Tired of wrestling with Python prototypes that fall short in production? Ever wondered how to harness PyTorch's power in C++ for real-time, high-performance AI? PyTorch for C and C++ Developers : Professional Machine Learning Solutions shows you how to take Python-style deep learning into the realm of native code. You'll set up LibTorch, master tensors and data pipelines, build and train custom neural networks, and deploy rock-solid inference services-all with clear, runnable examples and expert tips. What Sets This ...
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Tired of wrestling with Python prototypes that fall short in production? Ever wondered how to harness PyTorch's power in C++ for real-time, high-performance AI? PyTorch for C and C++ Developers : Professional Machine Learning Solutions shows you how to take Python-style deep learning into the realm of native code. You'll set up LibTorch, master tensors and data pipelines, build and train custom neural networks, and deploy rock-solid inference services-all with clear, runnable examples and expert tips. What Sets This Book Apart? This isn't a shallow overview. Each chapter delivers hands-on guidance and insights you can apply immediately: 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. You'll gain the confidence to write production-grade C++ code, achieve millisecond-level inference, and maintain robust CI/CD pipelines. Call-to-Action Ready to move beyond prototypes and build AI systems that run at native speed? Get your copy today and start writing the next generation of high-performance, professional-grade machine learning applications in C++!
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