Alvin Marsh (60 results)

Taschenbuch. Condition: Neu. Neuware - Deep Learning with PyTorch Playbook takes the fundamentals of PyTorch and moves into more advanced techniques for developing, training, evaluating, and optimizing deep learning models.This book focuses on the practical challenges that arise when moving from simple neural networks to more capable deep learning systems. Readers explore deeper architectures, convolutional neural networks, sequence models, modern training strategies, transfer learning, regularization, performance optimization, and techniques for building more reliable models.The emphasis is on understanding the complete deep learning workflow-from preparing data and designing architectures to training models efficiently, diagnosing problems, evaluating results, and improving performance.What You Will Learn- Design and implement advanced neural network architectures- Build convolutional neural networks for computer vision- Work with sequential and structured data- Apply regularization and techniques for improving generalization- Select and configure loss functions and optimizers- Use learning-rate strategies and training techniques- Apply transfer learning and pretrained models- Build more efficient and scalable training workflows- Diagnose overfitting, underfitting, and training instability- Improve model performance and computational efficiency- Evaluate deep learning models effectively- Structure practical PyTorch deep learning projectsThis book is intended for readers who have a basic understanding of PyTorch and want to develop stronger practical skills for building and optimizing modern deep learning models.…

Taschenbuch. Condition: Neu. Neuware - GitHub Copilot Automation and AI Development explores the next stage of AI-assisted software engineering: using GitHub Copilot to support intelligent development workflows, automate repetitive tasks, work with complex codebases, and build more efficient software development processes. As AI coding tools evolve beyond simple code completion, developers can use them for increasingly sophisticated tasks across the development lifecycle. This book examines how to approach AI-powered development as a workflow rather than a collection of isolated coding suggestions. You will learn how to combine Copilot's capabilities with structured instructions, project context, development tools, and automation techniques to create more productive and repeatable workflows.The book covers AI-assisted automation, coding agents, intelligent development workflows, codebase analysis, code generation, refactoring, testing, documentation, and project maintenance. You will learn how to break complex programming tasks into manageable steps, provide effective context, review AI-generated changes, and maintain appropriate control over automated development processes. You will also explore practical techniques for working with larger projects and existing repositories. These include understanding unfamiliar codebases, locating relevant files and components, analyzing dependencies, improving implementations, generating tests, updating documentation, and handling repetitive engineering tasks with AI assistance.A major focus of the book is responsible automation. Greater automation does not eliminate the need for developers to understand and evaluate the resulting code. You will learn how to establish review practices, validate AI-generated changes, consider security and reliability concerns, protect sensitive information, and maintain human oversight when applying AI to software engineering tasks. From individual development workflows to broader engineering processes, this playbook shows how GitHub Copilot can become part of a more intelligent and automated approach to building software. It is designed for developers who want to move beyond basic AI code completion and explore practical ways to integrate AI assistance into real-world development and automation. By the end of this book, you will understand how to design more capable AI-assisted development workflows, automate repetitive programming tasks, work more effectively with complex codebases, and use GitHub Copilot as a practical component of modern AI-powered software engineering.…

Taschenbuch. Condition: Neu. Neuware.

