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| title: Neural Network From Scratch (NumPy) | |
| emoji: 🧠 | |
| colorFrom: blue | |
| colorTo: indigo | |
| sdk: gradio | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| short_description: "Neural net by hand in NumPy: 97.7% on MNIST, no framework." | |
| # Neural Network From Scratch (NumPy) | |
| A multilayer perceptron written entirely by hand in NumPy: every forward and backward | |
| pass, the softmax cross-entropy, and the Adam optimizer. No PyTorch, no TensorFlow. | |
| It reaches **~97.7% accuracy on MNIST**, and its hand-written backprop is verified against | |
| **finite-difference gradients** in the test suite (so the chain rule is provably wired up | |
| correctly, not just "it seems to train"). Draw a digit, or load a real MNIST test image. | |
| Architecture: 784 → 256 → 128 → 10, ReLU activations, He initialisation, Adam. | |
| **Source & full docs:** https://github.com/LaelaZorana/nn-from-scratch | |