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[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) -> **স্থানীয়ভাবে ক্লোন করতে চান?** +> **অন্তর্নিহিত ক্লোন করতে চান?** -> এই রিপোজিটরিতে ৫০+ ভাষার অনুবাদ অন্তর্ভুক্ত রয়েছে যা ডাউনলোড সাইজ উল্লেখযোগ্যভাবে বৃদ্ধি করে। অনুবাদ ছাড়া ক্লোন করতে, sparse checkout ব্যবহার করুন: +> এই রিপোজিটরিতে ৫০+ ভাষার অনুবাদ রয়েছে যা ডাউনলোডের আকার অনেক বাড়িয়ে দেয়। অনুবাদ ছাড়া ক্লোন করতে, sparse checkout ব্যবহার করুন: > ```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' > ``` -> এটি আপনাকে কোর্স সম্পন্ন করার জন্য প্রয়োজনীয় সব কিছু দ্রুত গতিতে ডাউনলোড করার সুযোগ দেয়। +> এটি আপনাকে দ্রুত ডাউনলোডের মাধ্যমে কোর্স সম্পন্ন করতে যা যা প্রয়োজন তা দেবে। -**আপনি যদি অতিরিক্ত ভাষার সমর্থন চান, তারা এখানে তালিকাভুক্ত রয়েছে: [এখানে](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** +**আপনি যদি অতিরিক্ত অনুবাদ ভাষা চান, সেগুলি এখানে তালিকাভুক্ত আছে [here](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** -## সম্প্রদায়ে যোগদান করুন +## কমিউনিটিতে যোগদান করুন [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) ## আপনি যা শিখবেন -**[কোর্সের মাইন্ডম্যাপ](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** +**[কোর্সের মানচিত্র](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** এই কারিকুলামে আপনি শিখবেন: -* আর্টিফিশিয়াল ইন্টেলিজেন্সের বিভিন্ন পন্থা, যার মধ্যে "পুরানো ভালো" প্রতীকী পদ্ধতি সহ **জ্ঞান উপস্থাপন** এবং যুক্তি বিশ্লেষণ ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)) অন্তর্ভুক্ত। -* **নিউরাল নেটওয়ার্কস** এবং **ডীপ লার্নিং**, যা আধুনিক AI-এর মূল। আমরা দুইটি জনপ্রিয় ফ্রেমওয়ার্ক – [TensorFlow](http://Tensorflow.org) এবং [PyTorch](http://pytorch.org) ব্যবহার করে এই গুরুত্বপূর্ণ বিষয়গুলোর ধারণাসমূহ ব্যাখ্যা করব। -* চিত্র এবং পাঠ্য নিয়ে কাজ করার জন্য **নিউরাল আর্কিটেকচারস**। আমরা সাম্প্রতিক মডেলগুলি আলোচনা করব তবে সবচেয়ে আধুনিক বিষয়ে হয়তো কিছুটা কম থাকব। -* কম জনপ্রিয় AI পন্থাগুলো, যেমন **জেনেটিক অ্যালগরিদমস** এবং **মাল্টি-এজেন্ট সিস্টেমস**। +* কৃত্রিম বুদ্ধিমত্তার বিভিন্ন পদ্ধতি, যার মধ্যে রয়েছে "পুরোনো" প্রতীকী পদ্ধতি যেমন **জ্ঞান উপস্থাপন** এবং কারণ নির্ণয় ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence))। +* **নিউরাল নেটওয়ার্ক** এবং **ডীপ লার্নিং** যা আধুনিক AI-এর মূল ভিত্তি। আমরা এই গুরুত্বপূর্ণ বিষয়গুলির ধারণাগুলি জনপ্রিয় দুটি ফ্রেমওয়ার্ক - [TensorFlow](http://Tensorflow.org) এবং [PyTorch](http://pytorch.org) এ কোড ব্যবহার করে ব্যাখ্যা করব। +* ছবি এবং লেখার সাথে কাজ করার জন্য **নিউরাল আর্কিটেকচার**। আমরা সাম্প্রতিক মডেলগুলি কভার করব কিন্তু সাম্প্রতিকতম অবস্থা হয়তো কিছুটা কম থাকতে পারে। +* কম জনপ্রিয় AI পদ্ধতিগুলো, যেমন **জেনেটিক অ্যালগরিদম** এবং **মাল্টি-এজেন্ট সিস্টেম**। এই কারিকুলামে যা অন্তর্ভুক্ত নয়: -> [Microsoft Learn সংগ্রহে এই কোর্সের সমস্ত অতিরিক্ত সম্পদগুলি খুঁজুন](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) +> [এই কোর্সের জন্য সমস্ত অতিরিক্ত রিসোর্স আমাদের Microsoft Learn কালেকশনে খুঁজুন](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) -* **বিজনেসে AI** ব্যবহারের ব্যবসায়িক ক্ষেত্র। Microsoft Learn-এ [Introduction to AI for business users](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) শেখার পথ নিন, অথবা [AI Business School](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), যা [INSEAD](https://www.insead.edu/) এর সঙ্গে সহযোগিতায় তৈরি। -* ক্লাসিক **মেশিন লার্নিং**, যা আমাদের [Machine Learning for Beginners Curriculum](http://github.com/Microsoft/ML-for-Beginners) এ বিস্তারিত বর্ণিত হয়েছে। -* বাস্তবমুখী AI অ্যাপ্লিকেশন যা **[Cognitive Services](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)** ব্যবহার করে তৈরি। এর জন্য, Microsoft Learn থেকে [ভিশন](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [ন্যাচারাল ল্যাঙ্গুয়েজ প্রসেসিং](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[Generative AI with Azure OpenAI Service](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** এবং অন্যান্য মডিউল দিয়ে শুরু করা ভাল। -* নির্দিষ্ট ML এর **ক্লাউড ফ্রেমওয়ার্কস**, যেমন [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), অথবা [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum)। [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) এবং [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) শেখার পথ অনুসরণ করুন। -* **কনভার্সেশনাল AI** এবং **চ্যাট বটস**। একটি আলাদা [Create conversational AI solutions](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum) শেখার পথ আছে, এবং বিস্তারিত জানতে [এই ব্লগ পোস্টে](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) দেখুন। -* ডীপ লার্নিং-র পেছনের **গভীর গণিত**। এর জন্য, Ian Goodfellow, Yoshua Bengio এবং Aaron Courville-এর [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) বইটি সুপারিশ করব, যা অনলাইনে [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/) থেকেও পাওয়া যায়। +* **ব্যবসায় AI ব্যবহারের** ব্যবসায়িক কেস। আপনি Microsoft Learn এর [Introduction to AI for business users](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) লার্নিং পাথ বা [AI Business School](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), যা [INSEAD](https://www.insead.edu/) এর সঙ্গে সহযোগিতায় তৈরি, নিতে পারেন। +* **ক্লাসিক মেশিন লার্নিং**, যা আমাদের [Machine Learning for Beginners Curriculum](http://github.com/Microsoft/ML-for-Beginners) এ ভালভাবে বর্ণিত। +* **[Cognitive Services](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)** ব্যবহার করে তৈরি ব্যবহারিক AI অ্যাপ্লিকেশন। এ জন্য আমরা সুপারিশ করব Microsoft Learn এর [ভিশন](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [ন্যাচারাল ল্যাঙ্গুয়েজ প্রসেসিং](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[Azure OpenAI সার্ভিস এর সাথে জেনারেটিভ AI](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** এবং অন্যান্য মডিউল দিয়ে শুরু করার। +* নির্দিষ্ট ML **ক্লাউড ফ্রেমওয়ার্ক**, যেমন [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) অথবা [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum)। আপনি [Azure Machine Learning নিয়ে মেশিন লার্নিং সলিউশন তৈরি ও পরিচালনা](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) এবং [Azure Databricks এর সঙ্গে মেশিন লার্নিং সলিউশন তৈরি ও পরিচালনা](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum) লার্নিং পথ ব্যবহার করতে পারেন। +* **কনভারসেশনাল AI** এবং **চ্যাট বটস**। এর জন্য একটি আলাদা [কনভারসেশনাল AI সলিউশন তৈরি](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum) লার্নিং পাথ রয়েছে, এবং বিস্তারিত জানতে পারেন [এই ব্লগ পোষ্টে](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/)। +* ডীপ লার্নিং এর পেছনের **গভীর গণিত**। এর জন্য আমরা সুপারিশ করব Ian Goodfellow, Yoshua Bengio এবং Aaron Courville-এর [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) বইটি, যা অনলাইনে [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/) এ উপলব্ধ। -_এআই ইন দ্য ক্লাউড_ বিষয়গুলি পরিচিতির জন্য আপনি [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) শেখার পথ গ্রহণ করতে পারেন। +_ক্লাউডে AI_ বিষয়ে সহজ পরিচয়ের জন্য আপনি [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) লার্নিং পাথ নিতে পারেন। # বিষয়বস্তু -| | পাঠের লিঙ্ক | PyTorch/Keras/TensorFlow | ল্যাব | +| | পাঠ লিঙ্ক | PyTorch/Keras/TensorFlow | ল্যাব | | :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | -| 0 | [কোর্স সেটআপ](./lessons/0-course-setup/setup.md) | [আপনার ডেভেলপমেন্ট পরিবেশ সেটআপ করুন](./lessons/0-course-setup/how-to-run.md) | | -| I | [**AI পরিচিতি**](./lessons/1-Intro/README.md) | | | -| 01 | [AI এর পরিচিতি ও ইতিহাস](./lessons/1-Intro/README.md) | - | - | +| 0 | [কোর্স সেটআপ](./lessons/0-course-setup/setup.md) | [আপনার ডেভেলপমেন্ট এনভায়রনমেন্ট সেটআপ করুন](./lessons/0-course-setup/how-to-run.md) | | +| I | [**AI এর পরিচিতি**](./lessons/1-Intro/README.md) | | | +| 01 | [AI ইতিহাস ও পরিচিতি](./lessons/1-Intro/README.md) | - | - | | II | **প্রতীকী AI** | -| 02 | [জ্ঞান উপস্থাপন এবং এক্সপার্ট সিস্টেম](./lessons/2-Symbolic/README.md) | [এক্সপার্ট সিস্টেম](./lessons/2-Symbolic/Animals.ipynb) / [অন্তোলজি](./lessons/2-Symbolic/FamilyOntology.ipynb) /[কনসেপ্ট গ্রাফ](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | -| III | [**নিউরাল নেটওয়ার্ক পরিচিতি**](./lessons/3-NeuralNetworks/README.md) ||| -| 03 | [পারসেপট্রন](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [নোটবুক](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [ল্যাব](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | -| 04 | [মাল্টি-লেয়ারড পারসেপট্রন এবং আমাদের নিজস্ব ফ্রেমওয়ার্ক তৈরি](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [নোটবুক](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [ল্যাব](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | -| 05 | [ফ্রেমওয়ার্কস (PyTorch/TensorFlow) ওভারফিটিংয়ের পরিচিতি](./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) | [ল্যাব](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | -| IV | [**কম্পিউটার ভিশন**](./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)| [মাইক্রোসফ্ট আজুরে কম্পিউটার ভিশন অন্বেষণ করুন](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | +| 02 | [জ্ঞান উপস্থাপন ও এক্সপার্ট সিস্টেম](./lessons/2-Symbolic/README.md) | [এক্সপার্ট সিস্টেম](./lessons/2-Symbolic/Animals.ipynb) / [অন্টোলজি](./lessons/2-Symbolic/FamilyOntology.ipynb) /[ধারণা গ্রাফ](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | +| III | [**নিউরাল নেটওয়ার্কস এর পরিচিতি**](./lessons/3-NeuralNetworks/README.md) ||| +| 03 | [পারসেপ্ট্রন](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [নোটবুক](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [ল্যাব](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | +| 04 | [মাল্টি-লেয়ার্ড পারসেপ্ট্রন এবং আমাদের নিজের ফ্রেমওয়ার্ক তৈরি](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [নোটবুক](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [ল্যাব](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | +| 05 | [ফ্রেমওয়ার্কগুলোর পরিচিতি (PyTorch/TensorFlow) এবং ওভারফিটিং](./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) | [ল্যাব](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | +| IV | [**কম্পিউটার ভিশন**](./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)| [মাইক্রোসফট আজুরে কম্পিউটার ভিশন এক্সপ্লোর করুন](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | | 06 | [কম্পিউটার ভিশনের পরিচিতি। OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [নোটবুক](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [ল্যাব](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | -| 07 | [কনভলিউশনাল নিউরাল নেটওয়ার্কস](./lessons/4-ComputerVision/07-ConvNets/README.md) & [CNN আর্কিটেকচার](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [ল্যাব](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | -| 08 | [প্রি-ট্রেইন্ড নেটওয়ার্কস এবং ট্রান্সফার লার্নিং](./lessons/4-ComputerVision/08-TransferLearning/README.md) এবং [ট্রেনিং ট্রিকস](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [ল্যাব](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | -| 09 | [অটোএনকোডার এবং 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 | [জেনারেটিভ অ্যাডভার্সারিয়াল নেটওয়ার্কস এবং আর্টিস্টিক স্টাইল ট্রান্সফার](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | +| 07 | [কনভলিউশনাল নিউরাল নেটওয়ার্কস](./lessons/4-ComputerVision/07-ConvNets/README.md) & [CNN আর্কিটেকচারস](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [ল্যাব](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | +| 08 | [প্রি-ট্রেইনড নেটওয়ার্কস এবং ট্রান্সফার লার্নিং](./lessons/4-ComputerVision/08-TransferLearning/README.md) এবং [ট্রেনিং ট্রিক্স](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [ল্যাব](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | +| 09 | [অটোএনকোডারস এবং 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 | [জেনারেটিভ অ্যাডভারসেরিয়াল নেটওয়ার্কস ও আর্টিস্টিক স্টাইল ট্রান্সফার](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | | 11 | [অবজেক্ট ডিটেকশন](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [ল্যাব](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | -| 12 | [সেমেন্টিক সেগমেন্টেশন। ইউ-নেট](./lessons/4-ComputerVision/12-Segmentation/README.md) | [PyTorch](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb) | | -| V | [**ন্যাচারাল ল্যাঙ্গুয়েজ প্রসেসিং**](./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) | [মাইক্রোসফ্ট আজুরে ন্যাচারাল ল্যাঙ্গুয়েজ প্রসেসিং অন্বেষণ করুন](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| -| 13 | [টেক্সট প্রতিনিধিত্ব। বো/টিএফ-আইডিএফ](./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 | [সেমান্তিক শব্দ এমবেডিং। ওয়ার্ড২ভেক এবং গ্লোভ](./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 | [ল্যাঙ্গুয়েজ মডেলিং। নিজের এমবেডিং ট্রেনিং](./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) | [ল্যাব](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | -| 16 | [রিকারেন্ট নিউরাল নেটওয়ার্কস](./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 | [জেনারেটিভ রিকারেন্ট নেটওয়ার্কস](./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) | [ল্যাব](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | -| 18 | [ট্রান্সফরমার্স। 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) | | +| 12 | [সেমান্টিক সেগমেন্টেশন। ইউ-নেট](./lessons/4-ComputerVision/12-Segmentation/README.md) | [PyTorch](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb) | | +| V | [**প্রাকৃতিক ভাষা প্রক্রিয়াকরণ**](./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) | [মাইক্রোসফট আজুরে প্রাকৃতিক ভাষা প্রক্রিয়াকরণ এক্সপ্লোর করুন](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| +| 13 | [টেক্সট প্রতিনিধিত্ব। বাও/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 | [সেমান্টিক ওয়ার্ড এমবেডিংস। ওয়ার্ড2ভেক এবং গ্লোভ](./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 | [ভাষা মডেলিং। নিজের এমবেডিং ট্রেনিং](./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) | [ল্যাব](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | +| 16 | [রিকরণ্ট নিউরাল নেটওয়ার্কস](./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 | [জেনারেটিভ রিকরণ্ট নেটওয়ার্কস](./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) | [ল্যাব](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | +| 18 | [ট্রান্সফর্মার্স। 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 | [নেমড এন্টিটি রিকগনিশন](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [ল্যাব](./lessons/5-NLP/19-NER/lab/README.md) | -| 20 | [বড় ভাষা মডেল, প্রম্পট প্রোগ্রামিং এবং ফিউ-শট টাস্ক](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | -| VI | **অন্যান্য AI প্রযুক্তি** || | -| 21 | [জেনেটিক অ্যালগরিদম](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [নোটবুক](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | +| 20 | [বড় বড় ভাষার মডেল, প্রম্পট প্রোগ্রামিং এবং কিছু শট টাস্ক](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | +| VI | **অন্যান্য এআই কৌশল** || | +| 21 | [জেনেটিক অ্যালগোরিদমস](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [নোটবুক](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | | 22 | [ডিপ রিইনফোর্সমেন্ট লার্নিং](./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) | [ল্যাব](./lessons/6-Other/22-DeepRL/lab/README.md) | -| 23 | [মাল্টি-এজেন্ট সিস্টেম](./lessons/6-Other/23-MultiagentSystems/README.md) | | | -| VII | **AI নীতিশাস্ত্র** | | | -| 24 | [AI নীতিশাস্ত্র এবং দায়িত্বশীল AI](./lessons/7-Ethics/README.md) | [Microsoft Learn: দায়িত্বশীল AI নীতিমালা](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | -| IX | **অতিরিক্ত** | | | +| 23 | [মাল্টি-এজেন্ট সিস্টেমস](./lessons/6-Other/23-MultiagentSystems/README.md) | | | +| VII | **এআই নীতি** | | | +| 24 | [এআই নীতি এবং দায়িত্বশীল এআই](./lessons/7-Ethics/README.md) | [Microsoft Learn: দায়িত্বশীল এআই নীতিমালা](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | +| IX | **এক্সট্রাস** | | | | 25 | [মাল্টি-মোডাল নেটওয়ার্কস, CLIP এবং VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [নোটবুক](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | -## প্রতিটি পাঠে রয়েছে +## প্রতিটি পাঠে অন্তর্ভুক্ত -* প্রি-রিডিং মেটিরিয়াল -* কার্যকর Jupyter নোটবুক, যা প্রায়ই ফ্রেমওয়ার্কের নির্দিষ্ট (**PyTorch** বা **TensorFlow**)। কার্যকর নোটবুকটিতে অনেক তাত্ত্বিক বিষয়বস্তু থাকে, তাই বিষয়টি বুঝতে হলে আপনাকে কমপক্ষে একটি সংস্করণ (PyTorch বা TensorFlow) অবশ্যই দেখতে হবে। -* কিছু বিষয়ের জন্য **ল্যাব** রয়েছে, যা আপনাকে শেখা বিষয়বস্তু নির্দিষ্ট সমস্যায় প্রয়োগ করার সুযোগ দেয়। -* কিছু অংশে [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) মডিউলের লিঙ্ক রয়েছে যা সম্পর্কিত বিষয়গুলি আচ্ছাদিত করে। +* পূর্ব-পাঠ্য উপকরণ +* প্রয়োগযোগ্য জুপিটার নোটবুক, যা প্রায়শই ফ্রেমওয়ার্কের উপর নির্ভরশীল (**PyTorch** অথবা **TensorFlow**). প্রয়োগযোগ্য নোটবুকটিতে প্রচুর তাত্ত্বিক উপাদানও থাকে, তাই বিষয়টি বোঝার জন্য আপনাকে কমপক্ষে এক ধরনের নোটবুক (PyTorch অথবা TensorFlow) খতিয়ে দেখতে হবে। +* কিছু বিষয়ের জন্য **ল্যাব** উপলব্ধ, যা আপনাকে শেখা বিষয়বস্তু নির্দিষ্ট সমস্যায় প্রয়োগ করার সুযোগ দেয়। +* কিছু অধ্যায়ে [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) মডিউলগুলোর লিঙ্ক দেওয়া আছে, যা সংশ্লিষ্ট বিষয়গুলো কভার করে। ## শুরু করা যাক -### 🎯 AI নতুন? এখান থেকে শুরু করুন! +### 🎯 এআই তে নতুন? এখান থেকে শুরু করুন! -যদি আপনি সম্পূর্ণ নতুন হন AI এবং দ্রুত, ব্যবহারিক উদাহরণ চান, তাহলে আমাদের [**শুরুতেই-বান্ধব উদাহরণসমূহ**](./examples/README.md) দেখুন! এতে অন্তর্ভুক্ত: +আপনি যদি সম্পূর্ণ নতুন হয়ে থাকেন এবং দ্রুত, হাতে-কলমে উদাহরণ চান, তবে আমাদের [**শুরুকারীদের জন্য বন্ধুসুলভ উদাহরণসমূহ**](./examples/README.md) দেখুন! এর মধ্যে রয়েছে: -- 🌟 **হ্যালো AI ওয়ার্ল্ড** - আপনার প্রথম AI প্রোগ্রাম (প্যাটার্ন রেকগনিশন) -- 🧠 **সহজ নিউরাল নেটওয়ার্ক** - স্ক্র্যাচ থেকে নিউরাল নেটওয়ার্ক তৈরি করুন -- 🖼️ **ছবি শ্রেণীকরণকারী** - বিস্তারিত মন্তব্যসহ ছবির শ্রেণীবিভাগ করুন -- 💬 **পাঠ্যের অনুভূতি** - ইতিবাচক/নেতিবাচক পাঠ্য বিশ্লেষণ করুন +- 🌟 **হ্যালো এআই ওয়ার্ল্ড** - আপনার প্রথম এআই প্রোগ্রাম (প্যাটার্ন রিকগনিশন) +- 🧠 **সহজ নিউরাল নেটওয়ার্ক** - একদম শুরু থেকে নিউরাল নেটওয়ার্ক তৈরি +- 🖼️ **ইমেজ ক্লাসিফায়ার** - বিস্তারিত মন্তব্য সহ ছবি শ্রেণীবদ্ধকরণ +- 💬 **টেক্সট সেন্টিমেন্ট** - পজিটিভ/নেগেটিভ টেক্সট বিশ্লেষণ করুন -এই উদাহরণগুলি সম্পূর্ণ কারিকুলামের আগে আপনাকে এআই ধারণাগুলো বুঝতে সাহায্য করার জন্য ডিজাইন করা হয়েছে। +এই উদাহরণগুলি আপনাকে সম্পূর্ণ কারিকুলামে প্রবেশ করার আগে AI ধারণাগুলি বুঝতে সাহায্য করার জন্য ডিজাইন করা হয়েছে। ### 📚 সম্পূর্ণ কারিকুলাম সেটআপ -- আমরা আপনার ডেভেলপমেন্ট পরিবেশ সেটআপে সাহায্য করার জন্য একটি [সেটআপ পাঠ](./lessons/0-course-setup/setup.md) তৈরি করেছি। - শিক্ষার্থীদের জন্য, আমরা একটি [কারিকুলাম সেটআপ পাঠ](./lessons/0-course-setup/for-teachers.md)ও তৈরি করেছি! -- কিভাবে [কোড চালাবেন VSCode বা Codepace এ](./lessons/0-course-setup/how-to-run.md) +- আমরা আপনার ডেভেলপমেন্ট পরিবেশ সেটআপে সাহায্যের জন্য একটি [সেটআপ লেসন](./lessons/0-course-setup/setup.md) তৈরি করেছি। - শিক্ষকদের জন্য, আমরা একটি [কারিকুলাম সেটআপ লেসন](./lessons/0-course-setup/for-teachers.md)ও তৈরি করেছি! +- কিভাবে [VSCode বা Codespace-এ কোড চালাবেন](./lessons/0-course-setup/how-to-run.md) -এই ধাপগুলো অনুসরণ করুন: +এই ধাপগুলি অনুসরণ করুন: -রিপোজিটরিটি ফর্ক করুন: এই পৃষ্ঠার উপরের ডানদিকে "Fork" বোতামে ক্লিক করুন। +রেপোজিটরি ফর্ক করুন: এই পৃষ্ঠার উপরের-ডান কোনায় "Fork" বোতামে ক্লিক করুন। -রিপোজিটরিটি ক্লোন করুন: `git clone https://github.com/microsoft/AI-For-Beginners.git` +রেপোজিটরি ক্লোন করুন: `git clone https://github.com/microsoft/AI-For-Beginners.git` -ভুলবেন না এই রিপোতে স্টার (🌟) দিতে যাতে পরে সহজে খুঁজে পান। +পরে সহজে খুঁজে পাওয়ার জন্য এই রিপোতে স্টার (🌟) দেয়া ভুলবেন না। -## অন্যান্য শিক্ষার্থীদের সাথে পরিচিত হন +## অন্যান্য শিক্ষার্থীদের সাথে দেখা করুন -আমাদের [অফিশিয়াল এআই ডিসকর্ড সার্ভার](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) এ যোগ দিয়ে এই কোর্স নিচ্ছে এমন অন্যান্য শিক্ষার্থীদের সাথে পরিচিত ও নেটওয়ার্ক করুন এবং সহায়তা পান। +এই কোর্স নিচ্ছেন এমন অন্যান্য শিক্ষার্থীদের সাথে পরিচিত হতে এবং নেটওয়ার্ক গড়ে তুলতে আমাদের [সরকারি AI ডিসকর্ড সার্ভারে](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) যোগ দিন এবং সহায়তা নিন। -কোর্স তৈরি করার সময় যদি আপনার কোনো পণ্য প্রতিক্রিয়া বা প্রশ্ন থাকে, তাহলে আমাদের [Azure AI Foundry Developer Forum](https://aka.ms/foundry/forum) এ যান। +আপনার যদি প্রোডাক্ট ফিডব্যাক বা প্রশ্ন থাকে বানানোর সময়, আমাদের [Azure AI Foundry ডেভেলপার ফোরামে](https://aka.ms/foundry/forum) যান। ## কুইজ -> **কুইজ সম্পর্কে একটি নোট**: সকল কুইজগুলো Quiz-app ফোল্ডারে etc\quiz-app এ রাখা আছে, অথবা [অনলাইনে এখানে](https://ff-quizzes.netlify.app/) পাওয়া যাবে। এগুলো পাঠগনের মধ্য থেকে লিঙ্ক করা হয়েছে; কুইজ অ্যাপ স্থানীয়ভাবে চালানো যাবে অথবা Azure এ ডিপ্লয় করা যাবে; `quiz-app` ফোল্ডারের নির্দেশিকা অনুসরণ করুন। এগুলো ধীরে ধীরে স্থানীয়করণ করা হচ্ছে। +> **কুইজ সম্পর্কে একটি নোট**: সমস্ত কুইজ Quiz-app ফোল্ডারে etc\quiz-app এ রয়েছে, অথবা [অনলাইন এখানে](https://ff-quizzes.netlify.app/)। তারা লেসনের মধ্যে লিঙ্ক করা হয়েছে, কুইজ অ্যাপ স্থানীয়ভাবে চালানো যায় বা Azure এ ডিপ্লয় করা যায়; নির্দেশিকা অনুসরণ করুন `quiz-app` ফোল্ডারে। সেগুলি ধীরে ধীরে স্থানীয়করণ হচ্ছে। -## সাহায্য দরকার +## সাহায্য প্রয়োজন -আপনার কোনো পরামর্শ আছে কিংবা বানান বা কোড ত্রুটি পেয়েছেন? একটি ইস্যু রিপোর্ট করুন বা পুল রিকোয়েস্ট তৈরি করুন। +আপনার কি কোন পরামর্শ আছে বা বানান বা কোড ত্রুটি পেয়েছেন? একটি ইস্যু তুলুন অথবা একটি পুল রিকোয়েস্ট তৈরি করুন। ## বিশেষ ধন্যবাদ * **✍️ প্রধান লেখক:** [Dmitry Soshnikov](http://soshnikov.com), PhD * **🔥 সম্পাদক:** [Jen Looper](https://twitter.com/jenlooper), PhD -* **🎨 স্কেচনোট ইলাস্ট্রেটর:** [Tomomi Imura](https://twitter.com/girlie_mac) +* **🎨 স্কেচনোট চিত্রকর:** [Tomomi Imura](https://twitter.com/girlie_mac) * **✅ কুইজ নির্মাতা:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) -* **🙏 মূল অবদানকারীরা:** [Evgenii Pishchik](https://github.com/Pe4enIks) +* **🙏 প্রধান অবদানকারী:** [Evgenii Pishchik](https://github.com/Pe4enIks) ## অন্যান্য কারিকুলাম -আমাদের টিম অন্যান্য কারিকুলাম তৈরি করে! দেখে নিন: +আমাদের দল অন্যান্য কারিকুলাম উৎপাদন করে! দেখুন: ### LangChain @@ -197,7 +197,7 @@ _এআই ইন দ্য ক্লাউড_ বিষয়গুলি প --- -### মূল শেখা +### Core Learning [![ML for Beginners](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst) [![Data Science for Beginners](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst) [![AI for Beginners](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst) @@ -208,25 +208,25 @@ _এআই ইন দ্য ক্লাউড_ বিষয়গুলি প --- -### কপিলট সিরিজ +### Copilot Series [![Copilot for AI Paired Programming](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst) [![Copilot for C#/.NET](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst) [![Copilot Adventure](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst) -## সহায়তা গ্রহণ করুন +## সাহায্য পাওয়া -যদি আপনি আটকে যান বা AI অ্যাপ তৈরি করার বিষয়ে কোনো প্রশ্ন থাকে। MCP সম্পর্কে আলোচনা করতে সহপাঠী ও অভিজ্ঞ ডেভেলপারদের সাথে যোগ দিন। এটা একটি সহায়ক কমিউনিটি যেখানে প্রশ্ন করতে পারবেন এবং জ্ঞান বিনিময় করা হয়। +যদি আপনি আটকে যান বা AI অ্যাপ তৈরি করার সময় কোনো প্রশ্ন থাকে, MCP নিয়ে আলোচনা করার জন্য সহপাঠী ও অভিজ্ঞ ডেভেলপারদের সাথে যোগ দিন। এটি একটি সহায়ক কমিউনিটি যেখানে প্রশ্ন করা স্বাগত এবং জ্ঞান অবাধে ভাগ করা হয়। [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -আপনি যদি পণ্য মন্তব্য বা ত্রুটি সম্পর্কে জানাতে চান, তাহলে দেখুন: +আপনি যদি প্রোডাক্ট ফিডব্যাক বা বানানোর সময় ত্রুটি পান, যান: [![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) --- -**বিতর্কসম্বলিত বক্তব্য**: -এই নথিটি AI অনুবাদ সেবা [Co-op Translator](https://github.com/Azure/co-op-translator) ব্যবহার করে অনূদিত হয়েছে। আমরা যথাসাধ্য সঠিকতার চেষ্টা করি, তবে স্বয়ংক্রিয় অনুবাদে ত্রুটি বা ভুল থাকতে পারে তা দয়া করে বিবেচনা করুন। মূল নথিটি তার নিজস্ব ভাষায় প্রামাণিক উৎস হিসেবে গ্রহণ করা উচিত। গুরুত্বপূর্ণ তথ্যের ক্ষেত্রে পেশাদার মানব অনুবাদ সুপারিশ করা হয়। এই অনুবাদের ব্যবহারে সৃষ্ট কোনো ভুলবোঝাবুঝি বা ভুল ব্যাখ্যার জন্য আমরা দায়ী নয়। +**অস্বীকৃতি**: +এই নথিটি AI অনুবাদ সেবা [Co-op Translator](https://github.com/Azure/co-op-translator) ব্যবহার করে অনুবাদ করা হয়েছে। আমরা যথাসাধ্য সঠিকতার চেষ্টা করি, তবে স্বয়ংক্রিয় অনুবাদে ত্রুটি বা অসঙ্গতি থাকতে পারে। আসল নথিটির নিজস্ব ভাষার সংস্করণই কর্তৃত্বপূর্ণ উৎস হিসেবে বিবেচনা করা উচিত। গুরুত্বপূর্ণ তথ্যের জন্য পেশাদার মানব অনুবাদ সুপারিশ করা হয়। এই অনুবাদের ব্যবহার থেকে সৃষ্ট কোনো ভুল বোঝাবুঝি বা বিভ্রাটের জন্য আমরা দায়ী নই। \ No newline at end of file diff --git a/translations/bn/lessons/0-course-setup/how-to-run.md b/translations/bn/lessons/0-course-setup/how-to-run.md index 86a027ad..772502fe 100644 --- a/translations/bn/lessons/0-course-setup/how-to-run.md +++ b/translations/bn/lessons/0-course-setup/how-to-run.md @@ -1,21 +1,21 @@ -# কোড কীভাবে চালাবেন +# কোড কিভাবে চালাবেন -এই পাঠ্যক্রমে অনেক কার্যকর উদাহরণ এবং ল্যাব রয়েছে যা আপনি চালাতে চাইবেন। এটি করার জন্য, আপনাকে এই পাঠ্যক্রমের অংশ হিসেবে সরবরাহ করা জুপিটার নোটবুকে পাইথন কোড চালানোর ক্ষমতা থাকতে হবে। কোড চালানোর জন্য আপনার কাছে কয়েকটি বিকল্প রয়েছে: +এই পাঠ্যক্রমে অনেক এক্সিকিউটেবল উদাহরণ এবং ল্যাব আছে যেগুলি আপনি চালাতে চাইবেন। এটি করার জন্য, আপনাকে এই পাঠ্যক্রমের অংশ হিসেবে প্রদত্ত Jupyter Notebooks-এ Python কোড চালানোর ক্ষমতা থাকতে হবে। কোড চালানোর জন্য আপনার কাছে বেশ কয়েকটি অপশন আছে: -## আপনার কম্পিউটারে লোকালভাবে চালান +## আপনার কম্পিউটারে লোকালি চালানো -আপনার কম্পিউটারে কোড চালানোর জন্য, আপনার কম্পিউটারে পাইথনের একটি সংস্করণ ইনস্টল থাকতে হবে। আমি ব্যক্তিগতভাবে **[miniconda](https://conda.io/en/latest/miniconda.html)** ইনস্টল করার পরামর্শ দিই - এটি একটি হালকা ইনস্টলেশন যা বিভিন্ন পাইথন **ভার্চুয়াল এনভায়রনমেন্ট** এর জন্য `conda` প্যাকেজ ম্যানেজার সমর্থন করে। +আপনার কম্পিউটারে লোকালি কোড চালাতে হলে Python ইনস্টলেশন প্রয়োজন। একটি সুপারিশ হলো **[miniconda](https://conda.io/en/latest/miniconda.html)** ইনস্টল করা - এটি একটি তুলনামূলক লাইটওয়েট ইনস্টলেশন যা Python **ভার্চুয়াল এনভায়রনমেন্ট** গুলির জন্য `conda` প্যাকেজ ম্যানেজার সাপোর্ট করে। -Miniconda ইনস্টল করার পরে, আপনাকে রিপোজিটরি ক্লোন করতে হবে এবং এই কোর্সের জন্য ব্যবহৃত একটি ভার্চুয়াল এনভায়রনমেন্ট তৈরি করতে হবে: +miniconda ইনস্টল করার পর, রিপোজিটরি ক্লোন করুন এবং এই কোর্সের জন্য ব্যবহৃত একটি ভার্চুয়াল এনভায়রনমেন্ট তৈরি করুন: ```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 ``` -### পাইথন এক্সটেনশন সহ ভিজ্যুয়াল স্টুডিও কোড ব্যবহার করা +### Visual Studio Code সহ Python Extension ব্যবহার -সম্ভবত এই পাঠ্যক্রমটি ব্যবহার করার সেরা উপায় হল এটি [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) এ [Python Extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste) সহ খুলে নেওয়া। +এই পাঠ্যক্রমটি সর্বোত্তমভাবে ব্যবহৃত হয় যখন এটি [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) তে [Python Extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste) সহ খোলা হয়। -> **Note**: একবার আপনি রিপোজিটরি ক্লোন করে VS Code-এ ডিরেক্টরি খুললে, এটি আপনাকে স্বয়ংক্রিয়ভাবে পাইথন এক্সটেনশন ইনস্টল করার পরামর্শ দেবে। আপনাকে উপরে বর্ণিত Miniconda ইনস্টল করতেও হবে। +> **নোট**: একবার আপনি VS Code-এ ডিরেক্টরি ক্লোন এবং ওপেন করলে, এটি স্বয়ংক্রিয়ভাবে Python এক্সটেনশন ইনস্টল করার পরামর্শ দেবে। উপরে বর্ণিত মত miniconda ইনস্টল করাও প্রয়োজন হবে। -> **Note**: যদি VS Code আপনাকে রিপোজিটরিটি কন্টেইনারে পুনরায় খুলতে বলে, তবে আপনাকে এটি স্থানীয় পাইথন ইনস্টলেশন ব্যবহার করতে অস্বীকার করতে হবে। +> **নোট**: যদি VS Code আপনাকে রিপোজিটরি একটি কন্টেইনারে পুনরায় খোলার প্রস্তাব দেয়, তাহলে আপনাকে লোকাল Python ইনস্টলেশন ব্যবহার করার জন্য এটি অস্বীকার করতে হবে। -### ব্রাউজারে জুপিটার ব্যবহার করা +### ব্রাউজারে Jupyter ব্যবহার -আপনার নিজের কম্পিউটারে ব্রাউজার থেকে জুপিটার পরিবেশও ব্যবহার করতে পারেন। প্রকৃতপক্ষে, ক্লাসিক্যাল জুপিটার এবং জুপিটার হাব স্বয়ংক্রিয় সম্পূর্ণকরণ, কোড হাইলাইটিং ইত্যাদির সাথে বেশ সুবিধাজনক ডেভেলপমেন্ট পরিবেশ প্রদান করে। +আপনি ব্রাউজার থেকে আপনার নিজের কম্পিউটারে Jupyter পরিবেশ ব্যবহার করতেও পারেন। ক্লাসিক্যাল Jupyter এবং JupyterHub উভয়ই অটো-কমপ্লিশন, কোড হাইলাইটিং ইত্যাদি সহ একটি সুবিধাজনক ডেভেলপমেন্ট পরিবেশ প্রদান করে। -লোকালভাবে জুপিটার শুরু করতে, কোর্সের ডিরেক্টরিতে যান এবং নিম্নলিখিতটি চালান: +লোকালি Jupyter চালু করতে, কোর্সের ডিরেক্টরিতে যান এবং নিচের কমান্ড চালান: ```bash jupyter notebook ``` -অথবা + অথবা ```bash jupyterhub ``` -এরপর আপনি যেকোনো `.ipynb` ফাইলের দিকে যেতে পারেন, সেগুলি খুলতে পারেন এবং কাজ শুরু করতে পারেন। + তারপর আপনি `.ipynb` ফাইলগুলিতে যেকোনোটা খুলে কাজ শুরু করতে পারবেন। ### কন্টেইনারে চালানো -পাইথন ইনস্টলেশনের একটি বিকল্প হতে পারে কোডটি কন্টেইনারে চালানো। যেহেতু আমাদের রিপোজিটরিতে একটি বিশেষ `.devcontainer` ফোল্ডার রয়েছে যা এই রিপোর জন্য কন্টেইনার কীভাবে তৈরি করতে হবে তা নির্দেশ করে, VS Code আপনাকে কোডটি কন্টেইনারে পুনরায় খুলতে অফার করবে। এটি ডকার ইনস্টলেশন প্রয়োজন এবং আরও জটিল হতে পারে, তাই আমরা এটি আরও অভিজ্ঞ ব্যবহারকারীদের জন্য সুপারিশ করি। +Python ইনস্টলেশনের একটি বিকল্প হতে পারে কোডটি একটি কন্টেইনারে চালানো। আমাদের রিপোজিটরিগুলো একটি বিশেষ `.devcontainer` ফোল্ডার সরবরাহ করে যা এই রিপো জন্য কন্টেইনার কিভাবে বিল্ড করবেন তা নির্দেশ করে। VS Code কোডটি কন্টেইনারে পুনরায় খোলার সুযোগ দেয়। এর জন্য Docker ইনস্টলেশন প্রয়োজন, এবং এটি একটু জটিল, তাই আমরা এটি বেশি অভিজ্ঞ ব্যবহারকারীদের জন্য সুপারিশ করি। ## ক্লাউডে চালানো -যদি আপনি লোকালভাবে পাইথন ইনস্টল করতে না চান এবং কিছু ক্লাউড রিসোর্সে অ্যাক্সেস থাকে - একটি ভালো বিকল্প হতে পারে কোডটি ক্লাউডে চালানো। এটি করার কয়েকটি উপায় রয়েছে: +যদি আপনি Python লোকালি ইনস্টল করতে না চান, এবং আপনার কাছে কিছু ক্লাউড রিসোর্স অ্যাক্সেস থাকে, তাহলে কোড ক্লাউডে চালানোর ভালো বিকল্প রয়েছে। আপনি নিচের কয়েকটি উপায়ে এটি করতে পারেন: -* **[GitHub Codespaces](https://github.com/features/codespaces)** ব্যবহার করে, যা GitHub-এ আপনার জন্য তৈরি একটি ভার্চুয়াল পরিবেশ, যা VS Code ব্রাউজার ইন্টারফেসের মাধ্যমে অ্যাক্সেসযোগ্য। যদি আপনার Codespaces-এ অ্যাক্সেস থাকে, আপনি শুধু রিপোর **Code** বোতামে ক্লিক করুন, একটি কোডস্পেস শুরু করুন এবং দ্রুত চালানো শুরু করুন। -* **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)** ব্যবহার করে। [Binder](https://mybinder.org) হল ক্লাউডে বিনামূল্যে কম্পিউটিং রিসোর্স যা আপনাদের মতো লোকদের জন্য GitHub-এ কিছু কোড পরীক্ষা করার জন্য সরবরাহ করা হয়। ফ্রন্ট পেজে একটি বোতাম রয়েছে যা রিপোজিটরিটি Binder-এ খুলতে দেয় - এটি আপনাকে দ্রুত Binder সাইটে নিয়ে যাবে, যা অন্তর্নিহিত কন্টেইনার তৈরি করবে এবং জুপিটার ওয়েব ইন্টারফেসটি নির্বিঘ্নে শুরু করবে। +* **[GitHub Codespaces](https://github.com/features/codespaces)** ব্যবহার করা, যা GitHub-এ আপনার জন্য তৈরি একটি ভার্চুয়াল এনভায়রনমেন্ট, VS Code ব্রাউজার ইন্টারফেসের মাধ্যমে প্রবেশযোগ্য। যদি Codespaces অ্যাক্সেস থাকে, আপনি রিপোর **Code** বোতামে ক্লিক করে Codespace শুরু করতে পারেন এবং দ্রুত কোড চালাতে পারবেন। +* **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)** ব্যবহার করা। [Binder](https://mybinder.org) বিনামূল্যে কম্পিউটিং রিসোর্স সরবরাহ করে যারা GitHub-এ কিছু কোড পরীক্ষা করতে চান তাদের জন্য। মূল পৃষ্ঠায় একটি বাটন আছে যা রিপোজিটরিকে Binder-এ খুলতে দেয় - এটি দ্রুত আপনাকে Binder সাইটে নিয়ে যাবে, যেখানে একটি অন্তর্নিহিত কন্টেইনার তৈরি করে Jupyter ওয়েব ইন্টারফেস চালু করবে স্বচ্ছন্দে। -> **Note**: অপব্যবহার রোধ করতে, Binder কিছু ওয়েব রিসোর্সে অ্যাক্সেস ব্লক করেছে। এটি কিছু কোড কাজ করতে বাধা দিতে পারে, যা পাবলিক ইন্টারনেট থেকে মডেল এবং/অথবা ডেটাসেট নিয়ে আসে। আপনাকে কিছু বিকল্প খুঁজে বের করতে হতে পারে। এছাড়াও, Binder দ্বারা সরবরাহিত কম্পিউট রিসোর্সগুলি বেশ মৌলিক, তাই প্রশিক্ষণ ধীর হবে, বিশেষ করে পরবর্তী আরও জটিল পাঠে। +> **নোট**: অপব্যবহার এড়াতে, Binder কিছু ওয়েব রিসোর্স ব্লক করে রেখেছে। এটি কিছু কোড কাজ করতে বাধা দিতে পারে, যেগুলি পাবলিক ইন্টারনেট থেকে মডেল ও/অথবা ডেটাসেট ফেচ করে। আপনাকে কিছু সমাধান খুঁজে বের করতে হতে পারে। এছাড়া, Binder এর প্রদানকৃত কম্পিউটিং রিসোর্স যথেষ্ট বেসিক, তাই ট্রেনিং ধীর হবে, বিশেষ করে পরবর্তী, বেশি জটিল পাঠে। -## GPU সহ ক্লাউডে চালানো +## জিপিইউ সহ ক্লাউডে চালানো -এই পাঠ্যক্রমের কিছু পরবর্তী পাঠ GPU সমর্থন থেকে অনেক উপকৃত হবে, কারণ অন্যথায় প্রশিক্ষণ অত্যন্ত ধীর হবে। আপনি কয়েকটি বিকল্প অনুসরণ করতে পারেন, বিশেষ করে যদি আপনার কাছে [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) বা আপনার প্রতিষ্ঠানের মাধ্যমে ক্লাউডে অ্যাক্সেস থাকে: +এই পাঠ্যক্রমের কিছু পরবর্তী পাঠ GPU সাপোর্ট থেকে উল্লেখযোগ্যভাবে উপকৃত হবে। মডেল ট্রেনিং, উদাহরণস্বরূপ, অন্যথায় বেশ ধীর হতে পারে। কিছু বিকল্প অনুসরণ করতে পারেন, বিশেষত যদি আপনার ক্লাউড অ্যাক্সেস থাকে [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) এর মাধ্যমে, বা আপনার প্রতিষ্ঠান থেকে: -* [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) তৈরি করুন এবং জুপিটার এর মাধ্যমে এতে সংযোগ করুন। এরপর আপনি সরাসরি মেশিনে রিপো ক্লোন করতে পারেন এবং শেখা শুরু করতে পারেন। NC-সিরিজ VM-এ GPU সমর্থন রয়েছে। +* [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) তৈরি করুন এবং Jupyter এর মাধ্যমে এতে সংযোগ করুন। এরপর আপনি সরাসরি মেশিনে রিপো ক্লোন করে শিখতে শুরু করতে পারবেন। NC-সিরিজ VM গুলোতে GPU সাপোর্ট থাকে। -> **Note**: কিছু সাবস্ক্রিপশন, যার মধ্যে Azure for Students অন্তর্ভুক্ত, ডিফল্টভাবে GPU সমর্থন প্রদান করে না। আপনাকে টেকনিক্যাল সাপোর্ট রিকোয়েস্টের মাধ্যমে অতিরিক্ত GPU কোরের জন্য অনুরোধ করতে হতে পারে। +> **নোট**: কিছু সাবস্ক্রিপশন, Azure for Students সহ, ডিফল্টভাবে GPU সাপোর্ট দেয় না। অতিরিক্ত GPU কোর পেতে হতে পারে প্রযুক্তিগত সাপোর্ট অনুরোধের মাধ্যমে। -* [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) তৈরি করুন এবং সেখানে নোটবুক ফিচার ব্যবহার করুন। [এই ভিডিও](https://azure-for-academics.github.io/quickstart/azureml-papers/) দেখায় কীভাবে একটি রিপোজিটরি Azure ML নোটবুকে ক্লোন করতে হয় এবং এটি ব্যবহার শুরু করতে হয়। +* [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) তৈরি করুন এবং সেখানকার Notebook ফিচার ব্যবহার করুন। [এই ভিডিওটি](https://azure-for-academics.github.io/quickstart/azureml-papers/) দেখায় কিভাবে Azure ML নোটবুকে একটি রিপোজিটরি ক্লোন করে ব্যবহার শুরু করবেন। -আপনি Google Colab-ও ব্যবহার করতে পারেন, যা কিছু বিনামূল্যে GPU সমর্থন সহ আসে, এবং সেখানে জুপিটার নোটবুক আপলোড করে একে একে সেগুলি চালাতে পারেন। +আপনি Google Colab ও ব্যবহার করতে পারেন, যেখানে কিছু ফ্রি GPU সাপোর্ট আসে, এবং একে একে সেখানে Jupyter Notebooks আপলোড করে চালাতে পারবেন। +--- + + **অস্বীকৃতি**: -এই নথিটি AI অনুবাদ পরিষেবা [Co-op Translator](https://github.com/Azure/co-op-translator) ব্যবহার করে অনুবাদ করা হয়েছে। আমরা যথাসাধ্য সঠিকতা নিশ্চিত করার চেষ্টা করি, তবে অনুগ্রহ করে মনে রাখবেন যে স্বয়ংক্রিয় অনুবাদে ত্রুটি বা অসঙ্গতি থাকতে পারে। মূল ভাষায় থাকা নথিটিকে প্রামাণিক উৎস হিসেবে বিবেচনা করা উচিত। গুরুত্বপূর্ণ তথ্যের জন্য, পেশাদার মানব অনুবাদ সুপারিশ করা হয়। এই অনুবাদ ব্যবহারের ফলে কোনো ভুল বোঝাবুঝি বা ভুল ব্যাখ্যা হলে আমরা তার জন্য দায়ী থাকব না। \ No newline at end of file +এই ডকুমেন্টটি এআই অনুবাদ সেবা [Co-op Translator](https://github.com/Azure/co-op-translator) ব্যবহার করে অনূদিত হয়েছে। আমরা যথাসাধ্য সঠিকতার চেষ্টা করি, তবে স্বয়ংক্রিয় অনুবাদে ত্রুটি বা ভুল থাকতে পারে। মূল ভাষায় থাকা মূল ডকুমেন্টটিই কর্তৃত্বপ্রাপ্ত উৎস হিসেবে বিবেচিত হওয়া উচিত। গুরুত্বপূর্ণ তথ্যের জন্য পেশাদার মানব অনুবাদ প্রয়োজন। এই অনুবাদের ব্যবহারে সৃষ্ট কোনো ভুল বোঝাবুঝি বা ব্যাখ্যার জন্য আমরা দায়বদ্ধ নই। + \ No newline at end of file diff --git a/translations/bn/lessons/2-Symbolic/Animals.ipynb b/translations/bn/lessons/2-Symbolic/Animals.ipynb index 027c6051..2d9e6180 100644 --- a/translations/bn/lessons/2-Symbolic/Animals.ipynb +++ b/translations/bn/lessons/2-Symbolic/Animals.ipynb @@ -6,25 +6,25 @@ "collapsed": true }, "source": [ - "# প্রাণী বিশেষজ্ঞ সিস্টেম বাস্তবায়ন\n", + "# একটি প্রাণী বিশেষজ্ঞ ব্যবস্থা বাস্তবায়ন\n", "\n", "[AI for Beginners Curriculum](http://github.com/microsoft/ai-for-beginners) থেকে একটি উদাহরণ।\n", "\n", - "এই উদাহরণে, আমরা একটি সহজ জ্ঞান-ভিত্তিক সিস্টেম বাস্তবায়ন করব যা কিছু শারীরিক বৈশিষ্ট্যের উপর ভিত্তি করে একটি প্রাণী নির্ধারণ করবে। সিস্টেমটি নিম্নলিখিত AND-OR গাছ দ্বারা উপস্থাপন করা যেতে পারে (এটি পুরো গাছের একটি অংশ, আমরা সহজেই আরও কিছু নিয়ম যোগ করতে পারি):\n", + "এই নমুনায়, আমরা কিছু শারীরিক বৈশিষ্ট্যের উপর ভিত্তি করে একটি প্রাণী নির্ধারণ করার জন্য একটি সহজ জ্ঞান-ভিত্তিক ব্যবস্থা বাস্তবায়ন করব। এই ব্যবস্থা নিম্নলিখিত AND-OR গাছ দ্বারা প্রতিনিধিত্ব করা যেতে পারে (এটি সম্পূর্ণ গাছের একটি অংশ, আমরা সহজেই আরও কিছু নিয়ম যোগ করতে পারি):\n", "\n", - "![](../../../../translated_images/bn/AND-OR-Tree.5592d2c70187f283.webp)\n" + "![](../../../../../../translated_images/bn/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## আমাদের নিজস্ব এক্সপার্ট সিস্টেম শেল ব্যাকওয়ার্ড ইনফারেন্স সহ\n", + "## আমাদের নিজস্ব বিশেষজ্ঞ সিস্টেম শেল ব্যাকওয়ার্ড ইনফারেন্স সহ\n", "\n", - "চলুন প্রোডাকশন রুলের উপর ভিত্তি করে জ্ঞান উপস্থাপনার জন্য একটি সহজ ভাষা সংজ্ঞায়িত করার চেষ্টা করি। আমরা রুল সংজ্ঞায়িত করার জন্য Python ক্লাসগুলোকে কীওয়ার্ড হিসেবে ব্যবহার করব। মূলত তিন ধরনের ক্লাস থাকবে:\n", - "* `Ask` একটি প্রশ্নকে উপস্থাপন করে যা ব্যবহারকারীকে জিজ্ঞাসা করতে হবে। এটি সম্ভাব্য উত্তরগুলোর সেট ধারণ করে।\n", - "* `If` একটি নিয়মকে উপস্থাপন করে, এবং এটি শুধুমাত্র নিয়মের বিষয়বস্তু সংরক্ষণের জন্য একটি সিনট্যাকটিক সুগার।\n", - "* `AND`/`OR` হল ক্লাসগুলো যা গাছের AND/OR শাখাগুলোকে উপস্থাপন করে। এগুলো শুধু ভিতরে আর্গুমেন্টগুলোর তালিকা সংরক্ষণ করে। কোড সহজ করার জন্য, সমস্ত কার্যকারিতা প্যারেন্ট ক্লাস `Content`-এ সংজ্ঞায়িত করা হয়েছে।\n" + "চলুন প্রোডাকশন রুলসের ভিত্তিতে জ্ঞান উপস্থাপনার জন্য একটি সহজ ভাষা সংজ্ঞায়িত করার চেষ্টা করি। আমরা নিয়মগুলি সংজ্ঞায়িত করতে কীওয়ার্ড হিসাবে পাইথন ক্লাস ব্যবহার করব। মূলত ৩ ধরণের ক্লাস থাকবে:\n", + "* `Ask` একটি প্রশ্ন প্রতিনিধিত্ব করে যা ব্যবহারকারীর কাছে জিজ্ঞাসা করতে হবে। এটি সম্ভাব্য উত্তরগুলির সেট ধারণ করে।\n", + "* `If` একটি নিয়ম প্রতিনিধিত্ব করে, এবং এটি কেবল নিয়মের বিষয়বস্তু সংরক্ষণ করার জন্য একটি সিনট্যাকটিক সুগার।\n", + "* `AND`/`OR` হল গাছের AND/OR শাখাগুলি প্রতিনিধিত্ব করার জন্য ক্লাস। তারা কেবল ভিতরের আর্গুমেন্টগুলির তালিকা সংরক্ষণ করে। কোড সহজ করার জন্য, সমস্ত কার্যকারিতা প্যারেন্ট ক্লাস `Content`-এ সংজ্ঞায়িত।\n" ] }, { @@ -66,7 +66,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "আমাদের সিস্টেমে, কর্মরত মেমোরি **attribute-value pairs** হিসাবে **তথ্যগুলির** তালিকা ধারণ করবে। জ্ঞানভান্ডারকে একটি বড় ডিকশনারি হিসাবে সংজ্ঞায়িত করা যেতে পারে যা ক্রিয়াগুলিকে (নতুন তথ্য যা কর্মরত মেমোরিতে যোগ করা উচিত) শর্তগুলির সাথে মানচিত্র করে, যা AND-OR অভিব্যক্তি হিসাবে প্রকাশিত হয়। এছাড়াও, কিছু তথ্য `Ask` করা যেতে পারে।\n" + "আমাদের সিস্টেমে, ওয়ার্কিং মেমোরি থাকবে **ফ্যাক্টস** এর তালিকা যা **অ্যাট্রিবিউট-ভ্যালু পেয়ার** হিসেবে থাকবে। নলেজবেসকে এক বড় ডিকশনারি হিসাবে সংজ্ঞায়িত করা যেতে পারে যা অ্যাকশন (নতুন ফ্যাক্ট যা ওয়ার্কিং মেমোরিতে ইনসার্ট করা উচিত) গুলিকে শর্তের সাথে ম্যাপ করে, যা AND-OR এক্সপ্রেশন আকারে প্রকাশ করা হয়। এছাড়াও, কিছু ফ্যাক্টকে `Ask` করা যেতে পারে।\n" ] }, { @@ -99,13 +99,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "পিছনের দিকের অনুমান সম্পাদনের জন্য, আমরা `Knowledgebase` ক্লাসটি সংজ্ঞায়িত করব। এটি অন্তর্ভুক্ত করবে:\n", - "* কার্যকরী `memory` - একটি ডিকশনারি যা অ্যাট্রিবিউটগুলিকে মানগুলোর সাথে ম্যাপ করে\n", - "* Knowledgebase-এর `rules` - উপরে সংজ্ঞায়িত ফরম্যাটে\n", + "পিছনে ইনফারেন্স (backward inference) সম্পাদন করার জন্য, আমরা `Knowledgebase` ক্লাসটি সংজ্ঞায়িত করব। এটি থাকবে:\n", + "* কর্মরত `memory` - একটি ডিকশনারি যা বৈশিষ্ট্যগুলি মানের সাথে ম্যাপ করে\n", + "* উপরের নির্ধারিত ফরম্যাটে নলেজবেসের `rules`\n", "\n", - "দুটি প্রধান পদ্ধতি হল:\n", - "* `get` - একটি অ্যাট্রিবিউটের মান প্রাপ্ত করার জন্য, প্রয়োজন হলে অনুমান সম্পাদন করে। উদাহরণস্বরূপ, `get('color')` একটি রঙের স্লটের মান পাবে (প্রয়োজন হলে জিজ্ঞাসা করবে এবং পরে ব্যবহারের জন্য কার্যকরী মেমোরিতে মানটি সংরক্ষণ করবে)। যদি আমরা `get('color:blue')` জিজ্ঞাসা করি, এটি একটি রঙ জিজ্ঞাসা করবে এবং তারপর রঙের উপর নির্ভর করে `y`/`n` মানটি ফেরত দেবে।\n", - "* `eval` - আসল অনুমান সম্পাদন করে, অর্থাৎ AND/OR গাছটি অনুসন্ধান করে, সাব-গোলগুলো মূল্যায়ন করে ইত্যাদি।\n" + "দুটি প্রধান পদ্ধতি হলো:\n", + "* `get` একটি বৈশিষ্ট্যের মান পাওয়ার জন্য, প্রয়োজনে ইনফারেন্স সম্পাদন করে। উদাহরণস্বরূপ, `get('color')` রং স্লটের মান পাবে (যদি প্রয়োজন হয় তাহলে জিজ্ঞাসা করবে, এবং পরে ব্যবহার করার জন্য কর্মরত মেমরিতে মান সংরক্ষণ করবে)। যদি আমরা `get('color:blue')` জিজ্ঞাসা করি, এটি একটি রং সম্পর্কে প্রশ্ন করবে, এবং তারপর রঙের উপর ভিত্তি করে `y`/`n` মান ফেরত দেবে।\n", + "* `eval` প্রকৃত ইনফারেন্স সম্পাদন করে, অর্থাৎ AND/OR গাছ অনুসরণ করে, উপ-লক্ষ্যগুলি মূল্যায়ন করে ইত্যাদি।\n" ] }, { @@ -172,7 +172,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "এখন চলুন আমাদের প্রাণী জ্ঞানভাণ্ডার সংজ্ঞায়িত করি এবং পরামর্শ কার্যক্রম সম্পাদন করি। লক্ষ্য করুন যে এই কলটি আপনাকে প্রশ্ন করবে। আপনি `y`/`n` টাইপ করে হ্যাঁ-না প্রশ্নের উত্তর দিতে পারেন, অথবা দীর্ঘ বহুবিকল্প প্রশ্নের জন্য সংখ্যা (0..N) নির্দিষ্ট করে উত্তর দিতে পারেন।\n" + "এখন আমরা আমাদের প্রাণীজগতের জ্ঞানভাণ্ডার সংজ্ঞায়িত করব এবং পরামর্শ সম্পাদন করব। লক্ষ্য করুন যে এই কলটি আপনাকে প্রশ্ন করবে। আপনি হ্যাঁ-না প্রশ্নের জন্য `y`/`n` টাইপ করে উত্তর দিতে পারেন, অথবা দীর্ঘতর বহুনির্বাচনী প্রশ্নের জন্য সংখ্যা (0..N) নির্দিষ্ট করে উত্তর দিতে পারেন।\n" ] }, { @@ -229,11 +229,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## PyKnow ব্যবহার করে ফরোয়ার্ড ইনফারেন্স\n", + "## ফরওয়ার্ড ইনফারেন্সের জন্য Experta ব্যবহার করা\n", "\n", - "পরবর্তী উদাহরণে, আমরা জ্ঞান উপস্থাপনার জন্য একটি লাইব্রেরি, [PyKnow](https://github.com/buguroo/pyknow/) ব্যবহার করে ফরোয়ার্ড ইনফারেন্স বাস্তবায়নের চেষ্টা করব। **PyKnow** হলো একটি লাইব্রেরি যা পাইথনে ফরোয়ার্ড ইনফারেন্স সিস্টেম তৈরি করার জন্য ব্যবহৃত হয় এবং এটি ক্লাসিক্যাল পুরনো সিস্টেম [CLIPS](http://www.clipsrules.net/index.html)-এর মতো ডিজাইন করা হয়েছে।\n", + "পরবর্তী উদাহরণে, আমরা জ্ঞান উপস্থাপনার একটি লাইব্রেরি [Experta](https://github.com/nilp0inter/experta) ব্যবহার করে ফরওয়ার্ড ইনফারেন্স বাস্তবায়ন করার চেষ্টা করব। **Experta** হল পাইথনে ফরওয়ার্ড ইনফারেন্স সিস্টেম তৈরি করার জন্য একটি লাইব্রেরি, যা ঐতিহ্যবাহী পুরনো সিস্টেম [CLIPS](http://www.clipsrules.net/index.html) এর সাথে অনুরূপ হওয়ার জন্য ডিজাইন করা হয়েছে।\n", "\n", - "আমরা চাইলে নিজেরাই ফরোয়ার্ড চেইনিং বাস্তবায়ন করতে পারতাম, তেমন কোনো বড় সমস্যা ছাড়াই। তবে সাধারণ পদ্ধতিতে করা বাস্তবায়নগুলো সাধারণত খুব কার্যকর হয় না। আরও কার্যকরভাবে নিয়ম মেলানোর জন্য একটি বিশেষ অ্যালগরিদম [Rete](https://en.wikipedia.org/wiki/Rete_algorithm) ব্যবহার করা হয়।\n" + "আমরাও আমাদের নিজেরাই ফরওয়ার্ড চেইনিং বাস্তবায়ন করতে পারতাম বড় সমস্যাই ছাড়াই, কিন্তু সরল বাস্তবায়ন সাধারণত খুব দক্ষ নয়। আরও কার্যকর রুল ম্যাচিংয়ের জন্য একটি বিশেষ অ্যালগোরিদম [Rete](https://en.wikipedia.org/wiki/Rete_algorithm) ব্যবহৃত হয়।\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": [ - "আমরা আমাদের সিস্টেমকে `KnowledgeEngine` এর সাবক্লাস হিসাবে একটি ক্লাস হিসেবে সংজ্ঞায়িত করব। প্রতিটি নিয়ম একটি পৃথক ফাংশন দ্বারা সংজ্ঞায়িত করা হয় যা `@Rule` অ্যানোটেশন ব্যবহার করে, যা নির্ধারণ করে কখন নিয়মটি কার্যকর হবে। নিয়মের ভিতরে, আমরা `declare` ফাংশন ব্যবহার করে নতুন তথ্য যোগ করতে পারি, এবং সেই তথ্য যোগ করার ফলে ফরওয়ার্ড ইনফারেন্স ইঞ্জিন দ্বারা আরও কিছু নিয়ম কার্যকর হবে।\n" + "আমরা আমাদের সিস্টেমকে একটি ক্লাস হিসেবে সংজ্ঞায়িত করব যা `KnowledgeEngine` এর সাবক্লাস। প্রতিটি নিয়ম একটি পৃথক ফাংশনের মাধ্যমে সংজ্ঞায়িত হয় যার সাথে `@Rule` অ্যানোটেশন থাকে, যা নির্ধারণ করে কখন নিয়মটি কার্যকর হবে। নিয়মের ভিতরে, আমরা `declare` ফাংশন ব্যবহার করে নতুন তথ্য যোগ করতে পারি, এবং সেই তথ্য যুক্ত করার ফলে ফরওয়ার্ড ইনফারেন্স ইঞ্জিন দ্বারা আরও কিছু নিয়ম চালানো হবে।\n" ] }, { @@ -378,7 +377,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "একবার আমরা একটি জ্ঞানভাণ্ডার সংজ্ঞায়িত করলে, আমরা আমাদের কার্যকরী মেমোরি কিছু প্রাথমিক তথ্য দিয়ে পূরণ করি, এবং তারপর `run()` পদ্ধতি কল করি সিদ্ধান্ত গ্রহণের জন্য। এর ফলে আপনি দেখতে পাবেন যে নতুন সিদ্ধান্ত নেওয়া তথ্য কার্যকরী মেমোরিতে যোগ করা হয়েছে, যার মধ্যে প্রাণীর সম্পর্কে চূড়ান্ত তথ্যও রয়েছে (যদি আমরা সমস্ত প্রাথমিক তথ্য সঠিকভাবে সেট আপ করি)।\n" + "একবার আমরা একটি জ্ঞানভান্ডার সংজ্ঞায়িত করলে, আমরা আমাদের কার্যকরী মেমোরিতে কিছু প্রাথমিক তথ্য পূরণ করি, এবং তারপর তথ্যানুসন্ধান সম্পাদনের জন্য `run()` মেথডটি কল করি। আপনি ফলাফল হিসেবে দেখতে পাবেন যে নতুন অনুমেয় তথ্য কার্যকরী মেমোরিতে যুক্ত হচ্ছে, যার মধ্যে প্রাণী সম্পর্কে চূড়ান্ত তথ্যও রয়েছে (যদি আমরা সমস্ত প্রাথমিক তথ্য সঠিকভাবে সেট করি)।\n" ] }, { @@ -440,7 +439,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n---\n\n**অস্বীকৃতি**: \nএই নথিটি AI অনুবাদ পরিষেবা [Co-op Translator](https://github.com/Azure/co-op-translator) ব্যবহার করে অনুবাদ করা হয়েছে। আমরা যথাসম্ভব সঠিকতার জন্য চেষ্টা করি, তবে অনুগ্রহ করে মনে রাখবেন যে স্বয়ংক্রিয় অনুবাদে ত্রুটি বা অসঙ্গতি থাকতে পারে। মূল ভাষায় থাকা নথিটিকে প্রামাণিক উৎস হিসেবে বিবেচনা করা উচিত। গুরুত্বপূর্ণ তথ্যের জন্য, পেশাদার মানব অনুবাদ সুপারিশ করা হয়। এই অনুবাদ ব্যবহারের ফলে কোনো ভুল বোঝাবুঝি বা ভুল ব্যাখ্যা হলে আমরা দায়বদ্ধ থাকব না।\n" + "---\n\n\n**অস্বীকৃতি**:\nএই নথিটি AI অনুবাদ সেবা [Co-op Translator](https://github.com/Azure/co-op-translator) ব্যবহার করে অনুশীলিত হয়েছে। আমরা যথাসাধ্য সঠিকতার প্রতি যত্নশীল হলেও, স্বয়ংক্রিয় অনুবাদে ত্রুটি বা অসঙ্গতি থাকতে পারে তা অনুগ্রহ করে লক্ষ্য করুন। মূল নথিটি তার মাতৃভাষায় সচেতন উৎস হিসেবে বিবেচনা করা উচিত। গুরুত্বপূর্ণ তথ্যের জন্য পেশাদার মানুষের অনুবাদ গ্রহণ করা উচিত। এই অনুবাদের ব্যবহারের ফলে হওয়া কোনো ভুলবোঝাবুঝি বা ভুল অর্থ গ্রহণের জন্য আমরা দায়ী নই।\n\n" ] } ], @@ -467,8 +466,8 @@ "version": "3.11.2" }, "coopTranslator": { - "original_hash": "ab2bd97b0453415b89a469284609a8ce", - "translation_date": "2025-08-28T11:30:08+00:00", + "original_hash": "8ef43db4b9182239fd150a76bd494fdb", + "translation_date": "2026-01-15T12:52:14+00:00", "source_file": "lessons/2-Symbolic/Animals.ipynb", "language_code": "bn" } diff --git a/translations/bn/lessons/2-Symbolic/README.md b/translations/bn/lessons/2-Symbolic/README.md index 0e873aeb..0cad8d99 100644 --- a/translations/bn/lessons/2-Symbolic/README.md +++ b/translations/bn/lessons/2-Symbolic/README.md @@ -1,116 +1,116 @@ # জ্ঞান উপস্থাপন এবং বিশেষজ্ঞ সিস্টেম -![সিম্বলিক AI বিষয়বস্তুর সারাংশ](../../../../translated_images/bn/ai-symbolic.715a30cb610411a6.webp) +![সংকেতমূলক AI সামগ্রীর সারাংশ](../../../../../../translated_images/bn/ai-symbolic.715a30cb610411a6.webp) > স্কেচনোট [Tomomi Imura](https://twitter.com/girlie_mac) দ্বারা -কৃত্রিম বুদ্ধিমত্তার অনুসন্ধান মানুষের মতো করে পৃথিবীকে বোঝার জন্য জ্ঞান অনুসন্ধানের উপর ভিত্তি করে। কিন্তু এটি কীভাবে করা সম্ভব? +কৃত্রিম বুদ্ধিমত্তার জন্য যাত্রা জ্ঞানের অনুসন্ধানের উপর ভিত্তি করে, যেভাবে মানুষ বোঝে সেভাবে পৃথিবীকে অর্থ দেওয়ার জন্য। কিন্তু এটা কীভাবে করা যেতে পারে? -## [পূর্ব-লেকচার কুইজ](https://ff-quizzes.netlify.app/en/ai/quiz/3) +## [পূর্ব-অভিযান কুইজ](https://ff-quizzes.netlify.app/en/ai/quiz/3) -AI-এর প্রাথমিক দিনগুলোতে, বুদ্ধিমান সিস্টেম তৈরির জন্য টপ-ডাউন পদ্ধতি (পূর্ববর্তী পাঠে আলোচনা করা হয়েছে) জনপ্রিয় ছিল। ধারণাটি ছিল মানুষের কাছ থেকে জ্ঞান সংগ্রহ করে মেশিন-পঠনযোগ্য ফর্মে রূপান্তর করা এবং তারপর এটি ব্যবহার করে স্বয়ংক্রিয়ভাবে সমস্যার সমাধান করা। এই পদ্ধতি দুটি বড় ধারণার উপর ভিত্তি করে ছিল: +AI এর প্রাথমিক দিনে, বুদ্ধিমান সিস্টেম তৈরি করার জন্য শীর্ষ-থেকে-নিচের পদ্ধতি (পূর্ববর্তী পাঠে আলোচনা করা হয়েছে) জনপ্রিয় ছিল। ধারণাটি ছিল মানুষের কাছ থেকে জ্ঞান সংগ্রহ করে যেকোনো যন্ত্র-পাঠযোগ্য রূপে রূপান্তর করা, এবং তারপর এটি স্বয়ংক্রিয়ভাবে সমস্যা সমাধানে ব্যবহার করা। এই পদ্ধতিটি দুইটি বড় ধারণার উপর ভিত্তি করেছিল: * জ্ঞান উপস্থাপন * যুক্তি ## জ্ঞান উপস্থাপন -সিম্বলিক AI-এর একটি গুরুত্বপূর্ণ ধারণা হল **জ্ঞান**। এটি *তথ্য* বা *ডেটা* থেকে জ্ঞানকে আলাদা করা গুরুত্বপূর্ণ। উদাহরণস্বরূপ, কেউ বলতে পারে যে বইগুলো জ্ঞান ধারণ করে, কারণ বই পড়ে একজন বিশেষজ্ঞ হতে পারে। তবে, বইগুলোতে যা থাকে তা আসলে *ডেটা*, এবং বই পড়ে এবং এই ডেটাকে আমাদের বিশ্ব মডেলে সংযুক্ত করে আমরা এই ডেটাকে জ্ঞানে রূপান্তর করি। +সঙ্কেতমূলক AI এর একটি গুরুত্বপূর্ণ ধারণা হল **জ্ঞান**। এটি *তথ্য* বা *ডেটা* থেকে পৃথক করা গুরুত্বপূর্ণ। উদাহরণস্বরূপ, কেউ বলতে পারেন যে বইতে জ্ঞান থাকে, কারণ বই পড়ে একজন বিশেষজ্ঞ হওয়া যায়। তবে, আসলে বইয়ে যা থাকে তাকে *ডেটা* বলা হয়, আর বই পড়ে এই ডেটা আমাদের বিশ্ব মডেলে একত্রিত করে আমরা ডেটাকে জ্ঞান রূপে রুপান্তর করি। -> ✅ **জ্ঞান** হল এমন কিছু যা আমাদের মাথায় থাকে এবং আমাদের পৃথিবী সম্পর্কে বোঝার প্রতিনিধিত্ব করে। এটি একটি সক্রিয় **শেখার** প্রক্রিয়ার মাধ্যমে অর্জিত হয়, যা আমরা যে তথ্য পাই তা আমাদের সক্রিয় বিশ্ব মডেলে সংযুক্ত করে। +> ✅ **জ্ঞান** এমন কিছু যা আমাদের মস্তিষ্কে ধারণ, এবং যা আমাদের পৃথিবীর বোঝাপড়াকে প্রতিনিধিত্ব করে। এটি একটি সক্রিয় **শিক্ষণের** মাধ্যমে প্রাপ্ত হয়, যা আমরা প্রাপ্ত তথ্যের টুকরোকে আমাদের সক্রিয় বিশ্ব মডেলে যুক্ত করে। -আমরা প্রায়ই জ্ঞানকে কঠোরভাবে সংজ্ঞায়িত করি না, তবে আমরা এটি অন্যান্য সম্পর্কিত ধারণার সাথে [DIKW Pyramid](https://en.wikipedia.org/wiki/DIKW_pyramid) ব্যবহার করে সামঞ্জস্য করি। এতে নিম্নলিখিত ধারণাগুলো অন্তর্ভুক্ত রয়েছে: +সাধারণত আমরা জ্ঞানকে কঠোরভাবে সংজ্ঞায়িত করি না, কিন্তু এটি অন্যান্য সম্পর্কিত ধারণার সাথে সংযুক্ত করি [DIKW পিরামিড](https://en.wikipedia.org/wiki/DIKW_pyramid) ব্যবহার করে। এটি নিম্নলিখিত ধারণাগুলি অন্তর্ভুক্ত করে: -* **ডেটা** হল এমন কিছু যা শারীরিক মাধ্যমে উপস্থাপিত হয়, যেমন লিখিত পাঠ্য বা কথিত শব্দ। ডেটা মানুষের থেকে স্বাধীনভাবে বিদ্যমান এবং এটি মানুষের মধ্যে স্থানান্তরিত হতে পারে। -* **তথ্য** হল আমাদের মাথায় ডেটার ব্যাখ্যা। উদাহরণস্বরূপ, যখন আমরা *কম্পিউটার* শব্দটি শুনি, তখন আমাদের কিছু ধারণা থাকে এটি কী। -* **জ্ঞান** হল তথ্যকে আমাদের বিশ্ব মডেলে সংযুক্ত করার প্রক্রিয়া। উদাহরণস্বরূপ, একবার আমরা শিখি যে কম্পিউটার কী, তখন আমরা এটি কীভাবে কাজ করে, এর দাম কত এবং এটি কী কাজে ব্যবহার করা যায় তা সম্পর্কে ধারণা পেতে শুরু করি। এই আন্তঃসম্পর্কিত ধারণাগুলোর নেটওয়ার্ক আমাদের জ্ঞান গঠন করে। -* **বুদ্ধিমত্তা** হল আমাদের পৃথিবী সম্পর্কে বোঝার আরও একটি স্তর, এবং এটি *মেটা-জ্ঞান* প্রতিনিধিত্ব করে, যেমন জ্ঞান কখন এবং কীভাবে ব্যবহার করা উচিত তার ধারণা। +* **ডেটা** হল এমন কিছু যা শারীরিক মাধ্যমে উপস্থাপিত হয়, যেমন লিখিত লেখা বা মৌখিক শব্দ। ডেটা মানুষের থেকে স্বতন্ত্রভাবে উপস্থিত থাকে এবং মানুষের মধ্যে স্থানান্তরযোগ্য। +* **তথ্য** হল কিভাবে আমরা মস্তিষ্কে ডেটা ব্যাখ্যা করি। উদাহরণস্বরূপ, যখন আমরা *কম্পিউটার* শব্দ শুনি, আমাদের কিছু ধারণা থাকে এটি কী। +* **জ্ঞান** হল তথ্য যা আমাদের বিশ্ব মডেলে সংহত করা হয়েছে। উদাহরণস্বরূপ, একবার আমরা জানতে পারি কম্পিউটার কী, আমরা কম্পিউটার কীভাবে কাজ করে, এর মূল্য কত, এবং কী জন্য ব্যবহার করা যায় ইত্যাদি সম্পর্কে কিছু ধারণা পেতে শুরু করি। এই আন্তঃসংযুক্ত ধারণাগুলির নেটওয়ার্ক আমাদের জ্ঞান রূপায়িত করে। +* **জ্ঞানত্ত্ব** হল আমাদের পৃথিবীর বোঝাপড়ার আরও একটি স্তর, এবং এটি *মেটা-জ্ঞান* উপস্থাপন করে, যেমন কখন এবং কীভাবে জ্ঞান ব্যবহার করা উচিত তার কিছু ধারণা। - + -*চিত্র [উইকিপিডিয়া থেকে](https://commons.wikimedia.org/w/index.php?curid=37705247), Longlivetheux - নিজস্ব কাজ, CC BY-SA 4.0* +*ছবি [উইকিপিডিয়া থেকে](https://commons.wikimedia.org/w/index.php?curid=37705247), Longlivetheux - নিজস্ব কাজ, CC BY-SA 4.0* -তাই, **জ্ঞান উপস্থাপনের** সমস্যাটি হল কম্পিউটারের ভিতরে ডেটার আকারে জ্ঞান উপস্থাপন করার কার্যকর উপায় খুঁজে বের করা, যাতে এটি স্বয়ংক্রিয়ভাবে ব্যবহারযোগ্য হয়। এটি একটি স্পেকট্রামের মতো দেখা যেতে পারে: +সুতরাং, **জ্ঞান উপস্থাপন** সমস্যাটি হল কম্পিউটারের মধ্যে ডেটার রূপে জ্ঞান এমন একটি কার্যকর উপায় খুঁজে বের করা, যাতে এটি স্বয়ংক্রিয়ভাবে ব্যবহারযোগ্য হয়। এটি একটি স্পেকট্রাম হিসেবে দেখা যেতে পারে: -![জ্ঞান উপস্থাপনের স্পেকট্রাম](../../../../translated_images/bn/knowledge-spectrum.b60df631852c0217.webp) +![জ্ঞান উপস্থাপনের স্পেকট্রাম](../../../../../../translated_images/bn/knowledge-spectrum.b60df631852c0217.webp) -> চিত্র [Dmitry Soshnikov](http://soshnikov.com) দ্বারা +> ছবি [Dmitry Soshnikov](http://soshnikov.com) দ্বারা -* বাম দিকে, খুব সহজ ধরনের জ্ঞান উপস্থাপন রয়েছে যা কম্পিউটার দ্বারা কার্যকরভাবে ব্যবহার করা যেতে পারে। সবচেয়ে সহজটি হল অ্যালগরিদমিক, যেখানে জ্ঞান একটি কম্পিউটার প্রোগ্রামের মাধ্যমে উপস্থাপিত হয়। তবে, এটি জ্ঞান উপস্থাপনের সেরা উপায় নয়, কারণ এটি নমনীয় নয়। আমাদের মাথার ভিতরে থাকা জ্ঞান প্রায়ই অ্যালগরিদমিক নয়। -* ডান দিকে, প্রাকৃতিক পাঠ্যের মতো উপস্থাপন রয়েছে। এটি সবচেয়ে শক্তিশালী, তবে স্বয়ংক্রিয় যুক্তির জন্য ব্যবহার করা যায় না। +* বামে, খুবই সহজ ধরনের জ্ঞান উপস্থাপন রয়েছে যা কম্পিউটার দ্বারা কার্যকরভাবে ব্যবহার করা যায়। সবচেয়ে সহজটি হল অ্যালগরিদমিক, যেখানে জ্ঞান একটি কম্পিউটার প্রোগ্রামের মাধ্যমে উপস্থাপিত হয়। এটি সবচেয়ে ভাল উপায় নয় কারণ এটি নমনীয় নয়। আমাদের মস্তিষ্কের ভিতরের জ্ঞান প্রায়ই অ্যালগরিদমিক নয়। +* ডানদিকে, এমন কিছু উপস্থাপন রয়েছে যেমন প্রাকৃতিক লেখা। এটি সবচেয়ে শক্তিশালী, তবে স্বয়ংক্রিয় যুক্তির জন্য ব্যবহার করা যায় না। -> ✅ এক মিনিটের জন্য ভাবুন আপনি কীভাবে আপনার মাথায় জ্ঞান উপস্থাপন করেন এবং এটি নোটে রূপান্তর করেন। আপনার জন্য কোন নির্দিষ্ট ফরম্যাটটি ভাল কাজ করে যা মনে রাখার ক্ষেত্রে সহায়ক? +> ✅ এক মিনিট ভাবুন কিভাবে আপনি আপনার মাথায় জ্ঞান উপস্থাপন করেন এবং তা নোট হিসেবে রূপান্তরিত করেন। এমন কোন একটি নির্দিষ্ট ফরম্যাট কি আপনার জন্য retention এ সাহায্য করে? -## কম্পিউটার জ্ঞান উপস্থাপনের শ্রেণীবিন্যাস +## কম্পিউটার জ্ঞান উপস্থাপন শ্রেণীবদ্ধকরণ -আমরা বিভিন্ন কম্পিউটার জ্ঞান উপস্থাপনের পদ্ধতিগুলো নিম্নলিখিত বিভাগে শ্রেণীবদ্ধ করতে পারি: +আমরা বিভিন্ন কম্পিউটার জ্ঞান উপস্থাপনের পদ্ধতিগুলি নিম্নলিখিত শ্রেণীগুলিতে ভাগ করতে পারি: -* **নেটওয়ার্ক উপস্থাপন** আমাদের মাথার ভিতরে আন্তঃসম্পর্কিত ধারণাগুলোর একটি নেটওয়ার্ক থাকার উপর ভিত্তি করে। আমরা একই নেটওয়ার্কগুলোকে একটি গ্রাফ হিসেবে কম্পিউটারের ভিতরে পুনরুত্পাদন করতে পারি - একটি তথাকথিত **সেমান্টিক নেটওয়ার্ক**। +* **নেটওয়ার্ক উপস্থাপনগুলি** ভিত্তিভূত যা আমাদের মাথার ভিতরে আন্তঃসম্পর্কিত ধারণার একটি নেটওয়ার্ক রয়েছে। আমরা একই নেটওয়ার্ককে কম্পিউটারের মধ্যে গ্রাফ হিসেবে অনুলিপি করার চেষ্টা করতে পারি — একে বলা হয় **সেমান্টিক নেটওয়ার্ক**। -1. **অবজেক্ট-অ্যাট্রিবিউট-ভ্যালু ট্রিপলেট** বা **অ্যাট্রিবিউট-ভ্যালু পেয়ার**। যেহেতু একটি গ্রাফ কম্পিউটারের ভিতরে নোড এবং এজের তালিকা হিসেবে উপস্থাপিত হতে পারে, আমরা একটি সেমান্টিক নেটওয়ার্ককে একটি ট্রিপলেটের তালিকা হিসেবে উপস্থাপন করতে পারি, যেখানে অবজেক্ট, অ্যাট্রিবিউট এবং ভ্যালু থাকে। উদাহরণস্বরূপ, আমরা প্রোগ্রামিং ভাষা সম্পর্কে নিম্নলিখিত ট্রিপলেট তৈরি করি: +1. **অবজেক্ট-অ্যাট্রিবিউট-ভ্যালু ট্রিপলেট** অথবা **অ্যাট্রিবিউট-ভ্যালু পেয়ার**। যেহেতু একটি গ্রাফ কম্পিউটারের মধ্যে একটি তালিকা হিসাবে উপস্থাপন করা যায়, nods এবং edges নিয়ে, আমরা সেমান্টিক নেটওয়ার্ককে ট্রিপলেটের তালিকা দ্বারা উপস্থাপন করতে পারি, যা অবজেক্ট, অ্যাট্রিবিউট এবং ভ্যালু রয়েছে। উদাহরণস্বরূপ, আমরা প্রোগ্রামিং ভাষা সম্পর্কে নিম্নলিখিত ট্রিপলেটগুলি তৈরি করি: -Object | Attribute | Value +অবজেক্ট | অ্যাট্রিবিউট | মান -------|-----------|------ -Python | is | Untyped-Language -Python | invented-by | Guido van Rossum -Python | block-syntax | indentation -Untyped-Language | doesn't have | type definitions +Python | হল | Untyped-Language +Python | আবিষ্কার করেছিলেন | Guido van Rossum +Python | ব্লক-সিনট্যাক্স | ইনডেন্টেশন +Untyped-Language | নেই | টাইপ ডেফিনিশন -> ✅ ভাবুন কীভাবে ট্রিপলেটগুলো অন্যান্য ধরনের জ্ঞান উপস্থাপন করতে ব্যবহার করা যেতে পারে। +> ✅ ভাবুন কীভাবে ট্রিপলেট ব্যবহার করে অন্য ধরনের জ্ঞান উপস্থাপন করা যেতে পারে। -2. **হায়ারারকিক্যাল উপস্থাপন** জোর দেয় যে আমরা প্রায়ই আমাদের মাথার ভিতরে অবজেক্টগুলোর একটি শ্রেণীবিন্যাস তৈরি করি। উদাহরণস্বরূপ, আমরা জানি যে ক্যানারি একটি পাখি, এবং সব পাখির ডানা থাকে। আমরা এটাও জানি যে ক্যানারির সাধারণত কী রঙ হয় এবং তাদের উড়ার গতি কত। +2. **বয়সনিরক বা শ্রেণিবিন্যাসমূলক উপস্থাপন** জোর দেয় যে আমরা প্রায়ই আমাদের মাথায় অবজেক্টের একটি শ্রেণিবিন্যাস তৈরি করি। উদাহরণস্বরূপ, আমরা জানি যে কানারি একটি পাখি, এবং সব পাখির ডানা থাকে। আমাদের কিছু ধারণা থাকে কানারি সাধারণত কী রঙের, এবং তাদের উড়ন্ত গতি কত। - - **ফ্রেম উপস্থাপন** প্রতিটি অবজেক্ট বা অবজেক্টের শ্রেণীকে একটি **ফ্রেম** হিসেবে উপস্থাপন করার উপর ভিত্তি করে, যা **স্লট** ধারণ করে। স্লটগুলোতে সম্ভাব্য ডিফল্ট মান, মানের সীমাবদ্ধতা বা সংরক্ষিত পদ্ধতি থাকতে পারে যা স্লটের মান পেতে ডাকা যেতে পারে। সব ফ্রেম একটি শ্রেণীবিন্যাস গঠন করে যা অবজেক্ট-ওরিয়েন্টেড প্রোগ্রামিং ভাষার অবজেক্ট শ্রেণীবিন্যাসের মতো। - - **সিনারিও** হলো বিশেষ ধরনের ফ্রেম যা সময়ের সাথে সাথে unfold হওয়া জটিল পরিস্থিতি উপস্থাপন করে। + - **ফ্রেম উপস্থাপন** প্রতিটি অবজেক্ট বা অবজেক্ট শ্রেণীকে একটি **ফ্রেম** হিসেবে উপস্থাপন করে যা **স্লট** ধারণ করে। স্লটের সম্ভাব্য ডিফল্ট মান, মানের সীমাবদ্ধতা, বা সংরক্ষিত পদ্ধতি থাকতে পারে যেগুলি কল করে স্লটের মান পাওয়া যায়। সব ফ্রেম একত্রিত হয়ে অবজেক্ট-অরিয়েন্টেড প্রোগ্রামিং ভাষার অবজেক্ট শ্রেণিবিন্যাসের মতো একটি শ্রেণিবিন্যাস তৈরি করে। + - **পটভূমি** হলো বিশেষ ধরনের ফ্রেম যা সময়ের সাথে বিকাশমান জটিল পরিস্থিতি উপস্থাপন করে। **Python** -Slot | Value | Default value | Interval | ------|-------|---------------|----------| -Name | Python | | | -Is-A | Untyped-Language | | | -Variable Case | | CamelCase | | -Program Length | | | 5-5000 lines | -Block Syntax | Indent | | | +স্লট | মান | ডিফল্ট মান | ইন্টারভ্যাল | +-----|-------|---------------|------------| +নাম | Python | | | +মূল | Untyped-Language | | | +ভারিয়েবল কেস | | CamelCase | | +প্রোগ্রাম দৈর্ঘ্য | | | ৫-৫০০০ লাইন | +ব্লক সিনট্যাক্স | অন্তর | | | -3. **প্রসিডিউরাল উপস্থাপন** জ্ঞানকে এমন একটি ক্রিয়াকলাপের তালিকা হিসেবে উপস্থাপন করার উপর ভিত্তি করে যা একটি নির্দিষ্ট শর্ত ঘটলে কার্যকর করা যেতে পারে। - - প্রোডাকশন রুল হলো if-then বিবৃতি যা আমাদের সিদ্ধান্ত নিতে সাহায্য করে। উদাহরণস্বরূপ, একজন ডাক্তার একটি নিয়ম রাখতে পারেন যেখানে বলা হয় **যদি** রোগীর উচ্চ জ্বর থাকে **অথবা** রক্ত পরীক্ষায় উচ্চ C-reactive প্রোটিন থাকে **তাহলে** তার প্রদাহ আছে। একবার আমরা শর্তগুলোর একটি পূরণ করলে, আমরা প্রদাহ সম্পর্কে একটি সিদ্ধান্ত নিতে পারি এবং তারপর এটি আরও যুক্তিতে ব্যবহার করতে পারি। - - অ্যালগরিদমকে প্রসিডিউরাল উপস্থাপনের আরেকটি ফর্ম হিসেবে বিবেচনা করা যেতে পারে, যদিও সেগুলো প্রায় কখনোই সরাসরি জ্ঞান-ভিত্তিক সিস্টেমে ব্যবহার করা হয় না। +3. **প্রক্ৰিয়াগত উপস্থাপন** হল এমন উপস্থাপন যা জ্ঞানকে কর্ম-তালিকার মাধ্যমে উপস্থাপন করে যা নির্দিষ্ট শর্ত পূরণ হলে চালানো যায়। + - উৎপাদন নিয়মগুলি যা if-then বিবৃতি, এগুলো আমাদের সিদ্ধান্তে পৌঁছাতে সাহায্য করে। উদাহরণস্বরূপ, কোনো চিকিৎসকের এমন একটি নিয়ম থাকতে পারে যা বলে **যদি** কোনো রোগীর উচ্চ জ্বর থাকে **অথবা** রক্ত পরীক্ষায় C-reactive প্রোটিনের উচ্চ মাত্রা থাকে **তাহলে** তার প্রদাহ রয়েছে। শর্তগুলোর মধ্যে যখনই কোনটি পূরণ হয়, আমরা প্রদাহ সম্পর্কে সিদ্ধান্ত নিতে পারি, এবং পরে যুক্তি প্রয়োগে এটি ব্যবহার করি। + - অ্যালগরিদমকে আরেক ধরনের প্রক্ৰিয়াগত উপস্থাপন হিসাবে বিবেচনা করা যেতে পারে, যদিও এগুলি প্রায়শই জ্ঞান-ভিত্তিক সিস্টেমে সরাসরি ব্যবহার করা হয় না। -4. **লজিক** মূলত অ্যারিস্টটল দ্বারা প্রস্তাবিত হয়েছিল মানব জ্ঞানকে উপস্থাপন করার একটি উপায় হিসেবে। - - প্রেডিকেট লজিক একটি গাণিতিক তত্ত্ব হিসেবে খুব সমৃদ্ধ, তাই এটি গণনাযোগ্য নয়। তাই এর