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# AGENTS.md
## Projekti ülevaade

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[![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/)
@ -21,207 +12,208 @@ CO_OP_TRANSLATOR_METADATA:
[![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
# Tehisintellekt algajatele - õppekava
# Tehisintellekt algajatele – õppekava
|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../../../translated_images/et/ai-overview.0857791951d19500.webp)|
|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/et/ai-overview.0857791951d19500.webp)|
|:---:|
| Tehisintellekt algajatele - _Sketchnote autor [@girlie_mac](https://twitter.com/girlie_mac)_ |
| AI algajatele - _Sketchnote autorilt [@girlie_mac](https://twitter.com/girlie_mac)_ |
Avasta **Tehisintellekti** (AI) maailm meie 12-nädalase, 24-õpetunni õppekavaga! See sisaldab praktilisi tunde, viktoriine ja laboritöid. Õppekava on algajasõbralik ja käsitleb tööriistu nagu TensorFlow ja PyTorch ning ka tehisintellekti eetikat.
Avasta **tehisintellekti** (AI) maailm meie 12-nädalase ja 24-õppega õppekavaga! See hõlmab praktilisi tunde, teste ja laboritöid. Õppekava on algajasõbralik ja katab tööriistu nagu TensorFlow ja PyTorch ning ka AI eetikat.
### 🌐 Mitmekeelne tugi
#### Toetatud GitHub Actioni kaudu (automatiseeritud ja alati ajakohane)
#### Toetatud Github Actioni kaudu (automaatne ja alati ajakohane)
<!-- CO-OP TRANSLATOR LANGUAGES TABLE START -->
[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](./README.md) | [Finnish](../fi/README.md) | [French](../fr/README.md) | [German](../de/README.md) | [Greek](../el/README.md) | [Hebrew](../he/README.md) | [Hindi](../hi/README.md) | [Hungarian](../hu/README.md) | [Indonesian](../id/README.md) | [Italian](../it/README.md) | [Japanese](../ja/README.md) | [Kannada](../kn/README.md) | [Korean](../ko/README.md) | [Lithuanian](../lt/README.md) | [Malay](../ms/README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Nepali](../ne/README.md) | [Nigerian Pidgin](../pcm/README.md) | [Norwegian](../no/README.md) | [Persian (Farsi)](../fa/README.md) | [Polish](../pl/README.md) | [Portuguese (Brazil)](../br/README.md) | [Portuguese (Portugal)](../pt/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Romanian](../ro/README.md) | [Russian](../ru/README.md) | [Serbian (Cyrillic)](../sr/README.md) | [Slovak](../sk/README.md) | [Slovenian](../sl/README.md) | [Spanish](../es/README.md) | [Swahili](../sw/README.md) | [Swedish](../sv/README.md) | [Tagalog (Filipino)](../tl/README.md) | [Tamil](../ta/README.md) | [Telugu](../te/README.md) | [Thai](../th/README.md) | [Turkish](../tr/README.md) | [Ukrainian](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamese](../vi/README.md)
[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](./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)
> **Eelistad kloonimist lokaalselt?**
> **Eelista kloonimist kohalikult?**
> See hoidla sisaldab üle 50 keele tõlkeid, mis suurendab oluliselt allalaaditava faili suurust. Tõlgeteta kloonimiseks kasuta sparse checkout funktsiooni:
> See hoidla sisaldab 50+ keele tõlkeid, mis suurendab oluliselt allalaaditava faili suurust. Kui soovid kloonida ilma tõlgeteta, kasuta sparse checkouti:
> ```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'
> ```
> See annab sulle kõik vajaliku kursuse lõpetamiseks palju kiiremalt.
> See annab sulle kõik vajaliku kursuse läbimiseks palju kiiremalt.
<!-- CO-OP TRANSLATOR LANGUAGES TABLE END -->
**Kui soovid, et toetataks täiendavaid tõlkekeeli, siis need on loetletud [siin](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)**
**Kui soovid lisaks toetada tõlkeid, on toetatud keeled loetletud [siin](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)**
## Liitu kogukonnaga
[![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
## Mida sa õpib
## Mida sa õpid
**[Kursuse mõttekaart](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)**
Selles õppekavas õpid:
* Erinevaid tehisintellekti lähenemisi, sealhulgas "head vananenud" sümboolset lähenemist koos **Teadmiste representeerimise** ja loogikaga ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)).
* **Närvivõrke** ja **Sügavat õpet**, mis on kaasaegse AI tuum. Selgitame nende oluliste teemade taga olevaid kontseptsioone koodinäidete abil kahes populaarsemas raamistikus - [TensorFlow](http://Tensorflow.org) ja [PyTorch](http://pytorch.org).
* **Närviarhitektuure** piltide ja teksti töötlemiseks. Käsitleme uuemaid mudeleid, kuid võib-olla mitte täiesti uusimaid.
* Vähem populaarsed AI lähenemised, nagu **Geneetilised algoritmid** ja **Mitmeagendilised süsteemid**.
* Erinevaid tehisintellekti lähenemisi, kaasa arvatud „hea vana“ sümboolse lähenemise koos **teadmiste esitamise** ja järeldamisega ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)).
* **Neuraalvõrgud** ja **sügavõpe**, mis on moodsa AI tuum. Selgitame nende oluliste teemade kontseptsioone kasutades kahe populaarse raamistiku koodi - [TensorFlow](http://Tensorflow.org) ja [PyTorch](http://pytorch.org).
* **Neuraalarhitektuurid** piltide ja teksti töötlemiseks. Katame hiljutisi mudeleid, ent võime olla veidi maas tipptasemel teaduslikust arengust.
* Vähem levinud AI lähenemised, nagu **geneetilised algoritmid** ja **mitmeagendilised süsteemid**.
Mida me selles õppekavas ei käsitle:
Mida me selles õppekavas ei kata:
> [Leia kõik täiendavad selle kursuse ressursid meie Microsoft Learn'i kogumis](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)
> [Leia kõik selle kursuse lisamaterjalid meie Microsoft Learni kogumikust](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)
* Ärilised juhtumid **AI kasutamiseks äris**. Mõtle võtta [Sissejuhatus tehisintellekti jaoks ärikasutajatele](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) õppeteek Microsoft Learn'is või [AI ärikool](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), mis on välja töötatud koostöös [INSEAD](https://www.insead.edu/) organisatsiooniga.
* **Klassikaline masinõpe**, mis on põhjalikult kirjeldatud meie [Algajate masinõppe õppekavas](http://github.com/Microsoft/ML-for-Beginners).
* Praktilised tehisintellekti rakendused, mis on ehitatud kasutades **[Cognitive Services](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**. Soovitame alustada Microsoft Learni moodulitest [nägemi](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [loomuliku keele töötlemise](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[Generatiivse AI-ga Azure OpenAI Teenuse kaudu](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** ja teised.
* Spetsiifilised ML **pilverahastused**, nagu [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) või [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). Võiksid kaaluda [Masinõppe lahenduste loomist ja kasutamist Azure Machine Learninguga](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) ja [Masinõppe lahenduste loomist ja kasutamist Azure Databricksuga](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum) õppeteid.
* **Vestlev AI** ja **vestlusbotid**. Selleks on olemas eraldi [Loo vestlusAI lahendusi](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum) õppeteek ning saad lisaks vaadata [seda blogipostitust](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) detailsemalt.
* **Sügav matemaatika** sügava õppe taga. Selleks soovitame [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) raamatut autoritelt Ian Goodfellow, Yoshua Bengio ja Aaron Courville, mis on ka saadaval veebis aadressil [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/).
* Ärilisi juhtumeid AI kasutusest ettevõtluses. Kaalu [Sissejuhatust AI-sse ärikasutajatele](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) Microsoft Learni õppeteel või [AI ärikooli](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), mille on loonud koostöös [INSEAD](https://www.insead.edu/).
* **Klassikalist masinõpet**, mis on hästi kaetud meie [Algajate masinõppe õppekavas](http://github.com/Microsoft/ML-for-Beginners).
* Praktilisi AI rakendusi, mis põhinevad **[Cognitive Services](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)** teenustel. Soovitame alustada Microsoft Learni moodulitest [nägemine](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [loomulik keel](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[Generatiivne AI Azure OpenAI teenusega](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** ja teised.
* Spetsiifilisi pilvepõhiseid ML raamistikke, nagu [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) või [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). Soovitame kasutada õppeteid [Masinõpperakenduste loomine ja haldamine Azure Machine Learninguga](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) ja [Masinõpperakenduste loomine ja haldamine Azure Databricksi abil](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum).
* **Vestlusliku AI** ja **vestlusrobotite** teemasid. Selleks on eraldi [Vestluslike AI lahenduste loomine](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum) õpperada ning võid vaadata ka [seda blogipostitust](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) lisainfo saamiseks.
