I am reading about L2 Regularization. As far as I know we add a thing to the loss function that: $$J(w) = LOSS + \lambda w^T w$$
In the book Deep Learning by Goodfellow et al., they stated "minimizing J(w) results in a choice of weights that make a tradeoff between fitting the training data and being small".
$w^T w$. How is this related to "being small"? Why the weights now tend towards zero rather than any other values?