rstudio regression

Ufldl Study Notes and programming assignments: softmax regression (softmax regression) Ufldl provides a new tutorial, which is better than the previous one. Starting from the basics, the system is clear and has programming practices. In the high-quality deep learning group, you can learn DL directly without having to delve into other machine learning algorithms. So I started to do this recently. The tutori

The Elastic network regression algorithm is a regression algorithm for synthesizing lasso regression and ridge regression, which can control the effect of single coefficients by adding L1 regular and L2 regular term in loss function.ImportTensorFlow as TFImportNumPy as NPImportMatplotlib.pyplot as Plt fromSklearnImport

Problem 1. This section shows the final plsr related to component analysis and regression. Plsr feels that it has brought component analysis and regression to the extreme. The following describes the idea rather than the complete tutorial. Let's review the disadvantages of the earliest linear regression: if the number of samples m is less than the number of feat

The article is from Professor Andrew Ng of Stanford University's machine learning course, which is a personal study note for the course, subject to the contents of the original course. Thank Bo Master Rachel Zhang's personal notes, for me to do personal study notes provide a good reference and role models. §3. Logistic Regression of Logistic regression1 Classification classificationFirstly, the concept of classification problem is introduced-in

This article is from here, the content of this blog is Java Open source, distributed deep Learning Project deeplearning4j The introduction of learning documents. Introduction:in general, neural networks are often used for unsupervised learning, classification, and regression. That is, neural networks can help group unlabeled data, classify data, or output successive values after supervised training. The application of typical neural networks in c

Most of this series is from the Standford public class machine learning Andrew Teacher's explanation, add some of their own understanding, programming implementation and learning notes.Chapter I. Logistic regression1. Logistic regressionLogistic regression is a kind of supervised learning classification algorithm, compared with the previous linear regression algorithm, the difference is that it is a classif

Bounding-box regression Recently has been looking at detection-related paper, from rcnn, fast rcnn, faster rcnn, YOLO, R-FCN, SSD, to this year's CVPR newest yolo9000. These paper loss functions include a border regression, in addition to rcnn detailed introduction, the other paper are a stroke, or direct reference to rcnn the loss function is written out. The first three online explanations are more, the

1. PrefaceToday we introduce the famous logistic regression in machine learning. Don't look at his name "return", but it is actually a classification algorithm. It is called logistic regression mainly because it is a transformation from linear regression.2. Logical regression principle 2.1 origin of logistic regression

There are a lot of similar articles from other places, and I don't know who is the original one. Because there are fewer original articles and fewer errors, I have modified this article and made a proper key mark (the content shown on the horizontal line is not big white and complicated, the subsequent processes are classified based on the operators obtained above) Initial contact Logistic Regression Classifier is no secret. In classification, the le

Linear regression is the most typical regression problem, and its target value has a linear relationship with all the features . Linear regression is similar to logistic regression, where logistic regression is based on linear regression