⚡️ Backpropagation

Backpropagation is the algorithm that tells a neural network how to adjust its weights after making a mistake.

It pushes the error backward through the network.

Core idea: Measure error → send blame backward → update weights.

It's purpose is to minimize prediction error efficiently.

Where it is used:

🧠 Training neural networks
👁 Computer vision
🗣 NLP models
🔊 Speech systems
🤖 Deep learning

Simple flow:
1️⃣ Forward pass makes prediction
2️⃣ Loss measures error
3️⃣ Backward pass computes gradients
4️⃣ Optimizer updates weights

Things to know:

📉 Needs differentiable functions
⚡️ Uses chain rule from calculus
🧮 Works with gradient descent
🚨 Can suffer from vanishing gradients

✅Backprop = error feedback system for neural networks.