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Scala for Machine Learning, Second Edition - Second Edition

You're reading from  Scala for Machine Learning, Second Edition - Second Edition

Product type Book
Published in Sep 2017
Publisher Packt
ISBN-13 9781787122383
Pages 740 pages
Edition 2nd Edition
Languages
Toc

Table of Contents (27) Chapters close

Scala for Machine Learning Second Edition
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
1. Getting Started 2. Data Pipelines 3. Data Preprocessing 4. Unsupervised Learning 5. Dimension Reduction 6. Naïve Bayes Classifiers 7. Sequential Data Models 8. Monte Carlo Inference 9. Regression and Regularization 10. Multilayer Perceptron 11. Deep Learning 12. Kernel Models and SVM 13. Evolutionary Computing 14. Multiarmed Bandits 15. Reinforcement Learning 16. Parallelism in Scala and Akka 17. Apache Spark MLlib Basic Concepts References Index

Chapter 10


[10:1] Neural Network: A Review M. K. Gharate - PharmaInfo.net 2007

[10:2] Parallel Distributed Processing R. Rumelhart, J. McClelland - MIT Press 1986

[10:3] Pattern Recognition and Machine Learning Chap 5 Neural Networks: Introduction C. Bishop –Springer 2006

[10:4] Neural Network Models §3.3 Mathematical Model, §4.6 Lyapunov Theorem for Neural Networks P. De Wilde - Springer 1997

[10:5] Modern Multivariate Statistical Techniques §10.7 Multilayer Perceptrons (introduction) A.J. Izenman - Springer 2008

[10:6] Algorithms for initialization of neural network weights A. Pavelka, A Prochazka - Dept. of Computing and Control Engineering. Institute of Chemical Technology - http://dsp.vscht.cz/konference_matlab/matlab04/pavelka.pdf

[10:7] Design Patterns: Elements of Reusable Object-Oriented Software $Object creational pattern: builder E. Gamma, R. Helm, R. Johnson, J. Vlissides - Addison Wesley 1995

[10:8] Introduction to Machine Learning: Linear Discrimination §10.7.2 Multiple Classes. E. Alpaydin - MIT Press 2007

[10:9] Pattern Recognition and Machine Learning §5.3 Neural Networks: Error Backpropagation C. Bishop –Springer 2006

[10:10] Pattern Recognition and Machine Learning §5.2.4 Neural Networks: Gradient descent optimization C. Bishop –Springer 2006

[10:11] Introduction to Machine Learning §11.8.1 Multilayer Perceptrons: Improving Convergence. E. Alpaydin - MIT Press 2007

[10:12] The general inefficiency of batch training for gradient descent training D. R. Wilson, Fonix Corp. T. R. Martinez - Brigham Young University Elsevier 2003 - http://axon.cs.byu.edu/papers/Wilson.nn03.batch.pdf

[10:13] Regularization in Neural Networks CSE 574, §5 S. Srihari - University of New York, Buffalo - http://www.cedar.buffalo.edu/~srihari/CSE574/Chap5/Chap5.5-Regularization.pdf

[10:14] Stock Market Value Prediction Using Neural Networks M.P. Naeini, H. Taremian, H.B. Hashemi - 2010 International Conference on Computer Information Systems and Industrial Management Applications IEEE - http://people.cs.pitt.edu/~hashemi/papers/CISIM2010_HBHashemi.pdf

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