရား အဖြစ် အလယ်အလတ်အထိ ဖြန့်ဝေထားသော်လည်း အကောင်အထည်မဖော်နိုင်သော အချို့ အပိုင်းများ အဖြစ် ယခုမှာ Horn clauses (Prolog တွင် သုံးသော) ကို ဆောင်ရွက်လျက် ရှိသည်။ + - Descriptive Logic သည် object hierarchy များနှင့် knowledge representation တွေကို ကိုယ်စားပြုရန် အသုံးပြုသော logic မျိုးစားတစ်ရပ်ဖြစ်ပြီး semantic web ကဲ့သို့ တည်ဆောက်မှုများတွင် အသုံးပြုသည်။ -Symbolic AI ရဲ့ အစောပိုင်းအောင်မြင်မှုတွေထဲမှာ **expert systems** လို့ခေါ်တဲ့ စနစ်တွေ ပါဝင်ပါတယ်။ ဒီစနစ်တွေက အကန့်အသတ်ရှိတဲ့ ပြဿနာဒေသတစ်ခုမှာ ကျွမ်းကျင်သူတစ်ဦးအဖြစ် လုပ်ဆောင်ဖို့ ဖန်တီးထားတဲ့ computer systems တွေဖြစ်ပါတယ်။ ဒီစနစ်တွေမှာ **knowledge base** ကို လူသားကျွမ်းကျင်သူတစ်ဦး သို့မဟုတ် အများအပြားကနေ ထုတ်ယူပြီး **inference engine** ကို အသုံးပြုပြီး reasoning လုပ်ပါတယ်။ +## ကျွမ်းကျင်သူ စနစ်များ -![Human Architecture](../../../../translated_images/my/arch-human.5d4d35f1bba3ab1c.webp) | ![Knowledge-Based System](../../../../translated_images/my/arch-kbs.3ec5c150b09fa8da.webp) +Symbolic AI ၏ အစောပိုင်း အောင်မြင်မှုတစ်ခုမှာ **ကျွမ်းကျင်သူစနစ်များ** ဖြစ်သည် - အခြေအနေ တစ်ခုပေါ်တွင် ကျွမ်းကျင်သူတစ်ယောက်ကဲ့သို့ အလုပ်လုပ်နိုင်သော ကွန်ပျူတာစနစ်များ ဖြစ်သည်။ ၎င်းတို့သည် လူကြီးကျွမ်းကျင်သူတစ်ဦး သို့မဟုတ် ရှုပ်ထွေးမှုတစ်ခုပေါ်အခြေခံ၍ နှစ်ဆစ်စုထားခြင်းဖြစ်သော **သိမြင်မှု ဘဏ် (knowledge base)** ပါဝင်ပြီး၊ ၎င်း အပေါ်တွင် အချို့ ထုတ်ဖော်စဉ်းစားမှု လုပ်ဆောင်သော **inference engine** ပါဝင်သည်။ + +![Human Architecture](../../../../../../translated_images/my/arch-human.5d4d35f1bba3ab1c.webp) | ![Knowledge-Based System](../../../../../../translated_images/my/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ -လူသားရဲ့ ဦးနှောက်စနစ်ရဲ့ ရိုးရှင်းတဲ့ဖွဲ့စည်းမှု | အသိပညာအခြေခံစနစ်ရဲ့ ဖွဲ့စည်းမှု +လူမျိုးနာရးစနစ်၏ ရိုးရှင်းသည့် ဖွဲ့စည်းမှု | အသိပညာ အခြေပြုစနစ်၏ ဖွဲ့စည်းမှု -Expert systems တွေကို လူသားရဲ့ reasoning စနစ်လိုမျိုး ဖွဲ့စည်းထားပြီး **short-term memory** နဲ့ **long-term memory** ပါဝင်ပါတယ်။ အတူတူပဲ knowledge-based systems တွေမှာ အောက်ပါ components တွေကို ခွဲခြားထားပါတယ်- +ကျွမ်းကျင်သူစနစ်များသည် လူ၏ ထုတ်လွှင့်စိတ်ဓာတ်စနစ်ကဲ့သို့ **တိုတောင်းသော မေမရီ** နှင့် **ရှည်လျားသော မေမရီ** ပါဝင်သည်။ သဘောတူ အဖြစ် knowledge-based system များတွင် အောက်ပါ အစိတ်အပိုင်းများကို ခွဲခြားစဉ်းစားသည် - -* **Problem memory**: လက်ရှိဖြေရှင်းနေတဲ့ ပြဿနာအကြောင်း အသိပညာကို ပါဝင်ပါတယ်၊ ဥပမာအားဖြင့် လူနာရဲ့ အပူချိန် သို့မဟုတ် သွေးပေါင်ချိန်၊ သူမှာ အရောင်ရောင်ခြင်းရှိ/မရှိ စသည်တို့ပါဝင်ပါတယ်။ ဒီအသိပညာကို **static knowledge** လို့လည်း ခေါ်ပြီး လက်ရှိပြဿနာအခြေအနေကို snapshot အဖြစ် ဖော်ပြပါတယ်။ -* **Knowledge base**: ပြဿနာဒေသအကြောင်း ရေရှည်အသိပညာကို ကိုယ်စားပြုပါတယ်။ ဒါကို လူသားကျွမ်းကျင်သူတွေကနေ လက်နက်ဖြင့် ထုတ်ယူပြီး consultation တစ်ခုမှ consultation တစ်ခုအထိ မပြောင်းလဲပါဘူး။ ဒါက ပြဿနာအခြေအနေတစ်ခုမှ တစ်ခုကို navigation လုပ်နိုင်စေတဲ့အတွက် **dynamic knowledge** လို့လည်း ခေါ်ပါတယ်။ -* **Inference engine**: ပြဿနာအခြေအနေ space ထဲမှာ ရှာဖွေမှုလုပ်ငန်းစဉ်ကို စီမံခန့်ခွဲပြီး လိုအပ်တဲ့အခါ user ကို မေးခွန်းများမေးခြင်းကို လုပ်ဆောင်ပါတယ်။ ဒါကလည်း state တစ်ခုစီမှာ အသုံးပြုဖို့ rule တွေကို ရှာဖွေဖို့ တာဝန်ရှိပါတယ်။ +* **ပြဿနာ မေမရီ** - ယခု ဆောင်ရွက်နေသော ပြဿနာနှင့် များစွာစပ်ဆိုင်သော အသိပညာ ပါဝင်သည်၊ ဥပမာ လူနာ၏ အပူချိန် သို့မဟုတ် သွေးဖိအား၊ ရောင်ရမ်းမှုရှိသည် ဒါမှ မဟုတ် ဒါမဟုတ် ဆိုတာ။ ဤအသိပညာကို **static knowledge** ဟုလည်း ခေါ်ပြီး၊ ယခုစဉ်ဆက်တွင် ပြဿနာဖြေရှင်းနေ စဉ်အခြေအနေ၏ တစ်စိတ်တစ်ပိုင်းကို ကိုယ်စားပြုသည်။ +* **သိမြင်မှု ဘဏ်** - ပြဿနာအကွက်ဆိုင်ရာ ရှေ့ရာနှင့် ပတ်သက်ပြီး ရှည်လျားစွာ သိရှိထားသောအသိပညာဖြစ်သည်။ လူမှုကျွမ်းကျင်သူများထံမှ လက်ဖြင့် ဆွဲထုတ်ထားပြီး အကြံဥာဏ်အသစ် မပါဝင်ပါ။ ၎င်းကို **dynamic knowledge** လည်း ခေါ်သည်၊ ဘာကြောင့်ဆိုသော် ပြဿနာအခြေအနေတစ်ခုမှ နောက်တစ်ခုသို့ သွားနိုင်စေသည်။ +* **inference engine** - ပြဿနာအခြေအနေတစ်ခု အတွင်း ရှာဖွေရေးလုပ်ငန်းကို ဦးဆောင် တာဝန်ယူသည်၊ အရေးကြီးသောအခါ အသုံးပြုသူထံ မေးခွန်းများ မေးသည်။ ထိုအပြင် အသုံးပြုရန် ဥပဒေများကိုရှာဖွေသည်။ -ဥပမာအားဖြင့် အောက်ပါ expert system ကို သက်ရှိတစ်ခုရဲ့ ရုပ်ပိုင်းဆိုင်ရာလက္ခဏာအပေါ် အခြေခံပြီး သတ်မှတ်ခြင်းလုပ်ငန်းစဉ်အဖြစ် တွေးဆနိုင်ပါတယ်- +ဥပမာ အဖြစ်၊ တိရစ္ဆာန်ကို ထူးခြားသော ရုပ်ပိုင်းဆိုင်ရာလက္ခဏာများအရ သတ်မှတ်ရန် ဤအောက်ပါ ကျွမ်းကျင်သူစနစ်ကို တွေးကြည့်ပါ - -![AND-OR Tree](../../../../translated_images/my/AND-OR-Tree.5592d2c70187f283.webp) +![AND-OR Tree](../../../../../../translated_images/my/AND-OR-Tree.5592d2c70187f283.webp) -> Image by [Dmitry Soshnikov](http://soshnikov.com) +> ပုံကို [Dmitry Soshnikov](http://soshnikov.com) ဖန်တီးသည် -ဒီ diagram ကို **AND-OR tree** လို့ခေါ်ပြီး production rules တွေကို ကိုယ်စားပြုတဲ့ ဂရပ်ဖစ် representation ဖြစ်ပါတယ်။ Expert က knowledge ကို ထုတ်ယူတဲ့အစမှာ tree ကို ရေးဆွဲတာ အကျိုးရှိပါတယ်။ Knowledge ကို ကွန်ပျူတာထဲမှာ ဖော်ပြဖို့ rule တွေကို အသုံးပြုတာ ပိုအဆင်ပြေပါတယ်- +ဒီပုံသည် **AND-OR tree** ဟုခေါ်ပြီး ထုတ်လုပ်မှု ဆိုင်ရာ စည်းကမ်းများ စုစည်းမှု တစ်ခုကို ဂရပ်ပုံစံဖြင့် ဖော်ပြသည်။ သိမြင်မှု ရယူလိုစဉ် ဒီအပေါ်မှာ သစ်ငယ်ဆွဲခြင်းက အထောက်အကူဖြစ်သည်။ ကွန်ပျူတာထဲ တွင် အသိပညာ ကိုယ်စားပြုရာတွင် အသုံးပြုရန် ဥပဒေများကို ဆွဲထားသည့် ပုံစံ ပိုလွယ်ကူစေသည်။ ``` IF the animal eats meat @@ -121,44 +122,79 @@ OR (animal has sharp teeth THEN the animal is a carnivore ``` -သင်တွေ့ရတဲ့ rule ရဲ့ left-hand-side (LHS) conditions နဲ့ action တွေဟာ အခြေခံအားဖြင့် object-attribute-value (OAV) triplets တွေဖြစ်ပါတယ်။ **Working memory** က လက်ရှိဖြေရှင်းနေတဲ့ ပြဿနာကို ကိုယ်စားပြုတဲ့ OAV triplets တွေကို ပါဝင်ပါတယ်။ **Rules engine** က condition ကို ဖြည့်ဆည်းတဲ့ rule တွေကို ရှာပြီး အသုံးပြုပြီး working memory ထဲမှာ triplet အသစ်တစ်ခုကို ထည့်သွင်းပါတယ်။ -> ✅ သင်နှစ်သက်တဲ့ အကြောင်းအရာတစ်ခုအပေါ် AND-OR tree ကို ကိုယ်တိုင်ရေးဆွဲပါ! +ဥပဒေ၏ ဘယ်ဖက်ဘက်က အခြေအနေများရှိပြီး လက်ရှိ လုပ်ဆောင်ချက်သည် သုံးယူနစ် (OAV) ထပ်များဖြစ်ကြသည်ကို တွေ့ရမည်။ **အလုပ်လုပ်နေသော မေမရီ** ထဲတွင် လုပ်ဆောင်နေသည့် ပြဿနာနှင့်သက်ဆိုင်သော OAV စုစည်းမှု ရှိသည်။ **rules engine** သည် အခြေအနေ သိသာသော ဥပဒေများကို ရှာဖွေ၍ လုပ်ဆောင်ပြီး အသစ်သော OAV ကို အလုပ်လုပ်နေသော မေမရီအတွင်းထည့်သည်။ -### Forward vs. Backward Inference +> ✅ မိမိနှစ်သက်သော အကြောင်းအရာအပေါ် သင်၏ ကိုယ်ပိုင် AND-OR tree ကို ရေးကြည့်ပါ။ -အထက်မှာ ဖော်ပြထားတဲ့လုပ်ငန်းစဉ်ကို **forward inference** လို့ခေါ်ပါတယ်။ ဒါက working memory ထဲမှာ ပြဿနာအကြောင်း အချက်အလက်အစောပိုင်းနဲ့ စတင်ပြီး reasoning loop ကို အောက်ပါအတိုင်း လုပ်ဆောင်ပါတယ်- +### ရှေ့ဆက် နှင့် နောက်ပြန် ထုတ်ဖော်စဉ်းစားမှု -1. Target attribute ကို working memory ထဲမှာ တွေ့ရင် - ရပ်ပြီး ရလဒ်ကို ပေးပါ -2. လက်ရှိ condition ကို ဖြည့်ဆည်းတဲ့ rule တွေကို ရှာပါ - **conflict set** ကို ရယူပါ။ -3. **Conflict resolution** လုပ်ပါ - ဒီအဆင့်မှာ အကောင်အထည်ဖော်မယ့် rule တစ်ခုကို ရွေးပါ။ Conflict resolution strategy တွေက အမျိုးမျိုးရှိနိုင်ပါတယ်- - - Knowledge base ထဲမှာ အသုံးပြုနိုင်တဲ့ ပထမ rule ကို ရွေးပါ - - Random rule ကို ရွေးပါ - - *ပိုသေ -- XML အခြေခံထားသော ဘာသာစကားများ၏ မိသားစု - RDF (Resource Description Framework), RDFS (RDF Schema), OWL (Ontology Web Language)။ +အထက်ဖော်ပြခဲ့သည့် လုပ်ငန်းစဉ်ကို **ရှေ့ဆက် ထုတ်ဖော်စဉ်းစားမှု** ဟု ခေါ်သည်။ ယာယီ မေမျာတွင် ပြဿနာဆိုင်ရာ မူလဒေတာအချို့ ရှိပြီး ထို့နောက် အောက်ပါ ထုတ်ဖော်စဉ်းစားမှု လည်ပတ်မှု လုပ်ဆောင်သည်- -Semantic Web တွင် အဓိကအကြောင်းအရာတစ်ခုမှာ **Ontology** ဖြစ်သည်။ ၎င်းသည် တိကျသော နယ်ပယ်တစ်ခုကို တိကျသော အသိပညာကိုယ်စားပြုမှုဖြင့် ဖော်ပြခြင်းကို ဆိုလိုသည်။ အလွယ်ဆုံး Ontology သည် နယ်ပယ်အတွင်းရှိ အရာဝတ္ထုများ၏ အဆင့်ဆင့်အဆောက်အအုံဖြစ်နိုင်သော်လည်း၊ ပိုမိုရှုပ်ထွေးသော Ontology များတွင် သုံးနိုင်သော သတ်မှတ်ချက်များပါဝင်မည်ဖြစ်သည်။ +1. ရည်မှန်းသော attribute များသည် အလုပ်လုပ်နေသော မေမရီတွင် ရှိပါက ပြီးမြောက်ပါ။ +2. လက်ရှိ ထိမိသော အခြေအနေရှိသည့် ဥပဒေများအားလုံး ရှာဖွေပြီး **ယှဉ်ပြိုင်ရေးတန်း** (conflict set) ဖြစ်စေသည်။ +3. **ယှဉ်ပြိုင်ရေး ဖြေရှင်းမှု** စဉ်အတွင်း တစ်ကြောင်းကို ရွေးချယ်သည်။ ရွေးချယ်မှုလုပ်နည်းများမှာ - + - အသိပညာ ဘဏ်တွင် ပထမ ဥပဒေကို ရွေးချယ်ခြင်း။ + - မျှမှန်ရမည့် ဥပဒေတစ်ခုကို ရွေးချယ်ခြင်း။ + - ပိုမိုသတ်မှတ်သော ဥပဒေကို ရွေးချယ်ခြင်း၊ ဥပမာ LHS ၌ အခြေအနေများ အများဆုံးကို ဖြည့်ဆည်းနိုင်သော ဥပဒေ။ +4. ရွေးချယ်သော ဥပဒေကို လုပ်ဆောင်ပြီး ပြဿနာအခြေအနေသို့ အသစ်သော အသိပညာ တစ်စိတ်တစ်ပိုင်း ထည့်သည်။ +5. အဆင့် 1 ထံ ပြန်သွားခြင်း -Semantic Web တွင် အားလုံးကို triplets အခြေခံ၍ ကိုယ်စားပြုထားသည်။ အရာဝတ္ထုနှင့် ဆက်နွယ်မှုတိုင်းကို URI ဖြင့် တစ်ခုတည်းသာ သတ်မှတ်ထားသည်။ ဥပမာအားဖြင့် ဒီ AI Curriculum ကို Dmitry Soshnikov က ဇန်နဝါရီ ၁ ရက်၊ ၂၀၂၂ တွင် ဖန်တီးခဲ့သည်ဟု ပြောလိုပါက - သုံးနိုင်သော triplets များမှာ အောက်ပါအတိုင်းဖြစ်သည်။ +သို့သော် တခါတလေ ကျွန်ုပ်တို့သည် ပြဿနာအကြောင်း ရှင်းလင်းသော အသိပညာ မရှိဘဲ စတင်လိုလျှင် မေးခွန်းများ မေးကာ ဆုံးဖြတ်ချက် ရရန် ကြိုးစားနိုင်သည်။ ဥပမာ ဆေးဘက်ဆိုင်ရာ ချို့ယွင်းချက် သပ်ရပ် ရှာဖွေရေးတွင် လူနာ စောင်စေ့လိုပြုမှုများကို အစဉ်မပြုလုပ်ဘဲ လိုအပ်သည့်အချိန်တွင် ဆန်းစစ်စစ်ဆေးသည်။ - +ဤစဉ်းစားမှုကို **နောက်ပြန် ထုတ်ဖော်စဉ်းစားမှု** ဟုခေါ်ပြီး ရည်ရွယ်ချက် **goal** အား အခြေခံကာ အောက်ပါအတိုင်း ဆောင်ရွက်သည် - + +1. ရည်ရွယ်ချက်၏ တန်ဖိုးကို ပေးနိုင်သော ဥပဒေများအားလုံး ရွေးချယ်ခြင်း (RHS တွင် ရည်ရွယ်ချက်ပါဝင်သော) - conflict set +2. အဆိုပါ attribute အတွက် ဥပဒေမရှိပါက သို့မဟုတ် အသုံးပြုသူထံမေးရန် ဥပဒေရှိပါက တောင်းဆိုသည်။ +3. ယှဉ်ပြိုင်မှု ဖြေရှင်းနည်းဖြင့် စဉ်းစားမှု့ အကြံပြုချက် (hypothesis) အဖြစ် သုံးမည့် ဥပဒေကို ရွေးချယ်သည်။ +4. ဥပဒေ LHS တွင် ပါသော attribute များကို ရည်ရွယ်ချက် (goal) အဖြစ် ထပ်မံစမ်းသပ်သည်။ +5. တစ်နေရာစီတွင် မအောင်မြင်လျှင် အဆင့် 3 သို့ ပြန်သွားပြီး အခြားဥပဒေကို အသုံးပြုသည်။ + +> ✅ တချို့အခြေအနေများတွင် ရှေ့ဆက် ထုတ်ဖော်စဉ်းစားမှု ထိုက်သင့်မှုမည်သနည်း? နောက်ပြန် ထုတ်ဖော်စဉ်းစားမှုအတွက် သင့်မြတ်မှု မည်သနည်း? + +### ကျွမ်းကျင်သူစနစ်များ ထုတ်လုပ်ခြင်း + +ကျွမ်းကျင်သူစနစ်များ ကို အမျိုးမျိုးသော ကိရိယာဖြင့် ဖန်တီးနိုင်သည် - + +* အဆင့်မြင့် programming language တစ်ခုကိုတိုက်ရိုက် အသုံးပြု၍ ပရိုဂရမ်းချခြင်း။ ဒါကတော့ အကောင်းဆုံးနည်းမဟုတ်ပါ၊ ဒါကြောင့် အသိပညာ အခြေပြုစနစ်မှာ အသိပညာနှင့် ထုတ်ဖော်စဉ်းစားမှုကို ခွဲခြားထားပြီး အတွင်းလူနာနယ်ပယ် ကျွမ်းကျင်သူက အသုံးပြုနိုင်ရန် ဖြစ်သည်။ +* **expert systems shell** အသုံးပြုခြင်း။ ၎င်းသည် အသိပညာ ကိုယ်စားပြုမှု ဘာသာစကားဖြင့် အသိပညာ ထည့်သွင်းသည့် ထူးခြားခန်းမတစ်ခု ဖြစ်သည်။ + +## ✍️ လေ့ကျင့်ခန်း: တိရစ္ဆာန် ထုတ်ဖော်စဉ်းစားမှု + +ရှေ့ဆက် နှင့် နောက်ပြန် ထုတ်ဖော်စဉ်းစားမှု expert system တည်ဆောက်ခြင်းအတွက် [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) ကို ကြည့်ရှုပါ။ + +> **မှတ်ချက်** - ဤနမူနာသည် ရိုးရှင်းပြီး ဤအတိုင်း expert system က ပုံစံရနိုင်သည်ကို လက်တွေ့ဖော်ပြသည်။ ယင်းလိုစနစ်တစ်ခုကို ဖန်တီးခြင်းအခါတွင် ၂၀၀ ကျော် တည်းဖြတ်ခြင်းများ ရောက်ဆုံးသောအချိန်မှ စ၍ သိပ္ပံရည်မြှင့်သောပြုမှုကို တွေ့ကြုံရမည်။ စည်းကမ်းများမှာ စနစ်တကျမှုခက်ခဲလာသည်၊ သိပ္ပံဆိုင်ရာ အသိပညာအခြေပြုစနစ်များ၏ အဓိကအချက်မှာ ဆုံးဖြတ်ချက်တစ်ခုစီကို ရှင်းလင်းတတ်စေရန် အဆင်ပြေလျက်ရှိသည်။ + +## Ontologies နှင့် Semantic Web + +20 ရာစု အဆုံးပိုင်းတွင် Knowledge representation ကို Internet ရင်းမြစ်များတွင် မှတ်တမ်းတင်ရန် အစီအစဉ်တစ်ခု ရှိခဲ့သည်၊ ထိုကဲ့သို့ အမိန့်များအား အသေးစိတ် ရှာဖွေ၍ ရမည့် အချိန်။ ၎င်းအတွက် မူကြမ်းဟုခေါ်သည့် နေရာမှာ **Semantic Web** ဖြစ်ပြီး အောက်ပါ အယူအဆများ များစွာ အပေါ် အခြေခံသည်။ + +- **[description logics](https://en.wikipedia.org/wiki/Description_logic)** (DL) ကို အခြေခံထားသော အထူးသိမြင်မှု ကိုယ်စားပြုမှု။ ၎င်းသည် frame ကိုယ်စားပြုမှုနှင့် ဆင်တူသည်၊ အရာဝတ္ထုဟိုက်ယားကီနှင့်ပိုင်ဆိုင်သော်လည်း တူညီသော သဘောတရား နှင့်ထုတ်ဖော်စဉ်းစားမှုလည်း ပါဝင်သည်။ DL မျိုးစုံရှိပြီး ထုတ်ဖော်စဉ်းစားမှု၏ ပြည့်စုံမှုနှင့် algorithmic ပြဿနာ အဆင့်ကို ညှိနှိုင်းထားသည်။ +- မျှဝေထားသည့် အသိပညာ ကိုယ်စားပြုမှု၊ ဤအတွင်းမှာ အစဉ်ဆက် မပြတ်ဖြစ်သော ကွန်တစ်ကြားကွန်ရက်များအတွက် အရာဝတ္ထုတွေကို ဘာသာရပ်တစ်ခုတည်းဖြင့်ယူနစ်တစ်ခု (global URI) ဖြင့်ဖော်ပြထားသည်။ +- သိပ္ပံအခြေခံခြင်း XML အခြေပြုဘာသာစကားများ မျိုးဆက်တစ်ခု - RDF (Resource Description Framework), RDFS (RDF Schema), OWL (Ontology Web Language)။ + +Semantic Web ၏ အဓိကအယူအဆတစ်ခုမှာ **Ontology** ဖြစ်သည်။ ၎င်းသည် ပြဿနာဧရိယာကို တိကျအတိအကျ သတ်မှတ်ထားသည့် ပုံမှန်အသိပညာကိုယ်စားပြုမှုဖြင့် ရည်ညွှန်းသည်။ မူလ ontologies သည် ပြဿနာဧရိယာရှိ အရာဝတ္ထုများ၏ အဆင့်လိုက် ဖြစ်နိုင်သော်လည်း, ပိုမိုရှုပ်ထွေးသော ontologies များတွင် အနိမ့်ဆုံးနည်းလမ်းများအဖြစ် အသုံးပြုနိုင်သော စည်းမျဉ်းများလည်း ပါဝင်နိုင်သည်။ + +Semantic web တွင် တင်ဆက်မှုအားလုံးကို triplets များအား အခြေခံသည်။ အရာဝတ္ထုအချင်းချင်းနှင့် ဆက်နွယ်မှုတိုင်းကို URI ဖြင့် တစ်ကိုယ်ရည် ဖော်ပြသည်။ ဥပမာအားဖြင့်၊ ဒီ AI အတန်းတနိုင်ငံသည် Dmitry Soshnikov မှ သိမ်းဆည်းပြီး ဖြစ်သည်ဟု ဖော်ပြလိုပါက - အသုံးပြုနိုင်သည့် triplets များမှာ အောက်ပါအတိုင်းဖြစ်သည်- + + ``` -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 ``` -> ✅ ဒီမှာ `http://www.example.com/terms/creation-date` နှင့် `http://purl.org/dc/elements/1.1/creator` သည် *creator* နှင့် *creation date* ကို ကိုယ်စားပြုရန် အသိအမှတ်ပြုထားသော URI များဖြစ်သည်။ +> ✅ ဒီမှာ `http://www.example.com/terms/creation-date` နှင့် `http://purl.org/dc/elements/1.1/creator` သည် *creator* နှင့် *creation date* သိပ္ပံအယူအဆများကို ဖော်ပြရာတွင် ကျော်ကြားပြီး စံလမ်းညွှန် URIs ဖြစ်သည်။ -ပိုမိုရှုပ်ထွေးသော အခြေအနေတွင်၊ ဖန်တီးသူများ၏ စာရင်းကို သတ်မှတ်လိုပါက RDF တွင် သတ်မှတ်ထားသော ဒေတာဖွဲ့စည်းမှုများကို အသုံးပြုနိုင်သည်။ +ပိုမိုရှုပ်ထွေးသောအခြေအနေနှင့်၎င်း, ဖန်တီးသူများစာရင်းတစ်ခုသတ်မှတ်လိုပါက RDF တွင်သတ်မှတ်ထားသော ဒေတာဖွဲ့စည်းမှုများကို အသုံးပြုနိုင်သည်။ - + -> အထက်ပါ အကြမ်းဖျင်းပုံများကို [Dmitry Soshnikov](http://soshnikov.com) မှ ဖန်တီးထားသည်။ +> အထက်ပါ ပုံနှိပ်များကို [Dmitry Soshnikov](http://soshnikov.com) မှ ရေးဆွဲထားသည်။ -Semantic Web တည်ဆောက်မှု၏ တိုးတက်မှုသည် ရှာဖွေမှုအင်ဂျင်များနှင့် သဘာဝဘာသာစကားကို ကိုင်တွယ်နိုင်သော နည်းပညာများ၏ အောင်မြင်မှုကြောင့် တစ်စိတ်တစ်ပိုင်းနှေးကွေးခဲ့သည်။ သို့သော် အချို့နယ်ပယ်များတွင် Ontology များနှင့် အသိပညာအခြေခံများကို ထိန်းသိမ်းရန် အရေးကြီးသော ကြိုးပမ်းမှုများရှိနေဆဲဖြစ်သည်။ အောက်ပါစီမံကိန်းများသည် အရေးပါသည် - +Semantic Web ကို တည်ဆောက်ရာတွင် တိုးတက်မှုတချို့ကို ရှာဖွေမှုအင်ဂျင်များနှင့် သဘာဝဘာသာစကား ဆန်းစစ်နည်းများအောင်မြင်မှုကြောင့် နည်းနည်းကျဆင်းခဲ့သည်။ သို့သော် အချို့ နယ်ပယ်များတွင် သတင်းအချက်အလက် အခြေခံ Ontologies နှင့် အသိပညာ အခြေများကို ထိန်းသိမ်းရန် ကြိုးပမ်းမှုများ ရှိနေဆဲဖြစ်သည်။ တချို့ စီမံကိန်းများမှာ- -* [WikiData](https://wikidata.org/) သည် Wikipedia နှင့် ဆက်စပ်ထားသော စက်ဖြင့်ဖတ်နိုင်သော အသိပညာအခြေခံများ၏ စုစည်းမှုဖြစ်သည်။ ဒေတာအများစုကို Wikipedia *InfoBoxes* (Wikipedia စာမျက်နှာများအတွင်းရှိ ဖွဲ့စည်းထားသော အကြောင်းအရာများ) မှ ထုတ်ယူထားသည်။ SPARQL ဆိုသော Semantic Web အတွက် အထူး query ဘာသာစကားဖြင့် [query](https://query.wikidata.org/) Wikidata ကို ရှာဖွေနိုင်သည်။ လူများအကြား အများဆုံးတွေ့ရသော မျက်လုံးအရောင်များကို ပြသသည့် query တစ်ခုမှာ - +* [WikiData](https://wikidata.org/) သည် Wikipedia နှင့် ဆက်စပ်သော စက်မှတ်နိုင်သော အသိပညာအခြေများ စုစုပေါင်းဖြစ်သည်။ အလွန်မကြာခဏ Wikipedia *InfoBoxes* တွင် ရှာဖွေတွေ့ရှိသော ဒေတာများအများဆုံးဖြစ်သည်။ SPARQL ဆိုသည့် Semantic Web အတွက် ကွဲပြားသော မေးခြင်းဘာသာစကားဖြင့် [စစ်တမ်းခြင်းလုပ်](https://query.wikidata.org/) နိုင်သည်။ ဤနေရာတွင် လူ့မျက်လုံးအရောင်များထဲမှ လူကြိုက် အများဆုံးကို ဖော်ပြသည့် နမူနာမေးခွန်းတစ်ခုရှိသည်။ ```sparql #defaultView:BubbleChart @@ -172,47 +208,51 @@ WHERE GROUP BY ?eyeColorLabel ``` -* [DBpedia](https://www.dbpedia.org/) သည် WikiData နှင့် ဆင်တူသော ကြိုးပမ်းမှုတစ်ခုဖြစ်သည်။ +* [DBpedia](https://www.dbpedia.org/) သည် WikiData နှင့် ဆင်တူကြိုးပမ်းမှု တစ်ခု ဖြစ်သည်။ -> ✅ သင်၏ကိုယ်ပိုင် Ontology များကို တည်ဆောက်ရန် သို့မဟုတ် ရှိပြီးသား Ontology များကို ဖွင့်ရန် စမ်းသပ်လိုပါက [Protégé](https://protege.stanford.edu/) ဟုခေါ်သော အလွန်ကောင်းမွန်သော Ontology တည်းဖြတ်ရေးကိရိယာတစ်ခုရှိသည်။ ဒေါင်းလုဒ်လုပ်ပါ၊ သို့မဟုတ် အွန်လိုင်းတွင် အသုံးပြုပါ။ +> ✅ မိမိကိုယ်တိုင် ontologies များ တည်ဆောက် လေ့လာလိုသောလူများအတွက် [Protégé](https://protege.stanford.edu/) ဟုခေါ်သော သွယ်ဝိုက် အွန်တိုလို့ဂျီ တည်းဖြတ်ကိရိယာ ထူးကဲကောင်းမြဲသောကြီးပါသည်။ ဒေါင်းလုပ်ရယူပါ၊ ဒါမှမဟုတ် အွန်လိုင်းမှ အသုံးပြုပါ။ - + -*Web Protégé editor သည် Romanov Family Ontology ဖြင့် ဖွင့်ထားသည်။ Dmitry Soshnikov မှ Screenshot* +*Web Protégé တည်းဖြတ်ကိရိယာမှာ Romanov မျိုးဆက်အွန်တိုလို့ဂျီကို ဖွင့်ထားသည်။ Screenshots ကို Dmitry Soshnikov ပေးပို့သည်။* -## ✍️ လေ့ကျင့်မှု - မိသားစု Ontology +## ✍️ လေ့ကျင့်ခန်း - မျိုးဆက်အွန်တိုလို့ဂျီ -Semantic Web နည်းပညာများကို အသုံးပြု၍ မိသားစုဆက်နွယ်မှုများကို Reasoning လုပ်ပုံကို [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) တွင် ကြည့်ပါ။ GEDCOM ဖော်ပြချက်ပုံစံဖြင့် ကိုယ်စားပြုထားသော မိသားစုသစ်တစ်ခုနှင့် မိသားစုဆက်နွယ်မှု Ontology တစ်ခုကို အသုံးပြု၍ သတ်မှတ်ထားသော လူများအတွက် မိသားစုဆက်နွယ်မှုများ၏ ဂရပ်ဖွဲ့စည်းမှုကို တည်ဆောက်ပါ။ +Semantic Web နည်းပညာများဖြင့် မျိုးဆက် ဆက်နွယ်မှုများကို အတွေးရေးရာ သရုပ်ခေါ်ဖော်ပြပုံ နမူနာအတွက် [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) ကို ကြည့်ရှုပါ။ ကျွန်ုပ်တို့သည် GEDCOM ပွဲစဉ်ပုံစံတွင် ဖော်ပြထားသော မျိုးဆက်သမိုင်းဇယားနှင့် မျိုးဆက်ဆက်နွယ်မှု ontologies တစ်ခုကို အသုံးပြု၍ ပုံမှန် လူပုဂ္ဂိုလ်များအတွက် မျိုးဆက်ဆက်နွယ်မှု အားလုံး၏ ဂရပ် ဖန်တီးပါမည်။ ## Microsoft Concept Graph -အများဆုံးအခြေအနေများတွင် Ontology များကို လက်ဖြင့်သေချာစွာ ဖန်တီးထားသည်။ သို့သော် Unstructured Data (ဥပမာ - သဘာဝဘာသာစကားစာသားများ) မှ Ontology များကို **ထုတ်ယူ**နိုင်သည်။ +အများအားဖြင့် ontologies များကို လက်ဖြင့် စနစ်ကျစွာဖန်တီးသည်။ သို့သော် မတော်တဆ အဆင်မပြေ ဒေတာများ (ဥပမာသဘာဝဘာသာစကား စာသားများ) မှ ontologies များ **တူးဖော်** လုပ်နိုင်သည်။ -Microsoft Research မှ ကြိုးပမ်းမှုတစ်ခုဖြစ်ပြီး [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste) ကို ရလဒ်အဖြစ်ရရှိခဲ့သည်။ +Microsoft Research မှ စမ်းသပ်မှုတစ်ခု [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste) ကို ထုတ်လုပ်ခဲ့သည်။ -၎င်းသည် `is-a` အဆင့်ဆင့်ဆက်နွယ်မှုကို အသုံးပြု၍ အဖွဲ့ဖွဲ့စည်းထားသော အရာဝတ္ထုများ၏ စုစည်းမှုကြီးတစ်ခုဖြစ်သည်။ "Microsoft ဆိုတာဘာလဲ?" ဆိုသည့်မေးခွန်းကို "ကုမ္ပဏီ (0.87) နှင့် အမှတ်တံဆိပ် (0.75)" ဟုဖြေဆိုနိုင်သည်။ +အဓိက စုစည်းမှုမှာ `is-a` အမွေပုံသွား ဆက်နွယ်မှု အား အသုံးပြု၍ အဖွဲ့စည်းထားသော အရာဝတ္ထုများ ကြီးမားသော စုစည်းမှု ဖြစ်သည်။ ဥပမာ "What is Microsoft?" ဆိုသော မေးခွန်းမျိုးကို "ကုမ္ပဏီ probability 0.87 နဲ့ brand probability 0.75" ဆိုသလို ဖြေရှင်းနိုင်စေသည်။ -Graph ကို REST API အဖြစ် သို့မဟုတ် အဖွဲ့စည်းထားသော အရာဝတ္ထုများကို ဖော်ပြထားသော စာသားဖိုင်ကြီးအဖြစ် ရရှိနိုင်သည်။ +Graph သည် REST API အဖြစ် သို့မဟုတ် အကြီးစား ดาวน์โหลด ခွင့်ရှိသော စာသားဖိုင် အဖြစ် ရနိုင်သည်။ -## ✍️ လေ့ကျင့်မှု - Concept Graph +## ✍️ လေ့ကျင့်ခန်း - Concept Graph -Microsoft Concept Graph ကို အသုံးပြု၍ သတင်းဆောင်းပါးများကို အမျိုးအစားအတန်းများသို့ အုပ်စုဖွဲ့ပုံကို ကြည့်ရန် [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) notebook ကို စမ်းသပ်ပါ။ +Microsoft Concept Graph ကို သုံး၍ သတင်းဆောင်းပါးများကို အမျိုးအစားတချို့ အဖြစ်စုစုပေါင်းရန် သုံးကြည့်ရန် [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) notebook ကို စမ်းကြည့်ပါ။ -## နိဂုံး +## နိဂုံးချုပ် -ယနေ့ခေတ်တွင် AI ကို *Machine Learning* သို့မဟုတ် *Neural Networks* နှင့် အတူတူဖြစ်သည်ဟု ယူဆကြသည်။ သို့သော် လူသားတစ်ဦးသည် Neural Networks များက ကိုင်တွယ်နိုင်ခြင်းမရှိသေးသော တိကျသော Reasoning ကိုလည်း ပြသသည်။ အမှန်တကယ်သော စီမံကိန်းများတွင် တိကျသော Reasoning ကို ရှင်းလင်းချက်များလိုအပ်သော အလုပ်များကို ဆောင်ရွက်ရန် သို့မဟုတ် စနစ်၏ အပြုအမူကို ထိန်းချုပ်နိုင်သော နည်းလမ်းဖြင့် ပြောင်းလဲရန် အသုံးပြုနေဆဲဖြစ်သည်။ +ယနေ့ခေတ်တွင် AI ကို *Machine Learning* သို့မဟုတ် *Neural Networks* နှင့် ညီပေါ်စွာ ထင်မြင်ကြသည်။ သို့သော် လူသားများတွင် ရှေ့ကြည့်ထင်မြင်နိုင်မှု ထင်ရှားစွာ ရှိပြီး ၎င်းကို neural networks မည်သည့်ပုံစံဖြင့်မကာကွယ်နိုင်သေးပါ။ လက်တွေ့လုပ်ငန်းများတွင် ရှေ့ကြည့်အတည်ပြုချက်များကို အထောက်အပံ့ပြုရန် သို့မဟုတ် စနစ်၏ လုပ်ဆောင်မှုကို ထိန်းချုပ်ထားနိုင်စေရန် အသုံးပြုနေဆဲဖြစ်သည်။ ## 🚀 စိန်ခေါ်မှု -ဒီသင်ခန်းစာနှင့် ဆက်စပ်ထားသော Family Ontology notebook တွင် မိသားစုသစ်များ၏ ဆက်နွယ်မှုများကို စမ်းသပ်နိုင်ရန် အခွင့်အလမ်းရှိသည်။ မိသားစုသစ်တွင် လူများအကြား ဆက်နွယ်မှုအသစ်များကို ရှာဖွေကြည့်ပါ။ +ဒီသင်ခန်းစာနှင့်ပတ်သက်သော Family Ontology notebook တွင် မျိုးဆက်ဆက်နွယ်မှု အခြားအမျိုးအစားများအား လေ့လာရန် အခွင့်အလမ်း ရှိပါသည်။ မျိုးဆက်ဇယားထဲရှိ လူများအကြား ချိတ်ဆက်မှုအသစ်များ ရှာဖွေကြည့်ပါ။ -## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ai/quiz/4) +## [သင်ခန်းစာပြီးနောက် စမ်းသပ်မှု](https://ff-quizzes.netlify.app/en/ai/quiz/4) ## ပြန်လည်သုံးသပ်ခြင်းနှင့် ကိုယ်တိုင်လေ့လာခြင်း -လူသားများသည် အသိပညာကို အရေအတွက်သတ်မှတ်၍ စနစ်တကျဖော်ပြရန် ကြိုးပမ်းခဲ့သော နယ်ပယ်များကို ရှာဖွေပါ။ Bloom's Taxonomy ကို ကြည့်ရှုပါ၊ လူသားများသည် ၎င်းတို့၏ ကမ္ဘာကို နားလည်ရန် ကြိုးပမ်းခဲ့ပုံကို သမိုင်းကြောင်းအရ ပြန်လည်လေ့လာပါ။ Linnaeus ၏ အလုပ်ကို ကြည့်ရှု၍ အဖွဲ့ဖွဲ့စည်းမှုတစ်ခုကို ဖန်တီးပုံနှင့် Dmitri Mendeleev ၏ ဓာတုဒြပ်စင်များကို ဖော်ပြ၍ အုပ်စုဖွဲ့ပုံကို လေ့လာပါ။ သင်ရှာဖွေနိုင်သော အခြားစိတ်ဝင်စားဖွယ် ဥပမာများကို ရှာဖွေပါ။ +လူသားများ သင်၏အသိပညာကို အရေအတွက် ချမှတ်ခြင်းနှင့် စနစ်တကျသိမ်းဆည်းခြင်း ပြုလုပ်ခဲ့သော နယ်ပယ်များကို အင်တာနက်တွင် ရှာဖွေကြည့်ပါ။ Bloom's Taxonomy ကို ကြည့်ရှုပြီး လူသားများသည် စကလုံးတွင် မည်သို့ ကမ္ဘာအကြောင်း သိမြင်ခဲ့ကြသည်ကို သမိုင်းအရ ပြန်လည်လေ့လာပါ။ Linnaeus ၏ အသက်ဝင်ပုံစံများ သိပ္ပံအမျိုးအစား စနစ်တကျ ဖန်တီးမှုအလုပ်ကို ရှာဖွေကြည့်ပါ၊ Dmitri Mendeleev က ဓာတု အစိတ်အပိုင်းများကို ဖော်ပြခြင်းနှင့် အုပ်စုဖွဲ့ခြင်းပုံစံများကို မည်သို့ တီထွင်ခဲ့သည်ကို လေ့လာပါ။ အခြား စိတ်ဝင်စားဖွယ် ဥပမာများကို မည်သို့ ရှာတွေ့နိုင်ပါသလဲ? -**Assignment**: [Build an Ontology](assignment.md) +**တာဝန်**: [Ontology တည်ဆောက်ခြင်း](assignment.md) --- + +**သတိပြုချက်** +ဤစာတမ်းကို AI ဘာသာပြန်မှု ဝန်ဆောင်မှုဖြစ်သည့် [Co-op Translator](https://github.com/Azure/co-op-translator) ကို အသုံးပြု၍ ဘာသာပြန်ထားပါသည်။ တိကျမှုကို သေချာစေရန် ကြိုးစားသော်လည်း၊ စက်ပစ္စည်းအလိုအလျောက် ဘာသာပြန်မှုတွင် အမှားများ သို့မဟုတ် အတိအကျမရှိမှုများ ပါဝင်နိုင်ပါသည်။ မူရင်းစာတမ်းကို မိမိဘာသာစကားဖြင့်သာ ယုံကြည်သင့်သော မူရင်းအချက်အလက် အနေဖြင့် သတ်မှတ်သင့်ပါသည်။ အရေးကြီးသောအချက်အလက်များအတွက် လူ့လက်တွင် ရရှိနိုင်သော ကျွမ်းကျင်သူမှ ဘာသာပြန်မှုကို ထောက်ပြောအတွက် အကြံပြုပါသည်။ ဤဘာသာပြန်မှုကို အသုံးပြုခြင်းမှ ဖြစ်ပေါ်သော မတိကျမှုများ သို့မဟုတ် ဖွင့်ဆိုချက်မှားယွင်းမှုများအတွက် ကျွန်ုပ်တို့အကြောင်းပြချက်မရှိပါ။ + \ No newline at end of file diff --git a/translations/sl/README.md b/translations/sl/README.md index 4825f441..c3167c30 100644 --- a/translations/sl/README.md +++ b/translations/sl/README.md @@ -1,8 +1,8 @@ -[Arabščina](../ar/README.md) | [Bengalščina](../bn/README.md) | [Bolgarščina](../bg/README.md) | [Burmanščina (Mjanmar)](../my/README.md) | [Kitajsščina (poenostavljena)](../zh/README.md) | [Kitajsščina (tradicionalna, Hong Kong)](../hk/README.md) | [Kitajsščina (tradicionalna, Macao)](../mo/README.md) | [Kitajsščina (tradicionalna, Tajvan)](../tw/README.md) | [Hrvaščina](../hr/README.md) | [Češčina](../cs/README.md) | [Danščina](../da/README.md) | [Nizozemščina](../nl/README.md) | [Estonščina](../et/README.md) | [Finščina](../fi/README.md) | [Francoščina](../fr/README.md) | [Nemščina](../de/README.md) | [Grščina](../el/README.md) | [Hebrejščina](../he/README.md) | [Hindijščina](../hi/README.md) | [Madžarščina](../hu/README.md) | [Indonezijščina](../id/README.md) | [Italijanščina](../it/README.md) | [Japonščina](../ja/README.md) | [Kannada](../kn/README.md) | [Korejščina](../ko/README.md) | [Litvijščina](../lt/README.md) | [Malajščina](../ms/README.md) | 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(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](./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) -> **Raje želite klonirati lokalno?** +> **Raje klonirate lokalno?** -> Ta repozitorij vključuje več kot 50 prevodov jezikov, kar znatno poveča velikost prenosa. Za kloniranje brez prevodov uporabite izbiro sparse checkout: +> To skladišče vsebuje več kot 50 prevodov jezikov, kar bistveno poveča velikost prenosa. Za kloniranje brez prevodov uporabite 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' > ``` -> Tako dobite vse, kar potrebujete za dokončanje tečaja z veliko hitrejšim prenosom. +> Tako dobite vse, kar potrebujete za zaključek tečaja z veliko hitrejšim prenosom. -**Če želite, da so podprti nadaljnji prevodni jeziki, so ti navedeni [tukaj](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** +**Če želite podporo za dodatne jezike prevodov, so ti navedeni [tukaj](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** ## Pridružite se skupnosti [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) @@ -59,87 +58,87 @@ Raziščite svet **Umetne inteligence** (UI) z našim 12-tedenskim, 24-urno učn V tem učnem načrtu se boste naučili: -* Različne pristope k umetni inteligenci, vključno z "starim dobrim" simbolnim pristopom z **predstavo znanja** in sklepanjem ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). -* **Nevronske mreže** in **globoko učenje**, ki sta v jedru sodobne umetne inteligence. Koncepte teh pomembnih tem bomo ponazorili s kodo v dveh najbolj priljubljenih ogrodjih - [TensorFlow](http://Tensorflow.org) in [PyTorch](http://pytorch.org). -* **Nevronske arhitekture** za delo s slikami in besedilom. Pokrili bomo nedavne modele, čeprav morda nekoliko manj natančno najnaprednejše. -* Manj priljubljene pristope UI, kot so **genetski algoritmi** in **sistemi več agentov**. +* Različnih pristopov k umetni inteligenci, vključno z "dobrim starim" simbolnim pristopom s **predstavitvijo znanja** in sklepanjem ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). +* **Nevronske mreže** in **globoko učenje**, ki sta v središču sodobne UI. Koncepte teh pomembnih tem bomo ilustrirali z uporabo kode v dveh najpopularnejših okvirjih - [TensorFlow](http://Tensorflow.org) in [PyTorch](http://pytorch.org). +* **Nevronske arhitekture** za delo z slikami in besedilom. Pokrili bomo novejše modele, vendar morda nekoliko manj izpopolnjene najnovejše tehnologije. +* Manj priljubljene pristope UI, kot so **genetski algoritmi** in **sistemi z več agenti**. -Česar ta učni načrt ne pokriva: +Česar ta učni načrt ne bo pokrival: -> [Najdite vse dodatne vire za ta tečaj v naši zbirki Microsoft Learn](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) +> [Vse dodatne vire za ta tečaj poiščite v naši zbirki Microsoft Learn](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) -* Poslovni primeri uporabe **UI v poslu**. Razmislite o vpisu v pot učenja [Uvod v UI za poslovne uporabnike](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) na Microsoft Learn ali [AI poslovna šola](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), razvito v sodelovanju z [INSEAD](https://www.insead.edu/). -* **Klasično