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.

Related Content you might be interested in

Current Title:

Small book image
Large Scale Machine Learning with Python
Luca Massaron | Bastiaan Sjardin | Alberto Boschetti
Aug, 2016 | 420 pages
No titles found
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

Neural networks and regularization

Even though we didn't overtrain our model in our last example, it is necessary to think about regularization strategies for neural networks. Three of the most widely-used ways in which we can apply regularization to a neural network are as follows:

  • L1 and L2 regularization with weight decay as a parameter for the regularization strength

  • Dropout means that deactivating units within the neural network at random can force other units in the network to take over

    On the left hand, we see an architecture with dropout applied, randomly deactivating units in the network. On the right hand, we see an ordinary neural network (marked with X).

  • Averaging or ensembling multiple neural networks (each with different settings)

Let's try dropout for this model and see if works:

clf = Classifier(
    layers=[
    Layer("Rectifier", units=13),
    Layer("Rectifier", units=13),
    Layer("Softmax")],
    learning_rate=0.01,
    n_iter=2000,
    learning_rule='nesterov',
    regularize...
Previous Section
End of Section 3
Next Section