SmolML: A Machine Learning Library from Scratch in Python (No NumPy, No Dependencies)
Published:Dec 28, 2025 14:44
•1 min read
•r/learnmachinelearning
Analysis
This article introduces SmolML, a machine learning library created from scratch in Python without relying on external libraries like NumPy or scikit-learn. The project's primary goal is educational, aiming to help learners understand the underlying mechanisms of popular ML frameworks. The library includes core components such as autograd engines, N-dimensional arrays, various regression models, neural networks, decision trees, SVMs, clustering algorithms, scalers, optimizers, and loss/activation functions. The creator emphasizes the simplicity and readability of the code, making it easier to follow the implementation details. While acknowledging the inefficiency of pure Python, the project prioritizes educational value and provides detailed guides and tests for comparison with established frameworks.
Key Takeaways
- •SmolML is a Python-based ML library built from scratch, emphasizing educational value.
- •It provides implementations of core ML components without external dependencies, promoting understanding of underlying mechanisms.
- •The project offers detailed guides and tests for comparison with established ML frameworks.
Reference
“My goal was to help people learning ML understand what's actually happening under the hood of frameworks like PyTorch (though simplified).”