Mastering Large Language Models (LLMs)

This repository provides a detailed roadmap for mastering Large Language Models (LLMs)

The roadmap is divided into several sections, each focusing on a specific aspect of LLMs. Each section includes a list of resources for learning each topic, including books, online courses, and tutorials.

Let's embark on this exciting journey to master LLMs!

Table of Contents

• Mastering Large Language Models (LLMs)
  • Table of Contents
  • NLP Basics
  • Deep Learning for NLP
  • Transformers & Self-Attention for NLP
  • Large Language Models (LLMs) Courses
  • Building LLM Projects & Applications
  • Extra (Blogs)

NLP Basics

Before diving into LLMs, it's essential to have a solid understanding of Natural Language Processing (NLP). Here are some resources to help you get started:

1. Natural Language Processing with Python: This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. - Github

2. Practical Natural Language Processing: A practical guide to processing and understanding text data with NLP. - Github

3. Analytics Vidhya NLP tutorial: A comprehensive tutorial on NLP basics.

4. Github NLP tutorial: A collection of NLP resources.

5. Speech and Language Processing: This is an introductory textbook on NLP and computational linguistics, written by leading researchers in the field.

Deep Learning for NLP

Once you have a solid understanding of NLP basics, the next step is to learn about deep learning for NLP. Here are some resources to assist you:

1. Deep Learning Specialization: This specialization by Andrew Ng on Coursera will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology.

2. Deep Learning for NLP by Stanford University: This course provides a thorough introduction to cutting-edge research in Deep Learning for NLP. Through lectures, assignments, and a final project, students will learn the necessary skills to design, implement, and understand their own neural network models.

3. Applied Natural Language Processing by UC Berkeley: This course examines the use of natural language processing as a set of methods for exploring and reasoning about text as data, focusing especially on the applied side of NLP — using existing NLP methods and libraries in Python in new and creative ways (rather than exploring the core algorithms underlying them)

4. Natural Language Processing Specialization: This specialization by deeplearning.ai on Coursera will equip you with the machine learning basics and state-of-the-art deep learning techniques needed to build cutting-edge NLP systems.

5. Deep Learning Book: This book by Ian Goodfellow, Yoshua Bengio, and Aaron Courville provides a comprehensive introduction to the field of deep learning.

6. Deep Learning for Coders with fastai and PyTorch: This book provides a practical introduction to deep learning and artificial intelligence. It emphasizes the use of the fastai library, which simplifies the process of building complex models. - Free Book

7. Deep Learning for NLP (PyTorch tutorials): These tutorials provide a good starting point for understanding how to use PyTorch for NLP tasks.

Transformers & Self-Attention for NLP

The transformer model, which uses self-attention mechanisms, forms the backbone of most LLMs. Here are some resources to help you to understand transformers and self-attention mechanisms:

1. The Illustrated Transformer: This blog post by Jay Alammar provides a visual and intuitive understanding of how transformers work.

2. The Annotated Transformer: This blog post annotates the paper that introduced transformers, 'Attention is All You Need', and provides a line-by-line implementation in PyTorch.

3. Transformers for Natural Language Processing: This book explains the transformer architecture in depth and how it is used in real-world applications.

4. Hugging Face Transformers: Hugging Face provides a library of pre-trained transformer models for direct use in NLP tasks. Their documentation and tutorials are a great resource for learning how to use transformers in practice.

5. Attention Is All You Need (Original Paper): This is the original paper that introduced the transformer model. It's a bit dense, but it's a must-read if you want to understand the details of the model.

6. A Gentle Introduction to Transformers: This video by Luis Serrano provides a gentle and intuitive explanation of transformers.

Large Language Models (LLMs) Courses

1. COS 597G (Fall 2022): Understanding Large Language Models - Princeton University

2. CS324 lecture notes (Winter 2022) - Stanford University

Building LLM Projects & Applications

After understanding the theoretical aspects, it's time for some hands-on experience. Here are some resources to help you build projects and applications using LLMs:

1. Hugging Face Model Hub: The Hugging Face Model Hub is a repository of pre-trained models that you can use directly in your projects.

2. ChatGPT: ChatGPT is a state-of-the-art LLM developed by OpenAI. You can use the ChatGPT API to integrate it into your applications.

3. GPT-3 Sandbox: This is a platform where you can experiment with different applications of GPT-3.

4. Github GPT-3 Examples: This Github repository contains examples of applications built using GPT-3.

5. awesome-gpt3 Github: This Github repository contains examples of creative writing applications built using GPT-3.

6. GPT-3 Sandbox by OpenAI: This is a platform provided by OpenAI where you can experiment with different applications of GPT-3.

7. Full Stack LLM Bootcamp

8. LLM University! by cohere

9. DeepLearning.AI Short Courses:

   1. Chatgpt Prompt Engineering for Developers

   2. LangChain for LLM Application Development

   3. How Diffusion Models Work

   4. Building Systems with ChatGPT API

10. Nano GPT: Nano version of GPT made by Andrej Karpathy to understand how LLM models work from the ground up.

Extra (Blogs)

1. Lil’Log

2. Jay Alammar