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)

Use case—using LIME for interpretability

Deep learning models are known to be difficult to interpret. Some approaches to model interpretability, including LIME, allow us to gain some insights into how the model came to its conclusions. Before we demonstrate LIME, I will show how different data distributions and / or data leakage can cause problems when building deep learning models. We will reuse the deep learning churn model from Chapter 4, Training Deep Prediction Models, but we are going to make one change to the data. We are going to introduce a bad variable that is highly correlated to the y value. We will only include this variable in the data used to train and evaluate the model. A separate test set from the original data will be kept to represent the data the model will see in production, this will not have the bad variable in it. The creation of this bad variable...