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

什麼是機器學習 (ML)

Machine Learning (ML) - AI and technology concept illustration

© 2025 / unsplash.com

機器學習（Machine Learning, ML）是人工智慧的一個分支，涉及使用演算法和統計模型使電腦系統能夠執行特定任務，而無需明確的指示。它的核心在於通過數據驅動的方式，自動學習並改進自身的性能。

機器學習的運作方式通常分為監督學習、無監督學習和強化學習。監督學習中，系統通過標記的數據進行訓練，並預測未知數據的結果；而在無監督學習中，系統則需要在沒有標記的數據中發現模式。強化學習則通過試錯的方式進行學習，強調智能體與環境的互動。

機器學習的重要性體現在其對各行業的影響，尤其是在醫療、金融、零售等領域。它幫助企業提高效率、降低成本、提升客戶體驗。然而，機器學習也存在一些缺點，如對大規模數據的依賴、模型的可解釋性，以及潛在的偏見問題。

未來，機器學習將繼續發展，隨之而來的是更複雜的模型和演算法、更豐富的數據來源以及更高的計算能力。各行各業都在探索機器學習的創新應用，未來可能會出現顛覆性的技術和解決方案。

相關注意事項包括確保數據的質量和代表性、保護用戶隱私，以及對演算法進行適當的評估和監管。