Why Gradient Descent Became Stochastic
Towards Data Science4695 字 (约 19 分钟)
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The core reason gradient descent evolved into stochastic gradient descent (SGD) is computational scalability: as dataset size grows, batch gradient descent (BGD) becomes prohibitively expensive, while SGD updates parameters using only one or a few samples per iteration—reducing cost and leveraging noise to escape local minima; the article illustrates this via linear regression, deriving the closed-form solution from MSE and naturally motivating iterative optimization.
入选理由：线性回归中β₀=27315.74、β₁=9020.66的解析解可通过MSE对β₀/β₁求偏导并令其为0推导得出
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