Shuruucda Baadhista ee Kediska Shabakadda Neural

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

1. What pattern did Kaplan et al. report for language-model loss as model size, data, or compute increased?
   • Approximate power-law relationships over the tested ranges
   • A sudden guaranteed drop to zero
   • A fixed increase in loss for every larger model
   • Random oscillation with no measurable trend
2. Saddexdee qiyaasood ayaa sharciyada asalka ah ee miisaanku la xidhiidha luminta?
   • Cabbirka dufcada, ka-tagidda, iyo waayihii
   • Qiyaasta moodeelka, cabbirka xogta, iyo xisaabinta
   • Heerka waxbarashada, qudhunka miisaanka, iyo qoto dheer
   • GPU tirinta, xusuusta, iyo daahitaanka
3. How can a scaling-law fit help when planning a larger training run?
   • It guarantees the final benchmark score
   • It eliminates the need for validation
   • It can inform an extrapolation from smaller experiments, subject to the tested setup
   • It predicts results exactly for any dataset or architecture
4. Maxay u taagan tahay ereyga joogtada ah (aan la dhimi karin) ee qaacidada khasaaraha sharciga?
   • Cilad software
   • Heerka waxbarashada
   • Tirada GPU-yada
   • Xogta ku dhex jirta ee aan noocna ka adkaan karin
5. Why can absolute loss reductions become smaller as compute keeps increasing under a power-law fit?
   • The reducible loss term shrinks while the loss approaches its floor
   • The data is removed after each compute increase
   • The model stops using parameters
   • Power laws require loss to increase over time
6. Isbeddelkee ayaa garoonka ka riixaya wax ka sarreeya miisaan yar oo 'weyn ayaa ka wanaagsan'?
   • GPU-yadu waxay noqonayaan bilaash
   • yaraanta xogta tayada sare leh iyo tamarta iyo xadka kharashka
   • Qoraal tayo sare leh oo aan xad lahayn ayaa soo baxaya
   • Qallooca luminta oo isu beddela xariiq toosan oo toosan
7. What did the original Kaplan study say about architecture details such as width and depth within its tested range?
   • They always dominated model size, data, and compute
   • They made loss independent of data
   • They had relatively small effects compared with the studied scaling variables
   • They guaranteed the same performance for all architectures
8. What compute-allocation tendency did the original Kaplan paper report?
   • Every model should be trained to zero loss
   • Small models always use less compute at equal loss
   • Training data has no effect on compute allocation
   • Large models could be more sample-efficient and compute-efficient training could stop before convergence