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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

Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
by Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun

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