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DIDATTICO","\u003Ch1 class=\"fw-semibold text-uppercase\">Introduzione alle \u003Cspan class=\"text-color-primary-400\">Reti Convolutive\u003C/span>\u003C/h1>\n","\u003Cp class=\"font-size-body-1 text-color-grey-800\">\nArchietture Convolutive per la pattern-recognition in Computer-Vision, Natural Language Processing e analisi di segnali\u003C/p>\n","\u003Ch2>Descrizione\u003C/h2>\n\u003Cp class=\"font-size-body-1\">Introduzione e approfondimenti sulle principali architetture convolutive, per sfruttare la pattern-recognition nei task in cui applicare il Deep Learning. Focus sulle operazioni base di padding, striding e pooling e concetti teorici di base come il weight sharing, la convoluzione volumetrica, il local perceptive fields ed esempi su architetture come LeNet-5, Residual Networks e Google Inception. Approfondimento di concetti come funzioni di costo custom e feature-maps exctraction tramite tecniche di Neural Style Transfer\u003C/p>\n",{"title":789,"list":790,"link":783},"DOMANDE FREQUENTI",[791,794,797,800,803,806],{"title":792,"text":793},"Quali competenze sono necessarie per seguire il modulo Introduzione alle Reti Convolutive?","\u003Cp class=\"fw-regular font-size-body-1\">\u003Cspan class=\"text-color-grey-800\">Per seguire il modulo Introduzione alle Reti Convolutive sono necessarie competenze preliminari specifiche di Python, Regressione lineare e logistica.\u003C/span>\u003C/p>\n",{"title":795,"text":796},"Quali sono i principali argomenti trattati nel modulo Introduzione alle reti convolutive?","\u003Cp class=\"fw-regular font-size-body-1\">\u003Cspan class=\"text-color-grey-800\">Il modulo Introduzione alle Reti Convolutive copre i seguenti argomenti principali: reti neurali artificiali convolutive, convoluzione volumetrica, il padding, lo stride, filtri, kernel e parametri, local perceptive fields, weights sharing, campionamento.\u003C/span>\u003C/p>\n",{"title":798,"text":799},"Quali nozioni principali posso apprendere al termine del modulo Introduzione alle Reti Convolutive?","\u003Cp class=\"fw-regular font-size-body-1\">\u003Cspan class=\"text-color-grey-800\">Al termine del modulo Introduzione alle Reti Convolutive sarai in grado di comprendere e applicare le principali architetture convolutive per il Deep Learning, utilizzando tecniche come padding, striding e pooling, e concetti avanzati come il weight sharing, la convoluzione volumetrica, il local perceptive fields e tecniche di Neural Style Transfer.\u003C/span>\u003C/p>\n",{"title":801,"text":802},"Al termine del modulo sono previsti un esame finale e una certificazione?","\u003Cp class=\"fw-regular font-size-body-1\">\u003Cspan class=\"text-color-grey-800\">L’esame finale e la certificazione sono previsti per i corsi Data Masters, non per i singoli moduli.\u003C/span>\u003C/p>\n",{"title":804,"text":805},"Il materiale del modulo è scaricabile? Posso rivedere le lezioni?","\u003Cp class=\"fw-regular font-size-body-1\">\u003Cspan class=\"text-color-grey-800\">Tutti i materiali (slide, materiale per esercitazioni, ecc.) sono scaricabili, mentre tutte le lezioni possono essere riviste in qualsiasi momento.\u003C/span>\u003C/p>\n",{"title":807,"text":808},"In caso di dubbi e domande, è disponibile assistenza da parte dei docenti?","\u003Cp class=\"fw-regular font-size-body-1\">\u003Cspan class=\"text-color-grey-800\">Certamente, durante il modulo Introduzione alle reti convolutive è disponibile assistenza continua da parte dei nostri docenti per qualsiasi dubbio o domanda.