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 examined various methods for constructing ensembles of machine learning algorithms. The main purposes of creating ensembles are these:

  • Reducing the error of the elementary algorithms
  • Expanding the set of possible hypotheses
  • Increasing a probability of reaching the global optimum during optimizing

We saw that there are three main approaches for building ensembles: training elementary algorithms on various datasets and averaging the errors (bagging); consistently improving the results of the previous, weaker algorithms (boosting); and learning the meta-algorithm from the results of elementary algorithms (stacking). Note that the methods of building ensembles that we've covered, except stacking, require that the elementary algorithms belong to the same class, and this is one of the main requirements for ensembles. It is also believed that boosting...