Budd jeremy (55 risultati)

Paperback. Condizione: Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.

Condizione: New.

Paperback. Condizione: New. The use of differential equations on graphs as a framework for the mathematical analysis of images emerged about fifteen years ago and since then it has burgeoned, and with applications also to machine learning. The authors have written a bird's eye view of theoretical developments that will enable newcomers to quickly get a flavour of key results and ideas. Additionally, they provide an substantial bibliography which will point readers to where fuller details and other directions can be explored. This title is also available as open access on Cambridge Core.…

Condizione: As New. Unread book in perfect condition.

Paperback. Condizione: Very Good. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.

Condizione: good. A copy that has been read, remains in good condition. All pages are intact, and the cover is intact. The spine and cover show signs of wear. Pages can include notes and highlighting and show signs of wear, and the copy can include "From the library of" labels or previous owner inscriptions. 100% GUARANTEE! Shipped with delivery confirmation, if you're not satisfied with purchase please return item! Ships via media mail.…

Paperback. Condizione: Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.

Condizione: New.

Condizione: Very Good. Pages intact with possible writing/highlighting. Binding strong with minor wear. Dust jackets/supplements may not be included. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

Condizione: As New. Unread book in perfect condition.

Paperback. Condizione: New. Some of the most important and best lessons in a doctor's career are learnt from mistakes. However, an awareness of the common causes of medical errors and developing positive behaviours can reduce the risk of mistakes and litigation. Written for Foundation Year doctors, trainees and general practitioners, and unlike any other clinical management title available, Avoiding Errors in General Practice identifies and explains the most common errors likely to occur in an outpatient setting - so that you won't make them.   The first section in this brand new guide discusses the causes of errors in general practice. The second and largest section consists of case scenarios and includes expert and legal comment as well as clinical teaching points and strategies to help you engage in safer practice throughout your career. The final section discusses how to deal with complaints and the subsequent potential medico-legal consequences, helping to reduce your anxiety when dealing with the consequences of an error. Invaluable during the Foundation Years, Specialty Training and for Consultants, Avoiding Errors in General Practice is the perfect guide to help tackle the professional and emotional challenges of life as a GP. …

Condizione: New.

Condizione: As New. Unread book in perfect condition.

hardcover. Condizione: Fine.

Paperback. Condizione: Good. Good - . In good condition. No dust jacket.

Condizione: New.

Condizione: New.

Condizione: New. In English.

Condizione: As New. Unread book in perfect condition.

Paperback. Condizione: Brand New. 1st edition. 198 pages. 9.53x6.69x0.28 inches. In Stock.

Condizione: New.

Hardback. Condizione: New. The use of differential equations on graphs as a framework for the mathematical analysis of images emerged about fifteen years ago and since then it has burgeoned, and with applications also to machine learning. The authors have written a bird's eye view of theoretical developments that will enable newcomers to quickly get a flavour of key results and ideas. Additionally, they provide an substantial bibliography which will point readers to where fuller details and other directions can be explored. This title is also available as open access on Cambridge Core.…

hardcover. Condizione: New. Brand new book, sourced directly from publisher. Dispatch time is 24-48 hours from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

Condizione: New.

Paperback. Condizione: New. The use of differential equations on graphs as a framework for the mathematical analysis of images emerged about fifteen years ago and since then it has burgeoned, and with applications also to machine learning. The authors have written a bird's eye view of theoretical developments that will enable newcomers to quickly get a flavour of key results and ideas. Additionally, they provide an substantial bibliography which will point readers to where fuller details and other directions can be explored. This title is also available as open access on Cambridge Core.…

Condizione: As New. Unread book in perfect condition.

Condizione: New.

Condizione: New.

Condizione: New.

Hardback. Condizione: New. The burgeoning field of differential equations on graphs has experienced significant growth in the past decade, propelled by the use of variational methods in imaging and by its applications in machine learning. This text provides a detailed overview of the subject, serving as a reference for researchers and as an introduction for graduate students wishing to get up to speed. The authors look through the lens of variational calculus and differential equations, with a particular focus on graph-Laplacian-based models and the graph Ginzburg-Landau functional. They explore the diverse applications, numerical challenges, and theoretical foundations of these models. A meticulously curated bibliography comprising approximately 800 references helps to contextualise this work within the broader academic landscape. While primarily a review, this text also incorporates some original research, extending or refining existing results and methods. …