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 One-hot Encoding

One-hot Encoding 是一種常用的特徵表示方法，主要用於將分類數據轉換為計算機能夠理解的形式。在機器學習和數據挖掘中，數據的有效表示是模型成功的關鍵。One-hot Encoding 的基本思想是將每個類別值轉換為一個二進制向量，這種向量在類別項對應的位置上標記為1，而在其他位置標記為0。

這種方法的優點在於它能夠消除類別之間的順序關係，使模型能夠獨立處理每個類別。例如，考慮一個包含動物類別的數據集，如“貓”、“狗”和“鳥”。通過 One-hot Encoding，這些類別將被表示為三維數組：[1, 0, 0]、[0, 1, 0] 和 [0, 0, 1]。這種表示方式有助於提高模型的學習效果，尤其是在深度學習中。

儘管 One-hot Encoding 在許多場景中表現良好，但它也存在一些缺點。例如，當類別數量較多時，會導致稀疏矩陣的生成，從而增加計算複雜度和內存佔用。此外，One-hot Encoding 無法捕捉類別之間的關係，這在某些情況下可能影響模型性能。為了解決這些問題，研究人員提出了一些替代方法，如目標編碼（Target Encoding）和詞嵌入（Word Embedding）等。

未來的趨勢是結合使用 One-hot Encoding 和其他編碼方式，以便在保持有效性的同時，減少計算資源的消耗和模型的複雜性。總的來說，One-hot Encoding 是機器學習中處理分類數據的基礎技術，理解其原理和應用場景對於數據科學家至關重要。