* **Sügavas matemaatikas** sügava õppimise taga. Soovitame selleks [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) raamatut, autoriteks Ian Goodfellow, Yoshua Bengio ja Aaron Courville, mis on samuti saadaval veebis aadressil [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/).
Õrnema sissejuhatuse saamiseks _pilvetehisintellekti_ teemadesse võid kaaluda [Alustamist tehisintellektiga Azure'is](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum) õppeteed.
Õrnemaks sissejuhatuseks _AI pilves_ teemadesse võid kaaluda [Alustamist tehisintellektiga Azure'is](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum) õppeteel läbimist.
# Sisu
| | õppetunni link | PyTorch/Keras/TensorFlow | Labor |
| | Õpetuse link | PyTorch/Keras/TensorFlow | Labor |
| :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ |
| 0 | [Kursuse seadistus](./lessons/0-course-setup/setup.md) | [Arenduskeskkonna seadistamine](./lessons/0-course-setup/how-to-run.md) | |
| I | [**Sissejuhatus AI-sse**](./lessons/1-Intro/README.md) | | |
| 01 | [AI sissejuhatus ja ajalugu](./lessons/1-Intro/README.md) | - | - |
| 0 | [Kursuse seadistamine](./lessons/0-course-setup/setup.md) | [Arenduskeskkonna seadistamine](./lessons/0-course-setup/how-to-run.md) | |
| I | [**Intro tehisintellekti**](./lessons/1-Intro/README.md) | | |
| 01 | [Intro ja tehisintellekti ajalugu](./lessons/1-Intro/README.md) | - | - |
| II | **Sümboolne AI** |
| 02 | [Teadmiste representeerimine ja ekspertsüsteemid](./lessons/2-Symbolic/README.md) | [Ekspertsüsteemid](./lessons/2-Symbolic/Animals.ipynb) / [Ontoloogia](./lessons/2-Symbolic/FamilyOntology.ipynb) /[Kontseptsioonigraafik](./lessons/2-Symbolic/MSConceptGraph.ipynb) | |
| 02 | [Teadmiste esitamine ja ekspert süsteemid](./lessons/2-Symbolic/README.md) | [Ekspertsüsteemid](./lessons/2-Symbolic/Animals.ipynb) / [Ontoloogia](./lessons/2-Symbolic/FamilyOntology.ipynb) /[Kontseptsioonide graaf](./lessons/2-Symbolic/MSConceptGraph.ipynb) | |
| III | [**Sissejuhatus närvivõrkudesse**](./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 | [Mitmekihiline percetron ja oma raamistik](./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 | [Sissejuhatus raamistikesse (PyTorch/TensorFlow) ja üleõppimine](./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 | [**Arvutinägemine**](./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)| [Uuri arvutinägemist Microsoft Azure'is](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) |
| 06 | [Sissejuhatus arvutinägemisse. 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 | [Konvolutsioonilised närvivõrgud](./lessons/4-ComputerVision/07-ConvNets/README.md) & [CNN arhitektuurid](./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 | [Eeltreenitud võrgud ja teadmiste ülekandmine](./lessons/4-ComputerVision/08-TransferLearning/README.md) ja [Treeningtrikid](./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 | [Automaatkodeerijad ja VAE-d](./lessons/4-ComputerVision/09-Autoencoders/README.md) | [PyTorch](./lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb) | |
| 10 | [Generatiivsed vastanduvad võrgud & Kunstilise stiili ülekandmine](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | |
| 11 | [Objektituvastus](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [Lab](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) |
| 03 | [Perceptron](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [Märkmik](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [Labor](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) |
| 04 | [Mitmikkihtne perceptron ja oma raamistikuga alustamine](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [Märkmik](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [Labor](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) |
| 05 | [Raamistike sissejuhatus (PyTorch/TensorFlow) ja üleõppimine](./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) | [Labor](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) |
| IV | [**Arvutinägemine**](./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)| [Avasta arvutivisiooni Microsoft Azure'is](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) |
| 06 | [Sissejuhatus arvutinägemisse. OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [Märkmik](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [Labor](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) |
| 07 | [Konvolutsioonilised närvivõrgud](./lessons/4-ComputerVision/07-ConvNets/README.md) & [CNN arhitektuurid](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [Labor](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) |
| 08 | [Eeltreenitud võrgud ja ülekandeõpe](./lessons/4-ComputerVision/08-TransferLearning/README.md) ja [Treeningtrikid](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Labor](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) |
| 09 | [Autoenkoodrid ja VAEd](./lessons/4-ComputerVision/09-Autoencoders/README.md) | [PyTorch](./lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb) | |
| 10 | [Generatiivsed vastandvõrgustikud ja kunstiline stiiliohutus](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | |
| 11 | [Objektituvastus](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [Labor](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) |
| 12 | [Semantiline segmentimine. 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 | [**Loodusliku keele töötlemine**](./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) | [Uuri loodusliku keele töötlemist Microsoft Azure'is](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)|
| 13 | [Teksti representatsioon. 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 | [Semantilised sõna manused. Word2Vec ja 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 | [Keelemudelid. Oma manuste treenimine](./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 | [Korduvad närvivõrgud](./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 | [Generatiivsed korduvad võrgud](./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) |
| V | [**Loodusliku keele töötlemine**](./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) | [Avasta loodusliku keele töötlemist Microsoft Azure'is](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)|
| 13 | [Teksti esitlus. 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 | [Semantilised sõna pesaesitused. Word2Vec ja 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 | [Keelemudelid. Oma pesaesituste treenimine](./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) | [Labor](./lessons/5-NLP/15-LanguageModeling/lab/README.md) |
| 16 | [Tagasikorduvad närvivõrgud](./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 | [Generatiivsed tagasikorduvad võrgud](./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) | [Labor](./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 | [Nimetatud üksuste tuvastamine](./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 | [Suuured keelemudelid, promptide programmeerimine ja vähese andmestikuga ülesanded](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | |
| 19 | [Nimetatud üksuste äratundmine](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [Labor](./lessons/5-NLP/19-NER/lab/README.md) |
| 20 | [Suured keelemudelid, ülesannete programmeerimine ja vähese andmemahuga ülesanded](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | |
| VI | **Muud tehisintellekti tehnikad** || |
| 21 | [Geneetilised algoritmid](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [Notebook](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | |
| 22 | [Sügav tugevdatud õpe](./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) |
| 21 | [Geneetilised algoritmid](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [Märkmik](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | |
| 22 | [Sügav tugevdusõpe](./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) | [Labor](./lessons/6-Other/22-DeepRL/lab/README.md) |
| 23 | [Mitmeagendilised süsteemid](./lessons/6-Other/23-MultiagentSystems/README.md) | | |
| VII | **Tehisintellekti eetika** | | |
| 24 | [Eetika ja vastutustundlik tehisintellekt](./lessons/7-Ethics/README.md) | [Microsoft Learn: Vastutustundliku tehisintellekti põhimõtted](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | |
| 24 | [Tehisintellekti eetika ja vastutustundlik AI](./lessons/7-Ethics/README.md) | [Microsoft Learn: Vastutustundliku AI põhimõtted](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | |
| IX | **Lisad** | | |
| 25 | [Mitme-modaliga võrgud, CLIP ja VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [Notebook](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | |
| 25 | [Mitmemodaalvõrgud, CLIP ja VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [Märkmik](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | |
## Iga õppetund sisaldab
## Igas õppetükis on
* Eelnev lugemismaterjal
* Käivitatavad Jupyteri märkmikud, mis on tihti spetsiifilised raamistikule (**PyTorch** või **TensorFlow**). Käivitatav märkmik sisaldab ka palju teoreetilist materjali, nii et teema mõistmiseks tuleb läbi töötada vähemalt üks märkmiku versioon (kas PyTorch või TensorFlow).
* Mõne teema jaoks on saadaval **Laborid**, mis annavad sulle võimaluse proovida õpitut rakendada konkreetsele probleemile.
* Mõned osad sisaldavad linke [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) moodulitesse, mis käsitlevad seotud teemasid.
