Adversarial attacks on deep neural networks are starting to show up in a lot of literature. There are even examples of how to defeat a neural network by changing just the value of one pixel. Ensemble methods and distillation have been shown to be great tools for deploying models into production. Unfortunately, until now, some of these methods did not work well for deep learning. New methods have emerged that enable users to create and train deep learning ensembles that can be distilled into a fast single model, which can be hardened against adversarial attacks.
Alan Mosca leads you on a journey from a single model through all the various techniques that enable the construction of production-ready deep learning models. You’ll explore ensemble methods, distillation, adversarial training, and some novel optimization techniques, which have all been shown to help improve accuracy and increase robustness. Alan then shows you how these techniques can be used in the Toupee deep learning framework to create production-ready models.
Alan Mosca is the cofounder and CTO of nPlan and a part-time doctoral researcher at Birkbeck, University of London, where his research focuses on deep learning ensembles and improvements to optimization algorithms in deep learning. Previously, Alan worked at Wadhwani Asset Management, Jane Street Capital, and several software companies as well as on several consulting projects in machine learning and deep learning.
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