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0-9

1-shot learning|3D convolution|5G + AI|6DoF pose estimation|7D representation|8-bit quantization|2-stage detector|9-layer network|0-shot learning|4D data

A

AGI / Artificial General Intelligence|Attention|Autoencoder|Algorithm|Artificial Intelligence (AI)

B

Backpropagation|Batch Normalization|BERT|Bias|Boosting

C

Clustering|CNN / Convolutional Neural Network|Chatbot|Classifier / Classification|Cross-Validation

D

Data Augmentation|Deep Learning|Deepfake|Deterministic Model|Discriminative Model

E

Encoder|Embedding|Ensemble Learning|Epoch|Explainable AI (XAI)

F

Foundation Model|Fine-tuning|Fusion / Multimodal Fusion|Feature Extraction|Forward Propagation

G

Graph Neural Network (GNN)|GAN / Generative Adversarial Network|Generative AI|Gradient Descent|Grounding

H

Hierarchical Model|Hyperparameter|Hidden Layer|Hallucination|Heuristic

I

Instance / Sample|Instruction tuning|Intelligence Amplification / Augmentation|Interpretability|Imbalanced Data

J

JAX|Jittering|Joint Embedding|JSONL / JSON-lines|Juxtaposition

K

Kernel Trick|KL Divergence (Kullback–Leibler Divergence)|K-means Clustering|K-Shot Learning|Knowledge Distillation

L

Learning Rate|Large Language Model (LLM)|Latent Variable|Loss Function|LSTM / Long Short-Term Memory

M

Model|Machine Learning (ML)|Meta-learning|Multi-head Attention|Multimodal / Multimodality

N

Neural Network|Novelty Detection / Anomaly Detection|NLP / Natural Language Processing|NLU / Natural Language Understanding|Normalization

O

Objective Function|Optimizer|One-hot Encoding|Online Learning|Overfitting

P

Parameter|Prompt|Policy / Reinforcement Learning Policy|Pooling|Pretraining

Q

Query|Q-learning|Quality Estimation|Quantization|Queue / Buffer

R

Representation Learning|Regularization|Reinforcement Learning (RL)|Retrieval Augmented Generation (RAG)|RNN / Recurrent Neural Network

S

Sampling|Self-Supervised Learning|Sequence Modeling|Softmax|Supervised Learning

T

Tokenizer|Transfer Learning|Transformer|Tuning / Hyperparameter Tuning|Training Data

U

Universal Approximation Theorem|U-Net|Underfitting|Uncertainty Estimation|Unsupervised Learning

V

Validation Set|Vanishing / Exploding Gradient|Variational Autoencoder (VAE)|Vector Embedding|Vision Transformer (ViT)

W

Weak Supervision|Weight Decay|Whitening / Whitening Transformation|Word Embedding|Workflow

X

XOR problem|X-axis / feature axis|XAI / Explainable AI|XLM|XLNet

Y

Y-axis / feature axis|Y-transform / YUV|YAGNI (You Aren't Gonna Need It)|Yield (model yield / throughput)|Yoga of AI

Z

Zero-gradient phenomenon|Zero-centric / Zero-bias initialization|Z-score Normalization|Zero-shot Learning / Zero-shot inference|Zygosity in augmentation