SKU/Artículo: AMZ-B0FMQCXGW9
Machine Learning with C++: A Hands-on Guide to Implement, Train, and Optimize Intelligent Systems Using Modern C++ Libraries
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Paperback
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0.54 kg
0.54 kg
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Amazon
Amazon
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USA
USA
Sobre este producto
- Have you ever built a brilliant model, a true work of data science, only to see it struggle in the real world? It works perfectly in your notebook, but it's too slow to handle a live video stream, too resource-hungry for an embedded device, or its latency is too high for a real-time financial application. Your model is trapped, its potential locked away behind a performance bottleneck.This is the story for countless developers and data scientists. The tools that are perfect for rapid prototyping become a wall when it's time to deploy. But what if you could break through that wall? What if you could take that same powerful neural network and run it with the blistering speed needed for an autonomous drone? What if you could deploy your algorithm on a low-power device at the edge? That is the journey this book takes you on. It is the story of taking your machine learning skills out of the lab and into the high-stakes environments where every millisecond and every byte of memory counts. It’s about transforming your brilliant ideas into powerful, real-world applications that don't just work, but win. What's InsideThis book is packed with practical knowledge and hands-on projects. You will not just learn the theory; you will build and deploy working systems. Inside, you will master:Professional C++ Environment Setup: Configure your system from scratch with CMake and the vcpkg package manager.High-Performance Data Science: Master the Eigen library for the kind of fast linear algebra that powers all machine learning.Classical Model Implementation: Build, train, and evaluate fundamental regression and classification models like Support Vector Machines (SVMs) from the ground up.Unsupervised Learning: Discover hidden structure in your data with K-Means clustering and simplify complex datasets with Principal Component Analysis (PCA).Modern Deep Learning Deployment: Learn the industry-standard workflow for taking a model trained in any Python framework and running it with maximum speed in C++ using ONNX Runtime.Performance Optimization: Go beyond correctness and learn to make your code fast. Profile your application to find bottlenecks, and unlock hardware speed with multi-threading and an understanding of SIMD.The Capstone Project: Bring everything together to build a real-time object detection application that identifies objects from a live webcam feed. Who It's Meant ForThis book is for anyone who is serious about building production-grade machine learning applications. It is your guide if you are:A C++ developer looking to apply your programming skills to the exciting and rapidly growing field of machine learning.A Python data scientist or ML engineer who has hit a performance wall and needs to learn how to deploy your models in high-performance C++ environments.A student or self-taught programmer who wants to move beyond beginner tutorials and learn the skills required to build robust, efficient, and truly impressive AI systems. Stop leaving performance on the table. Don't let your best models remain as slow prototypes. It's time to learn the skills that bridge the gap between a working model and a production-ready system that can handle the demands of the real world.Your journey to becoming a high-performance machine learning practitioner starts now. Open the first page and begin to build.
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