You have a dataset containing information about sales performance for different regions in the last ten years.
Which type of data visualization would be most appropriate to compare the sales performance across regions on a year-by-year basis?

• A. Scatter plot
• B. Pie chart
• C. Line chart
• D. Bar chart

What is contrastive learning in the context of multimodal deep learning? Pick the 2 correct responses below.

• A. In a multimodal context, usually, contrastive learning decreases the similarity of representations across modalities for the same objects and increases the similarity of representations across modalities for different objects.
• B. In a multimodal context, usually, contrastive learning increases the similarity of representations across modalities for the same objects and decreases the similarity of representations across modalities for different objects.
• C. Contrastive learning is a technique used to train deep learning models by comparing similar and dissimilar inputs and optimizing the model to maximize the similarity between representations of similar inputs and minimize the similarity between representations of dissimilar inputs.
• D. In a multimodal context, usually, contrastive learning increases the similarity of representations across modalities for the different objects and decreases the similarity of representations across modalities for same objects.
• E. Contrastive learning is a technique used to manipulate and analyze multimodal data using Generative AI.

In machine learning, what is the purpose of data normalization?

• A. To convert data into a specific format for easier analysis.
• B. To reduce the dimensionality of the dataset.
• C. To increase the complexity of the dataset.
• D. To remove irrelevant data from the dataset.

In convolutional neural networks, we may use padding in both convolution and transposed convolution.
Which two (2) statements accurately describe padding in convolution and transposed convolution? Pick the 2 correct responses below.

• A. Padding in convolution increases the spatial dimensions of the input feature map, while padding in transposed convolution decreases the spatial dimensions of the output feature maps.
• B. In a convolution operation, padding is added to the output after it has been expanded with the stride. On the other hand, in a transposed convolution operation, padding is added to the input before it is expanded with stride.
• C. Padding in convolution enables convolution operations on the boundary pixels of the input. In transposed convolution, it removes rows and columns along the perimeter of the input after it is expanded with stride.
• D. Padding in convolution and transposed convolution serve the same purpose of reducing the convolutional neural network's memory requirement and computational cost of the convolutional neural network.
• E. Padding in convolution is used only when the input image is smaller than the filter size, while padding in transposed convolution is used only when the input image is larger than the filter size.