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Python Programming and Numerical Methods
Python Programming and Numerical Methods: A Guide for Engineers and Scientists introduces programming tools and numerical methods to engineering and science students, with the goal of helping the students to develop good computational problem-solving techniques through the use of numerical methods and the Python programming language. 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 that allows students to quickly apply results in practical settings.
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Conceptual Programming with Python
Thorsten and Isaac have written this book based on a programming course we teach for Master's Students at the School of Computer Science of the University of Nottingham. The book is intended for students with little or no background in programming coming from different backgrounds educationally as well as culturally. It is not mainly a Python course but we use Python as a vehicle to teach basic programming concepts. Hence, the words conceptual programming in the title. We cover basic concepts about data structures, imperative programming, recursion and backtracking, object-oriented programming, functional programming, game development and some basics of data science.
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Foundations of Python Network Programming
Foundations of Python Network Programming, Third Edition, covers all of the classic topics found in the second edition of this book, including network protocols, network data and errors, email, server architecture, and HTTP and web applications, plus updates for Python 3.Some of the new topics in this edition include:• Extensive coverage of the updated SSL support in Python 3• How to write your own asynchronous I/O loop.• An overview of the "asyncio" framework that comes with Python 3.4.• How the Flask web framework connects URLs to your Python code.• How cross-site scripting and cross-site request forgery can be used to attack your web site, and how to protect against them.• How a full-stack web framework like Django can automate the round trip from your database to the screen and back.If you're a Python programmer who needs a deep understanding of how to use Python for network-related tasks and applications, this is the book for you. From web application developers, to systems integrators, to system administrators—this book has everything that you need to know.
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Computer Science From Scratch
Computer science can feel unapproachable for those without a formal CS education. Fun Computer Science Projects in Python pulls back that curtain, illuminating several foundational CS concepts through creative, hands-on projects. Each of the 7 projects is presented in a code-centric tutorial that gently introduces topics like interpreters, emulators, and machine learning without getting bogged down by complex theory. The projects showcase advanced Python language features and clean code principles while exploring interesting algorithms. Chapters conclude with discussions of real-world applications of the topic and proposed exercises to extend the reader's skills. Covers Python 3.x
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Handbook of Logic in Artificial Intelligence and Logic Programming: Volume 2: Deduction Methodologies
Logic is now widely recognized to be one of the foundational disciplines of computing and has found applications in virtually all aspects of the subject, from software engineering and hardware to programming languages and artificial intelligence. There is a growing need for an in-depth survey of the applications of logic in Al and computer science. The Handbook of Logic in Articial Intelligence and Logic Programming and its companion, the Handbook of Logic in Computer Science, have been created in response to this need. We see the creation of the Handbook as a combination of authoritative exposition, comprehensive survey, and fundamental research exploring the underlying themes in the various areas. The intended audience is graduate students and researchers in the areas of A1 and logic, as well as other people interested in the subject. We assume as background some mathematical sophistication. Much of the material will be of interest to logicians and mathematicians. The tables of contents of the volumes were finalized after extensive discussions between handbook authors and second readers. This book is intended for theoretical computer scientists; logicians.Volume Co-ordinator:: S
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Intermediate C Programming
Revised for a new second edition, Intermediate C Programming provides a stepping-stone for intermediate-level students to go from writing short programs to writing real programs well. It shows students how to identify and eliminate bugs, write clean code, share code with others, and use standard Linux-based tools, such as ddd and valgrind.This second edition provides expanded coverage of these topics with new material focused on software engineering, including version control and unit testing. The text enhances their programming skills by explaining programming concepts and comparing common mistakes with correct programs. It also discusses how to use debuggers and the strategies for debugging as well as studies the connection between programming and discrete mathematics.Including additional student and instructor resources available online, this book is particularly appealing as a classroom resource.
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Hands-On GPU Programming with Python and CUDA
Build real-world applications with Python 2.7, CUDA 9, and CUDA 10. We suggest the use of Python 2.7 over Python 3.x, since Python 2.7 has stable support across all the libraries we use in this book.Key FeaturesExpand your background in GPU programming—PyCUDA, scikit-cuda, and NsightEffectively use CUDA libraries such as cuBLAS, cuFFT, and cuSolverApply GPU programming to modern data science applicationsBook DescriptionHands-On GPU Programming with Python and CUDA hits the ground running: you’ll start by learning how to apply Amdahl’s Law, use a code profiler to identify bottlenecks in your Python code, and set up an appropriate GPU programming environment. You’ll then see how to “query” the GPU’s features and copy arrays of data to and from the GPU’s own memory.As you make your way through the book, you’ll launch code directly onto the GPU and write full blown GPU kernels and device functions in CUDA C. You’ll get to grips with profiling GPU code effectively and fully test and debug your code using Nsight IDE. Next, you’ll explore some of the more well-known NVIDIA libraries, such as cuFFT and cuBLAS.With a solid background in place, you will now apply your new-found knowledge to develop your very own GPU-based deep neural network from scratch. You’ll then explore advanced topics, such as warp shuffling, dynamic parallelism, and PTX assembly. In the final chapter, you’ll see some topics and applications related to GPU programming that you may wish to pursue, including AI, graphics, and blockchain.By the end of this book, you will be able to apply GPU programming to problems related to data science and high-performance computing.What you will learnLaunch GPU code directly from PythonWrite effective and efficient GPU kernels and device functionsUse libraries such as cuFFT, cuBLAS, and cuSolverDebug and profile your code with Nsight and Visual ProfilerApply GPU programming to datascience problemsBuild a GPU-based deep neuralnetwork from scratchExplore advanced GPU hardware features, such as warp shufflingWho this book is forHands-On GPU Programming with Python and CUDA is for developers and data scientists who want to learn the basics of effective GPU programming to improve performance using Python code. You should have an understanding of first-year college or university-level engineering mathematics and physics, and have some experience with Python as well as in any C-based programming language such as C, C++, Go, or Java.
