News classification
Contact us
- Add: No. 9, North Fourth Ring Road, Haidian District, Beijing. It mainly includes face recognition, living detection, ID card recognition, bank card recognition, business card recognition, license plate recognition, OCR recognition, and intelligent recognition technology.
- Tel: 13146317170 廖经理
- Fax:
- Email: 398017534@qq.com
A basic overview of pattern recognition, artificial intelligence, depth learning, and machine learning
A basic overview of pattern recognition, artificial intelligence, depth learning, and machine learning
Introduction
Personally, I have made a simple understanding of several important concepts of artificial intelligence, such as artificial intelligence, data mining, form recognition, machine learning, deep learning, and understand their inclusion relationships and differences. Not necessarily comprehensive, only as a self created.
Artificial intelligence
.
Let's first figure out:
Obviously, we can see that AI has many central parts in the application category and machine learning, so many times we call AI and machine learning the same thing.
But AI is defined as: AI is the science and engineering that enables intelligent machines and computer programs to learn and deal with problems in the way of human intelligence. Usually, these include natural language processing and translation, visual perception and formal identification and decision making, but the number and complexity of applications are rapidly expanding.
Pattern Recognition
From 70s to 80s, the emphasis is how to make a computer program to do some looks very "smart" things, such as distinguishing between "3" and "B" or "3" and "8", a lot of time needs to specially design some manual classification rules, such as filtering, edge detection and morphological processing technology. (the birth of a smart program)
Data mining
Data mining: that is, data mining, is a very broad concept. Definition is from the mass of data to "discover" the hiding information, in accordance with the textbook statement, the data here is "large, incomplete, noisy, ambiguous, random data, information application" refers to the "implied law of people, but it is unknown beforehand, potential useful and ultimately understandable information and knowledge". This job BI (Business Intelligence) can do it, data analysis can be done, and even market operations can be done. You use Excel to dissect the data and find some useful information that can guide your business. So, data mining is more likely to be applied. In order to do a good job of data mining, enterprises have to set up a data warehouse.
Data mining is usually related to computer science, and through statistics, online analysis and disposal, information retrieval, machine learning, expert system (relying on past experience rule) and form recognition, etc, the above purpose is achieved.
machine learning
Machine learning: machine learning is the interdisciplinary subject of computer science and statistics. The basic purpose is to learn a x->y function (mapping) to do classification or regression work. Often and data mining together is because now a lot of data mining is the result of the work of machine learning algorithms to provide tools, such as advertising CTR estimates, PB level click log after the typical machine learning process to get a prediction model, so as to improve the Internet advertisement click rate and the rate of return personalized recommendation; or, after some machine learning algorithm analysis platform on the purchase and collection, reading log, get a referral model to predict the goods you love.
The idea of machine learning is not complicated. It is just an imitation of the learning process in human life. And in this whole process, the most critical is the data. Any relevant research data after learning algorithm exercise belong to machine learning, including many have been carried out for many years, such as linear regression (Linear Regression), the mean K (K-means, based on the objective function of clustering method prototype decision tree (Decision), Trees, using a graphic method of probability analysis, random forest () Random Forest, using a graphic method of probability analysis), PCA (Principal Component Analysis, principal component analysis, SVM (Support), Vector Machine, support vector machine (Artificial) and ANN Neural Networks, artificial neural network).
Deep learning
Deep learning: deep learning, a topic machine learning which now more fire (a sub machine learning set), itself is a neural network algorithm derived (e.g. convolutional neural network), the image classification and recognition of the voice and other rich media have very good effect, so the major research institutions and companies have invested a lot of manpower to do the related research and development.
The concept of Deep Learning is derived from the research of artificial neural networks. The multilayer perceptron with multiple hidden layers is a deep learning structure. Deep learning, through combination of low layer features, constitutes a more general level of high-level representation of attribute categories or features, in order to find out the scattered features of the data. Deep learning is a new field in machine learning. Its motive is to set up and imitate the neural network that human brain stops analyzing and learning. It simulates the mechanism of human brain to interpret data, such as image, voice and text.
summary
Data mining is a very broad concept. Most of the common methods of data mining come from the subject of machine learning, and also from database technology. Deep learning is a kind of analogy algorithm for machine learning, which is essentially the original neural network. AI is mostly referring to machine learning nowadays, and AI is far more than machine learning.
