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R 407.00
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Object-Oriented and Mixed Programming Paradigms
The area of computer graphics is characterized by rapid evolution. New techniques in hardware and software developments, e. g. , new rendering methods, have led to new ap plications and broader acceptance of graphics in fields such as scientific visualization, multi-media applications, computer aided design, and virtual reality systems. The evolving functionality and the growing complexity of graphics algorithms and sys tems make it more difficult for the application programmer to take full advantage of these systems. Conventional programming methods are no longer suited to manage the increasing complexity, so new programming paradigms and system architectures are re quired. One important step in this direction is the introduction and use of object-oriented methods. Intuition teils us that visible graphical entities are objects, and experience has indeed shown that object-oriented software techniques are quite useful for graphics. The expressiveness of object-oriented languages compared to pure procedurallanguages gives the graphics application programmer much better support when transforming his mental intentions into computer code. Moreover, object-oriented software development is a, weil founded technology, allowing software to be built from reusable and extensible compo nents. This book contains selected, reviewed and thoroughly revised vers ions of papers submit ted to and presented at the Fourth Eurographies Workshops on Object-Oriented Graphics, held on May 9-11, 1994 in Sintra, Portugal.
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Artificial Intelligence with Python Cookbook
Work through practical recipes to learn how to solve complex machine learning and deep learning problems using PythonKey FeaturesGet up and running with artificial intelligence in no time using hands-on problem-solving recipesExplore popular Python libraries and tools to build AI solutions for images, text, sounds, and imagesImplement NLP, reinforcement learning, deep learning, GANs, Monte-Carlo tree search, and much moreBook DescriptionArtificial intelligence (AI) plays an integral role in automating problem-solving. This involves predicting and classifying data and training agents to execute tasks successfully. This book will teach you how to solve complex problems with the help of independent and insightful recipes ranging from the essentials to advanced methods that have just come out of research.Artificial Intelligence with Python Cookbook starts by showing you how to set up your Python environment and taking you through the fundamentals of data exploration. Moving ahead, you’ll be able to implement heuristic search techniques and genetic algorithms. In addition to this, you'll apply probabilistic models, constraint optimization, and reinforcement learning. As you advance through the book, you'll build deep learning models for text, images, video, and audio, and then delve into algorithmic bias, style transfer, music generation, and AI use cases in the healthcare and insurance industries. Throughout the book, you’ll learn about a variety of tools for problem-solving and gain the knowledge needed to effectively approach complex problems.By the end of this book on AI, you will have the skills you need to write AI and machine learning algorithms, test them, and deploy them for production.What you will learnImplement data preprocessing steps and optimize model hyperparametersDelve into representational learning with adversarial autoencodersUse active learning, recommenders, knowledge embedding, and SAT solversGet to grips with probabilistic modeling with TensorFlow probabilityRun object detection, text-to-speech conversion, and text and music generationApply swarm algorithms, multi-agent systems, and graph networksGo from proof of concept to production by deploying models as microservicesUnderstand how to use modern AI in practiceWho this book is forThis AI machine learning book is for Python developers, data scientists, machine learning engineers, and deep learning practitioners who want to learn how to build artificial intelligence solutions with easy-to-follow recipes. You’ll also find this book useful if you’re looking for state-of-the-art solutions to perform different machine learning tasks in various use cases. Basic working knowledge of the Python programming language and machine learning concepts will help you to work with code effectively in this book.
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Practical Programming, 3e
Classroom-tested by tens of thousands of students, this new edition of the bestselling intro to programming book is for anyone who wants to understand computer science. Learn about design, algorithms, testing, and debugging. Discover the fundamentals of programming with Python 3.6--a language that's used in millions of devices. Write programs to solve real-world problems, and come away with everything you need to produce quality code. This edition has been updated to use the new language features in Python 3.6. No programming experience required! Incremental examples show you the steps and missteps that happen while developing programs, so you know what to expect when you tackle a problem on your own. Inspired by "How to Design Programs" (HtDP), discover a five-step recipe for designing functions, which helps you learn the concepts--and becomes an integral part of writing programs. In this detailed introduction to Python and to computer programming, find out exactly what happens when your programs are executed. Work with numbers, text, big data sets, and files using real-world examples. Create and use your own data types. Make your programs reliable, work with databases, download data from the web automatically, and build user interfaces. As you use the fundamental programming tools in this book, you'll see how to document and organize your code so that you and other programmers can more easily read and understand it. This new edition takes advantage of Python 3.6's new features, including type annotations on parameters, return types and variable declarations, and changes to string formatting. Most importantly, you'll learn how to think like a professional programmer. What You Need: You'll need to download Python 3.6, available from https://python.org. With that download comes IDLE, the editor we use for writing and running Python programs. (If you use Linux, you may need to install Python 3.6 and IDLE separately.)
