Book Image

Large Scale Machine Learning with Python

By : Bastiaan Sjardin, Alberto Boschetti
Book Image

Large Scale Machine Learning with Python

By: Bastiaan Sjardin, Alberto Boschetti

Overview of this book

Large Python machine learning projects involve new problems associated with specialized machine learning architectures and designs that many data scientists have yet to tackle. But finding algorithms and designing and building platforms that deal with large sets of data is a growing need. Data scientists have to manage and maintain increasingly complex data projects, and with the rise of big data comes an increasing demand for computational and algorithmic efficiency. Large Scale Machine Learning with Python uncovers a new wave of machine learning algorithms that meet scalability demands together with a high predictive accuracy. Dive into scalable machine learning and the three forms of scalability. Speed up algorithms that can be used on a desktop computer with tips on parallelization and memory allocation. Get to grips with new algorithms that are specifically designed for large projects and can handle bigger files, and learn about machine learning in big data environments. We will also cover the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python.
Table of Contents (17 chapters)
Large Scale Machine Learning with Python
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Preface
Index

The neural network architecture


Let's now focus on how neural networks are organized, starting from their architecture and a few definitions.

A network where the flow of learning is passed forward all the way to the outputs in one pass is referred to as a feedforward neural network.

A basic feedforward neural network can easily be depicted by a network diagram, as shown here:

In the network diagram, you can see that this architecture consists of an input layer, hidden layer, and output layer. The input layer contains the feature vectors (where each observation has n features), and the output layer consists of separate units for each class of the output vector in the case of classification and a single numerical vector in the case of regression.

The strength of the connections between the units is expressed through weights later to be passed to an activation function. The goal of an activation function is to transform its input to an output that makes binary decisions more separable.

These activation...