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links
http://karpathy.github.io/2015/05/21/rnn-effectiveness/
http://colah.github.io/
https://www.youtube.com/watch?v=M7smwHwdOIA
Stanford course: http://cs231n.github.io/
Papers about SW
weight initalization and co: dropout paper
training Very deep CNN's (100s of layers)
http://www.imm.dtu.dk/~abll/blog/simple_cnn/
LSTM: https://github.com/solidaqua/timit-lstm
Simple Neural Network model:
http://www.wildml.com/2015/09/implementing-a-neural-network-from-scratch/
(also recurrent, LSTM etc on that site)
audio- visual SR with HMM
beste convNets: RESNETS
Lasagne RNN
google LSTM
http://blog.dlib.net/2014/04/dlib-187-released-make-your-own-object.html DLIB
Speech Recognition TIMIT met Theano
Using TIMIT CTC
TIMIT
Nervana Systems (Neon): Binary Neural Networks https://github.com/NervanaSystems/neon/tree/master/examples/binary
Papers with interesting (background) information
http://www.embedded-vision.com/platinum-members/altera/embedded-vision-training/videos/pages/may-2015-embedded-vision-summit
Geoffrey Hinton: intro to Deep Learning: https://www.youtube.com/watch?v=GJdWESd543Y
https://tweakers.net/nieuws/115001/facebook-maakt-kunstmatige-intelligentie-voor-beeldherkenning-open-source.html
https://en.wikipedia.org/wiki/Hidden_Markov_model
https://en.wikipedia.org/wiki/Deep_learning#Deep_neural_networks
https://en.wikipedia.org/wiki/Convolutional_neural_network
https://en.wikipedia.org/wiki/Speech_recognition
http://pjreddie.com/darknet/hardware-guide/
https://registration.nvidia.com/ahr.aspx
http://timdettmers.com/2014/08/14/which-gpu-for-deep-learning/
Neural GPUs: https://www.youtube.com/watch?v=hVv4M0bTBJc
https://securewww.esat.kuleuven.be/helpdesk/display.php?DBname=itcalls&callid=26679