infovore + machinelearning   14

How Checkers Was Solved - The Atlantic
"From 1950 to 1990, Tinsley had been the world champion of checkers whenever he wanted to be. He’d occasionally retire to work on mathematics or devote himself to religious study, but he’d eventually return, beat everyone and become champion again. In that 40-year span, he lost five total games and never once dropped a match." Brilliant article from Alexis Madrigal on the race to solve draughts/checkers, one man and his computer, and another man and his faith.
ai  games  religion  checkers  draughts  machinelearning  journalism  alexismadrigal 
4 weeks ago by infovore
Machine Learning is Fun! — Medium
Only read part one so far, but is proving useful for at least wrapping my head around a few concepts.
machinelearning  programming 
july 2016 by infovore
Metacademy - Level-Up Your Machine Learning
Some useful reference points in here - bookmarking for when I actually have time to reutrn to it.
books  machinelearning  computing 
august 2014 by infovore
Kartlytics: Applying Big Data Analytics to Mario Kart - Blog - Joyent
"As serious intellectuals often do, we spent hours discussing these questions, what data we would want to collect to answer them, and even how we might go about collecting it. It sounded like a fun project, so I wrote a program that takes video captures of our Mario Kart 64 sessions and picks out when each race starts, which character is in each box on the screen, the rank of each player as the race progresses, and finally when the race finishes. Then I built a web client that lets us upload videos, record who played which character in each race, and browse the aggregated stats. The result is called Kartlytics, and now contains videos of over 230 races from over the last year and change." Yes, it's a plug for manta, but it's also a nifty piece of engineering.
analytics  ocr  computervision  machinelearning  mariokart 
august 2013 by infovore
zolrath/marky_markov · GitHub
"Marky Markov is an experiment in Markov Chain generation implemented in Ruby. It can be used both from the command-line and as a library within your code." It's very fast, and basically does all the work I've been doing on my projects by hand for me. But better.
markovchains  ruby  machinelearning  statistics  textgeneration 
june 2013 by infovore
Some pointers for Natural Language Processing / Machine Learning — Gist
MattB writes down his tips for language processing/machine learning; useful that somebody's done this.
machinelearning  naturallanguage  processing  programming 
october 2012 by infovore
Using Automated Rhyme Detection to Characterize Rhyming Style in Rap Music
"Imperfect and internal rhymes are two important features in rap music previously ignored in the music information retrieval literature. We developed a method of scoring potential rhymes using a probabilistic model based on phoneme frequencies in rap lyrics. We used this scoring scheme to automatically identify internal and line-final rhymes in song lyrics and demonstrated the performance of this method compared to rules-based models. We then calculated higher-level rhyme features and used them to compare rhyming styles in song lyrics from different genres, and for different rap artists. We found that these detected features corresponded to real- world descriptions of rhyming style and were strongly characteristic of different rappers, resulting in potential applications to style-based comparison, music recommendation, and authorship identification." Awesome, and something I am going to sit down and read properly.
rap  lyrics  rhyme  computation  machinelearning  paper  awesome 
march 2012 by infovore
dataists » Blog Archive » A Taxonomy of Data Science
"Both within the academy and within tech startups, we’ve been hearing some similar questions lately: Where can I find a good data scientist? What do I need to learn to become a data scientist? Or more succinctly: What is data science?" Great starting point; looking forward to more from the blog.
data  machinelearning  datascience  blog 
september 2010 by infovore
The Seven Secrets of Successful Data Scientists : Dataspora Blog
"...don’t confuse this kind of data exploration, where the goal is to size up the data, with building proper data plumbing, where you want robustness and maintainability. Perl and bash scripts are nice for the former, but can be a nightmare for building data pipelines." Lots of good stuff in this article; this was a highlight.
bigdata  data  datamining  statistics  machinelearning 
september 2010 by infovore
Moserware: Computing Your Skill
Excellent, detailed article on how Microsoft calculate TrueSkill - an algorithm for matching you to players about in your skill level. This is what is used every time you hit "game with strangers" on an XBL title, basically. Fascinating, detailed, not too challenging if you take it slow/steady - and the implementation is on github...
trueskill  machinelearning  programming  games  algorithms  probability  skill 
july 2010 by infovore
A New Theory of Awesomeness and Miracles, by James Bridle
"Being NOTES and SLIDES on a talk given at PLAYFUL 09, concerning CHARLES BABBAGE, HEATH ROBINSON, MENACE and MAGE" Awesome; shame I couldn't be there. I wondered where that link about Michie had come from a few weeks ago...
machinelearning  complexity  games  jamesbridle  literature  mathematics  donaldmichie  menace 
november 2009 by infovore
Guide to Getting Started in Machine Learning | A Beautiful WWW
"Someone at work recently asked how he should go about studying machine learning on his own. So I’m putting together a little guide." Ooh, useful. Lots of starting points for machine learning in R.
r  datamining  programming  machinelearning  statistics 
october 2009 by infovore
AI Ruby Plugins
"This page will maintain list of AI related libraries for the Ruby programming language." Some interesting stuff here, although it's all in varying degrees of maturity...
ruby  ai  machinelearning  collectiveintelligence  algorithms  software  libraries  gems 
october 2009 by infovore

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