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!

Building a model in Gorgonia with CUDA support

Building a model in Gorgonia with CUDA support that we do a few things first. We need to install Gorgonia's cu interface to CUDA, and then have a model ready to train!

Installing CUDA support for Gorgonia

To make use of CUDA, you need a computer with a GPU made by NVIDIA. Unfortunately, setting up CUDA to work with Gorgonia is a slightly more involved process, as it involves setting up a C compiler environment to work with Go, as well as a C compiler environment that works with CUDA. NVIDIA has kindly ensured that its compiler works with the common toolchain for each platform: Visual Studio on Windows, Clang-LLVM on macOS, and GCC on Linux.

Installing CUDA and ensuring that...