R Deep Learning Projects

More Information
Learn
  • Instrument Deep Learning models with packages such as deepnet, MXNetR, Tensorflow, H2O, Keras, and text2vec 
  • Apply neural networks to perform handwritten digit recognition using MXNet
  • Get the knack of CNN models, Neural Network API, Keras, and TensorFlow for traffic sign classification
  • Implement credit card fraud detection with Autoencoders 
  • Master reconstructing images using variational autoencoders 
  • Wade through sentiment analysis from movie reviews 
  • Run from past to future and vice versa with bidirectional Long Short-Term Memory (LSTM) networks 
  • Understand the applications of Autoencoder Neural Networks in clustering and dimensionality reduction
About

R is a popular programming language used by statisticians and mathematicians for statistical analysis, and is popularly used for deep learning. Deep Learning, as we all know, is one of the trending topics today, and is finding practical applications in a lot of domains.

This book demonstrates end-to-end implementations of five real-world projects on popular topics in deep learning such as handwritten digit recognition, traffic light detection, fraud detection, text generation, and sentiment analysis. You'll learn how to train effective neural networks in R—including convolutional neural networks, recurrent neural networks, and LSTMs—and apply them in practical scenarios. The book also highlights how neural networks can be trained using GPU capabilities. You will use popular R libraries and packages—such as MXNetR, H2O, deepnet, and more—to implement the projects.

By the end of this book, you will have a better understanding of deep learning concepts and techniques and how to use them in a practical setting.

Features
  • Master the different deep learning paradigms and build real-world projects related to text generation, sentiment analysis, fraud detection, and more 
  • Get to grips with R's impressive range of Deep Learning libraries and frameworks such as deepnet, MXNetR, Tensorflow, H2O, Keras, and text2vec 
  • Practical projects that show you how to implement different neural networks with helpful tips, tricks, and best practices
Page Count 258
Course Length 7 hours 44 minutes
ISBN 9781788478403
Date Of Publication 22 Feb 2018

Authors

Yuxi (Hayden) Liu

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 expertise in reinforcement learning to computational. He is an education enthusiast and the author of a series of ML books. His first book, 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. He also published five first-authored IEEE transaction and conference papers during his master's research at the University of Toronto.

Pablo Maldonado

Pablo Maldonado is an applied mathematician and data scientist who has had 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 a focus on mathematical game theory) at the Universite Pierre et Marie Curie in Paris, France.