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Hands-On Deep Learning with R

You're reading from Hands-On Deep Learning with R A practical guide to designing, building, and improving neural network models using R

Product type Paperback

Published in Apr 2020

Publisher Packt

ISBN-13 9781788996839

Length 330 pages

Edition 1st Edition

Languages

R

Tools

MXNet

Concepts

Deep Learning

Authors (2):

Rodger Devine

Michael Pawlus

View More author details

Table of Contents (16) Chapters

Preface

1. Section 1: Deep Learning Basics

2. Machine Learning Basics FREE CHAPTER

3. Setting Up R for Deep Learning

4. Artificial Neural Networks

5. Section 2: Deep Learning Applications

6. CNNs for Image Recognition

7. Multilayer Perceptron for Signal Detection

8. Neural Collaborative Filtering Using Embeddings

9. Deep Learning for Natural Language Processing

10. Long Short-Term Memory Networks for Stock Forecasting

11. Generative Adversarial Networks for Faces

12. Section 3: Reinforcement Learning

13. Reinforcement Learning for Gaming

14. Deep Q-Learning for Maze Solving

15. Other Books You May Enjoy

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Exploring MXNet

MXNet is a deep learning library designed by the Apache Software Foundation. It supports both imperative and symbolic programming. It is designed for speed by serializing functions with dependencies while running functions with no dependencies in parallel. It provides compatibility with CPU and GPU processors.

Available functions

MXNet provides the means to run a very extensive list of deep learning methods, including the following:

CNN
RNN
GAN
LSTM
Autoencoders
RBM/DBN
Reinforcement learning

Getting started with MXNet

For MXNet, we will use the same...

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Authors (2)

Pawlus

Pawlus

Michael Pawlus is a data scientist at The Ohio State University where he is currently part of the team building of the data science infrastructure for the Advancement department while also leading the implementation of innovative projects there. Prior to this, Michael was a data scientist at the University of Southern California. In addition to this work, Michael has chaired data science education conferences, published articles on the role of data science within fundraising and currently serves on committees where he is focused on providing a wider variety of educational offerings as well as increasing the diversity of content creators in this space. Michael holds degrees from Grand Valley State University and the University of Sheffield.

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Rodger Devine

Rodger Devine

Rodger Devine is the Associate Dean of External Affairs for Strategy and Innovation at the USC Dornsife College of Letters, Arts, and Sciences. Rodger's portfolio includes advancement operations, BI, leadership annual giving, program innovation, prospect development, and strategic information management. Prior to USC, Rodger served as the Director of Information, Analytics, and Annual Giving at the Michigan Ross School of Business. Rodger brings nearly 20 years of experience in software engineering, IT operations, BI, project management, organizational development, and leadership. Rodger completed his Masters in data science at the University of Michigan and is a doctoral student in the OCL program at the USC Rossier School of Education.

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