Active Learning

Active Learning is a machine learning strategy that enhances learning efficiency by allowing the model to actively select data that provide the most information. This approach aims to reduce the need for labeled data, especially in tasks where labeling is costly or time-consuming, such as image recognition and natural language processing.

AI Assisted Labeling

AI-assisted labeling is a method that utilizes artificial intelligence technologies to assist in labeling raw data. AI-assisted labeling uses machine learning algorithms to perform preliminary data analysis and predictions, providing automated labeling suggestions. Human annotators can quickly review and adjust these suggestions, enhancing the efficiency and accuracy of the annotation process.

Anchor Boxes

Anchor Boxes are rectangular boxes used in object detection to help locate and identify objects in an image. They have predefined sizes and ratios to assist the model in predicting the specific location and size of objects.

Anomaly Detection

Annotation

Artificial Intelligence

AI Data Services

Bias

Blur

Bounding Box

Backpropagation

Bayes' Theorem

ChatGPT

Classification

Class Boundary

Class Imbalance

Convolutional Neural Network (CNN)

COCO

Computer Vision

Computer Vision Ontology

Concept Drift

Confusion Matrix

Calibration Curve

Data Approximation

Data Augmentation

Data Error

Data Drift

Data Operations

Data Quality

Datasets

Debug

Decision Tree

Deep Learning

DICOM

Dynamic and Event-Based Classifications

Data Labeling

Data Mining

Dimensionally Reduction

Edge Detection

Epochs

Ensemble Learning

Entropy

F1 Score

False Positive Rate

Features

Feature Extraction

Feature Vector

Few Shot Learning

Frames Per Second (FPS)

Feature Engineering

Fuzzy Logic

Face Recognition

Generative Pre-Trained Transformer (GPT)

Ghost Frames

Greyscale

Ground Truth

Gradient Descent

Generalization

Generative Adversarial Network (GAN)

Garbage In, Gabage Out

Genetic Algorithm

Graphic Processing Unit (GPU)

Human in the Loop (HITL)

Human Pose Estimation

Hyperparameters

Heuristic

Image Annotation

Image Degredation

Imbalanced Dataset

Instance Segmentation

Interpolation

Intersection over Union (IoU)

Instance-based Learning

Intelligent Agent

Keypoints

K-Means Clustering

Knowledge Distillation

Kernel

Label

Label Errors

Learning Rate

Lifecycle

Logistic Regression

Labelled Data

Learning-to-Learn

Mean Average Precision (mAP)

Medical Image Segmentation

Micro-Models

Machine learning

MLOps

Model Accuracy

Model Parameters

Model Validation

Mean Square Error (MSE)

Model Evaluation

Multi-agent System

Monte Carlo

Machine Learning

Natural Language Processing (NLP)

Natural Language Processing (NLP) is a subfield of artificial intelligence (AI) and computational linguistics that focuses on the interaction between computers and human (natural) languages. It involves the development of algorithms and models that allow machines to process, understand, and generate human language in a way that is both meaningful and useful. NLP is a crucial technology in many applications, such as text analysis, speech recognition, machine translation, and chatbots.

Named Entity Recognition (NER)

Named Entity Recognition (NER) is a natural language processing (NLP) task that involves identifying and classifying named entities in text into predefined categories such as names of people, organizations, locations, dates, and other specific terms. NER is a key step in many NLP applications, including information extraction, question answering, and machine translation.

Noise

Noise refers to any random or unpredictable variations in the data that do not represent the underlying patterns or true relationships in the dataset. Noise can arise due to a variety of factors, including errors in data collection, sensor inaccuracies, human errors, or external factors that introduce irrelevant information into the dataset. Noise often reduces the quality and reliability of a model, making it harder for the algorithm to learn the true patterns and relationships in the data.

Normalization

Normalization refers to the process of adjusting the values of numerical data to a common scale, without distorting differences in the ranges of values. This is particularly important for models that are sensitive to the scale of input features, such as many machine learning algorithms and neural networks. By normalizing the data, we ensure that each feature contributes equally to the learning process, preventing any feature from dominating due to its larger scale.

Neural Networks

Neural Networks are a class of machine learning models inspired by the structure and functioning of the human brain. They are designed to recognize patterns and learn complex relationships in data by simulating the way neurons in the brain process information. Neural networks consist of layers of interconnected nodes (or "neurons"), where each connection has a weight that is adjusted during training to minimize errors and improve the model's predictions.

Object Detection

Object Localization

Object Tracking

One-Shot Learning

Openpose

Outlier Detection

Overfitting

PACS

Panoptic Segmentation

Pool based Sampling

Pre Trained Model

Precision

Population Stability Index (PSI)

Pattern Recognition

Predictive Modeling

Preprocessing

Query Strategy

Query Synthesis Methods

Q-Learning

Random Forest

Recall

Region-Based CNN

Region-Based CNN (R-CNN) is a deep learning method that combines Convolutional Neural Networks (CNNs) with Region Proposal Networks (RPNs), primarily used for object detection tasks in computer vision. R-CNN works by first generating a set of candidate regions (region proposals) from an image, then classifying and regressing each region to predict the location and class of objects within the image. It was one of the pioneering methods in object detection and significantly improved performance over traditional techniques.

