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 4. Neural Networks and Deep Learning

In this chapter, we will cover one of the most exciting fields in artificial intelligence and machine learning: Deep Learning. This chapter will walk through the most important concepts necessary to apply deep learning effectively. The topics that we will cover in this chapter are as follows:

  • Essential neural network theory

  • Running neural networks on the GPU or CPU

  • Parameter tuning for neural networks

  • Large scale deep learning on H2O

  • Deep learning with autoencoders (pretraining)

Deep learning emerged from the subfield of artificial intelligence that developed neural networks. Strictly speaking, any large neural network can be considered deep-learning. However, recent developments in deep architectures require more than setting up large neural networks. The difference between deep architectures and normal multilayer networks is that a deep architecture consists of multiple preprocessing and unsupervised steps that detect latent dimension in the data...