strojno učenje**, ki je dobro opisano v našem [Učnem načrtu strojnega učenja za začetnike](http://github.com/Microsoft/ML-for-Beginners). -* Praktične aplikacije UI, zgrajene z uporabo **[kognitivnih storitev](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**. Za to priporočamo začetek z moduli na Microsoft Learn za [vid](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [obdelavo naravnega jezika](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[generativno UI z Azure OpenAI Service](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** in druge. -* Specifična ML **oblakiška ogrodja**, kot so [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), ali [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). Razmislite o uporabi poti učenja [Izdelajte in upravljajte rešitve strojnega učenja z Azure Machine Learning](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) in [Izdelajte in upravljajte rešitve strojnega učenja z Azure Databricks](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum). -* **Pogovorna umetna inteligenca** in **klepetalniki**. Obstaja ločena pot učenja [Ustvarite rešitve pogovorne UI](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum), prav tako si lahko ogledate [ta blog objavo](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) za več podrobnosti. -* **Globoka matematika** za globoko učenje. Za to priporočamo [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) Ian Goodfellow, Yoshua Bengio in Aaron Courville, ki je na voljo tudi na spletu na [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/). +* Poslovnih primerov uporabe **UI v podjetjih**. Razmislite o vpisu na učni program [Uvod v UI za poslovne uporabnike](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) na Microsoft Learn ali [AI Business School](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), ki je nastal v sodelovanju z [INSEAD](https://www.insead.edu/). +* **Klasičnega strojnega učenja**, ki je dobro opisan v našem [učnem načrtu Strojno učenje za začetnike](http://github.com/Microsoft/ML-for-Beginners). +* Praktičnih UI aplikacij zgrajenih z uporabo **[Kognitivnih storitev](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**. Za to priporočamo začetek z moduli Microsoft Learn za [vid](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [obdelavo naravnega jezika](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[generativno UI z Azure OpenAI Service](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** in druge. +* Specifičnih ML **oblakovnih ogrodij**, kot so [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) ali [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). Razmislite o uporabi učnih poti [Izgradnja in upravljanje rešitev strojnega učenja z Azure Machine Learning](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) in [Izgradnja in upravljanje rešitev strojnega učenja z Azure Databricks](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum). +* **Konverzacijskih UI** in **klepetalnih robotov (Chat Botov)**. Obstaja posebej [Ustvarjanje konverzacijskih UI rešitev](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum) učna pot, lahko pa se tudi sklicujete na [ta blog objavo](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) za več podrobnosti. +* **Globoke matematike** za globoko učenje. Za to priporočamo [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) avtorjev Ian Goodfellow, Yoshua Bengio in Aaron Courville, ki je tudi na voljo spletno na [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/). -Za lahek uvod v teme _UI v oblaku_ lahko razmislite o poti učenja [Začni z umetno inteligenco na Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum). +Za mehkejši uvod v teme _UI v oblaku_ lahko razmislite o vpisu na učno pot [Začnite z umetno inteligenco na Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum). # Vsebina | | Povezava do lekcije | PyTorch/Keras/TensorFlow | Laboratorij | | :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | -| 0 | [Nastavitev tečaja](./lessons/0-course-setup/setup.md) | [Nastavi svoje razvojno okolje](./lessons/0-course-setup/how-to-run.md) | | +| 0 | [Nastavitev tečaja](./lessons/0-course-setup/setup.md) | [Nastavite svoje razvojno okolje](./lessons/0-course-setup/how-to-run.md) | | | I | [**Uvod v UI**](./lessons/1-Intro/README.md) | | | | 01 | [Uvod in zgodovina UI](./lessons/1-Intro/README.md) | - | - | | II | **Simbolna UI** | -| 02 | [Predstavitev znanja in ekspertni sistemi](./lessons/2-Symbolic/README.md) | [Ekspertni sistemi](./lessons/2-Symbolic/Animals.ipynb) / [Ontologija](./lessons/2-Symbolic/FamilyOntology.ipynb) /[Graf konceptov](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | +| 02 | [Predstavitev znanja in ekspertni sistemi](./lessons/2-Symbolic/README.md) | [Ekspertni sistemi](./lessons/2-Symbolic/Animals.ipynb) / [Ontologija](./lessons/2-Symbolic/FamilyOntology.ipynb) /[Konceptualni graf](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | | III | [**Uvod v nevronske mreže**](./lessons/3-NeuralNetworks/README.md) ||| | 03 | [Perceptron](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [Zvezek](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [Laboratorij](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | | 04 | [Večplastni perceptron in ustvarjanje lastnega ogrodja](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [Zvezek](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [Laboratorij](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | | 05 | [Uvod v ogrodja (PyTorch/TensorFlow) in prekomerno prileganje](./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) | [Laboratorij](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | -| IV | [**Računalniški vid**](./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)| [Razišči računalniški vid na Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | +| IV | [**Računalniški vid**](./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)| [Raziščite računalniški vid na Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | | 06 | [Uvod v računalniški vid. OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [Zvezek](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [Laboratorij](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | | 07 | [Konvolucijske nevronske mreže](./lessons/4-ComputerVision/07-ConvNets/README.md) & [Arhitekture 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) | [Laboratorij](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | -| 08 | [Predhodno izurjene mreže in prenosno učenje](./lessons/4-ComputerVision/08-TransferLearning/README.md) in [Triki za treniranje](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Laboratorij](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | -| 09 | [Avtoenkoderji in VAE-ji](./lessons/4-ComputerVision/09-Autoencoders/README.md) | [PyTorch](./lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb) | | +| 08 | [Predhodno usposobljene mreže in prenosno učenje](./lessons/4-ComputerVision/08-TransferLearning/README.md) in [Triki pri učenju](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Laboratorij](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | +| 09 | [Avtoenkoderji in 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 | [Generativne nasprotujoče si mreže in prenos umetniškega sloga](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | -| 11 | [Zaznavanje objektov](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [Laboratorij](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | +| 11 | [Zaznavanje predmetov](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [Laboratorij](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | | 12 | [Semantična segmentacija. 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 | [**Obdelava naravnega jezika**](./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) | [Razišči obdelavo naravnega jezika na Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| +| V | [**Obdelava naravnega jezika**](./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) | [Raziščite obdelavo naravnega jezika na Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| | 13 | [Predstavitev besedila. 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 | [Semantične vgradnje besed. Word2Vec in 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 | [Jezikovno modeliranje. Izurite svoje vgradnje](./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) | [Laboratorij](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | -| 16 | [Ponovljive nevronske mreže](./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 | [Generativne ponovljive mreže](./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) | [Laboratorij](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | -| 18 | [Transformerji. 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) | | +| 14 | [Semantične vdelave besed. Word2Vec in 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 | [Modeliranje jezika. Učenje lastnih vdelav](./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) | [Laboratorij](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | +| 16 | [Rekurentne nevronske mreže](./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 | [Generativne rekurentne mreže](./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) | [Laboratorij](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | +| 18 | [Transformers. BERT.](./lessons/5-NLP/18-Transformers/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb) /[TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersTF.ipynb) | | | 19 | [Prepoznavanje imenovanih entitet](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [Laboratorij](./lessons/5-NLP/19-NER/lab/README.md) | -| 20 | [Veliki jezikovni modeli, programiranje pozivov in naloge z nekaj primeri](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | -| VI | **Druge tehnike AI** || | +| 20 | [Veliki jezikovni modeli, programiranje pozivov in naloge z malo primeri](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | +| VI | **Druge AI tehnike** || | | 21 | [Genetski algoritmi](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [Zvezek](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | | 22 | [Globoko okrepljeno učenje](./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) | [Laboratorij](./lessons/6-Other/22-DeepRL/lab/README.md) | -| 23 | [Sistemi z več agenti](./lessons/6-Other/23-MultiagentSystems/README.md) | | | -| VII | **Etika AI** | | | -| 24 | [Etika AI in odgovorni AI](./lessons/7-Ethics/README.md) | [Microsoft Learn: Principi odgovorne AI](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | +| 23 | [Večagentni sistemi](./lessons/6-Other/23-MultiagentSystems/README.md) | | | +| VII | **Etika umetne inteligence** | | | +| 24 | [Etika AI in odgovorna AI](./lessons/7-Ethics/README.md) | [Microsoft Learn: Načela odgovorne AI](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | | IX | **Dodatki** | | | | 25 | [Večmodalne mreže, CLIP in VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [Zvezek](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | ## Vsaka lekcija vsebuje -* Predbralni material -* Izvedljive Jupyter zvezke, ki so pogosto specifični za ogrodje (**PyTorch** ali **TensorFlow**). Izvedljiv zvezek vsebuje tudi veliko teoretičnega gradiva, zato morate za razumevanje teme preiti skozi vsaj eno verzijo zvezka (bodisi PyTorch ali TensorFlow). -* **Laboratorije**, ki so na voljo za nekatere teme, in vam omogočajo, da poskusite uporabiti naučeno snov na konkretni problem. -* Nekateri odseki vsebujejo povezave do modulov [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum), ki pokrivajo sorodne teme. +* Predbrano gradivo +* Izvedljive Jupyter zvezke, ki so pogosto specifični za ogrodje (**PyTorch** ali **TensorFlow**). Izvedljivi zvezek vsebuje tudi veliko teoretičnega gradiva, zato je za razumevanje teme potrebno preiti vsaj eno verzijo zvezka (bodisi PyTorch ali TensorFlow). +* **Laboratorijske vaje** na voljo za nekatere teme, ki vam omogočajo, da poskusite uporabiti naučeno snov na konkretni problem. +* Nekateri razdelki vsebujejo povezave do modulov [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum), ki pokrivajo sorodne teme. ## Začetek -### 🎯 Novo pri AI? Začnite tukaj! +### 🎯 Novi v AI? Začnite tukaj! -Če ste popolnoma novi pri AI in želite hitre, praktične primere, si oglejte naše [**Primeri prijazni začetnikom**](./examples/README.md)! Ti vključujejo: +Če ste popolnoma novi v AI in želite hitre, praktične primere, preverite naše [**Primeri za začetnike**](./examples/README.md)! Ti vključujejo: -- 🌟 **Pozdravljen AI svet** - vaš