Book Image

Deep Learning Quick Reference

By : Mike Bernico
Book Image

Deep Learning Quick Reference

By: Mike Bernico

Overview of this book

Deep learning has become an essential necessity to enter the world of artificial intelligence. With this book deep learning techniques will become more accessible, practical, and relevant to practicing data scientists. It moves deep learning from academia to the real world through practical examples. You will learn how Tensor Board is used to monitor the training of deep neural networks and solve binary classification problems using deep learning. Readers will then learn to optimize hyperparameters in their deep learning models. The book then takes the readers through the practical implementation of training CNN's, RNN's, and LSTM's with word embeddings and seq2seq models from scratch. Later the book explores advanced topics such as Deep Q Network to solve an autonomous agent problem and how to use two adversarial networks to generate artificial images that appear real. For implementation purposes, we look at popular Python-based deep learning frameworks such as Keras and Tensorflow, Each chapter provides best practices and safe choices to help readers make the right decision while training deep neural networks. By the end of this book, you will be able to solve real-world problems quickly with deep neural networks.
Table of Contents (15 chapters)

Generative Adversarial Networks

While I've spent much of this book talking about networks that classify or estimate, in this chapter I get to show you some deep neural networks that have the ability to create. The Generative Adversarial Network (GAN), learns to do this through a sort of internal competition between two deep networks, which we will talk about next. In the case of Deep Convolutional General Adversarial Networks (DCGAN), which is the type of GAN I'm going to focus on in this chapter, the network learns to create images that resemble the images in the training dataset.

We will cover the following topics in this chapter:

  • An overview of the GAN
  • Deep Convolutional GAN architecture
  • How GANs can fail
  • Safe choices for a GAN
  • Generating MNIST images using a Keras GAN
  • Generating CIFAR-10 images using a Keras GAN