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

Neural networks and hyperparameter optimization


As the parameter space of neural networks and deep learning models is so wide, optimization is a hard task and computationally very expensive. A wrong neural network architecture can be a recipe for failure. These models can only be accurate if we apply the right parameters and choose the right architecture for our problem. Unfortunately, there are only a few applications that provide tuning methods. We found that the best parameter tuning method at the moment is randomized search, an algorithm that iterates over the parameter space at random sparing computational resources. The sknn library is really the only library that has this option. Let's walk through the parameter tuning methods with the following example based on the wine-quality dataset.

In this example, we first load the wine dataset. Than we apply transformation to the data, from where we tune our model based on chosen parameters. Note that this dataset has 13 features; we specify...