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Python Challenges
Augment your knowledge of Python with this entertaining learning guide, which features 100 exercises and programming puzzles and solutions. Python Challenges will help prepare you for your next exam or a job interview, and covers numerous practical topics such as strings, data structures, recursion, arrays, and more.Each topic is addressed in its own separate chapter, starting with an introduction to the basics and followed by 10 to 15 exercises of various degrees of difficulty, helping you to improve your programming skills effectively. Detailed sample solutions, including the algorithms used for all tasks, are included to maximize your understanding of each area. Author Michael Inden also describes alternative solutions and analyzes possible pitfalls and typical errors.Three appendices round out the book: the first covers the Python command line interpreter, which is often helpful for trying out the code snippets and examples in the book, followed by an overview of Pytest for unit testing and checking the solutions. The last explains the O notation for estimating performance.After reading this book, you'll be prepared to take the next step in your career or tackle your next personal project. All source code is freely available for download via the Apress website.What You Will Learn Improve your Python knowledge by solving enjoyable but challenging programming puzzles Solve mathematical problems, recursions, strings, arrays and moreManage data processing and data structures like lists, sets, mapsHandle advanced recursion as well as binary trees, sorting and searchingGamify key fundamentals for fun and easier reinforcement Who this book is for:Programmers, software developers who are either professionals or makers, as well as students and teachers. At least some prior experience with the Python programming is recommended.
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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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Learn Python 3 the Hard Way
You Will Learn Python 3! Zed Shaw has perfected the world’s best system for learning Python 3. Follow it and you will succeed—just like the millions of beginners Zed has taught to date! You bring the discipline, commitment, and persistence; the author supplies everything else. In Learn Python 3 the Hard Way, you’ll learn Python by working through 52 brilliantly crafted exercises. Read them. Type their code precisely. (No copying and pasting!) Fix your mistakes. Watch the programs run. As you do, you’ll learn how a computer works; what good programs look like; and how to read, write, and think about code. Zed then teaches you even more in 5+ hours of video where he shows you how to break, fix, and debug your code—live, as he’s doing the exercises. Install a complete Python environmentOrganize and write codeFix and break codeBasic mathematicsVariablesStrings and textInteract with usersWork with filesLooping and logicData structures using lists and dictionariesProgram designObject-oriented programmingInheritance and compositionModules, classes, and objectsPython packagingAutomated testingBasic game developmentBasic web development It’ll be hard at first. But soon, you’ll just get it—and that will feel great! This course will reward you for every minute you put into it. Soon, you’ll know one of the world’s most powerful, popular programming languages. You’ll be a Python programmer. This Book Is Perfect For Total beginners with zero programming experienceJunior developers who know one or two languagesReturning professionals who haven’t written code in yearsSeasoned professionals looking for a fast, simple, crash course in Python 3
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Applied Mathematics with Open-Source Software
Applied Mathematics with Open-source Software: Operational Research Problems with Python and R is aimed at a broad segment of readers who wish to learn how to use open-source software to solve problems in applied mathematics. The book has an innovative structure with 4 sections of two chapters covering a large range of applied mathematical techniques: probabilistic modelling, dynamical systems, emergent behaviour and optimisation. The pairs of chapters in each section demonstrate different families of solution approaches. Each chapter starts with a problem, gives an overview of the relevant theory, shows a solution approach in R and in Python, and finally gives wider context by including a number of published references. This structure will allow for maximum accessibility, with minimal prerequisites in mathematics or programming as well as giving the right opportunities for a reader wanting to delve deeper into a particular topic.Features An excellent resource for scholars of applied mathematics and operational research, and indeed any academics who want to learn how to use open-source software. Offers more general and accessible treatment of the subject than other texts, both in terms of programming language but also in terms of the subjects considered. The R and Python sections purposefully mirror each other so that a reader can read only the section that interests them. An accompanying open-source repository with source files and further examples is posted online at https://bit.ly/3kpoKSd.
