{"text_format":"2","workbook":"xls","n_sheets":1,"sheets":["report2"],"not_rendered":{"charts":0,"images":0,"macros":0,"external_links":1,"defined_names":2,"comments":0,"pivot_tables":0},"unopened":[],"bounds":{"document_bytes":16777216,"sheet_cells":100000,"run_cost":"one","rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0,"cell_chars":16384,"cell_head_chars":4096},"cells_cut":0,"cells_raw":0,"read":"whole","date_system":"1900","formulas":"cached_only"}

{"sheet":"report2","index":0,"n_rows":36,"n_cols":5,"rows_kept":36,"cut":false,"hidden":false,"merged":[[0,2,0,3],[0,4,0,5],[1,2,1,3],[1,4,1,5],[2,2,2,3],[2,4,2,5],[3,2,3,3],[4,2,4,3],[8,2,8,3],[9,2,9,3],[10,2,10,3],[11,2,11,3],[12,2,12,3],[13,2,13,3],[18,2,18,3],[18,4,18,5],[19,2,19,3],[19,4,19,5],[20,2,20,3],[20,4,20,5],[21,2,21,3],[22,2,22,3],[24,2,24,3],[25,2,25,3],[26,2,26,3],[27,2,27,3],[28,2,28,3],[29,2,29,3],[30,2,30,3],[34,2,34,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNINA9910984678103321"]
[null,null,"Autore",null,"Theodoridis Sergios"]
[null,null,"Titolo",null,"Machine Learning : A Bayesian and Optimization Perspective / Theodoridis, Sergios"]
[null,null,"Pubbl/distr/stampa",null,"Cambridge, MA, USA, : Academic Press, 2020"]
[null,null,"ISBN",null,"9780128188040"]
[{"$nulls":4},"0128188049"]
[{"$nulls":4},"9780128188033"]
[{"$nulls":4},"0128188030"]
[null,null,"Edizione",null,"[Second edition]"]
[null,null,"Descrizione fisica",null,"1 online resource (xxvii, 1031 pages) ; : illustrations"]
[null,null,"Disciplina",null,"006.31"]
[null,null,"Soggetti",null,"Machine learning"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,null,"Sommario/riassunto","Machine Learning: A Bayesian and Optimization Perspective, 2<sup>nd</sup> edition , gives a unified perspective on machine learning by covering both pillars of supervised learning, namely regression and classification. The book starts with the basics, including mean square, least squares and maximum likelihood methods, ridge regression, Bayesian decision theory classification, logistic regression, and decision trees. It then progresses to more recent techniques, covering sparse modelling methods, learning in reproducing kernel Hilbert spaces and support vector machines, Bayesian inference with a focus on the EM algorithm and its approximate inference variational versions, Monte Carlo methods, probabilistic graphical models focusing on Bayesian networks, hidden Markov models and particle filtering. Dimensionality reduction and latent variables modelling are also considered in depth.     This palette of techniques concludes with an extended chapter on neural networks and deep learning architectures. The book also covers the fundamentals of statistical parameter estimation, Wiener and Kalman filtering, convexity and convex optimization, including a chapter on stochastic approximation and the gradient descent family of algorithms, presenting related online learning techniques as well as concepts and algorithmic versions for distributed optimization.    Focusing on the physical reasoning behind the mathematics, without sacrificing rigor, all the various methods and "]
[{"$nulls":4},"techniques are explained in depth, supported by examples and problems, giving an invaluable resource to the student and researcher for understanding and applying machine learning concepts. Most of the chapters include typical case studies and computer exercises, both in MATLAB and Python.    The chapters are written to be as self-contained as possible, making the text suitable for different courses: pattern recognition, statistical/adaptive signal processing, statistical/Bayesian learning, as well as courses on sparse modeling, deep learning, and probabilistic graphical models.    New to this edition:         Complete re-write of the chapter on Neural Networks and Deep Learning to reflect the latest advances since the 1st edition. The chapter, starting from the basic perceptron and feed-forward neural networks concepts, now presents an in depth treatment of deep networks, including recent optimization algorithms, batch normalization, regularization techniques such as the dropout method, convolutional neural networks, recurrent neural networks, attention mechanisms, adversarial examples and training, capsule networks and generative architectures, such as restricted Boltzman machines (RBMs), variational autoencoders and generative adversarial networks (GANs).      Expanded treatment of Bayesian learning to include nonparametric Bayesian methods, with a focus on the Chinese restaurant and the Indian buffet processes."]
[null,"2.","Record Nr.",null,"UNINA9910967991203321"]
[null,null,"Autore",null,"Simek Nicole Jenette <1976->"]
[null,null,"Titolo",null,"Eating well, reading well : Maryse Conde and the ethics of interpretation / / Nicole Simek"]
[null,null,"Pubbl/distr/stampa",null,"Amsterdam ; ; New York, : Rodopi, 2008"]
[null,null,"ISBN",null,"94-012-0528-0"]
[{"$nulls":4},"1-4356-1362-7"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (236 p.)"]
[null,null,"Collana",null,"Francopolyphonies ; ; 7"]
[null,null,"Disciplina",null,"843.91409"]
[null,null,"Soggetti",null,"Literature and morals"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references (p. [207]-228) and index."]
[null,null,"Nota di contenuto",null,"Preliminary Material -- INTERPRETING THROUGH EXAMPLES -- READING HISTORY: THE EXAMPLE OF THE PAST AFTER GLOBALIZATION -- RUSING WITH THE CANON: INSOLENT IMITATION, PARODIC INTERTEXTUALITY -- WRITING VIOLENCE: COLLECTIVE TRAUMAS, SINGULAR PASTS -- THE CANNIBAL READER: DIGESTING THE OTHER, INTERPRETING COMMUNITY -- COMME UN INDIEN TUPINAMBA... -- BIBLIOGRAPHY -- INDEX."]
[null,null,null,"Sommario/riassunto","While rejecting a conception of literature as moral philosophy, or a device for imparting particular morals to the reader through exemplary characters and plots, Maryse Condé has displayed throughout her writing career a strong valorization of literature as ethical critique. This study examines her singular approach to literary commitment as a critical reworking of aesthetic models and modes of interpretation. Focusing on four dominant problematics in Condé’s work—history and globalization in La Belle Créole and Moi, Tituba sorcière...noire de Salem , intertextuality and reception in La migration des cœurs and Célanire cou-coupé , trauma and subjectivity in En attendant le bonheur and Desirada , community and ethics in Traversée de la mangrove and Histoire de la femme cannibale —this analysis proposes to elucidate how, and to what ends, Condé engages, and alters, approaches to reading, staging the problematic, yet pragmatic, need to read well . This hermeneutic imperative foregrounds the need to engage with texts, to cannibalize texts while recognizing their fundamental opacity and inexhaustibility, their resistance to the reader’s interpretive habits."]