项目作者: TrainingByPackt

项目描述 :
Design neural network models in R 3.5 using TensorFlow, Keras, and MXNet
高级语言: R
项目地址: git://github.com/TrainingByPackt/Deep-Learning-with-R-for-Beginners.git
创建时间: 2019-05-07T10:46:09Z
项目社区:https://github.com/TrainingByPackt/Deep-Learning-with-R-for-Beginners

开源协议:MIT 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