Notes and references on deep learning, from math prerequisites through transformers and LLMs. Written as a personal reference and kept current — each page is a short, opinionated summary with links to primary papers.
- Foundations: stats, linear algebra, calculus
- Machine Learning basics
- data and features
- gradient boosted decision trees
- training neural networks
- transformers
- LLM: large language models, RAG
- vision transformers (Legacy: cnn )
- reinforcement learning
- Stanford CS224R Deep Reinforcement Learning - Spring 2025
- Stanford CS229 Machine Learning - Spring 2026
- Stanford CS230 Deep Learning - Autumn 2025
- Stanford CS231 Deep Learning for Computer Vision - Spring 2025
- Stanford CS336 Language Models from scratch - Spring 2025
- Stanford CME295 Transformers and LLMs - Autumn 2025