How to explain Neural Networks to Your Grandfather?

What if I told you that cutting-edge voice assistants, facial recognition software, and self-driving cars can all be described using activities as basic as cake making and puzzle solving? Grandpa, hang on to your hat because I’m ready to do just that. I’m going to describe neural networks to you today in terminology that’s as crystal-clear as a sunny day.

Step 1: The Foundation – What is a Neural Network?

To begin, a neural network is a form of computer system that is supposed to resemble the human brain. Consider how our brain solves issues or learns new information. Isn’t it true that it won’t happen overnight? Slowly, we learn through recognising patterns, comprehending relationships, and remembering consequences. A neural network, similarly, learns from data to discover patterns, solve problems, and make judgements.

A neural network is made up of nodes or “artificial neurons,” just like our brain is made up of multiple linked cells called neurons. When these nodes are linked, they form a system capable of processing complicated data.

Step 2: How Does a Neural Network Work?

Think of it as teaching someone to bake a cake for the first time. Initially, they wouldn’t know the exact proportions of ingredients, baking time, or temperature settings. They might bake several cakes, each time adjusting these parameters based on the outcome until they get it right.

Similarly, a neural network starts with random values. It takes in input (data), processes it through multiple layers of these artificial neurons (where each does a simple computation), and gives an output. The network then checks how far off its output is from the expected result, and adjusts the calculations in the artificial neurons accordingly. This process is repeated with multiple pieces of data, and with each repetition, the network gets a little better at producing the right output. This is called “training the network”.

Step 3: Understanding the Structure of Neural Networks

The structure of a neural network can be thought of as a sports team. It has an “input layer” (like forwards in a football team), one or more “hidden layers” (midfielders), and an “output layer” (defenders). Each layer has its role, passing the ‘ball’ (information) to the next, trying to score a goal (solve a problem).

• The Input Layer: This is where the network receives information, just as forwards receive the ball at the start of the game. The type of data can vary, from images and text to numbers or sounds.
• The Hidden Layers: These are the layers in the middle, where the real processing happens. Each neuron in these layers performs a simple calculation on the data received and passes the result onto the next layer. This is much like the midfielders controlling the flow of the game.
• The Output Layer: This is where the final decision or prediction is made, like defenders deciding where to pass the ball to prevent the opponent from scoring.

Step 4: Learning Through Backpropagation

Remember the cake baking example? How we learned to adjust ingredients and baking time based on the outcome? A neural network does something similar through a process called “backpropagation”.

Backpropagation is a fancy word for a simple idea: when the network makes a mistake, it looks back at its steps (propagates back) to understand where it went wrong. It then makes a note to adjust the calculations in its artificial neurons to avoid the same mistake in the future.

In Conclusion

Just as your brain is capable of learning and improving with experience, neural networks are capable of learning from data and improving their performance over time. Whether it’s Siri understanding your commands,

Facebook tagging your friends in photos, or a self-driving car recognizing stop signs, it’s all thanks to these wonderfully complex neural networks.

So, next time you hear about AI or neural networks, you can picture a diligent student or a hard-working football team, continually learning and adapting to become better. Remember, Grandpa, understanding neural networks isn’t about knowing all the technical details but rather appreciating the magic of learning machines!