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    <description>recent bookmarks from infovore</description>
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      <rdf:Seq>	<rdf:li rdf:resource="https://minimaxir.com/2020/07/gpt3-expectations/"/>
	<rdf:li rdf:resource="https://gen.medium.com/the-bs-industrial-complex-of-phony-a-i-44bf1c0c60f8"/>
	<rdf:li rdf:resource="https://www.fast.ai/2019/01/24/course-v3/"/>
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	<rdf:li rdf:resource="https://www.theatlantic.com/technology/archive/2017/07/marion-tinsley-checkers/534111/"/>
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	<rdf:li rdf:resource="http://metacademy.org/roadmaps/cjrd/level-up-your-ml"/>
	<rdf:li rdf:resource="http://www.joyent.com/blog/introducing-kartlytics-mario-kart-64-analytics"/>
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	<rdf:li rdf:resource="https://gist.github.com/3888345"/>
	<rdf:li rdf:resource="https://kb.osu.edu/dspace/bitstream/handle/1811/48548/EMR000091a-Hirjee_Brown.pdf"/>
	<rdf:li rdf:resource="http://www.dataists.com/2010/09/a-taxonomy-of-data-science/"/>
	<rdf:li rdf:resource="http://dataspora.com/blog/the-seven-secrets-of-successful-data-scientists/"/>
	<rdf:li rdf:resource="http://www.moserware.com/2010/03/computing-your-skill.html"/>
	<rdf:li rdf:resource="http://shorttermmemoryloss.com/menace/"/>
	<rdf:li rdf:resource="http://abeautifulwww.com/2009/10/11/guide-to-getting-started-in-machine-learning/"/>
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  </channel><item rdf:about="https://minimaxir.com/2020/07/gpt3-expectations/">
    <title>Tempering Expectations for GPT-3 and OpenAI’s API | Max Woolf's Blog</title>
    <dc:date>2020-07-19T15:20:14+00:00</dc:date>
    <link>https://minimaxir.com/2020/07/gpt3-expectations/</link>
    <dc:creator>infovore</dc:creator><description><![CDATA["That demo got the attention of venture capitalists. And when a cool-looking magical thing gets the attention of venture capitalists, discourse tends to spiral out of control." Good, even-handed look at GPT3. It's both impressive and unexciting for me - there are so many underlying issues besides the 'magic', not to mention the relative failure rate, the complexity of any real-world deployment, and as ever, a lack of nuance in a lot of media about discussing text-generation. This lays out some of the points with the latter well.]]></description>
<dc:subject>ai gpt3 magicalthinking textgeneration machinelearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:infovore/b:61c111b3d7e6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:gpt3"/>
	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:magicalthinking"/>
	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:textgeneration"/>
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<item rdf:about="https://gen.medium.com/the-bs-industrial-complex-of-phony-a-i-44bf1c0c60f8">
    <title>The BS-Industrial Complex of Phony A.I. – GEN</title>
    <dc:date>2019-06-24T10:10:27+00:00</dc:date>
    <link>https://gen.medium.com/the-bs-industrial-complex-of-phony-a-i-44bf1c0c60f8</link>
    <dc:creator>infovore</dc:creator><description><![CDATA["In this way, Dynamic Yield is part of a generation of companies whose core technology, while extremely useful, is powered by artificial intelligence that is roughly as good as a 24-year-old analyst at Goldman Sachs with a big dataset and a few lines of Adderall."

This is good - and largely well written, bar an unnecessary cheap shot at one point. It overlaps with lots of what I have to teach students about AI: namely, those letter have become this huge suitcase concept for anything from gnarly machine learning problems and recurrent neural networks down to applied statistics and a splash of arithmetic. And meanwhile, everyone just keeps adding to this cyclone of nonsense as they try to out-claim one another. It's exhausting, and it pollutes the public sphere, such that inexperts - politicians, policymakers - get themselves tangled up about all the wrong things. Sigh.]]></description>
<dc:subject>ai artificialintelligence machinelearning bullshit suitcasewords marketing pr</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:infovore/b:bc2112218320/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:ai"/>
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<item rdf:about="https://www.fast.ai/2019/01/24/course-v3/">
    <title>Practical Deep Learning for Coders 2019 · fast.ai</title>
    <dc:date>2019-01-27T23:02:33+00:00</dc:date>
    <link>https://www.fast.ai/2019/01/24/course-v3/</link>
    <dc:creator>infovore</dc:creator><description><![CDATA[Highly recommended by SimonW. Possibly something to prod.]]></description>
<dc:subject>ai machinelearning ml programming python deeplearning classification</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:infovore/b:4862fbfe5ea5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:ml"/>
	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:deeplearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:classification"/>
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<item rdf:about="https://leapfrog.nl/blog/archives/2018/09/19/unboxing-at-behavior-design-amsterdam-16/">
    <title>‘Unboxing’ at Behavior Design Amsterdam #16 - Leapfroglog</title>
    <dc:date>2018-09-27T12:11:00+00:00</dc:date>
    <link>https://leapfrog.nl/blog/archives/2018/09/19/unboxing-at-behavior-design-amsterdam-16/</link>
    <dc:creator>infovore</dc:creator><description><![CDATA[This is very good stuff from Kars: from the challenges of designing with machine learning through to Value Sensitive Design and the complexity of good work.]]></description>
<dc:subject>karsalfrink design machinelearning ml ethics values</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:infovore/b:9cd32429cb65/</dc:identifier>
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<item rdf:about="https://lukeoakdenrayner.wordpress.com/2017/12/18/the-chestxray14-dataset-problems/">
    <title>Exploring the ChestXray14 dataset: problems – Luke Oakden-Rayner</title>
    <dc:date>2017-12-20T12:56:39+00:00</dc:date>
    <link>https://lukeoakdenrayner.wordpress.com/2017/12/18/the-chestxray14-dataset-problems/</link>
    <dc:creator>infovore</dc:creator><description><![CDATA[On the problems of machine-learning and medical data.]]></description>
<dc:subject>medicine ml machinelearning data computervision</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:infovore/b:f8d4cb5af349/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:medicine"/>
