An organization wants to develop a system that can identify objects in real-time from video feeds, regardless of the objects' positions or angles in the frames. Which neural network characteristic is crucial for this?
- Invariance to Translation
- Time Series Processing Capability
- Memory of Past Sequences
- Radial Basis Function Network
"Invariance to Translation" is crucial because it allows the network to recognize objects regardless of their position or orientation in the frames, a key requirement for real-time object detection.
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.
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.
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.
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.
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.
When an agent overly focuses on actions that have previously yielded rewards without exploring new possibilities, it might fall into a ________ trap.
- Exploitation
- Exploration
- Learning
- Reward
If an agent overly focuses on actions that have yielded rewards in the past, it falls into an exploitation trap, neglecting the exploration needed to find potentially better actions.
A machine learning model trained for predicting whether an email is spam or not has a very high accuracy of 99%. However, almost all emails (including non-spam) are classified as non-spam by the model. What could be a potential issue with relying solely on accuracy in this case?
- Data Imbalance
- Lack of Feature Engineering
- Overfitting
- Underfitting
The issue here is data imbalance, where the model is heavily biased toward the majority class (non-spam). Relying solely on accuracy in imbalanced datasets can be misleading as it doesn't account for the misclassification of the minority class (spam), which is a significant problem.
When the outcome variable is continuous and has a linear relationship with the predictor variables, you would use ________ regression.
- Linear
- Logistic
- Polynomial
- Ridge
Linear regression is used when there is a continuous outcome variable, and the relationship between the predictor variables and the outcome is linear. It's a fundamental technique in statistics and machine learning for regression tasks.
When a model is trained on one task and the learned features are used as a starting point for a model on a second task, it's known as ________.
- Transfer Learning
- Data Augmentation
- Ensemble Learning
- Gradient Boosting
Transfer learning is a technique where knowledge gained from one task is applied as the starting point for another task. This helps leverage pre-trained models and speeds up learning on the new task.