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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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Hands-On Deep Learning with Go

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
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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
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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
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Google
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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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Frequently bought together


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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
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
(2 Ratings)
5 star 50%
4 star 0%
3 star 0%
2 star 0%
1 star 50%
Patrick Barker Jan 09, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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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