Question: I am looking for some relatively simple data sets for testing and comparing different training methods for artificial neural networks. I would like data that won't take too much pre-processing to turn it into my input format of a list of inputs and outputs (normalized to 0-1). Any links appreciated.

Answer: 
<https://archive.ics.uci.edu/ml> is the University of California Irvine repository of machine learning datasets. It's a really great resource, and I believe that they are all in CSV files.
Some resources are

* The sinC function . 

```
       +----
       |   sin(x)
       |  -------        when x != 0
       |     x
sinC = |
       |
       |     1           otherwise
       +----

```
* The `sin(x)` function as @adrianbanks told.
* For testing some new modification to some algorithm the good old n-parity tests.
* The Iris dataset, semeion hand written digit data set etc, any other functions and a lot more.
* The UCI Machine Learning Repository: [archive.ics.uci.edu/ml/datasets.html](http://archive.ics.uci.edu/ml/datasets.html)
* Here is another resource having many regression datasets: [www.dcc.fc.up.pt/~ltorgo//Regression/DataSets.html](http://www.dcc.fc.up.pt/~ltorgo//Regression/DataSets.html) . You will get many of these from the UCI ML Repository.
* You can get data sets from <https://www.kaggle.com/> for various practical data sets.

I don't think you require a lot of pre-processing with these. Like for categorical variables, you can replace them with binary using a GUI text editor fast. For example the [Abalone](http://archive.ics.uci.edu/ml/datasets/Abalone) dataset has one categorical attribute, the Gender, which has three values "M" for male, "F" for female, "I" for infant. You can press Ctrl + R in your text editor and replace all occurrences of "M" with `1,0,0`, all occurrences of "F" with `0,1,0` and all occurrence of "I" with `0,0,1` (considering the file is in CSV format). This will make quick replacements of the categorical variables.

If you are in [R](http://www.r-project.org/), then you can use the `normalizeData` function which comes with the [RSNNS package](http://cran.r-project.org/web/packages/RSNNS/index.html) to scale and normalize your data in 0 and 1.

If you are in other environment like [octave](http://www.gnu.org/software/octave/) or [matlab](http://en.wikipedia.org/wiki/MATLAB), you can just invest some time to write your code. I am not aware of available functions in these environments, I use my code to scale and/or normalize the data.

When you use functions your work is made much easier, and once you prepare the data, save the modified data in a file.

Remember one thing, the target of training a neural-network is not just to train the network in a way such that it works good on a certain training set. The main target is to train the network such that it has best error for new data which the network haven't seen (directly or indirectly).