Analysis for Computer Scientists
Describes the fundamental concepts in analysis, covering real and complex numbers, trigonometry, sequences and series, functions, derivatives, integrals, and curves; Discusses important applications and advanced topics, such as fractals and L-systems, numerical integration, linear regression, and differential equations; Presents tools from vector and matrix algebra in the appendices, together with further information on continuity; Includes added material on hyperbolic functions, curves and surfaces in space, second-order differential equations, and the pendulum equation; Contains experiments, exercises, definitions, and propositions throughout the text; Supplies programming examples in Python, in addition to MATLAB; Provides supplementary resources at an associated website, including Java applets, code source files, and links to interactive online learning material.
Addressing the core needs of computer science students and researchers, this clearly written textbook is an essential resource for undergraduate-level courses on numerical analysis, and an ideal self-study tool for professionals seeking to enhance their analysis skills.
Table of contents
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|Author(s):||by Michael Oberguggenberger, Alexander Ostermann|
Take advantage of this course called Analysis for Computer Scientists to improve your Computer architecture skills and better understand computer.
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All you need to do is download the training document, open it and start learning computer for free.
This tutorial has been prepared for the beginners to help them understand basic computer Computer architecture. After completing this tutorial you will find yourself at a moderate level of expertise in computer from where you can take yourself to next levels.
This tutorial is designed for computer students who are completely unaware of computer concepts but they have basic understanding on Computer architecture training.
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