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|Python Programming: Your Beginner’s Guide To Easily Learn Python in 7 Days
Lowest new price: $16.95
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Author: iCode Academy
Are You Ready To Learn Python Easily? Python is one of the most popular of all the computer programming languages, simply because it is one of the easiest to learn. It is an all-purpose language that has a range of different applications, such as: ¥ Web development ¥ Mathematical and Scientific Computing ¥ Graphical user interface for the desktop Python has a very clean syntax and short code which makes it ideal for the beginner. Not only that, Python is fun because, rather than spending all your time worrying about the syntax, you get to spend time thinking about what your code is going to do instead. Python is an old language, developed in the 1980’s and being released for the first time in 1991. It was developed because Guido van Rossum wanted to create a language that was easy to understand and that could access the Amoeba system he was working on. That led to Python, an interpreted and extensible language that was named after Monty Python’s Flying Circus for no reason other than the creator was a fan! Python is so much easier to write and to read than many other languages and is one of the most feature-packed. Benefits include: ¥ It’s free and its open source – anyone can use Python and anyone can make changes to the source code and distribute it for themselves. ¥ Portable – Python programs can move from one platform to another can be run without the need to make any changes ¥ Embeddable and Extensible – Python code can be combined with bits of another computer language to produce a high-performance code and scripting abilities which are not always available with other languages out of the box ¥ Interpreted Language – Python does a lot of jobs, like garbage collection, memory management and so on automatically. Also, when your code is run, it will automatically be converted into a code that is understood by your computer system ¥ Libraries – Python contains a lot of libraries with much of the code needed to perform common tasks and cut down on the amount of code you need to write ¥ Object-Oriented - because everything that is in Python is an object, it is far easier to solve complex problems – each can be broken down into smaller problems through the creation of objects. Python is backed up with a large active community who are constantly striving to improve the language for beginners and experts alike. Use this community as they are of invaluable help to you. If you are quite ready, we are going to look at the basics of Python programming now. First, I will show you how to install it on your computer and then we will go through the basic concepts. There are going to be plenty of examples for you to input into Python and try or yourself. This is the best way to learn so I urge you to get your Python environment set up and work along with this book – you will learn so much more than if you just read it.
Here Is What You'll Learn After Downloading This Python Programming Book: ✓ Introduction Chapter 1: Installing Python and Setting up Your Environment ✓ Chapter 2: Python Keywords, Identifiers, and Operators Chapter 3: Python Flow Control ✓ Chapter 4: Python Functions Chapter 5: Python Datatypes ✓ Chapter 6: Python Lists Chapter 7: Python Tuples ✓ Chapter 8: Python Strings Chapter 9: Python Sets ✓ Chapter 10: Python Objects and Classes Chapter 11: Python Dictionary ✓ Chapter 12: Python Glossary
What Are You Waiting For?
Start Coding Python Programming Right Now!
|Matrix Analysis for Scientists and Engineers
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Author: Alan J. Laub
Brand: Brand: SIAM: Society for Industrial and Applied Mathematics
Matrix Analysis for Scientists and Engineers provides a blend of undergraduate- and graduate-level topics in matrix theory and linear algebra that relieves instructors of the burden of reviewing such material in subsequent courses that depend heavily on the language of matrices. Consequently, the text provides an often-needed bridge between undergraduate-level matrix theory and linear algebra and the level of matrix analysis required for graduate-level study and research. The text is sufficiently compact that the material can be taught comfortably in a one-quarter or one-semester course. Throughout the book, the author emphasizes the concept of matrix factorization to provide a foundation for a later course in numerical linear algebra. The author addresses connections to differential and difference equations as well as to linear system theory and encourages instructors to augment these examples with other applications of their own choosing.
