Transformer, BERT, and GPT: Including ChatGPT and Prompt Engineering

by Oswald Campesato

Overview

This book provides a comprehensive group of topics covering the details of the Transformer architecture, BERT models, and the GPT series, including GPT-3 and GPT-4. Spanning across ten chapters, it begins with foundational concepts such as the attention mechanism, then tokenization techniques, explores the nuances of Transformer and BERT architectures, and culminates in advanced topics related to the latest in the GPT series, including ChatGPT. Key chapters provide insights into the evolution and significance of attention in deep learning, the intricacies of the Transformer architecture, a two-part exploration of the BERT family, and hands-on guidance on working with GPT-3. The concluding chapters present an overview of ChatGPT, GPT-4, and visualization using generative AI. In addition to the primary topics, the book also covers influential AI organizations such as DeepMind, OpenAI, Cohere, Hugging Face, and more. Readers will gain a comprehensive understanding of the current landscape of NLP models, their underlying architectures, and practical applications. Features companion files with numerous code samples and figures from the book.

Product Details

ISBN-13:	9781683928980
Publisher:	Mercury Learning and Information
Publication date:	12/15/2023
Series:	MLI Generative AI Series
Pages:	364
Product dimensions:	6.00(w) x 9.00(h) x (d)
Age Range:	18 Years

About the Author

Oswald Campesato (San Francisco, CA) specializes in Deep Learning, Python, and GPT-4. He is the author/co-author of over thirty-five books including Python 3 Using ChatGPT / GPT-4, NLP for Developers, and Artificial Intelligence, Machine Learning and Deep Learning (all Mercury Learning).

Oswald Campesato (San Francisco, CA) specializes in Deep Learning, Python, and GPT-4. He is the author/co-author of over thirty-five books including Python 3 Using ChatGPT / GPT-4, NLP for Developers, and Artificial Intelligence, Machine Learning and Deep Learning (all Mercury Learning).

Table of Contents

1: The Attention Mechanism
2: Tokenization
3: Transformer Architecture Introduction
4: Transformer Architecture in Greater Depth
5: The BERT Family Introduction
6: The BERT Family in Greater Depth
7: Working with GPT-3 Introduction
8: Working with GPT-3 in Greater Depth
9: ChatGPT and GPT-4
10: Visualization with Generative AI
Index