{"id":73,"date":"2022-12-06T23:35:39","date_gmt":"2022-12-06T23:35:39","guid":{"rendered":"http:\/\/blog.vaniila-ai.catie-na.fr\/?p=73"},"modified":"2022-12-08T13:50:49","modified_gmt":"2022-12-08T13:50:49","slug":"cm-histoire-motivation-et-evolution-de-lapprentissage-profond","status":"publish","type":"post","link":"https:\/\/blog.vaniila-ai.catie-na.fr\/?p=73","title":{"rendered":"CM : Histoire, motivation, et \u00e9volution de l\u2019Apprentissage Profond"},"content":{"rendered":"

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CM : Histoire, motivation, et \u00e9volution de l\u2019Apprentissage Profond<\/span><\/h1>\n

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Site web du cours : <\/span>https:\/\/atcold.github.io\/pytorch-Deep…<\/a><\/yt-formatted-string><\/p>\n

Playlist : <\/span>http:\/\/bit.ly\/pDL-YouTube<\/a><\/yt-formatted-string><\/p>\n

Orateur : Yann Le Cun Traduction : Lo\u00efck Bourdois <\/span><\/yt-formatted-string><\/p>\n

Semaine 1 : <\/span>https:\/\/atcold.github.io\/pytorch-Deep…<\/a> <\/span><\/yt-formatted-string><\/p>\n

0:00:00<\/a> – Semaine 1 – Cours magistral Cours magistral partie A : <\/span>https:\/\/atcold.github.io\/pytorch-Deep…<\/a> Nous discutons de la motivation derri\u00e8re l’apprentissage profond. Nous commen\u00e7ons par l’histoire et l’inspiration de l’apprentissage profond. Nous discutons ensuite de l’histoire de la reconnaissance des motifs et introduisons la descente de gradient et son calcul par r\u00e9tropropagation. Enfin, nous discutons de la repr\u00e9sentation hi\u00e9rarchique du cortex visuel. <\/span><\/yt-formatted-string><\/p>\n

0:03:37<\/a> – L’inspiration de l’apprentissage profond et son histoire, l’apprentissage supervis\u00e9<\/span><\/yt-formatted-string><\/p>\n

<\/span>0:24:21<\/a> – Histoire de la reconnaissance des motifs et introduction \u00e0 la descente de gradient <\/span><\/yt-formatted-string><\/p>\n

0:38:56<\/a> – Calcul des gradients par r\u00e9tropropagation, repr\u00e9sentation hi\u00e9rarchique du cortex visuel <\/span><\/yt-formatted-string><\/p>\n

Cours magistral Partie B : <\/span>https:\/\/atcold.github.io\/pytorch-Deep…<\/a> Nous discutons d’abord de l’\u00e9volution des ConvNets, de Fukushima \u00e0 Le Cun et \u00e0 Alexnet. Nous abordons ensuite quelques applications des ConvNets, comme la segmentation d’images, les v\u00e9hicules autonomes et l’analyse d’images m\u00e9dicales. Nous discutons de la nature hi\u00e9rarchique des r\u00e9seaux profonds et des attributs des r\u00e9seaux profonds qui les rendent avantageux. Nous concluons par une discussion sur la g\u00e9n\u00e9ration et l’apprentissage de caract\u00e9ristiques\/repr\u00e9sentations. <\/span><\/yt-formatted-string><\/p>\n

0:49:25<\/a> – Evolution des ConvNets <\/span><\/yt-formatted-string><\/p>\n

1:05:55<\/a> – Apprentissage profond et extraction de caract\u00e9ristiques <\/span><\/yt-formatted-string><\/p>\n

1:19:27<\/a> – Apprendre les repr\u00e9sentations<\/span><\/yt-formatted-string><\/p>\n

 <\/p>\n

<\/span><\/p>\n

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