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Python Deep Learning

You're reading from   Python Deep Learning Understand how deep neural networks work and apply them to real-world tasks

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Product type Paperback
Published in Nov 2023
Publisher Packt
ISBN-13 9781837638505
Length 362 pages
Edition 3rd Edition
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Author (1):
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Ivan Vasilev Ivan Vasilev
Author Profile Icon Ivan Vasilev
Ivan Vasilev
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Table of Contents (17) Chapters Close

Preface 1. Part 1:Introduction to Neural Networks
2. Chapter 1: Machine Learning – an Introduction FREE CHAPTER 3. Chapter 2: Neural Networks 4. Chapter 3: Deep Learning Fundamentals 5. Part 2: Deep Neural Networks for Computer Vision
6. Chapter 4: Computer Vision with Convolutional Networks 7. Chapter 5: Advanced Computer Vision Applications 8. Part 3: Natural Language Processing and Transformers
9. Chapter 6: Natural Language Processing and Recurrent Neural Networks 10. Chapter 7: The Attention Mechanism and Transformers 11. Chapter 8: Exploring Large Language Models in Depth 12. Chapter 9: Advanced Applications of Large Language Models 13. Part 4: Developing and Deploying Deep Neural Networks
14. Chapter 10: Machine Learning Operations (MLOps) 15. Index 16. Other Books You May Enjoy

8

Exploring Large Language Models in Depth

In recent years, interest in transformers has skyrocketed in the academic world, industry, and even the general public. The state-of-the-art transformer-based architectures today are called large language models (LLMs). The most captivating feature is their text-generation capabilities, and the most popular example is ChatGPT (https://chat.openai.com/). But in their core lies the humble transformer we introduced in Chapter 7. Luckily, we already have a solid foundation of transformers. One remarkable aspect of this architecture is that it has changed little in the years since it was introduced. Instead, the capabilities of LLMs have grown with their size (the name gives it away), lending credibility to the phrase quantitative change leads to qualitative change.

The success of LLMs has further fueled the research in the area (or is it the other way around?). On the one hand, large industrial labs (such as Google, Meta, Microsoft, or OpenAI...

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