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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

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

"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

, 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

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

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

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,

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

When should do we use fine-tuning?It is typically used only if you have a large labeled training set; In this setting, fine-tuning can significantly improve the performance of your classifier. However, if you had a large unlabeled dataset (for unsupervised feature learning/pre-training) and only a relatively smal L labeled training Set, then fine-tuning was significantly less likely to help.Stacked Autoencoders (Training):Equivalent to capturing the c

despair. His style of being alone has influenced my view of the whole Tibetan minority, and there is no place to respect it. I thought, "I don't think I slept again tonight." ”I just climb out of bed straight start open source work, document open-source to GitHub a lot of ways, direct use of GitHub Markdown is too humble, the file organization is not beautiful, a website alone and some too. At the end, take a compromise and make a simple page with GitHub pages, and just do it. Eventually the wh

development set and should consider getting more data for it.(5) The eyeball data should be large enough to allow the algorithm to have enough error-disaggregated samples for you to analyze. For many applications, a BLACKBOXK development set containing 1000-10000 samples is sufficient.(6) If your development set is not large enough to be split in this way, use the eyeball development set for manual error analysis, model selection, and tuning parameters.20. Deviations and variances: two major so

diagnosis of benign or malignant tumors (this is a supervised learning problem), your decision gives a conclusion that determines the life and death of a patient. However, you might actually need to make multiple decisions in a row over time. For example, an unmanned helicopter's automatic flight, you make a wrong decision, he may not crash immediately, as long as you make the right decision, can be remedied, only if you have been making the wrong de

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 Study of large data sets 17.2 Stochastic gradient descent method 17.3miniature Batch gradient descent 17.4 stochastic gradient descent convergence 17.5 Online learning 17.6 mapping simplification and data parallelism

NG Machine Learning Video notes (ii)--Gradient descent algorithm interpretation and solving θ (Reproduced please attach this article link--linhxx) First, the interpretation gradient algorithmA gradient algorithm formula and a simplified cost function diagram, as shown in.1) Partial derivativeBy the know, at point A, its partial derivative is less than 0, so θ min

Download: https://pan.baidu.com/s/1Oeho172yfw1J6mCiXozQigTensorflow Machine Learning Practice Guide (Chinese Version pdf + English version PDF + Source Code)High-Definition Chinese PDF, 292 pages, with bookmarks, text can be copied and pasted;High Definition English

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

"Machine Learning Combat" (HD Chinese version pdf+ HD English pdf+ source code)HD Chinese and HD English comparison learning, with directory bookmarks, can be copied and pasted;The details are explained and the source code is provided.Download: https://pan.baidu.com/s/1s77wm

NG Machine Learning Video notes (11)--k - means algorithm theory(Reproduced please attach this article link--linhxx)I. OverviewK-Means (K-means) algorithm, is a unsupervised learning (unsupervised learning) algorithm, its core is clustering (clustering), that is, a set of in

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

Machine Learning-Overview of common matlab programming commands -- Summary from ng-ml-class octave/MATLAB tutorial CourseraA. basic operations and moving data around1 in command line mode, you can use Shift + press enter to append the next line to output 2 length command to apply to the matrix, and return a higher one-dimensional dimension3 help + command is the