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Author: John Lawlor
Publisher: Penguin
Published: 1991
Total Pages: 164
ISBN-13: 9781557880208
DOWNLOAD EBOOKOffers formulas and equations for calculating brake horsepower and torque, displacement, stroke, bore, compression ratio, and more
Author: Forbes Aird
Publisher: Motorbooks
Published: 2000
Total Pages: 144
ISBN-13: 9780760306963
DOWNLOAD EBOOKOffers formulas and equations for calculating brake horsepower and torque, displacement, stroke, bore, and compression ratio.
Author: John Lawlor
Publisher: HP Books
Published: 2011
Total Pages: 122
ISBN-13: 9781557885548
DOWNLOAD EBOOKSince 1991, John Lawlor's Auto Math Handbookhas been a standard reference for auto engineers, students, racers, and enthusiasts. The formulas, calculations, and equations in this book are the foundation for any car or engine building project. Engineer and racing engine builder Bill Hancock has updated and expanded the original edition with revised sections on- Displacement, bore, and stroke Brake horsepower and torque Air capacity and volumetric efficiency Center of gravity, weight distribution, and g force New sections on instrument error and calibration, rolling resistance, aerodynamics, planimeter usage, computer programs, and moment of inertia are presented in the same easy-to-read format using real-world applications.
Author: John Baechtel
Publisher: CarTech Inc
Published: 2011
Total Pages: 155
ISBN-13: 1934709476
DOWNLOAD EBOOKA reference book of math equations used in developing high-performance racing engines, including calculating engine displacement, compression ratio, torque and horsepower, intake and header size, carb size, VE and BSFC, injector sizing and piston speed. --book cover.
Author: Ilja N. Bronštejn
Publisher: Springer
Published: 2013-11-11
Total Pages: 989
ISBN-13: 3662256517
DOWNLOAD EBOOKAuthor: John Lawlor
Publisher:
Published: 2011
Total Pages: 0
ISBN-13:
DOWNLOAD EBOOKAuthor: Granino A. Korn
Publisher: Courier Corporation
Published: 2013-04-26
Total Pages: 1154
ISBN-13: 0486320235
DOWNLOAD EBOOKConvenient access to information from every area of mathematics: Fourier transforms, Z transforms, linear and nonlinear programming, calculus of variations, random-process theory, special functions, combinatorial analysis, game theory, much more.
Author: D. Stein
Publisher: Harper Collins
Published: 2009-03-17
Total Pages: 320
ISBN-13: 0061828688
DOWNLOAD EBOOKIn How Math Explains the World, mathematician Stein reveals how seemingly arcane mathematical investigations and discoveries have led to bigger, more world-shaking insights into the nature of our world. In the four main sections of the book, Stein tells the stories of the mathematical thinkers who discerned some of the most fundamental aspects of our universe. From their successes and failures, delusions, and even duels, the trajectories of their innovations—and their impact on society—are traced in this fascinating narrative. Quantum mechanics, space-time, chaos theory and the workings of complex systems, and the impossibility of a "perfect" democracy are all here. Stein's book is both mind-bending and practical, as he explains the best way for a salesman to plan a trip, examines why any thought you could have is imbedded in the number p , and—perhaps most importantly—answers one of the modern world's toughest questions: why the garage can never get your car repaired on time. Friendly, entertaining, and fun, How Math Explains the World is the first book by one of California's most popular math teachers, a veteran of both "math for poets" and Princeton's Institute for Advanced Studies. And it's perfect for any reader wanting to know how math makes both science and the world tick.
Author: Marc Peter Deisenroth
Publisher: Cambridge University Press
Published: 2020-04-23
Total Pages: 392
ISBN-13: 1108569323
DOWNLOAD EBOOKThe 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.
