Search results for "Python Programming for Mathematics"
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GPU-Accelerated Computing with Python 3 and CUDA
Accelerate your Python code on the GPU using CUDA, Numba, and modern libraries to solve real-world problems faster and more efficiently.Key FeaturesBuild a solid foundation in CUDA with Python, from kernel design to execution and debuggingOptimize GPU performance with efficient memory access, CUDA streams, and multi-GPU scalingUse JAX, CuPy, RAPIDS, and Numba to accelerate numerical computing and machine learningCreate practical GPU applications, from PDE solvers to image processing and transformersBook DescriptionWriting high-performance Python code doesn’t have to mean switching to C++. This book shows you how to accelerate Python applications using NVIDIA’s CUDA platform and a modern ecosystem of Python tools and libraries. Aimed at researchers, engineers, and data scientists, it offers a practical yet deep understanding of GPU programming and how to fully exploit modern GPU hardware.You’ll begin with the fundamentals of CUDA programming in Python using Numba-CUDA, learning how GPUs work and how to write, execute, and debug custom GPU kernels. Building on this foundation, the book explores memory access optimization, asynchronous execution with CUDA streams, and multi-GPU scaling using Dask-CUDA. Performance analysis and tuning are emphasized throughout, using NVIDIA Nsight profilers.You’ll also learn to use high-level GPU libraries such as JAX, CuPy, and RAPIDS to accelerate numerical Python workflows with minimal code changes. These techniques are applied to real-world examples, including PDE solvers, image processing, physical simulations, and transformer models.Written by experienced GPU practitioners, this hands-on guide emphasizes reproducible workflows using Python 3.10+, CUDA 12.3+, and tools like the Pixi package manager. By the end, you’ll have future-ready skills for building scalable GPU applications in Python.What you will learnUnderstand GPU execution, parallelism, and the CUDA programming modelWrite, launch, and debug custom CUDA kernels in Python with CUDAProfile GPU code with NVIDIA Nsight and optimize memory accessUse CUDA streams and async execution to overlap compute and transfersApply JAX, CuPy, and RAPIDS to numerical computing and machine learningScale GPU workloads across devices using Dask and multi-GPU strategiesAccelerate PDE solvers, simulations, and image processing on the GPUBuild, train, and run a transformer model from scratch on the GPUWho this book is forPython developers, (data) scientists, engineers, and researchers looking to accelerate numerical computations without switching to low-level languages. This book is ideal for those with experience in scientific Python (NumPy, Pandas, SciPy) and a basic understanding of computing fundamentals who want deeper control over performance in GPU environments.
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Mathematical Programming with Data Perturbations II, Second Edition
This book presents theoretical results, including an extension of constant rank and implicit function theorems, continuity and stability bounds results for infinite dimensional problems, and the interrelationship between optimal value conditions and shadow prices for stable and unstable programs.