prvi AI program (prepoznavanje vzorcev) -- 🧠 **Preprosta nevronska mreža** - postavite nevronsko mrežo iz nič -- 🖼️ **Klasifikator slik** - klasificirajte slike z obsežnimi komentarji -- 💬 **Čustvena analiza besedila** - Analiza pozitivnega/negativnega besedila +- 🌟 **Pozdrav AI svetu** - Vaš prvi AI program (prepoznavanje vzorcev) +- 🧠 **Preprosta nevronska mreža** - Zgradite nevronsko mrežo iz nič +- 🖼️ **Klasifikator slik** - Klasificirajte slike z obsežnimi komentarji +- 💬 **Sentiment besedila** - Analiza pozitivnega/negativnega besedila -Ti primeri so zasnovani, da vam pomagajo razumeti koncepte umetne inteligence, preden se poglobite v celoten učni načrt. +Ti primeri so zasnovani tako, da vam pomagajo razumeti koncepte umetne inteligence, preden se poglobite v celoten učni načrt. ### 📚 Nastavitev celotnega učnega načrta -- Ustvarili smo [uvodni učni modul](./lessons/0-course-setup/setup.md), ki vam pomaga pri nastavitvi razvojnega okolja. - Za učitelje smo prav tako pripravili [uvodni učni modul za učitelje](./lessons/0-course-setup/for-teachers.md)! -- Kako [pognati kodo v VSCode ali Codepace](./lessons/0-course-setup/how-to-run.md) +- Ustvarili smo [lekcijo za nastavitev](./lessons/0-course-setup/setup.md), ki vam pomaga pri nastavitvi razvojnega okolja. - Za učitelje smo ustvarili tudi [lekcijo za nastavitev učnih načrtov](./lessons/0-course-setup/for-teachers.md)! +- Kako [pognati kodo v VSCode ali Codespace](./lessons/0-course-setup/how-to-run.md) Sledite tem korakom: @@ -147,29 +146,29 @@ Razvejite repozitorij: Kliknite na gumb "Fork" v zgornjem desnem kotu te strani. Klonirajte repozitorij: `git clone https://github.com/microsoft/AI-For-Beginners.git` -Ne pozabite dati zvezdice (🌟) temu repozitoriju, da ga boste lažje našli kasneje. +Ne pozabite dodati zvezdico (🌟) temu repozitoriju, da ga boste lažje našli kasneje. ## Spoznajte druge učence -Pridružite se našemu [uradnemu AI Discord strežniku](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum), da spoznate in navežete stike z drugimi udeleženci tega tečaja ter dobite podporo. +Pridružite se našemu [uradnemu AI Discord strežniku](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum), da spoznate in se povežete z drugimi učenci tega tečaja ter pridobite podporo. Če imate povratne informacije o izdelku ali vprašanja med gradnjo, obiščite naš [Azure AI Foundry Developer Forum](https://aka.ms/foundry/forum) ## Kvizi -> **Opomba o kvizih**: Vsi kvizi so shranjeni v mapi Quiz-app znotraj etc\quiz-app, ali pa jih najdete [tukaj na spletu](https://ff-quizzes.netlify.app/). Do njih je mogoče dostopati iz učnih modulov. Aplikacijo za kvize lahko zaženete lokalno ali jo namestite v Azure; sledite navodilom v mapi `quiz-app`. Postopoma se lokalizirajo. +> **Opomba o kvizih**: Vsi kvizi so v mapi Quiz-app v etc\quiz-app ali [spletno tukaj](https://ff-quizzes.netlify.app/) Povezani so znotraj lekcij, aplikacijo kviza je mogoče pognati lokalno ali namestiti na Azure; sledite navodilom v mapi `quiz-app`. Postopoma se lokalizirajo. -## Iščemo pomoč +## Potrebna pomoč -Imate predloge ali ste našli napake v pravopisu ali kodi? Odprite vprašanje (issue) ali ustvarite pull request. +Imate predloge ali ste našli pravopisne ali kode napake? Odprite zadevo ali ustvarite pull request. ## Posebne zahvale * **✍️ Glavni avtor:** [Dmitry Soshnikov](http://soshnikov.com), PhD * **🔥 Urednik:** [Jen Looper](https://twitter.com/jenlooper), PhD -* **🎨 Ilustratorka skic:** [Tomomi Imura](https://twitter.com/girlie_mac) +* **🎨 Ilustrator skic:** [Tomomi Imura](https://twitter.com/girlie_mac) * **✅ Ustvarjalec kvizov:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) -* **🙏 Glavni prispevkarji:** [Evgenii Pishchik](https://github.com/Pe4enIks) +* **🙏 Glavni sodelavci:** [Evgenii Pishchik](https://github.com/Pe4enIks) ## Drugi učni načrti @@ -182,7 +181,7 @@ Naša ekipa ustvarja tudi druge učne načrte! Oglejte si: --- -### Azure / Edge / MCP / Agenti +### Azure / Edge / MCP / Agentje [![AZD za začetnike](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 za začetnike](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 za začetnike](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) @@ -190,7 +189,7 @@ Naša ekipa ustvarja tudi druge učne načrte! Oglejte si: --- -### Serija generativne umetne inteligence +### Serija generativne AI [![Generativna AI za začetnike](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) [![Generativna 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) [![Generativna 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) @@ -199,7 +198,7 @@ Naša ekipa ustvarja tudi druge učne načrte! Oglejte si: --- ### Osnovno učenje -[![Strojno učenje za začetnike](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) +[![ML za začetnike](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) [![Podatkovna znanost za začetnike](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 za začetnike](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) [![Kibernetska varnost za začetnike](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) @@ -210,14 +209,14 @@ Naša ekipa ustvarja tudi druge učne načrte! Oglejte si: --- ### Serija Copilot -[![Copilot za AI parno programiranje](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 za AI Parno programiranje](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 za 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 avantura](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 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) -## Iskanje pomoči +## Pridobivanje pomoči -Če zataknete ali imate vprašanja o ustvarjanju AI aplikacij, se pridružite sošolcem in izkušenim razvijalcem v razpravah o MCP. Gre za podporno skupnost, kjer so vprašanja dobrodošla in so znanja prosto deljena. +Če se zataknete ali imate vprašanja o ustvarjanju AI aplikacij. Pridružite se ostalim učencem in izkušenim razvijalcem v razpravah o MCP. To je podporna skupnost, kjer so vprašanja dobrodošla in kjer se znanje prostovoljno deli. [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) @@ -228,6 +227,6 @@ Naša ekipa ustvarja tudi druge učne načrte! Oglejte si: --- -**Omejitev odgovornosti**: -Ta dokument je bil preveden z uporabo storitve umetne inteligence za prevajanje [Co-op Translator](https://github.com/Azure/co-op-translator). Kljub prizadevanjem za natančnost vas opozarjamo, da avtomatizirani prevodi lahko vsebujejo napake ali netočnosti. Izvirni dokument v izvorni jezik se šteje za avtoritativni vir. Za pomembne informacije priporočamo strokovni prevod s strani človeka. Ne odgovarjamo za morebitna nerazumevanja ali napačne interpretacije, ki izhajajo iz uporabe tega prevoda. +**Omejitev odgovornosti**: +Ta dokument je bil preveden z uporabo storitve za avtomatski prevod AI [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, prosimo, upoštevajte, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Prvotni dokument v izvirnem jeziku velja za avtoritativni vir. Za ključne informacije priporočamo strokovni človeški prevod. Nismo odgovorni za morebitne nesporazume ali napačne interpretacije, ki izhajajo iz uporabe tega prevoda. \ No newline at end of file diff --git a/translations/sl/lessons/0-course-setup/how-to-run.md b/translations/sl/lessons/0-course-setup/how-to-run.md index 86c7e943..f75cb185 100644 --- a/translations/sl/lessons/0-course-setup/how-to-run.md +++ b/translations/sl/lessons/0-course-setup/how-to-run.md @@ -1,21 +1,21 @@ # Kako zagnati kodo -Ta učni načrt vsebuje veliko primerov kode in laboratorijskih vaj, ki jih boste želeli zagnati. Da bi to storili, potrebujete možnost izvajanja Python kode v Jupyter Notebookih, ki so del tega učnega načrta. Na voljo imate več možnosti za zagon kode: +Ta učni načrt vsebuje veliko izvajalnih primerov in laboratorijskih vaj, ki jih boste želeli zagnati. Da bi to naredili, morate imeti možnost izvajanja Python kode v Jupyter beležnicah, ki so vključene v ta učni načrt. Za izvajanje kode imate več možnosti: ## Zagon lokalno na vašem računalniku -Za zagon kode lokalno na vašem računalniku morate imeti nameščeno neko različico Pythona. Osebno priporočam namestitev **[miniconda](https://conda.io/en/latest/miniconda.html)** - gre za precej lahkotno namestitev, ki podpira upravitelja paketov `conda` za različna Python **virtualna okolja**. +Za lokalni zagon kode na vašem računalniku je potrebna namestitev Pythona. Ena izmed priporočil je namestitev **[miniconda](https://conda.io/en/latest/miniconda.html)** – gre za dokaj lahko namestitev, ki podpira upravitelja paketov `conda` za različna Python **virtualna okolja**. -Ko namestite minicondo, morate klonirati repozitorij in ustvariti virtualno okolje, ki ga boste uporabljali za ta tečaj: +Ko namestite minicondo, klonirajte repozitorij in ustvarite virtualno okolje, ki bo uporabljeno za ta tečaj: ```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 ``` -### Uporaba Visual Studio Code z razširitvijo za Python +### Uporaba Visual Studio Code z razširitvijo Python -Verjetno je najboljši način za uporabo učnega načrta, da ga odprete v [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) z [razširitvijo za Python](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste). +Ta učni načrt je najbolje uporabljati, ko ga odprete v [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) z [razširitvijo Python](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste). -> **Note**: Ko klonirate in odprete mapo v VS Code, vam bo samodejno predlagal namestitev razširitev za Python. Prav tako boste morali namestiti minicondo, kot je opisano zgoraj. +> **Opomba**: Ko klonirate in odprete mapo v VS Code, vam bo samodejno predlagal, da namestite Python razširitve. Prav tako boste morali namestiti minicondo, kot je opisano zgoraj. -> **Note**: Če vam VS Code predlaga, da ponovno odprete repozitorij v vsebniku, morate to zavrniti, da uporabite lokalno namestitev Pythona. +> **Opomba**: Če vam VS Code predlaga, da ponovno odprete repozitorij v vsebniku, to zavrnite, da uporabite lokalno namestitev Pythona. -### Uporaba Jupyterja v brskalniku +### Uporaba Jupyter v brskalniku -Jupyter okolje lahko uporabljate tudi neposredno iz brskalnika na vašem računalniku. Pravzaprav tako klasični Jupyter kot Jupyter Hub nudita precej priročno razvojno okolje z avtomatskim dopolnjevanjem, označevanjem kode itd. +Lahko uporabite tudi Jupyter okolje prek brskalnika na vašem računalniku. Tako klasični Jupyter kot JupyterHub nudita priročno razvojno okolje z avtomatskim dokončevanjem, poudarkom kode itd. -Za zagon Jupyterja lokalno pojdite v mapo tečaja in izvedite: +Za zagon Juypterja lokalno pojdite v mapo s tečajem in izvedite: ```bash jupyter notebook @@ -45,32 +45,36 @@ ali ```bash jupyterhub ``` -Nato lahko odprete katero koli `.ipynb` datoteko, jo odprete in začnete delati. +Nato lahko odprete katerokoli `.ipynb` datoteko in začnete z delom. ### Zagon v vsebniku -Alternativa namestitvi Pythona je zagon kode v vsebniku. Ker naš repozitorij vsebuje posebno mapo `.devcontainer`, ki določa, kako zgraditi vsebnik za ta repozitorij, vam bo VS Code ponudil možnost, da kodo ponovno odprete v vsebniku. To bo zahtevalo namestitev Dockerja in je nekoliko bolj zapleteno, zato to priporočamo bolj izkušenim uporabnikom. +Ena alternativa lokalni namestitvi Pythona je zagon kode v vsebniku. Ker naš repozitorij vsebuje posebno mapo `.devcontainer`, ki navodila za gradnjo vsebnika za ta repozitorij, VS Code ponuja možnost ponovnega odpiranja kode v vsebniku. To zahteva namestitev Dockerja in je nekoliko bolj zapleteno, zato priporočamo to bolj izkušenim uporabnikom. ## Zagon v oblaku -Če ne želite namestiti Pythona lokalno in imate dostop do nekaterih oblačnih virov, je dobra alternativa zagon kode v oblaku. Obstaja več načinov, kako to storiti: +Če ne želite namestiti Pythona lokalno in imate dostop do oblačnih virov, je dobra alternativa zagon kode v oblaku. Obstaja več načinov, kako to storite: -* Uporaba **[GitHub Codespaces](https://github.com/features/codespaces)**, ki je virtualno okolje, ustvarjeno za vas na GitHubu, dostopno prek brskalniškega vmesnika VS Code. Če imate dostop do Codespaces, lahko preprosto kliknete gumb **Code** v repozitoriju, zaženete Codespace in začnete delovati v trenutku. -* Uporaba **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**. [Binder](https://mybinder.org) je brezplačen računalniški vir v oblaku, namenjen ljudem, kot ste vi, za testiranje kode na GitHubu. Na začetni strani je gumb za odpiranje repozitorija v Binderju - to vas bo hitro pripeljalo na Binderjevo stran, ki bo zgradila osnovni vsebnik in brez težav zagnala