What we're about

Apache MXNet is a modern open-source deep learning framework used to train, and deploy deep neural networks. It is scalable, allowing for fast model training, and supports a flexible programming model and multiple languages (C++, Python, Julia, Matlab, Clojure, JavaScript, Go, R, Scala, Perl, Wolfram Language)

The MXNet library is portable and can scale to multiple GPUs and multiple machines. MXNet is supported by major Public Cloud providers including AWS and Azure. Amazon has chosen MXNet as its deep learning framework of choice at AWS. Currently, MXNet is supported by companies like Intel, Nvidia, Dato, Baidu, Microsoft, Wolfram Research, and research institutions such as Carnegie Mellon, MIT, the University of Washington, and the Hong Kong University of Science and Technology.

Upcoming events (1)

Deep Learning with MXNet Java API

1918 8th Ave

We will start serving hors d’oeuvres at 6.30pm. 7-8pm we will have a deep dive presentation about MXNet Java API. Afterwards, we will have a reception with wine, beer and more food. Abstract: AI is evolving rapidly, and much of the recent advancement is driven by Deep Learning, a machine learning technique inspired by the inner-working of the human brain. In this session, Qing Lan and Zach Kimberg will discuss what deep learning is, and the new capabilities it enables. We will dive into a few computer vision models that are demonstrating super-human performance, and to integrate these models into your existing Java system leveraging Apache MXNet - an open source deep learning framework – and it's Java API. In our session, we will show a brief introduction to DL, specifically about applications using MXNet Java to do inference with different DL models in Production systems. We would also host a hand-on workshop to run and deploy the DL models with MXNet Java API. By the end of the session, audience will learn how to leverage deep learning models in their Java-based systems, the various gotchas involved, and where/how to learn more. Our presenters: Qing Lan is a SDE in Amazon Deep Engine and one of the authors of MXNet Java API. He graduated with a M.S. in Computer Engineering from Columbia University in 2018. He is experienced in Deep Learning, Programming Language Compiler and distributed systems. Qing is also a Committer of Apache MXNet. Zach Kimberg is a SDE in Amazon Deep Engine and one of the authors of the MXNet Java API. He graduated from the University of Illinois at Urbana-Champaign with a Masters of Computer Science in 2018. His interests are in artificial general intelligence, decentralized systems, and programming languages. Please join to meet your fellow Deep Learning with Apache MXNet enthusiasts, to learn and discuss.

Past events (4)

Supervised learning for Natural Language Understanding

Photos (6)