A finance company wants to analyze sequences of stock prices to predict future market movements. Given the long sequences of data, which RNN variant would be more suited to capture potential long-term dependencies in the data?
- Simple RNN
- Bidirectional RNN
- Gated Recurrent Unit (GRU)
- Long Short-Term Memory (LSTM)
A Long Short-Term Memory (LSTM) is a suitable choice for capturing long-term dependencies in stock price sequences. LSTM's memory cell and gating mechanisms make it capable of handling long sequences and understanding potential trends in financial data.
Which technique involves setting a fraction of input units to 0 at each update during training time, which helps to prevent overfitting?
- Dropout
- Batch Normalization
- Data Augmentation
- Early Stopping
Dropout involves setting a fraction of input units to 0 during training, which helps prevent overfitting by making the model more robust and reducing reliance on specific neurons.
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.
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.
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.
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.
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.
In clustering problems where the assumption is that...
- K-Means
- Gaussian Mixture Model (GMM)
- Support Vector Machines
- Decision Trees
Gaussian Mixture Model (GMM) is a popular choice in clustering problems where data is assumed to be generated from a mixture of Gaussian distributions. It can model complex data distributions effectively.
In which algorithm is the outcome determined based on a majority vote from its neighbors?
- K-Nearest Neighbors (K-NN)
- Linear Regression
- Logistic Regression
- Principal Component Analysis (PCA)
K-Nearest Neighbors (K-NN) is a classification algorithm where the outcome is determined by majority voting among its nearest neighbors.
When using transfer learning, what part of the pre-trained model is typically fine-tuned for the new task?
- Last few layers
- First few layers
- All layers
- Random layers
In transfer learning, the last few layers are typically fine-tuned because they contain task-specific information, while the early layers retain more generic features.
Which evaluation metric would be least affected by a large number of true negatives in a dataset?
- Accuracy
- Precision
- Recall
- Specificity
Specificity is the evaluation metric least affected by a large number of true negatives in a dataset. It focuses on correctly identifying true negatives and is particularly relevant in situations where false positives should be minimized.
A medical diagnosis AI system provides a diagnosis but does not give any rationale or reasoning behind it. What aspect of machine learning is this system lacking?
- Interpretability
- Classification
- Model Complexity
- Feature Engineering
The system's lack of providing rationale or reasoning is a deficiency in interpretability. In medical AI, it's crucial for doctors to understand why a diagnosis was made to trust and make informed decisions based on the AI's recommendations.