কিছু সাবসেট সাধারণত ব্যবহৃত হয়, যেমন Prolog-এ ব্যবহৃত Horn clauses। - - ডেসক্রিপটিভ লজিক হলো লজিক্যাল সিস্টেমের একটি পরিবার যা অবজেক্টের শ্রেণীবিন্যাস এবং বিতরণকৃত জ্ঞান উপস্থাপন যেমন *সেমান্টিক ওয়েব* উপস্থাপন এবং যুক্তি করার জন্য ব্যবহৃত হয়। +4. **যুক্তি** মূলত অ্যারিস্টটল দ্বারা মানবতাত্ত্বিক সর্বজনীন জ্ঞান উপস্থাপনের উপায় হিসাবে প্রস্তাবিত হয়েছিল। + - প্রেডিকেট লজিক একটি গাণিতিক তত্ত্ব হিসাবে খুবই জটিল এবং গণনাযোগ্য নয়, তাই এর একটি সীমিত অংশই সাধারণত ব্যবহৃত হয়, যেমন Prolog এ ব্যবহৃত Horn ক্লজ। + - বর্ণনামূলক যুক্তি (Descriptive Logic) হল যুক্তির সিস্টেমের একটি পরিবার যা অবজেক্টের শ্রেণিবিন্যাস ও বিতরণকৃত জ্ঞান উপস্থাপন এবং যুক্তি করার জন্য ব্যবহৃত হয়, যেমন *সেমান্টিক ওয়েব*। ## বিশেষজ্ঞ সিস্টেম -সিম্বলিক AI-এর প্রাথমিক সাফল্যগুলোর মধ্যে একটি ছিল তথাকথিত **বিশেষজ্ঞ সিস্টেম** - কম্পিউটার সিস্টেম যা একটি সীমিত সমস্যার ক্ষেত্রে একজন বিশেষজ্ঞের মতো কাজ করার জন্য ডিজাইন করা হয়েছিল। এগুলো একটি **জ্ঞানভিত্তি** এবং একটি **ইনফারেন্স ইঞ্জিন** নিয়ে গঠিত ছিল যা এর উপর যুক্তি করত। +সঙ্কেতমূলক AI এর প্রথম সাফল্যগুলির মধ্যে ছিল তথাকথিত **বিশেষজ্ঞ সিস্টেম** — কম্পিউটার সিস্টেম যা নির্দিষ্ট সমস্যার ক্ষেত্রে বিশেষজ্ঞ হিসাবে কাজ করার জন্য ডিজাইন করা হয়েছিল। এদের ভিত্তি ছিল এক বা একাধিক মানব বিশেষজ্ঞের কাছ থেকে সংগৃহীত একটি **জ্ঞান ভিত্তি**, এবং এদের মধ্যে ছিল একটি **যুক্তি ইঞ্জিন** যা তার ওপর কিছু যুক্তি প্রয়োগ করত। -![মানব স্থাপত্য](../../../../translated_images/bn/arch-human.5d4d35f1bba3ab1c.webp) | ![জ্ঞানভিত্তিক সিস্টেম](../../../../translated_images/bn/arch-kbs.3ec5c150b09fa8da.webp) +![মানব স্থাপত্য](../../../../../../translated_images/bn/arch-human.5d4d35f1bba3ab1c.webp) | ![জ্ঞান-ভিত্তিক সিস্টেম](../../../../../../translated_images/bn/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ -মানব স্নায়ুতন্ত্রের সরলীকৃত কাঠামো | জ্ঞানভিত্তিক সিস্টেমের স্থাপত্য +মানব স্নায়ুতন্ত্রের সরলীকৃত কাঠামো | জ্ঞান-ভিত্তিক সিস্টেমের স্থাপত্য -বিশেষজ্ঞ সিস্টেমগুলো মানব যুক্তি ব্যবস্থার মতো তৈরি করা হয়, যা **স্বল্পমেয়াদী স্মৃতি** এবং **দীর্ঘমেয়াদী স্মৃতি** ধারণ করে। অনুরূপভাবে, জ্ঞানভিত্তিক সিস্টেমে আমরা নিম্নলিখিত উপাদানগুলো আলাদা করি: +বিশেষজ্ঞ সিস্টেমগুলি মানুষের যুক্তি ব্যবস্থার মতো গঠিত, যার মধ্যে থাকে **স্বল্প-মেয়াদী স্মৃতি** এবং **দীর্ঘ-মেয়াদী স্মৃতি**। অনুরূপভাবে, জ্ঞান-ভিত্তিক সিস্টেমে নিম্নলিখিত উপাদানগুলি চিহ্নিত করি: -* **সমস্যার স্মৃতি**: বর্তমানে সমাধান করা সমস্যার সম্পর্কে জ্ঞান ধারণ করে, যেমন রোগীর তাপমাত্রা বা রক্তচাপ, তার প্রদাহ আছে কিনা ইত্যাদি। এই জ্ঞানকে **স্থির জ্ঞান** বলা হয়, কারণ এটি বর্তমানে সমস্যার সম্পর্কে আমাদের জানা জ্ঞানের একটি স্ন্যাপশট ধারণ করে - তথাকথিত *সমস্যার অবস্থা*। -* **জ্ঞানভিত্তি**: সমস্যার ক্ষেত্র সম্পর্কে দীর্ঘমেয়াদী জ্ঞান উপস্থাপন করে। এটি মানব বিশেষজ্ঞদের কাছ থেকে ম্যানুয়ালি সংগ্রহ করা হয় এবং পরামর্শ থেকে পরামর্শে পরিবর্তিত হয় না। কারণ এটি আমাদের একটি সমস্যা অবস্থান থেকে অন্য অবস্থানে নেভিগেট করতে দেয়, এটিকে **গতিশীল জ্ঞান**ও বলা হয়। -* **ইনফারেন্স ইঞ্জিন**: পুরো প্রক্রিয়াটি পরিচালনা করে, প্রয়োজন হলে ব্যবহারকারীর কাছে প্রশ্ন জিজ্ঞাসা করে। এটি প্রতিটি অবস্থায় প্রয়োগ করার জন্য সঠিক নিয়ম খুঁজে বের করার জন্যও দায়ী। +* **সমস্যা স্মৃতি**: বর্তমানে সমাধানাধীন সমস্যার বিষয়ে জ্ঞান ধারণ করে, যেমন রোগীর তাপমাত্রা বা রক্তচাপ, প্রদাহ আছে কিনা ইত্যাদি। এই জ্ঞানকে **স্থিতিশীল জ্ঞান**ও বলা হয়, কারণ এটি সমস্যার বর্তমান অবস্থা বা *সমস্যা অবস্থা* এর একটি স্ন্যাপশট ধারণ করে। +* **জ্ঞানভিত্তি**: দীর্ঘমেয়াদী জ্ঞান যা একটি সমস্যা ক্ষেত্র সম্পর্কে। এটি মানব বিশেষজ্ঞদের কাছ থেকে ম্যানুয়ালি সংগৃহীত, এবং পরামর্শের সময় পরিবর্তিত হয় না। যেহেতু এটি আমাদের এক সমস্যা অবস্থা থেকে অন্যস্থানে যেতে দেয়, তাই এটিকে **গতিশীল জ্ঞান**ও বলা হয়। +* **যুক্তি ইঞ্জিন**: সমস্যার অবস্থা স্থান অনুসন্ধানের সম্পূর্ণ প্রক্রিয়া পরিচালনা করে, প্রয়োজনে ব্যবহারকারীর কাছে প্রশ্ন করে। এটি প্রতিটি অবস্থায় প্রযোজ্য সঠিক নিয়ম খুঁজে বের করার দায়িত্বেও থাকে। -একটি উদাহরণ হিসেবে, চলুন একটি বিশেষজ্ঞ সিস্টেমের কথা বিবেচনা করি যা শারীরিক বৈশিষ্ট্যের উপর ভিত্তি করে একটি প্রাণী নির্ধারণ করে: +উদাহরণস্বরূপ, চলুন নিচের শারীরিক বৈশিষ্ট্যের ভিত্তিতে একটি প্রাণী নির্ণয়কারী বিশেষজ্ঞ সিস্টেমটি দেখি: -![AND-OR গাছ](../../../../translated_images/bn/AND-OR-Tree.5592d2c70187f283.webp) +![এন্ড-অর গাছ](../../../../../../translated_images/bn/AND-OR-Tree.5592d2c70187f283.webp) -> চিত্র [Dmitry Soshnikov](http://soshnikov.com) দ্বারা +> ছবি [Dmitry Soshnikov](http://soshnikov.com) দ্বারা -এই ডায়াগ্রামটি **AND-OR গাছ** নামে পরিচিত, এবং এটি প্রোডাকশন রুলগুলোর একটি গ্রাফিকাল উপস্থাপন। বিশেষজ্ঞ থেকে জ্ঞান সংগ্রহের শুরুতে একটি গাছ আঁকা উপকারী। কম্পিউটারের ভিতরে জ্ঞান উপস্থাপন করতে নিয়ম ব্যবহার করা আরও সুবিধাজনক: +এই ডায়াগ্রামটিকে **এন্ড-অর গাছ** বলা হয়, এবং এটি উৎপাদন নিয়মগুলির একটি গ্রাফিকাল উপস্থাপন। বিশেষজ্ঞ থেকে জ্ঞান আহরণের শুরুতে একটি গাছ আঁকা উপকারী। কম্পিউটারের ভিতরে জ্ঞান উপস্থাপনের জন্য নিয়ম ব্যবহার করাই বেশি সুবিধাজনক: ``` IF the animal eats meat @@ -121,77 +121,78 @@ OR (animal has sharp teeth THEN the animal is a carnivore ``` -আপনি লক্ষ্য করবেন যে নিয়মের বাম দিকে শর্ত এবং ক্রিয়াগুলো মূলত অবজেক্ট-অ্যাট্রিবিউট-ভ্যালু (OAV) ট্রিপলেট। **ওয়ার্কিং মেমরি** সমস্যার সাথে সম্পর্কিত OAV ট্রিপলেটের সেট ধারণ করে যা বর্তমানে সমাধান করা হচ্ছে। একটি **রুল ইঞ্জিন** এমন নিয়ম খুঁজে বের করে যার শর্ত পূরণ হয়েছে এবং সেগুলো প্রয়োগ করে, ওয়ার্কিং মেমরিতে একটি নতুন ট্রিপলেট যোগ করে। +আপনি লক্ষ্য করবেন যে নিয়মের বাম-পাশের প্রতিটি শর্ত এবং কাজ মূলত অবজেক্ট-অ্যাট্রিবিউট-ভ্যালু (OAV) ট্রিপলেট। **কর্মরত স্মৃতি** হচ্ছে এমন OAV ট্রিপলেটের সেট যা বর্তমানে সমাধানাধীন সমস্যার সাথে সম্পর্কিত। একটি **নিয়ম ইঞ্জিন** এমন নিয়ম খুঁজে যা শর্ত পূরণ করে এবং তাদের প্রয়োগ করে, কর্মরত স্মৃতিতে আরও একটি ট্রিপলেট যুক্ত করে। -> ✅ আপনার পছন্দের একটি বিষয়ে নিজের AND-OR গাছ আঁকুন! +> ✅ আপনার পছন্দের বিষয়ে নিজস্ব AND-OR গাছ লেখুন! ### ফরওয়ার্ড বনাম ব্যাকওয়ার্ড ইনফারেন্স -উপরের প্রক্রিয়াটি **ফরওয়ার্ড ইনফারেন্স** নামে পরিচিত। এটি ওয়ার্কিং মেমরিতে সমস্যার প্রাথমিক ডেটা দিয়ে শুরু হয় এবং তারপর নিম্নলিখিত যুক্তি লুপটি কার্যকর করে: +উপরের প্রক্রিয়াটি **ফরওয়ার্ড ইনফারেন্স** নামে পরিচিত। এটি কর্মরত স্মৃতিতে উপলব্ধ সমস্যার কিছু প্রাথমিক তথ্য দিয়ে শুরু করে, এবং নিম্নলিখিত যুক্তি চক্র চলায়: -1. যদি লক্ষ্য অ্যাট্রিবিউটটি ওয়ার্কিং মেমরিতে উপস্থিত থাকে - থামুন এবং ফলাফল দিন -2. সমস্ত নিয়ম খুঁজুন যার শর্ত বর্তমানে পূরণ হয়েছে - **কনফ্লিক্ট সেট** পেতে। -3. **কনফ্লিক্ট রেজোলিউশন** সম্পাদন করুন - এই ধাপে কার্যকর করার জন্য একটি নিয়ম নির্বাচন করুন। বিভিন্ন কনফ্লিক্ট রেজোলিউশন কৌশল থাকতে পারে: - - জ্ঞানভিত্তিতে প্রথম প্রযোজ্য নিয়ম নির্বাচন করুন - - একটি র্যান্ডম নিয়ম নির্বাচন করুন - - একটি *আরও নির্দিষ্ট* নিয়ম নির্বাচন করুন, অর্থাৎ বাম দিকে (LHS) সবচেয়ে বেশি শর্ত পূরণ করে এমন নিয়ম -4. নির্বাচিত নিয়ম প্রয়োগ করুন এবং সমস্যার অবস্থায় নতুন জ্ঞানের অংশ যোগ করুন -5. ধাপ 1 থেকে পুনরাবৃত্তি করুন। +1. যদি লক্ষ্য অ্যাট্রিবিউট কর্মরত স্মৃতিতে থাকে — থেমে যান এবং ফলাফল দিন +2. সব নিয়ম খুঁজুন যার শর্ত পূরণ হয়েছে — **কনফ্লিক্ট সেট** সংগ্রহ করুন +3. **কনফ্লিক্ট রেজোলিউশন** সম্পাদন করুন — এমন একটি নিয়ম নির্বাচন করুন যা এই ধাপে কার্যকর হবে। বিভিন্ন কৌশল থাকতে পারে: + - প্রথম প্রযোজ্য নিয়ম নির্বাচন + - এলোমেলো নিয়ম নির্বাচন + - *বিশেষত* শর্ত পূরণকারী নিয়ম নির্বাচন, অর্থাৎ LHS এ সবচেয়ে বেশিবার শর্ত পূরণ করা নিয়ম +4. নির্বাচিত নিয়ম প্রয়োগ করুন এবং সমস্যা অবস্থাতে নতুন জ্ঞান যুক্ত করুন +5. ধাপ ১ থেকে পুনরাবৃত্তি করুন। -তবে, কিছু ক্ষেত্রে আমরা সমস্যার সম্পর্কে শূন্য জ্ঞান দিয়ে শুরু করতে চাই এবং এমন প্রশ্ন করতে চাই যা আমাদের সিদ্ধান্তে পৌঁছাতে সাহায্য করবে। উদাহরণস্বরূপ, যখন চিকিৎসা নির্ণয় করা হয়, আমরা সাধারণত রোগীর সমস্ত চিকিৎসা বিশ্লেষণ আগেই সম্পাদন করি না। বরং আমরা সিদ্ধান্ত নেওয়ার সময় বিশ্লেষণ করতে চাই। +কিন্তু কখনো কখনো আমরা সমস্যার সম্পর্কে শূন্য জ্ঞান থেকেই শুরু করতে চাই, এবং প্রশ্ন করে সিদ্ধান্তে পৌঁছাতে চাই। উদাহরণস্বরূপ, চিকিৎসা নির্ণয়ে আমরা সাধারণত রোগীর সব মেডিকেল বিশ্লেষণ আগেই করি না, বরং সিদ্ধান্ত নেওয়ার সময় প্রয়োজন অনুসারে করি। -এই প্রক্রিয়াটি **ব্যাকওয়ার্ড ইনফারেন্স** ব্যবহার করে মডেল করা যেতে পারে। এটি **লক্ষ্য** দ্বারা চালিত হয় - যে অ্যাট্রিবিউট মানটি আমরা খুঁজতে চাই: +এই প্রক্রিয়াটি **ব্যাকওয়ার্ড ইনফারেন্স** ব্যবহার করে মডেল করা যায়। এটি **লক্ষ্য** দ্বারা পরিচালিত — অ্যাট্রিবিউট মান যেটা আমরা খুঁজছি: -1. সমস্ত নিয়ম নির্বাচন করুন যা আমাদের লক্ষ্য মান দিতে পারে (অর্থাৎ লক্ষ্যটি ডান দিকে (RHS) রয়েছে) - একটি কনফ্লিক্ট সেট -1. যদি এই অ্যাট্রিবিউটের জন্য কোনো নিয়ম না থাকে, বা একটি নিয়ম থাকে যা বলে যে আমাদের ব্যবহারকারীর কাছ থেকে মান জিজ্ঞাসা করা উচিত - জিজ্ঞাসা করুন, অন্যথায়: -1. কনফ্লিক্ট রেজোলিউশন কৌশল ব্যবহার করে একটি নিয়ম নির্বাচন করুন যা আমরা *হাইপোথিসিস* হিসেবে ব্যবহার করব - আমরা এটি প্রমাণ করার চেষ্টা করব -1. নিয়মের বাম দিকে (LHS) সমস্ত অ্যাট্রিবিউটের জন্য প্রক্রিয়াটি পুনরাবৃত্তি করুন, লক্ষ্য হিসেবে সেগুলো প্রমাণ করার চেষ্টা করুন -1. যদি কোনো সময় প্রক্রিয়াটি ব্যর্থ হয় - ধাপ 3-এ অন্য নিয়ম ব্যবহার করুন। +1. সব নিয়ম নির্বাচন করুন যা লক্ষ্যের মান দিতে পারে (অর্থাৎ, RHS এ লক্ষ্য থাকে) — কনফ্লিক্ট সেট +2. যদি এই অ্যাট্রিবিউটের জন্য কোন নিয়ম না থাকে, বা এমন নিয়ম থাকে যা বলে ব্যবহারকারীর কাছ থেকে মান জিজ্ঞাসা করতে হবে — তাহলে জিজ্ঞাসা করুন, নাহলে: +3. কনফ্লিক্ট রেজোলিউশন কৌশল ব্যবহার করে একটি নিয়ম নির্বাচন করুন যা আমরা *হাইপোথিসিস* হিসাবে প্রমাণের চেষ্টা করব +4. নিয়মের LHS এ থাকা সব অ্যাট্রিবিউটের জন্য একই প্রক্রিয়া পুনরাবৃত্তি করুন, সেগুলো লক্ষ্য হিসাবে প্রমাণের চেষ্টা করুন +5. যদি যেকোন সময় প্রক্রিয়া ব্যর্থ হয় - ধাপ ৩ এ অন্য নিয়ম ব্যবহার করুন -> ✅ কোন পরিস্থিতিতে ফরওয়ার্ড ইনফারেন্স আরও উপযুক্ত? ব্যাকওয়ার্ড ইনফারেন্স কীভাবে? +> ✅ কোন পরিস্থিতিতে ফরওয়ার্ড ইনফারেন্স বেশি উপযুক্ত? ব্যাকওয়ার্ড ইনফারেন্সের ক্ষেত্রে কী হয়? ### বিশেষজ্ঞ সিস্টেম বাস্তবায়ন -বিশেষজ্ঞ সিস্টেম বিভিন্ন টুল ব্যবহার করে বাস্তবায়ন করা যেতে পারে: +বিভিন্ন সরঞ্জাম ব্যবহার করে বিশেষজ্ঞ সিস্টেম তৈরি করা যায়: -* কিছু উচ্চ-স্তরের প্রোগ্রামিং ভাষায় সরাসরি প্রোগ্রামিং করা। এটি সেরা ধারণা নয়, কারণ একটি জ্ঞানভিত্তিক সিস্টেমের প্রধান সুবিধা হল যে জ্ঞান যুক্তি থেকে পৃথক করা হয়, এবং সম্ভাব্যভাবে একটি সমস্যার ক্ষেত্রের বিশেষজ্ঞ নিয়ম লিখতে সক্ষম হওয়া উচিত যুক্তি প্রক্রিয়ার বিবরণ না বুঝে। -* **বিশেষজ্ঞ সিস্টেম শেল** ব্যবহার করা, অর্থাৎ একটি সিস্টেম যা বিশেষভাবে কিছু জ্ঞান উপস্থাপন ভাষা ব্যবহার করে জ্ঞান দ্বারা পূরণ করার জন্য ডিজাইন করা হয়েছে। +* কোনো উচ্চ-স্তরের প্রোগ্রামিং ভাষায় সরাসরি প্রোগ্রাম করে। এটি সবচেয়ে ভাল ধারণা নয়, কারণ জ্ঞান-ভিত্তিক সিস্টেমের প্রধান সুবিধাটি হচ্ছে জ্ঞান এবং যুক্তির প্রক্রিয়া আলাদা, এবং সম্ভাব্য সমস্যার বিশেষজ্ঞদের নিয়ম লেখার জন্য যুক্তির বিস্তারিত বুঝতে হবে না। +* **বিশেষজ্ঞ সিস্টেম শেল** ব্যবহার করে, অর্থাৎ এমন একটি সিস্টেম যা বিশেষজ্ঞ জ্ঞানসংগ্রহ ভাষা ব্যবহার করে জ্ঞান দ্বারা পূরণ করা হয়। -## ✍️ অনুশীলন: প্রাণী নির্ণয় +## ✍️ অনুশীলন: প্রাণী নিয়ম নির্ণয় -[Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) দেখুন ফরওয়ার্ড এবং ব্যাকওয়ার্ড ইনফারেন্স বিশেষজ্ঞ সিস্টেম বাস্তবায়নের একটি উদাহরণের জন্য। +[Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) এ ফরওয়ার্ড এবং ব্যাকওয়ার্ড ইনফারেন্স বিশেষজ্ঞ সিস্টেম বাস্তবায়নের উদাহরণ দেখুন। -> **নোট**: এই উদাহরণটি বেশ সহজ, এবং এটি কেবল একটি বিশেষজ্ঞ সিস্টেম কীভাবে দেখায় তার ধারণা দেয়। একবার আপনি এমন একটি সিস্টেম তৈরি করতে শুরু করলে, আপনি কেবল *বুদ্ধিমান* আচরণ লক্ষ্য করবেন যখন আপনি প্রায় ২০০+ নিয়মে পৌঁছাবেন। এক পর্যায়ে, নিয়মগুলো এত জটিল হয়ে যায় যে সবগুলো মনে রাখা কঠিন হয়ে যায়, এবং তখন আপনি ভাবতে পারেন কেন একটি সিস্টেম নির্দিষ্ট সিদ্ধান্ত নিচ্ছে। তবে, জ্ঞানভিত্তিক সিস্টেমের একটি গুরুত্বপূর্ণ বৈশিষ্ট্য হল যে আপনি সর্বদা *ব্যাখ্যা* করতে পারেন ঠিক কীভাবে কোনো সিদ্ধান্ত নেওয়া হয়েছে। +> **দ্রষ্টব্য**: এই উদাহরণটি বেশ সহজ, এবং কেবল বিশেষজ্ঞ সিস্টেমের ধারণা দেয়। যখন আপনি এমন একটি সিস্টেম তৈরি শুরু করবেন, আপনি সাধারণত ২০০+ নিয়ম পৌঁছানোর পর থেকেই এর *বুদ্ধিদীপ্ত* আচরণ লক্ষ্য করবেন। এক পর্যায়ে নিয়মগুলো এত জটিল হয়ে যায় যে সবগুলো মনে রাখা কঠিন হয়ে পড়ে, তখন আপনি ভাবতে পারেন কেন সিস্টেম কিছু সিদ্ধান্ত নিচ্ছে। তবে, জ্ঞান-ভিত্তিক সিস্টেমের গুরুত্বপূর্ণ বৈশিষ্ট্য হলো আপনি সবসময় *সঠিকভাবে ব্যাখ্যা* করতে পারেন যে কোনো সিদ্ধান্ত কীভাবে গৃহীত হয়েছে। ## অন্টোলজি এবং সেমান্টিক ওয়েব -২০ শতকের শেষে ইন্টারনেট রিসোর্সগুলোকে জ্ঞান উপস্থাপনের মাধ্যমে এনোটেট করার একটি উদ্যোগ ছিল, যাতে খুব নির্দিষ্ট প্রশ্নের সাথে সম্পর্কিত রিসোর্স খুঁজে পাওয়া সম্ভব হয়। এই উদ্যোগটি **সেমান্টিক ওয়েব** নামে পরিচিত ছিল, এবং এটি কয়েকটি ধারণার উপর নির্ভর করেছিল: +২০শ শতাব্দীর শেষদিকের একটি উদ্যোগ ছিল জ্ঞান উপস্থাপন ব্যবহার করে ইন্টারনেট রিসোর্সে অ্যানোটেশন করা, যাতে বিশেষ জিজ্ঞাসার জন্য রিসোর্স খুঁজে পাওয়া সম্ভব হয়। এই উদ্যোগকে বলা হয় **সেমান্টিক ওয়েব**, এবং এর ভিত্তি কিছু ধারণার উপর ছিল: -- **[ডেসক্রিপশন লজিক](https://en.wikipedia -- XML-ভিত্তিক ভাষার একটি পরিবার যা জ্ঞান বর্ণনার জন্য ব্যবহৃত হয়: RDF (Resource Description Framework), RDFS (RDF Schema), OWL (Ontology Web Language)। +- একটি বিশেষ জ্ঞান উপস্থাপন যা **[বর্ণনামূলক যুক্তি](https://en.wikipedia.org/wiki/Description_logic)** (DL) উপর নির্ভর করে। এটি ফ্রেম জ্ঞান উপস্থাপনের অনুরূপ কারণ এটি অবজেক্টের শ্রেণিবিন্যাস তৈরি করে এবং বৈশিষ্ট্যসহ উপস্থাপন করে, কিন্তু এর ফরমাল যুক্তির অর্থবোধকতা এবং ইনফারেন্স থাকে। DL এর একটি বিস্তৃত পরিবার রয়েছে যা প্রসারণক্ষমতা এবং ইনফারেন্সের অ্যালগরিদমিক জটিলতার মধ্যে সমতা রাখে। +- বিতরণকৃত জ্ঞান উপস্থাপন, যেখানে সব ধারণা একটি বিশ্বব্যাপী URI শনাক্তকারী দ্বারা উপস্থাপিত হয়, যা ইন্টারনেট জুড়ে জ্ঞান শ্রেণিবিন্যাস তৈরি সম্ভব করে। +- জ্ঞানের বর্ণনার জন্য XML-ভিত্তিক ভাষাগুলোর একটি পরিবার: RDF (Resource Description Framework), RDFS (RDF Schema), OWL (Ontology Web Language)। -সেমান্টিক ওয়েবের একটি মূল ধারণা হল **Ontology**। এটি একটি সমস্যা ক্ষেত্রের স্পষ্ট নির্দিষ্টকরণকে বোঝায়, যা কিছু আনুষ্ঠানিক জ্ঞান উপস্থাপনার মাধ্যমে করা হয়। সবচেয়ে সহজ Ontology হতে পারে একটি সমস্যা ক্ষেত্রের বস্তুগুলোর একটি শ্রেণিবিন্যাস, তবে আরও জটিল Ontology-তে এমন নিয়ম অন্তর্ভুক্ত থাকে যা থেকে সিদ্ধান্ত নেওয়া যায়। +সেমান্তিক ওয়েবে একটি মূল ধারণা হল **অন্টোলজি** ধারণা। এটি একটি সমস্যার ক্ষেত্রের স্পষ্ট উল্লেখ যা কিছু আনুষ্ঠানিক জ্ঞান উপস্থাপনার মাধ্যমে করা হয়। সবচেয়ে সহজ অন্টোলজি হতে পারে কেবল সমস্যার ক্ষেত্রের বস্তুর একটি শ্রেণিবিন্যাস, কিন্তু আরও জটিল অন্টোলজিগুলো এমন নিয়মগুলো অন্তর্ভুক্ত করবে যা অনুমান করার জন্য ব্যবহার করা যায়। -সেমান্টিক ওয়েবে, সমস্ত উপস্থাপনাই triplets-এর উপর ভিত্তি করে। প্রতিটি বস্তু এবং প্রতিটি সম্পর্ক URI দ্বারা অনন্যভাবে চিহ্নিত করা হয়। উদাহরণস্বরূপ, যদি আমরা বলতে চাই যে এই AI Curriculum Dmitry Soshnikov দ্বারা ১ জানুয়ারি, ২০২২-এ তৈরি করা হয়েছে - তাহলে আমরা নিম্নলিখিত triplets ব্যবহার করতে পারি: +সেমান্তিক ওয়েবে, সমস্ত উপস্থাপনাগুলো ট্রিপলেটের ওপর ভিত্তি করে। প্রতিটি বস্তু এবং প্রতিটি সম্পর্ক ইউনিকভাবে URI দ্বারা শনাক্ত করা হয়। উদাহরণস্বরূপ, যদি আমরা বলার চেষ্টা করি যে এই AI কারিকুলামটি Dmitry Soshnikov দ্বারা 1লা জানুয়ারি, 2022 তারিখে তৈরি হয়েছে - নিচের ট্রিপলেটগুলো আমরা ব্যবহার করতে পারি: - + ``` -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 ``` -> ✅ এখানে `http://www.example.com/terms/creation-date` এবং `http://purl.org/dc/elements/1.1/creator` হল *creator* এবং *creation date* ধারণাগুলো প্রকাশ করার জন্য কিছু সুপরিচিত এবং সর্বজনীনভাবে গ্রহণযোগ্য URI। +> ✅ এখানে `http://www.example.com/terms/creation-date` এবং `http://purl.org/dc/elements/1.1/creator` হল কিছু পরিচিত এবং সর্বজনীনভাবে গৃহীত URI যা *স্রষ্টা* এবং *তৈরির তারিখ* ধারণাগুলো প্রকাশ করতে ব্যবহৃত হয়। -আরও জটিল ক্ষেত্রে, যদি আমরা একটি creator-এর তালিকা সংজ্ঞায়িত করতে চাই, তাহলে আমরা RDF-এ সংজ্ঞায়িত কিছু ডেটা স্ট্রাকচার ব্যবহার করতে পারি। +একটু জটিল ক্ষেত্রে, যদি আমরা স্রষ্টাদের একটি তালিকা সংজ্ঞায়িত করতে চাই, আমরা RDF-তে সংজ্ঞায়িত কিছু ডেটা স্ট্রাকচার ব্যবহার করতে পারি। - + -> উপরের ডায়াগ্রামগুলো [Dmitry Soshnikov](http://soshnikov.com) দ্বারা তৈরি। +> উপরের চিত্রগুলি [Dmitry Soshnikov](http://soshnikov.com) এর -সেমান্টিক ওয়েব তৈরির অগ্রগতি কিছুটা ধীর হয়ে যায় সার্চ ইঞ্জিন এবং প্রাকৃতিক ভাষা প্রক্রিয়াকরণ কৌশলগুলোর সাফল্যের কারণে, যা পাঠ্য থেকে কাঠামোগত ডেটা বের করতে সক্ষম। তবে কিছু ক্ষেত্রে Ontology এবং জ্ঞানভিত্তিক ডেটাবেস বজায় রাখার জন্য এখনও উল্লেখযোগ্য প্রচেষ্টা রয়েছে। কয়েকটি উল্লেখযোগ্য প্রকল্প: +সেমান্তিক ওয়েব তৈরি করার প্রক্রিয়া কিছুটা ধীরগতিতে হয়েছে সার্চ ইঞ্জিন এবং প্রাকৃতিক ভাষা প্রক্রিয়াকরণ কৌশলগুলোর সাফল্যের কারণে, যা টেক্সট থেকে কাঠামোগত ডেটা বের করতে সক্ষম। তবে কিছু ক্ষেত্রেই অন্টোলজি এবং জ্ঞানভান্ডার রক্ষা করার জন্য উল্লেখযোগ্য প্রচেষ্টা চলছে। কিছু উল্লেখযোগ্য প্রকল্প: -* [WikiData](https://wikidata.org/) হল Wikipedia-এর সাথে যুক্ত মেশিন-পঠনযোগ্য জ্ঞানভিত্তিক ডেটাবেসের একটি সংগ্রহ। বেশিরভাগ ডেটা Wikipedia *InfoBoxes* থেকে সংগ্রহ করা হয়, যা Wikipedia পৃষ্ঠাগুলোর ভিতরে কাঠামোগত বিষয়বস্তুর অংশ। আপনি [SPARQL](https://query.wikidata.org/) ব্যবহার করে WikiData-তে প্রশ্ন করতে পারেন, যা সেমান্টিক ওয়েবের জন্য একটি বিশেষ প্রশ্ন ভাষা। এখানে একটি নমুনা প্রশ্ন রয়েছে যা মানুষের মধ্যে সবচেয়ে জনপ্রিয় চোখের রঙ প্রদর্শন করে: +* [WikiData](https://wikidata.org/) হল উইকিপিডিয়ার সাথে সম্পর্কিত যান্ত্রিক পাঠযোগ্য জ্ঞানভান্ডারের একটি সংগ্রহ। অধিকাংশ ডেটা উইকিপিডিয়ার *ইনফোবক্স* থেকে আহরণ করা হয়, যা উইকিপিডিয়া পৃষ্ঠাগুলোর মধ্যে কাঠামোগত বিষয়বস্তুর অংশ। আপনি [SPARQL](https://query.wikidata.org/) ব্যবহার করে উইকিডেটাতে প্রশ্ন করতে পারেন, এটি সেমান্তিক ওয়েবের জন্য একটি বিশেষ প্রশ্নভাষা। নিচে একটি উদাহরণ প্রশ্ন যা মানুষের মধ্যে সবচেয়ে জনপ্রিয় চোখের রঙ দেখায়: ```sparql #defaultView:BubbleChart @@ -205,47 +206,51 @@ WHERE GROUP BY ?eyeColorLabel ``` -* [DBpedia](https://www.dbpedia.org/) WikiData-এর মতো আরেকটি প্রচেষ্টা। +* [DBpedia](https://www.dbpedia.org/) আরেকটি উদ্যোগ যা WikiData এর মত। -> ✅ যদি আপনি নিজের Ontology তৈরি করতে বা বিদ্যমান Ontology খুলতে চান, তাহলে [Protégé](https://protege.stanford.edu/) নামক একটি চমৎকার ভিজ্যুয়াল Ontology এডিটর রয়েছে। এটি ডাউনলোড করুন বা অনলাইনে ব্যবহার করুন। +> ✅ আপনি যদি আপনার নিজস্ব অন্টোলজি তৈরির সঙ্গে পরীক্ষামূলক কাজ করতে চান, অথবা বিদ্যমান অন্টোলজি খুলতে চান, তাহলে একটি চমৎকার ভিজ্যুয়াল অন্টোলজি এডিটর রয়েছে যার নাম [Protégé](https://protege.stanford.edu/)। এটি ডাউনলোড করুন, অথবা অনলাইনে ব্যবহার করুন। - + -*Web Protégé এডিটর Romanov Family Ontology দিয়ে খোলা। Dmitry Soshnikov-এর স্ক্রিনশট* +*Web Protégé এডিটর ওপেন করেছে Romanov Family অন্টোলজি। স্ক্রিনশট: Dmitry Soshnikov* -## ✍️ অনুশীলন: একটি Family Ontology +## ✍️ ব্যায়াম: একটি পরিবার অন্টোলজি -[FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) দেখুন, যেখানে সেমান্টিক ওয়েব কৌশল ব্যবহার করে পারিবারিক সম্পর্ক নিয়ে যুক্তি করার একটি উদাহরণ রয়েছে। আমরা GEDCOM ফরম্যাটে সাধারণভাবে উপস্থাপিত একটি পারিবারিক গাছ এবং পারিবারিক সম্পর্কের Ontology ব্যবহার করে নির্দিষ্ট ব্যক্তিদের জন্য সমস্ত পারিবারিক সম্পর্কের একটি গ্রাফ তৈরি করব। +সেমান্তিক ওয়েব কৌশল ব্যবহার করে পরিবারিক সম্পর্ক নিরূপণ করার একটি উদাহরণ দেখতে পারেন [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) এ। আমরা GEDCOM সাধারণ ফরম্যাটে প্রদর্শিত একটি পরিবার বৃক্ষ এবং একটি পরিবারিক সম্পর্ক সহ অন্টোলজি গ্রহণ করে দেওয়া কিছু ব্যক্তির জন্য সব পরিবারিক সম্পর্কের গ্রাফ তৈরি করব। ## Microsoft Concept Graph -বেশিরভাগ ক্ষেত্রে, Ontology হাতে তৈরি করা হয়। তবে এটি **unstructured data** থেকে Ontology তৈরি করাও সম্ভব, যেমন প্রাকৃতিক ভাষার পাঠ্য থেকে। +অধিকাংশ ক্ষেত্রে, অন্টোলজিগুলো সাবধানে হাতে তৈরি করা হয়। তবে, এটি সম্ভব যান্ত্রিকভাবে অবিন্যস্ত ডেটা থেকে অন্টোলজি **খনন** করা, উদাহরণস্বরূপ প্রাকৃতিক ভাষার টেক্সট থেকে। -Microsoft Research-এর একটি প্রচেষ্টা এর ফলাফল ছিল [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste)। +মাইক্রোসফট রিসার্চ এর একটি প্রচেষ্টা ছিল যার ফলাফল হলো [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste)। -এটি `is-a` উত্তরাধিকার সম্পর্ক ব্যবহার করে একত্রিত করা সত্ত্বার একটি বড় সংগ্রহ। এটি "Microsoft কী?" এর মতো প্রশ্নের উত্তর দিতে সক্ষম - উত্তর হতে পারে "একটি কোম্পানি, সম্ভাবনা ০.৮৭, এবং একটি ব্র্যান্ড, সম্ভাবনা ০.৭৫"। +এটি একটি বৃহৎ সংগ্রহ যা `is-a` উত্তরাধিকার সম্পর্ক ব্যবহার করে একত্রিত প্রতিষ্ঠানের গ্রুপ। এটি এমন প্রশ্নের উত্তর দিতে সক্ষম যেমন "মাইক্রোসফট কী?" - উত্তরে আসবে "একটি কোম্পানি 0.87 সম্ভাবনা সহ, এবং একটি ব্র্যান্ড 0.75 সম্ভাবনা সহ"। -Graphটি REST API হিসেবে বা একটি বড় ডাউনলোডযোগ্য টেক্সট ফাইল হিসেবে উপলব্ধ, যেখানে সমস্ত entity pair তালিকাভুক্ত করা হয়েছে। +এই গ্রাফটি REST API হিসাবে অথবা একটি বড় ডাউনলোডযোগ্য টেক্সট ফাইল হিসাবে পাওয়া যায় যা সমস্ত সত্তার জোড়া তালিকাভুক্ত করে। -## ✍️ অনুশীলন: একটি Concept Graph +## ✍️ ব্যায়াম: একটি কনসেপ্ট গ্রাফ -[MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) নোটবুকটি চেষ্টা করুন, যেখানে Microsoft Concept Graph ব্যবহার করে সংবাদ প্রবন্ধগুলোকে বিভিন্ন বিভাগে গ্রুপ করার পদ্ধতি দেখানো হয়েছে। +[MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) নোটবুক ব্যবহার করে দেখুন কিভাবে মাইক্রোসফট কনসেপ্ট গ্রাফ ব্যবহার করে সংবাদ নিবন্ধগুলো কয়েকটি বিভাগে ভাগ করা যায়। ## উপসংহার -বর্তমানে, AI প্রায়ই *Machine Learning* বা *Neural Networks*-এর সমার্থক হিসেবে বিবেচিত হয়। তবে, একজন মানুষ স্পষ্ট যুক্তি প্রদর্শন করে, যা বর্তমানে Neural Networks দ্বারা পরিচালিত হয় না। বাস্তব প্রকল্পগুলোতে, স্পষ্ট যুক্তি এখনও এমন কাজ সম্পাদন করতে ব্যবহৃত হয় যা ব্যাখ্যা প্রয়োজন, বা সিস্টেমের আচরণ নিয়ন্ত্রিতভাবে পরিবর্তন করার ক্ষমতা প্রয়োজন। +বর্তমানে, AI কে প্রায়ই *মেশিন লার্নিং* বা *নিউরাল নেটওয়ার্ক* এর সমার্থক বলা হয়। তবে, একজন মানুষ স্পষ্ট যুক্তি প্রদর্শন করে, যা বর্তমানে নিউরাল নেটওয়ার্ক ডিল করছে না। বাস্তব প্রকল্পে, স্পষ্ট যুক্তি এখনও ব্যবহৃত হয় এমন কাজ করার জন্য যা ব্যাখ্যার প্রয়োজন বা সিস্টেমের আচরণ নিয়ন্ত্রিতভাবে পরিবর্তন করতে সক্ষম। ## 🚀 চ্যালেঞ্জ -এই পাঠের সাথে যুক্ত Family Ontology নোটবুকে, অন্যান্য পারিবারিক সম্পর্ক নিয়ে পরীক্ষা করার সুযোগ রয়েছে। পারিবারিক গাছে মানুষের মধ্যে নতুন সংযোগ আবিষ্কার করার চেষ্টা করুন। +এ লেসনের সাথে সংশ্লিষ্ট Family Ontology নোটবুকে অন্যান্য পরিবারিক সম্পর্ক দিয়ে পরীক্ষা করার সুযোগ রয়েছে। পরিবারের গাছের মধ্যে নতুন সম্পর্ক আবিষ্কার করার চেষ্টা করুন। ## [পোস্ট-লেকচার কুইজ](https://ff-quizzes.netlify.app/en/ai/quiz/4) ## পর্যালোচনা ও স্ব-অধ্যয়ন -ইন্টারনেটে গবেষণা করুন এবং এমন ক্ষেত্রগুলো আবিষ্কার করুন যেখানে মানুষ জ্ঞানকে পরিমাণগত এবং কোডিফাই করার চেষ্টা করেছে। Bloom's Taxonomy সম্পর্কে জানুন এবং ইতিহাসে ফিরে যান, যেখানে মানুষ তাদের পৃথিবীকে বোঝার চেষ্টা করেছে। Linnaeus-এর কাজ দেখুন, যিনি জীবের একটি শ্রেণিবিন্যাস তৈরি করেছিলেন, এবং Dmitri Mendeleev-এর কাজ পর্যবেক্ষণ করুন, যিনি রাসায়নিক উপাদানগুলো বর্ণনা এবং গ্রুপ করার একটি পদ্ধতি তৈরি করেছিলেন। আপনি আর কী আকর্ষণীয় উদাহরণ খুঁজে পেতে পারেন? +ইন্টারনেটে কিছু গবেষণা করুন এমন ক্ষেত্রগুলো জানার জন্য যেখানে মানুষ জ্ঞান পরিমাপ ও সংহত করার চেষ্টা করেছে। ব্লুমের ট্যাক্সোনোমি দেখুন, এবং ইতিহাসে ফিরে যান জানতে কিভাবে মানুষ তাদের বিশ্বকে বোধগম্য করার চেষ্টা করেছে। লিনিয়াসের কাজ অনুসন্ধান করুন জীবজগতের একটি শ্রেণিবিন্যাস তৈরির জন্য, এবং দেখুন কিভাবে Dmitri Mendeleev রাসায়নিক উপাদানগুলো বর্ণনা ও শ্রেণীবদ্ধ করার একটি পথ রচনা করেছেন। আর কি কি আকর্ষণীয় উদাহরণ আপনি খুঁজে পেতে পারেন? -**অ্যাসাইনমেন্ট**: [একটি Ontology তৈরি করুন](assignment.md) +**অ্যাসাইনমেন্ট**: [একটি অন্টোলজি তৈরি করুন](assignment.md) --- + +**দ্বিধাহীনতা**: +এই দস্তাবেজটি AI অনুবাদ সেবা [Co-op Translator](https://github.com/Azure/co-op-translator) ব্যবহার করে অনূদিত হয়েছে। যদিও আমরা সঠিকতার চেষ্টা করি, অনুগ্রহ করে জানুন যে স্বয়ংক্রিয় অনুবাদে ত্রুটি বা অসঙ্গতি থাকতে পারে। মূল দস্তাবেজের নিজস্ব ভাষার সংস্করণই প্রামাণিক উৎস হিসেবে বিবেচনা করা আবশ্যক। গুরুত্বপূর্ণ তথ্যের জন্য পেশাদার মানব অনুবাদের পরামর্শ দেওয়া হয়। এই অনুবাদের ব্যবহারে সৃষ্ট কোনো ভুলবোঝাবুঝি বা ভুল ব্যাখ্যার জন্য আমরা দায়বদ্ধ নই। + \ No newline at end of file diff --git a/translations/hi/README.md b/translations/hi/README.md index 7c7b4a88..c3eb963d 100644 --- a/translations/hi/README.md +++ b/translations/hi/README.md @@ -1,8 +1,8 @@ [Arabic](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgarian](../bg/README.md) | [Burmese (Myanmar)](../my/README.md) | [Chinese (Simplified)](../zh/README.md) | [Chinese (Traditional, Hong Kong)](../hk/README.md) | [Chinese (Traditional, Macau)](../mo/README.md) | [Chinese (Traditional, Taiwan)](../tw/README.md) | [Croatian](../hr/README.md) | [Czech](../cs/README.md) | [Danish](../da/README.md) | [Dutch](../nl/README.md) | [Estonian](../et/README.md) | [Finnish](../fi/README.md) | [French](../fr/README.md) | [German](../de/README.md) | [Greek](../el/README.md) | [Hebrew](../he/README.md) | [Hindi](./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) -> **स्थानीय रूप से क्लोन करना पसंद है?** +> **स्थानीय रूप से क्लोन करना पसंद करते हैं?** -> इस रिपॉजिटरी में 50+ भाषा अनुवाद शामिल हैं जो डाउनलोड साइज को काफी बढ़ाते हैं। अनुवादों के बिना क्लोन करने के लिए, sparse checkout का उपयोग करें: +> यह रिपॉजिटरी 50+ भाषा अनुवादों को शामिल करता है जो डाउनलोड साइज को काफी बढ़ाता है। बिना अनुवाद के क्लोन करने के लिए, sparse checkout का उपयोग करें: > ```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' > ``` -> यह आपको कोर्स पूरा करने के लिए आवश्यक सब कुछ बहुत तेजी से डाउनलोड करता है। +> यह आपको कोर्स पूरा करने के लिए आवश्यक सब कुछ देता है, और डाउनलोड बहुत तेज़ होता है। -**यदि आप अतिरिक्त अनुवाद भाषाओं को समर्थित देखना चाहते हैं, तो वे [यहाँ](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md) सूचीबद्ध हैं।** +**यदि आप अतिरिक्त भाषाओं के समर्थन की इच्छा रखते हैं तो सूची [यहाँ](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md) उपलब्ध है।** -## समुदाय में शामिल हों +## समुदाय से जुड़ें [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) ## आप क्या सीखेंगे @@ -58,121 +59,121 @@ CO_OP_TRANSLATOR_METADATA: इस पाठ्यक्रम में, आप सीखेंगे: -* आर्टिफिशियल इंटेलिजेंस के विभिन्न दृष्टिकोण, जिसमें "अच्छा पुराना" प्रतीकात्मक दृष्टिकोण शामिल है, जिसमें **ज्ञान प्रतिनिधित्त्व** और तर्क ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)) शामिल हैं। -* **न्यूरल नेटवर्क** और **डीप लर्निंग**, जो आधुनिक AI के मुख्य स्तंभ हैं। हम इन महत्वपूर्ण विषयों की अवधारणाओं को दो सबसे लोकप्रिय फ्रेमवर्क - [TensorFlow](http://Tensorflow.org) और [PyTorch](http://pytorch.org) में कोड के माध्यम से समझाएंगे। -* छवियों और टेक्स्ट के साथ काम करने के लिए **न्यूरल आर्किटेक्चर**। हम हाल के मॉडलों को कवर करेंगे लेकिन यह अत्याधुनिक में थोड़े कम हो सकते हैं। -* कम लोकप्रिय AI दृष्टिकोण, जैसे कि **जेनेटिक एल्गोरिदम** और **मल्टी-एजेंट सिस्टम**। +* आर्टिफिशियल इंटेलिजेंस के विभिन्न दृष्टिकोण, जिसमें "पुराना" प्रतीकात्मक तरीका शामिल है, जिसमें **ज्ञान प्रतिनिधित्व** और तर्कशक्ति ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)) शामिल हैं। +* **न्यूरल नेटवर्क्स** और **डीप लर्निंग**, जो आधुनिक AI के मूल में हैं। हम इन महत्वपूर्ण विषयों के पीछे की अवधारणाओं को कोड के माध्यम से दो सबसे लोकप्रिय फ्रेमवर्क्स - [TensorFlow](http://Tensorflow.org) और [PyTorch](http://pytorch.org) में समझाएंगे। +* छवियों और टेक्स्ट के साथ काम करने के लिए **न्यूरल आर्किटेक्चर**। हम वर्तमान मॉडल को कवर करेंगे लेकिन यह अत्याधुनिक स्तर पर थोड़ा कम हो सकता है। +* कम लोकप्रिय AI दृष्टिकोण, जैसे कि **जैविक एल्गोरिदम** और **मल्टी-एजेंट सिस्टम**। -इस पाठ्यक्रम में हम क्या नहीं कवर करेंगे: +इस पाठ्यक्रम में हम जो कवर नहीं करेंगे: -> [इस कोर्स के लिए सभी अतिरिक्त संसाधन हमारे Microsoft Learn संग्रह में खोजें](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) +> [इस कोर्स के सभी अतिरिक्त संसाधन हमारे Microsoft Learn संग्रह में देखें](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) -* **व्यवसाय में AI** के व्यावसायिक मामले। इसके लिए, Microsoft Learn पर [Introduction to AI for business users](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) शिक्षण पथ, या [AI Business School](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), जो [INSEAD](https://www.insead.edu/) के सहयोग से विकसित किया गया है, को देखें। -* **क्लासिक मशीन लर्निंग**, जिसका हमारे [Machine Learning for Beginners Curriculum](http://github.com/Microsoft/ML-for-Beginners) में अच्छा वर्णन है। -* **[कॉग्निटिव सर्विसेज](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)** का उपयोग करके बनाए गए व्यावहारिक AI अनुप्रयोग। इसके लिए, हम Microsoft Learn के मॉड्यूल जैसे [vision](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [प्राकृतिक भाषा प्रसंस्करण](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[Azure OpenAI सेवा के साथ जनरेटिव AI](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** और अन्य से शुरुआत करने की सलाह देते हैं। -* विशिष्ट एमएल **क्लाउड फ्रेमवर्क**, जैसे [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), या [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum)। [Azure Machine Learning के साथ मशीन लर्निंग समाधान बनाएं और संचालित करें](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) और [Azure Databricks के साथ मशीन लर्निंग समाधान बनाएं और संचालित करें](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum) शिक्षण पथों का उपयोग करने पर विचार करें। -* **संवादात्मक AI** और **चैट बॉट्स**। इसके लिए एक अलग [Create conversational AI solutions](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum) शिक्षण पथ है, और आप अधिक विवरण के लिए [इस ब्लॉग पोस्ट](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) को भी देख सकते हैं। -* डीप लर्निंग के पीछे की **गहरी गणित**। इसके लिए, हम Ian Goodfellow, Yoshua Bengio और Aaron Courville की [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) पुस्तक की सलाह देते हैं, जो ऑनलाइन [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/) पर भी उपलब्ध है। +* **व्यवसाय में AI** के व्यवसायिक मामले। Microsoft Learn पर [Introduction to AI for business users](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) लर्निंग पाथ या [AI Business School](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), जो [INSEAD](https://www.insead.edu/) के साथ सहयोग में विकसित किया गया है, लेने पर विचार करें। +* **क्लासिक मशीन लर्निंग**, जिसे हमारे [Machine Learning for Beginners Curriculum](http://github.com/Microsoft/ML-for-Beginners) में अच्छी तरह से वर्णित किया गया है। +* **[कॉग्निटिव सर्विसेज](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)** का उपयोग करके व्यावहारिक AI अनुप्रयोग। इसके लिए, हम Microsoft Learn के [vision](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [प्राकृतिक भाषा प्रसंस्करण](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[Azure OpenAI सेवा के साथ जनरेटिव AI](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** और अन्य मॉड्यूल से शुरुआत करने की सलाह देते हैं। +* विशिष्ट ML **क्लाउड फ्रेमवर्क**, जैसे [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), या [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum)। [Azure Machine Learning के साथ मशीन लर्निंग समाधान बनाएं और संचालित करें](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) और [Azure Databricks के साथ मशीन लर्निंग समाधान बनाएं और संचालित करें](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum) लर्निंग पाथ का उपयोग करने पर विचार करें। +* **संवादी AI** और **चैट बॉट्स**। इसके लिए एक अलग [Create conversational AI solutions](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum) लर्निंग पाथ है, और आप अधिक विवरण के लिए [यह ब्लॉग पोस्ट](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) भी देख सकते हैं। +* डीप लर्निंग के पीछे की **गहन गणित**। इसके लिए, हम Ian Goodfellow, Yoshua Bengio और Aaron Courville द्वारा लिखित [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) की सिफारिश करेंगे, जो ऑनलाइन [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/) पर भी उपलब्ध है। -_क्लाउड में AI_ विषयों के लिए एक कोमल परिचय के रूप में, आप [Azure पर आर्टिफिशियल इंटेलिजेंस के साथ शुरुआत करें](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum) सीखने का रास्ता ले सकते हैं। +_क्लाउड में AI_ विषयों की मुलायम शुरुआत के लिए आप [Azure पर आर्टिफिशियल इंटेलिजेंस के साथ शुरुआत करें](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum) लर्निंग पाथ लेने पर विचार कर सकते हैं। # सामग्री -| | पाठ लिंक | PyTorch/Keras/TensorFlow | लैब | +| | पाठ्य लिंक | PyTorch/Keras/TensorFlow | प्रयोगशाला | | :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | -| 0 | [कोर्स सेटअप](./lessons/0-course-setup/setup.md) | [अपने विकास वातावरण को सेटअप करें](./lessons/0-course-setup/how-to-run.md) | | -| I | [**AI का परिचय**](./lessons/1-Intro/README.md) | | | +| 0 | [कोर्स सेटअप](./lessons/0-course-setup/setup.md) | [अपना विकास वातावरण सेटअप करें](./lessons/0-course-setup/how-to-run.md) | | +| I | [**AI परिचय**](./lessons/1-Intro/README.md) | | | | 01 | [AI का परिचय और इतिहास](./lessons/1-Intro/README.md) | - | - | | II | **प्रतीकात्मक AI** | -| 02 | [ज्ञान प्रतिनिधित्व और विशेषज्ञ प्रणालियाँ](./lessons/2-Symbolic/README.md) | [विशेषज्ञ प्रणालियाँ](./lessons/2-Symbolic/Animals.ipynb) / [ऑन्टोलॉजी](./lessons/2-Symbolic/FamilyOntology.ipynb) /[धारणा ग्राफ](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | +| 02 | [ज्ञान प्रतिनिधित्व और विशेषज्ञ प्रणालियाँ](./lessons/2-Symbolic/README.md) | [विशेषज्ञ प्रणालियाँ](./lessons/2-Symbolic/Animals.ipynb) / [ऑन्टोलॉजी](./lessons/2-Symbolic/FamilyOntology.ipynb) /[कॉन्सेप्ट ग्राफ़](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | | III | [**न्यूरल नेटवर्क्स का परिचय**](./lessons/3-NeuralNetworks/README.md) ||| -| 03 | [परसेप्ट्रॉन](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [नोटबुक](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [प्रयोगशाला](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | -| 04 | [मल्टी-लेयर्ड परसेप्ट्रॉन और अपना फ्रेमवर्क बनाना](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [नोटबुक](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [प्रयोगशाला](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | -| 05 | [फ्रेमवर्क्स (PyTorch/TensorFlow) का परिचय और ओवरफिटिंग](./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) | [प्रयोगशाला](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | -| IV | [**कंप्यूटर विज़न**](./lessons/4-ComputerVision/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-77998-cacaste) / [TensorFlow](https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-77998-cacaste)| [Microsoft Azure पर कंप्यूटर विज़न एक्सप्लोर करें](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | +| 03 | [पर्सेप्ट्रॉन](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [नोटबुक](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [प्रयोगशाला](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | +| 04 | [मल्टी-लेयर्ड पर्सेप्ट्रॉन और हमारा अपना फ्रेमवर्क बनाना](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [नोटबुक](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [प्रयोगशाला](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | +| 05 | [फ़्रेमवर्क्स (PyTorch/TensorFlow) का परिचय और ओवरफिटिंग](./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) | [प्रयोगशाला](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | +| IV | [**कंप्यूटर विज़न**](./lessons/4-ComputerVision/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-77998-cacaste) / [TensorFlow](https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-77998-cacaste)| [Microsoft Azure पर कंप्यूटर विज़न का पता लगाएँ](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | | 06 | [कंप्यूटर विज़न का परिचय। OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [नोटबुक](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [प्रयोगशाला](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | -| 07 | [कन्वॉल्यूशनल न्यूरल नेटवर्क्स](./lessons/4-ComputerVision/07-ConvNets/README.md) & [CNN आर्किटेक्चर](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [प्रयोगशाला](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | -| 08 | [पूर्व-प्रशिक्षित नेटवर्क और ट्रांसफर लर्निंग](./lessons/4-ComputerVision/08-TransferLearning/README.md) और [ट्रेनिंग ट्रिक्स](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [प्रयोगशाला](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | +| 07 | [कन्वोल्यूशनल न्यूरल नेटवर्क्स](./lessons/4-ComputerVision/07-ConvNets/README.md) & [CNN वास्तुकलाएँ](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [प्रयोगशाला](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | +| 08 | [पूर्व-प्रशिक्षित नेटवर्क और ट्रांसफर लर्निंग](./lessons/4-ComputerVision/08-TransferLearning/README.md) और [प्रशिक्षण युक्तियाँ](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [प्रयोगशाला](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | | 09 | [ऑटोएन्कोडर्स और 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 | [जेनरेटिव एडवरसैरियल नेटवर्क्स & आर्टिस्टिक स्टाइल ट्रांसफर](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | +| 10 | [जनरेटिव एडवरसैरियल नेटवर्क्स और कलाकार शैली ट्रांसफर](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | | 11 | [ऑब्जेक्ट डिटेक्शन](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [प्रयोगशाला](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | -| 12 | [सेमांटिक सेगमेंटेशन। यू-नेट](./lessons/4-ComputerVision/12-Segmentation/README.md) | [PyTorch](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb) | | -| V | [**नैचुरल लैंग्वेज प्रोसेसिंग**](./lessons/5-NLP/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-77998-cacaste) /[TensorFlow](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-77998-cacaste) | [Microsoft Azure पर नैचुरल लैंग्वेज प्रोसेसिंग एक्सप्लोर करें](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| -| 13 | [टेक्स्ट प्रतिनिधित्व। बो/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 | [सेमांटिक शब्द एम्बेडिंग। Word2Vec और 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 | [भाषा मॉडलिंग। अपनी खुद की एम्बेडिंग्स का प्रशिक्षण](./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) | [प्रयोगशाला](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | -| 16 | [रकरेंट न्यूरल नेटवर्क्स](./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 | [जेनरेटिव रकरेंट नेटवर्क्स](./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) | [प्रयोगशाला](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | -| 18 | [ट्रांस्फॉर्मर्स। 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 | [नामित इकाई पहचान](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [प्रयोगशाला](./lessons/5-NLP/19-NER/lab/README.md) | -| 20 | [बड़े भाषा मॉडल, प्रॉम्प्ट प्रोग्रामिंग और फ्यू-शॉट टास्क](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | -| VI | **अन्य AI तकनीकें** || | -| 21 | [जनेटिक एल्गोरिदम](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [नोटबुक](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | -| 22 | [डीप रिइन्फोर्समेंट लर्निंग](./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) | [प्रयोगशाला](./lessons/6-Other/22-DeepRL/lab/README.md) | +| 12 | [सैमांटिक सेगमेंटेशन। यू-नेट](./lessons/4-ComputerVision/12-Segmentation/README.md) | [PyTorch](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb) | | +| V | [**प्राकृतिक भाषा प्रसंस्करण**](./lessons/5-NLP/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-77998-cacaste) /[TensorFlow](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-77998-cacaste) | [Microsoft Azure पर प्राकृतिक भाषा प्रसंस्करण का पता लगाएँ](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| +| 13 | [पाठ प्रतिनिधित्व। बो/टीएफ-आईडीएफ](./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 | [सैमांटिक शब्द एम्बेडिंग्स। Word2Vec और 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 | [भाषा मॉडलिंग। अपना खुद का एम्बेडिंग प्रशिक्षण](./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) | [प्रयोगशाला](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | +| 16 | [रैकरेंट न्यूरल नेटवर्क्स](./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 | [जनरेटिव रैकरेंट नेटवर्क्स](./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) | [प्रयोगशाला](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | +| 18 | [ट्रांसफॉर्मर्स। 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 | [नामित इकाई मान्यता](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [प्रयोगशाला](./lessons/5-NLP/19-NER/lab/README.md) | +| 20 | [बड़े भाषा मॉडल, प्रॉम्प्ट प्रोग्रामिंग और कुछ शॉट कार्य](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | +| VI | **अन्य एआई तकनीकें** || | +| 21 | [जेनेटिक एल्गोरिदम](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [नोटबुक](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | +| 22 | [डीप रिइंफोर्समेंट लर्निंग](./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) | [प्रयोगशाला](./lessons/6-Other/22-DeepRL/lab/README.md) | | 23 | [मल्टी-एजेंट सिस्टम्स](./lessons/6-Other/23-MultiagentSystems/README.md) | | | -| VII | **AI नैतिकता** | | | -| 24 | [AI नैतिकता और जिम्मेदार AI](./lessons/7-Ethics/README.md) | [Microsoft Learn: जिम्मेदार AI सिद्धांत](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | -| IX | **एक्सट्राज़** | | | -| 25 | [मल्टी-मोडल नेटवर्क्स, CLIP और VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [नोटबुक](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | +| VII | **एआई नैतिकता** | | | +| 24 | [एआई नैतिकता और ज़िम्मेदार एआई](./lessons/7-Ethics/README.md) | [Microsoft Learn: ज़िम्मेदार एआई सिद्धांत](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | +| IX | **अतिरिक्त** | | | +| 25 | [मल्टी-मॉडल नेटवर्क्स, CLIP और VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [नोटबुक](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | ## प्रत्येक पाठ में शामिल है * पूर्व-पठन सामग्री -* निष्पादन योग्य जुपिटर नोटबुक, जो अक्सर फ्रेमवर्क (**PyTorch** या **TensorFlow**) के लिए विशिष्ट होते हैं। निष्पादन योग्य नोटबुक में काफी सैद्धांतिक सामग्री भी होती है, इसलिए विषय को समझने के लिए आपको नोटबुक के कम से कम एक संस्करण (चाहे PyTorch या TensorFlow) को अवश्य पढ़ना चाहिए। -* कुछ विषयों के लिए उपलब्ध **प्रयोगशालाएँ**, जो आपको आपने सीखे हुए सामग्री को किसी विशिष्ट समस्या पर लागू करने का अवसर देती हैं। -* कुछ सेक्षन में [**एमएस लर्न**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) मॉड्यूल्स के लिंक होते हैं जो संबंधित विषयों को कवर करते हैं। +* निष्पादित जुपिटर नोटबुक्स, जो अक्सर फ्रेमवर्क (**PyTorch** या **TensorFlow**) के लिए विशिष्ट होती हैं। निष्पादित नोटबुक में बहुत सारी सैद्धांतिक सामग्री भी होती है, इसलिए विषय को समझने के लिए आपको कम से कम एक संस्करण (या तो PyTorch या TensorFlow) पढ़ना आवश्यक है। +* कुछ विषयों के लिए उपलब्ध **प्रयोगशालाएँ**, जो आपको सीखी गई सामग्री को किसी विशिष्ट समस्या पर लागू करने का अवसर देती हैं। +* कुछ अनुभागों में [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) मॉड्यूल्स के लिंक शामिल हैं जो संबंधित विषयों को कवर करते हैं। -## आरंभ करना +## शुरुआत करना -### 🎯 AI में नए हैं? यहां से शुरू करें! +### 🎯 एआई में नए हैं? यहाँ से शुरू करें! -यदि आप AI में बिल्कुल नए हैं और त्वरित, हैंड्स-ऑन उदाहरण चाहते हैं, तो हमारे [**शुरुआती-मित्रवत उदाहरण**](./examples/README.md) देखें! इनमें शामिल हैं: +अगर आप एआई में पूरी तरह से नया हैं और त्वरित, व्यावहारिक उदाहरण चाहते हैं, तो हमारे [**शुरुआत के अनुकूल उदाहरण**](./examples/README.md) देखें! इनमें शामिल हैं: -- 🌟 **हैलो AI वर्ल्ड** - आपका पहला AI प्रोग्राम (पैटर्न रिकग्निशन) -- 🧠 **सरल न्यूरल नेटवर्क** - नल से एक न्यूरल नेटवर्क बनाएं -- 🖼️ **इमेज क्लासीफायर** - चित्रों को वर्गीकृत करें विस्तृत टिप्पणियों के साथ -- 💬 **पाठ की भावना** - सकारात्मक/नकारात्मक पाठ का विश्लेषण करें +- 🌟 **हैलो एआई वर्ल्ड** - आपका पहला एआई प्रोग्राम (पैटर्न पहचान) +- 🧠 **सरल न्यूरल नेटवर्क** - खरोंच से न्यूरल नेटवर्क बनाएं +- 🖼️ **छवि वर्गीकर्ता** - टिप्पणियों के साथ छवियों को वर्गीकृत करें +- 💬 **टेक्स्ट सेंटिमेंट** - सकारात्मक/नकारात्मक टेक्स्ट का विश्लेषण करें -ये उदाहरण आपको पूर्ण पाठ्यक्रम में जाने से पहले AI अवधारणाओं को समझने में मदद करने के लिए डिज़ाइन किए गए हैं। +ये उदाहरण आपको पूरी पाठ्यक्रम में गोता लगाने से पहले AI अवधारणाओं को समझने में मदद करने के लिए डिज़ाइन किए गए हैं। ### 📚 पूर्ण पाठ्यक्रम सेटअप -- हमने आपके डेवलपमेंट वातावरण को सेटअप करने में मदद करने के लिए एक [सेटअप पाठ](./lessons/0-course-setup/setup.md) बनाया है। - शिक्षकों के लिए, हमने आपके लिए भी एक [पाठ्यक्रम सेटअप पाठ](./lessons/0-course-setup/for-teachers.md) बनाया है! -- VSCode या Codepace में कोड कैसे चलाएं यह जानने के लिए [यहां देखें](./lessons/0-course-setup/how-to-run.md) +- हमने आपके विकास पर्यावरण को सेटअप करने में मदद के लिए एक [सेटअप पाठ](./lessons/0-course-setup/setup.md) बनाया है। - शिक्षकों के लिए, हमने आपके लिए भी एक [पाठ्यक्रम सेटअप पाठ](./lessons/0-course-setup/for-teachers.md) बनाया है! +- VSCode या Codespace में [कोड कैसे चलाएं](./lessons/0-course-setup/how-to-run.md) इन चरणों का पालन करें: -रिपोजिटरी फोर्क करें: इस पेज के ऊपर-दाएँ कोने में "Fork" बटन पर क्लिक करें। +भंडार को फोर्क करें: इस पेज के शीर्ष-दाएँ कोने में "Fork" बटन पर क्लिक करें। -रिपोजिटरी क्लोन करें: `git clone https://github.com/microsoft/AI-For-Beginners.git` +भंडार क्लोन करें: `git clone https://github.com/microsoft/AI-For-Beginners.git` -इस रिपो को ⭐ स्टार देना न भूलें ताकि बाद में इसे आसानी से ढूंढा जा सके। +बाद में इसे आसानी से ढूंढने के लिए इस रिपॉजिटरी को स्टार (🌟) करना न भूलें। ## अन्य शिक्षार्थियों से मिलें -इस कोर्स को कर रहे अन्य शिक्षार्थियों से मिलने और नेटवर्किंग करने के लिए हमारे [आधिकारिक AI Discord सर्वर](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) में शामिल हों और सहायता प्राप्त करें। +इस कोर्स को कर रहे अन्य शिक्षार्थियों से मिलने और नेटवर्क बनाने के लिए हमारे [अधिकारिक AI डिस्कॉर्ड सर्वर](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) में शामिल हों और सहायता प्राप्त करें। -यदि आपको उत्पाद प्रतिक्रिया या प्रश्न हैं तो हमारे [Azure AI Foundry Developer Forum](https://aka.ms/foundry/forum) पर जाएं। +यदि आपके पास उत्पाद प्रतिक्रिया या प्रश्न हैं तो हमारे [Azure AI Foundry डेवलपर फोरम](https://aka.ms/foundry/forum) पर जाएँ। ## क्विज़ -> **क्विज़ के बारे में एक नोट**: सभी क्विज़ क्विज़-ऐप फ़ोल्डर में etc\quiz-app में संग्रहीत हैं, या आप इन्हें [ऑनलाइन यहां](https://ff-quizzes.netlify.app/) देख सकते हैं। ये क्विज़ पाठों के भीतर लिंक किए गए हैं; क्विज़ ऐप को स्थानीय रूप से चलाया जा सकता है या Azure पर तैनात किया जा सकता है; `quiz-app` फ़ोल्डर में निर्देशों का पालन करें। इन्हें धीरे-धीरे स्थानीयकृत किया जा रहा है। +> **क्विज़ के बारे में एक नोट**: सभी क्विज़ क्विज़-ऐप फ़ोल्डर में मौजूद हैं जो etc\quiz-app में है, या [यहाँ ऑनलाइन](https://ff-quizzes.netlify.app/)। इन्हें पाठ्यक्रम के भीतर लिंक किया गया है। क्विज़ ऐप को स्थानीय रूप से चलाया जा सकता है या Azure पर तैनात किया जा सकता है; `quiz-app` फ़ोल्डर में निर्देशों का पालन करें। इन्हें धीरे-धीरे स्थानीयकृत किया जा रहा है। ## सहायता चाहिए -क्या आपके पास सुझाव हैं या आपने वर्तनी या कोड त्रुटियां पाई हैं? एक मुद्दा उठाएं या पुल अनुरोध बनाएं। +क्या आपके पास सुझाव हैं या आपने वर्तनी या कोड की गलती पाई है? मुद्दा उठाएं या पुल रिक्वेस्ट बनाएं। ## विशेष धन्यवाद -* **✍️ प्राथमिक लेखक:** [Dmitry Soshnikov](http://soshnikov.com), PhD +* **✍️ प्रमुख लेखक:** [Dmitry Soshnikov](http://soshnikov.com), PhD * **🔥 संपादक:** [Jen Looper](https://twitter.com/jenlooper), PhD -* **🎨 स्केचनोट चित्रकार:** [Tomomi Imura](https://twitter.com/girlie_mac) +* **🎨 स्केचक नोट चित्रकार:** [Tomomi Imura](https://twitter.com/girlie_mac) * **✅ क्विज़ निर्माता:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) * **🙏 मुख्य योगदानकर्ता:** [Evgenii Pishchik](https://github.com/Pe4enIks) ## अन्य पाठ्यक्रम -हमारी टीम अन्य पाठ्यक्रम भी बनाती है! जांचें: +हमारी टीम अन्य पाठ्यक्रम भी उत्पादन करती है! देखें: ### LangChain @@ -181,7 +182,7 @@ _क्लाउड में AI_ विषयों के लिए एक क --- -### Azure / Edge / MCP / Agents +### Azure / Edge / MCP / एजेंट्स [![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) @@ -189,7 +190,7 @@ _क्लाउड में AI_ विषयों के लिए एक क --- -### Generative AI श्रृंखला +### Generative AI सीरीज [![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) @@ -208,19 +209,19 @@ _क्लाउड में AI_ विषयों के लिए एक क --- -### Copilot श्रृंखला +### कोपिलट सीरीज [![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) -## सहायता प्राप्त करना +## सहायता प्राप्त करें -यदि आप अटके हुए हैं या AI ऐप बनाने के संबंध में कोई प्रश्न है। MCP पर सहपाठी शिक्षार्थियों और अनुभवी डेवलपर्स के साथ चर्चा में शामिल हों। यह एक सहायक समुदाय है जहाँ प्रश्नों का स्वागत है और ज्ञान को स्वतंत्र रूप से साझा किया जाता है। +यदि आप अटके हैं या AI ऐप्लिकेशन बनाने के दौरान कोई प्रश्न हैं। MCP के बारे में चर्चाओं में साथ पढ़ने वाले और अनुभवी डेवलपर्स से जुड़ें। यह एक सहायक समुदाय है जहाँ प्रश्नों का स्वागत है और ज्ञान स्वतंत्र रूप से साझा किया जाता है। [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -यदि आपको उत्पाद प्रतिक्रिया या त्रुटियां मिलती हैं तो निर्माण के दौरान निम्नलिखित पर जाएं: +यदि आपके पास निर्माण के दौरान उत्पाद प्रतिक्रिया या त्रुटियां हैं तो जाएँ: [![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) @@ -228,5 +229,5 @@ _क्लाउड में AI_ विषयों के लिए एक क **अस्वीकरण**: -यह दस्तावेज़ AI अनुवाद सेवा [Co-op Translator](https://github.com/Azure/co-op-translator) का उपयोग करके अनूदित किया गया है। जबकि हम सटीकता के लिए प्रयासरत हैं, कृपया ध्यान रखें कि स्वचालित अनुवादों में त्रुटियाँ या असत्यताएँ हो सकती हैं। मूल दस्तावेज़ अपनी मूल भाषा में प्रामाणिक स्रोत माना जाना चाहिए। महत्वपूर्ण जानकारी के लिए, पेशेवर मानवीय अनुवाद की सिफारिश की जाती है। इस अनुवाद के उपयोग से उत्पन्न किसी भी गलतफहमी या गलत व्याख्या के लिए हम जिम्मेदार नहीं हैं। +यह दस्तावेज़ AI अनुवाद सेवा [Co-op Translator](https://github.com/Azure/co-op-translator) का उपयोग करके अनुवादित किया गया है। जबकि हम सटीकता के लिए प्रयास करते हैं, कृपया ध्यान दें कि स्वचालित अनुवाद में त्रुटियां या गलतियाँ हो सकती हैं। मूल दस्तावेज़ अपनी मूल भाषा में आधिकारिक स्रोत माना जाना चाहिए। महत्वपूर्ण जानकारी के लिए, पेशेवर मानव अनुवाद की सलाह दी जाती है। इस अनुवाद के उपयोग से उत्पन्न किसी भी गलतफहमी या गलत व्याख्या के लिए हम जिम्मेदार नहीं हैं। \ No newline at end of file diff --git a/translations/hi/lessons/0-course-setup/how-to-run.md b/translations/hi/lessons/0-course-setup/how-to-run.md index d51617fc..a12ee46e 100644 --- a/translations/hi/lessons/0-course-setup/how-to-run.md +++ b/translations/hi/lessons/0-course-setup/how-to-run.md @@ -1,21 +1,21 @@ # कोड कैसे चलाएं -यह पाठ्यक्रम कई निष्पादन योग्य उदाहरणों और प्रयोगशालाओं से भरा हुआ है, जिन्हें आप चलाना चाहेंगे। ऐसा करने के लिए, आपको इस पाठ्यक्रम के हिस्से के रूप में प्रदान किए गए Jupyter Notebooks में Python कोड चलाने की क्षमता की आवश्यकता होगी। कोड चलाने के लिए आपके पास कई विकल्प हैं: +इस पाठ्यक्रम में बहुत सारे निष्पादन योग्य उदाहरण और प्रयोगशालाएँ हैं जिन्हें आप चलाना चाहेंगे। ऐसा करने के लिए, आपके पास इस पाठ्यक्रम के हिस्से के रूप में प्रदान किए गए Jupyter नोटबुक में Python कोड निष्पादित करने की क्षमता होनी चाहिए। कोड चलाने के लिए आपके पास कई विकल्प हैं: -## अपने कंप्यूटर पर लोकल रूप से चलाएं +## अपने कंप्यूटर पर स्थानीय रूप से चलाएं -अपने कंप्यूटर पर कोड चलाने के लिए, आपके पास Python का कोई संस्करण इंस्टॉल होना चाहिए। मैं व्यक्तिगत रूप से **[miniconda](https://conda.io/en/latest/miniconda.html)** इंस्टॉल करने की सिफारिश करता हूं - यह एक हल्का इंस्टॉलेशन है जो `conda` पैकेज मैनेजर को विभिन्न Python **वर्चुअल एनवायरनमेंट्स** के लिए सपोर्ट करता है। +अपने कंप्यूटर पर स्थानीय रूप से कोड चलाने के लिए, Python इंस्टॉलेशन आवश्यक है। एक सिफारिश है **[miniconda](https://conda.io/en/latest/miniconda.html)** इंस्टॉल करना - यह एक हल्का इंस्टॉलेशन है जो विभिन्न Python **वर्चुअल वातावरणों** के लिए `conda` पैकेज मैनेजर का समर्थन करता है। -Miniconda इंस्टॉल करने के बाद, आपको रिपॉजिटरी को क्लोन करना होगा और इस कोर्स के लिए उपयोग किए जाने वाले वर्चुअल एनवायरनमेंट को बनाना होगा: +miniconda इंस्टॉल करने के बाद, रिपॉजिटरी क्लोन करें और इस कोर्स के लिए उपयोग किए जाने वाले वर्चुअल वातावरण को बनाएँ: ```bash git clone http://github.com/microsoft/ai-for-beginners @@ -26,51 +26,55 @@ conda activate ai4beg ### Python एक्सटेंशन के साथ Visual Studio Code का उपयोग करना -शायद इस पाठ्यक्रम का उपयोग करने का सबसे अच्छा तरीका यह है कि इसे [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) में [Python Extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste) के साथ खोलें। +यह पाठ्यक्रम सबसे अच्छा तब उपयोग किया जाता है जब आप इसे [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) में [Python एक्सटेंशन](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste) के साथ खोलते हैं। -> **Note**: एक बार जब आप रिपॉजिटरी को क्लोन करके VS Code में खोलते हैं, तो यह स्वचालित रूप से आपको Python एक्सटेंशन इंस्टॉल करने का सुझाव देगा। आपको ऊपर बताए गए अनुसार Miniconda भी इंस्टॉल करना होगा। +> **नोट**: एक बार जब आप रिपॉजिटरी क्लोन करके VS Code में डायरेक्टरी खोलेंगे, तो यह स्वचालित रूप से Python एक्सटेंशन्स इंस्टॉल करने का सुझाव देगा। आपको ऊपर वर्णित miniconda भी इंस्टॉल करना होगा। -> **Note**: यदि VS Code आपको रिपॉजिटरी को कंटेनर में फिर से खोलने का सुझाव देता है, तो आपको इसे अस्वीकार करना होगा और लोकल Python इंस्टॉलेशन का उपयोग करना होगा। +> **नोट**: यदि VS Code आपको रिपॉजिटरी को कंटेनर में पुनः खोलने का सुझाव देता है, तो स्थानीय Python इंस्टॉलेशन उपयोग करने के लिए इसे इनकार कर देना चाहिए। ### ब्राउज़र में Jupyter का उपयोग करना -आप अपने कंप्यूटर पर ब्राउज़र से सीधे Jupyter एनवायरनमेंट का उपयोग भी कर सकते हैं। वास्तव में, क्लासिकल Jupyter और Jupyter Hub दोनों ही ऑटो-कम्प्लीशन, कोड हाइलाइटिंग आदि के साथ एक सुविधाजनक विकास वातावरण प्रदान करते हैं। +आप अपने कंप्यूटर पर ब्राउज़र के माध्यम से Jupyter पर्यावरण का उपयोग भी कर सकते हैं। क्लासिक Jupyter और JupyterHub दोनों एक सुविधाजनक विकास वातावरण प्रदान करते हैं जिसमें ऑटो-कम्प्लीशन, कोड हाइलाइटिंग आदि शामिल हैं। -लोकल रूप से Jupyter शुरू करने के लिए, कोर्स की डायरेक्टरी में जाएं और निम्नलिखित कमांड चलाएं: +स्थानीय रूप से Jupyter शुरू करने के लिए, कोर्स की डायरेक्टरी में जाएं, और निष्पादित करें: ```bash jupyter notebook -``` -या +``` +या ```bash jupyterhub -``` -इसके बाद आप किसी भी `.ipynb` फाइल पर नेविगेट कर सकते हैं, उसे खोल सकते हैं और काम शुरू कर सकते हैं। +``` +आप इसके बाद `.ipynb` फ़ाइलों में नेविगेट कर सकते हैं, उन्हें खोल सकते हैं और काम करना शुरू कर सकते हैं। ### कंटेनर में चलाना -Python इंस्टॉलेशन का एक विकल्प कोड को कंटेनर में चलाना हो सकता है। चूंकि हमारी रिपॉजिटरी में एक विशेष `.devcontainer` फोल्डर है जो इस रिपॉजिटरी के लिए कंटेनर बनाने के निर्देश देता है, VS Code आपको कोड को कंटेनर में फिर से खोलने का सुझाव देगा। इसके लिए Docker इंस्टॉलेशन की आवश्यकता होगी और यह थोड़ा जटिल हो सकता है, इसलिए हम इसे अधिक अनुभवी उपयोगकर्ताओं के लिए सुझाते हैं। +Python इंस्टॉलेशन का एक विकल्प कोड को कंटेनर में चलाना हो सकता है। क्योंकि हमारी रिपॉजिटरी में एक विशेष `.devcontainer` फ़ोल्डर होता है जो इस रेपो के लिए कंटेनर बनाने के निर्देश देता है, VS Code कोड को कंटेनर में पुनः खोलने का अवसर देता है। इसके लिए Docker इंस्टॉलेशन आवश्यक होगा और यह अधिक जटिल होगा, इसलिए हम इसे अधिक अनुभवी उपयोगकर्ताओं को सुझाव देते हैं। ## क्लाउड में चलाना -यदि आप लोकल रूप से Python इंस्टॉल नहीं करना चाहते हैं और आपके पास कुछ क्लाउड संसाधनों तक पहुंच है, तो कोड को क्लाउड में चलाना एक अच्छा विकल्प हो सकता है। इसे करने के कई तरीके हैं: +यदि आप Python स्थानीय रूप से इंस्टॉल नहीं करना चाहते हैं, और आपके पास कुछ क्लाउड संसाधनों की पहुँच है - तो क्लाउड में कोड चलाना एक अच्छा विकल्प होगा। इसे करने के कई तरीके हैं: -* **[GitHub Codespaces](https://github.com/features/codespaces)** का उपयोग करना, जो GitHub पर आपके लिए एक वर्चुअल एनवायरनमेंट बनाता है और VS Code ब्राउज़र इंटरफेस के माध्यम से सुलभ होता है। यदि आपके पास Codespaces तक पहुंच है, तो आप बस रिपॉजिटरी में **Code** बटन पर क्लिक करें, एक Codespace शुरू करें, और तुरंत काम शुरू करें। -* **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)** का उपयोग करना। [Binder](https://mybinder.org) क्लाउड में मुफ्त कंप्यूटिंग संसाधन प्रदान करता है ताकि आप GitHub पर कुछ कोड आज़मा सकें। होम पेज पर एक बटन है जो रिपॉजिटरी को Binder में खोलने की अनुमति देता है - यह आपको जल्दी से Binder साइट पर ले जाएगा, जो अंतर्निहित कंटेनर बनाएगा और आपके लिए Jupyter वेब इंटरफेस को सहजता से शुरू करेगा। +* **[GitHub Codespaces](https://github.com/features/codespaces)** का उपयोग करना, जो GitHub पर आपके लिए बनाए गए एक वर्चुअल वातावरण है, जो VS Code ब्राउज़र इंटरफ़ेस के माध्यम से सुलभ है। यदि आपके पास Codespaces की पहुँच है, तो आप बस रेपो में **Code** बटन पर क्लिक करें, एक codespace शुरू करें, और तुरंत काम शुरू कर सकते हैं। +* **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)** का उपयोग करना। [Binder](https://mybinder.org) GitHub पर कुछ कोड टेस्ट करने के लिए क्लाउड में मुफ्त कंप्यूटिंग संसाधन प्रदान करता है। फ्रंट पेज पर रेपो को Binder में खोलने के लिए एक बटन है - यह आपको जल्दी से binder साइट पर ले जाएगा, जो एक कंटेनर बनाएगा और आपके लिए निर्बाध रूप से Jupyter वेब इंटरफ़ेस शुरू करेगा। -> **Note**: दुरुपयोग को रोकने के लिए, Binder में कुछ वेब संसाधनों तक पहुंच अवरुद्ध है। यह कुछ कोड को काम करने से रोक सकता है, जो सार्वजनिक इंटरनेट से मॉडल और/या डेटा सेट लाता है। आपको कुछ वैकल्पिक समाधान खोजने की आवश्यकता हो सकती है। इसके अलावा, Binder द्वारा प्रदान किए गए कंप्यूटिंग संसाधन काफी बुनियादी हैं, इसलिए प्रशिक्षण धीमा होगा, खासकर बाद के अधिक जटिल पाठों में। +> **नोट**: दुरुपयोग से बचने के लिए, Binder के पास कुछ वेब संसाधनों तक पहुँच अवरुद्ध है। यह कुछ कोड के काम करने में बाधा डाल सकता है, जो मॉडल और/या डेटासेट सार्वजनिक इंटरनेट से प्राप्त करता है। आपको कुछ समाधान खोजने पड़ सकते हैं। इसके अलावा, Binder द्वारा प्रदान किए गए कंप्यूट संसाधन काफी बुनियादी हैं, इसलिए प्रशिक्षण धीमा होगा, विशेष रूप से बाद के, अधिक जटिल पाठों में। ## GPU के साथ क्लाउड में चलाना -इस पाठ्यक्रम के कुछ बाद के पाठ GPU सपोर्ट से काफी लाभान्वित होंगे, क्योंकि अन्यथा प्रशिक्षण बहुत धीमा होगा। यदि आपके पास [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) या आपके संस्थान के माध्यम से क्लाउड तक पहुंच है, तो आप निम्नलिखित विकल्पों का उपयोग कर सकते हैं: +इस पाठ्यक्रम के कुछ बाद के पाठ GPU समर्थन से बहुत लाभान्वित होंगे। उदाहरण के लिए, मॉडल प्रशिक्षण अन्यथा बहुत धीमा हो सकता है। कुछ विकल्प हैं जिन्हें आप पालन कर सकते हैं, खासकर यदि आपके पास क्लाउड की पहुँच है, या तो [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) के माध्यम से, या अपने संस्थान के जरिए: -* [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) बनाएं और Jupyter के माध्यम से इससे कनेक्ट करें। आप फिर रिपॉजिटरी को सीधे मशीन पर क्लोन कर सकते हैं और सीखना शुरू कर सकते हैं। NC-सीरीज VMs में GPU सपोर्ट होता है। +* [Data Science वर्चुअल मशीन](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) बनाएँ और Jupyter के माध्यम से उससे कनेक्ट करें। आप फिर मशीन पर सीधे रेपो क्लोन कर सकते हैं, और सीखना शुरू कर सकते हैं। NC-श्रृंखला VM में GPU समर्थन होता है। -> **Note**: कुछ सब्सक्रिप्शन, जिनमें Azure for Students शामिल है, डिफ़ॉल्ट रूप से GPU सपोर्ट प्रदान नहीं करते हैं। आपको तकनीकी सहायता अनुरोध के माध्यम से अतिरिक्त GPU कोर का अनुरोध करना पड़ सकता है। +> **नोट**: कुछ सब्सक्रिप्शन, जिनमें Azure for Students शामिल है, बॉक्स से बाहर GPU समर्थन प्रदान नहीं करते हैं। आपको टेक्निकल सपोर्ट अनुरोध के जरिए अतिरिक्त GPU कोर प्राप्त करना पड़ सकता है। -* [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) बनाएं और वहां Notebook फीचर का उपयोग करें। [यह वीडियो](https://azure-for-academics.github.io/quickstart/azureml-papers/) दिखाता है कि Azure ML Notebook में रिपॉजिटरी को कैसे क्लोन करें और इसका उपयोग शुरू करें। +* [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) बनाएँ और वहां नोटबुक फीचर का उपयोग करें। [यह वीडियो](https://azure-for-academics.github.io/quickstart/azureml-papers/) दिखाता है कि Azure ML नोटबुक में रेपो को क्लोन कैसे करें और उपयोग शुरू करें। -आप Google Colab का भी उपयोग कर सकते हैं, जो कुछ मुफ्त GPU सपोर्ट के साथ आता है, और वहां Jupyter Notebooks को अपलोड करके उन्हें एक-एक करके निष्पादित कर सकते हैं। +आप Google Colab का भी उपयोग कर सकते हैं, जो कुछ मुफ्त GPU समर्थन के साथ आता है, और वहां Jupyter नोटबुक अपलोड कर के उन्हें एक-एक करके निष्पादित कर सकते हैं। -**अस्वीकरण**: -यह दस्तावेज़ AI अनुवाद सेवा [Co-op Translator](https://github.com/Azure/co-op-translator) का उपयोग करके अनुवादित किया गया है। जबकि हम सटीकता के लिए प्रयास करते हैं, कृपया ध्यान दें कि स्वचालित अनुवाद में त्रुटियां या अशुद्धियां हो सकती हैं। मूल भाषा में उपलब्ध मूल दस्तावेज़ को आधिकारिक स्रोत माना जाना चाहिए। महत्वपूर्ण जानकारी के लिए, पेशेवर मानव अनुवाद की सिफारिश की जाती है। इस अनुवाद के उपयोग से उत्पन्न किसी भी गलतफहमी या गलत व्याख्या के लिए हम उत्तरदायी नहीं हैं। \ No newline at end of file +--- + + +**अस्वीकरण**: +यह दस्तावेज़ एआई अनुवाद सेवा [Co-op Translator](https://github.com/Azure/co-op-translator) का उपयोग करके अनुवादित किया गया है। जबकि हम सटीकता के लिए प्रयासरत हैं, कृपया ध्यान दें कि स्वचालित अनुवादों में त्रुटियाँ या असत्यताएं हो सकती हैं। मूल दस्तावेज़ अपनी मूल भाषा में प्रामाणिक स्रोत माना जाना चाहिए। महत्वपूर्ण जानकारी के लिए, पेशेवर मानव अनुवाद की सिफारिश की जाती है। इस अनुवाद के उपयोग से उत्पन्न किसी भी गलतफहमी या गलत व्याख्या के लिए हम ज़िम्मेदार नहीं हैं। + \ No newline at end of file diff --git a/translations/hi/lessons/2-Symbolic/Animals.ipynb b/translations/hi/lessons/2-Symbolic/Animals.ipynb index 2ee2c539..e4740e94 100644 --- a/translations/hi/lessons/2-Symbolic/Animals.ipynb +++ b/translations/hi/lessons/2-Symbolic/Animals.ipynb @@ -6,25 +6,25 @@ "collapsed": true }, "source": [ - "# पशु विशेषज्ञ प्रणाली को लागू करना\n", + "# एक पशु विशेषज्ञ प्रणाली को लागू करना\n", "\n", "[AI for Beginners Curriculum](http://github.com/microsoft/ai-for-beginners) से एक उदाहरण।