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b/translated_images/yolo.a2648ec82ee8bb4ea27537677adb482fd4b733ca1705c561b6a24a85102dced5.ta.png deleted file mode 100644 index 061350e2..00000000 Binary files a/translated_images/yolo.a2648ec82ee8bb4ea27537677adb482fd4b733ca1705c561b6a24a85102dced5.ta.png and /dev/null differ diff --git a/translated_images/yolo.a2648ec82ee8bb4ea27537677adb482fd4b733ca1705c561b6a24a85102dced5.uk.png b/translated_images/yolo.a2648ec82ee8bb4ea27537677adb482fd4b733ca1705c561b6a24a85102dced5.uk.png deleted file mode 100644 index 8a6bdd8e..00000000 Binary files a/translated_images/yolo.a2648ec82ee8bb4ea27537677adb482fd4b733ca1705c561b6a24a85102dced5.uk.png and /dev/null differ diff --git a/translations/lt/README.md b/translations/lt/README.md index 7c64a6e7..dd997f27 100644 --- a/translations/lt/README.md +++ b/translations/lt/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](./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) -> **Pageidaujate klonuoti lokaliai?** +> **Norite klonuoti vietoje?** -> Šis saugykla apima daugiau nei 50 kalbų vertimų, kurie žymiai padidina atsisiuntimo dydį. Norėdami klonuoti be vertimų, naudokite sparse checkout: +> Šis saugykla apima daugiau nei 50 kalbų vertimų, dėl ko smarkiai padidėja atsisiuntimo dydis. Norėdami klonuoti be vertimų, naudokite ribotą patikrinimą: > ```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' > ``` -> Tai suteiks jums viską, ko reikia kursui užbaigti, žymiai greičiau atsisiunčiant. +> Tai suteikia viską, ko reikia kursui užbaigti, su daug greitesniu atsisiuntimu. **Jei norite, kad būtų palaikomos papildomos vertimų kalbos, jos yra išvardytos [čia](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** @@ -55,125 +55,125 @@ Atraskite **dirbtinio intelekto** (DI) pasaulį su mūsų 12 savaičių, 24 pamo ## Ko išmoksite -**[Kurso protokolas](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** +**[Kurso protų žemėlapis](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** Šioje mokymo programoje išmoksite: -* Skirtingus dirbtinio intelekto požiūrius, įskaitant „gerą seną“ simbolinį požiūrį su **žinių reprezentacija** ir loginio samprotavimo principais ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). -* **Neuroninius tinklus** ir **gilųjį mokymąsi**, kurie yra šiuolaikinio DI pagrindas. Demonstruosime svarbiausias šių temų sąvokas naudojant kodą dviejose populiariausiose aplinkose - [TensorFlow](http://Tensorflow.org) ir [PyTorch](http://pytorch.org). -* **Neuronines architektūras**, skirtas darbui su vaizdais ir tekstu. Apžvelgsime naujausius modelius, nors gali būti, kad būsime šiek tiek atsiliekantys nuo naujausių technologijų. +* Skirtingų dirbtinio intelekto požiūrių, įskaitant "senąjį gerą" simbolinį požiūrį su **žinių reprezentacija** ir samprotavimu ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). +* **Neuroninius tinklus** ir **gilųjį mokymąsi**, kurie yra šiuolaikinio DI pagrindas. Šias svarbias temas iliustruosime kodu dviem populiariausiais karkasais - [TensorFlow](http://Tensorflow.org) ir [PyTorch](http://pytorch.org). +* **Neuronines architektūras**, skirtas darbui su vaizdais ir tekstu. Aptarsime naujausius modelius, tačiau gali būti šiek tiek trūkumų pažangiausių technologijų srityje. * Mažiau populiarius DI metodus, tokius kaip **genetiniai algoritmai** ir **daugiagentinės sistemos**. -Ko mes neapimsime šioje mokymo programoje: +Ko šioje mokymo programoje neapimsime: -> [Raskite visas papildomas šio kurso medžiagas mūsų Microsoft Learn kolekcijoje](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) +> [Raskite visus papildomus šios programos išteklius mūsų Microsoft Learn kolekcijoje](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) -* Verslo atvejų panaudojimą **DI versle**. Apsvarstykite galimybę pasirinkti [Dirbtinio intelekto įvadą verslo naudotojams](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) Microsoft Learn arba [DI verslo mokyklą](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), kurį sukūrė bendradarbiaujant su [INSEAD](https://www.insead.edu/). -* **Klasikinį mašininį mokymąsi**, kuris gerai aprašytas mūsų [Mašininio mokymosi pradedantiesiems programoje](http://github.com/Microsoft/ML-for-Beginners). -* Praktines DI taikymas, naudojant **[Kognityvines paslaugas](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**. Tam rekomenduojame pradėti su Microsoft Learn moduliais apie [vaizdus](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [natūralios kalbos apdorojimą](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[generatyvinį DI su Azure OpenAI paslauga](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** ir kitus. -* Specifines ML **debesų sistemas**, tokias kaip [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) arba [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). Apsvarstykite galimybę naudoti mokymosi kelius [Kurti ir valdyti mašininio mokymosi sprendimus su Azure Machine Learning](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) ir [Kurti ir valdyti mašininio mokymosi sprendimus su Azure Databricks](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum). -* **Pašnekovų DI** ir **pokalbių robotus**. Tam yra atskiras mokymosi kelias [Sukurti pašnekovų DI sprendimus](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum), o taip pat galite pasiskaityti [šiame tinklaraščio įraše](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) dėl daugiau informacijos. -* **Gilusis matematikas**, slypintis giliajame mokymesi. Tam rekomenduojame knygą [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) autoriai Ian Goodfellow, Yoshua Bengio ir Aaron Courville, kuri taip pat prieinama internete adresu [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/). +* Verslo atvejų, kaip naudoti **DI versle**. Apsvarstykite galimybę imtis [Įvado į DI verslo naudotojams](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) mokymosi takelio Microsoft Learn arba [DI verslo mokyklos](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), sukurtos bendradarbiaujant su [INSEAD](https://www.insead.edu/). +* **Klasikinio mašininio mokymosi**, kuris išsamiai aprašytas mūsų [Mašininio mokymosi pradedantiesiems mokymo programoje](http://github.com/Microsoft/ML-for-Beginners). +* Praktinių DI programų, sukurtų naudojant **[Kognityvines paslaugas](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**. Tam rekomenduojame pradėti nuo Microsoft Learn modulių apie [vaizdų atpažinimą](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [natūralios kalbos apdorojimą](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[Generatyvinį DI su Azure OpenAI paslauga](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** ir kitus dalykus. +* Specifinių ML **debesijos karkasų**, tokių kaip [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) ar [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). Rekomenduojama naudoti mokymosi takelius [Kurti ir valdyti mašininio mokymosi sprendimus su Azure Machine Learning](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) bei [Kurti ir valdyti mašininio mokymosi sprendimus Azure Databricks](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum). +* **Pokalbinį DI** ir **pokalbių robotus**. Yra atskiras mokymosi takelis [Sukurti pokalbinio DI sprendimus](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum), be to, galite pasiskaityti [šį tinklaraščio įrašą](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) plačiau. +* **Giliąją matematiką**, slypinčią už giluminio mokymosi. Tam rekomenduojame [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) Ian Goodfellow, Yoshua Bengio ir Aaron Courville, kuri taip pat prieinama internete adresu [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/). -Norėdami švelnaus įvado į _DI debesų_ temas, galite apsvarstyti [Pradėti dirbtiniu intelektu Azure platformoje](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum) mokymosi kelią. +Norėdami švelnaus įvado į _DI debesyje_ temas, galite apsvarstyti [Pradėti dirbtinį intelektą Azure platformoje](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum) mokymosi takelį. # Turinys -| | Pamokos nuoroda | PyTorch/Keras/TensorFlow | Laboratorija | +| | Pamokos Nuoroda | PyTorch/Keras/TensorFlow | Laboratorija | | :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | -| 0 | [Kurso diegimas](./lessons/0-course-setup/setup.md) | [Sukurti vystymo aplinką](./lessons/0-course-setup/how-to-run.md) | | +| 0 | [Kurso Nustatymai](./lessons/0-course-setup/setup.md) | [Nustatykite savo kūrimo aplinką](./lessons/0-course-setup/how-to-run.md) | | | I | [**Įvadas į DI**](./lessons/1-Intro/README.md) | | | | 01 | [DI įvadas ir istorija](./lessons/1-Intro/README.md) | - | - | | II | **Simbolinis DI** | -| 02 | [Žinių reprezentacija ir ekspertinės sistemos](./lessons/2-Symbolic/README.md) | [Ekspertinės sistemos](./lessons/2-Symbolic/Animals.ipynb) / [Ontologija](./lessons/2-Symbolic/FamilyOntology.ipynb) /[Konceptų grafas](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | +| 02 | [Žinių reprezentacija ir ekspertų sistemos](./lessons/2-Symbolic/README.md) | [Ekspertų sistemos](./lessons/2-Symbolic/Animals.ipynb) / [Ontologija](./lessons/2-Symbolic/FamilyOntology.ipynb) /[Konceptų grafas](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | | III | [**Įvadas į neuroninius tinklus**](./lessons/3-NeuralNetworks/README.md) ||| -| 03 | [Perceptronas](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [Užrašų knygelė](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [Laboratorija](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | -| 04 | [Daugiasluoksnis perceptronas ir savo paties sistema](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [Užrašų knygelė](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [Laboratorija](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | -| 05 | [Įvadas į sistemas (PyTorch/TensorFlow) ir perpratimas](./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) | [Laboratorija](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | -| IV | [**Kompiuterinė rega**](./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)| [Tyrinėkite kompiuterinę regą Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | -| 06 | [Įvadas į kompiuterinę regą. OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [Užrašų knygelė](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [Laboratorija](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | -| 07 | [Konvoliuciniai neuroniniai tinklai](./lessons/4-ComputerVision/07-ConvNets/README.md) & [CNN architektūros](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [Laboratorija](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | -| 08 | [Iš anksto apmokyti tinklai ir perdirbimo mokymasis](./lessons/4-ComputerVision/08-TransferLearning/README.md) ir [Mokymosi triukai](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Laboratorija](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | -| 09 | [Autoenkoderiai ir 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 | [Generatyviniai priešpriešiniai tinklai ir meninio stiliaus perdavimas](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | -| 11 | [Objektų aptikimas](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [Laboratorija](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | +| 03 | [Perceptronas](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [Užrašų knygelė](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [Laboratorinis darbas](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | +| 04 | [Daugiasluoksnis perceptronas ir savo Framework kūrimas](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [Užrašų knygelė](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [Laboratorinis darbas](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | +| 05 | [Įvadas į Framework’us (PyTorch/TensorFlow) ir perpanaudojimą](./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) | [Laboratorinis darbas](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | +| IV | [**Kompiuterinė rega**](./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)| [Tyrinėkite Kompiuterinę regą Microsoft Azure platformoje](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | +| 06 | [Įvadas į Kompiuterinę regą. OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [Užrašų knygelė](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [Laboratorinis darbas](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | +| 07 | [Konvoliuciniai neuroniniai tinklai](./lessons/4-ComputerVision/07-ConvNets/README.md) & [Konvoliucinių tinklų architektūros](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [Laboratorinis darbas](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | +| 08 | [Iš anksto apmokyti tinklai ir perdavimo mokymasis](./lessons/4-ComputerVision/08-TransferLearning/README.md) ir [Mokymosi triukai](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Laboratorinis darbas](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | +| 09 | [Autoencoder’iai ir 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 | [Generatyviniai priešiniai tinklai ir meninio stiliaus perkėlimas](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | +| 11 | [Objektų aptikimas](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [Laboratorinis darbas](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | | 12 | [Semantinė segmentacija. U-Net](./lessons/4-ComputerVision/12-Segmentation/README.md) | [PyTorch](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb) | | -| V | [**Natūrali kalbos apdorojimas**](./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) | [Tyrinėkite natūralios kalbos apdorojimą Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| -| 13 | [Teksto atvaizdavimas. 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 | [Semantiniai žodžių įterpimai. Word2Vec ir 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 | [Kalbos modeliavimas. Mokykite savo įterpimus](./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) | [Laboratorija](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | -| 16 | [Rekurentiniai neuroniniai tinklai](./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 | [Generatyviniai rekurentiniai tinklai](./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) | [Laboratorija](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | -| 18 | [Transformatoriai. 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 | [Pavadintų entitetų atpažinimas](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [Laboratorija](./lessons/5-NLP/19-NER/lab/README.md) | -| 20 | [Dideli kalbos modeliai, užklausų programavimas ir keletas užduočių](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | +| V | [**Natūralios kalbos apdorojimas**](./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) | [Tyrinėkite natūralios kalbos apdorojimą Microsoft Azure platformoje](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| +| 13 | [Teksto reprezentacija. 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 | [Semantinės žodžių įterptinės reprezentacijos. Word2Vec ir 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 | [Kalbos modeliavimo pagrindai. Mokymasis savo įterptinių reprezentacijų](./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) | [Laboratorinis darbas](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | +| 16 | [Pasikartojantys neuroniniai tinklai](./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 | [Generatyviniai pasikartojantys tinklai](./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) | [Laboratorinis darbas](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | +| 18 | [Transformeriai. 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 | [Pavadintųjų asmenų atpažinimas](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [Laboratorinis darbas](./lessons/5-NLP/19-NER/lab/README.md) | +| 20 | [Dideli kalbos modeliai, komandinė programavimo kalba ir mažo duomenų kiekio užduotys](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | | VI | **Kitos DI technikos** || | | 21 | [Genetiniai algoritmai](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [Užrašų knygelė](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | -| 22 | [Gilus mokymasis su stiprinimu](./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) | [Laboratorija](./lessons/6-Other/22-DeepRL/lab/README.md) | -| 23 | [Daugiaveiksmai sistemos](./lessons/6-Other/23-MultiagentSystems/README.md) | | | -| VII | **DI etika** | | | +| 22 | [Giluminis sustiprintinis mokymasis](./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) | [Laboratorinis darbas](./lessons/6-Other/22-DeepRL/lab/README.md) | +| 23 | [Daugiaprograminės agentų sistemos](./lessons/6-Other/23-MultiagentSystems/README.md) | | | +| VII | **DIR Etika** | | | | 24 | [DI etika ir atsakingas DI](./lessons/7-Ethics/README.md) | [Microsoft Learn: Atsakingo DI principai](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | | IX | **Papildoma medžiaga** | | | | 25 | [Daugiapoliai tinklai, CLIP ir VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [Užrašų knygelė](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | ## Kiekviena pamoka apima -* Medžiagą prieš skaitymą -* Vykdomąsias Jupyter užrašų knygeles, kurios dažnai būna specifinės sistemai (**PyTorch** arba **TensorFlow**). Vykdoma užrašų knygelė taip pat turi daug teorinės medžiagos, todėl norint suprasti temą reikia pereiti bent vieną užrašų knygelės versiją (PyTorch arba TensorFlow). -* **Laboratorijas** kai kurioms temoms, kurios suteikia galimybę išbandyti tai, ką išmokote, sprendžiant konkrečią problemą. -* Kai kurios skiltys turi nuorodas į [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) modulius, apimančius susijusias temas. +* Išankstinę medžiagą skaitymui +* Vykdomas Jupyter užrašų knygeles, kurios dažnai būna specifinės konkrečiam framework’ui (**PyTorch** arba **TensorFlow**). Vykdoma užrašų knygelė taip pat turi daug teorinės medžiagos, todėl, norint suprasti temą, reikia pereiti bent vieną šios užrašų knygelės versiją (arba PyTorch, arba TensorFlow). +* Kai kurios temos turi **laboratorinius darbus**, kurie suteikia galimybę išbandyti įgytas žinias taikant jas konkrečioms problemoms. +* Kai kurios skiltys turi nuorodas į [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) modulius, kurie apima susijusias temas. ## Pradžia -### 🎯 Naujas DI srityje? Pradėkite čia! +### 🎯 Naujokas DI srityje? Pradėkite čia! -Jeigu esate visiškai naujas DI srityje ir norite greitai, praktiškai pavyzdžių, peržiūrėkite mūsų [**Pradedančiųjų pavyzdžius**](./examples/README.md)! Juose yra: +Jei visiškai nesate susipažinę su DI ir norite greitų, praktinių pavyzdžių, peržiūrėkite mūsų [**Pradedančiųjų pavyzdžius**](./examples/README.md)! Juose yra: -- 🌟 **Sveikas DI pasauli!** - Jūsų pirmoji DI programa (šablonų atpažinimas) -- 🧠 **Paprastas neuroninis tinklas** - Sukurkite neuroninį tinklą nuo nulio -- 🖼️ **Vaizdų klasifikatorius** - Klasifikuokite vaizdus su išsamiais komentarais -- 💬 **Teksto nuotaika** - Analizuokite teigiamą/neigiamą tekstą +- 🌟 **Sveikas, DI pasauli!** – Jūsų pirma DI programa (paternų atpažinimas) +- 🧠 **Paprastas neuroninis tinklas** – Sukurkite neuroninį tinklą nuo nulio +- 🖼️ **Vaizdų klasifikatorius** – Klasifikuokite vaizdus su išsamiais komentarais +- 💬 **Teksto nuotaika** - Analizuokite teigiamą/ neigiamą tekstą -Šie pavyzdžiai sukurti tam, kad padėtų jums suprasti DI sąvokas prieš pradedant visą mokymo programą. +Šie pavyzdžiai skirti padėti suprasti DI sąvokas prieš pradedant visą mokymo programą. -### 📚 Visos programos nustatymas +### 📚 Visos mokymo programos nustatymas -- Mes sukūrėme [nustatymo pamoką](./lessons/0-course-setup/setup.md), kad padėtume jums paruošti vystymo aplinką. - Mokytojams taip pat sukūrėme [programos nustatymo pamoką](./lessons/0-course-setup/for-teachers.md)! -- Kaip [paleisti kodą VSCode arba Codepace](./lessons/0-course-setup/how-to-run.md) +- Mes sukūrėme [nustatymo pamoką](./lessons/0-course-setup/setup.md), kad padėtume jums sukurti savo kūrimo aplinką. - Mokytojams taip pat sukūrėme [mokymo programos nustatymo pamoką](./lessons/0-course-setup/for-teachers.md)! +- Kaip [paleisti kodą VSCode arba Codespace](./lessons/0-course-setup/how-to-run.md) -Sekite šiuos žingsnius: +Vadovaukitės šiais žingsniais: -Šaknis (fork) saugyklą: Spustelėkite mygtuką "Fork" viršutiniame dešiniajame šio puslapio kampe. +Sukurkite šaką: Spustelėkite mygtuką „Fork“ šio puslapio viršutiniame dešiniajame kampe. -Klonuokite saugyklą: `git clone https://github.com/microsoft/AI-For-Beginners.git` +Klonuokite repozitoriją: `git clone https://github.com/microsoft/AI-For-Beginners.git` -Nepamirškite pažymėti (🌟) šį repo, kad vėliau būtų lengviau rasti. +Neužmirškite pažymėti žvaigždute (🌟) šį repo, kad vėliau jį lengviau rastumėte. -## Susipažinkite su kitais besimokančiais +## Susipažinkite su kitais mokiniais -Prisijunkite prie mūsų [oficialaus AI Discord serverio](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum), kad susitiktumėte ir bendrautumėte su kitais šio kurso dalyviais bei gautumėte pagalbą. +Prisijunkite prie mūsų [oficialaus DI Discord serverio](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum), kad susipažintumėte ir bendrautumėte su kitais šį kursą lankytais mokiniais ir gautumėte paramą. -Jei turite produktų atsiliepimų ar klausimų kurdami, apsilankykite mūsų [Azure AI Foundry kūrėjų forume](https://aka.ms/foundry/forum) +Jei turite atsiliepimų apie produktą ar klausimų statant, apsilankykite mūsų [Azure AI Foundry kūrėjų forume](https://aka.ms/foundry/forum) ## Testai -> **Pastaba apie testus**: Visi testai yra Quiz-app aplanke etc\quiz-app, arba [internete čia](https://ff-quizzes.netlify.app/) Jie yra susieti pamokose, o quiz programėlę galima paleisti vietoje arba diegti į Azure; vadovaukitės nurodymais `quiz-app` aplanke. Jie palaipsniui lokalizuojami. +> **Pastaba apie testus**: Visi testai yra saugomi Quiz-app aplanke etc\quiz-app, arba [internete čia](https://ff-quizzes.netlify.app/). Jie yra susieti su pamokomis, testų programėlę galima paleisti lokaliai arba išdiegti į Azure; sekite nurodymus `quiz-app` aplanke. Jie palaipsniui lokalizuojami. ## Reikalinga pagalba -Turite pasiūlymų ar radote rašybos ar kodo klaidų? Pateikite problemą arba sukurkite pull request. +Turite pasiūlymų arba radote rašybos ar kodo klaidų? Praneškite apie problemą arba pateikite pataisą. -## Specialūs padėkos žodžiai +## Specialūs dėkojimai * **✍️ Pagrindinis autorius:** [Dmitry Soshnikov](http://soshnikov.com), PhD * **🔥 Redaktorius:** [Jen Looper](https://twitter.com/jenlooper), PhD -* **🎨 Sketchnote iliustratorė:** [Tomomi Imura](https://twitter.com/girlie_mac) +* **🎨 Sąsiuvinio iliustratorė:** [Tomomi Imura](https://twitter.com/girlie_mac) * **✅ Testų kūrėja:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) * **🙏 Pagrindiniai bendradarbiai:** [Evgenii Pishchik](https://github.com/Pe4enIks) -## Kitos programos +## Kitos mokymo programos -Mūsų komanda kuria ir kitas programas! Peržiūrėkite: +Mūsų komanda kuria ir kitas mokymo programas! Peržiūrėkite: ### LangChain @@ -190,7 +190,7 @@ Mūsų komanda kuria ir kitas programas! Peržiūrėkite: --- -### Generatyvinio DI serija +### Generatyvinis DI serija [![Generatyvinis DI pradedantiesiems](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) [![Generatyvinis DI (.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) [![Generatyvinis DI (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) @@ -203,25 +203,25 @@ Mūsų komanda kuria ir kitas programas! Peržiūrėkite: [![Duomenų mokslas pradedantiesiems](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) [![DI pradedantiesiems](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) [![Kibernetinis saugumas pradedantiesiems](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung) -[![Interneto kūrimas pradedantiesiems](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst) +[![Tinklalapių kūrimas pradedantiesiems](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst) [![IoT pradedantiesiems](https://img.shields.io/badge/IoT%20for%20Beginners-14B8A6?style=for-the-badge&labelColor=E5E7EB&color=14B8A6)](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst) -[![XR plėtra pradedantiesiems](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst) +[![XR kūrimas pradedantiesiems](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst) --- ### Copilot serija -[![Copilot dirbtinio intelekto porinio programavimo įrankiui](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 DI porinius programavimui](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 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 nuotykiai](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) -## Pagalbos gavimas +## Gaukite pagalbos -Jei užstrigote ar turite klausimų apie DI programėlių kūrimą, prisijunkite prie kitų besimokančių ir patyrusių kūrėjų diskusijų apie MCP. Tai palaikanti bendruomenė, kur klausimai laukiami ir žinios dalijamos laisvai. +Jei užstrigote arba turite klausimų apie DI programėlių kūrimą. Prisijunkite prie kitų mokinių ir patyrusių kūrėjų diskusijų apie MCP. Tai palaikanti bendruomenė, kurioje laukiami klausimai ir laisvai dalinamasi žiniomis. [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -Jei turite produktų atsiliepimų ar klaidų kūrimo metu, apsilankykite: +Jei turite atsiliepimų apie produktą arba radote klaidų statant, apsilankykite: [![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) @@ -229,5 +229,5 @@ Jei turite produktų atsiliepimų ar klaidų kūrimo metu, apsilankykite: **Atsakomybės apribojimas**: -Šis dokumentas buvo išverstas naudojant dirbtinio intelekto vertimo paslaugą [Co-op Translator](https://github.com/Azure/co-op-translator). Nors stengiamės užtikrinti tikslumą, atkreipkite dėmesį, kad automatizuoti vertimai gali turėti klaidų ar netikslumų. Originalus dokumentas gimtąja kalba turėtų būti laikomas autoritetingu šaltiniu. Svarbios informacijos atvejais rekomenduojamas profesionalus žmogaus vertimas. Mes neatsakome už bet kokius nesusipratimus ar neteisingus aiškinimus, kilusius naudojant šį vertimą. +Šis dokumentas buvo išverstas naudojant dirbtinio intelekto vertimo paslaugą [Co-op Translator](https://github.com/Azure/co-op-translator). Nors stengiamės užtikrinti tikslumą, prašome atkreipti dėmesį, kad automatizuoti vertimai gali turėti klaidų ar netikslumų. Pirminis dokumentas originalia kalba laikomas autoritetingu šaltiniu. Svarbiai informacijai rekomenduojama naudotis profesionalaus žmogaus vertimu. Mes neatsakome už jokią painiavą ar neteisingą interpretaciją, kylančią dėl šio vertimo naudojimo. \ No newline at end of file diff --git a/translations/lt/lessons/0-course-setup/how-to-run.md b/translations/lt/lessons/0-course-setup/how-to-run.md index 1e903abb..f43ee328 100644 --- a/translations/lt/lessons/0-course-setup/how-to-run.md +++ b/translations/lt/lessons/0-course-setup/how-to-run.md @@ -1,21 +1,21 @@ # Kaip paleisti kodą -Ši mokymo programa apima daugybę vykdomų pavyzdžių ir laboratorijų, kurias norėsite išbandyti. Norėdami tai padaryti, jums reikės galimybės vykdyti Python kodą Jupyter Notebook aplinkoje, pateiktoje kaip šios mokymo programos dalis. Yra keletas būdų, kaip paleisti kodą: +Ši mokymo programa apima daug vykdomų pavyzdžių ir laboratorinių darbų, kuriuos norėsite paleisti. Tam jums reikalinga galimybė vykdyti Python kodą Jupyter užrašinėse, pateiktose kaip šios mokymo programos dalis. Turite keletą galimybių kodui paleisti: -## Paleidimas vietoje jūsų kompiuteryje +## Paleisti vietoje savo kompiuteryje -Norėdami paleisti kodą vietoje savo kompiuteryje, jums reikės įdiegti tam tikrą Python versiją. Asmeniškai rekomenduoju įdiegti **[miniconda](https://conda.io/en/latest/miniconda.html)** – tai lengvas diegimas, kuris palaiko `conda` paketų valdymą skirtingoms Python **virtualioms aplinkoms**. +Norėdami paleisti kodą vietoje savo kompiuteryje, reikia įdiegti Python. Vienas iš rekomenduojamų sprendimų yra įdiegti **[miniconda](https://conda.io/en/latest/miniconda.html)** – tai gana lengvas diegimas, palaikantis `conda` paketo tvarkyklę skirtingoms Python **virtualioms aplinkoms**. -Įdiegę miniconda, turite nukopijuoti saugyklą ir sukurti virtualią aplinką, kuri bus naudojama šiam kursui: +Įdiegę minicondą, klonuokite saugyklą ir susikurkite virtualią aplinką, kuri bus naudojama šiam kursui: ```bash git clone http://github.com/microsoft/ai-for-beginners @@ -26,15 +26,15 @@ conda activate ai4beg ### Naudojant Visual Studio Code su Python plėtiniu -Tikriausiai geriausias būdas naudoti mokymo programą yra ją atidaryti [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) su [Python plėtiniu](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste). +Ši mokymo programa geriausiai naudojama atidarius ją [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) su [Python plėtiniu](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste). -> **Note**: Kai nukopijuosite ir atidarysite katalogą VS Code, jis automatiškai pasiūlys įdiegti Python plėtinius. Taip pat turėsite įdiegti miniconda, kaip aprašyta aukščiau. +> **Pastaba**: Kai klonuojate ir atidarote katalogą VS Code, jis automatiškai pasiūlys įdiegti Python plėtinius. Taip pat turėsite įdiegti minicondą, kaip aprašyta aukščiau. -> **Note**: Jei VS Code pasiūlys atidaryti saugyklą konteineryje, turite atsisakyti šio pasiūlymo, kad galėtumėte naudoti vietinę Python diegimą. +> **Pastaba**: Jei VS Code pasiūlys jums iš naujo atidaryti saugyklą konteineryje, turėtumėte tai atmesti, kad naudotumėte vietinę Python diegimą. ### Naudojant Jupyter naršyklėje -Taip pat galite naudoti Jupyter aplinką tiesiai iš naršyklės savo kompiuteryje. Tiek klasikinis Jupyter, tiek Jupyter Hub suteikia patogią kūrimo aplinką su automatinio užbaigimo funkcija, kodo paryškinimu ir kt. +Taip pat galite naudoti Jupyter aplinką per naršyklę savo kompiuteryje. Tiek klasikinis Jupyter, tiek JupyterHub suteikia patogią kūrimo aplinką su automatinio pildymo galimybėmis, kodo paryškinimu ir kita. Norėdami paleisti Jupyter vietoje, eikite į kurso katalogą ir vykdykite: @@ -45,34 +45,36 @@ arba ```bash jupyterhub ``` -Tuomet galite naršyti po `.ipynb` failus, juos atidaryti ir pradėti dirbti. +Tada galite pereiti į bet kurį `.ipynb` failą, atidaryti jį ir pradėti darbą. ### Paleidimas konteineryje -Alternatyva Python diegimui būtų kodo paleidimas konteineryje. Kadangi mūsų saugykla turi specialų `.devcontainer` aplanką, kuris nurodo, kaip sukurti konteinerį šiai saugyklai, VS Code pasiūlys jums atidaryti kodą konteineryje. Tam reikės Docker diegimo, ir tai bus sudėtingiau, todėl rekomenduojame šį metodą labiau patyrusiems vartotojams. +Viena iš alternatyvų Python diegimui būtų kodą paleisti konteineryje. Kadangi mūsų saugykloje yra specialus `.devcontainer` aplankas, kuris nurodo, kaip sukurti konteinerį šiai saugyklai, VS Code siūlo galimybę iš naujo atidaryti kodą konteineryje. Tai reikalauja Docker diegimo ir yra sudėtingiau, todėl rekomenduojame tai labiau pažengusiems vartotojams. ## Paleidimas debesyje -Jei nenorite diegti Python vietoje ir turite prieigą prie tam tikrų debesų išteklių, gera alternatyva būtų paleisti kodą debesyje. Yra keletas būdų, kaip tai padaryti: +Jei nenorite diegti Python vietoje ir turite prieigą prie debesijos – gera alternatyva būtų paleisti kodą debesyje. Yra keletas būdų, kaip tai padaryti: -* Naudojant **[GitHub Codespaces](https://github.com/features/codespaces)**, kuris yra virtuali aplinka, sukurta jums GitHub platformoje, pasiekiama per VS Code naršyklės sąsają. Jei turite prieigą prie Codespaces, tiesiog spustelėkite **Code** mygtuką saugykloje, pradėkite Codespace ir greitai pradėkite darbą. -* Naudojant **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**. [Binder](https://mybinder.org) yra nemokami debesų kompiuterijos ištekliai, skirti žmonėms, kaip jūs, išbandyti kodą GitHub platformoje. Pagrindiniame puslapyje yra mygtukas, leidžiantis atidaryti saugyklą Binder – tai greitai nukels jus į Binder svetainę, kuri sukurs pagrindinį konteinerį ir sklandžiai paleis Jupyter interneto sąsają. +* Naudojant **[GitHub Codespaces](https://github.com/features/codespaces)** – tai virtuali aplinka, sukurta jums GitHub platformoje, pasiekiama per VS Code naršyklės sąsają. Jei turite prieigą prie Codespaces, tiesiog spustelėkite **Code** mygtuką saugykloje, paleiskite codespace ir pradėkite darbą vos per kelias minutes. +* Naudojant **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**. [Binder](https://mybinder.org) suteikia nemokamas skaičiavimo išteklių debesyje žmonėms, kaip jūs, norintiems išbandyti kodą iš GitHub. Pagrindiniame puslapyje yra mygtukas, leidžiantis atidaryti saugyklą Binder platformoje – tai greitai nuves į Binder svetainę, kuri sukurs fono konteinerį ir sklandžiai paleis Jupyter interneto sąsają. -> **Note**: Siekiant išvengti piktnaudžiavimo, Binder turi ribotą prieigą prie tam tikrų interneto išteklių. Tai gali trukdyti kai kuriam kodui, kuris atsisiunčia modelius ir/arba duomenų rinkinius iš viešojo interneto. Jums gali tekti rasti alternatyvius sprendimus. Be to, Binder teikiami kompiuteriniai ištekliai yra gana paprasti, todėl mokymas bus lėtas, ypač vėlesnėse sudėtingesnėse pamokose. +> **Pastaba**: Siekiant išvengti piktnaudžiavimo, Binder prieiga prie kai kurių interneto išteklių yra blokuojama. Tai gali trukdyti veikti kai kuriems kodams, kurie parsisiunčia modelius ir/ar duomenų rinkinius iš viešojo interneto. Gali tekti ieškoti sprendimų. Taip pat Binderyje teikiami skaičiavimo ištekliai yra pakankamai baziniai, todėl modelių mokymas bus lėtas, ypač vėlesniuose sudėtingesniuose pamokų etapuose. -## Paleidimas debesyje su GPU +## Paleidimas debesyje su GPU palaikymu -Kai kurios vėlesnės pamokos šioje mokymo programoje labai pasinaudotų GPU palaikymu, nes kitaip mokymas bus labai lėtas. Yra keletas variantų, kuriuos galite pasirinkti, ypač jei turite prieigą prie debesų per [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) arba per savo instituciją: +Kai kurios vėlesnės pamokos šioje mokymo programoje labai naudos GPU palaikymą. Pavyzdžiui, modelių mokymas kitu atveju gali būti labai lėtas. Galite rinktis kelis variantus, ypač jei turite prieigą prie debesijos per [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) arba per savo instituciją: -* Sukurkite [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) ir prisijunkite prie jos per Jupyter. Tuomet galite nukopijuoti saugyklą tiesiai į mašiną ir pradėti mokytis. NC serijos virtualios mašinos turi GPU palaikymą. +* Sukurkite [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) ir prisijunkite prie jos per Jupyter. Tuomet galite klonuoti saugyklą tiesiai į mašiną ir pradėti mokytis. NC serijos VM turi GPU palaikymą. -> **Note**: Kai kurios prenumeratos, įskaitant Azure for Students, iš karto nesuteikia GPU palaikymo. Jums gali tekti pateikti techninės pagalbos užklausą dėl papildomų GPU branduolių. +> **Pastaba**: Kai kurios prenumeratos, įskaitant Azure for Students, pagal nutylėjimą GPU palaikymo neteikia. Gali prireikti pateikti techninės pagalbos užklausą dėl papildomų GPU branduolių. -* Sukurkite [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) ir naudokite ten esančią Notebook funkciją. [Šis vaizdo įrašas](https://azure-for-academics.github.io/quickstart/azureml-papers/) parodo, kaip nukopijuoti saugyklą į Azure ML Notebook ir pradėti ją naudoti. +* Sukurkite [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) ir naudokite ten Notebook funkciją. [Šis vaizdo įrašas](https://azure-for-academics.github.io/quickstart/azureml-papers/) rodo, kaip klonuoti saugyklą į Azure ML užrašinę ir pradėti ją naudoti. -Taip pat galite naudoti Google Colab, kuris siūlo tam tikrą nemokamą GPU palaikymą, ir įkelti Jupyter Notebook failus ten, kad juos vykdytumėte po vieną. +Taip pat galite naudoti Google Colab, kuris suteikia nemokamą GPU palaikymą, ir įkelti Jupyter užrašines po vieną, kad jas vykdytumėte. --- + **Atsakomybės apribojimas**: -Šis dokumentas buvo išverstas naudojant AI vertimo paslaugą [Co-op Translator](https://github.com/Azure/co-op-translator). Nors siekiame tikslumo, prašome atkreipti dėmesį, kad automatiniai vertimai gali turėti klaidų ar netikslumų. Originalus dokumentas jo gimtąja kalba turėtų būti laikomas autoritetingu šaltiniu. Kritinei informacijai rekomenduojama naudoti profesionalų žmogaus vertimą. Mes neprisiimame atsakomybės už nesusipratimus ar klaidingus interpretavimus, atsiradusius dėl šio vertimo naudojimo. \ No newline at end of file +Šis dokumentas išverstas naudojant dirbtinio intelekto vertimo paslaugą [Co-op Translator](https://github.com/Azure/co-op-translator). Nors stengiamės užtikrinti tikslumą, prašome atkreipti dėmesį, kad automatizuoti vertimai gali turėti klaidų arba netikslumų. Originalus dokumentas gimtąja kalba turi būti laikomas autoritetingu šaltiniu. Kritinei informacijai rekomenduojama naudoti profesionalų žmogaus vertimą. Mes neprisiimame atsakomybės už bet kokius nesusipratimus ar neteisingus aiškinimus, kilusius dėl šio vertimo naudojimo. + \ No newline at end of file diff --git a/translations/lt/lessons/2-Symbolic/Animals.ipynb b/translations/lt/lessons/2-Symbolic/Animals.ipynb index 489174ea..302cc7b3 100644 --- a/translations/lt/lessons/2-Symbolic/Animals.ipynb +++ b/translations/lt/lessons/2-Symbolic/Animals.ipynb @@ -6,25 +6,25 @@ "collapsed": true }, "source": [ - "# Įgyvendinant gyvūnų ekspertų sistemą\n", + "# Gyvūnų Eksperto Sistemos Įgyvendinimas\n", "\n", - "Pavyzdys iš [AI pradedantiesiems mokymo programos](http://github.com/microsoft/ai-for-beginners).