Welcome to Ian Stewart's strange and magical world of mathematics! In Math Hysteria, Professor Stewart presents us with a wealth of magical puzzles, each one spun around an amazing tale: Counting the Cattle of the Sun; The Great Drain Robbery; and Preposterous Piratical Predicaments; to name but a few. Along the way, we also meet many curious characters: in short, these stori...(展开全部)

Welcome to Ian Stewart's strange and magical world of mathematics! In Math Hysteria, Professor Stewart presents us with a wealth of magical puzzles, each one spun around an amazing tale: Counting the Cattle of the Sun; The Great Drain Robbery; and Preposterous Piratical Predicaments; to name but a few. Along the way, we also meet many curious characters: in short, these stori...(展开全部)

肯·宾默尔是一个从数学转行的经济学家，他把一生贡献给了博弈论及其在经济学、进化生物学、心理学和道德哲学中的应用。他因为参与设计为英国纳税人赚了350亿美元的电信拍卖而闻名于世，但其主要研究贡献在于议价理论及其实验检验。他是英国科学院和美国艺术与科学研究院的院士。他著有12本著作和大约90篇研究论文。他是伦敦大学学院的经济学名誉教授。

肯·宾默尔是一个从数学转行的经济学家，他把一生贡献给了博弈论及其在经济学、进化生物学、心理学和道德哲学中的应用。他因为参与设计为英国纳税人赚了350亿美元的电信拍卖而闻名于世，但其主要研究贡献在于议价理论及其实验检验。他是英国科学院和美国艺术与科学研究院的院士。他著有12本著作和大约90篇研究论文。他是伦敦大学学院的经济学名誉教授。

Gardner's second collection of amusing, thought-provoking problems and puzzles from Loyd's Cyclopedia -- perhaps the most exciting collection of puzzles ever assembled in one volume. Arithmetic, algebra, speed and distance problems, game theory, counter and sliding block problems, similar topics. 166 problems. 150 original drawings, diagrams.

Gardner's second collection of amusing, thought-provoking problems and puzzles from Loyd's Cyclopedia -- perhaps the most exciting collection of puzzles ever assembled in one volume. Arithmetic, algebra, speed and distance problems, game theory, counter and sliding block problems, similar topics. 166 problems. 150 original drawings, diagrams.

Python, one of the world's most popular programming languages, has a number of powerful packages to help you tackle complex mathematical problems in a simple and efficient way. These core capabilities help programmers pave the way for building exciting applications in various domains, such as machine learning and data science, using knowledge in the computational mathematics domain.The book teaches you how to solve problems faced in a wide variety of mathematical fields, including calculus, probability, statistics and data science, graph theory, optimization, and geometry. You'll start by developing core skills and learning about packages covered in Python’s scientific stack, including NumPy, SciPy, and Matplotlib. As you advance, you'll get to grips with more advanced topics of calculus, probability, and networks (graph theory). After you gain a solid understanding of these topics, you'll discover Python's applications in data science and statistics, forecasting, geometry, and optimization. The final chapters will take you through a collection of miscellaneous problems, including working with specific data formats and accelerating code.By the end of this book, you'll have an arsenal of practical coding solutions that can be used and modified to solve a wide range of practical problems in computational mathematics and data science.

Python, one of the world's most popular programming languages, has a number of powerful packages to help you tackle complex mathematical problems in a simple and efficient way. These core capabilities help programmers pave the way for building exciting applications in various domains, such as machine learning and data science, using knowledge in the computational mathematics domain.The book teaches you how to solve problems faced in a wide variety of mathematical fields, including calculus, probability, statistics and data science, graph theory, optimization, and geometry. You'll start by developing core skills and learning about packages covered in Python’s scientific stack, including NumPy, SciPy, and Matplotlib. As you advance, you'll get to grips with more advanced topics of calculus, probability, and networks (graph theory). After you gain a solid understanding of these topics, you'll discover Python's applications in data science and statistics, forecasting, geometry, and optimization. The final chapters will take you through a collection of miscellaneous problems, including working with specific data formats and accelerating code.By the end of this book, you'll have an arsenal of practical coding solutions that can be used and modified to solve a wide range of practical problems in computational mathematics and data science.