Neural Networks#
A neural network learns a sequence of transformations from inputs to predictions. Dense layers combine the inputs using learned weights and biases, while nonlinear activation functions such as ReLU allow the network to learn relationships that cannot be represented by one linear transformation.
The example uses MNIST, a collection of small grayscale images of handwritten digits from 0 to 9. The task is to assign each image to the correct digit. The model keeps the structure of the source example:
Flatten → 200 ReLU → 150 ReLU → Dropout(0.5) → 10 logits
Both notebooks use the same data split, architecture, five-epoch training budget, and diagnostic figures.