regression solver

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

Ufldl Study Notes and programming assignments: Linear Regression (linear 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 tutorial

found on the internet there are a lot of principles to explain, in fact, this everyone will almost, very few provide code reference, I here Python directly realized, the back will also implement the neural network, regression tree and other types of machine learning algorithmsfirst to a small test sledgehammer, personal expression ability is not very good, we forgive briefly say your own understanding : train a linear

1 linear regression algorithmHttp://www.cnblogs.com/wangxin37/p/8297988.htmlThe term regression refers to the fact that we predict an accurate output value based on the previous data, for this example is the price, and there is another most common way to supervise learning, called classification, when we want to predict discrete output values, for example, we are looking for cancer tumors, and want to deter

Catalog Logistic regression principle Logistic regression code (Spark Python) Logistic regression principle See blog: http://www.cnblogs.com/itmorn/p/7890468.htmlBack to Catalog Logistic regression code (Spark Python) code data:https://pan.baidu.com/s/1jHWKG4I Pass

This is the study note of Andrew Ng's public course on machine learning. Examples of reality are spam/non-spam, tumors are benign or malignant, and so on. How to classify. I have accumulated an experience from high school mathematics. Assuming that the linear equation is f (x) = 0, then the point to the left of the line is taken to the left of the linear equation, resulting in the result So, if we can find such a line, so that its left point belongs to Class A, the right point belongs to Class B

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

Form: Use the sigmoid function: g(Z)= 1 1+ e? Z Its derivative is g- (Z)=(1?g(Z))g(Z) Assume: That If there is a sample of M, the likelihood function form is: Logarithmic form: Using gradient rise method to find its maximum valueDerivation: The update rules are: It can be found that the rules form and the LMS update rules are the same, however, their demarcation function hθ (x ) is completely different (the H (x) is a nonlinear function in