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Machine learning has attracted widespread attention and evolved into an enabling technology for a wide range of highly successful applications, such as intelligent computer vision, speech recognition, medical diagnosis, and more.
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Gunter Schlageter · 1978
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[Online; Retrieved in March 20, 2022 from https://data.stats.gov.cn
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[Online; Retrieved in March 19, 2022 from https://oag.ca.gov/privacy/ccpa
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[Online; Retrieved in March 19, 2022 from https://www.dataguidance.com/notes/japan-data-protection-overview
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[Online; Retrieved in March 19, 2022 from https://blog.didomi.io/en-us/canada-data-privacy-law
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