LLM Bootcamp - Spring 2023
What are the pre-requisites for this bootcamp?
Our goal is to get you 100% caught up to state-of-the-art and ready to build and deploy LLM apps, no matter what your level of experience with machine learning is.
- High-level intuitions for prompting
- Tips and tricks for effective prompting: decomposition/chain-of-thought, self-criticism, ensembling
- Gotchas: "few-shot learning" and tokenization
- Comparing and evaluating open source and proprietary models
- Iteration and prompt management
- Applying test-driven-development and continuous integration to LLMs
- General principles for user-centered design
- Emerging patterns in UX design for LUIs
- UX case studies: GitHub Copilot and Bing Chat
- Augmenting language model inputs with external knowledge
- Vector indices and embedding management systems
- Augmenting language model outputs with external tools
- Why is now the right time to build?
- Techniques and tools for the tinkering and discovery phase: ChatGPT, LangChain, Colab
- A simple stack for quickly launching augmented LLM applications
- Speed-run of ML fundamentals
- The Transformer architecture
- Notable LLMs and their datasets
- Can we build general purpose robots using multimodal models?
- Will models get bigger or smaller? Are we running out of data?
- How close are we to AGI? Can we make it safe?
- The "agent" design pattern: tool use, memory, reflection, and goals
- Challenges facing agents in production: controlling tool use, parsing outputs, handling large contexts, and more
- Exciting research projects with agents: AutoGPT, BabyAGI, CAMEL, and Generative Agents
- By Harrison Chase, co-creator of LangChain
We are deeply grateful to all of the sponsors who helped make this event happen.
Compute Credit Sponsors
We are excited to share this course with you for free.
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