English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

It is known that the learning rate is the most important hyper-parameter totune for training deep neural networks. This paper describes a new method forsetting the learning rate, named cyclical learning rates, which practicallyeliminates the need to experimentally find the best values and schedule for theglobal learning rates. Instead of monotonically decreasing the learning rate,this method lets the learning rate cyclically vary between reasonable boundaryvalues. Training with cyclical learning rates instead of fixed values achievesimproved classification accuracy without a need to tune and often in feweriterations. This paper also describes a simple way to estimate "reasonablebounds" -- linearly increasing the learning rate of the network for a fewepochs. In addition, cyclical learning rates are demonstrated on the CIFAR-10and CIFAR-100 datasets with ResNets, Stochastic Depth networks, and DenseNets,and the ImageNet dataset with the AlexNet and GoogLeNet architectures. Theseare practical tools for everyone who trains neural networks.

Cyclical Learning Rates for Training Neural Networks