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annotate doc/v2_planning/requirements.txt @ 1119:81ea57c6716d

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clarification to plugin.txt
author Yoshua Bengio <bengioy@iro.umontreal.ca>
date Tue, 14 Sep 2010 17:22:25 -0400
parents 4eda3f52ebef
children 1f5465622394
rev   line source
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1 ============
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2 Requirements
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3 ============
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4
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5
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6 Application Requirements
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7 ========================
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8
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9 Terminology and Abbreviations:
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10 ------------------------------
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11
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12 MLA - machine learning algorithm
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13
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14 learning problem - a machine learning application typically characterized by a
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15 dataset (possibly dataset folds) one or more functions to be learned from the
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16 data, and one or more metrics to evaluate those functions. Learning problems
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17 are the benchmarks for empirical model comparison.
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18
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19 n. of - number of
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20
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21 SGD - stochastic gradient descent
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22
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23 Users:
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24 ------
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25
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26 - New masters and PhD students in the lab should be able to quickly move into
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27 'production' mode without having to reinvent the wheel.
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28
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29 - Students in the two ML classes, able to play with the library to explore new
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30 ML variants. This means some APIs (e.g. Experiment level) must be really well
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31 documented and conceptually simple.
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32
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33 - Researchers outside the lab (who might study and experiment with our
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34 algorithms)
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35
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36 - Partners outside the lab (e.g. Bell, Ubisoft) with closed-source commercial
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37 projects.
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38
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39 Uses:
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40 -----
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41
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42 R1. reproduce previous work (our own and others')
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43
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44 R2. explore MLA variants by swapping components (e.g. optimization algo, dataset,
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45 hyper-parameters)
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46
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47 R3. analyze experimental results (e.g. plotting training curves, finding best
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48 models, marginalizing across hyper-parameter choices)
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49
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50 R4. disseminate (or serve as platform for disseminating) our own published algorithms
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51
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52 R5. provide implementations of common MLA components (e.g. classifiers, datasets,
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53 optimization algorithms, meta-learning algorithms)
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54
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55 R6. drive large scale parallizable computations (e.g. grid search, bagging,
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56 random search)
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57
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58 R7. provide implementations of standard pre-processing algorithms (e.g. PCA,
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59 stemming, Mel-scale spectrograms, GIST features, etc.)
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60
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61 R8. provide high performance suitable for large-scale experiments
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62
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63 R9. be able to use the most efficient algorithms in special case combinations of
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64 learning algorithm components (e.g. when there is a fast k-fold validation
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65 algorithm for a particular model family, the library should not require users
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66 to rewrite their standard k-fold validation script to use it)
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67
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68 R10. support experiments on a variety of datasets (e.g. movies, images, text,
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69 sound, reinforcement learning?)
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71 R11. support efficient computations on datasets larger than RAM and GPU memory
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72
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73 R12. support infinite datasets (i.e. generated on the fly)
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74
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75 R13. apply trained models "in production".
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76 - e.g. say you try many combinations of preprocessing, models and associated
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77 hyper-parameters, and want to easily be able to recover the full "processing
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78 pipeline" that performs best, and use it on real/test data later.
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79