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

Performance metrics for ML models

When we develop or implement a particular ML algorithm, we need to estimate how well it works. In other words, we need to estimate how well it solves our task. Usually, we use some numeric metrics for algorithm performance estimation. An example of such a metric could be a value of mean squared error that's been calculated for target and predicted values. We can use this value to estimate how distant our predictions are from the target values we used for training. Another use case for performance metrics is their use as objective functions in optimization processes. Some performance metrics are used for manual observations, though others can be used for optimization purposes too.

Performance metrics are different for each of the ML algorithms types. In Chapter 1, Introduction to Machine Learning with C++, we discussed that two main categories...