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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

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] Large Scale Machine Learning: Stochastic Gradient Descent Convergence A. Ng Stanford University https://class.coursera.org/ml-003/lecture/107

[A:11] Large-Scala Machine Learning with Stochastic Gradient Descent L. Bottou 2010 http://leon.bottou.org/publications/pdf/compstat-2010.pdf

[A:12] Overview of Quasi-Newton optimization methods Dept. Computer Science, University of Washington

[A:13] Lecture 2-3: Gradient and Hessian of Multivariate Function M. Zibulevsky 2013 http://www.youtube.com

[A:14] Introduction to the Lagrange Multiplier ediwm.com Video http://www.noodle.com/learn/details/334954/introduction-to-the-lagrange-multiplier

[A:15] A brief introduction to Dynamic Programming (DP) A. Kasibhatla, Nanocad Lab http://nanocad.ee.ucla.edu/pub/Main/SnippetTutorial/Amar_DP_Intro.pdf

[A:16] Financial ratios Wikipedia http://en.wikipedia.org/wiki/Financial_ratio

[A:17] Getting started in Technical Analysis §1 Charts: Forecasting Tool or Folklore? Schwager John Wiley & Sons 1999

[A:18] Getting started in Technical Analysis §4 Trading Ranges, Support & Resistance J Schwager John Wiley & Sons 1999

[A:19] Options: a personal seminar §1 Options: An Introduction, What is an Option S. Fullman New Your Institute of Finance, Simon Schuster 1992

[A:20] Options: a personal seminar §2 Purchasing Options S. Fullman New York Institute of Finance, Simon Schuster 1992

[A:21] List of financial data feeds: Wikipedia, the free encyclopedia Wikimedia foundation http://en.wikipedia.org/wiki/List_of_financial_data_feeds

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