Neural Networks

Gradient Descent

An optimisation method that updates parameters to reduce the model’s loss.

Gradient descent calculates how a small change in each model parameter would affect the loss. It then updates the parameters in the direction expected to reduce that loss.

The learning rate controls the size of each update. Variants such as stochastic gradient descent, momentum and adaptive optimisation improve speed, stability or performance on large datasets.

Also in Neural Networks

Perceptron Backpropagation Feed Forward Hopfield Network Boltzmann Machine Self Organising Maps

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