: Typically, you apply dropout after the activation function of hidden layers.
: By making the network "unreliable," you force it to learn redundant representations. No single neuron can become overly specialized or carry too much weight. DropOut-0.5.9a-pc.zip
: Dropout is only active during training. During evaluation or production (inference), all neurons are used, but their weights are scaled to account for the missing power during training. Best Practices for Implementation : Typically, you apply dropout after the activation
: It is most effective in large, complex networks where the risk of overfitting is high. all neurons are used
: For the best results, combine dropout with techniques like Max-Norm Regularization and decaying learning rates.