Volume II – Advanced Deep Learning with Modern C++
Architecting, Training, and Deploying Neural Systems with PyTorch C++, Flashlight, and ONNX
Master the cutting edge of high-performance deep learning engineering with Modern C++. This advanced volume takes you far beyond Python workflows, showing you how to design, optimize, and deploy neural systems directly in C++ using PyTorch’s C++ API, Facebook’s Flashlight framework, and ONNX Runtime.
From classic architectures like CNNs and RNNs to state-of-the-art Transformers, you’ll learn how to implement, train, and fine-tune models with full control over memory, performance, and execution. The book explores mixed-precision training, model quantization, distributed training strategies, and advanced optimization techniques tailored for production-grade systems.
You’ll also build complete inference pipelines, mastering ONNX export, runtime integration, CUDA acceleration, cuDNN optimizations, and TensorRT deployment for real-time, low-latency applications. Every chapter is designed to help experienced developers engineer deep learning systems that are fast, scalable, and production ready.
Keywords: deep learning with C++, PyTorch C++ frontend, neural network engineering, ONNX Runtime, Flashlight AI, CUDA optimization, Transformer models, CNNs, RNNs, model quantization, TensorRT deployment, GPU computing, high-performance AI systems.
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Paperback. Zustand: new. Paperback. Volume II - Advanced Deep Learning with Modern C++Architecting, Training, and Deploying Neural Systems with PyTorch C++, Flashlight, and ONNXMaster the cutting edge of high-performance deep learning engineering with Modern C++. This advanced volume takes you far beyond Python workflows, showing you how to design, optimize, and deploy neural systems directly in C++ using PyTorch's C++ API, Facebook's Flashlight framework, and ONNX Runtime.From classic architectures like CNNs and RNNs to state-of-the-art Transformers, you'll learn how to implement, train, and fine-tune models with full control over memory, performance, and execution. The book explores mixed-precision training, model quantization, distributed training strategies, and advanced optimization techniques tailored for production-grade systems.You'll also build complete inference pipelines, mastering ONNX export, runtime integration, CUDA acceleration, cuDNN optimizations, and TensorRT deployment for real-time, low-latency applications. Every chapter is designed to help experienced developers engineer deep learning systems that are fast, scalable, and production ready.Keywords: deep learning with C++, PyTorch C++ frontend, neural network engineering, ONNX Runtime, Flashlight AI, CUDA optimization, Transformer models, CNNs, RNNs, model quantization, TensorRT deployment, GPU computing, high-performance AI systems. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Bestandsnummer des Verkäufers 9798273859401
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