Neural networks
In our previous examples, we have discussed mainly regressions in the form 
. We have touched on using polynomials to fit more complex equations such as 
. However, as we add more features to our model, when to use a transformation of the original feature becomes a case of trial and error. Using neural networks, we are able to fit a much more complex function, y = f(X), to our data, without the need to engineer or transform our existing features.
Structure of neural networks
When we were learning the optimal value of 
, which minimized loss in our regressions, this is effectively the same as a one-layer neural network:
Figure 1.10 – One-layer neural network
Here, we take each of our features, 
, as an input, illustrated here by a node. We wish to learn the parameters, 
, which are represented as connections in this diagram. Our final sum of all the products between 
and 
gives us our final prediction, y:
A neural network...
