Wednesday, May 11, 2016

Ce soir: Paris Machine Learning Meetup #13 Season 3; Data augmentation methods in Deep Learning, Neural Turing Machines, Manga Recommendation

All the previous archives of the 40+ meetups are here.The hangout will be live:

We will be hosted by DojoEvents. Refreshements and food will be offered by Thank you to them for their support. There will be 2 free tickets to the next to win for people at the meetup.

 Follow our Twitter account, and / or use the following hashtag #MLParis during the meetup.

+ Franck Bardol, Igor Carron, Introduction

+ Pitches (2mins):

Vinay Kumar (
Role of Data augmentation methods in Deep Learning
With CNN architecture, Deep Learning models can predict/classify the data with better accuracy when compared any other methods. But instead of creating a new network architectures for new problem, data augmentation methods can help in using the same architecture and address to many tasks. In this talk, we will discuss about the various data augmentations we can use in Deep Learning and develop a model using 

Tristan Deleu (
Learning Algorithms (and more) with Neural Turing Machines
The idea of adding an external memory to neural networks has been increasingly popular over the past 2 years. In this talk, I will present you one of the earliest example of this family of models: the Neural Turing Machines. I will show you how they work, their early applications to algorithm learning as well as and recent developments for language understanding.

Jill-Jênn Vie (
Mangaki, recommandation de mangas open source
La plupart des sites marchands sont capables de nous recommander des produits correspondant à nos goûts. Mais comment cela fonctionne ?
Nous décrirons deux algorithmes permettant d'effectuer cela, ainsi qu'un test adaptatif de bienvenue pour l'élicitation des préférences.

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