🧠 Backpropagation Visualizer
Discover how neural networks learn through backpropagation
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0 / 5 completedThe Learning Mystery
Neural networks can recognize faces, understand speech, and beat world champions at games. But how do they learn these abilities? The answer lies in a powerful algorithm called backpropagation.
🎯 The Core Problem
Imagine you're teaching a neural network to recognize cats. After showing it a cat image, the network guesses "dog" (wrong!). Now comes the crucial question:
"Which weights in the network should be adjusted, and by how much?"
The Challenge
A typical neural network has millions of weights. Adjusting each one randomly would take forever and likely make things worse.
The Solution
Backpropagation uses calculus to calculate exactly how each weight contributed to the error, then adjusts them accordingly.
📊 What You'll Learn
- •Forward Pass: How data flows through the network to make predictions
- •Loss Calculation: Measuring how wrong the network's prediction is
- •Backward Pass: Computing gradients and propagating errors backward
- •Weight Updates: Adjusting parameters to reduce future errors
Backpropagation is the foundation of modern AI. Understanding it gives you insight into how ChatGPT, Stable Diffusion, and every other neural network actually learns from data.