Machine Learning and C++: From Algorithms to Production

The book "Machine Learning and C++" is a unique practical guide that bridges the gap between the world of Data Science and high-performance system programming. Unlike most books focused on Python, this work concentrates on implementing machine learning algorithms in C++ — a language that provides maximum control over memory, execution speed, and resources.

What is this book about?

The author systematically guides the reader from the basics of linear algebra and statistics to building complete ML pipelines. You will learn how to:

• Implement algorithms from scratch: linear and logistic regression, Support Vector Machines (SVM), decision trees, random forest, gradient boosting;
• Build a feedforward neural network and convolutional architectures in C++ without external frameworks;
• Optimize code using SIMD instructions, multithreading (OpenMP, std::thread), and cache management;
• Integrate models into existing C++ projects (games, robotics, high-load servers);
• Use the Eigen, Dlib, Shark, and TensorFlow C++ API libraries to accelerate development.

Who is this book for?

• C++ developers who want to incorporate machine learning into their projects without performance degradation;
• Data Scientists looking to move from prototyping in Python to production solutions in C++;
• Technical students studying both programming and ML;
• Embedded systems and robotics engineers, where every CPU cycle counts.

What will you learn?

• How to properly organize data for training in C++ (matrices, sparse structures, pipelines);
• The principles of gradient descent, backpropagation, and regularization — with full code implementation;
• Methods for evaluating model quality (cross-validation, metrics) without leaving the C++ environment;
• Ways to serialize trained models for loading at runtime;
• Real-world cases: time series forecasting, text classification, image processing.

The book contains over 50 code listings ready for compilation and exercises to reinforce the material. All examples are tested on GCC and Clang under Linux and Windows. This is not a reference book — it is a practical guide that will help you create fast and reliable ML systems in C++.

Perfect for those already familiar with the basics of C++ and who want to apply machine learning in real-world projects with strict speed and memory requirements.