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* [[Ian Goodfellow]], [[Yoshua Bengio]], [[Aaron Courville]] ('''2016'''). ''[http://www.deeplearningbook.org/ Deep Learning]''. [https://en.wikipedia.org/wiki/MIT_Press MIT Press]
* [[Volodymyr Mnih]], [[Adrià Puigdomènech Badia]], [[Mehdi Mirza]], [[Alex Graves]], [[Timothy Lillicrap]], [[Tim Harley]], [[David Silver]], [[Koray Kavukcuoglu]] ('''2016'''). ''Asynchronous Methods for Deep Reinforcement Learning''. [https://arxiv.org/abs/1602.01783 arXiv:1602.01783v2]
* [https://scholar.google.ca/citations?user=mZfgLA4AAAAJ&hl=en Vincent Dumoulin], [https://scholar.google.it/citations?user=kaAnZw0AAAAJ&hl=en Francesco Visin] ('''2016'''). ''A guide to convolution arithmetic for deep learning''. [https://arxiv.org/abs/1603.07285 arXiv:1603.07285]
* [https://en.wikipedia.org/wiki/Patricia_Churchland Patricia Churchland], [[Terrence J. Sejnowski]] ('''2016'''). ''[https://mitpress.mit.edu/books/computational-brain-0 The Computational Brain, 25th Anniversary Edition]''. [https://en.wikipedia.org/wiki/MIT_Press MIT Press]
* [[Audrūnas Gruslys]], [[Rémi Munos]], [[Ivo Danihelka]], [[Marc Lanctot]], [[Alex Graves]] ('''2016'''). ''Memory-Efficient Backpropagation Through Time''. [https://arxiv.org/abs/1606.03401v1 arXiv:1606.03401]
: [https://wiki.tum.de/display/lfdv/Convolutional+Neural+Networks#ConvolutionalNeuralNetworks-convolution Convolutional Neural Networks]
: [https://wiki.tum.de/display/lfdv/Deep+Residual+Networks Deep Residual Networks]
: [https://towardsdatascience.com/types-of-convolutions-in-deep-learning-717013397f4d An Introduction to different Types of Convolutions in Deep Learning] by [http://plpp.de/ Paul-Louis Pröve], July 22, 2017
: [https://towardsdatascience.com/squeeze-and-excitation-networks-9ef5e71eacd7 Squeeze-and-Excitation Networks] by [http://plpp.de/ Paul-Louis Pröve], October 17, 2017
* [https://en.wikipedia.org/wiki/Deep_learning Deep Learning from Wikipeadia]
* [http://www.scholarpedia.org/article/Deep_Learning Deep Learning - Scholarpedia] by [[Jürgen Schmidhuber]]
