andrew ng stanford machine learning

default is to use a hidden layer is a reasonable choice, but if you want to choose the most appropriate layer of hidden layer, you can also try to split the data into training sets, validation sets and test sets, and then try to use a hidden layer of neural network to train the model. Then try two, three hidden layers, and so on. Then see which neural network behaves best on the cross-validation set. That means you get three neural network models, one, two, and three hidden layers, respectively

This course comes to the end. I got a statement of accomplishment in Chinese Valentine's Day which is also called double seventh festival. In the last video, using sor Andrew Ng said a few words which impressed me a lot: And before wrapping up,There's just one last thing I wanted to say.Which is that: it was maybe not so long ago,That I was a student myself.And even today, you know, I still try to tak

11.1 What to do first11.2 Error AnalysisError measurement for class 11.3 skew11.4 The tradeoff between recall and precision11.5 Machine-Learning data 11.1 what to do firstIn the next video, I'll talk about the design of the machine learning system. These videos will talk about the major problems you will encounte

23:55:01 | category: foreign university courses | Tag: machine learning | font size subscription INSTRUCTOR: Andrew Ng Http://see.stanford.edu/see/courseinfo.aspx? Coll = 348ca38a-3a6d-4052-937d-cb017338d7b1 Http://www.stanford.edu/class/cs229/materials.html Lecture Notes 1 (PS) (PDF) Supervise

: 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

Original: http://blog.csdn.net/abcjennifer/article/details/7700772This column (machine learning) includes linear regression with single parameters, linear regression with multiple parameters, Octave Tutorial, Logistic Regression, regularization, neural network, design of the computer learning system, SVM (Support vector machines), clustering, dimensionality reduc

8. Support Vector machines (SVMs) Content 　　　　8. Support Vector machines (SVMs) 8.1 Optimization Objection 8.2 Large Margin Intuition 8.3 Mathematics Behind Large Margin classification 8.4 Kernels 8.5 Using a SVM 8.5.1 Multi-Class Classification 8.5.2 Logistic Regression vs. SVMs 8.1 Optimization ObjectionSupport Vector Machine (Support Vector MACHINE:SVM) is a very useful supervised

(that is, Xi in {1,..., | v|} Value in | V| is the vocabulary of the lexicon), n-word messages will be represented by a vector of length n, and the length of the vectors for different articles will probably not be the same.In the multiple event model, we assume that this is the case with the message: first determine whether this is a spam message through P (Y), and then independently determine each word by multiple distributions P (x|y). The probability of the final generation of the entire mes

Model (how to simulate)---strategy (risk function)-algorithm (optimization method)First section:Basic concepts and classifications of machine learningSection II:Linear regression, least squaresBatch gradient descent (BGD) and random gradient descent (SGD)Section III:Over-fitting, under-fittingNon-parametric learning algorithm: Local weighted regressionThe probability angle interprets the linear regression.

Tools used: NumPy and MatplotlibNumPy is the most basic Python programming library in the book. In addition to providing some advanced mathematical algorithms, it also has a very efficient vector and matrix operations function. These are particularly important for computational tasks for machine learning. Because both the characteristics of the data, or the batch design of parameters, are inseparable from t

is going when it is initialized, or we don't know where the driving direction is, only after the learning algorithm has been running long enough that the white section appears in the entire gray area, showing a specific direction of travel. This means that the neural network algorithm at this time has chosen a clear direction of travel, not like the beginning of the output of a faint light gray area, but the output of a white section.Stanford Univers

these matrices, and the θ superscript (j) becomes a wave matrix that controls the action from the first layer to the second or second to the third layer. The first hidden unit calculates its value in this way: A (2) 1 equals the S function or S-excitation function, also called the logical excitation function, which acts on the linear combination of this input. The second hidden unit equals the value of the S function on this linear combination. The parameter matrix controls the mapping from thr

SVM is considered by many people to be the best algorithm for supervised learning, and I was trying to learn this time last year. However, the face of long formulas and the awkward Chinese translation eventually gave up. After a year, see Andrew to explain SVM, finally have a more complete understanding of it, the general idea is this: 1. Introduce the concept of the interval and redefine the symbol; 2. Int

Open Course address: https://class.coursera.org/ml-003/class/index INSTRUCTOR: Andrew Ng1. unsupervised learning introduction (Introduction to unsupervised learning) We mentioned one of the two main branches of machine learning-supervised

Machine learning defines learning definitionArthur Samuel (1959). Machine Learning:field of study, gives computers the ability to learn without being explicitly programmed.There is no clear programming case to make the computer capable of learning the field of study.Four par

regression as shown below, (note that in matlab the vector subscript starts at 1, so the theta0 should be theta (1)).MATLAB implementation of the logistic regression the function code is as follows:function[J, Grad] =Costfunctionreg (Theta, X, y, Lambda)%costfunctionreg Compute Cost andgradient for logistic regression with regularization% J=Costfunctionreg (Theta, X, y, Lambda) computes the cost of using% theta as the parameter for regularized logistic re Gression andthe% Gradient of the cost w

,....} (A is the 1th word in the dictionary and Nip is the No. 35000 Word). So for naive Bayes, it can be expressed as the following matrix (the 1th element of the matrix is 1, and the No. 35000 element is also 1)in the multinomial event model, it is expressed as,. This means that the 1th word of the message is a, and the No. 35000 Word is nip. In this case, if the 3rd word in the message is a, the naive is unchanged, but the representation in the Multinomial event model will be x3=1. This allow

algorithm solves the problem of large optimization by decomposing it into several small optimization problems. These small optimization problems are often easy to solve, and the results of sequential solution are consistent with the results of solving them as a whole.The SMO works based on the coordinate ascent algorithm.1, coordinate ascentAssume that the optimization problem is:We select one of the parameters in turn to optimize this parameter, which causes the W function to grow fastest.The

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

returnWhen the classification problem is no longer two yuan but K yuan, that is, y∈{1,2,..., k}. We can solve this classification problem by constructing the generalized linear model. The following steps are described.Suppose y obeys exponential family distribution, φi = P (y = i;φ) and known. So. We also define.Also 1{} The condition for the representation in parentheses is the true value of the entire equation is 1, otherwise 0. So (T (y)) i = 1{y = i}. From the knowledge of probability theor