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Ukufunda kwenguxa

Mayelana Wikipedia

Ukufunda kwenguxa yibizo elibhekisela ekuxazululweni kwezinkinga ebezizovimba abahlelelisi ukuba bakwazi ukuthuthukisa imikholezima, kepha kunalokho lezo zinkinga zixazululwa ngokulekelela izinguxa ukuba "zivubukule" imikholezima yazo,[1] ngaphandle kokutshelwa ngokuqondile ukuba zenzeni yinona yimiphi imikholezima evela kumuntu. Kamuva, amaxhoxho weNzwa wokuzakhela aphehlwayo akwazile ukuqoqoda imiphumela yezindlela ezandulele.[2][3] Izinsondelo zokufunda kwenguxa ziye zasetshenziswa ezinongweni zolimi ezinkulu, umbono wesiCikizi, ukuhlonza iphimbo, ukuhlunga incwazuba, kwezolimo nakwezokwelapha, lapho kubiza imali eningi ukuthuthukisa imikholezima ezoqhogoya imisebenzi eyisidingo.[4][5]


Neural_network_with_dark_background

Iziseko zomchazazibalo wokufunda kwenguxa zitholakala ngezindlelasu zokuhlelelisa komchazazibalo. Ukumonyula imininingo omunye umkhakha ohlobene, ogxile kwisihlaziyo semininingo esihlwayayo esisebenzisa ukufundwa okungaqondisiwe.

Imithombo

[hlela | Hlela umthombo]
  1. Ethem Alpaydin (2020). Introduction to Machine Learning (Fourth ed.). MIT. pp. xix, 1–3, 13–18. ISBN 978-0262043793.
  2. "What is Machine Learning? | IBM". www.ibm.com (in i-English). Kulandwe ngomhlaka 2023-06-27.
  3. Zhou, Victor (2019-12-20). "Machine Learning for Beginners: An Introduction to Neural Networks". Medium (in i-English). Archived from the original on 2022-03-09. Kulandwe ngomhlaka 2021-08-15. Unknown parameter |url-status= ignored (help)
  4. Hu, Junyan; Niu, Hanlin; Carrasco, Joaquin; Lennox, Barry; Arvin, Farshad (2020). "Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning". IEEE Transactions on Vehicular Technology 69 (12): 14413–14423. doi:10.1109/tvt.2020.3034800. ISSN 0018-9545. http://dx.doi.org/10.1109/tvt.2020.3034800. Retrieved 2023-04-16. 
  5. Yoosefzadeh-Najafabadi, Mohsen; Hugh, Earl; Tulpan, Dan; Sulik, John; Eskandari, Milad (2021). "Application of Machine Learning Algorithms in Plant Breeding: Predicting Yield From Hyperspectral Reflectance in Soybean?". Front. Plant Sci. 11: 624273. doi:10.3389/fpls.2020.624273. PMC 7835636. PMID 33510761. http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=7835636.