Search results for "introduction to python programming"
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Programmer's Introduction to C# 2.0
* Written by a member of the original C# language design team and C# Program Manager; contains the expertise that pro C# programmers want. * Follows the successful first and second editions; one of the earliest practical books for developers using C# 2.0. * Introduces developers to critical new language elements introduced in C# 2.0, including generics, iterators, and partial classes. * Good introduction to C#.
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JavaTech, an Introduction to Scientific and Technical Computing with Java
JavaTech is a practical introduction to the Java programming language with an emphasis on the features that benefit technical computing. After presenting the basics of object-oriented programming in Java, it examines introductory topics such as graphical interfaces and thread processes. It goes on to review network programming and develops Web client-server examples for tasks such as monitoring remote devices. The focus then shifts to distributed computing with RMI. Finally, it examines how Java programs can access the local platform and interact with hardware. Topics include combining native code with Java, communication via serial lines, and programming embedded processors. An extensive web site supports the book with additional instructional materials. JavaTech demonstrates the ease with which Java can be used to create powerful network applications and distributed computing applications. It will be used as a textbook for programming courses, and by researchers who need to learn Java for a particular task.
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Introduction to Concurrency in Programming Languages
Exploring how concurrent programming can be assisted by language-level techniques, Introduction to Concurrency in Programming Languages presents high-level language techniques for dealing with concurrency in a general context. It provides an understanding of programming languages that offer concurrency features as part of the language definition.The book supplies a conceptual framework for different aspects of parallel algorithm design and implementation. It first addresses the limitations of traditional programming techniques and models when dealing with concurrency. The book then explores the current state of the art in concurrent programming and describes high-level language constructs for concurrency. It also discusses the historical evolution of hardware, corresponding high-level techniques that were developed, and the connection to modern systems, such as multicore and manycore processors. The remainder of the text focuses on common high-level programming techniques and their application to a range of algorithms. The authors offer case studies on genetic algorithms, fractal generation, cellular automata, game logic for solving Sudoku puzzles, pipelined algorithms, and more. Illustrating the effect of concurrency on programs written in familiar languages, this text focuses on novel language abstractions that truly bring concurrency into the language and aid analysis and compilation tools in generating efficient, correct programs. It also explains the complexity involved in taking advantage of concurrency with regard to program correctness and performance.
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Introduction to Microcontroller Programming for Power Electronics Control Applications
Microcontroller programming is not a trivial task. Indeed, it is necessary to set correctly the required peripherals by using programming languages like C/C++ or directly machine code. Nevertheless, MathWorks® developed a model-based workflow linked with an automatic code generation tool able to translate Simulink® schemes into executable files. This represents a rapid prototyping procedure, and it can be applied to many microcontroller boards available on the market. Among them, this introductory book focuses on the C2000 LaunchPadTM family from Texas InstrumentsTM to provide the reader basic programming strategies, implementation guidelines and hardware considerations for some power electronics-based control applications. Starting from simple examples such as turning on/off on-board LEDs, Analog-to-Digital conversion, waveform generation, or how a Pulse-Width-Modulation peripheral should be managed, the reader is guided through the settings of the specific MCU-related Simulink® blocks enabled for code translation. Then, the book proposes several control problems in terms of power management of RL and RLC loads (e.g., involving DC-DC converters) and closed-loop control of DC motors. The control schemes are investigated as well as the working principles of power converter topologies needed to drive the systems under investigation. Finally, a couple of exercises are proposed to check the reader’s understanding while presenting a processor-in-the loop (PIL) technique to either emulate the dynamics of complex systems or testing computational performance.Thus, this book is oriented to graduate students of electrical and automation and control engineering pursuing a curriculum in power electronics and drives, as well as to engineers and researchers who want to deepen their knowledge and acquire new competences in the design and implementations of control schemes aimed to the aforementioned application fields. Indeed, it is assumed that the reader is well acquainted with fundamentals of electrical machines and power electronics, as well as with continuous-time modeling strategies and linear control techniques. In addition, familiarity with sampled-data, discrete-time system analysis and embedded design topics is a plus. However, even if these competences are helpful, they are not essential, since this book provides some basic knowledge even to whom is approaching these topics for the first time. Key concepts are developed from scratch, including a brief review of control theory and modeling strategies for power electronic-based systems.