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Oxford Mathematics Primary Years Programme Teacher Book 1
Providing comprehensive coverage of the PYP Mathematics Scope and Sequence, these Teacher Books provide a wealth of additional teacher support to accompany the Oxford Mathematics Primary Years Programme Student and Practice and Mastery Books.· Cover the five strands of Number, Pattern and Function, Measurement, Shape and Space, and Data Handling using the PYP methodology of constructing, transferring and applying meaning · Measure progress using pre- and post-assessments· Plan individual, group and class activities thanks to extensive teaching support, including planning suggestions, worksheets and answers· Support and extend learning via reinforcement activities
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Introduction to Programming and Numerical Methods in MATLAB
An elementary first course for students in mathematics and engineeringPractical in approach: examples of code are provided for students to debug, and tasks – with full solutions – are provided at the end of each chapterIncludes a glossary of useful terms, with each term supported by an example of the syntaxes commonly encountered
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Oxford Mathematics Primary Years Programme Practice and Mastery Book 6
Provide opportunities to consolidate mathematical knowledge and skills and help learners to grow in confidence and independence with this colourful, inquiry-based Practice and Mastery Book.· Cover the five strands of Number, Pattern and Function, Measurement, Shape and Space, and Data Handling· Provide comprehensive coverage of the PYP Mathematics Scope and Sequence· Support and extend learning through reinforcement activities· Encourage learners to explore and apply knowledge using real-world problems
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Basic Mathematical Programming Theory
The subject of (static) optimization, also called mathematical programming, is one of the most important and widespread branches of modern mathematics, serving as a cornerstone of such scientific subjects as economic analysis, operations research, management sciences, engineering, chemistry, physics, statistics, computer science, biology, and social sciences. This book presents a unified, progressive treatment of the basic mathematical tools of mathematical programming theory. The authors expose said tools, along with results concerning the most common mathematical programming problems formulated in a finite-dimensional setting, forming the basis for further study of the basic questions on the various algorithmic methods and the most important particular applications of mathematical programming problems. This book assumes no previous experience in optimization theory, and the treatment of the various topics is largely self-contained. Prerequisites are the basic tools of differential calculus for functions of several variables, the basic notions of topology and of linear algebra, and the basic mathematical notions and theoretical background used in analyzing optimization problems. The book is aimed at both undergraduate and postgraduate students interested in mathematical programming problems but also those professionals who use optimization methods and wish to learn the more theoretical aspects of these questions.
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Introduction To Computational Mathematics (2nd Edition)
This unique book provides a comprehensive introduction to computational mathematics, which forms an essential part of contemporary numerical algorithms, scientific computing and optimization. It uses a theorem-free approach with just the right balance between mathematics and numerical algorithms. This edition covers all major topics in computational mathematics with a wide range of carefully selected numerical algorithms, ranging from the root-finding algorithm, numerical integration, numerical methods of partial differential equations, finite element methods, optimization algorithms, stochastic models, nonlinear curve-fitting to data modelling, bio-inspired algorithms and swarm intelligence. This book is especially suitable for both undergraduates and graduates in computational mathematics, numerical algorithms, scientific computing, mathematical programming, artificial intelligence and engineering optimization. Thus, it can be used as a textbook and/or reference book.
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Methods and Models in Mathematical Programming
This book focuses on mathematical modeling, describes the process of constructing and evaluating models, discusses the challenges and delicacies of the modeling process, and explicitly outlines the required rules and regulations so that the reader will be able to generalize and reuse concepts in other problems by relying on mathematical logic.Undergraduate and postgraduate students of different academic disciplines would find this book a suitable option preparing them for jobs and research fields requiring modeling techniques. Furthermore, this book can be used as a reference book for experts and practitioners requiring advanced skills of model building in their jobs.
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Advanced Python Programming
Write fast, robust, and highly reusable applications using Python's internal optimization, state-of-the-art performance-benchmarking tools, and cutting-edge librariesKey FeaturesBenchmark, profile, and accelerate Python programs using optimization toolsScale applications to multiple processors with concurrent programmingMake applications robust and reusable using effective design patternsBook DescriptionPython's powerful capabilities for implementing robust and efficient programs make it one of the most sought-after programming languages.In this book, you'll explore the tools that allow you to improve performance and take your Python programs to the next level.This book starts by examining the built-in as well as external libraries that streamline tasks in the development cycle, such as benchmarking, profiling, and optimizing. You'll then get to grips with using specialized tools such as dedicated libraries and compilers to increase your performance at number-crunching tasks, including training machine learning models.The book covers concurrency, a major solution to making programs more efficient and scalable, and various concurrent programming techniques such as multithreading, multiprocessing, and asynchronous programming.You'll also understand the common problems that cause undesirable behavior in concurrent programs.Finally, you'll work with a wide range of design patterns, including creational, structural, and behavioral patterns that enable you to tackle complex design and architecture challenges, making your programs more robust and maintainable.By the end of the book, you'll be exposed to a wide range of advanced functionalities in Python and be equipped with the practical knowledge needed to apply them to your use cases.What you will learnWrite efficient numerical code with NumPy, pandas, and XarrayUse Cython and Numba to achieve native performanceFind bottlenecks in your Python code using profilersOptimize your machine learning models with JAXImplement multithreaded, multiprocessing, and asynchronous programsSolve common problems in concurrent programming, such as deadlocksTackle architecture challenges with design patternsWho this book is forThis book is for intermediate to experienced Python programmers who are looking to scale up their applications in a systematic and robust manner. Programmers from a range of backgrounds will find this book useful, including software engineers, scientific programmers, and software architects.
