Kedis ku xidhan hagaha · Adag Heerka

Qarsoon Markov Model Kedis

Waxay hubisaa gobolada HMM, kala-guurka iyo qiiqa sii daaya, Horudhac iyo Viterbi algorithms, Barashada Baum-Welch iyo ku tiirsanaanta isku xigxiga.

Wadooyinka haga ee la xidhiidhaQaababka qarsoon ee Markov
Su'aal 1 ee 8

Waa maxay nooca 'qarsoon' ee ku jira Qaabka Qarsoon ee Markov?

Horudhac su'aal kasta8 su'aalo

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

  1. Waa maxay nooca 'qarsoon' ee ku jira Qaabka Qarsoon ee Markov?
    • Dawlad-goboleedyadu waa kuwo aan la arki karin oo laga soo qaatay wax-soo-saarka
    • Halbeegyada moodeelku waa qarsoodi
    • Indho-indheynta waa sir
    • Kala-guurka ayaa ah mid aan kala sooc lahayn oo aan la ogaan karin
  2. Waa maxay hantida Markov ee ay qaadatay HMM?
    • Dhammaan gobolada waxay sii daayaan calaamad isku mid ah
    • Gobolka soo socdaa wuxuu ku xidhan yahay oo kaliya xaalada hadda jirta
    • Indho-indhayntu way ka madaxbannaan yihiin dawladaha
    • Dawlad kastaa si isku mid ah ayay u badan tahay
  3. Algorithmkee hela isku xigxiga ugu macquulsan ee dawladaha qarsoon?
    • Algorithm-ka hore
    • Baum-Welch
    • Viterbi algorithm
    • Soo degid tartiib ah
  4. Waa kuwee saddexda qaybood ee itimaalka si buuxda u qeexaya HMM?
    • Ka hor, suurtogalnimada, iyo caddaynta
    • Micnaha, kala duwanaanshaha, iyo wada noolaanshaha
    • Gelida, qarsoon, iyo miisaanka soo saarka
    • Xaaladda hore, kala-guurka, iyo suurtogalnimada sii daynta
  5. Muxuu sameeyaa algorithm-ka Baum-Welch?
    • Wuxuu ku bartaa halbeegyada HMM xogta iyadoo la adeegsanayo rajo-kordhinta
    • Waxay siraysaa taxanaha indho-indheynta
    • Hela jidka keliya ee ay u badan tahay
    • Wuxuu ku xisaabiyaa shaxda kala-guurka saxda ah qaab xiran
  6. Which Transformer feature can directly relate tokens at distant positions in a sequence?
    • An HMM emission table
    • Self-attention over positions in the input
    • A fixed single-state transition
    • A requirement to process only one token ever
  7. What does the Forward algorithm compute for a sequence of observations?
    • Only the single most likely state path
    • The most likely parameter initialization
    • The total probability of the observations by summing over possible hidden-state paths
    • The mean of the emission probabilities
  8. A hidden state emits a word or acoustic symbol. Which HMM probability describes that relation?
    • The initial-state probability
    • The transition probability
    • The discount factor
    • The emission probability