machine learning by andrew ng coursera

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

11.1 What to do first11.2 Error AnalysisError measurement for class 11.3 skew11.4 The tradeoff between recall and precision11.5 Machine-Learning data11.1 what to do firstThe next video will talk about the design of the machine learning system. These videos will talk about the major problems you will encounter when desi

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

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

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