This project implements a neural network to classify handwritten digits from the MNIST dataset.
The main highlight is the visualization of the model parameters before and after training. This demonstrates how the network learns to represent each digit:
- Before training → weights are random and do not resemble any digit.
- After training → weights adapt and begin to resemble the digits they are meant to identify.
| Before Training | After Training |
|---|---|
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These images clearly show how the network parameters evolve to encode meaningful patterns, illustrating the learning process in action.