Book Image

Hands-On Deep Learning with Go

By : Gareth Seneque, Darrell Chua
Book Image

Hands-On Deep Learning with Go

By: Gareth Seneque, Darrell Chua

Overview of this book

Go is an open source programming language designed by Google for handling large-scale projects efficiently. The Go ecosystem comprises some really powerful deep learning tools such as DQN and CUDA. With this book, you'll be able to use these tools to train and deploy scalable deep learning models from scratch. This deep learning book begins by introducing you to a variety of tools and libraries available in Go. It then takes you through building neural networks, including activation functions and the learning algorithms that make neural networks tick. In addition to this, you'll learn how to build advanced architectures such as autoencoders, restricted Boltzmann machines (RBMs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and more. You'll also understand how you can scale model deployments on the AWS cloud infrastructure for training and inference. By the end of this book, you'll have mastered the art of building, training, and deploying deep learning models in Go to solve real-world problems.
Table of Contents (15 chapters)
Free Chapter
1
Section 1: Deep Learning in Go, Neural Networks, and How to Train Them
6
Section 2: Implementing Deep Neural Network Architectures
11
Section 3: Pipeline, Deployment, and Beyond!

Generative Models with Variational Autoencoders

In the previous chapter, we have looked into what DQN is and what types of predictions we can make around rewards or actions. In this chapter, we will look into how to build a VAE and about the advantages of a VAE over a standard autoencoder. We will also look into the effect of varying latent space dimensions on the network.

Let's take a look at another autoencoder. We've looked at autoencoders once before in Chapter 3, Beyond Basic Neural Networks – Autoencoders and RBMs, with a simple example, generating MNIST digits. Now we'll take a look at using it for a very different task—that is, generating new digits.

In this chapter, the following topics will be covered:

  • Introduction to variational autoencoders (VAEs)
  • Building a VAE on MNIST
  • Assessing the results and changing the latent dimensions
...