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end of file diff --git a/translations/hr/AGENTS.md b/translations/hr/AGENTS.md index 42c934cc..e6140394 100644 --- a/translations/hr/AGENTS.md +++ b/translations/hr/AGENTS.md @@ -1,12 +1,3 @@ - # AGENTS.md ## Pregled projekta diff --git a/translations/hr/README.md b/translations/hr/README.md index d6b4ef01..247e18ca 100644 --- a/translations/hr/README.md +++ b/translations/hr/README.md @@ -1,12 +1,3 @@ - [![GitHub license](https://img.shields.io/github/license/microsoft/AI-For-Beginners.svg)](https://github.com/microsoft/AI-For-Beginners/blob/main/LICENSE) [![GitHub contributors](https://img.shields.io/github/contributors/microsoft/AI-For-Beginners.svg)](https://GitHub.com/microsoft/AI-For-Beginners/graphs/contributors/) [![GitHub issues](https://img.shields.io/github/issues/microsoft/AI-For-Beginners.svg)](https://GitHub.com/microsoft/AI-For-Beginners/issues/) @@ -23,210 +14,212 @@ CO_OP_TRANSLATOR_METADATA: # Umjetna inteligencija za početnike - Kurikulum -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../../../translated_images/hr/ai-overview.0857791951d19500.webp)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/hr/ai-overview.0857791951d19500.webp)| |:---:| -| AI za početnike - _Sketchnote od [@girlie_mac](https://twitter.com/girlie_mac)_ | +| AI za početnike - _Sketchnote by [@girlie_mac](https://twitter.com/girlie_mac)_ | + +Istražite svijet **Umjetne inteligencije** (UI) s našim 12-tjednim, 24-lekcijskim kurikulumom! Uključuje praktične lekcije, kvizove i laboratorijske vježbe. Kurikulum je prilagođen početnicima i pokriva alate poput TensorFlowa i PyTorcha, kao i etiku u UI -Istražite svijet **Umjetne inteligencije** (UI) s našim 12-tjednim, 24-lekcijskim kurikulumom! Uključuje praktične lekcije, kvizove i radionice. Kurikulum je prilagođen početnicima i obuhvaća alate poput TensorFlow i PyTorch, kao i etiku u UI. ### 🌐 Podrška za više jezika -#### Podržano putem GitHub akcije (Automatski i uvijek ažurno) +#### Podržano preko GitHub Action (Automatski i uvijek ažurno) -[arapski](../ar/README.md) | [bengalski](../bn/README.md) | [bugarski](../bg/README.md) | [birmanski (Myanmar)](../my/README.md) | [kineski (pojednostavljeni)](../zh/README.md) | [kineski (tradicionalni, Hong Kong)](../hk/README.md) | [kineski (tradicionalni, Macau)](../mo/README.md) | [kineski (tradicionalni, Tajvan)](../tw/README.md) | [hrvatski](./README.md) | [češki](../cs/README.md) | [danski](../da/README.md) | [nizozemski](../nl/README.md) | [estonski](../et/README.md) | [finski](../fi/README.md) | [francuski](../fr/README.md) | [njemački](../de/README.md) | [grčki](../el/README.md) | [hebrejski](../he/README.md) | [hindski](../hi/README.md) | [mađarski](../hu/README.md) | [indonezijski](../id/README.md) | [talijanski](../it/README.md) | [japanski](../ja/README.md) | [kanada](../kn/README.md) | [korejski](../ko/README.md) | [litavski](../lt/README.md) | [malajski](../ms/README.md) | [malajalamski](../ml/README.md) | [maratijski](../mr/README.md) | [nepalski](../ne/README.md) | [nigerijski pidgin](../pcm/README.md) | [norveški](../no/README.md) | [persijski (farsi)](../fa/README.md) | [poljski](../pl/README.md) | [portugalski (Brazil)](../br/README.md) | [portugalski (Portugal)](../pt/README.md) | [pandžapski (Gurmukhi)](../pa/README.md) | [rumunjski](../ro/README.md) | [ruski](../ru/README.md) | [srpski (ćirilica)](../sr/README.md) | [slovački](../sk/README.md) | [slovenski](../sl/README.md) | [španjolski](../es/README.md) | [svahili](../sw/README.md) | [švedski](../sv/README.md) | [tagalog (filipinski)](../tl/README.md) | [tamil](../ta/README.md) | [telugu](../te/README.md) | [tajlandski](../th/README.md) | [turski](../tr/README.md) | [ukrajinski](../uk/README.md) | [urdu](../ur/README.md) | [vijetnamski](../vi/README.md) +[Arabic](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgarian](../bg/README.md) | [Burmese (Myanmar)](../my/README.md) | [Chinese (Simplified)](../zh-CN/README.md) | [Chinese (Traditional, Hong Kong)](../zh-HK/README.md) | [Chinese (Traditional, Macau)](../zh-MO/README.md) | [Chinese (Traditional, Taiwan)](../zh-TW/README.md) | [Croatian](./README.md) | [Czech](../cs/README.md) | [Danish](../da/README.md) | [Dutch](../nl/README.md) | [Estonian](../et/README.md) | [Finnish](../fi/README.md) | [French](../fr/README.md) | [German](../de/README.md) | [Greek](../el/README.md) | [Hebrew](../he/README.md) | [Hindi](../hi/README.md) | [Hungarian](../hu/README.md) | [Indonesian](../id/README.md) | [Italian](../it/README.md) | [Japanese](../ja/README.md) | [Kannada](../kn/README.md) | [Korean](../ko/README.md) | [Lithuanian](../lt/README.md) | [Malay](../ms/README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Nepali](../ne/README.md) | [Nigerian Pidgin](../pcm/README.md) | [Norwegian](../no/README.md) | [Persian (Farsi)](../fa/README.md) | [Polish](../pl/README.md) | [Portuguese (Brazil)](../pt-BR/README.md) | [Portuguese (Portugal)](../pt-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) -> **Želite li klonirati lokalno?** +> **Preferirate klonirati lokalno?** -> Ovo spremište uključuje više od 50 prijevoda jezika što znatno povećava veličinu preuzimanja. Za kloniranje bez prijevoda, koristite sparse checkout: +> Ovaj repozitorij sadrži više od 50 prijevoda jezika što značajno povećava veličinu preuzimanja. Za kloniranje bez prijevoda, koristite sparse checkout: > ```bash > git clone --filter=blob:none --sparse https://github.com/microsoft/AI-For-Beginners.git > cd AI-For-Beginners > git sparse-checkout set --no-cone '/*' '!translations' '!translated_images' > ``` -> Ovo vam daje sve što vam je potrebno za dovršetak tečaja znatno bržim preuzimanjem. +> Ovo vam daje sve što vam treba za završetak tečaja s mnogo bržim preuzimanjem. -**Ako želite dodatne podržane jezike za prijevod, pogledajte [ovdje](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** +**Ako želite dodatnu podršku za prevode, podržani jezici su navedeni [ovdje](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** ## Pridružite se zajednici [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) ## Što ćete naučiti -**[Mentalna mapa tečaja](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** +**[Mapa uma tečaja](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** U ovom kurikulumu naučit ćete: -* Različite pristupe umjetnoj inteligenciji, uključujući "dobri stari" simbolički pristup s **Reprezentacijom znanja** i zaključivanjem ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). -* **Neuronske mreže** i **duboko učenje**, koji su srž moderne UI. Objasnit ćemo koncepte iza ovih važnih tema koristeći kod u dva od najpopularnijih okvira - [TensorFlow](http://Tensorflow.org) i [PyTorch](http://pytorch.org). -* **Neuronske arhitekture** za rad sa slikama i tekstom. Pokrit ćemo nedavne modele, ali možda ćemo malo zaostajati za najnovijim dostignućima. -* Rjeđe popularne pristupe u UI, poput **genetskih algoritama** i **sustava s više agenata**. +* Različite pristupe umjetnoj inteligenciji, uključujući "dobar stari" simbolički pristup s **reprezentacijom znanja** i zaključivanjem ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). +* **Neuronske mreže** i **Duboko učenje**, koji su u središtu moderne UI. Koncepte iza ovih važnih tema prikazat ćemo pomoću koda u dva najpopularnija okvira - [TensorFlow](http://Tensorflow.org) i [PyTorch](http://pytorch.org). +* **Neuronske arhitekture** za rad sa slikama i tekstom. Pokrit ćemo nedavne modele, iako možda neće biti u potpunosti u vrhu suvremenih dostignuća. +* Manje popularne pristupe u UI, poput **genetskih algoritama** i **višekorisničkih sustava**. Što nećemo pokriti u ovom kurikulumu: -> [Pronađite sve dodatne resurse za ovaj tečaj u našoj kolekciji Microsoft Learn](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) +> [Pronađite sve dodatne resurse za ovaj tečaj u našoj Microsoft Learn kolekciji](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) -* Poslovne slučajeve korištenja **UI u poslovanju**. Razmislite o polaganju [Uvod u UI za poslovne korisnike](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) na Microsoft Learn, ili [AI Business School](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), razvijen u suradnji s [INSEAD](https://www.insead.edu/). -* **Klasično strojno učenje**, koje je dobro opisano u našem [Kurikulumu za strojno učenje za početnike](http://github.com/Microsoft/ML-for-Beginners). -* Praktične primjene UI izgrađene korištenjem **[Cognitive Services](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**. Za to preporučujemo da započnete s Microsoft Learn modulima za [viziju](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [obradu prirodnog jezika](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[generativnu umjetnu inteligenciju uz Azure OpenAI servis](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** i druge. -* Specifične okvire za ML u oblaku, poput [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-bethanycheum), [Microsoft Fabric](https://learn.microsoft.com/en-us/training/paths/get-started-fabric/?WT.mc_id=academic-77998-bethanycheum) ili [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). Razmislite o korištenju tečajeva [Izgradite i upravljajte ML rješenjima uz Azure Machine Learning](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) i [Izgradite i upravljajte ML rješenjima s Azure Databricks](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum). -* **Conversational AI** i **chat botove**. Postoji zaseban tečaj [Kreirajte konverzacijske AI aplikacije](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum), a možete se referirati i na [ovaj blog post](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) za više detalja. -* **Duboku matematiku** iza dubokog učenja. Za to preporučujemo [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) autora Iana Goodfellowa, Yoshua Bengioa i Aarona Courvillea, koji je također dostupan online na [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/). +* Poslovne slučajeve korištenja **UI u poslovanju**. Razmotrite polaganje [Uvod u UI za poslovne korisnike](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) putem Microsoft Learn ili [AI poslovnu školu](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), razvijenu u suradnji s [INSEAD](https://www.insead.edu/). +* **Klasično strojno učenje**, koje je dobro opisano u našem [Kurikulumu strojnog učenja za početnike](http://github.com/Microsoft/ML-for-Beginners). +* Praktične AI aplikacije izgrađene koristeći **[Cognitive Services](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**. Za njih preporučujemo da započnete s modulima Microsoft Learn za [vid](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [obradu prirodnog jezika](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[Generativnu umjetnu inteligenciju uz Azure OpenAI uslugu](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** i druge. +* Specifične ML **cloud okvire**, kao što su [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-bethanycheum), [Microsoft Fabric](https://learn.microsoft.com/en-us/training/paths/get-started-fabric/?WT.mc_id=academic-77998-bethanycheum) ili [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). Razmislite o korištenju [Gradite i upravljajte ML rješenjima uz Azure Machine Learning](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) i [Gradite i upravljajte ML rješenjima s Azure Databricks](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum) učnih staza. +* **Konverzacijski UI** i **chat botovi**. Postoji zasebna [Staza za stvaranje konverzacijskih AI rješenja](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum), a možete pogledati i [ovaj blog post](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) za dodatne detalje. +* **Duboku matematiku** iza dubokog učenja. Za to preporučujemo [Duboko učenje](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) autora Ian Goodfellow, Yoshua Bengio i Aaron Courville, koja je također dostupna online na [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/). -Za lagani uvod u teme _AI u oblaku_ možete uzeti u obzir tečaj [Počnite s umjetnom inteligencijom na Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum). +Za lagani uvod u teme _UI u oblaku_ možete razmotriti polaganje [Započinjemo s umjetnom inteligencijom na Azureu](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum) učne staze. # Sadržaj -| | Poveznica na lekciju | PyTorch/Keras/TensorFlow | Laboratorijske vježbe | -| :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------ | -| 0 | [Postavljanje tečaja](./lessons/0-course-setup/setup.md) | [Postavite svoje razvojno okruženje](./lessons/0-course-setup/how-to-run.md) | | -| I | [**Uvod u umjetnu inteligenciju**](./lessons/1-Intro/README.md) | | | +| | Poveznica lekcije | PyTorch/Keras/TensorFlow | Laboratorij | +| :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | +| 0 | [Postavljanje kursa](./lessons/0-course-setup/setup.md) | [Postavite svoje razvojno okruženje](./lessons/0-course-setup/how-to-run.md) | | +| I | [**Uvod u UI**](./lessons/1-Intro/README.md) | | | | 01 | [Uvod i povijest UI](./lessons/1-Intro/README.md) | - | - | -| II | **Simbolička umjetna inteligencija** | -| 02 | [Reprezentacija znanja i ekspertni sustavi](./lessons/2-Symbolic/README.md) | [Ekspertni sustavi](./lessons/2-Symbolic/Animals.ipynb) / [Ontologija](./lessons/2-Symbolic/FamilyOntology.ipynb) /[Graf pojmova](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | +| II | **Simbolička UI** | +| 02 | [Reprezentacija znanja i ekspertni sustavi](./lessons/2-Symbolic/README.md) | [Ekspertni sustavi](./lessons/2-Symbolic/Animals.ipynb) / [Ontologija](./lessons/2-Symbolic/FamilyOntology.ipynb) /[Graf koncepata](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | | III | [**Uvod u neuronske mreže**](./lessons/3-NeuralNetworks/README.md) ||| | 03 | [Perceptron](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [Bilježnica](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [Laboratorij](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | | 04 | [Višeslojni perceptron i stvaranje vlastitog okvira](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [Bilježnica](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [Laboratorij](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | -| 05 | [Uvod u okvire (PyTorch/TensorFlow) i prekomjerno uklapanje](./lessons/3-NeuralNetworks/05-Frameworks/README.md) | [PyTorch](./lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb) / [Keras](./lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Laboratorij](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | -| IV | [**Računalni vid**](./lessons/4-ComputerVision/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-77998-cacaste) / [TensorFlow](https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-77998-cacaste)| [Istraži računalni vid na Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | +| 05 | [Uvod u okvire (PyTorch/TensorFlow) i prenaučenost](./lessons/3-NeuralNetworks/05-Frameworks/README.md) | [PyTorch](./lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb) / [Keras](./lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Laboratorij](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | +| IV | [**Računalni vid**](./lessons/4-ComputerVision/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-77998-cacaste) / [TensorFlow](https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-77998-cacaste) | [Istražite računalni vid na Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | | 06 | [Uvod u računalni vid. OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [Bilježnica](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [Laboratorij](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | -| 07 | [Konvolucijske neuronske mreže](./lessons/4-ComputerVision/07-ConvNets/README.md) & [Arhitekture CNN-a](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [Laboratorij](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | -| 08 | [Unaprijed naučene mreže i transferno učenje](./lessons/4-ComputerVision/08-TransferLearning/README.md) i [Trikovi treniranja](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Laboratorij](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | -| 09 | [Autoenkoderi i VAE-ovi](./lessons/4-ComputerVision/09-Autoencoders/README.md) | [PyTorch](./lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb) | | -| 10 | [Generativne kontradiktorne mreže i prijenos umjetničkog stila](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | +| 07 | [Konvolucijske neuronske mreže](./lessons/4-ComputerVision/07-ConvNets/README.md) & [Arhitekture CNN](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [Laboratorij](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | +| 08 | [Unaprijed trenirane mreže i transfer učenja](./lessons/4-ComputerVision/08-TransferLearning/README.md) i [Trikovi u treniranju](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Laboratorij](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | +| 09 | [Autoenkoderi i VAEs](./lessons/4-ComputerVision/09-Autoencoders/README.md) | [PyTorch](./lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb) | | +| 10 | [Generativne suparničke mreže i prijenos umjetničkog stila](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | | 11 | [Detekcija objekata](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [Laboratorij](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | | 12 | [Semantička segmentacija. U-Net](./lessons/4-ComputerVision/12-Segmentation/README.md) | [PyTorch](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb) | | -| V | [**Obrada prirodnog jezika**](./lessons/5-NLP/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-77998-cacaste) /[TensorFlow](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-77998-cacaste) | [Istraži obradu prirodnog jezika na Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| -| 13 | [Predstavljanje teksta. BoW/TF-IDF](./lessons/5-NLP/13-TextRep/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb) | | -| 14 | [Semantičke vektorske reprezentacije riječi. Word2Vec i GloVe](./lessons/5-NLP/14-Embeddings/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb) | | -| 15 | [Modeliranje jezika. Treniranje vlastitih ugrađenih modela](./lessons/5-NLP/15-LanguageModeling/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-PyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-TF.ipynb) | [Laboratorij](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | +| V | [**Obrada prirodnog jezika**](./lessons/5-NLP/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-77998-cacaste) /[TensorFlow](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-77998-cacaste) | [Istražite obradu prirodnog jezika na Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| +| 13 | [Predstavljanje teksta. Bow/TF-IDF](./lessons/5-NLP/13-TextRep/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb) | | +| 14 | [Semantičke riječne ugrađenosti. Word2Vec i GloVe](./lessons/5-NLP/14-Embeddings/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb) | | +| 15 | [Modeliranje jezika. Treniranje vlastitih ugrađenosti](./lessons/5-NLP/15-LanguageModeling/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-PyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-TF.ipynb) | [Laboratorij](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | | 16 | [Rekurentne neuronske mreže](./lessons/5-NLP/16-RNN/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNTF.ipynb) | | | 17 | [Generativne rekurentne mreže](./lessons/5-NLP/17-GenerativeNetworks/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb) | [Laboratorij](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | | 18 | [Transformeri. BERT.](./lessons/5-NLP/18-Transformers/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb) /[TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersTF.ipynb) | | | 19 | [Prepoznavanje imenovanih entiteta](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [Laboratorij](./lessons/5-NLP/19-NER/lab/README.md) | -| 20 | [Veliki jezični modeli, programiranje na zahtjev i zadaci s malo primjera](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | +| 20 | [Veliki jezični modeli, programiranje upita i zadaci s malo primjera](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | | VI | **Ostale AI tehnike** || | | 21 | [Genetski algoritmi](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [Bilježnica](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | -| 22 | [Duboko učenje s pojačanjem](./lessons/6-Other/22-DeepRL/README.md) | [PyTorch](./lessons/6-Other/22-DeepRL/CartPole-RL-PyTorch.ipynb) /[TensorFlow](./lessons/6-Other/22-DeepRL/CartPole-RL-TF.ipynb) | [Laboratorij](./lessons/6-Other/22-DeepRL/lab/README.md) | +| 22 | [Duboko učenje s potkrepljenjem](./lessons/6-Other/22-DeepRL/README.md) | [PyTorch](./lessons/6-Other/22-DeepRL/CartPole-RL-PyTorch.ipynb) /[TensorFlow](./lessons/6-Other/22-DeepRL/CartPole-RL-TF.ipynb) | [Laboratorij](./lessons/6-Other/22-DeepRL/lab/README.md) | | 23 | [Sustavi s više agenata](./lessons/6-Other/23-MultiagentSystems/README.md) | | | -| VII | **Etika umjetne inteligencije** | | | -| 24 | [Etika umjetne inteligencije i odgovorna AI](./lessons/7-Ethics/README.md) | [Microsoft Learn: Načela odgovorne AI](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | +| VII | **AI etika** | | | +| 24 | [AI etika i odgovorni AI](./lessons/7-Ethics/README.md) | [Microsoft Learn: Principi odgovornog AI](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | | IX | **Dodatno** | | | -| 25 | [Višeslojne mreže, CLIP i VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [Bilježnica](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | +| 25 | [Višemodalne mreže, CLIP i VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [Bilježnica](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | ## Svaka lekcija sadrži -* Materijal za pred-čitanje -* Izvršljive Jupyter bilježnice, koje su često specifične za okvir (**PyTorch** ili **TensorFlow**). Izvršljiva bilježnica također sadrži mnogo teoretskog materijala, stoga da biste razumjeli temu potrebno je proći kroz barem jednu verziju bilježnice (bilo PyTorch ili TensorFlow). -* **Laboratorije** dostupne za neke teme, koje vam pružaju priliku da isprobate primjenu naučenog materijala na određenom problemu. -* Neki dijelovi sadrže poveznice na [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) module koji pokrivaju povezane teme. +* Materijal za predčitanje +* Izvršne Jupyter bilježnice, koje su često specifične za okvir (**PyTorch** ili **TensorFlow**). Izvršna bilježnica također sadrži mnogo teorijskog materijala, stoga za razumijevanje teme morate proći barem jednu verziju bilježnice (bilo PyTorch ili TensorFlow). +* **Laboratorije** dostupne za neke teme, koje vam daju priliku da isprobate primjenu naučenog materijala na određenom problemu. +* Neki odjeljci sadrže poveznice na module [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) koji pokrivaju povezane teme. ## Početak ### 🎯 Novi u AI? Počnite ovdje! -Ako ste potpuno novi u umjetnoj inteligenciji i želite brze, praktične primjere, pogledajte naše [**Primjerima prilagođene početnicima**](./examples/README.md)! Oni uključuju: +Ako ste potpuno novi u AI i želite brze, praktične primjere, pogledajte naše [**Primjere prilagođene početnicima**](./examples/README.md)! Oni uključuju: -- 🌟 **Pozdrav AI svijete** - Vaš prvi AI program (prepoznavanje uzoraka) -- 🧠 **Jednostavna neuronska mreža** - Izgradite neuronsku mrežu ispočetka -- 🖼️ **Klasifikator slika** - Klasificirajte slike s detaljnim komentarima -- 💬 **Sentiment teksta** - Analizirajte pozitivan/negativan tekst +- 🌟 **Hello AI World** - Vaš prvi AI program (prepoznavanje uzoraka) +- 🧠 **Jednostavna neuronska mreža** - Izgradite neuronsku mrežu od nule -Ovi primjeri osmišljeni su kako bi vam pomogli razumjeti AI koncepte prije nego započnete s cjelokupnim kurikulumom. +- 🖼️ **Klasifikator slika** - Klasificirajte slike s detaljnim komentarima +- 💬 **Sentiment teksta** - Analizirajte pozitivan/negativan tekst + +Ovi primjeri osmišljeni su da vam pomognu razumjeti AI koncepte prije nego što započnete s cjelokupnim kurikulumom. ### 📚 Postavljanje cjelokupnog kurikuluma -- Napravili smo [lekciju za postavljanje](./lessons/0-course-setup/setup.md) kako bismo vam pomogli pri postavljanju vašeg razvojog okruženja. - Za edukatore smo također kreirali [lekciju za postavljanje kurikuluma](./lessons/0-course-setup/for-teachers.md)! -- Kako [pokrenuti kod u VSCode-u ili Codespace-u](./lessons/0-course-setup/how-to-run.md) +- Izradili smo [lekciju za postavljanje](./lessons/0-course-setup/setup.md) koja će vam pomoći pri postavljanju razvojnog okruženja. - Za nastavnike smo također izradili [lekciju za postavljanje kurikuluma](./lessons/0-course-setup/for-teachers.md)! +- Kako [pokrenuti kod u VSCode ili Codespaceu](./lessons/0-course-setup/how-to-run.md) Slijedite ove korake: -Forkajte repositorij: Kliknite na gumb "Fork" u gornjem desnom kutu ove stranice. +Forkajte repozitorij: Kliknite na gumb "Fork" u gornjem desnom kutu ove stranice. -Klonirajte repositorij: `git clone https://github.com/microsoft/AI-For-Beginners.git` +Klonirajte repozitorij: `git clone https://github.com/microsoft/AI-For-Beginners.git` -Ne zaboravite označiti (🌟) ovaj repozitorij kako biste ga lakše pronašli kasnije. +Ne zaboravite označiti (🌟) ovaj repo da biste ga lakše pronašli kasnije. ## Upoznajte druge polaznike -Pridružite se našem [službenom AI Discord serveru](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) kako biste upoznali i povezali se s ostalim polaznicima ovog tečaja i dobili podršku. +Pridružite se našem [službenom AI Discord serveru](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) kako biste upoznali i povezali se s drugim polaznicima ovog tečaja i dobili podršku. Ako imate povratne informacije o proizvodu ili pitanja tijekom izrade, posjetite naš [Azure AI Foundry Developer Forum](https://aka.ms/foundry/forum) -## Kvizovi +## Kvizevi -> **Napomena o kvizovima**: Svi kvizovi nalaze se u mapi Quiz-app u etc\quiz-app, ili [Online Ovdje](https://ff-quizzes.netlify.app/) Povezani su iz lekcija, kviz aplikacija se može pokrenuti lokalno ili implementirati na Azure; slijedite upute u mapi `quiz-app`. Postupno se lokaliziraju. +> **Napomena o kvizovima**: Svi kvizovi nalaze se u mapi Quiz-app u etc\quiz-app, ili [online ovdje](https://ff-quizzes.netlify.app/) Povezani su kroz lekcije; aplikaciju kviza možete pokrenuti lokalno ili je distribuirati na Azure; slijedite upute u mapi `quiz-app`. Postupno se lokaliziraju. -## Tražimo pomoć +## Traži se pomoć -Imate li prijedloge ili ste pronašli pravopisne ili kodne greške? Otvorite issue ili kreirajte pull request. +Imate li prijedloge ili ste pronašli pravopisne ili programske pogreške? Otvorite issue ili napravite pull request. -## Posebne zahvale +## Posebna zahvalnost -* **✍️ Glavni autor:** [Dmitry Soshnikov](http://soshnikov.com), PhD -* **🔥 Urednik:** [Jen Looper](https://twitter.com/jenlooper), PhD -* **🎨 Ilustrator skica:** [Tomomi Imura](https://twitter.com/girlie_mac) -* **✅ Kreator kvizova:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) -* **🙏 Glavni suradnici:** [Evgenii Pishchik](https://github.com/Pe4enIks) +* **✍️ Glavni autor:** [Dmitry Soshnikov](http://soshnikov.com), PhD +* **🔥 Urednik:** [Jen Looper](https://twitter.com/jenlooper), PhD +* **🎨 Ilustrator sketchnote bilješki:** [Tomomi Imura](https://twitter.com/girlie_mac) +* **✅ Kreator kvizova:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) +* **🙏 Glavni suradnici:** [Evgenii Pishchik](https://github.com/Pe4enIks) ## Ostali kurikulumi Naš tim proizvodi i druge kurikulume! Pogledajte: -### LangChain -[![LangChain4j za početnike](https://img.shields.io/badge/LangChain4j%20for%20Beginners-22C55E?style=for-the-badge&&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchain4j-for-beginners) -[![LangChain.js za početnike](https://img.shields.io/badge/LangChain.js%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchainjs-for-beginners?WT.mc_id=m365-94501-dwahlin) +### LangChain +[![LangChain4j for Beginners](https://img.shields.io/badge/LangChain4j%20for%20Beginners-22C55E?style=for-the-badge&&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchain4j-for-beginners) +[![LangChain.js for Beginners](https://img.shields.io/badge/LangChain.js%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchainjs-for-beginners?WT.mc_id=m365-94501-dwahlin) --- -### Azure / Edge / MCP / Agent -[![AZD za početnike](https://img.shields.io/badge/AZD%20for%20Beginners-0078D4?style=for-the-badge&labelColor=E5E7EB&color=0078D4)](https://github.com/microsoft/AZD-for-beginners?WT.mc_id=academic-105485-koreyst) -[![Edge AI za početnike](https://img.shields.io/badge/Edge%20AI%20for%20Beginners-00B8E4?style=for-the-badge&labelColor=E5E7EB&color=00B8E4)](https://github.com/microsoft/edgeai-for-beginners?WT.mc_id=academic-105485-koreyst) -[![MCP za početnike](https://img.shields.io/badge/MCP%20for%20Beginners-009688?style=for-the-badge&labelColor=E5E7EB&color=009688)](https://github.com/microsoft/mcp-for-beginners?WT.mc_id=academic-105485-koreyst) -[![AI agenti za početnike](https://img.shields.io/badge/AI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst) +### Azure / Edge / MCP / Agent +[![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) --- - -### Serija