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È diventato il motore dei sistemi di \u003Ca href=\"https://www.snowflake.com/en/fundamentals/ai-programming-languages/\"\u003EAI moderna\u003C/a\u003E, favorendo progressi nella computer vision e nell’elaborazione del linguaggio naturale e generando testi convincenti e simili a quelli umani che alimentano i chatbot AI. Il deep learning costituisce inoltre la base di tecnologie autonome come veicoli a guida autonoma e robot intelligenti, che elaborano flussi di dati provenienti dai sensori in tempo reale per percepire l’ambiente circostante e prendere decisioni in frazioni di secondo.\u003C/p\u003E\n\u003Cp\u003EQuesta guida spiega cos’è il deep learning, perché è importante, e ne analizza vantaggi e limiti.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"is",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-76c1dfc7c8","additionalClasses":"headline-decoration","type":"heading2","lines":["Che cos’è il deep learning?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-05b063a406","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EIl deep learning è una forma avanzata di \u003Ca href=\"https://www.snowflake.com/en/fundamentals/machine-learning-frameworks/\"\u003Emachine learning\u003C/a\u003E che utilizza reti neurali multilivello per apprendere automaticamente pattern complessi direttamente dai \u003Ca href=\"https://www.snowflake.com/en/fundamentals/building-effective-machine-learning-pipelines/\"\u003Edati grezzi\u003C/a\u003E. A differenza degli algoritmi tradizionali di machine learning, non richiede che una persona indichi quali caratteristiche considerare, come bordi e colori in un’immagine o ricorrenze di parole in un testo. Si basa invece su reti con numerosi livelli di neuroni artificiali che determinano autonomamente quali caratteristiche siano rilevanti. Questo processo di autoapprendimento richiede dataset di addestramento molto più ampi per garantire che il \u003Ca href=\"https://www.snowflake.com/en/fundamentals/ml-models/\"\u003Emodello\u003C/a\u003E comprenda realmente i pattern nei dati e non si limiti a memorizzarli. Poiché la maggior parte delle reti neurali si basa su decine di livelli computazionali che eseguono calcoli simultaneamente, il deep learning richiede anche una potenza di calcolo significativamente superiore rispetto agli algoritmi tradizionali di machine learning.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_2060034519":{"columnClassNames":{"title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"work",":type":"snowflake-site/components/container",":items":{"title_v2_copy":{"id":"title-v2-bd4e7b46ae","additionalClasses":"headline-decoration","type":"heading2","lines":["Perché il deep learning è importante?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-78bdbd8d21","text":"\u003Cp\u003ELa capacità del deep learning di estrarre automaticamente pattern significativi da dati non strutturati consente alle organizzazioni di automatizzare attività prima impossibili o poco pratiche, come il rilevamento delle frodi in tempo reale, l’analisi di immagini mediche e la robotica di magazzino. Le organizzazioni che padroneggiano il deep learning possono valorizzare dati finora inutilizzati, automatizzare workflow complessi e individuare opportunità di mercato più rapidamente rispetto ai concorrenti, un vantaggio strategico in un’economia sempre più guidata dai dati.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2_copy","text_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},"container_copy_copy_":{"columnClassNames":{"container":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__373061683":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1985496925":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy_":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy_259718203":"aem-GridColumn aem-GridColumn--default--12","container_copy_289473020":"aem-GridColumn aem-GridColumn--default--12","container_copy":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1190438074":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"rel",":type":"snowflake-site/components/container",":items":{"container_copy_copy":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"use",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-8e4c19ea90","additionalClasses":"headline-decoration","type":"heading2","lines":["Esempi e casi d’uso del deep learning"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-fe158dcb55","text":"\u003Cp\u003EI modelli di deep learning sono già utilizzati in numerosi settori. Ecco alcuni esempi:\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ERilevamento delle frodi nel settore