Dyp læring

Hvordan dyp læring er en delmengde av maskinlæring og hvordan maskinlæring er en delmengde av kunstig intelligens (KI).

Dyp læring (engelsk: deep learning) er en læreprosess som en bruker til å trene opp dataalgoritmer ved hjelp av såkalte kunstige nevrale nettverk (også kjent som «nevrale nettverk»). Dybden i dyp læring viser til at de kunstige nevrale nettverkene har mange lag av beregningsenheter, såkalte nevroner. Data må gjennomgå mer enn to lag med ikke-lineære transformasjoner for at læringa og det nevrale nettverket kan karakteriseres som dypt.

Dyp læring er en sentral metode innen maskinlæring – hvor det er et prinsipp at datamaskiner skal tilegne seg kunnskap og lære om noe de ikke vet eller kan fra før fra de data de blir tilført.[1] Dyp læring er basert på et sett med algoritmer som forsøker å modellere abstraksjoner i data på høynivå ved å bruke mange beregningslag med komplekse strukturer, som består av affine og ikke-lineære transformasjoner.[2][3] Læreprosessen kan være styrt (også kalt veiledet, eller på engelsk: supervised), halv-styrt (delvis veiledet) eller ikke-styrt (ikke-veiledet). Dyp læring har hatt en stor innvirkning på områder som bildeklassifisering, datamaskinsyn (engelsk: computer vision), språkbehandling, biostatistikk og lydgjenkjenning.[3][4] Metodikken står bak en stor andel av nyvinningene det siste tiåret innen kunstig intelligens.

Litteratur

  • Bengio, Yoshua (2016). Deep learning. ISBN 9780262035613.  OCLC 955778308

Referanser

  1. ^ Tidemann, Axel. «Dyp læring» (på norsk). Store norske leksikon. Besøkt 30. mai 2019. 
  2. ^ LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey. «Deep learning» (på engelsk). Nature. s. 436-444. ISSN 0028-0836. doi:10.1038/nature14539. Besøkt 30. mai 2019. 
  3. ^ a b (Bengio)
  4. ^ Schmidhuber, Jürgen (1. januar 2015). «Deep learning in neural networks: An overview» (på engelsk). Neural Networks. s. 85–117. doi:10.1016/j.neunet.2014.09.003. Besøkt 30. mai 2019. 

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