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Generative AI Foundations in Python

You're reading from Generative AI Foundations in Python Discover key techniques and navigate modern challenges in LLMs

Product type Paperback

Published in Jul 2024

Publisher Packt

ISBN-13 9781835460825

Length 190 pages

Edition 1st Edition

Languages

Python

Tools

Transfer learning

Concepts

Artificial Intelligence

Author (1):

Carlos Rodriguez

View More author details

Table of Contents (13) Chapters

Preface

1. Part 1: Foundations of Generative AI and the Evolution of Large Language Models FREE CHAPTER

2. Chapter 1: Understanding Generative AI: An Introduction

3. Chapter 2: Surveying GenAI Types and Modes: An Overview of GANs, Diffusers, and Transformers

4. Chapter 3: Tracing the Foundations of Natural Language Processing and the Impact of the Transformer

5. Chapter 4: Applying Pretrained Generative Models: From Prototype to Production

6. Part 2: Practical Applications of Generative AI

7. Chapter 5: Fine-Tuning Generative Models for Specific Tasks

8. Chapter 6: Understanding Domain Adaptation for Large Language Models

9. Chapter 7: Mastering the Fundamentals of Prompt Engineering

10. Chapter 8: Addressing Ethical Considerations and Charting a Path Toward Trustworthy Generative AI

11. Index

Why subscribe?

12. Other Books You May Enjoy

Preface

Welcome to Generative AI Foundations in Python: Discover key techniques and navigate modern challenges in LLMs. This book offers an accessible introduction to generative AI and large language models (LLMs), guiding the reader from core principles to practical applications. It aims to present a balanced approach, offering theory and hands-on examples, providing a strong foundation for those seeking to understand and leverage generative AI in their respective disciplines and fields.

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Authors (1)

Carlos Rodriguez

Carlos Rodriguez

Carlos Rodriguez is the Director of AI risk at a major financial institution, where he oversees the validation of cutting-edge AI and machine learning models, including generative AI, to ensure that they remain trustworthy, unbiased, and compliant with stringent regulatory standards. With a degree in data science, numerous professional certifications, and two decades of experience in emerging technology, Carlos is a recognized expert in natural language processing and machine learning. Throughout his career, he has fostered and led high-performing machine learning engineering and data science teams specializing in natural language processing and AI risk, respectively. Known for his human-centered approach to AI, Carlos is a passionate autodidact who continuously expands his knowledge as a data scientist, machine learning practitioner, and risk executive. His current focus lies in developing a comprehensive framework for evaluating generative AI models within a regulatory setting, aiming to set new industry standards for responsible AI adoption and deployment.

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