Deep Learning with R

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.

Offers a hands on approach to deep learning while explaining the theory and mathematical concepts in an intuitive manner Broadens the understanding of advanced neural networks including ConvNets and Sequence models Covers deep learning frameworks

Autorentext

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.

Inhalt

Introduction to Machine Learning.- Introduction to Neural Networks .- Deep Neural Networks I .- Initialization of Network Parameters.- Optimization.- Deep Neural Networks - II.- Convolutional Neural Networks (ConvNets).- Recurrent Neural Networks (RNN) or Sequence Models.- Epilogue.