* Eelnevalt loetav materjal
* Teostatavad Jupyteri märkmikud, mis on tihti spetsiifilised raamistikule (**PyTorch** või **TensorFlow**). Teostatav märkmik sisaldab ka palju teoreetilist materjali, seega teema mõistmiseks tuleb läbida vähemalt üks versioon märkmikust (või PyTorch või TensorFlow).
* Mõne teema puhul on saadaval **laborid**, mis annavad võimaluse proovida õpitut rakendada konkreetsele probleemile.
* Mõned lõigud sisaldavad linke [**MS Lehti**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) moodulitele, mis käsitlevad seotud teemasid.
## Alustamine
### 🎯 Oled tehisintellektiga uus? Alusta siit!
### 🎯 Uus AI-s? Alusta siit!
Kui oled tehisintellektiga täiesti uus ja soovid kiireid, praktilisi näiteid, vaata meie [**Algajatele mõeldud näited**](./examples/README.md)! Need sisaldavad:
Kui oled täiesti uus AI maailmas ja soovid kiireid praktilisi näiteid, vaata meie [**Algajale sõbralikud näited**](./examples/README.md)! Nende hulka kuuluvad:
- 🌟 **Tere tulemast, AI maailm** - Sinu esimene tehisintellekti programm (mustriloendus)
- 🧠 **Lihtne närvivõrk** - Ehita närvivõrk nullist
- 🖼️ **Pildiklassifikaator** - Klassifitseeri pilte põhjalike kommentaaridega
- 💬 **Teksti sentiment** - Anadüüsi positiivset/negatiivset teksti
- 🌟 **Tere tulemast AI maailma** – Sinu esimene tehisintellekti programm (mustrimuster tunnustus)
- 🧠 **Lihtne närvivõrk** – Ehita närvivõrk nullist
Need näited on loodud selleks, et aidata teil mõista tehisintellekti kontseptsioone enne täielikku õppekava läbimist.
- 🖼️ **Pildiklassifikaator** - Pildista pilte üksikasjalike kommentaaridega
- 💬 **Teksti meeleolu** - Analüüsi positiivset/negatiivset teksti
Need näited on mõeldud aitamaks sul mõista tehisintellekti (AI) kontseptsioone enne kogu õppekava läbimist.
### 📚 Täieliku õppekava seadistamine
- Oleme loonud [seadistuse tunni](./lessons/0-course-setup/setup.md), et aidata teil arenduskeskkonda seadistada. - Õpetajatele oleme loonud ka [õppekava seadistuse tunni](./lessons/0-course-setup/for-teachers.md)!
- Kuidas [koodi käivitada VSCode’is või Codespaces](./lessons/0-course-setup/how-to-run.md)
- Oleme loonud [seadistustunni](./lessons/0-course-setup/setup.md), mis aitab sul oma arenduskeskkonda seadistada. - Õpetajatele oleme loonud ka [õppekava seadistustunni](./lessons/0-course-setup/for-teachers.md)!
- Kuidas [käivitada koodi VSCode-is või Codespace’is](./lessons/0-course-setup/how-to-run.md)
Järgige neid samme:
Järgi neid samme:
Varu repository: Klõpsake selle lehe paremas ülanurgas nuppu "Fork".
Repositooriumi forkimine: Klõpsa selle lehe paremas ülanurgas nuppu "Fork".
Klooni repository: `git clone https://github.com/microsoft/AI-For-Beginners.git`
Repositooriumi kloonimine: `git clone https://github.com/microsoft/AI-For-Beginners.git`
Ärge unustage sellele reposiidile tärni (🌟) panna, et hiljem seda lihtsam üles leida.
Ära unusta seda reposid tärnida (🌟), et seda hiljem lihtsamini leida.
## Tutvuge teiste õppijatega
## Kohtumine teiste õppijatega
Liituge meie [ametliku AI Discordi serveriga](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum), et kohtuda ja suhelda teiste selle kursuse õppijatega ning saada tuge.
Liitu meie ametliku [AI Discordi serveriga](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum), et kohtuda ja suhelda teiste selle kursuse õppijatega ning saada tuge.
Kui teil on toote tagasisidet või küsimusi ehitamise ajal, külastage meie [Azure AI Foundry arendajate foorumit](https://aka.ms/foundry/forum)
Kui sul on toodet puudutavaid kommentaare või küsimusi ehitamise ajal, külasta meie [Azure AI Foundry arendajate foorumit](https://aka.ms/foundry/forum)
## Testid
## Testid
> **Märkus testide kohta**: Kõik testid asuvad kaustas Quiz-app teekonnal etc\quiz-app või [Veebi kaudu siin](https://ff-quizzes.netlify.app/) Need on lingitud õppetundide sees; testi rakendust saab käivitada lokaalselt või paigaldada Azure’i; järgige juhiseid `quiz-app` kaustas. Neid kohandatakse järk-järgult erinevatele keeleversioonidele.
> **Märkus testide kohta**: Kõik testid asuvad Quiz-app kaustas aadressil etc\quiz-app või [veebis siin](https://ff-quizzes.netlify.app/). Neile viidatakse tundides, testirakendust saab käivitada lokaalselt või paigaldada Azuresse; järgi juhiseid `quiz-app` kaustas. Need on järk-järgult lokaliseeritud.
## Otsime abi
## Abi soovitud
Kas teil on ettepanekuid või olete leidnud õigekirja- või koodivigu? Tõstke esile probleem või looge tõmbepäring.
Kas sul on ettepanekuid või leidsid kirjavigu või koodivigu? Esita probleem või loo pull request.
## Eriti suur tänu
## Eriline tänu
* **✍️ Peaautor:** [Dmitry Soshnikov](http://soshnikov.com), PhD
* **✍️ Peamine autor:** [Dmitry Soshnikov](http://soshnikov.com), PhD
* **🔥 Toimetaja:** [Jen Looper](https://twitter.com/jenlooper), PhD
* **🎨 Sketšinote illustraator:** [Tomomi Imura](https://twitter.com/girlie_mac)
* **✅ Testide autor:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/)
* **🙏 Põhikontribuudid:** [Evgenii Pishchik](https://github.com/Pe4enIks)
* **🎨 Sketchnote illustraator:** [Tomomi Imura](https://twitter.com/girlie_mac)
* **✅ Testide looja:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/)
* **🙏 Põhijõud:** [Evgenii Pishchik](https://github.com/Pe4enIks)
## Muud õppekavad
Meie meeskond toodab ka teisi õppekavu! Vaadake:
Meie tiim toodab ka teisi õppekavu! Vaata neid:
<!-- CO-OP TRANSLATOR OTHER COURSES START -->
### LangChain
[![LangChain4j algajatele](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 algajatele](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 / Agentid
[![AZD algajatele](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 algajatele](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 algajatele](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 agentid algajatele](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 / Agendid
[![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)
---
### Generatiivne tehisintellekt seeria
[![Generatiivne AI algajatele](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)
[![Generatiivne 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)
[![Generatiivne 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)
[![Generatiivne 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)
### Generatiivse tehisintellekti sari
[![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)
---
### Põhijõudude õppimine
[![Masinõpe algajatele](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)
[![Andmeteadus algajatele](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 algajatele](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)
[![Küberjulgeolek algajatele](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)
[![Veebiarendus algajatele](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 algajatele](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 arendus algajatele](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)
### Põhiõpe
[![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)
---
### Copilot seeria
[![Copilot AI paar-programmeerimiseks](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst)
[![Copilot C#/.NET jaoks](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 seiklus](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst)
### Copilot sari
[![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)
<!-- CO-OP TRANSLATOR OTHER COURSES END -->
## Abi saamine
Kui teil jääb midagi arusaamatuks või teil on AI rakenduste loomise kohta küsimusi, liituge teiste õppijate ja kogenud arendajatega MCP teemal aruteludes. See on toetav kogukond, kus küsimusi julgesti esitada ja teadmisi vabalt jagada.
Kui sa jääd hätta või sul on küsimusi AI rakenduste ehitamise kohta, liitu kaasõppijate ja kogenud arendajatega MCP teemalises arutelus. See on toetav kogukond, kus küsimused on teretulnud ja teadmisi jagatakse vabalt.
[![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
Kui teil on kestvatoote tagasisidet või veateateid, külastage:
Kui sul on toodet puudutavaid kommentaare või vigasid ehitamise käigus, külasta:
[![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum)
@ -229,5 +221,5 @@ Kui teil on kestvatoote tagasisidet või veateateid, külastage:
<!-- CO-OP TRANSLATOR DISCLAIMER START -->
**Vastutusest loobumine**:
See dokument on tõlgitud kasutades tehisintellektil põhinevat tõlketeenust [Co-op Translator](https://github.com/Azure/co-op-translator). Kuigi püüame täpsust, olge teadlikud, et automaatsed tõlked võivad sisaldada vigu või ebatäpsusi. Originaaldokument oma emakeeles tuleb pidada autoriteetseks allikaks. Olulise teabe puhul soovitatakse kasutada professionaalset inimtõlget. Me ei vastuta selle tõlke kasutamisest tulenevate arusaamatuste või valesti mõistmiste eest.
See dokument on tõlgitud AI tõlketeenuse [Co-op Translator](https://github.com/Azure/co-op-translator) abil. Kuigi püüame täpsust, palun arvestage, et automatiseeritud tõlked võivad sisaldada vigu või ebatäpsusi. Originaaldokument tema algkeeles tuleks pidada autoriteetseks allikaks. Kriitilise teabe puhul soovitatakse kasutada professionaalset inimtõlget. Me ei vastuta mis tahes arusaamatuste ega valesti mõistmiste eest, mis võivad tekkida selle tõlke kasutamisest.
<!-- CO-OP TRANSLATOR DISCLAIMER END -->

