000			02333nam a22002177i 4500
999			_c44554 _d44554
020			_a9781484227336
020			_a1484227336
082	0	4	_a006.31 _bBEY
100	1		_aBeysolow, Taweh, II, _eaut
245	1	0	_aIntroduction to deep learning using R : a step-by-step guide to learning and implementing deep learning models using R
260			_aNew York? : _bApress, _c©2017.
300			_axix, 227 pages : _billustrations ;
490	1		_aFor professionals by professionals
500			_aIncludes index.
505	0		_aIntroduction to deep learning -- Mathematical review -- A review of optimization and machine learning -- Single and multilayer perceptron models -- Convolutional neural networks (CNNs) -- Recurrent neural networks (RNNs) -- Autoencoders, restricted boltzmann machines, and deep belief networks -- Experimental design and heuristics -- Hardware and software suggestions -- Machine learning example problems -- Deep learning and other example problems -- Closing statements.
520			_aUnderstand deep learning, the nuances of its different models, and where these models can be applied. The abundance of data and demand for superior products/services have driven the development of advanced computer science techniques, among them image and speech recognition. Introduction to Deep Learning Using R provides a theoretical and practical understanding of the models that perform these tasks by building upon the fundamentals of data science through machine learning and deep learning. This step-by-step guide will help you understand the disciplines so that you can apply the methodology in a variety of contexts. All examples are taught in the R statistical language, allowing students and professionals to implement these techniques using open source tools. What You Will Learn: • Understand the intuition and mathematics that power deep learning models • Utilize various algorithms using the R programming language and its packages • Use best practices for experimental design and variable selection • Practice the methodology to approach and effectively solve problems as a data scientist • Evaluate the effectiveness of algorithmic solutions and enhance their predictive power.
650		0	_aMachine learning.
650		0	_aBig data.
650		0	_aR (Computer program language)
942			_cREF