\n", "\n", - "इस उदाहरण में, हम एक सरल ज्ञान-आधारित प्रणाली को लागू करेंगे जो कुछ शारीरिक विशेषताओं के आधार पर किसी पशु का निर्धारण करती है। इस प्रणाली को निम्नलिखित AND-OR पेड़ द्वारा दर्शाया जा सकता है (यह पूरे पेड़ का एक हिस्सा है, हम आसानी से इसमें और नियम जोड़ सकते हैं):\n", + "इस नमूने में, हम कुछ शारीरिक लक्षणों के आधार पर एक पशु का निर्धारण करने के लिए एक सरल ज्ञान-आधारित प्रणाली लागू करेंगे। प्रणाली को निम्नलिखित AND-OR पेड़ द्वारा दर्शाया जा सकता है (यह पूरे पेड़ का एक हिस्सा है, हम आसानी से कुछ और नियम जोड़ सकते हैं):\n", "\n", - "![](../../../../lessons/2-Symbolic/images/AND-OR-Tree.png)\n" + "![](../../../../../../translated_images/hi/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## हमारा खुद का एक्सपर्ट सिस्टम शेल बैकवर्ड इंफरेंस के साथ\n", + "## हमारे अपने विशेषज्ञ प्रणालियों का शेल बैकवर्ड इनफेरेंस के साथ\n", "\n", - "आइए प्रोडक्शन रूल्स के आधार पर नॉलेज रिप्रेजेंटेशन के लिए एक सरल भाषा को परिभाषित करने की कोशिश करें। हम नियमों को परिभाषित करने के लिए Python क्लासेस का उपयोग करेंगे। मुख्य रूप से 3 प्रकार की क्लासेस होंगी:\n", - "* `Ask` एक प्रश्न को दर्शाता है जिसे उपयोगकर्ता से पूछा जाना है। इसमें संभावित उत्तरों का सेट होता है।\n", - "* `If` एक नियम को दर्शाता है, और यह केवल नियम की सामग्री को संग्रहीत करने के लिए एक सिंटैक्टिक शॉर्टकट है।\n", - "* `AND`/`OR` क्लासेस हैं जो ट्री की AND/OR शाखाओं को दर्शाती हैं। ये केवल अंदर दिए गए तर्कों की सूची को संग्रहीत करती हैं। कोड को सरल बनाने के लिए, सभी कार्यक्षमता पैरेंट क्लास `Content` में परिभाषित की गई है।\n" + "आइए उत्पादन नियमों पर आधारित ज्ञान प्रतिनिधित्व के लिए एक सरल भाषा परिभाषित करने का प्रयास करें। हम नियमों को परिभाषित करने के लिए कीवर्ड के रूप में Python कक्षाओं का उपयोग करेंगे। मूल रूप से तीन प्रकार की कक्षाएं होंगी:\n", + "* `Ask` एक प्रश्न को दर्शाती है जिसे उपयोगकर्ता से पूछा जाना आवश्यक है। इसमें संभावित उत्तरों का सेट होता है।\n", + "* `If` एक नियम को दर्शाती है, और यह केवल नियम की सामग्री संग्रहीत करने के लिए एक व्याकरणिक शर्करा है।\n", + "* `AND`/`OR` कक्षाएं पेड़ की AND/OR शाखाओं को दर्शाने के लिए हैं। वे केवल अंदर के तर्कों की सूची संग्रहीत करती हैं। कोड को सरल बनाने के लिए, सारी कार्यक्षमता पैरेंट क्लास `Content` में परिभाषित की गई है।\n" ] }, { @@ -66,7 +66,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "हमारे सिस्टम में, कार्यशील स्मृति में **तथ्यों** की सूची **गुण-वैल्यू जोड़ों** के रूप में होगी। ज्ञान आधार को एक बड़े शब्दकोश के रूप में परिभाषित किया जा सकता है जो क्रियाओं (नए तथ्य जो कार्यशील स्मृति में डाले जाने चाहिए) को शर्तों से जोड़ता है, जो AND-OR अभिव्यक्तियों के रूप में व्यक्त किए जाते हैं। साथ ही, कुछ तथ्यों को `पूछा` जा सकता है।\n" + "हमारे सिस्टम में, वर्किंग मेमोरी में **तथ्यों** की सूची होगी जो **विशेषता-मूल्य जोड़े** के रूप में होगी। ज्ञान आधार को एक बड़े शब्दकोश के रूप में परिभाषित किया जा सकता है जो क्रियाओं (नए तथ्य जिन्हें वर्किंग मेमोरी में सम्मिलित किया जाना चाहिए) को शर्तों से मैप करता है, जिन्हें AND-OR अभिव्यक्तियों के रूप में व्यक्त किया गया है। साथ ही, कुछ तथ्यों से `पूछा` भी जा सकता है।\n" ] }, { @@ -99,13 +99,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "पिछड़े निष्कर्षण (backward inference) को करने के लिए, हम `Knowledgebase` क्लास को परिभाषित करेंगे। इसमें शामिल होंगे:\n", - "* कार्यशील `memory` - एक डिक्शनरी जो गुणों (attributes) को उनके मानों (values) से जोड़ती है।\n", - "* Knowledgebase के `rules` - जैसा कि ऊपर परिभाषित किया गया है।\n", + "बैकवर्ड इनफेरेंस करने के लिए, हम `Knowledgebase` क्लास परिभाषित करेंगे। इसमें शामिल होंगे:\n", + "* वर्किंग `memory` - एक डिक्शनरी जो एट्रीब्यूट्स को मानों से मैप करती है\n", + "* नॉलेजबेस `rules` उपर बताए गए फॉर्मेट में\n", "\n", - "दो मुख्य विधियाँ (methods) हैं:\n", - "* `get` - किसी गुण का मान प्राप्त करने के लिए, यदि आवश्यक हो तो निष्कर्षण (inference) करते हुए। उदाहरण के लिए, `get('color')` किसी रंग के स्लॉट का मान प्राप्त करेगा (यह आवश्यक होने पर पूछेगा और कार्यशील मेमोरी में बाद के उपयोग के लिए मान को संग्रहीत करेगा)। यदि हम `get('color:blue')` पूछते हैं, तो यह रंग पूछेगा और फिर `y`/`n` मान लौटाएगा, जो रंग पर निर्भर करेगा।\n", - "* `eval` - वास्तविक निष्कर्षण करता है, यानी AND/OR ट्री को पार करता है, उप-लक्ष्यों (sub-goals) का मूल्यांकन करता है, आदि।\n" + "दो मुख्य मेथड हैं:\n", + "* `get` किसी एट्रीब्यूट का मान प्राप्त करने के लिए, आवश्यक होने पर इनफ़ेरेंस करता है। उदाहरण के लिए, `get('color')` किसी कलर स्लॉट का मान प्राप्त करेगा (यदि ज़रूरी हो तो पूछेगा, और बाद में उपयोग के लिए वर्किंग मेमोरी में मान संग्रहीत करेगा)। यदि हम `get('color:blue')` पूछें, तो यह कलर के लिए पूछेगा, और फिर कलर के आधार पर `y`/`n` मान लौटाएगा।\n", + "* `eval` वास्तविक इनफ़ेरेंस करता है, यानि AND/OR ट्री को ट्रैवर्स करता है, सब-गोल्स का मूल्यांकन करता है, आदि।\n" ] }, { @@ -172,7 +172,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "अब चलिए हमारे पशु ज्ञानकोष को परिभाषित करते हैं और परामर्श करते हैं। ध्यान दें कि यह कॉल आपसे प्रश्न पूछेगा। आप `y`/`n` टाइप करके हां-ना वाले प्रश्नों का उत्तर दे सकते हैं, या लंबे बहुविकल्पीय उत्तरों वाले प्रश्नों के लिए संख्या (0..N) निर्दिष्ट कर सकते हैं।\n" + "अब आइए अपने पशु ज्ञानकोष को परिभाषित करें और परामर्श करें। ध्यान दें कि यह कॉल आपसे प्रश्न पूछेगा। आप हाँ-नहीं प्रश्नों के लिए `y`/`n` टाइप करके उत्तर दे सकते हैं, या लंबे बहुविकल्पीय उत्तरों वाले प्रश्नों के लिए संख्या (0..N) निर्दिष्ट कर सकते हैं।\n" ] }, { @@ -229,11 +229,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## PyKnow का उपयोग करके Forward Inference\n", + "## फॉरवर्ड इनफेरेंस के लिए Experta का उपयोग करना\n", "\n", - "अगले उदाहरण में, हम ज्ञान प्रतिनिधित्व के लिए एक लाइब्रेरी [PyKnow](https://github.com/buguroo/pyknow/) का उपयोग करके forward inference को लागू करने की कोशिश करेंगे। **PyKnow** एक लाइब्रेरी है जो Python में forward inference सिस्टम बनाने के लिए डिज़ाइन की गई है, और यह पुराने क्लासिकल सिस्टम [CLIPS](http://www.clipsrules.net/index.html) के समान है।\n", + "अगले उदाहरण में, हम ज्ञान प्रतिनिधित्व के लिए लाइब्रेरियों में से एक, [Experta](https://github.com/nilp0inter/experta) का उपयोग करके फॉरवर्ड इनफेरेंस लागू करने का प्रयास करेंगे। **Experta** एक लाइब्रेरी है जो Python में फॉरवर्ड इनफेरेंस सिस्टम बनाने के लिए बनाई गई है, जिसे पारंपरिक पुराने सिस्टम [CLIPS](http://www.clipsrules.net/index.html) के समान डिज़ाइन किया गया है।\n", "\n", - "हम forward chaining को खुद भी बिना ज्यादा समस्या के लागू कर सकते थे, लेकिन साधारण (naive) कार्यान्वयन आमतौर पर बहुत प्रभावी नहीं होते। अधिक प्रभावी नियम मिलान (rule matching) के लिए एक विशेष एल्गोरिदम [Rete](https://en.wikipedia.org/wiki/Rete_algorithm) का उपयोग किया जाता है।\n" + "हम बिना किसी बड़ी समस्या के फॉरवर्ड चेनिंग स्वयं भी लागू कर सकते थे, लेकिन सामान्य कार्यान्वयन आमतौर पर बहुत कुशल नहीं होते। अधिक प्रभावी नियम मिलान के लिए एक विशेष एल्गोरिदम [Rete](https://en.wikipedia.org/wiki/Rete_algorithm) का उपयोग किया जाता है।\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": [ - "हम अपने सिस्टम को `KnowledgeEngine` का सबक्लास बनाकर एक क्लास के रूप में परिभाषित करेंगे। प्रत्येक नियम को `@Rule` एनोटेशन के साथ एक अलग फ़ंक्शन द्वारा परिभाषित किया जाता है, जो यह निर्दिष्ट करता है कि नियम कब सक्रिय होना चाहिए। नियम के अंदर, हम `declare` फ़ंक्शन का उपयोग करके नए तथ्य जोड़ सकते हैं, और उन तथ्यों को जोड़ने से आगे की अनुमान इंजन द्वारा कुछ और नियमों को कॉल किया जाएगा।\n" + "हम अपनी प्रणाली को `KnowledgeEngine` की एक उपवर्ग के रूप में परिभाषित करेंगे। प्रत्येक नियम को एक अलग फ़ंक्शन के रूप में परिभाषित किया जाता है जिसमें `@Rule` एनोटेशन होता है, जो निर्दिष्ट करता है कि नियम कब सक्रिय होना चाहिए। नियम के अंदर, हम `declare` फ़ंक्शन का उपयोग करके नए तथ्य जोड़ सकते हैं, और उन तथ्यों को जोड़ने से आगे की अनुमान इंजन द्वारा कुछ और नियमों को सक्रिय किया जाएगा।\n" ] }, { @@ -378,7 +377,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "एक बार जब हम एक ज्ञान आधार परिभाषित कर लेते हैं, तो हम अपनी कार्यशील स्मृति को कुछ प्रारंभिक तथ्यों से भरते हैं, और फिर `run()` विधि को कॉल करते हैं ताकि निष्कर्षण किया जा सके। आप परिणामस्वरूप देख सकते हैं कि नए निष्कर्षित तथ्य कार्यशील स्मृति में जोड़े जाते हैं, जिसमें जानवर के बारे में अंतिम तथ्य भी शामिल है (यदि हमने सभी प्रारंभिक तथ्यों को सही तरीके से सेट किया है)।\n" + "एक बार जब हमने एक नॉलेजबेस परिभाषित कर दिया, तो हम अपनी कार्यशील मेमोरी को कुछ प्रारंभिक तथ्यों से भरते हैं, और फिर इनफेरेंस करने के लिए `run()` मेथड को कॉल करते हैं। आप परिणामस्वरूप देख सकते हैं कि नए निष्कर्षित तथ्य कार्यशील मेमोरी में जोड़े जाते हैं, जिसमें जानवर के बारे में अंतिम तथ्य भी शामिल है (यदि हमने सभी प्रारंभिक तथ्य सही ढंग से सेट किए हैं)।\n" ] }, { @@ -440,7 +439,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n---\n\n**अस्वीकरण**: \nयह दस्तावेज़ AI अनुवाद सेवा [Co-op Translator](https://github.com/Azure/co-op-translator) का उपयोग करके अनुवादित किया गया है। जबकि हम सटीकता सुनिश्चित करने का प्रयास करते हैं, कृपया ध्यान दें कि स्वचालित अनुवाद में त्रुटियां या अशुद्धियां हो सकती हैं। मूल भाषा में उपलब्ध मूल दस्तावेज़ को प्रामाणिक स्रोत माना जाना चाहिए। महत्वपूर्ण जानकारी के लिए, पेशेवर मानव अनुवाद की सिफारिश की जाती है। इस अनुवाद के उपयोग से उत्पन्न किसी भी गलतफहमी या गलत व्याख्या के लिए हम उत्तरदायी नहीं हैं।\n" + "---\n\n\n**अस्वीकरण**: \nइस दस्तावेज़ का अनुवाद AI अनुवाद सेवा [Co-op Translator](https://github.com/Azure/co-op-translator) का उपयोग करके किया गया है। यद्यपि हम सटीकता के लिए प्रयासरत हैं, कृपया ध्यान रखें कि स्वचालित अनुवादों में त्रुटियां या अशुद्धियां हो सकती हैं। मूल दस्तावेज़ अपनी मूल भाषा में आधिकारिक स्रोत माना जाए। महत्वपूर्ण जानकारी के लिए पेशेवर मानव अनुवाद की सिफारिश की जाती है। हम इस अनुवाद के उपयोग से उत्पन्न किसी भी गलतफहमी या गलत व्याख्या के लिए जिम्मेदार नहीं हैं।\n\n" ] } ], @@ -467,8 +466,8 @@ "version": "3.11.2" }, "coopTranslator": { - "original_hash": "ab2bd97b0453415b89a469284609a8ce", - "translation_date": "2025-08-31T14:55:07+00:00", + "original_hash": "8ef43db4b9182239fd150a76bd494fdb", + "translation_date": "2026-01-15T12:51:44+00:00", "source_file": "lessons/2-Symbolic/Animals.ipynb", "language_code": "hi" } diff --git a/translations/hi/lessons/2-Symbolic/README.md b/translations/hi/lessons/2-Symbolic/README.md index fa77d648..b250d2bd 100644 --- a/translations/hi/lessons/2-Symbolic/README.md +++ b/translations/hi/lessons/2-Symbolic/README.md @@ -1,116 +1,116 @@ -# ज्ञान प्रतिनिधित्व और विशेषज्ञ प्रणाली +# ज्ञान प्रतिनिधित्व और विशेषज्ञ तंत्र -![सिंबोलिक AI सामग्री का सारांश](../../../../translated_images/hi/ai-symbolic.715a30cb610411a6.webp) +![चिन्हात्मक AI सामग्री का सारांश](../../../../../../translated_images/hi/ai-symbolic.715a30cb610411a6.webp) -> स्केच नोट [Tomomi Imura](https://twitter.com/girlie_mac) द्वारा +> स्केचनोट द्वारा [Tomomi Imura](https://twitter.com/girlie_mac) -कृत्रिम बुद्धिमत्ता की खोज ज्ञान की खोज पर आधारित है, ताकि दुनिया को उसी तरह समझा जा सके जैसे मनुष्य करते हैं। लेकिन इसे कैसे किया जा सकता है? +कृत्रिम बुद्धिमत्ता की खोज ज्ञान की खोज पर आधारित है, ताकि दुनिया को उसी तरह समझा जा सके जैसे मनुष्य करते हैं। लेकिन आप इसे कैसे कर सकते हैं? -## [प्री-लेक्चर क्विज़](https://ff-quizzes.netlify.app/en/ai/quiz/3) +## [पूर्व-व्याख्यान परीक्षा](https://ff-quizzes.netlify.app/en/ai/quiz/3) -AI के शुरुआती दिनों में, बुद्धिमान प्रणालियाँ बनाने के लिए टॉप-डाउन दृष्टिकोण (पिछले पाठ में चर्चा की गई) लोकप्रिय था। विचार यह था कि लोगों से ज्ञान निकालकर उसे मशीन-पढ़ने योग्य रूप में बदल दिया जाए, और फिर इसका उपयोग स्वचालित रूप से समस्याओं को हल करने के लिए किया जाए। यह दृष्टिकोण दो बड़े विचारों पर आधारित था: +AI के शुरुआती दिनों में, बुद्धिमान प्रणालियाँ बनाने के लिए शीर्ष-डाउन दृष्टिकोण (पिछले पाठ में चर्चा की गई) लोकप्रिय था। विचार यह था कि लोगों से ज्ञान को मशीन-पठनीय रूप में निकाला जाए, और फिर इसका उपयोग स्वचालित रूप से समस्याओं को हल करने के लिए किया जाए। यह दृष्टिकोण दो बड़े विचारों पर आधारित था: * ज्ञान प्रतिनिधित्व * तर्क ## ज्ञान प्रतिनिधित्व -सिंबोलिक AI में एक महत्वपूर्ण अवधारणा **ज्ञान** है। यह *सूचना* या *डेटा* से ज्ञान को अलग करना महत्वपूर्ण है। उदाहरण के लिए, कोई कह सकता है कि किताबें ज्ञान रखती हैं, क्योंकि कोई किताबें पढ़कर विशेषज्ञ बन सकता है। हालांकि, किताबों में जो होता है उसे वास्तव में *डेटा* कहा जाता है, और किताबें पढ़कर और इस डेटा को हमारे विश्व मॉडल में एकीकृत करके हम इसे ज्ञान में बदलते हैं। +सुंदर AI में एक महत्वपूर्ण अवधारणा है **ज्ञान**। ज्ञान को *सूचना* या *डेटा* से अलग करना महत्वपूर्ण है। उदाहरण के लिए, कोई कह सकता है कि पुस्तकों में ज्ञान होता है, क्योंकि कोई पुस्तक पढ़कर विशेषज्ञ बन सकता है। हालांकि, जो पुस्तकें वास्तव में रखती हैं उसे *डेटा* कहा जाता है, और पुस्तकों को पढ़कर तथा इस डेटा को हमारी विश्व मॉडल में समेकित करके हम इसे ज्ञान में बदलते हैं। -> ✅ **ज्ञान** वह है जो हमारे दिमाग में होता है और दुनिया के प्रति हमारी समझ को दर्शाता है। यह एक सक्रिय **सीखने** की प्रक्रिया के माध्यम से प्राप्त होता है, जो हमें प्राप्त होने वाली सूचनाओं को हमारे सक्रिय विश्व मॉडल में एकीकृत करता है। +> ✅ **ज्ञान** कुछ ऐसा है जो हमारे सिर में होता है और दुनिया के हमारे समझ का प्रतिनिधित्व करता है। इसे एक सक्रिय **अधिगम** प्रक्रिया के द्वारा प्राप्त किया जाता है, जो हमें जो सूचनाएँ मिलती हैं उन्हें हमारे सक्रिय विश्व मॉडल में समेकित करता है। -अक्सर, हम ज्ञान को सख्ती से परिभाषित नहीं करते हैं, बल्कि इसे [DIKW पिरामिड](https://en.wikipedia.org/wiki/DIKW_pyramid) के साथ अन्य संबंधित अवधारणाओं के साथ संरेखित करते हैं। इसमें निम्नलिखित अवधारणाएँ शामिल हैं: +अधिकतर, हम सख्ती से ज्ञान को परिभाषित नहीं करते, बल्कि इसे अन्य संबंधित अवधारणाओं के साथ [DIKW पिरामिड](https://en.wikipedia.org/wiki/DIKW_pyramid) का उपयोग करके समायोजित करते हैं। इसमें निम्न अवधारणाएँ शामिल हैं: -* **डेटा** वह है जो भौतिक माध्यम में प्रस्तुत किया जाता है, जैसे लिखित पाठ या बोले गए शब्द। डेटा मनुष्यों से स्वतंत्र रूप से मौजूद होता है और इसे लोगों के बीच साझा किया जा सकता है। -* **सूचना** वह है जो हम अपने दिमाग में डेटा की व्याख्या करते हैं। उदाहरण के लिए, जब हम शब्द *कंप्यूटर* सुनते हैं, तो हमें इसके बारे में कुछ समझ होती है। -* **ज्ञान** वह है जब सूचना हमारे विश्व मॉडल में एकीकृत हो जाती है। उदाहरण के लिए, एक बार जब हम सीखते हैं कि कंप्यूटर क्या है, तो हमें इसके काम करने, इसकी लागत और इसके उपयोग के बारे में कुछ विचार होने लगते हैं। इन आपस में जुड़े अवधारणाओं का नेटवर्क हमारा ज्ञान बनाता है। -* **बुद्धिमत्ता** हमारी दुनिया की समझ का एक और स्तर है, और यह *मेटा-ज्ञान* का प्रतिनिधित्व करता है, जैसे कि यह ज्ञान कब और कैसे उपयोग किया जाना चाहिए। +* **डेटा** कुछ ऐसा है जो भौतिक माध्यम में प्रदर्शित होता है, जैसे लिखित पाठ या बोले गए शब्द। डेटा मानव से स्वतंत्र रूप से मौजूद होता है और लोगों के बीच पारित किया जा सकता है। +* **जानकारी** वह है जो हम अपने मन में डेटा से व्याख्या करते हैं। उदाहरण के लिए, जब हम शब्द *कंप्यूटर* सुनते हैं, तब हमारे मन में इसकी कुछ समझ होती है। +* **ज्ञान** वह सूचना है जो हमारे विश्व मॉडल में समाहित होती है। उदाहरण के लिए, जब हम सीखते हैं कि कंप्यूटर क्या है, तब हम इसके काम करने के तरीके, इसकी लागत, और इसके उपयोग के बारे में विचार शुरू करते हैं। यह परस्पर जुड़े हुए अवधारणाओं का नेटवर्क हमारे ज्ञान का निर्माण करता है। +* **बुद्धिमत्ता** हमारी दुनिया की समझ का एक और स्तर है, और यह *मेटा-ज्ञान* का प्रतिनिधित्व करता है, यानी यह ज्ञान के उपयोग के बारे में कुछ विचार है कि इसे कब और कैसे प्रयोग करना चाहिए। - + -*छवि [विकिपीडिया से](https://commons.wikimedia.org/w/index.php?curid=37705247), Longlivetheux द्वारा - स्वयं का कार्य, CC BY-SA 4.0* +*छवि [विकिपीडिया से](https://commons.wikimedia.org/w/index.php?curid=37705247), लांग्लिवथ्यूक्स द्वारा - अपना कार्य, CC BY-SA 4.0* -इस प्रकार, **ज्ञान प्रतिनिधित्व** की समस्या यह है कि कंप्यूटर के अंदर डेटा के रूप में ज्ञान को प्रभावी ढंग से कैसे प्रस्तुत किया जाए, ताकि इसे स्वचालित रूप से उपयोग किया जा सके। इसे एक स्पेक्ट्रम के रूप में देखा जा सकता है: +इसलिए, **ज्ञान प्रतिनिधित्व** की समस्या कुछ प्रभावी तरीके से ज्ञान को कंप्यूटर के अंदर डेटा के रूप में प्रस्तुत करने की है, ताकि इसे स्वचालित रूप से उपयोग किया जा सके। इसे एक स्पेक्ट्रम के रूप में देखा जा सकता है: -![ज्ञान प्रतिनिधित्व स्पेक्ट्रम](../../../../translated_images/hi/knowledge-spectrum.b60df631852c0217.webp) +![ज्ञान प्रतिनिधित्व स्पेक्ट्रम](../../../../../../translated_images/hi/knowledge-spectrum.b60df631852c0217.webp) -> छवि [Dmitry Soshnikov](http://soshnikov.com) द्वारा +> चित्र [Dmitry Soshnikov](http://soshnikov.com) द्वारा -* बाईं ओर, बहुत सरल प्रकार के ज्ञान प्रतिनिधित्व हैं जिन्हें कंप्यूटर द्वारा प्रभावी ढंग से उपयोग किया जा सकता है। सबसे सरल प्रकार एल्गोरिदमिक है, जब ज्ञान को कंप्यूटर प्रोग्राम के रूप में प्रस्तुत किया जाता है। हालांकि, यह ज्ञान को प्रस्तुत करने का सबसे अच्छा तरीका नहीं है, क्योंकि यह लचीला नहीं है। हमारे दिमाग में ज्ञान अक्सर गैर-एल्गोरिदमिक होता है। -* दाईं ओर, प्राकृतिक पाठ जैसे प्रतिनिधित्व हैं। यह सबसे शक्तिशाली है, लेकिन इसे स्वचालित तर्क के लिए उपयोग नहीं किया जा सकता। +* बाईं ओर, बहुत सरल प्रकार के ज्ञान प्रतिनिधित्व हैं जिन्हें कंप्यूटर प्रभावी रूप से उपयोग कर सकते हैं। सबसे सरल है एल्गोरिदमिक, जहाँ ज्ञान कंप्यूटर प्रोग्राम द्वारा प्रतिनिधित्व किया जाता है। हालांकि, यह ज्ञान को प्रस्तुत करने का सबसे अच्छा तरीका नहीं है क्योंकि यह लचीला नहीं है। हमारे सिर में ज्ञान अक्सर गैर-एल्गोरिदमिक होता है। +* दाईं ओर, प्रतिनिधित्व जैसे प्राकृतिक पाठ हैं। यह सबसे शक्तिशाली है, लेकिन स्वचालित तर्क के लिए उपयोग नहीं किया जा सकता। -> ✅ एक मिनट के लिए सोचें कि आप अपने दिमाग में ज्ञान को कैसे प्रस्तुत करते हैं और इसे नोट्स में बदलते हैं। क्या कोई विशेष प्रारूप है जो आपको इसे याद रखने में मदद करता है? +> ✅ एक मिनट सोचें कि आप अपने सिर में ज्ञान को कैसे प्रस्तुत करते हैं और इसे नोट्स में कैसे परिवर्तित करते हैं। क्या कोई विशेष प्रारूप है जो आपके लिए स्मरण में सहायता करता है? -## कंप्यूटर ज्ञान प्रतिनिधित्व को वर्गीकृत करना +## कंप्यूटर ज्ञान प्रतिनिधित्व का वर्गीकरण -हम विभिन्न कंप्यूटर ज्ञान प्रतिनिधित्व विधियों को निम्नलिखित श्रेणियों में वर्गीकृत कर सकते हैं: +हम विभिन्न कंप्यूटर ज्ञान प्रतिनिधित्व विधियों को निम्नलिखित वर्गों में वर्गीकृत कर सकते हैं: -* **नेटवर्क प्रतिनिधित्व** इस तथ्य पर आधारित हैं कि हमारे दिमाग में आपस में जुड़े अवधारणाओं का एक नेटवर्क होता है। हम उसी नेटवर्क को कंप्यूटर के अंदर एक ग्राफ के रूप में पुन: उत्पन्न करने की कोशिश कर सकते हैं - जिसे **सामांतिक नेटवर्क** कहा जाता है। +* **नेटवर्क प्रतिनिधित्व** इस तथ्य पर आधारित होते हैं कि हमारे सिर में अवधारणाओं का एक नेटवर्क होता है। हम इसी नेटवर्क को कंप्यूटर के अंदर ग्राफ के रूप में पुन: प्रस्तुत करने की कोशिश कर सकते हैं - इसे कहा जाता है **सामार्थ्य नेटवर्क**। -1. **ऑब्जेक्ट-एट्रिब्यूट-वैल्यू ट्रिपलेट्स** या **एट्रिब्यूट-वैल्यू पेयर्स**। चूंकि एक ग्राफ को कंप्यूटर के अंदर नोड्स और एजेस की सूची के रूप में प्रस्तुत किया जा सकता है, हम एक सामांतिक नेटवर्क को ट्रिपलेट्स की सूची के रूप में प्रस्तुत कर सकते हैं, जिसमें ऑब्जेक्ट्स, एट्रिब्यूट्स और वैल्यूज शामिल हैं। उदाहरण के लिए, हम प्रोग्रामिंग भाषाओं के बारे में निम्नलिखित ट्रिपलेट्स बनाते हैं: +1. **ऑब्जेक्ट-अट्रिब्यूट-वैल्यू त्रय** या **एट्रिब्यूट-वैल्यू युग्म**। क्योंकि एक ग्राफ को कंप्यूटर में नोड्स और एज की सूची के रूप में प्रस्तुत किया जा सकता है, हम सेमांटिक नेटवर्क को त्रयों की सूची के रूप में प्रदर्शित कर सकते हैं, जिसमें वस्तुएं, गुण और मान होते हैं। उदाहरण के लिए, हम प्रोग्रामिंग भाषाओं के संबंध में निम्नलिखित त्रय बनाते हैं: -ऑब्जेक्ट | एट्रिब्यूट | वैल्यू ----------|------------|------ -Python | है | Untyped-Language -Python | आविष्कारक | Guido van Rossum +वस्तु | गुण | मान +-------|-----------|------ +Python | है | अतल-संकलन भाषा +Python | आविष्कार किया | गुइदो वैन रॉसम Python | ब्लॉक-सिंटैक्स | इंडेंटेशन -Untyped-Language | नहीं है | टाइप डिफिनिशन +अतल-संकलन भाषा | नहीं है | प्रकार परिभाषा -> ✅ सोचें कि ट्रिपलेट्स का उपयोग अन्य प्रकार के ज्ञान को प्रस्तुत करने के लिए कैसे किया जा सकता है। +> ✅ सोचें कि त्रयों का उपयोग अन्य प्रकार के ज्ञान को प्रस्तुत करने के लिए कैसे किया जा सकता है। -2. **हाइरार्किकल प्रतिनिधित्व** इस तथ्य पर जोर देते हैं कि हम अक्सर अपने दिमाग में वस्तुओं का एक पदानुक्रम बनाते हैं। उदाहरण के लिए, हम जानते हैं कि कैनरी एक पक्षी है, और सभी पक्षियों के पंख होते हैं। हमें यह भी पता है कि कैनरी का रंग आमतौर पर क्या होता है, और उनकी उड़ान की गति क्या होती है। +2. **हायरेरकी प्रतिनिधित्व** इस तथ्य पर जोर देते हैं कि हम अक्सर अपने सिर में वस्तुओं की एक पदानुक्रम बनाते हैं। उदाहरण के लिए, हमें पता है कि कैनरी एक पक्षी है, और सभी पक्षियों के पंख होते हैं। हमारे पास यह भी कुछ विचार होता है कि कैनरी आमतौर पर किस रंग की होती है, और उसकी उड़ान की गति क्या होती है। - - **फ्रेम प्रतिनिधित्व** प्रत्येक वस्तु या वस्तुओं के वर्ग को **फ्रेम** के रूप में प्रस्तुत करने पर आधारित है जिसमें **स्लॉट्स** होते हैं। स्लॉट्स में संभावित डिफ़ॉल्ट मान, मान प्रतिबंध, या संग्रहीत प्रक्रियाएँ हो सकती हैं जिन्हें स्लॉट का मान प्राप्त करने के लिए बुलाया जा सकता है। सभी फ्रेम एक पदानुक्रम बनाते हैं जो वस्तु-उन्मुख प्रोग्रामिंग भाषाओं में वस्तु पदानुक्रम के समान है। - - **परिदृश्य** फ्रेम का एक विशेष प्रकार हैं जो समय के साथ विकसित होने वाली जटिल स्थितियों का प्रतिनिधित्व करते हैं। + - **फ़्रेम प्रतिनिधित्व** प्रति वस्तु या वस्तु वर्ग को एक **फ़्रेम** के रूप में प्रस्तुत करने पर आधारित है जिसमें **स्लॉट्स** होते हैं। स्लॉट्स में संभावित डिफ़ॉल्ट मान, मान प्रतिबंध, या संग्रहीत प्रक्रियाएं हो सकती हैं जिन्हें कॉल करके स्लॉट का मान प्राप्त किया जा सकता है। सभी फ्रेम एक पदानुक्रम बनाते हैं जो ऑब्जेक्ट-ओरिएंटेड प्रोग्रामिंग भाषाओं में वस्तु पदानुक्रम के समान होता है। + - **परिदृश्य** विशेष प्रकार के फ्रेम होते हैं जो समय में विकसित होने वाली जटिल परिस्थितियों का प्रतिनिधित्व करते हैं। **Python** स्लॉट | मान | डिफ़ॉल्ट मान | अंतराल | -------|------|--------------|--------| +-----|-------|---------------|----------| नाम | Python | | | -है | Untyped-Language | | | -वेरिएबल केस | | CamelCase | | -प्रोग्राम लंबाई | | | 5-5000 लाइनें | +है-एक | अतल-संकलन भाषा | | | +चर केस | | कैमल केस | | +कार्यक्रम लंबाई | | | 5-5000 पंक्तियाँ | ब्लॉक सिंटैक्स | इंडेंट | | | -3. **प्रक्रियात्मक प्रतिनिधित्व** ज्ञान को क्रियाओं की सूची के रूप में प्रस्तुत करने पर आधारित हैं जिन्हें किसी निश्चित स्थिति में निष्पादित किया जा सकता है। - - उत्पादन नियम if-then कथन हैं जो हमें निष्कर्ष निकालने की अनुमति देते हैं। उदाहरण के लिए, एक डॉक्टर के पास एक नियम हो सकता है जो कहता है कि **यदि** किसी मरीज को तेज बुखार है **या** रक्त परीक्षण में C-रिएक्टिव प्रोटीन का उच्च स्तर है **तो** उसे सूजन है। एक बार जब हम इनमें से किसी एक स्थिति का सामना करते हैं, तो हम सूजन के बारे में निष्कर्ष निकाल सकते हैं, और फिर इसे आगे के तर्क में उपयोग कर सकते हैं। - - एल्गोरिदम को प्रक्रियात्मक प्रतिनिधित्व का एक और रूप माना जा सकता है, हालांकि वे लगभग कभी भी ज्ञान-आधारित प्रणालियों में सीधे उपयोग नहीं किए जाते। +3. **प्रक्रियात्मक प्रतिनिधित्व** ज्ञान को क्रियाओं की सूची के रूप में प्रस्तुत करने पर आधारित है, जो तब निष्पादित होती हैं जब कोई विशेष स्थिति उत्पन्न होती है। + - उत्पादन नियम if-then कथन होते हैं जो निष्कर्ष निकालने की अनुमति देते हैं। उदाहरण के लिए, डॉक्टर के पास एक नियम हो सकता है जिसमें कहा गया है कि **यदि** रोगी को उच्च तेज़ बुखार है **या** रक्त परीक्षण में सी-रिएक्टिव प्रोटीन का स्तर उच्च है, **तो** रोगी को सूजन है। जब हमें इन में से कोई भी शर्त मिलती है, हम सूजन के बारे में निष्कर्ष निकाल सकते हैं, और फिर इसका आगे के तर्क में उपयोग करते हैं। + - एल्गोरिदम को एक अन्य प्रकार के प्रक्रियात्मक प्रतिनिधित्व माना जा सकता है, हालांकि उन्हें ज्ञान आधारित प्रणालियों में सीधे उपयोग लगभग कभी नहीं किया जाता। -4. **तर्क** मूल रूप से अरस्तू द्वारा सार्वभौमिक मानव ज्ञान को प्रस्तुत करने के तरीके के रूप में प्रस्तावित किया गया था। - - प्रेडिकेट लॉजिक एक गणितीय सिद्धांत के रूप में बहुत समृद्ध है और इसलिए इसे गणना योग्य नहीं माना जाता है। इसलिए इसका कुछ उपसमुच्चय सामान्यतः उपयोग किया जाता है, जैसे कि Prolog में उपयोग किए जाने वाले Horn क्लॉज। - - वर्णनात्मक तर्क वस्तुओं के पदानुक्रम और वितरित ज्ञान प्रतिनिधित्व जैसे *सामांतिक वेब* के बारे में तर्क करने के लिए उपयोग किए जाने वाले तर्क प्रणालियों का एक परिवार है। +4. **तर्कशास्त्र** मूल रूप से अरस्तू द्वारा सार्वभौमिक मानव ज्ञान प्रस्तुत करने के लिए प्रस्तावित किया गया था। + - गणितीय सिद्धांत के रूप में प्रेडिकेट लॉजिक बहुत विस्तृत है, इसलिए इसका कुछ उपसमूह आमतौर पर उपयोग किया जाता है, जैसे कि प्रोलॉग में उपयोग किए जाने वाले हॉर्न क्लॉज। + - डिस्क्रिप्टिव लॉजिक तार्किक प्रणालियों का एक परिवार है जो वस्तुओं की पदानुक्रम और वितरित ज्ञान प्रतिनिधित्व जैसे *सामान्तिक वेब* के विषय में तर्क करने के लिए प्रयोग किया जाता है। -## विशेषज्ञ प्रणाली +## विशेषज्ञ तंत्र -सिंबोलिक AI की शुरुआती सफलताओं में से एक **विशेषज्ञ प्रणाली** थीं - कंप्यूटर प्रणाली जो किसी सीमित समस्या क्षेत्र में विशेषज्ञ के रूप में कार्य करने के लिए डिज़ाइन की गई थीं। ये **ज्ञान आधार** पर आधारित थीं जो एक या अधिक मानव विशेषज्ञों से निकाली गई थीं, और इनमें एक **तर्क इंजन** था जो इसके ऊपर कुछ तर्क करता था। +प्रतीकात्मक AI की प्रारंभिक सफलताओं में से एक थे तथाकथित **विशेषज्ञ तंत्र** - कंप्यूटर प्रणालियाँ जिन्हें किसी सीमित समस्या क्षेत्र में विशेषज्ञ के रूप में कार्य करने के लिए डिज़ाइन किया गया था। वे एक **ज्ञान आधार** पर आधारित थीं जो एक या अधिक मानव विशेषज्ञों से निकाली गई थी, और उनका एक **अनुमान इंजन** था जो उसके ऊपर कुछ तर्क करता था। -![मानव संरचना](../../../../translated_images/hi/arch-human.5d4d35f1bba3ab1c.webp) | ![ज्ञान-आधारित प्रणाली](../../../../translated_images/hi/arch-kbs.3ec5c150b09fa8da.webp) ----------------------------------------|-------------------------------------------- -मानव तंत्रिका प्रणाली की सरलीकृत संरचना | ज्ञान-आधारित प्रणाली की संरचना +![मानव वास्तुकला](../../../../../../translated_images/hi/arch-human.5d4d35f1bba3ab1c.webp) | ![ज्ञान-आधारित प्रणाली](../../../../../../translated_images/hi/arch-kbs.3ec5c150b09fa8da.webp) +---------------------------------------------|------------------------------------------------ +मानव तंत्रिका तंत्र की सरल संरचना | ज्ञान-आधारित प्रणाली की संरचना -विशेषज्ञ प्रणाली मानव तर्क प्रणाली की तरह बनाई जाती हैं, जिसमें **अल्पकालिक स्मृति** और **दीर्घकालिक स्मृति** होती है। इसी तरह, ज्ञान-आधारित प्रणालियों में हम निम्नलिखित घटकों को अलग करते हैं: +विशेषज्ञ तंत्र मानव तर्क प्रणाली की तरह बनाए जाते हैं, जिनमें **लघु-अवधि स्मृति** और **दीर्घ-अवधि स्मृति** होती है। इसी प्रकार, ज्ञान-आधारित प्रणालियों में हम निम्न घटकों को अलग करते हैं: -* **समस्या स्मृति**: वर्तमान में हल की जा रही समस्या के बारे में ज्ञान रखती है, जैसे कि मरीज का तापमान या रक्तचाप, क्या उसे सूजन है या नहीं, आदि। इस ज्ञान को **स्थिर ज्ञान** भी कहा जाता है, क्योंकि इसमें समस्या के बारे में वर्तमान में ज्ञात चीजों का स्नैपशॉट होता है - जिसे *समस्या स्थिति* कहा जाता है। -* **ज्ञान आधार**: समस्या क्षेत्र के बारे में दीर्घकालिक ज्ञान का प्रतिनिधित्व करता है। इसे मानव विशेषज्ञों से मैन्युअल रूप से निकाला जाता है, और परामर्श से परामर्श तक नहीं बदलता। क्योंकि यह हमें एक समस्या स्थिति से दूसरी में नेविगेट करने की अनुमति देता है, इसे **गतिशील ज्ञान** भी कहा जाता है। -* **तर्क इंजन**: समस्या स्थिति स्थान में खोज प्रक्रिया को व्यवस्थित करता है, जब आवश्यक हो तो उपयोगकर्ता से प्रश्न पूछता है। यह प्रत्येक स्थिति पर लागू होने वाले सही नियमों को खोजने के लिए भी जिम्मेदार है। +* **समस्या स्मृति**: इसमे उस समस्या के बारे में ज्ञान होता है जिसे वर्तमान में हल किया जा रहा है, जैसे रोगी का तापमान या रक्तचाप, क्या उसे सूजन है या नहीं, आदि। इस ज्ञान को **स्थैतिक ज्ञान** भी कहा जाता है, क्योंकि इसमें समस्या के बारे में वर्तमान में जो जानकारी है उसका स्नैपशॉट होता है - जिसे *समस्या स्थिति* कहते हैं। +* **ज्ञान आधार**: यह किसी समस्या क्षेत्र के बारे में दीर्घकालिक ज्ञान प्रस्तुत करता है। यह मानव विशेषज्ञों से मैन्युअली निकाला जाता है, और परामर्श से परामर्श बदलता नहीं है। चूंकि यह हमें एक समस्या स्थिति से दूसरी स्थिति में नेविगेट करने देता है, इसे **गतिशील ज्ञान** भी कहा जाता है। +* **अनुमान इंजन**: यह समस्या स्थिति के स्थान में खोज की पूरी प्रक्रिया का संचालन करता है, आवश्यकता पड़ने पर उपयोगकर्ता से प्रश्न पूछता है। यह प्रत्येक स्थिति पर लागू होने वाले सही नियम खोजने के लिए भी जिम्मेदार होता है। -उदाहरण के लिए, आइए एक जानवर की शारीरिक विशेषताओं के आधार पर उसे निर्धारित करने वाली विशेषज्ञ प्रणाली पर विचार करें: +उदाहरण के लिए, जानवर की शारीरिक विशेषताओं के आधार पर पहचान करने वाली निम्न विशेषज्ञ प्रणाली पर विचार करें: -![AND-OR ट्री](../../../../translated_images/hi/AND-OR-Tree.5592d2c70187f283.webp) +![AND-OR ट्री](../../../../../../translated_images/hi/AND-OR-Tree.5592d2c70187f283.webp) -> छवि [Dmitry Soshnikov](http://soshnikov.com) द्वारा +> चित्र [Dmitry Soshnikov](http://soshnikov.com) द्वारा -इस आरेख को **AND-OR ट्री** कहा जाता है, और यह उत्पादन नियमों के सेट का ग्राफिकल प्रतिनिधित्व है। विशेषज्ञ से ज्ञान निकालने की शुरुआत में ट्री बनाना उपयोगी होता है। कंप्यूटर के अंदर ज्ञान को प्रस्तुत करने के लिए नियमों का उपयोग करना अधिक सुविधाजनक होता है: +यह आरेख **AND-OR ट्री** कहलाता है, और यह उत्पादन नियमों के एक सेट का ग्राफिकल प्रतिनिधित्व है। विशेषज्ञ से ज्ञान निकालने के शुरुआत में ट्री बनाना उपयोगी होता है। कंप्यूटर के अंदर ज्ञान प्रस्तुत करने के लिए नियमों का उपयोग करना अधिक सुविधाजनक होता है: ``` IF the animal eats meat @@ -121,78 +121,78 @@ OR (animal has sharp teeth THEN the animal is a carnivore ``` -आप देख सकते हैं कि नियम के बाईं ओर की स्थिति और क्रिया वस्तु-एट्रिब्यूट-वैल्यू (OAV) ट्रिपलेट्स हैं। **कार्यशील स्मृति** उन OAV ट्रिपलेट्स का सेट रखती है जो वर्तमान में हल की जा रही समस्या से संबंधित हैं। **नियम इंजन** उन नियमों की खोज करता है जिनकी स्थिति संतुष्ट होती है और उन्हें लागू करता है, कार्यशील स्मृति में एक और ट्रिपलेट जोड़ता है। +आप देख सकते हैं कि नियम के बाएँ हाथ की प्रत्येक शर्त और क्रिया मूल रूप से ऑब्जेक्ट-अट्रिब्यूट-वैल्यू (OAV) त्रय हैं। **कार्य स्मृति** में OAV त्रयों का सेट होता है जो वर्तमान में हल की जा रही समस्या के अनुरूप होता है। **नियम इंजन** ऐसे नियम खोजता है जिनकी शर्त पूरी होती है और उन्हें लागू करता है, कार्य स्मृति में एक अन्य त्रय जोड़ता है। -> ✅ अपने पसंदीदा विषय पर अपना AND-OR ट्री बनाएं! +> ✅ अपनी पसंद के विषय पर अपना AND-OR ट्री बनाएं! -### फॉरवर्ड बनाम बैकवर्ड तर्क +### अग्रिम और प्रतिगामी अनुमान -ऊपर वर्णित प्रक्रिया को **फॉरवर्ड तर्क** कहा जाता है। यह कार्यशील स्मृति में समस्या के बारे में कुछ प्रारंभिक डेटा के साथ शुरू होता है, और फिर निम्नलिखित तर्क चक्र को निष्पादित करता है: +ऊपर वर्णित प्रक्रिया को **अग्रिम अनुमान** कहते हैं। यह समस्या के बारे में कुछ प्रारंभिक डेटा के साथ कार्य स्मृति में शुरू होती है, और फिर निम्नलिखित तर्क चक्र निष्पादित करती है: -1. यदि लक्ष्य विशेषता कार्यशील स्मृति में मौजूद है - रुकें और परिणाम दें -2. उन सभी नियमों की खोज करें जिनकी स्थिति वर्तमान में संतुष्ट है - **संघर्ष सेट** प्राप्त करें। -3. **संघर्ष समाधान** करें - उस नियम का चयन करें जिसे इस चरण में निष्पादित किया जाएगा। विभिन्न संघर्ष समाधान रणनीतियाँ हो सकती हैं: - - ज्ञान आधार में पहला लागू नियम चुनें +1. यदि लक्षित गुण (target attribute) कार्य स्मृति में मौजूद है - रोकें और परिणाम दें +2. उन सभी नियमों को खोजें जिनकी शर्त वर्तमान में पूरी हो चुकी है - **विरोध सेट** प्राप्त करें। +3. **विरोध निर्णय** करें - एक नियम चुनें जिसे इस चरण में निष्पादित किया जाएगा। विरोध निर्णय की विभिन्न रणनीतियाँ हो सकती हैं: + - ज्ञान आधार में पहला उपयुक्त नियम चुनें - एक यादृच्छिक नियम चुनें - - *अधिक विशिष्ट* नियम चुनें, यानी वह जो "बाईं ओर" (LHS) में सबसे अधिक स्थितियों को पूरा करता है + - एक *अधिक विशिष्ट* नियम चुनें, यानी जो "बाएं हाथ" (LHS) में सबसे अधिक शर्तों पर खरा उतरता हो 4. चयनित नियम लागू करें और समस्या स्थिति में नया ज्ञान जोड़ें -5. चरण 1 से पुनः आरंभ करें। +5. चरण 1 से दोहराएं। -हालांकि, कुछ मामलों में हम समस्या के बारे में खाली ज्ञान के साथ शुरू करना चाहते हैं, और ऐसे प्रश्न पूछना चाहते हैं जो हमें निष्कर्ष तक पहुँचने में मदद करें। उदाहरण के लिए, जब चिकित्सा निदान करते हैं, तो हम आमतौर पर सभी चिकित्सा विश्लेषण पहले से नहीं करते हैं। हम निर्णय लेने की आवश्यकता होने पर विश्लेषण करना चाहते हैं। +हालांकि, कुछ मामलों में हम समस्या के बारे में खाली ज्ञान से शुरू करना चाह सकते हैं, और ऐसे प्रश्न पूछना चाहते हैं जो हमें निष्कर्ष तक पहुंचाएं। उदाहरण के लिए, चिकित्सा निदान करते समय, हम आमतौर पर रोगी का निदान शुरू करने से पहले सारी जांच नहीं करते। हम उस समय जांच करते हैं जब निर्णय लेना आवश्यक होता है। -इस प्रक्रिया को **बैकवर्ड तर्क** का उपयोग करके मॉडल किया जा सकता है। यह **लक्ष्य** द्वारा संचालित होता है - वह विशेषता मान जिसे हम खोजने की कोशिश कर रहे हैं: +इस प्रक्रिया को **प्रतिगामी अनुमान** का उपयोग करके मॉडल किया जा सकता है। इसे **लक्ष्य** द्वारा संचालित किया जाता है - वह गुण मान जिसे हम ढूँढ़ रहे हैं: -1. उन सभी नियमों का चयन करें जो हमें लक्ष्य का मान दे सकते हैं (यानी लक्ष्य RHS ("दाईं ओर") पर हो) - एक संघर्ष सेट -1. यदि इस विशेषता के लिए कोई नियम नहीं है, या कोई नियम है जो कहता है कि हमें उपयोगकर्ता से मान पूछना चाहिए - पूछें, अन्यथा: -1. संघर्ष समाधान रणनीति का उपयोग करके एक नियम का चयन करें जिसे हम *परिकल्पना* के रूप में उपयोग करेंगे - हम इसे साबित करने की कोशिश करेंगे -1. नियम के LHS में सभी विशेषताओं के लिए प्रक्रिया को पुनरावर्ती रूप से दोहराएं, उन्हें लक्ष्य के रूप में साबित करने की कोशिश करें -1. यदि किसी भी बिंदु पर प्रक्रिया विफल हो जाती है - चरण 3 पर दूसरा नियम उपयोग करें। +1. सभी ऐसे नियम चुनें जो हमें लक्ष्य का मान दे सकते हैं (अर्थात् लक्ष्य RHS ("दाएँ हाथ") पर हो) - विरोध सेट +1. यदि इस गुण के लिए कोई नियम नहीं है, या कोई नियम कहता है कि इस मान के लिए उपयोगकर्ता से पूछा जाना चाहिए - तो पूछें, अन्यथा: +1. विरोध निर्णय रणनीति का उपयोग करके एक नियम चुनें जिसे हम *परिकल्पना* के रूप में उपयोग करेंगे - हम इसे साबित करने की कोशिश करेंगे +1. नियम के LHS के सभी गुणों के लिए पुनरावृत्त रूप से प्रक्रिया दोहराएं, उन्हें लक्ष्यों के रूप में साबित करने की कोशिश करते हुए +1. यदि कहीं भी प्रक्रिया असफल होती है - तो चरण 3 में कोई दूसरा नियम चुनें। -> ✅ किन स्थितियों में फॉरवर्ड तर्क अधिक उपयुक्त है? और बैकवर्ड तर्क? +> ✅ किन परिस्थितियों में अग्रिम अनुमान अधिक उपयुक्त होता है? और प्रतिगामी अनुमान के लिए क्या कहेंगे? -### विशेषज्ञ प्रणाली को लागू करना +### विशेषज्ञ तंत्र का कार्यान्वयन -विशेषज्ञ प्रणाली को विभिन्न उपकरणों का उपयोग करके लागू किया जा सकता है: +विशेषज्ञ तंत्र को विभिन्न उपकरणों का उपयोग करके लागू किया जा सकता है: -* उन्हें सीधे किसी उच्च स्तरीय प्रोग्रामिंग भाषा में प्रोग्राम करना। यह सबसे अच्छा विचार नहीं है, क्योंकि ज्ञान-आधारित प्रणाली का मुख्य लाभ यह है कि ज्ञान तर्क से अलग होता है, और संभावित रूप से समस्या क्षेत्र विशेषज्ञ को नियम लिखने में सक्षम होना चाहिए बिना तर्क प्रक्रिया के विवरण को समझे। -* **विशेषज्ञ प्रणाली शेल** का उपयोग करना, यानी एक प्रणाली जिसे विशेष रूप से किसी ज्ञान प्रतिनिधित्व भाषा का उपयोग करके ज्ञान से भरा जा सकता है। +* किसी उच्च स्तरीय प्रोग्रामिंग भाषा में सीधे प्रोग्राम करना। यह सबसे अच्छा विचार नहीं है, क्योंकि ज्ञान आधारित प्रणाली का मुख्य लाभ यह है कि ज्ञान और अनुमान अलग होते हैं, और संभावित रूप से समस्या क्षेत्र के विशेषज्ञ को अनुमान प्रक्रिया के विवरण को समझे बिना नियम लिखने में सक्षम होना चाहिए। +* **विशेषज्ञ तंत्र शेल** का उपयोग करना, यानी एक ऐसी प्रणाली जो विशेष रूप से कुछ ज्ञान प्रतिनिधित्व भाषा का उपयोग करके ज्ञान से भरी जा सके। -## ✍️ अभ्यास: जानवर तर्क +## ✍️ अभ्यास: पशु अनुमान -[Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) देखें, जिसमें फॉरवर्ड और बैकवर्ड तर्क विशेषज्ञ प्रणाली को लागू करने का उदाहरण दिया गया है। +आगे की जानकारी के लिए [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) देखें, जिसमें अग्रिम और प्रतिगामी अनुमान विशेषज्ञ तंत्र को लागू करने का उदाहरण है। -> **नोट**: यह उदाहरण काफी सरल है, और केवल यह विचार देता है कि विशेषज्ञ प्रणाली कैसी दिखती है। एक बार जब आप ऐसी प्रणाली बनाना शुरू करते हैं, तो आप केवल तभी *बुद्धिमान* व्यवहार देखेंगे जब आप लगभग 200+ नियमों तक पहुँचेंगे। एक बिंदु पर, नियम इतने जटिल हो जाते हैं कि सभी को याद रखना मुश्किल हो जाता है, और इस बिंदु पर आप सोच सकते हैं कि प्रणाली ने कुछ निर्णय क्यों लिए। हालांकि, ज्ञान-आधारित प्रणाली की महत्वपूर्ण विशेषता यह है कि आप हमेशा *समझा सकते हैं* कि किसी भी निर्णय को कैसे लिया गया। +> **सूचना**: यह उदाहरण काफी सरल है, और केवल यह विचार देता है कि एक विशेषज्ञ तंत्र कैसा दिखता है। जैसे ही आप ऐसी प्रणाली बनाने लगते हैं, आप केवल लगभग 200+ नियमों की संख्या तक पहुँचने के बाद ही इससे कुछ *बुद्धिमत्तापूर्ण* व्यवहार देखेंगे। एक बिंदु पर, नियम इतने जटिल हो जाते हैं कि उन्हें याद रखना कठिन हो जाता है, और उस समय आप सोचने लगते हैं कि सिस्टम कुछ निर्णय क्यों ले रहा है। हालांकि, ज्ञान आधारित प्रणालियों की महत्वपूर्ण विशेषता यह है कि आप हमेशा ठीक-ठीक समझा सकते हैं कि कोई भी निर्णय कैसे लिया गया। -## ओन्टोलॉजी और सामांतिक वेब +## ऑंटोलॉजी और सामान्तिक वेब -20वीं सदी के अंत में इंटरनेट संसाधनों को एनोटेट करने के लिए ज्ञान प्रतिनिधित्व का उपयोग करने की पहल की गई थी, ताकि बहुत विशिष्ट प्रश्नों के अनुरूप संसाधनों को ढूंढना संभव हो सके। इस आंदोलन को **सामांतिक वेब** कहा गया, और यह कई अवधारणाओं पर आधारित था: +20वीं सदी के अंत में एक पहल हुई जिसमें ज्ञान प्रतिनिधित्व का उपयोग इंटरनेट संसाधनों को एनोटेट करने के लिए किया गया, ताकि बहुत विशिष्ट प्रश्नों के अनुरूप संसाधन ढूंढना संभव हो सके। इस आंदोलन को **सामान्तिक वेब** कहा गया, और यह कई अवधारणाओं पर आधारित था: -- **[वर्णनात्मक तर्क](https://en.wikipedia.org/wiki/Description_logic)** (DL) पर आधारित एक विशेष ज्ञान प्रतिनिधित्व। यह फ्रेम ज्ञान प्रतिनिधित्व के समान है, क्योंकि यह गुणों के साथ वस्तुओं का पदानुक्रम बनाता है, लेकिन इसमें औपचारिक तर्कात्मक अर्थ और तर्क होता है। DL का एक पूरा परिवार है जो अभिव्यक्तता और तर्क की एल्गोरिदमिक जटिलता के बीच संतुलन बनाता है। -- वितरित ज्ञान प्रतिनिधित्व, जहाँ सभी अवधारणाओं को एक वैश्विक URI पहचानकर्ता द्वारा प्रस्तुत किया जाता है, जिससे इंटरनेट पर फैले ज्ञान पदानुक्रम बनाना संभव हो जाता है। +- **[डिस्क्रिप्शन लॉजिक](https://en.wikipedia.org/wiki/Description_logic)** (DL) पर आधारित एक विशेष ज्ञान प्रतिनिधित्व। यह फ्रेम ज्ञान प्रतिनिधित्व के समान है, क्योंकि यह गुणों वाले वस्तुओं की पदानुक्रम बनाता है, लेकिन इसका औपचारिक तार्किक अर्थ है और अनुमान लागू करता है। DL का एक पूरा परिवार है जो अभिव्यक्तिशीलता और अनुमान की एल्गोरिदमिक जटिलता के बीच संतुलन करता है। +- वितरित ज्ञान प्रतिनिधित्व, जहाँ सभी अवधारणाओं को एक वैश्विक URI पहचानकर्ता द्वारा प्रदर्शित किया जाता है, जिससे इंटरनेट पर फैली ज्ञान पदानुक्रम बनाना संभव होता है। - ज्ञान वर्णन के लिए XML-आधारित भाषाओं का एक परिवार: RDF (Resource Description Framework), RDFS (RDF Schema), OWL (Ontology Web Language)। -सेमांटिक वेब का एक मुख्य विचार **ऑन्टोलॉजी** का है। यह किसी समस्या क्षेत्र का स्पष्ट विनिर्देशन है, जिसे औपचारिक ज्ञान प्रतिनिधित्व के माध्यम से व्यक्त किया जाता है। सबसे सरल ऑन्टोलॉजी केवल समस्या क्षेत्र में वस्तुओं की एक पदानुक्रम हो सकती है, लेकिन अधिक जटिल ऑन्टोलॉजी में ऐसे नियम शामिल होंगे जो निष्कर्ष निकालने के लिए उपयोग किए जा सकते हैं। +सेमांटिक वेब में एक मुख्य अवधारणा है **ऑन्टोलॉजी**। इसका मतलब है किसी समस्या क्षेत्र का कुछ औपचारिक ज्ञान प्रतिनिधित्व का उपयोग करके स्पष्ट विनिर्देशन। सबसे सरल ऑन्टोलॉजी सिर्फ समस्या क्षेत्र में वस्तुओं की एक पदानुक्रम हो सकती है, लेकिन अधिक जटिल ऑन्टोलॉजी में नियम शामिल होंगे जिन्हें अनुमान के लिए उपयोग किया जा सकता है। -सेमांटिक वेब में, सभी प्रतिनिधित्व त्रिपलों पर आधारित होते हैं। प्रत्येक वस्तु और प्रत्येक संबंध को URI द्वारा अद्वितीय रूप से पहचाना जाता है। उदाहरण के लिए, यदि हम यह तथ्य बताना चाहते हैं कि यह AI पाठ्यक्रम Dmitry Soshnikov द्वारा 1 जनवरी, 2022 को विकसित किया गया है - तो यहां वे त्रिपल्स हैं जिन्हें हम उपयोग कर सकते हैं: +सेमांटिक वेब में, सभी प्रतिनिधित्व ट्रिपलेट्स पर आधारित हैं। प्रत्येक वस्तु और प्रत्येक संबंध को यूनिक रूप से URI द्वारा पहचाना जाता है। उदाहरण के लिए, यदि हम यह तथ्य व्यक्त करना चाहते हैं कि इस AI करिकुलम को Dmitry Soshnikov ने 1 जनवरी, 2022 को विकसित किया है - तो हम उपयोग कर सकते हैं निम्न ट्रिपलेट्स: - + ``` -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 ``` -> ✅ यहां `http://www.example.com/terms/creation-date` और `http://purl.org/dc/elements/1.1/creator` कुछ प्रसिद्ध और सार्वभौमिक रूप से स्वीकृत URIs हैं जो *creator* और *creation date* की अवधारणाओं को व्यक्त करते हैं। +> ✅ यहां `http://www.example.com/terms/creation-date` और `http://purl.org/dc/elements/1.1/creator` कुछ प्रसिद्ध और सार्वभौमिक रूप से स्वीकार किए गए URI हैं जो *creator* और *creation date* की अवधारणाओं को व्यक्त करते हैं। -एक अधिक जटिल मामले में, यदि हम रचनाकारों की एक सूची को परिभाषित करना चाहते हैं, तो हम RDF में परिभाषित कुछ डेटा संरचनाओं का उपयोग कर सकते हैं। +एक अधिक जटिल मामले में, यदि हम स्रष्टाओं की सूची परिभाषित करना चाहते हैं, तो हम RDF में परिभाषित कुछ डेटा संरचनाओं का उपयोग कर सकते हैं। - + -> ऊपर के आरेख [Dmitry Soshnikov](http://soshnikov.com) द्वारा। +> उपरोक्त आरेख [Dmitry Soshnikov](http://soshnikov.com) द्वारा -सेमांटिक वेब के निर्माण की प्रगति को खोज इंजन और प्राकृतिक भाषा प्रसंस्करण तकनीकों की सफलता ने धीमा कर दिया, जो पाठ से संरचित डेटा निकालने की अनुमति देती हैं। हालांकि, कुछ क्षेत्रों में अभी भी ऑन्टोलॉजी और ज्ञान आधार बनाए रखने के लिए महत्वपूर्ण प्रयास किए जा रहे हैं। कुछ उल्लेखनीय परियोजनाएं: +सेमांटिक वेब के विकास की गति खोज इंजन और प्राकृतिक भाषा प्रसंस्करण तकनीकों की सफलता से कुछ हद तक धीमी हुई, जो टेक्स्ट से संरचित डेटा निकालने की अनुमति देती हैं। हालांकि, कुछ क्षेत्रों में अभी भी ऑन्टोलॉजी और ज्ञान आधार बनाए रखने के लिए महत्वपूर्ण प्रयास हो रहे हैं। कुछ उल्लेखनीय प्रोजेक्ट्स: -* [WikiData](https://wikidata.org/) मशीन-पढ़ने योग्य ज्ञान आधारों का संग्रह है जो Wikipedia से जुड़ा हुआ है। अधिकांश डेटा Wikipedia *InfoBoxes* से निकाला गया है, जो Wikipedia पृष्ठों के अंदर संरचित सामग्री के टुकड़े हैं। आप [SPARQL](https://query.wikidata.org/) में WikiData को क्वेरी कर सकते हैं, जो सेमांटिक वेब के लिए एक विशेष क्वेरी भाषा है। यहां एक नमूना क्वेरी है जो मनुष्यों के बीच सबसे लोकप्रिय आंखों के रंग दिखाती है: +* [WikiData](https://wikidata.org/) विकिपीडिया से जुड़े मशीन-पठनीय ज्ञान आधारित का संग्रह है। अधिकांश डेटा विकिपीडिया *InfoBoxes* से निकाला जाता है, जो विकिपीडिया पृष्ठों के अंदर संरचित सामग्री के टुकड़े होते हैं। आप SPARQL में विकिडाटा [query](https://query.wikidata.org/) कर सकते हैं, जो सेमांटिक वेब के लिए एक विशेष क्वेरी भाषा है। यहाँ एक नमूना क्वेरी है जो मानवों में सबसे सामान्य आंखों के रंग दर्शाती है: ```sparql #defaultView:BubbleChart @@ -206,47 +206,51 @@ WHERE GROUP BY ?eyeColorLabel ``` -* [DBpedia](https://www.dbpedia.org/) WikiData के समान एक अन्य प्रयास है। +* [DBpedia](https://www.dbpedia.org/) भी WikiData के समान एक प्रयास है। -> ✅ यदि आप अपनी खुद की ऑन्टोलॉजी बनाने या मौजूदा ऑन्टोलॉजी खोलने के साथ प्रयोग करना चाहते हैं, तो एक शानदार दृश्य ऑन्टोलॉजी संपादक [Protégé](https://protege.stanford.edu/) है। इसे डाउनलोड करें, या इसे ऑनलाइन उपयोग करें। +> ✅ यदि आप अपनी खुद की ऑन्टोलॉजी बनाने या मौजूदा ऑन्टोलॉजियों को खोलने का प्रयोग करना चाहते हैं, तो एक शानदार दृश्य ऑन्टोलॉजी संपादक है जिसे [Protégé](https://protege.stanford.edu/) कहा जाता है। इसे डाउनलोड करें, या ऑनलाइन उपयोग करें। - + -*वेब Protégé संपादक Romanov Family ऑन्टोलॉजी के साथ खुला। Dmitry Soshnikov द्वारा स्क्रीनशॉट* +*Web Protégé संपादक रोमानोव परिवार ऑन्टोलॉजी के साथ खुला है। स्क्रीनशॉट Dmitry Soshnikov द्वारा* ## ✍️ अभ्यास: एक परिवार ऑन्टोलॉजी -[FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) देखें, जो सेमांटिक वेब तकनीकों का उपयोग करके पारिवारिक संबंधों के बारे में तर्क करने का एक उदाहरण है। हम सामान्य GEDCOM प्रारूप में प्रस्तुत एक परिवार वृक्ष और पारिवारिक संबंधों की एक ऑन्टोलॉजी लेंगे और दिए गए व्यक्तियों के सेट के लिए सभी पारिवारिक संबंधों का एक ग्राफ बनाएंगे। +सेमांटिक वेब तकनीकों का उपयोग कर परिवार के रिश्तों को तर्कसंगत बनाने का उदाहरण देखने के लिए [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) देखें। हम सामान्य GEDCOM फॉर्मेट में प्रस्तुत परिवार वृक्ष और परिवारिक संबंधों का एक ऑन्टोलॉजी लेंगे और दिए गए व्यक्तियों के लिए सभी पारिवारिक संबंधों का ग्राफ बनाएंगे। -## Microsoft Concept Graph +## माइक्रोसॉफ्ट कॉन्सेप्ट ग्राफ -अधिकांश मामलों में, ऑन्टोलॉजी को सावधानीपूर्वक हाथ से बनाया जाता है। हालांकि, यह भी संभव है कि **अनौपचारिक डेटा** से ऑन्टोलॉजी निकाली जाएं, उदाहरण के लिए, प्राकृतिक भाषा पाठों से। +अधिकांश मामलों में, ऑन्टोलॉजी हाथ से सावधानीपूर्वक बनाई जाती हैं। हालांकि, यह भी संभव है कि **खनन** किया जाए ऑन्टोलॉजी को असंरचित डेटा से, जैसे प्राकृतिक भाषा के पाठ से। -Microsoft Research द्वारा ऐसा ही एक प्रयास किया गया था, जिसके परिणामस्वरूप [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste) बना। +ऐसा एक प्रयास माइक्रोसॉफ्ट रिसर्च द्वारा किया गया था, जिससे [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste) बना। -यह `is-a` उत्तराधिकार संबंध का उपयोग करके समूहित संस्थाओं का एक बड़ा संग्रह है। यह "Microsoft क्या है?" जैसे प्रश्नों का उत्तर देने की अनुमति देता है - उत्तर कुछ इस प्रकार हो सकता है "एक कंपनी 0.87 संभावना के साथ, और एक ब्रांड 0.75 संभावना के साथ।" +यह इकाइयों का बड़ा संग्रह है जो `is-a` उत्तराधिकार सम्बन्ध का उपयोग करके समूहबद्ध की गई हैं। यह प्रकार के प्रश्नों का उत्तर देने में सक्षम है "Microsoft क्या है?" - उत्तर कुछ ऐसा होगा "एक कंपनी संभावना 0.87 के साथ, और एक ब्रांड संभावना 0.75 के साथ"। -ग्राफ़ REST API के रूप में उपलब्ध है, या एक बड़े डाउनलोड करने योग्य टेक्स्ट फ़ाइल के रूप में जो सभी इकाई जोड़े सूचीबद्ध करता है। +ग्राफ REST API के रूप में उपलब्ध है, या एक बड़ा डाउनलोड करने योग्य टेक्स्ट फ़ाइल के रूप में जो सभी इकाई जोड़ियों को सूचीबद्ध करता है। -## ✍️ अभ्यास: एक कॉन्सेप्ट ग्राफ़ +## ✍️ अभ्यास: एक कॉन्सेप्ट ग्राफ -[MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) नोटबुक आज़माएं, यह देखने के लिए कि हम Microsoft Concept Graph का उपयोग करके समाचार लेखों को कई श्रेणियों में कैसे समूहित कर सकते हैं। +देखें [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) नोटबुक यह देखने के लिए कि कैसे हम Microsoft Concept Graph का उपयोग समाचार artikle को कई वर्गों में समूहबद्ध करने के लिए कर सकते हैं। ## निष्कर्ष -आजकल, AI को अक्सर *मशीन लर्निंग* या *न्यूरल नेटवर्क्स* का पर्याय माना जाता है। हालांकि, एक मानव भी स्पष्ट तर्क प्रदर्शित करता है, जो कुछ ऐसा है जिसे वर्तमान में न्यूरल नेटवर्क द्वारा संभाला नहीं जा रहा है। वास्तविक दुनिया की परियोजनाओं में, स्पष्ट तर्क अभी भी उन कार्यों को करने के लिए उपयोग किया जाता है जिनमें स्पष्टीकरण की आवश्यकता होती है, या सिस्टम के व्यवहार को नियंत्रित तरीके से संशोधित करने की क्षमता होती है। +आजकल, AI को अक्सर *मशीन लर्निंग* या *न्यूरल नेटवर्क* के पर्याय के रूप में माना जाता है। हालांकि, एक मानव भी स्पष्ट तर्कशीलता प्रदर्शित करता है, जो कुछ ऐसा है जिसे वर्तमान में न्यूरल नेटवर्क संभाल नहीं पा रहे हैं। असली दुनिया की परियोजनाओं में, स्पष्ट तर्कशीलता का अभी भी उपयोग किया जाता है उन कार्यों को करने के लिए जिनके लिए स्पष्टीकरण आवश्यक होते हैं, या सिस्टम के व्यवहार को नियंत्रित तरीके से संशोधित करने की क्षमता हो। ## 🚀 चुनौती -इस पाठ से जुड़े Family Ontology नोटबुक में, अन्य पारिवारिक संबंधों के साथ प्रयोग करने का अवसर है। परिवार वृक्ष में लोगों के बीच नए संबंध खोजने का प्रयास करें। +इस पाठ से जुड़े परिवार ऑन्टोलॉजी नोटबुक में, अन्य पारिवारिक संबंधों के साथทดลอง करने का अवसर है। परिवार वृक्ष में लोगों के बीच नए संबंध खोजने की कोशिश करें। -## [पाठ के बाद क्विज़](https://ff-quizzes.netlify.app/en/ai/quiz/4) +## [पाठ के बाद प्रश्नोत्तरी](https://ff-quizzes.netlify.app/en/ai/quiz/4) -## समीक्षा और स्व-अध्ययन +## समीक्षा और आत्म-अध्ययन -इंटरनेट पर शोध करें और उन क्षेत्रों की खोज करें जहां मनुष्यों ने ज्ञान को मापने और कोडिफाई करने का प्रयास किया है। Bloom's Taxonomy पर एक नज़र डालें, और इतिहास में वापस जाएं यह जानने के लिए कि मनुष्यों ने अपनी दुनिया को समझने की कोशिश कैसे की। Linnaeus के कार्य का अन्वेषण करें, जिन्होंने जीवों की एक वर्गीकरण प्रणाली बनाई, और Dmitri Mendeleev के कार्य को देखें, जिन्होंने रासायनिक तत्वों को वर्णित और समूहित करने का एक तरीका बनाया। आप कौन से अन्य दिलचस्प उदाहरण खोज सकते हैं? +इंटरनेट पर कुछ खोज करें कि ऐसे कौन से क्षेत्र हैं जहां मनुष्यों ने ज्ञान को मापने और संहिताबद्ध करने का प्रयास किया है। Bloom's Taxonomy देखें, और इतिहास में जाएं यह सीखने के लिए कि मनुष्यों ने अपने संसार को समझने की कोशिश कैसे की। Linnaeus के कार्य को देखें जो जीवों की टैक्सोनॉमी बनाने के लिए था, और देखें कि Dmitri Mendeleev ने रासायनिक तत्वों का वर्णन और वर्गीकरण करने का तरीका कैसे बनाया। आप और कौन से रोचक उदाहरण पा सकते हैं? -**असाइनमेंट**: [ऑन्टोलॉजी बनाएं](assignment.md) +**आसाइन्मेंट**: [ऑन्टोलॉजी बनाएं](assignment.md) --- + +**अस्वीकरण**: +इस दस्तावेज़ का अनुवाद AI अनुवाद सेवा [Co-op Translator](https://github.com/Azure/co-op-translator) का उपयोग करके किया गया है। जबकि हम सटीकता के लिए प्रयासरत हैं, कृपया ध्यान दें कि स्वचालित अनुवादों में त्रुटियाँ या असत्यमयताएँ हो सकती हैं। मूल दस्तावेज़ को उसकी मूल भाषा में प्राधिकृत स्रोत माना जाना चाहिए। महत्वपूर्ण जानकारी के लिए, पेशेवर मानव अनुवाद की सिफारिश की जाती है। इस अनुवाद के उपयोग से उत्पन्न किसी भी गलतफहमी या मिथ्या व्याख्या के लिए हम जिम्मेदार नहीं हैं। + \ No newline at end of file diff --git a/translations/ko/README.md b/translations/ko/README.md index ac89e15a..688299a7 100644 --- a/translations/ko/README.md +++ b/translations/ko/README.md @@ -1,8 +1,8 @@ -[아랍어](../ar/README.md) | [벵골어](../bn/README.md) | [불가리아어](../bg/README.md) | [버마어 (미얀마)](../my/README.md) | [중국어 (간체)](../zh/README.md) | [중국어 (번체, 홍콩)](../hk/README.md) | [중국어 (번체, 마카오)](../mo/README.md) | [중국어 (번체, 대만)](../tw/README.md) | [크로아티아어](../hr/README.md) | [체코어](../cs/README.md) | [덴마크어](../da/README.md) | [네덜란드어](../nl/README.md) | [에스토니아어](../et/README.md) | [핀란드어](../fi/README.md) | [프랑스어](../fr/README.md) | [독일어](../de/README.md) | [그리스어](../el/README.md) | [히브리어](../he/README.md) | [힌디어](../hi/README.md) | [헝가리어](../hu/README.md) | [인도네시아어](../id/README.md) | [이탈리아어](../it/README.md) | [일본어](../ja/README.md) | [칸나다어](../kn/README.md) | [한국어](./README.md) | [리투아니아어](../lt/README.md) | [말레이어](../ms/README.md) | [말라얄람어](../ml/README.md) | 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[덴마크어](../da/README.md) | [네덜란드어](../nl/README.md) | [에스토니아어](../et/README.md) | [핀란드어](../fi/README.md) | [프랑스어](../fr/README.md) | [독일어](../de/README.md) | [그리스어](../el/README.md) | [히브리어](../he/README.md) | [힌디어](../hi/README.md) | [헝가리어](../hu/README.md) | [인도네시아어](../id/README.md) | [이탈리아어](../it/README.md) | [일본어](../ja/README.md) | [칸나다어](../kn/README.md) | [한국어](./README.md) | [리투아니아어](../lt/README.md) | [말레이어](../ms/README.md) | [말라얄람어](../ml/README.md) | [마라티어](../mr/README.md) | [네팔어](../ne/README.md) | [나이지리아 피진어](../pcm/README.md) | [노르웨이어](../no/README.md) | [페르시아어 (파르시)](../fa/README.md) | [폴란드어](../pl/README.md) | [포르투갈어 (브라질)](../br/README.md) | [포르투갈어 (포르투갈)](../pt/README.md) | [펀자브어 (구르무키)](../pa/README.md) | [루마니아어](../ro/README.md) | [러시아어](../ru/README.md) | [세르비아어 (키릴문자)](../sr/README.md) | [슬로바키아어](../sk/README.md) | [슬로베니아어](../sl/README.md) | [스페인어](../es/README.md) | [스와힐리어](../sw/README.md) | [스웨덴어](../sv/README.md) | [타갈로그어 (필리핀어)](../tl/README.md) | [타밀어](../ta/README.md) | [텔루구어](../te/README.md) | [타이어](../th/README.md) | [터키어](../tr/README.md) | [우크라이나어](../uk/README.md) | [우르두어](../ur/README.md) | [베트남어](../vi/README.md) -> **로컬에 복제하시겠습니까?** +> **로컬 복제를 선호하시나요?** -> 이 저장소에는 50개 이상의 번역본이 포함되어 있어 다운로드 크기가 크게 증가합니다. 번역본 없이 복제하려면 sparse checkout을 사용하세요: +> 이 저장소는 50개 이상의 언어 번역을 포함하고 있어 다운로드 크기가 상당히 큽니다. 번역 없이 복제하려면 sparse checkout을 사용하세요: > ```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' > ``` -> 이렇게 하면 훨씬 빠른 다운로드로 코스 완료에 필요한 모든 것을 얻을 수 있습니다. +> 이 방법은 훨씬 빠른 다운로드로 과정 완료에 필요한 모든 것을 제공합니다. -**추가 번역 언어 지원을 원하시면 [여기](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md) 목록을 참고하세요** +**추가 번역 언어 지원을 원하시면 [여기](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)를 참고하세요** -## 커뮤니티에 참여하세요 +## 커뮤니티에 참여하기 [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -## 학습 내용 +## 배울 내용 **[코스 마인드맵](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** -이 커리큘럼에서는 다음을 배우게 됩니다: +이 커리큘럼에서 배우는 내용: -* "좋은 옛날" 기호 기반 접근 방식으로 알려진 **지식 표현** 및 추론을 포함한 다양한 인공 지능 접근법 ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). -* 현대 AI의 핵심인 **신경망**과 **딥러닝**. 이 중요한 주제들의 개념을 두 가지 인기 프레임워크인 [TensorFlow](http://Tensorflow.org)와 [PyTorch](http://pytorch.org) 코드로 설명합니다. -* 이미지와 텍스트 작업을 위한 **신경망 구조**. 최신 모델을 다루지만 최신 최첨단 수준에는 약간 부족할 수 있습니다. -* 덜 대중적인 AI 접근법인 **유전 알고리즘**과 **다중 에이전트 시스템**. +* “좋은 오래된” 상징적 접근법인 **지식 표현**과 추론을 포함한 다양한 인공 지능 접근법 ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence))를 배웁니다. +* 현대 AI의 핵심인 **신경망**과 **딥러닝**. 이 중요한 주제들의 개념을 [TensorFlow](http://Tensorflow.org)와 [PyTorch](http://pytorch.org)라는 두 가지 인기 있는 프레임워크의 코드를 사용해 설명합니다. +* 이미지와 텍스트 작업을 위한 **신경망 구조**. 최신 모델을 다루지만 최신 상태를 완벽히 반영하지는 않을 수 있습니다. +* **유전 알고리즘**과 **다중 에이전트 시스템**과 같은 덜 알려진 AI 접근법. 이 커리큘럼에서 다루지 않는 내용: -> [Microsoft Learn 컬렉션에서 이 코스의 모든 추가 자료를 확인하세요](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) +> [Microsoft Learn 컬렉션에서 이 과정 관련 추가 자료 모두 보기](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) -* **비즈니스에서의 AI** 적용 사례. Microsoft Learn의 [비즈니스 사용자를 위한 AI 소개](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) 학습 경로나 [AI 비즈니스 스쿨](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum)을, [INSEAD](https://www.insead.edu/)와 협력하여 개발된 컨텐츠를 고려해보세요. -* 클래식 머신러닝, 이는 [초보자를 위한 머신러닝 커리큘럼](http://github.com/Microsoft/ML-for-Beginners)에 잘 설명되어 있습니다. -* **[인지 서비스](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**를 사용하여 구축한 실용적인 AI 애플리케이션. 이를 위해 Microsoft Learn의 [비전](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [자연어 처리](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[Azure OpenAI 서비스로 생성 AI](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** 모듈 등을 추천합니다. -* 특정 머신러닝 **클라우드 프레임워크**, 예를 들어 [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), 또는 [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). [Azure Machine Learning으로 머신러닝 솔루션 구축 및 운영](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) 및 [Azure Databricks로 머신러닝 솔루션 구축 및 운영](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum) 학습 경로를 고려해보세요. -* **대화형 AI** 및 **챗봇**. 별도의 [대화형 AI 솔루션 만들기](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum) 학습 경로가 있으며, 자세한 내용은 [이 블로그 글](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/)도 참고할 수 있습니다. -* 딥러닝의 **심층 수학**. 이를 위해 Ian Goodfellow, Yoshua Bengio, Aaron Courville가 저술한 [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618)을 추천하며, 온라인으로 [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/)에서도 이용 가능합니다. +* **비즈니스에서의 AI** 적용 사례. Microsoft Learn의 [비즈니스 사용자를 위한 AI 소개](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) 학습 경로나 [INSEAD](https://www.insead.edu/)와 협력하여 개발한 [AI 비즈니스 스쿨](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum)를 고려해 보세요. +* 저희 [초보자를 위한 머신러닝 커리큘럼](http://github.com/Microsoft/ML-for-Beginners)에 잘 설명된 **클래식 머신러닝**. +* **[인지 서비스](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**를 사용해 구축된 실제 AI 애플리케이션. 이를 위해 Microsoft Learn에서 [비전](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [자연어 처리](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[Azure OpenAI 서비스와 제너레이티브 AI](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** 등 모듈을 먼저 시작하는 것을 추천합니다. +* [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), 또는 [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum)와 같은 특정 ML 클라우드 프레임워크. [Azure Machine Learning으로 머신러닝 솔루션 구축 및 운영](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum)과 [Azure Databricks로 머신러닝 솔루션 구축 및 운영](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum) 학습 경로를 이용해 보세요. +* **대화형 AI** 및 **챗봇**. 별도의 [대화형 AI 솔루션 만들기](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum) 학습 경로가 있으며, 자세한 내용은 [이 블로그 글](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/)을 참고하세요. +* 딥러닝의 **심화 수학**. 