Full Stack Deep Learning - Course 2022
Course in Progress
Those who registered for the paid cohort option, please see here for details.
We will release lecture videos on Mondays at 6pm Pacific and lab videos on Wednesdays at 6pm Pacific on YouTube.
|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|
We review the purpose of the course and consider when it's a good (or bad!) idea to use ML. Published August 8, 2022.
We walk through the entire architecture of the application we will be building, from soup to nuts. Published July 25, 2022.
We review DNN architectures and work through basic model training with PyTorch + Lightning. Published August 10, 2022.
We tour the landscape of infrastructure and tooling for developing deep learning models. Published August 15, 2022.
We run, track, and manage model development experiments with Weights & Biases. Published August 17, 2022.
We look at tools and practices for testing software in general and ML models in particular. Published August 22, 2022.
We try out some Python testing tools and dissect a PyTorch trace to learn performance troubleshooting techniques. Published August 24, 2022.
We look at sourcing, storing, exploring, processing, labeling, and versioning data for deep learning. Published August 29, 2022.
We spin up a data annotation server and learn just how messy data really is. Published August 31, 2022.
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.
We create and deploy our ML-powered text recognition application with a simple web UI and a serverless model service. Published September 7, 2022.
We consider what it takes to build a continual learning system around an ML-powered application. Published September 12, 2022.
We add and review data logged by actual users of the FSDL Text Recognizer. Published September 14, 2022.
Building on Transformers, GPT-3, CLIP, StableDiffusion, and other Large Models. Published September 19, 2022.