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Clipping: Difference between revisions
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==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. | ||
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Clipping is a technique we use in machine learning to prevent the computer from making too many changes at once, similar to placing limits on how quickly it can alter things. This helps ensure that computers learn slowly and avoid making costly errors. | Clipping is a technique we use in machine learning to prevent the computer from making too many changes at once, similar to placing limits on how quickly it can alter things. This helps ensure that computers learn slowly and avoid making costly errors. | ||
[[Category:Terms]] [[Category:Machine learning terms]] | [[Category:Terms]] [[Category:Machine learning terms]] [[Category:not updated]] | ||