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Introduction to Sequential Monte Carlo
This book provides a general introduction to Sequential Monte Carlo (SMC) methods, also known as particle filters. These methods have become a staple for the sequential analysis of data in such diverse fields as signal processing, epidemiology, machine learning, population ecology, quantitative finance, and robotics.The coverage is comprehensive, ranging from the underlying theory to computational implementation, methodology, and diverse applications in various areas of science. This is achieved by describing SMC algorithms as particular cases of a general framework, which involves concepts such as Feynman-Kac distributions, and tools such as importance sampling and resampling. This general framework is used consistently throughout the book.Extensive coverage is provided on sequential learning (filtering, smoothing) of state-space (hidden Markov) models, as this remains an important application of SMC methods. More recent applications, such as parameter estimation of these models (through e.g. particle Markov chain Monte Carlo techniques) and the simulation of challenging probability distributions (in e.g. Bayesian inference or rare-event problems), are also discussed.The book may be used either as a graduate text on Sequential Monte Carlo methods and state-space modeling, or as a general reference work on the area. Each chapter includes a set of exercises for self-study, a comprehensive bibliography, and a “Python corner,” which discusses the practical implementation of the methods covered. In addition, the book comes with an open source Python library, which implements all the algorithms described in the book, and contains all the programs that were used to perform the numerical experiments.
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Introduction to MATLAB® Programming and Numerical Methods for Engineers
Assuming no prior background in linear algebra or real analysis, An Introduction to MATLAB® Programming and Numerical Methods for Engineers enables you to develop good computational problem solving techniques through the use of numerical methods and the MATLAB® programming environment. Part One introduces fundamental programming concepts, using simple examples to put new concepts quickly into practice. Part Two covers the fundamentals of algorithms and numerical analysis at a level allowing you to quickly apply results in practical settings.
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Introduction to Stochastic Programming
The aim of stochastic programming is to find optimal decisions in problems which involve uncertain data. This field is currently developing rapidly with contributions from many disciplines including operations research, mathematics, and probability. At the same time, it is now being applied in a wide variety of subjects ranging from agriculture to financial planning and from industrial engineering to computer networks. This textbook provides a first course in stochastic programming suitable for students with a basic knowledge of linear programming, elementary analysis, and probability. The authors aim to present a broad overview of the main themes and methods of the subject. Its prime goal is to help students develop an intuition on how to model uncertainty into mathematical problems, what uncertainty changes bring to the decision process, and what techniques help to manage uncertainty in solving the problems.In this extensively updated new edition there is more material on methods and examples including several new approaches for discrete variables, new results on risk measures in modeling and Monte Carlo sampling methods, a new chapter on relationships to other methods including approximate dynamic programming, robust optimization and online methods.The book is highly illustrated with chapter summaries and many examples and exercises. Students, researchers and practitioners in operations research and the optimization area will find it particularly of interest. Review of First Edition:"The discussion on modeling issues, the large number of examples used to illustrate the material, and the breadth of the coverage make 'Introduction to Stochastic Programming' an ideal textbook for the area." (Interfaces, 1998)
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Introduction to Object COBOL
Are You Ready to Meet the Demand for Object COBOL Programming? Now you can keep up-to-date with the newest standards in COBOL. With its use of straightforward language and real-world examples, An Introduction To Object COBOL is a concise and accessible introduction to using Object COBOL. You'll discover how object technology is applied and how the resulting Object COBOL code is constructed. All of the programs included in the text can be downloaded from the Wiley web site (www.wiley.com/college/doke). You are encouraged to execute and experiment with them. As you work with these programs and follow the related case study, you will gain important experience in using Object COBOL in a business environment. Inside these pages you will: * Keep up-to-date with the state-of-the-art in COBOL programming. * Understand the relationship of object technology to the business environment. * See how object technology is applied to real-life examples. * Receive a non-technical introduction to object technology without the use of intensive vocabulary.
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Introduction to C++ and Numerical Methods
In addition to being an introduction to C++, this text also provides clear explanations of the basics of numerical methods, and is unique for its coverage of numerical methods used in scientific and engineering computation. In addition there is a general discussion of some of the basic paradigms for writing good programs and detecting errors. The result is a brief yet comprehensive treatment of the subject.
