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

Feed-forward neural networks (FFNN)


The brain is a very powerful information processing engine that surpasses the reasoning ability of computers in domains such as learning, inductive reasoning, prediction, vision, and speech recognition. However, the simplest computing device has the capability to process very large datasets well beyond the ability of the human brain.

The biological background

In biology, a neural network is composed of groups of neurons interconnected by synapses [10:2], as shown in the following image:

Visualization of biological neurons and synapses

Neuroscientists have been especially interested in understanding how the billions of neurons in the brain can interact to provide human beings with parallel processing capabilities. The 1960s saw a new field of study emerging, known as connectionism. Connectionism marries cognitive psychology, artificial intelligence, and neuroscience. The goal was to create a model for mental phenomena. Although there are many forms of connectionism...