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<!-- BEGIN MICROSOFT SECURITY.MD V0.0.7 BLOCK -->
## Turvalisus

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# Microsofti avatud lähtekoodi käitumisjuhend
See projekt on omaks võtnud [Microsofti avatud lähtekoodi käitumisjuhendi](https://opensource.microsoft.com/codeofconduct/).

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# Kaastöö tegemine
See projekt ootab kaastöid ja ettepanekuid. Enamik kaastöid nõuab, et nõustuksite Kaastöö Litsentsilepinguga (CLA), mis kinnitab, et teil on õigus ja te tegelikult annate meile õiguse teie panust kasutada. Lisateabe saamiseks külastage https://cla.microsoft.com.

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# AI
## [Sissejuhatus tehisintellekti](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/1-Intro/README.md)

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# Tugi
## Kuidas esitada probleeme ja saada abi

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# Panusta tundide tõlkimisega
Ootame selle õppekava tundide tõlkeid!

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# Viktoriinid
Need viktoriinid on tehisintellekti õppekava eel- ja järeltestid aadressil https://aka.ms/ai-beginners

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# Algajatele sobivad tehisintellekti näited
Tere tulemast! See kataloog sisaldab lihtsaid ja iseseisvaid näiteid, mis aitavad sul alustada tehisintellekti ja masinõppega. Iga näide on loodud algajasõbralikuks, sisaldades üksikasjalikke kommentaare ja samm-sammulisi selgitusi.

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# Õpetajatele
Kas soovite seda õppekava oma klassis kasutada? Palun tehke seda julgelt!

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# Kuidas Koodi Käivitada
See õppekava sisaldab palju täidetavaid näiteid ja töötoad, mida soovite jooksutada. Selleks peate saama käivitada Python koodi Jupyter märkmikes, mis on selle õppekava osana esitatud. Koodi käivitamiseks on teil mitu võimalust:

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# Alustamine selle õppekavaga
## Kas oled õpilane?

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# Sissejuhatus tehisintellekti
![Sissejuhatuse kokkuvõte tehisintellekti teemal doodle'is](../../../../translated_images/et/ai-intro.bf28d1ac4235881c.webp)

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# Mängu Jam
Mängud on valdkond, mida on tugevalt mõjutanud tehisintellekti ja masinõppe areng. Selles ülesandes kirjuta lühike essee mängust, mis sulle meeldib ja mida on mõjutanud tehisintellekti areng. See peaks olema piisavalt vana mäng, et seda oleks mõjutanud mitut tüüpi arvutitöötlussüsteemid. Hea näide on male või Go, aga vaata ka videomänge nagu Pong või Pac-Man. Kirjuta essee, mis käsitleb mängu minevikku, olevikku ja tehisintellekti tulevikku.

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# Teadmus Representatsioon ja Ekspertsüsteemid
![Sümboolse tehisintellekti sisu kokkuvõte](../../../../../../translated_images/et/ai-symbolic.715a30cb610411a6.webp)
![Sümboolse tehisintellekti sisu kokkuvõte](../../../../translated_images/et/ai-symbolic.715a30cb610411a6.webp)
> Sketchnote autor [Tomomi Imura](https://twitter.com/girlie_mac)
@ -41,7 +32,7 @@ Tihti ei defineerita teadmust täpselt, vaid kooskõlastatakse see teiste seotud
Seega seisneb **teadmiste representatsiooni** probleem selles, et leida mõni tõhus viis teadmiste esindamiseks arvutis andmete kujul, et neid saaks automaatselt kasutada. Seda võib vaadelda spektrina:
![Teadmiste representatsiooni spekter](../../../../../../translated_images/et/knowledge-spectrum.b60df631852c0217.webp)
![Teadmiste representatsiooni spekter](../../../../translated_images/et/knowledge-spectrum.b60df631852c0217.webp)
> Pilt autor Dmitry Soshnikov [http://soshnikov.com](http://soshnikov.com)
@ -94,7 +85,7 @@ Ploki Süntaks | Taandumine | | |
Üks sümboolse tehisintellekti varajasi edusamme olid nn **ekspertsüsteemid** — arvutisüsteemid, mis olid loodud käituma nagu ekspert kitsas probleemivaldkonnas. Need põhinesid **teadmistebaasil**, mis oli kogutud ühelt või mitmelt inimeselt, ja sisaldasid **järeldusmootorit**, mis teostas järeldamist selle põhjal.
![Inimese arhitektuur](../../../../../../translated_images/et/arch-human.5d4d35f1bba3ab1c.webp) | ![Teadmistepõhise süsteemi arhitektuur](../../../../../../translated_images/et/arch-kbs.3ec5c150b09fa8da.webp)
![Inimese arhitektuur](../../../../translated_images/et/arch-human.5d4d35f1bba3ab1c.webp) | ![Teadmistepõhise süsteemi arhitektuur](../../../../translated_images/et/arch-kbs.3ec5c150b09fa8da.webp)
---------------------------------------------|------------------------------------------------
Inimese närvisüsteemi lihtsustatud struktuur | Teadmistepõhise süsteemi arhitektuur
@ -106,7 +97,7 @@ Ekspertsüsteemid on üles ehitatud inimese mõtlemise süsteemile sarnaselt, mi
Näiteks oluline ekspert­süsteemi näide on looma määramine füüsiliste omaduste põhjal:
![JA-VÕI puu](../../../../../../translated_images/et/AND-OR-Tree.5592d2c70187f283.webp)
![JA-VÕI puu](../../../../translated_images/et/AND-OR-Tree.5592d2c70187f283.webp)
> Pilt autor Dmitry Soshnikov [http://soshnikov.com](http://soshnikov.com)

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# Loo ontoloogia
Teadmistebaasi loomine seisneb mudeli kategoriseerimises, mis esindab fakte mingi teema kohta. Vali teema - näiteks inimene, koht või asi - ja loo selle teema mudel. Kasuta mõningaid selles õppetükis kirjeldatud tehnikaid ja mudeli loomise strateegiaid. Näiteks võiks luua elutoa ontoloogia, kus on mööbel, valgustid jne. Kuidas erineb elutuba köögist? Vannitoast? Kuidas sa tead, et tegemist on elutoaga, mitte söögitoaga? Kasuta ontoloogia loomiseks [Protégé](https://protege.stanford.edu/).

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# Sissejuhatus tehisnärvivõrkudesse: Perceptron
## [Eelloengu viktoriin](https://ff-quizzes.netlify.app/en/ai/quiz/5)

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# Mitmeklassiline klassifikatsioon perceptroniga
Laboriülesanne [AI algajatele õppekavast](https://github.com/microsoft/ai-for-beginners).

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# Sissejuhatus tehisnärvivõrkudesse. Mitmekihiline perceptron
Eelmises osas õppisite tundma kõige lihtsamat tehisnärvivõrgu mudelit – ühekihilist perceptronit, mis on lineaarne kahe klassi klassifitseerimise mudel.

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# MNIST klassifikatsioon meie enda raamistikuga
Laboriülesanne [AI algajatele õppekavast](https://github.com/microsoft/ai-for-beginners).

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# Neuraalvõrkude raamistikud
Nagu me juba õppinud oleme, on neuraalvõrkude tõhusaks treenimiseks vaja teha kahte asja:

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# Klassifikatsioon PyTorch/TensorFlow abil
Laboriülesanne [AI for Beginners Curriculum](https://github.com/microsoft/ai-for-beginners) materjalidest.

