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Some Starting Points for Deep Learning and RNNs

Bengio , LeCunn , Jordan , Hinton , Schmidhuber , Ng , de Freitas and OpenAI have done reddit AMA’s.  These are 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 summer school at IPAM have excellent tutorials, video lectures and programming exercises that should help you get started.


The online book by Nielsen , notes for CS231n , and blogs by Karpathy , Olah and Britz have 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. is a good place to dive into details.  I have a draft book in progress.


Theano , Torch , Caffe , ConvNet , TensorFlow , MXNet , CNTK , Veles , CGT , Neon , Chainer , Blocks and Fuel , Keras , Lasagne , Mocha.jl , Deeplearning4j , DeepLearnToolbox , Currennt , Project Oxford , Autograd ( for Torch ), Warp-CTC are some of the many deep learning software libraries and frameworks introduced in the last 10 years.   convnet-benchmarks and deepframeworks compare the performance of many existing packages. I am working on developing an alternative, Knet.jl , written in Julia supporting CNNs and RNNs on GPUs and supporting easy development of original architectures.  More software can be found at deeplearning.net .

Deeplearning.net and homepages of Bengio , Schmidhuber

have further information, background and links.

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