ai learning algorithms

This article is a translation of the article, but I did not translate the word by word, but some limitations, and added some of their own additions.Machine Learning (machines learning, ML) is what, as a mler, is often difficult to explain to everyone what is ML. Over time, it is found to understand or explain what machine learning can be, from the perspective of

In the previous discussion, we always given a sample x and then gave or didn't give the label Y. The samples are then fitted, classified, clustered, or reduced to a dimension. However, for many sequence decisions or control problems, it is difficult to have such a regular sample. For example, the four-legged robot control problem, at first did not know should let it move that leg, in the process of movement, also do not know how to let the robot automatically find the right direction. In additio

parallel. However, partial parallelism can be achieved by self-sampling SGBT.8, GBDTAdvantages: 1, can flexibly deal with various types of data, including continuous and discrete values, processing classification and regression problems, 2, in the relatively few parameters of the time, the forecast preparation rate can also be relatively high. This is relative to the SVM, 3, can be used to filter features.4, using some robust loss function, the robustness of outliers is very strong. such as Hub

hierarchical approach. So the clustering algorithm tries to find the intrinsic structure of the data in order to classify the data in the most common way. Common clustering algorithms include the K-means algorithm and the desired maximization algorithm (expectation maximization, EM). (8) Association Rules Learning Association rule Learning finds useful associa

Sort algorithms for algorithm learning: Hill sort, learning sort algorithm Hill Hill sortingAlso known as "downgrading incremental sorting", the basic idea is to divide the entire sequence of records to be sorted into several subsequences for direct insertion and sorting, respectively, when the record in the entire sequence is "basically ordered", the record is d

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In the introduction of recommendation system, we give the general framework of recommendation system. Obviously, the recommendation method is the most core and key part of the whole recommendation system, which determines the performance of the recommended system to a large extent. At present, the main recommended methods include: Based on content recommendation, collaborative filtering recommendation, recommendation based on association rules, based on utility recommendation, based on knowledge

Machine learning can be divided into several types according to different computational results. These different purposes determine that machine learning can be divided into different models and classifications in practical applications.As mentioned earlier , machine learning is a Cross - disciplinary subject in many fields and a new subject in many fields. , t

This article introduces several of the most popular machine learning algorithms. There are many machine learning algorithms. The difficulty is to classify methods. Here we will introduce two methods for thinking and classifying these algorithms. The first group of

. 7.5 910.5 . 13.5]]# n Powers of each element of the matrix: n=2mymatrix1 = Mat ([[[1,2,3],[4,5,6],[7,8,9]])print power (mymatrix1,2 1 4 9] [[49 6481]]# matrix multiplied by matrix mymatrix1 = Mat ([[1,2,3],[4,5,6],[7,8,9 = Mat ([[[1],[2],[3]])print mymatrix1*mymatrix2 output: [[[][+][50]]# Transpose of the matrix mymatrix1 = Mat ([[[1,2,3],[4,5,6],[7,8,9]])print mymatrix1. The transpose of the # Matrix to the transpose of the T # Matrix print mymatrix1 output results as follow

algorithms on the computer to perform the improvement of efficiency and accuracy.Computer Vision (Computer vision)Computer Vision = Image processing + machine learning. Image processing technology is used to process images as input into the machine learning model, and machine learning is responsible for identifying re

Machine learning is undoubtedly an important content in the field of data analysis now, people who engage in it work are in the usual work or manyor less will use machine learning algorithms.There are many algorithms for machine learning, but there are two types of big aspects: one is

Tags: basic machine learning Continue with the original algorithm: (5) Bayesian Method Bayesian algorithms are a class of algorithms based on Bayesian theorem. They are mainly used to solve classification and Regression Problems. Common algorithms include Naive Bayes, averaged one-dependence estimators, and Bayesi

Tags: basic machine learning Based on the similarity of functions and forms of algorithms, we can classify algorithms, such as tree-based algorithms and neural network-based algorithms. Of course, the scope of machine learning is

structure of linear tables, where the time complexity is O (1), regardless of the location of the data being stored or read. When inserting or deleting, the time complexity is O (n), indicating that it is more suitable for the number of elements stable, not often inserting and deleting elements.Advantages:① no need to add additional storage space to represent the logical relationship between elements in a table② can quickly access elements from anywhere in the tableDisadvantages:① Insert and de

Transferred from: http://www.dataguru.cn/article-10174-1.html Gradient descent algorithm is a very extensive optimization algorithm used in machine learning, and it is also the most commonly used optimization method in many machine learning algorithms. Almost every current advanced (State-of-the-art) machine Lear