Search results for "Master Multi-Paradigm Python Programming"
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Data Structures for Engineers and Scientists Using Python
The text covers the fundamentals of Python programming and the implementation of data structures using Python programming with the help of worked-out examples. It provides a learning tool for engineers as well as for researchers and scientists of advanced level. The text further discusses important concepts such as polynomial manipulation, sparse matrices, implementation of stack using the queue model and topological sorting.This book:Discusses the implementation of various data structures such as an array, stack, queue, tree and graph along with sorting and searching algorithms. Includes programming tips to highlight important concepts and help readers avoid common programming errors. Presents each concept of data structure with a different approach and implements the same using Python programming. Offers rich chapter-end pedagogy including objective-type questions (with answers), review questions and programming exercises to facilitate review. Covers fundamentals of Python up to object-oriented concepts including regular expression. It is primarily written for senior undergraduate, graduate students and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering and information technology.
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Game Programming with Code Angel
Program in Python on a Raspberry Pi or PC by developing six computer games. Each game project is split into several chapters of the book. Rather than taking you through programming techniques as standalone concepts, this book explains concepts as they are used within a game.You'll learn about variables; integer, real, Boolean and string data types; conditional if statements; fixed loops and conditional loops; modularity; arrays and lists; and predefined functions. You’ll also discover the PyGame library, which is popularly used in the development of 2D games.Key programming concepts are revisited in subsequent projects in the book to consolidate prior learning. Beyond teaching you how to code, this book explains the programming logic behind each project—exemplifying the process of designing and writing a computer game.All the projects in this book are supported by Code Angel (mycodeangel.com). Code Angel Code Angel largely serves students and new developers and the projects work by encouraging you to ‘Learn…then play’. Taking this approach, you'll be able to build fun 2D games and enjoy playing them by yourself or with friends. Developing games in this way keeps you engaged, gives a purpose as you work through each project, and offers a sense of achievement when each game is finished.What You'll Learn Integrate the fundamentals of the Python 3 programming languageProgram fun, classic computer games you can then playDevelop computational thinking skills and abilities that can be applied to other venturesWho This Book Is ForStudents, hobbyists, new developers or anyone wishing to learn how to design and write computer games.
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Network Programming and Automation Essentials
Unleash the power of automation by mastering network programming fundamentals using Python and Go best practicesPurchase of the print or Kindle book includes a free PDF eBookKey FeaturesUnderstand the fundamentals of network programming and automationLearn tips and tricks to transition from traditional networking to automated networksSolve everyday problems with automation frameworks in Python and GoBook DescriptionNetwork programming and automation, unlike traditional networking, is a modern-day skill that helps in configuring, managing, and operating networks and network devices. This book will guide you with important information, helping you set up and start working with network programming and automation.With Network Programming and Automation Essentials, you'll learn the basics of networking in brief. You'll explore the network programming and automation ecosystem, learn about the leading programmable interfaces, and go through the protocols, tools, techniques, and technologies associated with network programming. You'll also master network automation using Python and Go with hands-on labs and real network emulation in this comprehensive guide.By the end of this book, you'll be well equipped to program and automate networks efficiently.What you will learnUnderstand the foundation of network programmingExplore software-defined networks and related familiesRecognize the differences between Go and Python through comparisonLeverage the best practices of Go and PythonCreate your own network automation testing framework using network emulationAcquire skills in using automation frameworks and strategies for automationWho this book is forThis book is for network architects, network engineers, and software professionals looking to integrate programming into networks. Network engineers following traditional techniques can use this book to transition into modern-day network automation and programming. Familiarity with networking concepts is a prerequisite.
