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Data visualization in one of three most important steps in data mining (https://github.com/entaroadun/hnpickup#readme). Often times, it's impossible to understand data without proper visualization. I went looking for great tools to do that.
Two website list recent JS visualization frameworks:
- http://www.splashnology.com/article/15-awesome-free-javascript-charts/325/
- http://sixrevisions.com/javascript/20-fresh-javascript-data-visualization-libraries/
Most of them are available on Github. My three favorite:
Movies Recommendation:
- MovieLens - Movie Recommendation Data Sets http://www.grouplens.org/node/73
- Yahoo! - Movie, Music, and Images Ratings Data Sets http://webscope.sandbox.yahoo.com/catalog.php?datatype=r
- Jester - Movie Ratings Data Sets (Collaborative Filtering Dataset) http://www.ieor.berkeley.edu/~goldberg/jester-data/
- Cornell University - Movie-review data for use in sentiment-analysis experiments http://www.cs.cornell.edu/people/pabo/movie-review-data/
Music Recommendation:
- Last.fm - Music Recommendation Data Sets http://www.dtic.upf.edu/~ocelma/MusicRecommendationDataset/index.html
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#!/bin/bash -e | |
# NB: the file is named git-incr-build.sh to let Gist colorize, but you should name it git-incr-build | |
# Put this script in your PATH and call it this way: | |
# git incr-build clean install | |
# Can be combined with git pull --rebase and git push to make an "fast & safe push" script | |
function branchName { | |
git rev-parse --symbolic-full-name --abbrev-ref $1 | |
} |
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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 |