Hands-On Deep Learning with Go

Apply modern deep learning techniques to build and train deep neural networks using Gorgonia

Key Features

• 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

Book 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

Who this book is 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.

Table of Contents

1. Introduction to Deep Learning in Go
2. What Is a Neural Network and How Do I Train One?
3. Beyond Basic Neural Networks - Autoencoders and RBMs
4. CUDA - GPU-Accelerated Training
5. Next Word Prediction with Recurrent Neural Networks
6. Object Recognition with Convolutional Neural Networks
7. Maze Solving with Deep Q-Networks
8. Generative Models with Variational Autoencoders
9. Building a Deep Learning Pipeline
10. Scaling Deployment

Gareth Seneque is a machine learning engineer with 11 years' experience of building and deploying systems at scale in the finance and media industries. He became interested in deep learning in 2014 and is currently building a search platform within his organization, using neuro-linguistic programming and other machine learning techniques to generate content metadata and drive recommendations. He has contributed to a number of open source projects, including CoREBench and Gorgonia. He also has extensive experience with modern DevOps practices, using AWS, Docker, and Kubernetes to effectively distribute the processing of machine learning workloads.

Darrell Chua is a senior data scientist with more than 10 years' experience. He has developed models of varying complexity, from building credit scorecards with logistic regression to creating image classification models for trading cards. He has spent the majority of his time working with in fintech companies, trying to bring machine learning technologies into the world of finance. He has been programming in Go for several years and has been working on deep learning models for even longer. Among his achievements is the creation of numerous business intelligence and data science pipelines that enable the delivery of a top-of-the-line automated underwriting system, producing near-instant approval decisions.