Calculus for Everyone
Calculus for Everyone

Author: Mitch Stokes

Publisher:

Published: 2020-06

Total Pages: 0

ISBN-13: 9781944482541

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This book is for only two kinds of people: those who are interested in science and math, and those who aren't. And so, motivated by this powerful idea, Calculus for Everyone presents the mathematics of change in an extremely effective way for anyone with a first-year course in algebra. Yet it does so without dumbing calculus down. In fact, Calculus for Everyone is not only for students who would have never dreamt of taking calculus, it is also for those who have already taken a standard calculus course, as well as for those who will go on to take such a course Based on more than a decade of classroom experience, this book provides mastery of calculus's core by focusing on the foundational concepts of limits, derivatives, and integrals, explaining how all three are united in the fundamental theorem of calculus. Moreover, Calculus for Everyone explains how the story of calculus is central to Western culture, from Plato in ancient Greece, to today's modern physics. Indeed, this book explains why calculus is needed at all-and why it is needed so badly. By mastering the core of calculus-as well as seeing its meaning and significance-students will not only better understand math and science in general, but contemporary culture and their place in it.

Mathematics for Machine Learning
Mathematics for Machine Learning

Author: Marc Peter Deisenroth

Publisher: Cambridge University Press

Published: 2020-04-23

Total Pages: 392

ISBN-13: 1108569323

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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.

Calculus
Calculus

Author: Morris Kline

Publisher: Courier Corporation

Published: 2013-05-09

Total Pages: 962

ISBN-13: 0486134768

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Application-oriented introduction relates the subject as closely as possible to science with explorations of the derivative; differentiation and integration of the powers of x; theorems on differentiation, antidifferentiation; the chain rule; trigonometric functions; more. Examples. 1967 edition.

Calculus on Manifolds
Calculus on Manifolds

Author: Michael Spivak

Publisher: Westview Press

Published: 1965

Total Pages: 164

ISBN-13: 9780805390216

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This book uses elementary versions of modern methods found in sophisticated mathematics to discuss portions of "advanced calculus" in which the subtlety of the concepts and methods makes rigor difficult to attain at an elementary level.

Topics in Algebra
Topics in Algebra

Author: I. N. Herstein

Publisher: John Wiley & Sons

Published: 1991-01-16

Total Pages: 405

ISBN-13: 0471010901

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New edition includes extensive revisions of the material on finite groups and Galois Theory. New problems added throughout.

Probability
Probability

Author: David J. Morin

Publisher: Createspace Independent Publishing Platform

Published: 2016

Total Pages: 0

ISBN-13: 9781523318674

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Preface -- Combinatorics -- Probability -- Expectation values -- Distributions -- Gaussian approximations -- Correlation and regression -- Appendices.

Algebra
Algebra

Author: Thomas W. Hungerford

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 523

ISBN-13: 1461261015

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Finally a self-contained, one volume, graduate-level algebra text that is readable by the average graduate student and flexible enough to accommodate a wide variety of instructors and course contents. The guiding principle throughout is that the material should be presented as general as possible, consistent with good pedagogy. Therefore it stresses clarity rather than brevity and contains an extraordinarily large number of illustrative exercises.

A First Course in Calculus
A First Course in Calculus

Author: Serge Lang

Publisher: Springer Science & Business Media

Published: 2012-09-17

Total Pages: 741

ISBN-13: 1441985328

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This fifth edition of Lang's book covers all the topics traditionally taught in the first-year calculus sequence. Divided into five parts, each section of A FIRST COURSE IN CALCULUS contains examples and applications relating to the topic covered. In addition, the rear of the book contains detailed solutions to a large number of the exercises, allowing them to be used as worked-out examples -- one of the main improvements over previous editions.

A Programmer's Introduction to Mathematics
A Programmer's Introduction to Mathematics

Author: Jeremy Kun

Publisher:

Published: 2020-05-17

Total Pages: 400

ISBN-13:

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A Programmer's Introduction to Mathematics uses your familiarity with ideas from programming and software to teach mathematics. You'll learn about the central objects and theorems of mathematics, including graphs, calculus, linear algebra, eigenvalues, optimization, and more. You'll also be immersed in the often unspoken cultural attitudes of mathematics, learning both how to read and write proofs while understanding why mathematics is the way it is. Between each technical chapter is an essay describing a different aspect of mathematical culture, and discussions of the insights and meta-insights that constitute mathematical intuition. As you learn, we'll use new mathematical ideas to create wondrous programs, from cryptographic schemes to neural networks to hyperbolic tessellations. Each chapter also contains a set of exercises that have you actively explore mathematical topics on your own. In short, this book will teach you to engage with mathematics. A Programmer's Introduction to Mathematics is written by Jeremy Kun, who has been writing about math and programming for 10 years on his blog "Math Intersect Programming." As of 2020, he works in datacenter optimization at Google.The second edition includes revisions to most chapters, some reorganized content and rewritten proofs, and the addition of three appendices.