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

什麼是自然語言處理（NLP）

自然語言處理（NLP）是計算機科學和人工智慧領域的一個重要分支，專注於計算機與人類語言之間的互動。它旨在使計算機能夠有效地理解、解釋和生成自然語言。NLP的應用非常廣泛，包括機器翻譯、情感分析、文本分類和語音識別等。

NLP的歷史可以追溯到20世紀50年代，隨著技術的進步，特別是深度學習的興起，NLP取得了顯著的進展。現代NLP依賴於大量的語料庫和強大的計算能力，以提高語言處理的準確性和效率。

NLP通常通過幾個步驟進行：文本預處理（如去除標點和分詞）、特徵提取（如詞袋模型和TF-IDF）、模型訓練（使用機器學習算法）以及最終的預測或結果生成。隨著技術的發展，NLP系統的訓練越來越依賴於大型數據集和複雜的神經網絡架構。

NLP的典型場景包括智能客服、語音助手和社交媒體分析。未來，隨著人工智慧的進一步發展，NLP可能在教育、醫療和法律等領域發揮更大的作用。然而，NLP也面臨一些挑戰，如語言的多樣性與複雜性以及上下文理解的困難。

NLP的優點在於顯著提高信息處理的效率，減少人工干預，同時幫助企業洞察用戶情感和需求。然而，缺點是訓練NLP系統需要大量的數據，且可能存在偏見和錯誤的理解。

在使用NLP時，有幾個注意事項：首先，確保數據的多樣性和代表性，以減少模型的偏見；其次，持續監測和優化模型性能，以適應語言的變化和用戶需求的演變。