schmidt alix (20 Ergebnisse)

First Edition. First edition and first printing. Hardcover. A terrific collection of color photographs of a wide ranging group of artists. Includes portraits of artists like Tracey Emin, Thomas Demand, Christian Marclay, Catherine Opie, Richard Prince, and numerous others. A fine copy in cloth boards. No dust jacket as issued.

Hardcover. Zustand: New in publisher's shrink wrap. First edition. Small Square Folio. 151pp. Original tan cloth with black lettering on cover and spine. Profusely illustrated with color photographs. Many of the artist photographs and texts first appeared in V Magazine under the rubric Work in Progress. These photographs depict artists from all over the world in their natural environment, providing a view of their creative process. The artists portrait here include Laura Owens, Luc Tuymans, Thomas Demand, Philip-Lorca diCorcia, Doug Aitken, Bill Viola, Liz Larner, Sophie Calle and Paul McCarthy, among others.

Hardcover. First Edition. Quarto, 151 pages. In Very Good condition. Bound in publisher's tan cloth boards with black lettering. Mild general shelf wear. Textblock clean. Shelved on the Front Table. 1411496. Shelved Dupont Bookstore.

Zustand: As New. Unread book in perfect condition.

Hardcover without dustjacket as issued, 99 illustrations, as new condition; clean and crisp; no internal marks. Foreign shipping may be extra.

Zustand: As New. Unread book in perfect condition.

Zustand: New.

Zustand: New. 1st edition NO-PA16APR2015-KAP.

Zustand: New.

Zustand: New.

Hardback. Zustand: New. New copy - Usually dispatched within 4 working days.

Zustand: New. In.

Zustand: New.

Hardcover. Zustand: Brand New. 368 pages. 9.19x6.13x9.21 inches. In Stock.

first edition, small folio, tan cloth boards with black lettering to spine & front cover; 161pp, illus, VG+ (protected by plastic adhesive cover, slight marks on board edges where plastic has come off, slight soiling to page edges, v. good condition internally, photos are v. clean & bright) 161.

Buch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In competitive manufacturing industries, organizations embrace product development as a continuous investment strategy since both market share and profit margin stand to benefit. Formulating new or improved products has traditionally involved lengthy and expensive experimentation in laboratory or pilot plant settings. However, recent advancements in areas from data acquisition to analytics are synergizing to transform workflows and increase the pace of research and innovation. The Digital Transformation of Product Formulation offers practical guidance on how to implement data-driven, accelerated product development through concepts, challenges, and applications. In this book, you will read a variety of industrial, academic, and consulting perspectives on how to go about transforming your materials product design from a twentieth-century art to a twenty-first-century science.Presents a futuristic vision for digitally enabled product development, the role of data and predictive modeling, and how to avoid project pitfalls to maximize probability of successDiscusses data-driven materials design issues and solutions applicable to a variety of industries, including chemicals, polymers, pharmaceuticals, oil and gas, and food and beveragesAddresses common characteristics of experimental datasets, challenges in using this data for predictive modeling, and effective strategies for enhancing a dataset with advanced formulation information and ingredient characterizationCovers a wide variety of approaches to developing predictive models on formulation data, including multivariate analysis and machine learning methodsDiscusses formulation optimization and inverse design as natural extensions to predictive modeling for materials discovery and manufacturing design space definitionFeatures case studies and special topics, including AI-guided retrosynthesis, real-time statistical process monitoring, developing multivariate specifications regions for raw material quality properties, and enabling a digital-savvy and analytics-literate workforceThis book provides students and professionals from engineering and science disciplines with practical know-how in data-driven product development in the context of chemical products across the entire modeling lifecycle. 364 pp. Englisch.

Zustand: New. PRINT ON DEMAND.

HRD. Zustand: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

HRD. Zustand: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Buch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In competitive manufacturing industries, organizations embrace product development as a continuous investment strategy since both market share and profit margin stand to benefit. Formulating new or improved products has traditionally involved lengthy and expensive experimentation in laboratory or pilot plant settings. However, recent advancements in areas from data acquisition to analytics are synergizing to transform workflows and increase the pace of research and innovation. The Digital Transformation of Product Formulation offers practical guidance on how to implement data-driven, accelerated product development through concepts, challenges, and applications. In this book, you will read a variety of industrial, academic, and consulting perspectives on how to go about transforming your materials product design from a twentieth-century art to a twenty-first-century science.Presents a futuristic vision for digitally enabled product development, the role of data and predictive modeling, and how to avoid project pitfalls to maximize probability of successDiscusses data-driven materials design issues and solutions applicable to a variety of industries, including chemicals, polymers, pharmaceuticals, oil and gas, and food and beveragesAddresses common characteristics of experimental datasets, challenges in using this data for predictive modeling, and effective strategies for enhancing a dataset with advanced formulation information and ingredient characterizationCovers a wide variety of approaches to developing predictive models on formulation data, including multivariate analysis and machine learning methodsDiscusses formulation optimization and inverse design as natural extensions to predictive modeling for materials discovery and manufacturing design space definitionFeatures case studies and special topics, including AI-guided retrosynthesis, real-time statistical process monitoring, developing multivariate specifications regions for raw material quality properties, and enabling a digital-savvy and analytics-literate workforceThis book provides students and professionals from engineering and science disciplines with practical know-how in data-driven product development in the context of chemical products across the entire modeling lifecycle.