fundamentals of machine learning figured it must be because it would require cooperation between employees to successfully defraud the bank this made me realize that randomly removing different subset of neurons on each example would prevent conspiracies and thus reduce overfitting " the core idea is that introducing noise in the output values of layer can break up happenstance patterns that aren' significant (what hinton refers to as conspiracies)which the network will start memorizing if no noise is present in kerasyou can introduce dropout in network via the dropout layerwhich is applied to the output of the layer right before itmodel add(layers dropout( )let' add two dropout layers in the imdb network to see how well they do at reducing overfitting listing adding dropout he imdb net work model models sequential(model add(layers dense( activation='relu'input_shape=( ,))model add(layers dropout( )model add(layers dense( activation='relu')model add(layers dropout( )model add(layers dense( activation='sigmoid')figure shows plot of the results againthis is clear improvement over the reference network figure effect of dropout on validat ion loss to recapthese are the most common ways to prevent overfitting in neural networksget more training data reduce the capacity of the network add weight regularization add dropout see the reddit thread "amawe are the google brain team we' love to answer your questions about machine learning,licensed to