\n", + "Pavyzdys iš [AI pradedantiesiems mokymo plano](http://github.com/microsoft/ai-for-beginners).\n", "\n", - "Šiame pavyzdyje įgyvendinsime paprastą žinių pagrindu veikiančią sistemą, kuri nustatys gyvūną pagal tam tikras fizines savybes. Sistema gali būti pavaizduota šiuo AND-OR medžiu (tai yra tik dalis viso medžio, lengvai galime pridėti daugiau taisyklių):\n", + "Šiame pavyzdyje įgyvendinsime paprastą žinių pagrindu veikiančią sistemą, skirtą gyvūnui nustatyti pagal kai kurias fizines savybes. Sistema gali būti pavaizduota tokiu AND-OR medžiu (tai yra dalis viso medžio, galime lengvai pridėti dar keletą taisyklių):\n", "\n", - "![](../../../../lessons/2-Symbolic/images/AND-OR-Tree.png)\n" + "![](../../../../../../translated_images/lt/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Mūsų pačių ekspertinių sistemų apvalkalas su atvirkštine išvada\n", + "## Mūsų paties ekspertinių sistemų aplinka su atgaline išvada\n", "\n", - "Pabandykime apibrėžti paprastą kalbą žinių reprezentacijai, pagrįstą produkcinėmis taisyklėmis. Naudosime Python klases kaip raktinius žodžius taisyklėms apibrėžti. Iš esmės bus 3 tipų klasės:\n", - "* `Ask` reiškia klausimą, kurį reikia užduoti vartotojui. Ji turi galimų atsakymų rinkinį.\n", - "* `If` reiškia taisyklę ir yra tik sintaksinis patogumas taisyklės turiniui saugoti.\n", - "* `AND`/`OR` yra klasės, skirtos atstovauti AND/OR šakoms medyje. Jos tiesiog saugo argumentų sąrašą viduje. Siekiant supaprastinti kodą, visa funkcionalumas apibrėžtas pagrindinėje klasėje `Content`.\n" + "Pabandykime apibrėžti paprastą žinių reprezentavimo kalbą, pagrįstą gamybos taisyklėmis. Mes naudosime Python klases kaip raktinius žodžius taisyklėms apibrėžti. Iš esmės bus 3 klasių tipai:\n", + "* `Ask` reiškia klausimą, kurį reikia užduoti vartotojui. Jame yra galimų atsakymų rinkinys.\n", + "* `If` reiškia taisyklę ir yra tik sintaksinė cukraus forma taisyklės turiniui saugoti.\n", + "* `AND`/`OR` yra klasės, reprezentuojančios AND/OR medžio šakas. Jos tiesiog saugo argumentų sąrašą viduje. Kad supaprastintume kodą, visa funkcionalumas apibrėžtas tėvinėje klasėje `Content`.\n" ] }, { @@ -66,7 +66,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Mūsų sistemoje darbinė atmintis turėtų faktų sąrašą kaip **atributų-reikšmių poras**. Žinių bazė gali būti apibrėžta kaip didelis žodynas, kuris susieja veiksmus (naujus faktus, kuriuos reikia įterpti į darbinę atmintį) su sąlygomis, išreikštomis AND-OR išraiškomis. Taip pat kai kurių faktų galima `paklausti`.\n" + "Mūsų sistemoje darbo atmintyje būtų laikomas **faktų** sąrašas kaip **atributų-reikšmių poros**. Žinių bazę galima apibrėžti kaip vieną didelį žodyną, kuris susieja veiksmus (naujus faktus, kurie turėtų būti įterpti į darbo atmintį) su sąlygomis, išreikštomis AND-OR išraiškomis. Taip pat kai kurių faktų galima `Paklausti`.\n" ] }, { @@ -99,13 +99,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Norint atlikti atvirkštinę išvadą, mes apibrėšime klasę `Knowledgebase`. Ji apims:\n", - "* Darbinę `atmintį` - žodyną, kuris susieja atributus su reikšmėmis\n", - "* Žinių bazės `taisykles` pagal aukščiau apibrėžtą formatą\n", + "Norėdami atlikti atvirkštinę išvadą, apibrėšime klasę `Knowledgebase`. Ji turės:\n", + "* Darbinę `memory` – žodyną, susiejantį atributus su reikšmėmis\n", + "* Žinių bazės `rules` pagal aukščiau apibrėžtą formatą\n", "\n", "Du pagrindiniai metodai yra:\n", - "* `get`, skirtas gauti atributo reikšmę, atliekant išvadą, jei reikia. Pavyzdžiui, `get('color')` gautų spalvos reikšmę (jei reikia, paklaustų ir išsaugotų reikšmę vėlesniam naudojimui darbinėje atmintyje). Jei klausiame `get('color:blue')`, tai paklaus spalvos ir grąžins `y`/`n` reikšmę, priklausomai nuo spalvos.\n", - "* `eval` atlieka faktinę išvadą, t. y. pereina per AND/OR medį, įvertina sub-tikslus ir pan.\n" + "* `get`, skirtas gauti atributo reikšmę, jei reikia, atliekant išvadą. Pavyzdžiui, `get('color')` gautų spalvos reikšmę (jei reikia, paklaus ir išsaugos reikšmę vėlesniam naudojimui darbo atmintyje). Jei paklausime `get('color:blue')`, paklaus spalvos, o tada grąžins reikšmę `y`/`n` priklausomai nuo spalvos.\n", + "* `eval` atlieka pačią išvadą, t. y. keliauja AND/OR medžiu, įvertina po tikslus ir pan.\n" ] }, { @@ -172,7 +172,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Dabar apibrėžkime mūsų gyvūnų žinių bazę ir atlikime konsultaciją. Atkreipkite dėmesį, kad šis procesas užduos jums klausimus. Galite atsakyti įvesdami `y`/`n` atsakymus į taip-ne klausimus arba nurodydami skaičių (0..N) klausimams su ilgesniais daugybinio pasirinkimo atsakymais.\n" + "Dabar apibrėžkime mūsų gyvūnų žinių bazę ir atlikime konsultaciją. Atkreipkite dėmesį, kad šis kvietimas užduos jums klausimus. Galite atsakyti rašydami `y`/`n` už klausimus su atsakymais taip/ne, arba nurodydami numerį (0..N) už klausimus su ilgesniais daugybinio pasirinkimo atsakymais.\n" ] }, { @@ -229,11 +229,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Naudojant PyKnow priekinei išvadai\n", + "## Naudojimasis Experta pirmyn inferencijai\n", "\n", - "Kitame pavyzdyje bandysime įgyvendinti priekinę išvadą naudodami vieną iš žinių atvaizdavimo bibliotekų, [PyKnow](https://github.com/buguroo/pyknow/). **PyKnow** yra biblioteka, skirta kurti priekinių išvadų sistemas Python kalboje, kuri yra sukurta taip, kad būtų panaši į klasikinę seną sistemą [CLIPS](http://www.clipsrules.net/index.html).\n", + "Kitame pavyzdyje bandysime įgyvendinti pirmyn inferenciją naudodami vieną iš žinių reprezentacijos bibliotekų, [Experta](https://github.com/nilp0inter/experta). **Experta** yra biblioteka pirmyn inferencijos sistemoms kurti Python kalba, sukurta taip, kad būtų panaši į klasikines senas sistemas, tokias kaip [CLIPS](http://www.clipsrules.net/index.html).\n", "\n", - "Mes taip pat galėjome patys įgyvendinti priekinį grandinavimą be didelių problemų, tačiau naivūs įgyvendinimai paprastai nėra labai efektyvūs. Dėl efektyvesnio taisyklių atitikimo naudojamas specialus algoritmas [Rete](https://en.wikipedia.org/wiki/Rete_algorithm).\n" + "Mes taip pat galėjome įgyvendinti pirmyn grandininimą patys be didelių problemų, tačiau naivios įgyvendinimo metodikos dažniausiai nėra labai efektyvios. Dėl efektyvesnio taisyklių suderinimo naudojamas specialus algoritmas [Rete](https://en.wikipedia.org/wiki/Rete_algorithm).\n" ] }, { @@ -247,32 +247,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "Collecting git+https://github.com/buguroo/pyknow/\n", - " Cloning https://github.com/buguroo/pyknow/ to /tmp/pip-req-build-3cqeulyl\n", - " Running command git clone --filter=blob:none --quiet https://github.com/buguroo/pyknow/ /tmp/pip-req-build-3cqeulyl\n", - " Resolved https://github.com/buguroo/pyknow/ to commit 48818336f2e9a126f1964f2d8dc22d37ff800fe8\n", - " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hCollecting frozendict==1.2\n", - " Using cached frozendict-1.2.tar.gz (2.6 kB)\n", - " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hCollecting schema==0.6.7\n", - " Using cached schema-0.6.7-py2.py3-none-any.whl (14 kB)\n", - "Building wheels for collected packages: pyknow, frozendict\n", - " Building wheel for pyknow (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for pyknow: filename=pyknow-1.7.0-py3-none-any.whl size=34228 sha256=b7de5b09292c4007667c72f69b98d5a1b5f7324ff15f9dd8e077c3d5f7aade42\n", - " Stored in directory: /tmp/pip-ephem-wheel-cache-k7jpave7/wheels/81/1a/d3/f6c15dbe1955598a37755215f2a10449e7418500d7bd4b9508\n", - " Building wheel for frozendict (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for frozendict: filename=frozendict-1.2-py3-none-any.whl size=3148 sha256=2863d55c240d2409cddf05ccfe600591f8478681549fc97555c47c90dc6bb160\n", - " Stored in directory: /home/rg/.cache/pip/wheels/49/ac/f8/cb8120244e710bdb479c86198b03c7b08c3c2d3d2bf448fd6e\n", - "Successfully built pyknow frozendict\n", - "Installing collected packages: schema, frozendict, pyknow\n", - "Successfully installed frozendict-1.2 pyknow-1.7.0 schema-0.6.7\n" + "Collecting git+https://github.com/nilp0inter/experta\n", + " Cloning https://github.com/nilp0inter/experta to /tmp/pip-req-build-7qurtwk3\n", + " Running command git clone --filter=blob:none --quiet https://github.com/nilp0inter/experta /tmp/pip-req-build-7qurtwk3\n", + " Resolved https://github.com/nilp0inter/experta to commit c6d5834b123861f5ae09e7d07027dc98bec58741\n", + " Installing build dependencies ... \u001b[?25ldone\n", + "\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n", + "\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25hRequirement already satisfied: frozendict~=2.4.6 in /opt/conda/envs/ai4beg/lib/python3.12/site-packages (from experta==1.9.5.dev1) (2.4.7)\n", + "Collecting schema~=0.6.7 (from experta==1.9.5.dev1)\n", + " Downloading schema-0.6.8-py2.py3-none-any.whl.metadata (14 kB)\n", + "Downloading schema-0.6.8-py2.py3-none-any.whl (14 kB)\n", + "Building wheels for collected packages: experta\n", + " Building wheel for experta (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25h Created wheel for experta: filename=experta-1.9.5.dev1-py3-none-any.whl size=34804 sha256=888c459512a5e713f4b674caa9a0f96cfdf07ec0d6eb56cc318ce0653d218014\n", + " Stored in directory: /tmp/pip-ephem-wheel-cache-1eeii9zy/wheels/3d/e8/bb/22d7956359603fa8dd679aa09f5b8efb3f29991c3986fdc787\n", + "Successfully built experta\n", + "Installing collected packages: schema, experta\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2/2\u001b[0m [experta]\n", + "\u001b[1A\u001b[2KSuccessfully installed experta-1.9.5.dev1 schema-0.6.8\n" ] } ], "source": [ "import sys\n", - "!{sys.executable} -m pip install git+https://github.com/buguroo/pyknow/" + "!{sys.executable} -m pip install git+https://github.com/nilp0inter/experta" ] }, { @@ -283,15 +282,15 @@ }, "outputs": [], "source": [ - "from pyknow import *\n", - "#import pyknow" + "from experta import *\n", + "#import experta" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Mes apibrėšime savo sistemą kaip klasę, kuri paveldi `KnowledgeEngine`. Kiekviena taisyklė apibrėžiama atskira funkcija su `@Rule` anotacija, kuri nurodo, kada taisyklė turėtų būti vykdoma. Taisyklės viduje galime pridėti naujus faktus naudodami `declare` funkciją, o pridėjus tuos faktus, į priekį išvedimo variklis iškvies daugiau taisyklių.\n" + "Apibrėšime mūsų sistemą kaip klasę, paveldinčią iš `KnowledgeEngine`. Kiekviena taisyklė apibrėžiama atskira funkcija su `@Rule` anotacija, kuri nurodo, kada taisyklė turėtų būti aktyvuojama. Taisyklės viduje galime pridėti naujų faktų naudodami `declare` funkciją, o pridėjus tuos faktus, į priekį veikiantis spėliojimo variklis iškvies daugiau taisyklių.\n" ] }, { @@ -378,7 +377,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Kai apibrėžiame žinių bazę, užpildome darbinę atmintį pradiniais faktais, o tada iškviečiame `run()` metodą, kad atliktume išvadų darymą. Rezultate galite matyti, kad nauji išvesti faktai pridedami į darbinę atmintį, įskaitant galutinį faktą apie gyvūną (jei teisingai nustatėme visus pradinius faktus).\n" + "Kai apibrėžiame žinių bazę, užpildome darbo atmintį pradiniais faktais, o tada iškviečiame `run()` metodą, kad atliktume išvedimą. Kaip rezultatą galite matyti, kad nauji išvesti faktai pridedami prie darbo atminties, įskaitant galutinį faktą apie gyvūną (jei teisingai nustatėme visus pradinius faktus).\n" ] }, { @@ -440,7 +439,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n---\n\n**Atsakomybės apribojimas**: \nŠis dokumentas buvo išverstas naudojant AI vertimo paslaugą [Co-op Translator](https://github.com/Azure/co-op-translator). Nors siekiame tikslumo, prašome atkreipti dėmesį, kad automatiniai vertimai gali turėti klaidų ar netikslumų. Originalus dokumentas jo gimtąja kalba turėtų būti laikomas autoritetingu šaltiniu. Kritinei informacijai rekomenduojama profesionali žmogaus vertimo paslauga. Mes neprisiimame atsakomybės už nesusipratimus ar klaidingus interpretavimus, atsiradusius dėl šio vertimo naudojimo.\n" + "---\n\n\n**Atsakomybės apribojimas**:\nŠis dokumentas buvo išverstas naudojant dirbtinio intelekto vertimo paslaugą [Co-op Translator](https://github.com/Azure/co-op-translator). Nors siekiame tikslumo, prašome atkreipti dėmesį, kad automatiniai vertimai gali turėti klaidų ar netikslumų. Originalus dokumentas jo gimtąja kalba turėtų būti laikomas autoritetingu šaltiniu. Kritinei informacijai rekomenduojama naudoti profesionalų žmogaus vertimą. Mes neatsakome už jokias nesusipratimus ar neteisingus interpretavimus, kilusius iš šio vertimo naudojimo.\n\n" ] } ], @@ -467,8 +466,8 @@ "version": "3.11.2" }, "coopTranslator": { - "original_hash": "ab2bd97b0453415b89a469284609a8ce", - "translation_date": "2025-08-31T13:16:05+00:00", + "original_hash": "8ef43db4b9182239fd150a76bd494fdb", + "translation_date": "2026-01-16T06:41:58+00:00", "source_file": "lessons/2-Symbolic/Animals.ipynb", "language_code": "lt" } diff --git a/translations/lt/lessons/2-Symbolic/README.md b/translations/lt/lessons/2-Symbolic/README.md index 04815f5c..67eef4b2 100644 --- a/translations/lt/lessons/2-Symbolic/README.md +++ b/translations/lt/lessons/2-Symbolic/README.md @@ -1,116 +1,116 @@ -# Žinių Atvaizdavimas ir Ekspertinės Sistemos +# Žinių Atstovavimas ir Ekspertų Sistemos -![Santrauka apie simbolinį AI](../../../../translated_images/lt/ai-symbolic.715a30cb610411a6.webp) +![Santrauka apie simbolinį DI turinį](../../../../../../translated_images/lt/ai-symbolic.715a30cb610411a6.webp) -> Sketchnote sukūrė [Tomomi Imura](https://twitter.com/girlie_mac) +> Sketchnote autorius [Tomomi Imura](https://twitter.com/girlie_mac) -Dirbtinio intelekto siekis grindžiamas žinių paieška, siekiant suprasti pasaulį panašiai kaip žmonės. Bet kaip tai pasiekti? +Dirbtinio intelekto paieška yra paremta žinių ieškojimu, siekiant suprasti pasaulį panašiai kaip žmonės. Bet kaip tai galima padaryti? -## [Prieš paskaitą vykstantis testas](https://ff-quizzes.netlify.app/en/ai/quiz/3) +## [Prieš paskaitą - testas](https://ff-quizzes.netlify.app/en/ai/quiz/3) -Ankstyvaisiais AI laikais populiarus buvo "iš viršaus į apačią" požiūris į intelektualių sistemų kūrimą (aptartas ankstesnėje pamokoje). Idėja buvo išgauti žinias iš žmonių į mašininį formatą ir naudoti jas problemoms automatiškai spręsti. Šis požiūris buvo grindžiamas dviem pagrindinėmis idėjomis: +Ankstyvaisiais DI laikais buvo populiarus aukštesnio lygio požiūris į išmaniųjų sistemų kūrimą (aprašytas ankstesnėje pamokoje). Idėja buvo išgauti žinias iš žmonių į mašiniam supratimui tinkamą formą ir tuomet jas naudoti problemoms automatiškai spręsti. Šis požiūris buvo pagrįstas dviem didelėmis idėjomis: -* Žinių Atvaizdavimas -* Samprotavimas +* Žinių atstovavimas +* Išvedimas -## Žinių Atvaizdavimas +## Žinių atstovavimas -Vienas svarbiausių simbolinio AI konceptų yra **žinios**. Svarbu atskirti žinias nuo *informacijos* ar *duomenų*. Pavyzdžiui, galima sakyti, kad knygos turi žinių, nes jas studijuodami galime tapti ekspertais. Tačiau tai, ką knygos iš tikrųjų turi, vadinama *duomenimis*, o skaitydami knygas ir integruodami šiuos duomenis į savo pasaulio modelį, mes paverčiame juos žiniomis. +Vienas iš svarbių simbolinio DI terminų yra **žinios**. Svarbu atskirti žinias nuo *informacijos* ar *duomenų*. Pavyzdžiui, galima sakyti, kad knygos turi žinių, nes galima studijuoti knygas ir tapti ekspertu. Tačiau tai, ką knygos turi, iš tiesų vadinama *duomenimis*, o skaitydami knygas ir integruodami šiuos duomenis į savo pasaulio modelį mes juos paverčiame žiniomis. -> ✅ **Žinios** yra tai, kas yra mūsų galvoje ir atspindi mūsų pasaulio supratimą. Jos gaunamos aktyvaus **mokymosi** proceso metu, kuris integruoja gautą informaciją į mūsų aktyvų pasaulio modelį. +> ✅ **Žinios** yra tai, kas yra mūsų galvoje ir reprezentuoja mūsų pasaulio supratimą. Žinios įgyjamos aktyvaus **mokymosi** proceso metu, kuris integruoja gautą informaciją į mūsų aktyvų pasaulio modelį. -Dažniausiai mes griežtai neapibrėžiame žinių, bet jas susiejame su kitais susijusiais konceptais, naudodami [DIKW piramidę](https://en.wikipedia.org/wiki/DIKW_pyramid). Ji apima šiuos konceptus: +Dažniausiai mes griežtai neapibrėžiame žinių, tačiau jas susiejame su kitomis gretimomis sąvokomis naudodami [DIKW piramidę](https://en.wikipedia.org/wiki/DIKW_pyramid). Ji apima šias sąvokas: -* **Duomenys** yra tai, kas pateikiama fizinėje laikmenoje, pvz., rašytinis tekstas ar žodžiai. Duomenys egzistuoja nepriklausomai nuo žmonių ir gali būti perduodami tarp jų. -* **Informacija** yra tai, kaip mes interpretuojame duomenis savo galvoje. Pavyzdžiui, išgirdę žodį *kompiuteris*, turime tam tikrą supratimą, kas tai yra. -* **Žinios** yra informacija, integruota į mūsų pasaulio modelį. Pavyzdžiui, kai sužinome, kas yra kompiuteris, pradedame turėti idėjų apie tai, kaip jis veikia, kiek kainuoja ir kam gali būti naudojamas. Šis tarpusavyje susijusių konceptų tinklas sudaro mūsų žinias. -* **Išmintis** yra dar vienas mūsų pasaulio supratimo lygis, kuris atspindi *metažinias*, pvz., supratimą, kaip ir kada žinios turėtų būti naudojamos. +* **Duomenys** yra kažkas, kas yra atvaizduojama fizinėse laikmenose, pavyzdžiui, rašytinis tekstas ar kalbėti žodžiai. Duomenys egzistuoja nepriklausomai nuo žmogaus ir gali būti perduodami tarp žmonių. +* **Informacija** yra tai, kaip mes interpretuojame duomenis savo galvoje. Pavyzdžiui, kai išgirstame žodį *kompiuteris*, turime supratimą, kas tai yra. +* **Žinios** yra informacijos integracija į mūsų pasaulio modelį. Pavyzdžiui, kai sužinome, kas yra kompiuteris, pradedame turėti idėjų, kaip jis veikia, kiek kainuoja ir kam jis gali būti naudojamas. Šis sąvokų tinklas sudaro mūsų žinias. +* **Išmintis** yra dar aukštesnis mūsų pasaulio supratimo lygmuo, atspindintis *metazinias*, pvz., idėją, kaip ir kada naudoti žinias. - + -*Vaizdas [iš Vikipedijos](https://commons.wikimedia.org/w/index.php?curid=37705247), By Longlivetheux - Own work, CC BY-SA 4.0* +*Paveikslėlis [iš Vikipedijos](https://commons.wikimedia.org/w/index.php?curid=37705247), autorius Longlivetheux - Own work, CC BY-SA 4.0* -Taigi, **žinių atvaizdavimo** problema yra rasti efektyvų būdą atvaizduoti žinias kompiuteryje duomenų forma, kad jos būtų automatiškai naudojamos. Tai galima laikyti spektru: +Taigi, problema **žinių atstovavimas** reiškia efektyvų būdą reprezentuoti žinias kompiuterio viduje kaip duomenis, kad jos būtų automatiškai panaudojamos. Tai galima matyti kaip spektrą: -![Žinių atvaizdavimo spektras](../../../../translated_images/lt/knowledge-spectrum.b60df631852c0217.webp) +![Žinių atstovavimo spektras](../../../../../../translated_images/lt/knowledge-spectrum.b60df631852c0217.webp) -> Vaizdas sukurtas [Dmitry Soshnikov](http://soshnikov.com) +> Paveikslėlis autorius [Dmitry Soshnikov](http://soshnikov.com) -* Kairėje yra labai paprasti žinių atvaizdavimo tipai, kuriuos kompiuteriai gali efektyviai naudoti. Paprasčiausias yra algoritminis, kai žinios atvaizduojamos kompiuterio programoje. Tačiau tai nėra geriausias būdas atvaizduoti žinias, nes jis nėra lankstus. Žinios mūsų galvoje dažnai nėra algoritminės. -* Dešinėje yra tokie atvaizdavimo būdai kaip natūralus tekstas. Jis yra galingiausias, bet negali būti naudojamas automatiniam samprotavimui. +* Kairėje yra labai paprasti žinių atstovavimo tipai, kuriuos kompiuteriai gali efektyviai naudoti. Paprasčiausias yra algoritminis, kai žinios pateikiamos kompiuterinės programos forma. Tačiau tai nėra geriausias būdas atstovauti žinias, nes jis nėra lankstus. Žinios mūsų galvoje dažnai nėra algoritminės. +* Dešinėje yra tokios reprezentacijos kaip natūralus tekstas. Tai yra galingiausias būdas, bet negali būti naudojamas automatiškai samprotavimui. -> ✅ Pagalvokite minutę, kaip jūs atvaizduojate žinias savo galvoje ir paverčiate jas užrašais. Ar yra tam tikras formatas, kuris jums padeda geriau įsiminti? +> ✅ Pagalvokite akimirkai, kaip jūs reprezentuojate žinias savo galvoje ir konvertuojate jas į užrašus. Ar yra tam tikras formatas, kuris jums gerai padeda išlaikyti informaciją? -## Kompiuterinių Žinių Atvaizdavimo Klasifikavimas +## Kompiuterinių žinių atstovavimo klasifikavimas -Galime klasifikuoti skirtingus kompiuterinių žinių atvaizdavimo metodus į šias kategorijas: +Galime skirstyti skirtingus kompiuterinius žinių atstovavimo metodus į šias kategorijas: -* **Tinklo atvaizdavimai** yra pagrįsti tuo, kad mūsų galvoje yra tarpusavyje susijusių konceptų tinklas. Galime pabandyti atkurti tuos pačius tinklus kaip grafą kompiuteryje - vadinamąjį **semantinį tinklą**. +* **Tinklų atstovimai** remiasi tuo, kad mūsų galvoje yra tarpusavyje susijusių sąvokų tinklas. Galime bandyti atkurti tuos pačius tinklus kaip grafinį vaizdą kompiuteryje – taip vadinamą **semantinį tinklą**. -1. **Objekto-atributo-reikšmės trejetai** arba **atributo-reikšmės poros**. Kadangi grafas kompiuteryje gali būti atvaizduotas kaip mazgų ir kraštų sąrašas, semantinį tinklą galime atvaizduoti trejetų sąrašu, kuriame yra objektai, atributai ir reikšmės. Pavyzdžiui, sudarome šiuos trejetus apie programavimo kalbas: +1. **Objektas-Atributas-Reikšmė trejetai** arba **atributo-reikšmės poros**. Kadangi grafas kompiuteryje gali būti atvaizduojamas kaip mazgų ir kraštinių sąrašas, mes galime reprezentuoti semantinį tinklą kaip trejetų sąrašą, jame esančius objektus, atributus ir reikšmes. Pavyzdžiui, sudarome tokius trejetus apie programavimo kalbas: -Objektas | Atributas | Reikšmė ----------|-----------|-------- -Python | yra | Netipizuota kalba -Python | sukūrė | Guido van Rossum -Python | blokų sintaksė | įtraukimas -Netipizuota kalba | neturi | tipų apibrėžimų +Objektas | Atributas | Reikšmė +--------|-----------|--------- +Python | yra | Netipizuota kalba +Python | sukūrė | Guido van Rossum +Python | blokų sintaksė | įtraukimas +Netipizuota kalba | neturi | tipo aprašų -> ✅ Pagalvokite, kaip trejetai gali būti naudojami kitų tipų žinioms atvaizduoti. +> ✅ Pagalvokite, kaip trejetai gali būti naudojami kitų rūšių žinioms atstovauti. -2. **Hierarchiniai atvaizdavimai** pabrėžia tai, kad mes dažnai kuriame objektų hierarchiją savo galvoje. Pavyzdžiui, žinome, kad kanarėlė yra paukštis, o visi paukščiai turi sparnus. Taip pat turime tam tikrą supratimą, kokios spalvos dažniausiai būna kanarėlės ir koks jų skrydžio greitis. +2. **Hierarchiniai atstovimai** pabrėžia, kad dažnai mūsų galvoje sukuriame objektų hierarchiją. Pavyzdžiui, žinome, kad kanarėlė yra paukštis, o visi paukščiai turi sparnus. Taip pat turime tam tikrą supratimą, koks dažniausiai yra kanarėlės spalvos ir kokiu greičiu skraido. - - **Rėmo atvaizdavimas** yra pagrįstas kiekvieno objekto ar objektų klasės atvaizdavimu kaip **rėmo**, kuris turi **lizdus**. Lizdai turi galimas numatytas reikšmes, reikšmių apribojimus arba saugomas procedūras, kurias galima iškviesti norint gauti lizdo reikšmę. Visi rėmai sudaro hierarchiją, panašią į objektų hierarchiją objektinio programavimo kalbose. - - **Scenarijai** yra specialus rėmų tipas, kuris atvaizduoja sudėtingas situacijas, galinčias vystytis laikui bėgant. + - **Rėmelių atstovimas** (Frame representation) remiasi tuo, kad kiekvienas objektas arba objektų klasė yra atstovaujama kaip **rėmelis**, kuriame yra **vietos** (slots). Vietos turi galimas numatytąsias reikšmes, reikšmių apribojimus arba saugomas procedūras, kurias galima iškviesti norint gauti vietos reikšmę. Visi rėmeliai sudaro hierarchiją, panašią į objektų hierarchiją objektiniame programavime. + - **Scenarijai** yra specialus rėmelių tipas, kuris atstovauja sudėtingas situacijas, galinčias vystytis laike. **Python** -Lizdas | Reikšmė | Numatytoji reikšmė | Intervalas | --------|---------|--------------------|------------| +Vieta | Reikšmė | Numatytoji reikšmė | Intervalas +------|---------|--------------------|------------ Pavadinimas | Python | | | -Yra | Netipizuota kalba | | | -Kintamojo formatas | | CamelCase | | +Yra-tipass | Netipizuota kalba | | | +Kintamojo rašyba | | CamelCase | | Programos ilgis | | | 5-5000 eilučių | Blokų sintaksė | Įtraukimas | | | -3. **Procedūriniai atvaizdavimai** yra pagrįsti žinių atvaizdavimu kaip veiksmų sąrašu, kurį galima vykdyti, kai atsiranda tam tikra sąlyga. - - Produkcijos taisyklės yra if-then teiginiai, leidžiantys daryti išvadas. Pavyzdžiui, gydytojas gali turėti taisyklę, kuri sako, kad **JEI** pacientas turi aukštą temperatūrą **ARBA** aukštą C-reaktyvaus baltymo lygį kraujo tyrime **TADA** jis turi uždegimą. Kai susiduriame su viena iš sąlygų, galime padaryti išvadą apie uždegimą ir tada naudoti ją tolesniam samprotavimui. - - Algoritmai gali būti laikomi kita procedūrinio atvaizdavimo forma, nors jie beveik niekada nėra tiesiogiai naudojami žinių pagrindu veikiančiose sistemose. +3. **Procedūriniai atstovimai** remiasi žinių reprezentavimu kaip veiksmų sąrašu, kuriuos galima vykdyti, kai įvyksta tam tikra sąlyga. + - Produkcijos taisyklės yra if-tada teiginiai, leidžiantys daryti išvadas. Pavyzdžiui, gydytojas gali turėti taisyklę: **JEIGU** pacientui aukšta temperatūra **ARBA** aukštas C reaktyvaus baltymo lygis kraujo tyrime, **TADA** pacientas turi uždegimą. Susidūrus su viena iš sąlygų, galima priimti išvadą apie uždegimą ir tada ją naudoti tolesniam samprotavimui. + - Algoritmai gali būti laikomi dar vienu procedūrinės reprezentacijos formų, nors jie beveik niekada nenaudojami tiesiogiai žinių sistemose. -4. **Logika** buvo iš pradžių pasiūlyta Aristotelio kaip būdas atvaizduoti universalias žmonių žinias. - - Predikatų logika kaip matematinė teorija yra per daug turtinga, kad būtų skaičiuojama, todėl paprastai naudojamas jos pogrupis, pvz., Horn sąlygos, naudojamos Prolog. - - Aprašomoji logika yra loginių sistemų šeima, naudojama hierarchijų objektų atvaizdavimui ir samprotavimui apie paskirstytas žinių atvaizdavimo sistemas, tokias kaip *semantinis tinklas*. +4. **Logika** buvo pasiūlyta Aristotelio kaip būdas reprezentuoti universalias žmogaus žinias. + - Predikatų logika kaip matematinė teorija yra per plati, kad būtų tiesiogiai apskaičiuojama, todėl paprastai naudojamas jos poaibis, pavyzdžiui, Horn klausymai, naudojami Prolog kalboje. + - Aprašomoji logika yra loginių sistemų šeima, naudojama objektų hierarchijoms ir paskirstyto žinių atstovavimo, tokių kaip *semantinis internetas*, reprezentavimui ir samprotavimui. -## Ekspertinės Sistemos +## Ekspertų Sistemos -Vienas iš ankstyvųjų simbolinio AI pasiekimų buvo vadinamosios **ekspertinės sistemos** - kompiuterinės sistemos, sukurtos veikti kaip ekspertas tam tikroje ribotoje problemų srityje. Jos buvo pagrįstos **žinių baze**, išgauta iš vieno ar daugiau žmonių ekspertų, ir turėjo **išvadų variklį**, kuris atliko tam tikrą samprotavimą remdamasis šia baze. +Viena iš ankstyvųjų simbolinio DI sėkmių buvo vadinamosios **ekspertų sistemos** – kompiuterinės sistemos, sukurtos veikti kaip ekspertas tam tikroje ribotoje problemų srityje. Jos buvo pagrįstos iš vieno ar kelių žmogiškųjų ekspertų išgautomis **žinių bazėmis** ir turėjo **išvedimo variklį**, kuris atlikdavo tam tikrus samprotavimus. -![Žmogaus architektūra](../../../../translated_images/lt/arch-human.5d4d35f1bba3ab1c.webp) | ![Žinių pagrindu veikianti sistema](../../../../translated_images/lt/arch-kbs.3ec5c150b09fa8da.webp) +![Žmogaus architektūra](../../../../../../translated_images/lt/arch-human.5d4d35f1bba3ab1c.webp) | ![Žinių bazės sistema](../../../../../../translated_images/lt/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ -Supaprastinta žmogaus nervų sistemos struktūra | Žinių pagrindu veikiančios sistemos architektūra +Supaprastinta žmogaus nervų sistemos struktūra | Žinių bazės sistemos architektūra -Ekspertinės sistemos yra sukurtos panašiai kaip žmogaus samprotavimo sistema, kuri turi **trumpalaikę atmintį** ir **ilgalaikę atmintį**. Panašiai, žinių pagrindu veikiančiose sistemose išskiriame šiuos komponentus: +Ekspertų sistemos yra kuriamos panašiai kaip žmogaus mąstymo sistema, kuri turi **trumpalaikę atmintį** ir **ilgalaikę atmintį**. Panašiai žinių bazės sistemose išskiriami šie komponentai: -* **Problemos atmintis**: saugo žinias apie šiuo metu sprendžiamą problemą, pvz., paciento temperatūrą ar kraujospūdį, ar jis turi uždegimą ir pan. Šios žinios taip pat vadinamos **statinėmis žiniomis**, nes jos apima tai, ką šiuo metu žinome apie problemą - vadinamąją *problemos būseną*. -* **Žinių bazė**: atspindi ilgalaikes žinias apie problemų sritį. Ji rankiniu būdu išgaunama iš žmonių ekspertų ir nesikeičia nuo vienos konsultacijos iki kitos. Kadangi ji leidžia pereiti iš vienos problemos