Useful resources
A curated collection of courses, books, tutorials, and interactive visualizations that complement Statistical Learning and Machine Learning.
These are the same materials pointed to in class. The list grows as new material comes along. Filter by type to find what you need.
Slides, code, and readings from a PhD seminar on using LLMs and generative AI in business research.
The earlier edition of the same seminar; still a rich archive of foundational readings and demos.
A famously clear lecture series spanning deep learning fundamentals through self-attention, with full videos and slides.
Stanford's flagship NLP course, from word vectors to Transformers, with lecture videos and assignments.
The reference course for CNNs and computer vision; the course notes are excellent on their own.
A hands-on generative AI course with an emphasis on creative, real-world applications.
A full-semester treatment of how large language models are built, trained, and evaluated.
A graduate seminar on the science of large language models, built around close reading of key papers.
A mathematically rigorous take on language modeling, built up from probabilistic foundations.
An interactive textbook (and FA690 reference) where every concept comes with runnable code.
Home of "Neural Networks: Zero to Hero"; build backpropagation, GPT, and a tokenizer from scratch, line by line.
An interactive article showing what recurrent networks remember, and forget, as they read text.
A 3D, step-by-step walkthrough of every matrix multiplication inside a working GPT.
Train a small neural network in the browser and watch its decision boundary take shape.
An interactive walkthrough of convolutions, activations, and pooling inside a live CNN.
A bootcamp-style series of short, hands-on challenges for building LLM agents.