Machine learning C++ and high-performance computing converge in this hands-on guide for building fast AI models from the ground up. Lionel Eriksen teaches you to implement training, inference, and numerical routines where speed truly matters—close to the metal. From optimizing memory access to parallelizing algorithms with OpenMP and SIMD, you'll master techniques that make your models run faster than Python-based alternatives. Practical exercises cover gradient descent, backpropagation, and custom kernels for CPUs. No fluff—just C++ code that delivers real-world performance gains. Whether you're a beginner or seasoned developer, this book bridges theory and practice. Competitor authors: [placeholder] and [placeholder] offer similar topics, but Ibarra's focus on low-level optimization and practical implementation sets this apart.What You'll LearnBuild neural networks from scratch using raw C++ and EigenImplement gradient descent, backpropagation, and loss functionsOptimize memory layout and cache usage for faster trainingParallelize loops with OpenMP and vectorize with SIMD intrinsicsWrite custom numerical routines for matrix operationsProfile and debug performance bottlenecks with tools like perf and ValgrindDeploy models in embedded systems and low-latency applicationsWho This Book Is ForSoftware engineers, data scientists, and C++ developers who want to push AI performance beyond scripting languages. Ideal for those building real-time systems, game AI, or high-frequency trading models.Table of ContentsWhy C++ for Machine Learning?Setting Up Your Development EnvironmentData Structures for High PerformanceLinear Algebra Routines from ScratchImplementing Gradient DescentBuilding a Neural Network LayerTraining with BackpropagationParallelization with OpenMPVectorization with SIMDMemory Optimization TechniquesProfiling and BenchmarkingInference OptimizationDeploying to Embedded SystemsCase Study: Real-Time Object DetectionGet ready to write C++ that makes AI fly. No Python wrappers—just raw speed and full control.