Hands-On Machine Learning with C++

• Explore how to load and preprocess various data types to suitable C++ data structures
• Employ key machine learning algorithms with various C++ libraries
• Understand the grid-search approach to find the best parameters for a machine learning model
• Implement an algorithm for filtering anomalies in user data using Gaussian distribution
• Improve collaborative filtering to deal with dynamic user preferences
• Use C++ libraries and APIs to manage model structures and parameters
• Implement a C++ program to solve image classification tasks with LeNet architecture

C++ can make your machine learning models run faster and more efficiently. This handy guide will help you learn the fundamentals of machine learning (ML), 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.

This book will get you hands-on with tuning and optimizing a model for different use cases, assisting you with model selection and the measurement of performance. You’ll cover techniques such as product recommendations, ensemble learning, and anomaly detection using modern C++ libraries such as PyTorch C++ API, Caffe2, Shogun, Shark-ML, mlpack, and dlib. Next, you’ll explore neural networks and deep learning using examples such as image classification and sentiment analysis, which will help you solve various problems. Later, you’ll learn how to handle production and deployment challenges on mobile and cloud platforms, before discovering how to export and import models using the ONNX format.

By the end of this C++ book, you will have real-world machine learning and C++ knowledge, as well as the skills to use C++ to build powerful ML systems.

• Become familiar 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