이를 위해 Ian Goodfellow, Yoshua Bengio, Aaron Courville 저서인 [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) (온라인 [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/)에서도 이용 가능)을 추천합니다. -_클라우드 AI_ 주제에 대한 부드러운 소개를 원한다면 [Azure에서 인공 지능 시작하기](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum) 학습 경로를 고려해보세요. +_클라우드에서 AI 시작하기_를 위한 부드러운 입문으로는 [Azure에서 인공 지능 시작하기](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum) 학습 경로를 추천합니다. -# 콘텐츠 +# 내용 -| | 수업 링크 | PyTorch/Keras/TensorFlow | 실습 | +| | 강의 링크 | PyTorch/Keras/TensorFlow | 실험 | | :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | | 0 | [코스 설정](./lessons/0-course-setup/setup.md) | [개발 환경 설정](./lessons/0-course-setup/how-to-run.md) | | | I | [**AI 소개**](./lessons/1-Intro/README.md) | | | -| 01 | [인공지능 소개와 역사](./lessons/1-Intro/README.md) | - | - | -| II | **기호 기반 AI** | -| 02 | [지식 표현과 전문가 시스템](./lessons/2-Symbolic/README.md) | [전문가 시스템](./lessons/2-Symbolic/Animals.ipynb) / [온톨로지](./lessons/2-Symbolic/FamilyOntology.ipynb) /[개념 그래프](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | +| 01 | [AI 소개 및 역사](./lessons/1-Intro/README.md) | - | - | +| II | **상징적 AI** | +| 02 | [지식 표현 및 전문가 시스템](./lessons/2-Symbolic/README.md) | [전문가 시스템](./lessons/2-Symbolic/Animals.ipynb) / [온톨로지](./lessons/2-Symbolic/FamilyOntology.ipynb) /[개념 그래프](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | | III | [**신경망 소개**](./lessons/3-NeuralNetworks/README.md) ||| | 03 | [퍼셉트론](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [노트북](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [랩](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | -| 04 | [멀티 레이어 퍼셉트론과 직접 프레임워크 만들기](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [노트북](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [랩](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | -| 05 | [프레임워크 소개 (PyTorch/TensorFlow) 및 과적합](./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) | [랩](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | +| 04 | [다층 퍼셉트론과 나만의 프레임워크 만들기](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [노트북](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [랩](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | +| 05 | [프레임워크 소개 (PyTorch/TensorFlow) 와 과적합](./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) | [랩](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | | IV | [**컴퓨터 비전**](./lessons/4-ComputerVision/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-77998-cacaste) / [TensorFlow](https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-77998-cacaste)| [Microsoft Azure에서 컴퓨터 비전 탐색](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | -| 06 | [컴퓨터 비전 입문. OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [노트북](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [랩](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | +| 06 | [컴퓨터 비전 소개. OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [노트북](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [랩](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | | 07 | [합성곱 신경망](./lessons/4-ComputerVision/07-ConvNets/README.md) & [CNN 아키텍처](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [랩](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | -| 08 | [사전학습 네트워크와 전이학습](./lessons/4-ComputerVision/08-TransferLearning/README.md) 및 [학습 팁](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [랩](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | -| 09 | [오토인코더 및 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 | [생성적 적대 신경망 및 예술적 스타일 전송](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | +| 08 | [사전 학습된 네트워크와 전이 학습](./lessons/4-ComputerVision/08-TransferLearning/README.md) 그리고 [훈련 트릭](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [랩](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | +| 09 | [오토인코더와 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 | [생성적 적대 신경망 & 예술적 스타일 전이](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | | 11 | [객체 탐지](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [랩](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | -| 12 | [의미론적 분할. 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) | | +| 12 | [시맨틱 분할. 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 | [**자연어 처리**](./lessons/5-NLP/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-77998-cacaste) /[TensorFlow](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-77998-cacaste) | [Microsoft Azure에서 자연어 처리 탐색](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| | 13 | [텍스트 표현. 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 | [의미론적 단어 임베딩. Word2Vec 및 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 | [언어 모델링. 직접 임베딩 학습하기](./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) | [랩](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | +| 14 | [의미론적 단어 임베딩. Word2Vec와 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 | [언어 모델링. 나만의 임베딩 훈련하기](./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) | [랩](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | | 16 | [순환 신경망](./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 | [생성 순환 신경망](./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) | [랩](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | | 18 | [트랜스포머. 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 | [개체명 인식](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [랩](./lessons/5-NLP/19-NER/lab/README.md) | -| 20 | [대형 언어 모델, 프롬프트 프로그래밍 및 few-shot 작업](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | +| 19 | [명명된 개체 인식](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [랩](./lessons/5-NLP/19-NER/lab/README.md) | +| 20 | [대형 언어 모델, 프롬프트 프로그래밍 및 Few-Shot 작업](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | | VI | **기타 AI 기술** || | | 21 | [유전 알고리즘](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [노트북](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | | 22 | [심층 강화 학습](./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) | [랩](./lessons/6-Other/22-DeepRL/lab/README.md) | -| 23 | [멀티 에이전트 시스템](./lessons/6-Other/23-MultiagentSystems/README.md) | | | +| 23 | [다중 에이전트 시스템](./lessons/6-Other/23-MultiagentSystems/README.md) | | | | VII | **AI 윤리** | | | | 24 | [AI 윤리 및 책임 있는 AI](./lessons/7-Ethics/README.md) | [Microsoft Learn: 책임 있는 AI 원칙](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | | IX | **추가 자료** | | | | 25 | [멀티 모달 네트워크, CLIP 및 VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [노트북](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | -## 각 강의 내용 +## 각 강의는 다음을 포함합니다 -* 사전 학습 자료 -* 실행 가능한 Jupyter 노트북, 주로 특정 프레임워크(**PyTorch** 또는 **TensorFlow**)에 특화되어 있습니다. 실행 가능한 노트북에는 많은 이론 자료도 포함되어 있으므로 주제를 이해하기 위해서는 노트북 버전 중 하나(또는 PyTorch, TensorFlow)를 반드시 학습해야 합니다. -* 일부 주제에는 **랩**이 제공되어 학습한 내용을 특정 문제에 적용해 볼 수 있는 기회를 제공합니다. +* 예습 자료 +* 실행 가능한 Jupyter 노트북, 일반적으로 특정 프레임워크(**PyTorch** 또는 **TensorFlow**)에 맞춤화되어 있습니다. 실행 가능한 노트북에는 많은 이론적 내용도 포함되어 있어, 주제를 이해하려면 적어도 한 가지 버전의 노트북(PyTorch 또는 TensorFlow 중 하나)을 반드시 확인해야 합니다. +* 일부 주제에는 특정 문제에 대해 배운 내용을 적용해볼 수 있는 **랩**이 제공됩니다. * 일부 섹션에는 관련 주제를 다루는 [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) 모듈 링크가 포함되어 있습니다. ## 시작하기 -### 🎯 AI 초보자라면? 여기서 시작하세요! +### 🎯 AI가 처음인가요? 여기서 시작하세요! -AI가 완전 처음이고 빠른 실습 예제가 필요하다면, [**초보자 친화적 예제**](./examples/README.md)를 확인하세요! 여기에는 다음이 포함됩니다: +AI가 완전히 처음이고 빠르고 실습 중심의 예제를 원한다면, [**초보자용 예제**](./examples/README.md)를 확인해 보세요! 여기에는 다음이 포함됩니다: -- 🌟 **헬로우 AI 월드** - 첫 번째 AI 프로그램(패턴 인식) +- 🌟 **Hello AI World** - 첫 번째 AI 프로그램 (패턴 인식) - 🧠 **간단한 신경망** - 신경망을 처음부터 구축하기 -- 🖼️ **이미지 분류기** - 자세한 주석과 함께 이미지 분류하기 +- 🖼️ **이미지 분류기** - 자세한 설명과 함께 이미지 분류하기 - 💬 **텍스트 감정 분석** - 긍정/부정 텍스트 분석 이 예제들은 전체 커리큘럼을 시작하기 전에 AI 개념을 이해하는 데 도움이 되도록 설계되었습니다. ### 📚 전체 커리큘럼 설정 -- 개발 환경 설정에 도움이 되는 [설정 수업](./lessons/0-course-setup/setup.md)을 만들었습니다. - 교육자를 위해서도 [커리큘럼 설정 수업](./lessons/0-course-setup/for-teachers.md)을 제공하고 있습니다! -- VSCode 또는 Codepace에서 [코드 실행 방법](./lessons/0-course-setup/how-to-run.md) +- 개발 환경 설정을 돕기 위해 [설정 수업](./lessons/0-course-setup/setup.md)을 만들었습니다. - 교육자를 위해서도 [커리큘럼 설정 수업](./lessons/0-course-setup/for-teachers.md)을 마련했습니다! +- VSCode 또는 Codespace에서 [코드 실행 방법](./lessons/0-course-setup/how-to-run.md) -다음 단계를 따르세요: +다음 단계를 따라주세요: -레포지토리 포크: 이 페이지 오른쪽 상단의 "Fork" 버튼을 클릭하세요. +저장소 포크: 이 페이지 오른쪽 상단의 "Fork" 버튼을 클릭합니다. -레포지토리 클론: `git clone https://github.com/microsoft/AI-For-Beginners.git` +저장소 클론: `git clone https://github.com/microsoft/AI-For-Beginners.git` -나중에 더 쉽게 찾을 수 있도록 이 저장소에 별(🌟)을 달아 두세요. +이 저장소를 나중에 더 쉽게 찾을 수 있도록 별(🌟)을 눌러주세요. ## 다른 학습자 만나기 -[공식 AI Discord 서버](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum)에 참여하여 이 과정을 수강하는 다른 학습자들과 만나고 네트워크를 형성하며 지원을 받으세요. +이 과정을 수강하는 다른 학습자들과 만나고 네트워킹하며 지원을 받을 수 있는 [공식 AI Discord 서버](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum)에 참여하세요. -제품 피드백이나 질문이 있으면 [Azure AI Foundry 개발자 포럼](https://aka.ms/foundry/forum)을 방문하세요. +제품 피드백이나 질문이 있을 경우 [Azure AI Foundry 개발자 포럼](https://aka.ms/foundry/forum)을 방문하세요. ## 퀴즈 -> **퀴즈에 대한 안내**: 모든 퀴즈는 etc\quiz-app 내 Quiz-app 폴더에 포함되어 있으며, [온라인에서 여기](https://ff-quizzes.netlify.app/)에서 접근할 수 있습니다. 퀴즈 앱은 수업 내에서 링크되어 있으며 로컬에서 실행하거나 Azure에 배포할 수 있습니다; `quiz-app` 폴더의 지침을 따르세요. 점차적으로 현지화 작업이 진행 중입니다. +> **퀴즈에 대한 안내**: 모든 퀴즈는 etc\quiz-app 폴더 내 Quiz-app 폴더에 포함되어 있으며, [온라인에서 여기](https://ff-quizzes.netlify.app/)에서도 볼 수 있습니다. 퀴즈는 수업 내에서 연결되어 있으며 퀴즈 앱은 로컬에서 실행하거나 Azure에 배포할 수 있습니다. `quiz-app` 폴더 내 지침을 따르세요. 점차적으로 현지화 중입니다. ## 도움 요청 -제안 사항이 있거나 오타 또는 코드 오류를 발견하면 이슈를 제기하거나 풀 리퀘스트를 만들어 주세요. +제안 사항이 있거나 맞춤법 또는 코드 오류를 발견하셨나요? 이슈를 제기하거나 풀 리퀘스트를 만들어주세요. ## 특별 감사 * **✍️ 주요 저자:** [Dmitry Soshnikov](http://soshnikov.com), PhD * **🔥 편집자:** [Jen Looper](https://twitter.com/jenlooper), PhD -* **🎨 스케치 노트 일러스트레이터:** [Tomomi Imura](https://twitter.com/girlie_mac) +* **🎨 스케치노트 일러스트레이터:** [Tomomi Imura](https://twitter.com/girlie_mac) * **✅ 퀴즈 제작자:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) * **🙏 핵심 기여자:** [Evgenii Pishchik](https://github.com/Pe4enIks) ## 기타 커리큘럼 -저희 팀은 다른 커리큘럼도 제작합니다! 확인해 보세요: +저희 팀은 다른 커리큘럼도 제작합니다! 확인해보세요: ### LangChain @@ -181,7 +182,7 @@ AI가 완전 처음이고 빠른 실습 예제가 필요하다면, [**초보자 --- -### Azure / Edge / MCP / Agents +### Azure / Edge / MCP / 에이전트 [![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) @@ -216,11 +217,11 @@ AI가 완전 처음이고 빠른 실습 예제가 필요하다면, [**초보자 ## 도움 받기 -AI 앱 개발 중에 막히거나 질문이 있으면 MCP에 대해 토론하는 동료 학습자 및 경험 많은 개발자들과 함께하세요. 질문이 환영받고 지식이 자유롭게 공유되는 지원 커뮤니티입니다. +AI 앱 빌드 중 막히거나 질문이 있다면 MCP에 관한 토론에 참여하세요. 질문을 환영하고 지식을 자유롭게 공유하는 지원하는 커뮤니티입니다. [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -제품 피드백이나 개발 중 오류가 있으면 다음을 방문하세요: +제품 피드백이나 빌드 중 오류가 발생한 경우 방문하세요: [![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) @@ -228,5 +229,5 @@ AI 앱 개발 중에 막히거나 질문이 있으면 MCP에 대해 토론하는 **면책 조항**: -이 문서는 AI 번역 서비스 [Co-op Translator](https://github.com/Azure/co-op-translator)를 사용하여 번역되었습니다. 정확성을 위해 노력하고 있지만, 자동 번역에는 오류나 부정확성이 포함될 수 있음을 알려드립니다. 원문 문서를 권위 있는 출처로 간주해야 합니다. 중요한 정보의 경우 전문 인간 번역을 권장합니다. 이 번역의 사용으로 인해 발생하는 오해나 오해석에 대해 당사는 책임을 지지 않습니다. +이 문서는 AI 번역 서비스 [Co-op Translator](https://github.com/Azure/co-op-translator)를 사용하여 번역되었습니다. 정확성을 위해 노력하고 있으나, 자동 번역에는 오류나 부정확성이 포함될 수 있음을 유의해 주시기 바랍니다. 원문 문서는 권위 있는 출처로 간주되어야 합니다. 중요한 정보의 경우, 전문적인 인간 번역을 권장합니다. 본 번역 사용으로 인한 오해나 잘못된 해석에 대해 당사는 책임을 지지 않습니다. \ No newline at end of file diff --git a/translations/ko/lessons/0-course-setup/how-to-run.md b/translations/ko/lessons/0-course-setup/how-to-run.md index 20c87643..dba7a6b7 100644 --- a/translations/ko/lessons/0-course-setup/how-to-run.md +++ b/translations/ko/lessons/0-course-setup/how-to-run.md @@ -1,21 +1,21 @@ # 코드 실행 방법 -이 커리큘럼에는 실행 가능한 예제와 실습이 많이 포함되어 있으며, 이를 실행해 보고 싶을 것입니다. 이를 위해서는 이 커리큘럼의 일부로 제공된 Jupyter Notebook에서 Python 코드를 실행할 수 있는 환경이 필요합니다. 코드를 실행하는 방법에는 여러 가지가 있습니다: +이 커리큘럼에는 실행할 수 있는 많은 예제와 실습이 포함되어 있습니다. 이를 실행하려면 이 커리큘럼의 일부로 제공되는 Jupyter 노트북에서 Python 코드를 실행할 수 있어야 합니다. 코드를 실행하는 몇 가지 방법이 있습니다: -## 로컬 컴퓨터에서 실행 +## 컴퓨터에서 로컬로 실행 -코드를 로컬 컴퓨터에서 실행하려면 Python의 어떤 버전이든 설치되어 있어야 합니다. 개인적으로는 **[miniconda](https://conda.io/en/latest/miniconda.html)**를 설치하는 것을 추천합니다. 이는 가벼운 설치 방식으로, 다양한 Python **가상 환경**을 지원하는 `conda` 패키지 관리자를 제공합니다. +컴퓨터에서 코드를 로컬로 실행하려면 Python 설치가 필요합니다. 한 가지 권장 방법은 **[miniconda](https://conda.io/en/latest/miniconda.html)**를 설치하는 것입니다. 이는 비교적 가벼운 설치로, 다양한 Python **가상 환경**을 위한 `conda` 패키지 관리자를 지원합니다. -miniconda를 설치한 후, 저장소를 클론하고 이 강좌에서 사용할 가상 환경을 생성해야 합니다: +miniconda를 설치한 후, 저장소를 복제하고 이 과정에 사용할 가상 환경을 만드십시오: ```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 ``` -### Python 확장이 포함된 Visual Studio Code 사용 +### Python 확장 기능과 함께 Visual Studio Code 사용 -이 커리큘럼을 사용하는 가장 좋은 방법은 [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste)와 [Python Extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste)을 사용하여 여는 것입니다. +이 커리큘럼은 [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste)에서 [Python 확장 기능](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste)과 함께 열었을 때 가장 잘 사용됩니다. -> **Note**: 저장소를 클론하고 VS Code에서 디렉토리를 열면, Python 확장을 설치하라는 제안이 자동으로 표시됩니다. 위에서 설명한 대로 miniconda도 설치해야 합니다. +> **참고**: 저장소를 복제하고 VS Code에서 디렉터리를 열면 자동으로 Python 확장 기능 설치를 제안합니다. 위에서 설명한 대로 miniconda도 설치해야 합니다. -> **Note**: VS Code가 저장소를 컨테이너에서 다시 열 것을 제안하면, 로컬 Python 설치를 사용하기 위해 이를 거부해야 합니다. +> **참고**: VS Code에서 저장소를 컨테이너에서 다시 열도록 제안하면, 로컬 Python 설치를 사용하려면 이를 거부해야 합니다. ### 브라우저에서 Jupyter 사용 -브라우저에서 바로 Jupyter 환경을 사용할 수도 있습니다. 실제로, 클래식 Jupyter와 Jupyter Hub는 자동 완성, 코드 하이라이팅 등 편리한 개발 환경을 제공합니다. +브라우저에서 컴퓨터의 Jupyter 환경도 사용할 수 있습니다. 고전적인 Jupyter와 JupyterHub 모두 자동 완성, 코드 강조 등 편리한 개발 환경을 제공합니다. -로컬에서 Jupyter를 시작하려면, 강좌 디렉토리로 이동하여 다음 명령을 실행하세요: +로컬로 Jupyter를 시작하려면, 과정 디렉터리로 이동하여 다음을 실행하세요: ```bash jupyter notebook -``` -또는 +``` +또는 ```bash jupyterhub -``` -그런 다음 `.ipynb` 파일 중 하나로 이동하여 열고 작업을 시작할 수 있습니다. +``` +그런 다음 `.ipynb` 파일 중 아무거나 찾아 열어 작업을 시작할 수 있습니다. ### 컨테이너에서 실행 -Python을 설치하는 대신 컨테이너에서 코드를 실행하는 방법도 있습니다. 저장소에는 이 저장소를 위한 컨테이너를 빌드하는 방법을 지시하는 `.devcontainer` 폴더가 포함되어 있으므로, VS Code는 코드를 컨테이너에서 다시 열 것을 제안할 것입니다. 이를 위해 Docker 설치가 필요하며, 다소 복잡할 수 있으므로 더 숙련된 사용자에게 추천합니다. +Python 설치에 대한 대안으로 컨테이너에서 코드를 실행할 수 있습니다. 저장소는 이 저장소용 컨테이너 빌드 방법을 안내하는 특별한 `.devcontainer` 폴더를 제공하므로, VS Code는 코드를 컨테이너에서 다시 열 기회를 제공합니다. 이 방법은 Docker 설치가 필요하며 좀 더 복잡하므로, 숙련된 사용자에게 권장합니다. ## 클라우드에서 실행 -Python을 로컬에 설치하고 싶지 않거나 클라우드 리소스에 접근할 수 있다면, 클라우드에서 코드를 실행하는 것도 좋은 대안입니다. 이를 실행하는 몇 가지 방법이 있습니다: +로컬에 Python을 설치하고 싶지 않고, 클라우드 자원에 접근할 수 있다면 클라우드에서 코드를 실행하는 좋은 대안이 있습니다. 다음과 같은 방법들이 있습니다: -* **[GitHub Codespaces](https://github.com/features/codespaces)**를 사용하는 방법. 이는 GitHub에서 제공하는 가상 환경으로, VS Code 브라우저 인터페이스를 통해 접근할 수 있습니다. Codespaces에 접근할 수 있다면, 저장소의 **Code** 버튼을 클릭하고 Codespace를 시작하면 바로 실행할 수 있습니다. -* **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**를 사용하는 방법. [Binder](https://mybinder.org)는 GitHub에서 코드를 테스트하려는 사람들을 위해 제공되는 무료 클라우드 컴퓨팅 리소스입니다. 저장소의 첫 페이지에 Binder에서 저장소를 열 수 있는 버튼이 있으며, 이를 클릭하면 Binder 사이트로 이동하여 기본 컨테이너를 빌드하고 Jupyter 웹 인터페이스를 원활하게 시작할 수 있습니다. +* **[GitHub Codespaces](https://github.com/features/codespaces)** 사용하기: GitHub에서 가상 환경을 만들어 VS Code 브라우저 인터페이스를 통해 접근할 수 있습니다. Codespaces에 접근 권한이 있으면 저장소의 **Code** 버튼을 클릭해 codespace를 시작하고 빠르게 실행할 수 있습니다. +* **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)** 사용하기: [Binder](https://mybinder.org)는 GitHub상의 코드를 테스트할 수 있도록 클라우드에서 무료 컴퓨팅 자원을 제공합니다. 저장소 첫 페이지에 Binder에서 저장소를 열 수 있는 버튼이 있어, 이를 클릭하면 기저 컨테이너가 빌드되고 Jupyter 웹 인터페이스가 원활하게 시작됩니다. -> **Note**: 오용을 방지하기 위해 Binder는 일부 웹 리소스에 대한 접근을 차단합니다. 이는 모델이나 데이터를 공용 인터넷에서 가져오는 코드를 실행할 때 문제가 될 수 있습니다. 해결 방법을 찾아야 할 수도 있습니다. 또한, Binder에서 제공되는 컴퓨팅 리소스는 기본적이므로, 특히 더 복잡한 후반부 강의에서는 학습 속도가 느릴 수 있습니다. +> **참고**: 오용 방지를 위해 Binder에서는 일부 웹 자원 접근이 차단됩니다. 이로 인해 공개 인터넷에서 모델이나 데이터셋을 가져오는 일부 코드가 작동하지 않을 수 있습니다. 일부 우회 방법을 찾아야 할 수 있습니다. 또한 Binder에서 제공하는 컴퓨팅 자원은 초보 수준이므로, 특히 나중에 복잡한 수업에서는 훈련 속도가 느립니다. -## GPU를 활용한 클라우드 실행 +## GPU가 포함된 클라우드에서 실행 -이 커리큘럼의 후반부 강의 중 일부는 GPU 지원이 있으면 훨씬 더 효율적입니다. 그렇지 않으면 학습 속도가 매우 느릴 수 있습니다. 클라우드에 접근할 수 있는 경우, 특히 [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste)나 기관을 통해 접근할 수 있다면, 다음 옵션을 고려할 수 있습니다: +이 커리큘럼의 후반 수업 중 일부는 GPU 지원이 크게 도움이 됩니다. 예를 들어 모델 훈련은 그렇지 않으면 매우 느릴 수 있습니다. 특히 [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) 또는 기관을 통해 클라우드에 접근할 수 있다면 다음 옵션들을 고려할 수 있습니다: -* [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste)을 생성하고 Jupyter를 통해 연결합니다. 그런 다음, 해당 머신에 저장소를 클론하고 학습을 시작할 수 있습니다. NC 시리즈 VM은 GPU를 지원합니다. +* [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) 생성 후 Jupyter로 연결하기. 그 후 저장소를 머신에 복제하고 학습을 시작할 수 있습니다. NC 시리즈 VM에는 GPU 지원이 포함되어 있습니다. -> **Note**: Azure for Students를 포함한 일부 구독은 기본적으로 GPU 지원을 제공하지 않습니다. 추가 GPU 코어를 요청하려면 기술 지원 요청이 필요할 수 있습니다. +> **참고**: Azure for Students를 포함한 일부 구독은 기본적으로 GPU를 지원하지 않습니다. 추가 GPU 코어 요청을 기술 지원에 해야 할 수 있습니다. -* [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste)를 생성한 후, Notebook 기능을 사용합니다. [이 비디오](https://azure-for-academics.github.io/quickstart/azureml-papers/)는 Azure ML 노트북에 저장소를 클론하고 사용하는 방법을 보여줍니다. +* [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) 생성 후 노트북 기능 사용. [이 영상](https://azure-for-academics.github.io/quickstart/azureml-papers/)은 Azure ML 노트북에 저장소를 복제하고 사용하는 방법을 보여줍니다. -Google Colab도 사용할 수 있으며, 일부 무료 GPU 지원을 제공합니다. Jupyter Notebook을 업로드하여 하나씩 실행할 수 있습니다. +또한 일부 무료 GPU 지원이 포함된 Google Colab을 사용할 수도 있으며, Jupyter 노트북을 업로드해 차례대로 실행할 수 있습니다. +--- + + **면책 조항**: -이 문서는 AI 번역 서비스 [Co-op Translator](https://github.com/Azure/co-op-translator)를 사용하여 번역되었습니다. 정확성을 위해 최선을 다하고 있지만, 자동 번역에는 오류나 부정확성이 포함될 수 있습니다. 원본 문서의 원어 버전을 권위 있는 출처로 간주해야 합니다. 중요한 정보의 경우, 전문적인 인간 번역을 권장합니다. 이 번역 사용으로 인해 발생하는 오해나 잘못된 해석에 대해 책임을 지지 않습니다. \ No newline at end of file +이 문서는 AI 번역 서비스 [Co-op Translator](https://github.com/Azure/co-op-translator)를 이용하여 번역되었습니다. 정확성을 위해 최선을 다했지만, 자동 번역에는 오류나 부정확성이 포함될 수 있음을 유의하시기 바랍니다. 원문은 해당 언어의 원본 문서가 공식적인 출처로 간주되어야 합니다. 중요한 정보의 경우 전문적인 인간 번역을 권장합니다. 본 번역 사용으로 인한 오해나 잘못된 해석에 대해서는 책임을 지지 않습니다. + \ No newline at end of file diff --git a/translations/ko/lessons/2-Symbolic/Animals.ipynb b/translations/ko/lessons/2-Symbolic/Animals.ipynb index cff7207b..4fc6c476 100644 --- a/translations/ko/lessons/2-Symbolic/Animals.ipynb +++ b/translations/ko/lessons/2-Symbolic/Animals.ipynb @@ -8,23 +8,23 @@ "source": [ "# 동물 전문가 시스템 구현하기\n", "\n", - "[AI for Beginners Curriculum](http://github.com/microsoft/ai-for-beginners)에서 제공하는 예제입니다.\n", + "[AI for Beginners Curriculum](http://github.com/microsoft/ai-for-beginners)의 예제입니다.\n", "\n", - "이 예제에서는 몇 가지 신체적 특징을 기반으로 동물을 판별하는 간단한 지식 기반 시스템을 구현합니다. 이 시스템은 다음과 같은 AND-OR 트리로 표현될 수 있습니다 (이 트리는 전체 트리의 일부이며, 규칙을 더 추가하는 것도 간단합니다):\n", + "이 샘플에서는 몇 가지 신체적 특성에 기반하여 동물을 판별하는 간단한 지식 기반 시스템을 구현할 것입니다. 이 시스템은 다음과 같은 AND-OR 트리로 표현할 수 있습니다 (전체 트리의 일부이며, 더 많은 규칙을 쉽게 추가할 수 있습니다):\n", "\n", - "![](../../../../lessons/2-Symbolic/images/AND-OR-Tree.png)\n" + "![](../../../../../../translated_images/ko/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## 우리의 역추론 기반 전문가 시스템 셸\n", + "## 후방 추론을 사용하는 자체 전문가 시스템 셸\n", "\n", - "생산 규칙에 기반한 지식 표현을 위한 간단한 언어를 정의해 봅시다. 우리는 규칙을 정의하기 위해 Python 클래스들을 키워드로 사용할 것입니다. 기본적으로 세 가지 유형의 클래스가 있습니다:\n", - "* `Ask`는 사용자에게 물어봐야 할 질문을 나타냅니다. 이 클래스는 가능한 답변의 집합을 포함합니다.\n", - "* `If`는 규칙을 나타내며, 규칙의 내용을 저장하기 위한 단순한 문법적 설탕(syntactic sugar)입니다.\n", - "* `AND`/`OR`는 트리의 AND/OR 분기를 나타내는 클래스입니다. 이 클래스들은 내부에 인수 목록을 저장하는 역할만 합니다. 코드를 단순화하기 위해 모든 기능은 부모 클래스인 `Content`에 정의됩니다.\n" + "생산 규칙에 기반한 지식 표현을 위한 간단한 언어를 정의해 봅시다. 우리는 규칙을 정의하기 위해 Python 클래스를 키워드로 사용할 것입니다. 기본적으로 세 가지 유형의 클래스가 있습니다:\n", + "* `Ask`는 사용자에게 물어봐야 하는 질문을 나타냅니다. 가능한 답변 집합을 포함합니다.\n", + "* `If`는 규칙을 나타내며, 규칙 내용을 저장하기 위한 문법적 설탕 역할을 합니다.\n", + "* `AND`/`OR`는 트리의 AND/OR 분기를 나타내는 클래스입니다. 내부에 인수 목록을 저장합니다. 코드를 단순화하기 위해 모든 기능은 상위 클래스 `Content`에 정의되어 있습니다.\n" ] }, { @@ -66,7 +66,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "우리 시스템에서 작업 기억은 **속성-값 쌍**으로 된 **사실** 목록을 포함합니다. 지식 기반은 작업 기억에 삽입해야 할 새로운 사실(행동)을 조건에 매핑하는 하나의 큰 사전으로 정의될 수 있으며, 조건은 AND-OR 표현식으로 표현됩니다. 또한 일부 사실은 `Ask`될 수 있습니다.\n" + "우리 시스템에서 작업 기억은 **속성-값 쌍**으로 된 **사실들**의 목록을 포함합니다. 지식 베이스는 동작(작업 기억에 삽입되어야 하는 새로운 사실)을 조건에 매핑하는 하나의 큰 사전으로 정의할 수 있으며, 조건은 AND-OR 표현식으로 표현됩니다. 또한, 일부 사실은 `Ask`할 수 있습니다.\n" ] }, { @@ -99,13 +99,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "역추론을 수행하기 위해 `Knowledgebase` 클래스를 정의합니다. 이 클래스는 다음을 포함합니다:\n", + "역방향 추론을 수행하기 위해 `Knowledgebase` 클래스를 정의하겠습니다. 이 클래스는 다음을 포함합니다:\n", "* 작동 중인 `memory` - 속성을 값에 매핑하는 딕셔너리\n", - "* 위에서 정의된 형식의 Knowledgebase `rules`\n", + "* 위에서 정의한 형식의 Knowledgebase `rules`\n", "\n", - "주요 메서드 두 가지는 다음과 같습니다:\n", - "* `get` - 속성의 값을 얻고 필요하면 추론을 수행합니다. 예를 들어, `get('color')`는 색상 슬롯의 값을 가져옵니다(필요하면 질문을 하고, 이후 사용을 위해 작업 메모리에 값을 저장합니다). 만약 `get('color:blue')`를 요청하면 색상을 묻고, 색상에 따라 `y`/`n` 값을 반환합니다.\n", - "* `eval` - 실제 추론을 수행하며, AND/OR 트리를 탐색하고, 하위 목표를 평가하는 등의 작업을 합니다.\n" + "두 가지 주요 메서드는 다음과 같습니다:\n", + "* 필요한 경우 추론을 수행하여 속성의 값을 얻는 `get` 메서드입니다. 예를 들어, `get('color')`는 색상 슬롯의 값을 얻습니다(필요하면 묻고, 나중에 사용할 수 있도록 작동 메모리에 값을 저장합니다). `get('color:blue')`라고 묻는다면, 색상을 물어본 후 그 색상에 따라 `y`/`n` 값을 반환합니다.\n", + "* 실제 추론을 수행하는 `eval` 메서드로, AND/OR 트리를 탐색하고 하위 목표를 평가하는 등의 작업을 수행합니다.\n" ] }, { @@ -172,7 +172,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "이제 우리의 동물 지식 기반을 정의하고 상담을 수행해 봅시다. 이 호출은 당신에게 질문을 할 것입니다. 예/아니오 질문에는 `y`/`n`을 입력하여 답변할 수 있으며, 더 긴 다중 선택 질문에는 숫자(0..N)를 지정하여 답변할 수 있습니다.\n" + "이제 동물 지식 기반을 정의하고 상담을 수행해 보겠습니다. 이 호출은 질문을 할 것입니다. 예-아니오 질문에는 `y`/`n`으로 대답할 수 있고, 다지선다형 질문에는 0부터 N까지 숫자를 지정하여 대답할 수 있습니다.\n" ] }, { @@ -229,11 +229,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## PyKnow를 사용한 전방 추론\n", + "## Experta를 사용한 순방향 추론\n", "\n", - "다음 예제에서는 지식 표현을 위한 라이브러리 중 하나인 [PyKnow](https://github.com/buguroo/pyknow/)를 사용하여 전방 추론을 구현해 보겠습니다. **PyKnow**는 Python에서 전방 추론 시스템을 생성하기 위한 라이브러리로, 고전적인 시스템 [CLIPS](http://www.clipsrules.net/index.html)와 유사하게 설계되었습니다.\n", + "다음 예제에서는 지식 표현 라이브러리 중 하나인 [Experta](https://github.com/nilp0inter/experta)를 사용하여 순방향 추론을 구현해 보겠습니다. **Experta**는 파이썬에서 순방향 추론 시스템을 만들기 위한 라이브러리로, 고전적인 기존 시스템 [CLIPS](http://www.clipsrules.net/index.html)와 유사하게 설계되었습니다.\n", "\n", - "물론 우리가 직접 전방 연쇄를 구현할 수도 있지만, 단순한 구현은 보통 효율적이지 않습니다. 더 효과적인 규칙 매칭을 위해 특별한 알고리즘인 [Rete](https://en.wikipedia.org/wiki/Rete_algorithm)가 사용됩니다.\n" + "순방향 연결(forward chaining)을 직접 구현하는 것도 큰 문제 없이 가능했겠지만, 단순한 구현은 보통 효율적이지 않습니다. 보다 효과적인 규칙 매칭을 위해 특수한 알고리즘인 [Rete](https://en.wikipedia.org/wiki/Rete_algorithm)가 사용됩니다.\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": [ - "우리 시스템을 `KnowledgeEngine`을 서브클래스로 하는 클래스으로 정의할 것입니다. 각 규칙은 `@Rule` 주석으로 정의된 별도의 함수로 구성되며, 규칙이 실행되어야 할 시점을 지정합니다. 규칙 내부에서는 `declare` 함수를 사용하여 새로운 사실을 추가할 수 있으며, 이러한 사실을 추가하면 전방 추론 엔진에 의해 더 많은 규칙이 호출됩니다.\n" + "우리는 시스템을 `KnowledgeEngine`을 상속하는 클래스로 정의할 것입니다. 각 규칙은 언제 규칙이 실행될지를 지정하는 `@Rule` 주석이 붙은 별도의 함수로 정의됩니다. 규칙 내부에서는 `declare` 함수를 사용하여 새로운 사실을 추가할 수 있으며, 이러한 사실을 추가하면 순방향 추론 엔진에 의해 더 많은 규칙이 호출됩니다.\n" ] }, { @@ -378,7 +377,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "지식 기반을 정의한 후, 몇 가지 초기 사실로 작업 메모리를 채우고 `run()` 메서드를 호출하여 추론을 수행합니다. 결과적으로 새로운 추론된 사실들이 작업 메모리에 추가되며, 초기 사실을 올바르게 설정한 경우 동물에 대한 최종 사실도 포함됩니다.\n" + "지식 기반을 정의한 후, 초기 사실 몇 가지로 작업 기억을 채우고 `run()` 메서드를 호출하여 추론을 수행합니다. 그 결과로, 새로운 추론된 사실들이 작업 기억에 추가되는 것을 볼 수 있으며, (모든 초기 사실들을 올바르게 설정했다면) 동물에 관한 최종 사실도 포함됩니다.\n" ] }, { @@ -440,7 +439,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n---\n\n**면책 조항**: \n이 문서는 AI 번역 서비스 [Co-op Translator](https://github.com/Azure/co-op-translator)를 사용하여 번역되었습니다. 정확성을 위해 최선을 다하고 있으나, 자동 번역에는 오류나 부정확성이 포함될 수 있습니다. 원본 문서의 원어 버전을 신뢰할 수 있는 권위 있는 자료로 간주해야 합니다. 중요한 정보의 경우, 전문적인 인간 번역을 권장합니다. 이 번역 사용으로 인해 발생하는 오해나 잘못된 해석에 대해 책임을 지지 않습니다.\n" + "---\n\n\n**면책 조항**: \n본 문서는 AI 번역 서비스 [Co-op Translator](https://github.com/Azure/co-op-translator)를 사용하여 번역되었습니다. 정확성을 위해 최선을 다하고 있으나, 자동 번역에는 오류나 부정확성이 포함될 수 있음을 양지해 주시기 바랍니다. 원문 문서는 권위 있는 공식 자료로 간주되어야 합니다. 중요한 정보에 대해서는 전문적인 인간 번역을 권장합니다. 본 번역의 사용으로 인한 오해나 잘못된 해석에 대해서는 당사가 책임지지 않습니다.\n\n" ] } ], @@ -467,8 +466,8 @@ "version": "3.11.2" }, "coopTranslator": { - "original_hash": "ab2bd97b0453415b89a469284609a8ce", - "translation_date": "2025-08-31T13:15:39+00:00", + "original_hash": "8ef43db4b9182239fd150a76bd494fdb", + "translation_date": "2026-01-15T12:51:17+00:00", "source_file": "lessons/2-Symbolic/Animals.ipynb", "language_code": "ko" } diff --git a/translations/ko/lessons/2-Symbolic/README.md b/translations/ko/lessons/2-Symbolic/README.md index 032b2498..38c2058b 100644 --- a/translations/ko/lessons/2-Symbolic/README.md +++ b/translations/ko/lessons/2-Symbolic/README.md @@ -1,116 +1,116 @@ -# 지식 표현과 전문가 시스템 +# 지식 표현 및 전문가 시스템 -![Symbolic AI 내용 요약](../../../../translated_images/ko/ai-symbolic.715a30cb610411a6.webp) +![상징적 AI 콘텐츠 요약](../../../../../../translated_images/ko/ai-symbolic.715a30cb610411a6.webp) -> 스케치노트 제공: [Tomomi Imura](https://twitter.com/girlie_mac) +> 스케치노트 작성자: [Tomomi Imura](https://twitter.com/girlie_mac) -인공지능을 향한 탐구는 인간처럼 세상을 이해하기 위한 지식을 찾는 데 기반을 두고 있습니다. 하지만 이를 어떻게 구현할 수 있을까요? +인공 지능에 대한 탐구는 인간처럼 세상을 이해하기 위한 지식의 탐색에 기반합니다. 하지만 이것을 어떻게 해낼 수 있을까요? -## [강의 전 퀴즈](https://ff-quizzes.netlify.app/en/ai/quiz/3) +## [사전 강의 퀴즈](https://ff-quizzes.netlify.app/en/ai/quiz/3) -AI 초기에는 지능형 시스템을 만드는 데 있어 상향식 접근법(이전 강의에서 논의됨)이 인기를 끌었습니다. 이 접근법은 사람들로부터 지식을 추출하여 기계가 읽을 수 있는 형태로 변환한 뒤 이를 사용해 자동으로 문제를 해결하는 것이었습니다. 이 접근법은 두 가지 주요 아이디어에 기반을 두고 있습니다: +AI 초기에는 지능 시스템을 만들기 위한 탑다운 접근법(이전 수업에서 논의한)이 널리 쓰였습니다. 그 아이디어는 사람으로부터 지식을 추출하여 기계가 읽을 수 있는 형식으로 만든 후, 이를 자동으로 문제 해결에 활용하는 것이었습니다. 이 접근법은 두 가지 큰 개념에 기반합니다: * 지식 표현 * 추론 ## 지식 표현 -Symbolic AI에서 중요한 개념 중 하나는 **지식**입니다. 지식을 *정보*나 *데이터*와 구분하는 것이 중요합니다. 예를 들어, 책은 지식을 담고 있다고 말할 수 있지만, 사실 책이 담고 있는 것은 *데이터*입니다. 책을 읽고 이 데이터를 우리의 세계 모델에 통합함으로써 데이터를 지식으로 변환합니다. +상징적 AI에서 중요한 개념 중 하나가 **지식**입니다. 지식을 *정보*나 *데이터*와 구분하는 것이 중요합니다. 예를 들어, 책은 지식을 담고 있다고 말할 수 있는데, 책을 공부하면 전문가가 될 수 있기 때문입니다. 하지만 실제로 책이 담고 있는 것은 *데이터*라고 하며, 책을 읽고 이 데이터를 우리의 세계 모델에 통합함으로써 이 데이터를 지식으로 변환합니다. -> ✅ **지식**은 우리의 머릿속에 담겨 있으며 세상에 대한 우리의 이해를 나타냅니다. 이는 우리가 받은 정보를 능동적으로 **학습**하여 우리의 세계 모델에 통합함으로써 얻어집니다. +> ✅ **지식**은 우리의 머릿속에 들어 있고 세상에 대한 이해를 나타내는 것입니다. 이는 우리가 받은 다양한 정보를 적극적으로 배우는 **학습** 과정에서 얻어진 것으로, 세계에 대한 우리의 능동적인 모델에 이 정보를 통합하는 과정을 포함합니다. -대부분의 경우, 우리는 지식을 엄격히 정의하지 않고 [DIKW 피라미드](https://en.wikipedia.org/wiki/DIKW_pyramid)를 사용하여 관련 개념과 정렬합니다. 이 피라미드는 다음과 같은 개념을 포함합니다: +대부분의 경우 지식을 엄밀히 정의하지 않고, [DIKW 피라미드](https://en.wikipedia.org/wiki/DIKW_pyramid)를 통해 관련 개념들과 정렬합니다. 피라미드에는 다음과 같은 개념들이 포함됩니다: -* **데이터**는 물리적 매체에 표현된 것으로, 예를 들어 쓰여진 텍스트나 말로 표현된 단어입니다. 데이터는 인간과 독립적으로 존재하며 사람들 간에 전달될 수 있습니다. -* **정보**는 우리가 머릿속에서 데이터를 해석하는 방식입니다. 예를 들어, 우리가 *컴퓨터*라는 단어를 들으면 그것이 무엇인지에 대한 이해를 갖게 됩니다. -* **지식**은 정보가 우리의 세계 모델에 통합된 것입니다. 예를 들어, 컴퓨터가 무엇인지 배우면 컴퓨터가 어떻게 작동하는지, 비용이 얼마나 되는지, 무엇에 사용할 수 있는지에 대한 아이디어를 갖게 됩니다. 이러한 상호 연결된 개념의 네트워크가 우리의 지식을 형성합니다. -* **지혜**는 세상에 대한 우리의 이해의 또 다른 수준으로, *메타 지식*을 나타냅니다. 즉, 지식을 어떻게 그리고 언제 사용해야 하는지에 대한 개념입니다. +* **데이터**는 물리적 매체에 표현된 것으로, 예를 들어 쓰여진 텍스트나 말한 단어 등이 있습니다. 데이터는 인간과 독립적으로 존재하며, 사람들 사이에서 전달될 수 있습니다. +* **정보**는 데이터를 머릿속에서 해석하는 방식입니다. 예를 들어, "컴퓨터"라는 단어를 들으면 그것이 무엇인지에 대해 어느 정도 이해하게 됩니다. +* **지식**은 정보를 우리 세계 모델에 통합한 것입니다. 예를 들어, 컴퓨터가 무엇인지 알게 되면 어떻게 작동하는지, 비용은 얼마인지, 무엇에 사용되는지에 대한 아이디어를 갖게 됩니다. 이러한 상호 연관된 개념들의 네트워크가 지식을 형성합니다. +* **지혜**는 세상을 이해하는 또 다른 수준이며, 이는 *메타 지식*으로, 예를 들어 지식을 언제 어떻게 사용해야 하는지에 대한 개념을 나타냅니다. - + -*이미지 [출처: Wikipedia](https://commons.wikimedia.org/w/index.php?curid=37705247), By Longlivetheux - Own work, CC BY-SA 4.0* +*이미지 출처: [위키피디아](https://commons.wikimedia.org/w/index.php?curid=37705247), Longlivetheux 저작, CC BY-SA 4.0* -따라서 **지식 표현**의 문제는 컴퓨터 내부에서 데이터를 통해 지식을 효과적으로 표현하여 자동으로 사용할 수 있도록 하는 방법을 찾는 것입니다. 이는 다음과 같은 스펙트럼으로 볼 수 있습니다: +따라서, **지식 표현** 문제는 지식을 컴퓨터 내부에 데이터 형태로 효과적으로 표현하여 자동으로 활용할 수 있도록 하는 방법을 찾는 문제로 볼 수 있습니다. 이는 다음과 같은 스펙트럼으로 나타낼 수 있습니다: -![지식 표현 스펙트럼](../../../../translated_images/ko/knowledge-spectrum.b60df631852c0217.webp) +![지식 표현 스펙트럼](../../../../../../translated_images/ko/knowledge-spectrum.b60df631852c0217.webp) -> 이미지 제공: [Dmitry Soshnikov](http://soshnikov.com) +> 이미지 출처: [Dmitry Soshnikov](http://soshnikov.com) -* 왼쪽에는 컴퓨터가 효과적으로 사용할 수 있는 매우 간단한 유형의 지식 표현이 있습니다. 가장 간단한 것은 알고리즘으로, 지식이 컴퓨터 프로그램으로 표현됩니다. 그러나 이는 유연성이 부족하여 지식을 표현하는 최선의 방법은 아닙니다. 우리의 머릿속 지식은 종종 비알고리즘적입니다. -* 오른쪽에는 자연어 텍스트와 같은 표현이 있습니다. 이는 가장 강력하지만 자동 추론에는 사용할 수 없습니다. +* 왼쪽에는 컴퓨터가 효율적으로 사용할 수 있는 매우 단순한 종류의 지식 표현이 있습니다. 가장 단순한 형태는 알고리즘적 표현으로, 지식을 컴퓨터 프로그램으로 나타내는 것입니다. 하지만 이것은 유연하지 않아 지식을 표현하는 최선의 방법은 아닙니다. 우리의 머릿속 지식은 종종 비알고리즘적입니다. +* 오른쪽에는 자연어와 같은 표현이 있는데, 가장 강력하지만 자동 추론에는 사용할 수 없습니다. -> ✅ 자신의 머릿속 지식을 어떻게 노트로 변환하는지 생각해 보세요. 기억을 돕는 데 효과적인 특정 형식이 있나요? +> ✅ 잠깐 생각해보세요. 여러분은 머릿속의 지식을 어떻게 표현하고 메모로 변환하나요? 기억 유지에 특히 효과적인 형식이 있나요? ## 컴퓨터 지식 표현 분류 -컴퓨터 지식 표현 방법은 다음과 같은 범주로 분류할 수 있습니다: +다양한 컴퓨터 지식 표현 방법은 다음과 같이 분류할 수 있습니다: -* **네트워크 표현**은 우리의 머릿속에 상호 연결된 개념의 네트워크가 있다는 사실에 기반을 둡니다. 컴퓨터 내부에서 그래프로 이러한 네트워크를 재현할 수 있습니다. 이를 **의미 네트워크**라고 합니다. +* **네트워크 표현**은 우리 머릿속에 상호 연관된 개념의 네트워크가 존재한다는 점에 기반합니다. 컴퓨터 내에서 그래프로 같은 네트워크를 재현할 수 있으며, 이를 **시맨틱 네트워크**라고 합니다. -1. **객체-속성-값 삼중항** 또는 **속성-값 쌍**. 그래프는 컴퓨터 내부에서 노드와 엣지의 리스트로 표현될 수 있으므로, 의미 네트워크를 객체, 속성, 값으로 구성된 삼중항 리스트로 표현할 수 있습니다. 예를 들어, 프로그래밍 언어에 대한 다음 삼중항을 만들 수 있습니다: +1. **객체-속성-값 삼중항** 또는 **속성-값 쌍**. 그래프는 노드와 엣지 목록으로 컴퓨터 내 표현할 수 있으므로, 시맨틱 네트워크는 객체, 속성, 값을 포함하는 삼중항 목록으로 표현할 수 있습니다. 예를 들어, 프로그래밍 언어에 대해 다음과 같은 삼중항을 만듭니다: 객체 | 속성 | 값 ------|------|----- +-------|-----------|------ Python | is | Untyped-Language Python | invented-by | Guido van Rossum Python | block-syntax | indentation Untyped-Language | doesn't have | type definitions -> ✅ 삼중항이 다른 유형의 지식을 표현하는 데 어떻게 사용될 수 있는지 생각해 보세요. +> ✅ 삼중항이 다른 종류의 지식을 표현하는 데 어떻게 사용될 수 있을지 생각해보세요. -2. **계층적 표현**은 우리가 머릿속에서 종종 객체의 계층을 만든다는 사실을 강조합니다. 예를 들어, 카나리아는 새이고, 모든 새는 날개를 가지고 있다는 것을 알고 있습니다. 또한 카나리아의 일반적인 색상과 비행 속도에 대한 아이디어도 가지고 있습니다. +2. **계층적 표현**은 우리가 머릿속에 객체 계층을 만드는 경향에 집중합니다. 예를 들어, 카나리아는 새라는 것을 알고, 모든 새는 날개가 있다는 것도 압니다. 또한 카나리아의 보통 색깔과 비행 속도에 대해서도 어느 정도 알고 있습니다. - - **프레임 표현**은 각 객체 또는 객체 클래스가 **프레임**으로 표현되며, **슬롯**을 포함합니다. 슬롯은 가능한 기본값, 값 제한 또는 값을 얻기 위해 호출할 수 있는 저장된 절차를 가질 수 있습니다. 모든 프레임은 객체 지향 프로그래밍 언어의 객체 계층과 유사한 계층을 형성합니다. - - **시나리오**는 시간에 따라 전개될 수 있는 복잡한 상황을 나타내는 특별한 종류의 프레임입니다. + - **프레임 표현**은 각 객체 또는 객체 클래스를 **프레임**으로 나타내며, 프레임은 **슬롯**을 포함합니다. 슬롯에는 기본값, 값 제한, 또는 값을 얻기 위해 호출할 수 있는 저장된 절차가 있을 수 있습니다. 모든 프레임은 객체 지향 프로그래밍의 객체 계층과 유사한 계층 구조를 이룹니다. + - **시나리오**는 시간에 따라 전개될 수 있는 복잡한 상황을 나타내는 특별한 프레임 종류입니다. **Python** -슬롯 | 값 | 기본값 | 범위 | ------|----|--------|------| -이름 | Python | | | +슬롯 | 값 | 기본값 | 구간 | +-----|-------|---------------|----------| +Name | Python | | | Is-A | Untyped-Language | | | -변수 케이스 | | CamelCase | | -프로그램 길이 | | | 5-5000 줄 | -블록 구문 | Indent | | | +Variable Case | | CamelCase | | +Program Length | | | 5-5000 lines | +Block Syntax | Indent | | | -3. **절차적 표현**은 특정 조건이 발생했을 때 실행할 수 있는 행동 목록으로 지식을 표현하는 데 기반을 둡니다. - - 생산 규칙은 결론을 도출할 수 있는 if-then 문입니다. 예를 들어, 의사는 **만약** 환자가 고열이 있거나 **혹은** 혈액 검사에서 C-반응성 단백질 수치가 높다면 **그렇다면** 염증이 있다는 규칙을 가질 수 있습니다. 조건 중 하나를 충족하면 염증에 대한 결론을 내릴 수 있으며, 이를 추가 추론에 사용할 수 있습니다. - - 알고리즘은 또 다른 형태의 절차적 표현으로 간주될 수 있지만, 지식 기반 시스템에서는 거의 직접적으로 사용되지 않습니다. +3. **절차적 표현**은 특정 조건이 발생했을 때 실행할 수 있는 액션 목록으로 지식을 표현하는 방법입니다. + - 생산 규칙은 if-then 문장으로, 결론을 도출할 수 있게 해줍니다. 예를 들어, 의사에게 "만약 환자가 고열이 있거나 혈액 검사에서 C-반응성 단백질 수치가 높으면 염증이 있다"는 규칙이 있을 수 있습니다. 조건 하나가 만족되면 염증에 대해 결론을 내리고, 이후 추론에 사용할 수 있습니다. + - 알고리즘은 절차적 표현의 다른 형태로 볼 수 있지만, 지식 기반 시스템에서 직접 사용되는 경우는 거의 없습니다. -4. **논리**는 원래 아리스토텔레스가 인간의 보편적 지식을 표현하는 방법으로 제안했습니다. - - 술어 논리는 수학적 이론으로 너무 풍부하여 계산 가능하지 않으므로, Prolog에서 사용되는 Horn 절과 같은 일부 하위 집합이 일반적으로 사용됩니다. - - 기술 논리는 *의미 웹*과 같은 객체 계층 및 분산 지식 표현을 나타내고 추론하는 데 사용되는 논리 시스템의 가족입니다. +4. **논리**는 원래 아리스토텔레스가 보편적인 인간 지식을 표현하기 위해 제안한 방법입니다. + - 술어 논리는 수학 이론으로서 계산 가능성이 너무 높아, 일부 부분집합만 일반적으로 사용됩니다. 예를 들어 Prolog에서 사용하는 Horn 절이 있습니다. + - 기술 논리는 계층 구조와 분산된 지식 표현(예: *시맨틱 웹*)에 대해 표현하고 추론하기 위해 사용되는 논리 시스템 가족입니다. ## 전문가 시스템 -Symbolic AI의 초기 성공 사례 중 하나는 **전문가 시스템**이라 불리는 컴퓨터 시스템이었습니다. 이는 제한된 문제 영역에서 전문가처럼 행동하도록 설계되었습니다. 이러한 시스템은 인간 전문가로부터 추출된 **지식 기반**과 이를 기반으로 추론을 수행하는 **추론 엔진**을 포함하고 있었습니다. +상징적 AI의 초기 성공 중 하나는 **전문가 시스템**이었습니다. 전문가 시스템은 제한된 문제 영역에서 전문가처럼 행동하도록 설계된 컴퓨터 시스템입니다. 이는 한 명 이상의 인간 전문가로부터 추출한 **지식 기반**을 바탕으로 하고, 그 위에서 추론을 수행하는 **추론 엔진**을 포함합니다. -![인간 구조](../../../../translated_images/ko/arch-human.5d4d35f1bba3ab1c.webp) | ![지식 기반 시스템](../../../../translated_images/ko/arch-kbs.3ec5c150b09fa8da.webp) -----------------------------------|-------------------------------------- -인간 신경 시스템의 단순화된 구조 | 지식 기반 시스템의 구조 +![인간 구조](../../../../../../translated_images/ko/arch-human.5d4d35f1bba3ab1c.webp) | ![지식 기반 시스템](../../../../../../translated_images/ko/arch-kbs.3ec5c150b09fa8da.webp) +---------------------------------------------|------------------------------------------------ +간략화된 인간 신경 시스템 구조 | 지식 기반 시스템 아키텍처 -전문가 시스템은 **단기 기억**과 **장기 기억**을 포함하는 인간의 추론 시스템과 유사하게 구축됩니다. 마찬가지로, 지식 기반 시스템에서는 다음 구성 요소를 구분합니다: +전문가 시스템은 인간 추론 시스템처럼 **단기 기억**과 **장기 기억**을 포함합니다. 마찬가지로 지식 기반 시스템에서는 다음 구성 요소를 구분합니다: -* **문제 기억**: 현재 해결 중인 문제에 대한 지식을 포함합니다. 예를 들어, 환자의 체온이나 혈압, 염증 여부 등이 포함됩니다. 이 지식은 **정적 지식**이라고도 하며, 현재 문제에 대해 알고 있는 것의 스냅샷을 포함합니다. 이를 *문제 상태*라고 합니다. -* **지식 기반**: 문제 영역에 대한 장기적인 지식을 나타냅니다. 이는 인간 전문가로부터 수동으로 추출되며, 상담마다 변경되지 않습니다. 이는 한 문제 상태에서 다른 상태로 이동할 수 있도록 하므로 **동적 지식**이라고도 합니다. -* **추론 엔진**: 문제 상태 공간에서 검색 프로세스를 조율하며, 필요할 때 사용자에게 질문을 합니다. 또한 각 상태에 적용할 적절한 규칙을 찾는 역할을 합니다. +* **문제 기억**: 현재 해결 중인 문제에 대한 지식을 담고 있습니다. 예를 들어 환자의 체온, 혈압, 염증 여부 등이 해당합니다. 이 지식은 **정적 지식**이라고도 하는데, 이는 현재 문제에 대해 알고 있는 내용의 스냅샷, 즉 *문제 상태*를 포함하기 때문입니다. +* **지식 기반**: 문제 영역에 관한 장기 지식을 나타냅니다. 인간 전문가로부터 수동으로 추출하며 상담마다 변하지 않습니다. 문제 상태 간 탐색을 가능하게 하므로 **동적 지식**이라고도 불립니다. +* **추론 엔진**: 문제 상태 공간 탐색 과정을 조율하며, 필요할 때 사용자에게 질문합니다. 각 상태에 적용할 올바른 규칙을 찾는 역할도 합니다. -예를 들어, 동물의 신체적 특성을 기반으로 동물을 결정하는 다음 전문가 시스템을 고려해 보겠습니다: +예를 들어, 동물의 신체 특성에 기반해 동물을 판별하는 전문가 시스템을 고려해보겠습니다: -![AND-OR 트리](../../../../translated_images/ko/AND-OR-Tree.5592d2c70187f283.webp) +![AND-OR 트리](../../../../../../translated_images/ko/AND-OR-Tree.5592d2c70187f283.webp) -> 이미지 제공: [Dmitry Soshnikov](http://soshnikov.com) +> 이미지 출처: [Dmitry Soshnikov](http://soshnikov.com) -이 다이어그램은 **AND-OR 트리**라고 하며, 생산 규칙 집합의 그래픽 표현입니다. 전문가로부터 지식을 추출하는 초기 단계에서 트리를 그리는 것이 유용합니다. 컴퓨터 내부에서 지식을 표현하기 위해 규칙을 사용하는 것이 더 편리합니다: +이 다이어그램은 **AND-OR 트리**라고 불리며, 생산 규칙 세트를 그래픽으로 나타낸 것입니다. 전문가로부터 지식을 추출할 때 초기 단계에서 트리를 그리는 것이 유용합니다. 컴퓨터 내부에 지식을 표현할 때는 규칙을 사용하는 것이 더 편리합니다: ``` IF the animal eats meat @@ -121,78 +121,78 @@ OR (animal has sharp teeth THEN the animal is a carnivore ``` -각 규칙의 왼쪽 조건과 행동이 본질적으로 객체-속성-값(OAV) 삼중항이라는 점을 알 수 있습니다. **작업 기억**은 현재 해결 중인 문제에 해당하는 OAV 삼중항 집합을 포함합니다. **규칙 엔진**은 조건이 충족된 규칙을 찾아 적용하며, 작업 기억에 새로운 삼중항을 추가합니다. +규칙의 왼쪽 조건과 동작은 본질적으로 객체-속성-값(OAV) 삼중항임을 알 수 있습니다. **작업 기억**은 현재 해결 중인 문제에 해당하는 OAV 삼중항 집합을 포함합니다. **규칙 엔진**은 조건이 만족되는 규칙을 찾아 적용하며, 작업 기억에 새로운 삼중항을 추가합니다. -> ✅ 자신이 좋아하는 주제로 AND-OR 트리를 작성해 보세요! +> ✅ 관심 분야에 대해 자신의 AND-OR 트리를 만들어 보세요! -### 순방향 추론 vs. 역방향 추론 +### 순방향 추론 vs 역방향 추론 -위에서 설명한 프로세스는 **순방향 추론**이라고 합니다. 이는 작업 기억에 문제에 대한 초기 데이터로 시작하여 다음 추론 루프를 실행합니다: +위에서 설명한 과정은 **순방향 추론**이라고 합니다. 이는 작업 기억에 초기 문제 데이터가 주어지고, 다음 추론 루프를 실행합니다: -1. 목표 속성이 작업 기억에 있으면 중지하고 결과를 제공합니다. -2. 현재 조건이 충족된 모든 규칙을 찾아 **충돌 집합**을 얻습니다. -3. **충돌 해결**을 수행하여 이 단계에서 실행할 규칙을 선택합니다. 충돌 해결 전략은 다음과 같을 수 있습니다: - - 지식 기반에서 적용 가능한 첫 번째 규칙 선택 - - 임의의 규칙 선택 - - *더 구체적인* 규칙 선택, 즉 "왼쪽 조건"(LHS)에서 가장 많은 조건을 충족하는 규칙 -4. 선택된 규칙을 적용하고 문제 상태에 새로운 지식 조각을 삽입합니다. -5. 1단계부터 반복합니다. +1. 목표 속성이 작업 기억에 있으면 - 중단하고 결과를 반환 +2. 현재 조건이 만족되는 모든 규칙을 찾아 **충돌 집합**을 얻음 +3. **충돌 해결**을 수행하여 이 단계에서 실행할 한 규칙을 선택. 충돌 해결 전략에는 다음이 있을 수 있음: + - 지식 기반에서 처음 적용 가능한 규칙 선택 + - 임의 규칙 선택 + - *더 구체적인* 규칙 선택, 즉 왼쪽 조건(LHS)을 가장 많이 충족하는 규칙 선택 +4. 선택한 규칙 적용 후 문제 상태에 새로운 지식 삽입 +5. 1단계부터 반복 -하지만 경우에 따라 문제에 대한 지식이 없는 상태에서 시작하여 결론에 도달하는 데 도움이 되는 질문을 하고 싶을 수 있습니다. 예를 들어, 의료 진단을 할 때, 환자를 진단하기 전에 모든 의료 분석을 미리 수행하지 않습니다. 대신, 결정을 내려야 할 때 분석을 수행하고 싶어합니다. +하지만 어떤 경우에는 문제에 대한 지식이 전혀 없는 상태에서 시작해 결론에 도달하는 질문을 하고 싶을 수 있습니다. 예를 들어, 의료 진단 시 환자진단 전에 모든 검사를 미리 하지 않고, 결정할 필요가 있을 때 검사를 수행합니다. -이 프로세스는 **역방향 추론**을 사용하여 모델링할 수 있습니다. 이는 우리가 찾고자 하는 속성 값인 **목표**에 의해 구동됩니다: +이 과정은 **역방향 추론**으로 모델링할 수 있습니다. 이는 우리가 구하려는 **목표** 속성 값을 기반으로 합니다: -1. 목표 값을 제공할 수 있는 모든 규칙을 선택합니다(즉, 목표가 RHS("오른쪽 조건")에 있는 경우) - 충돌 집합 -1. 이 속성에 대한 규칙이 없거나 사용자에게 값을 요청해야 한다는 규칙이 있는 경우 값을 요청합니다. 그렇지 않으면: -1. 충돌 해결 전략을 사용하여 *가설*로 사용할 규칙을 선택합니다 - 이를 증명하려고 시도합니다. -1. 규칙의 LHS에 있는 모든 속성에 대해 반복적으로 프로세스를 수행하여 목표로 증명하려고 시도합니다. -1. 프로세스가 실패하면 3단계에서 다른 규칙을 사용합니다. +1. 목표 값(즉 오른쪽 조건 RHS에 목표가 있는 규칙)과 관련된 모든 규칙 선택 - 충돌 집합 형성 +2. 해당 속성에 규칙이 없거나 사용자에게 값을 물어야 한다는 규칙이 있으면 사용자에게 질문, 그렇지 않으면: +3. 충돌 해결 전략으로 한 규칙을 선택해 *가설*로 설정 - 이를 증명해봄 +4. 선택한 규칙 왼쪽 조건(LHS)에 있는 모든 속성을 목표로 설정하고 재귀적으로 증명 시도 +5. 과정이 실패하면 3단계에서 다른 규칙 사용 -> ✅ 순방향 추론이 더 적합한 상황은 어떤 경우인가요? 역방향 추론은 어떤 경우에 적합할까요? +> ✅ 어떤 상황에서 순방향 추론이 더 적합할까요? 역방향 추론은 어떻게 다를까요? ### 전문가 시스템 구현 -전문가 시스템은 다양한 도구를 사용하여 구현할 수 있습니다: +전문가 시스템은 다양한 도구로 구현할 수 있습니다: -* 고급 프로그래밍 언어로 직접 프로그래밍하기. 이는 최선의 방법이 아닙니다. 지식 기반 시스템의 주요 장점은 지식이 추론과 분리되어 있으며, 문제 영역 전문가가 추론 프로세스의 세부 사항을 이해하지 않고도 규칙을 작성할 수 있어야 한다는 점입니다. -* **전문가 시스템 셸** 사용, 즉 지식 표현 언어를 사용하여 지식으로 채워지도록 설계된 시스템. +* 고급 프로그래밍 언어로 직접 프로그래밍. 하지만 지식 기반 시스템의 주요 장점인 지식과 추론 분리가 어렵고, 문제 영역 전문가가 추론 세부사항을 모른 채 규칙을 작성하기 어렵기 때문에 최선의 방법은 아닙니다. +* **전문가 시스템 셸** 사용, 즉 지식 표현 언어를 활용해 지식을 채우도록 설계된 시스템. -## ✍️ 연습: 동물 추론 +## ✍️ 실습: 동물 추론 -[Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb)를 참조하여 순방향 및 역방향 추론 전문가 시스템 구현 예제를 확인하세요. +순방향 및 역방향 추론 전문가 시스템 구현 예제는 [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb)를 참고하세요. -> **참고**: 이 예제는 비교적 간단하며 전문가 시스템이 어떻게 생겼는지에 대한 아이디어만 제공합니다. 이러한 시스템을 만들기 시작하면 규칙이 약 200개 이상에 도달했을 때만 *지능적인* 행동을 관찰할 수 있습니다. 어느 시점에서 규칙이 너무 복잡해져 모든 규칙을 기억하기 어려워지고, 이 시점에서 시스템이 특정 결정을 내리는 이유에 대해 궁금해질 수 있습니다. 그러나 지식 기반 시스템의 중요한 특징은 시스템이 내린 모든 결정을 정확히 *설명*할 수 있다는 점입니다. +> **참고**: 이 예제는 다소 단순하며 전문가 시스템의 기본 아이디어를 제공합니다. 실제로 전문가 시스템을 구축하면 약 200개 이상의 규칙을 넘어서면서부터 "지능적인" 동작이 나타나기 시작합니다. 그 때부터는 모든 규칙을 머릿속에 담기 어려워지고, 시스템이 어떻게 특정 결정을 내리나 궁금해질 수 있습니다. 하지만 지식 기반 시스템의 중요한 특성은 어떤 결정이 어떻게 내려졌는지 **명확하게 설명할 수 있다는 점**입니다. -## 온톨로지와 의미 웹 +## 온톨로지와 시맨틱 웹 -20세기 말, 인터넷 자원을 주석 처리하여 매우 특정한 쿼리에 해당하는 자원을 찾을 수 있도록 하는 지식 표현을 사용하는 이니셔티브가 있었습니다. 이를 **의미 웹**이라고 하며, 다음 개념에 의존했습니다: +20세기 말에는 지식 표현을 이용해 인터넷 자원에 주석을 달아 매우 구체적인 질의에도 맞는 자원을 검색할 수 있게 하는 시도가 있었습니다. 이를 **시맨틱 웹**이라고 하며, 여러 개념에 기반합니다: -- **[기술 논리](https://en.wikipedia.org/wiki/Description_logic)**(DL)을 기반으로 한 특별한 지식 표현. 이는 객체의 계층과 속성을 구축하기 때문에 프레임 지식 표현과 유사하지만, 공식적인 논리적 의미와 추론을 가지고 있습니다. DL은 표현력과 추론의 알고리즘적 복잡성 사이의 균형을 맞추는 여러 가족이 있습니다. -- 모든 개념이 글로벌 URI 식별자로 표현되는 분산 지식 표현, 이를 통해 인터넷에 걸친 지식 계층을 생성할 수 있습니다. -- 지식 기술을 위한 XML 기반 언어들: RDF (Resource Description Framework), RDFS (RDF Schema), OWL (Ontology Web Language). +- **[기술 논리](https://en.wikipedia.org/wiki/Description_logic)**(DL)을 기반으로 한 특별한 지식 표현. 이는 객체들 간 계층 구조를 만들며 속성을 표현하는 프레임 지식 표현과 비슷하지만, 형식 논리 의미론과 추론을 갖추고 있습니다. 표현력과 알고리즘적 계산 복잡성 사이에 균형을 맞춘 DL 종류가 다양하게 존재합니다. +- 전 세계적으로 모든 개념에 글로벌 URI 식별자를 부여하여, 인터넷 전체에 걸친 지식 계층을 만드는 분산 지식 표현. +- 지식 기술을 위한 XML 기반 언어 패밀리: RDF(Resource Description Framework), RDFS(RDF Schema), OWL(Ontology Web Language). -시맨틱 웹(Semantic Web)의 핵심 개념 중 하나는 **온톨로지(Ontology)**입니다. 이는 문제 영역을 명시적으로 정의하기 위해 형식적인 지식 표현을 사용하는 것을 의미합니다. 가장 간단한 온톨로지는 문제 영역 내 객체들의 계층 구조일 수 있지만, 더 복잡한 온톨로지에는 추론에 사용할 수 있는 규칙들이 포함됩니다. +시맨틱 웹의 핵심 개념 중 하나는 **온톨로지** 개념입니다. 이는 어떤 공식적인 지식 표현을 사용하여 문제 영역을 명확하게 명세하는 것을 의미합니다. 가장 단순한 온톨로지는 문제 영역 내 개체들의 계층 구조일 수 있지만, 더 복잡한 온톨로지는 추론에 사용할 수 있는 규칙들을 포함합니다. -시맨틱 웹에서는 모든 표현이 삼중항(triplet)을 기반으로 합니다. 각 객체와 관계는 URI로 고유하게 식별됩니다. 예를 들어, 이 AI 커리큘럼이 Dmitry Soshnikov에 의해 2022년 1월 1일에 개발되었다는 사실을 표현하려면 다음과 같은 삼중항을 사용할 수 있습니다: +시맨틱 웹에서는 모든 표현이 삼중항(triplet)을 기반으로 합니다. 각 객체와 각 관계는 URI로 고유하게 식별됩니다. 예를 들어, 이 AI 커리큘럼이 2022년 1월 1일에 Dmitry Soshnikov에 의해 개발되었다는 사실을 표현하려면 다음과 같은 삼중항을 사용할 수 있습니다: - + ``` -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 ``` -> ✅ 여기서 `http://www.example.com/terms/creation-date`와 `http://purl.org/dc/elements/1.1/creator`는 *작성자*와 *작성 날짜* 개념을 표현하기 위해 널리 사용되고 인정받는 URI입니다. +> ✅ 여기서 `http://www.example.com/terms/creation-date` 와 `http://purl.org/dc/elements/1.1/creator` 는 *작성자*와 *작성 날짜* 개념을 표현하는 잘 알려져 있고 보편적으로 수용된 URI입니다. -더 복잡한 경우, 작성자 목록을 정의하려면 RDF에서 정의된 데이터 구조를 사용할 수 있습니다. +더 복잡한 경우, 작성자 목록을 정의하고 싶다면 RDF에 정의된 일부 데이터 구조를 사용할 수 있습니다. - + -> 위 다이어그램은 [Dmitry Soshnikov](http://soshnikov.com)가 작성한 것입니다. +> 위 다이어그램은 [Dmitry Soshnikov](http://soshnikov.com) 제공. -시맨틱 웹 구축의 진전은 검색 엔진과 자연어 처리 기술의 성공으로 인해 다소 지연되었습니다. 이러한 기술은 텍스트에서 구조화된 데이터를 추출할 수 있게 해줍니다. 그러나 일부 영역에서는 여전히 온톨로지와 지식 베이스를 유지하려는 중요한 노력이 이루어지고 있습니다. 주목할 만한 몇 가지 프로젝트는 다음과 같습니다: +시맨틱 웹 구축의 진전은 검색 엔진과 자연어 처리 기술의 발전으로 다소 둔화되었습니다. 이 기술들은 텍스트에서 구조화된 데이터를 추출할 수 있게 해줍니다. 하지만 일부 분야에서는 여전히 온톨로지와 지식 베이스를 유지하려는 상당한 노력이 있습니다. 주목할 만한 몇 가지 프로젝트는 다음과 같습니다: -* [WikiData](https://wikidata.org/)는 Wikipedia와 연계된 기계 판독 가능한 지식 베이스 모음입니다. 대부분의 데이터는 Wikipedia 페이지 내 구조화된 콘텐츠인 *InfoBox*에서 추출됩니다. [SPARQL](https://query.wikidata.org/)이라는 시맨틱 웹 전용 쿼리 언어를 사용해 WikiData를 쿼리할 수 있습니다. 다음은 인간의 가장 인기 있는 눈 색깔을 표시하는 샘플 쿼리입니다: +* [WikiData](https://wikidata.org/)는 위키백과와 연결된 기계 판독 가능한 지식 베이스 모음입니다. 대부분의 데이터는 위키백과 페이지 내 구조화된 콘텐츠 조각인 *InfoBoxes*에서 추출되었습니다. SPARQL이라는 시맨틱 웹을 위한 특수 쿼리 언어로 [wikidata를 쿼리](https://query.wikidata.org/)할 수 있습니다. 아래는 인간들 사이에서 가장 인기 있는 눈 색깔을 보여주는 샘플 쿼리입니다: ```sparql #defaultView:BubbleChart @@ -206,47 +206,51 @@ WHERE GROUP BY ?eyeColorLabel ``` -* [DBpedia](https://www.dbpedia.org/)는 WikiData와 유사한 또 다른 노력입니다. +* [DBpedia](https://www.dbpedia.org/) 역시 WikiData와 유사한 노력입니다. -> ✅ 온톨로지를 직접 구축하거나 기존 온톨로지를 열어보고 싶다면, [Protégé](https://protege.stanford.edu/)라는 훌륭한 시각적 온톨로지 편집기를 사용해 보세요. 다운로드하거나 온라인으로 사용할 수 있습니다. +> ✅ 온톨로지를 직접 만들거나 기존 온톨로지를 열어보고 싶다면, 훌륭한 시각적 온톨로지 편집기인 [Protégé](https://protege.stanford.edu/)를 이용해보세요. 다운로드하거나 온라인으로 사용할 수 있습니다. - + -*Web Protégé 편집기가 Romanov Family 온톨로지로 열려 있는 모습. Dmitry Soshnikov 제공 스크린샷* +*Web Protégé 편집기로 Romanov Family 온톨로지를 연 모습. 스크린샷 제공: Dmitry Soshnikov* ## ✍️ 연습: 가족 온톨로지 -[FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb)를 참고하여 시맨틱 웹 기술을 사용해 가족 관계를 추론하는 예제를 확인하세요. 일반적인 GEDCOM 형식으로 표현된 가족 트리와 가족 관계 온톨로지를 사용해 주어진 개인 집합에 대한 모든 가족 관계의 그래프를 구축합니다. +시맨틱 웹 기술을 사용해 가족 관계를 추론하는 예시를 보려면 [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb)를 참조하세요. 우리는 일반적인 GEDCOM 형식으로 표현된 가족 트리와 가족 관계 온톨로지를 사용해 주어진 개체 집합에 대한 모든 가족 관계 그래프를 구축할 것입니다. ## Microsoft Concept Graph -대부분의 경우, 온톨로지는 신중하게 수작업으로 작성됩니다. 그러나 자연어 텍스트와 같은 비구조화된 데이터에서 온톨로지를 **추출**하는 것도 가능합니다. +대부분의 경우, 온톨로지는 수작업으로 신중하게 생성됩니다. 그러나 비정형 데이터, 예를 들어 자연어 텍스트에서 온톨로지를 **추출**하는 것도 가능합니다. -이와 같은 시도 중 하나는 Microsoft Research에서 수행되었으며, [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste)라는 결과물을 낳았습니다. +이런 시도 중 하나가 Microsoft Research에서 이루어졌으며, [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste)로 결실을 맺었습니다. -이는 `is-a` 상속 관계를 사용해 그룹화된 대규모 엔터티 모음입니다. 이를 통해 "Microsoft는 무엇인가?"와 같은 질문에 답할 수 있습니다. 예를 들어, "회사일 확률 0.87, 브랜드일 확률 0.75"와 같은 답변을 제공합니다. +이 그래프는 `is-a` 상속 관계를 이용해 그룹화된 방대한 개체 모음입니다. "마이크로소프트가 무엇인가?"라는 질문에 "0.87 확률로 회사이고, 0.75 확률로 브랜드"라는 식으로 답할 수 있도록 합니다. -이 그래프는 REST API로 사용 가능하며, 모든 엔터티 쌍을 나열한 대규모 텍스트 파일로도 다운로드할 수 있습니다. +그래프는 REST API 또는 모든 엔티티 쌍을 나열한 대용량 텍스트 파일 형태로 이용 가능합니다. ## ✍️ 연습: 개념 그래프 -[MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) 노트북을 사용해 Microsoft Concept Graph를 활용하여 뉴스 기사를 여러 카테고리로 그룹화하는 방법을 확인해 보세요. +[MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) 노트북을 시도해보고 마이크로소프트 개념 그래프를 사용해 뉴스 기사를 여러 범주로 분류하는 방법을 살펴보세요. ## 결론 -오늘날 AI는 종종 *머신 러닝* 또는 *신경망*의 동의어로 간주됩니다. 그러나 인간은 명시적 추론(explicit reasoning)도 수행하며, 이는 현재 신경망이 처리하지 못하는 부분입니다. 실제 프로젝트에서는 설명이 필요하거나 시스템의 동작을 제어된 방식으로 수정해야 하는 작업을 수행하기 위해 여전히 명시적 추론이 사용됩니다. +오늘날 AI는 흔히 *머신러닝* 또는 *신경망*의 동의어로 간주됩니다. 그러나 인간은 명시적 추론도 수행하는데, 이는 현재 신경망이 다루지 못하는 영역입니다. 실제 프로젝트에서는 명시적 추론이 설명을 요구하거나 시스템 행동을 통제 가능한 방식으로 수정해야 하는 작업에 여전히 사용됩니다. ## 🚀 도전 과제 -이 강의와 관련된 Family Ontology 노트북에서 다른 가족 관계를 실험해 볼 기회가 있습니다. 가족 트리에서 사람들 간의 새로운 연결을 발견해 보세요. +이 강의와 연계된 Family Ontology 노트북에서 다른 가족 관계를 실험해볼 기회가 있습니다. 가족 트리 내 사람들 간에 새로운 연결을 발견해 보세요. ## [강의 후 퀴즈](https://ff-quizzes.netlify.app/en/ai/quiz/4) -## 복습 및 자기 학습 +## 복습 및 자습 -인터넷에서 인간이 지식을 정량화하고 체계화하려고 시도한 영역을 조사해 보세요. Bloom의 분류학(Bloom's Taxonomy)을 살펴보고, 인간이 세상을 이해하려고 노력했던 역사를 되짚어 보세요. Linnaeus가 생물 분류학을 만든 작업과 Dmitri Mendeleev가 화학 원소를 설명하고 그룹화하는 방법을 만든 과정을 탐구해 보세요. 또 다른 흥미로운 예제를 찾아보세요. +인터넷에서 인간이 지식을 정량화하고 체계화하려 노력한 분야를 조사해보세요. 블룸의 분류학(Bloom's Taxonomy)을 살펴보고, 인간이 세상을 이해하려 시도했던 역사적 과정을 공부해보세요. 생물 분류학을 만든 Linnaeus의 작업을 탐구하고, Dmitri Mendeleev가 화학 원소를 기술하고 분류한 방식을 관찰하세요. 또 어떤 흥미로운 사례를 찾을 수 있나요? **과제**: [온톨로지 구축하기](assignment.md) --- + +**면책 조항**: +이 문서는 AI 번역 서비스 [Co-op Translator](https://github.com/Azure/co-op-translator)를 사용하여 번역되었습니다. 정확성을 위해 노력하고 있지만, 자동 번역에는 오류나 부정확한 부분이 있을 수 있음을 유의하시기 바랍니다. 원본 문서는 원어로 된 문서가 권위 있는 자료로 간주되어야 합니다. 중요한 정보에 대해서는 전문적인 사람이 번역한 자료를 이용하시는 것을 권장합니다. 본 번역 사용으로 인해 발생하는 어떠한 오해나 잘못된 해석에 대해서도 당사는 책임을 지지 않습니다. + \ No newline at end of file diff --git a/translations/mr/README.md b/translations/mr/README.md index a7a9f987..aadc594c 100644 --- a/translations/mr/README.md +++ b/translations/mr/README.md @@ -1,8 +1,8 @@ -[अरेबिक](../ar/README.md) | [बंगाली](../bn/README.md) | [बल्गेरियन](../bg/README.md) | [म्यानमार (बर्मीस)](../my/README.md) | [चिनी 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[पंजाबी (गुरमुखी)](../pa/README.md) | [रोमानियन](../ro/README.md) | [रशियन](../ru/README.md) | [सर्बियन (सिरिलिक)](../sr/README.md) | [स्लोव्हाक](../sk/README.md) | [स्लोव्हेनियन](../sl/README.md) | [स्पॅनिश](../es/README.md) | [स्वाहिली](../sw/README.md) | [स्वीडिश](../sv/README.md) | [टागालॉग (फिलिपिनो)](../tl/README.md) | [तमिळ](../ta/README.md) | [तेलुगू](../te/README.md) | [थाई](../th/README.md) | [तुर्की](../tr/README.md) | [उक्रेनियन](../uk/README.md) | [उर्दू](../ur/README.md) | [व्हिएतनामी](../vi/README.md) +[Arabic](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgarian](../bg/README.md) | [Burmese (Myanmar)](../my/README.md) | [Chinese (Simplified)](../zh/README.md) | [Chinese (Traditional, Hong Kong)](../hk/README.md) | [Chinese (Traditional, Macau)](../mo/README.md) | [Chinese (Traditional, Taiwan)](../tw/README.md) | [Croatian](../hr/README.md) | [Czech](../cs/README.md) | [Danish](../da/README.md) | [Dutch](../nl/README.md) | [Estonian](../et/README.md) | [Finnish](../fi/README.md) | [French](../fr/README.md) | [German](../de/README.md) | [Greek](../el/README.md) | [Hebrew](../he/README.md) | [Hindi](../hi/README.md) | [Hungarian](../hu/README.md) | [Indonesian](../id/README.md) | [Italian](../it/README.md) | [Japanese](../ja/README.md) | [Kannada](../kn/README.md) | [Korean](../ko/README.md) | [Lithuanian](../lt/README.md) | [Malay](../ms/README.md) | [Malayalam](../ml/README.md) | [Marathi](./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) -> **स्थानिकरित्या क्लोन करायला प्राधान्य देता?** +> **स्थानिकरीत्या क्लोन करायचे प्राधान्य द्याल का?** -> या रिपॉझिटरीमध्ये ५०+ भाषांमध्ये अनुवादांचा समावेश आहे ज्यामुळे डाउनलोड आकार मोठा होतो. अनुवादांशिवाय क्लोन करण्यासाठी sparse checkout वापरा: +> या रिपॉझिटरीमध्ये ५०+ भाषा अनुवादांचा समावेश आहे ज्यामुळे डाउनलोड आकार मोठा होतो. अनुवादांशिवाय क्लोन करण्यासाठी sparse checkout वापरा: > ```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' > ``` -> यामुळे आपल्याला अभ्यासक्रम पूर्ण करण्यासाठी आवश्यक सर्व काही मिळेल आणि डाउनलोड जास्त जलद होईल. +> हे तुम्हाला कोर्स पूर्ण करण्यासाठी आवश्यक असलेली सर्व काही अधिक वेगवान डाउनलोडसह देते. -**जर आपणास अतिरिक्त भाषांमध्ये अनुवाद हवा असल्यास, ते [इथे](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md) दिलेले आहेत** +**जर तुम्हाला अतिरिक्त अनुवाद भाषा आवश्यक असतील तर त्या [येथे](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md) सूचीबद्ध आहेत** -## समुदायात सहभागी व्हा +## समुदायात सामील व्हा [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -## आपण काय शिकाल +## तुम्ही काय शिकाल -**[अभ्यासक्रमाचा माईंडमॅप](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** +**[कोर्सचा मांडलेला नकाशा](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** -या अभ्यासक्रमात, आपण शिकाल: +या अभ्यासक्रमात तुम्ही शिकाल: -* कृत्रिम बुद्धिमत्तेचे वेगवेगळे दृष्टिकोन, ज्यात "चांगल्या जुन्या" सांकेतिक दृष्टिकोनासह **ज्ञानाचे सादरीकरण** आणि तर्कशास्त्र ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)) समाविष्ट आहे. -* **न्यूरल नेटवर्क्स** आणि **डीप लर्निंग**, जे आधुनिक AI च्या मूलभूत घटक आहेत. आपण या महत्त्वाच्या विषयांचे संकल्पना दोन प्रसिध्द फ्रेमवर्क्स मध्ये कोड वापरून समजावून सांगू - [TensorFlow](http://Tensorflow.org) आणि [PyTorch](http://pytorch.org). -* प्रतिमा आणि मजकूरावर काम करण्यासाठी **न्यूरल आर्किटेक्चर्स**. आम्ही अलीकडील मॉडेल्सकडे लक्ष देऊ, पण सध्याच्या अत्याधुनिकीवर काहीसा अपूर्ण असू शकतो. -* कमी लोकप्रिय AI दृष्टिकोन, जसे की **जेनिटिक अल्गोरिदम्स** आणि **मल्टि-एजंट सिस्टीम्स**. +* कृत्रिम बुद्धिमत्तेचे वेगवेगळे दृष्टिकोन, ज्यात "चांगल्या जुन्या" सांकेतिक दृष्टिकोनासह **ज्ञान प्रतिनिधित्व** आणि न्यायाने ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). +* **न्यूरल नेटवर्क्स** आणि **डीप लर्निंग**, जे आधुनिक AI चे मुख्य आधार आहेत. आम्ही या महत्त्वाच्या विषयांमागील संकल्पना TensorFlow आणि PyTorch या दोन लोकप्रिय फ्रेमवर्कमधील कोड वापरून स्पष्ट करू. +* प्रतिमा आणि मजकूरावर काम करण्यासाठी **न्यूरल आर्किटेक्चर**. आम्ही अलीकडील मॉडेल्स कव्हर करू पण यामुळे अत्याधुनिक तंत्रज्ञानापासून थोडे काही कमी असू शकते. +* कमी लोकप्रिय AI दृष्टिकोन, जसे की **जैविक अल्गोरिदम** आणि **मल्टी-एजंट सिस्टिम्स**. -या अभ्यासक्रमात आपण जे समाविष्ट करणार नाही: +या अभ्यासक्रमात आम्ही काय समाविष्ट करणार नाही: -> [या अभ्यासक्रमासाठीच्या सर्व अतिरिक्त स्रोतांसाठी आमच्या Microsoft Learn संग्रहामध्ये पहा](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) +> [या कोर्ससाठी सर्व अतिरिक्त संसाधने Microsoft Learn संग्रहात येथे शोधा](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) -* **AI in Business** च्या बिझनेस केस. आपण Microsoft Learn वरील [Introduction to AI for business users](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) हा शिकण्याचा मार्ग किंवा [AI Business School](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), जो [INSEAD](https://www.insead.edu/) सहकार्याने विकसित झाला आहे, घेण्याचा विचार करू शकता. -* **क्लासिक मशीन लर्निंग**, जे आमच्या [Machine Learning for Beginners Curriculum](http://github.com/Microsoft/ML-for-Beginners) मध्ये चांगल्या प्रकारे वर्णन केलेले आहे. -* व्यावहारिक AI अनुप्रयोग जे **[Cognitive Services](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)** वापरून तयार केलेले आहेत. यासाठी, आम्ही Microsoft Learn चे पुढील मॉड्यूल वापरण्याची शिफारस करतो: [दृष्टी](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [नैसर्गिक भाषा प्रक्रिया](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[Azure OpenAI सेवा सह जनरेटिव्ह AI](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** आणि इतर. -* विशिष्ट ML **क्लाऊड फ्रेमवर्क्स**, जसे की [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), किंवा [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). [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) आणि [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) शिकण्याचे मार्ग वापरण्यावर विचार करा. -* **संवादी AI** आणि **चॅट बॉट्स**. एक वेगळा [Create conversational AI solutions](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum) शिकण्याचा मार्ग आहे, आणि अधिक तपशीलांसाठी आपण [हा ब्लॉग पोस्ट](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) देखील पहू शकता. -* डीप लर्निंगच्या मागील **सखोल गणित**. यासाठी, आम्ही Ian Goodfellow, Yoshua Bengio आणि Aaron Courville यांनी लिहिलेला [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) किताब आणि [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/) या वेबसाइटची शिफारस करू. +* **AI इन बिजनेस** च्या व्यवसायिक केसांचा विचार करणे. Microsoft Learn वरील [व्यवसाय वापरकर्त्यांसाठी AI परिचय](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) किंवा [AI Business School](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), जे [INSEAD](https://www.insead.edu/) सोबत सहकार्यात विकसित केले आहे, यांचा विचार करा. +* **क्लासिक मशीन लर्निंग**, ज्याचे वर्णन आमच्या [मशीन लर्निंग फॉर बिगिनर्स कॅरिक्युलम](http://github.com/Microsoft/ML-for-Beginners) मध्ये आहे. +* व्यावहारिक AI अनुप्रयोग **[कॉग्निटिव्ह सर्व्हिसेस](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)** वापरून. यासाठी आम्ही Microsoft Learn मधील [दृष्टी](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [नैसर्गिक भाषा प्रक्रिया](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[Azure OpenAI सेवा वापरून जनरेटिव्ह AI](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** आणि इतर मॉड्यूल सुरु करण्याची शिफारस करतो. +* विशिष्ट ML **क्लाउड फ्रेमवर्क्स**, जसे की [Azure मशीन लर्निंग](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), किंवा [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). [Azure ML सेवा वापरून मशीन लर्निंग सोल्यूशन्स तयार आणि चालवा](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) आणि [Azure Databricks वापरून मशीन लर्निंग सोल्यूशन्स तयार व चालवा](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum) शिकण्याच्या मार्गांचा विचार करा. +* **संवादात्मक AI** आणि **चॅट बॉट्स**. हा वेगळा [संवादात्मक AI सोल्यूशन्स तयार करा](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum) शिकण्याचा मार्ग आहे, तसेच तुम्ही [या ब्लॉग पोस्ट](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) ला अधिक तपशीलासाठी पाहू शकता. +* डीप लर्निंगच्या मागील **गहन गणिताचा** भाग. यासाठी Ian Goodfellow, Yoshua Bengio आणि Aaron Courville यांनी लिहिलेल्या [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) या पुस्तकाला आम्ही शिफारस करतो, जे ऑनलाइन https://www.deeplearningbook.org/ वर उपलब्ध आहे. -_क्लाउडमधील AI_ विषयांमध्ये सौम्य ओळखीकरिता आपण [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) शिकण्याचा मार्ग घेऊ शकता. +_क्लाउडमधील AI_ विषयांच्या सौम्य परिचयासाठी [Azure वर कृत्रिम बुद्धिमत्तीसह प्रारंभ करा](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum) शिकण्याचा मार्ग विचारात घ्या. # सामग्री -| | धड्याचा दुवा | PyTorch/Keras/TensorFlow | लॅब | +| | धडा लिंक | PyTorch/Keras/TensorFlow | प्रयोगशाळा | | :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | -| 0 | [कोर्स सेटअप](./lessons/0-course-setup/setup.md) | [आपले विकास वातावरण सेट करा](./lessons/0-course-setup/how-to-run.md) | | -| I | [**AI ची ओळख**](./lessons/1-Intro/README.md) | | | -| 01 | [AI चे परिचय आणि इतिहास](./lessons/1-Intro/README.md) | - | - | +| 0 | [कोर्स सेटअप](./lessons/0-course-setup/setup.md) | [तुमचे विकास वातावरण सेट करा](./lessons/0-course-setup/how-to-run.md) | | +| I | [**AI परिचय**](./lessons/1-Intro/README.md) | | | +| 01 | [AI ची ओळख आणि इतिहास](./lessons/1-Intro/README.md) | - | - | | II | **सांकेतिक AI** | -| 02 | [ज्ञान सादरीकरण आणि तज्ञ प्रणाली](./lessons/2-Symbolic/README.md) | [तज्ञ प्रणाली](./lessons/2-Symbolic/Animals.ipynb) / [ओन्टोलॉजी](./lessons/2-Symbolic/FamilyOntology.ipynb) /[संकल्पना ग्राफ](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | -| III | [**न्यूरल नेटवर्कची ओळख**](./lessons/3-NeuralNetworks/README.md) ||| -| 03 | [पर्सेप्ट्रॉन](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [नोटबुक](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [प्रयोगशाळा](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | -| 04 | [मल्टी-लेयर्ड पर्सेप्ट्रॉन आणि आपल्या स्वतःच्या फ्रेमवर्कची निर्मिती](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [नोटबुक](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [प्रयोगशाळा](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | -| 05 | [फ्रेमवर्क्सची ओळख (PyTorch/TensorFlow) आणि ओव्हरफिटिंग](./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) | [प्रयोगशाळा](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | -| IV | [**कंप्यूटर व्हिजन**](./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)| [मायक्रोसॉफ्ट अजूरवर कंप्यूटर व्हिजन एक्सप्लोर करा](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | -| 06 | [कंप्यूटर व्हिजनची ओळख. OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [नोटबुक](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [प्रयोगशाळा](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | -| 07 | [कन्व्होल्यूशनल न्यूरल नेटवर्क्स](./lessons/4-ComputerVision/07-ConvNets/README.md) & [CNN आर्किटेक्चर्स](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [प्रयोगशाळा](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | -| 08 | [पूर्व-प्रशिक्षित नेटवर्क्स आणि ट्रान्सफर लर्निंग](./lessons/4-ComputerVision/08-TransferLearning/README.md) आणि [प्रशिक्षण तंत्रे](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [प्रयोगशाळा](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | -| 09 | [ऑटोएन्कोडर्स आणि 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 | [जनरेटिव्ह अॅडव्हर्सेरियल नेटवर्क्स आणि आर्टिस्टिक स्टाइल ट्रान्सफर](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | +| 02 | [ज्ञान प्रतिनिधित्व आणि तज्ञ प्रणाली](./lessons/2-Symbolic/README.md) | [तज्ञ प्रणाली](./lessons/2-Symbolic/Animals.ipynb) / [ओन्टोलॉजी](./lessons/2-Symbolic/FamilyOntology.ipynb) /[संकल्पना ग्राफ](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | +| III | [**न्यूरल नेटवर्क्स परिचय**](./lessons/3-NeuralNetworks/README.md) ||| +| 03 | [परसेप्ट्रॉन](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [नोटबुक](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [प्रयोगशाळा](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | +| 04 | [मल्टि-लेयर्ड परसेप्ट्रॉन आणि आपला स्वतःचा फ्रेमवर्क तयार करणे](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [नोटबुक](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [प्रयोगशाळा](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | +| 05 | [फ्रेमवर्कसाठी परिचय (PyTorch/TensorFlow) आणि ओव्हरफिटिंग](./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) | [प्रयोगशाळा](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | +| IV | [**कंप्युटर व्हिजन**](./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)| [मायक्रोसॉफ्ट अझूरवर कंप्युटर व्हिजन एक्सप्लोर करा](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | +| 06 | [कंप्युटर व्हिजनसाठी परिचय. OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [नोटबुक](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [प्रयोगशाळा](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | +| 07 | [कॉनव्होल्यूशनल न्यूरल नेटवर्क्स](./lessons/4-ComputerVision/07-ConvNets/README.md) & [CNN आर्किटेक्चर्स](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [प्रयोगशाळा](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | +| 08 | [पूर्व-प्रशिक्षित नेटवर्क्स आणि ट्रान्सफर लर्निंग](./lessons/4-ComputerVision/08-TransferLearning/README.md) आणि [ट्रेनिंग ट्रिक्स](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [प्रयोगशाळा](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | +| 09 | [ऑटोएनकोडर्स आणि 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 | [जेनरेटिव अ‍ॅडव्हरसारियल नेटवर्क्स आणि आर्टिस्टिक स्टाइल ट्रान्सफर](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | | 11 | [ऑब्जेक्ट डिटेक्शन](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [प्रयोगशाळा](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | -| 12 | [सिमँटिक सेगमेंटेशन. 