finanziario\u003C/h3\u003E\n\u003Cp\u003EI sistemi di deep learning analizzano in tempo reale i pattern delle transazioni per identificare attività sospette che si discostano dal comportamento abituale dei clienti. Questi modelli possono segnalare le transazioni ad alto rischio per ulteriori verifiche o bloccarle automaticamente, contribuendo a ridurre le perdite dovute a frodi e a proteggere i clienti da addebiti non autorizzati. \u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EManutenzione predittiva nel manifatturiero\u003C/h3\u003E\n\u003Cp\u003EIl deep learning analizza i dati dei sensori dei macchinari industriali, come vibrazioni, temperatura e segnali acustici, per individuare segnali precoci di guasti imminenti. Questa capacità predittiva consente di pianificare la manutenzione durante i fermi programmati, riducendo i costi legati alle interruzioni impreviste, prolungando la vita utile delle apparecchiature e ottimizzando i costi di manutenzione.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ERaccomandazioni personalizzate nel retail\u003C/h3\u003E\n\u003Cp\u003ELe piattaforme e-commerce utilizzano il deep learning per analizzare la cronologia di navigazione, i modelli di acquisto e le affinità con altri clienti, così da suggerire prodotti di potenziale interesse. Mostrando suggerimenti più personalizzati, il deep learning può aumentare il coinvolgimento dei clienti e migliorare i tassi di conversione, in base all’implementazione e al contesto. \u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EImaging e diagnostica in campo medico\u003C/h3\u003E\n\u003Cp\u003EI modelli di deep learning addestrati su milioni di immagini mediche, come radiografie, TAC, risonanze magnetiche e fotografie retiniche, possono individuare patologie come tumori, malattie cardiache e disturbi oculari. Questa tecnologia accelera la diagnosi, riduce il rischio di errore umano e contribuisce a colmare la carenza globale di specialisti in aree meno servite. In attività specifiche e in alcuni studi, i modelli di deep learning hanno dimostrato prestazioni paragonabili a quelle dei clinici; l’efficacia nel mondo reale dipende dalla validazione, dall’integrazione nei workflow e dalla supervisione clinica. \u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EElaborazione del linguaggio naturale e chatbot\u003C/h3\u003E\n\u003Cp\u003EIl deep learning alimenta sistemi di AI conversazionale in grado di comprendere il linguaggio umano, permettendo ai chatbot di fornire assistenza clienti, rispondere a domande e completare transazioni senza intervento umano. Grazie all’apprendimento su grandi volumi di testi e conversazioni, questi sistemi sono sempre più capaci di gestire richieste complesse e fornire risposte naturali e utili.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EVeicoli autonomi e robotica\u003C/h3\u003E\n\u003Cp\u003EAuto a guida autonoma e robot utilizzano il deep learning per elaborare flussi video, dati lidar e input provenienti da sensori. Questo consente di comprendere l’ambiente, rilevare ostacoli e prendere decisioni di navigazione in tempo reale. La capacità di percepire il contesto consente ai sistemi autonomi di adattarsi a variazioni delle condizioni stradali, meteorologiche e del comportamento umano.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ERiconoscimento vocale ed elaborazione audio\u003C/h3\u003E\n\u003Cp\u003EI modelli di deep learning trasformano il parlato in testo con notevole accuratezza, alimentando assistenti vocali come Siri e Alexa e strumenti di accessibilità per persone con disabilità uditive. Questi sistemi imparano a gestire accenti diversi, rumori di fondo e vari stili di parlato, rendendo l’interazione vocale un’interfaccia efficace per un’ampia gamma di dispositivi e servizi.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy_":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"and",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-91114a0633","additionalClasses":"headline-decoration","type":"heading2","lines":["Come funziona il deep learning?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-497f8e2e71","text":"\u003Cp\u003EI modelli di deep learning si basano su reti complesse composte da migliaia di neuroni artificiali, operazioni matematiche che apprendono automaticamente pattern da esempi etichettati, regolando milioni di parametri interni per tentativi ed errori fino a prevedere o riconoscere con precisione nuovi dati mai visti prima.