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Author: Dennis D. Davis Publisher: Cengage Learning ISBN: 9780827367104 Category : Mathematics Languages : en Pages : 272
Book Description
This resource is written for numeracy learners working in steel, aluminum and other metals / plastics manufacturing roles. It is specifically targeted towards machinists / machine operators and covers realistic math problems that manufacturers encounter in the workplace. The resource begins with basic operators and moves onto more complex equations. Table of contents: * Whole numbers. * Common fractions. * Decimal fractions. * Direct measure. * Computed measure. * Percent and finance. * Graphs. * Shop formulas. * Ration and proportion. * Powers and roots. * Geometric forms and construction. * Trigonometry. * Appendix. Glossary. Odd numbered answers.
Author: Dennis D. Davis Publisher: Cengage Learning ISBN: 9780827367104 Category : Mathematics Languages : en Pages : 272
Book Description
This resource is written for numeracy learners working in steel, aluminum and other metals / plastics manufacturing roles. It is specifically targeted towards machinists / machine operators and covers realistic math problems that manufacturers encounter in the workplace. The resource begins with basic operators and moves onto more complex equations. Table of contents: * Whole numbers. * Common fractions. * Decimal fractions. * Direct measure. * Computed measure. * Percent and finance. * Graphs. * Shop formulas. * Ration and proportion. * Powers and roots. * Geometric forms and construction. * Trigonometry. * Appendix. Glossary. Odd numbered answers.
Author: Donna D. Boatwright Publisher: Delmar Pub ISBN: 9780827369740 Category : Mathematics Languages : en Pages : 266
Book Description
This book covers a variety of topics in mathematics as they relate to industrial technologies including manufacturing, electricity/electronics, graphics, communication, transportation, industrial management, materials and related science principles. Organized by topics, the main objective is to develop strong, logical problem-solving skills. ..A brief description of each math principle is presented with step-by-step examples. The explanations are designed to emphasize the correct use and application of math principles. Graphs, drawings and charts relating to the applications reinforce the use of the skills developed. ALSO AVAILABLE INSTRUCTOR SUPPLEMENTS CALL CUSTOMER SUPPORT TO ORDER Instructor's Guide, ISBN: 0-8273-6975-1
Author: C. R. MacCluer Publisher: ISBN: 9780486477022 Category : Mathematical models Languages : en Pages : 0
Book Description
Students learn how to solve problems they'll encounter in their professional lives with this concise single-volume treatment. It employs MATLAB and other strategies to explore typical industrial problems. 2000 edition.
Author: Mangey Ram Publisher: Academic Press ISBN: 012818907X Category : Mathematics Languages : en Pages : 421
Book Description
Advances in Mathematics for Industry 4.0 examines key tools, techniques, strategies, and methods in engineering applications. By covering the latest knowledge in technology for engineering design and manufacture, chapters provide systematic and comprehensive coverage of key drivers in rapid economic development. Written by leading industry experts, chapter authors explore managing big data in processing information and helping in decision-making, including mathematical and optimization techniques for dealing with large amounts of data in short periods. - Focuses on recent research in mathematics applications for Industry 4.0 - Provides insights on international and transnational scales - Identifies mathematics knowledge gaps for Industry 4.0 - Describes fruitful areas for further research in industrial mathematics, including forthcoming international studies and research
Author: Marc Peter Deisenroth Publisher: Cambridge University Press ISBN: 1108569323 Category : Computers Languages : en Pages : 392
Book Description
The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
Author: Ryuei Nishii Publisher: Springer ISBN: 4431550607 Category : Technology & Engineering Languages : en Pages : 497
Book Description
This book deals with one of the most novel advances in mathematical modeling for applied scientific technology, including computer graphics, public-key encryption, data visualization, statistical data analysis, symbolic calculation, encryption, error correcting codes, and risk management. It also shows that mathematics can be used to solve problems from nature, e.g., slime mold algorithms. One of the unique features of this book is that it shows readers how to use pure and applied mathematics, especially those mathematical theory/techniques developed in the twentieth century, and developing now, to solve applied problems in several fields of industry. Each chapter includes clues on how to use "mathematics" to solve concrete problems faced in industry as well as practical applications. The target audience is not limited to researchers working in applied mathematics and includes those in engineering, material sciences, economics, and life sciences.
Author: John G. Anderson Publisher: Industrial Press Inc. ISBN: 9780831111458 Category : Mathematics Languages : en Pages : 554
Book Description
Contains a larger, easier to read two-color format with improved flow between topics.Provides clear explanations that build on the strengths which have made this book a standard for more than 25 years.Includes an introduction to Statistics which is needed for many technical trades and not offered in most similar texts.Presents sufficient material for a very full one-semester course or for two standard lecture courses.
Author: Dennis D. Davis
Author: Dennis D. Davis
Author: Donna D. Boatwright
Author: Wayne R. Davis
Author: Edward G. Hoffman
Author: C. R. MacCluer
Author: Mangey Ram
Author: Marc Peter Deisenroth
Author: Ryuei Nishii
Author: 
Author: John G. Anderson