	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:ml"/>
	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:machinelearning"/>
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<item rdf:about="https://www.theatlantic.com/technology/archive/2017/07/marion-tinsley-checkers/534111/">
    <title>How Checkers Was Solved - The Atlantic</title>
    <dc:date>2017-07-19T18:27:15+00:00</dc:date>
    <link>https://www.theatlantic.com/technology/archive/2017/07/marion-tinsley-checkers/534111/</link>
    <dc:creator>infovore</dc:creator><description><![CDATA["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.]]></description>
<dc:subject>ai games religion checkers draughts machinelearning journalism alexismadrigal</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:infovore/b:da560d70369c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:ai"/>
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</item>
<item rdf:about="https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471#.tnj89uhgl">
    <title>Machine Learning is Fun! — Medium</title>
    <dc:date>2016-07-30T12:33:09+00:00</dc:date>
    <link>https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471#.tnj89uhgl</link>
    <dc:creator>infovore</dc:creator><description><![CDATA[Only read part one so far, but is proving useful for at least wrapping my head around a few concepts.]]></description>
<dc:subject>machinelearning programming</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:infovore/b:92327b18323d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:programming"/>
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</item>
<item rdf:about="https://github.com/karpathy/char-rnn">
    <title>karpathy/char-rnn: Multi-layer Recurrent Neural Networks (LSTM, GRU, RNN) for character-level language models in Torch</title>
    <dc:date>2016-03-12T11:51:14+00:00</dc:date>
    <link>https://github.com/karpathy/char-rnn</link>
    <dc:creator>infovore</dc:creator><description><![CDATA[As used by fullest.house - perhaps something to play with in due course.]]></description>
<dc:subject>machinelearning lua neuralnetworks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:infovore/b:800078b4ea8b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:lua"/>
	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:neuralnetworks"/>
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</item>
<item rdf:about="http://metacademy.org/roadmaps/cjrd/level-up-your-ml">
    <title>Metacademy - Level-Up Your Machine Learning</title>
    <dc:date>2014-08-19T19:33:12+00:00</dc:date>
    <link>http://metacademy.org/roadmaps/cjrd/level-up-your-ml</link>
    <dc:creator>infovore</dc:creator><description><![CDATA[Some useful reference points in here - bookmarking for when I actually have time to reutrn to it.]]></description>
<dc:subject>books machinelearning computing</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:infovore/b:e48d6c59d4b0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:books"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:computing"/>
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</item>
<item rdf:about="http://www.joyent.com/blog/introducing-kartlytics-mario-kart-64-analytics">
    <title>Kartlytics: Applying Big Data Analytics to Mario Kart - Blog - Joyent</title>
    <dc:date>2013-08-11T15:19:02+00:00</dc:date>
    <link>http://www.joyent.com/blog/introducing-kartlytics-mario-kart-64-analytics</link>
    <dc:creator>infovore</dc:creator><description><![CDATA["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.]]></description>
<dc:subject>analytics ocr computervision machinelearning mariokart</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:infovore/b:a7ced9aedb92/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:analytics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:ocr"/>
	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:computervision"/>
	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:machinelearning"/>
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</item>
<item rdf:about="https://github.com/zolrath/marky_markov">
    <title>zolrath/marky_markov · GitHub</title>
    <dc:date>2013-06-11T08:02:50+00:00</dc:date>
    <link>https://github.com/zolrath/marky_markov</link>
    <dc:creator>infovore</dc:creator><description><![CDATA["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.]]></description>
<dc:subject>markovchains ruby machinelearning statistics textgeneration</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:infovore/b:002085ac8f5c/</dc:identifier>
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</item>
<item rdf:about="https://gist.github.com/3888345">
    <title>Some pointers for Natural Language Processing / Machine Learning — Gist</title>
    <dc:date>2012-10-14T18:59:22+00:00</dc:date>
    <link>https://gist.github.com/3888345</link>
    <dc:creator>infovore</dc:creator><description><![CDATA[MattB writes down his tips for language processing/machine learning; useful that somebody's done this.]]></description>
<dc:subject>machinelearning naturallanguage processing programming</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:infovore/b:9daa2e52faa3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:naturallanguage"/>
	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:processing"/>
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</item>
<item rdf:about="https://kb.osu.edu/dspace/bitstream/handle/1811/48548/EMR000091a-Hirjee_Brown.pdf">
    <title>Using Automated Rhyme Detection to Characterize Rhyming Style in Rap Music</title>
    <dc:date>2012-03-23T16:21:57+00:00</dc:date>
    <link>https://kb.osu.edu/dspace/bitstream/handle/1811/48548/EMR000091a-Hirjee_Brown.pdf</link>
    <dc:creator>infovore</dc:creator><description><![CDATA["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.]]></description>
<dc:subject>rap lyrics rhyme computation machinelearning paper awesome</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:infovore/b:84604bb114b6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:rap"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:rhyme"/>
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</item>
<item rdf:about="http://www.dataists.com/2010/09/a-taxonomy-of-data-science/">
    <title>dataists » Blog Archive » A Taxonomy of Data Science</title>
    <dc:date>2010-09-28T09:53:04+00:00</dc:date>
    <link>http://www.dataists.com/2010/09/a-taxonomy-of-data-science/</link>
    <dc:creator>infovore</dc:creator><description><![CDATA["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.
