This is a great explanation of backpropagation. For those who just want the formulas, my personal favorite has been stanford's ufldl resource: http://ufldl.sta

This is a great explanation of backpropagation. For those who just want the formulas, my personal favorite has been stanford's ufldl resource: http://ufldl.stanford.edu/wiki/index.php/Backpropagation_Algorithm http://ufldl.stanford.edu/wiki/index.php/Backpropagation_Alg... The general intuition behind backprop is that, taking prediction error in to account (think how many labels it got wrong) How far off were the predictions? Based on that go back and penalize the weights that caused the error by that much. Multi layer perceptrons (as well as multi layer deep nets) have multiple layers whereupon you send the input through the network and make a guess. Then you basically keep updating the weights (iteratively via gradient descent, conjugate gradient, LBFGS,...) till it doesn't change much. It does this by conducting a search navigating using the cost: or objective function. For more in depth, obviously the above book covers this quite in depth. For those who want to just use deep learning, I will be giving talks at both OSCon and Hadoop Summit this year on distributed deep learning using 2 different frameworks I commit to [1] and [2]. Happy to answer questions! [1]: http://deeplearning4j.org/ http://deeplearning4j.org/ [2]: http://github.com/jpatanooga/Metronome/ http://github.com/jpatanooga/Metronome/