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 what clustering is and how it differs from classification. We saw different types of clustering methods, such as the partition-based, the spectral, the hierarchical, the density-based, and the model-based methods. Also, we observed that partition-based methods could be divided into more categories, such as the distance-based methods and the ones based on graph theory. We used implementations of these algorithms, including the k-means algorithm (the distance-based method), the GMM algorithm (the model-based method), the Newman modularity-based algorithm, and the Chinese Whispers algorithm for graph clustering. We also saw how to use the hierarchical and spectral clustering algorithm implementations in programs. We saw that the crucial issues for successful clustering are as follows:

  • The choice of the distance measure function
  • The initialization...