neural_net is a personal learning project focused on implementing a basic neural network from scratch using C++. The goal of this project is to gain a deep understanding of neural network architecture, training algorithms, and fundamental machine learning concepts.

Features

Custom neural network implementation in C++
Support for basic feed-forward neural network architecture
Configurable number of layers and neurons
Implementation of key activation functions
Simple data loading and preprocessing utilities
Basic performance metrics and evaluation

Prerequisites

C++17 or later
CMake (version 3.10 or higher)
A modern C++ compiler (GCC, Clang, or MSVC)

Installation

Clone the repository:

git clone https://github.com/aselimov/neural_net.git
cd neural_net

Create a build directory and compile:
```
mkdir build
cd build
cmake ..
make
```

Learning Objectives

Understand neural network architecture
Implement core machine learning algorithms
Practice advanced C++ programming techniques
Explore computational efficiency in ML implementations

Roadmap

Activation functions
Basic neural network structure
Backpropagation algorithm
Regularization techniques
Performance optimizations

License

This project is MIT licensed.

Contact

Alex Selimov - alex@alexselimov.com