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# Sissejuhatus tehisnärvivõrkudesse
![Kokkuvõte tehisnärvivõrkude sissejuhatuse sisust doodle'is](../../../../translated_images/et/ai-neuralnetworks.1c687ae40bc86e83.webp)

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# Sissejuhatus arvutinägemisse
[Arvutinägemine](https://wikipedia.org/wiki/Computer_vision) on valdkond, mille eesmärk on võimaldada arvutitel saavutada kõrgetasemeline arusaam digitaalsetest piltidest. See on üsna lai määratlus, kuna *arusaamine* võib tähendada mitmeid erinevaid asju, sealhulgas objekti leidmist pildilt (**objekti tuvastamine**), toimuvast aru saamist (**sündmuste tuvastamine**), pildi kirjeldamist tekstis või stseeni rekonstrueerimist 3D-s. Samuti on olemas eraldi ülesanded, mis on seotud inimeste piltidega: vanuse ja emotsioonide hindamine, näo tuvastamine ja identifitseerimine ning 3D poosi hindamine, kui nimetada vaid mõnda.

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# Liikumise tuvastamine optilise voolu abil
Laboriülesanne [AI algajatele mõeldud õppekavast](https://aka.ms/ai-beginners).

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# Tuntud CNN arhitektuurid
### VGG-16

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# Konvolutsioonilised närvivõrgud
Oleme varem näinud, et närvivõrgud on üsna head piltidega töötamisel ja isegi ühekihiline perceptron suudab MNIST andmestikus käsitsi kirjutatud numbreid mõistliku täpsusega ära tunda. Kuid MNIST andmestik on väga eriline, kuna kõik numbrid on pildi keskel, mis teeb ülesande lihtsamaks.

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# Lemmikloomade nägude klassifikatsioon
Laboriülesanne [AI for Beginners Curriculum](https://github.com/microsoft/ai-for-beginners) programmist.

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# Eelnevalt treenitud võrgud ja ülekandeõpe
CNN-ide treenimine võib võtta palju aega ja nõuda suurt hulka andmeid. Suur osa ajast kulub aga parimate madala taseme filtrite õppimisele, mida võrk saab kasutada mustrite tuvastamiseks piltidelt. Tekib loomulik küsimus – kas saaksime kasutada ühel andmestikul treenitud närvivõrku ja kohandada seda erinevate piltide klassifitseerimiseks ilma täieliku treenimisprotsessita?

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# Süvaõppe treenimise nipid
Kui närvivõrgud muutuvad sügavamaks, muutub nende treenimine üha keerulisemaks. Üks peamisi probleeme on nn [kaduvad gradiendid](https://en.wikipedia.org/wiki/Vanishing_gradient_problem) või [plahvatavad gradiendid](https://deepai.org/machine-learning-glossary-and-terms/exploding-gradient-problem#:~:text=Exploding%20gradients%20are%20a%20problem,updates%20are%20small%20and%20controlled.). [See postitus](https://towardsdatascience.com/the-vanishing-exploding-gradient-problem-in-deep-neural-networks-191358470c11) annab hea sissejuhatuse nendesse probleemidesse.

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# Oxfordi lemmikloomade klassifitseerimine ülekandeõppe abil
Laboriülesanne [AI for Beginners Curriculum](https://github.com/microsoft/ai-for-beginners) programmist.

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# Autoenkoodrid
CNN-ide treenimisel on üheks probleemiks see, et vajame palju märgistatud andmeid. Näiteks pildiklassifikatsiooni puhul peame pildid jagama erinevatesse klassidesse, mis on käsitsi tehtav töö.

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# Generatiivsed vastandlikud võrgud
Eelmises osas õppisime **generatiivsete mudelite** kohta: mudelid, mis suudavad luua uusi pilte, mis sarnanevad treeningandmestikus olevatele. VAE oli hea näide generatiivsest mudelist.

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# Objektide tuvastamine
Pildiklassifikatsiooni mudelid, millega oleme seni tegelenud, võtsid pildi ja andsid kategoorilise tulemuse, näiteks klassi 'number' MNIST-probleemis. Kuid paljudel juhtudel ei taha me lihtsalt teada, et pilt kujutab objekte – me tahame määrata nende täpse asukoha. Just seda eesmärki täidab **objektide tuvastamine**.

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# Peade tuvastamine Hollywood Heads andmestiku abil
Laboriülesanne [AI algajatele õppekavast](https://github.com/microsoft/ai-for-beginners).

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# Segmenteerimine
Oleme varem õppinud objektide tuvastamist, mis võimaldab meil leida objekte pildil, ennustades nende *piiravaid kaste*. Kuid mõnede ülesannete puhul ei vaja me ainult piiravaid kaste, vaid ka täpsemat objektide lokaliseerimist. Seda ülesannet nimetatakse **segmenteerimiseks**.

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# Inimkeha segmentimine
Laboriülesanne [AI for Beginners Curriculum](https://github.com/microsoft/ai-for-beginners) programmist.

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# Arvutinägemine
![Arvutinägemise sisu kokkuvõte visandina](../../../../translated_images/et/ai-computervision.6506ebebac3fbf76.webp)

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# Teksti esindamine tensoritena
## [Eelloengu viktoriin](https://ff-quizzes.netlify.app/en/ai/quiz/25)

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# Ülesanne: Märkmikud
Kasutades selle õppetunni juurde kuuluvaid märkmikke (kas PyTorch või TensorFlow versiooni), käivitage need uuesti, kasutades omaenda andmekogumit, näiteks Kaggle'ist, koos viitega. Kirjutage märkmik ümber, et rõhutada omaenda järeldusi. Proovige mõnda innovatiivset andmekogumit, mis võib osutuda üllatavaks, näiteks [see UFO-vaatluste andmekogum](https://www.kaggle.com/datasets/NUFORC/ufo-sightings) NUFORC-ist.

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# Sisestused
## [Eelloengu viktoriin](https://ff-quizzes.netlify.app/en/ai/quiz/27)

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# Ülesanne: Märkmikud
Kasutades selle õppetunni juurde kuuluvaid märkmikke (kas PyTorch või TensorFlow versiooni), käivitage need uuesti, kasutades oma andmekogumit, näiteks Kaggle'ist pärit andmeid, millele viitate korrektselt. Kirjutage märkmik ümber, et rõhutada omaenda järeldusi. Proovige teistsugust andmekogumit ja dokumenteerige oma järeldused, kasutades näiteks [neid Beatlesi laulusõnu](https://www.kaggle.com/datasets/jenlooper/beatles-lyrics).

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# Keelemudelid
Semantilised vektorid, nagu Word2Vec ja GloVe, on tegelikult esimene samm **keelemudelite** suunas – mudelite loomine, mis mingil moel *mõistavad* (või *esindavad*) keele olemust.

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# Skip-Gram mudeli treenimine
Laboriülesanne [AI for Beginners Curriculum](https://github.com/microsoft/ai-for-beginners) materjalidest.

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# Korduvad Neuraalvõrgud
## [Eelloengu viktoriin](https://ff-quizzes.netlify.app/en/ai/quiz/31)

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# Ülesanne: Märkmikud
Kasutades selle õppetunni juurde kuuluvaid märkmikke (kas PyTorch või TensorFlow versiooni), käivitage need uuesti, kasutades omaenda andmekogumit, näiteks Kaggle'ist, koos viitega. Kirjutage märkmik ümber, et rõhutada omaenda järeldusi. Proovige teistsugust andmekogumit ja dokumenteerige oma järeldused, kasutades teksti, näiteks [see Kaggle'i võistluse andmekogum ilmastiku säutsude kohta](https://www.kaggle.com/competitions/crowdflower-weather-twitter/data?select=train.csv).

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# Generatiivsed võrgud
## [Eelloengu viktoriin](https://ff-quizzes.netlify.app/en/ai/quiz/33)

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# Sõnataseme teksti genereerimine RNN-idega
Laboriülesanne [AI algajatele õppekavast](https://github.com/microsoft/ai-for-beginners).

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# Tähelepanu mehhanismid ja Transformerid
## [Eelloengu viktoriin](https://ff-quizzes.netlify.app/en/ai/quiz/35)

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# Ülesanne: Transformerid
Katseta Transformeritega HuggingFace platvormil! Proovi mõnda nende pakutavat skripti, et töötada erinevate mudelitega, mis on saadaval nende veebilehel: https://huggingface.co/docs/transformers/run_scripts. Kasuta ühte nende andmekogudest, seejärel impordi üks oma andmekogu sellest õppekavast või Kaggle'ist ja vaata, kas suudad genereerida huvitavaid tekste. Koosta märkmik oma leidudega.

