CNNs

What does CNN stand for in machine learning?

• A) Convolutional Neural Networks

• B) Convergent Network Nodes

• C) Computational Neural Nodes

• D) Cognitive Neural Networks

What is the purpose of a kernel in a CNN?

• A) Store data

• B) Perform matrix multiplication

• C) Extract features from input

• D) Generate output data

What type of layer in a CNN reduces the spatial dimensions of the input?

• A) Convolutional Layer

• B) Pooling Layer

• C) Dense Layer

• D) Activation Layer

What is "cross-correlation" in the context of CNNs?

• A) Rotating the input matrix

• B) Applying filters to extract features

• C) Normalizing the input data

• D) Adding biases to the layers

Which method is commonly used for data augmentation in CNNs?

• A) Cross-validation

• B) Random cropping

• C) Regularization

• D) Backpropagation

What is the role of backpropagation in CNNs?

• A) Forward propagating the input

• B) Adjusting weights and biases based on loss

• C) Increasing model complexity

• D) Reducing model parameters

What is a common generative AI application mentioned in the session?

• A) Parking occupancy detection

• B) Time-series prediction

• C) Spam filtering

• D) Image segmentation

Which activation function is commonly used in CNNs?

• A) Sigmoid

• B) ReLU

• C) Tanh

• D) Softmax

What is the "stride" in a convolutional operation?

• A) The number of filters applied

• B) The step size of the kernel movement

• C) The dimension of the output

• D) The size of the input data

Which layer type in CNNs is responsible for combining multiple feature maps?

• A) Dense layer

• B) Pooling layer

• C) Fully connected layer

• D) Output layer