02146nlm 22003135 450 99644265200331620211110085708.00-691-03601-220200229h1990---- fg engUSdrcnuChanges in the Roman empireessays in the ordinaryRamsay MacMullenPrinceton, NJPrinceton University Press1990Testo elettronico (PDF) (XIV, 399 p. : ill.)Princeton Legacy Library ;5435Base dati testualeScritta da uno dei più importanti storici dell'Impero Romano, questa raccolta di saggi sia nuovi che pubblicati in precedenza forma un quadro colorito della vita quotidiana nel mondo mediterraneo tra il 50 e il 450 d.C. Qui, ad esempio, l'autore applica l'analisi statistica ad ampi gruppi di persone su questioni che vanno dalla giustizia, alla medicina, al linguaggio. In tal modo è in grado di convalidare affermazioni generali sulle routine nel comportamento della gente comune e di rilevare all'interno di queste routine gli stessi cambiamenti che costituiscono la storia. Tale analisi mostra anche come questa epoca benefici degli stessi approcci storiografici che hanno chiarito con successo i fenomeni socioculturali in altri periodi. Attingendo dall'analisi statistica e da molti altri approcci storici, questi saggi sui costumi popolari nell'Impero Romano coprono argomenti come lingua e arte, acculturazione, pensiero e religione, sesso e genere, crudeltà e schiavitù e aspetti delle relazioni di classe e di potere. L'autore introduce la raccolta con alcuni saggi di metodo storico, in quanto attinenti alla ricchezza di documentazione e varietà riscontrabili nella regione e nel periodo prescelto.ACLS Humanities E-Book.Impero romanoVita socialeBNCF937.06MACMULLEN,$b Ramsay205377American Council of Learned Societies.cbaITcbaREICAT996442652003316EBERChanges in the Roman Empire175615UNISA01107nam1 22002893i 450 MIL044594820231121125547.020170620e19691841||||0itac50 bafrechz01i xxxe z01nRecueil de chants historiques francais, depuis le 12. jusqu'au 18. siecle avec des notices et une introductionLeroux de LincyGeneveSlatkine19692 v.21 cm.001MIL04679732000 1Leroux de Lincy1001MIL04679742000 2Leroux de Lincy284121Le Roux de Lincy, Adrien Jean VictorSBLV126236070224136ITIT-0120170620IT-FR0017 Biblioteca umanistica Giorgio ApreaFR0017 MIL0445948Biblioteca umanistica Giorgio Aprea 52MAG 7/460.1 52MAG 7/460.2 52Recueil de chants historiques francais, depuis le 12. jusqu'au 18. siecle avec des notices et une introduction3610572UNICAS03697oam 2200373 450 99632073450331620230323125548.00-262-33737-1(MiAaPQ)EBC6287197(PPN)25087847X(EXLCZ)99456000000000024620201210h20162016 uy 0engurcn#---uuuuuDeep learning /Ian Goodfellow, Yoshua Bengio and Aaron CourvilleCambridge, Massachusetts ;London, England :The MIT Press,[2016].©20161 online resource (xxii, 775 pages) illustrationsAdaptive computation and machine learning9780262035613 Includes bibliographical references (pages 711-766) and index.Introduction -- Applied math and machine learning basics. Linear algebra -- Probability and information theory -- Numerical computation -- Machine learning basics -- Deep networks: modern practices. Deep feedforward networks -- Regularization for deep learning -- Optimization for training deep models -- Convolutional networks -- Sequence modeling: recurrent and recursive nets -- Practical methodology -- Applications -- Deep learning research. Linear factor models -- Autoencoders -- Representation learning -- Structured probabilistic models for deep learning -- Monte Carlo methods -- Confronting the partition function -- Approximate inference -- Deep generative models.An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.Adaptive computation and machine learning.Machine learningMachine learning.006.31Goodfellow Ian752902Bengio YoshuaCourville AaronBOOK996320734503316Deep learning3068251UNISA