Although deep learning has received much acclaim due to its widespread empirical success, fundamental bottlenecks exist when attempting to develop deep learning applications at scale. One involves exploring the design space of a model family, which typically requires training tens to thousands of models with different hyperparameters. Model training itself is a second major bottleneck, as classical learning algorithms are often infeasible for the petabyte datasets that are fast becoming the norm.
Ameet Talwalkar shares research on addressing these two core scalability bottlenecks. Ameet first offers an overview of Hyperband, a novel algorithm for hyperparameter optimization that is simple, flexible, theoretically sound, and an order of magnitude faster than leading competitors. He then presents work aimed at understanding the underlying landscape of training deep learning models in parallel and distributed environments and introduces Paleo, an analytical performance model that can quickly and accurately model the expected scalability and performance of putative parallel and distributed deep learning systems.
Ameet Talwalkar is cofounder and chief scientist at Determined AI and an assistant professor in the School of Computer Science at Carnegie Mellon University. His research addresses scalability and ease-of-use issues in the field of statistical machine learning, with applications in computational genomics. Ameet led the initial development of the MLlib project in Apache Spark. He is the coauthor of the graduate-level textbook Foundations of Machine Learning (MIT Press) and teaches an award-winning MOOC on edX, Distributed Machine Learning with Apache Spark.
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