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

Manipulating images with the OpenCV and Dlib libraries

Many machine learning algorithms are related to computer vision problems. Such tasks are object detection in images, segmentation, image classification, and others. To be able to deal with such tasks, we need instruments for working with images. We usually need routines to load images to computer memory, as well as routines for image processing. For example, the standard operation is image scaling, because many machine learning algorithms are trained only on images of a specific size. This limitation follows from the algorithm structure or is a hardware requirement. For example, we cannot load large images to the graphics processing unit (GPU) memory because of its limited size.

Also, hardware requirements can lead to a limited range of numeric types our hardware supports, so we will need to change initial image representation...