Regression

Regression refers to a type of supervised learning task where the objective is to predict a continuous outcome variable (dependent variable) based on one or more input features (independent variables). The model learns the mapping from the input features to the continuous output variable, which can be used for tasks such as forecasting, trend analysis, or predicting future values.

Reinforcement Learning (RL)

Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by interacting with its environment. The agent takes actions in the environment, and in return, receives feedback in the form of rewards or penalties. The objective is to learn a policy that maximizes the cumulative reward over time. Unlike supervised or unsupervised learning, RL does not require labeled data but instead relies on the agent's experiences and feedback to improve its decision-making strategy.

Receiver Operating Characteristic（ROC）

ROC (Receiver Operating Characteristic curve) is a graphical representation used to evaluate the performance of binary classification models, particularly in scenarios where the classes are imbalanced. The ROC curve plots the True Positive Rate (TPR) (also known as sensitivity) against the False Positive Rate (FPR) (also known as the false positive rate) for different threshold values.

Scale Imbalance

Scale Imbalance refers to an issue that arises when there is an uneven distribution of data across different scales or sizes in a task, such as in image processing or computer vision. In tasks like object detection or segmentation, scale imbalance occurs when certain object sizes (e.g., small objects or large objects) are underrepresented or overrepresented in the dataset. This imbalance can lead to poor model performance on underrepresented scales. For example, if a model is trained on a dataset where small objects are much more common than large objects, the model may become biased toward detecting small objects and fail to accurately detect larger ones. Scale imbalance is a common challenge in machine learning and computer vision tasks that involve objects of varying sizes.

Segment Anything Model (SAM)

Segment Anything Model (SAM) is a cutting-edge computer vision model developed by Meta, designed to segment objects in images with minimal user input. SAM is trained to segment any object within an image by responding to simple interactions, such as a click or a box drawn around the object of interest. It does not require pre-defined object categories, making it highly flexible and capable of handling a wide variety of images and segmentation tasks. SAM is particularly notable for its efficiency and versatility in image segmentation.

Stream-based Sampling

Stream-based Sampling is a technique used in the context of data streams, particularly suited for real-time or large-scale data processing. In this approach, a model learns incrementally from the data stream as it arrives, rather than processing the entire dataset in a batch fashion. The goal of stream-based sampling is to select a representative subset of data from an ongoing stream so that the model can efficiently learn and update without requiring excessive memory or computation.

Supervised Learning

Supervised Learning is a type of machine learning where the algorithm learns from labeled training data. Each training sample consists of an input feature and a corresponding label, and the goal is to model the mapping between inputs and outputs. Once trained, the model can make predictions or classifications on new, unseen data.

Support Vector Machine

Support Vector Machine (SVM) is a supervised learning algorithm primarily used for classification and regression tasks. The goal of SVM is to find the optimal hyperplane that separates different classes of data points with the maximum margin. The key concept in SVM is the selection of support vectors, which are the data points that are most influential in determining the position and orientation of the hyperplane.● Classification: In classification tasks, SVM aims to find a hyperplane that can separate the data points of different classes with the largest margin.● Regression: In regression tasks, SVM tries to fit a regression line or curve that has the least deviation from the data points, within a specified margin of error.SVM is particularly effective in high-dimensional spaces and is well-suited for tasks with complex decision boundaries. It is also known for its effectiveness in situations with relatively small datasets.

Sentiment Analysis

Sentiment Analysis refers to the use of computational techniques to analyze and identify the sentiment conveyed in a piece of text. This can involve classifying text into categories such as positive, negative, or neutral sentiment, as well as assessing the intensity of the emotions expressed. Sentiment analysis is commonly used in areas like social media monitoring, customer feedback analysis, and brand management.

Training Data

Training Data refers to the dataset used to train a machine learning model. It includes input features and the corresponding target labels (in supervised learning). The training data helps the model learn underlying patterns and relationships in the data so it can make accurate predictions and classifications. The quality and quantity of training data directly impact the model's performance, and it often requires preprocessing, cleaning, and formatting to ensure it is suitable for training.

Transfer Learning

Transfer Learning is a machine learning method where a model trained on one task (source domain) is adapted and applied to a different but related task (target domain). The core idea is to transfer knowledge learned from the source domain to the target domain, allowing for faster training and better performance, especially when the target domain has limited data. Transfer learning is commonly used in fields like computer vision and natural language processing.

Transformers

Transformers are a deep learning model architecture introduced by Vaswani et al. in 2017, widely used for natural language processing tasks such as machine translation, text generation, and sentiment analysis. The key innovation of the Transformer model is the self-attention mechanism, which allows the model to capture long-range dependencies in the input data, overcoming the limitations of traditional RNNs and LSTMs for long sequence modeling. The Transformer architecture forms the foundation of many large language models, such as GPT and BERT.