\u003C/p\u003E\n\u003Cp\u003EOgni rete è composta da tre parti fondamentali: un livello di input in cui vengono inseriti i dati etichettati; più livelli nascosti di neuroni che analizzano e affinano progressivamente i dati; e un livello di output che restituisce la previsione finale. \u003C/p\u003E\n\u003Cp\u003EImmagina di voler addestrare una \u003Ca href=\"https://www.snowflake.com/it/fundamentals/neural-network/\"\u003Erete neurale\u003C/a\u003E a riconoscere se una foto raffigura un cane o un gatto. Si forniscono migliaia di immagini etichettate “cane” o “gatto”, lasciando che la rete individui autonomamente le differenze.\u003C/p\u003E\n\u003Cp\u003EIl primo livello nascosto può imparare a rilevare pattern semplici come bordi e angoli. Il secondo combina questi elementi in forme come cerchi e linee. Un terzo livello può riconoscere componenti come “orecchie appuntite” o “naso umido”, e così via. A ogni livello, la rete sviluppa una comprensione più sofisticata, passando dai pixel grezzi a concetti significativi.\u003C/p\u003E\n\u003Cp\u003EIl livello finale restituisce la previsione: una probabilità che l’immagine rappresenti un cane o un gatto. Se la rete sbaglia, , ovvero la previsione non corrisponde all’etichetta originale, riprova automaticamente, attribuendo maggiore peso ad alcune caratteristiche e minore ad altre. Ripete il processo finché non distingue correttamente tra cane e gatto con elevata accuratezza su dati di test separati, in base alla qualità e varietà dei dati di addestramento e alla progettazione del modello. \u003C/p\u003E\n\u003Cp\u003ELa rete apprende dai propri errori tramite un processo chiamato retropropagazione, analizzando a ritroso i livelli per identificare quali caratteristiche hanno contribuito maggiormente all’errore. Una funzione matematica di perdita indica quanto correggere quando la previsione è errata. Se l’errore è significativo, ad esempio con una previsione del 95% che una foto di un gatto sia un cane, il modello modifica in modo più marcato i pesi associati alle caratteristiche rilevanti. Se l’errore è lieve, ad esempio con una probabilità del 51%, le modifiche saranno meno rilevanti.\u003C/p\u003E\n\u003Cp\u003EEcco perché il deep learning è così potente: Una volta definito il processo di addestramento, il sistema individua automaticamente caratteristiche e rappresentazioni utili senza intervento manuale. La rete apprende ciò che conta. Fornendo più dati e maggiore potenza di calcolo, può apprendere pattern sempre più complessi, ampliando le possibilità dell’intelligenza artificiale.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy_259718203":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"how",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-3cc2615161","additionalClasses":"headline-decoration","type":"heading2","lines":["Tipi di modelli di deep learning"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-99da926082","text":"\u003Cp\u003EEsistono diverse architetture di deep learning, ciascuna progettata per specifici tipi di dati e attività. Ecco le principali.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EReti neurali convoluzionali (CNN)\u003C/h3\u003E\n\u003Cp\u003ELe CNN sono progettate per elaborare dati strutturati a griglia, come le immagini, individuando pattern quali bordi, texture e forme. Grazie alla capacità di comprendere le relazioni tra pixel adiacenti, eccellono in attività di computer vision come classificazione di immagini, rilevamento di oggetti, riconoscimento facciale e analisi di immagini mediche. Sono fondamentali per applicazioni che vanno dalle app fotografiche degli smartphone ai veicoli autonomi che rilevano pedoni e segnali stradali.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EReti neurali ricorrenti (RNN)\u003C/h3\u003E\n\u003Cp\u003ELe RNN sono ideate per attività in cui l’ordine dei dati è rilevante, come l’analisi di frasi in un documento o di fotogrammi in un video. La capacità di elaborare nuovi dati mantenendo memoria di quelli precedenti le rende utili per traduzione automatica, riconoscimento vocale e previsione di serie temporali. Sebbene i transformer abbiano in gran parte sostituito le RNN in molte attività linguistiche, restano utili con flussi di dati continui, come letture di sensori in tempo reale, o in contesti con risorse computazionali limitate.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EReti generative avversarie (GAN)\u003C/h3\u003E\n\u003Cp\u003ELe GAN sono composte da due reti neurali in competizione: un generatore che crea dati sintetici, come immagini false, e un discriminatore che tenta di distinguere tra dati reali e sintetici. Attraverso questo processo, il generatore diventa sempre più capace di produrre output realistici, rendendo le GAN efficaci nella creazione di immagini fotorealistiche, dati sintetici per l’addestramento e deepfake. Sono state utilizzate per creare opere artistiche, migliorare immagini a bassa risoluzione, generare volti realistici di persone inesistenti e progettare nuove molecole per la scoperta di farmaci.