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

Deep learning at scale with H2O

In previous sections, we covered neural networks and deep architectures running on a local computer and we found that neural networks are already highly vectorized but still computationally expensive. There is not much that we can do if we want to make the algorithm more scalable on a desktop computer other than utilizing Theano and GPU computing. So if we want to scale deep learning algorithms more drastically, we will need to find a tool that can run algorithms out-of-core instead of on a local CPU/GPU. H2O is, at this moment, the only open source out-of-core platform that can run deep learning algorithms quickly. It is also cross-platform; besides Python, there are APIs for R, Scala, and Java.

H2O is compiled on a Java-based platform developed for a wide range of data science-related tasks such as datahandling and machine learning. H2O runs on distributed and parallel CPUs in-memory so that data will be stored in the H2O cluster. The H2O platform—as of yet...