Deep Learning Medium Quiz 5 > Data AI Revolution

Deep Learning Medium Quiz 5

This medium-level quiz consists of 10 questions on deep learning topics such as vanishing gradients, advanced architectures like ResNet and GANs, and reinforcement learning. You will find both multiple-choice and true/false questions. This quiz is designed to test your intermediate understanding of deep learning concepts.

1 / 10

The Universal Approximation Theorem states that a neural network with a single hidden layer can approximate any continuous function given enough neurons.

False

True

2 / 10

In a neural network, the gradient vanishing problem can be completely avoided by using a high learning rate.

True

False

3 / 10

In a deep learning model, what is the primary reason for using Layer Normalization instead of Batch Normalization?

It adds non-linearity to the network

It reduces training time by a factor of 10

It increases the regularization effect, preventing overfitting

It works better with small batch sizes or even single samples

4 / 10

Which of the following techniques is used to prevent the vanishing gradient problem in deep neural networks?

Weight initialization methods like Xavier or He initialization

L2 regularization

Dropout

Using ReLU or Leaky ReLU activation functions

5 / 10

Which of the following techniques can be applied to a neural network model for dealing with imbalanced datasets?

Using a more complex architecture

Weighted loss functions

Under-sampling or over-sampling the data

Data augmentation

6 / 10

In a deep reinforcement learning setting, what is the purpose of experience replay?

To apply the dropout technique in reinforcement learning

To replay past rewards for better policy optimization

To store past experiences and randomly sample them during training

To allow the model to adjust its weights based on future actions

7 / 10

Which of the following is a key advantage of using a Residual Network (ResNet)?

It increases training speed by reducing the number of parameters

It allows the network to be deeper without suffering from vanishing gradients

It prevents overfitting by using convolutional layers

It uses LSTMs to process sequential data

8 / 10

What is the primary difference between the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures?

GRUs use fully connected layers, while LSTMs use convolutional layers

LSTMs can handle long-term dependencies, while GRUs cannot

LSTMs are generally more efficient in terms of memory usage

GRUs have fewer gates than LSTMs, making them faster to train

9 / 10

Which of the following best describes the purpose of a Generative Adversarial Network (GAN)?

It is primarily used for text classification tasks

It is a form of unsupervised learning used to label input data

It is used to generate new data samples that resemble a given dataset

It combines reinforcement learning with convolutional layers

10 / 10

The Softmax activation function is typically used in the output layer of a classification model with more than two classes.

False

True

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Deep Learning Medium Quiz 5

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