Apache Mahout

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June 25, 2008 by chase-me http://www.flickr.com/photos/sasy/2609508999
Page 1 and 2: Apache Mahout Large Scale Machine L
Page 3 and 4: Hello DAI!
Page 5 and 6: January 3, 2006 by Matt Callow http
Page 7 and 8: September 21, 2008, Rodrigo Galinde
Page 9 and 10: Machine learning - what's that?
Page 11: Archimedes model of nature
Page 15 and 16: Goals
Page 17 and 18: Open source machine learning: Chall
Page 19 and 20: http://www.fickr.com/photos/jaaronf
Page 21 and 22: Open source developers Developers w
Page 23 and 24: Developers world wide Developers in
Page 25 and 26: January 11, 2007, skreuzer http://w
Page 27 and 28: September 10, 2007 by .sanden. http
Page 29 and 30: We need a solution that: Is easy to
Page 32 and 33: Contributions need not be Hadoop ba
Page 34 and 35: Hadoop by example
Page 36 and 37: pattern=”http://[0-9A-Za-z\-_\.]*
Page 38 and 39: Assumptions: Moving data is expensi
Page 40 and 41: M A P | SHUFFLE | R E D U C E outpu
Page 42 and 43: r M A P | SHUFFLE | R E D U C E out
Page 44 and 45: What was left out ● ● ● ●
Page 46 and 47: Strategies for parallel machine lea
Page 48 and 49: Step 2: Parallelization strategies.
Page 54 and 55: What does Mahout have to offer.
Page 56 and 57: Discover groups of similar items
Page 62 and 63:
Until stable.
Page 64 and 65:
Until stable. Data intensive. Outpu
Page 66 and 67:
Assign items to defined categories.
Page 68 and 69:
Recommendation mining. ● Recommen
Page 70 and 71:
Release: 0.1 Big Thanks to those wh
Page 72 and 73:
What next? ● ● ● ● 2 nd Sum
Page 74 and 75:
Reasons: Paid, new developers. Prov
Page 76 and 77:
GSoC Timeline ● ● ● ● ●
Page 78 and 79:
Why go for Apache Mahout?
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Discuss ideas and problems online.
Page 82 and 83:
mahout-user@lucene.apache.org mahou
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mahout-user@lucene.apache.org mahou
Page 88 and 89:
Open questions. Gather data Extract
Page 90 and 91:
Algor. choice Parameters Gather dat
Page 92 and 93:
Algor. choice Parameters Train mode
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Apache Mahout

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