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

什麼是嵌入

嵌入是一個在多個領域中至關重要的概念，特別是在自然語言處理（NLP）和機器學習中。它指的是將高維數據（例如單詞或圖像）映射到低維空間的過程，從而使數據更易於計算。

在自然語言處理領域，單詞嵌入將單詞轉換為向量，使得相似意義的單詞在向量空間中彼此靠近。像 Word2Vec 和 GloVe 這樣的技術被廣泛使用。這些方法幫助模型理解單詞之間的關係和語義，從而增強文本分類和機器翻譯等任務。

嵌入不僅適用於單詞，還可以應用於其他數據類型，如圖像和用戶行為。在推薦系統中，用戶和物品的嵌入使得模型能夠根據用戶的偏好提供個性化的推薦。

展望未來，嵌入技術可能會發展到更高維度的表示，結合複雜的神經網絡架構，從而提高模型的性能。嵌入的可解釋性也將成為研究的重點，因為理解嵌入的工作原理對於改進模型和提升透明度至關重要。

嵌入的優點包括顯著減少數據的維度和計算複雜度，同時保留重要的語義信息。然而，缺點在於，嵌入訓練需要大量的數據和計算資源，如果數據不足，嵌入的質量可能會受到影響。

相關注意事項包括數據的預處理和選擇合適的嵌入方法。不同的任務可能需要不同類型的嵌入，因此在應用時需要進行評估和調整。