būsenos į kitą, ji taip pat vadinama **dinaminėmis žiniomis**. -* **Išvadų variklis**: koordinuoja visą procesą, ieškodamas problemos būsenos erdvėje, užduodamas klausimus vartotojui, kai reikia. Jis taip pat atsakingas už tinkamų taisyklių taikymą kiekvienai būsenai. +* **Problemos atmintis**: saugo žinias apie šiuo metu sprendžiamą problemą, pvz., temperatūrą ar paciento kraujospūdį, ar turi uždegimą ar ne. Šios žinios taip pat vadinamos **statinėmis žiniomis**, nes jose yra dabartinio problemos būsenos momentinė nuotrauka – taip vadinama *problemos būsena*. +* **Žinių bazė**: reprezentuoja ilgalaikes žinias apie problemų sritį. Ji yra rankiniu būdu ištraukta iš žmogiškųjų ekspertų ir nekinta nuo konsultacijos iki konsultacijos. Kadangi ši bazė leidžia judėti iš vienos problemos būsenos į kitą, ji taip pat vadinama **dinaminėmis žiniomis**. +* **Išvedimo variklis**: koordinuoja visą paieškos procesą problemų būsenų erdvėje ir, prireikus, užduoda klausimus vartotojui. Jis taip pat atsakingas už tinkamų taikomų taisyklių suradimą kiekvienai būsenai. -Kaip pavyzdį, apsvarstykime šią ekspertinę sistemą, skirtą gyvūnui nustatyti pagal jo fizines charakteristikas: +Pavyzdžiui, apsvarstykime ekspertų sistemą, kuri nustato gyvūną pagal jo fizines savybes: -![AND-OR medis](../../../../translated_images/lt/AND-OR-Tree.5592d2c70187f283.webp) +![AND-ARBO medis](../../../../../../translated_images/lt/AND-OR-Tree.5592d2c70187f283.webp) -> Vaizdas sukurtas [Dmitry Soshnikov](http://soshnikov.com) +> Paveikslėlis autorius [Dmitry Soshnikov](http://soshnikov.com) -Ši diagrama vadinama **AND-OR medžiu**, ir tai yra grafinis produkcijos taisyklių rinkinio atvaizdavimas. Medžio piešimas yra naudingas pradžioje, kai išgaunamos žinios iš eksperto. Norint atvaizduoti žinias kompiuteryje, patogiau naudoti taisykles: +Šis diagrama vadinama **AND-OR medis** ir yra grafinis gamybos taisyklių rinkinio atvaizdavimas. Medžio piešimas yra naudingas žinių iš ekspertų išgavimui pradžioje. Tačiau žinioms atstovauti kompiuteryje patogiau naudoti taisykles: ``` IF the animal eats meat @@ -121,71 +121,78 @@ OR (animal has sharp teeth THEN the animal is a carnivore ``` -Galite pastebėti, kad kiekviena sąlyga taisyklės kairėje pusėje ir veiksmas iš esmės yra objekto-atributo-reikšmės (OAR) trejetai. **Darbinė atmintis** saugo OAR trejetų rinkinį, kuris atitinka šiuo metu sprendžiamą problemą. **Taisyklių variklis** ieško taisyklių, kurių sąlyga yra patenkinta, ir jas taiko, pridėdamas naują trejetą į darbinę atmintį. +Galite pastebėti, kad kiekviena sąlyga dešinėje taisyklės pusėje ir veiksmas iš esmės yra objektas-atributas-reikšmė (OAV) trejetai. **Darbinė atmintis** saugo OAV trejetus, kurie atitinka šiuo metu sprendžiamą problemą. **Taisyklių variklis** ieško taisyklių, kurių sąlyga yra tenkinama, ir jas taiko, pridedant naują trejetą į darbinę atmintį. -> ✅ Sukurkite savo AND-OR medį apie jums patinkančią temą! +> ✅ Parašykite savo AND-OR medį patinkančia tema! -### Priekinė vs. Atgalinė Išvada +### Tiesioginis ir Atvirkštinis Išvedimas -Aukščiau aprašytas procesas vadinamas **priekine išvada**. Jis prasideda nuo tam tikrų pradinių duomenų apie problemą, esančių darbinėje atmintyje, ir tada vykdo šį samprotavimo ciklą: +Aprašytas procesas vadinamas **tiesioginiu išvedimu**. Jis prasideda nuo pirminių duomenų apie problemą, esančių darbinėje atmintyje, ir vykdo šį samprotavimo ciklą: -1. Jei tikslinis atributas yra darbinėje atmintyje - sustokite ir pateikite rezultatą -2. Ieškokite visų taisyklių, kurių sąlyga šiuo metu patenkinta - gaukite **konflikto rinkinį** taisyklių. -3. Atlikite **konflikto sprendimą** - pasirinkite vieną taisyklę, kuri bus vykdoma šiame žingsnyje. Gali būti įvairios konflikto sprendimo strategijos: - - Pasirinkite pirmą tinkamą taisyklę žinių bazėje +1. Jei norimas atributas yra darbinėje atmintyje – sustokite ir pateikite rezultatą +2. Ieškokite visų taisyklių, kurių sąlygos šiuo metu tenkinamos – gaunate **konflikto taisyklių rinkinį**. +3. Atlikite **konflikto sprendimą** – pasirinkite vieną taisyklę, kuri bus įvykdyta šiame žingsnyje. Yra skirtingos konflikto sprendimo strategijos: + - Pasirinkite pirmą taikomą taisyklę žinių bazėje - Pasirinkite atsitiktinę taisyklę - - Pasirinkite *specifiškesnę* taisyklę, t. y. tą, kuri atitinka daugiausiai sąlygų kairėje pusėje (LHS) + - Pasirinkite *specifiškesnę* taisyklę, t.y. atitinkančią daugiausiai sąlygų kairiajame šone (LHS) 4. Taikykite pasirinktą taisyklę ir įterpkite naują žinių dalį į problemos būseną 5. Kartokite nuo 1 žingsnio. -Tačiau kai kuriais atvejais galime norėti pradėti nuo tuščios žinių apie problemą ir užduoti klausimus, kurie padės pasiekti išvadą. Pavyzdžiui, atliekant medicininę diagnozę, paprastai neatliekame visų medicininių tyrimų iš anksto prieš pradedant diagnozuoti pacientą. Verčiau norime atlikti tyrimus, kai reikia priimti sprendimą. +Tačiau kai kuriais atvejais norime pradėti be jokių žinių apie problemą ir užduoti klausimus, kurie padėtų priartėti prie išvados. Pavyzdžiui, medicinos diagnozės metu mes įprastai nevykdome visų medicininių tyrimų iš anksto prieš pradedant diagnozuoti pacientą. Mes atliekame tyrimus tada, kai reikia priimti sprendimą. -Šį procesą galima modeliuoti naudojant **atgalinę išvadą**. Ji yra orientuota į **tikslą** - atributų reikšmę, kurią siekiame rasti: +Šis procesas gali būti modeliuojamas naudojant **atvirkštinį išvedimą**. Jį valdo **tikslo** paieška – atributo reikšmė, kurios norime rasti: -1. Pasirinkite visas taisykles, kurios gali suteikti mums tikslinę reikšmę (t. y. su tikslu dešinėje pusėje (RHS)) - konflikto rinkinį -1. Jei nėra taisyklių šiam atributui arba yra taisyklė, sakanti, kad reikšmę turėtume paklausti vartotojo - paklauskite jos, kitaip: -1. Naudokite konflikto sprendimo strategiją, kad pasirinktumėte vieną taisyklę, kurią naudosime kaip *hipotezę* - bandysime ją įrodyti -1. Pakartotinai kartokite procesą visiems atributams taisyklės kairėje pusėje, bandydami juos įrodyti kaip tikslus -1. Jei bet kuriuo metu procesas nepavyksta - naudokite kitą taisyklę 3 žingsnyje. +1. Pasirinkite visas taisykles, kurios gali pateikti tikslo reikšmę (t.y. taisykles, kuriose tikslas yra dešinėje) – konfliktų rinkinys +2. Jei nėra taisyklių šiam atributui arba yra taisyklė, kuri sako, kad reikia paklausti vartotojo reikšmės – paklauskite, kitaip: +3. Panaudokite konflikto sprendimo strategiją, kad pasirinktumėte vieną taisyklę, kuri bus laikoma *hipoteze* – ją bandysime įrodyti +4. Rekursyviai kartokite procesą visiems atributams taisyklės kairėje pusėje (LHS), bandydami įrodyti juos kaip tikslus +5. Jei procesas bet kada nepavyksta – eikite prie kitos taisyklės 3 žingsnyje. -> ✅ Kokiose situacijose priekinei išvadai teikiama pirmenybė? O kaip dėl atgalinės išvados? +> ✅ Kokiose situacijose labiau tinka tiesioginis, o kokiose – atvirkštinis išvedimas? -### Ekspertinių Sistemų Įgyvendinimas +### Ekspertų sistemų įgyvendinimas -Ekspertinės sistemos gali būti įgyvendintos naudojant įvairius įrankius: +Ekspertų sistemas galima įgyvendinti naudojant įvairius įrankius: -* Jas programuojant tiesiogiai aukšto lygio programavimo kalba. Tai nėra geriausia idėja, nes pagrindinis žinių pagrindu veikiančios sistemos privalumas yra tas, kad žinios yra atskirtos nuo išvadų, ir potencialiai problemų srities ekspertas turėtų galėti rašyti taisykles nesuprasdamas išvadų proceso detalių. -* Naudojant **ekspertinių sistemų apvalkalą**, t. y. sistemą, specialiai sukurtą žinioms užpildyti naudojant tam tikrą žinių atvaizdavimo kalbą. +* Programavimas tiesiogiai aukšto lygio programavimo kalba. Tai nėra geriausia idėja, nes pagrindinis žinių bazės sistemos privalumas yra žinių ir išvedimo atskyrimas, o potencialiai problemų srities ekspertas turėtų galėti rašyti taisykles nesiimdamas išvedimo proceso detalių. +* Naudojant **ekspertų sistemų apvalkalą** (shell), t.y. sistemą, specialiai sukurtą būti užpildyta žiniomis naudojant tam tikrą žinių atstovavimo kalbą. -## ✍️ Užduotis: Gyvūnų Išvada +## ✍️ Užduotis: Gyvūnų atpažinimas -Žr. [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) pavyzdį, kaip įgyvendinti priekinių ir atgalinių išvadų ekspertinę sistemą. +Žr. [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) kaip pavyzdį, įgyvendinant tiesioginio ir atvirkštinio išvedimo ekspertų sistemą. -> **Pastaba**: Šis pavyzdys yra gana paprastas ir tik suteikia idėją, kaip atrodo ekspertinė sistema. Kai pradėsite kurti tokią sistemą, pastebėsite tam tikrą *intelektualų* elgesį tik pasiekę tam tikrą taisyklių skaičių, apie 200+. Tam tikru momentu taisyklės tampa per sudėtingos, kad visas jas galėtumėte išlaikyti galvoje, ir tada galite pradėti stebėtis, kodėl sistema priima tam tikrus sprendimus. Tačiau svarbi žinių pagrindu veikiančių sistemų savybė yra ta, kad visada galite -- XML pagrindu sukurtų kalbų šeima žinių aprašymui: RDF (Resource Description Framework), RDFS (RDF Schema), OWL (Ontology Web Language). +> **Pastaba**: Šis pavyzdys yra gana paprastas ir tik suteikia idėją, kaip atrodo ekspertų sistema. Kai pradėsite kurti tokią sistemą, pastebėsite „protingą“ elgesį tik pasiekę tam tikrą taisyklių skaičių – apie 200+. Tam tikru momentu taisyklės tampa pernelyg sudėtingos, kad visas jas atsimintumėte, ir tuomet galite pradėti klausti, kodėl sistema priima tam tikrus sprendimus. Tačiau svarbi žinių bazės sistemų charakteristika yra ta, kad visada galite *paaiškinti*, kaip buvo priimtas bet koks sprendimas. -Pagrindinė Semantinio tinklo sąvoka yra **Ontologija**. Ji reiškia aiškų problemos srities apibrėžimą naudojant tam tikrą formalų žinių atvaizdavimą. Paprasčiausia ontologija gali būti tiesiog objektų hierarchija problemos srityje, tačiau sudėtingesnės ontologijos apima taisykles, kurios gali būti naudojamos išvadoms daryti. +## Ontologijos ir semantinis internetas -Semantiniame tinkle visi atvaizdavimai yra pagrįsti trigubais. Kiekvienas objektas ir kiekvienas ryšys yra unikalus ir identifikuojamas pagal URI. Pavyzdžiui, jei norime nurodyti faktą, kad šis AI mokymo planas buvo sukurtas Dmitrijaus Soshnikovo 2022 m. sausio 1 d., štai trigubai, kuriuos galime naudoti: +XX amžiaus pabaigoje kilo iniciatyva naudoti žinių atstovavimą žiniatinklio išteklių anotavimui, kad būtų įmanoma rasti išteklius, atitinkančius labai specifinius užklausimus. Ši iniciatyva vadinta **semantiniu internetu** ir remėsi keliomis sąvokomis: - +- Specialus žinių atstovavimo pagrindas, naudojantis **[aprašomąją logiką](https://en.wikipedia.org/wiki/Description_logic)** (DL). Ji panaši į rėmelių žinių atstovavimą, nes kuria objektų hierarchiją su savybėmis, tačiau turi formalią loginę semantiką ir išvedimą. Yra visa DL šeima, balansuojanti tarp išraiškingumo ir išvedimo algoritminio sudėtingumo. +- Paskirstytas žinių atstovavimas, kai visos sąvokos yra reprezentuojamos globaliu URI identifikatoriumi, leidžiančiu kurti žinių hierarchijas, apimančias visą internetą. +- XML pagrindu sukurtų kalbų šeima žinių aprašymui: RDF (Išteklių aprašymo pagrindas), RDFS (RDF schema), OWL (Ontologijų žiniatinklio kalba). + +Vienas pagrindinių Semantinio tinklo konceptų yra **Ontologijos** sąvoka. Tai aiškus problemos srities formalaus žinių atvaizdavimo aprašymas. Paprasčiausia ontologija gali būti vien hierarchija objektų problemos srityje, tačiau sudėtingesnės ontologijos apims taisykles, kurias galima naudoti išvedimui. + +Semantiniame tinkle visi atvaizdavimai yra paremti tripletais. Kiekvienas objektas ir kiekvienas ryšys yra unikalūs identifikuojami pagal URI. Pavyzdžiui, jei norime nurodyti faktą, kad šis DI mokymas buvo sukurtas Dmitry Soshnikov 2022 m. sausio 1 d. – štai tripletai, kuriuos galime panaudoti: + + ``` -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 ``` -> ✅ Čia `http://www.example.com/terms/creation-date` ir `http://purl.org/dc/elements/1.1/creator` yra gerai žinomi ir visuotinai priimti URI, skirti išreikšti *kūrėjo* ir *sukūrimo datos* sąvokas. +> ✅ Čia `http://www.example.com/terms/creation-date` ir `http://purl.org/dc/elements/1.1/creator` yra kai kurie gerai žinomi ir universaliai priimtini URI, išreiškiantys sąvokas *kūrėjas* ir *kūrimo data*. -Sudėtingesniu atveju, jei norime apibrėžti kūrėjų sąrašą, galime naudoti tam tikras RDF apibrėžtas duomenų struktūras. +Sudėtingesniu atveju, jei norime apibrėžti kūrėjų sąrašą, galime naudoti kai kurias RDF apibrėžtas duomenų struktūras. - + -> Aukščiau pateikti diagramų autoriai: [Dmitrijus Soshnikovas](http://soshnikov.com) +> Aukščiau pateikti diagramos – [Dmitry Soshnikov](http://soshnikov.com) -Semantinio tinklo kūrimo pažangą tam tikru mastu sulėtino paieškos sistemų ir natūralios kalbos apdorojimo technikų sėkmė, kurios leidžia išgauti struktūrizuotus duomenis iš teksto. Tačiau kai kuriose srityse vis dar dedamos reikšmingos pastangos ontologijoms ir žinių bazėms palaikyti. Keletas projektų, kuriuos verta paminėti: +Semantinio tinklo kūrimas kažkiek buvo sulėtėjęs dėl paieškos variklių ir natūralios kalbos apdorojimo technologijų sėkmės, leidžiančios iš tekstų išgauti struktūruotus duomenis. Tačiau kai kuriose srityse vis dar dedamos didelės pastangos palaikyti ontologijas ir žinių bazes. Keletas dėmesio vertų projektų: -* [WikiData](https://wikidata.org/) yra mašininio skaitymo žinių bazių kolekcija, susijusi su Wikipedia. Dauguma duomenų yra išgaunami iš Wikipedia *InfoBoxes*, struktūrizuoto turinio dalių Wikipedia puslapiuose. WikiData galite [užklausti](https://query.wikidata.org/) naudodami SPARQL, specialią užklausų kalbą Semantiniam tinklui. Štai pavyzdinė užklausa, rodanti populiariausias žmonių akių spalvas: +* [WikiData](https://wikidata.org/) – tai mašinomis skaitomos žinių bazės rinkinys, susijęs su Wikipedia. Didžioji dalis duomenų gaunama iš Wikipedia *InfoBox'ų*, struktūruoto turinio dalių Wikipedia puslapiuose. Galite [užklausti](https://query.wikidata.org/) wikidata naudojant SPARQL – specialią užklausų kalbą Semantiniam tinklui. Štai pavyzdinė užklausa, rodanti populiariausias akių spalvas tarp žmonių: ```sparql #defaultView:BubbleChart @@ -199,47 +206,51 @@ WHERE GROUP BY ?eyeColorLabel ``` -* [DBpedia](https://www.dbpedia.org/) yra dar viena iniciatyva, panaši į WikiData. +* [DBpedia](https://www.dbpedia.org/) – dar viena pastanga, panaši į WikiData. -> ✅ Jei norite eksperimentuoti kuriant savo ontologijas arba atidarant esamas, yra puikus vizualus ontologijų redaktorius, vadinamas [Protégé](https://protege.stanford.edu/). Atsisiųskite jį arba naudokite internetu. +> ✅ Jei norite eksperimentuoti kuriant savo ontologijas arba atidarant esamas, yra puikus vizualinis ontologijų redaktorius pavadintas [Protégé](https://protege.stanford.edu/). Atsisiųskite jį arba naudokite internetu. - + -*Web Protégé redaktorius atidarytas su Romanovų šeimos ontologija. Ekrano kopija: Dmitrijus Soshnikovas* +*Web Protégé redaktorius atidarytas su Romanovų šeimos ontologija. Nekšto Dmitry Soshnikov* -## ✍️ Užduotis: Šeimos ontologija +## ✍️ Užduotis: Šeimos Ontologija -Žr. [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) pavyzdį, kaip naudoti Semantinio tinklo technikas šeimos santykiams analizuoti. Mes paimsime šeimos medį, pateiktą įprastu GEDCOM formatu, ir šeimos santykių ontologiją, kad sukurtume visų šeimos santykių grafą tam tikram asmenų rinkiniui. +Peržiūrėkite [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb), kuriame pateiktas pavyzdys, kaip naudojamos Semantinio tinklo technikos, siekiant spręsti šeimos santykių problemas. Naudosime šeimos medį, pateiktą įprasta GEDCOM formatu, ir šeimos santykių ontologiją, kad sudarytume visų šeimos ryšių grafą pasirinktai asmenų grupei. -## Microsoft Concept Graph +## Microsoft Sąvokų Grafas -Daugeliu atvejų ontologijos yra kruopščiai kuriamos rankiniu būdu. Tačiau taip pat galima **išgauti** ontologijas iš nestruktūrizuotų duomenų, pavyzdžiui, iš natūralios kalbos tekstų. +Daugeliu atvejų ontologijos yra kruopščiai kuriamos ranka. Tačiau taip pat galima **iškasti** ontologijas iš nestruktūruotų duomenų, pavyzdžiui, natūralios kalbos tekstų. -Vienas toks bandymas buvo atliktas Microsoft Research ir rezultatas – [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste). +Vienas toks bandymas buvo atliktas Microsoft Research ir iš rezultatų gimė [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste). -Tai didelė subjektų kolekcija, sugrupuota naudojant `is-a` paveldėjimo ryšį. Ji leidžia atsakyti į klausimus, tokius kaip „Kas yra Microsoft?“ – atsakymas būtų kažkas panašaus į „kompanija su tikimybe 0.87, ir prekės ženklas su tikimybe 0.75“. +Tai didelė subjektų grupė, apjungta naudojant `is-a` paveldėjimo santykį. Jis leidžia atsakyti į klausimus, kaip „Kas yra Microsoft?" – kurių atsakymas būtų panašus į „įmonė su tikimybe 0.87, ir prekės ženklas su tikimybe 0.75“. -Grafas yra prieinamas kaip REST API arba kaip didelis atsisiunčiamas tekstinis failas, kuriame išvardyti visi subjektų poros. +Šis grafas prieinamas per REST API arba kaip didelis atsisiunčiamas tekstinis failas, kuriame išvardyti visi subjektų poros. -## ✍️ Užduotis: Konceptų grafas +## ✍️ Užduotis: Sąvokų Grafas -Išbandykite [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) užrašų knygelę, kad pamatytumėte, kaip galime naudoti Microsoft Concept Graph naujienų straipsniams suskirstyti į kelias kategorijas. +Išbandykite [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) užrašų knygelę, kad sužinotumėte, kaip Microsoft Sąvokų Grafas gali būti panaudotas naujienų straipsniams suskirstyti į kelias kategorijas. -## Išvada +## Išvados -Šiandien AI dažnai laikomas sinonimu *Mašininio mokymosi* arba *Neuroninių tinklų*. Tačiau žmogus taip pat demonstruoja aiškų samprotavimą, kuris šiuo metu nėra apdorojamas neuroninių tinklų. Realiuose projektuose aiškus samprotavimas vis dar naudojamas užduotims atlikti, kurioms reikia paaiškinimų arba galimybės kontroliuojamai keisti sistemos elgesį. +Šiandien DI dažnai laikomas sinonimu *Mašininiam mokymuisi* ar *Neuroniniams tinklams*. Tačiau žmogus taip pat demonstruoja aiškų samprotavimą, kurio šiuo metu neuroniniai tinklai negali atlikti. Realiose sistemose aiškus samprotavimas vis dar naudojamas atliekant užduotis, kurioms reikalingi paaiškinimai arba galimybė kontroliuojamai keisti sistemos elgseną. ## 🚀 Iššūkis -Šeimos ontologijos užrašų knygelėje, susijusioje su šia pamoka, yra galimybė eksperimentuoti su kitais šeimos santykiais. Pabandykite atrasti naujus ryšius tarp žmonių šeimos medyje. +Šeimos ontologijos užrašų knygelėje, susijusioje su pamoka, yra galimybė eksperimentuoti su kitais šeimos ryšiais. Pabandykite atrasti naujų ryšių tarp žmonių šeimos medyje. ## [Po paskaitos testas](https://ff-quizzes.netlify.app/en/ai/quiz/4) -## Apžvalga ir savarankiškas mokymasis +## Peržiūra ir savarankiškas mokymasis -Atlikite tyrimą internete, kad sužinotumėte sritis, kuriose žmonės bandė kiekybiškai įvertinti ir kodifikuoti žinias. Pažvelkite į Bloom'o taksonomiją ir grįžkite į istoriją, kad sužinotumėte, kaip žmonės bandė suprasti savo pasaulį. Išnagrinėkite Linėjaus darbą kuriant organizmų taksonomiją ir stebėkite, kaip Dmitrijus Mendelejevas sukūrė būdą cheminiams elementams aprašyti ir grupuoti. Kokius kitus įdomius pavyzdžius galite rasti? +Atlikite tyrimą internete, kad atrastumėte sritis, kuriose žmonės bandė kiekybiškai apibrėžti ir kodifikuoti žinias. Pažvelkite į Bloom taksonomiją ir grįžkite istorijoje, kaip žmonės stengėsi suprasti savo pasaulį. Tyrinėkite Linnaeus darbą organizmų taksonomijai kurti, ir stebėkite, kaip Dmitrijus Mendelejevas sukūrė cheminio elementų aprašymo ir grupavimo būdą. Kokius kitus įdomius pavyzdžius galite rasti? -**Užduotis**: [Sukurkite ontologiją](assignment.md) +**Namų darbas**: [Sukurkite Ontologiją](assignment.md) --- + +**Atsakomybės apribojimas**: +Šis dokumentas buvo išverstas naudojant dirbtinio intelekto vertimo paslaugą [Co-op Translator](https://github.com/Azure/co-op-translator). Nors siekiame tikslumo, prašome atkreipti dėmesį, kad automatiniai vertimai gali turėti klaidų ar netikslumų. Originalus dokumentas gimtąja kalba turi būti laikomas autoritetingu šaltiniu. Kritinei informacijai rekomenduojame naudoti profesionalų žmogaus vertimą. Mes neatsakome už bet kokius nesusipratimus ar neteisingus aiškinimus, kylančius dėl šio vertimo naudojimo. + \ No newline at end of file diff --git a/translations/ta/README.md b/translations/ta/README.md index a47c435c..26109bc3 100644 --- a/translations/ta/README.md +++ b/translations/ta/README.md @@ -1,8 +1,8 @@ [Arabic](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgarian](../bg/README.md) | [Burmese (Myanmar)](../my/README.md) | [Chinese (Simplified)](../zh/README.md) | [Chinese (Traditional, Hong Kong)](../hk/README.md) | [Chinese (Traditional, Macau)](../mo/README.md) | [Chinese (Traditional, Taiwan)](../tw/README.md) | [Croatian](../hr/README.md) | [Czech](../cs/README.md) | [Danish](../da/README.md) | [Dutch](../nl/README.md) | [Estonian](../et/README.md) | [Finnish](../fi/README.md) | [French](../fr/README.md) | [German](../de/README.md) | [Greek](../el/README.md) | [Hebrew](../he/README.md) | [Hindi](../hi/README.md) | [Hungarian](../hu/README.md) | [Indonesian](../id/README.md) | [Italian](../it/README.md) | [Japanese](../ja/README.md) | [Kannada](../kn/README.md) | [Korean](../ko/README.md) | [Lithuanian](../lt/README.md) | [Malay](../ms/README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Nepali](../ne/README.md) | [Nigerian Pidgin](../pcm/README.md) | [Norwegian](../no/README.md) | [Persian (Farsi)](../fa/README.md) | [Polish](../pl/README.md) | [Portuguese (Brazil)](../br/README.md) | [Portuguese (Portugal)](../pt/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Romanian](../ro/README.md) | [Russian](../ru/README.md) | [Serbian (Cyrillic)](../sr/README.md) | [Slovak](../sk/README.md) | [Slovenian](../sl/README.md) | [Spanish](../es/README.md) | [Swahili](../sw/README.md) | [Swedish](../sv/README.md) | [Tagalog (Filipino)](../tl/README.md) | [Tamil](./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) -> **உள்ளூரில் கிளோன் செய்வதற்கு விருப்பமா?** +> **உடனடியாக கிளோன் செய்ய விரும்புகிறீர்களா?** -> இந்த களஞ்சியத்தில் 50+ மொழிபெயர்ப்புகள் உள்ளன, இது பதிவிறக்கக் கையாள்மையை பெரியதாக மாற்றுகிறது. மொழிபெயர்ப்புகளின்றி கிளோன் செய்ய sparse checkout ஐப் பயன்படுத்தவும்: +> இந்த சேமிப்பகம் 50+ மொழிபெயர்ப்புகளை கொண்டுள்ளது, இது பதிவிறக்கம் அளவை குறிப்பிடத்தக்க அளவில் அதிகரிக்கிறது. மொழிபெயர்ப்புகள் இல்லாமல் கிளோன் செய்ய sparse checkout ஐப் பயன்படுத்தவும்: > ```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' > ``` -> இந்த வழியில் நீங்கள் படிப்பை முடிக்க தேவையான அனைத்தும் விரைவான பதிவிறக்கத்துடன் கிடைக்கும். +> இது உங்களுக்கு பாடத்திட்டத்தை விரைவான பதிவிறக்கத்துடன் முடிக்க தேவையான அனைத்தையும் வழங்கும். -**மேலும் மொழிபெயர்ப்புகளுக்கு ஆதரவு தேவையாயின், அத்தகைய மொழிகள் [இங்கே](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md) பட்டியலிடப்பட்டுள்ளன** +**மேலும் மொழி ஆதரவு விரும்பினால், அவை இங்கே பட்டியலிடப்பட்டுள்ளன [இங்கு](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** ## சமூகத்தில் சேரவும் [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -## நீங்கள் கற்றுக்கொள்ளப்போகிறீர்கள் +## நீங்கள் கற்றுக்கொள்ளப்போவது -**[பாடத்திட்டத்தின் மனப்படம்](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** +**[பாடத்தின் மனவரையறை வரைபடம்](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** -இந்த பாடத்திட்டத்தில், நீங்கள் கற்றுக்கொள்ளவிருப்பது: +இந்த பாடத்திட்டத்தில், நீங்கள் கற்றுக்கொள்ளப்போக, -* கலைநுண்ணறிவுக்கு வேறு-வேறு அணுகுமுறைகள், அதில் "நல்ல பழைய" குறிக்கோள் அடிப்படையிலான **அறிவுத்தொகுப்பு** மற்றும் காரணமறிதல் உள்ளன ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). -* நவீன AI இல் உள்ள மையமான **நரம்பியல் வலைப்பின்னல்கள்** மற்றும் **ஆழமான கற்றல்**. இந்த முக்கியமான தலைப்புகளின் பின்னணி கருத்துக்களை, இரண்டு மிகவும் பிரபலமான காரியமாக்கல் வடிவங்களில் - [TensorFlow](http://Tensorflow.org) மற்றும் [PyTorch](http://pytorch.org) ஆகியவற்றில் கோடுடன் எடுத்துக்காட்டுவோம். -* படங்கள் மற்றும் உரையுடன் வேலை செய்ய **நரம்பியல் கட்டமைப்புகள்**. சமீபத்திய மாதிரிகளை கவனிப்போம் ஆனால் நவீன நிலை-கலை பற்றிச் சற்று குறைவாக இருக்கலாம். -* அசலம் ஆகாத AI அணுகுமுறைகள், உதாரணமாக **ஜெனெட்டிக் ஆல்காரிதம்கள்** மற்றும் **பல-ஏஜென்ட் முறைமைகள்**. +* "நல்ல பழைய" குறியிட்டு (symbolic) முறையை உள்ளடக்கிய செயற்கை நுண்ணறிவுக்கான வெவ்வேறு அணுகுமுறைகள், இதில் **அறிவு பிரதிநிதித்துவம்** மற்றும் காரணமற்று (reasoning) உள்ளது ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). +* நவீன AI இன் காரணி ஆகும் **நியூரல் நெட்வொர்க்ஸ்** மற்றும் **டீப் லெர்னிங்**. இந்த முக்கிய தலைப்புகளுக்கான கருத்துக்களை நாம் இரண்டு பிரபலமான கட்டமைப்புகளில் உள்ள குறியீட்டுடன் விளக்கப்போகிறோம் - [TensorFlow](http://Tensorflow.org) மற்றும் [PyTorch](http://pytorch.org). +* படங்களுடன் மற்றும் உரையுடன் வேலை செய்ய **நியூரல் கட்டமைப்புகள்**. நாங்கள் சமீபத்திய மாதிரிகளை உள்ளடக்கும்; இருப்பினும், மிகவும் முன்னணி நிலைகளை சந்திக்கக் குறைவாக இருக்கலாம். +* குறைந்த பிரபலமான AI அணுகுமுறைகள், உதாரணமாக **ஜெனெடிக் ஆல்கொரிதம்கள்** மற்றும் **பல-செயலாளி அமைப்புகள்**. -இந்த பாடத்திட்டத்தில் நாம் பேசப் போவதில்லாதவை: +இந்த பாடத்திட்டத்தில் நாம் பரிசீலிக்காதவை: -> [இந்த பாடத்திட்டத்திற்கு அனைத்து கூடுதல் வளங்களையும் Microsoft Learn தொகுப்பில் காண்க](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) +> [Microsoft Learn தொகுப்பில் இந்த பாடத்திட்டத்திற்கு கூடுதல் மூலங்கள்](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) -* **AI வணிகத்தில்** பயன்படுத்தும் வணிக வழக்குகள். Microsoft Learn இல் [வணிகப் பயனாளர்களுக்கான AI அறிமுகம்](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) அல்லது [AI வணிகப்பள்ளி](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), [INSEAD](https://www.insead.edu/) உடன் ஒத்துழைத்து உருவாக்கப்பட்டது. -* நம் [தொடக்கக் பயனாளர்களுக்கான இயந்திரக் கற்றல் பாடத்திட்டம்](http://github.com/Microsoft/ML-for-Beginners) இல் முறையாக விவரிக்கப்பட்டுள்ள **சாதாரண இயந்திரக் கற்றல்**. -* **[அறிவாற்றல் சேவைகள்](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)** பயன்படுத்தி உருவாக்கப்பட்ட நடைமுறை AI பயன்பாடுகள். இதற்காக, Microsoft Learn இல் [காணொளி](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [இயல்பழ்ச்சி மொழி செயலாக்கம்](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[Azure OpenAI சேவையுடன் உருவாக்கும் AI](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** மற்றும் பிற முதன்மை வகுப்புகளை தொடங்க பரிந்துரைக்கிறோம். -* குறிப்பிட்ட ML **மேக வடிவமைப்புகள்**, உதாரணமாக [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), அல்லது [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). [Azure Machine Learning பயன்படுத்தி இயந்திரக் கற்றல் தீர்வுகளை உருவாக்கி இயக்கு](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) மற்றும் [Azure Databricks மூலம் இயந்திரக் கற்றல் தீர்வுகளை உருவாக்கி இயக்கு](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum) என்ற கற்றல் பாதைகளை பயன்படுத்து. -* **மொழிவழி AI** மற்றும் **அரட்டைப் பாட்டுகள்**. தனிப்பட்ட [மொழிவழி AI தீர்வுகளை உருவாக்கவும்](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum) கற்றல் பாதை உள்ளது, மேலும் விரிவாக [இந்த வலைப்பதிவையும்](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) பார்க்கலாம். -* ஆழ்கழிவுப் பின்னணியில் உள்ள **ஆழமான கணிதங்கள்**. இதற்காக, Ian Goodfellow, Yoshua Bengio மற்றும் Aaron Courville எழுதிய [ஆழமான கற்றல்](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) புத்தகத்தை பரிந்துரைக்கிறோம், இது ஆன்லைனிலும் கிடைக்கிறது [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/). +* வியாபாரத்தில் **AI பயன்பாடுகள்**. Microsoft Learn இல் உள்ள [வியாபார பயனாளர்களுக்கான AI அறிமுகம்](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) கற்றல் பாதை அல்லது [AI வியாபாரம் பள்ளி](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), [INSEAD](https://www.insead.edu/) உடன் இணைந்து உருவாக்கப்பட்டது, எடுத்துக் கொள்ள பரிந்துரைக்கப்படுகிறது. +* எங்கள் [தொடக்கநிலையினருக்கான இயந்திரக் கற்றல் பாடத்திட்டம்](http://github.com/Microsoft/ML-for-Beginners) இல் நன்றாக விளக்கப்பட்டுள்ள **முறைபூர்வ இயந்திரக் கற்றல்**. +* **[Cogntive Services](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)** மூலம் உருவாக்கப்பட்ட நடைமுறை AI பயன்பாடுகள். இதற்காக, Microsoft Learn இல் உள்ள [கண்](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [இயற்கை மொழி செயலாக்கம்](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[Azure OpenAI சேவையுடன் உருவாக்கும் AI](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** மற்றும் பிற மாட்ஹுகள் மூலம் படிக்க பரிந்துரைக்கப்படுகிறது. +* குறிப்பிட்ட ML **மேகம் தளம்**(Cloud Frameworks), உதாரணமாக [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), அல்லது [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). [Azure Machine Learning மூலம் இயந்திரக் கற்றல் தீர்வுகளை உருவாக்கி இயக்கவும்](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) மற்றும் [Azure Databricks உடன் இயந்திரக் கற்றல் தீர்வுகளை உருவாக்கி இயக்கவும்](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum) ஆகிய பாட பாதைகளைப் பயன்படுத்த பரிந்துரைக்கப்படுகிறது. +* **संवादी AI** மற்றும் **சாட் போட்ஸ்**. [சென்றொடர்புடைய AI தீர்வுகளை உருவாக்கு](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum) என தனித்தவிர் பாட பாதையும் உள்ளது, மேலும் விரிவாக அறிந்துகொள்ள [இந்த வலைப்பூக் கட்டுரையை](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) காணலாம். +* **ஆழமான கணிதவியல்** (Deep Mathematics) பழகும். இதற்காக, Ian Goodfellow, Yoshua Bengio மற்றும் Aaron Courville அவர்களின் [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) புத்தகத்தை பரிந்துரைக்கிறோம், இது இணையத்திலும் கிடைக்கிறது: [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/). -_மேகத்தில் AI_ பற்றிய மென்மையான அறிமுகத்திற்குத் [Azure இல் கலைநுண்ணறிவுடன் துவக்கம்](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum) கற்றல் பாதையை எடுத்துக் கொள்ளுங்கள். +_மேகத்தில் AI_ பற்றிய மென்மையான முன்னோட்டத்திற்கு [Azure இல் செயற்கை நுண்ணறிவை தொடங்குவது](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum) கற்றல் பாதையை எடுத்துக்கொள்ளலாம். # உள்ளடக்கம் -| | பாடத்திட்ட இணைப்பு | PyTorch/Keras/TensorFlow | ஆய்வுப் பணி | +| | பாட இணைப்பு | PyTorch/Keras/TensorFlow | ஆய்வுக் கூடம் | | :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | -| 0 | [பாடத்திட்ட அமைப்பு](./lessons/0-course-setup/setup.md) | [உங்கள் மேம்பாட்டு சூழலை அமைக்கவும்](./lessons/0-course-setup/how-to-run.md) | | +| 0 | [பாடத்தின் அமைப்பு](./lessons/0-course-setup/setup.md) | [உங்கள் மேம்பாட்டு சூழலை அமைக்கவும்](./lessons/0-course-setup/how-to-run.md) | | | I | [**AI அறிமுகம்**](./lessons/1-Intro/README.md) | | | -| 01 | [ஆறுபடியாகும் AI அறிமுகமும் வரலாறும்](./lessons/1-Intro/README.md) | - | - | -| II | **சின்னமூலம் AI** | -| 02 | [அறிவுத்தொகுப்பு மற்றும் நிபுணர் முறைமைகள்](./lessons/2-Symbolic/README.md) | [நிபுணர் முறைமைகள்](./lessons/2-Symbolic/Animals.ipynb) / [ஒன்டொலொஜி](./lessons/2-Symbolic/FamilyOntology.ipynb) /[கருத்துக் கோப்பு](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | -| III | [**நரம்பியல் வலைப்பின்னல்களின் அறிமுகம்**](./lessons/3-NeuralNetworks/README.md) ||| +| 01 | [AI அறிமுகம் மற்றும் வரலாறு](./lessons/1-Intro/README.md) | - | - | +| II | **குறியீட்டு AI** | +| 02 | [அறிவு பிரதிநிதித்துவம் மற்றும் நிபுணர் அமைப்புகள்](./lessons/2-Symbolic/README.md) | [நிபுணர் அமைப்புகள்](./lessons/2-Symbolic/Animals.ipynb) / [ஒன்டாலஜி](./lessons/2-Symbolic/FamilyOntology.ipynb) /[கருத்து வரைபடம்](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | +| III | [**நியூரல் நெட்வொர்க்குகளுக்கான அறிமுகம்**](./lessons/3-NeuralNetworks/README.md) ||| | 03 | [பெர்செப்ட்ரான்](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [நோட்புக்](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [லேப்](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | -| 04 | [பல அடுக்கு பெர்செப்ட்ரான் மற்றும் நம் சொந்த ஃப்ரேம்வொர்க்கை உருவாக்குதல்](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [நோட்புக்](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [லேப்](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | -| 05 | [ஃப்ரேம்வொர்க்குக்களுக்கு அறிமுகம் (PyTorch/TensorFlow) மற்றும் ஓவர்ஃபிட்டிங்](./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) | [லேப்](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | -| IV | [**கணினி பார்வை**](./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 Azureல் கணினி பார்வையை ஆராய்க](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | -| 06 | [கணினி பார்வைக்கு அறிமுகம். OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [நோட்புக்](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [லேப்](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | -| 07 | [கான்வலூஷனல் நியூரல் நெட்வொர்க்குகள்](./lessons/4-ComputerVision/07-ConvNets/README.md) & [CNN கட்டமைப்புகள்](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [லேப்](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | -| 08 | [முன்பே பயிற்சிப்பட்ட நெட்வொர்க்குகள் மற்றும் பரிமாற்ற கற்றல்](./lessons/4-ComputerVision/08-TransferLearning/README.md) மற்றும் [பயிற்சி முறைகள்](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [லேப்](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | -| 09 | [ஆட்டோஎன்கோடர்கள் மற்றும் VAEகள்](./lessons/4-ComputerVision/09-Autoencoders/README.md) | [PyTorch](./lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb) | | -| 10 | [ஏற்றுமதி போட்டி நெட்வொர்க்குகள் மற்றும் கலைக்கலை மாற்றம்](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | -| 11 | [உருப்படி கண்டுபிடித்தல்](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [லேப்](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | -| 12 | [சாதாரண பிரிவாக்கம். U-வலை](./lessons/4-ComputerVision/12-Segmentation/README.md) | [PyTorch](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb) | | -| V | [**உயிரியல் மொழி செயலாக்கம்**](./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 Azureல் உயிரியல் மொழி செயலாக்கத்தை ஆராய்க](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| +| 04 | [பல-செருகலுடைய பெர்செப்ட்ரான் மற்றும் எங்கள் சொந்த கட்டமைப்பை உருவாக்குதல்](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [நோட்புக்](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [லேப்](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | +| 05 | [கட்டமைப்புகளுக்கான அறிமுகம் (PyTorch/TensorFlow) மற்றும் மிகைப்படுத்தல்](./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) | [லேப்](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | +| IV | [**கணினி பார்வை**](./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)| [மைக்ரோசாப்ட் அசுரில் கணினி பார்வையை ஆராயவும்](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | +| 06 | [கணினி பார்வைக்கான அறிமுகம். OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [நோட்புக்](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [லேப்](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | +| 07 | [ஒற்றுமை நரம்பு வலைப்பின்னல்கள்](./lessons/4-ComputerVision/07-ConvNets/README.md) & [CNN கட்டமைப்புகள்](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [லேப்](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | +| 08 | [முன்னதாக பயிற்சி பெற்ற வலைப்பின்னல்கள் மற்றும் மாற்ற முறைப்பயிற்சி](./lessons/4-ComputerVision/08-TransferLearning/README.md) மற்றும் [பயிற்சி தர்பொழுக்கள்](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [லேப்](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | +| 09 | [ஆட்டோஎன்கோடர்கள் மற்றும் VAE-கள்](./lessons/4-ComputerVision/09-Autoencoders/README.md) | [PyTorch](./lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb) | | +| 10 | [உற்பத்தி எதிர்ப்புப் வலைப்பின்னல்கள் மற்றும் கலைப்பாணி மாற்றம்](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | +| 11 | [பொருள் கண்டறிதல்](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [லேப்](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | +| 12 | [பொருந்தும் பகிர்வு. 