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A-Z of Machine Learning and Computer Vision Terms

Activation Function

Active Learning

AI Assisted Labeling

Anomaly Detection

Artificial Intelligence (AI)

Backpropagation

Backpropagation

Batch Normalization

Bayesian Network

Binary Classification

Calibration Curve

Canonical Correlation Analysis (CCA)

Case-Based Reasoning

Chain of Thought (CoT)

Chi-Squared Automatic Interaction Detection (CHAID)

Class Boundary (Statistics & Machine Learning)

Class Imbalance

Collaborative Filtering

Computer Vision

Computer Vision Model

Conditional Random Field (CRF)

Confusion Matrix

Constrained Clustering

Contrastive Learning

Convolutional Neural Network (CNN)

Cross-entropy Loss

Cross-Validation

Data Approximation

Data Augmentation

Data Operations

Data Pre-processing

Decision Boundary

Deep Neural Networks

Deep Reinforcement Learning (DRL)

Dimensionality Reduction

Dimensionality Reduction

Dynamic and Event-Based Classifications

Embedding Spaces

Ensemble Learning

Expectation-Maximization Algorithm (EM)

Extreme Learning Machine

Factor Analysis

False Positive Rate

Feature Extraction

Feature Hashing

Feature Learning

Feature Scaling

Feature Selection

Fisher’s Linear Discriminant

Foundation Models

FP-Growth Algorithm

Frames Per Second (FPS)

Fully Connected Layer

Generative Adversarial Network (GAN)

Generative Adversarial Networks

B

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Dimensionality Reduction

Activation Function

K-Nearest Neighbor (KNN)

Support Vector Machines (SVMs)

Knowledge Transfer

Cross-entropy Loss

Convolutional Neural Network (CNN)

Deep Reinforcement Learning (DRL)

Zero-Shot Learning

YOLO Object Detection

Type I Error (False Positive)

XAI (Explainable AI)

Variational Autoencoders

Weight Decay (L2 Regularization)

Weak Supervision

Variance (Model Variance)

Unsupervised Learning

Pandas and NumPy

Transformers (Transformer Networks)

Type II Error (False Negative)

True Positive Rate (TPR)

Transfer Learning

Text Generation Inference

Surrogate Model

Stream-Based Selective Sampling

Supervised Learning

Sliding Window Attention

Semi-supervised Learning

Sentiment Analysis

Segment Anything Model (SAM)

Sensitivity and Specificity of Machine Learning

Semantic Segmentation

Self-Supervised Learning

Selective Sampling

Scale Imbalance

ROC (Receiver Operating Characteristic) Curve

Reinforcement Learning

Regularization Algorithms

Regression (Regression Analysis)

Region Proposal Network (RPN)

Region-Based CNN (R-CNN)

Recurrent Neural Network (RNN)

Recall (Sensitivity or True Positive Rate)

RAG Architecture

Query Synthesis Methods

Query Strategy (Active Learning)

Quantum Machine Learning

Prompt Engineering

Prompt Injection

Prompt Chaining

Predictive Model Validation

Pose Estimation

Pool-Based Sampling

Principal Component Analysis

Pattern Recognition

Parameter-Efficient Fine-Tuning (Prefix-Tuning)

Panoptic Segmentation

PACS (Picture Archiving and Communication System)

One-Shot Learning

Outlier Detection

Optimization Algorithms

Optical Character Recognition (OCR)

Object Tracking

Object Localization

Object Recognition

Object Detection

Neural Networks

Neural Style Transfer

Neural Architecture Search

Natural Language Processing (NLP)

Motion Estimation

Multi-Task Learning

Motion Detection

Model Validation

Model Parameters

Medical Image Segmentation