coursera machine learning ng

------------------------------------------------------------------------------------------------------------------ This article is excerpted from the courseware "Machine Learning" by Andrew Ng of Stanford University. Why is the Department of biological engineering at Ocean University the best? They are incomparable to other schools in studying the environment a

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 respons

Preface: This article describes Ng's notes about machine learning about SVM. I have also learned some SVM theories and used libsvm before. However, this time I have learned a lot about Ng's content, and I can vaguely see the process from Logistic model to SVM model. Basic Content: When using the linear model for classification, You can regard the parameter vector as a variable. If the cost function

Tags: 9.png update regular des mini RAC spam ORM ProofOrganize the machine learning course from Adrew Ng week3Directory: Two classification problems Model representation Decision Boundary Loss function Multi-Classification problem Over-fitting problems and regularization What is overfitting How to resol

=[] For C in Cs: # Select Model CLS = Logisticregression (c=c) # submit data to Model training Cls.fit (X_train, Y_train) Scores.append (Cls.score (X_test, Y_test)) # # Drawing Fig=plt.figure () Ax=fig.add_subplot (1,1,1) ax.pl OT (cs,scores) ax.set_xlabel (r "C") Ax.set_ylabel (r "Score") Ax.set_xscale (' Log ') Ax.set_title ("Logisticregression") plt.show () If __name__== ' __main__ ': X_train,x_test,y_train,y_test=load_data () # Generates a dataset for regression problems Test_logist

symmetric semi-definite matrixin the case where the data is non-linear:called L1 norm soft margin SVM. is a convex optimization problem. It allows an interval of less than 1, which allows for the categorization of errors. SMO algorithm:coordinate ascent algorithm:This algorithm has more iterations, but at some point the inner loop will be very fast if a parameter in W (A1,,, am) is very small at the cost of finding the optimal value. SMO:If only one α is solved as SVM, the other α is fixed. obt

: One-to-multiple ) Sometimes the problem is not as simple as determining whether a patient's tumor is malignant or benign. For example, determining whether the weather is sunny, cloudy, raining, Or snowing is necessary. We can use a line to separate binary classification. What about multiclass classification? There is a simple method, that is, to separate only one category at a time. There are several categories to construct several decision edge, that is, severalH (x): In th

After reading the first class in the Andrew Ng Open Class, I feel that machine learning is very high .. The 3D imaging technology used by the CAD Laboratory of the highest national key laboratory in the Department is almost the same as that used by Andrew Ng's clustering algorithm. After reading this, I think that I am now taking the high-end route → _ →Chapter 1

ascent):The path of the straight-line iterative optimization in the figure, you can see that each step will be further ahead of the optimal value, and that the forward route is parallel to the axis, because each step only optimizes one variable.This is like finding the extremum of a curve in the X-y coordinate system, but the curve function cannot be directly derivative, so what gradient descent method does not apply. However, after one variable is fixed, the other one can be obtained by deriva

http://blog.csdn.net/pipisorry/article/details/44850971Machine learning machines Learning-andrew NG Courses Study notesRecommender Systems recommendation System{An important application of machine learning}Problem formulation Problem PlanningNote:1. To allow 0 to 5 stars as

Percent Machine learning Online class-exercise 4 neural Network learning% instructions%------------% This file contains Co De that helps you get started on the% linear exercise. You'll need to complete the following functions% of this exericse:%% sigmoidgradient.m% randinitializeweights.m% nncost function.m%% for the exercise, you'll not need to the change any co