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Hands-On Deep Learning with Go
Hands-On Deep Learning with Go

Hands-On Deep Learning with Go: A practical guide to building and implementing neural network models using Go

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Profile Icon Seneque Profile Icon Chua
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$19.99 per month
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3 (2 Ratings)
Paperback Aug 2019 242 pages 1st Edition
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Arrow left icon
Profile Icon Seneque Profile Icon Chua
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Full star icon Full star icon Full star icon Empty star icon Empty star icon 3 (2 Ratings)
Paperback Aug 2019 242 pages 1st Edition
eBook
$24.99 $35.99
Paperback
$48.99
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Hands-On Deep Learning with Go

Introduction to Deep Learning in Go

This book will very quickly jump into the practicalities of implementing Deep Neural Networks (DNNs) in Go. Simply put, this book's title contains its aim. This means there will be a lot of technical detail, a lot of code, and (not too much) math. By the time you finally close this book or turn off your Kindle, you'll know how (and why) to implement modern, scalable DNNs and be able to repurpose them for your needs in whatever industry or mad science project you're involved in.

Our choice of Go reflects the maturing of the landscape of Go libraries built for the kinds of operations our DNNs perform. There is, of course, much debate about the trade-offs made when selecting languages or libraries, and we will devote a section of this chapter to our views and argue for the choices we've made.

However, what is code without context...

Introducing DL

We will now offer a high-level view of why DL is important and how it fits into the discussion about AI. Then, we will look at the historical development of DL, as well as current and future applications.

Why DL?

So, who are you, dear reader? Why are you interested in DL? Do you have your private vision for AI? Or do you have something more modest? What is your origin story?

In our survey of colleagues, teachers, and meetup acquaintances, the origin story of someone with a more formal interest in machines has a few common features. It doesn't matter much if you grew up playing games against the computer, an invisible enemy who sometimes glitched out, or if you chased down actual bots in id Software&apos...

Overview of ML in Go

This section will take a look at the ML ecosystem in Go, first discussing the essentials we want from a library, and then assessing each of the main Go ML libraries in turn.

Go's ML ecosystem has historically been quite limited. The language was introduced in 2009, well before the DL revolution that has brought many new programmers into the fold. You might assume that Go has seen the growth in libraries and tools that other languages have. History, instead, determined that many of the higher-level APIs for the mathematical operations that underpin our networks have appeared as Python libraries (or have complete Python bindings). There are numerous well-known examples of this, including PyTorch, Keras, TensorFlow, Theano, and Caffe (you get the idea).

Unfortunately, these libraries have either zero or incomplete bindings for Go. For example, TensorFlow...

Using Gorgonia

At the time of writing this book, there are two libraries that would typically be considered for DL in Go, TensorFlow and Gorgonia. However, while TensorFlow is definitely well regarded and has a full-featured API in Python, this is not the case in Go. As discussed previously, the Go bindings for TensorFlow are only suited to loading models that have already been created in Python, but not for creating models from scratch.

Gorgonia has been built from the ground up to be a Go library that is able to both train ML models and perform inference. This is a particularly valuable property, especially if you have an existing Go application or you are looking to build a Go application. Gorgonia allows you to develop, train, and maintain your DL model in your existing Go environment. For this book, we will be using Gorgonia exclusively to build models.

Before we go on to...

Summary

This chapter included a brief introduction to DL, both its history and applications. It was followed by a discussion of why Go is a great language for DL and demonstrated how the library we use in Gorgonia compares to other libraries in Go.

The next chapter will cover the magic that makes neural networks and DL work, which includes activation functions, network structure, and training algorithms.

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Key benefits

  • Gain a practical understanding of deep learning using Golang
  • Build complex neural network models using Go libraries and Gorgonia
  • Take your deep learning model from design to deployment with this handy guide

Description

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.

Who is this book for?

This book is for data scientists, machine learning engineers, and AI developers who want to build state-of-the-art deep learning models using Go. Familiarity with basic machine learning concepts and Go programming is required to get the best out of this book.

What you will learn

  • Explore the Go ecosystem of libraries and communities for deep learning
  • Get to grips with Neural Networks, their history, and how they work
  • Design and implement Deep Neural Networks in Go
  • Get a strong foundation of concepts such as Backpropagation and Momentum
  • Build Variational Autoencoders and Restricted Boltzmann Machines using Go
  • Build models with CUDA and benchmark CPU and GPU models

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Aug 08, 2019
Length: 242 pages
Edition : 1st
Language : English
ISBN-13 : 9781789340990
Vendor :
Google
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Product Details

Publication date : Aug 08, 2019
Length: 242 pages
Edition : 1st
Language : English
ISBN-13 : 9781789340990
Vendor :
Google
Category :
Languages :
Concepts :
Tools :

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Table of Contents

14 Chapters
Section 1: Deep Learning in Go, Neural Networks, and How to Train Them Chevron down icon Chevron up icon
Introduction to Deep Learning in Go Chevron down icon Chevron up icon
What Is a Neural Network and How Do I Train One? Chevron down icon Chevron up icon
Beyond Basic Neural Networks - Autoencoders and RBMs Chevron down icon Chevron up icon
CUDA - GPU-Accelerated Training Chevron down icon Chevron up icon
Section 2: Implementing Deep Neural Network Architectures Chevron down icon Chevron up icon
Next Word Prediction with Recurrent Neural Networks Chevron down icon Chevron up icon
Object Recognition with Convolutional Neural Networks Chevron down icon Chevron up icon
Maze Solving with Deep Q-Networks Chevron down icon Chevron up icon
Generative Models with Variational Autoencoders Chevron down icon Chevron up icon
Section 3: Pipeline, Deployment, and Beyond! Chevron down icon Chevron up icon
Building a Deep Learning Pipeline Chevron down icon Chevron up icon
Scaling Deployment Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
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(2 Ratings)
5 star 50%
4 star 0%
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1 star 50%
Patrick Barker Jan 09, 2020
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This book is a great overview to the tooling and methodologies of applying deep learning techniques in Go. It comes with easy to follow examples and gets you up and running with the right libraries.
Amazon Verified review Amazon
Terry Smith Dec 06, 2021
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
The very first NN code in the book actually didn't compile. One function returned the wrong data type and I had to overrule it. Also a comment was left uncommented.When I ran it it did not converge. Getting the actual code from github showed significant differences. That code compiled, ran and converged.The next adventure was the MINST code which was unrunnable because the people at gorgania, for whatever reason, removed the MINST code from their website. I got a 404.I will buy the next edition though. A little fixing and this book will be incredible.edit - Well, I kept going and the NEXT fiasco was the RBM on pages 81-93 had, after I keyed the whole thing into my JetBrains IDE had 81 errors. No, really.
Amazon Verified review Amazon
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