Quick Start Guide to Large Language Models : Strategies and Best Practices for Using ChatGPT and Other LLMs

Regular price $68.99
Unit price
per
  • Author:
    OZDEMIR Sinan
  • ISBN:
    9780138199197
  • Publication Date:
    September 2023
  • Edition:
    1
  • Pages:
    288
  • Binding:
    Paperback
  • Publisher:
    Pearson Education
  • Country of Publication:
    USA
Quick Start Guide to Large Language Models : Strategies and Best Practices for Using ChatGPT and Other LLMs
Quick Start Guide to Large Language Models : Strategies and Best Practices for Using ChatGPT and Other LLMs

Quick Start Guide to Large Language Models : Strategies and Best Practices for Using ChatGPT and Other LLMs

Regular price $68.99
Unit price
per
  • Author:
    OZDEMIR Sinan
  • ISBN:
    9780138199197
  • Publication Date:
    September 2023
  • Edition:
    1
  • Pages:
    288
  • Binding:
    Paperback
  • Publisher:
    Pearson Education
  • Country of Publication:
    USA

Description

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).

(0 in cart)
Shipping calculated at checkout.

You may also like

This is a Sample Product Title
Was $200.00 Now $100.00
  • 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).

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).