{"id":36249,"date":"2019-10-31T22:10:29","date_gmt":"2019-10-31T19:10:29","guid":{"rendered":"https:\/\/prohoster.info\/blog\/nejroset-v-stekle-ne-trebuet-elektropitaniya-raspoznayot-tsifry\/"},"modified":"2019-10-31T22:10:29","modified_gmt":"2019-10-31T19:10:29","slug":"nejroset-v-stekle-ne-trebuet-elektropitaniya-raspoznayot-tsifry","status":"publish","type":"post","link":"https:\/\/prohoster.info\/fr\/blog\/news\/nejroset-v-stekle-ne-trebuet-elektropitaniya-raspoznayot-tsifry","title":{"rendered":"Un r\u00e9seau neuronal dans le verre. Ne n\u00e9cessite pas d'alimentation \u00e9lectrique, reconna\u00eet les chiffres","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

\"Un
\n
\nNous connaissons tous cette capacit\u00e9 des r\u00e9seaux neuronaux \u00e0 reconna\u00eetre l'\u00e9criture manuscrite. Les bases de cette technologie existent depuis de nombreuses ann\u00e9es, mais ce n'est que relativement r\u00e9cemment qu'un bond en mati\u00e8re de puissance informatique et de traitement parall\u00e8le des donn\u00e9es a permis de transformer cette technologie en une solution tr\u00e8s pratique. Cependant, cette solution pratique sera fondamentalement pr\u00e9sent\u00e9e sous la forme d'un ordinateur num\u00e9rique modifiant sans cesse des bits, tout comme lors de l'ex\u00e9cution de n'importe quel programme. Mais dans le cas du r\u00e9seau neuronal d\u00e9velopp\u00e9 par des chercheurs des universit\u00e9s du Wisconsin, du MIT et de Columbia, la situation est diff\u00e9rente. Ils ont cr\u00e9\u00e9 un panneau de verre qui ne n\u00e9cessite pas d'alimentation \u00e9lectrique propre, mais qui est capable de reconna\u00eetre des chiffres manuscrits<\/a><\/noindex>.
\n<\/a><\/noindex>
\nCe verre contient des inclusions pr\u00e9cis\u00e9ment dispos\u00e9es, telles que des bulles d'air, des impuret\u00e9s de graph\u00e8ne et d'autres mat\u00e9riaux. Lorsque la lumi\u00e8re frappe le verre, des motifs d'onde complexes se forment, entra\u00eenant une intensification de la lumi\u00e8re dans l'une des dix zones. Chacune de ces zones correspond \u00e0 un chiffre. Par exemple, ci-dessous se trouvent deux exemples montrant comment la lumi\u00e8re se propage lors de la reconnaissance du chiffre \u00ab deux \u00bb.<\/p>\n

\"Un
\n
\nAvec un ensemble d'apprentissage de 5000 images, le r\u00e9seau neuronal est capable de reconna\u00eetre correctement 79 % des 1000 images d'entr\u00e9e. L'\u00e9quipe pense qu'elle pourrait am\u00e9liorer les r\u00e9sultats en contournant les limitations dues au processus de fabrication du verre. Ils ont commenc\u00e9 avec un design tr\u00e8s limit\u00e9 de l'appareil pour obtenir un prototype fonctionnel. Ensuite, ils pr\u00e9voient de continuer \u00e0 explorer diverses fa\u00e7ons d'am\u00e9liorer la qualit\u00e9 de la reconnaissance, tout en essayant de ne pas compliquer excessivement la technologie pour qu'elle puisse ensuite \u00eatre utilis\u00e9e en production. L'\u00e9quipe a \u00e9galement des projets pour cr\u00e9er un r\u00e9seau neuronal tridimensionnel dans le verre.
\n
Source : habr.com<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

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