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

Ensemble Learning

Anyone who works with data analysis and machine learning will come to understand that no method is ideal or universal. This is why there are so many methods. Researchers and enthusiasts have been searching for years for a compromise between the accuracy, simplicity, and interpretability of various models. Moreover, how can we increase the accuracy of the model, preferably without changing its essence? One way to improve the accuracy of models is to create and train model ensembles—that is, sets of models used to solve the same problem. The ensemble training methodology is the training of a final set of simple classifiers, with the subsequent merging of the results of their predictions into a single forecast of the aggregated algorithm.

This chapter describes what ensemble learning is, what types of ensembles exist, and how they can help to obtain better...