-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 (
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
regression.
The root number can also be selected based on the actual situation.Regular Equation
In addition to Iteration Methods, linear algebra can be used to directly calculate $ \ matrix {\ Theta} $.
For example, four groups of property price forecasts:
Least Squares
$ \ Theta = (\ matrix {x} ^ t \ matrix {x}) ^ {-1} \ matrix {x} ^ t \ matrix {y} $Gradient Descent, advantages and disadvantages of regular equations Gradient Descent:
Desired stride $ \ Alpha $;
Multiple iterations are requ
Original title:Given Arrays a[] and b[], each containing n distinct 2D points in the plane, design a subquadratic algorithm to count The number of points that is contained both in array a[] and array b[].The goal of the topic is to calculate the number of duplicate point, very simple, the code is as follows1 ImportJava.awt.Point;2 Importjava.util.Arrays;3 ImportJava.util.HashSet;4 ImportJava.util.Set;5 6 ImportEdu.princeton.cs.algs4.StdRandom;7 8 Public classplanepoints {9 PrivatesetNewHash
]; - } - System.out.println (arrays.tostring (aux)); the intL = 0; - intR =N; - for(intk = 0; k){ - if(l >= N) Break;//The array of auxiliary elements is exhausted, and the right side of the array does not need to be shifted. + Else if(R>=2*n) array[k]=aux[l++];//all elements of the right element of array are placed in the appropriate position, then simply move the elements of the auxiliary array to the right of the array - Els
Before the machine learning is very interested in the holiday cannot to see Coursera machine learning all the courses, collated notes in order to experience repeatedly.I. Introduction (Week 1)-What's machine learningThere is no unanimous answer to the definition of machine learning.Arthur Samuel (1959) gives a definition of machine learning:Machine learning is about giving computers the ability to learn without explicit programming.Samuel designed a c
Support Vector MachinesI have the some issues to state. First, there were some bugs in original code which is caused by versions. I don ' t know ...There is three pictures u need to draw a division boundary. The first calls ' VISUALIZEBOUNDARYLINEAR.M ' which is fine and the others which call ' visualizeboundary.m ' can notDraw boundaries. So I check out this file and change the code ' contour (X1, X2, Vals, [0 0], ' Color ', ' B '); ' to ' Contour (X1, X2, Vals, [0.1 0.1], ' LineColor ', ' B ')
This is a machine learning course that coursera on fire, and the instructor is Andrew Ng. In the process of looking at the neural network, I did find that I had a problem with a weak foundation and some basic concepts, so I wanted to take this course to find a leak. The current plan is to see the end of the neural network, the back is not necessarily seen.Of course, look at the process is still to do the notes to do homework, or read it is also a curs
Multivariate regressionReview simple linear regression: A feature, two correlation coefficients The actual application is much more complicated than this, such as1, house prices and housing area is not just a simple linear relationship.2, there are many factors affecting the price, not only the size of the house, but also many other factors. Now, in the first case, the price and the housing area are not simply linear, and may be two or polynomial:Two times function: Polynomial functions: P
?
This is determined by the characteristic value of the feature. There are two kinds of discrete value and continuous value, the distribution of discrete values is Poisson distribution, Bernoulli distribution, the distribution of continuous values is uniform distribution, normal distribution, chi-square distribution and so on. The reason why we assume the two eigenvalues of the above example is normal distribution is because the distribution of the majority of continuous-value variables
Questions -31 point Possible (graded)
Total time limit:
1000ms
Memory Limit:
65536kB
Describe
Write a two-dimensional array class Array2, so that the following program output is:
0,1,2,3,
4,5,6,7,
8,9,10,11,
Next
0,1,2,3,
4,5,6,7,
8,9,10,11,
Program:
#include
Add your code here
int main () { Array2 a (3,4); int i,j; fo
Overview
Cost Function and BackPropagation
Cost Function
BackPropagation algorithm
BackPropagation Intuition
Back propagation in practice
Implementation Note:unrolling Parameters
Gradient Check
Random initialization
Put It together
Application of Neural Networks
Autonomous Driving
Review
Log
2/10/2017:all the videos; Puzzled about Backprogation
2/11/2017:reviewed backpropaga
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