Taschenbuch. Condition: Neu. Neuware - Writing a CUDA program that works is only the beginning. Getting that program to use the GPU efficiently requires a different set of skills: measuring performance, identifying bottlenecks, understanding memory behavior, analyzing kernel execution, and making optimization decisions based on evidence rather than assumptions.CUDA Performance & Optimization For Beginners provides a practical introduction to the principles and techniques used to analyze and improve CUDA application performance.Building on fundamental CUDA programming concepts, the book introduces a systematic approach to performance optimization. You will learn how to measure GPU workloads, interpret profiling information, identify common performance bottlenecks, understand memory behavior, improve kernel execution, and evaluate whether an optimization actually produces meaningful results.You will explore how to: - Understand the fundamentals of CUDA performance- Measure and establish meaningful performance baselines- Profile CUDA applications and investigate bottlenecks- Analyze kernel execution and GPU utilization- Understand memory-access behavior and its effect on performance- Improve global, shared, and other forms of GPU memory usage- Reduce unnecessary data movement between CPU and GPU- Improve kernel organization and execution efficiency- Understand occupancy and resource utilization- Identify common causes of CUDA performance problems- Benchmark and validate optimization changes- Develop a repeatable approach to CUDA performance tuningRather than presenting optimization as a collection of isolated tricks, this book emphasizes a disciplined process: measure, analyze, optimize, and verify. This approach helps readers understand why a workload is slow and determine which changes are most likely to improve it.Whether you are learning CUDA optimization for the first time, improving existing GPU applications, or developing a stronger understanding of GPU performance engineering, this book provides a practical foundation for making CUDA workloads more efficient.Learn how to stop guessing about GPU performance-and start optimizing based on evidence.…

Taschenbuch. Condition: Neu. Neuware - GPU's have become an important part of modern computing, enabling applications to process large amounts of data through massive parallelism. CUDA provides developers with a powerful programming model for taking advantage of NVIDIA GPUs, but getting started can be challenging when concepts such as kernels, threads, blocks, grids, memory spaces, and synchronization are introduced all at once.GPU Programming with CUDA For Beginners provides a practical introduction to CUDA programming for readers who want to understand how GPU computing works and learn how to build CUDA applications from the ground up.Starting with the fundamentals of GPU architecture and parallel computing, the book progressively introduces the CUDA programming model, kernel execution, threads, thread blocks, grids, memory management, data transfer, synchronization, and common parallel programming patterns. Practical examples help connect these concepts to the way CUDA programs actually execute on a GPU.You will explore how to: - Understand the fundamentals of GPU computing and parallel execution- Set up a CUDA development environment- Write and launch CUDA kernels- Work with threads, blocks, grids, and execution hierarchies- Manage data between the CPU and GPU- Understand CUDA's different memory spaces- Use synchronization and coordinate parallel work- Design practical parallel algorithms- Identify common CUDA programming mistakes- Structure CUDA applications more effectively- Build a solid foundation for further CUDA development and optimizationThe goal is not simply to memorize CUDA functions. Instead, you will develop a practical mental model of how CUDA programs execute and how GPU-oriented programming differs from conventional CPU programming.Whether you are a programmer new to GPU computing, a software developer expanding into parallel programming, or a technical professional looking to understand CUDA, this book provides a structured starting point for building useful GPU programming skills.Build the fundamentals first. Then use them to write better GPU programs.…

Taschenbuch. Condition: Neu. Neuware - PyTorch Programming Playbook For Beginners provides a practical introduction to PyTorch for readers who want to move beyond machine learning theory and start building real models with code.The book begins with the foundations of PyTorch, explaining tensors, tensor operations, computational graphs, automatic differentiation, and the core concepts needed to understand how PyTorch works. It then progresses into neural networks, datasets, data loaders, loss functions, optimizers, training loops, validation, and model evaluation.Rather than treating PyTorch as a collection of disconnected APIs, the book develops an understanding of how the major components work together in a complete machine learning workflow. Readers learn how to prepare data, construct neural network architectures, train models, diagnose