Deep Learning 07: Hidden Layer

 You’re showing a computer a picture of a cat 🐱 and asking it to figure out what it is.

A hidden layer is like the thinking step between what the computer sees and what it decides.

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🎨 Example: Cat Picture

1. Input layer: Sees pixels (colors, brightness — just numbers).

2. Hidden layers: These are the detectives that look for patterns:

   • 1st hidden layer: Finds edges (lines, shapes).

   • 2nd hidden layer: Notices ears, eyes, fur.

   • 3rd hidden layer: Realizes “oh, those look like cat features!”

3. Output layer: Says “CAT!” 🐾

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🧠 Why “Hidden”?

Because you don’t see what’s happening inside — the neurons there are doing math that humans can’t easily interpret.
You only see the input (photo) and output (result).
What happens in the middle is hidden — but that’s where the real learning happens.

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⚙️ In Simple Terms:

Hidden layers are like:

“The secret steps your brain takes between seeing something and understanding what it is.”

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Would you like me to explain what happens inside a hidden layer (how it transforms inputs into patterns)?