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