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Paradigmi neurali quantistici e dinamiche di apprendimento

Paradigmi neurali quantistici e dinamiche di apprendimento

Ishwarya M. V. / Narayana K. E. / Prinslin L. L.

133,86 €
IVA incluido
Disponible
Editorial:
KS OmniScriptum Publishing
Año de edición:
2026
Materia
Empresa y gestión
ISBN:
9786209951251
133,86 €
IVA incluido
Disponible

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Sobre IBD

Le reti neurali, ispirate ai sistemi neurali biologici, sono costituite da strati interconnessi di neuroni artificiali che elaborano e trasmettono informazioni. Ogni neurone riceve segnali in ingresso, applica una trasformazione e passa il risultato agli strati successivi. Attraverso un processo noto come addestramento, le reti neurali regolano i loro parametri interni per ridurre al minimo gli errori e migliorare le prestazioni su compiti specifici. Questa capacità di adattamento le rende molto efficaci per applicazioni come il riconoscimento delle immagini, l’elaborazione del linguaggio naturale e l’analisi predittiva. La forza delle reti neurali risiede nella loro capacità di apprendere modelli complessi dai dati, rendendole strumenti indispensabili nella moderna intelligenza artificiale.L’integrazione dell’informatica quantistica e delle reti neurali è motivata dai limiti degli approcci classici quando si tratta di dati ad alta densità e di problemi di ottimizzazione complessi. Le reti neurali classiche richiedono notevoli risorse computazionali per l’addestramento, soprattutto quando le dimensioni della rete e il volume dei dati aumentano.

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