(1) How to comput the Cost function in Univirate/Multivariate Linear Regression; (2) How to comput the Batch Gradient Descent function in Univirate/Multivariate Linear Regression; (3) How to scale features by mean value and standard deviation; (4) Ho…
machine learning- linear regression with one variable(2) Linear regression with one variable = univariate linear regression: 由一个输入变量预测出一个output (regression problem预测连续的值). single input<--->single output training set:…
Question 1 Consider the problem of predicting how well a student does in her second year of college/university, given how well they did in their first year. Specifically, let x be equal to the number of "A" grades (including A-. A and A+ grades)…
ON THE EVOLUTION OF MACHINE LEARNING: FROM LINEAR MODELS TO NEURAL NETWORKS We recently interviewed Reza Zadeh (@Reza_Zadeh). Reza is a Consulting Professor in the Institute for Computational and Mathematical Engineering at Stanford University and a…
1. Sigmoid Function In Logisttic Regression, the hypothesis is defined as: where function g is the sigmoid function. The sigmoid function is defined as: 2.Cost function and gradient The cost function in logistic regression is: the gradient of the cos…
1.Multiple features So what the form of the hypothesis should be ? For convenience, define x0=1 At this time, the parameter in the model is a ( + 1)-dimensional vector, and any training instance is also a ( + 1)-dimensional vector. The dimension of t…
1.Model representation Our Training Set [训练集]: We will start with this ''Housing price prediction'' example first of fitting linear functions, and we will build on this to eventually have more complex models 2.Cost function 代价函数(平方误差函数):It figures ou…
