12 Feb 2016 14:30 - 15:30 Lecture Why deep learning ? Yann LeCun Deep learning 12 Feb 2016 14:30 - 15:30 Share Facebook Linkedin Copy url Audio-visual RSS
Friday 12 February 2016 Amphithéâtre Maurice Halbwachs, Site Marcelin Berthelot Open to all 14:30 - 15:30 Skip youtube video player Listen to audio Speaker(s) Yann LeCun Visiting Professor, Collège de France, Chief AI Scientist, Meta, Professor, NYU Events Previous Lecture 12 Feb 2016 14:30 - 15:30 Yann LeCun Why deep learning ? Lecture 19 Feb 2016 14:30 - 15:30 Yann LeCun Multi-layer networks and gradient backpropagation Seminar 19 Feb 2016 15:30 - 16:30 Stéphane Mallat Mathematical mysteries of convolutional neural networks Lecture 26 Feb 2016 11:00 - 12:00 Yann LeCun Deep learning in practice Seminar 26 Feb 2016 12:00 - 13:00 Yann Ollivier Optimization and training of recurrent networks Lecture 4 Mar 2016 11:00 - 12:00 Yann LeCun Convolutional networks Seminar 4 Mar 2016 12:00 - 13:00 Gabriel Synnaeve Speech recognition Lecture 25 Mar 2016 11:00 - 11:30 Yann LeCun Convolutional networks. Vision applications Seminar 25 Mar 2016 11:30 - 13:00 Cordelia Schmid et Samy Bengio Metric learning, structured prediction Lecture 1 Apr 2016 11:00 - 12:00 Yann LeCun Recurrent networks. Applications to natural language processing Seminar 1 Apr 2016 12:00 - 13:00 Holger Schwenk Translation and natural language processing Lecture 8 Apr 2016 11:00 - 12:00 Yann LeCun Reasoning, attention, memory Seminar 8 Apr 2016 12:00 - 13:00 Rob Fergus Deep Learning and Reasoning, Memory-Augmented Networks Lecture 15 Apr 2016 11:00 - 12:00 Yann LeCun Unsupervised learning Next See also Seminar related to the lecture: Deep learning : theory and practice Yann LeCun, chair Computer Sciences and Digital Technologies Deep learning
Seminar 19 Feb 2016 15:30 - 16:30 Stéphane Mallat Mathematical mysteries of convolutional neural networks
Seminar 25 Mar 2016 11:30 - 13:00 Cordelia Schmid et Samy Bengio Metric learning, structured prediction
Lecture 1 Apr 2016 11:00 - 12:00 Yann LeCun Recurrent networks. Applications to natural language processing