- Used Book in Good Condition
|Matrix Algebra Useful for Statistics (Wiley Series in Probability and Statistics)
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Author: Shayle R. Searle
A thoroughly updated guide to matrix algebra and it uses in statistical analysis and features SAS®, MATLAB®, and R throughout
This Second Edition addresses matrix algebra that is useful in the statistical analysis of data as well as within statistics as a whole. The material is presented in an explanatory style rather than a formal theorem-proof format and is self-contained. Featuring numerous applied illustrations, numerical examples, and exercises, the book has been updated to include the use of SAS, MATLAB, and R for the execution of matrix computations. In addition, André I. Khuri, who has extensive research and teaching experience in the field, joins this new edition as co-author. The Second Edition also:
- Contains new coverage on vector spaces and linear transformations and discusses computational aspects of matrices
- Covers the analysis of balanced linear models using direct products of matrices
- Analyzes multiresponse linear models where several responses can be of interest
- Includes extensive use of SAS, MATLAB, and R throughout
- Contains over 400 examples and exercises to reinforce understanding along with select solutions
- Includes plentiful new illustrations depicting the importance of geometry as well as historical interludes
Matrix Algebra Useful for Statistics, Second Edition is an ideal textbook for advanced undergraduate and first-year graduate level courses in statistics and other related disciplines. The book is also appropriate as a reference for independent readers who use statistics and wish to improve their knowledge of matrix algebra.
THE LATE SHAYLE R. SEARLE, PHD, was professor emeritus of biometry at Cornell University. He was the author of Linear Models for Unbalanced Data and Linear Models and co-author of Generalized, Linear, and Mixed Models, Second Edition, Matrix Algebra for Applied Economics, and Variance Components, all published by Wiley. Dr. Searle received the Alexander von Humboldt Senior Scientist Award, and he was an honorary fellow of the Royal Society of New Zealand.
ANDRÉ I. KHURI, PHD, is Professor Emeritus of Statistics at the University of Florida. He is the author of Advanced Calculus with Applications in Statistics, Second Edition and co-author of Statistical Tests for Mixed Linear Models, all published by Wiley. Dr. Khuri is a member of numerous academic associations, among them the American Statistical Association and the Institute of Mathematical Statistics.
|Structured Matrices in Mathematics, Computer Science, and Engineering I
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Brand: Brand: Amer Mathematical Society
Many important problems in applied sciences, mathematics, and engineering can be reduced to matrix problems. Moreover, various applications often introduce a special structure into the corresponding matrices, so that their entries can be described by a certain compact formula. Classic examples include Toeplitz matrices, Hankel matrices, Vandermonde matrices, Cauchy matrices, Pick matrices, Bezoutians, controllability and observability matrices, and others. Exploiting these and the more general structures often allows us to obtain elegant solutions to mathematical problems as well as to design more efficient practical algorithms for a variety of applied engineering problems.Structured matrices have been under close study for a long time and in quite diverse (and seemingly unrelated) areas, for example, mathematics, computer science, and engineering. Considerable progress has recently been made in all these areas, and especially in studying the relevant numerical and computational issues. In the past few years, a number of practical algorithms blending speed and accuracy have been developed. This significant growth is fully reflected in these volumes, which collect 38 papers devoted to the numerous aspects of the topic. The collection of the contributions to these volumes offers a flavor of the plethora of different approaches to attack structured matrix problems.The reader will find that the theory of structured matrices is positioned to bridge diverse applications in the sciences and engineering, deep mathematical theories, as well as computational and numerical issues. The presentation fully illustrates the fact that the techniques of engineers, mathematicians, and numerical analysts nicely complement each other, and they all contribute to one unified theory of structured matrices. The book is published in two volumes. The first contains articles on interpolation, system theory, signal and image processing, control theory, and spectral theory. Articles in the second volume are devoted to fast algorithms, numerical and iterative methods, and various applications.
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|Topics in Random Matrix Theory (Graduate Studies in Mathematics)
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Author: Terence Tao
The field of random matrix theory has seen an explosion of activity in recent years, with connections to many areas of mathematics and physics. However, this makes the current state of the field almost too large to survey in a single book. In this graduate text, we focus on one specific sector of the field, namely the spectral distribution of random Wigner matrix ensembles (such as the Gaussian Unitary Ensemble), as well as iid matrix ensembles. The text is largely self-contained and starts with a review of relevant aspects of probability theory and linear algebra. With over 200 exercises, the book is suitable as an introductory text for beginning graduate students seeking to enter the field.
|Matrix Algebra From a Statistician's Perspective
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Author: David A. Harville
Brand: David A Harville
A knowledge of matrix algebra is a prerequisite for the study of much of modern statistics, especially the areas of linear statistical models and multivariate statistics. This reference book provides the background in matrix algebra necessary to do research and understand the results in these areas. Essentially self-contained, the book is best-suited for a reader who has had some previous exposure to matrices. Solultions to the exercises are available in the author's "Matrix Algebra: Exercises and Solutions."