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 | [**இயற்கை மொழி செயலாக்கம்**](./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) | [மைக்ரோசாப்ட் அசுரில் இயற்கை மொழி செயலாக்கத்தை ஆராயவும்](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| | 13 | [உரை பிரதிநிதித்துவம். 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 | [செயல்முறை வார்த்தை நுழைவுகள். Word2Vec மற்றும் 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 | [மொழி மாதிரிகை. உங்கள் சொந்த நுழைவுகளை பயிற்சி செய்தல்](./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) | [லேப்](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | -| 16 | [புன்னவைக் நியூரல் நெட்வொர்க்குகள்](./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 | [செயற்கைவளர்ச்சி புன்னவைக் நெட்வொர்க்குகள்](./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) | [லேப்](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | +| 14 | [பொருள் வார்த்தை புகுத்தங்கள். Word2Vec மற்றும் 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 | [மொழி மாதிரிகள். உங்கள் சொந்த புகுத்தங்களை பயிற்றுவித்தல்](./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) | [லேப்](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | +| 16 | [மறுநடை நரம்பு வலைப்பின்னல்கள்](./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 | [உற்பத்தி மறுநடை வலைப்பின்னல்கள்](./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) | [லேப்](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | | 18 | [டிரான்ஸ்ஃபார்மர்கள். 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 | [பெயர் அடையாள மீறல்](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [லேப்](./lessons/5-NLP/19-NER/lab/README.md) | -| 20 | [பெரிய மொழி மாதிரிகள், பிராம்ட் நிரலாக்கம் மற்றும் குறைந்த-எடுத்துக் கொள்வது பணிகள்](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | -| VI | **மற்ற AI தொழில்நுட்பங்கள்** || | -| 21 | [புரொடக்கும் அல்கொரிதம்கள்](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [நோட்புக்](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | -| 22 | [ஆழ்ந்த ரீஇன்போர்ஸ்மென்ட் கற்றல்](./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) | [லேப்](./lessons/6-Other/22-DeepRL/lab/README.md) | -| 23 | [பல முகவர் அமைப்புகள்](./lessons/6-Other/23-MultiagentSystems/README.md) | | | -| VII | **AI நெறிமுறைகள்** | | | -| 24 | [AI நெறிமுறைகள் மற்றும் பொறுப்புள்ள AI](./lessons/7-Ethics/README.md) | [Microsoft Learn: பொறுப்புள்ள AI கொள்கைகள்](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | -| IX | **அதிகம்** | | | -| 25 | [பல-மாதிரி நெட்வொர்க்குகள், CLIP மற்றும் VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [நோட்புக்](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | +| 19 | [பெயர் உருப்படி හඳුනාத்தல்](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [லேப்](./lessons/5-NLP/19-NER/lab/README.md) | +| 20 | [பெரிய மொழி மாதிரிகள், ப்ராம்ட் நிரலாக்கம் மற்றும் குறைந்த-காட்சி பணிகள்](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | +| VI | **மற்ற AI நுட்பங்கள்** || | +| 21 | [மரபணு அணுகுமுறை](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [நோட்புக்](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | +| 22 | [ஆழ்ந்த பலவீனி கற்றல்](./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) | [லேப்](./lessons/6-Other/22-DeepRL/lab/README.md) | +| 23 | [பல-செயலாளர் அமைப்புகள்](./lessons/6-Other/23-MultiagentSystems/README.md) | | | +| VII | **AI நெறிமுறை** | | | +| 24 | [AI நெறிமுறை மற்றும் பொறுப்பிற்குரிய AI](./lessons/7-Ethics/README.md) | [மைக்ரோசாஃப்ட் கற்றல்: பொறுப்பிற்குரிய AI கொள்கைகள்](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | +| IX | **கூடுதல்** | | | +| 25 | [பல்வேறு மாதிரிகள், CLIP மற்றும் VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [நோட்புக்](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | -## ஒவ்வொரு பாடத்திட்டமும் கொண்டிருக்கும் +## ஒவ்வொரு பாடமும் கொண்டிருக்கிறது -* முன்னோக்கு வாசிப்பு பொருள் -* இயக்கக்கூடிய ஜூபிடர் நோட்புக்குகள், அவை பெரும்பாலும் குறிப்பிட்ட ஃப்ரேம்வொர்க் (**PyTorch** அல்லது **TensorFlow**)க்கு ஏற்றவையாக இருக்கும். இயக்கக்கூடிய நோட்புக் பெரிதும் கோட்பாட்டினைப் கொண்டுள்ளது, எனவே தலைப்பை புரிந்துகொள்ள நீங்கள் குறைந்தபட்சம் ஒரு நோட்புக் பதிப்பை (PyTorch அல்லது TensorFlow எதுவாக இருந்தாலும்) மேற்கொள்ள வேண்டும். -* சில பொருட்களுக்கு **லேப்கள்** கிடைக்கின்றன, அவைகள் நீங்கள் கற்றுக் கொண்ட பொருளை ஒரு குறிப்பிட்ட பிரச்சினைக்கு பயன்படுத்த முயற்சிக்கும் வாய்ப்பைக் கொடுக்கின்றன. -* சில பிரிவுகளில் தொடர்புடைய தலைப்புகளை உள்ளடக்கிய [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) மொடியுல்கள் இணைக்கப்பட்டுள்ளன. +* முன்படிப்பு பொருள் +* இயங்கக்கூடிய ஜூபைட்டர் நோட்புக்குகள், அவை பெரும்பாலும் கட்டமைப்பிற்கு ( **PyTorch** அல்லது **TensorFlow**) குறிப்பாக இருக்கும். இயங்கக்கூடிய நோட்புக் அதிகமாக ஆராய்ச்சி பொருள்களை உள்ளடக்கியது, ஆகவே தலைப்பை புரிந்துகொள்ள, நீங்கள் குறைந்தது ஒரு பதிப்பை (PyTorch அல்லது TensorFlow) முழுமையாகப் பார்க்க வேண்டும். +* சில தலைப்புகளுக்கு **லேப்கள்** உள்ளன, இவைகள் நீங்கள் கற்றுக்கொண்ட பொருளை ஒரு குறிப்பிட்ட பிரச்சனையில் பாவனை செய்ய முயற்சிக்க முடியும். +* சில பிரிவுகளில் தொடர்புடைய தலைப்புகளை உள்ளடக்கும் [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) பாடங்கள் உள்ளன. -## துவக்கம் +## தொடங்குவோம் -### 🎯 AI இல் புதியவர்? இங்கே தொடங்குங்கள்! +### 🎯 AI-க்கு புதியவரா? இங்கிருந்து ஆரம்பிக்கவும்! -நீங்கள் AI பற்றி முழுமையாக புதியவராக இருந்தால் மற்றும் விரைவான, கைமுறையாக எடுத்துக்காட்டுகள் தேவைப்படுமானால், எங்கள் [**ஆரம்பக மாணவர்களுக்கான எடுத்துக்காட்டுகள்**](./examples/README.md) பார்க்கவும்! இவை அடங்கும்: +நீங்கள் AI-வில் முழுமையாக புதியவராகவும் விரைவான, கைபடி எடுத்துக்காட்டு முறைமைகள் வேண்டும் என்றாலும், எங்கள் [**ஆரம்பக்காரர்கள்-Friendly எடுத்துக்காட்டுகள்**](./examples/README.md) காண்க! இதில் உள்ளன: -- 🌟 **ஹலோ AI உலகம்** - உங்கள் முதல் AI நிகழ்ச்சி (முறை அங்கீகாரம்) -- 🧠 **எளிய நியூரல் நெட்வொர்க்** - தொடக்கம் முதல் நியூரல் நெட்வொர்க்கை உருவாக்குதல் -- 🖼️ **பட வகைப்பாட்டு மென்பொருள்** - விரிவான குறிப்பு கொண்ட படங்களை வகைப்படுத்துதல் -- 💬 **உரை உணர்வு** - நேர்மறை/எதிர்மறை உரையை பகுப்பாய்வு செய்க +- 🌟 **வணக்கம் AI உலகம்** - உங்கள் முதல் AI நிகழ்ச்சி (வடிவம் அறிதல்) +- 🧠 **எளிய நரம்பு வலைப்பு** - ஆரம்பத்திலிருந்தே ஒரு நரம்பு வலைப்பை கட்டவும் +- 🖼️ **பட வகைப்படுத்தி** - விவரமான கருத்துக்களுடன் படங்களை வகைப்படுத்தவும் +- 💬 **உரை உணர்வு** - நேர்மறை/எதிர்மறை உரையை பகுப்பாய்வு செய்யவும் -இந்த உதாரணங்கள் முழு பாடத்திட்டத்தில் இறங்குவதற்கு முன் AI கருத்துக்களை புரிந்து கொள்ள உங்களை உதவ உருவாக்கப்பட்டவை. +இவை முழு பாடத்திட்டத்தின் ஆரம்பத்தில் AI கருத்துக்களை புரிந்து கொள்வதற்கு உதவ வடிவமைக்கப்பட்ட உதாரணங்கள். ### 📚 முழு பாடத்திட்ட அமைப்பு -- உங்கள் மேம்படுத்தும் சூழலை அமைக்க உதவ [அமைப்பு பாடம்](./lessons/0-course-setup/setup.md) நாம் உருவாக்கி உள்ளோம். - கல்வியாளர்களுக்கு, உங்கள் கூட [பாடத்திட்ட அமைப்பு பாடம்](./lessons/0-course-setup/for-teachers.md) உருவாக்கபட்டுள்ளது! -- [VSCode அல்லது Codepace இல் குறியீட்டை இயக்க](./lessons/0-course-setup/how-to-run.md) எப்படி என்பது. +- உங்கள் மேம்பாட்டு சூழலை அமைப்பதற்கு உதவ [அமைப்பு பாடம்](./lessons/0-course-setup/setup.md) உருவாக்கப்பட்டுள்ளது. - ஆசிரியர்களுக்கு, அவர்களுக்கும் உதவ [பாடத்திட்ட அமைப்பு பாடம்](./lessons/0-course-setup/for-teachers.md) உருவாக்கியுள்ளோம்! +- VSCode அல்லது Codespace இல் [குறியீட்டை இயக்குவது எப்படி](./lessons/0-course-setup/how-to-run.md) -இந்த படிகளை பின்பற்றவும்: +இந்த படிகளைக் கடைபிடிக்கவும்: -தொகுப்பை கிளோன் செய்க: இந்தப் பக்கத்தின் மேல் வலது முனையில் உள்ள "Fork" பொத்தானைக் கிளிக் செய்யவும். +அர்.இணைவை பிரித்து ரசிப்பது: இக்கணினியின் மேலே-வலது மூலையில் உள்ள "Fork" பொத்தானை சொடுக்கவும். -தொகுப்பை கிளோன் செய்க: `git clone https://github.com/microsoft/AI-For-Beginners.git` +அர்.இணைவை நகலெடுக்க: `git clone https://github.com/microsoft/AI-For-Beginners.git` -பின்னர் அதை எளிதில் கண்டுபிடிப்பதற்காக இந்த ரெப்போக்கு நட்சத்திரம் (🌟) வைப்பதை மறக்க வேண்டாம். +தயவுசெய்து பின்னர் எளிதில் கண்டுபிடிக்க இந்த ரெப்போவை நட்சத்திரப்படுத்த (🌟) மறக்க வேண்டாம். -## பிற கற்றுக்கொள்பவர்கள் +## மற்ற கற்றல் பயணிகளை சந்தியுங்கள் -இந்த பாடத்தை படிக்கும் பிற கற்றுக்கொள்பவர்களை சந்திக்கவும் வலைப்புள்ளி அமைப்பில் நமது [அதிகாரப்பூர்வ AI Discord சர்வர்](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) கு சேரவும் மற்றும் பாடத்தில்திருக்கும் ஆதரவை பெறவும். +இந்தப் படிப்பில் கலந்து கொள்வோர் மற்றும் ஆதரவு பெற [அது அதிகாரபூர்வ AI Discord சேவை](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) இணைந்து, மற்ற கற்றல் பயணிகளைக் காணுங்கள் மற்றும் தொடர்பு கொள்ளுங்கள். -உங்கள் தயாரிப்பு பின்னூட்டம் அல்லது கேள்விகள் இருந்தால், கட்டமைக்கும் போது நமது [Azure AI Foundry Developer Forum](https://aka.ms/foundry/forum) ஐப் பார். +உற்பத்தியாளர் கருத்து அல்லது கேள்விகளுக்காக, கட்டுமானத்தில் எமது [Azure AI Foundry Developer Forum](https://aka.ms/foundry/forum) பார்க்கவும் -## வினாக vragen +## வினாத்தாள்கள் -> **வினாக்களைப் பற்றி ஒரு குறிப்பு**: எல்லா வினாக்களும் Quiz-app கோப்புறையில் etc\quiz-app இல் உள்ளன, அல்லது [இணையத்தில் இங்கே](https://ff-quizzes.netlify.app/) கிடைக்கும். அவை பாடங்களில் இணைக்கப்பட்டுள்ளன. வினாக்களுக்கான செயலி உள்ளூர் அல்லது Azure இல் இயக்கப்படலாம்; `quiz-app` கோப்புறையிலுள்ள வழிமுறைகளை பின்பற்றவும். அவை படிப்படியாக உள்ளூர்துகொள்ளப்படுகின்றன. +> **வினாத்தாள்கள் பற்றிய குறிப்பு**: அனைத்து வினாத்தாள்களும் Quiz-app கோப்புறையில் etc\quiz-app என உள்ளன, அல்லது [இணையத்தில் இங்கே](https://ff-quizzes.netlify.app/) உள்ளது. அவை பாடங்களுக்குள் இணைக்கப்பட்டுள்ளன, வினாத்தாள் பயன்பாட்டை உள்ளூர் அல்லது Azure இல் இயக்கலாம்; `quiz-app` கோப்புறையில் உள்ள வழிமுறைகளை பின்பற்றவும். அவை படிப்படியாக உள்ளூர் மொழிகளுக்கு மாற்றப்படுகின்றன. ## உதவி தேவை -உங்களுக்கு பரிந்துரைகள் உள்ளதா அல்லது எழுத்துப்பிழைகள் அல்லது குறியீடு பிழைகள் கண்டுபிடித்தீர்களா? ஒரு கேள்வி எழுப்பவும் அல்லது ஒரு புல் கோரிக்கை உருவாக்கவும். +மின்னஞ்சலோ குறியீட்டுப் பிழைகளை நீங்கள் கண்டுபிடித்ததாக இருந்தால் அல்லது பரிந்துரைகள் இருந்தால், ஒரு பிரச்சனையை எழுப்பவும் அல்லது पुल் விண்ணப்பத்தை உருவாக்கவும். ## சிறப்பு நன்றி -* **✍️ பிரதான ஆசிரியர்:** [Dmitry Soshnikov](http://soshnikov.com), PhD -* **🔥 திருத்தியவர்:** [Jen Looper](https://twitter.com/jenlooper), PhD -* **🎨 ஸ்கெட்ச் நோட் ஓவியர்:** [Tomomi Imura](https://twitter.com/girlie_mac) -* **✅ வினா உருவாக்கியவர்:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) -* **🙏 முக்கிய பங்களிப்பாளர்கள்:** [Evgenii Pishchik](https://github.com/Pe4enIks) +* **✍️ பிரதான ஆசிரியர்:** [Dmitry Soshnikov](http://soshnikov.com), PhD +* **🔥 ஆசிரியர்:** [Jen Looper](https://twitter.com/jenlooper), PhD +* **🎨 வரைபடக் கலைஞர்:** [Tomomi Imura](https://twitter.com/girlie_mac) +* **✅ வினாத்தாள் உருவாக்குனர்:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) +* **🙏 கோர் பங்களிப்பாளர்கள்:** [Evgenii Pishchik](https://github.com/Pe4enIks) -## பிற பாடத்திட்டங்கள் +## மற்ற பாடத்திட்டங்கள் -எங்கள் அணியினர் பிற பாடத்திட்டங்களையும் உருவாக்குகின்றனர்! பாருங்கள்: +எமது குழு மற்ற பாடத்திட்டங்களையும் உருவாக்குகிறது! இதைப் பார்வையிடுங்கள்: ### LangChain @@ -197,7 +197,7 @@ _மேகத்தில் AI_ பற்றிய மென்மையான --- -### மூலக் கற்றல் +### 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) @@ -208,7 +208,7 @@ _மேகத்தில் AI_ பற்றிய மென்மையான --- -### கோப்பைலட் தொடர் +### 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) @@ -216,17 +216,17 @@ _மேகத்தில் AI_ பற்றிய மென்மையான ## உதவி பெறுதல் -நீங்கள் சிக்கிச்செய்கின்றீர்களா அல்லது AI செயலிகளைக் கட்டுமானம் செய்வதில் ஏதாவது கேள்விகள் உள்ளதா? MCP பற்றிய விவாதங்களில் பிற கற்றுக்கொள்பவர்கள் மற்றும் அனுபவம் வாய்ந்த டெவலப்பர்களுடன் சேருங்கள். கேள்விகள் வரவேற்கப்படுகின்றன மற்றும் அறிவு சுதந்திரமாக பகிரப்படுகிறது. +AI பயன்பாடுகளை உருவாக்கும்போது சிக்கல்களில் சிக்கினால் அல்லது ஏதேனும் கேள்விகள் இருந்தால், MCP குறித்து மற்ற கற்றல் பயணிகள் மற்றும் அனுபவசாலி மேம்படுத்திகள் சேர்ந்து விவாதிக்கின்றனர். இது ஒரு ஆதரவான சமூகமாகும், அங்கு கேள்விகள் வரவேற்கப்படுகின்றன மற்றும் அறிவு விடாமுயற்சியுடன் பகிரப்படுகிறது. [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -உங்கள் தயாரிப்பில் பின்னூட்டமோ பிழைகளோ இருந்தால், கீழ்க்கண்ட இடத்தைப் பார்க்கவும்: +உற்பத்தி தொடர்பான கருத்துக்கள் அல்லது பிழைகள் இருந்தால்: [![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) --- -**பிரதி பொறுப்பேற்கவில்லை**: -இந்த ஆவணம் AI மொழிபெயர்ப்பு சேவை [Co-op Translator](https://github.com/Azure/co-op-translator) மூலம் மொழிபெயர்க்கப்பட்டது. நாம் துல்லியத்திற்காக முயன்றாலும், தானாக செய்யப்பட்ட மொழிபெயர்ப்புகளில் பிழைகள் அல்லது தவறுகள் இருக்கக்கூடும் என்பதை கவனியுங்கள். ஆவணத்தின் இயல்புநிலை மொழியில் உள்ள மூலம் அங்கீகரிக்கப்பட்ட அதிகாரப்பூர்வமான தகவலாக கருதப்பட வேண்டும். முக்கியமான தகவல்களுக்கு, தொழில்முறை மனித மொழிபெயர்ப்பை பரிந்துரைக்கிறோம். இந்த மொழிபெயர்ப்பின் பயன்பாட்டில் ஏற்படும் எந்த தவறுகள் அல்லது தவறான புரிதல்களுக்கும் நாங்கள் பொறுப்பாளர்கள் அல்ல. +**அறிவுறுத்தல்**: +இந்த ஆவணம் AI மொழிபெயர்ப்பு சேவை [Co-op Translator](https://github.com/Azure/co-op-translator) மூலம் மொழிபெயர்க்கப்பட்டது. நாங்கள் துல்லியத்தைக் குறிக்க ஆசைப்படினாலும், தானாகம் மொழிபெயர்ப்புகளில் தவறுகள் அல்லது தவறிய விளக்கங்கள் இருக்க வாய்ப்பு உள்ளது என்பதை தயவுசெய்து கவனிக்கவும். அதன் தாய்மொழியில் உள்ள அசல் ஆவணம் அதிகாரப்பூர்வமான ஆதாரமாக கருதப்பட வேண்டும். முக்கியமான தகவலுக்கு, தகுதியான மனித மொழிபெயர்ப்பாளர் மூலம் மொழிபெயர்ப்பு செய்தல் பரிந்துரைக்கப்படுகின்றது. இந்த மொழிபெயர்ப்பை பயன்படுத்துவதால் ஏற்பட்ட எந்த தவறான புரிதலும் அல்லது தவறான விளக்கங்களுக்கும் நாங்கள் பொறுப்பு கொள்ளமாட்டோம். \ No newline at end of file diff --git a/translations/ta/lessons/0-course-setup/how-to-run.md b/translations/ta/lessons/0-course-setup/how-to-run.md index c1c8edbb..bd00c454 100644 --- a/translations/ta/lessons/0-course-setup/how-to-run.md +++ b/translations/ta/lessons/0-course-setup/how-to-run.md @@ -1,21 +1,21 @@ -# கோடுகளை இயக்குவது எப்படி +# குறியீட்டை எப்படி இயக்குவது -இந்த பாடத்திட்டத்தில் நீங்கள் இயக்க விரும்பும் நிறைய செயல்படக்கூடிய உதாரணங்கள் மற்றும் ஆய்வகங்கள் உள்ளன. இதை செய்ய, இந்த பாடத்திட்டத்தின் ஒரு பகுதியாக வழங்கப்படும் Jupyter Notebooks-ல் Python கோடுகளை இயக்கும் திறன் தேவைப்படும். கோடுகளை இயக்க பல விருப்பங்கள் உள்ளன: +இந்த பாடத்திட்டத்தில் நீங்கள் இயக்க விரும்பும் நிறைய செயலாக்கக் கூடிய உதாரணங்கள் மற்றும் 실습களைக் கொண்டுள்ளது. இதைச் செய்ய, நீங்கள் இந்த பாடத்திட்டத்தின் ஒரு பகுதியான Jupyter Notebooks இல் Python குறியீட்டை இயக்கும் திறனுடன் இருக்க வேண்டும். குறியீட்டை இயக்குவதற்கு உங்களுக்கு பலவிதமான விருப்பங்கள் உள்ளன: -## உங்கள் கணினியில் உள்ளூர் முறையில் இயக்கவும் +## உங்கள் கணினியில் உள்ளூர் முறையில் இயக்குதல் -கோடுகளை உங்கள் கணினியில் உள்ளூர் முறையில் இயக்க, Python இன் ஏதாவது ஒரு பதிப்பு நிறுவப்பட்டிருக்க வேண்டும். நான் **[miniconda](https://conda.io/en/latest/miniconda.html)**-ஐ நிறுவ பரிந்துரைக்கிறேன் - இது `conda` பேக்கேஜ் மேலாளர் மூலம் Python **மெய்நிகர் சூழல்களை** ஆதரிக்கும் எளிய நிறுவல். +உங்கள் கணினியில் உள்ளூர் முறையில் குறியீட்டை இயக்க Python நிறுவல் தேவைப்படும். ஒரு பரிந்துரையாக **[miniconda](https://conda.io/en/latest/miniconda.html)** ஐ நிறுவ பரிந்துரைக்கப்படுகின்றது - இது மிகவும் எளிமையான நிறுவல் ஆகும் மற்றும் Python இன் வேறு வேறு **உண்மை சூழல்கள் (virtual environments)** க்கான `conda` பாகேஜ் மேலாளரை ஆதரிக்கிறது. -Miniconda-ஐ நிறுவிய பிறகு, நீங்கள் repository-ஐ clone செய்து, இந்த பாடத்திட்டத்திற்காக பயன்படுத்த ஒரு மெய்நிகர் சூழலை உருவாக்க வேண்டும்: +miniconda ஐ நிறுவிய பிறகு, ரெப்போஸிட்டரியை கிளோன் செய்து இந்த பாடத்திட்டத்திற்கான ஒரு உண்மை சூழலை உருவாக்கவும்: ```bash git clone http://github.com/microsoft/ai-for-beginners @@ -24,19 +24,19 @@ conda env create --name ai4beg --file .devcontainer/environment.yml conda activate ai4beg ``` -### Python Extension உடன் Visual Studio Code பயன்படுத்துதல் +### Python விரிவாக்கத்துடன் Visual Studio Code பயன்படுத்துதல் -இந்த பாடத்திட்டத்தை பயன்படுத்த சிறந்த வழி [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste)-இல் [Python Extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste) உடன் திறப்பது. +இந்த பாடத்திட்டம் [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) இல் [Python Extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste) உடன் திறந்தபோது சிறந்த முறையில் பயன்படுத்தலாம். -> **குறிப்பு**: நீங்கள் repository-ஐ clone செய்து VS Code-இல் திறந்தவுடன், Python extensions-ஐ நிறுவ VS Code தானாகவே பரிந்துரைக்கும். மேலே விவரிக்கப்பட்ட Miniconda-ஐ நீங்கள் நிறுவ வேண்டும். +> **குறிப்பு**: நீங்கள் ரெப்போஸிட்டரியை கிளோன் செய்து VS Code இல் கோப்புறையை திறக்கும் போது, அது தானாக Python விரிவாக்கங்களை நிறுவ பரிந்துரைக்கும். மேலும், மேலே விளக்கப்பட்டபடி miniconda ஐ நிறுவ வேண்டியும் இருக்கும். -> **குறிப்பு**: VS Code repository-ஐ container-இல் மீண்டும் திறக்க பரிந்துரைத்தால், உள்ளூர் Python நிறுவலை பயன்படுத்த நீங்கள் அதை நிராகரிக்க வேண்டும். +> **குறிப்பு**: VS Code உங்களுக்கு ரெப்போஸிட்டரியை ஒரு கன்டைனரில் மீண்டும் திறக்க பரிந்துரைத்தால், உள்ளூர் Python நிறுவலைப் பயன்படுத்த இதை மறுத்தல் வேண்டும். ### உலாவியில் Jupyter பயன்படுத்துதல் -நீங்கள் உங்களது கணினியில் இருந்து உலாவியில் Jupyter சூழலை பயன்படுத்தலாம். உண்மையில், பாரம்பரிய Jupyter மற்றும் Jupyter Hub இரண்டும் auto-completion, code highlighting போன்ற வசதிகளுடன் மிகவும் வசதியான மேம்பாட்டு சூழலை வழங்குகின்றன. +உங்கள் கணினியில் உலாவி மூலம் ஒரு Jupyter சூழலை நீங்கள் பயன்படுத்தலாம். பாரம்பரிய Jupyter மற்றும் JupyterHub இரண்டும் தானாக பூட்டப்படுதல், குறியீட்டு ஒளிர்வு போன்ற வசதிகளுடன் ஒரு சிறந்த அபிவிருத்தி சூழலை வழங்குகின்றன. -உள்ளூர் முறையில் Jupyter-ஐ தொடங்க, பாடத்திட்டத்தின் கோப்பகத்திற்குச் செல்லவும், மற்றும் கீழே உள்ளதை இயக்கவும்: +Jupyter ஐ உள்ளூர் இயக்க, பாடத்திட்டத்தின் கோப்புறைக்குச் சென்று கீழ்காணும் கட்டளைகளை இயக்கவும்: ```bash jupyter notebook @@ -45,34 +45,37 @@ jupyter notebook ```bash jupyterhub ``` -பிறகு `.ipynb` கோப்புகளை எதையும் தேடி, திறந்து வேலை செய்ய தொடங்கலாம். +பின்னர் `.ipynb` கோப்புகளில் எந்தவொன்றையும் திறந்து, பணியாற்றத் தொடங்கலாம். -### Container-இல் இயக்குதல் +### கன்டைனரில் இயக்குதல் -Python நிறுவலுக்கு மாற்றாக, container-இல் கோடுகளை இயக்கலாம். எங்கள் repository-யில் `.devcontainer` கோப்பகம் உள்ளது, இது இந்த repository-க்கு container-ஐ உருவாக்க எப்படி என்பதை விளக்குகிறது. இதனால் VS Code container-இல் கோடுகளை மீண்டும் திறக்க பரிந்துரைக்கும். இது Docker நிறுவலை தேவைப்படும், மேலும் இது சிக்கலானது, எனவே இது அனுபவமுள்ள பயனர்களுக்கு பரிந்துரைக்கப்படுகிறது. +Python நிறுவலுக்கு மாற்றாக, குறியீட்டை கன்டைனரில் இயக்கு எடுத்துக்காட்டு உள்ளது. எங்கள் ரெப்போஸிட்டரியில் `.devcontainer` என்ற சிறப்பு அடைவுகள் உள்ளன, இது இந்த ரெப்போக்கான கன்டைனர் கட்டுமுறை வழிமுறைகள் அளிக்கிறது. VS Code குறியீட்டை கன்டைனரில் மீண்டும் திறக்க வாய்ப்பு வழங்குகிறது. இதற்கு Docker நிறுவல் தேவைப்படும் மற்றும் இதுவும் சற்று சிக்கலானது, ஆகவே இது அனுபவம் உள்ள பயனர்களுக்கே பரிந்துரைக்கப்படுகிறது. -## Cloud-இல் இயக்குதல் +## வகைப்படுத்தல்கள் -Python-ஐ உள்ளூர் முறையில் நிறுவ விரும்பாதவர்கள், மற்றும் சில cloud வளங்களுக்கு அணுகல் உள்ளவர்கள் - cloud-இல் கோடுகளை இயக்க ஒரு நல்ல மாற்று இருக்கும். இதை செய்ய சில வழிகள் உள்ளன: +Python ஐ உள்ளூர் நிறுவ விரும்பவில்லை ஆனால் சில மேக வளங்களைப் பெற்றுள்ளீர்கள் என்றால், குறியீட்டை மேகத்தில் இயக்குவது நல்ல வழி. இதற்காக பல முறைகள் உள்ளன: -* **[GitHub Codespaces](https://github.com/features/codespaces)**-ஐ பயன்படுத்துதல், இது GitHub-ல் உங்களுக்கு உருவாக்கப்பட்ட ஒரு மெய்நிகர் சூழல், VS Code உலாவி இடைமுகத்தின் மூலம் அணுகக்கூடியது. Codespaces-க்கு அணுகல் உள்ளவர்கள், repo-வில் **Code** பொத்தானை கிளிக் செய்து, codespace-ஐ தொடங்கி, உடனடியாக இயக்கலாம். -* **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**-ஐ பயன்படுத்துதல். [Binder](https://mybinder.org) என்பது GitHub-ல் சில கோடுகளை சோதிக்க cloud-இல் இலவச கணினி வளங்களை வழங்குகிறது. Repository-யை Binder-இல் திறக்க முன்னணி பக்கத்தில் ஒரு பொத்தான் உள்ளது - இது உங்களை Binder தளத்திற்குக் கொண்டு செல்லும், அங்கு underlying container-ஐ உருவாக்கி, Jupyter வலை இடைமுகத்தை தானாகவே தொடங்கும். +* **[GitHub Codespaces](https://github.com/features/codespaces)** பயன்படுத்தல் - இது GitHub இல் உங்களுக்கு உருவாக்கப்பட்ட ஒரு உண்மை சூழல் ஆகும், VS Code உலாவி இடைமுகத்தால் உள்நுழையப் பெறலாம். Codespaces கிடைத்தால், ரெப்போவில் **Code** பொத்தானை அழுத்தி, codespace ஐத் தொடக்கியவுடன் உடனுக்குடன் தொடங்கலாம். -> **குறிப்பு**: தவறாக பயன்படுத்துவதைத் தடுக்க, Binder சில வலை வளங்களுக்கு அணுகலைத் தடை செய்துள்ளது. இது public Internet-இல் இருந்து மாதிரிகள் மற்றும்/அல்லது தரவுத்தொகுப்புகளை பெறும் சில கோடுகள் செயல்படாமல் இருக்கலாம். நீங்கள் சில மாற்று வழிகளை தேட வேண்டியிருக்கும். மேலும், Binder வழங்கும் கணினி வளங்கள் அடிப்படையானவை, எனவே குறிப்பாக பின்னர் உள்ள சிக்கலான பாடங்களில் பயிற்சி மெதுவாக இருக்கும். +* **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)** பயன்படுத்துதல் - [Binder](https://mybinder.org) என்பது GitHub இல் உள்ள குறியீடை சோதிக்க மக்களுக்கு மேகத்தில் இலவச கணினிசார் வளங்களை வழங்குகிறது. முன்னணி பக்கத்தில் ஒரு பொத்தான் உள்ளது, இதன் மூலம் அந்த ரெப்போசிட்டரியை Binder இல் திறக்கலாம் - இது விரைவாக binder தளத்தை திறக்கும், அடிப்படை கன்டைனரை கட்டி, Jupyter இணைய முகப்பை ஆட்டோமாக ஆரம்பிக்கும். -## GPU உடன் Cloud-இல் இயக்குதல் +> **குறிப்பு**: தவறான பயன்பாட்டைத் தடுப்பதற்காக, Binder சில இணைய வளங்களுக்கு அணுகலைத் தடை செய்துள்ளது. இது சில குறியீடுகள், பொதுச்சேவையகங்கள் மற்றும் தரவுத்தொகுப்புகளைப் பெறும் போது வேலை செய்யாமற் போகலாம். நீங்கள் சில மாற்று வழிகளை தேட வேண்டியிருக்கலாம். மேலும், Binder வழங்கும் கணினிசார் வளங்கள் அடிப்படை மட்டமாகவே உள்ளதால், பயிற்சி மிகவும் மெதுவாக நடக்கும், குறிப்பாக பின்னர் சிக்கலான பாடங்களில். -இந்த பாடத்திட்டத்தின் சில பின்னர் உள்ள பாடங்கள் GPU ஆதரவைப் பெறுவதால் பயனுள்ளதாக இருக்கும், இல்லையெனில் பயிற்சி மிகவும் மெதுவாக இருக்கும். குறிப்பாக [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) அல்லது உங்கள் நிறுவனத்தின் மூலம் cloud-க்கு அணுகல் உள்ளவர்கள் பின்வரும் விருப்பங்களை பின்பற்றலாம்: +## GPU உடன் மேகத்தில் இயக்குதல் -* [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste)-ஐ உருவாக்கி, Jupyter மூலம் அதனை இணைக்கவும். பின்னர் repo-ஐ நேரடியாக அந்த இயந்திரத்தில் clone செய்து, கற்றல் தொடங்கலாம். NC-series VM-களில் GPU ஆதரவு உள்ளது. +இந்த பாடத்திட்டத்தின் சில பின்னுலக்க பாடங்கள் GPU ஆதரவுடன் பெரிதும் பயனடையும். மாதிரி பயிற்சி, உதாரணமாக, இல்லையெனில் மிகவும் மெதுவாக நடைபெறும். சில விருப்பங்கள் இருக்கின்றன, குறிப்பாக நீங்கள் மேகத்தில் [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) அல்லது உங்கள் கல்வி நிறுவனத்தின் வாயிலாக அணுகல் பெற்றிருந்தால்: -> **குறிப்பு**: Azure for Students உட்பட சில சந்தாக்கள், GPU ஆதரவை உடனடியாக வழங்காது. நீங்கள் தொழில்நுட்ப ஆதரவு கோரிக்கையின் மூலம் கூடுதல் GPU கோர்களை கோர வேண்டியிருக்கும். +* [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) உருவாக்கி Jupyter மூலம் அதனுடன் இணைவது. பின்னர் நீங்கள் ரெப்போவை நேரடியாக மின்னணு இயந்திரத்தில் கிளோன் செய்து изучு தொடங்கலாம். NC-வரிசை VM களில் GPU ஆதரவு உள்ளது. -* [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste)-ஐ உருவாக்கி, அங்கு Notebook அம்சத்தைப் பயன்படுத்தவும். [இந்த வீடியோ](https://azure-for-academics.github.io/quickstart/azureml-papers/) Azure ML notebook-இல் repository-யை clone செய்து அதை எப்படி பயன்படுத்துவது என்பதை காட்டுகிறது. +> **குறிப்பு**: சில சந்தாக்கள், Azure for Students உட்பட, பொதுவாக GPU ஆதரவை வழங்குவதில்லை. கூடுதல் GPU கோர்களை தொழில் நுட்ப ஆதரவு கோரிக்கையுடன் கேட்டு பெற வேண்டியிருக்கும். -நீங்கள் Google Colab-ஐ பயன்படுத்தலாம், இது சில இலவச GPU ஆதரவை வழங்குகிறது, மற்றும் Jupyter Notebooks-ஐ அங்கு upload செய்து, ஒவ்வொன்றாக அவற்றை இயக்கலாம். +* [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) உருவாக்கி, அங்கு உள்ள நோட்புக் வசதியைப் பயன்படுத்தவும். [இந்த வீடியோ](https://azure-for-academics.github.io/quickstart/azureml-papers/) எப்படி ரெப்போவை Azure ML நோட்புக்கில் கிளோன் செய்து பயன்படுத்துவது என்பதைக் காட்டுகிறது. + +மேலும், சில இலவச GPU ஆதரவுடன் கூடிய Google Colab ஐப் பயன்படுத்தி, Jupyter நோட்புக்குகளை அங்கே ஏற்றிக் கொண்டு ஒன்றரைவிடை இயக்கலாம். --- -**குறிப்பு**: -இந்த ஆவணம் [Co-op Translator](https://github.com/Azure/co-op-translator) என்ற AI மொழிபெயர்ப்பு சேவையைப் பயன்படுத்தி மொழிபெயர்க்கப்பட்டுள்ளது. நாங்கள் துல்லியத்திற்காக முயற்சிக்கின்றோம், ஆனால் தானியக்க மொழிபெயர்ப்புகளில் பிழைகள் அல்லது தவறான தகவல்கள் இருக்கக்கூடும் என்பதை தயவுசெய்து கவனத்தில் கொள்ளுங்கள். அதன் தாய்மொழியில் உள்ள மூல ஆவணம் அதிகாரப்பூர்வ ஆதாரமாக கருதப்பட வேண்டும். முக்கியமான தகவல்களுக்கு, தொழில்முறை மனித மொழிபெயர்ப்பு பரிந்துரைக்கப்படுகிறது. இந்த மொழிபெயர்ப்பைப் பயன்படுத்துவதால் ஏற்படும் எந்த தவறான புரிதல்கள் அல்லது தவறான விளக்கங்களுக்கு நாங்கள் பொறுப்பல்ல. \ No newline at end of file + +**மறுப்பு**: +இந்த ஆவணம் [Co-op Translator](https://github.com/Azure/co-op-translator) எனும் AI மொழிபெயர்ப்பு சேவையினைப் பயன்படுத்தி மொழிபெயர்க்கப்பட்டுள்ளது. நாம் சரியானதாக முயற்சித்தோமோ என்றாலும், தானியங்கி மொழிபெயர்ப்புகள் தவறுகள் அல்லது துல்லியமற்றவையாக இருக்கக்கூடும் என்பதை தயவுசெய்து கவனியுங்கள். மூல ஆவணம் அதன் பூர்வ மொழியில் அதிகாரப்பூர்வமான மூலமாக கருதப்பட வேண்டும். முக்கியமான தகவல்களுக்கு, தொழில்முறை மனித மொழிபெயர்ப்பு பரிந்துரைக்கப்படுகிறது. இந்த மொழிபெயர்ப்பைப் பயன்படுத்துவதால் ஏற்பட்ட எந்த தவறான புரிதல்கள் அல்லது தவறான விளக்கங்களுக்கும் நாங்கள் பொறுப்பேற்கவில்லை. + \ No newline at end of file diff --git a/translations/ta/lessons/2-Symbolic/Animals.ipynb b/translations/ta/lessons/2-Symbolic/Animals.ipynb index 5241a75f..407abeb1 100644 --- a/translations/ta/lessons/2-Symbolic/Animals.ipynb +++ b/translations/ta/lessons/2-Symbolic/Animals.ipynb @@ -6,25 +6,25 @@ "collapsed": true }, "source": [ - "# விலங்கு நிபுணர் அமைப்பை செயல்படுத்துதல்\n", + "# ஒரு விலங்கு நிபுணர் அமைப்பை செயல்படுத்துதல்\n", "\n", - "[AI for Beginners Curriculum](http://github.com/microsoft/ai-for-beginners) இலிருந்து எடுத்துக்காட்டு.\n", + "[AI for Beginners Curriculum](http://github.com/microsoft/ai-for-beginners) இல் இருந்து ஒரு உதாரணம்.\n", "\n", - "இந்த எடுத்துக்காட்டில், சில உடல் பண்புகளின் அடிப்படையில் விலங்குகளை தீர்மானிக்க ஒரு எளிய அறிவு அடிப்படையிலான அமைப்பை செயல்படுத்துவோம். இந்த அமைப்பு கீழே உள்ள AND-OR மரத்தால் பிரதிநிதித்துவம் செய்யப்படுகிறது (இது முழு மரத்தின் ஒரு பகுதி மட்டுமே, மேலும் சில விதிகளை எளிதாக சேர்க்கலாம்):\n", + "இந்த எடுத்துப்பாட்டில், சில உடல் பண்புகளை அடிப்படையாகக் கொண்டு ஒரு விலங்கை தீர்மானிக்க ஒரு எளிய அறிவு அடிப்படையிலான அமைப்பை செயல்படுத்துவோம். இந்த அமைப்பை பின்வரும் AND-OR மரமாக பிரதிபலிக்கலாம் (இது முழுமையான மரத்தின் ஒரு பகுதி, நாங்கள் எளிதில் சில கூடுதல் விதிகளையும் சேர்க்கலாம்):\n", "\n", - "![](../../../../translated_images/ta/AND-OR-Tree.5592d2c70187f283.webp)\n" + "![](../../../../../../translated_images/ta/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## பின்தொடர்பை பயன்படுத்தி நமது சொந்த நிபுணர் அமைப்பு\n", + "## நமதுயான நிபுணத்துவ அமைப்புகளுக்கான ஷெல் பின்வாங்கி தீர்வுடன்\n", "\n", - "உற்பத்தி விதிகளின் அடிப்படையில் அறிவு பிரதிநிதித்திற்கான ஒரு எளிய மொழியை வரையறுக்க முயலலாம். விதிகளை வரையறுக்க Python வகுப்புகளை முக்கிய வார்த்தைகளாக பயன்படுத்துவோம். மூன்று வகையான வகுப்புகள் இருக்கும்:\n", - "* `Ask` என்பது பயனரிடம் கேட்க வேண்டிய கேள்வியை பிரதிநிதித்துவப்படுத்துகிறது. இது சாத்தியமான பதில்களின் தொகுப்பை கொண்டுள்ளது.\n", - "* `If` என்பது ஒரு விதியை பிரதிநிதித்துவப்படுத்துகிறது, மேலும் இது விதியின் உள்ளடக்கத்தை சேமிக்க ஒரு சSyntaxic Sugar மட்டுமே.\n", - "* `AND`/`OR` என்பது மரத்தின் AND/OR கிளைகளை பிரதிநிதித்துவப்படுத்தும் வகுப்புகள். அவை உள்ளே உள்ள வாதங்களின் பட்டியலை மட்டும் சேமிக்கின்றன. குறியீட்டை எளிமைப்படுத்த, அனைத்து செயல்பாடுகளும் பெற்றோர் வகுப்பு `Content` இல் வரையறுக்கப்பட்டுள்ளன.\n" + "உற்பத்தி விதிகளின் அடிப்படையில் அறிவு பிரதிநிதித்துவத்திற்கு எளிய மொழியை வரையறுக்க முயலலாம். விதிகளை வரையறுக்க பைத்தான் வகுப்புகளை முக்கிய வார்த்தைகளாக பயன்படுத்துவோம். மூன்று வகையான வகுப்புகள் இருக்கின்றன:\n", + "* `Ask` என்பது பயனரிடம் கேட்க வேண்டிய கேள்வியை பிரதிநிதித்துவம் செய்யும். இது சாத்தியமான பதில்களின் தொகுப்பை கொண்டுள்ளது.\n", + "* `If` என்பது ஒரு விதியை பிரதிநிதித்துவம் செய்யும், மற்றும் அது விதியின் உள்ளடக்கத்தை சேமிப்பதற்கான சொல்லியல் சர்க்கரை மாதிரி உள்ளது\n", + "* `AND`/`OR` என்பது மரத்தின் AND/OR கிளைகளைக் குறிப்பிடும் வகுப்புகள். அவை உள்ளடக்க வாயிலான கணக்குகளை மட்டும் சேமிக்கின்றன. குறியுறுப்பை எளிமையாக்க, எல்லா செயல்பாடும் பெற்றோர் வகுப்பு `Content`-ல் வரையறுக்கப்பட்டுள்ளது\n" ] }, { @@ -66,7 +66,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "எங்கள் அமைப்பில், வேலை நினைவகம் **தகவல்களின்** பட்டியலை **அடையாளம்-மதிப்பு ஜோடிகளாக** கொண்டிருக்கும். அறிவகத்தை, செயல்களை (வேலை நினைவகத்தில் சேர்க்கப்பட வேண்டிய புதிய தகவல்கள்) நிபந்தனைகளுடன் இணைக்கும் ஒரு பெரிய அகராதியாக வரையறுக்கலாம், இது AND-OR வெளிப்பாடுகளாக வெளிப்படுத்தப்படும். மேலும், சில தகவல்கள் `கேட்கப்படலாம்`.\n" + "எமது கணினி அமைப்பில், செயல்பாட்டுக் கால நினைவகம் **அறிவியல் தரவுகளின்** பட்டியலை **பண்பு-மதிப்புப் பொருப்புகளாக** கொண்டிருக்கும். அறிவுத்தளம் ஒரே பெரிய அகராதியாக வரையறுக்கப்படலாம், அது செயல்களை (செயற்பாட்டுக் கால நினைவகத்தில் சேர்க்கப்படவேண்டிய புதிய அறிவியல் தரவுகள்) AND-OR வெளிப்பாடுகளாக வெளிப்படுத்தப்பட்ட நிபந்தனைகளுடன் இணைக்கிறது. மேலும், சில அறிவியல் தரவுகளை `Ask` செய்யலாம்.\n" ] }, { @@ -99,13 +99,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "பின்தங்கிய முடிவெடுப்பை செய்ய, நாங்கள் `Knowledgebase` வகுப்பை வரையறுக்கிறோம். இது கீழ்கண்டவற்றை கொண்டிருக்கும்:\n", - "* செயல்படும் `memory` - பண்புகளை மதிப்புகளுடன் இணைக்கும் ஒரு அகராதி\n", - "* மேலே வரையறுக்கப்பட்ட வடிவத்தில் உள்ள Knowledgebase `rules`\n", + "பின்னோக்கிய ஊகத்தை செய்ய, நாம் `Knowledgebase` வகுப்பை வரையறுப்போம். இது கொண்டிருக்க இருக்கும்:\n", + "* பணியாற்றும் `memory` - பண்புகளை மதிப்புகளுக்கு மையப்படுத்தும் அகராதி\n", + "* அறிவுத்தளம் `rules` முன்பு வரையறுக்கப்பட்ட வடிவத்தில்\n", "\n", - "முக்கியமான இரண்டு முறைகள்:\n", - "* `get` - ஒரு பண்பின் மதிப்பை பெற, தேவையானால் முடிவெடுப்பைச் செய்கிறது. உதாரணமாக, `get('color')` என்பது ஒரு நிறத்தின் ஸ்லாட்டின் மதிப்பை பெறும் (தேவையானால் கேட்கும், மேலும் வேலை நினைவகத்தில் மதிப்பை பின்னர் பயன்படுத்த சேமிக்கும்). நாம் `get('color:blue')` என்று கேட்டால், அது ஒரு நிறத்தை கேட்கும், பின்னர் அந்த நிறத்தைப் பொறுத்து `y`/`n` மதிப்பைத் திருப்பும்.\n", - "* `eval` - உண்மையான முடிவெடுப்பைச் செய்கிறது, அதாவது AND/OR மரத்தைச் சுற்றி, துணை இலக்குகளை மதிப்பீடு செய்கிறது, போன்றவை.\n" + "இரு முக்கிய முறைமைகள்:\n", + "* `get` பண்பின் மதிப்பை பெற, தேவையானபட்சத்தில் ஊகத்தைச் செய்கிறது. உதாரணமாக, `get('color')` ஒரு வண்ண வகை மதிப்பை பெறும் (தேவையானால் கேட்கும், மற்றும் பின்னர் பணியாற்றும் நினைவகத்தில் மதிப்பை சேமிக்கும்). நாம் `get('color:blue')` என்றால், அது வண்ணத்திற்காக கேட்கும், பின்னர் வண்ணத்தின் அடிப்படையில் `y`/`n` மதிப்பை தரும்.\n", + "* `eval` உண்மையான ஊகத்தை செய்கிறது, அதாவது AND/OR மரத்தை கடந்துமுழுக்குகிறது, துணை-நோக்குகளை மதிப்பாய்வு செய்கிறது, மற்றும் மேலும்.\n" ] }, { @@ -172,7 +172,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "இப்போது நமது விலங்கு அறிவுத்தொகுப்பை வரையறுத்து ஆலோசனையை மேற்கொள்வோம். இந்த அழைப்பு உங்களிடம் கேள்விகளை கேட்கும் என்பதை கவனிக்கவும். ஆம்-இல்லை கேள்விகளுக்கு `y`/`n` என تایப செய்து பதிலளிக்கலாம், அல்லது நீண்ட பல தேர்வு பதில்களுக்கான கேள்விகளுக்கு (0..N) எண் குறிப்பிடலாம்.\n" + "இப்போது நம் விலங்கு அறிவுக் கூறுகளை வரையறுக்கலாம் மற்றும் ஆலோசனையை முன்னெடுக்கலாம். இந்த அழைப்பு உங்களுக்குப் பின்வரும் கேள்விகளை கேட்கும் என்பதை கவனியுங்கள். நீங்கள் ஆம்-இல்லை கேள்விகளுக்கு `y`/`n` என்று எழுதுவதால் பதிலளிக்கலாம், அல்லது பல தேர்வுகள் உள்ள கேள்விகளுக்கு எண் (0..N) குறிப்பிடுவதால் பதில் அளிக்கலாம்.\n" ] }, { @@ -229,11 +229,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## PyKnow பயன்படுத்தி முன்னோக்கி தீர்மானம்\n", + "## முன்னோக்கி நுண்ணறிவுக்கு Experta பயன்பாடு\n", "\n", - "அடுத்த எடுத்துக்காட்டில், அறிவு பிரதிநிதித்துவத்திற்கான நூலகங்களில் ஒன்றை பயன்படுத்தி, [PyKnow](https://github.com/buguroo/pyknow/) மூலம் முன்னோக்கி தீர்மானத்தை செயல்படுத்த முயற்சிப்போம். **PyKnow** என்பது Python-ல் முன்னோக்கி தீர்மான அமைப்புகளை உருவாக்குவதற்கான ஒரு நூலகமாகும், இது பாரம்பரிய பழைய அமைப்பு [CLIPS](http://www.clipsrules.net/index.html) போன்றே வடிவமைக்கப்பட்டுள்ளது.