<p>Neural networks is simply one method. It is statistic in a sense that the neural network can be thought as a complicated function that is trained to fit the data in a best possible way.</p>

<p>As such it is not that academic approach as an engineering thing; things that work are used and people build intuition with experience on the matter. Thus it is hard to say anything about problem X, in case Y, if you have no experience. There is this thing called playing with data, that is supposed to give some intuition on the methods that could be applied</p>

<p>NN is the ultimate on that sense, that it is almost impossible to say anything too concrete about them because the number of layers / neurons per layer / NN-architecture all vary case by case.</p>

<p>Because of that, the NN gives an illusion that it would be something completely different, but it is not. It just happens to be so versatile that it has many applications. But in the end, it is just a method for regression. I recommend this article about common misconceptions: <a href="http://www.turingfinance.com/misconceptions-about-neural-networks/" rel="nofollow noreferrer">http://www.turingfinance.com/misconceptions-about-neural-networks/</a></p>