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 | [**नॅचरल लँग्वेज प्रोसेसिंग**](./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) | [मायक्रोसॉफ्ट अजूरवर नॅचरल लँग्वेज प्रोसेसिंग एक्सप्लोर करा](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| -| 13 | [टेक्स्ट रिप्रेझेंटेशन. बो/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 | [सिमँटिक वर्ड एम्बेडिंग्ज. Word2Vec आणि 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 | [लँग्वेज मॉडेलिंग. तुमचे स्वतःचे एम्बेडिंग्ज प्रशिक्षण](./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) | [प्रयोगशाळा](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | -| 16 | [रिकरंट न्यूरल नेटवर्क्स](./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 | [जनरेटिव्ह रिक्ररंट नेटवर्क्स](./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) | [प्रयोगशाळा](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | -| 18 | [ट्रान्सफॉर्मर्स. 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) | | +| 12 | [सेमांटिक सेगमेंटेशन. U-नेट](./lessons/4-ComputerVision/12-Segmentation/README.md) | [PyTorch](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb) | | +| V | [**नैसर्गिक भाषा प्रक्रिया**](./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) | [मायक्रोसॉफ्ट अझूरवर नैसर्गिक भाषा प्रक्रिया एक्सप्लोर करा](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| +| 13 | [टेक्स्ट प्रतिनिधित्व. बाओ/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 | [सेमांटिक शब्द एम्बेडिंग्ज. Word2Vec आणि 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 | [भाषा मॉडेलिंग. आपले स्वतःचे एम्बेडिंग्ज प्रशिक्षण द्या](./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) | [प्रयोगशाळा](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | +| 16 | [रकरंट न्यूरल नेटवर्क्स](./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 | [जेनरेटिव रकरंट नेटवर्क्स](./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) | [प्रयोगशाळा](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | +| 18 | [ट्रांसफॉर्मर्स. 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 | [नामित घटक ओळख](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [प्रयोगशाळा](./lessons/5-NLP/19-NER/lab/README.md) | -| 20 | [मोठे लँग्वेज मॉडेल्स, प्रॉम्प्ट प्रोग्रामिंग आणि फ्यू-शॉट टास्क्स](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | -| VI | **इतर AI तंत्रज्ञान** || | -| 21 | [जैविक अल्गोरिदम्स](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [नोटबुक](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | +| 20 | [मोठे भाषा मॉडेल्स, प्रॉम्प्ट प्रोग्रामिंग आणि फ्यु-शॉट टास्क](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | +| VI | **इतर AI तंत्रे** || | +| 21 | [जेनेटिक अल्गोरिदम्स](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [नोटबुक](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | | 22 | [डीप रिइन्फोर्समेंट लर्निंग](./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) | [प्रयोगशाळा](./lessons/6-Other/22-DeepRL/lab/README.md) | -| 23 | [मल्टी-एजंट सिस्टम्स](./lessons/6-Other/23-MultiagentSystems/README.md) | | | -| VII | **AI नैतिकता** | | | -| 24 | [AI नैतिकता आणि जबाबदार AI](./lessons/7-Ethics/README.md) | [Microsoft Learn: जबाबदार AI तत्त्वे](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | +| 23 | [मल्टि-एजंट सिस्टम्स](./lessons/6-Other/23-MultiagentSystems/README.md) | | | +| VII | **AI नीतिशास्त्र** | | | +| 24 | [AI नीतिशास्त्र आणि जबाबदार AI](./lessons/7-Ethics/README.md) | [Microsoft Learn: जबाबदार AI तत्त्वे](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | | IX | **अतिरिक्त** | | | -| 25 | [मल्टी-मॉडाल नेटवर्क्स, CLIP आणि VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [नोटबुक](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | +| 25 | [मल्टि-मोडल नेटवर्क्स, CLIP आणि VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [नोटबुक](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | -## प्रत्येक धड्यात काय असते +## प्रत्येक धडा यामध्ये असतो -* पूर्ववाचन साहित्य -* अंमलात आणता येणारे जुुपिटर नोटबुक्स, जे सहसा फ्रेमवर्कसाठी खास असतात (**PyTorch** किंवा **TensorFlow**). अंमलात आणता येणाऱ्या नोटबुकमध्ये भरपूर सैद्धांतिक साहित्य देखील असते, त्यामुळे विषय समजून घेण्यासाठी तुम्हाला कमीतकमी एक आवृत्ती पाहणे आवश्यक आहे (PyTorch किंवा TensorFlow पैकी कोणतीही). -* काही विषयांसाठी उपलब्ध **प्रयोगशाळा**, ज्यात तुम्हाला तुम्ही शिकलेले साहित्य एखाद्या विशिष्ट समस्येमध्ये वापरून पाहण्याची संधी दिली जाते. -* काही विभागांमध्ये संबंधित विषयांचा समावेश करणाऱ्या [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) मॉड्युल्ससाठी दुवे असतात. +* पूर्व-अभ्यास साहित्य +* अंमलात आणता येण्याजोगे जुपिटर नोटबुक्स, जे अनेकदा फ्रेमवर्कसाठी विशेष ( **PyTorch** किंवा **TensorFlow** ) असतात. अंमलात आणता येणाऱ्या नोटबुकमध्ये बरेच सैद्धांतिक साहित्य देखील असते, त्यामुळे विषय समजून घेण्यासाठी तुम्हाला नोटबुकच्या किमान एका आवृत्तीचा (PyTorch किंवा TensorFlow) अभ्यास करावा लागेल. +* काही विषयांसाठी **प्रयोगशाळा** उपलब्ध आहेत, ज्यामुळे तुम्हाला तुम्ही शिकलेल्या साहित्याचा उपयोग एखाद्या विशिष्ट समस्येवर करण्याची संधी मिळते. +* काही विभागांमध्ये संबंधित विषय झाकणाऱ्या [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) मॉड्यूल्ससाठी दुवे आहेत. -## सुरू करताना +## सुरुवात करणे -### 🎯 AI मध्ये नवीन आहात? येथे सुरू करा! +### 🎯 AI मध्ये नवीन आहात? येथे प्रारंभ करा! -जर तुम्ही AI मध्ये पूर्णपणे नवीन असाल आणि पटकन, व्यवहार्य उदाहरणे पाहू इच्छित असाल, तर आमचे [**सोपे उदाहरणे**](./examples/README.md) पाहा! त्यामध्ये समाविष्ट आहे: +जर तुम्ही पूर्णपणे AI मध्ये नवीन असाल आणि जलद, अभ्यासात्मक उदाहरणे पाहू इच्छित असाल, तर आमचे [**सुरुवातीसाठी सोपे उदाहरणे**](./examples/README.md) पाहा! यात समाविष्ट आहेत: - 🌟 **हॅलो AI वर्ल्ड** - तुमचा पहिला AI प्रोग्राम (पॅटर्न ओळख) -- 🧠 **साधे न्यूरल नेटवर्क** - न्यूरल नेटवर्क शून्यातून तयार करा -- 🖼️ **इमेज क्लासिफायर** - तपशीलवार टिप्पण्यांसह प्रतिमा वर्गीकरण -- 💬 **टेक्स्ट सेन्टिमेंट** - सकारात्मक/नकारात्मक मजकूराचे विश्लेषण करा +- 🧠 **साधा न्यूरल नेटवर्क** - शून्यातून न्यूरल नेटवर्क तयार करा +- 🖼️ **इमेज क्लासिफायर** - तपशीलवार टिपणीसह प्रतिमा वर्गीकरण करा +- 💬 **मजकूराची भावना** - सकारात्मक/नकारात्मक मजकूराचे विश्लेषण करा -हे उदाहरणे तुम्हाला पूर्ण अभ्यासक्रमात डोकावण्यापूर्वी AI संकल्पना समजून घेण्यास मदत करण्यासाठी डिझाइन केली आहेत. +हे उदाहरणे तुम्हाला संपूर्ण अभ्यासक्रमात प्रवेश करण्यापूर्वी AI संकल्पना समजून घेण्यासाठी तयार केली आहेत. -### 📚 पूर्ण अभ्यासक्रम सेटअप +### 📚 संपूर्ण अभ्यासक्रमाची स्थापना -- आम्ही तुमच्या विकास पर्यावरणाची सेटअप करण्यात मदत करण्यासाठी [सेटअप लेसन](./lessons/0-course-setup/setup.md) तयार केली आहे. - शिक्षकांसाठी, आम्ही तुमच्यासाठी एक [अभ्यासक्रम सेटअप लेसन](./lessons/0-course-setup/for-teachers.md) देखील तयार केली आहे! -- कसे [VSCode किंवा Codepace मध्ये कोड चालवायचा](./lessons/0-course-setup/how-to-run.md) +- आम्ही तुमच्या विकास पर्यावरणाच्या स्थापनेस मदत करण्यासाठी एक [सेटअप धडा](./lessons/0-course-setup/setup.md) तयार केला आहे. - शिक्षकांसाठी, आम्ही तुमच्यासाठी एक [अभ्यासक्रम सेटअप धडा](./lessons/0-course-setup/for-teachers.md) पण तयार केला आहे! +- VSCode किंवा Codespace मध्ये [कोड कसा चालवावा](./lessons/0-course-setup/how-to-run.md) -हे पावले अनुसरा: +या पायऱ्या अनुसरा: -रिपॉझिटरी फोर्क करा: या पानाच्या वरच्या उजव्या कोपऱ्यातील "Fork" बटणावर क्लिक करा. +रेपॉझिटरी फोर्क करा: या पृष्ठाच्या वरच्या- उजव्या कोपऱ्यातील "Fork" बटणावर क्लिक करा. -रिपॉझिटरी क्लोन करा: `git clone https://github.com/microsoft/AI-For-Beginners.git` +रेपॉझिटरी क्लोन करा: `git clone https://github.com/microsoft/AI-For-Beginners.git` -नंतर आरामात शोधण्यासाठी या रेपोला स्टार (🌟) देण्यास विसरू नका. +नंतर पुन्हा शोधण्यासाठी या रेपॉझिटरीला स्टार (🌟) देणे विसरू नका. -## इतर शिक्षार्थ्यांना भेटा +## इतर शिकणाऱ्यांशी भेटा -हा अभ्यासक्रम घेणाऱ्या इतर शिकणाऱ्यांशी भेटा आणि नेटवर्किंग करा आमच्या [अधिकृत AI डिस्कॉर्ड सर्व्हर](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) मध्ये सहभागी होऊन आणि समर्थन मिळवा. +आमच्या [अधिकृत AI डिस्कॉर्ड सर्व्हर](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) मध्ये सामील व्हा जेथे तुम्ही हा कोर्स करीत असलेल्या इतर शिकणाऱ्यांशी भेटू शकता आणि नेटवर्किंग करू शकता तसेच सहाय्यता मिळवा. -जर तुम्हाला उत्पादनाबद्दल अभिप्राय किंवा प्रश्न असतील तर आमच्या [Azure AI Foundry Developer Forum](https://aka.ms/foundry/forum) येथे भेट द्या. +तुमच्याकडे उत्पादनाबाबत अभिप्राय किंवा प्रश्न असल्यास आमच्या [Azure AI Foundry Developer Forum](https://aka.ms/foundry/forum) ला भेट द्या. -## क्विझेस +## प्रश्नमंजुषा -> **क्विझेसबद्दल एक टीप**: सर्व क्विझेस `etc\quiz-app` मधील Quiz-app फोल्डरमध्ये आहेत, किंवा [ऑनलाइन येथे](https://ff-quizzes.netlify.app/) उपलब्ध आहेत. त्या शिकवणींमध्ये लिंक केल्या आहेत, क्विझ अॅप स्थानिकपणे चालवता येतो किंवा Azure वर तैनात करता येतो; `quiz-app` फोल्डरमधील सूचना पाळा. त्या हळूहळू स्थानिकिकृत केल्या जात आहेत. +> **प्रश्नमंजुषा बद्दल एक नोंद**: सर्व प्रश्नमंजुषा etc\quiz-app मधील Quiz-app फोल्डरमध्ये आहेत, किंवा [ऑनलाइन येथे](https://ff-quizzes.netlify.app/) उपलब्ध आहेत. त्या धड्यांतून लिंक केलेल्या आहेत, प्रश्नमंजुषा अॅप स्थानिकरित्या चालवता येतो किंवा Azure वर तैनात करता येतो; `quiz-app` फोल्डरमधील सूचना अनुसरा. त्यांचा हळूहळू स्थानिकीकरण केले जात आहे. -## मदत हवी आहे +## सहाय्य हवे आहे -तुमच्याकडे सुचना आहेत का किंवा स्पेलिंग किंवा कोड त्रुटी सापडल्या का? समस्या नोंदवा किंवा पुल रिक्वेस्ट तयार करा. +तुमच्याकडे सूचना आहेत का किंवा तुम्हाला स्पेलिंग किंवा कोडमध्ये चुका सापडल्या का? समस्या उघडा किंवा पुल विनंती तयार करा. -## विशेष आभार +## विशेष धन्यवाद * **✍️ मुख्य लेखक:** [Dmitry Soshnikov](http://soshnikov.com), PhD * **🔥 संपादक:** [Jen Looper](https://twitter.com/jenlooper), PhD -* **🎨 स्केचनोट चित्रकार:** [Tomomi Imura](https://twitter.com/girlie_mac) -* **✅ क्विझ निर्माता:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) +* **🎨 स्केचनोट झाडण्यारा:** [Tomomi Imura](https://twitter.com/girlie_mac) +* **✅ प्रश्नमंजुषा तयार करणारा:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) * **🙏 मुख्य योगदानकर्ते:** [Evgenii Pishchik](https://github.com/Pe4enIks) ## इतर अभ्यासक्रम -आमची टीम इतर अभ्यासक्रम तयार करते! पाहा: +आमची टीम इतर अभ्यासक्रम तयार करते! पहा: ### LangChain @@ -197,7 +197,7 @@ _क्लाउडमधील AI_ विषयांमध्ये सौम --- -### Core Learning +### मुख्य शिका [![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) @@ -208,25 +208,25 @@ _क्लाउडमधील AI_ विषयांमध्ये सौम --- -### Copilot Series +### Copilot मालिका [![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) -## मदत कशी घ्यावी +## मदत मिळवा -जर तुम्हाला अडकले किंवा AI अ‍ॅप्स तयार करण्याबाबत कोणतेही प्रश्न असतील, तर सहअभ्यासक आणि अनुभवी विकासकांसोबत MCP बद्दल चर्चा करण्यासाठी या. हे एक सहायक समुदाय आहे जिथे प्रश्न विचारले जाऊ शकतात आणि ज्ञान मोकळेपणाने सामायिक केले जाते. +जर तुम्हाला कुठे अडचण येत असेल किंवा AI अॅप्स तयार करताना प्रश्न असतील तर MCP बद्दल चर्चा करण्यासाठी इतर शिकणाऱ्या आणि अनुभवी विकसकांसह सामील व्हा. हे एक सहाय्यपूर्ण समुदाय आहे जिथे प्रश्न विचारणे स्वागतार्ह आहे आणि ज्ञान मुक्तपणे वाटले जाते. [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -जर उत्पादनाबद्दल अभिप्राय किंवा त्रुटी असतील तर येथे भेट द्या: +जर तुम्हाला उत्पादनाबाबत अभिप्राय किंवा तयार करताना त्रुटी आढळल्या तर भेट द्या: [![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) --- -**स्वत:ची जबाबदारी**: -हा दस्तऐवज AI अनुवाद सेवा [Co-op Translator](https://github.com/Azure/co-op-translator) चा वापर करून अनुवादित केला गेला आहे. आम्ही अचूकतेसाठी प्रयत्नशील असलो तरी, कृपया लक्षात घ्या की स्वयंचलित अनुवादांमध्ये चुका किंवा अचूकतेचे त्रुटी असू शकतात. मूळ दस्तऐवज त्याच्या स्थानिक भाषेत अधिकृत स्रोत मानावा. महत्त्वाच्या माहितीकरिता व्यावसायिक मानवी अनुवाद करणे शिफारसीय आहे. या अनुवादाच्या वापरामुळे झालेल्या कोणत्याही गैरसमजुती किंवा चुकीच्या अर्थग्रहणासाठी आम्ही जबाबदार नाही. +**जबाबदारीची सूचना**: +हा दस्तऐवज AI भाषांतर सेवा [Co-op Translator](https://github.com/Azure/co-op-translator) वापरून भाषांतरित केला आहे. आम्ही अचूकतेसाठी प्रयत्न करतो, तरी कृपया लक्षात घ्या की स्वयंचलित भाषांतरांमध्ये चुका किंवा तफावत असू शकतात. मूळ दस्तऐवज त्याच्या मूळ भाषेत अधिकृत स्रोत मानला जातो. महत्त्वाच्या माहितीसाठी व्यावसायिक मानवी भाषांतराची शिफारस केली जाते. या भाषांतराच्या वापरामुळे उद्भवणाऱ्या कोणत्याही गैरसमजुतीसाठी किंवा चुकीच्या अर्थसंकल्पासाठी आम्ही जबाबदार नाही. \ No newline at end of file diff --git a/translations/mr/lessons/0-course-setup/how-to-run.md b/translations/mr/lessons/0-course-setup/how-to-run.md index dfa1f24e..4873b28e 100644 --- a/translations/mr/lessons/0-course-setup/how-to-run.md +++ b/translations/mr/lessons/0-course-setup/how-to-run.md @@ -1,21 +1,21 @@ # कोड कसा चालवायचा -या अभ्यासक्रमामध्ये तुम्हाला चालवता येतील असे अनेक उदाहरणे आणि प्रयोगशाळा आहेत, जे तुम्हाला चालवायचे असतील. हे करण्यासाठी, तुम्हाला या अभ्यासक्रमाचा भाग म्हणून दिलेले Jupyter Notebooks मध्ये Python कोड चालवण्याची क्षमता असणे आवश्यक आहे. कोड चालवण्यासाठी तुम्हाला काही पर्याय उपलब्ध आहेत: +या अभ्यासक्रमात चालवायच्या उदाहरणे आणि लॅब्स बऱ्याच आहेत. हे चालवण्यासाठी, तुम्हाला Jupyter Notebooks मध्ये Python कोड एक्सेक्यूट करण्याची क्षमता आवश्यक आहे जी या अभ्यासक्रमाचा भाग म्हणून दिली आहे. कोड चालवण्यासाठी तुम्हाकडे काही पर्याय आहेत: ## तुमच्या संगणकावर स्थानिकरित्या चालवा -कोड तुमच्या संगणकावर स्थानिकरित्या चालवण्यासाठी, तुमच्याकडे Python ची काही आवृत्ती स्थापित असणे आवश्यक आहे. मी वैयक्तिकरित्या **[miniconda](https://conda.io/en/latest/miniconda.html)** स्थापित करण्याची शिफारस करतो - हे एक हलके इंस्टॉलेशन आहे जे `conda` पॅकेज मॅनेजरला विविध Python **virtual environments** साठी समर्थन देते. +कोड तुमच्या संगणकावर स्थानिकरित्या चालवण्यासाठी, Python इन्स्टॉलेशन आवश्यक आहे. एक शिफारस म्हणजे **[miniconda](https://conda.io/en/latest/miniconda.html)** इन्स्टॉल करणे — हे तुलनेने हलके इन्स्टॉलेशन आहे जे `conda` पॅकेज मॅनेजरला विविध Python **वर्च्युअल एन्व्हायर्नमेंट्स** साठी समर्थन देते. -miniconda स्थापित केल्यानंतर, तुम्हाला रेपॉजिटरी क्लोन करावी लागेल आणि या अभ्यासक्रमासाठी वापरण्यासाठी एक virtual environment तयार करावी लागेल: +miniconda इन्स्टॉल केल्यानंतर, रेपॉजिटरी क्लोन करा आणि या अभ्यासक्रमासाठी वापरण्यासाठी एक वर्च्युअल एन्व्हायर्नमेंट तयार करा: ```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 ``` -### Visual Studio Code आणि Python Extension वापरणे +### Visual Studio Code सह Python Extension वापरणे -अभ्यासक्रमाचा सर्वोत्तम उपयोग करण्याचा मार्ग म्हणजे [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) आणि [Python Extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste) वापरून तो उघडणे. +हा अभ्यासक्रम [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) मध्ये [Python Extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste) सह उघडल्यावर सर्वोत्तम वापरला जातो. -> **Note**: एकदा तुम्ही रेपॉजिटरी क्लोन करून VS Code मध्ये उघडल्यावर, ते तुम्हाला Python extensions स्थापित करण्याची सूचना देईल. तुम्हाला वर वर्णन केल्याप्रमाणे miniconda देखील स्थापित करावे लागेल. +> **टीप**: एकदा तुम्ही रेपॉजिटरी क्लोन केली आणि VS Code मध्ये डिरेक्टरी उघडली की, तो आपोआप Python एक्स्टेंशन्स इन्स्टॉल करण्यास सुचवेल. तुम्हाला वरीलप्रमाणे miniconda सुद्धा इन्स्टॉल करावे लागेल. -> **Note**: जर VS Code तुम्हाला रेपॉजिटरी कंटेनरमध्ये पुन्हा उघडण्याची सूचना देत असेल, तर तुम्हाला स्थानिक Python इंस्टॉलेशन वापरण्यासाठी हे नाकारावे लागेल. +> **टीप**: जर VS Code तुम्हाला रेपॉजिटरी कंटेनरमध्ये पुन्हा उघडण्याचा सल्ला देतो, तर स्थानिक Python इन्स्टॉलेशन वापरण्यासाठी हे नाकारावे. -### ब्राउझरमध्ये Jupyter वापरणे +### ब्राउझरमधील Jupyter वापरणे -तुमच्या संगणकावरून थेट ब्राउझरमध्ये Jupyter environment वापरू शकता. प्रत्यक्षात, classical Jupyter आणि Jupyter Hub दोन्ही ऑटो-कम्प्लिशन, कोड हायलाइटिंग इत्यादीसह सोयीस्कर विकास वातावरण प्रदान करतात. +तुमच्या संगणकावरील ब्राउझरवरून सुद्धा Jupyter एन्व्हायर्नमेंट वापरू शकता. पारंपरिक Jupyter आणि JupyterHub दोन्ही ऑटो-कंप्लीशन, कोड हायलाइटिंग वगैरे सहज विकासासाठी सुविधा देतात. -Jupyter स्थानिकरित्या सुरू करण्यासाठी, अभ्यासक्रमाच्या डिरेक्टरीमध्ये जा आणि खालीलप्रमाणे चालवा: +Jupyter स्थानिकरित्या सुरू करण्यासाठी, कोर्सच्या डिरेक्टरीत जा आणि खालील आदेश चालवा: ```bash jupyter notebook @@ -45,32 +45,36 @@ jupyter notebook ```bash jupyterhub ``` -यानंतर तुम्ही कोणत्याही `.ipynb` फाइल्सकडे जाऊ शकता, ती उघडू शकता आणि काम सुरू करू शकता. +त्यानंतर तुम्ही कोणत्याही `.ipynb` फाईल्सवर जाऊन त्यांना उघडून काम सुरू करू शकता. -### कंटेनरमध्ये चालवणे +### कंटेनरमध्ये चालविणे -Python इंस्टॉलेशनचा एक पर्याय म्हणजे कोड कंटेनरमध्ये चालवणे. आमच्या रेपॉजिटरीमध्ये `.devcontainer` फोल्डर आहे जो या रेपॉजिटरीसाठी कंटेनर कसा तयार करायचा याचे निर्देश देतो, त्यामुळे VS Code तुम्हाला कोड कंटेनरमध्ये पुन्हा उघडण्याची ऑफर देईल. यासाठी Docker इंस्टॉलेशन आवश्यक आहे आणि हे अधिक क्लिष्ट असेल, त्यामुळे आम्ही हे अधिक अनुभवी वापरकर्त्यांसाठी शिफारस करतो. +Python इन्स्टॉलेशनचा एक पर्याय म्हणजे कोड कंटेनरमध्ये चालविणे. आमच्या रेपॉजिटरीमध्ये `.devcontainer` नावाची विशिष्ट फोल्डर आहे, जी कंटेनर कसा तयार करायचा यासाठी सूचना देते. VS Code ही संधी देते की कोड कंटेनरमध्ये पुन्हा उघडावा. यासाठी Docker इन्स्टॉलेशनची गरज असेल आणि प्रक्रिया तुलनेने अधिक क्लिष्ट आहे, त्यामुळे आम्ही हे जास्त अनुभवी वापरकर्त्यांसाठी सुचवतो. ## क्लाउडमध्ये चालवणे -जर तुम्हाला Python स्थानिकरित्या स्थापित करायचे नसेल आणि काही क्लाउड संसाधनांमध्ये प्रवेश असेल - तर कोड क्लाउडमध्ये चालवणे हा एक चांगला पर्याय असेल. तुम्ही हे करण्यासाठी काही मार्ग वापरू शकता: +जर तुम्हाला Python स्थानिकरित्या इन्स्टॉल करायचे नसेल, आणि तुमच्याकडे काही क्लाउड संसाधने असतील - तर कोड क्लाउडमध्ये चालविणे हा चांगला पर्याय आहे. हे करण्याचे काही मार्ग आहेत: -* **[GitHub Codespaces](https://github.com/features/codespaces)** वापरणे, जे GitHub वर तुमच्यासाठी तयार केलेले virtual environment आहे, जे VS Code ब्राउझर इंटरफेसद्वारे प्रवेशयोग्य आहे. जर तुम्हाला Codespaces मध्ये प्रवेश असेल, तर तुम्ही रेपॉजिटरीवरील **Code** बटणावर क्लिक करू शकता, Codespace सुरू करू शकता आणि लगेच चालवू शकता. -* **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)** वापरणे. [Binder](https://mybinder.org) हे क्लाउडमध्ये विनामूल्य संगणकीय संसाधने प्रदान करते, जे तुम्हाला GitHub वर काही कोड चाचणीसाठी वापरता येतील. मुख्य पृष्ठावर रेपॉजिटरी Binder मध्ये उघडण्यासाठी एक बटण आहे - हे तुम्हाला Binder साइटवर जलदपणे नेईल, जे अंतर्गत कंटेनर तयार करेल आणि Jupyter वेब इंटरफेस सुरळीतपणे सुरू करेल. +* **[GitHub Codespaces](https://github.com/features/codespaces)** वापरणे, जे GitHub वर तुमच्यासाठी तयार केलेले एक वर्च्युअल एन्व्हायर्नमेंट आहे, जे VS Code ब्राउझर इंटरफेसद्वारे सहज प्रवेश करता येते. जर तुमच्याकडे Codespaces वापरण्याचा प्रवेश असेल, तर फक्त रेपॉजिटरीतील **Code** बटणावर क्लिक करा, codespace सुरू करा आणि लगेचच काम सुरू करा. +* **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)** वापरणे. [Binder](https://mybinder.org) GitHub वरील कोड चाचणीसाठी लोकांसाठी क्लाउडमध्ये मोफत संगणकीय संसाधने ऑफर करते. मुख्य पृष्ठावर एक बटण आहे जे रेपॉजिटरी Binder मध्ये उघडते — हे जलदपणे तुम्हाला Binder साइटवर घेऊन जाईल, जिथे एक कंटेनर तयार होईल व तुम्हाला Jupyter वेब इंटरफेस सहज आणि तत्काळ सुरू होईल. -> **Note**: गैरवापर टाळण्यासाठी, Binder काही वेब संसाधनांमध्ये प्रवेश अवरोधित करते. यामुळे काही कोड कार्य करणे थांबू शकते, जे सार्वजनिक इंटरनेटवरून मॉडेल्स आणि/किंवा डेटासेट आणते. तुम्हाला काही पर्याय शोधावे लागतील. तसेच, Binder द्वारे प्रदान केलेले संगणकीय संसाधने खूप मूलभूत आहेत, त्यामुळे प्रशिक्षण हळू होईल, विशेषतः नंतरच्या अधिक जटिल धड्यांमध्ये. +> **टीप**: गैरवापर टाळण्यासाठी, Binder ला काही वेब संसाधनांपर्यंत प्रवेश बंद असू शकतो. त्यामुळे काही कोड जे सार्वजनिक इंटरनेटवरून मॉडेल्स आणि/किंवा डेटासेट डाउनलोड करतात ते काम करू शकणार नाही. यासाठी काही पर्याय शोधावे लागतील. तसेच, Binder कडून पुरवलेली संगणकीय संसाधने खूप प्राथमिक स्तरावर असल्याने, विशेषत: नंतरच्या अधिक क्लिष्ट धड्यांमध्ये प्रशिक्षण हळू होऊ शकते. -## GPU सह क्लाउडमध्ये चालवणे +## GPU सह क्लाउडमध्ये चालविणे -या अभ्यासक्रमातील काही नंतरचे धडे GPU समर्थनाचा मोठ्या प्रमाणावर फायदा घेतील, कारण अन्यथा प्रशिक्षण खूपच संथ होईल. तुम्ही काही पर्याय अनुसरू शकता, विशेषतः जर तुम्हाला [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) किंवा तुमच्या संस्थेद्वारे क्लाउडमध्ये प्रवेश असेल: +या अभ्यासक्रमातील काही पुढील धडे GPU समर्थनामुळे खूप लाभदायक होतील. उदाहरणार्थ, मॉडेल प्रशिक्षण खूप हळू होऊ शकते अन्यथा. काही पर्याय आहेत, विशेषतः जर तुमच्याकडे [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) किंवा तुमच्या संस्थेमार्फत क्लाउड प्रवेश असेल: -* [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) तयार करा आणि Jupyter द्वारे त्याला कनेक्ट करा. तुम्ही नंतर रेपॉजिटरी थेट मशीनवर क्लोन करू शकता आणि शिकणे सुरू करू शकता. NC-series VMs मध्ये GPU समर्थन आहे. +* [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) तयार करा आणि त्याला Jupyter द्वारे कनेक्ट करा. तुम्ही मग थेट त्या मशीनवर रेपॉजिटरी क्लोन करू शकता आणि शिकणे सुरू करू शकता. NC-श्रेणी VM ला GPU समर्थन आहे. -> **Note**: काही सबस्क्रिप्शन, ज्यामध्ये Azure for Students समाविष्ट आहे, GPU समर्थन डिफॉल्टने प्रदान करत नाहीत. तुम्हाला तांत्रिक समर्थन विनंतीद्वारे अतिरिक्त GPU कोरची विनंती करावी लागेल. +> **टीप**: काही सबस्क्रिप्शन्स, ज्यामध्ये Azure for Students समाविष्ट आहे, ती GPU समर्थन डिफॉल्टने देत नाहीत. तुम्हाला तांत्रिक समर्थन विनंतीद्वारे अतिरिक्त GPU कोर मागवावे लागतील. -* [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) तयार करा आणि तेथील Notebook फीचर वापरा. [हा व्हिडिओ](https://azure-for-academics.github.io/quickstart/azureml-papers/) Azure ML Notebook मध्ये रेपॉजिटरी कसे क्लोन करायचे आणि वापरण्यास सुरुवात कशी करायची हे दाखवतो. +* [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) तयार करा आणि तिथे नोटबुक फिचर वापरा. [हा व्हिडिओ](https://azure-for-academics.github.io/quickstart/azureml-papers/) कसा Azure ML नोटबुकमध्ये रेपॉजिटरी क्लोन करायचा आणि वापर सुरू करायचा हे दर्शवतो. -तुम्ही Google Colab देखील वापरू शकता, ज्यामध्ये काही विनामूल्य GPU समर्थन आहे, आणि Jupyter Notebooks तेथे अपलोड करून एक-एक करून चालवू शकता. +तुम्ही Google Colab देखील वापरू शकता, ज्यामध्ये काही मोफत GPU समर्थन असते, आणि Jupyter Notebooks स्थानिकरित्या अपलोड करून एक-एक करून अंमलात आणू शकता. -**अस्वीकरण**: -हा दस्तऐवज AI भाषांतर सेवा [Co-op Translator](https://github.com/Azure/co-op-translator) वापरून भाषांतरित करण्यात आला आहे. आम्ही अचूकतेसाठी प्रयत्नशील असलो तरी कृपया लक्षात ठेवा की स्वयंचलित भाषांतरे त्रुटी किंवा अचूकतेच्या अभावाने ग्रस्त असू शकतात. मूळ भाषेतील दस्तऐवज हा अधिकृत स्रोत मानला जावा. महत्त्वाच्या माहितीसाठी व्यावसायिक मानवी भाषांतराची शिफारस केली जाते. या भाषांतराचा वापर करून उद्भवलेल्या कोणत्याही गैरसमज किंवा चुकीच्या अर्थासाठी आम्ही जबाबदार नाही. \ No newline at end of file +--- + + +**सूचना**: +हा दस्ताऐवज AI अनुवाद सेवा [Co-op Translator](https://github.com/Azure/co-op-translator) वापरून अनुवादित करण्यात आला आहे. आम्ही अचूकतेसाठी प्रयत्नशील असलो तरी, कृपया लक्षात ठेवा की स्वयंचलित अनुवादांमध्ये चुका किंवा विसंगती असू शकतात. मूळ दस्ताऐवज त्याच्या स्थानिक भाषेत अधिकृत स्रोत मानला जावा. महत्त्वपूर्ण माहितीसाठी व्यावसायिक मानवी अनुवाद करण्याची शिफारस केली जाते. या अनुवादाचा वापर केल्यामुळे उद्भवलेल्या कोणत्याही गैरसमज किंवा चुकीच्या अर्थांच्या जबाबदारी आमची नाही. + \ No newline at end of file diff --git a/translations/mr/lessons/2-Symbolic/Animals.ipynb b/translations/mr/lessons/2-Symbolic/Animals.ipynb index d42466d4..bdeeeee0 100644 --- a/translations/mr/lessons/2-Symbolic/Animals.ipynb +++ b/translations/mr/lessons/2-Symbolic/Animals.ipynb @@ -6,25 +6,25 @@ "collapsed": true }, "source": [ - "# प्राणी तज्ज्ञ प्रणाली अंमलात आणणे\n", + "# प्राणी तज्ञ प्रणालीची अंमलबजावणी\n", "\n", "[AI for Beginners Curriculum](http://github.com/microsoft/ai-for-beginners) मधील एक उदाहरण.\n", "\n", - "या नमुन्यात, आपण काही शारीरिक वैशिष्ट्यांवर आधारित प्राणी ओळखण्यासाठी एक साधी ज्ञान-आधारित प्रणाली अंमलात आणू. ही प्रणाली खालील AND-OR झाडाद्वारे दर्शविली जाऊ शकते (हे संपूर्ण झाडाचा एक भाग आहे, आपण सहजपणे आणखी काही नियम जोडू शकतो):\n", + "या नमुन्यात, आपण काही भौतिक वैशिष्ट्यांवर आधारित प्राणी ठरवण्यासाठी एक सोपी ज्ञान-आधारित प्रणाली अंमलात आणू. प्रणाली खालील AND-OR वृक्षमुळे दर्शविली जाऊ शकते (हे संपूर्ण वृक्षकडील एक भाग आहे, आपण सहजपणे आणखी काही नियम जोडू शकतो):\n", "\n", - "![](../../../../translated_images/mr/AND-OR-Tree.5592d2c70187f283.webp)\n" + "![](../../../../../../translated_images/mr/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## मागील अनुमानासह आमची स्वतःची तज्ज्ञ प्रणाली शेल\n", + "## मागे जाणाऱ्या अनुमानांसह आपल्या स्वत: च्या तज्ञ प्रणाली शेल\n", "\n", - "चला उत्पादन नियमांवर आधारित ज्ञानाचे प्रतिनिधित्व करण्यासाठी एक साधी भाषा परिभाषित करण्याचा प्रयत्न करूया. नियम परिभाषित करण्यासाठी आम्ही Python वर्गांचा कीवर्ड म्हणून वापर करू. मुख्यतः 3 प्रकारचे वर्ग असतील:\n", - "* `Ask` वापरकर्त्याला विचारायच्या प्रश्नाचे प्रतिनिधित्व करते. यात संभाव्य उत्तरांचा संच असतो.\n", - "* `If` नियमाचे प्रतिनिधित्व करते, आणि नियमाच्या सामग्री साठवण्यासाठी फक्त एक सिंटॅक्टिक शुगर आहे.\n", - "* `AND`/`OR` हे वर्ग झाडाच्या AND/OR शाखांचे प्रतिनिधित्व करण्यासाठी आहेत. ते फक्त आतल्या युक्तिवादांची यादी साठवतात. कोड सुलभ करण्यासाठी, सर्व कार्यक्षमता पालक वर्ग `Content` मध्ये परिभाषित केली आहे.\n" + "चल, उत्पादन नियमांवर आधारित ज्ञान प्रतिनिधीकरणासाठी एक सोपी भाषा परिभाषित करण्याचा प्रयत्न करूया. नियम परिभाषित करण्यासाठी आम्ही Python वर्गांना कीवर्ड म्हणून वापरणार आहोत. मूलतः 3 प्रकारचे वर्ग असतील:\n", + "* `Ask` वापरकर्त्याला विचारायचा प्रश्न दर्शवतो. त्यात शक्य उत्तरे संच असतो.\n", + "* `If` नियम दर्शवतो, आणि तो केवळ नियमाच्या सामग्रीस संचयित करण्यासाठी एक वाक्यरचनात्मक साखर आहे\n", + "* `AND`/`OR` ही वृक्षाच्या AND/OR शाखांचे प्रतिनिधीत्व करणारे वर्ग आहेत. ते फक्त आतल्या तर्कांची यादी संग्रहित करतात. कोड सोपे करण्यासाठी, सर्व कार्यक्षमता पालक वर्ग `Content` मध्ये परिभाषित केली आहे\n" ] }, { @@ -66,7 +66,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "आमच्या प्रणालीमध्ये, कार्यरत स्मृतीमध्ये **गुणधर्म-मूल्य जोड्या** म्हणून **तथ्यांची** यादी असेल. ज्ञानकोशाला एक मोठा शब्दकोश म्हणून परिभाषित करता येईल, जो क्रिया (कार्यरत स्मृतीमध्ये समाविष्ट करावयाच्या नवीन तथ्ये) अटींशी नकाशित करतो, ज्या AND-OR अभिव्यक्ती म्हणून व्यक्त केल्या जातात. तसेच, काही तथ्ये `विचारली` जाऊ शकतात.\n" + "आपल्या प्रणालीमध्ये, कार्यशील स्मृतीमध्ये **घटनेची** यादी **गुणधर्म-मूल्य जोड्यां** स्वरूपात असेल. ज्ञानकोशाला एका मोठ्या शब्दकोशाप्रमाणे परिभाषित करता येईल जो क्रियांना (नवीन घटक जे कार्यशील स्मृतीमध्ये समाविष्ट केले जाणे आवश्यक आहे) अटींशी जोडतो, जे AND-OR अभिव्यक्तींमध्ये व्यक्त केल्या जातात. तसेच, काही घटक `Ask` केले जाऊ शकतात.\n" ] }, { @@ -99,13 +99,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "बॅकवर्ड इनफरन्स करण्यासाठी, आपण `Knowledgebase` नावाचा वर्ग परिभाषित करू. हा वर्ग खालील गोष्टींचा समावेश करेल:\n", - "* कार्यरत `memory` - एक डिक्शनरी जी गुणधर्मांना त्यांच्या मूल्यांशी नकाशित करते\n", - "* `rules` - वरीलप्रमाणे परिभाषित केलेल्या स्वरूपातील ज्ञानभांडार नियम\n", + "बॅकवॉर्ड इन्फरन्स करण्यासाठी, आपण `Knowledgebase` क्लास परिभाषित करू. यामध्ये असेल:\n", + "* कार्यरत `memory` - एक डिक्शनरी जी अॅट्रिब्युट्सना मूल्ये मॅप करते\n", + "* Knowledgebase मधील `rules` वरिभाषित स्वरूपात\n", "\n", - "मुख्य दोन पद्धती आहेत:\n", - "* `get` - एखाद्या गुणधर्माचे मूल्य प्राप्त करण्यासाठी, आवश्यक असल्यास इनफरन्स करणे. उदाहरणार्थ, `get('color')` हे रंगाच्या स्लॉटचे मूल्य मिळवेल (जर आवश्यक असेल तर विचारेल आणि नंतरच्या वापरासाठी कार्यरत मेमरीमध्ये मूल्य साठवेल). जर आपण `get('color:blue')` विचारले, तर ते रंग विचारेल आणि नंतर रंगावर आधारित `y`/`n` मूल्य परत करेल.\n", - "* `eval` - प्रत्यक्ष इनफरन्स करते, म्हणजे AND/OR ट्री ट्रॅव्हर्स करते, उप-लक्ष्यांचे मूल्यांकन करते, इत्यादी.\n" + "मुख्य दोन मेथड्स आहेत:\n", + "* `get` जे एका अॅट्रिब्युटचे मूल्य प्राप्त करते, गरज भासल्यास इन्फरन्स करतो. उदाहरणार्थ, `get('color')` रंगाच्या स्लॉटचे मूल्य मिळवेल (गरज भासल्यास विचारेल, आणि नंतरच्या वापरासाठी कार्यरत मेमरीमध्ये मूल्य साठवेल). जर आपण `get('color:blue')` विचारले, तर रंगासाठी विचारेल, आणि नंतर रंगानुसार `y`/`n` मूल्य परत करेल.\n", + "* `eval` प्रत्यक्ष इन्फरन्स करतो, म्हणजे AND/OR ट्रीमध्ये फिरतो, उप-लक्ष्यांचे मूल्यांकन करतो, इत्यादी.\n" ] }, { @@ -172,7 +172,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "आता आपण आपले प्राणी ज्ञानकोश परिभाषित करूया आणि सल्लामसलत करूया. लक्षात ठेवा की ही प्रक्रिया तुम्हाला प्रश्न विचारेल. तुम्ही `y`/`n` टाइप करून होय-नाही प्रश्नांची उत्तरे देऊ शकता, किंवा लांब बहुपर्यायी उत्तरांसाठी संख्या (0..N) निर्दिष्ट करून उत्तर देऊ शकता.\n" + "आता आपण आपला प्राणी ज्ञानसंच परिभाषित करूया आणि सल्ला प्रक्रिया पार पाडूया. लक्षात ठेवा की या कॉलमध्ये तुम्हाला प्रश्न विचारले जातील. तुम्ही हो वा नाही या प्रश्नांसाठी `y`/`n` टाइप करून उत्तर देऊ शकता, किंवा जास्त पर्याय असलेल्या प्रश्नांसाठी 0..N या क्रमांकावर उत्तर देऊ शकता.\n" ] }, { @@ -229,11 +229,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## PyKnow वापरून फॉरवर्ड इनफरन्स\n", + "## पुढील अनुमानासाठी Experta वापरणे\n", "\n", - "पुढील उदाहरणात, ज्ञान सादरीकरणासाठीच्या लायब्ररींपैकी एक, [PyKnow](https://github.com/buguroo/pyknow/) वापरून फॉरवर्ड इनफरन्स कसे अंमलात आणायचे ते पाहू. **PyKnow** ही Python मध्ये फॉरवर्ड इनफरन्स सिस्टीम तयार करण्यासाठीची लायब्ररी आहे, जी जुन्या पारंपरिक प्रणाली [CLIPS](http://www.clipsrules.net/index.html) सारखी डिझाइन केली गेली आहे.\n", + "पुढील उदाहरणात, आपण ज्ञान सादरीकरणासाठी असलेल्या लायब्ररींपैकी एका, [Experta](https://github.com/nilp0inter/experta) वापरून पुढील अनुमान अमलात आणण्याचा प्रयत्न करू. **Experta** ही पायथनमधील पुढील अनुमान प्रणाली तयार करण्यासाठीची लायब्ररी आहे, जी पारंपरिक जुन्या प्रणाली [CLIPS](http://www.clipsrules.net/index.html) प्रमाणे डिझाइन केलेली आहे.\n", "\n", - "आपण स्वतः फॉरवर्ड चेनिंग अंमलात आणू शकलो असतो, आणि त्यात फारसे अडचणीही आल्या नसत्या, पण साध्या अंमलबजावणी सहसा फार कार्यक्षम नसतात. अधिक प्रभावी नियम जुळवण्यासाठी एक विशेष अल्गोरिदम [Rete](https://en.wikipedia.org/wiki/Rete_algorithm) वापरला जातो.\n" + "आपणही अनेक अडचणीशिवाय पुढील चेनिंग स्वतः अमलात आणू शकतो होतो, पण साध्या अमलबजावणी सहसा फार कार्यक्षम नसतात. अधिक प्रभावी नियम जुळवणीसाठी एक विशेष अल्गोरिदम [Rete](https://en.wikipedia.org/wiki/Rete_algorithm) वापरला जातो.\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": [ - "आम्ही आमची प्रणाली `KnowledgeEngine` चे उपवर्ग असलेल्या वर्ग म्हणून परिभाषित करू. प्रत्येक नियम `@Rule` या अॅनोटेशनसह स्वतंत्र फंक्शनद्वारे परिभाषित केला जातो, जो नियम कधी कार्यान्वित होईल हे निर्दिष्ट करतो. नियमाच्या आत, आम्ही `declare` फंक्शनचा वापर करून नवीन तथ्ये जोडू शकतो, आणि ती तथ्ये जोडल्याने पुढील अनुमान इंजिनद्वारे आणखी काही नियम कार्यान्वित केले जातील.\n" + "आपण आपल्या सिस्टीमला `KnowledgeEngine` ची उपवर्ग वर्ग म्हणून परिभाषित करू. प्रत्येक नियम स्वतंत्र फंक्शनने परिभाषित केला जातो ज्यावर `@Rule` अ‍ॅनोटेशन असते, जे दर्शवते की नियम कधी लागू होईल. नियमाच्या आत, आपण `declare` फंक्शन वापरून नवीन तथ्ये जोडू शकतो, आणि त्या तथ्यांमुळे पुढील नियम फॉरवर्ड इन्फरन्स इंजिनद्वारे कॉल केले जातील.\n" ] }, { @@ -378,7 +377,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "एकदा आपण ज्ञानकोश परिभाषित केला की, आपण आपल्या कार्यरत स्मृतीत काही प्रारंभिक तथ्ये भरतो आणि नंतर `run()` पद्धत कॉल करून अनुमान प्रक्रिया करतो. परिणामी, आपण पाहू शकता की नवीन अनुमानित तथ्ये कार्यरत स्मृतीत जोडली जातात, ज्यामध्ये प्राण्याबद्दलचे अंतिम तथ्य समाविष्ट आहे (जर आपण सर्व प्रारंभिक तथ्ये योग्यरित्या सेट केली असतील तर).\n" + "एकदा आपण ज्ञानभांडार परिभाषित केल्यानंतर, आपण आपली कार्यरत स्मृती काही प्रारंभिक तथ्यांनी भरतो, आणि नंतर `run()` मेथड कॉल करतो ज्यामुळे तपासणी केली जाते. तुम्हाला परिणाम म्हणून दिसेल की नवीन निष्पन्न तथ्ये कार्यरत स्मृतीमध्ये जोडली जातात, ज्यात प्राण्याविषयी अंतिम तथ्यही समाविष्ट आहे (जर आपण सर्व प्रारंभिक तथ्ये योग्यरित्या सेट केली असतील तर).\n" ] }, { @@ -440,7 +439,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n---\n\n**अस्वीकरण**: \nहा दस्तऐवज AI भाषांतर सेवा [Co-op Translator](https://github.com/Azure/co-op-translator) चा वापर करून भाषांतरित करण्यात आला आहे. आम्ही अचूकतेसाठी प्रयत्नशील असलो तरी, कृपया लक्षात घ्या की स्वयंचलित भाषांतरांमध्ये त्रुटी किंवा अचूकतेचा अभाव असू शकतो. मूळ भाषेतील मूळ दस्तऐवज हा अधिकृत स्रोत मानला जावा. महत्त्वाच्या माहितीसाठी व्यावसायिक मानवी भाषांतराची शिफारस केली जाते. या भाषांतराचा वापर केल्यामुळे उद्भवणाऱ्या कोणत्याही गैरसमज किंवा चुकीच्या अर्थासाठी आम्ही जबाबदार राहणार नाही.\n" + "---\n\n\n**सूचना**:\nहा दस्तऐवज AI भाषांतर सेवा [Co-op Translator](https://github.com/Azure/co-op-translator) वापरून भाषांतरित केला आहे. आम्ही अचूकतेसाठी प्रयत्नशील असलो तरी, कृपया लक्षात ठेवा की स्वयंचलित भाषांतरांमध्ये चुका किंवा अचूकता नसण्याची शक्यता असते. मूळ दस्तऐवज त्याच्या मूळ भाषेत अधिकृत स्रोत मानला जावा. महत्त्वपूर्ण माहितीसाठी, व्यावसायिक मानवी भाषांतर करण्याचा सल्ला दिला जातो. या भाषांतराच्या वापरामुळे उद्भवणाऱ्या कोणत्याही गैरसमजुतीसाठी किंवा चुकीच्या अर्थासाठी आम्ही जबाबदार नाही.\n\n" ] } ], @@ -467,8 +466,8 @@ "version": "3.11.2" }, "coopTranslator": { - "original_hash": "ab2bd97b0453415b89a469284609a8ce", - "translation_date": "2025-08-28T08:34:45+00:00", + "original_hash": "8ef43db4b9182239fd150a76bd494fdb", + "translation_date": "2026-01-15T12:52:41+00:00", "source_file": "lessons/2-Symbolic/Animals.ipynb", "language_code": "mr" } diff --git a/translations/mr/lessons/2-Symbolic/README.md b/translations/mr/lessons/2-Symbolic/README.md index 385d8a33..eed69134 100644 --- a/translations/mr/lessons/2-Symbolic/README.md +++ b/translations/mr/lessons/2-Symbolic/README.md @@ -1,116 +1,116 @@ -# ज्ञानाचे प्रतिनिधित्व आणि तज्ज्ञ प्रणाली +# ज्ञान सादरीकरण आणि तज्ज्ञ प्रणाली -![Symbolic AI content चा सारांश](../../../../translated_images/mr/ai-symbolic.715a30cb610411a6.webp) +![Symbolic AI सामग्रीचा सारांश](../../../../../../translated_images/mr/ai-symbolic.715a30cb610411a6.webp) -> [Tomomi Imura](https://twitter.com/girlie_mac) यांचे स्केच नोट +> स्केचनोट [Tomomi Imura](https://twitter.com/girlie_mac) यांच्या कडून -कृत्रिम बुद्धिमत्तेचा शोध हा ज्ञान शोधण्यावर आधारित आहे, ज्यामुळे मानव जसा जगाचा अर्थ लावतो तसा संगणकही लावू शकेल. पण हे कसे साध्य करायचे? +कृत्रिम बुद्धिमत्तेचा शोध हा ज्ञान शोधण्यावर आधारित आहे, ज्यामुळे जग समजून घेणे मानवांप्रमाणे शक्य होते. पण हे कसे करता येईल? -## [पूर्व-व्याख्यान प्रश्नमंजुषा](https://ff-quizzes.netlify.app/en/ai/quiz/3) +## [पूर्व-व्याख्यान क्विझ](https://ff-quizzes.netlify.app/en/ai/quiz/3) -AI च्या सुरुवातीच्या काळात, बुद्धिमान प्रणाली तयार करण्यासाठी टॉप-डाउन दृष्टिकोन (मागील धड्यात चर्चा केली) लोकप्रिय होता. कल्पना अशी होती की लोकांकडून ज्ञान काढून ते मशीन-वाचनीय स्वरूपात रूपांतरित करावे आणि नंतर त्याचा वापर करून स्वयंचलितपणे समस्या सोडवाव्या. या दृष्टिकोनावर दोन मोठ्या कल्पनांवर आधारित होते: +AI च्या सुरुवातीच्या काळात, बुद्धिमान प्रणाली तयार करण्यासाठी टॉप-डाउन पद्धत (मागील धड्यातील चर्चेप्रमाणे) लोकप्रिय होती. कल्पना अशी होती की लोकांकडून ज्ञान काही मशीन-समजण्याजोग्या स्वरूपात काढावे, आणि नंतर त्याचा वापर स्वयंचलितपणे समस्या सोडवण्यासाठी करावा. ही पद्धत दोन मोठ्या कल्पनांवर आधारित होती: -* ज्ञानाचे प्रतिनिधित्व +* ज्ञान सादरीकरण * तर्कशक्ती -## ज्ञानाचे प्रतिनिधित्व +## ज्ञान सादरीकरण -Symbolic AI मधील एक महत्त्वाची संकल्पना म्हणजे **ज्ञान**. *माहिती* किंवा *डेटा* यापासून ज्ञान वेगळे कसे आहे हे समजून घेणे महत्त्वाचे आहे. उदाहरणार्थ, एखाद्याला असे म्हणता येईल की पुस्तके ज्ञानाने भरलेली असतात, कारण पुस्तके अभ्यास करून एखादी व्यक्ती तज्ज्ञ बनू शकते. परंतु, पुस्तके जे काही ठेवतात ते प्रत्यक्षात *डेटा* म्हणतात, आणि पुस्तके वाचून आणि हा डेटा आपल्या जगाच्या मॉडेलमध्ये समाकलित करून आपण हा डेटा ज्ञानामध्ये रूपांतरित करतो. +Symbolic AI मध्ये एक महत्वाचा संकल्पना म्हणजे **ज्ञान**. ज्ञान व *माहिती* किंवा *डेटा* यातील फरक ओळखणे महत्त्वाचे आहे. उदाहरणार्थ, आपण म्हणू शकतो की पुस्तके ज्ञान ठेवतात कारण पुस्तके अभ्यासून आपण तज्ज्ञ बनू शकतो. मात्र, जे पुस्तके ठेवतात ते प्रत्यक्षात *डेटा* म्हणतात, आणि पुस्तके वाचून आणि हा डेटा आपल्या जग मॉडेलमध्ये समाकलित करून आपण हा डेटा ज्ञानात रूपांतरित करतो. -> ✅ **ज्ञान** हे आपल्या डोक्यात असते आणि जगाबद्दलची आपली समज दर्शवते. हे सक्रिय **शिकण्याच्या** प्रक्रियेद्वारे प्राप्त होते, जे आपल्याला मिळालेल्या माहितीच्या तुकड्यांना आपल्या जगाच्या सक्रिय मॉडेलमध्ये समाकलित करते. +> ✅ **ज्ञान** म्हणजे आपल्या डोक्यात असणारी अशी गोष्ट जी आपल्या जगाच्या समजुतीचे प्रतिनिधित्व करते. ते सक्रिय **शिकण्याच्या** प्रक्रियेद्वारे मिळते, ज्यात आपण प्राप्त केलेली माहितीच्या तुकड्यांना आपल्या जागरूक जगाच्या मॉडेलमध्ये समाकलित करतो. -बहुतेक वेळा, आपण ज्ञानाचे कठोरपणे परिभाषित करत नाही, परंतु [DIKW Pyramid](https://en.wikipedia.org/wiki/DIKW_pyramid) चा वापर करून त्याला संबंधित संकल्पनांशी संरेखित करतो. यात खालील संकल्पना समाविष्ट आहेत: +बहुतेक वेळा, आम्ही ज्ञानाची अचूक व्याख्या करत नाही, पण त्याला [DIKW पिरॅमिड](https://en.wikipedia.org/wiki/DIKW_pyramid) वापरून इतर संबंधित संकल्पनांशी जुळवून घेतो. त्यात खालील संकल्पना आहेत: -* **डेटा** हे भौतिक माध्यमात सादर केलेले काहीतरी आहे, जसे की लिहिलेला मजकूर किंवा बोललेले शब्द. डेटा मानवांपासून स्वतंत्रपणे अस्तित्वात असतो आणि लोकांमध्ये हस्तांतरित केला जाऊ शकतो. -* **माहिती** म्हणजे आपण आपल्या डोक्यात डेटा कसा समजतो. उदाहरणार्थ, जेव्हा आपण *संगणक* हा शब्द ऐकतो, तेव्हा आपल्याला त्याबद्दल काही समज असते. -* **ज्ञान** म्हणजे माहिती आपल्या जगाच्या मॉडेलमध्ये समाकलित केली जाते. उदाहरणार्थ, एकदा आपण संगणक काय आहे हे शिकल्यावर, आपल्याला त्याचे कार्य कसे होते, त्याची किंमत किती आहे आणि त्याचा उपयोग कशासाठी होतो याबद्दल काही कल्पना येते. परस्पर संबंधित संकल्पनांचे हे जाळे आपले ज्ञान तयार करते. -* **शहाणपणा** हे आपल्या जगाच्या समजण्याचे आणखी एक स्तर आहे, आणि ते *मेटा-ज्ञान* दर्शवते, उदा. ज्ञान कसे आणि कधी वापरावे याबद्दल काही कल्पना. +* **डेटा** ही भौतिक माध्यमांमध्ये सादर केलेली गोष्ट आहे, जसे लिहिलेला मजकूर किंवा बोललेले शब्द. डेटा मानवांपासून स्वतंत्र अस्तित्वात असतो आणि लोकांमध्ये देवाणघेवाण होऊ शकतो. +* **माहिती** म्हणजे आपण डोक्यात डेटा कसा समजतो. उदाहरणार्थ, आपण *कंप्युटर* हा शब्द ऐकल्यावर त्याची काही समज मिळते. +* **ज्ञान** म्हणजे माहिती जी आपल्या जग मॉडेलमध्ये समाकलित होते. उदाहरणार्थ, एकदा आपल्याला समजले की कंप्युटर काय आहे, आपण त्याच्या कार्यप्रणाली, किमती आणि उपयोगाबाबत काही कल्पना तयार करतो. या परस्पर जोडलेल्या संकल्पनांचा जाळं म्हणजे आपले ज्ञान. +* **शहाणपणा** ही आपल्या जगाच्या समजुतीची अजून एक पातळी आहे, आणि ती *मेटा-ज्ञान* दर्शवते, उदा. ज्ञान कधी आणि कसे वापरले पाहिजे याचा काही अर्थ. - + -*Image [Wikipedia वरून](https://commons.wikimedia.org/w/index.php?curid=37705247), By Longlivetheux - Own work, CC BY-SA 4.0* +*प्रतिमा [विकिपीडिया](https://commons.wikimedia.org/w/index.php?curid=37705247) मधून, Longlivetheux - स्वतःचे काम, CC BY-SA 4.0* -म्हणून, **ज्ञानाचे प्रतिनिधित्व** करण्याची समस्या म्हणजे संगणकाच्या आत डेटा स्वरूपात ज्ञानाचे प्रतिनिधित्व करण्याचा काही प्रभावी मार्ग शोधणे, जेणेकरून ते स्वयंचलितपणे वापरता येईल. याकडे एक स्पेक्ट्रम म्हणून पाहिले जाऊ शकते: +म्हणून, **ज्ञान सादरीकरण** ची समस्या म्हणजे ज्ञान कॉम्प्युटरच्या आत डेटा स्वरूपात प्रभावीपणे सादर करण्याचा काही मार्ग शोधणे, ज्यामुळे ते स्वयंचलितपणे वापरता येईल. हा एक स्पेक्ट्रम म्हणून पाहिला जाऊ शकतो: -![ज्ञानाचे प्रतिनिधित्व स्पेक्ट्रम](../../../../translated_images/mr/knowledge-spectrum.b60df631852c0217.webp) +![ज्ञान सादरीकरण स्पेक्ट्रम](../../../../../../translated_images/mr/knowledge-spectrum.b60df631852c0217.webp) -> Image by [Dmitry Soshnikov](http://soshnikov.com) +> प्रतिमा [Dmitry Soshnikov](http://soshnikov.com) यांनी दिलेली -* डाव्या बाजूला, ज्ञानाचे अतिशय सोपे प्रकार आहेत जे संगणकाद्वारे प्रभावीपणे वापरले जाऊ शकतात. सर्वात सोपा प्रकार म्हणजे अल्गोरिदमिक, जेव्हा ज्ञान संगणक प्रोग्रामद्वारे सादर केले जाते. तथापि, हे ज्ञान सादर करण्याचा सर्वोत्तम मार्ग नाही, कारण ते लवचिक नाही. आपल्या डोक्यातील ज्ञान अनेकदा अल्गोरिदमिक नसते. -* उजव्या बाजूला, नैसर्गिक मजकूरासारख्या सादरीकरणे आहेत. ते सर्वात शक्तिशाली आहे, परंतु स्वयंचलित तर्कासाठी वापरता येत नाही. +* डावीकडे अतिशय सोपे ज्ञान सादरीकरण प्रकार असतात जे कॉम्प्युटरने प्रभावीपणे वापरले जाऊ शकतात. सर्वात सोपा प्रकार म्हणजे अल्गोरिद्मिक, जेथे ज्ञान कॉम्प्युटर प्रोग्रामने सादर केले जाते. मात्र, हे ज्ञान सादर करण्याचा सर्वोत्तम मार्ग नाही, कारण ते लवचिक नाही. आपल्या डोक्यातील ज्ञान अनेकदा अल्गोरिद्मिक नसते. +* उजवीकडे नैसर्गिक मजकूर सारखे सादरीकरणे आहेत. हे सर्वाधिक शक्तिशाली आहे, परंतु स्वयंचलित तर्कासाठी वापरता येत नाही. -> ✅ एक मिनिट विचार करा की तुम्ही तुमच्या डोक्यात ज्ञान कसे सादर करता आणि ते नोट्समध्ये रूपांतरित करता. तुमच्यासाठी स्मरणशक्ती सुधारण्यासाठी कोणते विशिष्ट स्वरूप चांगले कार्य करते? +> ✅ एक मिनिट विचार करा की आपण आपले ज्ञान डोक्यात कसे सादर करता आणि ते नोट्समध्ये कसे रूपांतरित करता. आपल्या लक्षात ठेवण्यास कसा प्रकार चांगला मदत करतो? -## संगणक ज्ञान प्रतिनिधित्व वर्गीकरण +## कॉम्प्युटर ज्ञान सादरीकरणांचे वर्गीकरण -आम्ही संगणक ज्ञान प्रतिनिधित्व पद्धती खालील श्रेणींमध्ये वर्गीकृत करू शकतो: +आपण वेगवेगळ्या कॉम्प्युटर ज्ञान सादरीकरण पद्धती खालील प्रकारांमध्ये वर्गीकरण करू शकतो: -* **नेटवर्क प्रतिनिधित्व** हे आपल्या डोक्यात परस्पर संबंधित संकल्पनांचे नेटवर्क असल्याच्या तथ्यावर आधारित आहे. आम्ही संगणकाच्या आत ग्राफ म्हणून समान नेटवर्क पुनरुत्पादित करण्याचा प्रयत्न करू शकतो - ज्याला **सामान्य नेटवर्क** म्हणतात. +* **नेटवर्क सादरीकरणे** ही तथ्यांवर आधारित आहेत की आपल्या डोक्यात एक परस्परसंबंधित संकल्पनांचा जाळं असतो. त्याच नेटवर्कना कॉम्प्युटरमध्ये ग्राफ म्हणून पुनरुत्पादित करण्याचा प्रयत्न करू शकतो - ज्याला **सेमान्टिक नेटवर्क** म्हणतात. -1. **ऑब्जेक्ट-अट्रिब्युट-वॅल्यू ट्रिपलेट्स** किंवा **अट्रिब्युट-वॅल्यू पेअर्स**. ग्राफ संगणकाच्या आत नोड्स आणि एजेसच्या यादी म्हणून सादर केला जाऊ शकतो, म्हणून आम्ही ऑब्जेक्ट्स, अट्रिब्युट्स आणि व्हॅल्यूज असलेल्या ट्रिपलेट्सच्या यादीद्वारे सामान्य नेटवर्क सादर करू शकतो. उदाहरणार्थ, आम्ही प्रोग्रामिंग भाषांबद्दल खालील ट्रिपलेट्स तयार करतो: +1. **ऑब्जेक्ट-अॅट्रिब्युट-व्हॅल्यू तुकडे** किंवा **अॅट्रिब्युट-व्हॅल्यू जोड्या**. ग्राफ कॉम्प्युटरमध्ये नोड्स आणि एजेसच्या यादी म्हणून सादर केला जाऊ शकतो, म्हणून आम्ही ऑब्जेक्ट, अॅट्रिब्युट आणि व्हॅल्यू असलेल्या तुकड्यांच्या यादीने सेमान्टिक नेटवर्क सादर करू शकतो. उदाहरणार्थ, खालील तुकडे प्रोग्रॅमिंग भाषांविषयी तयार केली आहेत: -Object | Attribute | Value +ऑब्जेक्ट | अॅट्रिब्युट | व्हॅल्यू -------|-----------|------ -Python | is | Untyped-Language -Python | invented-by | Guido van Rossum -Python | block-syntax | indentation -Untyped-Language | doesn't have | type definitions +Python | आहे | Untyped-Language +Python | तयार-कऱ्णारा | Guido van Rossum +Python | ब्लॉक-सिंटॅक्स | इन्डेंटेशन +Untyped-Language | नाही आहे | प्रकार व्याख्या -> ✅ विचार करा की ट्रिपलेट्स इतर प्रकारचे ज्ञान सादर करण्यासाठी कसे वापरले जाऊ शकतात. +> ✅ विचार करा की तुकडे कसे इतर प्रकारचे ज्ञान सादर करण्यासाठी वापरले जाऊ शकतात. -2. **हायरार्किकल प्रतिनिधित्व** हे दर्शवते की आपण आपल्या डोक्यात वस्तूंची श्रेणी तयार करतो. उदाहरणार्थ, आपल्याला माहित आहे की कॅनरी हा पक्षी आहे, आणि सर्व पक्ष्यांना पंख असतात. आपल्याला कॅनरीचा रंग कसा असतो, त्यांचा उड्डाणाचा वेग काय असतो याबद्दल काही कल्पना असते. +2. **हायअरार्किकल सादरीकरणे** यावर भर देतात की आपण अनेकदा डोक्यात ऑब्जेक्ट्सचा पदानुक्रम तयार करतो. उदाहरणार्थ, आपण जाणतो की कॅनरी हा पक्षी आहे, आणि सर्व पक्ष्यांना पंख असतात. आपल्याला कॅनरीचा रंग आणि त्याचा उड्डाण वेग काय असतो हेही माहिती आहे. - - **फ्रेम प्रतिनिधित्व** हे प्रत्येक वस्तू किंवा वस्तूंच्या वर्गाचे **फ्रेम** म्हणून प्रतिनिधित्व करण्यावर आधारित आहे ज्यामध्ये **स्लॉट्स** असतात. स्लॉट्समध्ये संभाव्य डीफॉल्ट मूल्ये, मूल्य निर्बंध किंवा साठवलेली प्रक्रिया असते जी स्लॉटचे मूल्य मिळवण्यासाठी कॉल केली जाऊ शकते. सर्व फ्रेम्स ऑब्जेक्ट-ओरिएंटेड प्रोग्रामिंग भाषांमधील ऑब्जेक्ट हायरार्कीप्रमाणे हायरार्की तयार करतात. - - **परिदृश्ये** ही फ्रेम्सचा एक विशेष प्रकार आहे जी वेळोवेळी उलगडणाऱ्या जटिल परिस्थितीचे प्रतिनिधित्व करते. + - **फ्रेम सादरीकरण** प्रत्येक ऑब्जेक्ट किंवा ऑब्जेक्ट्सच्या वर्गाला **फ्रेम** म्हणून सादर करण्यावर आधारित आहे ज्यात **स्लॉट्स** असतात. स्लॉट्सना शक्य तितक्या डीफॉल्ट मूल्ये, मूल्य प्रतिबंध किंवा स्टोर्ड प्रक्रियेचे कॉल करून मिळवले जाणारे मूल्य असू शकते. सर्व फ्रेम्स ऑब्जेक्ट-ओरिएंटेड प्रोग्रॅमिंग भाषांतील ऑब्जेक्ट पदानुक्रमाद्वारे एक पदानुक्रम तयार करतात. + - **परिदृश्ये** विशेष प्रकारचे फ्रेम्स आहेत जे वेळेनुसार विकसित होणाऱ्या गुंतागुंतीच्या परिस्थिती सादर करतात. **Python** -Slot | Value | Default value | Interval | +स्लॉट | मूल्य | डीफॉल्ट मूल्य | श्रेणी | -----|-------|---------------|----------| -Name | Python | | | -Is-A | Untyped-Language | | | -Variable Case | | CamelCase | | -Program Length | | | 5-5000 lines | -Block Syntax | Indent | | | +नाव | Python | | | +आहे | Untyped-Language | | | +व्हेरिएबल केस | | CamelCase | | +प्रोग्रॅम लांबी | | | 5-5000 ओळ्या | +ब्लॉक सिंटॅक्स | इन्डेंट | | | -3. **प्रोसीजरल प्रतिनिधित्व** हे अशा क्रियांच्या यादीद्वारे ज्ञान सादर करण्यावर आधारित आहे ज्या विशिष्ट परिस्थिती उद्भवल्यावर अंमलात आणल्या जाऊ शकतात. - - उत्पादन नियम हे if-then स्टेटमेंट्स आहेत जे आपल्याला निष्कर्ष काढण्यास अनुमती देतात. उदाहरणार्थ, डॉक्टरकडे असा नियम असू शकतो की **जर** रुग्णाला उच्च तापमान **किंवा** रक्त चाचणीत C-reactive प्रोटीनचे उच्च स्तर असतील **तर** त्याला जळजळ आहे. एकदा आपण परिस्थितींपैकी एकाला सामोरे गेलो की, आपण जळजळबद्दल निष्कर्ष काढू शकतो आणि नंतर त्याचा वापर पुढील तर्कासाठी करू शकतो. - - अल्गोरिदम्स हे प्रोसीजरल प्रतिनिधित्वाचा आणखी एक प्रकार मानले जाऊ शकते, जरी ते ज्ञान-आधारित प्रणालींमध्ये थेट वापरले जात नाहीत. +3. **प्रक्रियात्मक सादरीकरणे** ज्ञान अशा कृतींच्या यादीने सादर करतात ज्या विशेष परिस्थितीत कार्यान्वित केल्या जातात. + - उत्पादन नियम म्हणजे if-then विधान जे निष्कर्ष काढण्यास मदत करतात. उदाहरणार्थ, डॉक्टरकडे असा नियम असू शकतो की **जर** रुग्णाला ज्वार आहे **किंवा** रक्त तपासणीत C-reactive प्रथिनाचे प्रमाण जास्त आहे **तर** त्याला सूज आहे. या अटीपैकी एक पूर्ण झाली की आपण सूज याबाबत निष्कर्ष काढू शकतो आणि पुढील तर्कशक्तीसाठी त्याचा वापर करू शकतो. + - अल्गोरिदम्स प्रक्रियात्मक सादरीकरणाचा आणखी एक प्रकार मानले जातात, जरी त्यांचा ज्ञान-आधारित प्रणालींमध्ये थेट वापर जवळजवळ कधीच नसतो. -4. **तर्कशास्त्र** हे मूळतः अॅरिस्टॉटलने सार्वत्रिक मानवी ज्ञान सादर करण्याच्या मार्ग म्हणून प्रस्तावित केले होते. - - Predicate Logic ही एक गणितीय सिद्धांत आहे जी संगणनक्षम नसते, म्हणून सामान्यतः त्याचा काही उपसंच वापरला जातो, जसे की Prolog मध्ये वापरलेले Horn clauses. - - Descriptive Logic ही तर्कशास्त्र प्रणालींची कुटुंब आहे जी वस्तूंच्या हायरार्कीज आणि वितरित ज्ञान प्रतिनिधित्व जसे *सामान्य वेब* सादर करण्यासाठी आणि तर्क करण्यासाठी वापरली जाते. +4. **तर्कशास्त्र** मूळतः अरिस्टॉटलने सार्वत्रिक मानवी ज्ञान सादर करण्यासाठी प्रस्तावित केले. + - प्रदिक्ट लॉजिक एक गणितीय सैद्धांतिक प्रणाली आहे जी संगणनायोग्य नसल्यामुळे त्याचा काही उपसेट वापरला जातो, जसे की प्रोलॉगमधील हॉर्न क्लॉजेस. + - वर्णनात्मक तर्कशास्त्र (Descriptive Logic) हे लोकिक प्रणालींचे कौटुंबिक आहे जे ऑब्जेक्ट पदानुक्रम आणि म्हणून *सेमान्टिक वेब* सारख्या वितरित ज्ञान सादरीकरणासाठी वापरले जाते. ## तज्ज्ञ प्रणाली -Symbolic AI च्या सुरुवातीच्या यशांपैकी एक म्हणजे **तज्ज्ञ प्रणाली** - संगणक प्रणाली जी मर्यादित समस्या क्षेत्रात तज्ज्ञ म्हणून कार्य करण्यासाठी डिझाइन केली गेली होती. त्या **ज्ञान बेस** वर आधारित होत्या, जे एका किंवा अधिक मानवी तज्ज्ञांकडून काढले गेले होते, आणि त्यामध्ये **तर्क इंजिन** होते जे त्यावर काही तर्कशक्ती अंमलात आणत होते. +Symbolic AI चा एक प्रारंभिक यश म्हणजे **तज्ज्ञ प्रणाली** - संगणक प्रणाली जी काही मर्यादित समस्या क्षेत्रात तज्ज्ञ म्हणून काम करण्यासाठी तयार केली गेली होती. त्या मानवी तज्ज्ञांकडून घेतलेले **ज्ञान आधार** वापरत होत्या आणि त्यामध्ये काही तर्कशक्ती करणारी **अश्रित यंत्रणा** असायची. -![मानवी आर्किटेक्चर](../../../../translated_images/mr/arch-human.5d4d35f1bba3ab1c.webp) | ![ज्ञान-आधारित प्रणाली](../../../../translated_images/mr/arch-kbs.3ec5c150b09fa8da.webp) +![मानवी वास्तुकला](../../../../../../translated_images/mr/arch-human.5d4d35f1bba3ab1c.webp) | ![ज्ञान-आधारित प्रणाली](../../../../../../translated_images/mr/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ -मानवी न्यूरल प्रणालीची साधी रचना | ज्ञान-आधारित प्रणालीची आर्किटेक्चर +मानवी न्यूरल सिस्टमची साधी रूपरेषा | ज्ञान-आधारित प्रणालीची वास्तुकला -तज्ज्ञ प्रणाली मानवी तर्क प्रणालीसारखी तयार केली जाते, ज्यामध्ये **अल्पकालीन स्मृती** आणि **दीर्घकालीन स्मृती** असते. त्याचप्रमाणे, ज्ञान-आधारित प्रणालींमध्ये खालील घटक वेगळे केले जातात: +तज्ज्ञ प्रणाली मानवी तर्कशक्ती प्रणालीप्रमाणे तयार केली जातात, ज्यात **अल्पकालीन स्मृती** आणि **दीर्घकालीन स्मृती** असते. त्याचप्रमाणे, ज्ञान-आधारित प्रणालीमध्ये खालील घटक ओळखतो: -* **समस्या स्मृती**: सध्या सोडवली जात असलेल्या समस्येबद्दलचे ज्ञान ठेवते, उदा. रुग्णाचे तापमान किंवा रक्तदाब, त्याला जळजळ आहे की नाही इ. हे ज्ञान **स्थिर ज्ञान** म्हणून ओळखले जाते, कारण ते सध्या आपल्याला माहित असलेल्या समस्येचे स्नॅपशॉट ठेवते - ज्याला *समस्या स्थिती* म्हणतात. -* **ज्ञान बेस**: समस्या क्षेत्राबद्दल दीर्घकालीन ज्ञान सादर करते. हे मानवी तज्ज्ञांकडून मॅन्युअली काढले जाते आणि सल्लामसलत दरम्यान बदलत नाही. कारण ते आपल्याला एका समस्या स्थितीतून दुसऱ्या स्थितीत नेव्हिगेट करण्यास अनुमती देते, म्हणून याला **डायनॅमिक ज्ञान** असेही म्हणतात. -* **तर्क इंजिन**: समस्या स्थिती जागेत शोध प्रक्रिया समन्वयित करते, आवश्यक असल्यास वापरकर्त्याला प्रश्न विचारते. हे प्रत्येक स्थितीसाठी योग्य नियम शोधण्यास जबाबदार आहे. +* **समस्या स्मृती**: सध्या सोडवण्यात येणाऱ्या समस्येबद्दलचे ज्ञान ठेवते, उदा. रुग्णाचा तापमान किंवा रक्तदाब, त्याला सूज आहे की नाही, वगैरे. हे ज्ञान **स्थिर ज्ञान** म्हटले जाते कारण यात सध्या आपल्याला माहित असलेल्या समस्येची स्थिती - म्हणजे *समस्या स्थिती* - असते. +* **ज्ञान आधार**: विशिष्ट समस्या क्षेत्राबद्दलचे दीर्घकालीन ज्ञान सादर करतो. हे मानवी तज्ज्ञांकडून मॅन्युअली काढले जाते, आणि सल्लामसलतीमधून बदलत नाही. कारण हे एक समस्या स्थितीपासून दुसऱ्या स्थितीकडे नेण्यास अनुमती देते, ते **गतीशील ज्ञान** देखील म्हणतात. +* **तर्कशक्ती यंत्रणा**: समस्या स्थिती क्षेत्रात शोधण्याच्या संपूर्ण प्रक्रियेचे नियोजन करते, आवश्यक असल्यास वापरकर्त्याकडून प्रश्न विचारते. ती प्रत्येक स्थितीवर लागू होणारे योग्य नियम शोधण्याची जबाबदारी सांभाळते. -उदाहरण म्हणून, खालील तज्ज्ञ प्रणाली विचार करूया जी प्राण्याचे शारीरिक वैशिष्ट्यांवर आधारित निर्धारण करते: +उदाहरणार्थ, खालील वनस्पतींच्या शारीरिक वैशिष्ट्यांवरून प्राणी ओळखण्याची तज्ज्ञ प्रणाली पाहू: -![AND-OR Tree](../../../../translated_images/mr/AND-OR-Tree.5592d2c70187f283.webp) +![AND-OR झाड](../../../../../../translated_images/mr/AND-OR-Tree.5592d2c70187f283.webp) -> Image by [Dmitry Soshnikov](http://soshnikov.com) +> प्रतिमा [Dmitry Soshnikov](http://soshnikov.com) यांनी दिलेली -हा आकृती **AND-OR tree** म्हणून ओळखला जातो, आणि तो उत्पादन नियमांच्या संचाचे ग्राफिकल प्रतिनिधित्व आहे. तज्ज्ञाकडून ज्ञान काढण्याच्या सुरुवातीला झाड काढणे उपयुक्त आहे. संगणकाच्या आत ज्ञान सादर करण्यासाठी नियमांचा वापर करणे अधिक सोयीचे आहे: +हा आकडा **AND-OR झाड** म्हणतात, आणि तो उत्पादन नियमांच्या संचाचे ग्राफिकल सादरीकरण आहे. नेहमी तज्ज्ञांकडून ज्ञान काढताना झाड काढणे उपयुक्त असते. ज्ञान कंप्यूटरमध्ये सादर करण्यासाठी नियमांचा वापर करणे सोयीचे असते: ``` IF the animal eats meat @@ -121,75 +121,78 @@ OR (animal has sharp teeth THEN the animal is a carnivore ``` -आपण लक्षात घेऊ शकता की नियमाच्या डाव्या बाजूला असलेली प्रत्येक अट आणि क्रिया ही वस्तू-अट्रिब्युट-मूल्य (OAV) ट्रिपलेट्स आहेत. **वर्किंग मेमरी** सध्या सोडवली जात असलेल्या समस्येशी संबंधित OAV ट्रिपलेट्सचा संच ठेवते. **नियम इंजिन** अशा नियमांचा शोध घेतो ज्यासाठी अट पूर्ण होते आणि त्यांना लागू करते, कार्यरत मेमरीमध्ये आणखी एक ट्रिपलेट जोडते. +आपण नोंद घ्याल की नियमाच्या डाव्या बाजूच्या प्रत्येक अटी आणि क्रिया मूलतः ऑब्जेक्ट-अॅट्रिब्युट-व्हॅल्यू (OAV) तुकडे आहेत. **कार्य स्मृती** मध्ये सध्या सोडवण्यात येणाऱ्या समस्येशी संबंधित OAV तुकड्यांचा संच असतो. **नियम यंत्रणा** त्या नियमांसाठी शोध करते ज्यांची अट पूर्ण होते आणि ती लागू करते, कार्य स्मृतीत आणखी एक तुकडा जोडते. -> ✅ तुम्हाला आवडणाऱ्या विषयावर तुमचे स्वतःचे AND-OR tree तयार करा! +> ✅ तुमच्या आवडत्या विषयावर स्वतःचे AND-OR झाड तयार करा! -### फॉरवर्ड वि. बॅकवर्ड तर्क +### पुढील आणि मागील तर्कशक्ती -वर वर्णन केलेली प्रक्रिया **फॉरवर्ड तर्क** म्हणून ओळखली जाते. ती कार्यरत मेमरीमध्ये उपलब्ध असलेल्या समस्येबद्दल काही प्रारंभिक डेटा घेऊन सुरू होते आणि नंतर खालील तर्क लूप अंमलात आणते: +वरील प्रक्रियेला **पुढील तर्कशक्ती** म्हणतात. ती कार्य स्मृतीमध्ये उपलब्ध असलेल्या प्रारंभीच्या डेटाने सुरू होते, आणि नंतर खालील तर्कशक्तीची फेरफटका करते: -1. जर लक्ष्य अट कार्यरत मेमरीमध्ये उपस्थित असेल - थांबा आणि निकाल द्या -2. सध्या पूर्ण झालेल्या अटी असलेल्या सर्व नियमांचा शोध घ्या - **संघर्ष संच** प्राप्त करा. -3. **संघर्ष निराकरण** करा - या चरणावर अंमलात आणला जाणारा एक नियम निवडा. वेगवेगळ्या संघर्ष निराकरण रणनीती असू शकतात: - - ज्ञान बेसमधील लागू होणारा पहिला नियम निवडा +1. जर लक्ष्य अॅट्रिब्युट कार्य स्मृतीत असेल तर थांबा आणि निकाल द्या +2. सर्व नियम शोधा ज्यांची अट सध्या पूर्ण झाली आहे - **विरोध संच** प्राप्त करा. +3. **विरोध निवारण** करा - एका नियमाची निवड करा जो या टप्प्यात कार्यान्वित केला जाईल. विरोध निवारणासाठी वेगवेगळ्या धोरणे असू शकतात: + - ज्ञान आधारातील पहिला लागू होणारा नियम निवडा - यादृच्छिक नियम निवडा - - *अधिक विशिष्ट* नियम निवडा, म्हणजे डाव्या बाजूला (LHS) सर्वाधिक अटी पूर्ण करणारा नियम -4. निवडलेला नियम लागू करा आणि समस्या स्थितीत नवीन ज्ञानाचा तुकडा घाला -5. चरण 1 पासून पुन्हा सुरू करा. + - *अधिक विशिष्ट* नियम निवडा, म्हणजेच "डाव्या बाजू" (LHS) मधील जास्तीत जास्त अटी पूर्ण करणारा +4. निवडलेला नियम लागू करा आणि समस्येच्या स्थितीत नवीन ज्ञान घाला +5. चरण 1 पासून पुन्हा करा. -तथापि, काही प्रकरणांमध्ये आपण समस्येबद्दल रिक्त ज्ञानासह सुरुवात करू शकतो आणि निष्कर्षावर पोहोचण्यास मदत करणारे प्रश्न विचारू शकतो. उदाहरणार्थ, वैद्यकीय निदान करताना, रुग्णाचे निदान करण्यापूर्वी सर्व वैद्यकीय विश्लेषण आधीच अंमलात आणत नाही. त्याऐवजी, निर्णय घेण्याची गरज असताना विश्लेषण करणे अधिक योग्य ठरते. +परंतु, काही प्रकरणांत आपल्याला समस्येबद्दल शून्य ज्ञानाने सुरुवात करायची असते आणि निष्कर्षाकडे जायला मदत करणारे प्रश्न विचारायचे असतात. उदाहरणार्थ, वैद्यकीय निदान करताना, रुग्णाचे निदान करण्याआधी सर्व वैद्यकीय तपासण्या करत नाही. निर्णय घेण्याच्या क्षणी तपासण्या करायच्या असतात. -ही प्रक्रिया **बॅकवर्ड तर्क** वापरून मॉडेल केली जाऊ शकते. ती **लक्ष्य** चालवते - आपण शोधत असलेले अट मूल्य: +ही प्रक्रिया **मागील तर्कशक्ती** वापरून मॉडेल केली जाऊ शकते. ती **लक्ष्याने** प्रेरित असते - शोधायच्या असलेल्या अॅट्रिब्युट मूल्याने: -1. लक्ष्याचे मूल्य देऊ शकणारे सर्व नियम निवडा (उदा. लक्ष्य उजव्या बाजूला (RHS) असलेल्या नियमांसह) - संघर्ष संच -1. जर या अटसाठी कोणतेही नियम नसतील, किंवा वापरकर्त्याने मूल्य विचारावे असे सांगणारा नियम असेल - विचार करा, अन्यथा: -1. संघर्ष निराकरण रणनीती वापरून एक नियम निवडा जो आपण *गृहीत* म्हणून वापरू - आम्ही त्याचा पुरावा देण्याचा प्रयत्न करू -1. नियमाच्या डाव्या बाजूला असलेल्या सर्व अटींसाठी प्रक्रिया पुनरावृत्ती करा, त्यांना लक्ष्य म्हणून सिद्ध करण्याचा प्रयत्न करा -1. जर कोणत्याही टप्प्यावर प्रक्रिया अयशस्वी झाली - चरण 3 वर दुसरा नियम वापरा. +1. सर्व नियम निवडा जे लक्ष्याचे मूल्य देऊ शकतात (म्हणजे उजव्या बाजू ("right-hand-side") वर लक्ष्य असलेले) - विरोध संच +2. जर या अॅट्रिब्युटसाठी नियम नसतील किंवा कोणता तरी नियम असेल की वापरकर्त्याकडून मूल्य विचारावे - विचार करा, अन्यथा: +3. विरोध निवारण धोरण वापरून एक नियम निवडा ज्या आपण *कल्पना* म्हणून वापरू - आपण तो सिद्ध करू पाहू +4. नियमाच्या डाव्या बाजूतील सर्व अॅट्रिब्युटसाठी ही प्रक्रिया पुनरावृत्ती करा, त्यांना लक्ष्य म्हणून सिद्ध करण्याचा प्रयत्न करा +5. कुठल्याही टप्प्यावर प्रक्रिया अयशस्वी झाली तर 3 टप्प्यातलाही दुसरा नियम वापरा. -> ✅ कोणत्या परिस्थितीत फॉरवर्ड तर्क अधिक योग्य आहे? बॅकवर्ड तर्क कधी वापरावा? +> ✅ पुढील तर्कशक्ती कोणत्या परिस्थितीत अधिक योग्य आहे? मागील तर्कशक्ती कशी? -### तज्ज्ञ प्रणाली अंमलात आणणे +### तज्ज्ञ प्रणालीची अंमलबजावणी -तज्ज्ञ प्रणाली वेगवेगळ्या साधनांचा वापर करून अंमलात आणली जाऊ शकते: +तज्ज्ञ प्रणाली विविध साधनांचा वापर करून तयार करू शकतो: -* काही उच्च-स्तरीय प्रोग्रामिंग भाषेत थेट प्रोग्रामिंग करणे. ही सर्वोत्तम कल्पना नाही, कारण ज्ञान-आधारित प्रणालीचा मुख्य फायदा म्हणजे ज्ञान तर्कशक्तीपासून वेगळे आहे, आणि संभाव्यतः समस्या क्षेत्रातील तज्ज्ञाने तर्क प्रक्रियेच्या तपशीलांशिवाय नियम लिहिणे शक्य असावे. -* **तज्ज्ञ प्रणाली शेल** वापरणे, म्हणजे ज्ञान प्रतिनिधित्व भाषेचा वापर करून ज्ञानाने भरले जाण्यासाठी विशेषतः डिझाइन केलेली प्रणाली. +* काही उच्चस्तरीय प्रोग्रॅमिंग भाषेत थेट त्यांची प्रोग्रॅमिंग करणे. हे सर्वोत्तम कल्पना नाही कारण ज्ञान-आधारित प्रणालीची मुख्य फायदा म्हणजे ज्ञान तर्कशक्तीपासून वेगळे केलेले असते, आणि संभाव्य तज्ज्ञ नियम लिहू शकेल, ज्याला तर्कशक्ती प्रक्रियेचे तपशील समजू नये. +* **तज्ज्ञ प्रणाली शेल** वापरणे, म्हणजे असे सिस्टम जे ज्ञान सादरीकरण भाषेचा वापर करून ज्ञानाने भरले जातात. -## ✍️ व्यायाम: प्राणी तर्क +## ✍️ सराव: प्राणी तर्कशक्ती -[Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) पहा, ज्यामध्ये फॉरवर्ड आणि बॅकवर्ड तर्क तज्ज्ञ प्रणाली अंमलात आणण्याचे उदाहरण दिले आहे. +आगामी [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) मध्ये पुढील आणि मागील तर्कशक्ती वापरून तज्ज्ञ प्रणाली कशी अंमलात आणायची याचे उदाहरण पाहा. -> **टीप**: हे उदाहरण तुलनेने सोपे आहे आणि तज्ज्ञ प्रणाली कशी दिसते याची कल्पना देते. एकदा तुम्ही अशी प्रणाली तयार करण्यास सुरुवात केली की, तुम्हाला त्यामध्ये काही *बुद्धिमान* वर्तन दिसेल तेव्हा तुम्ही सुमारे 200+ नियमांपर्यंत पोहोचाल. एका टप्प्यावर, नियम इतके जटिल होतात की सर्व नियम लक्षात ठेवणे कठीण होते, आणि अशा वेळी तुम्हाला आश्चर्य वाटू शकते की प्रणाली विशिष्ट निर्णय का घेत आहे. तथापि, ज्ञान-आधारित प्रणालीचे महत्त्वाचे वैशिष्ट्य म्हणजे तुम्ही नेहमी *स्पष्टीकरण* देऊ शकता की कोणताही निर्णय कसा घेतला गेला. +> **टीप**: हे उदाहरण सोपे आहे आणि फक्त तज्ज्ञ प्रणाली कशी असते याची कल्पना देते. अशी प्रणाली तयार करायला सुरुवात केल्यावर, नियमांची संख्या सुमारे 200+ पोहोचली की तुम्हाला त्यातून काही *बुद्धिमान* वर्तन दिसू लागेल. काही काळापासून, नियम इतके जटिल होतात की ते लक्षात ठेवणे शक्य नाही, आणि तुम्हाला कधी तरी अशी प्रणाली निर्णय का घेत आहे याचा विचार करायला लागेल. तथापि, ज्ञान-आधारित प्रणालीची महत्त्वाची वैशिष्ट्ये म्हणजे तुम्ही नेहमीच कोणताही निर्णय कसा घेतला गेला याचे *स्पष्टीकरण* करू शकता. -## ऑंटोलॉजी आणि सामान्य वेब +## ऑंटोलॉजी आणि सेमान्टिक वेब -20व्या शतकाच्या शेवटी इंटरनेट संसाधनांचे वर्णन करण्यासाठी ज्ञान प्रतिनिधित्व वापरण्याची एक पुढाकार होती, जेणेकरून विशिष्ट क्वेरींशी संबंधित संसाधने शोधणे शक -- XML आधारित भाषांची एक कुटुंब: RDF (Resource Description Framework), RDFS (RDF Schema), OWL (Ontology Web Language). +२० व्या शतकामध्ये knowledge representation वापरून इंटरनेट स्रोतांना अ‍ॅनोटेट करण्याचा उपक्रम झाला, ज्यामुळे जास्त विशिष्ट प्रश्नांसाठी स्रोत शोधणे शक्य झाले. या चळवळीला **सेमान्टिक वेब** म्हणतात, आणि त्यामध्ये काही संकल्पना वापरल्या होत्या: -सिमॅंटिक वेबमधील एक मुख्य संकल्पना म्हणजे **Ontology**. याचा अर्थ काही औपचारिक ज्ञान प्रतिनिधित्व वापरून समस्या क्षेत्राची स्पष्ट तपशीलवार माहिती. सर्वात सोपी Ontology फक्त समस्या क्षेत्रातील वस्तूंची श्रेणी असू शकते, परंतु अधिक जटिल Ontologies मध्ये निष्कर्ष काढण्यासाठी वापरता येणारे नियम समाविष्ट असतात. +- **[वर्णनात्मक तर्कशास्त्र](https://en.wikipedia.org/wiki/Description_logic)** (DL) वर आधारित एक विशेष ज्ञान सादरीकरण. हे फ्रेम ज्ञान सादरीकरणासारखे आहे कारण ते ऑब्जेक्ट्सचा एक पदानुक्रम तयार करते ज्यात गुणधर्म असतात, पण त्याला औपचारिक तर्कशास्त्रीय अर्थ व तर्कशक्ती आहे. DL चा एक संपूर्ण कुटुंब आहे जे अभिव्यक्तीक्षमता आणि एल्गोरिदमिक तर्कशक्तीच्या गुंतागुंतीचा समतोल साधतो. +- वितरित ज्ञान सादरीकरण, जिथे सर्व संकल्पना जागतिक URI ओळखणाऱ्याने सादर केल्या जातात, ज्यामुळे इंटरनेटवर पसरलेल्या ज्ञान पदानुक्रमांची निर्मिती शक्य होते. +- ज्ञान वर्णनासाठी XML-आधारित भाषांची एक कुटुंब: RDF (Resource Description Framework), RDFS (RDF Schema), OWL (Ontology Web Language). -सिमॅंटिक वेबमध्ये, सर्व प्रतिनिधित्व त्रिकुटांवर आधारित असतात. प्रत्येक वस्तू आणि प्रत्येक संबंध URI द्वारे अद्वितीयपणे ओळखले जातात. उदाहरणार्थ, जर आपण असे सांगायचे असेल की हे AI Curriculum Dmitry Soshnikov यांनी 1 जानेवारी 2022 रोजी विकसित केले आहे - येथे आपण वापरू शकणारे त्रिकुट आहेत: +स्मॅंटिक वेबमधील एक मूलभूत संकल्पना म्हणजे **ऑन्टॉलॉजी**. हे काही औपचारिक ज्ञान प्रतिनिधित्व वापरून एखाद्या समस्या क्षेत्राचे स्पष्ट स्पष्टीकरण सूचित करते. सोपी ऑन्टॉलॉजी फक्त एखाद्या समस्या क्षेत्रातील वस्तूंची एक श्रेणी असू शकते, परंतु अधिक गुंतागुंतीच्या ऑन्टॉलॉजींमध्ये नियमांचा समावेश असेल जे अनुमानासाठी वापरले जाऊ शकतात. - +स्मॅंटिक वेबमध्ये, सर्व प्रतिनिधित्व त्रिपुटांवर आधारित असतात. प्रत्येक वस्तू आणि प्रत्येक नाते यांना युनिक URI ने ओळखले जाते. उदाहरणार्थ, जर आपण हे नमूद करू इच्छित असू की हा AI अभ्यासक्रम डिमिट्री सॉश्निकोव्ह यांनी 1 जानेवारी 2022 रोजी विकसित केला आहे - तर आपण वापरू शकता असे त्रिपुटे: + + ``` -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 ``` -> ✅ येथे `http://www.example.com/terms/creation-date` आणि `http://purl.org/dc/elements/1.1/creator` हे *creator* आणि *creation date* या संकल्पनांना व्यक्त करण्यासाठी काही सुप्रसिद्ध आणि सार्वत्रिकपणे स्वीकारलेले URI आहेत. +> ✅ येथे `http://www.example.com/terms/creation-date` आणि `http://purl.org/dc/elements/1.1/creator` हे *सर्जक* आणि *निर्मिती तारीख* या संकल्पना व्यक्त करण्यासाठी काही परिचित आणि सार्वत्रिकरीत्या स्वीकारलेले URI आहेत. -अधिक जटिल प्रकरणात, जर आपल्याला निर्मात्यांची यादी परिभाषित करायची असेल, तर आपण RDF मध्ये परिभाषित केलेल्या काही डेटा संरचना वापरू शकतो. +अधिक गुंतागुंतीच्या बाबतीत, जर आपल्याला सर्जकांची यादी परिभाषित करायची असेल, तर आपण RDF मध्ये व्याख्यायित काही डेटा संरचना वापरू शकतो. - + -> वरील आकृत्या [Dmitry Soshnikov](http://soshnikov.com) यांनी तयार केल्या आहेत. +> माथली आकृती डिमिट्री सॉश्निकोव्ह यांनी तयार केली आहे (http://soshnikov.com) -सिमॅंटिक वेब तयार करण्याची प्रगती शोध इंजिन आणि नैसर्गिक भाषा प्रक्रिया तंत्रांच्या यशामुळे काही प्रमाणात मंदावली, ज्यामुळे मजकुरातून संरचित डेटा काढणे शक्य झाले. तथापि, काही क्षेत्रांमध्ये Ontologies आणि ज्ञान बेस टिकवून ठेवण्यासाठी अजूनही महत्त्वपूर्ण प्रयत्न केले जात आहेत. काही उल्लेखनीय प्रकल्प: +स्मॅंटिक वेब बांधण्याचा प्रगती काहीशी मंदावत गेली कारण शोध इंजिन्स आणि नॅचरल लँग्वेज प्रोसेसिंग तंत्रज्ञानांनी मजकुरातून संरचित डेटा काढणे शक्य केले. तथापि, काही क्षेत्रांमध्ये ऑन्टॉलॉजी आणि ज्ञानसंग्रह राखण्यासाठी अजूनही महत्त्वपूर्ण प्रयत्न चालू आहेत. काही उल्लेखनीय प्रकल्प: -* [WikiData](https://wikidata.org/) हे Wikipedia शी संबंधित मशीन वाचण्यायोग्य ज्ञान बेसचे संग्रह आहे. बहुतेक डेटा Wikipedia *InfoBoxes* मधून, म्हणजेच Wikipedia पृष्ठांतील संरचित सामग्रीच्या तुकड्यांमधून काढला जातो. तुम्ही [SPARQL](https://query.wikidata.org/) वापरून WikiData क्वेरी करू शकता, जो सिमॅंटिक वेबसाठी एक विशेष क्वेरी भाषा आहे. येथे एक नमुना क्वेरी आहे जी मानवांमध्ये सर्वात लोकप्रिय डोळ्यांच्या रंगांचे प्रदर्शन करते: +* [WikiData](https://wikidata.org/) ही विकिपीडिया संबंधित मशीन-वाचनीय ज्ञानसंग्रहांची एक मालिका आहे. अधिकांश डेटा विकिपीडिया *इन्फोबॉक्सेस* मधून गोळा केलेला असतो, जो विकिपीडिया पानांतील संरचित माहितीचा तुकडा आहे. तुम्ही SPARQL, स्मॅंटिक वेबसाठी एक विशेष क्वेरी भाषा, वापरून [विकिडेटा क्वेरी](https://query.wikidata.org/) करू शकता. येथे मानवी लोकांमध्ये सर्वाधिक लोकप्रिय डोळ्यांच्या रंगांचा नमुना क्वेरी दिला आहे: ```sparql #defaultView:BubbleChart @@ -203,47 +206,51 @@ WHERE GROUP BY ?eyeColorLabel ``` -* [DBpedia](https://www.dbpedia.org/) हा WikiData सारखाच आणखी एक प्रयत्न आहे. +* [DBpedia](https://www.dbpedia.org/) हा विकिडेटा सारख्या प्रयत्नांपैकी एक आहे. -> ✅ जर तुम्हाला स्वतःचे Ontologies तयार करण्याचा किंवा विद्यमान Ontologies उघडण्याचा प्रयोग करायचा असेल, तर [Protégé](https://protege.stanford.edu/) नावाचा एक उत्कृष्ट व्हिज्युअल Ontology संपादक आहे. ते डाउनलोड करा किंवा ऑनलाइन वापरा. +> ✅ जर तुम्ही तुमच्याच ऑन्टॉलॉजी बांधण्याचा किंवा अस्तित्वात असलेल्या ऑन्टॉलॉजी उघडण्याचा प्रयोग करू इच्छित असाल, तर एक उत्कृष्ट दृष्टीमय ऑन्टॉलॉजी संपादक आहे ज्याला [Protégé](https://protege.stanford.edu/) म्हणतात. ते डाउनलोड करा किंवा ऑनलाइन वापरा. - + -*Web Protégé संपादक Romanov Family Ontology सह उघडलेला. Dmitry Soshnikov यांनी घेतलेला स्क्रीनशॉट* +*Web Protégé संपादक डिमिट्री सॉश्निकोव्ह यांच्या रुमानोव कुटुंब ऑन्टॉलॉजीसह उघडलेले. स्क्रीनशॉट डिमिट्री सॉश्निकोव्ह यांचे.