andrew ng stanford machine learning

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

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

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

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

Content Summary To now supervised learning has basically finished, this blog is mainly to write about the theory of machine learning, that is, when to use what learning algorithm, what kind of learning algorithms have what characteristics or advantages. At the time of fitti

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

Today formally began to learn the machine learning algorithm, the teacher first cited an example: a region of the house area and the price of a data set, then how to predict the price of a given housing area. What most of us can think of is to draw a scatter plot of the house area and price, and then fit the price on the area curve, then for a known housing area, you can get the predicted price on the fitte

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

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

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

This column (Machine learning) includes single parameter linear regression, multiple parameter linear regression, Octave Tutorial, Logistic regression, regularization, neural network, machine learning system design, SVM (Support vector machines Support vector machine), clust

This column (Machine learning) includes single parameter linear regression, multiple parameter linear regression, Octave Tutorial, Logistic regression, regularization, neural network, machine learning system design, SVM (Support vector machines Support vector machine), clust

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

For the performance of four different algorithms in different size data, it can be seen that with the increase of data volume, the performance of the algorithm tends to be close. That is, no matter how bad the algorithm, the amount of data is very large, the algorithm can perform well.When the amount of data is large, the learning algorithm behaves better:Using a larger set of training (which means that it is impossible to fit), the variance will be l

sixth week. Design of learning curve and machine learning system Learning Curve and machine learning System Design Key Words Learning curve, deviation variance diagnosis method, error a

This column (Machine learning) includes single parameter linear regression, multiple parameter linear regression, Octave Tutorial, Logistic regression, regularization, neural network, machine learning system design, SVM (Support vector machines Support vector machine), clust