andrew ng machine learning coursera videos

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

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 (Andrew

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

networks and overfitting: The following is a "small" Neural Network (which has few parameters and is easy to be unfitted ): It has a low computing cost. The following is a "big" Neural Network (which has many parameters and is easy to overfit ): It has a high computing cost. For the problem of Neural Network overfitting, it can be solved through the regularization (λ) method. References: Machine Learning