Neural Networks

ELI5 (Explain Like I'm 5)

Imagine making a pizza with a recipe that gets better every time you eat the result.

At first, the recipe might say:

• 1 cup of flour
• 1 tomato
• 1 pinch of salt

After you taste it and tell the cook “this is too bland," the cook adjusts the recipe by doing the following.

• adding a little more salt
• more cheese 
• maybe bake it a little longer

Each time you try the pizza and give feedback, the cook changes the recipe so the next pizza is a little closer to what you like.

A neural network works like that cook.

It starts with a recipe full of numbers (weights). Every time it makes a prediction, it checks how good it was, then adjusts its recipe. Over many tries, the recipe gets better and better at producing the right outcome.

Instead of ingredients, a neural network works with numbers and patterns. And instead of pizza, it might be identifying pictures, translating sentences, or spotting spam. But the basic idea is the same - to learn from feedback and improve over time.

Building Blocks of Neural Networks

Most ANNs have the following layers.

1. Input layer: This is where the raw data enters the system. If the network is analyzing an image, the input layer receives the pixel values. No processing happens here; it is simply the entry point.
2. Hidden layers: These layers sit between the input and output. In a process called forward propagation, neurons in these layers apply weights and biases to the data to identify features. Simple networks may have one hidden layer, while Deep Neural Networks have many.
3. Output layer: This is the final result. For a classification task (like email spam filtering), the output layer provides the probability that the email is "Spam" or "Not Spam."

Common applications of ANNs

ANNs power

• computer vision (self-driving cars via CNNs)
• NLP (chatbots with transformers)
• recommendation systems (Netflix), fraud detection (banks)
• medical diagnostics (tumor identification from scans).

Why neural networks matter in AI

Neural networks are powerful because they can:

• Handle complex, non-linear relationships
• Learn directly from raw data
• Scale well with more data and compute power

They are especially effective for unstructured data like images, audio, text, and video.