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

Ozdemir, Sinan

ISBN 10: 0138199191 / ISBN 13: 9780138199197

Editore: Addison-Wesley Professional, 2023

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The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.

Codice articolo GOR014397430

Dati bibliografici

• Titolo: Quick Start Guide to Large Language Models: Strategies and Best Practices for Using ChatGPT and Other LLMs (Addison-Wesley Data & Analytics Series)
• Editore: Addison-Wesley Professional
• Data di pubblicazione: 2023
• Lingua: Inglese
• ISBN 10: 0138199191
• ISBN 13: 9780138199197
• Rilegatura: Paperback
• Condizione: Very Good
• Peso: 490 grammi

Descrizione articolo

Riassunto

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 more
• Use APIs and Python to fine-tune and customize LLMs for your requirements
• Build a complete neural/semantic information retrieval system and attach to conversational LLMs for retrieval-augmented generation
• Master advanced prompt engineering techniques like output structuring, chain-ofthought, and semantic few-shot prompting
• Customize LLM embeddings to build a complete recommendation engine from scratch with user data
• Construct and fine-tune multimodal Transformer architectures using opensource LLMs
• Align 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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Informazioni sull?autore

Sinan Ozdemir is currently the founder and CTO of Shiba Technologies. Sinan is a former lecturer of Data Science at Johns Hopkins University and the author of multiple textbooks on data science and machine learning. Additionally, he is the founder of the recently acquired Kylie.ai, an enterprise-grade conversational AI platform with RPA capabilities. He holds a master's degree in Pure Mathematics from Johns Hopkins University and is based in San Francisco, CA.