Book Image

R Deep Learning Essentials. - Second Edition

By : Mark Hodnett, Joshua F. Wiley
Book Image

R Deep Learning Essentials. - Second Edition

By: Mark Hodnett, Joshua F. Wiley

Overview of this book

Deep learning is a powerful subset of machine learning that is very successful in domains such as computer vision and natural language processing (NLP). This second edition of R Deep Learning Essentials will open the gates for you to enter the world of neural networks by building powerful deep learning models using the R ecosystem. This book will introduce you to the basic principles of deep learning and teach you to build a neural network model from scratch. As you make your way through the book, you will explore deep learning libraries, such as Keras, MXNet, and TensorFlow, and create interesting deep learning models for a variety of tasks and problems, including structured data, computer vision, text data, anomaly detection, and recommendation systems. You’ll cover advanced topics, such as generative adversarial networks (GANs), transfer learning, and large-scale deep learning in the cloud. In the concluding chapters, you will learn about the theoretical concepts of deep learning projects, such as model optimization, overfitting, and data augmentation, together with other advanced topics. By the end of this book, you will be fully prepared and able to implement deep learning concepts in your research work or projects.
Table of Contents (13 chapters)

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.

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