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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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Toc

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

Part 3:
Natural Language Processing and Transformers

We’ll start this part with an introduction to natural language processing, which will serve as a backdrop for our discussion on recurrent networks and transformers. Transformers will be the main focus of this section because they represent one of the most significant deep learning advances in recent years. They are the foundation of large language models (LLM), such as ChatGPT. We’ll discuss their architecture and their core element – the attention mechanism. Then, we’ll discuss the properties of LLMs. Finally, we’ll focus on some advanced LLM applications, such as text and image generation, and learn how to build LLM-centered applications.

This part has the following chapters:

  • Chapter 6, Natural Language Processing and Recurrent Neural Networks
  • Chapter 7, The Attention Mechanism and Transformers
  • Chapter 8, Exploring Large Language Models in Depth
  • Chapter 9, Advanced...
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