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. When you use functions your work is made much easier, and once you prepare the data, save the modified data in a file. I am not aware of available functions in these environments, I use my code to scale and/or normalize the data. If you are in other environment like octave or matlab, you can just invest some time to write your code. If you are in R, then you can use the normalizeData function which comes with the RSNNS package to scale and normalize your data in 0 and 1. This will make quick replacements of the categorical variables. 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). For example the Abalone dataset has one categorical attribute, the Gender, which has three values 'M' for male, 'F' for female, 'I' for infant. Like for categorical variables, you can replace them with binary using a GUI text editor fast. I don't think you require a lot of pre-processing with these. You can get data sets from for various practical data sets.You will get many of these from the UCI ML Repository. Here is another resource having many regression datasets.The UCI Machine Learning Repository: /ml/datasets.html The Iris dataset, semeion hand written digit data set etc, any other functions and a lot more. For testing some new modification to some algorithm the good old n-parity tests.

Statistica neural networks 6.1.