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

1. Introduction to Machine Learning with C++
2. Data Processing
3. Measuring Performance and Selecting Models
4. Clustering
5. Anomaly Detection
6. Dimensionality Reduction
7. Classification
8. Recommender Systems
9. Ensemble Learning
10. Neural Networks for Image Classification
11. Sentiment Analysis with BERT and Transfer Learning
12. Exporting and Importing Models
13. Tracking and Visualizing ML Experiments
14. Deploying Models on a Mobile Platform