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

Preparing and preprocessing data

When working with time-series data, there are a number of data type formats to choose from and use for conversion. We have already used two of these formats, of which there are three that are most widely used. Let's briefly review these data types before moving on to our deep learning model.

When we wanted to add actual data as an overlay to our ARIMA model plot, we used the ts function to create a time-series data object. For this object, the index values must be integers. In the case of using the autolayer function with the arima plot, a time-series data object is required. This is one of the more simple time-series data types and it will look like a vector in your Environment tab. However, this only works with regular time series.

Another data type is zoo. The zoo data type will work with regular and irregular time series, and...