Deep Learning with R for Beginners

Deep Learning with R for Beginners

eBook Details:

  • Paperback: 612 pages
  • Publisher: WOW! eBook (May 20, 2019)
  • Language: English
  • ISBN-10: 1838642706
  • ISBN-13: 978-1838642709

eBook Description:

Deep Learning with R for Beginners: Explore the world of neural networks by building powerful deep learning models using the R ecosystem

Deep learning finds practical applications in several domains, while R is the preferred language for designing and deploying deep learning models.

This Learning Path introduces you to the basics of deep learning and even teaches you to build a neural network model from scratch. As you make your way through the chapters, you’ll explore deep learning libraries and understand how to create deep learning models for a variety of challenges, right from anomaly detection to recommendation systems. The book will then help you cover advanced topics, such as generative adversarial networks (GANs), transfer learning, and large-scale deep learning in the cloud, in addition to model optimization, overfitting, and data augmentation. Through real-world projects, you’ll also get up to speed with training convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory networks (LSTMs) in R.

  • Implement credit card fraud detection with autoencoders
  • Train neural networks to perform handwritten digit recognition using MXNet
  • Reconstruct images using variational autoencoders
  • Explore the applications of autoencoder neural networks in clustering and dimensionality reduction
  • Create natural language processing (NLP) models using Keras and TensorFlow in R
  • Prevent models from overfitting the data to improve generalizability
  • Build shallow neural network prediction models

By the end of this Learning Path Deep Learning with R for Beginners, you’ll be well versed with deep learning and have the skills you need to implement a number of deep learning concepts in your research work or projects.


2 Responses

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