Practice Questions
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What is the primary issue associated with the degradation problem in deep networks, and how can empirical validation help mitigate this issue?
Adding more layers can make a network perform worse on training data. Other options are incorrect because The problem is not about overfitting; Degrad...
How do residual connections in deep networks enhance training efficiency and mitigate the degradation problem?
Residual connections add a shortcut that lets the signal skip over layers. Other options are incorrect because Some think adding residuals means addin...
How does increasing the depth of a deep network potentially impact its performance metrics, particularly in terms of the degradation problem?
Adding more layers can make the model harder to train. Other options are incorrect because The idea that more depth always improves performance is a m...
Which of the following strategies most effectively addresses the degradation problem in deep networks while considering training efficiency, optimization techniques, and scalability issues?
Batch normalization normalizes each layer’s output, keeping values in a stable range and reducing internal covariate shift. Other options are incorrec...
In the context of deep learning, how does the degradation problem affect training efficiency and model complexity in neural networks?
The degradation problem makes deeper networks harder to train. Other options are incorrect because Some think deeper layers simplify training, but the...
Why does increasing the depth of a neural network often lead to performance degradation despite not being caused by overfitting?
When a network is very deep, the small changes in the output are multiplied many times during backpropagation. Other options are incorrect because Act...
Degradation Problem in Deep Networks : Performance degradation :: Residual Learning : ?
Residual learning adds shortcut links that let the signal pass directly to later layers. Other options are incorrect because Some think residual learn...
Which of the following statements correctly describe strategies to mitigate the degradation problem in deep networks? Select all that apply.
Skip connections let the gradient skip layers, so the signal can travel farther and the network learns easier. Other options are incorrect because Add...
Arrange the following steps in order to effectively address the degradation problem in deep networks: A) Implement residual connections, B) Increase network depth, C) Monitor training accuracy, D) Adjust learning rates accordingly.
First, you add more layers to explore deeper models. Other options are incorrect because Adding residual connections before making the network deeper ...
A data scientist is tasked with building a deep neural network to classify images of animals. They notice that as they increase the depth of the network, the accuracy of their model begins to degrade significantly. What is the most likely reason for this degradation, and what approach could they take to mitigate it?
When a network gets very deep, the signals that travel backward during training can become very weak, a problem called vanishing gradients. Other opti...
When considering the degradation problem in deep networks, which of the following scenarios best illustrates an approach to mitigate this issue?
Residual learning adds shortcut connections that let the signal travel directly from earlier layers to later ones. Other options are incorrect because...
The degradation problem in deep networks primarily refers to the issue where increasing network depth leads to performance ____, rather than overfitting.
When a network gets deeper, the signals that train it can get weaker and harder to pass through the layers. Other options are incorrect because Some p...
Why does increasing the depth of a neural network sometimes lead to worse performance, despite having more parameters?
When a network has many layers, the error signal that tells the model how to change its weights travels through each layer during training. Other opti...
What is the primary cause of the degradation problem in deep networks as they increase in depth?
When a network gets deeper, the path that learning signals travel becomes longer. Other options are incorrect because Many think more parameters mean ...
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