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This scarce antiquarian book is included in our special Legacy Reprint Series. In the interest of creating a more extensive selection of rare historical book reprints, we have chosen to reproduce this title even though it may possibly have occasional imperfections such as missing and blurred pages, missing text, poor pictures, markings, dark backgrounds and other reproduction issues beyond our control. Because this work is culturally important, we have made it available as a part of our commitment to protecting, preserving and promoting the world's literature.
"Making Mathematics with Needlework will inspire mathematicians, mathematics educators, and crafters; every chapter has an overview as well as sections on mathematics and mathematics education and detailed instructions for completing the chapter's project. All readers will be able to understand the overview sections, as they include introductions to the various fiber arts as well as summaries of the mathematical content. While the mathematics sections are written for mathematicians, the authors have made a special effort to make their work accessible to lay readers by providing definitions of mathematical terms and many diagrams. The project sections are written for crafters, so that non-mathematician readers can have a tangible experience with mathematical concepts."--BOOK JACKET.
This book will help those wishing to teach a course in technical writing, or who wish to write themselves.
William Clifford (1845-1879) was an important mathematician of his day. He is most remembered today for his invention of Clifford algebras, which are fundamental in modern differential geometry and mathematical physics. His ideas on the connection between energy and matter and the curvature of space were important in the eventual formulation of general relativity. Clifford was particularly interested in non-Euclidean geometry. However, in his relatively brief career, he made contributions to diverse fields of mathematics: elliptic functions, Riemann surfaces, biquaternions, motion in Euclidean and non-Euclidean space, spaces of constant curvature, syzygies, and so on. He was also well-known as a teacher and for his ideas on the philosophy of science. This work covers the life and mathematical work of Clifford, from his early education at Templeton (Exeter) to King's College (London), to Trinity (Cambridge) and ultimately to his professorship at University College (London)--a post which he occupied until the time of his death. Tucker discusses Clifford's Fellowship at the Royal Society and his Council post at the London Mathematical Society. His papers and talks are presented and peppered with entertaining anecdotes relating Clifford's associations with his private tutor, family members, and his wide circle of personal friends and professional colleagues.
This publication was made possible through a bequest from my beloved late wife. by the author which United together in this present collection are those works have not previously appeared in book form. The following are excepted: Vorlesungen fiber Differential und Integralrechnung (Lectures on Differential and Integral Calculus) Vols 1-3, Birkhauser Verlag, Basel (1965-1968); Aufgabensammlung zur Infinitesimalrechnung (Exercises in Infinitesimal Calculus) Vols 1, 2a, 2b, and 3, Birkhauser Verlag, Basel (1967-1977); two issues from Memorial des Sciences on Conformal Mapping (written together with C. Gattegno), Gauthier-Villars, Paris (1949); Solution of Equations in Euclidean and Banach Spaces, Academic Press, New York (1973); and Stu dien fiber den Schottkyschen Satz (Studies on Schottky's Theorem), Wepf & Co., Basel (1931). Where corrections have had to be implemented in the text of certain papers, references to these are made at the conclusion of each paper. In the few instances where this system does not, for technical reasons, seem appropriate, an asterisk in the page margin indicates wherever a correction is necessary and this is then given at the end of the paper. (There is one exception: the correc tions to the paper on page 561 are presented on page 722. The works are published in 6 volumes and are arranged under 16 topic headings. Within each heading, the papers are ordered chronologically according to the date of original publication."
An almost entirely self-taught mathematical genius, George Green (1793 –1841) is best known for Green's theorem, which is used in almost all computer codes that solve partial differential equations. He also published influential essays, or papers, in the fields of hydrodynamics, electricity, and magnetism. This collection comprises his most significant works. The first paper, "An Essay on the Application of Mathematical Analysis to the Theories of Electricity and Magnetism," which is also the longest and perhaps the most Important, appeared In 1828. It introduced the term potential as designating the result obtained by adding together the masses of all the particles of a system, each divided by its distance from a given point. Its three-part treatment first considers the properties of this function and then applies them, in the second and third parts, to the theories of magnetism and electricity. The following paper, "Mathematical Investigations concerning the Laws of the Equilibrium of Fluids analogous to the Electric Fluid," exhibits great analytical power, as does the next, "On the Determination of the Exterior and Interior Attractions of Ellipsoids of Variable Densities." Other highlights include the brief but absorbing paper, "On the Motion of Waves in a variable canal of small depth and width," and two of his most valuable memoirs, "On the Laws of Reflexlon and Refraction of Sound" and "On the Reflexlon and Refraction of Light at the common surface of two non-crystallized Media," which should be studied together.
Summary Deep Learning and the Game of Go teaches you how to apply the power of deep learning to complex reasoning tasks by building a Go-playing AI. After exposing you to the foundations of machine and deep learning, you'll use Python to build a bot and then teach it the rules of the game. Foreword by Thore Graepel, DeepMind Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology The ancient strategy game of Go is an incredible case study for AI. In 2016, a deep learning-based system shocked the Go world by defeating a world champion. Shortly after that, the upgraded AlphaGo Zero crushed the original bot by using deep reinforcement learning to master the game. Now, you can learn those same deep learning techniques by building your own Go bot! About the Book Deep Learning and the Game of Go introduces deep learning by teaching you to build a Go-winning bot. As you progress, you'll apply increasingly complex training techniques and strategies using the Python deep learning library Keras. You'll enjoy watching your bot master the game of Go, and along the way, you'll discover how to apply your new deep learning skills to a wide range of other scenarios! What's inside Build and teach a self-improving game AI Enhance classical game AI systems with deep learning Implement neural networks for deep learning About the Reader All you need are basic Python skills and high school-level math. No deep learning experience required. About the Author Max Pumperla and Kevin Ferguson are experienced deep learning specialists skilled in distributed systems and data science. Together, Max and Kevin built the open source bot BetaGo. Table of Contents PART 1 - FOUNDATIONS Toward deep learning: a machine-learning introduction Go as a machine-learning problem Implementing your first Go bot PART 2 - MACHINE LEARNING AND GAME AI Playing games with tree search Getting started with neural networks Designing a neural network for Go data Learning from data: a deep-learning bot Deploying bots in the wild Learning by practice: reinforcement learning Reinforcement learning with policy gradients Reinforcement learning with value methods Reinforcement learning with actor-critic methods PART 3 - GREATER THAN THE SUM OF ITS PARTS AlphaGo: Bringing it all together AlphaGo Zero: Integrating tree search with reinforcement learning