Jupyterjev spletni vmesnik. +* Uporaba **[GitHub Codespaces](https://github.com/features/codespaces)**, ki je virtualno okolje, ustvarjeno za vas na GitHubu, dostopno prek brskalniškega vmesnika VS Code. Če imate dostop do Codespaces, lahko preprosto kliknete gumb **Code** v repozitoriju, začnete codespace in kmalu zaženete okolje. +* Uporaba **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**. [Binder](https://mybinder.org) nudi brezplačne računalniške vire v oblaku, da lahko uporabniki, kot ste vi, preizkusijo kodo na GitHubu. Na začetni strani je gumb za odpiranje repozitorija v Binderju – s tem boste hitro preusmerjeni na Binder spletno mesto, ki bo zgradilo osnovni vsebnik in brez težav zagnalo Jupyter spletni vmesnik. -> **Note**: Da bi preprečili zlorabo, ima Binder dostop do nekaterih spletnih virov blokiran. To lahko prepreči delovanje kode, ki pridobiva modele in/ali podatkovne nabore z javnega interneta. Morda boste morali poiskati nekatere rešitve. Poleg tega so računalniški viri, ki jih ponuja Binder, precej osnovni, zato bo učenje počasno, zlasti pri kasnejših bolj zapletenih lekcijah. +> **Opomba**: Da bi preprečili zlorabe, Binder blokira dostop do določenih spletnih virov. To lahko prepreči delovanje nekaterih kod, ki nalagajo modele in/ali podatkovne nabore iz interneta. Morda boste morali poiskati kakšne rešitve. Prav tako so računalniški viri, ki jih nudi Binder, precej osnovni, zato bo učenje zanje počasnejše, zlasti v kasnejših, bolj zahtevnih lekcijah. ## Zagon v oblaku z GPU -Nekatere kasnejše lekcije v tem učnem načrtu bodo močno koristile podpori za GPU, saj bo sicer učenje boleče počasno. Na voljo imate nekaj možnosti, še posebej, če imate dostop do oblaka bodisi prek [Azure za študente](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) bodisi prek vaše institucije: +Nekatere kasnejše lekcije tega učnega načrta bi zelo koristile podporo GPU. Na primer, treniranje modelov je drugače lahko zelo počasen proces. Nekaj možnosti imate še posebej, če imate dostop do oblačnih virov preko [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) ali vaše institucije: -* Ustvarite [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) in se povežite z njo prek Jupyterja. Nato lahko klonirate repozitorij neposredno na stroj in začnete z učenjem. NC-serija VM-jev ima podporo za GPU. +* Ustvarite [virtualni stroj za znanost o podatkih](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) in se povežite z njim prek Jupyter. Nato lahko klonirate repozitorij neposredno na stroj in začnete z učenjem. NC-serije VM-jev imajo podporo za GPU. -> **Note**: Nekatere naročnine, vključno z Azure za študente, ne zagotavljajo podpore za GPU takoj. Morda boste morali zaprositi za dodatna GPU jedra prek tehnične podpore. +> **Opomba**: Nekatere naročnine, vključno z Azure for Students, ne zagotavljajo podpore za GPU privzeto. Morda boste morali preko tehnične podpore zaprositi za dodatna GPU jedra. -* Ustvarite [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) in nato uporabite funkcijo Notebook tam. [Ta video](https://azure-for-academics.github.io/quickstart/azureml-papers/) prikazuje, kako klonirati repozitorij v Azure ML Notebook in ga začeti uporabljati. +* Ustvarite [delovno okolje Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) in uporabite funkcijo beležnice tam. [Ta video](https://azure-for-academics.github.io/quickstart/azureml-papers/) prikazuje, kako klonirati repozitorij v Azure ML beležnico in začeti z uporabo. -Lahko uporabite tudi Google Colab, ki ponuja nekaj brezplačne podpore za GPU, in naložite Jupyter Notebooke tja, da jih izvajate enega za drugim. +Lahko uporabite tudi Google Colab, ki nudi nekaj brezplačne podpore za GPU, in naložite Jupyter beležnice, da jih izvajate eno za drugo. -**Omejitev odgovornosti**: -Ta dokument je bil preveden z uporabo storitve AI za prevajanje [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas prosimo, da upoštevate, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem maternem jeziku je treba obravnavati kot avtoritativni vir. Za ključne informacije priporočamo profesionalni človeški prevod. Ne prevzemamo odgovornosti za morebitna nesporazumevanja ali napačne razlage, ki bi nastale zaradi uporabe tega prevoda. \ No newline at end of file +--- + + +**Omejitev odgovornosti**: +To besedilo je bilo prevedeno z uporabo AI prevajalske storitve [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, upoštevajte, da lahko avtomatski prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem izvorno jeziku velja za zanesljiv vir. Za pomembne informacije priporočamo strokovni človeški prevod. Ne odgovarjamo za morebitna nesporazume ali napačne razlage, ki nastanejo zaradi uporabe tega prevoda. + \ No newline at end of file diff --git a/translations/sl/lessons/2-Symbolic/Animals.ipynb b/translations/sl/lessons/2-Symbolic/Animals.ipynb index c3472b6d..67e02ad8 100644 --- a/translations/sl/lessons/2-Symbolic/Animals.ipynb +++ b/translations/sl/lessons/2-Symbolic/Animals.ipynb @@ -8,23 +8,23 @@ "source": [ "# Implementacija strokovnega sistema za živali\n", "\n", - "Primer iz [Učnega načrta za začetnike v umetni inteligenci](http://github.com/microsoft/ai-for-beginners).\n", + "Primer iz [Učne vsebine AI za začetnike](http://github.com/microsoft/ai-for-beginners).\n", "\n", - "V tem primeru bomo implementirali preprost sistem, ki temelji na znanju, za določanje živali na podlagi nekaterih fizičnih značilnosti. Sistem lahko predstavimo z naslednjim AND-OR drevesom (to je del celotnega drevesa, zlahka lahko dodamo še več pravil):\n", + "V tem vzorcu bomo implementirali preprost sistem, ki temelji na znanju, da določimo žival glede na nekatere fizične značilnosti. Sistem je mogoče predstaviti z naslednjim AND-OR drevesom (to je del celotnega drevesa, lahko pa enostavno dodamo še več pravil):\n", "\n", - "![](../../../../translated_images/sl/AND-OR-Tree.5592d2c70187f283.webp)\n" + "![](../../../../../../translated_images/sl/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Naš lasten lupinski sistem strokovnega znanja z vzvratnim sklepanjem\n", + "## Naš lasten lupinski sistem strokovnih pravil z vzvratno inferenco\n", "\n", - "Poskusimo definirati preprost jezik za predstavitev znanja, ki temelji na produkcijskih pravilih. Za definiranje pravil bomo uporabili Python razrede kot ključne besede. Obstajale bodo tri osnovne vrste razredov:\n", + "Poskusimo definirati enostaven jezik za predstavitev znanja, ki temelji na produkcijskih pravilih. Kot ključne besede za definiranje pravil bomo uporabili Python razrede. Obstajali bi trije osnovni tipi razredov:\n", "* `Ask` predstavlja vprašanje, ki ga je treba zastaviti uporabniku. Vsebuje nabor možnih odgovorov.\n", - "* `If` predstavlja pravilo in je zgolj sintaktični sladkor za shranjevanje vsebine pravila.\n", - "* `AND`/`OR` sta razreda za predstavitev AND/OR vej drevesa. Preprosto shranjujeta seznam argumentov znotraj. Za poenostavitev kode je vsa funkcionalnost definirana v nadrejenem razredu `Content`.\n" + "* `If` predstavlja pravilo, in je le sintaktični dodatek za shranjevanje vsebine pravila\n", + "* `AND`/`OR` so razredi za predstavitev AND/OR vej drevesa. Samo shranjujejo seznam argumentov znotraj. Za poenostavitev kode je vsa funkcionalnost definirana v starševskem razredu `Content`\n" ] }, { @@ -66,7 +66,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "V našem sistemu bi delovni spomin vseboval seznam **dejstev** kot **pari atribut-vrednost**. Znanstvena baza je lahko opredeljena kot en velik slovar, ki preslika dejanja (nova dejstva, ki bi jih bilo treba vnesti v delovni spomin) v pogoje, izražene kot AND-OR izrazi. Prav tako je mogoče nekatera dejstva `vprašati`.\n" + "V našem sistemu bi delovni spomin vseboval seznam **dejstev** kot **parov atribut-vrednost**. Baza znanja je lahko definirana kot en velik slovar, ki preslika dejanja (nova dejstva, ki jih je treba vnesti v delovni spomin) na pogoje, izražene kot AND-OR izrazi. Prav tako je mogoče neka dejstva `Vprašati`.\n" ] }, { @@ -99,15 +99,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Za izvedbo povratnega sklepanja bomo definirali razred `Knowledgebase`. Vsebuje:\n", - "\n", - "* Delovni `spomin` - slovar, ki preslika atribute v vrednosti\n", - "* `Pravila` baze znanja v zgoraj določenem formatu\n", + "Za izvajanje vzvratnega sklepanja bomo definirali razred `Knowledgebase`. Ta bo vseboval:\n", + "* Delovni `memory` - slovar, ki preslika atribute v vrednosti\n", + "* Pravila Knowledgebase v zgoraj opisanem formatu\n", "\n", "Dve glavni metodi sta:\n", - "\n", - "* `get` za pridobitev vrednosti atributa, pri čemer po potrebi izvede sklepanje. Na primer, `get('color')` pridobi vrednost atributa barve (po potrebi vpraša in shrani vrednost za kasnejšo uporabo v delovnem spominu). Če vprašamo `get('color:blue')`, bo vprašal za barvo in nato vrnil vrednost `y`/`n`, odvisno od barve.\n", - "* `eval` izvaja dejansko sklepanje, tj. prehaja skozi AND/OR drevo, ocenjuje podcilje itd.\n" + "* `get` za pridobitev vrednosti atributa, pri čemer po potrebi opravi sklepanje. Na primer, `get('color')` bi pridobil vrednost atributa color (če je potrebno, bo vprašal in shranil vrednost za kasnejšo uporabo v delovnem spominu). Če vprašamo `get('color:blue')`, bo vprašal za color, nato pa vrnil vrednost `y`/`n` glede na barvo.\n", + "* `eval` opravi dejansko sklepanje, tj. prehodi po AND/OR drevesu, oceni podcilje itd.\n" ] }, { @@ -174,7 +172,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Zdaj določimo našo bazo znanja o živalih in izvedimo posvetovanje. Upoštevajte, da vas bo ta klic spraševal vprašanja. Odgovorite lahko z vnosom `y`/`n` za vprašanja z da-ne odgovori ali z navedbo številke (0..N) za vprašanja z daljšimi odgovori z več možnostmi.\n" + "Zdaj definirajmo našo bazo znanja o živalih in izvedimo posvetovanje. Upoštevajte, da vas bo ta klic spraševal. Odgovarjate lahko tako, da vnesete `y`/`n` za vprašanja z da/ne, ali s številko (0..N) za vprašanja z daljšimi večkratnimi možnostmi odgovora.\n" ] }, { @@ -231,11 +229,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Uporaba PyKnow za naprej sklepanje\n", + "## Uporaba Experta za naprej usmerjeno sklepanje\n", "\n", - "V naslednjem primeru bomo poskusili implementirati naprej sklepanje z uporabo ene izmed knjižnic za predstavitev znanja, [PyKnow](https://github.com/buguroo/pyknow/). **PyKnow** je knjižnica za ustvarjanje sistemov za naprej sklepanje v Pythonu, zasnovana tako, da je podobna klasičnemu staremu sistemu [CLIPS](http://www.clipsrules.net/index.html).\n", + "V naslednjem primeru bomo poskusili implementirati naprej usmerjeno sklepanje z uporabo ene izmed knjižnic za reprezentacijo znanja, [Experta](https://github.com/nilp0inter/experta). **Experta** je knjižnica za ustvarjanje sistemov za naprej usmerjeno sklepanje v Pythonu, ki je zasnovana tako, da je podobna klasičnemu staremu sistemu [CLIPS](http://www.clipsrules.net/index.html).\n", "\n", - "Naprej sklepanje bi lahko implementirali tudi sami brez večjih težav, vendar so naivne implementacije običajno manj učinkovite. Za bolj učinkovito ujemanje pravil se uporablja poseben algoritem [Rete](https://en.wikipedia.org/wiki/Rete_algorithm).\n" + "Naprej usmerjeno verižitev bi lahko tudi sami implementirali brez večjih težav, vendar naivne implementacije običajno niso zelo učinkovite. Za bolj učinkovito usklajevanje pravil se uporablja poseben algoritem [Rete](https://en.wikipedia.org/wiki/Rete_algorithm).\n" ] }, { @@ -249,32 +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" ] }, { @@ -285,15 +282,15 @@ }, "outputs": [], "source": [ - "from pyknow import *\n", - "#import pyknow" + "from experta import *\n", + "#import experta" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Naš sistem bomo definirali kot razred, ki podrazreduje `KnowledgeEngine`. Vsako pravilo je definirano z ločeno funkcijo z anotacijo `@Rule`, ki določa, kdaj naj se pravilo sproži. Znotraj pravila lahko dodamo nova dejstva z uporabo funkcije `declare`, dodajanje teh dejstev pa bo povzročilo, da bo sklepni stroj z naprej usmerjenim sklepanjem poklical še nekaj drugih pravil.\n" + "Sistem bomo definirali kot razred, ki podeduje `KnowledgeEngine`. Vsako pravilo je definirano z ločeno funkcijo z anotacijo `@Rule`, ki določa, kdaj naj se pravilo sproži. Znotraj pravila lahko z uporabo funkcije `declare` dodajamo nove dejstice, kar bo povzročilo, da bodo zaradi naprej delujočega inferenčnega mehanizma poklicana še nekatera druga pravila.