Bengio, LeCun, Jordan, Hinton, Schmidhuber, Ng, de Freitas and OpenAI had done Reddit AMA's. These is nice places-to-start to get a zeitgeist of the field. Hinton and Ng lectures at Coursera, UFLDL, cs224d and cs231n at Stanford, the deep learning course at udacity, and the sum Mer School at IPAM has excellent tutorials, video lectures and programming exercises that should help you get STARTED.&NB Sp The online book by Nielsen, notes for cs231n, and blogs by karpathy, Olah and Britz has clear explanations of MLPs, CNNs and Rnns. the tutorials at UFLDL and deeplearning.net give equations and code. The encyclopaedic book by Goodfellow et al was a good place-to-dive into details. i has a draft book in Progress. theano, Torch, Caffe, Convnet, TensorFlow, MXNet, CNTK, Veles, CGT, Neon, Chai NER, Blocks and Fuel, Keras, lasagne, MOCHA.JL, deeplearning4j, Deeplearntoolbox, Currennt, Project Oxford, Autograd (for Torch), WARP-CTC is some of the many deep learning software libraries and frameworks introduced in the last years. convnet-benchmarks and Deepframeworks Compare the performance of many existing packages. I am WOrking on developing a alternative, KNET.JL, written in Julia supporting CNNs and Rnns on GPUs and supporting easy Develo Pment of original architectures. more software can is found at deeplearning.net.
Deeplearning.net and homepages of Bengio, Schmidhuber have further information, background and links. From:http://www.denizyuret.com/2014/11/some-starting-points-for-deep-learning.html
Start learning deep learning and recurrent neural networks some starting points for deeper learning and Rnns
