Maswali yanayounganishwa na mwongozo · Rahisi Kiwango

Maswali ya Mitandao ya Neural

Vipimo vya majaribio, safu zilizofichwa, ReLU, masasisho ya daraja, tafsiri ya matokeo, na kwa nini mitandao mikubwa bado inahitaji majaribio ya uangalifu kwenye mifano mpya.

Njia za mwongozo zinazohusianaMitandao ya Neural
Swali 1 ya 8

In the toy neuron, input 0.8 is multiplied by 0.6 before a bias is added. What is the 0.6?

Hakiki kila swali8 maswali

Majibu na maelezo huonekana unapojibu maswali.

  1. In the toy neuron, input 0.8 is multiplied by 0.6 before a bias is added. What is the 0.6?
    • The bias added to every weighted sum
    • The weight applied to the first input
    • A held-out target label
    • The final output after activation
  2. What do hidden layers do between input and output?
    • Transform inputs into intermediate representations used by later layers
    • Store the original training labels unchanged
    • Select the final answer without computation
    • Replace the need for an output layer
  3. What role does ReLU play when applied to a value?
    • It computes the loss against a target
    • It updates weights using a learning rate
    • It assigns the correct class label
    • It returns zero for a negative input and passes a positive one through
  4. What does backpropagation provide to the training process?
    • The final parameter values after optimization
    • Gradients describing how parameters affect loss
    • A guarantee of the globally best solution
    • A separate test set for measuring generalization
  5. How should the brain analogy for neural networks be understood?
    • Artificial neurons are mathematical abstractions, not biological neurons
    • Modern networks reproduce brain cells in full detail
    • A successful network proves human-like understanding
    • Each network layer corresponds exactly to a brain region
  6. In the guide’s toy neuron, input 1.5 produces a weighted sum of -0.02. What does ReLU output?
    • 1.5 because ReLU preserves the input
    • -0.02 because ReLU preserves every sum
    • 0.02 because ReLU removes the negative sign
    • 0 because ReLU maps negative values to zero
  7. What happens when linear transformations are stacked without nonlinear activations?
    • Their composition is still linear
    • The stack automatically becomes nonlinear because it is deeper
    • Backpropagation turns the stack into a classifier
    • Each layer becomes an independent probability
  8. Why should a numeric output and category scores be interpreted differently?
    • A numeric output is always a confidence percentage
    • Category scores are guaranteed probabilities
    • Output meaning depends on the task and the chosen output layer
    • Every output layer returns a class label