Generativnog AI -[![Generativni AI za početnike](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst) -[![Generativni AI (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst) -[![Generativni AI (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst) -[![Generativni AI (JavaScript)](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst) + +### Serija Generativnog AI-a +[![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) --- - -### Osnovno učenje -[![ML za početnike](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst) -[![Data Science za početnike](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst) -[![AI za početnike](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst) -[![Cybersecurity za početnike](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung) -[![Web razvoj za početnike](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst) -[![IoT za početnike](https://img.shields.io/badge/IoT%20for%20Beginners-14B8A6?style=for-the-badge&labelColor=E5E7EB&color=14B8A6)](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst) -[![XR razvoj za početnike](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst) + +### Osnovno učenje +[![ML for Beginners](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst) +[![Data Science for Beginners](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst) +[![AI for Beginners](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst) +[![Cybersecurity for Beginners](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung) +[![Web Dev for Beginners](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst) +[![IoT for Beginners](https://img.shields.io/badge/IoT%20for%20Beginners-14B8A6?style=for-the-badge&labelColor=E5E7EB&color=14B8A6)](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst) +[![XR Development for Beginners](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst) --- - -### Serija Copilot -[![Copilot za AI programsko uparivanje](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst) -[![Copilot za C#/.NET](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst) -[![Copilot avantura](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst) + +### Serija Copilot +[![Copilot for AI Paired Programming](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst) +[![Copilot for C#/.NET](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst) +[![Copilot Adventure](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst) ## Dobivanje pomoći -Ako zapnete ili imate pitanja o izradi AI aplikacija, pridružite se drugim polaznicima i iskusnim developerima u diskusijama o MCP-u. To je podržavajuća zajednica gdje su pitanja dobrodošla, a znanje se slobodno dijeli. +Ako zapnete ili imate pitanja o izradi AI aplikacija, pridružite se kolegama polaznicima i iskusnim programerima u raspravama o MCP-u. To je podržavajuća zajednica u kojoj su pitanja dobrodošla i znanje se slobodno dijeli. [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -Ako imate povratne informacije o proizvodu ili greške tijekom izrade, posjetite: +Ako imate povratne informacije o proizvodu ili primijetite pogreške tijekom izrade, posjetite: [![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) --- -**Izjava o odricanju odgovornosti**: -Ovaj dokument je preveden korištenjem AI prevoditeljskog servisa [Co-op Translator](https://github.com/Azure/co-op-translator). Iako nastojimo osigurati točnost, imajte na umu da automatski prijevodi mogu sadržavati pogreške ili netočnosti. Izvorni dokument na izvornom jeziku treba smatrati autoritativnim izvorom. Za važne informacije preporučuje se profesionalni ljudski prijevod. Ne snosimo odgovornost za bilo kakve nesporazume ili pogrešna tumačenja nastala uporabom ovog prijevoda. +**Odricanje od odgovornosti**: +Ovaj dokument je preveden pomoću AI usluge za prevođenje [Co-op Translator](https://github.com/Azure/co-op-translator). Iako nastojimo osigurati točnost, imajte na umu da automatski prijevodi mogu sadržavati pogreške ili netočnosti. Izvorni dokument na izvornom jeziku smatra se službenim i autoritativnim izvorom. Za važne informacije preporučuje se profesionalni ljudski prijevod. Ne snosimo odgovornost za bilo kakve nesporazume ili pogrešna tumačenja proizašla iz korištenja ovog prijevoda. \ No newline at end of file diff --git a/translations/hr/SECURITY.md b/translations/hr/SECURITY.md index 0689e358..eb06be33 100644 --- a/translations/hr/SECURITY.md +++ b/translations/hr/SECURITY.md @@ -1,12 +1,3 @@ - ## Sigurnost Microsoft ozbiljno pristupa sigurnosti svojih softverskih proizvoda i usluga, uključujući sve repozitorije izvornog koda kojima upravljamo putem naših GitHub organizacija, kao što su [Microsoft](https://github.com/Microsoft), [Azure](https://github.com/Azure), [DotNet](https://github.com/dotnet), [AspNet](https://github.com/aspnet), [Xamarin](https://github.com/xamarin) i [naše GitHub organizacije](https://opensource.microsoft.com/). diff --git a/translations/hr/etc/CODE_OF_CONDUCT.md b/translations/hr/etc/CODE_OF_CONDUCT.md index 7e342161..414ed17b 100644 --- a/translations/hr/etc/CODE_OF_CONDUCT.md +++ b/translations/hr/etc/CODE_OF_CONDUCT.md @@ -1,12 +1,3 @@ - # Microsoftov Kodeks ponašanja za otvoreni izvor Ovaj projekt je usvojio [Microsoftov Kodeks ponašanja za otvoreni izvor](https://opensource.microsoft.com/codeofconduct/). diff --git a/translations/hr/etc/CONTRIBUTING.md b/translations/hr/etc/CONTRIBUTING.md index 5b650c48..61c64986 100644 --- a/translations/hr/etc/CONTRIBUTING.md +++ b/translations/hr/etc/CONTRIBUTING.md @@ -1,12 +1,3 @@ - # Doprinose Ovaj projekt pozdravlja doprinose i prijedloge. Većina doprinosa zahtijeva da se složite s Ugovorom o licenci za suradnike (CLA) kojim izjavljujete da imate pravo, i da zaista dajete, prava za korištenje vašeg doprinosa. Za detalje posjetite https://cla.microsoft.com. diff --git a/translations/hr/etc/Mindmap.md b/translations/hr/etc/Mindmap.md index fec8ca12..028060e8 100644 --- a/translations/hr/etc/Mindmap.md +++ b/translations/hr/etc/Mindmap.md @@ -1,12 +1,3 @@ - # AI ## [Uvod u AI](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/1-Intro/README.md) diff --git a/translations/hr/etc/SUPPORT.md b/translations/hr/etc/SUPPORT.md index 2cd3a1d2..9a5cc57e 100644 --- a/translations/hr/etc/SUPPORT.md +++ b/translations/hr/etc/SUPPORT.md @@ -1,12 +1,3 @@ - # Podrška ## Kako prijaviti probleme i dobiti pomoć diff --git a/translations/hr/etc/TRANSLATIONS.md b/translations/hr/etc/TRANSLATIONS.md index e250ebae..ea6c1c60 100644 --- a/translations/hr/etc/TRANSLATIONS.md +++ b/translations/hr/etc/TRANSLATIONS.md @@ -1,12 +1,3 @@ - # Sudjelujte u prevođenju lekcija Pozivamo vas da sudjelujete u prevođenju lekcija iz ovog kurikuluma! diff --git a/translations/hr/etc/quiz-app/README.md b/translations/hr/etc/quiz-app/README.md index fbf4ef20..90aeacb8 100644 --- a/translations/hr/etc/quiz-app/README.md +++ b/translations/hr/etc/quiz-app/README.md @@ -1,12 +1,3 @@ - # Kvizovi Ovi kvizovi su uvodni i završni kvizovi za AI kurikulum na https://aka.ms/ai-beginners diff --git a/translations/hr/examples/README.md b/translations/hr/examples/README.md index d5459a7d..9399178d 100644 --- a/translations/hr/examples/README.md +++ b/translations/hr/examples/README.md @@ -1,12 +1,3 @@ - # Primjeri umjetne inteligencije za početnike Dobrodošli! Ovaj direktorij sadrži jednostavne, samostalne primjere koji će vam pomoći da započnete s umjetnom inteligencijom i strojnim učenjem. Svaki primjer je osmišljen tako da bude prilagođen početnicima, uz detaljne komentare i objašnjenja korak po korak. diff --git a/translations/hr/lessons/0-course-setup/for-teachers.md b/translations/hr/lessons/0-course-setup/for-teachers.md index b10ed581..7b19234a 100644 --- a/translations/hr/lessons/0-course-setup/for-teachers.md +++ b/translations/hr/lessons/0-course-setup/for-teachers.md @@ -1,12 +1,3 @@ - # Za edukatore Želite li koristiti ovaj kurikulum u svojoj učionici? Slobodno ga koristite! diff --git a/translations/hr/lessons/0-course-setup/how-to-run.md b/translations/hr/lessons/0-course-setup/how-to-run.md index eacfec31..837cf679 100644 --- a/translations/hr/lessons/0-course-setup/how-to-run.md +++ b/translations/hr/lessons/0-course-setup/how-to-run.md @@ -1,12 +1,3 @@ - # Kako pokrenuti kod Ovaj nastavni plan sadrži mnogo izvršnih primjera i laboratorijskih vježbi koje želite pokrenuti. Da biste to učinili, morate imati mogućnost izvršavanja Python koda u Jupyter bilježnicama koje su dio ovog nastavnog plana. Imate nekoliko opcija za pokretanje koda: diff --git a/translations/hr/lessons/0-course-setup/setup.md b/translations/hr/lessons/0-course-setup/setup.md index acca7b94..d0ab9f56 100644 --- a/translations/hr/lessons/0-course-setup/setup.md +++ b/translations/hr/lessons/0-course-setup/setup.md @@ -1,12 +1,3 @@ - # Početak s ovim kurikulumom ## Jeste li student? diff --git a/translations/hr/lessons/1-Intro/README.md b/translations/hr/lessons/1-Intro/README.md index 1a0b7c03..ea57d3b1 100644 --- a/translations/hr/lessons/1-Intro/README.md +++ b/translations/hr/lessons/1-Intro/README.md @@ -1,12 +1,3 @@ - # Uvod u AI ![Sažetak sadržaja uvoda u AI u obliku crteža](../../../../translated_images/hr/ai-intro.bf28d1ac4235881c.webp) diff --git a/translations/hr/lessons/1-Intro/assignment.md b/translations/hr/lessons/1-Intro/assignment.md index f90429cf..1cdaf177 100644 --- a/translations/hr/lessons/1-Intro/assignment.md +++ b/translations/hr/lessons/1-Intro/assignment.md @@ -1,12 +1,3 @@ - # Game Jam Igre su područje koje je snažno pod utjecajem razvoja umjetne inteligencije (AI) i strojnog učenja (ML). U ovom zadatku napišite kratki rad o igri koja vam se sviđa, a koja je bila pod utjecajem evolucije AI-a. Trebala bi biti dovoljno stara da je bila pod utjecajem različitih vrsta računalnih sustava. Dobar primjer su šah ili Go, ali također razmotrite videoigre poput Ponga ili Pac-Mana. Napišite esej koji raspravlja o prošlosti, sadašnjosti i budućnosti igre u kontekstu AI-a. diff --git a/translations/hr/lessons/2-Symbolic/README.md b/translations/hr/lessons/2-Symbolic/README.md index 9cd4dbc3..cab2b648 100644 --- a/translations/hr/lessons/2-Symbolic/README.md +++ b/translations/hr/lessons/2-Symbolic/README.md @@ -1,15 +1,6 @@ - # Predstavljanje znanja i stručni sustavi -![Sažetak simboličke umjetne inteligencije](../../../../../../translated_images/hr/ai-symbolic.715a30cb610411a6.webp) +![Sažetak simboličke umjetne inteligencije](../../../../translated_images/hr/ai-symbolic.715a30cb610411a6.webp) > Sketchnote autora [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +32,7 @@ Najčešće ne definiramo strogo znanje, već ga usklađujemo s drugim povezanim Dakle, problem **predstavljanja znanja** je pronaći neki učinkovit način predstavljanja znanja unutar računala u obliku podataka kako bi bilo automatski upotrebljivo. To se može promatrati kao spektar: -![Spektar predstavljanja znanja](../../../../../../translated_images/hr/knowledge-spectrum.b60df631852c0217.webp) +![Spektar predstavljanja znanja](../../../../translated_images/hr/knowledge-spectrum.b60df631852c0217.webp) > Slika autora [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +85,7 @@ Sintaksa bloka | Indent | | | Jedan od ranih uspjeha simboličke AI bili su tzv. **stručni sustavi** - računalni sustavi dizajnirani da djeluju kao stručnjak u nekom ograničenom području problema. Temeljili su se na **bazi znanja** izvučenoj od jednog ili više ljudskih stručnjaka i sadržavali su **zaključni stroj** koji je vršio zaključivanje na temelju nje. -![Ljudska arhitektura](../../../../../../translated_images/hr/arch-human.5d4d35f1bba3ab1c.webp) | ![Sustav temeljen na znanju](../../../../../../translated_images/hr/arch-kbs.3ec5c150b09fa8da.webp) +![Ljudska arhitektura](../../../../translated_images/hr/arch-human.5d4d35f1bba3ab1c.webp) | ![Sustav temeljen na znanju](../../../../translated_images/hr/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ Pojednostavljena struktura ljudskog živčanog sustava | Arhitektura sustava temeljenog na znanju @@ -106,7 +97,7 @@ Stručni sustavi se grade poput ljudskog sustava zaključivanja, koji sadrži ** Kao primjer, razmotrimo sljedeći stručni sustav za određivanje životinje na temelju fizičkih karakteristika: -![AND-ILI stablo](../../../../../../translated_images/hr/AND-OR-Tree.5592d2c70187f283.webp) +![AND-ILI stablo](../../../../translated_images/hr/AND-OR-Tree.5592d2c70187f283.webp) > Slika autora [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/hr/lessons/2-Symbolic/assignment.md b/translations/hr/lessons/2-Symbolic/assignment.md index da90730e..f54ec7f2 100644 --- a/translations/hr/lessons/2-Symbolic/assignment.md +++ b/translations/hr/lessons/2-Symbolic/assignment.md @@ -1,12 +1,3 @@ - # Izgradnja ontologije Izgradnja baze znanja temelji se na kategorizaciji modela koji predstavlja činjenice o nekoj temi. Odaberite temu - poput osobe, mjesta ili stvari - i zatim izradite model te teme. Koristite neke od tehnika i strategija izgradnje modela opisane u ovoj lekciji. Primjer bi bio stvaranje ontologije dnevne sobe s namještajem, rasvjetom i slično. Kako se dnevna soba razlikuje od kuhinje? Kupaonice? Kako znate da je to dnevna soba, a ne blagovaonica? Koristite [Protégé](https://protege.stanford.edu/) za izradu svoje ontologije. diff --git a/translations/hr/lessons/3-NeuralNetworks/03-Perceptron/README.md b/translations/hr/lessons/3-NeuralNetworks/03-Perceptron/README.md index 56a069cc..e83e9dee 100644 --- a/translations/hr/lessons/3-NeuralNetworks/03-Perceptron/README.md +++ b/translations/hr/lessons/3-NeuralNetworks/03-Perceptron/README.md @@ -1,12 +1,3 @@ - # Uvod u neuronske mreže: Perceptron ## [Kviz prije predavanja](https://ff-quizzes.netlify.app/en/ai/quiz/5) @@ -15,7 +6,7 @@ Jedan od prvih pokušaja implementacije nečega sličnog modernoj neuronskoj mre | | | |--------------|-----------| -|Frank Rosenblatt | The Mark 1 Perceptron| +|Frank Rosenblatt | The Mark 1 Perceptron| > Slike [s Wikipedije](https://en.wikipedia.org/wiki/Perceptron) @@ -34,7 +25,7 @@ y(x) = f(wTx) gdje je f funkcija aktivacije koraka - + ## Treniranje perceptrona diff --git a/translations/hr/lessons/3-NeuralNetworks/03-Perceptron/lab/README.md b/translations/hr/lessons/3-NeuralNetworks/03-Perceptron/lab/README.md index 3c5ca881..c4e73a29 100644 --- a/translations/hr/lessons/3-NeuralNetworks/03-Perceptron/lab/README.md +++ b/translations/hr/lessons/3-NeuralNetworks/03-Perceptron/lab/README.md @@ -1,12 +1,3 @@ - # Višeklasna klasifikacija s perceptronom Laboratorijska vježba iz [AI for Beginners Curriculum](https://github.com/microsoft/ai-for-beginners). diff --git a/translations/hr/lessons/3-NeuralNetworks/04-OwnFramework/README.md b/translations/hr/lessons/3-NeuralNetworks/04-OwnFramework/README.md index 68eb5643..9a4a7f04 100644 --- a/translations/hr/lessons/3-NeuralNetworks/04-OwnFramework/README.md +++ b/translations/hr/lessons/3-NeuralNetworks/04-OwnFramework/README.md @@ -1,12 +1,3 @@ - # Uvod u neuronske mreže. Višeslojni perceptron U prethodnom dijelu naučili ste o najjednostavnijem modelu neuronske mreže - jednoslojnom perceptronu, linearnom modelu za klasifikaciju s dvije klase. @@ -65,7 +56,7 @@ Algoritam gradijentnog spuštanja ostaje isti, ali postaje teže izračunati gra Primijetite da je lijevi dio svih tih izraza isti, i stoga možemo učinkovito izračunavati derivacije počevši od funkcije gubitka i idući "unatrag" kroz računski graf. Stoga se metoda treniranja višeslojnog perceptrona naziva **povratna propagacija**, ili 'backprop'. -računski graf +računski graf > TODO: citiranje slike diff --git a/translations/hr/lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md b/translations/hr/lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md index 8721ac2a..abfb06e5 100644 --- a/translations/hr/lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md +++ b/translations/hr/lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md @@ -1,12 +1,3 @@ - # Klasifikacija MNIST-a s Našim Okvirom Laboratorijska vježba iz [AI za Početnike Kurikulum](https://github.com/microsoft/ai-for-beginners). diff --git a/translations/hr/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/hr/lessons/3-NeuralNetworks/05-Frameworks/README.md index 5836016c..d37103a8 100644 --- a/translations/hr/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/hr/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -1,12 +1,3 @@ - # Okviri za neuronske mreže Kao što smo već naučili, da bismo učinkovito trenirali neuronske mreže, moramo učiniti dvije stvari: diff --git a/translations/hr/lessons/3-NeuralNetworks/05-Frameworks/lab/README.md b/translations/hr/lessons/3-NeuralNetworks/05-Frameworks/lab/README.md index bf95f2af..10c8376e 100644 --- a/translations/hr/lessons/3-NeuralNetworks/05-Frameworks/lab/README.md +++ b/translations/hr/lessons/3-NeuralNetworks/05-Frameworks/lab/README.md @@ -1,12 +1,3 @@ - # Klasifikacija s PyTorch/TensorFlow Laboratorijska vježba iz [AI for Beginners Curriculum](https://github.com/microsoft/ai-for-beginners). diff --git a/translations/hr/lessons/3-NeuralNetworks/README.md b/translations/hr/lessons/3-NeuralNetworks/README.md index ad0949e3..c0e40dc8 100644 --- a/translations/hr/lessons/3-NeuralNetworks/README.md +++ b/translations/hr/lessons/3-NeuralNetworks/README.md @@ -1,12 +1,3 @@ - # Uvod u neuronske mreže ![Sažetak sadržaja o uvodu u neuronske mreže u obliku crteža](../../../../translated_images/hr/ai-neuralnetworks.1c687ae40bc86e83.webp) diff --git a/translations/hr/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/hr/lessons/4-ComputerVision/06-IntroCV/README.md index e8f871df..a27fffa5 100644 --- a/translations/hr/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/hr/lessons/4-ComputerVision/06-IntroCV/README.md @@ -1,12 +1,3 @@ - # Uvod u računalni vid [Računalni vid](https://wikipedia.org/wiki/Computer_vision) je disciplina čiji je cilj omogućiti računalima da steknu visok nivo razumijevanja digitalnih slika. Ovo je prilično široka definicija jer *razumijevanje* može značiti mnogo različitih stvari, uključujući pronalaženje objekta na slici (**detekcija objekata**), razumijevanje što se događa (**detekcija događaja**), opisivanje slike tekstom ili rekonstrukciju scene u 3D. Postoje i posebni zadaci vezani uz slike ljudi: procjena dobi i emocija, detekcija i identifikacija lica te procjena 3D poze, da spomenemo samo neke. @@ -115,7 +106,7 @@ Pročitajte više o optičkom toku [u ovom odličnom vodiču](https://learnopenc U ovom laboratoriju snimit ćete video s jednostavnim gestama, a vaš cilj je izdvojiti pokrete gore/dolje/lijevo/desno pomoću optičkog toka. -Okvir pokreta dlana +Okvir pokreta dlana --- diff --git a/translations/hr/lessons/4-ComputerVision/06-IntroCV/lab/README.md b/translations/hr/lessons/4-ComputerVision/06-IntroCV/lab/README.md index 2c1737e5..5f5fc458 100644 --- a/translations/hr/lessons/4-ComputerVision/06-IntroCV/lab/README.md +++ b/translations/hr/lessons/4-ComputerVision/06-IntroCV/lab/README.md @@ -1,12 +1,3 @@ - # Otkrivanje pokreta pomoću optičkog toka Laboratorijska vježba iz [AI za početnike kurikuluma](https://aka.ms/ai-beginners). diff --git a/translations/hr/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/hr/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 449c78fd..084a3973 100644 --- a/translations/hr/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/hr/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -1,12 +1,3 @@ - # Dobro poznate CNN arhitekture ### VGG-16 @@ -25,7 +16,7 @@ Kao što možete vidjeti, VGG slijedi tradicionalnu piramidalnu arhitekturu, koj ResNet je obitelj modela koju je predložio Microsoft Research 2015. godine. Glavna ideja ResNet-a je korištenje **rezidualnih blokova**: - + > Slika preuzeta iz [ovog rada](https://arxiv.org/pdf/1512.03385.pdf) @@ -37,7 +28,7 @@ Ovu mrežu možete zamisliti i kao mrežu koja prilagođava svoju složenost dat Google Inception arhitektura ide korak dalje i gradi svaki sloj mreže kao kombinaciju nekoliko različitih putova: - + > Slika preuzeta s [Researchgate](https://www.researchgate.net/figure/Inception-module-with-dimension-reductions-left-and-schema-for-Inception-ResNet-v1_fig2_355547454) diff --git a/translations/hr/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/hr/lessons/4-ComputerVision/07-ConvNets/README.md index b47bf7c7..3c68d7b5 100644 --- a/translations/hr/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/hr/lessons/4-ComputerVision/07-ConvNets/README.md @@ -1,12 +1,3 @@ - # Konvolucijske neuronske mreže Već smo vidjeli da su neuronske mreže prilično dobre u obradi slika, pa čak i perceptron s jednim slojem može prepoznati rukom pisane znamenke iz MNIST skupa podataka s razumnom točnošću. Međutim, MNIST skup podataka je vrlo specifičan, jer su sve znamenke centrirane unutar slike, što zadatak čini jednostavnijim. @@ -24,7 +15,7 @@ Za izdvajanje uzoraka koristit ćemo pojam **konvolucijskih filtera**. Kao što Na primjer, ako primijenimo 3x3 vertikalni i horizontalni rubni filter na znamenke iz MNIST skupa podataka, možemo dobiti istaknute dijelove (npr. visoke vrijednosti) gdje postoje vertikalni i horizontalni rubovi u našoj originalnoj slici. Tako se ta dva filtera mogu koristiti za "traženje" rubova. Slično tome, možemo dizajnirati različite filtere za traženje drugih niskorazinskih uzoraka: - + > Slika: [Leung-Malik Filter Bank](https://www.robots.ox.ac.uk/~vgg/research/texclass/filters.html) diff --git a/translations/hr/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/hr/lessons/4-ComputerVision/07-ConvNets/lab/README.md index b9213ea9..85238615 100644 --- a/translations/hr/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/hr/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -1,12 +1,3 @@ - # Klasifikacija lica kućnih ljubimaca Laboratorijska vježba iz [AI za početnike](https://github.com/microsoft/ai-for-beginners). diff --git a/translations/hr/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/hr/lessons/4-ComputerVision/08-TransferLearning/README.md index 43ab7887..1729122b 100644 --- a/translations/hr/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/hr/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -1,12 +1,3 @@ - # Pretrenirane mreže i prijenos učenja Treniranje CNN-a može zahtijevati puno vremena, a za taj zadatak potrebno je mnogo podataka. Međutim, velik dio vremena troši se na učenje najboljih niskorazinskih filtera koje mreža može koristiti za izdvajanje uzoraka iz slika. Postavlja se prirodno pitanje - možemo li koristiti neuronsku mrežu treniranu na jednom skupu podataka i prilagoditi je za klasifikaciju različitih slika bez potrebe za potpunim procesom treniranja? diff --git a/translations/hr/lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md b/translations/hr/lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md index 7d6f6f2e..2050eebe 100644 --- a/translations/hr/lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md +++ b/translations/hr/lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md @@ -1,12 +1,3 @@ - # Trikovi za treniranje dubokog učenja Kako neuronske mreže postaju dublje, proces njihovog treniranja postaje sve izazovniji. Jedan od glavnih problema su takozvani [nestajući gradijenti](https://en.wikipedia.org/wiki/Vanishing_gradient_problem) ili [eksplodirajući gradijenti](https://deepai.org/machine-learning-glossary-and-terms/exploding-gradient-problem#:~:text=Exploding%20gradients%20are%20a%20problem,updates%20are%20small%20and%20controlled.). [Ovaj članak](https://towardsdatascience.com/the-vanishing-exploding-gradient-problem-in-deep-neural-networks-191358470c11) pruža dobar uvod u te probleme. diff --git a/translations/hr/lessons/4-ComputerVision/08-TransferLearning/lab/README.md b/translations/hr/lessons/4-ComputerVision/08-TransferLearning/lab/README.md index 16db944c..21f53243 100644 --- a/translations/hr/lessons/4-ComputerVision/08-TransferLearning/lab/README.md +++ b/translations/hr/lessons/4-ComputerVision/08-TransferLearning/lab/README.md @@ -1,12 +1,3 @@ - # Klasifikacija Oxford kućnih ljubimaca koristeći prijenosno učenje Laboratorijska vježba iz [AI for Beginners Curriculum](https://github.com/microsoft/ai-for-beginners). diff --git a/translations/hr/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/hr/lessons/4-ComputerVision/09-Autoencoders/README.md index e6643cee..8366d8c6 100644 --- a/translations/hr/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/hr/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -1,12 +1,3 @@ - # Autoenkoderi Kod treniranja CNN-a, jedan od problema je potreba za velikom količinom označenih podataka. U slučaju klasifikacije slika, potrebno je razvrstati slike u različite klase, što zahtijeva ručni rad. @@ -46,7 +37,7 @@ Ukratko: * Uzorkujemo vektor `sample` iz distribucije N(zmean,exp(zlog\_sigma)) * Dekoder pokušava dekodirati originalnu sliku koristeći `sample` kao ulazni vektor - + > Slika iz [ovog blog posta](https://ijdykeman.github.io/ml/2016/12/21/cvae.html) autora Isaaka Dykemana @@ -57,13 +48,13 @@ Varijacijski autoenkoderi koriste složenu funkciju gubitka koja se sastoji od d Jedna važna prednost VAE-a je da nam omogućuju relativno lako generiranje novih slika, jer znamo iz koje distribucije uzorkovati latentne vektore. Na primjer, ako treniramo VAE s 2D latentnim vektorom na MNIST datasetu, možemo zatim mijenjati komponente latentnog vektora kako bismo dobili različite brojeve: -vaemnist +vaemnist > Slika autora [Dmitry Soshnikov](http://soshnikov.com) Primijetite kako se slike stapaju jedna u drugu, dok počinjemo dobivati latentne vektore iz različitih dijelova latentnog prostora parametara. Također možemo vizualizirati ovaj prostor u 2D: -vaemnist cluster +vaemnist cluster > Slika autora [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/hr/lessons/4-ComputerVision/10-GANs/README.md b/translations/hr/lessons/4-ComputerVision/10-GANs/README.md index 02630d34..53c48c63 100644 --- a/translations/hr/lessons/4-ComputerVision/10-GANs/README.md +++ b/translations/hr/lessons/4-ComputerVision/10-GANs/README.md @@ -1,12 +1,3 @@ - # Generativne suparničke mreže U prethodnom dijelu naučili smo o **generativnim modelima**: modelima koji mogu generirati nove slike slične onima iz skupa za treniranje. VAE je bio dobar primjer generativnog modela. @@ -17,7 +8,7 @@ Međutim, ako pokušamo generirati nešto zaista značajno, poput slike visoke r Glavna ideja GAN-a je imati dvije neuronske mreže koje se treniraju jedna protiv druge: - + > Slika: [Dmitry Soshnikov](http://soshnikov.com) @@ -41,7 +32,7 @@ Generator je malo složeniji. Možete ga smatrati obrnutim diskriminatorom. Poč > ✅ Budući da je konvolucijski sloj implementiran kao linearni filter koji prolazi kroz sliku, dekonvolucija je u suštini slična konvoluciji i može se implementirati koristeći istu logiku sloja. - + > Slika: [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/hr/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/hr/lessons/4-ComputerVision/11-ObjectDetection/README.md index abd4be20..d1234b10 100644 --- a/translations/hr/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/hr/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -1,12 +1,3 @@ - # Detekcija objekata Modeli za klasifikaciju slika s kojima smo se dosad susretali uzimaju sliku i proizvode kategorijski rezultat, poput klase 'broj' u MNIST problemu. Međutim, u mnogim slučajevima ne želimo samo znati da slika prikazuje objekte – želimo odrediti njihovu točnu lokaciju. Upravo to je cilj **detekcije objekata**. diff --git a/translations/hr/lessons/4-ComputerVision/11-ObjectDetection/lab/README.md b/translations/hr/lessons/4-ComputerVision/11-ObjectDetection/lab/README.md index e53f2c29..0967ab4c 100644 --- a/translations/hr/lessons/4-ComputerVision/11-ObjectDetection/lab/README.md +++ b/translations/hr/lessons/4-ComputerVision/11-ObjectDetection/lab/README.md @@ -1,12 +1,3 @@ - # Detekcija glava koristeći Hollywood Heads Dataset Laboratorijska vježba iz [AI for Beginners Curriculum](https://github.com/microsoft/ai-for-beginners). diff --git a/translations/hr/lessons/4-ComputerVision/12-Segmentation/README.md b/translations/hr/lessons/4-ComputerVision/12-Segmentation/README.md index 038f7c18..9f09449c 100644 --- a/translations/hr/lessons/4-ComputerVision/12-Segmentation/README.md +++ b/translations/hr/lessons/4-ComputerVision/12-Segmentation/README.md @@ -1,12 +1,3 @@ - # Segmentacija Ranije smo učili o Detekciji objekata, koja nam omogućuje lociranje objekata na slici predviđanjem njihovih *bounding boxova*. Međutim, za neke zadatke ne trebamo samo bounding boxove, već i precizniju lokalizaciju objekata. Taj zadatak zove se **segmentacija**. @@ -20,7 +11,7 @@ Segmentacija se može promatrati kao **klasifikacija piksela**, gdje za **svaki* Kod segmentacije instanci, ove ovce su različiti objekti, dok kod semantičke segmentacije sve ovce pripadaju jednoj klasi. - + > Slika iz [ovog blog posta](https://nirmalamurali.medium.com/image-classification-vs-semantic-segmentation-vs-instance-segmentation-625c33a08d50) @@ -29,7 +20,7 @@ Postoje različite neuronske arhitekture za segmentaciju, ali sve imaju istu str * **Encoder** izvlači značajke iz ulazne slike. * **Decoder** transformira te značajke u **masku slike**, iste veličine i s brojem kanala koji odgovara broju klasa. - + > Slika iz [ove publikacije](https://arxiv.org/pdf/2001.05566.pdf) @@ -43,7 +34,7 @@ U ovoj lekciji vidjet ćemo segmentaciju u praksi treniranjem mreže za prepozna > ✅ Ova tehnika je posebno