I'd like to be independent of commercial software for my scientific work. I find a dependence an commercial packages such as Matlab and its toolboxes unsatisfactory, because I do not know if I will have access to Matlab in the future, and because I don't like the language. Therefore, I'm looking for alternatives.

Fortunately, I'm quite fluent in Python (and I love the language), and with NumPy, SciPy, Matplotlib, Basemap, and NetCDF reading and writing routines, it satisfies most of my needs. Most — I still return to Matlab when I need to train satellite retrievals using feed-forward multi-layer perceptrons, e.g. te use Artificial Neural Networks.

As is not unusual with open-source software, there is more than one package that does neural networks. Considerably more than one:

* A while ago I tried [PyBrain](http://pypi.python.org/pypi/PyBrain/0.3), "the swiss army knife for neural networking", but I didn't succeed in getting any satisfactory results in a short time (both develop-time and run-time). Perhaps I didn't try hard enough, or perhaps it's not really geared toward my exact need.

* Just now I discovered  that there is a package called [neurolab](https://code.google.com/p/neurolab/), which looks promising: *a simple and powerful Neural Network Library for Python*, with an *API like Neural Network Toolbox (NNT) from MATLAB*.

* There is [FFnet](http://pypi.python.org/pypi/ffnet/0.7.1), *a fast and easy-to-use feed-forward neural network training solution for python*

* There is [simplenn](http://pypi.python.org/pypi/simplenn/0.1.1)

* There is [Peach](http://pypi.python.org/pypi/Peach/0.3.1), a *library for computational intelligence and machine learning*

* There are Python bindings to [FANN](http://leenissen.dk), the *Fast Artificial Neural Network* library, described as a [de facto standard in this StackOverflow post](http://stackoverflow.com/a/11477815/974555).

* There are probably others.

Has anyone gone through the effort of intercomparing the different options, based on criteria such as easy of use, speed, etc.? My own use case is satellite retrievals, e.g. fitting a strongly non-linear function of many variables. I am very much a *user* of neural nets; I am not interested in researching their inner workings.

[This question](http://stats.stackexchange.com/q/8817/12615) on Stats.SE is related, but with a different focus.