\n" ] }, { @@ -380,7 +377,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Ko enkrat definiramo bazo znanja, napolnimo delovni spomin z nekaterimi začetnimi dejstvi in nato pokličemo metodo `run()`, da izvedemo sklepanje. Kot rezultat lahko vidite, da so v delovni spomin dodana nova izpeljana dejstva, vključno s končnim dejstvom o živali (če smo pravilno nastavili vsa začetna dejstva).\n" + "Ko definiramo bazo znanja, napolnimo naš delovni pomnilnik z nekaj začetnimi dejstvi in nato pokličemo metodo `run()`, da izvedemo sklepanje. Vidite lahko, da se kot rezultat temu dodajo nova sklepanja dejstev v delovni pomnilnik, vključno s končnim dejstvom o živali (če smo pravilno nastavili vsa začetna dejstva).\n" ] }, { @@ -442,7 +439,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n---\n\n**Omejitev odgovornosti**: \nTa dokument je bil preveden z uporabo storitve za strojno prevajanje [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas opozarjamo, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem izvirnem jeziku je treba obravnavati kot avtoritativni vir. Za ključne informacije priporočamo strokovni prevod s strani človeka. Ne prevzemamo odgovornosti za morebitna nesporazumevanja ali napačne razlage, ki bi izhajale iz uporabe tega prevoda.\n" + "---\n\n\n**Opozorilo**:\nTa dokument je bil preveden z uporabo storitve za prevajanje z umetno inteligenco [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas opozarjamo, da avtomatizirani prevodi lahko vsebujejo napake ali netočnosti. Izvirni dokument v njegovem izvirnem jeziku velja za avtoritativni vir. Za pomembne informacije priporočamo strokovni človeški prevod. Ne odgovarjamo za kakršnekoli nesporazume ali napačne interpretacije, ki izhajajo iz uporabe tega prevoda.\n\n" ] } ], @@ -469,8 +466,8 @@ "version": "3.11.2" }, "coopTranslator": { - "original_hash": "ab2bd97b0453415b89a469284609a8ce", - "translation_date": "2025-08-30T07:42:18+00:00", + "original_hash": "8ef43db4b9182239fd150a76bd494fdb", + "translation_date": "2026-01-16T05:59:56+00:00", "source_file": "lessons/2-Symbolic/Animals.ipynb", "language_code": "sl" } diff --git a/translations/sl/lessons/2-Symbolic/README.md b/translations/sl/lessons/2-Symbolic/README.md index 12615b8e..4358212b 100644 --- a/translations/sl/lessons/2-Symbolic/README.md +++ b/translations/sl/lessons/2-Symbolic/README.md @@ -1,116 +1,116 @@ # Predstavitev znanja in ekspertni sistemi -![Povzetek vsebine simbolne umetne inteligence](../../../../translated_images/sl/ai-symbolic.715a30cb610411a6.webp) +![Povzetek vsebine simbolične umetne inteligence](../../../../../../translated_images/sl/ai-symbolic.715a30cb610411a6.webp) -> Sketchnote avtorja [Tomomi Imura](https://twitter.com/girlie_mac) +> Sketchnote avtorice [Tomomi Imura](https://twitter.com/girlie_mac) -Iskanje umetne inteligence temelji na iskanju znanja, da bi razumeli svet na podoben način, kot to počnejo ljudje. Toda kako to doseči? +Iskanje umetne inteligence temelji na iskanju znanja, da bi razumeli svet podobno kot ljudje. Kako pa se lahko tega lotimo? -## [Predhodni kviz pred predavanjem](https://ff-quizzes.netlify.app/en/ai/quiz/3) +## [Predpredavanje kviz](https://ff-quizzes.netlify.app/en/ai/quiz/3) -V zgodnjih dneh umetne inteligence je bil priljubljen pristop od zgoraj navzdol za ustvarjanje inteligentnih sistemov (o katerem smo govorili v prejšnji lekciji). Ideja je bila, da se znanje pridobi od ljudi v obliki, ki jo lahko berejo stroji, in nato uporabi za samodejno reševanje problemov. Ta pristop je temeljil na dveh velikih idejah: +V zgodnjih dneh umetne inteligence je bil priljubljen pristop od zgoraj navzdol za ustvarjanje inteligentnih sistemov (obravnavan v prejšnji lekciji). Ideja je bila pridobiti znanje od ljudi v neko obliko, ki jo lahko računalnik prebere, nato pa ga uporabiti za samodejno reševanje problemov. Ta pristop temelji na dveh velikih idejah: * Predstavitev znanja -* Razmišljanje +* Logično sklepanje ## Predstavitev znanja -Eden pomembnih konceptov v simbolni umetni inteligenci je **znanje**. Pomembno je razlikovati znanje od *informacij* ali *podatkov*. Na primer, lahko rečemo, da knjige vsebujejo znanje, ker jih lahko preučujemo in postanemo strokovnjaki. Vendar pa tisto, kar knjige dejansko vsebujejo, imenujemo *podatki*, in z branjem knjig ter vključevanjem teh podatkov v naš model sveta te podatke pretvorimo v znanje. +Eden pomembnih konceptov v simbolični umetni inteligenci je **znanje**. Pomembno je razlikovati med znanjem in *informacijami* ali *podatki*. Na primer, lahko rečemo, da knjige vsebujejo znanje, ker jih lahko preučimo in postanemo strokovnjaki. Vendar pa knjige dejansko vsebujejo *podatke*, in s branjem knjig ter vključevanjem teh podatkov v naš svetovni model te podatke pretvarjamo v znanje. -> ✅ **Znanje** je nekaj, kar je v naši glavi in predstavlja naše razumevanje sveta. Pridobljeno je z aktivnim procesom **učenja**, ki vključuje koščke informacij, ki jih prejmemo, v naš aktivni model sveta. +> ✅ **Znanje** je nekaj, kar imamo v glavi in predstavlja naše razumevanje sveta. Pridobimo ga z aktivnim **učenjem**, ki integrira informacije, ki jih prejmemo, v naš aktiven svetovni model. -Najpogosteje znanja ne definiramo strogo, ampak ga uskladimo z drugimi povezanimi koncepti z uporabo [DIKW piramide](https://en.wikipedia.org/wiki/DIKW_pyramid). Vsebuje naslednje koncepte: +Večinoma znanja ne definiramo strogo, temveč ga povežemo z drugimi sorodnimi pojmi s pomočjo [piramide DIKW](https://en.wikipedia.org/wiki/DIKW_pyramid). Ta vsebuje naslednje pojme: -* **Podatki** so nekaj, kar je predstavljeno na fizičnih medijih, kot so pisano besedilo ali govorjene besede. Podatki obstajajo neodvisno od ljudi in se lahko prenašajo med njimi. -* **Informacije** so način, kako interpretiramo podatke v naši glavi. Na primer, ko slišimo besedo *računalnik*, imamo neko razumevanje, kaj to je. -* **Znanje** je informacija, ki je vključena v naš model sveta. Na primer, ko se naučimo, kaj je računalnik, začnemo imeti ideje o tem, kako deluje, koliko stane in za kaj se lahko uporablja. Ta mreža medsebojno povezanih konceptov tvori naše znanje. -* **Modrost** je še ena raven našega razumevanja sveta in predstavlja *meta-znanje*, npr. neko idejo o tem, kako in kdaj naj se znanje uporabi. +* **Podatki** so nekaj, kar je predstavljeno v fizičnih medijih, kot so pisni tekst ali izgovorjene besede. Podatki obstajajo neodvisno od ljudi in jih je mogoče prenašati med ljudmi. +* **Informacije** so kako interpretiramo podatke v naši glavi. Na primer, ko slišimo besedo *računalnik*, imamo nek vpogled, kaj je to. +* **Znanje** so informacije, integrirane v naš svetovni model. Na primer, ko se naučimo, kaj je računalnik, začnemo imeti predstave, kako deluje, koliko stane in za kaj se lahko uporablja. Ta mreža medsebojno povezanih pojmov tvori naše znanje. +* **Modrost** je še ena raven našega razumevanja sveta in predstavlja *meta-znanje*, npr. neko predstavo o tem, kako in kdaj naj se znanje uporabi. - + -*Slika [iz Wikipedije](https://commons.wikimedia.org/w/index.php?curid=37705247), avtor Longlivetheux - lastno delo, CC BY-SA 4.0* +*Slika [iz Wikipedije](https://commons.wikimedia.org/w/index.php?curid=37705247), avtor Longlivetheux - Lastno delo, CC BY-SA 4.0* -Tako je problem **predstavitve znanja** najti učinkovit način za predstavitev znanja znotraj računalnika v obliki podatkov, da bi bilo samodejno uporabno. To lahko vidimo kot spekter: +Torej je problem **predstavitve znanja** najti učinkovit način predstavljanja znanja znotraj računalnika v obliki podatkov, da bo samodejno uporabno. To lahko vidimo kot spekter: -![Spekter predstavitve znanja](../../../../translated_images/sl/knowledge-spectrum.b60df631852c0217.webp) +![Spekter predstavitve znanja](../../../../../../translated_images/sl/knowledge-spectrum.b60df631852c0217.webp) > Slika avtorja [Dmitry Soshnikov](http://soshnikov.com) -* Na levi strani so zelo preproste vrste predstavitev znanja, ki jih računalniki lahko učinkovito uporabljajo. Najpreprostejša je algoritmična, kjer je znanje predstavljeno z računalniškim programom. To pa ni najboljši način za predstavitev znanja, ker ni prilagodljiv. Znanje v naši glavi je pogosto nealgoritmično. -* Na desni strani so predstavitve, kot je naravno besedilo. To je najmočnejše, vendar ga ni mogoče uporabiti za samodejno razmišljanje. +* Na levi strani so zelo preprosti tipi predstavitev znanja, ki jih lahko računalniki učinkovito uporabljajo. Najenostavnejša je algoritemska predstavitev, ko je znanje predstavljeno z računalniškim programom. Vendar to ni najboljši način predstavljanja znanja, ker ni prilagodljiv. Znanje v naši glavi je pogosto nealgoritemsko. +* Na desni strani so predstavitve, kot je naravni tekst. To je najmočnejše, vendar ne uporabno za avtomatsko sklepanje. -> ✅ Premislite za trenutek, kako predstavljate znanje v svoji glavi in ga pretvarjate v zapiske. Ali obstaja določen format, ki vam pomaga pri pomnjenju? +> ✅ Razmislite za trenutek, kako znanje predstavljate v svoji glavi in ga pretvarjate v zapiske. Ali obstaja kakšen poseben format, ki vam dobro služi pri pomnjenju? -## Razvrščanje računalniških predstavitev znanja +## Klasifikacija računalniških predstavitev znanja -Različne metode predstavitve znanja v računalnikih lahko razvrstimo v naslednje kategorije: +Različne metode predstavitve znanja lahko uvrstimo v naslednje kategorije: -* **Omrežne predstavitve** temeljijo na dejstvu, da imamo v glavi mrežo medsebojno povezanih konceptov. Poskusimo lahko reproducirati iste mreže kot graf znotraj računalnika - tako imenovano **semantično omrežje**. +* **Mrežne predstavitve** temeljijo na dejstvu, da imamo v glavi mrežo medsebojno povezanih pojmov. Poskušamo ustvariti enake mreže kot graf znotraj računalnika – tako imenovano **semantično mrežo**. -1. **Trojice objekt-lastnost-vrednost** ali **pari lastnost-vrednost**. Ker je graf mogoče predstaviti znotraj računalnika kot seznam vozlišč in povezav, lahko semantično omrežje predstavimo s seznamom trojic, ki vsebujejo objekte, lastnosti in vrednosti. Na primer, zgradimo naslednje trojice o programskih jezikih: +1. **Objekt-Atribut-Vrednost trojčki** ali **par atribut-vrednost**. Ker lahko graf predstavimo v računalniku kot seznam vozlišč in povezav, lahko semantično mrežo predstavimo s seznamom trojčkov, ki vsebujejo objekte, atribute in vrednosti. Na primer, ustvarimo naslednje trojčke o programskih jezikih: -Objekt | Lastnost | Vrednost --------|----------|--------- -Python | je | Ne-tipiziran jezik +Objekt | Atribut | Vrednost +-------|---------|--------- +Python | je | Nepojasnjen-Jezik Python | izumil | Guido van Rossum -Python | blok-sintaksa | zamik -Ne-tipiziran jezik | nima | definicij tipov +Python | sintaksa-bloka | zamik +Nepojasnjen-Jezik | nima | definicij tipov -> ✅ Premislite, kako lahko trojice uporabite za predstavitev drugih vrst znanja. +> ✅ Premislite, kako bi lahko trojčke uporabili za predstavitev drugih vrst znanja. -2. **Hierarhične predstavitve** poudarjajo dejstvo, da pogosto ustvarimo hierarhijo objektov v naši glavi. Na primer, vemo, da je kanarček ptica, in vse ptice imajo krila. Prav tako imamo neko idejo o tem, kakšne barve je običajno kanarček in kakšna je njegova hitrost letenja. +2. **Hierarhične predstavitve** poudarjajo, da pogosto ustvarjamo hierarhijo objektov v naši glavi. Na primer, vemo, da je kanarček ptica, in vse ptice imajo krila. Prav tako imamo predstavo, katere barve je kanarček in kakšna je njegova hitrost letenja. - - **Predstavitev okvirjev** temelji na predstavitvi vsakega objekta ali razreda objektov kot **okvirja**, ki vsebuje **reže**. Reže imajo možne privzete vrednosti, omejitve vrednosti ali shranjene postopke, ki jih je mogoče poklicati za pridobitev vrednosti reže. Vsi okvirji tvorijo hierarhijo, podobno hierarhiji objektov v objektno usmerjenih programskih jezikih. - - **Scenariji** so posebna vrsta okvirjev, ki predstavljajo kompleksne situacije, ki se lahko odvijajo skozi čas. + - **Predstavitev okvirjev** temelji na predstavljanju vsakega objekta ali razreda objektov kot **okvirja**, ki vsebuje **reže**. Reže imajo privzete vrednosti, omejitve vrednosti ali shranjene procese, ki jih lahko pokličemo za pridobitev vrednosti reže. Vsi okvirji tvorijo hierarhijo, podobno hierarhiji objektov v objektno usmerjenih programskih jezikih. + - **Scenariji** so posebna vrsta okvirjev, ki predstavljajo kompleksne situacije, ki se lahko razpletejo skozi čas. **Python** -Reža | Vrednost | Privzeta vrednost | Interval | ------|----------|-------------------|----------| +Reža | Vrednost | Privzeta vrednost | Interval +------|----------|-------------------|--------- Ime | Python | | | -Je | Ne-tipiziran jezik | | | -Primer