prikladna za ovu vrstu medicinskog snimanja, ali koje druge stvarne primjene možete zamisliti? -navi +navi > Slika iz PH2 baze podataka diff --git a/translations/hr/lessons/4-ComputerVision/12-Segmentation/lab/README.md b/translations/hr/lessons/4-ComputerVision/12-Segmentation/lab/README.md index c1cfe086..a12d4c3f 100644 --- a/translations/hr/lessons/4-ComputerVision/12-Segmentation/lab/README.md +++ b/translations/hr/lessons/4-ComputerVision/12-Segmentation/lab/README.md @@ -1,12 +1,3 @@ - # Segmentacija ljudskog tijela Laboratorijska vježba iz [AI for Beginners Curriculum](https://github.com/microsoft/ai-for-beginners). diff --git a/translations/hr/lessons/4-ComputerVision/README.md b/translations/hr/lessons/4-ComputerVision/README.md index f74f8956..234fe729 100644 --- a/translations/hr/lessons/4-ComputerVision/README.md +++ b/translations/hr/lessons/4-ComputerVision/README.md @@ -1,12 +1,3 @@ - # Računalni vid ![Sažetak sadržaja o računalnom vidu u obliku crteža](../../../../translated_images/hr/ai-computervision.6506ebebac3fbf76.webp) diff --git a/translations/hr/lessons/5-NLP/13-TextRep/README.md b/translations/hr/lessons/5-NLP/13-TextRep/README.md index db9edfcb..906ab8f9 100644 --- a/translations/hr/lessons/5-NLP/13-TextRep/README.md +++ b/translations/hr/lessons/5-NLP/13-TextRep/README.md @@ -1,12 +1,3 @@ - # Predstavljanje teksta kao tenzora ## [Kviz prije predavanja](https://ff-quizzes.netlify.app/en/ai/quiz/25) @@ -25,7 +16,7 @@ Naš cilj bit će klasificirati vijest u jednu od kategorija na temelju teksta. Ako želimo rješavati zadatke obrade prirodnog jezika (NLP) pomoću neuronskih mreža, trebamo način za predstavljanje teksta kao tenzora. Računala već predstavljaju tekstualne znakove kao brojeve koji se mapiraju na fontove na vašem ekranu koristeći kodiranja poput ASCII ili UTF-8. -Slika koja prikazuje dijagram mapiranja znaka na ASCII i binarnu reprezentaciju +Slika koja prikazuje dijagram mapiranja znaka na ASCII i binarnu reprezentaciju > [Izvor slike](https://www.seobility.net/en/wiki/ASCII) @@ -48,7 +39,7 @@ U nekim slučajevima možemo razmotriti korištenje tri-grama -- kombinacija tri Kod rješavanja zadataka poput klasifikacije teksta, trebamo biti u mogućnosti predstaviti tekst jednim vektorom fiksne veličine, koji ćemo koristiti kao ulaz za završni gusti klasifikator. Jedan od najjednostavnijih načina za to je kombiniranje svih pojedinačnih reprezentacija riječi, npr. njihovim zbrajanjem. Ako zbrojimo one-hot kodiranja svake riječi, dobit ćemo vektor frekvencija, koji pokazuje koliko se puta svaka riječ pojavljuje unutar teksta. Takva reprezentacija teksta naziva se **bag-of-words** (BoW). - + > Slika autora diff --git a/translations/hr/lessons/5-NLP/13-TextRep/assignment.md b/translations/hr/lessons/5-NLP/13-TextRep/assignment.md index 5877c00b..b51a3c5e 100644 --- a/translations/hr/lessons/5-NLP/13-TextRep/assignment.md +++ b/translations/hr/lessons/5-NLP/13-TextRep/assignment.md @@ -1,12 +1,3 @@ - # Zadatak: Bilježnice Koristeći bilježnice povezane s ovom lekcijom (bilo PyTorch ili TensorFlow verziju), ponovno ih pokrenite koristeći vlastiti skup podataka, možda neki s Kagglea, uz odgovarajuće navođenje izvora. Prepravite bilježnicu kako biste istaknuli vlastite zaključke. Isprobajte neke inovativne skupove podataka koji bi mogli biti iznenađujući, poput [ovog o viđenjima NLO-a](https://www.kaggle.com/datasets/NUFORC/ufo-sightings) od NUFORC-a. diff --git a/translations/hr/lessons/5-NLP/14-Embeddings/README.md b/translations/hr/lessons/5-NLP/14-Embeddings/README.md index 125d47ca..1db53b66 100644 --- a/translations/hr/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/hr/lessons/5-NLP/14-Embeddings/README.md @@ -1,12 +1,3 @@ - # Ugrađivanja ## [Pre-lecture kviz](https://ff-quizzes.netlify.app/en/ai/quiz/27) diff --git a/translations/hr/lessons/5-NLP/14-Embeddings/assignment.md b/translations/hr/lessons/5-NLP/14-Embeddings/assignment.md index 9479b6ca..ab6e9bb8 100644 --- a/translations/hr/lessons/5-NLP/14-Embeddings/assignment.md +++ b/translations/hr/lessons/5-NLP/14-Embeddings/assignment.md @@ -1,12 +1,3 @@ - # Zadatak: Bilježnice Koristeći bilježnice povezane s ovom lekcijom (bilo PyTorch ili TensorFlow verziju), ponovno ih pokrenite koristeći vlastiti skup podataka, možda neki s Kagglea, uz odgovarajuće navođenje izvora. Prepravite bilježnicu kako biste istaknuli vlastite zaključke. Isprobajte drugačiju vrstu skupa podataka i dokumentirajte svoja otkrića, koristeći tekst poput [ovih stihova Beatlesa](https://www.kaggle.com/datasets/jenlooper/beatles-lyrics). diff --git a/translations/hr/lessons/5-NLP/15-LanguageModeling/README.md b/translations/hr/lessons/5-NLP/15-LanguageModeling/README.md index cc2d1285..7f60f35f 100644 --- a/translations/hr/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/hr/lessons/5-NLP/15-LanguageModeling/README.md @@ -1,12 +1,3 @@ - # Modeliranje jezika Semantičke ugrađene reprezentacije, poput Word2Vec i GloVe, zapravo su prvi korak prema **modeliranju jezika** - stvaranju modela koji na neki način *razumiju* (ili *predstavljaju*) prirodu jezika. diff --git a/translations/hr/lessons/5-NLP/15-LanguageModeling/lab/README.md b/translations/hr/lessons/5-NLP/15-LanguageModeling/lab/README.md index c43dbb82..30c76203 100644 --- a/translations/hr/lessons/5-NLP/15-LanguageModeling/lab/README.md +++ b/translations/hr/lessons/5-NLP/15-LanguageModeling/lab/README.md @@ -1,12 +1,3 @@ - # Treniranje Skip-Gram modela Laboratorijska vježba iz [AI for Beginners Curriculum](https://github.com/microsoft/ai-for-beginners). diff --git a/translations/hr/lessons/5-NLP/16-RNN/README.md b/translations/hr/lessons/5-NLP/16-RNN/README.md index 45c2ef63..115f77d5 100644 --- a/translations/hr/lessons/5-NLP/16-RNN/README.md +++ b/translations/hr/lessons/5-NLP/16-RNN/README.md @@ -1,12 +1,3 @@ - # Rekurentne neuronske mreže ## [Kviz prije predavanja](https://ff-quizzes.netlify.app/en/ai/quiz/31) @@ -31,7 +22,7 @@ Pogledajmo kako je organizirana jednostavna RNN ćelija. Ona prihvaća prethodno Jednostavna RNN ćelija ima dvije matrice težina unutar sebe: jedna transformira ulazni simbol (nazovimo je W), a druga transformira ulazno stanje (H). U ovom slučaju izlaz mreže se računa kao σ(W×Xi+H×Si-1+b), gdje je σ funkcija aktivacije, a b dodatna pristranost. -Anatomija RNN ćelije +Anatomija RNN ćelije > Slika autora diff --git a/translations/hr/lessons/5-NLP/16-RNN/assignment.md b/translations/hr/lessons/5-NLP/16-RNN/assignment.md index 895d923d..b01280cd 100644 --- a/translations/hr/lessons/5-NLP/16-RNN/assignment.md +++ b/translations/hr/lessons/5-NLP/16-RNN/assignment.md @@ -1,12 +1,3 @@ - # Zadatak: Bilježnice Koristeći bilježnice povezane s ovom lekcijom (bilo PyTorch ili TensorFlow verziju), ponovno ih pokrenite koristeći vlastiti skup podataka, možda jedan s Kagglea, uz odgovarajuće navođenje izvora. Prepravite bilježnicu kako biste istaknuli vlastite zaključke. Isprobajte drugačiju vrstu skupa podataka i dokumentirajte svoja otkrića, koristeći tekst poput [ovog Kaggle natjecateljskog skupa podataka o vremenskim tweetovima](https://www.kaggle.com/competitions/crowdflower-weather-twitter/data?select=train.csv). diff --git a/translations/hr/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/hr/lessons/5-NLP/17-GenerativeNetworks/README.md index 3ebe9839..3e558297 100644 --- a/translations/hr/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/hr/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -1,12 +1,3 @@ - # Generativne mreže ## [Kviz prije predavanja](https://ff-quizzes.netlify.app/en/ai/quiz/33) @@ -36,7 +27,7 @@ Trenirat ćemo ovaj RNN da generira tekst korak po korak. Na svakom koraku uzet Tijekom generiranja teksta (tijekom inferencije), počinjemo s nekim **poticajem**, koji se prosljeđuje kroz RNN ćelije kako bi se generiralo njegovo međustanje, a zatim iz tog stanja počinje generiranje. Generiramo jedan znak po jedan, prosljeđujemo stanje i generirani znak sljedećoj RNN ćeliji kako bismo generirali sljedeći znak, sve dok ne generiramo dovoljno znakova. - + > Slika autora diff --git a/translations/hr/lessons/5-NLP/17-GenerativeNetworks/lab/README.md b/translations/hr/lessons/5-NLP/17-GenerativeNetworks/lab/README.md index ea8f349c..60a196a5 100644 --- a/translations/hr/lessons/5-NLP/17-GenerativeNetworks/lab/README.md +++ b/translations/hr/lessons/5-NLP/17-GenerativeNetworks/lab/README.md @@ -1,12 +1,3 @@ - # Generiranje teksta na razini riječi pomoću RNN-a Laboratorijska vježba iz [AI for Beginners Curriculum](https://github.com/microsoft/ai-for-beginners). diff --git a/translations/hr/lessons/5-NLP/18-Transformers/README.md b/translations/hr/lessons/5-NLP/18-Transformers/README.md index 7959e07c..2f7656be 100644 --- a/translations/hr/lessons/5-NLP/18-Transformers/README.md +++ b/translations/hr/lessons/5-NLP/18-Transformers/README.md @@ -1,12 +1,3 @@ - # Mehanizmi pažnje i transformeri ## [Pre-kviz predavanja](https://ff-quizzes.netlify.app/en/ai/quiz/35) @@ -56,7 +47,7 @@ Ideja pozicijskog kodiranja je sljedeća. * Ugrađivanje koje se trenira, slično ugrađivanju tokena. Ovo je pristup koji ovdje razmatramo. Primjenjujemo slojeve ugrađivanja na tokene i njihove pozicije, što rezultira vektorima ugrađivanja iste dimenzije, koje zatim zbrajamo. * Fiksna funkcija pozicijskog kodiranja, kako je predloženo u originalnom radu. - + > Slika autora diff --git a/translations/hr/lessons/5-NLP/18-Transformers/assignment.md b/translations/hr/lessons/5-NLP/18-Transformers/assignment.md index 3c41c617..d00ce5af 100644 --- a/translations/hr/lessons/5-NLP/18-Transformers/assignment.md +++ b/translations/hr/lessons/5-NLP/18-Transformers/assignment.md @@ -1,12 +1,3 @@ - # Zadatak: Transformeri Eksperimentirajte s Transformerima na HuggingFace! Isprobajte neke od skripti koje pružaju za rad s raznim modelima dostupnim na njihovoj stranici: https://huggingface.co/docs/transformers/run_scripts. Isprobajte jedan od njihovih skupova podataka, a zatim uvezite jedan svoj iz ovog kurikuluma ili s Kagglea i provjerite možete li generirati zanimljive tekstove. Izradite bilježnicu s vašim rezultatima. diff --git a/translations/hr/lessons/5-NLP/19-NER/README.md b/translations/hr/lessons/5-NLP/19-NER/README.md index 8fd14219..65ea9123 100644 --- a/translations/hr/lessons/5-NLP/19-NER/README.md +++ b/translations/hr/lessons/5-NLP/19-NER/README.md @@ -1,12 +1,3 @@ - # Prepoznavanje imenovanih entiteta Do sada smo se uglavnom fokusirali na jedan NLP zadatak - klasifikaciju. Međutim, postoje i drugi NLP zadaci koji se mogu ostvariti pomoću neuronskih mreža. Jedan od tih zadataka je **[Prepoznavanje imenovanih entiteta](https://wikipedia.org/wiki/Named-entity_recognition)** (NER), koji se bavi prepoznavanjem specifičnih entiteta unutar teksta, poput mjesta, imena osoba, vremenskih intervala, kemijskih formula i slično. @@ -17,7 +8,7 @@ Do sada smo se uglavnom fokusirali na jedan NLP zadatak - klasifikaciju. Međuti Pretpostavimo da želite razviti chatbot za prirodni jezik, sličan Amazon Alexi ili Google Asistentu. Inteligentni chatboti funkcioniraju tako da *razumiju* što korisnik želi, koristeći klasifikaciju teksta na ulaznoj rečenici. Rezultat te klasifikacije je takozvani **intencija**, koja određuje što chatbot treba učiniti. -Bot NER +Bot NER > Slika autora diff --git a/translations/hr/lessons/5-NLP/19-NER/lab/README.md b/translations/hr/lessons/5-NLP/19-NER/lab/README.md index f2acb769..84c4bbaf 100644 --- a/translations/hr/lessons/5-NLP/19-NER/lab/README.md +++ b/translations/hr/lessons/5-NLP/19-NER/lab/README.md @@ -1,12 +1,3 @@ - # NER Laboratorijska vježba iz [AI for Beginners Curriculum](https://github.com/microsoft/ai-for-beginners). diff --git a/translations/hr/lessons/5-NLP/20-LangModels/README.md b/translations/hr/lessons/5-NLP/20-LangModels/README.md index d6c1b354..5dd74cec 100644 --- a/translations/hr/lessons/5-NLP/20-LangModels/README.md +++ b/translations/hr/lessons/5-NLP/20-LangModels/README.md @@ -1,12 +1,3 @@ - # Pre-Trained Large Language Models U svim našim prethodnim zadacima trenirali smo neuronsku mrežu da obavlja određeni zadatak koristeći označene skupove podataka. Kod velikih transformacijskih modela, poput BERT-a, koristimo jezično modeliranje u samonadziranom načinu rada kako bismo izgradili jezični model, koji se zatim specijalizira za specifične zadatke uz dodatnu obuku prilagođenu domeni. Međutim, pokazalo se da veliki jezični modeli mogu riješiti mnoge zadatke i bez IKAKVE obuke prilagođene domeni. Obitelj modela sposobnih za to naziva se **GPT**: Generativni unaprijed trenirani transformator. diff --git a/translations/hr/lessons/5-NLP/README.md b/translations/hr/lessons/5-NLP/README.md index 3590ac81..e5fc75b5 100644 --- a/translations/hr/lessons/5-NLP/README.md +++ b/translations/hr/lessons/5-NLP/README.md @@ -1,12 +1,3 @@ - # Obrada Prirodnog Jezika ![Sažetak NLP zadataka u crtežu](../../../../translated_images/hr/ai-nlp.b22dcb8ca4707cea.webp) diff --git a/translations/hr/lessons/6-Other/21-GeneticAlgorithms/README.md b/translations/hr/lessons/6-Other/21-GeneticAlgorithms/README.md index ffd1ac77..c6a779c7 100644 --- a/translations/hr/lessons/6-Other/21-GeneticAlgorithms/README.md +++ b/translations/hr/lessons/6-Other/21-GeneticAlgorithms/README.md @@ -1,12 +1,3 @@ - # Genetski algoritmi ## [Pre-kviz predavanja](https://ff-quizzes.netlify.app/en/ai/quiz/41) diff --git a/translations/hr/lessons/6-Other/22-DeepRL/README.md b/translations/hr/lessons/6-Other/22-DeepRL/README.md index 277f4aca..b037c6ec 100644 --- a/translations/hr/lessons/6-Other/22-DeepRL/README.md +++ b/translations/hr/lessons/6-Other/22-DeepRL/README.md @@ -1,12 +1,3 @@ - # Duboko pojačano učenje Pojačano učenje (RL) smatra se jednim od osnovnih paradigmi strojnog učenja, uz nadzirano učenje i nenadzirano učenje. Dok se u nadziranom učenju oslanjamo na skup podataka s poznatim ishodima, RL se temelji na **učenju kroz rad**. Na primjer, kada prvi put vidimo računalnu igru, počinjemo igrati, čak i bez poznavanja pravila, i ubrzo poboljšavamo svoje vještine samo kroz proces igranja i prilagođavanja ponašanja. @@ -34,7 +25,7 @@ Vjerojatno ste svi vidjeli moderne uređaje za balansiranje poput *Segwaya* ili Pojednostavljena verzija balansiranja poznata je kao problem **CartPole**. U svijetu CartPole-a imamo horizontalni klizač koji se može kretati lijevo ili desno, a cilj je balansirati vertikalni štap na vrhu klizača dok se kreće. -cartpole +cartpole Za stvaranje i korištenje ovog okruženja potrebno je nekoliko linija Python koda: diff --git a/translations/hr/lessons/6-Other/22-DeepRL/lab/README.md b/translations/hr/lessons/6-Other/22-DeepRL/lab/README.md index 6b3f965a..5f920900 100644 --- a/translations/hr/lessons/6-Other/22-DeepRL/lab/README.md +++ b/translations/hr/lessons/6-Other/22-DeepRL/lab/README.md @@ -1,12 +1,3 @@ - ## Okruženje Okruženje Mountain Car sastoji se od automobila zarobljenog u dolini. Vaš cilj je iskočiti iz doline i doseći zastavu. Akcije koje možete poduzeti su ubrzavanje ulijevo, udesno ili ne raditi ništa. Možete promatrati položaj automobila duž x-osi i brzinu. diff --git a/translations/hr/lessons/6-Other/23-MultiagentSystems/README.md b/translations/hr/lessons/6-Other/23-MultiagentSystems/README.md index 120813a0..1ba2396c 100644 --- a/translations/hr/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/hr/lessons/6-Other/23-MultiagentSystems/README.md @@ -1,12 +1,3 @@ - # Višeagentski sustavi Jedan od mogućih načina postizanja inteligencije je takozvani **emergentni** (ili **sinergijski**) pristup, koji se temelji na činjenici da kombinirano ponašanje mnogih relativno jednostavnih agenata može rezultirati ukupno složenijim (ili inteligentnijim) ponašanjem sustava u cjelini. Teoretski, ovo se temelji na principima [kolektivne inteligencije](https://en.wikipedia.org/wiki/Collective_intelligence), [emergentizma](https://en.wikipedia.org/wiki/Global_brain) i [evolucijske kibernetike](https://en.wikipedia.org/wiki/Global_brain), koji tvrde da sustavi višeg nivoa dobivaju neku vrstu dodane vrijednosti kada se pravilno kombiniraju iz sustava nižeg nivoa (tzv. *princip prijelaza metasustava*). @@ -60,7 +51,7 @@ Možete [preuzeti](https://ccl.northwestern.edu/netlogo/download.shtml) i instal Sjajna stvar kod NetLoga je da sadrži biblioteku radnih modela koje možete isprobati. Idite na **File → Models Library**, i imate mnogo kategorija modela za odabir. -NetLogo Models Library +NetLogo Models Library > Snimka zaslona biblioteke modela Dmitryja Soshnikova diff --git a/translations/hr/lessons/6-Other/23-MultiagentSystems/assignment.md b/translations/hr/lessons/6-Other/23-MultiagentSystems/assignment.md index 6604a702..a3f3c4a7 100644 --- a/translations/hr/lessons/6-Other/23-MultiagentSystems/assignment.md +++ b/translations/hr/lessons/6-Other/23-MultiagentSystems/assignment.md @@ -1,12 +1,3 @@ - # Zadatak za NetLogo Odaberite jedan od modela iz NetLogo biblioteke i upotrijebite ga za simulaciju stvarne životne situacije što je moguće preciznije. Dobar primjer bio bi prilagoditi model Virus iz mape Alternative Visualizations kako bi pokazao kako se može koristiti za modeliranje širenja COVID-19. Možete li izraditi model koji oponaša stvarno širenje virusa? diff --git a/translations/hr/lessons/7-Ethics/README.md b/translations/hr/lessons/7-Ethics/README.md index 551e4243..d30862a1 100644 --- a/translations/hr/lessons/7-Ethics/README.md +++ b/translations/hr/lessons/7-Ethics/README.md @@ -1,12 +1,3 @@ - # Etička i odgovorna umjetna inteligencija Skoro ste završili ovaj tečaj i nadam se da sada jasno vidite da se umjetna inteligencija temelji na nizu formalnih matematičkih metoda koje nam omogućuju pronalaženje odnosa u podacima i treniranje modela za repliciranje nekih aspekata ljudskog ponašanja. U ovom trenutku povijesti smatramo umjetnu inteligenciju vrlo moćnim alatom za izdvajanje uzoraka iz podataka i primjenu tih uzoraka za rješavanje novih problema. diff --git a/translations/hr/lessons/README.md b/translations/hr/lessons/README.md index 88d59651..f6872f42 100644 --- a/translations/hr/lessons/README.md +++ b/translations/hr/lessons/README.md @@ -1,12 +1,3 @@ - # Pregled ![Pregled u crtežu](../../../translated_images/hr/ai-overview.0857791951d19500.webp) diff --git a/translations/hr/lessons/X-Extras/X1-MultiModal/README.md b/translations/hr/lessons/X-Extras/X1-MultiModal/README.md index fca85ca9..177db03e 100644 --- a/translations/hr/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/hr/lessons/X-Extras/X1-MultiModal/README.md @@ -1,12 +1,3 @@ - # Multi-modalne mreže Nakon uspjeha transformera u rješavanju zadataka obrade prirodnog jezika (NLP), iste ili slične arhitekture primijenjene su na zadatke računalnog vida. Sve je veći interes za izgradnju modela koji bi *kombinirali* sposobnosti vida i prirodnog jezika. Jedan od takvih pokušaja napravio je OpenAI, a naziva se CLIP i DALL.E. diff --git a/translations/hr/lessons/sketchnotes/LICENSE.md b/translations/hr/lessons/sketchnotes/LICENSE.md index 93c8485d..1f292dbe 100644 --- a/translations/hr/lessons/sketchnotes/LICENSE.md +++ b/translations/hr/lessons/sketchnotes/LICENSE.md @@ -1,12 +1,3 @@ - Priznanje-Dijeljenje pod istim uvjetima 4.0 Međunarodna ======================================================================= diff --git a/translations/hr/lessons/sketchnotes/README.md b/translations/hr/lessons/sketchnotes/README.md index 3ac72195..ffcb5ad6 100644 --- a/translations/hr/lessons/sketchnotes/README.md +++ b/translations/hr/lessons/sketchnotes/README.md @@ -1,12 +1,3 @@ - Sve sketchnoteovi kurikuluma mogu se preuzeti ovdje. 🎨 Autor: Tomomi Imura (Twitter: [@girlie_mac](https://twitter.com/girlie_mac), GitHub: [girliemac](https://github.com/girliemac)) diff --git a/translations/hr/troubleshoot.md b/translations/hr/troubleshoot.md index 1df06830..fd987e76 100644 --- a/translations/hr/troubleshoot.md +++ b/translations/hr/troubleshoot.md @@ -1,12 +1,3 @@ - # AI-For-Beginners Vodič za rješavanje problema Ovaj vodič pomaže u rješavanju uobičajenih problema koji se javljaju prilikom korištenja ili doprinosa [AI-For-Beginners](https://github.com/microsoft/AI-For-Beginners) repozitoriju. 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contributors](https://img.shields.io/github/contributors/microsoft/AI-For-Beginners.svg)](https://GitHub.com/microsoft/AI-For-Beginners/graphs/contributors/) [![GitHub issues](https://img.shields.io/github/issues/microsoft/AI-For-Beginners.svg)](https://GitHub.com/microsoft/AI-For-Beginners/issues/) @@ -21,158 +12,162 @@ CO_OP_TRANSLATOR_METADATA: [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -# စတင်လေ့လာသူများအတွက် အတုယူမှုအင်တဲလီဂျင့် - သင်ရိုးညွှန်းတမ်း +# လူသစ်များအတွက် အတုယူစက်ရုပ် - ပညာသင်အစီအစဉ် -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../../../translated_images/my/ai-overview.0857791951d19500.webp)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/my/ai-overview.0857791951d19500.webp)| |:---:| -| စတင်လေ့လာသူများအတွက် အတုယူမှုအင်တဲလီဂျင့် - _Sketchnote များကို [@girlie_mac](https://twitter.com/girlie_mac) မှဖန်တီးသည်_ | +| လူသစ်များအတွက် AI - _Sketchnote by [@girlie_mac](https://twitter.com/girlie_mac)_ | -ကျွန်ုပ်တို့ရဲ့ ၁၂ ပတ်၊ ၂၄ မိနစ် သင်ရိုးညွှန်းတမ်းဖြင့် **အတုယူမှုအင်တဲလီဂျင့်** (AI) ကမ္ဘာကြီးကို လေ့လာလိုက်ပါ! ၎င်းတွင် လက်တွေ့သင်ခန်းစာများ၊ စစ်တမ်းများနှင့် ဂုဏ်ရည်ခန်းများပါဝင်သည်။ သင်ရိုးညွှန်းတမ်းသည် စတင်လေ့လာသောသူများအတွက်လိုက်ဖက်ပြီး TensorFlow နဲ့ PyTorch ကဲ့သို့သော ကိရိယာများနှင့် AI ကျင့်ဝတ်များကိုလည်း ဖုံးကွယ်ပြသထားပါသည်။ +**အတုယူစက်ရုပ်** (AI) ၏ ကမ္ဘာကြီးကို ကျွန်ုပ်တို့၏ ၁၂ ပတ်၊ ၂၄ သင်ခန်းစာ ပညာသင်အစီအစဉ်ဖြင့် စူးစမ်းလေ့လာပါ! ၎င်းတွင် လက်တွေ့သင်ခန်းစာများ၊ စမ်းသပ်မေးခွန်းများနှင့် ပရိုဇက်များ ပါဝင်သည်။ ဒီအစီအစဉ်သည် လူသစ်များအတွက် လွယ်ကူစွာနားလည်နိုင်ပြီး TensorFlow နှင့် PyTorch ကဲ့သို့သော ကိရိယာများနှင့် AI တွင် ရိုးသားမှုအချက်များကိုလည်း ပါဝင်ပါသည်။ -### 🌐 ဘာသာစကားပေါင်းစုံ ထောက်ပံ့မှု -#### GitHub Action မှတဆင့် ထောက်ပံ့မှုရှိသည် (အလိုအလျောက်နှင့် အမြဲပြင်ဆင်ထား) +### 🌐 ဘာသာစကားစုံအထောက်အပံ့ + +#### GitHub Action မှတဆင့်ထောက်ပံ့ထားသည် (အလိုအလျောက် နှင့် အမြဲနောက်ဆုံးဗားရှင်း) -[အာရပ်](../ar/README.md) | [ဘင်္ဂါလီ](../bn/README.md) | [ဘူလ်ဂေးရီးယား](../bg/README.md) | [မြန်မာ (မြန်မာ)](./README.md) | [တရုတ် (ရိုးရိုး)](../zh/README.md) | [တရုတ် (ရိုးရိုး, ဟောင်ကောင်)](../hk/README.md) | [တရုတ် (ရိုးရိုး, မာစူ)](../mo/README.md) | [တရုတ် (ရိုးရိုး, တိုင်ဝမ်)](../tw/README.md) | [ခရိုက်ရှားဒီယား](../hr/README.md) | [ချက်](../cs/README.md) | [ဒိန်းမတ်](../da/README.md) | [ဒတ်ချ်](../nl/README.md) | [အက်စ်တိုးနီးယား](../et/README.md) | [ဖင်နစ်](../fi/README.md) | [ပြင်သစ်](../fr/README.md) | [ဂျာမာန်](../de/README.md) | [ဂရိ](../el/README.md) | [ဟီဘရူး](../he/README.md) | [ဟင်းဒီ](../hi/README.md) | [ဟန်ဂေရီးယား](../hu/README.md) | [အင်ဒိုနီးရှား](../id/README.md) | [အီတလီ](../it/README.md) | [ဂျပန်](../ja/README.md) | [ကနေဒါ](../kn/README.md) | [ကိုရီးယား](../ko/README.md) | [လစ္သူနီးယား](../lt/README.md) | [မလေး](../ms/README.md) | [မလေးလာမ်](../ml/README.md) | [မာရသီ](../mr/README.md) | [နီပေါလီ](../ne/README.md) | [နိုင်ဂျီးရီးယား ပစ်ဂင်](../pcm/README.md) | [နော်ဝေ](../no/README.md) | [ပါရှန် (ဖာဆီ)](../fa/README.md) | [ပိုလန်](../pl/README.md) | [ပေါ်တူဂီ (ဘရဇီးလ်)](../br/README.md) | [ပေါ်တူဂီ (ပေါ်ချီဂျီ)](../pt/README.md) | [ပန်ဇာဘီ (ဂျာမူခီ)](../pa/README.md) | [ရိုမေးနီးယား](../ro/README.md) | [ရုရှား](../ru/README.md) | [ဆားဘီးယား (စာရိုလစ်လစ်)](../sr/README.md) | [စလိုဗက်](../sk/README.md) | [စလိုဗေးနီးယား](../sl/README.md) | [စပိန်](../es/README.md) | [ဆွာဟီလီ](../sw/README.md) | [ဆွီဒင်](../sv/README.md) | [တာဂလိုဂ် (ဖိလစ်ပိုင်)](../tl/README.md) | [တမီးလ်](../ta/README.md) | [တယ်လူဂူ](../te/README.md) | [ထိုင်း](../th/README.md) | [တူရ်ကီ](../tr/README.md) | [ယူကရိန်း](../uk/README.md) | [ဥာဒူ](../ur/README.md) | [ဗီယက်နမ်](../vi/README.md) +[အာရဗီ](../ar/README.md) | [ဘင်္ဂါလီ](../bn/README.md) | [ဘူල්ဂေးရီးယား](../bg/README.md) | [မြန်မာ](./README.md) | [တရုတ် (ရိုးရိုး)](../zh-CN/README.md) | [တရုတ် (ရိုးရိုး, ဟောင်ကောင်)](../zh-HK/README.md) | [တရုတ် (ရိုးရိုး, မကာဝူ)](../zh-MO/README.md) | [တရုတ် (ရိုးရိုး, တိုင်ဝမ်)](../zh-TW/README.md) | [ခရို့ရှီးယား](../hr/README.md) | [ချက်](../cs/README.md) | [ဒိန်းမားခ်](../da/README.md) | [ဒါချ်](../nl/README.md) | [အက်စတိုနီးယား](../et/README.md) | [ဖင်နစ်](../fi/README.md) | [ပြင်သစ်](../fr/README.md) | [ဂျာမနီ](../de/README.md) | [ဂရိ](../el/README.md) | [ဟေဘရွူး](../he/README.md) | [ဟိန္ဒီ](../hi/README.md) | [ဟန်ဂေရီ](../hu/README.md) | [အင်ဒိုနီးရှား](../id/README.md) | [အီတလီ](../it/README.md) | [ဂျပန်](../ja/README.md) | [ကန်နာဒါ](../kn/README.md) | [ကိုရီးယား](../ko/README.md) | [လစ်သူဝေးနီးယား](../lt/README.md) | [မာလေး](../ms/README.md) | [မာလာရမ်](../ml/README.md) | [မာရသိ](../mr/README.md) | [နီပေါလီ](../ne/README.md) | [နိုင်ဂျီးရီးယား ပစ္ဂင်](../pcm/README.md) | [နော်ဝေ](../no/README.md) | [ပါရှန် (ဖာ ရ်စီ)](../fa/README.md) | [ပိုလန်](../pl/README.md) | [ပေါ်တူဂီ (ဘရာဇီးလ်)](../pt-BR/README.md) | [ပေါ်တူဂီ (ပိုတူဂီ)](../pt-PT/README.md) | [ပန်ဂျာဘီ (ဂူရူမူခီ)](../pa/README.md) | [ရိုမေးနီးယား](../ro/README.md) | [ရုရှား](../ru/README.md) | [ဆားဘီးယား (စီရီးလစ်)](../sr/README.md) | [စလိုဗက်](../sk/README.md) | [စလိုဗေးနီးယား](../sl/README.md) | [စပိန်](../es/README.md) | [ဆွာဟီလီ](../sw/README.md) | [ဆွီဒင်](../sv/README.md) | [တာဂလိုဂ် (ဖိလစ်ပိုင်)](../tl/README.md) | [တမီးလ်](../ta/README.md) | [တာလူဂူ](../te/README.md) | [ថៃ](../th/README.md) | [တူရကီ](../tr/README.md) | [ယူကရိန်း](../uk/README.md) | [ဥာဒူး](../ur/README.md) | [ဗီယက်နမ်](../vi/README.md) -> **ဒေသတွင်းမှာ Clone လုပ်ချင်ပါသလား?** +> **ဒေသတွင်း များကူးယူချင်ပါသလား?