Tags: get attention to bin www. Command line nbsp PAC Read Write codeRecently began to look at Coursera above the machine learning course, the above mentioned a software--octave, so I transferred the following blog.Do not know what is the specific reason, I download octave-4.2.1-w64-installer.exe, the speed is extremel

Octave Machine Learning Common commands
A, Basic operations and moving data around
1. Attach the next line of output with SHIFT + RETURN in command line mode
2. The length command returns a higher one-dimensional dimension when apply to the matrix
3. Help + command is a brief aid for displaying commands
4. doc + command is a detailed help document for displaying

Machine learning machines Learning-andrew NG Courses Study notesIf you want to build a large scale deployment of a learning algorithm, what people would often do is prototype and the Lang Uage is Octave.which is a great prototyping language. So you can sort of get your learning

blocks, followed by a parameter that determines how many barsEye (5)--Generates a 5*5 Unit matrixHelp Eye-View information about the eyeSize (a)--the command returns the number of rows and columns of a matrixSize (a,1)--the command returns the number of rows of a matrixSize (a,2)--the command returns the number of columns of a matrixLength (a)--Returns The largest dimension of a matrixPWD--View octave installation pathCD ' C:\User\Administrator\Des

Https://www.coursera.org/learn/machine-learning/exam/dbM1J/octave-matlab-tutorial
Octave Tutorial
5 questions
1.Suppose I first execute the following Octave commands:
A = [1 2; 3 4; 5 6];
B = [1 2 3; 4 5 6];
Which of the following is then valid

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

general P is a single number,p is a vector can be combined with multiple sub-graphs as a sub-graph.
Clear
Clc
X=-4*pi+eps:0.01:4*pi;
Y1=sin (x);
Y2=cos (x);
Y3=tan (x);
Figure
Subplot (2,2,1);p lot (x,y1), title (' Sin (x) ')
Subplot (2,2,2);p lot (x,y2), title (' cos (x) ')
Subplot (2,2,[3,4]);p lot (x,y3), title (' Tan (x) ')% merges two of the second row into one
Figure
Subplot (2,2,[1 2]);p lot (x,y1), title (' Sin (x) ')% merges two of the first row into one

Fourth Lesson plotting Data Drawing Datat = [0,0.01,0.98];y1 = sin (2*pi*4*t);y2 = cos (2*pi*4*t);Plot (t,y1);( drawing Figure 1)Hold on; ( Figure 1 does not disappear) Plot (T,y2, ' R ');( draw in red Figure 2)Xlable (' time ') ( horizontal axis name)Ylable (' value ') ( vertical axis name)Legend (' Sin ', ' cos ')(labeled two function curves)Title (' My Plot ')Print-dpng ' Myplot.png ' ( save image)CD '/home/flipped/desktop ' Print-dpng ' myplot.png ' ( save image to desktop)Close(image off)La

: Quarethisnumber(5) As you can see from this example,Octave differs from other languages in that the function can return two and more than two values. Example 2 : Calculate its cost function from a small number of datasetsThere is a file named "COSTFUCTIONJ.M" on the desktop with the following contents:Unction J = Costfuctionj (X, y, Thera)m = size (x,1);predictions = X*thera;Sqrerrors = (predictions-y). ^2;J = 1/(2*m) *sum (sqrerrors);Set X = [1 1;1

. DrawingT=[0:0.01:0.98]Y1=sin (2*pi*t)Plot (t,y1) % drawingOnY2=cos (2*pi*t)Plot (T,y2, ' R ')Xlabel (' time ')Ylabel (' value ')Legend (' Sin ', ' cos ') % legendTitle (' My Plot ')Print-dpng ' myplot.png ' % saved as picture fileClose % Closes the current diagramFigure (1) % Create a diagramCLF % Empty chart Current ContentsSubplot (1,2,2) % graph cut to 1*2 grid, draw 2nd gridAxis ([0.5 1-1 1]) % axis changed to x belongs to [0.5,1],y belonging to [ -1,1]Imagesc (The Magic ()), Colorbar,colo

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

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

[Introduction to machine learning] Li Hongyi Machine Learning notes-9 ("Hello World" of deep learning; exploring deep learning)
PDF
Video
Keras
Example application-handwriting Digit recognition
Step 1

WEEK1:Machine learning:
A computer program was said to learn from experience E with respect to some class of tasks T and performance measure P, if Its performance on tasks in T, as measured by P, improves with experience E.
Supervised learning:we already know what we correct output should look like.
Regression:try to map input variables to some continuous function.

IntroductionThe systematic learning machine learning course has benefited me a lot, and I think it is necessary to understand some basic problems, such as the category of machine learning algorithms.Why do you say that? I admit that, as a beginner, may not be in the early st

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

Objective:When looking for a job (IT industry), in addition to the common software development, machine learning positions can also be regarded as a choice, many computer graduate students will contact this, if your research direction is machine learning/data mining and so on, and it is very interested in, you can cons

learning in Hadoop that you can learn by yourself. If you are a novice in machine learning and big data learning, stick to learning Weka and learn a library wholeheartedly.
Scikit Learn: This is a machine

Original: http://blog.csdn.net/abcjennifer/article/details/7797502This column (machine learning) includes linear regression with single parameters, linear regression with multiple parameters, Octave Tutorial, Logistic Regression, regularization, neural network, design of the computer learning system, SVM (Support vecto

Machine learning is a comprehensive and applied discipline that can be used to solve problems in various fields such as computer vision/biology/robotics and everyday languages, as a result of research on artificial intelligence, and machine learning is designed to enable computers to have the ability to learn as humans

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