C++ can make your machine learning models run faster and more efficiently. This handy guide will help you learn the fundamentals of machine learning, showing you how to use C++ libraries to get the most out of your data. This book makes machine learning with C++ for beginners easy with its example-based approach, demonstrating how to implement supervised and unsupervised ML algorithms through real-world examples.
You’ll get hands-on experience with tuning and optimizing a model for different use cases, and get to grips with model selection and the measurement of performance. Next, you’ll cover techniques such as product recommendations, ensemble learning, anomaly detection, sentiment analysis, and object recognition using modern C++ libraries such as PyTorch C++ API, TensorFlow C++ API, Flashlight, mlpack, and dlib. You’ll also explore neural networks, deep learning, and transfer learning that allows you to use pre-trained models. The later chapters will teach you how to handle production and deployment challenges on mobile and cloud platforms, and how the ONNX model format can help you with such tasks. You’ll also learn how to extend existing deep learning frameworks with new operations.
By the end of this book, you will have real-world ML and C++ knowledge, as well as the skills to use C++ to build powerful ML systems.