  In this work we study the inverse quantum scattering via deep learning
regression, which is implemented via a Multilayer Perceptron. A step-by-step
method is provided in order to obtain the potential parameters. A circular
boundary-wall potential was chosen to exemplify the method. Detailed discussion
about the training is provided. A investigation with noisy data is presented
and it is observed that the neural network is useful to predict the potential
parameters.
