Key Features

Harness the ability to build algorithms for unsupervised data using deep learning concepts with R

Master the common problems faced such as overfitting of data, anomalous datasets, image recognition, and performance tuning while building the models

Build models relating to neural networks, prediction and deep prediction
Book Description
Deep learning is a branch of machine learning based on a set of algorithms that attempt to model highlevel abstractions in data by using model architectures. With the superb memory management and the full integration with multinode big data platforms, the H2O engine has become more and more popular among data scientists in the field of deep learning.
This book will introduce you to the deep learning package H2O with R and help you understand the concepts of deep learning. We will start by setting up important deep learning packages available in R and then move towards building models related to neural networks, prediction, and deep prediction, all of this with the help of reallife examples.
After installing the H2O package, you will learn about prediction algorithms. Moving ahead, concepts such as overfitting data, anomalous data, and deep prediction models are explained. Finally, the book will cover concepts relating to tuning and optimizing models.
What you will learn

Set up the R package H2O to train deep learning models

Understand the core concepts behind deep learning models

Use Autoencoders to identify anomalous data or outliers

Predict or classify data automatically using deep neural networks

Build generalizable models using regularization to avoid overfitting the training data
About the Author
Dr. Joshua F. Wiley is a lecturer at Monash University and a senior partner at Elkhart Group Limited, a statistical consultancy. He earned his PhD from the University of California, Los Angeles. His research focuses on using advanced quantitative methods to understand the complex interplays of psychological, social, and physiological processes in relation to psychological and physical health. In statistics and data science, Joshua focuses on biostatistics and is interested in reproducible research and graphical displays of data and statistical models. Through consulting at Elkhart Group Limited and his former work at the UCLA Statistical Consulting Group, Joshua has helped a wide array of clients, ranging from experienced researchers to biotechnology companies. He develops or codevelops a number of R packages including varian, a package to conduct Bayesian scalelocation structural equation models, and MplusAutomation, a popular package that links R to the commercial Mplus software.
Table of Contents

Getting Started with Deep Learning

Training a Prediction Model

Preventing Overfitting

Identifying Anomalous Data

Training Deep Prediction Models

Tuning and Optimizing Models

Bibliography