Summary and Info
About This Book Master the theory and algorithms behind numerical recipes and how they can be applied to real-world problems Learn to combine the most appropriate built-in functions from the SciPy stack by understanding the connection between the sources of your problem, volume of data, or computer architecture A comprehensive coverage of all the mathematical techniques needed to solve the presented topics, with a discussion of the relevant algorithms built in the SciPy stack Who This Book Is For 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 Learn Master relevant algorithms used in symbolic or numerical mathematics to address the approximation, interpolation, and optimization of scalar or multi-variate functions Develop different algorithms and strategies to effectively store and manipulate large matrices of data, with a view to solving various problems in numerical linear algebra Understand how to model physical problems with systems of differential equations and distinguish the factors that dictate the strategies to solve them numerically Perform statistical analysis, inference, data mining, and machine learning at higher level, and apply these to real-world problems Adapt valuable ideas in computational geometry like Delaunay triangulations, Voronoi diagrams, geometric query problems, or Bezier curves, and apply them to various engineering problems Familiarize yourself with different methods to represent and compress images, as well as techniques used in image processing, including edition, restoration, in painting, segmentation, or feature recognition In 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
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