Backpropagation is a key algorithm for training neural networks by minimizing errors.
Understanding Backpropagation
It calculates the gradient of the loss function, adjusting weights to improve accuracy.
How It Works
Inputs are processed through the network layers to produce an output prediction.
Forward Pass
The difference between predicted and actual outputs is computed, known as the loss.
Loss Calculation
Errors are propagated back through the network to update weights using the chain rule.
Backward Pass
Backpropagation often utilizes gradient descent to minimize loss and enhance learning.
Gradient Descent Optimization
This method enables efficient training of complex neural networks, making AI feasible.
Importance in Deep Learning