Residual neural network

A residual neural network (also referred to as a residual network or ResNet) is a deep learning architecture in which the layers learn residual functions with reference to the layer inputs. It was developed in 2015 for image recognition, and won the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) of that year.

Residual neural network - Wikipedia Jump to content From Wikipedia, the free encyclopedia Type of artificial neural network "ResNet" redirects here. For other uses, see ResNet (disambiguation) . A residual block in a deep residual network. Here, the residual connection skips two layers. A residual neural network (also referred to as a residual network or ResNet ) [ 1 ] is a deep learning architecture in which the layers learn residual functions with reference to the layer inputs. It was developed in 2015 for image recognition , and won the ImageNet Large Scale Visual Recognition Challenge ( IL

related reading

• What is Residual Connection? | Towards Data Sciencetowardsdatascience.com
• ResNets: Why do they perform better than Classic ConvNets? (Conceptual Analysis) | Towards Data Sciencetowardsdatascience.com
• 1512.03385arxiv.org
• The most cited paper of the century is a brilliant hackyoutube.com
• Aman's AI Journal • Primers • Ilya Sutskever's Top 30aman.ai
• Attention Residualsk-a.in
• Understanding LSTM Networks -- colah's blogcolah.github.io
• ImageNet Classification with Deep Convolutional Neural Networksproceedings.neurips.cc
• learning theorypeople.eecs.berkeley.edu
• AlexNet - Wikipediaen.wikipedia.org
• CS231n Deep Learning for Computer Visioncs231n.github.io
• Feature Visualizationdistill.pub