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# Nimega Entiteetide Tuvastamine
Siiani oleme peamiselt keskendunud ühele NLP ülesandele - klassifikatsioonile. Kuid on ka teisi NLP ülesandeid, mida saab lahendada närvivõrkude abil. Üks neist ülesannetest on **[Nimega Entiteetide Tuvastamine](https://wikipedia.org/wiki/Named-entity_recognition)** (NER), mis tegeleb konkreetsete entiteetide tuvastamisega tekstis, nagu näiteks kohad, isikunimed, kuupäevad ja ajavahemikud, keemilised valemid jne.

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# NER
Laboriülesanne [AI for Beginners Curriculum](https://github.com/microsoft/ai-for-beginners) kursusest.

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# Eeltreenitud suured keelemudelid
Kõikides meie varasemates ülesannetes treenisime närvivõrku, et see täidaks teatud ülesannet, kasutades märgistatud andmestikku. Suurte transformer-mudelite, nagu BERT, puhul kasutame keelemodelleerimist isejuhitud viisil, et luua keelemudel, mida seejärel spetsialiseeritakse konkreetsele ülesandele täiendava valdkonnapõhise treeninguga. Siiski on näidatud, et suured keelemudelid suudavad lahendada paljusid ülesandeid ka ILMA valdkonnapõhise treeninguta. Mudelite perekonda, mis seda suudavad, nimetatakse **GPT**: Generatiivne Eeltreenitud Transformer.

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# Loodusliku keele töötlemine
![NLP ülesannete kokkuvõte visandis](../../../../translated_images/et/ai-nlp.b22dcb8ca4707cea.webp)

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# Geneetilised algoritmid
## [Eelloengu viktoriin](https://ff-quizzes.netlify.app/en/ai/quiz/41)

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# Sügav Tugevdusõpe
Tugevdusõpe (RL) on üks põhilisi masinõppe paradigmasid, kõrvuti juhendatud ja juhendamata õppega. Kui juhendatud õppes tugineb õpe teadaolevate tulemustega andmekogule, siis RL põhineb **õppimisel läbi tegutsemise**. Näiteks, kui me esimest korda näeme arvutimängu, hakkame seda mängima, isegi kui me reegleid ei tea, ja peagi suudame oma oskusi parandada lihtsalt mängimise ja käitumise kohandamise kaudu.

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# Mäeauto treenimine põgenemiseks
Laboriülesanne [AI algajatele õppekavast](https://github.com/microsoft/ai-for-beginners).

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# Multiagent süsteemid
Üks võimalik viis intelligentsuse saavutamiseks on nn **emergentne** (või **sünergeetiline**) lähenemine, mis põhineb faktil, et paljude suhteliselt lihtsate agentide kombineeritud käitumine võib viia süsteemi kui terviku keerukama (või intelligentsema) käitumiseni. Teoreetiliselt põhineb see [kollektiivse intelligentsuse](https://en.wikipedia.org/wiki/Collective_intelligence), [emergentismi](https://en.wikipedia.org/wiki/Global_brain) ja [evolutsioonilise küberneetika](https://en.wikipedia.org/wiki/Global_brain) põhimõtetel, mis väidavad, et kõrgema taseme süsteemid saavutavad teatud lisaväärtuse, kui need on õigesti kombineeritud madalama taseme süsteemidest (nn *metasüsteemi ülemineku põhimõte*).

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# NetLogo Ülesanne
Võta üks NetLogo raamatukogu mudelitest ja kasuta seda, et simuleerida päriselu olukorda võimalikult täpselt. Hea näide oleks viiruse mudeli kohandamine Alternatiivsete Visualisatsioonide kaustas, et näidata, kuidas seda saab kasutada COVID-19 leviku modelleerimiseks. Kas suudad luua mudeli, mis jäljendab päriselu viiruse levikut?

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# Eetiline ja vastutustundlik tehisintellekt
Oled peaaegu lõpetanud selle kursuse, ja loodetavasti näed nüüd selgelt, et tehisintellekt põhineb mitmetel formaalsetel matemaatilistel meetoditel, mis võimaldavad meil leida seoseid andmetes ja treenida mudeleid, et jäljendada mõningaid inimkäitumise aspekte. Praegusel ajal peame tehisintellekti väga võimsaks tööriistaks, mis aitab andmetest mustreid välja tuua ja neid mustreid uute probleemide lahendamiseks rakendada.

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# Ülevaade
![Ülevaade visandina](../../../translated_images/et/ai-overview.0857791951d19500.webp)

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# Multi-modalvõrgud
Pärast transformer-mudelite edu NLP-ülesannete lahendamisel on sama või sarnaseid arhitektuure rakendatud ka arvutinägemise ülesannetes. Kasvab huvi luua mudeleid, mis *ühendaksid* nägemise ja loomuliku keele võimekused. Üks sellistest katsetest tehti OpenAI poolt ja seda nimetatakse CLIP ja DALL.E.

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Attribution-ShareAlike 4.0 Rahvusvaheline
=======================================================================

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Kõik õppekava visandmärkmed saab alla laadida siit.
🎨 Looja: Tomomi Imura (Twitter: [@girlie_mac](https://twitter.com/girlie_mac), GitHub: [girliemac](https://github.com/girliemac))

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# AI-For-Beginners Tõrkeotsingu Juhend
See juhend aitab lahendada levinud probleeme, mis võivad tekkida [AI-For-Beginners](https://github.com/microsoft/AI-For-Beginners) repositooriumi kasutamisel või sellele panustamisel. Iga probleem sisaldab tausta, sümptomeid, selgitusi ja samm-sammult lahendusi.

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<!--
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}
-->
# AGENTS.md
## Project Overview

