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!

Section 1: Deep Learning in Go, Neural Networks, and How to Train Them

This section introduces you to deep learning (DL) and the libraries in Go that are needed to design, implement, and train deep neural networks (DNNs). We also cover the implementation of an autoencoder for unsupervised learning, and a restricted Boltzmann machine (RBM) for a Netflix-style collaborative filtering system.

The following chapters are included in this section:

  • Chapter 1, Introduction to Deep Learning in Go
  • Chapter 2, What is a Neural Network and How Do I Train One?
  • Chapter 3, Beyond Basic Neural Networks - Autoencoders and Restricted Boltzmann Machines
  • Chapter 4, CUDA - GPU-Accelerated Training