  Neural networks with at least two hidden layers are called deep networks.
Recent developments in AI and computer programming in general has led to
development of tools such as Tensorflow, Keras, NumPy etc. making it easier to
model and draw conclusions from data. In this work we re-approach non-linear
regression with deep learning enabled by Keras and Tensorflow. In particular,
we use deep learning to parametrize a non-linear multivariate relationship
between inputs and outputs of an industrial sensor with an intent to optimize
the sensor performance based on selected key metrics.
