Machine Learning and C++: A Practical Guide

The book "Machine Learning and C++" is a unique guide for developers who want to master machine learning using the C++ language. Unlike popular Python libraries (scikit-learn, TensorFlow), C++ provides full control over memory, performance, and algorithm architecture. The author, Alexey Volkov, a senior developer with 10 years of experience in high-performance computing, shows how to create efficient ML models "from scratch."

What is this book about?

The book covers the complete cycle of developing ML solutions in C++: from data preparation to deploying models in production. You will learn:

• Fundamentals of machine learning — mathematical apparatus (linear algebra, statistics, optimization) with an emphasis on implementation in C++.
• Classic algorithms — linear and logistic regression, k-nearest neighbors method, decision trees, random forest.
• Gradient descent — various variants (SGD, Momentum, Adam) with manual implementation and performance comparison.
• Neural networks — fully connected layers, activation functions, backpropagation, regularization.
• Working with data — CSV parsing, normalization, one-hot encoding, handling missing values — all in pure C++.
• Code optimization — using SIMD instructions, multithreading (std::thread, OpenMP), and profiling to speed up training.

Who is this book for?

• C++ developers who want to integrate ML into their projects (games, robotics, finance, embedded systems).
• Data scientists wishing to understand the internal workings of algorithms and go beyond the "black boxes" of Python libraries.
• Students in technical fields studying machine learning and needing practical experience with a low-level language.
• Developers of high-load systems where execution speed is critical (recommendation systems, trading, real-time processing).

What will you learn after reading?

• How to implement ML algorithms without dependencies on third-party libraries.
• How to speed up model training by 10-100 times compared to Python counterparts.
• How to test and debug ML code using Google Test and Valgrind.
• How to integrate a trained model into an existing C++ project (API, serialization to JSON/binary).
• How to develop your own neural network architectures and optimize them for specific tasks.

The book contains more than 50 code examples, ready to compile (C++17/20), and practical exercises at the end of each chapter. All source code is available on GitHub. "Machine Learning and C++" is not just theory, but a tool for creating production-ready ML solutions in the fastest programming language.