andrew ng coursera machine learning notes

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[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

[Original] Andrew Ng Stanford Machine Learning (6) -- lecture 6_logistic Regression

function and the derivation of each parameter when using it. we implement the costfunction ourselves and pass in the response parameter. We can return the following two values at a time: For example, call the fminunc () function and use @ to input the pointer to the costfunction function. For the initialized Theta, you can also add options (gradobj = on indicates "Open the gradient target parameter ", that is, we will provide gradient parameters for this function ): 6.7 multi-category classifi

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

[Original] Andrew Ng Stanford Machine Learning (5) -- lecture 5 Ave ave tutorial-5.5 control statement: For, while, if statement

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, theta) 3 J = 2.33334 5> (1 ^ 2 + 2 ^ 2 + 3 ^ 2)/(2*3) % value of the cost function 6 ans = 2

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

[Machine Learning] Coursera notes-Support Vector machines

friends, but also hope to get the high people of God's criticism!        Preface  [Machine Learning] The Coursera Note series was compiled with notes from the course I studied at the Coursera learning (

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

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/test sets (Model selection and training/verific

Coursera Course "Machine learning" study notes (WEEK1)

This is a machine learning course that coursera on fire, and the instructor is Andrew Ng. In the process of looking at the neural network, I did find that I had a problem with a weak foundation and some basic concepts, so I wanted to take this course to find a leak. The curr

[Machine Learning] Coursera ml notes-Logistic regression (logistic Regression)

IntroductionThe Machine learning section records Some of the notes I've learned about the learning process, including linear regression, logistic regression, Softmax regression, neural networks, and SVM, and the main learning data from Standford

Coursera Machine Learning Study notes (i)

Before the machine learning is very interested in the holiday cannot to see Coursera machine learning all the courses, collated notes in order to experience repeatedly.I. Introduction (Week 1)-What's

Coursera Machine Learning Chapter 9th (UP) Anomaly Detection study notes

m>=10n and uses multiple Gaussian distributions.In practical applications, the original model is more commonly used, the average person will manually add additional variables.If the σ matrix is found to be irreversible in practical applications, there are 2 possible reasons for this:1. The condition of M greater than N is not satisfied.2. There are redundant variables (at least 2 variables are exactly the same, XI=XJ,XK=XI+XJ). is actually caused by the linear correlation of the characteristic

Coursera Machine Learning Study notes (12)

-Normal equationSo far, the gradient descent algorithm has been used in linear regression problems, but for some linear regression problems, the normal equation method is a better solution.The normal equation is solved by solving the following equations to find the parameters that make the cost function least:Assuming our training set feature matrix is x, our training set results are vector y, then the normal equation is used to solve the vector:The following table shows the data as an example:T

Coursera Machine Learning 5th Chapter Neural Networks:learning Study notes

)/∂ (θ (1) JK) is tested for gradients. After the partial derivative code does not have a problem, close the Gradient check section code.6. Use gradient descent or other advanced algorithms to perform reverse propagation to find the θ values for minimizing j (θ).This paper describes the gradient descent algorithm in neural networks: starting from the random initial point, descending step by step, until the local optimal value is obtained. Algorithms such as gradient descent can at least guarante

Coursera Machine Learning Study notes (ix)

-Feature ScalingWhen we are faced with multidimensional feature problems, we need to ensure that the multidimensional features have similar scales, which will help the gradient descent algorithm to converge faster.Take the housing price forecast problem as an example, assuming that the two characteristics we use, namely the size of the house and the number of rooms, the size value range is 0-2000 square feet, and the value of the room number is 0-5, which causes the gradient descent algorithm to

Coursera Machine Learning Study notes (v)

-Cost functionFor the training set and our assumptions, we will consider how to determine the coefficients in the assumptions.What we are going to do now is to choose the right parameters, and the selection of parameters directly affects the accuracy of the resulting straight line for the training set description. The difference between the predicted value and the actual value in the training set is the modeling error (Modeling error).the cost function is defined by calculating the sum of square

Coursera Machine Learning Study notes (13)

than or equal to 0, which is greater than or equal to 3 o'clock, the model predicts y = 1.We can draw a straight line, which is the dividing line of our model, separating the area predicted to 1 and the area predicted as 0.What kind of model would be appropriate if our data were to be presented in the following circumstances?Because curves are required to separate areas of y = 0 and y = 1, we need two-character:Assuming that the parameter is [-1 0 0 1 1], then we get the decision boundary is ex

Coursera Machine Learning Study notes (10)

-Learning RateIn the gradient descent algorithm, the number of iterations required for the algorithm convergence varies according to the model. Since we cannot predict in advance, we can plot the corresponding graphs of iteration times and cost functions to observe when the algorithm tends to converge.Of course, there are some ways to automatically detect convergence, for example, we compare the change value of a cost function with a predetermined thr

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