Transformer, BERT, and GPT

A comprehensive guide on Transformer, BERT, and GPT models, including ChatGPT and prompt engineering, essential for mastering advanced AI.

Key Features

• Detailed coverage of foundational concepts like attention mechanisms and tokenization techniques
• In-depth exploration of Transformer, BERT, and GPT architectures
• Practical guidance on using GPT-3 and ChatGPT for various NLP applications

Book Description

This book offers an in-depth exploration of the Transformer architecture, BERT models, and the GPT series, including GPT-3 and GPT-4. Beginning with foundational concepts like the attention mechanism and tokenization techniques, it delves into the intricacies of Transformer and BERT architectures. Advanced topics cover the latest developments in the GPT series, including ChatGPT. Key chapters provide insights into the evolution and significance of attention in deep learning, the nuances of Transformer architecture, a detailed exploration of the BERT family, and hands-on guidance on working with GPT-3.The journey continues with a comprehensive overview of ChatGPT, GPT-4, and visualization using generative AI. The book also discusses influential AI organizations such as DeepMind, OpenAI, Cohere, and Hugging Face. Readers will gain a thorough understanding of the current landscape of NLP models, their underlying architectures, and practical applications.Companion files with numerous code samples and figures from the book enhance the learning experience, providing practical tools and resources. This book is an essential guide for those seeking to master the latest advancements in natural language processing and generative AI.

What you will learn

• Understand Generative AI concepts
• Differentiate between Conversational and Generative AI
• Apply tokenization techniques
• Explore Transformer and BERT architectures
• Utilize GPT-3 and ChatGPT
• Implement practical NLP applications

Who this book is for

This book is ideal for those with basic machine learning knowledge or software developers interested in LLMs. Prior knowledge of Python is helpful. The content is designed for readers seeking detailed technical insights into NLP models.