Mcclarren ryan (57 résultats)

Etat : New.

Paperback or Softback. Etat : New. Radiation and You. Book.

Etat : As New. Unread book in perfect condition.

Etat : New.

Etat : As New. Unread book in perfect condition.

Etat : New.

Etat : New.

Taschenbuch. Etat : Neu. Neuware - Radiation is all around us: in the rocks under the ground, naturally in food, and raining down from outer space. Most of this radiation is natural, and some of it we use to make our lives better. This book teaches elementary age students the basics of the atom and radioactivity and shows where radiation can be found and how it can be used to improve our lives. A glossary gives definitions of key terms in the radiation and nuclear sciences. This book will teach children that: - Atoms are the building blocks of nature. - Radiation comes to us from natural and artificial sources. - Medicine and dentistry use radiation. - Too much radiation can be dangerous. - Radiation is used to give us energy, learn about dinosaurs, and understand how people lived thousands of years ago.…

Etat : New. &Uumlber den AutorRyan G. McClarren, PhD is a nuclear engineering educator and researcher. He uses computers to understand how radiation moves through nuclear reactors and other systems. When not pushing radiation around on a computer, .

Etat : As New. Unread book in perfect condition.

Etat : New.

Etat : New.

Etat : As New. Unread book in perfect condition.

Etat : New. 1st ed. 2021 edition NO-PA16APR2015-KAP.

Etat : As New. Unread book in perfect condition.

Paperback. Etat : Brand New. 260 pages. 9.25x6.10x0.71 inches. In Stock.

Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - All engineers and applied scientists will need to harness the power of machine learning to solve the highly complex and data intensive problems now emerging. This text teaches state-of-the-art machine learning technologies to students and practicing engineers from the traditionally 'analog' disciplines-mechanical, aerospace, chemical, nuclear, and civil. Dr. McClarren examines these technologies from an engineering perspective and illustrates their specific value to engineers by presenting concrete examples based on physical systems. The book proceeds from basic learning models to deep neural networks, gradually increasing readers' ability to apply modern machine learning techniques to their current work and to prepare them for future, as yet unknown, problems. Rather than taking a black box approach, the author teaches a broad range of techniques while conveying the kinds of problems best addressed by each. Examples and case studies in controls, dynamics, heat transfer, and other engineering applications are implemented in Python and the libraries scikit-learn and tensorflow, demonstrating how readers can apply the most up-to-date methods to their own problems. The book equally benefits undergraduate engineering students who wish to acquire the skills required by future employers, and practicing engineers who wish to expand and update their problem-solving toolkit.…

Etat : New.

Etat : New.

Etat : New.

Etat : New.

Etat : As New. Unread book in perfect condition.

Etat : New.

Etat : As New. Unread book in perfect condition.

Taschenbuch. Etat : Neu. Machine Learning for Engineers | Using data to solve problems for physical systems | Ryan G. McClarren | Taschenbuch | xiii | Englisch | 2022 | Springer | EAN 9783030703905 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

Etat : New.

Etat : New.

Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - All engineers and applied scientists will need to harness the power of machine learning to solve the highly complex and data intensive problems now emerging. This text teaches state-of-the-art machine learning technologies to students and practicing engineers from the traditionally 'analog' disciplines-mechanical, aerospace, chemical, nuclear, and civil. Dr. McClarren examines these technologies from an engineering perspective and illustrates their specific value to engineers by presenting concrete examples based on physical systems. The book proceeds from basic learning models to deep neural networks, gradually increasing readers' ability to apply modern machine learning techniques to their current work and to prepare them for future, as yet unknown, problems. Rather than taking a black box approach, the author teaches a broad range of techniques while conveying the kinds of problems best addressed by each. Examples and case studies in controls, dynamics, heat transfer, and other engineering applications are implemented in Python and the libraries scikit-learn and tensorflow, demonstrating how readers can apply the most up-to-date methods to their own problems. The book equally benefits undergraduate engineering students who wish to acquire the skills required by future employers, and practicing engineers who wish to expand and update their problem-solving toolkit.…

Etat : New.

Hardcover. Etat : Brand New. 260 pages. 9.25x6.10x0.87 inches. In Stock.