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EReti transformer\u003C/h3\u003E\n\u003Cp\u003EI transformer hanno rivoluzionato l’elaborazione del linguaggio naturale grazie a un meccanismo di attenzione che consente di focalizzarsi simultaneamente sulle parti più rilevanti dell’input, invece di elaborarlo in sequenza. Questa architettura alimenta i moderni \u003Ca href=\"https://www.snowflake.com/it/fundamentals/large-language-model/\"\u003Elarge language model\u003C/a\u003E come GPT e Claude, permettendo di comprendere il contesto in testi lunghi, generare contenuti simili a quelli umani e svolgere attività come traduzione e sintesi con elevata accuratezza. I transformer si sono dimostrati efficaci anche oltre il linguaggio, ad esempio nella computer vision e nella previsione della struttura delle proteine.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EAutoencoder\u003C/h3\u003E\n\u003Cp\u003EGli autoencoder comprimono i dati nelle caratteristiche essenziali e li ricostruiscono dalla forma compressa. Sono utili per individuare anomalie, poiché ciò che non viene ricostruito correttamente può indicare un’anomalia, per ripulire dati rumorosi e per ridurre dataset complessi ai loro elementi fondamentali. Questa capacità li rende utili nel rilevamento di frodi con carte di credito o nell’individuazione di difetti nei processi produttivi.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_289473020":{"columnClassNames":{"title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"equation",":type":"snowflake-site/components/container",":items":{"title_v2_copy":{"id":"title-v2-047a4e4fcb","additionalClasses":"headline-decoration","type":"heading2","lines":["Differenze chiave tra machine learning, deep learning e AI generativa"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-3a4c460404","text":"\u003Cp\u003ETre paradigmi correlati ma distinti dominano oggi lo sviluppo dei modelli di AI. Ecco le principali differenze. \u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EMachine learning\u003C/h3\u003E\n\u003Cp\u003EI modelli di machine learning apprendono pattern dai dati, ma in genere richiedono che una persona progetti ed estragga manualmente le caratteristiche rilevanti prima dell’addestramento. Sono efficaci con dati strutturati e dataset relativamente contenuti, ad esempio per credit scoring, segmentazione della clientela e sistemi di raccomandazione di base. Sono generalmente più interpretabili rispetto ai modelli di deep learning e richiedono meno risorse computazionali.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EDeep learning\u003C/h3\u003E\n\u003Cp\u003EIl deep learning utilizza reti neurali multilivello che individuano automaticamente le caratteristiche rilevanti, eliminando la necessità di feature engineering manuale. Eccelle con dati non strutturati come immagini, audio e testo, ma richiede grandi volumi di dati e notevoli risorse computazionali. Alimenta applicazioni come riconoscimento facciale, veicoli autonomi, diagnostica per immagini e sistemi di riconoscimento vocale.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EAI generativa\u003C/h3\u003E\n\u003Cp\u003EL’\u003Ca href=\"https://www.snowflake.com/en/fundamentals/generative-ai/\"\u003EAI generativa\u003C/a\u003E è un sottoinsieme del deep learning progettato per creare nuovi contenuti, come testi, immagini, musica, codice o video. Richiede dataset di dimensioni molto ampie e architetture come transformer e GAN per apprendere pattern sufficienti a generare output realistici e originali. È alla base di applicazioni come ChatGPT e Claude, DALL-E e Midjourney, GitHub Copilot e sistemi che generano dati sintetici o contenuti personalizzati su larga scala.\u003C/p\u003E\n\u003Cp\u003EOltre a questi tre paradigmi, ne esistono altri degni di nota. L’AI simbolica utilizza regole esplicite e logica programmata dall’essere umano, come nei sistemi esperti e nei chatbot rule-based. Nel reinforcement learning, gli agenti interagiscono con l’ambiente ricevendo ricompense o penalità in base alle azioni compiute. Questo modello è spesso implementato in sistemi di controllo robotici e motori di raccomandazione che imparano dall’engagement degli utenti. Gli algoritmi evolutivi si ispirano all’evoluzione biologica per migliorare progressivamente i modelli, ad esempio nella progettazione di reti neurali o nell’ottimizzazione della supply chain. L’AI neuro-simbolica combina reti neurali e ragionamento simbolico. È un paradigma emergente con applicazioni iniziali in ambito sanitario e cybersecurity.