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

Anomaly Detection

Anomaly detection is where we search for unexpected values in a given dataset. An anomaly is a system behavior deviation or data value deviation from the standard value. There are other names for anomalies, such as outliers, errors, deviations, and exceptions. They can occur in data that's of diverse nature and structure as a result of technical failures, accidents, deliberate hacks, and more.

There are many methods and algorithms we can use to search for anomalies in various types of data. These methods use different approaches to solve the same problem. There are unsupervised, supervised, and semi-supervised algorithms. However, in practice, unsupervised methods are the most popular. The unsupervised anomaly detection technique detects anomalies in unlabeled test datasets, under the assumption that most of the dataset is normal. It does this by searching...