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-Gradient descent for linear regressionHere we apply the gradient descent algorithm to the linear regression model, we first review the gradient descent algorithm and the linear regression model:We then expand the slope of the gradient descent algorithm to the partial derivative:In most cases, the linear regression model cost function is shaped like a convex body, so the local minimum value is equivalent to the global minimum:The following is the entire convergence and parameter determination pr

-Gradient descentThe gradient descent algorithm is an algorithm for calculating the minimum value of a function, and here we will use the gradient descent algorithm to find the minimum value of the cost function.The idea of a gradient descent is that we randomly select a combination of parameters and calculate the cost function at the beginning, and then we look for the next combination of parameters that will reduce the value of the cost function.We continue this process until a local minimum (

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use of MATLAB. *.4.gradientdescent.mfunction [Theta, j_history] =gradientdescent (X, y, theta, Alpha, num_iters)%gradientdescent performs gradient descent to learn theta% theta = gradientdescent (X, y, theta, Alpha, num_iters) up Dates theta by% taking num_iters gradient steps with learning rate alpha% Initialize Some useful valuesm= Length (y);%Number of training examplesj_history= Zeros (Num_iters,1); forITER =1: Num_iters% ====================== YOUR CODE here ======================% instru

Overview photo OCR problem Description and Pipeline sliding Windows getting Lots of data and Artificial data ceiling analysis:what part of the Pipeline to work on Next Review Lecture Slides Quiz:Application:Photo OCR Conclusion Summary and Thank You Log 4/20/2017:1.1, 1.2; Note Ocr? ... Coursera-machine Learning, Stanford:w

I've been procrastinating for the last time, and I'm going to keep it up today. Programming Title #: Calculating the sum of the edge elements of a matrix Source: POJ (Coursera statement: The exercises completed on POJ will not be counted into Coursera's final results. ) Note: Total time limit: 1000ms memory limit: 65536kB description Enter an integer matrix to compute the sum of elements at the edge of the matrix. The elements of the so-called matrix

Week 3 Quizhelp Center Warning:the hard deadline has passed. You can attempt it, but and you won't be. You are are welcome to try it as a learning exercise. In accordance with the Coursera Honor Code, I certify this answers here are I own work. Question 1 Assume you are using a Unigram language model to calculate the probabilities of phrases. Then, the probabilities of generating the phrases "study text mining" and "text mining study" are not equal, i

Week 3 Practice quizhelp Center Warning:the hard deadline has passed. You can attempt it, but and you won't be. You are are welcome to try it as a learning exercise. In accordance with the Coursera Honor Code, I certify this answers here are I own work. Question 1 are given a vocabulary composed of only three words: "text", "mining", and "the". Below are the probabilities of two of this three words given by a Unigram model: Word Probability Text 0.4 M

The course covers technology:Gradient descent, linear regression, supervised/unsupervised learning, classification/logistic regression, regularization, neural network, gradient test/numerical calculation, model selection/diagnosis, learning curve, evaluation metric, SVM, K-means clustering, PCA, Map Reduce Data Parallelism, etc...The course covers applications:Message classification, tumor diagnosis, handwriting recognition, autonomous driving, model optimization, OCR, etc...

1. What is a special course (specializations)?If you want to learn a major that you do not understand, you can study according to the special course arrangement. Coursera Special Course collects a field of curriculum, and according to the Order of teaching, it is very suitable for the new people who don't feel well.2. Program Design and algorithmThis special course is a computer Foundation course published by Peking University in

(Datasets) data (IRIS)#Exploratory Analysisnames (Iris) head (IRIS)#The following attempts to take Virginica,speal. The method of length is all wrongiris[,2]iris[iris$species=="virginica", 2]mean (iris[iris$species=="virginica", 2])##the above is Error,not correct##tapply (Test$sepal.length,test$species,mean)#using Species.mean to group vectors, this method is feasible, but the above method is necessary to look at the errorLibrary (Datasets) data (Mtcars) #以下为做某个题时的若干测试. And a trial-and-error l

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 video can be viewed or downloaded on Coursera