An iterative process is a repetitive sequence of steps that is performed multiple times, with each iteration building on the results of the previous one. In the context of training a neural network, this means that the model goes through a cycle of adjusting its parameters repeatedly to gradually improve performance.
Here’s how the iterative process applies to model training:
1. Initialize Parameters: At the start, the model’s parameters (weights and biases) are initialized, often with random values.
2. Forward Pass: In each iteration, the model makes predictions on a batch of training data using the current parameters.
3. Calculate Loss: The loss function calculates how far the predictions are from the actual values, giving us a measure of the model’s error.
4. Backpropagation: The gradients (i.e., the rate of change of the loss concerning each parameter) are calculated through backpropagation, identifying how each parameter influences the loss.
5. Update Parameters: Using these gradients, the model’s parameters are updated in the direction that reduces the loss.
6. Repeat: The process repeats for multiple iterations (or epochs, which means the entire dataset has been passed through the model once).
With each iteration, the model ideally gets closer to the optimal parameters that minimize the loss. This iterative process is at the heart of how a model “learns” from data.