Search results for "Python Arithmetic"
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Python for Scientific Computing and Artificial Intelligence
Python for Scientific Computing and Artificial Intelligence is split into 3 parts: in Section 1, the reader is introduced to the Python programming language and shown how Python can aid in the understanding of advanced High School Mathematics. In Section 2, the reader is shown how Python can be used to solve real-world problems from a broad range of scientific disciplines. Finally, in Section 3, the reader is introduced to neural networks and shown how TensorFlow (written in Python) can be used to solve a large array of problems in Artificial Intelligence (AI).This book was developed from a series of national and international workshops that the author has been delivering for over twenty years. The book is beginner friendly and has a strong practical emphasis on programming and computational modelling.Features:No prior experience of programming is requiredOnline GitHub repository available with codes for readers to practiceCovers applications and examples from biology, chemistry, computer science, data science, electrical and mechanical engineering, economics, mathematics, physics, statistics and binary oscillator computingFull solutions to exercises are available as Jupyter notebooks on the WebSupport MaterialGitHub Repository of Python Files and Notebooks: https://github.com/proflynch/CRC-Press/Solutions to All Exercises:Section 1: An Introduction to Python: https://drstephenlynch.github.io/webpages/SolutionsSection1.htmlSection 2: Python for Scientific Computing: https://drstephenlynch.github.io/webpages/SolutionsSection2.htmlSection 3: Artificial Intelligence: https://drstephenlynch.github.io/webpages/SolutionsSection3.html
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Python
This book is an introduction to programming concepts that uses Python 3 as the target language. It follows a practical just in time presentation – material is given to the student when it is needed. Many examples will be based on games because Python has become the language of choice for basic game development. Designed as a year 1 textbook for introduction to programming classes or for the hobbyist who wants to learn the fundamentals of programming, the text assumes no programming experience.FEATURES:Introduces programming concepts using Python 3.Covers basic computer concepts such as loops, strings, functions, files, graphics, multimedia, algorithms, classes, writing code, etc. Develops graphics, exercises, and user interface using Pygame.Includes many examples based on video game development.4 color throughout with game demos on the companion files.New chapters on parsing (14) and graphics (15).Instructor ancillaries for use as a textbook.
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Arithmetic
First published in 1927, as the second edition of a 1915 original, this book presents exercises in arithmetic aimed at school students. The text is divided into three main sections: Part I mainly covers integers; Part II covers fractions; Part III covers miscellaneous areas. Each section ends with revision papers and more exercises. This book will be of value to anyone with an interest in mathematics and the history of education.
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Python Algorithms
Python Algorithms, Second Edition explains the Python approach to algorithm analysis and design. Written by Magnus Lie Hetland, author of Beginning Python, this book is sharply focused on classical algorithms, but it also gives a solid understanding of fundamental algorithmic problem-solving techniques. The book deals with some of the most important and challenging areas of programming and computer science in a highly readable manner. It covers both algorithmic theory and programming practice, demonstrating how theory is reflected in real Python programs. Well-known algorithms and data structures that are built into the Python language are explained, and the user is shown how to implement and evaluate others.
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Pythonic Programming
Make your good Python code even better by following proven and effective pythonic programming tips. Avoid logical errors that usually go undetected by Python linters and code formatters, such as frequent data look-ups in long lists, improper use of local and global variables, and mishandled user input. Discover rare language features, like rational numbers, set comprehensions, counters, and pickling, that may boost your productivity. Discover how to apply general programming patterns, including caching, in your Python code. Become a better-than-average Python programmer, and develop self-documented, maintainable, easy-to-understand programs that are fast to run and hard to break.Python is one of the most popular and rapidly growing modern programming languages. With more than 200 standard libraries and even more third-party libraries, it reaches into the software development areas as diverse as artificial intelligence, bioinformatics, natural language processing, and computer vision. Find out how to improve your understanding of the spirit of the language by using one hundred pythonic tips to make your code safer, faster, and better documented.This programming style manual is a quick reference of helpful hints and a random source of inspiration. Choose the suitable data structures for searching and sorting jobs and become aware of how a wrong choice may cause your application to be completely ineffective. Understand global and local variables, class and instance attributes, and information-hiding techniques. Create functions with flexible interfaces. Manage intermediate computation results by caching them in files and memory to improve performance and reliability. Polish your documentation skills to make your code easy for other programmers to understand. As a bonus, discover Easter eggs cleverly planted in the standard library by its developers.Polish, secure, and speed-up your Python applications, and make them easier to maintain by following pythonic programming tips.What You Need:You will need a Python interpreter (ideally, version 3.4 or above) and the standard Python library that usually comes with the interpreter.
