In Data Science, _______ is the process of cleaning and structuring the data to make it suitable for analysis.
- Data Mining
- Data Integration
- Data Wrangling
- Data Ingestion
In Data Science, data wrangling is the process of cleaning and structuring data to prepare it for analysis. This includes tasks such as handling missing values, transforming data, and dealing with inconsistencies.
Which activation function maps any input to a value between 0 and 1?
- ReLU
- Sigmoid
- Tanh
- Softmax
The sigmoid activation function maps any input to a value between 0 and 1. It's commonly used in neural networks for binary classification problems and helps introduce non-linearity in the network's computations.
Overfitting can also be controlled by reducing the _______ of the neural network, which refers to the number of nodes and layers.
- Learning rate
- Epochs
- Capacity
- Batch size
Overfitting in neural networks can be controlled by reducing the capacity of the network, which refers to the number of nodes and layers. A simpler network is less likely to overfit as it has fewer parameters to learn and generalize more effectively.
In computer vision, detecting specific features or patterns in an image is often achieved using _______.
- Convolutional Neural Networks
- Principal Component Analysis
- Linear Regression
- Decision Trees
In computer vision, detecting specific features or patterns in an image is often achieved using Convolutional Neural Networks (CNNs). CNNs are well-suited for image feature extraction and are widely used in tasks like object detection and image classification.
The _______ activation function outputs values between 0 and 1 and can cause a vanishing gradient problem.
- ReLU
- Sigmoid
- Tanh
- Leaky ReLU
The blank should be filled with "Sigmoid." The Sigmoid activation function maps input values to the range of 0 to 1. It can cause the vanishing gradient problem, which makes training deep networks difficult due to its derivative approaching zero for extreme input values.
After clustering a dataset, you notice that some data points are far from their respective cluster centroids. What might these points represent, and how can they be addressed?
- Outliers
- Noise in the data
- Cluster prototypes
- Overfitting in the clustering algorithm
Data points that are far from their cluster centroids are likely outliers. Outliers can significantly impact clustering results. To address this issue, you can consider different strategies such as removing outliers, using robust clustering algorithms, or applying feature scaling and normalization to make the clusters less sensitive to outliers.
In a production environment, _______ allows for seamless updates of a machine learning model without any downtime.
- A/B testing
- Model versioning
- Continuous Integration
- Model deployment
Model versioning is a crucial aspect of model deployment. It enables organizations to update machine learning models without causing downtime. This is vital in real-world applications where models need to adapt to changing data and conditions.
The technique where spatial transformations are applied to input images to boost the performance and versatility of models is called _______ in computer vision.
- Edge Detection
- Data Augmentation
- Optical Flow
- Feature Extraction
Data augmentation involves applying spatial transformations to input images, such as rotation, flipping, or cropping, to increase the diversity of the training data. This technique enhances model generalization and performance.
In Matplotlib, the foundation for all visualizations is the _______ object, which provides the canvas where plots are drawn.
- Figure
- Canvas
- Plot
- Chart
Matplotlib uses the "Figure" object as the foundational canvas for all visualizations. It serves as the top-level container for plots, allowing you to add multiple subplots and customize various aspects of the visualizations.
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.