This algorithm uses a neural network built in Tensorflow to predict anomalies from transaction and/or sensor data feeds. The model outputs predictions and reconstruction errors for the observations that highlight potential anomalies. Using an adjustable lower order basis representation of the space, the model includes 2 hidden layers and default 0.01 learning rate for minimizing loss on a RMS objective function.