Calculus for Everyone

Calculus for Everyone
Author: Mitch Stokes
Publisher:
Total Pages: 0
Release: 2020-06
Genre: Mathematics
ISBN: 9781944482541

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
Total Pages: 392
Release: 2020-04-23
Genre: Computers
ISBN: 1108569323

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
Total Pages: 962
Release: 2013-05-09
Genre: Mathematics
ISBN: 0486134768

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
Total Pages: 164
Release: 1965
Genre: Science
ISBN: 9780805390216

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.

Probability

Probability
Author: David J. Morin
Publisher: Createspace Independent Publishing Platform
Total Pages: 0
Release: 2016
Genre: Probabilities
ISBN: 9781523318674

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

Topics in Algebra

Topics in Algebra
Author: I. N. Herstein
Publisher: John Wiley & Sons
Total Pages: 405
Release: 1991-01-16
Genre: Mathematics
ISBN: 0471010901

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

Algebra

Algebra
Author: Thomas W. Hungerford
Publisher: Springer Science & Business Media
Total Pages: 523
Release: 2012-12-06
Genre: Mathematics
ISBN: 1461261015

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 Programmer's Introduction to Mathematics

A Programmer's Introduction to Mathematics
Author: Jeremy Kun
Publisher:
Total Pages: 400
Release: 2020-05-17
Genre:
ISBN:

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.