Compressing deep learning models: rewinding (Ep.105)

<p>As a continuation of the <a href='https://datascienceathome.com/compressing-deep-learning-models-distillation-ep-104/'>previous episode</a> in this one I cover the topic about compressing deep learning models and explain another simple yet fantastic approach that can lead to much smaller models that still perform as good as the original one.</p> <p>Don't forget to join our <a href='https://join.slack.com/t/datascienceathome/shared_invite/zt-es8emg9c-6IAgTPZSYM53nIMMZwdpAw'>Slack channel</a> and discuss previous episodes or propose new ones.</p> <p>This episode is supported by <a href='https://pryml.io'>Pryml.io</a> Pryml is an enterprise-scale platform to synthesise data and deploy applications built on that data back to a production environment.</p> <p> </p> References <p class="title mathjax">Comparing Rewinding and Fine-tuning in Neural Network Pruning <a href='https://arxiv.org/abs/2003.02389'>https://arxiv.org/abs/2003.02389</a></p> <p> </p>