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)

How do auto-encoders work?

Auto-encoders are a form of dimensionality reduction technique. When they are used in this manner, they mathematically and conceptually have similarities to other dimensionality reduction techniques such as PCA. Auto-encoders consist of two parts: an encoder which creates a representation of the data, and a decoder which tries to reproduce or predict the inputs. Thus, the hidden layers and neurons are not maps between an input and some other outcome, but are self (auto)-encoding. Given sufficient complexity, auto-encoders can simply learn the identity function, and the hidden neurons will exactly mirror the raw data, resulting in no meaningful benefit. Similarly, in PCA, using all the principal components also provides no benefit. Therefore, the best auto-encoder is not necessarily the most accurate one, but one that reveals some meaningful structure...