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

Autoencoders and unsupervised learning


Up until now, we discussed neural networks with multiple layers and a wide variety of parameters to optimize. The current generation of neural networks that we often refer to as deep learning is capable of more; it is capable of learning new features automatically so that very little feature engineering and domain expertise is required. These features are created by unsupervised methods on unlabeled data later to be fed into a subsequent layer in a neural network. This method is referred to as (unsupervised) pretraining. This approach has been proven to be highly successful in image recognition, language learning, and even vanilla machine learning projects. The most important and dominant technique in recent years is called denoising autoencoders and algorithms based on Boltzmann techniques. Boltzmann machines, which were the building blocks for Deep Belief Networks (DBN), have lately fallen out of favor in the deep learning community because they turned...