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 BERT

BERT（雙向編碼器表示的轉換器）是谷歌於2018年發佈的一種自然語言處理（NLP）模型。其設計旨在更好地理解語言的上下文關係，通過雙向方法捕捉文本中單詞之間的相互關係。

BERT 的關鍵特性是其雙向性，這讓它能夠同時考慮單詞的左側和右側上下文。這種全面的理解使得 BERT 能夠比傳統的單向模型更有效地把握句子的細微含義。

BERT 對學術界和工業界都有顯著影響，提升了問答系統、情感分析和文本分類等多種應用的準確性。許多搜索引擎和聊天機器人開始應用 BERT，以增強其自然語言理解能力。

然而，BERT 也存在一些局限性，如對計算資源的高需求和處理速度較慢。此外，對於特定領域的語言理解，BERT 可能需要額外的微調才能達到最佳效果。