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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

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Product type Paperback
Published in Jul 2024
Publisher Packt
ISBN-13 9781835460825
Length 190 pages
Edition 1st Edition
Languages
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Author (1):
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Carlos Rodriguez Carlos Rodriguez
Author Profile Icon Carlos Rodriguez
Carlos Rodriguez
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Table of Contents (13) Chapters Close

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 12. Other Books You May Enjoy

Summary

In this chapter, we traced the evolution of generative AI, distinguished it from traditional ML, explored its evolution, discussed its risks and implications, and, hopefully, dispelled some common misconceptions. We contemplated some of the possibilities anchored by consideration for its responsible adoption.

As we move on to the next chapter, we will examine the fundamental architectures behind generative AI, giving us a foundational understanding of the key generative methods, including GANs, diffusion models, and transformers. These ML methods form the backbone of generative AI and have been instrumental in bringing about the remarkable advancements we see today.

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