Full Stack Deep Learning - 2022 Course
All the lecture and lab material is free forever. Just check out the links below.
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We released lecture videos on Mondays at 6pm Pacific and lab videos on Wednesdays at 6pm Pacific on YouTube.
|2022.07.25||Pre-Labs 1-3: CNNs, Transformers, and PyTorch+Lightning||-|
|2022.08.08||Lecture 1: Course Vision and When to Use ML||Lab Overview||-|
|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 Teams and Project Management||Work on project|
|2022.10.03||Lecture 9: Ethics||Work on project|
|2022.10.10||Project Presentations||Project due|
Pre-Labs 1-3: CNNs, Transformers, PyTorch Lightning
We review some prerequisites -- the DNN architectures we'll be using and basic model training with PyTorch -- and introduce PyTorch Lightning. Published August 10, 2022.
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.
We walk through the entire architecture of the application we will be building, from soup to nuts. Published July 25, 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 user feedback to our ML application and review data logged by actual users of the FSDL Text Recognizer. Published September 14, 2022.
Lecture 7: Foundation Models
We look at how to build on GPT-3, CLIP, StableDiffusion, and other large models. Published September 19, 2022.
Lecture 8: ML Teams and Project Management
We look at the structure of ML teams and projects, including how to hire or get hired on an ML team and how to build an ML-first organization. Published September 26, 2022.
Lecture 9: Ethics
We consider ethical concerns around buiding technlogy, building with machine learning, and building artificial intelligence. Published October 3, 2022.
This course was only possible with the support of our amazing TAs (in alphabetical order):
- Andrew Mendez is a Deep Learning Solutions Engineer at DeterminedAI, working on computer vision and NLP solutions for defense and autonomous vehicle companies. Previously Andrew worked as an ML Engineer at Clarifai and CACI.
- Daniel Hen is a Senior Data Scientist at Digital Turbine, working on Ad Tech and mobile solutions, as well as Big Data problems. Working with Spark, ML algorithms such as XGBoost, Computer Vision, and constantly learning new technology.
- James Le runs Data Relations and Partnerships at Superb AI, a data management platform for computer vision use cases. Outside work, he writes data-centric blog posts, hosts a data-focused podcast, and organizes in-person events for the data community.
- Saurabh Bipin Chandra is a Senior ML Scientist at Turnitin.
- Sayak Paul is as a Machine Learning Engineer at Carted on NLP and representation learning from HTML webpages. Besides work, he contributes to various open-source projects.
- Vishnu Rachakonda is a Data Scientist at firsthand.
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