Which NLP model captures the context of words by representing them as vectors?
- Word2Vec
- Regular Expressions
- Decision Trees
- Linear Regression
Word2Vec is a widely used NLP model that captures word context by representing words as vectors in a continuous space. It preserves the semantic meaning of words, making it a powerful tool for various NLP tasks like word embeddings and text analysis. The other options are not NLP models and do not capture word context in the same way.
The term "Data Science" is an interdisciplinary field that uses various methods and techniques from which of the following domains?
- Computer Science and Mathematics
- History and Art
- Literature and Geography
- Music and Philosophy
Data Science draws from Computer Science and Mathematics to develop analytical and computational techniques for data analysis. This interdisciplinary approach is essential for solving complex data-related problems.
In NLP tasks, transfer learning has gained popularity with models like _______ that provide pre-trained weights beneficial for multiple downstream tasks.
- BERT
- RecurrentNet
- RandomText
- GPT-3
Models like BERT (Bidirectional Encoder Representations from Transformers) have gained popularity in NLP for their pre-trained weights. These models can be fine-tuned for various downstream tasks, saving time and resources and achieving state-of-the-art results.
When you want to create a complex layered visualization by combining multiple plots, which Python library provides a FacetGrid class?
- Seaborn
- Matplotlib
- Plotly
- Pandas
Seaborn is a Python data visualization library that provides the FacetGrid class for creating complex layered visualizations by combining multiple plots. It allows you to create grid-like structures of subplots to visualize relationships between variables in your data, making it ideal for advanced visualization tasks.
Which role in Data Science is most likely to be involved in deploying machine learning models into production?
- Data Scientist
- Data Engineer
- Data Analyst
- Machine Learning Engineer
Machine Learning Engineers are responsible for developing and deploying machine learning models into production systems. They work closely with Data Scientists who create the models but specialize in the deployment process.
For real-time object detection in images or videos, the _______ algorithm is widely adopted.
- YOLO (You Only Look Once)
- R-CNN (Region-based Convolutional Neural Network)
- CNN (Convolutional Neural Network)
- HOG (Histogram of Oriented Gradients)
YOLO (You Only Look Once) is a popular algorithm for real-time object detection. It efficiently detects objects in images or videos, making it suitable for various applications, including self-driving cars and surveillance.
RNNs are particularly effective for tasks like _______ because they can retain memory from previous inputs in the sequence.
- Image classification
- Speech recognition
- Regression analysis
- Text formatting and styling
RNNs (Recurrent Neural Networks) are known for their ability to retain memory from previous inputs in a sequence, making them effective for tasks like speech recognition, where the order of input data and contextual information is crucial for accurate prediction. Speech recognition relies on capturing temporal dependencies in audio data, which RNNs excel at.
Raw logs from web servers, which might include a mix of text, images, and other file types, are considered _______ data.
- Structured data
- Unstructured data
- Semi-structured data
- Big data
Raw logs from web servers often contain unstructured data, as they can consist of a mix of text, images, and various file types that lack a specific format. Unstructured data is not organized in a traditional tabular structure.
Which NLP task involves determining the emotional tone behind a series of words?
- Sentiment Analysis
- Named Entity Recognition
- Part-of-Speech Tagging
- Machine Translation
Sentiment Analysis is an NLP task that involves determining the emotional tone or sentiment behind a series of words, often classifying it as positive, negative, or neutral. It's crucial for understanding public opinion and customer feedback.
Which type of network architecture is primarily used for image classification tasks in deep learning?
- Recurrent Neural Network (RNN)
- Convolutional Neural Network
- Long Short-Term Memory (LSTM)
- Feedforward Neural Network
Convolutional Neural Networks (CNNs) are specifically designed for image classification tasks. They use convolutional layers to capture spatial hierarchies in the input data, making them highly effective for image recognition and analysis.