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Deep Learning with R [electronic resource] /

By: Ghatak, Abhijit [author.].
Contributor(s): SpringerLink (Online service).
Material type: materialTypeLabelBookPublisher: Singapore : Springer Singapore : Imprint: Springer, 2019Description: XXIII, 245 p. 100 illus., 83 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9789811358500.Subject(s): Computer Science | Artificial intelligence | Computer science | Mathematical statistics | Artificial Intelligence | Mathematics of Computing | Programming Techniques | Statistics and Computing/Statistics ProgramsOnline resources: Click here to access online
Contents:
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.
In: Springer eBooksSummary: 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. .
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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.

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. .

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