itskonrad/miniMNIST-c resource

Health v2 · maintenance only

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1284 stars · 130 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A minimal neural network written in roughly 200 lines of dependency-free, standard-library-only C that trains a two-layer MLP (ReLU hidden layer, softmax output) with SGD and cross-entropy loss to classify handwritten digits from MNIST. It is a compact educational demonstration of neural network fundamentals, reaching about 98% accuracy in ~2.7 seconds per epoch, rather than a reusable ML library.

Use cases

• implement a neural network from scratch in C
• train a model on MNIST without any dependencies
• learn how backpropagation, ReLU, softmax, and cross-entropy work in plain code
• study a tiny readable codebase to understand SGD and mini-batch training
• see how fast a pure-C MLP can train on MNIST
• teaching example for an intro deep learning course

When to choose

• you want to read and learn from a very small from-scratch neural network implementation in C
• you need a dependency-free teaching or self-study example covering the full train loop (forward pass, loss, gradients, SGD)
• you want a quick MNIST baseline that compiles with a single gcc command and runs on CPU

When to avoid

• you need production ML capabilities such as GPU acceleration, convolutional layers, model saving/loading, or inference APIs
• you want a reusable library or framework to integrate into an application - it is a single monolithic program configured by editing constants in nn.c
• your task involves anything beyond MNIST-style dense inputs, e.g. images, text, or audio

Facets

learning-resource · maturity stable

machine-learning machine-learning deep-learning education tutorials c cross-platform cli windows neural-network mnist from-scratch handwritten-digit-recognition sgd softmax relu cross-entropy minimal-implementation standard-library-only educational linux macos

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