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

Configuring a data generator

Similar to ARIMA, for our LSTM model, we would like the model to use lagging historical data to predict actual data at a given point in time. However, in order to feed this data forward to an LSTM model, we must format the data so that a given number of columns contain all the lagging values and one column contains the target value. In the past, this was a slightly tedious process, but now we can use a data generator to make this task much simpler. In our case, we will use a time-series generator that produces a tensor that we can use for our LSTM model.

The arguments we will include when generating our data are the data objects we will use along with the target. In this case, we can use the same data object as values for both arguments. The reason this is possible has to do with the next argument, called length, which configures the time steps to...