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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 2. Data Pipelines FREE CHAPTER 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 8

[8:1] Machine Learning: A Probabilistic Perspective §23 Monte-Carlo inference K Murphy - MIT Press 2012

[8:2] Machine Learning: An Algorithmic Perspective $15.1.2 Gaussian Random Numbers S. Marsland – Chapman & Hall/CRC 2015

[8:3] Monte Carlo Method Wikipedia the free encyclopedia Wikimedia Foundation - https://en.wikipedia.org/wiki/Monte_Carlo_method

[8:4] Monte Carlo Integration - Hitotsubashi University 2009 - http://ta.twi.tudelft.nl/mf/users/oosterle/oosterlee/lec8-hit-2009.pdf

[8:5] Bootstrap: A Statistical Method K. Singh, M. Xie – Rutgers University -http://stat.rutgers.edu/home/mxie/RCPapers/bootstrap.pdf

[8:6] Machine Learning: A Probabilistic Perspective §24 Markov Chain Monte-Carlo inference K Murphy - MIT Press 2012

[8:7] Pattern Recognition and Machine Learning §11.2.2 The Metropolis-Hastings Algorithm C. Bishop –Springer 2006

[8:8] Bayesian Inference: Gibbs Sampling I. Yildirim - University of Rochester 2012 - http://www.mit...

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