spremenljivke | | CamelCase | | +Je-Kot | Nepojasnjen-Jezik | | | +Velikost Spremenljivke | | CamelCase | | Dolžina programa | | | 5-5000 vrstic | -Blok-sintaksa | Zamik | | | +Sintaksa bloka | Zamik | | | -3. **Postopkovne predstavitve** temeljijo na predstavitvi znanja s seznamom dejanj, ki jih je mogoče izvesti, ko se pojavi določen pogoj. - - Pravila produkcije so if-then izjave, ki nam omogočajo sklepanje. Na primer, zdravnik lahko ima pravilo, ki pravi, da **ČE** ima pacient visoko vročino **ALI** visoko raven C-reaktivnega proteina v krvnem testu **POTEM** ima vnetje. Ko naletimo na enega od pogojev, lahko sklepamo o vnetju in nato uporabimo to v nadaljnjem razmišljanju. - - Algoritmi se lahko štejejo za drugo obliko postopkovne predstavitve, čeprav se skoraj nikoli ne uporabljajo neposredno v sistemih, ki temeljijo na znanju. +3. **Proceduralne predstavitve** temeljijo na predstavljanju znanja kot seznama dejanj, ki se izvajajo v določenih pogojih. + - Produkcijska pravila so izjave če-potem, ki nam omogočajo sklepanje. Na primer, zdravnik ima pravilo, če ima pacient visoko vročino **ALI** visoko raven C-reaktivnega proteina v krvnem testu, **POTEM** ima vnetje. Ko je pogoj izpolnjen, naredimo sklep o vnetju in ga nato uporabimo v nadaljnjem sklepanju. + - Algoritmi so lahko obravnavani kot druga oblika proceduralnih predstavitev, čeprav jih skoraj nikoli ne uporabljamo neposredno v sistemih, ki temeljijo na znanju. -4. **Logika** je bila prvotno predlagana s strani Aristotela kot način za predstavitev univerzalnega človeškega znanja. - - Predikatna logika kot matematična teorija je prebogata, da bi bila računalniško izvedljiva, zato se običajno uporablja njen podmnožek, kot so Hornove klavzule, uporabljene v Prologu. - - Opisna logika je družina logičnih sistemov, ki se uporabljajo za predstavitev in razmišljanje o hierarhijah objektov ter razdeljenih predstavitvah znanja, kot je *semantični splet*. +4. **Logika** je bila prvotno predlagana od Aristotela kot način za predstavljanje univerzalnega človeškega znanja. + - Predikatna logika kot matematična teorija je prebogata, da bi bila računsko obvladljiva, zato se navadno uporablja neki podnabor, kot so Hornove klavzule uporabljene v Prologu. + - Deskriptivna logika je družina logičnih sistemov za predstavljanje in sklepanje o hierarhijah objektov in porazdeljenih predstavitvah znanja, kot je *semantični splet*. ## Ekspertni sistemi -Eden zgodnjih uspehov simbolne umetne inteligence so bili tako imenovani **ekspertni sistemi** - računalniški sistemi, zasnovani za delovanje kot strokovnjak na omejenem področju problemov. Temeljili so na **bazi znanja**, pridobljeni od enega ali več človeških strokovnjakov, in vsebovali **inferenčni mehanizem**, ki je izvajal razmišljanje na podlagi te baze. +Eden od zgodnjih uspehov simbolične umetne inteligence so bili tako imenovani **ekspertni sistemi** – računalniški sistemi, oblikovani za delovanje kot strokovnjak na omejenem področju. Temeljili so na **bazi znanja**, pridobljeni od enega ali več človeških strokovnjakov, in so vsebovali **inferenzni mehanizem**, ki je izvajal sklepanje na osnovi tega. -![Človeška arhitektura](../../../../translated_images/sl/arch-human.5d4d35f1bba3ab1c.webp) | ![Arhitektura sistema, ki temelji na znanju](../../../../translated_images/sl/arch-kbs.3ec5c150b09fa8da.webp) +![Človeška arhitektura](../../../../../../translated_images/sl/arch-human.5d4d35f1bba3ab1c.webp) | ![Sistem na podlagi znanja](../../../../../../translated_images/sl/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ -Poenostavljena struktura človeškega nevronskega sistema | Arhitektura sistema, ki temelji na znanju +Poenostavljena struktura človeškega živčnega sistema | Arhitektura sistema na podlagi znanja -Ekspertni sistemi so zgrajeni podobno kot človeški sistem razmišljanja, ki vsebuje **kratkoročni spomin** in **dolgoročni spomin**. Podobno v sistemih, ki temeljijo na znanju, razlikujemo naslednje komponente: +Ekspertni sistemi so zgrajeni podobno kot človeški sistem sklepanja, ki vsebuje **kratkoročni spomin** in **dolgoročni spomin**. Prav tako v sistemih na podlagi znanja ločimo naslednje komponente: -* **Spomin problema**: vsebuje znanje o problemu, ki se trenutno rešuje, npr. temperaturo ali krvni tlak pacienta, ali ima vnetje ali ne itd. To znanje se imenuje tudi **statično znanje**, ker vsebuje posnetek tega, kar trenutno vemo o problemu - tako imenovano *stanje problema*. -* **Baza znanja**: predstavlja dolgoročno znanje o področju problema. Ročno se pridobi od človeških strokovnjakov in se ne spreminja od posvetovanja do posvetovanja. Ker nam omogoča navigacijo od enega stanja problema do drugega, se imenuje tudi **dinamično znanje**. -* **Inferenčni mehanizem**: usklajuje celoten proces iskanja v prostoru stanja problema, postavlja vprašanja uporabniku, kadar je to potrebno. Prav tako je odgovoren za iskanje pravil, ki jih je treba uporabiti v vsakem stanju. +* **Spomin problema**: vsebuje znanje o problemu, ki se trenutno rešuje, npr. temperatura ali krvni tlak pacienta, ali ima vnetje ali ne itd. To znanje imenujemo tudi **statično znanje**, ker vsebuje trenutni posnetek tega, kar vemo o problemu – tako imenovano *stanje problema*. +* **Baza znanja**: predstavlja dolgoročno znanje o problemu. Je ročno pridobljena od strokovnjakov in se ne spreminja med posameznimi posveti. Ker omogoča premikanje med stanji problema, jo imenujemo tudi **dinamično znanje**. +* **Inferenzni mehanizem**: vodi celoten proces iskanja v prostoru stanj problema in postavlja vprašanja uporabniku po potrebi. Prav tako je odgovoren za izbiro pravil, ki se uporabijo v posameznih stanjih. -Kot primer si poglejmo naslednji ekspertni sistem za določanje živali na podlagi njihovih fizičnih značilnosti: +Kot primer si poglejmo ekspertni sistem za določanje živali na podlagi fizičnih lastnosti: -![AND-OR drevo](../../../../translated_images/sl/AND-OR-Tree.5592d2c70187f283.webp) +![AND-ALI Drevo](../../../../../../translated_images/sl/AND-OR-Tree.5592d2c70187f283.webp) > Slika avtorja [Dmitry Soshnikov](http://soshnikov.com) -Ta diagram se imenuje **AND-OR drevo** in je grafična predstavitev nabora produkcijskih pravil. Risanje drevesa je koristno na začetku pridobivanja znanja od strokovnjaka. Za predstavitev znanja znotraj računalnika je bolj priročno uporabiti pravila: +Ta diagram imenujemo **AND-OR drevo**, in je grafična predstavitev nabora produkcijskih pravil. Risanje drevesa je koristno na začetku procesa pridobivanja znanja od strokovnjaka. Za predstavitev znanja znotraj računalnika pa je bolj priročno uporabiti pravila: ``` IF the animal eats meat @@ -121,78 +121,78 @@ OR (animal has sharp teeth THEN the animal is a carnivore ``` -Opazite lahko, da vsak pogoj na levi strani pravila in dejanje v bistvu predstavljata trojice objekt-lastnost-vrednost (OAV). **Delovni spomin** vsebuje nabor OAV trojic, ki ustrezajo problemu, ki se trenutno rešuje. **Pravilni mehanizem** išče pravila, za katera je pogoj izpolnjen, in jih uporabi, pri čemer doda novo trojico v delovni spomin. +Opazimo lahko, da so vsak pogoj na levi strani pravila in ukrep v bistvu trojčki objekt-atribut-vrednost (OAV). **Delovni spomin** vsebuje množico OAV trojčkov, ki ustrezajo problemu, ki se trenutno rešuje. **Stroj pravil** išče pravila, katerih pogoj je izpolnjen, in jih uporabi, dodajajoč nov trojček v delovni spomin. -> ✅ Narišite svoje AND-OR drevo na temo, ki vam je všeč! +> ✅ Napišite svoje AND-OR drevo na katerokoli temo, ki vas zanima! -### Naprej proti nazaj sklepanje +### Napredno (forward) proti nazadnjemu (backward) sklepanju -Proces, opisan zgoraj, se imenuje **naprej sklepanje**. Začne se z nekaterimi začetnimi podatki o problemu, ki so na voljo v delovnem spominu, nato pa izvede naslednjo zanko razmišljanja: +Opisani proces imenujemo **napredno sklepanja**. Začne se z začetnimi podatki o problemu v delovnem spominu in izvaja naslednjo zanko sklepanja: -1. Če je ciljna lastnost prisotna v delovnem spominu - ustavi in poda rezultat -2. Poišči vsa pravila, katerih pogoj je trenutno izpolnjen - pridobi **konfliktni nabor** pravil. -3. Izvedi **reševanje konflikta** - izberi eno pravilo, ki bo izvedeno v tem koraku. Obstajajo različne strategije reševanja konfliktov: - - Izberi prvo uporabno pravilo v bazi znanja +1. Če je ciljna lastnost prisotna v delovnem spominu – ustavi se in da rezultat +2. Poišči vsa pravila, katerih pogoj je trenutno izpolnjen – dobljen je **množica konfliktov** pravil. +3. Izvedi **reševanje konflikta** – izberi eno pravilo, ki bo izvršeno v tej iteraciji. Obstaja več strategij za reševanje konflikta: + - Izberi prvo pravilo, ki se lahko uporabi iz baze znanja - Izberi naključno pravilo - - Izberi *bolj specifično* pravilo, tj. tisto, ki izpolnjuje največ pogojev na "levi strani" (LHS) + - Izberi *bolj specifično* pravilo, torej tisto, ki izpolnjuje največ pogojev na levi strani (LHS) 4. Uporabi izbrano pravilo in vstavi nov del znanja v stanje problema 5. Ponovi od koraka 1. -V nekaterih primerih pa želimo začeti z praznim znanjem o problemu in postavljati vprašanja, ki nam bodo pomagala priti do zaključka. Na primer, pri medicinski diagnozi običajno ne izvajamo vseh medicinskih analiz vnaprej, preden začnemo diagnosticirati pacienta. Raje želimo opraviti analize, ko je treba sprejeti odločitev. +Vendar pa v nekaterih primerih želimo začeti z mankom znanja o problemu in zastavljati vprašanja, ki nam pomagajo priti do zaključka. Na primer, pri medicinski diagnozi običajno ne opravimo vseh analiz vnaprej, ampak jih opravimo postopoma, ko je treba sprejeti odločitev. -Ta proces lahko modeliramo z uporabo **nazaj sklepanje**. Vodeno je z **ciljem** - vrednostjo lastnosti, ki jo iščemo: +Ta proces lahko modeliramo s **nazadnjim sklepanjem**. To se vodi z **ciljem** – atributno vrednostjo, ki jo želimo najti: -1. Izberi vsa pravila, ki nam lahko dajo vrednost cilja (tj. s ciljem na RHS ("desni strani")) - konfliktni nabor -1. Če ni pravil za to lastnost ali obstaja pravilo, ki pravi, da moramo vrednost vprašati uporabnika - vprašaj za to, sicer: -1. Uporabi strategijo reševanja konflikta za izbiro enega pravila, ki ga bomo uporabili kot *hipotezo* - poskusili jo bomo dokazati -1. Rekurzivno ponovi proces za vse lastnosti na LHS pravila, poskušajoč jih dokazati kot cilje -1. Če proces kadarkoli ne uspe - uporabi drugo pravilo v koraku 3. +1. Izberi vsa pravila, ki nam lahko dajo vrednost cilja (torej tista, kjer je cilj na desni strani pravila, RHS) – množica konfliktov +1. Če ni pravila za ta atribut ali obstaja pravilo, ki določa, da na vrednost vprašamo uporabnika – vprašaj uporabnika, sicer: +1. Uporabi strategijo reševanja konfliktov in izberi eno pravilo, ki ga bomo uporabili kot *hipotezo* – skušali jo bomo dokazati +1. Rekurzivno ponovi postopek za vse atribute na levi strani pravila, skušajoč jih dokazati kot cilje +1. Če proces kje zatakne – uporabi drugo pravilo v koraku 3. -> ✅ V katerih situacijah je naprej sklepanje bolj primerno? Kaj pa nazaj sklepanje? +> ✅ V katerih primerih je napredno sklepanje primernejše? Kakšno vlogo ima nazadnje sklepanje? ### Implementacija ekspertnih sistemov Ekspertni sistemi se lahko implementirajo z različnimi orodji: -* Neposredno programiranje v nekem visokonivojskem programskem jeziku. To ni najboljša ideja, ker je glavna prednost sistema, ki temelji na znanju, da je znanje ločeno od sklepanja, in potencialno bi moral strokovnjak za področje problema biti sposoben pisati pravila brez razumevanja podrobnosti procesa sklepanja. -* Uporaba **lupine ekspertnih sistemov**, tj. sistema, posebej zasnovanega za polnjenje z znanjem z uporabo nekega jezika za predstavitev znanja. +* Programiranje neposredno v visokoraven programski jezik. To ni najboljša ideja, ker glavna prednost sistema na podlagi znanja je ločitev znanja od sklepanja, in strokovnjak bi moral lahko pisati pravila brez razumevanja podrobnosti sklepanja. +* Uporaba **shell-a za ekspertne sisteme**, tj. sistema, posebej zasnovanega za vnos znanja z uporabo nekega jezika za predstavitev znanja. -## ✍️ Naloga: sklepanje o živalih +## ✍️ Vaja: Sklepanje o živalih -Oglejte si [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) za primer implementacije ekspertnega sistema za naprej in nazaj sklepanje. +Oglejte si [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) za primer implementacije ekspertnega sistema s naprednim in nazadnjim sklepanjem. -> **Opomba**: Ta primer je precej preprost in daje le idejo, kako izgleda ekspertni sistem. Ko začnete ustvarjati tak sistem, boste opazili neko *inteligentno* vedenje šele, ko dosežete