Which type of learning is characterized by an agent interacting with an environment and learning to make decisions based on rewards and penalties?
- Supervised Learning
- Reinforcement Learning
- Unsupervised Learning
- Semi-Supervised Learning
Reinforcement learning is the type of learning where an agent learns through interaction with an environment by receiving rewards and penalties.
Why might a deep learning practitioner use regularization techniques on a model?
- To make the model larger
- To simplify the model
- To prevent overfitting
- To increase training speed
Deep learning practitioners use regularization techniques to 'prevent overfitting.' Overfitting is when a model learns noise in the training data, and regularization helps in making the model more generalized and robust to new data.
Which NLP technique is often employed to extract structured information from unstructured medical notes?
- Sentiment Analysis
- Named Entity Recognition
- Part-of-Speech Tagging
- Machine Translation
Named Entity Recognition is an NLP technique used to identify and categorize entities (e.g., drugs, diseases) within unstructured medical text.
Which regression technique uses the logistic function (or sigmoid function) to squeeze the output between 0 and 1?
- Linear Regression
- Logistic Regression
- Poisson Regression
- Ridge Regression
Logistic Regression uses the logistic function (sigmoid function) to model the probability of a binary outcome. This function ensures that the output is constrained between 0 and 1, making it suitable for classification tasks.
In the context of Q-learning, what does the 'Q' stand for?
- Quality
- Quantity
- Question
- Quotient
In Q-learning, the 'Q' stands for Quality, representing the quality or expected return of taking a specific action in a given state.
Time series forecasting is crucial in fields like finance and meteorology because it helps in predicting stock prices and ________ respectively.
- Temperature
- Rainfall
- Crop yields
- Wind speed
Time series forecasting in meteorology is important for predicting variables like rainfall, not stock prices.
Experience replay, often used in DQNs, helps in stabilizing the learning by doing what?
- Reducing Correlation between Data
- Speeding up convergence
- Improving Exploration
- Saving Memory Space
Experience replay in DQNs reduces the correlation between consecutive data samples, which stabilizes learning by providing uncorrelated transitions for training.
An online retailer wants to create a hierarchical structure of product categories based on product descriptions and features. They want this hierarchy to be easily interpretable and visual. Which clustering approach would be most suitable?
- Hierarchical Clustering
- DBSCAN
- Gaussian Mixture Model (GMM)
- Affinity Propagation
For creating a hierarchical structure, Hierarchical Clustering is the most suitable approach. It builds a tree-like structure that is interpretable and can be easily visualized. This makes it ideal for organizing product categories based on descriptions and features.
In a situation where you have both numerical and categorical data, which clustering method might pose challenges, and why?
- Agglomerative Clustering
- DBSCAN Clustering
- Hierarchical Clustering
- K-Means Clustering
K-Means may pose challenges in such a situation because it calculates centroids using the mean, which isn't well-defined for categorical data. Other methods like hierarchical or DBSCAN may be more suitable.
While LSTMs have three gates, the GRU simplifies the model by using only ________ gates.
- 1
- 2
- 3
- 4
Gated Recurrent Units (GRUs) simplify the model by using only two gates: an update gate and a reset gate, as opposed to the three gates in LSTMs.
In convolutional neural networks, using weights from models trained on large datasets like ImageNet as a starting point for training on a new task is an application of ________.
- Transfer Learning
- Regularization
- Batch Normalization
- Data Augmentation
This application of transfer learning involves using pre-trained CNN models, like those on ImageNet, to initialize weights in a new model for a different task. It accelerates training and leverages existing knowledge.
What is the central idea behind using autoencoders for anomaly detection in data?
- Autoencoders learn a compressed data representation
- Autoencoders are trained on anomalies
- Autoencoders are rule-based
- Autoencoders use labeled data
Autoencoders for anomaly detection learn a compressed representation of normal data, and anomalies can be detected when the reconstruction error is high.