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The meetup will begin at 18:30 the early presentations at 19:00. The preliminary program is as follows:

6:30 p.m. Doors. Pizza, Champagne ....

7:00 p.m. Intro: Franck Bardol Igor Carron

7:05 p.m. Bastien Legras, Francois Sterin

7:30 p.m. Andrew Ng (remote from SF)

8:10 p.m. Muthu Muthukrishnan (remote)

8:30 p.m. Yaroslav Bulatov (remote)

Camille Couprie 8:45 p.m.

9:00 p.m. Sat Bessalah

Abstracts:
+ Bastien Legras, Google Cloud Platform Solution Engineer & Francois Sterin, Principal, Global Infrastructure

How Google Uses Machine Learning And Neural Networks To Optimize Data Centers, and how you can benefit from it with Google Cloud Platform "

+ Andrew Ng (Coursera, Baidu Chief Scientist), Deep Learning: Machine learning and AI via wide-scale neural networks
Andrew reads all the questions that you put here: http://www.google.com/moderator/ # 16 / e = 216d98

+ Muthu Muthukrishnan, On Sketching

+ Yaroslav Bulatov (Google SF), CNN and Google Streetview.

+ Camille Couprie (IFPEN) Semantic scene labeling using feature learning (Joint work with Farabet Clément, Laurent Najman and Yann LeCun)

In this talk, we address the problem of semantic year Assigning category to every pixel of an image, or video. We Introduce a model architecture That Allows us to learn hierarchies of multi-scale features while Being computationally efficient. We present results on different datasets, Including One That contains depth information, Which May be Handled in our trainable model very easily. As the output predictions May be noisy in videos, we propose a temporal smoothing method using minimum spanning trees, Providing an efficient solution for embedded, real-time applications.

+ Sam Bessalah Abstract algebra for stream learning.

A quick introduction into Some common algorithms and data structures to handle data in streaming fashion, like bloom filters, Hyperloglog gold min hashes. Then in a second hand how abstract algebra with monoids, groups or semi-groups help us build scalable analytical reason and beyond stream processing systems.