mit machine learning course online

=[] For C in Cs: # Select Model CLS = Logisticregression (c=c) # submit data to Model training Cls.fit (X_train, Y_train) Scores.append (Cls.score (X_test, Y_test)) # # Drawing Fig=plt.figure () Ax=fig.add_subplot (1,1,1) ax.pl OT (cs,scores) ax.set_xlabel (r "C") Ax.set_ylabel (r "Score") Ax.set_xscale (' Log ') Ax.set_title ("Logisticregression") plt.show () If __name__== ' __main__ ': X_train,x_test,y_train,y_test=load_data () # Generates a dataset for regression problems Test_logist

Hope to learn the gospel of the Children of Learning machine, the world's largest AI company Google launched a "machine learning Crash Course", not only the whole Chinese, but also free to listen to OH. The course is 15 hours, th

Public Course address:Https://class.coursera.org/ml-003/class/index INSTRUCTOR:Andrew Ng 1. Model Representation ( Model Creation ) Consider a question: what if we want to predict the price of a house in a given area based on the house price and area data? In fact, this is a linear regression problem. The given data is used as a training sample to train it to get a model that represents the relationship between price and area (actually a functi

Netease Open Course: 14th coursesNotes, 10 In the factor analysis mentioned earlier, the EM algorithm is used to find potential factor variables for dimensionality reduction. This article introduces another dimension reduction method, principal components analysis (PCA), which is more direct than factor analysis and easier to calculate. Principal component analysis is based on, In reality, for high-dimensional data, many dimensions are Disturb

columnsHttps://zhuanlan.zhihu.com/p/20878530?refer=intelligentunit Stanford cs231n Course Assignment # 1 Introduction-Smart Unit-Know the columnhttps://zhuanlan.zhihu.com/p/21441838 Stanford cs231n Course Assignment # 2 Introduction-Smart Unit-Know the columnhttps://zhuanlan.zhihu.com/p/21941485 Stanford cs231n Course Assignment # 3 Introduction-Smart Unit-Know

. 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

the value is, the closer the value of the evaluation function is to the midline position of the parabolic curve, that is, the closer it is to the minimum value. It can be represented by an example: Let's take a look at the meaning. When the value is too small, the update is slow, and the gradient descent algorithm will slow down in execution. When the value is too large, the gradient descent algorithm may exceed the target value (minimum value), leading to non-convergence, even divergence. As

July Algorithm-December machine Learning online Class -18th lesson Notes-Conditional random airport CRF July algorithm (julyedu.com) December machine Learning Online class study note http://www.julyedu.com1, logarithmic linear mod

July Algorithm--December machine Learning online Class -11th lesson notes-random forest and ascension July algorithm (julyedu.com) December machine Learning Online class study note http://www.julyedu.com?Random forest: Multiple tr

July Algorithm-December Machine Learning --17th lesson note-Hidden Markov model hmm July algorithm (julyedu.com) December machine Learning Online class study note http://www.julyedu.comHidden Markov modelThree parts: Probability calculation, parameter estimation, model predi

July Algorithm--December machine Learning online Class -11th lesson notes-random forest and ascension July algorithm (julyedu.com) December machine Learning Online class study note http://www.julyedu.com?Random forest: Multiple tr

July Algorithm-December machine Learning online Class -14th lesson Note-em Algorithm July algorithm (julyedu.com) December machine Learning Online class study note http://www.julyedu.com?EM expection Maxium Desired Maximum1 cited

July Algorithm--December machine Learning online Class -13th lesson notes-Bayesian network July algorithm (julyedu.com) December machine Learning Online class study note http://www.julyedu.com?1.1 The thought of Bayesian formula:

structure is as follows: { code:0, msg: { status:0, file_hash:string, file_name:string, result: { filename: Boolean }}} # Update LogJune 12, 2018 Deployment Add# Contact Information:Sevck#jdsec.com# MiscellaneousSimply say the architecture, use FLASK,MONGODB,RABBITMQFlask mainly to do the Web:/index, more simple instructions for use/put, upload task, return TaskID/result/MongoDB is primarily used to access task results:Put task will be the task ID, file attri