01782oam 2200493 450 991071410760332120181024131354.0(CKB)3780000000320625(OCoLC)988835886(EXLCZ)99378000000032062519800913d1957 ua 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierAnalysis of layoff, recall, and work-sharing procedures in union contractsWashington, D.C. :United States Department of Labor, Bureau of Labor Statistics,1957.1 online resource (iv, 35 pages) illustrationsBulletin ;no. 1209"From the Monthly Labor Review, December 1956 and January, February, and March 1957 issues, with additional tables.""March 1957.""The reports ... were prepared by Joseph W. Bloch, Robert Platt, and Rose Theodore"-- Page iii.Includes bibliographical references.Layoff systemsCollective labor agreementsUnited StatesCollective labor agreementsfastLayoff systemsfastUnited StatesfastLayoff systems.Collective labor agreementsCollective labor agreements.Layoff systems.Bloch Joseph W(Joseph Walter)1390459United States.Bureau of Labor Statistics,Z5AZ5AOCLCOOCLCFGPOBOOK9910714107603321Analysis of layoff, recall, and work-sharing procedures in union contracts3443178UNINA03176nam 22004573 450 991063769440332120240415084506.01-63828-053-3(CKB)5860000000282520(oapen)https://directory.doabooks.org/handle/20.500.12854/95746(MiAaPQ)EBC30191446(Au-PeEL)EBL30191446(OCoLC)1492945352(EXLCZ)99586000000028252020240415d2022 uy 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierConvex Optimization for Machine Learning1st ed.Norwell, MA :Now Publishers,2022.©2022.1 electronic resource (379 p.)NowOpen1-63828-052-5 This book covers an introduction to convex optimization, one of the powerful and tractable optimization problems that can be efficiently solved on a computer. The goal of the book is to help develop a sense of what convex optimization is, and how it can be used in a widening array of practical contexts with a particular emphasis on machine learning. The first part of the book covers core concepts of convex sets, convex functions, and related basic definitions that serve understanding convex optimization and its corresponding models. The second part deals with one very useful theory, called duality, which enables us to: (1) gain algorithmic insights; and (2) obtain an approximate solution to non-convex optimization problems which are often difficult to solve. The last part focuses on modern applications in machine learning and deep learning. A defining feature of this book is that it succinctly relates the “story” of how convex optimization plays a role, via historical examples and trending machine learning applications. Another key feature is that it includes programming implementation of a variety of machine learning algorithms inspired by optimization fundamentals, together with a brief tutorial of the used programming tools. The implementation is based on Python, CVXPY, and TensorFlow. This book does not follow a traditional textbook-style organization, but is streamlined via a series of lecture notes that are intimately related, centered around coherent themes and concepts. It serves as a textbook mainly for a senior-level undergraduate course, yet is also suitable for a first-year graduate course. Readers benefit from having a good background in linear algebra, some exposure to probability, and basic familiarity with Python.NowOpen SeriesOptimizationbicsscConvex Optimization, Deep Learning, Generative Adversarial Networks (GANs), TensorFlow, Supervised Learning, Wasserstein GAN, Strong Duality, Weak Duality, Computed TomographyOptimization006.31Suh Changho1310449MiAaPQMiAaPQMiAaPQBOOK9910637694403321Convex Optimization for Machine Learning3029832UNINA