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

In this chapter, we developed some TensorFlow models. We looked at TensorBoard, which is a great tool for visualizing and debugging deep learning models. We built a couple of models using TensorFlow, including a basic regression model and a Lenet model for computer vision models. From these examples, we saw that programming in TensorFlow was more complicated and error-prone than using the higher-level APIs (MXNet and Keras) that we used elsewhere in this book.

We then moved onto using TensorFlow estimators, which is a much easier interface than using TensorFlow. We then used that script in another package called tfruns, which stands for TensorFlow runs. This package allows us to call a TensorFlow estimators or Keras script with different flags each time. We used this for hyper-parameter selection, running, and evaluating multiple models. The TensorFlow runs have excellent...