Talks # 4: Sebastien Fischman - Pytorch-TabNet: Beating XGBoost on Tabular Data Using Deep Learning

Talks # 4:

Speaker: Sebastien Fischman (https://www.linkedin.com/in/sebastienfischman/)

Title : Pytorch-tabnet : Beating XGBoost on tabular data with deep learning?

Abstract: #DeepLearning has set up new benchmarks for Computer Vision, NLP, Speech, Reinforcement Learning in the past few years.
However tabular data competitions are still dominated by gradient boosted trees (GBTs) libraries like XGBoost, LightGBM and Catboost.
Tabnet is a new promising deep learning architecture based on sequential attention transformers proposed by Arik & Pfister that aims to fill the gap between GBTs and neural networks.

Pytorch-tabnet is an open source library that provides a scikit-like interface for training a TabNetClassifier or TabNetRegressor. It's ease of use allow any developer to quickly try a #TabNet architecture on any dataset, hopefully setting up new benchmarks.

Bio: Worked as a Data Scientist in France and Australia on very different topics:
- user segmentation based on their shopping habits for WoolWorth @Quantium
- real time bidding advertising @Tradelab
- stock market predictions based on sentiment analysis from social medias @SESAMm
- auto ML platform with explainable AI @DreamQuark
- now working on early stage cancer detection on new OCT-3D images @DamaeMedical

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Видео Talks # 4: Sebastien Fischman - Pytorch-TabNet: Beating XGBoost on Tabular Data Using Deep Learning канала Abhishek Thakur