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DIDATTICO","\u003Ch1 class=\"fw-semibold text-uppercase\">Deep \u003Cspan class=\"text-color-primary-400\">Learning\u003C/span>\u003C/h1>\n","\u003Cp class=\"font-size-body-1 text-color-grey-800\">\nModulo di approfondimento sul Deep Learning, con focus su particolari architetture ibride e alcuni strumenti utili all’addestramento.\u003C/p>\n","\u003Ch2>Descrizione\u003C/h2>\n\u003Cp class=\"font-size-body-1\">\nAnalisi e studio di particolari architetture ibride di Deep-Learning, come la cella ConvLSTM o le Highway-Networks.\u003Cbr />\nFocus sui limiti delle Reti Neurali Artificiali e descrizione di alcuni approcci per arginare questi limici, come NAC e NALU.\u003Cbr />\nFine-tuning automatico degli iper-parametri e analisi dell ándamento degli addestramenti tramite TensorBoard.\u003Cbr />\nApprofondimento sul funzionamento dei principali ottimizzatori.\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 Deep Learning?","\u003Cp class=\"fw-regular font-size-body-1\">\u003Cspan class=\"text-color-grey-800\">Per seguire il modulo Deep Learning sono necessarie competenze preliminari specifiche di Python, Reti convolutive, Reti ricorrenti e serie temporali.\u003C/span>\u003C/p>\n",{"title":795,"text":796},"Quali sono i principali argomenti trattati nel modulo Deep Learning?","\u003Cp class=\"fw-regular font-size-body-1\">\u003Cspan class=\"text-color-grey-800\">Il modulo Deep Learning copre i seguenti argomenti principali: architetture ibride, cella ConvLSTM, Highway Network e Fractal Network, Limiti ANN, NAC – Neural ACcumulator, Esercitazione NALU, Callback e TensorBoard.\u003C/span>\u003C/p>\n",{"title":798,"text":799},"Quali nozioni principali posso apprendere al termine del modulo Deep Learning?","\u003Cp class=\"fw-regular font-size-body-1\">\u003Cspan class=\"text-color-grey-800\">Al termine del modulo Deep Learning sarai in grado di 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