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Commits on May 15, 2014

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  mnielsen

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Commits on May 12, 2014

• Fixing mini-batch normalization

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Commits on May 7, 2014

• Fixing the normalization

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Commits on May 5, 2014

• Moving the old relu graphs

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• Tests removed, will be refactored

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  348e535

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• Lots of renaming to rationalize the organization of the code directory

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• Removing old file

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• Added save method and load function

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• Finished the results with multiple learning rates

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Commits on May 4, 2014

• Adding README for the replaced_by_d3 subdirectory

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  805a61a

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• Renamed many of the figures replaced by d3, adding first commit of a program to plot behaviour with different learning rates

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Commits on Apr 30, 2014

• Reverting likely erroneous changes to the matrix arithmetic

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  9d255a8

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• Improved documentation for network.py

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Commits on Apr 29, 2014

• Minor bugfixes and refactoring

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  49fa0fb

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• Renaming network_basic, and making minor fixes to network2.py

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Commits on Apr 28, 2014

• Regenerating graphs for 1/sqrt weight initialization

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Commits on Apr 27, 2014

• Removing old code

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  e5a940c

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Commits on Apr 26, 2014

• Added comparison graph for SVMs running over many training set sizes

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  c765a6e

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• Added fudge factor which improves the appearance of the rotated MNIST 5

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• Adding log plot showing what happens when more training data is used

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  9e9ab15

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• Adding figure of an MNIST digit and its rotated version

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Commits on Apr 25, 2014

• Adding a plot to show how more data improves training accuracy

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Commits on Apr 23, 2014

• Results for the regularized network trained on the full data set

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• Adding results for overfitting on full data set (much less is going on), plus a new graph

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Commits on Apr 22, 2014

• Modified overfitting.py to make it easier to run on varied data sets

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  9cc04c6

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• Make the number of epochs to train for a parameter

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  0fc3fd5

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• Getting the overfitting graphs right (earlier versions weren't initializing the weights correctly)

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  9faa5c0

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• Minor fixes

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  306059a

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Commits on Apr 21, 2014

• Minor improvements

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  d9bfc83

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Commits on Apr 18, 2014

• Results for weight initialization comparison experiment

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  a3cba56

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• Comparative weight initialization now working pretty well

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  189281c

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• First draft of program to contrast results between different approaches to weight initialization

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  48af3b3

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• Refactoring network2.py, and added new option for weight initialization

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  3d74d84

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Commits on Apr 14, 2014

• Fixed colors

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Commits on Apr 13, 2014

• Smoothing out the overfitting.py code

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