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2_copy","text_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},"container_copy_copy__1985496925":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"why",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-245d8bcb49","additionalClasses":"headline-decoration","type":"heading2","lines":["Vantaggi dei modelli di deep learning"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-7f13fbae47","text":"\u003Cp\u003EIl deep learning offre numerosi vantaggi rispetto ad altri paradigmi di AI. Ecco i principali.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EElevata accuratezza in attività complesse\u003C/h3\u003E\n\u003Cp\u003EIl deep learning può raggiungere prestazioni allo stato dell’arte in attività complesse, come , classificazione di immagini e riconoscimento vocale, in base a modello, dati e modalità di valutazione. È in grado di individuare caratteristiche sottili e relazioni difficilmente identificabili o programmabili manualmente, come ad esempio riconoscere i segni precoci di malattia nelle scansioni mediche o prevedere le strutture proteiche. Questo vantaggio aumenta con la complessità del problema, rendendo il deep learning l’approccio preferito per i problemi che in passato avevano sconfitto i metodi tradizionali.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EIdentificazione automatica delle caratteristiche rilevanti \u003C/h3\u003E\n\u003Cp\u003EIndividua automaticamente le caratteristiche importanti senza richiedere intervento manuale di esperti. Apprende rappresentazioni gerarchiche, dai bordi nei primi livelli a concetti di alto livello nei livelli successivi. Questa automazione riduce notevolmente i tempi di sviluppo e consente di affrontare problemi in domini in cui gli esperti umani potrebbero anche non sapere cosa è rilevante.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EScalabilità su grandi volumi di dati\u003C/h3\u003E\n\u003Cp\u003ELe prestazioni migliorano in modo prevedibile con l’aumento dei dati di addestramento, mentre i tradizionali algoritmi di machine learning spesso si bloccano dopo un certo punto. Le organizzazioni con accesso a grandi volumi di dati possono ottenere vantaggi competitivi significativi investendo in una maggiore raccolta di dati e modelli più grandi. La relazione tra volume dei dati e prestazioni crea un ulteriore vantaggio per le organizzazioni che possono raccogliere ed elaborare informazioni su vasta scala.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EDecisioni in tempo reale \u003C/h3\u003E\n\u003Cp\u003EUna volta addestrati, i modelli possono elaborare informazioni e generare previsioni molto rapidamente. Sono quindi adatti ai veicoli autonomi che devono rilevare gli ostacoli e reagire immediatamente, ai sistemi di rilevamento delle frodi che valutano le transazioni non appena si verificano e agli assistenti vocali che rispondono ai comandi vocali senza ritardi evidenti. Le moderne ottimizzazioni hardware e tecniche di compressione dei modelli continuano a migliorare la velocità di inferenza, ampliando la gamma di applicazioni in tempo reale.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EEfficacia con dati non strutturati\u003C/h3\u003E\n\u003Cp\u003EA differenza degli algoritmi tradizionali, gestiscono efficacemente tipi di dati non strutturati privi di una chiara organizzazione tabulare, come immagini, video, audio, testo e flussi di sensori. Consentono di valorizzare grandi volumi di dati prima inutilizzabili: email, registrazioni del servizio clienti, filmati delle telecamere di sicurezza e post sui social media. Rendendo i dati precedentemente inutilizzabili accessibili per l’analisi, il deep learning consente di ottenere categorie di applicazioni e insight completamente nuove.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EAdattabilità a nuove attività\u003C/h3\u003E\n\u003Cp\u003EModelli addestrati su un’attività possono essere riadattati a compiti correlati con dati aggiuntivi limitati, riducendo notevolmente i dati e il tempo necessari per le nuove applicazioni. Ad esempio, un modello addestrato per riconoscere oggetti di uso quotidiano può essere ottimizzato per identificare condizioni mediche specifiche, utilizzando un numero di immagini mediche molto inferiore a quello richiesto per l’addestramento da zero. Questa tecnica, nota come transfer learning, accelera i cicli di sviluppo sfruttando i modelli esistenti come punto di partenza e rendendo il deep learning più accessibile anche quando i dati specifici del settore sono limitati.