The updated edition of Applying Math with Python will help you solve complex problems in a wide variety of mathematical fields in simple and efficient ways. Old recipes have been revised for new libraries and several recipes have been added to demonstrate new tools such as JAX.
You'll start by refreshing your knowledge of several core mathematical fields and learn about packages covered in Python's scientific stack, including NumPy, SciPy, and Matplotlib. As you progress, you'll gradually get to grips with more advanced topics of calculus, probability, and networks (graph theory). Once you’ve developed a solid base in these topics, you’ll have the confidence to set out on math adventures with Python as you explore 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.
Become familiar with basic Python packages, tools, and libraries for solving mathematical problems
Explore real-world applications of mathematics to reduce a problem in optimization
Understand the core concepts of applied mathematics and their application in computer science
Find out how to choose the most suitable package, tool, or technique to solve a problem
Implement basic mathematical plotting, change plot styles, and add labels to plots using Matplotlib
Get to grips with probability theory with the Bayesian inference and Markov Chain Monte Carlo (MCMC) methods