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Quantum Chemistry and Computing for the Curious
Learn quantum chemistry and computing with updated Qiskit workflows, noise-aware simulations, and quantum machine learning techniques using Python, with hands-on learning, turning complex concepts into practical skills.Key FeaturesExplore new chapters on quantum machine learning and advanced algorithmsApply noise-aware quantum chemistry with error mitigation techniquesWork with updated Qiskit workflows and modern Python examplesBook DescriptionBuild a solid foundation in quantum chemistry and quantum computing using modern tools, updated frameworks, and practical Python examples. In its second edition, this book enhances the original with new chapters and refreshed workflows aligned with the latest advancements.You begin with core principles of quantum mechanics, quantum information, and molecular Hamiltonians, updated to incorporate the latest Qiskit capabilities. You then implement hybrid algorithms such as VQE using improved Python workflows. New to this edition, you will explore noise aware quantum chemistry, including error mitigation techniques and optimizer behavior in realistic simulations. The book also introduces quantum machine learning for molecular prediction and a new generation of quantum algorithms for chemistry, including Quantum Phase Estimation (QPE), Quantum Imaginary Time Evolution (QITE), quantum Lanczos and subspace methods, and sampling based approaches such as Sample based Quantum Diagonalization (SQD), Sample based Krylov Quantum Diagonalization (SKQD), and SqDRIFT, which combines SKQD with a qDRIFT style randomized compilation of the Hamiltonian propagator.By the end of this book, you will be able to model molecular systems and apply modern quantum techniques with confidence.What you will learnUnderstand quantum mechanics and molecular systemsBuild quantum circuits using Qiskit and PythonImplement VQE for molecular energy estimationApply error mitigation in noisy quantum systemsUse optimizers for stable hybrid quantum workflowsDevelop quantum machine learning models for moleculesExplore advanced algorithms beyond VQEWho this book is forProfessionals interested in chemistry and computer science at the early stages of learning or interested in a career of quantum computational chemistry and quantum computing, including advanced high school and college students. Helpful to have high school level chemistry, mathematics (algebra), and programming. An introductory level of understanding Python is sufficient to read the code presented to illustrate quantum chemistry and computing.
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Python Book
The Python Book Discover the power of one of the fastest growing programming languages in the world with this insightful new resource The Python Book delivers an essential introductory guide to learning Python for anyone who works with data but does not have experience in programming. The author, an experienced data scientist and Python programmer, shows readers how to use Python for data analysis, exploration, cleaning, and wrangling. Readers will learn what in the Python language is important for data analysis, and why. The Python Book offers readers a thorough and comprehensive introduction to Python that is both simple enough to be ideal for a novice programmer, yet robust to be useful for those more experienced in the language. The book assists budding programmers to gradually increase their skills as they move through the book, always with an understanding of what they are covering and why it is useful. Used by major companies like Google, Facebook, Instagram, Spotify, and more, Python promises to remain central to the programming landscape for years to come. Containing a thorough discussion of Python programming topics like variables, equalities and comparisons, tuple and dictionary data types, while and for loops, and if statements, readers will also learn: How to use highly useful Python programming libraries, including Pandas and MatplotlibHow to write Python functions and classesHow to write and use Python scriptsTo deal with different data types within Python Perfect for statisticians, computer scientists, software programmers, and practitioners working in private industry and medicine, The Python Book will also be of interest to students in any of the aforementioned fields. As it assumes no programming experience or knowledge, the book is ideal for those who work with data and want to learn to use Python to enhance their work.
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Python Bootcamp
Python Bootcamp is a concise guide to becoming proficient in the Python programming language. This book enables you to write and comprehend engaging Python programs quickly. The book is organized into three main parts. Part I: Foundations lays the groundwork for your programming journey. You'll begin with setting up your programming environment and introducing essential programming concepts. Next, you'll explore strings, integers, floats, booleans, lists, tuples, and dictionaries, all while learning to create interactive applications. Part II: Building Smart Programs equips you with the tools to enhance your coding skills. You'll learn to make decisions in your programs and learn how to repeat actions efficiently. You'll learn more complex structures and learn about Functions and Modules to create reusable code. Further, you will be introduced to error management, and how to read from and write to files. Part III: Introduction to OOP offers a brief overview of object-oriented programming. You'll explore Classes and Objects and learn about Inheritance, allowing for code reuse and organization. Each chapter is designed to be completed in short time, making this book perfect for readers who feel intimidated by lengthy, complex programming texts yet wish to learn quickly and effectively. What you will learn: Understand the fundamental features of Python 3Learn to use Jupyter Notebook with the Anaconda distribution.Discover tools that can improve your coding skills.Explore Object-Oriented Programming (OOP) with Python. Who this book is for: Software developers, and software architects. Readers with basic programming knowledge will find it quick and easy to understand.
