Deep Learning with R Cookbook

Deep Learning with R Cookbook

eBook Details:

  • Paperback: 328 pages
  • Publisher: WOW! eBook (February 21, 2020)
  • Language: English
  • ISBN-10: 1789805678
  • ISBN-13: 978-1789805673

eBook Description:

Deep Learning with R Cookbook: Tackle the complex challenges faced while building end-to-end deep learning models using modern R libraries

Deep learning (DL) has evolved in recent years with developments such as generative adversarial networks (GANs), variational autoencoders (VAEs), and deep reinforcement learning. This book will get you up and running with R 3.5.x to help you implement DL techniques.

The book starts with the various DL techniques that you can implement in your apps. A unique set of recipes will help you solve binomial and multinomial classification problems, and perform regression and hyperparameter optimization. To help you gain hands-on experience of concepts, the book features recipes for implementing convolutional neural networks (CNNs), recurrent neural networks (RNNs), and Long short-term memory (LSTMs) networks, as well as sequence-to-sequence models and reinforcement learning. You’ll then learn about high-performance computation using GPUs, along with learning about parallel computation capabilities in R. Later, you’ll explore libraries, such as MXNet, that are designed for GPU computing and state-of-the-art DL. Finally, you’ll discover how to solve different problems in NLP, object detection, and action identification, before understanding how to use pre-trained models in DL apps.

  • Work with different datasets for image classification using CNNs
  • Apply transfer learning to solve complex computer vision problems
  • Use RNNs and their variants such as LSTMs and Gated Recurrent Units (GRUs) for sequence data generation and classification
  • Implement autoencoders for DL tasks such as dimensionality reduction, denoising, and image colorization
  • Build deep generative models to create photorealistic images using GANs and VAEs
  • Use MXNet to accelerate the training of DL models through distributed computing

By the end of this Deep Learning with R Cookbook book, you’ll have comprehensive knowledge of DL and DL packages, and be able to develop effective solutions for different DL problems.


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