Book Image

Machine Learning with Apache Spark Quick Start Guide

By : Jillur Quddus
Book Image

Machine Learning with Apache Spark Quick Start Guide

By: Jillur Quddus

Overview of this book

Every person and every organization in the world manages data, whether they realize it or not. Data is used to describe the world around us and can be used for almost any purpose, from analyzing consumer habits to fighting disease and serious organized crime. Ultimately, we manage data in order to derive value from it, and many organizations around the world have traditionally invested in technology to help process their data faster and more efficiently. But we now live in an interconnected world driven by mass data creation and consumption where data is no longer rows and columns restricted to a spreadsheet, but an organic and evolving asset in its own right. With this realization comes major challenges for organizations: how do we manage the sheer size of data being created every second (think not only spreadsheets and databases, but also social media posts, images, videos, music, blogs and so on)? And once we can manage all of this data, how do we derive real value from it? The focus of Machine Learning with Apache Spark is to help us answer these questions in a hands-on manner. We introduce the latest scalable technologies to help us manage and process big data. We then introduce advanced analytical algorithms applied to real-world use cases in order to uncover patterns, derive actionable insights, and learn from this big data.
Table of Contents (10 chapters)

Artificial neural networks

As we studied in Chapter 3, Artificial Intelligence and Machine Learning, an artificial neural network (ANN) is a connected group of artificial neurons that is aggregated into three types of linked neural layers—the input layer, zero or more hidden layers, and the output layer. A monolayer ANN consists of just one layer of links between the input nodes and output nodes, while multilayer ANNs are characterized by the segmentation of artificial neurons across multiple linked layers.

An ANN where signals are propagated in one direction only—that is, the signals are received by the input layer and forwarded to the next layer for processing—are called feedforward networks. ANNs where a signal may be propagated back to artificial neurons or neural layers that have already processed that signal are called feedback networks.

Backwards propagation...