A-Z of Machine Learning and Computer Vision Terms

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PyTorch
PyTorch
Q
Q
Quantum Machine Learning
Quantum Machine Learning
Query Strategy (Active Learning)
Query Strategy (Active Learning)
Query Synthesis Methods
Query Synthesis Methods
R
R
RAG Architecture
RAG Architecture
ROC (Receiver Operating Characteristic) Curve
ROC (Receiver Operating Characteristic) Curve
Random Forest
Random Forest
Recall (Sensitivity or True Positive Rate)
Recall (Sensitivity or True Positive Rate)
Recurrent Neural Network (RNN)
Recurrent Neural Network (RNN)
Region-Based CNN (R-CNN)
Region-Based CNN (R-CNN)
Regression (Regression Analysis)
Regression (Regression Analysis)
Regularization Algorithms
Regularization Algorithms
Reinforcement Learning
Reinforcement Learning
Responsible AI
Responsible AI
S
S
Scale Imbalance
Scale Imbalance
Scikit-Learn
Scikit-Learn
Segment Anything Model (SAM)
Segment Anything Model (SAM)
Selective Sampling
Selective Sampling
Self-Supervised Learning
Self-Supervised Learning
Semantic Segmentation
Semantic Segmentation
Semi-supervised Learning
Semi-supervised Learning
Sensitivity and Specificity of Machine Learning
Sensitivity and Specificity of Machine Learning
Sentiment Analysis
Sentiment Analysis
Sliding Window Attention
Sliding Window Attention
Stream-Based Selective Sampling
Stream-Based Selective Sampling
Supervised Learning
Supervised Learning
Support Vector Machine (SVM)
Support Vector Machine (SVM)
Surrogate Model
Surrogate Model
Synthetic Data
Synthetic Data
T
T
Tabular Data
Tabular Data
Text Generation Inference
Text Generation Inference
Training Data
Training Data
Transfer Learning
Transfer Learning
Transformers (Transformer Networks)
Transformers (Transformer Networks)
Triplet Loss
Triplet Loss
True Positive Rate (TPR)
True Positive Rate (TPR)
Type I Error (False Positive)
Type I Error (False Positive)
Type II Error (False Negative)
Type II Error (False Negative)
U
U
Unsupervised Learning
Unsupervised Learning
V
V
Variance (Model Variance)
Variance (Model Variance)
Variational Autoencoders
Variational Autoencoders
W
W
Weak Supervision
Weak Supervision
Weight Decay (L2 Regularization)
Weight Decay (L2 Regularization)
X
X
XAI (Explainable AI)
XAI (Explainable AI)
XGBoost
XGBoost
Y
Y
YOLO (You Only Look Once)
YOLO (You Only Look Once)
Yolo Object Detection
Yolo Object Detection
Z
Z
Zero-Shot Learning
Zero-Shot Learning
N

Natural Language Processing (NLP)

Natural Language Processing (NLP) is the field of AI that focuses on interactions between computers and human (natural) languages. It involves enabling computers to understand, interpret, generate, and learn from human language in a useful way. Common tasks in NLP include: text classification (e.g., sentiment analysis, spam detection), machine translation (translating between languages), named entity recognition (finding person names, locations, etc., in text), syntactic parsing (grammatical analysis of sentences), language modeling (predicting next word or modeling text generation), question answering, summarization, speech recognition and synthesis (though speech is often considered separate “speech processing”), and so on. Techniques in NLP range from rule-based and statistical methods to modern neural approaches (like Transformers powering models such as BERT, GPT). Key to NLP is handling the ambiguity, variability, and context-dependence of language. Data like large text corpora and linguistic annotations are used to train language models and classifiers.

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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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