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Advanced Deep Learning with Python

You're reading from   Advanced Deep Learning with Python Design and implement advanced next-generation AI solutions using TensorFlow and PyTorch

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Product type Paperback
Published in Dec 2019
Publisher Packt
ISBN-13 9781789956177
Length 468 pages
Edition 1st 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. Section 1: Core Concepts
2. The Nuts and Bolts of Neural Networks FREE CHAPTER 3. Section 2: Computer Vision
4. Understanding Convolutional Networks 5. Advanced Convolutional Networks 6. Object Detection and Image Segmentation 7. Generative Models 8. Section 3: Natural Language and Sequence Processing
9. Language Modeling 10. Understanding Recurrent Networks 11. Sequence-to-Sequence Models and Attention 12. Section 4: A Look to the Future
13. Emerging Neural Network Designs 14. Meta Learning 15. Deep Learning for Autonomous Vehicles 16. Other Books You May Enjoy

Generative Models

In the previous two chapters (Chapter 4, Advanced Convolutional Networks, and Chapter 5, Object Detection and Image Segmentation), we focused on supervised computer vision problems, such as classification and object detection. In this chapter, we'll discuss how to create new images with the help of unsupervised neural networks. After all, it's a lot better knowing that you don't need labeled data. More specifically, we'll talk about generative models.

This chapter will cover the following topics:

  • Intuition and justification of generative models
  • Introduction to Variational Autoencoders (VAEs)
  • Introduction to Generative Adversarial Networks (GANs)
  • Types of GAN
  • Introducing to artistic style transfer
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