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)

References/further reading

These papers are classical deep learning papers in this domain. Some of them document winning approaches to ImageNet competitions. I encourage you to download and read all of them. You may not understand them at first, but their importance will become more evident as you continue on your journey in deep learning.

  • Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. ImageNet Classification with Deep Convolutional Neural Networks. Advances in neural information processing systems. 2012.
  • Szegedy, Christian, et al. Going Deeper with Convolutions. Cvpr, 2015.
  • LeCun, Yann, et al. Learning Algorithms for Classification: A Comparison on Handwritten Digit Recognition. Neural networks: the statistical mechanics perspective 261 (1995): 276.
  • Zeiler, Matthew D., and Rob Fergus. Visualizing and Understanding Convolutional Networks. European conference on computer...