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

Chapter 5. Deep Learning with TensorFlow

In this chapter, we will focus on TensorFlow and cover the following topics:

  • Basic TensorFlow operations

  • Machine learning from scratch with TensorFlow—regression, SGD classifier, and neural network

  • Deep learning with SkFlow

  • Incremental deep learning with large files

  • Convolutional Neural Networks with Keras

The TensorFlow framework was introduced at the time of writing this book and already has proven to be a great addition to the machine learning landscape.

TensorFlow was started by the Google Brain Team consisting of most of the researchers that worked on important developments in deep learning in the recent decade (Geoffrey Hinton, Samy Bengio, and others). It is basically a next-generation development of an earlier generation of frameworks called DistBelief, a platform for distributed deep neural networks. Contrary to TensorFlow, DistBelief is not open source. Interesting examples of successful DistBelief projects are the reversed image search engine...