Deep Learning 06: Neural Network

 You’re teaching a computer to recognize cats in pictures.

A neural network is like a brain made of tiny math “neurons” that all work together to learn patterns.

Here’s the ELI10 (Explain Like I’m 10) version:

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🧠 The Brain Analogy

• Think of neurons as little decision makers.

• Each neuron looks at numbers (like pixel brightness), does some math, and passes a result to the next layer of neurons.

• The last layer gives the final answer — like “This is a cat” or “Not a cat.”

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🧩 The Layers

1. Input Layer: The raw data — e.g., all the pixels in a photo.

2. Hidden Layers: The “thinking” part — neurons here detect patterns like edges, eyes, or fur.

3. Output Layer: The decision — e.g., cat 🐱 or not-cat ❌.

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⚙️ How It Learns

• The network guesses (say, “cat”).

• It checks if it’s right or wrong.

• If wrong, it adjusts the neuron connections (these connections are called weights).

• Repeat this many, many times until it gets really good at guessing.

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🔁 Simple Analogy

It’s like learning to throw a basketball:

• You throw (make a guess).

• You see if it goes in (check error).

• You adjust your aim (update weights).

• After enough tries, you get accurate.

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So in short:

A neural network is a system that learns from examples by adjusting how strongly its “neurons” talk to each other, so it can recognize patterns or make predictions — kind of like a mini digital brain.