problems, improve performance, and organize PyTorch projects effectively.What You Will Learn- Understand PyTorch tensors and tensor operations- Work with CPU and GPU computation- Use automatic differentiation and autograd- Build neural networks with torch.nn- Prepare datasets and create data pipelines- Implement training and validation loops- Work with loss functions and optimizers- Monitor and evaluate model performance- Save and load trained models- Identify and troubleshoot common training problems- Build practical neural network projects- Develop a foundation for more advanced deep learning with PyTorchWritten for beginners while maintaining practical depth, this book provides the foundation needed to confidently move into more advanced deep learning techniques and real-world PyTorch development. …

Taschenbuch. Condition: Neu. Neuware - Graph Neural Networks with PyTorch For Beginners introduces the concepts and practical techniques needed to understand and build Graph Neural Networks using PyTorch-based tools and workflows.Traditional neural networks work naturally with images, sequences, and other structured data, but many real-world problems involve relationships between entities. Social networks, recommendation systems, knowledge graphs, molecular structures, fraud networks, and transportation systems can all be represented as graphs. Graph Neural Networks provide a powerful approach for learning from both the features of individual entities and the relationships connecting them.This book introduces graph concepts before progressively exploring graph representations, message passing, graph convolution, node classification, link prediction, graph classification, graph attention, graph datasets, training workflows, and practical GNN applications.What You Will Learn- Understand graphs, nodes, edges, features, and graph representations- Learn the foundations of Graph Neural Networks- Understand message-passing neural networks- Build GNN models with PyTorch-based tooling- Work with graph datasets and graph structures- Perform node classification and link prediction- Build graph-level classification models- Explore graph convolution and attention mechanisms- Train, evaluate, and improve GNN models- Handle common challenges in graph machine learning- Apply GNNs to practical machine learning problems- Develop a foundation for more advanced graph learning and GraphRAG systemsDesigned for readers who already have some familiarity with Python and basic machine learning concepts, this book provides an accessible path into graph-based deep learning while maintaining a strong emphasis on practical implementation.By the end of the book, readers will understand how Graph Neural Networks work, how to implement them, and how to approach real-world problems where relationships and connectivity are as important as the data attached to individual entities.…

Taschenbuch. Condition: Neu. Neuware - GitHub Copilot Development Playbook is a practical guide to using AI assistance throughout the modern software development lifecycle. Rather than treating GitHub Copilot as simply a tool for completing lines of code, this book explores how developers can use AI to improve coding, debugging, testing, refactoring, documentation, code review, and everyday development workflows.The book begins with the foundations of AI-assisted programming and the role of GitHub Copilot in software development. You will learn how to configure and use Copilot effectively, understand how AI-generated suggestions work, and provide the context and instructions needed to obtain more useful results. From generating functions and classes to explaining unfamiliar code, Copilot can become a practical part of your development process when used with the right techniques.You will explore practical approaches to prompting and code generation, including how to describe programming tasks clearly, refine generated code, work with existing codebases, and evaluate AI suggestions before incorporating them into a project. The book also covers debugging and troubleshooting, helping you use Copilot to investigate errors, understand unexpected behavior, identify potential solutions, and improve existing implementations.Beyond writing code, this playbook examines how Copilot can support testing, refactoring, documentation, code review, and software maintenance. You will learn how to use AI assistance without surrendering engineering judgment, while developing workflows that keep your code understandable, maintainable, secure, and aligned with project requirements.Whether you are learning a new programming language, building applications, maintaining an existing project, or looking for ways to reduce repetitive development work, this book provides practical techniques for incorporating GitHub Copilot into real development scenarios.By the end of this book, you will have a stronger understanding of AI-assisted software development and a practical framework for using GitHub Copilot to write, understand, test, debug, improve, and maintain software more effectively.…

Taschenbuch. Condition: Neu. Neuware.

Taschenbuch. Condition: Neu. Neuware.

