https://github.com/trainingbypackt/deep-learning-with-r-for-beginners

Design neural network models in R 3.5 using TensorFlow, Keras, and MXNet
https://github.com/trainingbypackt/deep-learning-with-r-for-beginners

computer-vision deep-learning keras machine-learning mxnet r

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Design neural network models in R 3.5 using TensorFlow, Keras, and MXNet

• Host: GitHub
• URL: https://github.com/trainingbypackt/deep-learning-with-r-for-beginners
• Owner: TrainingByPackt
• License: mit
• Created: 2019-05-07T10:46:09.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2019-05-17T06:29:17.000Z (over 5 years ago)
• Last Synced: 2023-03-02T20:56:42.954Z (almost 2 years ago)
• Topics: computer-vision, deep-learning, keras, machine-learning, mxnet, r
• Language: R
• Size: 10.1 MB
• Stars: 2
• Watchers: 2
• Forks: 8
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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# Deep Learning with R for Beginners

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.

By the end of this Learning Path, 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.

Deep Learning with R for Beginners  by **Joshua F. Wiley**, **Mark Hodnett**, **Yuxi (Hayden) Liu** and **Pablo Maldonado**

## What you will learn

* 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

### Hardware requirements

For an optimal student experience, we recommend the following hardware configuration:

* **Processor**: Intel Core i7 or equivalent

* **Memory**: 8 GB RAM

* **Storage**: 15 GB available space

### Software requirements

You’ll also need the following software installed in advance:

* **Operating system**: Windows 7, 8.1 or 10 64-bit, macOS High Sierra or Linux

* **Browser**: Google Chrome, Latest Version