TL;DR: Dive deep into the world of Large Language Models (LLMs) with a structured learning path. From foundational mathematics to practical application development, this guide covers essential resources, courses, and tools to master LLMs, including implementing models from scratch, prompt engineering, and fine-tuning.
Exploring the World of Large Language Models
As we delve deeper into the remarkable world of Large Language Models (LLMs), understanding these foundational frameworks is crucial for anyone looking to excel in AI, particularly with OpenAI’s GPT and similar models. Here, I present a curated roadmap to mastering LLMs, combining video tutorials, hands-on coding, and comprehensive guides.
Start with the Basics: Introductory Resources
Begin your journey with insightful presentations:
- Watch the Intro to Large Language Models by Andrej to grasp the core concepts.
- Dive into Large Language Models in Five Formulas by Alexander Rush from Cornell Tech.
Neural Networks: Zero to Hero
Andrej Karpathy's series, Neural Networks: Zero to Hero, is a must-watch. It covers everything from coding backpropagation to building GPT models from scratch. For those eager to explore more, check out his latest video on building a GPT Tokenizer.
Free LLM Bootcamp
Full Stack Deep Learning offers a free LLM Bootcamp that covers prompt engineering, LLMOps, and launching an LLM app quickly.
Building with LLMs: Application Development
If you're ready to build applications using LLMs, these resources are invaluable:
- Watch Application Development using Large Language Models by Andrew Ng.
- Read Building LLM applications for production by Huyen Chip.
- Explore Patterns for Building LLM-based Systems & Products by Eugene Yan.
- Utilize the OpenAI Cookbook for practical recipes.
- Kickstart your projects with Vercel AI templates.
Engage in Hackathons
Participate in weekly AI hackathons at lablab.ai. Let me know if you want to collaborate!
Deepen Your Understanding: Read Essential Papers
Sebastian Raschka’s Understanding Large Language Models is a comprehensive article listing crucial papers to read. Follow his substack, Ahead of AI.
Writing Transformers from Scratch
- Dive into The Transformer Family Version 2.0 for an overview.
- Key papers include Attention Is All You Need and The Illustrated Transformer.
- Engage with comprehensive guides and blogs to implement transformers from scratch.
Learning to Run Open-Source Models
Utilize ollama to get started with models like Llama 2.
Mastering Prompt Engineering
Explore Prompt Engineering | Lil’Log and enroll in courses like ChatGPT Prompt Engineering for Developers.
Fine-tuning LLMs
- Follow the Hugging Face fine-tuning guide.
- Check out Fine-Tuning — The GenAI Guidebook.
Understanding RAG
Explore articles on Retrieval Augmented Generation (RAG) such as A Comprehensive Guide for Building RAG-based LLM Applications.
By leveraging these resources, you'll not only enhance your understanding of LLMs but also gain practical skills to innovate and lead in the realm of AI. Dive in, and let the journey of discovery begin!
