We have almost come to the end of our journey in deep learning with R. This chapter is a bit of a mixed bag of topics. We will begin this chapter by revisiting an image classification task and building a complete image classification solution image files rather than tabular data. We will then move on to explaining transfer learning, where you can use an existing model on a new dataset. Next we discuss an important consideration in any machine learning project - how will your model be used in deployment, that is, production? We will show how to create a REST API that allows any programming language to call a deep learning model in R to predict on new data. We will then move on to briefly discussing two other deep learning topics: Generative Adversarial Networks and reinforcement learning. Finally, we will close this chapter and the book by providing...

#### R Deep Learning Essentials. - Second Edition

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#### R Deep Learning Essentials. - Second Edition

##### By:

#### 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)

Preface

Free Chapter

Getting Started with Deep Learning

Training a Prediction Model

Deep Learning Fundamentals

Training Deep Prediction Models

Image Classification Using Convolutional Neural Networks

Tuning and Optimizing Models

Natural Language Processing Using Deep Learning

Deep Learning Models Using TensorFlow in R

Anomaly Detection and Recommendation Systems

Running Deep Learning Models in the Cloud

The Next Level in Deep Learning

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