Implement supervised and unsupervised machine learning (ML) algorithms using C++ libraries such as PyTorch C++ API, TensorFlow C++ API, Flashlight, mlpack, and dlib with the help of real-world examples and datasets Key Features Familiarize yourself with data processing, performance measuring, and model selection using various C++ libraries Implement practical machine learning and deep learning techniques to build smart models Deploy machine learning models to work on mobile and embedded devices Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionC++ 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.What you will learn Find out how to load and pre-process various data types to suitable C++ data structures Employ key machine learning algorithms with various C++ libraries Understand how to find the best parameters for a machine learning model Use anomaly detection for filtering user data Apply collaborative filtering to deal with dynamic user preferences Use C++ libraries and APIs to manage model structures and parameters Build a C++ program for object detection with advanced neural networks Extend machine learning frameworks with custom operators written in C++ Who this book is forIf you want to get started with machine learning algorithms and techniques using the popular C++ language, then this C++ machine learning book is for you. Aside from being a useful first course in machine learning with C++, this book will also appeal to data analysts, data scientists, and machine learning developers looking to implement different machine learning models in production using varied datasets and examples. Working knowledge of the C++ programming language is needed to get started with this book.