Implementing neural network layers for foundational problem types

In Chapters 2 to 7, although many types of NN layers were introduced, the core layers for the problem types were either not used or not explained. Here, we will go through each of them for clarity and intuition.

Implementing the binary classification layer

Binary means two options for categorical data. Note that this does not necessarily mean a strict rule for the categories to be true or false nor positive or negative in the raw data. The two options can be in any format possible in terms of raw data, in strings, numbers, or symbols. However, note that NNs can always only produce numerical outputs. This means that the target itself has to be represented numerically, for which the optimal numbers are the binary values of zero and one. This means that the data column to be used as a target for training with only two unique values must go through preprocessing to map itself into zero or one.

Generally, there are...