Book Image

Scala for Machine Learning - Second Edition

Book Image

Scala for Machine Learning - 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

Pros and cons


An increasing number of organizations are adopting Spark as their distributed data processing platform for real-time, or pseudo real-time operations.

There are several reasons for the fast adoption of Spark:

  • Supported by a large and dedicated community of developers

  • In-memory persistency is ideal for iterative computation found in machine learning and statistical inference algorithms

  • Excellent performance and scalability that can be extended with the streaming library for pseudo-real time computation or infinite loop

  • Apache Spark leverages Scala functional capabilities and a large number of open source Java libraries

  • Spark can leverage the Mesos or Yarn cluster managers, which reduces the complexity of defining fault-tolerance and load balancing between worker nodes

  • Spark is to be integrated with commercial Hadoop vendors such as Cloudera

However, no platform is perfect and Spark is no exception. The most common complaints or concerns regarding Spark are:

  • Creating a Spark application...