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

Measuring distance in clustering

A metric or a distance measure is an essential concept in clustering because it is used to determine the similarity between objects. However, before applying a distance measure to objects, we have to make a vector of object characteristics; usually, this is a set of numerical values such as human height or weight. Also, some algorithms can work with categorical object features (or characteristics). The standard practice is to normalize feature values. Normalization ensures that each feature gives the same impact in a distance measure calculation. There are many distance measure functions that can be used in the scope of the clustering task. The most popular ones used for numerical properties are Euclidean distance, Squared Euclidean distance, Manhattan distance, and Chebyshev distance. The following subsections describe them in detail.