We already know that AI problems
are represented by mathematical models.
The architecture of a machine learning
model can be described as a network
made up of layers of interconnected nodes.
Let’s take a closer look at the structure of the model.
An Artificial Neural Network, or ANN,
is modeled after some concepts learned
from the workings of the human brain –
the original neural network.
In an artificial neural network, there are
layers of interconnected artificial neurons.
One layer is the input layer, where data
flows in to the artificial neural network.
The output layer is where the final results are generated.
In between there are one or more hidden
layers of neurons, where an activation function is
applied to the data and the resulting
output is transmitted to the next layer.