- Matrix Algebra From a Statistician s Perspective
|Iterative Methods for Sparse Linear Systems, Second Edition
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Author: Yousef Saad
Brand: Brand: Society for Industrial and Applied Mathematics
Tremendous progress has been made in the scientific and engineering disciplines regarding the use of iterative methods for linear systems. The size and complexity of linear and nonlinear systems arising in typical applications has grown, meaning that using direct solvers for the three-dimensional models of these problems is no longer effective. At the same time, parallel computing, becoming less expensive and standardized, has penetrated these application areas. Iterative methods are easier than direct solvers to implement on parallel computers but require approaches and solution algorithms that are different from classical methods. This second edition gives an in-depth, up-to-date view of practical algorithms for solving large-scale linear systems of equations, including a wide range of the best methods available today. A new chapter on multigrid techniques has been added, whilst material throughout has been updated, removed or shortened. Numerous exercises have been added, as well as an updated and expanded bibliography.
- Used Book in Good Condition
|Quaternions and Rotation Sequences: A Primer with Applications to Orbits, Aerospace and Virtual Reality
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Author: J. B. Kuipers
Brand: Brand: Princeton University Press
Ever since the Irish mathematician William Rowan Hamilton introduced quaternions in the nineteenth century--a feat he celebrated by carving the founding equations into a stone bridge--mathematicians and engineers have been fascinated by these mathematical objects. Today, they are used in applications as various as describing the geometry of spacetime, guiding the Space Shuttle, and developing computer applications in virtual reality. In this book, J. B. Kuipers introduces quaternions for scientists and engineers who have not encountered them before and shows how they can be used in a variety of practical situations.
The book is primarily an exposition of the quaternion, a 4-tuple, and its primary application in a rotation operator. But Kuipers also presents the more conventional and familiar 3 x 3 (9-element) matrix rotation operator. These parallel presentations allow the reader to judge which approaches are preferable for specific applications. The volume is divided into three main parts. The opening chapters present introductory material and establish the book's terminology and notation. The next part presents the mathematical properties of quaternions, including quaternion algebra and geometry. It includes more advanced special topics in spherical trigonometry, along with an introduction to quaternion calculus and perturbation theory, required in many situations involving dynamics and kinematics. In the final section, Kuipers discusses state-of-the-art applications. He presents a six degree-of-freedom electromagnetic position and orientation transducer and concludes by discussing the computer graphics necessary for the development of applications in virtual reality.
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|The Theory of Matrices in Numerical Analysis
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Author: Alston Scott Householder
This text explores aspects of matrix theory that are most useful in developing and appraising computational methods for solving systems of linear equations and for finding characteristic roots. Suitable for advanced undergraduates and graduate students, it assumes an understanding of the general principles of matrix algebra, including the Cayley-Hamilton theorem, characteristic roots and vectors, and linear dependence.
An introductory chapter covers the Lanczos algorithm, orthogonal polynomials, and determinantal identities. Succeeding chapters examine norms, bounds, and convergence; localization theorems and other inequalities; and methods of solving systems of linear equations. The final chapters illustrate the mathematical principles underlying linear equations and their interrelationships. Topics include methods of successive approximation, direct methods of inversion, normalization and reduction of the matrix, and proper values and vectors. Each chapter concludes with a helpful set of references and problems.
|The Oxford Handbook of Random Matrix Theory (Oxford Handbooks)
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Author: Gernot Akemann
With a foreword by Freeman Dyson, the handbook brings together leading mathematicians and physicists to offer a comprehensive overview of random matrix theory, including a guide to new developments and the diverse range of applications of this approach.
In part one, all modern and classical techniques of solving random matrix models are explored, including orthogonal polynomials, exact replicas or supersymmetry. Further, all main extensions of the classical Gaussian ensembles of Wigner and Dyson are introduced including sparse, heavy tailed, non-Hermitian or multi-matrix models. In the second and larger part, all major applications are covered, in disciplines ranging from physics and mathematics to biology and engineering. This includes standard fields such as number theory, quantum chaos or quantum chromodynamics, as well as recent developments such as partitions, growth models, knot theory, wireless communication or bio-polymer folding.
The handbook is suitable both for introducing novices to this area of research and as a main source of reference for active researchers in mathematics, physics and engineering.
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