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Deep Learning with R for Beginners

You're reading from   Deep Learning with R for Beginners Design neural network models in R 3.5 using TensorFlow, Keras, and MXNet

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Product type Course
Published in May 2019
Publisher Packt
ISBN-13 9781838642709
Length 612 pages
Edition 1st Edition
Languages
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Authors (4):
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Mark Hodnett Mark Hodnett
Author Profile Icon Mark Hodnett
Mark Hodnett
Pablo Maldonado Pablo Maldonado
Author Profile Icon Pablo Maldonado
Pablo Maldonado
Joshua F. Wiley Joshua F. Wiley
Author Profile Icon Joshua F. Wiley
Joshua F. Wiley
Yuxi (Hayden) Liu Yuxi (Hayden) Liu
Author Profile Icon Yuxi (Hayden) Liu
Yuxi (Hayden) Liu
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Toc

Table of Contents (23) Chapters Close

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

Contributors

About the authors

Mark Hodnett is a data scientist with over 20 years of industry experience in software development, business intelligence systems, and data science. He has worked in a variety of industries, including CRM systems, retail loyalty, IoT systems, and accountancy. He holds a master's in data science and an MBA. He works in Cork, Ireland, as a senior data scientist with AltViz.

 

 

 

 

 

 

 

Joshua F. Wiley is a lecturer at Monash University, conducting quantitative research on sleep, stress, and health. He earned his Ph.D. from the University of California, Los Angeles and completed postdoctoral training in primary care and prevention. In statistics and data science, Joshua focuses on biostatistics and is interested in reproducible research and graphical displays of data and statistical models. He develops or co-develops a number of R packages including Varian, a package to conduct Bayesian scale-location structural equation models, and MplusAutomation, a popular package that links R to the commercial Mplus software.

Yuxi (Hayden) Liu is an experienced data scientist who's focused on developing machine learning and deep learning models and systems. He has worked in a variety of data-driven domains and has applied his machine learning expertise to computational advertising, recommendation, and network anomaly detection. He published five first-authored IEEE transaction and conference papers during his master's research at the University of Toronto. He is an education enthusiast and the author of a series of machine learning books. His first book, the first edition of Python Machine Learning By Example, was a #1 bestseller on Amazon India in 2017 and 2018. His other books include R Deep Learning Projects and Hands-On Deep Learning Architectures with Python published by Packt.

 

 

 

 

 

 

Pablo Maldonado is an applied mathematician and data scientist with a taste for software development since his days of programming BASIC on a Tandy 1000. As an academic and business consultant, he spends a great deal of his time building applied artificial intelligence solutions for text analytics, sensor and transactional data, and reinforcement learning. Pablo earned his Ph.D. in applied mathematics (with focus on mathematical game theory) at the Universite Pierre et Marie Curie in Paris, France.

 

 

 

 

 

 

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