With the deepening influence of artificial intelligence (AI) technology on various industries, we also hear more about "machine learning", "deep learning", "enhanced learning", and "neural network" in news or reports. "The words are a bit of a fantasy for non-professionals." This article has combed the 12 keywords including these, and hopes to help readers understand more clearly the connotation and potential of this artificial intelligence technology.
1, machine learning
Professor Tom Michel is a computer science and machine learning department at Carnegie Mellon University. According to his definition in Machine Learning, machine learning is "to study how to build computer programs that can be automatically improved based on experience." . Machine learning is interdisciplinary in nature, using computer science, statistics, and artificial intelligence, as well as knowledge from other disciplines. The main product of machine learning research is algorithms that can help with automatic improvement based on experience. These algorithms can be used in a variety of industries, including computer vision, artificial intelligence, and data mining.
The meaning of classification is to create models and classify data into different categories. These models are created by entering a training database with pre-marked categories for the algorithm to learn. Then, enter the category unlabeled database in the model and let the model predict the category of the new data based on the knowledge it has learned from the training database.
Because such algorithms require explicit category marking, classification is a form of "supervised learning."
Regression is closely tied to classification. Classification is the prediction of discrete categories, while regression is applicable when the prediction "category" consists of consecutive numbers. Linear regression is an example of regression techniques.
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