Seneque Gareth - Hands-On Deep Learning with Go a practical guide to building and implementing neural network models using Go

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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.

Xuanyi Chew is the primary author of Gorgonia. In his day job, he is the chief data scientist of a rapidly growing local start-up in Sydney . At night, he works on his hobbies of building deep learning AI (using Gorgonia), furthering his hopes of one day building an AGI. He wants to make Go the primary ecosystem for machine learning work and would love your help.

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Go is an open source programming language designed by Google to handle huge projects efficiently. It makes building reliable, simple, and efficient software straightforward and easy.

This book immediately jumps into the practicalities of implementing Deep Neural Networks (DNNs) in Go. Simply put, the 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 the 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.

This book is for data scientists, machine learning engineers, and deep learning aspirants who are looking to inject deep learning into their Go applications. Familiarity with machine learning and basic Golang code is expected in order to get the most out of this book.

, Introduction to Deep Learning in Go , introduces the history and applications of deep learning. This chapter also gives an overview of ML with Go.

, What is a Neural Network and How Do I Train One? , covers how to build a simple neural network and how to inspect a graph, as well as many of the commonly used activation functions. This chapter also discusses some of the different options for gradient descent algorithms and optimizations for your neural network.

, Beyond Basic Neural Networks Autoencoders and RBMs , shows how to build a simple multilayer neural network and an autoencoder. This chapter also explores the design and implementation of a probabilistic graphical model, an RBM, used in an unsupervised manner to create a recommendation engine for films.