Well,<p>It&#x27;s not just training that implies utility maximization, basic internal processes of a neural network, such as back propagation, are based on the utility function maximization. Back propagation is an approach that allows the network to do gradient descent on a utility function by propagating errors through the system.[1] Back propagation has been the way that deep networks have become tunable and effective. Without a utility function, it&#x27;s hard to see how one would tune them.<p>And it&#x27;s hard to think of what &quot;mean maximization&quot; could be but maximizing a utility function based on meaning.<p>And while you can construct a neural network that learns from a single example, the nn framework is fundamentally based on learning from multiple examples so such a construct is mostly meaningless.<p>But the point about machine learning being limited by the training methodology is good, it&#x27;s just I&#x27;m pretty sure you&#x27;d different algorithms if you used a different methodology, the existing algorithms are used specifically because they fit the methodology.<p>[1]<a href="https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Backpropagation" rel="nofollow">https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Backpropagation</a>