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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 7

[7:1] A tutorial on Hidden Markov Models and Selected Applications in Speech Recognition, L. Rabiner - Proceedings of the IEEE Volume 77, Feb 1989 - http://www.cs.ubc.ca/~murphyk/Bayes/rabiner.pdf

[7:2] CRF Java library v 1.3 S. Sarawagi - Indian Institute of Technology, Bombay 2008 - http://crf.sourceforge.net/

[7:3] Introduction to Machine Learning §13.2 Discrete Markov Processes E. Alpaydin - The MIT Press 2004

[7:4] A Revealing Introduction to Hidden Markov Models M. Stamp - Dept. of Computer Science, San Jose State University 2012 - http://www.cs.sjsu.edu/~stamp/RUA/HMM.pdf

[7:5] A brief introduction to Dynamic Programming A. Kasibhatla - Nanocad Lab University of California, Los Angeles 2010 -http://nanocad.ee.ucla.edu/pub/Main/SnippetTutorial/Amar_DP_Intro.pdf

[7:6] Pattern Recognition and Machine Learning §13.2.1 Maximum Likelihood for the HMM C. Bishop –Springer 2006

[7:7] Dynamic Programing in Machine learning – Part 3: Viterbi Algorithm and Machine...

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