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03460nam a22004575i 4500 |
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978-1-4842-2734-3 |
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170720s2017 xxu| s |||| 0|eng d |
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|a 9781484227343
|9 978-1-4842-2734-3
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|a 10.1007/978-1-4842-2734-3
|2 doi
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|a HF5548.125-HF5548.6
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|a BUS070030
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|a 658.4038
|2 23
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|a Beysolow II, Taweh.
|e author.
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|a Introduction to Deep Learning Using R
|h [electronic resource] :
|b A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R /
|c by Taweh Beysolow II.
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|a Berkeley, CA :
|b Apress :
|b Imprint: Apress,
|c 2017.
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|a XIX, 227 p. 106 illus., 53 illus. in color.
|b online resource.
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|a text
|b txt
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|a computer
|b c
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|a online resource
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|a text file
|b PDF
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|a Chapter 1: What is Deep Learning? -- Chapter 2: Mathematical Review -- Chapter 3: A Review of Optimization and Machine Learning -- Chapter 4: Single and Multi-Layer Perceptron Models -- Chapter 5: Convolutional Neural Networks (CNNs) -- Chapter 6: Recurrent Neural Networks (RNNs) -- Chapter 7: Autoencoders, Restricted Boltzmann Machines, and Deep Belief Networks -- Chapter 8: Experimental Design and Heuristics -- Chapter 9: Deep Learning and Machine Learning Hardware/Software Suggestions -- Chapter 10: Machine Learning Example Problems -- Chapter 11: Deep Learning and Other Example Problems -- Chapter 12: Closing Statements.-.
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|a Understand 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.
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|a Business.
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|a Big data.
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|a Programming languages (Electronic computers).
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|a Computers.
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|a Business and Management.
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|a Big Data/Analytics.
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|a Computing Methodologies.
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|a Programming Languages, Compilers, Interpreters.
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|a SpringerLink (Online service)
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|t Springer eBooks
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|i Printed edition:
|z 9781484227336
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|u http://dx.doi.org/10.1007/978-1-4842-2734-3
|z Full Text via HEAL-Link
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|a ZDB-2-BUM
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|a Business and Management (Springer-41169)
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