Sam Morley - Applying Math with Python: Practical recipes for solving computational math problems using Python programming and its libraries

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Sam Morley is an experienced lecturer in mathematics and a researcher in pure mathematics. He is currently a research software engineer at the University of Oxford working on the DataSig project. He was previously a lecturer in mathematics at the University of East Anglia and Nottingham Trent University. His research interests lie in functional analysis, especially Banach algebras. Sam has a firm commitment to providing high-quality, inclusive, and enjoyable teaching, with the aim of inspiring his students and spreading his enthusiasm for mathematics.

Bryan Johns is an experienced data scientist and mathematician. Since completing his PhD in mathematics, Bryan has been working as a data scientist, where he has been using Python to deliver machine learning solutions to some of today's most intractable business problems. Bryan has worked in the financial services and consulting industries, as well as serving as a data science mentor for the next generation of data scientists. In his free time, Bryan enjoys surfing, skiing, sailing, and other activities that start with "s." Bryan lives in San Diego, California, with his wife, one-year-old son, and two mischievous cats.

Valeriy Babushkin is the senior director of data science at X5 Retail Group, where he leads a team of 80+ people in the fields of machine learning, data analysis, computer vision, natural language processing, R&D, and A/B testing. Valeriy is a Kaggle competition grandmaster and attending lecturer at the National Research Institute Higher School of Economics and the Central Bank of Kazakhstan. He is a technical reviewer of AI Crash Course and Hands-On Reinforcement Learning with Python, Second Edition, published by Packt.

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Python is a powerful and flexible programming language that is fun and easy to learn. It is the programming language of choice for many professionals, hobbyists, and scientists. The power of Python comes from its large ecosystem of packages and friendly community, and from its ability to communicate seamlessly with compiled extension modules. This means that Python is ideal for solving problems of all kinds, and mathematical problems in particular.

Mathematics is usually associated with calculations and equations, but in reality, these are very small parts of a much larger subject. At its core, mathematics is about solving problems, and the logical, structured approach to solutions. Once you explore past the equations, calculations, derivatives, and integrals, you discover a vast world of beautiful, elegant structures.

This book is an introduction to solving mathematical problems using Python. It provides an introduction to some of the basic concepts from mathematics and how to use Python to work with these concepts and templates for solving a variety of mathematical problems across a large number of topics within mathematics. The first few chapters focus on core skills such as working with NumPy arrays, plotting, calculus, and probability. These topics are very important throughout mathematics, and act as the foundation for the rest of the book. In the remaining chapters, we discuss more practical problems, covering topics such as data analysis and statistics, networks, regression and forecasting, optimization, and game theory. We hope that this book provides a basis for solving mathematical problems and the tools for you to further explore the world of mathematics.