All the lecture and lab material is free forever. To be among the first to hear about future iterations of the course, simply enter your email below, follow us on Twitter, or subscribe to our YouTube channel. We released lecture videos on Mondays at 6pm Pacific and lab videos on Wednesdays at 6pm Pacific on YouTube. We review some prerequisites -- the DNN architectures we'll be using and basic model training with PyTorch -- and introduce PyTorch Lightning. We review the purpose of the course and consider when it's a good (or bad!) idea to use ML.

We are teaching a major update of the course Spring 2021 as an official UC Berkeley course and as an online course, with all lectures and labs available for free. There are many great courses to learn how to train deep neural networks. However, training the model is just one part of shipping a deep learning project. The course is aimed at people who already know the basics of deep learning and want to understand the rest of the process of creating production deep learning systems. While we cover the basics of deep learning (backpropagation, convolutional neural networks, recurrent neural networks, transformers, etc), we expect these lectures to be mostly review.

The week of February 15, we cover RNNs and applications in Natural Language Processing, and start doing sequence processing in lab.

Full Stack Deep Learning covers the full lifecycle of an AI application, from ideation through deployment but it does not cover theory or model fitting.