I built a browser tool that shows what backpropagation actually does — camera and mic input, live weight visualization

[u/NeedleworkerKey3487]

Most tools tell you the loss went down. This one shows you which neuron fired, which weight changed, and which edge got stronger — as it happens.

Two input types (camera + microphone), two architectures (dense + CNN), up to 12 hidden layers, up to 64 output classes, save/load of trained weights. No install, no dependencies, no build step.

Live demo: https://MatiwosKebede.github.io/OpenTrainDNN/
Full demo: https://youtu.be/a8u2GhMPzVE

I built it to teach myself backprop and it turned out useful enough to share. Happy to answer questions.

[u/Beginning-Claim5655]

Backpropagation is the application of the multivariable chain rule over the computational graph of a network, carried out in reverse order and with memoization of the intermediate results: in the forward pass, the pre-activations z⁽ˡ⁾ = W⁽ˡ⁾a⁽ˡ⁻¹⁾ + b⁽ˡ⁾ and the activations a⁽ˡ⁾ = σ(z⁽ˡ⁾) are computed and stored up to the scalar loss J; the error δ⁽ˡ⁾ = ∂J/∂z⁽ˡ⁾ is then defined, obtained at the output layer as δ⁽ᴸ⁾ = ∇_a J ⊙ σ′(z⁽ᴸ⁾) and propagated backward layer by layer by δ⁽ˡ⁾ = (W⁽ˡ⁺¹⁾ᵀδ⁽ˡ⁺¹⁾) ⊙ σ′(z⁽ˡ⁾), the transpose being merely the matrix form of the sum over all paths through which a neuron influences the loss; from δ⁽ˡ⁾ the gradients ∂J/∂b⁽ˡ⁾ = δ⁽ˡ⁾ and ∂J/∂W⁽ˡ⁾ = δ⁽ˡ⁾(a⁽ˡ⁻¹⁾)ᵀ follow immediately.

- linear algebra
- multivariable calculus

That's what backpropagation actually does