Exercise:Vectorization

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(Vectorization)
 
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== Vectorization ==
== Vectorization ==
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In the previous problem set, we implemented a sparse autoencoder for patches taken from natural images. In this problem set, you will vectorize your code to make it run much faster, and further adapt your sparse autoencoder to work on images of handwritten digits.  Our network for learning from handwritten digits will be much larger than the one we'd trained on the natural images, and so using the original implementation would have been painfully slow.  But with a vectorized implementation of the autoencoder, you will be able to get this to run in a reasonable amount of computation time.  
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In the previous problem set, we implemented a sparse autoencoder for patches taken from natural images. In this problem set, you will vectorize your code to make it run much faster, and further adapt your sparse autoencoder to work on images of handwritten digits.  Your network for learning from handwritten digits will be much larger than the one you'd trained on the natural images, and so using the original implementation would have been painfully slow.  But with a vectorized implementation of the autoencoder, you will be able to get this to run in a reasonable amount of computation time.  
=== Support Code/Data ===
=== Support Code/Data ===
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The following additional files are required for this exercise:
The following additional files are required for this exercise:
* [http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz MNIST Dataset (Training Images)]
* [http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz MNIST Dataset (Training Images)]
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* [http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz MNIST Dataset (Training Labels)]
* [[Using the MNIST Dataset | Support functions for loading MNIST in Matlab ]]
* [[Using the MNIST Dataset | Support functions for loading MNIST in Matlab ]]
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As with the first problem, the autoencoder should learn edge features. Your code should run in under 10 minutes on a reasonably fast machine. If it takes significantly longer, check your code and ensure that it is vectorized.
As with the first problem, the autoencoder should learn edge features. Your code should run in under 10 minutes on a reasonably fast machine. If it takes significantly longer, check your code and ensure that it is vectorized.
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[[Category:Exercises]]
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[[Category:Exercises]] -->
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{{Vectorized Implementation}}

Latest revision as of 11:00, 26 May 2011

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