Little Bits of Artificial Intelligence

1. Imagine: A Curious Kid Learning to Identify Fruits

Meet Arya, a curious kid.

Arya is trying to learn how to recognize fruits — say, apples, bananas, and oranges. But she’s just starting out and needs to be trained.

To help Arya learn, we show her lots of examples.

Basic Building Blocks of a Neural Network = How Arya Learns

1. Input Layer = Arya’s Eyes

This is where data enters the brain. For Arya, it could be:

• The color of the fruit
• The shape
• The texture
• The size

These are like little clues Arya gets when she looks at a fruit.

2. Hidden Layers = Arya’s Brain Thinking

This is where magic happens. Arya’s brain tries to connect the clues:

• “If it’s yellow and long → might be a banana”
• “If it’s red and round → could be an apple”

This thinking happens in layers — each layer gets better at understanding based on the previous one. She may first look at color, then shape, then match both to get a better guess. Think of this like a group of teachers passing the fruit along a chain. Each teacher adds a little more insight before handing it to the next one.

3. Weights and Biases = Arya’s Guessing Strategy

Arya doesn’t treat every clue equally.

• Color might be more important than size.
• So she gives more weight to color.

These weights are like how much Arya trusts a clue. Over time, she adjusts them if she’s wrong — learning from mistakes. “Oops! That yellow thing was an orange, not a banana… maybe color alone isn’t enough.”

4. Activation Function = Arya’s Decision Switch

After Arya adds up her clues, she decides:

• “Yes, this is a banana!”
• Or “Hmm… I’m not so sure.”

This step adds a spark of logic to her thinking — kind of like a filter that decides when she’s confident enough to make a call.

5. Output Layer = Arya’s Answer

Now Arya gives her answer: Apple, Banana, or Orange.

Just like the final output of a neural network: it tells you the predicted result.

6. Feedback = Learning from Mistakes

Each time Arya gets it wrong, you correct her: “No Arya, that wasn’t an apple — it was a tomato!”

She updates how much she trusts each clue, adjusts her strategy, and improves over time.

That’s exactly how a neural network learns — through trial and error.