AI

What is “gradient boosting” in machine learning? a) An ensemble technique that builds models sequentially to minimize errors by focusing on the residuals b) A method to tune hyperparameters c) A technique to reduce the dimensionality of input data d) A method to cluster data points Answer: a) An ensemble technique that builds models sequentially […]

What is “model overfitting” in machine learning? a) When a model performs well on training data but poorly on unseen test data b) When a model performs equally well on training and test data c) When a model fails to learn the data patterns d) When a model underfits the data Answer: a) When a

What is “feature scaling” in machine learning? a) A technique to standardize the range of independent variables in the dataset b) A method to extract important features from the data c) A process to increase the size of the dataset d) A method to reduce the dimensionality of the dataset Answer: a) A technique to

What is “data augmentation” in deep learning? a) A technique to artificially increase the size of the training dataset by applying transformations to the original data b) A method to cluster data points c) A process for splitting the data into training and test sets d) A method for feature extraction Answer: a) A technique

What is “early stopping” in machine learning? a) A regularization technique used to prevent overfitting by stopping training when the model’s performance on validation data starts to deteriorate b) A method to reduce the size of a dataset c) A technique to improve model accuracy d) A way to tune hyperparameters Answer: a) A regularization

What is “cross-validation” used for in machine learning? a) To evaluate the performance of a model on different subsets of data b) To cluster data points c) To reduce the dimensionality of the dataset d) To generate new features from existing data Answer: a) To evaluate the performance of a model on different subsets of

What is the “curse of dimensionality” in machine learning? a) The phenomenon where the performance of algorithms deteriorates as the number of features increases b) The problem of having too few data points in a dataset c) The challenge of training neural networks with very large datasets d) The issue of overfitting in small datasets

What is “Random Forest” in machine learning? a) An ensemble learning technique that combines multiple decision trees b) A clustering method for high-dimensional data c) A dimensionality reduction algorithm d) A method to split the dataset into training and testing sets Answer: a) An ensemble learning technique that combines multiple decision trees Explanation: Random Forest

What is “Boosting” in machine learning? a) An ensemble learning technique that builds models sequentially, each correcting the errors of the previous model b) A method to tune hyperparameters c) A technique for reducing the size of a dataset d) A way to normalize data Answer: a) An ensemble learning technique that builds models sequentially,

What is “Bagging” in machine learning? a) An ensemble learning technique where multiple models are trained on random subsets of the data b) A method to reduce dimensionality c) A technique to cluster data d) A process for tuning hyperparameters Answer: a) An ensemble learning technique where multiple models are trained on random subsets of

What is “word embedding” in natural language processing (NLP)? a) A technique to represent words as continuous vectors in a high-dimensional space b) A method to cluster similar words together c) A way to convert text into binary format d) A method to translate text from one language to another Answer: a) A technique to

What is “one-hot encoding” in machine learning? a) A technique to represent categorical variables as binary vectors b) A method to reduce the number of features in a dataset c) A process to split the data into training and test sets d) A way to balance an imbalanced dataset Answer: a) A technique to represent

What is the “kernel trick” in support vector machines (SVMs)? a) A method to transform data into higher dimensions for non-linear classification b) A technique for reducing the dimensionality of input data c) A regularization method for deep learning d) A way to preprocess data for clustering Answer: a) A method to transform data into

What is a “support vector machine” (SVM) used for in AI? a) Classification and regression tasks b) Data clustering c) Dimensionality reduction d) Image generation Answer: a) Classification and regression tasks Explanation: Support Vector Machines (SVMs) are supervised learning models used for classification and regression tasks. They work by finding the optimal hyperplane that best

What is the purpose of the “softmax” function in neural networks? a) To convert raw model outputs into probabilities b) To update weights in the network c) To reduce the dimensionality of the input d) To cluster data points Answer: a) To convert raw model outputs into probabilities Explanation: The softmax function is commonly used

What is “batch normalization” in deep learning? a) A technique to standardize the inputs to each layer, improving training stability b) A method to reduce the size of the training dataset c) A technique to combine multiple models d) A method to tune hyperparameters Answer: a) A technique to standardize the inputs to each layer,

What is the main challenge with training deep neural networks? a) Vanishing and exploding gradients b) Lack of training data c) High-dimensional input data d) High accuracy on training data Answer: a) Vanishing and exploding gradients Explanation: The vanishing and exploding gradient problem is one of the major challenges in training deep neural networks. This

What is a “recurrent neural network” (RNN) commonly used for? a) Sequence data such as time series or natural language b) Image classification c) Clustering data d) Reducing dimensionality of datasets Answer: a) Sequence data such as time series or natural language Explanation: Recurrent Neural Networks (RNNs) are a class of neural networks that are

What is “transfer learning” in machine learning? a) Reusing a pre-trained model on a new, similar task b) Training multiple models simultaneously c) Generating new features from existing data d) Transferring data between different machines Answer: a) Reusing a pre-trained model on a new, similar task Explanation: Transfer learning is a technique in machine learning

What is “Principal Component Analysis” (PCA) used for in machine learning? a) To reduce the dimensionality of a dataset by identifying the most important features b) To cluster data points into different groups c) To optimize the performance of neural networks d) To increase the number of features in a dataset Answer: a) To reduce

What is “dimensionality reduction” in machine learning? a) A process of reducing the number of features in the dataset b) A technique to increase the size of the dataset c) A method to balance the training and test sets d) A process to generate new features from existing ones Answer: a) A process of reducing

What is “clustering” in unsupervised learning? a) Grouping similar data points together without labels b) Predicting the next data point in a sequence c) Classifying data based on predefined categories d) Reinforcing learning through feedback Answer: a) Grouping similar data points together without labels Explanation: Clustering is an unsupervised learning technique where the goal is

What is a “generative adversarial network” (GAN)? a) A class of neural networks used for generating new data by competing against each other b) A method to reduce the dimensionality of data c) A type of reinforcement learning algorithm d) A network architecture used to classify data Answer: a) A class of neural networks used

What is the main goal of reinforcement learning in AI? a) To learn an optimal strategy through trial and error by receiving rewards or penalties b) To classify data into categories based on labeled data c) To reduce the dimensionality of data d) To cluster similar data points together Answer: a) To learn an optimal

What is a “convolutional neural network” (CNN) primarily used for? a) Image recognition and processing b) Time-series forecasting c) Text-based analysis d) Reinforcement learning tasks Answer: a) Image recognition and processing Explanation: Convolutional Neural Networks (CNNs) are specialized types of neural networks designed for image recognition and processing tasks. They are particularly effective at extracting