An intuitive guide to Artificial Neural Networks

What is Neural Network (biological intuition)?
It will be nice if we give mother nature a round of applause and start with biological intuition. As the name suggests Neural Networks are information architecture inspired by our brain’s communication networks. A human brain on average contains about trillions of neural connections or synapses and these synapses help acquire, store, and retrieve day-to-day learning and memories. When any learning occurs in form of either explicit memory (facts, events) or implicit (skills, tasks) it stores the results in the form of new neural connections, when recalled that memory or skill from storage again, the result is these connections simultaneously firing together.
Math intuition
Now that have we reflected upon how amazing nature is, lets move on to towards how can we copy this architecture.

In the above figure, X1 to X3 represents our input neuron (feature), where in order for the output node to be activated (in our case memory recall), all three inputs (X) need to fire simultaneously.

Each of X variable’s connection carries a “weight” with it — think of it as the importance of that variable X. In machine learning, the weight can also be called the feature importance. Think of the weights as the measurement of the electrical impulse and the potency of the voltage defines how important the variable X is in playing a role for the output node (prediction). In Artificial Neural Networks (ANN) the weights are redefined and updated after every training session. Neural Networks are effective in measuring feature interactions and hidden patterns thus suitable for unsupervised learning.
or as phrased by Siegrid Löwel:
“Cells that fire together, wire together”
The output node carries an aggregated sum of all the interactions that go through the weight filter. Please keep in mind this is a simplified neural learning model; complex computational ANNs can consist of thousands of nodes with hundreds of features with million connections and interactions.
This marks the end of Part 1, where we talked about how signals from input variable X propagates and if the “intensity” or “weight” of their signal plays significant outcome in output neuron (prediction), then the weight positively increases so does the feature importance.
