sotte

If you're interested in the generalization ability of neural networks I can recommend the following paper: "Intriguing properties of neural networks" http://arxiv.org/pdf/1312.6199v4.pdf

TLDR: The authors create adversarial examples, i.e., they slightly modify the original images, which look exactly the same to humans but neural networks can't classify the images correctly anymore. What does that imply on the ability to generalize? :)

On a more general note: NN are always treated as something magic. I think a "sober view" is that NN are a special way to parameterize non-liner functions. This is neither good nor bad but it's easy to see when you look at, for example, a 2 layer NN:

$f(x) = w_2^T \sigma(W_1 \sigma (W_0 x))$

Here is the video of the talk: http://www.fosslc.org/drupal/content/everything-you-need-kno...

Does anybody know docopt? I really like it because you simply write the help/usage as text and docopt automatically generates the parser for it.

Take a look at the example in the README: https://github.com/docopt/docopt