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Learn Quantum Computing with Python and IBM Quantum
A step-by-step guide to working with programs that exploit quantum computing principles, with the help of IBM Quantum, Qiskit, and PythonKey FeaturesUnderstand the difference between classical computers and quantum computersWork with key quantum computational principles such as superposition, interference, and entanglement, and observe how they are leveraged on the IBM Quantum systemsRun your own quantum experiments and applications by integrating with Qiskit and PythonBook DescriptionThe IBM Quantum Platform was built to enable developers to learn the basics of quantum computing by providing access to high performant quantum computers and provide documentation and courses to help get up to speed with the latest features in quantum computing. Updated with new examples and changes to the platform, this edition begins with an introduction to the IBM Quantum Platform and the Quantum Information Science Kit (Qiskit) SDK. You will become well versed in the IBM Quantum Composer interface as well as running your quantum circuits on a real quantum computer. Along the way, you’ll learn some of the fundamental principles regarding quantum mechanics, qubits, quantum gates, quantum circuits, and the error mitigation techniques that are used to perform operations on qubits. As you build on your knowledge, you’ll understand the functionality of IBM Qiskit and the developer-focused resources so you can create your own quantum algorithms. You’ll learn how to monitor and optimize your quantum circuits. Lastly, you’ll look at the fundamental quantum algorithms and understand how they can be applied effectively. By the end of this quantum computing book, you'll know how to build quantum algorithms and will have gained a practical understanding of quantum computation that you can apply to your research or business. What you will learnGet familiar with the features within the IBM Quantum PlatformCreate and visualize quantum gates and circuitsOperate quantum gates on qubits using the IBM Quantum ComposerInstall and run your quantum circuits on an IBM Quantum computerDiscover Qiskit and its many features such as the Qiskit RuntimeGet to grips with fundamental quantum algorithms and error mitigation techniques to help you get startedUnderstand the new era of quantum utility and how this moves us closer towards quantum advantageWho this book is forThis book is for Python developers who are looking to learn quantum computing from the ground up and put their knowledge to use in practical situations with the help of the IBM Quantum Platform and Qiskit. Some background in computer science and high school-level physics and math is required.
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Advanced Machine Learning with Python
Solve challenging data science problems by mastering cutting-edge machine learning techniques in PythonAbout This Book• Resolve complex machine learning problems and explore deep learning• Learn to use Python code for implementing a range of machine learning algorithms and techniques• A practical tutorial that tackles real-world computing problems through a rigorous and effective approachWho This Book Is ForThis title is for Python developers and analysts or data scientists who are looking to add to their existing skills by accessing some of the most powerful recent trends in data science. If you've ever considered building your own image or text-tagging solution, or of entering a Kaggle contest for instance, this book is for you!Prior experience of Python and grounding in some of the core concepts of machine learning would be helpful.What You Will Learn• Compete with top data scientists by gaining a practical and theoretical understanding of cutting-edge deep learning algorithms• Apply your new found skills to solve real problems, through clearly-explained code for every technique and test• Automate large sets of complex data and overcome time-consuming practical challenges• Improve the accuracy of models and your existing input data using powerful feature engineering techniques• Use multiple learning techniques together to improve the consistency of results• Understand the hidden structure of datasets using a range of unsupervised techniques• Gain insight into how the experts solve challenging data problems with an effective, iterative, and validation-focused approach• Improve the effectiveness of your deep learning models further by using powerful ensembling techniques to strap multiple models togetherIn DetailDesigned to take you on a guided tour of the most relevant and powerful machine learning techniques in use today by top data scientists, this book is just what you need to push your Python algorithms to maximum potential. Clear examples and detailed code samples demonstrate deep learning techniques, semi-supervised learning, and more - all whilst working with real-world applications that include image, music, text, and financial data.The machine learning techniques covered in this book are at the forefront of commercial practice. They are applicable now for the first time in contexts such as image recognition, NLP and web search, computational creativity, and commercial/financial data modeling. Deep Learning algorithms and ensembles of models are in use by data scientists at top tech and digital companies, but the skills needed to apply them successfully, while in high demand, are still scarce.This book is designed to take the reader on a guided tour of the most relevant and powerful machine learning techniques. Clear descriptions of how techniques work and detailed code examples demonstrate deep learning techniques, semi-supervised learning and more, in real world applications. We will also learn about NumPy and Theano.By this end of th
