{"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\/da\/blog\/news\/nejroset-v-stekle-ne-trebuet-elektropitaniya-raspoznayot-tsifry","title":{"rendered":"Neuro-net i glas. Kr\u00e6ver ikke str\u00f8m, genkender cifre","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

\"Neuro-net
\n
\nVi kender alle til neurale netv\u00e6rks evne til at genkende h\u00e5ndskrevet tekst. Grundlaget for denne teknologi har eksisteret i mange \u00e5r, men f\u00f8rst for nylig har springet i computerkraft og parallel databehandling gjort det muligt at skabe en meget praktisk l\u00f8sning ud af denne teknologi. Alligevel vil denne praktiske l\u00f8sning i sin kerne blive pr\u00e6senteret som en digital computer, der gentagne gange \u00e6ndrer bits, pr\u00e6cis som ved udf\u00f8relsen af ethvert andet program. Men i tilf\u00e6lde af det neurale net, der er udviklet af forskere fra universiteterne Wisconsin, MIT og Columbia, er det anderledes. De har skabt en glaspanel, der ikke kr\u00e6ver egen str\u00f8m, men som samtidig er i stand til at genkende h\u00e5ndskrevne cifre<\/a><\/noindex>.
\n<\/a><\/noindex>
\nDette glas indeholder pr\u00e6cist placerede indskud, s\u00e5som luftbobler, grafen og andre materialer. N\u00e5r lys rammer glasset, opst\u00e5r der komplekse b\u00f8lge-m\u00f8nstre, som resulterer i, at lyset i en af ti omr\u00e5der bliver mere intenst. Hver af disse omr\u00e5der svarer til et bestemt tal. For eksempel er der nedenfor to eksempler, der viser, hvordan lyset spreder sig, n\u00e5r tallet \u201eto\u201d genkendes.<\/p>\n

\"Neuro-net
\n
\nVed hj\u00e6lp af et tr\u00e6ningss\u00e6t p\u00e5 5000 billeder er det neurale net i stand til korrekt at genkende 79% af 1000 indg\u00e5ende billeder. Teamet mener, at de kunne forbedre resultatet, hvis de kunne omg\u00e5 de begr\u00e6nsninger, der skyldes producentens proces af glasset. De startede med et meget begr\u00e6nset design af enheden for at f\u00e5 en fungerende prototype. Derefter planl\u00e6gger de at forts\u00e6tte med at unders\u00f8ge forskellige m\u00e5der at forbedre genkendelseskvaliteten p\u00e5, samtidig med at de fors\u00f8ger ikke at komplicere teknologien un\u00f8digt, s\u00e5 den senere kan anvendes i produktionen. Teamet har ogs\u00e5 planer om at skabe et 3D neuralt netv\u00e6rk i glas.
\n
Kilde: habr.com<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

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