I am just testing out a simple neural network with a single neuron. To classify if a number X between 1..10 is greater than a number N. N is a constant for example N=3.
Given my input X and a constant neuron 1. My output is (w1*X+w2) where w are weights.
But what I'm finding is that some values of N lead to faster training than others.
In particular the training leads to an equation w1*X+w2>0 and the neural network learns by gradually getting better values for the weights. Different values of N will give different ratios w1/w2.
This ratio it seems is related to how fast the neural network will learn.
Will it always be easier/harder to classify if a number N>=5 than say N>=2 or N>=9 ?
Also there is a redundancy in the equation w1*X+w2>0 which since we can multiply w1 and w2 by a constant. How can we remove this redundancy?