** -> ဤ repository တွင် ဘာသာစကား ၅၀ ကျော်သော ဘာသာပြန်ချက်များပါရှိပြီး ဒါကြောင့် ဒေါင်းလုတ်အရွယ်အစား အများကြီးတိုးပွားပါသည်။ ဘာသာပြန်ချက် မပါမှုဖြင့် clone လုပ်လိုပါက sparse checkout ကို အသုံးပြုပါ: +> ဒီ repository မှာ ဘာသာစကား ၅၀ ကျော် စာနဲ့အတူ ပါဝင်တဲ့ အတွက် ဒေါင်းလုဒ် အရွယ်အစား ကြီးတယ်။ ဘာသာစကားများမပါဘဲ ကူးယူချင်ရင် sparse checkout ကို အသုံးပြုပါ: > ```bash > git clone --filter=blob:none --sparse https://github.com/microsoft/AI-For-Beginners.git > cd AI-For-Beginners > git sparse-checkout set --no-cone '/*' '!translations' '!translated_images' > ``` -> ဒါက သင်အတန်းလေ့လာပြီးမြန်ဆန်စွာ ဒေါင်းလုတ်လုပ်နိုင်မှာ ဖြစ်ပါတယ်။ +> ဒါကြောင့် သင်သင်ယူဖို့ လိုအပ်သမျှ အားလုံးကို ပိုမြန်တဲ့ ဒေါင်းလုဒ်နဲ့ ရနိုင်ပါပြီ။ -**ထပ်မံဘာသာပြန်စကားများ ထောက်ပံ့လိုပါက [ဒီနေရာ](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md) တွင်စာရင်းပြထားပါသည်။** +**အခြား ဘာသာစကားများ ထောက်ပံ့လိုပါက၊ ဤနေရာတွင် စာရင်းပြုထားသည် [here](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** + +## အသိုင်းအဝိုင်းတွင် ပူးပေါင်းပါ -## အသိုင်းအဝိုင်းတွင် ပါဝင်ပါ [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -## သင်တတ်မည့်အရာများ +## သင်ယူမည့်အချက်များ **[သင်တန်း၏ စိတ်ကူးမြေပုံ](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** -ဤသင်ရိုးညွှန်းတမ်းတွင် သင်သည် လေ့လာမည်မှာ – +ဒီအစီအစဉ်တွင် သင်တန်းသားများ အောက်ဖော်ပြပါအချက်များကို သင်ယူမည်ဖြစ်ပါသည်- -* အတုယူမှုအင်တဲလီဂျင့် (AI) ဆိုင်ရာ မတူကွဲပြားသောနည်းလမ်းများ၊ ပုံမှန်ရိုးရာ "အဟောင်း" သင်္ကေတနည်းလမ်းနှင့် **အကြောင်းအရာ ဖော်ပြချက်** နဲ့ သဘောထားခြင်း ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)) ပါဝင်သည်။ -* ခေတ်မီ AI ၏ အဓိကဖြစ်သော **နာယူးရယ်ကွန်ရက်များ** နှင့် **နက်ကြီးသင်ယူခြင်း**။ မူလတန်းအတွက် များသောအားဖြင့် အဓိက သဘောတရားများကို ပရိုဂရမ်ကို အသုံးပြု၍ ထုတ်ပြသမယ် – နာမည်ကြီးသော Framework နှစ်ခု ဖြစ်သည့် [TensorFlow](http://Tensorflow.org) နှင့် [PyTorch](http://pytorch.org) တွင်ဖြစ်သည်။ -* ပုံရိပ်နှင့် စာသားများကို ကိုင်တွယ်သုံးစွဲနိုင်ဖို့ **နာယူးရယ်ဖွဲ့စည်းမှုများ**။ နောက်ဆုံးပေါ်မော်ဒယ်များကို ပါဝင်ကာ ပြီးမြောက်မှု လောက်မရှိသော်လည်း ပါဝင်သည်။ -* နည်းနည်း နာမည်ကျော် မဟုတ်သော AI နည်းလမ်းများကဲ့သို့ **ဂျီနက်တစ် အယ်လ်ဂိုရစ်သမ်များ** နှင့် **Multi-Agent Systems** လည်း ပါဝင်သည်။ +* အတုယူစက်ရုပ်၏ ကွဲပြားသောနည်းလမ်းများ၊ အထူးသဖြင့် အမှတ်အသားပြ နည်းလမ်းဖြစ်သော **အသိအမှတ်ပြုမှု** နှင့် စဉ်းစားခြင်း ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence))။ +* ခေတ်မှီ AI ၏ အခြေခံတည်နေရာဖြစ်သော **နွယ်နက်၀ါးများ** နှင့် **နက်ရှိုင်းသင်ယူမှု**။ ထိုဂိမ်းဝိုင်းကို ပိုမိုနားလည်ရလွယ်ကူစေရန် အဓိက framework ၂ ခုဖြစ်သော [TensorFlow](http://Tensorflow.org) နှင့် [PyTorch](http://pytorch.org) တွင် ကုဒ်များဖြင့် ရှင်းလင်းပြသမည်။ +* ပုံနှင့် စာသားကို လုပ်ဆောင်ရာ၌ အသုံးပြုသော **နွယ်နက် ပုံစံများ**။ မကြာသေးမီက မော်ဒယ်များကို ရှင်းလင်းထားသော်လည်း နောက်ဆုံးနည်းပညာအတိုင်းမဖြစ်နိုင်သေးပါ။ +* အနည်းသော အသုံးပြုမှုရှိသော AI နည်းလမ်းများဖြစ်သော **ဗီဇဆိုင်ရာ အယ်လဂေါရီသမ်များ** နှင့် **အဖွဲ့ဝင်စနစ်များ**။ -ဤသင်ရိုးညွှန်းတမ်းတွင် မပါဝင်သည့် အရာများက +ဒီအစီအစဉ်တွင် မပါဝင်မည့် အကြောင်းအရာများ- -> [ဤသင်တန်း၏ အပိုဆောင်း အရင်းအမြစ်များအား Microsoft Learn စုစည်းမှုတွင် ရှာဖွေပါ](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) +> [ဒီသင်တန်းနှင့် ပတ်သက်သော အပိုဆောင်းအရင်းအမြစ်များကို မြန်မာ Microsoft Learn စုစည်းမှုတွင် ရှာဖွေပါ](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) -* **AI ကို စီးပွားရေးထဲတွင် အသုံးပြုခြင်း** စီးပွားရေးဆိုင်ရာကိစ္စများ။ Microsoft Learn မှ [စီးပွားရေးအသုံးပြုသူများအတွက် AI မိတ်ဆက်](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) သင်ကြားမှုလမ်းကြောင်း သို့မဟုတ် [AI စီးပွားရေးကျောင်း](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum) ကို [INSEAD](https://www.insead.edu/) နှင့် ပူးပေါင်းဖန်တီးထားသည်။ -* ပထမအဆင့် မော်ကွန်းသင်ယူမှု **စတင်ခြင်း** အား အောက်ပါ [Machine Learning for Beginners Curriculum](http://github.com/Microsoft/ML-for-Beginners) တွင် ပြည့်စုံစွာ ဖော်ပြထားသည်။ -* **[Cognitive Services](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)** အသုံးပြု၍ တည်ဆောက်ထားသော လက်တွေ့ AI လျှောက်လွှာများ။ ဤအတွက် Microsoft Learn တွင် [မြင်ကွင်း](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [သဘာဝဘာသာစကား သီးခြားစီစစ်ခြင်း](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[Azure OpenAI ဝန်ဆောင်မှုဖြင့် စိတ်မဖြစ်သော AI ဖန်တီးခြင်း](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** နှင့် အခြား ဒါမှမဟုတ် မော်ဂျူးများကို စတင်လေ့လာရန် အကြံပြုပါသည်။ -* အထူးပြု ML **Cloud Frameworks** များကဲ့သို့ [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-bethanycheum), [Microsoft Fabric](https://learn.microsoft.com/en-us/training/paths/get-started-fabric/?WT.mc_id=academic-77998-bethanycheum), သို့မဟုတ် [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum) တို့ပါဝင်သည်။ [Build and operate machine learning solutions with Azure Machine Learning](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) နှင့် [Build and Operate Machine Learning Solutions with Azure Databricks](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum) သင်ကြားပေးမှု လမ်းကြောင်းများကို အသုံးပြုဖို့ တိုက်တွန်းပါသည်။ -* **စကားပြော AI** နှင့် **စကားပြော ဘော့များ**။ ပါဝင်သင်ကြားမှု ပြုသည့် [Create conversational AI solutions](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum) ကိုသင်ယူနိုင်ပြီး၊ အသေးစိတ်အတွက် [ဒီဘလော့ဂ်စာတမ်း](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) ကိုလည်း အကြံပြုပါသည်။ -* နက်ကြီးသင်ယူမှုအတွက် ကျယ်ပြန့်သော **ဂဏန်းဆိုင်ရာ သင်္ချာများ**။ ဤအတွက် Ian Goodfellow, Yoshua Bengio နှင့် Aaron Courville ၏ [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) စာအုပ်ကို အကြံပြုလို 있으며၊ အွန်လိုင်းတွင် [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/) မှလည်း ရနိုင်ပါသည်။ +* **စီးပွားရေးသုံး AI** ၏ စီးပွားရေးကိစ္စများ။ Microsoft Learn တွင် ပါဝင်သော [စီးပွားရေးသုံး AI ကို မိတ်ဆက်ခြင်း](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) သင်ကြားမှုလမ်းကြောင်း သို့မဟုတ် [AI စီးပွားရေးကျောင်း](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum) ကို INSEAD နှင့် ပူးပေါင်းတီထွင်ထားသည်ကို သင်ယူပါ။ +* ကျွန်ုပ်တို့၏ [လူသစ်များအတွက် စက်သင်ယူမှု](http://github.com/Microsoft/ML-for-Beginners) တွင် အသေးစားနည်းလမ်းဖြစ်သည့် **စက်သင်ယူမှု ပုံမှန်နည်းလမ်း** မပါဝင်။ +* **[Cognitive Services](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)** အသုံးပြုပြီး တီထွင်ထားသော လက်တွေ့ AI အက်ပလီကေးရှင်းများ။ ဤအတွက် Microsoft Learn တွင် [မြင်ကွင်း](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum)၊ [သဘာဝဘာသာစကား ပြုလုပ်မှု](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum)၊ **[Azure OpenAI Service ဖြင့် ဂျီနရေးတစ် AI](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** နှင့် အခြားများ စသော မော်ဂျူးများဖြင့် စတင်ရန် တိုက်တွန်းပါသည်။ +* အထူးသတ်မှတ်ထားသော ML **Cloud Frameworks**, ဥပမာ [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-bethanycheum), [Microsoft Fabric](https://learn.microsoft.com/en-us/training/paths/get-started-fabric/?WT.mc_id=academic-77998-bethanycheum), သို့မဟုတ် [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum)။ သင်ယူရန် [Build and operate machine learning solutions with Azure Machine Learning](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) နှင့် [Build and Operate Machine Learning Solutions with Azure Databricks](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum) သင်ယူမှုလမ်းကြောင်းများကို အသုံးပြုပါ။ +* **စကားပြော AI** နှင့် **စကားပြော Bot များ**။ သီးခြား [စကားပြော AI ဖြေရှင်းချက်များ ဖန်တီးခြင်း](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum) သင်ကြားမှု လမ်းကြောင်းရှိပြီး၊ ပိုပြီး အသေးစိတ်အတွက် [ဤ ဘလော့ဂ်ပို့စ်](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) ကိုလည်း ပြန်လည်ကြည့်ရှုနိုင်သည်။ +* နက်ရှိုင်းသင်ယူမှုတွင် ပါဝင်သော **နက်ရှိုင်းသင်ယူမှု သင်္ချာကိန်းဂဏန်းများ**। ဤအတွက် Ian Goodfellow, Yoshua Bengio နှင့် Aaron Courville ရေးသားသော [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) ဝတ္ထုကို လမ်းညွှန်အဖြစ် အသုံးပြုရန် ညိတ်ဆက်ထားပြီး [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/) တွင် အွန်လိုင်းလည်း ရရှိနိုင်ပါသည်။ -_cloud_ အတွင်းရှိ _AI_ အကြောင်းအရာများကို မျက်လုံးပိတ်ကာ အနည်းငယ် မိတ်ဆက်ကြည့်လိုလျှင် [Get started with artificial intelligence on Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum) သင်ကြားမှုလမ်းကြောင်းကို ဆင်ခြင်ကြည့်ရှုနိုင်ပါသည်။ +_Cloud တွင် AI_ အကြောင်း လွယ်ကူစွာ မိတ်ဆက်ရန်အတွက် [Azure ပေါ်တွင် အတုယူစက်ရုပ်ဖြင့် စတင်ရန်](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum) သင်ကြားမှု လမ်းကြောင်းကို အကြံပြုပါသည်။ -# အကြောင်းအရာများ +# အကြောင်းအရာ -| | သင်ခန်းစာ လင့်ခ် | PyTorch/Keras/TensorFlow | ဂုဏ်ရည်ခန်း | +| | သင်ခန်းစာ လင့်ခ် | PyTorch/Keras/TensorFlow | လေ့ကျင့်ခန်း | | :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | -| 0 | [သင်တန်း စတင်ခြင်း](./lessons/0-course-setup/setup.md) | [သင့် ဖွံ့ဖြိုးတိုးတက်မှု ပတ်ဝန်းကျင် စတင်ဆောင်ရွက်ခြင်း](./lessons/0-course-setup/how-to-run.md) | | +| 0 | [သင်တန်း စတင်ခြင်း](./lessons/0-course-setup/setup.md) | [ဖန်တီးမှု ပတ်ဝန်းကျင် ပြင်ဆင်ခြင်း](./lessons/0-course-setup/how-to-run.md) | | | I | [**AI မိတ်ဆက်**](./lessons/1-Intro/README.md) | | | -| 01 | [AI ၏ မိတ်ဆက်နှင့် သမိုင်း](./lessons/1-Intro/README.md) | - | - | +| 01 | [AI မိတ်ဆက်နှင့် သမိုင်း](./lessons/1-Intro/README.md) | - | - | | II | **သင်္ကေတ AI** | -| 02 | [အကြောင်းအရာ ဖော်ပြခြင်းနှင့် ကျွမ်းကျင်သူ စနစ်များ](./lessons/2-Symbolic/README.md) | [ကျွမ်းကျင်သူ စနစ်များ](./lessons/2-Symbolic/Animals.ipynb) / [အောင့်တောလီ](./lessons/2-Symbolic/FamilyOntology.ipynb) /[အယူအဆဇယား](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | -| III | [**နာယူးရယ်ကွန်ရက် မိတ်ဆက်**](./lessons/3-NeuralNetworks/README.md) ||| -| 03 | [Perceptron](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [Notebook](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [Lab](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | -| 04 | [Multi-Layered Perceptron and Creating our own Framework](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [Notebook](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [Lab](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | -| 05 | [Intro to Frameworks (PyTorch/TensorFlow) and Overfitting](./lessons/3-NeuralNetworks/05-Frameworks/README.md) | [PyTorch](./lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb) / [Keras](./lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Lab](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | -| IV | [**ကွန်ပျူတာဗွေရှင်း**](./lessons/4-ComputerVision/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-77998-cacaste) / [TensorFlow](https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-77998-cacaste)| [Explore Computer Vision on Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | -| 06 | [ကွန်ပျူတာဗွေရှင်းနဲ့နိဒါန်း။ OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [Notebook](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [Lab](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | -| 07 | [Convolutional Neural Networks](./lessons/4-ComputerVision/07-ConvNets/README.md) & [CNN Architectures](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [Lab](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | -| 08 | [Pre-trained Networks and Transfer Learning](./lessons/4-ComputerVision/08-TransferLearning/README.md) and [Training Tricks](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Lab](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | -| 09 | [Autoencoders and VAEs](./lessons/4-ComputerVision/09-Autoencoders/README.md) | [PyTorch](./lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb) | | -| 10 | [Generative Adversarial Networks & Artistic Style Transfer](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | -| 11 | [Object Detection](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [Lab](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | -| 12 | [Semantic Segmentation. U-Net](./lessons/4-ComputerVision/12-Segmentation/README.md) | [PyTorch](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb) | | -| V | [**သဘာဝဘာသာစကားကုမန့်ဆုံးခြင်း**](./lessons/5-NLP/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-77998-cacaste) /[TensorFlow](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-77998-cacaste) | [Explore Natural Language Processing on Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| -| 13 | [စာသားဖော်ပြချက်။ Bow/TF-IDF](./lessons/5-NLP/13-TextRep/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb) | | -| 14 | [အဓိပ္ပါယ်အရ စကားလုံးထိုးသွင်းပါတယ်။ Word2Vec နဲ့ GloVe](./lessons/5-NLP/14-Embeddings/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb) | | -| 15 | [ဘာသာစကားပုံစံ။ ကိုယ်ပိုင်အပ်ဒိတ်ထိုးသွင်းခြင်းလေ့ကျင့်ခြင်း](./lessons/5-NLP/15-LanguageModeling/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-PyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-TF.ipynb) | [Lab](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | -| 16 | [Recurrent Neural Networks](./lessons/5-NLP/16-RNN/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNTF.ipynb) | | -| 17 | [Generative Recurrent Networks](./lessons/5-NLP/17-GenerativeNetworks/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb) | [Lab](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | +| 02 | [အသိအမှတ်ပြုမှုနှင့် ကျွမ်းကျင်မှု စနစ်များ](./lessons/2-Symbolic/README.md) | [ကျွမ်းကျင်မှု စနစ်များ](./lessons/2-Symbolic/Animals.ipynb) / [ဇာတိတော်အခြေခံ စနစ်](./lessons/2-Symbolic/FamilyOntology.ipynb) /[အယူအဆ အကြောင်းပြု ရုပ်ပုံ](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | +| III | [**နယူးရယ်ကွန်ယက်များသို့နိဒါန်း**](./lessons/3-NeuralNetworks/README.md) ||| +| 03 | [ပာစက်ထရွန်](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [Notebook](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [Lab](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | +| 04 | [အလွယ်တကူအဆင့်များပါ ပာစက်ထရွန်နှင့် ကျွန်ုပ်တို့၏ကိုယ့်စိတ်ကြိုက် Framework တည်ဆောက်ခြင်း](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [Notebook](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [Lab](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | +| 05 | [Framework များသို့နိဒါန်း (PyTorch/TensorFlow) နှင့် အလွန်တက်မှု](./lessons/3-NeuralNetworks/05-Frameworks/README.md) | [PyTorch](./lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb) / [Keras](./lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Lab](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | +| IV | [**ကွန်ပျူတာမြင်ကွင်း**](./lessons/4-ComputerVision/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-77998-cacaste) / [TensorFlow](https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-77998-cacaste)| [Microsoft Azure တွင် ကွန်ပျူတာမြင်ကွင်း စမ်းသပ်ရန်](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | +| 06 | [ကွန်ပျူတာမြင်ကွင်းနှင့် မိတ်ဆက်ခြင်း။ OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [Notebook](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [Lab](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | +| 07 | [ကွန်ဗောလုရှင်းနယူးရယ်ကွန်ယက်များ](./lessons/4-ComputerVision/07-ConvNets/README.md) & [CNN စက်ဆောက်ပုံများ](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [Lab](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | +| 08 | [ကြိုတင်လေ့လာပြီးသောကွန်ယက်များနှင့် ကူးပြောင်းသင်ယူခြင်း](./lessons/4-ComputerVision/08-TransferLearning/README.md) နှင့် [လေ့ကျင့်ရေးနည်းပညာများ](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Lab](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | +| 09 | [အော်တိုအင်ကိုးဒါများနှင့် VAE များ](./lessons/4-ComputerVision/09-Autoencoders/README.md) | [PyTorch](./lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb) | | +| 10 | [ဖန်တီးမှုဆန်ဆန် အပြိုင်တန်းကွန်ယက်များနှင့် အနုပညာစတိုင်ကူးပြောင်းခြင်း](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | +| 11 | [အရာဝတ္ထုစမ်းသပ်ခြင်း](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [Lab](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | +| 12 | [သိပ္ပံဘာသာဖြင့် Segmentation။ U-Net](./lessons/4-ComputerVision/12-Segmentation/README.md) | [PyTorch](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb) | | +| V | [**ဘာသာစကားသဘာဝလုပ်ငန်းဆောင်တာ**](./lessons/5-NLP/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-77998-cacaste) /[TensorFlow](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-77998-cacaste) | [Microsoft Azure တွင် ဘာသာစကားသဘာဝလုပ်ငန်းဆောင်တာ စူးစမ်းခြင်း](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| +| 13 | [စာသားကို ကိုယ်စားပြုခြင်း။ Bow/TF-IDF](./lessons/5-NLP/13-TextRep/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb) | | +| 14 | [စာလုံး၏အဓိပ္ပာယ်အရသွင်ပြင်ဆက်စပ်မှု။ Word2Vec နှင့် 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 | [ဘာသာစကားမော်ဒယ်ရေးခြင်း။ ကိုယ့်ကိုယ်ကို embedding များလေ့ကျင့်ခြင်း](./lessons/5-NLP/15-LanguageModeling/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-PyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-TF.ipynb) | [Lab](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | +| 16 | [ပြန်လည်ဖြတ်သန်းမှုနယူးရယ်ကွန်ယက်များ](./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 | [ဖန်တီးရေးပြန်လည်ဖြတ်သန်းမှုကွန်ယက်များ](./lessons/5-NLP/17-GenerativeNetworks/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb) | [Lab](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | | 18 | [Transformers. BERT.](./lessons/5-NLP/18-Transformers/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb) /[TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersTF.ipynb) | | -| 19 | [အမည်ရှိနယ်မြေအသိအမှတ်ပြုခြင်း](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [Lab](./lessons/5-NLP/19-NER/lab/README.md) | -| 20 | [ကြီးမားသောဘာသာစကားပုံစံများ၊ ကမ်းလှမ်းချက်ပရိုဂရမ်းမင်းနှင့် အနည်းငယ်-သွားတာလှုပ်ရှားမှုများ](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | +| 19 | [အမည်သတ်မှတ်ထားသောအဖွဲ့အစည်း အသိအမှတ်ပြုခြင်း](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [Lab](./lessons/5-NLP/19-NER/lab/README.md) | +| 20 | [အကြီးစားဘာသာစကားမော်ဒယ်များ၊ Prompt Programming နှင့် နည်းနည်းသင်ကြားမှုလုပ်ငန်းဆောင်တာများ](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | | VI | **အခြား AI နည်းပညာများ** || | -| 21 | [ဂျင်နက်တစ် အယ်လ်ဂိုရီသမ်များ](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [Notebook](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | -| 22 | [နက်ရှိုင်းသော အားပေးသင်ကြားမှု လေ့လာမှု](./lessons/6-Other/22-DeepRL/README.md) | [PyTorch](./lessons/6-Other/22-DeepRL/CartPole-RL-PyTorch.ipynb) /[TensorFlow](./lessons/6-Other/22-DeepRL/CartPole-RL-TF.ipynb) | [Lab](./lessons/6-Other/22-DeepRL/lab/README.md) | -| 23 | [အများပြည်သူအေဂျင့်စနစ်များ](./lessons/6-Other/23-MultiagentSystems/README.md) | | | -| VII | **AI သမာဓိ** | | | -| 24 | [AI သမာဓိနှင့် တာဝန်ခံ AI](./lessons/7-Ethics/README.md) | [Microsoft Learn: Responsible AI Principles](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | -| IX | **အပိုဆောင်းများ** | | | -| 25 | [မူလတန်းစုံကွန်ရက်များ၊ CLIP နှင့် VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [Notebook](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | +| 21 | [ဂျင်နိုက် အယ်လဂိုရီသမ်များ](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [Notebook](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | +| 22 | [နက်ရှိုက် အခြေခံတဲ့ ပြန်လည်အားဖြည့်မှုသင်ယူခြင်း](./lessons/6-Other/22-DeepRL/README.md) | [PyTorch](./lessons/6-Other/22-DeepRL/CartPole-RL-PyTorch.ipynb) /[TensorFlow](./lessons/6-Other/22-DeepRL/CartPole-RL-TF.ipynb) | [Lab](./lessons/6-Other/22-DeepRL/lab/README.md) | +| 23 | [Multi-Agent Systems](./lessons/6-Other/23-MultiagentSystems/README.md) | | | +| VII | **AI မြင်ကွင်းများနှင့် သမာဓိ** | | | +| 24 | [AI မြင်ကွင်းများနှင့် တာဝန်ရှိမှု AI](./lessons/7-Ethics/README.md) | [Microsoft Learn: တာဝန်ရှိသော AI 원칙များ](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | +| IX | **ထပ်ဆောင်းအကြောင်းအရာများ** | | | +| 25 | [Multi-Modal Networks, CLIP နှင့် VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [Notebook](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | -## သင်ခန်းစာတိုင်းတွင်ပါဝင်သောအရာများ +## လက်တွေ့သင်ခန်းစာတိုင်းတွင် ပါဝင်သည်များ -* မင်္ဂလာဆောင်စာအုပ်ပုံစံစာမျက်နှာများ -* စနစ်အသီးသီးအတွက် အထူးသီးသန့်ဖြစ်သော Jupyter Notebooks များ (**PyTorch** သို့မဟုတ် **TensorFlow** ဖြစ်ကြောင်း) ပါဝင်သည်။ ကိုယ့်ဘာသာ အကြောင်းအရာကိုနားလည်ရန်အတွက် နောက်ထပ်အနည်းဆုံးတစ်ခုခု Notebook (PyTorch သို့မဟုတ် TensorFlow) ကို ဖတ်ပါ။ -* အချို့သောခေါင်းစဉ်များအတွက် **Labs** များ ရရှိနိုင်ပြီး သင်လေ့လာထားသော အကြောင်းအရာများကို အကောင်အထည်ဖော်ရန်အခွင့်အရေးပေးသည်။ -* အချို့သောအပိုင်းများတွင် သက်ဆိုင်ရာခေါင်းစဉ်များကို ဖုံးကွယ်ထားသည့် [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) modules များသို့ ချိတ်ဆက်ထားသည်။ +* ကြိုတင်ဖတ်ရှုရန် စာရင်း +* လုပ်ဆောင်နိုင်သော Jupyter Notebooks များ၊ မကြာခဏ Framework အရောက်ကျသည့် (**PyTorch** သို့မဟုတ် **TensorFlow**) ဖြစ်သည်။ လုပ်ဆောင်နိုင်သော notebook တွင် သဘောတရားဆိုင်ရာများလည်းပါရှိသည့်အတွက် မည်သူမဆို ခေါင်းစဉ်နားလည်ရန် သာမန်အားဖြင့် notebook တစ်ခုခု (PyTorch သို့ TensorFlow) ကို ကြည့်ရှုရန် လိုအပ်သည်။ +* အချို့ခေါင်းစဉ်များအတွက် သင်တန်းပြန်လည်လုပ်ဆောင်နိုင်သော **Labs** များ၊ သင်သင်ယူခဲ့သော ကိစ္စအကြောင်းအရာကို တိကျသောပြဿနာတစ်ခုတွင် အသုံးပြု၍ ကြိုးစားလေ့လာနိုင်သောအခွင့်အလမ်းပေးသည်။ +* အပိုင်းတချို့တွင် [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) မိုဒျူးများသို့ ချိတ်ဆက်ထားသည်။ -## စတင်လုပ်ဆောင်ခြင်း +## စတင်ရန် -### 🎯 AI အသစ်လား? ဒီမှာစပါ! +### 🎯 AI အသစ်တက်သူများအတွက်! ဒီနေရာကနေ စတင်ပါ။ -သင် AI ကိုမပြည့်စုံသော အသစ်တစ်ယောက်ဖြစ်ပြီး လွယ်ကူမြန်ဆန်သော လက်တွေ့နမူနာများလိုလျှင် ကျွန်ုပ်တို့ရဲ့ [**စတင်ရန်အဆင်ပြေသောနမူနာများ**](./examples/README.md) ကို ကြည့်ပါ! ၎င်းတွင်ပါဝင်သည်မှာ- +AI အသစ်တက်သူများအတွက် လျင်မြန်သေချာသော လက်တွေ့ ဥပမာများကို ကျွန်ုပ်တို့၏ [**စိတ်ကြိုက်အကြီးအကျယ် ဥပမာများ**](./examples/README.md) တွင် စူးစမ်းနိုင်ပါသည်။ ထည့်သွင်းထားသည်မှာ - -- 🌟 **မင်္ဂလာပါ AI ကမ္ဘာ** - သင့်ရဲ့ ပထမဆုံး AI ပရိုဂရမ်း (ပုံစံသိရှိခြင်း) -- 🧠 **ရိုးရှင်းသောနည်းလမ်း Networks** - စတင်ဖန်တီးသော နယူးရယ်နက်ဝက် -- 🖼️ **ပုံရိပ်စာတန်းသတ်မှတ်စက်** - အသေးစိတ် မှတ်ချက်များဖြင့် ပုံရိပ်များ သတ်မှတ်ခြင်း -- 💬 **စာသားခံစားချက်** - ဂရုတစိုက်/မဂြိုဟ် စာသားကိုစစ်ဆေးပါ +- 🌟 **Hello AI World** - သင်၏ပထမဆုံး AI ပရိုဂရမ် (ပုံစံအသိအမှတ်ပြုခြင်း) +- 🧠 **ရိုးရှင်းသောနယူးရယ်ကွန်ယက်** - နယူးရယ်ကွန်ယက်ကို အစကနေတည်ဆောက်ခြင်း -ဤဥပမာများသည် သင်အား AI အတွေးအခေါ်များကို နားလည်စေရန်နှင့် အပြည့်အစုံ သင်ရိုးအစီအစဉ်ထဲ ဝင်ရောက်ရန်အတွက် အထောက်အကူပြုရန် ရည်ရွယ်ပါသည်။ +- 🖼️ **ပုံခွဲခြားစနစ်** - အဒီၤပုံများကို အသေးစိတ် မှတ်ချက်များနှင့် ခွဲခြားခြင်း +- 💬 **စာသားခံစားချက်** - အပြု/အမပြု စာသားများကို ခွဲခြမ်းစိတ်ဖြာခြင်း -### 📚 အပြည့်အစုံ သင်ရိုးအစီအစဉ် တပ်ဆင်ခြင်း +ဤဥပမာများကို သင်၏ AI မှတ်ယူချက်များကို နားလည်ရန်အတွက် ဖန်တီးထားပြီး မိမိတို့လေ့လာရေးအစီအစဉ်ကို စတင်လေ့လာရန် ပြင်ဆင်ပေးထားသည်။ -- သင်၏ ဖွံ့ဖြိုးရေး ပတ်ဝန်းကျင်ကို တပ်ဆင်ရာတွင် ကူညီရန် အတွက် [တပ်ဆင်ခြင်းသင်ခန်းစာ](./lessons/0-course-setup/setup.md) တစ်ခု ပြုလုပ်ထားပါသည်။ - ပညာသင်ကြားသူများအတွက်လည်း သင့်အတွက် [သင်ရိုးစီစဉ်ခြင်း သင်ခန်းစာ](./lessons/0-course-setup/for-teachers.md) တစ်ခု ပြုလုပ်ထားပါသည်။ -- VSCode သို့မဟုတ် Codespace တွင် [ကုဒ်မှတ်တမ်းများကို အကောင်အထည်ဖော်နည်း](./lessons/0-course-setup/how-to-run.md) +### 📚 အပြည့်အစုံ သင်ရိုးညွှန်းတမ်း ပြင်ဆင်ခြင်း -အောက်ပါအဆင့်များကို လိုက်နာပါ။ +- ဖန်တီးထားသည့် [setup lesson](./lessons/0-course-setup/setup.md) မှတဆင့် သင်၏ ဖွံဖြိုးတိုးတက်မှု ပတ်ဝန်းကျင်ကို ပြင်ဆင်နိုင်ပါသည်။ +- အတန်းအတွက် သင်ကြားသူများအတွက်လည်း [curricula setup lesson](./lessons/0-course-setup/for-teachers.md) ဖန်တီးထားသည်။ +- [VSCode သို့မဟုတ် Codespace တွင် အကောင်အထည်ဖော်နည်း](./lessons/0-course-setup/how-to-run.md) -Repository ကို Fork လုပ်ပါ- ဤ စာမျက်နှာ၏ ညာဘက်ထိပ်ရှိ "Fork" ခလုတ်ကို နှိပ်ပါ။ +အောက်ပါ အဆင့်များကို လိုက်နာပါ - -Repository ကို Clone လုပ်ပါ- `git clone https://github.com/microsoft/AI-For-Beginners.git` +Repository ကို ဖောက်သည် - ဤစာမျက်နှာ၏ ညာဘက်အပေါ်တွင်ရှိသည့် "Fork" ခလုတ်ကို နှိပ်ပါ။ -နောက်မှရှာဖွေရန်အဆင်ပြေသည့်အတွက် ဤ repo ကို စတား (🌟) ခြင်းမမေ့ပါနှင့်။ +Repository ကို clone လုပ်ပါ - `git clone https://github.com/microsoft/AI-For-Beginners.git` -## အခြား သင်ယူသူများနှင့် တွေ့ဆုံခြင်း +ပြီးလျှင် repo ကို စတား (🌟) ပေးပါ။ ရှာဖွေဖို့ လွယ်ကူစေပါမည်။ -ဤ သင်တန်းကို လေ့လာနေသူများနှင့် တွေ့ဆုံ၍ ပူးပေါင်းဆွေးနွေးနိုင်ရန်အတွက် ကျွန်ုပ်တို့၏ [တရားဝင် AI Discord ဆာဗာ](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) တက်ရောက်ပါ။ +## အခြားလေ့လာသူများနှင့် တွေ့ဆုံခြင်း -ထုတ်ကုန်ဆိုင်ရာ အကြံပြုချက်များ သို့မဟုတ် မေးခွန်းများ ရှိပါက ကျွန်ုပ်တို့၏ [Azure AI Foundry Developer Forum](https://aka.ms/foundry/forum) မှာ လည်းလာဆွေးနွေးနိုင်ပါသည်။ +ဒီသင်ကြားမှုကို လေ့လာနေသူများနှင့် တွေ့ဆုံပြီး ဆွေးနွေးရန် ကျွန်ုပ်တို့ရဲ့ [တရားဝင် AI Discord server](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) ကို ဝင်ရောက်ပါ။ -## စမ်းသပ်မေးခွန်းများ +ထပ်မံ ဒီဇိုင်းဆောက်လုပ်စဉ် တွေ့ရှိလာသော ထုတ်ကုန်မေးမြန်းချက်များ သို့မဟုတ် အကြံပြုချက်များရှိပါက ကျွန်ုပ်တို့ရဲ့ [Azure AI Foundry Developer Forum](https://aka.ms/foundry/forum) သို့ သွားရောက်ပါ။ -> **စမ်းသပ်မေးခွန်းများအကြောင်း မှတ်ချက်**: စမ်းသပ်မေးခွန်းအားလုံးကို Quiz-app ဖိုလ်ဒါတွင်း etc\quiz-app မှာပါရှိပြီး [ဤနေရာတွင် အွန်လိုင်း](https://ff-quizzes.netlify.app/) မှာလည်း ကြည့်ရှုနိုင်ပါသည်။ စမ်းသပ်မေးခွန်း app ကို ဒေသတွင်းတွင် အကောင်အထည်ဖော်နိုင်ပြီး Azure သို့ deployment လုပ်နိုင်ပါသည်။ `quiz-app` ဖိုလ်ဒါအတွင်း လမ်းညွှန်ချက်များကို လိုက်နာပါ။ အစီအစဉ်များကို တဖြည်းဖြည်း ဘာသာပြန်ဆောင်ရွက်နေပါသည်။ +## စမ်းသပ်စစ်ဆေးမှုများ (Quizzes) -## ကူညီလိုသူ +> **Quiz များအကြောင်း မှတ်ချက်**: All quizzes are contained in the Quiz-app folder in etc\quiz-app, or [Online Here](https://ff-quizzes.netlify.app/) သင်ခန်းစာအတွင်းပိုင်းမှ ချိတ်ဆက်ထားပါသည်။ Quiz App ကို ဒေသခံတွင် သို့မဟုတ် Azure သို့ တပ်ဆင်အသုံးပြုနိုင်ပါသည်။ `quiz-app` ဖိုလ်ဒါအတွင်းရှိပြသနာများကို လိုက်နာပါ။ Quiz များကို မြန်မြန်ဆန်ဆန် ပြည်တွင်းဘာသာဖြင့် ပြင်ဆင်နေပါသည်။ -အကြံပြုချက်များရှိပါသလား၊ လက်လွတ်စကားလုံးများ သို့မဟုတ် ကုဒ်အမှားများကို တွေ့ရှိပါသလား? ပြဿနာတင်ပါ သို့မဟုတ် pull request တင်ပါ။ +## အကူအညီလိုအပ်ပါသည် -## အထူးကျေးဇူးတင်၏ +အကြံပြုချက်များရှိပါသလား သို့မဟုတ် စာလုံးပေါင်း ချွတ်ယွင်းမှု သို့မဟုတ် ကုဒ်အမှားများတွေ့ရှိပါသလား? ပြဿနာတင်ရန် သို့မဟုတ် pull request တင်ရန် ဆောင်ရွက်ပါ။ -* **✍️ အဓိကရေးသားသူ**: [Dmitry Soshnikov](http://soshnikov.com), PhD -* **🔥 တည်းဖြတ်သူ**: [Jen Looper](https://twitter.com/jenlooper), PhD -* **🎨 အမှတ်အသားပုံဆွဲသူ**: [Tomomi Imura](https://twitter.com/girlie_mac) -* **✅ စမ်းသပ်မေးခွန်း ဖန်တီးသူ**: [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) -* **🙏 အဓိက ပံ့ပိုးသူများ**: [Evgenii Pishchik](https://github.com/Pe4enIks) +## အထူးကျေးဇူးတင်ခြင်း -## အခြား သင်ရိုးအစီအစဉ်များ +* **✍️ အဓိကရေးသားသူ:** [Dmitry Soshnikov](http://soshnikov.com), PhD +* **🔥 အယ်ဒီတာ:** [Jen Looper](https://twitter.com/jenlooper), PhD +* **🎨 Sketchnote ပန်းချီဆရာ:** [Tomomi Imura](https://twitter.com/girlie_mac) +* **✅ စမ်းသပ်မှု ဖန်တီးသူ:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) +* **🙏 အဓိက ပါဝင်ဆောင်ရွက်သူများ:** [Evgenii Pishchik](https://github.com/Pe4enIks) -ကျွန်ုပ်တို့၏အဖွဲ့သည် အခြား သင်ရိုးအစီအစဉ်များကို လည်း ထုတ်လုပ်ပါသည်။ ကြည့်ရှုပါ။ +## အခြား သင်ရိုးညွှန်းတမ်းများ + +ကျွန်ုပ်တို့အသင်းအဖွဲ့သည် အခြားသင်ရိုးညွှန်းတမ်းများကို ထုတ်လုပ်သည်။ ဖော်ပြပါကိုကြည့်ရှုပါ - ### LangChain @@ -214,19 +209,19 @@ Repository ကို Clone လုပ်ပါ- `git clone https://github.com/mic [![Copilot Adventure](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst) -## ကူညီမှု ရယူခြင်း +## အကူအညီရယူနည်း -AI apps ဖန်တီးရာတွင် ပိတ်ဆို့မှုရှိပါက သို့မဟုတ် မေးခွန်းများရှိပါက MCP အကြောင်း ဆွေးနွေးရန်အတွက် အခြား သင်ယူသူများနှင့် အတွေ့အကြုံရှိ ဖွံ့ဖြိုးသူများနှင့် ပူးပေါင်းဆွေးနွေးနိုင်ပါသည်။ ဒီသည် မေးခွန်းများကို ကြိုဆိုပြီး အသိပညာကို လွတ်လပ်စွာ မျှဝေနိုင်သော ပံ့ပိုးကူညီမှုရှိသော အသိုင်းအဝိုင်းဖြစ်ပါသည်။ +AI အက်ပ်များ ဖန်တီးရာတွင် အခက်အခဲရှိပါက MCP အတွင်း လေ့လာသူများနှင့် အတွေ့အကြုံရှိသူများနှင့် ဆွေးနွေးနိုင်ပါသည်။ မေးခွန်းများသာမက ပညာအကြောင်းအရာများကိုလည်း မျှဝေကြသည့် ပံ့ပိုးမှုအဖွဲ့အစည်းဖြစ်ပါသည်။ [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -ထုတ်ကုန်ဆိုင်ရာ တုံ့ပြန်ချက်များ သို့မဟုတ် ပြဿနာများ ရှိပါက လည်းလေ့လာရန်: +ထုတ်ကုန်ပြန်လည်တုံ့ပြန်ချက် သို့မဟုတ် ဖန်တီးရာတွင် အမှားများရိှပါက သွားရောက်စစ်ဆေးပါ - [![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) --- -**အကြောင်းကြားချက်** -ဤစာတမ်းကို AI ဘာသာပြန်ဝန်ဆောင်မှုဖြစ်သော [Co-op Translator](https://github.com/Azure/co-op-translator) အသုံးပြု၍ ဘာသာပြန်ထားပါသည်။ ကျွန်ုပ်တို့သည် မှန်ကန်မှုအတွက် ကြိုးစားတတ်သော်လည်း၊ အလိုအလျှောက် ဘာသာပြန်ခြင်းသည် အမှားများ သို့မဟုတ် မှားယွင်းမှုများ ပါရှိနိုင်ကြောင်း ကျေးဇူးပြု၍ သိရှိထားပေးပါရန် တိုက်တွန်းအပ်ပါသည်။ ဆက်စပ်ဘာသာစကားဖြင့် မူရင်းစာတမ်းကို စွဲဆိုနိုင်သော အတည်ပြုရရှိသော အရင်းအမြစ်အဖြစ် သတ်မှတ်စဉ်းစားသင့်ပါသည်။ အရေးကြီးသော သတင်းအချက်အလက်များအတွက်တော့ ပရော်ဖက်ရှင်နယ် လူသားဘာသာပြန်ခြင်းကို အကြံပြုအပ်ပါသည်။ ဤ ဘာသာပြန်ချက်ကို အသုံးပြုရာမှ ဖြစ်ပေါ်နိုင်သော နားမလည်မှုများ သို့မဟုတ် မှားယွင်းဖတ်ရှုမှုများအတွက် ကျွန်ုပ်တို့ အာမခံချက် မရှိပါ။ +**အဆိုပြုချက်** +ဤစာတမ်းကို AI ဘာသာပြန်ဆဲဝစ်စ် [Co-op Translator](https://github.com/Azure/co-op-translator) ဖြင့် ဘာသာပြန်ထားပါသည်။ ကျွန်ုပ်တို့သည် မှန်ကန်မှုအတွက် ကြိုးပမ်းပါသော်လည်း အလိုအလျှောက် ဘာသာပြန်ချက်များတွင် အမှားလည်း ရှိနိုင်မှုကို သတိပြုပါရန် မေတ္တာရပ်ခံအပ်ပါသည်။ မူရင်းစာတမ်းကို မိမိဘာသာစကားဖြင့် ထုတ်ပြန်ထားသည့်ပုံစံကို တရားဝင်သော အရင်းအမြစ်အဖြစ် သတ်မှတ်စဉ်းစားသင့်ပါသည်။ အရေးကြီးသော အချက်အလက်များအတွက် သက်ဆိုင်ရာ ပညာရှင် လူသား ဘာသာပြန်ခြင်းကို အကြံပြုပါသည်။ ဤဘာသာပြန်ချက်ကို အသုံးပြုသည့်နေရာများမှ ဖြစ်ပေါ်နိုင်သော နားမလည်မှုများ သို့မဟုတ် မမှန်ကန်စွာ ခွဲခြားနားလည်မှုများအတွက် ကျွန်ုပ်တို့ တာဝန်မယူပါ။ \ No newline at end of file diff --git a/translations/my/SECURITY.md b/translations/my/SECURITY.md index d5ad28ff..f770c69d 100644 --- a/translations/my/SECURITY.md +++ b/translations/my/SECURITY.md @@ -1,12 +1,3 @@ - ## လုံခြုံရေး Microsoft သည် ၎င်း၏ ဆော့ဖ်ဝဲထုတ်ကုန်များနှင့် ဝန်ဆောင်မှုများ၏ လုံခြုံရေးကို အလေးထားဆောင်ရွက်ပြီး၊ ၎င်းတွင် [Microsoft](https://github.com/Microsoft), [Azure](https://github.com/Azure), [DotNet](https://github.com/dotnet), [AspNet](https://github.com/aspnet), [Xamarin](https://github.com/xamarin) နှင့် [ကျွန်ုပ်တို့၏ GitHub အဖွဲ့အစည်းများ](https://opensource.microsoft.com/) အပါအဝင် GitHub အဖွဲ့အစည်းများမှ စီမံခန့်ခွဲထားသော အရင်းအမြစ်ကုဒ်ရုံများအားလုံး ပါဝင်သည်။ diff --git a/translations/my/etc/CODE_OF_CONDUCT.md b/translations/my/etc/CODE_OF_CONDUCT.md index af30b943..1613a704 100644 --- a/translations/my/etc/CODE_OF_CONDUCT.md +++ b/translations/my/etc/CODE_OF_CONDUCT.md @@ -1,12 +1,3 @@ - # Microsoft Open Source Code of Conduct ဒီပရောဂျက်သည် [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/) ကို လက်ခံထားပါသည်။ diff --git a/translations/my/etc/CONTRIBUTING.md b/translations/my/etc/CONTRIBUTING.md index aaaa95fb..a838cab0 100644 --- a/translations/my/etc/CONTRIBUTING.md +++ b/translations/my/etc/CONTRIBUTING.md @@ -1,12 +1,3 @@ - # အထောက်အပံ့ပေးခြင်း ဒီပရောဂျက်ဟာ အထောက်အပံ့ပေးမှုနဲ့ အကြံပြုချက်တွေကို ကြိုဆိုပါတယ်။ အများစုသော အထောက်အပံ့ပေးမှုတွေဟာ Contributor License Agreement (CLA) ကို သဘောတူဖို့ လိုအပ်ပါတယ်။ ဒါဟာ သင့်အနေဖြင့် သင့်အထောက်အပံ့ကို အသုံးပြုခွင့်ပေးဖို့ အခွင့်အရေးရှိတယ်၊ အမှန်တကယ်ပေးတယ်ဆိုတာကို ကြေညာတဲ့ အချက်လက်စာချုပ်တစ်ခုဖြစ်ပါတယ်။ အသေးစိတ်အချက်အလက်များကို https://cla.microsoft.com မှာ ကြည့်ရှုနိုင်ပါတယ်။ diff --git a/translations/my/etc/Mindmap.md b/translations/my/etc/Mindmap.md index 4762ad02..062431aa 100644 --- a/translations/my/etc/Mindmap.md +++ b/translations/my/etc/Mindmap.md @@ -1,12 +1,3 @@ - # AI ## [AI အကြောင်းအရာမိတ်ဆက်](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/1-Intro/README.md) diff --git a/translations/my/etc/SUPPORT.md b/translations/my/etc/SUPPORT.md index 63aeebc4..a4670bf4 100644 --- a/translations/my/etc/SUPPORT.md +++ b/translations/my/etc/SUPPORT.md @@ -1,12 +1,3 @@ - # အထောက်အပံ့ ## ပြဿနာများကို တင်ပြခြင်းနှင့် အကူအညီရယူရန် diff --git a/translations/my/etc/TRANSLATIONS.md b/translations/my/etc/TRANSLATIONS.md index 994111ca..7f7a9fa6 100644 --- a/translations/my/etc/TRANSLATIONS.md +++ b/translations/my/etc/TRANSLATIONS.md @@ -1,12 +1,3 @@ - # သင်ခန်းစာများကို ဘာသာပြန်ခြင်းဖြင့် အထောက်အကူပြုပါ ဒီသင်ခန်းစာများအတွက် ဘာသာပြန်မှုများကို ကြိုဆိုပါသည်! diff --git a/translations/my/etc/quiz-app/README.md b/translations/my/etc/quiz-app/README.md index 9955f42d..89034e64 100644 --- a/translations/my/etc/quiz-app/README.md +++ b/translations/my/etc/quiz-app/README.md @@ -1,12 +1,3 @@ - # မေးခွန်းများ ဒီမေးခွန်းများဟာ AI သင်ခန်းစာများအတွက် [!NOTE] သင်ခန်းစာမတိုင်မီနှင့်ပြီးနောက် မေးခွန်းများဖြစ်ပါတယ်။ https://aka.ms/ai-beginners မှာတွေ့နိုင်ပါတယ်။ diff --git a/translations/my/examples/README.md b/translations/my/examples/README.md index 464758c0..791423b7 100644 --- a/translations/my/examples/README.md +++ b/translations/my/examples/README.md @@ -1,12 +1,3 @@ - # AI စတင်လေ့လာသူများအတွက် နမူနာများ ကြိုဆိုပါတယ်! ဒီ directory မှာ AI နဲ့ machine learning ကို စတင်လေ့လာဖို့အတွက် လွယ်ကူပြီး တစ်ခုချင်းစီ standalone နမူနာများ ပါဝင်ပါတယ်။ နမူနာတစ်ခုချင်းစီကို စတင်လေ့လာသူများအတွက် သက်သာစေဖို့ အကြောင်းအရာများကို အသေးစိတ် ရှင်းပြထားပြီး အဆင့်ဆင့် လမ်းညွှန်ချက်များ ပါဝင်ပါတယ်။ diff --git a/translations/my/lessons/0-course-setup/for-teachers.md b/translations/my/lessons/0-course-setup/for-teachers.md index 8dd833ba..8faa0669 100644 --- a/translations/my/lessons/0-course-setup/for-teachers.md +++ b/translations/my/lessons/0-course-setup/for-teachers.md @@ -1,12 +1,3 @@ - # ဆရာများအတွက် ဒီသင်ရိုးကို သင့်အတန်းထဲမှာ အသုံးပြုချင်ပါသလား? ကျေးဇူးပြု၍ အသုံးပြုလိုက်ပါ။ diff --git a/translations/my/lessons/0-course-setup/how-to-run.md b/translations/my/lessons/0-course-setup/how-to-run.md index 07b2f5c4..76ad6f99 100644 --- a/translations/my/lessons/0-course-setup/how-to-run.md +++ b/translations/my/lessons/0-course-setup/how-to-run.md @@ -1,12 +1,3 @@ - # နည်းလမ်း အတိုင်း ကုတ်ကို လည်ပတ်ရန် ဒီ သင်ခန်းစာဟာ အလုပ်လုပ်နိုင်တဲ့ ဥပမာတွေ နဲ့ လက်တွေ့လေ့ကျင့်ခန်းတွေ များစွာ ပါဝင်ပြီး သင် လည်ပတ်ချင်မယ်။ ဒါကို လုပ်ဖို့ သင် လိုအပ်တာက ဒီသင်ခန်းစာ၏ အစိတ်အပိုင်းတစ်ခုအနေဖြင့် ပေးထားတဲ့ Jupyter Notebooks ထဲမှာ Python ကုတ်ကို လည်ပတ်နိုင်စွမ်းရှိဖို့ ဖြစ်ပါတယ်။ ကုတ်ကို လည်ပတ်ဖို့ အနည်းငယ် ရွေးချယ်စရာရှိပါတယ်- diff --git a/translations/my/lessons/0-course-setup/setup.md