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Asynchronous Programming in Python
Take a practical journey through the core principles, concepts, and applications of asynchronous programming in Python across real-world environments and technologies.Key FeaturesUnderstand core principles and theory behind async programming in PythonMeasure the impact of async techniques in practical, real-world use casesApply async patterns in software design and data-oriented architecturesBook DescriptionAsynchronous programming is one of the most effective but often misunderstood techniques for building fast, scalable, and responsive systems in Python. While it can significantly improve performance, efficiency, and sustainability, using async without a clear understanding of its trade-offs can lead to fragile designs and hard-to-debug issues.This book offers a structured approach to applying asynchronous programming in Python. It begins with a conceptual framework to help you distinguish between synchronous and asynchronous execution models, and shows how async relates to other concurrency strategies such as multithreading and multiprocessing. From there, you will explore the core tools available for building async applications in Python.You will also learn how to measure the impact of async programming in practical scenarios, profile and debug asynchronous code, and evaluate performance improvements using real-world metrics. The final chapters focus on applying async techniques to common cloud-based systems, such as web frameworks, database interactions, and data-pipelines tools.Designed for developers looking to apply async programming with confidence, this book blends real-world examples with core concepts to help you write efficient, maintainable Python code.What you will learnUse generators, coroutines and async/await to build scalable Python functionsExplore event loops to manage concurrency and orchestrate async flowCompare concurrency models to choose the right async strategyOptimize I/O-intensive programs to improve system throughput and efficiencyBuild async services using real-world APIs and popular Python librariesApply structured concurrency and design patterns for cleaner async designTest and debug async Python code to ensure reliability and stabilityWho this book is forThis book will help Python developers who want to understand and apply the asynchronous programming model in application development, data analysis, and orchestration scenarios. Junior developers, data engineers,, and tech leads will also benefit from the application design examples.
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Metaprogramming with Python
A practical approach to metaprogramming with real-world examples that enables the development of advanced frameworks, libraries, and applications using PythonKey FeaturesLearn applied metaprogramming through a simple step-by-step approachWork with easily understandable examples and explanations that take you deep into the theory of metaprogrammingGet practical experience in writing reusable code with real-world examplesBook DescriptionEffective and reusable code makes your application development process seamless and easily maintainable. With Python, you will have access to advanced metaprogramming features that you can use to build high-performing applications. The book starts by introducing you to the need and applications of metaprogramming, before navigating the fundamentals of object-oriented programming. Next, you will learn about simple decorators, work with metaclasses, and later focus on introspection and reflection. You'll also delve into generics and typing before defining templates for algorithms. As you progress, you will understand your code using abstract syntax trees and explore method resolution order. This Python book also shows you how to create your own dynamic objects before structuring the objects through design patterns. Finally, you will learn simple code-generation techniques along with discovering best practices and eventually building your own applications. By the end of this learning journey, you'll have acquired the skills and confidence you need to design and build reusable high-performing applications that can solve real-world problems.What you will learnUnderstand the programming paradigm of metaprogramming and its needRevisit the fundamentals of object-oriented programmingDefine decorators and work with metaclassesEmploy introspection and reflection on your codeApply generics, typing, and templates to enhance your codeGet to grips with the structure of your code through abstract syntax trees and the behavior through method resolution orderCreate dynamic objects and generate dynamic codeUnderstand various design patterns and best practicesWho this book is forIf you are an intermediate-level Python programmer looking to enhance your coding skills by developing reusable and advanced frameworks, then this book is for you. Basic knowledge of Python programming will help you get the most out of this learning journey.