Soft cover. Condition: Very Good. 1st Edition. Very Good, approaching Near Fine; see scans and description. Chicago: Educational Foundation for Nuclear Science, 1985. The November, 1985 issue of The Bulletin of the Atomic Scientists. Anita Kunz cover art. The famous and historic Doomsday Clock - shown on each cover since 1947, two years after the publication's inception - here shows the time to be just 3 minutes of midnight as of late 1985. That's about as close as it had been to that point. Quarto, illustrated staple-bound wraps, 57 pp. A stout Very Good; subscriber's label at front cover, faint age-toning at perimeter of the white covers; no other exterior flaws, and the interior is quite literally as new and immaculate. See scans. Firmly bound, contents bright, tipped-in cardstock offers remain untouched. A sturdy example; see all scans. Established in 1945 by biophysicist Eugene Rabinowitch and physicist Hyman Goldsmith in response to a correctly-perceived demand for nuclear information at the time by the general public, The Bulletin of the Atomic Scientists is without doubt the most historically significant non-technical publication on the subject of "'global security and public policy issues related to the dangers posed by nuclear and other weapons of mass destruction, climate change,[2] and emerging technologies and diseases". Hence - a geopolitical instrument that became more than a nuclear watchdog. Feature articles in this now-vintage 1987 issue: U.S.-Soviet Summit (11th); Weapons Design Impedes Test Ban; Spent Nuclear Fuel and Waste; Gorbachev's Policy Innovations; Sea Provocations; Les Aspin; Pugwash Council . More, of course. A more-activist-than-average issue. Contributors include Harrison Brown (Editor); William M. Arkin; John Isaacs; Hugh E. DeWitt; Gerald E. Marsh; Coit D. Blacker; Archie Brown; Alan B. Sherr; Sidney N. Graybeal; Michael Krepon; Jack N. Barkenbus; Alvin M. Weinberg; Marcelo Alonso; William F. Lawless; Paul Good. Very scarce as the original monthly softcover issue. Ships in a new, sturdy, protective box - not a bag. LPR44. …

PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condition: new. Paperback. Turn Windsurf AI into a practical development partner for building, testing, refactoring, and maintaining real-world software.AI-assisted programming becomes significantly more powerful when it is treated as an engineering workflow rather than a simple code-generation shortcut. The quality of the result depends on how projects are structured, how requirements are communicated, how AI agents receive context, and how generated code is reviewed, tested, and refined.Windsurf AI Programming Playbook takes a practical, workflow-oriented approach to AI-powered software development. Building on the fundamentals of working with Windsurf AI, this book explores techniques for managing larger development tasks, guiding AI coding agents, establishing project-specific rules, working with existing codebases, and maintaining control over increasingly complex applications. You will learn how to break software projects into manageable tasks, provide AI with the right context, improve generated implementations, investigate bugs, refactor code, create testing workflows, and integrate version control into an AI-assisted development process. The book also examines ways to make development more systematic through reusable instructions, project conventions, automation, and iterative workflows.The goal is not simply to produce code faster. It is to develop a repeatable engineering process in which AI accelerates implementation while human judgment remains responsible for architecture, correctness, security, testing, and maintainability.Inside the Book, You Will Learn How To: Structure effective Windsurf AI development workflowsGuide AI coding agents through complex programming tasksBreak large projects into specifications, tasks, and manageable iterationsProvide project context that improves AI-generated resultsCreate and maintain project-specific rules and conventionsWork efficiently with unfamiliar and existing codebasesGenerate, review, refactor, and improve application codeDiagnose bugs and use AI-assisted debugging techniquesDesign practical testing workflows for AI-generated implementationsIntegrate Git and version control into AI-assisted developmentManage changes and reduce the risk of unwanted AI modificationsAutomate repetitive development activitiesImprove consistency across larger projectsCombine human code review with AI-assisted implementationPrepare applications for deployment and continued maintenanceDevelop reliable workflows that remain useful as AI development tools evolveWho This Book Is For This playbook is intended for developers, software engineers, technical learners, and AI-assisted programmers who already understand basic development concepts and want to use Windsurf AI more effectively.It is particularly useful for developers who want to move beyond simple prompt-and-generate workflows and establish a more disciplined approach to planning, implementation, testing, refactoring, version control, and application delivery.Build faster without abandoning good engineering practices. Use AI for acceleration while keeping architecture, quality, and control at the center of development. