R Deep Learning Cookbook by Dr. PKS Prakash
English | 4 Aug. 2017 | ISBN: 1787121089 | ASIN: B071NDMWN2 | 288 Pages | AZW3 | 6.91 MB
Powerful, independent recipes to build deep learning models in different application areas using R libraries
About This Book
Master intricacies of R deep learning packages such as mxnet & tensorflow
Learn application on deep learning in different domains using practical examples from text, image and speech
Guide to set-up deep learning models using CPU and GPU
Who This Book Is For
Data science professionals or analysts who have performed machine learning tasks and now want to explore deep learning and want a quick reference that could address the pain points while implementing deep learning. Those who wish to have an edge over other deep learning professionals will find this book quite useful.
What You Will Learn
Build deep learning models in different application areas using TensorFlow, H2O, and MXnet.
Analyzing a Deep boltzmann machine
Setting up and Analysing Deep belief networks
Building supervised model using various machine learning algorithms
Set up variants of basic convolution function
Represent data using Autoencoders.
Explore generative models available in Deep Learning.
Discover sequence modeling using Recurrent nets
Learn fundamentals of Reinforcement Leaning
Learn the steps involved in applying Deep Learning in text mining
Explore application of deep learning in signal processing
Utilize Transfer learning for utilizing pre-trained model
Train a deep learning model on a GPU
In Detail
Deep Learning is the next big thing. It is a part of machine learning. It's favorable results in applications with huge and complex data is remarkable. Simultaneously, R programming language is very popular amongst the data miners and statisticians.
This book will help you to get through the problems that you face during the execution of different tasks and Understand hacks in deep learning, neural networks, and advanced machine learning techniques. It will also take you through complex deep learning algorithms and various deep learning packages and libraries in R. It will be starting with different packages in Deep Learning to neural networks and structures. You will also encounter the applications in text mining and processing along with a comparison between CPU and GPU performance.
By the end of the book, you will have a logical understanding of Deep learning and different deep learning packages to have the most appropriate solutions for your problems.
Style and approach
Collection of hands-on recipes that would act as your all-time reference for your deep learning needs
