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My way of entry, first from the application phenomenon, summed up the law to reverse the essence. It is wrong to plunge into theoretical books.The foreigner's advanced, still manifests in the inheritance aspect. No way, we are under a lot of pressure, there are good things tucked away. Everybody's bitter.I started from the introduction, with the basic concept, see XXX. Know the ten algorithms, you can start.Open source components start, infer.netThe e

and is easily downloaded and modified by the reader.The following books will not be introduced, share the graphic coverHere is still to recommend my own built Python development Learning Group: 725479218, the group is the development of Python, if you are learning Python, small series welcome you to join, everyone is the software Development Party, not regularly share dry goods (only Python software develo

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, example to get the classification result of Class 1The same input is transferred to different nodes and the results are different because the respective nodes have different weights and biasThis is forward propagation.10. MarkovVideoMarkov Chains is made up of state and transitionsChestnuts, according to the phrase ' The quick brown fox jumps over the lazy dog ', to get Markov chainStep, set each word to a state, and then calculate the probability of transitions between statesThis is the proba

'); Plt.ylabel ('True Positive rate') Plt.title ('ROC curve for AdaBoost horse colic detection system') Ax.axis ([0,1,0,1]) plt.show ()Print "The area under the Curve is:", Ysum*xstepDaboost is the most popular meta-algorithm and one of the most powerful tools in machine learning.The combination of different algorithms can also be the same algorithm in different settings of the integration, can also be different parts of the data set assigned to diffe

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http://blog.csdn.net/pipisorry/article/details/44850971Machine learning machines Learning-andrew NG Courses Study notesRecommender Systems recommendation System{An important application of machine learning}Problem formulation Problem PlanningNote:1. To allow 0 to 5 stars as Well,because This just makes some of the math

Percent Machine learning Online class-exercise 4 neural Network learning% instructions%------------% This file contains Co De that helps you get started on the% linear exercise. You'll need to complete the following functions% of this exericse:%% sigmoidgradient.m% randinitializeweights.m% nncost function.m%% for the exercise, you'll not need to the change any co

2.7.x,python 3.3.X and Python 3.4.X four series packages, which is a legacy of other distributions. Therefore, in various operating systems, whether it is Linux, or Windows, MAC, it is recommended anaconda!Since Anacoda is a collection of Python science and technology packages, different packages follow the same protocol, and you can see http://docs.continuum.io/anaconda/licenses.htmlAnacoda common documents are as follows:Anaconda Official documentsConda Official documentsMy Anaconda Landscape

: 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 2;1 3] (the first column is the x0 value, th

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