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Full Stack Deep Learning - Course 2022

Course in Progress

Those who registered for the paid cohort option, please see here for details.

And everyone else – all the material is free! Simply enter your email below, follow us on Twitter, or subscribe to our YouTube channel to get updates week-by-week.


We will release lecture videos on Mondays at 6pm Pacific and lab videos on Wednesdays at 6pm Pacific on YouTube.

Week Lecture Lab Project
2022.08.08 Lecture 1: Course Vision and When to Use ML Labs 1-3: CNNs, Transformers, PyTorch Lightning -
2022.08.15 Lecture 2: Development Infrastructure & Tooling Lab 4: Experiment Management -
2022.08.22 Lecture 3: Troubleshooting & Testing Lab 5: Troubleshooting & Testing -
2022.08.29 Lecture 4: Data Management Lab 6: Data Annotation Start forming groups
2022.09.05 Lecture 5: Deployment Lab 7: Web Deployment Group proposals due
2022.09.12 Lecture 6: Continual Learning Lab 8: Model Monitoring Work on project
2022.09.19 Lecture 7: Foundation Models Work on project
2022.09.26 Lecture 8: ML Project Management Work on project
2022.10.03 Lecture 9: Ethics Work on project
2022.10.10 Project Presentations Project due

Detailed Contents

Lecture 1: Course Vision and When to Use ML

We review the purpose of the course and consider when it's a good (or bad!) idea to use ML. Published August 8, 2022.

Lab Overview

We walk through the entire architecture of the application we will be building, from soup to nuts. Published July 25, 2022.

Labs 1-3: CNNs, Transformers, PyTorch Lightning

We review DNN architectures and work through basic model training with PyTorch + Lightning. Published August 10, 2022.

Lecture 2: Development Infrastructure & Tooling

We tour the landscape of infrastructure and tooling for developing deep learning models. Published August 15, 2022.

Lab 4: Experiment Management

We run, track, and manage model development experiments with Weights & Biases. Published August 17, 2022.

Lecture 3: Troubleshooting & Testing

We look at tools and practices for testing software in general and ML models in particular. Published August 22, 2022.

Lab 5: Troubleshooting & Testing

We try out some Python testing tools and dissect a PyTorch trace to learn performance troubleshooting techniques. Published August 24, 2022.

Lecture 4: Data Management

We look at sourcing, storing, exploring, processing, labeling, and versioning data for deep learning. Published August 29, 2022.

Lab 6: Data Annotation

We spin up a data annotation server and learn just how messy data really is. Published August 31, 2022.

Lecture 5: Data Management

We do a lightning tour of all the ways models are deployed and do a deep dive on running models as web services. Published September 5, 2022.

Lab 7: Web Deployment

We create and deploy our ML-powered text recognition application with a simple web UI and a serverless model service. Published September 7, 2022.

Lecture 6: Continual Learning

We consider what it takes to build a continual learning system around an ML-powered application. Published September 12, 2022.

Lab 8: Model Monitoring

We add and review data logged by actual users of the FSDL Text Recognizer. Published September 14, 2022.

Lecture 7: Foundation Models

Building on Transformers, GPT-3, CLIP, StableDiffusion, and other Large Models. Published September 19, 2022.