R Deep Learning Essentials

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 high-level abstractions in data by using model architectures. With the superb memory management and the full integration with multi-node 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 real-life 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