18 Questions
What issue do saturated neurons cause in deep learning?
Saturated neurons cause the gradient to vanish.
Why does the vanishing gradient problem restrict the possible update directions?
Saturated neurons cause the gradient to vanish.
What is the impact of non-zero centered sigmoid activation functions on backpropagation?
Sigmoids are not zero centered.
In which quadrant is it not possible for all gradients to be negative?
Quadrant in which all gradients are -ve (Not possible).
What is the consequence of having saturated neurons in terms of the gradients of the weights?
All gradients at a layer are either all positive or all negative.
How do the characteristics of the sigmoid activation function impact weight initialization?
Sigmoids are not zero centered.
Why do saturated neurons cause the gradient to vanish?
Saturated neurons lead to very small gradients which can vanish during backpropagation.
What is the issue with sigmoid activation functions not being zero-centered?
Non-zero-centered activation functions like sigmoid can lead to zigzag paths during optimization.
How does the vanishing gradient problem affect weight updates in deep learning?
In the presence of vanishing gradients, weight updates become very small or negligible, hindering the learning process.
Explain why weight initialization is crucial in deep learning.
Proper weight initialization helps in avoiding issues like vanishing gradients and accelerates convergence during training.
What is the significance of using appropriate activation functions in neural networks?
Choosing the right activation functions can prevent issues like vanishing gradients and improve the overall performance of the model.
How does the backpropagation algorithm help in training neural networks?
Backpropagation calculates the gradients of the loss function with respect to the weights, enabling the model to update weights and learn from the errors.
Why do saturated neurons cause the gradient to vanish?
Because the gradient becomes close to zero, leading to no learning happening in the network.
Why is it a problem if the gradient vanishes during training?
A vanishing gradient means the network stops learning, as the weights do not get updated effectively, hindering convergence.
How does the non-zero centered nature of sigmoids impact gradient descent?
The non-zero centered nature of sigmoids can lead to biased gradients, affecting the convergence and training of neural networks.
What happens to the gradient if the common term in the gradient calculation is positive?
Both gradients, ∇w1 and ∇w2, will be positive.
Why is weight initialization crucial in neural networks?
Proper weight initialization helps prevent issues like vanishing or exploding gradients at the start of training.
How do vanishing gradients impact the training process?
Vanishing gradients make it difficult for the network to learn deep hierarchical representations, affecting the training of deep neural networks.
This quiz covers concepts related to neuron gradients, vanishing gradient problem, and the impact of saturated neurons in deep learning networks. Topics include the calculation of gradients for weights, the influence of positive and negative terms on gradients, and the effect of saturation on gradient computation.
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