http://code.activestate.com/recipes/tags/propagation/new/2014-09-11T06:25:14-07:00ActiveState Code Recipes2014-09-11T06:25:14-07:00Alexander Pletzerhttp://code.activestate.com/recipes/users/4190754/http://code.activestate.com/recipes/578932-teach-your-computer-a-few-tricks/

<p style="color: grey"> Python recipe 578932 by <a href="/recipes/users/4190754/">Alexander Pletzer</a> (<a href="/recipes/tags/anl/">anl</a>, <a href="/recipes/tags/back/">back</a>, <a href="/recipes/tags/network/">network</a>, <a href="/recipes/tags/neural/">neural</a>, <a href="/recipes/tags/propagation/">propagation</a>). </p> <p>Following is an artifical neural network program that takes any number of inputs and any number of hidden layers, and spits out an output. It applies back propagation with regularization to minimize the cost function. A gradient descent algorithm tries to find the minimum of the cost function in the landscape of weights. </p>

2012-05-30T17:09:49-07:00David Adlerhttp://code.activestate.com/recipes/users/4182015/http://code.activestate.com/recipes/578148-simple-back-propagation-neural-network-in-python-s/

<p style="color: grey"> Python recipe 578148 by <a href="/recipes/users/4182015/">David Adler</a> (<a href="/recipes/tags/back/">back</a>, <a href="/recipes/tags/back_propagation/">back_propagation</a>, <a href="/recipes/tags/neural/">neural</a>, <a href="/recipes/tags/neural_network/">neural_network</a>, <a href="/recipes/tags/propagation/">propagation</a>, <a href="/recipes/tags/python/">python</a>). </p> <p>This is a slightly different version of this <a href="http://arctrix.com/nas/python/bpnn.py" rel="nofollow">http://arctrix.com/nas/python/bpnn.py</a></p>