Sebastian Raschka
Description A deep dive into the architecture and implementation of AI models capable of logical deduction and multi-step reasoning. It explains how t...
This is a general audience deep dive into the Large Language Model (LLM) AI technology that powers ChatGPT and related products. It is covers the full...
Edward Raff, Drew Farris, Stella Biderman
Publisher
Simon and Schuster
Published
2025
Duration
6 hr 39 min
ISBN
9781638357605
Genres
Description An examination of the internal mechanics of Large Language Models (LLMs) such as GPT and Gemini. It covers the technical transition from a natural language prompt to a text completion, detailing the training, optimization, and deployment processes.Key Topics: Tokenization, transformer architecture (attention mechanisms, positional encoding), supervised fine-tuning, Reinforcement Learning from Human Feedback (RLHF), and Retrieval-Augmented Generation (RAG). About the Technology: LLMs are prediction engines that process billions of documents to identify patterns and generate human-like text. They utilize decoder-only transformer architectures to predict subsequent tokens in an autoregressive manner. About the Book: The guide explains why LLMs make errors, how to evaluate their performance, and how to navigate the ethical and security issues inherent in their deployment. It also explores LLM applications beyond text, such as in computer vision and code generation. About the Reader: No prior background in machine learning is required; the text is designed for a general technical audience. About the Author: Authored by machine learning researchers at Booz Allen Hamilton, including Stella Biderman, Drew Farris, and Edward Raff.
