Название: Modern C++ for Machine Learning: A Comprehensive Guide to Building Production-Ready AI Systems
Автор: Aarav Joshi
Издательство: 101 Books
Год: 2025
Страниц: 2531
Язык: английский
Формат: epub (true)
Размер: 34.8 MB

Modern C++ for Machine Learning: A Comprehensive Guide to Building Production-Ready AI Systems is an essential resource for developers, data scientists, and engineers who want to harness the power of modern C++ for Machine Learning applications. This comprehensive guide takes you through the complete machine learning lifecycle, from foundational concepts to advanced deployment strategies.

The book covers cutting-edge topics including Deep Learning architectures, transformer models, computer vision, natural language processing, and MLOps practices. You'll master essential C++ libraries like Eigen, OpenCV, TensorFlow C++ API, and PyTorch C++ frontend while learning to implement everything from basic clustering algorithms to sophisticated neural networks.

What sets this book apart is its focus on production-ready systems. You'll explore advanced topics like federated learning, quantum machine learning concepts, AutoML, and explainable AI. The comprehensive coverage includes real-time inference optimization, distributed model serving, GPU acceleration with CUDA, and mobile deployment strategies.

Each chapter provides hands-on examples with detailed implementations, performance optimization techniques, and best practices for scalable systems. Whether you're building recommendation systems, implementing computer vision applications, or deploying models on edge devices, this book provides the practical knowledge needed to create robust, high-performance machine learning solutions using modern C++.

Machine Learning has transformed from a specialized academic discipline to a fundamental technology powering countless applications across industries. This section explores the diverse landscape of machine learning paradigms, examining how different approaches solve unique problems. We’ll investigate supervised, unsupervised, reinforcement, and semi-supervised learning techniques, along with Deep Learning fundamentals. The machine learning workflow provides a structured approach to developing AI solutions, while real-world applications demonstrate the practical impact across domains like finance, healthcare, autonomous systems, and more. Understanding these foundations is essential for C++ developers looking to implement efficient, production-ready Machine Learning systems that address complex challenges in the modern technological landscape.

Machine Learning systems learn from data through different approaches, each suited to specific problem types. Supervised learning, the most common paradigm, trains models on labeled data where both inputs and desired outputs are provided. In regression problems, models predict continuous values, such as housing prices based on features like square footage, location, and amenities. Classification tasks involve assigning discrete categories, like determining whether an email is spam or legitimate.

By the end, you'll have the expertise to design, implement, and deploy enterprise-grade AI systems that leverage C++'s performance advantages for demanding machine learning applications.