Build Your Own Neural Network in C++: Learn AI from Scratch Step by Step with No Libraries and Only School Math: Code a Digit Reader and a Tiny Language ... in Modern C++20 (Code Pathway C++ Book 3)
English | October 4, 2026 | ASIN: B0HLWY4JMT | 448 pages | Epub | 630.75 KB
Build a neural network from an empty file, in modern C++20, and finally understand how AI works.Most AI tutorials hand you a library and a black box. You call fit(), a number goes down, and you learn nothing about why. This book takes the opposite path. You write every piece yourself in plain C++: the neuron, the layers, the training loop, backpropagation, softmax and the optimizer. No libraries. No Python. Only the math you did at school.What you will buildA single neuron that makes a decision, then learns to make it better on its ownA network that solves XOR, the puzzle a single neuron can never solveThe Digit Reader, a network that reads small hand-drawn digits, including messy ones and one you draw yourself. It grows across the whole book, and by the end it saves what it learned to a file and loads it back in an instant.A tiny language model that learns from a paragraph of text and then writes new text in the same style, one character at a timeEvery program was tested before this book was publishedAll 220 programs were compiled with g++ -std=c++20 -Wall -Wextra -pedantic -Werror and run, and the output printed in the book was compared automatically against what each program actually prints. Every program is printed in full, with all its training data built in, so there is nothing to download and no link that can break. The book even includes its own small random number generator, so your training runs print the same numbers as the book on any compiler.Math you can read as codeEvery formula appears as a line of C++ first and in plain words second. Gradient descent is a loop that walks downhill. Backpropagation is taught slowly, with every number printed for one small example and then checked by nudging each weight. If you can read a for loop, you can follow it.The 20 chapters1. What a Neural Network Really Is · 2. One Neuron · 3. Teaching a Neuron to Learn · 4. Loss Functions · 5. Gradient Descent · 6. Sigmoid, Tanh and ReLU · 7. A Matrix Class · 8. Layers and the Forward Pass · 9. Backpropagation · 10. Solving XOR · 11. Weight Initialization and Learning Rate · 12. Loading, Scaling and Splitting Data · 13. Softmax and Cross-Entropy · 14. The Digit Reader · 15. Overfitting · 16. Mini-Batches, Momentum and Adam · 17. Saving and Loading a Trained Network · 18. Performance C++ for AI · 19. Convolution · 20. A Tiny Language ModelAlso insideA quick quiz, five exercises and tested solutions in every chapterAn "I Want To." Finder that takes you straight to the answer for tasks like "write the Adam optimizer" or "stop softmax from overflowing"A chapter map at the start of each chapter saying what you need firstA glossary and a full indexWho this is forProgrammers who know basic C++ (variables, loops, functions, vectors and simple classes) and want to understand AI from the inside. Students who want a project that shows real understanding. Developers who can run machine learning code but cannot yet explain it.What it is not: this is not a guide to PyTorch or TensorFlow, it does not use a graphics card, and it does not build a chatbot. The networks here are small enough to train in seconds and read in full. The ideas are the same ones large systems use, and the last chapter explains honestly how your tiny language model relates to the big ones.New to C++? Start with C++ Programming for Beginners, Book 1 of the Code Pathway series.
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