How a network computes, why gradients vanish, and what makes depth trainable
calc2.15 established that the derivative of a composition is a product of Jacobians, J = Jᴸ ⋯ J² J¹. Matrix multiplication is associative, so you may bracket that product however you like and the answer is identical. The cost is not identical, and the gap is not small.
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