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Introduction to Machine Learning with R
Machine learning is an intimidating subject until you know the fundamentals. If you understand basic coding concepts, this introductory guide will help you gain a solid foundation in machine learning principles. Using the R programming language, you’ll first start to learn with regression modelling and then move into more advanced topics such as neural networks and tree-based methods. Finally, you’ll delve into the frontier of machine learning, using the caret package in R. Once you develop a familiarity with topics such as the difference between regression and classification models, you’ll be able to solve an array of machine learning problems. Author Scott V. Burger provides several examples to help you build a working knowledge of machine learning. Explore machine learning models, algorithms, and data training Understand machine learning algorithms for supervised and unsupervised cases Examine statistical concepts for designing data for use in models Dive into linear regression models used in business and science Use single-layer and multilayer neural networks for calculating outcomes Look at how tree-based models work, including popular decision trees Get a comprehensive view of the machine learning ecosystem in R Explore the powerhouse of tools available in R’s caret package
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Introduction to Stochastic Programming
The aim of stochastic programming is to find optimal decisions in problems which involve uncertain data. This field is currently developing rapidly with contributions from many disciplines including operations research, mathematics, and probability. At the same time, it is now being applied in a wide variety of subjects ranging from agriculture to financial planning and from industrial engineering to computer networks. This textbook provides a first course in stochastic programming suitable for students with a basic knowledge of linear programming, elementary analysis, and probability. The authors aim to present a broad overview of the main themes and methods of the subject. Its prime goal is to help students develop an intuition on how to model uncertainty into mathematical problems, what uncertainty changes bring to the decision process, and what techniques help to manage uncertainty in solving the problems.In this extensively updated new edition there is more material on methods and examples including several new approaches for discrete variables, new results on risk measures in modeling and Monte Carlo sampling methods, a new chapter on relationships to other methods including approximate dynamic programming, robust optimization and online methods.The book is highly illustrated with chapter summaries and many examples and exercises. Students, researchers and practitioners in operations research and the optimization area will find it particularly of interest. Review of First Edition:"The discussion on modeling issues, the large number of examples used to illustrate the material, and the breadth of the coverage make 'Introduction to Stochastic Programming' an ideal textbook for the area." (Interfaces, 1998)
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Introduction to Linear Programming with MATLAB
This book is based on the lecture notes of the author delivered to the students at the Institute of Science, Banaras Hindu University, India. It covers simplex, revised simplex, two-phase method, duality, dual simplex, complementary slackness, transportation and assignment problems with good number of examples, clear proofs, MATLAB codes and homework problems. The book will be useful for both students and practitioners.
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Introduction to PHP for Scientists and Engineers
The best way to become acquainted with a subject is to write a book about it. —Benjamin Disraeli i. Background The purpose of this book is provide an introduction to using a server-side programming language to solve some kinds of computing problems that cannot be solved with a client-side language such as JavaScript. The language is PHP (originally created in 1994 by Danish/Icelandic programmer Rasmus Lerdorf as “Personal Home Page Tools” for dealing with his own web site). The PHP language does not have a formal specification, as C does, for example. It is developed and maintained by a User Group of volunteers and is, essentially, defined by the most recently available free download. Although this might seem to be a shaky foundation on which to make a commitment to learning a programming language, PHP has a very large world-wide base of users and applications, which ensures its role into the foreseeable future. This book should not be considered as a PHP reference source and it does not deal exhaustively even with those elements of the PHP language used in the book. (This should be considered a blessing by the casual programmer. ) If you need more information, there is a huge amount of information online about PHP. Hopefully, this book will help you filter this information to focus on solving typical science and engineering problems. An excellent online source for information about PHP is http://www. php. net/manual/en/index. php, maintained by the PHP 1 Documentation Group.
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Introduction to Data Science
An Introduction to Data Science is an easy-to-read, gentle introduction for advanced undergraduate, certificate, and graduate students coming from a wide range of backgrounds into the world of data science. After introducing the basic concepts of data science, the book builds on these foundations to explain data science techniques using the R programming language and RStudio® from the ground up. Short chapters allow instructors to group concepts together for a semester course and provide students with manageable amounts of information for each concept. By taking students systematically through the R programming environment, the book takes the fear out of data science and familiarizes students with the environment so they can be successful when performing advanced functions. The authors cover statistics from a conceptual standpoint, focusing on how to use and interpret statistics, rather than the math behind the statistics. This text then demonstrates how to use data effectively and efficiently to construct models, predict outcomes, visualize data, and make decisions. Accompanying digital resources provide code and datasets for instructors and learners to perform a wide range of data science tasks.
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Introduction to Coding Theory
It is gratifying that this textbook is still sufficiently popular to warrant a third edition. I have used the opportunity to improve and enlarge the book. When the second edition was prepared, only two pages on algebraic geometry codes were added. These have now been removed and replaced by a relatively long chapter on this subject. Although it is still only an introduction, the chapter requires more mathematical background of the reader than the remainder of this book. One of the very interesting recent developments concerns binary codes defined by using codes over the alphabet 7l.4• There is so much interest in this area that a chapter on the essentials was added. Knowledge of this chapter will allow the reader to study recent literature on 7l. -codes. 4 Furthermore, some material has been added that appeared in my Springer Lec ture Notes 201, but was not included in earlier editions of this book, e. g. Generalized Reed-Solomon Codes and Generalized Reed-Muller Codes. In Chapter 2, a section on "Coding Gain" ( the engineer's justification for using error-correcting codes) was added. For the author, preparing this third edition was a most welcome return to mathematics after seven years of administration. For valuable discussions on the new material, I thank C.P.l.M.Baggen, I. M.Duursma, H.D.L.Hollmann, H. C. A. van Tilborg, and R. M. Wilson. A special word of thanks to R. A. Pellikaan for his assistance with Chapter 10.
