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)

Summary

This chapter covered topics that are critical to success in deep learning projects. These included the different types of evaluation metric that can be used to evaluate the model. We looked at some issues that can come up in data preparation, including if you only have a small amount of data to train on and how to create different splits in the data, that is, how to create proper train, test, and validation datasets. We looked at two important issues that can cause the model to perform poorly in production, different data distributions, and data leakage. We saw how data augmentation can be used to improve an existing model by creating artificial data and looked at tuning hyperparameters in order to improve the performance of a deep learning model. We closed the chapter by examining a use case where we simulated a problem with different data distributions/data leakage and...