All the Mathematics You Missed
Title | All the Mathematics You Missed PDF eBook |
Author | Thomas A. Garrity |
Publisher | 清华大学出版社有限公司 |
Pages | 380 |
Release | 2004 |
Genre | Mathematics |
ISBN | 9787302090854 |
1089 and All that
Title | 1089 and All that PDF eBook |
Author | D. J. Acheson |
Publisher | Oxford University Press, USA |
Pages | 200 |
Release | 2002 |
Genre | Mathematics |
ISBN | 9780198516231 |
This excellent book, written by the established author David Acheson, makes mathematics accessible to everyone. Providing an entertaining and witty overview of the subject, the text includes several fascinating puzzles, and is accompanied by numerous illustrations and sketches by world famouscartoonists. This unusual book is one of the most readable explanations of mathematics available.
The Joy of X
Title | The Joy of X PDF eBook |
Author | Steven Henry Strogatz |
Publisher | Houghton Mifflin Harcourt |
Pages | 333 |
Release | 2012 |
Genre | Mathematics |
ISBN | 0547517653 |
A delightful tour of the greatest ideas of math, showing how math intersects with philosophy, science, art, business, current events, and everyday life, by an acclaimed science communicator and regular contributor to the "New York Times."
Mathematics Education for a New Era
Title | Mathematics Education for a New Era PDF eBook |
Author | Keith Devlin |
Publisher | CRC Press |
Pages | 220 |
Release | 2011-02-25 |
Genre | Computers |
ISBN | 1439867712 |
Stanford mathematician and NPR Math Guy Keith Devlin explains why, fun aside, video games are the ideal medium to teach middle-school math. Aimed primarily at teachers and education researchers, but also of interest to game developers who want to produce videogames for mathematics education, Mathematics Education for a New Era: Video Games as a Med
The New Mathematics
Title | The New Mathematics PDF eBook |
Author | Irving Adler |
Publisher | John Day Company, Incorporated |
Pages | 234 |
Release | 1972 |
Genre | Mathematics |
ISBN |
Everything You Need to Ace Math in One Big Fat Notebook
Title | Everything You Need to Ace Math in One Big Fat Notebook PDF eBook |
Author | Workman Publishing |
Publisher | Workman Publishing Company |
Pages | 265 |
Release | 2018-02-06 |
Genre | Juvenile Nonfiction |
ISBN | 1523505443 |
It’s the revolutionary math study guide just for middle school students from the brains behind Brain Quest. Everything You Need to Ace Math . . . covers everything to get a student over any math hump: fractions, decimals, and how to multiply and divide them; ratios, proportions, and percentages; geometry; statistics and probability; expressions and equations; and the coordinate plane and functions. The BIG FAT NOTEBOOK™ series is built on a simple and irresistible conceit—borrowing the notes from the smartest kid in class. There are five books in all, and each is the only book you need for each main subject taught in middle school: Math, Science, American History, English Language Arts, and World History. Inside the reader will find every subject’s key concepts, easily digested and summarized: Critical ideas highlighted in neon colors. Definitions explained. Doodles that illuminate tricky concepts in marker. Mnemonics for memorable shortcuts. And quizzes to recap it all. The BIG FAT NOTEBOOKS meet Common Core State Standards, Next Generation Science Standards, and state history standards, and are vetted by National and State Teacher of the Year Award–winning teachers. They make learning fun and are the perfect next step for every kid who grew up on Brain Quest.
Mathematics for Machine Learning
Title | Mathematics for Machine Learning PDF eBook |
Author | Marc Peter Deisenroth |
Publisher | Cambridge University Press |
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.