Abstract

Recent developments in neural network approaches (more known now as "deep learning") have dramatically changed the landscape of several research fields such as image classification, object detection, speech recognition, machine translation, self-driving cars and many more. Due its promise of leveraging large (sometimes even small) amounts of data in an end-to-end manner, i.e. train a model to extract features by itself and to learn from them, deep learning is increasingly appealing to other fields as well. This course is an introduction to deep learning using the PyTorch library in Python with a focus on graph neural networks. 

Bio

Dr. Lelarge is a researcher at INRIA leading the DYOGENE Research team which is part of the computer science department of Ecole Normale Superieure, (Paris, France). He is also a lecturer in deep learning at Ecole Polytechnique (Palaiseau, France) and Ecole Normale Superieure. He graduated from Ecole Polytechnique, qualified as an engineer at Ecole Nationale Superieure des Telecommunications (Paris) and received a PhD in Applied Mathematics from Ecole Polytechnique in 2005. His research interests include machine learning, deep learning, graphs and data analytics. 
Dr. Lelarge received the NetGCoop 2011 Best Paper Award with his PhD student E. Coupechoux, was awarded the 2012 SIGMETRICS rising star researcher award and the 2015 Best Publication in Applied Probability Award with Mohsen Bayati and Andrea Montanari for their work on compressed sensing.