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Commits on Jul 2, 2017

• README: MIT license

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Commits on Sep 12, 2015

• cache lookups in innermost training loop

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Commits on Sep 6, 2015

• clean up unused var

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Commits on Sep 5, 2015

• slight refactor: presize delta vectors and store in a hash

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Commits on Sep 4, 2015

• Refactor building of matrixes: better method name. Use Hash as container instead of Array

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Commits on Jan 17, 2015

• move .gitignore to examples/

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• mpg linear regression example: calculate r-squared

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• No need to check for weights in #train - check will happen when #run is called

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Commits on Dec 9, 2014

• README: add link to neuralnetworksanddeeplearning.com

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• mpg data: eliminate samples with unknown values

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Commits on Dec 8, 2014

• make examples executable

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• mpg example: don't bother showing pre-training examples

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• mnist example: just stop at 1%

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Commits on Dec 2, 2014

• Add simple regression example

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• Classification, not prediction

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• Show mse as a percentage

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Commits on Nov 26, 2014

• Revert to error = actual - ideal

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• Error function should be ideal - actual, not other way around

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Commits on Nov 25, 2014

• simplify #sign

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Commits on Nov 24, 2014

• Use Math.exp in place of Math::E**

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Commits on Nov 23, 2014

• cleanup

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Commits on Nov 22, 2014

• Add ImageGrid class. Output MNIST weights after training as png

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• Mnist example: enable setting hidden neuron count via ARGV

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Commits on Nov 21, 2014

• README: add link to Ruby ML resources gist

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• README improvements

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• break out neuron_error calcuation

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• move examples into directory

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• train: separate arrays for inputs and expected outputs

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Commits on Nov 13, 2014

• just use require

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Commits on Nov 9, 2014

• Examples: show error rate and mse

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Commits on Nov 7, 2014

• update README

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• Enable marshal dump and load

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• move methods around

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• Do delta and gradient calculation in same method

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• Remove XOR example

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