À propos de ce groupe

The best way to become an expert in machine learning is to learn from the best. Our group meets twice a week for peer-to-peer discussions on machine learning. Tuesday meetups are focused on applied machine learning. Friday meetups are discussions on a pre-selected research paper. Papers may include image processing, AGI, NLP or other machine learning topics. The papers and our discussions are in English. Meetings are limited to 25 people so our peer-to-peer style discussions don't become overwhelming.

Hi, I'm Robert Salita, the Paris group's organizer. I coordinate meetings with Paul-Louis (Algolia). I'm a veteran of the successful San Francisco Deep Learning Study Group. By day, I'm a developer tool and compiler writer by day. By night, I'm a machine learning insomniac.

Don't worry about not fully understanding the research papers. Even top San Francisco members would comment that they only understood 75% of the paper. Papers are often short on key details, don't make source code available, or lack ways to experiment with or duplicate findings.

We use the ParisML slack group for discussions. Ask the event's host for an invite.

Événements à venir (3)

🤖 ONLINE - Imbalanced Classification #1 - Concept and applications

Details We meet every Friday for a peer-to-peer discussion of a pre-selected machine learning research paper. The papers and discussion is in English. This session is part of 2 sessions in which we will discuss imbalanced classification. The sessions are the following: 1/ Concept and applications: we will discuss the concept of imbalanced classification, the issues it involves and how to overcome them 2/ Recent advances and implementation: we will discuss the last strategies and practical implementations. Resources: COMING SOON

🤖 ONLINE - Imbalanced Classification #2 - Recent advanced and implementation

Details We meet every Friday for a peer-to-peer discussion of a pre-selected machine learning research paper. The papers and discussion is in English. This session is part of 2 sessions in which we will discuss imbalanced classification. The sessions are the following: 1/ Concept and applications: we will discuss the concept of imbalanced classification, the issues it involves and how to overcome them 2/ Recent advances and implementation: we will discuss the last strategies and practical implementations. Resources: COMING SOON

🤖 ONLINE - Data streaming technologies. What's the point?

Événement en ligne

We meet every Friday for a peer-to-peer discussion of a pre-selected machine learning research paper. The papers and discussion is in English. Resources: COMING SOON

Événements passés (171)

ONLINE - ML from End to End #6 - Deployment

Événement en ligne

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