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)

What is unsupervised learning?

So far, we have focused on models and techniques that broadly fall under the category of supervised learning. Supervised learning is supervised because the task is for the machine to learn the relationship between a set of variables or features and one or more outcomes. For example, in Chapter 4, Training Deep Prediction Models, we wanted to predict whether someone would visit a store in the next 14 days. In this chapter, we will delve into methods of unsupervised learning. In contrast with supervised learning, where there is an outcome variable(s) or labeled data is being used, unsupervised learning does not use any outcomes or labeled data. Unsupervised learning uses only input features for learning. A common example of unsupervised learning is cluster analysis, such as k-means clustering, where the machine learns hidden or latent clusters in the...