GenAI

1. Which of the following best describes the relationship among AI, ML, and DL?
   
   

• ML is a subset of DL, which is a subset of AI

• DL is a subset of ML, which is a subset of AI

• AI is a subset of ML, which is a subset of DL

• AI, ML, and DL are unrelated fields

Why is Deep Learning (DL) often preferred for complex data like images and audio?

• It requires less data to train than traditional ML

• It uses simpler models for faster computation

• It can automatically extract features from raw data

• It only works with structured tabular data

What gives Neural Networks their "deep" power?

• Ability to use binary data

• Multiple hidden layers that extract hierarchical features

• Only one layer of neurons

• Simple linear processing of inputs

Which is a common tool used for implementing sequence models?

• Recurrent Neural Networks (RNNs)

• K-Means Clustering

• Convolutional Layers

• Decision Trees

What is the key innovation introduced by Transformers compared to RNNs?

• Use of convolution instead of recurrence

• Use of self-attention to process input sequences in parallel

• Removal of activation functions

• Strict sequential processing of data

What role does attention play in neural networks?

• It prevents overfitting during training

• It simplifies the training process by reducing weights

• It compresses the input data

• It allows the model to focus on important parts of the input sequence

Which technique is most associated with Deep Vision?

• Recurrent layers

• Convolutional Neural Networks (CNNs)

• Decision Trees

• Generative Adversarial Networks (GANs)

What is a key ethical concern when deploying Deep Learning models?

• Speed of computation

• Bias in predictions and lack of explainability

• Training cost

• Inability to automate tasks

  Which tool is used in both image generation and data augmentation?

• CNN

• GAN

• RNN

• Transformer

Which of the following is a deep generative model?

• Logistic Regression

• Random Forest

• Generative Adversarial Network (GAN)

• K-Means Clustering