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!

Maze Solving with Deep Q-Networks

Imagine for a moment that your data is not a discrete body of text or a carefully cleaned set of records from your organization's data warehouse. Perhaps you would like to train an agent to navigate an environment. How would you begin to solve this problem? None of the techniques that we have covered so far are suitable for such a task. We need to think about how we can train our model in quite a different way to make this problem tractable. Additionally, with use cases where the problem can be framed as an agent exploring and attaining a reward from an environment, from game playing to personalized news recommendations, Deep Q-Networks (DQNs) are useful tools in our arsenal of deep learning techniques.

Reinforcement learning (RL) has been described by Yann LeCun (who was instrumental in the development of Convolutional Neural Networks (CNNs...