How to understand the `Net pruing` via complexity penalty <p>In Chapter6.10.3 'Net pruning', page53 of <strong>An introduction to neural networks __ Kevin Gurney</strong>. It introduce the <code>complexity penalty</code> into the back-propagation training algorithm. The <code>complexity penalty</code> is like as follow:</p> <p>$$ E_c=\sum_{i}w_i $$<br> $$ E = E_t + \lambda E_c $$ </p> <p><code>Et</code> is error used so far based on input-output differences.<br> Then performing gradient descent on this total risk E. </p> <p><strong>My question</strong> : After doing derivation. The <code>complexity penalty</code> will dissapear. How can it affect the training</p>