Book Image

R Deep Learning Essentials

By : Joshua F. Wiley
Book Image

R Deep Learning Essentials

By: Joshua F. Wiley

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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R Deep Learning Essentials
Joshua F. Wiley
Mar, 2016 | 170 pages
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Table of Contents (14 chapters)
R Deep Learning Essentials
R Deep Learning Essentials
Credits
Credits
About the Author
About the Author
About the Reviewer
About the Reviewer
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Getting Started with Deep Learning
Getting Started with Deep Learning
What is deep learning?
R packages for deep learning
Connecting R and H2O
Summary
2
Training a Prediction Model
Training a Prediction Model
Neural networks in R
The problem of overfitting data – the consequences explained
Use case – build and apply a neural network
Summary
3
Preventing Overfitting
Preventing Overfitting
L1 penalty
L2 penalty
Ensembles and model averaging
Use case – improving out-of-sample model performance using dropout
Summary
4
Identifying Anomalous Data
Identifying Anomalous Data
Getting started with unsupervised learning
How do auto-encoders work?
Training an auto-encoder in R
Use case – building and applying an auto-encoder model
Fine-tuning auto-encoder models
Summary
5
Training Deep Prediction Models
Training Deep Prediction Models
Getting started with deep feedforward neural networks
Common activation functions – rectifiers, hyperbolic tangent, and maxout
Picking hyperparameters
Training and predicting new data from a deep neural network
Use case – training a deep neural network for automatic classification
Summary
6
Tuning and Optimizing Models
Tuning and Optimizing Models
Dealing with missing data
Solutions for models with low accuracy
Summary
Bibliography
Bibliography
Index
Index

Appendix A. Bibliography

The following are the references for all the citations throughout the book:

  • Anguita, D., Ghio, A., Oneto, L., Parra, X., and Reyes-Ortiz, J. L. (2013). A Public Domain Dataset for Human Activity Recognition Using Smartphones. 21th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013. Bruges (Belgium), 24-26 April 2013.

  • Bengio, Y. (2012). Practical Recommendations for Gradient-Based Training of Deep Architectures. In Neural Networks: Tricks of the Trade (pp. 437-478). Springer Berlin Heidelberg. (Also on the arXiv: http://arxiv.org/pdf/1206.5533.pdf)

  • Bengio, Y., Courville, A., and Vincent, P. (2013). Representation Learning: A Review and New Perspectives. Pattern Analysis and Machine Intelligence, IEEE Transactions, 35(8), 1798-1828.

  • Bergmeir, C., and Benítez, J. M. (2012). Neural Networks in R Using the Stuttgart Neural Network Simulator: RSNNS. Journal of Statistical Software, 46(7), 1-26.

  • Bishop, C. M. (2006). Pattern Recognition and Machine Learning, Springer.

  • Goodfellow, I. J., Warde-Farley, D., Mirza, M., Courville, A., and Bengio, Y. (2013). Maxout Networks. arXiv preprint arXiv:1302.4389.

  • Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Second Edition. Springer.

  • Hinton, G. E., Osindero, S., and Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18 (7), 1527-1554.

  • Kuhn, M. (2008). Building Predictive Models in R Using the caret Package. Journal of Statistical Software, 28 (5), 1-26.

  • Kuhn, M. and Johnson, K. (2013). Applied Predictive Modeling. New York: Springer.

  • Lichman, M. (2013). UCI Machine Learning Repository (http://archive.ics.uci.edu/ml). Irvine, CA: University of California, School of Information and Computer Science.

  • Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective. MIT press.

  • Nair, V., and Hinton, G. E. (2010). Rectified Linear Units Improve Restricted Boltzmann Machines. In Proceedings of the 27th International Conference on Machine Learning (ICML-10) (pp. 807-814).

  • Riedmiller, M., and Braun, H. (1993). A Direct Adaptive Method for Faster Backpropagation Learning: The RPROP Algorithm. In Neural Networks, 1993, IEEE International Conference.

  • Schmidhuber, J. (2015). Deep Learning in Neural Networks: An Overview. Neural Networks, 61, 85-117.

  • Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. (2014). Dropout: A Simple Way to Prevent Neural Networks from Overfitting. The Journal of Machine Learning Research, 15(1), 1929-1958.

  • Venables, W. N. and Ripley, B. D. (2002). Modern Applied Statistics with S-Plus. Fourth Edition. Springer.

  • Vincent, P., Larochelle, H., Bengio, Y., and Manzagol, P. A. (2008, July). Extracting and Composing Robust Features with Denoising Autoencoders. In Proceedings of the 25th International Conference on Machine Learning (pp. 1096-1103). ACM.

  • Zeiler, M. D. (2012). ADADELTA: An Adaptive Learning Rate Method. arXiv preprint arXiv:1212.5701.

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