machine learning stanford university andrew ng

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Andrew ng Machine Learning (i): Linear regression

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 to change a variable into multiple variables; 3. Normalization of the input range.SummaryL

[Checked (vid only)] Cousera-machine Learning by Andrew Ng

Tags: video LSE tun assign DDE INI got the NTSJust finished watching all videos of this course-thank your Andrew for elaborating all basic ML concepts\algorithms in an Easy to understand.I watched most of the course videos on BART, and unfortunately I didn ' t has a chance to work on programming assignments- But again, just following videos helps a ton. All topics is so well organized and internally related. I ' ve got so many ' ah-ha ' moments, and a

Notes of machine learning (Andrew Ng), Week, Linear Regression

updated, and a final θj value is obtained.The entire derivative is calculated as follows:Vector representation of ④ hypothesis function, cost function and gradient descent algorithmSuppose the vector of the function is represented as follows:The cost function is represented as follows:The vectorization of θ using the gradient descent algorithm is represented as follows:(There is an error in the original formula, the formula after the first equals should not be divided by M, corrected here)The c

Machine learning (Andrew Ng) Notes (b): Linear regression model & gradient descent algorithm

for linear regressionWe take the formula of the cost function J into the gradient descent algorithm, then use the concept of partial derivative to simplify the formula, and finally we can get the formula. The specific derivation requires some knowledge of calculus.We can actually use them directly. That is, the algorithm is probably written like this, we use these two formulas to constantly revise the value of two parameters, until the function J reached a minimum value. Now that we have this f

Loss function-Andrew ng machine Learning public Lesson Note 1.2

"linear regression, gradient descent"The regular equationThe training features are represented as X-matrices, the results are expressed as Y-vectors, and the linear regression model is still the same, and the loss function is unchanged. Then θ can be derived directly from the following formula:The derivation process involves the knowledge of linear algebra, where the linear algebra knowledge is not expanded in detail.Set m as the number of training samples; x is the independent variable in the

Logistic regression-andrew ng machine Learning public Lesson Note 1.4

, according to the biased formula:y=lnx y'=1/x. The second step is to attribute G ' (z) = g (z) (1-g (z)) according to the derivation of G (Z). The third step is the normal transformation. So we get the update direction of each iteration of the gradient rise, then the iteration of Theta represents the formula: This expression looks exactly the same as the LMS algorithm's expression, but the gradient rise is two different algorithms than the LMS, because it represents a nonlinear function. Two

Andrew ng Machine learning note +weka correlation algorithm implementation (four) SVM and primitive duality problem

problem of the original problem. Relative to the original problem is only the change of the order of Min and Max, here to take the equal sign. Conditions such as the following descriptive narrations:① If a constrained inequality GI is a convex (convex) function (a linear function belongs to a convex function)② constrained equation hi are affine (affine) functions (Shaped like H (w) =wtx+b)③ and exists W makes for all I,gi (W) In these if, there must be ω?,α?,β, so that Omega is the solution of

Coursera open course notes: "Advice for applying machine learning", 10 class of machine learning at Stanford University )"

Stanford University machine Learning lesson 10 "Neural Networks: Learning" study notes. This course consists of seven parts: 1) Deciding what to try next (decide what to do next) 2) Evaluating a hypothesis (Evaluation hypothesis) 3) Model selection and training/validation/te

Machine learning Note (ii)-from Andrew Ng's instructional video

Omit the use of octave end, later use to see itWeek Three:Logistic Regression:For 0-1 categoriesHypothesis representation:: Sigmoid function or Logistic functionDecision Boundary:Theta's Transpose * small x>=0 is boundaryMay:non-linear decision boundaries, constructing the polynomial of XCost function:Simplified cost function and gradient descent:Because Y has only two values, merging:To find the least biased guide:(The denominator should be ignored)Advanced Optimization:Conjugate gradient,bfgs,

Andrew ng Machine Learning (ii): Logistic regression

category by two, and get N classifiers.When testing is required, input the data into each classifier, selecting one of the largest probabilities as the output.SummaryLogistic regression is built on the basis of linear regression. The model is: the probability that the output is 1 through the sigmoid function. The application should conform to the Bernoulli distribution in the output.The gradient descent algorithm is also useful, and there are some more efficient algorithms. At first, you can us