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@ -1,12 +1,3 @@
<!--
CO_OP_TRANSLATOR_METADATA:
{
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}
-->
[![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/)
@ -23,32 +14,31 @@ CO_OP_TRANSLATOR_METADATA:
# Artificial Intelligence for Beginners - A Curriculum
|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../../../translated_images/pcm/ai-overview.0857791951d19500.webp)|
|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/pcm/ai-overview.0857791951d19500.webp)|
|:---:|
| AI For Beginners - _Sketchnote by [@girlie_mac](https://twitter.com/girlie_mac)_ |
Explore di world of **Artificial Intelligence** (AI) wit our 12-week, 24-lesson curriculum! E get practical lessons, quizzes, and labs. Di curriculum na beginner-friendly and e cover tools like TensorFlow and PyTorch, plus ethics for AI
Explore di world of **Artificial Intelligence** (AI) wit our 12-week, 24-lesson curriculum! E get practical lessons, quizzes, and labs. Di curriculum easy for beginner and e cover tools like TensorFlow and PyTorch, plus ethics for AI
### 🌐 Multi-Language Support
#### Supported via GitHub Action (Automated & Always Up-to-Date)
<!-- CO-OP TRANSLATOR LANGUAGES TABLE START -->
[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](./README.md) | [Norwegian](../no/README.md) | [Persian (Farsi)](../fa/README.md) | [Polish](../pl/README.md) | [Portuguese (Brazil)](../br/README.md) | [Portuguese (Portugal)](../pt/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Romanian](../ro/README.md) | [Russian](../ru/README.md) | [Serbian (Cyrillic)](../sr/README.md) | [Slovak](../sk/README.md) | [Slovenian](../sl/README.md) | [Spanish](../es/README.md) | [Swahili](../sw/README.md) | [Swedish](../sv/README.md) | [Tagalog (Filipino)](../tl/README.md) | [Tamil](../ta/README.md) | [Telugu](../te/README.md) | [Thai](../th/README.md) | [Turkish](../tr/README.md) | [Ukrainian](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamese](../vi/README.md)
[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](./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)
> **Prefer to Clone Locally?**
> Dis repository get 50+ language translations wey full ground, na im dey make di download size big. If you want clone without translations, use sparse checkout:
> Dis repository get 50+ language translations wey dey make di download size big well well. If you wan clone without di translations, use 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'
> ```
> Dis go give you everything wey you need to complete di course wit much faster download.
> Dis go give you everything wey you need to complete di course wit faster download.
<!-- CO-OP TRANSLATOR LANGUAGES TABLE END -->
**If you want make dem add extra translation languages, dem list dey [here](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)**
**If you want make any oda translations language dem support dey listed [here](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)**
## Join di Community
[![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
@ -57,25 +47,25 @@ Explore di world of **Artificial Intelligence** (AI) wit our 12-week, 24-lesson
**[Mindmap of the Course](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)**
For this curriculum, you go learn:
For dis curriculum, you go learn:
* Different ways to do Artificial Intelligence, including di "good old" symbolic way wit **Knowledge Representation** and reasoning ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)).
* **Neural Networks** and **Deep Learning**, wey be di main tin for modern AI. We go show di concepts behind these important topics wit code for two popular frameworks - [TensorFlow](http://Tensorflow.org) and [PyTorch](http://pytorch.org).
* **Neural Architectures** for work wit images and text. We go cover recent models but e fit no too strong for the newest ones.
* Different ways to do Artificial Intelligence, including di "good old" symbolic way with **Knowledge Representation** and reasoning ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)).
* **Neural Networks** and **Deep Learning**, wey be di main thing for modern AI. We go show di ideas behind dis important tins using code for two popular frameworks - [TensorFlow](http://Tensorflow.org) and [PyTorch](http://pytorch.org).
* **Neural Architectures** for working wit pictures and text. We go cover recent models but e fit no too complete for di state-of-the-art.
* Less popular AI ways, like **Genetic Algorithms** and **Multi-Agent Systems**.
Wetin we no go talk for dis curriculum:
Wetin we no go cover for dis curriculum:
> [Find all additional resources for this course in our Microsoft Learn collection](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)
* Business tins wey dey use **AI for Business**. Try check [Introduction to AI for business users](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) learning path for Microsoft Learn, or [AI Business School](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), wey dem develop together wit [INSEAD](https://www.insead.edu/).
* Business cases of how to use **AI in Business**. You fit try [Introduction to AI for business users](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) learning path for Microsoft Learn, or [AI Business School](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), wey dem develop join body wit [INSEAD](https://www.insead.edu/).
* **Classic Machine Learning**, wey well explain for our [Machine Learning for Beginners Curriculum](http://github.com/Microsoft/ML-for-Beginners).
* Practical AI work wey build using **[Cognitive Services](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**. For these, we advise say start wit Microsoft Learn modules for [vision](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [natural language processing](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[Generative AI with Azure OpenAI Service](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** and others.
* Specific ML **Cloud Frameworks**, like [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), or [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). Try use [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) and [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) learning paths.
* **Conversational AI** and **Chat Bots**. Dem get separate [Create conversational AI solutions](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum) learning path, plus you fit also check [dis blog post](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) for more info.
* **Deep Mathematics** behind deep learning. For this one, we recommend [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) by Ian Goodfellow, Yoshua Bengio and Aaron Courville, wey also dey online at [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/).
* Practical AI applications wey wey build using **[Cognitive Services](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**. For dis one, we recommend say you start wit modules wey dey Microsoft Learn for [vision](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [natural language processing](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[Generative AI with Azure OpenAI Service](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** and oda modules.
* Specific ML **Cloud Frameworks**, like [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), or [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). You fit try [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) and [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) learning paths.
* **Conversational AI** and **Chat Bots**. E get separate [Create conversational AI solutions](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum) learning path, you fit still check [dis blog post](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) for more details.
* **Deep Mathematics** behind deep learning. For dis one, we recommend say you check [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) by Ian Goodfellow, Yoshua Bengio and Aaron Courville, wey still dey available online at [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/).
For soft soft introduction to _AI in the Cloud_ topics, you fit try di [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) Learning Path.
If you want gentle introduction to _AI in the Cloud_ topics you fit consider taking di [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) Learning Path.
# Content
@ -119,49 +109,50 @@ For soft soft introduction to _AI in the Cloud_ topics, you fit try di [Get star
## Each lesson contains
* Pre-reading material
* Executable Jupyter Notebooks, wey dey often specific to the framework (**PyTorch** or **TensorFlow**). The executable notebook sef get plenty theoretical material, so to understand the topic you need to go through at least one version of the notebook (either PyTorch or TensorFlow).
* **Labs** wey dey available for some topics, wey go give you chance to try apply wetin you don learn for a specific problem.
* Some sections get links to [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) modules wey cover related topics.
* Executable Jupyter Notebooks, wey dem sabi run, wey dey often relate to framework (**PyTorch** or **TensorFlow**). Di executable notebook also get plenty theoretical material, so to sabi di topic you go need go through at least one version of di notebook (either PyTorch or TensorFlow).
* **Labs** wey dey for some topics, wey go give you chance to try apply di material wey you don learn to one particular wahala.
* Some sections get links to [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) modules wey dey cover related topics.
## Getting Started
### 🎯 New to AI? Start Here!
If you dey completely new to AI and you want quick, hands-on examples, check out our [**Beginner-Friendly Examples**](./examples/README.md)! Dem include:
If you never sabi AI before and you want beta, hands-on examples quick quick, check our [**Beginner-Friendly Examples**](./examples/README.md)! Dem get:
- 🌟 **Hello AI World** - Your first AI program (pattern recognition)
- 🧠 **Simple Neural Network** - Build neural network from scratch
- 🖼️ **Image Classifier** - Classify images wit detailed comments
- 💬 **Text Sentiment** - Analyse positive/negative text
Dem examples dem design to help you sabi AI concepts before you go enter the full curriculum.
- 🖼️ **Image Classifier** - Classify image dem wit detailed comments
- 💬 **Text Sentiment** - Analyze positive/negative text
Dem examples na to help you sabi AI concepts before you jump enter di full curriculum.
### 📚 Full Curriculum Setup
- We don create [setup lesson](./lessons/0-course-setup/setup.md) to help you setup your development environment. - For Educators, we don create [curricula setup lesson](./lessons/0-course-setup/for-teachers.md) for una too!
- We don create [setup lesson](./lessons/0-course-setup/setup.md) to help you set up your development environment. - For Educators, we don create [curricula setup lesson](./lessons/0-course-setup/for-teachers.md) for una too!
- How to [Run the code for VSCode or Codespace](./lessons/0-course-setup/how-to-run.md)
Follow these steps:
Fork the Repository: Click the "Fork" button for the top-right corner of dis page.
Fork the Repository: Click the "Fork" button for top-right corner of dis page.
Clone the Repository: `git clone https://github.com/microsoft/AI-For-Beginners.git`
No forget to star (🌟) dis repo so e go easy for you to find am later.
No forget to star (🌟) dis repo make e easy for you find later.
## Meet other Learners
Join our [official AI Discord server](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) to meet and network wit other learners wey dey do dis course and get help.
Join our [official AI Discord server](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) to meet and network wit other learners wey dey do dis course and get support.
If you get product feedback or questions as you dey build, visit our [Azure AI Foundry Developer Forum](https://aka.ms/foundry/forum)
If you get product feedback or questions as you dey build visit our [Azure AI Foundry Developer Forum](https://aka.ms/foundry/forum)
## Quizzes
> **A note about quizzes**: All quizzes dey inside the Quiz-app folder for etc\quiz-app, or [Online Here](https://ff-quizzes.netlify.app/) Dem link from inside the lessons, the quiz app fit run locally or fit deploy for Azure; follow instruction wey dey the `quiz-app` folder. Dem dey slowly dey localize.
> **A note about quizzes**: All quizzes dey for Quiz-app folder inside etc\quiz-app, or [Online Here](https://ff-quizzes.netlify.app/) Dem link from within di lessons, di quiz app fit run locally or fit deploy to Azure; follow di instruction for `quiz-app` folder. Dem dey slowly dey localize.
## Help Wanted
You get suggestions or you see spelling or code errors? Raise issue or create pull request.
You get suggestions or you see any spelling or code errors? Raise issue or create pull request.
## Special Thanks
@ -217,17 +208,17 @@ Our team dey produce other curricula! Check am out:
## Getting Help
If you jam wall or get any question about building AI apps. Join other learners and experienced devs for talks about MCP. Na supportive community wey questions dey welcome and dem dey share knowledge freely.
If you jam stuck or get any questions about building AI apps. Join other learners and experienced developers for discussions about MCP. Na support community wey questions dey welcome and knowledge dey freely shared.
[![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
If you get product feedback or errors while you dey build, visit:
If you get product feedback or errors as you dey build visit:
[![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)
---
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**Disclaimer**:
Dis document na so AI translation service [Co-op Translator](https://github.com/Azure/co-op-translator) translate am. Even though we dey try make am correct, abeg sabi say automated translations fit get errors or wahala. Make you always trust the original document wey dey the original language as the real correct source. If na important info, e better make person wey sabi do human translation help you. We no go take responsibility if any misunderstanding or wrong meaning show because of this translation.
**Disclaimer**:
Dis document e don translate wit AI translation service wey dem dey call [Co-op Translator](https://github.com/Azure/co-op-translator). Even tho we dey try make am correct, abeg make you sabi say automatic translation fit get some errors or wahala. The og document wey e dey original language na im you suppose trust pass. If na serious info, e better make person wey sabi translate am well well translate am. We no go carry any blame for any meaning wey you no understand well or any mistake wey come from dis translation.
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## Security
Microsoft dey take di security of dia software products and services serious, e include all di source code repositories wey dem dey manage through dia GitHub organizations, wey include [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), and [our GitHub organizations](https://opensource.microsoft.com/).

