A supervised machine learning algorithm is applied to the visual representations of the energy
deposits in two orthogonal views of the calorimeter of ISS-CREAM. Convolutional Neural Net-
works (CNNs) backed by Tensorflow are used to calibrate the sampled energy of the calorimeter
and reconstruct the total primary energy of cosmic rays (CR), as well as for CR identification.
The CNN regression models are trained on detailed Monte Carlo simulated events reproducing the
behavior of the ISS-CREAM instrument suite, and the results indicate that a calorimeter energy
reconstruction resolution of as good as 25% is achieved. The energy sampled in the calorimeter
is determined with a resolution as good as 8%. The CNN classification model can reach a CR
identification accuracy of up to 93%. The CR primary energy reconstruction results from machine
learning methods are consistent with a simple scaling of the sampled energy. The increased accuracy of this CNN energy reconstruction comes from the additional information of the longitudinal
and lateral energy deposit profiles. This machine learning approach is widely applicable to a range
of particle physics and astrophysics problems.