Stanford University public Class machine learning: Machines Learning System Design | Error metrics for skewed classes (definition of skew class issues and evaluation measures for skew class issues: precision ratio (precision) and recall rate (recall))

classification model, which gives us a better evaluation value and gives us a more direct way to evaluate the good and bad of the model. One last thing to keep in mind, in the definition of precision and recall, we define precision and recall rates, and we habitually use Y=1 to show that this class appears very little. So if we try to detect a very rare situation, like cancer. I hope it's a rare situation where precision and recall are defined as Y=1 rather than y=0, as some of the fewer classe

Stanford University public Class machine learning: Advice for applying machines learning-deciding to try next (how to determine the most appropriate and correct method when designing a machine learning system)

feeling that most people choose to choose one of these methods casually, such as they would say "let's find some more data" and then spend six months collecting a lot of data, and then maybe another person said, " Let's find some more features in the data from these houses. " A lot of people spend at least six months to complete one of their random choices, and after six months or more, they regret to find that they have chosen a way of no return.Stanford U

Stanford University public Class machine learning: Advice for applying machines learning-evaluatin a phpothesis (how to evaluate the assumptions given by the learning algorithm and how to prevent overfitting or lack of fit)

assumptions tend to be 0, but the actual labels are 1, both of which indicate a miscarriage of judgment. Otherwise, we define the error value as 0, at which point the value is assumed to correctly classify the sample Y.Then, we can use the error rate errors to define the test error, that is, 1/mtest times the error rate errors of H (i) (xtest) and Y (i) (sum from I=1 to Mtest).Stanford University public Cl

Stanford University public Class machine learning: Advice for applying machines learning | Learning curves (Improved learning algorithm: the relationship between high and high variance and learning curve)

to the right in this image. We can generally see the two learning curves, the two curves of blue and red are approaching each other. Therefore, if we extend the curve to the right, it seems that the training set error is likely to increase gradually. The cross-validation set error will continue to decline. Of course, we are most concerned with cross-validation set errors or test set errors. So from this picture, we can basically predict that if we co

Stanford University Machine Learning public Class (II): Supervised learning application and gradient descent

mathematical expression was unfolded using Taylor's formula, and looked a bit ugly, so we compared the Taylor expansion in the case of a one-dimensional argument.You know what's going on with the Taylor expansion in multidimensional situations.in the [1] type, the higher order infinitesimal can be ignored, so the [1] type is taken to the minimum value,should maketake the minimum-this is the dot product (quantity product) of two vectors, and in what case is the value minimal? look at the two vec

Stanford ng Machine Learning Lecture Notes-Referral system (Recommender systems)

and the computational optimization of the problem is discussed.Collaborativefiltering algorithm:We can iteratively optimize the theta and eigenvectors, but this performance is relatively low, so now consider improving the performance of the algorithm. At the same time, two kinds of methods are solved.is to combine the two method optimization functions to get the overall objective function.Algorithm Flowchart:Exercises:Vectorization Low Rank matrix factorization:The main thing here is to constru

Stanford ng Machine Learning course: Anomaly Detection

learning.In fact, these two states are not completely divided, for example, if we are trading in a lot of fraud, then we study the problem from anomaly detection to supervise learning.Exercise: Intuitive judgment of two situationsChoosingwhat Features to useThe previous approach is to assume that the data satisfies the Gaussian distribution, and also mentions that if the distribution is not Gaussian distribution, the above method can be used, but if we convert the distribution to approximate Ga

Stanford University public Class machine learning: Machines Learning System Design | Trading off precision and recall (F score formula: How to balance (trade-off) precision and recall values in a learning algorithm)

take an average of this evaluation mode.It is a useful algorithm to use the F-score algorithm to evaluate both precision and recall rates . The PR of the molecule determines that the precision ratio (P) and recall (R) must be large at the same time to ensure that the F score values are larger. If the precision ratio or recall rate is very low, close to 0, the direct result of the PR value is very low, approaching 0, that is, F score is also very low.At this point we compare three algorithms, we

Stanford University-machine learning public class-2. Supervised learning applications • Gradient descent

be able to find the global optimal solution.When the training sample is very large, each update parameter needs to traverse all the sample calculation total error, so that the learning speed is too slow; this time the random gradient descent algorithm that calculates the error update parameters of a sample is usually more thanThe batch gradient descent method is faster. (Theoretically, there is no guarantee that the random gradient descent can conver

Stanford University public Class machine learning: Advice for applying machines learning | Deciding what to try Next (Revisited) (for high-deviation, high-variance resolution and the choice of hidden layers)

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

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