Apache MXNet, a next-generation deep learning framework designed for both efficiency and flexibility, allows you to mix the flavors of symbolic programming and imperative programming to maximize efficiency and productivity. At its core, it’s a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly with a graph optimization layer on top that makes symbolic execution fast and memory efficient. The library is portable and lightweight, and it is suitable for deployment from multiple GPUs and multiple machines to embedded systems such as smartphones and embedded GPUs.
Anima Anandkumar provides hands-on experience on how to use Apache MXNet with preconfigured Deep Learning AMIs and CloudFormation Templates to help speed your development.
Anima Anandkumar is a principal scientist at Amazon Web Services. Anima is currently on leave from UC Irvine, where she is an associate professor. Her research interests are in the areas of large-scale machine learning, nonconvex optimization, and high-dimensional statistics. In particular, she has been spearheading the development and analysis of tensor algorithms. Previously, she was a postdoctoral researcher at MIT and a visiting researcher at Microsoft Research New England. Anima is the recipient of several awards, including the Alfred. P. Sloan fellowship, the Microsoft faculty fellowship, the Google research award, the ARO and AFOSR Young Investigator awards, the NSF CAREER Award, the Early Career Excellence in Research Award at UCI, the Best Thesis Award from the ACM SIGMETRICS society, the IBM Fran Allen PhD fellowship, and several best paper awards. She has been featured in a number of forums, such as the Quora ML session, Huffington Post, Forbes, and O’Reilly Media. Anima holds a BTech in electrical engineering from IIT Madras and a PhD from Cornell University.
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