PyTorch in Munich at Microsoft

Name: PyTorch in Munich at Microsoft
Start: 2019-11-15T18:30:00+01:00
End: 2019-11-15T21:30:00+01:00
Location: Microsoft Deutschland GmbH

Hosted By

Thomas V. and Holger K.

Details

We’re thrilled to announce our next meetup about PyTorch in applied deep learning with two awesome speakers. We will meet at the Microsoft Munich HQ.

Speakers: Adam Paszke & Carla Gil

Talk 1: Adam Paszke: Exciting Times in PyTorch

PyTorch grew to be one of the most popular libraries used for machine learning thanks to all of you. However, we’re not really done with it just yet. Even though many of the improvements might feel incremental, there are some changes that I think can have a profound impact on how we write and express the same computations in some time. In this talk I’ll cover the most exciting (in my opinion) improvement coming up — dispatcher changes — which doesn’t sound super interesting alone, but is actually fascinating and is at the very core of everything we do in PyTorch. After that, I’ll move on to a project that is a little bit independent of the core, but might make it a lot easier to deal with shapes in your programs.

Bio: Adam is one of the original authors of PyTorch. His background is in Computer Science and Mathematics.

Talk 2: Carla Gil: Deployment for the edge

Carla will walk us through the design and implementation of a Parking Space Detection System which will be linked to a Telegram Bot to provide the information about the detected available parking spots on the street. She will show us how by leveranging the features and tools made available by PyTorch, a smooth transition from the development to the deployment of Deep Learning models on edge devices can be achieved.

Bio: Carla uses Pytorch to develop and deploy ML algorithms in her current position at BMW Group. She has gathered experience in the field of ML by working in different companies. She was Team Lead during the development of Samsung's Voice Assistant for the German market and has developed multiple deep learning algorithms to solve problems in the fields of Computer Vision and Biometrics.

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