多元(多变量)梯度下降与特征缩放、学习率 Gradient Descent for Multiple Variables (Feature Scaling、Learning Rate),程序员大本营,技术文章内容聚合第一站。
一、Linear Regression with multiple variables (多元线性回归): 二、Gradient descent for multiple variables(多元梯度下降法) (1)Gradient descent for multiple variables 偏导数项展开: (2)Feature Scaling(特征缩放) 原因:若特征规模差别很大(如x1:0-2000,x2:1-5),得到的代价函数可能会不光滑,导致梯度下降收...
The gradient descent equation itself is generally the same form; we just have to repeat it for our 'n' features: In other words: The following
The gradient descent equation itself is generally the same form; we just have to repeat it for our 'n' features: In other words: The following image compares gradient descent with one variable to gradient descent with multiple variables:
Once again，使特征值在一个相近的范围，从而使梯度下降更快。只要范围相近就OK。归一化（mean normalization）：特征值的范围的标准差做分母（也可以是最大值减去最小值），然后每个特征值减去它们的的平均值做分子。（因为只要使得特征值范围相近就OK）目的是使特征在一个相近的范围，更快的收敛。上图...
第2周--Multiple Features 第2周--GradientDescentfor Multiple Variables 第2周--GradientDescentin Practice I - Feature Scaling 第2周--GradientDescentin Practice II -LearningRate 第2周--Features and [李宏毅-机器学习]梯度下降Graident Descent
week two(1)—Multiple Features Gradient Descent 1 Multiple Features 2 Gradient Descent For Multiple Variables 3.1 Gradient Descent in Practice I - Feature Scaling
The MSE cost function includes multiple variables, so let’s look at one more simple minimization example before going back to the cost function.Let’s take the function:J(θ)=θ12+θ22J(θ)=θ12+θ22 When there are multiple variables in the minimization objective, gradient descent ...
(alpha).11%12% Your taskisto first make sure that your functions -13%computeCost and gradientDescent already work with14%thisstarter code and support multiple variables.15%16% After that,tryrunning gradient descent with17%different values of alpha and see which one gives18%you the best ...
Tags Functions Gradient Multiple Multiple variables Variables Oct 19, 2007 #1 Niles 1,834 0 Homework Statement The gradient of f(x,y) = x^2-x+y is: gradient_f(x,y) = (2x-1 ; 1). To find gradient_f(x,y), I set 2x-1 = 0 and 1 = 0 - so there are no points, where...