coursera

-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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.*h);% =========================================================================EndFormula:Note the 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_it

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

5.1 Section cost FunctionThe cost function of a neural network.Review some of the concepts in neural networks:L the total number of layers of the neural network.Number of units of the SL-L layer (excluding deviation units).Category 2 Classification

You can access the Google drive containing all of the current and in-progress lecture slides for this course through the L Ink below. Lecture Slides You could find it helpful to either bookmark this page or download the slides for easy

You can access the Google drive containing all of the current and in-progress lecture slides for this course through the L Ink below. Lecture Slides You could find it helpful to either bookmark this page or download the slides for easy

This section describes the core of machine learning, the fundamental problem-the feasibility of learning. As we all know about machine learning, the ability to measure whether a machine learning algorithm is learning is not how the model behaves on

Extremely light of a semester finally passed, summer vacation intends to learn the big step down this machine learning techniques.The first lesson is the introduction of SVM, although I have learned it before, but I heard a feeling is very rewarding.

This section is about overfitting, listening to the understanding of overfitting more profound than before.First introduced the overfitting, the consequence is that Ein is very small, and eout is very large. Then the causes of overfitting are

This section is about regularization, in the optimization of the use of regularization, in class when the teacher a word, not too much explanation. After listening to this class,To understand the difference between a good university and a pheasant

-Feature ScalingWhen we are faced with multidimensional feature problems, we need to ensure that the multidimensional features have similar scales, which will help the gradient descent algorithm to converge faster.Take the housing price forecast

-Cost functionFor the training set and our assumptions, we will consider how to determine the coefficients in the assumptions.What we are going to do now is to choose the right parameters, and the selection of parameters directly affects the

Vi. Logistic Regression (Week 3)-ClassificationIn the classification problem, what we try to predict is whether the result belongs to a certain class (for example, correct or error). Examples of classification problems include determining whether an

Anomaly Detection and Recommender SystemsThis week's programming job is divided into two parts: anomaly detection and referral system.Anomaly Detection: The essence is to use the Gaussian distribution of the sample to the special value to estimate

The origin of Neural network Considering a nonlinear classification, when the number of features is very small, the logical regression can be completed, but when the feature number becomes larger, the higher order term will be exponential growth,