\n", + "அடுத்த உதாரணத்தில், அறிவு பிரதிநிதித்துவத்திற்கு பயன்படுத்தப்படும் புத்தகங்களிலொன்றான [Experta](https://github.com/nilp0inter/experta) யை பயின்று முன் நோக்கி நுண்ணறிவை செயல்படுத்த முயற்சிப்போம். **Experta** என்பது Python இல் முன் நோக்கி நுண்ணறிவு அமைப்புகளை உருவாக்குவதற்கான ஒரு நூலகமாகும், இது பாரம்பரிய பழைய அமைப்பான [CLIPS](http://www.clipsrules.net/index.html) போன்றதாக வடிவமைக்கப்பட்டுள்ளது.\n", "\n", - "நாம் முன்னோக்கி சங்கிலியை (forward chaining) எளிதாகவே நம்மால் செயல்படுத்த முடியும், ஆனால் சாதாரண செயல்பாடுகள் பெரும்பாலும் மிகவும் திறமையானவை அல்ல. மேலும் திறமையான விதி பொருத்தத்திற்காக [Rete](https://en.wikipedia.org/wiki/Rete_algorithm) என்ற சிறப்பு الگورிதம் பயன்படுத்தப்படுகிறது.\n" + "நாம் நேரடியாக முன் தொடர் செயல்முறையையும் செயல்படுத்த முடியும், ஆனால் சாதாரண செயலாக்கங்கள் பொதுவாக மிகச் சிறந்த திறன்மிக்கவை அல்ல. பொறுப்பான விதி பொருத்தத்திற்காக ஒரு சிறப்பு அல்காரிதம் [Rete](https://en.wikipedia.org/wiki/Rete_algorithm) பயன்படுத்தப்படுகிறது.\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": [ - "நாம் எங்கள் அமைப்பை `KnowledgeEngine` ஐ துணை வகுப்பாகக் கொண்ட ஒரு வகுப்பாக வரையறுப்போம். ஒவ்வொரு விதியும் `@Rule` குறிப்பு கொண்ட தனி செயல்பாட்டால் வரையறுக்கப்படுகிறது, இது எந்த நேரத்தில் விதி செயல்பட வேண்டும் என்பதை குறிப்பிடுகிறது. விதிக்குள், `declare` செயல்பாட்டைப் பயன்படுத்தி புதிய உண்மைகளைச் சேர்க்கலாம், மேலும் அந்த உண்மைகளைச் சேர்ப்பது முன்னோக்கி தீர்மான இயந்திரத்தால் மேலும் சில விதிகள் அழைக்கப்படுவதற்கு காரணமாக இருக்கும்.\n" + "நாம் எங்கள் முறைமைக்கான வகுப்பை `KnowledgeEngine` என்பதன் துணை வகுப்பாக வரையறுப்போம். ஒவ்வொரு விதிமுறையும் `@Rule` அலங்காரத்துடன் தனித்தனியான פונ்க்ஷன் மூலம் வரையறுக்கப்படுகிறது, இது விதி எப்போது இயங்க வேண்டும் என்பதைக் குறிப்பிடுகிறது. விதியின் உள்ளே, நாம் `declare` פונ்க்ஷன் பயன்படுத்தி புதிய உண்மைகளைச் சேர்க்கலாம், அந்த உண்மைகளைச் சேர்த்தால் முன்னேற்றக் கணத்துப் பொறியால் சில கூடுதல் விதிகள் அழைக்கப்படும்.\n" ] }, { @@ -378,7 +377,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "ஒரு அறிவுத்தொகுப்பை வரையறுத்த பிறகு, சில ஆரம்ப உண்மைகளை கொண்டு செயல்முறை நினைவகத்தை நிரப்பி, பின்னர் `run()` முறைமையை அழைத்து முடிவுகளை மேற்கொள்ளலாம். இதன் விளைவாக, புதிய முடிவான உண்மைகள் செயல்முறை நினைவகத்தில் சேர்க்கப்படும், அதில் இறுதியாக விலங்கைப் பற்றிய உண்மையும் அடங்கும் (நாம் அனைத்து ஆரம்ப உண்மைகளையும் சரியாக அமைத்தால்).\n" + "ஒரு அறிவுத்தளத்தை анықித்துவிட்டவுடன், எங்கள் செயற்பாட்டு நினைவகத்தை சில ஆரம்ப உண்மைகளால் நிரப்பி, பிறகு ஊகிக்கையின் செயல்பாட்டை நடத்தியதற்காக `run()` முறைமையைக் கூப்பிடுகிறோம். நாம் முழுமையாக அனைத்து ஆரம்ப உண்மைகளையும் சரியாக அமைத்தால், இறுதியில் அந்த விலங்கின் பற்றிய சரியான உண்மைகள் செயற்பாட்டு நினைவகத்தில் புதிதாக சேர்க்கப்படுவதை நீங்கள் காணலாம்.\n" ] }, { @@ -440,7 +439,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n---\n\n**குறிப்பு**: \nஇந்த ஆவணம் [Co-op Translator](https://github.com/Azure/co-op-translator) என்ற AI மொழிபெயர்ப்பு சேவையை பயன்படுத்தி மொழிபெயர்க்கப்பட்டுள்ளது. எங்கள் தரச்சிறப்பிற்காக முயற்சிப்பதுடன், தானியங்கி மொழிபெயர்ப்புகளில் பிழைகள் அல்லது தவறுகள் இருக்கக்கூடும் என்பதை கவனத்தில் கொள்ளவும். அதன் தாய்மொழியில் உள்ள மூல ஆவணம் அதிகாரப்பூர்வ ஆதாரமாக கருதப்பட வேண்டும். முக்கியமான தகவல்களுக்கு, தொழில்முறை மனித மொழிபெயர்ப்பு பரிந்துரைக்கப்படுகிறது. இந்த மொழிபெயர்ப்பைப் பயன்படுத்துவதால் ஏற்படும் எந்த தவறான புரிதல்கள் அல்லது தவறான விளக்கங்களுக்கு நாங்கள் பொறுப்பல்ல.\n" + "---\n\n\n**பொருள் வழங்கல்**: \nஇந்த ஆவணம் AI மொழிபெயர்ப்பு சேவை [Co-op Translator](https://github.com/Azure/co-op-translator) மூலம் மொழிபெயர்க்கப்பட்டுள்ளது. சரியானதற்காக我们 முயற்சிக்கிறோம், இருப்பினும் தானாக生成ப்பட்ட மொழிபெயர்ப்புகளில் பிழைகள் அல்லது தவறுகள் இருக்கக்கூடும் என்பதைக் கவனத்தில் கொள்ளவும். அசல் ஆவணம் அதன் சொந்த மொழியில் அங்கீகாரம் பெறும் ஆதாரமாக கருதப்பட வேண்டும். முக்கியமான தகவல்களுக்கு, தொழில்முறை மனித மொழிபெயர்ப்பு பரிந்துரைக்கப்படுகிறது. இந்த மொழிபெயர்ப்பின் பயன்பாட்டால் தோன்றும் எந்த தவறானப் புரிதல்கள் அல்லது தவறான விளக்கங்களுக்குமான பொறுப்பும் நாம் ஏற்கவில்லை.\n\n" ] } ], @@ -467,8 +466,8 @@ "version": "3.11.2" }, "coopTranslator": { - "original_hash": "ab2bd97b0453415b89a469284609a8ce", - "translation_date": "2025-10-11T12:35:24+00:00", + "original_hash": "8ef43db4b9182239fd150a76bd494fdb", + "translation_date": "2026-01-16T06:42:24+00:00", "source_file": "lessons/2-Symbolic/Animals.ipynb", "language_code": "ta" } diff --git a/translations/ta/lessons/2-Symbolic/README.md b/translations/ta/lessons/2-Symbolic/README.md index c5b0dc63..b5356b9a 100644 --- a/translations/ta/lessons/2-Symbolic/README.md +++ b/translations/ta/lessons/2-Symbolic/README.md @@ -1,76 +1,76 @@ -# அறிவு பிரதிநிதித்துவம் மற்றும் நிபுணர் அமைப்புகள் +# அறிவுத்திறன் பிரதிநிதித்துவமும் நிபுணர் அமைப்புகளும் -![சின்னவியல் AI உள்ளடக்கத்தின் சுருக்கம்](../../../../translated_images/ta/ai-symbolic.715a30cb610411a6.webp) +![குறியீட்டு AI உள்ளடக்கத்தின் சுருக்கம்](../../../../../../translated_images/ta/ai-symbolic.715a30cb610411a6.webp) -> [Tomomi Imura](https://twitter.com/girlie_mac) அவர்களின் சின்னவியல் குறிப்பு +> ஸ்கெட்ச் நோட் [டோமோமி இமுரா](https://twitter.com/girlie_mac) அவர்களால் -மனிதர்கள் உலகத்தை புரிந்து கொள்ளும் முறையைப் போலவே, செயற்கை நுண்ணறிவை உருவாக்கும் தேடல் அறிவைத் தேடுவதில் அடிப்படையாக உள்ளது. ஆனால், இதை எவ்வாறு செய்ய முடியும்? +கைமுறையாக செயல்படும் புத்திசாலித்தனத்தை உருவாக்கும் முறையை மனிதர்களைப் போல உலகத்தை உணர்ந்து கொள்ளும் அறிவைக் கண்டு பிடிப்பதில்தான் அடிப்படையாக கொண்டுள்ளது. ஆனால் இது எவ்வாறு செய்யப்பட வேண்டும்? -## [முன்-வகுப்பு வினாடி வினா](https://ff-quizzes.netlify.app/en/ai/quiz/3) +## [முன்னர்-வழக்கு வினாத்தாள்](https://ff-quizzes.netlify.app/en/ai/quiz/3) -AI-யின் ஆரம்ப காலங்களில், புத்திசாலி அமைப்புகளை உருவாக்குவதற்கான மேல்-கீழ் அணுகுமுறை (முந்தைய பாடத்தில் விவாதிக்கப்பட்டது) பிரபலமாக இருந்தது. இந்த அணுகுமுறை இரண்டு முக்கிய கருத்துக்களை அடிப்படையாகக் கொண்டது: +ஆய்வியல் புத்திசாலித்தனத்தின் தொடக்க நாட்களில், மேலே இருந்து கீழே நோக்கும் முறையான புத்திசாலி அமைப்புகளை உருவாக்கும் வழிமுறை (முந்தைய பாடத்தில் விவாதிக்கப்பட்டது) பிரபலமாக இருந்தது. மனிதர்களிடமிருந்து அறிவைப் பெறும் விதமாகக் கொண்டு அதை இயந்திரப் படமாக மாற்றி, தானாக பிரச்சனைகளை தீர்க்க பயன்படுத்துதல். இந்த முறையானது இரண்டு பெரிய கோட்பாடுகளின் அடிப்படையில் இருந்தது: -* அறிவு பிரதிநிதித்துவம் -* காரணம் +* அறிவுரைப் பிரதிநிதித்துவம் +* காரணியம் -## அறிவு பிரதிநிதித்துவம் +## அறிவுரைப் பிரதிநிதித்துவம் -சின்னவியல் AI-யில் முக்கியமான கருத்துகளில் ஒன்று **அறிவு**. *தகவல்* அல்லது *தரவு* என்பதிலிருந்து அறிவை வேறுபடுத்துவது முக்கியம். உதாரணமாக, புத்தகங்கள் அறிவை உள்ளடக்கியவை என்று கூறலாம், ஏனெனில் புத்தகங்களைப் படித்து நிபுணராக மாறலாம். ஆனால், புத்தகங்களில் உள்ளவை உண்மையில் *தரவு* என்று அழைக்கப்படுகிறது, மேலும் புத்தகங்களைப் படித்து, இந்த தரவுகளை நமது உலக மாதிரியில் ஒருங்கிணைப்பதன் மூலம், இந்த தரவை அறிவாக மாற்றுகிறோம். +குறியீட்டு AI இல் முக்கியமான கருத்துக்களில் ஒன்று **அறிவுரைக்** ஆகும். அறிவுரையை *தரவு* அல்லது *தகவல்* என்பவற்றிலிருந்து வேறுபடுத்துவது முக்கியம். எடுத்துக்காட்டாக, புத்தகங்கள் அறிவுறையை கொண்டிருக்கும் என்று சொல்லலாம், ஏனென்றால் புத்தகங்களை படித்து நிபுணர் ஆகலாம். இருப்பினும், புத்தகங்களில் உள்ளவை உண்மையில் *தரவு* என்று அழைக்கப்படுகின்றன, புத்தகங்களைப் படித்து அந்த தரவுகளை உலக மாதிரியில் ஒருங்கிணைத்தால் அந்த தரவை அறிவுரையாக மாற்றுவோம். -> ✅ **அறிவு** என்பது நமது தலையில் உள்ளதொன்றாகும், மேலும் அது உலகத்தைப் பற்றிய நமது புரிதலை பிரதிநிதித்துவப்படுத்துகிறது. இது ஒரு செயலில் **கற்றல்** செயல்முறையால் பெறப்படுகிறது, இது நாங்கள் பெறும் தகவல்களின் துண்டுகளை நமது உலக மாதிரியில் ஒருங்கிணைக்கிறது. +> ✅ **அறிவுரை** என்பது நமது தலைக்குள் உள்ளதும், உலகத்தை எவ்வாறு புரிந்துகொள்கின்றோம் என்பதைக் குறிக்கும். இது ஒரு செயலில் நடைபெறும் **கற்றல்** செயல்முறையால் பெறப்படும், நாம் பெற்ற தகவல்களை உலக மாதிரியில் ஒருங்கிணைக்கும் முறையாகும். -அறிவை நாங்கள் பெரும்பாலும் கடுமையாக வரையறுக்கவில்லை, ஆனால் அதை [DIKW Pyramid](https://en.wikipedia.org/wiki/DIKW_pyramid) மூலம் தொடர்புடைய கருத்துகளுடன் இணைக்கிறோம். இது பின்வரும் கருத்துக்களை உள்ளடக்கியது: +அதிகமாக, அறிவுரையை கடுமையாக வரையறுக்கவில்லை, ஆனால் அதனை தொடர்புடைய பிற கருத்துகளுடன் [DIKW ஹரிஷேகரம்](https://en.wikipedia.org/wiki/DIKW_pyramid) பயன்படுத்தி ஒத்திசைவேற்று உள்ளோம். இதில் உள்ள கருத்துக்கள்: -* **தரவு** என்பது எழுதப்பட்ட உரை அல்லது பேசப்பட்ட வார்த்தைகள் போன்ற உடல் ஊடகங்களில் பிரதிநிதித்துவப்படுத்தப்படும் ஒன்றாகும். தரவு மனிதர்களிடமிருந்து சுயாதீனமாக உள்ளது மற்றும் மனிதர்களுக்கு இடையே பரிமாறப்படலாம். -* **தகவல்** என்பது நாங்கள் நமது தலையில் தரவை எப்படி விளக்குகிறோம் என்பதைக் குறிக்கிறது. உதாரணமாக, *கணினி* என்ற வார்த்தையை நாம் கேட்கும்போது, அது என்ன என்பதைப் பற்றிய சில புரிதல்கள் நமக்கு உள்ளன. -* **அறிவு** என்பது தகவலை நமது உலக மாதிரியில் ஒருங்கிணைப்பதாகும். உதாரணமாக, ஒரு கணினி என்ன என்பதை நாங்கள் கற்றுக்கொண்ட பிறகு, அது எப்படி செயல்படுகிறது, அதன் விலை எவ்வளவு, மற்றும் அது எதற்காக பயன்படுத்தப்படலாம் என்பதற்கான சில கருத்துக்கள் நமக்கு உருவாகின்றன. இந்த தொடர்புடைய கருத்துக்களின் வலை நமது அறிவை உருவாக்குகிறது. -* **ஞானம்** என்பது உலகத்தைப் பற்றிய நமது புரிதலின் மேலும் ஒரு நிலையாகும், மேலும் இது *மெட்டா-அறிவை* பிரதிநிதித்துவப்படுத்துகிறது, உதாரணமாக, அறிவு எப்போது மற்றும் எப்படி பயன்படுத்தப்பட வேண்டும் என்பதற்கான கருத்து. +* **தரவு** என்பது பழைய எழுத்து அல்லது வாய்மொழிகள் போன்ற உடல் வாயிலாக பிரதிநிதித்துவம் செய்யப்படுகின்றது. தரவு மனிதர்களுக்கு சாராது இருப்பதுடன் பரிமாறப்படக்கூடியதாகும். +* **தகவல்** என்பது அந்த தரவை நமது மனதில் நாம் எப்படி அர்த்தமாற்றம் செய்கிறோம் என்பதாகும். உதாரணத்திற்கு, *கணினி* என்கிற வார்த்தையை கேட்டால், அதற்கு எவ்வாறு புரிதல் உண்டு. +* **அறிவுரை** என்பது தகவலை உலக மாதிரியில் ஒருங்கிணைக்கும் செயலாகும். உதாரணத்திற்கு, கணினி என்றால், அது எப்படி வேலை செய்கிறது, விலை என்ன, என்ன பயன்பாடுகள் என்பது பற்றி கற்றுக்கொள்கிறோம். இத்தகைய தொடர்புடைய கருத்துக்களின் வலை நமது அறிவுரையை உருவாக்குகிறது. +* **அறிவுமை** என்பது உலகத்தைப் பற்றி மேலதிக புரிந்துகொள்ளல் மட்டமாகும், இது *மெட்டஅறிவு* என்று அழைக்கப்படலாம், உதாரணமாக அறிவுரையை எப்போது மற்றும் எப்படி பயன்படுத்த வேண்டும் என்பது குறித்து ஒரு கருத்து. -*படம் [விக்கிப்பீடியாவில் இருந்து](https://commons.wikimedia.org/w/index.php?curid=37705247), Longlivetheux - சொந்த வேலை, CC BY-SA 4.0* +*படம் [விக்கிப்பீடியாவிலிருந்து](https://commons.wikimedia.org/w/index.php?curid=37705247), லாங்ளிவ்தெக்ஸ் உருவாக்கம், CC BY-SA 4.0* -அதனால், **அறிவு பிரதிநிதித்துவம்** என்ற பிரச்சினை என்பது கணினியில் தரவின் வடிவத்தில் அறிவை பிரதிநிதித்துவப்படுத்துவதற்கான சில பயனுள்ள வழிகளை கண்டுபிடிப்பதாகும், இதை தானாகவே பயன்படுத்த முடியும். இது ஒரு வரம்பாகக் காணப்படுகிறது: +இந்த வகையில், **அறிவுரைப் பிரதிநிதித்துவம்** பிரச்சனையை ஒரு கணினியின் உள்ளே தரவு வடிவில் அறிவுரையை பிரதிநிதித்துவம் செய்யக்கூடிய சிறந்த முறையை கண்டறிவதற்கான பிரச்சனை என பார்க்கலாம். இது ஒரு வரம்பு போலும் பார்க்கலாம்: -![அறிவு பிரதிநிதித்துவ வரம்பு](../../../../translated_images/ta/knowledge-spectrum.b60df631852c0217.webp) +![அறிவுரைப் பிரதிநிதித்துவ வரம்பு](../../../../../../translated_images/ta/knowledge-spectrum.b60df631852c0217.webp) -> [Dmitry Soshnikov](http://soshnikov.com) அவர்களின் படங்கள் +> படம் [ட்மிட்ரீ சோஷ்னிகோவ்](http://soshnikov.com) அவர்களின் படைப்பு -* இடதுபுறத்தில், கணினிகள் மூலம் பயனுள்ளதாக பயன்படுத்தக்கூடிய மிகவும் எளிய வகையான அறிவு பிரதிநிதித்துவங்கள் உள்ளன. மிகவும் எளிமையானது ஒரு கணினி நிரலால் பிரதிநிதித்துவப்படுத்தப்படும் அல்காரிதமிக் அறிவு. இது, எனினும், அறிவை பிரதிநிதித்துவப்படுத்துவதற்கான சிறந்த வழி அல்ல, ஏனெனில் இது நெகிழ்வானது அல்ல. நமது தலையில் உள்ள அறிவு பெரும்பாலும் அல்காரிதமிக் அல்ல. -* வலதுபுறத்தில், இயற்கை உரை போன்ற பிரதிநிதித்துவங்கள் உள்ளன. இது மிகவும் சக்திவாய்ந்தது, ஆனால் தானாகவே காரணம் காண்பதற்கு பயன்படுத்த முடியாது. +* இடது பக்கம், கணினிகள் பயனடையக்கூடிய போதிரமான வகை அறிவுரைப் பிரதிநிதித்துவங்கள் உள்ளன. மிகவும் எளிமையானது என்பது கணினி திட்டமிடலின் மூலம் அறிவுரை பிரதிநிதித்துவம் செய்யப்படுவது ஆகும். இது, இருப்பினும், மிகவும் எளிமையாக இல்லாது, ஏனென்றால் இது நெடுங்கால பாதுகாப்பிற்குப் பொருத்தமில்லாமல் இருக்கும். நமது தலைவிரைகளின் அறிவுரை பெரும்பாலும் கூட்டு நிரல் அல்லாத வகையில் இருக்கும். +* வலது பக்கம், இயற்கை உரை போன்ற பிரதிநிதித்துவங்கள் உள்ளன. அவை மிகவும் சக்திவாய்ந்தவை, ஆனால் தானாகத் காரணீயம் செய்ய இயலாது. -> ✅ உங்கள் தலையில் அறிவை எப்படி பிரதிநிதித்துவப்படுத்துகிறீர்கள் மற்றும் அதை குறிப்புகளாக மாற்றுகிறீர்கள் என்பதை ஒரு நிமிடம் யோசிக்கவும். உங்கள் நினைவில் வைத்துக்கொள்ள உதவுவதற்கான ஒரு குறிப்பிட்ட வடிவம் உங்களுக்கு வேலை செய்கிறதா? +> ✅ சில நินைவில் அந்த அறிவுரையை நீங்கள் உங்கள் தலைவில் எவ்வாறு பிரதிநிதித்துவம் செய்கிறீர்கள் மற்றும் அதை குறிப்புகளாக மாற்றுகையில் எவ்வாறு செய்வதை நினைத்துப் பாருங்கள். இது நினைவில் பாதுகாப்பதற்கு ஏதேனும் சிறந்த வடிவமா? -## கணினி அறிவு பிரதிநிதித்துவங்களை வகைப்படுத்துதல் +## கணினி அறிவுரைப் பிரதிநிதித்துவ வகைப்படுத்தல் -கணினி அறிவு பிரதிநிதித்துவ முறைகளை பின்வரும் வகைகளில் வகைப்படுத்தலாம்: +நாம் பல்வேறு கணினி அறிவுறைய பிரதிநிதித்துவ முறைகளை பின்வரும் வகைகளாக வகைப்படுத்தலாம்: -* **வலை பிரதிநிதித்துவங்கள்** நமது தலையில் தொடர்புடைய கருத்துக்களின் வலை உள்ளது என்பதை அடிப்படையாகக் கொண்டவை. நாங்கள் அதே வலைகளை ஒரு **semantic network** எனக் கணினியில் ஒரு கிராஃப் ஆக மீண்டும் உருவாக்க முயற்சிக்கலாம். +* **பிணைய பிரதிநிதித்துவங்கள்** என்பது நமது தலைவிற்குள் உள்ள தொடர்புடைய கருத்துக்களின் பிணையம் கொண்டிருப்பதை அடிப்படையாக்கொண்டது. அதே பிணையங்களை கணினி உள்ளே ஒரு கிராப் போல உருவாக்க முயலலாம் - இது **அர்த்தமுள்ள பிணையம்** என்று அழைக்கப்படும். -1. **வஸ்து-அடிப்படை-மதிப்பு மூன்றெடுப்புகள்** அல்லது **அடிப்படை-மதிப்பு ஜோடிகள்**. ஒரு கிராஃப் கணினியில் நொடுகள் மற்றும் விளிம்புகளின் பட்டியலாக பிரதிநிதித்துவப்படுத்தப்படலாம், எனவே ஒரு semantic network ஐ மூன்றெடுப்புகளின் பட்டியலாக பிரதிநிதித்துவப்படுத்தலாம், இதில் பொருட்கள், அடிப்படைகள் மற்றும் மதிப்புகள் உள்ளன. உதாரணமாக, நிரலாக்க மொழிகள் பற்றிய பின்வரும் மூன்றெடுப்புகளை நாங்கள் உருவாக்குகிறோம்: +1. **பொருள்-குணம்-மதிப்பு மூன்றுகூட்டங்கள்** அல்லது **குண-மதிப்பு ஜோடிகள்**. ஒரு கிராப் கணினியின் நுட்பத்தில் உருப்படிகளை மற்றும் இணைப்புகளை பட்டியலாகக் கொண்டிருப்பதால், அர்த்தபூர்வமான பிணையத்தை மூன்றுகூட்டங்களின் பட்டியலாக பிரதிநிதித்துவம் செய்யலாம், இதில் பொருள், குணம் மற்றும் மதிப்பு அடங்கும். உதாரணமாக பின்வருமாறு ப்ரோகிராமிங் மொழிகள் பற்றிய மூன்றுகூட்டங்களை கெட்டெடுப்போம்: Object | Attribute | Value -------|-----------|------ -Python | is | Untyped-Language -Python | invented-by | Guido van Rossum -Python | block-syntax | indentation -Untyped-Language | doesn't have | type definitions +Python | ஆகும் | Untyped-Language +Python | கண்டுபிடித்தவர் | Guido van Rossum +Python | தொகுதி இலக்கம் | பின்னிழைப்பு +Untyped-Language | இல்லை | வகை வரையறைகள் -> ✅ மூன்றெடுப்புகள் மற்ற வகையான அறிவை பிரதிநிதித்துவப்படுத்த எப்படி பயன்படுத்தப்படலாம் என்று யோசிக்கவும். +> ✅ மூன்றுகூட்டங்களை மற்ற அறிவுரைகளை பிரதிநிதித்துவம் செய்ய எவ்வாறு பயன்படுத்தலாம் என்று யோசிக்கவும். -2. **அடுக்கு பிரதிநிதித்துவங்கள்** நாங்கள் நமது தலையில் பொருட்களின் ஒரு அடுக்கை உருவாக்குகிறோம் என்பதை வலியுறுத்துகின்றன. உதாரணமாக, நாங்கள் கானரி ஒரு பறவை என்று அறிகிறோம், மேலும் அனைத்து பறவைகளுக்கும் இறகுகள் உள்ளன. மேலும், கானரியின் நிறம் மற்றும் அதன் பறக்கும் வேகம் பற்றிய சில கருத்துக்கள் நமக்கு உள்ளன. +2. **அடுக்கு பிரதிநிதித்துவங்கள்** என்பது அதிகாக நாம் ஒரு அடுக்கு முறை பொருட்களை தலைவிற்க்குள் உருவாக்குவதாகும். உதாரணமாக, கானரி என்பது பறவை என்றும் அனைத்து பறவைகளுக்கும் அண்டு இருப்பதாகவும் நாம் அறிவோம். மேலும் கானரி இயல்பாக எந்த நிறமுடையது மற்றும் அதன் பறப்புத் வேகம் என்ன என்பதையும் அறிவோம். - - **Frame representation** என்பது ஒவ்வொரு பொருள் அல்லது பொருட்களின் வகுப்பையும் **frame** ஆக பிரதிநிதித்துவப்படுத்துவதில் அடிப்படையாக உள்ளது, இது **slots** ஐ கொண்டுள்ளது. Slots க்கு இயல்புநிலை மதிப்புகள், மதிப்பு கட்டுப்பாடுகள் அல்லது ஒரு slot இன் மதிப்பை பெற அழைக்கக்கூடிய சேமிக்கப்பட்ட செயல்முறைகள் இருக்கலாம். அனைத்து frames க்கும் object-oriented programming மொழிகளில் object hierarchy போன்ற ஒரு hierarchy உள்ளது. - - **Scenarios** என்பது நேரத்தில் unfold ஆகக்கூடிய சிக்கலான சூழல்களை பிரதிநிதித்துவப்படுத்தும் frames இன் ஒரு சிறப்பு வகை. + - **வடிவமைப்புப் பிரதிநிதித்துவம்** என்பது ஒவ்வொரு பொருள் அல்லது வகுப்பு ஒன்றையும் ஒரு **வடிவமைப்பு** என நிரப்புவதாகும் இதில் **ஸ்லாட்கள்** இருக்கும். ஸ்லாட்களுக்கு இயல்பான மதிப்புகள், மதிப்பு வரம்புகள், அல்லது மதிப்பை பெற அழைக்கப்படும் சேமிக்கப்பட்ட செயல்முறைகள் இருக்கலாம். அனைத்து வடிவமைப்புகளும் பொருள் அடுக்கு முறைபோல இணைகின்றன, பொருள்-அடிப்படையிலான நிரலாக்க மொழிகளுக்கு ஒத்தவை. + - **நிகழ்வுகள்** என்பது நேரத்தில் விரிவடைவதற்கான சிக்கலான சூழல்களை பிரதிநிதித்துவம் செய்யும் சிறப்பு வடிவமைப்புகள். **Python** @@ -79,38 +79,38 @@ Slot | Value | Default value | Interval | Name | Python | | | Is-A | Untyped-Language | | | Variable Case | | CamelCase | | -Program Length | | | 5-5000 lines | +Program Length | | | 5-5000 வரிசைகள் | Block Syntax | Indent | | | -3. **செயல்முறை பிரதிநிதித்துவங்கள்** என்பது ஒரு குறிப்பிட்ட நிலை ஏற்படும் போது செயல்படுத்தக்கூடிய செயல்களின் பட்டியலால் அறிவை பிரதிநிதித்துவப்படுத்துவதில் அடிப்படையாக உள்ளது. - - **Production rules** என்பது முடிவுகளை வரையறுக்க அனுமதிக்கும் if-then அறிக்கைகள். உதாரணமாக, ஒரு மருத்துவர் **IF** ஒரு நோயாளிக்கு அதிக காய்ச்சல் **OR** இரத்த பரிசோதனையில் அதிக அளவிலான C-reactive protein இருந்தால் **THEN** அவருக்கு ஒரு அழற்சி உள்ளது என்று கூறும் ஒரு விதியை வைத்திருக்கலாம். நாங்கள் ஒரு நிலையை சந்திக்கும் போது, ​​அழற்சியைப் பற்றிய ஒரு முடிவை எடுக்க முடியும், பின்னர் அதை மேலும் காரணம் காண்பதற்குப் பயன்படுத்தலாம். - - **Algorithms** என்பது அறிவு அடிப்படையிலான அமைப்புகளில் நேரடியாகப் பயன்படுத்தப்படுவதில்லை என்றாலும், மற்றொரு வகையான செயல்முறை பிரதிநிதித்துவமாகக் கருதப்படலாம். +3. **செயல்முறை பிரதிநிதித்துவங்கள்** என்பது குறிப்பிட்ட நிபந்தனை வந்தபோது செயலாக்கக்கூடிய நடவடிக்கைகள் பட்டியலாக அறிவுரையை பிரதிநிதித்துவம் செய்வதாகும். + - உற்பத்தி விதிகள் என்பது "எனில்-அப்பின்" (if-then) வகை கூற்றுகள் ஆகும், அவை தீர்வுகளை செல்லச் செய்கின்றன. உதாரணமாக, ஒரு மருத்துவர் ஒருவர் நோயாளிக்கு உயர் காய்ச்சல் அல்லது இரத்தத்தில் C-ரியாக்டிவ் புரோட்டீன் அளவு அதிகமாக இருந்தால் உள்ளபோது **எனில்** ஒரு விதி வைத்திருக்கலாம் அதாவது நோயாளிக்கு வீக்கம் உள்ளது என்ற முடிவை எடுக்கலாம். நிபந்தனைகளில் ஒன்று சந்திப்பானதும், வீக்கம் பற்றிய முடிவை எடுத்து, மேலும் காரணீயம் செய்யலாம். + - ஆல்காரிதங்கள் ஒரு விதமான செயல்முறை பிரதிநிதித்துவம் என கருதப்படலாம், இருப்பினும் அவை அறிவுரை அடிப்படையிலான அமைப்புகளில் நேரடியாக ஒருபோதும் பயன்படுவதில்லை. -4. **Logic** என்பது மனிதர்களின் உலகளாவிய அறிவை பிரதிநிதித்துவப்படுத்தும் ஒரு வழியாக அரிஸ்டாட்டில் மூலம் முதலில் முன்மொழியப்பட்டது. - - **Predicate Logic** என்பது கணித கோட்பாடாக கணினி செயல்பட முடியாத அளவுக்கு செறிவாக உள்ளது, எனவே பொதுவாக அதன் ஒரு subset மட்டுமே பயன்படுத்தப்படுகிறது, உதாரணமாக Prolog இல் பயன்படுத்தப்படும் Horn clauses. - - **Descriptive Logic** என்பது *semantic web* போன்ற விநியோகிக்கப்பட்ட அறிவு பிரதிநிதித்துவங்களைப் பற்றிய hierarchies ஐ பிரதிநிதித்துவப்படுத்த மற்றும் காரணம் காண பயன்படுத்தப்படும் தர்க்க முறைமைகளின் குடும்பமாகும். +4. **தர்க்கம்** முதலில் Արிஸ்டாட்டில் முன்மொழியப்பெற்ற மனித அறிவுரையின் பரவலான பிரதிநிதித்துவமாகும். + - கணிதத் தேர்ச்சி Predicate Logic கணிசமாக வளமானது, எனவே அதன் எதையாவது துணுக்கை பொதுவாகப் பயன்படுத்துகிறோம், உதாரணமாக Prolog இல் பயன்படுத்தப்படும் ஹார்ன் கிளாஸ்கள். + - விளக்க Logic என்பது பொருள் அடுக்குகளை பிரதிநிதித்துவமும் காரணீயமும் செய்ய பயன்படும் தர்க்கக் குடும்பமாகும், இது *semantic web* போன்ற பகிரப்பட்ட அறிவுரைப் பிரதிநிதித்துவங்களில் பயன்படுத்தப்படுகிறது. ## நிபுணர் அமைப்புகள் -சின்னவியல் AI-யின் ஆரம்ப வெற்றிகளில் ஒன்று **நிபுணர் அமைப்புகள்** - குறிப்பிட்ட பிரச்சினை துறையில் நிபுணராக செயல்பட வடிவமைக்கப்பட்ட கணினி அமைப்புகள். அவை **knowledge base** மற்றும் **inference engine** ஆகியவற்றின் அடிப்படையில் உருவாக்கப்பட்டன. +குறியீட்டு AI இன் ஆரம்ப வெற்றிகளில் ஒன்று **நிபுணர் அமைப்புகள்** எனப்படும், குறிப்பிட்ட பிரச்சனையில் நிபුණராக செயல்பட வடிவமைக்கப்பட்ட கணினி அமைப்புகள். இவை மனித நிபுணர்களிடமிருந்து ஒரு அல்லது பலரிடமிருந்து பெற்ற **அறிவு அங்கம்** மற்றும் அதற்கு மேலே சில காரணீயம் செய்யும் **காரண இயந்திரம்** கொண்டவை. -![மனித அமைப்பு](../../../../translated_images/ta/arch-human.5d4d35f1bba3ab1c.webp) | ![அறிவு அடிப்படையிலான அமைப்பு](../../../../translated_images/ta/arch-kbs.3ec5c150b09fa8da.webp) +![மனித கட்டமைப்பு](../../../../../../translated_images/ta/arch-human.5d4d35f1bba3ab1c.webp) | ![அறிவு அடிப்படையிலான அமைப்பு](../../../../../../translated_images/ta/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ -மனித நரம்பு அமைப்பின் எளிமையான அமைப்பு | அறிவு அடிப்படையிலான அமைப்பின் கட்டமைப்பு +மனித நியூரல் அமைப்பின் எளிமையான உருவகம் | அறிவுரைப் பிரச்சனை அமைப்பின் கட்டமைப்பு -நிபுணர் அமைப்புகள் மனித reasoning அமைப்பைப் போலவே கட்டமைக்கப்பட்டுள்ளன, இதில் **short-term memory** மற்றும் **long-term memory** உள்ளது. அதேபோல, அறிவு அடிப்படையிலான அமைப்புகளில் பின்வரும் கூறுகளை வேறுபடுத்துகிறோம்: +நிபுணர் அமைப்புகள் மனித காரணும் அமைப்பைப் போலவேச் செய்வது, இதில் **குறுகியகால நினைவகம்** மற்றும் **நீண்டகால நினைவகம்** உள்ளன. அதேபோல் அறிவுரைப் பிரச்சனை அமைப்புகளில் பின்வரும் கூறுகளை பிரித்து பார்க்கிறோம்: -* **Problem memory**: தற்போது தீர்க்கப்படும் பிரச்சினை பற்றிய அறிவை உள்ளடக்கியது, அதாவது ஒரு நோயாளியின் வெப்பநிலை அல்லது இரத்த அழுத்தம், அவருக்கு அழற்சி உள்ளதா இல்லையா என்பதற்கான தகவல். இந்த அறிவு **static knowledge** என்றும் அழைக்கப்படுகிறது, ஏனெனில் இது தற்போது பிரச்சினை பற்றிய snapshot ஐ உள்ளடக்கியது - *problem state*. -* **Knowledge base**: ஒரு பிரச்சினை துறையைப் பற்றிய நீண்டகால அறிவை பிரதிநிதித்துவப்படுத்துகிறது. இது மனித நிபுணர்களிடமிருந்து கையேடு மூலம் எடுக்கப்படுகிறது, மேலும் ஆலோசனைக்கு ஆலோசனை மாற்றம் செய்யப்படாது. இது ஒரு problem state இல் இருந்து மற்றொன்றுக்கு செல்ல அனுமதிக்கிறது, எனவே இது **dynamic knowledge** என்றும் அழைக்கப்படுகிறது. -* **Inference engine**: இது problem state space இல் தேடல் செயல்முறையை ஒழுங்குபடுத்துகிறது, தேவையான போது பயனரிடம் கேள்விகள் கேட்கிறது. இது ஒவ்வொரு நிலைக்கு பொருந்தக்கூடிய சரியான விதிகளை கண்டுபிடிப்பதற்கும் பொறுப்பாக உள்ளது. +* **பிரச்சனை நினைவகம்**: தற்பொழுது தீர்த்துவரும் பிரச்சனை பற்றிய அறிவுரையை கொண்டுள்ளது, உதாரணமாக நோயாளியின் வெப்பம், இரத்த அழுத்தம் மற்றும் வீக்கம் இருப்பது போன்றவை. இதை **நிலையான அறிவுரை** என்றும் அழைக்கலாம், ஏனெனில் இது பிரச்சனை பற்றிய தற்போதைய நிலையை காட்டும் - இதை *பிரச்சனை நிலை* என்று சொல்லப்படுகிறது. +* **அறிவு அங்கம்**: பிரச்சனைச் சூழலின் நீண்டகால அறிவுரையை பிரதிநிதித்துவம் செய்கிறது. இது மனித நிபுணர்களிடமிருந்து கைமுறை எடுத்துவைக்கப்படுகிறது மற்றும் ஆலோசனையில் மாறாது. ஒன்று பிரச்சனை நிலையிலிருந்து மற்ற நிலைக்கு செல்வதற்கு வழி அளிக்கும் என்பதால் **மையமான அறிவுரை** என்பதும் ஆகும். +* **காரண இயந்திரம்**: பிரச்சனை நிலை இடைவெளியில் தேடலை ஒருங்கிணைக்கிறது, தேவையான போது பயனரிடம் கேள்விகள் கேட்கிறது. ஒவ்வொரு நிலையிலும் போதுமான விதிகளைக் கண்டறிந்து பயன்படுத்துவது அவளை பயன்பாட்டின் பொறுப்பு ஆகும். -உதாரணமாக, ஒரு விலங்கின் உடல் பண்புகளை அடிப்படையாகக் கொண்டு அதைத் தீர்மானிக்கும் பின்வரும் நிபுணர் அமைப்பைப் பார்ப்போம்: +உதாரணமாக, ஒரு நிபுணர் அமைப்பு, ஒரு உயிரினத்தை அதன் உடலியல் பண்புகளின் அடிப்படையில் கண்டறிக்கும் அமைப்பை எடுத்துக்கொள்வோம்: -![AND-OR Tree](../../../../translated_images/ta/AND-OR-Tree.5592d2c70187f283.webp) +![AND-OR மரம்](../../../../../../translated_images/ta/AND-OR-Tree.5592d2c70187f283.webp) -> [Dmitry Soshnikov](http://soshnikov.com) அவர்களின் படங்கள் +> படம் [ட்மிட்ரீ சோஷ்னிகோவ்](http://soshnikov.com) உருவாக்கப்பட்டது -இந்த வரைபடம் **AND-OR tree** என்று அழைக்கப்படுகிறது, மேலும் இது **production rules** இன் ஒரு தொகுப்பின் ஒரு கிராஃபிகல் பிரதிநிதித்துவமாகும். நிபுணரிடமிருந்து அறிவை எடுக்கும் தொடக்கத்தில் ஒரு tree ஐ வரையுவது பயனுள்ளதாக இருக்கும். கணினியில் அறிவை பிரதிநிதித்துவப்படுத்த, விதிகளைப் பயன்படுத்துவது வசதியாக இருக்கும்: +இந்த வரைபடம் **AND-OR மரம்** என்று அழைக்கப்பட며, இது உற்பத்தி விதிகளின் காட்சிமுறைப் பிரதிநிதித்துவமாகும். நிபுணரிடமிருந்து அறிவுரையை எடுக்கும் நேரத்தில் மரம் வரைதல் பயனுள்ளதாக இருக்கும். ஆனால் கணினியில் அறிவுரையை பதிவுசெய்ய விதிகளை பயன்படுத்துவது வசதியாகும்: ``` IF the animal eats meat @@ -121,73 +121,78 @@ OR (animal has sharp teeth THEN the animal is a carnivore ``` -நீங்கள் கவனிக்கலாம், விதியின் இடது பக்கம் உள்ள ஒவ்வொரு நிலை மற்றும் செயல் அடிப்படையில் **object-attribute-value (OAV) triplets** ஆகும். **Working memory** தற்போதைய பிரச்சினையை தீர்க்கும் OAV triplets க்கான தொகுப்பை உள்ளடக்கியது. **Rules engine** ஒரு நிலை திருப்தி செய்யப்படும் விதிகளைத் தேடுகிறது மற்றும் அவற்றைச் செயல்படுத்துகிறது, மேலும் ஒரு புதிய triplet ஐ working memory இல் சேர்க்கிறது. +வலது பக்கத்தில் உள்ள விதி மற்றும் நடவடிக்கை என்பது பொதுவாக பொருள்-குணம்-மதிப்பு (OAV) மூன்றுகூட்டங்களின் வடிவில் இருக்கும். **செயல்பாட்டு நினைவகம்** என்பது தற்போது தீர்க்கப்படும் பிரச்சனையை குறிக்கும் OAV மூன்றுகூட்டங்களின் தொகுப்பாகும். ஒரு **விதி இயந்திரம்** தற்பொள்ளும் நிபந்தனை நிறைவடையும் விதிகளைத் தேடுகிறது மற்றும் அவற்றை பயன்படுத்தி புதிய மூன்றுகூட்டங்களை செயல்பாட்டு நினைவகத்தில் சேர்க்கிறது. -> ✅ உங்களுக்கு பிடித்த ஒரு தலைப்பில் உங்கள் சொந்த AND-OR tree ஐ எழுதுங்கள்! +> ✅ நீங்கள் விரும்பும் தலைப்பில் உங்கள் சொந்த AND-OR மரத்தை எழுதுங்கள்! -### Forward vs. Backward Inference +### முன்னேற்ற மற்றும் பின்னோட்ட காரணீயம் -மேலே விவரிக்கப்பட்ட செயல்முறை **forward inference** என்று அழைக்கப்படுகிறது. இது working memory இல் கிடைக்கும் பிரச்சினை பற்றிய சில ஆரம்ப தரவுகளுடன் தொடங்குகிறது, பின்னர் பின்வரும் reasoning loop ஐ செயல்படுத்துகிறது: +மேலே விவரிக்கப்பட்ட செயல்முறை **முன்னேற்ற காரணீயம்** என்று அழைக்கப்படுகிறது. இது செயல்பாட்டு நினைவகத்தில் உள்ள பிரச்சனையைப் பற்றிய ஆரம்ப தரவோடு துவங்குகிறது, பின்னர் பின்வரும் காரணிய செயலையும் செய்கிறது: -1. இலக்கு attribute working memory இல் உள்ளதா என்பதைச் சரிபார்க்கவும் - முடித்து முடிவைத் தரவும் -2. தற்போதைய நிலை திருப்தி செய்யப்படும் அனைத்து விதிகளையும் தேடுங்கள் - **conflict set** ஐப் பெறுங்கள். -3. **Conflict resolution** ஐச் செய்யவும் - இந்த படியில் செயல்படுத்தப்படும் ஒரு விதியைத் தேர்ந்தெடுக்கவும். conflict resolution strategies பலவகையானவை இருக்கலாம்: - - Knowledge base இல் பொருந்தக்கூடிய முதல் விதியைத் தேர்ந்தெடுக்கவும் - - ஒரு random விதியைத் தேர்ந்தெடுக்கவும் - - *மேலும் குறிப்பிட்ட* விதியைத் தேர்ந்தெடுக்கவும், அதாவது "left-hand-side" (LHS) இல் அதிகமான நிலைகளை சந்திக்கும் விதி -4. தேர்ந்தெடுக்கப்பட்ட விதியைச் செயல்படுத்தவும் மற்றும் problem state இல் புதிய அறிவு துண்டைச் சேர்க்கவும் -5. படி 1 இல் இருந்து மீண்டும் தொடங்கவும். +1. இலக்க குணம் செயல்பாட்டு நினைவகத்தில் இருந்தால் நிறுத்தி முடிவை வழங்கு +2. தற்போதைய நிலையில் நிறைவேற்றப்படும் அனைத்து விதிகளையும் தேடு - **சண்டை தொகுப்பு** பெறுக +3. **சண்டை தீர்வு** செய் - அதாவது இந்த கட்டத்தில் செயல்படுத்த ஒரே விதியை தேர்ந்தெடு. சண்டைத் தீர்விற்கு பல வழிகள் இருக்கலாம்: + - அறிவுக் களத்தில் முதன்முதலில் பொருந்தும் விதியைத் தேர்ந்தெடு + - எடிக்கமாய் அளவிடப்பட்ட விதியை தேர்ந்தெடு + - *மிகவும் தனிப்பட்ட* விதியை தேர்ந்தெடு, அதாவது "இடது பக்கம்"(LHS) அதிகமான நிபந்தனைகளை பூர்த்தி செய்யுகிறது +4. தேர்ந்தெடுக்கப்பட்ட விதியை செயல்படுத்து மற்றும் பிரச்சனை நிலைக்கு புதிய அறிவைச் சேர்க்கவும் +5. படி 1 இடைவரை செயல்பாடு தொடர்க -எனினும், சில சந்தர்ப்பங்களில், பிரச்சினை பற்றிய அறிவு இல்லாமல் தொடங்க விரும்புகிறோம், மேலும் முடிவுக்கு வர உதவக்கூடிய கேள்விகளை கேட்க விரும்புகிறோம். உதாரணமாக, மருத்துவ நோயறிதலில், நோயாளியை நோயறிதல் செய்யத் தொடங்குவதற்கு முன் அனைத்து மருத்துவ பரிசோதனைகளையும் முன்னதாகச் செய்ய மாட்டோம். ஒரு முடிவை எடுக்க வேண்டிய போது பரிசோதனைகளைச் செய்ய விரும்புகிறோம். +எனினும் சில சமயங்களில், பிரச்சனை பற்றி ஆரம்ப அறிவுரை இல்லாமல் துவங்கி முடிவை அடைய உதவும் கேள்விகளை கேட்க விரும்பலாம். உதாரணமாக மருத்துவ கணிப்பு செய்யும் போது, நோயாளி கண்டறிதலைத் தொடங்கும் முன் எல்லா மருத்துவ பரிசோதனைகளையும் செய்யவேண்டியதில்லை, முடிவு எடுக்கும்போதுவே அவற்றை செய்கிறோம். -இந்த செயல்முறையை **backward inference** ஐப் பயன்படுத்தி மாதிரியாக்கலாம். இது **goal** மூலம் இயக்கப்படுகிறது - நாம் கண்டுபிடிக்க முயற்சிக்கும் attribute value: +இச்செயலை **பின்னோட்ட காரணீயம்** எனக் கூறலாம். இது **கோல்** - இதைத் தேடுகின்ற attribute மதிப்பின் அடிப்படையில் நடக்கிறது: -1. ஒரு goal இன் மதிப்பை நமக்கு வழங்கக்கூடிய அனைத்து விதிகளையும் தேர்ந்தெடுக்கவும் (அதாவது goal ஐ RHS ("right-hand-side") இல் கொண்டது) - conflict set -1. இந்த attribute க்கு விதிகள் இல்லை அல்லது பயனரிடமிருந்து மதிப்பை கேட்க வேண்டும் என்று கூறும் விதி இருந்தால் - கேட்கவும், இல்லையெனில்: -1. conflict resolution strategy ஐப் பயன்படுத்தி நாங்கள் *hypothesis* ஆகப் பயன்படுத்தும் ஒரு விதியைத் தேர்ந்தெடுக்கவும் - அதை நிரூபிக்க முயற்சிக்கவும் -1. goal களை நிரூபிக்க முயற்சித்து, விதியின் LHS இல் உள்ள அனைத்து attributes க்கும் செயல்முறையை மீண்டும் மீண்டும் செய்யவும் -1. எந்த நேரத்திலும் செயல்முறை தோல்வியடையுமானால் - படி 3 இல் மற்றொரு விதியைப் பயன்படுத்தவும். +1. கோலுக்கான மதிப்பைக் கொடுத்து கொள்ளக்கூடிய அனைத்து விதிகளையும் தேர்வு செய் (அதாவது RHS ("வலது பக்கம்") இல் கோல் உள்ளன) - சண்டை தொகுப்பு +2. அந்த attribute கான விதி இல்லையெனில், அல்லது பயன்படுத்துபவரிடமிருந்து மதிப்பைக் கேட்க சொல்வதுபோன்ற விதி இருந்தால் - கேளோ, இல்லையெனில்: +3. சண்டை தீர்வு முறையை பயன்படுத்தி ஒரு விதியை தேர்வு செய், அதை *கருதுகோள்* என பயன்படுத்துவோம் - அதை நிரூபிப்பதற்கு முயலுவோம் +4. அன்றைய விதியின் LHS இல் உள்ள அனைத்து அம்சங்களுக்கும் மீண்டும் இதே செயல்முறையை திரும்ப திரும்ப செய்யவும், அவற்றையும் கோல் என நிரூபிப்பதற்கு முயலுதல் +5. எதையாவது போது இந்த செயல்பாடு தோல்வியடைத்தால் - படி 3 இல் வேறு விதியை பயன்படுத்து -> ✅ எந்த சூழல்களில் forward inference அதிகம் பொருத்தமாக இருக்கும்? backward inference எப்படி? +> ✅ எந்த சூழ்நிலைகளில் முன்னேற்ற காரணியம் பொருத்தமானது? பின்னோட்ட காரணியம் பற்றி எப்படி? -### நிபுணர் அமைப்புகளை செயல்படுத்துதல் +### நிபுணர் அமைப்புகளை அமல்படுத்தல் -நிபுணர் அமைப்புகள் பல கருவிகளைப் பயன்படுத்தி செயல்படுத்தப்படலாம்: +நிபுணர் அமைப்புகளை வெவ்வேறு கருவிகளுடன் உருவாக்கலாம்: -* சில உயர் நிலை நிரலாக்க மொழியில் நேரடியாக நிரலாக்கம். இது சிறந்த யோசனை அல்ல, ஏனெனில் ஒரு problem domain expert inference செயல்முறையின் விவரங்களைப் புரிந்துகொள்ளாமல் விதிகளை எழுத முடியும் என்பதே அறிவு அடிப்படையிலான அமைப்பின் முக்கிய நன்மையாகும். -* **Expert systems shell** ஐப் பயன்படுத்துதல், அதாவது ஒரு knowledge representation language ஐப் பயன்படுத்தி அறிவு நிரப்புவதற்காக குறிப்பாக வடிவமைக்கப்பட்ட ஒரு அமைப்பு. +* ஒரு உயர் நிலை நிரலாக்க மொழியில் நேரடியாக நிரலாக்குதல். இது சிறந்த யோசனையல்ல, ஏனென்றால் அறிவுரைக் களையும் காரணியையும் பிரித்துவைக்க முடியும் என்பதே அறிவுரைக் நிறுவல் அமைப்புகளின் முதன்மை நன்மை, மேலும் பிரச்சனைத் துறை நிபுணர் அறிவுரைகளை காரணிய விவரங்களைப் புரிந்துகொள்ளாமல் விதிகளை எழுத முடியும். +* **நிபுணர் அமைப்புக் கோட்டை** (expert systems shell) பயன்படுத்துதல், அதாவது அறிவுரைகளை உருவாக்க ஒரு குறிப்பிட்ட அறிவுரைப் பிரதிநிதித்துவ மொழியைப் பயன்படுத்துவதற்கான அமைப்பு. -## ✍️ பயிற்சி: விலங்கு inference +## ✍️ பயிற்சி: உயிரினம் தொடர்பான காரணீயம் -[Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) ஐ forward மற்றும் backward inference expert system ஐ செயல்படுத்துவதற்கான உதாரணமாகப் பார்க்கவும். +[Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) ஐ பாருங்கள், முன்னோட்டமும் பின்னோட்ட காரணிய அமைப்பை உருவாக்கிய உதாரணம். -> **குறிப்பு**: இந்த உதாரணம் மிகவும் எளிமையானது, மேலும் ஒரு நிபுணர் அமைப்பு எப்படி இருக்கும் என்பதைப் பற்றிய கருத்தை மட்டுமே வழங்குகிறது. நீங்கள் ஒரு அமைப்பை உருவாக்கத் தொடங்கும் போது, ​​200+ விதிகள் வரை சென்ற பிறகே சில *புத்திசாலி* நடத்தை அதிலிருந்து நீங்கள் கவனிக்க ஆரம்பிப்பீர்கள். ஒரு கட்டத்தில், விதிகள் மிகவும் சிக்கலாகி, அவற்றை அனைத்தையும் மனதில் வைத்திருக்க முடியாது, மேலும் இந்த கட்டத்தில் ஒரு அமைப்பு ஏன் குறிப்பிட்ட முடிவுகளை எடுக்கிறது என்று நீங்கள் ஆச்சரியப்படலாம். எனினும், அறிவு அடிப்படையிலான அமைப்பின் முக்கியமான பண்புகள் என்னவென்றால், எந்த முடிவும் எவ்வாறு எடுக்கப்பட்டது என்பதை நீங்கள் எப்போதும் *விளக்க* முடியும். +> **குறிப்பு**: இந்த உதாரணம் மிகவும் எளிமையானது, மற்றும் நிபுணர் அமைப்பின் உருவாக்கம் எப்படி இருக்கிறது என்பதற்கு மட்டும் ஒரு கருத்து தருகிறது. நீங்கள் இந்த அமைப்பைப் பயன்படுத்த ஆரம்பிக்கும் போது, சுமார் 200+ விதிகளுக்கு மட்டுமே நிபுணர் அமைப்பின் *புத்திசாலி* செயல்பாடு தோன்றும். சில நேரங்களில் விதிகள் எல்லாம் மனதில் வைக்கப்பட முடியாத அளவு சிக்கலாக இருக்கும், அப்பொழுது அமைப்பு ஏன் அவ்வாறாய் முடிவு செய்கிறது என்று நீங்கள் ஆச்சரியப்படக்கூடும். இருப்பினும், அறிவுரைப் பிரதிநிதித்துவ அமைப்புகளின் முக்கிய தன்மையே *எவ்வாறு* எந்த முடிவும் எடுக்கப்படுகிறதோ அதை எப்போதும் விளக்க முடியும் என்பதே ஆகும். -## Ontologies -- XML அடிப்படையிலான மொழிகளின் குடும்பம்: RDF (Resource Description Framework), RDFS (RDF Schema), OWL (Ontology Web Language). +## ஆன்டாலஜிகள் மற்றும் அர்த்தமுள்ள வலை -Semantic Web இல் முக்கியமான கருத்து **Ontology** ஆகும். இது ஒரு பிரச்சினை துறையை தெளிவாகக் குறிப்பிடுவதற்கான ஒரு முறையான அறிவு பிரதிநிதித்துவத்தை குறிக்கிறது. மிக எளிய Ontology என்பது ஒரு பிரச்சினை துறையில் உள்ள பொருட்களின் ஒரு வரிசையாக இருக்கலாம், ஆனால் மேலும் சிக்கலான Ontology-கள் inference செய்ய பயன்படும் விதிகளை உள்ளடக்கும். +20ஆம் நூற்றாண்டின் இறுதியில், இணைய வளங்களை குறிப்பிட்டு, மிகவும் குறிப்பிட்ட கேள்விகளுக்கு பொருந்தும் வளங்களை தேட முடியும் என்ற நோக்கத்தில் அறிவுரைப் பிரதிநிதித்துவத்தை பயன்படுத்த முன்முயற்சி ஒன்று துவங்கப்பட்டது. இதை **அர்த்த மிக்க வலை (Semantic Web)** என அழைக்கப்பட்டது, மேலும் இதனுடன் தொடர்புடைய சில கருத்துக்கள் கீழ்வருமாறு: -Semantic Web இல், அனைத்து பிரதிநிதித்துவங்களும் triplets அடிப்படையில் அமைக்கப்பட்டுள்ளன. ஒவ்வொரு பொருளும் மற்றும் ஒவ்வொரு தொடர்பும் URI மூலம் தனித்துவமாக அடையாளம் காணப்படுகிறது. உதாரணமாக, இந்த AI Curriculum-ஐ Dmitry Soshnikov ஜனவரி 1, 2022 அன்று உருவாக்கியதாகக் கூற விரும்பினால், நாம் பயன்படுத்தக்கூடிய triplets இவை: +- **[விளக்கத் தர்க்கங்கள்](https://en.wikipedia.org/wiki/Description_logic)** (DL) அடிப்படையிலான ஒரு சிறப்பு அறிவுரைப் பிரதிநிதித்துவம். இது வடிவமைப்பு அறிவுரைக் பிரதிநிதித்துவத்திற்கு இணையாக உள்ளது, ஏனெனில் இது அம்சங்களைக் கொண்ட பொருள் அடுக்குகளை உருவாக்கிறது, ஆனால் இதற்கு அதிகாரப்பூர்வ தர்க்க அர்த்தவியல் மற்றும் காரணியம் உள்ளது. DL களின் முழு குடும்பம் பிரதிநிதித்துவத்தின் விளக்கத்தன்மையும் காரணிக்கும் ஆல்கோரிதமுறையின் சிக்கல்களையும் சமன்செய்கிறது. +- இணையத்தைச் சூழ்ந்த அறிவுறையை உருவாக்கும் முறையாக உலகளாவிய URI அடையாளம் மூலம் அனைத்து கருத்துக்களும் பிரதிநிதித்துவம் செய்யப்படுகின்றன, இது பகிரப்பட்ட