Book Image

Hands-On Deep Learning with R

By : Michael Pawlus, Rodger Devine
Book Image

Hands-On Deep Learning with R

By: Michael Pawlus, Rodger Devine

Overview of this book

Deep learning enables efficient and accurate learning from a massive amount of data. This book will help you overcome a number of challenges using various deep learning algorithms and architectures with R programming. This book starts with a brief overview of machine learning and deep learning and how to build your first neural network. You’ll understand the architecture of various deep learning algorithms and their applicable fields, learn how to build deep learning models, optimize hyperparameters, and evaluate model performance. Various deep learning applications in image processing, natural language processing (NLP), recommendation systems, and predictive analytics will also be covered. Later chapters will show you how to tackle recognition problems such as image recognition and signal detection, programmatically summarize documents, conduct topic modeling, and forecast stock market prices. Toward the end of the book, you will learn the common applications of GANs and how to build a face generation model using them. Finally, you’ll get to grips with using reinforcement learning and deep reinforcement learning to solve various real-world problems. By the end of this deep learning book, you will be able to build and deploy your own deep learning applications using appropriate frameworks and algorithms.
Table of Contents (16 chapters)
1
Section 1: Deep Learning Basics
5
Section 2: Deep Learning Applications
12
Section 3: Reinforcement Learning

Creating an RBM

So far, we have extracted elements from text, added metadata, and created term clusters to discover latent topics. We will now identify latent features by using a deep learning model known as an RBM. As you may recall, we have discovered latent topics in the text by looking for term co-occurrence within a given window size. In this case, we will go back to using a neural network approach. The RBM is half the typical neural network. Instead of taking data through hidden layers to an output layer, the RBM model just takes the data to the hidden layers and this is the output. The end result is similar to factor analysis or principal component analysis. Here, we will begin the process of finding each of the 20 Newsgroups in the dataset and throughout the rest of this chapter, we will make modifications to the model to improve its performance.

To get started with building...