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In this repo you will find a roadmap to master Large Language Models

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# 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)](#mastering-large-language-models-llms)
  - [Table of Contents](#table-of-contents)
  - [NLP Basics](#nlp-basics)
  - [Deep Learning for NLP](#deep-learning-for-nlp)
  - [Transformers \& Self-Attention for NLP](#transformers--self-attention-for-nlp)
  - [Large Language Models (LLMs) Courses](#large-language-models-llms-courses)
  - [Building LLM Projects \& Applications](#building-llm-projects--applications)
  - [Extra (Blogs)](#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](https://www.amazon.com/Natural-Language-Processing-Python-Analyzing/dp/0596516495): 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](https://github.com/Sturzgefahr/Natural-Language-Processing-with-Python-Analyzing-Text-with-the-Natural-Language-Toolkit)

2. [Practical Natural Language Processing](https://www.amazon.com/Practical-Natural-Language-Processing-Pragmatic/dp/1492054054): A practical guide to processing and understanding text data with NLP. - [Github](https://github.com/practical-nlp/practical-nlp-code)

3. [Analytics Vidhya NLP tutorial](https://www.analyticsvidhya.com/blog/2017/01/ultimate-guide-to-understand-implement-natural-language-processing-codes-in-python/): A comprehensive tutorial on NLP basics.

4. [Github NLP tutorial](https://github.com/keon/awesome-nlp): A collection of NLP resources.

5. [Speech and Language Processing](https://web.stanford.edu/~jurafsky/slp3/): 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](https://www.coursera.org/specializations/deep-learning): 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](http://web.stanford.edu/class/cs224n/): 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](https://people.ischool.berkeley.edu/~dbamman/info256.html): 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](https://www.coursera.org/specializations/natural-language-processing): 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](http://www.deeplearningbook.org/): 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](https://www.amazon.com/Deep-Learning-Coders-fastai-PyTorch/dp/1492045527): 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](https://fastai.github.io/fastbook2e/)

7. [Deep Learning for NLP (PyTorch tutorials)](https://pytorch.org/tutorials/beginner/deep_learning_nlp_tutorial.html): 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](http://jalammar.github.io/illustrated-transformer/): This blog post by Jay Alammar provides a visual and intuitive understanding of how transformers work.

2. [The Annotated Transformer](http://nlp.seas.harvard.edu/2018/04/03/attention.html): 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](https://www.amazon.com/Transformers-Natural-Language-Processing-Understanding/dp/1800565790): This book explains the transformer architecture in depth and how it is used in real-world applications.

4. [Hugging Face Transformers](https://huggingface.co/learn/nlp-course/chapter1/1): 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)](https://arxiv.org/abs/1706.03762): 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](https://www.youtube.com/watch?v=4Bdc55j80l8): 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](https://www.cs.princeton.edu/courses/archive/fall22/cos597G/)
   
2. [CS324 lecture notes (Winter 2022) - Stanford University](https://stanford-cs324.github.io/winter2022/lectures/)

## 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](https://huggingface.co/models): The Hugging Face Model Hub is a repository of pre-trained models that you can use directly in your projects.

2. [ChatGPT](https://openai.com/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](https://gpt3demo.com/): This is a platform where you can experiment with different applications of GPT-3.

4. [Github GPT-3 Examples](https://github.com/openai/gpt-3): This Github repository contains examples of applications built using GPT-3.

5. [awesome-gpt3 Github](https://github.com/elyase/awesome-gpt3): This Github repository contains examples of creative writing applications built using GPT-3.

6. [GPT-3 Sandbox by OpenAI](https://beta.openai.com/): This is a platform provided by OpenAI where you can experiment with different applications of GPT-3.
   
7. [Full Stack LLM Bootcamp](https://fullstackdeeplearning.com/llm-bootcamp/)
   
8. [LLM University! by cohere](https://docs.cohere.com/docs/llmu)

9.  [DeepLearning.AI Short Courses:](https://www.deeplearning.ai/short-courses/)
   
    1.  [Chatgpt Prompt Engineering for Developers](./0_Chatgpt_Prompt_Engineering_for_Developers)

2.  [LangChain for LLM Application Development](./1_LangChain_for_LLM_Application_Development)

3.  [How Diffusion Models Work](./2_How_Diffusion_Models_Work)

4.  [Building Systems with ChatGPT API](./3_Building_Systems_with_ChatGPT_API)

10. [Nano GPT](https://github.com/karpathy/nanoGPT): Nano version of GPT made by Andrej Karpathy to understand how LLM models work from the ground up.

## Extra (Blogs)
1. [Lil’Log](https://lilianweng.github.io/)
   
2. [Jay Alammar](https://jalammar.github.io/)

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