coursera machine learning review

used to measure the difference in the direction of two vectors, which is borrowed from the machine learning to measure the difference between sample vectors.(1) The angle cosine formula of vector A (x1,y1) and Vector B (x2,y2) in two-dimensional space:(2) Angle cosine of two n-dimensional sample points a (x11,x12,..., x1n) and B (x21,x22,..., x2n)Similarly, for two n-dimensional sample points a (x11,x12,..

as:If the covariance matrix is a unit matrix (the independent distribution of each sample vector), the formula becomes:That's the Euclidean distance.If the covariance matrix is a diagonal matrix, the formula becomes the normalized Euclidean distance.(2) The advantages and disadvantages of Markov distance: dimension independent, exclude the interference between the correlations between variables.(3) MATLAB calculation (1 2), (1 3), (2 2), (3 1) of the Markov distance between 22X = [1 2; 1 3; 2 2

\):The chain rules are updated as follows:\[\begin{split}\frac{c_0}{\partial \omega_{jk}^{(L)}}= \frac{\partial z_j^{(L)}}{\partial \omega_{jk}^{(l)}}\ Frac{\partial a_j^{(L)}}{\partial z_j^{(l)}}\frac{\partial c_0}{\partial a_j^{(L)}}\=a^{l-1}_k \sigma\prime (z^ {(l)}_j) 2 (a^{(l)}_j-y_j) \end{split}\]And to push this formula to other layers ( \frac{c}{\partial \omega_{jk}^{(L)}}\) , only the \ (\frac{\partial c}{\partial a_j^{) in the formula is required ( L)}}\) .Summarized as follows:Therefo

Last night written in-depth Java Virtual machine learning-the loading mechanism of the class to 1:30, because the next day to work, did not write the demo in the previous article, today take time to fill in the example of last night to explain.Here I first posted yesterday's two copies of the code, look again:classsingleton{Private StaticSingleton Singleton =NewSingleton ();//location of the first piece of

angle? You should be cautious about it. The cosine of the angle in the ry can be used to measure the difference between two vector directions. This concept is used in machine learning to measure the difference between sample vectors. (1) cosine formula of the angle between vector A (x1, Y1) and vector B (X2, Y2) in two-dimensional space: (2) Two n-dimensional sample points A (X11, X12 ,..., X1n) and B (X2

direction of two vectors, which is borrowed from the machine learning to measure the difference between sample vectors.(1) The angle cosine formula of vector A (x1,y1) and Vector B (x2,y2) in two-dimensional space:(2) Angle cosine of two n-dimensional sample points a (x11,x12,..., x1n) and B (x21,x22,..., x2n)Similarly, for two n-dimensional sample points a (x11,x12,..., x1n) and B (x21,x22,..., x2n), a co

. So, why is this so? The reason is simple: there are highly correlated variables in the data (up to 0.987 of the x1,x2 correlation), and the two variables are so similar, like two parallel vectors, that is, they're collinear . Popular, because two software is too similar, so that cannot judge who can contribute greater user satisfaction, the two 10:0 open, 5:5 Open, 0:10 open almost no difference. As can be seen from the above results, the standard error of β1 reached 2.3947 and β2 reached 2.4

http://blog.csdn.net/ppn029012/article/details/8908104 Machine Learning---2. From maximum likelihood to view linear regression classification: Mathematics machine Study 2013-05-10 00:34 3672 people read comments (15) Collection Report MLE machine learning Directory (?) [+]

recently in the "machine learning Combat" in the study of some basic algorithms, for a pure novice I also found on the Internet to write information, the following on the book I see Plus on other blog content to do a summary, blog please refer to http://www.cnblogs.com/ Baiyishaonian/p/4567446.htmlK-Nearest Neighbor algorithmThe K-Nearest neighbor algorithm is used to measure the distance between different

from:http://blog.csdn.net/lsldd/article/details/41551797In this series of articles, it is mentioned that the use of Python to start machine learning (3: Data fitting and generalized linear regression) refers to the regression algorithm for numerical prediction. The logistic regression algorithm is essentially regression, but it introduces logic functions to help classify it. It is found in practice that log

Version: Centos-6.5-i386-minimal virtual machine: after installing VMware 11.1.2, the NIC information is not visible, as follows: We edit the configuration information for the NIC Etho: the onboot option Value to Yes: save to exit and restart the Network service: Review the networking configuration again: complete. Note that if it fails, the configuration options for the virtual

noise in the activities as a regularizer). Presumably, for an implicit unit that uses a logical function, its output must be between 0 and 1, and now we use a binary function in the forward direction instead of the logic function in the hidden unit, the random output 0 or 1, the output is computed. Then in the reverse, we use the correct method to do the correction. The resulting model may have a poor performance on the training set, and the training speed is slower, but its performance on the

classification, Y only 1 and-1, the two cases of the error curve drawn out, found in fact classification line is always in the regression line below.Therefore, the conclusion is that Linear regression can be used as a slightly looser upper bound on binary classification problems.The trade-off object here is the efficiency of the algorithm and the tightness of the error bound .Here, Lin presents a practical approach: in practice, you can even do a regression to get an initialization parameter va

The book "Java Virtual machine concurrency programming" is really brief encounter. I've only scratched the surface of concurrent programming, and this book gives me a whole new understanding of concurrent programming. So put the knowledge points in the book to take notes, in order to review the use later.Concurrency and parallelismCarefully speaking, concurrency and parallelism are two different concepts. B

Review and summary of the related articles on generative antagonism learning (generative adversarial network, GAN). Article: Generative adversarial Nets (2014) [Paper][code]Ian Goodfellow's first article about generative confrontation learning, groundbreaking work.-This paper proposes to estimate the generation model by the confrontation network.-The theory expou

; kesi greater than 1 means the wrong, the more to the other end of the hyperplance)(3) can also be converted into standard QP problem, easy to solveNext, the idea of Hard-margin dual SVM is used, and the Soft-margin SVM is primal→dual.Because the inequality constraints become two classes, the natural introduction of two Largrange, and then the transformation of the hard-margin of thinking, the transformation into dual problem solving.First, the derivation of the Kesi, the optimization of the ob

components are 0.5 and 1 respectively) X = [0 0; 1 0; 0 2] D = Pdist (X, ' Seuclidean ', [0.5,1]) Results: D = 2.0000 2.0000 2.8284 6. Markov distance (Mahalanobis Distance) (1) Markov distance definition There are m sample vectors x1~xm, the covariance matrix is denoted as s, the mean values are denoted as vector μ, and the Markov distances of sample vectors x to u are expressed as: Where the Markov distance between the Vector XI and XJ is defined as: If the covariance matrix is a unit mat

It is mentioned in this series that using Python to start machine learning (3: Data fitting and generalized linear regression) mentions the regression algorithm for numerical prediction. The logical regression algorithm is essentially regression, but it introduces a logical function to help classify it. The practice found that the logical regression in the field of text classification performance is also ve