Mercurial > pylearn
annotate doc/v2_planning/existing_python_ml_libraries.txt @ 1069:16ea3e5c5a7a
api_optimization: Couple questions
author | Olivier Delalleau <delallea@iro> |
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date | Fri, 10 Sep 2010 10:28:34 -0400 |
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1 Committee members: GD, DWF, IG, DE |
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2 |
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3 This committee will investigate the possibility of interfacing and/or |
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4 borrowing from other Python machine learning libraries that exist out there. |
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5 Some questions that we need to answer: |
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6 |
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7 * How much should we try to interface with other libraries? |
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8 * What parts can we and should we implement ourselves and what should we leave |
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9 to the other libraries? |
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10 |
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11 Preliminary list of libraries to look at: |
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12 |
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13 * Pybrain Razvan |
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14 * MDP Ian |
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15 * Orange (http://www.ailab.si/orange/) Ian (but could trade) |
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16 * PyML (http://pyml.sourceforge.net/) |
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18 * APGL (http://packages.python.org/apgl/) Dumitru | |
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19 * MontePython (http://montepython.sourceforge.net/) Guillaume (but could trade) |
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20 * Shogun python bindings |
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21 * libsvm python bindings Ian (but could trade) |
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22 * scikits.learn Guillaume (but could trade) |
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23 |
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24 Also check out http://scipy.org/Topical_Software#head-fc5493250d285f5c634e51be7ba0f80d5f4d6443 |
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25 - scipy.org's ``topical software'' section on Artificial Intelligence and |
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26 Machine Learning |