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strive to be able to find the right path to progress, in order to successfully achieve the learning objectives. Not only that, the students in the study of the help is not limited to the student learning method design, and open various classes, extracurricular classes, for students to design a set of tangible "progressive" Step. It seems to be believed that as l

In a twinkling of an eye, I started school for two months. In the middle, I had to spend a month or so on learning discrete mathematics because of some disgusting things, summarize the learning content, methods, and books.First, what exactly does discrete mathematics contain? Traditional lecture methods include mathema

a set language, that is.
3. Comprehensive knowledge
The knowledge of each chapter of high school mathematics is not isolated. There is a close relationship between the chapter and chapter, and between the chapter and the section. We need to use it comprehensively.
For example, after learning about the solution to the inequality, let's look at the following questions:
Three inequalities are known:
To make

Mathematics in machine learning (1)-Regression (regression), gradient descent (gradient descent)Copyright Notice:This article is owned by Leftnoteasy and published in Http://leftnoteasy.cnblogs.com. If reproduced, please specify the source, without the consent of the author to use this article for commercial purposes, will be held accountable for its legal responsibility.Objective:Last wrote a about Bayesia

wenwang !"
I was surprised to hear this song, because it was called "Wen Wang Cao", and he never said it to Confucius!
What kind of learning spirit is this?
This is a kind of spirit that thoroughly learns knowledge! We should not only learn the concepts and skills of people, but also the methods and methods, and also the ideological realm of people. Without a thorough understanding, it is difficult to understand the knowledge.

Copyright Notice:This article is owned by Leftnoteasy and published in Http://leftnoteasy.cnblogs.com. If reproduced, please specify the source, without the consent of the author to use this article for commercial purposes, will be held accountable for its legal responsibility.Objective:Last wrote a about Bayesian probability theory of mathematics, the recent time is relatively tight, coding task is heavier, but still take time to read some machine

average value is minimal. We make:The above-mentioned Z and B are taken into the reduced-dimensional expression:The expression of the above-loaded J is obtained:Then using the Laplace multiplier (a little bit here), we can get the expression of the projection base we want:Here is another characteristic value expression, we want the first m vector is actually here the largest m eigenvalues corresponding to the eigenvector. Prove this can also see, we J can be translated into:That is, when the er

Mathematics in Machine learning (5)-powerful matrix singular value decomposition (SVD) and its applicationCopyright Notice:This article is published by Leftnoteasy in Http://leftnoteasy.cnblogs.com, this article can be reproduced or part of the use, but please indicate the source, if there is a problem, please contact [email protected]Objective:Last time I wrote about PCA and LDA, there are two general impl

, Each time the map-reduce completes, it involves the operation of writing files and reading files. Personal speculation in the Google Cloud computing system, in addition to map-reduce, there should be a similar to the MPI model, that is, the node is to maintain communication between the data is resident in memory, the calculation model than Map-reduce in the number of iterations to solve a lot of times, much faster.The Lanczos iteration is a method t

(such as GBDT) are typical of the method, today mainly talk about the gradient boosting method (this is a little different from the traditional boosting) some mathematical basis, With this mathematical basis, the application above can be seen Freidman gradient boosting machine.This article requires the reader to learn basic college mathematics, as well as the ba

likelihood solution. For finite data sets, the posteriori mean of parameter μ is always between the transcendental average and the maximum likelihood estimate of μ.SummarizeAs we can see, the posterior distribution becomes an increasingly steep peak shape as the observational data increases. This is shown by the variance of the beta distributions, when a and b approach infinity, the variance of the beta distribution tends to be nearly 0. At a macro level, when we observe more data, the uncertai

expression:The expression of the above-loaded J is obtained:Then using the Laplace multiplier (a little bit here), we can get the expression of the projection base we want:Here is another characteristic value expression, we want the first m vector is actually here the largest m eigenvalues corresponding to the eigenvector. Prove this can also see, we J can be translated into:That is, when the error J is composed of the smallest d-m eigenvalues, J obtains the minimum value. It's the same as the

expression:The expression of the above-loaded J is obtained:Then using the Laplace multiplier (a little bit here), we can get the expression of the projection base we want:Here is another characteristic value expression, we want the first m vector is actually here the largest m eigenvalues corresponding to the eigenvector. Prove this can also see, we J can be translated into:That is, when the error J is composed of the smallest d-m eigenvalues, J obtains the minimum value. It's the same as the

(such as GBDT) are typical of the method, today mainly talk about the gradient boosting method (this is a little different from the traditional boosting) some mathematical basis, With this mathematical basis, the application above can be seen Freidman gradient boosting machine.This article requires the reader to learn basic college mathematics, as well as the ba

-method, but there is another problem: If the numerator and denominator can all get any value, then there will be an infinite solution, we will limit the denominator to 1 (this is a very important technique using the Laplace multiplier method. It will also be used in the PCA mentioned below. If you forget it, please review the high number) and use it as the restriction condition of the Laplace multiplier

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Author: leftnoteasy
regression and gradient descent:
Regression in mathematics is given a set of points, can be used to fit a curve, if the curve is a straight line, that is called linear regression, if the curve is a two-time curve, is called two regression, regression there are many variants, such as locally weighted regression, logistic regression , wait, this is going to be in the back.
Using

4. ubiquitous BayesHere we will give some practical examples to illustrate the universality of Bayesian method, which is mainly focused on machine learning because I am not an economic student, otherwise, you can find a bunch of examples of economics.4.1 Chinese Word SegmentationBayesian is one of the core methods of machine learning. For example, Bayesian is use

This article mainly introduces C # based on the pure mathematical method recursive implementation of currency digital conversion Chinese function, involving C # for string traversal, conversion and mathematical operations related operation skills, the need for friends can refer to the following
In this paper, we describe the function of C # Recursive implementation of Chinese currency digital conversion based on pure mathematical

Learning notes of machine learning practice: Classification Method Based on Naive Bayes,
Probability is the basis of many machine learning algorithms. A small part of probability knowledge is used in the decision tree generation process, that is, to count the number of times a feature obtains a specific value in a dat

are pros and cons. This also gives us a large part of the time to explore.I began to develop a learning plan, collect information, watch the video, hope to understand these things in theory, slowly to practice them, and finally use. Well, it looks good. The idea is really hardships, and I finally failed to make it through the road. Theoretical knowledge involves, probability theory, Mathematical statistics, advanced

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