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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

Early approaches in NLP

Before the widespread use of neural networks (NNs) in language processing, NLP was largely grounded in methods that counted words. Two particularly notable techniques were count vectors and Term Frequency-Inverse Document Frequency (TF-IDF). In essence, count vectors tallied up how often each word appeared in a document. Building on this, Dadgar et al. applied the TF-IDF algorithm (historically used for information retrieval) to text classification in 2016. This method assigned weights to words based on their significance in one document relative to their occurrence across a collection of documents. These count-based methods were successful for tasks such as searching and categorizing. However, they presented a key limitation in that they could not capture the semantic relationships between words, meaning they could not interpret the nuanced meanings of words in context. This challenge paved the way for exploring NNs, offering a deeper and more nuanced way to...

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