This website blew my mind. It’s a completely open-source and free collection of information about how to build LLM-powered apps.
See below for a description of the course and the link to the full course:
We put together a two-day program based on emerging best practices and the latest research results to help you make the transition to building LLM apps with confidence.
We ran that program as an in-person bootcamp in San Francisco in April 2023. Now, we're releasing the recorded lectures, for free!
Subscribe here to get access to the first 500 ideas from our blog. For just one coffee a month, you can have access to more than $500 billion dollars of ideas. What's not to love?
They also offer a Deep Learning Course:
Detailed Contents
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.
Lab Overview
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.
Contributed by: Michael Bervell (Billion Dollar Startup Ideas)