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Introduction to Meshfree Methods and Their Programming
The finite difference method (FDM) hasbeen used tosolve differential equation systems for centuries. The FDM works well for problems of simple geometry and was widely used before the invention of the much more efficient, robust finite element method (FEM). FEM is now widely used in handling problems with complex geometry. Currently, we are using and developing even more powerful numerical techniques aiming to obtain more accurate approximate solutions in a more convenient manner for even more complex systems. The meshfree or meshless method is one such phenomenal development in the past decade, and is the subject of this book. There are many MFree methods proposed so far for different applications. Currently, three monographs on MFree methods have been published. Mesh Free Methods, Moving Beyond the Finite Element Method d by GR Liu (2002) provides a systematic discussion on basic theories, fundamentals for MFree methods, especially on MFree weak-form methods. It provides a comprehensive record of well-known MFree methods and the wide coverage of applications of MFree methods to problems of solids mechanics (solids, beams, plates, shells, etc.) as well as fluid mechanics. The Meshless Local Petrov-Galerkin (MLPG) Method d by Atluri and Shen (2002) provides detailed discussions of the meshfree local Petrov-Galerkin (MLPG) method and itsvariations. Formulations and applications of MLPG are well addressed in their book.
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Introduction to Bayesian Inference, Methods and Computation
These lecture notes provide a rapid, accessible introduction to Bayesian statistical methods. The course covers the fundamental philosophy and principles of Bayesian inference, including the reasoning behind the prior/likelihood model construction synonymous with Bayesian methods, through to advanced topics such as nonparametrics, Gaussian processes and latent factor models. These advanced modelling techniques can easily be applied using computer code samples written in Python and Stan which are integrated into the main text. Importantly, the reader will learn methods for assessing model fit, and to choose between rival modelling approaches.
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Introduction to GameGuru
GameGuru is an entry-level engine designed to be easy to use as well as being extremely accessible for the user. This book gives users the information needed to access the full depth of features available in the program. Details on how to perform more complex tasks are not found easily anywhere else or in any of the GameGuru documentation. This book will cover all of the common topics including building levels, coding, AI and more.Key Features The only book the fully covers the GameGuru engine. Includes robust documentation to perform complex tasks that are not outlined anywhere else. Includes level building, coding, AI and more. Included are scripts and demo maps for readers to learn from. GameGuru is the ultimate start-to-finish guide
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Strumenti open source per l'analisi dei dati fisici
Lo scopo di questo libro di testo è quello di fornire competenze informatiche per analizzare dati raccolti tramite diversi tipi di esperimenti. In generale, l'analisi dei dati è complementare alle attività sperimentali e/o teoriche, ma, di fatto, una parte fondamentale del processo formativo nei corsi di laurea scientifica (come Fisica, Matematica, Chimica, Biologia e Ingegneria) consiste in diverse attività di laboratorio, raccogliendo dati e poi analizzandoli e interpretandoli. Sono disponibili diversi strumenti di analisi per questo scopo, tra cui quelli commerciali e open source. Alcuni di essi consentono di analizzare i dati in modo intuitivo e possono essere utili per il primo approccio di uno studente a un problema di analisi dei dati, ma, allo stesso tempo, rappresentano un limite alle reali possibilità che gli studenti possono raggiungere utilizzando le potenzialità di calcolo offerte da una buona conoscenza dei linguaggi di programmazione. Per questo motivo, almeno un corso di informatica è generalmente presente nei corsi di laurea scientifica, così come nei laboratori sperimentali in cui parte del tempo di formazione è dedicato all'analisi e alla visualizzazione dei dati.Una parte del libro è dedicata a fornire i rudimenti della programmazione in C++, Python e altri linguaggi open source. Nella seconda parte, Root, un potente framework di analisi dei dati open source, e l'universo delle librerie Python saranno introdotti come uno strumento completo per l'analisi dei dati, così come la loro applicazione ed esempi riguardanti esperimenti di fisica eseguiti in laboratorio.