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Applied Math with Python
A step-by-step guide for using Python to transform abstract mathematical concepts into effective, on-the-ground scripts that solve real-world business problems Applied Math with Python: Solve Real-World Problems with Python-Based Solutions is a detailed, step-by-step guide for business professionals, analysts, and data scientists interested in using Python to perform crucial organizational tasks: optimizing inefficient supply chains, calculating probabilities, forecasting financial performance, mining customer data for new insights, and more. Author, researcher, and Assistant Professor of Finance at the University of North Florida, Blake Rayfield uses practical examples and hands-on exercises to explain how to combine concepts from optimization, probability, statistics, and other branches of mathematics with the Python language to solve difficult, common business problems. You’ll discover how marketing managers can use Python to create useful customer segments, how to model revenue growth, and how to allocate limited resources in a product launch or expansion. Inside the book: Modular, plug-and-play strategies for solving hard problems in Python in situations where a spreadsheet is inadequateInstructions for building effective, scalable Python scripts incorporating many of the most powerful Python libraries, including pandas, NumPy, matplotlib, seaborn, scikit-learn, and PlotlyStart-to-finish coverage for business professionals – from building a Python scripting environment on your local computer or in a cloud environment to designing, writing, testing, and running a functional script Perfect for entrepreneurs, analysts, managers, and professionals working in AI, data science, and finance, Applied Math with Python is an expert guide for transforming abstract mathematical concepts into useful, repeatable, scalable solutions you can put to work immediately in your team and in your organization.
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Computer Arithmetic in Practice
Computer Arithmetic in Practice - Exercises and Programming is a simple, brief introductory volume for undergraduate and graduate students at university courses interested in understanding the foundation of computers. It is focused on numeric data formats and capabilities of computer to perform basic arithmetic operations. It discusses mainly such topics as: Basic concepts of computer architecture Assembly language programming skills Data formats used to express integer and real numbers Algorithms of basic arithmetic operations Short overview of nonlinear functions evaluation Discussion on limited number representation and computer arithmetic Exercises and programming tasks.This book provides an accessible overview of common data formats used to write numbers in programming languages and how the computer performs four basic arithmetic operations from the point of view of the processor instruction set. The book is primarily didactic in nature, therefore the theoretical information is enriched with many numerical examples and exercises to be solved using a "sheet of paper and a pencil". Answers are provided for most of the tasks. The theoretical discussed issues are illustrated by listings of algorithms presenting the way hot to implement arithmetic operations in low level language. taking. It allows to develop the skills of optimal programming taking into considerations the computer architecture and limitations. Creating software using low level language programming, despite the initial difficulties, gives the ability to control the code and create efficient applications. This allows for effective consolidation of knowledge and acquisition of practical skills, required at the stage of education, mainly a specialist in the field of information technology, electronics, telecommunications, other related disciplines, or at the level of general education with introduction to information technology. It may be also useful for engineers interested in their own professional development and teachers as well.Sławomir Gryś is a university professor at Częstochowa University of Technology, Poland. He has conducted many courses focused on analog electronics, logical devices, foundations of computer architecture and organization, low-level programming techniques in assembly and C languages for embedded systems, image processing and recognition. He is the author or co-author of several scientific monographs, book chapters, academic textbook, patent and more than 60 papers in journals and domestic and international conference proceedings in Poland, Germany, Canada and Australia.
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Computational Probability with Python
Probability theory provides the foundation for understanding uncertainty in fields ranging from finance and insurance to engineering and data science. This textbook is designed for undergraduate actuarial students seeking a clear, practical, and mathematically sound introduction to the subject. Requiring only a first course in calculus, it develops core ideas in a structured and accessible way, making it suitable for a one-semester course as well as for independent study.What sets this book apart is its balanced approach between intuition and rigor. Rather than relying heavily on measure-theoretic formalism or focusing solely on computational techniques, it introduces essential concepts—such as probability spaces—in a gradual and approachable manner. By combining theoretical development with computational tools, the text bridges the gap between abstract understanding and real-world application, preparing students for both academic study and professional practice.Key features:A balanced presentation that introduces foundational concepts of probability without heavy measure-theoretic machineryClear, step-by-step development starting from set theory and building toward probability models and applicationsNumerous practice questions adapted from Society of Actuaries Probability (P) exam materialsSelected solutions with detailed explanations to support self-study and deeper understandingIntegrated Python simulations in every section to illustrate concepts and verify resultsAccompanying Jupyter notebooks available online for interactive learning and experimentationBy combining mathematical rigor, practical exercises, and modern computational tools, this book offers a solid introduction to probability theory. It is ideal for actuarial students preparing for professional exams, while also serving as a valuable resource for students in related fields who seek a solid and engaging foundation in probability.
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