அறிவுரைப் பிரதிநிதித்துவங்களை உருவாக்க உதவுகிறது. +- அறிவு விவரணத்திற்கான XML அடிப்படையிலான ஒரு குடும்பம்: RDF (Resource Description Framework), RDFS (RDF சுற்று), OWL (Ontology Web Language). + +Semantic Web-இல் ஒரு முக்கியக் கருத்து **Ontology** என்ற கருத்து ஆகும். இது ஒரு பிரச்சினை துறை பற்றி எளிய அறிவியல் காட்டுகை வரையறுக்கும் ஒரு தெளிவான கணிப்பாகும். எளிய ontology என்பது பிரச்சினை துறையில் உள்ள பொருட்களின் ஒழுங்கு அட்டவணையாக இருக்கலாம், ஆனால் திட்டமிட்ட ontologies இல் inference க்குப் பயன்படுத்தக்கூடிய விதிமுறைகள் அடக்கம் இருக்கும். + +Semantic Web-இல் எல்லா வடிவமைப்புகளும் மூன்று பகுதிகளின் அடிப்படையில் இருக்கும். ஒவ்வொரு பொருளும் மற்றும் ஒவ்வொரு தொடர்பும் தனித்துவமாக URI மூலம் அடையாளப்படுத்தப்படுகின்றன. உதாரணமாக, இந்த AI பாடத்திட்டம் Dmitry Soshnikov அவரால் 2022 ஜனவரி 1-ஆம் தேதி உருவாக்கப்பட்டதை தெரிவித்துக் கொள்ள வேண்டுமானால் நாம் பயன்படுத்தக்கூடிய மூன்று பகுதிகள் இங்கே: ``` -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 ``` -> ✅ இங்கு `http://www.example.com/terms/creation-date` மற்றும் `http://purl.org/dc/elements/1.1/creator` ஆகியவை *creator* மற்றும் *creation date* ஆகிய கருத்துகளை வெளிப்படுத்துவதற்கான சில பரவலாக ஏற்றுக்கொள்ளப்பட்ட URI-கள். +> ✅ இங்கே `http://www.example.com/terms/creation-date` மற்றும் `http://purl.org/dc/elements/1.1/creator` என்பது *உருவாக்குனர்* மற்றும் *உருவாக்க தேதி* ஆகிய கருத்துக்களை வெளிப்படுத்த someசிறப்பான மற்றும் அனைத்து இடங்களிலும் ஏற்றுக்கொள்ளப்பட்ட URI கள். -சிக்கலான சூழலில், உருவாக்குநர்களின் பட்டியலை வரையறுக்க விரும்பினால், RDF-ல் வரையறுக்கப்பட்ட சில தரவமைப்புகளை பயன்படுத்தலாம். +மேல் நிலை பிரकरणத்தில், ஒரு உருவாக்குனரின் பட்டியலை வரையறுக்க RDF இல் வரையறுக்கப்பட்ட தரவுத் தொகுதிகளை பயன்படுத்தலாம். -> மேலே உள்ள வரைபடங்கள் [Dmitry Soshnikov](http://soshnikov.com) மூலம். +> மேலே உள்ள வரைபடங்களை [Dmitry Soshnikov](http://soshnikov.com) உருவாக்கினார் -Semantic Web உருவாக்கத்தின் முன்னேற்றம் தேடுபொறிகள் மற்றும் இயற்கை மொழி செயலாக்க நுட்பங்களின் வெற்றியால் தடைப்பட்டுவிட்டது, இது உரையிலிருந்து அமைந்த தரவுகளை எடுக்க அனுமதிக்கிறது. இருப்பினும், சில துறைகளில் Ontology-கள் மற்றும் அறிவு அடுக்குகளை பராமரிக்க குறிப்பிடத்தக்க முயற்சிகள் இன்னும் உள்ளன. குறிப்பிடத்தக்க சில திட்டங்கள்: +Semantic Web உருவாக்கத்தின் முன்னேற்றம் வெற்றி பெற்ற தேடல் பொறிகள் மற்றும் இயற்கை மொழி செயலாக்க நுட்பங்களால் கொஞ்சம் மந்தமடைந்தது, இவை உரையிலிருந்து கட்டமைக்கப்படுத்தப்பட்ட தரவை எடுக்க உதவுகின்றன. இருப்பினும், சில துறைகளில் ontology களையும் அறிவுத் தளங்களையும் பராமரிப்பதில் இன்னும் முக்கிய முயற்சிகள் உண்டு. சில குறிப்பிடத்தக்க திட்டங்கள்: -* [WikiData](https://wikidata.org/) என்பது Wikipedia-க்கு தொடர்புடைய இயந்திரம் வாசிக்கக்கூடிய அறிவு அடுக்குகளின் தொகுப்பாகும். பெரும்பாலான தரவுகள் Wikipedia *InfoBoxes*-இலிருந்து, Wikipedia பக்கங்களின் உள்ளடக்கத்தில் இருந்து எடுக்கப்படுகிறது. நீங்கள் [SPARQL](https://query.wikidata.org/) மூலம் WikiData-ஐ கேள்வி கேட்கலாம், இது Semantic Web-க்கு ஒரு சிறப்பு கேள்வி மொழியாகும். மனிதர்களிடையே மிகவும் பிரபலமான கண் நிறங்களை காட்டும் ஒரு மாதிரி கேள்வி இதோ: +* [WikiData](https://wikidata.org/) என்பது Wikipedia உடன் தொடர்புடைய கணினி வாசிக்கக்கூடிய அறிவுத் தளங்களின் தொகுப்பு ஆகும். பெரும்பாலான தரவு Wikipedia இன் *InfoBoxes* இலிருந்து பெறப்பட்டது, அவை Wikipedia பக்கங்களில் உள்ள கட்டமைக்கப்பட்ட உள்ளடக்க பகுதிகள். நீங்கள் SPARQL என்ற Semantic Web இற்கான சிறப்பு விசாரணை மொழியில் wikidata ஐ [விசாரிக்க](https://query.wikidata.org/) முடியும். மனிதர்களின் மிகவும் பிரபலமான கண் வண்ணங்களை காட்டும் ஒரு மாதிரி விசாரணை இங்கே: ```sparql #defaultView:BubbleChart @@ -201,49 +206,51 @@ WHERE GROUP BY ?eyeColorLabel ``` -* [DBpedia](https://www.dbpedia.org/) என்பது WikiData-க்கு ஒத்த ஒரு முயற்சி. +* [DBpedia](https://www.dbpedia.org/) என்பது WikiData உடன் ஒத்த முயற்சியாகும். -> ✅ உங்கள் சொந்த Ontology-களை உருவாக்க அல்லது ஏற்கனவே உள்ளவற்றைத் திறக்க முயற்சிக்க விரும்பினால், [Protégé](https://protege.stanford.edu/) என்ற ஒரு சிறந்த காட்சி Ontology தொகுப்பியைப் பயன்படுத்தலாம். இதைப் பதிவிறக்கவும் அல்லது ஆன்லைனில் பயன்படுத்தவும். +> ✅ உங்களுக்கேற்ற ontologies உருவாக்க அல்லது உள்ள ontologiesஐ திறக்க விரும்பினால், [Protégé](https://protege.stanford.edu/) என்ற சிறந்த காணொளி ontology தொகுப்பாளரை பயன்படுத்தலாம். அதை பதிவிறக்கவும், அல்லது ஆன்லைனில் பயன்படுத்தவும். -*Romanov குடும்ப Ontology-யுடன் திறந்த Web Protégé தொகுப்பி. Dmitry Soshnikov மூலம் எடுத்த படக்காட்சி* +*Web Protégé தொகுப்பாளரை Romanov குடும்ப ontologyயுடன் திறந்துள்ளது. Dmitry Soshnikov அவர்களின் ஸ்கிரீன்ஷாட்* ## ✍️ பயிற்சி: குடும்ப Ontology -Semantic Web நுட்பங்களைப் பயன்படுத்தி குடும்ப உறவுகளைப் பற்றி ஆராய [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) ஐப் பார்க்கவும். பொதுவான GEDCOM வடிவத்தில் பிரதிநிதித்துவப்படுத்தப்பட்ட குடும்ப மரம் மற்றும் குடும்ப உறவுகளின் Ontology-யை எடுத்து, கொடுக்கப்பட்ட நபர்களின் தொகுப்புக்கான அனைத்து குடும்ப உறவுகளின் ஒரு வரைபடத்தை உருவாக்குவோம். +இணைக்கப்பட்டுள்ள [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) ஐ பாருங்கள், இது குடும்ப உறவுகளைப் பற்றி காரணமறிதல் செய்ய Semantic Web நுட்பங்களைப் பயன்படுத்தும் உதாரணமாகும். பொதுவான GEDCOM வடிவில் ஜனங்கள் பிரதிநிதித்துவம் செய்யப்பட்ட குடும்ப மரத்தை மற்றும் குடும்ப உறவுகளின் ontology ஐ எடுத்து கொடுக்கப்பட்ட நபர்களுக்கான அனைத்து குடும்ப உறவுகளின் ஒரு வரைபடத்தை உருவாக்கி பார்க்கிறோம். -## Microsoft Concept Graph +## Microsoft கருத்து வரைபடம் -பொதுவாக, Ontology-கள் கவனமாக கையால் உருவாக்கப்படுகின்றன. இருப்பினும், இயற்கை மொழி உரைகளிலிருந்து, உதாரணமாக, அமைப்பற்ற தரவிலிருந்து Ontology-களை **கோர** முடியும். +பெரும்பாலான நேரங்களில் ontologies கையேடு முறையில் கவனமாக உருவாக்கப்படுகின்றன. இருந்தாலும், இயற்கை மொழி உரைப்பிரிவுகளிலிருந்து ontology களை **கருவூலம்** செய்யவும் முடியும். -Microsoft Research மூலம் மேற்கொள்ளப்பட்ட ஒரு முயற்சி [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste) ஆகும். +அப்படியான ஒரு முயற்சி Microsoft Researchஇல் செய்யபட்டது மற்றும் [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste) எனும் உருவாகியது. -இது `is-a` மரபு உறவுகளைப் பயன்படுத்தி குழுவாக்கப்பட்ட பொருட்களின் ஒரு பெரிய தொகுப்பாகும். "Microsoft என்ன?" போன்ற கேள்விகளுக்கு பதிலளிக்க இது உதவுகிறது - பதில் "ஒரு நிறுவனம் (Probability 0.87) மற்றும் ஒரு பிராண்ட் (Probability 0.75)" போன்றதாக இருக்கும். +இது `is-a` என்பதை அடிப்படையாகக் கொண்டு ஒருங்கிணைக்கப்பட்ட பரபரப்பான பல பொருட்களின் ஒரு பெரிய தொகுப்பாகும். இது "Microsoft என்ன?" போன்ற கேள்விகளுக்கு பதில் அளிக்க உதவும் - பதில் "ஒரு நிறுவனம் 0.87 சாத்தியக்கூறுடன் மற்றும் ஒரு பிராண்ட் 0.75 சாத்தியக்கூறுடன்" என இருக்கும். -இந்த Graph REST API ஆகவும், அல்லது அனைத்து entity ஜோடிகளையும் பட்டியலிடும் ஒரு பெரிய பதிவிறக்கக்கூடிய உரை கோப்பாகவும் கிடைக்கிறது. +இந்த வரைபடம் REST API ஆகவும், அல்லது அனைத்து பொருள் ஜோடிகளையும் பட்டியலிடும் ஒரு பெரிய பதிவிறக்கம் செய்யக்கூடிய உரை கோப்பாகவும் கிடைக்கிறது. -## ✍️ பயிற்சி: ஒரு Concept Graph +## ✍️ பயிற்சி: கருத்து வரைபடம் -செய்தி கட்டுரைகளை பல வகைகளில் குழுவாக்க Microsoft Concept Graph-ஐ எப்படி பயன்படுத்தலாம் என்பதைப் பார்க்க [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) நோட்புக் முயற்சிக்கவும். +[MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) நோட்புக் முயற்சியிடுங்கள், Microsoft கருத்து வரைபடத்தைப் பயன்படுத்தி செய்தி கட்டுரைகளை பல பிரிவுகளாக எப்படி பகுப்பாய்வு செய்யலாம் என்பது பார்க்க. ## முடிவு -இன்றைய காலத்தில், AI பெரும்பாலும் *Machine Learning* அல்லது *Neural Networks* என்பதற்கான ஒத்த பொருளாகக் கருதப்படுகிறது. இருப்பினும், மனிதன் தெளிவான காரணங்களை வெளிப்படுத்துகிறான், இது தற்போதைய Neural Networks கையாள முடியாத ஒன்றாகும். உண்மையான உலக திட்டங்களில், விளக்கங்கள் தேவைப்படும் அல்லது அமைப்பின் நடத்தை கட்டுப்படுத்தப்பட்ட முறையில் மாற்றுவதற்கான பணிகளைச் செய்ய தெளிவான காரணங்கள் இன்னும் பயன்படுத்தப்படுகின்றன. +இன்றைய நிலையில், AI பெரும்பாலும் *படையல் கற்றல்* அல்லது *நியூரல் நெட்வொர்க்* என்ற வார்த்தைக்கு சமமென கருதப்படுகிறது. இருப்பினும், மனிதன் தெளிவான காரணமறிதலைவும் வெளிப்படுத்துகிறான், இது இப்போது நியூரல் நெட்வொர்க் மூலம் நிறைவேற்றப்படவில்லை. உண்மையான உலகத் திட்டங்களில், விளக்கங்களுக்காக அல்லது அமைப்பின் நடத்தை கட்டுப்பாடாக மாற்றக்கூடிய வகையில் விளக்கக்கூடிய காரணமறிதல் இன்னும் பயன்படுத்தப்படுகிறது. ## 🚀 சவால் -இந்த பாடத்துடன் தொடர்புடைய குடும்ப Ontology நோட்புக்கில், குடும்ப மரத்தில் உள்ள மற்ற குடும்ப உறவுகளுடன் பரிசோதிக்க வாய்ப்பு உள்ளது. குடும்ப மரத்தில் உள்ள மக்களுக்கிடையே புதிய தொடர்புகளை கண்டறிய முயற்சிக்கவும். +இந்த பாடத்துடன் இணைக்கப்பட்ட குடும்ப Ontology நோட்புக்கில், மற்ற குடும்ப உறவுகளை முயற்சி செய்யும் வாய்ப்பு உள்ளது. குடும்ப மரத்தில் உள்ள மனிதர்களுக்கு இடையேயான புதிய இணைப்புக்களை கண்டுபிடிக்க முயற்சிக்கவும். -## [பாடத்திற்குப் பிந்தைய வினாடி வினா](https://ff-quizzes.netlify.app/en/ai/quiz/4) +## [பாடப்பின்னர் வினாடி வினா](https://ff-quizzes.netlify.app/en/ai/quiz/4) -## மதிப்பீடு மற்றும் சுயபயிற்சி +## மதிப்பாய்வு & சுயபடிப்பு -மனிதர்கள் அறிவை அளவிடவும் குறியிடவும் முயற்சித்துள்ள பகுதிகளை கண்டறிய இணையத்தில் சில ஆராய்ச்சிகளைச் செய்யவும். Bloom's Taxonomy-ஐப் பாருங்கள், மேலும் மனிதர்கள் தங்கள் உலகத்தைப் புரிந்துகொள்ள முயற்சித்த வரலாற்றைத் திரும்பிப் பாருங்கள். Linnaeus-ன் வேலைகளை ஆராய்ந்து, உயிரினங்களின் ஒரு Taxonomy உருவாக்கவும், மற்றும் Dmitri Mendeleev வேதியியல் கூறுகளை விவரிக்கவும் குழுவாக்கவும் ஒரு வழியை உருவாக்கிய விதத்தை கவனிக்கவும். நீங்கள் மேலும் எந்த 흥미로운 உதாரணங்களை கண்டறிய முடியும்? +வலைப்பின்னலில் ஆராய்ச்சி செய்து மனிதர்கள் அறிவை எவ்வாறு அளவிட முயற்சி செய்தனர் மற்றும் குறியிட முயற்சி செய்தனர் என்பதை கற்றுக்கொள்ளுங்கள். Bloom இன் வர்க்காக்கலைப் பற்றி பாருங்கள், மற்றும் மறுவாரிசை நோக்கி மனிதர்கள் தங்களுடைய உலகத்தை எவ்வாறு உணர முயன்றனர் என்பதையும் அறியுங்கள். உயிரினங்களுக்கான வர்க்காக்கலை உருவாக்க லின்னியஸ் செய்த பணியை ஆராயவும், இரசாயன கூறுகளுக்கான வகைப்பாட்டை உருவாக்க மென்டலீவ் செய்த முறையையும் கவனியுங்கள். நீங்கள் பிற சிறந்த உதாரணங்களை கண்டுபிடிக்க முடியுமா? -**பணி**: [Ontology உருவாக்கவும்](assignment.md) +**பணி**: [ஒரு Ontology உருவாக்கவும்](assignment.md) --- -**குறிப்பு**: -இந்த ஆவணம் AI மொழிபெயர்ப்பு சேவை [Co-op Translator](https://github.com/Azure/co-op-translator) பயன்படுத்தி மொழிபெயர்க்கப்பட்டுள்ளது. நாங்கள் துல்லியத்திற்காக முயற்சிக்கின்றோம், ஆனால் தானியங்கி மொழிபெயர்ப்புகளில் பிழைகள் அல்லது தவறான தகவல்கள் இருக்கக்கூடும் என்பதை கவனத்தில் கொள்ளவும். அதன் தாய்மொழியில் உள்ள மூல ஆவணம் அதிகாரப்பூர்வ ஆதாரமாக கருதப்பட வேண்டும். முக்கியமான தகவல்களுக்கு, தொழில்முறை மனித மொழிபெயர்ப்பு பரிந்துரைக்கப்படுகிறது. இந்த மொழிபெயர்ப்பைப் பயன்படுத்துவதால் ஏற்படும் எந்த தவறான புரிதல்கள் அல்லது தவறான விளக்கங்களுக்கு நாங்கள் பொறுப்பல்ல. \ No newline at end of file + +**வெறுமனே அறிவிப்பு**: +இந்த ஆவணம் AI மொழிபெயர்ப்பு சேவை [Co-op Translator](https://github.com/Azure/co-op-translator) பயன்படுத்தி மொழிமாற்றம் செய்யப்பட்டுள்ளது. நாம் துல்லியம் பெற்றுக்கொள்ள முயற்சிப்பதாலும், தானாக செய்யப்பட்ட மொழிபெயர்ப்புகளில் தவறுகள் அல்லது பிழைகள் இருக்கக்கூடும் என்பதை கவனிக்கவும். அந்த ஆவணத்தின் மூல மொழியில் உள்ள அசல் ஆவணம் அதிகாரப்பூர்வயானதாக கருதப்பட வேண்டும். முக்கியமான தகவல்களுக்கு, தொழில்முறை மனித மொழிபெயர்ப்பை பரிந்துரைக்கிறோம். இந்த மொழிபெயர்ப்பின் பயன்பாட்டால் ஏற்படும் எந்த தவறுபாடுகளுக்கும் அல்லது தவறான புரிதலுக்கும் நாம் பொறுப்பேற்கமாட்டோம். + \ No newline at end of file diff --git a/translations/uk/README.md b/translations/uk/README.md index b4e1e321..c1a845fa 100644 --- a/translations/uk/README.md +++ b/translations/uk/README.md @@ -1,8 +1,8 @@ [Arabic](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgarian](../bg/README.md) | [Burmese (Myanmar)](../my/README.md) | [Chinese (Simplified)](../zh/README.md) | [Chinese (Traditional, Hong Kong)](../hk/README.md) | [Chinese (Traditional, Macau)](../mo/README.md) | [Chinese (Traditional, Taiwan)](../tw/README.md) | [Croatian](../hr/README.md) | [Czech](../cs/README.md) | [Danish](../da/README.md) | [Dutch](../nl/README.md) | [Estonian](../et/README.md) | [Finnish](../fi/README.md) | [French](../fr/README.md) | [German](../de/README.md) | [Greek](../el/README.md) | [Hebrew](../he/README.md) | [Hindi](../hi/README.md) | [Hungarian](../hu/README.md) | [Indonesian](../id/README.md) | [Italian](../it/README.md) | [Japanese](../ja/README.md) | [Kannada](../kn/README.md) | [Korean](../ko/README.md) | [Lithuanian](../lt/README.md) | [Malay](../ms/README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Nepali](../ne/README.md) | [Nigerian Pidgin](../pcm/README.md) | [Norwegian](../no/README.md) | [Persian (Farsi)](../fa/README.md) | [Polish](../pl/README.md) | [Portuguese (Brazil)](../br/README.md) | [Portuguese (Portugal)](../pt/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Romanian](../ro/README.md) | [Russian](../ru/README.md) | [Serbian (Cyrillic)](../sr/README.md) | [Slovak](../sk/README.md) | [Slovenian](../sl/README.md) | [Spanish](../es/README.md) | [Swahili](../sw/README.md) | [Swedish](../sv/README.md) | [Tagalog (Filipino)](../tl/README.md) | [Tamil](../ta/README.md) | [Telugu](../te/README.md) | [Thai](../th/README.md) | [Turkish](../tr/README.md) | [Ukrainian](./README.md) | [Urdu](../ur/README.md) | [Vietnamese](../vi/README.md) > **Віддаєте перевагу клонувати локально?** -> Цей репозиторій включає понад 50 мовних перекладів, що значно збільшує розмір завантаження. Щоб клонувати без перекладів, використовуйте sparse checkout: +> Цей репозиторій містить понад 50 мовних перекладів, що значно збільшує розмір завантаження. Щоб клонувати без перекладів, використовуйте sparse checkout: > ```bash > git clone --filter=blob:none --sparse https://github.com/microsoft/AI-For-Beginners.git > cd AI-For-Beginners > git sparse-checkout set --no-cone '/*' '!translations' '!translated_images' > ``` -> Це дасть вам все необхідне для проходження курсу з набагато швидшим завантаженням. +> Це дасть вам усе необхідне для проходження курсу з набагато швидшим завантаженням. -**Якщо ви хочете, щоб були підтримані додаткові мови перекладів, вони перераховані [тут](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** +**Якщо ви хочете, щоб були підтримані додаткові мови перекладу, вони перелічені [тут](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** ## Приєднуйтесь до спільноти [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -## Чого ви навчитесь +## Чого ви навчитеся **[Ментальна карта курсу](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** -У цій програмі ви вивчите: +У цій навчальній програмі ви дізнаєтесь: -* Різні підходи до Штучного Інтелекту, включаючи "старий добрий" символічний підхід з **Представленням Знань** та логічним виведенням ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). -* **Нейронні мережі** та **Глибинне навчання**, які є ядром сучасного AI. Ми проілюструємо концепції цих важливих тем, використовуючи код у двох найпопулярніших фреймворках – [TensorFlow](http://Tensorflow.org) і [PyTorch](http://pytorch.org). -* **Нейронні архітектури** для роботи з зображеннями та текстом. Ми розглянемо новітні моделі, хоча вони можуть дещо відставати від останніх досягнень. -* Менш популярні підходи в AI, такі як **Генетичні алгоритми** та **Багатоагентні системи**. +* Різні підходи до штучного інтелекту, включаючи «старий добрий» символічний підхід із **репрезентацією знань** та логічним виведенням ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). +* **Нейронні мережі** та **глибинне навчання**, які лежать в основі сучасного ШІ. Ми продемонструємо концепції цих важливих тем на прикладах коду у двох найпопулярніших фреймворках - [TensorFlow](http://Tensorflow.org) та [PyTorch](http://pytorch.org). +* **Нейронні архітектури** для роботи з зображеннями та текстом. Ми розглянемо останні моделі, хоча можливо трохи поступимося сучасній передовій. +* Менш популярні підходи в ШІ, такі як **генетичні алгоритми** та **багатоагентні системи**. -Чого ми не розглядатимемо у цій програмі: +Чого ми не охоплюватимемо у цій навчальній програмі: -> [Знайдіть всі додаткові ресурси для цього курсу в нашій колекції Microsoft Learn](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) +> [Знайдіть усі додаткові ресурси для цього курсу у нашій колекції Microsoft Learn](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) -* Бізнес-кейси використання **AI у бізнесі**. Рекомендуємо пройти [Вступ до AI для бізнес-користувачів](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) на Microsoft Learn або [AI Business School](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), розроблений у співпраці з [INSEAD](https://www.insead.edu/). -* **Класичне машинне навчання**, яке добре описане у нашій [Програмі для початківців з машинного навчання](http://github.com/Microsoft/ML-for-Beginners). -* Практичні застосування AI, побудовані з використанням **[Cognitive Services](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**. Для цього рекомендуємо почати з модулів Microsoft Learn по [зору](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [обробці природної мови](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[Генеративний AI з Azure OpenAI Service](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** та інші. -* Специфічні ML **хмарні фреймворки**, такі як [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) або [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). Розгляньте використання навчальних шляхів [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) та [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). -* **Розмовний AI** та **Чат-боти**. Є окремий навчальний шлях [Create conversational AI solutions](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum), а також ви можете звернутися до [цього блогу](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) для детальнішої інформації. -* **Глибока математика** за глибоким навчанням. Для цього рекомендуємо книгу [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) авторів Ян Гудфеллоу, Йошуа Бенджіо та Аарон Куорвіль, яка також доступна онлайн за адресою [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/). +* Бізнес-кейси використання **ШІ у бізнесі**. Розгляньте можливість проходження навчальної траєкторії [Вступ до ШІ для бізнес-користувачів](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) на Microsoft Learn або [AI Business School](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), розроблений у співпраці з [INSEAD](https://www.insead.edu/). +* **Класичне машинне навчання**, яке добре описано у нашій навчальній програмі [Машинне навчання для початківців](http://github.com/Microsoft/ML-for-Beginners). +* Практичні застосування ШІ, створені за допомогою **[Когнітивних сервісів](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**. Для цього рекомендуємо почати з модулів Microsoft Learn для [зору](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [обробки природної мови](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[генеративного ШІ за допомогою Azure OpenAI Service](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** та інших. +* Специфічні ML **хмарні фреймворки**, такі як [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) або [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). Розгляньте використання навчальних шляхів [Створення та експлуатація машинного навчання з Azure Machine Learning](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) і [Створення та експлуатація машинного навчання з Azure Databricks](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum). +* **Розмовний ШІ** та **чат-боти**. Існує окремий навчальний шлях [Створення розмовних рішень ШІ](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum), а також ви можете звернутися до [цього блогу](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) для більшої деталізації. +* **Глибока математика** глибинного навчання. Для цього ми рекомендуємо книгу [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) авторів Іена Гудфеллоу, Йошуа Бенджіо та Аарона Курвіль, яка також доступна онлайн за [адресою](https://www.deeplearningbook.org/). -Для плавного введення у теми _AI у хмарі_ рекомендуємо навчальний шлях [Початок роботи зі штучним інтелектом на Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum). +Для плавного вступу до тематики _ШІ у хмарі_ ви можете розглянути навчальний шлях [Початок роботи з штучним інтелектом на Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum). # Зміст | | Посилання на урок | PyTorch/Keras/TensorFlow | Лабораторна робота | | :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | -| 0 | [Налаштування курсу](./lessons/0-course-setup/setup.md) | [Налаштуйте ваше середовище розробки](./lessons/0-course-setup/how-to-run.md) | | -| I | [**Вступ до AI**](./lessons/1-Intro/README.md) | | | -| 01 | [Вступ та історія AI](./lessons/1-Intro/README.md) | - | - | -| II | **Символічний AI** | -| 02 | [Представлення знань та експертні системи](./lessons/2-Symbolic/README.md) | [Експертні системи](./lessons/2-Symbolic/Animals.ipynb) / [Онтологія](./lessons/2-Symbolic/FamilyOntology.ipynb) /[Граф концептів](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | +| 0 | [Налаштування курсу](./lessons/0-course-setup/setup.md) | [Налаштуйте своє середовище розробки](./lessons/0-course-setup/how-to-run.md) | | +| I | [**Вступ до ШІ**](./lessons/1-Intro/README.md) | | | +| 01 | [Вступ та історія ШІ](./lessons/1-Intro/README.md) | - | - | +| II | **Символічний ШІ** | +| 02 | [Репрезентація знань та експертні системи](./lessons/2-Symbolic/README.md) | [Експертні системи](./lessons/2-Symbolic/Animals.ipynb) / [Онтологія](./lessons/2-Symbolic/FamilyOntology.ipynb) /[Граф понять](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | | III | [**Вступ до нейронних мереж**](./lessons/3-NeuralNetworks/README.md) ||| -| 03 | [Персептрон](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [Ноутбук](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [Лабораторна](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | -| 04 | [Багатошаровий персептрон та створення власного фреймворку](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [Ноутбук](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [Лабораторна](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | +| 03 | [Перцептрон](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [Зошит](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [Лабораторна](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | +| 04 | [Багатошаровий перцептрон та створення власного фреймворку](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [Зошит](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [Лабораторна](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | | 05 | [Вступ до фреймворків (PyTorch/TensorFlow) та перенавчання](./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) | [Лабораторна](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | -| IV | [**Комп’ютерний зір**](./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)| [Дослідіть комп’ютерний зір на Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | -| 06 | [Вступ до комп’ютерного зору. OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [Ноутбук](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [Лабораторна](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | +| IV | [**Комп’ютерний зір**](./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)| [Дослідіть Комп’ютерний зір на Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | +| 06 | [Вступ до комп’ютерного зору. OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [Зошит](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [Лабораторна](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | | 07 | [Згорткові нейронні мережі](./lessons/4-ComputerVision/07-ConvNets/README.md) & [Архітектури CNN](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [Лабораторна](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | -| 08 | [Попередньо навчені мережі та перенавчання](./lessons/4-ComputerVision/08-TransferLearning/README.md) і [Трюки тренування](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Лабораторна](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | -| 09 | [Автокодировувачі та 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 | [Генеративні змагальні мережі та перенесення художнього стилю](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | +| 08 | [Попередньо навчені мережі та перенавчання](./lessons/4-ComputerVision/08-TransferLearning/README.md) та [Трюки тренування](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Лабораторна](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | +| 09 | [Автокодировщики та 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 | [Генеративно-змагальні мережі та стильове перенесення мистецтва](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | | 11 | [Виявлення об’єктів](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [Лабораторна](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | | 12 | [Семантична сегментація. 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 | [**Обробка природної мови**](./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) | [Дослідіть обробку природної мови на Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| | 13 | [Представлення тексту. 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 | [Семантичні векторні подання слів. Word2Vec і 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 | [Моделювання мови. Навчання власних ембеддингів](./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) | [Лабораторна](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | +| 14 | [Семантичні векторні уявлення слів. Word2Vec і 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 | [Моделювання мови. Навчання власних векторних уявлень](./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) | [Лабораторна](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | | 16 | [Рекурентні нейронні мережі](./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 | [Генеративні рекурентні мережі](./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) | [Лабораторна](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | | 18 | [Трансформери. BERT.](./lessons/5-NLP/18-Transformers/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb) /[TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersTF.ipynb) | | | 19 | [Розпізнавання іменованих сутностей](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [Лабораторна](./lessons/5-NLP/19-NER/lab/README.md) | -| 20 | [Великі мовні моделі, програмування промптів та few-shot завдання](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | -| VI | **Інші техніки ШІ** || | -| 21 | [Генетичні алгоритми](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [Ноутбук](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | -| 22 | [Глибоке підкріплювальне навчання](./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) | [Лабораторна](./lessons/6-Other/22-DeepRL/lab/README.md) | -| 23 | [Багатокористувацькі системи](./lessons/6-Other/23-MultiagentSystems/README.md) | | | +| 20 | [Великі мовні моделі, програмування підказок і завдання з малою кількістю прикладів](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | +| VI | **Інші методи ШІ** || | +| 21 | [Генетичні алгоритми](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [Зошит](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | +| 22 | [Глибинне підкріплювальне навчання](./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) | [Лабораторна](./lessons/6-Other/22-DeepRL/lab/README.md) | +| 23 | [Системи з багатьма агентами](./lessons/6-Other/23-MultiagentSystems/README.md) | | | | VII | **Етика ШІ** | | | -| 24 | [Етика ШІ та відповідальний ІІ](./lessons/7-Ethics/README.md) | [Microsoft Learn: принципи відповідального ІІ](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | +| 24 | [Етика ШІ та відповідальний ШІ](./lessons/7-Ethics/README.md) | [Microsoft Learn: Принципи відповідального ШІ](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | | IX | **Додатково** | | | -| 25 | [Мультимодальні мережі, CLIP та VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [Ноутбук](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | +| 25 | [Мультимодальні мережі, CLIP та VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [Зошит](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | ## Кожен урок містить -* Матеріали для передчитання -* Виконувані Jupyter ноутбуки, які часто специфічні для фреймворку (**PyTorch** або **TensorFlow**). Виконуваний ноутбук також містить багато теоретичного матеріалу, тому для розуміння теми потрібно пройти принаймні одну версію ноутбука (або PyTorch, або TensorFlow). -* **Лабораторні роботи** доступні для деяких тем, які дають можливість спробувати застосувати вивчений матеріал до конкретної задачі. -* Деякі розділи містять посилання на модулі [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum), що охоплюють відповідні теми. +* Попередній матеріал для читання +* Виконувані зошити Jupyter, які часто специфічні для фреймворку (**PyTorch** або **TensorFlow**). Виконуваний зошит також містить багато теоретичного матеріалу, тому для розуміння теми потрібно пройти хоча б один варіант зошита (або PyTorch, або TensorFlow). +* **Лабораторні роботи** для деяких тем, які дають можливість спробувати застосувати вивчений матеріал до конкретної задачі. +* Деякі розділи містять посилання на модулі [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum), що охоплюють суміжні теми. ## Початок роботи @@ -130,68 +129,68 @@ CO_OP_TRANSLATOR_METADATA: Якщо ви зовсім новачок у ШІ і хочете швидкі практичні приклади, перегляньте наші [**Приклади для початківців**](./examples/README.md)! Вони включають: - 🌟 **Hello AI World** - Ваша перша програма ШІ (розпізнавання шаблонів) -- 🧠 **Проста нейронна мережа** - Побудуйте нейронну мережу з нуля -- 🖼️ **Класифікатор зображень** - Класифікація зображень з детальними коментарями -- 💬 **Аналіз настрою тексту** - Аналіз позитивного/негативного тексту +- 🧠 **Проста нейронна мережа** - Побудова нейронної мережі з нуля +- 🖼️ **Класифікатор зображень** - Класифікація зображень з докладними коментарями +- 💬 **Аналіз настрою тексту** — аналіз позитивного/негативного тексту -Ці приклади призначені, щоб допомогти вам зрозуміти концепції ШІ перед тим, як зануритись у повний курс. +Ці приклади розроблені, щоб допомогти вам зрозуміти концепції ШІ перед тим, як зануритися у повний навчальний курс. -### 📚 Налаштування повного курсу +### 📚 Налаштування повного навчального курсу -- Ми створили [урок налаштування](./lessons/0-course-setup/setup.md), щоб допомогти вам налаштувати ваше середовище розробки. - Для викладачів ми також створили [урок налаштування навчальної програми](./lessons/0-course-setup/for-teachers.md)! -- Як [запустити код у VSCode або Codepace](./lessons/0-course-setup/how-to-run.md) +- Ми створили [урок з налаштування](./lessons/0-course-setup/setup.md), щоб допомогти вам із налаштуванням вашого середовища розробки. - Для викладачів ми також створили [урок з налаштування навчальних програм](./lessons/0-course-setup/for-teachers.md)! +- Як [запустити код у VSCode або Codespace](./lessons/0-course-setup/how-to-run.md) Дотримуйтесь цих кроків: -Форкніть Репозиторій: Натисніть кнопку "Fork" у верхньому правому куті цієї сторінки. +Форкніть репозиторій: натисніть кнопку "Fork" у верхньому правому куті цієї сторінки. -Клонуйте Репозиторій: `git clone https://github.com/microsoft/AI-For-Beginners.git` +Клонуйте репозиторій: `git clone https://github.com/microsoft/AI-For-Beginners.git` -Не забудьте додати зірочку (🌟) цьому репозиторію, щоб легше було його знайти пізніше. +Не забудьте поставити зірочку (🌟) цьому репозиторію, щоб легше було його знайти пізніше. -## Знайомтесь з іншими учнями +## Познайомтесь з іншими учнями -Приєднуйтесь до нашого [офіційного Discord-сервера ШІ](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum), щоб зустрітися і поспілкуватись з іншими учнями курсу та отримати підтримку. +Приєднуйтесь до нашого [офіційного AI Discord сервера](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum), щоб зустріти й поспілкуватися з іншими учнями цього курсу та отримати підтримку. -Якщо у вас є відгуки про продукт або питання під час розробки, завітайте на наш [форум розробників Azure AI Foundry](https://aka.ms/foundry/forum) +Якщо у вас є відгуки про продукт або питання під час створення, відвідайте наш [Azure AI Foundry Developer Forum](https://aka.ms/foundry/forum) ## Вікторини -> **Примітка щодо вікторин**: Всі вікторини знаходяться у папці Quiz-app у etc\quiz-app, або [онлайн тут](https://ff-quizzes.netlify.app/). Вони пов’язані з уроками, додаток вікторин можна запускати локально або розгортати в Azure; дотримуйтесь інструкцій у папці `quiz-app`. Вікторини поступово локалізуються. +> **Примітка про вікторини**: Всі вікторини знаходяться у папці Quiz-app в etc\quiz-app, або [онлайн тут](https://ff-quizzes.netlify.app/). Вони пов’язані з уроками, застосунок вікторин можна запускати локально або розгортати на Azure; дотримуйтеся інструкцій у папці `quiz-app`. Вікторини поступово локалізуються. ## Потрібна допомога -У вас є пропозиції або ви знайшли помилки у тексті чи коді? Створіть issue або pull request. +Чи маєте пропозиції або знайшли орфографічні чи кодові помилки? Створіть issue або pull request. ## Особлива подяка * **✍️ Головний автор:** [Dmitry Soshnikov](http://soshnikov.com), PhD * **🔥 Редактор:** [Jen Looper](https://twitter.com/jenlooper), PhD -* **🎨 Ілюстратор замальовок:** [Tomomi Imura](https://twitter.com/girlie_mac) +* **🎨 Ілюстратор скетчнотів:** [Tomomi Imura](https://twitter.com/girlie_mac) * **✅ Творець