Implement State-of-the-art Techniques to Visualize Solutions to Challenging Problems in Scientific Computing, With the Use of the SciPy StackeBook - 2015
If you are a professional with a proficiency in Python and familiarity with IPython, this book is for you. Some basic knowledge of numerical methods in scientific computing would be helpful.What You Will LearnMaster relevant algorithms used in symbolic or numerical mathematics to address the approximation, interpolation, and optimization of scalar or multi-variate functionsDevelop different algorithms and strategies to effectively store and manipulate large matrices of data, with a view to solving various problems in numerical linear algebraUnderstand how to model physical problems with systems of differential equations and distinguish the factors that dictate the strategies to solve them numericallyPerform statistical analysis, inference, data mining, and machine learning at higher level, and apply these to real-world problemsAdapt valuable ideas in computational geometry like Delaunay triangulations, Voronoi diagrams, geometric query problems, or Bezier curves, and apply them to various engineering problemsFamiliarize yourself with different methods to represent and compress images, as well as techniques used in image processing, including edition, restoration, inpainting, segmentation, or feature recognitionIn Detail
The SciPy stack is a collection of open source libraries of the powerful scripting language Python, together with its interactive shells. This environment offers a cutting-edge platform for numerical computation, programming, visualization and publishing, and is used by some of the world's leading mathematicians, scientists, and engineers.
This book goes beyond a mere description of the different built-in functions coded in the libraries from the SciPy stack. It presents you with a solid mathematical and computational background to help you identify the right tools for each problem in scientific computing and visualization.
You will gain an insight into the best practices with numerical methods depending on the amount or type of data, properties of the mathematical tools employed, or computer architecture, among other factors.