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Programming in Two Semesters
This highly accessible textbook teaches programming from first principles. In common with many programming courses, it uses Python as the introductory programming language before going on to use Java as the vehicle for more advanced programming concepts.The first part, which teaches Python, covers fundamental programming concepts, such as data types and control structures and functions. It introduces more complex data types such as lists and dictionaries and also deals with file handling. It introduces object-oriented concepts and ends with a case study bringing together all the topics of the first semester. The second part uses Java to teach advanced concepts and centres around object-oriented programming, teaching key object-oriented concepts such as inheritance and polymorphism. The semester again ends with an advanced case study bringing together all the topics of the second semester.Topics and features:Assumes no prior knowledge, and makes the transition from Python to Java a smooth processFeatures numerous exercises and also an illustrative case study for each language Examines procedural and object-oriented methodologies, as well as design principles Covers such advanced topics as interfaces and lambda expressions, exceptions and Collections Includes a chapter on graphics programming in Python using Tkinter Introduces the latest Java technology for graphical interfaces, JavaFXExplains design concepts using UML notation Offering a gentle introduction to the field and assuming no prerequisite background, Programming in Two Semesters is the ideal companion to undergraduate modules in software development or programming. In addition, it will serve as a strong primer for professionals looking to strengthen their knowledge of programming with these languages.
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Mathematics and Python Programming
This book contains 120 Python programs and more than 110 illustrations useful both to students of science of the first university courses, as well as high school students and teachers. All the programs and graphics of the book have been developed using free software, which you can obtain free and legally on the internet, so it is not necessary to buy any software. Python is currently the most popular language for teaching introductory computer science courses at top-ranked U.S. universities. You can freely write Python programs whether you are a Windows user or whether you prefer Mac or Linux. A free pdf excerpt is available at our website www.pysamples.com. The buyers of the book can also download the code of the Python programs. The book consists of a detailed introduction to Python, followed by ten chapters of mathematics with its corresponding Python programs, results and graphs.
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Mobile Applications Development
The book covers the concepts of Python programming language along with mobile application development. Starting from fundamentals, the book continues with the explanation of mobile app development using Kivy framework. All the chapters offer questions and exercises for to better understanding of the subject. This second revised and updated edition covers the most recent developments in Kivy since the publishing of the first edition.
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Scientific Scripting with Python
Learn how to automate your scientific research with Python. This book teaches you the basics of the popular Python scripting language, before delving deep into all aspects of processing data. Python is a simple, yet powerful, programming language similar to Perl, Tcl, and Ruby. It is heavily used in scientific research, and by companies like Google. This book places a strong emphasis on practical techniques for handling data. Although targeted specifically at scientists and engineers, anyone who needs to automate their work flow would find it useful.
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Testing Python
Fundamental testing methodologies applied to the popular Python language Testing Python; Applying Unit Testing, TDD, BDD and Acceptance Testing is the most comprehensive book available on testing for one of the top software programming languages in the world. Python is a natural choice for new and experienced developers, and this hands-on resource is a much needed guide to enterprise-level testing development methodologies. The book will show you why Unit Testing and TDD can lead to cleaner, more flexible programs. Unit Testing and Test-Driven Development (TDD) are increasingly must-have skills for software developers, no matter what language they work in. In enterprise settings, it's critical for developers to ensure they always have working code, and that's what makes testing methodologies so attractive. This book will teach you the most widely used testing strategies and will introduce to you to still others, covering performance testing, continuous testing, and more. Learn Unit Testing and TDD—important development methodologies that lie at the heart of Agile developmentEnhance your ability to work with Python to develop powerful, flexible applications with clean codeDraw on the expertise of author David Sale, a leading UK developer and tech commentatorGet ahead of the crowd by mastering the underappreciated world of Python testing Knowledge of software testing in Python could set you apart from Python developers using outmoded methodologies. Python is a natural fit for TDD and Testing Python is a must-read text for anyone who wants to develop expertise in Python programming.
