Dropout in neural networks

Dropout is regularization in neural networks to avoid overfitting of the data. Typically, dropout is 0.2 (80 percentage of neurons present randomly all the time) in initial layers and 0.5 in middle layers. One intuitive way to understand the dropout concept would be with the office team, in which a few team members are good with communication with clients though they are not good with technical details, whereas a few are good with technical knowledge but do not have good enough communication skills. Let's say some of the members take leave from the office, and then other members try to fill the shoes of others for the completion of work. In this way, team members who are good with communication will also learn technical details similarly; a few other team members who are good with technical knowledge also learn communication with clients. In this way, all team members will become independent and robust enough to perform all types of work, which is good for the...