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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...

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.

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

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

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Patrick Barker Jan 09, 2020

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.

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Terry Smith Dec 06, 2021

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.

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

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Hands-On Deep Learning with Go

Introduction to Deep Learning in Go

Introducing DL

Why DL?

Overview of ML in Go

Using Gorgonia

Summary

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

What do you get with Print?

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Who is this book for?

What you will learn

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What do you get with Print?

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