Scala for Machine Learning Second Edition

Patrick R. Nicolas

  • 出版商: Packt Publishing
  • 出版日期: 2017-09-26
  • 售價: $2,510
  • 貴賓價: 9.5$2,385
  • 語言: 英文
  • 頁數: 740
  • 裝訂: Paperback
  • ISBN: 1787122387
  • ISBN-13: 9781787122383
  • 相關分類: JVM 語言Machine Learning
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Key Features

  • Explore a broad variety of data processing, machine learning, and genetic algorithms through diagrams, mathematical formulation, and updated source code in Scala
  • Take your expertise in Scala programming to the next level by creating and customizing AI applications
  • Experiment with different techniques and evaluate their benefits and limitations using real-world applications in a tutorial style

Book Description

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 thefunctional 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.

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
  • Dive into neural networks and some deep learning architecture
  • Apply some basic multiarm-bandit algorithms
  • 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



  • 通過圖表、數學公式和Scala的最新源代碼,探索各種數據處理、機器學習和遺傳算法

  • 通過創建和自定義人工智能應用程序,將您在Scala編程方面的專業知識提升到更高水平

  • 以教程風格的真實應用程序為例,實驗不同技術並評估其優點和限制





最後,本書還包括Scala和Akka中並行計算的全面概述,以及Apache Spark及其ML庫的介紹。通過基於最新版本Scala的更新代碼和全面的示例,本書將確保您在Scala機器學習方面不僅具有堅實的基礎知識。


  • 構建科學計算的動態工作流程

  • 利用開源庫從時間序列中提取模式

  • 編寫自己的分類、聚類或進化算法

  • 進行Spark的相對性能調優和評估

  • 掌握用於序列數據的概率模型

  • 嘗試高級技術,如正則化和核化

  • 深入研究神經網絡和一些深度學習架構

  • 應用基本的多臂搶劫算法

  • 使用Scala並行集合、Akka演員和Apache Spark集群解決大數據問題

  • 將關鍵學習策略應用於金融市場的技術分析