Abstract: In current years, Deep Learning has change into the go-to answer for a broad range of purposes, usually outperforming state-of-the-art. However, it’s important, for each theoreticians and practitioners, to gain a deeper understanding of the difficulties and limitations associated with common approaches and algorithms. We describe 4 kinds of easy issues, for which the gradient-based mostly algorithms commonly used in deep learning both fail or undergo from significant difficulties. We illustrate the failures by means of sensible experiments, and supply theoretical insights explaining their source, and the way they is perhaps remedied.
Theano — An open supply machine learning library for Python supported by the University of Montreal and Yoshua Bengio’s workforce. Scientists have fed an artificially clever system with Daily Mail articles so it might probably learn the way pure language works. While it isn’t fairly HAL 9000, it is a worrying thought for any left wing tecchies. … Read more