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

최적화기란 무엇인가

‘최적화기’라는 용어는 컴퓨터 과학, 데이터 분석 및 머신 러닝 등 다양한 분야에서 효율성을 높이고 결과를 극대화하기 위해 설계된 알고리즘 및 도구를 의미합니다.

기술적 맥락에서 최적화기는 주어진 문제에 대한 최상의 솔루션을 찾기 위해 매개변수를 조정합니다. 예를 들어, 머신 러닝에서는 최적화기가 모델의 가중치를 조정하여 예측 오류를 최소화합니다. 운영 연구에서는 최적화기가 복잡한 의사 결정 문제를 해결하여 효율성을 높이고 비용을 절감합니다.

인공지능과 빅데이터가 발전함에 따라 최적화 기술은 더욱 정교하고 지능적으로 발전할 것으로 예상됩니다. 최적화기의 응용 범위는 전통적인 산업을 넘어 의료, 금융 및 환경 보호 등 여러 분야로 확대될 것입니다.

최적화기를 사용할 때는 과적합이나 계산 자원의 낭비를 피하기 위해 적절한 알고리즘과 도구를 선택하는 것이 중요합니다.