Book Image

Large Scale Machine Learning with Python

By : Luca Massaron, Bastiaan Sjardin, Alberto Boschetti
Book Image

Large Scale Machine Learning with Python

By: Luca Massaron, 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.

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Large Scale Machine Learning with Python
Luca Massaron | Bastiaan Sjardin | Alberto Boschetti
Aug, 2016 | 420 pages
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Table of Contents (17 chapters)
Large Scale Machine Learning with Python
Large Scale Machine Learning with Python
Credits
Credits
About the Authors
About the Authors
About the Reviewer
About the Reviewer
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
First Steps to Scalability
First Steps to Scalability
Explaining scalability in detail
Python for large scale machine learning
Python packages
Summary
2
Scalable Learning in Scikit-learn
Scalable Learning in Scikit-learn
Out-of-core learning
Streaming data from sources
Stochastic learning
Feature management with data streams
Summary
3
Fast SVM Implementations
Fast SVM Implementations
Datasets to experiment with on your own
Support Vector Machines
Feature selection by regularization
Including non-linearity in SGD
Hyperparameter tuning
Summary
4
Neural Networks and Deep Learning
Neural Networks and Deep Learning
The neural network architecture
Neural networks and regularization
Neural networks and hyperparameter optimization
Neural networks and decision boundaries
Deep learning at scale with H2O
Deep learning and unsupervised pretraining
Deep learning with theanets
Autoencoders and unsupervised learning
Summary
5
Deep Learning with TensorFlow
Deep Learning with TensorFlow
TensorFlow installation
Machine learning on TensorFlow with SkFlow
Keras and TensorFlow installation
Convolutional Neural Networks in TensorFlow through Keras
CNN's with an incremental approach
GPU Computing
Summary
6
Classification and Regression Trees at Scale
Classification and Regression Trees at Scale
Bootstrap aggregation
Random forest and extremely randomized forest
Fast parameter optimization with randomized search
CART and boosting
XGBoost
Out-of-core CART with H2O
Summary
7
Unsupervised Learning at Scale
Unsupervised Learning at Scale
Unsupervised methods
Feature decomposition – PCA
PCA with H2O
Clustering – K-means
K-means with H2O
LDA
Summary
8
Distributed Environments – Hadoop and Spark
Distributed Environments – Hadoop and Spark
From a standalone machine to a bunch of nodes
Setting up the VM
The Hadoop ecosystem
Spark
Summary
9
Practical Machine Learning with Spark
Practical Machine Learning with Spark
Setting up the VM for this chapter
Sharing variables across cluster nodes
Data preprocessing in Spark
Machine learning with Spark
Summary
Introduction to GPUs and Theano
Introduction to GPUs and Theano
GPU computing
Theano – parallel computing on the GPU
Installing Theano
Index
Index

About the Authors

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.

Luca Massaron is a data scientist and marketing research director who is specialized in multivariate statistical analysis, machine learning, and customer insight, with over a decade of experience in solving real-world problems and generating value for stakeholders by applying reasoning, statistics, data mining, and algorithms. From being a pioneer of Web audience analysis in Italy to achieving the rank of a top ten Kaggler, he has always been very passionate about everything regarding data and its analysis, and also about demonstrating the potential of data-driven knowledge discovery to both experts and non-experts. Favoring simplicity over unnecessary sophistication, he believes that a lot can be achieved in data science just by doing the essentials.

I would like to thank Yukiko and Amelia for their continued support, help, and loving patience.

Alberto Boschetti is a data scientist with expertise in signal processing and statistics. He holds a PhD in telecommunication engineering and currently lives and works in London. In his work projects, he faces challenges that span from natural language processing (NLP) and machine learning to 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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