Representation Learning

This research topic is very important in machine learning and a first step for all kinds of machine learning algorithms. Having a robust representation definitely plays a vital role for good performance/accuracy.

Recent progress of representation learning is using deep architecture (normally referring to deep learning). Here I collect some of very good papers worth reading:

1) Representation Learning: A Review and New Perspectives (Yoshua Bengio et al., 2014)

2) Deep Learning of Representations for Unsupervised and Transfer Learning (Yoshua Bengio et al., 2012)

3) ...

Competitive Programming Book

Link: https://sites.google.com/site/stevenhalim/
Intro: for programming contests

VisuAlgo

Link: http://visualgo.net/

Intro:  a tool to help his students better understand data structures and algorithms, by allowing them to learn the basics on their own and at their own pace.

CMPH

Link: http://cmph.sourceforge.net/

Intro: A perfect hash function maps a static set of n keys into a set of m integer numbers without collisions, where m is greater than or equal to n. If m is equal to n, the function is called minimal.

Open-source implementation of Boostexter

Link: http://code.google.com/p/icsiboost/

Intro: Boosting is a meta-learning approach that aims at combining an ensemble of weak classifiers to form a strong classifier. Adaptive Boosting (Adaboost) is a greedy search for a linear combination of classifiers by overweighting the examples that are misclassified by each classifier. icsiboost implements Adaboost over stumps (one-level decision trees) on discrete and continuous attributes (words and real values)

LEDA

A tool supporting learning algorithms
http://www.cs.sunysb.edu/%7Ealgorith/implement/LEDA/implement.shtml

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Cheers,
Vu

Topic modeling toolkit

MALLET: http://mallet.cs.umass.edu/
A famous tool supporting various algorithms in exploring latent topics in raw texts.

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Cheers,
Vu