Handbook Of Machine Foundations

The performance, safety and stability of machines depends largely on their design, manufacturing and interaction with environment. Machine foundations should be designed in such a way that the dynamic forces transmitted to the soil through the foundation, eliminating all potentially harmful forces. This handbook is designed primarily for the practising engineers engaged in design of machine foundations. It covers basic fundamentals for understanding and evaluating dynamic response of machine foundation systems with emphasis is on detailed dynamic analysis for response evaulation. Use of commercially available Finite Element packages, for analysis and design of the foundation, is recommended. Theory is supported by results from practice in the form of examples.

Intelligent business analytics is an emerging technology that has become a mainstream market adopted broadly across industries, organizations, and geographic regions. Intelligent business analytics is a current focus for research and development across academia and industries and must be examined and considered thoroughly so businesses can apply the technology appropriately. The Handbook of Research on Foundations and Applications of Intelligent Business Analytics examines the technologies and applications of intelligent business analytics and discusses the foundations of intelligent analytics such as intelligent mining, intelligent statistical modeling, and machine learning. Covering topics such as augmented analytics and artificial intelligence systems, this major reference work is ideal for scholars, engineers, professors, practitioners, researchers, industry professionals, academicians, and students.

This text brings together traditional and new concepts and procedures for analyzing and designing dynamically loaded structures.

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.

An intuitive approach to machine learning covering key concepts, real-world applications, and practical Python coding exercises.

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.

In his introduction to this book, George R. Harrison, Dean Emeritus of M.I.T.'s School of Science, writes as follows: "Basic to man's behavior is his ability to determine, modify, and adapt to his environment. This he has been able to do in proportion to his skill at making measurements, and fundamental to all other measuring operations is his ability to determine locations in the material world. Thus the science of mechanical measurements is a fundamental one. It is this science, and the art which accompanies and informs it, with which this book is concerned." This is the third book produced by the , Inc., of Bridgeport, Connecticut. Like all of its products, the book is marked by a clean precision of design and execution. The firm has built a worldwide reputation since 1924, both as a manufacturer of special tooling to extremely close accuracies and of machine tools that make possible a very high degree of precision. Wayne R. Moore has assembled in the 350 pages of Foundations of Mechanical Accuracythe company's intimate knowledge of and experience with mechanical accuracy, and how to achieve it. He has illustrated his text with over 500 original photographs and drawings. This book tells how to attain precision in manufacturing to millionths of an inch and how to control such precision by appropriate measuring techniques. The book is divided into four main sections: geometry, standards of length, dividing the circle, and roundness. A fifth section covers "Universal Measuring Machine Techniques and Applications." The book is printed in two colors throughout, and interspersed with full-page, full-color plates.