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Author: Erik Oberg
Publisher:
ISBN: 9780831124922
Category : Mechanical engineering
Languages : en
Pages : 0
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Book Description
Author: Erik Oberg
Publisher:
ISBN: 9780831124922
Category : Mechanical engineering
Languages : en
Pages : 0
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Book Description
Author:
Publisher:
ISBN:
Category : English imprints
Languages : en
Pages : 1140
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Book Description
Author: P. Srinivasulu
Publisher:
ISBN:
Category : MACHINERY
Languages : en
Pages : 264
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Book Description
Author: Joseph Edward Shigley
Publisher: McGraw-Hill Professional Publishing
ISBN:
Category : Technology & Engineering
Languages : en
Pages : 1672
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Book Description
The latest ideas in machine analysis and design have led to a major revision of the field's leading handbook. New chapters cover ergonomics, safety, and computer-aided design, with revised information on numerical methods, belt devices, statistics, standards, and codes and regulations. Key features include: *new material on ergonomics, safety, and computer-aided design; *practical reference data that helps machines designers solve common problems--with a minimum of theory. *current CAS/CAM applications, other machine computational aids, and robotic applications in machine design. This definitive machine design handbook for product designers, project engineers, design engineers, and manufacturing engineers covers every aspect of machine construction and operations. Voluminous and heavily illustrated, it discusses standards, codes and regulations; wear; solid materials, seals; flywheels; power screws; threaded fasteners; springs; lubrication; gaskets; coupling; belt drive; gears; shafting; vibration and control; linkage; and corrosion.
Author:
Publisher:
ISBN:
Category : American literature
Languages : en
Pages : 710
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Book Description
Author: Avrim Blum
Publisher: Cambridge University Press
ISBN: 1108617360
Category : Computers
Languages : en
Pages : 433
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Book Description
This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data.
Author: Keith Frankish
Publisher: Cambridge University Press
ISBN: 0521871425
Category : Computers
Languages : en
Pages : 367
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Book Description
An authoritative, up-to-date survey of the state of the art in artificial intelligence, written for non-specialists.
Author: Marc Peter Deisenroth
Publisher: Cambridge University Press
ISBN: 1108569323
Category : Computers
Languages : en
Pages : 392
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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:
Publisher:
ISBN:
Category : Periodicals
Languages : en
Pages : 1268
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Book Description
Author:
Publisher:
ISBN:
Category : Buildings
Languages : en
Pages : 240
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Book Description
