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? Why do we care about this seemingly convoluted mix of linear algebra, calculus, statistics, and probability? Why use computers to recognize things in images or identify aberrant patterns in financial data? And, perhaps most importantly, what do the approaches to these tasks have in common? The initial sections of this book will try to provide some of this context.

Scientific endeavor, when broken up into the disciplines that represent their institutional and industry specialization, is governed by an idea of progress. By this, we mean a kind of momentum, a moving forward, toward some end. For example, the ideal goal of medicine is to be able to identify and cure any ailment or disease. Physicists aim to understand completely the fundamental laws of nature. Progress trends in this general direction. Science is itself an optimization method. So, what might the ultimate goal of Machine Learning (ML) be?

We'll be upfront. We think it's the creation of Artificial General Intelligence (AGI). That's the prize: a general-purpose learning computer to take care of the jobs and leave life to people. As we will see when we cover the history of Deep Learning (DL) in detail, founders of the top Artificial Intelligence (AI) labs agree that AGI represents a meta-solution to many of the complex problems in our world today, from economics to medicine to government.

This chapter will cover the following topics:

• Why DL?
• DL—history applications
• Overview of ML in Go 
• Using Gorgonia