Compressing deep learning models: distillation (Ep.104)

Using large deep learning models on limited hardware or edge devices is definitely prohibitive. There are methods to compress large models by orders of magnitude and maintain similar accuracy during inference.In this episode I explain one of the first methods: knowledge distillation Come join us on Slack ReferenceDistilling the Knowledge in a Neural Network https://arxiv.org/abs/1503.02531Knowledge Distillation and Student-Teacher Learning for Visual Intelligence: A Review and New Outlooks https://arxiv.org/abs/2004.05937 This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit datascienceathome.substack.com