I have implemented an MLP. Now, I want to train it to solve simple tasks.
Are there any data sets to train the MLP on simple tasks, that is, tasks with a small number of inputs and outputs?
I would like to train it to solve problems which are slightly more complex than the XOR problem.
There are a ton of sample datasets our there you can play with. A bunch of good ones install with R in the datasets package. Luckily you can download them independently if you're not an R user. Try https://vincentarelbundock.github.io/Rdatasets/datasets.html
You might also be interested in the MNIST database which is one of the canonical databases used in handwriting recognition research.
Beyond that, you can look at / ask on http://datasets.reddit.com and/or http://opendata.reddit.com and you'll find all sorts of useful datasets.
And finally, don't overlook the UCI Machine Learning Repository.
A popular dataset is the fisher iris dataset. It consists of 150 samples each with a dimensionality of 4. You can find it at http://archive.ics.uci.edu/ml/datasets/Iris
If you want to solve a multi-class classification problem, you could use the famous iris flower dataset, which was introduced by Fisher in 1936. In this dataset, each flower has (only) $4$ features (the inputs), namely
There are $3$ classes (the outputs)
And there are a total of $150$ observations (or records).
The iris flower dataset is available in sklearn. See, for example, Iris plants dataset.
To search for other datasets, you can also use https://toolbox.google.com/datasetsearch.
After almost three years the question is still relevant.
Let me add some too:
The datasets from the above link can be used for benchmarking deep learning algorithms.
An image dataset which is inspired by CIFAR-10 dataset
