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. 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.

Transformer, BERT, and GPT3 PDF

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