In DQNs, the target Q-values are updated less frequently than the predicted Q-values to ensure stability, using a concept known as ________ networks.
- Target
- Control
- Reactive
- Neural
In Deep Q Networks (DQNs), the target Q-values are updated less frequently to stabilize learning. This concept is known as 'target' networks, which involves having separate target Q-networks.
A retail store wants to recommend products to customers based on their purchase history. They want to find products that other customers with similar purchase histories have bought. Which algorithm is apt for this recommendation system?
- Apriori Algorithm
- Collaborative Filtering
- Linear Regression
- Principal Component Analysis (PCA)
Collaborative Filtering is ideal for recommending products based on user behavior and finding items preferred by users with similar purchase histories. It leverages user-item interaction patterns for recommendations.
Which machine learning algorithm works by recursively splitting the data set into subsets based on the value of features until it reaches a certain stopping criterion?
- Decision Trees
- K-Means Clustering
- Linear Regression
- Neural Networks
Decision Trees work by recursively splitting the dataset into subsets based on feature values. This process continues until a stopping criterion, such as the maximum depth of the tree, is met. Decision Trees are used for both classification and regression tasks.
You're working with a large dataset of facial images. You want to reduce the dimensionality of the images while preserving their primary features for facial recognition. Which neural network structure would you employ?
- Autoencoder
- Convolutional Neural Network
- Recurrent Neural Network
- Generative Adversarial Network
Autoencoders are used to reduce the dimensionality of data while preserving essential features. They are commonly employed in facial recognition for feature extraction.