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Maya Programming with Python Cookbook
Master complex workflows and conquer the world with Python and MayaAbout This Book• Improve your modelling skills and reduce your scripting problems using Python in Maya• Learn to communicate with web applications using Python for easier team development• A quick and practical answer to every problem you can have whilst scripting in Maya with PythonWho This Book Is ForThis book is for Python developers who have just started scripting with Maya.What You Will Learn• Find out how to use Python scripting to automate tedious tasks• Create functional user interfaces to make scripts easy to share with others• Add new functionality to Maya via the power of scripting• Import and export arbitrary data into and out of Maya• Improve your workflow, and that of your team• Create custom create custom controls to make rigs that are easy to work with• Implement a system to render 3D assets for isometric games• Use script jobs to trigger actions automatically in response to user interaction• Open a command port to allow other applications to communicate with MayaIn DetailMaya is a 3D graphics and animation software, used to develop interactive 3D applications and games with stupendous visual effects. The Maya Programming with Python Cookbook is all about creating fast, powerful automation systems with minimum coding using Maya Python. With the help of insightful and essential recipes, this book will help you improve your modelling skills. Expand your development options and overcome scripting problems encountered whilst developing code in Maya. Right from the beginning, get solutions to complex development concerns faced when implementing as parts of build.Style and approachThis book is comprised of a set of practical recipes, grouped under specific topics, which can be referred to independently or in sequence. These recipes provide quick solutions to common problems, and cover most of the real-world scenarios that developers are likely to face when working with Maya.
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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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R 602.00
Concise Introduction to Programming in Python
A Concise Introduction to Programming in Python, Second Edition provides a hands-on and accessible introduction to writing software in Python, with no prior programming experience required.The Second Edition was thoroughly reorganized and rewritten based on classroom experience to incorporate: A spiral approach, starting with turtle graphics, and then revisiting concepts in greater depth using numeric, textual, and image data Clear, concise explanations written for beginning students, emphasizing core principles A variety of accessible examples, focusing on key concepts Diagrams to help visualize new concepts New sections on recursion and exception handling, as well as an earlier introduction of lists, based on instructor feedbackThe text offers sections designed for approximately one class period each, and proceeds gradually from procedural to object-oriented design. Examples, exercises, and projects are included from diverse application domains, including finance, biology, image processing, and textual analysis. It also includes a brief "How-To" sections that introduce optional topics students may be interested in exploring.The text is written to be read, making it a good fit in flipped classrooms. Designed for either classroom use or self-study, all example programs and solutions to odd-numbered exercises (except for projects) are available at: http://www.central.edu/go/conciseintro/.
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Architecting Intelligent Agents in Azure
Architecting Intelligent Agents in Azure takes you from your first interaction with an AI agent to deploying a fully orchestrated, production-ready system that can reason, remember, collaborate, and act. Using the Microsoft Agent Framework and Python, this book walks you through every engineering layer behind modern agentic systems, from grounding and memory to tools, semantic recall, and autonomous action. Across the chapters, you'll build Thain, an Azure-native feedback agent that evolves into a multi-agent system with the ability to retrieve knowledge, coordinate tasks, and refine its performance. With a balanced mix of architecture, hands-on code, and cloud patterns, you'll learn how Azure Cosmos DB, Azure AI Search, Azure Monitor, and serverless components come together to form intelligent, self-improving enterprise agents. Whether you're an AI engineer, Azure developer, or solution architect, this book offers a practical, end-to-end guide to building agents that can reason, recall, collaborate, and grow over time. What You Will Learn: Build a fully functioning agent using Python and the Microsoft Agent Framework Implement reasoning loops, short-term memory, and tool-based actions Add persistent memory using Azure Cosmos DB and semantic recall using Azure AI Search Create safe and governed agents with telemetry, observability, and policy enforcement Integrate external systems through tools for tickets, documents, notifications, and workflows Orchestrate collaborative multi-agent systems with shared memory Deploy agentic workloads using Azure Functions, CI/CD pipelines, and cloud-native architecture Optimize cost, scale, and performance for enterprise production environments Who This Book Is For: AI engineers and developers building real agentic systems and evaluating the Microsoft Agent Framework, vector search, and Azure AI services Cloud and Azure engineers looking to integrate AI capabilities deeply into existing or new applications Solution architects designing AI-native or AI-augmented enterprise platforms Full-stack engineers transitioning into AI engineering and wanting an end-to-end practical pathway into agentic systems
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Mastering Parallel Programming with R
