Khan, Arshad

Business Intelligence and Data Analysis in the Age of AI

Campesato, Oswald

Beginning Python 3 with Claude 3

Fortino, Andres

Case Studies in Excel, R, and ChatGPT

Data Analysis for Business Decisions

Zherlitsyn, Dmytro

Financial Data Analysis Using Python

A Prompt-based Exploration of LLMs

Large Language Models for Developers

Beginning Python 3 with Grok 2

Herman, Erik

Using Analytics and Case Studies

Data Science for Decision Makers

Goralski, Walter

Understanding AI, IoT, 6G and The Infrastructure Revolution

Erik, Herman L.

Theory, Applications and Ethics

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CAMPESATO, OSWALD

CSS3 and SVG with Meta AI

MLI Generative AI Series

Frontmatter

Contents

Preface

About the Contributor

Chapter 1: The Generative AI Landscape

Chapter 2: Prompt Engineering (1)

Chapter 3: Prompt Engineering (2)

Chapter 4: Well-Known LLMs and APIs

Chapter 5: Fine-Tuning LLMs (1)

Chapter 6: LLMs and Fine-Tuning (2)

Chapter 7: What is Tokenization?

Chapter 8: Attention Mechanism

Chapter 9: LLMs and Quantization (1)

Chapter 10: LLMs and Quantization (2)

Index

This book offers a thorough exploration of Large Language Models (LLMs), guiding developers through the evolving landscape of generative AI and equipping them with the skills to utilize LLMs in practical applications. Designed for developers with a foundational understanding of machine learning, this book covers essential topics such as prompt engineering techniques, fine-tuning methods, attention mechanisms, and quantization strategies to optimize and deploy LLMs.

This book offers a thorough exploration of Large Language Models (LLMs), guiding developers through the evolving landscape of generative AI and equipping them...

Format

Large Language Models for Developers

A Prompt-based Exploration of LLMs

Oswald Campesato

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