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)

Image classification models

We covered image classification in Chapter 5, Image Classification Using Convolutional Neural Networks. In that chapter, we described convolutional and pooling layers that are essential for deep learning tasks involving images. We also built a number of models on a simple dataset, the MNIST dataset. Here, we are going to look at some advanced topics in image classification. First, we will build a complete image classification model using image files as input. We will look at callbacks, which are a great aid in building complex deep learning models. A call-back function will be used to persist (save) a model to file, which will be loaded back later. We then use this model in our next example, which is transfer learning. This is where you use some of the layers in a pre-trained model on new data.

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