Deep Learning with R

1. Front Matter

   Pages i-xxiii

   PDF

2. Introduction to Machine Learning

   • Abhijit Ghatak

   Pages 1-21

3. Introduction to Neural Networks

   • Abhijit Ghatak

   Pages 23-63

4. Deep Neural Networks-I

   • Abhijit Ghatak

   Pages 65-86

5. Initialization of Network Parameters

   • Abhijit Ghatak

   Pages 87-102

6. Optimization

   • Abhijit Ghatak

   Pages 103-147

7. Deep Neural Networks-II

   • Abhijit Ghatak

   Pages 149-170

8. Convolutional Neural Networks (ConvNets)

   • Abhijit Ghatak

   Pages 171-206

9. Recurrent Neural Networks (RNN) or Sequence Models

   • Abhijit Ghatak

   Pages 207-237

10. Epilogue

    • Abhijit Ghatak

    Pages 239-241

11. Back Matter

    Pages 243-245

    PDF

 Deep Learning with R introduces deep learning and neural networks using the R programming language. The book builds on the understanding of the theoretical and mathematical constructs and enables the reader to create applications on computer vision, natural language processing and transfer learning.  

The book starts with an introduction to machine learning and moves on to describe the basic architecture, different activation functions, forward propagation, cross-entropy loss and backward propagation of a simple neural network. It goes on to create different code segments to construct deep neural networks. It discusses in detail the initialization of network parameters, optimization techniques, and some of the common issues surrounding neural networks such as dealing with NaNs and the vanishing/exploding gradient problem. Advanced variants of multilayered perceptrons namely, convolutional neural networks and sequence models are explained, followed by application to different use cases. The book makes extensive use of the Keras and TensorFlow frameworks. 

• Artificial Intelligence
• Statistics
• Deep neural networks
• Regularization and hyper-parameter tuning
• Convolutional neural networks and sequence models

“This is a very useful book in the domain of deep learning and the author has done a great job of bringing all the paradigms and libraries together to illustrate how they work for real big data. I am glad to have this book on my shelf.” (Anna Bartkowiak, ISCB News, Vol. 68, December, 2019)

• Kolkata, India

  Abhijit Ghatak

Abhijit Ghatak is a Data Scientist and holds an M.E. in Engineering and M.S. in Data Science from Stevens Institute of Technology, USA. He began his career as a submarine engineer officer in the Indian Navy and worked on various data-intensive projects involving submarine operations and construction. Thereafter he has worked in academia, technology companies and as a research scientist in the area of Internet of Things (IoT) and pattern recognition for the European Union (EU). He has published several papers in the areas of engineering and machine learning and is currently a consultant in the area of machine learning and deep learning. His research interests include IoT, stream analytics and design of deep learning systems.

Policies and ethics