Book Image

Deep Learning with R for Beginners

By : Mark Hodnett, Joshua F. Wiley, Yuxi (Hayden) Liu, Pablo Maldonado
Book Image

Deep Learning with R for Beginners

By: Mark Hodnett, Joshua F. Wiley, Yuxi (Hayden) Liu, Pablo Maldonado

Overview of this book

Deep learning has a range of practical applications in several domains, while R is the preferred language for designing and deploying deep learning models. This Learning Path introduces you to the basics of deep learning and even teaches you to build a neural network model from scratch. As you make your way through the chapters, you’ll explore deep learning libraries and understand how to create deep learning models for a variety of challenges, right from anomaly detection to recommendation systems. The Learning Path will then help you cover advanced topics, such as generative adversarial networks (GANs), transfer learning, and large-scale deep learning in the cloud, in addition to model optimization, overfitting, and data augmentation. Through real-world projects, you’ll also get up to speed with training convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory networks (LSTMs) in R. By the end of this Learning Path, you’ll be well-versed with deep learning and have the skills you need to implement a number of deep learning concepts in your research work or projects.
Table of Contents (23 chapters)
Title Page
Copyright and Credits
About Packt
Contributors
Preface
Index

Tuning hyperparameters


All machine learning algorithms have hyper-parameters or settings that can change how they operate. These hyper-parameters can improve the accuracy of a model or reduce the training time. We have seen some of these hyper-parameters in previous chapters, particularly Chapter 3, Deep Learning Fundamentals, where we looked at the hyper-parameters that can be set in the mx.model.FeedForward.create function. The techniques in this section can help us find better values for the hyper-parameters.

Selecting hyper-parameters is not a magic bullet; if the raw data quality is poor or if there is not enough data to support training, then tuning hyper-parameters will only get you so far. In these cases, either acquiring additional variables/features that can be used as predictors and/or additional cases may be required.

Grid search

For more information on tuning hyper-parameters, see Bengio, Y. (2012), particularly Section 3, Hyperparameters, which discusses the selection and characteristics...