Q-learning is a type of ________ learning algorithm that aims to find the best action to take given a current state.
- Reinforcement
- Supervised
- Unsupervised
- Semi-supervised
Q-learning is a type of reinforcement learning that focuses on finding the best action to take in a given state to maximize cumulative rewards.
When aiming to reduce both bias and variance, one might use techniques like ________ to regularize a model.
- Cross-Validation
- Data Augmentation
- Dropout
- L1 Regularization
L1 regularization is a technique used to reduce both bias and variance in a machine learning model. It does so by adding a penalty term to the model's loss function, which encourages the model to use fewer features, thus reducing complexity and variance. Dropout, Cross-Validation, and Data Augmentation are techniques but are not primarily used for regularization.
What does the "G" in GRU stand for when referring to a type of RNN?
- Gated
- Global
- Gradient
- Graph
The "G" in GRU stands for "Gated." GRU is a type of RNN that uses gating mechanisms to control information flow, making it capable of handling sequences efficiently.
One of the challenges in training deep RNNs is the ________ gradient problem, which affects the network's ability to learn long-range dependencies.
- Vanishing
- Exploding
- Overfitting
- Regularization
The vanishing gradient problem refers to the issue where gradients in deep RNNs become too small during training, making it challenging to capture long-range dependencies.
For a medical test, it's crucial to minimize the number of false negatives. Which metric would be particularly important to optimize in this context?
- Accuracy
- F1 Score
- Precision
- Recall
In the context of a medical test, minimizing false negatives is vital because you want to avoid missing actual positive cases. This emphasizes the importance of optimizing recall, which measures the ability of the test to correctly identify all positive cases, even at the expense of more false positives.
In hierarchical clustering, the ________ method involves merging the closest clusters in each iteration.
- Agglomerative
- Divisive
- DBSCAN
- OPTICS
In hierarchical clustering, the Agglomerative method starts with individual data points as clusters and iteratively merges the closest clusters, creating a hierarchy. "Agglomerative" is the correct option, representing the bottom-up approach of this clustering method.
In the context of GANs, the generator tries to produce fake data, while the discriminator tries to ________ between real and fake data.
- Differentiate
- Discriminate
- Generate
- Classify
The discriminator in a GAN is responsible for distinguishing or discriminating between real and fake data, not generating or differentiating.
What type of neural network is designed for encoding input data into a compressed representation and then decoding it back to its original form?
- Convolutional Neural Network (CNN)
- Recurrent Neural Network (RNN)
- Autoencoder
- Long Short-Term Memory (LSTM)
Autoencoders are neural networks designed for this task. They consist of an encoder network that compresses input data into a compact representation and a decoder network that reconstructs the original data from this representation.
What is the main challenge faced by NLP systems when processing clinical notes in electronic health records?
- Variability in clinical language
- Availability of data
- Lack of computational resources
- Precision and recall
Clinical notes often use varied and context-specific language, making it challenging for NLP systems to accurately interpret and extract information from electronic health records. This variability can impact system accuracy.
Which term refers to using a model that has already been trained on a large dataset and fine-tuning it for a specific task?
- Model adaptation
- Model transformation
- Model modification
- Fine-tuning
Fine-tuning is the process of taking a pre-trained model and adjusting it to perform a specific task. It's a crucial step in transfer learning, where the model adapts its features and parameters to suit the new task.