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Large Scale Machine Learning with Python

You're reading from Large Scale Machine Learning with Python Learn to build powerful machine learning models quickly and deploy large-scale predictive applications

Product type Paperback

Published in Aug 2016

Publisher Packt

ISBN-13 9781785887215

Length 420 pages

Edition 1st Edition

Languages

Python

Tools

Hadoop

Concepts

Machine Learning

Authors (3):

Alberto Boschetti

Bastiaan Sjardin

Luca Massaron

View More author details

Table of Contents (12) Chapters

Preface

1. First Steps to Scalability FREE CHAPTER

2. Scalable Learning in Scikit-learn

3. Fast SVM Implementations

4. Neural Networks and Deep Learning

5. Deep Learning with TensorFlow

6. Classification and Regression Trees at Scale

7. Unsupervised Learning at Scale

8. Distributed Environments – Hadoop and Spark

9. Practical Machine Learning with Spark

A. Introduction to GPUs and Theano

Index

The Hadoop ecosystem

Apache Hadoop is a very popular software framework for distributed storage and distributed processing on a cluster. Its strengths are in the price (it's free), flexibility (it's open source, and although being written in Java, it can by used by other programming languages), scalability (it can handle clusters composed by thousands of nodes), and robustness (it was inspired by a published paper from Google and has been around since 2011), making it the de facto standard to handle and process big data. Moreover, lots of other projects from the Apache foundation extend its functionalities.

Architecture

Logically, Hadoop is composed of two pieces: distributed storage (HDFS) and distributed processing (YARN and MapReduce). Although the code is very complex, the overall architecture is fairly easy to understand. A client can access both storage and processing through two dedicated modules; they are then in charge of distributing the job across all theorking nodes:

All the Hadoop...

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Authors (3)

Sjardin

Sjardin

Bastiaan Sjardin is a data scientist and founder with a background in artificial intelligence and mathematics. He has a MSc degree in cognitive science obtained at the University of Leiden together with on campus courses at Massachusetts Institute of Technology (MIT). In the past 5 years, he has worked on a wide range of data science and artificial intelligence projects. He is a frequent community TA at Coursera in the social network analysis course from the University of Michigan and the practical machine learning course from Johns Hopkins University. His programming languages of choice are Python and R. Currently, he is the cofounder of Quandbee (http://www.quandbee.com/), a company providing machine learning and artificial intelligence applications at scale.

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Luca Massaron

Luca Massaron

Having joined Kaggle over 10 years ago, Luca Massaron is a Kaggle Grandmaster in discussions and a Kaggle Master in competitions and notebooks. In Kaggle competitions he reached no. 7 in the worldwide rankings. On the professional side, Luca is a data scientist with more than a decade of experience in transforming data into smarter artifacts, solving real-world problems, and generating value for businesses and stakeholders. He is a Google Developer Expert(GDE) in machine learning and the author of best-selling books on AI, machine learning, and algorithms.

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Alberto Boschetti

Alberto Boschetti

Alberto Boschetti is a data scientist with expertise in signal processing and statistics. He holds a Ph.D. in telecommunication engineering and currently lives and works in London. In his work projects, he faces challenges ranging from natural language processing (NLP) and behavioral analysis to machine learning and distributed processing. He is very passionate about his job and always tries to stay updated about the latest developments in data science technologies, attending meet-ups, conferences, and other events.

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