Explore Products

Best Sellers

New Releases

Books

Videos

Audiobooks

Learning Hub

Free Learning

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

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

Leave a review - let other readers know what you think

Exploring ReinforcementLearning and RBM

The ReinforcementLearning and RBM packages differ from the libraries already covered in two important ways: first, they are specialized packages that have functions for only one specific deep learning task instead of attempting to support myriad deep learning options, and second, they are completely written in R and have no additional language dependencies. This can be an advantage as the complexity of the previous libraries means that the packages can break when changes happen outside the package. The support pages for these libraries are full of examples of installation FAQs and troubleshooting instructions, as well as some cases where a given package may suddenly stop working or become deprecated. In these cases, we encourage you to continue searching CRAN and other sites as the R community is well known for its dynamic, evolving, and...

The rest of the chapter is locked

A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.

Unlock this book and the full library FREE for 7 days

Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of

Start free trial

Renews at $19.99/month. Cancel anytime

Authors (2)

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.

See other products by Pawlus

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.

See other products by Rodger Devine