b/translations/my/lessons/0-course-setup/setup.md index f9cf8036..b4cf6523 100644 --- a/translations/my/lessons/0-course-setup/setup.md +++ b/translations/my/lessons/0-course-setup/setup.md @@ -1,12 +1,3 @@ - # ဒီသင်ရိုးကို စတင်အသုံးပြုခြင်း ## သင်ကျောင်းသားလား? diff --git a/translations/my/lessons/1-Intro/README.md b/translations/my/lessons/1-Intro/README.md index 8f99c27e..a5a0e6ca 100644 --- a/translations/my/lessons/1-Intro/README.md +++ b/translations/my/lessons/1-Intro/README.md @@ -1,12 +1,3 @@ - # AI အကြောင်းအကျဉ်း ![AI အကြောင်းအကျဉ်းအကြောင်းအရာကို ရေးဆွဲထားသော ပုံ](../../../../translated_images/my/ai-intro.bf28d1ac4235881c.webp) diff --git a/translations/my/lessons/1-Intro/assignment.md b/translations/my/lessons/1-Intro/assignment.md index 750a8f41..0804e282 100644 --- a/translations/my/lessons/1-Intro/assignment.md +++ b/translations/my/lessons/1-Intro/assignment.md @@ -1,12 +1,3 @@ - # Game Jam ဂိမ်းများသည် AI နှင့် ML တိုးတက်မှုများ၏ သက်ရောက်မှုကို အလွန်ရရှိထားသော နယ်ပယ်တစ်ခုဖြစ်သည်။ ဤအလုပ်မှာ သင်နှစ်သက်သော ဂိမ်းတစ်ခုကို AI တိုးတက်မှုများ၏ သက်ရောက်မှုကြောင့် အကျိုးသက်ရောက်မှုရှိခဲ့သော ဂိမ်းအကြောင်းကို အတိုချုံးစာတမ်းရေးပါ။ ဂိမ်းသည် ကွန်ပျူတာလုပ်ဆောင်မှုစနစ်အမျိုးမျိုး၏ သက်ရောက်မှုကို ရရှိခဲ့သော ရှေးဟောင်းဂိမ်းတစ်ခုဖြစ်ရမည်။ ကောင်းသော ဥပမာများမှာ Chess သို့မဟုတ် Go ဖြစ်ပြီး pong သို့မဟုတ် Pac-Man ကဲ့သို့သော ဗီဒီယိုဂိမ်းများကိုလည်း ကြည့်ပါ။ ဂိမ်း၏ အတိတ်၊ ပစ္စုပ္ပန်နှင့် AI အနာဂတ်ကို ဆွေးနွေးသော စာတမ်းရေးပါ။ diff --git a/translations/my/lessons/2-Symbolic/README.md b/translations/my/lessons/2-Symbolic/README.md index 00ff8ab7..7aecbd59 100644 --- a/translations/my/lessons/2-Symbolic/README.md +++ b/translations/my/lessons/2-Symbolic/README.md @@ -1,15 +1,6 @@ - # အသိပညာ ကိုယ်စားပြုခြင်းနှင့် ကျွမ်းကျင်သူ စနစ်များ -![Summary of Symbolic AI content](../../../../../../translated_images/my/ai-symbolic.715a30cb610411a6.webp) +![Summary of Symbolic AI content](../../../../translated_images/my/ai-symbolic.715a30cb610411a6.webp) > Sketchnote ကို [Tomomi Imura](https://twitter.com/girlie_mac) က ဖန်တီးထားသည်။ @@ -42,7 +33,7 @@ Symbolic AI တွင် အရေးကြီးသော အယူအဆတစ ထို့ကြောင့် **အသိပညာ ကိုယ်စားပြုခြင်း** ပြဿနာမှာ ကွန်ပျူတာထဲတွင် ဒေတာ ပုံစံဖြင့် အသိပညာကို ထိရောက်စွာ ကိုယ်စားပြုနိုင်ရန် နည်းလမ်းတစ်ခု ရှာဖွေခြင်း ဖြစ်သည်။ ၎င်းကို အောက်ပါအတိုင်း အမျိုးအစားများဖြင့် ဖော်ပြနိုင်သည် - -![Knowledge representation spectrum](../../../../../../translated_images/my/knowledge-spectrum.b60df631852c0217.webp) +![Knowledge representation spectrum](../../../../translated_images/my/knowledge-spectrum.b60df631852c0217.webp) > ပုံကို [Dmitry Soshnikov](http://soshnikov.com) ဖန်တီးသည် @@ -95,7 +86,7 @@ Block သဘောစနစ် | Indent | | | Symbolic AI ၏ အစောပိုင်း အောင်မြင်မှုတစ်ခုမှာ **ကျွမ်းကျင်သူစနစ်များ** ဖြစ်သည် - အခြေအနေ တစ်ခုပေါ်တွင် ကျွမ်းကျင်သူတစ်ယောက်ကဲ့သို့ အလုပ်လုပ်နိုင်သော ကွန်ပျူတာစနစ်များ ဖြစ်သည်။ ၎င်းတို့သည် လူကြီးကျွမ်းကျင်သူတစ်ဦး သို့မဟုတ် ရှုပ်ထွေးမှုတစ်ခုပေါ်အခြေခံ၍ နှစ်ဆစ်စုထားခြင်းဖြစ်သော **သိမြင်မှု ဘဏ် (knowledge base)** ပါဝင်ပြီး၊ ၎င်း အပေါ်တွင် အချို့ ထုတ်ဖော်စဉ်းစားမှု လုပ်ဆောင်သော **inference engine** ပါဝင်သည်။ -![Human Architecture](../../../../../../translated_images/my/arch-human.5d4d35f1bba3ab1c.webp) | ![Knowledge-Based System](../../../../../../translated_images/my/arch-kbs.3ec5c150b09fa8da.webp) +![Human Architecture](../../../../translated_images/my/arch-human.5d4d35f1bba3ab1c.webp) | ![Knowledge-Based System](../../../../translated_images/my/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ လူမျိုးနာရးစနစ်၏ ရိုးရှင်းသည့် ဖွဲ့စည်းမှု | အသိပညာ အခြေပြုစနစ်၏ ဖွဲ့စည်းမှု @@ -107,7 +98,7 @@ Symbolic AI ၏ အစောပိုင်း အောင်မြင်မှ ဥပမာ အဖြစ်၊ တိရစ္ဆာန်ကို ထူးခြားသော ရုပ်ပိုင်းဆိုင်ရာလက္ခဏာများအရ သတ်မှတ်ရန် ဤအောက်ပါ ကျွမ်းကျင်သူစနစ်ကို တွေးကြည့်ပါ - -![AND-OR Tree](../../../../../../translated_images/my/AND-OR-Tree.5592d2c70187f283.webp) +![AND-OR Tree](../../../../translated_images/my/AND-OR-Tree.5592d2c70187f283.webp) > ပုံကို [Dmitry Soshnikov](http://soshnikov.com) ဖန်တီးသည် diff --git a/translations/my/lessons/2-Symbolic/assignment.md b/translations/my/lessons/2-Symbolic/assignment.md index 7165f59a..84e2d3e9 100644 --- a/translations/my/lessons/2-Symbolic/assignment.md +++ b/translations/my/lessons/2-Symbolic/assignment.md @@ -1,12 +1,3 @@ - # အွန်တိုလိုဂျီ တည်ဆောက်ခြင်း အသိပညာအခြေခံကို တည်ဆောက်ခြင်းသည် တစ်ခုသော ခေါင်းစဉ်နှင့် ပတ်သက်သော အချက်အလက်များကို ကိုယ်စားပြုထားသော မော်ဒယ်ကို အမျိုးအစားခွဲခြင်းနှင့် ဆိုင်သည်။ လူတစ်ဦး၊ နေရာတစ်ခု သို့မဟုတ် အရာဝတ္ထုတစ်ခုကဲ့သို့သော ခေါင်းစဉ်တစ်ခုကို ရွေးချယ်ပြီး ထိုခေါင်းစဉ်၏ မော်ဒယ်ကို တည်ဆောက်ပါ။ ဒီသင်ခန်းစာတွင် ဖော်ပြထားသော နည်းလမ်းများနှင့် မော်ဒယ်တည်ဆောက်မှု မဟာဗျူဟာများကို အသုံးပြုပါ။ ဥပမာအားဖြင့်၊ ပရိဘောဂများ၊ မီးအလင်းများ စသဖြင့် ပါဝင်သော ဧည့်ခန်းတစ်ခန်း၏ အွန်တိုလိုဂျီတစ်ခုကို ဖန်တီးခြင်းဖြစ်နိုင်သည်။ ဧည့်ခန်းသည် မီးဖိုချောင်နှင့် ဘယ်လိုကွာခြားသလဲ။ ရေချိုးခန်းနဲ့ရော? ဧည့်ခန်းဟာ ဘာကြောင့် ထမင်းစားခန်းမဟုတ်ဘူးလို့ သိနိုင်သလဲ? သင့်အွန်တိုလိုဂျီကို တည်ဆောက်ရန် [Protégé](https://protege.stanford.edu/) ကို အသုံးပြုပါ။ diff --git a/translations/my/lessons/3-NeuralNetworks/03-Perceptron/README.md b/translations/my/lessons/3-NeuralNetworks/03-Perceptron/README.md index 099ad5f2..2a00e37c 100644 --- a/translations/my/lessons/3-NeuralNetworks/03-Perceptron/README.md +++ b/translations/my/lessons/3-NeuralNetworks/03-Perceptron/README.md @@ -1,12 +1,3 @@ - # နယူးရယ်နက်ဝက်များအကြောင်း: Perceptron ## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ai/quiz/5) @@ -15,7 +6,7 @@ CO_OP_TRANSLATOR_METADATA: | | | |--------------|-----------| -|Frank Rosenblatt | The Mark 1 Perceptron| +|Frank Rosenblatt | The Mark 1 Perceptron| > ပုံများ [Wikipedia မှ](https://en.wikipedia.org/wiki/Perceptron) @@ -34,7 +25,7 @@ y(x) = f(wTx) f ဟာ step activation function ဖြစ်ပါတယ်။ - + ## Perceptron ကို Training လုပ်ခြင်း diff --git a/translations/my/lessons/3-NeuralNetworks/03-Perceptron/lab/README.md b/translations/my/lessons/3-NeuralNetworks/03-Perceptron/lab/README.md index 3881e7ae..19a9a3ef 100644 --- a/translations/my/lessons/3-NeuralNetworks/03-Perceptron/lab/README.md +++ b/translations/my/lessons/3-NeuralNetworks/03-Perceptron/lab/README.md @@ -1,12 +1,3 @@ - # Multi-Class Classification with Perceptron [AI for Beginners Curriculum](https://github.com/microsoft/ai-for-beginners) မှ Lab Assignment။ diff --git a/translations/my/lessons/3-NeuralNetworks/04-OwnFramework/README.md b/translations/my/lessons/3-NeuralNetworks/04-OwnFramework/README.md index 0a6b52ac..d621bb6a 100644 --- a/translations/my/lessons/3-NeuralNetworks/04-OwnFramework/README.md +++ b/translations/my/lessons/3-NeuralNetworks/04-OwnFramework/README.md @@ -1,12 +1,3 @@ - # နယူးရယ်နက်ဝါ့ခ်များကို မိတ်ဆက်ခြင်း။ Multi-Layered Perceptron ယခင်အပိုင်းတွင် သင်သည် အလွယ်ဆုံး နယူးရယ်နက်ဝါ့ခ် မော်ဒယ် - တစ်လွှာတည်းရှိသော perceptron, linear two-class classification မော်ဒယ်ကို လေ့လာခဲ့ပါသည်။ @@ -66,7 +57,7 @@ Gradient descent algorithm သည် အတူတူပင်ဖြစ်သေ ဤ expression များ၏ ဘယ်ဘက်ဆုံးအပိုင်းသည် အတူတူဖြစ်ပြီး loss function မှ စတင်၍ computational graph ကို "နောက်ပြန်" သွားသောအတိုင်း derivatives တွက်ချက်နိုင်သည်။ ထို့ကြောင့် multi-layered perceptron training နည်းလမ်းကို **backpropagation** သို့မဟုတ် 'backprop' ဟုခေါ်သည်။ -compute graph +compute graph > TODO: image citation diff --git a/translations/my/lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md b/translations/my/lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md index 72e6d24f..45e31c07 100644 --- a/translations/my/lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md +++ b/translations/my/lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md @@ -1,12 +1,3 @@ - # MNIST ကိုယ်တိုင်ဖွဲ့စည်းထားသော Framework ဖြင့် ခွဲခြားခြင်း [AI for Beginners Curriculum](https://github.com/microsoft/ai-for-beginners) မှ Lab Assignment။ diff --git a/translations/my/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/my/lessons/3-NeuralNetworks/05-Frameworks/README.md index 0b8f6165..e9b5e0cf 100644 --- a/translations/my/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/my/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -1,12 +1,3 @@ - # Neural Network Frameworks ကျွန်ုပ်တို့သိရှိပြီးသားအတိုင်း၊ နယူးရယ်နက်ဝက်များကို ထိရောက်စွာလေ့ကျင့်နိုင်ရန်အတွက် အောက်ပါအရာနှစ်ခုကို လုပ်ဆောင်ရမည်ဖြစ်သည်- diff --git a/translations/my/lessons/3-NeuralNetworks/05-Frameworks/lab/README.md b/translations/my/lessons/3-NeuralNetworks/05-Frameworks/lab/README.md index 65a01f03..9359064b 100644 --- a/translations/my/lessons/3-NeuralNetworks/05-Frameworks/lab/README.md +++ b/translations/my/lessons/3-NeuralNetworks/05-Frameworks/lab/README.md @@ -1,12 +1,3 @@ - # PyTorch/TensorFlow ဖြင့် အမျိုးအစားခွဲခြားခြင်း [AI for Beginners Curriculum](https://github.com/microsoft/ai-for-beginners) မှ Lab Assignment။ diff --git a/translations/my/lessons/3-NeuralNetworks/README.md b/translations/my/lessons/3-NeuralNetworks/README.md index ce68712a..b1ee0d8a 100644 --- a/translations/my/lessons/3-NeuralNetworks/README.md +++ b/translations/my/lessons/3-NeuralNetworks/README.md @@ -1,12 +1,3 @@ - # နယူးရယ်နက်ဝါ့ခ်များအကြောင်း အကျဉ်းချုပ် ![နယူးရယ်နက်ဝါ့ခ်များအကြောင်း အကျဉ်းချုပ်ကို ရေးဆွဲထားသော ပုံ](../../../../translated_images/my/ai-neuralnetworks.1c687ae40bc86e83.webp) diff --git a/translations/my/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/my/lessons/4-ComputerVision/06-IntroCV/README.md index f6bd5f13..829458f2 100644 --- a/translations/my/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/my/lessons/4-ComputerVision/06-IntroCV/README.md @@ -1,12 +1,3 @@ - # ကွန်ပျူတာဗီရှင်းအကြောင်းမိတ်ဆက် [ကွန်ပျူတာဗီရှင်း](https://wikipedia.org/wiki/Computer_vision) ဆိုတာက ကွန်ပျူတာတွေကို ဒစ်ဂျစ်တယ်ပုံရိပ်တွေကို အဆင့်မြင့်နားလည်မှုရရှိစေဖို့ ရည်ရွယ်တဲ့ အပိုင်းတစ်ခုဖြစ်ပါတယ်။ ဒီအဓိပ္ပါယ်က အတော်လေးကျယ်ပြန့်ပါတယ်၊ အကြောင်းမူတည်ပြီး *နားလည်မှု* ဆိုတာ အမျိုးမျိုးဖြစ်နိုင်ပါတယ်။ ဥပမာအားဖြင့် ပုံထဲမှာ အရာဝတ္ထုတစ်ခုကို ရှာဖွေခြင်း (**object detection**), ဖြစ်ပျက်နေတဲ့အရာကို နားလည်ခြင်း (**event detection**), ပုံကို စာသားနဲ့ ဖော်ပြခြင်း, ဒါမှမဟုတ် 3D အနေအထားနဲ့ ရှုခင်းကို ပြန်လည်တည်ဆောက်ခြင်း။ လူနဲ့ဆိုင်တဲ့ ပုံရိပ်တွေကို အထူးလုပ်ငန်းတွေပါရှိပါတယ် - အသက်အရွယ်နဲ့ ခံစားချက်ခန့်မှန်းခြင်း, မျက်နှာရှာဖွေခြင်းနဲ့ မှတ်ပုံတင်ခြင်း, 3D အနေအထားခန့်မှန်းခြင်း စသည်ဖြင့်။ @@ -117,7 +108,7 @@ Optical flow အကြောင်းကို [ဒီအလွန်ကော ဒီ lab မှာ လက်သွားလက်လာရဲ့ ဗီဒီယိုကို ရိုက်ကူးပြီး optical flow ကို အသုံးပြုပြီး အပေါ်/အောက်/ဘယ်/ညာ လှုပ်ရှားမှုတွေကို ရှာဖွေဖို့ ရည်ရွယ်ပါတယ်။ -Palm Movement Frame +Palm Movement Frame --- diff --git a/translations/my/lessons/4-ComputerVision/06-IntroCV/lab/README.md b/translations/my/lessons/4-ComputerVision/06-IntroCV/lab/README.md index e089c07e..7e37b69a 100644 --- a/translations/my/lessons/4-ComputerVision/06-IntroCV/lab/README.md +++ b/translations/my/lessons/4-ComputerVision/06-IntroCV/lab/README.md @@ -1,12 +1,3 @@ - # Optical Flow ကို အသုံးပြု၍ လှုပ်ရှားမှုများကို ရှာဖွေခြင်း [AI for Beginners Curriculum](https://aka.ms/ai-beginners) မှ Lab Assignment။ diff --git a/translations/my/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/my/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 2ee77b53..0e064d1a 100644 --- a/translations/my/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/my/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -1,12 +1,3 @@ - # လူသိများသော CNN အဆောက်အအုံများ ### VGG-16 @@ -25,7 +16,7 @@ VGG သည် convolution-pooling layers များ၏ အစဉ်အတိ ResNet သည် Microsoft Research မှ 2015 ခုနှစ်တွင် တင်ပြခဲ့သော model များ၏ မိသားစုဖြစ်သည်။ ResNet ၏ အဓိကအကြောင်းအရာမှာ **residual blocks** ကို အသုံးပြုခြင်းဖြစ်သည်- - + > [ဒီစာတမ်း](https://arxiv.org/pdf/1512.03385.pdf) မှရရှိသော ပုံ @@ -37,7 +28,7 @@ Identity pass-through ကို အသုံးပြုရသည့် အက Google Inception architecture သည် ဤအကြောင်းအရာကို တစ်ဆင့်ပိုမိုတိုးတက်စေပြီး network layer တစ်ခုစီကို path များစွာ၏ ပေါင်းစပ်အဖြစ် တည်ဆောက်သည်- - + > [Researchgate](https://www.researchgate.net/figure/Inception-module-with-dimension-reductions-left-and-schema-for-Inception-ResNet-v1_fig2_355547454) မှရရှိသော ပုံ diff --git a/translations/my/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/my/lessons/4-ComputerVision/07-ConvNets/README.md index 09df45c5..249f8968 100644 --- a/translations/my/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/my/lessons/4-ComputerVision/07-ConvNets/README.md @@ -1,12 +1,3 @@ - # Convolutional Neural Networks မကြာသေးမီက ကျွန်တော်တို့ neural networks တွေဟာ ပုံတွေကို ကောင်းကောင်းကိုင်တွယ်နိုင်ပြီး၊ တစ်လွှာတည်းသော perceptron ကတောင် MNIST dataset ထဲက လက်ရေးအက္ခရာဂဏန်းတွေကို တော်တော်လေးတိကျမှုရှိရှိနဲ့ မှတ်မိနိုင်တယ်ဆိုတာကို မြင်ခဲ့ပါတယ်။ သို့သော်လည်း MNIST dataset ဟာ အထူးတလည်ဖြစ်ပြီး၊ အက္ခရာဂဏန်းတွေဟာ ပုံထဲမှာ အလယ်မှာထားရှိထားတာကြောင့် အလုပ်ကို ပိုမိုလွယ်ကူစေပါတယ်။ @@ -24,7 +15,7 @@ Patterns တွေကို ရှာဖွေဖို့ **convolutional filte ဥပမာ၊ MNIST digits တွေကို 3x3 vertical edge နဲ့ horizontal edge filters တွေကို အသုံးပြုရင်၊ မူရင်းပုံထဲမှာ vertical နဲ့ horizontal edges ရှိတဲ့နေရာတွေမှာ highlight (ဥပမာ high values) တွေကို ရနိုင်ပါတယ်။ ဒါကြောင့် အဲဒီ filter နှစ်ခုကို edges တွေကို "ရှာဖွေ"ဖို့ အသုံးပြုနိုင်ပါတယ်။ အဲဒီလိုပဲ၊ အခြား low-level patterns တွေကို ရှာဖွေဖို့ filter တွေကို design လုပ်နိုင်ပါတယ်။ - + > Image of [Leung-Malik Filter Bank](https://www.robots.ox.ac.uk/~vgg/research/texclass/filters.html) diff --git a/translations/my/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/my/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 1d3df1b8..411c4472 100644 --- a/translations/my/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/my/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -1,12 +1,3 @@ - # အိမ်မွေးတိရစ္ဆာန်မျက်နှာများ အမျိုးအစားခွဲခြားခြင်း [AI for Beginners Curriculum](https://github.com/microsoft/ai-for-beginners) မှ Lab Assignment။ diff --git a/translations/my/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/my/lessons/4-ComputerVision/08-TransferLearning/README.md index 2863df68..935b4ec1 100644 --- a/translations/my/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/my/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -1,12 +1,3 @@ - # Pre-trained Networks and Transfer Learning CNN များကို သင်ကြားရန် အချိန်များစွာ လိုအပ်ပြီး အချက်အလက်များစွာ လိုအပ်ပါသည်။ သို့သော် အချိန်အများစုမှာ ပုံများမှ pattern များကို ထုတ်ယူရန် အသုံးပြုနိုင်သော အနိမ့်အဆင့် filter များကို သင်ယူရန် အသုံးပြုသည်။ သဘာဝအတိုင်း မေးခွန်းတစ်ခု ထွက်ပေါ်လာသည် - တစ်ခုသော dataset တွင် သင်ကြားထားသော neural network ကို အသုံးပြု၍ အခြားပုံများကို အပြည့်အဝ သင်ကြားမှုမလိုအပ်ဘဲ ခွဲခြားနိုင်မည်လား? diff --git a/translations/my/lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md b/translations/my/lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md index ccf91afb..b5277cda 100644 --- a/translations/my/lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md +++ b/translations/my/lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md @@ -1,12 +1,3 @@ - # အနက်ရှိုင်းသော သင်ကြားမှု လေ့ကျင့်မှု နည်းလမ်းများ နယူးရယ်နက်ဝက်များ ပိုမိုနက်ရှိုင်းလာသည်နှင့်အမျှ၊ ၎င်းတို့ကို လေ့ကျင့်ခြင်းလုပ်ငန်းစဉ်သည် ပိုမိုခက်ခဲလာသည်။ အဓိကပြဿနာတစ်ခုမှာ [vanishing gradients](https://en.wikipedia.org/wiki/Vanishing_gradient_problem) သို့မဟုတ် [exploding gradients](https://deepai.org/machine-learning-glossary-and-terms/exploding-gradient-problem#:~:text=Exploding%20gradients%20are%20a%20problem,updates%20are%20small%20and%20controlled.) ဖြစ်သည်။ [ဒီပို့စ်](https://towardsdatascience.com/the-vanishing-exploding-gradient-problem-in-deep-neural-networks-191358470c11) သည် ၎င်းပြဿနာများအပေါ် အကျဉ်းချုပ်ကောင်းတစ်ခုကို ပေးသည်။ diff --git a/translations/my/lessons/4-ComputerVision/08-TransferLearning/lab/README.md b/translations/my/lessons/4-ComputerVision/08-TransferLearning/lab/README.md index 09e047b8..82c1f5ad 100644 --- a/translations/my/lessons/4-ComputerVision/08-TransferLearning/lab/README.md +++ b/translations/my/lessons/4-ComputerVision/08-TransferLearning/lab/README.md @@ -1,12 +1,3 @@ - # Oxford အိမ်မွေးတိရစ္ဆာန်များကို Transfer Learning အသုံးပြု၍ ခွဲခြားခြင်း [AI for Beginners Curriculum](https://github.com/microsoft/ai-for-beginners) မှ Lab Assignment။ diff --git a/translations/my/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/my/lessons/4-ComputerVision/09-Autoencoders/README.md index 8a2ee488..9e011bb3 100644 --- a/translations/my/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/my/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -1,12 +1,3 @@ - # အော်တိုအင်ကိုဒါများ CNN များကို လေ့ကျင့်ရာတွင် တစ်ခုသော ပြဿနာမှာ အမှတ်အသားပြုထားသော ဒေတာများ များစွာ လိုအပ်သည်။ ပုံရိပ်များကို အမျိုးအစားအလိုက် ခွဲခြားရန် လိုအပ်သောအခါ၊ ၎င်းသည် လက်ဖြင့်လုပ်ဆောင်ရသော အလုပ်ဖြစ်သည်။ @@ -46,7 +37,7 @@ VAE သည် latent parameters များ၏ *statistical distribution* က * Distribution N(zmean,exp(zlog\_sigma)) မှ sample vector ကို ယူသည် * Decoder သည် sample ကို input vector အဖြစ် အသုံးပြု၍ မူရင်းပုံရိပ်ကို ပြန်လည်ဖန်တီးရန် ကြိုးစားသည် - + > ပုံရိပ် - [Isaak Dykeman ၏ ဘလော့](https://ijdykeman.github.io/ml/2016/12/21/cvae.html) @@ -57,13 +48,13 @@ Variational auto-encoders တွင် loss function အစိတ်အပို VAE များ၏ အရေးကြီးသော အားသာချက်တစ်ခုမှာ latent vectors များကို sample လုပ်ရန် distribution ကို သိရှိထားသောကြောင့် အသစ်သော ပုံရိပ်များကို အလွယ်တကူ ဖန်တီးနိုင်ခြင်းဖြစ်သည်။ ဥပမာအားဖြင့် MNIST dataset ကို 2D latent vector ဖြင့် VAE ကို လေ့ကျင့်ပါက၊ latent vector ၏ components များကို အပြောင်းအလဲလုပ်၍ အမျိုးမျိုးသော digit များကို ရနိုင်သည် - -vaemnist +vaemnist > ပုံရိပ် - [Dmitry Soshnikov](http://soshnikov.com) latent parameter space ၏ အခြားသော အပိုင်းများမှ latent vectors များကို ရယူသည့်အခါ၊ ပုံရိပ်များသည် အချင်းချင်း ပေါင်းစည်းနေသည်ကို တွေ့နိုင်သည်။ ၎င်း space ကို 2D တွင်လည်း မြင်နိုင်သည် - -vaemnist cluster +vaemnist cluster > ပုံရိပ် - [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/my/lessons/4-ComputerVision/10-GANs/README.md b/translations/my/lessons/4-ComputerVision/10-GANs/README.md index 97ecaf73..f050cda1 100644 --- a/translations/my/lessons/4-ComputerVision/10-GANs/README.md +++ b/translations/my/lessons/4-ComputerVision/10-GANs/README.md @@ -1,12 +1,3 @@ - # Generative Adversarial Networks ယခင်အပိုင်းတွင် **generative models** အကြောင်းကို လေ့လာခဲ့ပါသည်။ Generative models ဆိုသည်မှာ သင်ကြားမှု dataset ထဲရှိပုံများနှင့် ဆင်တူသော ပုံအသစ်များကို ဖန်တီးနိုင်သော မော်ဒယ်များဖြစ်သည်။ VAE သည် generative model တစ်ခုအနေဖြင့် ကောင်းမွန်သော ဥပမာတစ်ခုဖြစ်သည်။ @@ -17,7 +8,7 @@ CO_OP_TRANSLATOR_METADATA: GANs ၏ အဓိကအကြောင်းအရာမှာ နေရာတစ်ခုတွင် neural networks နှစ်ခုကို တစ်ခုနှင့်တစ်ခု ယှဉ်ပြိုင်၍ သင်ကြားခြင်းဖြစ်သည်။ - + > ပုံကို [Dmitry Soshnikov](http://soshnikov.com) မှ ဖန်တီးထားသည်။ @@ -41,7 +32,7 @@ Generator သည် အနည်းငယ်ပိုမိုရှုပ်ထ > ✅ Convolution layer ကို linear filter အဖြစ် ပုံကို ဖြတ်သန်း၍ အကောင်အထည်ဖော်သည့်အတွက် deconvolution သည် convolution နှင့် ဆင်တူပြီး အတူတူသော layer logic ကို အသုံးပြု၍ အကောင်အထည်ဖော်နိုင်သည်။ - + > ပုံကို [Dmitry Soshnikov](http://soshnikov.com) မှ ဖန်တီးထားသည်။ diff --git a/translations/my/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/my/lessons/4-ComputerVision/11-ObjectDetection/README.md index d6b02eee..ef052e63 100644 --- a/translations/my/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/my/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -1,12 +1,3 @@ - # Object Detection ယခင်က ကျွန်တော်တို့ handling လုပ်ခဲ့တဲ့ image classification models တွေဟာ ပုံတစ်ပုံကိုယူပြီး categorical ရလဒ်တစ်ခုထုတ်ပေးခဲ့ပါတယ်၊ ဥပမာ MNIST ပြဿနာမှာ 'number' class တစ်ခုလိုပါပဲ။ သို့သော် အများဆုံးအခြေအနေတွေမှာ ပုံတစ်ပုံမှာ object တွေရှိတယ်ဆိုတာကိုသိရုံမကဘဲ၊ အဲ့ဒီ object တွေရှိတဲ့တိကျတဲ့နေရာကိုသိချင်ပါတယ်။ ဒါဟာ **object detection** ရဲ့အဓိကရည်ရွယ်ချက်ပဲဖြစ်ပါတယ်။ diff --git a/translations/my/lessons/4-ComputerVision/11-ObjectDetection/lab/README.md b/translations/my/lessons/4-ComputerVision/11-ObjectDetection/lab/README.md index aa687844..9cf22463 100644 --- a/translations/my/lessons/4-ComputerVision/11-ObjectDetection/lab/README.md +++ b/translations/my/lessons/4-ComputerVision/11-ObjectDetection/lab/README.md @@ -1,12 +1,3 @@ - # Head Detection using Hollywood Heads Dataset [AI for Beginners Curriculum](https://github.com/microsoft/ai-for-beginners) မှ Lab Assignment။ diff --git a/translations/my/lessons/4-ComputerVision/12-Segmentation/README.md b/translations/my/lessons/4-ComputerVision/12-Segmentation/README.md index 08ba72b7..9548f729 100644 --- a/translations/my/lessons/4-ComputerVision/12-Segmentation/README.md +++ b/translations/my/lessons/4-ComputerVision/12-Segmentation/README.md @@ -1,12 +1,3 @@ - # အပိုင်းခွဲခြားခြင်း ယခင်က ကျွန်တော်တို့ Object Detection အကြောင်းကို လေ့လာခဲ့ပြီး၊ *bounding boxes* ကိုခန့်မှန်းခြင်းဖြင့် ပုံထဲမှာရှိတဲ့ objects တွေကို ရှာဖွေနိုင်ခဲ့ပါတယ်။ သို့သော် တချို့သောအလုပ်များအတွက် bounding boxes ပဲမဟုတ်ဘဲ၊ ပိုမိုတိကျတဲ့ object localization လည်းလိုအပ်ပါတယ်။ ဒီအလုပ်ကို **segmentation** လို့ခေါ်ပါတယ်။ @@ -20,7 +11,7 @@ Segmentation ကို **pixel classification** အနေနဲ့ကြည့ ဥပမာအားဖြင့် instance segmentation မှာ ဒီသိုးတွေက object အနေနဲ့ ခွဲခြားထားပြီး၊ semantic segmentation မှာတော့ သိုးအားလုံးကို class တစ်ခုအနေနဲ့သာ ဖော်ပြထားပါတယ်။ - + > ပုံကို [ဒီ blog post](https://nirmalamurali.medium.com/image-classification-vs-semantic-segmentation-vs-instance-segmentation-625c33a08d50) မှရယူထားပါသည်။ @@ -29,7 +20,7 @@ Segmentation အတွက် neural architectures မျိုးစုံရှ * **Encoder** က input image မှ feature တွေကို extract လုပ်ပေးပါတယ်။ * **Decoder** က feature တွေကို **mask image** အဖြစ်ပြောင်းပေးပြီး၊ အရွယ်အစားတူညီပြီး channel အရေအတွက်က class အရေအတွက်နဲ့ကိုက်ညီပါတယ်။ - + > ပုံကို [ဒီ publication](https://arxiv.org/pdf/2001.05566.pdf) မှရယူထားပါသည်။ @@ -43,7 +34,7 @@ Segmentation အတွက် အသုံးပြုတဲ့ loss function က > ✅ ဒီနည်းလမ်းက ဆေးဘက်ပုံရိပ်အမျိုးအစားအတွက် အထူးသင့်လျော်ပါတယ်၊ ဒါပေမယ့် သင်အခြားသော အကွက်များမှာလည်း အသုံးချနိုင်မယ့် နည်းလမ်းတွေကို စဉ်းစားနိုင်ပါသလား? -navi +navi > ပုံကို PH2 Database မှရယူထားပါသည်။ diff --git a/translations/my/lessons/4-ComputerVision/12-Segmentation/lab/README.md b/translations/my/lessons/4-ComputerVision/12-Segmentation/lab/README.md index ddcce9f5..3e4d6a60 100644 --- a/translations/my/lessons/4-ComputerVision/12-Segmentation/lab/README.md +++ b/translations/my/lessons/4-ComputerVision/12-Segmentation/lab/README.md @@ -1,12 +1,3 @@ - # လူ့ခန္ဓာကိုယ် အပိုင်းခွဲခြားခြင်း လက်တွေ့လေ့ကျင့်ရေး အလုပ်စီမံကိန်း - [AI for Beginners Curriculum](https://github.com/microsoft/ai-for-beginners) မှ။ diff --git a/translations/my/lessons/4-ComputerVision/README.md b/translations/my/lessons/4-ComputerVision/README.md index 972ae173..2012a6db 100644 --- a/translations/my/lessons/4-ComputerVision/README.md +++ b/translations/my/lessons/4-ComputerVision/README.md @@ -1,12 +1,3 @@ - # ကွန်ပျူတာမြင်ကြည့်မှု ![ကွန်ပျူတာမြင်ကြည့်မှုအကြောင်းအရာကို ရေးဆွဲထားသောပုံ](../../../../translated_images/my/ai-computervision.6506ebebac3fbf76.webp) diff --git a/translations/my/lessons/5-NLP/13-TextRep/README.md b/translations/my/lessons/5-NLP/13-TextRep/README.md index bb048c66..5d021fb7 100644 --- a/translations/my/lessons/5-NLP/13-TextRep/README.md +++ b/translations/my/lessons/5-NLP/13-TextRep/README.md @@ -1,12 +1,3 @@ - # တိုကင်ဆာများကို Tensor အဖြစ်ဖော်ပြခြင်း ## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ai/quiz/25) @@ -25,7 +16,7 @@ CO_OP_TRANSLATOR_METADATA: Neural network များဖြင့် သဘာဝဘာသာစကားလုပ်ငန်းများ (NLP) ကို ဖြေရှင်းလိုပါက၊ စာသားကို tensor အဖြစ်ဖော်ပြရန် နည်းလမ်းတစ်ခုလိုအပ်သည်။ ကွန်ပျူတာများသည် ASCII သို့မဟုတ် UTF-8 ကဲ့သို့သော encoding များကို အသုံးပြု၍ စာသားအက္ခရာများကို နံပါတ်များအဖြစ် ဖော်ပြထားပြီး သင့် screen ပေါ်တွင် font များအဖြစ် ပြသထားသည်။ -အက္ခရာကို ASCII နှင့် binary ဖော်ပြချက်များသို့ mapping ပြုလုပ်ထားသော diagram ကို ပြသထားသော ပုံ +အက္ခရာကို ASCII နှင့် binary ဖော်ပြချက်များသို့ mapping ပြုလုပ်ထားသော diagram ကို ပြသထားသော ပုံ > [ပုံရင်းအရင်းအမြစ်](https://www.seobility.net/en/wiki/ASCII) @@ -50,7 +41,7 @@ Neural network များဖြင့် သဘာဝဘာသာစကား စာသားအမျိုးအစားခွဲခြင်းကဲ့သို့သော အလုပ်များကို ဖြေရှင်းရာတွင်၊ စာသားကို fixed-size vector တစ်ခုအဖြစ် ဖော်ပြနိုင်ရမည်။ ဤ vector ကို နောက်ဆုံး dense classifier သို့ input အဖြစ် အသုံးပြုမည်။ အလွယ်ဆုံးနည်းလမ်းတစ်ခုမှာ စကားလုံးတစ်ခုစီ၏ representation များကို ပေါင်းစည်းခြင်းဖြစ်သည်။ စကားလုံးတစ်ခုစီ၏ one-hot encoding များကို ပေါင်းစည်းပါက၊ စကားလုံးတစ်ခုစီသည် စာသားအတွင်း ရှိသောအကြိမ်ရေကို ဖော်ပြသော frequency vector ကို ရရှိမည်။ ဤ representation ကို **bag of words** (BoW) ဟုခေါ်သည်။ - + > ပုံရေးသားသူ diff --git a/translations/my/lessons/5-NLP/13-TextRep/assignment.md b/translations/my/lessons/5-NLP/13-TextRep/assignment.md index a99d0fbe..2b68dda8 100644 --- a/translations/my/lessons/5-NLP/13-TextRep/assignment.md +++ b/translations/my/lessons/5-NLP/13-TextRep/assignment.md @@ -1,12 +1,3 @@ - # အလုပ်တာဝန်: Notebooks ဒီသင်ခန်းစာနဲ့ဆက်စပ်နေတဲ့ notebooks (PyTorch version ဒါမှမဟုတ် TensorFlow version) တွေကို သင့်ရဲ့ကိုယ်ပိုင်ဒေတာစနစ်နဲ့ ပြန်လည်အသုံးပြုပါ၊ ဥပမာ Kaggle ကနေရရှိတဲ့ ဒေတာတစ်ခုကို Attribution နဲ့အတူ အသုံးပြုနိုင်ပါတယ်။ သင့်ရဲ့ကိုယ်ပိုင် ရှာဖွေတွေ့ရှိချက်တွေကို အထောက်အထားပြဖို့ notebook ကို ပြန်ရေးဆွဲပါ။ အံ့သြဖွယ်ဖြစ်နိုင်တဲ့ ဒေတာစနစ်တွေကို စမ်းသပ်ကြည့်ပါ၊ ဥပမာ [UFO တွေ့မြင်မှုများအကြောင်း ဒီဒေတာ](https://www.kaggle.com/datasets/NUFORC/ufo-sightings) NUFORC မှ ရရှိနိုင်ပါတယ်။ diff --git a/translations/my/lessons/5-NLP/14-Embeddings/README.md b/translations/my/lessons/5-NLP/14-Embeddings/README.md index 592d1c3c..23463a10 100644 --- a/translations/my/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/my/lessons/5-NLP/14-Embeddings/README.md @@ -1,12 +1,3 @@ - # Embeddings ## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ai/quiz/27) diff --git a/translations/my/lessons/5-NLP/14-Embeddings/assignment.md b/translations/my/lessons/5-NLP/14-Embeddings/assignment.md index d386b54c..c970a15c 100644 --- a/translations/my/lessons/5-NLP/14-Embeddings/assignment.md +++ b/translations/my/lessons/5-NLP/14-Embeddings/assignment.md @@ -1,12 +1,3 @@ - # အလုပ်အမိန့်: နိုတ်ဘွတ်များ ဒီသင်ခန်းစာနှင့်ဆက်စပ်နေသော နိုတ်ဘွတ်များ (PyTorch version သို့မဟုတ် TensorFlow version) ကို သင့်ရဲ့ dataset ကို အသုံးပြုပြီး ပြန်လည်ရိုက်ပါ။ Kaggle က dataset တစ်ခုကို attribution ဖြင့် အသုံးပြုနိုင်ပါတယ်။ သင့်ရဲ့ ရလဒ်များကို အထောက်အထားပြရန် နိုတ်ဘွတ်ကို ပြန်ရေးပါ။ အခြား dataset အမျိုးအစားတစ်ခုကို စမ်းသပ်ပြီး သင့်ရဲ့ ရလဒ်များကို documentation ဖြင့် ဖော်ပြပါ။ ဥပမာ [ဒီ Beatles သီချင်းစာသားများ](https://www.kaggle.com/datasets/jenlooper/beatles-lyrics) ကဲ့သို့သော စာသားများကို အသုံးပြုနိုင်ပါတယ်။ diff --git a/translations/my/lessons/5-NLP/15-LanguageModeling/README.md b/translations/my/lessons/5-NLP/15-LanguageModeling/README.md index 700c4133..c58f9425 100644 --- a/translations/my/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/my/lessons/5-NLP/15-LanguageModeling/README.md @@ -1,12 +1,3 @@ - # Language Modeling Semantic embeddings, Word2Vec နှင့် GloVe က **ဘာသာစကားမော်ဒယ်** ဖန်တီးခြင်းဆီသို့ ပထမအဆင့်အဖြစ် သွားရောက်နေသည်။ ၎င်းသည် ဘာသာစကား၏ သဘာဝကို *နားလည်* (သို့မဟုတ် *ကိုယ်စားပြု*) နိုင်သော မော်ဒယ်များ ဖန်တီးရန် ရည်ရွယ်သည်။ diff --git a/translations/my/lessons/5-NLP/15-LanguageModeling/lab/README.md b/translations/my/lessons/5-NLP/15-LanguageModeling/lab/README.md index c06f0ef1..6ac3bbdf 100644 --- a/translations/my/lessons/5-NLP/15-LanguageModeling/lab/README.md +++ b/translations/my/lessons/5-NLP/15-LanguageModeling/lab/README.md @@ -1,12 +1,3 @@ - # Skip-Gram မော်ဒယ်ကို လေ့ကျင့်ခြင်း [AI for Beginners Curriculum](https://github.com/microsoft/ai-for-beginners) မှ လက်တွေ့လေ့ကျင့်ရေးအလုပ်များ။ diff --git a/translations/my/lessons/5-NLP/16-RNN/README.md b/translations/my/lessons/5-NLP/16-RNN/README.md index b93e0155..e404b117 100644 --- a/translations/my/lessons/5-NLP/16-RNN/README.md +++ b/translations/my/lessons/5-NLP/16-RNN/README.md @@ -1,12 +1,3 @@ - # Recurrent Neural Networks ## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ai/quiz/31) @@ -31,7 +22,7 @@ State vectors S0,...,Sn များကို network အ ရိုးရှင်းသော RNN cell တစ်ခုတွင် weight matrices နှစ်ခုပါဝင်သည်- input symbol ကို transform လုပ်သော W နှင့် input state ကို transform လုပ်သော H တစ်ခု။ ဤအခြေအနေတွင် network ၏ output ကို σ(W×Xi+H×Si-1+b) အဖြစ်တွက်ချက်သည်၊ σ သည် activation function ဖြစ်ပြီး b သည် bias ဖြစ်သည်။ -RNN Cell Anatomy +RNN Cell Anatomy > ပုံကို စာရေးသူမှ ဖန်တီးသည် diff --git a/translations/my/lessons/5-NLP/16-RNN/assignment.md b/translations/my/lessons/5-NLP/16-RNN/assignment.md index 9ec32987..9ccad172 100644 --- a/translations/my/lessons/5-NLP/16-RNN/assignment.md +++ b/translations/my/lessons/5-NLP/16-RNN/assignment.md @@ -1,12 +1,3 @@ - # အလုပ်ပေးချက်: နိုတ်ဘွတ်များ ဒီသင်ခန်းစာနှင့်ဆက်စပ်နေသော နိုတ်ဘွတ်များ (PyTorch version သို့မဟုတ် TensorFlow version) ကို သင့်ရဲ့ dataset ကို အသုံးပြုပြီး ပြန်လည်ရိုက်ပါ။ Kaggle မှ dataset တစ်ခုကို attribution ဖြင့် အသုံးပြုနိုင်ပါသည်။ သင့်ရဲ့ ရလဒ်များကို အထောက်အထားပြရန် နိုတ်ဘွတ်ကို ပြန်ရေးပါ။ အခြား dataset အမျိုးအစားတစ်ခုကို စမ်းသပ်ပြီး သင့်ရဲ့ ရလဒ်များကို documentation ဖြင့် မှတ်တမ်းတင်ပါ။ ဥပမာအားဖြင့် [ဒီ Kaggle ပြိုင်ပွဲ dataset (ရာသီဥတုနှင့်ပတ်သက်သော Twitter)](https://www.kaggle.com/competitions/crowdflower-weather-twitter/data?select=train.csv) ကို အသုံးပြုနိုင်ပါသည်။ diff --git a/translations/my/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/my/lessons/5-NLP/17-GenerativeNetworks/README.md index 24500b2f..e487df66 100644 --- a/translations/my/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/my/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -1,12 +1,3 @@ - # Generative networks ## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ai/quiz/33) @@ -36,7 +27,7 @@ Recurrent Neural Networks (RNNs) နှင့် Long Short Term Memory Cells (L စာသားထုတ်လုပ်ခြင်း (inference အတွင်း) တွင် **prompt** တစ်ခုကို RNN cells မှတဆင့် hidden state ကို ရယူပြီး၊ ထို့နောက် generation ကို စတင်မည်။ စာလုံးတစ်လုံးစီကို အဆင့်ဆင့် ထုတ်လုပ်ပြီး၊ state နှင့် ထုတ်လုပ်ထားသော စာလုံးကို နောက် RNN cell သို့ ပေးပို့ကာ နောက် character