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Deep Learning with R for Beginners
Deep Learning with R for Beginners

Deep Learning with R for Beginners: Design neural network models in R 3.5 using TensorFlow, Keras, and MXNet

By Mark Hodnett , Joshua F. Wiley , Yuxi (Hayden) Liu , Pablo Maldonado
$15.99 per month
Book May 2019 pages 1st Edition

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Key benefits

  • Get to grips with the fundamentals of deep learning and neural networks
  • Use R 3.5 and its libraries and APIs to build deep learning models for computer vision and text processing
  • Implement effective deep learning systems in R with the help of end-to-end projects

Description

Deep learning has a range of 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 Learning Path 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.

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

Product Details

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Publication date : May 20, 2019
Length pages
Edition : 1st Edition
Language : English
ISBN-13 : 9781838642709
Category :
Languages :
Concepts :

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Product Details


Publication date : May 20, 2019
Length pages
Edition : 1st Edition
Language : English
ISBN-13 : 9781838642709
Category :
Languages :
Concepts :

Table of Contents

23 Chapters
Title Page Chevron down icon Chevron up icon
Copyright and Credits Chevron down icon Chevron up icon
About Packt Chevron down icon Chevron up icon
Contributors Chevron down icon Chevron up icon
Preface Chevron down icon Chevron up icon
1. Getting Started with Deep Learning Chevron down icon Chevron up icon
2. Training a Prediction Model Chevron down icon Chevron up icon
3. Deep Learning Fundamentals Chevron down icon Chevron up icon
4. Training Deep Prediction Models Chevron down icon Chevron up icon
5. Image Classification Using Convolutional Neural Networks Chevron down icon Chevron up icon
6. Tuning and Optimizing Models Chevron down icon Chevron up icon
7. Natural Language Processing Using Deep Learning Chevron down icon Chevron up icon
8. Deep Learning Models Using TensorFlow in R Chevron down icon Chevron up icon
9. Anomaly Detection and Recommendation Systems Chevron down icon Chevron up icon
10. Running Deep Learning Models in the Cloud Chevron down icon Chevron up icon
11. The Next Level in Deep Learning Chevron down icon Chevron up icon
12. Handwritten Digit Recognition using Convolutional Neural Networks Chevron down icon Chevron up icon
13. Traffic Signs Recognition for Intelligent Vehicles Chevron down icon Chevron up icon
14. Fraud Detection with Autoencoders Chevron down icon Chevron up icon
15. Text Generation using Recurrent Neural Networks Chevron down icon Chevron up icon
16. Sentiment Analysis with Word Embedding Chevron down icon Chevron up icon
1. Other Books You May Enjoy Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

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