used in these previous works are readily applied to the estimation of the gas fraction from large input datasets. In this work, we will train and validate the ANN using galaxies drawn from the ALFALFA.70 data release, matched to galaxies in the SDSS DR7. The trained network can then be applied to all galaxies in the DR7 (not covered by ALFALFA.70) in order to predict the gas fraction based on their optical properties.

The layout of the paper is as follows. In Section \[sec-sample\] we describe the samples that are used for training the ANN and the method used for rejecting galaxies that may have their 21 cm fluxes contaminated by the presence of a close companion within the Arecibo beam. The results in the following sections are then presented with respect to three main objectives. First, we will explore whether non-linear methods are able to reduce the scatter in  scaling relations below what has been previously achieved for linear calibrations for a given set of input parameters (Section \[sec-example\]). Specifically, we use the same optical input parameters as Zhang et al. (2009) in order to compare the scatter of their linear calibration with that of the non-linear methods. Although 