ကို ထုတ်လုပ်မည်။ ထိုနောက် လိုအပ်သော စာလုံးများကို ထုတ်လုပ်ပြီးမှ ရပ်မည်။ - + > ပုံကို စာရေးသူမှ ဖန်တီးထားသည် diff --git a/translations/my/lessons/5-NLP/17-GenerativeNetworks/lab/README.md b/translations/my/lessons/5-NLP/17-GenerativeNetworks/lab/README.md index b0aa5383..4896894c 100644 --- a/translations/my/lessons/5-NLP/17-GenerativeNetworks/lab/README.md +++ b/translations/my/lessons/5-NLP/17-GenerativeNetworks/lab/README.md @@ -1,12 +1,3 @@ - # RNN များကို အသုံးပြု၍ စကားလုံးအဆင့် စာသားထုတ်လုပ်ခြင်း [AI for Beginners Curriculum](https://github.com/microsoft/ai-for-beginners) မှ လက်တွေ့လေ့ကျင့်မှု။ diff --git a/translations/my/lessons/5-NLP/18-Transformers/README.md b/translations/my/lessons/5-NLP/18-Transformers/README.md index 1d1e9fd0..86696099 100644 --- a/translations/my/lessons/5-NLP/18-Transformers/README.md +++ b/translations/my/lessons/5-NLP/18-Transformers/README.md @@ -1,12 +1,3 @@ - # အာရုံစိုက်မှု Mechanisms နှင့် Transformers ## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ai/quiz/35) @@ -56,7 +47,7 @@ Positional encoding ၏ အယူအဆမှာ အောက်ပါအတိ * Token embedding ကဲ့သို့ trainable embedding ကို အသုံးပြုခြင်း။ ဒီနည်းလမ်းကို ဒီမှာ သုံးပါမည်။ Token နှင့် position နှစ်ခုစလုံးကို embedding layers တွင် ထည့်သွင်းပြီး၊ အချင်းချင်းတူညီသော dimension ရရှိသော embedding vectors ကို ထည့်ပေါင်းပါသည်။ * Original paper တွင် အကြံပြုထားသော fixed position encoding function ကို အသုံးပြုခြင်း။ - + > ပုံကို အတောအတွင်းရေးသားသူမှ ဖန်တီးထားသည် diff --git a/translations/my/lessons/5-NLP/18-Transformers/assignment.md b/translations/my/lessons/5-NLP/18-Transformers/assignment.md index 2aaafb9e..9edf6076 100644 --- a/translations/my/lessons/5-NLP/18-Transformers/assignment.md +++ b/translations/my/lessons/5-NLP/18-Transformers/assignment.md @@ -1,12 +1,3 @@ - # တာဝန်: Transformers HuggingFace တွင် Transformers ကို စမ်းသပ်ကြည့်ပါ! သူတို့၏ ဝက်ဘ်ဆိုဒ် https://huggingface.co/docs/transformers/run_scripts တွင် ရရှိနိုင်သော မော်ဒယ်များနှင့် အလုပ်လုပ်ရန် ပံ့ပိုးထားသော စာတမ်းများကို စမ်းသပ်ကြည့်ပါ။ သူတို့၏ ဒေတာစနစ်တစ်ခုကို စမ်းသပ်ပြီး၊ ထို့နောက် သင်၏ သင်ရိုးညွှန်းတစ်ခုမှ သို့မဟုတ် Kaggle မှ သင်၏ကိုယ်ပိုင် ဒေတာစနစ်တစ်ခုကို တင်သွင်းပြီး စိတ်ဝင်စားဖွယ် စာသားများကို ထုတ်လုပ်နိုင်မလား ကြည့်ပါ။ သင်၏ ရလဒ်များနှင့်အတူ notebook တစ်ခု ထုတ်လုပ်ပါ။ diff --git a/translations/my/lessons/5-NLP/19-NER/README.md b/translations/my/lessons/5-NLP/19-NER/README.md index 81c41f67..7471867c 100644 --- a/translations/my/lessons/5-NLP/19-NER/README.md +++ b/translations/my/lessons/5-NLP/19-NER/README.md @@ -1,12 +1,3 @@ - # Named Entity Recognition အခုအချိန်ထိ ကျွန်တော်တို့အဓိကထားပြီး လေ့လာခဲ့တာက NLP task တစ်ခုဖြစ်တဲ့ - classification ပဲဖြစ်ပါတယ်။ သို့သော် neural networks ကို အသုံးပြု၍ ပြုလုပ်နိုင်သော အခြားသော NLP task များလည်း ရှိပါသည်။ အဲဒီ task များထဲမှ တစ်ခုက **[Named Entity Recognition](https://wikipedia.org/wiki/Named-entity_recognition)** (NER) ဖြစ်ပြီး၊ စာသားထဲမှာ ရှိတဲ့ အထူး entity များကို ရှာဖွေသိရှိရန် အသုံးပြုသည်။ ဥပမာအားဖြင့် နေရာများ၊ လူအမည်များ၊ ရက်စွဲ-အချိန်ကာလများ၊ ဓာတုဖော်မြူလာများ စသည်တို့ဖြစ်ပါသည်။ @@ -17,7 +8,7 @@ CO_OP_TRANSLATOR_METADATA: ဥပမာအားဖြင့် Amazon Alexa သို့မဟုတ် Google Assistant ကဲ့သို့သော သဘာဝဘာသာစကား chat bot တစ်ခုကို ဖွံ့ဖြိုးတိုးတက်စေလိုသည်ဟု ဆိုပါစို့။ အတတ်နိုင်ဆုံး chat bot များသည် အသုံးပြုသူ၏လိုအပ်ချက်ကို *နားလည်*ရန် input စာကြောင်းအပေါ် text classification ပြုလုပ်ခြင်းဖြင့် အလုပ်လုပ်သည်။ အဲဒီ classification ရလဒ်ကို **intent** ဟုခေါ်ပြီး၊ chat bot သည် ဘာလုပ်ရမည်ကို သတ်မှတ်ပေးသည်။ -Bot NER +Bot NER > ပုံကိုရေးသားသူမှ diff --git a/translations/my/lessons/5-NLP/19-NER/lab/README.md b/translations/my/lessons/5-NLP/19-NER/lab/README.md index 856749c5..41a510a3 100644 --- a/translations/my/lessons/5-NLP/19-NER/lab/README.md +++ b/translations/my/lessons/5-NLP/19-NER/lab/README.md @@ -1,12 +1,3 @@ - # NER လေ့လာရေးအတွက် [AI for Beginners Curriculum](https://github.com/microsoft/ai-for-beginners) မှ Lab Assignment။ diff --git a/translations/my/lessons/5-NLP/20-LangModels/README.md b/translations/my/lessons/5-NLP/20-LangModels/README.md index 3f570832..570a5330 100644 --- a/translations/my/lessons/5-NLP/20-LangModels/README.md +++ b/translations/my/lessons/5-NLP/20-LangModels/README.md @@ -1,12 +1,3 @@ - # ကြိုတင်လေ့ကျင့်ထားသော အကြီးစားဘာသာစကားမော်ဒယ်များ ယခင်အလုပ်များအားလုံးတွင် ကျွန်ုပ်တို့သည် အမှတ်အသားပြုထားသော ဒေတာအစုအဝေးကို အသုံးပြု၍ neural network ကို သတ်မှတ်ထားသော အလုပ်တစ်ခုကို လုပ်ဆောင်ရန် လေ့ကျင့်နေခဲ့သည်။ BERT ကဲ့သို့သော အကြီးစား transformer မော်ဒယ်များတွင် self-supervised နည်းလမ်းဖြင့် ဘာသာစကားမော်ဒယ်တစ်ခုကို တည်ဆောက်ရန် language modelling ကို အသုံးပြုသည်။ ထို့နောက် အထူးသဖြင့် domain-specific လေ့ကျင့်မှုများဖြင့် အထူးပြုထားသော downstream task များအတွက် အသုံးပြုသည်။ သို့သော် အကြီးစားဘာသာစကားမော်ဒယ်များသည် domain-specific လေ့ကျင့်မှုမရှိဘဲ အလုပ်များစွာကို ဖြေရှင်းနိုင်သည်ဟု သက်သေပြထားသည်။ အလုပ်များစွာကို လုပ်ဆောင်နိုင်သော မော်ဒယ်များ၏ မိသားစုကို **GPT** (Generative Pre-Trained Transformer) ဟုခေါ်သည်။ diff --git a/translations/my/lessons/5-NLP/README.md b/translations/my/lessons/5-NLP/README.md index 87153baf..0fbf398a 100644 --- a/translations/my/lessons/5-NLP/README.md +++ b/translations/my/lessons/5-NLP/README.md @@ -1,12 +1,3 @@ - # သဘာဝဘာသာစကားလုပ်ငန်းဆောင်တာ ![NLP လုပ်ငန်းဆောင်တာများကို ရေးဆွဲထားသော ပုံ](../../../../translated_images/my/ai-nlp.b22dcb8ca4707cea.webp) diff --git a/translations/my/lessons/6-Other/21-GeneticAlgorithms/README.md b/translations/my/lessons/6-Other/21-GeneticAlgorithms/README.md index 40801f07..03f6b8b9 100644 --- a/translations/my/lessons/6-Other/21-GeneticAlgorithms/README.md +++ b/translations/my/lessons/6-Other/21-GeneticAlgorithms/README.md @@ -1,12 +1,3 @@ - # ဂျင်နက်တစ် အယ်လဂိုရီသမ်များ ## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ai/quiz/41) diff --git a/translations/my/lessons/6-Other/22-DeepRL/README.md b/translations/my/lessons/6-Other/22-DeepRL/README.md index 1016136b..99de01d6 100644 --- a/translations/my/lessons/6-Other/22-DeepRL/README.md +++ b/translations/my/lessons/6-Other/22-DeepRL/README.md @@ -1,12 +1,3 @@ - # အနက်ရှိုင်းသော Reinforcement Learning Reinforcement learning (RL) သည် supervised learning နှင့် unsupervised learning အနက် machine learning ရဲ့ အခြေခံ paradigm တစ်ခုအဖြစ် သတ်မှတ်ထားသည်။ Supervised learning တွင် ရလဒ်များကို သိရှိထားသော dataset ကို အခြေခံပြီး လေ့လာရသလို RL တွင် **လုပ်ဆောင်ခြင်းမှတစ်ဆင့် လေ့လာခြင်း** ကို အခြေခံသည်။ ဥပမာအားဖြင့် ပထမဆုံး computer game ကို မြင်တွေ့သောအခါ ကျွန်ုပ်တို့သည် အစပျိုးက စတင်ကစားပြီး game ရဲ့ rule များကို မသိရှိဘဲဖြစ်စေ ကစားရင်း ကျွန်ုပ်တို့၏ ကျွမ်းကျင်မှုကို တိုးတက်စေသည်။ @@ -34,7 +25,7 @@ Segway သို့မဟုတ် Gyroscooters ကဲ့သို့သော Balancing ရဲ့ simplified version ကို **CartPole** problem ဟု သိထားသည်။ CartPole world တွင် horizontal slider တစ်ခုရှိပြီး left သို့မဟုတ် right သို့ ရွှေ့နိုင်သည်။ ၎င်း slider ရဲ့ အပေါ်တွင် vertical pole ကို balance လုပ်ရန် ရည်ရွယ်ထားသည်။ -a cartpole +a cartpole ဒီ environment ကို ဖန်တီးပြီး အသုံးပြုရန် Python code အချို့လိုအပ်သည် - diff --git a/translations/my/lessons/6-Other/22-DeepRL/lab/README.md b/translations/my/lessons/6-Other/22-DeepRL/lab/README.md index 3c60c46b..477556e8 100644 --- a/translations/my/lessons/6-Other/22-DeepRL/lab/README.md +++ b/translations/my/lessons/6-Other/22-DeepRL/lab/README.md @@ -1,12 +1,3 @@ - ## ပတ်ဝန်းကျင် Mountain Car ပတ်ဝန်းကျင်သည် ကားတစ်စီးကို တောင်ကြားထဲတွင် ပိတ်မိနေသောအခြေအနေကို ဖော်ပြထားသည်။ သင့်ရည်မှန်းချက်မှာ တောင်ကြားထဲကနေ ခုန်ထွက်ပြီး အလံကို ရောက်ရှိရန်ဖြစ်သည်။ သင့်အနေဖြင့် လုပ်ဆောင်နိုင်သော လှုပ်ရှားမှုများမှာ ဘယ်ဘက်သို့ အရှိန်မြှင့်ခြင်း၊ ညာဘက်သို့ အရှိန်မြှင့်ခြင်း၊ သို့မဟုတ် ဘာမှ မလုပ်ခြင်းဖြစ်သည်။ သင်သည် ကား၏ x-ဦးတည်ချက်တလျှောက်ရှိနေသော တည်နေရာနှင့် အရှိန်ကို ကြည့်ရှုနိုင်သည်။ diff --git a/translations/my/lessons/6-Other/23-MultiagentSystems/README.md b/translations/my/lessons/6-Other/23-MultiagentSystems/README.md index 0758491a..796f2516 100644 --- a/translations/my/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/my/lessons/6-Other/23-MultiagentSystems/README.md @@ -1,12 +1,3 @@ - # Multi-Agent Systems တစ်ခုတည်းသောနည်းလမ်းဖြင့် ဉာဏ်ရည်ရှိမှုကို ရရှိစေခြင်းသည် **ပေါ်ပေါက်မှု** (သို့မဟုတ် **synergetic**) နည်းလမ်းအပေါ် အခြေခံထားသည်။ ၎င်းသည် အလွန်ရိုးရှင်းသော agent များစွာ၏ ပေါင်းစပ်အပြုအမူသည် စနစ်တစ်ခုလုံး၏ ပိုမိုရှုပ်ထွေးသော (သို့မဟုတ် ဉာဏ်ရည်ရှိသော) အပြုအမူကို ဖြစ်ပေါ်စေသည်ဟု ဆိုသည်။ သီအိုရီအရ၊ ၎င်းသည် [Collective Intelligence](https://en.wikipedia.org/wiki/Collective_intelligence), [Emergentism](https://en.wikipedia.org/wiki/Global_brain) နှင့် [Evolutionary Cybernetics](https://en.wikipedia.org/wiki/Global_brain) ၏ မူဝါဒများအပေါ် အခြေခံထားသည်။ ၎င်းတို့က အဆင့်မြင့်စနစ်များသည် အဆင့်နိမ့်စနစ်များမှ သင့်တော်စွာ ပေါင်းစည်းခြင်းဖြင့် တန်ဖိုးတစ်ခုခုကို ထပ်မံရရှိနိုင်သည်ဟု ဆိုသည် (*principle of metasystem transition* ဟုခေါ်သည်)။ @@ -60,7 +51,7 @@ NetLogo ကို [download](https://ccl.northwestern.edu/netlogo/download.shtml NetLogo ၏ အထူးကောင်းမွန်မှုမှာ ၎င်းတွင် စမ်းသပ်နိုင်သော working models များ library ပါဝင်သည်။ **File → Models Library** သို့ သွားပါ၊ ၎င်းတွင် models အမျိုးအစားများစွာ ရွေးချယ်နိုင်သည်။ -NetLogo Models Library +NetLogo Models Library > Dmitry Soshnikov ၏ models library screenshot diff --git a/translations/my/lessons/6-Other/23-MultiagentSystems/assignment.md b/translations/my/lessons/6-Other/23-MultiagentSystems/assignment.md index 399dddab..290620b1 100644 --- a/translations/my/lessons/6-Other/23-MultiagentSystems/assignment.md +++ b/translations/my/lessons/6-Other/23-MultiagentSystems/assignment.md @@ -1,12 +1,3 @@ - # NetLogo အလုပ်ပေးချက် NetLogo ရဲ့ စာကြည့်တိုက်ထဲမှာရှိတဲ့ မော်ဒယ်တစ်ခုကို ယူပြီး အမှန်တကယ်ဖြစ်ပျက်နေတဲ့ အခြေအနေတစ်ခုကို အနီးစပ်ဆုံး simulation လုပ်ပါ။ ကောင်းတဲ့ ဥပမာတစ်ခုက Alternative Visualizations ဖိုလ်ဒါထဲမှာရှိတဲ့ Virus မော်ဒယ်ကို COVID-19 ပျံ့နှံ့မှုကို မော်ဒယ်တစ်ခုအဖြစ် အသုံးပြုနိုင်ပုံကို ပြသဖို့ ပြင်ဆင်ခြင်းဖြစ်ပါတယ်။ အမှန်တကယ် ရောဂါပျံ့နှံ့မှုကို အတုယူတဲ့ မော်ဒယ်တစ်ခုကို တည်ဆောက်နိုင်ပါသလား? diff --git a/translations/my/lessons/7-Ethics/README.md b/translations/my/lessons/7-Ethics/README.md index 17b9bd68..7b5ae97b 100644 --- a/translations/my/lessons/7-Ethics/README.md +++ b/translations/my/lessons/7-Ethics/README.md @@ -1,12 +1,3 @@ - # အကျင့်သင့် AI နှင့် တာဝန်ရှိမှုရှိသော AI ဒီသင်တန်းကို အဆုံးသတ်လိုက်ဖို့နီးပါပြီ၊ အခုအချိန်မှာတော့ AI ဟာ ဒေတာထဲက ဆက်စပ်မှုတွေကို ရှာဖွေပြီး လူသားအပြုအမူတချို့ကို ပြန်လည်တူညီအောင် သင်ကြားပေးနိုင်တဲ့ သင်္ချာနည်းလမ်းတွေကို အခြေခံထားတဲ့ အရာတစ်ခုဖြစ်တယ်ဆိုတာ ရှင်းလင်းစွာနားလည်နိုင်ပြီလို့ မျှော်လင့်ပါတယ်။ ယနေ့ခေတ်မှာ AI ကို ဒေတာထဲက ပုံစံတွေကို ရှာဖွေပြီး ပြဿနာအသစ်တွေကို ဖြေရှင်းဖို့ အသုံးချနိုင်တဲ့ အလွန်အစွမ်းထက်တဲ့ ကိရိယာတစ်ခုအဖြစ် သတ်မှတ်ထားပါတယ်။ diff --git a/translations/my/lessons/README.md b/translations/my/lessons/README.md index 41de2207..dc41ccf4 100644 --- a/translations/my/lessons/README.md +++ b/translations/my/lessons/README.md @@ -1,12 +1,3 @@ - # အကျဉ်းချုပ် ![အကျဉ်းချုပ်ကို ရေးဆွဲထားသော ပုံ](../../../translated_images/my/ai-overview.0857791951d19500.webp) diff --git a/translations/my/lessons/X-Extras/X1-MultiModal/README.md b/translations/my/lessons/X-Extras/X1-MultiModal/README.md index 9373acca..2ed1fab6 100644 --- a/translations/my/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/my/lessons/X-Extras/X1-MultiModal/README.md @@ -1,12 +1,3 @@ - # Multi-Modal Networks NLP အလုပ်များကို ဖြေရှင်းရန် transformer models အောင်မြင်ပြီးနောက်၊ အတူတူ သို့မဟုတ် ဆင်တူသော architecture များကို computer vision အလုပ်များတွင်လည်း အသုံးပြုလာကြသည်။ Vision နှင့် သဘာဝဘာသာစကားစွမ်းရည်များကို *ပေါင်းစပ်* လုပ်နိုင်မည့် မော်ဒယ်များကို တည်ဆောက်ရန် စိတ်ဝင်စားမှုများလာသည်။ OpenAI မှ CLIP နှင့် DALL.E ဟုခေါ်သော ကြိုးစားမှုတစ်ခုကို ပြုလုပ်ခဲ့သည်။ diff --git a/translations/my/lessons/sketchnotes/LICENSE.md b/translations/my/lessons/sketchnotes/LICENSE.md index 625306c6..c6d018b0 100644 --- a/translations/my/lessons/sketchnotes/LICENSE.md +++ b/translations/my/lessons/sketchnotes/LICENSE.md @@ -1,12 +1,3 @@ - Attribution-ShareAlike 4.0 International ======================================================================= diff --git a/translations/my/lessons/sketchnotes/README.md b/translations/my/lessons/sketchnotes/README.md index 0bbdf021..d9b151f9 100644 --- a/translations/my/lessons/sketchnotes/README.md +++ b/translations/my/lessons/sketchnotes/README.md @@ -1,12 +1,3 @@ - အခြေခံသင်ခန်းစာများ၏ အကျဉ်းချုပ်များကို ဒီနေရာမှာ ဒေါင်းလုဒ်လုပ်နိုင်ပါတယ်။ 🎨 ဖန်တီးသူ - Tomomi Imura (Twitter: [@girlie_mac](https://twitter.com/girlie_mac), GitHub: [girliemac](https://github.com/girliemac)) diff --git a/translations/my/troubleshoot.md b/translations/my/troubleshoot.md index e752f067..a57da98d 100644 --- a/translations/my/troubleshoot.md +++ b/translations/my/troubleshoot.md @@ -1,12 +1,3 @@ - # AI-For-Beginners ပြဿနာဖြေရှင်းလမ်းညွှန် ဤလမ်းညွှန်သည် [AI-For-Beginners](https://github.com/microsoft/AI-For-Beginners) repository ကို အသုံးပြုခြင်း သို့မဟုတ် အထောက်အကူပြုခြင်းအတွင်း တွေ့ကြုံရသော ပုံမှန်ပြဿနာများကို ဖြေရှင်းရန် ကူညီပေးပါသည်။ ပြဿနာတစ်ခုစီတွင် နောက်ခံအချက်အလက်များ၊ ရောဂါလက္ခဏာများ၊ ရှင်းလင်းချက်များနှင့် အဆင့်လိုက်ဖြေရှင်းနည်းများ ပါဝင်သည်။ diff --git a/translations/sl/.co-op-translator.json b/translations/sl/.co-op-translator.json new file mode 100644 index 00000000..60351201 --- /dev/null +++ b/translations/sl/.co-op-translator.json @@ -0,0 +1,398 @@ +{ + "AGENTS.md": { + "original_hash": "6b11a37115944252ab3ed04e358d830d", + "translation_date": "2025-10-03T09:31:10+00:00", + "source_file": "AGENTS.md", + "language_code": "sl" + }, + "README.md": { + "original_hash": 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c3167c30..0bbdb43b 100644 --- a/translations/sl/README.md +++ b/translations/sl/README.md @@ -1,12 +1,3 @@ - [![GitHub license](https://img.shields.io/github/license/microsoft/AI-For-Beginners.svg)](https://github.com/microsoft/AI-For-Beginners/blob/main/LICENSE) [![GitHub contributors](https://img.shields.io/github/contributors/microsoft/AI-For-Beginners.svg)](https://GitHub.com/microsoft/AI-For-Beginners/graphs/contributors/) [![GitHub issues](https://img.shields.io/github/issues/microsoft/AI-For-Beginners.svg)](https://GitHub.com/microsoft/AI-For-Beginners/issues/) @@ -23,31 +14,32 @@ CO_OP_TRANSLATOR_METADATA: # Umetna inteligenca za začetnike - učni načrt -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../../../translated_images/sl/ai-overview.0857791951d19500.webp)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/sl/ai-overview.0857791951d19500.webp)| |:---:| -| AI Za začetnike - _Sketchnote avtorja [@girlie_mac](https://twitter.com/girlie_mac)_ | +| AI za začetnike - _Skiznot avtorice [@girlie_mac](https://twitter.com/girlie_mac)_ | + +Raziščite svet **umetne inteligence** (UI) z našim 12-tedenskim učnim načrtom s 24 lekcijami! Vključuje praktične lekcije, kvize in delavnice. Učni načrt je prijazen začetnikom in pokriva orodja, kot sta TensorFlow in PyTorch, ter tudi etiko v UI. -Raziskujte svet **umetne inteligence** (UI) z našim 12-tedenskim učnim načrtom s 24 lekcijami! Vključuje praktične lekcije, kvize in laboratorijske vaje. Učni načrt je prijazen do začetnikov in pokriva orodja, kot so TensorFlow in PyTorch, ter etiko v UI. ### 🌐 Podpora za več jezikov -#### Podprto preko GitHub Actions (avtomatizirano in vedno posodobljeno) +#### Podprto preko GitHub Action (avtomatizirano in vedno posodobljeno) -[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](./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) +[Arabic](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgarian](../bg/README.md) | [Burmese (Myanmar)](../my/README.md) | [Chinese (Simplified)](../zh-CN/README.md) | [Chinese (Traditional, Hong Kong)](../zh-HK/README.md) | [Chinese (Traditional, Macau)](../zh-MO/README.md) | [Chinese (Traditional, Taiwan)](../zh-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)](../pt-BR/README.md) | [Portuguese (Portugal)](../pt-PT/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Romanian](../ro/README.md) | [Russian](../ru/README.md) | [Serbian (Cyrillic)](../sr/README.md) | [Slovak](../sk/README.md) | [Slovenian](./README.md) | [Spanish](../es/README.md) | [Swahili](../sw/README.md) | [Swedish](../sv/README.md) | [Tagalog (Filipino)](../tl/README.md) | [Tamil](../ta/README.md) | [Telugu](../te/README.md) | [Thai](../th/README.md) | [Turkish](../tr/README.md) | [Ukrainian](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamese](../vi/README.md) > **Raje klonirate lokalno?** -> To skladišče vsebuje več kot 50 prevodov jezikov, kar bistveno poveča velikost prenosa. Za kloniranje brez prevodov uporabite sparse checkout: +> Ta repozitorij vključuje več kot 50 prevodov, kar znatno poveča velikost prenosa. Za klon brez prevodov uporabite sparse checkout: > ```bash > git clone --filter=blob:none --sparse https://github.com/microsoft/AI-For-Beginners.git > cd AI-For-Beginners > git sparse-checkout set --no-cone '/*' '!translations' '!translated_images' > ``` -> Tako dobite vse, kar potrebujete za zaključek tečaja z veliko hitrejšim prenosom. +> Tako dobite vse, kar potrebujete za dokončanje tečaja z veliko hitrejšim prenosom. -**Če želite podporo za dodatne jezike prevodov, so ti navedeni [tukaj](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** +**Če želite podpreti dodatne jezike prevodov, so podprti jeziki navedeni [tukaj](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** ## Pridružite se skupnosti [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) @@ -58,23 +50,23 @@ Raziskujte svet **umetne inteligence** (UI) z našim 12-tedenskim učnim načrto V tem učnem načrtu se boste naučili: -* Različnih pristopov k umetni inteligenci, vključno z "dobrim starim" simbolnim pristopom s **predstavitvijo znanja** in sklepanjem ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). -* **Nevronske mreže** in **globoko učenje**, ki sta v središču sodobne UI. Koncepte teh pomembnih tem bomo ilustrirali z uporabo kode v dveh najpopularnejših okvirjih - [TensorFlow](http://Tensorflow.org) in [PyTorch](http://pytorch.org). -* **Nevronske arhitekture** za delo z slikami in besedilom. Pokrili bomo novejše modele, vendar morda nekoliko manj izpopolnjene najnovejše tehnologije. -* Manj priljubljene pristope UI, kot so **genetski algoritmi** in **sistemi z več agenti**. +* Različnih pristopov k umetni inteligenci, vključno z "dobrim starim" simbolnim pristopom s **predstavljanjem znanja** in sklepanjem ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). +* **Nevronskih mrež** in **globokega učenja**, ki so jedro sodobne UI. Pojasnili bomo koncepte za tema pomembnima področjema z uporabo kode v dveh najbolj priljubljenih okvirjih - [TensorFlow](http://Tensorflow.org) in [PyTorch](http://pytorch.org). +* **Nevronskih arhitektur** za delo s slikami in besedilom. Pokrili bomo najnovejše modele, čeprav morda ne povsem najnovejše dosežke. +* Manj priljubljene pristope k UI, kot so **genetski algoritmi** in **večagentni sistemi**. -Česar ta učni načrt ne bo pokrival: +Kaj ne bomo zajemali v tem učnem načrtu: -> [Vse dodatne vire za ta tečaj poiščite v naši zbirki Microsoft Learn](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) +> [Poiščite vse dodatne vire za ta tečaj v naši zbirki Microsoft Learn](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) -* Poslovnih primerov uporabe **UI v podjetjih**. Razmislite o vpisu na učni program [Uvod v UI za poslovne uporabnike](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) na Microsoft Learn ali [AI Business School](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), ki je nastal v sodelovanju z [INSEAD](https://www.insead.edu/). -* **Klasičnega strojnega učenja**, ki je dobro opisan v našem [učnem načrtu Strojno učenje za začetnike](http://github.com/Microsoft/ML-for-Beginners). -* Praktičnih UI aplikacij zgrajenih z uporabo **[Kognitivnih storitev](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**. Za to priporočamo začetek z moduli Microsoft Learn za [vid](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [obdelavo naravnega jezika](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[generativno UI z Azure OpenAI Service](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** in druge. -* Specifičnih ML **oblakovnih ogrodij**, kot so [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-bethanycheum), [Microsoft Fabric](https://learn.microsoft.com/en-us/training/paths/get-started-fabric/?WT.mc_id=academic-77998-bethanycheum) ali [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). Razmislite o uporabi učnih poti [Izgradnja in upravljanje rešitev strojnega učenja z Azure Machine Learning](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) in [Izgradnja in upravljanje rešitev strojnega učenja z Azure Databricks](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum). -* **Konverzacijskih UI** in **klepetalnih robotov (Chat Botov)**. Obstaja posebej [Ustvarjanje konverzacijskih UI rešitev](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum) učna pot, lahko pa se tudi sklicujete na [ta blog objavo](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) za več podrobnosti. -* **Globoke matematike** za globoko učenje. Za to priporočamo [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) avtorjev Ian Goodfellow, Yoshua Bengio in Aaron Courville, ki je tudi na voljo spletno na [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/). +* Poslovnih primerov uporabe **UI v poslovanju**. Razmislite o opravilu učne poti [Uvod v UI za poslovne uporabnike](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) na Microsoft Learn ali [AI poslovno šolo](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), razvito v sodelovanju z [INSEAD](https://www.insead.edu/). +* **Klasičnega strojnega učenja**, ki je dobro opisan v našem [Učnem načrtu strojnega učenja za začetnike](http://github.com/Microsoft/ML-for-Beginners). +* Praktičnih UI aplikacij, zgrajenih z uporabo **[Kognitivnih storitev](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**. Za to priporočamo, da začnete z moduli Microsoft Learn za [vid](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [obdelavo naravnega jezika](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[generativno UI z Azure OpenAI Service](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** in druge. +* Specifične ML **oblake ogrodja**, kot so [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-bethanycheum), [Microsoft Fabric](https://learn.microsoft.com/en-us/training/paths/get-started-fabric/?WT.mc_id=academic-77998-bethanycheum), ali [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). Uporabite učni poti [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) in [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). +* **Pogovorno UI** in **pogovorne bote**. Obstaja ločena učna pot [Ustvarite pogovorne UI rešitve](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum), prav tako pa si lahko ogledate [to objavo na blogu](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) za več podrobnosti. +* **Globoko matematiko** za globoko učenje. Za to priporočamo [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) avtorjev Ian Goodfellow, Yoshua Bengio in Aaron Courville, ki je na voljo tudi na spletu na [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/). -Za mehkejši uvod v teme _UI v oblaku_ lahko razmislite o vpisu na učno pot [Začnite z umetno inteligenco na Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum). +Za prijazen uvod v teme _UI v oblaku_ razmislite o opravljanju učne poti [Začni z umetno inteligenco na Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum). # Vsebina @@ -84,91 +76,92 @@ Za mehkejši uvod v teme _UI v oblaku_ lahko razmislite o vpisu na učno pot [Za | I | [**Uvod v UI**](./lessons/1-Intro/README.md) | | | | 01 | [Uvod in zgodovina UI](./lessons/1-Intro/README.md) | - | - | | II | **Simbolna UI** | -| 02 | [Predstavitev znanja in ekspertni sistemi](./lessons/2-Symbolic/README.md) | [Ekspertni sistemi](./lessons/2-Symbolic/Animals.ipynb) / [Ontologija](./lessons/2-Symbolic/FamilyOntology.ipynb) /[Konceptualni graf](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | +| 02 | [Predstavljanje znanja in ekspertni sistemi](./lessons/2-Symbolic/README.md) | [Ekspertni sistemi](./lessons/2-Symbolic/Animals.ipynb) / [Ontologija](./lessons/2-Symbolic/FamilyOntology.ipynb) /[Graf konceptov](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | | III | [**Uvod v nevronske mreže**](./lessons/3-NeuralNetworks/README.md) ||| | 03 | [Perceptron](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [Zvezek](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [Laboratorij](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | | 04 | [Večplastni perceptron in ustvarjanje lastnega ogrodja](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [Zvezek](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [Laboratorij](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | -| 05 | [Uvod v ogrodja (PyTorch/TensorFlow) in prekomerno prileganje](./lessons/3-NeuralNetworks/05-Frameworks/README.md) | [PyTorch](./lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb) / [Keras](./lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Laboratorij](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | -| IV | [**Računalniški vid**](./lessons/4-ComputerVision/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-77998-cacaste) / [TensorFlow](https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-77998-cacaste)| [Raziščite računalniški vid na Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | +| 05 | [Uvod v ogrodja (PyTorch/TensorFlow) in prenaučenje](./lessons/3-NeuralNetworks/05-Frameworks/README.md) | [PyTorch](./lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb) / [Keras](./lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Laboratorij](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | +| IV | [**Računalniški vid**](./lessons/4-ComputerVision/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-77998-cacaste) / [TensorFlow](https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-77998-cacaste)| [Raziskuj računalniški vid na Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | | 06 | [Uvod v računalniški vid. OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [Zvezek](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [Laboratorij](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | -| 07 | [Konvolucijske nevronske mreže](./lessons/4-ComputerVision/07-ConvNets/README.md) & [Arhitekture CNN](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [Laboratorij](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | -| 08 | [Predhodno usposobljene mreže in prenosno učenje](./lessons/4-ComputerVision/08-TransferLearning/README.md) in [Triki pri učenju](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Laboratorij](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | -| 09 | [Avtoenkoderji in VAEs](./lessons/4-ComputerVision/09-Autoencoders/README.md) | [PyTorch](./lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb) | | -| 10 | [Generativne nasprotujoče si mreže in prenos umetniškega sloga](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | +| 07 | [Konvolucijske nevronske mreže](./lessons/4-ComputerVision/07-ConvNets/README.md) & [CNN arhitekture](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [Laboratorij](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | +| 08 | [Predhodno usposobljene mreže in prenosno učenje](./lessons/4-ComputerVision/08-TransferLearning/README.md) in [Triki pri usposabljanju](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Laboratorij](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | +| 09 | [Avtoenkoderji in VAE-ji](./lessons/4-ComputerVision/09-Autoencoders/README.md) | [PyTorch](./lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb) | | +| 10 | [Generativne nasprotujoče mreže in prenos umetniškega stila](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | | 11 | [Zaznavanje predmetov](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [Laboratorij](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | | 12 | [Semantična segmentacija. U-Net](./lessons/4-ComputerVision/12-Segmentation/README.md) | [PyTorch](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb) | | -| V | [**Obdelava naravnega jezika**](./lessons/5-NLP/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-77998-cacaste) /[TensorFlow](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-77998-cacaste) | [Raziščite obdelavo naravnega jezika na Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| +| V | [**Obdelava naravnega jezika**](./lessons/5-NLP/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-77998-cacaste) /[TensorFlow](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-77998-cacaste) | [Raziskuj obdelavo naravnega jezika na Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| | 13 | [Predstavitev besedila. Bow/TF-IDF](./lessons/5-NLP/13-TextRep/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb) | | -| 14 | [Semantične vdelave besed. Word2Vec in GloVe](./lessons/5-NLP/14-Embeddings/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb) | | -| 15 | [Modeliranje jezika. Učenje lastnih vdelav](./lessons/5-NLP/15-LanguageModeling/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-PyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-TF.ipynb) | [Laboratorij](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | +| 14 | [Semantični vgrajeni zapisi besed. Word2Vec in GloVe](./lessons/5-NLP/14-Embeddings/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb) | | +| 15 | [Modeliranje jezika. Usposabljanje lastnih vgrajenih