R Deep Learning Essentials, Second Edition

This is the code repository for R Deep Learning Essentials, Second Edition, published by Packt.

A step-by-step guide to building deep learning models using TensorFlow, Keras, and MXNet

What is this book about?

Deep learning is a powerful subset of machine learning that is very successful in domains such as computer vision and natural language processing (NLP). This second edition of R Deep Learning Essentials will open the gates for you to enter the world of neural networks by building powerful deep learning models using the R ecosystem.

This book covers the following exciting features: <First 5 What you'll learn points>

Build shallow neural network prediction models
Prevent models from overfitting the data to improve generalizability
Explore techniques for finding the best hyperparameters for deep learning models
Create NLP models using Keras and TensorFlow in R
Use deep learning for computer vision tasks

If you feel this book is for you, get your copy today!

Instructions and Navigations

All of the code is organized into folders. For example, Chapter02.

The code will look like the following:

x <- layer_dot(list(cust_flat, prod_flat), axes = 1)
x <- layer_add(list(x, ub_flat))
x <- layer_add(list(x, mb_flat))

Following is what you need for this book: This second edition of R Deep Learning Essentials is 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 is necessary to get the most out of this book.

With the following software and hardware list you can run all code files present in the book (Chapter 1-11).

Software and Hardware List

Chapter	Software required	OS required
All	R version 3.3.0	Windows, Mac OS X, and Linux (Any)
All	Rstudio Desktop 0.99.903	Windows, Mac OS X, and Linux (Any)

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.

Get to Know the Author

Mark Hodnett 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.

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