Courses
Build a Large Language Model (From Scratch) Course
- Main course: free LLMs-from-Scratch YouTube playlist
- Source code repository
- Alternative: Manning course with additional structure, bonus lectures, and ad-free viewing
PyTorch in One Hour: From Tensors to Training Neural Networks on Multiple GPUs
This written tutorial aims to introduce you to the most essential topics of the popular open-source deep learning library, PyTorch, in about one hour of reading time. My primary goal is to get you up to speed with the essentials so that you can get started with using and implementing deep neural networks, such as large language models (LLMs).
This tutorial covers the following topics:
- An overview of the PyTorch deep learning library
- Setting up an environment and workspace for deep learning
- Tensors as a fundamental data structure for deep learning
- The mechanics of training deep neural networks
- Training models on GPUs
Deep Learning Fundamentals – Learning Deep Learning With a Modern Open Source Stack
- A modern class consisting of 10 units with bite-sized videos
- It is more concise than my university class but also covers additional topics: multi-GPU training, self-supervised learning, setting up effective hyperparameter sweeps, learning rate scheduling, and many more
- Code examples are in PyTorch; some units use the Lightning Trainer for extra functionality
Introduction to Deep Learning
- Video recordings of an introductory deep learning course
- Code examples are in PyTorch
Introduction to Machine Learning
- Video recordings of an introductory machine learning course
- Code examples are in scikit-learn and MLxtend
Lightning Bits: Engineering for Researchers
- A series of short videos with William Falcon teaching to become more productive in your machine learning and AI research
- Covering fundamental tools like IDEs, Git, the terminal, and more
Courses taught at UW-Madison
Below is a list of courses I taught at the University of Wisconsin-Madison as former professor in the Department of Statistics.
Fall 2021
Spring 2021
Fall 2020
- STAT 451: Introduction to Machine Learning and Statistical Pattern Classification
- STAT 571: Statistical Methods for Bioscience I