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

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This book presents a comprehensive group of topics covering details of transformer architecture, BERT models, and the GPT series, including GPT-3 and GPT-4. Spanning ten chapters, it begins with foundational concepts such as the attention mechanism, then tokenization techniques, explores the nuances of the Transformer and BERT architectures, and concludes with advanced topics related to the latest developments in the GPT series, including ChatGPT. Key chapters provide insight into the evolution and importance of attention in deep learning, the intricacies of the Transformer architecture, a two-part exploration of the BERT family, and a practical guide to working with GPT-3. The final chapters provide an overview of ChatGPT, GPT-4, and generative AI visualization. In addition to the main topics, the book also covers influential AI organizations such as DeepMind, OpenAI, Cohere, Hugging Face, and others. Readers will gain a comprehensive understanding of the current landscape of NLP models, their underlying architectures, and practical applications. Contains accompanying files with numerous code examples and pictures from the book.
Although this book is for educational purposes only, some knowledge of Python 3.x will certainly be helpful for the code examples. Knowledge of other programming languages (such as Java) can also be helpful due to familiarity with programming concepts and constructs. The less technical knowledge you have, the more diligence it will take to understand the various topics that are covered. If you want to make sure you can learn the material in this book, look at some of the code examples to get an idea of what's familiar and what's new to you.
Features:
Contains an extensive group of sections covering details of the Transformer architecture, BERT models, and the GPT series, including GPT-3 and GPT-4.
Contains accompanying files with numerous code examples and pictures from the book.
Target Audience:
This book is primarily intended for people with basic knowledge of machine learning or software developers interested in working with LLMS. In particular, this book is intended for readers who are accustomed to searching the Internet for more detailed information on technical topics. If you are a beginner, there are other books that may be more suitable for you, and you can find them by searching online. This book is also intended for an international audience of readers with a very diverse range of experiences across different age groups. In addition, this book uses standard English expressions rather than colloquialisms, which may confuse such readers. This book provides a convenient and meaningful learning experience for its intended readers.​

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