The Classification Pipeline

1. Input Preprocessing

   The image is converted to grayscale, resized to 28×28 pixels, and normalized to match the MNIST dataset format.

2. Feature Extraction

   The neural network extracts meaningful features from the image through multiple layers of computation.

3. Classification

   The final layer produces logits which are converted to probabilities using softmax activation. The highest probability indicates the predicted digit.

Key Neural Network Concepts

Dense Layers

Fully connected layers that learn patterns by adjusting weights between all neurons.

ReLU Activation

A non-linear function that helps the network learn complex patterns.

Softmax

Converts the final layer outputs into probabilities that sum to 1.