blocks

I know some more. Some of them are made as libraries, some are just code examples, where you however could extract out some relevant code. * PyLearn, LISA labs, http://deeplearning.net/software/pylearn2/ * LSTM, http://deeplearning.net/tutorial/lstm.html#lstm * LSTM, https://github.com/skaae/nntools * LSTM, https://github.com/JonathanRaiman/theano_lstm * LSTM, https://github.com/mohammadpz/Recurrent-Neural-Networks * LSTM, http://christianherta.de/lehre/dataScience/machineLearning/n... * LSTM, https://gist.github.com/jpuigcerver/9358036 * LSTM + CTC, https://github.com/kastnerkyle/net * Speech modeling, LSTM, https://github.com/kastnerkyle/speech_density * FF + RNN, https://github.com/lmjohns3/theano-nets * FF + RNN, https://github.com/lmjohns3/theanets Speech: https://github.com/lmjohns3/arrnn-experiment/blob/master/tas... * FF, https://

I know of 4 projects for deep learning based on Theano. Keras, Blocks and Lasagne all seem to share the same goal of being more libraries than framework. You can use only one part (e.g. a Layer implementation, training algo) without having to pull in everything : https://github.com/bartvm/blocks https://github.com/benanne/Lasagne Then there is pylearn2, which look more like a framework and seems to be a good candidate for becoming the GPU-accelerated scikit-learn : https://github.com/lisa-lab/pylearn2 I have started using blocks and did some tests with pylearn2. Anybody with more experience want to share the strength/weaknesses of each of those projects ?