advanced machine learning with scikit learn

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How to learn machine learning algorithms

of thumb for using the algorithm?) What kinds of problems can the algorithm solve? (What classes of problem are the algorithm well suited?) What are the resources that describe the relevant algorithms? (What is useful resources for learning more about the algorithm?) Where is the source of the algorithm? (What is the primary references or resources in which the algorithm is first described?)In daily l

Machine learning Week 3-advanced-optimization

, which isparameterized by its second argument C. Here Myfun isA MATLAB file function such asfunction [F,g]=Myfun (x,c) F= C*x (1)^2+2*x (1) *x (2) + x (2)^2; %function G= [2*c*x (1) +2*x (2) %Gradient2*x (1) +2*x (2)]; To optimize fora specific value of C, first assign the value to C. Then create a one-argument anonymous function, that captures, the value of C and calls Myfun with the arguments. Finally, pass. Thisanonymous function to Fminunc:c=3; %define parameter first options=

A simple and easy-to-learn machine learning algorithm--density-based clustering algorithm Dbscan

= tmp+ (x (I,k)-X (J,k)). ^2; End Dis (i,j) = sqrt (TMP); Dis (j,i) = Dis (I,J); End End End Epsilon function[Plain]View PlainCopy function [Eps]=epsilon (X,K) % Function: [Eps]=epsilon (X,k) % % Aim: % analytical to estimating neighborhood radius for DBSCAN % % Input: % X-data matrix (m,n); M-objects, N-variables % K-number of objects in a neighborhood of a object % (minimal number of objects considered as a cluster) [M,n]=size (x); Eps= ((

Starting today to learn the pattern recognition and machine learning (PRML), chapter 5.2-5.3,neural Networks Neural network training (BP algorithm)

). In fact, the calculation of numerical methods can not take advantage of the previous useful information, each derivative needs to be calculated independently, the calculation can not be simplified.But the interesting thing is that the numerical derivative is useful in another place--gradient check! We can use the results of the central differences and the derivative of the BP algorithm to compare, in order to determine whether the BP algorithm execution is correct.Starting today to

Robot Learning Cornerstone Machine Learn Cornerstone (machines learining foundations) Job 2 q16-18 C + + implementation

e_out = mu * lambda + (1-LAMBDA) * (1-MU), lambda = 0.8,mu brought in to get answers(3) Answer: 0.5+0.3*s* (|theta|-1)2.17th, 18 questions(1) Test instructions:The 17th question means that in [ -1,1] take 20 points, separated into 21 intervals as the theta of the range of values, each classification has 42 hyphothesis, enumerate all the possible conditions to find the smallest hyphothesis e_in, record the smallest e_inThe 18th question means to calculate the e_out by the formula of the 16th que

A simple and easy-to-learn machine learning algorithm--BP neural network of Neural network

(J,:)); End Outputoftest (:, m) = W2 ' *hiddentestoutput+b2;end%% result analysis% based on network output find out what kind of data belongs to m=1:400 Output_fore (m) =find (outputoftes T (:, M) ==max (Outputoftest (:, M))), END%BP Network prediction Error ERROR=OUTPUT_FORE-OUTPUT1 (n (1601:2000)) '; K=zeros (1,4); % find out which category of the inferred error belongs to which class for i=1:400 if error (i) ~=0 [B,c]=max (Testoutput (i,:)); Switch C Case 1 K (1) =k (1) +1

Starting today to learn the pattern recognition and machine learning (PRML), chapter 5.2-5.3,neural Networks Neural network training (BP algorithm)

the above accuracy problems:But the calculation is almost twice times the amount of (5.68). In fact, the calculation of numerical methods can not take advantage of the previous useful information, each derivative needs to be calculated independently, the calculation can not be simplified.But the interesting thing is that the numerical derivative is useful in another place--gradient check! We can use the results of the central differences and the derivative of the BP algorithm to compare, in ord

cs281:advanced Machine Learning second section probability theory probability theory

some examples of beta functions:It is of the following nature:Pareto DistributionThe Pareto principle must have heard it, is the famous long tail theory, Pareto distribution expression is as follows:Here are some examples: the left image shows the Pareto distribution under different parameter configurations.Some of the properties are as follows:ReferenceprmlmlapCopyright NOTICE: This article for Bo Master original article, without Bo Master permission not reproduced. cs281:

Machine learning Combat-Learn to read Python code (5)

) p (CI)/P (W)Calculate a specific document W belongs to C0 (insulting document) or C1 (non-insulting document), statistics the probability of each word in this document in two different categories, quantified by the Bayesian formula, that is, each word in a particular document in the p0v or p1v to find the corresponding word probability, Multiply these probabilities, i.e. P (W0|CI) p (W1|CI) p (w2|ci). P (WN|CI), multiplied by P (CI), the final result is two probability values, the probability

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