Assignment Task: Multilayer perceptrons are perceptrons stacked one on top of another. In a single layer, we determine the number of outputs by the number of neurons we choose to train.
Experiment with TensorFlow playground, its regularization settings are critical, which force the model to learn a simpler representation. Are more layers and neurons with carefully set regularization settings able to learn better? Could it be these datasets are too simple?