Marsh Alvin (48 results)

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Taschenbuch. Condition: Neu. Neuware.

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 - 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.

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 - 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.

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 - 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.…