https://github.com/shamspias/transformers-and-large-language-models-from-basics-to-frontier-research
Dive into the transformative world of NLP with this guide on Transformers. Journey from the roots of NLP to advanced Transformer variants like BERT and GPT. Discover their architecture, practical applications, ethical considerations, and future prospects. A comprehensive resource for AI enthusiasts and experts alike.
https://github.com/shamspias/transformers-and-large-language-models-from-basics-to-frontier-research
ai-book artificial-intelligence gpt-3 gpt-4 gpt-4-32k gpt-book llm llm-book llms llms-book machine-learning ml-book-code nlp-machine-learning self-attention
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Dive into the transformative world of NLP with this guide on Transformers. Journey from the roots of NLP to advanced Transformer variants like BERT and GPT. Discover their architecture, practical applications, ethical considerations, and future prospects. A comprehensive resource for AI enthusiasts and experts alike.
- Host: GitHub
- URL: https://github.com/shamspias/transformers-and-large-language-models-from-basics-to-frontier-research
- Owner: shamspias
- License: other
- Created: 2023-08-28T07:32:55.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2023-08-28T09:03:54.000Z (about 2 years ago)
- Last Synced: 2025-03-26T12:11:49.611Z (6 months ago)
- Topics: ai-book, artificial-intelligence, gpt-3, gpt-4, gpt-4-32k, gpt-book, llm, llm-book, llms, llms-book, machine-learning, ml-book-code, nlp-machine-learning, self-attention
- Homepage:
- Size: 18.6 KB
- Stars: 5
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
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README
# Transformers and Large Language Models: From Basics to Frontier Research
Welcome to the official GitHub repository for the book "Transformers and Large Language Models: From Basics to Frontier Research". This repository contains all the supplementary materials, code examples, and additional resources referenced in the book.
## Table of Contents
1. [Introduction](#introduction)
2. [How to Use This Repository](#how-to-use-this-repository)
3. [Code Examples](#code-examples)
4. [Feedback and Contributions](#feedback-and-contributions)
5. [License](#license)
## Introduction
This book aims to provide readers with a comprehensive understanding of the Transformer architecture, its applications, and the future prospects of NLP. Whether you're an AI enthusiast, researcher, or practitioner, you'll find valuable insights and hands-on examples here.
### [Book Index](book/index.md)
## How to Use This Repository
- **Reading**: Each chapter's content is available in markdown format.
- **Code**: Navigate to the `code` directory for practical examples and projects.
- **Datasets**: Essential datasets used in the book are stored in the `datasets` directory.
## Code Examples
All code examples are written in Python and make use of popular libraries such as TensorFlow and PyTorch. Ensure you have the required dependencies installed. Each code directory contains a `requirements.txt` for easy setup.
## Feedback and Contributions
We value your feedback and contributions! If you have suggestions or find errors:
1. Raise an issue detailing your feedback or the error you've found.
2. If you wish to contribute directly, fork the repository, make your changes, and submit a pull request.
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE.md) file for details.