Train your Deep Learning model faster and sharper: Snapshot Ensembling -- M models for the cost of 1

Deep neural networks have many, many learnable parameters that are used to make inferences. Often, this poses a problem in two ways: Sometimes, the model does not make very accurate predictions. It also takes a long time to train them. The papers can be found here (Snapshot ensembles) and here (FreezeOut). This article assumes some familiarity with neural networks, including aspects like SGD, minima, optimisation, etc. Editor: this post describes Snapshot ensembles, and here is the second part which explains Freezout.