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The Full-Stack LLM Bootcamp

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!


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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)

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