Scala for Machine Learning

Medizin, Wissenschaft & Technik

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Buchbeschreibung zu „Scala for Machine Learning“

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 designs, biometrics, and trading strategies, to detection of genetic anomalies.

The book begins with an introduction 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.

Next, you'll learn about data preprocessing and filtering techniques. Following this, you'll move on to clustering and dimension reduction, Naive Bayes, regression models, sequential data, regularization and kernelization, support vector machines, neural networks, generic algorithms, and re-enforcement learning. A review of the Akka framework and Apache Spark clusters concludes the tutorial.

Über Patrick R. Nicolas

Patrick R. Nicolas is a lead R&D engineer at Dell in Santa Clara, California. He has 25 years of experience in software engineering and building large-scale applications in C++, Java, and Scala, and has held several managerial positions. His interests include real-time analytics, modeling, and optimization.


Packt Publishing




ca. 435

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