вікторин:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) * **🙏 Основні учасники:** [Evgenii Pishchik](https://github.com/Pe4enIks) ## Інші навчальні програми -Наша команда створює й інші навчальні програми! Перегляньте: +Наша команда створює інші навчальні програми! Ознайомтесь із ними: ### LangChain -[![LangChain4j для початківців](https://img.shields.io/badge/LangChain4j%20for%20Beginners-22C55E?style=for-the-badge&&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchain4j-for-beginners) -[![LangChain.js для початківців](https://img.shields.io/badge/LangChain.js%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchainjs-for-beginners?WT.mc_id=m365-94501-dwahlin) +[![LangChain4j for Beginners](https://img.shields.io/badge/LangChain4j%20for%20Beginners-22C55E?style=for-the-badge&&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchain4j-for-beginners) +[![LangChain.js for Beginners](https://img.shields.io/badge/LangChain.js%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchainjs-for-beginners?WT.mc_id=m365-94501-dwahlin) --- ### Azure / Edge / MCP / Агенти -[![AZD для початківців](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 для початківців](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 для початківців](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) -[![Агенти ШІ для початківців](https://img.shields.io/badge/AI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst) +[![AZD for Beginners](https://img.shields.io/badge/AZD%20for%20Beginners-0078D4?style=for-the-badge&labelColor=E5E7EB&color=0078D4)](https://github.com/microsoft/AZD-for-beginners?WT.mc_id=academic-105485-koreyst) +[![Edge AI for Beginners](https://img.shields.io/badge/Edge%20AI%20for%20Beginners-00B8E4?style=for-the-badge&labelColor=E5E7EB&color=00B8E4)](https://github.com/microsoft/edgeai-for-beginners?WT.mc_id=academic-105485-koreyst) +[![MCP for Beginners](https://img.shields.io/badge/MCP%20for%20Beginners-009688?style=for-the-badge&labelColor=E5E7EB&color=009688)](https://github.com/microsoft/mcp-for-beginners?WT.mc_id=academic-105485-koreyst) +[![AI Agents for Beginners](https://img.shields.io/badge/AI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst) --- ### Серія Generative AI -[![Generative AI для початківців](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 for Beginners](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst) [![Generative AI (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst) [![Generative AI (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst) [![Generative AI (JavaScript)](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst) @@ -199,35 +198,35 @@ CO_OP_TRANSLATOR_METADATA: --- ### Основне навчання -[![Машинне навчання для початківців](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) -[![Наука про дані для початківців](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) -[![ШІ для початківців](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) -[![Кібербезпека для початківців](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung) -[![Веб-розробка для початківців](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst) -[![Інтернет речей для початківців](https://img.shields.io/badge/IoT%20for%20Beginners-14B8A6?style=for-the-badge&labelColor=E5E7EB&color=14B8A6)](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst) -[![Розробка XR для початківців](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst) +[![ML for Beginners](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst) +[![Data Science for Beginners](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst) +[![AI for Beginners](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst) +[![Cybersecurity for Beginners](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung) +[![Web Dev for Beginners](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst) +[![IoT for Beginners](https://img.shields.io/badge/IoT%20for%20Beginners-14B8A6?style=for-the-badge&labelColor=E5E7EB&color=14B8A6)](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst) +[![XR Development for Beginners](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst) --- ### Серія Copilot -[![Copilot для парного програмування з ШІ](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 для 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 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) ## Отримання допомоги -Якщо ви застрягли або маєте питання щодо створення ШІ-додатків, приєднуйтесь до інших учнів і досвідчених розробників у дискусіях про MCP. Це підтримуюча спільнота, де питання вітаються, а знання вільно поширюються. +Якщо ви застрягли або маєте питання щодо створення AI-застосунків, приєднуйтесь до інших учнів і досвідчених розробників у обговореннях про MCP. Це підтримуюча спільнота, де вітаються питання і відбувається вільний обмін знаннями. [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -Якщо у вас є відгуки про продукт або помилки під час розробки, завітайте на: +Якщо у вас є відгуки про продукт або помилки під час створення – відвідайте: [![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) --- -**Відмова від відповідальності**: -Цей документ було перекладено за допомогою сервісу штучного інтелекту [Co-op Translator](https://github.com/Azure/co-op-translator). Хоча ми прагнемо до точності, просимо враховувати, що автоматичні переклади можуть містити помилки або неточності. Орігінальний документ мовою оригіналу слід вважати авторитетним джерелом. Для критично важливої інформації рекомендується професійний людський переклад. Ми не несемо відповідальності за будь-які непорозуміння чи неправильні тлумачення, що виникли внаслідок використання цього перекладу. +**Відмова від відповідальності**: +Цей документ був перекладений за допомогою автоматичного перекладача [Co-op Translator](https://github.com/Azure/co-op-translator). Хоча ми прагнемо до точності, будь ласка, майте на увазі, що автоматичний переклад може містити помилки або неточності. Оригінальний документ рідною мовою слід вважати авторитетним джерелом. Для важливої інформації рекомендується звертатися до професійного людського перекладу. Ми не несемо відповідальності за будь-які непорозуміння чи неправильні тлумачення, що виникли внаслідок використання цього перекладу. \ No newline at end of file diff --git a/translations/uk/lessons/0-course-setup/how-to-run.md b/translations/uk/lessons/0-course-setup/how-to-run.md index 93be588b..6cdd9dcf 100644 --- a/translations/uk/lessons/0-course-setup/how-to-run.md +++ b/translations/uk/lessons/0-course-setup/how-to-run.md @@ -1,21 +1,21 @@ # Як запустити код -Цей курс містить багато прикладів коду та лабораторних робіт, які ви, ймовірно, захочете виконати. Для цього вам потрібно мати можливість запускати Python-код у Jupyter Notebooks, які є частиною цього курсу. У вас є кілька варіантів для запуску коду: +Цей навчальний курс містить багато виконуваних прикладів і лабораторних робіт, які ви захочете запустити. Для цього вам потрібна можливість виконувати код Python у Jupyter Notebook, які є частиною цього курсу. Ви маєте кілька варіантів запуску коду: ## Запуск локально на вашому комп'ютері -Щоб запустити код локально на вашому комп'ютері, вам потрібно мати встановлену якусь версію Python. Особисто я рекомендую встановити **[miniconda](https://conda.io/en/latest/miniconda.html)** — це досить легка установка, яка підтримує менеджер пакетів `conda` для різних **віртуальних середовищ** Python. +Щоб запустити код локально на вашому комп'ютері, потрібна установка Python. Однією з рекомендацій є встановлення **[miniconda](https://conda.io/en/latest/miniconda.html)** - це досить легка установка, яка підтримує пакетний менеджер `conda` для різних **віртуальних середовищ** Python. -Після встановлення miniconda вам потрібно клонувати репозиторій і створити віртуальне середовище для цього курсу: +Після встановлення miniconda склонуйте репозиторій і створіть віртуальне середовище для використання в цьому курсі: ```bash git clone http://github.com/microsoft/ai-for-beginners @@ -26,52 +26,55 @@ conda activate ai4beg ### Використання Visual Studio Code з розширенням Python -Мабуть, найкращий спосіб використовувати цей курс — відкрити його у [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) з [розширенням Python](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste). +Цей курс найкраще використовувати, відкриваючи його у [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) з [розширенням Python](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste). -> **Примітка**: Після того, як ви клонували і відкрили директорію у VS Code, програма автоматично запропонує вам встановити розширення Python. Вам також потрібно буде встановити miniconda, як описано вище. +> **Примітка**: Після того, як ви склонуєте і відкриєте каталог у VS Code, він автоматично запропонує встановити розширення Python. Також вам доведеться встановити miniconda, як описано вище. -> **Примітка**: Якщо VS Code запропонує вам відкрити репозиторій у контейнері, відмовтеся від цього, щоб використовувати локальну установку Python. +> **Примітка**: Якщо VS Code запропонує вам пере-відкрити репозиторій у контейнері, варто відмовитися, щоб використовувати локальну інсталяцію Python. ### Використання Jupyter у браузері -Ви також можете використовувати середовище Jupyter прямо у браузері на вашому комп'ютері. Насправді, як класичний Jupyter, так і Jupyter Hub забезпечують досить зручне середовище розробки з автозавершенням, підсвічуванням коду тощо. +Ви також можете використовувати оточення Jupyter у браузері на власному комп’ютері. І класичний Jupyter, і JupyterHub забезпечують зручне середовище розробки з автозаповненням, підсвічуванням коду тощо. -Щоб запустити Jupyter локально, перейдіть до директорії курсу і виконайте: +Щоб запустити Jupyter локально, перейдіть у каталог курсу і виконайте: ```bash jupyter notebook -``` -або +``` +або ```bash jupyterhub -``` -Після цього ви зможете перейти до будь-якого з `.ipynb` файлів, відкрити їх і почати працювати. +``` +Після цього ви можете перейти до будь-якого з файлів `.ipynb`, відкрити їх і почати роботу. ### Запуск у контейнері -Альтернативою встановленню Python може бути запуск коду у контейнері. Оскільки наш репозиторій містить спеціальну папку `.devcontainer`, яка вказує, як створити контейнер для цього репозиторію, VS Code запропонує вам відкрити код у контейнері. Це вимагатиме встановлення Docker і буде трохи складніше, тому ми рекомендуємо цей варіант більш досвідченим користувачам. +Однією з альтернатив встановленню Python є запуск коду у контейнері. Оскільки наш репозиторій містить спеціальну папку `.devcontainer`, що інструктує, як збудувати контейнер для цього репозиторію, VS Code пропонує можливість заново відкрити код у контейнері. Це вимагає встановлення Docker і є більш складним, тому ми рекомендуємо це більш досвідченим користувачам. ## Запуск у хмарі -Якщо ви не хочете встановлювати Python локально і маєте доступ до хмарних ресурсів, хорошою альтернативою буде запуск коду у хмарі. Є кілька способів зробити це: +Якщо ви не хочете встановлювати Python локально і маєте доступ до хмарних ресурсів — гарною альтернативою буде запуск коду у хмарі. Існує кілька способів зробити це: -* Використання **[GitHub Codespaces](https://github.com/features/codespaces)**, що є віртуальним середовищем, створеним для вас на GitHub, доступним через інтерфейс браузера VS Code. Якщо у вас є доступ до Codespaces, просто натисніть кнопку **Code** у репозиторії, запустіть Codespace і починайте працювати. +* Використання **[GitHub Codespaces](https://github.com/features/codespaces)**, який є віртуальним середовищем, створеним для вас на GitHub і доступним через інтерфейс VS Code у браузері. Якщо у вас є доступ до Codespaces, ви можете просто натиснути кнопку **Code** у репозиторії, стартувати codespace і почати роботу без зволікань. +* Використання **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**. [Binder](https://mybinder.org) пропонує безкоштовні обчислювальні ресурси у хмарі, щоб ви могли випробувати код на GitHub. На головній сторінці є кнопка для відкриття репозиторію в Binder — вона швидко перекине вас на сайт Binder, який побудує базовий контейнер і плавно запустить веб-інтерфейс Jupyter. -* Використання **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**. [Binder](https://mybinder.org) надає безкоштовні обчислювальні ресурси у хмарі для тестування коду на GitHub. На головній сторінці є кнопка для відкриття репозиторію у Binder — це швидко перенесе вас на сайт Binder, який побудує контейнер і запустить веб-інтерфейс Jupyter для вас. +> **Примітка**: Щоб запобігти зловживанням, до деяких веб-ресурсів доступ від Binder обмежений. Це може перешкодити роботі деяких частин коду, що завантажують моделі і/або набори даних з публічного Інтернету. Вам можливо доведеться шукати обхідні шляхи. Також обчислювальні ресурси в Binder досить базові, тому навчання буде повільним, особливо у пізніших, більш складних уроках. -> **Примітка**: Щоб запобігти зловживанням, Binder блокує доступ до деяких веб-ресурсів. Це може завадити роботі коду, який завантажує моделі та/або набори даних з Інтернету. Вам, можливо, доведеться знайти обхідні шляхи. Також обчислювальні ресурси, надані Binder, досить базові, тому навчання буде повільним, особливо у пізніших, складніших уроках. +## Запуск у хмарі з GPU -## Запуск у хмарі з підтримкою GPU +Деякі пізніші уроки цього курсу значно виграють від підтримки GPU. Наприклад, тренування моделей може бути вкрай повільним без GPU. Є кілька варіантів, особливо якщо у вас є доступ до хмари через [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) або через ваш навчальний заклад: -Деякі з пізніших уроків цього курсу значно виграють від підтримки GPU, оскільки інакше навчання буде дуже повільним. Є кілька варіантів, особливо якщо у вас є доступ до хмари через [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) або через вашу установу: +* Створити [Віртуальну машину для Data Science](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) і підключитись до неї через Jupyter. Ви можете клонувати репозиторій безпосередньо на машину і почати навчання. VM серії NC підтримують GPU. -* Створіть [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) і підключіться до неї через Jupyter. Ви можете клонувати репозиторій прямо на цю машину і почати навчання. Віртуальні машини серії NC мають підтримку GPU. +> **Примітка**: Деякі підписки, включно з Azure for Students, за замовчуванням не надають підтримку GPU. Можливо, треба буде подати технічне звернення для отримання додаткових GPU-ядер. -> **Примітка**: Деякі підписки, включаючи Azure for Students, не надають підтримку GPU за замовчуванням. Вам, можливо, доведеться подати запит на додаткові GPU-ядра через технічну підтримку. +* Створити [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) і використовувати функцію Notebook там. [Цей відеоогляд](https://azure-for-academics.github.io/quickstart/azureml-papers/) показує, як клонувати репозиторій у Azure ML notebook і почати працювати. -* Створіть [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) і використовуйте функцію Notebook там. [Це відео](https://azure-for-academics.github.io/quickstart/azureml-papers/) показує, як клонувати репозиторій у блокнот Azure ML і почати його використовувати. +Ви також можете скористатися Google Colab, який має часткову безкоштовну підтримку GPU, і завантажити туди Jupyter Notebooks для поетапного виконання. -Ви також можете скористатися Google Colab, який надає безкоштовну підтримку GPU, і завантажити туди Jupyter Notebooks для виконання їх по черзі. +--- -**Відмова від відповідальності**: -Цей документ був перекладений за допомогою сервісу автоматичного перекладу [Co-op Translator](https://github.com/Azure/co-op-translator). Хоча ми прагнемо до точності, звертаємо вашу увагу, що автоматичні переклади можуть містити помилки або неточності. Оригінальний документ на його рідній мові слід вважати авторитетним джерелом. Для критично важливої інформації рекомендується професійний людський переклад. Ми не несемо відповідальності за будь-які непорозуміння або неправильні тлумачення, що виникли внаслідок використання цього перекладу. \ No newline at end of file + +**Застереження**: +Цей документ був перекладений з використанням сервісу автоматичного перекладу [Co-op Translator](https://github.com/Azure/co-op-translator). Хоча ми прагнемо до точності, зверніть увагу, що автоматичні переклади можуть містити помилки або неточності. Оригінальний документ рідною мовою слід вважати авторитетним джерелом. Для критично важливої інформації рекомендується звертатись до професійного людського перекладу. Ми не несемо відповідальності за будь-які непорозуміння або неправильні тлумачення, що виникли внаслідок використання цього перекладу. + \ No newline at end of file diff --git a/translations/uk/lessons/2-Symbolic/Animals.ipynb b/translations/uk/lessons/2-Symbolic/Animals.ipynb index cf584026..b34cd867 100644 --- a/translations/uk/lessons/2-Symbolic/Animals.ipynb +++ b/translations/uk/lessons/2-Symbolic/Animals.ipynb @@ -6,25 +6,25 @@ "collapsed": true }, "source": [ - "# Реалізація експертної системи для визначення тварин\n", + "# Впровадження експертної системи для тварин\n", "\n", - "Приклад із [AI for Beginners Curriculum](http://github.com/microsoft/ai-for-beginners).\n", + "Приклад з [AI for Beginners Curriculum](http://github.com/microsoft/ai-for-beginners).\n", "\n", - "У цьому прикладі ми реалізуємо просту систему на основі знань для визначення тварини за деякими фізичними характеристиками. Система може бути представлена наступним деревом AND-OR (це частина всього дерева, ми легко можемо додати ще кілька правил):\n", + "У цьому прикладі ми реалізуємо просту систему на основі знань, щоб визначити тварину за деякими фізичними характеристиками. Система може бути представлена наступним AND-OR деревом (це частина всього дерева, ми легко можемо додати ще правила):\n", "\n", - "![](../../../../translated_images/uk/AND-OR-Tree.5592d2c70187f283.webp)\n" + "![](../../../../../../translated_images/uk/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Наш власний оболонка експертних систем із зворотним висновком\n", + "## Власна оболонка експертних систем з зворотнім виведенням\n", "\n", - "Спробуймо визначити просту мову для представлення знань на основі продукційних правил. Ми будемо використовувати класи Python як ключові слова для визначення правил. Існує три основні типи класів:\n", - "* `Ask` представляє питання, яке потрібно задати користувачеві. Воно містить набір можливих відповідей.\n", - "* `If` представляє правило і є просто синтаксичним скороченням для збереження змісту правила.\n", - "* `AND`/`OR` — це класи для представлення гілок дерева AND/OR. Вони просто зберігають список аргументів всередині. Для спрощення коду вся функціональність визначена в батьківському класі `Content`.\n" + "Спробуємо визначити просту мову для представлення знань на основі правил продукції. Ми використаємо класи Python як ключові слова для визначення правил. Загалом буде 3 типи класів:\n", + "* `Ask` представляє питання, яке потрібно поставити користувачу. Він містить набір можливих відповідей.\n", + "* `If` представляє правило і є просто синтаксичним цукром для збереження змісту правила.\n", + "* `AND`/`OR` — це класи для представлення гілок AND/OR дерева. Вони просто зберігають список аргументів усередині. Для спрощення коду весь функціонал визначено в батьківському класі `Content`\n" ] }, { @@ -66,7 +66,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "У нашій системі робоча пам'ять міститиме список **фактів** як **пари атрибут-значення**. База знань може бути визначена як один великий словник, який відображає дії (нові факти, які слід вставити в робочу пам'ять) на умови, виражені як AND-OR вирази. Також деякі факти можуть бути `Запитані`.\n" + "У нашій системі робоча пам’ять міститиме список **фактів** як **пари атрибут-значення**. Базу знань можна визначити як один великий словник, який відображає дії (нові факти, які слід вставити до робочої пам'яті) у умови, виражені у вигляді AND-OR виразів. Також деякі факти можна `Ask`-запитувати.\n" ] }, { @@ -99,13 +99,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Щоб виконати зворотний висновок, ми визначимо клас `Knowledgebase`. Він буде містити:\n", - "* Робочу `пам'ять` - словник, який відображає атрибути на значення\n", - "* `Правила` бази знань у форматі, визначеному вище\n", + "Для виконання зворотного виведення визначимо клас `Knowledgebase`. Він міститиме:\n", + "* Робочу `memory` - словник, який відображає атрибути у значення\n", + "* Правила бази знань `rules` у форматі, визначеному вище\n", "\n", "Два основні методи:\n", - "* `get` для отримання значення атрибута, виконуючи висновок, якщо це необхідно. Наприклад, `get('color')` отримує значення слоту кольору (запитує, якщо потрібно, і зберігає значення для подальшого використання в робочій пам'яті). Якщо ми запитаємо `get('color:blue')`, він запитає про колір, а потім поверне значення `y`/`n` залежно від кольору.\n", - "* `eval` виконує фактичний висновок, тобто проходить дерево AND/OR, оцінює підцілі тощо.\n" + "* `get` для отримання значення атрибута, виконуючи виведення за потреби. Наприклад, `get('color')` отримає значення кольору (за потреби запитає, і збереже значення для подальшого використання в робочій пам’яті). Якщо ми виконаємо `get('color:blue')`, він запитає про колір, а потім поверне значення `y`/`n` залежно від кольору.\n", + "* `eval` виконує власне виведення, тобто обходить дерево AND/OR, оцінює підцілі тощо.\n" ] }, { @@ -172,7 +172,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Тепер давайте визначимо нашу базу знань про тварин і проведемо консультацію. Зверніть увагу, що цей виклик буде задавати вам питання. Ви можете відповідати, вводячи `y`/`n` для питань з відповіддю \"так-ні\", або вказуючи число (0..N) для питань з довшими варіантами відповідей.\n" + "Тепер давайте визначимо нашу базу знань про тварин та проведемо консультацію. Зверніть увагу, що цей виклик задасть вам питання. Ви можете відповісти, набравши `y`/`n` для питань із відповіддю так або ні, або вказати номер (0..N) для питань з довшими варіантами відповідей.\n" ] }, { @@ -229,11 +229,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Використання PyKnow для прямого виведення\n", + "## Використання Experta для прямого виведення\n", "\n", - "У наступному прикладі ми спробуємо реалізувати пряме виведення, використовуючи одну з бібліотек для представлення знань, [PyKnow](https://github.com/buguroo/pyknow/). **PyKnow** — це бібліотека для створення систем прямого виведення на Python, яка розроблена так, щоб бути схожою на класичну стару систему [CLIPS](http://www.clipsrules.net/index.html).\n", + "У наступному прикладі ми спробуємо реалізувати пряме виведення за допомогою однієї з бібліотек для представлення знань, [Experta](https://github.com/nilp0inter/experta). **Experta** — це бібліотека для створення систем прямого виведення на Python, яка розроблена так, щоб бути схожою на класичну стару систему [CLIPS](http://www.clipsrules.net/index.html).\n", "\n", - "Ми могли б також реалізувати прямий ланцюжок самостійно без особливих труднощів, але наївні реалізації зазвичай не дуже ефективні. Для більш ефективного зіставлення правил використовується спеціальний алгоритм [Rete](https://en.wikipedia.org/wiki/Rete_algorithm).\n" + "Ми також могли б реалізувати пряме виведення самостійно без особливих проблем, але наївні реалізації зазвичай не дуже ефективні. Для більш ефективного зіставлення правил використовується спеціальний алгоритм [Rete](https://en.wikipedia.org/wiki/Rete_algorithm).\n" ] }, { @@ -247,32 +247,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "Collecting git+https://github.com/buguroo/pyknow/\n", - " Cloning https://github.com/buguroo/pyknow/ to /tmp/pip-req-build-3cqeulyl\n", - " Running command git clone --filter=blob:none --quiet https://github.com/buguroo/pyknow/ /tmp/pip-req-build-3cqeulyl\n", - " Resolved https://github.com/buguroo/pyknow/ to commit 48818336f2e9a126f1964f2d8dc22d37ff800fe8\n", - " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hCollecting frozendict==1.2\n", - " Using cached frozendict-1.2.tar.gz (2.6 kB)\n", - " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hCollecting schema==0.6.7\n", - " Using cached schema-0.6.7-py2.py3-none-any.whl (14 kB)\n", - "Building wheels for collected packages: pyknow, frozendict\n", - " Building wheel for pyknow (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for pyknow: filename=pyknow-1.7.0-py3-none-any.whl size=34228 sha256=b7de5b09292c4007667c72f69b98d5a1b5f7324ff15f9dd8e077c3d5f7aade42\n", - " Stored in directory: /tmp/pip-ephem-wheel-cache-k7jpave7/wheels/81/1a/d3/f6c15dbe1955598a37755215f2a10449e7418500d7bd4b9508\n", - " Building wheel for frozendict (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for frozendict: filename=frozendict-1.2-py3-none-any.whl size=3148 sha256=2863d55c240d2409cddf05ccfe600591f8478681549fc97555c47c90dc6bb160\n", - " Stored in directory: /home/rg/.cache/pip/wheels/49/ac/f8/cb8120244e710bdb479c86198b03c7b08c3c2d3d2bf448fd6e\n", - "Successfully built pyknow frozendict\n", - "Installing collected packages: schema, frozendict, pyknow\n", - "Successfully installed frozendict-1.2 pyknow-1.7.0 schema-0.6.7\n" + "Collecting git+https://github.com/nilp0inter/experta\n", + " Cloning https://github.com/nilp0inter/experta to /tmp/pip-req-build-7qurtwk3\n", + " Running command git clone --filter=blob:none --quiet https://github.com/nilp0inter/experta /tmp/pip-req-build-7qurtwk3\n", + " Resolved https://github.com/nilp0inter/experta to commit c6d5834b123861f5ae09e7d07027dc98bec58741\n", + " Installing build dependencies ... \u001b[?25ldone\n", + "\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n", + "\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25hRequirement already satisfied: frozendict~=2.4.6 in /opt/conda/envs/ai4beg/lib/python3.12/site-packages (from experta==1.9.5.dev1) (2.4.7)\n", + "Collecting schema~=0.6.7 (from experta==1.9.5.dev1)\n", + " Downloading schema-0.6.8-py2.py3-none-any.whl.metadata (14 kB)\n", + "Downloading schema-0.6.8-py2.py3-none-any.whl (14 kB)\n", + "Building wheels for collected packages: experta\n", + " Building wheel for experta (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25h Created wheel for experta: filename=experta-1.9.5.dev1-py3-none-any.whl size=34804 sha256=888c459512a5e713f4b674caa9a0f96cfdf07ec0d6eb56cc318ce0653d218014\n", + " Stored in directory: /tmp/pip-ephem-wheel-cache-1eeii9zy/wheels/3d/e8/bb/22d7956359603fa8dd679aa09f5b8efb3f29991c3986fdc787\n", + "Successfully built experta\n", + "Installing collected packages: schema, experta\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2/2\u001b[0m [experta]\n", + "\u001b[1A\u001b[2KSuccessfully installed experta-1.9.5.dev1 schema-0.6.8\n" ] } ], "source": [ "import sys\n", - "!{sys.executable} -m pip install git+https://github.com/buguroo/pyknow/" + "!{sys.executable} -m pip install git+https://github.com/nilp0inter/experta" ] }, { @@ -283,15 +282,15 @@ }, "outputs": [], "source": [ - "from pyknow import *\n", - "#import pyknow" + "from experta import *\n", + "#import experta" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Ми визначимо нашу систему як клас, що успадковує `KnowledgeEngine`. Кожне правило визначається окремою функцією з анотацією `@Rule`, яка вказує, коли правило має спрацювати. Всередині правила ми можемо додавати нові факти за допомогою функції `declare`, і додавання цих фактів призведе до виклику інших правил за допомогою механізму прямого висновку.\n" + "Ми визначимо нашу систему як клас, що наслідує `KnowledgeEngine`. Кожне правило визначається окремою функцією з анотацією `@Rule`, яка вказує, коли правило має спрацювати. Всередині правила ми можемо додавати нові факти за допомогою функції `declare`, і додавання цих фактів призведе до виклику додаткових правил за допомогою механізму прямого виведення.\n" ] }, { @@ -378,7 +377,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Як тільки ми визначили базу знань, ми заповнюємо робочу пам'ять деякими початковими фактами, а потім викликаємо метод `run()`, щоб виконати висновок. Ви можете побачити, що в результаті до робочої пам'яті додаються нові виведені факти, включаючи остаточний факт про тварину (якщо ми правильно налаштували всі початкові факти).\n" + "Після того, як ми визначили базу знань, ми наповнюємо нашу робочу пам’ять деякими початковими фактами, а потім викликаємо метод `run()` для виконання виведення. Ви можете побачити в результаті, що нові виведені факти додаються до робочої пам’яті, включно з остаточним фактом про тварину (якщо ми правильно встановили всі початкові факти).\n" ] }, { @@ -440,7 +439,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n---\n\n**Відмова від відповідальності**: \nЦей документ був перекладений за допомогою сервісу автоматичного перекладу [Co-op Translator](https://github.com/Azure/co-op-translator). Хоча ми прагнемо до точності, будь ласка, майте на увазі, що автоматичні переклади можуть містити помилки або неточності. Оригінальний документ на його рідній мові слід вважати авторитетним джерелом. Для критичної інформації рекомендується професійний людський переклад. Ми не несемо відповідальності за будь-які непорозуміння або неправильні тлумачення, що виникають внаслідок використання цього перекладу.\n" + "---\n\n\n**Відмова від відповідальності**: \nЦей документ було перекладено за допомогою сервісу автоматичного перекладу [Co-op Translator](https://github.com/Azure/co-op-translator). Хоча ми прагнемо до точності, майте на увазі, що автоматичні переклади можуть містити помилки чи неточності. Оригінальний документ рідною мовою слід вважати авторитетним джерелом. Для критично важливої інформації рекомендується користуватися послугами професійного людського перекладу. Ми не несемо відповідальності за будь-які непорозуміння або неправильне тлумачення, що виникли внаслідок використання цього перекладу.