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# Microsoft Open Source Code of Conduct
Dis project don adopt di [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).

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# Contribut
Dis project dey welcome contribut and suggestion. Most contribut go need make you gree to Contributor License Agreement (CLA) wey go show say you get di right to, and you dey really give us di right to use wetin you contribute. For more detail, go check https://cla.microsoft.com.

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# AI
## [Intro to AI](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/1-Intro/README.md)

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# Support
## How to take report for wahala and get help

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# Help us translate di lessons
We go happy if you fit help us translate di lessons wey dey dis curriculum!

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# Quizzes
Dis quizzes na di pre- and post-lecture quizzes for di AI curriculum wey dey for https://aka.ms/ai-beginners

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# Beginner-Friendly AI Examples
Welcome! Dis directory get simple, standalone examples wey go help you start wit AI and machine learning. Each example dey designed make e easy for beginners wit detailed comments and step-by-step explanation.

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# For Educators
You wan use dis curriculum for your classroom? Abeg feel free!

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# How to Run the Code
Dis curriculum get plenti executable examples and labs wey you go like run. To fit do dis, you need beta skill to execute Python code for Jupyter Notebooks wey dem provide as part of dis curriculum. You get plenty ways to run the code:

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# How to Start wit Dis Curricula
## You be Student?

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# Introduction to AI
![Summary of Introduction of AI content in a doodle](../../../../translated_images/pcm/ai-intro.bf28d1ac4235881c.webp)

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# Game Jam
Games na one area wey AI and ML don really change well well. For dis assignment, make you write small paper about one game wey you like wey AI don help change as e dey grow. The game suppose don dey old well well so e fit don use different types of computer processing systems. Good example na Chess or Go, but you fit also check video games like pong or Pac-Man. Write essay wey go talk about how the game take start, how e dey now, and wetin AI fit do for am for future.

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# Knowledge Representation and Expert Systems
![Summary of Symbolic AI content](../../../../../../translated_images/pcm/ai-symbolic.715a30cb610411a6.webp)
![Summary of Symbolic AI content](../../../../translated_images/pcm/ai-symbolic.715a30cb610411a6.webp)
> Sketchnote by [Tomomi Imura](https://twitter.com/girlie_mac)
@ -41,7 +32,7 @@ Most times, we no define knowledge sharp-sharp, but we dey relate am to other re
So, di problem of **knowledge representation** na to find how to effectively represent knowledge inside computer as data, to make am automatically usable. E fit be like spectrum:
![Knowledge representation spectrum](../../../../../../translated_images/pcm/knowledge-spectrum.b60df631852c0217.webp)
![Knowledge representation spectrum](../../../../translated_images/pcm/knowledge-spectrum.b60df631852c0217.webp)
> Image by [Dmitry Soshnikov](http://soshnikov.com)
@ -94,7 +85,7 @@ Block Syntax | Indent | | |
One of early successes of symbolic AI na the so-called **expert systems** - computer systems wey dem design to act like expert inside limited problem domain. Dem dey based on **knowledge base** wey dem comot from one or more human experts, and e get **inference engine** wey dey reason on top am.
![Human Architecture](../../../../../../translated_images/pcm/arch-human.5d4d35f1bba3ab1c.webp) | ![Knowledge-Based System](../../../../../../translated_images/pcm/arch-kbs.3ec5c150b09fa8da.webp)
![Human Architecture](../../../../translated_images/pcm/arch-human.5d4d35f1bba3ab1c.webp) | ![Knowledge-Based System](../../../../translated_images/pcm/arch-kbs.3ec5c150b09fa8da.webp)
---------------------------------------------|------------------------------------------------
Simplified structure of a human neural system | Architecture of a knowledge-based system
@ -106,7 +97,7 @@ Expert systems be like human reasoning system, wey get **short-term memory** and
Example, make we check dis expert system for identifying animal based on physical characteristics:
![AND-OR Tree](../../../../../../translated_images/pcm/AND-OR-Tree.5592d2c70187f283.webp)
![AND-OR Tree](../../../../translated_images/pcm/AND-OR-Tree.5592d2c70187f283.webp)
> Image by [Dmitry Soshnikov](http://soshnikov.com)

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# Build Ontology
To build knowledge base na to arrange model wey go show facts about one topic. Choose one topic - like person, place, or thing - then build model for dat topic. Use some techniques and model-building strategies wey dem describe for dis lesson. Example fit be to create ontology for living room wey get furniture, lights, and other things. Wetin make living room different from kitchen? From bathroom? How you go take sabi say na living room and no be dining room? Use [Protégé](https://protege.stanford.edu/) to build your ontology.

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# Introduction to Neural Networks: Perceptron
## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ai/quiz/5)

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# Multi-Class Classification wit Perceptron
Lab Assignment from [AI for Beginners Curriculum](https://github.com/microsoft/ai-for-beginners).

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# Introduction to Neural Networks. Multi-Layered Perceptron
For di last section, you don learn about di simplest neural network model - one-layered perceptron, wey be linear two-class classification model.

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# MNIST Classification wit Our Own Framework
Lab Assignment from [AI for Beginners Curriculum](https://github.com/microsoft/ai-for-beginners).

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# Neural Network Frameworks
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# Classification wit PyTorch/TensorFlow
Lab Assignment from [AI for Beginners Curriculum](https://github.com/microsoft/ai-for-beginners).

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# Introduction to Neural Networks
![Summary of Intro Neural Networks content in a doodle](../../../../translated_images/pcm/ai-neuralnetworks.1c687ae40bc86e83.webp)

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# Introduction to Computer Vision
[Computer Vision](https://wikipedia.org/wiki/Computer_vision) na area wey dey try make computer sabi wetin dey happen for digital pictures. Dis definition dey wide well-well, because *understanding* fit mean plenty things, like to find object for picture (**object detection**), sabi wetin dey happen (**event detection**), describe picture with text, or even build scene for 3D. E still get special work wey concern human pictures: age and emotion estimation, face detection and identification, and 3D pose estimation, among others.

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# Detect Movements wit Optical Flow
Lab Assignment from [AI for Beginners Curriculum](https://aka.ms/ai-beginners).

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# Well-Known CNN Architectures
### VGG-16

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# Convolutional Neural Networks
We don see before say neural networks dey good wella for handling images, even one-layer perceptron fit sabi recognize handwritten digits from MNIST dataset with beta accuracy. But MNIST dataset dey special, all di digits dey center for di image, wey make di work easy.

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# Classification of Pets Faces
Lab Assignment from [AI for Beginners Curriculum](https://github.com/microsoft/ai-for-beginners).

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# Pre-trained Networks and Transfer Learning
To train CNNs dey fit take plenty time, and e go need plenty data to do am. But most of di time, na to learn di best low-level filters wey network fit use to sabi patterns from images. Di question wey go come be - we fit use neural network wey dem don train for one dataset and adjust am to classify different images without to train am from scratch?

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# Deep Learning Training Tricks
As neural networks dey go deeper, e dey harder to train dem. One big wahala wey fit happen na [vanishing gradients](https://en.wikipedia.org/wiki/Vanishing_gradient_problem) or [exploding gradients](https://deepai.org/machine-learning-glossary-and-terms/exploding-gradient-problem#:~:text=Exploding%20gradients%20are%20a%20problem,updates%20are%20small%20and%20controlled.). [Dis post](https://towardsdatascience.com/the-vanishing-exploding-gradient-problem-in-deep-neural-networks-191358470c11) explain well well about dis problems.

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# Classification of Oxford Pets using Transfer Learning
Lab Assignment from [AI for Beginners Curriculum](https://github.com/microsoft/ai-for-beginners).

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