Kedis ku xidhan hagaha · Dhexdhexaad Heerka

Kediska Koodhka Barashada Mashiinka Imtixaanka Cutubka

Dooro imtixaanada ku haboon isbadalka, khasaaraha, qandaraasyada tensor-ka iyo habdhaqanka xad dhaafka ah ee yar adoon ku jahawareerin hubinta koodka iyo guud ahaan.

Wadooyinka haga ee la xidhiidhaKoodhka Barashada Mashiinka Imtixaanka Cutubka
Su'aal 1 ee 8

Waa maxay xaqiijinta ku habboon ee transformer-ka hor-qaadista?

Horudhac su'aal kasta8 su'aalo

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

  1. Waa maxay xaqiijinta ku habboon ee transformer-ka hor-qaadista?
    • Expected output shape, column order and known transformed values
    • Held-out accuracy on a deployment-like population
    • Recall across rare production subgroups
    • End-to-end latency under peak service load
  2. Maxay tahay sababta loo isbarbar dhigo hirgelinta khasaaraha xisaabinta gacanta?
    • It proves the model will generalize
    • It can catch formula or reduction errors on a known example
    • It replaces training data quality checks
    • It guarantees the optimizer converges
  3. Waa maxay tijaabada yar-yar ee overfit ka caawisaa ogaanshaha?
    • Fairness across all subgroups
    • Reliable performance on unseen data
    • Potential training-path or gradient bugs
    • Production latency under load
  4. Sidee tijaabooyinka u isbarbar dhigaan natiijooyinka tirada dhijitaalka ah?
    • Skip checks for all model outputs
    • Require exact bitwise equality on every device
    • Round every value to an integer
    • Use explicit tolerances appropriate to numerical precision
  5. Waa maxay hubinta ku habboon ee tijaabada interface-ka moodelka?
    • A statistical calibration curve on a held-out set
    • One prediction per input row and clear rejection of malformed features
    • Subgroup performance estimates with confidence intervals
    • Training loss after a full retraining job
  6. Why separate unit tests from model-quality evaluation?
    • Quality metrics test only syntax
    • Unit tests cannot execute Python functions
    • Code correctness does not establish statistical usefulness
    • Model validation needs no data
  7. What can a finite-gradient assertion establish?
    • The model is calibrated
    • The gradients are mathematically optimal
    • The model has no bias
    • A basic gradient path exists for expected parameters
  8. Where should slow full-dataset training usually be placed?
    • In a separate integration or training validation stage
    • In every tiny unit test
    • Inside a feature-name assertion
    • In a package lockfile