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Introduction to Python and Computer Programming
This book introduces Python programming language and fundamental concepts in algorithms and computing. Its target audience includes students and engineers with little or no background in programming, who need to master a practical programming language and learn the basic thinking in computer science/programming. The main contents come from lecture notes for engineering students from all disciplines, and has received high ratings. Its materials and ordering have been adjusted repeatedly according to classroom reception. Compared to alternative textbooks in the market, this book introduces the underlying Python implementation of number, string, list, tuple, dict, function, class, instance and module objects in a consistent and easy-to-understand way, making assignment, function definition, function call, mutability and binding environments understandable inside-out. By giving the abstraction of implementation mechanisms, this book builds a solid understanding of the Python programming language.
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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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CAD Based Programming for Sensory Robots
This book contains 26 papers presented at the NATO Advanced Research Workshop on "CAD Based Programming for Sensory Robots," held in IL CIOCCa, Italy, July 4-6, 1988. CAD based robot programming is considered to be the process where CAD (Computer Based) models are used to develop robot programs. If the program is generated, at least partially, by a programmer interacting, for example, with a computer graph i c d sp i 1 ay of the robot and its workce 11 env ironment, the process is referred to as graphical off-line programming. On the other hand, if the robot program is generated automatically, for example, by a computer, then the process is referred to as automatic robot programmi ng. The key element here is the use of CAD models both for interact i ve and automat i c generat i on of robot programs. CAD based programmi ng, therefore, bri ngs together computer based model i ng and robot programmi ng and as such cuts across several discipl ines including geometric model ing, robot programming, kinematic and dynamic modeling, artificial intelligence, sensory monitoring and so-on.
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Action Programming Languages
Artificial systems that think and behave intelligently are one of the most exciting and challenging goals of Artificial Intelligence. Action Programming is the art and science of devising high-level control strategies for autonomous systems which employ a mental model of their environment and which reason about their actions as a means to achieve their goals. Applications of this programming paradigm include autonomous software agents, mobile robots with high-level reasoning capabilities, and General Game Playing. These lecture notes give an in-depth introduction to the current state-of-the-art in action programming. The main topics are knowledge representation for actions, procedural action programming, planning, agent logic programs, and reactive, behavior-based agents. The only prerequisite for understanding the material in these lecture notes is some general programming experience and basic knowledge of classical first-order logic. Table of Contents: Introduction / Mathematical Preliminaries / Procedural Action Programs / Action Programs and Planning / Declarative Action Programs / Reactive Action Programs / Suggested Further Reading
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Bayesian Analysis with Python - Third Edition
Learn the fundamentals of Bayesian modeling using state-of-the-art Python libraries, such as PyMC, ArviZ, Bambi, and more, guided by an experienced Bayesian modeler who contributes to these librariesKey Features: Conduct Bayesian data analysis with step-by-step guidanceGain insight into a modern, practical, and computational approach to Bayesian statistical modelingEnhance your learning with best practices through sample problems and practice exercisesPurchase of the print or Kindle book includes a free PDF eBook.Book Description: The third edition of Bayesian Analysis with Python serves as an introduction to the main concepts of applied Bayesian modeling using PyMC, a state-of-the-art probabilistic programming library, and other libraries that support and facilitate modeling like ArviZ, for exploratory analysis of Bayesian models; Bambi, for flexible and easy hierarchical linear modeling; PreliZ, for prior elicitation; PyMC-BART, for flexible non-parametric regression; and Kulprit, for variable selection.In this updated edition, a brief and conceptual introduction to probability theory enhances your learning journey by introducing new topics like Bayesian additive regression trees (BART), featuring updated examples. Refined explanations, informed by feedback and experience from previous editions, underscore the book's emphasis on Bayesian statistics. You will explore various models, including hierarchical models, generalized linear models for regression and classification, mixture models, Gaussian processes, and BART, using synthetic and real datasets.By the end of this book, you will possess a functional understanding of probabilistic modeling, enabling you to design and implement Bayesian models for your data science challenges. You'll be well-prepared to delve into more advanced material or specialized statistical modeling if the need arises.What You Will Learn: Build probabilistic models using PyMC and BambiAnalyze and interpret probabilistic models with ArviZAcquire the skills to sanity-check models and modify them if necessaryBuild better models with prior and posterior predictive checksLearn the advantages and caveats of hierarchical modelsCompare models and choose between alternative onesInterpret results and apply your knowledge to real-world problemsExplore common models from a unified probabilistic perspectiveApply the Bayesian framework's flexibility for probabilistic thinkingWho this book is for: If you are a student, data scientist, researcher, or developer looking to get started with Bayesian data analysis and probabilistic programming, this book is for you. The book is introductory, so no previous statistical knowledge is required, although some experience in using Python and scientific libraries like NumPy is expected.Table of Contents
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