Personally, I have made a simple understanding of several important concepts of artificial intelligence, such as artificial intelligence, data mining, form recognition, machine learning, deep learning, and understand their inclusion relationships and differences. Not necessarily comprehensive, only as a self created.
Artificial intelligence
.
Let's first figure out:
Obviously, we can see that AI has many central parts in the application category and machine learning, so many times we call AI and machine learning the same thing.
But AI is defined as: AI is the science and engineering that enables intelligent machines and computer programs to learn and deal with problems in the way of human intelligence. Usually, these include natural language processing and translation, visual perception and formal identification and decision making, but the number and complexity of applications are rapidly expanding.
Pattern Recognition
From 70s to 80s, the emphasis is how to make a computer program to do some looks very "smart" things, such as distinguishing between "3" and "B" or "3" and "8", a lot of time needs to specially design some manual classification rules, such as filtering, edge detection and morphological processing technology. (the birth of a smart program)
Data mining
Data mining: that is, data mining, is a very broad concept. Definition is from the mass of data to "discover" the hiding information, in accordance with the textbook statement, the data here is "large, incomplete, noisy, ambiguous, random data, information application" refers to the "implied law of people, but it is unknown beforehand, potential useful and ultimately understandable information and knowledge". This job BI (Business Intelligence) can do it, data analysis can be done, and even market operations can be done. You use Excel to dissect the data and find some useful information that can guide your business. So, data mining is more likely to be applied. In order to do a good job of data mining, enterprises have to set up a data warehouse.
Data mining is usually related to computer science, and through statistics, online analysis and disposal, information retrieval, machine learning, expert system (relying on past experience rule) and form recognition, etc, the above purpose is achieved.
machine learning
Machine learning: machine learning is the interdisciplinary subject of computer science and statistics. The basic purpose is to learn a x->y function (mapping) to do classification or regression work. Often and data mining together is because now a lot of data mining is the result of the work of machine learning algorithms to provide tools, such as advertising CTR estimates, PB level click log after the typical machine learning process to get a prediction model, so as to improve the Internet advertisement click rate and the rate of return personalized recommendation; or, after some machine learning algorithm analysis platform on the purchase and collection, reading log, get a referral model to predict the goods you love.
The idea of machine learning is not complicated. It is just an imitation of the learning process in human life. And in this whole process, the most critical is the data. Any relevant research data after learning algorithm exercise belong to machine learning, including many have been carried out for many years, such as linear regression (Linear Regression), the mean K (K-means, based on the objective function of clustering method prototype decision tree (Decision), Trees, using a graphic method of probability analysis, random forest () Random Forest, using a graphic method of probability analysis), PCA (Principal Component Analysis, principal component analysis, SVM (Support), Vector Machine, support vector machine (Artificial) and ANN Neural Networks, artificial neural network).
Deep learning
Deep learning: deep learning, a topic machine learning which now more fire (a sub machine learning set), itself is a neural network algorithm derived (e.g. convolutional neural network), the image classification and recognition of the voice and other rich media have very good effect, so the major research institutions and companies have invested a lot of manpower to do the related research and development.
The concept of Deep Learning is derived from the research of artificial neural networks. The multilayer perceptron with multiple hidden layers is a deep learning structure. Deep learning, through combination of low layer features, constitutes a more general level of high-level representation of attribute categories or features, in order to find out the scattered features of the data. Deep learning is a new field in machine learning. Its motive is to set up and imitate the neural network that human brain stops analyzing and learning. It simulates the mechanism of human brain to interpret data, such as image, voice and text.
summary
Data mining is a very broad concept. Most of the common methods of data mining come from the subject of machine learning, and also from database technology. Deep learning is a kind of analogy algorithm for machine learning, which is essentially the original neural network. AI is mostly referring to machine learning nowadays, and AI is far more than machine learning.