Neural Networks vs Deep Neural Networks: Advanced Concepts Quiz Quiz

1. Difference in Architecture Depth

   Which of the following best distinguishes a deep neural network from a shallow neural network in terms of architectural depth?

   1. A neural network with a single hidden layer is considered deep.
   2. A deep neural network typically contains two or more hidden layers.
   3. A shallow network always uses convolutional layers.
   4. Depth in a neural network refers to the number of input nodes.
   5. All neural networks with more than two output nodes are deep.

2. Feature Representation

   In the context of learning feature hierarchies, why are deep neural networks generally preferred over shallow neural networks for image recognition tasks?

   1. They require less data for training due to fewer parameters.
   2. They are always resilient to overfitting regardless of complexity.
   3. Deep neural networks can automatically learn increasingly abstract representations of features across multiple layers.
   4. Shallow networks inherently avoid the vanishing gradient problem.
   5. Deep networks only use linear activation functions.

3. Universal Approximation

   Given a fixed number of neurons, how do deep neural networks compare to shallow neural networks regarding the efficiency of approximating complex functions?

   1. Shallow networks can represent any function more efficiently than deep ones.
   2. Both deep and shallow networks require the same number of neurons to approximate highly complex functions.
   3. Deep neural networks can approximate some complex functions using exponentially fewer neurons than shallow networks.
   4. Only shallow networks can handle non-linear functions.
   5. Deep networks cannot represent periodic functions at all.

4. Training Challenges

   Which specific challenge often arises when training deep neural networks but is less problematic in shallow neural networks?

   1. Exponential growth in training speed with more layers.
   2. Vanishing or exploding gradients during backpropagation.
   3. Guaranteed convergence to the global minimum.
   4. Inevitable memorization of the training data.
   5. Complete absence of hyperparameters.

5. Regularization Techniques

   Which regularization technique is particularly crucial for deep neural networks to prevent overfitting but is generally less critical for shallow neural networks?

   1. Early weight initialization
   2. Stochastic gradient boosting
   3. Dropout randomly disabling units during training
   4. Linear regression transformation
   5. Radial basis function initialization