Unlock the full potential of machine learning and deep learning with modern C++ in this hands-on guide by Min Jae-Lin. Hands-On Machine Learning with Modern C++ teaches you how to build, train, and deploy high-performance ML models using popular C++ libraries such as PyTorch C++ API, Flashlight, Blaze, mlpack, and dlib.

This comprehensive book covers supervised and unsupervised learning, clustering, dimensionality reduction, anomaly detection, recommender systems, and ensemble learning, with practical examples and real-world datasets. Learn to implement neural networks, perform sentiment analysis with transformers, and optimize hyperparameters with Optuna. Track and visualize experiments using MLflow and deploy your models on mobile and embedded devices using ONNX.

Inside, you'll find:

Step-by-step guidance on data processing, feature engineering, and model evaluation.

Practical examples of classification, regression, and anomaly detection in C++.

Techniques to build smart recommender systems and real-time object detection pipelines.

Hands-on deep learning projects for image classification and natural language processing.

Strategies for hyperparameter tuning, experiment tracking, and production-ready deployment.

Perfect for software engineers, ML engineers, data scientists, and C++ developers, this book equips you with the skills to implement real-world machine learning solutions with performance and efficiency in mind. Whether you're building AI systems, deploying models on edge devices, or exploring advanced C++ ML libraries, this guide provides the knowledge and practical expertise you need to succeed.

By the end of this book, you'll have production-level C++ machine learning skills, hands-on experience with modern ML frameworks, and the confidence to deploy AI systems in real-world applications.