Isku xirka Gobolka Echo iyo Kediska Xisaabinta Kaydka

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

1. Shabakadda Gobolka Echo, miisaankee baa dhab ahaan loo tababaray?
   • Kaliya wax soo saarka (akhrinta) miisaannada
   • Dhammaan miisaannada soo noqnoqda ee gudaha kaydka
   • Kaliya miisaannada wax gelinta
   • Ma jiro miisaan waligeed loo tababaray
2. Which condition expresses the echo state property for two runs with the same input history?
   • Their recurrent weights must all be trained by backpropagation
   • Their reservoir states eventually become independent of different initial states
   • Their output weights must have spectral radius exactly one
   • Their hidden states must equal the raw input at every step
3. Waa maxay sababta tababarka ESN uu aad uga dhaqso badan yahay tabobarka LSTM caadiga ah?
   • ESN-yadu ma isticmaalaan neurons-yada gabi ahaanba
   • ESN-yadu waxay ku shaqeeyaan kaliya kombayuutarrada quantum-ka
   • ESN-yadu waxay ka fogaadaan faafinta-xilli-waqti-ilaa iyagoo xallinaya akhrinta toosan
   • ESN-yadu uma baahna xog gelin
4. How is spectral radius commonly used when tuning an echo-state reservoir?
   • It directly sets the number of readout classes
   • It guarantees fading memory for every recurrent matrix
   • It measures the number of trainable reservoir weights
   • It is a recurrent-feedback scale heuristic for balancing rich dynamics and stability, not a complete guarantee of the echo state property
5. Mashiinnada Dawladda ee dareeraha ah, nooca spiking-neuron ee xisaabinta kaydka, ayaa uu keenay?
   • Wolfgang Maass
   • Geoffrey Hinton
   • Andrew Ng
   • Yoshua Bengio
6. What does the fixed reservoir compute before the ESN readout produces an output?
   • A normalized probability over all possible weights
   • A high-dimensional dynamic state representation of recent inputs
   • A learned transition table for every future sequence
   • A separate trained policy for each output class
7. What method is commonly used to fit an Echo State Network’s readout weights?
   • Backpropagation through all fixed recurrent weights
   • Dynamic routing by agreement
   • Linear least-squares regression, often with ridge regularization
   • A softmax over reservoir eigenvalues
8. What does the spectral radius of a recurrent weight matrix mean?
   • The sum of all matrix entries
   • The number of nonzero output weights
   • The average magnitude of the input sequence
   • The largest absolute value among its eigenvalues