[{"command":"insert","method":null,"selector":".course-modal-ajax-placeholder-962040","data":"\u003Cdiv class=\u0022container content-rte\u0022\u003E\n  \u003Cdiv class=\u0022row\u0022\u003E\n    \u003Cdiv class=\u0022box col-md-6 bg-primary text-white\u0022\u003E\n      \u003Ch2 class=\u0022h1 text-left\u0022\u003E  Foundations of Deep Learning\n\u003C\/h2\u003E\n                  \u003Cdiv class=\u0022field-spacing\u0022\u003E\n        \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EECTS :\n          3\n        \u003C\/div\u003E\n      \u003C\/div\u003E\n                  \u003Cdiv class=\u0022field-spacing\u0022\u003E\n        \u003Cdiv class=\u0022field__label h3 dash\u0022\u003ESemestre calendaire :\n          Semestre 1\n        \u003C\/div\u003E\n      \u003C\/div\u003E\n                    \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003ELangue d\u0026#039;enseignement\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003EAnglais\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n        \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EEnseignement \u00e0 distance\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003Enon\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n      \u003C\/div\u003E\n    \u003Cdiv class=\u0022box col-md-6\u0022\u003E\n                    \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EProgramme \/ plan \/ contenus\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003E\u003Cp\u003ECourse outline\u0026nbsp;\u003C\/p\u003E\n\u003Cul\u003E\n\u003Cli\u003EIntroduction to Neural Networks: Perceptrons, capacity of a single neuron, Linear and logistic regression, etc.\u003C\/li\u003E\n\u003Cli\u003EApproximation with neural networks, Barron\u2019s theorem\u0026nbsp;\u003C\/li\u003E\n\u003Cli\u003ETraining of neural networks, Generalization, Rademacher complexity\u0026nbsp;\u003C\/li\u003E\n\u003Cli\u003EVarious Neural Networks architectures (RNN, CNN, etc.)\u003C\/li\u003E\n\u003Cli\u003EExample of modern architectural variations for communications.\u003C\/li\u003E\n\u003C\/ul\u003E\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n        \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EObjectifs d\u0026#039;apprentissage\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003E\u003Cp\u003EThis is a course on the principles of representation learning in general and deep learning in particular. Deep learning has recently been responsible for a large number of impressive empirical gains across a wide array of applications including most dramatically in object recognition and detection in images, natural language processing and speech recognition.\u0026nbsp;\u003C\/p\u003E\n\u003Cp\u003EThis course explores recent theories that explain the empirical success of deep learning. Furthermore, advanced neural networks architectures (RNN, CNN, etc.) will be explained and implementation using Python will be discussed.\u003C\/p\u003E\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n        \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EOrganisation g\u00e9n\u00e9rale et modalit\u00e9s p\u00e9dagogiques\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003E\u003Cp\u003EThis course is composed of 6 lectures (18h) and two lab sessions (6h).\u003C\/p\u003E\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n                        \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EBibliographie\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003E\u003Cp\u003E1. Deep Learning, by Ian Goodfellow, Yoshua Bengio and Aaron Courville, Adaptive Computation and Machine Learning series, MIT Press, November 2016, ISBN-10: 0262035618.\u0026nbsp;\u003C\/p\u003E\n\u003Cp\u003E2. Machine Learning: A Probabilistic Perspective, by Kevin P. Murphy, Adaptive Computation and Machine Learning series, MIT Press, ISBN-10: 0262018020.\u0026nbsp;\u003C\/p\u003E\n\u003Cp\u003E3. Machine Learning, by Tom M. Mitchell, Mcgraw-Hill Series in Computer Science, McGraw-Hill Education, ISBN-10: 0070428077.\u0026nbsp;\u003C\/p\u003E\n\u003Cp\u003E4. Deep Learning - Methods and Applications by Li Deng and Dong Yu\u0026nbsp;\u003Ca rel=\u0022nofollow noopener noreferrer\u0022 target=\u0022_blank\u0022 href=\u0022http:\/\/research.microsoft.com\/pubs\/219984\/BOOK2014.pdf\u0022\u003E\u003Cstrong\u003Ehttp:\/\/research.microsoft.com\/pubs\/219984\/BOOK2014.pdf\u003C\/strong\u003E\u003C\/a\u003E.\u003C\/p\u003E\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n        \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003ENature de l\u0026#039;\u00e9valuation\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003EEvaluation Continue non Int\u00e9grale\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n      \u003C\/div\u003E\n  \u003C\/div\u003E\n\u003C\/div\u003E\n","settings":null}]