R Deep Learning Essentials
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.
|Course Length||5 hours 6 minutes|
|Date Of Publication||29 Mar 2016|
|Getting started with deep feedforward neural networks|
|Common activation functions – rectifiers, hyperbolic tangent, and maxout|
|Training and predicting new data from a deep neural network|
|Use case – training a deep neural network for automatic classification|