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

Artificial Intelligence (AI)Algorithm Attention Autoencoder AGI / Artificial General Intelligence

B

BERT Boosting Backpropagation Batch Normalization Bias

C

Classifier / Classification Cross-Validation Clustering CNN / Convolutional Neural Network Chatbot

D

Discriminative Model Deterministic Model Deep Learning Data Augmentation Deepfake

E

Epoch Encoder Ensemble Learning Explainable AI (XAI)Embedding

F

Foundation Model Forward Propagation Fusion / Multimodal Fusion Feature Extraction Fine-tuning

G

GAN / Generative Adversarial Network Grounding Generative AI Gradient Descent Graph Neural Network (GNN)

H

Hierarchical Model Hyperparameter Hallucination Heuristic Hidden Layer

I

Interpretability Instruction tuning Imbalanced Data Instance / Sample Intelligence Amplification / Augmentation

J

JAX JSONL / JSON-lines Juxtaposition Jittering Joint Embedding

K

K-Shot Learning Kernel Trick KL Divergence (Kullback–Leibler Divergence)Knowledge Distillation K-means Clustering

L

LSTM / Long Short-Term Memory Large Language Model (LLM)Latent Variable Loss Function Learning Rate

M

Multimodal / Multimodality Multi-head Attention Machine Learning (ML)Model Meta-learning

N

NLU / Natural Language Understanding Normalization Neural Network Novelty Detection / Anomaly Detection NLP / Natural Language Processing

O

One-hot Encoding Overfitting Objective Function Optimizer Online Learning

P

Prompt Parameter Pretraining Policy / Reinforcement Learning Policy Pooling

Q

Q-learning Query Queue / Buffer Quantization Quality Estimation

R

RNN / Recurrent Neural Network Representation Learning Retrieval Augmented Generation (RAG)Reinforcement Learning (RL)Regularization

S

Softmax Sampling Supervised Learning Sequence Modeling Self-Supervised Learning

T

Tokenizer Transformer Tuning / Hyperparameter Tuning Transfer Learning Training Data

U

U-Net Uncertainty Estimation Underfitting Universal Approximation Theorem Unsupervised Learning

V

Vision Transformer (ViT)Variational Autoencoder (VAE)Vector Embedding Vanishing / Exploding Gradient Validation Set

W

Weight Decay Word Embedding Workflow Whitening / Whitening Transformation Weak Supervision

X

X-axis / feature axis XLM XLNet XAI / Explainable AI XOR problem

Y

Yoga of AI Y-transform / YUV Yield (model yield / throughput)Y-axis / feature axis YAGNI (You Aren't Gonna Need It)

Z

Z-score Normalization Zero-gradient phenomenon Zero-shot Learning / Zero-shot inference Zero-centric / Zero-bias initialization Zygosity in augmentation

什么是 Tokenizer

Tokenizer - AI and technology concept illustration

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Tokenizer 是自然语言处理（NLP）和编程语言解析中的一个重要组成部分。它负责将输入文本分解为更小的单元，通常是单词、子词或符号，以便进行进一步处理。

Tokenization 是文本处理的第一步，为各种算法和模型奠定基础，尤其是在机器学习和深度学习上下文中。不同的语言和应用需要不同类型的 tokenizer；例如，基于空格的 tokenizer 适用于英文，而基于字符的 tokenizer 在处理中文时则更为有效。

Tokenization 的重要性在于它为文本数据的分析和处理提供结构化的信息。通过将文本分解为 token，算法能够更容易地识别模式、提取特征并生成预测。因此，选择合适的 tokenizer 对于确保模型性能至关重要。

随着人工智能和机器学习的不断发展，tokenization 方法也在不断演变。许多现代模型使用了基于子词的 tokenization 方法，如字节对编码（BPE）或 WordPiece，这些方法能够有效应对稀有词和新词，提高模型的泛化能力。