\n\n" ] } ], @@ -467,8 +466,8 @@ "version": "3.11.2" }, "coopTranslator": { - "original_hash": "ab2bd97b0453415b89a469284609a8ce", - "translation_date": "2025-08-30T09:41:49+00:00", + "original_hash": "8ef43db4b9182239fd150a76bd494fdb", + "translation_date": "2026-01-16T06:41:30+00:00", "source_file": "lessons/2-Symbolic/Animals.ipynb", "language_code": "uk" } diff --git a/translations/uk/lessons/2-Symbolic/README.md b/translations/uk/lessons/2-Symbolic/README.md index e2ce8685..9dc136bd 100644 --- a/translations/uk/lessons/2-Symbolic/README.md +++ b/translations/uk/lessons/2-Symbolic/README.md @@ -1,116 +1,116 @@ # Представлення знань та експертні системи -![Резюме змісту символічного AI](../../../../translated_images/uk/ai-symbolic.715a30cb610411a6.webp) +![Підсумок змісту Символічного AI](../../../../../../translated_images/uk/ai-symbolic.715a30cb610411a6.webp) -> Скетчноут від [Tomomi Imura](https://twitter.com/girlie_mac) +> Лаконічна замітка від [Tomomi Imura](https://twitter.com/girlie_mac) -Пошук штучного інтелекту базується на прагненні до знань, щоб розуміти світ так, як це роблять люди. Але як це можна реалізувати? +Пошук штучного інтелекту базується на пошуку знань, щоб розуміти світ так само, як і люди. Але як це зробити? -## [Тест перед лекцією](https://ff-quizzes.netlify.app/en/ai/quiz/3) +## [Попередній тест](https://ff-quizzes.netlify.app/en/ai/quiz/3) -На ранніх етапах розвитку AI популярним був підхід "зверху вниз" до створення інтелектуальних систем (обговорювався в попередньому уроці). Ідея полягала в тому, щоб витягти знання від людей у форму, яку може обробляти машина, і потім використовувати їх для автоматичного вирішення задач. Цей підхід базувався на двох великих ідеях: +У ранні дні AI популярним був підхід зверху вниз до створення інтелектуальних систем (обговорений у попередньому уроці). Ідея полягала у вилученні знань від людей у форму, зрозумілу машиною, а потім автоматичному вирішенні проблем на її основі. Цей підхід базувався на двох великих ідеях: * Представлення знань * Міркування ## Представлення знань -Одним із важливих концептів у символічному AI є **знання**. Важливо розрізняти знання від *інформації* або *даних*. Наприклад, можна сказати, що книги містять знання, тому що їх можна вивчати і ставати експертом. Однак те, що міститься в книгах, насправді називається *даними*, і, читаючи книги та інтегруючи ці дані у нашу модель світу, ми перетворюємо дані на знання. +Однією з важливих концепцій у Символічному AI є **знання**. Важливо відрізняти знання від *інформації* або *даних*. Наприклад, можна сказати, що книги містять знання, тому що, вивчаючи книги, можна стати експертом. Проте те, що міститься в книгах, фактично називається *даними*, а читаючи книги та інтегруючи ці дані у нашу модель світу, ми перетворюємо дані на знання. -> ✅ **Знання** — це те, що міститься у нашій голові і представляє наше розуміння світу. Воно отримується через активний процес **навчання**, який інтегрує отриману інформацію у нашу активну модель світу. +> ✅ **Знання** — це те, що міститься в нашій свідомості і представляє наше розуміння світу. Воно отримується через активний процес **навчання**, що інтегрує отриману інформацію у наш активний світогляд. -Найчастіше ми не строго визначаємо знання, але узгоджуємо їх з іншими пов'язаними концептами за допомогою [піраміди DIKW](https://en.wikipedia.org/wiki/DIKW_pyramid). Вона містить наступні концепти: +Здебільшого ми не даємо суворого визначення знання, але узгоджуємо його з іншими спорідненими поняттями за допомогою [піраміди DIKW](https://en.wikipedia.org/wiki/DIKW_pyramid). Вона містить наступні поняття: -* **Дані** — це те, що представлено на фізичних носіях, таких як написаний текст або вимовлені слова. Дані існують незалежно від людей і можуть передаватися між ними. -* **Інформація** — це те, як ми інтерпретуємо дані у нашій голові. Наприклад, коли ми чуємо слово *комп'ютер*, ми маємо певне розуміння, що це таке. -* **Знання** — це інформація, інтегрована у нашу модель світу. Наприклад, коли ми дізнаємося, що таке комп'ютер, ми починаємо мати уявлення про те, як він працює, скільки коштує і для чого його можна використовувати. Ця мережа взаємопов'язаних концептів формує наші знання. -* **Мудрість** — це ще один рівень нашого розуміння світу, який представляє *мета-знання*, наприклад, уявлення про те, як і коли знання слід використовувати. +* **Дані** — це те, що представлено на фізичному носії, наприклад, у вигляді написаного тексту або усних слів. Дані існують незалежно від людей і можуть передаватися між ними. +* **Інформація** — це те, як ми інтерпретуємо дані в голові. Наприклад, коли ми чуємо слово *комп’ютер*, ми маємо якесь розуміння, що це таке. +* **Знання** — це інформація, інтегрована у нашу світову модель. Наприклад, коли ми дізнаємося, що таке комп’ютер, ми маємо уявлення про те, як він працює, скільки коштує і для чого використовується. Це мережа взаємозалежних понять, що формують наші знання. +* **Мудрість** — це щабель вищого рівня розуміння світу, яка представляє *мета-знання*, тобто уявлення про те, коли і як слід застосовувати знання. - + -*Зображення [з Вікіпедії](https://commons.wikimedia.org/w/index.php?curid=37705247), By Longlivetheux - Own work, CC BY-SA 4.0* +*Зображення [з Вікіпедії](https://commons.wikimedia.org/w/index.php?curid=37705247), автор Longlivetheux - власна робота, CC BY-SA 4.0* -Таким чином, проблема **представлення знань** полягає у пошуку ефективного способу представлення знань у комп'ютері у формі даних, щоб зробити їх автоматично придатними для використання. Це можна розглядати як спектр: +Отже, задача **представлення знань** полягає у знаходженні ефективного способу представляти знання всередині комп’ютера у формі даних, щоб їх можна було автоматично використовувати. Це можна уявити як спектр: -![Спектр представлення знань](../../../../translated_images/uk/knowledge-spectrum.b60df631852c0217.webp) +![Спектр представлення знань](../../../../../../translated_images/uk/knowledge-spectrum.b60df631852c0217.webp) > Зображення від [Dmitry Soshnikov](http://soshnikov.com) -* Ліворуч знаходяться дуже прості типи представлення знань, які можуть ефективно використовуватися комп'ютерами. Найпростіший — алгоритмічний, коли знання представлені комп'ютерною програмою. Однак це не найкращий спосіб представлення знань, оскільки він не є гнучким. Знання у нашій голові часто не є алгоритмічними. -* Праворуч знаходяться представлення, такі як природний текст. Це найпотужніший спосіб, але він не може бути використаний для автоматичного міркування. +* Зліва розташовані дуже прості типи представлення знань, які можуть ефективно використовуватися комп’ютерами. Найпростіший — алгоритмічний, коли знання представлені комп’ютерною програмою. Проте це не найкращий спосіб представлення знань, бо він не гнучкий. Знання в нашій голові нерідко неалгоритмічні. +* Справа — представлення, такі як природний текст. Він найпотужніший, але не придатний для автоматичних міркувань. -> ✅ Подумайте хвилину про те, як ви представляєте знання у своїй голові і перетворюєте їх на нотатки. Чи є якийсь формат, який добре працює для вас, щоб допомогти у запам'ятовуванні? +> ✅ Подумайте хвилину, як ви представляєте знання у своїй голові і конвертуєте його у нотатки. Чи є певний формат, який сприяє кращому запам’ятовуванню? -## Класифікація комп'ютерних представлень знань +## Класифікація комп’ютерних методів представлення знань -Ми можемо класифікувати різні методи представлення знань у комп'ютері за наступними категоріями: +Ми можемо класифікувати різні методи представлення знань у комп’ютері за такими категоріями: -* **Мережеві представлення** базуються на тому, що у нашій голові є мережа взаємопов'язаних концептів. Ми можемо спробувати відтворити такі ж мережі у вигляді графа у комп'ютері — так званої **семантичної мережі**. +* **Мережева репрезентація** базується на тому, що в нашій свідомості існує мережа взаємопов’язаних понять. Ми можемо спробувати відтворити такі ж мережі у вигляді графа в комп’ютері — так званої **семантичної мережі**. -1. **Трійки об'єкт-атрибут-значення** або **пари атрибут-значення**. Оскільки граф може бути представлений у комп'ютері як список вузлів і ребер, ми можемо представити семантичну мережу списком трійок, що містять об'єкти, атрибути і значення. Наприклад, ми створюємо наступні трійки про мови програмування: +1. **Триплети об’єкт-атрибут-значення** або **пари атрибут-значення**. Оскільки граф можна представити у комп’ютері у вигляді списку вузлів і ребер, ми можемо представити семантичну мережу як список триплетів, що містять об’єкти, атрибути та значення. Наприклад, побудуємо наступні триплети про мови програмування: -Об'єкт | Атрибут | Значення +Об’єкт | Атрибут | Значення -------|---------|--------- -Python | є | Мова без типізації -Python | створений | Guido van Rossum +Python | є | Не типізованою мовою +Python | винайшов | Guido van Rossum Python | синтаксис блоку | відступи -Мова без типізації | не має | визначень типів +Не типізована мова | не має | визначень типів -> ✅ Подумайте, як трійки можуть бути використані для представлення інших типів знань. +> ✅ Подумайте, як за допомогою триплетів можна представити інші типи знань. -2. **Ієрархічні представлення** підкреслюють той факт, що ми часто створюємо ієрархію об'єктів у нашій голові. Наприклад, ми знаємо, що канарка — це птах, і всі птахи мають крила. Ми також маємо уявлення про те, якого кольору зазвичай канарка і яка її швидкість польоту. +2. **Ієрархічні представлення** підкреслюють, що ми часто створюємо ієрархію об’єктів у своїй свідомості. Наприклад, ми знаємо, що канарейка — це птах, і всі птахи мають крила. Ми також маємо уявлення про колір канарейки і її швидкість польоту. - - **Представлення у вигляді фреймів** базується на представленні кожного об'єкта або класу об'єктів як **фрейму**, який містить **слоти**. Слоти мають можливі значення за замовчуванням, обмеження значень або збережені процедури, які можна викликати для отримання значення слота. Усі фрейми формують ієрархію, схожу на ієрархію об'єктів у мовах програмування, орієнтованих на об'єкти. - - **Сценарії** — це особливий вид фреймів, які представляють складні ситуації, що можуть розгортатися у часі. + - **Фреймова репрезентація** базується на представленні кожного об’єкта або класу об’єктів як **фрейму**, що містить **слоти**. Слоти можуть мати можливі значення за замовчуванням, обмеження значень або збережені процедури, які можна викликати для отримання значення слоту. Усі фрейми формують ієрархію, подібну до ієрархії об’єктів в об’єктно-орієнтованих мовах програмування. + - **Сценарії** — це особливий вид фреймів, які представляють складні ситуації, що можуть розгортатися в часі. **Python** -Слот | Значення | Значення за замовчуванням | Інтервал | ------|----------|---------------------------|----------| +Слот | Значення | Значення за замовчуванням | Інтервал +-----|----------|---------------------------|---------- Назва | Python | | | -Є | Мова без типізації | | | -Регістр змінних | | CamelCase | | +Є-Типом | Не типізована мова | | | +Регістр змінної | | CamelCase | | Довжина програми | | | 5-5000 рядків | -Синтаксис блоку | Відступ | | | +Синтаксис блоку | Відступи | | | -3. **Процедурні представлення** базуються на представленні знань списком дій, які можуть бути виконані за певних умов. - - Правила продукції — це if-then твердження, які дозволяють робити висновки. Наприклад, лікар може мати правило, яке говорить, що **ЯКЩО** у пацієнта висока температура **АБО** високий рівень С-реактивного білка у аналізі крові **ТОДІ** у нього запалення. Як тільки ми стикаємося з однією з умов, ми можемо зробити висновок про запалення, а потім використовувати його у подальших міркуваннях. - - Алгоритми можна вважати ще однією формою процедурного представлення, хоча вони майже ніколи не використовуються безпосередньо у системах, заснованих на знаннях. +3. **Процедурні представлення** базуються на представленні знань як списку дій, які можна виконати за певної умови. + - Правила продукції — це умова-оператор if-then, які дозволяють робити висновки. Наприклад, у лікаря може бути правило: **ЯКЩО** у пацієнта висока температура **АБО** високий рівень C-реактивного білка у крові, **ТО** у нього є запалення. Як тільки одна з умов виконується, можна зробити висновок про запалення і використати його у подальших міркуваннях. + - Алгоритми можна вважати іншою формою процедурного представлення, хоча їх майже ніколи безпосередньо не використовують у системах, що базуються на знаннях. 4. **Логіка** була спочатку запропонована Арістотелем як спосіб представлення універсальних людських знань. - - Логіка предикатів як математична теорія є занадто багатою, щоб бути обчислюваною, тому зазвичай використовується її підмножина, така як клаузи Хорна, що використовуються у Prolog. - - Описова логіка — це сімейство логічних систем, які використовуються для представлення і міркування про ієрархії об'єктів у розподілених представленнях знань, таких як *семантична мережа*. + - Предикатна логіка як математична теорія надто багата для обчислення, тому зазвичай використовують її підмножину, наприклад, горнові клаузи, які застосовуються у Prolog. + - Описова логіка — це сімейство логічних систем для представлення і міркувань про ієрархії об’єктів, розподілені представлення знань, такі як *семантична павутина*. ## Експертні системи -Одним із ранніх успіхів символічного AI були так звані **експертні системи** — комп'ютерні системи, які були розроблені для того, щоб діяти як експерт у певній обмеженій області задач. Вони базувалися на **базі знань**, отриманій від одного або кількох людських експертів, і містили **мотор виведення**, який виконував певні міркування на її основі. +Одним із ранніх успіхів символічного AI були так звані **експертні системи** — комп’ютерні системи, які розроблялися для імітації експерта в деякій обмеженій предметній області. Вони базувалися на **базі знань**, вилученій від одного чи більше експертів-людей, та містили **вивідний механізм**, який виконував певні міркування на її основі. -![Архітектура людини](../../../../translated_images/uk/arch-human.5d4d35f1bba3ab1c.webp) | ![Система на основі знань](../../../../translated_images/uk/arch-kbs.3ec5c150b09fa8da.webp) ----------------------------------------------|------------------------------------------------ -Спрощена структура людської нервової системи | Архітектура системи на основі знань +![Інфраструктура людини](../../../../../../translated_images/uk/arch-human.5d4d35f1bba3ab1c.webp) | ![Система, що базується на знаннях](../../../../../../translated_images/uk/arch-kbs.3ec5c150b09fa8da.webp) +---------------------------------------------------|------------------------------------------------------ +Спрощена структура нейронної системи людини | Архітектура системи, що базується на знаннях -Експертні системи побудовані подібно до системи людського мислення, яка містить **короткострокову пам'ять** і **довгострокову пам'ять**. Аналогічно, у системах на основі знань ми розрізняємо наступні компоненти: +Експертні системи побудовані за зразком людської системи міркувань, що містить **короткочасну пам’ять** і **довготривалу пам’ять**. Аналогічно в системах на основі знань розрізняють такі компоненти: -* **Пам'ять задачі**: містить знання про задачу, яка зараз вирішується, тобто температуру або кров'яний тиск пацієнта, чи є у нього запалення тощо. Ці знання також називаються **статичними знаннями**, оскільки вони містять знімок того, що ми зараз знаємо про задачу — так званий *стан задачі*. -* **База знань**: представляє довгострокові знання про область задач. Вона отримується вручну від людських експертів і не змінюється від консультації до консультації. Оскільки вона дозволяє переходити від одного стану задачі до іншого, її також називають **динамічними знаннями**. -* **Мотор виведення**: координує весь процес пошуку у просторі станів задачі, задаючи питання користувачеві, коли це необхідно. Він також відповідає за пошук правильних правил, які слід застосувати до кожного стану. +* **Проблемна пам’ять**: містить знання про проблему, яка наразі розв’язується, напр., температуру або артеріальний тиск пацієнта, чи є в нього запалення тощо. Ці знання також називають **статичними знаннями**, тому що вони містять снимок того, що ми зараз знаємо про проблему — так званий *стан проблеми*. +* **База знань**: подає довготермінові знання про предметну область. Вона отримується вручну від експертів і не змінюється під час консультацій. Оскільки вона дозволяє переміщатися від одного стану проблеми до іншого, її також називають **динамічними знаннями**. +* **Вивідний механізм**: координує увесь процес пошуку у просторі станів проблеми, задаючи користувачу питання за потреби. Він також відповідає за пошук правил, які слід застосувати до кожного стану. -Наприклад, розглянемо наступну експертну систему для визначення тварини на основі її фізичних характеристик: +Як приклад розглянемо експертну систему визначення тварини на основі її фізичних характеристик: -![AND-OR дерево](../../../../translated_images/uk/AND-OR-Tree.5592d2c70187f283.webp) +![AND-OR дерево](../../../../../../translated_images/uk/AND-OR-Tree.5592d2c70187f283.webp) > Зображення від [Dmitry Soshnikov](http://soshnikov.com) -Ця діаграма називається **AND-OR деревом**, і вона є графічним представленням набору правил продукції. Малювання дерева корисне на початку отримання знань від експерта. Для представлення знань у комп'ютері зручніше використовувати правила: +Цю діаграму називають **AND-OR деревом**, і це графічне представлення набору правил продукції. Малювати дерево корисно на початку вилучення знань від експерта. Для подання знань у комп’ютері зручніше використати правила: ``` IF the animal eats meat @@ -121,77 +121,78 @@ OR (animal has sharp teeth THEN the animal is a carnivore ``` -Ви можете помітити, що кожна умова на лівій стороні правила і дія фактично є трійками об'єкт-атрибут-значення (OAV). **Робоча пам'ять** містить набір трійок OAV, які відповідають задачі, що зараз вирішується. **Мотор правил** шукає правила, для яких умова задовольняється, і застосовує їх, додаючи ще одну трійку до робочої пам'яті. +Ви можете помітити, що кожна умова з лівої частини правила та дія насправді є триплетом об’єкт-атрибут-значення (OAV). **Робоча пам’ять** містить набір триплетів OAV, що відповідають проблемі, яка наразі розв’язується. **Механізм правил** шукає правила, для яких умова виконується, і застосовує їх, додаючи новий триплет до робочої пам’яті. -> ✅ Намалюйте власне AND-OR дерево на тему, яка вам подобається! +> ✅ Намалюйте власне AND-OR дерево на тему, що вам подобається! -### Пряме vs. зворотне виведення +### Прямий проти зворотного виводу -Описаний вище процес називається **прямим виведенням**. Він починається з деяких початкових даних про задачу, доступних у робочій пам'яті, і потім виконує наступний цикл міркувань: +Описаний вище процес називають **прямим виводом**. Він починається з деяких початкових даних про проблему, що є у робочій пам’яті, і потім виконує цикл міркувань: -1. Якщо цільовий атрибут присутній у робочій пам'яті — зупинитися і надати результат -2. Шукати всі правила, умови яких зараз задовольняються — отримати **конфліктний набір** правил. -3. Виконати **розв'язання конфлікту** — вибрати одне правило, яке буде виконано на цьому кроці. Можуть бути різні стратегії розв'язання конфлікту: - - Вибрати перше застосовне правило у базі знань +1. Якщо цільовий атрибут присутній у робочій пам’яті — зупинись і дай результат +2. Знайди всі правила, умова яких наразі виконується — отримаємо **набір конфліктних правил**. +3. Виконай **розв’язання конфлікту** — обери одне правило для виконання на цьому кроці. Існують різні стратегії розв’язання конфлікту: + - Вибрати перше застосовне правило в базі знань - Вибрати випадкове правило - - Вибрати *більш специфічне* правило, тобто те, яке задовольняє найбільше умов на "лівій стороні" (LHS) -4. Застосувати вибране правило і вставити новий шматок знань у стан задачі + - Вибрати *більш специфічне* правило, тобто те, що задовольняє найбільше умов у лівій частині (LHS) +4. Застосувати вибране правило і вставити новий шматок знань у стан проблеми 5. Повторити з кроку 1. -Однак у деяких випадках ми можемо захотіти почати з порожніх знань про задачу і задавати питання, які допоможуть нам дійти до висновку. Наприклад, при медичній діагностиці ми зазвичай не виконуємо всі медичні аналізи заздалегідь перед початком діагностики пацієнта. Ми скоріше хочемо виконувати аналізи, коли потрібно прийняти рішення. +Однак у деяких випадках ми можемо захотіти почати з порожніх знань про проблему і задавати питання, які допоможуть прийти до висновку. Наприклад, при медичній діагностиці ми зазвичай не робимо усіх аналізів наперед, а лише за потребою. -Цей процес можна змоделювати за допомогою **зворотного виведення**. Він керується **ціллю** — значенням атрибута, яке ми шукаємо: +Цей процес можна змоделювати за допомогою **зворотного виводу**. Він керується **метою** — значенням атрибута, яке ми хочемо знайти: -1. Вибрати всі правила, які можуть надати нам значення цілі (тобто з ціллю на RHS ("правій стороні")) — конфліктний набір -1. Якщо немає правил для цього атрибута або є правило, яке говорить, що ми повинні запитати значення у користувача — запитати його, інакше: -1. Використовувати стратегію розв'язання конфлікту, щоб вибрати одне правило, яке ми будемо використовувати як *гіпотезу* — ми спробуємо її довести -1. Рекурсивно повторити процес для всіх атрибутів на LHS правила, намагаючись довести їх як цілі -1. Якщо у будь-який момент процес зазнає невдачі — використовувати інше правило на кроці 3. +1. Вибрати всі правила, які можуть дати значення мети (тобто з метою у правій частині правила) — створити набір конфліктів +2. Якщо немає правил для цього атрибута, або є правило, що належить запросити значення у користувача — запитай його, інакше: +3. Використати стратегію розв’язання конфлікту, щоб вибрати правило, яке використовуватиметься як *гіпотеза* — спробуємо її довести +4. Рекурсивно повторити процес для усіх атрибутів із лівої частини правила, намагаючись довести їх як цілі +5. Якщо будь-який етап провалюється — використати інше правило на кроці 3. -> ✅ У яких ситуаціях пряме виведення є більш доречним? А як щодо зворотного виведення? +> ✅ У яких ситуаціях прямий вивід є більш доречним? А як щодо зворотного? ### Реалізація експертних систем -Експертні системи можуть бути реалізовані за допомогою різних інструментів: +Експертні системи можна реалізувати різними способами: -* Програмування їх безпосередньо у якомусь високорівневому мовному програмуванні. Це не найкраща ідея, оскільки головна перевага системи на основі знань полягає у тому, що знання відокремлені від виведення, і потенційно експерт у предметній області повинен мати можливість писати правила без розуміння деталей процесу виведення. -* Використання **оболонки експертних систем**, тобто системи, спеціально розробленої для заповнення знаннями за допомогою якоїсь мови представлення знань. +* Програмування їх безпосередньо на якійсь мові високого рівня. Це не найкраща ідея, бо головна перевага систем на основі знань у тому, що знання відокремлені від виводу, і експерт предметної області потенційно міг би писати правила, не знаючи деталей процесу виводу. +* Використання **експертної оболонки**, тобто системи, спеціально розробленої для наповнення знаннями за допомогою спеціальної мови представлення знань. -## ✍️ Вправа: Виведення про тварин +## ✍️ Вправа: Виведення тварини -Дивіться [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) для прикладу реалізації експертної системи з прямим і зворотним виведенням. +Дивіться [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) для прикладу реалізації системи експертного виводу вперед і назад. -> **Примітка**: Цей приклад досить простий і лише дає уявлення про те, як виглядає експертна система. Як тільки ви почнете створювати таку систему, ви помітите деяку *інтелектуальну* поведінку лише тоді, коли досягнете певної кількості правил, приблизно 200+. У якийсь момент правила стають занадто складними, щоб тримати всі їх у голові, і в цей момент ви можете почати задаватися питанням, чому система приймає певні рішення. Однак важливою характеристикою систем на основі знань є те, що ви завжди можете *пояснити*, як було прийнято будь-яке з рішень. +> **Примітка**: цей приклад досить простий і лише дає уявлення про те, як виглядає експертна система. Коли ви почнете створювати таку систему, ви помітите деяку *інтелектуальну* поведінку лише після досягнення певної кількості правил, близько 200+. З часом правила стають надто складними, щоб тримати їх усі в голові, і ви можете задаватися питанням, чому система приймає ті чи інші рішення. Важливою характеристикою систем, що базуються на знаннях, є те, що ви завжди можете *пояснити*, як було прийнято будь-яке рішення. -## Онтології та семантична мережа +## Онтології та Семантична Павутина -Наприкінці 20-го століття була ініціатива використати представлення знань для анотації ресурсів Інтернету, щоб стало можливим знаходити ресурси, які відповідають дуже специфічним запитам. Цей рух називався **Семантична мережа**, і він базувався на кількох концептах: +Наприкінці XX століття з’явилася ініціатива використовувати представлення знань для анотування ресурсів Інтернету, щоб можна було знаходити ресурси, що відповідають дуже специфічним запитам. Ця ініціатива називалася **Семантичною Павутиною** і базувалася на таких концепціях: -- Спе -- Сімейство мов на основі XML для опису знань: RDF (Resource Description Framework), RDFS (RDF Schema), OWL (Ontology Web Language). +- Спеціальне представлення знань на основі **[описової логіки](https://en.wikipedia.org/wiki/Description_logic)** (DL). Вона схожа на фреймове представлення знань, бо створює ієрархію об’єктів із властивостями, але має формальну логічну семантику та виводи. Існує ціла родина DL, які балансують між експресивністю і алгоритмічною складністю виводів. +- Розподілене представлення знань, де всі поняття представлені глобальним URI-ідентифікатором, що робить можливим створення ієрархій знань, що охоплюють Інтернет. +- Родина мов на основі XML для опису знань: RDF (Resource Description Framework), RDFS (RDF Schema), OWL (Ontology Web Language). -Основним поняттям у Семантичному вебі є поняття **Онтології**. Воно стосується явної специфікації предметної області за допомогою формального представлення знань. Найпростіша онтологія може бути просто ієрархією об'єктів у предметній області, але складніші онтології включатимуть правила, які можна використовувати для висновків. +Основна концепція у Семантичному вебі — це поняття **Онтології**. Воно означає явну специфікацію предметної області за допомогою формального представлення знань. Найпростіша онтологія може бути лише ієрархією об’єктів у предметній області, але більш складні онтології включатимуть правила, які можна використовувати для логічних висновків. -У семантичному вебі всі представлення базуються на трійках. Кожен об'єкт і кожне відношення унікально ідентифікуються за допомогою URI. Наприклад, якщо ми хочемо заявити факт, що цей навчальний курс з AI був розроблений Дмитром Сошниковим 1 січня 2022 року, ось трійки, які ми можемо використати: +У семантичному вебі всі представлення базуються на трійках. Кожен об’єкт і кожне відношення унікально ідентифікуються URI. Наприклад, якщо ми хочемо вказати факт, що цей AI Curriculum був розроблений Дмитром Сошниковим 1 січня 2022 року — ось трійки, які ми можемо використати: - + ``` -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 ``` -> ✅ Тут `http://www.example.com/terms/creation-date` і `http://purl.org/dc/elements/1.1/creator` є добре відомими та загальноприйнятими URI для вираження понять *автор* і *дата створення*. +> ✅ Тут `http://www.example.com/terms/creation-date` і `http://purl.org/dc/elements/1.1/creator` — це деякі відомі і загальноприйняті URI для вираження понять *творець* і *дата створення*. -У більш складному випадку, якщо ми хочемо визначити список авторів, ми можемо використати деякі структури даних, визначені в RDF. +У більш складному випадку, якщо ми хочемо визначити список творців, ми можемо використати деякі структури даних, визначені в RDF. - + -> Діаграми вище створені [Дмитром Сошниковим](http://soshnikov.com) +> Схеми вище від [Dmitry Soshnikov](http://soshnikov.com) -Прогрес у створенні Семантичного вебу був дещо уповільнений через успіх пошукових систем і технік обробки природної мови, які дозволяють витягувати структуровані дані з тексту. Однак у деяких областях все ще докладаються значні зусилля для підтримки онтологій і баз знань. Кілька проектів, які варто відзначити: +Прогрес у побудові Семантичного веба певною мірою сповільнився через успіх пошукових систем і методів обробки природної мови, які дозволяють отримувати структуровані дані з тексту. Однак у деяких сферах все ще докладаються значні зусилля для підтримки онтологій та баз знань. Декілька проектів, які варто відзначити: -* [WikiData](https://wikidata.org/) — це колекція машинно читаних баз знань, пов'язаних із Вікіпедією. Більшість даних добувається з *InfoBoxes* Вікіпедії — структурованого контенту всередині сторінок Вікіпедії. Ви можете [запитувати](https://query.wikidata.org/) WikiData за допомогою SPARQL, спеціальної мови запитів для Семантичного вебу. Ось приклад запиту, який показує найпоширеніші кольори очей серед людей: +* [WikiData](https://wikidata.org/) — це колекція машинно-зчитуваних баз знань, пов’язана з Вікіпедією. Більша частина даних здобувається з Вікіпедійних *інфобоксів* — фрагментів структурованого контенту всередині сторінок Вікіпедії. Ви можете [запитувати](https://query.wikidata.org/) wikidata мовою SPARQL — спеціальною мовою запитів для Семантичного веба. Ось приклад запиту, який показує найпопулярніші кольори очей серед людей: ```sparql #defaultView:BubbleChart @@ -205,47 +206,52 @@ WHERE GROUP BY ?eyeColorLabel ``` -* [DBpedia](https://www.dbpedia.org/) — ще одна ініціатива, схожа на WikiData. +* [DBpedia](https://www.dbpedia.org/) — це ще одна подібна ініціатива, як WikiData. -> ✅ Якщо ви хочете експериментувати зі створенням власних онтологій або відкриттям існуючих, є чудовий візуальний редактор онтологій під назвою [Protégé](https://protege.stanford.edu/). Завантажте його або використовуйте онлайн. +> ✅ Якщо ви хочете експериментувати із побудовою власних онтологій або відкривати існуючі, існує чудовий візуальний редактор онтологій під назвою [Protégé](https://protege.stanford.edu/). Завантажте його або використовуйте онлайн. - + -*Веб-редактор Protégé відкритий з онтологією родини Романових. Скриншот Дмитра Сошникова* +*Веб-редактор Protégé відкритий з онтологією сім’ї Романових. Знімок екрану Дмитра Сошникова* -## ✍️ Вправа: Онтологія родини +## ✍️ Завдання: Онтологія сім’ї -Дивіться [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) для прикладу використання технік Семантичного вебу для аналізу сімейних відносин. Ми візьмемо родинне дерево, представлене у загальному форматі GEDCOM, і онтологію сімейних відносин, щоб побудувати графік усіх сімейних зв'язків для заданого набору осіб. -## Граф концепцій Microsoft +Дивіться [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) для прикладу використання технік Семантичного веба для розуміння сімейних відносин. Ми візьмемо дерево родини, представлене у загальному форматі GEDCOM, та онтологію сімейних зв’язків і побудуємо граф усіх сімейних зв’язків для заданої множини осіб. -У більшості випадків онтології ретельно створюються вручну. Однак також можливо **добувати** онтології з неструктурованих даних, наприклад, з текстів природної мови. +## Microsoft Concept Graph -Одна з таких спроб була здійснена Microsoft Research і призвела до створення [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste). +У більшості випадків онтології ретельно створюють вручну. Однак також можливо **видобувати** онтології з неструктурованих даних, наприклад, з текстів природної мови. -Це велика колекція сутностей, згрупованих за допомогою відношення успадкування `is-a`. Вона дозволяє відповідати на запитання типу "Що таке Microsoft?" — відповідь може бути щось на кшталт "компанія з ймовірністю 0.87, і бренд з ймовірністю 0.75". +Один із таких спроб здійснила Microsoft Research, що призвело до створення [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste). -Граф доступний як REST API або як великий текстовий файл для завантаження, який містить усі пари сутностей. +Це велика колекція сутностей, згрупованих за допомогою відношення успадкування `is-a`. Вона дозволяє відповідати на питання типу «Що таке Microsoft?» — відповідь буде на кшталт «компанія з ймовірністю 0.87 і бренд з ймовірністю 0.75». -## ✍️ Вправа: Граф концепцій +Граф доступний або через REST API, або у вигляді великого текстового файлу для завантаження, який містить усі пари сутностей. -Спробуйте блокнот [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb), щоб побачити, як ми можемо використовувати Microsoft Concept Graph для групування новинних статей у кілька категорій. +## ✍️ Завдання: Граф понять + +Спробуйте ноутбук [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb), щоб побачити, як можна використовувати Microsoft Concept Graph для групування новинних статей за кількома категоріями. ## Висновок -Сьогодні штучний інтелект часто вважається синонімом *машинного навчання* або *нейронних мереж*. Однак людина також демонструє явне мислення, яке наразі не обробляється нейронними мережами. У реальних проектах явне мислення все ще використовується для виконання завдань, які потребують пояснень або можливості змінювати поведінку системи контрольованим чином. +Сьогодні ШІ часто вважають синонімом *машинного навчання* або *нейронних мереж*. Однак людина також здійснює явне міркування, що наразі не охоплюється нейронними мережами. У реальних проєктах явне міркування все ще застосовується для виконання завдань, які потребують пояснень або можливості керованої зміни поведінки системи. ## 🚀 Виклик -У блокноті Family Ontology, пов'язаному з цим уроком, є можливість експериментувати з іншими сімейними відносинами. Спробуйте знайти нові зв'язки між людьми в родинному дереві. +В ноутбуці Онтології сім’ї, пов’язаному з цим уроком, є можливість експериментувати з іншими сімейними відносинами. Спробуйте виявити нові зв’язки між людьми у дереві сім’ї. -## [Тест після лекції](https://ff-quizzes.netlify.app/en/ai/quiz/4) +## [Післялекційний тест](https://ff-quizzes.netlify.app/en/ai/quiz/4) -## Огляд і самостійне навчання +## Огляд і самостоятельне вивчення -Проведіть дослідження в інтернеті, щоб виявити області, де люди намагалися кількісно оцінити та кодувати знання. Ознайомтеся з таксономією Блума і поверніться в історію, щоб дізнатися, як люди намагалися зрозуміти свій світ. Дослідіть роботу Ліннея зі створення таксономії організмів і спостерігайте, як Дмитро Менделєєв створив спосіб опису та групування хімічних елементів. Які ще цікаві приклади ви можете знайти? +Проведіть дослідження в Інтернеті, щоб виявити сфери, де люди намагалися кількісно оцінити та кодувати знання. Ознайомтеся з Таксономією Блума і поверніться в історію, щоб дізнатися, як люди намагалися осмислити свій світ. Вивчіть роботу Ліннея зі створення таксономії організмів і спостерігайте, як Дмитро Менделєєв створив спосіб опису і групування хімічних елементів. Які ще цікаві приклади ви можете знайти? -**Завдання**: [Створіть онтологію](assignment.md) +**Завдання**: [Побудувати онтологію](assignment.md) --- + +**Відмова від відповідальності**: +Цей документ був перекладений із використанням сервісу автоматичного перекладу [Co-op Translator](https://github.com/Azure/co-op-translator). Хоча ми прагнемо до точності, зверніть увагу, що автоматичні переклади можуть містити помилки або неточності. Оригінальний документ рідною мовою слід вважати авторитетним джерелом інформації. Для критично важливої інформації рекомендується звертатися до професійного людського перекладу. Ми не несемо відповідальності за будь-які непорозуміння або неправильне тлумачення, що виникли внаслідок використання цього перекладу. + \ No newline at end of file