เครื่องมือเพิ่มประสิทธิภาพ: SGD, โมเมนตัม, RMSProp และ Adam

Optimizers: how a neural network reaches the minimum

All optimizers solve the same problem: update weights so loss goes down. The difference is how they use the gradient. SGD is direct: a step opposite to the gradient. Momentum adds inertia: it remembers past steps. Adam is adaptive: it scales the step per parameter from gradient history. That adaptivity is why Adam is the default for transformers.

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