MXNet is a fully featured, flexibly programmable, and ultrascalable deep learning framework supporting state of the art deep learning models. It provides both low-level control and high-level APIs, allowing developers to mix imperative and symbolic programming models and to code in their language of choice (including Python, Scala, Java, C++, and R). Besides its computational and memory efficiency, MXNet is lightweight and portable and can run on various systems, including edge devices. It supports distributed training on multi-GPUs across multiple hosts and achieves high scalability.
Join Nathalie Rauschmayr for an overview of MXNet and its core capabilities.
Nathalie Rauschmayr is a machine learning scientist at AWS, where she helps customers develop deep learning applications. She has a research background in high-performance computing, having conducted research in several international research organizations including the German Aerospace Center, the European Organization for Nuclear Research (CERN), and Lawrence Livermore National Laboratory (LLNL).
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