Pytorch Programming Series (18 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.

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 - 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.…

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.…

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. …

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.…

Condition: New. Print on Demand.

Condition: New. Print on Demand.

Condition: New. Print on Demand.

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 our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

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 our UK warehouse or from our Australian or US warehouses, 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 our UK warehouse or from our Australian or US warehouses, depending on stock availability.…