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

An overview of ensemble learning

The training of an ensemble of models is understood to be the procedure of training a final set of elementary algorithms, whose results are then combined to form the forecast of an aggregated classifier. The model ensemble's purpose is to improve the accuracy of the prediction of the aggregated classifier, particularly when compared with the accuracy of every single elementary classifier. It is intuitively clear that combining simple classifiers can give a more accurate result than each simple classifier separately. Despite that, simple classifiers can be sufficiently accurate on particular datasets, but at the same time, they can make mistakes on different datasets.

An example of ensembles is Condorcet's jury theorem (1784). A jury must come to a correct or incorrect consensus, and each juror has an independent opinion. If the probability...