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condition: new. Paperback. Graph Neural Networks with PyTorch For Beginners introduces the concepts and practical techniques needed to understand and build Graph Neural Networks using PyTorch-based tools and workflows.Traditional neural networks work naturally with images, sequences, and other structured data, but many real-world problems involve relationships between entities. Social networks, recommendation systems, knowledge graphs, molecular structures, fraud networks, and transportation systems can all be represented as graphs. Graph Neural Networks provide a powerful approach for learning from both the features of individual entities and the relationships connecting them.This book introduces graph concepts before progressively exploring graph representations, message passing, graph convolution, node classification, link prediction, graph classification, graph attention, graph datasets, training workflows, and practical GNN applications.What You Will LearnUnderstand graphs, nodes, edges, features, and graph representationsLearn the foundations of Graph Neural NetworksUnderstand message-passing neural networksBuild GNN models with PyTorch-based toolingWork with graph datasets and graph structuresPerform node classification and link predictionBuild graph-level classification modelsExplore graph convolution and attention mechanismsTrain, evaluate, and improve GNN modelsHandle common challenges in graph machine learningApply GNNs to practical machine learning problemsDevelop a foundation for more advanced graph learning and GraphRAG systemsDesigned for readers who already have some familiarity with Python and basic machine learning concepts, this book provides an accessible path into graph-based deep learning while maintaining a strong emphasis on practical implementation.By the end of the book, readers will understand how Graph Neural Networks work, how to implement them, and how to approach real-world problems where relationships and connectivity are as important as the data attached to individual entities. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condition: new. Paperback. GitHub Copilot Development Playbook is a practical guide to using AI assistance throughout the modern software development lifecycle. Rather than treating GitHub Copilot as simply a tool for completing lines of code, this book explores how developers can use AI to improve coding, debugging, testing, refactoring, documentation, code review, and everyday development workflows.The book begins with the foundations of AI-assisted programming and the role of GitHub Copilot in software development. You will learn how to configure and use Copilot effectively, understand how AI-generated suggestions work, and provide the context and instructions needed to obtain more useful results. From generating functions and classes to explaining unfamiliar code, Copilot can become a practical part of your development process when used with the right techniques.You will explore practical approaches to prompting and code generation, including how to describe programming tasks clearly, refine generated code, work with existing codebases, and evaluate AI suggestions before incorporating them into a project. The book also covers debugging and troubleshooting, helping you use Copilot to investigate errors, understand unexpected behavior, identify potential solutions, and improve existing implementations.Beyond writing code, this playbook examines how Copilot can support testing, refactoring, documentation, code review, and software maintenance. You will learn how to use AI assistance without surrendering engineering judgment, while developing workflows that keep your code understandable, maintainable, secure, and aligned with project requirements.Whether you are learning a new programming language, building applications, maintaining an existing project, or looking for ways to reduce repetitive development work, this book provides practical techniques for incorporating GitHub Copilot into real development scenarios.By the end of this book, you will have a stronger understanding of AI-assisted software development and a practical framework for using GitHub Copilot to write, understand, test, debug, improve, and maintain software more effectively. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condition: new. Paperback. Build, code, test, and improve real-world applications with Windsurf AI using practical AI-assisted development workflows. Software development is changing rapidly as AI coding tools make it possible to plan projects, generate code, understand existing applications, debug problems, and accelerate repetitive development tasks. But effective AI-assisted programming requires more than simply asking an AI tool to write code. You need to understand how to communicate requirements, provide useful context, review generated code, test implementations, and guide the development process.Windsurf AI Coding For Beginners provides a practical introduction to building software with Windsurf AI. The book shows you how to approach development with an AI-first mindset while maintaining the engineering practices needed to create reliable and maintainable applications. You will learn how to use natural-language instructions to work with AI coding agents, plan projects, create specifications, generate