Master the robust features of R parallel programming to accelerate your data science computationsAbout This Book• Create R programs that exploit the computational capability of your cloud platforms and computers to the fullest• Become an expert in writing the most efficient and highest performance parallel algorithms in R• Get to grips with the concept of parallelism to accelerate your existing R programsWho This Book Is ForThis book is for R programmers who want to step beyond its inherent single-threaded and restricted memory limitations and learn how to implement highly accelerated and scalable algorithms that are a necessity for the performant processing of Big Data. No previous knowledge of parallelism is required. This book also provides for the more advanced technical programmer seeking to go beyond high level parallel frameworks.What You Will Learn• Create and structure efficient load-balanced parallel computation in R, using R's built-in parallel package• Deploy and utilize cloud-based parallel infrastructure from R, including launching a distributed computation on Hadoop running on Amazon Web Services (AWS)• Get accustomed to parallel efficiency, and apply simple techniques to benchmark, measure speed and target improvement in your own code• Develop complex parallel processing algorithms with the standard Message Passing Interface (MPI) using RMPI, pbdMPI, and SPRINT packages• Build and extend a parallel R package (SPRINT) with your own MPI-based routines• Implement accelerated numerical functions in R utilizing the vector processing capability of your Graphics Processing Unit (GPU) with OpenCL• Understand parallel programming pitfalls, such as deadlock and numerical instability, and the approaches to handle and avoid them• Build a task farm master-worker, spatial grid, and hybrid parallel R programsIn DetailR is one of the most popular programming languages used in data science. Applying R to big data and complex analytic tasks requires the harnessing of scalable compute resources.Mastering Parallel Programming with R presents a comprehensive and practical treatise on how to build highly scalable and efficient algorithms in R. It will teach you a variety of parallelization techniques, from simple use of R's built-in parallel package versions of lapply(), to high-level AWS cloud-based Hadoop and Apache Spark frameworks. It will also teach you low level scalable parallel programming using RMPI and pbdMPI for message passing, applicable to clusters and supercomputers, and how to exploit thousand-fold simple processor GPUs through ROpenCL. By the end of the book, you will understand the factors that influence parallel efficiency, including assessing code performance and implementing load balancing; pitfalls to avoid, including deadlock and numerical instability issues; how to structure your code and data for the most appropriate type of parallelism for your problem domain; and how to extract the maximum performance from your R code running
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Deep Learning with Python
Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is often acquired by practitioners by reading source code, manuals, and posting questions on community forums, which tends to be a slow and a painful process. Deep Learning with Python allows you to ramp up to such practical know-how in a short period of time and focus more on the domain, models, and algorithms.This book briefly covers the mathematical prerequisites and fundamentals of deep learning, making this book a good starting point for software developers who want to get started in deep learning. A brief survey of deep learning architectures is also included.Deep Learning with Python also introduces you to key concepts of automatic differentiation and GPU computation which, while not central to deep learning, are critical when it comes to conducting large scale experiments. What You Will Learn Leverage deep learning frameworks in Python namely, Keras, Theano, and Caffe Gain the fundamentals of deep learning with mathematical prerequisites Discover the practical considerations of large scale experiments Take deep learning models to productionWho This Book Is ForSoftware developers who want to try out deep learning as a practical solution to a particular problem. Software developers in a data science team who want to take deep learning models developed by data scientists to production.
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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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Python Scripting for ArcGIS Pro
Unlock the power of Python in ArcGIS® Pro with this definitive, easy-to-follow guide designed for users with limited programming or scripting experience.Get started learning to write Python scripts to automate tasks in ArcGIS Pro with Python Scripting for ArcGIS Pro. This book begins with the fundamentals of Python programming and then dives into how to write useful Python scripts that work with spatial data in ArcGIS Pro. You’ll learn how to use geoprocessing tools; describe, create, and update data; and execute specialized tasks. With step-by-step instructions, practical examples, and insightful guidance, you’ll be able to write scripts that will automate and improve your ArcGIS Pro workflows.This third edition has been revised for ArcGIS Pro 3.2 and Python 3.9.18 and includes updated images; a fully updated chapter 2; and expanded chapters 4, 8, 9, and 10.The key topics you will learn include:Python fundamentalsSetting up a Python editorAutomating geoprocessing tasks using ArcPy™Exploring and manipulating spatial and tabular dataWorking with geometries using cursorsWorking with rasters and map algebraMap scriptingDebugging and error handlingHelpful points to remember, key terms, and review questions are included at the end of each chapter to reinforce your understanding of Python. Corresponding data and tutorials are available online.Whether you’re new to Python or already have some experience, Python Scripting for ArcGIS Pro is the go-to resource for learning the versatility of Python coding to solve problems and enhance productivity and efficiency in ArcGIS Pro.
