# calculating output error
dA = perceptron_inputs[-1] - target.reshape(-1, 1)

#a scaling factor for the batch size.
#you want changes to be an average across all batches
#so we divide by m once we've aggregated all changes.
m = len(target)

for i in reversed(range(len(self.weights))):
  dZ = dA #simplified for now

  # calculating change to weights
  dW = np.dot(perceptron_inputs[i].T, dZ) / m
  # calculating change to bias
  db = np.sum(dZ, axis=0, keepdims=True) / m

  # keeping track of required changes
  weight_changes.append(dW)
  bias_changes.append(db)
  ...