Dropout is a relatively novel approach to regularization that is particularly valuable for large and complex deep neural networks. For a much more detailed exploration of dropout in deep neural networks, see Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. (2014). The concept behind dropout is actually quite straightforward. During the training of the model, units (for example, inputs, hidden neurons, and so on) are probabilistically dropped along with all connections to and from them. For example, Figure 3.3 is an example of what might happen at each step of training for a model where hidden neurons and their connections are probabilistically dropped with a probability of 1/3. The grayed out and dashed neurons and connections are the ones that were dropped. Importantly, it is not that some neurons are dropped during the entirety of training, but that they are only dropped for a step/update...
R Deep Learning Essentials
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R Deep Learning Essentials
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Overview of this book
<p>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.</p>
<p>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.</p>
<p>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.</p>
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Table of Contents (14 chapters)
R Deep Learning Essentials
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Free Chapter
Getting Started with Deep Learning
Training a Prediction Model
Preventing Overfitting
Identifying Anomalous Data
Training Deep Prediction Models
Tuning and Optimizing Models
Bibliography
Index
Customer Reviews