Overview

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.

Full Product Details

9798246857113

Table of Contents

Reviews

Author Information

Tab Content 6

Countries Available