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Li Hongyi machine learning introduction -2022.07.11
2022-07-19 15:09:00 【ww9878】
Machine learning introduction summary :
Machine learning is to make machines have the ability to learn .
Understand machines from two perspectives :
1. personification : The language can be recognized through the program , Or identify categories .
2. Pragmatic : Find the right function , Identify data by function
The process of machine learning :
1. Set a series of functions
2. Training materials : The machine judges the quality of the function according to the quality of the training data
(1)inputs
(2)outputs
(1,2 Training process )
3. Choose the best function
Enter information to start training , Output training results ( Testing process )
The task of machine learning revolves around two categories :
1.regression: Enter the value , The output is used to make future predictions based on existing data
2.classification:
(1)Birary Classification: Such as yes or no Simple classification of
(2)Multi-Classification: Classify different materials correctly , Not limited to two categories . Such as : Classification of news articles , It can be divided into politics , economic , Literature, etc .
Machine learning classification :
1.supervised Learning: Through a large number of label To learn
2.Semi_supervised Learing: Information has label and unlabel To learn
3.Transfer Learning: Through a large number of different label Materials to learn , To judge the effect of irrelevant material learning
4.Unsupervised Learning: Information unlabel, have only outputs No, Inputs Let the machine learn under such circumstances , To see the effect of learning
5.Reinforcement Learning: Don't tell the machine the correct answer , Only score the learned behavior of the machine , Let the machine learn from evaluation
6.Structured Learning: Let the machine output structural things . For example, will ‘ machine learning ’ Translated into English .
summary : Machine learning is mainly through determining the behavior of learning , The problem to be solved , Use different methods to determine how the machine will solve the problem .
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