\u003C/span>\u003C/p>\n",[810,813,817,820],{"icon":811,"caption":71,"text":812},"icon-level","Da Beginner ad Advanced",{"icon":814,"caption":815,"text":816},"icon-round-check","PREREQUISITI","Python per la ds, Dalla regressione logistica alle reti neurali, Intro al ml e regressione lineare",{"icon":818,"caption":70,"text":819},"icon-calendar","5 ore",{"icon":821,"caption":822,"text":823},"icon-lessons","LEZIONI","100% on demand",[825],{"title":826,"items":827},"Introduzione alle Reti Convolutive",[828,831,833,835,837,839,841,843,845,847,849,851,853,855,857,859,861,863,865,867,869,871],{"title":829,"text":830},"Introduzione alle Reti Neurali Artificiali Convolutive","\u003Cp>\u003Cspan class=\"text-color-grey-700\">Nessun dettaglio\u003C/span>\u003C/p>\n",{"title":832,"text":830},"Generalità sulla Convoluzione",{"title":834,"text":830},"Esempio di Filtraggio",{"title":836,"text":830},"Il Padding",{"title":838,"text":830},"Lo Stride",{"title":840,"text":830},"Convoluzione Volumetrica",{"title":842,"text":830},"Filtri, Kernel e Parametri",{"title":844,"text":830},"Local Perceptive Fields",{"title":846,"text":830},"Weights Sharing",{"title":848,"text":830},"Downsampling e Upsampling",{"title":850,"text":830},"Addestramento di una rete neurale convolutiva",{"title":852,"text":830},"Esercitazione CNN e Keras",{"title":854,"text":830},"Esercitazione LeNet-5 in Keras",{"title":856,"text":830},"[OPZIONALE] Esercitazione LeNet-5 in PyTorch",{"title":858,"text":830},"Esercitazione Spam Detection",{"title":860,"text":830},"Residual Networks",{"title":862,"text":830},"Esercitazione Residual Networks in Keras",{"title":864,"text":830},"[OPZIONALE] Esercitazione Residual Networks in PyTorch",{"title":866,"text":830},"1x1 Convolution",{"title":868,"text":830},"Inception Block e GoogLeNet",{"title":870,"text":830},"Esercitazione Inception",{"title":872,"text":830},"Risorse esterne \u003Cspan class=\"lesson-badge gray\">\u003Cspan class=\"bracket\">[\u003C/span>APPROFONDIMENTO\u003Cspan class=\"bracket\">]\u003C/span>\u003C/span>",{"list":874,"intro":1068},[875,995],{"id":535,"date":876,"title":531,"description":877,"url":533,"image":878,"video_preview":886,"video_poster":887,"video_embed":893,"duration":894,"prerequisite":895,"projects":896,"live":897,"salary":898,"regular_price":899,"offer_price":900,"taxonomies":901,"info":961,"level":271,"type":515},"2025-07-29T10:32:28+02:00","Un Percorso di Carriera avanzato, pratico e strutturato per acquisire le competenze chiave della Data Science e padroneggiare le principali tecniche di Machine Learning per diventare il punto di riferimento nel tuo team.",{"id":879,"title":880,"filename":881,"filesize":882,"url":883,"alt":54,"description":54,"caption":54,"mime_type":748,"width":884,"height":885},27456,"datascientist","datascientist.jpg",229085,"https://website-prod.datamasters.it/wp-content/uploads/2024/06/datascientist.jpg",1600,900,"https://website-prod.datamasters.it/wp-content/uploads/2024/06/data-scientist-loop.mp4",{"id":888,"title":889,"filename":890,"filesize":891,"url":892,"alt":54,"description":54,"caption":54,"mime_type":748,"width":884,"height":885},27484,"datascientist2","datascientist2.jpg",227074,"https://website-prod.datamasters.it/wp-content/uploads/2024/06/datascientist2.jpg","\u003Ciframe src=\"https://player.vimeo.com/video/1101159294?badge=0&autopause=0&player_id=0&app_id=58479&autoplay=1\" frameborder=\"0\" allow=\"autoplay; 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