PACKT - R Deep Learning Projects
|Publication Date:||22 February 2018|
5 real-world projects to help you master deep learning concepts
About This Book
• 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
Who This Book Is For
Machine 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.
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
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.
Style and approach
This book's unique, learn-as-you-do approach ensures the reader builds on his understanding of deep learning progressively with each project. This book is designed in such a way that implementing each project will empower you with a unique skillset and enable you to implement the next project more confidently.