Deep Learning Quiz

Which algorithms below use neural networks? (select all that apply)

• CNN

• K-means clustering

• Transformers

• Deep decision trees

What are the key components of a layer in a basic neural network? (Select all that apply)

• Weights and biases

• Activation functions

• Loss functions

• Learning rate

What is an epoch in the context of training neural networks?

• The number of layers in a neural network.

• The number of times the learning algorithm goes through the entire training dataset.

• The number of neurons in each layer of the network.

• The number of batches the data is divided into during training.

In a neural network for image recognition (e.g., identifying objects in photos), what is the role of different layers in the network? For example, how might the first few layers process a photo of a cat compared to the deeper layers?

• The first few layers recognize high-level features like the entire cat, while deeper layers focus on individual pixels.

• The first few layers detect simple features like edges and textures, while deeper layers recognize more complex patterns like shapes, eyes, or entire objects.

• All layers process the image in the same way, with each layer improving accuracy by repeating the same computation.

• The first few layers detect colors, while deeper layers detect objects based solely on size.

Which of the following about ANN and CNN is true?

• ANNs are only used for regression tasks, while CNNs are used for classification.

• CNNs use convolutional layers to process data with a grid-like topology, such as images.

• ANNs use recurrent connections to handle sequential data, while CNNs do not.

• CNNs require less memory than standard ANNs for the same number of neurons

Which of the following is true about Recurrent Neural Networks (RNNs)?

• RNNs are well-suited for processing fixed-size input data.

• RNNs are specialized for image recognition.

• RNNs are ideal for handling sequential data, such as time series or text.

• RNNs do not use backward propagation

What is the primary function of gradient descent in neural networks? (Select all that apply)

• To initialize the network's weights.

• To calculate the model's accuracy on a test dataset.

• To optimize the network by minimizing the loss function.

• To compute the weights iteratively in the direction of steepest descent to find the global minimum.

What are some of the problems with the algorithms prior to Transformers, such as RNN, LSTM? (select all that apply)

• Vanishing gradients

• Exploding gradients

• slow learning rate

• Limited memory

How do Transformers overcome the limitation of RNNs in processing long sequences of data?

• By using convolutional layers to extract hierarchical features.

• By using attention mechanisms that allow the model to weigh all input positions equally.

• By relying solely on recurrence to capture long-range dependencies.

• By implementing recurrent connections at multiple layers.

What are the key advantages of transformer architectures over RNNs, LSTMs? (select all that apply)

• Parallel processing of input sequences

• Better handling of long-range dependencies

• Lower computational complexity for all sequence lengths

• No vanishing gradient problem