North American plan for animal and pandemic influenza

• Record Nr.: UNINA9910703349703321
• Pubbl/distr/stampa: [Ottawa, Ont., : Public Safety Canada], 2012; [Washington, D.C.] : , : [North America Leaders Summit] : , : [U.S. Dept. of Health and Human Services], , [2012]
• Descrizione fisica: 1 electronic text (ii, 68 p.) : digital file
• Disciplina: 614.5/18/097
• Soggetti: H1N1 influenza - Prevention - North America; Epidemics - Prevention - North America; Influenza, Human - North America; Influenza A Virus, H1N1 Subtype; Disease Outbreaks - North America; Grippe A (H1N1) - Prévention - Amérique du Nord; Épidémies - Prévention - Amérique du Nord; Grippe humaine - Amérique du Nord; Sous-type H1N1 du virus de la grippe A; Flambées de maladies - Amérique du Nord; Avian influenza; Health; Human activities; Influenza; Influenza a virus subtype h1n1; Influenza a virus subtype h5n1; Influenza pandemic; Infection; Health sciences; Public health
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: "Released on April 2, 2012".; Issued as part of the Canadian Electronic Library, Documents collection, Canadian health research collection, and Canadian public policy ; collection.
• Nota di bibliografia: Includes bibliographical references.
• Nota di contenuto: List of acronyms -- Executive summary -- Chapter 1. Introduction: The animal and pandemic influenza threat; North American cooperation to address the threat -- Chapter 2. Emergency coordination and communications: Overview of federal emergency management structures; International legal framework; North American coordination; Joint exercises and training; Animal and pandemic influenza communications; Emergency response assistance -- Chapter 3. Animal influenza: Current framework for managing livestock and poultry diseases of national importance; Moving towards a framework for animal influenza: a North American phased approach -- Chapter 4. Pandemic influenza: Surveillance, epidemiology and laboratory practices; Medical countermeasures; Personnel exchange; Public health measures -- Chapter 5: Border health measures: Trilateral Working Group on Border Issues; Air travel; Maritime travel; Land borders -- Chapter 6: Critical infrastructure protection: The North American framework; Critical infrastructure sectors; Improving critical infrastructure resilience; Pandemic preparedness and response management for critical infrastructure -- Annex I. Terms of reference for the North American Senior Coordinating Body and the Health Security Working Group -- Annex II. Avian influenza -- Annex III. Chief veterinary officers agreement.

Deep learning : foundations and concepts / / by Christopher M. Bishop, Hugh Bishop

• Record Nr.: UNINA9910760286403321
• Autore: Bishop Christopher M.
• Pubbl/distr/stampa: Cham : , : Springer, , [2024]
• ISBN: 9783031454684; 3031454685; 9783031454677
• Descrizione fisica: 1 online resource (656 pages)
• Disciplina: 733
• Soggetti: Artificial intelligence; Machine learning; Artificial intelligence - Data processing; Artificial Intelligence; Machine Learning; Data Science; Aprenentatge profund (Aprenentatge automàtic)
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Preface -- The Deep Learning Revolution -- Probabilities -- Standard Distributions -- Single-layer Networks: Regression -- Single-layer Networks: Classification -- Deep Neural Networks -- Gradient Descent -- Backpropagation -- Regularization -- Convolutional Networks -- Structured Distributions -- Transformers -- Graph Neural Networks -- Sampling -- Discrete Latent Variables -- Continuous Latent Variables -- Generative Adversarial Networks -- Normalizing Flows -- Autoencoders -- Diffusion Models -- Appendix A Linear Algebra -- Appendix B Calculus of Variations -- Appendix C Lagrange Multipliers -- Biblyography -- Index.
• Sommario/riassunto: This book offers a comprehensive introduction to the central ideas that underpin deep learning. It is intended both for newcomers to machine learning and for those already experienced in the field. Covering key concepts relating to contemporary architectures and techniques, this essential book equips readers with a robust foundation for potential ; future specialization. The field of deep learning is undergoing rapid evolution, and therefore this book focusses on ideas that are likely to endure the test of time. The book is organized into numerous bite-sized chapters, each exploring a distinct topic, and the narrative follows a linear progression, with each chapter building upon content from its predecessors. This structure is well-suited to teaching a two-semester undergraduate or postgraduate machine learning course, while remaining equally relevant to those engaged in active research or in self-study. A full understanding of machine learning requires some mathematical background and so the book includes a self-contained introduction to probability theory. However, the focus of the book is on conveying a clear understanding of ideas, with emphasis on the real-world practical value of techniques rather than on abstract theory. Complex concepts are therefore presented from multiple complementary perspectives including textual descriptions, diagrams, mathematical formulae, and pseudo-code. Chris Bishop is a Technical Fellow at Microsoft and is the Director of Microsoft Research AI4Science. He is a Fellow of Darwin College Cambridge, a Fellow of the Royal Academy of Engineering, and a Fellow of the Royal Society. Hugh Bishop is an Applied Scientist at Wayve, a deep learning autonomous driving company in London, where he designs and trains deep neural networks. He completed his MPhil in Machine Learning and Machine Intelligence at Cambridge University. “Chris Bishop wrote a terrific textbook on neural networks in 1995 and has a deep knowledge of the field and its core ideas. His many years of experience in explaining neural networks have made him extremely skillful at presenting complicated ideas in the simplest possible way and it is a delight to see these skills applied to the revolutionary new developments in the field.” -- Geoffrey Hinton "With the recent explosion of deep learning and AI as a research topic, and the quickly growing importance of AI applications, a modern textbook on the topic was badly needed. The "New Bishop" masterfully fills the gap, covering algorithms for supervised and unsupervised learning, modern deep learning architecture families, as well as how to apply all of this to various application areas." – Yann LeCun “This excellent and very educational book will bring the reader up to date with the main concepts and advances in deep learning with a solid anchoring inprobability. These concepts are powering current industrial AI systems and are likely to form the basis of further advances towards artificial general intelligence.” -- Yoshua Bengio.