by Michele Laurelli
An algorithm for training neural networks by calculating gradients of the loss function with respect to weights.
Backpropagation uses the chain rule to efficiently compute gradients layer by layer, from output to input. These gradients guide weight updates during training through gradient descent optimization.
Training deep neural networks
Optimizing convolutional layers
Fine-tuning language models
An optimization algorithm that iteratively adjusts parameters to minimize a loss function by following the gradient.
A computational model inspired by biological neural networks, consisting of interconnected nodes (neurons) that process information.
A function that measures the difference between predicted and actual values, guiding model optimization.