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Synopsis
LEARN PYTORCH – 2026 Edition: Master the Creation of Deep Learning Models with Flexibility and Efficiency
This 2026 edition of LEARN PYTORCH has been fully revised and reorganized according to the TECHWRITER 2.3 protocol, focused on scalability, conceptual precision, and practical applicability in professional environments.
Aimed at students, professionals, and teams, the book covers everything from environment setup (Linux, Windows, macOS, Colab, clusters) to the implementation of advanced models, including tensor engineering, neural networks (CNNs, RNNs, Transformers, GANs), data handling (DataLoader, augmentation, normalization), distributed training, debugging, optimization, export, APIs, and multi-platform deployment.
It includes integration and practical examples with PyTorch Lightning, torchvision, torchaudio, Hugging Face, FastAI, scikit-learn, TensorBoard, ONNX, TorchServe, AWS, Azure, Google Cloud, Docker, Kubernetes, CI/CD, edge, IoT, and Prometheus.
Highlights:
- Deep learning environments: CPU, GPU, TPU
- Neural networks, datasets, DataLoader, and pipelines
- Optimizers, loss functions, and regularization
- Deployment with TorchScript, ONNX, TorchServe, APIs
- Automation, versioning, compliance, and MLOps
- Edge/IoT, quantization, pruning, compression
- Practical case studies and operational checklist
- Common error resolution and best practices
By the end, the reader will have an objective roadmap to implement, optimize, and maintain deep learning solutions with PyTorch, fully prepared for modern environments and global integrations, with a strong focus on practical results and operational mastery.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
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LEARN PYTORCH - 2026 Edition (Paperback)
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Paperback. Etat : new. Paperback. LEARN PYTORCH - 2026 Edition: Master the Creation of Deep Learning Models with Flexibility and EfficiencyThis 2026 edition of LEARN PYTORCH has been fully revised and reorganized according to the TECHWRITER 2.3 protocol, focused on scalability, conceptual precision, and practical applicability in professional environments.Aimed at students, professionals, and teams, the book covers everything from environment setup (Linux, Windows, macOS, Colab, clusters) to the implementation of advanced models, including tensor engineering, neural networks (CNNs, RNNs, Transformers, GANs), data handling (DataLoader, augmentation, normalization), distributed training, debugging, optimization, export, APIs, and multi-platform deployment.It includes integration and practical examples with PyTorch Lightning, torchvision, torchaudio, Hugging Face, FastAI, scikit-learn, TensorBoard, ONNX, TorchServe, AWS, Azure, Google Cloud, Docker, Kubernetes, CI/CD, edge, IoT, and Prometheus.Highlights: Deep learning environments: CPU, GPU, TPUNeural networks, datasets, DataLoader, and pipelinesOptimizers, loss functions, and regularizationDeployment with TorchScript, ONNX, TorchServe, APIsAutomation, versioning, compliance, and MLOpsEdge/IoT, quantization, pruning, compressionPractical case studies and operational checklistCommon error resolution and best practicesBy the end, the reader will have an objective roadmap to implement, optimize, and maintain deep learning solutions with PyTorch, fully prepared for modern environments and global integrations, with a strong focus on practical results and operational mastery. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9798246857113
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LEARN PYTORCH - 2026 Edition (Paperback)
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Paperback. Etat : new. Paperback. LEARN PYTORCH - 2026 Edition: Master the Creation of Deep Learning Models with Flexibility and EfficiencyThis 2026 edition of LEARN PYTORCH has been fully revised and reorganized according to the TECHWRITER 2.3 protocol, focused on scalability, conceptual precision, and practical applicability in professional environments.Aimed at students, professionals, and teams, the book covers everything from environment setup (Linux, Windows, macOS, Colab, clusters) to the implementation of advanced models, including tensor engineering, neural networks (CNNs, RNNs, Transformers, GANs), data handling (DataLoader, augmentation, normalization), distributed training, debugging, optimization, export, APIs, and multi-platform deployment.It includes integration and practical examples with PyTorch Lightning, torchvision, torchaudio, Hugging Face, FastAI, scikit-learn, TensorBoard, ONNX, TorchServe, AWS, Azure, Google Cloud, Docker, Kubernetes, CI/CD, edge, IoT, and Prometheus.Highlights: Deep learning environments: CPU, GPU, TPUNeural networks, datasets, DataLoader, and pipelinesOptimizers, loss functions, and regularizationDeployment with TorchScript, ONNX, TorchServe, APIsAutomation, versioning, compliance, and MLOpsEdge/IoT, quantization, pruning, compressionPractical case studies and operational checklistCommon error resolution and best practicesBy the end, the reader will have an objective roadmap to implement, optimize, and maintain deep learning solutions with PyTorch, fully prepared for modern environments and global integrations, with a strong focus on practical results and operational mastery. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9798246857113
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