Triplet Loss

Triplet Loss is a loss function commonly used in metric learning, especially in tasks like facial recognition and image retrieval. The triplet loss learns an embedding space by minimizing the distance relationships within a triplet of samples. A triplet consists of three components: an anchor sample, a positive sample (similar to the anchor), and a negative sample (dissimilar to the anchor). The goal of triplet loss is to minimize the distance between the anchor and positive sample while maximizing the distance between the anchor and negative sample.

True Positive Rate (TPR)

True Positive Rate (TPR), also known as Recall, is a performance metric for classification models that measures the proportion of actual positive samples correctly identified by the model. A higher TPR indicates that the model is better at detecting positive samples.

Type 1 Errors

A Type 1 Error, also known as a False Positive, occurs when a model incorrectly classifies a negative instance as positive. In other words, the model "wrongly predicts" the occurrence of an event.

Type 2 Errors

A Type 2 Error, also known as a False Negative, occurs when a model incorrectly classifies a positive instance as negative. In other words, the model fails to detect an event that actually occurred.

Unsupervised Learning

Unsupervised Learning is a type of machine learning where the algorithm learns patterns and structures from data that is not labeled. In other words, the task of unsupervised learning is to find inherent structures or patterns in the data without the guidance of labeled outputs. Unlike supervised learning, which requires a set of labeled training data, unsupervised learning extracts information and relationships directly from the raw data. Common unsupervised learning tasks include clustering (e.g., K-means), dimensionality reduction (e.g., Principal Component Analysis (PCA)), and association rule learning. The key characteristic of unsupervised learning is that there is no "correct answer" or "target label" to guide the algorithm. Instead, the algorithm learns from the distribution and features of the data itself. Unsupervised learning is often used in applications like data preprocessing, feature engineering, data compression, and anomaly detection.

Underfitting

Underfitting refers to a scenario in machine learning where a model is too simple to capture the underlying patterns or relationships in the training data, resulting in poor performance on both the training set and test set. Underfitting typically occurs when: The model is too simplistic, such as using too few features or a very simple model (e.g., applying linear regression to a complex dataset). There is insufficient data or the features do not provide enough information for learning. The model's complexity is not high enough to capture the intricate patterns in the data. Underfitting is usually characterized by both high training error and high test error, indicating that the model fails to learn from the training data. To address underfitting, you can try increasing the model's complexity, adding more features, or using more powerful algorithms.

Variance

Variance is a statistical measure of the dispersion or spread of a dataset. It represents the average squared deviation of each data point from the mean of the dataset. A higher variance indicates that the data points are more spread out from the mean, while a lower variance suggests that the data points are closer to the mean. Variance is a foundational concept in statistics and probability theory, widely used in various fields such as data analysis, quality control, and financial analysis. In machine learning, variance is often discussed in the context of the bias-variance tradeoff, where high variance indicates that a model is overly complex and prone to overfitting, while low variance suggests that the model is more general and less sensitive to fluctuations in the data.

Validation Set

A Validation Set is a subset of data used to evaluate the performance of a machine learning or deep learning model during the training process. It is typically separated from the training set and is used for periodic evaluation to help tune hyperparameters and select the best model. The primary purpose of the validation set is to monitor the model's generalization ability, detect overfitting, and ensure that the model performs well on data it hasn't seen before. The validation set is not used for training but is essential for model selection and tuning based on its performance.

Video Annotation

Video Annotation refers to the process of adding labels, tags, or metadata to the content of a video for later analysis, model training, or other data processing tasks. It may involve marking specific objects, actions, events, or other significant features within the video. Video annotation is widely used in computer vision and video analysis tasks, such as object detection, action recognition, scene segmentation, etc. The annotation can be done manually or using automated tools and algorithms to improve efficiency, especially in training deep learning models for video content understanding.

XGBoost

XGBoost (Extreme Gradient Boosting) is an efficient, flexible, and scalable machine learning algorithm primarily used for classification, regression, and other tasks. Based on the Gradient Boosting Decision Trees (GBDT) algorithm, XGBoost incorporates several optimizations, such as regularization, pruning, and parallel computation, making it faster and more efficient, especially for large datasets. XGBoost is widely used in machine learning competitions like Kaggle and is highly regarded for its performance in structured data tasks like financial risk prediction and click-through rate prediction.

YOLO (You Only Look Once)

YOLO (You Only Look Once) is an efficient object detection algorithm designed to detect and classify multiple objects in an image in real-time. The core idea of YOLO is to reformulate the object detection task into a regression problem, where a neural network predicts both the locations (bounding boxes) and the class labels of objects in a single forward pass. YOLO's main advantage is its speed and efficiency, making it suitable for real-time applications and large-scale datasets. It is widely used in fields like autonomous driving, surveillance, and intelligent robotics.

Zero Shot Learning

Zero Shot Learning (ZSL) refers to a machine learning technique that enables models to recognize or perform tasks related to classes or categories they have never seen before. Unlike traditional machine learning approaches that rely on labeled data for every possible class, zero-shot learning leverages relationships between known classes or external sources of information (such as textual descriptions or attributes) to infer properties of unseen classes. This approach is widely used in fields like image recognition and natural language processing, especially in scenarios where obtaining labeled data for every potential class is impractical.