Glossary

0-9

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

A

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

B

Batch Normalization Boosting Backpropagation Bias BERT

C

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

D

Deterministic Model Data Augmentation Deep Learning Discriminative Model Deepfake

E

Explainable AI (XAI)Ensemble Learning Encoder Embedding Epoch

F

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

G

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

H

Hierarchical Model Hidden Layer Hyperparameter Hallucination Heuristic

I

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

J

Juxtaposition Joint Embedding Jittering JAX JSONL / JSON-lines

K

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

L

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

M

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

N

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

O

Objective Function Overfitting Online Learning Optimizer One-hot Encoding

P

Parameter Policy / Reinforcement Learning Policy Prompt Pretraining Pooling

Q

Quantization Query Queue / Buffer Quality Estimation Q-learning

R

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

S

Supervised Learning Sampling Sequence Modeling Self-Supervised Learning Softmax

T

Transfer Learning Tokenizer Tuning / Hyperparameter Tuning Transformer Training Data

U

Uncertainty Estimation Unsupervised Learning Underfitting Universal Approximation Theorem U-Net

V

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

W

Whitening / Whitening Transformation Weak Supervision Word Embedding Workflow Weight Decay

X

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

Y

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

Z

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

Y축/특징 축이란 무엇인가

Y축 또는 특징 축은 차트 및 데이터 시각화에서 중요한 구성 요소입니다. 일반적으로 데이터의 한 차원 또는 특징을 나타내어 사용자가 서로 다른 데이터 포인트 간의 관계를 시각적으로 표현할 수 있게 합니다.

통계 및 수학에서 Y축은 2차원 좌표계에서 데이터를 표시하고 비교하는 데 필수적입니다. 예를 들어, 산점도에서 Y축은 매출을 나타내고 X축은 시간을 나타내어 시청자가 시간에 따른 추세를 이해할 수 있도록 도와줍니다.

데이터 시각화 도구가 발전함에 따라 Y축의 기능은 더 많은 데이터 분석 형태와 상호작용을 지원하도록 확장될 수 있습니다. 디자이너는 정보를 효과적으로 전달하기 위해 Y축 레이블의 명확성과 일관성을 보장해야 합니다.