Backpropagation, intuitively

After this lesson you can: Explain how blame for an error is shared backwards through the network.

First: Layers and activation functions, Gradient descent

Watch

• Backpropagation, intuitively | Deep Learning Chapter 3 - 3Blue1Brown, 13 min. Exact intuitive backprop lesson from a strong visual teaching channel, at a focused concept-friendly length.
• Neural Networks Pt. 2: Backpropagation Main Ideas - StatQuest with Josh Starmer, 18 min. Main-ideas framing suits engineers who know gradient descent but need the backward blame-sharing picture.
• What is Back Propagation - IBM Technology, 8 min. A concise IBM overview can reinforce the vocabulary without turning this into a math lecture.

Notes

Backpropagation is the bookkeeping that tells gradient descent how each weight contributed to the final error. A neural network makes a prediction by passing numbers forward through layers; the loss says how wrong that prediction was. Backprop then walks backward from that loss, asking: if this value had been a little higher or lower, would the loss have improved? That local sensitivity becomes the blame signal passed to the previous layer.

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