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

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

B

BERT Boosting Bias Backpropagation Batch Normalization

C

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

D

Deepfake Discriminative Model Data Augmentation Deep Learning Deterministic Model

E

Epoch Explainable AI (XAI)Embedding Encoder Ensemble Learning

F

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

G

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

H

Hierarchical Model Hallucination Heuristic Hyperparameter Hidden Layer

I

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

J

JAX JSONL / JSON-lines Juxtaposition Jittering Joint Embedding

K

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

L

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

M

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

N

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

O

One-hot Encoding Optimizer Objective Function Online Learning Overfitting

P

Prompt Pooling Parameter Policy / Reinforcement Learning Policy Pretraining

Q

Q-learning Quality Estimation Query Quantization Queue / Buffer

R

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

S

Softmax Sampling Sequence Modeling Supervised Learning Self-Supervised Learning

T

Tokenizer Transformer Training Data Tuning / Hyperparameter Tuning Transfer Learning

U

U-Net Uncertainty Estimation Underfitting Unsupervised Learning Universal Approximation Theorem

V

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

W

Weight Decay Workflow Weak Supervision Whitening / Whitening Transformation Word Embedding

X

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

Y

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

Z

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

什麼是通用逼近定理

通用逼近定理（Universal Approximation Theorem，簡稱 UA Theorem）是神經網絡與函數逼近理論中的一個基本結果。它表明，具有足夠隱藏層的前饋神經網絡可以逼近任何連續函數。

該定理最早由 George Cybenko 在 1989 年提出，之後經過多次擴展。其核心思想是，儘管神經網絡的結構可能複雜，但只要網絡的隱藏層足夠多，就能以任意精度逼近任何連續函數。

UA Theorem 的重要性在於它為深度學習的成功提供了理論依據，表明神經網絡是一種強大的函數逼近工具。這一發現推動了神經網絡的廣泛應用，特別是在圖像識別、自然語言處理等領域。

該定理通常應用於前饋神經網絡，特別是單隱層的網絡。通過適當的激活函數（如 sigmoid 或 ReLU），網絡能夠捕捉輸入與輸出之間的複雜關係。

隨著深度學習技術的進步，UA Theorem 的應用將進一步擴展到更複雜的模型和算法中，特別是生成對抗網絡（GANs）和強化學習框架。

儘管該定理的理論適用性廣泛，但在實際應用中，網絡的訓練過程可能面臨過擬合、收斂速度慢等問題。因此，理解 UA Theorem 對於從事機器學習和深度學習研究的人员至關重要，特別是在設計和優化神經網絡時。