The dimensionless third-order nonlinear Schr(o)dinger equation (alias the Hirota equation) is investigated via deep leaning neural networks.In this paper,we use the physics-informed neural networks (PINNs) deep learning method to explore the data-driven solutions (e.g.bright soliton,breather,and rogue waves) of the Hirota equation when the two types of the unperturbated and perturbated (a 2％ noise) training data are considered.Moreover,we use the PINNs deep learning to study the data-driven discovery of parameters appearing in the Hirota equation with the aid of bright solitons.