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


Machine learning (ML) is a popular approach to solve different kinds of problems. ML allows you to deal with various tasks without knowing a direct algorithm to solve them. The key feature of ML algorithms is their ability to learn solutions by using a set of training samples, or even without them. Nowadays, ML is a widespread approach used in various areas of industry. Examples of areas where ML outperforms classical direct algorithms include computer vision, natural language processing, and recommender systems.

This book is a handy guide to help you learn the fundamentals of ML, showing you how to use C++ libraries to get the most out of data. C++ can make your ML models run faster and more efficiently compared to other approaches that use interpreted languages, such as Python. Also, C++ allows you to significantly reduce the negative performance impact of data conversion between different languages used in the ML model because you have direct access to core algorithms and raw data.