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

Summary


In this chapter, we have come quite a long way covering the TensorFlow landscape and its corresponding methods. We got acquainted with how to set up basic regressors, classifiers, and single-hidden layer neural networks. Even though the programming TensorFlow operations are relatively straightforward, for off-the-shelf machine learning tasks, TensorFlow might be a little bit too tedious. This is exactly where SkFlow comes in, a higher-level library with an interface quite similar to Scikit-learn. For incremental or even out-of-core solutions, SkFlow provides a partial fit method, which can easily be set up. Other large scale solutions are either restricted to GPU applications or are at a premature stage. So for now, we have to settle for incremental learning strategies when it comes to scalable solutions.

We also provided an introduction to Convolutional Neural Networks and saw how they can be set up in Keras.