Machine Learning and C++: A Practical Guide

The book "Machine Learning and C++" is the first practical guide of its kind, showing how to implement machine learning algorithms in pure C++ without using Python. Unlike most ML books focused on Python and its libraries, this edition emphasizes high performance, low-level control, and embedding models into existing C++ projects.

What is this book about?

The author guides the reader step by step from basic machine learning concepts to creating their own neural networks. You will learn how to:

• Implement linear and logistic regression using the Eigen library for matrix operations;
• Build decision trees and random forests without external dependencies;
• Use OpenCV for image preprocessing and feature extraction;
• Integrate the TensorFlow C++ API to load and run pre-trained models;
• Optimize code using multithreading (OpenMP) and SIMD instructions;
• Deploy trained models into production — from embedded systems to high-load servers.

Who is this book for?

• C++ developers who want to add ML to their toolkit;
• Engineers working on high-performance systems (robotics, finance, gaming, IoT);
• Students and researchers who want to understand the inner workings of ML algorithms without "black boxes";
• Anyone tired of Python and looking for faster, more predictable solutions for machine learning.

What will you learn after reading?

• How to write your own machine learning library from scratch;
• How to efficiently work with large volumes of data using C++ features (smart pointers, move semantics, STL containers);
• How to profile and accelerate ML algorithms: from cache optimization to GPU computing via CUDA;
• How to integrate C++ ML models into web applications (via REST API) or mobile applications (via JNI);
• Real-world examples: anomaly detection in logs, stock price prediction, image classification on Raspberry Pi.

The book contains over 50 ready-to-use code examples available for download. All examples are tested on GCC and Clang under Linux, macOS, and Windows. The author uses the modern C++17/20 standard, making the code current and safe.

If you want to go beyond Python and learn to create truly fast and compact ML solutions — this book is for you.