and modify code, troubleshoot errors, refactor implementations, and test applications. You will also explore project rules, codebase context, Git-based workflows, iterative development, and deployment practices that help turn AI-generated ideas into functioning software. Rather than focusing on a single temporary feature or software version, this book emphasizes fundamental workflows and transferable skills that can remain useful as AI-assisted development continues to evolve.Inside the Book, You Will Learn How To: Understand AI-assisted programming and the Windsurf AI development workflowSet up and navigate an AI-powered coding environmentUse natural language to create and modify applicationsWork effectively with AI coding agentsWrite clearer prompts, requirements, and development instructionsPlan projects using specifications, tasks, and structured workflowsGenerate, review, understand, and improve AI-assisted codeDebug errors and guide AI through troubleshootingRefactor existing implementationsCreate and use automated testsEstablish project rules and coding conventionsWork with existing codebasesUse Git and version control alongside AI-assisted developmentIterate between planning, coding, testing, and refinementPrepare applications for deployment and maintenanceRecognize the limitations of AI-generated code and apply sound engineering judgmentWho This Book Is For This book is ideal for beginners exploring AI-assisted programming, aspiring developers who want to build applications more efficiently, and experienced developers looking to incorporate AI coding agents into their workflows.Whether you are building your first AI-assisted application, experimenting with rapid prototyping, or looking for a structured introduction to AI-powered development, this book gives you a practical foundation for working productively with Windsurf AI.Learn the fundamentals, build with confidence, and develop a practical workflow for AI-assisted software development. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condition: new. Paperback. GPU's have become an important part of modern computing, enabling applications to process large amounts of data through massive parallelism. CUDA provides developers with a powerful programming model for taking advantage of NVIDIA GPUs, but getting started can be challenging when concepts such as kernels, threads, blocks, grids, memory spaces, and synchronization are introduced all at once.GPU Programming with CUDA For Beginners provides a practical introduction to CUDA programming for readers who want to understand how GPU computing works and learn how to build CUDA applications from the ground up.Starting with the fundamentals of GPU architecture and parallel computing, the book progressively introduces the CUDA programming model, kernel execution, threads, thread blocks, grids, memory management, data transfer, synchronization, and common parallel programming patterns. Practical examples help connect these concepts to the way CUDA programs actually execute on a GPU.You will explore how to: Understand the fundamentals of GPU computing and parallel executionSet up a CUDA development environmentWrite and launch CUDA kernelsWork with threads, blocks, grids, and execution hierarchiesManage data between the CPU and GPUUnderstand CUDA's different memory spacesUse synchronization and coordinate parallel workDesign practical parallel algorithmsIdentify common CUDA programming mistakesStructure CUDA applications more effectivelyBuild a solid foundation for further CUDA development and optimizationThe goal is not simply to memorize CUDA functions. Instead, you will develop a practical mental model of how CUDA programs execute and how GPU-oriented programming differs from conventional CPU programming.Whether you are a programmer new to GPU computing, a software developer expanding into parallel programming, or a technical professional looking to understand CUDA, this book provides a structured starting point for building useful GPU programming skills.Build the fundamentals first. Then use them to write better GPU programs. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condition: new. Paperback. PyTorch Programming Playbook For Beginners provides a practical introduction to PyTorch for readers who want to move beyond machine learning theory and start building real models with code.The book begins with the foundations of PyTorch, explaining tensors, tensor operations, computational graphs, automatic differentiation, and the core concepts needed to understand how PyTorch works. It then progresses into neural networks, datasets, data loaders, loss functions, optimizers, training loops, validation, and model evaluation.Rather than treating PyTorch as a collection of disconnected APIs, the book develops an understanding of how the major components work together in a complete machine learning workflow. Readers learn how to prepare data, construct neural network architectures, train models, diagnose problems, improve performance, and organize PyTorch projects effectively.What You Will LearnUnderstand PyTorch tensors and tensor operationsWork with CPU and GPU computationUse automatic differentiation and autogradBuild neural networks with torch.nnPrepare datasets and create data pipelinesImplement training and validation loopsWork with loss functions and optimizersMonitor and evaluate model performanceSave and load trained modelsIdentify and troubleshoot common training problemsBuild practical neural network projectsDevelop a foundation for more advanced deep learning with PyTorchWritten for beginners while maintaining practical depth, this book provides the foundation needed to confidently move into more advanced deep learning techniques and real-world PyTorch development. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. …

PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condition: new. Paperback. Writing a CUDA program that works is only the beginning. Getting that program to use the GPU efficiently requires a different set of skills: measuring performance, identifying bottlenecks, understanding memory behavior, analyzing kernel execution, and making optimization decisions based on evidence rather than assumptions.CUDA Performance & Optimization For Beginners provides a practical introduction to the principles and techniques used to analyze and improve CUDA application performance.Building on fundamental CUDA programming concepts, the book introduces a systematic approach to performance optimization. You will learn how to measure GPU workloads, interpret profiling information, identify common performance bottlenecks, understand memory behavior, improve kernel execution, and evaluate whether an optimization actually produces meaningful results.You will explore how to: Understand the fundamentals of CUDA performanceMeasure and establish meaningful performance baselinesProfile CUDA applications and investigate bottlenecksAnalyze kernel execution and GPU utilizationUnderstand memory-access behavior and its effect on performanceImprove global, shared, and other forms of GPU memory usageReduce unnecessary data movement between CPU and GPUImprove kernel organization and execution efficiencyUnderstand occupancy and resource utilizationIdentify common causes of CUDA performance problemsBenchmark and validate optimization changesDevelop a repeatable approach to CUDA performance tuningRather than presenting optimization as a collection of isolated tricks, this book emphasizes a disciplined process: measure, analyze, optimize, and verify. This approach helps readers understand why a workload is slow and determine which changes are most likely to improve it.Whether you are learning CUDA optimization for the first time, improving existing GPU applications, or developing a stronger understanding of GPU performance engineering, this book provides a practical foundation for making CUDA workloads more efficient.Learn how to stop guessing about GPU performance-and start optimizing based on evidence. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condition: new. Paperback. Deep Learning with PyTorch Playbook takes the fundamentals of PyTorch and moves into more advanced techniques for developing, training, evaluating, and optimizing deep learning models.This book focuses on the practical challenges that arise when moving from simple neural networks to more capable deep learning systems. Readers explore deeper architectures, convolutional neural networks, sequence models, modern training strategies, transfer learning, regularization, performance optimization, and techniques for building more reliable models.The emphasis is on understanding the complete deep learning workflow-from preparing data and designing architectures to training models efficiently, diagnosing problems, evaluating results, and improving performance.What You Will LearnDesign and implement advanced neural network architecturesBuild convolutional neural networks for computer visionWork with sequential and structured dataApply regularization and techniques for improving generalizationSelect and configure loss functions and optimizersUse learning-rate strategies and training techniquesApply transfer learning and pretrained modelsBuild more efficient and scalable training workflowsDiagnose overfitting, underfitting, and training instabilityImprove model performance and computational efficiencyEvaluate deep learning models effectivelyStructure practical PyTorch deep learning projectsThis book is intended for readers who have a basic understanding of PyTorch and want to develop stronger practical skills for building and optimizing modern deep learning models. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condition: new. Paperback. Master advanced AI-assisted development workflows for building, understanding, testing, automating, and deploying production-oriented software with Windsurf AI. AI coding agents are becoming an increasingly important part of modern software engineering. Their greatest value, however, goes beyond generating individual functions or completing isolated coding tasks. When properly integrated into a development workflow, AI can assist with project planning, codebase exploration, implementation, debugging, testing, refactoring, documentation, automation, and continuous improvement. Mastering Windsurf AI for Software Development explores advanced strategies for using Windsurf AI across the software development lifecycle. The book focuses on applying AI coding agents to more complex projects while maintaining the engineering discipline required for reliable, maintainable, and production-oriented