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.
Considering the sensitivity of healthcare data, what is a primary concern when applying machine learning to electronic health records?
- Data Privacy and Security
- Model Accuracy
- Data Collection and Storage
- Interoperability and Integration
Healthcare data is highly sensitive, and maintaining privacy and security is paramount when applying machine learning to electronic health records. This involves complying with regulations like HIPAA and implementing encryption and access controls.
Given a scenario where computational resources are limited, but there's a need to process high-resolution images for feature detection, what approach might be taken in the design of the neural network to balance performance and computational efficiency?
- Use Transfer Learning
- Increase Network Depth
- Add More Neurons
- Use Recurrent Connections
Transfer Learning can balance performance and computational efficiency by leveraging pre-trained models on high-resolution images, reducing the need for extensive training.
In hierarchical clustering, as the name suggests, the data is grouped into a hierarchy of clusters. What visualization is commonly used to represent this hierarchy?
- Bar Chart
- Dendrogram
- Heatmap
- Scatter Plot
A dendrogram is commonly used in hierarchical clustering to visualize the hierarchical structure of clusters, showing the merging and splitting of clusters.
For the k-NN algorithm, what could be a potential drawback of using a very large value of k?
- Decreased Model Sensitivity
- Improved Generalization
- Increased Computational Cost
- Reduced Memory Usage
A large value of k in k-NN can make the model less sensitive to local patterns, leading to a loss in predictive accuracy due to averaging over more neighbors.
How does the architecture of a CNN ensure translational invariance?
- CNNs use weight sharing in convolutional layers, making features invariant to translation
- CNNs utilize pooling layers to reduce feature maps size
- CNNs randomly initialize weights to break translational invariance
- CNNs use a large number of layers for translation invariance
CNNs ensure translational invariance by sharing weights in convolutional layers, allowing learned features to detect patterns regardless of their location in the image. This is a key property of CNNs.
For binary classification tasks, which regression outputs a probability score between 0 and 1?
- Lasso Regression
- Linear Regression
- Logistic Regression
- Support Vector Regression
Logistic Regression outputs probability scores between 0 and 1, making it suitable for binary classification. It uses the logistic function to model the probability of the positive class.
If you want to visualize high-dimensional data in a 2D or 3D space, which of the following techniques would be suitable?
- Principal Component Analysis
- Regression Analysis
- Naive Bayes
- Linear Discriminant Analysis
Principal Component Analysis (PCA) is suitable for visualizing high-dimensional data in a lower-dimensional space. It identifies the directions of maximum variance, making data more manageable for visualization.
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.