How a network computes, why gradients vanish, and what makes depth trainable
You now own the dense layer completely. Forward it computes Wx + b, backward it returns Wᵀδ to the layer below and δxᵀ for its own weights, and a whole stack of them trains by dl.15. Almost all of modern machine learning is two constrained versions of that one layer.
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▶ Bridge: Weight Sharing, Convolution and Attention