Kediska Autoencoders

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

1. Waa maxay ujeedada 'qoorta dhalada' (koodka qarsoon) ee ku jira autoencoder?
   • In lagu qasbo shabakada inay gasho matalaad is haysta oo qabsata qaab dhismeedka muhiimka ah
   • Si loo kordhiyo xaddi badan oo la kordhiyo awoodda model
   • Si loo kaydiyo calaamadaha kala soocida la kormeero
   • Si loo yareeyo tababbarka xasilloonida wanaagsan
2. Waa maxay sababta autoencoders loogu tixgalinayo inay is-maamulaan?
   • Waxay u baahan yihiin xog-ururin sumadeysan oo waaweyn
   • Bartilmaameedka tababbarku waa tallo-gelinta lafteeda, markaa looma baahna calaamado dibadda ah
   • Bini'aadamku wuxuu dib-u-dhiska kasta ku calaamadiyaa gacan
   • Waxay kaliya ku shaqeeyaan sawirro hore loo soocay
3. Sidee buu u diidaa autoencoder uga duwan yahay kan caadiga ah?
   • Waxa ay gebi ahaanba ka saartaa furaha
   • Waxa kaliya oo ay ku shaqeysaa xogta qoraalka
   • Waxaa loo tababbaray in dib loo dhiso tallo nadiif ah oo laga soo qaatay nooca kharriban
   • Uma baahnid lakabka dhalo
4. Maxaa ka dhigaya kala duwanaanshaha autoencoder (VAE) inuu awoodo inuu soo saaro xog cusub?
   • Waxay xafidaysaa tusaale kasta oo tababar ah
   • Ma isticmaasho wax-kaliya
   • Waxay ku beddeshaa shabakadaha neerfaha geedaha go'aanka
   • Waxay barataa meel siman, meel qarsoon oo macquul ah oo aad muunad ka qaadan karto
5. Atoencoder toosan oo lagu tababaray qalad dhexdhexaad ah oo isku wareeg ah ayaa si dhow u shabbahay farsamada caadiga ah?
   • Falanqaynta qaybta aasaasiga ah (PCA)
   • K-macnaheedu waa kooxaysi
   • Go'aan geedaha
   • Dib-u-noqoshada saadka
6. Waa maxay sababta autoencoders ay si fiican ugu habboon yihiin ogaanshaha cilladaha?
   • Waxay calaamadiyaan cillado xilliga tababarka
   • Waxay si fiican dib u dhisaan qaababka caadiga ah laakiin waxay muujinayaan khaladaad waaweyn oo naadir ah, qaababka aan la arki karin
   • Waxay iska indho-tiraan khaladka dib-u-dhiska gabi ahaanba
   • Kaliya waxay soo saaraan kala soocida binary
7. Which reconstruction loss does the guide associate with binary-pixel data?
   • Luminta margin saddex-geesoodka ah
   • Temporal-difference error
   • Cross-entropy
   • Cosine annealing loss
8. Why can nonlinear activations make an autoencoder more expressive than a linear one?
   • They force the latent code to equal the input size
   • They remove the decoder from the model
   • They guarantee zero reconstruction error on unseen data
   • They allow it to learn nonlinear or curved structures in the data