The essence of machine learning is pattern recognition. One part can be used for forecasting (supervised learning, unsupervised learning), the other type is used directly for decision making (enhanced learning), a core task of machine learning is pattern recognition, we can usually use pattern recognition to our future research system. Categorize and predict various possible future outcomes.
Perhaps the concept of embarrassment explains that you may not be able to understand it for half an hour. The best way to understand this technology is to try it yourself, and this is the purpose of Google's content.
Recently, we always listen to machine learning technology machine learning technology, then what is machine learning technology? How does it work? Perhaps the concept of embarrassment explains that you may not be able to understand it for half an hour, and the best way to understand this technology is to try it yourself.
Now Google has launched an experimental browser teaching content called Teachable Machine. It uses a simple two-minute content to tell us intuitively through video and pictures. What can modern artificial intelligence technology do? Temporarily unable to do so.
Teachable Machine allows us to use our webcam to train a very basic artificial intelligence program. With the click of buttons including green, purple, orange, etc., the machine can record everything it sees through the camera. Once "it" learns enough content, it automatically outputs whatever you like (a gif, a sound, or some text), after we train the objects or activities it sees. For example, I teach it to identify plants in the room, and make corresponding gif animations, and also automatically play the guitar in a gesture-controlled manner.
All of this is very interesting and shows some of the basic principles of machine learning technology. First of all, such programs are implemented through instance learning. Look first, then find the pattern and remember. Second, it requires a lot of examples to learn. The third and most important thing is that it is still superficial and easy to cultivate.
For example, at first I taught it to identify plants in my home. In fact, I just taught it to identify a fuzzy pixel group. It doesn't see aloe vera, and then it is associated with why young people like to raise plants indoors? Maybe it's because you can't afford a big house, and you'll be tagged with it. All machines are basically identified by what they see, and other extra information needs to be programmed into the program.
We have a better understanding of machine learning technology through Google's Teaching Machine, and next time we can better understand the advancement of artificial intelligence or machine learning technology. But as it sees more and more things, the creation of algorithms is not as smart as our brains think. In other words, it is still learning.
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