Categories

Tag

derivatives

View Cards
Mode Paginated
Per page Per batch 24
Technology

Essential Differentiation Rules for Neural Networks

The essential differentiation rules are a handful of formulas that turn any function into its derivative: the power rule, the product and quotient rules, and the rules for the exponential and the logarithm. With them, plus the chain rule, a neural network computes gradients and learns by adjusting its weights.

6 min
Technology

Derivatives, the Rate of Change That Teaches the Network

A derivative measures the rate of change of a function: how much its output varies when the input changes a little. In a neural network, that slope tells us in which direction and how strongly to adjust each weight to reduce the error, and it is the foundation of gradient descent and backpropagation.

7 min