Implementation of Karpathy's micrograd in Mojo 🔥

yet another scalar autograd engine - featuring complex numbers and fixed DAG

🎓 super small and cute neural net engine, using only numpy

A simple vector valued autograd engine aimed to be between micrograd and tinygrad with a nn library.

Simple auto gradient project that supports basic operations written in C++ from scratch.

A small, elegant and visulized implementation of reverse mode autograd that is distinct from Karpathy's micrograd. Zero dependency.

A tiny scalar-valued autograd engine and a neural net library on top of it with PyTorch-like API

Nanograd is even smaller than micrograd | Autograd implementation with vanilla Python

# The is a micrograd-mini. Inspired by Andrej Karpathy.

This repository is a clean, educational reimplementation of Andrej Karpathy's micrograd project. It includes: - A minimal autograd engine (`Value` class) built from scratch - Neuron, Layer, and MLP abstractions built directly on top of `Value` - Manual training loop using scalar-based backpropagation along with Visualization

An unapologetic mock-off of the awesome micrograd.

A lean, but mean, autograd engine! (⚠️ not active anymore, since this was more of a educational project for me.)

numpy written in python from scratch | moved to delveopers/Axon

Micrograd is a small automatic differentiation engine and neural network library built with Python, designed for educational purposes. It demonstrates core concepts like automatic differentiation, backpropagation, and neural network building blocks, with testing against PyTorch.

Implementation of Neural Network service with automatic gradient calculations