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!

Building a Deep Learning Pipeline

So far, for the various deep learning architectures we've discussed, we have assumed that our input data is static. We have had fixed sets of movie reviews, images, or text to process.

In the real world, whether your organization or project includes data from self-driving cars, IoT sensors, security cameras, or customer-product usage, your data generally changes over time. Therefore, you need a way of integrating this new data so that you can update your models. The structure of the data may change too, and in the case of customer or audience data, there may be new transformations you need to apply to the data. Also, dimensions may be added or removed in order to test whether they impact the quality of your predictions, are no longer relevant, or fall foul of privacy legislation. What do we do in these scenarios?

This is where a tool such...