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 looked at the most important concepts behind deep learning together with scalable solutions.

We took away some of the black-boxiness by learning how to construct the right architecture for any given task and worked through the mechanics of forward propagation and backpropagation. Updating the weights of a neural network is a hard task, regular stochastic gradient descent can result in getting stuck in global minima or overshooting. More sophisticated algorithms like momentum, ADAGRAD, RPROP and RMSProp can provide solutions. Even though neural networks are harder to train than other machine learning methods, they have the power of transforming feature representations and can learn any given function (universal approximation theorem). We also dived into large scale deep learning with H2O, and even utilized the very hot topic of parameter optimization for deep learning.

Unsupervised pre-training with auto-encoders can increase accuracy of any given deep network and...