Vectorization

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When working with learning algorithms, having a faster piece of code often  
When working with learning algorithms, having a faster piece of code often  
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means that you'll make progress faster on your research.  For example, if your
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means that you'll make progress faster on your project.  For example, if your
learning algorithm takes 20 minutes to run to completion, that means you can
learning algorithm takes 20 minutes to run to completion, that means you can
"try" up to 3 new ideas per hour.  But if your code takes 20 hours to
"try" up to 3 new ideas per hour.  But if your code takes 20 hours to
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run, that pretty much means you can "try" only one idea a day, since that's
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run, that means you can "try" only one idea a day, since that's
how long you have to wait to get feedback from your program.  In this latter
how long you have to wait to get feedback from your program.  In this latter
case, if you can speed up your code so that it takes only 10 hours to run,  
case, if you can speed up your code so that it takes only 10 hours to run,  
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that can literally double your productivity as a researcher!   
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that can literally double your personal productivity!   
'''Vectorization''' refers to a powerful way to speed up your algorithms.  
'''Vectorization''' refers to a powerful way to speed up your algorithms.  
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you'll find that it usually isn't much effort to vectorize your code. Doing  
you'll find that it usually isn't much effort to vectorize your code. Doing  
so will make your code run much faster and, in some cases, simplify it too.
so will make your code run much faster and, in some cases, simplify it too.
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{{Vectorized Implementation}}
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{{Languages|矢量化编程|中文}}

Latest revision as of 13:07, 7 April 2013

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