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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

Chapter 3

[3:1] Programming in Scala 3rd edition §21.6 Context bounds, M. Odersky, L. Spoon, B. Venners - Artima 2016

[3:2] Moving Averages, Wikipedia, the free encyclopedia Wikimedia Foundation - http://en.wikipedia.org/wiki/Moving_average_model

[3:3] Forecasting with weighted moving averages P. Kumar, Kushmanda Manpower 2011

[3:4] Practical Guide to Data Smoothing & Filtering, T. Van Den Bogert - 1996 -http://isbweb.org/software/sigproc/bogert/filter.pdf

[3:5] Spectral density estimation, Wikipedia, the free encyclopedia Wikimedia Foundation - http://en.wikipedia.org/wiki/Spectral_estimation

[3:6] The Fast Fourier Transform and Its Applications E O. Brigham - Prentice-Hall 1988

[3:7] Fourier Transform Tutorial: Learn difficult engineering concepts through interactive flash programs - http://www.fourier-series.com/f-transform/

[3:8] The Cooley-Tukey Fast Fourier Transform Algorithm, C. S. Burrus OpenStax -http://cnx.org/content/m16334/latest/

[3:9] Apache Commons Math 3.3 API, Apache...

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