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

什么是嵌入

嵌入是一个在多个领域中至关重要的概念，特别是在自然语言处理（NLP）和机器学习中。它指的是将高维数据（例如单词或图像）映射到低维空间的过程，从而使数据更易于计算。

在自然语言处理领域，单词嵌入将单词转换为向量，使得相似意义的单词在向量空间中彼此靠近。像 Word2Vec 和 GloVe 这样的技术被广泛使用。这些方法帮助模型理解单词之间的关系和语义，从而增强文本分类和机器翻译等任务。

嵌入不仅适用于单词，还可以应用于其他数据类型，如图像和用户行为。在推荐系统中，用户和物品的嵌入使得模型能够根据用户的偏好提供个性化的推荐。

展望未来，嵌入技术可能会发展到更高维度的表示，结合复杂的神经网络架构，从而提高模型的性能。嵌入的可解释性也将成为研究的重点，因为理解嵌入的工作原理对于改进模型和提升透明度至关重要。

嵌入的优点包括显著减少数据的维度和计算复杂度，同时保留重要的语义信息。然而，缺点在于，嵌入训练需要大量的数据和计算资源，如果数据不足，嵌入的质量可能会受到影响。

相关注意事项包括数据的预处理和选择合适的嵌入方法。不同的任务可能需要不同类型的嵌入，因此在应用时需要进行评估和调整。