Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.

Materials for my scikit-learn tutorial

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training

A curated list of Large Language Model resources, covering model training, serving, fine-tuning, and building LLM applications.

The LLM's practical guide: From the fundamentals to deploying advanced LLM and RAG apps to AWS using LLMOps best practices