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Mastering Financial Markets with Python
Master Technical Analysis in the Age of Python and Innovation Financial markets are evolving, and so should your strategies. Mastering Financial Markets with Python: New Horizons in Technical Analysis bridges the gap between traditional methods and the new era of data-driven analysis. This book equips you with the skills to design, implement, and evaluate advanced technical analysis techniques all through a practical, code-first approach. Inside, you will learn how to: Rethink and modernize classic indicators with Python. Build advanced volatility and moving average indicators. Detect and analyze harmonic and reversal patterns automatically. Detect and analyze timing patterns automatically. Apply enhanced Fibonacci techniques and alternative charting systems. Develop a new generation of technical indicators tailored to modern markets. This book provides hands-on tools to master today's fast-moving financial landscape. By combining innovation with Python, you'll gain a deeper understanding of markets and build smarter, more adaptive strategies. Why buy it now? Because trading edges are fleeting, and those who master Python-driven technical analysis will be the ones shaping the markets of tomorrow.
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Introduction to HTML and JavaScript
Dual-use technological writing at its best. This book presents HTML and JavaScript in a way that uniquely meets the needs of students in both engineering and the sciences. The author shows how to create simple client-side applications for scientific and engineering calculations. Complete HTML/JavaScript examples with science/engineering applications are used throughout to guide the reader comprehensively through the subject. The book gives the reader a sufficient understanding of HTML and JavaScript to write their online applications. This book emphasises basic programming principles in a modern Web-oriented environment, making it suitable for an introductory programming course for non-computer science majors. It is also ideal for self-study.
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Introduction to Linear Programming and Game Theory
Praise for the Second Edition: "This is quite a well-done book: very tightly organized, better-than-average exposition, and numerous examples, illustrations, and applications." —Mathematical Reviews of the American Mathematical Society An Introduction to Linear Programming and Game Theory, Third Edition presents a rigorous, yet accessible, introduction to the theoretical concepts and computational techniques of linear programming and game theory. Now with more extensive modeling exercises and detailed integer programming examples, this book uniquely illustrates how mathematics can be used in real-world applications in the social, life, and managerial sciences, providing readers with the opportunity to develop and apply their analytical abilities when solving realistic problems. This Third Edition addresses various new topics and improvements in the field of mathematical programming, and it also presents two software programs, LP Assistant and the Solver add-in for Microsoft Office Excel, for solving linear programming problems. LP Assistant, developed by coauthor Gerard Keough, allows readers to perform the basic steps of the algorithms provided in the book and is freely available via the book's related Web site. The use of the sensitivity analysis report and integer programming algorithm from the Solver add-in for Microsoft Office Excel is introduced so readers can solve the book's linear and integer programming problems. A detailed appendix contains instructions for the use of both applications. Additional features of the Third Edition include: A discussion of sensitivity analysis for the two-variable problem, along with new examples demonstrating integer programming, non-linear programming, and make vs. buy models Revised proofs and a discussion on the relevance and solution of the dual problem A section on developing an example in Data Envelopment Analysis An outline of the proof of John Nash's theorem on the existence of equilibrium strategy pairs for non-cooperative, non-zero-sum games Providing a complete mathematical development of all presented concepts and examples, Introduction to Linear Programming and Game Theory, Third Edition is an ideal text for linear programming and mathematical modeling courses at the upper-undergraduate and graduate levels. It also serves as a valuable reference for professionals who use game theory in business, economics, and management science.
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Introduction to Fortran 90 for Scientific Computing
Fortran was one of the earliest programming languages and is still the most important language for scientific and engineering computation. It has evolved considerably over the last 35 years and this book provides an introduction to its latest standard: Fortran 90. The general organization of this text is based on a companion volume, An Introduction to FORTRAN for Scientific Computing, which covered Fortran 77 with some discussion of Fortran 90 features. Ortega begins with a general introduction to computing, then introduces the basic constructs of the Fortran language: variables, assignment statements, the IF statement, repetition by DO loops, arrays, functions and subroutines, and formatted input/output. Only the simplest forms of these constructs are introduced, but even these are enough for students to begin writing fairly sophisticated programs. To develop good programming habits early on, Ortega discusses programming techniques--such as top-down step-wise refinement, and the important question of detecting errors--alongside the factual material right from the beginning. By the end of Chapter 3, students will have covered most of Fortran 77 and many of the simpler added features of Fortran 90. In Chapter 4, Ortega addresses the more advanced features of Fortran 90: derived types, modules, interface blocks, overloading, and pointers, and concludes with a summary of how Fortran 77 differs from Fortran 90. Development of this text took place in many forms as a first-year programming course taught at the University of Virginia.
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