Variational autoencoders (VAEs) introduce a probabilistic spin to autoencoders by associating a ________ with the encoded representations.
- Probability Distribution
- Singular Value Decomposition
- Principal Component
- Regression Function
VAEs introduce a probabilistic element to autoencoders by associating a probability distribution (typically Gaussian) with the encoded representations. This allows for generating new data points.
Which regression technique is primarily used for predicting a continuous outcome variable (like house price)?
- Decision Tree Regression
- Linear Regression
- Logistic Regression
- Polynomial Regression
Linear Regression is the most common technique for predicting a continuous outcome variable, such as house prices. It establishes a linear relationship between input features and the output.
The Actor-Critic model combines value-based and ________ methods to optimize its decision-making process.
- Policy-Based
- Model-Free
- Model-Based
- Q-Learning
The Actor-Critic model combines value-based (critic) and model-free (actor) methods to optimize decision-making. The critic evaluates actions using value functions, and the actor selects actions based on this evaluation, thus combining two approaches for improved learning.
For text classification problems, the ________ variant of Naive Bayes is often used.
- K-Means
- Multinomial
- Random Forest
- SVM
In text classification, the Multinomial variant of Naive Bayes is commonly used due to its suitability for modeling discrete data like word counts.
For a non-linearly separable dataset, which property of SVMs allows them to classify the data?
- Feature selection
- Kernel functions
- Large training dataset
- Parallel processing
SVMs can classify non-linearly separable data using kernel functions, which map the data into a higher-dimensional space where it becomes linearly separable.
In the context of reinforcement learning, what does the term "gradient" in "Policy Gradient Methods" refer to?
- The direction of steepest ascent in the policy space
- A mathematical term used to describe the rate of change
- The probability distribution of actions
- The value function
In "Policy Gradient Methods," the "gradient" refers to the direction in the policy space that increases the expected reward. It guides policy updates to maximize reward.
In ________ learning, the algorithm isn't provided with the correct answers but discovers them through exploration and exploitation.
- Reinforcement
- Semi-supervised
- Supervised
- Unsupervised
Reinforcement learning involves exploration and exploitation strategies, where the algorithm learns by trial and error and discovers correct answers over time. It doesn't start with pre-defined correct answers.
In the context of text classification, Naive Bayes often works well because it can handle what type of data?
- High-Dimensional and Sparse Data
- Images and Videos
- Low-Dimensional and Dense Data
- Numeric Data
Naive Bayes is effective with high-dimensional and sparse data as it assumes independence between features, making it suitable for text data with numerous attributes.
In CNNs, the ________ layer is responsible for detecting features in an image.
- Convolutional
- Pooling
- Fully Connected
- Input
The 'Convolutional' layer in a Convolutional Neural Network (CNN) is responsible for detecting features in an image using convolution operations.
Which ensemble method combines multiple decision trees and aggregates their results for improved accuracy and reduced overfitting?
- Logistic Regression
- Naive Bayes
- Principal Component Analysis (PCA)
- Random Forest
Random Forest is an ensemble method that combines multiple decision trees. It aggregates their results through techniques like bagging and boosting to achieve better accuracy and reduce overfitting. Random Forest is a popular choice for various machine learning tasks.