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R Machine Learning Projects

You're reading from  R Machine Learning Projects

Product type Book
Published in Jan 2019
Publisher Packt
ISBN-13 9781789807943
Pages 334 pages
Edition 1st Edition
Languages
Author (1):
Dr. Sunil Kumar Chinnamgari Dr. Sunil Kumar Chinnamgari
Profile icon Dr. Sunil Kumar Chinnamgari

Table of Contents (12) Chapters

Preface 1. Exploring the Machine Learning Landscape 2. Predicting Employee Attrition Using Ensemble Models 3. Implementing a Jokes Recommendation Engine 4. Sentiment Analysis of Amazon Reviews with NLP 5. Customer Segmentation Using Wholesale Data 6. Image Recognition Using Deep Neural Networks 7. Credit Card Fraud Detection Using Autoencoders 8. Automatic Prose Generation with Recurrent Neural Networks 9. Winning the Casino Slot Machines with Reinforcement Learning 10. The Road Ahead
11. Other Books You May Enjoy

Introduction to the MXNet framework

MXNet is a super-powerful open source deep learning framework that is built to ease the development of deep learning algorithms. It is used to define, train, and deploy deep neural networks. MXNet is lean, flexible, and ultra-scalable, that is, it allows fast model training and supports a flexible programming model with multiple languages. The problem with existing deep learning frameworks, such as Torch7, Theano, and Caffe, is that users need to learn another system or a different programming flavor.

However, MXNet resolves this issue by supporting multiple languages, such as C++, Python, R, Julia, and Perl. This eliminates the need for users to learn a new language; therefore, they can use the framework and simplify network definitions. MXNet models are able to fit in small amounts of memory and they can be trained on CPUs, GPUs, and on multiple...

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