Riivo riivo

General Background and Overview

Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
Models and Issues in Data Stream Systems
Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
[Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t

This simple script will take a picture of a whiteboard and use parts of the ImageMagick library with sane defaults to clean it up tremendously.

The script is here:

#!/bin/bash
convert "$1" -morphology Convolve DoG:15,100,0 -negate -normalize -blur 0x1 -channel RBG -level 60%,91%,0.1 "$2"

If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.

	"""
	Implementation of pairwise ranking using scikit-learn LinearSVC

	Reference:

	"Large Margin Rank Boundaries for Ordinal Regression", R. Herbrich,
	T. Graepel, K. Obermayer 1999

	"Learning to rank from medical imaging data." Pedregosa, Fabian, et al.,
	Machine Learning in Medical Imaging 2012.

	"""
	Implementation of pairwise ranking using scikit-learn LinearSVC

	Reference: "Large Margin Rank Boundaries for Ordinal Regression", R. Herbrich,
	T. Graepel, K. Obermayer.

	Authors: Fabian Pedregosa <[email protected]>
	Alexandre Gramfort <[email protected]>
	"""

	"""
	(C) August 2013, Mathieu Blondel
	# License: BSD 3 clause

	This is a Numba-based reimplementation of the block coordinate descent solver
	(without line search) described in the paper:

	Block Coordinate Descent Algorithms for Large-scale Sparse Multiclass
	Classification. Mathieu Blondel, Kazuhiro Seki, and Kuniaki Uehara.
	Machine Learning, May 2013.

	// Just before switching jobs:
	// Add one of these.
	// Preferably into the same commit where you do a large merge.
	//
	// This started as a tweet with a joke of "C++ pro-tip: #define private public",
	// and then it quickly escalated into more and more evil suggestions.
	// I've tried to capture interesting suggestions here.
	//
	// Contributors: @r2d2rigo, @joeldevahl, @msinilo, @_Humus_,
	// @YuriyODonnell, @rygorous, @cmuratori, @mike_acton, @grumpygiant,

	# Copyright (C) 2013 Istituto per l'Interscambio Scientifico I.S.I.
	# You can contact us by email ([email protected]) or write to:
	# ISI Foundation, Via Alassio 11/c, 10126 Torino, Italy.
	#
	# This work is licensed under a Creative Commons 4.0
	# Attribution-NonCommercial-ShareAlike License
	# You may obtain a copy of the License at
	# http://creativecommons.org/licenses/by-nc-sa/4.0/
	#
	# This program was written by Andre Panisson <[email protected]> at

	"""
	A deep neural network with or w/o dropout in one file.

	License: Do What The Fuck You Want to Public License http://www.wtfpl.net/
	"""

	import numpy, theano, sys, math
	from theano import tensor as T
	from theano import shared
	from theano.tensor.shared_randomstreams import RandomStreams

	'''
	Non-parametric computation of entropy and mutual-information

	Adapted by G Varoquaux for code created by R Brette, itself
	from several papers (see in the code).

	This code is maintained at https://github.com/mutualinfo/mutual_info
	Please download the latest code there, to have improvements and
	bug fixes.