  We propose machine learning inspired methods for computing numerical
Calabi-Yau (Ricci flat K\"ahler) metrics, and implement them using
Tensorflow/Keras. We compare them with previous work, and find that they are
far more accurate for manifolds with little or no symmetry. We also discuss
issues such as overparameterization and choice of optimization methods.
