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
About the Authors
About the Reviewer

Keras and TensorFlow installation

Previously, we have seen practical examples of the SkFlow wrapper for TensorFlow applications. For a more sophisticated approach to neural networks and deep learning where we have more control over parameters, we propose Keras ( This package was originally developed within the Theano framework, but recently is also adapted to TensorFlow. This way, we can use Keras as a higher abstract package on top of TensorFlow. Keep in mind though that Keras is slightly less straightforward than SkFlow in its methods. Keras can run on both GPU and CPU, which makes this package really flexible when porting it to different environments.

Let's first install Keras and make sure that it utilizes the TensorFlow backend.

Installation works simply using pip in the command line:

$pip install Keras

Keras is originally built on top of Theano, so we need to specify Keras to utilize TensorFlow instead. In order to do this, we first need to run Keras once on its default...