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Quick Python Book, Fourth Edition
A fast-paced introduction to Python for intermediate developers–now with coverage of generative AI!For over 25 years, The Quick Python Book has been one of the best Python books money can buy. It concisely covers programming basics, while introducing Python's comprehensive standard library and unique features in depth and detail. In this fourth edition, you'll find new coverage of AI coding tools like Copilot and Google's Colaboratory (Colab), and develop a mindset that can make the most of AI. The Quick Python Book, Fourth Edition includes: Python syntax, data structures, and best practicesPython as an object oriented languageCommon Python librariesBasic data handling with PythonUsing AI code generation tools with Python Whether you're new to Python or looking to advance your basic skills, The Quick Python Book, Fourth Edition will get you writing effective Python code fast. Python authority and former Chair of the Python Software Foundation Board or Directors Naomi Ceder has returned to author this extensively revised fourth edition. With the personal touch of a skilled teacher, Naomi beautifully balances details of the language with the insights and advice you need to handle any task. About the technology: System automation. High-performance web apps. Cloud and back-end services. Cutting edge AI. No matter what you're building, it pays to know how to read and write Python! The Quick Python Book has helped over 100,000 developers get up to speed with the Python programming language. This revised Fourth Edition, fully updated for Python 3.13, explores the latest features and libraries and shows you how to code smarter with AI tools like ChatGPT.
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Python GUI Programming Cookbook
Over 90 recipes to help you develop widgets, forms, layouts, charts, and much more using the latest features of Python 3Key FeaturesUse object-oriented programming to develop impressive GUIs in PythonCreate interesting charts to visually represent data using MatplotlibDevelop GUIs with the latest versions of tkinter, PyQt5, and wxPython frameworksBook DescriptionPython is a multi-domain, interpreted programming language that is easy to learn and implement. With its wide support for frameworks to develop GUIs, you can build interactive and beautiful GUI-based applications easily using Python. This third edition of Python GUI Programming Cookbook follows a task-based approach to help you create effective GUIs with the smallest amount of code. Every recipe in this book builds upon the last to create an entire, real-life GUI application. These recipes also help you solve problems that you might encounter while developing GUIs. This book mainly focuses on using Python’s built-in tkinter GUI framework. You'll learn how to create GUIs in Python using simple programming styles and object-oriented programming (OOP). As you add more widgets and expand your GUI, you will learn how to connect to networks, databases, and graphical libraries that greatly enhance the functionality of your GUI. You’ll also learn how to use threading to ensure that your GUI doesn't become unresponsive. Toward the end, you’ll learn about the versatile PyQt GUI framework, which comes along with its own visual editor that allows you to design GUIs using drag and drop features. By the end of the book, you’ll be an expert in designing Python GUIs and be able to develop a variety of GUI applications with ease.What you will learnCreate amazing GUIs with Python's built-in tkinter moduleCustomize GUIs using layout managers to arrange GUI widgetsAdvance from the typical waterfall coding style to an OOP style using PythonDevelop beautiful charts using the free Matplotlib Python moduleUse threading in a networked environment to make GUIs responsiveDiscover ways to connect GUIs to a MySQL databaseUnderstand how unit tests can be created and internationalize GUIDelve into the world of GUI creation using PyQt5Who this book is forIf you’re a programmer or developer looking to enhance your Python skills by writing powerful GUI applications, this book is for you. Familiarity with the Python programming language is necessary to get the most out of the book.
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Student's Guide to the Study, Practice, and Tools of Modern Mathematics
A Student‘s Guide to the Study, Practice, and Tools of Modern Mathematics provides an accessible introduction to the world of mathematics. It offers tips on how to study and write mathematics as well as how to use various mathematical tools, from LaTeX and Beamer to Mathematica and Maple to MATLAB and R. Along with a color insert, the text includes exercises and challenges to stimulate creativity and improve problem solving abilities. The first section of the book covers issues pertaining to studying mathematics. The authors explain how to write mathematical proofs and papers, how to perform mathematical research, and how to give mathematical presentations. The second section focuses on the use of mathematical tools for mathematical typesetting, generating data, finding patterns, and much more. The text describes how to compose a LaTeX file, give a presentation using Beamer, create mathematical diagrams, use computer algebra systems, and display ideas on a web page. The authors cover both popular commercial software programs and free and open source software, such as Linux and R. Showing how to use technology to understand mathematics, this guide supports students on their way to becoming professional mathematicians. For beginning mathematics students, it helps them study for tests and write papers. As time progresses, the book aids them in performing advanced activities, such as computer programming, typesetting, and research.
