#14 Paris Women in Machine Learning & Data Science: Deep Neural Network, Prediction with GCP & WiMLDS

WiMLDS Paris
Apr 28, 2019 · 4 min read
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Paris was a #WomenInTech city on the 24th of April 2019. While Chloe Condon 🎀 was speaking at Datadog HQ for a Duchess.fr meetup, Malt kindly welcomed the 14th edition of the WiMLDS Paris meetup! “La Malterie” was packed and 90 people listened to Manon Ansart, Giulia Bianchi and the Paris WiMLDS team.

Our very own Natalie Cernecka introduced the session by sharing news from WiMLDS Paris and worldwide.

📈 We shared the last updates about the global WiMLDS community! 24 new chapters have been created since the beginning of 2019. Look at these impressive numbers ⬇️

🚀 We are extremely proud to announce that the WiMLDS Paris meetup is one of the 16 finalists of the Women in Tech Challenge, in the Science & Research category! The ceremony will take place on the 15th of May 2019 in Paris. Don’t forget to register if you want to attend and root for us 👋!

📊 On the 10, 11 & 12th of May 2019, our very own Caroline Chavier will present WiMLDS Paris in Strasbourg during the Villagedeladata week-end! You can still join if you are in the area and want to take part in Data Science, Machine Learning discussions and hackathons.

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📌 On the 21st of May 2019, Chloé-Agathe Azencott and Caroline Chavier will speak about WiMLDS Paris during the 4-day-conference organized at Jussieu to celebrate Nicole El Karoui’s 75th birthday. Join us!

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21st of May 2019 program + Full program + Gallery of Women in Mathematics throughout Europe

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Manon Ansart presenting

The night was kicked-off by Manon Ansart with a talk about: “Machine Learning for automatic diagnosis: why your deep neural network might not work” introducing her work on the Alzheimer disease. Manon is currently doing a PhD with the Aramis Lab (Sorbonne Université, Inserm, ICM, CNRS, Inria).

The early diagnosis of neurodegenerative diseases is crucial, as it could lead to early treatment and better chances of stopping the process. Machine learning algorithms provide an opportunity to detect these diseases earlier through automatic diagnosis, but their application to the medical domain is not straightforward. From data set size to interpretability, Manon Ansart explained why the beautiful, trendy, and complex solution we can first think about might not be the best one.

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Then, Giulia Bianchi, Data Scientist at Xebia, introduced how a data scientist can exploit Google Cloud Platform’s potential to train its own model.

With the release of Google Cloud AutoML, Google Cloud Platform provides yet another out-of-the-box AI managed service. But this doesn’t mean that data scientists have no say in training and deploying customised machine learning models in the cloud. There are services, such as Cloud ML Engine, devoted to this specific goal.

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The Paris WiMLDS team : Chloé, Marina, Caroline, & Natalie

Finally, the WiMLDS Paris team shared anecdotes, figures, goals and initiatives led by WiMLDS on both the global and local scale.

The team also encouraged the Meetup members to volunteer and help the organizers. If you are interested, email us at paris@wimlds.org

📯 Make sure to join our Meetup page to subscribe to our next events. They have not been announced yet. However, here is a sneak-peak 🌟 ⬇️ of our very first meta-meetup with Melanie Warrick in collaboration with Ladies of Code, Women Who Go Paris and Paris Data Ladies 🔥 :

If you want to keep posted about our activities, you are welcome to:

📑check our Google spreadsheet if you want to speak 📣, host 💙, or help 🌠

🔗follow our Twitter account, Meetup page, Instagram or LinkedIn page

📩send an email to the Paris WiMLDS team to keep in touch >paris@wimlds.org

📍join our Slack channel for more discussions about machine learning, data science, and diversity in tech!

🎬 Follow our WiMLDS Paris Youtube channel

📸 WiMLDS has an Instagram account and a global LinkedIn page!

🔥 Feel free to share your company or lab’s job positions for free on WiMLDS’ website.

A special thanks to Morgane Dalbergue for the pictures!

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