Multi-Layer Perceptron 편집하기 (부분)

==Training a Multi-Layer Perceptron==
Training an MLP involves several steps:

1. '''Forward Propagation''': Input data is passed through each layer, producing predictions based on current weights. 2. '''Loss Calculation''': The loss function computes the error between predicted and actual values. 3. '''Backward Propagation''': The error is propagated backward through the network, calculating the gradients for each weight. 4. '''Weight Update''': The optimizer adjusts the weights and biases based on the gradients, reducing the error iteratively.

This process is repeated for multiple epochs until the model converges on minimal error or reaches a set number of iterations.