📚 AI Learning Resources

Reddit's largest ML community. Research papers, discussions, and industry news.

Active Discord community for ML practitioners. Get help, share projects, and discuss latest research.

Build neural networks from scratch in Python. Covers backprop, GPT, tokenization, and more.

Beautiful visual explanations of neural networks and deep learning fundamentals by Grant Sanderson.

Practical guide to building AI applications in production, covering MLOps, evaluation, and deployment.

The definitive textbook on deep learning theory, covering math foundations, modern architectures, and research methods.

Aurélien Géron's practical guide to ML with Scikit-Learn, Keras, and TensorFlow. Industry standard textbook.

Collection of practical examples and best practices for using OpenAI APIs effectively.

Learn to build, deploy, and manage LLM-powered applications from scratch.

Comprehensive guide to prompt engineering techniques for various AI models and use cases.

Official tutorials for building LLM applications with LangChain, including RAG, agents, and chains.

Learn NLP using the Transformers library. Covers tokenization, fine-tuning, and model deployment.

Google's fast-paced introduction to machine learning with TensorFlow APIs and real-world case studies.

Stanford's flagship ML course covering supervised learning, unsupervised learning, and reinforcement learning.

Top-down approach to deep learning. Build real projects from day one without heavy math prerequisites.

Andrew Ng's comprehensive deep learning course covering neural networks, CNNs, RNNs, and more.