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Python Scripting for Computational Science
With a primary focus on examples and applications of relevance to computational scientists, this brilliantly useful book shows computational scientists how to develop tailored, flexible, and human-efficient working environments built from small scripts written in the easy-to-learn, high-level Python language. All the tools and examples in this book are open source codes. This third edition features lots of new material. It is also released after a comprehensive reorganization of the text. The author has inserted improved examples and tools and updated information, as well as correcting any errors that crept in to the first imprint.
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Mastering Python for Finance
Take your financial skills to the next level by mastering cutting-edge mathematical and statistical financial applicationsKey FeaturesExplore advanced financial models used by the industry and ways of solving them using PythonBuild state-of-the-art infrastructure for modeling, visualization, trading, and moreEmpower your financial applications by applying machine learning and deep learningBook DescriptionThe second edition of Mastering Python for Finance will guide you through carrying out complex financial calculations practiced in the industry of finance by using next-generation methodologies. You will master the Python ecosystem by leveraging publicly available tools to successfully perform research studies and modeling, and learn to manage risks with the help of advanced examples.You will start by setting up your Jupyter notebook to implement the tasks throughout the book. You will learn to make efficient and powerful data-driven financial decisions using popular libraries such as TensorFlow, Keras, Numpy, SciPy, and sklearn. You will also learn how to build financial applications by mastering concepts such as stocks, options, interest rates and their derivatives, and risk analytics using computational methods. With these foundations, you will learn to apply statistical analysis to time series data, and understand how time series data is useful for implementing an event-driven backtesting system and for working with high-frequency data in building an algorithmic trading platform. Finally, you will explore machine learning and deep learning techniques that are applied in finance.By the end of this book, you will be able to apply Python to different paradigms in the financial industry and perform efficient data analysis.What you will learnSolve linear and nonlinear models representing various financial problemsPerform principal component analysis on the DOW index and its componentsAnalyze, predict, and forecast stationary and non-stationary time series processesCreate an event-driven backtesting tool and measure your strategiesBuild a high-frequency algorithmic trading platform with PythonReplicate the CBOT VIX index with SPX options for studying VIX-based strategiesPerform regression-based and classification-based machine learning tasks for predictionUse TensorFlow and Keras in deep learning neural network architectureWho this book is forIf you are a financial or data analyst or a software developer in the financial industry who is interested in using advanced Python techniques for quantitative methods in finance, this is the book you need! You will also find this book useful if you want to extend the functionalities of your existing financial applications by using smart machine learning techniques. Prior experience in Python is required.
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Python For ArcGIS
This book introduces Python scripting for geographic information science (GIS) workflow optimization using ArcGIS. It builds essential programming skills for automating GIS analysis. Over 200 sample Python scripts and 175 classroom-tested exercises reinforce the learning objectives. Readers will learn to: • Write and run Python in the ArcGIS Python Window, the PythonWin IDE, and the PyScripter IDE • Work with Python syntax and data types • Call ArcToolbox tools, batch process GIS datasets, and manipulate map documents using the arcpy package • Read and modify proprietary and ASCII text GIS data • Parse HTML web pages and KML datasets • Create Web pages and fetch GIS data from Web sources. • Build user-interfaces with the native Python file dialog toolkit or the ArcGIS Script tools and PyToolboxes Python for ArcGIS is designed as a primary textbook for advanced-level students in GIS. Researchers, government specialists and professionals working in GIS will also find this book useful as a reference.
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