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endfunction
Initializes the matrix for the preceding dataset. Call a function to calculate the value of the cost function.
1> X = [1 1; 1 2; 1 3]; 2> Y = [1; 2; 3]; 3> Theta = [0; 1]; % records is 0, 1 h (x) = x. The value of the cost function is 04> J = costfunctionj (X, Y, theta) 5 J = 0.
1> Theta = [0; 0]; % values is 0, 0 h (x) = 0. data cannot be fitted at this time. 2> J = costfunctionj (X, Y, th
Public Course address:Https://class.coursera.org/ml-003/class/index
INSTRUCTOR:Andrew Ng 1. Cost Function (
Cost functions
)
The last lecture introduced the multiclass classification problem. The difference between the multiclass classification problem and the binary classification problem lies in that there are multiple output units, which are summarized as follows:
At the same time, we also
is more than one, the Newton method iterates over the rule:Newton's method usually has a faster convergence rate than the batch gradient, and it takes a much smaller number of iterations to get close to the minimum value. However, when the parameters of the model are many (n), the computational cost of the Hessian matrix will be large, resulting in a slower convergence rate, but when the number of arguments is not long, the Newton method is usually m
This is what we have learned (except decision tree)Here is a typical decision tree algorithm, with four places to choose from:Then introduced a cart algorithm: By decision Stump divided into two categories, the criterion for measuring subtree is that the data are divided into two categories, the purity of these two types of data (purifying).The following is a measure of purity:Finally, when to stop:Decision tree may be overfitting, reducing the number of Ein and leaves (indicating the complexity
In this section, a linear model is introduced, and several linear models are compared, and the linear regression and the logistic regression are used for classification by the conversion error function.More important is this diagram, which explains why you can use linear regression or a logistic regression to replace linear classificationThen the stochastic gradient descent method is introduced, which is an improvement to the gradient descent method, which greatly improves the efficiency.Finally
This section is about the nuclear svm,andrew Ng's handout, which is also well-spoken.The first is kernel trick, which uses nuclear techniques to simplify the calculation of low-dimensional features by mapping high-dimensional features. The handout also speaks of the determination of the kernel function, that is, what function K can use kernel trick.In addition, the kernel function can measure the similarity of two features, the greater the value, the more similar.Next is the polynomial Kernel, w
-Polynomial regressionSince linear regression does not apply to all data, sometimes we need to use curves to fit our data, for example, with two-times polynomial:Or three-time polynomial:Usually we need to look at the data before deciding what model to try to fit.After that, we can make:The two-time polynomial is then converted to a linear regression model.It is worth noting that if we adopt a polynomial regression model, feature scaling is necessary before the gradient descent algorithm is run.
based on the minimum mean variance. The closer to the predicted point, the heavier the weight, which is to use the points near the check to give higher weights. The most common is the Gaussian nucleus. The weights corresponding to the Gaussian nuclei are as follows:In (Formula 2), the only thing we need to make sure is that it's a user-specified parameter that determines how much weight is given to nearby points.Therefore, as shown in (Equation 3), local weighted linear regression is a non-para
17.1 Study of large data sets17.2 Random Gradient Descent method17.3 Miniature Batch Gradient descent17.4 Stochastic gradient descent convergence17.5 Online Learning17.6 mapping simplification and data parallelism
17.1 Learning from large data sets
17.2random Gradient Descent method
17.3miniature Batch gradient descent
17.4stochastic gradient descent convergence
17.5Online Learning
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
calculates the accuracy of the entire system at this time:
As shown in, text recognition consists of four parts. Now we can find the system accuracy after optimization for each part. The question is, how can we improve the accuracy of the entire system? We can see from the table that, if we have optimized the text moderation part, the accuracy will be72%Add89%If we optimize the character segmentation, the accuracy is only from89%To90%If character recognition is optimized90%To100%In contr
the value is, the closer the value of the evaluation function is to the midline position of the parabolic curve, that is, the closer it is to the minimum value. It can be represented by an example:
Let's take a look at the meaning. When the value is too small, the update is slow, and the gradient descent algorithm will slow down in execution. When the value is too large, the gradient descent algorithm may exceed the target value (minimum value), leading to non-convergence, even divergence. As
An introductory tutorial on machine learning with a higher degree of identity, by Andrew Ng of Stanford. NetEase public class with Chinese and English subtitles teaching video resources (http://open.163.com/special/opencourse/ machinelearning.html), handout stamp here: http://cs229.stanford.edu/materials.htmlThere are a variety of similar course
It is decided that machine learning is under system learning, and Stanford courseware is the main line.
Notes1 is part of the http://www.stanford.edu/class/cs229/notes/cs229-notes1.pdf about Regression 1. Linear Regression
For example, if the House Price is predicted and the data cannot be found on the Internet, use
Since the end of last year to learn Andrew Ng's machine learning public class, in accordance with its courseware to try to achieve some of the algorithm to deepen understanding, but in this process encountered some problems, or for the implementation of the program, or to understand the algorithm. So prepare to organize this course and document your understanding, either right or wrong, to discuss together.
Terryj.sejnowski. (c) function interval and geometric interval of support vector machineto understand support vector machines (vectormachine), you must first understand the function interval and the geometry interval. Assume that the dataset is linearly divided. first change the symbol, the category y desirable value from {0,1} to { -1,1}, assuming that the function g is:The objective function H also consists of:Into:wherein, Equation 15 x,θεRn+1, and X0=1. In Equation 16, x,ωεRN,b replaces the
Welcome and Introductionoverviewreadinglog
9/9 videos and quiz completed;
10/29 Review;
Note1.1 Welcome
1) What are machine learning?
Machine learning are the science of getting compters to learn, without being explicitly programmed.
1.2 Introduction
Linear reg
single-variable learning method for the parameter, and the new algorithm on the right is the Multivariable learning method.(iii), Gradient descent for multiple variables-feature ScalingIt is important to normalized feature, so feature scaling is used, all feature will be normalized to the [ -1,1] interval:Normalization method: Xi= (xi-μi)/σi(iv), Gradient descent for multiple variables-
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