TY  - BOOK
AU  - Ghatak,Abhijit
TI  - Deep Learning with R
SN  - 9789811358500
AV  - Q334-342 
U1  - 006.3 23
PY  - 2019///
CY  - Singapore
PB  - Springer Singapore, Imprint: Springer
KW  - Artificial intelligence
KW  - Computer science—Mathematics
KW  - Computer programming
KW  - Statistics 
KW  - Artificial Intelligence
KW  - Mathematics of Computing
KW  - Programming Techniques
KW  - Statistics and Computing/Statistics Programs
N1  -  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
N2  - 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.
UR  - https://doi.org/10.1007/978-981-13-5850-0
ER  -