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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 2:
Deep Neural Networks for Computer Vision

In this part, we’ll introduce convolutional neural networks (CNNs) – a type of neural network suitable for computer vision applications. Building on top of the first three chapters, we’ll discuss the rationale behind CNNs, their building blocks, and their architecture. We’ll also outline the most popular CNN models in use today. Finally, we’ll focus on the advanced applications of CNNs – object detection, image segmentation, and image generation.

This part has the following chapters:

  • Chapter 4, Computer Vision with Convolutional Networks
  • Chapter 5, Advanced Computer Vision Applications
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