Mark Hodnett - Deep Learning with R for Beginners

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First published: May 2019

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ISBN 978-1-83864-270-9

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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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Deep learning finds practical applications in several domains and R is a preferred language to design and deploy deep learning models.
This Learning Path introduces you to the basics of deep learning and teaches you to build a neural network model from scratch. As you make your way through the concepts, youll explore deep learning libraries and create deep learning models for a variety of problems, such as anomaly detection and recommendation systems. Youll cover advanced topics, such as generative adversarial networks (GANs), transfer learning, and large-scale deep learning in the cloud. Before it ends, this Learning Path teaches you advanced topics, such as model optimization, overfitting, and data augmentation. Through real-world projects, youll learn how to train convolutional neural networks, recurrent neural networks, and LSTMs in R.
By the end of this Learning Path, youll have a better understanding of deep learning concepts and will be able to implement deep learning concepts in your research work or projects.

This Learning Path includes content from the following Packt products:

• R Deep Learning Essentials - Second Edition by Joshua F. Wiley and Mark Hodnett
• R Deep Learning Projects by Yuxi (Hayden) Liu and Pablo Maldonado

This Learning Path is ideal for aspiring data scientists, data analysts, machine learning developers, and deep learning enthusiasts who are well versed in machine learning concepts and are looking to explore the deep learning paradigm using R. Fundamental understanding of the R language and familiarity with basic concepts of deep learning are necessary to get the most out of this Learning Path.