* -## ✍️ व्यायाम: एक कुटुंब Ontology +## ✍️ सराव: एक कुटुंब ऑन्टॉलॉजी -[FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) पहा, ज्यामध्ये सिमॅंटिक वेब तंत्रांचा वापर करून कुटुंबातील नातेसंबंधांबद्दल विचार करण्याचे उदाहरण दिले आहे. आपण सामान्य GEDCOM स्वरूपात प्रतिनिधित्व केलेल्या कुटुंबाच्या झाडाचा आणि कुटुंबातील नातेसंबंधांची Ontology घेऊन दिलेल्या व्यक्तींच्या संचासाठी सर्व कुटुंब नातेसंबंधांचा ग्राफ तयार करू. +कुटुंब संबंधांवर विचार करण्यासाठी स्मॅंटिक वेब तंत्रांचा वापर कसा करायचा याचा एक उदाहरण पाहण्यासाठी [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) पहा. आपण सर्वसामान्य GEDCOM स्वरूपात सादर केलेले एक कुटुंब वृक्ष आणि कुटुंब संबंधांची ऑन्टॉलॉजी घेऊन दिलेल्या व्यक्तींच्या समूहासाठी सर्व कुटुंब संबंधांची एक ग्राफ तयार करू. -## Microsoft Concept Graph +## मायक्रोसॉफ्ट संकल्पना ग्राफ -बहुतेक प्रकरणांमध्ये, Ontologies काळजीपूर्वक हाताने तयार केल्या जातात. तथापि, नैसर्गिक भाषा मजकुरासारख्या असंरचित डेटामधून Ontologies **माइन** करणे देखील शक्य आहे. +अधिकांश प्रकरणांमध्ये, ऑन्टॉलॉजी काळजीपूर्वक हाताने तयार केल्या जातात. परंतु, हे ही शक्य आहे की **असंगठित डेटा** जसे की नैसर्गिक भाषा मजकूरातून ऑन्टॉलॉजी **मायन** करणे. -Microsoft Research ने केलेल्या अशाच एका प्रयत्नातून [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste) तयार झाले. +अशा प्रयत्नाचा उपयोग मायक्रोसॉफ्ट रिसर्चने केला आणि त्याचा निकाल म्हणून [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste) तयार केला. -हे `is-a` वारसा संबंध वापरून गटबद्ध केलेल्या घटकांचा मोठा संग्रह आहे. हे "Microsoft म्हणजे काय?" यासारख्या प्रश्नांची उत्तरे देण्यास अनुमती देते - उत्तर असे काहीतरी असेल: "एक कंपनी 0.87 संभाव्यतेसह, आणि एक ब्रँड 0.75 संभाव्यतेसह". +हे एक मोठे संग्रह आहे ज्यात संस्थांचा `is-a` वारसाहक्काच्या नात्याने गटबद्ध केला जातो. हे "मायक्रोसॉफ्ट काय आहे?" या प्रश्नाचे उत्तर देऊ शकते - उत्तर असे काहीतरी आहे की "0.87 शक्यतेने एक कंपनी आहे, आणि 0.75 शक्यतेने एक ब्रँड आहे". -Graph REST API म्हणून किंवा सर्व घटक जोड्या सूचीबद्ध करणाऱ्या मोठ्या डाउनलोड करण्यायोग्य मजकूर फाइल म्हणून उपलब्ध आहे. +ग्राफ REST API किंवा मोठ्या टेक्स्ट फाईल म्हणून उपलब्ध आहे ज्यात सर्व संस्था जोड्यांची यादी असते. -## ✍️ व्यायाम: एक Concept Graph +## ✍️ सराव: एक संकल्पना ग्राफ -[MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) नोटबुक वापरून पहा, ज्यामध्ये Microsoft Concept Graph वापरून बातम्यांच्या लेखांना विविध श्रेणींमध्ये गटबद्ध कसे करता येईल हे दाखवले आहे. +[MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) नोटबुक वापरून पाहा, ज्या मध्ये आपण कसे मायक्रोसॉफ्ट संकल्पना ग्राफ वापरून बातम्यांच्या लेखांना विविध श्रेण्यांमध्ये वर्गीकृत करू शकतो ते दाखवले आहे. ## निष्कर्ष -आजकाल, AI ला अनेकदा *Machine Learning* किंवा *Neural Networks* चे समानार्थी मानले जाते. तथापि, मानव देखील स्पष्ट विचार प्रदर्शित करतो, जे सध्या Neural Networks द्वारे हाताळले जात नाही. वास्तविक जगातील प्रकल्पांमध्ये, स्पष्ट विचार अजूनही अशा कार्यांसाठी वापरला जातो ज्यासाठी स्पष्टीकरण आवश्यक असते, किंवा प्रणालीच्या वर्तनात नियंत्रित पद्धतीने बदल करण्याची क्षमता असते. +आजकाल, AI ला बर्‍याच वेळा *मशीन लर्निंग* किंवा *न्यूरल नेटवर्क्स* यांचा पर्याय मानले जाते. तथापि, माणूसही स्पष्ट तार्किक विचार करतो, जो सध्या न्यूरल नेटवर्क्सद्वारे हाताळला जात नाही. वास्तविक जगातील प्रकल्पांमध्ये, स्पष्ट तार्किक विचार त्या कामांसाठी वापरला जातो जे स्पष्टीकरणे आवश्यक असतात किंवा प्रणालीच्या वर्तनात नियंत्रित पद्धतीने बदल करण्यास सक्षम असणे आवश्यक असते. ## 🚀 आव्हान -या धड्याशी संबंधित Family Ontology नोटबुकमध्ये, कुटुंबाच्या झाडातील इतर नातेसंबंधांसह प्रयोग करण्याची संधी आहे. कुटुंबाच्या झाडातील लोकांमधील नवीन संबंध शोधण्याचा प्रयत्न करा. +या धड्याशी संबंधित कुटुंब ऑन्टॉलॉजी नोटबुकमध्ये, तुम्हाला इतर कुटुंब संबंधांसह प्रयोग करण्याची संधी आहे. कुटुंब वृक्षातील लोकांमध्ये नवीन संबंध शोधण्याचा प्रयत्न करा. -## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ai/quiz/4) +## [धड्यानंतर क्विझ](https://ff-quizzes.netlify.app/en/ai/quiz/4) -## पुनरावलोकन आणि स्व-अभ्यास +## पुनरावलोकन आणि स्वअध्ययन -इंटरनेटवर संशोधन करा आणि अशा क्षेत्रांचा शोध घ्या जिथे मानवांनी ज्ञानाचे प्रमाण आणि कोडिफिकेशन करण्याचा प्रयत्न केला आहे. Bloom's Taxonomy बद्दल माहिती मिळवा आणि इतिहासात जाऊन मानवांनी त्यांच्या जगाचा अर्थ लावण्याचा प्रयत्न कसा केला हे जाणून घ्या. Linnaeus ने जीवसृष्टींची वर्गवारी तयार करण्यासाठी केलेले कार्य आणि Dmitri Mendeleev ने रासायनिक घटकांचे वर्णन आणि गटबद्ध करण्याचा मार्ग कसा तयार केला हे पाहा. तुम्हाला आणखी कोणते मनोरंजक उदाहरणे सापडतात? +जागतिक इंटरनेटवर संशोधन करा आणि शोधा की माणसांनी ज्ञानाचं मापन आणि कोडिफिकेशन कुठल्या क्षेत्रात कसे केले आहे. ब्लूम्स टॅक्सोनॉमी पहा, आणि इतिहासात परत जा वाचा की माणसांनी त्यांच्या जगाचा अर्थ लावण्यासाठी कसा प्रयत्न केला. लिनियस यांनी जीवसृष्टीची टॅक्सोनॉमी कशी तयार केली याचा अभ्यास करा, आणि डिमिट्री मेंडेलिव्ह यांनी रासायनिक घटकांचे वर्णन व वर्गीकरण कसे केले ते पाहा. आणखी कोणती मनोरंजक उदाहरणे तुम्हाला सापडतात? -**असाइनमेंट**: [Build an Ontology](assignment.md) +**असाइनमेंट**: [एक ऑन्टॉलॉजी तयार करा](assignment.md) --- + +**अस्वीकरण**: +हा दस्तऐवज AI भाषांतर सेवा [Co-op Translator](https://github.com/Azure/co-op-translator) चा वापर करून भाषांतरित केला गेला आहे. आम्ही अचूकतेसाठी प्रयत्न करतो, तरी कृपया लक्षात ठेवा की स्वयंचलित भाषांतरांमध्ये चुका किंवा अचूकतेचा अभाव असू शकतो. मूळ दस्तऐवज त्याच्या मूळ भाषेत अधिकृत स्रोत मानला पाहिजे. महत्वाची माहिती असल्यास, व्यावसायिक मानवी भाषांतर करण्याचा सल्ला दिला जातो. या भाषांतराच्या वापरामुळे झालेल्या कोणत्याही गैरसमजुती किंवा गैरव्याख्यांसाठी आम्ही जबाबदार नाही. + \ No newline at end of file diff --git a/translations/ne/README.md b/translations/ne/README.md index aef74274..3472c4ca 100644 --- a/translations/ne/README.md +++ b/translations/ne/README.md @@ -1,8 +1,8 @@ [Arabic](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgarian](../bg/README.md) | [Burmese (Myanmar)](../my/README.md) | [Chinese (Simplified)](../zh/README.md) | [Chinese (Traditional, Hong Kong)](../hk/README.md) | [Chinese (Traditional, Macau)](../mo/README.md) | [Chinese (Traditional, Taiwan)](../tw/README.md) | [Croatian](../hr/README.md) | [Czech](../cs/README.md) | [Danish](../da/README.md) | [Dutch](../nl/README.md) | [Estonian](../et/README.md) | [Finnish](../fi/README.md) | [French](../fr/README.md) | [German](../de/README.md) | [Greek](../el/README.md) | [Hebrew](../he/README.md) | [Hindi](../hi/README.md) | [Hungarian](../hu/README.md) | [Indonesian](../id/README.md) | [Italian](../it/README.md) | [Japanese](../ja/README.md) | [Kannada](../kn/README.md) | [Korean](../ko/README.md) | [Lithuanian](../lt/README.md) | [Malay](../ms/README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Nepali](./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) -> **स्थानीय रूपमा क्लोन गर्न चाहनुहुन्छ?** +> **स्थानीय रूपमा क्लोन गर्न मन लाग्छ?** -> यो रिपोजिटरीमा ५०+ भाषा अनुवादहरू समावेश छन् जसले डाउनलोड आकारलाई उल्लेखनीय रूपमा बढाउँछ। अनुवादहरू बिना क्लोन गर्न, sparse checkout प्रयोग गर्नुहोस्: +> यस रिपोजिटरीमा ५०+ भाषा अनुवादहरू समावेश छन् जसले डाउनलोड आकार उल्लेखनीय रूपमा बढाउँछ। अनुवादहरू बिना क्लोन गर्न sparse checkout प्रयोग गर्नुहोस्: > ```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' > ``` -> यसले तपाईंलाई पाठ्यक्रम पूरा गर्न आवश्यक सबै कुरा छिटो डाउनलोडको साथ दिन्छ। +> यसले तपाईंलाई कोर्स पूरा गर्न आवश्यक सबै सामग्री धेरै छिटो डाउनलोड गर्न दिन्छ। -**यदि तपाईंले थप अनुवाद भाषाहरू समर्थन गर्न चाहनुहुन्छ भने उल्लेखित छन् [यहाँ](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** +**यदि तपाईं थप अनुवाद भाषाहरूलाई समर्थन गर्न चाहनुहुन्छ भने ती [यहाँ](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md) सूचीबद्ध छन्** -## समुदायमा सामेल हुनुहोस् +## कम्युनिटीमा सहभागी हुनुहोस् [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -## तपाईंले के सिक्नुहुनेछ +## तपाईं के सिक्नुहुनेछ -**[पाठ्यक्रमको माइन्डम्याप](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** +**[कोर्सको माइन्डम्याप](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** यस पाठ्यक्रममा, तपाईंले सिक्नुहुनेछ: -* कृत्रिम बुद्धिमत्ताको विभिन्न दृष्टिकोणहरू, जसमा "राम्रो पुरानो" प्रतीकात्मक दृष्टिकोण **ज्ञान प्रतिनिधित्व** र तर्कसंगत उपयोग सहित ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence))। -* **न्यूरल नेटवर्कहरू** र **गहिरो सिकाइ**, जुन आधुनिक AI को मूल तत्व हो। हामी यी महत्वपूर्ण विषयहरूका अवधारणाहरू दुई लोकप्रिय फ्रेमवर्कहरूमा कोड प्रयोग गरेर देखाउनेछौं - [TensorFlow](http://Tensorflow.org) र [PyTorch](http://pytorch.org)। -* तस्वीर र पाठसँग काम गर्न **न्यूरल वास्तुकला**। हामी हालका मोडेलहरू समेट्नेछौं तर यो सायद अत्याधुनिकमा अलिकति कम हुन सक्छ। -* कम प्रचलित AI दृष्टिकोणहरू, जस्तै **जेनेटिक एल्गोरिदमहरू** र **बहु-एजेन्ट प्रणालीहरू**। +* कृत्रिम बुद्धिमत्ताका विभिन्न दृष्टिकोणहरू, जसमा "पुरानो" प्रतीकात्मक दृष्टिकोण सहित **ज्ञान प्रतिनिधित्व** र तर्क ({GOFAI}). +* **न्यूरल नेटवर्कहरू** र **गहिरो सिकाइ**, जुन आधुनिक AI को केन्द्रमा छन्। हामी यी महत्त्वपूर्ण विषयहरूका सैद्धान्तिक पक्षहरूलाई दुई लोकप्रिय फ्रेमवर्कहरूमा कोडको माध्यमबाट देखाउनेछौं - [TensorFlow](http://Tensorflow.org) र [PyTorch](http://pytorch.org)। +* छविहरू र पाठसँग काम गर्न **न्यूरल आर्किटेक्चरहरू**। हामी हालका मोडेलहरू समेट्नेछौं तर सायद सबैभन्दा नयाँ अवस्था (state-of-the-art) मा केही कमी रहला। +* कम लोकप्रिय AI दृष्टिकोणहरू, जस्तै **जेनेटिक एल्गोरिदमहरू** र **बहु-एजेन्ट प्रणालीहरू**। -यो पाठ्यक्रममा हामीले समेट्नेछैनौं: +यस पाठ्यक्रममा हामी के समेट्ने छैनौं: -> [यस पाठ्यक्रमका सबै अतिरिक्त स्रोतहरू हाम्रो Microsoft Learn संग्रहमा पाउनुहोस्](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) +> [यस कोर्सका सबै थप स्रोतहरू Microsoft Learn को संग्रहमा फेला पार्नुहोस्](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) -* व्यवसायमा **AI को प्रयोगका** केसहरू। [आफ्नो Microsoft Learn बाट [Introduction to AI for business users](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) अध्ययन पथ लिने वा [AI Business School](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum) लिन सिफारिस गरिन्छ जुन [INSEAD](https://www.insead.edu/) सँग सहकार्यमा विकास गरिएको हो। -* **परम्परागत मेसिन लर्निङ**, जुन हाम्रो [Machine Learning for Beginners Curriculum](http://github.com/Microsoft/ML-for-Beginners) मा राम्रोसँग वर्णन गरिएको छ। -* **[कग्निटिभ सर्भिसेस](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)** प्रयोग गरेर तयार पारिएका व्यवहारिक AI अनुप्रयोगहरू। यसको लागि, हामी तपाईंलाई Microsoft Learn बाट [vision](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [प्राकृतिक भाषा प्रशोधन](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[Azure OpenAI सेवा संग जनरेटिभ AI](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** र अन्य विषयहरू भएको मोड्युलहरूसँग सुरु गर्न सल्लाह दिन्छौं। -* विशेष ML **क्लाउड फ्रेमवर्कहरू**, जस्तै [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), वा [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum)। [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) र [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) सिकाइ पथहरू प्रयोग गर्ने विचार गर्नुहोस्। -* **संवादात्मक AI** र **च्याट बोटहरू**। यसमा फरक [Create conversational AI solutions](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum) सिकाइ पथ छ, र थप विवरणका लागि तपाईं [यो ब्लग पोस्ट](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) पनि हेर्न सक्नुहुन्छ। -* गहिरो सिकाइको पछाडि रहेको **गाढा गणित**। यसको लागि, हामी Ian Goodfellow, Yoshua Bengio र Aaron Courville द्वारा लिखित [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) पुस्तक सिफारिस गर्नेछौं, जुन अनलाइन पनि उपलब्ध छ [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/) मा। +* **बिजनेसमा AI को प्रयोग**। यसका लागि [Introduction to AI for business users](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) Microsoft Learn मा लिनुहोस्, वा [AI Business School](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum) जुन [INSEAD](https://www.insead.edu/) सँग सहकार्यमा विकास गरिएको छ। +* **क्लासिक मशीन लर्निङ** जुन हाम्रो [Machine Learning for Beginners Curriculum](http://github.com/Microsoft/ML-for-Beginners) मा राम्रोसँग वर्णन गरिएको छ। +* **व्यावहारिक AI अनुप्रयोगहरू**, जो **[Cognitive Services](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)** प्रयोग गरेर बनाइन्छ। यसका लागि Microsoft Learn का [दृष्टि](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [प्राकृतिक भाषा प्रक्रिया](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[Azure OpenAI सेवा सँग जनरेटिभ AI](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** लगायतका मोड्युलहरू सुरु गर्न सिफारिस गरिन्छ। +* विशिष्ट ML **क्लाउड फ्रेमवर्कहरू**, जस्तै [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), वा [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum)। यी सिक्न [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) र [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) लिन सकिन्छ। +* **संवादी AI** र **च्याट बोटहरू**। एक अलग [Create conversational AI solutions](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum) सिकाइ पथ छ, र थप विवरणका लागि [यस ब्लग पोस्ट](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) पनि हेर्न सक्नुहुन्छ। +* गहिरो सिकाइ पछाडि रहेको **गहिरो गणित**। यसको लागि हामी [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) लेखक Ian Goodfellow, Yoshua Bengio र Aaron Courville द्वारा लेखिएको सिफारिस गर्छौं, जुन अनलाइन पनि उपलब्ध छ [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/) मा। -_क्लाउडमा AI_ विषयहरूको सौम्य परिचयका लागि, तपाईंले [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) सिकाइ पथ लिन सक्नुहुन्छ। +_क्लाउडमा AI_ विषयहरूमा सौम्य परिचयका लागि [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) सिकाइ पथ लिन सक्नुहुन्छ। # सामग्री -| | Lesson Link | PyTorch/Keras/TensorFlow | Lab | +| | पाठ लिंक | PyTorch/Keras/TensorFlow | प्रयोगशाला | | :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | -| 0 | [कोर्स सेटअप](./lessons/0-course-setup/setup.md) | [आफ्नो विकास वातावरण सेटअप गर्नुहोस्](./lessons/0-course-setup/how-to-run.md) | | +| 0 | [कोर्स सेटअप](./lessons/0-course-setup/setup.md) | [तपाईंको विकास वातावरण सेटअप गर्नुहोस्](./lessons/0-course-setup/how-to-run.md) | | | I | [**AI परिचय**](./lessons/1-Intro/README.md) | | | | 01 | [AI को परिचय र इतिहास](./lessons/1-Intro/README.md) | - | - | -| II | **प्रतीकात्मक AI** | -| 02 | [ज्ञान प्रतिनिधित्व र विशेषज्ञ प्रणालीहरू](./lessons/2-Symbolic/README.md) | [विशेषज्ञ प्रणालीहरू](./lessons/2-Symbolic/Animals.ipynb) / [ओन्टोलोजी](./lessons/2-Symbolic/FamilyOntology.ipynb) /[सङ्कल्पना ग्राफ](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | -| III | [**न्यूरल नेटवर्कको परिचय**](./lessons/3-NeuralNetworks/README.md) ||| +| II | **प्रतिनिधित्वात्मक AI** | +| 02 | [ज्ञान प्रतिनिधित्व र विशेषज्ञ प्रणालीहरू](./lessons/2-Symbolic/README.md) | [विशेषज्ञ प्रणालीहरू](./lessons/2-Symbolic/Animals.ipynb) / [अन्तरङ्ग](./lessons/2-Symbolic/FamilyOntology.ipynb) /[अवधारणा ग्राफ](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | +| III | [**न्यूरल नेटवर्कहरूको परिचय**](./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 | [मल्टि-लेयर्ड पर्सेप्ट्रन र हाम्रो आफ्नै फ्रेमवर्क बनाउने](./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 | [फ्रेमवर्कहरूको परिचय (PyTorch/TensorFlow) र ओभरफिटिङ](./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 | [**कम्प्युटर भिजन**](./lessons/4-ComputerVision/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-77998-cacaste) / [TensorFlow](https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-77998-cacaste)| [Microsoft Azure मा कम्प्युटर भिजन अन्वेषण गर्नुहोस्](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | -| 06 | [कम्प्युटर भिजनको परिचय। 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 | [कन्भोलुसनल न्युरल नेटवर्कहरू](./lessons/4-ComputerVision/07-ConvNets/README.md) & [CNN आर्किटेक्चरहरू](./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 | [प्री-ट्रेन्ड नेटवर्कहरू र ट्रान्सफर लर्निङ](./lessons/4-ComputerVision/08-TransferLearning/README.md) र [ट्रेनिङ ट्रिक्स](./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 | [अटोएन्कोडरहरू र VAE हरू](./lessons/4-ComputerVision/09-Autoencoders/README.md) | [PyTorch](./lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb) | | -| 10 | [जेनेरेटिभ एडभरसैरियल नेटवर्कहरू र आर्टिस्टिक स्टाइल ट्रान्सफर](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | -| 11 | [अब्जेक्ट डिटेक्सन](./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 | [सेम्यान्टिक सेग्मेन्टेसन। 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 | [**प्राकृतिक भाषा प्रशोधन**](./lessons/5-NLP/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-77998-cacaste) /[TensorFlow](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-77998-cacaste) | [Microsoft Azure मा प्राकृतिक भाषा प्रशोधन अन्वेषण गर्नुहोस्](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| -| 13 | [पाठ प्रतिनिधित्व। 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 | [सेम्यान्टिक शब्द इम्बेडिङ। Word2Vec र 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 | [भाषा मोडेलिङ। आफ्नै इम्बेडिङहरू तालिम दिनुहोस्](./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 | [रिकरेन्ट न्युरल नेटवर्कहरू](./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 | [जेनेरेटिभ रिकरेन्ट नेटवर्कहरू](./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) | +| 04 | [मल्टि-लेयर्ड पर्सेप्ट्रोन र हाम्रो आफ्नै फ्रेमवर्क सिर्जना गर्ने](./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 | [फ्रेमवर्कमा परिचय (PyTorch/TensorFlow) र ओभरफिटिङ](./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 | [**कम्प्युटर भिजन**](./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)| [माइक्रोसफ्ट अज्युरमा कम्प्युटर भिजन अन्वेषण गर्नुहोस्](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | +| 06 | [कम्प्युटर भिजनमा परिचय। 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 | [कन्वोलुसनल न्यूरल नेटवर्कहरू](./lessons/4-ComputerVision/07-ConvNets/README.md) & [CNN आर्किटेक्चरहरू](./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 | [पूर्व-प्रशिक्षित नेटवर्कहरू र ट्रान्सफर लर्निङ](./lessons/4-ComputerVision/08-TransferLearning/README.md) र [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 | [अटोएन्कोडरहरू र VAEहरू](./lessons/4-ComputerVision/09-Autoencoders/README.md) | [PyTorch](./lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb) | | +| 10 | [जनरेटिभ एड्भर्सेरियल नेटवर्कहरू र कलात्मक शैली ट्रान्सफर](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | +| 11 | [वस्तु पहिचान](./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 | [सेम्यान्टिक सेग्मेन्टेशन। 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 | [**प्राकृतिक भाषा प्रशोधन**](./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) | [माइक्रोसफ्ट अज्युरमा प्राकृतिक भाषा प्रशोधन अन्वेषण गर्नुहोस्](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| +| 13 | [पाठ प्रतिनिधित्व। बो/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 | [सेम्यान्टिक शब्द एम्बेडिङहरू। Word2Vec र 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 | [भाषा मोडलिङ। आफ्नै एम्बेडिङ प्रशिक्षण](./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 | [रकरेन्ट न्यूरल नेटवर्कहरू](./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 | [जनरेटिभ रकरेन्ट नेटवर्कहरू](./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 | [ट्रान्सफर्मरहरू। 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 | [नामित इकाई पहिचान](./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 | [ठूला भाषा मोडेलहरू, प्रॉम्प्ट प्रोग्रामिङ र फ्यू-शट कार्यहरू](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | +| 20 | [ठूला भाषा मोडलहरू, प्रम्प्ट प्रोग्रामिङ र केहि-शट कार्यहरू](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | | VI | **अन्य AI प्रविधिहरू** || | | 21 | [जेनेटिक एल्गोरिदमहरू](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [Notebook](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | -| 22 | [डीप रिइन्फोर्समेन्ट लर्निङ](./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 | [मल्टि-एजेन्ट सिस्टमहरू](./lessons/6-Other/23-MultiagentSystems/README.md) | | | -| VII | **AI नैतिकता** | | | -| 24 | [AI नैतिकता र जिम्मेवार AI](./lessons/7-Ethics/README.md) | [Microsoft Learn: जिम्मेवार AI सिद्धान्तहरू](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | -| IX | **थप विषयहरू** | | | +| 22 | [गहिरो सुदृढीकरण सिकाइ](./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 | [मल्टि-एजेन्ट प्रणालीहरू](./lessons/6-Other/23-MultiagentSystems/README.md) | | | +| VII | **AI एथिक्स** | | | +| 24 | [AI एथिक्स र जिम्मेवार 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 | **अतिरिक्तहरू** | | | | 25 | [मल्टि-मोडल नेटवर्कहरू, CLIP र VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [Notebook](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | -## प्रत्येक पाठमा समावेश छन् +## प्रत्येक पाठमा समावेश छ -* पूर्व-पढाइ सामग्री -* कार्यान्वयन योग्य जुपिटर नोटबुकहरू, जुन प्रायः फ्रेमवर्क (**PyTorch** वा **TensorFlow**) विशेष हुन्छन्। कार्यान्वयनयोग्य नोटबुकमा धेरै सैद्धान्तिक सामग्री पनि हुन्छ, त्यसैले विषयलाई बुझ्नको लागि कम्तीमा एउटा संस्करण (PyTorch वा TensorFlow मध्ये कुनै एक) पढ्नु आवश्यक छ। -* केही विषयहरूका लागि उपलब्ध **लैब**हरू, जसले तपाईंलाई सिकेको सामग्रीलाई विशेष समस्यामा लागू गरी हेर्ने अवसर दिन्छ। -* केही खण्डहरूमा सम्बन्धित विषयहरू समेटिएका [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) मोड्युलहरूका लिंकहरू हुन्छन्। +* पूर्व-पठन सामग्री +* कार्यान्वयन योग्य जुपिटर नोटबुकहरू, जुन प्रायः फ्रेमवर्क अनुसार विशेष हुन्छन् (**PyTorch** वा **TensorFlow**). कार्यान्वयन योग्य नोटबुकमा धेरै सैद्धान्तिक सामग्री पनि हुन्छ, त्यसैले विषय बुझ्न तपाईंले कम्तिमा एक संस्करणको नोटबुक (PyTorch वा TensorFlow) अवश्य हेर्नु पर्छ। +* केही विषयहरूका लागि उपलब्ध **प्रयोगशालाहरू**, जसले तपाईंलाई सिकेको सामग्रीलाई कुनै विशेष समस्यामा लागू गर्ने अवसर दिन्छ। +* केही खण्डहरूमा सम्बन्धित विषयहरू समेटिएका [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) मोड्यूलहरूको लिंकहरू छन्। -## सुरूवात गर्दै +## सुरु गर्ने तरिका -### 🎯 AI मा नयाँ हुनुहुन्छ? यहाँबाट सुरु गर्नुहोस्! +### 🎯 AI मा नयाँ? यहाँबाट सुरु गर्नुहोस्! -यदि तपाईं पूर्ण रूपमा AI मा नयाँ हुनुहुन्छ र चाँडो, प्रत्यक्ष उदाहरणहरू चाहनुहुन्छ भने, हाम्रो [**शुरुआती मैत्री उदाहरणहरू**](./examples/README.md) हेर्नुहोस्! यीमा समावेश छन्: +यदि तपाईं पूर्ण रूपमा AI मा नयाँ हुनुहुन्छ र छिटो, प्रयोगात्मक उदाहरणहरू खोज्दै हुनुहुन्छ भने, हाम्रा [**शुरुवाती मैत्री उदाहरणहरू**](./examples/README.md) हेर्नुहोस्! यीमा समावेश छन्: -- 🌟 **Hello AI World** - तपाईंको पहिलो AI प्रोग्राम (ढाँचा पहिचान) -- 🧠 **साधारण न्युरल नेटवर्क** - आरम्भदेखि एक न्युरल नेटवर्क बनाउनुहोस् -- 🖼️ **छवि वर्गीकरणकर्ता** - विस्तृत टिप्पणी सहित छविहरू वर्गीकरण गर्नुहोस् +- 🌟 **Hello AI World** - तपाईंको पहिलो AI प्रोग्राम (प्याटर्न मान्यता) +- 🧠 **साधारण न्यूरल नेटवर्क** - शुन्यबाट न्यूरल नेटवर्क बनाउने +- 🖼️ **छवि वर्गीकर्ता** - विस्तृत टिप्पणीहरू सहित छविहरू वर्गीकरण गर्ने - 💬 **पाठ भावना** - सकारात्मक/नकारात्मक पाठ विश्लेषण गर्नुहोस् -यी उदाहरणहरू तपाईलाई पूर्ण पाठ्यक्रममा डुब्नुभन्दा अघि एआई अवधारणाहरू बुझ्न मद्दत गर्न डिजाइन गरिएको हुन्। +यी उदाहरणहरू तपाईंलाई पूर्ण पाठ्यक्रममा डुब्नुअघि AI अवधारणाहरू बुझ्न सहयोग गर्न डिजाइन गरिएको हो। ### 📚 पूर्ण पाठ्यक्रम सेटअप -- तपाईको विकास वातावरण सेटअप गर्न मद्दत गर्न हामीले [सेटअप पाठ](./lessons/0-course-setup/setup.md) तयार पारेका छौं। - शिक्षकहरूको लागि, हामीले तपाईको लागि [पाठ्यक्रम सेटअप पाठ](./lessons/0-course-setup/for-teachers.md) पनि बनाएका छौं! -- कसरी [VSCode वा Codepace मा कोड चलाउने](./lessons/0-course-setup/how-to-run.md) +- हामीले तपाईंलाई विकास वातावरण सेटअप गर्न सहयोगको लागि [सेटअप पाठ](./lessons/0-course-setup/setup.md) सिर्जना गरेका छौं। - शिक्षकहरूका लागि, हामीले तपाईंको लागि पनि [पाठ्यक्रम सेटअप पाठ](./lessons/0-course-setup/for-teachers.md) तयार पारेका छौं! +- कसरी [VSCode वा Codespace मा कोड चलाउने](./lessons/0-course-setup/how-to-run.md) यी चरणहरू पालन गर्नुहोस्: -रिपोजिटोरी फोर्क गर्नुहोस्: यस पृष्ठको माथिल्लो-दायाँ कुनामा "Fork" बटनमा क्लिक गर्नुहोस्। +रिपोजिटोरी फोर्क गर्नुहोस्: यस पृष्ठको माथिल्लो-दायाँ कुनामा रहेको "Fork" बटनमा क्लिक गर्नुहोस्। रिपोजिटोरी क्लोन गर्नुहोस्: `git clone https://github.com/microsoft/AI-For-Beginners.git` -यस रिपोमा तारा (🌟) दिन नबिर्सनुहोस् ताकि पछि सजिलै फेला पार्न सक्नुहुनेछ। +पछि फेला पार्न सजिलो बनाउन यो रिपोमा स्टार (🌟) दिन नबिर्सनुहोस्। -## अन्य सिक्नेहरूसँग भेट्नुहोस् +## अन्य शिक्षार्थीहरूलाई भेट्नुहोस् -यस कोर्स लिने अन्य सिक्नेहरूसँग भेट गर्न र नेटवर्किङ गर्न हाम्रो [आधिकारिक AI Discord सर्भर](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) मा सामेल हुनुहोस् र समर्थन पाउनुहोस्। +हाम्रो [आधिकारिक AI डिस्कार्ड सर्भर](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) मा सहभागी भएर यस कोर्स लिने अन्य शिक्षार्थीहरूसँग भेट्नुहोस् र नेटवर्क बनाउनुहोस् र सहयोग पाउनुहोस्। -तपाईंसँग उत्पादन प्रतिक्रिया वा जिज्ञासा भएमा हाम्रो [Azure AI Foundry Developer Forum](https://aka.ms/foundry/forum) भ्रमण गर्नुहोस्। +यदि तपाईंले उत्पादन प्रतिक्रिया वा प्रश्नहरू हुनुहुन्छ भने हाम्रो [Azure AI Foundry Developer Forum](https://aka.ms/foundry/forum) भ्रमण गर्नुहोस् -## क्विजहरू +## क्विजहरू -> **क्विजहरू बारे टिप्पणी**: सबै क्विजहरू Quiz-app फोल्डरमा रहेको etc\quiz-app मा छन्, वा [यहाँ अनलाइन](https://ff-quizzes.netlify.app/) उपलब्ध छन्। तिनीहरू पाठहरूबाट लिंक गरिएका छन्। क्विज एप्लिकेशन स्थानीय रूपमा चलाउन वा Azure मा डिप्लोय गर्न सकिन्छ; `quiz-app` फोल्डरमा रहेका निर्देशनहरू पालना गर्नुहोस्। तिनीहरू क्रमशः स्थानीयकरण हुँदैछन्। +> **क्विजहरू बारे एउटा नोट**: सबै क्विजहरू Quiz-app फोल्डरमा etc\quiz-app मा समावेश छन्, वा [यहाँ अनलाइन](https://ff-quizzes.netlify.app/) उपलब्ध छन्। तिनीहरू पाठहरूबाट लिंक गरिएको छन्, क्विज एप स्थानीय रूपमा चलाउन वा Azure मा डिप्लोय गर्न सकिन्छ; `quiz-app` फोल्डरमा निर्देशनहरू अनुसरण गर्नुहोस्। तिनीहरू क्रमशः स्थानीयकरण हुँदैछन्। -## सहयोग आवश्यक +## सहयोग चाहिन्छ -के तपाईंसँग सुझाव वा टाइप गलत या कोड त्रुटिहरू भेट्टाउनुभएको छ? समस्या उठाउनुहोस् वा पुल अनुरोध सिर्जना गर्नुहोस्। +के तपाईंसँग सुझावहरू छन् वा वर्तनी वा कोड त्रुटिहरू भेट्टाउनुभयो? समस्या उठाउनुहोस् वा पुल अनुरोध सिर्जना गर्नुहोस्। ## विशेष धन्यवाद @@ -168,11 +168,11 @@ _क्लाउडमा AI_ विषयहरूको सौम्य पर * **🔥 सम्पादक:** [Jen Looper](https://twitter.com/jenlooper), PhD * **🎨 स्केचनोट चित्रकार:** [Tomomi Imura](https://twitter.com/girlie_mac) * **✅ क्विज सिर्जनाकर्ता:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) -* **🙏 प्रमुख योगदानकर्ताहरू:** [Evgenii Pishchik](https://github.com/Pe4enIks) +* **🙏 मूल योगदानकर्ता:** [Evgenii Pishchik](https://github.com/Pe4enIks) ## अन्य पाठ्यक्रमहरू -हाम्रो टोलीले अन्य पाठ्यक्रमहरू उत्पादन गर्दछ! जाँच गर्नुहोस्: +हाम्रो टोलीले अन्य पाठ्यक्रमहरू उत्पादन गर्छ! जाँच गर्नुहोस्: ### LangChain @@ -189,7 +189,7 @@ _क्लाउडमा AI_ विषयहरूको सौम्य पर --- -### जनरेटिभ AI सिरिज +### जनरेटिभ AI शृंखला [![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) @@ -197,7 +197,7 @@ _क्लाउडमा AI_ विषयहरूको सौम्य पर --- -### आधारभूत सिकाइ +### कोर सिकाइ [![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) @@ -208,25 +208,25 @@ _क्लाउडमा AI_ विषयहरूको सौम्य पर --- -### कोपिलट सिरिज +### कोपिलट श्रृंखला [![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) -## मद्दत पाउनुहोस् +## सहयोग पाउनुहोस् -यदि तपाईं अड्कनु हुन्छ वा AI एपहरू निर्माण गर्दा कुनै प्रश्नहरू छन् भने। MCP सम्बन्धी छलफलमा अन्य सिक्नेहरू र अनुभवी विकासकर्ताहरूमा सामेल हुनुहोस्। यो एक सहयोगी समुदाय हो जहाँ प्रश्नहरू स्वागतयोग्य छन् र ज्ञान स्वतन्त्र रूपमा साझा गरिन्छ। +यदि तपाईं अड्किनुभयो वा AI अनुप्रयोगहरू निर्माण गर्दा कुनै प्रश्न छ भने। MCP सम्बन्धी छलफलहरूमा सहपाठी शिक्षार्थीहरू र अनुभवी विकासकर्ताहरूसँग सहभागी हुनुहोस्। यो एक सहायक समुदाय हो जहाँ प्रश्नहरू स्वागतयोग्य छन् र ज्ञान स्वतन्त्र रूपमा साझा गरिन्छ। [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -यदि तपाईंलाई उत्पादन प्रतिक्रिया वा निर्माण गर्दा त्रुटिहरू छन् भने भ्रमण गर्नुहोस्: +यदि निर्माण गर्दा तपाईंसँग उत्पादन प्रतिक्रिया वा त्रुटिहरू छन् भने: [![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) --- -**अस्वीकरण**: -यस दस्तावेजलाई AI अनुवाद सेवा [Co-op Translator](https://github.com/Azure/co-op-translator) प्रयोग गरी अनुवाद गरिएको हो। हामी शुद्धताका लागि प्रयासरत छौं, तर कृपया ध्यान दिनुहोस् कि स्वचालित अनुवादहरूमा त्रुटिहरू वा असम्मानहरू हुन सक्छन्। मूल भाषामा रहेको दस्तावेजलाई अधिकारभूत स्रोतको रूपमा मानिनुपर्छ। महत्त्वपूर्ण जानकारीका लागि व्यावसायिक मानव अनुवादलाई सिफारिस गरिन्छ। यस अनुवादको प्रयोगबाट हुने कुनै पनि भ्रान्ति वा गलत व्याख्याका लागि हामी जिम्मेवार हुने छैनौं। +**अस्वीकरण**: +यो कागजात AI अनुवाद सेवा [Co-op Translator](https://github.com/Azure/co-op-translator) प्रयोग गरी अनुवाद गरिएको हो। हामी शुद्धताको लागि प्रयास गर्छौं, तर कृपया ध्यान दिनुहोस् कि स्वचालित अनुवादमा त्रुटि वा गलतफहमी हुन सक्दछ। मूल कागजात यसको मूल भाषामा आधिकारिक स्रोत मानिनुपर्छ। महत्वपूर्ण जानकारीका लागि व्यावसायिक मानवीय अनुवाद सिफारिस गरिन्छ। यस अनुवादको प्रयोगबाट उत्पन्न कुनै पनि गलतफहमी वा व्याख्यामा हामी जिम्मेवार हुनेछैनौं। \ No newline at end of file diff --git a/translations/ne/lessons/0-course-setup/how-to-run.md b/translations/ne/lessons/0-course-setup/how-to-run.md index dd22de75..682fe5ce 100644 --- a/translations/ne/lessons/0-course-setup/how-to-run.md +++ b/translations/ne/lessons/0-course-setup/how-to-run.md @@ -1,21 +1,21 @@ # कोड कसरी चलाउने -यो पाठ्यक्रममा धेरै चलाउन मिल्ने उदाहरणहरू र प्रयोगशालाहरू छन् जुन तपाईं चलाउन चाहनुहुन्छ। यसलाई गर्नको लागि, तपाईंलाई यो पाठ्यक्रमको भागको रूपमा प्रदान गरिएका Jupyter Notebooks मा Python कोड चलाउन सक्ने क्षमता चाहिन्छ। कोड चलाउनका लागि तपाईंसँग केही विकल्पहरू छन्: +यो पाठ्यक्रममा धेरै कार्यान्वयनयोग्य उदाहरणहरू र प्रयोगशाला छन् जुन तपाईंले चलाउन चाहनुहुन्छ। यसलाई गर्नका लागि, तपाईंलाई यो पाठ्यक्रमको एक हिस्सा स्वरूप उपलब्ध गराइएको Jupyter नोटबुकहरूमा Python कोड चलाउने क्षमता आवश्यक छ। तपाईंलाई कोड चलाउनका लागि कयौं विकल्पहरू उपलब्ध छन्: -## आफ्नो कम्प्युटरमा स्थानीय रूपमा चलाउनुहोस् +## तपाईको कम्प्युटरमा स्थानीय रूपमा चलाउने -कोडलाई आफ्नो कम्प्युटरमा स्थानीय रूपमा चलाउनको लागि, तपाईंलाई Python को केही संस्करण स्थापना गर्न आवश्यक छ। म व्यक्तिगत रूपमा **[miniconda](https://conda.io/en/latest/miniconda.html)** स्थापना गर्न सिफारिस गर्छु - यो हल्का स्थापना हो जसले विभिन्न Python **भर्चुअल वातावरणहरू** को लागि `conda` प्याकेज प्रबन्धकलाई समर्थन गर्दछ। +तपाईंको कम्प्युटरमा कोड स्थानीय रूपमा चलाउन Python स्थापना आवश्यक हुन्छ। एउटा सिफारिस भनेको **[miniconda](https://conda.io/en/latest/miniconda.html)** स्थापना गर्नु हो - यो हल्का स्थापना हो जुन विभिन्न Python **भर्चुअल वातावरणहरू** को लागि `conda` प्याकेज म्यानेजरलाई समर्थन गर्छ। -Miniconda स्थापना गरेपछि, तपाईंले रिपोजिटरी क्लोन गर्नुपर्नेछ र यो पाठ्यक्रमको लागि प्रयोग गर्न भर्चुअल वातावरण सिर्जना गर्नुपर्नेछ: +miniconda स्थापना गरेपछि, रिपोजिटरी क्लोन गर्नुहोस् र यस कोर्सका लागि प्रयोग गरिने भर्चुअल वातावरण सिर्जना गर्नुहोस्: ```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 र Python Extension प्रयोग गर्दै +### Python विस्तारसँग Visual Studio Code प्रयोग गर्दै -शायद पाठ्यक्रम प्रयोग गर्ने सबैभन्दा राम्रो तरिका भनेको यसलाई [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) मा [Python Extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste) सहित खोल्नु हो। +यो पाठ्यक्रम [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) मा [Python विस्तार](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste) सहित खोल्दा सबैभन्दा राम्रो प्रयोग हुन्छ। -> **Note**: जब तपाईं रिपोजिटरी क्लोन गरेर VS Code मा खोल्नुहुन्छ, यसले तपाईंलाई Python एक्सटेन्सन स्थापना गर्न सुझाव दिनेछ। तपाईंले माथि वर्णन गरिएको अनुसार Miniconda पनि स्थापना गर्नुपर्नेछ। +> **Note**: तपाइँले जब रिपोजिटरी क्लोन गरी VS Code मा खोल्नुहुन्छ, तब यसले तपाईंलाई Python विस्तारहरू स्थापना गर्न स्वचालित सल्लाह दिनेछ। माथि वर्णन गरेअनुसार miniconda पनि स्थापित गर्नुपर्ने हुन्छ। -> **Note**: यदि VS Code ले रिपोजिटरीलाई कन्टेनरमा पुनः खोल्न सुझाव दिन्छ भने, तपाईंले स्थानीय Python स्थापना प्रयोग गर्न यसलाई अस्वीकार गर्नुपर्नेछ। +> **Note**: यदि VS Code ले रिपोजिटरीलाई कन्टेनरमा पुन: खोल्न सुझाव दिन्छ भने, स्थानीय Python स्थापना प्रयोग गर्नको लागि तपाईंले यो अस्वीकार गर्नुपर्छ। ### ब्राउजरमा Jupyter प्रयोग गर्दै -तपाईं आफ्नो कम्प्युटरमा ब्राउजरबाट Jupyter वातावरण पनि प्रयोग गर्न सक्नुहुन्छ। वास्तवमा, क्लासिकल Jupyter र Jupyter Hub दुवैले स्वतः-पूर्णता, कोड हाइलाइटिङ, आदि सहित धेरै सुविधाजनक विकास वातावरण प्रदान गर्छन्। +तपाईं आफ्नै कम्प्युटरमा ब्राउजरबाट पनि Jupyter वातावरण प्रयोग गर्न सक्नुहुन्छ। दुवै क्लासिकल Jupyter र JupyterHub ले स्वत: पूर्ति, कोड हाइलाइटिङ, आदि सहित सुविधाजनक विकास वातावरण प्रदान गर्छन्। -स्थानीय रूपमा Jupyter सुरु गर्नको लागि, पाठ्यक्रमको डाइरेक्टरीमा जानुहोस् र निम्न आदेश चलाउनुहोस्: +स्थानीय रूपमा Jupyter सुरु गर्न, कोर्सको डाइरेक्टरीमा जानुहोस् र यसलाई कार्यान्वयन गर्नुहोस्: ```bash jupyter notebook ``` -वा + अथवा ```bash jupyterhub ``` -त्यसपछि तपाईं कुनै पनि `.ipynb` फाइलहरूमा नेभिगेट गर्न सक्नुहुन्छ, तिनीहरू खोल्न सक्नुहुन्छ र काम सुरु गर्न सक्नुहुन्छ। + तपाईं त्यसपछि कुनै पनि `.ipynb` फाइलमा नेभिगेट गरी खोल्न र काम सुरु गर्न सक्नुहुन्छ। -### कन्टेनरमा चलाउँदै +### कन्टेनरमा चलाउने -Python स्थापना गर्ने अर्को विकल्प भनेको कोडलाई कन्टेनरमा चलाउनु हो। हाम्रो रिपोजिटरीमा विशेष `.devcontainer` फोल्डर समावेश छ जसले यस रिपोजिटरीको लागि कन्टेनर कसरी निर्माण गर्ने भनेर निर्देशन दिन्छ, त्यसैले VS Code ले तपाईंलाई कोडलाई कन्टेनरमा पुनः खोल्न प्रस्ताव गर्नेछ। यसले Docker स्थापना आवश्यक पर्नेछ, र यो अलि जटिल हुनेछ, त्यसैले हामी यसलाई अनुभवी प्रयोगकर्ताहरूलाई सिफारिस गर्छौं। +Python स्थापना गर्ने एउटा विकल्प भनेको कन्टेनरमा कोड चलाउनु हो। हाम्रो रिपोजिटरीमा एउटा विशेष `.devcontainer` फोल्डर रहेको छ जसले यस रिपोको लागि कन्टेनर कसरी बनाउने निर्देशन दिन्छ, त्यसैले VS Code ले कोडलाई कन्टेनरमा पुन: खोल्ने मौका दिन्छ। यसका लागि Docker स्थापना आवश्यक हुन्छ, र यो जटिल पनि हुन सक्छ, त्यसैले हामी यसलाई बढी अनुभवी प्रयोगकर्ताहरूलाई सिफारिस गर्छौं। -## क्लाउडमा चलाउँदै +## क्लाउडमा चलाउने -यदि तपाईं Python स्थानीय रूपमा स्थापना गर्न चाहनुहुन्न, र केही क्लाउड स्रोतहरूमा पहुँच छ भने - क्लाउडमा कोड चलाउने राम्रो विकल्प हुनेछ। तपाईंले यसलाई गर्नका लागि केही तरिकाहरू छन्: +यदि तपाईंले Python स्थानीय रूपमा स्थापना गर्न चाहनुहुन्न र तपाईंलाई केही क्लाउड स्रोतहरू पहुँचयोग्य छन् भने—कोड क्लाउडमा चलाउनु राम्रो विकल्प हो। तपाईंले यसलाई गर्नका लागि विभिन्न तरिकाहरू छन्: -* **[GitHub Codespaces](https://github.com/features/codespaces)** प्रयोग गर्दै, जुन GitHub मा तपाईंको लागि सिर्जना गरिएको भर्चुअल वातावरण हो, VS Code ब्राउजर इन्टरफेस मार्फत पहुँचयोग्य। यदि तपाईंलाई Codespaces मा पहुँच छ भने, तपाईंले रिपोजिटरीमा **Code** बटन क्लिक गर्न सक्नुहुन्छ, Codespace सुरु गर्न सक्नुहुन्छ, र तुरुन्तै चलाउन सुरु गर्न सक्नुहुन्छ। -* **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)** प्रयोग गर्दै। [Binder](https://mybinder.org) निःशुल्क कम्प्युटिङ स्रोत हो जुन GitHub मा केही कोड परीक्षण गर्न चाहने व्यक्तिहरूका लागि प्रदान गरिएको हो। फ्रन्ट पेजमा Binder मा रिपोजिटरी खोल्ने बटन छ - यसले तपाईंलाई छिट्टै Binder साइटमा लैजानेछ, जसले आधारभूत कन्टेनर निर्माण गर्नेछ र Jupyter वेब इन्टरफेसलाई सहज रूपमा सुरु गर्नेछ। +* **[GitHub Codespaces](https://github.com/features/codespaces)** प्रयोग गर्दै, जुन GitHub मा तपाईंको लागि सिर्जना गरिएको एक भर्चुअल वातावरण हो, VS Code ब्राउजर इन्टरफेसमार्फत पहुँचयोग्य। यदि तपाईंलाई Codespaces पहुँच छ भने, रिपोमा रहेको **Code** बटनमा क्लिक गरेर Codespace सुरु गर्न सक्नुहुन्छ र तुरुन्तै काम सुरु गर्न सक्नुहुन्छ। +* **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)** प्रयोग गर्दै। [Binder](https://mybinder.org) ले GitHub मा केही कोड परीक्षण गर्नका लागि क्लाउडमा निःशुल्क कम्प्युटिंग स्रोतहरू प्रदान गर्छ। मुख्य पृष्ठमा रिपोजिटरी Binder मा खोल्ने बटन हुन्छ - यसले छिटो तपाईंलाई Binder साइटमा लग्नेछ, जुन एउटा अन्तर्निहित कन्टेनर निर्माण गर्नेछ र तपाईंका लागि सहज रूपमा Jupyter वेब इन्टरफेस सुरु गर्नेछ। -> **Note**: दुरुपयोग रोक्नको लागि, Binder ले केही वेब स्रोतहरूमा पहुँच रोक्छ। यसले सार्वजनिक इन्टरनेटबाट मोडेलहरू र/वा डेटासेटहरू ल्याउने कोडलाई काम गर्न रोक्न सक्छ। तपाईंले केही वैकल्पिक उपायहरू खोज्नुपर्ने हुन सक्छ। साथै, Binder द्वारा प्रदान गरिएका कम्प्युटिङ स्रोतहरू धेरै आधारभूत छन्, त्यसैले प्रशिक्षण ढिलो हुनेछ, विशेष गरी पछि जटिल पाठहरूमा। +> **Note**: दुरुपयोग रोक्न Binder लाई केही वेब स्रोतहरूमा पहुँच अवरुद्ध गरिएको छ। यसले केही कोड काम नगर्न सक्छ जुन सार्वजनिक इन्टरनेटबाट मोडेलहरू र/वा डेटासेटहरू ल्याउँछ। तपाईंले केही समाधानहरू खोज्नुपर्ने हुन सक्छ। साथै, Binder द्वारा प्रदान गरिएका कम्प्युट स्रोतहरू धेरै आधारभूत हुन्छन्, त्यसैले प्रशिक्षण विशेष गरी पछिल्ला, जटिल पाठहरूमा ढिलो हुनेछ। -## GPU सहित क्लाउडमा चलाउँदै +## GPU सहित क्लाउडमा चलाउने -यो पाठ्यक्रमका केही पछिल्ला पाठहरू GPU समर्थनबाट धेरै लाभान्वित हुनेछन्, किनभने अन्यथा प्रशिक्षण अत्यन्तै ढिलो हुनेछ। तपाईंले केही विकल्पहरू अनुसरण गर्न सक्नुहुन्छ, विशेष गरी यदि तपाईं [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) मार्फत, वा आफ्नो संस्थामार्फत क्लाउडमा पहुँच छ भने: +यस पाठ्यक्रमका केही पछि आएका पाठहरूले GPU समर्थनबाट ठूलो फाइदा उठाउन सक्दछन्। उदाहरणका लागि मोडेल प्रशिक्षण यदि GPU नहुँदा धेरै सुस्त हुन सक्छ। तपाईंले केही विकल्पहरू अपनाउन सक्नुहुन्छ, विशेष गरी तपाईंले क्लाउडमा पहुँच छ भने, वा [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) मार्फत, वा आफ्नो संस्थानमार्फत: -* [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) सिर्जना गर्नुहोस् र Jupyter मार्फत यसमा जडान गर्नुहोस्। त्यसपछि तपाईंले रिपोजिटरीलाई सोही मेसिनमा क्लोन गर्न सक्नुहुन्छ र सिक्न सुरु गर्न सक्नुहुन्छ। NC-शृंखला VM हरूमा GPU समर्थन छ। +* [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) सिर्जना गरी Jupyter मार्फत यसमा जडान गर्नुहोस्। त्यसपछि तपाईं यस मेसिनमा रिपो क्लोन गरेर सिक्न सुरु गर्न सक्नुहुन्छ। NC-series VM हरू GPU समर्थन गर्छन्। -> **Note**: केही सदस्यताहरू, Azure for Students सहित, GPU समर्थन स्वतः प्रदान गर्दैन। तपाईंले प्राविधिक समर्थन अनुरोध मार्फत थप GPU कोरहरू अनुरोध गर्नुपर्ने हुन सक्छ। +> **Note**: केही सदस्यताहरू, जस्तै Azure for Students, GPU समर्थन डिफल्ट रूपमा उपलब्ध गराउँदैनन्। तपाईंले प्राविधिक सहायता अनुरोध गरेर थप GPU कोर माग्नुपर्ने हुन सक्छ। -* [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) सिर्जना गर्नुहोस् र त्यहाँको Notebook सुविधा प्रयोग गर्नुहोस्। [यो भिडियो](https://azure-for-academics.github.io/quickstart/azureml-papers/) ले Azure ML Notebook मा रिपोजिटरी क्लोन गरेर प्रयोग गर्न सुरु गर्ने तरिका देखाउँछ। +* [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) सिर्जना गरी त्यहाँका Notebook सुविधा प्रयोग गर्नुहोस्। [यो भिडियो](https://azure-for-academics.github.io/quickstart/azureml-papers/) ले Azure ML नोटबुकमा रिपोजिटरी क्लोन गरी सुरु गर्ने तरिका देखाउँछ। -तपाईं Google Colab पनि प्रयोग गर्न सक्नुहुन्छ, जसले केही निःशुल्क GPU समर्थन प्रदान गर्दछ, र त्यहाँ Jupyter Notebooks अपलोड गरेर तिनीहरूलाई एक-एक गरेर चलाउन सक्नुहुन्छ। +तपाईं Google Colab पनि प्रयोग गर्न सक्नुहुन्छ, जससँग केही निःशुल्क GPU समर्थन हुन्छ, र त्यहाँ Jupyter नोटबुकहरू अपलोड गरेर एक-एक गरी ती कार्यान्वयन गर्न सक्नुहुन्छ। -**अस्वीकरण**: -यो दस्तावेज़ AI अनुवाद सेवा [Co-op Translator](https://github.com/Azure/co-op-translator) प्रयोग गरी अनुवाद गरिएको हो। हामी यथासम्भव सही अनुवाद प्रदान गर्न प्रयास गर्छौं, तर कृपया ध्यान दिनुहोस् कि स्वचालित अनुवादमा त्रुटिहरू वा अशुद्धताहरू हुन सक्छन्। यसको मूल भाषामा रहेको मूल दस्तावेज़लाई आधिकारिक स्रोत मानिनुपर्छ। महत्वपूर्ण जानकारीका लागि, व्यावसायिक मानव अनुवाद सिफारिस गरिन्छ। यस अनुवादको प्रयोगबाट उत्पन्न हुने कुनै पनि गलतफहमी वा गलत व्याख्याका लागि हामी जिम्मेवार हुने छैनौं। \ No newline at end of file +--- + + +**अस्वीकरण**: +यस दस्तावेजलाई AI अनुवाद सेवा [Co-op Translator](https://github.com/Azure/co-op-translator) को प्रयोग गरी अनुवाद गरिएको हो। हामी शुद्धताका लागि प्रयासरत छौं भने पनि, कृपया बुझ्नुस् कि स्वचालित अनुवादमा त्रुटि वा असंगतिले हुन सक्छ। मूल भाषामा रहेको दस्तावेजलाई अधिकारिक स्रोतको रूपमा लिनुहोस्। महत्वपूर्ण जानकारीका लागि व्यावसायिक मानवीय अनुवाद सिफारिस गरिन्छ। यस अनुवादको प्रयोगबाट हुने कुनै पनि गलतफहमी वा गलत व्याख्याका लागि हामी जिम्मेवार छैनौं। + \ No newline at end of file diff --git a/translations/ne/lessons/2-Symbolic/Animals.ipynb b/translations/ne/lessons/2-Symbolic/Animals.ipynb index fce7642f..2a71eda5 100644 --- a/translations/ne/lessons/2-Symbolic/Animals.ipynb +++ b/translations/ne/lessons/2-Symbolic/Animals.ipynb @@ -6,25 +6,25 @@ "collapsed": true }, "source": [ - "# जनावर विशेषज्ञ प्रणाली कार्यान्वयन\n", + "# जनावर विशेषज्ञ प्रणाली कार्यान्वयन गर्दै\n", "\n", - "[AI for Beginners Curriculum](http://github.com/microsoft/ai-for-beginners) बाट लिएको एक उदाहरण।\n", + "[AI for Beginners Curriculum](http://github.com/microsoft/ai-for-beginners) बाट एक उदाहरण।\n", "\n", - "यस नमूनामा, हामी केही भौतिक विशेषताहरूको आधारमा जनावर पहिचान गर्नको लागि एक साधारण ज्ञान-आधारित प्रणाली कार्यान्वयन गर्नेछौं। प्रणालीलाई निम्न AND-OR रूखद्वारा प्रतिनिधित्व गर्न सकिन्छ (यो सम्पूर्ण रूखको एक भाग हो, हामी सजिलै थप नियमहरू थप्न सक्छौं):\n", + "यस नमूनामा, हामी केही भौतिक विशेषताहरूमा आधारित जनावर निर्धारण गर्न एक साधारण ज्ञान-आधारित प्रणाली कार्यान्वयन गर्नेछौं। प्रणालीलाई निम्न AND-OR रूखद्वारा प्रतिनिधित्व गर्न सकिन्छ (यो सम्पूर्ण रूखको एक भाग हो, हामी सजिलै केहि थप नियमहरू थप्न सक्छौं):\n", "\n", - "![](../../../../translated_images/ne/AND-OR-Tree.5592d2c70187f283.webp)\n" + "![](../../../../../../translated_images/ne/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## हाम्रो आफ्नै विशेषज्ञ प्रणाली शेल ब्याकवर्ड इन्फरेन्सको साथ\n", + "## हाम्रो आफ्नै विशेषज्ञ प्रणाली शेल पछाडि अन्वेषणसहित\n", "\n", - "उत्पादन नियमहरूमा आधारित ज्ञान प्रतिनिधित्वको लागि एउटा साधारण भाषा परिभाषित गर्ने प्रयास गरौं। हामी नियमहरू परिभाषित गर्न Python कक्षाहरूलाई कुञ्जीशब्दको रूपमा प्रयोग गर्नेछौं। मुख्यतया ३ प्रकारका कक्षाहरू हुनेछन्:\n", - "* `Ask` एउटा प्रश्नलाई प्रतिनिधित्व गर्छ जुन प्रयोगकर्तालाई सोध्न आवश्यक छ। यसमा सम्भावित उत्तरहरूको सेट समावेश हुन्छ।\n", - "* `If` एउटा नियमलाई प्रतिनिधित्व गर्छ, र यो नियमको सामग्री भण्डारण गर्नको लागि मात्र एक प्रकारको सिंट्याक्टिक सुगर हो।\n", - "* `AND`/`OR` कक्षाहरू रूखको AND/OR शाखाहरूलाई प्रतिनिधित्व गर्नका लागि हुन्। यीले भित्रका तर्कहरूको सूची मात्र भण्डारण गर्छन्। कोडलाई सरल बनाउन, सबै कार्यक्षमता अभिभावक कक्षा `Content` मा परिभाषित गरिएको छ।\n" + "उत्पादन नियमहरूमा आधारित ज्ञान प्रतिनिधित्वको लागि सरल भाषा परिभाषित गर्ने प्रयास गरौं। हामी नियमहरू परिभाषित गर्न कुञ्जीशब्दहरू रूपमा Python कक्षाहरू प्रयोग गर्नेछौं। आधारभूत रूपमा ३ प्रकारका कक्षाहरू हुनेछन्ः\n", + "* `Ask` प्रयोगकर्तालाई सोध्नुपर्ने प्रश्न प्रतिनिधित्व गर्छ। यसले सम्भावित उत्तरहरूको सेट समावेश गर्छ।\n", + "* `If` नियम प्रतिनिधित्व गर्छ, र यो नियमको सामग्री राख्नको लागि केवल एक व्याकरणिक सजावट हो\n", + "* `AND`/`OR` रूखका AND/OR शाखाहरूलाई प्रतिनिधित्व गर्नका लागि कक्षाहरू हुन्। यी भित्रका तर्कहरूको सूची मात्र राख्छन्। कोडलाई सरल बनाउन सबै कार्यक्षमताहरू अभिभावक कक्षा `Content` मा परिभाषित छन्।\n" ] }, { @@ -66,7 +66,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "हाम्रो प्रणालीमा, कार्य स्मृतिमा **तथ्यहरू** को सूची **गुण-मान जोडीहरू** को रूपमा समावेश हुनेछ। ज्ञान आधारलाई एउटा ठूलो शब्दकोशको रूपमा परिभाषित गर्न सकिन्छ जसले क्रियाहरू (नयाँ तथ्यहरू जुन कार्य स्मृतिमा समावेश गर्नुपर्छ) लाई सर्तहरूसँग नक्सा गर्दछ, जुन AND-OR अभिव्यक्तिहरूको रूपमा व्यक्त गरिन्छ। साथै, केही तथ्यहरूलाई `सोध्न` सकिन्छ।\n" + "हाम्रो प्रणालीमा, कार्य स्मृति मा **तथ्यहरू** को सूची **गुण-अर्थ जोडीहरूको रूपमा** समावेश हुन्छ। ज्ञानभण्डारलाई एउटा ठूलो शब्दकोशको रूपमा परिभाषित गर्न सकिन्छ जसले क्रियाकलापहरू (नयाँ तथ्यहरू जुन कार्य स्मृतिमा थपिनुपर्छ) लाई सशर्तहरूमा नक्साङ्कन गर्छ, जुन AND-OR अभिव्यक्तिहरूको रूपमा व्यक्त गरिएको हुन्छ। साथै, केही तथ्यहरूलाई `पूछ्न` सकिन्छ।\n" ] }, { @@ -99,13 +99,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "पछाडि तर्फको अनुमान गर्नको लागि, हामी `Knowledgebase` नामक कक्षा परिभाषित गर्नेछौं। यसले समावेश गर्नेछ:\n", - "* कार्यरत `memory` - एउटा शब्दकोश जसले विशेषताहरूलाई मानहरूसँग नक्सा बनाउँछ\n", - "* Knowledgebase `rules` - माथि परिभाषित गरिएको ढाँचामा\n", + "पछाडिको निरुपण गर्नका लागि, हामी `Knowledgebase` कक्षा परिभाषित गर्नेछौं। यसले समावेश गर्नेछ:\n", + "* कार्यरत `memory` - एउटा शब्दकोश जसले विशेषताहरूलाई मानहरूमा नक्सांकन गर्छ\n", + "* माथि परिभाषित स्वरूपमा Knowledgebase `rules`\n", "\n", - "दुई मुख्य विधिहरू छन्:\n", - "* `get` - कुनै विशेषताको मान प्राप्त गर्न प्रयोग गरिन्छ, आवश्यक परे अनुमान गर्दै। उदाहरणका लागि, `get('color')` ले रंग स्लटको मान प्राप्त गर्नेछ (आवश्यक परे सोध्नेछ, र पछि प्रयोगको लागि कार्यरत मेमोरीमा मान भण्डारण गर्नेछ)। यदि हामी `get('color:blue')` सोध्छौं भने, यो रंगको लागि सोध्नेछ, र त्यसपछि रंगको आधारमा `y`/`n` मान फर्काउनेछ।\n", - "* `eval` - वास्तविक अनुमान प्रदर्शन गर्ने विधि हो, अर्थात् AND/OR ट्रीको यात्रा गर्ने, उप-लक्ष्यहरूको मूल्यांकन गर्ने, आदि।\n" + "दुई मुख्य विधिहरू हुन्:\n", + "* `get` विशेषताको मान प्राप्त गर्न, आवश्यक परेमा निरुपण गर्दै। उदाहरणका लागि, `get('color')` ले रंग स्लटको मान प्राप्त गर्नेछ (आवश्यक परेमा सोध्नेछ, र पछि प्रयोगको लागि मान कार्यरत स्मृतिमा भण्डारण गर्नेछ)। यदि हामी `get('color:blue')` सोध्यौं भने, यो रंग सोध्नेछ, र त्यसपछि रंग अनुसार `y`/`n` मान फर्काउनेछ।\n", + "* `eval` वास्तविक निरुपण सञ्चालन गर्दछ, अर्थात AND/OR रुखमा यात्रा गर्ने, उप-लक्ष्यहरू मूल्याङ्कन गर्ने आदि।\n" ] }, { @@ -172,7 +172,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "अब हामी हाम्रो जनावर ज्ञानको आधार परिभाषित गरौं र परामर्श सञ्चालन गरौं। ध्यान दिनुहोस् कि यो कलले तपाईंलाई प्रश्नहरू सोध्नेछ। तपाईं `y`/`n` टाइप गरेर हो-होइन प्रश्नहरूको उत्तर दिन सक्नुहुन्छ, वा लामो बहुविकल्पीय उत्तरहरू भएका प्रश्नहरूको लागि संख्या (0..N) निर्दिष्ट गरेर उत्तर दिन सक्नुहुन्छ।\n" + "अब हामी हाम्रो जनावर ज्ञान आधार परिभाषित गर्छौं र परामर्श सम्पन्न गर्छौं। ध्यान दिनुहोस् कि यो कलले तपाईंलाई प्रश्न सोध्नेछ। तपाईंले हो-होइन प्रश्नहरूको लागि `y`/`n` टाइप गरेर उत्तर दिन सक्नुहुन्छ, वा लामो बहुविकल्पी उत्तरहरू भएका प्रश्नहरूको लागि संख्या (0..N) निर्धारण गरेर उत्तर दिन सक्नुहुन्छ।\n" ] }, { @@ -229,11 +229,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## PyKnow प्रयोग गरेर फर्वार्ड इन्फरेन्स\n", + "## अग्रगामी अनुमानका लागि Experta को प्रयोग\n", "\n", - "अर्को उदाहरणमा, हामी ज्ञान प्रतिनिधित्वका लागि उपलब्ध पुस्तकालयहरूमध्ये एक, [PyKnow](https://github.com/buguroo/pyknow/) प्रयोग गरेर फर्वार्ड इन्फरेन्स कार्यान्वयन गर्ने प्रयास गर्नेछौं। **PyKnow** एउटा पुस्तकालय हो जसले Python मा फर्वार्ड इन्फरेन्स प्रणालीहरू बनाउनका लागि प्रयोग गरिन्छ, जुन पुरानो क्लासिकल प्रणाली [CLIPS](http://www.clipsrules.net/index.html) जस्तै डिजाइन गरिएको छ।\n", + "अर्को उदाहरणमा, हामी ज्ञान प्रतिनिधित्वका लागि उपलब्ध पुस्तकालयहरूमध्ये एक, [Experta](https://github.com/nilp0inter/experta) को प्रयोग गरेर अग्रगामी अनुमान कार्यान्वयन गर्ने प्रयास गर्नेछौं। **Experta** पायथनमा अग्रगामी अनुमान प्रणालीहरू सिर्जना गर्नको लागि एक पुस्तकालय हो, जसलाई पुरानो कक्षा प्रणाली [CLIPS](http://www.clipsrules.net/index.html) सँग समान डिजाइन गरिएको छ।\n", "\n", - "हामीले फर्वार्ड चेइनिङ आफैं पनि धेरै समस्याबिना कार्यान्वयन गर्न सक्थ्यौं, तर साधारण कार्यान्वयनहरू प्रायः धेरै प्रभावकारी हुँदैनन्। नियम मिलानलाई अझ प्रभावकारी बनाउनका लागि एउटा विशेष एल्गोरिदम [Rete](https://en.wikipedia.org/wiki/Rete_algorithm) प्रयोग गरिन्छ।\n" + "हामीले अगाडि श्रृंखला पनि सजिलै आफैं कार्यान्वयन गर्न सक्थ्यौं, तर साधारण कार्यान्वयनहरू प्रायः धेरै कुशल हुँदैनन्। नियम मिलानको प्रभावकारीतालाई बढाउनका लागि विशेष एल्गोरिदम [Rete](https://en.wikipedia.org/wiki/Rete_algorithm) प्रयोग गरिन्छ।\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": [ - "हामी हाम्रो प्रणालीलाई `KnowledgeEngine` को उपवर्गको रूपमा कक्षा रूपमा परिभाषित गर्नेछौं। प्रत्येक नियम `@Rule` एनोटेशनसहितको छुट्टै कार्यद्वारा परिभाषित गरिन्छ, जसले नियम कहिले कार्यान्वयन हुनुपर्छ भनेर निर्दिष्ट गर्दछ। नियमभित्र, हामी `declare` कार्य प्रयोग गरेर नयाँ तथ्यहरू थप्न सक्छौं, र ती तथ्यहरू थप्दा अगाडि निष्कर्ष इन्जिनद्वारा केही थप नियमहरू बोलाइनेछ।\n" + "हामी हाम्रो प्रणालीलाई `KnowledgeEngine` को सबक्लास हुने क्लासको रूपमा परिभाषित गर्नेछौं। प्रत्येक नियमलाई `@Rule` एनोटेशन भएको अलग फंक्शनद्वारा परिभाषित गरिन्छ, जसले नियम कहिले लागू हुने हो भनी निर्दिष्ट गर्छ। नियमभित्र, हामी `declare` फंक्शनको प्रयोग गरेर नयाँ तथ्यहरू थप्न सक्छौं, र ती तथ्यहरू थप्दा अग्रगामी अनुमान इन्जिनद्वारा केही अरु नियमहरू लागू हुनेछन्।\n" ] }, { @@ -378,7 +377,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "एक पटक हामीले ज्ञान आधार परिभाषित गरेपछि, हामी हाम्रो कार्यरत स्मृतिलाई केही प्रारम्भिक तथ्यहरूद्वारा भरिन्छौं, र त्यसपछि `run()` विधिलाई कल गरेर निष्कर्ष निकाल्ने कार्य गर्दछौं। परिणामस्वरूप, तपाईंले देख्न सक्नुहुन्छ कि नयाँ निष्कर्ष निकालिएका तथ्यहरू कार्यरत स्मृतिमा थपिएका छन्, जसमा जनावरको अन्तिम तथ्य पनि समावेश छ (यदि हामीले सबै प्रारम्भिक तथ्यहरू सही रूपमा सेटअप गरेका छौं भने)।\n" + "एक पटक हामीले ज्ञानको आधार परिभाषित गरेपछि, हामीले हाम्रो कार्य सम्झनामा केही प्रारम्भिक तथ्यहरू राख्छौं, र त्यसपछि inference प्रदर्शन गर्न `run()` मेथड कल गर्छौं। तपाईंले नतिजाको रूपमा देख्न सक्नुहुन्छ कि नयाँ अनुमानित तथ्यहरू कार्य सम्झनामा थपिन्छन्, जसमा जनावरबारे अन्तिम तथ्य पनि समावेश छ (यदि हामीले सबै प्रारम्भिक तथ्यहरू ठीकसँग सेट अप गरेका छौं भने)।\n" ] }, { @@ -440,7 +439,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n---\n\n**अस्वीकरण**: \nयो दस्तावेज़ AI अनुवाद सेवा [Co-op Translator](https://github.com/Azure/co-op-translator) प्रयोग गरी अनुवाद गरिएको हो। हामी यथासम्भव सटीकता सुनिश्चित गर्न प्रयास गर्छौं, तर कृपया ध्यान दिनुहोस् कि स्वचालित अनुवादहरूमा त्रुटिहरू वा अशुद्धताहरू हुन सक्छन्। यसको मूल भाषामा रहेको मूल दस्तावेज़लाई आधिकारिक स्रोत मानिनुपर्छ। महत्त्वपूर्ण जानकारीका लागि, व्यावसायिक मानव अनुवाद सिफारिस गरिन्छ। यस अनुवादको प्रयोगबाट उत्पन्न हुने कुनै पनि गलतफहमी वा गलत व्याख्याका लागि हामी जिम्मेवार हुने छैनौं। \n" + "---\n\n\n**अस्वीकरण**:\nयो दस्तावेज AI अनुवाद सेवा [Co-op Translator](https://github.com/Azure/co-op-translator) को प्रयोग गरेर अनुवाद गरिएको हो। हामी शुद्धताका लागि प्रयासरत छौं, तर कृपया बुझ्नुहोस् कि स्वचालित अनुवादहरूमा त्रुटि वा अशुद्धता हुन सक्छ। मूल दस्तावेज यसको मातृ भाषामा अधिकृत स्रोत मानिनुपर्छ। महत्वपूर्ण जानकारीको लागि, व्यावसायिक मानव अनुवाद सिफारिस गरिन्छ। यस अनुवादको प्रयोगबाट उत्पन्न कुनै पनि भ्रम वा गलत व्याख्याहरूको लागि हामी उत्तरदायी छैनौं।