Papers and documents that you might find interesting to read as background material

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# Deep Learning Papers and Documents

Listly by Patrice Kerremans

An ensemble of papers and documents that form the foundation of current day Deep LearningDeep learning (also known as deep structured learning or hie... More

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

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

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Introduction to Convolutional Neural Networks by Jianxin Wu

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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.

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Understanding Convolutional Neural Networks with A Mathematical Model by C.-C. Jay Kuo

(original: https://arxiv.org/pdf/1609.04112.pdf)

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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)

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

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# The 9 Deep Learning Papers You Need To Know About

Listly by Patrice Kerremans

From the superb introduction to Convolutional Neural Networks guide "A Beginner's Guide To Understanding Convolutional Neural Networks" by "Adit Deshpande": "The 9 Deep Learning Papers You Need To Know About"

(If you count well you'll see there are 11 PDFs, not 9 🙂 )

Paper accepted and presented at the Neural Information Processing Systems Conference (http://nips.cc/)

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