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PyTorch for C and C++ Developers: Professional Machine Learning Solutions

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Chapter 1 – Getting Started with PyTorch C++ (LibTorch): Install LibTorch, configure CMake, and verify your environment for seamless development.
Chapter 2 – PyTorch Tensors and Data Operations in C++: Create, manipulate, and batch data with efficient tensor code.
Chapter 3 – Building Neural Networks with PyTorch C++ API: Define custom modules, implement feedforward, CNN, and RNN architectures, and manage parameters.
Chapter 4 – Training Workflows and Optimization: Write professional training loops, track validation metrics, and integrate callbacks for logging and checkpoints.
Chapter 5 – Saving, Loading, and Deploying Models: Script, serialize, and serve your models—including loading Python-trained TorchScript modules in C++.
Chapter 6 – Advanced Topics and Best Practices: Leverage CUDA, multithreading, memory-management tricks, and profiling to squeeze out every drop of performance.
Chapter 7 – Real-World Projects and Integrations: Power computer vision, NLP pipelines, Qt/OpenCV GUIs, and RESTful AI APIs with complete C++ examples.
Chapter 8 – Production-Ready AI with PyTorch C++: Automate builds, monitor model health, secure your service, and scale on Kubernetes.

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PyTorch for C and C++ Developers: Professional Machine Learning Solutions

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