Quick Start Guide to Large Language Models: Strategies and Best Practices for Using ChatGPT and Other LLMs (Addison-Wesley Data & Analytics Series)

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The Practical, Step-by-Step Guide to Using LLMs at Scale in Projects and Products

Large Language Models (LLMs) like ChatGPT are demonstrating breathtaking capabilities, but their size and complexity have deterred many practitioners from applying them. In Quick Start Guide to Large Language Models, pioneering data scientist and AI entrepreneur Sinan Ozdemir clears away those obstacles and provides a guide to working with, integrating, and deploying LLMs to solve practical problems.

Ozdemir brings together all you need to get started, even if you have no direct experience with LLMs: step-by-step instructions, best practices, real-world case studies, hands-on exercises, and more. Along the way, he shares insights into LLMs’ inner workings to help you optimize model choice, data formats, parameters, and performance. You’ll find even more resources on the companion website, including sample datasets and code for working with open- and closed-source LLMs such as those from OpenAI (GPT-4 and ChatGPT), Google (BERT, T5, and Bard), EleutherAI (GPT-J and GPT-Neo), Cohere (the Command family), and Meta (BART and the LLaMA family).

Learn key concepts: pre-training, transfer learning, fine-tuning, attention, embeddings, tokenization, and moreUse APIs and Python to fine-tune and customize LLMs for your requirementsBuild a complete neural/semantic information retrieval system and attach to conversational LLMs for retrieval-augmented generationMaster advanced prompt engineering techniques like output structuring, chain-ofthought, and semantic few-shot promptingCustomize LLM embeddings to build a complete recommendation engine from scratch with user dataConstruct and fine-tune multimodal Transformer architectures using opensource LLMsAlign LLMs using Reinforcement Learning from Human and AI Feedback (RLHF/RLAIF)Deploy prompts and custom fine-tuned LLMs to the cloud with scalability and evaluation pipelines in mind

“By balancing the potential of both open- and closed-source models, Quick Start Guide to Large Language Models stands as a comprehensive guide to understanding and using LLMs, bridging the gap between theoretical concepts and practical application.”
–Giada Pistilli, Principal Ethicist at HuggingFace

“A refreshing and inspiring resource. Jam-packed with practical guidance and clear explanations that leave you smarter about this incredible new field.”
–Pete Huang, author of The Neuron

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From the Publisher

Why You Should Read This Book

This book accommodates varying levels of experience in machine learning, making it accessible to both those with prior knowledge and beginners who can code in Python. It offers flexibility in how deeply you engage with its content, allowing you to focus on practical aspects, experiment with code, or delve into the theoretical aspects without coding. The chapters in this book build upon each other, with knowledge and skills from previous sections aiding in later ones. Expect challenges along the way, as overcoming obstacles is part of the learning process. Just like the author’s experience developing a visual question-answering system, you may face frustrations and setbacks but will eventually achieve breakthroughs. Embrace these challenges, for they lead to moments of triumph in your learning journey.

Publisher ‏ : ‎ Addison-Wesley Professional; 1st edition (September 21, 2023)
Language ‏ : ‎ English
Paperback ‏ : ‎ 288 pages
ISBN-10 ‏ : ‎ 0138199191
ISBN-13 ‏ : ‎ 978-0138199197
Item Weight ‏ : ‎ 1 pounds
Dimensions ‏ : ‎ 6.9 x 0.56 x 9.2 inches