The field of machine learning has taken a dramatic twist in recent times,
with the rise of the Artificial Neural Network (ANN). These biologically
inspired computational models are able to far exceed the performance of
previous forms of artificial intelligence in common machine learning tasks. One
of the most impressive forms of ANN architecture is that of the Convolutional
Neural Network (CNN). CNNs are primarily used to solve difficult image-driven
pattern recognition tasks and with their precise yet simple architecture,
offers a simplified method of getting started with ANNs.
This document provides a brief introduction to CNNs, discussing recently
published papers and newly formed techniques in developing these brilliantly
fantastic image recognition models. This introduction assumes you are familiar
with the fundamentals of ANNs and machine learning.
%0 Generic
%1 oshea2015introduction
%A O'Shea, Keiron
%A Nash, Ryan
%D 2015
%K imported
%T An Introduction to Convolutional Neural Networks
%U http://arxiv.org/abs/1511.08458
%X The field of machine learning has taken a dramatic twist in recent times,
with the rise of the Artificial Neural Network (ANN). These biologically
inspired computational models are able to far exceed the performance of
previous forms of artificial intelligence in common machine learning tasks. One
of the most impressive forms of ANN architecture is that of the Convolutional
Neural Network (CNN). CNNs are primarily used to solve difficult image-driven
pattern recognition tasks and with their precise yet simple architecture,
offers a simplified method of getting started with ANNs.
This document provides a brief introduction to CNNs, discussing recently
published papers and newly formed techniques in developing these brilliantly
fantastic image recognition models. This introduction assumes you are familiar
with the fundamentals of ANNs and machine learning.
@misc{oshea2015introduction,
abstract = {{The field of machine learning has taken a dramatic twist in recent times,
with the rise of the Artificial Neural Network (ANN). These biologically
inspired computational models are able to far exceed the performance of
previous forms of artificial intelligence in common machine learning tasks. One
of the most impressive forms of ANN architecture is that of the Convolutional
Neural Network (CNN). CNNs are primarily used to solve difficult image-driven
pattern recognition tasks and with their precise yet simple architecture,
offers a simplified method of getting started with ANNs.
This document provides a brief introduction to CNNs, discussing recently
published papers and newly formed techniques in developing these brilliantly
fantastic image recognition models. This introduction assumes you are familiar
with the fundamentals of ANNs and machine learning.}},
added-at = {2017-07-19T15:29:59.000+0200},
archiveprefix = {arXiv},
author = {O'Shea, Keiron and Nash, Ryan},
biburl = {https://www.bibsonomy.org/bibtex/211afd1d93412e27401a375360f0d6626/andreashdez},
citeulike-article-id = {14024294},
citeulike-linkout-0 = {http://arxiv.org/abs/1511.08458},
citeulike-linkout-1 = {http://arxiv.org/pdf/1511.08458},
day = 2,
eprint = {1511.08458},
interhash = {612d22a6ba396f63b37507300da78aba},
intrahash = {11afd1d93412e27401a375360f0d6626},
keywords = {imported},
month = dec,
posted-at = {2016-04-29 18:03:04},
priority = {0},
timestamp = {2017-07-19T15:31:02.000+0200},
title = {{An Introduction to Convolutional Neural Networks}},
url = {http://arxiv.org/abs/1511.08458},
year = 2015
}