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Scala for Machine Learning, Second Edition

You're reading from   Scala for Machine Learning, Second Edition Build systems for data processing, machine learning, and deep learning

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781787122383
Length 740 pages
Edition 2nd Edition
Languages
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Author (1):
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Patrick R. Nicolas Patrick R. Nicolas
Author Profile Icon Patrick R. Nicolas
Patrick R. Nicolas
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Table of Contents (21) Chapters Close

Preface 1. Getting Started FREE CHAPTER 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 A. Basic Concepts B. References Index

References

[A:1] Daily scala: Enumeration J. Eichar 2009 http://daily-scala.blogspot.com/2009/08/enumerations.html

[A:2] Matrices and Linear Transformations 2nd edition C. Cullen, Dover Books on Mathematics 1990

[A:3] Linear Algebra: A Modern Introduction D. Poole, BROOKS/COLE CENGAGE Learning 2010

[A:4] Matrix decomposition for regression analysis D. Bates 2007 http://www.stat.wisc.edu/courses/st849-bates/lectures/Orthogonal.pdf

[A:5] Eigenvalues and Eigenvectors of Symmetric Matrices I. Mateev 2013 http://www.slideshare.net/vanchizzle/eigenvalues-and-eigenvectors-of-symmetric-matrices

[A:6] Linear Algebra Done Right 2nd edition §5 Eigenvalues and Eigenvectors S. Axler, Springer 2000

[A:7] First Order Predicate Logic S. Kaushik, CSE India Institute of Technology, Delhi http://www.cse.iitd.ac.in/~saroj/LFP/LFP_2013/L4.pdf

[A:8] Matrix Recipes J. Movellan 2005

[A:9] Gradient descent: Wikipedia, the free encyclopedia Wikimedia foundation http://en.wikipedia.org/wiki/Gradient_descent

[A:10...

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