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

Machine learning on TensorFlow with SkFlow


Now that we have seen the basic operations of TensorFlow, let's dive into the higher-level applications built on top of TensorFlow to make machine learning a little more practical. SkFlow is the first application that we will cover. In SkFlow, we don't have to specify types and placeholders. We can load and manage data in the same way that we would do with Scikit-learn and NumPy. Let's install the package with pip.

The safest way is to install the package from GitHub directly:

$ pip install git+git://github.com/tensorflow/skflow.git

SkFlow has three main classes of learning algorithms: linear classifiers, linear regression, and neural networks. A linear classifier is basically a simple SGD (multi) classifier, and neural networks is where SkFlow excels. It provides relatively easy-to-use wrappers for very deep neural networks, recurrent networks, and Convolutional Neural Networks. Unfortunately, other algorithms such as Random Forest, gradient boosting...