Interface administrators, Administrators (Semantic MediaWiki), Curators (Semantic MediaWiki), Editors (Semantic MediaWiki), Suppressors, Administrators
7,785
edits
Clipping: Difference between revisions
m
Text replacement - " unstable " to " unstable "
No edit summary |
m (Text replacement - " unstable " to " unstable ") |
||
| Line 4: | Line 4: | ||
==The Need for Clipping== | ==The Need for Clipping== | ||
Machine learning algorithms like [[stochastic gradient descent]] (SGD) are commonly employed to update the weights of a neural network during training. SGD works by computing the [[gradient]] of the [[loss function]] with respect to the network's weights and adjusting them in the direction of that negative gradient in order to minimize [[loss]]. However, if this gradient is very large, it could cause weights to change rapidly, leading to unstable behavior. | Machine learning algorithms like [[stochastic gradient descent]] (SGD) are commonly employed to update the weights of a neural network during training. SGD works by computing the [[gradient]] of the [[loss function]] with respect to the network's weights and adjusting them in the direction of that negative gradient in order to minimize [[loss]]. However, if this gradient is very large, it could cause weights to change rapidly, leading to [[unstable]] behavior. | ||
This problem is especially prevalent in [[deep neural network]]s, which can have millions of weights that make it difficult to keep them under control during training. Clipping offers a straightforward solution by restricting the range of the weights and preventing them from growing too large. | This problem is especially prevalent in [[deep neural network]]s, which can have millions of weights that make it difficult to keep them under control during training. Clipping offers a straightforward solution by restricting the range of the weights and preventing them from growing too large. | ||