Machine Learning (ML)
Batch Learning
Online Learning
Instance-based Learning
Model-based Learning
Epoch
Batch Size
Learning Rate
Loss Function
Mean Squared Error (MSE)
Mean Absolute Error (MAE)
Root Mean Squared Error (RMSE)
Log Loss
Hinge Loss
L1 Regularization (Lasso)
L2 Regularization (Ridge)
Elastic Net
Stochastic Gradient Descent (SGD)
Mini-batch Gradient Descent
Adam Optimizer
Adagrad
RMSprop
Early Stopping
Dropout
Bagging
Boosting
AdaBoost
XGBoost
LightGBM
CatBoost
Feature Selection
Feature Scaling
Data Augmentation
Imbalanced Data
SMOTE (Synthetic Minority Over-sampling Technique)
Undersampling
Oversampling
Cross-Entropy
Data Splitting
Train/Test Split
Validation Set
Holdout Set
Grid Search
Random Search
Bayesian Optimization
Model Deployment
Model Monitoring
Model Drift
Model Interpretability
Shapley Values
LIME (Local Interpretable Model-agnostic Explanations)
Partial Dependence Plot
ICE (Individual Conditional Expectation) Plot
ROC-AUC Score
PR-AUC Score
Learning Curves
Bias-Variance Tradeoff
Monte Carlo Simulation
Artificial Intelligence (AI)
Autonomous Vehicles
Smart Home Devices
AI Ethics
Moral Machine
Superintelligence
Narrow AI
General AI
Strong AI
Weak AI
Machine Learning Pipeline
AI Workflow
AI Ethics Board
AI Policy
AI Regulation
AI Bias
AI Fairness
AI Transparency
AI Accountability
AI Privacy
Data Privacy
Data Security
Federated Learning
Distributed AI
Collaborative Filtering
Recommender Systems
Personalization
Content-based Filtering
Multi-Agent Systems
Swarm Intelligence
Fuzzy Logic
Neural Architecture Search (NAS)
Quantum Computing
Quantum Machine Learning
Bioinformatics
Health Informatics
Predictive Analytics
Prescriptive Analytics
Diagnostic Analytics
Descriptive Analytics
Edge AI
Cloud AI
AI as a Service (AIaaS)
Digital Twin
Smart Cities
Sentiment Analysis
Emotion Recognition
Face Recognition
Object Detection
Scene Understanding
Video Analysis
Activity Recognition
Behavioral Analysis
Large Language Models (LLMs)
Bidirectional Transformer
Uni-directional Transformer
Autoencoding Transformer
Autoregressive Transformer
Encoder-Decoder Architecture
Seq2Seq (Sequence to Sequence)
Transformer-XL
XLNet
RoBERTa (Robustly optimized BERT approach)
ALBERT (A Lite BERT)
T5 (Text-to-Text Transfer Transformer)
BART (Bidirectional and Auto-Regressive Transformers)
DistilBERT
Electra
XLNet
Longformer
GPT-2
GPT-3
GPT-4
DALL-E
CLIP
BPE (Byte-Pair Encoding)
WordPiece
SentencePiece
Unigram Language Model
Transformer Layers
Hidden Layers
Feedforward Neural Network
Layer Normalization
Residual Connection
Linear Transformation
Softmax Function
Logits
Greedy Search
Top-k Sampling
Top-p (Nucleus) Sampling
Transformer Block
Language Modeling
Text Summarization
Text Classification
Named Entity Recognition (NER)
Part-of-Speech Tagging
Dependency Parsing
Coreference Resolution
Semantic Role Labeling
Dialogue Systems
Conversational AI
Question Answering
Knowledge Graphs
Retrieval-Augmented Generation (RAG)
Data Labeling
Token Embeddings
Sentence Embeddings
Contextual Embeddings
Bidirectional Encoder
Monolingual Model
Multilingual Model
Code-Mixed Language Model
Training Corpus
Pretraining Corpus
Training Epochs
Gradient Clipping
Mixed Precision Training
Batch Normalization
Model Parallelism
Data Parallelism
Attention Heads
Cross-Attention
Causal Masking
Beam Width
Log-Likelihood
Vocabulary Size
Model Capacity
Parameter Tuning
Computational Graph
Model Architecture
Transformer
Attention Mechanism
Self-Attention
Multi-Head Attention
Encoder
Decoder
Feedforward Neural Network
Residual Connections
Layer Normalization
Positional Encoding
Transformer Layers
Hidden Layers
Training and Optimization
Pretraining
Fine-tuning
Transfer Learning
Epoch
Batch Size
Learning Rate
Gradient Descent
Stochastic Gradient Descent (SGD)
Adam Optimizer
Loss Function
Cross-Entropy Loss
Backpropagation
Gradient Clipping
Mixed Precision Training
Data and Tokenization
Dataset
Training Corpus
Tokenization
Token
Subword Tokenization
Byte-Pair Encoding (BPE)
WordPiece
SentencePiece
Vocabulary Size
Embedding
Token Embeddings
Positional Embeddings
Model Evaluation
Perplexity
Log-Likelihood
Evaluation Metrics
Validation Set
Test Set
Cross-Validation
Overfitting
Underfitting
Inference and Generation
Inference
Text Generation
Text Completion
Language Modeling
Context Window
Beam Search
Sampling
Top-k Sampling
Top-p (Nucleus) Sampling
Temperature
Logits
Softmax
Scaling and Performance
Model Size
Parameters
Computational Cost
Memory Efficiency
Model Parallelism
Data Parallelism
Scaling Laws
Advanced Techniques
Zero-Shot Learning
Few-Shot Learning
Prompt Engineering
Contextual Understanding
Causal Language Model
Masked Language Model
Retrieval-Augmented Generation (RAG)
Knowledge Distillation
Neural Architecture Search (NAS)
Practical Considerations
Model Deployment
Inference Latency
Model Monitoring
Model Drift
Ethical AI
Explainable AI
Bias Mitigation
Data Privacy
Model Security
Applications
Text Summarization
Text Classification
Named Entity Recognition (NER)
Part-of-Speech Tagging
Dependency Parsing
Coreference Resolution
Semantic Role Labeling
Question Answering
Dialogue Systems
Conversational AI