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How Gpt Works Meap V01 Chapters 1 To 3 Of 10 Drew Farris Edward Raff

  • SKU: BELL-56617728
How Gpt Works Meap V01 Chapters 1 To 3 Of 10 Drew Farris Edward Raff
$ 31.00 $ 45.00 (-31%)

4.1

70 reviews

How Gpt Works Meap V01 Chapters 1 To 3 Of 10 Drew Farris Edward Raff instant download after payment.

Publisher: Manning Publications
File Extension: PDF
File size: 2.48 MB
Pages: 60
Author: Drew Farris, Edward Raff, Stella Biderman for Booz Allen Hamilton
ISBN: 9781633437081, 1633437086
Language: English
Year: 2024
Edition: Chapters 1 to 3 of 10

Product desciption

How Gpt Works Meap V01 Chapters 1 To 3 Of 10 Drew Farris Edward Raff by Drew Farris, Edward Raff, Stella Biderman For Booz Allen Hamilton 9781633437081, 1633437086 instant download after payment.

How GPT Works is an introduction to LLMs that explores OpenAI’s GPT models. The book takes you inside ChatGPT, showing how a prompt becomes text output. In clear, plain language, this illuminating book shows you when and why LLMs make errors, and how you can account for inaccuracies in your AI solutions. Once you know how LLMs work, you’ll be ready to start exploring the bigger questions of AI, such as how LLMs “think” differently that humans, how to best design LLM-powered systems that work well with human operators, and what ethical, legal, and security issues can—and will—arise from AI automation.
 
Learn how large language models like GPT and Gemini work under the hood in plain English.
 
How GPT Works translates years of expert research on Large Language Models into a readable, focused introduction to working with these amazing systems. It explains clearly how LLMs function, introduces the optimization techniques to fine tune them, and shows how to create pipelines and processes to ensure your AI applications are efficient and error-free.
 
In How GPT Works you will learn how to:
    Test and evaluate LLMs
    Use human feedback, supervised fine tuning, and Retrieval augmented generation (RAG)
    Reducing the risk of bad outputs, high-stakes errors, and automation bias
    Human-computer interaction systems
    Combine LLMs with traditional ML

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