Book Image

Hands-On Deep Learning with Go

By : Gareth Seneque, Darrell Chua
Book Image

Hands-On Deep Learning with Go

By: Gareth Seneque, Darrell Chua

Overview of this book

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.
Table of Contents (15 chapters)
Free Chapter
1
Section 1: Deep Learning in Go, Neural Networks, and How to Train Them
6
Section 2: Implementing Deep Neural Network Architectures
11
Section 3: Pipeline, Deployment, and Beyond!

Scaling Deployment

Now that we've been introduced to a tool that manages data pipelines, it's time to peer completely under the hood. Our models ultimately run on the kinds of hardware we talked about in Chapter 5, Next Word Prediction with Recurrent Neural Networks, abstracted through many layers of software until we get to the point where we can use code such as go build --tags=cuda.

Our deployment of the image recognition pipeline built on top of Pachyderm was local. We did it in a way that was functionally identical to deploying it to cloud resources, without getting into the detail of what that would look like. This detail will now be our focus.

By the end of this chapter, you should be able to do the following:

  • Identify and understand cloud resources, including those specific to our platform example (AWS)
  • Know how to migrate your local deployment to the cloud
  • ...