Book Image

Hands-On Machine Learning with C++

By : Kirill Kolodiazhnyi
Book Image

Hands-On Machine Learning with C++

By: Kirill Kolodiazhnyi

Overview of this book

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.
Table of Contents (19 chapters)
Section 1: Overview of Machine Learning
Section 2: Machine Learning Algorithms
Section 3: Advanced Examples
Section 4: Production and Deployment Challenges


In this chapter, we considered how to load data from CSV, JSON, and HDF5 formats. We saw how to convert the loaded data in the objects suitable to use in different machine learning frameworks. We used the libraries' APIs to convert raw C++ arrays into matrices and higher-level datasets' objects for machine learning algorithms. We looked at how to load and process images with the OpenCV and Dlib libraries. We became familiar with the data normalization process, which is very important for many machine learning algorithms. Also, we saw which normalization techniques are available in machine learning libraries, and we implemented some normalization approaches with linear algebra functions from the Eigen library.

In the following chapter, we will see how to measure a model's performance on different types of data. We will look at special techniques that help...