gamification learning theory

I. Introduction Recently I have written many learning notes about machine learning, which often involves the knowledge of probability theory. Here I will summarize and review all the knowledge about probability theory for your convenience and share it with many bloggers, I hope that with the help of this blog post, you

learned. Therefore, in the model application process, it is necessary to continuously predict the learning parameters, then, the parameters are constantly adjusted based on the current predicted values and actual values. We provide a boundary for the online learning residual of the sensor algorithm. Label set used. Sensor parameters.NYesXBecause one of the parameters represents a constant term in the funct

Probability-based classification method: Naive BayesianBayesian decision theoryNaive Bayes is part of the Bayesian decision theory, so let's take a quick and easy look at Bayesian decision theory before we talk about naive Bayes.The core idea of Bayesian decision-making theory : Choose the decision with the highest probability. For example, we graduate to choose

NG Machine Learning Video notes (11)--k - means algorithm theory(Reproduced please attach this article link--linhxx)I. OverviewK-Means (K-means) algorithm, is a unsupervised learning (unsupervised learning) algorithm, its core is clustering (clustering), that is, a set of input, through the K-means algorithm classifica

Up to now, we have learned how the common machine learning algorithms work and how the learning steps are implemented. However, the application background of machine learning is diverse. To do practical engineering, you must learn how to evaluate the quality of a learning model based on specific problems, and how to ra

Support vector machine-SVM must be familiar with machine learning, Because SVM has always occupied the role of machine learning before deep learning emerged. His theory is very elegant, and there are also many variant Release versions, such as latent-SVM and structural-SVM. In this section, let's take a look at the SVM

In the last 20 blog posts, many machine learning algorithms have been involved, and the appetite has been exhausted. I decided to officially start the system-based Machine Learning Theory and try to enter the practical stage, cover: Professor Yaser Abu-Mostafa of Caltech focuses on traditional statistical theory. Pro

to close to 0 (when the coefficients are almost no longer adjusted, then the iterations are meaningless), we constrain alpha to be more than a slightly larger constant term, see code.2) Each iteration, change the order of optimization of the sample. That is, randomly selecting samples to update the regression coefficients. This reduces cyclical fluctuations, because the order of the samples changes so that each iteration is no longer cyclical.The pseudo code of the improved stochastic gradient

Data structure hash sum one: Theory study Chapter Data structure Hash sum two: Program Learning Chapter Data Structure Hash Summary three: Practice basic article Data Structure Hash Summary four: program advanced article Data Structure Hash Summary five: Nginx in the hash (version 0.1) Reprint please indicate the source http://blog.csdn.net/yankai0219/article/details/8185796 0,

, easy to read later]KNN calculates the distance between the input instance and each training instance, when the training set is large, the computation is very time consuming, in order to improve the efficiency of KNN search, we can consider using special structure to store the training data to reduce the number of distance calculation. [see reference 1, this data is not very small, temporarily did not see]2.k-nearest neighbor classification algorithmFigure 1 from Reference 23.KNN featuresThe da

) Else: Y_new.append (0) Y_new = Np.asarray (y_new) indicesminority = Np.where (y_new = = minorityclasslabel) [0] return indicesminority, Y_new def generatesamples (X, y, Minorityclasslabel = 1, K =5,beta = 0.3): syntheticdata_x = [] Syntheticdata_y = [] indicesminority, y_new = get_indicesminority (y) Ymin = y[indicesminority] Xmin = x[indicesminority] Rlist = Getris (X, Y_new, indicesminority, Minorityclasslabel, K) ML, MS = Get_clas

What is iptables, a learning and practice example of iptables knowledge theory? Without explanation, go to the topic. The command structure of iptables is iptables [-ttable] command [match] [target]. Next, we will introduce the learning and practice of [-ttable] part [-tt iptables knowledge theory. for example, what is

", "database", etc. support for dependent test methods, parallel tests, load tests, local failures flexible plug-in API multi-threaded test TestNG write the test process in three steps: Write the business logic for the test and insert the testng in the code annotation Add test information to the Testng.xml file or to the Pom.xml file Run TestNG The difference between TestNG and junit4+ The design goals are different. TestNG is an open

In the past year or two, the emerging generation model generative adversarial Networks (GAN) has made a big progress in generating tasks. Although there are many problems such as the instability of GAN in the course of being presented, the researchers have improved it from the aspects of model, training technique and theory respectively. This article aims to comb these related work. Although most of the time, supervised

scheduling timing can be divided into1. Preemptive scheduling: high-priority processes preempt the processor and leave the process being processed in the ready queue.2. Non-preemptive scheduling: When the highest priority of a process in the ready queue is higher than the highest priority of a process running in the processor, it does not leave the running process out of the processor, but rather the header of the high-priority queue at the ready.1.11 Dead LockMultiple processes are deadlocked

still returns to the parent shell without affecting the parent shell's original environment.The child shell, like the parent shell, opens three files: standard input (keyboard), standard output (screen), standard error (screen), and the child shell inherits the contents of several variable values of the parent shell, which are called environment variablesThe child shell then opens the child shell:View is currently located in several layers of shell:echo $SHLVL OR PS AXFThis article is from the

already an answer or no solution{Now=q.front (); Take the first element of the team to expandThe order of if and for can be reversed, depending on the situationif (Reach target condition){the corresponding operation;Return}Perform the required actions, and then access the points adjacent to the first element of the team;After it is found, mark it and put the qualifying points into the queue;The first element of the team is out of the team because it has been accessed and the required operation

are the same, there are differences, we can not only define entities, we can also build data model, that is EF (entityframework), also is ORM (Object Relational Mapping Framework/ Data persistence framework) An object-oriented operation framework for manipulating data in a data table based on an entity object, and the underlying invocation of ADO.The third one is viewsRequests sent by the client browser are returned to the client by the controller by selecting the appropriate view after the met

No Free LunchThe NFL theorem shows that no learning algorithm can always produce the most accurate learner in any field. No matter what learning algorithm is used, there is at least one objective function, which can make the random guessing algorithm a better algorithm.Howding InequalitiesWhat does the howding inequality mean?What is PAC?Algorithm theory--the fea

)); Node T; intto ; scanf ("%d", N); for(inti = n; I >=1; --i) for(intj =1; J j) scanf ("%d", A[i][j]); for(intj =1; J j) { for(inti =1; I i) {t.create (J, I); to=Hash[t]; Add (To, a[i][j]); P[j][i]=sum (to); } }}voidWork () {intm, u, v; scanf ("%d", m); for(inti =1; I i) {scanf ("%d%d", u, v); printf ("%lld\n", P[u][v]); }}intMain () {//freopen ("test.in", "R", stdin); //freopen ("Test.out", "w", stdout);init (); intT; scanf ("%d", T); for(intTimes =1; Times Times ) {printf