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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

Tracing the Foundations of Natural Language Processing and the Impact of the Transformer

The transformer architecture is a key advancement that underpins most modern generative language models. Since its introduction in 2017, it has become a fundamental part of natural language processing (NLP), enabling models such as Generative Pre-trained Transformer 4 (GPT-4) and Claude to advance text generation capabilities significantly. A deep understanding of the transformer architecture is crucial for grasping the mechanics of modern large language models (LLMs).

In the previous chapter, we explored generative modeling techniques, including generative adversarial networks (GANs), diffusion models, and autoregressive (AR) transformers. We discussed how Transformers can be leveraged to generate images from text. However, transformers are more than just one generative approach among many; they form the basis for nearly all state-of-the-art generative language models.

In this chapter, we...

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