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The Maths Behind over 60 everyday phenomena. Have you ever wondered why traffic jams often turn out to have no cause when you get to the end of the queue? There's a mathematical explanation for that. Or ever considered whether some lotteries might be easier to win than others? There's a formula for that too. If you've ever been curious about the mathematical strings that hold our world together, then look no further than The Maths Behind. This intriguing and illuminating book takes a scientific view of your everyday world, and can give you the answers to all the niggling questions in your life, along with many you never even thought to ask. From the science behind roller coasters, to the maths behind how to consistently win at Monopoly (and become very unpopular with your family), this is a fascinating look at the mathematical forces that run beneath our everyday transactions.
"A compilation of everyday events analyzed for their probability of occurring. The odds are determined using mathematical equations and science. An entertaining illustrated reference to the role of mathematics in everyday life. Topics examined are: the human condition, sports and games, traveling from A to B, digital technology, chance and coincidence, and more."--
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.
Accompanying CD-ROM includes musical selections.
Learn about the most important mathematical ideas, theorems, and movements in The Maths Book. Part of the fascinating Big Ideas series, this book tackles tricky topics and themes in a simple and easy to follow format. Learn about Maths in this overview guide to the subject, great for novices looking to find out more and experts wishing to refresh their knowledge alike! The Maths Book brings a fresh and vibrant take on the topic through eye-catching graphics and diagrams to immerse yourself in. This captivating book will broaden your understanding of Maths, with: - More than 85 ideas and events key to the development of mathematics - Packed with facts, charts, timelines and graphs to help explain core concepts - A visual approach to big subjects with striking illustrations and graphics throughout - Easy to follow text makes topics accessible for people at any level of understanding The Maths Book is a captivating introduction to the world's most famous theorems, mathematicians and movements, aimed at adults with an interest in the subject and students wanting to gain more of an overview. Charting the development of maths around the world from Babylon to Bletchley Park, this book explains how maths help us understand everything from patterns in nature to artificial intelligence. Your Maths Questions, Simply Explained What is an imaginary number? Can two parallel lines ever meet? How can maths help us predict the future? This engaging overview explores answers to big questions like these and how they contribute to our understanding of maths. If you thought it was difficult to learn about topics like algebra and statistics, The Maths Book presents key information in an easy to follow layout. Learn about the history of maths, from ancient ideas such as magic squares and the abacus to modern cryptography, fractals, and the final proof of Fermat's Last Theorem. The Big Ideas Series With millions of copies sold worldwide, The Maths Book is part of the award-winning Big Ideas series from DK. The series uses striking graphics along with engaging writing, making big topics easy to understand. r to understand.
This book is for anyone who wishes to illustrate their mathematical ideas, which in our experience means everyone. It is organized by material, rather than by subject area, and purposefully emphasizes the process of creating things, including discussions of failures that occurred along the way. As a result, the reader can learn from the experiences of those who came before, and will be inspired to create their own illustrations. Topics illustrated within include prime numbers, fractals, the Klein bottle, Borromean rings, tilings, space-filling curves, knot theory, billiards, complex dynamics, algebraic surfaces, groups and prime ideals, the Riemann zeta function, quadratic fields, hyperbolic space, and hyperbolic 3-manifolds. Everyone who opens this book should find a type of mathematics with which they identify. Each contributor explains the mathematics behind their illustration at an accessible level, so that all readers can appreciate the beauty of both the object itself and the mathematics behind it.
"A mathematician pulls back the curtain and reveals the hidden patterns--from dating sites to divorce, sex to marriage--behind the rituals of love ... applying mathematical formulas to the most common yet complex questions pertaining to love: What's the chance of finding love? What's the probability that it will last? How do online dating algorithms work, exactly? Can game theory help us decide who to approach in a bar? At what point in your dating life should you settle down?"--Amazon.com.
A guide to the theory behind bond math formulas Bond Math explores the ideas and assumptions behind commonly used statistics on risk and return for individual bonds and on fixed income portfolios. But this book is much more than a series of formulas and calculations; the emphasis is on how to think about and use bond math. Author Donald J. Smith, a professor at Boston University and an experienced executive trainer, covers in detail money market rates, periodicity conversions, bond yields to maturity and horizon yields, the implied probability of default, after-tax rates of return, implied forward and spot rates, and duration and convexity. These calculations are used on traditional fixed-rate and zero-coupon bonds, as well as floating-rate notes, inflation-indexed securities, and interest rate swaps. Puts bond math in perspective through discussions of bond portfolios and investment strategies. Critiques the Bloomberg Yield Analysis (YA) page, indicating which numbers provide reliable information for making decisions about bonds, which are meaningless data, and which can be very misleading to investors Filled with thought-provoking insights and practical advice, this book puts the intricacies of bond math into a clear and logical order.
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Math and Architectures of Deep Learning bridges the gap between theory and practice, laying out the math of deep learning side by side with practical implementations in Python and PyTorch. You'll peer inside the "black box" to understand how your code is working, and learn to comprehend cutting-edge research you can turn into practical applications. Math and Architectures of Deep Learning sets out the foundations of DL usefully and accessibly to working practitioners. Each chapter explores a new fundamental DL concept or architectural pattern, explaining the underpinning mathematics and demonstrating how they work in practice with well-annotated Python code. You'll start with a primer of basic algebra, calculus, and statistics, working your way up to state-of-the-art DL paradigms taken from the latest research. Learning mathematical foundations and neural network architecture can be challenging, but the payoff is big. You'll be free from blind reliance on pre-packaged DL models and able to build, customize, and re-architect for your specific needs. And when things go wrong, you'll be glad you can quickly identify and fix problems.