What is the primary objective of Generative Adversarial Networks (GANs)?
- Data Classification
- Data Generation
- Data Storage
- Data Analysis
The primary objective of GANs is data generation. GANs consist of a generator that creates data samples to closely resemble real data, aiding in tasks like image generation.
How does NLP handle the nuances and variations in medical terminologies across different healthcare systems?
- Named Entity Recognition and Mapping
- Machine Translation of Medical Terms
- Simple Text Matching
- Manual Reconciliation
NLP utilizes techniques like Named Entity Recognition to identify and map medical terms to a standardized vocabulary, addressing the nuances and variations in medical terminologies across different healthcare systems. This enables consistent analysis and retrieval of information.
A bank uses a machine learning model for loan approvals. However, it's observed that individuals from certain ethnic backgrounds are consistently getting rejected more than others, despite having similar financial profiles. This raises concerns related to which aspect of machine learning?
- Fairness & Bias
- Accuracy
- Data Preprocessing
- Model Training
This situation pertains to fairness and bias in machine learning, specifically, unfair outcomes or discrimination against certain ethnic groups, which highlights ethical concerns. It's important to address these biases for equitable decision-making.
The output of a GAN, after training, is a/an ________ that closely resembles the real data.
- Image
- Noise
- Anomaly
- Vector
The output of a GAN is typically an image, which is generated to closely resemble the real data it was trained on.
RNNs are particularly suitable for tasks like ________ because of their ability to handle sequences.
- Sentiment Analysis
- Image Classification
- Sequence Prediction
- Audio Recognition
RNNs excel in tasks that involve sequences, such as sequence prediction, where the previous elements influence the future ones.
In the multi-armed bandit problem, the challenge is to balance between exploration of arms and ________ of the best-known arm.
- Exploitation
- Reward accumulation
- Arm selection
- Probability estimation
The multi-armed bandit problem involves the trade-off between exploration (trying new arms) and exploitation (selecting the best-known arm).
Ensuring that a machine learning model does not unintentionally favor or discriminate against certain groups is ensuring its ________.
- Fairness
- Accuracy
- Efficiency
- Robustness
Ensuring fairness in machine learning models means preventing biases and discrimination in model predictions across different groups.
The drive to make machine learning models more transparent and understandable is often termed as the quest for model ________.
- Interpretability
- Complexity
- Unpredictability
- Accuracy
Model interpretability focuses on making models more transparent, understandable, and interpretable, enhancing trust and insight.
In the context of PCA, what do the principal components represent?
- Clustered Data
- Error in Data
- Features of Data
- Variance of Data Explained
Principal components represent the directions in the data space where the variance of data is maximized. They capture the most significant information and reduce the dimensionality of data.
Which type of machine learning is primarily concerned with using labeled data to make predictions?
- Reinforcement Learning
- Semi-Supervised Learning
- Supervised Learning
- Unsupervised Learning
Supervised Learning is focused on using labeled data to make predictions. In this approach, the algorithm learns from examples with known outcomes, making it a key technique for tasks like classification and regression.
In a logistic regression model, if the odds ratio for a predictor is greater than 1, what does this indicate about the relationship between the predictor and the response variable?
- A negative relationship
- A positive relationship
- An inverse relationship
- There is no relationship
If the odds ratio for a predictor in logistic regression is greater than 1, it indicates a positive relationship between the predictor and the response variable. In other words, an increase in the predictor's value is associated with an increase in the odds of the event occurring.
The ________ in LSTMs help prevent the vanishing gradient problem common in traditional RNNs.
- Forget Gates
- Input Gates
- Output Gates
- Update Gates
The "Forget Gates" in Long Short-Term Memory (LSTM) networks allow them to retain and discard information from the past, addressing vanishing gradients.