Michael Shearer – författare

This textbook provides beginning graduate students and advanced undergraduates with an accessible introduction to the rich subject of partial differential equations (PDEs). It presents a rigorous and clear explanation of the more elementary theoretical aspects of PDEs, while also drawing connections to deeper analysis and applications. The book serves as a needed bridge between basic undergraduate texts and more advanced books that require a significant background in functional analysis. Topics include first order equations and the method of characteristics, second order linear equations, wave and heat equations, Laplace and Poisson equations, and separation of variables. The book also covers fundamental solutions, Green's functions and distributions, beginning functional analysis applied to elliptic PDEs, traveling wave solutions of selected parabolic PDEs, and scalar conservation laws and systems of hyperbolic PDEs.* Provides an accessible yet rigorous introduction to partial differential equations* Draws connections to advanced topics in analysis* Covers applications to continuum mechanics* An electronic solutions manual is available only to professors* An online illustration package is available to professors

Entity resolution is a key analytic technique that enables you to identify multiple data records that refer to the same real-world entity. With this hands-on guide, product managers, data analysts, and data scientists will learn how to add value to data by cleansing, analyzing, and resolving datasets using open source Python libraries and cloud APIs.

Author Michael Shearer shows you how to scale up your data matching processes and improve the accuracy of your reconciliations. You''ll be able to remove duplicate entries within a single source and join disparate data sources together when common keys aren''t available. Using real-world data examples, this book helps you gain practical understanding to accelerate the delivery of real business value.

With entity resolution, you''ll build rich and comprehensive data assets that reveal relationships for marketing and risk management purposes, key to harnessing the full potential of ML and AI. This book covers:

Challenges in deduplicating and joining datasetsExtracting, cleansing, and preparing datasets for matchingText matching algorithms to identify equivalent entitiesTechniques for deduplicating and joining datasets at scaleMatching datasets containing persons and organizationsEvaluating data matchesOptimizing and tuning data matching algorithmsEntity resolution using cloud APIsMatching using privacy-enhancing technologies

Entity resolution is a key analytic technique that enables you to identify multiple data records that refer to the same real-world entity. With this hands-on guide, product managers, data analysts, and data scientists will learn how to add value to data by cleansing, analyzing, and resolving datasets using open source Python libraries and cloud APIs.

Author Michael Shearer shows you how to scale up your data matching processes and improve the accuracy of your reconciliations. You''ll be able to remove duplicate entries within a single source and join disparate data sources together when common keys aren''t available. Using real-world data examples, this book helps you gain practical understanding to accelerate the delivery of real business value.

With entity resolution, you''ll build rich and comprehensive data assets that reveal relationships for marketing and risk management purposes, key to harnessing the full potential of ML and AI. This book covers:

Challenges in deduplicating and joining datasetsExtracting, cleansing, and preparing datasets for matchingText matching algorithms to identify equivalent entitiesTechniques for deduplicating and joining datasets at scaleMatching datasets containing persons and organizationsEvaluating data matchesOptimizing and tuning data matching algorithmsEntity resolution using cloud APIsMatching using privacy-enhancing technologies

Entity resolution is a key analytic technique that enables you to identify multiple data records that refer to the same real-world entity. With this hands-on guide, product managers, data analysts, and data scientists will learn how to add value to data by cleansing, analyzing, and resolving datasets using open source Python libraries and cloud APIs. Author Michael Shearer shows you how to scale up your data matching processes and improve the accuracy of your reconciliations. You'll be able to remove duplicate entries within a single source and join disparate data sources together when common keys aren't available. Using real-world data examples, this book helps you gain practical understanding to accelerate the delivery of real business value. With entity resolution, you'll build rich and comprehensive data assets that reveal relationships for marketing and risk management purposes, key to harnessing the full potential of ML and AI. This book covers: Challenges in deduplicating and joining datasets Extracting, cleansing, and preparing datasets for matching Text matching algorithms to identify equivalent entities Techniques for deduplicating and joining datasets at scale Matching datasets containing persons and organizations Evaluating data matches Optimizing and tuning data matching algorithms Entity resolution using cloud APIs Matching using privacy-enhancing technologies About the AuthorMichael Shearer is the Group Head of Compliance Product Management for HSBC. Since joining HSBC in 2014 he has led the delivery of financial crime risk capabilities for the bank, including industry-leading artificial intelligence and network analytics platforms. Prior to HSBC Michael spent 20 years in UK government service where he led the delivery of international projects to acquire and process large volumes of highly sensitive data. Michael is a Chartered Engineer. He was educated at Queen's University Belfast where he gained a Master's degree in Electrical and Electronic Engineering with distinction.

This IMA Volume in Mathematics and its Applications NONLINEAR EVOLUTION EQUATIONS THAT CHANGE TYPE is based on the proceedings of a workshop which was an integral part of the 1988-89 IMA program on NONLINEAR WAVES. The workshop focussed on prob­ lems of ill-posedness and change of type which arise in modeling flows in porous materials, viscoelastic fluids and solids and phase changes. We thank the Coordinat­ ing Committee: James Glimm, Daniel Joseph, Barbara Lee Keyfitz, Andrew Majda, Alan Newell, Peter Olver, David Sattinger and David Schaeffer for planning and implementing an exciting and stimulating year-long program. We especially thank the workshop organizers, Barbara Lee Keyfitz and Michael Shearer, for their efforts in bringing together many of the major figures in those research fields in which theories for nonlinear evolution equations that change type are being developed. A vner Friedman Willard Miller, J r. ix PREFACE During the winter and spring quarters of the 1988/89 IMA Program on Non­ linear Waves, the issue of change of type in nonlinear partial differential equations appeared frequently. Discussion began with the January 1989 workshop on Two­ Phase Waves in Fluidized Beds, Sedimentation and Granular Flow; some of the papers in the proceedings of that workshop present strategies designed to avoid the appearance of change of type in models for multiphase fluid flow.