Learning-Hub

📚 AI / ML / GenAI Learning Resources

A personal collection of high-quality resources for learning AI, machine learning, deep learning, and generative AI.
⭐ This list will be continuously updated as I come across new valuable materials.

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🔹 Core Learning Paths

• Minitorch
  A minimal PyTorch-like library and educational framework for learning deep learning from scratch.

• PyTorch in One Hour: From Tensors to Training Neural Networks on Multiple GPUs
  A good blog to learn Pytorch basics.

• Neural Networks and Deep Learning (Andrew Ng)
  Foundational course on deep learning, neural networks, and backpropagation.

• 100 Days of Deep Learning (Campus X)
  Practical machine learning and data science tutorials.

• Mathematical Foundations of Generative AI (IIT Madras)
  GenAI basics

• Introduction to large language models (IIT Madras)
  Great Introduction to LLMs

• D2AI
  Deep Learning for AI and Computer Vision — practical tutorials and concepts.

• Stanford CS336: Large Language Models
  Stanford’s course on LLMs — architectures, alignment, and applications.

• AI for Beginners (Microsoft) — ⭐ 38.2k
  Beginner-friendly curriculum to understand AI concepts.

• Neural Networks: Zero to Hero (Andrej Karpathy) — ⭐ 14.2k
  A hands-on introduction to neural networks, backpropagation, and training.

• ML for Beginners (Microsoft) — ⭐ 73.4k
  Everything you need to know to start building GenAI apps.

• Made With ML — ⭐ 40k
  Learn how to deliver real value with machine learning projects.

• AI Agents for Beginners (Microsoft) — ⭐ 28.1k
  Learn how to build and deploy AI agents.

• AI Engineering Hub — ⭐ 12.8k
  Tutorials on LLMs, RAGs, and real-world AI agent applications.

• Computer Science courses with video lectures — ⭐ 69.2k
  Tutorials that cover almost every topic under Computer Science.

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🔹 Cheat Sheets & Reference

• Machine Learning Cheat Sheet — ⭐ 7.8k
  Quick reference for common ML algorithms and concepts.

• Awesome Machine Learning — ⭐ 68.7k
  A curated list of awesome machine learning frameworks, libraries, and software.

• Machine Learning Q and AI Book (Sebastian Raschka)
  A modern book combining machine learning theory with practical questions. Useful for revision.

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🔹 Visualizers

• Neural Network Playground
  An interactive tool to see neural networks learn and make decisions.

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✍ Future Additions

I will add more resources here as I discover and review them!
Feel free to suggest new entries via issues or pull requests if this repository is public.