quiz bu lëkkale ak tegtal · Yomb Niveau

Quiz jàng bu xóot

Saytu reso yu bari couche, lim parametre, nonlinearité, tàggat ak inference, yamale ak joxe leeral ci poñ model yi.

Yooni njiit yu jëm ciJàng bu xóot
Laaj 1 de 8

What does “deep” describe in a deep-learning model?

Xoolaatal laaj bu nekk8 laaj

Sooy def quiz bi, tontu yi ak leeral yi dañuy feeñ.

  1. What does “deep” describe in a deep-learning model?
    • A neural-network architecture with multiple layers between input and output
    • A model that understands topics at a profound human level
    • A system trained only on unusually large datasets
    • A method that guarantees higher task accuracy
  2. A network has 2 inputs, hidden layers of 3 and 2 units, and 1 output. With a bias for each hidden/output unit, how many weights and biases are there?
    • 14
    • 20
    • 18
    • 23
  3. Why use nonlinear activations between hidden layers?
    • They convert every hidden unit into a labeled training example
    • They guarantee the network will generalize to unseen data
    • Without them, stacking linear transformations still produces a linear transformation
    • They remove the need for learned weights
  4. During inference, what normally happens to the trained model’s parameters?
    • They are retrained after every input
    • They are replaced by the labels in the prompt
    • They are reset before each output
    • The model uses them to process an input without necessarily updating them
  5. A model fits its training examples closely but performs poorly on comparable held-out data. What pattern does this show?
    • Soppi
    • Underfitting
    • Good generalization
    • A guarantee that the labels are correct
  6. Why compare a deeper network with a simpler baseline on held-out examples?
    • Because simpler models always outperform neural networks
    • To see whether added complexity improves the task enough to justify its practical costs
    • Because deeper models cannot be evaluated on new data
    • To avoid checking error patterns
  7. A classifier returns a score of 0.9. What does that alone establish?
    • The prediction is correct 90% of the time on every task
    • The model has been calibrated on deployment data
    • It is a model score, not proof of a calibrated 90% chance of correctness
    • The system understands the example with 90% certainty
  8. What does adding more layers alone prove about a model’s understanding or usefulness?
    • That it understands inputs like a person
    • That it will outperform a simpler model
    • That its errors are harmless
    • Nothing by itself; evaluate it on the intended task and relevant examples