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 how to save and load model parameters in different ML frameworks. We saw that all the frameworks we used in the Shogun, Shark-ML, Dlib, and PyTorch libraries have an API for model parameter serialization. Usually, these are quite simple functions that work with model objects and some input and output streams. Also, we discussed another type of serialization API that can be used to save and load the overall model architecture. At the time of writing, the frameworks we used don't fully support such functionality. The Shogun toolkit can load neural network architectures from the JSON descriptions, but can't export them. The Dlib library can export neural networks in XML format but can't load them. The PyTorch C++ API lacks a model architecture that supports exporting, but it can load and evaluate model architectures that have been...