R Deep Learning Projects

Master the techniques to design and develop neural network models in R
 
 
Packt Publishing Limited
  • erschienen am 22. Februar 2018
 
  • Buch
  • |
  • Softcover
  • |
  • 258 Seiten
978-1-78847-840-3 (ISBN)
 
5 real-world projects to help you master deep learning concepts

Key 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

Book DescriptionR 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.

What you will 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

Who this book is forMachine learning professionals and data scientists looking to master deep learning by implementing practical projects in R will find this book a useful resource. A knowledge of R programming and the basic concepts of deep learning is required to get the best out of this book.
  • Englisch
  • Birmingham
  • |
  • Großbritannien
  • Höhe: 235 mm
  • |
  • Breite: 191 mm
  • |
  • Dicke: 14 mm
  • 451 gr
978-1-78847-840-3 (9781788478403)
1788478401 (1788478401)
Yuxi (Hayden) Liu is currently an applied research scientist focused on developing machine learning models and systems for given learning tasks. He has worked for a few years as a data scientist, and applied his machine learning expertise in computational advertising. He earned his degree from the University of Toronto, and published five first-authored IEEE transaction and conference papers during his research. His first book, titled Python Machine Learning By Example, was ranked the #1 bestseller in Amazon India in 2017. He is also a machine learning education enthusiast. 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 PhD in applied mathematics (with focus on mathematical game theory) at the Universite Pierre et Marie Curie in Paris, France.
Table of Contents

Handwritten Digit Recognition using Convolutional Neural Networks
Traffic Signs Recognition for Intelligent Vehicles
Fraud Detection with Autoencoders
Text Generation using Recurrent Neural Networks
Sentiment Analysis with Word Embedding

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