第一章:Simple Linear Regression 1.领域知识在lR中有什么用? feature extraction的时候需要对这个领域的理解。 2.线性回归的点方程和线方程表示? 3.梯度下降计算loss时是计算所有样本点的loss还是部分点的loss? 4.什么是凸函数? 5.可以用梯度=0来解LR嘛?可以解其它ML模型嘛? 6.目前数学界对凸优化和非凸优化...
这种实际叫做Stepwise Regression。 8.LASSO的重要性? 写《The Element of Statistical Learning》的作者专门为LASSO写了一本书,在本课程中,Emily老师说"LASSO is really fundamentally changed the field of machine learning and statistic." 9.什么是Elastic Net? 占坑 10.为什么LASSO感觉比Ridge更重要? 因为LASSO...
%matplotlib widgetimportmatplotlib.pyplotaspltfromlab_utils_commonimportplot_data, sigmoid, dlc plt.style.use('./deeplearning.mplstyle') X_train = np.array([[0.5,1.5], [1,1], [1.5,0.5], [3,0.5], [2,2], [1,2.5]])#(m,n)y_train = np.array([0,0,0,1,1,1])#(m,)fig,ax...
下图给出了上一小节中给出的梯度下降算法,其中:参数α称为学习速率,它控制我们以多大的幅度更新参数θj。本小节将要给这个式子一个直观的认识,并且介绍式中的两部分(learning rate和derivative)的作用以及为什么当把这两部分放一起时整个更新过程是有意义的。 以一个稍微简单的情况为例:代价函数J只有一个参数θ1,...
ridge regression 机器学习 machine learning regression 深度学习的课程笔记,参考李宏毅机器学习课程 一、定义 回归是通过输入特征向量来找到函数并输出数值标量。 例如,深度学习应用于自动驾驶领域。我们在无人车上输入每个传感器的数据,例如路况、测量的车辆距离等,并结合回归模型输出方向盘角度。
Logistic regression is another technique borrowed by machine learning from the field of statistics. It is the go-to method for binary classification problems (problems with two class values). In this post, you will discover the logistic regression algorithm for machine learning. After reading this ...
Regression is a vital tool for estimating investing outcomes based on various inputs. Regression is a vital tool for predicting outcomes in investing and other pursuits. Find out what it means when applied to machine learning.
Why is regression in machine learning important? Regression is an essential concept not only for machine learning experts, but also for all business leaders, as it is a foundational technique inpredictive analytics, said Nick Kramer, vice president of applied solutions at global consulting firm SSA...
「Machine Learning」线性回归认识 不难,根本都不难~😧 1、初识线性回归 所谓线性回归(Linear Regression),其最本质的特点就是可以用来根据已有的数据探究一个(或者多个)自变量与因变量之间的线性关系,从而对未知自变量所对应因变量进行预测。以单个自变量为例:...
第3 个问题:A cat photo classification model predicts 1 if it's a cat, and 0 if it's not a cat. For a particular photograph, the logistic regression model outputs g(z)g(z) (a number between 0 and 1). Which of these would be a reasonable criteria to decide whether to predict if...