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

Cleaning text to remove noise

The next step we will take to prepare for text analysis is doing some preliminary cleaning. This is a common way to get started, regardless of what machine learning method will be applied later. When working with text, there are several terms and patterns that will not provide meaningful information. Some of these terms are generally not useful and steps to remove these pieces of text data can be used every time, while others will be more context-dependent.

As previously noted, there are collections of terms referred to as stop words. These terms have no information value and can usually be removed. To remove stop words from our data, we use the following code:

word_tokens <- word_tokens %>%
filter(!word %in% stop_words$word)

After running the preceding code, our row count goes down from 3.5 million to 1.7 million. In effect, our data (word_tokens...