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

Exploring the applications of anomaly detection

There are two areas in data analysis that look for anomalies: outlier detection and novelty detection.

A new object or novelty is an object that differs in its properties from the objects in the training dataset. Unlike an outlier, the new object is not in the dataset itself, but it can appear at any point after a system has started working. Its task is to detect when it appears. For example, if we were to analyze existing temperature measurements and identify abnormally high or low values, then we would be detecting outliers. On the other hand, if we were to create an algorithm that, for every new measurement, evaluates the temperature's similarity to past values and identifies significantly unusual ones, then we are detecting novelties.

The reasons for outliers appearing include data errors, the presence of noise, misclassified...