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Multicriteria Portfolio Construction with Python
This book covers topics in portfolio management and multicriteria decision analysis (MCDA), presenting a transparent and unified methodology for the portfolio construction process. The most important feature of the book includes the proposed methodological framework that integrates two individual subsystems, the portfolio selection subsystem and the portfolio optimization subsystem. An additional highlight of the book includes the detailed, step-by-step implementation of the proposed multicriteria algorithms in Python. The implementation is presented in detail; each step is elaborately described, from the input of the data to the extraction of the results. Algorithms are organized into small cells of code, accompanied by targeted remarks and comments, in order to help the reader to fully understand their mechanics. Readers are provided with a link to access the source code through GitHub.This Work may also be considered as a reference which presents the state-of-art research on portfolio construction with multiple and complex investment objectives and constraints. The book consists of eight chapters. A brief introduction is provided in Chapter 1. The fundamental issues of modern portfolio theory are discussed in Chapter 2. In Chapter 3, the various multicriteria decision aid methods, either discrete or continuous, are concisely described. In Chapter 4, a comprehensive review of the published literature in the field of multicriteria portfolio management is considered. In Chapter 5, an integrated and original multicriteria portfolio construction methodology is developed. Chapter 6 presents the web-based information system, in which the suggested methodological framework has been implemented. In Chapter 7, the experimental application of the proposed methodology is discussed and in Chapter 8, the authors provide overall conclusions.The readership of the book aims to be a diverse group, including fund managers, risk managers, investment advisors, bankers, private investors, analytics scientists, operations researchers scientists, and computer engineers, to name just several. Portions of the book may be used as instructional for either advanced undergraduate or post-graduate courses in investment analysis, portfolio engineering, decision science, computer science, or financial engineering.
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Programming for Computations - Python
This book is published open access under a CC BY 4.0 license. This book presents computer programming as a key method for solving mathematical problems. This second edition of the well-received book has been extensively revised: All code is now written in Python version 3.6 (no longer version 2.7). In addition, the two first chapters of the previous edition have been extended and split up into five new chapters, thus expanding the introduction to programming from 50 to 150 pages. Throughout the book, the explanations provided are now more detailed, previous examples have been modified, and new sections, examples and exercises have been added. Also, a number of small errors have been corrected. The book was inspired by the Springer book TCSE 6: A Primer on Scientific Programming with Python (by Langtangen), but the style employed is more accessible and concise, in keeping with the needs of engineering students. The book outlines the shortest possible path from no previous experience with programming to a set of skills that allows students to write simple programs for solving common mathematical problems with numerical methods in the context of engineering and science courses. The emphasis is on generic algorithms, clean program design, the use of functions, and automatic tests for verification.
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Python Pocket Reference
Updated for both Python 3.4 and 2.7, this convenient pocket guide is the perfect on-the-job quick reference. You'll find concise, need-to-know information on Python types and statements, special method names, built-in functions and exceptions, commonly used standard library modules, and other prominent Python tools. The handy index lets you pinpoint exactly what you need. Written by Mark Lutz - widely recognized as the world's leading Python trainer - Python Pocket Reference is an ideal companion to O'Reilly's classic Python tutorials, Learning Python and Programming Python, also written by Mark. This fifth edition covers: Built-in object types, including numbers, lists, dictionaries, and more Statements and syntax for creating and processing objects Functions and modules for structuring and reusing code Python's object-oriented programming tools Built-in functions, exceptions, and attributes Special operator overloading methods Widely used standard library modules and extensions Command-line options and development tools Python idioms and hints The Python SQL Database API
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Distributed Programming Paradigms with Cryptography Applications
This monograph describes a programming methodology based upon programming paradigms and generic programs and demonstates how distributed application programs can be developed by simple substitution of data structures and sequential procedures. The author introduces generic programs for two paradigms and shows how to derive new distributed programs for several applications related to the RSA cryptosystem, including RSA enciphering and deciphering, prime number generation, and factoring.The book is well-written and easily accessible to the non-expert. The work is an appealing combination of the theory and practice of parallel computing and provides a clear introduction to the theory and practice of RSA cryptography.
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