Meetup

    Cardiologist-Level Arrhythmia Detection With Convolutional Neural Networks image

    Next Meetup:

    Cardiologist-Level Arrhythmia Detection With Convolutional Neural Networks

    For this meetup we'll be reviewing work by Andrew Ng and others from the Stanford ML Group. Our presenter will be Kelvin Ross. Kelvin is a director with IntelliHQ, a healthcare AI firm based in Queensland, Australia. He’ll be presenting the paper Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks describing an algorithm developed by Stanford researchers that has been able to exceed human expert performance in identifying cardiac arrhythmias based on raw ECG readings. He'll also be covering the broader issues associated with data capture and labelling, as well as longer term heart-rate variability, which can be used as a predictor or early warning for sepsis, fatigue, shock, concussion, heart attack, stroke, and more.

    Details for the meetup follow. We hope to see you there!

    Topic:Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks
    Presenter:Kelvin Ross
    Date:Tuesday, Jun 12th US / Wednesday, Jun 13th Australia
    Time:5:00 PM US Pacific Time / 8:00 PM Eastern Time / 11 AM AEDT.
    (Check HERE for your timezone.)

    If you're not yet registered for the meetup, please sign up below. Scroll down to view the meetup archives.


    Upcoming Meetups:

    • Quantum Machine Learning - July 2018

    Next Meetup:

    Cardiologist-Level Arrhythmia Detection With Convolutional Neural Networks

    For this meetup we'll be reviewing work by Andrew Ng and others from the Stanford ML Group. Our presenter will be Kelvin Ross. Kelvin is a director with IntelliHQ, a healthcare AI firm based in Queensland, Australia. He’ll be presenting the paper Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks describing an algorithm developed by Stanford researchers that has been able to exceed human expert performance in identifying cardiac arrhythmias based on raw ECG readings. He'll also be covering the broader issues associated with data capture and labelling, as well as longer term heart-rate variability, which can be used as a predictor or early warning for sepsis, fatigue, shock, concussion, heart attack, stroke, and more.

    Details for the meetup follow. We hope to see you there!

    Topic:Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks
    Presenter:Kelvin Ross
    Date:Tuesday, Jun 12th US / Wednesday, Jun 13th Australia
    Time:5:00 PM US Pacific Time / 8:00 PM Eastern Time / 11 AM AEDT.
    (Check HERE for your timezone.)

    If you're not yet registered for the meetup, please sign up below. Scroll down to view the meetup archives.


    Upcoming Meetups:

    • Quantum Machine Learning - July 2018
    Cardiologist-Level Arrhythmia Detection With Convolutional Neural Networks image

    Join the Meetup!

    Tell us a bit about yourself:

    If other, please elaborate:

    I'm willing to lead a discussion around a paper. (Note, you don't need to be an expert to do this):

    What do you hope to learn by joining this group? Are there any particular papers you'd like to read or present?

    Tell us about your experience with ML/AI research papers:

    This form collects your name and email address so that we can correspond with you. For more information about how we use and protect your data, please see our privacy policy.

    By submitting this form and joining the meetup, you'll automatically receive notices, reminder emails, and follow-ups about meetup events (about 3-4 emails per month).

    Meetup Archive

    YOLO9000: Better, Stronger, Faster – TWiML Online Meetup #8 – May 2018

    150 150 This Week in Machine Learning & AI

    In this month’s community segment we chatted about the recent demo of Google’s Duplex, a automated appointment making chatbot. We talk about the implications of bots mimicking humans, if this tech qualifies as worthy of a passing Turing test grade and more. Community member @NicT also shares a medium post he wrote, inspired by our…

    Trust in AI – TWiML Online Meetup #7 – April 2018

    150 150 This Week in Machine Learning & AI

    In this month’s community segment we chatted about explainability, Carlos Guestrin’s LIME paper, Europe’s attempt to ban “untrustworthy” AI systems and finally, Community member Nicolas Teague shares a blog post he wrote entitled “A Sight for Obscured Eye, Adversary, Optics, and Illusions,” which explores the parallels between computer vision adversarial examples & human vision optical…

    Playing Atari with Deep Reinforcement Learning – TWiML Online Meetup #6 – March 2018

    150 150 This Week in Machine Learning & AI

    This video is a recap of our March 2018 TWiML Online Meetup. In our community segment we had a very fun and wide ranging discussion about freezing your brain, ML and AI in the healthcare space, and more. Community member Nicholas Teague,‏ who goes by @NicT on twitter, also briefly spoke about his essay “A…

    Using Deep Learning and Google Street View to Estimate the Demographic Makeup of Neighborhoods Across the United States – TWiML Online Meetup #5 – January 2017

    150 150 This Week in Machine Learning & AI

    This is a recap of the TWiML Online Meetup, held on Jan 16, 2018, we focus on the paper “Using deep learning and Google Street View to estimate the demographic makeup of neighborhoods across the United States” by Microsoft Research post doctoral researcher Timnit Gebru. We recap some of the major ML/AI Resolutions for 2018,…

    Understanding Deep Learning Requires Rethinking Generalization – TWiML Online Meetup #4 – December 2017

    150 150 This Week in Machine Learning & AI

    In this recap of the TWiML Online Meetup, held on Dec 13, 2017, we focus on the paper “Understanding Deep Learning Requires Rethinking Generalization” by Google Brain researchers Chiyuan Zhang, Samy Bengio and others. We also recap some of the major ML and AI advancements of the year, and take a look ahead to some…

    Learning Long-Term Dependencies with Gradient Descent is Difficult – TWiML Online Meetup #2 – September 2017

    150 150 This Week in Machine Learning & AI

    This is a recording of the TWiML Online Meetup group. This month we discuss the paper “Learning Long-Term Dependencies with Gradient Descent is Difficult” by Yoshua Bengio & company, one of the classic papers on Recurrent Neural Networks. Huge thank you to listener Nikola Kučerová for presenting. Make sure you Like this video, and subscribe…

    Learning From Simulated & Unsupervised Images through Adversarial Training – TWiML Online Meetup #1 – August 2017

    150 150 This Week in Machine Learning & AI

    This is a recap of the first monthly TWiML Online Meetup, held on Aug 16 2017. The focus of the meetup was the CVPR best-paper-award-winner “Learning From Simulated and and Unsupervised Images through Adversarial Training” by researchers from Apple (Link Below). Thanks again to community members Josh Manela who did a great job presenting this…