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Backpropagation

Backpropagation is how a neural network learns from its mistakes. After a wrong answer it works backward through the layers and calculates how much each connection contributed to the error, so gradient descent knows which weights to adjust and by how much. Nearly every modern network is trained this way, including large language models.

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Reference Guide · 20 min read

How Neural Networks Work

The canonical 784-16-16-10 MNIST network as an explorable 3D machine: 16 stations across 4 acts, a 16-step guided journey from pixels to the brain question, all 12,960 weight connections really drawn, and a training row where gradient descent is really computed on an illustrative loss terrain, and a Train mode where the network genuinely learns 10,000 real MNIST digits in your browser.

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Backpropagation calculus | Deep Learning Chapter 4 video · 3Blue1Brown
Backpropagation, intuitively | Deep Learning Chapter 3 video · 3Blue1Brown
The spelled-out intro to neural networks and backpropagation: building micrograd video · Andrej Karpathy
Backpropagation (Wikipedia) guide · Wikipedia
Backpropagation algorithm visual explanation post · Hacker News
Backpropagation is not just the chain-rule, then what is it? post · r/MachineLearning
Is it possible to train a neural network without backpropagation? post · Cross Validated
What do you think is the best way to understand backpropagation? post · r/MachineLearning

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Backpropagation

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