Overview

C++ Machine Learning: Turbocharge AI Workflows with High-Performance Training, On-Device Inference & Low-Level Tuning is your definitive guide for building end-to-end AI systems that marry the raw speed of C++ with the flexibility of modern ML. From orchestrating massive distributed training jobs to squeezing deep learning models onto microcontrollers, you'll master every layer-software, hardware, and tooling-to deliver blazing-fast, production-ready solutions. What You'll Learn ✔ High-Performance Training - Leverage C++ tensor libraries and CUDA/cuDNN integrations to implement custom neural network kernels. - Scale across multi-GPU clusters with MPI, NCCL, and asynchronous pipelines for maximum throughput. - Build distributed data loaders, sharded optimizers, and gradient accumulation schemes to handle billion-parameter models. ✔ On-Device Inference - Embed optimized runtimes (ONNX Runtime, TensorRT, TVM) directly into your C++ applications. - Exploit quantization (INT8/4-bit), pruning, and graph fusion to cut latency and memory footprint. - Use SIMD/NEON intrinsics and custom microkernels to achieve real-time inference on CPUs and edge accelerators. ✔ Low-Level Tuning & Profiling - Apply loop unrolling, cache blocking, and prefetch directives to maximize data locality. - Harness advanced allocators, memory pools, and lock-free buffers for predictable performance under load. - Profile end-to-end pipelines with Intel VTune, Linux perf, and custom tracers to pinpoint and eliminate bottlenecks. ✔ Bridging C++ with Python and DevOps - Integrate C++ inference libraries with Python front-ends via Pybind11 and custom bindings. - Automate CI/CD pipelines for continuous benchmarking, cross-compilation, and firmware updates. - Embed unit tests and fuzzing harnesses to ensure robustness across hardware generations. Who This Book Is For - Machine learning engineers who need maximum performance and resource control. - C++ developers transitioning into AI and data science domains. - Embedded and IoT architects deploying vision, speech, or control models on constrained devices. - Infrastructure teams building scalable training clusters, inference microservices, or hybrid CPU/GPU/FPGA platforms. With hands-on examples, real-world case studies, and complete code listings, C++ Machine Learning arms you with the patterns, tools, and confidence to push AI from prototype to production-on any scale and in any environment.

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