Which tool or technique is often used to make complex machine learning models more understandable for humans?
- Explainable AI (XAI)
- Reinforcement Learning
- Principal Component Analysis
- Gradient Boosting
Explainable AI (XAI) techniques are employed to simplify complex machine learning models, making them interpretable, and providing insights into model decisions.
One of the applications of NLP in healthcare is to assist in ________, which involves the conversion of voice-recorded notes into text format.
- Transcription
- Speech Recognition
- Note Parsing
- Audio Conversion
NLP (Natural Language Processing) can be employed for transcription tasks in healthcare, where voice-recorded notes from medical professionals are converted into text format. This conversion makes the notes more accessible and searchable for medical records.
A deep learning model is overfitting to the training data, capturing noise and making it perform poorly on the validation set. Which technique might be employed to address this problem?
- Regularization Techniques
- Data Augmentation
- Gradient Descent Algorithms
- Hyperparameter Tuning
Regularization techniques, like L1 or L2 regularization, are used to prevent overfitting by adding penalties to the model's complexity, encouraging it to generalize better and avoid capturing noise.
How do activation functions, like the ReLU (Rectified Linear Unit), contribute to the operation of a neural network?
- They introduce non-linearity into the model
- They reduce the model's accuracy
- They increase model convergence
- They control the learning rate
Activation functions introduce non-linearity to the model, allowing neural networks to approximate complex, non-linear relationships in data. ReLU is popular due to its simplicity and ability to mitigate the vanishing gradient problem.
Which algorithm is based on the principle that similar data points are likely to have similar output values?
- Decision Tree
- K-Means
- Naive Bayes
- Support Vector Machine
K-Means is a clustering algorithm based on the principle that data points in the same cluster are similar, making it useful for data grouping.
Which of the following techniques is primarily used for dimensionality reduction in datasets with many features?
- Apriori Algorithm
- Breadth-First Search (BFS)
- Linear Regression
- Principal Component Analysis (PCA)
Principal Component Analysis (PCA) is a dimensionality reduction technique used to reduce the number of features while preserving data variance.
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.
A common activation function used in CNNs that helps introduce non-linearity is ________.
- Sigmoid
- ReLU
- Linear
- Tanh
The ReLU (Rectified Linear Unit) activation function is widely used in CNNs for its ability to introduce non-linearity into the model, crucial for learning complex patterns.
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).
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.