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!

Vanilla RNNs

According to their more utopian description, RNNs are able to do something that the networks we've covered so far cannot: remember. More precisely, in a simple network with a single hidden layer, the network's output, as well as the state of that hidden layer, are combined with the next element in a training sequence to form the input for a new network (with its own trainable, hidden state). A vanilla RNN can be visualized as follows:

Let's unpack this a bit. The two networks in the preceding diagram are two different representations of the same thing. One is in a Rolled state, which is simply an abstract representation of the computation graph, where an infinite number of timesteps is represented by (t). We then use the Unrolled RNN as we feed the network data and train it.

For a given forward pass, this network takes two inputs, where X is a representation...