Neural networks require too much data to train. A classification network may require thousands of examples in a single class for it to identify it in unseen data. Due to this, sometimes it is not feasible to create ANN models for fringe applications.
Neural networks are not interpretable. The user needs to input data into the network and it outputs the required output, but the work that goes into processing the input and giving an output is not understandable to human beings.
The power required to train the neural network is extremely high compared to the amount of power that a human brain uses (around 20 Watts) to do almost the same things such as image classification.