\n\n" ] } ], @@ -467,8 +466,8 @@ "version": "3.11.2" }, "coopTranslator": { - "original_hash": "ab2bd97b0453415b89a469284609a8ce", - "translation_date": "2025-08-28T08:35:22+00:00", + "original_hash": "8ef43db4b9182239fd150a76bd494fdb", + "translation_date": "2026-01-15T12:53:10+00:00", "source_file": "lessons/2-Symbolic/Animals.ipynb", "language_code": "ne" } diff --git a/translations/ne/lessons/2-Symbolic/README.md b/translations/ne/lessons/2-Symbolic/README.md index b5cf0e9a..b7a33593 100644 --- a/translations/ne/lessons/2-Symbolic/README.md +++ b/translations/ne/lessons/2-Symbolic/README.md @@ -1,116 +1,116 @@ -# ज्ञान प्रतिनिधित्व र विशेषज्ञ प्रणाली +# ज्ञान प्रस्तुतीकरण र विशेषज्ञ प्रणालीहरू -![सिम्बोलिक AI सामग्रीको सारांश](../../../../translated_images/ne/ai-symbolic.715a30cb610411a6.webp) +![संकेतिक AI सामग्रीको सारांश](../../../../../../translated_images/ne/ai-symbolic.715a30cb610411a6.webp) -> स्केच नोट [Tomomi Imura](https://twitter.com/girlie_mac) द्वारा +> स्केचनोट द्वारा [टोमोमी इमुरा](https://twitter.com/girlie_mac) -कृत्रिम बुद्धिमत्ताको खोजी ज्ञानको खोजीमा आधारित छ, जसले मानिसहरूले संसारलाई बुझ्ने तरिकालाई नक्कल गर्न प्रयास गर्दछ। तर, यो कसरी गर्न सकिन्छ? +कृत्रिम बुद्धिमत्ताको खोज ज्ञानको खोजमा आधारित छ, जसले संसारलाई मानिसहरूले जस्तै बुझ्न सक्छ। तर तपाईंले यो कसरी गर्ने? -## [पूर्व-व्याख्यान क्विज](https://ff-quizzes.netlify.app/en/ai/quiz/3) +## [पूर्व-लेक्चर क्विज](https://ff-quizzes.netlify.app/en/ai/quiz/3) -AI को सुरुवाती दिनहरूमा, बुद्धिमान प्रणालीहरू निर्माण गर्नको लागि शीर्ष-तल दृष्टिकोण (पछिल्लो पाठमा छलफल गरिएको) लोकप्रिय थियो। विचार यो थियो कि मानिसहरूबाट ज्ञान निकालेर यसलाई मेसिन-पढ्न सकिने स्वरूपमा रूपान्तरण गरियोस्, र त्यसपछि यसलाई स्वचालित रूपमा समस्याहरू समाधान गर्न प्रयोग गरियोस्। यो दृष्टिकोण दुई ठूला विचारहरूमा आधारित थियो: +AI को प्रारम्भिक दिनहरूमा, बुद्धिमान प्रणालीहरू बनाउन माथिबाट तलको तरिका (अघिल्लो पाठमा छलफल गरिएको) लोकप्रिय थियो। विचार यस्तो थियो कि मानिसहरूबाट ज्ञान निकालेर कम्प्युटरले पढ्न सक्ने रूपान्तरण गर्ने, अनि त्यसलाई समस्या समाधानमा स्वतः प्रयोग गर्ने। यो तरिका दुई ठूलो विचारमा आधारित थियो: -* ज्ञान प्रतिनिधित्व +* ज्ञान प्रस्तुतीकरण * तर्क -## ज्ञान प्रतिनिधित्व +## ज्ञान प्रस्तुतीकरण -सिम्बोलिक AI को महत्त्वपूर्ण अवधारणाहरू मध्ये एक हो **ज्ञान**। *सूचना* वा *डाटा* बाट ज्ञानलाई फरक पार्नु महत्त्वपूर्ण छ। उदाहरणका लागि, किताबहरूले ज्ञान समावेश गर्छन् भन्ने कुरा भन्न सकिन्छ, किनकि किताबहरू अध्ययन गरेर विशेषज्ञ बन्न सकिन्छ। तर, किताबहरूले समावेश गरेको कुरा वास्तवमा *डाटा* हो, र किताबहरू पढेर र यस डाटालाई हाम्रो संसारको मोडेलमा समाहित गरेर हामी यसलाई ज्ञानमा रूपान्तरण गर्छौं। +संकेतिक AI मा एउटा महत्त्वपूर्ण अवधारणा **ज्ञान** हो। ज्ञानलाई *सूचना* वा *डेटा* बाट अलग पार्न महत्त्वपूर्ण छ। उदाहरणका लागि, किताबहरूमा ज्ञान हुन्छ भन्न सकिन्छ किनकि किताब पढेर एक विशेषज्ञ बन्न सकिन्छ। तर किताबहरूले भएको वस्तु वास्तवमा *डेटा* हो, जुन पढेर र विश्व मोडेलमा मिसाएर हामी त्यो डेटालाई ज्ञानमा परिवर्तन गर्छौं। -> ✅ **ज्ञान** हाम्रो मस्तिष्कमा समावेश गरिएको कुरा हो जसले संसारको हाम्रो बुझाइलाई प्रतिनिधित्व गर्छ। यो एक सक्रिय **अध्ययन** प्रक्रियाद्वारा प्राप्त गरिन्छ, जसले हामीले प्राप्त गरेको सूचनाका टुक्राहरूलाई हाम्रो सक्रिय संसारको मोडेलमा समाहित गर्छ। +> ✅ **ज्ञान** हाम्रो टाउकोमा भएको र संसारको हाम्रो बुझाइलाई प्रतिनिधित्व गर्ने कुरा हो। यो सक्रिय **अध्ययन** प्रक्रियाबाट प्राप्त हुन्छ, जसले हामीले प्राप्त गरेको सूचना टुक्रा हाम्रो सक्रिय संसार मोडेलमा समाहित गर्दछ। -प्रायः, हामी ज्ञानलाई कडाइका साथ परिभाषित गर्दैनौं, तर यसलाई [DIKW पिरामिड](https://en.wikipedia.org/wiki/DIKW_pyramid) प्रयोग गरेर अन्य सम्बन्धित अवधारणाहरूको साथ मिलाउँछौं। यसमा निम्न अवधारणाहरू समावेश छन्: +अधिकांश अवस्थामा, हामी ज्ञानलाई कडाइले परिभाषित गर्दैनौं, तर यसलाई सम्बन्धित अन्य अवधारणासँग [DIKW पिरामिड](https://en.wikipedia.org/wiki/DIKW_pyramid) को प्रयोग गरेर मिलाउँछौं। यसमा निम्न अवधारणाहरू छन्: -* **डाटा** भौतिक माध्यममा प्रतिनिधित्व गरिएको कुरा हो, जस्तै लेखिएको पाठ वा बोलेको शब्द। डाटा मानिसहरूबाट स्वतन्त्र रूपमा अस्तित्वमा रहन्छ र मानिसहरू बीचमा आदानप्रदान गर्न सकिन्छ। -* **सूचना** भनेको हाम्रो मस्तिष्कमा डाटाको व्याख्या हो। उदाहरणका लागि, जब हामी *कम्प्युटर* शब्द सुन्छौं, हामीलाई यसको के हो भन्ने केही बुझाइ हुन्छ। -* **ज्ञान** भनेको हाम्रो संसारको मोडेलमा समाहित गरिएको सूचना हो। उदाहरणका लागि, जब हामी कम्प्युटर के हो भनेर सिक्छौं, हामीलाई यसको काम गर्ने तरिका, मूल्य, र यसको उपयोगका बारेमा केही विचार आउँछ। यी आपसमा सम्बन्धित अवधारणाहरूको जालोले हाम्रो ज्ञान बनाउँछ। -* **बुद्धिमत्ता** भनेको हाम्रो संसारको बुझाइको अर्को स्तर हो, र यसले *मेटा-ज्ञान* प्रतिनिधित्व गर्छ, जस्तै ज्ञान कहिले र कसरी प्रयोग गरिनु पर्छ भन्ने धारणा। +* **डेटा** भनेको भौतिक माध्यममा प्रतिनिधित्व गरिएका कुरा हुन्, जस्तै लेखिएको पाठ वा बोलेका शब्दहरू। डेटा मानिसहरूबाट स्वतन्त्र हुन्छ र मानिसहरूबीच साटासाट गर्न सकिन्छ। +* **सूचना** भनेको हामीले टाउकोमा डाटालाई कसरी व्याख्या गर्छौं भन्ने हो। उदाहरणका लागि, जब हामीले *कम्प्युटर* शब्द सुन्छौं, हामीलाई यसको केही बुझाइ हुन्छ। +* **ज्ञान** भनेको सूचना हाम्रो विश्व मोडेलमा समाहित हुनु हो। उदाहरणका लागि, एक पटक हामी कम्प्युटर के हो भनेर जान्दा, हामीलाई यसको काम गर्ने तरिका, लागत र प्रयोगबारे केही विचार आउँछ। यी अन्तरसम्बन्धित अवधारणाहरूको जालले हाम्रो ज्ञान बनाउँछ। +* **बुुद्धिमत्ता** भनेको हाम्रो बुझाइको एक स्तर हो, र यो *मेटा-ज्ञान* लाई प्रतिनिधित्व गर्छ, जस्तै ज्ञान कहिले र कसरी प्रयोग गर्ने भन्ने केही धारणा। - + -*छवि [विकिपिडियाबाट](https://commons.wikimedia.org/w/index.php?curid=37705247), Longlivetheux द्वारा - आफ्नै काम, CC BY-SA 4.0* +*चित्र [विकिपीडिया बाट](https://commons.wikimedia.org/w/index.php?curid=37705247), Longlivetheux द्वारा - आफ्नै काम, CC BY-SA 4.0* -त्यसैले, **ज्ञान प्रतिनिधित्व** को समस्या भनेको कम्प्युटर भित्र ज्ञानलाई डाटाको रूपमा प्रतिनिधित्व गर्ने केही प्रभावकारी तरिका पत्ता लगाउनु हो, ताकि यसलाई स्वचालित रूपमा प्रयोग गर्न सकियोस्। यसलाई एक स्पेक्ट्रमको रूपमा हेर्न सकिन्छ: +त्यसैले, **ज्ञान प्रस्तुतीकरण** को समस्या कम्प्युटर भित्र डेटा को रूपमा ज्ञानलाई प्रभावकारी रूपमा प्रस्तुत गर्ने तरिका खोज्नु हो, जसले स्वतः प्रयोग सक्षम बनाओस्। यसलाई एक स्पेक्ट्रमको रूपमा हेर्न सकिन्छ: -![ज्ञान प्रतिनिधित्व स्पेक्ट्रम](../../../../translated_images/ne/knowledge-spectrum.b60df631852c0217.webp) +![ज्ञान प्रस्तुतीकरण स्पेक्ट्रम](../../../../../../translated_images/ne/knowledge-spectrum.b60df631852c0217.webp) -> छवि [Dmitry Soshnikov](http://soshnikov.com) द्वारा +> चित्र द्वारा [डमित्री सोस्नीकोव](http://soshnikov.com) -* बाँया तर्फ, कम्प्युटरहरूले प्रभावकारी रूपमा प्रयोग गर्न सक्ने धेरै सरल प्रकारका ज्ञान प्रतिनिधित्वहरू छन्। सबैभन्दा सरल प्रकार एल्गोरिदमिक हो, जहाँ ज्ञान कम्प्युटर प्रोग्रामद्वारा प्रतिनिधित्व गरिन्छ। तर, यो ज्ञान प्रतिनिधित्व गर्ने सबैभन्दा राम्रो तरिका होइन, किनकि यो लचिलो छैन। हाम्रो मस्तिष्कभित्रको ज्ञान प्रायः गैर-एल्गोरिदमिक हुन्छ। -* दायाँ तर्फ, प्राकृतिक पाठ जस्ता प्रतिनिधित्वहरू छन्। यो सबैभन्दा शक्तिशाली हो, तर स्वचालित तर्कका लागि प्रयोग गर्न सकिँदैन। +* बाँया तर्फ, कम्प्युटरहरूले प्रभावकारी रूपमा प्रयोग गर्न सक्ने सरल प्रकारका ज्ञान प्रस्तुतीकरणहरू छन्। सबैभन्दा सरल हो एल्गोरिदमिक, जहाँ ज्ञान कम्प्युटर प्रोग्राममार्फत प्रतिनिधित्व हुन्छ। तर यो ज्ञानलाई प्रतिनिधित्व गर्ने सबैभन्दा राम्रो तरिका होइन, किनकि यो लचिलो हुँदैन। हाम्रो टाउको भित्रको ज्ञान प्रायः गैर-एल्गोरिदमिक हुन्छ। +* दाहिने तर्फ, प्राकृतिक पाठ जस्ता प्रस्तुतीकरणहरू छन्। यो सबैभन्दा शक्तिशाली हो, तर स्वतः तर्कका लागि प्रयोग गर्न सकिँदैन। -> ✅ एक मिनेट सोच्नुहोस् कि तपाईं आफ्नो मस्तिष्कमा ज्ञान कसरी प्रतिनिधित्व गर्नुहुन्छ र यसलाई नोट्समा रूपान्तरण गर्नुहुन्छ। के कुनै विशेष स्वरूप छ जसले तपाईंलाई सम्झनामा सहयोग पुर्‍याउँछ? +> ✅ एउटा मिनेट सोच्नुहोस् कि तपाईंले आफ्नो टाउकोमा ज्ञानलाई कसरी प्रतिनिधित्व गर्नुहुन्छ र नोटहरूमा रूपान्तरण गर्नुहुन्छ। के त्यहाँ कुनै विशेष ढाँचा छ जसले तपाईंलाई सम्झनामा मद्दत गर्छ? -## कम्प्युटर ज्ञान प्रतिनिधित्व वर्गीकरण +## कम्प्युटर ज्ञान प्रस्तुतीकरणहरू वर्गीकरण -हामी कम्प्युटर ज्ञान प्रतिनिधित्वका विभिन्न विधिहरूलाई निम्न वर्गहरूमा वर्गीकृत गर्न सक्छौं: +हामी विभिन्न कम्प्युटर ज्ञान प्रस्तुतीकरण विधिहरूलाई निम्न वर्गहरूमा वर्गीकरण गर्न सक्छौं: -* **नेटवर्क प्रतिनिधित्वहरू** हाम्रो मस्तिष्कभित्र आपसमा सम्बन्धित अवधारणाहरूको जालोको आधारमा बनाइन्छ। हामी कम्प्युटर भित्र समान जालोहरूलाई ग्राफको रूपमा पुन: उत्पादन गर्न सक्छौं - जसलाई **सामन्तिक नेटवर्क** भनिन्छ। +* **नेटवर्क प्रस्तुतीकरणहरू** हाम्रो टाउकोमा अन्तरसम्बन्धित अवधारणाहरूको नेटवर्क भएको तथ्यमा आधारित छन्। हामी समान नेटवर्कलाई कम्प्युटर भित्र ग्राफको रूपमा पुन: निर्मित गर्न सक्दछौं - यसलाई भनिन्छ **सामान्तरिक नेटवर्क**। -1. **वस्तु-गुण-मान त्रिपलेटहरू** वा **गुण-मान जोडीहरू**। चूँकि ग्राफलाई कम्प्युटर भित्र नोड र किनारहरूको सूचीको रूपमा प्रतिनिधित्व गर्न सकिन्छ, हामी सामन्तिक नेटवर्कलाई वस्तु, गुण, र मानहरू समावेश गर्ने त्रिपलेटहरूको सूचीद्वारा प्रतिनिधित्व गर्न सक्छौं। उदाहरणका लागि, हामी प्रोग्रामिङ भाषाहरूको बारेमा निम्न त्रिपलेटहरू निर्माण गर्छौं: +1. **बस्तु-विशेषता-मूल्य त्रिपल्टहरू** वा **विशेषता-मूल्य जोडीहरू**। ग्राफ कम्प्युटर भित्र नोडहरू र एजहरूको सूचीको रूपमा प्रतिनिधित्व गर्न सकिन्छ, त्यसैले हामी त्रिपल्टहरूको सूची मार्फत सामाजिक नेटवर्क प्रतिनिधित्व गर्न सक्छौं, जसमा वस्तुहरू, विशेषताहरू, र मूल्यहरू हुन्छन्। उदाहरणका लागि, प्रोग्रामिङ भाषाहरूबारे हामीले निम्न त्रिपल्टहरू बनाउँछौं: -वस्तु | गुण | मान +Object | Attribute | Value -------|-----------|------ Python | हो | Untyped-Language -Python | आविष्कारक | Guido van Rossum -Python | ब्लक-सिन्ट्याक्स | इनडेन्टेशन +Python | बनाएको-ले | Guido van Rossum +Python | ब्लक-सिन्ट्याक्स | इनडेन्टेसन Untyped-Language | छैन | प्रकार परिभाषा -> ✅ सोच्नुहोस् कि त्रिपलेटहरू अन्य प्रकारका ज्ञान प्रतिनिधित्व गर्न कसरी प्रयोग गर्न सकिन्छ। +> ✅ सोच्नुहोस् कि अरु प्रकारका ज्ञान प्रतिनिधित्व गर्न त्रिपल्टहरू कसरी प्रयोग गर्न सकिन्छ। -2. **हाइरार्किकल प्रतिनिधित्वहरू** यस तथ्यलाई जोड दिन्छ कि हामी प्रायः हाम्रो मस्तिष्कभित्र वस्तुहरूको हाइरार्की सिर्जना गर्छौं। उदाहरणका लागि, हामीलाई थाहा छ कि क्यानरी एक चरा हो, र सबै चराहरूको पखेटा हुन्छ। हामीलाई क्यानरीको सामान्य रंग र यसको उडान गति पनि थाहा छ। +2. **श्रृंखलात्मक प्रस्तुतीकरणहरू** यो तथ्यमा जोड दिन्छ कि हामी प्रायः हाम्रो टाउकोमा वस्तुहरूको हाइरार्की निर्माण गर्छौं। उदाहरणका लागि, हामीलाई थाहा छ कि क्यानेरी एक चर हो, र सबै चरहरूलाई पंख हुन्छ। हामीसँग क्यानरी के रंगको हुन्छ र यसको उडान गति कति हुन्छ भन्ने केही विचार पनि हुन्छ। - - **फ्रेम प्रतिनिधित्व** प्रत्येक वस्तु वा वस्तुहरूको वर्गलाई **फ्रेम** को रूपमा प्रतिनिधित्व गर्नेमा आधारित छ, जसमा **स्लटहरू** हुन्छन्। स्लटहरूमा सम्भावित डिफल्ट मानहरू, मान प्रतिबन्धहरू, वा मान प्राप्त गर्न सकिने भण्डारण प्रक्रियाहरू हुन्छन्। सबै फ्रेमहरूले वस्तु-उन्मुख प्रोग्रामिङ भाषाहरूमा वस्तु हाइरार्की जस्तै हाइरार्की बनाउँछन्। - - **परिदृश्यहरू** समयको साथमा खुल्ने जटिल परिस्थितिहरूलाई प्रतिनिधित्व गर्ने विशेष प्रकारका फ्रेमहरू हुन्। + - **फ्रेम प्रस्तुतीकरण** प्रत्येक वस्तु वा वस्तु वर्गलाई एक **फ्रेम** को रूपमा प्रतिनिधित्व गर्नेमा आधारित छ, जुन **स्लटहरू** समावेश गर्दछ। स्लटहरूले सम्भावित पूर्वनिर्धारित मूल्यहरू, मूल्य प्रतिबन्धहरू, वा मान प्राप्त गर्न सकिने प्रक्रियाहरू हुन्छन्। सबै फ्रेमहरूले वस्तु-उन्मुख प्रोग्रामिङ भाषाहरूमा वस्तु पदानुक्रम जस्तै हाइरार्की बनाउँछन्। + - **परिदृश्यहरू** जटिल अवस्थाहरू प्रतिनिधित्व गर्ने विशेष प्रकारका फ्रेमहरू हुन् जसले समयसँग unfolding हुनसक्ने अवस्था देखाउँछन्। **Python** -स्लट | मान | डिफल्ट मान | अन्तराल | +Slot | Value | Default value | Interval | -----|-------|---------------|----------| -नाम | Python | | | -हो | Untyped-Language | | | -भेरिएबल केस | | CamelCase | | -प्रोग्राम लम्बाइ | | | 5-5000 लाइनहरू | -ब्लक सिन्ट्याक्स | इनडेन्ट | | | +Name | Python | | | +Is-A | Untyped-Language | | | +Variable Case | | CamelCase | | +Program Length | | | 5-5000 lines | +Block Syntax | Indent | | | -3. **प्रक्रियात्मक प्रतिनिधित्वहरू** ज्ञानलाई कार्यहरूको सूचीद्वारा प्रतिनिधित्व गर्नेमा आधारित छ, जुन निश्चित अवस्थामा कार्यान्वयन गर्न सकिन्छ। - - उत्पादन नियमहरू यदि-तब कथनहरू हुन् जसले हामीलाई निष्कर्ष निकाल्न अनुमति दिन्छ। उदाहरणका लागि, एक डाक्टरसँग नियम हुन सक्छ जसले भन्छ कि **यदि** बिरामीलाई उच्च ज्वरो छ **वा** रक्त परीक्षणमा उच्च स्तरको C-reactive प्रोटीन छ **तब** उसलाई सूजन छ। एकपटक हामीले कुनै एक अवस्था भेट्टायौं भने, हामी सूजनको बारेमा निष्कर्ष निकाल्न सक्छौं, र त्यसपछि यसलाई थप तर्कमा प्रयोग गर्न सक्छौं। - - एल्गोरिदमहरू प्रक्रियात्मक प्रतिनिधित्वको अर्को रूप मान्न सकिन्छ, यद्यपि तिनीहरू प्रायः ज्ञान-आधारित प्रणालीहरूमा प्रत्यक्ष रूपमा प्रयोग गरिँदैनन्। +3. **प्रक्रियागत प्रस्तुतीकरणहरू** ज्ञानलाई क्रियाहरूको सूचीको रूपमा प्रतिनिधित्व गर्नेमा आधारित छन्, जुन कुनै निश्चित सर्त पूर्ति हुँदा चलाउन सकिन्छ। + - उत्पादन नियमहरू if-then कथनहरू हुन् जसले निष्कर्ष निकाल्न मद्दत गर्छ। उदाहरणका लागि, डाक्टरसँग नियम हुन सक्छ कि **यदि** बिरामीलाई ज्वरो बढी छ **वा** रगत परीक्षणमा C-रिएक्टिव प्रोटीन धेरै छ **भने** उनको सूजन छ। सर्तमा भेटिएपछि हामी सूजनको निष्कर्ष निकाल्न सक्छौं र थप तर्कमा प्रयोग गर्न सक्छौं। + - एल्गोरिदमहरूलाई अर्को प्रक्रियागत प्रस्तुतीकरणको रूपमा सोच्न सकिन्छ, यद्यपि तिनीहरू ज्ञान-आधारित प्रणालीमा प्रायः प्रत्यक्ष प्रयोग हुँदैनन्। -4. **तर्क** मूल रूपमा अरिस्टोटलले सार्वभौमिक मानव ज्ञान प्रतिनिधित्व गर्ने तरिका रूपमा प्रस्ताव गरेका थिए। - - प्रेडिकेट तर्क एक गणितीय सिद्धान्त हो जुन कम्प्युटेबल हुन धेरै समृद्ध छ, त्यसैले सामान्यतया यसको केही उपसमुच्ची प्रयोग गरिन्छ, जस्तै Prolog मा प्रयोग गरिने Horn क्लजहरू। - - वर्णनात्मक तर्क वस्तुहरूको हाइरार्की र वितरित ज्ञान प्रतिनिधित्वहरू जस्तै *सामन्तिक वेब* को बारेमा प्रतिनिधित्व र तर्क गर्न प्रयोग गरिने तर्क प्रणालीहरूको परिवार हो। +4. **तर्क** अरिस्टोटलले मानव ज्ञानलाई प्रतिनिधित्व गर्ने तरिकाको रूपमा सुरुमा प्रस्ताव गरेका थिए। + - Predicate Logic गणितीय सिद्धान्तका रूपमा कम्प्युटेबल हुन अति समृद्ध छ, त्यसैले यसबाट केही उपसमूह मात्र प्रयोग हुन्छ, जस्तै Prolog मा प्रयोग हुने Horn कलेजहरू। + - Descriptive Logic एउटा तर्क प्रणालीहरूको परिवार हो जसले वस्तुहरूको हाइरार्की र वितरित ज्ञान प्रस्तुतीकरण जस्तै *सामान्तरिक वेब* को तर्क र प्रतिनिधित्व गर्दछ। ## विशेषज्ञ प्रणालीहरू -सिम्बोलिक AI को प्रारम्भिक सफलताहरू मध्ये एक **विशेषज्ञ प्रणालीहरू** थिए - कम्प्युटर प्रणालीहरू जसलाई सीमित समस्या क्षेत्रमा विशेषज्ञको रूपमा कार्य गर्न डिजाइन गरिएको थियो। तिनीहरू **ज्ञान आधार** मा आधारित थिए, जुन एक वा बढी मानव विशेषज्ञहरूबाट निकालिएको थियो, र तिनीहरूमा **तर्क इन्जिन** समावेश थियो जसले यसमा केही तर्क प्रदर्शन गर्थ्यो। +संकेतिक AI का प्रारम्भिक सफलताहरू मध्ये एक थिए भनिने **विशेषज्ञ प्रणालीहरू** - कम्प्युटर प्रणालीहरू जुन सीमित समस्या क्षेत्रमा एक विशेषज्ञको रूपमा काम गर्न डिजाइन गरिएका थिए। तिनीहरू कसै एक वा बढी मानव विशेषज्ञबाट निकालेको **ज्ञान आधार** मा आधारित थिए, र तिनीहरू सँग **तर्क इञ्जिन** थियो जसले त्यस माथि केही तर्क गर्छ। -![मानव वास्तुकला](../../../../translated_images/ne/arch-human.5d4d35f1bba3ab1c.webp) | ![ज्ञान-आधारित प्रणाली](../../../../translated_images/ne/arch-kbs.3ec5c150b09fa8da.webp) +![मानव संरचना](../../../../../../translated_images/ne/arch-human.5d4d35f1bba3ab1c.webp) | ![ज्ञान-आधारित प्रणाली](../../../../../../translated_images/ne/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ -मानव न्युरल प्रणालीको सरलीकृत संरचना | ज्ञान-आधारित प्रणालीको वास्तुकला +मानव स्नायु प्रणालीको सरल संरचना | ज्ञान-आधारित प्रणालीको वास्तुकला -विशेषज्ञ प्रणालीहरू मानव तर्क प्रणाली जस्तै निर्माण गरिन्छ, जसमा **छोटो-समय स्मृति** र **लामो-समय स्मृति** हुन्छ। त्यस्तै, ज्ञान-आधारित प्रणालीहरूमा हामी निम्न घटकहरू छुट्याउँछौं: +विशेषज्ञ प्रणालीहरू मानव तर्क प्रणाली जस्तै बनाइन्छन्, जसमा **छोटो अवधिको स्मृति** र **दीर्घकालीन स्मृति** हुन्छ। त्यस्तै, ज्ञान-आधारित प्रणालीहरूमा हामी निम्न घटकहरू छुट्याउँछौं: -* **समस्या स्मृति**: हाल समाधान भइरहेको समस्याको बारेमा ज्ञान समावेश गर्दछ, जस्तै बिरामीको तापक्रम वा रक्तचाप, उसलाई सूजन छ कि छैन, आदि। यस ज्ञानलाई **स्थिर ज्ञान** पनि भनिन्छ, किनकि यसले हालको समस्याको बारेमा हामीलाई थाहा भएको कुराको स्न्यापशट समावेश गर्दछ - तथाकथित *समस्या अवस्था*। -* **ज्ञान आधार**: समस्या क्षेत्रको बारेमा लामो-समय ज्ञान प्रतिनिधित्व गर्दछ। यो मानव विशेषज्ञहरूबाट म्यानुअल रूपमा निकालिएको हो, र परामर्शबाट परामर्शमा परिवर्तन हुँदैन। किनकि यसले हामीलाई एक समस्या अवस्थाबाट अर्कोमा नेभिगेट गर्न अनुमति दिन्छ, यसलाई **गतिशील ज्ञान** पनि भनिन्छ। -* **तर्क इन्जिन**: समस्या अवस्था स्थानमा खोजी गर्ने सम्पूर्ण प्रक्रियालाई समन्वय गर्दछ, आवश्यक पर्दा प्रयोगकर्तासँग प्रश्न सोध्छ। यो प्रत्येक अवस्थामा लागू गर्नुपर्ने सही नियमहरू फेला पार्न पनि जिम्मेवार छ। +* **समस्या स्मृति**: हाल समाधान भइरहेको समस्याको ज्ञान समावेश गर्दछ, उदाहरणका लागि बिरामीको तापक्रम वा रक्तचाप, के उनीमा सूजन छ वा छैन आदि। यसलाई **स्थिर ज्ञान** पनि भनिन्छ किनभने यसले अहिलेको समस्या अवस्थाको स्न्यापशट समेट्छ - तथाकथित *समस्या अवस्था*। +* **ज्ञान आधार**: समस्या क्षेत्रमा दीर्घकालीन ज्ञान प्रतिनिधित्व गर्दछ। यो मानव विशेषज्ञहरूबाट म्यानुअली निकालिन्छ र परामर्शहरूको बीच परिवर्तन हुँदैन। किनभने यसले एउटा समस्या अवस्थाबाट अर्कोमा नेविगेट गर्न अनुमति दिन्छ, यसलाई **गतिशील ज्ञान** पनि भनिन्छ। +* **तर्क इञ्जिन**: समस्या अवस्थाको ठाउँमा खोजी गर्ने सम्पूर्ण प्रक्रियालाई संचालित गर्छ, आवश्यक परे प्रयोगकर्ताबाट प्रश्न सोध्छ। यसले प्रत्येक अवस्थामा लागु गर्नुपर्ने नियमहरू पनि खोज्छ। -उदाहरणका लागि, निम्न विशेषज्ञ प्रणालीलाई विचार गरौं जसले शारीरिक विशेषताहरूको आधारमा जनावर निर्धारण गर्दछ: +उदाहरणका लागि, एउटा जनावरको शारीरिक विशेषताहरूका आधारमा जनावर पहिचान गर्ने विशेषज्ञ प्रणाली लिऔं: -![AND-OR ट्री](../../../../translated_images/ne/AND-OR-Tree.5592d2c70187f283.webp) +![AND-OR वृक्ष](../../../../../../translated_images/ne/AND-OR-Tree.5592d2c70187f283.webp) -> छवि [Dmitry Soshnikov](http://soshnikov.com) द्वारा +> चित्र द्वारा [डमित्री सोस्नीकोव](http://soshnikov.com) -यो चित्रलाई **AND-OR ट्री** भनिन्छ, र यो उत्पादन नियमहरूको सेटको ग्राफिकल प्रतिनिधित्व हो। विशेषज्ञबाट ज्ञान निकाल्नको सुरुवातमा ट्री बनाउनु उपयोगी हुन्छ। कम्प्युटर भित्र ज्ञान प्रतिनिधित्व गर्न नियमहरू प्रयोग गर्नु अधिक सुविधाजनक हुन्छ: +यो चित्रलाई **AND-OR वृक्ष** भनिन्छ, र यो उत्पादन नियमहरूको सेटको ग्राफिकल प्रतिनिधित्व हो। ज्ञान एक्स्ट्र्याक्ट गर्दा सुरुमा वृक्ष बनाउनु उपयोगी हुन्छ। कम्प्युटर भित्र ज्ञान प्रतिनिधित्व गर्न नियमहरू प्रयोग गर्नु अधिक सुविधाजनक हुन्छ: ``` IF the animal eats meat @@ -121,78 +121,78 @@ OR (animal has sharp teeth THEN the animal is a carnivore ``` -तपाईंले देख्न सक्नुहुन्छ कि नियमको बाँया-पक्षमा प्रत्येक अवस्था र कार्य वस्तु-गुण-मान (OAV) त्रिपलेटहरू हुन्। **कार्य स्मृति** OAV त्रिपलेटहरूको सेट समावेश गर्दछ जुन हाल समाधान भइरहेको समस्यासँग मेल खान्छ। **नियम इन्जिन** नियमहरूको खोजी गर्दछ जसको अवस्था पूरा भएको छ र तिनीहरूलाई लागू गर्दछ, कार्य स्मृतिमा अर्को त्रिपलेट थप्दै। +तपाईंले देख्न सक्नुहुन्छ कि नियमको बाँया पट्टि प्रत्येक सर्त र क्रिया वस्तु-विशेषता-मूल्य (OAV) त्रिपल्टहरू हुन्। **कार्य स्मृति** समाधान भइरहेको समस्यासँग मिल्दोजुल्दो OAV त्रिपल्टहरू समेट्छ। **नियम इञ्जिन** सर्त पुरा हुने नियमहरू खोज्छ र तिनीहरूलाई लागू गर्छ, र कार्य स्मृतिमा अर्को त्रिपल्ट थप्छ। -> ✅ तपाईंलाई मन पर्ने विषयमा आफ्नो AND-OR ट्री लेख्नुहोस्! +> ✅ आफू मन पर्ने विषयमा आफ्नो AND-OR वृक्ष लेख्नुहोस्! -### अगाडि बनाम पछाडि तर्क +### अग्रगामी बनाम पछाडि तर्क -माथि वर्णन गरिएको प्रक्रिया **अगाडि तर्क** भनिन्छ। यो कार्य स्मृतिमा उपलब्ध समस्याको प्रारम्भिक डाटाबाट सुरु हुन्छ, र त्यसपछि निम्न तर्क लूप कार्यान्वयन गर्दछ: +माथि वर्णित प्रक्रिया **अग्रगामी तर्क** भनिन्छ। यसले कुनै प्रारम्भिक डेटा काम स्मृतिमा रहेको मान्छ र तलको तर्क लूप चलाउँछ: -1. यदि लक्ष्य गुण कार्य स्मृतिमा उपस्थित छ भने - रोक्नुहोस् र परिणाम दिनुहोस् -2. सबै नियमहरूको खोजी गर्नुहोस् जसको अवस्था हाल पूरा भएको छ - **संघर्ष सेट** प्राप्त गर्नुहोस्। -3. **संघर्ष समाधान** गर्नुहोस् - यस चरणमा कार्यान्वयन गरिने एक नियम चयन गर्नुहोस्। विभिन्न संघर्ष समाधान रणनीतिहरू हुन सक्छ: - - ज्ञान आधारमा लागू गर्न सकिने पहिलो नियम चयन गर्नुहोस् - - कुनै पनि नियम चयन गर्नुहोस् - - *अधिक विशिष्ट* नियम चयन गर्नुहोस्, अर्थात् "बाँया-पक्ष" (LHS) मा सबैभन्दा धेरै अवस्थाहरू पूरा गर्ने नियम -4. चयन गरिएको नियम लागू गर्नुहोस् र समस्या अवस्थामा नयाँ ज्ञानको टुक्रा समावेश गर्नुहोस् -5. चरण 1 बाट दोहोर्याउनुहोस्। +1. यदि लक्षित विशेषता काम स्मृतिमा छ भने - रोक्नुहोस् र परिणाम दिनुहोस् +2. सबै नियमहरू जो अहिलेको सर्त पुरा गर्छन् खोज्नुहोस् - **विवाद सेट** प्राप्त गर्नुहोस्। +3. **विवाद समाधान** गर्नुहोस् - एक नियम छनोट गर्नुहोस् जुन यस चरणमा लागू हुन्छ। विभिन्न विवाद समाधान रणनीतिहरू हुन सक्छन्: + - नियम आधारमा पहिलो लागू हुन सक्ने नियम चयन गर्नुहोस् + - कुनैपनि नियम बेतरतीब चयन गर्नुहोस् + - *विशेष* नियम चयन गर्नुहोस्, जस्तै सबैभन्दा धेरै सर्तहरू पूरा गर्ने नियम “बाँया पट्टि” (LHS) +4. छनोट गरिएका नियम लागू गरेर समस्या अवस्थामा नयाँ ज्ञान थप्नुहोस् +5. चरण 1 बाट दोहोर्‍याउनुहोस्। -तर, केही अवस्थामा हामी समस्याको बारेमा खाली ज्ञानबाट सुरु गर्न चाहन्छौं, र प्रश्नहरू सोध्न चाहन्छौं जसले हामीलाई निष्कर्षमा पुग्न मद्दत गर्दछ। उदाहरणका लागि, जब चिकित्सा निदान गरिन्छ, हामी सामान्यतया बिरामीको निदान सुरु गर्नु अघि सबै चिकित्सा विश्लेषणहरू अग्रिम रूपमा कार्यान्वयन गर्दैनौं। हामी निर्णय लिन आवश्यक पर्दा विश्लेषण गर्न चाहन्छौं। +तर कहिलेकाहीं हामी सुरुमा समस्याबारे खाली ज्ञान राख्न चाहन्छौं र प्रश्न सोधेर निष्कर्षमा पुग्न चाहन्छौं। जस्तै, चिकित्सा निदान गर्दा हामी सबै मेडिकल परीक्षण पहिले नै नगरेका हुन्छौं। जब निर्णय लिनुपर्ने हुन्छ तब मात्र परीक्षण गर्छौं। -यो प्रक्रिया **पछाडि तर्क** प्रयोग गरेर मोडेल गर्न सकिन्छ। यो **लक्ष्य** द्वारा संचालित हुन्छ - हामी खोज्दै गरेको गुण मान: +यो प्रक्रिया **पछाडि तर्क** प्रयोग गरेर मोडेल गर्न सकिन्छ। यो **लक्ष्य** द्वारा सञ्चालित हुन्छ - खोजिने विशेषता मूल्य: -1. लक्ष्यको मान दिन सक्ने सबै नियमहरू चयन गर्नुहोस् (अर्थात् लक्ष्य दायाँ-पक्षमा (RHS) भएको) - एक संघर्ष सेट -1. यदि यो गुणको लागि कुनै नियम छैन, वा प्रयोगकर्ताबाट मान सोध्नुपर्ने नियम छ भने - सोध्नुहोस्, अन्यथा: -1. संघर्ष समाधान रणनीति प्रयोग गरेर हामीले *परिकल्पना* को रूपमा प्रयोग गर्ने एक नियम चयन गर्नुहोस् - हामी यसलाई प्रमाणित गर्ने प्रयास गर्नेछौं -1. नियमको बाँया-पक्षमा रहेका सबै गुणहरूको लागि प्रक्रिया पुनः दोहोर्याउनुहोस्, तिनीहरूलाई लक्ष्यको रूपमा प्रमाणित गर्ने प्रयास गर्दै -1. यदि कुनै पनि बिन्दुमा प्रक्रिया असफल हुन्छ भने - चरण 3 मा अर्को नियम प्रयोग गर्नुहोस्। +1. ती सबै नियमहरू छनोट गर्नुहोस् जसले लक्ष्यको मूल्य दिन सक्छन् (अर्थात् लक्ष्य RHS ("दायाँ पट्टि") मा छन्) - विवाद सेट +2. यदि यस विशेषताका लागि नियम छैन, वा कुनै नियमले प्रयोगकर्ताबाट मूल्य सोध्नुपर्ने भन्छ भने - सोध्नुहोस्, अन्यथा: +3. विवाद समाधान नीति प्रयोग गरी एउटा नियम छनोट गर्नुहोस् जुन हामी *परिकल्पना* को रूपमा प्रयोग गर्नेछौं - हामी यसलाई प्रमाणित गर्ने प्रयास गर्नेछौं +4. नियमको LHS का सबै विशेषताहरूका लागि प्रक्रियालाई पुनरावृत्ति गरेर तिनीहरूलाई पनि लक्ष्य प्रमाणित गर्ने प्रयास गर्नुहोस् +5. प्रक्रिया कुनै अवस्थामा असफल भएमा चरण 3 मा अर्को नियम प्रयोग गर्नुहोस्। -> ✅ कुन परिस्थितिमा अगाडि तर्क उपयुक्त हुन्छ? पछाडि तर्कको बारेमा के भन्नुहुन्छ? +> ✅ कुन अवस्थामा अग्रगामी तर्क धेरै उपयुक्त हुन्छ? पछाडि तर्क कस्तो हुन्छ? -### विशेषज्ञ प्रणाली कार्यान्वयन +### विशेषज्ञ प्रणालीहरु कार्यान्वयन -विशेषज्ञ प्रणालीहरू विभिन्न उपकरणहरू प्रयोग गरेर कार्यान्वयन गर्न सकिन्छ: +विशेषज्ञ प्रणालीहरू निम्न उपकरणहरू प्रयोग गरेर कार्यान्वयन गर्न सकिन्छ: -* तिनीहरूलाई कुनै उच्च-स्तरीय प्रोग्रामिङ भाषामा प्रत्यक्ष रूपमा प्रोग्राम गर्नुहोस्। यो सबैभन्दा राम्रो विचार होइन, किनकि ज्ञान-आधारित प्रणालीको मुख्य फाइदा भनेको ज्ञान तर्कबाट अलग छ, र सम्भावित रूपमा समस्या क्षेत्र विशेषज्ञले तर्क प्रक्रियाको विवरण नबुझी नियमहरू लेख्न सक्षम हुनुपर्छ। -* **विशेषज्ञ प्रणाली खोल** प्रयोग गर्नुहोस्, अर्थात् ज्ञान प्रतिनिधित्व भाषा प्रयोग गरेर ज्ञानले भरिने गरी विशेष रूपमा डिजाइन गरिएको प्रणाली। +* कुनै उच्च स्तरको प्रोग्रामिङ भाषामा सिधै प्रोग्राम गर्नु। यो राम्रो विचार होइन किनभने ज्ञान-आधारित प्रणालीको मुख्य फाइदा ज्ञान र तर्क अलग हुनु हो, र सम्भावित रूपमा समस्या क्षेत्र विशेषज्ञले तर्क प्रक्रिया नबुझिकन नियमहरू लेख्न सक्नुपर्छ। +* **विशेषज्ञ प्रणाली शेल** प्रयोग गर्नु, अर्थात् ज्ञान प्रस्तुतीकरण भाषाको प्रयोग गरेर ज्ञान प्रविष्टिका लागि विशेष डिजाइन गरिएको प्रणाली। ## ✍️ अभ्यास: जनावर तर्क -[Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) हेर्नुहोस्, जसले अगाडि र पछाडि तर्क विशेषज्ञ प्रणाली कार्यान्वयनको उदाहरण दिन्छ। +अग्रगामी र पछाडि तर्क विशेषज्ञ प्रणाली कार्यान्वयनको उदाहरणका लागि हेर्नुहोस् [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb)। -> **नोट**: यो उदाहरण धेरै सरल छ, र विशेषज्ञ प्रणाली कस्तो देखिन्छ भन्ने विचार मात्र दिन्छ। एकपटक तपाईंले यस्तो प्रणाली सिर्जना गर्न सुरु गरेपछि, तपाईंले यसबाट *बुद्धिमान* व्यवहार मात्र देख्नुहुनेछ जब तपाईंले लगभग 200+ नियमहरूमा पुग्नुहुन्छ। कुनै बिन्दुमा, नियमहरू धेरै जटिल हुन्छन् जसले तिनीहरूलाई सबैलाई सम्झन असम्भव बनाउँछ, र यस बिन्दुमा तपाईंले प्रणालीले किन निश्चित निर्णयहरू लिन्छ भन्ने बारेमा सोच्न थाल्न सक्नुहुन्छ। तर, ज्ञान-आधारित प्रणालीहरूको महत्त्वपूर्ण विशेषता भनेको तपाईंले कुनै पनि निर्णयहरू कसरी गरिएका छन् भन्ने कुरा सटीक रूपमा *व्याख्या* गर्न सक्नुहुन्छ। +> **नोट**: यो उदाहरण सामान्य छ र मात्र विशेषज्ञ प्रणाली कस्तो हुन्छ भनेर विचार दिन्छ। यस्तो प्रणाली बनाउँदा तपाईंले २००+ नियम पुगेपछि मात्र केही *बुद्धिमान* व्यवहार देख्नुहुनेछ। कुनै बिन्दुमा नियमहरू धेरै जटिल हुन्छन् र सबै याद राख्न गाह्रो हुन्छ, त्यस बेला तपाईंले किन कुनै निर्णय प्रणालीले लिन्छ भनेर सोच्नु पर्ने हुन्छ। तर ज्ञान-आधारित प्रणालीहरूको महत्वपूर्ण विशेषता भनेको तपाईं सधैं स्पष्ट रूपमा *व्याख्या* गर्न सक्नुहुनेछ कि निर्णय कसरी लिइयो। -## ओन्टोलोजीहरू र सामन्तिक वेब +## ओन्टोलोजीहरू र सामान्तरिक वेब -20 औं शताब्दीको अन्त्यमा इन्टरनेट स्रोतहरूलाई ज्ञान प्रतिनिधित्व प्रयोग गरेर एनोटेट गर्ने पहल थियो, ताकि धेरै विशिष्ट प्रश्नहरूको जवाफ दिने स्रोतहरू फेला पार्न सकियोस्। यसलाई **सामन्तिक वेब** भनिन्छ, र यसले केही अवधारणाहरूमा भर पर्छ: +आधुनिक युगको अन्त्यमा इन्टरनेट स्रोतहरूलाई एनोटेट गर्न ज्ञान प्रस्तुतीकरण प्रयोग गर्ने पहल थियो, जसले विशिष्ट क्वेरीहरूसँग मेल खाने स्रोतहरू खोज्न सम्भव बनायो। यसलाई **सामान्तरिक वेब** भनियो, र यसले निम्न अवधारणाहरूमा आधारित थियो: -- **[वर्णनात्मक तर्क](https://en.wikipedia.org/wiki/Description_logic)** (DL) मा आधारित विशेष ज्ञान प्रतिनिधित्व। यो फ्रेम ज्ञान प्रतिनिधित्वसँग मिल्दोजुल्दो छ, किनकि यो वस्तुहरूको हाइरार्की निर्माण गर्दछ, तर यसमा औपचारिक तर्कात्मक अर्थ र तर्क छ। DL को सम्पूर्ण परिवार छ जसले अभिव्यक्तिलाई सन्तुलन गर्दछ र तर्कको एल्गोरिदमिक जटिलता। -- वितरित ज्ञान प्रतिनिधित्व, जहाँ सबै अवधारणाहरूलाई एक वैश्विक URI पहिचानद्वारा प्रतिनिधित्व गरिन्छ, जसले इन्टरनेटमा फैलिएको ज्ञान हाइरार्की सिर्जना गर्न सम्भव बनाउँछ। -- ज्ञानको वर्णनका लागि XML-आधारित भाषाहरूको परिवार: RDF (Resource Description Framework), RDFS (RDF Schema), OWL (Ontology Web Language)। +- विशेष ज्ञान प्रस्तुतीकरण, विशेषगरी **[वर्णनात्मक तर्क](https://en.wikipedia.org/wiki/Description_logic)** (DL) मा आधारित। यसले फ्रेम ज्ञान प्रस्तुतीकरण जस्तै वस्तुहरूको हाइरार्की बनाउँछ गुणहरू सहित, तर यसमा औपचारिक तर्कीय अर्थ र तर्क समावेश हुन्छ। DL को एउटा परिवार छ जुन अभिव्यक्तिशीलता र तर्कको एल्गोरिदमिक जटिलताको बीच सन्तुलन गर्छ। +- वितरित ज्ञान प्रस्तुतीकरण, जहाँ सबै अवधारणाहरूलाई वैश्विक URI अनुकरणकर्ताले प्रतिनिधित्व गर्छ, जसले इन्टरनेटभर फैलिएको ज्ञान हाइरार्कीहरू सिर्जना गर्न सम्भव बनाउँछ। +- ज्ञान वर्णनाका लागि XML-आधारित भाषाहरूको परिवार: RDF (Resource Description Framework), RDFS (RDF Schema), OWL (Ontology Web Language)। -सेम्यान्टिक वेबको मुख्य अवधारणामध्ये एक हो **Ontology**। यसले कुनै समस्या क्षेत्रलाई औपचारिक ज्ञान प्रतिनिधित्वको प्रयोग गरेर स्पष्ट रूपमा निर्दिष्ट गर्ने प्रक्रियालाई जनाउँछ। सबैभन्दा साधारण Ontology भनेको समस्या क्षेत्रभित्रका वस्तुहरूको एक पदानुक्रम मात्र हुन सक्छ, तर जटिल Ontologyहरूले तर्क गर्न प्रयोग गर्न सकिने नियमहरू समावेश गर्छन्। +Semantic Web मा एक मुख्य अवधारणा हो **Ontology** को अवधारणा। यसले केही औपचारिक ज्ञान प्रतिनिधित्व प्रयोग गरी समस्याको डोमेनको स्पष्ट निर्दिष्टता जनाउँछ। सबैभन्दा सरल ontology समस्या डोमेनमा वस्तुहरूको मात्र एक पदानुक्रम हुन सक्छ, तर जटिल ontologies मा नियमहरू समावेश हुनेछन् जुन inference को लागि प्रयोग गर्न सकिन्छ। -सेम्यान्टिक वेबमा, सबै प्रतिनिधित्वहरू त्रिप्लेटहरूमा आधारित हुन्छन्। प्रत्येक वस्तु र प्रत्येक सम्बन्ध URI द्वारा अद्वितीय रूपमा पहिचान गरिन्छ। उदाहरणका लागि, यदि हामी यो तथ्य व्यक्त गर्न चाहन्छौं कि यो AI पाठ्यक्रम Dmitry Soshnikov द्वारा जनवरी १, २०२२ मा विकास गरिएको हो - यहाँ हामीले प्रयोग गर्न सक्ने त्रिप्लेटहरू छन्: +Semantic Web मा, सबै प्रतिनिधित्व त्रिपलहरूमा आधारित हुन्छन्। प्रत्येक वस्तु र प्रत्येक सम्बन्धलाई URI द्वारा विशिष्ट रूपमा पहिचान गरिन्छ। उदाहरणका लागि, यदि हामी भन्न चाहन्छौं कि यो AI Curriculum डिमिट्री सोष्निकोभले १ जनवरी २०२२ मा विकास गरेको छ - यहाँ प्रयोग गर्न सकिने त्रिपलहरू छन्: - + ``` -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 ``` -> ✅ यहाँ `http://www.example.com/terms/creation-date` र `http://purl.org/dc/elements/1.1/creator` केही प्रख्यात र विश्वव्यापी रूपमा स्वीकार गरिएका URI हरू हुन्, जसले *creator* र *creation date* का अवधारणाहरू व्यक्त गर्छन्। +> ✅ यहाँ `http://www.example.com/terms/creation-date` र `http://purl.org/dc/elements/1.1/creator` केही प्रसिद्ध र सार्वभौमिक रूपमा स्वीकार गरिएका URI हरू हुन् जसले *creator* र *creation date* को अवधारणाहरू व्यक्त गर्छन्। -अझ जटिल अवस्थामा, यदि हामी सर्जकहरूको सूची परिभाषित गर्न चाहन्छौं भने, हामी RDF मा परिभाषित केही डाटा संरचनाहरू प्रयोग गर्न सक्छौं। +थप जटिल अवस्थामा, यदि हामी निर्माता हरूको सूची परिभाषित गर्न चाहन्छौं भने, हामी RDF मा परिभाषित केही डाटा संरचनाहरू प्रयोग गर्न सक्छौं। - + -> माथिका आरेखहरू [Dmitry Soshnikov](http://soshnikov.com) द्वारा। +> माथिका डायग्रामहरू [डिमिट्री सोष्निकोभ](http://soshnikov.com) द्वारा -सेम्यान्टिक वेब निर्माणको प्रगति केही हदसम्म खोज इन्जिनहरूको सफलता र प्राकृतिक भाषा प्रशोधन प्रविधिहरूको कारण सुस्त भयो, जसले पाठबाट संरचित डाटा निकाल्न अनुमति दिन्छ। तर, केही क्षेत्रमा Ontology र ज्ञान आधारहरू कायम राख्न अझै महत्त्वपूर्ण प्रयासहरू भइरहेका छन्। केही उल्लेखनीय परियोजनाहरू: +Semantic Web बनाउने प्रगति खोजी इन्जिन र प्राकृतिक भाषा प्रशोधन प्रविधिहरूको सफलताले केहि हदसम्म ढिलो बनायो, जसले पाठबाट संरचित डाटा निकाल्न अनुमति दिन्छ। यद्यपि केही क्षेत्रहरूमा अझै पनि ontologies र ज्ञान आधारहरूलाई कायम राख्न महत्त्वपूर्ण प्रयासहरू भएका छन्। केही ध्यान दिन योग्य परियोजनाहरू: -* [WikiData](https://wikidata.org/) Wikipedia सँग सम्बन्धित मेसिन-पढ्न मिल्ने ज्ञान आधारहरूको संग्रह हो। अधिकांश डाटा Wikipedia *InfoBoxes* बाट निकालिएको हो, जुन Wikipedia पृष्ठहरूभित्रका संरचित सामग्रीका टुक्राहरू हुन्। तपाईं [SPARQL](https://query.wikidata.org/) प्रयोग गरेर WikiData मा प्रश्न गर्न सक्नुहुन्छ, जुन सेम्यान्टिक वेबका लागि विशेष प्रश्न भाषा हो। यहाँ एउटा नमूना प्रश्न छ, जसले मानिसहरूमा सबैभन्दा लोकप्रिय आँखा रङहरू देखाउँछ: +* [WikiData](https://wikidata.org/) विकिपिडियासँग सम्बन्धित मेसिन पठनीय ज्ञान आधारहरूको संग्रह हो। डाटाको अधिकांश भाग विकिपिडिया *InfoBoxes* बाट निकालिएको हो, विकिपिडिया पृष्ठहरू भित्रको संरचित सामग्रीका टुक्राहरू। तपाईं SPARQL मा wikidata [query](https://query.wikidata.org/) गर्न सक्नुहुन्छ, Semantic Web को लागि विशेष क्वेरी भाषा हो। यहाँ एउटा नमूना क्वेरी छ जुन मानिसहरू बीच सबैभन्दा लोकप्रिय आँखाको रंग देखाउँछ: ```sparql #defaultView:BubbleChart @@ -206,47 +206,51 @@ WHERE GROUP BY ?eyeColorLabel ``` -* [DBpedia](https://www.dbpedia.org/) WikiData जस्तै अर्को प्रयास हो। +* [DBpedia](https://www.dbpedia.org/) अरूको जस्तै WikiData को प्रयास हो। -> ✅ यदि तपाईं आफ्नै Ontology निर्माण गर्न, वा विद्यमान Ontology खोल्न प्रयोग गर्न चाहनुहुन्छ भने, [Protégé](https://protege.stanford.edu/) नामक उत्कृष्ट दृश्य Ontology सम्पादक छ। यसलाई डाउनलोड गर्नुहोस्, वा अनलाइन प्रयोग गर्नुहोस्। +> ✅ यदि तपाईं आफ्नै ontologies बनाउन अभ्यास गर्न चाहनुहुन्छ वा अवस्थित ontologies खोल्न चाहनुहुन्छ भने, त्यहाँ एउटा उत्कृष्ट भिजुअल ontology सम्पादक छ जसलाई [Protégé](https://protege.stanford.edu/) भनिन्छ। यसलाई डाउनलोड गर्नुहोस्, वा अनलाइन प्रयोग गर्नुहोस्। - + -*Web Protégé सम्पादक Romanov Family Ontology सँग खुला। Dmitry Soshnikov द्वारा स्क्रिनसट।* +*Web Protégé सम्पादक रोमानोव परिवार ontology खोलिएको छ। स्क्रिनसट डिमिट्री सोष्निकोभ द्वारा* ## ✍️ अभ्यास: परिवार Ontology -[FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) हेर्नुहोस्, जसले सेम्यान्टिक वेब प्रविधिहरू प्रयोग गरेर परिवार सम्बन्धहरूको बारेमा तर्क गर्न सिकाउँछ। हामी सामान्य GEDCOM ढाँचामा प्रतिनिधित्व गरिएको परिवार वृक्ष र परिवार सम्बन्धहरूको Ontology लिनेछौं र दिइएको व्यक्तिहरूको सेटका लागि सबै परिवार सम्बन्धहरूको ग्राफ निर्माण गर्नेछौं। +Semantic Web प्रविधिहरू प्रयोग गरी परिवार सम्बन्धहरूको तर्क गर्नको लागि उदाहरणका लागि [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) हेर्नुहोस्। हामी साझा GEDCOM ढाँचामा परिवारको रुख लिनेछौं र परिवार सम्बन्धहरूको ontology लिनेछौं र दिइएको व्यक्तिहरूको समूहका लागि सबै पारिवारिक सम्बन्धहरूको ग्राफ निर्माण गर्नेछौं। ## Microsoft Concept Graph -धेरैजसो अवस्थामा, Ontologyहरू सावधानीपूर्वक हातले निर्माण गरिन्छन्। तर, यो पनि सम्भव छ कि **असंरचित डाटा** बाट Ontology निकाल्न सकिन्छ, जस्तै प्राकृतिक भाषा पाठहरूबाट। +अधिकांश अवस्थामा, ontologies सावधानीपूर्वक हातले सिर्जना गरिन्छन्। यद्यपि, यो सम्भव पनि छ कि **खनिज** ungstructured डाटाबाट, उदाहरणका लागि प्राकृतिक भाषा पाठहरूबाट ontologies निकाल्न। -Microsoft Research द्वारा गरिएको यस्तो प्रयासले [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste) निर्माण गर्‍यो। +त्यस्तो एउटा प्रयास Microsoft Research द्वारा गरिएको थियो, र परिणामस्वरूप आएको छ [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste)। -यो `is-a` उत्तराधिकार सम्बन्ध प्रयोग गरेर समूहबद्ध गरिएका संस्थाहरूको ठूलो संग्रह हो। यसले "Microsoft के हो?" जस्ता प्रश्नहरूको उत्तर दिन अनुमति दिन्छ - उत्तर "कम्पनी (०.८७ सम्भावना) र ब्रान्ड (०.७५ सम्भावना)" जस्ता हुन सक्छ। +यो धेरै संस्थाहरूको संग्रह हो जुन `is-a` विरासत सम्बन्ध प्रयोग गरी सँगै समूहबद्ध गरिएको छ। यसले यस प्रकारका प्रश्नहरूको उत्तर दिन अनुमति दिन्छ "Microsoft के हो?" - जवाफ केही यस प्रकार हो "एक कम्पनी संभावना 0.87 संग, र एक ब्रान्ड संभावना 0.75 संग"। -Graph REST API को रूपमा उपलब्ध छ, वा सबै संस्थाहरूको जोडी सूचीबद्ध गरिएको ठूलो डाउनलोड गर्न मिल्ने पाठ फाइलको रूपमा। +यो ग्राफ REST API को रूपमा उपलब्ध छ, वा सबै संस्थाहरूका जोडीहरूको ठूलो डाउनलोडयोग्य पाठ फाइलको रूपमा। -## ✍️ अभ्यास: Concept Graph +## ✍️ अभ्यास: एक Concept Graph -[MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) नोटबुक प्रयास गर्नुहोस्, जसले Microsoft Concept Graph प्रयोग गरेर समाचार लेखहरूलाई विभिन्न श्रेणीहरूमा समूह गर्न सिकाउँछ। +[MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) नोटबुक प्रयास गर्नुहोस् र हेर्नुहोस् कि कसरी Microsoft Concept Graph संग समाचार सामग्रीहरूलाई केही वर्गहरूमा समूहबद्ध गर्न सकिन्छ। ## निष्कर्ष -आजकल, AI लाई प्रायः *Machine Learning* वा *Neural Networks* को पर्यायवाची मानिन्छ। तर, मानिसहरूले स्पष्ट तर्क प्रदर्शन गर्छन्, जुन हाल Neural Networks द्वारा ह्यान्डल गरिएको छैन। वास्तविक संसारका परियोजनाहरूमा, स्पष्ट तर्क अझै पनि व्याख्या आवश्यक पर्ने कार्यहरू गर्न, वा प्रणालीको व्यवहारलाई नियन्त्रण गरिएको तरिकामा परिमार्जन गर्न प्रयोग गरिन्छ। +आजकल, AI प्रायः *Machine Learning* वा *Neural Networks* को पर्यायको रूपमा लिइन्छ। तर, मानिसहरूले पनि स्पष्ट तर्क प्रदर्शन गर्छन्, जुन अहिले न्युरेल नेटवर्कहरूले सामना गरिरहेका छैनन्। वास्तविक संसारका परियोजनाहरूमा, स्पष्ट तर्क अझै पनि ती कार्यहरू गर्न प्रयोग गरिन्छ जसले व्याख्या गर्न आवश्यक पर्छ, वा प्रणालीको व्यवहार नियन्त्रित तरिकाले परिवर्तन गर्न सकिन्छ। ## 🚀 चुनौती -यस पाठसँग सम्बन्धित Family Ontology नोटबुकमा, अन्य परिवार सम्बन्धहरूसँग प्रयोग गर्ने अवसर छ। परिवार वृक्षमा मानिसहरू बीच नयाँ सम्बन्धहरू पत्ता लगाउने प्रयास गर्नुहोस्। +यस पाठसँग सम्बन्धित परिवार Ontology नोटबुकमा, अन्य पारिवारिक सम्बन्धहरूसँग प्रयोग गर्ने अवसर छ। परिवार रुखमा मानिसहरूबीच नयाँ सम्बन्धहरूको खोजी गर्नुहोस्। -## [पाठ-पछिको प्रश्नोत्तरी](https://ff-quizzes.netlify.app/en/ai/quiz/4) +## [पाठपछिको प्रश्नोत्तरी](https://ff-quizzes.netlify.app/en/ai/quiz/4) -## समीक्षा र आत्म-अध्ययन +## समीक्षा र स्व-अध्ययन -इन्टरनेटमा अनुसन्धान गरेर मानिसहरूले ज्ञानलाई परिमाणित र कोडिफाइ गर्न प्रयास गरेका क्षेत्रहरू पत्ता लगाउनुहोस्। Bloom's Taxonomy को अध्ययन गर्नुहोस्, र इतिहासमा फर्केर हेर्नुहोस् कि मानिसहरूले आफ्नो संसारलाई बुझ्न कसरी प्रयास गरे। Linnaeus को जीवहरूको वर्गीकरण निर्माण गर्ने काम अन्वेषण गर्नुहोस्, र Dmitri Mendeleev ले रासायनिक तत्वहरूको वर्णन र समूहबद्ध गर्न बनाएको तरिका अवलोकन गर्नुहोस्। तपाईंले अरू के चाखलाग्दा उदाहरणहरू फेला पार्न सक्नुहुन्छ? +इन्टरनेटमा अनुसन्धान गरी ती क्षेत्रहरू पत्ता लगाउनुहोस् जहाँ मानिसहरूले ज्ञानको मात्रमापन र कोडिफिकेसन प्रयास गरेका छन्। ब्लूमको ट्याक्सोनोमी हेर्नुहोस्, र इतिहासमा फर्केर जानुहोस् कि मानिसहरूले आफ्नो संसार कसरी बुझ्न कोशिस गरेका थिए। लिनायसले जीवहरूको ट्याक्सोनोमी कसरी बनायो अन्वेषण गर्नुहोस्, र डिमिट्री मेंडेलिभले कसरी रासायनिक तत्वहरूको वर्णन र समूह गर्नु भयो अवलोकन गर्नुहोस्। तपाईं अरू कस्ता रोचक उदाहरणहरू फेला पार्न सक्नुहुन्छ? -**कार्य**: [Ontology निर्माण गर्नुहोस्](assignment.md) +**असाइन्मेन्ट**: [Build an Ontology](assignment.md) --- + +**अस्वीकरण**: +यस दस्तावेज़लाई AI अनुवाद सेवा [Co-op Translator](https://github.com/Azure/co-op-translator) प्रयोग गरी अनुवाद गरिएको हो। हामी सटीकताको प्रयास गर्छौं, तर कृपया जानकारि राख्नुहोस् कि स्वचालित अनुवादमा त्रुटिहरु वा अशुद्धिहरु हुन सक्छन्। मूल दस्तावेज आफ्नो मूल भाषामा आधिकारिक श्रोत मानिनुपर्छ। महत्वपूर्ण जानकारीको लागि, व्यावसायिक मानव अनुवाद सिफारिस गरिन्छ। यस अनुवादको प्रयोगबाट सिर्जित कुनै पनि गलतफहमी वा गलत व्याख्याको लागि हामी जिम्मेवार छैनौं। + \ No newline at end of file diff --git a/translations/pa/README.md b/translations/pa/README.md index ae294ed6..874d82f5 100644 --- a/translations/pa/README.md +++ b/translations/pa/README.md @@ -1,8 +1,8 @@ [Arabic](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgarian](../bg/README.md) | [Burmese (Myanmar)](../my/README.md) | [Chinese (Simplified)](../zh/README.md) | [Chinese (Traditional, Hong Kong)](../hk/README.md) | [Chinese (Traditional, Macau)](../mo/README.md) | [Chinese (Traditional, Taiwan)](../tw/README.md) | [Croatian](../hr/README.md) | [Czech](../cs/README.md) | [Danish](../da/README.md) | [Dutch](../nl/README.md) | [Estonian](../et/README.md) | [Finnish](../fi/README.md) | [French](../fr/README.md) | [German](../de/README.md) | [Greek](../el/README.md) | [Hebrew](../he/README.md) | [Hindi](../hi/README.md) | [Hungarian](../hu/README.md) | [Indonesian](../id/README.md) | [Italian](../it/README.md) | [Japanese](../ja/README.md) | [Kannada](../kn/README.md) | [Korean](../ko/README.md) | [Lithuanian](../lt/README.md) | [Malay](../ms/README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Nepali](../ne/README.md) | [Nigerian Pidgin](../pcm/README.md) | [Norwegian](../no/README.md) | [Persian (Farsi)](../fa/README.md) | [Polish](../pl/README.md) | [Portuguese (Brazil)](../br/README.md) | [Portuguese (Portugal)](../pt/README.md) | [Punjabi (Gurmukhi)](./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) -> **ਕੀ ਤੁਸੀਂ ਸਥਾਨਕ ਤੌਰ 'ਤੇ ਕਲੋਨ ਕਰਨਾ ਚਾਹੁੰਦੇ ਹੋ?** +> **ਕੀ ਤੁਸੀਂ ਲੋਕਲ ਕਲੋਨ ਪ੍ਰਾਥਮਿਕਤਾ ਦਿੰਦੇ ਹੋ?** -> ਇਹ ਰਿਪੋਜ਼ਿਟਰੀ ਵਿੱਚ 50+ ਭਾਸ਼ਾਈ ਅਨੁਵਾਦ ਸ਼ਾਮਲ ਹਨ, ਜੋ ਡਾਊਨਲੋਡ ਦਾ ਆਕਾਰ ਕਾਫੀ ਵਧਾ ਦਿੰਦੇ ਹਨ। ਬਿਨਾਂ ਅਨੁਵਾਦਾਂ ਦੇ ਕਲੋਨ ਕਰਨ ਲਈ, sparse checkout ਵਰਤੋਂ ਕਰੋ: +> ਇਹ ਰਿਪੋ ਵਿੱਚ 50+ ਭਾਸ਼ਾਈ ਅਨੁਵਾਦ ਸ਼ਾਮਲ ਹਨ ਜੋ ਡਾਊਨਲੋਡ ਦਾ ਆਕਾਰ ਕਾਫੀ ਵਧਾ ਦਿੰਦੇ ਹਨ। ਬਿਨਾਂ ਅਨੁਵਾਦਾਂ ਦੇ ਕਲੋਨ ਕਰਨ ਲਈ, ਸਪਾਰਸ ਚੇਕਆਊਟ ਵਰਤੋਂ ਕਰੋ: > ```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' > ``` -> ਇਸ ਨਾਲ ਤੁਹਾਨੂੰ ਉਹ ਸਭ ਕੁਝ ਮਿਲੇਗਾ ਜੋ ਕੋਰਸ ਨੂੰ ਤੇਜ਼ ਡਾਊਨਲੋਡ ਨਾਲ ਪੂਰਾ ਕਰਨ ਲਈ ਚਾਹੀਦਾ ਹੈ। +> ਇਸ ਨਾਲ ਤੁਹਾਨੂੰ ਕੋਰਸ ਨੂੰ ਪੂਰਾ ਕਰਨ ਲਈ ਸਾਰਾ ਕੁਝ ਮਿਲਦਾ ਹੈ ਪਰ ਡਾਊਨਲੋਡ ਬਹੁਤ ਤੇਜ਼ ਹੁੰਦਾ ਹੈ। -**ਜੇ ਤੁਸੀਂ ਵਾਧੂ ਅਨੁਵਾਦਾਂ ਦੀ ਸਹਾਇਤਾ ਚਾਹੁੰਦੇ ਹੋ, ਤਾਂ ਉਹ [ਇਥੇ](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md) ਦਿੱਤੀ ਗਈ ਹੈ।** +**ਜੇ ਤੁਸੀਂ ਹੋਰ ਅਨੁਵਾਦ ਭਾਸ਼ਾਵਾਂ ਦੀ ਮੰਗ ਕਰਦੇ ਹੋ ਤਾਂ ਉਹਨਾਂ ਨੂੰ [ਇੱਥੇ](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md) ਵੇਖੋ** ## ਕਮਿਊਨਿਟੀ ਵਿੱਚ ਸ਼ਾਮਿਲ ਹੋਵੋ [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) ## ਤੁਸੀਂ ਕੀ ਸਿੱਖੋਗੇ -**[ਕੋਰਸ ਦੀ ਮਾਈਂਡਮੈਪ](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** +**[ਕੋਰਸ ਦਾ ਮਾਈਂਡਮੈਪ](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** ਇਸ ਕੋਰਸ ਵਿੱਚ, ਤੁਸੀਂ ਸਿੱਖੋਗੇ: -* ਕ੍ਰਿਤਰਿਮ ਬੁੱਧੀ ਦੇ ਵੱਖ-ਵੱਖ ਰਸਤੇ, ਜਿਸ ਵਿੱਚ "ਚੰਗਾ ਪੁਰਾਣਾ" ਪ੍ਰਤੀਕਾਦਾਰ ਢੰਗ ਸ਼ਾਮਲ ਹੈ, ਜਿਸ ਵਿੱਚ **ਜਾਣਕਾਰੀ ਪ੍ਰਸਤੁਤੀ** ਅਤੇ ਤਰਕਸ਼ੀਲਤਾ ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)) ਹੈ। -* **ਨਿਊਰਲ ਨੈੱਟਵਰਕ** ਅਤੇ **ਡੀਪ ਲਰਨਿੰਗ**, ਜੋ ਆਧੁਨਿਕ AI ਦੇ ਮੁੱਖ ਹਨ। ਅਸੀਂ ਕੋਡ ਦੇ ਜ਼ਰੀਏ ਇਹਨਾਂ ਮਹੱਤਵਪੂਰਨ ਮੁੱਦਿਆਂ ਦੇ ਮੂਲ ਸਿਧਾਂਤ ਦੋ ਪ੍ਰਸਿੱਧ ਫ੍ਰੇਮਵਰਕ - [TensorFlow](http://Tensorflow.org) ਅਤੇ [PyTorch](http://pytorch.org) ਵਿੱਚ ਸਮਝਾਵਾਂਗੇ। -* ਚਿੱਤਰ ਅਤੇ ਲਿਖਤ ਨਾਲ ਕੰਮ ਕਰਨ ਲਈ **ਨਿਊਰਲ ਢਾਂਚੇ**। ਅਸੀਂ ਹਾਲੀਆ ਮਾਡਲ ਕਵਰ ਕਰਾਂਗੇ ਪਰ ਸਮਕਾਲੀ ਪ੍ਰਗਟਾਵਾਂ ਵਿੱਚ ਕੁਝ ਘਾਟ ਹੋ ਸਕਦੀ ਹੈ। -* ਘੱਟ ਲੋਕਪ੍ਰਿਯ AI ਢੰਗ, ਜਿਵੇਂ ਕਿ **ਜੀਨੈਟਿਕ ਅਲਗੋਰਿਥਮ** ਅਤੇ **ਮਲਟੀ-ਏਜੰਟ ਸਿਸਟਮ**। +* ਕ੍ਰਿਤ੍ਰਿਮ ਬੁੱਧੀ ਦੇ ਵੱਖ-ਵੱਖ ਦਾੜ੍ਹ, ਜਿਸ ਵਿੱਚ "ਚੰਗਾ ਪੁਰਾਣਾ" ਪ੍ਰਤੀਕਾਤਮਕ (symbolic) ਰਵੱਈਆ ਸੰਝੇਦਾਰੀ ਰਿਪ੍ਰੈਜ਼ੈਂਟੇਸ਼ਨ ਅਤੇ ਤਰਕ ਵਿਗਿਆਨ ਨਾਲ ਸਹਿਤ ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence))। +* **ਨਿਊਰਲ ਨੈੱਟਵਰਕਸ** ਅਤੇ **ਡੀਪ ਲਰਨਿੰਗ**, ਜੋ ਆਧੁਨਿਕ AI ਦੇ ਕੇਂਦਰ ਹਨ। ਅਸੀਂ ਇਹ ਮੁੱਖ ਮੌਜੂਦਾ ਵਿਸ਼ਿਆਂ ਨੂੰ ਦੋ ਪ੍ਰਸਿੱਧ ਫਰੇਮਵਰਕਸ - [TensorFlow](http://Tensorflow.org) ਅਤੇ [PyTorch](http://pytorch.org) ਵਿੱਚ ਕੋਡ ਦੀ ਵਰਤੋਂ ਨਾਲ ਵਿਵਰਣ ਕਰਾਂਗੇ। +* ਚਿੱਤਰਾਂ ਅਤੇ ਲਿਖਤ ਨਾਲ ਕੰਮ ਕਰਨ ਲਈ **ਨਿਊਰਲ ਆਰਕੀਟੈਕਚਰਜ਼**। ਅਸੀਂ ਹਾਲੀਆ ਮਾਡਲਜ਼ ਕਵਰ ਕਰਾਂਗੇ ਪਰ ਸਾਇੰਸ ਦੇ ਸਬ ਤੋਂ ਅਗਲੇ ਦਰਜੇ ਵਿੱਚ ਸ਼ਾਇਦ ਕੁਝ ਘਾਟ ਹੋ ਸਕਦੀ ਹੈ। +* ਘੱਟ ਜਾਣੀਆਂ ਜਾਂਦੀਆਂ AI ਪਹੁੰਚਾਂ, ਜਿਵੇਂ ਕਿ **ਜੀਨੈਟਿਕ ਐਲਗੋਰਿਥਮਸ** ਅਤੇ **ਮਲਟੀ-ਏਜੰਟ ਸਿਸਟਮਜ਼**। -ਇਸ ਕੋਰਸ ਵਿੱਚ ਜੋ ਕੁਝ ਨਹੀਂ ਕਵਰ ਕੀਤਾ ਜਾਵੇਗਾ: +ਇਸ ਕੋਰਸ ਵਿੱਚ ਅਸੀਂ ਕੀ ਨਹੀਂ ਕਵਰ ਕਰਾਂਗੇ: -> [ਇਸ ਕੋਰਸ ਲਈ ਸਾਡੇ Microsoft Learn ਕਲੈਕਸ਼ਨ ਵਿੱਚ ਸਾਰੇ ਵਾਧੂ ਸਰੋਤ ਲੱਭੋ](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) +> [ਇਸ ਕੋਰਸ ਲਈ ਸਾਰੇ ਵਾਧੂ ਸਾਧਨਾਂ ਨੂੰ ਸਾਡੇ ਮਾਈਕ੍ਰੋਸਾਫਟ ਲਰਨ ਕਲੇਕਸ਼ਨ ਵਿੱਚ ਲੱਭੋ](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) -* **ਕਾਰੋਬਾਰ ਵਿੱਚ AI** ਦੇ ਵਆਪਾਰਕ ਕੇਸ। ਇਸ ਲਈ ਮਾਈਕ੍ਰੋਸਾਫਟ ਲਰਨ 'ਤੇ [Introduction to AI for business users](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) ਸਿੱਖਣ ਦਾ ਰਸਤਾ ਜਾਂ [AI Business School](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum) ਜੋ [INSEAD](https://www.insead.edu/) ਦੇ ਸਾਥ ਵਿਕਸਤ ਕੀਤਾ ਗਿਆ ਹੈ, ਦੀ ਸਿਫਾਰਸ਼ ਕੀਤੀ ਜਾਂਦੀ ਹੈ। -* **ਕਲਾਸਿਕ ਮਸ਼ੀਨ ਲਰਨਿੰਗ**, ਜੋ ਸਾਡੇ [Machine Learning for Beginners Curriculum](http://github.com/Microsoft/ML-for-Beginners) ਵਿੱਚ ਵਧੀਆ ਤਰੀਕੇ ਨਾਲ ਦਰਸਾਇਆ ਗਿਆ ਹੈ। -* **[ਕਾਗਨੀਟਿਵ ਸੇਵਾਵਾਂ](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)** ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਤਿਆਰ ਕੀਤੇ ਕ੍ਰਿਆਸ਼ੀਲ AI ਐਪਲੀਕੇਸ਼ਨ। ਇਸ ਲਈ, ਅਸੀਂ Microsoft Learn ਦੇ ਮਾਡਿਊਲ [vision](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [natural language processing](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[Generative AI with Azure OpenAI Service](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** ਅਤੇ ਹੋਰ ਤੋਂ ਸ਼ੁਰੂਆਤ ਕਰਨ ਦੀ ਸਿਫਾਰਸ਼ ਕਰਦੇ ਹਾਂ। -* ਖਾਸ ML **ਕਲਾਊਡ ਫ੍ਰੇਮਵਰਕ**, ਜਿਵੇਂ [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), ਜਾਂ [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum)। [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) ਅਤੇ [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) ਰਸਤੇ ਵਰਤੋਂ ਕਰਨ 'ਤੇ ਵਿਚਾਰ ਕਰੋ। -* **ਗੱਲਬਾਤੀ AI** ਅਤੇ **ਚੈਟ ਬੋਟ**। ਇੱਕ ਵੱਖਰਾ [Create conversational AI solutions](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum) ਸਿੱਖਣ ਦਾ ਰਸਤਾ ਹੈ, ਅਤੇ ਤੁਸੀਂ ਹੋਰ ਜਾਣਕਾਰੀ ਲਈ [ਇਸ ਬਲੌਗ ਪੋਸਟ](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) ਨੂੰ ਵੀ ਦੇਖ ਸਕਦੇ ਹੋ। -* ਡੀਪ ਲਰਨਿੰਗ ਦੇ ਪਿੱਛੇ **ਗਹਿਰਾ ਗਣਿਤ**। ਇਸ ਲਈ ਅਸੀਂ ਈਆਨ ਗੁੱਡਫੈਲੋ, ਯੋਸ਼ੂਆ ਬੇਂਗਿਓ ਅਤੇ ਐਰੋਨ ਕੋਰਵਿਲ ਦੁਆਰਾ ਲਿਖਿਤ [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) ਦੀ ਸਿਫਾਰਸ਼ ਕਰਾਂਗੇ, ਜੋ ਆਨਲਾਈਨ [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/) 'ਤੇ ਵੀ ਉਪਲਬਧ ਹੈ। +* **ਕਾਰੋਬਾਰ ਵਿੱਚ AI** ਦੀ ਵਰਤੋਂ ਦੇ ਕਾਰੋਬਾਰੀ ਮਾਮਲੇ। ਇਸ ਲਈ, ਤੁਸੀਂ Microsoft Learn 'ਤੇ [ਕਾਰੋਬਾਰੀ ਵਰਤੋਂਕਾਰਾਂ ਲਈ AI ਦਾ ਪਰੀਚਯ](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) ਲਰਨਿੰਗ ਪਾਥ ਜਾਂ [AI ਬਿਜ਼ਨਿਸ ਸਕੂਲ](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum) ਜੋ [INSEAD](https://www.insead.edu/) ਨਾਲ ਸਹਿਯੋਗ ਵਿੱਚ ਵਿਕਸਿਤ ਹੈ, ਲੈ ਸਕਦੇ ਹੋ। +* **ਕਲਾਸਿਕ ਮਸ਼ੀਨ ਲਰਨਿੰਗ**, ਜੋ ਸਾਡੇ [ਮਸ਼ੀਨ ਲਰਨਿੰਗ ਫਾਰ ਬਿਗਿਨਰਜ਼ ਕੋਰਸ](http://github.com/Microsoft/ML-for-Beginners) ਵਿੱਚ ਵਧੀਆ ਦੱਸਿਆ ਗਿਆ ਹੈ। +* **ਕੌਗਨਿਟਿਵ ਸਰਵਿਸਿਜ਼** ਦੀ ਵਰਤੋਂ ਨਾਲ ਬਣੇ ਕਾਰਗਰ AI ਐਪਲੀਕੇਸ਼ਨ। ਇਸ ਲਈ, ਅਸੀਂ Microsoft Learn 'ਤੇ [ਦ੍ਰਿਸ਼ਟੀ](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [ਕੁਦਰਤੀ ਭਾਸ਼ਾ ਪ੍ਰਕਿਰਿਆ](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[Azure OpenAI ਸੇਵਾ ਨਾਲ ਜਨਰੇਟਿਵ AI](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** ਅਤੇ ਹੋਰ ਮਾਡਿਊਲ ਨਾਲ ਸ਼ੁਰੂ ਕਰਨ ਦੀ ਸਿਫਾਰਿਸ਼ ਕਰਦੇ ਹਾਂ। +* ਵਿਸ਼ੇਸ਼ ਮਸ਼ੀਨ ਲਰਨਿੰਗ **ਕਲਾਉਡ ਫਰੇਮਵਰਕਸ**, ਜਿਵੇਂ ਕਿ [Azure ਮਸ਼ੀਨ ਲਰਨਿੰਗ](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-bethanycheum), [Microsoft ਫੈਬਰਿਕ](https://learn.microsoft.com/en-us/training/paths/get-started-fabric/?WT.mc_id=academic-77998-bethanycheum), ਜਾਂ [Azure ਡੇਟਾਬ੍ਰਿਕਸ](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum)। ਤੁਸੀਂ [Azure ਮਸ਼ੀਨ ਲਰਨਿੰਗ ਨਾਲ ਮਸ਼ੀਨ ਲਰਨਿੰਗ ਹੱਲ ਬਣਾਓ ਤੇ ਚਲਾਓ](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) ਅਤੇ [Azure ਡੇਟਾਬ੍ਰਿਕਸ ਨਾਲ ਮਸ਼ੀਨ ਲਰਨਿੰਗ ਹੱਲ ਬਣਾਓ ਅਤੇ ਚਲਾਓ](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum) ਲਰਨਿੰਗ ਪਾਥ ਵਰਤ ਸਕਦੇ ਹੋ। +* **ਸੰਵਾਦਾਤਮਕ AI** ਅਤੇ **ਚੈਟ ਬੌਟਸ**। ਇਸ ਲਈ ਇੱਕ ਵੱਖਰਾ [ਸੰਵਾਦਾਤਮਕ AI ਹੱਲ ਬਣਾਓ](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum) ਲਰਨਿੰਗ ਪਾਥ ਹੈ, ਅਤੇ ਤੁਸੀਂ ਵਧੇਰੇ ਜਾਣਕਾਰੀ ਲਈ [ਇਸ ਬਲੌਗ ਪੋਸਟ](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) ਨੂੰ ਵੀ ਦੇਖ ਸਕਦੇ ਹੋ। +* ਡੀਪ ਲਰਨਿੰਗ ਦਾ **ਗਣਿਤਵਿਗਿਆਨ**। ਇਸ ਲਈ ਸਾਡੀ ਸਿਫਾਰਿਸ਼ ਹੈ [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) ਜਿਹੜੀ Ian Goodfellow, Yoshua Bengio ਅਤੇ Aaron Courville ਨੇ ਲਿਖੀ ਹੈ, ਜੋ ਆਨਲਾਈਨ [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/) 'ਤੇ ਵੀ ਉਪਲਬਧ ਹੈ। -_ਕਲਾਊਡ ਵਿੱਚ AI_ ਟੋਪਿਕਸ ਲਈ ਨਰਮ ਪਰਚਾਇ ਕਰਨ ਲਈ ਤੁਸੀਂ [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) ਸਿੱਖਣ ਦਾ ਰਸਤਾ ਲੈ ਸਕਦੇ ਹੋ। +_ਕਲਾਉਡ ਵਿੱਚ AI_ ਦੇ ਇੱਕ ਹੌਲੀ ਪਰੀਚਯ ਲਈ ਤੁਸੀਂ [Azure 'ਤੇ ਕ੍ਰਿਤ੍ਰਿਮ ਬੁੱਧੀ ਨਾਲ ਸ਼ੁਰੂਆਤ ਕਰੋ](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum) ਲਰਨਿੰਗ ਪਾਥ ਵੀ ਲੈ ਸਕਦੇ ਹੋ। # ਸਮੱਗਰੀ -| | ਪਾਠ ਦਾ ਲਿੰਕ | PyTorch/Keras/TensorFlow | ਲੈਬ | +| | ਪਾਠ ਲਿੰਕ | PyTorch/Keras/TensorFlow | ਪ੍ਰਯੋਗਸ਼ਾਲਾ | | :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | -| 0 | [ਕੋਰਸ ਸੈੱਟਅੱਪ](./lessons/0-course-setup/setup.md) | [ਆਪਣੇ ਵਿਕਾਸ ਵਾਲੇ ਵਾਤਾਵਰਣ ਦੀ ਸੈੱਟਅੱਪ ਕਰੋ](./lessons/0-course-setup/how-to-run.md) | | -| I | [**AI ਦਾ ਪਰਿੱਚੈ**](./lessons/1-Intro/README.md) | | | -| 01 | [AI ਦਾ ਪਰਿੱਚੈ ਅਤੇ ਇਸ ਦਾ ਇਤਿਹਾਸ](./lessons/1-Intro/README.md) | - | - | -| II | **ਪ੍ਰਤੀਕਾਦਾਰ AI** | -| 02 | [ਜਾਣਕਾਰੀ ਪ੍ਰਸਤੁਤੀ ਅਤੇ ਵਿਸ਼ੇਸ਼ਜ੍ਞ ਸਿਸਟਮ](./lessons/2-Symbolic/README.md) | [ਵਿਸ਼ੇਸ਼ਜ੍ਞ ਸਿਸਟਮ](./lessons/2-Symbolic/Animals.ipynb) / [ਓਨਟੋਲੋਜੀ](./lessons/2-Symbolic/FamilyOntology.ipynb) /[ਸੰਕਲਪ ਗ੍ਰਾਫ](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | -| III | [**ਨਿਊਰਲ ਨੈੱਟਵਰਕ ਦਾ ਪਰਿੱਚੈ**](./lessons/3-NeuralNetworks/README.md) ||| -| 03 | [ਪਰਸੈਪਟ੍ਰੋਨ](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [ਨੋਟਬੁੱਕ](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [ਲੈਬ](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | -| 04 | [ਮਲਟੀ-ਲੇਅਰਡ ਪਰਸੈਪਟ੍ਰੋਨ ਅਤੇ ਆਪਣਾ ਫਰੇਮਵਰਕ ਬਣਾਉਣਾ](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [ਨੋਟਬੁੱਕ](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [ਲੈਬ](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | -| 05 | [ਫਰੇਮਵਰਕਸ ਦਾ ਪਰਿਚਯ (PyTorch/TensorFlow) ਅਤੇ ਓਵਰਫਿਟਿੰਗ](./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) | [ਲੈਬ](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | -| IV | [**ਕੰਪਿਊਟਰ ਵਿਜ਼ਨ**](./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)| [ਮਾਈਕ੍ਰੋਸੌਫਟ ਏਜ਼ਰ 'ਤੇ ਕੰਪਿਊਟਰ ਵਿਜ਼ਨ ਖੋਜੋ](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | +| 0 | [ਕੋਰਸ ਸੈੱਟਅਪ](./lessons/0-course-setup/setup.md) | [ਆਪਣੇ ਵਿਕਾਸ ਦਾ ਮਾਹੌਲ ਸੈੱਟ ਕਰੋ](./lessons/0-course-setup/how-to-run.md) | | +| I | [**AI ਦਾ ਪਰਚਯ**](./lessons/1-Intro/README.md) | | | +| 01 | [AI ਦਾ ਪਰਚਯ ਅਤੇ ਇਤਿਹਾਸ](./lessons/1-Intro/README.md) | - | - | +| II | **ਪ੍ਰਤੀਕਾਤਮਕ AI** | +| 02 | [ਜਾਣਕਾਰੀ ਦੀ ਪ੍ਰਤੀਨਿਧੀ ਅਤੇ ਨਿਪੁੰਨ ਸਿਸਟਮ](./lessons/2-Symbolic/README.md) | [ਨਿਪੁੰਨ ਸਿਸਟਮ](./lessons/2-Symbolic/Animals.ipynb) / [ਓਨਟੋਲੋਜੀ](./lessons/2-Symbolic/FamilyOntology.ipynb) /[ਸੰਕਲਪ ਗਰਾਫ](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | +| III | [**ਨਿਊਰਲ ਨੈੱਟਵਰਕਸ ਦਾ ਪਰਚਯ**](./lessons/3-NeuralNetworks/README.md) ||| +| 03 | [ਪਰਸੀਪਟ੍ਰੌਨ](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [ਨੋਟਬੁੱਕ](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [ਲੈਬ](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | +| 04 | [ਮਲਟੀ-ਲੇਅਰਡ ਪਰਸੀਪਟ੍ਰੌਨ ਅਤੇ ਆਪਣਾ ਖੁਦ ਦਾ ਫਰੇਮਵਰਕ ਬਣਾਉਣਾ](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [ਨੋਟਬੁੱਕ](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [ਲੈਬ](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | +| 05 | [ਫਰੇਮਵਰਕਸ ਦਾ ਪਰਚਿਆ (PyTorch/TensorFlow) ਅਤੇ ਓਵਰਫਿਟਿੰਗ](./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) | [ਲੈਬ](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | +| IV | [**ਕੰਪਿਊਟਰ ਵਿਜ਼ਨ**](./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)| [ਮਾਇਕ੍ਰੋਸਾਫਟ ਐਜ਼ਯੁਰ 'ਤੇ ਕੰਪਿਊਟਰ ਵਿਜ਼ਨ ਦੀ ਖੋਜ ਕਰੋ](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | | 06 | [ਕੰਪਿਊਟਰ ਵਿਜ਼ਨ ਦਾ ਪਰਿਚਯ। OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [ਨੋਟਬੁੱਕ](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [ਲੈਬ](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | -| 07 | [ਕਨਵੋਲੂਸ਼ਨਲ ਨਿਊਰਲ ਨੈੱਟਵਰਕਸ](./lessons/4-ComputerVision/07-ConvNets/README.md) & [CNN ਆਰਕੀਟੈਕਚਰਸ](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [ਲੈਬ](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | -| 08 | [ਪ੍ਰੀ-ਟਰੇਨਡ ਨੈੱਟਵਰਕਸ ਅਤੇ ਟਰਾਂਸਫਰ ਲਰਨਿੰਗ](./lessons/4-ComputerVision/08-TransferLearning/README.md) ਅਤੇ [ਟਰੇਨਿੰਗ ਟ੍ਰਿਕਸ](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [ਲੈਬ](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | -| 09 | [ਆਟੋਏਂਕੋਡਰ ਅਤੇ 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 | [ਜਨੇਰੇਟਿਵ ਐਡਵਰਸਰੀਅਲ ਨੈੱਟਵਰਕਸ ਅਤੇ ਕਲਾ ਸਟਾਈਲ ਟ੍ਰਾਂਸਫਰ](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | -| 11 | [ਉੱਬਜੈਕਟ ਡਿਟੈਕਸ਼ਨ](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [ਲੈਬ](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | -| 12 | [ਸੈਮੈਂਟਿਕ ਸੈਗਮੈਂਟੇਸ਼ਨ। 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 | [**ਕੁਦਰਤੀ ਭਾਸ਼ਾ ਪ੍ਰੋਸੈਸਿੰਗ**](./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) | [ਮਾਈਕ੍ਰੋਸੌਫਟ ਏਜ਼ਰ 'ਤੇ ਕੁਦਰਤੀ ਭਾਸ਼ਾ ਪ੍ਰੋਸੈਸਿੰਗ ਖੋਜੋ](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| -| 13 | [ਟੈਕਸਟ ਪ੍ਰਤੀਨਿਧਿਤਾ। ਬੋ/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 | [ਸੈਮੈਂਟਿਕ ਵਰਡ ਐਮਬੈੱਡਿੰਗਸ। Word2Vec ਅਤੇ 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 | [ਭਾਸ਼ਾ ਮਾਡਲਿੰਗ। ਆਪਣੀਆਂ ਐਮਬੈੱਡਿੰਗਸ ਨੂੰ ਟਰੇਨ ਕਰਨਾ](./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) | [ਲੈਬ](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | -| 16 | [ਰਿਕਰਨਟ ਨਿਊਰਲ ਨੈੱਟਵਰਕਸ](./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 | [ਜਨੇਰੇਟਿਵ ਰਿਕਰਨਟ ਨੈੱਟਵਰਕਸ](./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) | [ਲੈਬ](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | -| 18 | [ਟ੍ਰਾਂਸਫਰਮਰ। 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 | [ਨਾਮਿਤ ਏਂਟੀਟੀ ਪਹਿਚਾਣ](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [ਲੈਬ](./lessons/5-NLP/19-NER/lab/README.md) | -| 20 | [ਵੱਡੇ ਭਾਸ਼ਾ ਮਾਡਲ, ਪ੍ਰਾਂਪਟ ਪ੍ਰੋਗ੍ਰਾਮਿੰਗ ਅਤੇ ਫਿਊ-ਸ਼ਾਟ ਟਾਸਕ](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | -| VI | **ਹੋਰ ਏਆਈ ਤਕਨੀਕਾਂ** || | -| 21 | [ਜੀਟੈਟਿਕ ਅਲਗੋਰਿਦਮ](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [ਨੋਟਬੁੱਕ](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | +| 07 | [ਕਨਵੋਲੁਸ਼ਨਲ ਨਿਊਰਲ ਨੈੱਟਵਰਕਸ](./lessons/4-ComputerVision/07-ConvNets/README.md) & [CNN ਆਰਕੀਟੈਕਚਰਸ](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [ਲੈਬ](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | +| 08 | [ਪ्री-ਟ੍ਰੇਨਡ ਨੈੱਟਵਰਕਸ ਅਤੇ ਟ੍ਰਾਂਸਫਰ ਲਰਨਿੰਗ](./lessons/4-ComputerVision/08-TransferLearning/README.md) ਅਤੇ [ਟ੍ਰੇਨਿੰਗ ਟ੍ਰਿਕਸ](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [ਲੈਬ](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | +| 09 | [ਆਟੋਐਨਕੋਡਰਸ ਅਤੇ 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 | [ਜਨਰੇਟਿਵ ਐਡਵਰਸਰੀਅਲ ਨੈੱਟਵਰਕਸ ਅਤੇ ਆਰਟੀਸਟਿਕ ਸਟਾਈਲ ਟ੍ਰਾਂਸਫਰ](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | +| 11 | [ਓਬਜੈਕਟ ਡਿਟੈਕਸ਼ਨ](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [ਲੈਬ](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | +| 12 | [ਸੇਮੈਂਟਿਕ ਸੈਗਮੈਂਟੇਸ਼ਨ। 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 | [**ਕੁਦਰਤੀ ਭਾਸ਼ਾ ਪ੍ਰੋਸੈਸਿੰਗ**](./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) | [ਮਾਇਕ੍ਰੋਸਾਫਟ ਐਜ਼ਯੁਰ 'ਤੇ ਕੁਦਰਤੀ ਭਾਸ਼ਾ ਪ੍ਰੋਸੈਸਿੰਗ ਦੀ ਖੋਜ ਕਰੋ](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| +| 13 | [ਟੈਕਸਟ ਪ੍ਰਤੀਨਿਧਿਤਾ। 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 | [ਸੇਮੈਂਟਿਕ ਵਰਡ ਐਮਬੈਡਿੰਗਸ। Word2Vec ਅਤੇ 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 | [ਭਾਸ਼ਾ ਮਾਡਲਿੰਗ। ਆਪਣੀਆਂ ਐਮਬੈਡਿੰਗਜ਼ ਸਿੱਖਣਾ](./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) | [ਲੈਬ](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | +| 16 | [ਰੀਕਰਨਟ ਨਿਊਰਲ ਨੈੱਟਵਰਕਸ](./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 | [ਜਨਰੇਟਿਵ ਰੀਕਰਨਟ ਨੈੱਟਵਰਕਸ](./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) | [ਲੈਬ](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | +| 18 | [ਟ੍ਰਾਂਸਫਰਮਰਜ਼। 