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Hands-On Machine Learning with C++: Build, train, and deploy end-to-end machine learning and deep learning pipelines

Hands-On Machine Learning with C++: Build, train, and deploy end-to-end machine learning and deep learning pipelines

Hands-On Machine Learning with C++: Build, train, and deploy end-to-end machine learning and deep learning pipelines Paperback / softback - 2025

by Kirill Kolodiazhnyi

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Paperback / softback. New. Implement supervised and unsupervised machine learning (ML) algorithms using C++ libraries such as PyTorch C++ API, TensorFlow C++ API, Flashlight, mlpack, and dlib with the help of real-world examples and datasets Key Features Familiarize yourself with data processing, performance measuring, and model selection using various C++ libraries Implement practical machine learning and deep learning techniques to build smart models Deploy machine learning models to work on mobile and embedded devices Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionC++ can make your machine learning models run faster and more efficiently. This handy guide will help you learn the fundamentals of machine learning, 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. You’ll get hands-on experience with tuning and optimizing a model for different use cases, and get to grips with model selection and the measurement of performance. Next, you’ll cover techniques such as product recommendations, ensemble learning, anomaly detection, sentiment analysis, and object recognition using modern C++ libraries such as PyTorch C++ API, TensorFlow C++ API, Flashlight, mlpack, and dlib. You’ll also explore neural networks, deep learning, and transfer learning that allows you to use pre-trained models. The later chapters will teach you how to handle production and deployment challenges on mobile and cloud platforms, and how the ONNX model format can help you with such tasks. You’ll also learn how to extend existing deep learning frameworks with new operations. By the end of this book, you will have real-world ML and C++ knowledge, as well as the skills to use C++ to build powerful ML systems.What you will learn Find out how to load and pre-process various data types to suitable C++ data structures Employ key machine learning algorithms with various C++ libraries Understand how to find the best parameters for a machine learning model Use anomaly detection for filtering user data Apply collaborative filtering to deal with dynamic user preferences Use C++ libraries and APIs to manage model structures and parameters Build a C++ program for object detection with advanced neural networks Extend machine learning frameworks with custom operators written in C++ Who this book is forIf you want to get started with machine learning algorithms and techniques using the popular C++ language, then this C++ machine learning book is for you. Aside from being a useful first course in machine learning with C++, this book will also appeal to data analysts, data scientists, and machine learning developers looking to implement different machine learning models in production using varied datasets and examples. Working knowledge of the C++ programming language is needed to get started with this book.
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From the publisher

Apply supervised and unsupervised machine learning algorithms using C++ libraries, such as PyTorch C++ API, Flashlight, Blaze, mlpack, and dlib using real-world examples and datasets

Key Features:

- Familiarize yourself with data processing, performance measuring, and model selection using various C++ libraries

- Implement practical machine learning and deep learning techniques to build smart models

- Deploy machine learning models to work on mobile and embedded devices

- Purchase of the print or Kindle book includes a free PDF eBook

Book Description:

Written by a seasoned software engineer with several years of industry experience, this book will teach you the basics of machine learning (ML) and show you how to use C++ libraries, along with helping you create supervised and unsupervised ML models.

You'll gain hands-on experience in tuning and optimizing a model for various use cases, enabling you to efficiently select models and measure performance. The chapters cover techniques such as product recommendations, ensemble learning, anomaly detection, sentiment analysis, and object recognition using modern C++ libraries. You'll also learn how to overcome production and deployment challenges on mobile platforms, and see how the ONNX model format can help you accomplish these tasks.

This new edition has been updated with key topics such as sentiment analysis implementation using transfer learning and transformer-based models, as well as tracking and visualizing ML experiments with MLflow. An additional section shows you how to use Optuna for hyperparameter selection. The section on model deployment into mobile platform now includes a detailed explanation of real-time object detection for Android with C++.

By the end of this C++ book, you'll have real-world machine learning and C++ knowledge, as well as the skills to use C++ to build powerful ML systems.

What You Will Learn:

- Employ key machine learning algorithms using various C++ libraries

- Load and pre-process different data types to suitable C++ data structures

- Find out how to identify the best parameters for a machine learning model

- Use anomaly detection for filtering user data

- Apply collaborative filtering to manage dynamic user preferences

- Utilize C++ libraries and APIs to manage model structures and parameters

- Implement C++ code for object detection using a modern neural network

Who this book is for:

This book is for beginners looking to explore machine learning algorithms and techniques using C++. This book is also valuable for data analysts, scientists, and developers who want to implement machine learning models in production. Working knowledge of C++ is needed to make the most of this book.

Table of Contents

- Introduction to Machine Learning with C++

- Data Processing

- Measuring Performance and Selecting Models

- Clustering

- Anomaly Detection

- Dimensionality Reduction

- Classification

- Recommender Systems

- Ensemble Learning

- Neural Networks for Image Classification

- Sentiment Analysis with BERT and Transfer Learning

- Exporting and Importing Models

- Tracking and Visualizing ML Experiments

- Deploying Models on a Mobile Platform

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