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Expert Python Programming
Refine your Python programming skills and build professional grade applications with this comprehensive guideKey FeaturesCreate manageable code that can run in various environments with different sets of dependenciesImplement effective Python data structures and algorithms to write optimized codeDiscover the exciting new features of Python 3.7Book DescriptionPython is a dynamic programming language that's used in a wide range of domains thanks to its simple yet powerful nature. Although writing Python code is easy, making it readable, reusable, and easy to maintain is challenging. Complete with best practices, useful tools, and standards implemented by professional Python developers, the third edition of Expert Python Programming will help you overcome this challenge.The book will start by taking you through the new features in Python 3.7. You'll then learn the advanced components of Python syntax, in addition to understanding how to apply concepts of various programming paradigms, including object-oriented programming, functional programming, and event-driven programming. This book will also guide you through learning the best naming practices, writing your own distributable Python packages, and getting up to speed with automated ways of deploying your software on remote servers. You’ll discover how to create useful Python extensions with C, C++, Cython, and CFFI. Furthermore, studying about code management tools, writing clear documentation, and exploring test-driven development will help you write clean code.By the end of the book, you will have become an expert in writing efficient and maintainable Python code.What you will learnExplore modern ways of setting up repeatable and consistent development environmentsPackage Python code effectively for community and production useLearn modern syntax elements of Python programming such as f-strings, enums, and lambda functionsDemystify metaprogramming in Python with metaclassesWrite concurrent code in PythonExtend Python with code written in different languagesIntegrate Python with code written in different languagesWho this book is forThis book will appeal to you if you’re a programmer looking to take your Python knowledge to the next level by writing efficient code and learning the latest features of version 3.7 and above.
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Arithmetik in Rechenanlagen
Dieses Buch entstand aus einer Reihe von Vorlesungen und Seminaren an der Universitat Saarbrlicken. Es enthalt eine vergleichende Be- schreibung von schnellen und kostenglinstigen Algorithmen zur Reali- sierung arithmetischer Operationen in Digitalrechnern. Obwohl fast aIle Informatiker und nicht wenige Wissenschaftler an- derer Fachrichtungen mit diesen Problemen mehr oder weniger inten- siv konfrontiert werden, behandeln deutschsprachige Fachblicher mit allgemeinerer Zielsetzung diese Fragen nur am Rande oder in ver- gleichsweise kurzen Kapiteln; dies flihrt zwangslaufig zu einer Be- schrankung auf die Beschreibung der bekanntesten und einfachsten Verfahren. In der englischsprachigen Literatur gibt es zwar mehre- re ausgezeichnete Standardwerke (zu erwahnen ist hier in erster Li- nie Flores "The Logic of computer Arithmetic"), doch sind diese oft nur schwer zuganglich und meist alteren Datums; viele wichtige Re- sultate wird man dort also vergeblich suchen. Der Mangel an neueren Blichern liber Rechnerarithmetik kann nicht dar auf zurlickgeflihrt werden, daB aIle interessierenden Probleme be- reits in Standardwerken abgehandelt sind: die Anzahl und die Rele- vanz der in jlingster Zeit veroffentlichten Arbeiten beweist das Ge- genteil. Die Hauptursache daflir liegt nach meiner Ansicht vielmehr in der oft uneinheitlichen Darstellung und der zu starken Beschran- kung vieler Arbeiten auf ein bestimmtes Maschinenkonzept oder auf eine zur Zeit verfligbare bzw. in Entwicklung befindliche Baustein- serie, wodurch eine zusammenfassende Ubersicht sehr erschwert wird.
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