SVMs aim to maximize the margin, which is the distance between the decision boundary and the nearest ______ from any class.
- Decision Tree
- Hyperplane
- Outlier
- Support Vector
SVMs aim to maximize the margin, which is the distance between the decision boundary and the nearest support vector from any class. Support vectors play a crucial role in defining the decision boundary.
Which algorithm is commonly used for blind source separation or separating mixed signals?
- Principal Component Analysis (PCA)
- Support Vector Machine (SVM)
- K-Means Clustering
- Decision Trees
Principal Component Analysis (PCA) is commonly used for blind source separation, reducing the dimensionality of data to separate mixed signals. PCA identifies the principal components or directions of maximum variance in the data.
t-SNE is a technique primarily used for what kind of task in machine learning?
- Dimensionality Reduction
- Image Classification
- Anomaly Detection
- Reinforcement Learning
t-SNE (t-distributed Stochastic Neighbor Embedding) is primarily used for dimensionality reduction, reducing high-dimensional data to a lower-dimensional representation for visualization and analysis.
Which of the following RNN variants uses both a forget gate and an input gate to regulate the flow of information?
- LSTM (Long Short-Term Memory)
- GRU (Gated Recurrent Unit)
- Elman Network
- Jordan Network
The LSTM (Long Short-Term Memory) variant uses both a forget gate and an input gate to manage information flow. These gates allow it to control which information to forget or remember, making it highly effective in learning and retaining information over long sequences.
A financial institution wants to predict whether a loan applicant is likely to default on their loan. They have a mix of numerical data (like income, age) and categorical data (like occupation, marital status). Which algorithm might be well-suited for this task due to its ability to handle both types of data?
- Decision Tree
- Random Forest
- Support Vector Machine
- k-Nearest Neighbors
The Random Forest algorithm is well-suited for this task because it can handle both numerical and categorical data effectively. It combines multiple decision trees and takes a vote to make predictions, making it robust and accurate for such mixed data.
In Q-learning, the update rule involves a term known as the learning rate, represented by the symbol ________.
- Alpha
- Delta
- Sigma
- Theta
In Q-learning, the learning rate is represented by 'alpha.' It controls the step size for updates and impacts the convergence and stability of the learning algorithm.
Which type of autoencoder is designed specifically for generating data that is similar but not identical to the training data?
- Variational Autoencoder
- Denoising Autoencoder
- Contractive Autoencoder
- Sparse Autoencoder
Variational Autoencoders (VAEs) are designed for generating data that is similar but not identical to the training data. They generate data from a learned distribution, enabling the generation of new and similar data points by sampling from this distribution.
A neural network that contains more than one hidden layer is often referred to as a ________.
- Multilayer
- Deep
- Complex
- Advanced
A neural network with more than one hidden layer is commonly referred to as a 'Deep' neural network, emphasizing its depth and capacity for learning complex patterns.
In deep learning, ________ refers to the concept of using a model trained on a large dataset and adapting it to a specific task.
- Transfer Learning
- Supervised Learning
- Reinforcement Learning
- Unsupervised Learning
Transfer Learning is a technique where a pre-trained model is fine-tuned for a specific task. It leverages knowledge learned from one domain for another.
When visualizing clusters in high-dimensional data...
- Principal Component Analysis (PCA)
- t-Distributed Stochastic Neighbor Embedding (t-SNE)
- Linear Regression
- Random Forest
t-SNE (t-Distributed Stochastic Neighbor Embedding) is beneficial for visualizing clusters in high-dimensional data because it emphasizes non-linear similarities, making it suitable for complex data structures.
What is the primary advantage of using a Convolutional Neural Network (CNN) over a standard feed-forward neural network for image classification tasks?
- CNNs can automatically learn hierarchical features from images
- CNNs require fewer training examples than feed-forward networks
- CNNs have a simpler architecture than feed-forward networks
- CNNs are less computationally intensive than feed-forward networks
Convolutional Neural Networks (CNNs) excel in image tasks due to their ability to automatically learn hierarchical features like edges, textures, and shapes. This hierarchical feature learning makes them more effective in image classification tasks.
Which of the following best describes the dilemma faced in the multi-armed bandit problem?
- Balancing exploration (trying different actions) and exploitation (using the best-known action)
- Choosing the arm with the highest mean reward
- Maximizing rewards from a single arm
- Choosing arms randomly
The multi-armed bandit problem revolves around the exploration-exploitation trade-off, where you must balance trying new actions (exploration) with exploiting the known best action (exploitation) to maximize cumulative rewards.