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Updated by Patrice Kerremans on Jan 13, 2019
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Deep Learning Papers and Documents

An ensemble of papers and documents that form the foundation of current day Deep Learning

Gradient-Based Learning Applied to Document Recognition by Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner.

Efficient BackProp by Yann LeCun, Léon Bottou, Geneviève B. Orr, and Klaus-Robert Müller

Introduction to Convolutional Neural Networks by Jianxin Wu

Convolution Matrix

Here is a mathematician's domain. Most of filters are using convolution
matrix. With the Convolution Matrix filter, if the fancy takes you, you
can build a custom filter.

Understanding Convolutional Neural Networks with A Mathematical Model by C.-C. Jay Kuo
(original: https://arxiv.org/pdf/1609.04112.pdf)

Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
by Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
(original: https://arxiv.org/pdf/1502.01852.pdf)

Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition by Dominik Scherer, Andreas Müller, and Sven Behnke