R Deep Learning Cookbook

Dr. PKS Prakash, Achyutuni Sri Krishna Rao

  • 出版商: Packt Publishing - ebooks Account
  • 出版日期: 2017-08-04
  • 定價: USD $44.99
  • 售價: $1,710
  • 語言: 英文
  • 頁數: 288
  • 裝訂: Paperback
  • ISBN: 1787121089
  • ISBN-13: 9781787121089
  • 相關標籤: RDeep Learning

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產品描述

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. Its favorable results in application with huge and complex data is remarkable. Simultaneously, R programming language is very popular amongst the data miners and statisticians. With the growth in Deep Learning, the inter relation between R and deep learning is growing tremendously as they are very compatible with each other in attaining the various results.

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 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 H20, MXnet.
  • Analyzing a Deep boltzmann machine
  • Setting up and Analysing Deep belief networks
  • Generating a RNN-RBM hybrid model for sequence generation
  • 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.
  • Implement Branching Program Machines for structured or sequential outputs
  • Discover sequence modeling using Recurrent and Recursive nets
  • Learn the steps involved in applying Deep Learning in text mining
  • Train a deep learning model on a GPU