We will now explore a different kind of unsupervised learning technique that, in our example, is capable of working with data that reflects a given group of users' preferences for particular pieces of content. This section will introduce new concepts around network architecture and probability distributions, as well as how they can be used in practical implementations of recommendation systems, specifically for recommending films that a given user may find interesting.
Hands-On Deep Learning with Go
By :
Hands-On Deep Learning with Go
By:
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)
Preface
Free Chapter
Section 1: Deep Learning in Go, Neural Networks, and How to Train Them
Introduction to Deep Learning in Go
What Is a Neural Network and How Do I Train One?
Beyond Basic Neural Networks - Autoencoders and RBMs
CUDA - GPU-Accelerated Training
Section 2: Implementing Deep Neural Network Architectures
Next Word Prediction with Recurrent Neural Networks
Object Recognition with Convolutional Neural Networks
Maze Solving with Deep Q-Networks
Generative Models with Variational Autoencoders
Section 3: Pipeline, Deployment, and Beyond!
Building a Deep Learning Pipeline
Scaling Deployment
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