This is the code repository for Hands-On Deep Learning with R, published by Packt.

A practical guide to designing, building, and improving neural network models using R

Deep learning enables efficient and accurate learning from data. Developers working with R will be able to put their knowledge to work with this practical guide to deep learning. The book provides a hands-on approach to implementation and associated methodologies that will have you up-and-running, and productive in no time.

This book covers the following exciting features:

• Design a feedforward neural network to see how the activation function computes an output
• Create an image recognition model using convolutional neural networks (CNNs)
• Prepare data, decide hidden layers and neurons and train your model with the backpropagation algorithm
• Apply text cleaning techniques to remove uninformative text using NLP
• Build, train, and evaluate a GAN model for face generation
• Understand the concept and implementation of reinforcement learning in R

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

All of the code is organized into folders.

The code will look like the following:

linear_fits <- function(w, to_add = TRUE, line_type = 1) {curve(-w[1] /
w[2] * x - w[3] / w[2], xlim = c(-1, 2), ylim = c(-1, 2), col = "black",lty
= line_type, lwd = 2, xlab = "Input Value A", ylab = "Input Value B", add = to_add)}

Following is what you need for this book: This book is for data scientists, machine learning engineers, and deep learning developers who are familiar with machine learning and are looking to enhance their knowledge of deep learning using practical examples. Anyone interested in increasing the efficiency of their machine learning applications and exploring various options in R will also find this book useful. Basic knowledge of machine learning techniques and working knowledge of the R programming language is expected.

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

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

• Advanced Deep Learning with R [Packt] [Amazon]

• Machine Learning with R Cookbook [Packt] [Amazon]

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.

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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If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost.
Simply click on the link to claim your free PDF.

https://packt.link/free-ebook/9781788996839