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 | [ਨਾਮਤੌਰ ਅਮਲ ਪਛਾਣ](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [ਲੈਬ](./lessons/5-NLP/19-NER/lab/README.md) | +| 20 | [ਵੱਡੇ ਭਾਸ਼ਾ ਮਾਡਲ, ਪ੍ਰਾਂਪਟ ਪ੍ਰੋਗ੍ਰਾਮਿੰਗ ਅਤੇ ਕੁਝ-ਸ਼ਾਟ ਟਾਸਕ](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | +| VI | **ਹੋਰ AI ਤਕਨੀਕਾਂ** || | +| 21 | [ජੀਨටਿਕ ਅਲਗੋਰੀਦਮਸ](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [ਨੋਟਬੁੱਕ](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | | 22 | [ਡੀਪ ਰੀਇਨਫੋਰਸਮੈਂਟ ਲਰਨਿੰਗ](./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) | [ਲੈਬ](./lessons/6-Other/22-DeepRL/lab/README.md) | -| 23 | [ਮਲਟੀ-ਏਜੰਟ ਸਿਸਟਮ](./lessons/6-Other/23-MultiagentSystems/README.md) | | | -| VII | **ਏਆਈ ਨੈਤਿਕਤਾ** | | | -| 24 | [ਏਆਈ ਨੈਤਿਕਤਾ ਅਤੇ ਜ਼ਿੰਮੇਵਾਰ ਏਆਈ](./lessons/7-Ethics/README.md) | [Microsoft Learn: ਜ਼ਿੰਮੇਵਾਰ ਏਆਈ ਸਿਧਾਂਤ](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | -| IX | **ਇਕਸਟਰਾ** | | | +| 23 | [ਮਲਟੀ-ਏਜੰਟ ਸਿਸਟਮਸ](./lessons/6-Other/23-MultiagentSystems/README.md) | | | +| VII | **AI ਨੈਤੀਕਤਾ** | | | +| 24 | [AI ਨੈਤੀਕਤਾ ਅਤੇ ਜ਼ਿੰਮੇਵਾਰ AI](./lessons/7-Ethics/README.md) | [Microsoft Learn: ਜ਼ਿੰਮੇਵਾਰ AI ਨੀਤੀਆਂ](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | +| IX | **ਵਾਧੂ** | | | | 25 | [ਮਲਟੀ-ਮੋਡਲ ਨੈੱਟਵਰਕਸ, CLIP ਅਤੇ VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [ਨੋਟਬੁੱਕ](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | ## ਹਰ ਪਾਠ ਵਿੱਚ ਸ਼ਾਮਲ ਹੈ -* ਪੜ੍ਹਾਈ ਤੋਂ ਪਹਿਲਾਂ ਮਾਡਰੀਅਲ -* ਚੱਲਣ ਵਾਲੇ ਜੂਪਾਈਟਰ ਨੋਟਬੁੱਕ, ਜੋ ਅਕਸਰ ਫਰੇਮਵਰਕ ( **PyTorch** ਜਾਂ **TensorFlow** ) ਨਾਲ ਸੰਬੰਧਿਤ ਹੁੰਦੇ ਹਨ। ਚੱਲਣ ਵਾਲਾ ਨੋਟਬੁੱਕ ਬਹੁਤ ਸਾਰੀ ਸਿਧਾਂਤਕ ਸਮਗਰੀ ਵੀ ਰੱਖਦਾ ਹੈ, ਇਸ ਲਈ ਵਿਸ਼ੇ ਨੂੰ ਸਮਝਣ ਲਈ ਤੁਹਾਨੂੰ ਨੋਟਬੁੱਕ ਦਾ ਘੱਟੋ-ਘੱਟ ਇੱਕ ਵਰਜ਼ਨ (ਚਾਹੇ PyTorch ਜਾਂ TensorFlow) ਦੇਖਣਾ ਚਾਹੀਦਾ ਹੈ। -* ਕੁਝ ਵਿਸ਼ਿਆਂ ਲਈ **ਲੈਬਸ**, ਜੋ ਤੁਸੀਂ ਸਿੱਖੇ ਸਮਗਰੀ ਨੂੰ ਕਿਸੇ ਨਿਰਦਿਸ਼ਟ ਸਮੱਸਿਆ 'ਤੇ ਲਾਗੂ ਕਰਨ ਦੀ ਕੋਸ਼ਿਸ਼ ਕਰਨ ਦਾ ਮੌਕਾ ਦਿੰਦੇ ਹਨ। -* ਕੁਝ ਹਿੱਸਿਆਂ ਵਿੱਚ [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) ਮਾਡਿਊਲਾਂ ਦੇ ਲਿੰਕ ਹਨ ਜੋ ਸੰਬੰਧਤ ਵਿਸ਼ਿਆਂ ਨੂੰ ਢੱਕਦੇ ਹਨ। +* ਪੜ੍ਹਨ ਤੋਂ ਪਹਿਲਾਂ ਦਾ ਸਮਗਰੀ +* ਚਲਾਉਣ ਯੋਗ ਜੂਪੀਟਰ ਨੋਟਬੁੱਕ, ਜੋ ਅਕਸਰ ਫਰੇਮਵਰਕ ( **PyTorch** ਜਾਂ **TensorFlow**) ਲਈ ਖਾਸ ਹੁੰਦੇ ਹਨ। ਚਲਾਉਣ ਯੋਗ ਨੋਟਬੁੱਕ ਵਿੱਚ ਕਾਫੀ ਸਿਧਾਂਤਕ ਸਮੱਗਰੀ ਵੀ ਹੁੰਦੀ ਹੈ, ਇਸ ਲਈ ਵਿਸ਼ੇ ਨੂੰ ਸਮਝਣ ਲਈ ਤੁਹਾਨੂੰ ਘੱਟੋ-ਘੱਟ ਇੱਕ ਵਰਜਨ (ਚਾਹੇ PyTorch ਜਾਂ TensorFlow) ਦੇਖਣਾ ਜ਼ਰੂਰੀ ਹੈ। +* ਕੁਝ ਵਿਸ਼ਿਆਂ ਲਈ **ਲੈਬ** ਉਪਲਬਧ ਹਨ, ਜੋ ਤੁਹਾਨੂੰ ਸਿੱਖੇ ਸਮੱਗਰੀ ਨੂੰ ਕਿਸੇ ਖ਼ਾਸ ਸਮੱਸਿਆ ਤੇ ਲਾਗੂ ਕਰਨ ਦਾ ਮੌਕਾ ਦਿੰਦੇ ਹਨ। +* ਕੁਝ ਭਾਗਾਂ ਵਿੱਚ [**ਐਮਐਸ ਲਰਨ**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) ਮੌਡੀਊਲਾਂ ਦੇ ਲਿੰਕ ਹਨ ਜੋ ਸਬੰਧਤ ਵਿਸ਼ਿਆਂ ਨੂੰ ਕਵਰ ਕਰਦੇ ਹਨ। -## ਸ਼ੁਰੂਆਤ ਕਿਵੇਂ ਕਰੀਏ +## ਸ਼ੁਰੂ ਕਰਨਾ -### 🎯 ਨਵਾਂ ਏਆਈ ਸਿੱਖਣਾ? ਇੱਥੋਂ ਸ਼ੁਰੂ ਕਰੋ! +### 🎯 AI ਵਿੱਚ ਨਵਾਂ ਹੋ? ਇੱਥੋਂ ਸ਼ੁਰੂ ਕਰੋ! -ਜੇ ਤੁਸੀਂ ਏਆਈ ਵਿੱਚ ਬਿਲਕੁਲ ਨਵੇਂ ਹੋ ਅਤੇ ਤੁਰੰਤ, ਹੱਥੋਂ ਹੱਥ ਉਦਾਹਰਨਾਂ ਚਾਹੁੰਦੇ ਹੋ, ਤਾਂ ਸਾਡਾ [**ਬਿਗਿਨਰ-ਫ੍ਰੈਂਡਲੀ ਉਦਾਹਰਨਾਂ**](./examples/README.md) ਵੇਖੋ! ਇਸ ਵਿੱਚ ਸ਼ਾਮਲ ਹਨ: +ਜੇ ਤੁਸੀਂ ਬਿਲਕੁਲ ਨਵੇਂ ਹੋ ਅਤੇ ਤੇਜ਼, ਅਮਲਯੋਗ ਉਦਾਹਰਨਾਂ ਚਾਹੁੰਦੇ ਹੋ, ਤਾਂ ਸਾਡੀਆਂ [**ਸ਼ੁਰੂਆਤੀ-ਦੋਸਤ ਉਦਾਹਰਨਾਂ**](./examples/README.md) ਦਾ ਜਾਇਜ਼ਾ ਲਓ! ਇਸ ਵਿੱਚ ਹਨ: -- 🌟 **ਹੈਲੋ ਏਆਈ ਵਰਲਡ** - ਤੁਹਾਡਾ ਪਹਿਲਾ ਏਆਈ ਪ੍ਰੋਗਰਾਮ (ਪੈਟਰਨ ਪਛਾਣ) -- 🧠 **ਸਧਾਰਣ ਨਿਊਰਲ ਨੈੱਟਵਰਕ** - ਖ਼ਾਲੀ ਕਾਗਜ਼ ਤੋਂ ਨਿਊਰਲ ਨੈੱਟਵਰਕ ਬਣਾਓ -- 🖼️ **ਇਮੇਜ ਕਲਾਸੀਫਾਇਰ** - ਵਿਸਥਾਰ ਨਾਲ ਟਿੱਪਣੀਆਂ ਨਾਲ ਤਸਵੀਰਾਂ ਦੀ ਵਰਗੀਕਰਨ ਕਰਨਾ -- 💬 **ਪਾਠ ਭਾਵਨਾ** - ਸਕਾਰਾਤਮਕ/ਨਕਾਰਾਤਮਕ ਪਾਠ ਦਾ ਵਿਸ਼ਲੇਸ਼ਣ ਕਰੋ +- 🌟 **ਹੈਲੋ AI ਵਰਲਡ** - ਤੁਹਾਡਾ ਪਹਿਲਾ AI ਪ੍ਰੋਗ੍ਰਾਮ (ਪੈਟਰਨ ਪਛਾਣ) +- 🧠 **ਸਰਲ ਨਿਊਰਲ ਨੈੱਟਵਰਕ** - ਖ਼ਾਲੀ ਤੋਂ ਨਿਊਰਲ ਨੈੱਟਵਰਕ ਬਣਾਓ +- 🖼️ **ਚਿੱਤਰ ਵర్గੀਕਰਤਾ** - ਵਿਸਥਾਰ ਨਾਲ ਟਿੱਪਣੀਆਂ ਵਾਲੇ ਚਿੱਤਰਾਂ ਦੀ ਵਰਗੀਕਰਨ +- 💬 **ਟੈਕਸਟ ਸੈਂਟੀਮੈਂਟ** - ਸਕਾਰਾਤਮਕ/ਨਕਾਰਾਤਮਕ ਟੈਕਸਟ ਦਾ ਵਿਸ਼ਲੇਸ਼ਣ ਕਰੋ -ਇਹ ਉਦਾਹਰਣ ਤੁਹਾਨੂੰ ਪੂਰੇ ਕੋਰਸ ਵਿੱਚ ਮਿਲਣ ਤੋਂ ਪਹਿਲਾਂ ਏਆਈ ਸੰਕਲਪਾਂ ਨੂੰ ਸਮਝਣ ਵਿੱਚ ਮਦਦ ਕਰਨ ਲਈ ਤਿਆਰ ਕੀਤੇ ਗਏ ਹਨ। +ਇਹ ਉਦਾਹਰਨ ਤੁਹਾਨੂੰ ਪੂਰੇ ਕੋਰਸ ਵਿੱਚ ਕਦਮ ਰੱਖਣ ਤੋਂ ਪਹਿਲਾਂ AI ਸੰਕਲਪਾਂ ਨੂੰ ਸਮਝਣ ਵਿੱਚ ਮਦਦ ਲਈ ਡਿਜ਼ਾਈਨ ਕੀਤੀਆਂ ਗਈਆਂ ਹਨ। ### 📚 ਪੂਰਾ ਕੋਰਸ ਸੈਟਅੱਪ -- ਅਸੀਂ ਤੁਹਾਡੇ ਵਿਕਾਸ ਮਾਹੌਲ ਨੂੰ ਸੈਟਅੱਪ ਕਰਨ ਵਿੱਚ ਮਦਦ ਲਈ ਇੱਕ [ਸੈਟਅੱਪ ਪਾਠ](./lessons/0-course-setup/setup.md) ਬਣਾਇਆ ਹੈ। - ਸਿੱਖਿਆਦਾਤਿਆਂ ਲਈ, ਅਸੀਂ ਇੱਕ [ਕਰਿਕੁਲਮ ਸੈਟਅੱਪ ਪਾਠ](./lessons/0-course-setup/for-teachers.md) ਵੀ ਬਣਾਇਆ ਹੈ! -- ਕਿਸੇ VSCode ਜਾਂ Codepace ਵਿੱਚ ਕੋਡ ਕਿਵੇਂ ਚਲਾਉਣਾ ਹੈ ਇਹ ਜਾਣਨ ਲਈ [ਇੱਥੇ ਕਲਿੱਕ ਕਰੋ](./lessons/0-course-setup/how-to-run.md) +- ਅਸੀਂ ਤੁਹਾਡੇ ਵਿਕਾਸ ਮਾਹੌਲ ਨੂੰ ਸੈਟਅੱਪ ਕਰਨ ਵਿੱਚ ਮਦਦ ਲਈ ਇੱਕ [ਸੈਟਅੱਪ ਲੈਸਨ](./lessons/0-course-setup/setup.md) ਬਣਾਇਆ ਹੈ। - ਸਿੱਖਿਆਦਾਤਿਆਂ ਲਈ, ਅਸੀਂ ਤੁਹਾਡੇ ਲਈ ਵੀ ਇੱਕ [ਕੋਰਸ ਸੈਟਅੱਪ ਲੈਸਨ](./lessons/0-course-setup/for-teachers.md) ਬਣਾਇਆ ਹੈ! +- VSCode ਜਾਂ Codespace ਵਿੱਚ ਕੋਡ ਕਿਵੇਂ ਚਲਾਉਣਾ ਹੈ ਦੇਖੋ: [Run the code in a VSCode or a Codespace](./lessons/0-course-setup/how-to-run.md) -ਇਹ ਕਦਮ ਅਨੁਸਰਣ ਕਰੋ: +ਇਹ ਕਦਮਾਂ ਦੀ ਪਾਲਣਾ ਕਰੋ: -ਰਿਪੋਜ਼ਿਟਰੀ Fork ਕਰੋ: ਇਸ ਪੇਜ ਦੇ ਸਿੱਟੇ-ਸਿਰੇ ਉੱਪਰ "Fork" ਬਟਨ ਉੱਤੇ ਕਲਿੱਕ ਕਰੋ। +ਰਿਪੋਜ਼ਟਰੀ ਨੂੰ Fork ਕਰੋ: ਇਸ ਪੇਜ ਦੇ ਉਪਰ-ਸੱਜੇ ਕੋਨੇ 'Fork' ਬਟਨ 'ਤੇ ਕਲਿਕ ਕਰੋ। -ਰਿਪੋਜ਼ਿਟਰੀ Clone ਕਰੋ: `git clone https://github.com/microsoft/AI-For-Beginners.git` +ਰਿਪੋਜ਼ਟਰੀ ਕਲੋਨ ਕਰੋ: `git clone https://github.com/microsoft/AI-For-Beginners.git` -ਇਸ ਰਿਪੋ ਨੂੰ ਬਾਅਦ ਵਿੱਚ ਆਸਾਨੀ ਨਾਲ ਲੱਭਣ ਲਈ ਇਸ ਨੂੰ ਸਿਤਾਰਾ (🌟) ਦੇਣਾ ਨਾ ਭੁੱਲੋ। +ਇਸ ਰਿਪੋ ਨੂੰ ਬਾਅਦ ਵਿੱਚ ਅਸਾਨੀ ਨਾਲ ਲੱਭਣ ਲਈ ਸਟਾਰ (🌟) ਕਰਨਾ ਨਾ ਭੁੱਲੋ। ## ਹੋਰ ਸਿੱਖਣ ਵਾਲਿਆਂ ਨਾਲ ਮਿਲੋ -ਸਾਡੇ [ਅਧਿਕਾਰਿਕ ਏਆਈ ਡਿਸਕੋਰਡ ਸਰਵਰ](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) ਵਿੱਚ ਸ਼ਾਮਿਲ ਹੋਵੋ ਅਤੇ ਇਸ ਕੋਰਸ ਵਿੱਚ ਸਿੱਖ ਰਹੇ ਹੋਰ ਸਿੱਖਣ ਵਾਲਿਆਂ ਨਾਲ ਮਿਲੋ ਅਤੇ ਸਹਾਇਤਾ ਪ੍ਰਾਪਤ ਕਰੋ। +ਸਾਡੇ [ਆਧਿਕਾਰਿਕ AI ਡਿਸਕੋਰਡ ਸਰਵਰ](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) ਵਿੱਚ ਸ਼ਾਮਲ ਹੋਵੋ ਅਤੇ ਇਸ ਕੋਰਸ ਲੈ ਰਹੇ ਹੋਰ ਸਿੱਖਣ ਵਾਲਿਆਂ ਨਾਲ ਮਿਲੋ ਅਤੇ ਆਪਣੇ ਸਵਾਲਾਂ ਲਈ ਸਹਿਯੋਗ ਪ੍ਰਾਪਤ ਕਰੋ। -ਜੇ ਤੁਹਾਡੇ ਕੋਲ ਪ੍ਰੋਡਕਟ ਫੀਡਬੈਕ ਜਾਂ ਸਵਾਲ ਹਨ ਤਾਂ ਬਿਲਡਿੰਗ ਕਰਦੇ ਸਮੇਂ ਸਾਡੇ [Azure AI Foundry Developer Forum](https://aka.ms/foundry/forum) ਨੂੰ ਵੇਖੋ। +ਜੇ ਤੁਹਾਨੂੰ ਪ੍ਰੋਡਕਟ ਫੀਡਬੈਕ ਜਾਂ ਸਵਾਲ ਹਨ ਜਦੋਂ ਤੁਸੀਂ ਬਿਲਡ ਕਰ ਰਹੇ ਹੋ, ਤਾਂ ਸਾਡੇ [Azure AI Foundry Developer Forum](https://aka.ms/foundry/forum) 'ਤੇ ਜਾਓ। -## ਕੁਇਜ਼ +## ਕੁਇਜ਼ਜ਼ -> **ਕੁਇਜ਼ ਬਾਰੇ ਇੱਕ ਨੋਟ**: ਸਾਰੇ ਕੁਇਜ਼ Quiz-app ਫੋਲਡਰ ਵਿੱਚ etc\quiz-app ਵਿੱਚ ਹਨ, ਜਾਂ [ਹੇਠਾਂ ਆਨਲਾਈਨ](https://ff-quizzes.netlify.app/) ਵੀ ਹਨ। ਇਹ ਪਾਠਾਂ ਵਿੱਚ ਲਿੰਕ ਕੀਤੇ ਗਏ ਹਨ। ਕੁਇਜ਼ ਐਪ ਨੂੰ ਸਥਾਨਕ ਤੌਰ ਤੇ ਚਲਾਇਆ ਜਾਂਦਾ ਹੈ ਜਾਂ Azure' ਤੇ ਡਿਪਲੋਇ ਕੀਤਾ ਜਾ ਸਕਦਾ ਹੈ; `quiz-app` ਫੋਲਡਰ ਵਿੱਚ ਦਿੱਤੇ ਨਿਰਦੇਸ਼ਾਂ ਨੂੰ ਫਾਲੋ ਕਰੋ। ਇਹ ਧੀਰੇ-ਧੀਰੇ ਸਥਾਨਕਕ੍ਰਿਤ ਕੀਤਾ ਜਾ ਰਿਹਾ ਹੈ। +> **ਕੁਇਜ਼ ਲਈ ਇੱਕ ਨੋਟ**: ਸਾਰੇ ਕੁਇਜ਼ਜ਼ Quiz-app ਫੋਲਡਰ ਵਿੱਚ etc\quiz-app ਵਿੱਚ ਹਨ ਜਾਂ [ਆਨਲਾਈਨ ਇੱਥੇ](https://ff-quizzes.netlify.app/). ਇਹ ਲੈਸਨਾਂ ਦੇ ਅੰਦਰ ਲਿੰਕ ਕੀਤੇ ਗਏ ਹਨ। ਕੁਇਜ਼ ਐਪ ਨੂੰ ਲੋਕਲ ਵਿੱਚ ਚਲਾਇਆ ਜਾ ਸਕਦਾ ਹੈ ਜਾਂ Azure 'ਤੇ ਡਿਪਲੋਇ ਕੀਤਾ ਜਾ ਸਕਦਾ ਹੈ; `quiz-app` ਫੋਲਡਰ ਵਿੱਚ ਦਿੱਤੀਆਂ ਹਦਾਇਤਾਂ ਨੂੰ ਪਾਲਣਾ ਕਰੋ। ਇਹ ਧੀਰੇ-ਧੀਰੇ ਅਨੇਕ ਭਾਸ਼ਾਵਾਂ ਵਿੱਚ ਉਪਲਬਧ ਹੋ ਰਹੇ ਹਨ। -## ਮਦਦ ਦੀ ਲੋੜ ਹੈ? +## ਮਦਦ ਦੀ ਲੋੜ -ਕੀ ਤੁਹਾਡੇ ਕੋਲ ਸੁਝਾਵ ਹਨ ਜਾਂ ਤੁਸੀਂ ਕੋਈ ਵਿਆਕਰਣ ਜਾਂ ਕੋਡ ਦੀਆਂ ਗਲਤੀਆਂ ਲੱਭੀਆਂ ਹਨ? ਇੱਕ ਇਸ਼ੂ ਉੱਠਾਓ ਜਾਂ ਪੁਰ ਰੀਕਵੈਸਟ ਬਣਾਓ। +ਕੀ ਤੁਹਾਡੇ ਕੋਲ ਸੁਝਾਅ ਹਨ ਜਾਂ ਕੋਈ ਸਪੈਲਿੰਗ ਜਾਂ ਕੋਡ ਗਲਤੀਆਂ ਮਿਲੀਆਂ ਹਨ? ਕੋਈ ਮੁੱਦਾ ਉਠਾਓ ਜਾਂ ਪੁਲ ਰਿਕਵੇਸਟ ਬਣਾਓ। ## ਖ਼ਾਸ ਧੰਨਵਾਦ -* **✍️ ਮੁੱਖ ਲੇਖਕ:** [ਡਿਮਿਤਰੀ ਸੋਸ਼ਨਿਕੋਵ](http://soshnikov.com), PhD -* **🔥 ਸੰਪਾਦਕ:** [ਜੇਨ ਲੂਪਰ](https://twitter.com/jenlooper), PhD -* **🎨 ਸਕੈਚਨੋਟ ਇਲਸਟਰੈਟਰ:** [ਟੋਮੋਮੀ ਇਮੁਰਾ](https://twitter.com/girlie_mac) -* **✅ ਕੁਇਜ਼ ਸਿਰਜਕ:** [ਲੇਟੀਫਾਹ ਬੈਲੋ](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) -* **🙏 ਮੁੱਖ ਯੋਗਦਾਤਾ:** [ਏਵਗੇਨੀ ਪਿਸ਼ਚਿਕ](https://github.com/Pe4enIks) +* **✍️ ਪ੍ਰਧਾਨ ਲੇਖਕ:** [Dmitry Soshnikov](http://soshnikov.com), PhD +* **🔥 ਸੰਪਾਦਕ:** [Jen Looper](https://twitter.com/jenlooper), PhD +* **🎨 ਸਕੈਚਨੋਟ ਚિત્રਕਾਰ:** [Tomomi Imura](https://twitter.com/girlie_mac) +* **✅ ਕੁਇਜ਼ ਸਿਰਜਣਹਾਰ:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) +* **🙏 ਮੁੱਖ ਯੋਗਦਾਨਕਾਰ:** [Evgenii Pishchik](https://github.com/Pe4enIks) ## ਹੋਰ ਕੋਰਸ -ਸਾਡੀ ਟੀਮ ਹੋਰ ਕੋਰਸ ਵੀ ਬਣਾਉਂਦੀ ਹੈ! ਵੇਖੋ: +ਸਾਡੀ ਟੀਮ ਹੋਰ ਕੋਰਸ ਤਿਆਰ ਕਰਦੀ ਹੈ! ਵੇਖੋ: ### LangChain @@ -197,7 +198,7 @@ _ਕਲਾਊਡ ਵਿੱਚ AI_ ਟੋਪਿਕਸ ਲਈ ਨਰਮ ਪਰਚ --- -### Core Learning +### ਕੋਰ ਲਰਨਿੰਗ [![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) @@ -208,25 +209,25 @@ _ਕਲਾਊਡ ਵਿੱਚ AI_ ਟੋਪਿਕਸ ਲਈ ਨਰਮ ਪਰਚ --- -### Copilot Series +### ਕੋਪਾਇਲਟ ਸੀਰੀਜ਼ [![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) -## ਮਦਦ ਪ੍ਰਾਪਤ ਕਰੋ +## ਮਦਦ ਲੈਣਾ -ਜੇ ਤੁਸੀਂ ਫਸ ਜਾਂਦੇ ਹੋ ਜਾਂ ਏਆਈ ਐਪਸ ਬਣਾਉਣ ਬਾਰੇ ਕੋਈ ਵੀ ਸਵਾਲ ਹੈ, ਤਾਂ MCP ਬਾਰੇ ਗੱਲਬਾਤ ਵਿੱਚ ਹੋਰ ਸਿੱਖਣ ਵਾਲਿਆਂ ਅਤੇ ਅਨੁਭਵੀ ਡਿਵੈਲਪਰਾਂ ਨਾਲ ਜੁੜੋ। ਇਹ ਇੱਕ ਸਮਰਥਕ ਕੰਮਿਊਨਿਟੀ ਹੈ ਜਿੱਥੇ ਸਵਾਲਾਂ ਦਾ ਸਵਾਗਤ ਹੈ ਅਤੇ ਗਿਆਨ ਖੁੱਲ੍ਹ ਕੇ ਸਾਂਝਾ ਕੀਤਾ ਜਾਂਦਾ ਹੈ। +ਜੇ ਤੁਸੀਂ ਫਸ ਜਾਂਦੇ ਹੋ ਜਾਂ AI ਐਪ ਬਣਾਉਣ ਸਬੰਧੀ ਕੋਈ ਵੀ ਸਵਾਲ ਹੈ, ਤਾਂ MCP ਬਾਰੇ ਚਰਚਾ ਕਰਨ ਲਈ ਹੋਰ ਸਿੱਖਣ ਵਾਲਿਆਂ ਅਤੇ ਅਨੁਭਵੀ ਵਿਕਾਸਕਾਰਾਂ ਦੀ ਵਰਤੋਂ ਕਰੋ। ਇਹ ਇੱਕ ਸਹਾਇਕ ਭਾਈਚਾਰਾ ਹੈ ਜਿੱਥੇ ਸਵਾਲਾ ਦਾ ਸਵਾਗਤ ਕੀਤਾ ਜਾਂਦਾ ਹੈ ਅਤੇ ਗਿਆਨ ਖੁੱਲ੍ਹੇ ਦਿਲ ਨਾਲ ਸਾਂਝਾ ਕੀਤਾ ਜਾਂਦਾ ਹੈ। [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -ਜੇ ਤੁਸੀਂ ਪ੍ਰੋਡਕਟ ਫੀਡਬੈਕ ਜਾਂ ਬਿਲਡਿੰਗ ਦੇ ਸਮੇਂ ਕੋਈ ਗਲਤੀ ਵੇਖੋ ਤਾਂ ਇੱਥੇ ਵੇਖੋ: +ਜੇ ਤੁਹਾਨੂੰ ਬਣਾਉਣ ਦੌਰਾਨ ਪ੍ਰੋਡਕਟ ਫੀਡਬੈਕ ਜਾਂ ਗਲਤੀਆਂ ਮਿਲਦੀਆਂ ਹਨ ਤਾਂ ਇਸਤੇ: [![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) --- -**ਟਾਲੜੀ ਸੂਚਨਾ**: -ਇਹ ਦਸਤਾਵੇਜ਼ AI ਅਨੁਵਾਦ ਸੇਵਾ [Co-op Translator](https://github.com/Azure/co-op-translator) ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਅਨੁਵਾਦ ਕੀਤਾ ਗਿਆ ਹੈ। ਜਿਥੇ ਅਸੀਂ ਸ਼ੁੱਧਤਾ ਲਈ ਯਤਨ ਕਰਦੇ ਹਾਂ,ਕਿਰਪਾ ਕਰਕੇ ਨੋਟ ਕਰੋ ਕਿ ਆਟੋਮੇਟਿਕ ਅਨੁਵਾਦ ਵਿੱਚ ਗਲਤੀਆਂ ਜਾਂ ਅਸਪਸ਼ਟਤਾਵਾਂ ਹੋ ਸਕਦੀਆਂ ਹਨ। ਮੂਲ ਦਸਤਾਵੇਜ਼ ਨੂੰ ਇਸ ਦੀ ਮੂਲ ਭਾਸ਼ਾ ਵਿੱਚ ਅਧਿਕਾਰਕ ਸਰੋਤ ਵਜੋਂ ਮੰਨਿਆ ਜਾਣਾ ਚਾਹੀਦਾ ਹੈ। ਜ਼ਰੂਰੀ ਜਾਣਕਾਰੀ ਲਈ, ਪੇਸ਼ੇਵਰ ਮਨੁੱਖੀ ਅਨੁਵਾਦ ਦੀ ਸਿਫ਼ਾਰਸ਼ ਕੀਤੀ ਜਾਂਦੀ ਹੈ। ਸਾਡੇ ਉਤੇ ਇਸ ਅਨੁਵਾਦ ਦੀ ਵਰਤੋਂ ਨਾਲ ਹੋਣ ਵਾਲੀਆਂ ਕਿਸੇ ਵੀ ਗਲਤਫਹਮੀ ਜਾਂ ਭੁਲੇਖੇ ਲਈ ਜ਼ਿੰਮੇਵਾਰੀ ਨਹੀਂ ਲੱਗਦੀ। +**ਅਸਵੀਕਾਰੋਪੱਤਰ**: +ਇਹ ਦਸਤਾਵੇਜ਼ AI ਅਨੁਵਾਦ ਸੇਵਾ [Co-op Translator](https://github.com/Azure/co-op-translator) ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਅਨੁਵਾਦ ਕੀਤਾ ਗਿਆ ਹੈ। ਜਦੋਂ ਕਿ ਅਸੀਂ ਸਹੀਤਾ ਲਈ ਯਤਨਸ਼ੀਲ ਹਾਂ, ਕਿਰਪਾ ਕਰਕੇ ਧਿਆਨ ਰੱਖੋ ਕਿ ਸਵੈਚਾਲਿਤ ਅਨੁਵਾਦਾਂ ਵਿੱਚ ਗਲਤੀਆਂ ਜਾਂ ਅਸਮਰੱਥਤਾਵਾਂ ਹੋ ਸਕਦੀਆਂ ਹਨ। ਮੂਲ ਦਸਤਾਵੇਜ਼ ਆਪਣੇ ਮੂਲ ਭਾਸ਼ਾ ਵਿੱਚ ਅਧਿਕਾਰਿਕ ਸਰੋਤ ਮੰਨਿਆ ਜਾਣਾ ਚਾਹੀਦਾ ਹੈ। ਜ਼ਰੂਰੀ ਜਾਣਕਾਰੀ ਲਈ ਪ੍ਰੋਫੈਸ਼ਨਲ ਮਨੁੱਖੀ ਅਨੁਵਾਦ ਦੀ ਸਲਾਹ ਦਿੱਤੀ ਜਾਂਦੀ ਹੈ। ਅਸੀਂ ਇਸ ਅਨੁਵਾਦ ਦੇ ਵਰਤੋਂ ਨਾਲ ਪੈਦਾ ਹੋਣ ਵਾਲੀਆਂ ਕਿਸੇ ਵੀ ਗਲਤਫਹਿਮੀਆਂ ਜਾਂ ਭੁਲ-ਵੇਖਿਆਂ ਲਈ ਜਵਾਬਦੇਹ ਨਹੀਂ ਹਾਂ। \ No newline at end of file diff --git a/translations/pa/lessons/0-course-setup/how-to-run.md b/translations/pa/lessons/0-course-setup/how-to-run.md index a214e338..aa1d1ebe 100644 --- a/translations/pa/lessons/0-course-setup/how-to-run.md +++ b/translations/pa/lessons/0-course-setup/how-to-run.md @@ -1,21 +1,21 @@ -# ਕੋਡ ਕਿਵੇਂ ਚਲਾਉਣਾ ਹੈ +# ਕੋਡ ਨੂੰ ਕਿਵੇਂ ਚਲਾਉਣਾ ਹੈ -ਇਸ ਕੋਰਸ ਵਿੱਚ ਕਈ ਚਲਣਯੋਗ ਉਦਾਹਰਨਾਂ ਅਤੇ ਪ੍ਰਯੋਗਸ਼ਾਲਾਵਾਂ ਸ਼ਾਮਲ ਹਨ, ਜਿਨ੍ਹਾਂ ਨੂੰ ਤੁਸੀਂ ਚਲਾਉਣਾ ਚਾਹੋਗੇ। ਇਹ ਕਰਨ ਲਈ, ਤੁਹਾਨੂੰ ਇਸ ਕੋਰਸ ਦੇ ਹਿੱਸੇ ਵਜੋਂ ਦਿੱਤੇ ਗਏ Jupyter Notebooks ਵਿੱਚ Python ਕੋਡ ਚਲਾਉਣ ਦੀ ਸਮਰੱਥਾ ਹੋਣੀ ਚਾਹੀਦੀ ਹੈ। ਕੋਡ ਚਲਾਉਣ ਲਈ ਤੁਹਾਡੇ ਕੋਲ ਕਈ ਵਿਕਲਪ ਹਨ: +ਇਸ ਸਿੱਕਲ ਵਿੱਚ ਬਹੁਤ ਸਾਰੇ ਚਲਾਏ ਜਾ ਸਕਣ ਵਾਲੇ ਉਦਾਹਰਨਾਂ ਅਤੇ ਲੈਬ ਹਨ ਜੋ ਤੁਸੀਂ ਚਲਾਉਣਾ ਚਾਹੁੰਦੇ ਹੋ। ਇਹ ਕਰਨ ਲਈ, ਤੁਹਾਨੂੰ ਇਸ ਸਿੱਕਲ ਦੇ ਹਿੱਸੇ ਵਜੋਂ ਦਿੱਤੇ ਗਏ Jupyter Notebooks ਵਿੱਚ Python ਕੋਡ ਚਲਾਉਣ ਦੀ ਸਮਰੱਥਾ ਦੀ ਲੋੜ ਹੈ। ਕੋਡ ਚਲਾਉਣ ਲਈ ਤੁਹਾਡੇ ਕੋਲ ਕਈ ਵਿਕਲਪ ਹਨ: -## ਆਪਣੇ ਕੰਪਿਊਟਰ 'ਤੇ ਸਥਾਨਕ ਤੌਰ 'ਤੇ ਚਲਾਓ +## ਤੁਹਾਡੇ ਕੰਪਿਊਟਰ 'ਤੇ ਸਥਾਨਕ ਤੌਰ 'ਤੇ ਚਲਾਉਣਾ -ਕੋਡ ਨੂੰ ਆਪਣੇ ਕੰਪਿਊਟਰ 'ਤੇ ਸਥਾਨਕ ਤੌਰ 'ਤੇ ਚਲਾਉਣ ਲਈ, ਤੁਹਾਡੇ ਕੋਲ Python ਦਾ ਕੋਈ ਸੰਸਕਰਣ ਇੰਸਟਾਲ ਹੋਣਾ ਚਾਹੀਦਾ ਹੈ। ਮੈਂ ਵਿਸ਼ੇਸ਼ ਤੌਰ 'ਤੇ **[miniconda](https://conda.io/en/latest/miniconda.html)** ਇੰਸਟਾਲ ਕਰਨ ਦੀ ਸਿਫਾਰਸ਼ ਕਰਦਾ ਹਾਂ - ਇਹ ਇੱਕ ਹਲਕਾ ਇੰਸਟਾਲੇਸ਼ਨ ਹੈ ਜੋ ਵੱਖ-ਵੱਖ Python **ਵਰਚੁਅਲ ਇਨਵਾਇਰਮੈਂਟਸ** ਲਈ `conda` ਪੈਕੇਜ ਮੈਨੇਜਰ ਨੂੰ ਸਹਾਇਕ ਬਣਾਉਂਦਾ ਹੈ। +ਤੁਹਾਡੇ ਕੰਪਿਊਟਰ 'ਤੇ ਸਥਾਨਕ ਤੌਰ 'ਤੇ ਕੋਡ ਚਲਾਉਣ ਲਈ, Python ਦੀ ਇੰਸਟਾਲੇਸ਼ਨ ਲੋੜੀਂਦੀ ਹੈ। ਇੱਕ ਸੁਝਾਅ ਹੈ ਕਿ **[miniconda](https://conda.io/en/latest/miniconda.html)** ਇੰਸਟਾਲ ਕਰੋ - ਇਹ ਇੱਕ ਹਲਕੀ ਫੁਲਕੀ ਇੰਸਟਾਲੇਸ਼ਨ ਹੈ ਜੋ ਵੱਖ-ਵੱਖ Python **ਵਰਚੂਅਲ ਮਾਹੌਲਾਂ** ਲਈ `conda` ਪੈਕੇਜ ਮੈਨੇਜਰ ਦਾ ਸਮਰਥਨ ਕਰਦਾ ਹੈ। -Miniconda ਇੰਸਟਾਲ ਕਰਨ ਤੋਂ ਬਾਅਦ, ਤੁਹਾਨੂੰ ਰਿਪੋਜ਼ਟਰੀ ਕਲੋਨ ਕਰਨੀ ਹੋਵੇਗੀ ਅਤੇ ਇਸ ਕੋਰਸ ਲਈ ਵਰਤਣ ਲਈ ਇੱਕ ਵਰਚੁਅਲ ਇਨਵਾਇਰਮੈਂਟ ਬਣਾਉਣਾ ਹੋਵੇਗਾ: +miniconda ਇੰਸਟਾਲ ਕਰਨ ਤੋਂ ਬਾਅਦ, ਰਿਪੋਜ਼ਿਟਰੀ ਨੂੰ ਕਲੋਨ ਕਰੋ ਅਤੇ ਇਸ ਕੋਰਸ ਲਈ ਵਰਤਣ ਵਾਲਾ ਇੱਕ ਵਰਚੂਅਲ ਮਾਹੌਲ ਬਣਾਓ: ```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 ``` -### Python ਐਕਸਟੈਂਸ਼ਨ ਨਾਲ Visual Studio Code ਵਰਤਣਾ +### Visual Studio Code ਨੂੰ Python ਐਕਸਟੈਂਸ਼ਨ ਨਾਲ ਵਰਤਣਾ -ਸਭ ਤੋਂ ਵਧੀਆ ਤਰੀਕਾ ਇਹ ਹੈ ਕਿ ਇਸ ਕੋਰਸ ਨੂੰ [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) ਵਿੱਚ [Python Extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste) ਨਾਲ ਖੋਲ੍ਹੋ। +ਇਹ ਸਿੱਕਲ ਸਭ ਤੋਂ ਵਧੀਆ [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) ਵਿੱਚ [Python Extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste) ਨਾਲ ਖੋਲ੍ਹ ਕੇ ਵਰਤੀ ਜਾਂਦੀ ਹੈ। -> **Note**: ਜਦੋਂ ਤੁਸੀਂ ਰਿਪੋਜ਼ਟਰੀ ਨੂੰ ਕਲੋਨ ਕਰਕੇ VS Code ਵਿੱਚ ਖੋਲ੍ਹਦੇ ਹੋ, ਤਾਂ ਇਹ ਤੁਹਾਨੂੰ Python ਐਕਸਟੈਂਸ਼ਨ ਇੰਸਟਾਲ ਕਰਨ ਦੀ ਸਿਫਾਰਸ਼ ਕਰੇਗਾ। ਤੁਹਾਨੂੰ ਉਪਰ ਦਿੱਤੇ ਗਏ ਤਰੀਕੇ ਅਨੁਸਾਰ Miniconda ਵੀ ਇੰਸਟਾਲ ਕਰਨੀ ਪਵੇਗੀ। +> **ਨੋਟ**: ਜਦੋਂ ਤੁਸੀਂ ਰਿਪੋ ਨੂੰ ਕਲੋਨ ਕਰਕੇ VS Code ਵਿੱਚ ਡਾਇਰੈਕਟਰੀ ਖੋਲ੍ਹਦੇ ਹੋ, ਤਾਂ ਇਹ ਆਟੋਮੈਟਿਕ ਤੌਰ ਤੇ ਤੁਹਾਨੂੰ Python ਐਕਸਟੈਂਸ਼ਨ ਇੰਸਟਾਲ ਕਰਨ ਦੀ ਸਿਫਾਰਿਸ਼ ਕਰੇਗਾ। ਤੁਹਾਨੂੰ ਉਪਰ ਦਸੇ अनुसार miniconda ਵੀ ਇੰਸਟਾਲ ਕਰਨਾ ਪਵੇਗਾ। -> **Note**: ਜੇਕਰ VS Code ਤੁਹਾਨੂੰ ਰਿਪੋਜ਼ਟਰੀ ਨੂੰ ਕੰਟੇਨਰ ਵਿੱਚ ਦੁਬਾਰਾ ਖੋਲ੍ਹਣ ਦੀ ਸਿਫਾਰਸ਼ ਕਰਦਾ ਹੈ, ਤਾਂ ਤੁਹਾਨੂੰ ਇਸਨੂੰ ਸਥਾਨਕ Python ਇੰਸਟਾਲੇਸ਼ਨ ਵਰਤਣ ਲਈ ਅਸਵੀਕਾਰ ਕਰਨਾ ਚਾਹੀਦਾ ਹੈ। +> **ਨੋਟ**: ਜੇ VS Code ਤੁਹਾਨੂੰ ਰਿਪੋ ਨੂੰ ਕੰਟੇਨਰ ਵਿੱਚ ਦੁਬਾਰਾ ਖੋਲ੍ਹਣ ਲਈ ਬੋਲਦਾ ਹੈ, ਤਾਂ ਤੁਸੀਂ ਇਹ ਥੱਲੇ ਗਲਾ ਨਾਹਮੰਨ ਕੇ ਲੋਕਲ Python ਇੰਸਟਾਲੇਸ਼ਨ ਵਰਤਣ ਲਈ ਇਨਕਾਰ ਕਰਨਾ ਚਾਹੀਦਾ ਹੈ। -### ਬ੍ਰਾਊਜ਼ਰ ਵਿੱਚ Jupyter ਵਰਤਣਾ +### ਬਰਾਊਜ਼ਰ ਵਿੱਚ Jupyter ਵਰਤਣਾ -ਤੁਸੀਂ ਆਪਣੇ ਕੰਪਿਊਟਰ 'ਤੇ ਬ੍ਰਾਊਜ਼ਰ ਤੋਂ ਸਿੱਧੇ Jupyter ਇਨਵਾਇਰਮੈਂਟ ਵੀ ਵਰਤ ਸਕਦੇ ਹੋ। ਦਰਅਸਲ, ਕਲਾਸਿਕ Jupyter ਅਤੇ Jupyter Hub ਦੋਵੇਂ ਆਟੋ-ਕੰਪਲੀਸ਼ਨ, ਕੋਡ ਹਾਈਲਾਈਟਿੰਗ ਆਦਿ ਨਾਲ ਕਾਫ਼ੀ ਸੁਵਿਧਾਜਨਕ ਵਿਕਾਸ ਵਾਤਾਵਰਣ ਪ੍ਰਦਾਨ ਕਰਦੇ ਹਨ। +ਤੁਸੀਂ ਆਪਣੇ ਕੰਪਿਊਟਰ ਦੇ ਬਰਾਊਜ਼ਰ ਵਿਚੋਂ ਵੀ Jupyter محیط ਵਰਤ ਸਕਦੇ ਹੋ। ਕਲਾਸਿਕਲ Jupyter ਅਤੇ JupyterHub ਦੋਹਾਂ ਆਟੋ-ਕੰਪਲੀਸ਼ਨ, ਕੋਡ ਹਾਈਲਾਈਟਿੰਗ ਆਦਿ ਨਾਲ ਇੱਕ ਸਹੂਲਤਮੰਦ ਵਿਕਾਸ ਪ੍ਰਦਾਤਾ ਹੈ। -ਸਥਾਨਕ ਤੌਰ 'ਤੇ Jupyter ਸ਼ੁਰੂ ਕਰਨ ਲਈ, ਕੋਰਸ ਦੀ ਡਾਇਰੈਕਟਰੀ 'ਤੇ ਜਾਓ ਅਤੇ ਇਹ ਚਲਾਓ: +Jupyter ਸਥਾਨਕ ਤੌਰ 'ਤੇ ਸ਼ੁਰੂ ਕਰਨ ਲਈ, ਕੋਰਸ ਦੀ ਡਾਇਰੈਕਟਰੀ 'ਤੇ ਜਾਓ ਅਤੇ ਇਹ ਚਲਾਓ: ```bash jupyter notebook ``` -ਜਾਂ + ਜਾਂ ```bash jupyterhub ``` -ਫਿਰ ਤੁਸੀਂ ਕਿਸੇ ਵੀ `.ipynb` ਫਾਈਲ 'ਤੇ ਜਾ ਸਕਦੇ ਹੋ, ਇਸਨੂੰ ਖੋਲ੍ਹ ਸਕਦੇ ਹੋ ਅਤੇ ਕੰਮ ਸ਼ੁਰੂ ਕਰ ਸਕਦੇ ਹੋ। +ਇਸ ਤੋਂ ਬਾਅਦ, ਤੁਸੀਂ `.ipynb` ਫਾਇਲਾਂ ਵਿੱਚੋਂ ਕਿਸੇ ਵੀ ਇਕ 'ਤੇ ਜਾ ਸਕਦੇ ਹੋ, ਉਹ ਖੋਲ੍ਹ ਕੇ ਕੰਮ ਕਰਨਾ ਸ਼ੁਰੂ ਕਰ ਸਕਦੇ ਹੋ। ### ਕੰਟੇਨਰ ਵਿੱਚ ਚਲਾਉਣਾ -Python ਇੰਸਟਾਲੇਸ਼ਨ ਦਾ ਇੱਕ ਵਿਕਲਪ ਇਹ ਹੈ ਕਿ ਕੋਡ ਨੂੰ ਕੰਟੇਨਰ ਵਿੱਚ ਚਲਾਇਆ ਜਾਵੇ। ਕਿਉਂਕਿ ਸਾਡੀ ਰਿਪੋਜ਼ਟਰੀ ਵਿੱਚ ਇੱਕ ਵਿਸ਼ੇਸ਼ `.devcontainer` ਫੋਲਡਰ ਸ਼ਾਮਲ ਹੈ ਜੋ ਇਸ ਰਿਪੋਜ਼ਟਰੀ ਲਈ ਕੰਟੇਨਰ ਬਣਾਉਣ ਦੇ ਨਿਰਦੇਸ਼ ਦਿੰਦਾ ਹੈ, VS Code ਤੁਹਾਨੂੰ ਕੋਡ ਨੂੰ ਕੰਟੇਨਰ ਵਿੱਚ ਦੁਬਾਰਾ ਖੋਲ੍ਹਣ ਦੀ ਪੇਸ਼ਕਸ਼ ਕਰੇਗਾ। ਇਸ ਲਈ Docker ਇੰਸਟਾਲੇਸ਼ਨ ਦੀ ਲੋੜ ਹੋਵੇਗੀ, ਅਤੇ ਇਹ ਕੁਝ ਜਟਿਲ ਹੋ ਸਕਦਾ ਹੈ, ਇਸ ਲਈ ਅਸੀਂ ਇਸਨੂੰ ਅਨੁਭਵੀ ਯੂਜ਼ਰਾਂ ਲਈ ਸਿਫਾਰਸ਼ ਕਰਦੇ ਹਾਂ। +Python ਇੰਸਟਾਲੇਸ਼ਨ ਦਾ ਇੱਕ ਵਿਕਲਪ ਕੰਟੇਨਰ ਵਿੱਚ ਕੋਡ ਚਲਾਉਣਾ ਵੀ ਹੋ ਸਕਦਾ ਹੈ। ਸਾਡੀ ਰਿਪੋਜ਼ਿਟਰੀ ਵਿੱਚ ਇੱਕ ਖ਼ਾਸ `.devcontainer` ਫੋਲਡਰ ਹੁੰਦਾ ਹੈ ਜੋ ਇਹ ਧਰਾਉਂਦਾ ਹੈ ਕਿ ਇਸ ਰਿਪੋ ਲਈ ਕੰਟੇਨਰ ਕਿਵੇਂ ਬਣਾਉਣਾ ਹੈ, ਇਸ ਨਾਲ VS Code ਤੁਹਾਨੂੰ ਕੋਡ ਨੂੰ ਕੰਟੇਨਰ ਵਿੱਚ ਦੁਬਾਰਾ ਖੋਲ੍ਹਣ ਦਾ ਮੌਕਾ ਦਿੰਦਾ ਹੈ। ਇਹ ਲਈ Docker ਇੰਸਟਾਲੇਸ਼ਨ ਤਾਂ ਲੋੜੀਂਦੀ ਹੈ, ਅਤੇ ਇਹ ਥੋੜਾ ਘਣੇਰੇ ਪੱਧਰ ਦਾ ਹੁੰਦਾ ਹੈ, ਇਸ ਲਈ ਅਸੀਂ ਇਸਨੂੰ ਜ਼ਿਆਦਾ ਅਨੁਭਵੀ ਵਰਤੋਂਕਾਰਾਂ ਲਈ ਸਿਫਾਰਸ਼ ਕਰਦੇ ਹਾਂ। ## ਕਲਾਉਡ ਵਿੱਚ ਚਲਾਉਣਾ -ਜੇਕਰ ਤੁਸੀਂ Python ਨੂੰ ਸਥਾਨਕ ਤੌਰ 'ਤੇ ਇੰਸਟਾਲ ਨਹੀਂ ਕਰਨਾ ਚਾਹੁੰਦੇ, ਅਤੇ ਤੁਹਾਡੇ ਕੋਲ ਕੁਝ ਕਲਾਉਡ ਸਰੋਤਾਂ ਦੀ ਪਹੁੰਚ ਹੈ - ਤਾਂ ਇੱਕ ਵਧੀਆ ਵਿਕਲਪ ਕਲਾਉਡ ਵਿੱਚ ਕੋਡ ਚਲਾਉਣਾ ਹੋਵੇਗਾ। ਇਸ ਲਈ ਕਈ ਤਰੀਕੇ ਹਨ: +ਜੇ ਤੁਸੀਂ Python ਸਥਾਨਕ ਤੌਰ 'ਤੇ ਇੰਸਟਾਲ ਨਹੀਂ ਕਰਨਾ ਚਾਹੁੰਦੇ, ਅਤੇ ਤੁਹਾਡੇ ਕੋਲ ਕੁਝ ਕਲਾਉਡ ਜ਼ਰੀਏ ਹਨ - ਤਾਂ ਇੱਕ ਵਧੀਆ ਵਿਕਲਪ ਕੋਡ ਕਲਾਉਡ ਵਿੱਚ ਚਲਾਉਣਾ ਹੋਵੇਗਾ। ਤੁਸੀਂ ਇਹ ਕੁਝ ਤਰੀਕਿਆਂ ਨਾਲ ਕਰ ਸਕਦੇ ਹੋ: -* **[GitHub Codespaces](https://github.com/features/codespaces)** ਵਰਤਣਾ, ਜੋ GitHub 'ਤੇ ਤੁਹਾਡੇ ਲਈ ਬਣਾਇਆ ਗਿਆ ਇੱਕ ਵਰਚੁਅਲ ਵਾਤਾਵਰਣ ਹੈ, ਜੋ VS Code ਬ੍ਰਾਊਜ਼ਰ ਇੰਟਰਫੇਸ ਰਾਹੀਂ ਪਹੁੰਚਯੋਗ ਹੈ। ਜੇਕਰ ਤੁਹਾਡੇ ਕੋਲ Codespaces ਦੀ ਪਹੁੰਚ ਹੈ, ਤਾਂ ਤੁਸੀਂ ਸਿਰਫ **Code** ਬਟਨ 'ਤੇ ਕਲਿੱਕ ਕਰਕੇ Codespace ਸ਼ੁਰੂ ਕਰ ਸਕਦੇ ਹੋ ਅਤੇ ਤੁਰੰਤ ਕੰਮ ਸ਼ੁਰੂ ਕਰ ਸਕਦੇ ਹੋ। -* **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)** ਵਰਤਣਾ। [Binder](https://mybinder.org) ਇੱਕ ਮੁਫ਼ਤ ਕਲਾਉਡ ਸਰੋਤ ਹੈ ਜੋ ਤੁਹਾਡੇ ਵਰਗੇ ਲੋਕਾਂ ਲਈ GitHub 'ਤੇ ਕੁਝ ਕੋਡ ਟੈਸਟ ਕਰਨ ਲਈ ਉਪਲਬਧ ਹੈ। ਮੁੱਖ ਪੰਨੇ 'ਤੇ ਇੱਕ ਬਟਨ ਹੈ ਜੋ ਰਿਪੋਜ਼ਟਰੀ ਨੂੰ Binder ਵਿੱਚ ਖੋਲ੍ਹਣ ਲਈ ਹੈ - ਇਹ ਤੁਹਾਨੂੰ ਜਲਦੀ Binder ਸਾਈਟ 'ਤੇ ਲੈ ਜਾਵੇਗਾ, ਜੋ ਅਧਾਰਭੂਤ ਕੰਟੇਨਰ ਬਣਾਏਗਾ ਅਤੇ ਤੁਹਾਡੇ ਲਈ Jupyter ਵੈੱਬ ਇੰਟਰਫੇਸ ਸ਼ੁਰੂ ਕਰੇਗਾ। +* **[GitHub Codespaces](https://github.com/features/codespaces)** ਵਰਤਕੇ, ਜੋ GitHub 'ਤੇ ਤੁਹਾਡੇ ਲਈ ਬਣਾਇਆ ਗਿਆ ਇੱਕ ਵਰਚੂਅਲ ਮਾਹੌਲ ਹੈ, ਜੋ VS Code ਦੇ ਬਰਾਊਜ਼ਰ ਇੰਟਰਫੇਸ ਤੋਂ ਪਹੁੰਚਯੋਗ ਹੈ। ਜੇ ਤੁਹਾਡੇ ਕੋਲ Codespaces ਦੀ ਪਹੁੰਚ ਹੈ, ਤਾਂ ਤੁਸੀਂ ਸਿਰਫ਼ ਰਿਪੋ ਵਿੱਚ **Code** ਬਟਨ ਤੇ ਕਲਿਕ ਕਰੋ, ਇੱਕ codespace ਸ਼ੁਰੂ ਕਰੋ ਅਤੇ ਬਿਨਾਂ ਦੇਰ ਦੇ ਚੱਲ ਪੈ ਜਾਓ। +* **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)** ਵਰਤਕੇ। [Binder](https://mybinder.org) ਮੁਫ਼ਤ ਕਮਪਿਊਟਿੰਗ ਸਰੋਤ ਮੁਹੱਈਆ ਕਰਵਾਉਂਦਾ ਹੈ ਜੋ ਲੋਕਾਂ ਨੂੰ GitHub 'ਤੇ ਕੁਝ ਕੋਡ ਚੈੱਕ ਕਰਨ ਲਈ ਕਲਾਉਡ ਵਿੱਚ ਸੁਵਿਧਾ ਦਿੰਦਾ ਹੈ। ਅੱਗੇ ਵਾਲੇ ਪੰਨੇ 'ਤੇ ਇੱਕ ਬਟਨ ਹੈ ਜੋ ਰਿਪੋਜ਼ਿਟਰੀ ਨੂੰ ਬਾਈਂਡਰ ਵਿੱਚ ਖੋਲ੍ਹਦਾ ਹੈ - ਇਹ ਤੁਹਾਨੂੰ ਜਲਦੀ ਨਾਲ ਬਾਈਂਡਰ ਸਾਈਟ ਤੇ ਲਿਜਾਵੇਗਾ, ਜੋ ਅੰਦਰਲੀ ਕੰਟੇਨਰ ਬਣਾ ਕੇ ਤੁਹਾਡੇ ਲਈ ਬਿਨਾਂ ਰੁਕਾਵਟ Jupyter ਵੈੱਬ ਇੰਟਰਫੇਸ ਸ਼ੁਰੂ ਕਰੇਗਾ। -> **Note**: ਗਲਤ ਵਰਤੋਂ ਨੂੰ ਰੋਕਣ ਲਈ, Binder ਨੂੰ ਕੁਝ ਵੈੱਬ ਸਰੋਤਾਂ ਤੱਕ ਪਹੁੰਚ ਰੋਕੀ ਗਈ ਹੈ। ਇਹ ਕੁਝ ਕੋਡ ਨੂੰ ਕੰਮ ਕਰਨ ਤੋਂ ਰੋਕ ਸਕਦਾ ਹੈ, ਜੋ ਜਨਤਕ ਇੰਟਰਨੈਟ ਤੋਂ ਮਾਡਲਾਂ ਅਤੇ/ਜਾਂ ਡਾਟਾਸੈਟ ਲਿਆਉਂਦਾ ਹੈ। ਤੁਹਾਨੂੰ ਕੁਝ ਵੱਖਰੇ ਹੱਲ ਲੱਭਣ ਦੀ ਲੋੜ ਹੋ ਸਕਦੀ ਹੈ। ਇਸ ਤੋਂ ਇਲਾਵਾ, Binder ਦੁਆਰਾ ਪ੍ਰਦਾਨ ਕੀਤੇ ਗਏ ਕੰਪਿਊਟ ਸਰੋਤ ਕਾਫ਼ੀ ਮੂਲਭੂਤ ਹਨ, ਇਸ ਲਈ ਖਾਸ ਕਰਕੇ ਬਾਅਦ ਦੇ ਜਟਿਲ ਪਾਠਾਂ ਵਿੱਚ ਟ੍ਰੇਨਿੰਗ ਧੀਮੀ ਹੋਵੇਗੀ। +> **ਨੋਟ**: ਗਲਤ ਉਦੇਸ਼ ਤੋਂ ਬਚਣ ਲਈ, ਬਾਈਂਡਰ ਨੂੰ ਕੁਝ ਵੈੱਬ ਸਰੋਤਾਂ ਤੱਕ ਪਹੁੰਚ ਰੋਕ ਦਿੱਤੀ ਗਈ ਹੈ। ਇਸ ਨਾਲ ਕੁਝ ਕੋਡ, ਜੋ ਮਾਡਲ ਜਾਂ ਡਾਟਾਸੈੱਟ ਪਬਲਿਕ ਇੰਟਰਨੈਟ ਤੋਂ ਫੈਚ ਕਰਦੇ ਹਨ, ਕੰਮ ਨਾ ਕਰ ਸਕੈ। ਤੁਹਾਨੂੰ ਕੁਝ ਵੱਖਰੇ ਤਰੀਕੇ ਲੱਭਣੇ ਪੈ ਸਕਦੇ ਹਨ। ਇਸ ਦੇ ਨਾਲ, ਬਾਈਂਡਰ ਦੇ ਕੰਪਿਊਟਿੰਗ ਸਰੋਤ ਬਹੁਤ ਬੁਨਿਆਦੀ ਹਨ, ਇਸ ਲਈ ਸਿੱਖਣ ਵਾਲੀਆਂ ਦਹਾਕਿਆਂ ਵਿੱਚ ਖਾਸ ਕਰਕੇ ਟ੍ਰੇਨਿੰਗ ਸਕੂਣ ਹੋ ਸਕਦੀ ਹੈ। -## GPU ਦੇ ਨਾਲ ਕਲਾਉਡ ਵਿੱਚ ਚਲਾਉਣਾ +## ਕਲਾਉਡ ਵਿੱਚ GPU ਨਾਲ ਚਲਾਉਣਾ -ਇਸ ਕੋਰਸ ਦੇ ਕੁਝ ਬਾਅਦ ਦੇ ਪਾਠਾਂ ਵਿੱਚ GPU ਸਹਾਇਤਾ ਤੋਂ ਕਾਫ਼ੀ ਲਾਭ ਹੋਵੇਗਾ, ਕਿਉਂਕਿ ਨਹੀਂ ਤਾਂ ਟ੍ਰੇਨਿੰਗ ਬਹੁਤ ਹੀ ਧੀਮੀ ਹੋਵੇਗੀ। ਕੁਝ ਵਿਕਲਪ ਹਨ ਜਿਨ੍ਹਾਂ ਨੂੰ ਤੁਸੀਂ ਅਪਣਾਉ ਸਕਦੇ ਹੋ, ਖਾਸ ਕਰਕੇ ਜੇਕਰ ਤੁਹਾਡੇ ਕੋਲ [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) ਜਾਂ ਆਪਣੇ ਸੰਸਥਾਨ ਰਾਹੀਂ ਕਲਾਉਡ ਦੀ ਪਹੁੰਚ ਹੈ: +ਇਸ ਸਿੱਕਲ ਦੇ ਬਾਅਦਲੇ ਕੁਝ ਪਾਠਾਂ ਨੂੰ GPU ਸਹਾਇਤਾ ਨਾਲ ਵੱਡਾ ਫ਼ਾਇਦਾ ਹੋਵੇਗਾ। ਮਾਡਲ ਟ੍ਰੇਨਿੰਗ, ਉਦਾਹਰਨ ਵਜੋਂ, ਨਹੀਂ ਤਾਂ ਬਹੁਤ ਧੀਮੀ ਹੋ ਸਕਦੀ ਹੈ। ਕੁਝ ਵਿਕਲਪ ਹਨ, ਖ਼ਾਸ ਕਰਕੇ ਜੇ ਤੁਹਾਡੇ ਕੋਲ ਕਲਾਉਡ ਪਹੁੰਚ ਹੈ ਜਾਂ ਤਾਂ [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) ਤੋਂ ਜਾਂ ਆਪਣੇ ਸੰਸਥਾਨ ਤੋਂ: -* [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) ਬਣਾਓ ਅਤੇ ਇਸਨੂੰ Jupyter ਰਾਹੀਂ ਕਨੈਕਟ ਕਰੋ। ਫਿਰ ਤੁਸੀਂ ਰਿਪੋਜ਼ਟਰੀ ਨੂੰ ਸਿੱਧੇ ਮਸ਼ੀਨ 'ਤੇ ਕਲੋਨ ਕਰ ਸਕਦੇ ਹੋ ਅਤੇ ਸਿੱਖਣਾ ਸ਼ੁਰੂ ਕਰ ਸਕਦੇ ਹੋ। NC-ਸਿਰੀਜ਼ VMs ਵਿੱਚ GPU ਸਹਾਇਤਾ ਹੁੰਦੀ ਹੈ। +* [ਡਾਟਾ ਸਾਇੰਸ ਵਰਚੂਅਲ ਮਸ਼ੀਨ](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) ਬਣਾਓ ਅਤੇ Jupyter ਰਾਹੀਂ ਇਸ ਨਾਲ ਜੁੜੋ। ਤੁਸੀਂ ਫਿਰ ਰਿਪੋ ਨੂੰ ਸੀਧਾ ਮਸ਼ੀਨ 'ਤੇ ਕਲੋਨ ਕਰ ਸਕਦੇ ਹੋ ਅਤੇ ਸਿੱਖਣਾ ਸ਼ੁਰੂ ਕਰ ਸਕਦੇ ਹੋ। NC-ਸਿਰੀਜ਼ VM ਵਿੱਚ GPU ਸਹਾਇਤਾ ਹੈ। -> **Note**: ਕੁਝ ਸਬਸਕ੍ਰਿਪਸ਼ਨ, ਜਿਵੇਂ ਕਿ Azure for Students, ਡਿਫਾਲਟ ਰੂਪ ਵਿੱਚ GPU ਸਹਾਇਤਾ ਪ੍ਰਦਾਨ ਨਹੀਂ ਕਰਦੇ। ਤੁਹਾਨੂੰ ਤਕਨੀਕੀ ਸਹਾਇਤਾ ਬੇਨਤੀ ਰਾਹੀਂ ਵਾਧੂ GPU ਕੋਰਜ਼ ਦੀ ਬੇਨਤੀ ਕਰਨ ਦੀ ਲੋੜ ਹੋ ਸਕਦੀ ਹੈ। +> **ਨੋਟ**: ਕੁਝ ਸਬਸਕ੍ਰਿਪਸ਼ਨ, ਜਿਵੇਂ ਕਿ Azure for Students, ਬਾਹਰਲੇ ਬਾਕਸ ਤੋੜ ਤੇ GPU ਸਹਾਇਤਾ ਨਹੀਂ ਦਿੰਦੇ। ਤੁਹਾਨੂੰ ਤਕਨੀਕੀ ਸਹਾਇਤਾ ਅਨੁਰੋਧ ਨਾਲ ਵਾਧੂ GPU ਕੋਰਾਂ ਦੀ ਮੰਗ ਕਰਨੀ ਪੈ ਸਕਦੀ ਹੈ। -* [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) ਬਣਾਓ ਅਤੇ ਫਿਰ ਉੱਥੇ ਨੋਟਬੁੱਕ ਫੀਚਰ ਵਰਤੋ। [ਇਹ ਵੀਡੀਓ](https://azure-for-academics.github.io/quickstart/azureml-papers/) ਦਿਖਾਉਂਦੀ ਹੈ ਕਿ ਰਿਪੋਜ਼ਟਰੀ ਨੂੰ Azure ML ਨੋਟਬੁੱਕ ਵਿੱਚ ਕਿਵੇਂ ਕਲੋਨ ਕਰਨਾ ਹੈ ਅਤੇ ਇਸਨੂੰ ਵਰਤਣਾ ਸ਼ੁਰੂ ਕਰਨਾ ਹੈ। +* [Azure ਮਸ਼ੀਨ ਲਰਨਿੰਗ ਵਰਕਸਪੇਸ](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) ਬਣਾਓ ਅਤੇ ਫਿਰ ਉੱਥੇ ਨੋਟਬੁੱਕ ਫੀਚਰ ਦੀ ਵਰਤੋਂ ਕਰੋ। [ਇਹ ਵੀਡੀਓ](https://azure-for-academics.github.io/quickstart/azureml-papers/) ਦਿਖਾਉਂਦੀ ਹੈ ਕਿ ਕਿਵੇਂ ਰਿਪੋ ਨੂੰ Azure ML ਨੋਟਬੁੱਕ ਵਿੱਚ ਕਲੋਨ ਕਰਕੇ ਇਸ ਦੀ ਵਰਤੋਂ ਸ਼ੁਰੂ ਕਰਨੀ ਹੈ। -ਤੁਸੀਂ Google Colab ਵੀ ਵਰਤ ਸਕਦੇ ਹੋ, ਜੋ ਕੁਝ ਮੁਫ਼ਤ GPU ਸਹਾਇਤਾ ਦੇ ਨਾਲ ਆਉਂਦਾ ਹੈ, ਅਤੇ ਉੱਥੇ Jupyter Notebooks ਨੂੰ ਇੱਕ-ਇੱਕ ਕਰਕੇ ਅਪਲੋਡ ਕਰਕੇ ਚਲਾ ਸਕਦੇ ਹੋ। +ਤੁਸੀਂ ਗੂਗਲ ਕੋਲੈਬ ਵੀ ਵਰਤ ਸਕਦੇ ਹੋ, ਜਿਸ ਵਿੱਚ ਕੁਝ ਮੁਫਤ GPU ਸਹਾਇਤਾ ਹੁੰਦੀ ਹੈ, ਅਤੇ ਉਥੇ Jupyter Notebooks ਇਕ ਇਕ ਕਰਕੇ ਅਪਲੋਡ ਕਰ ਕੇ ਚਲਾ ਸਕਦੇ ਹੋ। -**ਅਸਵੀਕਤੀ**: -ਇਹ ਦਸਤਾਵੇਜ਼ AI ਅਨੁਵਾਦ ਸੇਵਾ [Co-op Translator](https://github.com/Azure/co-op-translator) ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਅਨੁਵਾਦ ਕੀਤਾ ਗਿਆ ਹੈ। ਜਦੋਂ ਕਿ ਅਸੀਂ ਸਹੀਤਾ ਲਈ ਯਤਨਸ਼ੀਲ ਹਾਂ, ਕਿਰਪਾ ਕਰਕੇ ਧਿਆਨ ਦਿਓ ਕਿ ਸਵੈਚਾਲਿਤ ਅਨੁਵਾਦਾਂ ਵਿੱਚ ਗਲਤੀਆਂ ਜਾਂ ਅਸੁਚਤਤਾਵਾਂ ਹੋ ਸਕਦੀਆਂ ਹਨ। ਮੂਲ ਦਸਤਾਵੇਜ਼ ਨੂੰ ਇਸਦੀ ਮੂਲ ਭਾਸ਼ਾ ਵਿੱਚ ਅਧਿਕਾਰਤ ਸਰੋਤ ਮੰਨਿਆ ਜਾਣਾ ਚਾਹੀਦਾ ਹੈ। ਮਹੱਤਵਪੂਰਨ ਜਾਣਕਾਰੀ ਲਈ, ਪੇਸ਼ੇਵਰ ਮਨੁੱਖੀ ਅਨੁਵਾਦ ਦੀ ਸਿਫਾਰਸ਼ ਕੀਤੀ ਜਾਂਦੀ ਹੈ। ਇਸ ਅਨੁਵਾਦ ਦੀ ਵਰਤੋਂ ਤੋਂ ਪੈਦਾ ਹੋਣ ਵਾਲੇ ਕਿਸੇ ਵੀ ਗਲਤਫਹਿਮੀ ਜਾਂ ਗਲਤ ਵਿਆਖਿਆ ਲਈ ਅਸੀਂ ਜ਼ਿੰਮੇਵਾਰ ਨਹੀਂ ਹਾਂ। \ No newline at end of file +--- + + +**ਤਰਜਮਾ ਸੂਚਨਾ**: +ਇਸ ਦਸਤਾਵੇਜ਼ ਨੂੰ AI ਅਨੁਵਾਦ ਸੇਵਾ [Co-op Translator](https://github.com/Azure/co-op-translator) ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਅਨੁਵਾਦ ਕੀਤਾ ਗਿਆ ਹੈ। ਹਾਲਾਂਕਿ ਅਸੀਂ ਸਹੀਤਾ ਲਈ ਕੋਸ਼ਿਸ਼ ਕਰਦੇ ਹਾਂ, ਕਿਰਪਾ ਕਰਕੇ ਜਾਣਦੇ ਰਹੋ ਕਿ ਆਟੋਮੇਟਿਡ ਅਨੁਵਾਦਾਂ ਵਿੱਚ ਗਲਤੀਆਂ ਜਾਂ ਅਸਮਰਥਤਾਵਾਂ ਹੋ ਸਕਦੀਆਂ ਹਨ। ਇਸ ਦਾ ਮੂਲ ਦਸਤਾਵੇਜ਼ ਉਸ ਦੀ ਮੂਲ ਭਾਸ਼ਾ ਵਿੱਚ ਹੀ ਪ੍ਰਮਾਣਿਕ ਸਰੋਤ ਮੰਨਿਆ ਜਾਣਾ ਚਾਹੀਦਾ ਹੈ। ਅਹਿਮ ਜਾਣਕਾਰੀ ਲਈ, ਪ੍ਰੋਫੈਸ਼ਨਲ ਮਨੁੱਖੀ ਅਨੁਵਾਦ ਦੀ ਸਿਫਾਰਿਸ਼ ਕੀਤੀ ਜਾਂਦੀ ਹੈ। ਇਸ ਅਨੁਵਾਦ ਦੀ ਵਰਤੋਂ ਤੋਂ ਹੋਣ ਵਾਲੀਆਂ ਕਿਸੇ ਵੀ ਗਲਤਫ਼ਹਿਮੀਆਂ ਜਾਂ ਗਲਤ ਵਿਆਖਿਆਵਾਂ ਲਈ ਅਸੀਂ ਜ਼ਿੰਮੇਵਾਰ ਨਹੀਂ ਹਾਂ। + \ No newline at end of file diff --git a/translations/pa/lessons/2-Symbolic/Animals.ipynb b/translations/pa/lessons/2-Symbolic/Animals.ipynb index 3657dd6d..65c49ef1 100644 --- a/translations/pa/lessons/2-Symbolic/Animals.ipynb +++ b/translations/pa/lessons/2-Symbolic/Animals.ipynb @@ -6,25 +6,25 @@ "collapsed": true }, "source": [ - "# ਜਾਨਵਰ ਮਾਹਰ ਪ੍ਰਣਾਲੀ ਨੂੰ ਲਾਗੂ ਕਰਨਾ\n", + "# Implementing an Animal Expert System\n", "\n", - "[AI for Beginners Curriculum](http://github.com/microsoft/ai-for-beginners) ਤੋਂ ਇੱਕ ਉਦਾਹਰਨ।\n", + "An example from [AI for Beginners Curriculum](http://github.com/microsoft/ai-for-beginners).\n", "\n", - "ਇਸ ਉਦਾਹਰਨ ਵਿੱਚ, ਅਸੀਂ ਕੁਝ ਭੌਤਿਕ ਲੱਛਣਾਂ ਦੇ ਆਧਾਰ 'ਤੇ ਜਾਨਵਰ ਦੀ ਪਛਾਣ ਕਰਨ ਲਈ ਇੱਕ ਸਧਾਰਣ ਗਿਆਨ-ਅਧਾਰਿਤ ਪ੍ਰਣਾਲੀ ਲਾਗੂ ਕਰਾਂਗੇ। ਇਸ ਪ੍ਰਣਾਲੀ ਨੂੰ ਹੇਠਾਂ ਦਿੱਤੇ AND-OR ਟ੍ਰੀ ਦੁਆਰਾ ਦਰਸਾਇਆ ਜਾ ਸਕਦਾ ਹੈ (ਇਹ ਪੂਰੇ ਟ੍ਰੀ ਦਾ ਇੱਕ ਹਿੱਸਾ ਹੈ, ਅਸੀਂ ਆਸਾਨੀ ਨਾਲ ਹੋਰ ਨਿਯਮ ਸ਼ਾਮਲ ਕਰ ਸਕਦੇ ਹਾਂ):\n", + "In this sample, we will implement a simple knowledge-based system to determine an animal based on some physical characteristics. The system can be represented by the following AND-OR tree (this is a part of the whole tree, we can easily add some more rules):\n", "\n", - "![](../../../../translated_images/pa/AND-OR-Tree.5592d2c70187f283.webp)\n" + "![](../../../../../../translated_images/pa/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## ਪਿੱਛੇ ਵੱਲ ਤਰਕ ਨਾਲ ਸਾਡਾ ਆਪਣਾ ਐਕਸਪਰਟ ਸਿਸਟਮ ਸ਼ੈੱਲ\n", + "## ਸਾਡੇ ਆਪਣੇ ਮਾਹਿਰ ਸਿਸਟਮ ਸ਼ੈੱਲ ਬੈਕਵਰਡ ਇਨਫਰੈਂਸ ਦੇ ਨਾਲ\n", "\n", - "ਆਓ ਉਤਪਾਦਨ ਨਿਯਮਾਂ ਦੇ ਆਧਾਰ 'ਤੇ ਗਿਆਨ ਪ੍ਰਸਤੁਤੀ ਲਈ ਇੱਕ ਸਧਾਰਣ ਭਾਸ਼ਾ ਨੂੰ ਪਰਿਭਾਸ਼ਿਤ ਕਰਨ ਦੀ ਕੋਸ਼ਿਸ਼ ਕਰੀਏ। ਅਸੀਂ ਨਿਯਮਾਂ ਨੂੰ ਪਰਿਭਾਸ਼ਿਤ ਕਰਨ ਲਈ Python ਕਲਾਸਾਂ ਨੂੰ ਕੁੰਜੀਆਂ ਦੇ ਰੂਪ ਵਿੱਚ ਵਰਤਾਂਗੇ। ਮੁੱਖ ਤੌਰ 'ਤੇ 3 ਕਿਸਮ ਦੀਆਂ ਕਲਾਸਾਂ ਹੋਣਗੀਆਂ:\n", - "* `Ask` ਇੱਕ ਸਵਾਲ ਨੂੰ ਦਰਸਾਉਂਦੀ ਹੈ ਜੋ ਯੂਜ਼ਰ ਤੋਂ ਪੁੱਛਿਆ ਜਾਣਾ ਹੈ। ਇਸ ਵਿੱਚ ਸੰਭਾਵਿਤ ਜਵਾਬਾਂ ਦਾ ਸੈੱਟ ਸ਼ਾਮਲ ਹੁੰਦਾ ਹੈ।\n", - "* `If` ਇੱਕ ਨਿਯਮ ਨੂੰ ਦਰਸਾਉਂਦੀ ਹੈ, ਅਤੇ ਇਹ ਨਿਯਮ ਦੀ ਸਮੱਗਰੀ ਨੂੰ ਸਟੋਰ ਕਰਨ ਲਈ ਸਿਰਫ਼ ਇੱਕ ਸਿੰਟੈਕਟਿਕ ਸ਼ੁਗਰ ਹੈ।\n", - "* `AND`/`OR` ਕਲਾਸਾਂ ਹਨ ਜੋ ਰੁੱਖ ਦੀਆਂ AND/OR ਸ਼ਾਖਾਵਾਂ ਨੂੰ ਦਰਸਾਉਂਦੀਆਂ ਹਨ। ਇਹ ਸਿਰਫ਼ ਦਲੀਲਾਂ ਦੀ ਸੂਚੀ ਨੂੰ ਅੰਦਰ ਸਟੋਰ ਕਰਦੀਆਂ ਹਨ। ਕੋਡ ਨੂੰ ਸਧਾਰਣ ਬਣਾਉਣ ਲਈ, ਸਾਰੀ ਕਾਰਗੁਜ਼ਾਰੀ ਮੂਲ ਕਲਾਸ `Content` ਵਿੱਚ ਪਰਿਭਾਸ਼ਿਤ ਕੀਤੀ ਗਈ ਹੈ।\n" + "ਆਓ ਇੱਕ ਸਰਲ ਭਾਸ਼ਾ ਵਿਆਖਿਆ ਕਰਨ ਦੀ ਕੋਸ਼ਿਸ਼ ਕਰੀਏ ਜੋ ਉਤਪਾਦਕ ਨਿਯਮਾਂ 'ਤੇ ਅਧਾਰਤ ਗਿਆਨ ਪ੍ਰਤੀਨਿਧਤਾ ਲਈ ਹੈ। ਅਸੀਂ ਨਿਯਮਾਂ ਨੂੰ ਪਰਿਭਾਸ਼ਿਤ ਕਰਨ ਲਈ ਕੀਵਰਡ ਵਜੋਂ Python ਕਲਾਸਾਂ ਦੀ ਵਰਤੋਂ ਕਰਾਂਗੇ। ਮੂਲ ਰੂਪ ਵਿੱਚ 3 ਕਿਸਮ ਦੀਆਂ ਕਲਾਸਾਂ ਹੋਣਗੀਆਂ: \n", + "* `Ask` ਵਰਤੋਂਕਾਰ ਨੂੰ ਪੁੱਛੇ ਜਾਣ ਵਾਲੇ ਪ੍ਰਸ਼ਨ ਨੂੰ ਦਰਸਾਉਂਦਾ ਹੈ। ਇਸ ਵਿੱਚ ਸੰਭਾਵਿਤ ਜਵਾਬਾਂ ਦਾ ਸੈੱਟ ਹੁੰਦਾ ਹੈ। \n", + "* `If` ਇੱਕ ਨਿਯਮ ਨੂੰ ਦਰਸਾਉਂਦਾ ਹੈ, ਅਤੇ ਇਹ ਸਿਰਫ ਨਿਯਮ ਦੀ ਸਮੱਗਰੀ ਨੂੰ ਸਟੋਰ ਕਰਨ ਲਈ ਇੱਕ ਸਿੰਟੈਕਟਿਕ ਸੁਗਰ ਹੈ। \n", + "* `AND`/`OR` ਟ੍ਰੀ ਦੇ AND/OR ਸ਼ਾਖਾਵਾਂ ਨੂੰ ਦਰਸਾਉਣ ਲਈ ਕਲਾਸਾਂ ਹਨ। ਉਹ ਸਿਰਫ ਅੰਦਰਲੇ ਦਲੀਲਾਂ ਦੀ ਸੂਚੀ ਸਟੋਰ ਕਰਦੀਆਂ ਹਨ। ਕੋਡ ਨੂੰ ਸਧਾਰਨ ਬਣਾਉਣ ਲਈ, ਸਾਰੀ ਕਾਰਗੁਜਾਰੀ ਮਾਤਰ ਮਾਪੇ ਕਲਾਸ `Content` ਵਿੱਚ ਪਰਿਭਾਸ਼ਿਤ ਹੈ।\n" ] }, { @@ -66,7 +66,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "ਸਾਡੇ ਸਿਸਟਮ ਵਿੱਚ, ਵਰਕਿੰਗ ਮੈਮੋਰੀ ਵਿੱਚ **ਤੱਥਾਂ** ਦੀ ਸੂਚੀ **ਗੁਣ-ਮੁੱਲ ਜੋੜਿਆਂ** ਵਜੋਂ ਸ਼ਾਮਲ ਹੋਵੇਗੀ। ਗਿਆਨ ਅਧਾਰ ਨੂੰ ਇੱਕ ਵੱਡੇ ਸ਼ਬਦਕੋਸ਼ ਵਜੋਂ ਪਰਿਭਾਸ਼ਿਤ ਕੀਤਾ ਜਾ ਸਕਦਾ ਹੈ ਜੋ ਕਾਰਵਾਈਆਂ (ਨਵੇਂ ਤੱਥ ਜੋ ਵਰਕਿੰਗ ਮੈਮੋਰੀ ਵਿੱਚ ਸ਼ਾਮਲ ਕੀਤੇ ਜਾਣੇ ਚਾਹੀਦੇ ਹਨ) ਨੂੰ ਸ਼ਰਤਾਂ ਨਾਲ ਜੋੜਦਾ ਹੈ, ਜੋ AND-OR ਅਭਿਵੈਕਤੀਆਂ ਵਜੋਂ ਪ੍ਰਗਟ ਕੀਤੀਆਂ ਜਾਂਦੀਆਂ ਹਨ। ਇਸ ਤੋਂ ਇਲਾਵਾ, ਕੁਝ ਤੱਥਾਂ ਨੂੰ `ਪੁੱਛਿਆ` ਜਾ ਸਕਦਾ ਹੈ।\n" + "ਸਾਡੇ ਸਿਸਟਮ ਵਿੱਚ, ਵਰਕਿੰਗ ਮੇਮੋਰੀ ਵਿੱਚ **ਤੱਥਾਂ** ਦੀ ਸੂਚੀ **ਗੁਣ-ਮੂਲ੍ਯ ਜੋੜਿਆਂ** ਦੇ ਰੂਪ ਵਿੱਚ ਹੋਵੇਗੀ। ਗਿਆਨਭੰਡਾਰ ਨੂੰ ਇੱਕ ਵੱਡੇ ਸ਼ਬਦਕੋਸ਼ ਵਜੋਂ ਪਰਿਭਾਸ਼ਿਤ ਕੀਤਾ ਜਾ ਸਕਦਾ ਹੈ ਜੋ ਕਾਰਵਾਈਆਂ (ਨਵੇਂ ਤੱਥ ਜੋ ਵਰਕਿੰਗ ਮੇਮੋਰੀ ਵਿੱਚ ਸ਼ਾਮਲ ਕੀਤੇ ਜਾਣੇ ਚਾਹੀਦੇ ਹਨ) ਨੂੰ ਸ਼ਰਤਾਂ ਨਾਲ ਜੋੜਦਾ ਹੈ, ਜੋ AND-OR ਪ੍ਰਗਟਾਵਾਂ ਦੇ ਰੂਪ ਵਿੱਚ ਪ੍ਰਗਟ ਕੀਤੀਆਂ ਜਾਂਦੀਆਂ ਹਨ। ਇਸਤੋਂ ਇਲਾਵਾ, ਕੁਝ ਤੱਥਾਂ ਨੂੰ `Ask` ਕੀਤਾ ਜਾ ਸਕਦਾ ਹੈ।\n" ] }, { @@ -99,13 +99,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "ਪਿੱਛੇ ਵੱਲ ਤਰਕ ਕਰਨ ਲਈ, ਅਸੀਂ `Knowledgebase` ਕਲਾਸ ਨੂੰ ਪਰਿਭਾਸ਼ਿਤ ਕਰਾਂਗੇ। ਇਹ ਵਿੱਚ ਸ਼ਾਮਲ ਹੋਵੇਗਾ:\n", - "* ਕੰਮ ਕਰਨ ਵਾਲੀ `memory` - ਇੱਕ ਡਿਕਸ਼ਨਰੀ ਜੋ ਗੁਣਾਂ ਨੂੰ ਮੁੱਲਾਂ ਨਾਲ ਜੋੜਦੀ ਹੈ\n", - "* Knowledgebase ਦੇ `rules` - ਜਿਵੇਂ ਉਪਰ ਦਿੱਤੇ ਫਾਰਮੈਟ ਵਿੱਚ ਪਰਿਭਾਸ਼ਿਤ ਕੀਤਾ ਗਿਆ ਹੈ\n", + "ਪਿੱਛੇਲੇ ਤਹਿ ਅਨੁਮਾਨ ਕਰਨ ਲਈ, ਅਸੀਂ `Knowledgebase` ਕਲਾਸ ਨੂੰ ਪਰਿਭਾਸ਼ਿਤ ਕਰਾਂਗੇ। ਇਸ ਵਿੱਚ ਸ਼ਾਮਲ ਹੋਵੇਗਾ:\n", + "* ਕਾਰਜਕੁਸ਼ਲ `memory` - ਇੱਕ ਡਿਕਸ਼ਨਰੀ ਜੋ ਗੁਣਾਂ ਨੂੰ ਮੁੱਲਾਂ ਨਾਲ ਮੈਪ ਕਰਦੀ ਹੈ\n", + "* Knowledgebase `rules` ਉਪਰ ਦਿੱਤੇ ਫਾਰਮੈਟ ਵਿੱਚ\n", "\n", - "ਦੋ ਮੁੱਖ ਤਰੀਕੇ ਹਨ:\n", - "* `get` - ਕਿਸੇ ਗੁਣ ਦਾ ਮੁੱਲ ਪ੍ਰਾਪਤ ਕਰਨ ਲਈ, ਜੇ ਲੋੜ ਹੋਵੇ ਤਾਂ ਤਰਕ ਕਰਦੇ ਹੋਏ। ਉਦਾਹਰਣ ਲਈ, `get('color')` ਰੰਗ ਸਲਾਟ ਦਾ ਮੁੱਲ ਪ੍ਰਾਪਤ ਕਰੇਗਾ (ਇਹ ਲੋੜ ਪੈਣ 'ਤੇ ਪੁੱਛੇਗਾ, ਅਤੇ ਕੰਮ ਕਰਨ ਵਾਲੀ ਮੈਮੋਰੀ ਵਿੱਚ ਬਾਅਦ ਲਈ ਮੁੱਲ ਸਟੋਰ ਕਰੇਗਾ)। ਜੇ ਅਸੀਂ `get('color:blue')` ਪੁੱਛਦੇ ਹਾਂ, ਤਾਂ ਇਹ ਰੰਗ ਬਾਰੇ ਪੁੱਛੇਗਾ, ਅਤੇ ਫਿਰ ਰੰਗ ਦੇ ਅਧਾਰ 'ਤੇ `y`/`n` ਮੁੱਲ ਵਾਪਸ ਕਰੇਗਾ।\n", - "* `eval` ਅਸਲ ਤਰਕ ਕਰਦਾ ਹੈ, ਜਿਵੇਂ AND/OR ਟ੍ਰੀ ਨੂੰ ਪਾਰ ਕਰਨਾ, ਉਪ-ਲਕਸ਼ਾਂ ਦਾ ਮੁਲਾਂਕਣ ਕਰਨਾ, ਆਦਿ।\n" + "ਦੋ ਮੁੱਖ ਢੰਗ ਹਨ:\n", + "* `get` ਇੱਕ ਗੁਣ ਦਾ ਮੁੱਲ ਪ੍ਰਾਪਤ ਕਰਨ ਲਈ, ਜੇ ਲੋੜ ਹੋਵੇ ਤਾਂ ਅਨੁਮਾਨ ਕਰਦਾ ਹੈ। ਉਦਾਹਰਨ ਵਜੋਂ, `get('color')` ਰੰਗ ਸਲਾਟ ਦਾ ਮੁੱਲ ਲਏਗਾ (ਜੇ ਲੋੜ ਹੋਵੇ ਤਾਂ ਪੁੱਛੇਗਾ, ਅਤੇ ਬਾਅਦ ਵਿੱਚ ਵਰਤਣ ਲਈ ਇਸ ਮੁੱਲ ਨੂੰ ਕਾਰਜਕੁਸ਼ਲ ਮੈਮੋਰੀ ਵਿੱਚ ਸਟੋਰ ਕਰੇਗਾ)। ਜੇ ਅਸੀਂ `get('color:blue')` ਪੁੱਛਦੇ ਹਾਂ, ਤਾਂ ਇਹ ਰੰਗ ਬਾਰੇ ਪੁੱਛੇਗਾ, ਅਤੇ ਫਿਰ ਰੰਗ ਦੇ ਆਧਾਰ 'ਤੇ `y`/`n` ਮੁੱਲ ਵਾਪਸ ਕਰੇਗਾ।\n", + "* `eval` ਅਸਲ ਅਨੁਮਾਨ ਨੂੰ ਕਰਦਾ ਹੈ, ਜਿਵੇਂ ਕਿ AND/OR ਟ੍ਰੀ ਵਿੱਚ ਤਰਾਵਰ ਕਰਨਾ, ਉਪ-ਲਕੜੀਆਂ ਦਾ ਮੁਲਾਂਕਣ ਕਰਨਾ, ਆਦਿ।\n" ] }, { @@ -172,7 +172,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "ਆਓ ਹੁਣ ਅਸੀਂ ਆਪਣੇ ਜਾਨਵਰਾਂ ਦੇ ਗਿਆਨ ਅਧਾਰ ਨੂੰ ਪਰਿਭਾਸ਼ਿਤ ਕਰੀਏ ਅਤੇ ਸਲਾਹ-ਮਸ਼ਵਰਾ ਕਰੀਏ। ਧਿਆਨ ਦਿਓ ਕਿ ਇਹ ਕਾਲ ਤੁਹਾਨੂੰ ਸਵਾਲ ਪੁੱਛੇਗੀ। ਤੁਸੀਂ ਹਾਂ-ਨਹੀਂ ਵਾਲੇ ਸਵਾਲਾਂ ਲਈ `y`/`n` ਟਾਈਪ ਕਰਕੇ ਜਵਾਬ ਦੇ ਸਕਦੇ ਹੋ, ਜਾਂ ਲੰਬੇ ਬਹੁ-ਚੋਣ ਵਾਲੇ ਜਵਾਬਾਂ ਲਈ ਨੰਬਰ (0..N) ਦਰਸਾ ਸਕਦੇ ਹੋ।\n" + "ਹੁਣ ਆਉਂਦੇ ਹਾਂ ਆਪਣੇ ਜੀਵ-ਜੰਤੂ ਗਿਆਨਸੰਦਰਭ ਨੂੰ ਪਰਿਭਾਸ਼ਿਤ ਕਰੀਏ ਅਤੇ ਸਲਾਹ-ਮਸ਼ਵਰੇ ਦੀ ਕਾਰਵਾਈ ਕਰੀਏ। ਧਿਆਨ ਦਿਓ ਕਿ ਇਹ ਕਾਲ ਤੁਹਾਡੇ ਤੋਂ سوال ਪੁੱਛੇਗੀ। ਤੁਸੀਂ ਹਾਂ-ਨ੍ਹਾਂ ਵਾਲੇ ਸਵਾਲਾਂ ਲਈ `y`/`n` ਟਾਈਪ ਕਰਕੇ ਜਵਾਬ ਦੇ ਸਕਦੇ ਹੋ, ਜਾਂ ਜਦੋਂ ਵਧੇਰੇ ਵਿਕਲਪੀ ਉੱਤਰ ਵਾਲੇ ਸਵਾਲ ਹون ਤਾਂ ਨੰਬਰ (0..N) ਦੱਸ ਕੇ ਜਵਾਬ ਦੇ ਸਕਦੇ ਹੋ।\n" ] }, { @@ -229,11 +229,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## PyKnow ਦੀ ਵਰਤੋਂ ਨਾਲ ਫਾਰਵਰਡ ਇਨਫਰੈਂਸ\n", + "## ਫਾਰਵਰਡ ਇੰਫਰੈਂਸ ਲਈ Experta ਵਰਤਣਾ\n", "\n", - "ਅਗਲੇ ਉਦਾਹਰਨ ਵਿੱਚ, ਅਸੀਂ ਗਿਆਨ ਪ੍ਰਸਤੁਤੀ ਲਈ ਇੱਕ ਲਾਇਬ੍ਰੇਰੀ [PyKnow](https://github.com/buguroo/pyknow/) ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਫਾਰਵਰਡ ਇਨਫਰੈਂਸ ਨੂੰ ਲਾਗੂ ਕਰਨ ਦੀ ਕੋਸ਼ਿਸ਼ ਕਰਾਂਗੇ। **PyKnow** ਪਾਇਥਨ ਵਿੱਚ ਫਾਰਵਰਡ ਇਨਫਰੈਂਸ ਸਿਸਟਮ ਬਣਾਉਣ ਲਈ ਇੱਕ ਲਾਇਬ੍ਰੇਰੀ ਹੈ, ਜੋ ਪੁਰਾਣੇ ਕਲਾਸਿਕ ਸਿਸਟਮ [CLIPS](http://www.clipsrules.net/index.html) ਦੇ ਸਮਾਨ ਬਣਾਈ ਗਈ ਹੈ।\n", + "ਅਗਲੇ ਉਦਾਹਰਨ ਵਿੱਚ, ਅਸੀਂ ਗਿਆਨ ਪ੍ਰਤੀਨਿਧਿਤਾ ਲਈ ਇੱਕ ਲਾਇਬਰੇਰੀ, [Experta](https://github.com/nilp0inter/experta) ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਫਾਰਵਰਡ ਇੰਫਰੈਂਸ ਲਾਗੂ ਕਰਨ ਦੀ ਕੋਸ਼ਿਸ਼ ਕਰਾਂਗੇ। **Experta** ਪਾਇਥਨ ਵਿੱਚ ਫਾਰਵਰਡ ਇੰਫਰੈਂਸ ਸਿਸਟਮ ਬਣਾਉਣ ਲਈ ਇੱਕ ਲਾਇਬਰੇਰੀ ਹੈ, ਜੋ ਪਰੰਪਰਾਗਤ ਪੁਰਾਣੇ ਸਿਸਟਮ [CLIPS](http://www.clipsrules.net/index.html) ਨਾਲ ਮਿਲਦੀ-ਜੁਲਦੀ ਬਣਾਈ ਗਈ ਹੈ।\n", "\n", - "ਅਸੀਂ ਬਿਨਾਂ ਕਿਸੇ ਵੱਡੀ ਮੁਸ਼ਕਲ ਦੇ ਫਾਰਵਰਡ ਚੇਨਿੰਗ ਨੂੰ ਖੁਦ ਵੀ ਲਾਗੂ ਕਰ ਸਕਦੇ ਸੀ, ਪਰ ਸਧਾਰਨ ਤਰੀਕੇ ਆਮ ਤੌਰ 'ਤੇ ਬਹੁਤ ਪ੍ਰਭਾਵਸ਼ਾਲੀ ਨਹੀਂ ਹੁੰਦੇ। ਜ਼ਿਆਦਾ ਪ੍ਰਭਾਵਸ਼ਾਲੀ ਨਿਯਮ ਮੈਚਿੰਗ ਲਈ ਇੱਕ ਖਾਸ ਐਲਗੋਰਿਦਮ [Rete](https://en.wikipedia.org/wiki/Rete_algorithm) ਦੀ ਵਰਤੋਂ ਕੀਤੀ ਜਾਂਦੀ ਹੈ।\n" + "ਸਾਨੂੰ ਫਾਰਵਰਡ ਚੇਨਿੰਗ ਨੂੰ ਆਪਣੇ ਆਪ ਲਾਗੂ ਕਰਨ ਵਿੱਚ ਵੀ ਜ਼ਿਆਦਾ ਸਮੱਸਿਆਵਾਂ ਨਹੀਂ ਆਂਦੀਆਂ, ਪਰ ਸਧਾਰਣ ਲਾਗੂ ਕਰਨਾ ਅਕਸਰ ਬਹੁਤ ਪ੍ਰਭਾਵਸ਼ਾਲੀ ਨਹੀਂ ਹੁੰਦਾ। ਜ਼ਿਆਦਾ ਪ੍ਰਭਾਵਸ਼ਾਲੀ ਨਿਯਮ ਮੇਲ ਖਾਤਮ ਕਰਨ ਲਈ ਇੱਕ ਵਿਸ਼ੇਸ਼ ਅਲਗੋਰਿਦਮ [Rete](https://en.wikipedia.org/wiki/Rete_algorithm) ਦੀ ਵਰਤੋਂ ਕੀਤੀ ਜਾਂਦੀ ਹੈ।\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": [ - "ਅਸੀਂ ਆਪਣੇ ਸਿਸਟਮ ਨੂੰ `KnowledgeEngine` ਦੀ ਸਬਕਲਾਸ ਵਜੋਂ ਇੱਕ ਕਲਾਸ ਦੇ ਰੂਪ ਵਿੱਚ ਪਰਿਭਾਸ਼ਿਤ ਕਰਾਂਗੇ। ਹਰ ਨਿਯਮ ਨੂੰ `@Rule` ਐਨੋਟੇਸ਼ਨ ਵਾਲੇ ਇੱਕ ਵੱਖਰੇ ਫੰਕਸ਼ਨ ਦੁਆਰਾ ਪਰਿਭਾਸ਼ਿਤ ਕੀਤਾ ਜਾਂਦਾ ਹੈ, ਜੋ ਦੱਸਦਾ ਹੈ ਕਿ ਨਿਯਮ ਕਦੋਂ ਚਲਾਇਆ ਜਾਣਾ ਚਾਹੀਦਾ ਹੈ। ਨਿਯਮ ਦੇ ਅੰਦਰ, ਅਸੀਂ `declare` ਫੰਕਸ਼ਨ ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਨਵੇਂ ਤੱਥ ਸ਼ਾਮਲ ਕਰ ਸਕਦੇ ਹਾਂ, ਅਤੇ ਉਹ ਤੱਥ ਸ਼ਾਮਲ ਕਰਨ ਨਾਲ ਅੱਗੇ ਵਧਣ ਵਾਲੇ ਤਰਕ ਇੰਜਣ ਦੁਆਰਾ ਹੋਰ ਨਿਯਮਾਂ ਨੂੰ ਕਾਲ ਕਰਨ ਦਾ ਕਾਰਨ ਬਣੇਗਾ।\n" + "ਅਸੀਂ ਆਪਣੀ ਸਿਸਟਮ ਨੂੰ ਇੱਕ ਕਲਾਸ ਵਜੋਂ ਪਰਿਭਾਸ਼ਿਤ ਕਰਾਂਗੇ ਜੋ `KnowledgeEngine` ਨੂੰ ਸਬਕਲਾਸ ਕਰਦੀ ਹੈ। ਹਰ ਨਿਯਮ ਨੂੰ ਇੱਕ ਵੱਖਰੇ ਫੰਕਸ਼ਨ ਨਾਲ ਪਰਿਭਾਸ਼ਿਤ ਕੀਤਾ ਜਾਂਦਾ ਹੈ ਜਿਸ ਵਿੱਚ `@Rule` ਐਨੋਟੇਸ਼ਨ ਹੁੰਦੀ ਹੈ, ਜੋ ਦੱਸਦੀ ਹੈ ਕਿ ਨਿਯਮ ਕਦੋਂ ਲਾਗੂ ਹੋਣਾ ਚਾਹੀਦਾ ਹੈ। ਨਿਯਮ ਦੇ ਅੰਦਰ, ਅਸੀਂ `declare` ਫੰਕਸ਼ਨ ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਨਵੇਂ ਤੱਥ ਸ਼ਾਮਿਲ ਕਰ ਸਕਦੇ ਹਾਂ, ਅਤੇ ਉਹ ਤੱਥ ਸ਼ਾਮਿਲ ਕਰਨ ਨਾਲ ਕੁਝ ਹੋਰ ਨਿਯਮਾਂ ਨੂੰ ਫਾਰਵਰਡ ਇਨਫਰੈਂਸ ਇੰਜਣ ਵੱਲੋਂ ਕਾਲ ਕੀਤਾ ਜਾਵੇਗਾ।\n" ] }, { @@ -378,7 +377,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "ਜਦੋਂ ਅਸੀਂ ਇੱਕ ਗਿਆਨ ਅਧਾਰ ਨੂੰ ਪਰਿਭਾਸ਼ਿਤ ਕਰ ਲੈਂਦੇ ਹਾਂ, ਤਾਂ ਅਸੀਂ ਕੁਝ ਪ੍ਰਾਰੰਭਿਕ ਤੱਥਾਂ ਨਾਲ ਆਪਣੀ ਵਰਕਿੰਗ ਮੈਮੋਰੀ ਨੂੰ ਭਰਦੇ ਹਾਂ, ਅਤੇ ਫਿਰ ਨਿਰੀਖਣ ਕਰਨ ਲਈ `run()` ਵਿਧੀ ਨੂੰ ਕਾਲ ਕਰਦੇ ਹਾਂ। ਨਤੀਜੇ ਵਜੋਂ ਤੁਸੀਂ ਦੇਖ ਸਕਦੇ ਹੋ ਕਿ ਨਵੇਂ ਅਨੁਮਾਨਿਤ ਤੱਥ ਵਰਕਿੰਗ ਮੈਮੋਰੀ ਵਿੱਚ ਸ਼ਾਮਲ ਕੀਤੇ ਜਾਂਦੇ ਹਨ, ਜਿਸ ਵਿੱਚ ਜਾਨਵਰ ਬਾਰੇ ਅੰਤਿਮ ਤੱਥ ਵੀ ਸ਼ਾਮਲ ਹੈ (ਜੇਕਰ ਅਸੀਂ ਸਾਰੇ ਪ੍ਰਾਰੰਭਿਕ ਤੱਥ ਸਹੀ ਤਰ੍ਹਾਂ ਸੈਟ ਕੀਤੇ ਹਨ)।\n" + "ਜਦੋਂ ਅਸੀਂ ਇੱਕ ਗਿਆਨ ਅਧਾਰ ਨੂੰ ਪਰਿਭਾਸ਼ਿਤ ਕਰ ਲੈਂਦੇ ਹਾਂ, ਅਸੀਂ ਆਪਣੀ ਕਾਰਗਰ ਯਾਦاشت ਨੂੰ ਕੁਝ ਸ਼ੁਰੂਆਤੀ ਤੱਥਾਂ ਨਾਲ ਭਰਦੇ ਹਾਂ, ਅਤੇ ਫਿਰ ਇੰਫਰੇਂਸ ਕਰਨ ਲਈ `run()` ਮੇਥਡ ਨੂੰ ਕਾਲ ਕਰਦੇ ਹਾਂ। ਤੁਸੀਂ ਦੇਖ ਸਕਦੇ ਹੋ ਕਿ ਨਵੇਂ ਇੰਫਰੇਂਸ ਕੀਤੇ ਤੱਥ ਕਾਰਗਰ ਯਾਦاشت ਵਿੱਚ ਸ਼ਾਮِل ਕੀਤੇ ਜਾਂਦੇ ਹਨ, ਜਿਸ ਵਿੱਚ ਜਾਨਵਰ ਬਾਰੇ ਆਖਰੀ ਤੱਥ ਵੀ ਸ਼ਾਮِل ਹੈ (ਜੇਕਰ ਅਸੀਂ ਸਾਰੇ ਸ਼ੁਰੂਆਤੀ ਤੱਥ ਸਹੀ ਤਰ੍ਹਾਂ ਸੈੱਟ ਕੀਤੇ ਹਨ)।\n" ] }, { @@ -440,7 +439,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n---\n\n**ਅਸਵੀਕਰਤੀ**: \nਇਹ ਦਸਤਾਵੇਜ਼ AI ਅਨੁਵਾਦ ਸੇਵਾ [Co-op Translator](https://github.com/Azure/co-op-translator) ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਅਨੁਵਾਦ ਕੀਤਾ ਗਿਆ ਹੈ। ਜਦੋਂ ਕਿ ਅਸੀਂ ਸਹੀ ਹੋਣ ਦਾ ਯਤਨ ਕਰਦੇ ਹਾਂ, ਕਿਰਪਾ ਕਰਕੇ ਧਿਆਨ ਦਿਓ ਕਿ ਸਵੈਚਾਲਿਤ ਅਨੁਵਾਦਾਂ ਵਿੱਚ ਗਲਤੀਆਂ ਜਾਂ ਅਸੁੱਤਰ ਹੋ ਸਕਦੇ ਹਨ। ਇਸ ਦੀ ਮੂਲ ਭਾਸ਼ਾ ਵਿੱਚ ਮੌਜੂਦ ਮੂਲ ਦਸਤਾਵੇਜ਼ ਨੂੰ ਪ੍ਰਮਾਣਿਕ ਸਰੋਤ ਮੰਨਿਆ ਜਾਣਾ ਚਾਹੀਦਾ ਹੈ। ਮਹੱਤਵਪੂਰਨ ਜਾਣਕਾਰੀ ਲਈ, ਪੇਸ਼ੇਵਰ ਮਨੁੱਖੀ ਅਨੁਵਾਦ ਦੀ ਸਿਫਾਰਸ਼ ਕੀਤੀ ਜਾਂਦੀ ਹੈ। ਇਸ ਅਨੁਵਾਦ ਦੇ ਪ੍ਰਯੋਗ ਤੋਂ ਪੈਦਾ ਹੋਣ ਵਾਲੇ ਕਿਸੇ ਵੀ ਗਲਤਫਹਿਮੀ ਜਾਂ ਗਲਤ ਵਿਆਖਿਆ ਲਈ ਅਸੀਂ ਜ਼ਿੰਮੇਵਾਰ ਨਹੀਂ ਹਾਂ। \n" + "---\n\n\n**ਅਸਪਸ਼ਟੀਕਰਨ**: \nਇਹ ਦਸਤਾਵੇਜ਼ AI ਅਨੁਵਾਦ ਸੇਵਾ [Co-op Translator](https://github.com/Azure/co-op-translator) ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਅਨੁਵਾਦ ਕੀਤਾ ਗਿਆ ਹੈ। ਜਦੋਂ ਕਿ ਅਸੀਂ ਸਹੀਤਾ ਲਈ ਯਤਨ ਕਰਦੇ ਹਾਂ, ਕਿਰਪਾ ਕਰਕੇ ਇਹ ਗੱਲ ਧਿਆਨ ਵਿੱਚ ਰੱਖੋ ਕਿ ਆਟੋਮੈਟਿਕ ਅਨੁਵਾਦਾਂ ਵਿੱਚ ਗਲਤੀਆਂ ਜਾਂ ਅਕੁਰੇਟਤਾ ਦੀ ਘਾਟ ਹੋ ਸਕਦੀ ਹੈ। ਮੂਲ ਦਸਤਾਵੇਜ਼ ਆਪਣੇ ਮੂਲ ਭਾਸ਼ਾ ਵਿੱਚ ਅਧਿਕਾਰਕ ਸਰੋਤ ਮੰਨਾ ਜਾਣਾ ਚਾਹੀਦਾ ਹੈ। ਢੁਕਵੀਂ ਜਾਣਕਾਰੀ ਲਈ ਪੇਸ਼ੇਵਰ ਮਨੁੱਖੀ ਅਨੁਵਾਦ ਦੀ ਸਿਫਾਰਸ਼ ਕੀਤੀ ਜਾਂਦੀ ਹੈ। ਇਸ ਅਨੁਵਾਦ ਦੇ ਉਪਯੋਗ ਤੋਂ ਜਨਮ ਲੈਣ ਵਾਲੀਆਂ ਕਿਸੇ ਵੀ ਗਲਤਫਹਮੀਆਂ ਜਾਂ ਗਲਤ ਵਿਆਖਿਆਂ ਲਈ ਅਸੀਂ ਜ਼ਿੰਮੇਵਾਰ ਨਹੀਂ ਹਾਂ।\n\n" ] } ], @@ -467,8 +466,8 @@ "version": "3.11.2" }, "coopTranslator": { - "original_hash": "ab2bd97b0453415b89a469284609a8ce", - "translation_date": "2025-08-28T08:36:04+00:00", + "original_hash": "8ef43db4b9182239fd150a76bd494fdb", + "translation_date": "2026-01-15T12:53:53+00:00", "source_file": "lessons/2-Symbolic/Animals.ipynb", "language_code": "pa" } diff --git a/translations/pa/lessons/2-Symbolic/README.md b/translations/pa/lessons/2-Symbolic/README.md index fdd870d0..7576adfd 100644 --- a/translations/pa/lessons/2-Symbolic/README.md +++ b/translations/pa/lessons/2-Symbolic/README.md @@ -1,116 +1,116 @@ -# ਗਿਆਨ ਪ੍ਰਸਤੁਤੀ ਅਤੇ ਮਾਹਰ ਪ੍ਰਣਾਲੀਆਂ +# ਗਿਆਨ ਪ੍ਰਤੀਨਿਧਿਤਾ ਅਤੇ ਮਾਹਿਰ ਪ੍ਰਣਾਲੀਆਂ -![ਸੰਕੇਤਕ AI ਸਮੱਗਰੀ ਦਾ ਸਾਰ](../../../../translated_images/pa/ai-symbolic.715a30cb610411a6.webp) +![ਸੰਕੇਤਕ AI ਸਮੱਗਰੀ ਦਾ ਸਾਰ](../../../../../../translated_images/pa/ai-symbolic.715a30cb610411a6.webp) -> ਸਕੈਚਨੋਟ [Tomomi Imura](https://twitter.com/girlie_mac) ਦੁਆਰਾ +> ਸਕੈਚਨੋਟ ਦੁਆਰਾ [ਟੋਮੋਮੀ ਇਮੁਰਾ](https://twitter.com/girlie_mac) -ਕ੍ਰਿਤ੍ਰਿਮ ਬੁੱਧੀ ਦੀ ਖੋਜ ਦਾ ਮਕਸਦ ਗਿਆਨ ਦੀ ਖੋਜ ਹੈ, ਦੁਨੀਆ ਨੂੰ ਸਮਝਣ ਲਈ ਜਿਵੇਂ ਮਨੁੱਖ ਕਰਦੇ ਹਨ। ਪਰ ਇਹ ਕਿਵੇਂ ਕੀਤਾ ਜਾ ਸਕਦਾ ਹੈ? +ਕୃਤਿਮ ਬੁੱਧਿਮਤਾ ਦੀ ਖੋਜ ਗਿਆਨ ਲਈ ਇੱਕ ਤਲਾਸ਼ 'ਤੇ ਆਧਾਰਿਤ ਹੈ, ਤਾਂ ਜੋ ਦੁਨੀਆ ਨੂੰ ਮਨੁੱਖਾਂ ਵਰਗਾ ਸਮਝਿਆ ਜਾ ਸਕੇ। ਪਰ ਤੁਸੀਂ ਇਹ ਕਿਵੇਂ ਕਰ ਸਕਦੇ ਹੋ? -## [ਪ੍ਰੀ-ਲੈਕਚਰ ਕਵਿਜ਼](https://ff-quizzes.netlify.app/en/ai/quiz/3) +## [ਪੂਰਵ-ਵਿਆਖਿਆਣ ਕਵੀਜ਼](https://ff-quizzes.netlify.app/en/ai/quiz/3) -AI ਦੇ ਸ਼ੁਰੂਆਤੀ ਦਿਨਾਂ ਵਿੱਚ, ਬੁੱਧੀਮਾਨ ਪ੍ਰਣਾਲੀਆਂ ਬਣਾਉਣ ਲਈ ਟੌਪ-ਡਾਊਨ ਪਹੁੰਚ (ਪਿਛਲੇ ਪਾਠ ਵਿੱਚ ਚਰਚਾ ਕੀਤੀ ਗਈ) ਪ੍ਰਸਿੱਧ ਸੀ। ਇਹ ਵਿਚਾਰ ਸੀ ਕਿ ਮਨੁੱਖਾਂ ਤੋਂ ਗਿਆਨ ਨੂੰ ਮਸ਼ੀਨ-ਪੜ੍ਹਨਯੋਗ ਰੂਪ ਵਿੱਚ ਕੱਢਿਆ ਜਾਵੇ ਅਤੇ ਫਿਰ ਇਸਨੂੰ ਸਵੈਚਾਲਿਤ ਤਰੀਕੇ ਨਾਲ ਸਮੱਸਿਆਵਾਂ ਹੱਲ ਕਰਨ ਲਈ ਵਰਤਿਆ ਜਾਵੇ। ਇਸ ਪਹੁੰਚ ਦੇ ਪਿੱਛੇ ਦੋ ਵੱਡੇ ਵਿਚਾਰ ਸਨ: +AI ਦੇ ਸ਼ੁਰੂਆਤੀ ਦਿਨਾਂ ਵਿੱਚ, ਬੁੱਧਿਮਾਨ ਸਿਸਟਮ ਬਣਾਉਣ ਲਈ ਉੱਪਰ-ਥੱਲੇ ਪਧਤੀ (ਜੋ ਪਿਛਲੇ ਪਾਠ ਵਿੱਚ ਚਰਚਾ ਕੀਤੀ ਗਈ ਸੀ) ਲੋਕਪਰੀਯ ਸੀ। ਵਿਚਾਰ ਇਹ ਸੀ ਕਿ ਲੋਕਾਂ ਤੋਂ ਗਿਆਨ ਨੂੰ ਮਸ਼ੀਨ-ਸਮਝਣਯੋਗ ਰੂਪ ਵਿੱਚ ਕੱਢਿਆ ਜਾਵੇ, ਅਤੇ ਫਿਰ ਇਸ ਨੂੰ ਆਟੋਮੈਟਿਕ ਤੌਰ 'ਤੇ ਸਮੱਸਿਆਵਾਂ ਹੱਲ ਕਰਨ ਲਈ ਵਰਤਿਆ ਜਾਵੇ। ਇਹ ਪਧਤੀ ਦੋ ਵੱਡੇ ਵਿਚਾਰਾਂ 'ਤੇ ਆਧਾਰਿਤ ਸੀ: -* ਗਿਆਨ ਪ੍ਰਸਤੁਤੀ -* ਤਰਕ +* ਗਿਆਨ ਪ੍ਰਤੀਨਿਧਿਤਾ +* ਤਰਕ ਕਰਨ -## ਗਿਆਨ ਪ੍ਰਸਤੁਤੀ +## ਗਿਆਨ ਪ੍ਰਤੀਨਿਧਿਤਾ -ਸੰਕੇਤਕ AI ਵਿੱਚ ਇੱਕ ਮਹੱਤਵਪੂਰਨ ਧਾਰਨਾ **ਗਿਆਨ** ਹੈ। ਇਹ ਜਾਣਨਾ ਜਰੂਰੀ ਹੈ ਕਿ ਗਿਆਨ ਨੂੰ *ਜਾਣਕਾਰੀ* ਜਾਂ *ਡਾਟਾ* ਤੋਂ ਕਿਵੇਂ ਵੱਖ ਕੀਤਾ ਜਾਵੇ। ਉਦਾਹਰਣ ਲਈ, ਕੋਈ ਕਹਿ ਸਕਦਾ ਹੈ ਕਿ ਕਿਤਾਬਾਂ ਵਿੱਚ ਗਿਆਨ ਹੁੰਦਾ ਹੈ, ਕਿਉਂਕਿ ਕਿਤਾਬਾਂ ਪੜ੍ਹ ਕੇ ਕੋਈ ਮਾਹਰ ਬਣ ਸਕਦਾ ਹੈ। ਹਾਲਾਂਕਿ, ਕਿਤਾਬਾਂ ਵਿੱਚ ਜੋ ਕੁਝ ਹੁੰਦਾ ਹੈ, ਉਸਨੂੰ ਅਸਲ ਵਿੱਚ *ਡਾਟਾ* ਕਿਹਾ ਜਾਂਦਾ ਹੈ, ਅਤੇ ਕਿਤਾਬਾਂ ਪੜ੍ਹ ਕੇ ਅਤੇ ਇਸ ਡਾਟਾ ਨੂੰ ਸਾਡੇ ਦੁਨੀਆ ਦੇ ਮਾਡਲ ਵਿੱਚ ਸ਼ਾਮਲ ਕਰਕੇ ਅਸੀਂ ਇਸਨੂੰ ਗਿਆਨ ਵਿੱਚ ਬਦਲਦੇ ਹਾਂ। +ਸੰਕੇਤਕ AI ਵਿੱਚ ਇੱਕ ਮਹੱਤਵਪੂਰਨ ਧਾਰਨਾ ਹੈ **ਗਿਆਨ**। ਗਿਆਨ ਨੂੰ *ਸੂਚਨਾ* ਜਾਂ *ਡੇਟਾ* ਤੋਂ ਵੱਖਰਾ ਕਰਨਾ ਜ਼ਰੂਰੀ ਹੈ। ਉਦਾਹਰਨ ਵਜੋਂ, ਕਿਹਾ ਜਾ ਸਕਦਾ ਹੈ ਕਿ ਕਿਤਾਬਾਂ ਵਿੱਚ ਗਿਆਨ ਹੁੰਦਾ ਹੈ, ਕਿਉਂਕਿ ਕਿਤਾਬਾਂ ਪੜ੍ਹ ਕੇ ਕੋਈ ਮਾਹਿਰ ਬਣ ਸਕਦਾ ਹੈ। ਹਾਲਾਂਕਿ, ਕਿਤਾਬਾਂ ਵਿੱਚ ਜੋ ਹੁੰਦਾ ਹੈ ਉਹ ਅਸਲ ਵਿੱਚ *ਡੇਟਾ* ਹੋਂਦਾ ਹੈ, ਅਤੇ ਕਿਤਾਬਾਂ ਪੜ੍ਹ ਕੇ ਅਤੇ ਇਸ ਡੇਟਾ ਨੂੰ ਆਪਣੀ ਦੁਨੀਆ ਦੇ ਮਾਡਲ ਵਿੱਚ ਇੱਕਠਾ ਕਰਕੇ ਅਸੀਂ ਇਸ ਡੇਟਾ ਨੂੰ ਗਿਆਨ ਵਿੱਚ ਬਦਲ ਦੇਂਦੇ ਹਾਂ। -> ✅ **ਗਿਆਨ** ਉਹ ਚੀਜ਼ ਹੈ ਜੋ ਸਾਡੇ ਮਨ ਵਿੱਚ ਹੁੰਦੀ ਹੈ ਅਤੇ ਦੁਨੀਆ ਦੀ ਸਾਡੀ ਸਮਝ ਨੂੰ ਦਰਸਾਉਂਦੀ ਹੈ। ਇਹ ਇੱਕ ਸਰਗਰਮ **ਸਿੱਖਣ** ਪ੍ਰਕਿਰਿਆ ਦੁਆਰਾ ਪ੍ਰਾਪਤ ਹੁੰਦੀ ਹੈ, ਜੋ ਸਾਨੂੰ ਮਿਲਣ ਵਾਲੀ ਜਾਣਕਾਰੀ ਦੇ ਟੁਕੜਿਆਂ ਨੂੰ ਸਾਡੇ ਸਰਗਰਮ ਮਾਡਲ ਵਿੱਚ ਸ਼ਾਮਲ ਕਰਦੀ ਹੈ। +> ✅ **ਗਿਆਨ** ਉਹ ਕੁਝ ਹੁੰਦਾ ਹੈ ਜੋ ਸਾਡੀ ਸਿਰ ਵਿੱਚ ਹੋੰਦਾ ਹੈ ਅਤੇ ਦੁਨੀਆ ਦੀ ਸਾਡੇ ਸਮਝ ਦਾ ਪ੍ਰਤੀਨਿਧਿਤਾ ਕਰਦਾ ਹੈ। ਇਹ ਇੱਕ ਸਰਗਰਮ **ਸਿੱਖਣ** ਪ੍ਰਕਿਰਿਆ ਦੁਆਰਾ ਪ੍ਰਾਪਤ ਹੁੰਦਾ ਹੈ, ਜੋ ਮਿਲਣ ਵਾਲੀਆਂ ਸੂਚਨਾਵਾਂ ਨੂੰ ਸਾਡੇ ਸਰਗਰਮ ਦੁਨੀਆ ਮਾਡਲ ਵਿੱਚ ਜੁੜਦਾ ਹੈ। -ਅਕਸਰ, ਅਸੀਂ ਗਿਆਨ ਨੂੰ ਸਖ਼ਤੀ ਨਾਲ ਪਰਿਭਾਸ਼ਿਤ ਨਹੀਂ ਕਰਦੇ, ਪਰ ਅਸੀਂ ਇਸਨੂੰ [DIKW Pyramid](https://en.wikipedia.org/wiki/DIKW_pyramid) ਦੇ ਨਾਲ ਸੰਬੰਧਿਤ ਧਾਰਨਾਵਾਂ ਨਾਲ ਸਥਿਤ ਕਰਦੇ ਹਾਂ। ਇਸ ਵਿੱਚ ਹੇਠਾਂ ਦਿੱਤੀਆਂ ਧਾਰਨਾਵਾਂ ਸ਼ਾਮਲ ਹਨ: +ਸਭ ਤੋਂ ਵੱਧ, ਅਸੀਂ ਕਮਜ਼ੋਰ ਤੌਰ ਤੇ ਗਿਆਨ ਨੂੰ ਪਰਿਭਾਸ਼ਿਤ ਕਰਦੇ ਹਾਂ, ਪਰ ਅਸੀਂ ਇਸ ਨੂੰ ਹੋਰ ਸੰਬੰਧਿਤ ਧਾਰਨਾਵਾਂ ਨਾਲ [DIKW ਪਿਰਾਮਿਡ](https://en.wikipedia.org/wiki/DIKW_pyramid) ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਮਿਲਾਂਦੇ ਹਾਂ। ਇਸ ਵਿੱਚ ਨਿਮਨਲਿਖਤ ਧਾਰਨਾਵਾਂ ਹੁੰਦੀਆਂ ਹਨ: -* **ਡਾਟਾ** ਉਹ ਚੀਜ਼ ਹੈ ਜੋ ਭੌਤਿਕ ਮੀਡੀਆ ਵਿੱਚ ਦਰਸਾਈ ਜਾਂਦੀ ਹੈ, ਜਿਵੇਂ ਕਿ ਲਿਖਤ ਪਾਠ ਜਾਂ ਬੋਲੀਆਂ ਗੱਲਾਂ। ਡਾਟਾ ਮਨੁੱਖਾਂ ਤੋਂ ਅਜ਼ਾਦ ਹੁੰਦਾ ਹੈ ਅਤੇ ਇਸਨੂੰ ਲੋਕਾਂ ਵਿੱਚ ਸਾਂਝਾ ਕੀਤਾ ਜਾ ਸਕਦਾ ਹੈ। -* **ਜਾਣਕਾਰੀ** ਉਹ ਹੈ ਜੋ ਅਸੀਂ ਆਪਣੇ ਮਨ ਵਿੱਚ ਡਾਟਾ ਦੀ ਵਿਆਖਿਆ ਕਰਦੇ ਹਾਂ। ਉਦਾਹਰਣ ਲਈ, ਜਦੋਂ ਅਸੀਂ ਸ਼ਬਦ *ਕੰਪਿਊਟਰ* ਸੁਣਦੇ ਹਾਂ, ਤਾਂ ਸਾਡੇ ਕੋਲ ਇਸ ਬਾਰੇ ਕੁਝ ਸਮਝ ਹੁੰਦੀ ਹੈ। -* **ਗਿਆਨ** ਉਹ ਜਾਣਕਾਰੀ ਹੈ ਜੋ ਸਾਡੇ ਦੁਨੀਆ ਦੇ ਮਾਡਲ ਵਿੱਚ ਸ਼ਾਮਲ ਕੀਤੀ ਜਾਂਦੀ ਹੈ। ਉਦਾਹਰਣ ਲਈ, ਜਦੋਂ ਅਸੀਂ ਸਿੱਖ ਲੈਂਦੇ ਹਾਂ ਕਿ ਕੰਪਿਊਟਰ ਕੀ ਹੈ, ਤਾਂ ਅਸੀਂ ਇਸਦੇ ਕੰਮ ਕਰਨ ਦੇ ਤਰੀਕੇ, ਕੀਮਤ ਅਤੇ ਇਸਦੇ ਉਪਯੋਗ ਬਾਰੇ ਕੁਝ ਵਿਚਾਰ ਬਣਾਉਣ ਲੱਗਦੇ ਹਾਂ। ਇਹ ਪਰਸਪਰ ਸੰਬੰਧਿਤ ਧਾਰਨਾਵਾਂ ਦਾ ਜਾਲ ਸਾਡੇ ਗਿਆਨ ਨੂੰ ਬਣਾਉਂਦਾ ਹੈ। -* **ਬੁੱਧੀ** ਸਾਡੇ ਦੁਨੀਆ ਦੀ ਸਮਝ ਦਾ ਇੱਕ ਹੋਰ ਪੱਧਰ ਹੈ, ਅਤੇ ਇਹ *ਮੈਟਾ-ਗਿਆਨ* ਨੂੰ ਦਰਸਾਉਂਦੀ ਹੈ, ਜਿਵੇਂ ਕਿ ਗਿਆਨ ਨੂੰ ਕਿਵੇਂ ਅਤੇ ਕਦੋਂ ਵਰਤਿਆ ਜਾਣਾ ਚਾਹੀਦਾ ਹੈ। +* **ਡੇਟਾ** ਕੁਝ ਐਸੀ ਚੀਜ਼ ਹੈ ਜੋ ਭੌਤਿਕ ਮਾਧਿਅਮ ਵਿੱਚ ਪ੍ਰਤੀਨਿਧਿਤ ਕੀਤੀ ਜਾਂਦੀ ਹੈ, ਜਿਵੇਂ ਕਿ ਲਿਖਤੀ ਪਾਠ ਜਾਂ ਬੋਲੀ ਗਈਆਂ ਸ਼ਬਦ। ਡੇਟਾ ਮਨੁੱਖਾਂ ਤੋਂ ਸੁਤੰਤਰ ਹੁੰਦਾ ਹੈ ਅਤੇ ਲੋਕਾਂ ਵਿੱਚ ਸਾਂਝਾ ਕੀਤਾ ਜਾ ਸਕਦਾ ਹੈ। +* **ਸੂਚਨਾ** ਉਹ ਹੈ ਜਿਸ ਤਰ੍ਹਾਂ ਅਸੀਂ ਡੇਟਾ ਨੂੰ ਆਪਣੇ ਸਿਰ ਵਿੱਚ ਵਿਆਖਿਆ ਕਰਦੇ ਹਾਂ। ਉਦਾਹਰਨ ਵਜੋਂ, ਜਦੋਂ ਅਸੀਂ *ਕੰਪਿਊਟਰ* ਸ਼ਬਦ ਸੁਣਦੇ ਹਾਂ, ਤਾਂ ਸਾਨੂੰ ਕੁਝ ਸਮਝ ਹੁੰਦੀ ਹੈ ਕਿ ਇਹ ਕੀ ਹੈ। +* **ਗਿਆਨ** ਉਹ ਹੈ ਜਦ ਸੂਚਨਾ ਸਾਡੇ ਦੁਨੀਆ ਮਾਡਲ ਵਿੱਚ ਸ਼ਾਮਲ ਹੁੰਦੀ ਹੈ। ਉਦਾਹਰਨ ਵਜੋਂ, ਜਦੋਂ ਅਸੀਂ ਸਿੱਖ ਲੈਂਦੇ ਹਾਂ ਕਿ ਕੰਪਿਊਟਰ ਕੀ ਹੈ, ਤਾਂ ਸਾਨੂੰ ਇਸ ਬਾਰੇ ਕੁਝ ਵਿਚਾਰ ਮਿਲਦੇ ਨਿਵੱਸ, ਕਿੰਨਾ ਮਹਿਲਾ ਹੈ, ਅਤੇ ਇਸ ਨੂੰ ਕਿਸ ਲਈ ਵਰਤਿਆ ਜਾ ਸਕਦਾ ਹੈ। ਇਹ ਪਰਸਪਰ ਜੁੜੀਆਂ ਹੋਈਆਂ ਧਾਰਨਾਵਾਂ ਦਾ ਜਾਲ ਸਾਡਾ ਗਿਆਨ ਬਣਾਉਂਦਾ ਹੈ। +* **ਬੁੱਧਿਮਤਾ** ਇੱਕ ਹੋਰ ਸਤਰ ਹੈ ਸਾਡੇ ਦੁਨੀਆ ਦੀ ਸਮਝ ਦਾ, ਜੋ *ਮੇਟਾ-ਗਿਆਨ* ਨੂੰ ਦਰਸਾਉਂਦੀ ਹੈ, ਉਦਾਹਰਨ ਵਜੋਂ, ਇਹ ਕਿਹੜੇ ਤਰੀਕੇ ਨਾਲ ਅਤੇ ਕਦੋਂ ਗਿਆਨ ਦੀ ਵਰਤੋਂ ਕਰਨੀ ਚਾਹੀਦੀ ਹੈ। - + -*ਚਿੱਤਰ [ਵਿਕੀਪੀਡੀਆ ਤੋਂ](https://commons.wikimedia.org/w/index.php?curid=37705247), Longlivetheux ਦੁਆਰਾ - ਆਪਣਾ ਕੰਮ, CC BY-SA 4.0* +*ਚਿੱਤਰ [ਵਿਕੀਪੀਡੀਆ ਤੋਂ](https://commons.wikimedia.org/w/index.php?curid=37705247), ਲੇਖਕ Longlivetheux - ਆਪਣਾ ਕੰਮ, CC BY-SA 4.0* -ਇਸ ਤਰ੍ਹਾਂ, **ਗਿਆਨ ਪ੍ਰਸਤੁਤੀ** ਦੀ ਸਮੱਸਿਆ ਇਹ ਹੈ ਕਿ ਕੰਪਿਊਟਰ ਵਿੱਚ ਡਾਟਾ ਦੇ ਰੂਪ ਵਿੱਚ ਗਿਆਨ ਨੂੰ ਦਰਸਾਉਣ ਦਾ ਕੁਝ ਪ੍ਰਭਾਵਸ਼ਾਲੀ ਤਰੀਕਾ ਲੱਭਿਆ ਜਾਵੇ, ਤਾਂ ਜੋ ਇਹ ਸਵੈਚਾਲਿਤ ਤਰੀਕੇ ਨਾਲ ਵਰਤਿਆ ਜਾ ਸਕੇ। ਇਸਨੂੰ ਇੱਕ ਸਪੈਕਟ੍ਰਮ ਵਜੋਂ ਦੇਖਿਆ ਜਾ ਸਕਦਾ ਹੈ: +ਇਸ ਤਰ੍ਹਾਂ, **ਗਿਆਨ ਪ੍ਰਤੀਨਿਧਿਤਾ** ਸਮੱਸਿਆ ਇਹ ਹੈ ਕਿ ਕਿਵੇਂ ਕੰਪਿਊਟਰ ਵਿੱਚ ਡੇਟਾ ਦੇ ਰੂਪ ਵਿੱਚ ਗਿਆਨ ਨੂੰ ਪ੍ਰਭਾਵਸ਼ਾਲੀ ਤਰੀਕੇ ਨਾਲ ਪ੍ਰਤੀਨਿਧਿਤ ਕੀਤਾ ਜਾਵੇ ਤਾਂ ਜੋ ਇਹ ਆਟੋਮੈਟਿਕ ਤੌਰ 'ਤੇ ਵਰਤੋਂ ਯੋਗ ਹੋਵੇ। ਇਸਨੂੰ ਇੱਕ ਸਪੈਕਟਰਮ ਵਜੋਂ ਵੇਖਿਆ ਜਾ ਸਕਦਾ ਹੈ: -![ਗਿਆਨ ਪ੍ਰਸਤੁਤੀ ਸਪੈਕਟ੍ਰਮ](../../../../translated_images/pa/knowledge-spectrum.b60df631852c0217.webp) +![ਗਿਆਨ ਪ੍ਰਤੀਨਿਧਿਤਾ ਸਪੈਕਟਰਮ](../../../../../../translated_images/pa/knowledge-spectrum.b60df631852c0217.webp) -> ਚਿੱਤਰ [Dmitry Soshnikov](http://soshnikov.com) ਦੁਆਰਾ +> ਚਿੱਤਰ [ਦਿਮਿਤਰੀ ਸੋਸ਼ਨਿਕੋਵ](http://soshnikov.com) ਵੱਲੋਂ -* ਖੱਬੇ ਪਾਸੇ, ਬਹੁਤ ਸਧਾਰਨ ਕਿਸਮਾਂ ਦੀਆਂ ਗਿਆਨ ਪ੍ਰਸਤੁਤੀਆਂ ਹਨ ਜੋ ਕੰਪਿਊਟਰਾਂ ਦੁਆਰਾ ਪ੍ਰਭਾਵਸ਼ਾਲੀ ਤਰੀਕੇ ਨਾਲ ਵਰਤੀਆਂ ਜਾ ਸਕਦੀਆਂ ਹਨ। ਸਭ ਤੋਂ ਸਧਾਰਨ ਇੱਕ ਐਲਗੋਰਿਦਮਿਕ ਹੈ, ਜਦੋਂ ਗਿਆਨ ਨੂੰ ਕੰਪਿਊਟਰ ਪ੍ਰੋਗਰਾਮ ਦੁਆਰਾ ਦਰਸਾਇਆ ਜਾਂਦਾ ਹੈ। ਹਾਲਾਂਕਿ, ਇਹ ਗਿਆਨ ਨੂੰ ਦਰਸਾਉਣ ਦਾ ਸਭ ਤੋਂ ਵਧੀਆ ਤਰੀਕਾ ਨਹੀਂ ਹੈ, ਕਿਉਂਕਿ ਇਹ ਲਚਕਦਾਰ ਨਹੀਂ ਹੈ। ਸਾਡੇ ਮਨ ਵਿੱਚ ਗਿਆਨ ਅਕਸਰ ਗੈਰ-ਐਲਗੋਰਿਦਮਿਕ ਹੁੰਦਾ ਹੈ। -* ਸੱਜੇ ਪਾਸੇ, ਕੁਦਰਤੀ ਪਾਠ ਵਰਗੀਆਂ ਪ੍ਰਸਤੁਤੀਆਂ ਹਨ। ਇਹ ਸਭ ਤੋਂ ਸ਼ਕਤੀਸ਼ਾਲੀ ਹੈ, ਪਰ ਸਵੈਚਾਲਿਤ ਤਰਕ ਲਈ ਵਰਤੀਆਂ ਨਹੀਂ ਜਾ ਸਕਦੀਆਂ। +* ਖੱਬੇ ਪਾਸੇ ਉਹ ਬਹੁਤ ਸਧਾਰਣ ਕਿਸਮਾਂ ਦੀਆਂ ਗਿਆਨ ਪ੍ਰਤੀਨਿਧਿਤਾਵਾਂ ਹੁੰਦੀਆਂ ਹਨ ਜਿਹਨੂੰ ਕੰਪਿਊਟਰ ਪ੍ਰਭਾਵਸ਼ਾਲੀ ਤਰੀਕੇ ਨਾਲ ਵਰਤ ਸਕਦੇ ਹਨ। ਸਭ ਤੋਂ ਸਧਾਰਣ ਹੈ ਅਲਗੋਰਿਦਮਿਕ ਜਦੋਂ ਗਿਆਨ ਇੱਕ ਕੰਪਿਊਟਰ ਪ੍ਰੋਗਰਾਮ ਦੁਆਰਾ ਪ੍ਰਤੀਨਿਧਿਤ ਹੁੰਦਾ ਹੈ। ਇਸ ਤਰੀਕੇ ਨਾਲ ਗਿਆਨ ਪ੍ਰਤੀਨਿਧਿਤ ਕਰਨਾ ਸਭ ਤੋਂ ਵਧੀਆ ਨਹੀਂ ਹੈ ਕਿਉਂਕਿ ਇਹ ਲਚਕੀਲਾ ਨਹੀਂ ਹੁੰਦਾ। ਸਾਡੀ ਸਿਰ ਵਿੱਚ ਗਿਆਨ ਅਕਸਰ ਗੈਰ-ਅਲਗੋਰਿਦਮਿਕ ਹੁੰਦਾ ਹੈ। +* ਸੱਜੇ ਪਾਸੇ ਐਸੀਆਂ ਪ੍ਰਤੀਨਿਧਿਤਾਵਾਂ ਹਨ ਜਿਵੇਂ ਕੁਦਰਤੀ ਪਾਠ। ਇਹ ਸਭ ਤੋਂ ਸ਼ਕਤੀਸ਼ਾਲੀ ਹੈ, ਪਰ ਆਟੋਮੈਟਿਕ ਤਰਕ ਲਈ ਵਰਤੀ ਨਹੀਂ ਜਾ ਸਕਦੀ। -> ✅ ਇੱਕ ਮਿੰਟ ਲਈ ਸੋਚੋ ਕਿ ਤੁਸੀਂ ਆਪਣੇ ਮਨ ਵਿੱਚ ਗਿਆਨ ਨੂੰ ਕਿਵੇਂ ਦਰਸਾਉਂਦੇ ਹੋ ਅਤੇ ਇਸਨੂੰ ਨੋਟਸ ਵਿੱਚ ਬਦਲਦੇ ਹੋ। ਕੀ ਕੋਈ ਖਾਸ ਫਾਰਮੈਟ ਹੈ ਜੋ ਤੁਹਾਡੇ ਲਈ ਯਾਦਸ਼ਕਤੀ ਵਿੱਚ ਮਦਦਗਾਰ ਹੁੰਦਾ ਹੈ? +> ✅ ਇਕ ਮਿੰਟ ਲਈ ਸੋਚੋ ਕਿ ਤੁਸੀਂ ਆਪਣੀ ਸਿਰ ਵਿੱਚ ਗਿਆਨ ਨੂੰ ਕਿਵੇਂ ਪ੍ਰਤੀਨਿਧਿਤ ਕਰਦੇ ਹੋ ਅਤੇ ਇਸਨੂੰ ਨੋਟਾਂ ਵਿੱਚ ਕਿਵੇਂ ਬਦਲਦੇ ਹੋ। ਕੀ ਤੁਹਾਡੇ ਲਈ ਕੋਈ ਵਿਸ਼ੇਸ਼ ਫਾਰਮੈਟ ਹੈ ਜੋ ਯਾਦਸ਼ਤ ਲਈ ਮਦਦਗਾਰ ਹੁੰਦਾ ਹੈ? -## ਕੰਪਿਊਟਰ ਗਿਆਨ ਪ੍ਰਸਤੁਤੀਆਂ ਦਾ ਵਰਗੀਕਰਨ +## ਕੰਪਿਊਟਰ ਗਿਆਨ ਪ੍ਰਤੀਨਿਧਿਤਾਵਾਂ ਦੀ ਵਰਗੀਕਰਨ -ਅਸੀਂ ਵੱਖ-ਵੱਖ ਕੰਪਿਊਟਰ ਗਿਆਨ ਪ੍ਰਸਤੁਤੀ ਤਰੀਕਿਆਂ ਨੂੰ ਹੇਠਾਂ ਦਿੱਤੀਆਂ ਸ਼੍ਰੇਣੀਆਂ ਵਿੱਚ ਵਰਗੀਕਰ ਸਕਦੇ ਹਾਂ: +ਅਸੀਂ ਵੱਖ-ਵੱਖ ਕੰਪਿਊਟਰ ਗਿਆਨ ਪ੍ਰਤੀਨਿਧਿਤਾ ਢੰਗਾਂ ਨੂੰ ਹੇਠ ਲਿਖੀਆਂ ਸ਼੍ਰੇਣੀਆਂ ਵਿੱਚ ਵੰਡ ਸਕਦੇ ਹਾਂ: -* **ਨੈਟਵਰਕ ਪ੍ਰਸਤੁਤੀਆਂ** ਇਸ ਧਾਰਨਾ 'ਤੇ ਆਧਾਰਿਤ ਹਨ ਕਿ ਸਾਡੇ ਮਨ ਵਿੱਚ ਪਰਸਪਰ ਸੰਬੰਧਿਤ ਧਾਰਨਾਵਾਂ ਦਾ ਇੱਕ ਨੈਟਵਰਕ ਹੁੰਦਾ ਹੈ। ਅਸੀਂ ਕੰਪਿਊਟਰ ਵਿੱਚ ਇੱਕ ਗ੍ਰਾਫ ਵਜੋਂ ਉਹੀ ਨੈਟਵਰਕ ਬਣਾਉਣ ਦੀ ਕੋਸ਼ਿਸ਼ ਕਰ ਸਕਦੇ ਹਾਂ - ਜਿਸਨੂੰ **ਸੈਮੈਂਟਿਕ ਨੈਟਵਰਕ** ਕਿਹਾ ਜਾਂਦਾ ਹੈ। +* **ਨੈੱਟਵਰਕ ਪ੍ਰਤੀਨਿਧਿਤਾਵਾਂ** ਅਧਾਰਿਤ ਹੁੰਦੀਆਂ ਹਨ ਇਸ ਗੱਲ 'ਤੇ ਕਿ ਸਾਡੇ ਸਿਰ ਵਿੱਚ ਪਰਸਪਰ ਜੁੜੇ ਧਾਰਨਾਵਾਂ ਦਾ ਜਾਲ ਹੁੰਦਾ ਹੈ। ਅਸੀਂ ਇਹੇ ਨੈੱਟਵਰਕ ਕੰਪਿਊਟਰ ਵਿੱਚ ਇੱਕ ਗ੍ਰਾਫ ਵਜੋਂ ਦੁਹਰਾਉਣ ਦੀ ਕੋਸ਼ਿਸ਼ ਕਰ ਸਕਦੇ ਹਾਂ - ਜਿਸਨੂੰ ਕਿਹਾ ਜਾਂਦਾ ਹੈ **ਸੈਮਾਂਟਿਕ ਨੈੱਟਵਰਕ**। -1. **ਵਸਤੂ-ਗੁਣ-ਮੁੱਲ ਤ੍ਰਿਪlets** ਜਾਂ **ਗੁਣ-ਮੁੱਲ ਜੋੜੇ**। ਕਿਉਂਕਿ ਗ੍ਰਾਫ ਨੂੰ ਕੰਪਿਊਟਰ ਵਿੱਚ ਨੋਡ ਅਤੇ ਐਜ ਦੀ ਸੂਚੀ ਵਜੋਂ ਦਰਸਾਇਆ ਜਾ ਸਕਦਾ ਹੈ, ਅਸੀਂ ਸੈਮੈਂਟਿਕ ਨੈਟਵਰਕ ਨੂੰ ਤ੍ਰਿਪlets ਦੀ ਸੂਚੀ ਦੁਆਰਾ ਦਰਸਾ ਸਕਦੇ ਹਾਂ, ਜਿਸ ਵਿੱਚ ਵਸਤੂਆਂ, ਗੁਣ ਅਤੇ ਮੁੱਲ ਸ਼ਾਮਲ ਹਨ। ਉਦਾਹਰਣ ਲਈ, ਅਸੀਂ ਪ੍ਰੋਗਰਾਮਿੰਗ ਭਾਸ਼ਾਵਾਂ ਬਾਰੇ ਹੇਠਾਂ ਦਿੱਤੇ ਤ੍ਰਿਪlets ਬਣਾਉਂਦੇ ਹਾਂ: +1. **ਵਸਤੂ-ਵਿਸ਼ੇਸ਼ਤਾ-ਮੁੱਲ ਤ੍ਰਿਪੇਟ** ਜਾਂ **ਵਿਸ਼ੇਸ਼ਤਾ-ਮੁੱਲ ਜੋੜੇ**। ਕਿਉਂਕਿ ਗ੍ਰਾਫ ਕੰਪਿਊਟਰ ਵਿੱਚ ਨੋਡਜ਼ ਅਤੇ ਐਜਜ਼ ਦੀ ਸੂਚੀ ਵਜੋਂ ਪ੍ਰਤੀਨਿਧਿਤ ਕੀਤਾ ਜਾ ਸਕਦਾ ਹੈ, ਅਸੀਂ ਸੈਮਾਂਟਿਕ ਨੈੱਟਵਰਕ ਨੂੰ ਤ੍ਰਿਪੇਟ ਦੀ ਸੂਚੀ ਵਜੋਂ ਪ੍ਰਤੀਨਿਧਿਤ ਕਰ ਸਕਦੇ ਹਾਂ, ਜਿਸ ਵਿੱਚ ਵਸਤੂਆਂ, ਵਿਸ਼ੇਸ਼ਤਾਵਾਂ ਅਤੇ ਮੁੱਲ ਹੁੰਦੇ ਹਨ। ਉਦਾਹਰਨ ਵਜੋਂ, ਅਸੀਂ ਪ੍ਰੋਗ੍ਰਾਮਿੰਗ ਭਾਸ਼ਾਵਾਂ ਬਾਰੇ ਹੇਠ ਲਿਖੇ ਤ੍ਰਿਪੇਟ ਬਣਾਉਂਦੇ ਹਾਂ: -Object | Attribute | Value +ਵਸਤੂ | ਵਿਸ਼ੇਸ਼ਤਾ | ਮੁੱਲ -------|-----------|------ -Python | is | Untyped-Language -Python | invented-by | Guido van Rossum -Python | block-syntax | indentation -Untyped-Language | doesn't have | type definitions +Python | ਹੈ | Untyped-Language +Python | ਦੀ ਖੋਜ | Guido van Rossum ਨੇ ਕੀਤੀ +Python | ਬਲਾਕ-ਸਿੰਟੈਕਸ | ਇੰਡੈਂਟੇਸ਼ਨ +Untyped-Language | ਨਹੀਂ ਹੈ | ਟਾਈਪ ਪਰਿਭਾਸ਼ਾਵਾਂ -> ✅ ਸੋਚੋ ਕਿ ਤ੍ਰਿਪlets ਨੂੰ ਹੋਰ ਕਿਸਮਾਂ ਦੇ ਗਿਆਨ ਨੂੰ ਦਰਸਾਉਣ ਲਈ ਕਿਵੇਂ ਵਰਤਿਆ ਜਾ ਸਕਦਾ ਹੈ। +> ✅ ਸੋਚੋ ਕਿ ਤ੍ਰਿਪੇਟ ਹੋਰ ਕਿਸਮਾਂ ਦੇ ਗਿਆਨ ਨੂੰ ਪ੍ਰਤੀਨਿਧਿਤ ਕਰਨ ਲਈ ਕਿਵੇਂ ਵਰਤੇ ਜਾ ਸਕਦੇ ਹਨ। -2. **ਹਾਇਰਾਰਕੀਕਲ ਪ੍ਰਸਤੁਤੀਆਂ** ਇਸ ਗੱਲ ਨੂੰ ਜ਼ੋਰ ਦਿੰਦੀ ਹਨ ਕਿ ਅਸੀਂ ਅਕਸਰ ਆਪਣੇ ਮਨ ਵਿੱਚ ਵਸਤੂਆਂ ਦੀ ਇੱਕ ਹਾਇਰਾਰਕੀ ਬਣਾਉਂਦੇ ਹਾਂ। ਉਦਾਹਰਣ ਲਈ, ਅਸੀਂ ਜਾਣਦੇ ਹਾਂ ਕਿ ਕੈਨਰੀ ਇੱਕ ਪੰਛੀ ਹੈ, ਅਤੇ ਸਾਰੇ ਪੰਛੀਆਂ ਦੇ ਪੰਖ ਹੁੰਦੇ ਹਨ। ਸਾਡੇ ਕੋਲ ਇਹ ਵੀ ਵਿਚਾਰ ਹੁੰਦਾ ਹੈ ਕਿ ਕੈਨਰੀ ਦਾ ਰੰਗ ਕਿਹੜਾ ਹੁੰਦਾ ਹੈ ਅਤੇ ਉਹ ਕਿੰਨੀ ਤੇਜ਼ੀ ਨਾਲ ਉੱਡਦੇ ਹਨ। +2. **ਹਾਇਰਾਰਕੀਕ ਪ੍ਰਤੀਨਿਧਿਤਾਵਾਂ** ਇਹ ਦਰਸਾਉਂਦੀਆਂ ਹਨ ਕਿ ਅਸੀਂ ਅਕਸਰ ਵਸਤੂਆਂ ਦੀ ਇੱਕ ਹਾਇਰਾਰਕੀ ਸਿਰਜਦੇ ਹਾਂ। ਉਦਾਹਰਨ ਵਜੋਂ, ਅਸੀਂ ਜਾਣਦੇ ਹਾਂ ਕਿ ਕੈਨੇਰੀ ਇੱਕ ਪੰਛੀ ਹੈ, ਅਤੇ ਸਾਰੇ ਪੰਛੀਆਂ ਦੇ ਪੰਛ ਹੁੰਦੇ ਹਨ। ਸਾਡੇ ਕੋਲ ਕੈਨੇਰੀ ਦਾ ਰੰਗ ਅਤੇ ਉਡਾਣ ਦੀ ਗਤੀ ਬਾਰੇ ਵੀ ਧਿਆਨ ਹੁੰਦਾ ਹੈ। - - **ਫਰੇਮ ਪ੍ਰਸਤੁਤੀ** ਇਸ ਧਾਰਨਾ 'ਤੇ ਆਧਾਰਿਤ ਹੈ ਕਿ ਹਰ ਵਸਤੂ ਜਾਂ ਵਸਤੂਆਂ ਦੇ ਵਰਗ ਨੂੰ **ਫਰੇਮ** ਵਜੋਂ ਦਰਸਾਇਆ ਜਾਵੇ ਜੋ **ਸਲਾਟ** ਸ਼ਾਮਲ ਕਰਦੇ ਹਨ। ਸਲਾਟਾਂ ਵਿੱਚ ਸੰਭਾਵਿਤ ਡਿਫਾਲਟ ਮੁੱਲ, ਮੁੱਲ ਪਾਬੰਦੀਆਂ, ਜਾਂ ਸਟੋਰ ਕੀਤੇ ਪ੍ਰਕਿਰਿਆਵਾਂ ਹੁੰਦੀਆਂ ਹਨ ਜੋ ਸਲਾਟ ਦਾ ਮੁੱਲ ਪ੍ਰਾਪਤ ਕਰਨ ਲਈ ਬੁਲਾਈ ਜਾ ਸਕਦੀਆਂ ਹਨ। ਸਾਰੇ ਫਰੇਮ ਇੱਕ ਹਾਇਰਾਰਕੀ ਬਣਾਉਂਦੇ ਹਨ ਜੋ ਵਸਤੂ-ਅਧਾਰਿਤ ਪ੍ਰੋਗਰਾਮਿੰਗ ਭਾਸ਼ਾਵਾਂ ਵਿੱਚ ਵਸਤੂ ਹਾਇਰਾਰਕੀ ਦੇ ਸਮਾਨ ਹੁੰਦੀ ਹੈ। - - **ਦ੍ਰਿਸ਼** ਇੱਕ ਵਿਸ਼ੇਸ਼ ਕਿਸਮ ਦੇ ਫਰੇਮ ਹੁੰਦੇ ਹਨ ਜੋ ਜਟਿਲ ਸਥਿਤੀਆਂ ਨੂੰ ਦਰਸਾਉਂਦੇ ਹਨ ਜੋ ਸਮੇਂ ਦੇ ਨਾਲ ਖੁਲ੍ਹ ਸਕਦੀਆਂ ਹਨ। + - **ਫਰੇਮ ਪ੍ਰਤੀਨਿਧਿਤਾ** ਹਰ ਵਸਤੂ ਜਾਂ ਵਸਤੂਆਂ ਦੀ ਵਰਗ ਨੂੰ ਇੱਕ **ਫਰੇਮ** ਵਜੋਂ ਦਰਸਾਉਂਦੀ ਹੈ ਜਿਸ ਵਿੱਚ **ਸਲਾਟ** ਹੁੰਦੇ ਹਨ। ਸਲਾਟز ਦੇ ਸੰਭਾਵਿਤ ਡਿਫਾਲਟ ਮੁੱਲ, ਮੁੱਲ ਦੀਆਂ ਪਾਬੰਦੀਆਂ ਜਾਂ ਸਟੋਰ ਕੀਤੀਆਂ ਪ੍ਰਕਿਰਿਆਵਾਂ ਹੁੰਦੀਆਂ ਹਨ ਜਿਨ੍ਹਾਂ ਨੂੰ ਸਲਾਟ ਦਾ ਮੁੱਲ ਪ੍ਰਾਪਤ ਕਰਨ ਲਈ ਕਾਲ ਕੀਤਾ ਜਾ ਸਕਦਾ ਹੈ। ਸਾਰੇ ਫਰੇਮ ਇੱਕ ਹਾਇਰਾਰਕੀ ਬਣਾਉਂਦੇ ਹਨ ਜੋ ਵਸਤੂ-ਮੁਖੀ ਪ੍ਰੋਗ੍ਰਾਮਿੰਗ ਭਾਸ਼ਾਵਾਂ ਦੇ ਵਸਤੂ ਹਾਇਰਾਰਕੀ ਵਾਂਗ ਹੈ। + - **ਦ੍ਰਿਸ਼** ਇੱਕ ਖ਼ਾਸ ਕਿਸਮ ਦੇ ਫਰੇਮ ਹੁੰਦੇ ਹਨ ਜੋ ਸਮੇਂ ਵਿੱਚ ਵਿਸ਼ਤ੍ਰਿਤ ਹੋ ਸਕਣ ਵਾਲੀਆਂ ਸੁਕੁਲਤ ਸਥਿਤੀਆਂ ਨੂੰ ਦਰਸਾਉਂਦੇ ਹਨ। **Python** -Slot | Value | Default value | Interval | +ਸਲਾਟ | ਮੁੱਲ | ਡਿਫਾਲਟ ਮੁੱਲ | ਸੀਮਾ | -----|-------|---------------|----------| -Name | Python | | | -Is-A | Untyped-Language | | | -Variable Case | | CamelCase | | -Program Length | | | 5-5000 lines | -Block Syntax | Indent | | | +ਨਾਂ | Python | | | +ਇੱਕ-ਹੈ | Untyped-Language | | | +ਵੈਰੀਏਬਲ ਕੇਸ | | CamelCase | | +ਪ੍ਰੋਗਰਾਮ ਦੀ ਲੰਬਾਈ | | | 5-5000 ਲਾਈਨਾਂ | +ਬਲਾਕ ਸਿੰਟੈਕਸ | ਇੰਡੈਂਟ | | | -3. **ਪ੍ਰਕਿਰਿਆਵਾਦੀ ਪ੍ਰਸਤੁਤੀਆਂ** ਇਸ ਧਾਰਨਾ 'ਤੇ ਆਧਾਰਿਤ ਹਨ ਕਿ ਗਿਆਨ ਨੂੰ ਕਾਰਵਾਈਆਂ ਦੀ ਸੂਚੀ ਦੁਆਰਾ ਦਰਸਾਇਆ ਜਾ ਸਕਦਾ ਹੈ ਜੋ ਕਿਸੇ ਖਾਸ ਸਥਿਤੀ ਵਿੱਚ ਹੋ ਸਕਦੀਆਂ ਹਨ। - - ਉਤਪਾਦਨ ਨਿਯਮ if-then ਬਿਆਨ ਹੁੰਦੇ ਹਨ ਜੋ ਸਾਨੂੰ ਨਤੀਜੇ ਕੱਢਣ ਦੀ ਆਗਿਆ ਦਿੰਦੇ ਹਨ। ਉਦਾਹਰਣ ਲਈ, ਇੱਕ ਡਾਕਟਰ ਦੇ ਕੋਲ ਇੱਕ ਨਿਯਮ ਹੋ ਸਕਦਾ ਹੈ ਜੋ ਕਹਿੰਦਾ ਹੈ ਕਿ **ਜੇਕਰ** ਮਰੀਜ਼ ਨੂੰ ਉੱਚ ਬੁਖਾਰ **ਜਾਂ** ਖੂਨ ਟੈਸਟ ਵਿੱਚ C-reactive ਪ੍ਰੋਟੀਨ ਦੀ ਉੱਚ ਪੱਧਰ **ਤਾਂ** ਉਸਨੂੰ ਸੂਜਨ ਹੈ। ਜਦੋਂ ਅਸੀਂ ਸਥਿਤੀ ਵਿੱਚੋਂ ਇੱਕ ਦਾ ਸਾਹਮਣਾ ਕਰਦੇ ਹਾਂ, ਅਸੀਂ ਸੂਜਨ ਬਾਰੇ ਨਤੀਜਾ ਕੱਢ ਸਕਦੇ ਹਾਂ, ਅਤੇ ਫਿਰ ਇਸਨੂੰ ਅਗਲੇ ਤਰਕ ਵਿੱਚ ਵਰਤ ਸਕਦੇ ਹਾਂ। - - ਐਲਗੋਰਿਦਮ ਨੂੰ ਪ੍ਰਕਿਰਿਆਵਾਦੀ ਪ੍ਰਸਤੁਤੀ ਦਾ ਇੱਕ ਹੋਰ ਰੂਪ ਮੰਨਿਆ ਜਾ ਸਕਦਾ ਹੈ, ਹਾਲਾਂਕਿ ਇਹਨਾਂ ਨੂੰ ਗਿਆਨ-ਅਧਾਰਤ ਪ੍ਰਣਾਲੀਆਂ ਵਿੱਚ ਸਿੱਧੇ ਤੌਰ 'ਤੇ ਕਦੇ ਵੀ ਵਰਤਿਆ ਨਹੀਂ ਜਾਂਦਾ। +3. **ਪ੍ਰਕਿਰਿਆਤਮਕ ਪ੍ਰਤੀਨਿਧਿਤਾਵਾਂ** ਗਿਆਨ ਨੂੰ ਕਾਰਵਾਈਆਂ ਦੀ ਸੂਚੀ ਵਜੋਂ ਦਰਸਾਉਂਦੀਆਂ ਹਨ ਜੋ ਕਿਸੇ ਖਾਸ ਸਥਿਤੀ ਵਿੱਚ ਚਲਾਈਆਂ ਜਾ ਸਕਦੀਆਂ ਹਨ। + - ਪ੍ਰੋਡਕਸ਼ਨ ਨਿਯਮ ਜੇ-ਤਦ ਬਿਆਨਾਂ ਹਨ ਜੋ ਸਾਨੂੰ ਨਤੀਜੇ ਕੱਢਣ ਦੀ ਆਗਿਆ ਦਿੰਦੇ ਹਨ। ਉਦਾਹਰਨ ਵਜੋਂ, ਇੱਕ ਡਾਕਟਰ ਕੋਲ ਇੱਕ ਨਿਯਮ ਹੋ ਸਕਦਾ ਹੈ ਜੋ ਕਹਿੰਦਾ ਹੈ ਕਿ **ਜੇ** ਮਰੀਜ਼ ਨੂੰ ਉੱਚ ਜ਼ੁਕਾਮ ਹੋਵੇ **ਜਾਂ** ਖੂਨ ਵਿੱਚ C-ਰਿਏਕਟਿਵ ਪ੍ਰੋਟੀਨ ਦੀ ਉੱਚ ਵਰਦਾਨ ਹੋਵੇ **ਤਦ** ਉਹਨਾਂ ਨੂੰ ਸੋਜ ਹੋਈ ਹੈ। ਜਦੋਂ ਅਸੀਂ ਹਾਲਤ ਵਿੱਚੋਂ ਇੱਕ ਦਾ ਸਾਹਮਣਾ ਕਰਦੇ ਹਾਂ, ਅਸੀਂ ਸੋਜ ਬਾਰੇ ਨਤੀਜਾ ਕੱਢ ਸਕਦੇ ਹਾਂ ਅਤੇ ਫਿਰ ਅੱਗੇ ਤਰਕ ਕਰਨ ਵਿੱਚ ਇਸਨੂੰ ਵਰਤਦੇ ਹਾਂ। + - ਅਲਗੋਰਿਦਮ ਪ੍ਰਕਿਰਿਆਤਮਕ ਪ੍ਰਤੀਨਿਧਿਤਾ ਦਾ ਇੱਕ ਹੋਰ ਰੂਪ ਹੋ ਸਕਦੇ ਹਨ, ਹਾਲਾਂਕਿ ਉਹ ਗਿਆਨ ਅਧਾਰਿਤ ਪ੍ਰਣਾਲੀਆਂ ਵਿੱਚ ਸਿੱਧਾ ਵਰਤੇ ਨਹੀਂ ਜਾਂਦੇ। -4. **ਤਰਕ** ਅਰਸਤੂ ਦੁਆਰਾ ਮਨੁੱਖੀ ਗਿਆਨ ਨੂੰ ਦਰਸਾਉਣ ਦੇ ਇੱਕ ਤਰੀਕੇ ਵਜੋਂ ਮੂਲ ਰੂਪ ਵਿੱਚ ਪ੍ਰਸਤਾਵਿਤ ਕੀਤਾ ਗਿਆ ਸੀ। - - Predicate Logic ਇੱਕ ਗਣਿਤਕ ਸਿਧਾਂਤ ਵਜੋਂ ਬਹੁਤ ਹੀ ਸਮਰੱਥ ਹੈ, ਇਸ ਲਈ ਇਹ ਗਣਨਯੋਗ ਨਹੀਂ ਹੈ। ਇਸ ਲਈ, ਇਸਦਾ ਕੁਝ ਉਪਸੈੱਟ ਆਮ ਤੌਰ 'ਤੇ ਵਰਤਿਆ ਜਾਂਦਾ ਹੈ, ਜਿਵੇਂ ਕਿ Prolog ਵਿੱਚ ਵਰਤੀਆਂ Horn clauses। - - Descriptive Logic ਉਹ ਤਰਕ ਪ੍ਰਣਾਲੀਆਂ ਦਾ ਪਰਿਵਾਰ ਹੈ ਜੋ ਵਸਤੂਆਂ ਦੀਆਂ ਹਾਇਰਾਰਕੀਜ਼ ਅਤੇ ਵੰਡੇ ਗਿਆਨ ਪ੍ਰਸਤੁਤੀਆਂ ਜਿਵੇਂ *ਸੈਮੈਂਟਿਕ ਵੈੱਬ* ਨੂੰ ਦਰਸਾਉਣ ਅਤੇ ਤਰਕ ਕਰਨ ਲਈ ਵਰਤੀਆਂ ਜਾਂਦੀਆਂ ਹਨ। +4. **ਤਰਕਸ਼ਾਸਤਰ** ਮੂਲ ਰੂਪ ਵਿੱਚ ਅਰਿਸਟੋਟਲ ਵੱਲੋਂ ਸੁਝਾਇਆ ਗਿਆ ਸੀ ਜੋ ਮਨੁੱਖੀ ਵਿਸ਼ਵ ਗਿਆਨ ਨੂੰ ਦਰਸਾਉਣ ਲਈ ਇੱਕ ਤਰੀਕਾ ਸੀ। + - ਪ੍ਰੇਡੀਕੇਟ ਲੋਜਿਕ ਇੱਕ ਗਣਿਤੀ ਸਿਧਾਂਤ ਵਜੋਂ ਬਹੁਤ ਧਨਾਧਿਕ ਹੈ ਜਿਸਨੂੰ ਗਣਨਾ ਯੋਗ ਬਣਾਉਣਾ ਮੁਸ਼ਕਲ ਹੈ, ਇਸ ਲਈ ਇਸ ਦਾ ਕੁਝ ਹਿੱਸਾ ਆਮ ਤੌਰ 'ਤੇ ਵਰਤਿਆ ਜਾਂਦਾ ਹੈ, ਜਿਵੇਂ ਪ੍ਰੋਲੌਗ ਵਿੱਚ ਵਰਤੇ ਜਾਣ ਵਾਲੇ ਹਾਰਨ ਕ੍ਰੌਸ। + - ਵਰਣਨਾਤਮਕ ਤਰਕਸ਼ਾਸਤਰ ਤਰਕਸ਼ਾਸਤਰੀ ਪ੍ਰਣਾਲੀਆਂ ਦੀ ਇੱਕ ਪਰਿਵਾਰ ਹੈ ਜੋ ਵਸਤੂਆਂ ਦੀ ਹਾਇਰਾਰਕੀ ਅਤੇ ਵਿਤਰਿਤ ਗਿਆਨ ਦਾ ਪ੍ਰਤੀਨਿਧਾਨ ਅਤੇ ਤਰਕ ਕਰਨ ਲਈ ਵਰਤੀ ਜਾਂਦੀ ਹੈ, ਜਿਵੇਂ ਕਿ *ਸੈਮਾਂਟਿਕ ਵੈੱਬ*। -## ਮਾਹਰ ਪ੍ਰਣਾਲੀਆਂ +## ਮਾਹਿਰ ਪ੍ਰਣਾਲੀਆਂ -ਸੰਕੇਤਕ AI ਦੀਆਂ ਸ਼ੁਰੂਆਤੀ ਸਫਲਤਾਵਾਂ ਵਿੱਚੋਂ ਇੱਕ **ਮਾਹਰ ਪ੍ਰਣਾਲੀਆਂ** ਸਨ - ਕੰਪਿਊਟਰ ਪ੍ਰਣਾਲੀਆਂ ਜੋ ਕੁਝ ਸੀਮਿਤ ਸਮੱਸਿਆ ਖੇਤਰ ਵਿੱਚ ਮਾਹਰ ਵਜੋਂ ਕੰਮ ਕਰਨ ਲਈ ਡਿਜ਼ਾਈਨ ਕੀਤੀਆਂ ਗਈਆਂ ਸਨ। ਇਹਨਾਂ ਦਾ ਆਧਾਰ **ਗਿਆਨ ਅਧਾਰ** ਸੀ ਜੋ ਇੱਕ ਜਾਂ ਵੱਧ ਮਨੁੱਖੀ ਮਾਹਰਾਂ ਤੋਂ ਕੱਢਿਆ ਗਿਆ ਸੀ, ਅਤੇ ਇਹਨਾਂ ਵਿੱਚ ਇੱਕ **ਤਰਕ ਇੰਜਣ** ਸੀ ਜੋ ਇਸ 'ਤੇ ਕੁਝ ਤਰਕ ਕਰਦਾ ਸੀ। +ਸੰਕੇਤਕ AI ਦੀਆਂ ਪਹਿਲੀਆਂ ਸਫਲਤਾਵਾਂ ਵਿੱਚੋਂ ਇੱਕ ਸਨ **ਮਾਹਿਰ ਪ੍ਰਣਾਲੀਆਂ** - ਐਸੀਆਂ ਕੰਪਿਊਟਰ ਪ੍ਰਣਾਲੀਆਂ ਜਿਹੜੀਆਂ ਕਿਸੇ ਸੀਮਤ ਸਮੱਸਿਆ ਖੇਤਰ ਵਿੱਚ ਮਾਹਿਰ ਵਜੋਂ ਕੰਮ ਕਰਨ ਲਈ ਤਿਆਰ ਕੀਤੀਆਂ ਗਈਆਂ ਸਨ। ਇਹ ਇਕ ਜਾਂ ਵੱਧ ਮਨੁੱਖੀ ਮਾਹਿਰਾਂ ਤੋਂ ਕੱਢੇ ਗਿਆਨ ਦੇ **ਗਿਆਨ ਬੇਸ** ਤੇ ਆਧਾਰਿਤ ਹੁੰਦੀਆਂ ਸਨ, ਅਤੇ ਇਹਨਾਂ ਵਿੱਚ ਇੱਕ **ਤਰਕ ਯੰਤਰ** ਹੁੰਦਾ ਹੈ ਜੋ ਇਸ 'ਤੇ ਕੁਝ ਤਰਕ ਕਰਦਾ ਹੈ। -![ਮਨੁੱਖੀ ਆਰਕੀਟੈਕਚਰ](../../../../translated_images/pa/arch-human.5d4d35f1bba3ab1c.webp) | ![ਗਿਆਨ-ਅਧਾਰਤ ਪ੍ਰਣਾਲੀ](../../../../translated_images/pa/arch-kbs.3ec5c150b09fa8da.webp) +![ਮਨੁੱਖੀ ਸੰਰਚਨਾ](../../../../../../translated_images/pa/arch-human.5d4d35f1bba3ab1c.webp) | ![ਗਿਆਨ-ਅਧਾਰਿਤ ਪ੍ਰਣਾਲੀ](../../../../../../translated_images/pa/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ -ਮਨੁੱਖੀ ਨਰਵ ਪ੍ਰਣਾਲੀ ਦੀ ਸਰਲ ਰਚਨਾ | ਗਿਆਨ-ਅਧਾਰਤ ਪ੍ਰਣਾਲੀ ਦੀ ਆਰਕੀਟੈਕਚਰ +ਮਨੁੱਖੀ ਨਿuroਲ ਪ੍ਰਣਾਲੀ ਦੀ ਸਧਾਰਿਤ ਬਣਤਰ | ਗਿਆਨ-ਅਧਾਰਿਤ ਪ੍ਰਣਾਲੀ ਦੀ ਸੰਰਚਨਾ -ਮਾਹਰ ਪ੍ਰਣਾਲੀਆਂ ਮਨੁੱਖੀ ਤਰਕ ਪ੍ਰਣਾਲੀ ਵਾਂਗ ਬਣਾਈਆਂ ਜਾਂਦੀਆਂ ਹਨ, ਜਿਸ ਵਿੱਚ **ਛੋਟੇ ਸਮੇਂ ਦੀ ਯਾਦ** ਅਤੇ **ਲੰਬੇ ਸਮੇਂ ਦੀ ਯਾਦ** ਸ਼ਾਮਲ ਹੁੰਦੀ ਹੈ। ਇਸੇ ਤਰ੍ਹਾਂ, ਗਿਆਨ-ਅਧਾਰਤ ਪ੍ਰਣਾਲੀਆਂ ਵਿੱਚ ਅਸੀਂ ਹੇਠਾਂ ਦਿੱਤੇ ਘਟਕਾਂ ਨੂੰ ਵੱਖ ਕਰਦੇ ਹਾਂ: +ਮਾਹਿਰ ਪ੍ਰਣਾਲੀਆਂ ਮਨੁੱਖੀ ਤਰਕ ਪ੍ਰਣਾਲੀ ਵਾਂਗ ਬਣਾਈਆਂ ਜਾਂਦੀਆਂ ਹਨ, ਜਿਸ ਵਿੱਚ **ਟਕਸਾਲ-ਸਮੌਖਿਕ ਯਾਦਾਸ਼ਤ** ਅਤੇ **ਲੰਬੀ ਅਤੇ ਸਮੌਖਿਕ ਯਾਦਾਸ਼ਤ** ਹੁੰਦੀ ਹੈ। ਇਸੇ ਤਰ੍ਹਾਂ, ਗਿਆਨ-ਅਧਾਰਿਤ ਪ੍ਰਣਾਲੀਆਂ ਵਿੱਚ ਅਸੀਂ ਹੇਠ ਲਿਖੇ ਹਿੱਸਿਆਂ ਨੂੰ ਵੱਖ ਕਰਦੇ ਹਾਂ: -* **ਸਮੱਸਿਆ ਯਾਦ**: ਮੌਜੂਦਾ ਸਮੱਸਿਆ ਬਾਰੇ ਗਿਆਨ ਨੂੰ ਸ਼ਾਮਲ ਕਰਦੀ ਹੈ, ਜਿਵੇਂ ਕਿ ਮਰੀਜ਼ ਦਾ ਤਾਪਮਾਨ ਜਾਂ ਰਕਤ ਦਬਾਅ, ਉਸਨੂੰ ਸੂਜਨ ਹੈ ਜਾਂ ਨਹੀਂ, ਆਦਿ। ਇਸ ਗਿਆਨ ਨੂੰ **ਸਥਿਰ ਗਿਆਨ** ਵੀ ਕਿਹਾ ਜਾਂਦਾ ਹੈ, ਕਿਉਂਕਿ ਇਹ ਉਹ ਸਨੈਪਸ਼ਾਟ ਸ਼ਾਮਲ ਕਰਦਾ ਹੈ ਜੋ ਅਸੀਂ ਮੌਜੂਦਾ ਸਮੱਸਿਆ ਬਾਰੇ ਜਾਣਦੇ ਹਾਂ - ਜਿਸਨੂੰ *ਸਮੱਸਿਆ ਸਥਿਤੀ* ਕਿਹਾ ਜਾਂਦਾ ਹੈ। -* **ਗਿਆਨ ਅਧਾਰ**: ਸਮੱਸਿਆ ਖੇਤਰ ਬਾਰੇ ਲੰਬੇ ਸਮੇਂ ਦਾ ਗਿਆਨ ਦਰਸਾਉਂਦਾ ਹੈ। ਇਹ ਮਨੁੱਖੀ ਮਾਹਰਾਂ ਤੋਂ ਹੱਥੋਂ-ਹੱਥ ਕੱਢਿਆ ਜਾਂਦਾ ਹੈ, ਅਤੇ ਸਲਾਹ-ਮਸ਼ਵਰੇ ਤੋਂ ਸਲਾਹ-ਮਸ਼ਵਰੇ ਤੱਕ ਨਹੀਂ ਬਦਲਦਾ। ਕਿਉਂਕਿ ਇਹ ਸਾਨੂੰ ਇੱਕ ਸਮੱਸਿਆ ਸਥਿਤੀ ਤੋਂ ਦੂਜੇ ਵਿੱਚ ਜਾਣ ਲਈ ਸਹਾਇਕ ਹੁੰਦਾ ਹੈ, ਇਸਨੂੰ **ਗਤੀਸ਼ੀਲ ਗਿਆਨ** ਵੀ ਕਿਹਾ ਜਾਂਦਾ ਹੈ। -* **ਤਰਕ ਇੰਜਣ**: ਸਮੱਸਿਆ ਸਥਿਤੀ ਸਪੇਸ ਵਿੱਚ ਖੋਜ ਕਰਨ ਦੀ ਪੂਰੀ ਪ੍ਰਕਿਰਿਆ ਨੂੰ ਸੰਗਠਿਤ ਕਰਦਾ ਹੈ, ਜ਼ਰੂਰਤ ਪੈਣ 'ਤੇ ਉਪਭੋਗਤਾ ਤੋਂ ਸਵਾਲ ਪੁੱਛਦਾ ਹੈ। ਇਹ ਹਰ ਸਥਿਤੀ 'ਤੇ ਲਾਗੂ ਕੀਤੇ ਜਾਣ ਵਾਲੇ ਸਹੀ ਨਿਯਮਾਂ ਨੂੰ ਲੱਭਣ ਲਈ ਵੀ ਜ਼ਿੰਮੇਵਾਰ ਹੁੰਦਾ ਹੈ। +* **ਸਮੱਸਿਆ ਯਾਦਾਸ਼ਤ**: ਉਸ ਸਮੱਸਿਆ ਬਾਰੇ ਗਿਆਨ ਸੰਭਾਲਦੀ ਹੈ ਜੋ ਇਸ ਸਮੇਂ ਹੱਲ ਕੀਤੀ ਜਾ ਰਹੀ ਹੈ, ਜਿਵੇਂ ਮਰੀਜ਼ ਦਾ ਤਾਪਮਾਨ ਜਾਂ ਖੂਨ ਦਾ ਦਬਾਅ, ਕੀ ਉਸਨੂੰ ਸੋਜ ਹੈ ਜਾਂ ਨਹੀਂ। ਇਹ ਗਿਆਨ **ਸਟੈਟਿਕ ਗਿਆਨ** ਕਹਿੰਦੇ ਹਨ ਕਿਉਂਕਿ ਇਸ ਵਿੱਚ ਸਮੱਸਿਆ ਦਾ ਸਨੈਪਸ਼ਾਟ ਹੁੰਦਾ ਹੈ - ਜਿਸਨੂੰ ਕਿਹਾ ਜਾਂਦਾ ਹੈ *ਸਮੱਸਿਆ ਸਥਿਤੀ*। +* **ਗਿਆਨ ਬੇਸ**: ਸਮੱਸਿਆ ਖੇਤਰ ਬਾਰੇ ਲੰਬੇ ਸਮੇਂ ਦਾ ਗਿਆਨ ਦਰਸਾਉਂਦਾ ਹੈ। ਇਹ ਮਨੁੱਖੀ ਮਾਹਿਰਾਂ ਤੋਂ ਹੱਥੋਂ ਕੱਢਿਆ ਗਿਆ ਹੁੰਦਾ ਹੈ ਅਤੇ ਪਰਾਮਰਸ਼ ਤੋਂ ਪਰਾਮਰਸ਼ ਤੱਕ ਨਹੀਂ ਬਦਲਦਾ। ਕਿਉਂਕਿ ਇਹ ਸਾਨੂੰ ਇੱਕ ਸਮੱਸਿਆ ਸਥਿਤੀ ਤੋਂ ਦੂਜੇ ਤੱਕ ਜਾਣ ਦੀ ਆਗਿਆ ਦਿੰਦਾ ਹੈ, ਇਸਨੂੰ **ਗਤਿਵਿਧਿਗਿਆਨ** ਵੀ ਕਹਿੰਦੇ ਹਨ। +* **ਤਰਕ ਯੰਤਰ**: ਸਮੱਸਿਆ ਸਥਿਤੀ ਦੀ ਖੋਜ ਕਰਨ ਦੀ ਸਾਰੀ ਪ੍ਰਕਿਰਿਆ ਨੂੰ ਸੰਚਾਲਿਤ ਕਰਦਾ ਹੈ, ਜਦ ਲੋੜ ਹੋਵੇ, ਉਪਭੋਗਤਾ ਨਾਲ ਸਵਾਲ ਕਰਦਾ ਹੈ। ਇਹ ਹਰ ਸਥਿਤੀ 'ਤੇ ਲਾਗੂ ਕਰਨ ਲਈ ਸਹੀ ਨਿਯਮ ਲੱਭਣ ਦਾ ਵੀ ਜ਼ਿੰਮੇਵਾਰ ਹੁੰਦਾ ਹੈ। -ਉਦਾਹਰਣ ਵਜੋਂ, ਆਓ ਹੇਠਾਂ ਦਿੱਤੀ ਮਾਹਰ ਪ੍ਰਣਾਲੀ ਨੂੰ ਵੇਖੀਏ ਜੋ ਕਿਸੇ ਜਾਨਵਰ ਨੂੰ ਇਸਦੇ ਭੌਤਿਕ ਲੱਛਣਾਂ ਦੇ ਆਧਾਰ 'ਤੇ ਨਿਰਧਾਰਤ ਕਰਦੀ ਹੈ: +ਇਸਦਾ ਉਦਾਹਰਨ ਵਜੋਂ, ਆਓ ਕਿਸੇ ਜਾਨਵਰ ਨੂੰ ਉਸ ਦੀ ਭੌਤਿਕ ਵਿਸ਼ੇਸ਼ਤਾਵਾਂ ਦੇ ਆਧਾਰ 'ਤੇ ਸੰਪੂਰਨ ਕਰਨ ਵਾਲੀ ਮਾਹਿਰ ਪ੍ਰਣਾਲੀ ਬਾਰੇ ਸੋਚੀਏ: -![AND-OR ਟ੍ਰੀ](../../../../translated_images/pa/AND-OR-Tree.5592d2c70187f283.webp) +![AND-OR ਟ੍ਰੀ](../../../../../../translated_images/pa/AND-OR-Tree.5592d2c70187f283.webp) -> ਚਿੱਤਰ [Dmitry Soshnikov](http://soshnikov.com) ਦੁਆਰਾ +> ਚਿੱਤਰ [ਦਿਮਿਤਰੀ ਸੋਸ਼ਨਿਕੋਵ](http://soshnikov.com) ਵੱਲੋਂ -ਇਸ ਡਾਇਗ੍ਰਾਮ ਨੂੰ **AND-OR ਟ੍ਰੀ** ਕਿਹਾ ਜਾਂਦਾ ਹੈ, ਅਤੇ ਇਹ ਉਤਪਾਦਨ ਨਿਯਮਾਂ ਦੇ ਇੱਕ ਸੈੱਟ ਦੀ ਗ੍ਰਾਫਿਕਲ ਪ੍ਰਸਤੁਤੀ ਹੈ। ਮਾਹਰ ਤੋਂ ਗਿਆਨ ਕੱਢਣ ਦੀ ਸ਼ੁਰੂਆਤ ਵਿੱਚ ਇੱਕ ਟ੍ਰੀ ਬਣਾਉਣਾ ਲਾਭਦਾਇਕ ਹੁੰਦਾ ਹੈ। ਕੰਪਿਊਟਰ ਵਿੱਚ ਗਿਆਨ ਨੂੰ ਦਰਸਾਉਣ ਲਈ ਨਿਯਮਾਂ ਦੀ ਵਰਤੋਂ ਕਰਨਾ ਜ਼ਿਆਦਾ ਸੁਵਿਧਾਜਨਕ ਹੁੰਦਾ ਹੈ: +ਇਹ ਡਾਇਗ੍ਰਾਮ ਨੂੰ **AND-OR ਟ੍ਰੀ** ਕਿਹਾ ਜਾਂਦਾ ਹੈ, ਅਤੇ ਇਹ ਪ੍ਰੋਡਕਸ਼ਨ ਨਿਯਮਾਂ ਦੇ ਗ੍ਰਾਫਿਕ ਰੂਪ ਵਿੱਚ ਪ੍ਰਤੀਨਿਧਿਤ ਕਰਦਾ ਹੈ। ਨਿਯਮ ਸੂਤਰ ਬਾਹਰ ਕੱਢਣ ਦੀ ਸ਼ੁਰੂਆਤ ਵਿੱਚ ਟ੍ਰੀ ਡਰਾਂ ਕਰਨਾ ਲਾਭਕਾਰੀ ਹੁੰਦਾ ਹੈ। ਕਮਪਿਊਟਰ ਵਿੱਚ ਗਿਆਨ ਪ੍ਰਤੀਨਿਧਿਤ ਕਰਨ ਲਈ ਨਿਯਮ ਵਰਤਣਾ ਜ਼ਿਆਦਾ ਸੁਵਿਧਾਜਨਕ ਹੈ: ``` IF the animal eats meat @@ -121,33 +121,78 @@ OR (animal has sharp teeth THEN the animal is a carnivore ``` -ਤੁਹਾਨੂੰ ਨੋਟਿਸ ਹੋਵੇਗਾ ਕਿ ਨਿਯਮ ਦੇ ਖੱਬੇ ਪਾਸੇ ਦੀ ਸਥਿਤੀ ਅਤੇ ਕਾਰਵਾਈ ਅਸਲ ਵਿੱਚ ਵਸਤੂ-ਗੁਣ-ਮੁੱਲ (OAV) ਤ੍ਰਿਪlets ਹਨ। **ਕੰਮ ਯਾਦ** OAV ਤ੍ਰਿਪlets ਦੇ ਸੈੱਟ ਨੂੰ ਸ਼ਾਮਲ ਕਰਦੀ ਹੈ ਜੋ ਮੌਜੂਦਾ ਸਮੱਸਿਆ ਨੂੰ ਦਰਸਾਉਂਦੀ ਹੈ। **ਨਿਯਮ ਇੰਜਣ** ਉਹ ਨਿਯਮ ਲੱਭਦਾ ਹੈ ਜਿਨ੍ਹਾਂ ਦੀ ਸਥਿਤੀ ਸੰਤੁਸ਼ਟ ਹੈ ਅਤੇ ਉਹਨਾਂ ਨੂੰ ਲਾਗੂ ਕਰਦਾ ਹੈ, ਕੰਮ ਯਾਦ ਵਿੱਚ ਇੱਕ ਹੋਰ ਤ੍ਰਿਪlet ਸ਼ਾਮਲ ਕਰਦਾ ਹੈ। +ਤੁਸੀਂ ਨੋਟ ਕਰ ਸਕਦੇ ਹੋ ਕਿ ਨਿਯਮ ਦੇ ਖੱਬੇ ਪਾਸੇ ਦੀ ਹਰ ਸਥਿਤੀ ਅਤੇ ਕਾਰਵਾਈ ਦਰਅਸਲ ਵਸਤੂ-ਵਿਸ਼ੇਸ਼ਤਾ-ਮੁੱਲ (OAV) ਤ੍ਰਿਪੇਟ ਹਨ। **ਕਾਮ ਕਰਨ ਵਾਲੀ ਯਾਦਾਸ਼ਤ** ਉਹ OAV ਤ੍ਰਿਪੇਟ ਦੇ ਸੈੱਟ ਨੂੰ ਸੰਭਾਲਦੀ ਹੈ ਜੋ ਸਮੱਸਿਆ ਜਿਸ 'ਤੇ ਕੰਮ ਕੀਤਾ ਜਾ ਰਿਹਾ ਹੈ, ਉਸ ਨਾਲ ਸਬੰਧਤ ਹਨ। **ਨਿਯਮ ਇੰਜਣ** ਉਹ ਨਿਯਮ ਲੱਭਦਾ ਹੈ ਜਿਨ੍ਹਾਂ ਦੀ ਸਥਿਤੀ ਪੂਰੀ ਹੁੰਦੀ ਹੈ ਅਤੇ ਉਹਨਾਂ ਨੂੰ ਲਾਗੂ ਕਰਦਾ ਹੈ, ਜੋ ਕਾਮ ਕਰਨ ਵਾਲੀ ਯਾਦਾਸ਼ਤ ਵਿੱਚ ਹੋਰ ਤ੍ਰਿਪੇਟ ਸ਼ਾਮਲ ਕਰਦਾ ਹੈ। -> ✅ ਆਪਣੇ ਮਨਪਸੰਦ ਵਿਸ਼ੇ ' -- XML-ਅਧਾਰਿਤ ਭਾਸ਼ਾਵਾਂ ਦਾ ਇੱਕ ਪਰਿਵਾਰ ਜੋ ਗਿਆਨ ਦੇ ਵਰਣਨ ਲਈ ਵਰਤਿਆ ਜਾਂਦਾ ਹੈ: RDF (Resource Description Framework), RDFS (RDF Schema), OWL (Ontology Web Language)। +> ✅ ਆਪਣੇ ਪਸੰਦੀਂ ਵਿਸ਼ੇ ਤੇ ਆਪਣਾ AND-OR ਟ੍ਰੀ ਬਣਾਓ! -ਸੈਮੈਂਟਿਕ ਵੈੱਬ ਵਿੱਚ ਇੱਕ ਮੁੱਖ ਧਾਰਨਾ **Ontology** ਦੀ ਹੈ। ਇਹ ਕਿਸੇ ਸਮੱਸਿਆ ਖੇਤਰ ਦੀ ਸਪਸ਼ਟ ਵਿਸ਼ੇਸ਼ਤਾ ਨੂੰ ਦਰਸਾਉਂਦੀ ਹੈ ਜੋ ਕਿ ਕਿਸੇ ਰਸਮੀ ਗਿਆਨ ਪ੍ਰਸਤੁਤੀਕਰਨ ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਕੀਤੀ ਜਾਂਦੀ ਹੈ। ਸਭ ਤੋਂ ਸਧਾਰਨ Ontology ਸਿਰਫ਼ ਸਮੱਸਿਆ ਖੇਤਰ ਵਿੱਚ ਵਸਤੂਆਂ ਦੀ ਇੱਕ ਹਾਇਰਾਰਕੀ ਹੋ ਸਕਦੀ ਹੈ, ਪਰ ਜ਼ਿਆਦਾ ਜਟਿਲ Ontologies ਵਿੱਚ ਉਹ ਨਿਯਮ ਸ਼ਾਮਲ ਹੋ ਸਕਦੇ ਹਨ ਜੋ ਤਰਕ ਲਈ ਵਰਤੇ ਜਾ ਸਕਦੇ ਹਨ। +### ਅੱਗੇਵਾਲੀ ਅਤੇ ਪਿੱਛੇਵਾਲੀ ਤਰਕ -ਸੈਮੈਂਟਿਕ ਵੈੱਬ ਵਿੱਚ, ਸਾਰੇ ਪ੍ਰਸਤੁਤੀਕਰਨ ਤਿੰਨ-ਤੱਕੀ (triplets) ਅਧਾਰਿਤ ਹੁੰਦੇ ਹਨ। ਹਰ ਵਸਤੂ ਅਤੇ ਹਰ ਸੰਬੰਧ URI ਦੁਆਰਾ ਵਿਲੱਖਣ ਤਰੀਕੇ ਨਾਲ ਪਛਾਣੇ ਜਾਂਦੇ ਹਨ। ਉਦਾਹਰਣ ਲਈ, ਜੇ ਅਸੀਂ ਇਹ ਦੱਸਣਾ ਚਾਹੁੰਦੇ ਹਾਂ ਕਿ ਇਹ AI Curriculum ਦਿਮਿਤਰੀ ਸੋਸ਼ਨਿਕੋਵ ਦੁਆਰਾ 1 ਜਨਵਰੀ, 2022 ਨੂੰ ਵਿਕਸਿਤ ਕੀਤਾ ਗਿਆ ਸੀ - ਇੱਥੇ ਉਹ ਤਿੰਨ-ਤੱਕੀ ਹਨ ਜੋ ਅਸੀਂ ਵਰਤ ਸਕਦੇ ਹਾਂ: +ਉਪਰ ਦਿੱਤੇ ਗਏ ਪ੍ਰਕਿਰਿਆ ਨੂੰ **ਅੱਗੇਵਾਲੀ ਤਰਕ** ਕਹਿੰਦੇ ਹਨ। ਇਹ ਸਮੱਸਿਆ ਬਾਰੇ ਸਰਵਪ੍ਰਥਮ ਡੇਟਾ ਨਾਲ ਸ਼ੁਰੂ ਹੁੰਦੀ ਹੈ ਜੋ ਕਾਮ ਕਰਨ ਵਾਲੀ ਯਾਦਾਸ਼ਤ ਵਿੱਚ ਹੁੰਦਾ ਹੈ, ਅਤੇ ਫਿਰ ਹੇਠਾਂ ਦਿੱਤਾ ਗਿਆ ਤਰਕ ਲੂਪ ਚਲਾਉਂਦੀ ਹੈ: - +1. ਜੇ ਲਕੜੀ ਦਾ ਲੱਛਣ ਕਾਮ ਕਰਨ ਵਾਲੀ ਯਾਦਾਸ਼ਤ ਵਿੱਚ ਮੌਜੂਦ ਹੈ - ਰੋਕੋ ਅਤੇ ਨਤੀਜਾ ਦਿਓ +2. ਸਾਰਿਆਂ ਨਿਯਮਾਂ ਦੀ ਖੋਜ ਕਰੋ ਜਿਨ੍ਹਾਂ ਦੀ ਸਥਿਤੀ ਇਸ ਵੇਲੇ ਸੱਚੀ ਹੈ - ਨਿਯਮਾਂ ਦਾ **ਟਕਰਾਅ ਸੈੱਟ** ਪ੍ਰਾਪਤ ਕਰੋ। +3. **ਟਕਰਾਅ ਨਿਵਾਰਣ** ਕਰੋ - ਇਕ ਨਿਯਮ ਚੁਣੋ ਜੋ ਇਸ ਕਦਮ 'ਤੇ ਲਾਗੂ ਕੀਤਾ ਜਾਵੇ। ਵੱਖ-ਵੱਖ ਟਕਰਾਅ ਨਿਵਾਰਣ ਦੀਆਂ ਯੋਜਨਾਵਾਂ ਹੋ ਸਕਦੀਆਂ ਹਨ: + - ਗਿਆਨ ਬੇਸ ਵਿੱਚ ਪਹਿਲਾ ਲਾਗੂ ਯੋਗ ਨਿਯਮ ਚੁਣਨਾ + - ਰੈਂਡਮ ਨਿਯਮ ਚੁਣਨਾ + - ਇੱਕ *ਵਧੇਰੇ ਵਿਸ਼ੇਸ਼* ਨਿਯਮ ਚੁਣਨਾ, ਜਿਵੇਂ ਜੋ "ਖੱਬੇ-ਪਾਸੇ" (LHS) ਵਿੱਚ ਸਭ ਤੋਂ ਜ਼ਿਆਦਾ ਸਥਿਤੀਆਂ ਨੂੰ ਪੂਰਾ ਕਰਦਾ ਹੋਵੇ +4. ਚੁਣਿਆ ਨਿਯਮ ਲਾਗੂ ਕਰੋ ਅਤੇ ਸਮੱਸਿਆ ਸਥਿਤੀ ਵਿੱਚ ਨਵਾਂ ਗਿਆਨ ਸ਼ਾਮਲ ਕਰੋ +5. ਕਦਮ 1 ਤੋਂ ਦੁਹਰਾਓ। + +ਹਾਲਾਂਕਿ ਕਿਸੇ ਖਾਸ ਹਾਲਾਤ ਵਿੱਚ ਅਸੀਂ ਸਮੱਸਿਆ ਬਾਰੇ ਖਾਲੀ ਗਿਆਨ ਨਾਲ ਸ਼ੁਰੂ ਕਰਨਾ ਚਾਹੁੰਦੇ ਹਾਂ, ਅਤੇ ਸਵਾਲ ਪੁੱਛਣੀ ਚਾਹੁੰਦੇ ਹਾਂ ਜੋ ਸਾਡੇ ਨੂੰ ਨਤੀਜੇ ਤੇ ਲੈ ਜਾਵੇ। ਉਦਾਹਰਨ ਵਜੋਂ, ਮੈਡੀਕਲ ਨਿਧਾਰਨ ਦੌਰਾਨ, ਅਸੀਂ ਆਮ ਤੌਰ 'ਤੇ ਸਾਰੇ ਮੈਡੀਕਲ ਟੈਸਟ ਪਹਿਲਾਂ ਨਹੀਂ ਕਰਦੇ। ਅਸੀਂ ਉਹ ਟੈਸਟ ਕਰਵਾਏ ਜਾਂਦੇ ਹਾਂ ਜਦੋਂ ਫ਼ੈਸਲਾ ਲੈਣਾ ਲਾਜ਼ਮੀ ਹੁੰਦਾ ਹੈ। + +ਇਸ ਪ੍ਰਕਿਰਿਆ ਨੂੰ **ਪਿਛੇਵਾਲੀ ਤਰਕ** ਨਾਲ ਮਾਡਲ ਕੀਤਾ ਜਾ ਸਕਦਾ ਹੈ। ਇਹ **ਲਕੜੀ** ਦੁਆਰਾ ਸੰਚਾਲਿਤ ਹੁੰਦੀ ਹੈ - ਉਹ ਵਿਸ਼ੇਸ਼ਤਾ ਜਿਹਦੀ ਅਸੀਂ ਲੱਭਣਾ ਚਾਹੁੰਦੇ ਹਾਂ: + +1. ਸਾਰੇ ਨਿਯਮ ਚੁਣੋ ਜੋ ਸਾਨੂੰ ਲਕੜੀ ਦਾ ਮੁੱਲ ਦੇ ਸਕਦੇ ਹਨ (ਯਾਨੀ RHS 'ਤੇ ਲਕੜੀ ਹੈ) - ਟਕਰਾਅ ਸੈੱਟ +1. ਜੇ ਇਸ ਵਿਸ਼ੇਸ਼ਤਾ ਲਈ ਕੋਈ ਨਿਯਮ ਨਹੀਂ ਹੈ, ਜਾਂ ਕਿਸੇ ਨਿਯਮ ਵਿੱਚ ਕਹਿੰਦਾ ਹੈ ਕਿ ਸਾਨੂੰ ਉਪਭੋਗਤਾ ਤੋਂ ਮੁੱਲ ਪੁੱਛਣਾ ਚਾਹੀਦਾ ਹੈ - ਤਾਂ ਪੁੱਛੋ, ਨਹੀਂ ਤਾਂ: +1. ਟਕਰਾਅ ਨਿਵਾਰਣ ਨੀਤੀ ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਇੱਕ ਨਿਯਮ ਚੁਣੋ ਜੋ ਅਸੀਂ *ਅਨੁਮਾਨ* ਵਜੋਂ ਵਰਤਾਂਗੇ - ਅਸੀਂ ਇਸ ਪ੍ਰਮਾਣਿਤ ਕਰਨ ਦੀ ਕੋਸ਼ਿਸ਼ ਕਰਾਂਗੇ +1. ਨਿਯਮ ਦੇ ਖੱਬੇ ਪਾਸੇ (LHS) ਦੀਆਂ ਸਮੂਹ ਵਿਸ਼ੇਸ਼ਤਾਵਾਂ ਲਈ ਵਾਪਸੀ ਪ੍ਰਕਿਰਿਆ ਦੁਹਰਾਓ, ਉਨ੍ਹਾਂ ਨੂੰ ਲਕੜੀਆਂ ਵਜੋਂ ਪਰਖਦੇ ਹੋਏ +1. ਜੇ ਕਿਸੇ ਵੀ ਪੜਾਅ 'ਤੇ ਪ੍ਰਕਿਰਿਆ ਅਸਫਲ ਰਹੇ - ਕਦਮ 3 'ਤੇ ਕੋਈ ਹੋਰ ਨਿਯਮ ਵਰਤੋਂ। + +> ✅ ਕਿਹੜੀਆਂ ਸਥਿਤੀਆਂ ਵਿੱਚ ਅੱਗੇਵਾਲੀ ਤਰਕ ਜ਼ਿਆਦਾ ਉਚਿਤ ਹੈ? ਪਿੱਛੇਵਾਲੀ ਤਰਕ ਬਾਰੇ ਕੀ ਸੋਚਦੇ ਹੋ? + +### ਮਾਹਿਰ ਪ੍ਰਣਾਲੀਆਂ ਦਾ ਲਾਗੂ ਕਰਨਾ + +ਮਾਹਿਰ ਪ੍ਰਣਾਲੀਆਂ ਵੱਖ-ਵੱਖ ਸੰਦਾਂ ਦੀ ਵਰਤੋਂ ਨਾਲ ਲਾਗੂ ਕੀਤੀਆਂ ਜਾ ਸਕਦੀਆਂ ਹਨ: + +* ਕਿਸੇ ਉੱਚ-ਸਤਿਹ ਪ੍ਰੋਗ੍ਰਾਮਿੰਗ ਭਾਸ਼ਾ ਵਿੱਚ ਸਿੱਧਾ ਕਾਰਜਕਾਰੀ ਬਣਾਉਣਾ। ਇਹ ਸਭ ਤੋਂ ਵਧੀਆ ਵਿਚਾਰ ਨਹੀਂ ਹੈ ਕਿਉਂਕਿ ਗਿਆਨ-ਅਧਾਰਿਤ ਪ੍ਰਣਾਲੀ ਦਾ ਮੁੱਖ ਲਾਭ ਇਹ ਹੈ ਕਿ ਗਿਆਨ ਤਰਕ ਤੋਂ ਵੱਖਰਾ ਹੁੰਦਾ ਹੈ, ਅਤੇ ਸੰਭਵ ਹੈ ਕਿ ਸਮੱਸਿਆ ਖੇਤਰ ਦਾ ਮਾਹਿਰ ਟਰਕ ਪ੍ਰਕਿਰਿਆ ਨੂੰ ਸਮਝਣ ਬਗੈਰ ਨਿਯਮ ਲਿਖ ਸਕੇ। +* **ਮਾਹਿਰ ਪ੍ਰਣਾਲੀ ਸ਼ੈੱਲ** ਦੀ ਵਰਤੋਂ ਕਰਨਾ, ਜੋ ਇਕ ਵਿਸ਼ੇਸ਼ ਤੌਰ 'ਤੇ ਗਿਆਨ ਪ੍ਰਤੀਨਿਧਿਤਾ ਭਾਸ਼ਾ ਦੀ ਵਰਤੋਂ ਨਾਲ ਗਿਆਨ ਨਾਲ ਭਰਣ ਲਈ ਬਣਾਈ ਗਈ ਪ੍ਰਣਾਲੀ ਹੈ। + +## ✍️ ਅਭਿਆਸ: ਜਾਨਵਰ ਤਰਕ + +ਅੱਗੇਵਾਲੀ ਅਤੇ ਪਿੱਛੇਵਾਲੀ ਤਰਕ ਵਾਲੀ ਮਾਹਿਰ ਪ੍ਰਣਾਲੀ ਲਾਗੂ ਕਰਨ ਦਾ ਉਦਾਹਰਨ ਵੇਖਣ ਲਈ [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) ਵੇਖੋ। + +> **ਨੋਟ**: ਇਹ ਉਦਾਹਰਨ ਕਾਫੀ ਸਧਾਰਣ ਹੈ, ਅਤੇ ਸਿਰਫ਼ ਮਾਹਿਰ ਪ੍ਰਣਾਲੀ ਦੇ ਚਿਹਰੇ ਦੀ ਜਾਣਕਾਰੀ ਦਿੰਦੀ ਹੈ। ਜਦੋਂ ਤੁਸੀਂ ਐਸੀ ਪ੍ਰਣਾਲੀ ਬਣਾਉਣੀ ਸ਼ੁਰੂ ਕਰਦੇ ਹੋ ਤਾਂ ਤੁਸੀਂ ਇਹ ਹੁਸ਼ਿਆਰ ਵਰਤੋਂ ਦੇਖੋਗੇ ਜਦੋਂ ਨਿਯਮ ਲਗਭਗ 200+ ਹੋ ਜਾਣ। ਕਿਸੇ ਸਮੇਂ, ਨਿਯਮ ਬਹੁਤ ਜਟਿਲ ਹੋ ਜਾਂਦੇ ਹਨ ਅਤੇ ਤੁਸੀਂ ਸੋਚਨ ਲੱਗਦੇ ਹੋ ਕਿ ਸਿਸਟਮ ਕਿਸ ਤਰਾਂ ਫੈਸਲੇ ਕਰ ਰਿਹਾ ਹੈ। ਪਰ, ਗਿਆਨ-ਅਧਾਰਿਤ ਪ੍ਰਣਾਲੀਆਂ ਦੀ ਸਭ ਤੋਂ ਮਹੱਤਵਪੂਰਨ ਖਾਸੀਅਤ ਇਹ ਹੈ ਕਿ ਤੁਸੀਂ ਹਮੇਸ਼ਾ ਕੋਈ ਵੀ ਫੈਸਲਾ ਕਿਵੇਂ ਬਣਿਆ ਯਕੀਨ ਦੇ ਨਾਲ ਸਮਝਾ ਸਕਦੇ ਹੋ। + +## ਔਂਟੋਲੋਜੀਆਂ ਅਤੇ ਸੈਮਾਂਟਿਕ ਵੈੱਬ + +20ਵੀਂ ਸਦੀ ਦੇ ਅਖੀਰ ਵਿੱਚ ਐਸਾ ਉੱਦਮ ਹੋਇਆ ਕਿ ਗਿਆਨ ਪ੍ਰਤੀਨਿਧਿਤਾ ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਇੰਟਰਨੈੱਟ ਸਰੋਤਾਂ ਨੂੰ ਟੈਗ ਕੀਤਾ ਜਾਵੇ ਤਾਂ ਜੋ ਬਹੁਤ ਵਿਸ਼ੇਸ਼ ਪ੍ਰਸ਼ਨਾਂ ਨਾਲ ਮੈਚ ਕਰਨ ਵਾਲੇ ਸਰੋਤ ਲਭੇ ਜਾ ਸਕਣ। ਇਸ ਮੁਹਿੰਮ ਨੂੰ **ਸੈਮਾਂਟਿਕ ਵੈੱਬ** ਕਿਹਾ ਗਿਆ, ਅਤੇ ਇਸ ਵਿੱਚ ਕਈ ਧਾਰਨਾਵਾਂ ਸੀ: + +- ਇੱਕ ਖ਼ਾਸ ਗਿਆਨ ਪ੍ਰਤੀਨਿਧਿਤਾ ਜੋ **[ਵਰਨਨਾਤਮਕ ਤਰਕਸ਼ਾਸਤਰ](https://en.wikipedia.org/wiki/Description_logic)** (DL) 'ਤੇ ਆਧਾਰਿਤ ਸੀ। ਇਹ ਫਰੇਮ ਗਿਆਨ ਪ੍ਰਤੀਨਿਧਿਤਾ ਦੇ ਸਮਾਨ ਹੈ ਕਿਉਂਕਿ ਇਹ ਵਸਤੂਆਂ ਦੀ ਇੱਕ ਹਾਇਰਾਰਕੀ ਬਣਾਂਦਾ ਹੈ ਜਿਨ੍ਹਾਂ ਦੀਆਂ ਸੰਪਤੀਆਂ ਹੁੰਦੀਆਂ ਹਨ, ਪਰ ਇਸਦੇ ਕੋਲ ਅਧਿਕਾਰਿਕ ਤਰਕ ਸ਼ਾਸਤਰੀ ਸੇਮਾਂਟਿਕਸ ਅਤੇ ਤਰਕ ਹੁੰਦਾ ਹੈ। DL ਦਾ ਇੱਕ ਪੂਰਾ ਪਰਿਵਾਰ ਹੈ ਜੋ ਤਰਕ ਦੀ ਵਿਅਕਤੀਗਤਤਾ ਅਤੇ ਅਲਗੋਰਿਦਮਿਕ ਸංකੂਚਨ ਨੂੰ ਬਾਲੈਂਸ ਕਰਦਾ ਹੈ। +- ਵਿਤਰਿਤ ਗਿਆਨ ਪ੍ਰਤੀਨਿਧਿਤਾ, ਜਿੱਥੇ ਸਾਰੇ ਧਾਰਣਾ ਇੱਕ ਵਿਸ਼ਵ ਵਿਆਪੀ URI ਪਹਚਾਣ ਨਾਲ ਦਰਸਾਈਆਂ ਜਾਂਦੀਆਂ ਹਨ, ਜੋ ਇੰਟਰਨੈੱਟ 'ਤੇ ਵਿਸ਼ੇਵਾਂ ਦੀ ਹਾਇਰਾਰਕੀ ਬਣਾਉਣਾ ਸੰਭਵ ਬਨਾਉਂਦਾ ਹੈ। +- ਜਾਣਕਾਰੀ ਵਰਣਨ ਲਈ XML-ਆਧਾਰਿਤ ਭਾਸ਼ਾਵਾਂ ਦਾ ਪਰਿਵਾਰ: RDF (ਰਿਸੋਰਸ ਡਿਸਕ੍ਰਿਪਸ਼ਨ ਫਰੇਮਵਰਕ), RDFS (RDF ਸਕੀਮਾ), OWL (ਓਂਟੋਲੋਜੀ ਵੈੱਬ ਭਾਸ਼ਾ)। + +ਸੈਮੈਂਟਿਕ ਵੈੱਬ ਵਿੱਚ ਇੱਕ ਮੁੱਖ ਧਾਰਨਾ **ਓਂਟੋਲੋਜੀ** ਦੀ ਧਾਰਨਾ ਹੈ। ਇਹ ਕਿਸੇ ਸਮੱਸਿਆ ਖੇਤਰ ਦੀ ਖੁੱਲ੍ਹੀ ਸਪષ્ટ ਵਿਸ਼ੇਸ਼ਤਾ ਲਈ ਹੁੰਦੀ ਹੈ, ਜੋ ਕੁਝ ਰਾਸ਼ਟਰੀ ਗਿਆਨ ਪ੍ਰਸਤੁਤੀ ਬਾਰੇ ਵਰਤੋਂ ਕਰਦੀ ਹੈ। ਸਭ ਤੋਂ ਸਾਦਾ ਓਂਟੋਲੋਜੀ ਸਿਰਫ ਸਮੱਸਿਆ ਖੇਤਰ ਵਿੱਚ ਵਸਤੂਆਂ ਦੀ ਇੱਕ ਹੇਰਾਰਕੀ ਹੋ ਸਕਦੀ ਹੈ, ਪਰ ਜ਼ਿਆਦਾ ਜਟਿਲ ਓਂਟੋਲੋਜੀਆਂ ਵਿੱਚ ਅਜਿਹੇ ਨਿਯਮ ਸ਼ਾਮਲ ਹੁੰਦੇ ਹਨ ਜੋ ਤਰਕ ਲਾਗੂ ਕਰਨ ਲਈ ਵਰਤੇ ਜਾ ਸਕਦੇ ਹਨ। + +ਸੈਮੈਂਟਿਕ ਵੈੱਬ ਵਿੱਚ, ਸਾਰੀਆਂ ਪ੍ਰਸਤੁਤੀਆਂ ਤ੍ਰਿਪਲੈਟਸ ’ਤੇ ਆਧਾਰਿਤ ਹੁੰਦੀਆਂ ਹਨ। ਹਰ ਵਸਤੂ ਅਤੇ ਹਰ ਸੰਬੰਧ ਨੂੰ ਯੂਨੀਕ ਤੌਰ ’ਤੇ URI ਨਾਲ ਪਹਚਾਣਿਆ ਜਾਂਦਾ ਹੈ। ਉਦਾਹਰਨ ਵਜੋਂ, ਜੇ ਅਸੀਂ ਇਹ ਕਹਿਣਾ ਚਾਹੁੰਦੇ ਹਾਂ ਕਿ ਇਹ AI ਕੋਰਸ Дмитрий ਸошников ਵੱਲੋਂ 1 ਜਨਵਰੀ, 2022 ਨੂੰ ਵਿਕਸਤ ਕੀਤਾ गया ਹੈ, ਤਾਂ ਇੱਥੇ ਤ੍ਰਿਪਲੈਟ ਹਨ ਜਿਨ੍ਹਾਂ ਦਾ ਇਸਤੇਮਾਲ ਕਰ ਸਕਦੇ ਹਾਂ: + + ``` -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 ``` -> ✅ ਇੱਥੇ `http://www.example.com/terms/creation-date` ਅਤੇ `http://purl.org/dc/elements/1.1/creator` ਕੁਝ ਪ੍ਰਸਿੱਧ ਅਤੇ ਵਿਸ਼ਵ-ਪ੍ਰਸਿੱਧ URI ਹਨ ਜੋ *creator* ਅਤੇ *creation date* ਦੇ ਅਰਥ ਨੂੰ ਦਰਸਾਉਂਦੇ ਹਨ। +> ✅ ਇੱਥੇ `http://www.example.com/terms/creation-date` ਅਤੇ `http://purl.org/dc/elements/1.1/creator` ਕੁਝ ਪ੍ਰਸਿੱਧ ਅਤੇ ਵਿਸ਼ਵਪ੍ਰਸਿੱਧ URIs ਹਨ ਜੋ *ਸਿਰਜਣਹਾਰ* ਅਤੇ *ਤਾਰੀਖ ਬਣਾਉਣ* ਦੇ ਅਰਥ ਨੂੰ ਵਿਅਕਤ ਕਰਨ ਲਈ ਵਰਤੇ ਜਾਂਦੇ ਹਨ। -ਇੱਕ ਹੋਰ ਜਟਿਲ ਮਾਮਲੇ ਵਿੱਚ, ਜੇ ਅਸੀਂ ਰਚਨਾਕਾਰਾਂ ਦੀ ਸੂਚੀ ਨੂੰ ਪਰਿਭਾਸ਼ਿਤ ਕਰਨਾ ਚਾਹੁੰਦੇ ਹਾਂ, ਤਾਂ ਅਸੀਂ RDF ਵਿੱਚ ਪਰਿਭਾਸ਼ਿਤ ਕੁਝ ਡਾਟਾ ਸਟ੍ਰਕਚਰਾਂ ਦੀ ਵਰਤੋਂ ਕਰ ਸਕਦੇ ਹਾਂ। +ਇੱਕ ਜ਼ਿਆਦਾ ਜਟਿਲ ਹਾਲਤ ਵਿੱਚ, ਜੇ ਅਸੀਂ ਸਿਰਜਣਹਾਰਾਂ ਦੀ ਸੂਚੀ ਪਰਿਭਾਸ਼ਿਤ ਕਰਨੀ ਹੈ, ਤਾਂ ਅਸੀਂ RDF ਵਿੱਚ ਪਰਿਭਾਸ਼ਿਤ ਕੁਝ ਡੇਟਾ ਸਟ੍ਰਕਚਰਾਂ ਨੂੰ ਵਰਤ ਸਕਦੇ ਹਾਂ। - + -> ਉਪਰੋਕਤ ਡਾਇਗ੍ਰਾਮ [ਦਿਮਿਤਰੀ ਸੋਸ਼ਨਿਕੋਵ](http://soshnikov.com) ਦੁਆਰਾ। +> ਉੱਪਰ ਦਿੱਤੀ ਡਾਇਗ੍ਰਾਮਾਂ Dmitry Soshnikov ਵੱਲੋਂ -ਸੈਮੈਂਟਿਕ ਵੈੱਬ ਬਣਾਉਣ ਦੀ ਪ੍ਰਗਤੀ ਨੂੰ ਖੋਜ ਇੰਜਨਾਂ ਅਤੇ ਕੁਦਰਤੀ ਭਾਸ਼ਾ ਪ੍ਰੋਸੈਸਿੰਗ ਤਕਨੀਕਾਂ ਦੀ ਸਫਲਤਾ ਦੁਆਰਾ ਕੁਝ ਹੱਦ ਤੱਕ ਰੋਕਿਆ ਗਿਆ, ਜੋ ਪਾਠ ਤੋਂ ਸੰਰਚਿਤ ਡਾਟਾ ਕੱਢਣ ਦੀ ਆਗਿਆ ਦਿੰਦੇ ਹਨ। ਹਾਲਾਂਕਿ, ਕੁਝ ਖੇਤਰਾਂ ਵਿੱਚ Ontologies ਅਤੇ ਗਿਆਨ ਅਧਾਰਾਂ ਨੂੰ ਬਣਾਉਣ ਲਈ ਅਜੇ ਵੀ ਮਹੱਤਵਪੂਰਨ ਯਤਨ ਕੀਤੇ ਜਾ ਰਹੇ ਹਨ। ਕੁਝ ਪ੍ਰੋਜੈਕਟ ਜਿਨ੍ਹਾਂ ਦਾ ਜ਼ਿਕਰ ਕਰਨਾ ਯੋਗ ਹੈ: +ਸੈਮੈਂਟਿਕ ਵੈੱਬ ਬਣਾਉਣ ਦੀ ਪ੍ਰਗਟਾਊ ਕੁਝ ਹੱਦ ਤੱਕ ਖੋਜ ਇੰਜਨਾਂ ਅਤੇ ਕੁਦਰਤੀ ਭਾਸ਼ਾ ਪ੍ਰਕਿਰਿਆ ਦੀਆਂ ਤਕਨੀਕਾਂ ਦੀ ਸਫਲਤਾ ਕਾਰਨ ਹੌਲੀ ਪਈ, ਜੋ ਲਿਖਤੀ ਪਾਠ ਤੋਂ ਸਠਿਕ ਡਾਟਾ ਕੱਢਣ ਦੀ ਆਗਿਆ ਦਿੰਦੀਆਂ ਹਨ। ਫਿਰ ਵੀ, ਕੁਝ ਖੇਤਰਾਂ ਵਿੱਚ ਓਂਟੋਲੋਜੀਆਂ ਅਤੇ ਗਿਆਨ ਅਧਾਰਾਂ ਨੂੰ ਬਰਕਰਾਰ ਰੱਖਣ ਲਈ ਮਹੱਤਵਪੂਰਣ ਕੌਸ਼ਿਸ਼ਾਂ ਜਾਰੀ ਹਨ। ਕੁਝ ਪ੍ਰੋਜੈਕਟ ਜੇਹੜੇ ਝੱਲਣ ਯੋਗ ਹਨ: -* [WikiData](https://wikidata.org/) ਇੱਕ ਮਸ਼ੀਨ-ਪੜ੍ਹਨਯੋਗ ਗਿਆਨ ਅਧਾਰਾਂ ਦਾ ਸੰਗ੍ਰਹਿ ਹੈ ਜੋ Wikipedia ਨਾਲ ਜੁੜਿਆ ਹੋਇਆ ਹੈ। ਜ਼ਿਆਦਾਤਰ ਡਾਟਾ Wikipedia *InfoBoxes* ਤੋਂ ਖੋਜਿਆ ਜਾਂਦਾ ਹੈ, ਜੋ Wikipedia ਪੰਨਿਆਂ ਦੇ ਅੰਦਰ ਸੰਰਚਿਤ ਸਮੱਗਰੀ ਦੇ ਟੁਕੜੇ ਹਨ। ਤੁਸੀਂ [SPARQL](https://query.wikidata.org/) ਵਿੱਚ WikiData ਨੂੰ ਪੁੱਛਗਿੱਛ ਕਰ ਸਕਦੇ ਹੋ, ਜੋ ਕਿ ਸੈਮੈਂਟਿਕ ਵੈੱਬ ਲਈ ਇੱਕ ਵਿਸ਼ੇਸ਼ ਪੁੱਛਗਿੱਛ ਭਾਸ਼ਾ ਹੈ। ਇੱਥੇ ਇੱਕ ਉਦਾਹਰਣੀ ਪੁੱਛਗਿੱਛ ਹੈ ਜੋ ਮਨੁੱਖਾਂ ਵਿੱਚ ਸਭ ਤੋਂ ਪ੍ਰਸਿੱਧ ਅੱਖਾਂ ਦੇ ਰੰਗ ਦਿਖਾਉਂਦੀ ਹੈ: +* [WikiData](https://wikidata.org/) ਵਿੱਕੀਪੀਡੀਆ ਨਾਲ ਸੰਬੰਧਤ ਮਸ਼ੀਨ ਪੜ੍ਹਨ ਯੋਗ ਗਿਆਨ ਬੇਸਾਂ ਦਾ ਇਕ ਇਕੱਠ ਹੈ। ਬਹੁਤ ਸਾਰਾ ਡਾਟਾ ਵਿਕੀਪੀਡੀਆ *ਇੰਫੋਬੌਕਸ* ਤੋਂ ਖੋਜਿਆ ਜਾਂਦਾ ਹੈ, ਜੋ ਕਿ ਵਿਕੀਪੀਡੀਆ ਪੰਨਿਆਂ ਵਿੱਚ ਸਮਰਚਿਤ ਸਮੱਗਰੀ ਦੇ ਟੁਕੜੇ ਹੁੰਦੇ ਹਨ। ਤੁਸੀਂ [ਪ੍ਰਸ਼ਨ](https://query.wikidata.org/) ਕਰ ਸਕਦੇ ਹੋ SPARQL ਵਿੱਚ, ਜੋ ਕਿ ਸੈਮੈਂਟਿਕ ਵੈੱਬ ਲਈ ਇੱਕ ਖ਼ਾਸ ਪੁੱਛਗਿੱਛ ਭਾਸ਼ਾ ਹੈ। ਇਹ ਇੱਕ ਨਮੂਨਾ ਪੁੱਛਗਿੱਛ ਹੈ ਜੋ ਮਨੁੱਖਾਂ ਵਿੱਚ ਸਭ ਤੋਂ ਪ੍ਰਸਿੱਧ ਅੱਖ ਰੰਗ ਦਿਖਾਉਂਦੀ ਹੈ: ```sparql #defaultView:BubbleChart @@ -161,47 +206,51 @@ WHERE GROUP BY ?eyeColorLabel ``` -* [DBpedia](https://www.dbpedia.org/) WikiData ਦੇ ਸਮਾਨ ਇੱਕ ਹੋਰ ਯਤਨ ਹੈ। +* [DBpedia](https://www.dbpedia.org/) ਵ੍ਹਿਕੀਡੇਟਾ ਨਾਲ ਮਿਲਦਾ ਜੁਲਦਾ ਇੱਕ ਹੋਰ ਯਤਨ ਹੈ। -> ✅ ਜੇ ਤੁਸੀਂ ਆਪਣੀਆਂ Ontologies ਬਣਾਉਣ ਜਾਂ ਮੌਜੂਦਾ Ontologies ਨੂੰ ਖੋਲ੍ਹਣ ਨਾਲ ਪ੍ਰਯੋਗ ਕਰਨਾ ਚਾਹੁੰਦੇ ਹੋ, ਤਾਂ ਇੱਕ ਸ਼ਾਨਦਾਰ ਵਿਜ਼ੂਅਲ Ontology ਸੰਪਾਦਕ [Protégé](https://protege.stanford.edu/) ਹੈ। ਇਸਨੂੰ ਡਾਊਨਲੋਡ ਕਰੋ ਜਾਂ ਆਨਲਾਈਨ ਵਰਤੋ। +> ✅ ਜੇ ਤੁਸੀਂ ਆਪਣੀਆਂ ਖੁਦ ਦੀਆਂ ਓਂਟੋਲੋਜੀਆਂ ਬਣਾਉਣ ਜਾਂ ਮੌਜੂਦਾ ਓਂਟੋਲੋਜੀਆਂ ਖੋਲ੍ਹਣ ਵਿਚ ਪ੍ਰਯੋਗ ਕਰਨਾ ਚਾਹੁੰਦੇ ਹੋ, ਤਾਂ ਇੱਕ ਵਧੀਆ ਵਿਜ਼ੂਅਲ ਓਂਟੋਲੋਜੀ ਸੰਪਾਦਕ ਹੈ ਜੋ [Protégé](https://protege.stanford.edu/) ਕਹਾਂਦੇ ਹਨ। ਇਸ ਨੂੰ ਡਾਊਨਲੋਡ ਕਰੋ, ਜਾਂ ਆਨਲਾਈਨ ਵਰਤੋ। - + -*Web Protégé ਸੰਪਾਦਕ Romanov Family Ontology ਨਾਲ ਖੁੱਲ੍ਹਾ। ਸਕ੍ਰੀਨਸ਼ਾਟ ਦਿਮਿਤਰੀ ਸੋਸ਼ਨਿਕੋਵ ਦੁਆਰਾ।* +*Web Protégé ਸੰਪਾਦਕ ਰੋਮਾਨੋਵ ਪਰਿਵਾਰ ਦੀ ਓਂਟੋਲੋਜੀ ਨਾਲ ਖੁੱਲ੍ਹਾ। ਸਕਰੀਨਸ਼ਾਟ Dmitry Soshnikov ਵੱਲੋਂ* -## ✍️ ਅਭਿਆਸ: ਇੱਕ ਪਰਿਵਾਰ Ontology +## ✍️ ਅਭਿਆਸ: ਪਰਿਵਾਰ ਓਂਟੋਲੋਜੀ -[FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) ਨੂੰ ਦੇਖੋ, ਜੋ ਸੈਮੈਂਟਿਕ ਵੈੱਬ ਤਕਨੀਕਾਂ ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਪਰਿਵਾਰਕ ਸੰਬੰਧਾਂ ਬਾਰੇ ਤਰਕ ਕਰਨ ਦਾ ਉਦਾਹਰਣ ਹੈ। ਅਸੀਂ GEDCOM ਫਾਰਮੈਟ ਵਿੱਚ ਪ੍ਰਸਤੁਤ ਇੱਕ ਪਰਿਵਾਰਕ ਰੁੱਖ ਅਤੇ ਪਰਿਵਾਰਕ ਸੰਬੰਧਾਂ ਦੀ Ontology ਲੈ ਕੇ ਦਿੱਤੇ ਗਏ ਵਿਅਕਤੀਆਂ ਦੇ ਸੈੱਟ ਲਈ ਸਾਰੇ ਪਰਿਵਾਰਕ ਸੰਬੰਧਾਂ ਦਾ ਇੱਕ ਗ੍ਰਾਫ ਬਣਾਵਾਂਗੇ। +ਸੈਮੈਂਟਿਕ ਵੈੱਬ ਤਕਨੀਕਾਂ ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਪਰਿਵਾਰਕ ਸੰਬੰਧਾਂ ਬਾਰੇ ਤਰਕ ਕਰਨ ਲਈ ਉਦਾਹਰਨ ਵੇਖਣ ਲਈ [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) ਵੇਖੋ। ਅਸੀਂ ਇੱਕ ਪਰਿਵਾਰਕ ਵੂੰਝੀ ਨੂੰ ਜੋ ਆਮ GEDCOM ਫਾਰਮੈਟ ਵਿੱਚ ਦਰਸਾਇਆ ਗਿਆ ਹੈ ਅਤੇ ਪਰਿਵਾਰਕ ਸੰਬੰਧਾਂ ਦੀ ਓਂਟੋਲੋਜੀ ਨੂੰ ਲੈ ਕੇ ਦਿੱਤੇ ਗਏ ਵਿਅਕਤੀਆਂ ਲਈ ਸਾਰੇ ਪਰਿਵਾਰਕ ਸੰਬੰਧਾਂ ਦਾ ਗ੍ਰਾਫ ਬਣਾਵਾਂਗੇ। ## Microsoft Concept Graph -ਜ਼ਿਆਦਾਤਰ ਮਾਮਲਿਆਂ ਵਿੱਚ, Ontologies ਹੱਥੋਂ ਧਿਆਨਪੂਰਵਕ ਬਣਾਈਆਂ ਜਾਂਦੀਆਂ ਹਨ। ਹਾਲਾਂਕਿ, ਇਹ ਵੀ ਸੰਭਵ ਹੈ ਕਿ **ਅਸੰਰਚਿਤ ਡਾਟਾ** ਤੋਂ Ontologies ਨੂੰ ਖੋਜਿਆ ਜਾਵੇ, ਉਦਾਹਰਣ ਲਈ, ਕੁਦਰਤੀ ਭਾਸ਼ਾ ਦੇ ਪਾਠਾਂ ਤੋਂ। +ਅਕਸਰ, ਓਂਟੋਲੋਜੀਆਂ ਧਿਆਨ ਨਾਲ ਹੱਥ ਨਾਲ ਬਣਾਈਆਂ ਜਾਂਦੀਆਂ ਹਨ। ਪਰ, ਇਹ ਸੰਭਵ ਹੈ ਕਿ ਅਸੀਂ ਪ੍ਰਭਾਵਹੀਨ ਡਾਟਾ, ਉਦਾਹਰਨ ਵਜੋਂ ਕੁਦਰਤੀ ਭਾਸ਼ਾ ਟੈਕਸਟ ਵਰਗਾ ਡਾਟਾ ਤੋਂ ਓਂਟੋਲੋਜੀਆਂ **ਖੋਜ** ਕਰ ਸਕੀਏ। -ਇੱਕ ਇਸ ਤਰ੍ਹਾਂ ਦਾ ਯਤਨ Microsoft Research ਦੁਆਰਾ ਕੀਤਾ ਗਿਆ ਸੀ, ਜਿਸ ਦਾ ਨਤੀਜਾ [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste) ਰੂਪ ਵਿੱਚ ਨਿਕਲਿਆ। +ਇੱਕ ਅਜਿਹਾ ਯਤਨ Microsoft Research ਵੱਲੋਂ ਕੀਤਾ ਗਿਆ ਸੀ, ਜਿਸ ਦਾ ਨਤੀਜਾ [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste) ਹੈ। -ਇਹ ਵਸਤੂਆਂ ਦਾ ਇੱਕ ਵੱਡਾ ਸੰਗ੍ਰਹਿ ਹੈ ਜੋ `is-a` ਵਿਰਾਸਤ ਸੰਬੰਧ ਦੁਆਰਾ ਇਕੱਠੇ ਕੀਤੇ ਗਏ ਹਨ। ਇਹ "Microsoft ਕੀ ਹੈ?" ਵਰਗੇ ਸਵਾਲਾਂ ਦੇ ਜਵਾਬ ਦੇਣ ਦੀ ਆਗਿਆ ਦਿੰਦਾ ਹੈ - ਜਵਾਬ ਕੁਝ ਇਸ ਤਰ੍ਹਾਂ ਹੋ ਸਕਦਾ ਹੈ "ਇੱਕ ਕੰਪਨੀ 0.87 ਸੰਭਾਵਨਾ ਨਾਲ, ਅਤੇ ਇੱਕ ਬ੍ਰਾਂਡ 0.75 ਸੰਭਾਵਨਾ ਨਾਲ।" +ਇਹ ਇਕ ਵੱਡਾ ਇਹਾੜਾ ਹੈ ਜਿਸ ਵਿੱਚ ਇਕ ਜਿਹੜੇ ਨੌਕਰੀਏ `is-a` ਵਾਰਿਸਤાની ਸੰਬੰਧ ਨਾਂਲ ਜੋੜੇ ਗਏ ਹਨ। ਇਹ ਇੰਝ ਸਵਾਲਾਂ ਦੇ ਜਵਾਬ ਦੇ ਸਕਦਾ ਹੈ "Microsoft ਕੀ ਹੈ?" - ਜਵਾਬ ਕੁਝ ਇਸ ਤਰ੍ਹਾਂ ਹੋ ਸਕਦਾ ਹੈ "ਇੱਕ ਕੰਪਨੀ ਜਿਸ ਦੇ ਹੋਣ ਦੀ ਸੰਭਾਵਨਾ 0.87 ਹੈ, ਅਤੇ ਇੱਕ ਬ੍ਰਾਂਡ ਜਿਸ ਦੀ ਸੰਭਾਵਨਾ 0.75 ਹੈ"। -ਗ੍ਰਾਫ REST API ਦੇ ਰੂਪ ਵਿੱਚ ਉਪਲਬਧ ਹੈ, ਜਾਂ ਇੱਕ ਵੱਡੀ ਡਾਊਨਲੋਡ ਕਰਨਯੋਗ ਟੈਕਸਟ ਫਾਈਲ ਦੇ ਰੂਪ ਵਿੱਚ ਜੋ ਸਾਰੇ ਵਸਤੂ ਜੋੜੇ ਦੀ ਸੂਚੀ ਦਿੰਦੀ ਹੈ। +ਇਹ ਗ੍ਰਾਫ REST API ਵਜੋਂ ਉਪਲਬਧ ਹੈ, ਜਾਂ ਇੱਕ ਵੱਡੀ ਡਾਊਨਲੋਡ ਕਰਨ ਯੋਗ ਟੈਕਸਟ ਫਾਈਲ ਵਜੋਂ, ਜੋ ਸਾਰੇ ਇਕਾਈ ਜੋੜੇ ਦਰਸਾਉਂਦੀ ਹੈ। -## ✍️ ਅਭਿਆਸ: ਇੱਕ Concept Graph +## ✍️ ਅਭਿਆਸ: ਇੱਕ ਸੰਕਲਪ ਗ੍ਰਾਫ -[MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) ਨੋਟਬੁੱਕ ਨੂੰ ਅਜ਼ਮਾਓ ਤਾਂ ਜੋ ਅਸੀਂ ਦੇਖ ਸਕੀਏ ਕਿ Microsoft Concept Graph ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਖ਼ਬਰਾਂ ਦੇ ਲੇਖਾਂ ਨੂੰ ਕਈ ਸ਼੍ਰੇਣੀਆਂ ਵਿੱਚ ਕਿਵੇਂ ਸਮੂਹਬੱਧ ਕੀਤਾ ਜਾ ਸਕਦਾ ਹੈ। +ਵੱਖ-ਵੱਖ ਖਬਰਾਂ ਦੇ ਲੇਖਾਂ ਨੂੰ ਕਈ ਸ਼੍ਰੇਣੀਆਂ ਵਿੱਚ ਵੰਡਣ ਲਈ Microsoft Concept Graph ਨੂੰ ਕਿਵੇਂ ਵਰਤਿਆ ਜਾ ਸਕਦਾ ਹੈ, ਇਹ ਵੇਖਣ ਲਈ [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) ਨੋਟਬੁੱਕ ਕੋਸ਼ਿਸ਼ ਕਰੋ। -## ਨਿਸ਼ਕਰਸ਼ +## ਨਤੀਜਾ -ਅੱਜਕਲ, AI ਨੂੰ ਅਕਸਰ *Machine Learning* ਜਾਂ *Neural Networks* ਦਾ ਪਰਯਾਇ ਮੰਨਿਆ ਜਾਂਦਾ ਹੈ। ਹਾਲਾਂਕਿ, ਇੱਕ ਮਨੁੱਖ ਸਪਸ਼ਟ ਤਰਕ ਵੀ ਪ੍ਰਦਰਸ਼ਿਤ ਕਰਦਾ ਹੈ, ਜੋ ਕਿ ਕੁਝ ਅਜਿਹਾ ਹੈ ਜੋ Neural Networks ਦੁਆਰਾ ਵਰਤਿਆ ਨਹੀਂ ਜਾ ਰਿਹਾ। ਅਸਲ ਦੁਨੀਆ ਦੇ ਪ੍ਰੋਜੈਕਟਾਂ ਵਿੱਚ, ਸਪਸ਼ਟ ਤਰਕ ਅਜੇ ਵੀ ਉਹ ਕੰਮ ਕਰਨ ਲਈ ਵਰਤਿਆ ਜਾਂਦਾ ਹੈ ਜਿਨ੍ਹਾਂ ਨੂੰ ਵਿਆਖਿਆਵਾਂ ਦੀ ਲੋੜ ਹੁੰਦੀ ਹੈ, ਜਾਂ ਸਿਸਟਮ ਦੇ ਵਿਹਾਰ ਨੂੰ ਨਿਯੰਤਰਿਤ ਢੰਗ ਨਾਲ ਬਦਲਣ ਦੀ ਯੋਗਤਾ ਦੀ ਲੋੜ ਹੁੰਦੀ ਹੈ। +ਅੱਜਕੱਲ੍ਹ, AI ਨੂੰ ਅਕਸਰ *ਮਸ਼ੀਨ ਲਰਨਿੰਗ* ਜਾਂ *ਨਿਊਰਲ ਨੈਟਵਰਕਸ* ਦੇ ਸਮਾਨ ਸਮਝਿਆ ਜਾਂਦਾ ਹੈ। ਫਿਰ ਵੀ, ਇੱਕ ਮਨੁੱਖ ਸਪਸ਼ਟ ਤਰਕ ਦਿਖਾਉਂਦਾ ਹੈ, ਜੋ ਕਿ ਅਜੇ ਤੱਕ ਨਿਊਰਲ ਨੈਟਵਰਕਸ ਦਾ ਹਿੱਸਾ ਨਹੀਂ ਹੈ। ਅਸਲੀ ਦੁਨੀਆ ਦੇ ਪ੍ਰੋਜੈਕਟਾਂ ਵਿੱਚ, ਸਪਸ਼ਟ ਤਰਕਵੀਚਾਰ ਅਜੇ ਵੀ ਉਹ ਕੰਮ ਕਰਨ ਲਈ ਵਰਤੀ ਜਾਂਦੀ ਹੈ ਜਿਨ੍ਹਾਂ ਲਈ ਵਿਆਖਿਆ ਦੀ ਲੋੜ ਹੋਵੇ ਜਾਂ ਪ੍ਰਣਾਲੀ ਦੇ ਵਿਹਾਰ ਵਿੱਚ ਨਿਯੰਤਰਿਤ ਤਰੀਕੇ ਨਾਲ ਸੋਧ ਕਰਨ ਯੋਗ ਹੋਣਾ ਜਰੂਰੀ ਹੈ। ## 🚀 ਚੁਣੌਤੀ -ਇਸ ਪਾਠ ਨਾਲ ਜੁੜੇ Family Ontology ਨੋਟਬੁੱਕ ਵਿੱਚ, ਹੋਰ ਪਰਿਵਾਰਕ ਸੰਬੰਧਾਂ ਨਾਲ ਪ੍ਰਯੋਗ ਕਰਨ ਦਾ ਮੌਕਾ ਹੈ। ਪਰਿਵਾਰਕ ਰੁੱਖ ਵਿੱਚ ਲੋਕਾਂ ਦੇ ਵਿਚਕਾਰ ਨਵੇਂ ਸੰਬੰਧਾਂ ਦੀ ਖੋਜ ਕਰਨ ਦੀ ਕੋਸ਼ਿਸ਼ ਕਰੋ। +ਇਸ ਪਾਠ ਨਾਲ ਸੰਬੰਧਿਤ Family Ontology ਨੋਟਬੁੱਕ ਵਿੱਚ ਹੋਰ ਪਰਿਵਾਰਕ ਸੰਬੰਧਾਂ ਨਾਲ ਪ੍ਰਯੋਗ ਕਰਨ ਦਾ ਮੌਕਾ ਹੈ। ਪਰਿਵਾਰਕ ਵੂੰਝੀ ਵਿੱਚ ਲੋਕਾਂ ਦੇ ਨਵੇਂ ਸੰਬੰਧ ਲੱਭਣ ਦੀ ਕੋਸ਼ਿਸ਼ ਕਰੋ। -## [ਪਾਠ-ਪ੍ਰਸ਼ਨੋਤਰੀ](https://ff-quizzes.netlify.app/en/ai/quiz/4) +## [ਪੋਸਟ-ਲੈਕਚਰ ਕਵਿਜ਼](https://ff-quizzes.netlify.app/en/ai/quiz/4) -## ਸਮੀਖਿਆ ਅਤੇ ਸਵੈ ਅਧਿਐਨ +## ਸਮੀਖਿਆ ਅਤੇ ਖੁਦ ਅਧਿਐਨ -ਇੰਟਰਨੈਟ 'ਤੇ ਕੁਝ ਖੋਜ ਕਰੋ ਤਾਂ ਜੋ ਉਹ ਖੇਤਰ ਪਤਾ ਲਗ ਸਕਣ ਜਿੱਥੇ ਮਨੁੱਖਾਂ ਨੇ ਗਿਆਨ ਨੂੰ ਮਾਪਣ ਅਤੇ ਕੋਡਿਫਾਈ ਕਰਨ ਦੀ ਕੋਸ਼ਿਸ਼ ਕੀਤੀ। Bloom's Taxonomy ਨੂੰ ਦੇਖੋ, ਅਤੇ ਇਤਿਹਾਸ ਵਿੱਚ ਵਾਪਸ ਜਾਓ ਤਾਂ ਜੋ ਇਹ ਸਿੱਖਿਆ ਜਾ ਸਕੇ ਕਿ ਮਨੁੱਖਾਂ ਨੇ ਆਪਣੀ ਦੁਨੀਆ ਨੂੰ ਸਮਝਣ ਦੀ ਕੋਸ਼ਿਸ਼ ਕਿਵੇਂ ਕੀਤੀ। Linnaeus ਦੇ ਕੰਮ ਨੂੰ ਦੇਖੋ ਜਿਨ੍ਹਾਂ ਨੇ ਜੀਵਾਂ ਦੀ ਇੱਕ Taxonomy ਬਣਾਈ, ਅਤੇ ਇਹ ਦੇਖੋ ਕਿ Dmitri Mendeleev ਨੇ ਰਸਾਇਣਕ ਤੱਤਾਂ ਨੂੰ ਵਰਣਨ ਅਤੇ ਸਮੂਹਬੱਧ ਕਰਨ ਦਾ ਇੱਕ ਤਰੀਕਾ ਕਿਵੇਂ ਬਣਾਇਆ। ਕੀ ਤੁਸੀਂ ਹੋਰ ਦਿਲਚਸਪ ਉਦਾਹਰਣ ਲੱਭ ਸਕਦੇ ਹੋ? +ਇੰਟਰਨੈੱਟ ’ਤੇ ਖੋਜ ਕਰਕੇ ਉਹ ਖੇਤਰ ਪਤਾ ਲਗਾਓ ਜਿੱਥੇ ਮਨੁੱਖਾਂ ਨੇ ਗਿਆਨ ਨੂੰ ਮਾਪਣ ਅਤੇ ਕੈਟਲੌਗ ਕਰਨ ਦੀ ਕੋਸ਼ਿਸ਼ ਕੀਤੀ ਹੈ। Bloom’s Taxonomy ਨੂੰ ਵੇਖੋ, ਅਤੇ ਇਤਿਹਾਸ ਵਿੱਚ ਵਾਪਸ ਜਾ ਕੇ ਦੇਖੋ ਕਿ ਮਨੁੱਖਾਂ ਨੇ ਆਪਣੇ ਸੰਸਾਰ ਨੂੰ ਸਮਝਣ ਲਈ ਕਿਵੇਂ ਕੋਸ਼ਿਸ਼ ਕੀਤੀ। Linnaeus ਦੇ ਕੰਮ ਨੂੰ ਵੇਖੋ, ਜਿਸ ਵਿੱਚ ਜੀਵਾਂ ਦੀ ਇੱਕ ਟੈਕਸੋਨੋਮੀ ਬਣਾਈ ਗਈ, ਅਤੇ Dmitri Mendeleev ਦੇ ਕੰਮ ਨੂੰ ਅਧਿਐਨ ਕਰੋ ਜਿੱਥੇ ਉਹਨਾਂ ਨੇ ਰਸਾਇਣਿਕ ਤੱਤਾਂ ਨੂੰ ਵਰਣਿਤ ਅਤੇ ਗਰੁੱਪ ਕਰਨਾ ਸਿਖਾਇਆ। ਹੋਰ ਕਿਹੜੇ ਦਿਲਚਸਪ ਉਦਾਹਰਨ ਤੁਸੀਂ ਲੱਭ ਸਕਦੇ ਹੋ? -**ਅਸਾਈਨਮੈਂਟ**: [Ontology ਬਣਾਓ](assignment.md) +**ਐਸਾਈਨਮੈਂਟ**: [ਇੱਕ ਓਂਟੋਲੋਜੀ ਬਣਾਓ](assignment.md) --- + +**ਅਸਵੀਕਾਰੋਪਣ**: +ਇਹ ਦਸਤਾਵੇਜ਼ AI ਅਨੁਵਾਦ ਸੇਵਾ [Co-op Translator](https://github.com/Azure/co-op-translator) ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਅਨੁਵਾਦਿਤ ਕੀਤਾ ਗਿਆ ਹੈ। ਜਦੋਂ ਕਿ ਅਸੀਂ ਸਚਾਈ ਲਈ ਕੋਸ਼ਿਸ਼ ਕਰਦੇ ਹਾਂ, ਕਿਰਪਾ ਕਰਕੇ ਧਿਆਨ ਦਿਓ ਕਿ ਆਟੋਮੈਟਿਕ ਅਨੁਵਾਦਾਂ ਵਿੱਚ ਗਲਤੀਆਂ ਜਾਂ ਅਸੁਚਿਤਾਂ ਹੋ ਸਕਦੀਆਂ ਹਨ। ਮੂਲ ਦਸਤਾਵੇਜ਼ ਆਪਣੀ ਮੂਲ ਭਾਸ਼ਾ ਵਿੱਚ ਹੀ ਪ੍ਰਮਾਣਿਕ ਸਰੋਤ ਮੰਨਿਆ ਜਾਣਾ ਚਾਹੀਦਾ ਹੈ। ਜ਼ਰੂਰੀ ਜਾਣਕਾਰੀ ਲਈ, ਪੇਸ਼ੇਵਰ ਮਨੁੱਖੀ ਅਨੁਵਾਦ ਦੀ ਸਿਫ਼ਾਰਸ਼ ਕੀਤੀ ਜਾਂਦੀ ਹੈ। ਇਹ ਅਨੁਵਾਦ ਵਰਤੋਂ ਨਾਲ ਹੋਣ ਵਾਲੀਆਂ ਕਿਸੇ ਵੀ ਗਲਤਫਹਿਮੀਆਂ ਜਾਂ ਗਲਤ ਵਿਆਖਿਆਵਾਂ ਲਈ ਅਸੀਂ ਜ਼ਿੰਮੇਵਾਰ ਨਹੀਂ ਹਾਂ। + \ No newline at end of file