software. You will explore advanced approaches to working with large and unfamiliar codebases, managing complex development tasks, creating effective project rules, improving AI-generated implementations, coordinating iterative development, and integrating testing and version control into AI-assisted workflows. You will also learn how to use AI more strategically for automation, code analysis, refactoring, documentation, deployment preparation, and ongoing project maintenance. The emphasis throughout the book is on engineering judgment and workflow design. AI can accelerate implementation, but developers remain responsible for evaluating generated code, identifying errors, protecting sensitive information, validating behavior, managing dependencies, and ensuring that applications meet their technical and operational requirements.Inside the Book, You Will Learn How To: Apply advanced Windsurf AI workflows across the software development lifecycleWork effectively with complex and large-scale codebasesGuide AI coding agents through multi-step development tasksImprove AI performance through project context, rules, conventions, and structured instructionsAnalyze unfamiliar applications and identify areas for improvementRefactor and modernize existing implementationsBuild comprehensive testing and validation workflowsUse AI-assisted debugging for complex application problemsIntegrate Git and disciplined change management into AI-powered developmentAutomate repetitive coding and engineering tasksImprove development consistency across larger projectsUse AI for code analysis, documentation, and maintenanceCombine AI-generated implementations with human architectural decision-makingPrepare applications for deployment and production environmentsManage the limitations, risks, and reliability challenges of AI-generated codeDevelop sustainable AI-assisted engineering practices as tools and models evolveWho This Book Is For This book is designed for developers, software engineers, technical leads, advanced learners, and experienced programmers who want to move beyond basic AI coding assistance and use Windsurf AI as part of a more sophisticated software engineering workflow.It is particularly suited to readers who want to work with larger projects, existing codebases, automated testing, structured development processes, and production-oriented applications while using AI to accelerate their work. Whether you are modernizing an existing project, building a complex application, or developing an AI-assisted engineering workflow for long-term use, this book provides practical strategies for integrating Windsurf AI into serious software development. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condition: new. Paperback. GitHub Copilot Automation and AI Development explores the next stage of AI-assisted software engineering: using GitHub Copilot to support intelligent development workflows, automate repetitive tasks, work with complex codebases, and build more efficient software development processes. As AI coding tools evolve beyond simple code completion, developers can use them for increasingly sophisticated tasks across the development lifecycle. This book examines how to approach AI-powered development as a workflow rather than a collection of isolated coding suggestions. You will learn how to combine Copilot's capabilities with structured instructions, project context, development tools, and automation techniques to create more productive and repeatable workflows.The book covers AI-assisted automation, coding agents, intelligent development workflows, codebase analysis, code generation, refactoring, testing, documentation, and project maintenance. You will learn how to break complex programming tasks into manageable steps, provide effective context, review AI-generated changes, and maintain appropriate control over automated development processes. You will also explore practical techniques for working with larger projects and existing repositories. These include understanding unfamiliar codebases, locating relevant files and components, analyzing dependencies, improving implementations, generating tests, updating documentation, and handling repetitive engineering tasks with AI assistance.A major focus of the book is responsible automation. Greater automation does not eliminate the need for developers to understand and evaluate the resulting code. You will learn how to establish review practices, validate AI-generated changes, consider security and reliability concerns, protect sensitive information, and maintain human oversight when applying AI to software engineering tasks. From individual development workflows to broader engineering processes, this playbook shows how GitHub Copilot can become part of a more intelligent and automated approach to building software. It is designed for developers who want to move beyond basic AI code completion and explore practical ways to integrate AI assistance into real-world development and automation. By the end of this book, you will understand how to design more capable AI-assisted development workflows, automate repetitive programming tasks, work more effectively with complex codebases, and use GitHub Copilot as a practical component of modern AI-powered software engineering. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…