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Blender Python API
Understand Blender's Python API to allow for precision 3D modeling and add-on development. Follow detailed guidance on how to create precise geometries, complex texture mappings, optimized renderings, and much more.This book is a detailed, user-friendly guide to understanding and using Blender’s Python API for programmers and 3D artists. Blender is a popular open source 3D modeling software used in advertising, animation, data visualization, physics simulation, photorealistic rendering, and more. Programmers can produce extremely complex and precise models that would be impossible to replicate by hand, while artists enjoy numerous new community-built add-ons.The Blender Python API is an unparalleled programmable visualization environment. Using the API is made difficult due to its complex object hierarchy and vast documentation. Understanding the Blender Python API clearly explains the interface. You will become familiar with data structures and low-level concepts in both modeling and rendering with special attention given to optimizing procedurally generated models. In addition, the book:Discusses modules of the API as analogs to human input modes in BlenderReviews low-level and data-level manipulation of 3D objects in Blender PythonDetails how to deploy and extend projects with external librariesProvides organized utilities of novel and mature API abstractions for general use in add-on developmentWhat You’ll LearnGenerate 3D data visualizations in Blender to better understand multivariate data and mathematical patterns.Create precision object models in Blender of architectural models, procedurally generated landscapes, atomic models, etc.Develop and distribute a Blender add-on, with special consideration given to careful development practicesPick apart Blender’s 3D viewport and Python source code to learn about API behaviorsDevelop a practical knowledge of 3D modeling and rendering conceptsHave a practical reference to an already powerful and vast APIWho This Book Is ForPython programmers with an interest in data science, game development, procedural generation, and open-source programming as well as programmers of all types with a need to generate precise 3D models. Also for 3D artists with an interest in programming or with programming experience and Blender artists regardless of programming experience.
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Exercises in Programming Style
The first edition of Exercises in Programming Style was honored as an ACM Notable Book and praised as "The best programming book of the decade." This new edition retains the same presentation but has been upgraded to Python 3, and there is a new section on neural network styles.Using a simple computational task (term frequency) to illustrate different programming styles, Exercises in Programming Style helps readers understand the various ways of writing programs and designing systems. It is designed to be used in conjunction with code provided on an online repository. The book complements and explains the raw code in a way that is accessible to anyone who regularly practices the art of programming. The book can also be used in advanced programming courses in computer science and software engineering programs.The book contains 40 different styles for writing the term frequency task. The styles are grouped into ten categories: historical, basic, function composition, objects and object interactions, reflection and metaprogramming, adversity, data-centric, concurrency, interactivity, and neural networks. The author states the constraints in each style and explains the example programs. Each chapter first presents the constraints of the style, next shows an example program, and then gives a detailed explanation of the code. Most chapters also have sections focusing on the use of the style in systems design as well as sections describing the historical context in which the programming style emerged.
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Advanced Python Scripting for ArcGIS Pro
Tackle complex spatial data tasks effortlessly with this easy-to-follow guide to writing specialized Python scripts and developing tools for spatial data in ArcGIS® Pro.Advanced Python Scripting for ArcGIS Pro follows up on the topics explained in Python Scripting for ArcGIS Pro (Esri Press, 2024) and is now updated for ArcGIS Pro 3.2.Intended for users who have a good foundation in Python, this book explores how to develop scripts into tools and notebooks to share with others, use third-party packages, and learn other more specialized tasks. By the end of this book, you’ll be confident in writing more advanced scripts, developing them into tools and notebooks, and sharing them with others.The key topics you will learn include:Creating custom functions and classesWriting specialized scripts using ArcPy™Creating Python script tools and Python toolboxesSharing scripts and toolsManaging Python packages and environmentsMigrating scripts from Python 2 to 3NumPy, Pandas, and MatplotlibCreating and using notebooksArcGIS API for Python and Jupyter NotebookHelpful points to remember, key terms, and review questions are included at the end of each chapter to reinforce your understanding of Python. Companion data and tutorials are available online.Packed with advanced techniques and practical examples, Advanced Python Scripting for ArcGIS Pro is perfect for more experienced ArcGIS Pro users who are looking to upgrade their Python skills and enhance their workflows.
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