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EApprendimento continuo \u003C/h3\u003E\n\u003Cp\u003EI sistemi di deep learning possono essere continuamente aggiornati con nuovi dati, adattandosi a pattern in evoluzione per migliorare l’accuratezza nel tempo e gestire nuove condizioni senza un riaddestramento completo. Ciò consente ai modelli distribuiti in produzione di migliorare man mano che incontrano più esempi del mondo reale, adattandosi naturalmente ai cambiamenti nel comportamento degli utenti, nelle condizioni di mercato o nei fattori ambientali. I sistemi di deep learning risultano quindi più robusti e sostenibili nel tempo rispetto ai sistemi statici.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy":{"columnClassNames":{"title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"types",":type":"snowflake-site/components/container",":items":{"title_v2_copy":{"id":"title-v2-c6f9d83f46","additionalClasses":"headline-decoration","type":"heading2","lines":["Svantaggi dei modelli di deep learning"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-77e9424fb4","text":"\u003Cp\u003ENonostante i vantaggi, i modelli di deep learning presentano anche enormi sfide in termini di costo, consumo di energia, interpretabilità e potenziale utilizzo improprio. Ecco i principali svantaggi del deep learning.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EElevato fabbisogno di potenza di calcolo \u003C/h3\u003E\n\u003Cp\u003EL’addestramento richiede notevole potenza di calcolo, spesso con hardware specializzati costosi come le GPU in esecuzione per giorni o settimane. Il loro consumo energetico può essere enorme: l’addestramento di modelli di grandi dimensioni può richiedere un notevole dispendio di energia e i requisiti variano ampiamente in base alle dimensioni del modello, all’hardware e alla durata dell’addestramento. Anche l’implementazione di modelli per l’inferenza in tempo reale su vasta scala richiede costanti investimenti in infrastrutture e risorse di calcolo, rendendo il deep learning economicamente impraticabile per alcune applicazioni e organizzazioni più piccole.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ENecessità di grandi dataset etichettati\u003C/h3\u003E\n\u003Cp\u003EI modelli di deep learning richiedono tipicamente da migliaia a milioni di esempi di addestramento etichettati per funzionare bene, e la creazione di queste etichette spesso richiede un notevole impegno umano e competenze specifiche. In ambiti specialistici, come l’imaging medico o la diagnosi delle malattie rare, in cui gli esperti devono rivedere e annotare manualmente ogni esempio, ottenere una quantità sufficiente di dati etichettati può essere estremamente difficile o costoso. Questo requisito dei dati crea un problema di partenza a freddo in cui il deep learning non può essere applicato in modo efficace senza prima investire molto nella raccolta e nell’etichettatura dei dati, rendendo le applicazioni avanzate fuori dalla portata delle organizzazioni senza notevoli risorse di dati.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ERischio di overfitting\u003C/h3\u003E\n\u003Cp\u003EPossono memorizzare i dati di addestramento invece di apprendere pattern generalizzabili. Un modello overfit offre ottime prestazioni negli esempi di addestramento, ma fallisce quando si verificano situazioni nuove e leggermente diverse, come un sistema di riconoscimento facciale che funziona perfettamente in laboratorio ma fatica a gestire condizioni di illuminazione o angolazioni della fotocamera diverse in produzione. Per prevenire l’overfitting servono tecniche come la regolarizzazione, l’abbandono e il test di convalida, ma nonostante queste garanzie, i modelli possono comunque apprendere correlazioni spurie che non reggono nel mondo reale.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EScarsa interpretabilità \u003C/h3\u003E\n\u003Cp\u003EÈ spesso difficile spiegare perché un modello abbia preso una decisione, il che risulta problematico per le applicazioni in cui le spiegazioni sono legalmente o eticamente necessarie. Ad esempio, un sistema di approvazione dei prestiti basato sul deep learning potrebbe rifiutare un richiedente senza essere in grado di spiegare quali fattori hanno spinto tale decisione, potenzialmente violando leggi sul prestito equo o perpetuando distorsioni nascoste. Questo “problema della scatola nera” in settori regolamentati come l’assistenza sanitaria e la finanza rende anche difficile il debug dei modelli quando non funzionano o verificare che stiano prendendo decisioni per i motivi giusti.