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The efficiency of the transport of angular momentum and chemical elementsinside intermediate-mass stars lacks proper calibration, thereby introducinguncertainties on a star's evolutionary pathway. Improvements require betterestimation of stellar masses, evolutionary stages, and internal mixingproperties. We aim to develop a neural network approach for asteroseismicmodelling and test its capacity to provide stellar masses, ages, andovershooting parameter for a sample of 37 $\gamma$ Doradus stars. Here, ourgoal is to perform the parameter estimation from modelling of individualperiods measured for dipole modes with consecutive radial order. We havetrained neural networks to predict theoretical pulsation periods of high-ordergravity modes as well as the luminosity, effective temperature, and surfacegravity for a given mass, age, overshooting parameter, diffusive envelopemixing, metallicity, and near-core rotation frequency. We have applied ourneural networks for Computing Pulsation Periods and Photospheric Observables,C-3PO, to our sample and compute grids of stellar pulsation models for theestimated parameters. We present the near-core rotation rates (from literature)as a function of the inferred stellar age and critical rotation rate. We assessthe rotation rates of the sample near the start of the main sequence assumingrigid rotation. Furthermore, we measure the extent of the core overshoot regionand find no correlation with mass, age, or rotation. The neural networkapproach developed in this study allows for the derivation of stellarproperties dominant for stellar evolution -- such as mass, age, and extent ofcore-boundary mixing. It also opens a path for future estimation of mixingprofiles throughout the radiative envelope, with the aim to infer thoseprofiles for large samples of $\gamma$ Doradus stars.

Constraining stellar evolution theory with asteroseismology of $\gamma$ Doradus stars using deep learning