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 learned what ML is, how it differs from other computer algorithms, and how it became so popular. We also became familiar with the necessary mathematical background required to begin to work with ML algorithms. We looked at software libraries that provide APIs for linear algebra, and also implemented our first ML algorithm—linear regression.

There are other linear algebra libraries for C++. Moreover, the popular deep learning frameworks use their own implementations of linear algebra libraries. For example, the MXNet framework is based on the mshadow library, and the PyTorch framework is based on the ATen library. Some of these libraries can use GPU or special CPU instructions for speeding up calculations. Such features do not usually change the API but require some additional library initialization settings or explicit object conversion to different backends such as CPUs or GPUs.

In the next two chapters, we will learn more about available software tools that are necessary to implement more complicated algorithms, and we will also learn more theoretical background on how to manage ML algorithms.