Functional representations

Before we march forth in our journey to understand, build, and master neural networks, we must at least refresh our perception of some fundamental ML concepts. For example, it is important to understand that you are never modeling a phenomenon completely. You are only functionally representing a part of it. This helps you think about data intuitively, forming but a small piece in the large puzzle, represented by a general phenomenon that you are trying to understand. This also helps you realize that times change. The importance of features, as well as surrounding environments, are both subject to such change, eroding the predictive power of your model. Such intuition is naturally built with practice and domain knowledge.

In the following section, we will briefly refresh our memory with some classic pitfalls of ML use cases, with a few simple scenario-driven examples. This is important to do as we will notice these same problems reappear when we undertake our main journey of understanding and applying neural networks to various use cases.