karpathy/micrograd

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

17273 stars · 2760 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A tiny scalar-valued autograd engine implementing reverse-mode automatic differentiation over a dynamically built DAG, with a small PyTorch-like neural network library on top. It is intentionally minimal (~150 lines total) and designed primarily for educational purposes to understand backpropagation and neural network training.

Use cases

• learn how backpropagation works from scratch
• understand reverse-mode automatic differentiation
• train a small MLP binary classifier
• visualize computation graphs and gradients with graphviz
• teach a deep learning fundamentals course
• prototype scalar-valued gradient computations

When to choose

• you want to learn or teach how autograd and backpropagation work internally
• you need a minimal, readable codebase to study rather than a production tool
• you're building tiny educational neural nets like binary classifiers on toy datasets
• you want a dependency-free starting point for understanding frameworks like PyTorch

When to avoid

• you need performance or GPU acceleration for real training workloads
• you need tensor operations, batching, or vectorized computation - it only works on scalars
• you're building production machine learning systems - use PyTorch, JAX, or TensorFlow instead
• you need a full-featured neural network library with optimizers, layers, and data loaders

Facets

library · maturity stable

machine-learning deep-learning machine-learning deep-learning education tutorials python cross-platform autograd backpropagation neural-networks educational reverse-mode-autodiff pytorch-like-api education

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markdown · JSON · MCP: product_card(name="karpathy/micrograd")