dgrtwo

library(tidyverse)

# Data is downloaded from here:
# https://www.kaggle.com/c/digit-recognizer
kaggle_data <- read_csv("~/Downloads/train.csv")

pixels_gathered <- kaggle_data %>%
  mutate(instance = row_number()) %>%
  gather(pixel, value, -label, -instance) %>%
  extract(pixel, "pixel", "(\\d+)", convert = TRUE)

raineorshine

An Aesthetic Comparison of Human-Readable
Hashing Functions

The following compares the output of several creative hash functions designed for human readability.

sha1's are merely used as arbitrary, longer, distributed input values.

zacharyvoase/humanhash

input	1 word output	2 word output	3 word output

calstad

Quick List of Resources for Topological Data Analysis with Emphasis on Machine Learning

This is just a quick list of resourses on TDA that I put together for @rickasaurus after he was asking for links to papers, books, etc on Twitter and is by no means an exhaustive list.

Survey Papers

Both Carlsson's and Ghrist's survey papers offer a very good introduction to the subject

• Topology and Data by Gunnar Carlsson
• Barcodes: The Persistent Topology of Data by Robert Ghrist

Other Papers and Web Resources

• Extracting insights from the shape of complex data using topology A good introductory paper in Nature on the Mapper algorithm.

mblondel

"""
Multiclass SVMs (Crammer-Singer formulation).

A pure Python re-implementation of:

Large-scale Multiclass Support Vector Machine Training via Euclidean Projection onto the Simplex.
Mathieu Blondel, Akinori Fujino, and Naonori Ueda.
ICPR 2014.
http://www.mblondel.org/publications/mblondel-icpr2014.pdf
"""

ehedaya

   Wordlist ver 0.732 - EXPECT INCOMPATIBLE CHANGES;
  acrobat  africa   alaska   albert   albino   album    
  alcohol  alex     alpha    amadeus  amanda   amazon   
  america  analog   animal   antenna  antonio  apollo   
  april    aroma    artist   aspirin  athlete  atlas    
  banana   bandit   banjo    bikini   bingo    bonus    
  camera   canada   carbon   casino   catalog  cinema   
  citizen  cobra    comet    compact  complex  context  
  credit   critic   crystal  culture  david    delta    
  dialog   diploma  doctor   domino   dragon   drama    

omangin

""" Non-negative matrix factorization for I divergence

    This code was implements Lee and Seung's multiplicative updates algorithm
    for NMF with I divergence cost.

    Lee D. D., Seung H. S., Learning the parts of objects by non-negative
      matrix factorization. Nature, 1999
"""
# Author: Olivier Mangin <olivier.mangin@inria.fr>

dmglab

---

Note: this is a summary of different git workflows putting together to a small git bible. references are in between the text

---

How to Branch

try to keep your hacking out of the master and create feature branches. the [feature-branch workflow][4] is a good median between noobs (i have no idea how to branch) and git veterans (let's do some rocket sience with git branches!). everybody get the idea!

Basic usage examples

debasishg

General Background and Overview

1. 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.
2. Models and Issues in Data Stream Systems
3. 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
4. Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
5. [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t

jboner

Latency Comparison Numbers (~2012)
----------------------------------
L1 cache reference                           0.5 ns
Branch mispredict                            5   ns
L2 cache reference                           7   ns                      14x L1 cache
Mutex lock/unlock                           25   ns
Main memory reference                      100   ns                      20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy             3,000   ns        3 us
Send 1K bytes over 1 Gbps network       10,000   ns       10 us
Read 4K randomly from SSD*             150,000   ns      150 us          ~1GB/sec SSD

agramfort

"""
Non-Negative Garotte implementation with the scikit-learn
"""

# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
#         Jaques Grobler (__main__ script) <jaques.grobler@inria.fr>
#
# License: BSD Style.

import numpy as np