Deriving gradients using the backpropagation idea
From Ufldl
(→Introduction) |
|||
| Line 1: | Line 1: | ||
== Introduction == | == Introduction == | ||
| - | In the section on the [[Backpropagation Algorithm | backpropagation algorithm]], you were briefly introduced to backpropagation as a means of deriving gradients for learning in the sparse autoencoder. It turns out that together with matrix calculus, this provides a powerful method and intuition for deriving gradients for more complex matrix functions (functions from matrices to the reals, or symbolically, from <math>\mathbb{R}^{r \times c} \rightarrow \mathbb{R}</math>. | + | In the section on the [[Backpropagation Algorithm | backpropagation algorithm]], you were briefly introduced to backpropagation as a means of deriving gradients for learning in the sparse autoencoder. It turns out that together with matrix calculus, this provides a powerful method and intuition for deriving gradients for more complex matrix functions (functions from matrices to the reals, or symbolically, from <math>\mathbb{R}^{r \times c} \rightarrow \mathbb{R}</math>). |
First, recall the backpropagation idea, which we present in a modified form appropriate for our purposes below: | First, recall the backpropagation idea, which we present in a modified form appropriate for our purposes below: | ||