določeno število pravil, okoli 200+. Na neki točki postanejo pravila preveč zapletena, da bi jih lahko vse imeli v mislih, in takrat se lahko začnete spraševati, zakaj sistem sprejema določene odločitve. Vendar pa je pomembna značilnost sistemov, ki temeljijo na znanju, da lahko vedno *pojasnite*, kako je bila sprejeta katera koli odločitev. +> **Opomba**: Ta primer je dokaj enostaven in daje zgolj idejo, kako izgleda ekspertni sistem. Ko začnete ustvarjati tak sistem, boste opazili *inteligentno* vedenje šele, ko dosežete določeno število pravil, nekje več kot 200+. V neki točki pravila postanejo preveč zapletena, da bi jih vse shranili v spomin, in v tem trenutku se boste morda spraševali, zakaj sistem sprejema določene odločitve. Vendar pa je pomembna lastnost sistemov na podlagi znanja, da vedno lahko *razložite*, kako so bile sprejete odločitve. -## Ontologije in semantični splet +## Ontologije in Semantični splet -Ob koncu 20. stoletja je bila pobuda za uporabo predstavitve znanja za označevanje internetnih virov, da bi bilo mogoče najti vire, ki ustrezajo zelo specifičnim poizvedbam. Ta gibanje se je imenovalo **semantični splet**, in temeljilo je na več konceptih: +Ob koncu 20. stoletja je bila pobuda uporabe predstavitve znanja za označevanje internetnih virov, da bi bilo mogoče najti vire, ki ustrezajo zelo specifičnim poizvedbam. Ta pobuda se imenuje **Semantični splet** in temelji na več konceptih: -- Posebna predstavitev znanja, ki temelji na **[opisni logiki](https://en.wikipedia.org/wiki/Description_logic)** (DL). Podobna je predstavitvi znanja z okvirji, ker gradi hierarhijo objektov z lastnostmi, vendar ima formalno logično semantiko in sklepanje. Obstaja cela družina DL-jev, ki uravnotežijo med izraznostjo in algoritmično kompleksnostjo sklepanja. -- Razdeljena predstavitev znanja, kjer so vsi koncepti predstavljeni z globalnim URI identifikatorjem, kar omogoča ustvarjanje hierarhij znanja, ki se raztezajo po internetu. -- Družina jezikov, ki temeljijo na XML za opis znanja: RDF (Resource Description Framework), RDFS (RDF Schema), OWL (Ontology Web Language). +- Posebna predstavitev znanja, ki temelji na **[deskriptivni logiki](https://en.wikipedia.org/wiki/Description_logic)** (DL). Podobna je predstavitvi okvirjev, ker gradi hierarhijo objektov s lastnostmi, vendar ima formalno logično semantiko in sklepanje. Obstaja cela družina DL-jev, ki uravnotežujejo med izraznostjo in algoritmično kompleksnostjo sklepanja. +- Porazdeljena predstavitev znanja, kjer so vsi pojmi predstavljeni z globalnim URI identifikatorjem, kar omogoča ustvarjanje hierarhij znanja, ki segajo čez internet. +- Družina jezikov, temelječih na XML za opis znanja: RDF (Resource Description Framework), RDFS (RDF shema), OWL (Ontology Web Language). -Osrednji koncept v semantičnem spletu je koncept **ontologije**. Nanaša se na eksplicitno specifikacijo problematičnega področja z uporabo formalne predstavitve znanja. Najenostavnejša ontologija je lahko zgolj hierarhija objektov v problematičnem področju, bolj kompleksne ontologije pa vključujejo pravila, ki jih je mogoče uporabiti za sklepanje. +Osrednji koncept v Semantičnem spletu je koncept **ontologije**. Nanaša se na eksplicitno specifikacijo problematičnega področja z uporabo formalne predstavitve znanja. Najpreprostejša ontologija je lahko le hierarhija objektov znotraj problematičnega področja, vendar kompleksnejše ontologije vključujejo pravila, ki jih lahko uporabimo za sklepanja. -V semantičnem spletu so vse predstavitve osnovane na trojicah. Vsak objekt in vsaka relacija sta enolično identificirana z URI. Na primer, če želimo navesti dejstvo, da je ta učni načrt umetne inteligence razvil Dmitry Soshnikov 1. januarja 2022, lahko uporabimo naslednje trojice: +V semantičnem spletu so vse predstavitve osnovane na trojicah. Vsak objekt in vsak odnos sta enolično določena z URI. Na primer, če želimo navesti dejstvo, da je bil ta AI kurikulum razvit s strani Dmitryja Soshnikova 1. januarja 2022, lahko uporabimo naslednje trojice: - + ``` -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 ``` -> ✅ Tukaj sta `http://www.example.com/terms/creation-date` in `http://purl.org/dc/elements/1.1/creator` dobro poznana in splošno sprejeta URI-ja za izražanje konceptov *ustvarjalec* in *datum ustvarjanja*. +> ✅ Tukaj sta `http://www.example.com/terms/creation-date` in `http://purl.org/dc/elements/1.1/creator` dobro znana in univerzalno sprejeta URI za izražanje pojmov *ustvarjalec* in *datum nastanka*. -V bolj kompleksnem primeru, če želimo definirati seznam ustvarjalcev, lahko uporabimo nekatere podatkovne strukture, definirane v RDF. +V bolj zapletenem primeru, če želimo definirati seznam ustvarjalcev, lahko uporabimo nekatere podatkovne strukture, definirane v RDF. - + -> Zgornji diagrami avtorja [Dmitry Soshnikov](http://soshnikov.com) +> Diagrami zgoraj avtorja [Dmitry Soshnikov](http://soshnikov.com) -Napredek pri gradnji semantičnega spleta je bil nekoliko upočasnjen zaradi uspeha iskalnikov in tehnik obdelave naravnega jezika, ki omogočajo pridobivanje strukturiranih podatkov iz besedila. Vendar pa na nekaterih področjih še vedno obstajajo pomembna prizadevanja za vzdrževanje ontologij in baz znanja. Nekaj projektov, ki jih je vredno omeniti: +Napredek pri gradnji Semantičnega spleta je bil nekako upočasnjen zaradi uspeha iskalnikov in tehnik za obdelavo naravnega jezika, ki omogočajo izluščitev strukturiranih podatkov iz besedil. Kljub temu na nekaterih področjih še vedno potekajo pomembna prizadevanja za vzdrževanje ontologij in baz znanja. Nekaj projektov, vrednih omembe: -* [WikiData](https://wikidata.org/) je zbirka strojno berljivih baz znanja, povezanih z Wikipedijo. Večina podatkov je pridobljena iz Wikipedijinih *InfoBoxov*, delov strukturirane vsebine znotraj strani Wikipedije. Wikidata lahko [poizvedujete](https://query.wikidata.org/) v SPARQL, posebnem jeziku za poizvedbe v semantičnem spletu. Tukaj je primer poizvedbe, ki prikazuje najbolj priljubljene barve oči med ljudmi: +* [WikiData](https://wikidata.org/) je zbirka strojno berljivih baz znanja, povezanih z Wikipedijo. Večina podatkov je izkopanih iz *InfoBoxov* v Wikipediji, kosov strukturirane vsebine na straneh Wikipedije. S wikidatami lahko [poizvedujete](https://query.wikidata.org/) v SPARQL, posebnem poizvedbenem jeziku za Semantični splet. Tukaj je vzorčna poizvedba, ki prikaže najbolj priljubljene barve oči med ljudmi: ```sparql #defaultView:BubbleChart @@ -206,47 +206,52 @@ WHERE GROUP BY ?eyeColorLabel ``` -* [DBpedia](https://www.dbpedia.org/) je še en projekt, podoben WikiData. +* [DBpedia](https://www.dbpedia.org/) je še eno podobno prizadevanje kot WikiData. -> ✅ Če želite eksperimentirati z gradnjo lastnih ontologij ali odpiranjem obstoječih, obstaja odličen vizualni urejevalnik ontologij, imenovan [Protégé](https://protege.stanford.edu/). Prenesite ga ali ga uporabite na spletu. +> ✅ Če želite eksperimentirati z gradnjo lastnih ontologij ali odpiranjem obstoječih, obstaja odličen vizualni urejevalnik ontologij, imenovan [Protégé](https://protege.stanford.edu/). Prenesite ga ali uporabite na spletu. - + -*Spletni urejevalnik Protégé odprt z ontologijo družine Romanov. Posnetek zaslona avtorja Dmitry Soshnikov* +*Web urejevalnik Protégé odprt z ontologijo družine Romanovih. Posnetek zaslona avtor Dmitry Soshnikov* -## ✍️ Naloga: Ontologija družine +## ✍️ Vaja: Ontologija družine -Oglejte si [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) za primer uporabe tehnik semantičnega spleta za sklepanje o družinskih odnosih. Vzeli bomo družinsko drevo, predstavljeno v običajnem formatu GEDCOM, in ontologijo družinskih odnosov ter zgradili graf vseh družinskih odnosov za določen nabor posameznikov. + +Oglejte si [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) za primer uporabe tehnik Semantičnega spleta za sklepanja o družinskih odnosih. Vzeli bomo družinsko drevo, predstavljeno v običajnem formatu GEDCOM, in ontologijo družinskih odnosov ter zgradili graf vseh družinskih povezav za dano množico posameznikov. ## Microsoft Concept Graph -V večini primerov so ontologije skrbno ustvarjene ročno. Vendar pa je mogoče ontologije tudi **pridobiti** iz nestrukturiranih podatkov, na primer iz besedil v naravnem jeziku. +V večini primerov so ontologije skrbno ročno ustvarjene. Vendar pa je tudi možno **izluščiti** ontologije iz nestrukturiranih podatkov, na primer iz besedil naravnega jezika. -Eden takšnih poskusov je bil izveden s strani Microsoft Research in je privedel do [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste). +Tak poskus je izvedel Microsoft Research, ki je rezultiral v [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste). -Gre za veliko zbirko entitet, združenih z dedno relacijo `is-a`. Omogoča odgovarjanje na vprašanja, kot je "Kaj je Microsoft?" - odgovor pa je nekaj takega: "podjetje s verjetnostjo 0,87 in blagovna znamka s verjetnostjo 0,75". +To je velika zbirka entitet, združenih skupaj z uporabo dednega odnosa `je vrstniški` (is-a). Omogoča odgovore na vprašanja, kot je "Kaj je Microsoft?" - odgovor pa je nekaj v smislu "podjetje s pripadnostjo verjetnosti 0.87 in blagovna znamka s pripadnostjo verjetnosti 0.75". -Graf je na voljo bodisi kot REST API bodisi kot velik prenosljiv besedilni dokument, ki navaja vse pare entitet. +Graf je na voljo bodisi kot REST API, bodisi kot velika prenosljiva besedilna datoteka, ki navaja vse pare entitet. -## ✍️ Naloga: Graf konceptov +## ✍️ Vaja: Konceptualni graf -Preizkusite zvezek [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb), da vidite, kako lahko uporabimo Microsoft Concept Graph za razvrščanje novic v več kategorij. +Preizkusite beležko [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) in si oglejte, kako lahko uporabimo Microsoft Concept Graph za združevanje novičarskih člankov v več kategorij. ## Zaključek -Danes se umetna inteligenca pogosto obravnava kot sinonim za *strojno učenje* ali *nevronske mreže*. Vendar pa človek izkazuje tudi eksplicitno sklepanje, kar trenutno ne obravnavajo nevronske mreže. Pri projektih v resničnem svetu se eksplicitno sklepanje še vedno uporablja za izvajanje nalog, ki zahtevajo razlage ali možnost nadzorovanega spreminjanja vedenja sistema. +Danes se AI pogosto obravnava kot sopomenka za *strojno učenje* ali *nevralne mreže*. Vendar pa tudi človek izvaja eksplicitno sklepanje, ki ga trenutno nevralne mreže ne obdelujejo. V resničnih projektih se eksplicitno sklepanje še vedno uporablja za izvajanje nalog, ki zahtevajo razlage ali možnost nadzorovane spremembe vedenja sistema. ## 🚀 Izziv -V zvezku Ontologija družine, povezanem s to lekcijo, je priložnost za eksperimentiranje z drugimi družinskimi odnosi. Poskusite odkriti nove povezave med ljudmi v družinskem drevesu. +V beležki Ontologija družine, povezani s to lekcijo, je možnost eksperimentiranja z drugimi družinskimi povezavami. Poskusite odkriti nove povezave med ljudmi v družinskem drevesu. -## [Kvizi po predavanju](https://ff-quizzes.netlify.app/en/ai/quiz/4) +## [Preizkus po predavanju](https://ff-quizzes.netlify.app/en/ai/quiz/4) ## Pregled in samostojno učenje -Raziskujte na internetu, da odkrijete področja, kjer so ljudje poskušali kvantificirati in kodificirati znanje. Oglejte si Bloomovo taksonomijo in se vrnite v zgodovino, da se naučite, kako so ljudje poskušali razumeti svoj svet. Raziščite delo Linnaeusa pri ustvarjanju taksonomije organizmov in opazujte način, kako je Dmitri Mendeleev ustvaril način za opisovanje in razvrščanje kemijskih elementov. Katere druge zanimive primere lahko najdete? +Raziskujte po internetu, da odkrijete področja, kjer so ljudje poskušali kvantificirati in kodificirati znanje. Oglejte si Bloomovo taksonomijo in se vrnite nazaj v zgodovino, da spoznate, kako so ljudje poskušali razumeti svoj svet. Raziščite delo Linnaeusa pri ustvarjanju taksonomije organizmov in opazujte, kako je Dmitrij Mendelejev ustvaril način za opisovanje in razvrščanje kemičnih elementov. Katere druge zanimive primere lahko najdete? -**Naloga**: [Zgradite ontologijo](assignment.md) +**Naloga**: [Izdelajte ontologijo](assignment.md) --- + +**Izjava o omejitvi odgovornosti**: +Ta dokument je bil preveden z uporabo storitve za strojno prevajanje [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas prosimo, da upoštevate, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem izvirnem jeziku šteje kot avtoritativni vir. Za kritične informacije priporočamo strokovni človeški prevod. Ne odgovarjamo za morebitne nesporazume ali napačne razlage, ki izhajajo iz uporabe tega prevoda. + \ No newline at end of file