Quiz zu wiederkehrenden neuronalen Netzen

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1. What recurrent information does a conventional unidirectional RNN combine with each new input?
   • Only the previous printed prediction with no internal state
   • A fresh unrelated dataset at every step
   • The previous hidden state
   • No parameters or intermediate information
2. How should an RNN hidden state be understood?
   • An evolving internal representation that may summarize earlier inputs
   • A guaranteed lossless archive of every earlier input
   • The network's entire training dataset on disk
   • Only a final class label with no intermediate role
3. Which training difficulty can arise from propagating RNN gradients through many time steps?
   • A guarantee that every hidden state grows without bound
   • Vanishing or exploding gradient magnitudes
   • An automatic change from speech to image inputs
   • Permanent deletion of the training objective
4. Wie verbessern LSTMs und GRUs einfache RNNs?
   • Sie entfernen den gesamten Speicher aus dem Netzwerk
   • Sie funktionieren nur mit Zahlen, nicht mit Text
   • Sie trainieren ohne jegliche Daten
   • Mithilfe von Gates steuern sie, welche Informationen aufbewahrt, aktualisiert oder verworfen werden sollen
5. Für welche Art von Daten sind RNNs speziell konzipiert?
   • Sequentielle Daten wie Text, Sprache und Zeitreihen
   • Einzelne Standbilder ohne Bestellung
   • Zufällige ungeordnete Zahlen
   • Nur dreidimensionale Gebäudepläne
6. What training-computation difference does the original transformer architecture offer compared with a conventional sequential RNN?
   • Guaranteed fully parallel autoregressive generation
   • A guarantee that no training examples are needed
   • More parallel computation across training positions and direct attention interactions
   • A model with no learned parameters
7. In the stated linear recurrent illustration h-new equals one-half h-previous plus x, what is h-new for h-previous two and x three?
   • Fünf
   • Eins
   • Sieben
   • Vier
8. Why does a fixed-size RNN inference state not establish constant memory for full-sequence training?
   • Training never uses intermediate values
   • Backpropagation through time may store or recompute information across many steps
   • Every model deletes its parameters before learning
   • A fixed state guarantees every historical input is retained exactly