Dropout Regularization
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May 1, 2024
3 minute read
Dropout regularization is a technique used to reduce overfitting in machine learning models, especially neural networks. It involves randomly dropping out units (neurons) from the network during training, which helps prevent the model from learning too much from the training data and becoming too specific to it. This can improve the model's ability to generalize to new, unseen data.
Why Learn About Dropout Regularization?
There are several reasons why you might want to learn about dropout regularization:
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Find a path to becoming a Dropout Regularization. Learn more at:
OpenCourser.com/topic/1waj6o/dropout