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ESollevano forti preoccupazioni etiche\u003C/h3\u003E\n\u003Cp\u003EPoiché i modelli di deep learning imparano dai dati storici, inevitabilmente assorbono e amplificano i bias presenti in tali dati, perpetuando potenzialmente la discriminazione nelle assunzioni, nei prestiti, nella giustizia penale e in altri domini sensibili. Un sistema di riconoscimento facciale addestrato principalmente sui volti più chiari avrà scarsi risultati sugli individui con pelle scura e uno strumento di screening del curriculum addestrato sulle decisioni di assunzione storiche potrebbe discriminare le donne o le minoranze. Sollevano inoltre preoccupazioni etiche sulla sua capacità di generare deepfake, il ruolo nel rendere possibile la sorveglianza di massa e l’utilizzo con le armi autonome.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2_copy","text_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},"container":{"columnClassNames":{},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"advantages",":type":"snowflake-site/components/container",":items":{},":itemsOrder":[],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},"container_copy_copy__1190438074":{"columnClassNames":{"container_copy_2083166428":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","container_copy_20831":"aem-GridColumn aem-GridColumn--default--12","container_copy":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"customer",":type":"snowflake-site/components/container",":items":{"container_copy":{"columnClassNames":{},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"limitation",":type":"snowflake-site/components/container",":items":{},":itemsOrder":[],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},"container_copy_2083166428":{"columnClassNames":{"title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"conslusion",":type":"snowflake-site/components/container",":items":{"title_v2_copy":{"id":"title-v2-54a316fa4c","additionalClasses":"headline-decoration","type":"heading2","lines":["Conclusione"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-2d32edb13e","text":"\u003Cp\u003EIl deep learning ha trasformato l’intelligenza artificiale consentendo alle macchine di apprendere automaticamente schemi complessi dai dati grezzi, sbloccando funzionalità impossibili con i tradizionali approcci e potenziando le innovazioni in tutti i settori, dall’assistenza sanitaria ai sistemi autonomi. Le organizzazioni che lo adottano possono valorizzare grandi volumi di dati non strutturati, automatizzare decisioni complesse e individuare opportunità invisibili ai concorrenti. \u003C/p\u003E\n\u003Cp\u003EÈ ormai un’infrastruttura essenziale per l’economia moderna. Con la crescita esponenziale dei dati e la maggiore accessibilità della potenza di calcolo, la competenza nel deep learning diventa un fattore distintivo per qualsiasi organizzazione che desideri competere in un futuro basato sull’AI. La domanda non è più se adottarlo, ma quanto rapidamente sviluppare competenze, infrastrutture e risorse dati per sfruttarne il potenziale trasformativo.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2_copy","text_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},"container_copy_20831":{"columnClassNames":{"container":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","simple_snowflake_acc":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"faq",":type":"snowflake-site/components/container",":items":{"title_v2_copy":{"id":"title-v2-4b4f2325aa","additionalClasses":"headline-decoration","type":"heading2","lines":["FAQ sul deep learning"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"simple_snowflake_acc":{"id":"simple-snowflake-accordion-28f6b9f53e","showDivider":false,"accordionItemsList":[{"title":"Qual è la differenza tra deep learning e AI generativa?","richText":"\u003Cp\u003EIl deep learning è un approccio al machine learning che utilizza reti neurali multilivello per apprendere pattern dai dati. L’AI generativa è un sottoinsieme specifico focalizzato sulla creazione di nuovi contenuti come testo, immagini, musica, codice o video. Entrambi utilizzano reti neurali, ma sono ottimizzati per obiettivi diversi: comprendere o creare.\u003C/p\u003E\n"},{"title":"È necessario essere esperti di matematica o saper programmare per comprendere il deep learning?","richText":"\u003Cp\u003ENon è necessario essere esperti di matematica per comprenderne i principi. Per sviluppare modelli occorrono competenze di programmazione, solitamente Python, e conoscenze di base di calcolo, algebra lineare e statistica.\u003C/p\u003E\n"},{"title":"È davvero utile per problemi del mondo reale?","richText":"\u003Cp\u003EIl deep learning risolve problemi reali in ambiti come diagnostica medica e veicoli autonomi. 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