  Deep learning is the state-of-the-art in fields such as visual object
recognition and speech recognition. This learning uses a large number of layers
and a huge number of units and connections. Therefore, overfitting is a serious
problem with it, and the dropout which is a kind of regularization tool is
used. However, in online learning, the effect of dropout is not well known.
This paper presents our investigation on the effect of dropout in online
learning. We analyzed the effect of dropout on convergence speed near the
singular point. Our results indicated that dropout is effective in online
learning. Dropout tends to avoid the singular point for convergence speed near
that point.
