AI Models Quiz

Which of the following is a common loss function used in classification problems?

• Mean Squared Error (MSE)

• Cross-Entropy Loss

• Hinge Loss

• Mean Absolute Error (MAE)

In Natural Language Processing, what does the term "tokenization" refer to?

• Converting text into lowercase

• Splitting text into individual words or subwords

• Removing stop words from the text

• Stemming and lemmatization

Which of the following is a popular dataset used for training computer vision models?

• MNIST

• IMDB

• UCI Machine Learning Repository

• Reuters-21578

What is the main difference between supervised and unsupervised learning?

• Supervised learning uses labeled data, while unsupervised learning uses unlabeled data

• Supervised learning is faster than unsupervised learning

• Unsupervised learning requires more data than supervised learning

• Supervised learning is used for clustering, while unsupervised learning is used for classification

Which of the following is a type of recurrent neural network (RNN)?

• Convolutional Neural Network (CNN)

• Long Short-Term Memory (LSTM)

• Generative Adversarial Network (GAN)

• Autoencoder

In the context of deep learning, what does the term "dropout" refer to?

• A technique to increase the learning rate

• A method to prevent overfitting by randomly setting some neurons' output to zero during training

• A way to reduce the number of layers in a neural network

• A method to initialize the weights of a neural network

Which algorithm is commonly used for dimensionality reduction in machine learning?

• K-means clustering

• Principal Component Analysis (PCA)

• Decision Trees

• Support Vector Machines (SVM)

In computer vision, what is the purpose of using convolutional layers in a CNN?

• To reduce the dimensionality of the input data

• To detect and learn spatial hierarchies of features

• To perform data augmentation

• To normalize the input data

What is the main advantage of using transfer learning in deep learning models?

• It allows models to be trained from scratch

• It reduces the amount of labeled data required for training

• It increases the complexity of the model

• It ensures the model will not overfit

Which of the following is an example of a generative model in machine learning?

• Support Vector Machine (SVM)

• Generative Adversarial Network (GAN)

• Random Forest

• K-Nearest Neighbors (KNN)