I am looking for an open source neural network library. So far, I have looked at FANN, WEKA, and OpenNN. Are the others that I should look at? The criteria, of course, is documentation, examples, and ease of use.
OpenNN (Open Neural Networks Library) is a software library written in the C++ programming language which implements neural networks, a main area of deep learning research. The library is open-source, licensed under the GNU Lesser General Public License.
Google released TensorFlow as an open source technology in 2015 under an Apache 2.0 license. Since then, the framework has gained a variety of adherents beyond Google.
NeuroLab is a simple and powerful Neural Network Library for Python. This library contains based neural networks, train algorithms and flexible framework to create and explore other networks.
TensorFlow is a free and open-source software library for machine learning and artificial intelligence.
Last update: 2020/03/24 (I will update this answer from time to time...)
Because neural networks are quite popular in research and industry at the moment ("deep learning") there are many research libraries available. Most of them are kind of easy to set up, integrate, and use. Although not as easy as the libraries mentioned above. They provide leading edge functionality and high performance (with GPUs etc.). Most of these libraries also have automatic differentiation. You can easily specify new architectures, loss functions etc. and don't have to specify the backpropagation manually.
A performance comparison for GPU-accelerated libraries can be found here (a bit outdated unfortunately). A comparison of GPUs and library versions can be found here.
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