Book Image

Scala for Machine Learning, Second Edition - Second Edition

Book Image

Scala for Machine Learning, Second Edition - Second Edition

Overview of this book

The discovery of information through data clustering and classification is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, engineering design, logistics, manufacturing, and trading strategies, to detection of genetic anomalies. The book is your one stop guide that introduces you to the functional capabilities of the Scala programming language that are critical to the creation of machine learning algorithms such as dependency injection and implicits. You start by learning data preprocessing and filtering techniques. Following this, you'll move on to unsupervised learning techniques such as clustering and dimension reduction, followed by probabilistic graphical models such as Naïve Bayes, hidden Markov models and Monte Carlo inference. Further, it covers the discriminative algorithms such as linear, logistic regression with regularization, kernelization, support vector machines, neural networks, and deep learning. You’ll move on to evolutionary computing, multibandit algorithms, and reinforcement learning. Finally, the book includes a comprehensive overview of parallel computing in Scala and Akka followed by a description of Apache Spark and its ML library. With updated codes based on the latest version of Scala and comprehensive examples, this book will ensure that you have more than just a solid fundamental knowledge in machine learning with Scala.
Table of Contents (27 chapters)
Scala for Machine Learning Second Edition
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Index

References


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[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

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

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[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