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Deep learning with R

By: Ghatak, Abhijit Material type: Text

TextPublication details: Switzerland Springer 2019 Description: xxiii, 245 pISBN: 9789811358494Subject(s): Machine learning | R (Computer program language) | Artificial intelligence | Computer programming | Computer science--MathematicsDDC classification: 006.31 Summary: About this book 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.

List(s) this item appears in: IT & Decision Sciences | Finance & Accounting

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Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Book	Indian Institute of Management LRC General Stacks	IT & Decisions Sciences	006.31 GHA (Browse shelf(Opens below))	1	Available		002784

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Previous	006.31 FEN Machine learning with Python for everyone	006.31 FOR Data smart: using data science to transform Information into insight	006.31 FOR Applied machine learning	006.31 GHA Deep learning with R	006.31 GIA Explainable AI with python	006.31 GRI Time series forecasting using deep learning: combining PyTorch, RNN, TCN and deep neural network models to provide production-ready prediction solutions	006.31 KEL Fundamentals of machine learning for predictive data analytics:	Next

About this book
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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