gradient descent linear regression

This topic (Machine Learning) including Single-parameter linear regression, multi-parameter linear regression, Octave tutorial, logistic regression, regularization, neural network, machine learning system design, SVM (Support Vector Machines support vector machine), clusteri

0) The purpose of the recursive descent algorithm is to approximate the minimum value of the function by continually iterating, thus finding the parameter 1) the logistic regression is actually a classifier, using the existing sample to train the Sigmoid function.(1) The general form of the sigmoid function:(2) Graph of the sigmoid function:(3) Prediction function:For example, there is a sample X, he has 10

http://blog.csdn.net/pipisorry/article/details/43529845Machine learning machines Learning-andrew NG Courses Study notesMultivariate linear regression multivariable linear programming(linear regression works with multiple variables or with multiple features)multiple Features

The gradient descent method is a first-order optimization algorithm, also known as the fastest descent method. I have never paid attention to such algorithms before. Recently, When I was attending a machine learning course at Stanford University, I encountered the problem of Solving Linear

calculate the cost function value at this timeEnd% observe the change in cost function value with the number of iterations% plot (J);% observed fitting conditionsStem (x1,y);P2=x*theta;Hold on;Plot (X1,P2);7. Actual UseWhen you actually use linear regression, the input data is optimized first. Includes: 1. Remove redundant and unrelated variables; 2. For nonlinear relationships, polynomial fitting is used

) \]\[\frac{\partial}{\partial\omega_j}j (W) = \sum_{i=1}^{n} (f (x_i)-y_i) \cdot x_{i,j}\] All \ (w_i\) updates at the same time, where \ (\alpha\) is the learning rate/update step Some derivatives: Batch processing gradient descent Every update takes advantage of all data Iteration is slow under large samples Random

The theory of logistic regression has been described in the previous article, in the solution of parameters can be used Newton iterative, you can find this method seemsToo complex, today we introduce another method, called gradient descent . Of course, the minimum value is the gradient drop, and the maximum value is re

= Load (' Ex2x.dat '); y = Load (' Ex2y.dat '); Plot (x, y, ' * ') xlabel (' height ') ylabel (' age ') x = [Ones (size (x), 1), x]; W=INV (x ' *x) *x ' *y hold on%plot (x,0.0639*x+0.7502) plot (x (:, 2), 0.0639*x (:, 2) +0.7502)% corrected code Using gradient descend Process solution: % Exercise 2 Linear Regression% Data is roughly based on $ CDC growth figu

the actual value? Therefore, the cost function is used to evaluate.The cost function after vectorization:Generally, the number of training samples is expressed in m (size of training set), X (i) represents the first sample, and Y (i) represents the predicted result of the sample I.It can be seen that the cost function is very similar to the concept of "minimum mean variance". J (θ) is the theta function.Obviously, the smaller the cost function, the better the model. The goal, therefore, is to

The theory of logistic regression has been described in the previous article, in the solution of parameters can be used Newton iterative, you can find this method seems Too complex, today we introduce another method, called gradient descent . Of course, the minimum value is the gradient drop, and the maximum value is r

The theory of logistic regression has been described in the previous article, in the solution of parameters can be used Newton iterative, you can find this method seemsToo complex, today we introduce another method, called gradient descent . Of course, the minimum value is the gradient drop, and the maximum value is re

As a fan of machine learning, he has recently been studying with Andrew Ng's machines learning. In the first part of the handout, Ng first explains what is called supervised learning, secondly, the linear model solved by least squares, the logistics regression of the response function by using the SIGMOD function, and then, using these two models, a widely used exponential distribution family is introduced.

Result Print("model parameters: \n%s"% Sess.run (model["Model_params"])) Print("Iteration count:%s"%Step)Print("loss function Value:%s"%loss)defrun ():"""Program Entry""" #dimension represents the number of independent variables, and NUM represents the number of data in the dataset. Dimension = 30Num= 10000#generate model data randomlyX, Y =generatelineardata (dimension, num)#Defining the ModelModel =Createlinearmodel (Dimension)#Estimating model parameters using the

distribution.How to adjust θ so that J (θ) obtains the minimum value there are many methods, this article will focus on the gradient descent method and the normal equation method.Gradient DescentAfter the linear regression model is selected, the model can be used for prediction only if the parameter θ is determined. H

=Tf.reduce_mean (Tf.abs (A)) L2_a_loss=Tf.reduce_mean (Tf.square (A)) E1_term=tf.multiply (elastic_p1,l1_a_loss) e2_term=tf.multiply (Elastic_p2,l2_a_loss)#here A is an irregular shape that corresponds to the array form of the 3,1 loss also expands the arrays formLoss=tf.expand_dims (Tf.add (Tf.add (Tf.reduce_mean (Tf.square (y_target-model_out)), e1_term), e2_term), 0)#Initialize Variablesinit=Tf.global_variables_initializer () sess.run (init)#Gradient