What Is Dropout Layer? Table Of Contents: What Is Dropout Layer? What Happens In Training Stage ? What Happens In Testing Stage ? Why We Need To Scale The Weights After Training, When Using Dropouts? (1) What Is Dropout Layer ? The Dropout Layer is a regularization technique used in deep learning neural networks to prevent overfitting. Overfitting occurs when a model performs exceptionally well on the training data but fails to generalize well to new, unseen data. The Dropout Layer works by randomly “dropping out” (i.e., temporarily deactivating) a proportion of the neurons in a neural network during the

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