Machine Learning resources

Great resources for anyone who would like to learn more about Machine Learning:

  • Apache Mahout – machine learning library for Hadoop.
  • brain – Neural networks in JavaScript.
  • Cloudera Oryx – real-time large-scale machine learning.
  • Concurrent Pattern – machine learning library for Cascading.
  • convnetjs – Deep Learning in Javascript. Train Convolutional Neural Networks (or ordinary ones) in your browser.
  • Decider – Flexible and Extensible Machine Learning in Ruby.
  • etcML – text classification with machine learning.
  • Etsy Conjecture – scalable Machine Learning in Scalding.
  • H2O – statistical, machine learning and math runtime for Hadoop.
  • MLbase – distributed machine learning libraries for the BDAS stack.
  • MLPNeuralNet – Fast multilayer perceptron neural network library for iOS and Mac OS X.
  • nupic – Numenta Platform for Intelligent Computing: a brain-inspired machine intelligence platform, and biologically accurate neural network based on cortical learning algorithms.
  • PredictionIO – machine learning server built on Hadoop, Mahout and Cascading.
  • scikit-learn – scikit-learn: machine learning in Python.
  • Spark MLlib – a Spark implementation of some common machine learning (ML) functionality.
  • Vowpal Wabbit – learning system sponsored by Microsoft and Yahoo!.
  • WEKA – suite of machine learning software.

Get the full list here »

Face and eyes detection using Haar Cascades

OpenCV algorithm is currently using the following Haar-like features which are the input to the basic classifiers:

Haar-like features

Haar-like features

Cascading is a particular case of ensemble learning based on the concatenation of several classifiers, using all information collected from the output from a given classifier as additional information for the next classifier in the cascade. Unlike voting or stacking ensembles, which are multi-expert systems, cascading is a multistage one.

stages Pictures source

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