zapisov](./lessons/5-NLP/15-LanguageModeling/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-PyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-TF.ipynb) | [Laboratorij](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | | 16 | [Rekurentne nevronske mreže](./lessons/5-NLP/16-RNN/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNTF.ipynb) | | | 17 | [Generativne rekurentne mreže](./lessons/5-NLP/17-GenerativeNetworks/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb) | [Laboratorij](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | -| 18 | [Transformers. BERT.](./lessons/5-NLP/18-Transformers/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb) /[TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersTF.ipynb) | | -| 19 | [Prepoznavanje imenovanih entitet](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [Laboratorij](./lessons/5-NLP/19-NER/lab/README.md) | -| 20 | [Veliki jezikovni modeli, programiranje pozivov in naloge z malo primeri](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | -| VI | **Druge AI tehnike** || | +| 18 | [Transformatorji. BERT.](./lessons/5-NLP/18-Transformers/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb) /[TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersTF.ipynb) | | +| 19 | [Prepoznavanje poimenovanih entitet](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [Laboratorij](./lessons/5-NLP/19-NER/lab/README.md) | +| 20 | [Veliki jezikovni modeli, programiranje navodil in naloge z malo primeri](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | +| VI | **Druge tehnike umetne inteligence** || | | 21 | [Genetski algoritmi](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [Zvezek](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | -| 22 | [Globoko okrepljeno učenje](./lessons/6-Other/22-DeepRL/README.md) | [PyTorch](./lessons/6-Other/22-DeepRL/CartPole-RL-PyTorch.ipynb) /[TensorFlow](./lessons/6-Other/22-DeepRL/CartPole-RL-TF.ipynb) | [Laboratorij](./lessons/6-Other/22-DeepRL/lab/README.md) | -| 23 | [Večagentni sistemi](./lessons/6-Other/23-MultiagentSystems/README.md) | | | +| 22 | [Globoko učenje z okrepljenjem](./lessons/6-Other/22-DeepRL/README.md) | [PyTorch](./lessons/6-Other/22-DeepRL/CartPole-RL-PyTorch.ipynb) /[TensorFlow](./lessons/6-Other/22-DeepRL/CartPole-RL-TF.ipynb) | [Laboratorij](./lessons/6-Other/22-DeepRL/lab/README.md) | +| 23 | [Sistemi z več agenti](./lessons/6-Other/23-MultiagentSystems/README.md) | | | | VII | **Etika umetne inteligence** | | | -| 24 | [Etika AI in odgovorna AI](./lessons/7-Ethics/README.md) | [Microsoft Learn: Načela odgovorne AI](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | +| 24 | [Etika umetne inteligence in odgovorna umetna inteligenca](./lessons/7-Ethics/README.md) | [Microsoft Learn: Načela odgovorne umetne inteligence](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | | IX | **Dodatki** | | | | 25 | [Večmodalne mreže, CLIP in VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [Zvezek](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | ## Vsaka lekcija vsebuje -* Predbrano gradivo -* Izvedljive Jupyter zvezke, ki so pogosto specifični za ogrodje (**PyTorch** ali **TensorFlow**). Izvedljivi zvezek vsebuje tudi veliko teoretičnega gradiva, zato je za razumevanje teme potrebno preiti vsaj eno verzijo zvezka (bodisi PyTorch ali TensorFlow). -* **Laboratorijske vaje** na voljo za nekatere teme, ki vam omogočajo, da poskusite uporabiti naučeno snov na konkretni problem. +* Material za predhodno branje +* Izvršljive Jupyter zvezke, ki so pogosto specifični za ogrodje (**PyTorch** ali **TensorFlow**). Izvršljivi zvezek vsebuje tudi veliko teoretičnega gradiva, zato je za razumevanje teme potrebno prebrati vsaj eno različico zvezka (bodisi PyTorch ali TensorFlow). +* **Laboratorije** na voljo za nekatere teme, ki vam omogočajo, da preizkusite uporabo naučenega gradiva na določenem problemu. * Nekateri razdelki vsebujejo povezave do modulov [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum), ki pokrivajo sorodne teme. -## Začetek +## Začni -### 🎯 Novi v AI? Začnite tukaj! +### 🎯 Nov v AI? Začni tukaj! -Če ste popolnoma novi v AI in želite hitre, praktične primere, preverite naše [**Primeri za začetnike**](./examples/README.md)! Ti vključujejo: +Če ste popolnoma novi v AI in želite hitre, praktične primere, si oglejte naše [**Primere za začetnike**](./examples/README.md)! Ti vključujejo: -- 🌟 **Pozdrav AI svetu** - Vaš prvi AI program (prepoznavanje vzorcev) -- 🧠 **Preprosta nevronska mreža** - Zgradite nevronsko mrežo iz nič -- 🖼️ **Klasifikator slik** - Klasificirajte slike z obsežnimi komentarji -- 💬 **Sentiment besedila** - Analiza pozitivnega/negativnega besedila +- 🌟 **Pozdravljen AI svet** - vaš prvi program AI (prepoznavanje vzorcev) +- 🧠 **Preprosta nevronska mreža** - zgradite nevronsko mrežo iz nič -Ti primeri so zasnovani tako, da vam pomagajo razumeti koncepte umetne inteligence, preden se poglobite v celoten učni načrt. +- 🖼️ **Razvrščevalnik slik** - Razvrsti slike z obširnimi komentarji +- 💬 **Sentiment besedila** - Analiza pozitivnega/negativnega besedila + +Ti primeri so oblikovani tako, da vam pomagajo razumeti pojme umetne inteligence, preden se lotite celotnega učnega načrta. ### 📚 Nastavitev celotnega učnega načrta -- Ustvarili smo [lekcijo za nastavitev](./lessons/0-course-setup/setup.md), ki vam pomaga pri nastavitvi razvojnega okolja. - Za učitelje smo ustvarili tudi [lekcijo za nastavitev učnih načrtov](./lessons/0-course-setup/for-teachers.md)! -- Kako [pognati kodo v VSCode ali Codespace](./lessons/0-course-setup/how-to-run.md) +- Ustvarili smo [nastavitveno lekcijo](./lessons/0-course-setup/setup.md), ki vam pomaga pri nastavitvi razvojnega okolja. - Za izobraževalce smo pripravili tudi [lekcijo o nastavitvi učnih načrtov](./lessons/0-course-setup/for-teachers.md)! +- Kako [pognati kodo v VSCode ali Codespace](./lessons/0-course-setup/how-to-run.md) Sledite tem korakom: -Razvejite repozitorij: Kliknite na gumb "Fork" v zgornjem desnem kotu te strani. +Odvežite repozitorij: Kliknite na gumb "Fork" v zgornjem desnem kotu te strani. -Klonirajte repozitorij: `git clone https://github.com/microsoft/AI-For-Beginners.git` +Klonirajte repozitorij: `git clone https://github.com/microsoft/AI-For-Beginners.git` -Ne pozabite dodati zvezdico (🌟) temu repozitoriju, da ga boste lažje našli kasneje. +Ne pozabite tej repozitorij dati zvezdico (🌟), da ga boste lažje našli kasneje. -## Spoznajte druge učence +## Spoznajte druge udeležence -Pridružite se našemu [uradnemu AI Discord strežniku](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum), da spoznate in se povežete z drugimi učenci tega tečaja ter pridobite podporo. +Pridružite se našemu [uradnemu AI Discord strežniku](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum), da spoznate in se povežete z drugimi udeleženci tečaja ter dobite podporo. -Če imate povratne informacije o izdelku ali vprašanja med gradnjo, obiščite naš [Azure AI Foundry Developer Forum](https://aka.ms/foundry/forum) +Če imate povratne informacije o izdelku ali vprašanja med razvojem, obiščite naš [Azure AI Foundry Developer Forum](https://aka.ms/foundry/forum) ## Kvizi -> **Opomba o kvizih**: Vsi kvizi so v mapi Quiz-app v etc\quiz-app ali [spletno tukaj](https://ff-quizzes.netlify.app/) Povezani so znotraj lekcij, aplikacijo kviza je mogoče pognati lokalno ali namestiti na Azure; sledite navodilom v mapi `quiz-app`. Postopoma se lokalizirajo. +> **Opomba o kvizih**: Vsi kvizi so nameščeni v mapi Quiz-app v etc\quiz-app ali [spletno tukaj](https://ff-quizzes.netlify.app/). Povezani so znotraj lekcij, kviz aplikacijo je mogoče pognati lokalno ali namestiti v Azure; sledite navodilom v mapi `quiz-app`. Postopoma se lokalizirajo. -## Potrebna pomoč +## Iščemo pomoč -Imate predloge ali ste našli pravopisne ali kode napake? Odprite zadevo ali ustvarite pull request. +Imate predloge ali ste našli pravopisne ali kodne napake? Odprite issue ali ustvarite pull request. ## Posebne zahvale -* **✍️ Glavni avtor:** [Dmitry Soshnikov](http://soshnikov.com), PhD -* **🔥 Urednik:** [Jen Looper](https://twitter.com/jenlooper), PhD -* **🎨 Ilustrator skic:** [Tomomi Imura](https://twitter.com/girlie_mac) -* **✅ Ustvarjalec kvizov:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) -* **🙏 Glavni sodelavci:** [Evgenii Pishchik](https://github.com/Pe4enIks) +* **✍️ Glavni avtor:** [Dmitry Soshnikov](http://soshnikov.com), PhD +* **🔥 Urednik:** [Jen Looper](https://twitter.com/jenlooper), PhD +* **🎨 Ilustrator sketchnotov:** [Tomomi Imura](https://twitter.com/girlie_mac) +* **✅ Ustvarjalec kvizov:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) +* **🙏 Glavni sodelavci:** [Evgenii Pishchik](https://github.com/Pe4enIks) ## Drugi učni načrti @@ -176,57 +169,57 @@ Naša ekipa ustvarja tudi druge učne načrte! Oglejte si: ### LangChain -[![LangChain4j za začetnike](https://img.shields.io/badge/LangChain4j%20for%20Beginners-22C55E?style=for-the-badge&&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchain4j-for-beginners) -[![LangChain.js za začetnike](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 / Agentje -[![AZD za začetnike](https://img.shields.io/badge/AZD%20for%20Beginners-0078D4?style=for-the-badge&labelColor=E5E7EB&color=0078D4)](https://github.com/microsoft/AZD-for-beginners?WT.mc_id=academic-105485-koreyst) -[![Edge AI za začetnike](https://img.shields.io/badge/Edge%20AI%20for%20Beginners-00B8E4?style=for-the-badge&labelColor=E5E7EB&color=00B8E4)](https://github.com/microsoft/edgeai-for-beginners?WT.mc_id=academic-105485-koreyst) -[![MCP za začetnike](https://img.shields.io/badge/MCP%20for%20Beginners-009688?style=for-the-badge&labelColor=E5E7EB&color=009688)](https://github.com/microsoft/mcp-for-beginners?WT.mc_id=academic-105485-koreyst) -[![AI agenti za začetnike](https://img.shields.io/badge/AI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst) +### Azure / Edge / MCP / Agenti +[![AZD for Beginners](https://img.shields.io/badge/AZD%20for%20Beginners-0078D4?style=for-the-badge&labelColor=E5E7EB&color=0078D4)](https://github.com/microsoft/AZD-for-beginners?WT.mc_id=academic-105485-koreyst) +[![Edge AI for Beginners](https://img.shields.io/badge/Edge%20AI%20for%20Beginners-00B8E4?style=for-the-badge&labelColor=E5E7EB&color=00B8E4)](https://github.com/microsoft/edgeai-for-beginners?WT.mc_id=academic-105485-koreyst) +[![MCP for Beginners](https://img.shields.io/badge/MCP%20for%20Beginners-009688?style=for-the-badge&labelColor=E5E7EB&color=009688)](https://github.com/microsoft/mcp-for-beginners?WT.mc_id=academic-105485-koreyst) +[![AI Agents for Beginners](https://img.shields.io/badge/AI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst) --- - + ### Serija generativne AI -[![Generativna AI za začetnike](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst) -[![Generativna AI (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst) -[![Generativna AI (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst) -[![Generativna 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) +[![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) --- - + ### Osnovno učenje -[![ML za začetnike](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst) -[![Podatkovna znanost za začetnike](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst) -[![AI za začetnike](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst) -[![Kibernetska varnost za začetnike](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung) -[![Spletni razvoj za začetnike](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst) -[![IoT za začetnike](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) -[![Razvoj XR za začetnike](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) --- - + ### Serija Copilot -[![Copilot za AI Parno programiranje](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst) -[![Copilot za 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) -## Pridobivanje pomoči +## Dobite pomoč -Če se zataknete ali imate vprašanja o ustvarjanju AI aplikacij. Pridružite se ostalim učencem in izkušenim razvijalcem v razpravah o MCP. To je podporna skupnost, kjer so vprašanja dobrodošla in kjer se znanje prostovoljno deli. +Če se zataknete ali imate kakršnakoli vprašanja glede ustvarjanja AI aplikacij. Pridružite se ostalim udeležencem in izkušenim razvijalcem v razpravah o MCP. Gre za podporno skupnost, kjer so vprašanja dobrodošla in se znanje prosto deli. [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -Če imate povratne informacije o izdelku ali napake med gradnjo, obiščite: +Če imate povratne informacije o izdelku ali napake med razvojem, obiščite: [![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) --- -**Omejitev odgovornosti**: -Ta dokument je bil preveden z uporabo storitve za avtomatski prevod AI [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, prosimo, upoštevajte, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Prvotni dokument v izvirnem jeziku velja za avtoritativni vir. Za ključne informacije priporočamo strokovni človeški prevod. Nismo odgovorni za morebitne nesporazume ali napačne interpretacije, ki izhajajo iz uporabe tega prevoda. +**Omejitev odgovornosti**: +Ta dokument je bil preveden z uporabo storitve za avtomatski prevod AI [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas opozarjamo, da avtomatski prevodi lahko vsebujejo napake ali netočnosti. Izvirni dokument v njegovem maternem jeziku naj velja za zanesljiv vir. Za ključne informacije priporočamo strokovni človeški prevod. Nismo odgovorni za morebitna nesporazume ali napačne interpretacije, ki izhajajo iz uporabe tega prevoda. \ No newline at end of file diff --git a/translations/sl/SECURITY.md b/translations/sl/SECURITY.md index a1f25f40..8932daa6 100644 --- a/translations/sl/SECURITY.md +++ b/translations/sl/SECURITY.md @@ -1,12 +1,3 @@ - ## Varnost Microsoft jemlje varnost svojih programsko-opremskih izdelkov in storitev resno, kar vključuje vse repozitorije izvorne kode, ki jih upravljajo naše GitHub organizacije, med katerimi so [Microsoft](https://github.com/Microsoft), [Azure](https://github.com/Azure), [DotNet](https://github.com/dotnet), [AspNet](https://github.com/aspnet), [Xamarin](https://github.com/xamarin) in [naše GitHub organizacije](https://opensource.microsoft.com/). diff --git a/translations/sl/etc/CODE_OF_CONDUCT.md b/translations/sl/etc/CODE_OF_CONDUCT.md index 2433ce6a..e4674ef4 100644 --- a/translations/sl/etc/CODE_OF_CONDUCT.md +++ b/translations/sl/etc/CODE_OF_CONDUCT.md @@ -1,12 +1,3 @@ - # Microsoftov kodeks ravnanja za odprtokodno programsko opremo Ta projekt je sprejel [Microsoftov kodeks ravnanja za odprtokodno programsko opremo](https://opensource.microsoft.com/codeofconduct/). diff --git a/translations/sl/etc/CONTRIBUTING.md b/translations/sl/etc/CONTRIBUTING.md index a2be96ea..16f6b337 100644 --- a/translations/sl/etc/CONTRIBUTING.md +++ b/translations/sl/etc/CONTRIBUTING.md @@ -1,12 +1,3 @@ - # Prispevanje Ta projekt pozdravlja prispevke in predloge. Večina prispevkov zahteva, da se strinjate s Pogodbo o licenciranju prispevkov (CLA), s katero potrjujete, da imate pravico in dejansko podeljujete pravice za uporabo vašega prispevka. Za podrobnosti obiščite https://cla.microsoft.com. diff --git a/translations/sl/etc/Mindmap.md b/translations/sl/etc/Mindmap.md index 4395e7b7..69942475 100644 --- a/translations/sl/etc/Mindmap.md +++ b/translations/sl/etc/Mindmap.md @@ -1,12 +1,3 @@ - # Umetna inteligenca (UI) ## [Uvod v UI](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/1-Intro/README.md) diff --git a/translations/sl/etc/SUPPORT.md b/translations/sl/etc/SUPPORT.md index c6bb2f29..1aa7fc29 100644 --- a/translations/sl/etc/SUPPORT.md +++ b/translations/sl/etc/SUPPORT.md @@ -1,12 +1,3 @@ - # Podpora ## Kako prijaviti težave in dobiti pomoč diff --git a/translations/sl/etc/TRANSLATIONS.md b/translations/sl/etc/TRANSLATIONS.md index f2518c57..19e8a702 100644 --- a/translations/sl/etc/TRANSLATIONS.md +++ b/translations/sl/etc/TRANSLATIONS.md @@ -1,12 +1,3 @@ - # Prispevajte s prevajanjem lekcij Z veseljem sprejemamo prevode lekcij v tem učnem načrtu! diff --git a/translations/sl/etc/quiz-app/README.md b/translations/sl/etc/quiz-app/README.md index a98095d9..dfe348e5 100644 --- a/translations/sl/etc/quiz-app/README.md +++ b/translations/sl/etc/quiz-app/README.md @@ -1,12 +1,3 @@ - # Kvizi Ti kvizi so pred- in po-predavanji kvizi za učni načrt umetne inteligence na https://aka.ms/ai-beginners diff --git a/translations/sl/examples/README.md b/translations/sl/examples/README.md index ffb15afd..a2914fc4 100644 --- a/translations/sl/examples/README.md +++ b/translations/sl/examples/README.md @@ -1,12 +1,3 @@ - # Primeri umetne inteligence za začetnike Dobrodošli! Ta imenik vsebuje preproste, samostojne primere, ki vam bodo pomagali začeti z umetno inteligenco in strojno učenje. Vsak primer je zasnovan tako, da je prijazen začetnikom, z natančnimi komentarji in razlagami korak za korakom. diff --git a/translations/sl/lessons/0-course-setup/for-teachers.md b/translations/sl/lessons/0-course-setup/for-teachers.md index 086084d0..d6e019c3 100644 --- a/translations/sl/lessons/0-course-setup/for-teachers.md +++ b/translations/sl/lessons/0-course-setup/for-teachers.md @@ -1,12 +1,3 @@ - # Za učitelje Bi radi uporabili ta učni načrt v svojem razredu? Kar izvolite! diff --git a/translations/sl/lessons/0-course-setup/how-to-run.md b/translations/sl/lessons/0-course-setup/how-to-run.md index f75cb185..03a2ea2f 100644 --- a/translations/sl/lessons/0-course-setup/how-to-run.md +++ b/translations/sl/lessons/0-course-setup/how-to-run.md @@ -1,12 +1,3 @@ - # Kako zagnati kodo Ta učni načrt vsebuje veliko izvajalnih primerov in laboratorijskih vaj, ki jih boste želeli zagnati. Da bi to naredili, morate imeti možnost izvajanja Python kode v Jupyter beležnicah, ki so vključene v ta učni načrt. Za izvajanje kode imate več možnosti: diff --git a/translations/sl/lessons/0-course-setup/setup.md b/translations/sl/lessons/0-course-setup/setup.md index c5d545f2..705440cb 100644 --- a/translations/sl/lessons/0-course-setup/setup.md +++ b/translations/sl/lessons/0-course-setup/setup.md @@ -1,12 +1,3 @@ - # Začetek s tem učnim načrtom ## Ste študent? diff --git a/translations/sl/lessons/1-Intro/README.md b/translations/sl/lessons/1-Intro/README.md index 53a868f3..d0f8880b 100644 --- a/translations/sl/lessons/1-Intro/README.md +++ b/translations/sl/lessons/1-Intro/README.md @@ -1,12 +1,3 @@ - # Uvod v umetno inteligenco ![Povzetek vsebine uvoda v umetno inteligenco v skici](../../../../translated_images/sl/ai-intro.bf28d1ac4235881c.webp) diff --git a/translations/sl/lessons/1-Intro/assignment.md b/translations/sl/lessons/1-Intro/assignment.md index da6cea6a..272ee8a5 100644 --- a/translations/sl/lessons/1-Intro/assignment.md +++ b/translations/sl/lessons/1-Intro/assignment.md @@ -1,12 +1,3 @@ - # Game Jam Igre so področje, ki je močno pod vplivom razvoja umetne inteligence (UI) in strojnega učenja (SU). V tej nalogi napišite kratko razpravo o igri, ki vam je všeč in ki je bila pod vplivom razvoja UI. Igra naj bo dovolj stara, da je bila pod vplivom več vrst računalniških procesnih sistemov. Dober primer sta šah ali Go, vendar si oglejte tudi video igre, kot sta Pong ali Pac-Man. Napišite esej, ki obravnava preteklost, sedanjost in prihodnost igre v povezavi z UI. diff --git a/translations/sl/lessons/2-Symbolic/README.md b/translations/sl/lessons/2-Symbolic/README.md index 4358212b..ba963543 100644 --- a/translations/sl/lessons/2-Symbolic/README.md +++ b/translations/sl/lessons/2-Symbolic/README.md @@ -1,15 +1,6 @@ - # Predstavitev znanja in ekspertni sistemi -![Povzetek vsebine simbolične umetne inteligence](../../../../../../translated_images/sl/ai-symbolic.715a30cb610411a6.webp) +![Povzetek vsebine simbolične umetne inteligence](../../../../translated_images/sl/ai-symbolic.715a30cb610411a6.webp) > Sketchnote avtorice [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +32,7 @@ Večinoma znanja ne definiramo strogo, temveč ga povežemo z drugimi sorodnimi Torej je problem **predstavitve znanja** najti učinkovit način predstavljanja znanja znotraj računalnika v obliki podatkov, da bo samodejno uporabno. To lahko vidimo kot spekter: -![Spekter predstavitve znanja](../../../../../../translated_images/sl/knowledge-spectrum.b60df631852c0217.webp) +![Spekter predstavitve znanja](../../../../translated_images/sl/knowledge-spectrum.b60df631852c0217.webp) > Slika avtorja [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +85,7 @@ Sintaksa bloka | Zamik | | | Eden od zgodnjih uspehov simbolične umetne inteligence so bili tako imenovani **ekspertni sistemi** – računalniški sistemi, oblikovani za delovanje kot strokovnjak na omejenem področju. Temeljili so na **bazi znanja**, pridobljeni od enega ali več človeških strokovnjakov, in so vsebovali **inferenzni mehanizem**, ki je izvajal sklepanje na osnovi tega. -![Človeška arhitektura](../../../../../../translated_images/sl/arch-human.5d4d35f1bba3ab1c.webp) | ![Sistem na podlagi znanja](../../../../../../translated_images/sl/arch-kbs.3ec5c150b09fa8da.webp) +![Človeška arhitektura](../../../../translated_images/sl/arch-human.5d4d35f1bba3ab1c.webp) | ![Sistem na podlagi znanja](../../../../translated_images/sl/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ Poenostavljena struktura človeškega živčnega sistema | Arhitektura sistema na podlagi znanja @@ -106,7 +97,7 @@ Ekspertni sistemi so zgrajeni podobno kot človeški sistem sklepanja, ki vsebuj Kot primer si poglejmo ekspertni sistem za določanje živali na podlagi fizičnih lastnosti: -![AND-ALI Drevo](../../../../../../translated_images/sl/AND-OR-Tree.5592d2c70187f283.webp) +![AND-ALI Drevo](../../../../translated_images/sl/AND-OR-Tree.5592d2c70187f283.webp) > Slika avtorja [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/sl/lessons/2-Symbolic/assignment.md b/translations/sl/lessons/2-Symbolic/assignment.md index 965d15e6..d83fe0ee 100644 --- a/translations/sl/lessons/2-Symbolic/assignment.md +++ b/translations/sl/lessons/2-Symbolic/assignment.md @@ -1,12 +1,3 @@ - # Zgradite ontologijo Gradnja baze znanja pomeni kategorizacijo modela, ki predstavlja dejstva o določeni temi. Izberite temo - na primer osebo, kraj ali stvar - in nato zgradite model te teme. Uporabite nekatere tehnike in strategije gradnje modelov, opisane v tej lekciji. Primer bi bil ustvarjanje ontologije dnevne sobe s pohištvom, lučmi in podobno. Kako se dnevna soba razlikuje od kuhinje? Kopalnice? Kako veste, da gre za dnevno sobo in ne jedilnico? Uporabite [Protégé](https://protege.stanford.edu/) za gradnjo svoje ontologije. diff --git a/translations/sl/lessons/3-NeuralNetworks/03-Perceptron/README.md b/translations/sl/lessons/3-NeuralNetworks/03-Perceptron/README.md index 30c5d0c2..44b4a6ab 100644 --- a/translations/sl/lessons/3-NeuralNetworks/03-Perceptron/README.md +++ b/translations/sl/lessons/3-NeuralNetworks/03-Perceptron/README.md @@ -1,12 +1,3 @@ - # Uvod v nevronske mreže: Perceptron ## [Predhodni kviz](https://ff-quizzes.netlify.app/en/ai/quiz/5) @@ -15,7 +6,7 @@ Eden prvih poskusov implementacije nečesa podobnega sodobni nevronski mreži je | | | |--------------|-----------| -|Frank Rosenblatt | The Mark 1 Perceptron| +|Frank Rosenblatt | The Mark 1 Perceptron| > Slike [iz Wikipedije](https://en.wikipedia.org/wiki/Perceptron) @@ -34,7 +25,7 @@ y(x) = f(wTx) kjer je f funkcija aktivacije s korakom. - + ## Učenje perceptrona diff --git a/translations/sl/lessons/3-NeuralNetworks/03-Perceptron/lab/README.md b/translations/sl/lessons/3-NeuralNetworks/03-Perceptron/lab/README.md index 36a24115..29a27d99 100644 --- a/translations/sl/lessons/3-NeuralNetworks/03-Perceptron/lab/README.md +++ b/translations/sl/lessons/3-NeuralNetworks/03-Perceptron/lab/README.md @@ -1,12 +1,3 @@ - # Večrazredna klasifikacija s perceptronom Laboratorijska naloga iz [Učnega načrta za začetnike v umetni inteligenci](https://github.com/microsoft/ai-for-beginners). diff --git a/translations/sl/lessons/3-NeuralNetworks/04-OwnFramework/README.md b/translations/sl/lessons/3-NeuralNetworks/04-OwnFramework/README.md index b6b62448..20888b0e 100644 --- a/translations/sl/lessons/3-NeuralNetworks/04-OwnFramework/README.md +++ b/translations/sl/lessons/3-NeuralNetworks/04-OwnFramework/README.md @@ -1,12 +1,3 @@ - # Uvod v nevronske mreže. Večplastni perceptron V prejšnjem poglavju ste spoznali najpreprostejši model nevronske mreže - enoplastni perceptron, linearen model za klasifikacijo dveh razredov. @@ -65,7 +56,7 @@ Algoritem gradientnega spusta ostane enak, vendar je izračun gradientov bolj za Opazite, da je skrajno levi del vseh teh izrazov enak, zato lahko učinkovito izračunamo odvode, začenši s funkcijo izgube in gremo "nazaj" skozi računski graf. Zato se metoda učenja večplastnega perceptrona imenuje **povratno razširjanje** ali 'backprop'. -računski graf +računski graf > TODO: navedba vira slike diff --git a/translations/sl/lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md b/translations/sl/lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md index 06d94f9d..3c797dbe 100644 --- a/translations/sl/lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md +++ b/translations/sl/lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md @@ -1,12 +1,3 @@ - # MNIST klasifikacija z našim lastnim ogrodjem Laboratorijska naloga iz [Učnega načrta za začetnike v umetni inteligenci](https://github.com/microsoft/ai-for-beginners). diff --git a/translations/sl/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/sl/lessons/3-NeuralNetworks/05-Frameworks/README.md index ce2b35fa..28fbcfa5 100644 --- a/translations/sl/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/sl/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -1,12 +1,3 @@ - # Okvirji za nevronske mreže Kot smo že spoznali, za učinkovito učenje nevronskih mrež moramo narediti dve stvari: diff --git a/translations/sl/lessons/3-NeuralNetworks/05-Frameworks/lab/README.md b/translations/sl/lessons/3-NeuralNetworks/05-Frameworks/lab/README.md index 4a90f242..a1f21479 100644 --- a/translations/sl/lessons/3-NeuralNetworks/05-Frameworks/lab/README.md +++ b/translations/sl/lessons/3-NeuralNetworks/05-Frameworks/lab/README.md @@ -1,12 +1,3 @@ - # Klasifikacija s PyTorch/TensorFlow Laboratorijska naloga iz [Učnega načrta za začetnike v umetni inteligenci](https://github.com/microsoft/ai-for-beginners). diff --git a/translations/sl/lessons/3-NeuralNetworks/README.md b/translations/sl/lessons/3-NeuralNetworks/README.md index c4b1276a..468d2aa4 100644 --- a/translations/sl/lessons/3-NeuralNetworks/README.md +++ b/translations/sl/lessons/3-NeuralNetworks/README.md @@ -1,12 +1,3 @@ - # Uvod v nevronske mreže ![Povzetek vsebine uvoda v nevronske mreže v skici](../../../../translated_images/sl/ai-neuralnetworks.1c687ae40bc86e83.webp) diff --git a/translations/sl/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/sl/lessons/4-ComputerVision/06-IntroCV/README.md index a3bec85d..7e0dcbec 100644 --- a/translations/sl/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/sl/lessons/4-ComputerVision/06-IntroCV/README.md @@ -1,12 +1,3 @@ - # Uvod v računalniški vid [Računalniški vid](https://wikipedia.org/wiki/Computer_vision) je področje, katerega cilj je omogočiti računalnikom, da pridobijo visok nivo razumevanja digitalnih slik. To je precej široka definicija, saj lahko *razumevanje* pomeni veliko različnih stvari, vključno z iskanjem objekta na sliki (**prepoznavanje objektov**), razumevanjem dogajanja (**prepoznavanje dogodkov**), opisovanjem slike z besedilom ali rekonstrukcijo prizora v 3D. Obstajajo tudi posebne naloge, povezane s človeškimi slikami: ocenjevanje starosti in čustev, prepoznavanje obrazov ter določanje 3D drže, če naštejemo le nekaj primerov. @@ -115,7 +106,7 @@ Preberite več o optičnem toku [v tem odličnem vodiču](https://learnopencv.co V tej nalogi boste posneli video s preprostimi gestami, vaš cilj pa bo izluščiti premike gor/dol/levo/desno z uporabo optičnega toka. -Okvir premika dlani +Okvir premika dlani --- diff --git a/translations/sl/lessons/4-ComputerVision/06-IntroCV/lab/README.md b/translations/sl/lessons/4-ComputerVision/06-IntroCV/lab/README.md index dde156fb..139c29f9 100644 --- a/translations/sl/lessons/4-ComputerVision/06-IntroCV/lab/README.md +++ b/translations/sl/lessons/4-ComputerVision/06-IntroCV/lab/README.md @@ -1,12 +1,3 @@ - # Zaznavanje gibanja z optičnim tokom Laboratorijska naloga iz [učnega načrta AI za začetnike](https://aka.ms/ai-beginners). diff --git a/translations/sl/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/sl/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 6df5cdba..0b01da9b 100644 --- a/translations/sl/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/sl/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -1,12 +1,3 @@ - # Dobro poznane arhitekture CNN ### VGG-16 @@ -25,7 +16,7 @@ Kot lahko vidite, VGG sledi tradicionalni piramidni arhitekturi, ki je zaporedje ResNet je družina modelov, ki jih je leta 2015 predlagal Microsoft Research. Glavna ideja ResNet-a je uporaba **rezidualnih blokov**: - + > Slika iz [tega članka](https://arxiv.org/pdf/1512.03385.pdf) @@ -37,7 +28,7 @@ Mrežo si lahko predstavljate tudi kot sposobno prilagajanja svoje kompleksnosti Arhitektura Google Inception to idejo nadgradi in zgradi vsak sloj mreže kot kombinacijo več različnih poti: - + > Slika iz [Researchgate](https://www.researchgate.net/figure/Inception-module-with-dimension-reductions-left-and-schema-for-Inception-ResNet-v1_fig2_355547454) diff --git a/translations/sl/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/sl/lessons/4-ComputerVision/07-ConvNets/README.md index 6954e149..fc77b3dc 100644 --- a/translations/sl/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/sl/lessons/4-ComputerVision/07-ConvNets/README.md @@ -1,12 +1,3 @@ - # Konvolucijske nevronske mreže Že prej smo videli, da so nevronske mreže precej dobre pri obdelavi slik, in celo enoslojni perceptron je sposoben prepoznati ročno napisane številke iz podatkovne zbirke MNIST z razumno natančnostjo. Vendar pa je podatkovna zbirka MNIST zelo posebna, saj so vse številke centrirane znotraj slike, kar nalogo poenostavi. @@ -24,7 +15,7 @@ Za ekstrakcijo vzorcev bomo uporabili koncept **konvolucijskih filtrov**. Kot ve Na primer, če uporabimo 3x3 filtre za navpične in vodoravne robove na številkah iz MNIST, lahko dobimo poudarke (npr. visoke vrednosti) tam, kjer so v izvirni sliki navpični in vodoravni robovi. Tako lahko ta dva filtra uporabimo za "iskanje" robov. Podobno lahko oblikujemo različne filtre za iskanje drugih nizkoročnih vzorcev: - + > Slika: [Leung-Malik Filter Bank](https://www.robots.ox.ac.uk/~vgg/research/texclass/filters.html) diff --git a/translations/sl/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/sl/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 2d7f9601..c7e7dc3e 100644 --- a/translations/sl/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/sl/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -1,12 +1,3 @@ - # Razvrščanje obrazov hišnih ljubljenčkov Laboratorijska naloga iz [Učnega načrta za začetnike v AI](https://github.com/microsoft/ai-for-beginners). diff --git a/translations/sl/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/sl/lessons/4-ComputerVision/08-TransferLearning/README.md index e0a23ce9..2e3eb24c 100644 --- a/translations/sl/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/sl/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -1,12 +1,3 @@ - # Vnaprej naučeni modeli in prenos učenja Učenje CNN-jev lahko zahteva veliko časa, poleg tega pa je za to nalogo potrebnih veliko podatkov. Velik del časa se porabi za učenje najboljših nizkoročnih filtrov, ki jih mreža lahko uporabi za prepoznavanje vzorcev iz slik. Pojavi se naravno vprašanje – ali lahko uporabimo nevronsko mrežo, naučeno na enem naboru podatkov, in jo prilagodimo za razvrščanje drugih slik, ne da bi morali izvesti celoten proces učenja? diff --git a/translations/sl/lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md b/translations/sl/lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md index e9190b22..3d5bc8be 100644 --- a/translations/sl/lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md +++ b/translations/sl/lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md @@ -1,12 +1,3 @@ - # Triki za učenje globokega učenja Ko postajajo nevronske mreže globlje, postaja proces njihovega učenja vse bolj zahteven. Ena glavnih težav so tako imenovani [izginjajoči gradienti](https://en.wikipedia.org/wiki/Vanishing_gradient_problem) ali [eksplodirajoči gradienti](https://deepai.org/machine-learning-glossary-and-terms/exploding-gradient-problem#:~:text=Exploding%20gradients%20are%20a%20problem,updates%20are%20small%20and%20controlled.). [Ta objava](https://towardsdatascience.com/the-vanishing-exploding-gradient-problem-in-deep-neural-networks-191358470c11) ponuja dober uvod v te težave. diff --git a/translations/sl/lessons/4-ComputerVision/08-TransferLearning/lab/README.md b/translations/sl/lessons/4-ComputerVision/08-TransferLearning/lab/README.md index 23a8d09e..9ef150fd 100644 --- a/translations/sl/lessons/4-ComputerVision/08-TransferLearning/lab/README.md +++ b/translations/sl/lessons/4-ComputerVision/08-TransferLearning/lab/README.md @@ -1,12 +1,3 @@ - # Klasifikacija hišnih ljubljenčkov iz Oxforda z uporabo prenosa učenja Laboratorijska naloga iz [Učnega načrta za začetnike v umetni inteligenci](https://github.com/microsoft/ai-for-beginners). diff --git a/translations/sl/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/sl/lessons/4-ComputerVision/09-Autoencoders/README.md index 8a5c526d..c737ec95 100644 --- a/translations/sl/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/sl/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -1,12 +1,3 @@ - # Avtoenkoderji Pri treniranju CNN-jev je ena od težav, da potrebujemo veliko označenih podatkov. Pri klasifikaciji slik moramo slike razvrstiti v različne razrede, kar zahteva ročno delo. @@ -46,7 +37,7 @@ Povzetek: * Vzamemo vzorec `sample` iz porazdelitve N(zmean,exp(zlog\_sigma)) * Dekodirnik poskuša dekodirati izvirno sliko z uporabo `sample` kot vhodnega vektorja - + > Slika iz [tega bloga](https://ijdykeman.github.io/ml/2016/12/21/cvae.html) avtorja Isaaka Dykemana @@ -57,13 +48,13 @@ Variacijski avtoenkoderji uporabljajo kompleksno funkcijo izgube, ki je sestavlj Ena pomembna prednost VAE-jev je, da omogočajo relativno enostavno generiranje novih slik, ker vemo, iz katere porazdelitve vzeti latentne vektorje. Na primer, če treniramo VAE z 2D latentnim vektorjem na MNIST, lahko nato spreminjamo komponente latentnega vektorja, da dobimo različne številke: -vaemnist +vaemnist > Slika avtorja [Dmitry Soshnikov](http://soshnikov.com) Opazite, kako se slike prelivajo ena v drugo, ko začnemo pridobivati latentne vektorje iz različnih delov latentnega prostora parametrov. Ta prostor lahko vizualiziramo tudi v 2D: -vaemnist cluster +vaemnist cluster > Slika avtorja [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/sl/lessons/4-ComputerVision/10-GANs/README.md b/translations/sl/lessons/4-ComputerVision/10-GANs/README.md index fa837d31..b98703e4 100644 --- a/translations/sl/lessons/4-ComputerVision/10-GANs/README.md +++ b/translations/sl/lessons/4-ComputerVision/10-GANs/README.md @@ -1,12 +1,3 @@ - # Generativne nasprotujoče si mreže V prejšnjem poglavju smo spoznali **generativne modele**: modele, ki lahko ustvarijo nove slike, podobne tistim v učnem naboru podatkov. VAE je bil dober primer generativnega modela. @@ -17,7 +8,7 @@ V prejšnjem poglavju smo spoznali **generativne modele**: modele, ki lahko ustv Glavna ideja GAN-a je, da imamo dve nevronski mreži, ki se učita ena proti drugi: - + > Slika avtorja [Dmitry Soshnikov](http://soshnikov.com) @@ -41,7 +32,7 @@ Generator je nekoliko bolj zahteven. Lahko si ga predstavljate kot obrnjenega di > ✅ Ker je konvolucijski sloj implementiran kot linearni filter, ki prehaja skozi sliko, je dekonvolucija v bistvu podobna konvoluciji in jo je mogoče implementirati z isto logiko sloja. - + > Slika avtorja [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/sl/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/sl/lessons/4-ComputerVision/11-ObjectDetection/README.md index 8daccc06..9e5ffccb 100644 --- a/translations/sl/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/sl/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -1,12 +1,3 @@ - # Zaznavanje objektov Modeli za klasifikacijo slik, s katerimi smo se doslej ukvarjali, so vzeli sliko in podali kategorialni rezultat, na primer razred 'številka' v problemu MNIST. Vendar pa v mnogih primerih ne želimo le vedeti, da slika prikazuje objekte – želimo določiti njihovo natančno lokacijo. To je pravzaprav bistvo **zaznavanja objektov**. diff --git a/translations/sl/lessons/4-ComputerVision/11-ObjectDetection/lab/README.md b/translations/sl/lessons/4-ComputerVision/11-ObjectDetection/lab/README.md index bf72f051..64bfba2c 100644 --- a/translations/sl/lessons/4-ComputerVision/11-ObjectDetection/lab/README.md +++ b/translations/sl/lessons/4-ComputerVision/11-ObjectDetection/lab/README.md @@ -1,12 +1,3 @@ - # Zaznavanje glav z uporabo Hollywood Heads Dataset Laboratorijska naloga iz [učnega načrta AI za začetnike](https://github.com/microsoft/ai-for-beginners). diff --git a/translations/sl/lessons/4-ComputerVision/12-Segmentation/README.md b/translations/sl/lessons/4-ComputerVision/12-Segmentation/README.md index f0430e74..898ff5b0 100644 --- a/translations/sl/lessons/4-ComputerVision/12-Segmentation/README.md +++ b/translations/sl/lessons/4-ComputerVision/12-Segmentation/README.md @@ -1,12 +1,3 @@ - # Segmentacija Prej smo se učili o zaznavanju objektov, ki nam omogoča lociranje objektov na sliki z napovedovanjem njihovih *omejevalnih okvirjev*. Vendar pa za nekatere naloge ne potrebujemo le omejevalnih okvirjev, temveč tudi bolj natančno lokalizacijo objektov. Ta naloga se imenuje **segmentacija**. @@ -20,7 +11,7 @@ Segmentacijo lahko obravnavamo kot **klasifikacijo pikslov**, kjer moramo za **v Pri segmentaciji instanc so te ovce različni objekti, medtem ko pri semantični segmentaciji vse ovce predstavljajo en razred. - + > Slika iz [tega bloga](https://nirmalamurali.medium.com/image-classification-vs-semantic-segmentation-vs-instance-segmentation-625c33a08d50) @@ -29,7 +20,7 @@ Obstajajo različne nevronske arhitekture za segmentacijo, vendar imajo vse enak * **Kodirnik** izlušči značilnosti iz vhodne slike. * **Dekodirnik** pretvori te značilnosti v **sliko maske**, ki ima enako velikost in število kanalov, ki ustrezajo številu razredov. - + > Slika iz [tega prispevka](https://arxiv.org/pdf/2001.05566.pdf) @@ -43,7 +34,7 @@ V tej lekciji bomo videli segmentacijo v praksi, ko bomo trenirali omrežje za p > ✅ Ta tehnika je še posebej primerna za tovrstno medicinsko slikanje, vendar katere druge aplikacije v resničnem svetu si lahko zamislite? -navi +navi > Slika iz PH2 baze podatkov diff --git a/translations/sl/lessons/4-ComputerVision/12-Segmentation/lab/README.md b/translations/sl/lessons/4-ComputerVision/12-Segmentation/lab/README.md index 80e8932b..af27bae5 100644 --- a/translations/sl/lessons/4-ComputerVision/12-Segmentation/lab/README.md +++ b/translations/sl/lessons/4-ComputerVision/12-Segmentation/lab/README.md @@ -1,12 +1,3 @@ - # Segmentacija človeškega telesa Laboratorijska naloga iz [Učnega načrta za začetnike v AI](https://github.com/microsoft/ai-for-beginners). diff --git a/translations/sl/lessons/4-ComputerVision/README.md b/translations/sl/lessons/4-ComputerVision/README.md index df55f7b1..7581c0ac 100644 --- a/translations/sl/lessons/4-ComputerVision/README.md +++ b/translations/sl/lessons/4-ComputerVision/README.md @@ -1,12 +1,3 @@ - # Računalniški vid ![Povzetek vsebine o računalniškem vidu v skici](../../../../translated_images/sl/ai-computervision.6506ebebac3fbf76.webp) diff --git a/translations/sl/lessons/5-NLP/13-TextRep/README.md b/translations/sl/lessons/5-NLP/13-TextRep/README.md index f7e72a73..ed530c10 100644 --- a/translations/sl/lessons/5-NLP/13-TextRep/README.md +++ b/translations/sl/lessons/5-NLP/13-TextRep/README.md @@ -1,12 +1,3 @@ - # Predstavljanje besedila kot tenzorjev ## [Predhodni kviz](https://ff-quizzes.netlify.app/en/ai/quiz/25) @@ -25,7 +16,7 @@ Naš cilj bo razvrstiti novico v eno od kategorij na podlagi besedila. Če želimo reševati naloge obdelave naravnega jezika (NLP) z nevronskimi mrežami, potrebujemo način za predstavljanje besedila kot tenzorjev. Računalniki že predstavljajo besedilne znake kot številke, ki se preslikajo v pisave na vašem zaslonu z uporabo kodiranj, kot sta ASCII ali UTF-8. -Slika prikazuje diagram, ki preslika znak v ASCII in binarno predstavitev +Slika prikazuje diagram, ki preslika znak v ASCII in binarno predstavitev > [Vir slike](https://www.seobility.net/en/wiki/ASCII) @@ -48,7 +39,7 @@ V nekaterih primerih lahko razmislimo o uporabi tri-gramov -- kombinacij treh be Pri reševanju nalog, kot je razvrščanje besedila, moramo biti sposobni predstaviti besedilo z enim vektorjem fiksne velikosti, ki ga bomo uporabili kot vhod za končni gosti klasifikator. Eden najpreprostejših načinov za to je združiti vse posamezne predstavitve besed, npr. z njihovim seštevanjem. Če seštejemo enovrstične kodiranja vsake besede, bomo dobili vektor frekvenc, ki prikazuje, kolikokrat se vsaka beseda pojavi v besedilu. Takšna predstavitev besedila se imenuje **vreča besed** (BoW). - + > Slika avtorja diff --git a/translations/sl/lessons/5-NLP/13-TextRep/assignment.md b/translations/sl/lessons/5-NLP/13-TextRep/assignment.md index 98b459bb..e1f185a7 100644 --- a/translations/sl/lessons/5-NLP/13-TextRep/assignment.md +++ b/translations/sl/lessons/5-NLP/13-TextRep/assignment.md @@ -1,12 +1,3 @@ - # Naloga: Zvezki Z uporabo zvezkov, povezanih s to lekcijo (bodisi PyTorch ali TensorFlow različice), jih ponovno zaženite z lastnim naborom podatkov, morda s Kaggle, uporabljenim z ustreznim navajanjem vira. Prepišite zvezek, da poudarite svoje ugotovitve. Preizkusite nekaj inovativnih naborov podatkov, ki bi lahko bili presenetljivi, na primer [ta o opažanjih NLP-jev](https://www.kaggle.com/datasets/NUFORC/ufo-sightings) iz NUFORC. diff --git a/translations/sl/lessons/5-NLP/14-Embeddings/README.md b/translations/sl/lessons/5-NLP/14-Embeddings/README.md index 59428818..d4c2660e 100644 --- a/translations/sl/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/sl/lessons/5-NLP/14-Embeddings/README.md @@ -1,12 +1,3 @@ - # Vdelave ## [Predhodni kviz](https://ff-quizzes.netlify.app/en/ai/quiz/27) diff --git a/translations/sl/lessons/5-NLP/14-Embeddings/assignment.md b/translations/sl/lessons/5-NLP/14-Embeddings/assignment.md index 884880c0..60f0ba8e 100644 --- a/translations/sl/lessons/5-NLP/14-Embeddings/assignment.md +++ b/translations/sl/lessons/5-NLP/14-Embeddings/assignment.md @@ -1,12 +1,3 @@ - # Naloga: Zvezki Z uporabo zvezkov, povezanih s to lekcijo (bodisi različice PyTorch ali TensorFlow), jih ponovno zaženite z lastnim naborom podatkov, morda z enim izmed Kagglovih, uporabljenim z ustreznim navajanjem vira. Prepišite zvezek, da poudarite svoje ugotovitve. Preizkusite drugačen tip nabora podatkov in dokumentirajte svoje ugotovitve, z uporabo besedila, kot so [ti Beatlesovi verzi](https://www.kaggle.com/datasets/jenlooper/beatles-lyrics). diff --git a/translations/sl/lessons/5-NLP/15-LanguageModeling/README.md b/translations/sl/lessons/5-NLP/15-LanguageModeling/README.md index 385c9eb5..42aab687 100644 --- a/translations/sl/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/sl/lessons/5-NLP/15-LanguageModeling/README.md @@ -1,12 +1,3 @@ - # Jezikovno modeliranje Semantične vektorske predstavitve, kot sta Word2Vec in GloVe, so pravzaprav prvi korak k **jezikovnemu modeliranju** – ustvarjanju modelov, ki nekako *razumejo* (ali *predstavljajo*) naravo jezika. diff --git a/translations/sl/lessons/5-NLP/15-LanguageModeling/lab/README.md b/translations/sl/lessons/5-NLP/15-LanguageModeling/lab/README.md index 4182c366..13fab033 100644 --- a/translations/sl/lessons/5-NLP/15-LanguageModeling/lab/README.md +++ b/translations/sl/lessons/5-NLP/15-LanguageModeling/lab/README.md @@ -1,12 +1,3 @@ - # Usposabljanje modela Skip-Gram Laboratorijska naloga iz [učnega načrta AI za začetnike](https://github.com/microsoft/ai-for-beginners). diff --git a/translations/sl/lessons/5-NLP/16-RNN/README.md b/translations/sl/lessons/5-NLP/16-RNN/README.md index 916ebf1c..c9ce5f40 100644 --- a/translations/sl/lessons/5-NLP/16-RNN/README.md +++ b/translations/sl/lessons/5-NLP/16-RNN/README.md @@ -1,12 +1,3 @@ - # Rekurentne nevronske mreže ## [Predhodni kviz](https://ff-quizzes.netlify.app/en/ai/quiz/31) @@ -31,7 +22,7 @@ Poglejmo, kako je organizirana preprosta RNN celica. Sprejme prejšnje stanje S< Preprosta RNN celica ima znotraj dve matriki uteži: ena transformira vhodni simbol (imenujmo jo W), druga pa transformira vhodno stanje (H). V tem primeru se izhod mreže izračuna kot σ(W×Xi+H×Si-1+b), kjer je σ aktivacijska funkcija, b pa dodatna pristranskost. -Anatomija RNN celice +Anatomija RNN celice > Slika avtorja diff --git a/translations/sl/lessons/5-NLP/16-RNN/assignment.md b/translations/sl/lessons/5-NLP/16-RNN/assignment.md index f45f7c4e..9b11d14e 100644 --- a/translations/sl/lessons/5-NLP/16-RNN/assignment.md +++ b/translations/sl/lessons/5-NLP/16-RNN/assignment.md @@ -1,12 +1,3 @@ - # Naloga: Zvezki Z uporabo zvezkov, povezanih s to lekcijo (bodisi različice PyTorch ali TensorFlow), jih ponovno zaženite z lastnim naborom podatkov, morda s Kaggle, uporabljenim z ustreznim navajanjem vira. Prepišite zvezek, da poudarite svoje ugotovitve. Preizkusite drugačen tip nabora podatkov in dokumentirajte svoje ugotovitve, z uporabo besedila, kot je [ta nabor podatkov iz Kaggle tekmovanja o vremenskih tvitih](https://www.kaggle.com/competitions/crowdflower-weather-twitter/data?select=train.csv). diff --git a/translations/sl/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/sl/lessons/5-NLP/17-GenerativeNetworks/README.md index a1b670d0..d85fda8c 100644 --- a/translations/sl/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/sl/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -1,12 +1,3 @@ - # Generativne mreže ## [Predhodni kviz](https://ff-quizzes.netlify.app/en/ai/quiz/33) @@ -36,7 +27,7 @@ To RNN bomo trenirali za generiranje besedila korak za korakom. Na vsakem koraku Pri generiranju besedila (med inferenco) začnemo z nekim **pozivom**, ki ga prenesemo skozi RNN celice za generiranje vmesnega stanja, nato pa se začne generiranje. Generiramo en znak naenkrat, stanje in generirani znak pa prenesemo v drugo RNN celico za generiranje naslednjega, dokler ne generiramo dovolj znakov. - + > Slika avtorja diff --git a/translations/sl/lessons/5-NLP/17-GenerativeNetworks/lab/README.md b/translations/sl/lessons/5-NLP/17-GenerativeNetworks/lab/README.md index 3f598c88..38f0301e 100644 --- a/translations/sl/lessons/5-NLP/17-GenerativeNetworks/lab/README.md +++ b/translations/sl/lessons/5-NLP/17-GenerativeNetworks/lab/README.md @@ -1,12 +1,3 @@ - # Generiranje besedil na ravni besed z uporabo RNN-jev Laboratorijska naloga iz [Učnega načrta za začetnike v umetni inteligenci](https://github.com/microsoft/ai-for-beginners). diff --git a/translations/sl/lessons/5-NLP/18-Transformers/README.md b/translations/sl/lessons/5-NLP/18-Transformers/README.md index 649f496a..b3f03ff5 100644 --- a/translations/sl/lessons/5-NLP/18-Transformers/README.md +++ b/translations/sl/lessons/5-NLP/18-Transformers/README.md @@ -1,12 +1,3 @@ - # Mehanizmi pozornosti in transformatorji ## [Predavanje kviz](https://ff-quizzes.netlify.app/en/ai/quiz/35) @@ -56,7 +47,7 @@ Ideja kodiranja položaja je naslednja. * Učljivo ugnezdenje, podobno ugnezdenju tokenov. To je pristop, ki ga obravnavamo tukaj. Na vrhu tako tokenov kot njihovih položajev uporabimo plasti ugnezdenja, kar rezultira v vektorjih ugnezdenja enakih dimenzij, ki jih nato seštejemo. * Fiksna funkcija kodiranja položaja, kot je predlagano v izvirnem članku. - + > Slika avtorja diff --git a/translations/sl/lessons/5-NLP/18-Transformers/assignment.md b/translations/sl/lessons/5-NLP/18-Transformers/assignment.md index 6db928be..caa8142a 100644 --- a/translations/sl/lessons/5-NLP/18-Transformers/assignment.md +++ b/translations/sl/lessons/5-NLP/18-Transformers/assignment.md @@ -1,12 +1,3 @@ - # Naloga: Transformatorji Preizkusite transformatorje na HuggingFace! Poskusite nekaj skriptov, ki jih ponujajo za delo z različnimi modeli, ki so na voljo na njihovi strani: https://huggingface.co/docs/transformers/run_scripts. Preizkusite enega od njihovih naborov podatkov, nato pa uvozite enega svojega iz tega učnega načrta ali s Kaggle in preverite, ali lahko ustvarite zanimiva besedila. Pripravite beležnico s svojimi ugotovitvami. diff --git a/translations/sl/lessons/5-NLP/19-NER/README.md b/translations/sl/lessons/5-NLP/19-NER/README.md index adbeb555..0a3a2335 100644 --- a/translations/sl/lessons/5-NLP/19-NER/README.md +++ b/translations/sl/lessons/5-NLP/19-NER/README.md @@ -1,12 +1,3 @@ - # Prepoznavanje imenovanih entitet Do sedaj smo se večinoma osredotočali na eno nalogo NLP - klasifikacijo. Vendar pa obstajajo tudi druge naloge NLP, ki jih je mogoče doseči z nevronskimi mrežami. Ena od teh nalog je **[Prepoznavanje imenovanih entitet](https://wikipedia.org/wiki/Named-entity_recognition)** (NER), ki se ukvarja s prepoznavanjem specifičnih entitet v besedilu, kot so kraji, imena oseb, časovni intervali, kemijske formule in podobno. @@ -17,7 +8,7 @@ Do sedaj smo se večinoma osredotočali na eno nalogo NLP - klasifikacijo. Venda Recimo, da želite razviti klepetalni bot, podoben Amazon Alexa ali Google Assistant. Inteligentni klepetalni boti delujejo tako, da *razumejo*, kaj uporabnik želi, s klasifikacijo besedila vhodnega stavka. Rezultat te klasifikacije je tako imenovani **namen**, ki določa, kaj naj klepetalni bot naredi. -Bot NER +Bot NER > Slika avtorja diff --git a/translations/sl/lessons/5-NLP/19-NER/lab/README.md b/translations/sl/lessons/5-NLP/19-NER/lab/README.md index ecc38c83..fdffc466 100644 --- a/translations/sl/lessons/5-NLP/19-NER/lab/README.md +++ b/translations/sl/lessons/5-NLP/19-NER/lab/README.md @@ -1,12 +1,3 @@ - # NER Laboratorijska naloga iz [učnega načrta AI za začetnike](https://github.com/microsoft/ai-for-beginners). diff --git a/translations/sl/lessons/5-NLP/20-LangModels/README.md b/translations/sl/lessons/5-NLP/20-LangModels/README.md index 663dc912..8d967862 100644 --- a/translations/sl/lessons/5-NLP/20-LangModels/README.md +++ b/translations/sl/lessons/5-NLP/20-LangModels/README.md @@ -1,12 +1,3 @@ - # Vnaprej naučeni veliki jezikovni modeli Pri vseh naših prejšnjih nalogah smo trenirali nevronsko mrežo za izvajanje določene naloge z uporabo označenega nabora podatkov. Pri velikih transformacijskih modelih, kot je BERT, uporabljamo jezikovno modeliranje na samonadzorovan način za izdelavo jezikovnega modela, ki ga nato specializiramo za specifične naloge z dodatnim usposabljanjem na področju specifičnih podatkov. Vendar pa je bilo dokazano, da lahko veliki jezikovni modeli rešujejo številne naloge tudi brez KAKRŠNEGA KOLI specifičnega usposabljanja. Družina modelov, ki to zmore, se imenuje **GPT**: Generativni vnaprej naučeni transformator. diff --git a/translations/sl/lessons/5-NLP/README.md b/translations/sl/lessons/5-NLP/README.md index c88a43d2..29568bad 100644 --- a/translations/sl/lessons/5-NLP/README.md +++ b/translations/sl/lessons/5-NLP/README.md @@ -1,12 +1,3 @@ - # Obdelava naravnega jezika ![Povzetek nalog NLP v skici](../../../../translated_images/sl/ai-nlp.b22dcb8ca4707cea.webp) diff --git a/translations/sl/lessons/6-Other/21-GeneticAlgorithms/README.md b/translations/sl/lessons/6-Other/21-GeneticAlgorithms/README.md index 9dd46338..60857b55 100644 --- a/translations/sl/lessons/6-Other/21-GeneticAlgorithms/README.md +++ b/translations/sl/lessons/6-Other/21-GeneticAlgorithms/README.md @@ -1,12 +1,3 @@ - # Genetski algoritmi ## [Predhodni kviz](https://ff-quizzes.netlify.app/en/ai/quiz/41) diff --git a/translations/sl/lessons/6-Other/22-DeepRL/README.md b/translations/sl/lessons/6-Other/22-DeepRL/README.md index ce7cd121..dbb3e738 100644 --- a/translations/sl/lessons/6-Other/22-DeepRL/README.md +++ b/translations/sl/lessons/6-Other/22-DeepRL/README.md @@ -1,12 +1,3 @@ - # Globoko okrepljeno učenje Okrepljeno učenje (RL) velja za enega osnovnih paradigm strojnega učenja, poleg nadzorovanega in nenadzorovanega učenja. Medtem ko se pri nadzorovanem učenju zanašamo na podatkovne nabore z znanimi rezultati, je RL osnovano na **učenju skozi izkušnje**. Na primer, ko prvič vidimo računalniško igro, začnemo igrati, čeprav ne poznamo pravil, in kmalu izboljšamo svoje spretnosti zgolj z igranjem in prilagajanjem svojega vedenja. @@ -34,7 +25,7 @@ Verjetno ste že videli sodobne naprave za uravnoteženje, kot so *Segway* ali * Poenostavljena različica uravnoteženja je znana kot problem **CartPole**. V svetu CartPole imamo horizontalni drsnik, ki se lahko premika levo ali desno, cilj pa je uravnotežiti navpični drog na vrhu drsnika med njegovim premikanjem. -cartpole +cartpole Za ustvarjanje in uporabo tega okolja potrebujemo nekaj vrstic kode v Pythonu: diff --git a/translations/sl/lessons/6-Other/22-DeepRL/lab/README.md b/translations/sl/lessons/6-Other/22-DeepRL/lab/README.md index 87afe2bf..0e02f24b 100644 --- a/translations/sl/lessons/6-Other/22-DeepRL/lab/README.md +++ b/translations/sl/lessons/6-Other/22-DeepRL/lab/README.md @@ -1,12 +1,3 @@ - ## Okolje Okolje Mountain Car vključuje avto, ujet v dolini. Vaš cilj je skočiti iz doline in doseči zastavo. Dejanja, ki jih lahko izvedete, so pospeševanje v levo, v desno ali nič. Opazujete lahko položaj avtomobila na x-osi in hitrost. diff --git a/translations/sl/lessons/6-Other/23-MultiagentSystems/README.md b/translations/sl/lessons/6-Other/23-MultiagentSystems/README.md index 4a46646a..26a94320 100644 --- a/translations/sl/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/sl/lessons/6-Other/23-MultiagentSystems/README.md @@ -1,12 +1,3 @@ - # Večagentni sistemi Eden od možnih načinov doseganja inteligence je tako imenovani **emergentni** (ali **sinergijski**) pristop, ki temelji na dejstvu, da lahko kombinirano vedenje mnogih relativno preprostih agentov privede do bolj kompleksnega (ali inteligentnega) vedenja sistema kot celote. Teoretično to temelji na principih [kolektivne inteligence](https://en.wikipedia.org/wiki/Collective_intelligence), [emergentizma](https://en.wikipedia.org/wiki/Global_brain) in [evolucijske kibernetike](https://en.wikipedia.org/wiki/Global_brain), ki pravijo, da višjenivojski sistemi pridobijo določeno dodano vrednost, ko so pravilno sestavljeni iz nižjenivojskih sistemov (tako imenovani *princip prehoda metasistema*). @@ -60,7 +51,7 @@ NetLogo lahko [prenesete](https://ccl.northwestern.edu/netlogo/download.shtml) i Odlična stvar pri NetLogo je, da vsebuje knjižnico delujočih modelov, ki jih lahko preizkusite. Pojdite na **File → Models Library**, kjer imate na voljo številne kategorije modelov. -NetLogo Models Library +NetLogo Models Library > Posnetek zaslona knjižnice modelov avtorja Dmitry Soshnikov diff --git a/translations/sl/lessons/6-Other/23-MultiagentSystems/assignment.md b/translations/sl/lessons/6-Other/23-MultiagentSystems/assignment.md index b9ca08a2..ccf43ca5 100644 --- a/translations/sl/lessons/6-Other/23-MultiagentSystems/assignment.md +++ b/translations/sl/lessons/6-Other/23-MultiagentSystems/assignment.md @@ -1,12 +1,3 @@ - # Naloga NetLogo Izberite enega od modelov iz knjižnice NetLogo in ga uporabite za čim bolj natančno simulacijo resnične situacije. Dober primer bi bil prilagoditev modela Virus v mapi Alternative Visualizations, da pokažete, kako ga je mogoče uporabiti za modeliranje širjenja COVID-19. Ali lahko ustvarite model, ki posnema širjenje virusa v resničnem življenju? diff --git a/translations/sl/lessons/7-Ethics/README.md b/translations/sl/lessons/7-Ethics/README.md index efe27129..961fa8b4 100644 --- a/translations/sl/lessons/7-Ethics/README.md +++ b/translations/sl/lessons/7-Ethics/README.md @@ -1,12 +1,3 @@ - # Etična in odgovorna umetna inteligenca Skoraj ste zaključili ta tečaj in upam, da zdaj jasno vidite, da je umetna inteligenca (UI) osnovana na številnih formalnih matematičnih metodah, ki nam omogočajo iskanje povezav v podatkih in učenje modelov za posnemanje določenih vidikov človeškega vedenja. V tem trenutku zgodovine umetno inteligenco obravnavamo kot zelo močno orodje za pridobivanje vzorcev iz podatkov in uporabo teh vzorcev za reševanje novih težav. diff --git a/translations/sl/lessons/README.md b/translations/sl/lessons/README.md index 1301e8bf..aa59b635 100644 --- a/translations/sl/lessons/README.md +++ b/translations/sl/lessons/README.md @@ -1,12 +1,3 @@ - # Pregled ![Pregled v skici](../../../translated_images/sl/ai-overview.0857791951d19500.webp) diff --git a/translations/sl/lessons/X-Extras/X1-MultiModal/README.md b/translations/sl/lessons/X-Extras/X1-MultiModal/README.md index c394e458..e86b2104 100644 --- a/translations/sl/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/sl/lessons/X-Extras/X1-MultiModal/README.md @@ -1,12 +1,3 @@ - # Multi-modalna omrežja Po uspehu modelov transformatorjev pri reševanju nalog NLP so bile iste ali podobne arhitekture uporabljene tudi za naloge računalniškega vida. Narašča zanimanje za gradnjo modelov, ki bi *združevali* sposobnosti vida in naravnega jezika. Eden takšnih poskusov je bil izveden s strani OpenAI, imenovan CLIP in DALL.E. diff --git a/translations/sl/lessons/sketchnotes/LICENSE.md b/translations/sl/lessons/sketchnotes/LICENSE.md index e276f47f..c4643b87 100644 --- a/translations/sl/lessons/sketchnotes/LICENSE.md +++ b/translations/sl/lessons/sketchnotes/LICENSE.md @@ -1,12 +1,3 @@ - Priznanje-Deljenje pod enakimi pogoji 4.0 Mednarodna ======================================================================= diff --git a/translations/sl/lessons/sketchnotes/README.md b/translations/sl/lessons/sketchnotes/README.md index f9fd5ae1..77000142 100644 --- a/translations/sl/lessons/sketchnotes/README.md +++ b/translations/sl/lessons/sketchnotes/README.md @@ -1,12 +1,3 @@ - Vse skice učnega načrta lahko prenesete tukaj. 🎨 Ustvarila: Tomomi Imura (Twitter: [@girlie_mac](https://twitter.com/girlie_mac), GitHub: [girliemac](https://github.com/girliemac)) diff --git a/translations/sl/troubleshoot.md b/translations/sl/troubleshoot.md index 4e8e062b..47492008 100644 --- a/translations/sl/troubleshoot.md +++ b/translations/sl/troubleshoot.md @@ -1,12 +1,3 @@ - # Vodnik za odpravljanje težav pri AI-For-Beginners Ta vodnik vam pomaga rešiti pogoste težave, ki se pojavijo pri uporabi ali prispevanju v repozitorij [AI-For-Beginners](https://github.com/microsoft/AI-For-Beginners). Vsaka težava vključuje ozadje, simptome, razlage in korake za rešitev.