Dropout is a regularization technique for neural networks that prevents overfitting by preventing neurons from becoming too reliant on each other. 

During training, some neurons are randomly dropped out from each layer, meaning that they are not used to calculate the output of that layer. 

This forces the other neurons in the layer to learn to work together more robustly, and makes the network less likely to overfit to the training data.

In this example, the dropout rate is set to 0.5, meaning that half of the neurons in each layer will be dropped out during training. 

This will force the other neurons in the layer to learn to work together more robustly.

Dropout is a very powerful regularization technique, and can be used to significantly improve the performance of neural networks.

Benefits of using dropout

• Prevents overfitting
• Improves the generalization performance of neural networks
• Makes neural networks more robust to noise in the data
• Can reduce the number of parameters in a neural network, which can make it faster to train