What is Dropout?
Dropout is a regularization technique which involves randomly ignoring or “dropping out” some layer outputs during training, used in deep neural networks to prevent overfitting.
Dropout is implemented per-layer in various types of layers like dense fully connected, convolutional, and recurrent layers, excluding the output layer. The dropout probability specifies the chance of dropping outputs, with different probabilities for input and hidden layers that prevents any one neuron from becoming too specialized or overly dependent on the presence of specific features in the training data.
Dropout Regularization in Deep Learning
Training a model excessively on available data can lead to overfitting, causing poor performance on new test data. Dropout regularization is a method employed to address overfitting issues in deep learning. This blog will delve into the details of how dropout regularization works to enhance model generalization.
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