You could try the following improvements to speed up neural network training: - Resilient Propagation (RPROP), it significantly speeds up training for full bat

You could try the following improvements to speed up neural network training: - Resilient Propagation (RPROP), it significantly speeds up training for full batch learning: http://davinci.fmph.uniba.sk/~uhliarik4/recognition/resources/rprop/rb_1993_rprop.pdf http://davinci.fmph.uniba.sk/~uhliarik4/recognition/resource... - RMSProp, introduced by Geoffrey Hinton, also speeds up training but can also be used for mini-batch learning: https://class.coursera.org/neuralnets-2012-001/lecture/67 https://class.coursera.org/neuralnets-2012-001/lecture/67 (sign up to view the video) Please consider more datasets when benchmarking methods: - MNIST ( 70k 28x28 pixel images of handwritten digits ): http://yann.lecun.com/exdb/mnist/ http://yann.lecun.com/exdb/mnist/ . There are several wrappers for Python on github. - UCI Machine Learning Repository: http://archive.ics.uci.edu/ml/datasets.html http://archive.ics.uci.edu/ml/datasets.html