]]></description>
<dc:subject>data machinelearning datascience blog</dc:subject>
<dc:identifier>https://pinboard.in/u:infovore/b:853f6908d46d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:datascience"/>
	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:blog"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://dataspora.com/blog/the-seven-secrets-of-successful-data-scientists/">
    <title>The Seven Secrets of Successful Data Scientists : Dataspora Blog</title>
    <dc:date>2010-09-03T09:18:24+00:00</dc:date>
    <link>http://dataspora.com/blog/the-seven-secrets-of-successful-data-scientists/</link>
    <dc:creator>infovore</dc:creator><description><![CDATA["...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.
]]></description>
<dc:subject>bigdata data datamining statistics machinelearning</dc:subject>
<dc:identifier>https://pinboard.in/u:infovore/b:33d020ec7bef/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:bigdata"/>
	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:datamining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:machinelearning"/>
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</item>
<item rdf:about="http://www.moserware.com/2010/03/computing-your-skill.html">
    <title>Moserware: Computing Your Skill</title>
    <dc:date>2010-07-21T13:36:31+00:00</dc:date>
    <link>http://www.moserware.com/2010/03/computing-your-skill.html</link>
    <dc:creator>infovore</dc:creator><description><![CDATA[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...
]]></description>
<dc:subject>trueskill machinelearning programming games algorithms probability skill</dc:subject>
<dc:identifier>https://pinboard.in/u:infovore/b:66ad594bd640/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:trueskill"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:games"/>
	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:probability"/>
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</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://shorttermmemoryloss.com/menace/">
    <title>A New Theory of Awesomeness and Miracles, by James Bridle</title>
    <dc:date>2009-11-02T14:11:35+00:00</dc:date>
    <link>http://shorttermmemoryloss.com/menace/</link>
    <dc:creator>infovore</dc:creator><description><![CDATA["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...
]]></description>
<dc:subject>machinelearning complexity games jamesbridle literature mathematics donaldmichie menace</dc:subject>
<dc:identifier>https://pinboard.in/u:infovore/b:ad54ae82b6f6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:games"/>
	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:jamesbridle"/>
	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:literature"/>
	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:mathematics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:donaldmichie"/>
	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:menace"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://abeautifulwww.com/2009/10/11/guide-to-getting-started-in-machine-learning/">
    <title>Guide to Getting Started in Machine Learning | A Beautiful WWW</title>
    <dc:date>2009-10-20T14:21:45+00:00</dc:date>
    <link>http://abeautifulwww.com/2009/10/11/guide-to-getting-started-in-machine-learning/</link>
    <dc:creator>infovore</dc:creator><description><![CDATA["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.
]]></description>
<dc:subject>r datamining programming machinelearning statistics</dc:subject>
<dc:identifier>https://pinboard.in/u:infovore/b:9908c39e5b0a/</dc:identifier>
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</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://web.media.mit.edu/~dustin/rubyai.html">
    <title>AI Ruby Plugins</title>
    <dc:date>2009-10-16T15:01:53+00:00</dc:date>
    <link>http://web.media.mit.edu/~dustin/rubyai.html</link>
    <dc:creator>infovore</dc:creator><description><![CDATA["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...
]]></description>
<dc:subject>ruby ai machinelearning collectiveintelligence algorithms software libraries gems</dc:subject>
<dc:identifier>https://pinboard.in/u:infovore/b:2e5ae1ff2d2e/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:collectiveintelligence"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:software"/>
	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:libraries"/>
	<rdf:li rdf:resource="https://pinboard.in/u:infovore/t:gems"/>
</rdf:Bag></taxo:topics>
</item>
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