Ebook: Transformer, BERT, and GPT3 : Including ChatGPT and Prompt Engineering

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.

Although this book is introductory in nature, some knowledge of Python 3.x with certainly be helpful for the code samples. Knowledge of other programming languages (such as Java) can also be helpful because of the exposure to programming concepts and constructs. The less technical knowledge that you have, the more diligence will be required in order to understand the various topics that are covered. If you want to be sure that you can grasp the material in this book, glance through some of the code samples to get an idea of how much is familiar to you and how much is new for you.

Features:
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.
Features companion files with numerous code samples and figures from the book.

The target audience:
This book is intended primarily for people who have a basic knowledge of Machine Learning or software developers who are interested in working with LLMs. Specifically, this book is for readers who are accustomed to searching online for more detailed information about technical topics. If you are a beginner, there are other books that may be more suitable for you, and you can find them by performing an online search. This book is also intended to reach an international audience of readers with highly diverse backgrounds in various age groups. In addition, this book uses standard English rather than colloquial expressions that might be confusing to those readers. This book provide a comfortable and meaningful learning experience for the intended readers.