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

Evaluation


Before applying our multilayer perceptron to understand fluctuations in the currency market exchanges, let's get acquainted with some of the key learning parameters introduced in the first section.

Execution profile

Let's look at the convergence of the training of the multiple layer perceptron. The monitor trait (refer to the Training section under Helper classes in the Appendix) collects and displays some execution parameters. We selected to extract the profile for the convergence of the multiple layer perceptron using the difference of the backpropagation errors between two consecutive episodes (or epochs).

The test profiles the convergence of the MLP using a learning rate, ? = 0.03, and a momentum factor of a = 0.3 for a multilayer perceptron with two input values, one hidden layer with three nodes, and one output value. The test relies on synthetically generated random values:

Execution profile for cumulative errors for MLP

Impact of learning rate

The purpose of the first exercise...