You are viewing content from a past/completed QCon -

Presentation: Debuggable Deep Learning

Track: Deep Learning in Practice

Location: Cyril Magnin II

Duration: 3:20pm - 4:00pm

Day of week:

Slides: Download Slides

This presentation is now available to view on InfoQ.com

Watch video with transcript

Abstract

Deep Learning is often called a black box, so how can we diagnose and fix problems in a Deep Neural Network (DNN)? Engineers at Cardiogram explain how they systematically debugged DeepHeart, a DNN that detects cardiovascular disease from heart rate data. You'll leave this talk with an arsenal of tools for debugging DNNs, including Jacobian analysis, TensorBoard, and "DNN Unit Tests".

Speaker: Mantas Matelis

Software Engineer @Cardiogram

Mantas Matelis is an engineer at Cardiogram, a startup that applies deep learning to wearable data. Previously, Mantas interned at Airbnb, Quora, and Apple. He holds a Bachelors in Computer Science from the University of Waterloo.

Find Mantas Matelis at

Speaker page

https://linkedin.com/in/mantasmatelis

Speaker: Avesh Singh

Software Engineer & Technical Lead @Cardiogram

Avesh Singh is an engineer at Cardiogram, a startup that applies deep learning to wearable data. Previously, Avesh worked at Nest Labs and Google. He holds a a BS and MS in computer science from Carnegie Mellon University.

Find Avesh Singh at

Speaker page

https://www.linkedin.com/in/avesh-singh/