I would also like to mention my project: pydeeplearn. You can find it here https://github.com/mihaelacr/pydeeplearn https://github.com/mihaelacr/pydeeplearn. I

I would also like to mention my project: pydeeplearn. You can find it here https://github.com/mihaelacr/pydeeplearn https://github.com/mihaelacr/pydeeplearn. I think it's main advantage is that it uses theano under the hood but the user does not need to know theano at all. The most complete implementations are those of RBMs and DBNs, but I also have CNNs. The library has support for adversarial training, as presented in the paper "Explaining and Harnessing Adversarial Examples" by Ian J. Goodfellow, Jonathon Shlens, Christian Szegedy. I recently also integrated spearmint into the library, so hyperparameter optimization comes in for free.