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 One-hot Encoding

One-hot Encoding 是一种常用的特征表示方法，主要用于将分类数据转换为计算机能够理解的形式。在机器学习和数据挖掘中，数据的有效表示是模型成功的关键。One-hot Encoding 的基本思想是将每个类别值转换为一个二进制向量，这种向量在类别项对应的位置上标记为1，而在其他位置标记为0。

这种方法的优点在于它能够消除类别之间的顺序关系，使模型能够独立处理每个类别。例如，考虑一个包含动物类别的数据集，如“猫”、“狗”和“鸟”。通过 One-hot Encoding，这些类别将被表示为三维数组：[1, 0, 0]、[0, 1, 0] 和 [0, 0, 1]。这种表示方式有助于提高模型的学习效果，尤其是在深度学习中。

尽管 One-hot Encoding 在许多场景中表现良好，但它也存在一些缺点。例如，当类别数量较多时，会导致稀疏矩阵的生成，从而增加计算复杂度和内存占用。此外，One-hot Encoding 无法捕捉类别之间的关系，这在某些情况下可能影响模型性能。为了解决这些问题，研究人员提出了一些替代方法，如目标编码（Target Encoding）和词嵌入（Word Embedding）等。

未来的趋势是结合使用 One-hot Encoding 和其他编码方式，以便在保持有效性的同时，减少计算资源的消耗和模型的复杂性。总的来说，One-hot Encoding 是机器学习中处理分类数据的基础技术，理解其原理和应用场景对于数据科学家至关重要。