# gamification learning theory

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### Python Machine LearningTheory and Practice (6) Support Vector Machine and python LearningTheory

Python Machine Learning Theory and Practice (6) Support Vector Machine and python Learning Theory In the previous section, the theory of SVM is basically pushed down, and the goal of finding the maximum interval is finally converted to the problem of solving the alpha of the

### Python Machine LearningTheory and Practice (5) Support Vector Machine and python LearningTheory

Python Machine Learning Theory and Practice (5) Support Vector Machine and python Learning Theory Support vector machine-SVM must be familiar with machine learning, Because SVM has always occupied the role of machine learning befo

### Python Machine LearningTheory and Practice (4) Logistic regression and python LearningTheory

Python Machine Learning Theory and Practice (4) Logistic regression and python Learning Theory From this section, I started to go to "regular" machine learning. The reason is "regular" because it starts to establish a value function (cost function) and then optimizes the val

### 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 permissio

### Cs281:advanced Machine Learning Section II Information Theory information theory

Entropy of information theoryIf the discrete random variable has a P (X) distribution, then X carries the entropy (amount of information):The reason for using log2 as a base is to make it easy to measure how many bits the information can be represented by. Because 1 bit is not 0 or 1. It can be deduced from the above formula that when the probability of K states is the same, the greater the entropy of the random variable x carries. As indicated by the Bernoulli distribution the entropy carries t

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### Thoughts on the learning of the theory of Network Theory

, following this idea, if we sum up more basic principles and the relationship between them (instead of computer models), it will be easier to solve many problems of invention. in fact, the application of the theory of Network Technology (ITIL) in the Soviet Union and the West is very successful, and the benefits are very obvious. Under the guidance of this method, the patent is estimated to be several millions in size, it is a required skill for prod

### April 23 Python Learning summary socket UDP and operating system theory, multi-channel theory

have disks, and in many systems, one disk saves files for many users at the same time. Allocating disk space and documenting who is using which disk block is a typical task for operating system resource management. The combination of these two approaches is a multi-channel technique.The biggest problem with spatial multiplexing is that memory must be split between programs, which needs to be implemented at the hardware level and controlled by the operating system. If the memory is not split, on

### [SignalR Learning Series] 1. SignalR theory introduction, signalr Theory

[SignalR Learning Series] 1. SignalR theory introduction, signalr TheoryWhat is SignalR? ASP. NET SignalR is a development library that allows ASP. NET developers to easily add instant messaging functions to their programs. The instant messaging function can send data directly from the server to the online client without waiting for the client to request data to return data. SignalR provides a simple api to

### "Turn" "Hu Yu Learning (theory) computer" Post and two supplement

Today's recommended is an old post, the Nanjing University of Mr. Sir's "Hu Yu" and later two additional posts. Count is more than 10 years ago post, I know the post from the Nanjing University BBS, Baidu a bit, but did not turn to the original. But Baidu to a lot of irresponsible reprint, I think I this is not a copy of it. That year, I have a very respectable seniors to recommend this article, let me benefit (seniors young and arrogant because this matter also ate a lot of losses, he himself h

### Machine learning-Bayesian theory _ Machine learning

computationally complex, they can still measure other algorithms as a criterion for optimal decision making. Bayes Law In machine learning, we are usually interested in determining the best assumptions in the hypothetical space H given the training data d. The so-called best hypothesis, one approach is to define it as a priori probability in the given dataset D and h of the different assumptions of the knowledge offending possible (most probable) hyp

### Today I will start learning pattern recognition and machine learning (PRML), Chapter 1.2, probability theory (I)

Original writing. For reprint, please indicate that this article is from:Http://blog.csdn.net/xbinworld, Bin Column Pattern Recognition and machine learning (PRML), Chapter 1.2, probability theory (I) This section describes the essence of probability theory in the entire book, highlighting an uncertainty understanding. I think it is slow. I want to take a loo

### Learningtheory Experience risk minimization--andrew ng machine Learning notes (vii)

Content Summary To now supervised learning has basically finished, this blog is mainly to write about the theory of machine learning, that is, when to use what learning algorithm, what kind of learning algorithms have what characteristics or advantages. At the time of fitti

### Introduction to metacognitive theory and learning

From: http://blog.sina.com.cn/s/blog_50f98dfa0100a86r.html Introduction to metacognitive theory and learning, edited by bloggers The most important teaching is to teach students to learn, and the most important learning is to learn. In recent years, many large, medium, and primary schools in China have carried out teaching of

### Model selection of learningtheory--andrew ng machine Learning notes (eight)

-validation approach. Cross-validation A simple idea to solve the above model selection problem is that I use 70% of the data to train each model, with 30% of the data for training error calculation, and then we compare the training errors of each model, we can choose the training error is relatively small model. If you do not refer to these errors (learn the theory of experience risk minimization--andrew ng machine

### Summary of probability theorylearning (road map)

In the recent learning Pattern recognition and machine learning often use the knowledge of probability theory, simply re-review the knowledge of probability theory. The most important point of learning probability theory is not th

### Stanford CS229 Machine Learning course Note six: Learningtheory, model selection and regularization

Anyone who knows a little bit about supervised machine learning will know that we first train the training model, then test the model effect on the test set, and finally deploy the algorithm on the unknown data set. However, our goal is to hope that the algorithm has a good classification effect on the unknown data set (that is, the lowest generalization error), why the model with the least training error will also be effective in controlling the gene

### Xu Zi rain: SEO learning to first theory again combat and pay attention to the deep level of improvement

Hello everyone, I am the Phantom of the Rain. In front of you to share a lot about SEO knowledge, there are several is about SEO learning, but many people for learning SEO or have their own set of methods, may be introduced before the method for everyone is not feasible suggestions, Today, I would like to tell you that I have a little bit of SEO ideas: seo learning

### Today, we will start learning pattern recognition and machine learning (PRML), Chapter 1.2, probability theory (I)

Original writing, reproduced please indicate the source of http://www.cnblogs.com/xbinworld/archive/2013/04/25/3041505.html Today I will start learning pattern recognition and machine learning (PRML), Chapter 1.2, probability theory (I) This section describes the essence of probability theory in the entire bo

### Machine learning Algorithms Study Notes (3)--learningtheory

we invent a new learning model or algorithm, then cross-validation can be used to evaluate the model. In NLP, for example, we focus our training on part of the training and part of the test.Reference documents[1] machine learning Open Class by Andrew Ng in Stanford http://openclassroom.stanford.edu/MainFolder/CoursePage.php? Course=machinelearning[2] Yu Zheng, Licia Capra, Ouri Wolfson, Hai Yang. Urban com

### Machine Learning---Computational learningtheory

and feasible.1, we trained the mapping relationship with the ideal complete data on the error rate is approximately equal to 0 (that is, we use some training data is OK)2, let the error rate is approximately equal to 0 of the probability is equal to 1 (that is, no matter what kind of training data we take no effect)Proves the two contents, then we can say that the method of learning is correct and feasible.How does it prove about 0? In fact, just pro

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