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 discussed how to estimate the ML model's performance and what metrics can be used for such estimation. We considered different metrics for regression and classification tasks and what characteristics they have. We have also seen how performance metrics can be used to determine the model's behavior, and also looked at the bias and variance characteristics. We looked at some high bias (underfitting) and high variance (overfitting) problems and considered how to solve them. We also learned about the regularization approaches, which are often used to deal with overfitting. We then studied what validation is and how it is used in the cross-validation technique. We saw that the cross-validation technique allows us to estimate model performance while training limited data. In the last section, we combined an evaluation metric and cross-validation...