In past articles, we have seen how neural networks approximate non-linearities and the reason for their extraordinary efficiency. In this article, let us expand our understanding by exploring loss functions. Firstly, what exactly is a loss function, and what purpose does it serve?
To answer our question, let us recall how we represent neural networks. A simple neural network that maps a set of inputs X to a set of outputs Y is represented as follows:
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