R Deep Learning Cookbook: Solve complex neural net problems with TensorFlow, H2O and MXNet (Paperback) (ISBN-13: 9781787121089)

Powerful, independent recipes to build deep learning models in different application areas using R libraries

Key Features:

• 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

Book Description:

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.

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

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.