In order to make the gradient decent faster ,we can use this formular .

After updating the xi ,we could use gradient decent faster.
NOTICE : If we want to predict some other values , the input use the above normalization method.
In order to know whether our gradient decent is working or not. We can plot the cost function and interation is the x axis. every iteration should lead to the cost function going down. If it doesn't go down, maybe the alpha(For error square cost function) is too big .
本文介绍了一种改进梯度下降法的方法,通过特定公式更新参数以提高算法效率。文中强调了正确选择学习率的重要性,并建议通过绘制代价函数随迭代次数变化的曲线来监测算法是否正常运行。
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