Book Image

Python Deep Learning - Second Edition

By : Ivan Vasilev, Daniel Slater, Gianmario Spacagna, Peter Roelants, Valentino Zocca
Book Image

Python Deep Learning - Second Edition

By: Ivan Vasilev, Daniel Slater, Gianmario Spacagna, Peter Roelants, Valentino Zocca

Overview of this book

With the surge in artificial intelligence in applications catering to both business and consumer needs, deep learning is more important than ever for meeting current and future market demands. With this book, you’ll explore deep learning, and learn how to put machine learning to use in your projects. This second edition of Python Deep Learning will get you up to speed with deep learning, deep neural networks, and how to train them with high-performance algorithms and popular Python frameworks. You’ll uncover different neural network architectures, such as convolutional networks, recurrent neural networks, long short-term memory (LSTM) networks, and capsule networks. You’ll also learn how to solve problems in the fields of computer vision, natural language processing (NLP), and speech recognition. You'll study generative model approaches such as variational autoencoders and Generative Adversarial Networks (GANs) to generate images. As you delve into newly evolved areas of reinforcement learning, you’ll gain an understanding of state-of-the-art algorithms that are the main components behind popular games Go, Atari, and Dota. By the end of the book, you will be well-versed with the theory of deep learning along with its real-world applications.
Table of Contents (12 chapters)

Generating Images with GANs and VAEs

"What I cannot create, I do not understand."- Richard Feynman

This quote is often cited in the same sentence as generative models, and for good reason. In the previous two chapters (Chapter 4, Computer Vision with Convolutional Networks and Chapter 5, Advanced Computer Vision), we focused on supervised computer vision problems, such as classification and object detection. Now, 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
  • Variational autoencoders
  • Generative Adversarial networks