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[null,"1.","Record Nr.",null,"UNISA996320734503316"]
[null,null,"Autore",null,"Goodfellow Ian"]
[null,null,"Titolo",null,"Deep learning / / Ian Goodfellow, Yoshua Bengio and Aaron Courville"]
[null,null,"Pubbl/distr/stampa",null,"Cambridge, Massachusetts ; ; London, England : , : The MIT Press, , [2016]"]
[{"$nulls":4},"©2016"]
[null,null,"ISBN",null,"0-262-33737-1"]
[null,null,"Descrizione fisica",null,"1 online resource (xxii, 775 pages) : illustrations"]
[null,null,"Collana",null,"Adaptive computation and machine learning"]
[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 (pages 711-766) and index."]
[null,null,"Nota di contenuto",null,"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."]
[null,null,null,"Sommario/riassunto","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 "]
[{"$nulls":4},"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."]
[null,"2.","Record Nr.",null,"UNINA9910731470403321"]
[null,null,"Autore",null,"Lossio-Ventura Juan Antonio"]
[null,null,"Titolo",null,"Information Management and Big Data : 9th Annual International Conference, SIMBig 2022, Lima, Peru, November 16–18, 2022, Proceedings / / edited by Juan Antonio Lossio-Ventura, Jorge Valverde-Rebaza, Eduardo Díaz, Hugo Alatrista-Salas"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023"]
[null,null,"ISBN",null,"3-031-35445-1"]
[null,null,"Edizione",null,"[1st ed. 2023.]"]
[null,null,"Descrizione fisica",null,"1 online resource (287 pages)"]
[null,null,"Collana",null,"Communications in Computer and Information Science, , 1865-0937 ; ; 1837"]
[null,null,"Disciplina",null,"004.6"]
[null,null,"Soggetti",null,"Computer networks"]
[{"$nulls":4},"Computer engineering"]
[{"$nulls":4},"Artificial intelligence"]
[{"$nulls":4},"Education - Data processing"]
[{"$nulls":4},"Social sciences - Data processing"]
[{"$nulls":4},"Image processing - Digital techniques"]
[{"$nulls":4},"Computer vision"]
[{"$nulls":4},"Computer Communication Networks"]
[{"$nulls":4},"Computer Engineering and Networks"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Computers and Education"]
[{"$nulls":4},"Computer Application in Social and Behavioral Sciences"]
[{"$nulls":4},"Computer Imaging, Vision, Pattern Recognition and Graphics"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"A Preliminary Analysis of Twitter's LGBTQ + Discussions -- User-agnostic Model for Prediction of Retweets Based on Social Neighborhood Information  -- Segmentation and classification of pages for digitized documents of the Public Prosecutor's Oce -- A Semantic Query Engine for Knowledge Rich Legal Digital Libraries -- Getting Quechua Closer to Final Users through Knowledge Graphs -- Using features based on elongation to enhance sentiment analysis -- Image Generation from sketches and text-guided attribute edition -- ADRAS: Airborne Disease Risk Assessment System for Closed Environments -- Predictive Sentiment Analysis Model regarding the variation of the "]
[{"$nulls":4},"Dollar Exchange Rate -- Maximising Influence Spread in Complex Networks by Utilising Community-based Driver Nodes as Seeds -- Peak Anomaly Detection from Environmental Sensor-Generated Watershed Time Series Data -- Gas Sensors and Machine Learning for Quality Evaluation of Grape Spirits (Pisco) -- Optimal layer selection on Deep Convolutional Neural Networks using backward freezing and binary Search -- Profiling Public Service Accessibility based on the Public Transport Infrastructure -- Multiple Scale Comparative Analysis of Classical, Dynamic and Intelligent Edge Detection Schemes -- Soil Organic Carbon Prediction using Digital Color Sensor in Peru -- Ideation of Computational Thinking Programs by Assembling Code Snippets from the Web -- Smart doorbell with Telegram notification for multifamily dwellings -- Performance analysis of machine learning for food fraud prediction."]
[null,null,null,"Sommario/riassunto","This book constitutes the refereed proceedings of the 9th Annual International Conference on Information Management and Big Data, SIMBig 2022, held in Lima, Peru, during November 16–18, 2022. The 18 full papers and 1 short paper included in this book were carefully reviewed and selected from 50 submissions. The volume presented novel methods for the analysis and management of large data, in fields like Artificial Intelligence (AI), Data Science, Machine Learning, Natural Language Processing, Semantic Web, Data-driven Software Engineering, Health Informatics."]