Book Image

R Deep Learning Essentials. - Second Edition

By : Mark Hodnett, Joshua F. Wiley
Book Image

R Deep Learning Essentials. - Second Edition

By: Mark Hodnett, Joshua F. Wiley

Overview of this book

Deep learning is a powerful subset of machine learning that is very successful in domains such as computer vision and natural language processing (NLP). This second edition of R Deep Learning Essentials will open the gates for you to enter the world of neural networks by building powerful deep learning models using the R ecosystem. This book will introduce you to the basic principles of deep learning and teach you to build a neural network model from scratch. As you make your way through the book, you will explore deep learning libraries, such as Keras, MXNet, and TensorFlow, and create interesting deep learning models for a variety of tasks and problems, including structured data, computer vision, text data, anomaly detection, and recommendation systems. You’ll cover advanced topics, such as generative adversarial networks (GANs), transfer learning, and large-scale deep learning in the cloud. In the concluding chapters, you will learn about the theoretical concepts of deep learning projects, such as model optimization, overfitting, and data augmentation, together with other advanced topics. By the end of this book, you will be fully prepared and able to implement deep learning concepts in your research work or projects.
Table of Contents (13 chapters)

Deep Learning Fundamentals

In the previous chapter, we created some machine learning models using neural network packages in R. This chapter will look at some of the fundamentals of neural networks and deep learning by creating a neural network using basic mathematical and matrix operations. This application sample will be useful for explaining some key parameters in deep learning algorithms and some of the optimizations that allow them to train on large datasets. We will also demonstrate how to evaluate different hyper-parameters for models to find the best set. In the previous chapter, we briefly looked at the problem of overfitting; this chapter goes into that topic in more depth and looks at how you can overcome this problem. It includes an example use case using dropout, the most common regularization technique in deep learning.

This chapter covers the following topics:

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