Which activation function can alleviate the vanishing gradient problem to some extent?
- Sigmoid
- ReLU (Rectified Linear Unit)
- Tanh (Hyperbolic Tangent)
- Leaky ReLU
The ReLU activation function is known for mitigating the vanishing gradient problem, which is a common issue in deep learning. ReLU allows gradients to flow more freely during backpropagation, making it easier to train deep neural networks.
In Tableau, you can connect to various data sources and create a unified view known as a _______.
- Dashboard
- Workbook
- Storyboard
- Data source
In Tableau, a "Workbook" is where you can connect to various data sources, design visualizations, and create a unified view of your data. It serves as a container for creating and organizing your data visualizations and analyses.
In L2 regularization, the penalty is proportional to the _______ of the magnitude of the coefficients.
- Square
- Absolute
- Exponential
- Logarithmic
In L2 regularization (Ridge), the penalty is proportional to the square of the magnitude of the coefficients. This regularization technique adds a penalty term to the loss function based on the sum of squared coefficients, which helps prevent overfitting by discouraging large coefficients.
Data formats that allow for tagging or marking up of data, giving it some level of organization, often fall into the _______ data category.
- Raw data
- Structured data
- NoSQL data
- XML data
Data formats like XML allow for tagging or marking up data, which gives it a structured organization. XML is an example of semi-structured data, where data elements are labeled, providing some level of organization.
Random Forest is an ensemble technique that builds multiple:
- Decision trees
- Linear regression models
- Neural networks
- Support vector machines
Random Forest is an ensemble method that builds multiple decision trees. These decision trees are used to make predictions and are combined to create a more accurate and robust model. Random Forest is known for its ability to reduce overfitting and provide reliable predictions, making it a popular choice in machine learning.
For large-scale image recognition tasks, which type of network, having multiple layers between input and output, is typically used?
- Convolutional Neural Network (CNN)
- Recurrent Neural Network (RNN)
- Long Short-Term Memory (LSTM) Network
- Gated Recurrent Unit (GRU) Network
Convolutional Neural Networks (CNNs) are commonly used in large-scale image recognition tasks. CNNs have multiple layers that can automatically learn hierarchical features from images, making them well-suited for this purpose.
In image processing, what technique is commonly used to detect edges in an image?
- Histogram Equalization
- Fourier Transform
- Canny Edge Detection
- K-Means Clustering
Canny Edge Detection is a widely used technique for edge detection in images. It applies multiple filters to detect edges with varying intensities, providing information about the location and strength of edges in the image.
What is the primary purpose of transfer learning in the context of deep learning for computer vision?
- Training a model from scratch
- Fine-tuning a pre-trained model
- Reducing the number of layers in a neural network
- Converting images into text
Transfer learning in computer vision involves fine-tuning a pre-trained model to adapt it for a new task. It leverages knowledge from a source task to improve performance on a target task, making it more efficient and effective than training from scratch.
When evaluating models for a multi-class classification problem, which method computes the average metric score for each class, considering the other classes as the negative class?
- Micro-averaging
- Macro-averaging
- Weighted averaging
- Mini-batch averaging
Macro-averaging computes the average metric score for each class, treating all other classes as the "negative" class. It provides an equal weight to each class and is useful when you want to assess the model's overall performance while giving equal importance to each class, regardless of class size. Macro-averaging can be particularly useful in imbalanced multi-class classification problems.
A company has built a highly accurate model for detecting objects in urban scenes. They now want to adapt this model for rural scenes. Instead of training a new model from scratch, how can they utilize their existing model?
- Fine-tuning the existing model
- Rewriting the entire model
- Ignoring the existing model and starting from scratch
- Hiring more data scientists for the rural project
To adapt the model for rural scenes, fine-tuning the existing model is a practical approach. Fine-tuning involves training the model on the new rural scene data, which allows the model to leverage its knowledge from the urban scene while adapting to rural conditions.