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Until last time, I briefly learned about the history of artificial intelligence and artificial intelligence. Now let's find out what machine learning is in earnest.

Machine Learning

= Self-learning technology to improve performance

Let's first pick up the relationship between artificial intelligence and machine learning. Machine learning is a field of artificial intelligence, a technology that allows machines to learn and perform better on their own. This technology is a small category of artificial intelligence.

Machine learning is called machine learning in Korean. What is this machine learning? To put it simply, the program gives input to the program, checks the output, adjusts the logic according to the output, and improves itself by modifying the logic to produce better results when the program gives the next input.

Self-correcting logic?

Non-Communists and those who have done some coding can be a little confused meaning up there. You're modifying logic on your own? The traditional program we know is that humans plan logic and print out 1 if the input value a is greater than 10! Otherwise, print zero!!

input a 
if( a > 10 ){ 
  print : 1 
}else{ 
  print : 0
}

If you squeeze and spin logic like this, it's only by that logic. By the way, this logic is being modified by a computer? That's right. If you say something, the program itself doesn't change the source itself. The method is to save the figures through mathematical modeling and update them to find the best value.

For example, I entered 3 in the input, but the result is 0? I entered 11 in the second input, but the result is 1? I entered 10 in the third input, but the result is 0? Based on the results of the three inputs, we found that if the input was greater than 10, we would return 1 and if it was smaller, we would return 0. Then the program sets the standard as input>10. The program modifies this reference value by itself through the mathematical model mentioned above, even if the output and other inputs later.

To sum up, if the traditional program of the past was for the user to enter the rule and produce the output, machine learning is for the user to enter the output and make the rule with his own thread. Do you understand a little bit now?!

There are many kinds of machine learning. Logistic regression, linear regression, artificial neural network, etc. Let's just go over the learning types of machine learning.

Meaning of training in machine learning

In machine learning, the word training is used a lot. No, it's no exaggeration to say it's a necessity. So what does the word training mean in machine learning? It's exactly what we know and what training means. If you take a quick look at how machine learning works, it gives input and the program checks the input and output and modifies the rules on its own. This one-time course is one-time training.

If it gives 100 inputs and outputs and the rule changes 100 times, it is said to have trained 100 times. In other words, training is the process of giving various inputs (problems) and outputs (answers) and modifying standards so that the program can improve itself. Literally, machine learning is a machine learning process, so training is essential in this learning process.

instructional learning
= supervised leading
= train models with input and target

 

Map learning is literally human guiding programs if you understand them. In the example of the above data, the program gives past data on whether it actually rained, depending on the amount of clouds, which is called training data. We put in a total of 7 data, 67% of the cloud was not enough, 31% was not enough, 99% was raining. The program trains a total of seven data sequentially.


Eventually, after seven training sessions, Plgram creates a standard of rain if the amount of clouds is approximately 70%, or less. There are only seven data, but when hundreds of millions of training data come in, this standard becomes very sophisticated.

It is guidance that gives data on past inputs and outputs, and identifies patterns in this data to create a baseline. And if you put a new input into this trained data, it's the map learning that shows the expected results!

→Limits of map learning

The most important thing in guidance is training data. It takes a huge amount of training data to become a sophisticated program, and the process of collecting or creating such data is not easy, and it is also problematic when there is data with large errors.

The biggest problem is that the model is not updated in real time when new inputs come in. New training should include newly entered data, which means that once a model is made, it will only return the expected value for that model and it will be difficult to adapt to environmental changes.

non-map learning
= unsupervised leading
= Training with targetless data

As opposed to map learning, there is no target data. In other words, there is only an input and no result data. There is a group classification as an example of using this Bizzy learning.

It is like no target because no group exists until a group is created, and there may be any group. Without a target, the program creates a cluster by classifying the inputs accordingly.

This is the way to learn Bizzy.

reinforcement learning
= reforestation leading
= reward and punish the result.

If you look at the instruction I saw earlier, they used inputs to create rules. However, when the nature of the input changed, there was a limit to not being able to adapt. However, using Ganghwa learning can solve the problem above to some extent.

Reinforcement studies evaluate targets with input. So if it is right, it rewards it, and if it is wrong, it punishes it and adjusts the rules it has. This allows the rules to adapt to the environment by reward and punishment even if the input's personality changes later.

Making the above contents into a single model is called an agent. The agent's goal is to be rewarded as much as possible. Google's AlphaGo was also created using such reinforcement learning.

[Deep Learning] #3 Let's find out what machine learning is! / Relationship with Artificial Intelligence / Map Learning / Non-Map Learning / Reinforcement Learning

 

#DeepLearning #MachineLearning #ArtificialIntelligence #Relationship #Learningforguidance #Learningfornontraditionallearning #Studyingforstrength

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