A-Z of Machine Learning and Computer Vision Terms

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Ghost Frames
Ghost Frames
Gradient Descent
Gradient Descent
Greyscale
Greyscale
Ground Truth
Ground Truth
H
H
Hierarchical Clustering
Hierarchical Clustering
Histogram of Oriented Gradients (HOG)
Histogram of Oriented Gradients (HOG)
Human Pose Estimation
Human Pose Estimation
Human in the Loop (HITL)
Human in the Loop (HITL)
Hyperparameter Tuning
Hyperparameter Tuning
Hyperparameters
Hyperparameters
I
I
Image Annotation
Image Annotation
Image Augmentation
Image Augmentation
Image Captioning
Image Captioning
Image Classification
Image Classification
Image Degradation
Image Degradation
Image Generation
Image Generation
Image Processing
Image Processing
Image Recognition
Image Recognition
Image Restoration
Image Restoration
Image Segmentation
Image Segmentation
Imbalanced Data
Imbalanced Data
Imbalanced Dataset
Imbalanced Dataset
In-Context Learning
In-Context Learning
Instance Segmentation
Instance Segmentation
Instance Segmentation
Instance Segmentation
Interpolation
Interpolation
Interpretability
Interpretability
Intersection over Union (IoU)
Intersection over Union (IoU)
J
J
Jaccard Index
Jaccard Index
Jupyter Notebooks
Jupyter Notebooks
K
K
K-Means Clustering
K-Means Clustering
Keypoints
Keypoints
Knowledge Graphs
Knowledge Graphs
L
L
LIDAR
LIDAR
Label
Label
Label Errors
Label Errors
Large Language Model (LLM)
Large Language Model (LLM)
Latent Dirichlet Allocation (LDA)
Latent Dirichlet Allocation (LDA)
Latent Space
Latent Space
Learning Rate
Learning Rate
Linear Discriminant Analysis (LDA)
Linear Discriminant Analysis (LDA)
Linear Regression
Linear Regression
Logistic Regression
Logistic Regression
Long Short-Term Memory (LSTM)
Long Short-Term Memory (LSTM)
Loss Function
Loss Function
M
M
Machine Learning (ML)
Machine Learning (ML)
Manifold Learning
Manifold Learning
Markov Chains
Markov Chains
Mean Average Precision (mAP)
Mean Average Precision (mAP)
Mean Squared Error (MSE)
Mean Squared Error (MSE)
Medical Image Segmentation
Medical Image Segmentation
Micro-Models
Micro-Models
Model Accuracy
Model Accuracy
Model Parameters
Model Parameters
Model Validation
Model Validation
Motion Detection
Motion Detection
Motion Estimation
Motion Estimation
Multi-Task Learning
Multi-Task Learning
N
N
NIfTI
NIfTI
Natural Language Processing (NLP)
Natural Language Processing (NLP)
Neural Architecture Search
Neural Architecture Search
Neural Networks
Neural Networks
Neural Style Transfer
Neural Style Transfer
Noise
Noise
Normalization
Normalization
O
O
Object Detection
Object Detection
Object Localization
Object Localization
Object Recognition
Object Recognition
Object Tracking
Object Tracking
One-Shot Learning
One-Shot Learning
Optical Character Recognition (OCR)
Optical Character Recognition (OCR)
Optimization Algorithms
Optimization Algorithms
Outlier Detection
Outlier Detection
Overfitting
Overfitting
P
P
PACS (Picture Archiving and Communication System)
PACS (Picture Archiving and Communication System)
PR AUC
PR AUC
Pandas and NumPy
Pandas and NumPy
Panoptic Segmentation
Panoptic Segmentation
Parameter-Efficient Fine-Tuning (Prefix-Tuning)
Parameter-Efficient Fine-Tuning (Prefix-Tuning)
Pattern Recognition
Pattern Recognition
Perceptron
Perceptron
Pixel
Pixel
Pool-Based Sampling
Pool-Based Sampling
Pooling
Pooling
Pose Estimation
Pose Estimation
Precision
Precision
Predictive Model Validation
Predictive Model Validation
Principal Component Analysis
Principal Component Analysis
Prompt Chaining
Prompt Chaining
Prompt Engineering
Prompt Engineering
Prompt Injection
Prompt Injection
D

Data Error

A data error refers to inaccuracies or inconsistencies in a dataset that can arise during collection, entry, transmission, or storage. Common types of data errors include: missing values, duplicates, outliers, and inconsistent formats or codings. For example, a sensor might drop readings (missing data), a database might have the same record twice (duplicate), or a human might mistype an entry (inaccuracy). Data errors can lead to incorrect model training or analysis results, as they distort the true underlying patterns. Therefore, data preprocessing steps like cleaning, validation, outlier analysis, and imputation are crucial to handle errors. Ensuring data quality is fundamental because “garbage in, garbage out” – poor data yields poor models or decisions.

Further Reading

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Class Boundary (Statistics & Machine Learning)
Long Short-Term Memory (LSTM)
Recurrent Neural Network (RNN)
Large Language Model (LLM)
Chain of Thought (CoT)
Foundation Models
Semantic Segmentation
Variance (Model Variance)
XAI (Explainable AI)
YOLO (You Only Look Once)
Weight Decay (L2 Regularization)
Text Generation Inference
True Positive Rate (TPR)
Type II Error (False Negative)
Type I Error (False Positive)
Triplet Loss
Transformers (Transformer Networks)
Tabular Data
Stream-Based Selective Sampling
Synthetic Data
Training Data
Support Vector Machine (SVM)
Sentiment Analysis
Surrogate Model
Supervised Learning
Semi-supervised Learning
Selective Sampling
Sliding Window Attention
Sensitivity and Specificity of Machine Learning
Scikit-Learn
Segment Anything Model (SAM)
Regularization Algorithms
ROC (Receiver Operating Characteristic) Curve
Scale Imbalance
Responsible AI
Regression (Regression Analysis)
Region-Based CNN (R-CNN)
Recall (Sensitivity or True Positive Rate)
Random Forest
RAG Architecture
Query Synthesis Methods
Query Strategy (Active Learning)
Predictive Model Validation
Prompt Injection
Prompt Engineering
PyTorch
Prompt Chaining
PR AUC
Precision
Pooling
Pose Estimation
Pool-Based Sampling
Pattern Recognition
Perceptron
Pixel
Parameter-Efficient Fine-Tuning (Prefix-Tuning)
Pandas and NumPy
Panoptic Segmentation
PACS (Picture Archiving and Communication System)
Overfitting
Outlier Detection
Object Tracking
Optical Character Recognition (OCR)
One-Shot Learning
Object Localization
Object Detection
Normalization
Noise
NIfTI
Natural Language Processing (NLP)
Neural Networks
Multi-Task Learning
Motion Detection
Motion Estimation
Model Validation
Latent Dirichlet Allocation (LDA)
Medical Image Segmentation
Model Parameters
Model Accuracy
Micro-Models
Mean Squared Error (MSE)
Keypoints
Mean Average Precision (mAP)
Machine Learning (ML)
Loss Function
Linear Regression
Linear Discriminant Analysis (LDA)
LIDAR
Learning Rate
Label Errors
Label
Intersection over Union (IoU)
Interpolation
Interpretability
Imbalanced Dataset
Instance Segmentation
Image Processing
Image Restoration
Image Segmentation
Image Recognition

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