Kediska Faafinta

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

1. What quantity does backpropagation compute for a neural network's relevant weights?
   • Derivatives of the loss with respect to those weights
   • A causal percentage of total error uniquely assigned to each weight
   • The architecture drawing without any calculation
   • A permanently updated set of weights without an optimizer
2. Xeerkee xisaabeed ayaa xuddun u ah faafinta?
   • Aragtida Pythagorean
   • Aragtida Bayes
   • Xeerka silsiladda calculus
   • Qaaciddada quadratic
3. Why can reverse-mode backpropagation compute many parameter derivatives efficiently?
   • It avoids all local derivatives
   • It reuses graph structure and relevant intermediate results
   • It calculates only the output prediction again
   • It guarantees every implementation has exactly one forward pass of runtime
4. Siday u kala horreeyaan ayay faafinta dib-u-celinta u xisaabisaa gradients?
   • Laga soo bilaabo lakabka wax soo saarka ee dhinaca lakabka wax soo saarka
   • Dhammaan lakabyada isku mar oo aan lahayn amar
   • Kaliya lakabka qarsoon ee dhexe
   • Laga soo bilaabo lakabka wax soo saarka ee gadaal u socda lakabka wax gelinta
5. Does computing backpropagation gradients by itself establish that model weights were updated?
   • Yes; every backward computation automatically applies a learning rate
   • Yes; gradient values always replace the weights directly
   • No; the optimizer update is a separate step
   • Only if the network has no parameters
6. In a conventional cached-backpropagation workflow, what does the forward computation provide for later derivative calculations?
   • Relevant intermediate values that can be retained for the backward computation
   • A completed optimizer update without any update rule
   • A guarantee that all later evaluation errors disappear
   • A permanent deletion of the training objective
7. On one composed backpropagation path, local scalar derivatives are three and four. What is their chain-rule product?
   • Toddoba
   • Mid
   • Three quarters
   • Laba iyo toban
8. A shared parameter has loss-gradient contributions two and five through two paths. What combined contribution is accumulated?
   • Toban
   • Toddoba
   • Saddex
   • Two fifths