Scala for Machine Learning

Patrick R. Nicolas

  • 出版商: Packt Publishing - ebooks Account
  • 出版日期: 2014-12-31
  • 售價: $1,981
  • 貴賓價: 9.5$1,882
  • 語言: 英文
  • 頁數: 420
  • 裝訂: Paperback
  • ISBN: 1783558741
  • ISBN-13: 9781783558742
  • 相關標籤: Scala機器學習
  • 相關分類: JVM 語言機器學習

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商品描述

Leverage Scala and Machine Learning to construct and study systems that can learn from data

About This Book

  • Explore a broad variety of data processing, machine learning, and genetic algorithms through diagrams, mathematical formulation, and source code
  • Leverage your expertise in Scala programming to create and customize AI applications with your own scalable machine learning algorithms
  • Experiment with different techniques, and evaluate their benefits and limitations using real-world financial applications, in a tutorial style

Who This Book Is For

Are you curious about AI? All you need is a good understanding of the Scala programming language, a basic knowledge of statistics, a keen interest in Big Data processing, and this book!

What You Will Learn

  • Build dynamic workflows for scientific computing
  • Leverage open source libraries to extract patterns from time series
  • Write your own classification, clustering, or evolutionary algorithm
  • Perform relative performance tuning and evaluation of Spark
  • Master probabilistic models for sequential data
  • Experiment with advanced techniques such as regularization and kernelization
  • Solve big data problems with Scala parallel collections, Akka actors, and Apache Spark clusters
  • Apply key learning strategies to a technical analysis of financial markets

In Detail

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.