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

Understanding dimension reduction algorithms with various С++ libraries

Let's look at how to use dimensionality reduction algorithms in practice. All of these examples use the same dataset, which contains four normally distributed 2D point sets that have been transformed with Swiss roll mapping, , into a 3D space. The following graph shows the result of this mapping. You can find the original dataset and mapping details at

This dataset is labeled. Each of the normally distributed parts has its own labels, and we can see these labels as a certain color on the result. We use these colors to show transformation results for each of the algorithms we'll be using in the following samples. This gives us an idea of how the algorithm works. The following sections provide concrete examples of how to use the Dlib...