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Computer Science

Machine Learning

Activation Functions in Neural Networks

Bias-Variance Tradeoff

Computer Vision Fundamental Concepts

Cross-validation Techniques

Data Preprocessing for Machine Learning

Deep Learning Fundamentals

Dimensionality Reduction Methods

Data Imbalance Handling

Ensemble Learning Techniques

Evaluation Metrics for Classification

Feature Engineering Techniques

Generative Models

Gradient Descent Variants

Hyperparameter Tuning

Machine Learning Best Practices

Loss Functions Explained

Machine Learning Security and Privacy

Machine Learning Pipelines

Machine Learning Frameworks

Model Explainability and Interpretability

Model Selection Criteria

Multiclass Classification Strategies

Natural Language Processing Terminology

Neural Network Architectures

Performance Optimization in Machine Learning

Time Series Analysis Techniques

Reinforcement Learning Concepts

Statistical Tests for Model Comparison

Computer Vision Fundamental Concepts

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Pixel

The smallest addressable element in an image, representing the color and intensity at a specific point.

Feature Detection

The process of identifying and locating key characteristics in an image, such as edges, corners, or blobs, useful for understanding the content of the image.

Edge Detection

A technique to locate the edges of objects within images, crucial for object recognition, segmentation, and tracking.

Convolutional Neural Network (CNN)

A deep learning architecture especially effective for processing data with a grid-like topology, such as images for tasks including image classification and recognition.

Image Segmentation

The process of dividing an image into different regions or segments to simplify or change its representation for easier analysis.

Object Recognition

The task of identifying and classifying various objects within an image into predefined categories.

Depth Perception

The ability of a vision system to perceive the world in three dimensions (3D) and the distance of an object from the camera.

Image Classification

The task of categorizing and labeling an image as belonging to a particular class from a predefined set.

Optical Flow

The pattern of apparent motion of objects, surfaces, and edges in a visual scene caused by the relative motion between an observer and the scene.

Semantic Segmentation

The process of partitioning an image into parts and assigning a label to each part, where the label corresponds to a class of objects.

Histogram of Oriented Gradients (HOG)

A feature descriptor used for object detection in computer vision. It counts occurrences of gradient orientation in localized portions of an image.

Color Space

A specific organization of colors that helps in image processing tasks to process colors in a way that makes them more understandable to humans or machines.

Super-Resolution

The process of increasing the resolution of an image by using algorithms to predict high-resolution details from low-resolution images.

Scale-Invariant Feature Transform (SIFT)

An algorithm in computer vision to detect and describe local features in images for tasks such as object recognition or matching.

Principal Component Analysis (PCA) in CV

A statistical technique used in computer vision to reduce the dimensionality of image data by transforming to a new set of variables (principal components) that are uncorrelated.

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